diff --git a/.bumpversion.cfg b/.bumpversion.cfg index 30c3988b4d..9ec439a095 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,6 +1,6 @@ [bumpversion] parse = (?P\d+)\.(?P\d+)\.(?P\d+)|dev -current_version = 0.27.2 +current_version = 0.33.0 [bumpversion:file:setup.py] diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs index 996e88a867..b9998dd0d2 100644 --- a/.git-blame-ignore-revs +++ b/.git-blame-ignore-revs @@ -6,3 +6,5 @@ 38cc6712a6f701703074a7a7c82ce0252fe869ee # Fix last isort issues and update Black to 22.1.0 8158d3eaef9d9f6e04f219b029e306d1f1be46d5 +# Change Python target-version to 3.9 and update Ruff to 0.7.2 +18e2e3fd7d82d239ab24807fcc1033094ea09940 diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index 70ab5e667a..da97001c93 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -5,7 +5,7 @@ # the repo. Unless a later match takes precedence, # @global-owner1 and @global-owner2 will be requested for # review when someone opens a pull request. -* @hrzn @dennisbader @brunnedu +* @dennisbader @madtoinou @hrzn # Custom CODEOWNERS can be set up for branches with specific # patterns, you can find more info here: diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 4692b54e67..47a1e330bf 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -17,8 +17,8 @@ Steps to reproduce the behavior, preferably code snippet. A clear and concise description of what you expected to happen. **System (please complete the following information):** - - Python version: [e.g. 3.8] - - darts version [e.g. 0.24.0] + - Python version: [e.g. 3.10] + - darts version [e.g. 0.31.0] **Additional context** Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md new file mode 100644 index 0000000000..5c5ce2fc2a --- /dev/null +++ b/.github/ISSUE_TEMPLATE/question.md @@ -0,0 +1,16 @@ +--- +name: General question +about: Ask clarification about the documentation or a feature +title: "[QUESTION]" +labels: question, triage +assignees: '' + +--- + +**Describe the issue linked to the documentation** +A detailed description of the issue you are facing. Please include any specific sections of/link to the documentation or methods you are struggling with. + +If relevant, include a code snippet to describe your attempt. + +**Additional context** +Add any other context about the problem here. diff --git a/.github/RELEASE_TEMPLATE/release_body.md b/.github/RELEASE_TEMPLATE/release_body.md new file mode 100644 index 0000000000..d4b5f9834a --- /dev/null +++ b/.github/RELEASE_TEMPLATE/release_body.md @@ -0,0 +1,3 @@ +We are pleased to announce the release of a new Darts version. + +You can find a list with all changes in the [release notes](https://unit8co.github.io/darts/release_notes/RELEASE_NOTES.html). diff --git a/.github/codecov.yml b/.github/codecov.yml new file mode 100644 index 0000000000..35cde5cd5e --- /dev/null +++ b/.github/codecov.yml @@ -0,0 +1,4 @@ +coverage: + status: + project: off + patch: off diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 0e4febd841..48047b2301 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,3 +1,8 @@ +Checklist before merging this PR: +- [ ] Mentioned all issues that this PR fixes or addresses. +- [ ] Summarized the updates of this PR under **Summary**. +- [ ] Added an entry under **Unreleased** in the [Changelog](../CHANGELOG.md). + Fixes #. diff --git a/.github/workflows/develop.yml b/.github/workflows/develop.yml index 1ba127cf23..bf62fc285d 100644 --- a/.github/workflows/develop.yml +++ b/.github/workflows/develop.yml @@ -9,129 +9,130 @@ jobs: lint: runs-on: ubuntu-latest steps: - - name: "1. Clone repository" - uses: actions/checkout@v2 + - name: "Clone repository" + uses: actions/checkout@v4 - - name: "2. Set up Python 3.9" - uses: actions/setup-python@v1 + - name: "Set up Python 3.10" + uses: actions/setup-python@v5 with: - python-version: '3.9' + python-version: '3.10' - # downloading gradle multiple times in parallel can yield to connection errors - - name: "3. Cache gradle distribution" - uses: actions/cache@v2 - with: - path: ~/.gradle/wrapper/dists - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} - - - name: "3.1 Cache gradle packages" - uses: actions/cache@v2 - with: - path: ~/.gradle/caches - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} + - name: "Install Dev Dependencies" + run: | + python -m pip install --upgrade pip + pip install -r requirements/dev.txt - - name: "4. Lint" + - name: "Lint" run: | - ./gradlew lint + pre-commit run --all-files + tests: runs-on: ${{ matrix.os }} strategy: matrix: - os: [macos-latest, ubuntu-latest] - python-version: ['3.9'] + os: [macos-13, ubuntu-latest] + python-version: ['3.10'] flavour: ['all'] steps: - - name: "1. Clone repository" - uses: actions/checkout@v2 + - name: "Clone repository" + uses: actions/checkout@v4 - - name: "2. Set up Python ${{ matrix.python-version }}" - uses: actions/setup-python@v1 + - name: "Set up Python ${{ matrix.python-version }}" + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - # downloading gradle multiple times in parallel can yield to connection errors - - name: "3. Cache gradle distribution" - uses: actions/cache@v2 + # use `uv` to retrieve the latest dependency versions + - name: "Compile Dependency Versions" + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + if [ "${{ matrix.os }}" == "macos-13" ]; then + source $HOME/.local/bin/env + fi + uv pip compile requirements/dev-all.txt > requirements-latest.txt + + - name: "Cache python environment" + uses: actions/cache@v4 + id: pythonenv-cache with: - path: ~/.gradle/wrapper/dists - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} + path: ${{ env.pythonLocation }} + key: ${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('requirements/*.txt', 'requirements-latest.txt') }} - - name: "3.1 Cache gradle packages" - uses: actions/cache@v2 - with: - path: ~/.gradle/caches - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} + - name: "Setup Pip" + run: | + python -m pip install --upgrade pip - - name: "4. Setup pip" + - name: "Install Dependencies" run: | - ./gradlew installPipLatest + # install latest dependencies (potentially updating cached dependencies) + pip install -U -r requirements/dev-all.txt - - name: "5. Install libomp (for LightGBM)" + - name: "Install libomp (for LightGBM)" run: | ./.github/scripts/libomp-${{ runner.os }}.sh - - name: "6. Attach cache for pip" - uses: actions/cache@v1 - id: cache - with: - path: ~/.cache/pip - key: tests-${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements-latest.txt') }} - - - name: "7. Tests" + - name: "Run tests" run: | - ./gradlew "test_${{matrix.flavour}}" + pytest --durations=50 --cov=darts --cov-config=.coveragerc --cov-report=xml darts/tests - - name: "8. Codecov upload" + - name: "Codecov upload" if: ${{ matrix.flavour == 'all' }} - uses: codecov/codecov-action@v2 + uses: codecov/codecov-action@v4 with: fail_ci_if_error: true - files: ./coverage.xml + token: ${{ secrets.CODECOV_TOKEN }} docs: runs-on: ubuntu-latest steps: - - name: "1. Clone repository" - uses: actions/checkout@v2 + - name: "Clone repository" + uses: actions/checkout@v4 - - name: "2. Set up Python 3.9" - uses: actions/setup-python@v1 + - name: "Set up Python 3.10" + uses: actions/setup-python@v5 with: - python-version: '3.9' + python-version: '3.10' - - name: "3. Install pandoc" + # use `uv` to retrieve the latest dependency versions + - name: "Compile Dependency Versions" run: | - sudo apt-get install -y pandoc + curl -LsSf https://astral.sh/uv/install.sh | sh + uv pip compile requirements/dev-all.txt > requirements-latest.txt - # downloading gradle multiple times in parallel can yield to connection errors - - name: "4. Cache gradle distribution" - uses: actions/cache@v2 + # only restore cache but do not upload + - name: "Restore cached python environment" + uses: actions/cache/restore@v4 + id: pythonenv-cache with: - path: ~/.gradle/wrapper/dists - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} + path: ${{ env.pythonLocation }} + key: ${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('requirements/*.txt', 'requirements-latest.txt') }} - - name: "4.1 Cache gradle packages" - uses: actions/cache@v2 - with: - path: ~/.gradle/caches - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} + - name: "Install pandoc" + run: | + sudo apt-get install -y pandoc - - name: "5. Setup pip" + - name: "Setup Pip" run: | - ./gradlew setupPip + python -m pip install --upgrade pip - - name: "6. Attach cache for pip" - uses: actions/cache@v1 - id: cache - with: - path: ~/.cache/pip - key: tests-${{ runner.os }}-3.9-pip-${{ hashFiles('requirements/core.txt', 'requirements/release.txt') }} + - name: "Install Dependencies" + run: | + # install latest dependencies (potentially updating cached dependencies) + pip install -U -r requirements/dev-all.txt - - name: "7. Build docs" + - name: "Install libomp (for LightGBM)" run: | - ./gradlew buildDocs + ./.github/scripts/libomp-${{ runner.os }}.sh + + - name: "Install Locally" + run: | + pip install . + + - name: "Build docs" + run: | + make --directory ./docs build-all-docs check-examples: runs-on: ubuntu-latest @@ -139,27 +140,46 @@ jobs: matrix: example-name: [03-FFT-examples.ipynb, 04-RNN-examples.ipynb, 00-quickstart.ipynb, 02-data-processing.ipynb, 01-multi-time-series-and-covariates.ipynb] steps: - - name: "1. Clone repository" - uses: actions/checkout@v2 + - name: "Clone repository" + uses: actions/checkout@v4 - - name: "2. Set up Python 3.9" - uses: actions/setup-python@v1 + - name: "Set up Python 3.10" + uses: actions/setup-python@v5 with: - python-version: '3.9' + python-version: '3.10' - # downloading gradle multiple times in parallel can yield to connection errors - - name: "3. Cache gradle distribution" - uses: actions/cache@v2 - with: - path: ~/.gradle/wrapper/dists - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} + # use `uv` to retrieve the latest dependency versions + - name: "Compile Dependency Versions" + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + uv pip compile requirements/dev-all.txt > requirements-latest.txt - - name: "3.1 Cache gradle packages" - uses: actions/cache@v2 + # only restore cache but do not upload + - name: "Restore cached python environment" + uses: actions/cache/restore@v4 + id: pythonenv-cache with: - path: ~/.gradle/caches - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} + path: ${{ env.pythonLocation }} + key: ${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('requirements/*.txt', 'requirements-latest.txt') }} + + - name: "Setup Pip" + run: | + python -m pip install --upgrade pip + + - name: "Install Dependencies" + run: | + # install latest dependencies (potentially updating cached dependencies) + pip install -U -r requirements/dev-all.txt + + - name: "Install libomp (for LightGBM)" + run: | + ./.github/scripts/libomp-${{ runner.os }}.sh + + - name: "Install Locally" + run: | + pip install . - - name: "4. Run examples ${{matrix.example-name}}" + - name: "Run example ${{matrix.example-name}}" + working-directory: ./examples run: | - ./gradlew checkExample -PexampleName=${{matrix.example-name}} + papermill ${{matrix.example-name}} ${{matrix.example-name}} diff --git a/.github/workflows/doc.yml b/.github/workflows/doc.yml index 3da8fd10ee..3e9a56472f 100644 --- a/.github/workflows/doc.yml +++ b/.github/workflows/doc.yml @@ -7,49 +7,54 @@ jobs: deploy-docs: runs-on: ubuntu-latest steps: - - name: "1. Clone repository" - uses: actions/checkout@v2 + - name: "Clone repository" + uses: actions/checkout@v4 - - name: "2. Set up Python 3.9" - uses: actions/setup-python@v1 + - name: "Set up Python 3.10" + uses: actions/setup-python@v5 with: - python-version: '3.9' + python-version: '3.10' - - name: "3. Install pandoc" + # use `uv` to retrieve the latest dependency versions + - name: "Compile Dependency Versions" run: | - sudo apt-get install -y pandoc + curl -LsSf https://astral.sh/uv/install.sh | sh + uv pip compile requirements/dev-all.txt > requirements-latest.txt - # downloading gradle multiple times in parallel can yield to connection errors - - name: "4. Cache gradle distribution" - uses: actions/cache@v2 + # only restore cache but do not upload + - name: "Restore cached python environment" + uses: actions/cache/restore@v4 + id: pythonenv-cache with: - path: ~/.gradle/wrapper/dists - key: release-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} + path: ${{ env.pythonLocation }} + key: ${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('requirements/*.txt', 'requirements-latest.txt') }} - - name: "4.1 Cache gradle packages" - uses: actions/cache@v2 - with: - path: ~/.gradle/caches - key: release-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} + - name: "Install pandoc" + run: | + sudo apt-get install -y pandoc - - name: "5. Setup pip" + - name: "Setup Pip" run: | - ./gradlew setupPip + python -m pip install --upgrade pip - - name: "6. Attach cache for pip" - uses: actions/cache@v1 - id: cache - with: - path: ~/.cache/pip - key: release-${{ runner.os }}-pip-${{ hashFiles('requirements/core.txt', 'requirements/release.txt') }} - restore-keys: | - release-${{ runner.os }}-pip- + - name: "Install Dependencies" + run: | + # install latest dependencies (potentially updating cached dependencies) + pip install -U -r requirements/dev-all.txt + + - name: "Install libomp (for LightGBM)" + run: | + ./.github/scripts/libomp-${{ runner.os }}.sh + + - name: "Install Locally" + run: | + pip install . - - name: "7. Build docs" + - name: "Build docs" run: | - ./gradlew buildDocs + make --directory ./docs build-all-docs - - name: "8. Publish documentation to gh-pages" + - name: "Publish documentation to gh-pages" uses: s0/git-publish-subdir-action@v2.2.0 env: REPO: self diff --git a/.github/workflows/merge.yml b/.github/workflows/merge.yml index d8b5ae732f..b74cd0a26f 100644 --- a/.github/workflows/merge.yml +++ b/.github/workflows/merge.yml @@ -9,152 +9,169 @@ jobs: lint: runs-on: ubuntu-latest steps: - - name: "1. Clone repository" - uses: actions/checkout@v2 + - name: "Clone repository" + uses: actions/checkout@v4 - - name: "2. Set up Python 3.10" - uses: actions/setup-python@v1 + - name: "Set up Python 3.11" + uses: actions/setup-python@v5 with: - python-version: '3.10' - - # downloading gradle multiple times in parallel can yield to connection errors - - name: "3. Cache gradle distribution" - uses: actions/cache@v2 - with: - path: ~/.gradle/wrapper/dists - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} + python-version: '3.11' - - name: "3.1 Cache gradle packages" - uses: actions/cache@v2 - with: - path: ~/.gradle/caches - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} + - name: "Install Dev Dependencies" + run: | + python -m pip install --upgrade pip + pip install -r requirements/dev.txt - - name: "4. Lint" + - name: "Lint" run: | - ./gradlew lint + pre-commit run --all-files tests: runs-on: ${{ matrix.os }} strategy: matrix: - os: [macos-latest, ubuntu-latest] - python-version: ['3.8', '3.10'] + os: [macos-13, ubuntu-latest] + python-version: ['3.9', '3.11'] flavour: ['core', 'torch', 'all'] steps: - - name: "1. Clone repository" - uses: actions/checkout@v2 + - name: "Clone repository" + uses: actions/checkout@v4 - - name: "2. Set up Python ${{ matrix.python-version }}" - uses: actions/setup-python@v1 + - name: "Set up Python ${{ matrix.python-version }}" + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - # downloading gradle multiple times in parallel can yield to connection errors - - name: "3. Cache gradle distribution" - uses: actions/cache@v2 - with: - path: ~/.gradle/wrapper/dists - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} - - - name: "3.1 Cache gradle packages" - uses: actions/cache@v2 - with: - path: ~/.gradle/caches - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} - - - name: "4. Setup pip" + - name: "Setup pip" run: | - ./gradlew setupPip + python -m pip install --upgrade pip - - name: "5. Install libomp (for LightGBM)" + - name: "Install Dependencies Flavour ${{ matrix.flavour }}" + run: | + if [ "${{ matrix.flavour }}" == "core" ]; then + pip install -r requirements/core.txt -r requirements/dev.txt + elif [ "${{ matrix.flavour }}" == "torch" ]; then + pip install -r requirements/core.txt -r requirements/torch.txt -r requirements/dev.txt + elif [ "${{ matrix.flavour }}" == "all" ]; then + pip install -r requirements/core.txt -r requirements/torch.txt -r requirements/notorch.txt -r requirements/dev.txt + fi + + - name: "Install libomp (for LightGBM)" run: | ./.github/scripts/libomp-${{ runner.os }}.sh - - name: "6. Tests" + - name: "Run tests" run: | - ./gradlew "test_${{matrix.flavour}}" + if [ "${{ matrix.flavour }}" == "all" ]; then + pytest --durations=50 --cov=darts --cov-config=.coveragerc --cov-report=xml darts/tests + else + pytest --durations=50 + fi - - name: "7. Codecov upload" + - name: "Codecov upload" if: ${{ matrix.flavour == 'all' }} - uses: codecov/codecov-action@v2 + uses: codecov/codecov-action@v4 with: fail_ci_if_error: true - files: ./coverage.xml + token: ${{ secrets.CODECOV_TOKEN }} check-examples: runs-on: ubuntu-latest strategy: matrix: - example-name: [00-quickstart.ipynb, 01-multi-time-series-and-covariates.ipynb, 02-data-processing.ipynb, 03-FFT-examples.ipynb, 04-RNN-examples.ipynb, 05-TCN-examples.ipynb, 06-Transformer-examples.ipynb, 07-NBEATS-examples.ipynb, 08-DeepAR-examples.ipynb, 09-DeepTCN-examples.ipynb, 10-Kalman-filter-examples.ipynb, 11-GP-filter-examples.ipynb, 12-Dynamic-Time-Warping-example.ipynb, 13-TFT-examples.ipynb, 15-static-covariates.ipynb, 16-hierarchical-reconciliation.ipynb, 18-TiDE-examples.ipynb, 19-EnsembleModel-examples.ipynb, 20-RegressionModel-examples.ipynb] + example-name: [00-quickstart.ipynb, 01-multi-time-series-and-covariates.ipynb, 02-data-processing.ipynb, 03-FFT-examples.ipynb, 04-RNN-examples.ipynb, 05-TCN-examples.ipynb, 06-Transformer-examples.ipynb, 07-NBEATS-examples.ipynb, 08-DeepAR-examples.ipynb, 09-DeepTCN-examples.ipynb, 10-Kalman-filter-examples.ipynb, 11-GP-filter-examples.ipynb, 12-Dynamic-Time-Warping-example.ipynb, 13-TFT-examples.ipynb, 15-static-covariates.ipynb, 16-hierarchical-reconciliation.ipynb, 18-TiDE-examples.ipynb, 19-EnsembleModel-examples.ipynb, 20-RegressionModel-examples.ipynb, 21-TSMixer-examples.ipynb, 22-anomaly-detection-examples.ipynb, 23-Conformal-Prediction-examples.ipynb] steps: - - name: "1. Clone repository" - uses: actions/checkout@v2 + - name: "Clone repository" + uses: actions/checkout@v4 - - name: "2. Set up Python 3.9" - uses: actions/setup-python@v1 + - name: "Set up Python 3.10" + uses: actions/setup-python@v5 with: - python-version: '3.9' + python-version: '3.10' - # downloading gradle multiple times in parallel can yield to connection errors - - name: "3. Cache gradle distribution" - uses: actions/cache@v2 - with: - path: ~/.gradle/wrapper/dists - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} + # use `uv` to retrieve the latest dependency versions + - name: "Compile Dependency Versions" + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + uv pip compile requirements/dev-all.txt > requirements-latest.txt - - name: "3.1 Cache gradle packages" - uses: actions/cache@v2 + # only restore cache but do not upload + - name: "Restore cached python environment" + uses: actions/cache/restore@v4 + id: pythonenv-cache with: - path: ~/.gradle/caches - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} + path: ${{ env.pythonLocation }} + key: ${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('requirements/*.txt', 'requirements-latest.txt') }} - # TODO: why is this a matrix? there is no pip caching, and we restart this for each item in the matrix - - name: "4. Run examples ${{matrix.example-name}}" + - name: "Setup Pip" run: | - ./gradlew checkExample -PexampleName=${{matrix.example-name}} + python -m pip install --upgrade pip + + - name: "Install Dependencies" + run: | + # install latest dependencies (potentially updating cached dependencies) + pip install -U -r requirements/dev-all.txt + + - name: "Install libomp (for LightGBM)" + run: | + ./.github/scripts/libomp-${{ runner.os }}.sh + + - name: "Install Locally" + run: | + pip install . + + - name: "Run example ${{matrix.example-name}}" + working-directory: ./examples + run: | + papermill ${{matrix.example-name}} ${{matrix.example-name}} docs: runs-on: ubuntu-latest - steps: - - name: "1. Clone repository" - uses: actions/checkout@v2 + - name: "Clone repository" + uses: actions/checkout@v4 - - name: "2. Set up Python 3.9" - uses: actions/setup-python@v1 + - name: "Set up Python 3.10" + uses: actions/setup-python@v5 with: - python-version: '3.9' + python-version: '3.10' - - name: "3. Install pandoc" + # use `uv` to retrieve the latest dependency versions + - name: "Compile Dependency Versions" run: | - sudo apt-get install -y pandoc + curl -LsSf https://astral.sh/uv/install.sh | sh + uv pip compile requirements/dev-all.txt > requirements-latest.txt - # downloading gradle multiple times in parallel can yield to connection errors - - name: "4. Cache gradle distribution" - uses: actions/cache@v2 + # only restore cache but do not upload + - name: "Restore cached python environment" + uses: actions/cache/restore@v4 + id: pythonenv-cache with: - path: ~/.gradle/wrapper/dists - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} + path: ${{ env.pythonLocation }} + key: ${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('requirements/*.txt', 'requirements-latest.txt') }} - - name: "4.1 Cache gradle packages" - uses: actions/cache@v2 - with: - path: ~/.gradle/caches - key: tests-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} + - name: "Install pandoc" + run: | + sudo apt-get install -y pandoc - - name: "5. Setup pip" + - name: "Setup Pip" run: | - ./gradlew setupPip + python -m pip install --upgrade pip - - name: "6. Attach cache for pip" - uses: actions/cache@v1 - id: cache - with: - path: ~/.cache/pip - key: release-${{ runner.os }}-3.9-pip-${{ hashFiles('requirements/core.txt', 'requirements/release.txt') }} + - name: "Install Dependencies" + run: | + # install latest dependencies (potentially updating cached dependencies) + pip install -U -r requirements/dev-all.txt + + - name: "Install libomp (for LightGBM)" + run: | + ./.github/scripts/libomp-${{ runner.os }}.sh + + - name: "Install Locally" + run: | + pip install . - - name: "7. Build docs" + - name: "Build docs" run: | - ./gradlew buildDocs + make --directory ./docs build-all-docs diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index f0cfef2590..ac451a8c74 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -12,34 +12,25 @@ jobs: runs-on: ubuntu-latest steps: - name: "1. Clone repository" - uses: actions/checkout@v2 + uses: actions/checkout@v4 with: - token: ${{ secrets.RELEASE_WORKFLOW_TOKEN_NEW }} + token: ${{ secrets.RELEASE_WORKFLOW_TOKEN_NEW_FINE_GRAINED }} fetch-depth: '1' - - name: "2. Set up Python 3.9" - uses: actions/setup-python@v1 + - name: "2. Set up Python 3.10" + uses: actions/setup-python@v5 with: - python-version: '3.9' + python-version: '3.10' - - name: "3. Update pip" + - name: "Setup Pip" run: | python -m pip install --upgrade pip - - name: "4. Attach cache for pip" - uses: actions/cache@v1 - id: cache - with: - path: ~/.cache/pip - key: release-${{ runner.os }}-pip-${{ hashFiles('requirements/release.txt') }} - restore-keys: | - release-${{ runner.os }}-pip- - - - name: "5. Install release dependencies" + - name: "Install release dependencies" run: | pip install -q -r requirements/release.txt - - name: "6. Determine next version" + - name: "Determine next version" uses: hrzn/github-tag-action@master id: bump_dry env: @@ -47,11 +38,11 @@ jobs: DRY_RUN: true BUMP_TYPE: ${{ github.event.inputs.bump_type}} - - name: "7. Bump version" + - name: "Bump version" run: | bump2version --new-version ${{ steps.bump_dry.outputs.new_tag }} patch - - name: "8. Commit new version" + - name: "Commit new version" uses: stefanzweifel/git-auto-commit-action@v4.1.6 with: commit_message: Release ${{ steps.bump_dry.outputs.new_tag }} @@ -60,7 +51,7 @@ jobs: commit_user_name: Unit8 Bot commit_user_email: info@unit8.co - - name: "9. Publish new tag" + - name: "Publish new tag" uses: hrzn/github-tag-action@master env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} @@ -73,15 +64,16 @@ jobs: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} with: tag_name: ${{ steps.bump_dry.outputs.new_tag }} - release_name: Release ${{steps.bump_dry.outputs.part}} ${{ steps.bump_dry.outputs.new_tag }} + release_name: Darts ${{steps.bump_dry.outputs.part}} ${{ steps.bump_dry.outputs.new_tag }} draft: false + body_path: .github/RELEASE_TEMPLATE/release_body.md deploy-docker: needs: [release] runs-on: ubuntu-latest steps: - name: "1. Clone repository" - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: "2. Determine current version" uses: hrzn/github-tag-action@master @@ -91,83 +83,76 @@ jobs: DRY_RUN: true BUMP_TYPE: ${{ github.event.inputs.bump_type}} - - name: "3. Login to docker hub" - run: docker login -u $DOCKER_HUB_USER -p $DOCKER_HUB_TOKEN - env: - DOCKER_HUB_USER: ${{ secrets.DOCKER_HUB_USER }} - DOCKER_HUB_TOKEN: ${{ secrets.DOCKER_HUB_TOKEN }} + - name: "Set up QEMU" + uses: docker/setup-qemu-action@v3 - # downloading gradle multiple times in parallel can yield to connection errors - - name: "4. Cache gradle distribution" - uses: actions/cache@v2 - with: - path: ~/.gradle/wrapper/dists - key: release-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} + - name: "Set up Docker Buildx" + uses: docker/setup-buildx-action@v3 - - name: "4.1 Cache gradle packages" - uses: actions/cache@v2 + - name: "Login to Docker Hub" + uses: docker/login-action@v3 with: - path: ~/.gradle/caches - key: release-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} - - #check build.gradle file for explanation of next steps - - name: "5. Publish image with tag corresponding to current version" - run: | - ./gradlew dockerPushVersion -P version=${{ steps.bump_dry.outputs.tag }} - - - name: "6. Publish image with tag 'latest' if not hotfix" - if: ${{ !contains(github.event.head_commit.message, '#hotfix') }} - run: | - ./gradlew dockerPushLatest + username: ${{ secrets.DOCKER_HUB_USER }} + password: ${{ secrets.DOCKER_HUB_TOKEN }} + - name: "Build and push" + uses: docker/build-push-action@v6 + with: + push: true + tags: unit8/darts:${{ steps.bump_dry.outputs.tag }}, unit8/darts:latest deploy-docs: runs-on: ubuntu-latest needs: [release] steps: - - name: "1. Clone repository" - uses: actions/checkout@v2 + - name: "Clone repository" + uses: actions/checkout@v4 - - name: "2. Set up Python 3.9" - uses: actions/setup-python@v1 + - name: "Set up Python 3.10" + uses: actions/setup-python@v5 with: - python-version: '3.9' + python-version: '3.10' - - name: "3. Install pandoc" + # use `uv` to retrieve the latest dependency versions + - name: "Compile Dependency Versions" run: | - sudo apt-get install -y pandoc + curl -LsSf https://astral.sh/uv/install.sh | sh + uv pip compile requirements/dev-all.txt > requirements-latest.txt - # downloading gradle multiple times in parallel can yield to connection errors - - name: "4. Cache gradle distribution" - uses: actions/cache@v2 + # only restore cache but do not upload + - name: "Restore cached python environment" + uses: actions/cache/restore@v4 + id: pythonenv-cache with: - path: ~/.gradle/wrapper/dists - key: release-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties') }} + path: ${{ env.pythonLocation }} + key: ${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('requirements/*.txt', 'requirements-latest.txt') }} - - name: "4.1 Cache gradle packages" - uses: actions/cache@v2 - with: - path: ~/.gradle/caches - key: release-${{ runner.os }}-gradle-${{ hashFiles('gradle/wrapper/gradle-wrapper.properties', 'build.gradle') }} + - name: "Install pandoc" + run: | + sudo apt-get install -y pandoc - - name: "5. Setup pip" + - name: "Setup Pip" run: | - ./gradlew setupPip + python -m pip install --upgrade pip - - name: "6. Attach cache for pip" - uses: actions/cache@v1 - id: cache - with: - path: ~/.cache/pip - key: release-${{ runner.os }}-pip-${{ hashFiles('requirements/core.txt', 'requirements/release.txt') }} - restore-keys: | - release-${{ runner.os }}-pip- + - name: "Install Dependencies" + run: | + # install latest dependencies (potentially updating cached dependencies) + pip install -U -r requirements/dev-all.txt + + - name: "Install libomp (for LightGBM)" + run: | + ./.github/scripts/libomp-${{ runner.os }}.sh + + - name: "Install Locally" + run: | + pip install . - - name: "7. Build docs" + - name: "Build docs" run: | - ./gradlew buildDocs + make --directory ./docs build-all-docs - - name: "8. Publish documentation to gh-pages" + - name: "Publish documentation to gh-pages" uses: s0/git-publish-subdir-action@v2.2.0 env: REPO: self diff --git a/.github/workflows/update-cache.yml b/.github/workflows/update-cache.yml new file mode 100644 index 0000000000..3243bc4dc2 --- /dev/null +++ b/.github/workflows/update-cache.yml @@ -0,0 +1,50 @@ +name: update-cache + +on: + push: + branches: + - master + +jobs: + # This workflow updates the python environment cache so that other workflows in different branches have access to it + build-cache: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [macos-13, ubuntu-latest] + python-version: ['3.10'] + flavour: ['all'] + + steps: + - name: "Clone repository" + uses: actions/checkout@v4 + + - name: "Set up Python ${{ matrix.python-version }}" + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + + # use `uv` to retrieve the latest dependency versions + - name: "Compile Dependency Versions" + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + if [ "${{ matrix.os }}" == "macos-13" ]; then + source $HOME/.local/bin/env + fi + uv pip compile requirements/dev-all.txt > requirements-latest.txt + + - name: "Cache python environment" + uses: actions/cache@v4 + id: pythonenv-cache + with: + path: ${{ env.pythonLocation }} + key: ${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('requirements/*.txt', 'requirements-latest.txt') }} + + - name: "Setup Pip" + run: | + python -m pip install --upgrade pip + + - name: "Install Latest Dependencies" + run: | + # install latest dependencies (potentially updating cached dependencies) + pip install -U -r requirements/dev-all.txt diff --git a/.gitignore b/.gitignore index 453913f0b7..58c86b40ce 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,7 @@ docs/source/examples docs/source/userguide/ docs/source/quickstart/ docs/source/README.rst +docs/source/release_notes/ docs/source/generated_api darts.egg-info/ build/ @@ -16,9 +17,11 @@ runs/ htmlcov coverage.xml .darts +darts_logs/ docs_env .DS_Store .gradle +.venv # used by CI to build with latest versions of dependencies requirements-latest.txt diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8300af26cc..77c0747395 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,23 +1,32 @@ -repos: - - repo: https://github.com/psf/black - rev: 22.3.0 - hooks: - - id: black-jupyter - language_version: python3 +default_language_version: + python: python3 - - repo: https://github.com/PyCQA/flake8 - rev: 4.0.1 - hooks: - - id: flake8 - language_version: python3 +ci: + autofix_prs: true + autoupdate_commit_msg: "[pre-commit.ci] pre-commit suggestions" + autoupdate_schedule: quarterly + # submodules: true - - repo: https://github.com/pycqa/isort - rev: 5.11.5 +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.6.0 hooks: - - id: isort + - id: end-of-file-fixer + exclude_types: [csv] + - id: trailing-whitespace + - id: check-json + - id: check-yaml + exclude: "conda_recipe/darts/meta.yaml" + - id: check-toml + - id: detect-private-key - - repo: https://github.com/asottile/pyupgrade - rev: v2.31.0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.7.2 hooks: - - id: pyupgrade - args: ['--py37-plus'] + # try to fix what is possible + - id: ruff + args: ["--fix"] + # perform formatting updates + - id: ruff-format + # validate if all is fine with preview mode + - id: ruff diff --git a/CHANGELOG.md b/CHANGELOG.md index f02a87a52e..c81360df7b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,4 +1,3 @@ - # Changelog We do our best to avoid the introduction of breaking changes, @@ -6,56 +5,420 @@ but cannot always guarantee backwards compatibility. Changes that may **break co ## [Unreleased](https://github.com/unit8co/darts/tree/master) -[Full Changelog](https://github.com/unit8co/darts/compare/0.27.2...master) +[Full Changelog](https://github.com/unit8co/darts/compare/0.33.0...master) + +### For users of the library: + +**Improved** + +- Added ONNX support for torch-based models with method `TorchForecastingModel.to_onnx()`. Check out [this example](https://unit8co.github.io/darts/userguide/gpu_and_tpu_usage.html#exporting-model-to-onnx-format-for-inference) from the user guide on how to export and load a model for inference. [#2620](https://github.com/unit8co/darts/pull/2620) by [Antoine Madrona](https://github.com/madtoinou) +- Made method `ForecastingModel.untrained_model()` public. Use this method to get a new (untrained) model instance created with the same parameters. [#2684](https://github.com/unit8co/darts/pull/2684) by [Timon Erhart](https://github.com/turbotimon) +- `TimeSeries.plot()` now supports setting the color for each component in the series. Simply pass a list / sequence of colors with length matching the number of components as parameters "c" or "colors". [#2680](https://github.com/unit8co/darts/pull/2680) by [Jules Authier](https://github.com/authierj) +- Made it possible to run the quickstart notebook `00-quickstart.ipynb` locally. [#2691](https://github.com/unit8co/darts/pull/2691) by [Jules Authier](https://github.com/authierj) + +**Fixed** + +- 🔴 / 🟢 Fixed a bug which raised an error when loading torch models that were saved with Darts versions < 0.33.0. This is a breaking change and models saved with version 0.33.0 will not be loadable anymore. [#2692](https://github.com/unit8co/darts/pull/2692) by [Dennis Bader](https://github.com/dennisbader). + +**Dependencies** + +### For developers of the library: + +## [0.33.0](https://github.com/unit8co/darts/tree/0.33.0) (2025-02-14) + +### For users of the library: + +**Improved** + +- Improvements to `TimeSeries`: + - Added more resampling methods to `TimeSeries.resample()`. This allows to aggregate values when down-sampling and to fill or keep the holes when up-sampling. [#2654](https://github.com/unit8co/darts/pull/2654) by [Jonas Blanc](https://github.com/jonasblanc) + - Added the `title` attribute to `TimeSeries.plot()`. This allows to set a title for the plot. [#2639](https://github.com/unit8co/darts/pull/2639) by [Jonathan Koch](https://github.com/jonathankoch99). + - Added general function `darts.slice_intersect()` to intersect a sequence of `TimeSeries` along the time index. [#2592](https://github.com/unit8co/darts/pull/2592) by [Yoav Matzkevich](https://github.com/ymatzkevich). +- Improvements to Model Saving & Loading: + - Added parameter `clean: bool` to `ForecastingModel.save()` to store a cleaned version of the model (removes training data from global models, and Lightning Trainer-related parameters from torch models). [#2649](https://github.com/unit8co/darts/pull/2649) by [Jonas Blanc](https://github.com/jonasblanc). + - Added parameter `pl_trainer_kwargs` to `TorchForecastingModel.load()` to setup a new Lightning Trainer used to configure the model for downstream tasks (e.g. prediction). [#2649](https://github.com/unit8co/darts/pull/2649) by [Jonas Blanc](https://github.com/jonasblanc). +- Improvements to Anomaly Detection: + - Added parameter `component_wise` to `show_anomalies()` to separately plot each component in multivariate series. [#2544](https://github.com/unit8co/darts/pull/2544) by [He Weilin](https://github.com/cnhwl). + - Improved the documentation of how `WindowedAnomalyScorer` extract the training data from the input series. [#2674](https://github.com/unit8co/darts/pull/2674) by [Dennis Bader](https://github.com/dennisbader). +- Other improvemets: + - Added new forecasting model: `StatsForecastAutoTBATS`, wrapping [AutoTBATS](https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotbats) from Nixtla's `statsforecasts` library. [#2611](https://github.com/unit8co/darts/pull/2611) by [He Weilin](https://github.com/cnhwl). + - Added new time aggregated metric `wmape()` (Weighted Mean Absolute Percentage Error). [#2544](https://github.com/unit8co/darts/pull/2648) by [He Weilin](https://github.com/cnhwl). + +**Fixed** + +- Fixed a bug which raised an error when loading a torch model with `torch>=2.6.0`. [#2658](https://github.com/unit8co/darts/pull/2658) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a bug when performing optimized historical forecasts with `stride=1` using a `RegressionModel` with `output_chunk_shift>=1` and `output_chunk_length=1`, where the forecast time index was not properly shifted. [#2634](https://github.com/unit8co/darts/pull/2634) by [Mattias De Charleroy](https://github.com/MattiasDC). +- Fixed a bug where global naive models could not be used in ensemble models. [#2666](https://github.com/unit8co/darts/pull/2666) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a bug with global naive models where some `supports_*` properties were wrongly defined as methods. [#2666](https://github.com/unit8co/darts/pull/2666) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a bug in `LightGBMModel`, `XGBModel`, and `CatBoostModel` which raised an error when calling `fit()` with `val_sample_weight`. [#2626](https://github.com/unit8co/darts/pull/2626) by [Kylin Schmidt](https://github.com/kylinschmidt). +- Fixed the `ShapExplainer` `summary_plot` title where Horizon does not include `output_chunk_shift`. [#2647](https://github.com/unit8co/darts/pull/2647) by [He Weilin](https://github.com/cnhwl). + +**Dependencies** + +- Removed the upper version cap on `sklearn<1.6.0` since `xboost` added support in version `2.1.4`. [#2665](https://github.com/unit8co/darts/pull/2665) by [Dennis Bader](https://github.com/dennisbader). + +### For developers of the library: + +- Bumped `jinja2` from 3.1.4 to 3.1.5 (release requirement). [#2672](https://github.com/unit8co/darts/pull/2672) by dapendabot. + +## [0.32.0](https://github.com/unit8co/darts/tree/0.32.0) (2024-12-21) + +### For users of the library: + +**Improved** + +- 🚀🚀 Introducing Conformal Prediction: You can now add calibrated prediction intervals to any pre-trained global forecasting model with our first two conformal prediction models : [#2552](https://github.com/unit8co/darts/pull/2552) by [Dennis Bader](https://github.com/dennisbader). + - `ConformalNaiveModel`: Uses past point forecast errors to produce calibrated forecast intervals with a specified coverage probability. + - `ConformalQRModel`: Combines quantile regression (or any probabilistic model) with conformal prediction techniques. It adjusts the quantile estimates to generate calibrated prediction intervals with a specified coverage probability. + - Both models offer the following support: + - identical API as forecasting models + - use any pre-trained global forecasting model as the base forecaster + - uni and multivariate forecasts + - single and multiple series forecasts + - single and multi-horizon forecasts + - generate a single or multiple calibrated prediction intervals + - direct quantile value predictions (interval bounds) or sampled predictions from these quantile values + - covariates based on the underlying forecasting model + - Check out our [conformal prediction notebook](https://unit8co.github.io/darts/examples/23-Conformal-Prediction-examples.html) for detailed information and usage examples! +- Improvements to backtesting with `ForecastingModel` (`historical_forecasts()`, `backtest()`, `residuals()`, and `gridsearch()`): + - 🚀🚀 Added support for data transformers and pipelines. Use argument `data_transformers` to automatically apply any `DataTransformer` and/or `Pipeline` to the input series without data-leakage (optional fit on historic window of input series, transform the input series, and inverse transform the forecasts). [#2529](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou) and [Jan Fidor](https://github.com/JanFidor) + - Improved `start` handling. If `start` is not within the trainable / forecastable points, uses the closest valid start point that is a round multiple of `stride` ahead of start. Raises a ValueError, if no valid start point exists. This guarantees that all historical forecasts are `n * stride` points away from start, and will simplify many downstream tasks. [#2560](https://github.com/unit8co/darts/issues/2560) by [Dennis Bader](https://github.com/dennisbader). + - Added support for `overlap_end=True` to `residuals()`. This computes historical forecasts and residuals that can extend further than the end of the target series. Guarantees that all returned residual values have the same length per forecast (the last residuals will contain missing values, if the forecasts extended further into the future than the end of the target series). [#2552](https://github.com/unit8co/darts/pull/2552) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to `metrics`: Added three new quantile interval metrics (plus their aggregated versions) : [#2552](https://github.com/unit8co/darts/pull/2552) by [Dennis Bader](https://github.com/dennisbader). + - Interval Winkler Score `iws()`, and Mean Interval Winkler Scores `miws()` (time-aggregated) ([source](https://otexts.com/fpp3/distaccuracy.html)) + - Interval Coverage `ic()` (binary if observation is within the quantile interval), and Mean Interval Coverage `mic()` (time-aggregated) + - Interval Non-Conformity Score for Quantile Regression `incs_qr()`, and Mean ... `mincs_qr()` (time-aggregated) ([source](https://arxiv.org/pdf/1905.03222)) +- Added `series_idx` argument to `DataTransformer` that allows users to use only a subset of the transformers when `global_fit=False` and severals series are used. [#2529](https://github.com/unit8co/darts/pull/2529) by [Antoine Madrona](https://github.com/madtoinou) +- Updated the Documentation URL of `Statsforecast` models. [#2610](https://github.com/unit8co/darts/pull/2610) by [He Weilin](https://github.com/cnhwl). + +**Fixed** + +- Fixed a bug when initiating a `RegressionModel` with `lags_past_covariates` as dict and `lags_future_covariates` as some other type (not dict) and `output_chunk_shift>0`, [#2652](https://github.com/unit8co/darts/issues/2652) by [Jules Authier](https://github.com/authierj). +- Fixed a bug which raised an error when computing residuals (or backtest with "per time step" metrics) on multiple series with corresponding historical forecasts of different lengths. [#2604](https://github.com/unit8co/darts/pull/2604) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a bug when using `darts.utils.data.tabularization.create_lagged_component_names()` with target `lags=None`, that did not return any lagged target label component names. [#2576](https://github.com/unit8co/darts/pull/2576) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a bug when using `num_samples > 1` with a deterministic regression model and the optimized `historical_forecasts()` method, which did not raise an exception. [#2576](https://github.com/unit8co/darts/pull/2588) by [Antoine Madrona](https://github.com/madtoinou). +- Fixed a bug when performing optimized historical forecasts with `RegressionModel` and `last_points_only=False`, where the forecast index generation could result in out-of-bound dates. [#2623](https://github.com/unit8co/darts/pull/2623) by [Dennis Bader](https://github.com/dennisbader). +- Fixed the failing docker image deployment. [#2583](https://github.com/unit8co/darts/pull/2583) by [Dennis Bader](https://github.com/dennisbader). + +**Dependencies** + +- 🔴 Removed support for Python 3.8. The new minimum Python version is 3.9. [#2586](https://github.com/unit8co/darts/pull/2586) by [Dennis Bader](https://github.com/dennisbader). +- We set an upper version cap on `sklkearn<=1.5.0` until `xgboost` officially supports version `1.6.0`. [#2618](https://github.com/unit8co/darts/pull/2618) by [Dennis Bader](https://github.com/dennisbader). + +### For developers of the library: + +**Improved** + +- Improvements to CI/CD : [#2584](https://github.com/unit8co/darts/pull/2584) by [Dennis Bader](https://github.com/dennisbader). + - updated all workflows with most recent GitHub actions versions + - improved caching across `master` branch and its children + - fixed failing docker deployment + - removed `gradle` dependency in favor of native GitHub actions plugins. +- Updated ruff to v0.7.2 and target-version to python39, also fixed various typos. [#2589](https://github.com/unit8co/darts/pull/2589) by [Greg DeVosNouri](https://github.com/gdevos010) and [Antoine Madrona](https://github.com/madtoinou). +- Replaced the deprecated `torch.nn.utils.weight_norm` function with `torch.nn.utils.parametrizations.weight_norm`. [#2593](https://github.com/unit8co/darts/pull/2593) by [Saeed Foroutan](https://github.com/SaeedForoutan). + +## [0.31.0](https://github.com/unit8co/darts/tree/0.31.0) (2024-10-13) + +### For users of the library: + +**Improved** + +- Improvements to `metrics`: + - Added support for computing metrics on one or multiple quantiles `q`, either from probabilistic or quantile forecasts. [#2530](https://github.com/unit8co/darts/pull/2530) by [Dennis Bader](https://github.com/dennisbader). + - Added quantile interval metrics `miw` (Mean Interval Width, time aggregated) and `iw` (Interval Width, per time step / non-aggregated) which compute the width of quantile intervals `q_intervals` (expected to be a tuple or sequence of tuples with (lower quantile, upper quantile)). [#2530](https://github.com/unit8co/darts/pull/2530) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to `backtest()` and `residuals()`: + - Added support for computing backtest and residuals on one or multiple quantiles `q` in the `metric_kwargs`, either from probabilistic or quantile forecasts. [#2530](https://github.com/unit8co/darts/pull/2530) by [Dennis Bader](https://github.com/dennisbader). + - Added support for parameters `enable_optimization` and `predict_likelihood_parameters`. [#2530](https://github.com/unit8co/darts/pull/2530) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to `TimeSeries`: + - Added support for broadcasting TimeSeries on component and sample level for arithmetic operations. [#2476](https://github.com/unit8co/darts/pull/2476) by [Joel L.](https://github.com/Joelius300). + - Added property `TimeSeries.shape` to get the shape of the time series. [#2530](https://github.com/unit8co/darts/pull/2530) by [Dennis Bader](https://github.com/dennisbader). +- Other improvements: + - Added a new anomaly detector `IQRDetector`, that allows to detect anomalies using the Interquartile Range algorithm. [#2441](https://github.com/unit8co/darts/pull/2441) by [Igor Urbanik](https://github.com/u8-igor). + - Added hyperparameters `temporal_hidden_size_past/future` controlling the hidden layer sizes for the feature encoders in `TiDEModel`. [#2408](https://github.com/unit8co/darts/pull/2408) by [eschibli](https://github.com/eschibli). + - Added hyperparameter `activation` to `BlockRNNModel` to specify the activation function in case of a multi-layer output network. [#2504](https://github.com/unit8co/darts/pull/2504) by [Szymon Cogiel](https://github.com/SzymonCogiel). + - Helper function `darts.utils.utils.generate_index()` now accepts datetime strings as `start` and `end` parameters to generate the pandas DatetimeIndex. [#2522](https://github.com/unit8co/darts/pull/2522) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to the documentation: + - Made README's forecasting model support table more colorblind-friendly. [#2433](https://github.com/unit8co/darts/pull/2433) by [Jatin Shridhar](https://github.com/jatins). + - Updated the Ray Tune Hyperparameter Optimization example in the [user guide](https://unit8co.github.io/darts/userguide/hyperparameter_optimization.html) to work with the latest `ray` versions (`>=2.31.0`). [#2459](https://github.com/unit8co/darts/pull/2459) by [He Weilin](https://github.com/cnhwl). + - Indicate that `multi_models=False` induce a lags shift for each step in `output_chunk_length` in `RegressionModel` and `LinearRegressionModel`. [#2511](https://github.com/unit8co/darts/pull/2511) by [Antoine Madrona](https://github.com/madtoinou). + - Added reference to `timeseries_generation.datetime_attribute_timeseries` in `TimeSeries.add_datetime_attribute` (0-indexing of encoding is enforced). [#2511](https://github.com/unit8co/darts/pull/2511) by [Antoine Madrona](https://github.com/madtoinou). + +**Fixed** + +- Fixes to `RegressionModel`: + - Fixed a bug when performing probabilistic optimized historical forecasts (`num_samples>1, retrain=False, enable_optimization=True`) with regression models, where reshaping the array resulted in a wrong order of samples across components and forecasts. [#2534](https://github.com/unit8co/darts/pull/2534) by [Dennis Bader](https://github.com/dennisbader). + - Fixed a bug when predicting with `predict_likelihood_parameters=True`, `n > 1` and a `RegressionModel` with `multi_models=False` that uses a `likelihood`. The prediction now works without raising an exception. [#2545](https://github.com/unit8co/darts/pull/2545) by [Dennis Bader](https://github.com/dennisbader). + - Fixed a bug when using `historical_forecasts()` with a pre-trained `RegressionModel` that has no target lags `lags=None` but uses static covariates. [#2426](https://github.com/unit8co/darts/pull/2426) by [Dennis Bader](https://github.com/dennisbader). + - Fixed a bug when using `fit()` with a `RegressionModel` that uses an underlying `model` which does not support `sample_weight`. [#2445](https://github.com/unit8co/darts/pull/2445) by [He Weilin](https://github.com/cnhwl). + - Fixed a bug when using `save()` and `load()` with a `RegressionEnsembleModel` that ensembles any `TorchForecastingModel`. [#2437](https://github.com/unit8co/darts/pull/2437) by [GeorgeXiaojie](https://github.com/GeorgeXiaojie). + - Fixed a bug with `xgboost>=2.1.0`, where multi output regression was not properly handled. [#2426](https://github.com/unit8co/darts/pull/2426) by [Dennis Bader](https://github.com/dennisbader). +- Fixes to `TimeSeries`: + - Fixed a bug when plotting a probabilistic multivariate series with `TimeSeries.plot()`, where all confidence intervals (starting from 2nd component) had the same color as the median line. [#2532](https://github.com/unit8co/darts/pull/2532) by [Dennis Bader](https://github.com/dennisbader). + - Fixed a bug when using `TimeSeries.from_group_dataframe()` with a `time_col` of type integer, where the resulting time index was wrongly converted to a DatetimeIndex. [#2512](https://github.com/unit8co/darts/pull/2512) by [Alessio Pellegrini](https://github.com/AlessiopSymplectic) + - Fixed a bug where passing an empty array to `TimeSeries.prepend/append_values()` raised an error. [#2522](https://github.com/unit8co/darts/pull/2522) by [Alessio Pellegrini](https://github.com/AlessiopSymplectic) + - Fixed a bug with `TimeSeries.prepend/append_values()`, where the name of the (time) index was lost. [#2522](https://github.com/unit8co/darts/pull/2522) by [Alessio Pellegrini](https://github.com/AlessiopSymplectic) +- Other fixes: + - Fixed a bug when using `ShapExplainer.explain()` with some selected `target_components` and a regression model that natively supports multi output regression: The target components were not properly mapped. [#2428](https://github.com/unit8co/darts/pull/2428) by [Dennis Bader](https://github.com/dennisbader). + - Fixed a bug with `CrostonModel`, which actually does not support future covariates. [#2511](https://github.com/unit8co/darts/pull/2511) by [Antoine Madrona](https://github.com/madtoinou). + - Fixed the comment of `scorers_are_univariate` in class `AnomalyModel`. [#2452](https://github.com/unit8co/darts/pull/2542) by [He Weilin](https://github.com/cnhwl). + +**Dependencies** + +- Bumped release requirements versions for jupyterlab and dependencies : [#2515](https://github.com/unit8co/darts/pull/2515) by [Dennis Bader](https://github.com/dennisbader). + - Bumped `ipython` from 8.10.0 to 8.18.1 + - Bumped `ipykernel` from 5.3.4 to 6.29.5 + - Bumped `ipywidgets` from 7.5.1 to 8.1.5 + - Bumped `jupyterlab` from 4.0.11 to 4.2.5 + +### For developers of the library: + +## [0.30.0](https://github.com/unit8co/darts/tree/0.30.0) (2024-06-19) + +### For users of the library: + +**Improved** + +- 🚀🚀 All `GlobalForecastingModel` now support being trained with sample weights (regression-, ensemble-, and neural network models) : [#2404](https://github.com/unit8co/darts/pull/2404), [#2410](https://github.com/unit8co/darts/pull/2410), [#2417](https://github.com/unit8co/darts/pull/2417) and [#2418](https://github.com/unit8co/darts/pull/2418) by [Anton Ragot](https://github.com/AntonRagot) and [Dennis Bader](https://github.com/dennisbader). + - Added parameters `sample_weight` and `val_sample_weight` to `fit()`, `historical_forecasts()`, `backtest()`, `residuals`, and `gridsearch()` to apply weights to each observation, label (each step in the output chunk), and target component in the training and evaluation set. Supported by both deterministic and probabilistic models. The sample weight can either be `TimeSeries` themselves or built-in weight generators "linear" and "exponential" decay. In case of a `TimeSeries` it is handled identically as the covariates (e.g. pass multiple weight series with multiple target series, relevant time frame extraction is handled automatically for you, ...). You can find an example [here](https://unit8co.github.io/darts/quickstart/00-quickstart.html#Sample-Weights). +- 🚀🚀 Improvements to the Anomaly Detection Module through major refactor. The refactor includes major performance optimization for the majority of processes and improvements to the API, consistency, reliability, and the documentation. Some of these necessary changes come at the cost of breaking changes : [#1477](https://github.com/unit8co/darts/pull/1477) by [Dennis Bader](https://github.com/dennisbader), [Samuele Giuliano Piazzetta](https://github.com/piaz97), [Antoine Madrona](https://github.com/madtoinou), [Julien Herzen](https://github.com/hrzn), [Julien Adda](https://github.com/julien12234). + - Added an [example notebook](https://unit8co.github.io/darts/examples/22-anomaly-detection-examples.html) that showcases how to use Darts for Time Series Anomaly Detection. + - Added a new dataset `TaxiNewYorkDataset` for anomaly detection with the number of taxi passengers in New York from the years 2014 and 2015. + - `FittableWindowScorer` (KMeans, PyOD, and Wasserstein Scorers) now accept any of darts ["per-time" step metrics](https://unit8co.github.io/darts/generated_api/darts.metrics.html) as difference function `diff_fn`. + - `ForecastingAnomalyModel` is now much faster in generating forecasts (input for the scorers) thanks to optimized historical forecasts. We also added more control over the historical forecasts generation through additional parameters in all model methods. + - 🔴 Breaking changes: + - `FittableWindowScorer` (KMeans, PyOD, and Wasserstein Scorers) now expects `diff_fn` to be one of Darts "per-time" step metrics + - `ForecastingAnomalyModel` : `model` is now enforced to be a `GlobalForecastingModel` + - `*.eval_accuracy()` : (Aggregators, Detectors, Filtering/Forecasting Anomaly Models, Scorers) + - renamed method to `eval_metric()` : + - renamed params `actual_anomalies` to `anomalies`, and `anomaly_score` to `pred_scores` + - `*.show_anomalies()` : (Filtering/Forecasting Anomaly Models, Scorers) + - renamed params `actual_anomalies` to `anomalies` + - `*.fit()` (Filtering/Forecasting Anomaly Models) + - renamed params `actual_anomalies` to `anomalies` + - `Scorer.*_from_prediction()` (Scorers) + - renamed method `eval_accuracy_from_prediction()` to `eval_metric_from_prediction()` + - renamed params `actual_series` to `series`, and `actual_anomalies` to `anomalies` + - `darts.ad.utils.eval_accuracy_from_scores` : + - renamed function to `eval_metric_from_scores` + - renamed params `actual_anoamlies` to `anomalies`, and `anomaly_score` to `pred_scores` + - `darts.ad.utils.eval_accuracy_from_binary_prediction` : + - renamed function to `eval_metric_from_binary_prediction` + - renamed params `actual_anoamlies` to `anomalies`, and `binary_pred_anomalies` to `pred_anomalies` + - `darts.ad.utils.show_anomalies_from_scores` : + - renamed params `series` to `actual_series`, `actual_anomalies` to `anomalies`, `model_output` to `pred_series`, and `anomaly_scores` to `pred_scores` +- Improvements to `TorchForecastingModel` : [#2295](https://github.com/unit8co/darts/pull/2295) by [Bohdan Bilonoh](https://github.com/BohdanBilonoh). + - Added `dataloader_kwargs` parameters to `fit*()`, `predict*()`, and `find_lr()` for more control over the PyTorch `DataLoader` setup. + - 🔴 Removed parameter `num_loader_workers` from `fit*()`, `predict*()`, `find_lr()`. You can now set the parameter through the `dataloader_kwargs` dict. +- Improvements to `DataTransformers` : + - Significant speed up when using `fit`, `fit_transform`, `transform`, and `inverse_transform` on a large number of series. The component masking logic was moved into the parallelized transform methods. [#2401](https://github.com/unit8co/darts/pull/2401) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to `TimeSeries` : [#1477](https://github.com/unit8co/darts/pull/1477) by [Dennis Bader](https://github.com/dennisbader). + - New method `with_times_and_values()`, which returns a new series with a new time index and new values but with identical columns and metadata as the series called from (static covariates, hierarchy). + - New method `slice_intersect_times()`, which returns the sliced time index of a series, where the index has been intersected with another series. + - Method `with_values()` now also acts on array-like `values` rather than only on numpy arrays. +- Improvements to quick start notebook : [#2418](https://github.com/unit8co/darts/pull/2418) by [Dennis Bader](https://github.com/dennisbader). + - Added examples for using sample weights, forecast start shifting, direct likelihood parameter predictions. + - Enhanced examples for historical forecasts, backtest and residuals. + +**Fixed** + +- Fixed a bug when using a `RegressionModel` (that supports validation series) with a validation set: encoders, static covariates, and component-specific lags are now correctly applied to the validation set. [#2383](https://github.com/unit8co/darts/pull/2383) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a bug where `darts.utils.utils.n_steps_between()` did not work properly with custom business frequencies. This affected metrics computation. [#2357](https://github.com/unit8co/darts/pull/2357) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a bug when calling `predict()` with a `MixedCovariatesTorchModel` (e.g. TiDE, N/DLinear, ...), `n n * 20 times faster** than before for series with `n` components/columns. This boosts direct metric computations as well as backtest and residuals computation! + - Added new metrics: + - Time aggregated metric `merr()` (Mean Error) + - Time aggregated scaled metrics `rmsse()`, and `msse()` : The (Root) Mean Squared Scaled Error. + - "Per time step" metrics that return a metric score per time step: `err()` (Error), `ae()` (Absolute Error), `se()` (Squared Error), `sle()` (Squared Log Error), `ase()` (Absolute Scaled Error), `sse` (Squared Scaled Error), `ape()` (Absolute Percentage Error), `sape()` (symmetric Absolute Percentage Error), `arre()` (Absolute Ranged Relative Error), `ql` (Quantile Loss) + - All scaled metrics (`mase()`, ...) now accept `insample` series that can be overlapping into `pred_series` (before they had to end exactly one step before `pred_series`). Darts will handle the correct time extraction for you. + - Improvements to the documentation: + - Added a summary list of all metrics to the [metrics documentation page](https://unit8co.github.io/darts/generated_api/darts.metrics.html) + - Standardized the documentation of each metric (added formula, improved return documentation, ...) + - 🔴 Breaking changes: + - Improved metric output consistency based on the type of input `series`, and the applied reductions. For some scenarios, the output type changed compared to previous Darts versions. You can find a detailed description in the [metric API documentation](https://unit8co.github.io/darts/generated_api/darts.metrics.metrics.html#darts.metrics.metrics.mae). + - Renamed metric parameter `reduction` to `component_reduction`. + - Renamed metric parameter `inter_reduction` to `series_reduction`. + - `quantile_loss()` : + - Renamed to `mql()` (Mean Quantile Loss). + - Renamed quantile parameter `tau` to `q`. + - The metric is now multiplied by a factor `2` to make the loss more interpretable (e.g. for `q=0.5` it is identical to the `MAE`) + - `rho_risk()` : + - Renamed to `qr()` (Quantile Risk). + - Renamed quantile parameter `rho` to `q`. + - Scaled metrics do not allow seasonality inference anymore with `m=None`. + - Custom metrics using decorators `multi_ts_support` and `multivariate_support` must now act on multivariate series (possibly containing missing values) instead of univariate series. + - **Historical Forecasts**: + - 🔴 Improved historical forecasts output consistency based on the type of input `series` : If `series` is a sequence, historical forecasts will now always return a sequence/list of the same length (instead of trying to reduce to a `TimeSeries` object). You can find a detailed description in the [historical forecasts API documentation](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel.historical_forecasts). + - **Backtest**: + - Metrics are now computed only once on all `series` and `historical_forecasts`, significantly speeding things up when using a large number of `series`. + - Added support for scaled metrics as `metric` (such as `ase`, `mase`, ...). No extra code required, backtest extracts the correct `insample` series for you. + - Added support for passing additional metric (-specific) arguments with parameter `metric_kwargs`. This allows for example to parallelize the metric computation with `n_jobs`, customize the metric reduction with `*_reduction`, specify seasonality `m` for scaled metrics, etc. + - 🔴 Breaking changes: + - Improved backtest output consistency based on the type of input `series`, `historical_forecast`, and the applied backtest reduction. For some scenarios, the output type changed compared to previous Darts versions. You can find a detailed description in the [backtest API documentation](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel.backtest). + - `reduction` callable now acts on `axis=1` rather than `axis=0` to aggregate the metrics per series. + - Backtest will now raise an error when user supplied `historical_forecasts` don't have the expected format based on input `series` and the `last_points_only` value. + - **Residuals**: While the default behavior of `residuals()` remains identical, the method is now very similar to `backtest()` but that it computes any "per time step" `metric` on `historical_forecasts` : + - Added support for multivariate `series`. + - Added support for all `historical_forecasts()` parameters to generate the historical forecasts for the residuals computation. + - Added support for pre-computed historical forecasts with parameter `historical_forecasts`. + - Added support for computing the residuals with any of Darts' "per time step" metric with parameter `metric` (e.g. `err()`, `ae()`, `ape()`, ...). By default, uses `err()` (Error). + - Added support for passing additional metric arguments with parameter `metric_kwargs`. This allows for example to parallelize the metric computation with `n_jobs`, specify seasonality `m` for scaled metrics, etc. + - 🔴 Improved residuals output and consistency based on the type of input `series` and `historical_forecast`. For some scenarios, the output type changed compared to previous Darts versions. You can find a detailed description in the [residuals API documentation](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel.residuals). +- Improvements to `TimeSeries` : + - `from_group_dataframe()` now supports parallelized creation over the `pandas.DataFrame` groups. This can be enabled with parameter `n_jobs`. [#2292](https://github.com/unit8co/darts/pull/2292) by [Bohdan Bilonoha](https://github.com/BohdanBilonoh). + - New method `slice_intersect_values()`, which returns the sliced values of a series, where the time index has been intersected with another series. [#2284](https://github.com/unit8co/darts/pull/2284) by [Dennis Bader](https://github.com/dennisbader). + - Performance boost for methods: `slice_intersect()`, `has_same_time_as()`. [#2284](https://github.com/unit8co/darts/pull/2284) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to forecasting models: + - Improvements to `RNNModel`, [#2329](https://github.com/unit8co/darts/pull/2329) by [Dennis Bader](https://github.com/dennisbader): + - 🔴 Enforce `training_length>input_chunk_length` since otherwise, during training the model is never run for as many iterations as it will during prediction. + - Historical forecasts now correctly infer all possible prediction start points for untrained and pre-trained `RNNModel`. + - Added a progress bar to `RegressionModel` when performing optimized historical forecasts (`retrain=False` and no autoregression) to display the series-level progress. [#2320](https://github.com/unit8co/darts/pull/2320) by [Dennis Bader](https://github.com/dennisbader). + - Renamed private `ForecastingModel._is_probabilistic` property to public `supports_probabilistic_prediction`. [#2269](https://github.com/unit8co/darts/pull/2269) by [Felix Divo](https://github.com/felixdivo). +- Other improvements: + - All `InvertibleDataTransformer` now supports parallelized inverse transformation for `series` being a list of lists of `TimeSeries` (`Sequence[Sequence[TimeSeries]]`). This type represents the output of `historical_forecasts()` when using multiple series with `last_points_only=False`. [#2267](https://github.com/unit8co/darts/pull/2267) by [Alicja Krzeminska-Sciga](https://github.com/alicjakrzeminska). + - Added [release notes](https://unit8co.github.io/darts/release_notes/RELEASE_NOTES.html) to the Darts Documentation. [#2333](https://github.com/unit8co/darts/pull/2333) by [Dennis Bader](https://github.com/dennisbader). + - 🔴 Moved around utils functions to clearly separate Darts-specific from non-Darts-specific logic, [#2284](https://github.com/unit8co/darts/pull/2284) by [Dennis Bader](https://github.com/dennisbader): + - Moved function `generate_index()` from `darts.utils.timeseries_generation` to `darts.utils.utils` + - Moved functions `retain_period_common_to_all()`, `series2seq()`, `seq2series()`, `get_single_series()` from `darts.utils.utils` to `darts.utils.ts_utils`. + +**Fixed** + +- Fixed the order of the features when using component-specific lags so that they are grouped by values, then by components (before, they were grouped by components, then by values). [#2272](https://github.com/unit8co/darts/pull/2272) by [Antoine Madrona](https://github.com/madtoinou). +- Fixed a bug when using a dropout with a `TorchForecastingModel` and pytorch lightning versions >= 2.2.0, where the dropout was not properly activated during training. [#2312](https://github.com/unit8co/darts/pull/2312) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a bug when performing historical forecasts with an untrained `TorchForecastingModel` and using covariates, where the historical forecastable time index generation did not take the covariates into account. [#2329](https://github.com/unit8co/darts/pull/2329) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a bug in `quantile_loss`, where the loss was computed on all samples rather than only on the predicted quantiles. [#2284](https://github.com/unit8co/darts/pull/2284) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a segmentation fault that some users were facing when importing a `LightGBMModel`. [#2304](https://github.com/unit8co/darts/pull/2304) by [Dennis Bader](https://github.com/dennisbader). +- Fixed type hint warning "Unexpected argument" when calling `historical_forecasts()` caused by the `_with_sanity_checks` decorator. The type hinting is now properly configured to expect any input arguments and return the output type of the method for which the sanity checks are performed for. [#2286](https://github.com/unit8co/darts/pull/2286) by [Dennis Bader](https://github.com/dennisbader). + +### For developers of the library: + +- Fixed failing docs build by adding new dependency `lxml_html_clean` for `nbsphinx`. [#2303](https://github.com/unit8co/darts/pull/2303) by [Dennis Bader](https://github.com/dennisbader). +- Bumped `black` from 24.1.1 to 24.3.0. [#2308](https://github.com/unit8co/darts/pull/2308) by [Dennis Bader](https://github.com/dennisbader). +- Bumped `codecov-action` from v2 to v4 and added codecov token as repository secret for codecov upload authentication in CI pipelines. [#2309](https://github.com/unit8co/darts/pull/2309) and [#2312](https://github.com/unit8co/darts/pull/2312) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to linting, switch from `flake8` to Ruff. [#2323](https://github.com/unit8co/darts/pull/2323) by [Jirka Borovec](https://github.com/borda). + +## [0.28.0](https://github.com/unit8co/darts/tree/0.28.0) (2024-03-05) + +### For users of the library: + +**Improved** + +- Improvements to `GlobalForecastingModel` : + - 🚀🚀🚀 All global models (regression and torch models) now support shifted predictions with model creation parameter `output_chunk_shift`. This will shift the output chunk for training and prediction by `output_chunk_shift` steps into the future. [#2176](https://github.com/unit8co/darts/pull/2176) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to `TimeSeries`, [#2196](https://github.com/unit8co/darts/pull/2196) by [Dennis Bader](https://github.com/dennisbader): - 🚀🚀🚀 Significant performance boosts for several `TimeSeries` methods resulting increased efficiency across the entire `Darts` library. Up to 2x faster creation times for series indexed with "regular" frequencies (e.g. Daily, hourly, ...), and >100x for series indexed with "special" frequencies (e.g. "W-MON", ...). Affects: - - All `TimeSeries` creation methods + - All `TimeSeries` creation methods - Additional boosts for slicing with integers and Timestamps - Additional boosts for `from_group_dataframe()` by performing some of the heavy-duty computations on the entire DataFrame, rather than iteratively on the group level. - Added option to exclude some `group_cols` from being added as static covariates when using `TimeSeries.from_group_dataframe()` with parameter `drop_group_cols`. +- 🚀 New global baseline models that use fixed input and output chunks for prediction. This offers support for univariate, multivariate, single and multiple target series prediction, one-shot- or autoregressive/moving forecasts, optimized historical forecasts, batch prediction, prediction from datasets, and more. [#2261](https://github.com/unit8co/darts/pull/2261) by [Dennis Bader](https://github.com/dennisbader). + - `GlobalNaiveAggregate` : Computes an aggregate (using a custom or built-in `torch` function) for each target component over the last `input_chunk_length` points, and repeats the values `output_chunk_length` times for prediction. Depending on the parameters, this model can be equivalent to `NaiveMean` and `NaiveMovingAverage`. + - `GlobalNaiveDrift` : Takes the slope of each target component over the last `input_chunk_length` points and projects the trend over the next `output_chunk_length` points for prediction. Depending on the parameters, this model can be equivalent to `NaiveDrift`. + - `GlobalNaiveSeasonal` : Takes the target component value at the `input_chunk_length`th point before the end of the target `series`, and repeats the values `output_chunk_length` times for prediction. Depending on the parameters, this model can be equivalent to `NaiveSeasonal`. +- Improvements to `TorchForecastingModel` : + - Added support for additional lr scheduler configuration parameters for more control ("interval", "frequency", "monitor", "strict", "name"). [#2218](https://github.com/unit8co/darts/pull/2218) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to `RegressionModel`, [#2246](https://github.com/unit8co/darts/pull/2246) by [Antoine Madrona](https://github.com/madtoinou): + - Added a `get_estimator()` method to access the underlying estimator + - Added attribute `lagged_label_names` to identify the forecasted step and component of each estimator + - Updated the docstring of `get_multioutout_estimator()` +- Other improvements: + - Added argument `keep_names` to `WindowTransformer` and `window_transform` to indicate whether the original component names should be kept. [#2207](https://github.com/unit8co/darts/pull/2207) by [Antoine Madrona](https://github.com/madtoinou). + - Added new helper function `darts.utils.utils.n_steps_between()` to efficiently compute the number of time steps (periods) between two points with a given frequency. Improves efficiency for regression model tabularization by avoiding `pd.date_range()`. [#2176](https://github.com/unit8co/darts/pull/2176) by [Dennis Bader](https://github.com/dennisbader). + - 🔴 Changed the default `start` value in `ForecastingModel.gridsearch()` from `0.5` to `None`, to make it consistent with `historical_forecasts` and other methods. [#2243](https://github.com/unit8co/darts/pull/2243) by [Thomas Kientz](https://github.com/thomktz). + - Improvements to `ARIMA` documentation: Specified possible `p`, `d`, `P`, `D`, `trend` advanced options that are available in statsmodels. More explanations on the behaviour of the parameters were added. [#2142](https://github.com/unit8co/darts/pull/2142) by [MarcBresson](https://github.com/MarcBresson). **Fixed** + +- Fixed a bug when using `RegressionModel` with `lags=None`, some `lags_*covariates`, and the covariates starting after or at the same time as the first predictable time step; the lags were not extracted from the correct indices. [#2176](https://github.com/unit8co/darts/pull/2176) by [Dennis Bader](https://github.com/dennisbader). +- Fixed a bug when calling `window_transform` on a `TimeSeries` with a hierarchy. The hierarchy is now only preserved for single transformations applied to all components, or removed otherwise. [#2207](https://github.com/unit8co/darts/pull/2207) by [Antoine Madrona](https://github.com/madtoinou). - Fixed a bug in probabilistic `LinearRegressionModel.fit()`, where the `model` attribute was not pointing to all underlying estimators. [#2205](https://github.com/unit8co/darts/pull/2205) by [Antoine Madrona](https://github.com/madtoinou). - Raise an error in `RegressionEsembleModel` when the `regression_model` was created with `multi_models=False` (not supported). [#2205](https://github.com/unit8co/darts/pull/2205) by [Antoine Madrona](https://github.com/madtoinou). -- Fixed a bug in `coefficient_of_variaton()` with `intersect=True`, where the coefficient was not computed on the intersection. [#2202](https://github.com/unit8co/darts/pull/2202) by [Antoine Madrona](https://github.com/madtoinou). +- Fixed a bug in `coefficient_of_variation()` with `intersect=True`, where the coefficient was not computed on the intersection. [#2202](https://github.com/unit8co/darts/pull/2202) by [Antoine Madrona](https://github.com/madtoinou). +- Fixed a bug in `gridsearch()` with `use_fitted_values=True`, where the model was not properly instantiated for sanity checks. [#2222](https://github.com/unit8co/darts/pull/2222) by [Antoine Madrona](https://github.com/madtoinou). +- Fixed a bug in `TimeSeries.append/prepend_values()`, where the component names and the hierarchy were dropped. [#2237](https://github.com/unit8co/darts/pull/2237) by [Antoine Madrona](https://github.com/madtoinou). +- Fixed a bug in `get_multioutput_estimator()`, where the index of the estimator was incorrectly calculated. [#2246](https://github.com/unit8co/darts/pull/2246) by [Antoine Madrona](https://github.com/madtoinou). +- 🔴 Fixed a bug in `datetime_attribute_timeseries()`, where 1-indexed attributes were not properly handled. Also, 0-indexing is now enforced for all the generated encodings. [#2242](https://github.com/unit8co/darts/pull/2242) by [Antoine Madrona](https://github.com/madtoinou). + +**Dependencies** + +- Removed upper version cap (<=v2.1.2) for PyTorch Lightning. [#2251](https://github.com/unit8co/darts/pull/2251) by [Dennis Bader](https://github.com/dennisbader). ### For developers of the library: -## [0.27.2](https://github.com/unit8co/darts/tree/0.27.2) (2023-01-21) +- Updated pre-commit hooks to the latest version using `pre-commit autoupdate`. Change `pyupgrade` pre-commit hook argument to `--py38-plus`. [#2228](https://github.com/unit8co/darts/pull/2228) by [MarcBresson](https://github.com/MarcBresson). +- Bumped dev dependencies to newest versions, [#2248](https://github.com/unit8co/darts/pull/2248) by [Dennis Bader](https://github.com/dennisbader): + - black[jupyter]: from 22.3.0 to 24.1.1 + - flake8: from 4.0.1 to 7.0.0 + - isort: from 5.11.5 to 5.13.2 + - pyupgrade: 2.31.0 from to v3.15.0 + +## [0.27.2](https://github.com/unit8co/darts/tree/0.27.2) (2024-01-21) + ### For users of the library: + **Improved** + - Added `darts.utils.statistics.plot_ccf` that can be used to plot the cross correlation between a time series (e.g. target series) and the lagged values of another time series (e.g. covariates series). [#2122](https://github.com/unit8co/darts/pull/2122) by [Dennis Bader](https://github.com/dennisbader). -- Improvements to `TimeSeries`: Improved the time series frequency inference when using slices or pandas DatetimeIndex as keys for `__getitem__`. [#2152](https://github.com/unit8co/darts/pull/2152) by [DavidKleindienst](https://github.com/DavidKleindienst). +- Improvements to `TimeSeries` : Improved the time series frequency inference when using slices or pandas DatetimeIndex as keys for `__getitem__`. [#2152](https://github.com/unit8co/darts/pull/2152) by [DavidKleindienst](https://github.com/DavidKleindienst). **Fixed** + - Fixed a bug when using a `TorchForecastingModel` with `use_reversible_instance_norm=True` and predicting with `n > output_chunk_length`. The input normalized multiple times. [#2160](https://github.com/unit8co/darts/pull/2160) by [FourierMourier](https://github.com/FourierMourier). ### For developers of the library: ## [0.27.1](https://github.com/unit8co/darts/tree/0.27.1) (2023-12-10) + ### For users of the library: + **Improved** + - 🔴 Added `CustomRNNModule` and `CustomBlockRNNModule` for defining custom RNN modules that can be used with `RNNModel` and `BlockRNNModel`. The custom `model` must now be a subclass of the custom modules. [#2088](https://github.com/unit8co/darts/pull/2088) by [Dennis Bader](https://github.com/dennisbader). **Fixed** + - Fixed a bug in historical forecasts, where some `fit/predict_kwargs` were not passed to the underlying model's fit/predict methods. [#2103](https://github.com/unit8co/darts/pull/2103) by [Dennis Bader](https://github.com/dennisbader). - Fixed an import error when trying to create a `TorchForecastingModel` with PyTorch Lightning v<2.0.0. [#2087](https://github.com/unit8co/darts/pull/2087) by [Eschibli](https://github.com/eschibli). - Fixed a bug when creating a `RNNModel` with a custom `model`. [#2088](https://github.com/unit8co/darts/pull/2088) by [Dennis Bader](https://github.com/dennisbader). ### For developers of the library: + - Added a folder `docs/generated_api` to define custom .rst files for generating the documentation. [#2115](https://github.com/unit8co/darts/pull/2115) by [Dennis Bader](https://github.com/dennisbader). ## [0.27.0](https://github.com/unit8co/darts/tree/0.27.0) (2023-11-18) + ### For users of the library: + **Improved** -- Improvements to `TorchForecastingModel`: + +- Improvements to `TorchForecastingModel` : - 🚀🚀 We optimized `historical_forecasts()` for pre-trained `TorchForecastingModel` running up to 20 times faster than before (and even more when tuning the batch size)!. [#2013](https://github.com/unit8co/darts/pull/2013) by [Dennis Bader](https://github.com/dennisbader). - Added callback `darts.utils.callbacks.TFMProgressBar` to customize at which model stages to display the progress bar. [#2020](https://github.com/unit8co/darts/pull/2020) by [Dennis Bader](https://github.com/dennisbader). - All `InferenceDataset`s now support strided forecasts with parameters `stride`, `bounds`. These datasets can be used with `TorchForecastingModel.predict_from_dataset()`. [#2013](https://github.com/unit8co/darts/pull/2013) by [Dennis Bader](https://github.com/dennisbader). -- Improvements to `RegressionModel`: +- Improvements to `RegressionModel` : - New example notebook for the `RegressionModels` explaining features such as (component-specific) lags, `output_chunk_length` in relation with `multi_models`, multivariate support, and more. [#2039](https://github.com/unit8co/darts/pull/2039) by [Antoine Madrona](https://github.com/madtoinou). - `XGBModel` now leverages XGBoost's native Quantile Regression support that was released in version 2.0.0 for improved probabilistic forecasts. [#2051](https://github.com/unit8co/darts/pull/2051) by [Dennis Bader](https://github.com/dennisbader). - Improvements to `LocalForecastingModel` @@ -66,10 +429,11 @@ but cannot always guarantee backwards compatibility. Changes that may **break co - Other improvements: - Added support for time index time zone conversion with parameter `tz` before generating/computing holidays and datetime attributes. Support was added to all Time Axis Encoders, standalone encoders and forecasting models' `add_encoders`, time series generation utils functions `holidays_timeseries()` and `datetime_attribute_timeseries()`, and `TimeSeries` methods `add_datetime_attribute()` and `add_holidays()`. [#2054](https://github.com/unit8co/darts/pull/2054) by [Dennis Bader](https://github.com/dennisbader). - Added new data transformer: `MIDAS`, which uses mixed-data sampling to convert `TimeSeries` from high frequency to low frequency (and back). [#1820](https://github.com/unit8co/darts/pull/1820) by [Boyd Biersteker](https://github.com/Beerstabr), [Antoine Madrona](https://github.com/madtoinou) and [Dennis Bader](https://github.com/dennisbader). - - Added new dataset `ElectricityConsumptionZurichDataset`: The dataset contains the electricity consumption of households in Zurich, Switzerland from 2015-2022 on different grid levels. We also added weather measurements for Zurich which can be used as covariates for modelling. [#2039](https://github.com/unit8co/darts/pull/2039) by [Antoine Madrona](https://github.com/madtoinou) and [Dennis Bader](https://github.com/dennisbader). + - Added new dataset `ElectricityConsumptionZurichDataset` : The dataset contains the electricity consumption of households in Zurich, Switzerland from 2015-2022 on different grid levels. We also added weather measurements for Zurich which can be used as covariates for modelling. [#2039](https://github.com/unit8co/darts/pull/2039) by [Antoine Madrona](https://github.com/madtoinou) and [Dennis Bader](https://github.com/dennisbader). - Adapted the example notebooks to properly apply data transformers and avoid look-ahead bias. [#2020](https://github.com/unit8co/darts/pull/2020) by [Samriddhi Singh](https://github.com/SimTheGreat). **Fixed** + - Fixed a bug when calling `historical_forecasts()` and `overlap_end=False` that did not generate the last possible forecast. [#2013](https://github.com/unit8co/darts/pull/2013) by [Dennis Bader](https://github.com/dennisbader). - Fixed a bug when calling optimized `historical_forecasts()` for a `RegressionModel` trained with varying component-specific lags. [#2040](https://github.com/unit8co/darts/pull/2040) by [Antoine Madrona](https://github.com/madtoinou). - Fixed a bug when using encoders with `RegressionModel` and series with a non-evenly spaced frequency (e.g. Month Begin). This raised an error during lagged data creation when trying to divide a pd.Timedelta by the ambiguous frequency. [#2034](https://github.com/unit8co/darts/pull/2034) by [Antoine Madrona](https://github.com/madtoinou). @@ -79,24 +443,27 @@ but cannot always guarantee backwards compatibility. Changes that may **break co - Fixed a bug when using `DLinearModel` and `NLinearModel` on multivariate series with static covariates shared across components and `use_static_covariates=True`. [#2070](https://github.com/unit8co/darts/pull/2070) by [Antoine Madrona](https://github.com/madtoinou). ### For developers of the library: + No changes. ## [0.26.0](https://github.com/unit8co/darts/tree/0.26.0) (2023-09-16) + ### For users of the library: **Improved** -- Improvements to `RegressionModel`: [#1962](https://github.com/unit8co/darts/pull/1962) by [Antoine Madrona](https://github.com/madtoinou). + +- Improvements to `RegressionModel`, [#1962](https://github.com/unit8co/darts/pull/1962) by [Antoine Madrona](https://github.com/madtoinou): - 🚀🚀 All models now support component/column-specific lags for target, past, and future covariates series. -- Improvements to `TorchForecastingModel`: +- Improvements to `TorchForecastingModel` : - 🚀 Added `RINorm` (Reversible Instance Norm) as an input normalization option for all models except `RNNModel`. Activate it with model creation parameter `use_reversible_instance_norm`. [#1969](https://github.com/unit8co/darts/pull/1969) by [Dennis Bader](https://github.com/dennisbader). - 🔴 Added past covariates feature projection to `TiDEModel` with parameter `temporal_width_past` following the advice of the model architect. Parameter `temporal_width` was renamed to `temporal_width_future`. Additionally, added the option to bypass the feature projection with `temporal_width_past/future=0`. [#1993](https://github.com/unit8co/darts/pull/1993) by [Dennis Bader](https://github.com/dennisbader). -- Improvements to `EnsembleModel`: [#1815](https://github.com/unit8co/darts/pull/#1815) by [Antoine Madrona](https://github.com/madtoinou) and [Dennis Bader](https://github.com/dennisbader). +- Improvements to `EnsembleModel`, [#1815](https://github.com/unit8co/darts/pull/#1815) by [Antoine Madrona](https://github.com/madtoinou) and [Dennis Bader](https://github.com/dennisbader): - 🔴 Renamed model constructor argument `models` to `forecasting_models`. - 🚀🚀 Added support for pre-trained `GlobalForecastingModel` as `forecasting_models` to avoid re-training when ensembling. This requires all models to be pre-trained global models. - - 🚀 Added support for generating the `forecasting_model` forecasts (used to train the ensemble model) with historical forecasts rather than direct (auto-regressive) predictions. Enable it with `train_using_historical_forecasts=True` at model creation. + - 🚀 Added support for generating the `forecasting_model` forecasts (used to train the ensemble model) with historical forecasts rather than direct (auto-regressive) predictions. Enable it with `train_using_historical_forecasts=True` at model creation. - Added an example notebook for ensemble models. -- Improvements to historical forecasts, backtest and gridsearch: [#1866](https://github.com/unit8co/darts/pull/1866) by [Antoine Madrona](https://github.com/madtoinou). - - Added support for negative `start` values to start historical forecasts relative to the end of the target series. +- Improvements to historical forecasts, backtest and gridsearch, [#1866](https://github.com/unit8co/darts/pull/1866) by [Antoine Madrona](https://github.com/madtoinou): + - Added support for negative `start` values to start historical forecasts relative to the end of the target series. - Added a new argument `start_format` that allows to use an integer `start` either as the index position or index value/label for `series` indexed with a `pd.RangeIndex`. - Added support for `TimeSeries` with a `RangeIndex` starting at a negative integer. - Other improvements: @@ -105,6 +472,7 @@ No changes. - Added method `TimeSeries.cumsum()` to get the cumulative sum of the time series along the time axis. [#1988](https://github.com/unit8co/darts/pull/1988) by [Eliot Zubkoff](https://github.com/Eliotdoesprogramming). **Fixed** + - Fixed a bug in `TimeSeries.from_dataframe()` when using a pandas.DataFrame with `df.columns.name != None`. [#1938](https://github.com/unit8co/darts/pull/1938) by [Antoine Madrona](https://github.com/madtoinou). - Fixed a bug in `RegressionEnsembleModel.extreme_lags` when the forecasting models have only covariates lags. [#1942](https://github.com/unit8co/darts/pull/1942) by [Antoine Madrona](https://github.com/madtoinou). - Fixed a bug when using `TFTExplainer` with a `TFTModel` running on GPU. [#1949](https://github.com/unit8co/darts/pull/1949) by [Dennis Bader](https://github.com/dennisbader). @@ -116,19 +484,23 @@ No changes. ### For developers of the library: **Improved** + - Refactored all tests to use pytest instead of unittest. [#1950](https://github.com/unit8co/darts/pull/1950) by [Dennis Bader](https://github.com/dennisbader). ## [0.25.0](https://github.com/unit8co/darts/tree/0.25.0) (2023-08-04) + ### For users of the library: **Installation** + - 🔴 Removed Prophet, LightGBM, and CatBoost dependencies from PyPI packages (`darts`, `u8darts`, `u8darts[torch]`), and conda-forge packages (`u8darts`, `u8darts-torch`) to avoid installation issues that some users were facing (installation on Apple M1/M2 devices, ...). [#1589](https://github.com/unit8co/darts/pull/1589) by [Julien Herzen](https://github.com/hrzn) and [Dennis Bader](https://github.com/dennisbader). - The models are still supported by installing the required packages as described in our [installation guide](https://github.com/unit8co/darts/blob/master/INSTALL.md#enabling-optional-dependencies). - - The Darts package including all dependencies can still be installed with PyPI package `u8darts[all]` or conda-forge package `u8darts-all`. + - The Darts package including all dependencies can still be installed with PyPI package `u8darts[all]` or conda-forge package `u8darts-all`. - Added new PyPI flavor `u8darts[notorch]`, and conda-forge flavor `u8darts-notorch` which are equivalent to the old `u8darts` installation (all dependencies except neural networks). - 🔴 Removed support for Python 3.7 [#1864](https://github.com/unit8co/darts/pull/1864) by [Dennis Bader](https://github.com/dennisbader). **Improved** + - General model improvements: - 🚀🚀 Optimized `historical_forecasts()` for `RegressionModel` when `retrain=False` and `forecast_horizon <= output_chunk_length` by vectorizing the prediction. This can run up to 700 times faster than before! [#1885](https://github.com/unit8co/darts/pull/1885) by [Antoine Madrona](https://github.com/madtoinou). - Improved efficiency of `historical_forecasts()` and `backtest()` for all models giving significant process time reduction for larger number of predict iterations and series. [#1801](https://github.com/unit8co/darts/pull/1801) by [Dennis Bader](https://github.com/dennisbader). @@ -138,12 +510,12 @@ No changes. - Added method `generate_fit_predict_encodings()` to generate the encodings (from `add_encoders` at model creation) required for training and prediction. [#1925](https://github.com/unit8co/darts/pull/1925) by [Dennis Bader](https://github.com/dennisbader). - Added support for `PathLike` to the `save()` and `load()` functions of all non-deep learning based models. [#1754](https://github.com/unit8co/darts/pull/1754) by [Simon Sudrich](https://github.com/sudrich). - Added model property `ForecastingModel.supports_multivariate` to indicate whether the model supports multivariate forecasting. [#1848](https://github.com/unit8co/darts/pull/1848) by [Felix Divo](https://github.com/felixdivo). -- Improvements to `EnsembleModel`: +- Improvements to `EnsembleModel` : - Model creation parameter `forecasting_models` now supports a mix of `LocalForecastingModel` and `GlobalForecastingModel` (single `TimeSeries` training/inference only, due to the local models). [#1745](https://github.com/unit8co/darts/pull/1745) by [Antoine Madrona](https://github.com/madtoinou). - Future and past covariates can now be used even if `forecasting_models` have different covariates support. The covariates passed to `fit()`/`predict()` are used only by models that support it. [#1745](https://github.com/unit8co/darts/pull/1745) by [Antoine Madrona](https://github.com/madtoinou). - `RegressionEnsembleModel` and `NaiveEnsembleModel` can generate probabilistic forecasts, probabilistics `forecasting_models` can be sampled to train the `regression_model`, updated the documentation (stacking technique). [#1692](https://github.com/unit8co/darts/pull/1692) by [Antoine Madrona](https://github.com/madtoinou). - Improvements to `Explainability` module: - - 🚀🚀 New forecasting model explainer: `TFTExplainer` for `TFTModel`. You can now access and visualize the trained model's feature importances and self attention. [#1392](https://github.com/unit8co/darts/issues/1392) by [Sebastian Cattes](https://github.com/Cattes) and [Dennis Bader](https://github.com/dennisbader). + - 🚀🚀 New forecasting model explainer: `TFTExplainer` for `TFTModel`. You can now access and visualize the trained model's feature importances and self attention. [#1392](https://github.com/unit8co/darts/pull/1392) by [Sebastian Cattes](https://github.com/Cattes) and [Dennis Bader](https://github.com/dennisbader). - Added static covariates support to `ShapeExplainer`. [#1803](https://github.com/unit8co/darts/pull/1803) by [Anne de Vries](https://github.com/anne-devries) and [Dennis Bader](https://github.com/dennisbader). - Improvements to documentation [#1904](https://github.com/unit8co/darts/pull/1904) by [Dennis Bader](https://github.com/dennisbader): - made model sections in README.md, covariates user guide and forecasting model API Reference more user friendly by adding model links and reorganizing them into model categories. @@ -151,10 +523,11 @@ No changes. - Other improvements: - Improved static covariates column naming when using `StaticCovariatesTransformer` with a `sklearn.preprocessing.OneHotEncoder`. [#1863](https://github.com/unit8co/darts/pull/1863) by [Anne de Vries](https://github.com/anne-devries). - Added `MSTL` (Season-Trend decomposition using LOESS for multiple seasonalities) as a `method` option for `extract_trend_and_seasonality()`. [#1879](https://github.com/unit8co/darts/pull/1879) by [Alex Colpitts](https://github.com/alexcolpitts96). - - Added `RINorm` (Reversible Instance Norm) as a new input normalization option for `TorchForecastingModel`. So far only `TiDEModel` supports it with model creation parameter `use_reversible_instance_norm`. [#1865](https://github.com/unit8co/darts/issues/1856) by [Alex Colpitts](https://github.com/alexcolpitts96). - - Improvements to `TimeSeries.plot()`: custom axes are now properly supported with parameter `ax`. Axis is now returned for downstream tasks. [#1916](https://github.com/unit8co/darts/pull/1916) by [Dennis Bader](https://github.com/dennisbader). + - Added `RINorm` (Reversible Instance Norm) as a new input normalization option for `TorchForecastingModel`. So far only `TiDEModel` supports it with model creation parameter `use_reversible_instance_norm`. [#1865](https://github.com/unit8co/darts/pull/1856) by [Alex Colpitts](https://github.com/alexcolpitts96). + - Improvements to `TimeSeries.plot()` : custom axes are now properly supported with parameter `ax`. Axis is now returned for downstream tasks. [#1916](https://github.com/unit8co/darts/pull/1916) by [Dennis Bader](https://github.com/dennisbader). **Fixed** + - Fixed an issue not considering original component names for `TimeSeries.plot()` when providing a label prefix. [#1783](https://github.com/unit8co/darts/pull/1783) by [Simon Sudrich](https://github.com/sudrich). - Fixed an issue with the string representation of `ForecastingModel` when using array-likes at model creation. [#1749](https://github.com/unit8co/darts/pull/1749) by [Antoine Madrona](https://github.com/madtoinou). - Fixed an issue with `TorchForecastingModel.load_from_checkpoint()` not properly loading the loss function and metrics. [#1759](https://github.com/unit8co/darts/pull/1759) by [Antoine Madrona](https://github.com/madtoinou). @@ -163,37 +536,40 @@ No changes. - Fixed `TimeSeries.__getitem__()` for series with a RangeIndex with start != 0 and freq != 1. [#1868](https://github.com/unit8co/darts/pull/1868) by [Dennis Bader](https://github.com/dennisbader). - Fixed an issue where `DTWAlignment.plot_alignment()` was not plotting the alignment plot of series with a RangeIndex correctly. [#1880](https://github.com/unit8co/darts/pull/1880) by [Ahmet Zamanis](https://github.com/AhmetZamanis) and [Dennis Bader](https://github.com/dennisbader). - Fixed an issue when calling `ARIMA.predict()` and `num_samples > 1` (probabilistic forecasting), where the start point of the simulation was not anchored to the end of the target series. [#1893](https://github.com/unit8co/darts/pull/1893) by [Dennis Bader](https://github.com/dennisbader). -- Fixed an issue when using `TFTModel.predict()` with `full_attention=True` where the attention mask was not applied properly. [#1392](https://github.com/unit8co/darts/issues/1392) by [Dennis Bader](https://github.com/dennisbader). +- Fixed an issue when using `TFTModel.predict()` with `full_attention=True` where the attention mask was not applied properly. [#1392](https://github.com/unit8co/darts/pull/1392) by [Dennis Bader](https://github.com/dennisbader). ### For developers of the library: **Improvements** -- Refactored the `ForecastingModelExplainer` and `ExplainabilityResult` to simplify implementation of new explainers. [#1392](https://github.com/unit8co/darts/issues/1392) by [Dennis Bader](https://github.com/dennisbader). -- Adapted all unit tests to run successfully on M1 devices. [#1933](https://github.com/unit8co/darts/issues/1933) by [Dennis Bader](https://github.com/dennisbader). + +- Refactored the `ForecastingModelExplainer` and `ExplainabilityResult` to simplify implementation of new explainers. [#1392](https://github.com/unit8co/darts/pull/1392) by [Dennis Bader](https://github.com/dennisbader). +- Adapted all unit tests to run successfully on M1 devices. [#1933](https://github.com/unit8co/darts/pull/1933) by [Dennis Bader](https://github.com/dennisbader). ## [0.24.0](https://github.com/unit8co/darts/tree/0.24.0) (2023-04-12) + ### For users of the library: **Improved** + - General model improvements: - New baseline forecasting model `NaiveMovingAverage`. [#1557](https://github.com/unit8co/darts/pull/1557) by [Janek Fidor](https://github.com/JanFidor). - New models `StatsForecastAutoCES`, and `StatsForecastAutoTheta` from Nixtla's statsforecasts library as local forecasting models without covariates support. AutoTheta supports probabilistic forecasts. [#1476](https://github.com/unit8co/darts/pull/1476) by [Boyd Biersteker](https://github.com/Beerstabr). - Added support for future covariates, and probabilistic forecasts to `StatsForecastAutoETS`. [#1476](https://github.com/unit8co/darts/pull/1476) by [Boyd Biersteker](https://github.com/Beerstabr). - - Added support for logistic growth to `Prophet` with parameters `growth`, `cap`, `floor`. [#1419](https://github.com/unit8co/darts/pull/1419) by [David Kleindienst](https://github.com/DavidKleindienst). + - Added support for logistic growth to `Prophet` with parameters `growth`, `cap`, `floor`. [#1419](https://github.com/unit8co/darts/pull/1419) by [David Kleindienst](https://github.com/DavidKleindienst). - Improved the model string / object representation style similar to scikit-learn models. [#1590](https://github.com/unit8co/darts/pull/1590) by [Janek Fidor](https://github.com/JanFidor). - 🔴 Renamed `MovingAverage` to `MovingAverageFilter` to avoid confusion with new `NaiveMovingAverage` model. [#1557](https://github.com/unit8co/darts/pull/1557) by [Janek Fidor](https://github.com/JanFidor). -- Improvements to `RegressionModel`: - - Optimized lagged data creation for fit/predict sets achieving a drastic speed-up. [#1399](https://github.com/unit8co/darts/pull/1399) by [Matt Bilton](https://github.com/mabilton). - - Added support for categorical past/future/static covariates to `LightGBMModel` with model creation parameters `categorical_*_covariates`. [#1585](https://github.com/unit8co/darts/pull/1585) by [Rijk van der Meulen](https://github.com/rijkvandermeulen). - - Added lagged feature names for better interpretability; accessible with model property `lagged_feature_names`. [#1679](https://github.com/unit8co/darts/pull/1679) by [Antoine Madrona](https://github.com/madtoinou). - - 🔴 New `use_static_covariates` option for all models: When True (default), models use static covariates if available at fitting time and enforce identical static covariate shapes across all target `series` used for training or prediction; when False, models ignore static covariates. [#1700](https://github.com/unit8co/darts/pull/1700) by [Dennis Bader](https://github.com/dennisbader). -- Improvements to `TorchForecastingModel`: +- Improvements to `RegressionModel` : + - Optimized lagged data creation for fit/predict sets achieving a drastic speed-up. [#1399](https://github.com/unit8co/darts/pull/1399) by [Matt Bilton](https://github.com/mabilton). + - Added support for categorical past/future/static covariates to `LightGBMModel` with model creation parameters `categorical_*_covariates`. [#1585](https://github.com/unit8co/darts/pull/1585) by [Rijk van der Meulen](https://github.com/rijkvandermeulen). + - Added lagged feature names for better interpretability; accessible with model property `lagged_feature_names`. [#1679](https://github.com/unit8co/darts/pull/1679) by [Antoine Madrona](https://github.com/madtoinou). + - 🔴 New `use_static_covariates` option for all models: When True (default), models use static covariates if available at fitting time and enforce identical static covariate shapes across all target `series` used for training or prediction; when False, models ignore static covariates. [#1700](https://github.com/unit8co/darts/pull/1700) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to `TorchForecastingModel` : - New methods `load_weights()` and `load_weights_from_checkpoint()` for loading only the weights from a manually saved model or checkpoint. This allows to fine-tune the pre-trained models with different optimizers or learning rate schedulers. [#1501](https://github.com/unit8co/darts/pull/1501) by [Antoine Madrona](https://github.com/madtoinou). - New method `lr_find()` that helps to find a good initial learning rate for your forecasting problem. [#1609](https://github.com/unit8co/darts/pull/1609) by [Levente Szabados](https://github.com/solalatus) and [Dennis Bader](https://github.com/dennisbader). - Improved the [user guide](https://unit8co.github.io/darts/userguide/torch_forecasting_models.html) and added new sections about saving/loading (checkpoints, manual save/load, loading weights only), and callbacks. [#1661](https://github.com/unit8co/darts/pull/1661) by [Antoine Madrona](https://github.com/madtoinou). - 🔴 Replaced `":"` in save file names with `"_"` to avoid issues on some operating systems. For loading models saved on earlier Darts versions, try to rename the file names by replacing `":"` with `"_"`. [#1501](https://github.com/unit8co/darts/pull/1501) by [Antoine Madrona](https://github.com/madtoinou). - - 🔴 New `use_static_covariates` option for `TFTModel`, `DLinearModel` and `NLinearModel`: When True (default), models use static covariates if available at fitting time and enforce identical static covariate shapes across all target `series` used for training or prediction; when False, models ignore static covariates. [#1700](https://github.com/unit8co/darts/pull/1700) by [Dennis Bader](https://github.com/dennisbader). -- Improvements to `TimeSeries`: + - 🔴 New `use_static_covariates` option for `TFTModel`, `DLinearModel` and `NLinearModel` : When True (default), models use static covariates if available at fitting time and enforce identical static covariate shapes across all target `series` used for training or prediction; when False, models ignore static covariates. [#1700](https://github.com/unit8co/darts/pull/1700) by [Dennis Bader](https://github.com/dennisbader). +- Improvements to `TimeSeries` : - Added support for integer indexed input to `from_*` factory methods, if index can be converted to a pandas.RangeIndex. [#1527](https://github.com/unit8co/darts/pull/1527) by [Dennis Bader](https://github.com/dennisbader). - Added support for integer indexed input with step sizes (freq) other than 1. [#1527](https://github.com/unit8co/darts/pull/1527) by [Dennis Bader](https://github.com/dennisbader). - Optimized time series creation with `fill_missing_dates=True` achieving a drastic speed-up . [#1527](https://github.com/unit8co/darts/pull/1527) by [Dennis Bader](https://github.com/dennisbader). @@ -204,6 +580,7 @@ No changes. - New `quantile_loss()` (pinball loss) metric for probabilistic forecasts. [#1559](https://github.com/unit8co/darts/pull/1559) by [Janek Fidor](https://github.com/JanFidor). **Fixed** + - Fixed an issue in `BottomUp/TopDownReconciliator` where the order of the series components was not taken into account. [#1592](https://github.com/unit8co/darts/pull/1592) by [David Kleindienst](https://github.com/DavidKleindienst). - Fixed an issue with `DLinearModel` not supporting even numbered `kernel_size`. [#1695](https://github.com/unit8co/darts/pull/1695) by [Antoine Madrona](https://github.com/madtoinou). - Fixed an issue with `RegressionEnsembleModel` not using future covariates during training. [#1660](https://github.com/unit8co/darts/pull/1660) by [Rajesh Balakrishnan](https://github.com/Rajesh4AI). @@ -222,14 +599,16 @@ No changes. ### For developers of the library: **Improvements** + - Option to skip slow tests locally with `pytest . --no-cov -m "not slow"`. [#1625](https://github.com/unit8co/darts/pull/1625) by [Blazej Nowicki](https://github.com/BlazejNowicki). - Major refactor of data transformers which simplifies implementation of new transformers. [#1409](https://github.com/unit8co/darts/pull/1409) by [Matt Bilton](https://github.com/mabilton). - ## [0.23.1](https://github.com/unit8co/darts/tree/0.23.1) (2023-01-12) + Patch release **Fixed** + - Fix an issue in `TimeSeries` which made it incompatible with Python 3.7. [#1449](https://github.com/unit8co/darts/pull/1449) by [Dennis Bader](https://github.com/dennisbader). - Fix an issue with static covariates when series have variable lengths with `RegressionModel`s. @@ -244,11 +623,12 @@ Patch release - Fix an issue with `slice_n_points` functions on integer indexes. [#1482](https://github.com/unit8co/darts/pull/1482) by [Julien Herzen](https://github.com/hrzn). - ## [0.23.0](https://github.com/unit8co/darts/tree/0.23.0) (2022-12-23) + ### For users of the library: **Improved** + - 🚀🚀🚀 Brand new Darts module dedicated to anomaly detection on time series: `darts.ad`. More info on the API doc page: https://unit8co.github.io/darts/generated_api/darts.ad.html. [#1256](https://github.com/unit8co/darts/pull/1256) by [Julien Adda](https://github.com/julien12234) @@ -268,7 +648,7 @@ Patch release - New window transformation capabilities: `TimeSeries.window_transform()` and a new `WindowTransformer` which allow to easily create window features. [#1269](https://github.com/unit8co/darts/pull/1269) by [Eliane Maalouf](https://github.com/eliane-maalouf). -- 🔴 Improvements to `TorchForecastingModels`: Load models directly to CPU that were trained on GPU. Save file size reduced. +- 🔴 Improvements to `TorchForecastingModels` : Load models directly to CPU that were trained on GPU. Save file size reduced. Improved PyTorch Lightning Trainer handling fixing several minor issues. Removed deprecated methods `load_model` and `save_model` [#1371](https://github.com/unit8co/darts/pull/1371) by [Dennis Bader](https://github.com/dennisbader). @@ -297,10 +677,10 @@ Patch release - Allow the creation of empty `TimeSeries` [#1359](https://github.com/unit8co/darts/pull/1359) by [Antoine Madrona](https://github.com/madtoinou). - **Fixed** + - Fixed edge case in ShapExplainer for regression models where covariates series > target series - [#1310](https://https://github.com/unit8co/darts/pull/1310) by [Rijk van der Meulen](https://github.com/rijkvandermeulen) + [#1310](https://github.com/unit8co/darts/pull/1310) by [Rijk van der Meulen](https://github.com/rijkvandermeulen) - Fixed a bug in `TimeSeries.resample()` [#1350](https://github.com/unit8co/darts/pull/1350) by [Antoine Madrona](https://github.com/madtoinou). - Fixed splitting methods when split point is not in the series @@ -314,38 +694,41 @@ Patch release - Fixed treatment of stochastic models in ensemble models [#1423](https://github.com/unit8co/darts/pull/1423) by [Eliane Maalouf](https://github.com/eliane-maalouf). - ## [0.22.0](https://github.com/unit8co/darts/tree/0.22.0) (2022-10-04) + ### For users of the library: **Improved** + - New explainability feature. The class `ShapExplainer` in `darts.explainability` can provide Shap-values explanations of the importance of each lag and each dimension in producing each forecasting lag for `RegressionModel`s. [#909](https://github.com/unit8co/darts/pull/909) by [Maxime Dumonal](https://github.com/dumjax). - New model: `StatsForecastsETS`. Similarly to `StatsForecastsAutoARIMA`, this model offers the ETS model from Nixtla's `statsforecasts` library as a local forecasting model supporting future covariates. [#1171](https://github.com/unit8co/darts/pull/1171) by [Julien Herzen](https://github.com/hrzn). - Added support for past and future covariates to `residuals()` function. [#1223](https://github.com/unit8co/darts/pull/1223) by [Eliane Maalouf](https://github.com/eliane-maalouf). - Added support for retraining model(s) every `n` iteration and on custom conditions in `historical_forecasts` method of `ForecastingModel`s. [#1139](https://github.com/unit8co/darts/pull/1139) by [Francesco Bruzzesi](https://github.com/fbruzzesi). - Added support for beta-NLL in `GaussianLikelihood`s, as proposed in [this paper](https://arxiv.org/abs/2203.09168). [#1162](https://github.com/unit8co/darts/pull/1162) by [Julien Herzen](https://github.com/hrzn). -- New LayerNorm alternatives, RMSNorm and LayerNormNoBias [#1113](https://github.com/unit8co/darts/issues/1113) by [Greg DeVos](https://github.com/gdevos010). +- New LayerNorm alternatives, RMSNorm and LayerNormNoBias [#1113](https://github.com/unit8co/darts/pull/1113) by [Greg DeVos](https://github.com/gdevos010). - 🔴 Improvements to encoders: improve fitting behavior of encoders' transformers and solve a couple of issues. Remove support for absolute index encoding. [#1257](https://github.com/unit8co/darts/pull/1257) by [Dennis Bader](https://github.com/dennisbader). -- Overwrite min_train_series_length for Catboost and LightGBM [#1214](https://https://github.com/unit8co/darts/pull/1214) by [Anne de Vries](https://github.com/anne-devries). +- Overwrite min_train_series_length for Catboost and LightGBM [#1214](https://github.com/unit8co/darts/pull/1214) by [Anne de Vries](https://github.com/anne-devries). - New example notebook showcasing and end-to-end example of hyperparameter optimization with Optuna [#1242](https://github.com/unit8co/darts/pull/1242) by [Julien Herzen](https://github.com/hrzn). - New user guide section on hyperparameter optimization with Optuna and Ray Tune [#1242](https://github.com/unit8co/darts/pull/1242) by [Julien Herzen](https://github.com/hrzn). - Documentation on model saving and loading. [#1210](https://github.com/unit8co/darts/pull/1210) by [Amadej Kocbek](https://github.com/amadejkocbek). - 🔴 `torch_device_str` has been removed from all torch models in favor of Pytorch Lightning's `pl_trainer_kwargs` method [#1244](https://github.com/unit8co/darts/pull/1244) by [Greg DeVos](https://github.com/gdevos010). **Fixed** + - An issue with `add_encoders` in `RegressionModel`s when fit/predict were called with a single target series. [#1193](https://github.com/unit8co/darts/pull/1193) by [Dennis Bader](https://github.com/dennisbader). - Some issues with integer-indexed series. [#1191](https://github.com/unit8co/darts/pull/1191) by [Julien Herzen](https://github.com/hrzn). - A bug when using the latest versions (>=1.1.1) of Prophet. [#1208](https://github.com/unit8co/darts/pull/1208) by [Julien Herzen](https://github.com/hrzn). - An issue with calling `fit_transform()` on reconciliators. [#1165](https://github.com/unit8co/darts/pull/1165) by [Julien Herzen](https://github.com/hrzn). - A bug in `GaussianLikelihood` object causing issues with confidence intervals. [#1162](https://github.com/unit8co/darts/pull/1162) by [Julien Herzen](https://github.com/hrzn). - An issue which prevented plotting `TimeSeries` of length 1. [#1206](https://github.com/unit8co/darts/issues/1206) by [Julien Herzen](https://github.com/hrzn). -- Type hinting for ExponentialSmoothing model [#1185](https://https://github.com/unit8co/darts/pull/1185) by [Rijk van der Meulen](https://github.com/rijkvandermeulen) +- Type hinting for ExponentialSmoothing model [#1185](https://github.com/unit8co/darts/pull/1185) by [Rijk van der Meulen](https://github.com/rijkvandermeulen) ## [0.21.0](https://github.com/unit8co/darts/tree/0.21.0) (2022-08-12) ### For users of the library: **Improved** + - New model: Catboost, incl `quantile`, `poisson` and `gaussian` likelihoods support. [#1007](https://github.com/unit8co/darts/pull/1007), [#1044](https://github.com/unit8co/darts/pull/1044) by [Jonas Racine](https://github.com/jonasracine). - Extension of the `add_encoders` option to `RegressionModel`s. It is now straightforward to add calendar based or custom past or future covariates to these models, similar to torch models. [#1093](https://github.com/unit8co/darts/pull/1093) by [Dennis Bader](https://github.com/dennisbader). - Introduction of `StaticCovariatesTransformer`, categorical static covariate support for `TFTModel`, example and user-guide updates on static covariates. [#1081](https://github.com/unit8co/darts/pull/1081) by [Dennis Bader](https://github.com/dennisbader). @@ -364,6 +747,7 @@ Patch release - Small readability improvements to user guide. [#1039](https://github.com/unit8co/darts/pull/1039), [#1046](https://github.com/unit8co/darts/pull/1046/files) by [Ryan Russell](https://github.com/ryanrussell) **Fixed** + - Fixed an error when loading torch forecasting models. [#1124](https://github.com/unit8co/darts/pull/1124) by [Dennis Bader](https://github.com/dennisbader). - 🔴 renamed `ignore_time_axes` into `ignore_time_axis` in `TimeSeries.concatenate()`. [#1073](https://github.com/unit8co/darts/pull/1073/files) by [Thomas KIENTZ](https://github.com/thomktz) - Propagate static covs and hierarchy in missing value filler. [#1076](https://github.com/unit8co/darts/pull/1076) by [Julien Herzen](https://github.com/hrzn). @@ -377,6 +761,7 @@ Patch release ### For users of the library: **Improved** + - Added support for static covariates in `TimeSeries` class. [#966](https://github.com/unit8co/darts/pull/966) by [Dennis Bader](https://github.com/dennisbader). - Added support for static covariates in TFT model. [#966](https://github.com/unit8co/darts/pull/966) by [Dennis Bader](https://github.com/dennisbader). - Support for storing hierarchy of components in `TimeSeries` (in view of hierarchical reconciliation) [#1012](https://github.com/unit8co/darts/pull/1012) by [Julien Herzen](https://github.com/hrzn). @@ -384,17 +769,18 @@ Patch release - Added support for Monte Carlo Dropout, as a way to capture model uncertainty with torch models at inference time. [#1013](https://github.com/unit8co/darts/pull/1013) by [Julien Herzen](https://github.com/hrzn). - New datasets: ETT and Electricity. [#617](https://github.com/unit8co/darts/pull/617) by [Greg DeVos](https://github.com/gdevos010) -- New dataset: [Uber TLC](https://github.com/fivethirtyeight/uber-tlc-foil-response). [#1003](https://github.com/unit8co/darts/pull/1003) by [Greg DeVos](https://github.com/gdevos010). +- New dataset, [Uber TLC](https://github.com/fivethirtyeight/uber-tlc-foil-response). [#1003](https://github.com/unit8co/darts/pull/1003) by [Greg DeVos](https://github.com/gdevos010). - Model Improvements: Option for changing activation function for NHiTs and NBEATS. NBEATS support for dropout. NHiTs Support for AvgPooling1d. [#955](https://github.com/unit8co/darts/pull/955) by [Greg DeVos](https://github.com/gdevos010). -- Implemented ["GLU Variants Improve Transformer"](https://arxiv.org/abs/2002.05202) for transformer based models (transformer and TFT). [#959](https://github.com/unit8co/darts/issues/959) by [Greg DeVos](https://github.com/gdevos010). +- Implemented ["GLU Variants Improve Transformer"](https://arxiv.org/abs/2002.05202) for transformer based models (transformer and TFT). [#968](https://github.com/unit8co/darts/pull/968) by [Greg DeVos](https://github.com/gdevos010). - Added support for torch metrics during training and validation. [#996](https://github.com/unit8co/darts/pull/996) by [Greg DeVos](https://github.com/gdevos010). - Better handling of logging [#1010](https://github.com/unit8co/darts/pull/1010) by [Dustin Brunner](https://github.com/brunnedu). - Better support for Python 3.10, and dropping `prophet` as a dependency (`Prophet` model still works if `prophet` package is installed separately) [#1023](https://github.com/unit8co/darts/pull/1023) by [Julien Herzen](https://github.com/hrzn). - Option to avoid global matplotlib configuration changes. -[#924](https://github.com/unit8co/darts/pull/924) by [Mike Richman](https://github.com/zgana). + [#924](https://github.com/unit8co/darts/pull/924) by [Mike Richman](https://github.com/zgana). - 🔴 `HNiTSModel` renamed to `HNiTS` [#1000](https://github.com/unit8co/darts/pull/1000) by [Greg DeVos](https://github.com/gdevos010). **Fixed** + - A bug with `tail()` and `head()` [#942](https://github.com/unit8co/darts/pull/942) by [Julien Herzen](https://github.com/hrzn). - An issue with arguments being reverted for the `metric` function of gridsearch and backtest [#989](https://github.com/unit8co/darts/pull/989) by [Clara Grotehans](https://github.com/ClaraGrthns). - An error checking whether `fit()` has been called in global models [#944](https://github.com/unit8co/darts/pull/944) by [Julien Herzen](https://github.com/hrzn). @@ -403,13 +789,15 @@ Patch release ### For developers of the library: **Fixed** -- An issue with LinearLR scheduler in tests. [#928](https://github.com/unit8co/darts/pull/928) by [Dennis Bader](https://github.com/dennisbader). +- An issue with LinearLR scheduler in tests. [#928](https://github.com/unit8co/darts/pull/928) by [Dennis Bader](https://github.com/dennisbader). ## [0.19.0](https://github.com/unit8co/darts/tree/0.19.0) (2022-04-13) + ### For users of the library: **Improved** + - New model: `NHiTS` implementing the N-HiTS model. [#898](https://github.com/unit8co/darts/pull/898) by [Julien Herzen](https://github.com/hrzn). - New model: `StatsForecastAutoARIMA` implementing the (faster) AutoARIMA version of @@ -424,20 +812,22 @@ Patch release - Improved user guide with more sections. [#905](https://github.com/unit8co/darts/pull/905) by [Julien Herzen](https://github.com/hrzn). - New notebook showcasing transfer learning and training forecasting models on large time - series datasets. [#885](https://github.com/unit8co/darts/pull/885) + series datasets. [#885](https://github.com/unit8co/darts/pull/885) by [Julien Herzen](https://github.com/hrzn). - **Fixed** + - Some issues with PyTorch Lightning >= 1.6.0 [#888](https://github.com/unit8co/darts/pull/888) by [Julien Herzen](https://github.com/hrzn). ## [0.18.0](https://github.com/unit8co/darts/tree/0.18.0) (2022-03-22) + ### For users of the library: **Improved** + - `LinearRegressionModel` and `LightGBMModel` can now be probabilistic, supporting quantile - and poisson regression. [#831](https://github.com/unit8co/darts/pull/831), + and poisson regression. [#831](https://github.com/unit8co/darts/pull/831), [#853](https://github.com/unit8co/darts/pull/853) by [Gian Wiher](https://github.com/gnwhr). - New models: `BATS` and `TBATS`, based on [tbats](https://github.com/intive-DataScience/tbats). [#816](https://github.com/unit8co/darts/pull/816) by [Julien Herzen](https://github.com/hrzn). @@ -447,7 +837,7 @@ Patch release by [@gsamaras](https://github.com/gsamaras). - Added train and validation loss to PyTorch Lightning progress bar. [#825](https://github.com/unit8co/darts/pull/825) by [Dennis Bader](https://github.com/dennisbader). -- More losses available in `darts.utils.losses` for PyTorch-based models: +- More losses available in `darts.utils.losses` for PyTorch-based models: `SmapeLoss`, `MapeLoss` and `MAELoss`. [#845](https://github.com/unit8co/darts/pull/845) by [Julien Herzen](https://github.com/hrzn). - Improvement to the seasonal decomposition [#862](https://github.com/unit8co/darts/pull/862). @@ -460,17 +850,20 @@ Patch release [#825](https://github.com/unit8co/darts/pull/825) by [Dennis Bader](https://github.com/dennisbader). **Fixed** + - Fixed some issues with encoders in `fit_from_dataset()`. [#829](https://github.com/unit8co/darts/pull/829) by [Julien Herzen](https://github.com/hrzn). - Fixed an issue with covariates slicing for `DualCovariatesForecastingModels`. [#858](https://github.com/unit8co/darts/pull/858) by [Dennis Bader](https://github.com/dennisbader). - ## [0.17.1](https://github.com/unit8co/darts/tree/0.17.1) (2022-02-17) + Patch release ### For users of the library: + **Fixed** + - Fixed issues with (now deprecated) `torch_device_str` parameter, and improved documentation related to using devices with PyTorch Lightning. [#806](https://github.com/unit8co/darts/pull/806) by [Dennis Bader](https://github.com/dennisbader). @@ -478,14 +871,15 @@ Patch release by [Dennis Bader](https://github.com/dennisbader). - Fixed an issue with the periodic basis functions of N-BEATS. [#804](https://github.com/unit8co/darts/pull/804) by [Vladimir Chernykh](https://github.com/vladimir-chernykh). -- Relaxed requirements for `pandas`; from `pandas>=1.1.0` to `pandas>=1.0.5`. +- Relaxed requirements for `pandas`; from `pandas>=1.1.0` to `pandas>=1.0.5`. [#800](https://github.com/unit8co/darts/pull/800) by [@adelnick](https://github.com/adelnick). - ## [0.17.0](https://github.com/unit8co/darts/tree/0.17.0) (2022-02-15) + ### For users of the library: **Improved** + - 🚀 Support for [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning): All deep learning models are now implemented using PyTorch Lightning. This means that many more features are now available via PyTorch Lightning trainers functionalities; such as tailored callbacks, or multi-GPU training. @@ -493,10 +887,10 @@ Patch release - The `RegressionModel`s now accept an `output_chunk_length` parameter; meaning that they can be trained to predict more than one time step in advance (and used auto-regressively to predict on longer horizons). [#761](https://github.com/unit8co/darts/pull/761) by [Dustin Brunner](https://github.com/brunnedu). -- 🔴 `TimeSeries` "simple statistics" methods (such as `mean()`, `max()`, `min()` etc, ...) have been refactored +- 🔴 `TimeSeries` "simple statistics" methods (such as `mean()`, `max()`, `min()` etc, ...) have been refactored to work natively on stochastic `TimeSeries`, and over configurable axes. [#773](https://github.com/unit8co/darts/pull/773) by [Gian Wiher](https://github.com/gnwhr). -- 🔴 `TimeSeries` now support only pandas `RangeIndex` as an integer index, and does not support `Int64Index` anymore, +- 🔴 `TimeSeries` now support only pandas `RangeIndex` as an integer index, and does not support `Int64Index` anymore, as it became deprecated with pandas 1.4.0. This also now brings the guarantee that `TimeSeries` do not have missing "dates" even when indexed with integers. [#777](https://github.com/unit8co/darts/pull/777) by [Julien Herzen](https://github.com/hrzn). @@ -510,21 +904,24 @@ Patch release which allows chaining calls. [#741](https://github.com/unit8co/darts/pull/741) by [Julien Herzen](https://github.com/hrzn). - **Fixed** -- Fixed an issue with tensorboard and gridsearch when `model_name` is provided. - [#759](https://github.com/unit8co/darts/issues/759) by [@gdevos010](https://github.com/gdevos010). + +- Fixed an issue with tensorboard and gridsearch when `model_name` is provided. + [#760](https://github.com/unit8co/darts/pull/760) by [@gdevos010](https://github.com/gdevos010). - Fixed issues with pip-tools. [#762](https://github.com/unit8co/darts/pull/762) by [Tomas Van Pottelbergh](https://github.com/tomasvanpottelbergh). ### For developers of the library: + - Some linting checks have been added to the CI pipeline. [#749](https://github.com/unit8co/darts/pull/749) by [Tomas Van Pottelbergh](https://github.com/tomasvanpottelbergh). ## [0.16.1](https://github.com/unit8co/darts/tree/0.16.1) (2022-01-24) + Patch release ### For users of the library: + - Fixed an incompatibility with latest version of Pandas ([#752](https://github.com/unit8co/darts/pull/752)) by [Julien Herzen](https://github.com/hrzn). - Fixed non contiguous error when using lstm_layers > 1 on GPU. ([#740](https://github.com/unit8co/darts/pull/740)) @@ -533,17 +930,18 @@ Patch release by [Dustin Brunner](https://github.com/brunnedu). ### For developers of the library: + - Added flake8 tests to CI pipelines ([#749](https://github.com/unit8co/darts/pull/749), [#748](https://github.com/unit8co/darts/pull/748), [#745](https://github.com/unit8co/darts/pull/745)) by [Tomas Van Pottelbergh](https://github.com/tomasvanpottelbergh) and [Dennis Bader](https://github.com/dennisbader). - ## [0.16.0](https://github.com/unit8co/darts/tree/0.16.0) (2022-01-13) ### For users of the library: **Improved** + - The [documentation page](https://unit8co.github.io/darts/index.html) has been revamped and now contains a brand new Quickstart guide, as well as a User Guide section, which will be populated over time. - The [API documentation](https://unit8co.github.io/darts/generated_api/darts.html) has been revamped and improved, @@ -551,27 +949,31 @@ Patch release - The datasets building procedure has been improved in `RegressionModel`, which yields dramatic speed improvements. **Added** + - The `KalmanFilter` can now do system identification using `fit()` (using [nfoursid](https://github.com/spmvg/nfoursid)). **Fixed** + - Catch a [potentially problematic case](https://github.com/unit8co/darts/issues/724) in ensemble models. - Fixed support for `ReduceLROnPlateau` scheduler. - ### For developers of the library: + - We have switched to [black](https://black.readthedocs.io/en/stable/) for code formatting (this is checked by the CI pipeline). - ## [0.15.0](https://github.com/unit8co/darts/tree/0.15.0) (2021-12-24) + ### For users of the library: **Added**: + - On-the-fly encoding of position and calendar information in Torch-based models. Torch-based models now accept an option `add_encoders` parameter, specifying how to use certain calendar and position information as past and/or future covariates on the-fly. Example: + ``` from darts.dataprocessing.transformers import Scaler add_encoders={ @@ -582,6 +984,7 @@ Patch release 'transformer': Scaler() } ``` + This will add a cyclic encoding of the month as future covariates, add some datetime attributes as past and future covariates, an absolute/relative position (index), and even some custom mapping of the index (such as a function of the year). A `Scaler` will @@ -598,12 +1001,12 @@ Patch release - `TimeSeries.map()` and mappers data transformers now work on stochastic `TimeSeries`. - Granger causality function: `utils.statistics.granger_causality_tests` can test if one univariate `TimeSeries` "granger causes" another. -- New stationarity tests for univariate `TimeSeries`: `darts.utils.statistics.stationarity_tests`, +- New stationarity tests for univariate `TimeSeries` : `darts.utils.statistics.stationarity_tests`, `darts.utils.statistics.stationarity_test_adf` and `darts.utils.statistics.stationarity_test_kpss`. - New test coverage badge 🦄 - **Fixed**: + - Fixed various issues in different notebooks. - Fixed a bug handling frequencies in Prophet model. - Fixed an issue causing `PastCovariatesTorchModels` (such as `NBEATSModel`) prediction @@ -613,81 +1016,89 @@ Patch release - Fixed an issue causing `residuals()` to fail for Torch-based models. ### For developers of the library: + - Updated the [contribution guidelines](https://github.com/unit8co/darts/blob/master/CONTRIBUTING.md) - The unit tests have been re-organised with submodules following that of the library. - All relative import paths have been removed and replaced by absolute paths. - pytest and pytest-cov are now used to run tests and compute coverage. - ## [0.14.0](https://github.com/unit8co/darts/tree/0.14.0) (2021-11-28) + ### For users of the library: **Added**: + - Probabilistic N-BEATS: The `NBEATSModel` can now produce probabilistic forecasts, -in a similar way as all the other deep learning models in Darts (specifying a `likelihood` -and predicting with `num_samples` >> 1). + in a similar way as all the other deep learning models in Darts (specifying a `likelihood` + and predicting with `num_samples` >> 1). - We have improved the speed of the data loaing functionalities for PyTorch-based models. -This should speedup training, typically by a few percents. + This should speedup training, typically by a few percents. - Added `num_loader_workers` parameters to `fit()` and `predict()` methods of PyTorch-based models, -in order to control the `num_workers` of PyTorch DataLoaders. This can sometimes result in drastic speedups. + in order to control the `num_workers` of PyTorch DataLoaders. This can sometimes result in drastic speedups. - New method `TimeSeries.astype()` which allows to easily case (e.g. between `np.float64` and `np.float32`). - Added `dtype` as an option to the time series generation modules. - Added a small [performance guide](https://github.com/unit8co/darts/blob/master/guides/performance.md) for -PyTorch-based models. + PyTorch-based models. - Possibility to specify a (relative) time index to be used as future covariates in the TFT Model. -Future covariates don't have to be specified when this is used. + Future covariates don't have to be specified when this is used. - New TFT example notebook. - Less strict dependencies: we have loosened the required dependencies versions. **Fixed**: + - A small fix on the Temporal Fusion Transformer `TFTModel`, which should improve performance. - A small fix in the random state of some unit tests. - Fixed a typo in Transformer example notebook. - ## [0.13.1](https://github.com/unit8co/darts/tree/0.13.1) (2021-11-08) + ### For users of the library: **Added**: + - Factory methods in `TimeSeries` are now `classmethods`, which makes inheritance of `TimeSeries` more convenient. **Fixed**: + - An issue which was causing some of the flavours installations not to work ## [0.13.0](https://github.com/unit8co/darts/tree/0.13.0) (2021-11-07) + ### For users of the library: **Added**: -- New forecasting model: [Temporal Fusion Transformer](https://arxiv.org/abs/1912.09363) (`TFTModel`). + +- New forecasting model, [Temporal Fusion Transformer](https://arxiv.org/abs/1912.09363) (`TFTModel`). A new deep learning model supporting both past and future covariates. - Improved support for Facebook Prophet model (`Prophet`): - - Added support for fit & predict with future covariates. For instance: - `model.fit(train, future_covariates=train_covariates)` and - `model.predict(n=len(test), num_sample=1, future_covariates=test_covariates)` - - Added stochastic forecasting, for instance: `model.predict(n=len(test), num_samples=200)` - - Added user-defined seasonalities either at model creation with kwarg - `add_seasonality` (`Prophet(add_seasonality=kwargs_dict)`) or pre-fit with - `model.add_seasonality(kwargs)`. For more information on how to add seasonalities, - see the [Prophet docs](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet.html). - - Added possibility to predict and return the base model's raw output with `model.predict_raw()`. - Note that this returns a pd.DataFrame `pred_df`, which will not be supported for further - processing with the Darts API. But it is possible to access Prophet's methods such as - plots with `model.model.plot_compenents(pred_df)`. + - Added support for fit & predict with future covariates. For instance: + `model.fit(train, future_covariates=train_covariates)` and + `model.predict(n=len(test), num_sample=1, future_covariates=test_covariates)` + - Added stochastic forecasting, for instance: `model.predict(n=len(test), num_samples=200)` + - Added user-defined seasonalities either at model creation with kwarg + `add_seasonality` (`Prophet(add_seasonality=kwargs_dict)`) or pre-fit with + `model.add_seasonality(kwargs)`. For more information on how to add seasonalities, + see the [Prophet docs](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet.html). + - Added possibility to predict and return the base model's raw output with `model.predict_raw()`. + Note that this returns a pd.DataFrame `pred_df`, which will not be supported for further + processing with the Darts API. But it is possible to access Prophet's methods such as + plots with `model.model.plot_compenents(pred_df)`. - New `n_random_samples` in `gridsearch()` method, which allows to specify a number of (random) hyper parameters combinations to be tried, in order mainly to limit the gridsearch time. - Improvements in the checkpointing and saving of Torch models. - - Now models don't save checkpoints by default anymore. Set `save_checkpoints=True` to enable them. - - Models can be manually saved with `YourTorchModel.save_model(file_path)` - (file_path pointing to the .pth.tar file). - - Models can be manually loaded with `YourTorchModel.load_model(file_path)` or - the original method `YourTorchModel.load_from_checkpoint()`. + - Now models don't save checkpoints by default anymore. Set `save_checkpoints=True` to enable them. + - Models can be manually saved with `YourTorchModel.save_model(file_path)` + (file_path pointing to the .pth.tar file). + - Models can be manually loaded with `YourTorchModel.load_model(file_path)` or + the original method `YourTorchModel.load_from_checkpoint()`. - New `QuantileRegression` Likelihood class in `darts.utils.likelihood_models`. Allows to apply quantile regression loss, and get probabilistic forecasts on all deep learning models supporting likelihoods. Used by default in the Temporal Fusion Transformer. **Fixed:** + - Some issues with `darts.concatenate()`. - Fixed some bugs with `RegressionModel`s applied on multivariate series. - An issue with the confidence bounds computation in ACF plot. @@ -696,9 +1107,11 @@ Future covariates don't have to be specified when this is used. - Some rendering issues with bullet points lists in examples. ## [0.12.0](https://github.com/unit8co/darts/tree/0.12.0) (2021-09-25) + ### For users of the library: **Added**: + - Improved probabilistic forecasting with neural networks - Now all neural networks based forecasting models (except `NBEATSModel`) support probabilistic forecasting, by providing the `likelihood` parameter to the model's constructor method. @@ -713,33 +1126,38 @@ Future covariates don't have to be specified when this is used. - New rho-risk metric for probabilistic forecasts. - New method `darts.utils.statistics.plot_hist()` to plot histograms of time series data (e.g. backtest errors). - New argument `fillna_value` to `TimeSeries` factory methods, allowing to specify a value to fill missing dates -(instead of `np.nan`). + (instead of `np.nan`). - Synthetic `TimeSeries` generated with `darts.utils.timeseries_generation` methods can now be integer-index -(just pass an integer instead of a timestamp for the `start` argument). + (just pass an integer instead of a timestamp for the `start` argument). - Removed some deprecation warnings - Updated conda installation instructions **Fixed:** -- Removed [extra 1x1 convolutions](https://github.com/unit8co/darts/issues/470) in TCN Model. + +- Removed [extra 1x1 convolutions](https://github.com/unit8co/darts/pull/471) in TCN Model. - Fixed an issue with linewidth parameter when plotting `TimeSeries`. - Fixed a column name issue in datetime attribute time series. ### For developers of the library: + - We have removed the `develop` branch. - We force sklearn<1.0 has we have observed issues with pmdarima and sklearn==1.0 ## [0.11.0](https://github.com/unit8co/darts/tree/0.11.0) (2021-09-04) + ### For users of the library: **Added:** + - New model: `LightGBMModel` is a new regression model. Regression models allow to predict future values -of the target, given arbitrary lags of the target as well as past and/or future covariates. `RegressionModel` -already works with any scikit-learn regression model, and now `LightGBMModel` does the same with LightGBM. -If you want to activate LightGBM support in Darts, please read the detailed install notes on -the [README](https://github.com/unit8co/darts/blob/master/README.md) carefully. + of the target, given arbitrary lags of the target as well as past and/or future covariates. `RegressionModel` + already works with any scikit-learn regression model, and now `LightGBMModel` does the same with LightGBM. + If you want to activate LightGBM support in Darts, please read the detailed install notes on + the [README](https://github.com/unit8co/darts/blob/master/README.md) carefully. - Added stride support to gridsearch **Fixed:** + - A bug which was causing issues when training on a GPU with a validation set - Some issues with custom-provided RNN modules in `RNNModel`. - Properly handle `kwargs` in the `fit` function of `RegressionModel`s. @@ -747,92 +1165,99 @@ the [README](https://github.com/unit8co/darts/blob/master/README.md) carefully. - An issue causing errors in the FFT notebook ## [0.10.1](https://github.com/unit8co/darts/tree/0.10.1) (2021-08-19) + ### For users of the library: **Fixed:** + - A bug with memory pinning that was causing issues with training models on GPUs. **Changed:** + - Clarified conda support on the README ## [0.10.0](https://github.com/unit8co/darts/tree/0.10.0) (2021-08-13) + ### For users of the library: **Added:** -- 🔴 Improvement of the covariates support. Before, some models were accepting a `covariates` (or `exog`) -argument, but it wasn't always clear whether this represented "past-observed" or "future-known" covariates. -We have made this clearer. Now all covariate-aware models support `past_covariates` and/or `future_covariates` argument -in their `fit()` and `predict()` methods, which makes it clear what series is used as a past or future covariate. -We recommend [this article](https://medium.com/unit8-machine-learning-publication/time-series-forecasting-using-past-and-future-external-data-with-darts-1f0539585993) -for more information and examples. - -- 🔴 Significant improvement of `RegressionModel` (incl. `LinearRegressionModel` and `RandomForest`). -These models now support training on multiple (possibly multivariate) time series. They also support both -`past_covariates` and `future_covariates`. It makes it easier than ever to fit arbitrary regression models (e.g. from -scikit-learn) on multiple series, to predict the future of a target series based on arbitrary lags of the target and -the past/future covariates. The signature of these models changed: It's not using "`exog`" keyword arguments, but -`past_covariates` and `future_covariates` instead. +- 🔴 Improvement of the covariates support. Before, some models were accepting a `covariates` (or `exog`) + argument, but it wasn't always clear whether this represented "past-observed" or "future-known" covariates. + We have made this clearer. Now all covariate-aware models support `past_covariates` and/or `future_covariates` argument + in their `fit()` and `predict()` methods, which makes it clear what series is used as a past or future covariate. + We recommend [this article](https://medium.com/unit8-machine-learning-publication/time-series-forecasting-using-past-and-future-external-data-with-darts-1f0539585993) + for more information and examples. +- 🔴 Significant improvement of `RegressionModel` (incl. `LinearRegressionModel` and `RandomForest`). + These models now support training on multiple (possibly multivariate) time series. They also support both + `past_covariates` and `future_covariates`. It makes it easier than ever to fit arbitrary regression models (e.g. from + scikit-learn) on multiple series, to predict the future of a target series based on arbitrary lags of the target and + the past/future covariates. The signature of these models changed: It's not using "`exog`" keyword arguments, but + `past_covariates` and `future_covariates` instead. - Dynamic Time Warping. There is a brand new `darts.dataprocessing.dtw` submodule that -implements Dynamic Time Warping between two `TimeSeries`. It's also coming with a new `dtw` -metric in `darts.metrics`. We recommend going over the -[new DTW example notebook](https://github.com/unit8co/darts/blob/master/examples/13-Dynamic-Time-Warping-example.ipynb) -for a good overview of the new functionalities - + implements Dynamic Time Warping between two `TimeSeries`. It's also coming with a new `dtw` + metric in `darts.metrics`. We recommend going over the + [new DTW example notebook](https://github.com/unit8co/darts/blob/master/examples/13-Dynamic-Time-Warping-example.ipynb) + for a good overview of the new functionalities - Conda forge installation support (fully supported with Python 3.7 only for now). You can now -`conda install u8darts-all`. - + `conda install u8darts-all`. - `TimeSeries.from_csv()` allows to obtain a `TimeSeries` from a CSV file directly. - - Optional cyclic encoding of the datetime attributes future covariates; for instance it's now possible to call -`my_series.add_datetime_attribute('weekday', cyclic=True)`, which will add two columns containing a sin/cos -encoding of the weekday. - + `my_series.add_datetime_attribute('weekday', cyclic=True)`, which will add two columns containing a sin/cos + encoding of the weekday. - Default seasonality inference in `ExponentialSmoothing`. If left to `None`, the `seasonal_periods` is inferred -from the `freq` of the provided series. - + from the `freq` of the provided series. - Various documentation improvements. **Fixed:** + - Now transformations and forecasting maintain the columns' names of the `TimeSeries`. -The generation module `darts.utils.timeseries_generation` also comes with better default columns names. + The generation module `darts.utils.timeseries_generation` also comes with better default columns names. - Some issues with our Docker build process - A bug with GPU usage **Changed:** + - For probabilistic PyTorch based models, the generation of multiple samples (and series) at prediction time is now -vectorized, which improves inference performance. + vectorized, which improves inference performance. ## [0.9.1](https://github.com/unit8co/darts/tree/0.9.1) (2021-07-17) + ### For users of the library: **Added:** + - Improved `GaussianProcessFilter`, now handling missing values, and better handling -time series indexed by datetimes. + time series indexed by datetimes. - Improved Gaussian Process notebook. **Fixed:** + - `TimeSeries` now supports indexing using `pandas.Int64Index` and not just `pandas.RangeIndex`, -which solves some indexing issues. + which solves some indexing issues. - We have changed all factory methods of `TimeSeries` to have `fill_missing_dates=False` by -default. This is because in some cases inferring the frequency for missing dates and -resampling the series is causing significant performance overhead. + default. This is because in some cases inferring the frequency for missing dates and + resampling the series is causing significant performance overhead. - Fixed backtesting to make it work with integer-indexed series. - Fixed a bug that was causing inference to crash on GPUs for some models. - Fixed the default folder name, which was causing issues on Windows systems. - We have slightly improved the documentation rendering and fixed the titles -of the documentation pages for `RNNModel` and `BlockRNNModel` to distinguish them. + of the documentation pages for `RNNModel` and `BlockRNNModel` to distinguish them. **Changed:** + - The dependencies are not pinned to some exact versions anymore. ### For developers of the library: + - We have fixed the building process. ## [0.9.0](https://github.com/unit8co/darts/tree/0.9.0) (2021-07-09) + ### For users of the library: **Added:** + - Multiple forecasting models can now produce probabilistic forecasts by specifying a `num_samples` parameter when calling `predict()`. Stochastic forecasts are stored by utilizing the new `samples` dimension in the refactored `TimeSeries` class (see 'Changed' section). Models supporting probabilistic predictions so far are `ARIMA`, `ExponentialSmoothing`, `RNNModel` and `TCNModel`. - Introduced `LikelihoodModel` class which is used by probabilistic `TorchForecastingModel` classes in order to make predictions in the form of parametrized distributions of different types. - Added new abstract class `TorchParametricProbabilisticForecastingModel` to serve as parent class for probabilistic models. @@ -844,8 +1269,8 @@ of the documentation pages for `RNNModel` and `BlockRNNModel` to distinguish the - `ForecastingModel.gridsearch` now makes use of parallel computation. - Introduced a new `force_reset` parameter to `TorchForecastingModel.__init__()` which, if left to False, will prevent the user from overriding model data with the same name and directory. - **Fixed:** + - Solved bug occurring when training `NBEATSModel` on a GPU. - Fixed crash when running `NBEATSModel` with `log_tensorboard=True` - Solved bug occurring when training a `TorchForecastingModel` instance with a `batch_size` bigger than the available number of training samples. @@ -853,17 +1278,22 @@ of the documentation pages for `RNNModel` and `BlockRNNModel` to distinguish the - Other minor bug fixes **Changed:** -- 🔴 The `TimeSeries` class has been refactored to support stochastic time series representation by adding an additional dimension to a time series, namely `samples`. A time series is now based on a 3-dimensional `xarray.DataArray` with shape `(n_timesteps, n_components, n_samples)`. This overhaul also includes a change of the constructor which is incompatible with the old one. However, factory methods have been added to create a `TimeSeries` instance from a variety of data types, including `pd.DataFrame`. Please refer to the documentation of `TimeSeries` for more information. -- 🔴 The old version of `RNNModel` has been renamed to `BlockRNNModel`. + +- 🔴 The `TimeSeries` class has been refactored to support stochastic time series representation by adding an additional dimension to a time series, namely `samples`. A time series is now based on a 3-dimensional `xarray.DataArray` with shape `(n_timesteps, n_components, n_samples)`. This overhaul also includes a change of the constructor which is incompatible with the old one. However, factory methods have been added to create a `TimeSeries` instance from a variety of data types, including `pd.DataFrame`. Please refer to the documentation of `TimeSeries` for more information. +- 🔴 The old version of `RNNModel` has been renamed to `BlockRNNModel`. - The `historical_forecast()` and `backtest()` methods of `ForecastingModel` have been reorganized a bit by making use of new wrapper methods to fit and predict models. - Updated `README.md` to reflect the new additions to the library. ## [0.8.1](https://github.com/unit8co/darts/tree/0.8.1) (2021-05-22) + **Fixed:** + - Some fixes in the documentation **Changed:** + - The way to instantiate Dataset classes; datasets should now be used like this + ``` from darts.datasets import AirPassengers ts: TimeSeries = AirPassengers().load() @@ -872,131 +1302,152 @@ ts: TimeSeries = AirPassengers().load() ## [0.8.0](https://github.com/unit8co/darts/tree/0.8.0) (2021-05-21) ### For users of the library: + **Added:** + - `RandomForest` algorithm implemented. Uses the scikit-learn `RandomForestRegressor` to predict future values from (lagged) exogenous -variables and lagged values of the target. + variables and lagged values of the target. - `darts.datasets` is a new submodule allowing to easily download, cache and import some commonly used time series. - Better support for processing sequences of `TimeSeries`. * The Transformers, Pipelines and metrics have been adapted to be used on sequences of `TimeSeries` - (rather than isolated series). + (rather than isolated series). * The inference of neural networks on sequences of series has been improved - There is a new utils function `darts.utils.model_selection.train_test_split` which allows to split a `TimeSeries` -or a sequence of `TimeSeries` into train and test sets; either along the sample axis or along the time axis. -It also optionally allows to do "model-aware" splitting, where the split reclaims as much data as possible for the -training set. + or a sequence of `TimeSeries` into train and test sets; either along the sample axis or along the time axis. + It also optionally allows to do "model-aware" splitting, where the split reclaims as much data as possible for the + training set. - Our implementation of N-BEATS, `NBEATSModel`, now supports multivariate time series, as well as covariates. **Changed** + - `RegressionModel` is now a user exposed class. It acts as a wrapper around any regression model with a `fit()` and `predict()` -method. It enables the flexible usage of lagged values of the target variable as well as lagged values of multiple exogenous -variables. Allowed values for the `lags` argument are positive integers or a list of positive integers indicating which lags -should be used during training and prediction, e.g. `lags=12` translates to training with the last 12 lagged values of the target variable. -`lags=[1, 4, 8, 12]` translates to training with the previous value, the value at lag 4, lag 8 and lag 12. -- 🔴 `StandardRegressionModel` is now called `LinearRegressionModel`. It implements a linear regression model -from `sklearn.linear_model.LinearRegression`. Users who still need to use the former `StandardRegressionModel` with -another sklearn model should use the `RegressionModel` now. + method. It enables the flexible usage of lagged values of the target variable as well as lagged values of multiple exogenous + variables. Allowed values for the `lags` argument are positive integers or a list of positive integers indicating which lags + should be used during training and prediction, e.g. `lags=12` translates to training with the last 12 lagged values of the target variable. + `lags=[1, 4, 8, 12]` translates to training with the previous value, the value at lag 4, lag 8 and lag 12. +- 🔴 `StandardRegressionModel` is now called `LinearRegressionModel`. It implements a linear regression model + from `sklearn.linear_model.LinearRegression`. Users who still need to use the former `StandardRegressionModel` with + another sklearn model should use the `RegressionModel` now. **Fixed** + - We have fixed a bug arising when multiple scalers were used. - We have fixed a small issue in the TCN architecture, which makes our implementation follow the original paper -more closely. + more closely. ### For developers of the library: + **Added:** + - We have added some [contribution guidelines](https://github.com/unit8co/darts/blob/master/CONTRIBUTE.md). ## [0.7.0](https://github.com/unit8co/darts/tree/0.7.0) (2021-04-14) [Full Changelog](https://github.com/unit8co/darts/compare/0.6.0...0.7.0) + ### For users of the library: **Added:** + - `darts` Pypi package. It is now possible to `pip install darts`. The older name `u8darts` is still maintained -and provides the different flavours for lighter installs. + and provides the different flavours for lighter installs. - New forecasting model available: VARIMA (Vector Autoregressive moving average). - Support for exogeneous variables in ARIMA, AutoARIMA and VARIMA (optional `exog` parameter in `fit()` and `predict()` -methods). + methods). - New argument `dummy_index` for `TimeSeries` creation. If a series is just composed of a sequence of numbers -without timestamps, setting this flag will allow to create a `TimeSeries` which uses a "dummy time index" behind the -scenes. This simplifies the creation of `TimeSeries` in such cases, and makes it possible to use all forecasting models, -except those that explicitly rely on dates. + without timestamps, setting this flag will allow to create a `TimeSeries` which uses a "dummy time index" behind the + scenes. This simplifies the creation of `TimeSeries` in such cases, and makes it possible to use all forecasting models, + except those that explicitly rely on dates. - New method `TimeSeries.diff()` returning differenced `TimeSeries`. - Added an example of `RegressionEnsembleModel` in intro notebook. **Changed:** + - Improved N-BEATS example notebook. - Methods `TimeSeries.split_before()` and `split_after()` now also accept integer or float arguments (in addition to -timestamp) for the breaking point (e.g. specify 0.8 in order to obtain a 80%/20% split). + timestamp) for the breaking point (e.g. specify 0.8 in order to obtain a 80%/20% split). - Argument `value_cols` no longer has to be provided if not necessary when creating a `TimeSeries` from a `DataFrame`. - Update of dependency requirements to more recent versions. **Fixed:** + - Fix issue with MAX_TORCH_SEED_VALUE on 32-bit architectures (https://github.com/unit8co/darts/issues/235). - Corrected a bug in TCN inference, which should improve accuracy. - Fix historical forecasts not returning last point. - Fixed bug when calling the `TimeSeries.gaps()` function for non-regular time frequencies. - Many small bug fixes. - ## [0.6.0](https://github.com/unit8co/darts/tree/0.6.0) (2021-02-02) [Full Changelog](https://github.com/unit8co/darts/compare/0.5.0...0.6.0) + ### For users of the library: + **Added:** + - `Pipeline.invertible()` a getter which returns whether the pipeline is invertible or not. - `TimeSeries.to_json()` and `TimeSeries.from_json()` methods to convert `TimeSeries` to/from a `JSON` string. - New base class `GlobalForecastingModel` for all models supporting training on multiple time series, as well -as covariates. All PyTorch models are now `GlobalForecastingModel`s. + as covariates. All PyTorch models are now `GlobalForecastingModel`s. - As a consequence of the above, the `fit()` function of PyTorch models (all neural networks) can optionally be called -with a sequence of time series (instead of a single time series). + with a sequence of time series (instead of a single time series). - Similarly, the `predict()` function of these models also accepts a specification of which series should be forecasted - A new `TrainingDataset` base class. - Some implementations of `TrainingDataset` containing some slicing logic for the training of neural networks on -several time series. + several time series. - A new `TimeSeriesInferenceDataset` base class. - An implementation `SimpleInferenceDataset` of `TimeSeriesInferenceDataset`. - All PyTorch models have a new `fit_from_dataset()` method which allows to directly fit the model from a specified -`TrainingDataset` instance (instead of using a default instance when going via the :func:`fit()` method). + `TrainingDataset` instance (instead of using a default instance when going via the :func:`fit()` method). - A new explanatory notebooks for global models: -https://github.com/unit8co/darts/blob/master/examples/02-multi-time-series-and-covariates.ipynb + https://github.com/unit8co/darts/blob/master/examples/02-multi-time-series-and-covariates.ipynb **Changed:** -- 🔴 removed the arguments `training_series` and `target_series` in `ForecastingModel`s. Please consult -the API documentation of forecasting models to see the new signatures. -- 🔴 removed `UnivariateForecastingModel` and `MultivariateForecastingModel` base classes. This distinction does -not exist anymore. Instead, now some models are "global" (can be trained on multiple series) or "local" (they cannot). -All implementations of `GlobalForecastingModel`s support multivariate time series out of the box, except N-BEATS. + +- 🔴 removed the arguments `training_series` and `target_series` in `ForecastingModel`s. Please consult + the API documentation of forecasting models to see the new signatures. +- 🔴 removed `UnivariateForecastingModel` and `MultivariateForecastingModel` base classes. This distinction does + not exist anymore. Instead, now some models are "global" (can be trained on multiple series) or "local" (they cannot). + All implementations of `GlobalForecastingModel`s support multivariate time series out of the box, except N-BEATS. - Improved the documentation and README. - Re-ordered the example notebooks to improve the flow of examples. **Fixed:** + - Many small bug fixes. - Unit test speedup by about 15x. ## [0.5.0](https://github.com/unit8co/darts/tree/0.5.0) (2020-11-09) [Full Changelog](https://github.com/unit8co/darts/compare/0.4.0...0.5.0) + ### For users of the library: + **Added:** + - Ensemble models, a new kind of `ForecastingModel` which allows to ensemble multiple models to make predictions: - `EnsembleModel` is the abstract base class for ensemble models. Classes deriving from `EnsembleModel` must implement the `ensemble()` method, which takes in a `List[TimeSeries]` of predictions from the constituent models, and returns the ensembled prediction (a single `TimeSeries` object) - `RegressionEnsembleModel`, a concrete implementation of `EnsembleModel `which allows to specify any regression model (providing `fit()` and `predict()` methods) to use to ensemble the constituent models' predictions. -- A new method to `TorchForecastingModel`: `untrained_model()` returns the model as it was initially created, allowing to retrain the exact same model from scratch. Works both when specifying a `random_state` or not. +- A new method to `TorchForecastingModel` : `untrained_model()` returns the model as it was initially created, allowing to retrain the exact same model from scratch. Works both when specifying a `random_state` or not. - New `ForecastingModel.backtest()` and `RegressionModel.backtest()` functions which by default compute a single error score from the historical forecasts the model would have produced. - A new `reduction` parameter allows to specify whether to compute the mean/median/… of errors or (when `reduction` is set to `None`) to return a list of historical errors. - The previous `backtest()` functionality still exists but has been renamed `historical_forecasts()` - Added a new `last_points_only` parameter to `historical_forecasts()`, `backtest()` and `gridsearch()` **Changed:** -- 🔴 Renamed `backtest()` into `historical_forecasts()` + +- 🔴 Renamed `backtest()` into `historical_forecasts()` - `fill_missing_values()` and `MissingValuesFiller` used to remove the variable names when used with `fill='auto'` – not anymore. - Modified the default plotting style to increase contrast and make plots lighter. **Fixed:** + - Small mistake in the `NaiveDrift` model implementation which caused the first predicted value to repeat the last training value. ### For developers of the library: + **Changed:** + - `@random_method` decorator now always assigns a `_random_instance` field to decorated methods (seeded with a random seed). This doesn't change the observed behavior, but allows to deterministically "reset" `TorchForecastingModel` by saving `_random_instance` along with the other parameters of the model upon creation. ## [0.4.0](https://github.com/unit8co/darts/tree/0.4.0) (2020-10-28) @@ -1004,10 +1455,12 @@ All implementations of `GlobalForecastingModel`s support multivariate time serie [Full Changelog](https://github.com/unit8co/darts/compare/0.3.0...0.4.0) ### For users of the library: + **Added:** -- Data (pre) processing abilities using `DataTransformer`, `Pipeline`: + +- Data (pre) processing abilities using `DataTransformer`, `Pipeline` : - `DataTransformer` provide a unified interface to apply transformations on `TimeSeries`, using their `transform()` method - - `Pipeline`: + - `Pipeline` : - allow chaining of `DataTransformers` - provide `fit()`, `transform()`, `fit_transform()` and `inverse_transform()` methods. - Implementing your own data transformers: @@ -1025,7 +1478,8 @@ All implementations of `GlobalForecastingModel`s support multivariate time serie - `NBEATSModel`, an implementation based on the N-BEATS architecture described in [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://openreview.net/forum?id=r1ecqn4YwB) by Boris N. Oreshkin et al. (2019) **Changed:** -- 🔴 Removed `cols` parameter from `map()`. Using indexing on `TimeSeries` is preferred. + +- 🔴 Removed `cols` parameter from `map()`. Using indexing on `TimeSeries` is preferred. ```python # Assuming a multivariate TimeSeries named series with 3 columns or variables. # To apply fn to columns with names '0' and '2': @@ -1035,9 +1489,9 @@ All implementations of `GlobalForecastingModel`s support multivariate time serie #new syntax series[['0', '2']].map(fn) # returns a time series with only 2 columns ``` -- 🔴 Renamed `ScalerWrapper` into `Scaler` -- 🔴 Renamed the `preprocessing` module into `dataprocessing` -- 🔴 Unified `auto_fillna()` and `fillna()` into a single `fill_missing_value()` function +- 🔴 Renamed `ScalerWrapper` into `Scaler` +- 🔴 Renamed the `preprocessing` module into `dataprocessing` +- 🔴 Unified `auto_fillna()` and `fillna()` into a single `fill_missing_value()` function ```python #old syntax fillna(series, fill=0) @@ -1054,8 +1508,10 @@ All implementations of `GlobalForecastingModel`s support multivariate time serie ``` ### For developers of the library + **Changed:** -- GitHub release workflow is now triggered manually from the GitHub "Actions" tab in the repository, providing a `#major`, `#minor`, or `#patch` argument. [\#211](https://github.com/unit8co/darts/pull/211) + +- GitHub release workflow is now triggered manually from the GitHub "Actions" tab in the repository, providing a `#major`, `#minor`, or `#patch` argument. [#211](https://github.com/unit8co/darts/pull/211) - (A limited number of) notebook examples are now run as part of the GitHub PR workflow. ## [0.3.0](https://github.com/unit8co/darts/tree/0.3.0) (2020-10-05) @@ -1063,21 +1519,22 @@ All implementations of `GlobalForecastingModel`s support multivariate time serie [Full Changelog](https://github.com/unit8co/darts/compare/0.2.3...0.3.0) ### For users of the library: + **Added:** -- Better indexing on TimeSeries (support for column/component indexing) [\#150](https://github.com/unit8co/darts/pull/150) -- New `FourTheta` forecasting model [\#123](https://github.com/unit8co/darts/pull/123), [\#156](https://github.com/unit8co/darts/pull/156) -- `map()` method for TimeSeries [\#121](https://github.com/unit8co/darts/issues/121), [\#166](https://github.com/unit8co/darts/pull/166) -- Further improved the backtesting functions [\#111](https://github.com/unit8co/darts/pull/111): +- Better indexing on TimeSeries (support for column/component indexing) [#150](https://github.com/unit8co/darts/pull/150) +- New `FourTheta` forecasting model [#123](https://github.com/unit8co/darts/pull/123), [#156](https://github.com/unit8co/darts/pull/156) +- `map()` method for TimeSeries [#163](https://github.com/unit8co/darts/pull/163), [#166](https://github.com/unit8co/darts/pull/166) +- Further improved the backtesting functions [#111](https://github.com/unit8co/darts/pull/111): - Added support for multivariate TimeSeries and models - Added `retrain` and `stride` parameters -- Custom style for matplotlib plots [\#191](https://github.com/unit8co/darts/pull/191) -- sMAPE metric [\#129](https://github.com/unit8co/darts/pull/129) -- Option to specify a `random_state` at model creation using the `@random_method` decorator on models using neural networks to allow reproducibility of results [\#118](https://github.com/unit8co/darts/pull/118) +- Custom style for matplotlib plots [#191](https://github.com/unit8co/darts/pull/191) +- sMAPE metric [#129](https://github.com/unit8co/darts/pull/129) +- Option to specify a `random_state` at model creation using the `@random_method` decorator on models using neural networks to allow reproducibility of results [#118](https://github.com/unit8co/darts/pull/118) **Changed:** -- 🔴 **Refactored backtesting** [\#184](https://github.com/unit8co/darts/pull/184) +- 🔴 **Refactored backtesting** [#184](https://github.com/unit8co/darts/pull/184) - Moved backtesting functionalities inside `ForecastingModel` and `RegressionModel` ```python # old syntax: @@ -1093,7 +1550,7 @@ All implementations of `GlobalForecastingModel`s support multivariate time serie regression_model.backtest(*args, **kwargs) ``` - Consequently removed the `backtesting` module -- 🔴 `ForecastingModel` `fit()` **method syntax** using TimeSeries indexing instead of additional parameters [\#161](https://github.com/unit8co/darts/pull/161) +- 🔴 `ForecastingModel` `fit()` **method syntax** using TimeSeries indexing instead of additional parameters [#161](https://github.com/unit8co/darts/pull/161) ```python # old syntax: multivariate_model.fit(multivariate_series, target_indices=[0, 1]) @@ -1109,24 +1566,28 @@ All implementations of `GlobalForecastingModel`s support multivariate time serie ``` **Fixed:** -- Solved issue of TorchForecastingModel.predict(n) throwing an error at n=1. [\#108](https://github.com/unit8co/darts/pull/108) -- Fixed MASE metrics [\#129](https://github.com/unit8co/darts/pull/129) -- \[BUG\] ForecastingModel.backtest: Can bypass sanity checks [\#188](https://github.com/unit8co/darts/issues/188) -- ForecastingModel.backtest\(\) fails if forecast\_horizon isn't provided [\#186](https://github.com/unit8co/darts/issues/186) + +- Solved issue of TorchForecastingModel.predict(n) throwing an error at n=1. [#108](https://github.com/unit8co/darts/pull/108) +- Fixed MASE metrics [#129](https://github.com/unit8co/darts/pull/129) +- BUG ForecastingModel.backtest: Can bypass sanity checks [#189](https://github.com/unit8co/darts/pull/189) +- `ForecastingModel.backtest()` fails if `forecast_horizon` isn't provided [#186](https://github.com/unit8co/darts/issues/186) ### For developers of the library **Added:** -- Gradle to build docs, docker image, run tests, … [\#112](https://github.com/unit8co/darts/pull/112), [\#127](https://github.com/unit8co/darts/pull/127), [\#159](https://github.com/unit8co/darts/pull/159) -- M4 competition benchmark and notebook to the examples [\#138](https://github.com/unit8co/darts/pull/138) -- Check of test coverage [\#141](https://github.com/unit8co/darts/pull/141) + +- Gradle to build docs, docker image, run tests, … [#112](https://github.com/unit8co/darts/pull/112), [#127](https://github.com/unit8co/darts/pull/127), [#159](https://github.com/unit8co/darts/pull/159) +- M4 competition benchmark and notebook to the examples [#138](https://github.com/unit8co/darts/pull/138) +- Check of test coverage [#141](https://github.com/unit8co/darts/pull/141) **Changed:** -- Dependencies' versions are now fixed [\#173](https://github.com/unit8co/darts/pull/173) -- Workflow: tests trigger on Pull Request [\#165](https://github.com/unit8co/darts/pull/165) + +- Dependencies' versions are now fixed [#173](https://github.com/unit8co/darts/pull/173) +- Workflow: tests trigger on Pull Request [#165](https://github.com/unit8co/darts/pull/165) **Fixed:** -- Passed the `freq` parameter to the `TimeSeries` constructor in all TimeSeries generating functions [\#157](https://github.com/unit8co/darts/pull/157) + +- Passed the `freq` parameter to the `TimeSeries` constructor in all TimeSeries generating functions [#157](https://github.com/unit8co/darts/pull/157) ## Older releases diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 25df10c37c..83ec290b23 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -48,31 +48,39 @@ and discuss it with some of the core team. * `refactor/` * … * Work on your update -7. Check that your code passes all the tests and design new unit tests if needed: `./gradlew test_all`. -8. Verify your tests coverage by running `./gradlew coverageTest` - * Additionally you can generate an xml report and use VSCode Coverage gutter to identify untested - lines with `./coverage.sh xml` -9. If your contribution introduces a non-negligible change, add it to `CHANGELOG.md` under the "Unreleased" section. +7. Check that your code passes all the tests and design new unit tests if needed: `pytest`. +8. If your contribution introduces a non-negligible change, add it to `CHANGELOG.md` under the "Unreleased" section. You can already refer to the pull request. In addition, for tracking contributions we are happy if you provide your full name (if you want to) and link to your Github handle. Example: ``` - Added new feature XYZ. [#001](https://https://github.com/unit8co/darts/pull/001) by [](https://github.com/). ``` -10. Create a pull request from your new branch into the **master** branch. +9. Create a pull request from your new branch into the **master** branch. +10. `Codecov` will add a test coverage report in the pull request. Make sure your test cover all changed lines. +### Build the Documentation Locally + +You can build the documentation locally using `make`: + +```bash +# make sure your latest changes are installed +pip install . +# build the docs +make --directory=./docs build-all-docs +``` +After that docs will be available in `./docs/build/html` directory. You can just open `./docs/build/html/index.html` using your favourite browser. ### Code Formatting and Linting -Darts uses [Black](https://black.readthedocs.io/en/stable/index.html) with default values for automatic code formatting, along with [flake8](https://flake8.pycqa.org/en/latest/) and [isort](https://pycqa.github.io/isort/). +Darts uses [Black via Ruff](https://docs.astral.sh/ruff/formatter/) with default values for automatic code formatting, along with [ruff](https://docs.astral.sh/ruff/). As part of the checks on pull requests, it is checked whether the code still adheres to the code style. To ensure you don't need to worry about formatting and linting when contributing, it is recommended to set up at least one of the following: - Integration in git (recommended): 1. Install the pre-commit hook using `pre-commit install` - 2. This will install Black, isort and pyupgrade formatting and flake8 linting hooks - 3. The formatters will automatically fix all files and flake8 will highlight any potential problems before committing + 2. This will install `ruff` linting hooks + 3. The formatters will automatically fix all files and in case of some non-trivial case `ruff` will highlight any remaining problems before committing - Integration in your editor: - - For [Black](https://black.readthedocs.io/en/stable/integrations/editors.html) - For other integrations please look at the documentation for your editor ### Development environment on Mac with Apple Silicon M1 processor (arm64 architecture) @@ -80,5 +88,5 @@ To ensure you don't need to worry about formatting and linting when contributing Please follow the procedure described in [INSTALL.md](https://github.com/unit8co/darts/blob/master/INSTALL.md#test-environment-appple-m1-processor) to set up a x_64 emulated environment. For the development environment, instead of installing Darts with `pip install darts`, instead go to the darts cloned repo location and install the packages with: `pip install -r requirements/dev-all.txt`. -If necessary, follow the same steps to setup libomp for lightgbm. -Finally, verify your overall environment setup by successfully running all unitTests with gradlew or pytest. +If necessary, follow the same steps to setup libomp for lightgbm. +Finally, verify your overall environment setup by successfully running all unitTests with `pytest`. diff --git a/Dockerfile b/Dockerfile index 160fbec0a8..37444e88e2 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,9 +1,4 @@ -FROM ubuntu:latest - -# setup packages -RUN apt-get update -y -RUN apt-get install -y python3 python-is-python3 python3-pip default-jre -RUN pip install --upgrade pip +FROM python:3.10 # install python requirements before copying the rest of the files # this way we can cache the requirements and not have to reinstall them @@ -21,4 +16,4 @@ RUN pip install -e . # assuming you are working from inside your darts directory: # docker build . -t darts-test:latest -# docker run -it -v $(pwd)/:/app/ darts-test:latest bash \ No newline at end of file +# docker run -it -v $(pwd)/:/app/ darts-test:latest bash diff --git a/INSTALL.md b/INSTALL.md index c69a29ce54..00d5fa825b 100644 --- a/INSTALL.md +++ b/INSTALL.md @@ -5,7 +5,7 @@ Below, we detail how to install Darts using either `conda` or `pip`. ## From PyPI Install Darts with all models except the ones from optional dependencies (Prophet, LightGBM, CatBoost, see more on that [here](#enabling-optional-dependencies)): `pip install darts`. -If this fails on your platform, please follow the official installation +If this fails on your platform, please follow the official installation guide for [PyTorch](https://pytorch.org/get-started/locally/), then try installing Darts again. As some dependencies are relatively big or involve non-Python dependencies, @@ -37,8 +37,8 @@ As some models have relatively heavy dependencies, we provide four conda-forge p ## Other Information ### Enabling Optional Dependencies -As of version 0.25.0, the default `darts` package does not install Prophet, CatBoost, and LightGBM dependencies anymore, because their -build processes were too often causing issues. We continue supporting the model wrappers `Prophet`, `CatBoostModel`, and `LightGBMModel` in Darts though. If you want to use any of them, you will need to manually install the corresponding packages (or install a Darts flavor as described above). +As of version 0.25.0, the default `darts` package does not install Prophet, CatBoost, and LightGBM dependencies anymore, because their +build processes were too often causing issues. We continue supporting the model wrappers `Prophet`, `CatBoostModel`, and `LightGBMModel` in Darts though. If you want to use any of them, you will need to manually install the corresponding packages (or install a Darts flavor as described above). #### Prophet Install the `prophet` package (version 1.1.1 or more recent) using the [Prophet install guide](https://facebook.github.io/prophet/docs/installation.html#python) @@ -73,30 +73,3 @@ jupyter lab --ip 0.0.0.0 --no-browser --allow-root ``` Then copy and paste the URL provided by the docker container into your browser to access Jupyter notebook. - - -## Tests - -The gradle setup works best when used in a python environment, but the only requirement is to have `pip` installed for Python 3+ - -To run all tests at once just run -```bash -./gradlew test_all -``` - -alternatively you can run -```bash -./gradlew unitTest_all # to run only unittests -./gradlew coverageTest # to run coverage -./gradlew lint # to run linter -``` - -To run the tests for specific flavours of the library, replace `_all` with `_core`, `_prophet`, `_pmdarima` or `_torch`. - -## Documentation - -To build documentation locally just run -```bash -./gradlew buildDocs -``` -After that docs will be available in `./docs/build/html` directory. You can just open `./docs/build/html/index.html` using your favourite browser. \ No newline at end of file diff --git a/README.md b/README.md index 2d3f335742..46b04eb0f1 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ --- [![PyPI version](https://badge.fury.io/py/u8darts.svg)](https://badge.fury.io/py/darts) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/u8darts-all.svg)](https://anaconda.org/conda-forge/u8darts-all) -![Supported versions](https://img.shields.io/badge/python-3.8+-blue.svg) +![Supported versions](https://img.shields.io/badge/python-3.9+-blue.svg) [![Docker Image Version (latest by date)](https://img.shields.io/docker/v/unit8/darts?label=docker&sort=date)](https://hub.docker.com/r/unit8/darts) ![GitHub Release Date](https://img.shields.io/github/release-date/unit8co/darts) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/unit8co/darts/release.yml?branch=master) @@ -19,8 +19,8 @@ on time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The forecasting models can all be used in the same way, using `fit()` and `predict()` functions, similar to scikit-learn. The library also makes it easy to backtest models, -combine the predictions of several models, and take external data into account. -Darts supports both univariate and multivariate time series and models. +combine the predictions of several models, and take external data into account. +Darts supports both univariate and multivariate time series and models. The ML-based models can be trained on potentially large datasets containing multiple time series, and some of the models offer a rich support for probabilistic forecasting. @@ -50,7 +50,7 @@ fledged anomaly detection models. ## Quick Install -We recommend to first setup a clean Python environment for your project with Python 3.8+ using your favorite tool +We recommend to first setup a clean Python environment for your project with Python 3.9+ using your favorite tool ([conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html "conda-env"), [venv](https://docs.python.org/3/library/venv.html), [virtualenv](https://virtualenv.pypa.io/en/latest/) with or without [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)). @@ -59,7 +59,7 @@ Once your environment is set up you can install darts using pip: pip install darts -For more details you can refer to our +For more details you can refer to our [installation instructions](https://github.com/unit8co/darts/blob/master/INSTALL.md). ## Example Usage @@ -164,9 +164,9 @@ series.plot() The `PyODScorer` makes it trivial to use PyOD detectors on time series. * **Multivariate Support:** `TimeSeries` can be multivariate - i.e., contain multiple time-varying - dimensions instead of a single scalar value. Many models can consume and produce multivariate series. + dimensions/columns instead of a single scalar value. Many models can consume and produce multivariate series. -* **Multiple series training (global models):** All machine learning based models (incl. all neural networks) +* **Multiple Series Training (Global Models):** All machine learning based models (incl. all neural networks) support being trained on multiple (potentially multivariate) series. This can scale to large datasets too. * **Probabilistic Support:** `TimeSeries` objects can (optionally) represent stochastic @@ -174,10 +174,13 @@ series.plot() flavours of probabilistic forecasting (such as estimating parametric distributions or quantiles). Some anomaly detection scorers are also able to exploit these predictive distributions. -* **Past and Future Covariates support:** Many models in Darts support past-observed and/or future-known +* **Conformal Prediction Support:** Our conformal prediction models allow to generate probabilistic forecasts with + calibrated quantile intervals for any pre-trained global forecasting model. + +* **Past and Future Covariates Support:** Many models in Darts support past-observed and/or future-known covariate (external data) time series as inputs for producing forecasts. -* **Static Covariates support:** In addition to time-dependent data, `TimeSeries` can also contain +* **Static Covariates Support:** In addition to time-dependent data, `TimeSeries` can also contain static data for each dimension, which can be exploited by some models. * **Hierarchical Reconciliation:** Darts offers transformers to perform reconciliation. @@ -186,9 +189,16 @@ series.plot() * **Regression Models:** It is possible to plug-in any scikit-learn compatible model to obtain forecasts as functions of lagged values of the target series and covariates. +* **Training with Sample Weights:** All global models support being trained with sample weights. They can be + applied to each observation, forecasted time step and target column. + +* **Forecast Start Shifting:** All global models support training and prediction on a shifted output window. + This is useful for example for Day-Ahead Market forecasts, or when the covariates (or target series) are reported + with a delay. + * **Explainability:** Darts has the ability to *explain* some forecasting models using Shap values. -* **Data processing:** Tools to easily apply (and revert) common transformations on +* **Data Processing:** Tools to easily apply (and revert) common transformations on time series data (scaling, filling missing values, differencing, boxcox, ...) * **Metrics:** A variety of metrics for evaluating time series' goodness of fit; @@ -211,53 +221,61 @@ Here's a breakdown of the forecasting models currently implemented in Darts. We on bringing more models and features. -| Model | Sources | Target Series Support:

Univariate/
Multivariate | Covariates Support:

Past-observed/
Future-known/
Static | Probabilistic Forecasting:

Sampled/
Distribution Parameters | Training & Forecasting on Multiple Series | -|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------------------|--------------------------------------------------------------------------|-------------------------------------------| -| **Baseline Models**
([LocalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms)) | | | | | | -| [NaiveMean](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean) | | 🟩 🟩 | 🟥 🟥 🟥 | 🟥 🟥 | 🟥 | -| [NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal) | | 🟩 🟩 | 🟥 🟥 🟥 | 🟥 🟥 | 🟥 | -| [NaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift) | | 🟩 🟩 | 🟥 🟥 🟥 | 🟥 🟥 | 🟥 | -| [NaiveMovingAverage](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage) | | 🟩 🟩 | 🟥 🟥 🟥 | 🟥 🟥 | 🟥 | -| **Statistical / Classic Models**
([LocalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms)) | | | | | | -| [ARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA) | | 🟩 🟥 | 🟥 🟩 🟥 | 🟩 🟥 | 🟥 | -| [VARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA) | | 🟥 🟩 | 🟥 🟩 🟥 | 🟩 🟥 | 🟥 | -| [AutoARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.auto_arima.html#darts.models.forecasting.auto_arima.AutoARIMA) | | 🟩 🟥 | 🟥 🟩 🟥 | 🟥 🟥 | 🟥 | -| [StatsForecastAutoArima](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA) (faster AutoARIMA) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | 🟩 🟥 | 🟥 🟩 🟥 | 🟩 🟥 | 🟥 | -| [ExponentialSmoothing](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing) | | 🟩 🟥 | 🟥 🟥 🟥 | 🟩 🟥 | 🟥 | -| [StatsforecastAutoETS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | 🟩 🟥 | 🟥 🟩 🟥 | 🟩 🟥 | 🟥 | -| [StatsforecastAutoCES](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | 🟩 🟥 | 🟥 🟥 🟥 | 🟥 🟥 | 🟥 | -| [BATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.BATS) and [TBATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.TBATS) | [TBATS paper](https://robjhyndman.com/papers/ComplexSeasonality.pdf) | 🟩 🟥 | 🟥 🟥 🟥 | 🟩 🟥 | 🟥 | -| [Theta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta) and [FourTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta) | [Theta](https://robjhyndman.com/papers/Theta.pdf) & [4 Theta](https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R) | 🟩 🟥 | 🟥 🟥 🟥 | 🟥 🟥 | 🟥 | -| [StatsForecastAutoTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | 🟩 🟥 | 🟥 🟥 🟥 | 🟩 🟥 | 🟥 | -| [Prophet](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet) | [Prophet repo](https://github.com/facebook/prophet) | 🟩 🟥 | 🟥 🟩 🟥 | 🟩 🟥 | 🟥 | -| [FFT](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT) (Fast Fourier Transform) | | 🟩 🟥 | 🟥 🟥 🟥 | 🟥 🟥 | 🟥 | -| [KalmanForecaster](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster) using the Kalman filter and N4SID for system identification | [N4SID paper](https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf) | 🟩 🟩 | 🟥 🟩 🟥 | 🟩 🟥 | 🟥 | -| [Croston](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.croston.html#darts.models.forecasting.croston.Croston) method | | 🟩 🟥 | 🟥 🟩 🟥 | 🟥 🟥 | 🟥 | -| **Regression Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | | -| [RegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel): generic wrapper around any sklearn regression model | | 🟩 🟩 | 🟩 🟩 🟩 | 🟥 🟥 | 🟩 | -| [LinearRegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel) | | 🟩 🟩 | 🟩 🟩 🟩 | 🟩 🟩 | 🟩 | -| [RandomForest](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest) | | 🟩 🟩 | 🟩 🟩 🟩 | 🟥 🟥 | 🟩 | -| [LightGBMModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel) | | 🟩 🟩 | 🟩 🟩 🟩 | 🟩 🟩 | 🟩 | -| [XGBModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel) | | 🟩 🟩 | 🟩 🟩 🟩 | 🟩 🟩 | 🟩 | -| [CatBoostModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel) | | 🟩 🟩 | 🟩 🟩 🟩 | 🟩 🟩 | 🟩 | -| **PyTorch (Lightning)-based Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | | -| [RNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel) (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | [DeepAR paper](https://arxiv.org/abs/1704.04110) | 🟩 🟩 | 🟥 🟩 🟥 | 🟩 🟩 | 🟩 | -| [BlockRNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel) (incl. LSTM and GRU) | | 🟩 🟩 | 🟩 🟥 🟥 | 🟩 🟩 | 🟩 | -| [NBEATSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel) | [N-BEATS paper](https://arxiv.org/abs/1905.10437) | 🟩 🟩 | 🟩 🟥 🟥 | 🟩 🟩 | 🟩 | -| [NHiTSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel) | [N-HiTS paper](https://arxiv.org/abs/2201.12886) | 🟩 🟩 | 🟩 🟥 🟥 | 🟩 🟩 | 🟩 | -| [TCNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel) | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4) | 🟩 🟩 | 🟩 🟥 🟥 | 🟩 🟩 | 🟩 | -| [TransformerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel) | | 🟩 🟩 | 🟩 🟥 🟥 | 🟩 🟩 | 🟩 | -| [TFTModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel) (Temporal Fusion Transformer) | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html) | 🟩 🟩 | 🟩 🟩 🟩 | 🟩 🟩 | 🟩 | -| [DLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel) | [DLinear paper](https://arxiv.org/pdf/2205.13504.pdf) | 🟩 🟩 | 🟩 🟩 🟩 | 🟩 🟩 | 🟩 | -| [NLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel) | [NLinear paper](https://arxiv.org/pdf/2205.13504.pdf) | 🟩 🟩 | 🟩 🟩 🟩 | 🟩 🟩 | 🟩 | -| [TiDEModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel) | [TiDE paper](https://arxiv.org/pdf/2304.08424.pdf) | 🟩 🟩 | 🟩 🟩 🟩 | 🟩 🟩 | 🟩 | -| **Ensemble Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)): Model support is dependent on ensembled forecasting models and the ensemble model itself | | | | | | -| [NaiveEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel) | | 🟩 🟩 | 🟩 🟩 🟩 | 🟩 🟩 | 🟩 | -| [RegressionEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel) | | 🟩 🟩 | 🟩 🟩 🟩 | 🟩 🟩 | 🟩 | - +| Model | Sources | Target Series Support:

Univariate/
Multivariate | Covariates Support:

Past-observed/
Future-known/
Static | Probabilistic Forecasting:

Sampled/
Distribution Parameters | Training & Forecasting on Multiple Series | +|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------------------|--------------------------------------------------------------------------|-------------------------------------------| +| **Baseline Models**
([LocalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms)) | | | | | | +| [NaiveMean](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [NaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [NaiveMovingAverage](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| **Statistical / Classic Models**
([LocalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms)) | | | | | | +| [ARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA) | | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [VARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA) | | 🔴 ✅ | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [AutoARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.auto_arima.html#darts.models.forecasting.auto_arima.AutoARIMA) | | ✅ 🔴 | 🔴 ✅ 🔴 | 🔴 🔴 | 🔴 | +| [StatsForecastAutoArima](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA) (faster AutoARIMA) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [ExponentialSmoothing](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing) | | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 | +| [StatsforecastAutoETS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [StatsforecastAutoCES](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [BATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.BATS) and [TBATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.TBATS) | [TBATS paper](https://robjhyndman.com/papers/ComplexSeasonality.pdf) | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 | +| [StatsForecastAutoTBATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_tbats.html#darts.models.forecasting.sf_auto_tbats.StatsForecastAutoTBATS) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 | +| [Theta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta) and [FourTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta) | [Theta](https://robjhyndman.com/papers/Theta.pdf) & [4 Theta](https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R) | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [StatsForecastAutoTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta) | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast) | ✅ 🔴 | 🔴 🔴 🔴 | ✅ 🔴 | 🔴 | +| [Prophet](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet) | [Prophet repo](https://github.com/facebook/prophet) | ✅ 🔴 | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [FFT](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT) (Fast Fourier Transform) | | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| [KalmanForecaster](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster) using the Kalman filter and N4SID for system identification | [N4SID paper](https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf) | ✅ ✅ | 🔴 ✅ 🔴 | ✅ 🔴 | 🔴 | +| [Croston](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.croston.html#darts.models.forecasting.croston.Croston) method | | ✅ 🔴 | 🔴 🔴 🔴 | 🔴 🔴 | 🔴 | +| **Global Baseline Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | | +| [GlobalNaiveAggregate](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveAggregate) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ | +| [GlobalNaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveDrift) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ | +| [GlobalNaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveSeasonal) | | ✅ ✅ | 🔴 🔴 🔴 | 🔴 🔴 | ✅ | +| **Regression Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | | +| [RegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel): generic wrapper around any sklearn regression model | | ✅ ✅ | ✅ ✅ ✅ | 🔴 🔴 | ✅ | +| [LinearRegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [RandomForest](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest) | | ✅ ✅ | ✅ ✅ ✅ | 🔴 🔴 | ✅ | +| [LightGBMModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [XGBModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [CatBoostModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| **PyTorch (Lightning)-based Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)) | | | | | | +| [RNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel) (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | [DeepAR paper](https://arxiv.org/abs/1704.04110) | ✅ ✅ | 🔴 ✅ 🔴 | ✅ ✅ | ✅ | +| [BlockRNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel) (incl. LSTM and GRU) | | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | +| [NBEATSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel) | [N-BEATS paper](https://arxiv.org/abs/1905.10437) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | +| [NHiTSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel) | [N-HiTS paper](https://arxiv.org/abs/2201.12886) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | +| [TCNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel) | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4) | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | +| [TransformerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel) | | ✅ ✅ | ✅ 🔴 🔴 | ✅ ✅ | ✅ | +| [TFTModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel) (Temporal Fusion Transformer) | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [DLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel) | [DLinear paper](https://arxiv.org/pdf/2205.13504.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [NLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel) | [NLinear paper](https://arxiv.org/pdf/2205.13504.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [TiDEModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel) | [TiDE paper](https://arxiv.org/pdf/2304.08424.pdf) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [TSMixerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tsmixer_model.html#darts.models.forecasting.tsmixer_model.TSMixerModel) | [TSMixer paper](https://arxiv.org/pdf/2303.06053.pdf), [PyTorch Implementation](https://github.com/ditschuk/pytorch-tsmixer) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| **Ensemble Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)): Model support is dependent on ensembled forecasting models and the ensemble model itself | | | | | | +| [NaiveEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [RegressionEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel) | | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| **Conformal Models**
([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)): Model support is dependent on the forecasting model used | | | | | | +| [ConformalNaiveModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalNaiveModel) | [Conformalized Prediction](https://arxiv.org/pdf/1905.03222) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | +| [ConformalQRModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalQRModel) | [Conformalized Quantile Regression](https://arxiv.org/pdf/1905.03222) | ✅ ✅ | ✅ ✅ ✅ | ✅ ✅ | ✅ | ## Community & Contact -Anyone is welcome to join our [Gitter room](https://gitter.im/u8darts/darts) to ask questions, make proposals, +Anyone is welcome to join our [Gitter room](https://gitter.im/u8darts/darts) to ask questions, make proposals, discuss use-cases, and more. If you spot a bug or have suggestions, GitHub issues are also welcome. If what you want to tell us is not suitable for Gitter or Github, diff --git a/build.gradle b/build.gradle deleted file mode 100644 index 5331b46920..0000000000 --- a/build.gradle +++ /dev/null @@ -1,186 +0,0 @@ -buildscript { - repositories { - maven { url "https://plugins.gradle.org/m2/" } - gradlePluginPortal() - } -} - -plugins { - id "com.palantir.docker" version "0.27.0" - id "com.palantir.docker-run" version "0.27.0" -} - -// needed for palantir plugin -task build { -} - -// docker & docker run -docker { - name "unit8/darts" - - // ./gradlew dockerPushVersion will push image with tag ${version} - // ${version} is property passed from command line during workflow - tag "version", "unit8/darts:${version}" - - // ./gradlew dockerPushLatest will push image with tag 'latest' - tag "latest", "unit8/darts:latest" - - dockerfile file("${project.rootDir}/Dockerfile") - // needed files for docker and to build library - files "README.md", "setup.py", "setup.cfg" - copySpec.with { - from(".") { - include "examples/**" - into "." - } - from(".") { - include "darts/**" - into "." - } - from(".") { - include "requirements/**" - into "." - } - } -} - -dockerRun { - name "unit8_darts" - image "unit8/darts:latest" - ports "8888:8888" - daemonize false - clean true -} - -// setup requirements -task setupPip(type: Exec) { - commandLine "python", "-m", "pip", "install", "--upgrade", "pip" -} - -task installPipLatest { - dependsOn setupPip - doLast { - exec { - commandLine "pip", "install", "pip-tools" - } - exec { - commandLine "pip-compile", "requirements/core.txt", "requirements/notorch.txt", "requirements/torch.txt", "-o", "requirements-latest.txt" - } - exec { - commandLine "pip", "install", "-r", "requirements-latest.txt" - } - } -} - -void createPipInstallTask(String flavour) { - String taskName = "pip_" + flavour; - String taskArgument = "requirements/" + flavour + ".txt"; - task (taskName, type: Exec) { - commandLine "pip", "install", "-q", "-r", taskArgument - } -} - -String[] flavours = ["core", "dev", "notorch", "torch", "release"]; - -for(String flavour : flavours) { - createPipInstallTask(flavour); -} - -task installLocally(type:Exec) { - commandLine "pip", "install", "." -} - -task pipInstall() { - doFirst { - setupPip - } - dependsOn pip_core, pip_dev, pip_notorch, pip_torch, pip_release -} - -task lint_black(type: Exec) { - dependsOn pip_dev - commandLine "black", "--check", "." -} - -task lint_flake8(type: Exec) { - dependsOn pip_dev - commandLine "flake8" -} - -task lint_isort(type: Exec) { - dependsOn pip_dev - commandLine "isort", "--check", "." -} - -task lint { - dependsOn lint_black, lint_flake8, lint_isort -} - -void createPipRelatedTask(String flavour) { - String taskName = "unitTest_" + flavour; - String taskArgument = "pip_" + flavour; - task (taskName, type: Exec) { - dependsOn(taskArgument) - dependsOn pip_core - dependsOn pip_dev - commandLine "pytest", "--durations=50", "--cov=darts", "--cov-config=.coveragerc", "--cov-report=xml", "darts/tests" - } - - taskName = "test_" + flavour; - String taskArgument1 = "unitTest_" + flavour; - task (taskName) { - dependsOn(taskArgument1) - dependsOn lint - } -} - -flavours = ["core", "torch"]; - -for(String flavour : flavours) { - createPipRelatedTask(flavour); -} - -task unitTest_all(type: Exec) { - dependsOn installPipLatest, pip_dev - doFirst { - installPipLatest - } - commandLine "pytest", "--durations=50", "--cov=darts", "--cov-config=.coveragerc", "--cov-report=xml", "darts/tests" -} - -task test_all() { - dependsOn unitTest_all - dependsOn lint -} - -def exampleName=project.properties["exampleName"] ?: "" - -task checkExample(type: Exec) { - dependsOn pipInstall, installLocally - workingDir "./examples" - doFirst { - exec { - commandLine "echo", "Installed packages" - } - exec { - commandLine "pip", "list" - } - } - // exampleName must be passed with -PexampleName=FFT-examples.ipynb - commandLine "papermill", exampleName, exampleName -} - -// Documentation build -void docSteps() { - exec { - commandLine "make", "--directory", "./docs", "build-all-docs" - } -} - -task buildDocs() { - dependsOn pip_notorch, pip_release, installLocally - // dependsOn cleanDocs - doLast { - docSteps() - } -} diff --git a/conda_recipe/darts/meta.yaml b/conda_recipe/darts/meta.yaml index 714887eb3f..7375216e81 100644 --- a/conda_recipe/darts/meta.yaml +++ b/conda_recipe/darts/meta.yaml @@ -2,7 +2,7 @@ package: name: "darts" - version: "0.27.2" + version: "0.33.0" source: # root folder, not the package diff --git a/conda_recipe/environment.yml b/conda_recipe/environment.yml index 98005d0d18..f8579619ac 100644 --- a/conda_recipe/environment.yml +++ b/conda_recipe/environment.yml @@ -1,6 +1,6 @@ # conda-specific dependencies for the dev environment name: darts-dev dependencies: - - python>=3.8 + - python>=3.9 - conda-build - conda-verify diff --git a/darts/__init__.py b/darts/__init__.py index 2f75c40cfe..8a944caf60 100644 --- a/darts/__init__.py +++ b/darts/__init__.py @@ -8,9 +8,9 @@ import matplotlib as mpl from matplotlib import cycler -from .timeseries import TimeSeries, concatenate +from darts.timeseries import TimeSeries, concatenate, slice_intersect -__version__ = "0.27.2" +__version__ = "0.33.0" colors = cycler( color=["black", "003DFD", "b512b8", "11a9ba", "0d780f", "f77f07", "ba0f0f"] @@ -41,3 +41,5 @@ if os.getenv("DARTS_CONFIGURE_MATPLOTLIB", "1") != "0": mpl.rcParams.update(u8plots_mplstyle) + +__all__ = ["TimeSeries", "concatenate", "slice_intersect"] diff --git a/darts/ad/__init__.py b/darts/ad/__init__.py index 3996373070..939435d630 100644 --- a/darts/ad/__init__.py +++ b/darts/ad/__init__.py @@ -5,38 +5,38 @@ A suite of tools for performing anomaly detection and classification on time series. -* `Anomaly Scorers `_ - are at the core of the anomaly detection module. They - produce anomaly scores time series, either for single series (``score()``), - or for series accompanied by some predictions (``score_from_prediction()``). - Scorers can be trainable (e.g., ``KMeansScorer``) or not (e.g., ``NormScorer``). - -* `Anomaly Models `_ - offer a convenient way to produce anomaly scores from any of Darts - forecasting models (``ForecastingAnomalyModel``) or filtering models (``FilteringAnomalyModel``), - by comparing models' predictions with actual observations. - These classes take as parameters one Darts model, and one or multiple scorers, and can be readily - used to produce anomaly scores with the ``score()`` method. - -* `Anomaly Detectors `_: - transform raw time series (such as anaomly scores) into binary anomaly time series. - -* `Anomaly Aggregators `_: - combine multiple binary anomaly time series (in the form of multivariate time series) - into a single binary anomaly time series applying boolean logic. +- `Anomaly Scorers `_ are at the core of the + anomaly detection module. They produce anomaly scores time series, either for single series (`score()`), + or for series accompanied by some predictions (`score_from_prediction()`). Scorers can be trainable + (e.g., :class:`~darts.ad.scorers.kmeans_scorer.KMeansScorer`) or not + (e.g., :class:`~darts.ad.scorers.norm_scorer.NormScorer`). + +- `Anomaly Models `_ offer a convenient way + to produce anomaly scores from any of Darts forecasting models + (:class:`~darts.ad.anomaly_model.forecasting_am.ForecastingAnomalyModel`) or filtering models + (:class:`~darts.ad.anomaly_model.filtering_am.FilteringAnomalyModel`), by comparing models' predictions with actual + observations. These classes take as parameters one Darts model, and one or multiple scorers, and can be readily used + to produce anomaly scores with the `score()` method. + +- `Anomaly Detectors `_: transform raw time + series (such as anomaly scores) into binary anomaly time series. + +- `Anomaly Aggregators `_: combine multiple + binary anomaly time series (in the form of multivariate time series) into a single binary anomaly time series + applying boolean logic. """ # anomaly aggregators -from .aggregators import AndAggregator, EnsembleSklearnAggregator, OrAggregator +from darts.ad.aggregators import AndAggregator, EnsembleSklearnAggregator, OrAggregator # anomaly models -from .anomaly_model import FilteringAnomalyModel, ForecastingAnomalyModel +from darts.ad.anomaly_model import FilteringAnomalyModel, ForecastingAnomalyModel # anomaly detectors -from .detectors import QuantileDetector, ThresholdDetector +from darts.ad.detectors import QuantileDetector, ThresholdDetector # anomaly scorers -from .scorers import ( +from darts.ad.scorers import ( CauchyNLLScorer, DifferenceScorer, ExponentialNLLScorer, @@ -49,3 +49,24 @@ PyODScorer, WassersteinScorer, ) + +__all__ = [ + "AndAggregator", + "EnsembleSklearnAggregator", + "OrAggregator", + "FilteringAnomalyModel", + "ForecastingAnomalyModel", + "QuantileDetector", + "ThresholdDetector", + "CauchyNLLScorer", + "DifferenceScorer", + "ExponentialNLLScorer", + "GammaNLLScorer", + "GaussianNLLScorer", + "KMeansScorer", + "LaplaceNLLScorer", + "NormScorer", + "PoissonNLLScorer", + "PyODScorer", + "WassersteinScorer", +] diff --git a/darts/ad/aggregators/__init__.py b/darts/ad/aggregators/__init__.py index 324b54b24b..85e564f37b 100644 --- a/darts/ad/aggregators/__init__.py +++ b/darts/ad/aggregators/__init__.py @@ -2,17 +2,24 @@ Anomaly Aggregators ------------------- -An anomaly aggregator can take multiple detected anomalies -(in the form of binary TimeSeries, as coming from an anomaly detector) -and combine them into one. It can typically be used to combine -the detections of multiple models into one final detection. +An anomaly aggregator can take multiple detected anomalies (in the form of binary TimeSeries, as coming from an anomaly +detector) and combine them into one. It can typically be used to combine the detections of multiple models into one +final detection. -The key method is ``predict()``, which takes as input one (or multiple) -multivariate binary TimeSeries where each component represents the -detection of a single model, and returns one (or multiple) univariate -binary TimeSeries representing the final detection. +The key method is `predict()`, which takes as input one (or multiple) multivariate binary TimeSeries where each +component represents the detection of a single model, and returns one (or multiple) univariate binary TimeSeries +representing the final detection. """ -from .and_aggregator import AndAggregator -from .ensemble_sklearn_aggregator import EnsembleSklearnAggregator -from .or_aggregator import OrAggregator +from darts.ad.aggregators.aggregators import Aggregator, FittableAggregator +from darts.ad.aggregators.and_aggregator import AndAggregator +from darts.ad.aggregators.ensemble_sklearn_aggregator import EnsembleSklearnAggregator +from darts.ad.aggregators.or_aggregator import OrAggregator + +__all__ = [ + "Aggregator", + "FittableAggregator", + "AndAggregator", + "EnsembleSklearnAggregator", + "OrAggregator", +] diff --git a/darts/ad/aggregators/aggregators.py b/darts/ad/aggregators/aggregators.py index b9980922e2..e8fc3abd53 100644 --- a/darts/ad/aggregators/aggregators.py +++ b/darts/ad/aggregators/aggregators.py @@ -9,20 +9,40 @@ # - decision tree # - create show_all_combined (info about correlation, and from what path did # the anomaly alarm came from) - -from abc import ABC, abstractmethod -from typing import Any, Sequence, Union +import sys import numpy as np +if sys.version_info >= (3, 11): + from typing import Self +else: + from typing_extensions import Self +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal + +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import Optional, Union + from darts import TimeSeries -from darts.ad.utils import _to_list, eval_accuracy_from_binary_prediction -from darts.logging import raise_if_not +from darts.ad.utils import ( + _assert_fit_called, + _check_input, + eval_metric_from_binary_prediction, + series2seq, +) +from darts.logging import get_logger, raise_log + +logger = get_logger(__name__) class Aggregator(ABC): - def __init__(self, *args: Any, **kwargs: Any) -> None: - pass + """Base class for Aggregators.""" + + def __init__(self): + self.width_trained_on: Optional[int] = None @abstractmethod def __str__(self): @@ -30,13 +50,27 @@ def __str__(self): pass @abstractmethod - def _predict_core(self): - """returns the aggregated results""" + def _predict_core(self, series: Sequence[TimeSeries]) -> Sequence[TimeSeries]: + """Aggregates the sequence of multivariate binary series given as + input into a sequence of univariate binary series. assuming the input is + in the correct shape. + + Parameters + ---------- + series + The sequence of multivariate binary series to aggregate + + Returns + ------- + TimeSeries + Sequence of aggregated results + """ pass - @abstractmethod def predict( - self, series: Union[TimeSeries, Sequence[TimeSeries]] + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + name: str = "series", ) -> Union[TimeSeries, Sequence[TimeSeries]]: """Aggregates the (sequence of) multivariate binary series given as input into a (sequence of) univariate binary series. @@ -44,257 +78,140 @@ def predict( Parameters ---------- series - The (sequence of) multivariate binary series to aggregate + The (sequence of) multivariate binary series to aggregate. + name + The name of `series`. Returns ------- TimeSeries - (Sequence of) aggregated results + (Sequence of) aggregated results. """ - pass - - def _check_input(self, series: Union[TimeSeries, Sequence[TimeSeries]]): - """ - Checks for input if: - - it is a (sequence of) multivariate series (width>1) - - (sequence of) series must be: - * a deterministic TimeSeries - * binary (only values equal to 0 or 1) - """ - - list_series = _to_list(series) - - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_series]), - "all series in `series` must be of type TimeSeries.", - ) - - raise_if_not( - all([s.width > 1 for s in list_series]), - "all series in `series` must be multivariate (width>1).", - ) - - raise_if_not( - all([s.is_deterministic for s in list_series]), - "all series in `series` must be deterministic (number of samples=1).", - ) - - raise_if_not( - all( - [ - np.array_equal( - s.values(copy=False), s.values(copy=False).astype(bool) - ) - for s in list_series - ] - ), - "all series in `series` must be binary (only 0 and 1 values).", - ) - - return list_series - - def eval_accuracy( + called_with_single_series = isinstance(series, TimeSeries) + series = _check_input( + series, + name=name, + width_expected=self.width_trained_on, + check_deterministic=True, + check_binary=True, + check_multivariate=True, + ) + pred = self._predict_core(series) + return pred[0] if called_with_single_series else pred + + def eval_metric( self, - actual_anomalies: Sequence[TimeSeries], - series: Sequence[TimeSeries], + anomalies: Union[TimeSeries, Sequence[TimeSeries]], + series: Union[TimeSeries, Sequence[TimeSeries]], window: int = 1, - metric: str = "recall", + metric: Literal["recall", "precision", "f1", "accuracy"] = "recall", ) -> Union[float, Sequence[float]]: """Aggregates the (sequence of) multivariate series given as input into one (sequence of) - series and evaluates the results against true anomalies. + series and evaluates the results against the ground truth anomaly labels. Parameters ---------- - actual_anomalies - The (sequence of) ground truth of the anomalies (1 if it is an anomaly and 0 if not) + anomalies + The (sequence of) binary ground truth anomaly labels (1 if it is an anomaly and 0 if not). series - The (sequence of) multivariate binary series to aggregate + The (sequence of) predicted multivariate binary series to aggregate. window (Sequence of) integer value indicating the number of past samples each point represents in the (sequence of) series. The parameter will be used by the - function ``_window_adjustment_anomalies()`` in darts.ad.utils to transform - actual_anomalies. + function `_window_adjustment_anomalies()` in darts.ad.utils to transform + anomalies. metric - Metric function to use. Must be one of "recall", "precision", - "f1", and "accuracy". - Default: "recall" + The name of the metric function to use. Must be one of "recall", "precision", "f1", and "accuracy". + Default: "recall". Returns ------- Union[float, Sequence[float]] - (Sequence of) score for the (sequence of) series + (Sequence of) score for the (sequence of) series. """ - - list_actual_anomalies = _to_list(actual_anomalies) - - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_actual_anomalies]), - "all series in `actual_anomalies` must be of type TimeSeries.", + pred_anomalies = self.predict(series) + return eval_metric_from_binary_prediction( + anomalies=anomalies, + pred_anomalies=pred_anomalies, + window=window, + metric=metric, ) - raise_if_not( - all([s.is_deterministic for s in list_actual_anomalies]), - "all series in `actual_anomalies` must be deterministic (number of samples=1).", - ) - raise_if_not( - all([s.width == 1 for s in list_actual_anomalies]), - "all series in `actual_anomalies` must be univariate (width=1).", - ) - - raise_if_not( - len(list_actual_anomalies) == len(_to_list(series)), - "`actual_anomalies` and `series` must contain the same number of series.", - ) - - preds = self.predict(series) - - return eval_accuracy_from_binary_prediction( - list_actual_anomalies, preds, window, metric - ) - - -class NonFittableAggregator(Aggregator): - "Base class of Aggregators that do not need training." +class FittableAggregator(Aggregator): + """Base class for Aggregators that require training.""" - def __init__(self) -> None: + def __init__(self): super().__init__() + self._fit_called = False - # indicates if the Aggregator is trainable or not - self.trainable = False - - def predict( - self, series: Union[TimeSeries, Sequence[TimeSeries]] - ) -> Union[TimeSeries, Sequence[TimeSeries]]: - """Aggregates the (sequence of) multivariate binary series given as - input into a (sequence of) univariate binary series. + @abstractmethod + def _fit_core(self, anomalies: Sequence[np.ndarray], series: Sequence[np.ndarray]): + """Fits the aggregator, assuming the input is in the correct shape. Parameters ---------- + anomalies + The (sequence of) binary ground truth anomaly labels (1 if it is an anomaly and 0 if not). series - The (sequence of) multivariate binary series to aggregate - - Returns - ------- - TimeSeries - (Sequence of) aggregated results + The (sequence of) multivariate binary anomalies (predicted labels) to aggregate. """ - list_series = self._check_input(series) - - if isinstance(series, TimeSeries): - return self._predict_core(list_series)[0] - else: - return self._predict_core(list_series) - - -class FittableAggregator(Aggregator): - "Base class of Aggregators that do need training." - - def __init__(self) -> None: - super().__init__() - - # indicates if the Aggregator is trainable or not - self.trainable = True - - # indicates if the Aggregator has been trained yet - self._fit_called = False - - def _assert_fit_called(self): - """Checks if the Aggregator has been fitted before calling its `score()` function.""" - - raise_if_not( - self._fit_called, - f"The Aggregator {self.__str__()} has not been fitted yet. Call `fit()` first.", - ) + pass def fit( self, - actual_anomalies: Union[TimeSeries, Sequence[TimeSeries]], + anomalies: Union[TimeSeries, Sequence[TimeSeries]], series: Union[TimeSeries, Sequence[TimeSeries]], - ): - """Fit the aggregators on the (sequence of) multivariate binary series. + ) -> Self: + """Fit the aggregators on the (sequence of) multivariate binary anomaly series. If a list of series is given, they must have the same number of components. Parameters ---------- - actual_anomalies - The (sequence of) ground truth of the anomalies (1 if it is an anomaly and 0 if not) + anomalies + The (sequence of) binary ground truth anomaly labels (1 if it is an anomaly and 0 if not). series - The (sequence of) multivariate binary series + The (sequence of) multivariate binary series (predicted labels) to aggregate. """ - list_series = self._check_input(series) - self.width_trained_on = list_series[0].width - - raise_if_not( - all([s.width == self.width_trained_on for s in list_series]), - "all series in `list_series` must have the same number of components.", - ) - - list_actual_anomalies = _to_list(actual_anomalies) - - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_actual_anomalies]), - "all series in `actual_anomalies` must be of type TimeSeries.", - ) - - raise_if_not( - all([s.is_deterministic for s in list_actual_anomalies]), - "all series in `actual_anomalies` must be deterministic (width=1).", - ) - - raise_if_not( - all([s.width == 1 for s in list_actual_anomalies]), - "all series in `actual_anomalies` must be univariate (width=1).", - ) - - raise_if_not( - len(list_actual_anomalies) == len(list_series), - "`actual_anomalies` and `series` must contain the same number of series.", - ) - - same_intersection = list( - zip( - *[ - [anomalies.slice_intersect(series), series.slice_intersect(series)] - for (anomalies, series) in zip(list_actual_anomalies, list_series) - ] + pred_width = series2seq(series)[0].width + series = _check_input( + series, + name="series", + width_expected=pred_width, + check_deterministic=True, + check_binary=True, + check_multivariate=True, + ) + self.width_trained_on = pred_width + + anomalies = _check_input( + anomalies, + name="anomalies", + width_expected=1, + check_deterministic=True, + check_binary=True, + check_multivariate=False, + ) + if len(anomalies) != len(series): + raise_log( + ValueError( + "`anomalies` and `series` must contain the same number of series." + ), + logger=logger, ) - ) - list_actual_anomalies = list(same_intersection[0]) - list_series = list(same_intersection[1]) - - ret = self._fit_core(list_actual_anomalies, list_series) + anomalies_vals, series_vals = [], [] + for anom, pred_anom in zip(anomalies, series): + anomalies_vals.append(anom.slice_intersect_values(pred_anom)[:, :, 0]) + series_vals.append(pred_anom.slice_intersect_values(anom)[:, :, 0]) + self._fit_core(anomalies_vals, series_vals) self._fit_called = True - return ret + return self def predict( - self, series: Union[TimeSeries, Sequence[TimeSeries]] + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + name: str = "series", ) -> Union[TimeSeries, Sequence[TimeSeries]]: - """Aggregates the (sequence of) multivariate binary series given as - input into a (sequence of) univariate binary series. - - Parameters - ---------- - series - The (sequence of) multivariate binary series to aggregate - - Returns - ------- - TimeSeries - (Sequence of) aggregated results - """ - self._assert_fit_called() - list_series = self._check_input(series) - - raise_if_not( - all([s.width == self.width_trained_on for s in list_series]), - "all series in `series` must have the same number of components as the data" - + " used for training the detector model, number of components in training:" - + f" {self.width_trained_on}.", - ) - - if isinstance(series, TimeSeries): - return self._predict_core(list_series)[0] - else: - return self._predict_core(list_series) + _assert_fit_called(self._fit_called, name="Aggregator") + return super().predict(series=series, name=name) diff --git a/darts/ad/aggregators/and_aggregator.py b/darts/ad/aggregators/and_aggregator.py index 1e12bc6efc..f48d2c8ae7 100644 --- a/darts/ad/aggregators/and_aggregator.py +++ b/darts/ad/aggregators/and_aggregator.py @@ -1,25 +1,50 @@ """ AND Aggregator -------------- - -Aggregator that identifies a time step as anomalous if all the components -are flagged as anomalous (logical AND). """ -from typing import Sequence +from collections.abc import Sequence from darts import TimeSeries -from darts.ad.aggregators.aggregators import NonFittableAggregator +from darts.ad.aggregators.aggregators import Aggregator +from darts.utils.utils import _parallel_apply + +class AndAggregator(Aggregator): + def __init__(self, n_jobs: int = 1) -> None: + """AND Aggregator -class AndAggregator(NonFittableAggregator): - def __init__(self) -> None: + Aggregator that identifies a time step as anomalous if all the components are flagged as anomalous + (logical AND). + + Parameters + ---------- + n_jobs + The number of jobs to run in parallel. Defaults to `1` (sequential). Setting the parameter to `-1` means + using all the available processors. + """ super().__init__() + self._n_jobs = n_jobs - def __str__(self): + def __str__(self) -> str: return "AndAggregator" - def _predict_core(self, series: Sequence[TimeSeries]) -> Sequence[TimeSeries]: - return [ - s.sum(axis=1).map(lambda x: (x >= s.width).astype(s.dtype)) for s in series - ] + def _predict_core( + self, series: Sequence[TimeSeries], *args, **kwargs + ) -> Sequence[TimeSeries]: + def _compononents_and(s: TimeSeries): + return TimeSeries.from_times_and_values( + times=s.time_index, + values=(s.all_values(copy=False).sum(axis=1) >= s.width).astype( + s.dtype + ), + columns=["components_sum"], + ) + + return _parallel_apply( + [(s,) for s in series], + _compononents_and, + n_jobs=1, + fn_args=args, + fn_kwargs=kwargs, + ) diff --git a/darts/ad/aggregators/ensemble_sklearn_aggregator.py b/darts/ad/aggregators/ensemble_sklearn_aggregator.py index e053819d29..aaf4b8868c 100644 --- a/darts/ad/aggregators/ensemble_sklearn_aggregator.py +++ b/darts/ad/aggregators/ensemble_sklearn_aggregator.py @@ -1,12 +1,9 @@ """ Ensemble scikit-learn aggregator -------------------------------- - -Aggregator wrapped around the Ensemble model of sklearn. -`sklearn https://scikit-learn.org/stable/modules/ensemble.html`_. """ -from typing import Sequence +from collections.abc import Sequence import numpy as np from sklearn.ensemble import BaseEnsemble @@ -17,8 +14,17 @@ class EnsembleSklearnAggregator(FittableAggregator): - def __init__(self, model) -> None: + def __init__(self, model: BaseEnsemble) -> None: + """Ensemble scikit-learn aggregator + + Aggregator wrapped around the sklearn ensemble model `sklearn ensemble model + `_. + Parameters + ---------- + model + The sklearn ensemble model. + """ raise_if_not( isinstance(model, BaseEnsemble), f"Scorer is expecting a model of type BaseEnsemble (from sklearn ensemble), \ @@ -28,36 +34,25 @@ def __init__(self, model) -> None: self.model = model super().__init__() - def __str__(self): + def __str__(self) -> str: return "EnsembleSklearnAggregator: {}".format( self.model.__str__().split("(")[0] ) - def _fit_core( - self, - actual_anomalies: Sequence[TimeSeries], - series: Sequence[TimeSeries], - ): - - X = np.concatenate( - [s.all_values(copy=False).reshape(len(s), -1) for s in series], - axis=0, - ) - + def _fit_core(self, anomalies: Sequence[np.ndarray], series: Sequence[np.ndarray]): + X = np.concatenate(series, axis=0) y = np.concatenate( - [s.all_values(copy=False).reshape(len(s)) for s in actual_anomalies], + [s.flatten() for s in anomalies], axis=0, ) - self.model.fit(y=y, X=X) - return self def _predict_core(self, series: Sequence[TimeSeries]) -> Sequence[TimeSeries]: - + # assume that parallelization occurs at sklearn model level return [ TimeSeries.from_times_and_values( s.time_index, - self.model.predict((s).all_values(copy=False).reshape(len(s), -1)), + self.model.predict(s.values(copy=False)), ) for s in series ] diff --git a/darts/ad/aggregators/or_aggregator.py b/darts/ad/aggregators/or_aggregator.py index 5737839630..784ab1e771 100644 --- a/darts/ad/aggregators/or_aggregator.py +++ b/darts/ad/aggregators/or_aggregator.py @@ -1,24 +1,49 @@ """ OR Aggregator ------------- - -Aggregator that identifies a time step as anomalous if any of the components -is flagged as anomalous (logical OR). """ - -from typing import Sequence +from collections.abc import Sequence from darts import TimeSeries -from darts.ad.aggregators.aggregators import NonFittableAggregator +from darts.ad.aggregators.aggregators import Aggregator +from darts.utils.utils import _parallel_apply + +class OrAggregator(Aggregator): + def __init__(self, n_jobs: int = 1) -> None: + """OR Aggregator -class OrAggregator(NonFittableAggregator): - def __init__(self) -> None: + Aggregator that identifies a time step as anomalous if any of the components + is flagged as anomalous (logical OR). + + Parameters + ---------- + n_jobs + The number of jobs to run in parallel. Defaults to `1` (sequential). Setting the parameter to `-1` means + using all the available processors. + """ super().__init__() - def __str__(self): + self._n_jobs = n_jobs + + def __str__(self) -> str: return "OrAggregator" - def _predict_core(self, series: Sequence[TimeSeries]) -> Sequence[TimeSeries]: - return [s.sum(axis=1).map(lambda x: (x > 0).astype(s.dtype)) for s in series] + def _predict_core( + self, series: Sequence[TimeSeries], *args, **kwargs + ) -> Sequence[TimeSeries]: + def _compononents_or(s: TimeSeries): + return TimeSeries.from_times_and_values( + times=s.time_index, + values=(s.all_values(copy=False).sum(axis=1) > 0).astype(s.dtype), + columns=["components_sum"], + ) + + return _parallel_apply( + [(s,) for s in series], + _compononents_or, + n_jobs=1, + fn_args=args, + fn_kwargs=kwargs, + ) diff --git a/darts/ad/anomaly_model/__init__.py b/darts/ad/anomaly_model/__init__.py index 50af79dbc6..6900600889 100644 --- a/darts/ad/anomaly_model/__init__.py +++ b/darts/ad/anomaly_model/__init__.py @@ -2,28 +2,30 @@ Anomaly Models -------------- -Anomaly models make it possible to use any of Darts' forecasting -or filtering models to detect anomalies in time series. +Anomaly models make it possible to use any of Darts' forecasting or filtering models to detect anomalies in time series. -The basic idea is to compare the predictions produced by a fitted model (the forecasts -or the filtered series) with the actual observations, and to emit an anomaly score -describing how "different" the observations are from the predictions. +The basic idea is to compare the predictions produced by a fitted model (the forecasts or the filtered series) with the +actual observations, and to emit an anomaly score describing how "different" the observations are from the predictions. -An anomaly model takes as parameters a model and one or multiple scorer objects. -The key method is ``score()``, which takes as input one (or multiple) -time series and produces one or multiple anomaly scores time series, for each provided series. +An anomaly model takes as parameters a model and one or multiple scorer objects. The key method is `score()`, which +takes as input one (or multiple) time series and produces one or multiple anomaly scores time series, for each provided +series. -:class:`ForecastingAnomalyModel` works with Darts forecasting models, and :class:`FilteringAnomalyModel` -works with Darts filtering models. -The anomaly models can also be fitted by calling :func:`fit()`, which trains the scorer(s) -(in case some are trainable), and potentially the model as well. +:class:`~darts.ad.anomaly_model.forecasting_am.ForecastingAnomalyModel` works with Darts forecasting models, and +:class:`~darts.ad.anomaly_model.filtering_am.FilteringAnomalyModel` works with Darts filtering models. The anomaly +models can also be fitted by calling :func:`fit()`, which trains the scorer(s) (in case some are trainable), and +potentially the model as well. -The function :func:`eval_accuracy()` is the same as :func:`score()`, but outputs the score of an agnostic -threshold metric ("AUC-ROC" or "AUC-PR"), between the predicted anomaly score time series, and some known binary -ground-truth time series indicating the presence of actual anomalies. -Finally, the function :func:`show_anomalies()` can also be used to visualize the predictions -(in-sample predictions and anomaly scores) of the anomaly model. +The function :func:`eval_metric()` is the same as :func:`score()`, but outputs the score of an agnostic threshold +metric ("AUC-ROC" or "AUC-PR"), between the predicted anomaly score time series, and some known binary ground-truth +time series indicating the presence of actual anomalies. Finally, the function :func:`show_anomalies()` can also be +used to visualize the predictions (in-sample predictions and anomaly scores) of the anomaly model. """ -from .filtering_am import FilteringAnomalyModel -from .forecasting_am import ForecastingAnomalyModel +from darts.ad.anomaly_model.filtering_am import FilteringAnomalyModel +from darts.ad.anomaly_model.forecasting_am import ForecastingAnomalyModel + +__all__ = [ + "FilteringAnomalyModel", + "ForecastingAnomalyModel", +] diff --git a/darts/ad/anomaly_model/anomaly_model.py b/darts/ad/anomaly_model/anomaly_model.py index a86d122249..63655db40c 100644 --- a/darts/ad/anomaly_model/anomaly_model.py +++ b/darts/ad/anomaly_model/anomaly_model.py @@ -2,119 +2,204 @@ Anomaly models base classes """ +import sys from abc import ABC, abstractmethod -from typing import Dict, Sequence, Union +from collections.abc import Sequence +from typing import Literal, Optional, Union + +if sys.version_info >= (3, 11): + from typing import Self +else: + from typing_extensions import Self from darts.ad.scorers.scorers import AnomalyScorer from darts.ad.utils import ( - _to_list, - eval_accuracy_from_scores, + _assert_same_length, + _check_input, + eval_metric_from_scores, show_anomalies_from_scores, ) -from darts.logging import raise_if_not +from darts.logging import get_logger, raise_log from darts.timeseries import TimeSeries +logger = get_logger(__name__) + class AnomalyModel(ABC): """Base class for all anomaly models.""" def __init__(self, model, scorer): + self.scorers = [scorer] if not isinstance(scorer, Sequence) else scorer + if not all([isinstance(s, AnomalyScorer) for s in self.scorers]): + raise_log( + ValueError( + "all scorers must be of instance `darts.ad.scorers.AnomalyScorer`." + ), + logger=logger, + ) + self.model = model - self.scorers = _to_list(scorer) + def fit( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + allow_model_training: bool, + **kwargs, + ) -> Self: + """Fit the underlying forecasting/filtering model (if applicable) and the fittable scorers.""" + # interrupt training if nothing to fit + if not allow_model_training and not self.scorers_are_trainable: + return self - raise_if_not( - all([isinstance(s, AnomalyScorer) for s in self.scorers]), - "all scorers must be of instance darts.ad.scorers.AnomalyScorer.", + # check input series and covert to sequences + series, kwargs = self._process_input_series(series, **kwargs) + self._fit_core( + series=series, allow_model_training=allow_model_training, **kwargs ) + return self - self.scorers_are_trainable = any(s.trainable for s in self.scorers) - self.univariate_scoring = any(s.univariate_scorer for s in self.scorers) + @abstractmethod + def score( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + return_model_prediction: bool = False, + **kwargs, + ) -> Union[TimeSeries, Sequence[TimeSeries]]: + """Compute anomaly score(s) for the given series. - self.model = model + Predicts the given target time series with the forecasting model, and applies the scorer(s) + on the prediction and the target input time series. - def _check_univariate(self, actual_anomalies): - """Checks if `actual_anomalies` contains only univariate series, which - is required if any of the scorers returns a univariate score. + Parameters + ---------- + series + The (sequence of) series to score on. + return_model_prediction + Whether to return the forecasting/filtering model prediction along with the anomaly scores. + **kwargs + Additional parameters passed to `AnomalyModel.predict_series()` + + Returns + ------- + TimeSeries + A single `TimeSeries` for a single `series` with a single anomaly scorers. + Sequence[TimeSeries] + A sequence of `TimeSeries` for: + + - a single `series` with multiple anomaly scorers. + - a sequence of `series` with a single anomaly scorer. + Sequence[Sequence[TimeSeries]] + A sequence of sequences of `TimeSeries` for a sequence of `series` and multiple anomaly scorers. + The outer sequence is over the series, and inner sequence is over the scorers. """ + called_with_single_series = isinstance(series, TimeSeries) + # check input series and covert to sequences + series, kwargs = self._process_input_series(series, **kwargs) + # predict / filter `series` + pred = self.predict_series(series=series, **kwargs) - if self.univariate_scoring: - raise_if_not( - all([s.width == 1 for s in actual_anomalies]), - "Anomaly model contains scorer {} that will return".format( - [s.__str__() for s in self.scorers if s.univariate_scorer] - ) - + " a univariate anomaly score series (width=1). Found a" - + " multivariate `actual_anomalies`. The evaluation of the" - + " accuracy cannot be computed. If applicable, think about" - + " setting the scorer parameter `componenet_wise` to True.", - ) + scores = list( + zip(*[sc.score_from_prediction(series, pred) for sc in self.scorers]) + ) - @abstractmethod - def fit( - self, series: Union[TimeSeries, Sequence[TimeSeries]] - ) -> Union[TimeSeries, Sequence[TimeSeries]]: - pass + if called_with_single_series: + scores = scores[0] + if len(scores) == 1: + # there's only one scorer + scores = scores[0] + pred = pred[0] - @abstractmethod - def score( - self, series: Union[TimeSeries, Sequence[TimeSeries]] - ) -> Union[TimeSeries, Sequence[TimeSeries]]: - pass + if return_model_prediction: + return scores, pred - @abstractmethod - def eval_accuracy( - self, - actual_anomalies: Union[TimeSeries, Sequence[TimeSeries]], - series: Union[TimeSeries, Sequence[TimeSeries]], - ) -> Union[TimeSeries, Sequence[TimeSeries]]: - pass + return scores @abstractmethod - def show_anomalies(self, series: TimeSeries): + def predict_series( + self, series: Sequence[TimeSeries], **kwargs + ) -> Sequence[TimeSeries]: + """Abstract method to implement the generation of predictions for the input `series`.""" pass - def _show_anomalies( + def eval_metric( self, - series: TimeSeries, - model_output: TimeSeries = None, - anomaly_scores: Union[TimeSeries, Sequence[TimeSeries]] = None, - names_of_scorers: Union[str, Sequence[str]] = None, - actual_anomalies: TimeSeries = None, - title: str = None, - metric: str = None, - ): - """Internal function that plots the results of the anomaly model. - Called by the function show_anomalies(). - """ + anomalies: Union[TimeSeries, Sequence[TimeSeries]], + series: Union[TimeSeries, Sequence[TimeSeries]], + metric: Literal["AUC_ROC", "AUC_PR"] = "AUC_ROC", + **kwargs, + ) -> Union[ + dict[str, float], + dict[str, Sequence[float]], + Sequence[dict[str, float]], + Sequence[dict[str, Sequence[float]]], + ]: + """Compute the accuracy of the anomaly scores computed by the model. - if title is None: - title = f"Anomaly results ({self.model.__class__.__name__})" + Predicts the `series` with the underlying forecasting/filtering model, and applies the scorer(s) on the + predicted time series and the given target time series. Returns the score(s) of an agnostic threshold metric, + based on the anomaly score given by the scorer(s). - if names_of_scorers is None: - names_of_scorers = [s.__str__() for s in self.scorers] + Parameters + ---------- + anomalies + The (sequence of) ground truth binary anomaly series (`1` if it is an anomaly and `0` if not). + series + The (sequence of) series to predict anomalies on. + metric + The name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". + **kwargs + Additional parameters passed to the `score()` method. - list_window = [s.window for s in self.scorers] + Returns + ------- + Dict[str, float] + A dictionary with the resulting metrics for single univariate `series`, with keys representing the + anomaly scorer(s), and values representing the metric values. + Dict[str, Sequence[float]] + Same as for `Dict[str, float]` but for multivariate `series`, and anomaly scorers that treat series + components/columns independently (by nature of the scorer or if `component_wise=True`). + Sequence[Dict[str, float]] + Same as for `Dict[str, float]` but for a sequence of univariate series. + Sequence[Dict[str, Sequence[float]]] + Same as for `Dict[str, float]` but for a sequence of multivariate series. + """ - return show_anomalies_from_scores( + def _check_univariate(s: TimeSeries): + """Checks if `anomalies` contains only univariate series, which + is required if any of the scorers returns a univariate score. + """ + if self.scorers_are_univariate and not s.width == 1: + raise_log( + ValueError( + f"Anomaly model contains scorer {[s.__str__() for s in self.scorers if s.is_univariate]} " + f"that will return a univariate anomaly score series (width=1). Found a multivariate " + f"`anomalies`. The evaluation of the accuracy cannot be computed. If applicable, " + f"think about setting the scorer parameter `componenet_wise` to True." + ), + logger=logger, + ) + + called_with_single_series = isinstance(series, TimeSeries) + # deterministic `series` + series = _check_input( series, - model_output=model_output, - anomaly_scores=anomaly_scores, - window=list_window, - names_of_scorers=names_of_scorers, - actual_anomalies=actual_anomalies, - title=title, - metric=metric, + name="series", + check_deterministic=True, + ) + # deterministic, binary anomalies, (possibly univariate) + anomalies = _check_input( + anomalies, + name="anomalies", + check_deterministic=True, + check_binary=True, + extra_checks=_check_univariate, ) + _assert_same_length(series, anomalies, "series", "anomalies") - def _eval_accuracy_from_scores( - self, - list_actual_anomalies: Sequence[TimeSeries], - list_anomaly_scores: Sequence[TimeSeries], - metric: str, - ) -> Union[Sequence[Dict[str, float]], Sequence[Dict[str, Sequence[float]]]]: - """Internal function that computes the accuracy of the anomaly scores - computed by the model. Called by the function eval_accuracy(). - """ + pred_scores = self.score(series=series, **kwargs) + + # compute metric for anomaly scores windows = [s.window for s in self.scorers] # create a list of unique names for each scorer that @@ -134,15 +219,141 @@ def _eval_accuracy_from_scores( name_scorers.append(name) - acc = [] - for anomalies, scores in zip(list_actual_anomalies, list_anomaly_scores): - acc.append( - eval_accuracy_from_scores( - actual_anomalies=anomalies, - anomaly_score=scores, + metric_vals = [] + for anomalies, scores in zip(anomalies, pred_scores): + metric_vals.append( + eval_metric_from_scores( + anomalies=anomalies, + pred_scores=scores, window=windows, metric=metric, ) ) + metric_vals_pred_scores = [ + dict(zip(name_scorers, scorer_values)) for scorer_values in metric_vals + ] + + return ( + metric_vals_pred_scores[0] + if called_with_single_series + else metric_vals_pred_scores + ) + + def show_anomalies( + self, + series: TimeSeries, + anomalies: TimeSeries = None, + predict_kwargs: Optional[dict] = None, + names_of_scorers: Union[str, Sequence[str]] = None, + title: str = None, + metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None, + component_wise: bool = False, + **score_kwargs, + ): + """Plot the results of the anomaly model. + + Computes the score on the given series input and shows the different anomaly scores with respect to time. + + The plot will be composed of the following: + + - the series itself with the output of the forecasting model. + - the anomaly score for each scorer. The scorers with different windows will be separated. + - the actual anomalies, if given. + + It is possible to: + + - add a title to the figure with the parameter `title` + - give personalized names for the scorers with `names_of_scorers` + - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), if the actual anomalies are provided. + + Parameters + ---------- + series + The series to visualize anomalies from. + anomalies + The ground truth of the anomalies (1 if it is an anomaly and 0 if not). + predict_kwargs + Optionally, some additional parameters passed to `AnomalyModel.predict_series()`. + names_of_scorers + Name of the scores. Must be a list of length equal to the number of scorers in the anomaly_model. + title + Title of the figure. + metric + Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". + score_kwargs + parameters for the `score()` method. + component_wise + If True, will separately plot each component in case of multivariate anomaly detection. + """ + series = _check_input(series, name="series", num_series_expected=1)[0] + predict_kwargs = predict_kwargs if predict_kwargs is not None else {} + pred_scores, pred_series = self.score( + series, + return_model_prediction=True, + **predict_kwargs, + **score_kwargs, + ) + + if title is None: + title = f"Anomaly results ({self.model.__class__.__name__})" + + if names_of_scorers is None: + names_of_scorers = [s.__str__() for s in self.scorers] + + list_window = [s.window for s in self.scorers] + + return show_anomalies_from_scores( + series=series, + anomalies=anomalies, + pred_series=pred_series, + pred_scores=pred_scores, + window=list_window, + names_of_scorers=names_of_scorers, + title=title, + metric=metric, + component_wise=component_wise, + ) + + @property + def scorers_are_univariate(self): + """Whether any of the Scorers is univariate.""" + return any(s.is_univariate for s in self.scorers) + + @property + def scorers_are_trainable(self): + """Whether any of the Scorers is trainable.""" + return any(s.is_trainable for s in self.scorers) + + @abstractmethod + def _fit_core( + self, + series: Sequence[TimeSeries], + allow_model_training: bool, + **kwargs, + ): + """Abstract method to implement the model and scorer training.""" + pass + + def _fit_scorers( + self, list_series: Sequence[TimeSeries], list_pred: Sequence[TimeSeries] + ): + """Train the fittable scorers using model forecasts""" + for scorer in self.scorers: + if scorer.is_trainable: + scorer.fit_from_prediction(list_series, list_pred) - return [dict(zip(name_scorers, scorer_values)) for scorer_values in acc] + @staticmethod + def _process_input_series( + series: Union[TimeSeries, Sequence[TimeSeries]], **kwargs + ): + """Checks input series and coverts series and covariates in `kwargs` to sequences.""" + series = _check_input(series, name="series") + for cov_name in ["past_covariates", "future_covariates"]: + cov = kwargs.pop(cov_name, None) + if cov is not None: + cov = _check_input(cov, name=cov_name) + _assert_same_length(series, cov, "series", cov_name) + kwargs[cov_name] = cov + return series, kwargs diff --git a/darts/ad/anomaly_model/filtering_am.py b/darts/ad/anomaly_model/filtering_am.py index 9679bc2906..678f3f7d60 100644 --- a/darts/ad/anomaly_model/filtering_am.py +++ b/darts/ad/anomaly_model/filtering_am.py @@ -2,17 +2,23 @@ Filtering Anomaly Model ----------------------- -A ``FilteringAnomalyModel`` wraps around a Darts filtering model and one or +A `FilteringAnomalyModel` wraps around a Darts filtering model and one or several anomaly scorer(s) to compute anomaly scores by comparing how actuals deviate from the model's predictions (filtered series). """ -from typing import Dict, Sequence, Union +import sys +from collections.abc import Sequence +from typing import Literal, Optional, Union + +if sys.version_info >= (3, 11): + from typing import Self +else: + from typing_extensions import Self from darts.ad.anomaly_model.anomaly_model import AnomalyModel from darts.ad.scorers.scorers import AnomalyScorer -from darts.ad.utils import _assert_same_length, _to_list -from darts.logging import get_logger, raise_if_not +from darts.logging import get_logger, raise_log from darts.models.filtering.filtering_model import FilteringModel from darts.timeseries import TimeSeries @@ -34,26 +40,24 @@ def __init__( function of the model will be sufficient to train it to satisfactory performance on series without anomalies. Calling :func:`fit()` on the anomaly model will fit the underlying filtering model only - if ``allow_model_training`` is set to ``True`` upon calling ``fit()``. + if `allow_model_training` is set to `True` upon calling `fit()`. In addition, calling :func:`fit()` will also fit the fittable scorers, if any. Parameters ---------- - filter - A filtering model from Darts that will be used to filter the actual time series + model + A Darts `FilteringModel` used to filter the actual time series. scorer - One or multiple scorer(s) that will be used to compare the actual and predicted time series in order - to obtain an anomaly score ``TimeSeries``. - If a list of `N` scorer is given, the anomaly model will call each - one of the scorers and output a list of `N` anomaly scores ``TimeSeries``. + One or multiple scorer(s) used to compare the actual and predicted time series in order to obtain an + anomaly score `TimeSeries`. If a list of scorers, + :meth:`~darts.ad.anomaly_model.filtering_am.FilteringAnomalyModel.score` will output anomaly scores for + each scorer. """ - - raise_if_not( - isinstance(model, FilteringModel), - f"`model` must be a darts.models.filtering not a {type(model)}.", - ) - self.filter = model - + if not isinstance(model, FilteringModel): + raise_log( + ValueError("`model` must be a Darts `FilteringModel`."), + logger=logger, + ) super().__init__(model=model, scorer=scorer) def fit( @@ -61,11 +65,11 @@ def fit( series: Union[TimeSeries, Sequence[TimeSeries]], allow_model_training: bool = False, **filter_fit_kwargs, - ): + ) -> Self: """Fit the underlying filtering model (if applicable) and the fittable scorers, if any. - Train the filter (if not already fitted and `allow_filter_training` is set to True) - and the scorer(s) on the given time series. + Train the filter (if not already fitted and `allow_model_training` is `True`) and the fittable scorer(s) on the + given time series. The filter model will be applied to the given series, and the results will be used to train the scorer(s). @@ -73,135 +77,22 @@ def fit( Parameters ---------- series - The (sequence of) series to be trained on. + The (sequence of) series to train on (generally assumed to be anomaly-free). allow_model_training - Boolean value that indicates if the filtering model needs to be fitted on the given series. - If set to False, the model needs to be already fitted. - Default: False - filter_fit_kwargs - Parameters to be passed on to the filtering model ``fit()`` method. + Whether the filtering model should be fitted on the given series. If `False`, the model must already be + fitted. + **filter_fit_kwargs + Additional parameters passed to the filtering model's `fit()` method. Returns ------- self - Fitted model + Fitted model. """ - # TODO: add support for covariates (see eg. Kalman Filter) - - raise_if_not( - type(allow_model_training) is bool, - f"`allow_filter_training` must be Boolean, found type: {type(allow_model_training)}.", - ) - - # checks if model does not need training and all scorer(s) are not fittable - if not allow_model_training and not self.scorers_are_trainable: - logger.warning( - f"The filtering model {self.model.__class__.__name__} is not required to be trained" - + " because the parameter `allow_filter_training` is set to False, and no scorer" - + " fittable. The ``.fit()`` function has no effect." - ) - return - - list_series = _to_list(series) - - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_series]), - "all input `series` must be of type Timeseries.", - ) - - if allow_model_training: - # fit filtering model - if hasattr(self.filter, "fit"): - # TODO: check if filter is already fitted (for now fit it regardless -> only Kalman) - raise_if_not( - len(list_series) == 1, - f"Filter model {self.model.__class__.__name__} can only be fitted on a" - + " single time series, but multiple are provided.", - ) - - self.filter.fit(list_series[0], **filter_fit_kwargs) - else: - raise ValueError( - "`allow_filter_training` was set to True, but the filter" - + f" {self.model.__class__.__name__} has no fit() method." - ) - else: - # TODO: check if Kalman is fitted or not - # if not raise error "fit filter before, or set `allow_filter_training` to TRUE" - pass - - if self.scorers_are_trainable: - list_pred = [self.filter.filter(series) for series in list_series] - - # fit the scorers - for scorer in self.scorers: - if hasattr(scorer, "fit"): - scorer.fit_from_prediction(list_series, list_pred) - - return self - - def show_anomalies( - self, - series: TimeSeries, - actual_anomalies: TimeSeries = None, - names_of_scorers: Union[str, Sequence[str]] = None, - title: str = None, - metric: str = None, - **score_kwargs, - ): - """Plot the results of the anomaly model. - - Computes the score on the given series input and shows the different anomaly scores with respect to time. - - The plot will be composed of the following: - - - the series itself with the output of the filtering model - - the anomaly score of each scorer. The scorer with different windows will be separated. - - the actual anomalies, if given. - - It is possible to: - - - add a title to the figure with the parameter `title` - - give personalized names for the scorers with `names_of_scorers` - - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), if the actual anomalies are given - - Parameters - ---------- - series - The series to visualize anomalies from. - actual_anomalies - The ground truth of the anomalies (1 if it is an anomaly and 0 if not) - names_of_scorers - Name of the scorers. Must be a list of length equal to the number of scorers in the anomaly_model. - title - Title of the figure - metric - Optionally, Scoring function to use. Must be one of "AUC_ROC" and "AUC_PR". - Default: "AUC_ROC" - score_kwargs - parameters for the `.score()` function - """ - - if isinstance(series, Sequence): - raise_if_not( - len(series) == 1, - f"`show_anomalies` expects one series, found a sequence of length {len(series)} as input.", - ) - - series = series[0] - - anomaly_scores, model_output = self.score( - series, return_model_prediction=True, **score_kwargs - ) - - return self._show_anomalies( - series, - model_output=model_output, - anomaly_scores=anomaly_scores, - names_of_scorers=names_of_scorers, - actual_anomalies=actual_anomalies, - title=title, - metric=metric, + return super().fit( + series=series, + allow_model_training=allow_model_training, + **filter_fit_kwargs, ) def score( @@ -209,140 +100,182 @@ def score( series: Union[TimeSeries, Sequence[TimeSeries]], return_model_prediction: bool = False, **filter_kwargs, - ): - """Compute the anomaly score(s) for the given series. + ) -> Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]]: + """Compute the anomaly score(s) for the given (sequence of) series. Predicts the given target time series with the filtering model, and applies the scorer(s) to compare the predicted (filtered) series and the provided series. - Outputs the anomaly score(s) of the provided time series. - Parameters ---------- series - The (sequence of) series to score. + The (sequence of) series to score on. return_model_prediction - Boolean value indicating if the prediction of the model should be returned along the anomaly score - Default: False - filter_kwargs - parameters of the Darts `.filter()` filtering model + Whether to return the filtering model prediction along with the anomaly scores. + **filter_kwargs + Additional parameters passed to the filtering model's `filter()` method. Returns ------- - Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] - Anomaly scores series generated by the anomaly model scorers - - - ``TimeSeries`` if `series` is a series, and the anomaly model contains one scorer. - - ``Sequence[TimeSeries]`` - - * If `series` is a series, and the anomaly model contains multiple scorers, - returns one series per scorer. - * If `series` is a sequence, and the anomaly model contains one scorer, - returns one series per series in the sequence. - - ``Sequence[Sequence[TimeSeries]]`` if `series` is a sequence, and the anomaly - model contains multiple scorers. - The outer sequence is over the series, and inner sequence is over the scorers. + TimeSeries + A single `TimeSeries` for a single `series` with a single anomaly scorers. + Sequence[TimeSeries] + A sequence of `TimeSeries` for: + + - a single `series` with multiple anomaly scorers. + - a sequence of `series` with a single anomaly scorer. + Sequence[Sequence[TimeSeries]] + A sequence of sequences of `TimeSeries` for a sequence of `series` and multiple anomaly scorers. + The outer sequence is over the series, and inner sequence is over the scorers. """ - raise_if_not( - type(return_model_prediction) is bool, - f"`return_model_prediction` must be Boolean, found type: {type(return_model_prediction)}.", + return super().score( + series=series, + return_model_prediction=return_model_prediction, + **filter_kwargs, ) - list_series = _to_list(series) + def predict_series( + self, series: Sequence[TimeSeries], **kwargs + ) -> Sequence[TimeSeries]: + """Filters the given sequence of target time series with the filtering model. - # TODO: vectorize this call later on if we have any filtering models allowing this - list_pred = [self.filter.filter(s, **filter_kwargs) for s in list_series] - - scores = list( - zip( - *[ - sc.score_from_prediction(list_series, list_pred) - for sc in self.scorers - ] - ) - ) - - if len(scores) == 1 and not isinstance(series, Sequence): - # there's only one series - scores = scores[0] - if len(scores) == 1: - # there's only one scorer - scores = scores[0] - - if len(list_pred) == 1: - list_pred = list_pred[0] - - if return_model_prediction: - return scores, list_pred - else: - return scores + Parameters + ---------- + series + The sequence of series to filter. + **kwargs + Additional parameters passed to the filtering model's `filter()` method. + """ + return [self.model.filter(s, **kwargs) for s in series] - def eval_accuracy( + def eval_metric( self, - actual_anomalies: Union[TimeSeries, Sequence[TimeSeries]], + anomalies: Union[TimeSeries, Sequence[TimeSeries]], series: Union[TimeSeries, Sequence[TimeSeries]], - metric: str = "AUC_ROC", + metric: Literal["AUC_ROC", "AUC_PR"] = "AUC_ROC", **filter_kwargs, ) -> Union[ - Dict[str, float], - Dict[str, Sequence[float]], - Sequence[Dict[str, float]], - Sequence[Dict[str, Sequence[float]]], + dict[str, float], + dict[str, Sequence[float]], + Sequence[dict[str, float]], + Sequence[dict[str, Sequence[float]]], ]: - """Compute the accuracy of the anomaly scores computed by the model. + """Compute a metric for the anomaly scores computed by the model. - Predicts the `series` with the filtering model, and applies the - scorer(s) on the filtered time series and the given target time series. Returns the - score(s) of an agnostic threshold metric, based on the anomaly score given by the scorer(s). + Predicts the `series` with the filtering model, and applies the scorer(s) on the filtered time series + and the given target time series. Returns the score(s) of an agnostic threshold metric, based on the anomaly + score given by the scorer(s). Parameters ---------- - actual_anomalies - The (sequence of) ground truth of the anomalies (1 if it is an anomaly and 0 if not) + anomalies + The (sequence of) ground truth binary anomaly series (`1` if it is an anomaly and `0` if not). series The (sequence of) series to predict anomalies on. metric - Optionally, Scoring function to use. Must be one of "AUC_ROC" and "AUC_PR". - Default: "AUC_ROC" - filter_kwargs - parameters of the Darts `.filter()` filtering model + The name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". + **filter_kwargs + Additional parameters passed to the filtering model's `filter()` method. Returns ------- - Union[Dict[str, float], Dict[str, Sequence[float]], Sequence[Dict[str, float]], - Sequence[Dict[str, Sequence[float]]]] - Score for the time series. - A (sequence of) dictionary with the keys being the name of the scorers, and the values being the - metric results on the (sequence of) `series`. If the scorer treats every dimension independently - (by nature of the scorer or if its component_wise is set to True), the values of the dictionary - will be a Sequence containing the score for each dimension. + Dict[str, float] + A dictionary with the resulting metrics for single univariate `series`, with keys representing the + anomaly scorer(s), and values representing the metric values. + Dict[str, Sequence[float]] + Same as for `Dict[str, float]` but for multivariate `series`, and anomaly scorers that treat series + components/columns independently (by nature of the scorer or if `component_wise=True`). + Sequence[Dict[str, float]] + Same as for `Dict[str, float]` but for a sequence of univariate series. + Sequence[Dict[str, Sequence[float]]] + Same as for `Dict[str, float]` but for a sequence of multivariate series. """ - list_series, list_actual_anomalies = _to_list(series), _to_list( - actual_anomalies + return super().eval_metric( + anomalies=anomalies, + series=series, + metric=metric, + **filter_kwargs, ) - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_series]), - "all input `series` must be of type Timeseries.", - ) + def show_anomalies( + self, + series: TimeSeries, + anomalies: TimeSeries = None, + names_of_scorers: Union[str, Sequence[str]] = None, + title: str = None, + metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None, + **score_kwargs, + ): + """Plot the results of the anomaly model. - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_actual_anomalies]), - "all input `actual_anomalies` must be of type Timeseries.", - ) + Computes the score on the given series input and shows the different anomaly scores with respect to time. + + The plot will be composed of the following: - _assert_same_length(list_series, list_actual_anomalies) - self._check_univariate(list_actual_anomalies) + - the series itself with the output of the forecasting model. + - the anomaly score for each scorer. The scorers with different windows will be separated. + - the actual anomalies, if given. + + It is possible to: - list_anomaly_scores = self.score(series=list_series, **filter_kwargs) + - add a title to the figure with the parameter `title` + - give personalized names for the scorers with `names_of_scorers` + - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), if the actual anomalies are provided. - acc_anomaly_scores = self._eval_accuracy_from_scores( - list_actual_anomalies=list_actual_anomalies, - list_anomaly_scores=list_anomaly_scores, + Parameters + ---------- + series + The series to visualize anomalies from. + anomalies + The ground truth of the anomalies (1 if it is an anomaly and 0 if not). + names_of_scorers + Name of the scores. Must be a list of length equal to the number of scorers in the anomaly_model. + title + Title of the figure. + metric + Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". + score_kwargs + parameters for the `score()` method. + """ + return super().show_anomalies( + series=series, + anomalies=anomalies, + predict_kwargs=None, + names_of_scorers=names_of_scorers, + title=title, metric=metric, + **score_kwargs, ) - if len(acc_anomaly_scores) == 1 and not isinstance(series, Sequence): - return acc_anomaly_scores[0] + def _fit_core( + self, + series: Sequence[TimeSeries], + allow_model_training: bool, + **model_fit_kwargs, + ): + """Fit the filters (if applicable) and scorers.""" + # TODO: add support for covariates (see eg. Kalman Filter) + if allow_model_training and hasattr(self.model, "fit"): + # TODO: check if filter is already fitted (for now fit it regardless -> only Kalman) + if len(series) > 1: + raise_log( + ValueError( + f"Filter model {self.model.__class__.__name__} can only be fitted " + f"on a single time series, but multiple are provided." + ), + logger=logger, + ) + self.model.fit(series[0], **model_fit_kwargs) else: - return acc_anomaly_scores + # TODO: check if Kalman is fitted or not + # if not raise error "fit filter before, or set `allow_model_training` to TRUE" + pass + + if self.scorers_are_trainable: + pred = self.predict_series(series) + # fit the scorers + self._fit_scorers(series, pred) diff --git a/darts/ad/anomaly_model/forecasting_am.py b/darts/ad/anomaly_model/forecasting_am.py index cab0eaf683..8b4339cd9c 100644 --- a/darts/ad/anomaly_model/forecasting_am.py +++ b/darts/ad/anomaly_model/forecasting_am.py @@ -2,23 +2,27 @@ Forecasting Anomaly Model ------------------------- -A ``ForecastingAnomalyModel`` wraps around a Darts forecasting model and one or several anomaly +A `ForecastingAnomalyModel` wraps around a Darts forecasting model and one or several anomaly scorer(s) to compute anomaly scores by comparing how actuals deviate from the model's forecasts. """ # TODO: # - put start default value to its minimal value (wait for the release of historical_forecast) +import sys +from collections.abc import Sequence +from typing import Literal, Optional, Union -import inspect -from typing import Dict, Optional, Sequence, Union +if sys.version_info >= (3, 11): + from typing import Self +else: + from typing_extensions import Self import pandas as pd from darts.ad.anomaly_model.anomaly_model import AnomalyModel from darts.ad.scorers.scorers import AnomalyScorer -from darts.ad.utils import _assert_same_length, _assert_timeseries, _to_list -from darts.logging import get_logger, raise_if_not -from darts.models.forecasting.forecasting_model import ForecastingModel +from darts.logging import get_logger, raise_log +from darts.models.forecasting.forecasting_model import GlobalForecastingModel from darts.timeseries import TimeSeries logger = get_logger(__name__) @@ -27,19 +31,20 @@ class ForecastingAnomalyModel(AnomalyModel): def __init__( self, - model: ForecastingModel, + model: GlobalForecastingModel, scorer: Union[AnomalyScorer, Sequence[AnomalyScorer]], ): """Forecasting-based Anomaly Detection Model - The forecasting model may or may not be already fitted. The underlying assumption is that `model` - should be able to accurately forecast the series in the absence of anomalies. For this reason, - it is recommended to either provide a model that has already been fitted and evaluated to work - appropriately on a series without anomalies, or to ensure that a simple call to the :func:`fit()` - method of the model will be sufficient to train it to satisfactory performance on a series without anomalies. + The forecasting model must be a `GlobalForecastingModel` that may or may not be already fitted. The + underlying assumption is that `model` should be able to accurately forecast the series in the absence of + anomalies. For this reason, it is recommended to either provide a model that has already been fitted and + evaluated to work appropriately on a series without anomalies, or to ensure that a simple call to the + :func:`fit()` method of the model will be sufficient to train it to satisfactory performance on a series + without anomalies. The pre-trained model will be used to generate forecasts when calling :func:`score()`. - Calling :func:`fit()` on the anomaly model will fit the underlying forecasting model only - if ``allow_model_training`` is set to ``True`` upon calling ``fit()``. + Calling :func:`fit()` on the anomaly model will fit the underlying forecasting model only if + `allow_model_training` is set to `True` upon calling `fit()`. In addition, calling :func:`fit()` will also fit the fittable scorers, if any. Parameters @@ -48,17 +53,16 @@ def __init__( An instance of a Darts forecasting model. scorer One or multiple scorer(s) that will be used to compare the actual and predicted time series in order - to obtain an anomaly score ``TimeSeries``. + to obtain an anomaly score `TimeSeries`. If a list of `N` scorers is given, the anomaly model will call each - one of the scorers and output a list of `N` anomaly scores ``TimeSeries``. + one of the scorers and output a list of `N` anomaly scores `TimeSeries`. """ - - raise_if_not( - isinstance(model, ForecastingModel), - f"Model must be a darts ForecastingModel not a {type(model)}.", - ) + if not isinstance(model, GlobalForecastingModel): + raise_log( + ValueError("`model` must be a Darts `GlobalForecastingModel`."), + logger=logger, + ) self.model = model - super().__init__(model=model, scorer=scorer) def fit( @@ -68,288 +72,84 @@ def fit( future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, allow_model_training: bool = False, forecast_horizon: int = 1, - start: Union[pd.Timestamp, float, int] = 0.5, + start: Union[pd.Timestamp, float, int] = None, + start_format: Literal["position", "value"] = "value", num_samples: int = 1, + verbose: bool = False, + show_warnings: bool = True, + enable_optimization: bool = True, **model_fit_kwargs, - ): + ) -> Self: """Fit the underlying forecasting model (if applicable) and the fittable scorers, if any. - Train the model (if not already fitted and ``allow_model_training`` is set to True) and the - scorer(s) (if fittable) on the given time series. + Train the forecasting model (if not already fitted and `allow_model_training` is `True`) and the fittable + scorer(s) on the given time series. - Once the model is fitted, the series historical forecasts are computed, - representing what would have been forecasted by this model on the series. - - The prediction and the series are then used to train the scorer(s). + We use the trained forecasting model to compute historical forecasts for the input `series`. + The scorer(s) are then trained on these forecasts along with the input `series`. Parameters ---------- series - One or multiple (if the model supports it) target series to be - trained on (generally assumed to be anomaly-free). + The (sequence of) series to train on (generally assumed to be anomaly-free). past_covariates - Optional past-observed covariate series or sequence of series. This applies only if the model - supports past covariates. + Optionally, a (sequence of) past-observed covariate series or sequence of series. This applies only to + models that support past covariates. future_covariates - Optional future-known covariate series or sequence of series. This applies only if the model - supports future covariates. + Optionally, a (sequence of) future-known covariate series or sequence of series. This applies only to + models that support future covariates. allow_model_training - Boolean value that indicates if the forecasting model needs to be fitted on the given series. - If set to False, the model needs to be already fitted. - Default: False + Whether the forecasting model should be fitted on the given series. If `False`, the model must already be + fitted. forecast_horizon The forecast horizon for the predictions. start The first point of time at which a prediction is computed for a future time. - This parameter supports 3 different data types: ``float``, ``int`` and ``pandas.Timestamp``. - In the case of ``float``, the parameter will be treated as the proportion of the time series + This parameter supports 3 different data types: `float`, `int` and `pandas.Timestamp`. + In the case of `float`, the parameter will be treated as the proportion of the time series that should lie before the first prediction point. - In the case of ``int``, the parameter will be treated as an integer index to the time index of + In the case of `int`, the parameter will be treated as an integer index to the time index of `series` that will be used as first prediction time. - In case of ``pandas.Timestamp``, this time stamp will be used to determine the first prediction time + In case of `pandas.Timestamp`, this time stamp will be used to determine the first prediction time directly. - Default: 0.5 + start_format + Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a + `pd.RangeIndex`. + If set to 'position', `start` corresponds to the index position of the first predicted point and can range + from `(-len(series), len(series) - 1)`. + If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: `'value'` num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for - deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + enable_optimization + Whether to use the optimized version of historical_forecasts when supported and available. + Default: ``True``. model_fit_kwargs - Parameters to be passed on to the forecast model ``fit()`` method. + Parameters to be passed on to the forecast model `fit()` method. Returns ------- self Fitted model """ - - raise_if_not( - type(allow_model_training) is bool, - f"`allow_model_training` must be Boolean, found type: {type(allow_model_training)}.", - ) - - # checks if model does not need training and all scorer(s) are not fittable - if not allow_model_training and not self.scorers_are_trainable: - logger.warning( - f"The forecasting model {self.model.__class__.__name__} won't be trained" - + " because the parameter `allow_model_training` is set to False, and no scorer" - + " is fittable. ``.fit()`` method has no effect." - ) - return - - list_series = _to_list(series) - - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_series]), - "all input `series` must be of type Timeseries.", - ) - - list_past_covariates = self._prepare_covariates( - past_covariates, list_series, "past" - ) - list_future_covariates = self._prepare_covariates( - future_covariates, list_series, "future" - ) - - model_fit_kwargs["past_covariates"] = list_past_covariates - model_fit_kwargs["future_covariates"] = list_future_covariates - - # remove None elements from dictionary - model_fit_kwargs = {k: v for k, v in model_fit_kwargs.items() if v} - - # fit forecasting model - if allow_model_training: - # the model has not been trained yet - - fit_signature_series = ( - inspect.signature(self.model.fit).parameters["series"].annotation - ) - - # checks if model can be trained on multiple time series or only on a time series - # TODO: check if model can accept multivariate timeseries, raise error if given and model cannot - if "Sequence[darts.timeseries.TimeSeries]" in str(fit_signature_series): - self.model.fit(series=list_series, **model_fit_kwargs) - else: - raise_if_not( - len(list_series) == 1, - f"Forecasting model {self.model.__class__.__name__} only accepts a single time series" - + " for the training phase and not a sequence of multiple of time series.", - ) - self.model.fit(series=list_series[0], **model_fit_kwargs) - else: - raise_if_not( - self.model._fit_called, - f"Model {self.model.__class__.__name__} needs to be trained, consider training " - + "it beforehand or setting " - + "`allow_model_training` to True (default: False). " - + "The model will then be trained on the provided series.", - ) - - # generate the historical_forecast() prediction of the model on the train set - if self.scorers_are_trainable: - # check if the window size of the scorers are lower than the max size allowed - self._check_window_size(list_series, start) - - list_pred = [] - for idx, series in enumerate(list_series): - - if list_past_covariates is not None: - past_covariates = list_past_covariates[idx] - - if list_future_covariates is not None: - future_covariates = list_future_covariates[idx] - - list_pred.append( - self._predict_with_forecasting( - series, - past_covariates=past_covariates, - future_covariates=future_covariates, - forecast_horizon=forecast_horizon, - start=start, - num_samples=num_samples, - ) - ) - - # fit the scorers - for scorer in self.scorers: - if hasattr(scorer, "fit"): - scorer.fit_from_prediction(list_series, list_pred) - - return self - - def _prepare_covariates( - self, - covariates: Union[TimeSeries, Sequence[TimeSeries]], - series: Sequence[TimeSeries], - name_covariates: str, - ) -> Sequence[TimeSeries]: - """Convert `covariates` into Sequence, if not already, and checks if their length is equal to the one of `series`. - - Parameters - ---------- - covariates - Covariate ("future" or "past") of `series`. - series - The series to be trained on. - name_covariates - Internal parameter for error message, a string indicating if it is a "future" or "past" covariates. - - Returns - ------- - Sequence[TimeSeries] - Covariate time series - """ - - if covariates is not None: - list_covariates = _to_list(covariates) - - for covariates in list_covariates: - _assert_timeseries( - covariates, name_covariates + "_covariates input series" - ) - - raise_if_not( - len(list_covariates) == len(series), - f"Number of {name_covariates}_covariates must match the number of given " - + f"series, found length {len(list_covariates)} and expected {len(series)}.", - ) - - return list_covariates if covariates is not None else None - - def show_anomalies( - self, - series: TimeSeries, - past_covariates: Optional[TimeSeries] = None, - future_covariates: Optional[TimeSeries] = None, - forecast_horizon: int = 1, - start: Union[pd.Timestamp, float, int] = 0.5, - num_samples: int = 1, - actual_anomalies: TimeSeries = None, - names_of_scorers: Union[str, Sequence[str]] = None, - title: str = None, - metric: str = None, - ): - """Plot the results of the anomaly model. - - Computes the score on the given series input and shows the different anomaly scores with respect to time. - - The plot will be composed of the following: - - - the series itself with the output of the forecasting model. - - the anomaly score for each scorer. The scorers with different windows will be separated. - - the actual anomalies, if given. - - It is possible to: - - - add a title to the figure with the parameter `title` - - give personalized names for the scorers with `names_of_scorers` - - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), - if the actual anomalies are provided. - - Parameters - ---------- - series - The series to visualize anomalies from. - past_covariates - An optional past-observed covariate series or sequence of series. This applies only if the model - supports past covariates. - future_covariates - An optional future-known covariate series or sequence of series. This applies only if the model - supports future covariates. - forecast_horizon - The forecast horizon for the predictions. - start - The first point of time at which a prediction is computed for a future time. - This parameter supports 3 different data types: ``float``, ``int`` and ``pandas.Timestamp``. - In the case of ``float``, the parameter will be treated as the proportion of the time series - that should lie before the first prediction point. - In the case of ``int``, the parameter will be treated as an integer index to the time index of - `series` that will be used as first prediction time. - In case of ``pandas.Timestamp``, this time stamp will be used to determine the first prediction time - directly. - num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for - deterministic models. - actual_anomalies - The ground truth of the anomalies (1 if it is an anomaly and 0 if not) - names_of_scorers - Name of the scores. Must be a list of length equal to the number of scorers in the anomaly_model. - title - Title of the figure - metric - Optionally, Scoring function to use. Must be one of "AUC_ROC" and "AUC_PR". - Default: "AUC_ROC" - """ - - if isinstance(series, Sequence): - raise_if_not( - len(series) == 1, - f"`show_anomalies` expects one series, found a list of length {len(series)} as input.", - ) - - series = series[0] - - raise_if_not( - isinstance(series, TimeSeries), - f"`show_anomalies` expects an input of type TimeSeries, found type: {type(series)}.", - ) - - anomaly_scores, model_output = self.score( - series, + return super().fit( + series=series, past_covariates=past_covariates, future_covariates=future_covariates, + allow_model_training=allow_model_training, forecast_horizon=forecast_horizon, start=start, + start_format=start_format, num_samples=num_samples, - return_model_prediction=True, - ) - - return self._show_anomalies( - series, - model_output=model_output, - anomaly_scores=anomaly_scores, - names_of_scorers=names_of_scorers, - actual_anomalies=actual_anomalies, - title=title, - metric=metric, + verbose=verbose, + show_warnings=show_warnings, + enable_optimization=enable_optimization, + **model_fit_kwargs, ) def score( @@ -358,317 +158,429 @@ def score( past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, forecast_horizon: int = 1, - start: Union[pd.Timestamp, float, int] = 0.5, + start: Union[pd.Timestamp, float, int] = None, + start_format: Literal["position", "value"] = "value", num_samples: int = 1, + verbose: bool = False, + show_warnings: bool = True, + enable_optimization: bool = True, return_model_prediction: bool = False, - ) -> Union[TimeSeries, Sequence[TimeSeries]]: + ) -> Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]]: """Compute anomaly score(s) for the given series. Predicts the given target time series with the forecasting model, and applies the scorer(s) - on the prediction and the target input time series. Outputs the anomaly score of the given - input time series. + on the prediction and the target input time series. Parameters ---------- series The (sequence of) series to score on. past_covariates - An optional past-observed covariate series or sequence of series. This applies only if the model - supports past covariates. + Optionally, a (sequence of) past-observed covariate series or sequence of series. This applies only to + models that support past covariates. future_covariates - An optional future-known covariate series or sequence of series. This applies only if the model - supports future covariates. + Optionally, a (sequence of) future-known covariate series or sequence of series. This applies only to + models that support future covariates. forecast_horizon The forecast horizon for the predictions. start The first point of time at which a prediction is computed for a future time. - This parameter supports 3 different data types: ``float``, ``int`` and ``pandas.Timestamp``. - In the case of ``float``, the parameter will be treated as the proportion of the time series + This parameter supports 3 different data types: `float`, `int` and `pandas.Timestamp`. + In the case of `float`, the parameter will be treated as the proportion of the time series that should lie before the first prediction point. - In the case of ``int``, the parameter will be treated as an integer index to the time index of + In the case of `int`, the parameter will be treated as an integer index to the time index of `series` that will be used as first prediction time. - In case of ``pandas.Timestamp``, this time stamp will be used to determine the first prediction time - directly. Default: 0.5 + In case of `pandas.Timestamp`, this time stamp will be used to determine the first prediction time + directly. + start_format + Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a + `pd.RangeIndex`. + If set to 'position', `start` corresponds to the index position of the first predicted point and can range + from `(-len(series), len(series) - 1)`. + If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: `'value'` num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for - deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + enable_optimization + Whether to use the optimized version of historical_forecasts when supported and available. + Default: ``True``. return_model_prediction - Boolean value indicating if the prediction of the model should be returned along the anomaly score - Default: False + Whether to return the forecasting model prediction along with the anomaly scores. Returns ------- - Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] - Anomaly scores series generated by the anomaly model scorers - - - ``TimeSeries`` if `series` is a series, and the anomaly model contains one scorer. - - ``Sequence[TimeSeries]`` - - * if `series` is a series, and the anomaly model contains multiple scorers, - returns one series per scorer. - * if `series` is a sequence, and the anomaly model contains one scorer, - returns one series per series in the sequence. - - ``Sequence[Sequence[TimeSeries]]`` if `series` is a sequence, and the anomaly - model contains multiple scorers. The outer sequence is over the series, - and inner sequence is over the scorers. - """ - raise_if_not( - type(return_model_prediction) is bool, - f"`return_model_prediction` must be Boolean, found type: {type(return_model_prediction)}.", - ) - - raise_if_not( - self.model._fit_called, - f"Model {self.model} has not been trained. Please call ``.fit()``.", - ) - - list_series = _to_list(series) - - list_past_covariates = self._prepare_covariates( - past_covariates, list_series, "past" - ) - list_future_covariates = self._prepare_covariates( - future_covariates, list_series, "future" - ) - - # check if the window size of the scorers are lower than the max size allowed - self._check_window_size(list_series, start) - - list_pred = [] - for idx, s in enumerate(list_series): - - if list_past_covariates is not None: - past_covariates = list_past_covariates[idx] - - if list_future_covariates is not None: - future_covariates = list_future_covariates[idx] - - list_pred.append( - self._predict_with_forecasting( - s, - past_covariates=past_covariates, - future_covariates=future_covariates, - forecast_horizon=forecast_horizon, - start=start, - num_samples=num_samples, - ) - ) - - scores = list( - zip( - *[ - sc.score_from_prediction(list_series, list_pred) - for sc in self.scorers - ] - ) - ) - - if len(scores) == 1 and not isinstance(series, Sequence): - # there's only one series - scores = scores[0] - if len(scores) == 1: - # there's only one scorer - scores = scores[0] - - if len(list_pred) == 1: - list_pred = list_pred[0] - - if return_model_prediction: - return scores, list_pred - else: - return scores - - def _check_window_size( - self, series: Sequence[TimeSeries], start: Union[pd.Timestamp, float, int] - ): - """Checks if the parameters `window` of the scorers are smaller than the maximum window size allowed. - The maximum size allowed is equal to the output length of the .historical_forecast() applied on `series`. - It is defined by the parameter `start` and the series’ length. + TimeSeries + A single `TimeSeries` for a single `series` with a single anomaly scorers. + Sequence[TimeSeries] + A sequence of `TimeSeries` for: - Parameters - ---------- - series - The series given to the .historical_forecast() - start - Parameter of the .historical_forecast(): first point of time at which a prediction is computed - for a future time. + - a single `series` with multiple anomaly scorers. + - a sequence of `series` with a single anomaly scorer. + Sequence[Sequence[TimeSeries]] + A sequence of sequences of `TimeSeries` for a sequence of `series` and multiple anomaly scorers. + The outer sequence is over the series, and inner sequence is over the scorers. """ - # biggest window of the anomaly_model scorers - max_window = max(scorer.window for scorer in self.scorers) - - for s in series: - max_possible_window = ( - len(s.drop_before(s.get_timestamp_at_point(start))) + 1 - ) - raise_if_not( - max_window <= max_possible_window, - f"Window size {max_window} is greater than the targeted series length {max_possible_window}," - + f" must be lower or equal. Reduce window size, or reduce start value (start: {start}).", - ) + return super().score( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=forecast_horizon, + start=start, + start_format=start_format, + num_samples=num_samples, + verbose=verbose, + show_warnings=show_warnings, + enable_optimization=enable_optimization, + return_model_prediction=return_model_prediction, + ) - def _predict_with_forecasting( + def predict_series( self, - series: TimeSeries, - past_covariates: Optional[TimeSeries] = None, - future_covariates: Optional[TimeSeries] = None, + series: Sequence[TimeSeries], + past_covariates: Optional[Sequence[TimeSeries]] = None, + future_covariates: Optional[Sequence[TimeSeries]] = None, forecast_horizon: int = 1, start: Union[pd.Timestamp, float, int] = None, + start_format: Literal["position", "value"] = "value", num_samples: int = 1, - ) -> TimeSeries: - - """Compute the historical forecasts that would have been obtained by this model on the `series`. + verbose: bool = False, + show_warnings: bool = True, + enable_optimization: bool = True, + ) -> Sequence[TimeSeries]: + """Computes the historical forecasts that would have been obtained by the underlying forecasting model + on `series`. - `retrain` is set to False if possible (this is not supported by all models). If set to True, it will always + `retrain` is set to `False` if possible (this is not supported by all models). If set to `True`, it will always re-train the model on the entire available history, Parameters ---------- series - The target time series to use to successively train and evaluate the historical forecasts. + The sequence of series to score on. past_covariates - An optional past-observed covariate series or sequence of series. This applies only if the model - supports past covariates. + Optionally, a sequence of past-observed covariate series or sequence of series. This applies only to + models that support past covariates. future_covariates - An optional future-known covariate series or sequence of series. This applies only if the model - supports future covariates. + Optionally, a sequence of future-known covariate series or sequence of series. This applies only to + models that support future covariates. forecast_horizon - The forecast horizon for the predictions + The forecast horizon for the predictions. start The first point of time at which a prediction is computed for a future time. - This parameter supports 3 different data types: ``float``, ``int`` and ``pandas.Timestamp``. - In the case of ``float``, the parameter will be treated as the proportion of the time series + This parameter supports 3 different data types: `float`, `int` and `pandas.Timestamp`. + In the case of `float`, the parameter will be treated as the proportion of the time series that should lie before the first prediction point. - In the case of ``int``, the parameter will be treated as an integer index to the time index of + In the case of `int`, the parameter will be treated as an integer index to the time index of `series` that will be used as first prediction time. - In case of ``pandas.Timestamp``, this time stamp will be used to determine the first prediction time + In case of `pandas.Timestamp`, this time stamp will be used to determine the first prediction time directly. + start_format + Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a + `pd.RangeIndex`. + If set to 'position', `start` corresponds to the index position of the first predicted point and can range + from `(-len(series), len(series) - 1)`. + If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: `'value'` num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for - deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + enable_optimization + Whether to use the optimized version of historical_forecasts when supported and available. + Default: ``True``. Returns ------- - TimeSeries - Single ``TimeSeries`` instance created from the last point of each individual forecast. + Sequence[TimeSeries] + A sequence of `TimeSeries` with the historical forecasts for each series (with `last_points_only=True`). """ + if not self.model._fit_called: + raise_log( + ValueError( + f"Forecasting `model` {self.model} has not been trained yet. Call `fit()` before." + ), + logger=logger, + ) + return self.model.historical_forecasts( + series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=forecast_horizon, + stride=1, + retrain=False, + last_points_only=True, + start=start, + start_format=start_format, + num_samples=num_samples, + verbose=verbose, + show_warnings=show_warnings, + enable_optimization=enable_optimization, + ) - # TODO: raise an exception. We only support models that do not need retrain - # checks if model accepts to not be retrained in the historical_forecasts() - if self.model._supports_non_retrainable_historical_forecasts: - # default: set to False. Allows a faster computation. - retrain = False - else: - retrain = True - - historical_forecasts_param = { - "past_covariates": past_covariates, - "future_covariates": future_covariates, - "forecast_horizon": forecast_horizon, - "start": start, - "retrain": retrain, - "num_samples": num_samples, - "stride": 1, - "last_points_only": True, - "verbose": False, - } - - return self.model.historical_forecasts(series, **historical_forecasts_param) - - def eval_accuracy( + def eval_metric( self, - actual_anomalies: Union[TimeSeries, Sequence[TimeSeries]], + anomalies: Union[TimeSeries, Sequence[TimeSeries]], series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, forecast_horizon: int = 1, - start: Union[pd.Timestamp, float, int] = 0.5, + start: Union[pd.Timestamp, float, int] = None, + start_format: Literal["position", "value"] = "value", num_samples: int = 1, - metric: str = "AUC_ROC", + verbose: bool = False, + show_warnings: bool = True, + enable_optimization: bool = True, + metric: Literal["AUC_ROC", "AUC_PR"] = "AUC_ROC", ) -> Union[ - Dict[str, float], - Dict[str, Sequence[float]], - Sequence[Dict[str, float]], - Sequence[Dict[str, Sequence[float]]], + dict[str, float], + dict[str, Sequence[float]], + Sequence[dict[str, float]], + Sequence[dict[str, Sequence[float]]], ]: """Compute the accuracy of the anomaly scores computed by the model. - Predicts the `series` with the forecasting model, and applies the - scorer(s) on the predicted time series and the given target time series. Returns the - score(s) of an agnostic threshold metric, based on the anomaly score given by the scorer(s). + Predicts the `series` with the forecasting model, and applies the scorer(s) on the predicted time series + and the given target time series. Returns the score(s) of an agnostic threshold metric, based on the anomaly + score given by the scorer(s). Parameters ---------- - actual_anomalies - The (sequence of) ground truth of the anomalies (1 if it is an anomaly and 0 if not) + anomalies + The (sequence of) ground truth binary anomaly series (`1` if it is an anomaly and `0` if not). series The (sequence of) series to predict anomalies on. past_covariates - An optional past-observed covariate series or sequence of series. This applies only - if the model supports past covariates. + Optionally, a (sequence of) past-observed covariate series or sequence of series. This applies only to + models that support past covariates. future_covariates - An optional future-known covariate series or sequence of series. This applies only - if the model supports future covariates. + Optionally, a (sequence of) future-known covariate series or sequence of series. This applies only to + models that support future covariates. forecast_horizon The forecast horizon for the predictions. start The first point of time at which a prediction is computed for a future time. - This parameter supports 3 different data types: ``float``, ``int`` and ``pandas.Timestamp``. - In the case of ``float``, the parameter will be treated as the proportion of the time series + This parameter supports 3 different data types: `float`, `int` and `pandas.Timestamp`. + In the case of `float`, the parameter will be treated as the proportion of the time series that should lie before the first prediction point. - In the case of ``int``, the parameter will be treated as an integer index to the time index of + In the case of `int`, the parameter will be treated as an integer index to the time index of `series` that will be used as first prediction time. - In case of ``pandas.Timestamp``, this time stamp will be used to determine the first prediction time + In case of `pandas.Timestamp`, this time stamp will be used to determine the first prediction time directly. + start_format + Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a + `pd.RangeIndex`. + If set to 'position', `start` corresponds to the index position of the first predicted point and can range + from `(-len(series), len(series) - 1)`. + If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: `'value'` num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 for - deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + enable_optimization + Whether to use the optimized version of historical_forecasts when supported and available. + Default: ``True``. metric - Optionally, Scoring function to use. Must be one of "AUC_ROC" and "AUC_PR". - Default: "AUC_ROC" + The name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". Returns ------- - Union[Dict[str, float], Dict[str, Sequence[float]], Sequence[Dict[str, float]], - Sequence[Dict[str, Sequence[float]]]] - Score for the time series. - A (sequence of) dictionary with the keys being the name of the scorers, and the values being the - metric results on the (sequence of) `series`. If the scorer treats every dimension independently - (by nature of the scorer or if its component_wise is set to True), the values of the dictionary - will be a Sequence containing the score for each dimension. + Dict[str, float] + A dictionary with the resulting metrics for single univariate `series`, with keys representing the + anomaly scorer(s), and values representing the metric values. + Dict[str, Sequence[float]] + Same as for `Dict[str, float]` but for multivariate `series`, and anomaly scorers that treat series + components/columns independently (by nature of the scorer or if `component_wise=True`). + Sequence[Dict[str, float]] + Same as for `Dict[str, float]` but for a sequence of univariate series. + Sequence[Dict[str, Sequence[float]]] + Same as for `Dict[str, float]` but for a sequence of multivariate series. """ - - list_actual_anomalies = _to_list(actual_anomalies) - list_series = _to_list(series) - - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_series]), - "all input `series` must be of type Timeseries.", - ) - - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_actual_anomalies]), - "all input `actual_anomalies` must be of type Timeseries.", - ) - - _assert_same_length(list_actual_anomalies, list_series) - self._check_univariate(list_actual_anomalies) - - list_anomaly_scores = self.score( - series=list_series, + return super().eval_metric( + anomalies=anomalies, + series=series, past_covariates=past_covariates, future_covariates=future_covariates, forecast_horizon=forecast_horizon, start=start, + start_format=start_format, num_samples=num_samples, + verbose=verbose, + show_warnings=show_warnings, + enable_optimization=enable_optimization, + metric=metric, ) - acc_anomaly_scores = self._eval_accuracy_from_scores( - list_actual_anomalies=list_actual_anomalies, - list_anomaly_scores=list_anomaly_scores, + def show_anomalies( + self, + series: TimeSeries, + past_covariates: Optional[TimeSeries] = None, + future_covariates: Optional[TimeSeries] = None, + forecast_horizon: int = 1, + start: Union[pd.Timestamp, float, int] = None, + start_format: Literal["position", "value"] = "value", + num_samples: int = 1, + verbose: bool = False, + show_warnings: bool = True, + enable_optimization: bool = True, + anomalies: TimeSeries = None, + names_of_scorers: Union[str, Sequence[str]] = None, + title: str = None, + metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None, + component_wise: bool = False, + **score_kwargs, + ): + """Plot the results of the anomaly model. + + Computes the score on the given series input and shows the different anomaly scores with respect to time. + + The plot will be composed of the following: + + - the series itself with the output of the forecasting model. + - the anomaly score for each scorer. The scorers with different windows will be separated. + - the actual anomalies, if given. + + It is possible to: + + - add a title to the figure with the parameter `title` + - give personalized names for the scorers with `names_of_scorers` + - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), if the actual anomalies are provided. + + Parameters + ---------- + series + The series to visualize anomalies from. + past_covariates + Optionally, a past-observed covariate series or sequence of series. This applies only to + models that support past covariates. + future_covariates + Optionally, a future-known covariate series or sequence of series. This applies only to models that support + future covariates. + forecast_horizon + The forecast horizon for the predictions. + start + The first point of time at which a prediction is computed for a future time. + This parameter supports 3 different data types: `float`, `int` and `pandas.Timestamp`. + In the case of `float`, the parameter will be treated as the proportion of the time series + that should lie before the first prediction point. + In the case of `int`, the parameter will be treated as an integer index to the time index of + `series` that will be used as first prediction time. + In case of `pandas.Timestamp`, this time stamp will be used to determine the first prediction time + directly. + start_format + Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a + `pd.RangeIndex`. + If set to 'position', `start` corresponds to the index position of the first predicted point and can range + from `(-len(series), len(series) - 1)`. + If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: `'value'` + num_samples + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + enable_optimization + Whether to use the optimized version of historical_forecasts when supported and available. + Default: ``True``. + anomalies + The ground truth of the anomalies (1 if it is an anomaly and 0 if not). + names_of_scorers + Name of the scores. Must be a list of length equal to the number of scorers in the anomaly_model. + title + Title of the figure. + metric + Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". + component_wise + If True, will separately plot each component in case of multivariate anomaly detection. + score_kwargs + parameters for the `score()` method. + """ + predict_kwargs = { + "past_covariates": past_covariates, + "future_covariates": future_covariates, + "forecast_horizon": forecast_horizon, + "start": start, + "start_format": start_format, + "num_samples": num_samples, + "verbose": verbose, + "show_warnings": show_warnings, + "enable_optimization": enable_optimization, + } + return super().show_anomalies( + series=series, + anomalies=anomalies, + predict_kwargs=predict_kwargs, + names_of_scorers=names_of_scorers, + title=title, metric=metric, + component_wise=component_wise, + **score_kwargs, ) - if len(acc_anomaly_scores) == 1 and not isinstance(series, Sequence): - return acc_anomaly_scores[0] - else: - return acc_anomaly_scores + def _fit_core( + self, + series: Sequence[TimeSeries], + past_covariates: Optional[Sequence[TimeSeries]] = None, + future_covariates: Optional[Sequence[TimeSeries]] = None, + allow_model_training: bool = False, + forecast_horizon: int = 1, + start: Union[pd.Timestamp, float, int] = 0.5, + start_format: Literal["position", "value"] = "value", + num_samples: int = 1, + verbose: bool = False, + show_warnings: bool = True, + enable_optimization: bool = True, + **model_fit_kwargs, + ): + """Fit the forecasting model (if applicable) and scorers.""" + # fit forecasting model + if allow_model_training: + self.model._fit_wrapper( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + **model_fit_kwargs, + ) + elif not self.model._fit_called: + raise_log( + ValueError( + f"With `allow_model_training=False`, the underlying model `{self.model.__class__.__name__}` " + f"must have already been trained. Either train it before or set `allow_model_training=True` " + f"(model will trained from scratch on the provided series)." + ), + logger=logger, + ) + + # generate the historical_forecast() prediction of the model on the train set + if self.scorers_are_trainable: + historical_forecasts = self.predict_series( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=forecast_horizon, + start=start, + start_format=start_format, + num_samples=num_samples, + verbose=verbose, + show_warnings=show_warnings, + enable_optimization=enable_optimization, + ) + # fit the scorers + self._fit_scorers(series, historical_forecasts) diff --git a/darts/ad/detectors/__init__.py b/darts/ad/detectors/__init__.py index 820b0f71c6..5ed84593f4 100644 --- a/darts/ad/detectors/__init__.py +++ b/darts/ad/detectors/__init__.py @@ -2,22 +2,28 @@ Anomaly Detectors ----------------- -Detectors provide binary anomaly classification on time series. -They can typically be used to transform anomaly scores time series into binary anomaly time series. +Detectors provide binary anomaly classification on time series. They can typically be used to transform anomaly scores +time series into binary anomaly time series. -Some detectors are trainable. For instance, ``QuantileDetector`` emits a binary anomaly for -every time step where the observed value(s) are beyond the quantile(s) observed -on the training series. +Some detectors are trainable. For instance, :class:`~darts.ad.detectors.quantile_detector.QuantileDetector` emits a +binary anomaly for every time step where the observed value(s) are beyond the quantile(s) observed on the training +series. -The main functions are ``fit()`` (for the trainable detectors), ``detect()`` and ``eval_accuracy()``. +The main functions are `fit()` (for the trainable detectors), `detect()` and `eval_metric()`. -``fit()`` trains the detector over the history of one or multiple time series. It can -for instance be called on series containing anomaly scores (or even raw values) during normal times. -The function ``detect()`` takes an anomaly score time series as input, and applies the detector -to obtain binary predictions. The function ``eval_accuracy()`` returns the accuracy metric -("accuracy", "precision", "recall" or "f1") between a binary prediction time series and some known +`fit()` trains the detector over the history of one or multiple time series. It can for instance be called on series +containing anomaly scores (or even raw values) during normal times. The function `detect()` takes an anomaly score +time series as input, and applies the detector to obtain binary predictions. The function `eval_metric()` returns +the accuracy metric ("accuracy", "precision", "recall" or "f1") between a binary prediction time series and some known binary ground truth time series indicating the presence of anomalies. """ -from .quantile_detector import QuantileDetector -from .threshold_detector import ThresholdDetector +from darts.ad.detectors.iqr_detector import IQRDetector +from darts.ad.detectors.quantile_detector import QuantileDetector +from darts.ad.detectors.threshold_detector import ThresholdDetector + +__all__ = [ + "IQRDetector", + "QuantileDetector", + "ThresholdDetector", +] diff --git a/darts/ad/detectors/detectors.py b/darts/ad/detectors/detectors.py index 88f3b3cc7a..496b1d21ea 100644 --- a/darts/ad/detectors/detectors.py +++ b/darts/ad/detectors/detectors.py @@ -4,124 +4,111 @@ # TODO: # - check error message and add name of variable in the message error -# - rethink the positionning of fun _check_param() # - add possibility to input a list of param rather than only one number # - add more complex detectors # - create an ensemble fittable detector +import sys from abc import ABC, abstractmethod -from typing import Any, Sequence, Union +from collections.abc import Sequence +from typing import Any, Literal, Optional, Union + +if sys.version_info >= (3, 11): + from typing import Self +else: + from typing_extensions import Self + +import numpy as np from darts import TimeSeries -from darts.ad.utils import eval_accuracy_from_binary_prediction -from darts.logging import raise_if_not +from darts.ad.utils import ( + _assert_fit_called, + _check_input, + eval_metric_from_binary_prediction, +) +from darts.logging import get_logger, raise_log +from darts.utils.ts_utils import series2seq + +logger = get_logger(__name__) class Detector(ABC): """Base class for all detectors""" def __init__(self, *args: Any, **kwargs: Any) -> None: - pass + self.width_trained_on: Optional[int] = None def detect( self, series: Union[TimeSeries, Sequence[TimeSeries]], + name: str = "series", ) -> Union[TimeSeries, Sequence[TimeSeries]]: """Detect anomalies on given time series. Parameters ---------- series - series on which to detect anomalies. + The (sequence of) series on which to detect anomalies. + name + The name of `series`. Returns ------- Union[TimeSeries, Sequence[TimeSeries]] - binary prediciton (1 if considered as an anomaly, 0 if not) + binary prediction (1 if considered as an anomaly, 0 if not) """ - - list_series = [series] if not isinstance(series, Sequence) else series - - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_series]), - "all series in `series` must be of type TimeSeries.", + called_with_single_series = isinstance(series, TimeSeries) + series = _check_input( + series, + name=name, + width_expected=self.width_trained_on, + check_deterministic=True, ) - - raise_if_not( - all([s.is_deterministic for s in list_series]), - "all series in `series` must be deterministic (number of samples equal to 1).", - ) - detected_series = [] - for s in list_series: - detected_series.append(self._detect_core(s)) + for s in series: + detected_series.append(self._detect_core(s, name=name)) + return detected_series[0] if called_with_single_series else detected_series - if len(detected_series) == 1 and not isinstance(series, Sequence): - return detected_series[0] - else: - return detected_series - - @abstractmethod - def _detect_core(self, input: Any) -> Any: - pass - - def eval_accuracy( + def eval_metric( self, - actual_anomalies: Union[TimeSeries, Sequence[TimeSeries]], - anomaly_score: Union[TimeSeries, Sequence[TimeSeries]], + anomalies: Union[TimeSeries, Sequence[TimeSeries]], + pred_scores: Union[TimeSeries, Sequence[TimeSeries]], window: int = 1, - metric: str = "recall", + metric: Literal["recall", "precision", "f1", "accuracy"] = "recall", ) -> Union[float, Sequence[float], Sequence[Sequence[float]]]: """Score the results against true anomalies. Parameters ---------- - actual_anomalies - The ground truth of the anomalies (1 if it is an anomaly and 0 if not). - anomaly_score - Series indicating how anomoulous each window of size w is. + anomalies + The (sequence of) ground truth binary anomaly series (`1` if it is an anomaly and `0` if not). + pred_scores + The (sequence of) of estimated anomaly score series indicating how anomalous each window of size w is. window - Integer value indicating the number of past samples each point represents - in the anomaly_score. + Integer value indicating the number of past samples each point represents in the `pred_scores`. metric - Metric function to use. Must be one of "recall", "precision", - "f1", and "accuracy". - Default: "recall" + The name of the metric function to use. Must be one of "recall", "precision", "f1", and "accuracy". + Default: "recall". Returns ------- Union[float, Sequence[float], Sequence[Sequence[float]]] Metric results for each anomaly score """ - - if isinstance(anomaly_score, Sequence): - raise_if_not( - all([isinstance(s, TimeSeries) for s in anomaly_score]), - "all series in `anomaly_score` must be of type TimeSeries.", - ) - - raise_if_not( - all([s.is_deterministic for s in anomaly_score]), - "all series in `anomaly_score` must be deterministic (number of samples equal to 1).", - ) - else: - raise_if_not( - isinstance(anomaly_score, TimeSeries), - f"Input `anomaly_score` must be of type TimeSeries, found {type(anomaly_score)}.", - ) - - raise_if_not( - anomaly_score.is_deterministic, - "Input `anomaly_score` must be deterministic (number of samples equal to 1).", - ) - - return eval_accuracy_from_binary_prediction( - actual_anomalies, self.detect(anomaly_score), window, metric + return eval_metric_from_binary_prediction( + anomalies=anomalies, + pred_anomalies=self.detect(pred_scores), + window=window, + metric=metric, ) + @abstractmethod + def _detect_core(self, series: TimeSeries, name: str = "series") -> TimeSeries: + pass + class FittableDetector(Detector): - """Base class of Detectors that need training.""" + """Base class of Detectors that require training.""" def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) @@ -130,90 +117,170 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: def detect( self, series: Union[TimeSeries, Sequence[TimeSeries]], + name: str = "series", ) -> Union[TimeSeries, Sequence[TimeSeries]]: - """Detect anomalies on given time series. + _assert_fit_called(self._fit_called, name="Detector") + return super().detect(series, name=name) + + def fit(self, series: Union[TimeSeries, Sequence[TimeSeries]]) -> Self: + """Trains the detector on the given time series. Parameters ---------- series - series on which to detect anomalies. + Time (sequence of) series to be used to train the detector. Returns ------- - Union[TimeSeries, Sequence[TimeSeries]] - binary prediciton (1 if considered as an anomaly, 0 if not) + self + Fitted Detector. """ - - list_series = [series] if not isinstance(series, Sequence) else series - - raise_if_not( - self._fit_called, - "The Detector has not been fitted yet. Call `fit()` first.", + width = series2seq(series)[0].width + series = _check_input( + series, + name="series", + width_expected=width, + check_deterministic=True, + check_binary=False, + check_multivariate=False, ) + self.width_trained_on = width + self._fit_core(series) + self._fit_called = True + return self - raise_if_not( - all([self.width_trained_on == s.width for s in list_series]), - "all series in `series` must have the same number of components as the data " - + "used for training the detector model, number of components in training: " - + f" {self.width_trained_on}.", - ) + def fit_detect( + self, series: Union[TimeSeries, Sequence[TimeSeries]] + ) -> Union[TimeSeries, Sequence[TimeSeries]]: + """Trains the detector and detects anomalies on the same series. + + Parameters + ---------- + series + Time series to be used for training and be detected for anomalies. - return super().detect(series) + Returns + ------- + Union[TimeSeries, Sequence[TimeSeries]] + Binary prediction (1 if considered as an anomaly, 0 if not) + """ + self.fit(series) + return self.detect(series, name="series") @abstractmethod - def _fit_core(self, input: Any) -> Any: + def _fit_core(self, series: Sequence[TimeSeries]) -> None: pass - def fit(self, series: Union[TimeSeries, Sequence[TimeSeries]]) -> None: - """Trains the detector on the given time series. + +class _BoundedDetectorMixin(ABC): + """ + A class containing functions supporting bounds-based detection, to be used as a mixin for some + `Detector` subclasses. + """ + + @staticmethod + def _prepare_boundaries( + lower_bound_name: str, + upper_bound_name: str, + lower_bound: Optional[Union[Sequence[float], float]] = None, + upper_bound: Optional[Union[Sequence[float], float]] = None, + ) -> tuple[list[Optional[float]], list[Optional[float]]]: + """ + Process the boundaries argument and perform some sanity checks Parameters ---------- - series - Time series to be used to train the detector. + lower_bound_name + Name of the lower bound + upper_bound_name + Name of the upper bound + lower_bound + (Sequence of) numerical bound below which a value is regarded as anomaly. + If a sequence, must match the dimensionality of the series + this detector is applied to. + upper_bound + (Sequence of) numerical bound above which a value is regarded as anomaly. + If a sequence, must match the dimensionality of the series + this detector is applied to. Returns ------- - self - Fitted Detector. + lower_bound + Lower bounds, as a list of values (at least one not None value) + upper_bound + Upper bounds, as a list of values (at least one not None value) """ + if lower_bound is None and upper_bound is None: + raise_log( + ValueError( + f"`{lower_bound_name} and `{upper_bound_name}` cannot both be `None`." + ), + logger=logger, + ) - list_series = [series] if not isinstance(series, Sequence) else series - - raise_if_not( - all([isinstance(s, TimeSeries) for s in list_series]), - "all series in `series` must be of type TimeSeries.", - ) + def _prep_boundaries(boundaries) -> list[Optional[float]]: + """Convert boundaries to List""" + return ( + boundaries.tolist() + if isinstance(boundaries, np.ndarray) + else ( + [boundaries] if not isinstance(boundaries, Sequence) else boundaries + ) + ) - raise_if_not( - all([s.is_deterministic for s in list_series]), - "all series in `series` must be deterministic (number of samples equal to 1).", - ) + # convert to list + lower_bound = _prep_boundaries(lower_bound) + upper_bound = _prep_boundaries(upper_bound) - self.width_trained_on = list_series[0].width + if all([lo is None for lo in lower_bound]) and all([ + hi is None for hi in upper_bound + ]): + raise_log( + ValueError("All provided upper and lower bounds values are None."), + logger=logger, + ) - raise_if_not( - all([s.width == self.width_trained_on for s in list_series]), - "all series in `series` must have the same number of components.", + # match the lengths of the boundaries + lower_bound = ( + lower_bound * len(upper_bound) if len(lower_bound) == 1 else lower_bound + ) + upper_bound = ( + upper_bound * len(lower_bound) if len(upper_bound) == 1 else upper_bound ) - self._fit_called = True - return self._fit_core(list_series) + if not len(lower_bound) == len(upper_bound): + raise_log( + ValueError( + f"Parameters `{lower_bound_name}` and `{upper_bound_name}` " + f"must be of the same length `n`, found " + f"`{lower_bound_name}`: n={len(lower_bound)} and " + f"`{upper_bound_name}`: n={len(upper_bound)}." + ), + logger=logger, + ) + if not all([ + lb is None or ub is None or lb <= ub + for (lb, ub) in zip(lower_bound, upper_bound) + ]): + raise_log( + ValueError( + f"All values in `{lower_bound_name}` must be lower or equal" + f"to their corresponding value in `{upper_bound_name}`." + ), + logger=logger, + ) + return lower_bound, upper_bound - def fit_detect( - self, series: Union[TimeSeries, Sequence[TimeSeries]] - ) -> Union[TimeSeries, Sequence[TimeSeries]]: - """Trains the detector and detects anomalies on the same series. + @staticmethod + def _expand_threshold(series: TimeSeries, threshold: list[float]) -> list[float]: + return threshold * series[0].width if len(threshold) == 1 else threshold - Parameters - ---------- - series - Time series to be used for training and be detected for anomalies. + @property + @abstractmethod + def low_threshold(self): + pass - Returns - ------- - Union[TimeSeries, Sequence[TimeSeries]] - Binary prediciton (1 if considered as an anomaly, 0 if not) - """ - self.fit(series) - return self.detect(series) + @property + @abstractmethod + def high_threshold(self): + pass diff --git a/darts/ad/detectors/iqr_detector.py b/darts/ad/detectors/iqr_detector.py new file mode 100644 index 0000000000..3549024be8 --- /dev/null +++ b/darts/ad/detectors/iqr_detector.py @@ -0,0 +1,82 @@ +""" +Interquartile Range (IQR) Detector +----------------- + +Flags anomalies that are beyond the IQR (between the third and the first quartile) +of historical data by some factor of it's difference (typically 1.5). +This is similar to a threshold-based detector, but the thresholds are +computed as distances from the IQR of historical data when the detector is fitted. +""" + +from collections.abc import Sequence +from typing import Union + +import numpy as np + +from darts.ad.detectors.quantile_detector import QuantileDetector +from darts.ad.detectors.threshold_detector import ThresholdDetector +from darts.logging import get_logger, raise_log +from darts.timeseries import TimeSeries + +logger = get_logger(__name__) + + +class IQRDetector(QuantileDetector): + def __init__(self, scale: Union[Sequence[float], float] = 1.5) -> None: + """IQR Detector + + Flags values that lie outside of the interquartile range (IQR) + by more than a certain factor of IQR's value as anomalies. + The factor is passed in the `scale` parameter. + + If a single value is provided for `scale`, + this same value will be used across all components of the series. + + If a sequences of values is given for the `scale` parameter, + it's length must match the dimensionality of the series passed. + + Parameters + ---------- + scale + (Sequence of) scale(s) used to indicate what distance from the IQR constitutes an anomaly. + Defaults to `1.5`. Must be non-negative. If a sequence, must match the dimensionality of the series + this detector is applied to. + """ + + # Parent QuantileDetector will compute Q1 and Q3 thresholds + super().__init__(low_quantile=0.25, high_quantile=0.75) + + self.scale = np.array(scale) + if self.scale.ndim == 0: + self.scale = np.expand_dims(self.scale, 0) + + if not np.issubdtype(self.scale.dtype, np.number) or (self.scale < 0.0).any(): + raise_log( + ValueError("All values in `scale` must be non-negative numbers."), + logger=logger, + ) + + def _fit_core(self, series: Sequence[TimeSeries]) -> None: + super()._fit_core(series) + + if len(self.scale) > 1 and len(self.scale) != series[0].width: + raise_log( + ValueError( + "The number of components of input must be equal to the number " + "of values given for `scale`. Found number of components " + f"equal to {series[0].width} and expected {len(self.scale)}." + ), + logger=logger, + ) + + low_threshold = np.array(self.detector.low_threshold) + high_threshold = np.array(self.detector.high_threshold) + + IQR = high_threshold - low_threshold + + low_threshold -= self.scale * IQR + high_threshold += self.scale * IQR + + self.detector = ThresholdDetector( + low_threshold=list(low_threshold), high_threshold=list(high_threshold) + ) diff --git a/darts/ad/detectors/quantile_detector.py b/darts/ad/detectors/quantile_detector.py index 471850990b..97fcb962d4 100644 --- a/darts/ad/detectors/quantile_detector.py +++ b/darts/ad/detectors/quantile_detector.py @@ -7,33 +7,36 @@ computed as quantiles of historical data when the detector is fitted. """ -from typing import Sequence, Union +from collections.abc import Sequence +from typing import Optional, Union import numpy as np -from darts.ad.detectors.detectors import FittableDetector +from darts.ad.detectors.detectors import FittableDetector, _BoundedDetectorMixin from darts.ad.detectors.threshold_detector import ThresholdDetector -from darts.logging import raise_if, raise_if_not +from darts.logging import get_logger, raise_log from darts.timeseries import TimeSeries +logger = get_logger(__name__) -class QuantileDetector(FittableDetector): + +class QuantileDetector(FittableDetector, _BoundedDetectorMixin): def __init__( self, low_quantile: Union[Sequence[float], float, None] = None, high_quantile: Union[Sequence[float], float, None] = None, ) -> None: - """ - Flags values that are either - below or above the `low_quantile` and `high_quantile` - quantiles of historical data, respectively. + """Quantile Detector - If a single value is provided for `low_quantile` or `high_quantile`, this same - value will be used across all components of the series. + Flags values that are either below or above the `low_quantile` and `high_quantile` quantiles + of historical data, respectively. + + If a single value is provided for `low_quantile` or `high_quantile`, this same value will be + used across all components of the series. If sequences of values are given for the parameters `low_quantile` and/or `high_quantile`, they must be of the same length, matching the dimensionality of the series passed - to ``fit()``, or have a length of 1. In the latter case, this single value will be used + to `fit()`, or have a length of 1. In the latter case, this single value will be used across all components of the series. If either `low_quantile` or `high_quantile` is None, the corresponding bound will not be used. @@ -49,100 +52,45 @@ def __init__( (Sequence of) quantile of historical data above which a value is regarded as anomaly. Must be between 0 and 1. If a sequence, must match the dimensionality of the series this detector is applied to. - - Attributes - ---------- - low_threshold - The (sequence of) lower quantile values. - high_threshold - The (sequence of) upper quantile values. """ super().__init__() - - raise_if( - low_quantile is None and high_quantile is None, - "At least one parameter must be not None (`low` and `high` are both None).", + low_quantile, high_quantile = self._prepare_boundaries( + lower_bound=low_quantile, + upper_bound=high_quantile, + lower_bound_name="low_quantile", + upper_bound_name="high_quantile", ) - - def _prep_quantile(q): - return ( - q.tolist() - if isinstance(q, np.ndarray) - else [q] - if not isinstance(q, Sequence) - else q - ) - - low = _prep_quantile(low_quantile) - high = _prep_quantile(high_quantile) - - for q in (low, high): - raise_if_not( - all([x is None or 0 <= x <= 1 for x in q]), - "Quantiles must be between 0 and 1, or None.", - ) - - self.low_quantile = low * len(high) if len(low) == 1 else low - self.high_quantile = high * len(low) if len(high) == 1 else high - - # the quantiles parameters are now sequences of the same length, - # possibly containing some None values, but at least one non-None value - + for q in (low_quantile, high_quantile): + if not all([x is None or 0 <= x <= 1 for x in q]): + raise_log( + ValueError("All quantiles must be between 0 and 1, or None."), + logger=logger, + ) + self.low_quantile = low_quantile + self.high_quantile = high_quantile # We'll use an inner Threshold detector once the quantiles are fitted - self.detector = None - - # A few more checks: - raise_if_not( - len(self.low_quantile) == len(self.high_quantile), - "Parameters `low_quantile` and `high_quantile` must be of the same length," - + f" found `low`: {len(self.low_quantile)} and `high`: {len(self.high_quantile)}.", - ) - - raise_if( - all([lo is None for lo in self.low_quantile]) - and all([hi is None for hi in self.high_quantile]), - "All provided quantile values are None.", - ) - - raise_if_not( - all( - [ - l <= h - for (l, h) in zip(self.low_quantile, self.high_quantile) - if ((l is not None) and (h is not None)) - ] - ), - "all values in `low_quantile` must be lower than or equal" - + "to their corresponding value in `high_quantile`.", - ) - - def _fit_core(self, list_series: Sequence[TimeSeries]) -> None: + self.detector: Optional[ThresholdDetector] = None + def _fit_core(self, series: Sequence[TimeSeries]) -> None: # if len(low) > 1 and len(high) > 1, then check it matches input width: - raise_if( - len(self.low_quantile) > 1 - and len(self.low_quantile) != list_series[0].width, - "The number of components of input must be equal to the number" - + " of values given for `high_quantile` or/and `low_quantile`. Found number of " - + f"components equal to {list_series[0].width} and expected {len(self.low_quantile)}.", - ) + if len(self.low_quantile) > 1 and len(self.low_quantile) != series[0].width: + raise_log( + ValueError( + "The number of components of input must be equal to the number " + "of values given for `high_quantile` or/and `low_quantile`. Found number of " + f"components equal to {series[0].width} and expected {len(self.low_quantile)}." + ), + logger=logger, + ) # otherwise, make them the right length - self.low_quantile = ( - self.low_quantile * list_series[0].width - if len(self.low_quantile) == 1 - else self.low_quantile - ) - self.high_quantile = ( - self.high_quantile * list_series[0].width - if len(self.high_quantile) == 1 - else self.high_quantile - ) + self.low_quantile = self._expand_threshold(series[0], self.low_quantile) + self.high_quantile = self._expand_threshold(series[0], self.high_quantile) - # concatenate everything along time axis + # concatenate everything along the time axis np_series = np.concatenate( - [series.all_values(copy=False) for series in list_series], axis=0 + [series.all_values(copy=False) for series in series], axis=0 ) # move sample dimension to position 1 @@ -154,20 +102,26 @@ def _fit_core(self, list_series: Sequence[TimeSeries]) -> None: # Compute 2 thresholds (low, high) for each component: # TODO: we could make this more efficient when low_quantile or high_quantile contain a single value - self.low_threshold = [ + low_threshold = [ np.quantile(np_series[:, i], q=lo, axis=0) if lo is not None else None for i, lo in enumerate(self.low_quantile) ] - self.high_threshold = [ + high_threshold = [ np.quantile(np_series[:, i], q=hi, axis=0) if hi is not None else None for i, hi in enumerate(self.high_quantile) ] self.detector = ThresholdDetector( - low_threshold=self.low_threshold, high_threshold=self.high_threshold + low_threshold=low_threshold, high_threshold=high_threshold ) - return self + def _detect_core(self, series: TimeSeries, name: str = "series") -> TimeSeries: + return self.detector.detect(series, name=name) + + @property + def low_threshold(self): + return self.detector.low_threshold if self.detector is not None else None - def _detect_core(self, series: TimeSeries) -> TimeSeries: - return self.detector.detect(series) + @property + def high_threshold(self): + return self.detector.high_threshold if self.detector is not None else None diff --git a/darts/ad/detectors/threshold_detector.py b/darts/ad/detectors/threshold_detector.py index 6643c72f37..1d89d90a99 100644 --- a/darts/ad/detectors/threshold_detector.py +++ b/darts/ad/detectors/threshold_detector.py @@ -7,22 +7,26 @@ identifies time points as anomalous when values are beyond the thresholds. """ -from typing import Sequence, Union +from collections.abc import Sequence +from typing import Union import numpy as np -from darts.ad.detectors.detectors import Detector -from darts.logging import raise_if, raise_if_not +from darts.ad.detectors.detectors import Detector, _BoundedDetectorMixin +from darts.logging import get_logger, raise_log from darts.timeseries import TimeSeries +logger = get_logger(__name__) -class ThresholdDetector(Detector): + +class ThresholdDetector(Detector, _BoundedDetectorMixin): def __init__( self, low_threshold: Union[int, float, Sequence[float], None] = None, high_threshold: Union[int, float, Sequence[float], None] = None, ) -> None: - """ + """Threshold Detector + Flags values that are either below or above the `low_threshold` and `high_threshold`, respectively. @@ -31,7 +35,7 @@ def __init__( If sequences of values are given for the parameters `low_threshold` and/or `high_threshold`, they must be of the same length, matching the dimensionality of the series passed - to ``detect()``, or have a length of 1. In the latter case, this single value will be used + to `detect()`, or have a length of 1. In the latter case, this single value will be used across all components of the series. If either `low_threshold` or `high_threshold` is None, the corresponding bound will not be used. @@ -40,97 +44,44 @@ def __init__( Parameters ---------- low_threshold - (Sequence of) lower bounds. - If a sequence, must match the dimensionality of the series - this detector is applied to. + (Sequence of) lower bounds. If a sequence, must match the dimensionality of the series this + detector is applied to. high_threshold - (Sequence of) upper bounds. - If a sequence, must match the dimensionality of the series - this detector is applied to. + (Sequence of) upper bounds. If a sequence, must match the dimensionality of the series this + detector is applied to. """ - - # TODO: could we refactor some code common between ThresholdDetector and QuantileDetector? - super().__init__() - - raise_if( - low_threshold is None and high_threshold is None, - "At least one parameter must be not None (`low` and `high` are both None).", + low_threshold, high_threshold = self._prepare_boundaries( + lower_bound=low_threshold, + upper_bound=high_threshold, + lower_bound_name="low_threshold", + upper_bound_name="high_threshold", ) - - def _prep_thresholds(q): - return ( - q.tolist() - if isinstance(q, np.ndarray) - else [q] - if not isinstance(q, Sequence) - else q + self._low_threshold = low_threshold + self._high_threshold = high_threshold + + def _detect_core(self, series: TimeSeries, name: str = "series") -> TimeSeries: + if len(self.low_threshold) > 1 and len(self.low_threshold) != series.width: + raise_log( + ValueError( + f"The number of components for each series in `{name}` must be " + f"equal to the number of threshold values. Found number of " + f"components equal to {series.width} and expected {len(self.low_threshold)}." + ), + logger=logger, ) - low = _prep_thresholds(low_threshold) - high = _prep_thresholds(high_threshold) - - self.low_threshold = low * len(high) if len(low) == 1 else low - self.high_threshold = high * len(low) if len(high) == 1 else high - - # the threshold parameters are now sequences of the same length, - # possibly containing some None values, but at least one non-None value - - raise_if_not( - len(self.low_threshold) == len(self.high_threshold), - "Parameters `low_threshold` and `high_threshold` must be of the same length," - + f" found `low`: {len(self.low_threshold)} and `high`: {len(self.high_threshold)}.", - ) - - raise_if( - all([lo is None for lo in self.low_threshold]) - and all([hi is None for hi in self.high_threshold]), - "All provided threshold values are None.", - ) - - raise_if_not( - all( - [ - l <= h - for (l, h) in zip(self.low_threshold, self.high_threshold) - if ((l is not None) and (h is not None)) - ] - ), - "all values in `low_threshold` must be lower than or equal" - + "to their corresponding value in `high_threshold`.", - ) - - def _detect_core(self, series: TimeSeries) -> TimeSeries: - raise_if_not( - series.is_deterministic, "This detector only works on deterministic series." - ) - - raise_if( - len(self.low_threshold) > 1 and len(self.low_threshold) != series.width, - "The number of components of input must be equal to the number" - + " of threshold values. Found number of " - + f"components equal to {series.width} and expected {len(self.low_threshold)}.", - ) - # if length is 1, tile it to series width: - low_threshold = ( - self.low_threshold * series.width - if len(self.low_threshold) == 1 - else self.low_threshold - ) - high_threshold = ( - self.high_threshold * series.width - if len(self.high_threshold) == 1 - else self.high_threshold - ) + low_threshold = self._expand_threshold(series[0], self.low_threshold) + high_threshold = self._expand_threshold(series[0], self.high_threshold) # (time, components) - np_series = series.all_values(copy=False).squeeze(-1) + np_series = series.values(copy=False) def _detect_fn(x, lo, hi): # x of shape (time,) for 1 component - return (x < (np.NINF if lo is None else lo)) | ( - x > (np.Inf if hi is None else hi) + return (x < (-np.inf if lo is None else lo)) | ( + x > (np.inf if hi is None else hi) ) detected = np.zeros_like(np_series, dtype=int) @@ -141,5 +92,12 @@ def _detect_fn(x, lo, hi): low_threshold[component_idx], high_threshold[component_idx], ) + return series.with_values(np.expand_dims(detected, -1).astype(series.dtype)) + + @property + def low_threshold(self): + return self._low_threshold - return TimeSeries.from_times_and_values(series.time_index, detected) + @property + def high_threshold(self): + return self._high_threshold diff --git a/darts/ad/scorers/__init__.py b/darts/ad/scorers/__init__.py index b0eec1298d..429280bf08 100644 --- a/darts/ad/scorers/__init__.py +++ b/darts/ad/scorers/__init__.py @@ -2,78 +2,96 @@ Anomaly Scorers --------------- -Scorers are at the core of the anomaly detection module. They -produce anomaly scores time series, either for series directly (``score()``), -or for series accompanied by some predictions (``score_from_prediction()``). - -The higher an anomaly score is, the more "anomalous" the corresponding -time period is. Scorers can work over time windows, and the length of the window is related -to the time scale over which anomalies are expected to occur. -The interpretability of the anomaly score is dependent on the scorer. - -The function ``score_from_prediction()`` works by taking some "difference" (or "residual") -between the prediction and the actual series (captured by the ``"diff_fn"`` parameter). -Some scorers are trainable (e.g., ``KMeansScorer``, which learns clusters over historical data), -in which case the ``score()`` function can be used to score new series. -Other scorers are not trainable (e.g., ``NormScorer``, which simply takes the Lp-norm between -predicted values and actual values over windows). In this latter case ``score()`` cannot be -used and scoring is only possible using ``score_from_prediction()``. - -Some scorers can handle probabilistic predictions from models (at the moment all the "NLL" scorers), -while others handle deterministic predictions (e.g., ``KMeansScorer``). - -As an example, the ``KMeansScorer``, which is trainable, can be applied using the functions: - -- ``fit()`` and ``score()``: directly on a series to uncover the relationships between the different - dimensions (over timesteps within windows and/or over dimensions of multivariate series). -- ``fit_from_prediction`` and ``score_from_prediction``: which will compute a difference (residuals) - between the prediction (coming e.g., from a forecasting model) and the series itself. - When scoring, the scorer will attribute a higher score to residuals that are distant - from the clusters found during the training phase. - +Scorers are at the core of the anomaly detection module. They produce anomaly scores time series, either for series +directly (`score()`), or for series accompanied by some predictions (`score_from_prediction()`). + +The higher an anomaly score is, the more "anomalous" the corresponding time period is. Scorers can work over time +windows, and the length of the window is related to the time scale over which anomalies are expected to occur. The +interpretability of the anomaly score is dependent on the scorer. + +The function `score_from_prediction()` works by taking some "difference" (or "residual") between the prediction and +the actual series (captured by the `"diff_fn"` parameter). Some scorers are trainable +(e.g., :class:`~darts.ad.scorers.kmeans_scorer.KMeansScorer`, which learns clusters over historical data), in which +case the `score()` function can be used to score new series. Other scorers are not trainable +(e.g., :class:`~darts.ad.scorers.norm_scorer.NormScorer`, which simply takes the Lp-norm between predicted values and +actual values over windows). In this latter case `score()` cannot be used and scoring is only possible using +`score_from_prediction()`. + +Some scorers can handle probabilistic predictions from models (at the moment all the "NLL" scorers), while others +handle deterministic predictions (e.g., :class:`~darts.ad.scorers.kmeans_scorer.KMeansScorer`). + +As an example, the :class:`~darts.ad.scorers.kmeans_scorer.KMeansScorer`, which is trainable, can be applied using the +functions: + +- `fit()` and `score()`: directly on a series to uncover the relationships between the different dimensions + (over timesteps within windows and/or over dimensions of multivariate series). +- `fit_from_prediction` and `score_from_prediction`: which will compute a difference (residuals) between the + prediction (coming e.g., from a forecasting model) and the series itself. When scoring, the scorer will attribute a + higher score to residuals that are distant from the clusters found during the training phase. + Note that `Anomaly Models `_ can be used to conveniently combine any of Darts forecasting and filtering models with one or multiple scorers. Most of the scorers have the following main parameters: - `window`: - Integer value indicating the size of the window W used by the scorer to transform the series into - an anomaly score. A scorer will slice the given series into subsequences of size W and returns - a value indicating how anomalous these subset of W values are. The window size should be commensurate - to the expected durations of the anomalies one is looking for. + Integer value indicating the size of the window W used by the scorer to transform the series into an anomaly score. + A scorer will slice the given series into subsequences of size W and returns a value indicating how anomalous these + subset of W values are. A post-processing step will convert this anomaly score into a point-wise anomaly score + (see definition of `window_transform`). The window size should be commensurate to the expected durations of the + anomalies one is looking for. - `component_wise`: - boolean parameter indicating how the scorer should behave with multivariate series. If set to - True, the model will treat each series dimension independently. If set to False, the model will - consider the dimensions jointly in the considered `window` W to compute the score. - + Boolean parameter indicating how the scorer should behave with multivariate series. If set to `True`, the model will + treat each series dimension independently. If set to `False`, the model will consider the dimensions jointly in the + considered `window` W to compute the score. +- `window_transform`: + Boolean value that indicates if the scorer needs to post-process its output when the `window` parameter exceeds 1. + If set to `True`, the scores for each point can be assigned by aggregating the anomaly scores for each window the + point is included in. It returns a point-wise anomaly score. If set to `False`, the score is returned without this + post-processing step and is a window-wise anomaly score. Default: True Other useful functions are: -- ``eval_accuracy_from_prediction()`` - Takes as input two (sequence of) series, computes all the anomaly scores, and - returns the value of an agnostic threshold metric (AUC-ROC or AUC-PR) based on some known ground truth - of anomalies. The returned value is between 0 and 1, with 1 indicating that the scorer could perfectly - separate the anomalous point from the normal ones. +- `eval_metric_from_prediction()` + Takes as input two (sequence of) series, computes all the anomaly scores, and returns the value of an agnostic + threshold metric (AUC-ROC or AUC-PR) based on some known ground truth of anomalies. The returned value is between 0 + and 1, with 1 indicating that the scorer could perfectly separate the anomalous point from the normal ones. -- ``fit_from_prediction()`` - Takes two (sequence of) series as input and fits the scorer. This task is dependent on the scorer, - but as a general case the scorer will calibrate its scoring function based on the training series that is - considered to be anomaly-free. This training phase will allow the scorer to detect anomalies during - the scoring phase, by comparing the series to score with the anomaly-free series seen during training. +- `fit_from_prediction()` + Takes two (sequence of) series as input and fits the scorer. This task is dependent on the scorer, but as a general + case the scorer will calibrate its scoring function based on the training series that is considered to be + anomaly-free. This training phase will allow the scorer to detect anomalies during the scoring phase, by comparing + the series to score with the anomaly-free series seen during training. More details can be found in the API documentation of each scorer. """ -from .difference_scorer import DifferenceScorer -from .kmeans_scorer import KMeansScorer -from .nll_cauchy_scorer import CauchyNLLScorer -from .nll_exponential_scorer import ExponentialNLLScorer -from .nll_gamma_scorer import GammaNLLScorer -from .nll_gaussian_scorer import GaussianNLLScorer -from .nll_laplace_scorer import LaplaceNLLScorer -from .nll_poisson_scorer import PoissonNLLScorer -from .norm_scorer import NormScorer -from .pyod_scorer import PyODScorer -from .scorers import FittableAnomalyScorer, NonFittableAnomalyScorer -from .wasserstein_scorer import WassersteinScorer +from darts.ad.scorers.difference_scorer import DifferenceScorer +from darts.ad.scorers.kmeans_scorer import KMeansScorer +from darts.ad.scorers.nll_cauchy_scorer import CauchyNLLScorer +from darts.ad.scorers.nll_exponential_scorer import ExponentialNLLScorer +from darts.ad.scorers.nll_gamma_scorer import GammaNLLScorer +from darts.ad.scorers.nll_gaussian_scorer import GaussianNLLScorer +from darts.ad.scorers.nll_laplace_scorer import LaplaceNLLScorer +from darts.ad.scorers.nll_poisson_scorer import PoissonNLLScorer +from darts.ad.scorers.norm_scorer import NormScorer +from darts.ad.scorers.pyod_scorer import PyODScorer +from darts.ad.scorers.scorers import AnomalyScorer, FittableAnomalyScorer +from darts.ad.scorers.wasserstein_scorer import WassersteinScorer + +__all__ = [ + "DifferenceScorer", + "KMeansScorer", + "CauchyNLLScorer", + "ExponentialNLLScorer", + "GammaNLLScorer", + "GaussianNLLScorer", + "LaplaceNLLScorer", + "PoissonNLLScorer", + "NormScorer", + "PyODScorer", + "AnomalyScorer", + "FittableAnomalyScorer", + "WassersteinScorer", +] diff --git a/darts/ad/scorers/difference_scorer.py b/darts/ad/scorers/difference_scorer.py index 191f7254b7..54bbef3e59 100644 --- a/darts/ad/scorers/difference_scorer.py +++ b/darts/ad/scorers/difference_scorer.py @@ -7,22 +7,24 @@ returns a multivariate series. """ -from darts.ad.scorers.scorers import NonFittableAnomalyScorer -from darts.timeseries import TimeSeries +import numpy as np +from darts.ad.scorers.scorers import AnomalyScorer -class DifferenceScorer(NonFittableAnomalyScorer): + +class DifferenceScorer(AnomalyScorer): def __init__(self) -> None: - super().__init__(univariate_scorer=False, window=1) + """Difference Scorer""" + super().__init__(is_univariate=False, window=1) def __str__(self): return "Difference" def _score_core_from_prediction( self, - actual_series: TimeSeries, - pred_series: TimeSeries, - ) -> TimeSeries: - self._assert_deterministic(actual_series, "actual_series") - self._assert_deterministic(pred_series, "pred_series") - return actual_series - pred_series + vals: np.ndarray, + pred_vals: np.ndarray, + ) -> np.ndarray: + vals = self._extract_deterministic_values(vals, "series") + pred_vals = self._extract_deterministic_values(pred_vals, "pred_series") + return vals - pred_vals diff --git a/darts/ad/scorers/kmeans_scorer.py b/darts/ad/scorers/kmeans_scorer.py index 1cbe77b5ab..8236968789 100644 --- a/darts/ad/scorers/kmeans_scorer.py +++ b/darts/ad/scorers/kmeans_scorer.py @@ -9,70 +9,73 @@ .. [1] https://en.wikipedia.org/wiki/K-means_clustering """ -from typing import Sequence - import numpy as np -from numpy.lib.stride_tricks import sliding_window_view from sklearn.cluster import KMeans -from darts.ad.scorers.scorers import FittableAnomalyScorer -from darts.logging import raise_if_not -from darts.timeseries import TimeSeries +from darts import metrics +from darts.ad.scorers.scorers import WindowedAnomalyScorer +from darts.logging import get_logger +from darts.metrics.metrics import METRIC_TYPE + +logger = get_logger(__name__) -class KMeansScorer(FittableAnomalyScorer): +class KMeansScorer(WindowedAnomalyScorer): def __init__( self, window: int = 1, k: int = 8, component_wise: bool = False, - diff_fn="abs_diff", + window_agg: bool = True, + diff_fn: METRIC_TYPE = metrics.ae, **kwargs, ) -> None: - """ - When calling ``fit(series)``, a moving window is applied, which results in a set of vectors of size `W`, - where `W` is the window size. The `k`-means model is trained on these vectors. The ``score(series)`` function + """k-means Scorer + + When calling `fit(series)`, a moving window is applied, which results in a set of vectors of size `W`, + where `W` is the window size. The `k`-means model is trained on these vectors. The `score(series)` function applies the same moving window and returns the distance to the closest of the `k` centroids for each vector of size `W`. - Alternatively, the scorer has the functions ``fit_from_prediction()`` and ``score_from_prediction()``. + Alternatively, the scorer has the functions `fit_from_prediction()` and `score_from_prediction()`. Both require two series (actual and prediction), and compute a "difference" series by applying the - function ``diff_fn`` (default: absolute difference). The resulting series is then passed to the - functions ``fit()`` and ``score()``, respectively. + function `diff_fn` (default: absolute difference). The resulting series is then passed to the + functions `fit()` and `score()`, respectively. `component_wise` is a boolean parameter indicating how the model should behave with multivariate inputs - series. If set to True, the model will treat each component independently by fitting a different - `k`-means model for each dimension. If set to False, the model concatenates the dimensions in + series. If set to `True`, the model will treat each component independently by fitting a different + `k`-means model for each dimension. If set to `False`, the model concatenates the dimensions in each windows of length `W` and computes the score using only one underlying `k`-means model. - **Training with** ``fit()``: + **Training with** `fit()`: - The input can be a series (univariate or multivariate) or multiple series. The series will be sliced - into equal size subsequences. The subsequence will be of size `W` * `D`, with: + The input can be a series (univariate or multivariate) or multiple series. The series will be partitioned + into equal size subsequences. Each subsequence has size `W * D` (features), where: - * `W` being the size of the window given as a parameter `window` - * `D` being the dimension of the series (`D` = 1 if univariate or if `component_wise` is set to True) + - `W` is the size of the window given as a parameter `window` + - `D` is the dimension of the series (`D` = 1 if univariate or if `component_wise` is set to `True`) - For a series of length `N`, (`N` - `W` + 1)/W subsequences will be generated. If a list of series is given - of length L, each series will be partitioned into subsequences, and the results will be concatenated into - an array of length L * number of subsequences of each series. + For a series of length `N`, `(N - W + 1)` subsequences will be generated. The final array `X` passed to the + underlying scorer has shape `(N - W + 1, W * D)`; or in other terms (number of samples, number of features). + If a list of series is given of length L, each series `i` is partitioned, and all `X_i` are concatenated along + the sample axis. The `k`-means model will be fitted on the generated subsequences. The model will find `k` clusters in the vector space of dimension equal to the length of the subsequences (`D` * `W`). - If `component_wise` is set to True, the algorithm will be applied to each dimension independently. For each + If `component_wise` is set to `True`, the algorithm will be applied to each dimension independently. For each dimension, a `k`-means model will be trained. - **Computing score with** ``score()``: + **Computing score with** `score()`: The input can be a series (univariate or multivariate) or a sequence of series. The given series must have the same dimension `D` as the data used to train the `k`-means model. For each series, if the series is multivariate of dimension `D`: - * if `component_wise` is set to False: it returns a univariate series (dimension=1). It represents + - if `component_wise` is set to `False`: it returns a univariate series (dimension=1). It represents the anomaly score of the entire series in the considered window at each timestamp. - * if `component_wise` is set to True: it returns a multivariate series of dimension `D`. Each dimension + - if `component_wise` is set to `True`: it returns a multivariate series of dimension `D`. Each dimension represents the anomaly score of the corresponding component of the input. If the series is univariate, it returns a univariate series regardless of the parameter @@ -88,117 +91,43 @@ def __init__( Size of the window used to create the subsequences of the series. k The number of clusters to form as well as the number of centroids to generate by the KMeans model. - diff_fn - Optionally, reduction function to use if two series are given. It will transform the two series into one. - This allows the KMeansScorer to apply KMeans on the original series or on its residuals (difference - between the prediction and the original series). - Must be one of "abs_diff" and "diff" (defined in ``_diff_series()``). - Default: "abs_diff" component_wise - Boolean value indicating if the score needs to be computed for each component independently (True) - or by concatenating the component in the considered window to compute one score (False). - Default: False + Boolean value indicating if the score needs to be computed for each component independently (`True`) + or by concatenating the component in the considered window to compute one score (`False`). + Default: `False`. + window_agg + Boolean indicating whether the anomaly score for each time step is computed by + averaging the anomaly scores for all windows this point is included in. + If `False`, the anomaly score for each point is the anomaly score of its trailing window. + Default: `True`. + diff_fn + The differencing function to use to transform the predicted and actual series into one series. + The scorer is then applied to this series. Must be one of Darts per-time-step metrics (e.g., + :func:`~darts.metrics.metrics.ae` for the absolute difference, :func:`~darts.metrics.metrics.err` for the + difference, :func:`~darts.metrics.metrics.se` for the squared difference, ...). + By default, uses the absolute difference (:func:`~darts.metrics.metrics.ae`). kwargs Additional keyword arguments passed to the internal scikit-learn KMeans model(s). """ - - raise_if_not( - type(component_wise) is bool, - f"Parameter `component_wise` must be Boolean, found type: {type(component_wise)}.", - ) - self.component_wise = component_wise - self.kmeans_kwargs = kwargs self.kmeans_kwargs["n_clusters"] = k # stop warning about default value of "n_init" changing from 10 to "auto" in sklearn 1.4 if "n_init" not in self.kmeans_kwargs: self.kmeans_kwargs["n_init"] = 10 + self.model = KMeans(**self.kmeans_kwargs) + super().__init__( - univariate_scorer=(not component_wise), window=window, diff_fn=diff_fn + is_univariate=(not component_wise), + window=window, + window_agg=window_agg, + diff_fn=diff_fn, ) def __str__(self): return "k-means Scorer" - def _fit_core( - self, - list_series: Sequence[TimeSeries], - ): - - list_np_series = [series.all_values(copy=False) for series in list_series] - - if not self.component_wise: - self.model = KMeans(**self.kmeans_kwargs) - self.model.fit( - np.concatenate( - [ - sliding_window_view(ar, window_shape=self.window, axis=0) - .transpose(0, 3, 1, 2) - .reshape(-1, self.window * len(ar[0])) - for ar in list_np_series - ], - axis=0, - ) - ) - else: - models = [] - for component_idx in range(self.width_trained_on): - model = KMeans(**self.kmeans_kwargs) - model.fit( - np.concatenate( - [ - sliding_window_view( - ar[:, component_idx], window_shape=self.window, axis=0 - ) - .transpose(0, 2, 1) - .reshape(-1, self.window) - for ar in list_np_series - ], - axis=0, - ) - ) - models.append(model) - self.models = models - - def _score_core(self, series: TimeSeries) -> TimeSeries: - raise_if_not( - self.width_trained_on == series.width, - "Input must have the same number of components as the data used for" - + " training the KMeans model, found number of components equal to" - + f" {series.width} and expected {self.width_trained_on}.", - ) - - np_series = series.all_values(copy=False) - np_anomaly_score = [] - - if not self.component_wise: - # return distance to the clostest centroid - np_anomaly_score.append( - self.model.transform( - sliding_window_view(np_series, window_shape=self.window, axis=0) - .transpose(0, 3, 1, 2) - .reshape(-1, self.window * series.width) - ).min(axis=1) - ) # only return the closest distance out of the k ones (k centroids) - else: - for component_idx in range(self.width_trained_on): - score = ( - self.models[component_idx] - .transform( - sliding_window_view( - np_series[:, component_idx], - window_shape=self.window, - axis=0, - ) - .transpose(0, 2, 1) - .reshape(-1, self.window) - ) - .min(axis=1) - ) - - np_anomaly_score.append(score) - - return TimeSeries.from_times_and_values( - series.time_index[self.window - 1 :], list(zip(*np_anomaly_score)) - ) + def _model_score_method(self, model, data: np.ndarray) -> np.ndarray: + """Wrapper around model inference method""" + # only return the closest distance out of the k ones (k centroids) + return model.transform(data).min(axis=1) diff --git a/darts/ad/scorers/nll_cauchy_scorer.py b/darts/ad/scorers/nll_cauchy_scorer.py index 6ef9754fe2..b2ddda107c 100644 --- a/darts/ad/scorers/nll_cauchy_scorer.py +++ b/darts/ad/scorers/nll_cauchy_scorer.py @@ -16,18 +16,14 @@ class CauchyNLLScorer(NLLScorer): def __init__(self, window: int = 1) -> None: + """NLL Cauchy Scorer""" super().__init__(window=window) def __str__(self): return "CauchyNLLScorer" def _score_core_nllikelihood( - self, - deterministic_values: np.ndarray, - probabilistic_estimations: np.ndarray, + self, vals: np.ndarray, pred_vals: np.ndarray ) -> np.ndarray: - - params = np.apply_along_axis(cauchy.fit, axis=1, arr=probabilistic_estimations) - return -cauchy.logpdf( - deterministic_values, loc=params[:, 0], scale=params[:, 1] - ) + params = np.apply_along_axis(cauchy.fit, axis=1, arr=pred_vals) + return -cauchy.logpdf(vals, loc=params[:, 0], scale=params[:, 1]) diff --git a/darts/ad/scorers/nll_exponential_scorer.py b/darts/ad/scorers/nll_exponential_scorer.py index 1a16894347..5b252a8e74 100644 --- a/darts/ad/scorers/nll_exponential_scorer.py +++ b/darts/ad/scorers/nll_exponential_scorer.py @@ -16,22 +16,28 @@ class ExponentialNLLScorer(NLLScorer): def __init__(self, window: int = 1) -> None: + """NLL Exponential Scorer + + Parameters + ---------- + window + Integer value indicating the size of the window W used by the scorer to transform the series into an + anomaly score. A scorer will slice the given series into subsequences of size W and returns a value + indicating how anomalous these subset of W values are. A post-processing step will convert this anomaly + score into a point-wise anomaly score (see definition of `window_transform`). The window size should be + commensurate to the expected durations of the anomalies one is looking for. + """ super().__init__(window=window) def __str__(self): return "ExponentialNLLScorer" def _score_core_nllikelihood( - self, - deterministic_values: np.ndarray, - probabilistic_estimations: np.ndarray, + self, vals: np.ndarray, pred_vals: np.ndarray ) -> np.ndarray: - # This is the ML estimate for 1/lambda, which is what scipy expects as scale. - mu = np.mean(probabilistic_estimations, axis=1) - + mu = np.mean(pred_vals, axis=1) # This is ML estimate for the loc - see: # https://github.com/scipy/scipy/blob/de80faf9d3480b9dbb9b888568b64499e0e70c19/scipy/stats/_continuous_distns.py#L1705 - loc = np.min(probabilistic_estimations, axis=1) - - return -expon.logpdf(deterministic_values, scale=mu, loc=loc) + loc = np.min(pred_vals, axis=1) + return -expon.logpdf(vals, scale=mu, loc=loc) diff --git a/darts/ad/scorers/nll_gamma_scorer.py b/darts/ad/scorers/nll_gamma_scorer.py index 40dc113c3c..5a4b217b62 100644 --- a/darts/ad/scorers/nll_gamma_scorer.py +++ b/darts/ad/scorers/nll_gamma_scorer.py @@ -16,18 +16,24 @@ class GammaNLLScorer(NLLScorer): def __init__(self, window: int = 1) -> None: + """NLL Gamma Scorer + + Parameters + ---------- + window + Integer value indicating the size of the window W used by the scorer to transform the series into an + anomaly score. A scorer will slice the given series into subsequences of size W and returns a value + indicating how anomalous these subset of W values are. A post-processing step will convert this anomaly + score into a point-wise anomaly score (see definition of `window_transform`). The window size should be + commensurate to the expected durations of the anomalies one is looking for. + """ super().__init__(window=window) def __str__(self): return "GammaNLLScorer" def _score_core_nllikelihood( - self, - deterministic_values: np.ndarray, - probabilistic_estimations: np.ndarray, + self, vals: np.ndarray, pred_vals: np.ndarray ) -> np.ndarray: - - params = np.apply_along_axis(gamma.fit, axis=1, arr=probabilistic_estimations) - return -gamma.logpdf( - deterministic_values, a=params[:, 0], loc=params[:, 1], scale=params[:, 2] - ) + params = np.apply_along_axis(gamma.fit, axis=1, arr=pred_vals) + return -gamma.logpdf(vals, a=params[:, 0], loc=params[:, 1], scale=params[:, 2]) diff --git a/darts/ad/scorers/nll_gaussian_scorer.py b/darts/ad/scorers/nll_gaussian_scorer.py index 56eb86300b..da6b7deb31 100644 --- a/darts/ad/scorers/nll_gaussian_scorer.py +++ b/darts/ad/scorers/nll_gaussian_scorer.py @@ -16,17 +16,25 @@ class GaussianNLLScorer(NLLScorer): def __init__(self, window: int = 1) -> None: + """NLL Gaussian Scorer + + Parameters + ---------- + window + Integer value indicating the size of the window W used by the scorer to transform the series into an + anomaly score. A scorer will slice the given series into subsequences of size W and returns a value + indicating how anomalous these subset of W values are. A post-processing step will convert this anomaly + score into a point-wise anomaly score (see definition of `window_transform`). The window size should be + commensurate to the expected durations of the anomalies one is looking for. + """ super().__init__(window=window) def __str__(self): return "GaussianNLLScorer" def _score_core_nllikelihood( - self, - deterministic_values: np.ndarray, - probabilistic_estimations: np.ndarray, + self, vals: np.ndarray, pred_vals: np.ndarray ) -> np.ndarray: - - mu = np.mean(probabilistic_estimations, axis=1) - std = np.std(probabilistic_estimations, axis=1) - return -norm.logpdf(deterministic_values, loc=mu, scale=std) + mu = np.mean(pred_vals, axis=1) + std = np.std(pred_vals, axis=1) + return -norm.logpdf(vals, loc=mu, scale=std) diff --git a/darts/ad/scorers/nll_laplace_scorer.py b/darts/ad/scorers/nll_laplace_scorer.py index 342dab53ef..6f267ccb49 100644 --- a/darts/ad/scorers/nll_laplace_scorer.py +++ b/darts/ad/scorers/nll_laplace_scorer.py @@ -16,26 +16,29 @@ class LaplaceNLLScorer(NLLScorer): def __init__(self, window: int = 1) -> None: + """NLL Laplace Scorer + + Parameters + ---------- + window + Integer value indicating the size of the window W used by the scorer to transform the series into an + anomaly score. A scorer will slice the given series into subsequences of size W and returns a value + indicating how anomalous these subset of W values are. A post-processing step will convert this anomaly + score into a point-wise anomaly score (see definition of `window_transform`). The window size should be + commensurate to the expected durations of the anomalies one is looking for. + """ super().__init__(window=window) def __str__(self): return "LaplaceNLLScorer" def _score_core_nllikelihood( - self, - deterministic_values: np.ndarray, - probabilistic_estimations: np.ndarray, + self, vals: np.ndarray, pred_vals: np.ndarray ) -> np.ndarray: - # ML estimate for the Laplace loc - loc = np.median(probabilistic_estimations, axis=1) - + loc = np.median(pred_vals, axis=1) # ML estimate for the Laplace scale # see: https://github.com/scipy/scipy/blob/de80faf9d3480b9dbb9b888568b64499e0e70c19/scipy # /stats/_continuous_distns.py#L4846 - scale = ( - np.sum(np.abs(probabilistic_estimations.T - loc), axis=0).T - / probabilistic_estimations.shape[1] - ) - - return -laplace.logpdf(deterministic_values, loc=loc, scale=scale) + scale = np.sum(np.abs(pred_vals.T - loc), axis=0).T / pred_vals.shape[1] + return -laplace.logpdf(vals, loc=loc, scale=scale) diff --git a/darts/ad/scorers/nll_poisson_scorer.py b/darts/ad/scorers/nll_poisson_scorer.py index df5ee411b8..bd1c97bd0a 100644 --- a/darts/ad/scorers/nll_poisson_scorer.py +++ b/darts/ad/scorers/nll_poisson_scorer.py @@ -16,16 +16,24 @@ class PoissonNLLScorer(NLLScorer): def __init__(self, window: int = 1) -> None: + """NLL Poisson Scorer + + Parameters + ---------- + window + Integer value indicating the size of the window W used by the scorer to transform the series into an + anomaly score. A scorer will slice the given series into subsequences of size W and returns a value + indicating how anomalous these subset of W values are. A post-processing step will convert this anomaly + score into a point-wise anomaly score (see definition of `window_transform`). The window size should be + commensurate to the expected durations of the anomalies one is looking for. + """ super().__init__(window=window) def __str__(self): return "PoissonNLLScorer" def _score_core_nllikelihood( - self, - deterministic_values: np.ndarray, - probabilistic_estimations: np.ndarray, + self, vals: np.ndarray, pred_vals: np.ndarray ) -> np.ndarray: - - mu = np.mean(probabilistic_estimations, axis=1) - return -poisson.logpmf(deterministic_values, mu=mu) + mu = np.mean(pred_vals, axis=1) + return -poisson.logpmf(vals, mu=mu) diff --git a/darts/ad/scorers/norm_scorer.py b/darts/ad/scorers/norm_scorer.py index 6764960994..af2d0057d4 100644 --- a/darts/ad/scorers/norm_scorer.py +++ b/darts/ad/scorers/norm_scorer.py @@ -11,28 +11,27 @@ import numpy as np -from darts.ad.scorers.scorers import NonFittableAnomalyScorer -from darts.logging import raise_if_not -from darts.timeseries import TimeSeries +from darts.ad.scorers.scorers import AnomalyScorer -class NormScorer(NonFittableAnomalyScorer): +class NormScorer(AnomalyScorer): def __init__(self, ord=None, component_wise: bool = False) -> None: - """ - Returns the elementwise norm of a given order between two series' values. + """Norm Scorer + + Returns the element-wise norm of a given order between two series' values. - If `component_wise` is False, the norm is computed between vectors + If `component_wise` is `False`, the norm is computed between vectors made of the series' components (one norm per timestamp). - If `component_wise` is True, for any `ord` this effectively amounts to computing the absolute + If `component_wise` is `True`, for any `ord` this effectively amounts to computing the absolute value of the difference. The scoring function expects two series. If the two series are multivariate of width `w`: - * if `component_wise` is set to False: it returns a univariate series (width=1). - * if `component_wise` is set to True: it returns a multivariate series of width `w`. + - if `component_wise` is set to `False`: it returns a univariate series (width=1). + - if `component_wise` is set to `True`: it returns a multivariate series of width `w`. If the two series are univariate, it returns a univariate series regardless of the parameter `component_wise`. @@ -42,41 +41,27 @@ def __init__(self, ord=None, component_wise: bool = False) -> None: ord Order of the norm. Options are listed under 'Notes' at: . - Default: None + Default: `None` component_wise - Whether to compare components of the two series in isolation (True), or jointly (False). - Default: False + Whether to compare components of the two series in isolation (`True`), or jointly (`False`). + Default: `False` """ - - raise_if_not( - type(component_wise) is bool, - f"`component_wise` must be Boolean, found type: {type(component_wise)}.", - ) - self.ord = ord - self.component_wise = component_wise - super().__init__(univariate_scorer=(not component_wise), window=1) + super().__init__(is_univariate=(not component_wise), window=1) def __str__(self): return f"Norm (ord={self.ord})" def _score_core_from_prediction( self, - actual_series: TimeSeries, - pred_series: TimeSeries, - ) -> TimeSeries: - - self._assert_deterministic(actual_series, "actual_series") - self._assert_deterministic(pred_series, "pred_series") - - diff = actual_series - pred_series - - if self.component_wise: - return diff.map(lambda x: np.abs(x)) - + vals: np.ndarray, + pred_vals: np.ndarray, + ) -> np.ndarray: + vals = self._extract_deterministic_values(vals, "series") + pred_vals = self._extract_deterministic_values(pred_vals, "pred_series") + diff = vals - pred_vals + if not self.is_univariate: + diff = np.abs(diff) else: - diff_np = diff.all_values(copy=False) - - return TimeSeries.from_times_and_values( - diff.time_index, np.linalg.norm(diff_np, ord=self.ord, axis=1) - ) + diff = np.linalg.norm(diff, ord=self.ord, axis=1) + return diff diff --git a/darts/ad/scorers/pyod_scorer.py b/darts/ad/scorers/pyod_scorer.py index 0a90235bd2..d7cea35f43 100644 --- a/darts/ad/scorers/pyod_scorer.py +++ b/darts/ad/scorers/pyod_scorer.py @@ -1,33 +1,33 @@ """ -PyODScorer +PyOD Scorer ----- This scorer can wrap around detection algorithms of PyOD. `PyOD https://pyod.readthedocs.io/en/latest/#`_. """ -from typing import Sequence - import numpy as np -from numpy.lib.stride_tricks import sliding_window_view from pyod.models.base import BaseDetector -from darts.ad.scorers.scorers import FittableAnomalyScorer +from darts import metrics +from darts.ad.scorers.scorers import WindowedAnomalyScorer from darts.logging import get_logger, raise_if_not -from darts.timeseries import TimeSeries +from darts.metrics.metrics import METRIC_TYPE logger = get_logger(__name__) -class PyODScorer(FittableAnomalyScorer): +class PyODScorer(WindowedAnomalyScorer): def __init__( self, model: BaseDetector, window: int = 1, component_wise: bool = False, - diff_fn="abs_diff", + window_agg: bool = True, + diff_fn: METRIC_TYPE = metrics.ae, ) -> None: - """ + """PyOD Scorer + When calling ``fit(series)``, a moving window is applied, which results in a set of vectors of size `W`, where `W` is the window size. The PyODScorer model is trained on these vectors. The ``score(series)`` function will apply the same moving window and return the predicted raw anomaly score of each vector. @@ -38,25 +38,26 @@ def __init__( functions ``fit()`` and ``score()``, respectively. `component_wise` is a boolean parameter indicating how the model should behave with multivariate inputs - series. If set to True, the model will treat each series dimension independently by fitting a different - PyODScorer model for each dimension. If set to False, the model concatenates the dimensions in + series. If set to `True`, the model will treat each series dimension independently by fitting a different + PyODScorer model for each dimension. If set to `False`, the model concatenates the dimensions in each windows of length `W` and compute the score using only one underlying PyODScorer model. **Training with** ``fit()``: The input can be a series (univariate or multivariate) or multiple series. The series will be partitioned - into equal size subsequences. The subsequence will be of size `W` * `D`, with: + into equal size subsequences. Each subsequence has size `W * D` (features), where: - * `W` being the size of the window given as a parameter `window` - * `D` being the dimension of the series (`D` = 1 if univariate or if `component_wise` is set to True) + - `W` is the size of the window given as a parameter `window` + - `D` is the dimension of the series (`D` = 1 if univariate or if `component_wise` is set to `True`) - For a series of length `N`, (`N` - `W` + 1)/W subsequences will be generated. If a list of series is given - of length L, each series will be partitioned into subsequences, and the results will be concatenated into - an array of length L * number of subsequences of each series. + For a series of length `N`, `(N - W + 1)` subsequences will be generated. The final array `X` passed to the + underlying scorer has shape `(N - W + 1, W * D)`; or in other terms (number of samples, number of features). + If a list of series is given of length L, each series `i` is partitioned, and all `X_i` are concatenated along + the sample axis. The PyOD model will be fitted on the generated subsequences. - If `component_wise` is set to True, the algorithm will be applied to each dimension independently. For each + If `component_wise` is set to `True`, the algorithm will be applied to each dimension independently. For each dimension, a PyOD model will be trained. **Computing score with** ``score()``: @@ -66,9 +67,9 @@ def __init__( For each series, if the series is multivariate of dimension `D`: - * if `component_wise` is set to False: it returns a univariate series (dimension=1). It represents + - if `component_wise` is set to `False`: it returns a univariate series (dimension=1). It represents the anomaly score of the entire series in the considered window at each timestamp. - * if `component_wise` is set to True: it returns a multivariate series of dimension `D`. Each dimension + - if `component_wise` is set to `True`: it returns a multivariate series of dimension `D`. Each dimension represents the anomaly score of the corresponding component of the input. If the series is univariate, it returns a univariate series regardless of the parameter @@ -84,111 +85,39 @@ def __init__( The (fitted) PyOD BaseDetector model. window Size of the window used to create the subsequences of the series. - diff_fn - Optionally, reduced function to use if two series are given. It will transform the two series into one. - This allows the KMeansScorer to apply PyODScorer on the original series or on its residuals (difference - between the prediction and the original series). - Must be one of "abs_diff" and "diff" (defined in ``_diff_series()``). - Default: "abs_diff" component_wise - Boolean value indicating if the score needs to be computed for each component independently (True) - or by concatenating the component in the considered window to compute one score (False). - Default: False + Boolean value indicating if the score needs to be computed for each component independently (`True`) + or by concatenating the component in the considered window to compute one score (`False`). + Default: `False`. + window_agg + Boolean indicating whether the anomaly score for each time step is computed by + averaging the anomaly scores for all windows this point is included in. + If `False`, the anomaly score for each point is the anomaly score of its trailing window. + Default: `True`. + diff_fn + The differencing function to use to transform the predicted and actual series into one series. + The scorer is then applied to this series. Must be one of Darts per-time-step metrics (e.g., + :func:`~darts.metrics.metrics.ae` for the absolute difference, :func:`~darts.metrics.metrics.err` for the + difference, :func:`~darts.metrics.metrics.se` for the squared difference, ...). + By default, uses the absolute difference (:func:`~darts.metrics.metrics.ae`). """ raise_if_not( isinstance(model, BaseDetector), f"model must be a PyOD BaseDetector, found type: {type(model)}", + logger, ) self.model = model - - raise_if_not( - type(component_wise) is bool, - f"Parameter `component_wise` must be Boolean, found type: {type(component_wise)}.", - ) - self.component_wise = component_wise - super().__init__( - univariate_scorer=(not component_wise), window=window, diff_fn=diff_fn + is_univariate=(not component_wise), + window=window, + window_agg=window_agg, + diff_fn=diff_fn, ) def __str__(self): return "PyODScorer (model {})".format(self.model.__str__().split("(")[0]) - def _fit_core(self, list_series: Sequence[TimeSeries]): - - list_np_series = [series.all_values(copy=False) for series in list_series] - - # TODO: can we factorize code in common bteween PyODScorer and KMeansScorer? - - if not self.component_wise: - self.model.fit( - np.concatenate( - [ - sliding_window_view(ar, window_shape=self.window, axis=0) - .transpose(0, 3, 1, 2) - .reshape(-1, self.window * len(ar[0])) - for ar in list_np_series - ] - ) - ) - else: - models = [] - for component_idx in range(self.width_trained_on): - - model_width = self.model - model_width.fit( - np.concatenate( - [ - sliding_window_view( - ar[:, component_idx], window_shape=self.window, axis=0 - ) - .transpose(0, 2, 1) - .reshape(-1, self.window) - for ar in list_np_series - ] - ) - ) - models.append(model_width) - self.models = models - - def _score_core(self, series: TimeSeries) -> TimeSeries: - - raise_if_not( - self.width_trained_on == series.width, - "Input must have the same number of components as the data used for training" - + " the PyODScorer model {},".format(self.model.__str__().split("(")[0]) - + f" found number of components equal to {series.width} and expected " - + f"{self.width_trained_on}.", - ) - - np_series = series.all_values(copy=False) - np_anomaly_score = [] - - if not self.component_wise: - - np_anomaly_score.append( - self.model.decision_function( - sliding_window_view(np_series, window_shape=self.window, axis=0) - .transpose(0, 3, 1, 2) - .reshape(-1, self.window * series.width) - ) - ) - else: - - for component_idx in range(self.width_trained_on): - score = self.models[component_idx].decision_function( - sliding_window_view( - np_series[:, component_idx], - window_shape=self.window, - axis=0, - ) - .transpose(0, 2, 1) - .reshape(-1, self.window) - ) - - np_anomaly_score.append(score) - - return TimeSeries.from_times_and_values( - series.time_index[self.window - 1 :], list(zip(*np_anomaly_score)) - ) + def _model_score_method(self, model, data: np.ndarray) -> np.ndarray: + """Wrapper around model inference method""" + return model.decision_function(data) diff --git a/darts/ad/scorers/scorers.py b/darts/ad/scorers/scorers.py index de2f878aab..3fadee463a 100644 --- a/darts/ad/scorers/scorers.py +++ b/darts/ad/scorers/scorers.py @@ -6,23 +6,36 @@ # - add stride for Scorers like Kmeans and Wasserstein # - add option to normalize the windows for kmeans? capture only the form and not the values. - +import copy +import sys from abc import ABC, abstractmethod -from typing import Any, Sequence, Union +from collections.abc import Sequence +from typing import Optional, Union + +if sys.version_info >= (3, 11): + from typing import Self +else: + from typing_extensions import Self +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal import numpy as np -from darts import TimeSeries +from darts import TimeSeries, metrics from darts.ad.utils import ( _assert_same_length, - _assert_timeseries, - _intersect, + _check_input, _sanity_check_two_series, - _to_list, - eval_accuracy_from_scores, + eval_metric_from_scores, show_anomalies_from_scores, ) -from darts.logging import get_logger, raise_if_not +from darts.logging import get_logger, raise_log +from darts.metrics.metrics import METRIC_TYPE +from darts.utils.data.tabularization import create_lagged_data +from darts.utils.ts_utils import series2seq +from darts.utils.utils import _build_tqdm_iterator, _parallel_apply logger = get_logger(__name__) @@ -30,155 +43,147 @@ class AnomalyScorer(ABC): """Base class for all anomaly scorers""" - def __init__(self, univariate_scorer: bool, window: int) -> None: - - raise_if_not( - type(window) is int, - f"Parameter `window` must be an integer, found type {type(window)}.", - ) - - raise_if_not( - window > 0, - f"Parameter `window` must be stricly greater than 0, found size {window}.", - ) - - self.window = window - - self.univariate_scorer = univariate_scorer - - def _check_univariate_scorer(self, actual_anomalies: Sequence[TimeSeries]): - """Checks if `actual_anomalies` contains only univariate series when the scorer has the - parameter 'univariate_scorer' set to True. - - 'univariate_scorer' is: - True -> when the function of the scorer ``score(series)`` (or, if applicable, - ``score_from_prediction(actual_series, pred_series)``) returns a univariate - anomaly score regardless of the input `series` (or, if applicable, `actual_series` - and `pred_series`). - False -> when the scorer will return a series that has the - same number of components as the input (can be univariate or multivariate). + def __init__(self, is_univariate: bool, window: int) -> None: """ - - if self.univariate_scorer: - raise_if_not( - all([isinstance(s, TimeSeries) for s in actual_anomalies]), - "all series in `actual_anomalies` must be of type TimeSeries.", - ) - - raise_if_not( - all([s.width == 1 for s in actual_anomalies]), - f"Scorer {self.__str__()} will return a univariate anomaly score series (width=1)." - + " Found a multivariate `actual_anomalies`." - + " The evaluation of the accuracy cannot be computed between the two series.", + Parameters + ---------- + is_univariate + Whether the scorer is a univariate scorer. + window + Integer value indicating the size of the window W used by the scorer to transform the series into an + anomaly score. A scorer will slice the given series into subsequences of size W and returns a value + indicating how anomalous these subset of W values are. A post-processing step will convert this anomaly + score into a point-wise anomaly score (see definition of `window_transform`). The window size should be + commensurate to the expected durations of the anomalies one is looking for. + """ + if window <= 0: + raise_log( + ValueError( + f"Parameter `window` must be strictly greater than 0, found `{window}`." + ), + logger=logger, ) + self.window = window + self._is_univariate = is_univariate - def _check_window_size(self, series: TimeSeries): - """Checks if the parameter window is less or equal than the length of the given series""" - - raise_if_not( - self.window <= len(series), - f"Window size {self.window} is greater than the targeted series length {len(series)}, " - + "must be lower or equal. Decrease the window size or increase the length series input" - + " to score on.", - ) - - @property - def is_probabilistic(self) -> bool: - """Whether the scorer expects a probabilistic prediction for its first input.""" - return False - - def _assert_stochastic(self, series: TimeSeries, name_series: str): - "Checks if the series is stochastic (number of samples is higher than one)." + def score_from_prediction( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + ) -> Union[TimeSeries, Sequence[TimeSeries]]: + """Computes the anomaly score on the two (sequence of) series. - raise_if_not( - series.is_stochastic, - f"Scorer {self.__str__()} is expecting `{name_series}` to be a stochastic timeseries" - + f" (number of samples must be higher than 1, found: {series.n_samples}).", - ) + If a pair of sequences is given, they must contain the same number + of series. The scorer will score each pair of series independently + and return an anomaly score for each pair. - def _assert_deterministic(self, series: TimeSeries, name_series: str): - "Checks if the series is deterministic (number of samples is equal to one)." + Parameters + ---------- + series: + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. - if not series.is_deterministic: - logger.warning( - f"Scorer {self.__str__()} is expecting `{name_series}` to be a (sequence of) deterministic" - + f" timeseries (number of samples must be equal to 1, found: {series.n_samples}). The " - + "series will be converted to a deterministic series by taking the median of the samples.", + Returns + ------- + Union[TimeSeries, Sequence[TimeSeries]] + (Sequence of) anomaly score time series + """ + called_with_single_series = isinstance(series, TimeSeries) + series, pred_series = series2seq(series), series2seq(pred_series) + name, pred_name = "series", "pred_series" + _assert_same_length(series, pred_series, name, pred_name) + + pred_scores = [] + for actual, pred in zip(series, pred_series): + _sanity_check_two_series(actual, pred, name, pred_name) + index = actual.slice_intersect_times(pred, copy=False) + self._check_window_size(index) + scores = self._score_core_from_prediction( + vals=actual.slice_intersect_values(pred), + pred_vals=pred.slice_intersect_values(actual), + ) + scores = TimeSeries.from_times_and_values( + values=scores, + times=index, ) - series = series.quantile_timeseries(quantile=0.5) - - return series - @abstractmethod - def __str__(self): - """returns the name of the scorer""" - pass + if self.window > 1: + # apply a moving average with window size `self.window` to the anomaly scores starting at `self.window`; + # series of length `n` will be transformed into a series of length `n-self.window+1`. + scores = scores.window_transform( + transforms={ + "window": self.window, + "function": "mean", + "mode": "rolling", + "min_periods": self.window, + }, + treat_na="dropna", + ) + pred_scores.append(scores) + return pred_scores[0] if called_with_single_series else pred_scores - def eval_accuracy_from_prediction( + def eval_metric_from_prediction( self, - actual_anomalies: Union[TimeSeries, Sequence[TimeSeries]], - actual_series: Union[TimeSeries, Sequence[TimeSeries]], + anomalies: Union[TimeSeries, Sequence[TimeSeries]], + series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], - metric: str = "AUC_ROC", + metric: Literal["AUC_ROC", "AUC_PR"] = "AUC_ROC", ) -> Union[float, Sequence[float], Sequence[Sequence[float]]]: - """Computes the anomaly score between `actual_series` and `pred_series`, and returns the score + """Computes the anomaly score between `series` and `pred_series`, and returns the score of an agnostic threshold metric. Parameters ---------- - actual_anomalies - The (sequence of) ground truth of the anomalies (1 if it is an anomaly and 0 if not) - actual_series + anomalies + The (sequence of) ground truth binary anomaly series (`1` if it is an anomaly and `0` if not). + series The (sequence of) actual series. pred_series The (sequence of) predicted series. metric - Optionally, metric function to use. Must be one of "AUC_ROC" and "AUC_PR". - Default: "AUC_ROC" + The name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". Returns ------- - Union[float, Sequence[float], Sequence[Sequence[float]]] - Score of an agnostic threshold metric for the computed anomaly score - - ``float`` if `actual_series` and `actual_series` are univariate series (dimension=1). - - ``Sequence[float]`` - - * if `actual_series` and `actual_series` are multivariate series (dimension>1), - returns one value per dimension, or - * if `actual_series` and `actual_series` are sequences of univariate series, - returns one value per series - - ``Sequence[Sequence[float]]]`` if `actual_series` and `actual_series` are sequences - of multivariate series. Outer Sequence is over the sequence input and the inner - Sequence is over the dimensions of each element in the sequence input. + float + A single metric value for a single univariate `series`. + Sequence[float] + A sequence of metric values for: + + - a single multivariate `series`. + - a sequence of univariate `series`. + Sequence[Sequence[float]] + A sequence of sequences of metric values for a sequence of multivariate `series`. + The outer sequence is over the series, and inner sequence is over the series' components/columns. """ - actual_anomalies = _to_list(actual_anomalies) - self._check_univariate_scorer(actual_anomalies) - - anomaly_score = self.score_from_prediction(actual_series, pred_series) - - return eval_accuracy_from_scores( - actual_anomalies, anomaly_score, self.window, metric + self._check_univariate_scorer(anomalies) + pred_scores = self.score_from_prediction(series, pred_series) + return eval_metric_from_scores( + anomalies=anomalies, + pred_scores=pred_scores, + window=self.window, + metric=metric, ) - @abstractmethod - def score_from_prediction(self, actual_series: Any, pred_series: Any) -> Any: - pass - def show_anomalies_from_prediction( self, - actual_series: TimeSeries, + series: TimeSeries, pred_series: TimeSeries, scorer_name: str = None, - actual_anomalies: TimeSeries = None, + anomalies: TimeSeries = None, title: str = None, - metric: str = None, + metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None, + component_wise: bool = False, ): """Plot the results of the scorer. Computes the anomaly score on the two series. And plots the results. The plot will be composed of the following: - - the actual_series and the pred_series. + - the series and the pred_series. - the anomaly score of the scorer. - the actual anomalies, if given. @@ -190,197 +195,275 @@ def show_anomalies_from_prediction( Parameters ---------- - actual_series + series The actual series to visualize anomalies from. pred_series - The predicted series of `actual_series`. - actual_anomalies + The predicted series of `series`. + anomalies The ground truth of the anomalies (1 if it is an anomaly and 0 if not) scorer_name Name of the scorer. title Title of the figure metric - Optionally, Scoring function to use. Must be one of "AUC_ROC" and "AUC_PR". - Default: "AUC_ROC" + Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". + component_wise + If True, will separately plot each component in case of multivariate anomaly detection. """ - if isinstance(actual_series, Sequence): - raise_if_not( - len(actual_series) == 1, - "``show_anomalies_from_prediction`` expects only one series for `actual_series`," - + f" found a list of length {len(actual_series)} as input.", - ) - - actual_series = actual_series[0] - - raise_if_not( - isinstance(actual_series, TimeSeries), - "``show_anomalies_from_prediction`` expects an input of type TimeSeries," - + f" found type {type(actual_series)} for `actual_series`.", - ) - - if isinstance(pred_series, Sequence): - raise_if_not( - len(pred_series) == 1, - "``show_anomalies_from_prediction`` expects one series for `pred_series`," - + f" found a list of length {len(pred_series)} as input.", - ) - - pred_series = pred_series[0] - - raise_if_not( - isinstance(pred_series, TimeSeries), - "``show_anomalies_from_prediction`` expects an input of type TimeSeries," - + f" found type: {type(pred_series)} for `pred_series`.", - ) - - anomaly_score = self.score_from_prediction(actual_series, pred_series) + series = _check_input(series, name="series", num_series_expected=1)[0] + pred_series = _check_input( + pred_series, name="pred_series", num_series_expected=1 + )[0] + pred_scores = self.score_from_prediction(series, pred_series) if title is None: - title = f"Anomaly results by scorer {self.__str__()}" + title = f"Anomaly results by scorer {str(self)}" if scorer_name is None: - scorer_name = [f"anomaly score by {self.__str__()}"] + scorer_name = [f"anomaly score by {str(self)}"] return show_anomalies_from_scores( - actual_series, - model_output=pred_series, - anomaly_scores=anomaly_score, + series=series, + anomalies=anomalies, + pred_series=pred_series, + pred_scores=pred_scores, window=self.window, names_of_scorers=scorer_name, - actual_anomalies=actual_anomalies, title=title, metric=metric, + component_wise=component_wise, ) + @property + def is_probabilistic(self) -> bool: + """Whether the scorer expects a probabilistic prediction as the first input.""" + return False -class NonFittableAnomalyScorer(AnomalyScorer): - """Base class of anomaly scorers that do not need training.""" - - def __init__(self, univariate_scorer, window) -> None: - super().__init__(univariate_scorer=univariate_scorer, window=window) + @property + def is_univariate(self) -> bool: + """Whether the Scorer is a univariate scorer.""" + return self._is_univariate - # indicates if the scorer is trainable or not - self.trainable = False + @property + def is_trainable(self) -> bool: + """Whether the scorer is trainable.""" + return False @abstractmethod - def _score_core_from_prediction(self, series: Any) -> Any: + def __str__(self): + """returns the name of the scorer""" pass - def score_from_prediction( + @abstractmethod + def _score_core_from_prediction( self, - actual_series: Union[TimeSeries, Sequence[TimeSeries]], - pred_series: Union[TimeSeries, Sequence[TimeSeries]], - ) -> Union[TimeSeries, Sequence[TimeSeries]]: - """Computes the anomaly score on the two (sequence of) series. - - If a pair of sequences is given, they must contain the same number - of series. The scorer will score each pair of series independently - and return an anomaly score for each pair. + vals: np.ndarray, + pred_vals: np.ndarray, + ) -> np.ndarray: + pass - Parameters - ---------- - actual_series: - The (sequence of) actual series. - pred_series - The (sequence of) predicted series. + def _check_univariate_scorer( + self, anomalies: Union[TimeSeries, Sequence[TimeSeries]] + ): + """Checks if `anomalies` contains only univariate series when the scorer has the + parameter 'is_univariate' set to True. - Returns - ------- - Union[TimeSeries, Sequence[TimeSeries]] - (Sequence of) anomaly score time series + 'is_univariate' is: + True -> when the function of the scorer `score(series)` (or, if applicable, + `score_from_prediction(series, pred_series)`) returns a univariate + anomaly score regardless of the input `series` (or, if applicable, `series` + and `pred_series`). + False -> when the scorer will return a series that has the + same number of components as the input (can be univariate or multivariate). """ - list_actual_series, list_pred_series = _to_list(actual_series), _to_list( - pred_series + + def _check_univariate(s: TimeSeries): + """Checks if `anomalies` contains only univariate series, which + is required if any of the scorers returns a univariate score. + """ + if self.is_univariate and not s.width == 1: + raise_log( + ValueError( + f"Scorer {str(self)} will return a univariate anomaly score series (width=1). " + f"Found a multivariate `anomalies`. " + f"The evaluation of the accuracy cannot be computed between the two series." + ), + logger=logger, + ) + + _ = _check_input(anomalies, name="anomalies", extra_checks=_check_univariate) + + def _check_window_size(self, series: Sequence): + """Checks if the parameter window is less or equal than the length of the given series""" + if not self.window <= len(series): + raise_log( + ValueError( + f"Window size {self.window} is greater than the targeted series length {len(series)}, " + f"must be lower or equal. Decrease the window size or increase the length series " + f"input to score on." + ), + logger=logger, + ) + + def _assert_stochastic(self, series: np.ndarray, name_series: str): + """Checks if the series is stochastic (number of samples is larger than one).""" + if not series.shape[2] > 1: + raise_log( + ValueError( + f"Scorer {str(self)} is expecting `{name_series}` to be a stochastic " + f"timeseries (number of samples must be higher than 1, found: {series.shape[2]}).", + ), + logger=logger, + ) + + def _extract_deterministic_series(self, series: TimeSeries, name_series: str): + """Extract a deterministic series from `series` (quantile=0.5 if `series` is probabilistic).""" + if series.is_deterministic: + return series + + logger.warning( + f"Scorer {str(self)} is expecting `{name_series}` to be a (sequence of) deterministic " + f"timeseries (number of samples must be equal to 1, found: {series.n_samples}). The series " + f"will be converted to a deterministic series by taking the median of the samples.", ) - _assert_same_length(list_actual_series, list_pred_series) - - anomaly_scores = [] - - for s1, s2 in zip(list_actual_series, list_pred_series): - _sanity_check_two_series(s1, s2) - s1, s2 = _intersect(s1, s2) - self._check_window_size(s1) - self._check_window_size(s2) - anomaly_scores.append(self._score_core_from_prediction(s1, s2)) - - if ( - len(anomaly_scores) == 1 - and not isinstance(pred_series, Sequence) - and not isinstance(actual_series, Sequence) - ): - return anomaly_scores[0] - else: - return anomaly_scores + return series.quantile_timeseries(quantile=0.5) + def _extract_deterministic_values(self, series: np.ndarray, name_series: str): + """Extract deterministic values from `series` (quantile=0.5 if `series` is probabilistic).""" + if series.shape[2] == 1: + return series -class FittableAnomalyScorer(AnomalyScorer): - """Base class of scorers that do need training.""" + logger.warning( + f"Scorer {str(self)} is expecting `{name_series}` to be a (sequence of) deterministic " + f"timeseries (number of samples must be equal to 1, found: {series.shape[2]}). The series " + f"will be converted to a deterministic series by taking the median of the samples.", + ) + return np.expand_dims(np.quantile(series, q=0.5, axis=2), -1) - def __init__(self, univariate_scorer, window, diff_fn="abs_diff") -> None: - super().__init__(univariate_scorer=univariate_scorer, window=window) - # indicates if the scorer is trainable or not - self.trainable = True +class FittableAnomalyScorer(AnomalyScorer): + """Base class of scorers that require training.""" + + def __init__( + self, + is_univariate: bool, + window: int, + window_agg: bool, + diff_fn: METRIC_TYPE = metrics.ae, + n_jobs: int = 1, + ) -> None: + """ + Parameters + ---------- + is_univariate + Whether the scorer is a univariate scorer. + window + Integer value indicating the size of the window W used by the scorer to transform the series into an + anomaly score. A scorer will slice the given series into subsequences of size W and returns a value + indicating how anomalous these subset of W values are. A post-processing step will convert this anomaly + score into a point-wise anomaly score (see definition of `window_transform`). The window size should be + commensurate to the expected durations of the anomalies one is looking for. + window_agg + Whether to transform/aggregate window-wise anomaly scores into a point-wise anomaly scores. + diff_fn + The differencing function to use to transform the predicted and actual series into one series. + The scorer is then applied to this series. Must be one of Darts per-time-step metrics (e.g., + :func:`~darts.metrics.metrics.ae` for the absolute difference, :func:`~darts.metrics.metrics.err` for the + difference, :func:`~darts.metrics.metrics.se` for the squared difference, ...). + By default, uses the absolute difference (:func:`~darts.metrics.metrics.ae`). + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a `Sequence[TimeSeries]` is + passed as input, parallelising operations regarding different `TimeSeries`. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + """ + super().__init__(is_univariate=is_univariate, window=window) + if diff_fn not in metrics.TIME_DEPENDENT_METRICS: + valid_metrics = [m.__name__ for m in metrics.TIME_DEPENDENT_METRICS] + raise_log( + ValueError( + f"`diff_fn` must be one of Darts 'per time step' metrics " + f"{valid_metrics}. Found `{diff_fn}`" + ), + logger=logger, + ) + self.diff_fn = diff_fn + self.window_agg = window_agg + self._n_jobs = n_jobs # indicates if the scorer has been trained yet self._fit_called = False + self.width_trained_on: Optional[int] = None - # function used in ._diff_series() to convert 2 time series into 1 - if diff_fn in {"abs_diff", "diff"}: - self.diff_fn = diff_fn - else: - raise ValueError(f"Metric should be 'diff' or 'abs_diff', found {diff_fn}") + def fit( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + ) -> Self: + """Fits the scorer on the given time series. - def check_if_fit_called(self): - """Checks if the scorer has been fitted before calling its `score()` function.""" + If a sequence of series, the scorer is fitted on the concatenation of the sequence. + + The assumption is that `series` is generally anomaly-free. - raise_if_not( - self._fit_called, - f"The Scorer {self.__str__()} has not been fitted yet. Call ``fit()`` first.", + Parameters + ---------- + series + The (sequence of) series with no anomalies. + + Returns + ------- + self + Fitted Scorer. + """ + width = series2seq(series)[0].width + series = _check_input( + series, + name="series", + width_expected=width, + extra_checks=self._check_window_size, ) + self.width_trained_on = width + self._fit_core(series) + self._fit_called = True + return self - def eval_accuracy( + def fit_from_prediction( self, - actual_anomalies: Union[TimeSeries, Sequence[TimeSeries]], series: Union[TimeSeries, Sequence[TimeSeries]], - metric: str = "AUC_ROC", - ) -> Union[float, Sequence[float], Sequence[Sequence[float]]]: - """Computes the anomaly score of the given time series, and returns the score - of an agnostic threshold metric. + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + ): + """Fits the scorer on the two (sequences of) series. + + The function `diff_fn` passed as a parameter to the scorer, will transform `pred_series` and `series` + into one series. By default, `diff_fn` will compute the absolute difference (Default: + :func:`~darts.metrics.metrics.ae`). If `pred_series` and `series` are sequences, `diff_fn` will be + applied to all pairwise elements of the sequences. + + The scorer will then be fitted on this (sequence of) series. If a sequence of series is given, + the scorer will be fitted on the concatenation of the sequence. + + The scorer assumes that the (sequence of) series is anomaly-free. + + If any of the series is stochastic (with `n_samples>1`), `diff_fn` is computed on quantile `0.5`. Parameters ---------- - actual_anomalies - The ground truth of the anomalies (1 if it is an anomaly and 0 if not) series - The (sequence of) series to detect anomalies from. - metric - Optionally, metric function to use. Must be one of "AUC_ROC" and "AUC_PR". - Default: "AUC_ROC" + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. Returns ------- - Union[float, Sequence[float], Sequence[Sequence[float]]] - Score of an agnostic threshold metric for the computed anomaly score - - ``float`` if `series` is a univariate series (dimension=1). - - ``Sequence[float]`` - - * if `series` is a multivariate series (dimension>1), returns one - value per dimension, or - * if `series` is a sequence of univariate series, returns one value - per series - - ``Sequence[Sequence[float]]]`` if `series` is a sequence of multivariate - series. Outer Sequence is over the sequence input and the inner Sequence - is over the dimensions of each element in the sequence input. + self + Fitted Scorer. """ - actual_anomalies = _to_list(actual_anomalies) - self._check_univariate_scorer(actual_anomalies) - anomaly_score = self.score(series) - - return eval_accuracy_from_scores( - actual_anomalies, anomaly_score, self.window, metric - ) + series = _check_input(series, "series") + pred_series = _check_input(pred_series, "pred_series") + diff_series = self._diff_series(series, pred_series) + self.fit(diff_series) + self._fit_called = True def score( self, @@ -401,31 +484,107 @@ def score( Union[TimeSeries, Sequence[TimeSeries]] (Sequence of) anomaly score time series """ + self._check_fit_called() - self.check_if_fit_called() + called_with_single_series = isinstance(series, TimeSeries) + series = _check_input( + series, name="series", extra_checks=self._check_window_size + ) + series = [self._extract_deterministic_series(s, "series") for s in series] - list_series = _to_list(series) + pred_scores = self._score_core(series) + return pred_scores[0] if called_with_single_series else pred_scores - anomaly_scores = [] - for s in list_series: - _assert_timeseries(s) - self._check_window_size(s) - anomaly_scores.append( - self._score_core(self._assert_deterministic(s, "series")) - ) + def score_from_prediction( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + ) -> Union[TimeSeries, Sequence[TimeSeries]]: + """Computes the anomaly score on the two (sequence of) series. - if len(anomaly_scores) == 1 and not isinstance(series, Sequence): - return anomaly_scores[0] - else: - return anomaly_scores + The function `diff_fn` passed as a parameter to the scorer, will transform `pred_series` and `series` + into one "difference" series. By default, `diff_fn` will compute the absolute difference + (Default: :func:`~darts.metrics.metrics.ae`). + If series and pred_series are sequences, `diff_fn` will be applied to all pairwise elements + of the sequences. + + The scorer will then transform this series into an anomaly score. If a sequence of series is given, + the scorer will score each series independently and return an anomaly score for each series in the sequence. + + Parameters + ---------- + series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + + Returns + ------- + Union[TimeSeries, Sequence[TimeSeries]] + (Sequence of) anomaly score time series + """ + self._check_fit_called() + + called_with_single_series = isinstance(series, TimeSeries) + series = _check_input(series, "series") + pred_series = _check_input(pred_series, "pred_series") + + diff = self._diff_series(series, pred_series) + pred_scores = self.score(diff) + return pred_scores[0] if called_with_single_series else pred_scores + + def eval_metric( + self, + anomalies: Union[TimeSeries, Sequence[TimeSeries]], + series: Union[TimeSeries, Sequence[TimeSeries]], + metric: Literal["AUC_ROC", "AUC_PR"] = "AUC_ROC", + ) -> Union[float, Sequence[float], Sequence[Sequence[float]]]: + """Computes the anomaly score of the given time series, and returns the score + of an agnostic threshold metric. + + Parameters + ---------- + anomalies + The (sequence of) ground truth binary anomaly series (`1` if it is an anomaly and `0` if not). + series + The (sequence of) series to detect anomalies from. + metric + The name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". + + Returns + ------- + float + A single score/metric for univariate `series` series (with only one component/column). + Sequence[float] + A sequence (list) of scores for: + + - multivariate `series` series (multiple components). Gives a score for each component. + - a sequence (list) of univariate `series` series. Gives a score for each series. + Sequence[Sequence[float]] + A sequence of sequences of scores for a sequence of multivariate `series` series. + Gives a score for each series (outer sequence) and component (inner sequence). + """ + anomalies = series2seq(anomalies) + self._check_univariate_scorer(anomalies) + pred_scores = self.score(series) + window = 1 if self.window_agg else self.window + return eval_metric_from_scores( + anomalies=anomalies, + pred_scores=pred_scores, + window=window, + metric=metric, + ) def show_anomalies( self, series: TimeSeries, - actual_anomalies: TimeSeries = None, + anomalies: TimeSeries = None, scorer_name: str = None, title: str = None, - metric: str = None, + metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None, + component_wise: bool = False, ): """Plot the results of the scorer. @@ -446,307 +605,369 @@ def show_anomalies( ---------- series The series to visualize anomalies from. - actual_anomalies - The ground truth of the anomalies (1 if it is an anomaly and 0 if not) + anomalies + The (sequence of) ground truth binary anomaly series (`1` if it is an anomaly and `0` if not). scorer_name Name of the scorer. title Title of the figure metric - Optionally, Scoring function to use. Must be one of "AUC_ROC" and "AUC_PR". - Default: "AUC_ROC" + Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". + component_wise + If True, will separately plot each component in case of multivariate anomaly detection. """ - - if isinstance(series, Sequence): - raise_if_not( - len(series) == 1, - "``show_anomalies`` expects one series for `series`," - + f" found a list of length {len(series)} as input.", - ) - - series = series[0] - - raise_if_not( - isinstance(series, TimeSeries), - "``show_anomalies`` expects an input of type TimeSeries," - + f" found type {type(series)} for `series`.", - ) - - anomaly_score = self.score(series) + series = _check_input(series, name="series", num_series_expected=1)[0] + pred_scores = self.score(series) if title is None: - title = f"Anomaly results by scorer {self.__str__()}" + title = f"Anomaly results by scorer {str(self)}" if scorer_name is None: - scorer_name = f"anomaly score by {self.__str__()}" + scorer_name = f"anomaly score by {str(self)}" + + if self.window_agg: + window = 1 + else: + window = self.window return show_anomalies_from_scores( - series, - anomaly_scores=anomaly_score, - window=self.window, + series=series, + anomalies=anomalies, + pred_scores=pred_scores, + window=window, names_of_scorers=scorer_name, - actual_anomalies=actual_anomalies, title=title, metric=metric, + component_wise=component_wise, ) - def score_from_prediction( - self, - actual_series: Union[TimeSeries, Sequence[TimeSeries]], - pred_series: Union[TimeSeries, Sequence[TimeSeries]], - ) -> Union[TimeSeries, Sequence[TimeSeries]]: - """Computes the anomaly score on the two (sequence of) series. - - The function ``diff_fn`` passed as a parameter to the scorer, will transform `pred_series` and `actual_series` - into one "difference" series. By default, ``diff_fn`` will compute the absolute difference - (Default: "abs_diff"). - If actual_series and pred_series are sequences, ``diff_fn`` will be applied to all pairwise elements - of the sequences. - - The scorer will then transform this series into an anomaly score. If a sequence of series is given, - the scorer will score each series independently and return an anomaly score for each series in the sequence. - - Parameters - ---------- - actual_series - The (sequence of) actual series. - pred_series - The (sequence of) predicted series. - - Returns - ------- - Union[TimeSeries, Sequence[TimeSeries]] - (Sequence of) anomaly score time series - """ + @property + def is_trainable(self) -> bool: + """Whether the Scorer is trainable.""" + return True - self.check_if_fit_called() + @abstractmethod + def _fit_core(self, series: Sequence[TimeSeries], *args, **kwargs): + pass - list_actual_series, list_pred_series = _to_list(actual_series), _to_list( - pred_series - ) - _assert_same_length(list_actual_series, list_pred_series) - - anomaly_scores = [] - for (s1, s2) in zip(list_actual_series, list_pred_series): - _sanity_check_two_series(s1, s2) - s1 = self._assert_deterministic(s1, "actual_series") - s2 = self._assert_deterministic(s2, "pred_series") - diff = self._diff_series(s1, s2) - self._check_window_size(diff) - anomaly_scores.append(self.score(diff)) - - if ( - len(anomaly_scores) == 1 - and not isinstance(pred_series, Sequence) - and not isinstance(actual_series, Sequence) - ): - return anomaly_scores[0] - else: - return anomaly_scores + @abstractmethod + def _score_core( + self, series: Sequence[TimeSeries], *args, **kwargs + ) -> Sequence[TimeSeries]: + pass - def fit( + def _score_core_from_prediction( self, - series: Union[TimeSeries, Sequence[TimeSeries]], - ): - """Fits the scorer on the given time series input. + vals: np.ndarray, + pred_vals: np.ndarray, + ) -> np.ndarray: + pass - If sequence of series is given, the scorer will be fitted on the concatenation of the sequence. + def _diff_series( + self, + series: Sequence[TimeSeries], + pred_series: Sequence[TimeSeries], + ) -> Sequence[TimeSeries]: + """Applies the `diff_fn` to two sequences of time series. Converts two time series into 1. - The assumption is that the series `series` used for training are generally anomaly-free. + Each series-pair in series and pred_series must: + - have a non-empty time intersection + - be of the same width W Parameters ---------- series - The (sequence of) series with no anomalies. + A sequence of time series + pred_series + A sequence of predicted time series to compute `diff_fn` on. Returns ------- - self - Fitted Scorer. + Sequence[TimeSeries] + A sequence of series of width W from the difference between `series` and `pred_series`. """ - list_series = _to_list(series) - - for idx, s in enumerate(list_series): - _assert_timeseries(s) - - if idx == 0: - self.width_trained_on = s.width - else: - raise_if_not( - s.width == self.width_trained_on, - "series in `series` must have the same number of components," - + f" found number of components equal to {self.width_trained_on}" - + f" at index 0 and {s.width} at index {idx}.", - ) - self._check_window_size(s) - - self._assert_deterministic(s, "series") + residuals = self.diff_fn(series, pred_series, component_reduction=None) + out = [] + for s1, s2, res in zip(series, pred_series, residuals): + time_index = s2.slice_intersect_times(s1, copy=False) + out.append(s2.with_times_and_values(times=time_index, values=res)) + return out + + def _fun_window_agg( + self, scores: Sequence[TimeSeries], window: int + ) -> Sequence[TimeSeries]: + """ + Transforms a window-wise anomaly score into a point-wise anomaly score. - self._fit_core(list_series) - self._fit_called = True + When using a window of size `W`, a scorer will return an anomaly score + with values that represent how anomalous each past `W` is. If the parameter + `window_agg` is set to `True` (default value), the scores for each point + can be assigned by aggregating the anomaly scores for each window the point + is included in. - def fit_from_prediction( - self, - actual_series: Union[TimeSeries, Sequence[TimeSeries]], - pred_series: Union[TimeSeries, Sequence[TimeSeries]], - ): - """Fits the scorer on the two (sequence of) series. + This post-processing step is equivalent to a rolling average of length window + over the anomaly score series. The return anomaly score represents the abnormality + of each timestamp. + """ + # TODO: can we use window_transform here? + scores_point_wise = [] + for score in scores: + score_vals = score.all_values(copy=False) + mean_score = np.empty(score_vals.shape) + for idx_point in range(len(score)): + # "look ahead window" to account for the "look behind window" of the scorer + mean_score[idx_point] = score_vals[idx_point : idx_point + window].mean( + axis=0 + ) + score_point_wise = score.with_times_and_values(score.time_index, mean_score) + scores_point_wise.append(score_point_wise) + return scores_point_wise - The function ``diff_fn`` passed as a parameter to the scorer, will transform `pred_series` and `actual_series` - into one series. By default, ``diff_fn`` will compute the absolute difference (Default: "abs_diff"). - If `pred_series` and `actual_series` are sequences, ``diff_fn`` will be applied to all pairwise elements - of the sequences. + def _check_fit_called(self): + """Checks if the scorer has been fitted before calling its `score()` function.""" + if not self._fit_called: + raise_log( + ValueError( + f"The Scorer {str(self)} has not been fitted yet. Call `fit()` first." + ), + logger=logger, + ) - The scorer will then be fitted on this (sequence of) series. If a sequence of series is given, - the scorer will be fitted on the concatenation of the sequence. - The scorer assumes that the (sequence of) actual_series is anomaly-free. +class WindowedAnomalyScorer(FittableAnomalyScorer): + """Base class for anomaly scorers that rely on windows to detect anomalies""" + def __init__( + self, + is_univariate: bool, + window: int, + window_agg: bool, + diff_fn: METRIC_TYPE, + ) -> None: + """ Parameters ---------- - actual_series - The (sequence of) actual series. - pred_series - The (sequence of) predicted series. - - Returns - ------- - self - Fitted Scorer. + is_univariate + Whether the scorer is a univariate scorer. If `True` and when using multivariate series, the scores are + computed on the concatenated components/columns in the considered window to compute one score. + window + Integer value indicating the size of the window W used by the scorer to transform the series into an + anomaly score. A scorer slices the given series into subsequences of size W and returns a value + indicating how anomalous these subsets of W values are. A post-processing step will convert the anomaly + scores into point-wise anomaly scores (see definition of `window_transform`). The window size should be + commensurate to the expected durations of the anomalies one is looking for. + window_agg + Whether to transform/aggregate window-wise anomaly scores into point-wise anomaly scores. + diff_fn + The differencing function to use to transform the predicted and actual series into one series. + The scorer is then applied to this series. Must be one of Darts per-time-step metrics (e.g., + :func:`~darts.metrics.metrics.ae` for the absolute difference, :func:`~darts.metrics.metrics.err` for the + difference, :func:`~darts.metrics.metrics.se` for the squared difference, ...). + By default, uses the absolute difference (:func:`~darts.metrics.metrics.ae`). """ - list_actual_series, list_pred_series = _to_list(actual_series), _to_list( - pred_series + super().__init__( + is_univariate=is_univariate, + window=window, + window_agg=window_agg, + diff_fn=diff_fn, ) - _assert_same_length(list_actual_series, list_pred_series) - - list_fit_series = [] - for s1, s2 in zip(list_actual_series, list_pred_series): - _sanity_check_two_series(s1, s2) - s1 = self._assert_deterministic(s1, "actual_series") - s2 = self._assert_deterministic(s2, "pred_series") - list_fit_series.append(self._diff_series(s1, s2)) - - self.fit(list_fit_series) - self._fit_called = True @abstractmethod - def _fit_core(self, series: Any) -> Any: + def _model_score_method(self, model, data: np.ndarray) -> np.ndarray: + """Wrapper around model inference method""" pass - @abstractmethod - def _score_core(self, series: Any) -> Any: - pass + def _fit_core(self, series: Sequence[TimeSeries], *args, **kwargs): + """Train one sub-model for each component when self.is_univariate=False and series is multivariate""" + if self.is_univariate or series[0].width == 1: + self.model.fit(self._tabularize_series(series, component_wise=False)) + return + + tabular_data = self._tabularize_series(series, component_wise=True) + # parallelize fitting of the component-wise models + fit_iterator = zip(tabular_data, [None] * len(tabular_data)) + input_iterator = _build_tqdm_iterator( + fit_iterator, verbose=False, desc=None, total=tabular_data.shape[1] + ) + self.model = _parallel_apply( + input_iterator, + copy.deepcopy(self.model).fit, + n_jobs=self._n_jobs, + fn_args=args, + fn_kwargs=kwargs, + ) - def _diff_series(self, series_1: TimeSeries, series_2: TimeSeries) -> TimeSeries: - """Applies the ``diff_fn`` to the two time series. Converts two time series into 1. + def _score_core( + self, series: Sequence[TimeSeries], *args, **kwargs + ) -> Sequence[TimeSeries]: + """Apply the scorer (sub) model scoring method on the series components""" + _ = _check_input(series, "series", width_expected=self.width_trained_on) + if self.is_univariate or series[0].width == 1: + # n series * (time, components, samples) -> (n series * (time - (window - 1)),) + score_vals = self._model_score_method( + model=self.model, + data=self._tabularize_series(series, component_wise=False), + ) + # (n series * (time - (window - 1)),) -> (components=1, n series * (time - (window - 1))) + score_vals = np.expand_dims(score_vals, 0) + else: + # parallelize scoring of components by the corresponding sub-model + score_iterator = zip( + self.model, + self._tabularize_series(series, component_wise=True), + ) + input_iterator = _build_tqdm_iterator( + score_iterator, verbose=False, desc=None, total=len(self.model) + ) + # n series * (time, components, samples) -> (components, n series * (time - (window - 1))) + score_vals = np.array( + _parallel_apply( + input_iterator, + self._model_score_method, + n_jobs=self._n_jobs, + fn_args=args, + fn_kwargs=kwargs, + ) + ) + # (components, n series * (time - (window - 1))) -> n series * (time - (window - 1), components) + score_series = self._convert_tabular_to_series(series, score_vals) + if self.window > 1 and self.window_agg: + return self._fun_window_agg(score_series, self.window) + else: + return score_series - series_1 and series_2 must: - - have a non empty time intersection - - be of the same width W + def _tabularize_series( + self, series: Sequence[TimeSeries], component_wise: bool + ) -> np.ndarray: + """Internal function called by WindowedAnomalyScorer `fit()` and `score()` functions. - Parameters - ---------- - series_1 - 1st time series - series_2: - 2nd time series + Transforms a sequence of series into tabular data of size window `W`. The parameter `component_wise` + indicates how the rolling window must treat the different components if the series is multivariate. + If set to `False`, the rolling window will be done on each component independently. If set to `True`, + the `N` components will be concatenated to create windows of size `W` * `N`. The resulting tabular + data of each series are concatenated. Returns ------- - TimeSeries - series of width W + np.ndarray + For `component_wise=True`, an array of shape (components, time - (window - 1), window). + The component dimension is in first place for easy parallelization over all component-wise models. + For `component_wise=False`, an array of shape (time - (window - 1), window * components). """ - series_1, series_2 = _intersect(series_1, series_2) - - if self.diff_fn == "abs_diff": - return (series_1 - series_2).map(lambda x: np.abs(x)) - elif self.diff_fn == "diff": - return series_1 - series_2 + # n series * (time, components, sample) -> (time - (window - 1), window * components) + data = create_lagged_data( + target_series=series, + lags=[i for i in range(-self.window, 0)], + uses_static_covariates=False, + is_training=False, + concatenate=True, + )[0].squeeze(-1) + + # bring into required model input shape + if component_wise: + # (time - (window - 1), window * components) -> (time - (window - 1), window, components) + data = data.reshape((-1, self.window, series[0].width)) + # (time - (window - 1), window, components) -> (components, time - (window - 1), window) + d_time, d_wind, d_comp = (0, 1, 2) + data = np.moveaxis(data, [d_time, d_comp], [d_wind, d_time]) + return data + + def _convert_tabular_to_series( + self, series: Sequence[TimeSeries], score_vals: np.ndarray + ) -> Sequence[TimeSeries]: + """Converts generated anomaly score from `np.ndarray` into a sequence of series. For efficiency reasons, + the anomaly scores were computed in one go (for each component if `component_wise=True`). If a list of series + is given, each series will be concatenated by its components. The function aims to split the anomaly score at + the proper indexes to create an anomaly score for each series. + """ + if not self.is_univariate or self.is_univariate and series[0].width == 1: + # number of input components matches output components, we can generate a new series + # with the same attrs, and component names + create_fn = "with_times_and_values" else: - # found an non-existent diff_fn - raise ValueError( - f"Metric should be 'diff' or 'abs_diff', found {self.diff_fn}" + # otherwise, create a clean new series + create_fn = "from_times_and_values" + + # (components, n series * (time - (window - 1))) -> (n series * (time - (window - 1)), components) + score_vals = score_vals.T + result = [] + idx = 0 + # (n series * (time - (window - 1)), components) -> n series * (time - (window - 1), components) + for s in series: + result.append( + getattr(s, create_fn)( + times=s._time_index[self.window - 1 :], + values=score_vals[idx : idx + len(s) - self.window + 1, :], + ) ) + idx += len(s) - self.window + 1 + return result -class NLLScorer(NonFittableAnomalyScorer): +class NLLScorer(AnomalyScorer): """Parent class for all LikelihoodScorer""" def __init__(self, window) -> None: - super().__init__(univariate_scorer=False, window=window) + """ + Parameters + ---------- + window + Integer value indicating the size of the window W used by the scorer to transform the series into an + anomaly score. A scorer will slice the given series into subsequences of size W and returns a value + indicating how anomalous these subset of W values are. A post-processing step will convert this anomaly + score into a point-wise anomaly score (see definition of `window_transform`). The window size should be + commensurate to the expected durations of the anomalies one is looking for. + """ + super().__init__(is_univariate=False, window=window) + + @property + def is_probabilistic(self) -> bool: + return True def _score_core_from_prediction( self, - actual_series: TimeSeries, - pred_series: TimeSeries, - ) -> TimeSeries: + vals: np.ndarray, + pred_vals: np.ndarray, + ) -> np.ndarray: """For each timestamp of the inputs: - - the parameters of the considered distribution are fitted on the samples of the probabilistic time series - - the negative log-likelihood of the determinisitc time series values are computed + + - the parameters of the considered distribution are fitted on the samples of the probabilistic time series + - the negative log-likelihood of the deterministic time series values are computed If the series is multivariate, the score will be computed on each component independently. Parameters ---------- - actual_series: - A determinisict time series (number of samples per timestamp must be equal to 1) - pred_series - A probabilistic time series (number of samples per timestamp must be higher than 1) + vals + The values of a deterministic time series (number of samples per timestamp must be equal to 1) + pred_vals + The values of a probabilistic time series (number of samples per timestamp must be higher than 1) + time_index + The time index intersection between `series` and `pred_series`. Returns ------- TimeSeries """ - actual_series = self._assert_deterministic(actual_series, "actual_series") - self._assert_stochastic(pred_series, "pred_series") - - np_actual_series = actual_series.all_values(copy=False) - np_pred_series = pred_series.all_values(copy=False) + vals = self._extract_deterministic_values(vals, "series") + self._assert_stochastic(pred_vals, "pred_series") np_anomaly_scores = [] - for component_idx in range(pred_series.width): + for component_idx in range(pred_vals.shape[1]): np_anomaly_scores.append( self._score_core_nllikelihood( - # shape actual: (time_steps, ) - # shape pred: (time_steps, samples) - np_actual_series[:, component_idx].squeeze(-1), - np_pred_series[:, component_idx], + vals[:, component_idx].squeeze(-1), + pred_vals[:, component_idx], ) ) - - anomaly_scores = TimeSeries.from_times_and_values( - pred_series.time_index, list(zip(*np_anomaly_scores)) - ) - - def _window_adjustment_series(series: TimeSeries) -> TimeSeries: - """Slides a window of size self.window along the input series, and replaces the value of - the input time series by the mean of the values contained in the window (past self.window - points, including itself). - A series of length N will be transformed into a series of length N-self.window+1. - """ - - if self.window == 1: - # the process results in replacing every value by itself -> return directly the series - return series - else: - return series.window_transform( - transforms={ - "window": self.window, - "function": "mean", - "mode": "rolling", - "min_periods": self.window, - }, - treat_na="dropna", - ) - - return _window_adjustment_series(anomaly_scores) - - @property - def is_probabilistic(self) -> bool: - return True + return np.array(np_anomaly_scores).T @abstractmethod - def _score_core_nllikelihood(self, input_1: Any, input_2: Any) -> Any: + def _score_core_nllikelihood( + self, vals: np.ndarray, pred_vals: np.ndarray + ) -> np.ndarray: """For each timestamp, the corresponding distribution is fitted on the probabilistic time-series input_2, and returns the negative log-likelihood of the deterministic time-series input_1 given the distribution. diff --git a/darts/ad/scorers/wasserstein_scorer.py b/darts/ad/scorers/wasserstein_scorer.py index a332cb4173..8166e0adf4 100644 --- a/darts/ad/scorers/wasserstein_scorer.py +++ b/darts/ad/scorers/wasserstein_scorer.py @@ -1,5 +1,5 @@ """ -WassersteinScorer +Wasserstein Scorer ----- Wasserstein Scorer (distance function defined between probability distributions) [1]_. @@ -11,71 +11,75 @@ .. [1] https://en.wikipedia.org/wiki/Wasserstein_metric """ -from typing import Sequence +from collections.abc import Sequence import numpy as np -from numpy.lib.stride_tricks import sliding_window_view from scipy.stats import wasserstein_distance -from darts.ad.scorers.scorers import FittableAnomalyScorer -from darts.logging import get_logger, raise_if_not +from darts import metrics +from darts.ad.scorers.scorers import WindowedAnomalyScorer +from darts.logging import get_logger +from darts.metrics.metrics import METRIC_TYPE from darts.timeseries import TimeSeries logger = get_logger(__name__) -class WassersteinScorer(FittableAnomalyScorer): +class WassersteinScorer(WindowedAnomalyScorer): def __init__( self, window: int = 10, component_wise: bool = False, - diff_fn="abs_diff", + window_agg: bool = True, + diff_fn: METRIC_TYPE = metrics.ae, ) -> None: - """ - When calling ``fit(series)``, a moving window is applied, which results in a set of vectors of size `W`, + """Wasserstein Scorer + + When calling `fit(series)`, a moving window is applied, which results in a set of vectors of size `W`, where `W` is the window size. These vectors are kept in memory, representing the training - distribution. The ``score(series)`` function will apply the same moving window. + distribution. The `score(series)` function will apply the same moving window. The Wasserstein distance is computed between the training distribution and each vector, resulting in an anomaly score. - Alternatively, the scorer has the functions ``fit_from_prediction()`` and ``score_from_prediction()``. + Alternatively, the scorer has the functions `fit_from_prediction()` and `score_from_prediction()`. Both require two series (actual and prediction), and compute a "difference" series by applying the - function ``diff_fn`` (default: absolute difference). The resulting series is then passed to the - functions ``fit()`` and ``score()``, respectively. + function `diff_fn` (default: absolute difference). The resulting series is then passed to the + functions `fit()` and `score()`, respectively. `component_wise` is a boolean parameter indicating how the model should behave with multivariate inputs - series. If set to True, the model will treat each series dimension independently. If set to False, the model + series. If set to `True`, the model will treat each series dimension independently. If set to `False`, the model concatenates the dimensions in each windows of length `W` and computes a single score for all dimensions. - **Training with** ``fit()``: + **Training with** `fit()`: The input can be a series (univariate or multivariate) or multiple series. The series will be partitioned - into equal size subsequences. The subsequence will be of size `W` * `D`, with: + into equal size subsequences. Each subsequence has size `W * D` (features), where: - * `W` being the size of the window given as a parameter `window` - * `D` being the dimension of the series (`D` = 1 if univariate or if `component_wise` is set to True) + - `W` is the size of the window given as a parameter `window` + - `D` is the dimension of the series (`D` = 1 if univariate or if `component_wise` is set to `True`) - For a series of length `N`, (`N` - `W` + 1)/W subsequences will be generated. If a list of series is given - of length L, each series will be partitioned into subsequences, and the results will be concatenated into - an array of length L * number of subsequences of each series. + For a series of length `N`, `(N - W + 1)` subsequences will be generated. The final array `X` passed to the + underlying scorer has shape `(N - W + 1, W * D)`; or in other terms (number of samples, number of features). + If a list of series is given of length L, each series `i` is partitioned, and all `X_i` are concatenated along + the sample axis. The arrays will be kept in memory, representing the training data distribution. In practice, the series or list of series can for instance represent residuals than can be considered independent and identically distributed (iid). - If `component_wise` is set to True, the algorithm will be applied to each dimension independently. For each + If `component_wise` is set to `True`, the algorithm will be applied to each dimension independently. For each dimension, a PyOD model will be trained. - **Computing score with** ``score()``: + **Computing score with** `score()`: The input can be a series (univariate or multivariate) or a sequence of series. The given series must have the same dimension `D` as the data used to train the PyOD model. For each series, if the series is multivariate of dimension `D`: - * if `component_wise` is set to False: it returns a univariate series (dimension=1). It represents + - if `component_wise` is set to `False`: it returns a univariate series (dimension=1). It represents the anomaly score of the entire series in the considered window at each timestamp. - * if `component_wise` is set to True: it returns a multivariate series of dimension `D`. Each dimension + - if `component_wise` is set to `True`: it returns a multivariate series of dimension `D`. Each dimension represents the anomaly score of the corresponding component of the input. If the series is univariate, it returns a univariate series regardless of the parameter @@ -90,17 +94,21 @@ def __init__( window Size of the sliding window that represents the number of samples in the testing distribution to compare with the training distribution in the Wasserstein function - diff_fn - Optionally, reduced function to use if two series are given. It will transform the two series into one. - This allows the WassersteinScorer to compute the Wasserstein distance on the original series or on its - residuals (difference between the prediction and the original series). - Must be one of "abs_diff" and "diff" (defined in ``_diff_series()``). - Default: "abs_diff" component_wise - Boolean value indicating if the score needs to be computed for each component independently (True) - or by concatenating the component in the considered window to compute one score (False). - Default: False - + Boolean value indicating if the score needs to be computed for each component independently (`True`) + or by concatenating the component in the considered window to compute one score (`False`). + Default: `False`. + window_agg + Boolean indicating whether the anomaly score for each time step is computed by + averaging the anomaly scores for all windows this point is included in. + If `False`, the anomaly score for each point is the anomaly score of its trailing window. + Default: `True`. + diff_fn + The differencing function to use to transform the predicted and actual series into one series. + The scorer is then applied to this series. Must be one of Darts per-time-step metrics (e.g., + :func:`~darts.metrics.metrics.ae` for the absolute difference, :func:`~darts.metrics.metrics.err` for the + difference, :func:`~darts.metrics.metrics.se` for the squared difference, ...). + By default, uses the absolute difference (:func:`~darts.metrics.metrics.ae`). """ # TODO: @@ -108,84 +116,36 @@ def __init__( # only one sample # - check if there is an equivalent Wasserstein distance for d-D distributions (currently only accepts 1D) - if type(window) is int: + if type(window) is int: # noqa: E721 if window > 0 and window < 10: logger.warning( f"The `window` parameter WassersteinScorer is smaller than 10 (w={window})." + " The value represents the window length rolled on the series given as" - + " input in the ``score`` function. At each position, the w values will" + + " input in the `score` function. At each position, the w values will" + " constitute a subset, and the Wasserstein distance between the subset" + " and the train distribution will be computed. To better represent the" + " constituted test distribution, the window parameter should be larger" + " than 10." ) - - raise_if_not( - type(component_wise) is bool, - f"Parameter `component_wise` must be Boolean, found type: {type(component_wise)}.", - ) - self.component_wise = component_wise - super().__init__( - univariate_scorer=(not component_wise), window=window, diff_fn=diff_fn + is_univariate=(not component_wise), + window=window, + window_agg=window_agg, + diff_fn=diff_fn, ) def __str__(self): return "WassersteinScorer" - def _fit_core( - self, - list_series: Sequence[TimeSeries], - ): - self.training_data = np.concatenate( - [s.all_values(copy=False) for s in list_series] - ).squeeze(-1) - - if not self.component_wise: - self.training_data = self.training_data.flatten() - - def _score_core(self, series: TimeSeries) -> TimeSeries: - raise_if_not( - self.width_trained_on == series.width, - "Input must have the same number of components as the data used for" - + " training the Wasserstein model, found number of components equal" - + f" to {series.width} and expected {self.width_trained_on}.", + def _fit_core(self, series: Sequence[TimeSeries], *args, **kwargs): + """The training values are considered as the scorer model""" + self.model = np.concatenate([s.all_values(copy=False) for s in series]).squeeze( + -1 ) - np_series = series.all_values(copy=False) - np_anomaly_score = [] + if self.is_univariate or series[0].width == 1: + self.model = self.model.flatten() - if not self.component_wise: - np_anomaly_score = [ - wasserstein_distance(self.training_data, window_samples) - for window_samples in sliding_window_view( - np_series, window_shape=self.window, axis=0 - ) - .transpose(0, 3, 1, 2) - .reshape(-1, self.window * series.width) - ] - - return TimeSeries.from_times_and_values( - series.time_index[self.window - 1 :], np_anomaly_score - ) - - else: - for component_idx in range(self.width_trained_on): - score = [ - wasserstein_distance( - self.training_data[component_idx, :], window_samples - ) - for window_samples in sliding_window_view( - np_series[:, component_idx], - window_shape=self.window, - axis=0, - ) - .transpose(0, 2, 1) - .reshape(-1, self.window) - ] - - np_anomaly_score.append(score) - - return TimeSeries.from_times_and_values( - series.time_index[self.window - 1 :], list(zip(*np_anomaly_score)) - ) + def _model_score_method(self, model, data: np.ndarray) -> np.ndarray: + """Wrapper around model inference method""" + return [wasserstein_distance(model, window_samples) for window_samples in data] diff --git a/darts/ad/utils.py b/darts/ad/utils.py index 507178c5fb..4395afdfeb 100644 --- a/darts/ad/utils.py +++ b/darts/ad/utils.py @@ -2,18 +2,23 @@ Utils for Anomaly Detection --------------------------- -Common functions used by anomaly_model.py, scorers.py, aggregators.py and detectors.py +Common functions used throughout the Anomaly Detection module. """ # TODO: -# - change structure of eval_accuracy_from_scores and eval_accuracy_from_binary_prediction (a lot of repeated code) # - migrate metrics function to darts.metric # - check error message # - create a zoom option on anomalies for a show function -# - add an option visualize: "by window", "unique", "together" +# - add an option to visualize: "by window", "unique", "together" # - create a normalize option in plot function (norm every anomaly score btw 1 and 0) -> to be seen on the same plot -from typing import Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Callable, Optional, Union + +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal import matplotlib.pyplot as plt import numpy as np @@ -27,184 +32,175 @@ ) from darts import TimeSeries -from darts.logging import get_logger, raise_if, raise_if_not +from darts.logging import get_logger, raise_log +from darts.utils.ts_utils import series2seq logger = get_logger(__name__) -def _assert_binary(series: TimeSeries, name_series: str): - """Checks if series is a binary timeseries (1 and 0)" - - Parameters - ---------- - series - series to check for. - name_series - name str of the series. - """ - - raise_if_not( - np.array_equal( - series.values(copy=False), - series.values(copy=False).astype(bool), - ), - f"Input series {name_series} must be a binary time series.", - ) - - -def eval_accuracy_from_scores( - actual_anomalies: Union[TimeSeries, Sequence[TimeSeries]], - anomaly_score: Union[TimeSeries, Sequence[TimeSeries]], +def eval_metric_from_scores( + anomalies: Union[TimeSeries, Sequence[TimeSeries]], + pred_scores: Union[TimeSeries, Sequence[TimeSeries]], window: Union[int, Sequence[int]] = 1, - metric: str = "AUC_ROC", + metric: Literal["AUC_ROC", "AUC_PR"] = "AUC_ROC", ) -> Union[float, Sequence[float], Sequence[Sequence[float]]]: - """Scores the results against true anomalies. + """Computes a score/metric between anomaly scores against true anomalies. - `actual_anomalies` and `anomaly_score` must have the same shape. - `actual_anomalies` must be binary and have values belonging to the two classes (0 and 1). + `anomalies` and `pred_scores` must have the same shape. + `anomalies` must be binary and have values belonging to the two classes (0 and 1). - If one series is given for `actual_anomalies` and `anomaly_score` contains more than - one series, the function will consider `actual_anomalies` as the ground truth anomalies for - all scores in `anomaly_score`. + If one series is given for `anomalies` and `pred_scores` contains more than + one series, the function will consider `anomalies` as the ground truth anomalies for + all scores in `pred_scores`. Parameters ---------- - actual_anomalies - The ground truth of the anomalies (1 if it is an anomaly and 0 if not). - anomaly_score - Series indicating how anomoulous each window of size w is. + anomalies + The (sequence of) ground truth binary anomaly series (`1` if it is an anomaly and `0` if not). + pred_scores + The (sequence of) of estimated anomaly score series indicating how anomalous each window of size w is. window - Integer value indicating the number of past samples each point represents - in the anomaly_score. The parameter will be used by the function - ``_window_adjustment_anomalies()`` to transform actual_anomalies. - If a list is given. the length must match the number of series in anomaly_score - and actual_anomalies. If only one window is given, the value will be used for every - series in anomaly_score and actual_anomalies. + Integer value indicating the number of past samples each point represents in the `pred_scores`. + The parameter will be used to transform `anomalies`. + If a list of integers, the length must match the number of series in `pred_scores`. + If an integer, the value will be used for every series in `pred_scores` and `anomalies`. metric - Optionally, Scoring function to use. Must be one of "AUC_ROC" and "AUC_PR". - Default: "AUC_ROC" + The name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Default: "AUC_ROC". Returns ------- - Union[float, Sequence[float], Sequence[Sequence[float]]] - Score of the anomalies score prediction - * ``float`` if `anomaly_score` is a univariate series (dimension=1). - * ``Sequence[float]`` - - * if `anomaly_score` is a multivariate series (dimension>1), - returns one value per dimension. - * if `anomaly_score` is a sequence of univariate series, returns one - value per series - * ``Sequence[Sequence[float]]`` if `anomaly_score` is a sequence of - multivariate series. Outer Sequence is over the sequence input, and the inner - Sequence is over the dimensions of each element in the sequence input. + float + A single score/metric for univariate `pred_scores` series (with only one component/column). + Sequence[float] + A sequence (list) of scores for: + + - multivariate `pred_scores` series (multiple components). Gives a score for each component. + - a sequence (list) of univariate `pred_scores` series. Gives a score for each series. + Sequence[Sequence[float]] + A sequence of sequences of scores for a sequence of multivariate `pred_scores` series. + Gives a score for each series (outer sequence) and component (inner sequence). """ - - raise_if_not( - metric in {"AUC_ROC", "AUC_PR"}, - "Argument `metric` must be one of 'AUC_ROC', 'AUC_PR'", + return _eval_metric( + anomalies=anomalies, + pred_series=pred_scores, + window=window, + metric=metric, + pred_is_binary=False, ) - metric_fn = roc_auc_score if metric == "AUC_ROC" else average_precision_score - list_actual_anomalies, list_anomaly_scores, list_window = ( - _to_list(actual_anomalies), - _to_list(anomaly_score), - _to_list(window), - ) - - if len(list_actual_anomalies) == 1 and len(list_anomaly_scores) > 1: - list_actual_anomalies = list_actual_anomalies * len(list_anomaly_scores) - - _assert_same_length(list_actual_anomalies, list_anomaly_scores) - - if len(list_window) == 1: - list_window = list_window * len(actual_anomalies) - else: - raise_if_not( - len(list_window) == len(list_actual_anomalies), - "The list of windows must be the same length as the list of `anomaly_score` and" - + " `actual_anomalies`. There must be one window value for each series." - + f" Found length {len(list_window)}, expected {len(list_actual_anomalies)}.", - ) - sol = [] - for idx, (s_anomalies, s_score) in enumerate( - zip(list_actual_anomalies, list_anomaly_scores) - ): +def eval_metric_from_binary_prediction( + anomalies: Union[TimeSeries, Sequence[TimeSeries]], + pred_anomalies: Union[TimeSeries, Sequence[TimeSeries]], + window: Union[int, Sequence[int]] = 1, + metric: Literal["recall", "precision", "f1", "accuracy"] = "recall", +) -> Union[float, Sequence[float], Sequence[Sequence[float]]]: + """Computes a score/metric between predicted anomalies against true anomalies. - _assert_binary(s_anomalies, "actual_anomalies") + `pred_anomalies` and `anomalies` must have: - sol.append( - _eval_accuracy_from_data( - s_anomalies, s_score, list_window[idx], metric_fn, metric - ) - ) + - identical dimensions (number of time steps and number of components/columns), + - binary values belonging to the two classes (`1` if it is an anomaly and `0` if not) - if len(sol) == 1 and not isinstance(anomaly_score, Sequence): - return sol[0] - else: - return sol + If one series is given for `anomalies` and `pred_anomalies` contains more than + one series, the function will consider `anomalies` as the true anomalies for + all scores in `pred_scores`. + Parameters + ---------- + anomalies + The (sequence of) ground truth binary anomaly series (`1` if it is an anomaly and `0` if not). + pred_anomalies + The (sequence of) predicted binary anomaly series. + window + Integer value indicating the number of past samples each point represents in the `pred_scores`. + The parameter will be used to transform `anomalies`. + If a list of integers, the length must match the number of series in `pred_scores`. + If an integer, the value will be used for every series in `pred_scores` and `anomalies`. + metric + The name of the metric function to use. Must be one of "recall", "precision", "f1", and "accuracy". + Default: "recall". -def eval_accuracy_from_binary_prediction( - actual_anomalies: Union[TimeSeries, Sequence[TimeSeries]], - binary_pred_anomalies: Union[TimeSeries, Sequence[TimeSeries]], - window: Union[int, Sequence[int]] = 1, - metric: str = "recall", -) -> Union[float, Sequence[float], Sequence[Sequence[float]]]: - """Score the results against true anomalies. + Returns + ------- + float + A single score for univariate `pred_anomalies` series (with only one component/column). + Sequence[float] + A sequence (list) of scores for: + + - multivariate `pred_anomalies` series (multiple components). Gives a score for each component. + - a sequence (list) of univariate `pred_anomalies` series. Gives a score for each series. + Sequence[Sequence[float]] + A sequence of sequences of scores for a sequence of multivariate `pred_anomalies` series. + Gives a score for each series (outer sequence) and component (inner sequence). + """ + return _eval_metric( + anomalies=anomalies, + pred_series=pred_anomalies, + window=window, + metric=metric, + pred_is_binary=True, + ) - checks that `pred_anomalies` and `actual_anomalies` are the same: - - type, - - length, - - number of components - - binary and has values belonging to the two classes (1 and 0) - If one series is given for `actual_anomalies` and `pred_anomalies` contains more than - one series, the function will consider `actual_anomalies` as the true anomalies for - all scores in `anomaly_score`. +def _eval_metric( + anomalies: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + window: Union[int, Sequence[int]], + metric: Literal["AUC_ROC", "AUC_PR", "recall", "precision", "f1", "accuracy"], + pred_is_binary: bool, +): + """Computes a score/metric between anomaly scores or binary predicted anomalies against true + anomalies. Parameters ---------- - actual_anomalies - The (sequence of) ground truth of the anomalies (1 if it is an anomaly and 0 if not) - binary_pred_anomalies - Anomaly predictions. + anomalies + The (sequence of) ground truth binary anomaly series (`1` if it is an anomaly and `0` if not). + pred_series + The (sequence of) anomaly scores or predicted binary anomaly series. window - Integer value indicating the number of past samples each point represents - in the pred_anomalies. The parameter will be used to transform actual_anomalies. - If a list is given. the length must match the number of series in pred_anomalies - and actual_anomalies. If only one window is given, the value will be used for every - series in pred_anomalies and actual_anomalies. + Integer value indicating the number of past samples each point represents in the `pred_scores`. + The parameter will be used to transform `anomalies`. + If a list of integers, the length must match the number of series in `pred_scores`. + If an integer, the value will be used for every series in `pred_scores` and `anomalies`. metric - Optionally, Scoring function to use. Must be one of "recall", "precision", - "f1", and "accuracy". - Default: "recall" + The name of the scoring function to use. Must be one of "recall", "precision", + "f1", and "accuracy" if `pred_is_binary` is `True`. Otherwise, must be one of "AUC_ROC", "AUC_PR". + pred_is_binary + Whether `pred_series` refers predicted binary anomalies or anomaly scores. Returns ------- - Union[float, Sequence[float], Sequence[Sequence[float]]] - Score of the anomalies prediction - - * ``float`` if `binary_pred_anomalies` is a univariate series (dimension=1). - * ``Sequence[float]`` - - * if `binary_pred_anomalies` is a multivariate series (dimension>1), - returns one value per dimension. - * if `binary_pred_anomalies` is a sequence of univariate series, returns one - value per series - * ``Sequence[Sequence[float]]`` if `binary_pred_anomalies` is a sequence of - multivariate series. Outer Sequence is over the sequence input, and the inner - Sequence is over the dimensions of each element in the sequence input. + float + A single score for univariate `pred_series` series (with only one component/column). + Sequence[float] + A sequence (list) of scores for: + + - multivariate `pred_series` series (multiple components). Gives a score for each component. + - a sequence (list) of univariate `pred_series` series. Gives a score for each series. + Sequence[Sequence[float]] + A sequence of sequences of scores for a sequence of multivariate `pred_series` series. + Gives a score for each series (outer sequence) and component (inner sequence). """ - - raise_if_not( - metric in {"recall", "precision", "f1", "accuracy"}, - "Argument `metric` must be one of 'recall', 'precision', " - "'f1' and 'accuracy'.", + metrics_exp = ( + {"recall", "precision", "f1", "accuracy"} + if pred_is_binary + else {"AUC_ROC", "AUC_PR"} ) + if metric not in metrics_exp: + raise_log( + ValueError(f"Argument `metric` must be one of {metrics_exp}"), + logger=logger, + ) - if metric == "recall": + if metric == "AUC_ROC": + metric_fn = roc_auc_score + elif metric == "AUC_PR": + metric_fn = average_precision_score + elif metric == "recall": metric_fn = recall_score elif metric == "precision": metric_fn = precision_score @@ -213,173 +209,339 @@ def eval_accuracy_from_binary_prediction( else: metric_fn = accuracy_score - list_actual_anomalies, list_binary_pred_anomalies, list_window = ( - _to_list(actual_anomalies), - _to_list(binary_pred_anomalies), - _to_list(window), + called_with_single_series = isinstance(pred_series, TimeSeries) + anomalies = series2seq(anomalies) + pred_series = series2seq(pred_series) + window = [window] if not isinstance(window, Sequence) else window + + if len(anomalies) == 1 and len(pred_series) > 1: + anomalies = anomalies * len(pred_series) + + name = "anomalies" + pred_name = "pred_anomalies" if pred_is_binary else "pred_scores" + _assert_same_length( + anomalies, + pred_series, + name, + pred_name, ) - if len(list_actual_anomalies) == 1 and len(list_binary_pred_anomalies) > 1: - list_actual_anomalies = list_actual_anomalies * len(list_binary_pred_anomalies) - - _assert_same_length(list_actual_anomalies, list_binary_pred_anomalies) - - if len(list_window) == 1: - list_window = list_window * len(actual_anomalies) + if len(window) == 1: + window = window * len(anomalies) else: - raise_if_not( - len(list_window) == len(list_actual_anomalies), - "The list of windows must be the same length as the list of `pred_anomalies` and" - + " `actual_anomalies`. There must be one window value for each series." - + f" Found length {len(list_window)}, expected {len(list_actual_anomalies)}.", - ) + if len(window) != len(anomalies): + raise_log( + ValueError( + f"The list of windows must be the same length as the list of `{pred_name}` and " + f"`{name}`. There must be one window value for each series. " + f"Found length {len(window)}, expected {len(anomalies)}." + ), + logger=logger, + ) sol = [] - for idx, (s_anomalies, s_pred) in enumerate( - zip(list_actual_anomalies, list_binary_pred_anomalies) - ): + for s_anomalies, s_pred, s_window in zip(anomalies, pred_series, window): + _assert_timeseries(s_pred, name=pred_name) + _assert_timeseries(s_anomalies, name=name) + _assert_binary(s_anomalies, name) + if pred_is_binary: + _assert_binary(s_pred, pred_name) + + # if s_window > 1, the anomalies will be adjusted so that it can be compared timewise with s_pred + s_anomalies = _max_pooling(s_anomalies, s_window) + + _sanity_check_two_series(s_pred, s_anomalies, pred_name, name) + + s_pred_vals = s_pred.slice_intersect_values(s_anomalies, copy=False) + s_anomalies_vals = s_anomalies.slice_intersect_values(s_pred, copy=False) + + if not len(s_pred_vals) == len(s_anomalies_vals): + raise_log( + ValueError( + f"The two time series `{pred_name}` and `{name}` " + f"must have at least a partially overlapping time index." + ), + logger=logger, + ) - _assert_binary(s_pred, "pred_anomalies") - _assert_binary(s_anomalies, "actual_anomalies") + if not pred_is_binary: # `pred_series` is an anomaly score + nr_anomalies_per_component = s_anomalies_vals.sum(axis=0).flatten() - sol.append( - _eval_accuracy_from_data( - s_anomalies, s_pred, list_window[idx], metric_fn, metric + if nr_anomalies_per_component.min() == 0: + raise_log( + ValueError( + f"`{name}` does not contain anomalies. {metric} cannot be computed." + ), + logger=logger, + ) + if nr_anomalies_per_component.max() == len(s_anomalies_vals): + add_txt = ( + "" + if s_window <= 1 + else f" Consider decreasing the window size (window={s_window})" + ) + raise_log( + ValueError( + f"`{name}` only contains anomalies. {metric} cannot be computed." + + add_txt + ), + logger=logger, + ) + + # TODO: could we vectorize this? + metrics = [] + for component_idx in range(s_pred.width): + metrics.append( + metric_fn( + s_anomalies_vals[:, component_idx], + s_pred_vals[:, component_idx], + ) ) - ) + sol.append(metrics if len(metrics) > 1 else metrics[0]) - if len(sol) == 1 and not isinstance(binary_pred_anomalies, Sequence): - return sol[0] - else: - return sol + return sol[0] if called_with_single_series else sol + + +def show_anomalies_from_scores( + series: TimeSeries, + anomalies: TimeSeries = None, + pred_series: TimeSeries = None, + pred_scores: Union[TimeSeries, Sequence[TimeSeries]] = None, + window: Union[int, Sequence[int]] = 1, + names_of_scorers: Union[str, Sequence[str]] = None, + title: str = None, + metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None, + component_wise: bool = False, +): + """Plot the results generated by an anomaly model. + The plot will be composed of the following: + - the actual series itself with the output of the model (if given) + - the anomaly score of each scorer. The scorer with different windows will be separated. + - the actual anomalies, if given. -def _eval_accuracy_from_data( - s_anomalies: TimeSeries, - s_data: TimeSeries, - window: int, - metric_fn, - metric_name: str, -) -> Union[float, Sequence[float]]: - """Internal function for: - - ``eval_accuracy_from_binary_prediction()`` - - ``eval_accuracy_from_scores()`` + If `pred_series` is stochastic (i.e., if it has multiple samples), the function will plot: + - the mean per timestamp + - the quantile 0.95 for an upper bound + - the quantile 0.05 for a lower bound - Score the results against true anomalies. + Possible to: + - add a title to the figure with the parameter `title` + - give personalized names for the scorers with `names_of_scorers` + - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), if the actual anomalies is given Parameters ---------- - actual_anomalies - The ground truth of the anomalies (1 if it is an anomaly and 0 if not) - s_data - series prediction + series + The actual series to visualize anomalies from. + anomalies + The ground truth of the anomalies (1 if it is an anomaly and 0 if not). + pred_series + Output of the model given as input the `series` (can be stochastic). + pred_scores + Output of the scorers given the output of the model and `series`. window - Integer value indicating the number of past samples each point represents - in the anomaly_score. The parameter will be used by the function - ``_window_adjustment_anomalies()`` to transform s_anomalies. - metric_fn - Function to use. Can be "average_precision_score", "roc_auc_score", "accuracy_score", - "f1_score", "precision_score" and "recall_score". - metric_name - Name str of the function to use. Can be "AUC_PR", "AUC_ROC", "accuracy", - "f1", "precision" and "recall". - - Returns - ------- - Union[float, Sequence[float]] - Score of the anomalies prediction - - float -> if `s_data` is a univariate series (dimension=1). - - Sequence[float] -> if `s_data` is a multivariate series (dimension>1), - returns one value per dimension. + Window parameter for each anomaly scores. + Default: 1. If a list of anomaly scores is given, the same default window will be used for every score. + names_of_scorers + Name of the scores. Must be a list of length equal to the number of scorers in the anomaly_model. + Only effective when `pred_scores` is not `None`. + title + Title of the figure + metric + Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the + Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores). + Only effective when `pred_scores` is not `None`. + Default: "AUC_ROC". + component_wise + If True, will separately plot each component in case of multivariate anomaly detection. """ + series = _check_input( + series, + name="series", + num_series_expected=1, + check_multivariate=component_wise, + )[0] - _assert_timeseries(s_data, "Prediction series input") - _assert_timeseries(s_anomalies, "actual_anomalies input") - - # if window > 1, the anomalies will be adjusted so that it can be compared timewise with s_data - s_anomalies = _max_pooling(s_anomalies, window) - - _sanity_check_two_series(s_data, s_anomalies) + if title is None and pred_scores is not None: + title = "Anomaly results" - s_data, s_anomalies = _intersect(s_data, s_anomalies) + nbr_plots = 1 + if anomalies is not None: + nbr_plots = nbr_plots + 1 + elif metric is not None: + raise_log( + ValueError("`anomalies` must be given in order to calculate a metric."), + logger=logger, + ) - if metric_name == "AUC_ROC" or metric_name == "AUC_PR": + pred_scores = series2seq(pred_scores) + if pred_scores is not None: + if names_of_scorers is not None: + names_of_scorers = ( + [names_of_scorers] + if isinstance(names_of_scorers, str) + else names_of_scorers + ) + if len(names_of_scorers) != len(pred_scores): + raise_log( + ValueError( + f"The number of names in `names_of_scorers` must match the " + f"number of anomaly score given as input, found " + f"{len(names_of_scorers)} and expected {len(pred_scores)}." + ), + logger=logger, + ) - nr_anomalies_per_component = ( - s_anomalies.sum(axis=0).values(copy=False).flatten() - ) + window = [window] if isinstance(window, int) else window + if not all([w > 0 for w in window]): + raise_log( + ValueError( + "Parameter `window` must be a positive integer, " + "or a sequence of positive integers." + ), + logger=logger, + ) + window = window if len(window) > 1 else window * len(pred_scores) + if len(window) != len(pred_scores): + raise_log( + ValueError( + f"The number of window in `window` must match the " + f"number of anomaly score given as input. One window " + f"value for each series. Found length {len(window)}, " + f"and expected {len(pred_scores)}." + ), + logger=logger, + ) - raise_if( - nr_anomalies_per_component.min() == 0, - f"`actual_anomalies` does not contain anomalies. {metric_name} cannot be computed.", - ) + if not all([w < len(s) for (w, s) in zip(window, pred_scores)]): + raise_log( + ValueError( + "Parameter `window` must be an integer or sequence of integers " + "with value(s) smaller than the length of the corresponding series " + "in `pred_scores`." + ), + logger=logger, + ) - raise_if( - nr_anomalies_per_component.max() == len(s_anomalies), - f"`actual_anomalies` only contains anomalies. {metric_name} cannot be computed." - + ["", f" Consider decreasing the window size (window={window})"][ - window > 1 - ], - ) + nbr_plots += len(set(window)) + + series_width = series.n_components + if pred_series is not None: + pred_series = _check_input( + pred_series, + name="pred_series", + width_expected=series_width, + num_series_expected=1, + check_multivariate=component_wise, + )[0] + + if anomalies is not None and component_wise: + anomalies = _check_input( + anomalies, + name="anomalies", + width_expected=series_width, + num_series_expected=1, + check_binary=True, + check_multivariate=component_wise, + )[0] + + if pred_scores is not None and component_wise: + for pred_score in pred_scores: + _ = _check_input( + pred_score, + name="pred_score", + width_expected=series_width, + num_series_expected=1, + check_multivariate=component_wise, + )[0] + + plots_per_ts = nbr_plots * series_width if component_wise else nbr_plots + height_ratios = ([2] + [1] * (nbr_plots - 1)) * (plots_per_ts // nbr_plots) + height_total = 2 * sum(height_ratios) + fig, axs = plt.subplots( + nrows=plots_per_ts, + figsize=(8, height_total), + sharex=True, + gridspec_kw={"height_ratios": height_ratios}, + ) - # TODO: could we vectorize this? - metrics = [] - for component_idx in range(s_data.width): - metrics.append( - metric_fn( - s_anomalies.all_values(copy=False)[:, component_idx], - s_data.all_values(copy=False)[:, component_idx], + for i in range(series_width if component_wise else 1): + if component_wise: + series_ = series[series.components[i]] + anomalies_ = ( + anomalies[anomalies.components[i]] if anomalies is not None else None + ) + pred_series_ = ( + pred_series[pred_series.components[i]] + if pred_series is not None + else None ) + pred_scores_ = ( + [pc[pc.components[i]] for pc in pred_scores] + if pred_scores is not None + else None + ) + else: + series_ = series + anomalies_ = anomalies + pred_series_ = pred_series + pred_scores_ = pred_scores + + _plot_series_and_anomalies( + series=series_, + anomalies=anomalies_, + pred_series=pred_series_, + pred_scores=pred_scores_, + window=window, + names_of_scorers=names_of_scorers, + metric=metric, + axs=axs, + index_ax=i * nbr_plots, ) + # make title fit nicely on plot + title_height = 0.1 + title_y = 1 - title_height / height_total - if len(metrics) == 1: - return metrics[0] - else: - return metrics + fig.suptitle(title, y=title_y) + fig.tight_layout() -def _intersect( - series_1: TimeSeries, - series_2: TimeSeries, -) -> Tuple[TimeSeries, TimeSeries]: - """Returns the sub-series of series_1 and of series_2 that share the same time index. - (Intersection in time of the two time series) +def _assert_binary(series: TimeSeries, name: str): + """Checks if series is a binary timeseries (1 and 0)" Parameters ---------- - series_1 - 1st time series - series_2: - 2nd time series - - Returns - ------- - Tuple[TimeSeries, TimeSeries] + series + series to check for. + name + name of the series. """ - new_series_1 = series_1.slice_intersect(series_2) - raise_if( - len(new_series_1) == 0, - "Time intersection between the two series must be non empty.", - ) - - return new_series_1, series_2.slice_intersect(series_1) + vals = series.values(copy=False) + if not np.array_equal(vals, vals.astype(bool)): + raise_log( + ValueError(f"Input series `{name}` must have binary values only."), + logger=logger, + ) -def _assert_timeseries(series: TimeSeries, message: str = None): +def _assert_timeseries(series: TimeSeries, name: str = "series"): """Checks if given input is of type Darts TimeSeries""" - - raise_if_not( - isinstance(series, TimeSeries), - "{} must be type darts.timeseries.TimeSeries and not {}.".format( - message if message is not None else "Series input", type(series) - ), - ) + if not isinstance(series, TimeSeries): + raise_log( + ValueError( + f"all series in `{name}` must be `TimeSeries`. Received {type(series)}." + ), + logger=logger, + ) def _sanity_check_two_series( series_1: TimeSeries, series_2: TimeSeries, + name_series_1: str, + name_series_2: str, ): """Performs sanity check on the two given inputs @@ -396,21 +558,18 @@ def _sanity_check_two_series( 2nd time series """ - _assert_timeseries(series_1) - _assert_timeseries(series_2) + _assert_timeseries(series_1, name=name_series_1) + _assert_timeseries(series_2, name=name_series_2) # check if the two inputs time series have the same number of components - raise_if_not( - series_1.width == series_2.width, - "Series must have the same number of components," - + f" found {series_1.width} and {series_2.width}.", - ) - - # check if the time intersection between the two inputs time series is not empty - raise_if_not( - len(series_1.time_index.intersection(series_2.time_index)) > 0, - "Series must have a non-empty intersection timestamps.", - ) + if series_1.width != series_2.width: + raise_log( + ValueError( + f"The series from `{name_series_1}` and `{name_series_2}` must have the " + f"same number of components, found {series_1.width} and {series_2.width}." + ), + logger=logger, + ) def _max_pooling(series: TimeSeries, window: int) -> TimeSeries: @@ -432,276 +591,244 @@ def _max_pooling(series: TimeSeries, window: int) -> TimeSeries: ------- Binary TimeSeries """ - - raise_if_not( - isinstance(window, int), - f"Parameter `window` must be of type int, found {type(window)}.", - ) - - raise_if_not( - window > 0, - f"Parameter `window` must be stricly greater than 0, found size {window}.", - ) - - raise_if_not( - window < len(series), - "Parameter `window` must be smaller than the length of the input series, " - + f" found window size {(window)}, and max size {len(series)}.", - ) + if window <= 0: + raise_log( + ValueError( + f"Parameter `window` must be strictly greater than 0, found size {window}." + ), + logger=logger, + ) + if window >= len(series): + raise_log( + ValueError( + f"Parameter `window` must be smaller than the length of the " + f"input series, found window size {window}, and max size {len(series)}." + ), + logger=logger, + ) if window == 1: # the process results in replacing every value by itself -> return directly the series return series - else: - return series.window_transform( - transforms={ - "window": window, - "function": "max", - "mode": "rolling", - "min_periods": window, - }, - treat_na="dropna", - ) - - -def _to_list(series: Union[TimeSeries, Sequence[TimeSeries]]) -> Sequence[TimeSeries]: - """If not already, it converts the input into a sequence - - Parameters - ---------- - series - single TimeSeries, or a sequence of TimeSeries - Returns - ------- - Sequence[TimeSeries] - """ - - return [series] if not isinstance(series, Sequence) else series + return series.window_transform( + transforms={ + "window": window, + "function": "max", + "mode": "rolling", + "min_periods": window, + }, + treat_na="dropna", + ) def _assert_same_length( list_series_1: Sequence[TimeSeries], list_series_2: Sequence[TimeSeries], + name_series_1: str, + name_series_2: str, ): """Checks if the two sequences contain the same number of TimeSeries.""" - raise_if_not( - len(list_series_1) == len(list_series_2), - "Sequences of series must be of the same length, found length:" - + f" {len(list_series_1)} and {len(list_series_2)}.", - ) - + if len(list_series_1) != len(list_series_2): + raise_log( + ValueError( + f"Number of `{name_series_2}` must match the number of given " + f"`{name_series_1}`, found length {len(list_series_2)} and " + f"expected {len(list_series_1)}." + ), + logger=logger, + ) -def show_anomalies_from_scores( - series: TimeSeries, - model_output: TimeSeries = None, - anomaly_scores: Union[TimeSeries, Sequence[TimeSeries]] = None, - window: Union[int, Sequence[int]] = 1, - names_of_scorers: Union[str, Sequence[str]] = None, - actual_anomalies: TimeSeries = None, - title: str = None, - metric: str = None, -): - """Plot the results generated by an anomaly model. - The plot will be composed of the following: - - the series itself with the output of the model (if given) - - the anomaly score of each scorer. The scorer with different windows will be separated. - - the actual anomalies, if given. - - If model_output is stochastic (i.e., if it has multiple samples), the function will plot: - - the mean per timestamp - - the quantile 0.95 for an upper bound - - the quantile 0.05 for a lower bound +def _plot_series(series, ax_id, linewidth, label_name, **kwargs): + """Internal function called by `show_anomalies_from_scores()` - Possible to: - - add a title to the figure with the parameter `title` - - give personalized names for the scorers with `names_of_scorers` - - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), if the actual anomalies is given + Plot the series on the given axes ax_id. Parameters ---------- series - The series to visualize anomalies from. - model_output - Output of the model given as input the series (can be stochastic). - anomaly_scores - Output of the scorers given the output of the model and the series. - window - Window parameter for each anomaly scores. - Default: 1. If a list of anomaly scores is given, the same default window will be used for every score. - names_of_scorers - Name of the scores. Must be a list of length equal to the number of scorers in the anomaly_model. - actual_anomalies - The ground truth of the anomalies (1 if it is an anomaly and 0 if not) - title - Title of the figure - metric - Optionally, Scoring function to use. Must be one of "AUC_ROC" and "AUC_PR". - Default: "AUC_ROC" + The series to plot. + ax_id + The axis the series will be plotted on. + linewidth + Thickness of the line. + label_name + Name that will appear in the legend. """ + for i, c in enumerate(series._xa.component[:10]): + comp = series._xa.sel(component=c) - raise_if_not( - isinstance(series, TimeSeries), - f"Input `series` must be of type TimeSeries, found {type(series)}.", - ) - - if title is None: - if anomaly_scores is not None: - title = "Anomaly results" - else: - raise_if_not( - isinstance(title, str), - f"Input `title` must be of type str, found {type(title)}.", - ) - - nbr_plots = 1 - - if model_output is not None: - raise_if_not( - isinstance(model_output, TimeSeries), - f"Input `model_output` must be of type TimeSeries, found {type(model_output)}.", - ) - - if actual_anomalies is not None: - raise_if_not( - isinstance(actual_anomalies, TimeSeries), - f"Input `actual_anomalies` must be of type TimeSeries, found {type(actual_anomalies)}.", - ) + if comp.sample.size > 1: + central_series = comp.mean(dim="sample") + low_series = comp.quantile(q=0.05, dim="sample") + high_series = comp.quantile(q=0.95, dim="sample") + else: + central_series = comp - nbr_plots = nbr_plots + 1 - else: - raise_if_not( - metric is None, - "`actual_anomalies` must be given in order to calculate a metric.", + label_to_use = ( + (label_name + ("_" + str(i) if len(series.components) > 1 else "")) + if label_name != "" + else "" + str(str(c.values)) ) - if anomaly_scores is not None: + central_series.plot(ax=ax_id, linewidth=linewidth, label=label_to_use, **kwargs) - if isinstance(anomaly_scores, Sequence): - for idx, s in enumerate(anomaly_scores): - raise_if_not( - isinstance(s, TimeSeries), - f"Elements of anomaly_scores must be of type TimeSeries, found {type(s)} at index {idx}.", - ) - else: - raise_if_not( - isinstance(anomaly_scores, TimeSeries), - f"Input `anomaly_scores` must be of type TimeSeries or Sequence, found {type(actual_anomalies)}.", + if comp.sample.size > 1: + ax_id.fill_between( + series.time_index, low_series, high_series, alpha=0.25, **kwargs ) - anomaly_scores = [anomaly_scores] - if names_of_scorers is not None: - if isinstance(names_of_scorers, str): - names_of_scorers = [names_of_scorers] - elif isinstance(names_of_scorers, Sequence): - for idx, name in enumerate(names_of_scorers): - raise_if_not( - isinstance(name, str), - f"Elements of names_of_scorers must be of type str, found {type(name)} at index {idx}.", - ) - else: - raise ValueError( - f"Input `names_of_scorers` must be of type str or Sequence, found {type(names_of_scorers)}." - ) +def _check_input( + series: Union[TimeSeries, Sequence[TimeSeries]], + name: str, + width_expected: Optional[int] = None, + check_deterministic: bool = False, + check_binary: bool = False, + check_multivariate: bool = False, + num_series_expected: Optional[int] = None, + extra_checks: Optional[Callable] = None, +): + """ + Input `series` checks used for Aggregators, Detectors, ... - raise_if_not( - len(names_of_scorers) == len(anomaly_scores), - "The number of names in `names_of_scorers` must match the number of anomaly score " - + f"given as input, found {len(names_of_scorers)} and expected {len(anomaly_scores)}.", - ) + - `series` must be (sequence of) series with length (`num_series_expected`) where each series must: + - have width `width_expected` if it is not `None` + - be deterministic if `check_deterministic=True` + - be binary if `check_binary=True` + - be multivariate if `check_multivariate=True` - if isinstance(window, int): - window = [window] - elif isinstance(window, Sequence): - for idx, w in enumerate(window): - raise_if_not( - isinstance(w, int), - f"Every window must be of type int, found {type(w)} at index {idx}.", - ) - else: - raise ValueError( - f"Input `window` must be of type int or Sequence, found {type(window)}." - ) - - raise_if_not( - all([w > 0 for w in window]), - "All windows must be positive integer.", - ) + By default, all checks except the `TimeSeries` check are disabled. - if len(window) == 1: - window = window * len(anomaly_scores) + Parameters + ---------- + series + A (sequence of) multivariate series. + name + The name of the series. + width_expected + Optionally, the expected number of components/width of each series. + check_multivariate + Whether to check if all series are multivariate. + """ + series = series2seq(series) + if num_series_expected is not None and len(series) != num_series_expected: + if num_series_expected == 1: + err_txt = f"`{name}` must be single `TimeSeries` or a sequence of `TimeSeries` of length `1`." else: - raise_if_not( - len(window) == len(anomaly_scores), - "The number of window in `window` must match the number of anomaly score given as input. One " - + f"window value for each series. Found length {len(window)}, and expected {len(anomaly_scores)}.", - ) - - raise_if_not( - all([w < len(s) for (w, s) in zip(window, anomaly_scores)]), - "All windows must be smaller than the length of their corresponding score.", + err_txt = f"`{name}` must be a sequence of `TimeSeries` of length `{num_series_expected}`." + raise_log( + ValueError(err_txt), + logger=logger, ) - - nbr_plots = nbr_plots + len(set(window)) - else: - if window is not None: - logger.warning( - "The parameter `window` is given, but the input `anomaly_scores` is None." + for s in series: + if not isinstance(s, TimeSeries): + raise_log( + ValueError(f"all series in `{name}` must be of type `TimeSeries`."), + logger=logger, ) - - if names_of_scorers is not None: - logger.warning( - "The parameter `names_of_scorers` is given, but the input `anomaly_scores` is None." + if check_deterministic and not s.is_deterministic: + raise_log( + ValueError( + f"all series in `{name}` must be deterministic (number of samples=1)." + ), + logger=logger, ) - - if metric is not None: - logger.warning( - "The parameter `metric` is given, but the input `anomaly_scores` is None." + if check_binary: + _assert_binary(s, name=name) + if check_multivariate and s.width <= 1: + raise_log( + ValueError(f"all series in `{name}` must be multivariate (width>1)."), + logger=logger, ) + if width_expected is not None and s.width != width_expected: + raise_log( + ValueError( + f"all series in `{name}` must have `{width_expected}` component(s) (width={width_expected})." + ), + logger=logger, + ) + if extra_checks is not None: + extra_checks(s) + return series + + +def _assert_fit_called(fit_called: bool, name: str): + """Checks that `fit_called` is `True`.""" + if not fit_called: + raise_log( + ValueError( + f"The `{name}` has not been fitted yet. Call `{name}.fit()` first." + ), + logger=logger, + ) - fig, axs = plt.subplots( - nbr_plots, - figsize=(8, 4 + 2 * (nbr_plots - 1)), - sharex=True, - gridspec_kw={"height_ratios": [2] + [1] * (nbr_plots - 1)}, - squeeze=False, - ) - - index_ax = 0 - _plot_series(series=series, ax_id=axs[index_ax][0], linewidth=0.5, label_name="") +def _plot_series_and_anomalies( + series: TimeSeries, + anomalies: TimeSeries, + pred_series: TimeSeries, + pred_scores: Sequence[TimeSeries], + window: Sequence[int], + names_of_scorers: Sequence[str], + metric: str, + axs: plt.Axes, + index_ax: int, +): + """Helper function to plot series and anomalies. - if model_output is not None: + Parameters + ---------- + series + The actual series to visualize anomalies from. + anomalies + The ground truth of the anomalies (1 if it is an anomaly and 0 if not). + pred_series + Output of the model given as input the `series` (can be stochastic). + pred_scores + Output of the scorers given the output of the model and `series`. + window + Window parameter for each anomaly scores. + names_of_scorers + Name of the scores. + metric + The name of the metric function to use. + axs + The axes to plot on. + index_ax + The index of the current axis. + """ + _plot_series(series=series, ax_id=axs[index_ax], linewidth=0.5, label_name="") + if pred_series is not None: _plot_series( - series=model_output, - ax_id=axs[index_ax][0], + series=pred_series, + ax_id=axs[index_ax], linewidth=0.5, label_name="model output", ) - axs[index_ax][0].set_title("") - - if actual_anomalies is not None or anomaly_scores is not None: - axs[index_ax][0].set_xlabel("") + axs[index_ax].set_title("") - axs[index_ax][0].legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2) + if anomalies is not None or pred_scores is not None: + axs[index_ax].set_xlabel("") - if anomaly_scores is not None: + axs[index_ax].legend(loc="upper center", bbox_to_anchor=(0.5, 1.1), ncol=2) + if pred_scores is not None: dict_input = {} - for idx, (score, w) in enumerate(zip(anomaly_scores, window)): - + for idx, (score, w) in enumerate(zip(pred_scores, window)): dict_input[idx] = {"series_score": score, "window": w, "name_id": idx} - current_window = window[0] - index_ax = index_ax + 1 - - for elem in sorted(dict_input.items(), key=lambda x: x[1]["window"]): + for index, elem in enumerate( + sorted(dict_input.items(), key=lambda x: x[1]["window"]) + ): + if index == 0: + current_window = elem[1]["window"] + index_ax = index_ax + 1 idx = elem[1]["name_id"] w = elem[1]["window"] @@ -712,9 +839,9 @@ def show_anomalies_from_scores( if metric is not None: value = round( - eval_accuracy_from_scores( - anomaly_score=anomaly_scores[idx], - actual_anomalies=actual_anomalies, + eval_metric_from_scores( + anomalies=anomalies, + pred_scores=pred_scores[idx], window=w, metric=metric, ), @@ -730,77 +857,29 @@ def show_anomalies_from_scores( _plot_series( series=elem[1]["series_score"], - ax_id=axs[index_ax][0], + ax_id=axs[index_ax], linewidth=0.5, label_name=label, ) - axs[index_ax][0].legend( - loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2 - ) - axs[index_ax][0].set_title(f"Window: {str(w)}", loc="left") - axs[index_ax][0].set_title("") - axs[index_ax][0].set_xlabel("") - - if actual_anomalies is not None: + axs[index_ax].legend(loc="upper center", bbox_to_anchor=(0.5, 1.19), ncol=2) + axs[index_ax].set_title(f"Window: {str(w)}", loc="left") + axs[index_ax].set_title("") + axs[index_ax].set_xlabel("") + if anomalies is not None: _plot_series( - series=actual_anomalies, - ax_id=axs[index_ax + 1][0], + series=anomalies, + ax_id=axs[index_ax + 1], linewidth=1, label_name="anomalies", color="red", ) - axs[index_ax + 1][0].set_title("") - axs[index_ax + 1][0].set_ylim([-0.1, 1.1]) - axs[index_ax + 1][0].set_yticks([0, 1]) - axs[index_ax + 1][0].set_yticklabels(["no", "yes"]) - axs[index_ax + 1][0].legend( - loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2 - ) + axs[index_ax + 1].set_title("") + axs[index_ax + 1].set_ylim([-0.1, 1.1]) + axs[index_ax + 1].set_yticks([0, 1]) + axs[index_ax + 1].set_yticklabels(["no", "yes"]) + axs[index_ax + 1].legend(loc="upper center", bbox_to_anchor=(0.5, 1.2), ncol=2) else: - axs[index_ax][0].set_xlabel("timestamp") - - fig.suptitle(title) - - -def _plot_series(series, ax_id, linewidth, label_name, **kwargs): - """Internal function called by ``show_anomalies_from_scores()`` - - Plot the series on the given axes ax_id. - - Parameters - ---------- - series - The series to plot. - ax_id - The axis the series will be ploted on. - linewidth - Thickness of the line. - label_name - Name that will appear in the legend. - """ - - for i, c in enumerate(series._xa.component[:10]): - comp = series._xa.sel(component=c) - - if comp.sample.size > 1: - central_series = comp.mean(dim="sample") - low_series = comp.quantile(q=0.05, dim="sample") - high_series = comp.quantile(q=0.95, dim="sample") - else: - central_series = comp - - label_to_use = ( - (label_name + ("_" + str(i) if len(series.components) > 1 else "")) - if label_name != "" - else "" + str(str(c.values)) - ) - - central_series.plot(ax=ax_id, linewidth=linewidth, label=label_to_use, **kwargs) - - if comp.sample.size > 1: - ax_id.fill_between( - series.time_index, low_series, high_series, alpha=0.25, **kwargs - ) + axs[index_ax].set_xlabel("timestamp") diff --git a/darts/dataprocessing/__init__.py b/darts/dataprocessing/__init__.py index acceace885..06030ccdf7 100644 --- a/darts/dataprocessing/__init__.py +++ b/darts/dataprocessing/__init__.py @@ -3,4 +3,6 @@ --------------- """ -from .pipeline import Pipeline +from darts.dataprocessing.pipeline import Pipeline + +__all__ = ["Pipeline"] diff --git a/darts/dataprocessing/dtw/__init__.py b/darts/dataprocessing/dtw/__init__.py index 163fad72d0..6b90d70e78 100644 --- a/darts/dataprocessing/dtw/__init__.py +++ b/darts/dataprocessing/dtw/__init__.py @@ -3,6 +3,23 @@ -------------------------- """ -from .cost_matrix import CostMatrix -from .dtw import DTWAlignment, dtw -from .window import CRWindow, Itakura, NoWindow, SakoeChiba, Window +from darts.dataprocessing.dtw.cost_matrix import CostMatrix +from darts.dataprocessing.dtw.dtw import DTWAlignment, dtw +from darts.dataprocessing.dtw.window import ( + CRWindow, + Itakura, + NoWindow, + SakoeChiba, + Window, +) + +__all__ = [ + "CostMatrix", + "DTWAlignment", + "dtw", + "CRWindow", + "Itakura", + "NoWindow", + "SakoeChiba", + "Window", +] diff --git a/darts/dataprocessing/dtw/_plot.py b/darts/dataprocessing/dtw/_plot.py index 03dc7c6104..78c76fccab 100644 --- a/darts/dataprocessing/dtw/_plot.py +++ b/darts/dataprocessing/dtw/_plot.py @@ -1,4 +1,4 @@ -from typing import Tuple, Union +from typing import Union import numpy as np import xarray as xr @@ -71,7 +71,7 @@ def plot( interpolation="none", origin="lower", extent=[0, self.n, 0, self.m], - **args_cost + **args_cost, ) show_path = True @@ -102,7 +102,7 @@ def plot_alignment( new_plot: bool = False, series1_y_offset: float = 0, series2_y_offset: float = 0, - components: Union[Tuple[Union[str, int], Union[str, int]]] = (0, 0), + components: Union[tuple[Union[str, int], Union[str, int]]] = (0, 0), args_line: dict = {}, args_series1: dict = {}, args_series2: dict = {}, diff --git a/darts/dataprocessing/dtw/cost_matrix.py b/darts/dataprocessing/dtw/cost_matrix.py index 6b5bbc444c..415967e5f3 100644 --- a/darts/dataprocessing/dtw/cost_matrix.py +++ b/darts/dataprocessing/dtw/cost_matrix.py @@ -1,13 +1,12 @@ import array from abc import ABC, abstractmethod from itertools import repeat -from typing import Tuple import numpy as np -from .window import CRWindow, Window +from darts.dataprocessing.dtw.window import CRWindow, Window -Elem = Tuple[int, int] +Elem = tuple[int, int] class CostMatrix(ABC): diff --git a/darts/dataprocessing/dtw/dtw.py b/darts/dataprocessing/dtw/dtw.py index 3b27cd2242..4f26153cc4 100644 --- a/darts/dataprocessing/dtw/dtw.py +++ b/darts/dataprocessing/dtw/dtw.py @@ -11,12 +11,11 @@ import xarray as xr from darts import TimeSeries +from darts.dataprocessing.dtw.cost_matrix import CostMatrix +from darts.dataprocessing.dtw.window import CRWindow, NoWindow, Window from darts.logging import get_logger, raise_if, raise_if_not from darts.timeseries import DIMS -from .cost_matrix import CostMatrix -from .window import CRWindow, NoWindow, Window - logger = get_logger(__name__) SeriesValue = Union[np.ndarray, np.floating] @@ -27,7 +26,6 @@ def _dtw_cost_matrix( x: np.ndarray, y: np.ndarray, dist: DistanceFunc, window: Window ) -> CostMatrix: - dtw = CostMatrix._from_window(window) dtw.fill(np.inf) diff --git a/darts/dataprocessing/dtw/window.py b/darts/dataprocessing/dtw/window.py index 8f2d93467d..7f211f3265 100644 --- a/darts/dataprocessing/dtw/window.py +++ b/darts/dataprocessing/dtw/window.py @@ -6,7 +6,6 @@ import array from abc import ABC, abstractmethod from math import atan, tan -from typing import Tuple import numpy as np @@ -36,7 +35,7 @@ def __len__(self) -> int: pass @abstractmethod - def column_index(self, elem: Tuple[int, int]) -> int: + def column_index(self, elem: tuple[int, int]) -> int: """Gives the number of active grid cells before row element j, in column i. Parameters @@ -101,7 +100,7 @@ class NoWindow(Window): def __len__(self): return self.n * self.m + 1 # include (0,0) element - def column_index(self, elem: Tuple[int, int]): + def column_index(self, elem: tuple[int, int]): return elem[1] - 1 def column_length(self, column: int) -> int: @@ -215,7 +214,7 @@ def add_range(self, column: int, start: int, end: int): self.column_ranges[start_idx] = start self.column_ranges[end_idx] = end - def add(self, elem: Tuple[int, int]): + def add(self, elem: tuple[int, int]): """Marks a grid cell as active. Parameters @@ -230,7 +229,7 @@ def column_length(self, column: int) -> int: start, end = self.column_ranges[column] return gtz(end - start) - def column_index(self, elem: Tuple[int, int]) -> int: + def column_index(self, elem: tuple[int, int]) -> int: i, j = elem start, end = self.column_ranges[i] @@ -239,7 +238,7 @@ def column_index(self, elem: Tuple[int, int]) -> int: else: return j - start - def __contains__(self, elem: Tuple[int, int]) -> bool: + def __contains__(self, elem: tuple[int, int]) -> bool: i, j = elem start, end = self.column_ranges[i] return start <= j < end diff --git a/darts/dataprocessing/encoders/__init__.py b/darts/dataprocessing/encoders/__init__.py index beaf75e0f4..fe8fda6e7b 100644 --- a/darts/dataprocessing/encoders/__init__.py +++ b/darts/dataprocessing/encoders/__init__.py @@ -3,7 +3,7 @@ ------------------ """ -from .encoders import ( +from darts.dataprocessing.encoders.encoders import ( FutureCallableIndexEncoder, FutureCyclicEncoder, FutureDatetimeAttributeEncoder, @@ -14,3 +14,15 @@ PastIntegerIndexEncoder, SequentialEncoder, ) + +__all__ = [ + "FutureCallableIndexEncoder", + "FutureCyclicEncoder", + "FutureDatetimeAttributeEncoder", + "FutureIntegerIndexEncoder", + "PastCallableIndexEncoder", + "PastCyclicEncoder", + "PastDatetimeAttributeEncoder", + "PastIntegerIndexEncoder", + "SequentialEncoder", +] diff --git a/darts/dataprocessing/encoders/encoder_base.py b/darts/dataprocessing/encoders/encoder_base.py index 771fc5b04b..777570c4cc 100644 --- a/darts/dataprocessing/encoders/encoder_base.py +++ b/darts/dataprocessing/encoders/encoder_base.py @@ -4,7 +4,8 @@ """ from abc import ABC, abstractmethod -from typing import List, Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Literal, Optional, Union import numpy as np import pandas as pd @@ -12,15 +13,10 @@ from darts import TimeSeries from darts.dataprocessing.transformers import FittableDataTransformer from darts.logging import get_logger, raise_if, raise_log -from darts.utils.timeseries_generation import generate_index - -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal +from darts.utils.utils import generate_index SupportedIndex = Union[pd.DatetimeIndex, pd.RangeIndex] -EncoderOutputType = Optional[Union[Sequence[TimeSeries], List[TimeSeries]]] +EncoderOutputType = Optional[Union[Sequence[TimeSeries], list[TimeSeries]]] logger = get_logger(__name__) @@ -49,7 +45,7 @@ def __init__( self, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_covariates: Optional[List[int]] = None, + lags_covariates: Optional[list[int]] = None, ): """:class:`CovariatesIndexGenerator` generates a time index for covariates at training and inference / prediction time with methods :func:`generate_train_idx()`, and :func:`generate_inference_idx()`. @@ -115,7 +111,7 @@ def __init__( @abstractmethod def generate_train_idx( self, target: TimeSeries, covariates: Optional[TimeSeries] = None - ) -> Tuple[SupportedIndex, pd.Timestamp]: + ) -> tuple[SupportedIndex, pd.Timestamp]: """ Generates/extracts time index (or integer index) for covariates at model training time. @@ -134,7 +130,7 @@ def generate_train_idx( @abstractmethod def generate_inference_idx( self, n: int, target: TimeSeries, covariates: Optional[TimeSeries] = None - ) -> Tuple[SupportedIndex, pd.Timestamp]: + ) -> tuple[SupportedIndex, pd.Timestamp]: """ Generates/extracts time index (or integer index) for covariates at model inference / prediction time. @@ -155,7 +151,7 @@ def generate_inference_idx( def generate_train_inference_idx( self, n: int, target: TimeSeries, covariates: Optional[TimeSeries] = None - ) -> Tuple[SupportedIndex, pd.Timestamp]: + ) -> tuple[SupportedIndex, pd.Timestamp]: """ Generates/extracts time index (or integer index) for covariates for training and inference / prediction. @@ -200,7 +196,7 @@ def _verify_scenario( self, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_covariates: Optional[List[int]] = None, + lags_covariates: Optional[list[int]] = None, ): # LocalForecastingModel, or model agnostic (only supported by future covariates) is_scenario_a = ( @@ -276,8 +272,7 @@ class PastCovariatesIndexGenerator(CovariatesIndexGenerator): def generate_train_idx( self, target: TimeSeries, covariates: Optional[TimeSeries] = None - ) -> Tuple[SupportedIndex, pd.Timestamp]: - + ) -> tuple[SupportedIndex, pd.Timestamp]: super().generate_train_idx(target, covariates) # the returned index depends on the following cases: @@ -318,8 +313,7 @@ def generate_train_idx( def generate_inference_idx( self, n: int, target: TimeSeries, covariates: Optional[TimeSeries] = None - ) -> Tuple[SupportedIndex, pd.Timestamp]: - + ) -> tuple[SupportedIndex, pd.Timestamp]: super().generate_inference_idx(n, target, covariates) # for prediction (`n` is given) with past covariates the returned index depends on the following cases: @@ -377,8 +371,7 @@ class FutureCovariatesIndexGenerator(CovariatesIndexGenerator): def generate_train_idx( self, target: TimeSeries, covariates: Optional[TimeSeries] = None - ) -> Tuple[SupportedIndex, pd.Timestamp]: - + ) -> tuple[SupportedIndex, pd.Timestamp]: super().generate_train_idx(target, covariates) # the returned index depends on the following cases: @@ -428,8 +421,7 @@ def generate_train_idx( def generate_inference_idx( self, n: int, target: TimeSeries, covariates: Optional[TimeSeries] = None - ) -> Tuple[SupportedIndex, pd.Timestamp]: - + ) -> tuple[SupportedIndex, pd.Timestamp]: super().generate_inference_idx(n, target, covariates) # for prediction (`n` is given) with future covariates the returned index depends on the following cases: @@ -805,7 +797,7 @@ def encode_train_inference( @property @abstractmethod - def accept_transformer(self) -> List[bool]: + def accept_transformer(self) -> list[bool]: """Whether the `SingleEncoder` sub class accepts to be transformed.""" pass @@ -849,7 +841,7 @@ class SequentialEncoderTransformer: inference dataset covariates. User-supplied covariates are not transformed.""" def __init__( - self, transformer: FittableDataTransformer, transform_mask: List[bool] + self, transformer: FittableDataTransformer, transform_mask: list[bool] ): """ Parameters @@ -864,7 +856,7 @@ def __init__( self.transform_mask: np.ndarray = np.array(transform_mask) self._fit_called: bool = False - def transform(self, covariates: List[TimeSeries]) -> List[TimeSeries]: + def transform(self, covariates: list[TimeSeries]) -> list[TimeSeries]: """This method applies transformation to the non-transformed encoded covariates output of `SequentialEncoder._encode_sequence()` after being merged with user-defined covariates. The transformer is fitted when `transform()` is called for the first time. This ensures proper transformation of train, validation @@ -899,7 +891,7 @@ def transform(self, covariates: List[TimeSeries]) -> List[TimeSeries]: transformed = covariates return transformed - def _update_mask(self, covariates: List[TimeSeries]) -> None: + def _update_mask(self, covariates: list[TimeSeries]) -> None: """if user supplied additional covariates to model.fit() or model.predict(), `self.transform_mask` has to be updated as user-defined covariates should not be transformed. These covariates are always located in the first `n_diff = covariates[0].width - len(self.transform_mask)` components of each TimeSeries in in diff --git a/darts/dataprocessing/encoders/encoders.py b/darts/dataprocessing/encoders/encoders.py index 09b3414593..51e2878020 100644 --- a/darts/dataprocessing/encoders/encoders.py +++ b/darts/dataprocessing/encoders/encoders.py @@ -157,7 +157,8 @@ """ import copy -from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Callable, Optional, Union import numpy as np import pandas as pd @@ -176,11 +177,9 @@ from darts.dataprocessing.transformers import FittableDataTransformer from darts.logging import get_logger, raise_if, raise_if_not from darts.timeseries import DIMS -from darts.utils.timeseries_generation import ( - datetime_attribute_timeseries, - generate_index, -) -from darts.utils.utils import seq2series, series2seq +from darts.utils.timeseries_generation import datetime_attribute_timeseries +from darts.utils.ts_utils import seq2series, series2seq +from darts.utils.utils import generate_index SupportedTimeSeries = Union[TimeSeries, Sequence[TimeSeries]] logger = get_logger(__name__) @@ -246,7 +245,7 @@ def _encode( ) @property - def accept_transformer(self) -> List[bool]: + def accept_transformer(self) -> list[bool]: """`CyclicTemporalEncoder` should not be transformed. Returns two elements for sine and cosine waves.""" return [False, False] @@ -271,7 +270,7 @@ def __init__( attribute: str, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_covariates: Optional[List[int]] = None, + lags_covariates: Optional[list[int]] = None, tz: Optional[str] = None, ): """ @@ -320,7 +319,7 @@ def __init__( attribute: str, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_covariates: Optional[List[int]] = None, + lags_covariates: Optional[list[int]] = None, tz: Optional[str] = None, ): """ @@ -406,7 +405,7 @@ def _encode( ) @property - def accept_transformer(self) -> List[bool]: + def accept_transformer(self) -> list[bool]: """`DatetimeAttributeEncoder` accepts transformations""" return [True] @@ -431,7 +430,7 @@ def __init__( attribute: str, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_covariates: Optional[List[int]] = None, + lags_covariates: Optional[list[int]] = None, tz: Optional[str] = None, ): """ @@ -480,7 +479,7 @@ def __init__( attribute: str, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_covariates: Optional[List[int]] = None, + lags_covariates: Optional[list[int]] = None, tz: Optional[str] = None, ): """ @@ -579,7 +578,7 @@ def _encode( ).astype(np.dtype(dtype)) @property - def accept_transformer(self) -> List[bool]: + def accept_transformer(self) -> list[bool]: """`IntegerIndexEncoder` accepts transformations. Note that transforming 'relative' `IntegerIndexEncoder` will return the absolute position (in the transformed space).""" return [True] @@ -608,7 +607,7 @@ def __init__( attribute: str, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_covariates: Optional[List[int]] = None, + lags_covariates: Optional[list[int]] = None, **kwargs, ): """ @@ -652,7 +651,7 @@ def __init__( attribute: str, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_covariates: Optional[List[int]] = None, + lags_covariates: Optional[list[int]] = None, **kwargs, ): """ @@ -729,7 +728,7 @@ def _encode( ).astype(np.dtype(dtype)) @property - def accept_transformer(self) -> List[bool]: + def accept_transformer(self) -> list[bool]: """`CallableIndexEncoder` accepts transformations.""" return [True] @@ -756,7 +755,7 @@ def __init__( attribute: Callable, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_covariates: Optional[List[int]] = None, + lags_covariates: Optional[list[int]] = None, **kwargs, ): """ @@ -803,7 +802,7 @@ def __init__( attribute: Callable, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_covariates: Optional[List[int]] = None, + lags_covariates: Optional[list[int]] = None, **kwargs, ): """ @@ -848,11 +847,11 @@ class SequentialEncoder(Encoder): def __init__( self, - add_encoders: Dict, + add_encoders: dict, input_chunk_length: Optional[int] = None, output_chunk_length: Optional[int] = None, - lags_past_covariates: Optional[List[int]] = None, - lags_future_covariates: Optional[List[int]] = None, + lags_past_covariates: Optional[list[int]] = None, + lags_future_covariates: Optional[list[int]] = None, takes_past_covariates: bool = False, takes_future_covariates: bool = False, ) -> None: @@ -953,9 +952,9 @@ def __init__( self.lags_future_covariates = lags_future_covariates # encoders - self._past_encoders: List[SingleEncoder] = [] + self._past_encoders: list[SingleEncoder] = [] self._past_components: pd.Index = pd.Index([]) - self._future_encoders: List[SingleEncoder] = [] + self._future_encoders: list[SingleEncoder] = [] self._future_components: pd.Index = pd.Index([]) # transformer @@ -973,7 +972,7 @@ def encode_train( future_covariates: Optional[SupportedTimeSeries] = None, encode_past: bool = True, encode_future: bool = True, - ) -> Tuple[ + ) -> tuple[ Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]] ]: """Returns encoded index for all past and/or future covariates for training. @@ -1036,7 +1035,7 @@ def encode_inference( future_covariates: Optional[SupportedTimeSeries] = None, encode_past: bool = True, encode_future: bool = True, - ) -> Tuple[ + ) -> tuple[ Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]] ]: """Returns encoded index for all past and/or future covariates for inference/prediction. @@ -1088,7 +1087,7 @@ def encode_train_inference( future_covariates: Optional[SupportedTimeSeries] = None, encode_past: bool = True, encode_future: bool = True, - ) -> Tuple[ + ) -> tuple[ Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]] ]: """Returns encoded index for all past and/or future covariates for training and inference/prediction. @@ -1147,7 +1146,7 @@ def _launch_encoder( n: Optional[int] = None, encode_past: bool = True, encode_future: bool = True, - ) -> Tuple[Sequence[TimeSeries], Sequence[TimeSeries]]: + ) -> tuple[Sequence[TimeSeries], Sequence[TimeSeries]]: """Launches the encode sequence for past covariates and future covariates for either training, inference/prediction or training and inference/prediction depending on `encoder_method`. """ @@ -1199,7 +1198,7 @@ def _encode_sequence( covariates_type: str, encoder_method: _EncoderMethod, n: Optional[int] = None, - ) -> List[TimeSeries]: + ) -> list[TimeSeries]: """Sequentially encodes the index of all input target/covariates TimeSeries with the corresponding `encoder_method`. """ @@ -1245,17 +1244,17 @@ def _encode_sequence( return encoded_sequence @property - def past_encoders(self) -> List[SingleEncoder]: + def past_encoders(self) -> list[SingleEncoder]: """Returns the past covariates encoders""" return self._past_encoders @property - def future_encoders(self) -> List[SingleEncoder]: + def future_encoders(self) -> list[SingleEncoder]: """Returns the future covariates encoders""" return self._future_encoders @property - def encoders(self) -> Tuple[List[SingleEncoder], List[SingleEncoder]]: + def encoders(self) -> tuple[list[SingleEncoder], list[SingleEncoder]]: """Returns a tuple of (past covariates encoders, future covariates encoders)""" return self.past_encoders, self.future_encoders @@ -1274,7 +1273,7 @@ def future_components(self) -> pd.Index: return self._future_components @property - def components(self) -> Tuple[pd.Index, pd.Index]: + def components(self) -> tuple[pd.Index, pd.Index]: """Returns the covariates component names generated by `SequentialEncoder.past_encoders` and `SequentialEncoder.future_encoders`. A tuple of (past encoded components, future encoded components). Only available after calling `SequentialEncoder.encode_train()` @@ -1282,7 +1281,7 @@ def components(self) -> Tuple[pd.Index, pd.Index]: return self.past_components, self.future_components @property - def encoding_n_components(self) -> Tuple[int, int]: + def encoding_n_components(self) -> tuple[int, int]: """Returns the number of components generated by `SequentialEncoder.past_encoders` and `SequentialEncoder.future_encoders`. """ @@ -1307,12 +1306,12 @@ def future_transformer(self) -> SequentialEncoderTransformer: def transformers( self, - ) -> Tuple[SequentialEncoderTransformer, SequentialEncoderTransformer]: + ) -> tuple[SequentialEncoderTransformer, SequentialEncoderTransformer]: """Returns a tuple of (past transformer, future transformer).""" return self.past_transformer, self.future_transformer @property - def encoder_map(self) -> Dict: + def encoder_map(self) -> dict: """Mapping between encoder identifier string (from parameters at model creations) and the corresponding future or past covariates encoder""" mapper = { @@ -1327,7 +1326,7 @@ def encoder_map(self) -> Dict: } return mapper - def _setup_encoders(self, params: Dict) -> None: + def _setup_encoders(self, params: dict) -> None: """Sets up/Initializes all past and future encoders and an optional transformer from `add_encoder` parameter used at model creation. @@ -1366,7 +1365,7 @@ def _setup_encoders(self, params: Dict) -> None: ] self.encoding_available = True - def _setup_transformer(self, params: Dict) -> None: + def _setup_transformer(self, params: dict) -> None: """Sets up/Initializes an optional transformer from `add_encoder` parameter used at model creation. Parameters @@ -1389,7 +1388,7 @@ def _setup_transformer(self, params: Dict) -> None: copy.deepcopy(transformer), transform_future_mask ) - def _process_input_encoders(self, params: Dict) -> Tuple[List, List]: + def _process_input_encoders(self, params: dict) -> tuple[list, list]: """Processes input and returns two lists of tuples `(encoder_id, attribute)` from relevant encoder parameters at model creation. @@ -1497,8 +1496,8 @@ def _process_input_encoders(self, params: Dict) -> Tuple[List, List]: return past_encoders, future_encoders def _process_input_transformer( - self, params: Dict - ) -> Tuple[Optional[FittableDataTransformer], List, List]: + self, params: dict + ) -> tuple[Optional[FittableDataTransformer], list, list]: """Processes input params used at model creation and returns tuple of one transformer object and two masks that specify which past / future encoders accept being transformed. @@ -1536,7 +1535,7 @@ def _process_input_transformer( return transformer, transform_past_mask, transform_future_mask @staticmethod - def _process_timezone(params: Dict) -> Optional[str]: + def _process_timezone(params: dict) -> Optional[str]: """Processes input params used at model creation for time zone specification, and returns the time zone. Parameters @@ -1552,6 +1551,6 @@ def _process_timezone(params: Dict) -> Optional[str]: @property def requires_fit(self) -> bool: - return any( - [enc.requires_fit for cov_enc in self.encoders for enc in cov_enc] - ) or any([tf is not None for tf in self.transformers()]) + return any([ + enc.requires_fit for cov_enc in self.encoders for enc in cov_enc + ]) or any([tf is not None for tf in self.transformers()]) diff --git a/darts/dataprocessing/pipeline.py b/darts/dataprocessing/pipeline.py index d43899eb13..e79dd7f084 100644 --- a/darts/dataprocessing/pipeline.py +++ b/darts/dataprocessing/pipeline.py @@ -2,8 +2,10 @@ Pipeline -------- """ + +from collections.abc import Iterator, Sequence from copy import deepcopy -from typing import Iterator, Sequence, Union +from typing import Optional, Union from darts import TimeSeries from darts.dataprocessing.transformers import ( @@ -88,6 +90,16 @@ def __init__( isinstance(t, InvertibleDataTransformer) for t in self._transformers ) + self._fittable = any( + isinstance(t, FittableDataTransformer) for t in self._transformers + ) + + self._global_fit = all( + t._global_fit + for t in self._transformers + if isinstance(t, FittableDataTransformer) + ) + if verbose is not None: for transformer in self._transformers: transformer.set_verbose(verbose) @@ -147,7 +159,9 @@ def fit_transform( return data def transform( - self, data: Union[TimeSeries, Sequence[TimeSeries]] + self, + data: Union[TimeSeries, Sequence[TimeSeries]], + series_idx: Optional[Union[int, Sequence[int]]] = None, ) -> Union[TimeSeries, Sequence[TimeSeries]]: """ For each data transformer in pipeline transform data. Then transformed data is passed to next transformer. @@ -156,6 +170,9 @@ def transform( ---------- data (`Sequence` of) `TimeSeries` to be transformed. + series_idx + Optionally, the index(es) of each series corresponding to their positions within the series used to fit + the transformer (to retrieve the appropriate transformer parameters). Returns ------- @@ -163,16 +180,19 @@ def transform( Transformed data. """ for transformer in self._transformers: - data = transformer.transform(data) + data = transformer.transform(data, series_idx=series_idx) return data def inverse_transform( - self, data: Union[TimeSeries, Sequence[TimeSeries]], partial: bool = False + self, + data: Union[TimeSeries, Sequence[TimeSeries]], + partial: bool = False, + series_idx: Optional[Union[int, Sequence[int]]] = None, ) -> Union[TimeSeries, Sequence[TimeSeries]]: """ For each data transformer in the pipeline, inverse-transform data. Then inverse transformed data is passed to the next transformer. Transformers are traversed in reverse order. Raises value error if not all of the - transformers are invertible and ``partial`` is set to False. Set ``partial`` to True for inverting only the + transformers are invertible and ``partial`` is set to `False`. Set ``partial`` to True for inverting only the InvertibleDataTransformer in the pipeline. Parameters @@ -182,6 +202,9 @@ def inverse_transform( partial If set to `True`, the inverse transformation is applied even if the pipeline is not fully invertible, calling `inverse_transform()` only on the `InvertibleDataTransformer`s + series_idx + Optionally, the index(es) of each series corresponding to their positions within the series used to fit + the transformer (to retrieve the appropriate transformer parameters). Returns ------- @@ -196,14 +219,18 @@ def inverse_transform( ) for transformer in reversed(self._transformers): - data = transformer.inverse_transform(data) + data = transformer.inverse_transform(data, series_idx=series_idx) return data else: for transformer in reversed(self._transformers): if isinstance(transformer, InvertibleDataTransformer): - data = transformer.inverse_transform(data) + data = transformer.inverse_transform( + data, + series_idx=series_idx, + ) return data + @property def invertible(self) -> bool: """ Returns whether the pipeline is invertible or not. @@ -216,6 +243,34 @@ def invertible(self) -> bool: """ return self._invertible + @property + def fittable(self) -> bool: + """ + Returns whether the pipeline is fittable or not. + A pipeline is fittable if at least one of the transformers in the pipeline is fittable. + + Returns + ------- + bool + `True` if the pipeline is fittable, `False` otherwise + """ + return self._fittable + + @property + def _fit_called(self) -> bool: + """ + Returns whether all the transformers in the pipeline were fitted (when applicable). + + Returns + ------- + bool + `True` if all the fittable transformers are fitted, `False` otherwise + """ + return all( + (not isinstance(t, FittableDataTransformer)) or t._fit_called + for t in self._transformers + ) + def __getitem__(self, key: Union[int, slice]) -> "Pipeline": """ Gets subset of Pipeline based either on index or slice with indexes. diff --git a/darts/dataprocessing/transformers/__init__.py b/darts/dataprocessing/transformers/__init__.py index 5080760af3..225e394915 100644 --- a/darts/dataprocessing/transformers/__init__.py +++ b/darts/dataprocessing/transformers/__init__.py @@ -3,19 +3,43 @@ ----------------- """ -from .base_data_transformer import BaseDataTransformer -from .boxcox import BoxCox -from .diff import Diff -from .fittable_data_transformer import FittableDataTransformer -from .invertible_data_transformer import InvertibleDataTransformer -from .mappers import InvertibleMapper, Mapper -from .midas import MIDAS -from .missing_values_filler import MissingValuesFiller -from .reconciliation import ( +from darts.dataprocessing.transformers.base_data_transformer import BaseDataTransformer +from darts.dataprocessing.transformers.boxcox import BoxCox +from darts.dataprocessing.transformers.diff import Diff +from darts.dataprocessing.transformers.fittable_data_transformer import ( + FittableDataTransformer, +) +from darts.dataprocessing.transformers.invertible_data_transformer import ( + InvertibleDataTransformer, +) +from darts.dataprocessing.transformers.mappers import InvertibleMapper, Mapper +from darts.dataprocessing.transformers.midas import MIDAS +from darts.dataprocessing.transformers.missing_values_filler import MissingValuesFiller +from darts.dataprocessing.transformers.reconciliation import ( BottomUpReconciliator, MinTReconciliator, TopDownReconciliator, ) -from .scaler import Scaler -from .static_covariates_transformer import StaticCovariatesTransformer -from .window_transformer import WindowTransformer +from darts.dataprocessing.transformers.scaler import Scaler +from darts.dataprocessing.transformers.static_covariates_transformer import ( + StaticCovariatesTransformer, +) +from darts.dataprocessing.transformers.window_transformer import WindowTransformer + +__all__ = [ + "BaseDataTransformer", + "BoxCox", + "Diff", + "FittableDataTransformer", + "InvertibleDataTransformer", + "InvertibleMapper", + "Mapper", + "MIDAS", + "MissingValuesFiller", + "BottomUpReconciliator", + "MinTReconciliator", + "TopDownReconciliator", + "Scaler", + "StaticCovariatesTransformer", + "WindowTransformer", +] diff --git a/darts/dataprocessing/transformers/base_data_transformer.py b/darts/dataprocessing/transformers/base_data_transformer.py index 4ba79fbd81..2dd7f6dd52 100644 --- a/darts/dataprocessing/transformers/base_data_transformer.py +++ b/darts/dataprocessing/transformers/base_data_transformer.py @@ -3,19 +3,59 @@ --------------------------- """ +import copy from abc import ABC, abstractmethod -from typing import Any, Generator, List, Mapping, Optional, Sequence, Union +from collections.abc import Generator, Iterable, Mapping, Sequence +from functools import wraps +from typing import Any, Optional, Union import numpy as np -import xarray as xr from darts import TimeSeries -from darts.logging import get_logger, raise_if, raise_if_not +from darts.logging import get_logger, raise_log from darts.utils import _build_tqdm_iterator, _parallel_apply +from darts.utils.ts_utils import SeriesType, get_series_seq_type, series2seq logger = get_logger(__name__) +def component_masking(transformer_method): + """Applies component masking to the series fed to any `transform` method, and then reverts the masking for the + final output series. + """ + + @wraps(transformer_method) + def transform_wrapper( + cls, series: TimeSeries, params: Mapping[str, Any], *args, **kwargs + ): + kwargs = copy.deepcopy(kwargs) + # `mask_components` and `component_mask` must be in `kwargs` + mask_components = kwargs.pop("mask_components") + mask_components_apply_only = kwargs.pop("mask_components_apply_only") + component_mask = kwargs.pop("component_mask") + + # remove non-transform columns + if mask_components and component_mask is not None: + series_proc = BaseDataTransformer.apply_component_mask( + series, component_mask, return_ts=True + ) + else: + series_proc = series + if component_mask is not None: + kwargs["component_mask"] = component_mask + + out = transformer_method(cls, series_proc, params, *args, **kwargs) + + # add back non-transformed columns + if mask_components and not mask_components_apply_only: + out = BaseDataTransformer.unapply_component_mask( + series, out, component_mask + ) + return out + + return transform_wrapper + + class BaseDataTransformer(ABC): def __init__( self, @@ -168,7 +208,8 @@ def set_verbose(self, value: bool): value New verbosity status """ - raise_if_not(isinstance(value, bool), "Verbosity status must be a boolean.") + if not isinstance(value, bool): + raise_log(ValueError("Verbosity status must be a boolean."), logger=logger) self._verbose = value @@ -180,10 +221,16 @@ def set_n_jobs(self, value: int): value New n_jobs value. Set to `-1` for using all the available cores. """ - - raise_if_not(isinstance(value, int), "n_jobs must be an integer") + if not isinstance(value, int): + raise_log(ValueError("n_jobs must be an integer"), logger=logger) self._n_jobs = value + @classmethod + @component_masking + def _ts_transform(cls, *args, **kwargs): + """Applies component masking to `ts_transform`.""" + return cls.ts_transform(*args, **kwargs) + @staticmethod @abstractmethod def ts_transform(series: TimeSeries, params: Mapping[str, Any]) -> TimeSeries: @@ -257,8 +304,9 @@ def transform( series: Union[TimeSeries, Sequence[TimeSeries]], *args, component_mask: Optional[np.array] = None, + series_idx: Optional[Union[int, Sequence[int]]] = None, **kwargs, - ) -> Union[TimeSeries, List[TimeSeries]]: + ) -> Union[TimeSeries, list[TimeSeries]]: """Transforms a (sequence of) of series by calling the user-implemeneted `ts_transform` method. In case a ``Sequence[TimeSeries]`` is passed as input data, this function takes care of @@ -281,6 +329,9 @@ def transform( attribute was set to `True` when instantiating `BaseDataTransformer`, then the component mask will be automatically applied to each `TimeSeries` input. Otherwise, `component_mask` will be provided as an addition keyword argument to `ts_transform`. See 'Notes' for further details. + series_idx + Optionally, the index(es) of each series corresponding to their positions within the series used to fit + the transformer (to retrieve the appropriate transformer parameters). kwargs Additional keyword arguments for each :func:`ts_transform()` method call @@ -312,45 +363,39 @@ def transform( # Take note of original input for unmasking purposes: if isinstance(series, TimeSeries): - input_series = [series] data = [series] + if series_idx: + transformer_selector = self._process_series_idx(series_idx) + else: + transformer_selector = [0] else: - input_series = series data = series - - if self._mask_components: - data = [ - self.apply_component_mask(ts, component_mask, return_ts=True) - for ts in data - ] - else: - kwargs["component_mask"] = component_mask + if series_idx: + transformer_selector = self._process_series_idx(series_idx) + else: + transformer_selector = range(len(series)) input_iterator = _build_tqdm_iterator( - zip(data, self._get_params(n_timeseries=len(data))), + zip(data, self._get_params(transformer_selector=transformer_selector)), verbose=self._verbose, desc=desc, total=len(data), ) + # apply & unapply component masking to the transform method + kwargs["mask_components"] = self._mask_components + kwargs["mask_components_apply_only"] = False + kwargs["component_mask"] = component_mask + transformed_data = _parallel_apply( - input_iterator, self.__class__.ts_transform, self._n_jobs, args, kwargs + input_iterator, self._ts_transform, self._n_jobs, args, kwargs ) - - if self._mask_components: - unmasked = [] - for ts, transformed_ts in zip(input_series, transformed_data): - unmasked.append( - self.unapply_component_mask(ts, transformed_ts, component_mask) - ) - transformed_data = unmasked - return ( transformed_data[0] if isinstance(series, TimeSeries) else transformed_data ) def _get_params( - self, n_timeseries: int + self, transformer_selector: Iterable ) -> Generator[Mapping[str, Any], None, None]: """ Creates generator of dictionaries containing fixed parameter values @@ -359,11 +404,11 @@ def _get_params( parallel jobs. Called by `transform` and `inverse_transform`, if `Transformer` does *not* inherit from `FittableTransformer`. """ - self._check_fixed_params(n_timeseries) + self._check_fixed_params(transformer_selector) - def params_generator(n_timeseries, fixed_params, parallel_params): + def params_generator(transformer_selector, fixed_params, parallel_params): fixed_params_copy = fixed_params.copy() - for i in range(n_timeseries): + for i in transformer_selector: for key in parallel_params: fixed_params_copy[key] = fixed_params[key][i] if fixed_params_copy: @@ -373,27 +418,51 @@ def params_generator(n_timeseries, fixed_params, parallel_params): yield params return None - return params_generator(n_timeseries, self._fixed_params, self._parallel_params) + return params_generator( + transformer_selector, self._fixed_params, self._parallel_params + ) - def _check_fixed_params(self, n_timeseries: int) -> None: + def _check_fixed_params(self, transformer_selector: Iterable) -> None: """ Raises `ValueError` if `self._parallel_params` specifies a `key` in `self._fixed_params` that should be distributed, but - `len(self._fixed_params[key])` does not equal `n_timeseries`. + `len(self._fixed_params[key])` does not equal to the number of time series + (the maximum value + 1 from `transformer_selector`). """ for key in self._parallel_params: - raise_if( - n_timeseries > len(self._fixed_params[key]), - f"{n_timeseries} TimeSeries were provided " - f"but only {len(self._fixed_params[key])} {key} values " - f"were specified upon initialising {self.name}.", - ) + n_timeseries_ = max(transformer_selector) + 1 + if n_timeseries_ > len(self._fixed_params[key]): + raise_log( + ValueError( + f"{n_timeseries_} TimeSeries were provided " + f"but only {len(self._fixed_params[key])} {key} values " + f"were specified upon initialising {self.name}." + ), + logger=logger, + ) + elif n_timeseries_ < len(self._fixed_params[key]): + logger.warning( + f"Only {n_timeseries_} TimeSeries were provided " + f"which is lower than the number of {key} values " + f"(n={len(self._fixed_params[key])}) that were specified " + f"upon initialising {self.name}." + ) return None + @staticmethod + def _process_series_idx(series_idx: Union[int, Sequence[int]]) -> Sequence[int]: + """Convert the `series_idx` to a Sequence[int]. + + Note: the validity of the entries in series_idx is checked in _get_params(). + """ + return [series_idx] if isinstance(series_idx, int) else series_idx + @staticmethod def apply_component_mask( - series: TimeSeries, component_mask: Optional[np.ndarray] = None, return_ts=False - ) -> np.ndarray: + series: TimeSeries, + component_mask: Optional[np.ndarray] = None, + return_ts: bool = False, + ) -> Union[TimeSeries, Sequence[TimeSeries], np.ndarray, Sequence[np.ndarray]]: """ Extracts components specified by `component_mask` from `series` @@ -415,36 +484,49 @@ def apply_component_mask( specified by `component_mask` remaining. """ + sequence_type_in = get_series_seq_type(series) + called_with_single_series = sequence_type_in == SeriesType.SINGLE + series = series2seq(series) + if component_mask is None: - masked = series.copy() if return_ts else series.all_values() - else: - raise_if_not( - isinstance(component_mask, np.ndarray) and component_mask.dtype == bool, - f"`component_mask` must be a boolean `np.ndarray`, not a {type(component_mask)}.", - logger, - ) - raise_if_not( - series.width == len(component_mask), - "mismatch between number of components in `series` and length of `component_mask`", - logger, - ) - masked = series.all_values(copy=False)[:, component_mask, :] if return_ts: - # Remove masked components from coords: - coords = dict(series._xa.coords) - coords["component"] = coords["component"][component_mask] - new_xa = xr.DataArray( - masked, dims=series._xa.dims, coords=coords, attrs=series._xa.attrs + out = series.copy() + else: + out = [series_.all_values() for series_ in series] + return out[0] if called_with_single_series else out + + if not ( + isinstance(component_mask, np.ndarray) and component_mask.dtype == bool + ): + raise_log( + ValueError( + f"`component_mask` must be a boolean `np.ndarray`, not a {type(component_mask)}." + ), + logger=logger, + ) + + out = [] + for series_ in series: + if not series_.width == len(component_mask): + raise_log( + ValueError( + "mismatch between number of components in `series` and length of `component_mask`" + ), + logger=logger, ) - masked = TimeSeries(new_xa) - return masked + if return_ts: + out_ = series_[series_.columns[component_mask].tolist()] + else: + out_ = series_.all_values(copy=False)[:, component_mask, :] + out.append(out_) + return out[0] if called_with_single_series else out @staticmethod def unapply_component_mask( - series: TimeSeries, - vals: Union[np.ndarray, TimeSeries], + series: Union[TimeSeries, Sequence[TimeSeries]], + vals: Union[np.ndarray, Sequence[np.ndarray], TimeSeries, Sequence[TimeSeries]], component_mask: Optional[np.ndarray] = None, - ) -> Union[np.ndarray, TimeSeries]: + ) -> Union[np.ndarray, Sequence[np.ndarray], TimeSeries, Sequence[TimeSeries]]: """ Adds back components previously removed by `component_mask` in `apply_component_mask` method. @@ -465,28 +547,44 @@ def unapply_component_mask( `TimeSeries` (if `vals` is a `TimeSeries`) or `np.ndarray` (if `vals` is an `np.ndarray`) with those components previously removed by `component_mask` now 'added back'. """ - if component_mask is None: - unmasked = vals - else: - raise_if_not( - isinstance(component_mask, np.ndarray) and component_mask.dtype == bool, - "If `component_mask` is given, must be a boolean np.ndarray`", - logger, + return vals + + if not ( + isinstance(component_mask, np.ndarray) and component_mask.dtype == bool + ): + raise_log( + ValueError( + "If `component_mask` is given, must be a boolean np.ndarray`" + ), + logger=logger, ) - raise_if_not( - series.width == len(component_mask), - "mismatch between number of components in `series` and length of `component_mask`", - logger, - ) - unmasked = series.all_values() - if isinstance(vals, TimeSeries): - unmasked[:, component_mask, :] = vals.all_values() + + sequence_type_in = get_series_seq_type(series) + called_with_single_series = sequence_type_in == SeriesType.SINGLE + series = series2seq(series) + if called_with_single_series: + vals = [vals] + + out = [] + for series_, vals_ in zip(series, vals): + if not series_.width == len(component_mask): + raise_log( + ValueError( + "mismatch between number of components in `series` and length of `component_mask`" + ), + logger=logger, + ) + unmasked = series_.all_values() + if isinstance(vals_, TimeSeries): + unmasked[:, component_mask, :] = vals_.all_values() # Remove timepoints not present in transformed data: - unmasked = series.slice_intersect(vals).with_values(unmasked) + unmasked = series_.slice_intersect(vals_).with_values(unmasked) else: - unmasked[:, component_mask, :] = vals - return unmasked + unmasked[:, component_mask, :] = vals_ + + out.append(unmasked) + return out[0] if called_with_single_series else out @staticmethod def stack_samples(vals: Union[np.ndarray, TimeSeries]) -> np.ndarray: @@ -560,10 +658,13 @@ def unstack_samples( if series is not None: n_samples = series.n_samples else: - raise_if( - all(x is None for x in [n_timesteps, n_samples]), - "Must specify either `n_timesteps`, `n_samples`, or `series`.", - ) + if all(x is None for x in [n_timesteps, n_samples]): + raise_log( + ValueError( + "Must specify either `n_timesteps`, `n_samples`, or `series`." + ), + logger=logger, + ) n_components = vals.shape[-1] if n_timesteps is not None: reshaped_vals = vals.reshape(n_timesteps, -1, n_components) diff --git a/darts/dataprocessing/transformers/boxcox.py b/darts/dataprocessing/transformers/boxcox.py index af840ee5ca..5c417b878c 100644 --- a/darts/dataprocessing/transformers/boxcox.py +++ b/darts/dataprocessing/transformers/boxcox.py @@ -3,24 +3,23 @@ ------------------- """ -from typing import Any, Mapping, Optional, Sequence, Union - -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal +from collections.abc import Mapping, Sequence +from typing import Any, Literal, Optional, Union import numpy as np import pandas as pd from scipy.special import inv_boxcox from scipy.stats import boxcox, boxcox_normmax +from darts.dataprocessing.transformers.fittable_data_transformer import ( + FittableDataTransformer, +) +from darts.dataprocessing.transformers.invertible_data_transformer import ( + InvertibleDataTransformer, +) from darts.logging import get_logger, raise_if from darts.timeseries import TimeSeries -from .fittable_data_transformer import FittableDataTransformer -from .invertible_data_transformer import InvertibleDataTransformer - logger = get_logger(__name__) @@ -135,7 +134,7 @@ def ts_fit( series: Union[TimeSeries, Sequence[TimeSeries]], params: Mapping[str, Any], *args, - **kwargs + **kwargs, ) -> Union[Sequence[float], pd.Series]: lmbda, method = params["fixed"]["_lmbda"], params["fixed"]["_optim_method"] # If `global_fit` is `True`, then `series` will be ` Sequence[TimeSeries]`; @@ -164,7 +163,6 @@ def ts_fit( def ts_transform( series: TimeSeries, params: Mapping[str, Any], **kwargs ) -> TimeSeries: - lmbda = params["fitted"] vals = BoxCox.stack_samples(series) @@ -178,7 +176,6 @@ def ts_transform( def ts_inverse_transform( series: TimeSeries, params: Mapping[str, Any], **kwargs ) -> TimeSeries: - lmbda = params["fitted"] vals = BoxCox.stack_samples(series) diff --git a/darts/dataprocessing/transformers/diff.py b/darts/dataprocessing/transformers/diff.py index fab8f01e7d..5d6b785425 100644 --- a/darts/dataprocessing/transformers/diff.py +++ b/darts/dataprocessing/transformers/diff.py @@ -3,16 +3,20 @@ ------------------------ """ -from typing import Any, Mapping, Sequence, Union +from collections.abc import Mapping, Sequence +from typing import Any, Union import numpy as np +from darts.dataprocessing.transformers.fittable_data_transformer import ( + FittableDataTransformer, +) +from darts.dataprocessing.transformers.invertible_data_transformer import ( + InvertibleDataTransformer, +) from darts.logging import get_logger, raise_if, raise_if_not from darts.timeseries import TimeSeries -from .fittable_data_transformer import FittableDataTransformer -from .invertible_data_transformer import InvertibleDataTransformer - logger = get_logger(__name__) diff --git a/darts/dataprocessing/transformers/fittable_data_transformer.py b/darts/dataprocessing/transformers/fittable_data_transformer.py index e037d3ad40..4a7ee52d52 100644 --- a/darts/dataprocessing/transformers/fittable_data_transformer.py +++ b/darts/dataprocessing/transformers/fittable_data_transformer.py @@ -4,16 +4,19 @@ """ from abc import abstractmethod -from typing import Any, Generator, List, Mapping, Optional, Sequence, Union +from collections.abc import Generator, Iterable, Mapping, Sequence +from typing import Any, Optional, Union import numpy as np from darts import TimeSeries -from darts.logging import get_logger, raise_if, raise_if_not +from darts.dataprocessing.transformers.base_data_transformer import ( + BaseDataTransformer, + component_masking, +) +from darts.logging import get_logger, raise_log from darts.utils import _build_tqdm_iterator, _parallel_apply -from .base_data_transformer import BaseDataTransformer - logger = get_logger(__name__) @@ -173,6 +176,12 @@ class should first inherit from `FittableDataTransformer` and then from `Inverti self._fitted_params = None # stores the fitted parameters/objects self._global_fit = global_fit + @classmethod + @component_masking + def _ts_fit(cls, *args, **kwargs): + """Applies component masking to `ts_fit`.""" + return cls.ts_fit(*args, **kwargs) + @staticmethod @abstractmethod def ts_fit( @@ -256,18 +265,14 @@ def fit( if isinstance(series, TimeSeries): data = [series] + transformer_selector = [0] else: data = series + transformer_selector = range(len(series)) - if self._mask_components: - data = [ - self.apply_component_mask(ts, component_mask, return_ts=True) - for ts in data - ] - else: - kwargs["component_mask"] = component_mask - - params_iterator = self._get_params(n_timeseries=len(data), calling_fit=True) + params_iterator = self._get_params( + transformer_selector=transformer_selector, calling_fit=True + ) fit_iterator = ( zip(data, params_iterator) if not self._global_fit @@ -278,19 +283,39 @@ def fit( fit_iterator, verbose=self._verbose, desc=desc, total=n_jobs ) + # apply component masking to the fit method + kwargs["mask_components"] = self._mask_components + kwargs["mask_components_apply_only"] = True + kwargs["component_mask"] = component_mask + self._fitted_params = _parallel_apply( - input_iterator, self.__class__.ts_fit, self._n_jobs, args, kwargs + input_iterator, self._ts_fit, self._n_jobs, args, kwargs ) - return self + def transform( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + *args, + component_mask: Optional[np.array] = None, + series_idx: Optional[Union[int, Sequence[int]]] = None, + **kwargs, + ) -> Union[TimeSeries, list[TimeSeries]]: + return super().transform( + series=series, + *args, + component_mask=component_mask, + series_idx=series_idx if not self._global_fit else None, + **kwargs, + ) + def fit_transform( self, series: Union[TimeSeries, Sequence[TimeSeries]], *args, component_mask: Optional[np.array] = None, **kwargs, - ) -> Union[TimeSeries, List[TimeSeries]]: + ) -> Union[TimeSeries, list[TimeSeries]]: """Fit the transformer to the (sequence of) series and return the transformed input. Parameters @@ -315,7 +340,7 @@ def fit_transform( ).transform(series, *args, component_mask=component_mask, **kwargs) def _get_params( - self, n_timeseries: int, calling_fit: bool = False + self, transformer_selector: Iterable, calling_fit: bool = False ) -> Generator[Mapping[str, Any], None, None]: """ Overrides `_get_params` of `BaseDataTransformer`. Creates generator of dictionaries containing @@ -327,14 +352,18 @@ def _get_params( `transform` and `inverse_transform`. """ # Call `_check_fixed_params` of `BaseDataTransformer`: - self._check_fixed_params(n_timeseries) - fitted_params = self._get_fitted_params(n_timeseries, calling_fit) + self._check_fixed_params(transformer_selector) + fitted_params = self._get_fitted_params(transformer_selector, calling_fit) def params_generator( - n_jobs, fixed_params, fitted_params, parallel_params, global_fit + transformer_selector_, + fixed_params, + fitted_params, + parallel_params, + global_fit, ): fixed_params_copy = fixed_params.copy() - for i in range(n_jobs): + for i in transformer_selector_: for key in parallel_params: fixed_params_copy[key] = fixed_params[key][i] params = {} @@ -348,37 +377,53 @@ def params_generator( params = None yield params - n_jobs = n_timeseries if not (calling_fit and self._global_fit) else 1 + transformer_selector_ = ( + transformer_selector if not (calling_fit and self._global_fit) else [0] + ) return params_generator( - n_jobs, + transformer_selector_, self._fixed_params, fitted_params, self._parallel_params, self._global_fit, ) - def _get_fitted_params(self, n_timeseries: int, calling_fit: bool) -> Sequence[Any]: + def _get_fitted_params( + self, transformer_selector: Iterable, calling_fit: bool + ) -> Sequence[Any]: """ Returns `self._fitted_params` if `calling_fit = False`, otherwise returns an empty tuple. If `calling_fit = False`, also checks that `self._fitted_params`, which is a - sequence of values, contains exactly `n_timeseries` values; if not, a `ValueError` is thrown. + sequence of values, contains exactly `transformer_selector` values; if not, a `ValueError` is thrown. """ if not calling_fit: - raise_if_not( - self._fit_called, - ("Must call `fit` before calling `transform`/`inverse_transform`."), - ) + if not self._fit_called: + raise_log( + ValueError( + "Must call `fit` before calling `transform`/`inverse_transform`." + ), + logger=logger, + ) fitted_params = self._fitted_params else: fitted_params = tuple() if not self._global_fit and fitted_params: - raise_if( - n_timeseries > len(fitted_params), - ( - f"{n_timeseries} TimeSeries were provided " - f"but only {len(fitted_params)} TimeSeries " - f"were specified upon training {self.name}." - ), - ) + n_timeseries_ = max(transformer_selector) + 1 + if n_timeseries_ > len(fitted_params): + raise_log( + ValueError( + f"{n_timeseries_} TimeSeries were provided " + f"but only {len(fitted_params)} TimeSeries " + f"were specified upon training {self.name}." + ), + logger=logger, + ) + elif n_timeseries_ < len(fitted_params): + logger.warning( + f"Only {n_timeseries_} TimeSeries (lists) were provided " + f"which is lower than the number of series (n={len(fitted_params)}) " + f"used to fit {self.name}. This can result in a mismatch between the " + f"series and the underlying transformers." + ) return fitted_params diff --git a/darts/dataprocessing/transformers/invertible_data_transformer.py b/darts/dataprocessing/transformers/invertible_data_transformer.py index fbd9e0e61a..5e5c73eafd 100644 --- a/darts/dataprocessing/transformers/invertible_data_transformer.py +++ b/darts/dataprocessing/transformers/invertible_data_transformer.py @@ -4,16 +4,19 @@ """ from abc import abstractmethod -from typing import Any, List, Mapping, Optional, Sequence, Union +from collections.abc import Mapping, Sequence +from typing import Any, Optional, Union import numpy as np from darts import TimeSeries -from darts.logging import get_logger, raise_if_not +from darts.dataprocessing.transformers.base_data_transformer import ( + BaseDataTransformer, + component_masking, +) +from darts.logging import get_logger, raise_log from darts.utils import _build_tqdm_iterator, _parallel_apply -from .base_data_transformer import BaseDataTransformer - logger = get_logger(__name__) @@ -171,6 +174,12 @@ def __init__( mask_components=mask_components, ) + @classmethod + @component_masking + def _ts_inverse_transform(cls, *args, **kwargs): + """Applies component masking to `ts_inverse_transform`.""" + return cls.ts_inverse_transform(*args, **kwargs) + @staticmethod @abstractmethod def ts_inverse_transform( @@ -245,14 +254,15 @@ def ts_inverse_transform( def inverse_transform( self, - series: Union[TimeSeries, Sequence[TimeSeries]], + series: Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]], *args, component_mask: Optional[np.array] = None, + series_idx: Optional[Union[int, Sequence[int]]] = None, **kwargs, - ) -> Union[TimeSeries, List[TimeSeries]]: + ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: """Inverse transforms a (sequence of) series by calling the user-implemented `ts_inverse_transform` method. - In case a sequence is passed as input data, this function takes care of parallelising the + In case a sequence or list of lists is passed as input data, this function takes care of parallelising the transformation of multiple series in the sequence at the same time. Additionally, if the `mask_components` attribute was set to `True` when instantiating `InvertibleDataTransformer`, then any provided `component_mask`s will be automatically applied to each input `TimeSeries`; @@ -263,18 +273,28 @@ def inverse_transform( Parameters ---------- series - the (sequence of) series be inverse-transformed. + The series to inverse-transform. + If a single `TimeSeries`, returns a single series. + If a sequence of `TimeSeries`, returns a list of series. The series should be in the same order as the + sequence used to fit the transformer. + If a list of lists of `TimeSeries`, returns a list of lists of series. This can for example be the output + of `ForecastingModel.historical_forecasts()` when using multiple series. Each inner list should contain + `TimeSeries` related to the same series. The order of inner lists should be the same as the sequence used + to fit the transformer. args Additional positional arguments for the :func:`ts_inverse_transform()` method component_mask : Optional[np.ndarray] = None Optionally, a 1-D boolean np.ndarray of length ``series.n_components`` that specifies which components of the underlying `series` the inverse transform should consider. + series_idx + Optionally, the index(es) of each series corresponding to their positions within the series used to fit + the transformer (to retrieve the appropriate transformer parameters). kwargs Additional keyword arguments for the :func:`ts_inverse_transform()` method Returns ------- - Union[TimeSeries, List[TimeSeries]] + Union[TimeSeries, List[TimeSeries], List[List[TimeSeries]]] Inverse transformed data. Notes @@ -295,54 +315,69 @@ def inverse_transform( `component_masks` will be passed as a keyword argument `ts_inverse_transform`; the user can then manually specify how the `component_mask` should be applied to each series. """ - if hasattr(self, "_fit_called"): - raise_if_not( - self._fit_called, - "fit() must have been called before inverse_transform()", - logger, + if hasattr(self, "_fit_called") and not self._fit_called: + raise_log( + ValueError("fit() must have been called before inverse_transform()"), + logger=logger, ) desc = f"Inverse ({self._name})" # Take note of original input for unmasking purposes: + called_with_single_series = False + called_with_sequence_series = False if isinstance(series, TimeSeries): - input_series = [series] data = [series] - else: - input_series = series + if series_idx: + transformer_selector = self._process_series_idx(series_idx) + else: + transformer_selector = [0] + called_with_single_series = True + elif isinstance(series[0], TimeSeries): # Sequence[TimeSeries] data = series - - if self._mask_components: - data = [ - self.apply_component_mask(ts, component_mask, return_ts=True) - for ts in data - ] - else: - kwargs["component_mask"] = component_mask + if series_idx: + transformer_selector = self._process_series_idx(series_idx) + else: + transformer_selector = range(len(series)) + called_with_sequence_series = True + else: # Sequence[Sequence[TimeSeries]] + data = [] + transformer_selector = [] + if series_idx: + iterator_ = zip(self._process_series_idx(series_idx), series) + else: + iterator_ = enumerate(series) + for idx, series_list in iterator_: + data.extend(series_list) + transformer_selector += [idx] * len(series_list) input_iterator = _build_tqdm_iterator( - zip(data, self._get_params(n_timeseries=len(data))), + zip(data, self._get_params(transformer_selector=transformer_selector)), verbose=self._verbose, desc=desc, - total=len(data), + total=len(transformer_selector), ) + # apply & unapply component masking to the transform method + kwargs["mask_components"] = self._mask_components + kwargs["mask_components_apply_only"] = False + kwargs["component_mask"] = component_mask + transformed_data = _parallel_apply( input_iterator, - self.__class__.ts_inverse_transform, + self._ts_inverse_transform, self._n_jobs, args, kwargs, ) - if self._mask_components: - unmasked = [] - for ts, transformed_ts in zip(input_series, transformed_data): - unmasked.append( - self.unapply_component_mask(ts, transformed_ts, component_mask) - ) - transformed_data = unmasked - - return ( - transformed_data[0] if isinstance(series, TimeSeries) else transformed_data - ) + if called_with_single_series: + return transformed_data[0] + elif called_with_sequence_series: + return transformed_data + else: + cum_len = np.cumsum([0] + [len(s_) for s_ in series]) + return [ + transformed_data[cum_len[i] : cum_len[i + 1]] + for i in range(len(cum_len) - 1) + ] diff --git a/darts/dataprocessing/transformers/mappers.py b/darts/dataprocessing/transformers/mappers.py index 881f5bc38c..ad0fed6d83 100644 --- a/darts/dataprocessing/transformers/mappers.py +++ b/darts/dataprocessing/transformers/mappers.py @@ -3,17 +3,19 @@ --------------------------- """ -from typing import Any, Callable, Mapping, Union +from collections.abc import Mapping +from typing import Any, Callable, Union import numpy as np import pandas as pd +from darts.dataprocessing.transformers.base_data_transformer import BaseDataTransformer +from darts.dataprocessing.transformers.invertible_data_transformer import ( + InvertibleDataTransformer, +) from darts.logging import get_logger from darts.timeseries import TimeSeries -from .base_data_transformer import BaseDataTransformer -from .invertible_data_transformer import InvertibleDataTransformer - logger = get_logger(__name__) MapperFn = Union[ diff --git a/darts/dataprocessing/transformers/midas.py b/darts/dataprocessing/transformers/midas.py index bba870a31e..2253eac0c1 100644 --- a/darts/dataprocessing/transformers/midas.py +++ b/darts/dataprocessing/transformers/midas.py @@ -2,7 +2,9 @@ Mixed-data sampling (MIDAS) Transformer --------------------------------------- """ -from typing import Any, Dict, List, Mapping, Optional, Sequence, Union + +from collections.abc import Mapping, Sequence +from typing import Any, Optional, Union import numpy as np import pandas as pd @@ -14,7 +16,7 @@ ) from darts.logging import get_logger, raise_log from darts.timeseries import _finite_rows_boundaries -from darts.utils.timeseries_generation import generate_index +from darts.utils.utils import generate_index logger = get_logger(__name__) @@ -53,14 +55,14 @@ def __init__( Whether to remove the NaNs from the start and the end of the transformed series. drop_static_covariates If set to `True`, the statics covariates of the input series won't be transferred to the output. - This migth be useful for multivariate series with component-specific static covariates. + This might be useful for multivariate series with component-specific static covariates. name A specific name for the scaler n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is - passed as input to a method, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + passed as input to a method, parallelizing operations regarding different ``TimeSeries``. Defaults to `1` (sequential). Setting the parameter to `-1` means using all the available processors. - Note: for a small amount of data, the parallelisation overhead could end up increasing the total + Note: for a small amount of data, the parallelization overhead could end up increasing the total required amount of time. verbose Optionally, whether to print operations progress @@ -114,7 +116,7 @@ def ts_fit( params: Mapping[str, Any], *args, **kwargs, - ) -> Union[Dict[str, Any], List[Dict[str, Any]]]: + ) -> Union[dict[str, Any], list[dict[str, Any]]]: """MIDAS needs the high frequency period name in order to easily reverse_transform TimeSeries, the parallelization is handled by `transform` and/or `inverse_transform` (see InvertibleDataTransformer.__init__() docstring). @@ -140,13 +142,11 @@ def ts_fit( ), logger=logger, ) - fitted_params.append( - { - "high_freq": high_freq, - "start": ts.start_time(), - "end": ts.end_time(), - } - ) + fitted_params.append({ + "high_freq": high_freq, + "start": ts.start_time(), + "end": ts.end_time(), + }) return fitted_params[0] if is_single_series else fitted_params @staticmethod diff --git a/darts/dataprocessing/transformers/missing_values_filler.py b/darts/dataprocessing/transformers/missing_values_filler.py index 9d26ffe2b6..c4a6d6e8d9 100644 --- a/darts/dataprocessing/transformers/missing_values_filler.py +++ b/darts/dataprocessing/transformers/missing_values_filler.py @@ -3,14 +3,14 @@ --------------------- """ -from typing import Any, Mapping, Union +from collections.abc import Mapping +from typing import Any, Union from darts import TimeSeries +from darts.dataprocessing.transformers.base_data_transformer import BaseDataTransformer from darts.logging import get_logger, raise_if, raise_if_not from darts.utils.missing_values import fill_missing_values -from .base_data_transformer import BaseDataTransformer - logger = get_logger(__name__) diff --git a/darts/dataprocessing/transformers/reconciliation.py b/darts/dataprocessing/transformers/reconciliation.py index bcba40ecc1..1ee22e2622 100644 --- a/darts/dataprocessing/transformers/reconciliation.py +++ b/darts/dataprocessing/transformers/reconciliation.py @@ -9,8 +9,8 @@ It can be added to a ``TimeSeries`` using e.g., the :meth:`TimeSeries.with_hierarchy` method. """ - -from typing import Any, Mapping, Optional +from collections.abc import Mapping +from typing import Any, Optional import numpy as np @@ -18,8 +18,10 @@ BaseDataTransformer, FittableDataTransformer, ) +from darts.logging import get_logger, raise_if_not from darts.timeseries import TimeSeries -from darts.utils.utils import raise_if_not + +logger = get_logger(__name__) def _get_summation_matrix(series: TimeSeries): @@ -38,6 +40,7 @@ def _get_summation_matrix(series: TimeSeries): raise_if_not( series.has_hierarchy, "The provided series must have a hierarchy defined for reconciliation to be performed.", + logger=logger, ) hierarchy = series.hierarchy components_seq = list(series.components) @@ -46,8 +49,8 @@ def _get_summation_matrix(series: TimeSeries): n = len(components_seq) S = np.zeros((n, m)) - components_indexes = {c: i for i, c in enumerate(components_seq)} - leaves_indexes = {l: i for i, l in enumerate(leaves_seq)} + components_indexes = {comp: i for i, comp in enumerate(components_seq)} + leaves_indexes = {leaf: i for i, leaf in enumerate(leaves_seq)} def increment(cur_node, leaf_idx): """ @@ -85,7 +88,7 @@ class BottomUpReconciliator(BaseDataTransformer): def get_projection_matrix(series): leaves_seq = list(series.bottom_level_components) n, m = series.n_components, len(leaves_seq) - leaves_indexes = {l: i for i, l in enumerate(leaves_seq)} + leaves_indexes = {leaf: i for i, leaf in enumerate(leaves_seq)} G = np.zeros((m, n)) for i, c in enumerate(series.components): if c in leaves_indexes: diff --git a/darts/dataprocessing/transformers/scaler.py b/darts/dataprocessing/transformers/scaler.py index 4262b21853..5f9dcb9d5d 100644 --- a/darts/dataprocessing/transformers/scaler.py +++ b/darts/dataprocessing/transformers/scaler.py @@ -3,18 +3,22 @@ ------ """ +from collections.abc import Mapping, Sequence from copy import deepcopy -from typing import Any, Mapping, Sequence, Union +from typing import Any, Union import numpy as np from sklearn.preprocessing import MinMaxScaler +from darts.dataprocessing.transformers.fittable_data_transformer import ( + FittableDataTransformer, +) +from darts.dataprocessing.transformers.invertible_data_transformer import ( + InvertibleDataTransformer, +) from darts.logging import get_logger, raise_log from darts.timeseries import TimeSeries -from .fittable_data_transformer import FittableDataTransformer -from .invertible_data_transformer import InvertibleDataTransformer - logger = get_logger(__name__) @@ -114,7 +118,6 @@ def __init__( def ts_transform( series: TimeSeries, params: Mapping[str, Any], **kwargs ) -> TimeSeries: - transformer = params["fitted"] tr_out = transformer.transform(Scaler.stack_samples(series)) @@ -139,7 +142,7 @@ def ts_fit( series: Union[TimeSeries, Sequence[TimeSeries]], params: Mapping[str, Any], *args, - **kwargs + **kwargs, ) -> Any: transformer = deepcopy(params["fixed"]["transformer"]) # If `global_fit` is `True`, then `series` will be ` Sequence[TimeSeries]`; diff --git a/darts/dataprocessing/transformers/static_covariates_transformer.py b/darts/dataprocessing/transformers/static_covariates_transformer.py index 76a2f0373f..4917c7f83e 100644 --- a/darts/dataprocessing/transformers/static_covariates_transformer.py +++ b/darts/dataprocessing/transformers/static_covariates_transformer.py @@ -2,25 +2,25 @@ Static Covariates Transformer ------ """ -from collections import OrderedDict -from typing import Any, Dict, List, Optional, Sequence, Tuple -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal +from collections import OrderedDict +from collections.abc import Sequence +from typing import Any, Literal, Optional import numpy as np import pandas as pd from scipy.sparse import csr_matrix from sklearn.preprocessing import MinMaxScaler, OrdinalEncoder +from darts.dataprocessing.transformers.fittable_data_transformer import ( + FittableDataTransformer, +) +from darts.dataprocessing.transformers.invertible_data_transformer import ( + InvertibleDataTransformer, +) from darts.logging import get_logger, raise_log from darts.timeseries import TimeSeries -from .fittable_data_transformer import FittableDataTransformer -from .invertible_data_transformer import InvertibleDataTransformer - logger = get_logger(__name__) @@ -29,8 +29,8 @@ def __init__( self, transformer_num=None, transformer_cat=None, - cols_num: Optional[List[str]] = None, - cols_cat: Optional[List[str]] = None, + cols_num: Optional[list[str]] = None, + cols_cat: Optional[list[str]] = None, name="StaticCovariatesTransformer", n_jobs: int = 1, verbose: bool = False, @@ -149,7 +149,7 @@ def __init__( @staticmethod def ts_fit( - series: Sequence[TimeSeries], params: Dict[str, Dict[str, Any]], *args, **kwargs + series: Sequence[TimeSeries], params: dict[str, dict[str, Any]], *args, **kwargs ): """ Collates static covariates of all provided `TimeSeries` and fits the following parameters: @@ -256,9 +256,9 @@ def _create_component_masks( """ Returns a boolean array indicating which components of the UNTRANSFORMED `stat_covs` are numerical and a boolean array indicating which components - of the UNTRANSFORMED `stat_covs` are categoical. + of the UNTRANSFORMED `stat_covs` are categorical. - It's important to recognise that these masks only apply to the UNTRANSFORMED + It's important to recognize that these masks only apply to the UNTRANSFORMED static covariates since some transformations can generate multiple new components from a single component (e.g. one-hot encoding). """ @@ -290,9 +290,9 @@ def _create_category_mappings( ).shape[-1] # transformer generates same number of features -> make a 1-1 column map if n_cat_out == sum(mask_cat): - col_map_cat = inv_col_map_cat = OrderedDict( - {col: [col] for col in cols_cat} - ) + col_map_cat = inv_col_map_cat = OrderedDict({ + col: [col] for col in cols_cat + }) # transformer generates more features (i.e. OneHotEncoder) -> create a 1-many column map else: col_map_cat = OrderedDict() @@ -317,15 +317,15 @@ def _create_category_mappings( def _create_inv_component_masks( mask_num: np.ndarray, mask_cat: np.ndarray, - cat_mapping: Dict[str, str], + cat_mapping: dict[str, str], cols_cat: Sequence[str], ): """ Returns a boolean array indicating which components of the TRANSFORMED `stat_covs` are numerical and a boolean array indicating which components - of the TRANSFORMED `stat_covs` are categoical. + of the TRANSFORMED `stat_covs` are categorical. - It's important to recognise that these masks only apply to the UNTRANSFORMED + It's important to recognize that these masks only apply to the UNTRANSFORMED static covariates since some transformations can generate multiple new components from a single component (e.g. one-hot encoding). """ @@ -356,7 +356,7 @@ def _create_inv_component_masks( @staticmethod def ts_transform( - series: TimeSeries, params: Dict[str, Any], *args, **kwargs + series: TimeSeries, params: dict[str, Any], *args, **kwargs ) -> TimeSeries: return StaticCovariatesTransformer._transform_static_covs( series, params["fitted"], method="transform" @@ -364,7 +364,7 @@ def ts_transform( @staticmethod def ts_inverse_transform( - series: TimeSeries, params: Dict[str, Any], *args, **kwargs + series: TimeSeries, params: dict[str, Any], *args, **kwargs ) -> TimeSeries: return StaticCovariatesTransformer._transform_static_covs( series, params["fitted"], method="inverse_transform" @@ -373,7 +373,7 @@ def ts_inverse_transform( @staticmethod def _transform_static_covs( series: TimeSeries, - fitted_params: Dict[str, Any], + fitted_params: dict[str, Any], method: Literal["transform", "inverse_transform"], ): """ @@ -422,7 +422,7 @@ def _transform_static_covs( @staticmethod def _extract_static_covs( series: TimeSeries, mask_num: np.ndarray, mask_cat: np.ndarray - ) -> Tuple[np.array, np.array]: + ) -> tuple[np.array, np.array]: """ Extracts all static covariates from a `TimeSeries`, and then extracts the numerical and categorical components to transform from these static covariates. @@ -437,7 +437,7 @@ def _add_back_static_covs( vals_cat: np.ndarray, mask_num: np.ndarray, mask_cat: np.ndarray, - col_map_cat: Dict[str, str], + col_map_cat: dict[str, str], ) -> pd.DataFrame: """ Adds transformed static covariates back to original `TimeSeries`. The categorical component diff --git a/darts/dataprocessing/transformers/window_transformer.py b/darts/dataprocessing/transformers/window_transformer.py index ab16f64e3e..885898ee4e 100644 --- a/darts/dataprocessing/transformers/window_transformer.py +++ b/darts/dataprocessing/transformers/window_transformer.py @@ -3,7 +3,8 @@ ------------------ """ -from typing import Any, List, Mapping, Optional, Union +from collections.abc import Mapping +from typing import Any, Optional, Union from darts.dataprocessing.transformers import BaseDataTransformer from darts.logging import get_logger @@ -15,11 +16,12 @@ class WindowTransformer(BaseDataTransformer): def __init__( self, - transforms: Union[dict, List[dict]], + transforms: Union[dict, list[dict]], treat_na: Optional[Union[str, Union[int, float]]] = None, forecasting_safe: Optional[bool] = True, keep_non_transformed: Optional[bool] = False, include_current: Optional[bool] = True, + keep_names: Optional[bool] = False, name: str = "WindowTransformer", n_jobs: int = 1, verbose: bool = False, @@ -123,11 +125,15 @@ def __init__( keep_non_transformed ``False`` to return the transformed components only, ``True`` to return all original components along - the transformed ones. Default is ``False``. + the transformed ones. Default is ``False``. If the series has a hierarchy, must be set to ``False``. include_current ``True`` to include the current time step in the window, ``False`` to exclude it. Default is ``True``. + keep_names + Whether the transformed components should keep the original component names or. Must be set to ``False`` + if `keep_non_transformed = True` or the number of transformation is greater than 1. + name A specific name for the transformer. @@ -147,6 +153,7 @@ def __init__( self.treat_na = treat_na self.forecasting_safe = forecasting_safe self.include_current = include_current + self.keep_names = keep_names super().__init__(name, n_jobs, verbose) @staticmethod diff --git a/darts/datasets/__init__.py b/darts/datasets/__init__.py index eb5dd9a6a2..79ee95b761 100644 --- a/darts/datasets/__init__.py +++ b/darts/datasets/__init__.py @@ -5,20 +5,15 @@ A few popular time series datasets """ -import os from pathlib import Path -from typing import List, Literal, Optional import numpy as np import pandas as pd from darts import TimeSeries +from darts.datasets.dataset_loaders import DatasetLoaderCSV, DatasetLoaderMetadata from darts.logging import get_logger, raise_if_not -from darts.utils.utils import _build_tqdm_iterator - -from .dataset_loaders import DatasetLoaderCSV, DatasetLoaderMetadata - -pd_above_v22 = pd.__version__ >= "2.2" +from darts.utils.utils import _build_tqdm_iterator, freqs """ Overall usage of this package: @@ -493,6 +488,32 @@ def __init__(self): ) +class TaxiNewYorkDataset(DatasetLoaderCSV): + """ + Taxi Passengers in New York, from 2014-07 to 2015-01. + The data consists of aggregated total number of + taxi passengers into 30 minute buckets. + Univariate series. + Source: [1]_ + + References + ---------- + .. [1] https://www.kaggle.com/code/julienjta/nyc-taxi-traffic-analysis + """ + + def __init__(self): + super().__init__( + metadata=DatasetLoaderMetadata( + "taxi_new_york_passengers.csv", + uri=_DEFAULT_PATH + "/taxi_new_york_passengers.csv", + hash="0a81adf1b74354a8ec18c30e9e8fe5f0", + header_time="time", + format_time="%Y-%m-%d %H:%M:%S", + freq="30min", + ), + ) + + class ElectricityDataset(DatasetLoaderCSV): """ Measurements of electric power consumption in one household with 15 minute sampling rate. @@ -515,12 +536,13 @@ def __init__(self, multivariate: bool = True): Parameters ---------- multivariate: bool - Whether to return a single multivariate timeseries - if False returns a list of univariate TimeSeries. Default is True. + Whether to return a single multivariate timeseries - if False returns a list of univariate TimeSeries. + Default is True. """ def pre_proces_fn(extracted_dir, dataset_path): with open(Path(extracted_dir, "LD2011_2014.txt")) as fin: - with open(dataset_path, "wt", newline="\n") as fout: + with open(dataset_path, "w", newline="\n") as fout: for line in fin: fout.write(line.replace(",", ".").replace(";", ",")) @@ -536,7 +558,7 @@ def pre_proces_fn(extracted_dir, dataset_path): ) ) - def _to_multi_series(self, series: pd.DataFrame) -> List[TimeSeries]: + def _to_multi_series(self, series: pd.DataFrame) -> list[TimeSeries]: """ Load the electricity dataset as a list of univariate series, one for each household. """ @@ -549,8 +571,8 @@ def _to_multi_series(self, series: pd.DataFrame) -> List[TimeSeries]: # filter column down to the period of recording srs = srs.replace(0.0, np.nan) - start_date = min(srs.fillna(method="ffill").dropna().index) - end_date = max(srs.fillna(method="bfill").dropna().index) + start_date = min(srs.ffill().dropna().index) + end_date = max(srs.bfill().dropna().index) active_range = (srs.index >= start_date) & (srs.index <= end_date) srs = srs[active_range].fillna(0.0) @@ -586,7 +608,8 @@ def __init__(self, sample_freq: str = "hourly", multivariate: bool = True): sample_freq: str The sampling frequency of the data. Can be "hourly" or "daily". Default is "hourly". multivariate: bool - Whether to return a single multivariate timeseries - if False returns a list of univariate TimeSeries. Default is True. + Whether to return a single multivariate timeseries - if False returns a list of univariate TimeSeries. + Default is True. """ valid_sample_freq = ["daily", "hourly"] raise_if_not( @@ -604,7 +627,7 @@ def pre_proces_fn(extracted_dir, dataset_path): ) output_dict = {} - freq_setting = "1H" if "hourly" in str(dataset_path) else "1D" + freq_setting = "1" + freqs["h"] if "hourly" in str(dataset_path) else "1D" time_series_of_locations = list(df.groupby(by="locationID")) for locationID, df in time_series_of_locations: df.sort_index() @@ -622,9 +645,11 @@ def pre_proces_fn(extracted_dir, dataset_path): uri="https://github.com/fivethirtyeight/uber-tlc-foil-response/raw/" "63bb878b76f47f69b4527d50af57aac26dead983/" "uber-trip-data/uber-raw-data-janjune-15.csv.zip", - hash="9ed84ebe0df4bc664748724b633b3fe6" - if sample_freq == "hourly" - else "24f9fd67e4b9e53f0214a90268cd9bee", + hash=( + "9ed84ebe0df4bc664748724b633b3fe6" + if sample_freq == "hourly" + else "24f9fd67e4b9e53f0214a90268cd9bee" + ), header_time="Pickup_date", format_time="%Y-%m-%d %H:%M:%S", pre_process_zipped_csv_fn=pre_proces_fn, @@ -632,7 +657,7 @@ def pre_proces_fn(extracted_dir, dataset_path): ) ) - def _to_multi_series(self, series: pd.DataFrame) -> List[TimeSeries]: + def _to_multi_series(self, series: pd.DataFrame) -> list[TimeSeries]: """ load the Uber TLC dataset as a list of univariate timeseries, one for each locationID. """ @@ -644,8 +669,8 @@ def _to_multi_series(self, series: pd.DataFrame) -> List[TimeSeries]: srs = series[label] # filter column down to the period of recording - start_date = min(srs.fillna(method="ffill").dropna().index) - end_date = max(srs.fillna(method="bfill").dropna().index) + start_date = min(srs.ffill().dropna().index) + end_date = max(srs.bfill().dropna().index) active_range = (srs.index >= start_date) & (srs.index <= end_date) srs = srs[active_range] @@ -665,15 +690,18 @@ class ILINetDataset(DatasetLoaderCSV): Components Descriptions: - * % WEIGHTED ILI: Combined state-specific data of patients visit to healthcare providers for ILI reported each week weighted by state population - * % UNWEIGHTED ILI: Combined state-specific data of patients visit to healthcare providers for ILI reported each week unweighted by state population + * % WEIGHTED ILI: Combined state-specific data of patients visit to healthcare providers for ILI reported each week + weighted by state population + * % UNWEIGHTED ILI: Combined state-specific data of patients visit to healthcare providers for ILI reported each + week unweighted by state population * AGE 0-4: Number of patients between 0 and 4 years of age * AGE 25-49: Number of patients between 25 and 49 years of age * AGE 25-64: Number of patients between 25 and 64 years of age * AGE 5-24: Number of patients between 5 and 24 years of age * AGE 50-64: Number of patients between 50 and 64 years of age * AGE 65: Number of patients above (>=65) 65 years of age - * ILITOTAL: Total number of ILI patients. For this system, ILI is defined as fever (temperature of 100°F [37.8°C] or greater) and a cough and/or a sore throat + * ILITOTAL: Total number of ILI patients. For this system, ILI is defined as fever (temperature of 100°F [37.8°C] + or greater) and a cough and/or a sore throat * NUM. OF PROVIDERS: Number of outpatient healthcare providers * TOTAL PATIENTS: Total number of patients @@ -700,7 +728,7 @@ def __init__(self, multivariate: bool = True): ) ) - def _to_multi_series(self, series: pd.DataFrame) -> List[TimeSeries]: + def _to_multi_series(self, series: pd.DataFrame) -> list[TimeSeries]: """ Load the ILINetDataset dataset as a list of univariate timeseries. """ @@ -709,8 +737,9 @@ def _to_multi_series(self, series: pd.DataFrame) -> List[TimeSeries]: class ExchangeRateDataset(DatasetLoaderCSV): """ - The collection of the daily exchange rates of eight foreign countries, including Australia, British, Canada, Switzerland, China, Japan, New Zealand, - and Singapore, ranging from 1990 to 2016. Unfortunately, there were some inconsistencies concerning the dates, so the resulting TimeSeries is integer-indexed. + The collection of the daily exchange rates of eight foreign countries, including Australia, British, Canada, + Switzerland, China, Japan, New Zealand, and Singapore, ranging from 1990 to 2016. Unfortunately, + there were some inconsistencies concerning the dates, so the resulting TimeSeries is integer-indexed. Source: [1]_ References @@ -723,7 +752,8 @@ def __init__(self, multivariate: bool = True): Parameters ---------- multivariate: bool - Whether to return a single multivariate timeseries - if False returns a list of univariate TimeSeries. Default is True. + Whether to return a single multivariate timeseries - if False returns a list of univariate TimeSeries. + Default is True. """ super().__init__( metadata=DatasetLoaderMetadata( @@ -735,7 +765,7 @@ def __init__(self, multivariate: bool = True): ) ) - def _to_multi_series(self, series: pd.DataFrame) -> List[TimeSeries]: + def _to_multi_series(self, series: pd.DataFrame) -> list[TimeSeries]: """ Load the ExchangeRateDataset dataset as a list of univariate timeseries, one for each country. """ @@ -744,8 +774,9 @@ def _to_multi_series(self, series: pd.DataFrame) -> List[TimeSeries]: class TrafficDataset(DatasetLoaderCSV): """ - The data in this repo is a collection of 48 months (2015-2016) hourly data from the California Department of Transportation. The data describes - the road occupancy rates (between 0 and 1) measured by 862 different sensors on San Francisco Bay area freeways. The raw data is in http://pems.dot.ca.gov. + The data in this repo is a collection of 48 months (2015-2016) hourly data from the California Department + of Transportation. The data describes the road occupancy rates (between 0 and 1) measured by 862 different sensors + on San Francisco Bay area freeways. The raw data is in http://pems.dot.ca.gov. Source: [1]_ References @@ -758,7 +789,8 @@ def __init__(self, multivariate: bool = True): Parameters ---------- multivariate: bool - Whether to return a single multivariate timeseries - if False returns a list of univariate TimeSeries. Default is True. + Whether to return a single multivariate timeseries - if False returns a list of univariate TimeSeries. + Default is True. """ super().__init__( metadata=DatasetLoaderMetadata( @@ -767,12 +799,12 @@ def __init__(self, multivariate: bool = True): hash="a2105f364ef70aec06c757304833f72a", header_time="Date", format_time="%Y-%m-%d %H:%M:%S", - freq="1H", + freq="1" + freqs["h"], multivariate=multivariate, ) ) - def _to_multi_series(self, series: pd.DataFrame) -> List[TimeSeries]: + def _to_multi_series(self, series: pd.DataFrame) -> list[TimeSeries]: """ Load the TrafficDataset dataset as a list of univariate timeseries, one for each ID. """ @@ -797,7 +829,8 @@ def __init__(self, multivariate: bool = True): Parameters ---------- multivariate: bool - Whether to return a single multivariate timeseries - if False returns a list of univariate TimeSeries. Default is True. + Whether to return a single multivariate timeseries - if False returns a list of univariate TimeSeries. + Default is True. """ super().__init__( metadata=DatasetLoaderMetadata( @@ -811,7 +844,7 @@ def __init__(self, multivariate: bool = True): ) ) - def _to_multi_series(self, series: pd.DataFrame) -> List[TimeSeries]: + def _to_multi_series(self, series: pd.DataFrame) -> list[TimeSeries]: """ Load the WeatherDataset dataset as a list of univariate timeseries, one for weather indicator. """ @@ -888,12 +921,8 @@ def pre_process_dataset(dataset_path): df.index.name = "Timestamp" df.to_csv(self._get_path_dataset()) - # pandas v2.2.0 introduced some changes - hash_expected = ( - "485d81e9902cc0ccb1f86d7e01fb37cd" - if pd_above_v22 - else "a019125b7f9c1afeacb0ae60ce7455ef" - ) + # pandas v2.2.0 introduced a bug that was fixed in v2.2.1; the expected hash for 2.2.0 + # is "485d81e9902cc0ccb1f86d7e01fb37cd" # hash value for dataset with weather data super().__init__( metadata=DatasetLoaderMetadata( @@ -903,7 +932,7 @@ def pre_process_dataset(dataset_path): "ewz_stromabgabe_netzebenen_stadt_zuerich/" "download/ewz_stromabgabe_netzebenen_stadt_zuerich.csv" ), - hash=hash_expected, + hash="a019125b7f9c1afeacb0ae60ce7455ef", header_time="Timestamp", freq="15min", pre_process_csv_fn=pre_process_dataset, diff --git a/darts/datasets/dataset_loaders.py b/darts/datasets/dataset_loaders.py index 01473bdfa6..c1e8e87839 100644 --- a/darts/datasets/dataset_loaders.py +++ b/darts/datasets/dataset_loaders.py @@ -5,7 +5,7 @@ from abc import ABC, abstractmethod from dataclasses import dataclass from pathlib import Path -from typing import Callable, List, Optional, Union +from typing import Callable, Optional, Union import pandas as pd import requests @@ -203,8 +203,7 @@ def __init__( def _load_from_disk( self, path_to_file: Path, metadata: DatasetLoaderMetadata - ) -> Union[TimeSeries, List[TimeSeries]]: - + ) -> Union[TimeSeries, list[TimeSeries]]: df = pd.read_csv(path_to_file) if metadata.header_time is not None: df = self._format_time_column(df) diff --git a/darts/explainability/__init__.py b/darts/explainability/__init__.py index 88c0ef0c5b..a5288a4858 100644 --- a/darts/explainability/__init__.py +++ b/darts/explainability/__init__.py @@ -3,17 +3,16 @@ -------------- """ -from darts.logging import get_logger - -logger = get_logger(__name__) - from darts.explainability.explainability_result import ( ShapExplainabilityResult, TFTExplainabilityResult, _ExplainabilityResult, ) from darts.explainability.shap_explainer import ShapExplainer +from darts.logging import get_logger +from darts.models.utils import NotImportedModule +logger = get_logger(__name__) try: from darts.explainability.tft_explainer import TFTExplainer except ModuleNotFoundError: @@ -22,3 +21,12 @@ 'To enable them, install "darts", "u8darts[torch]" or "u8darts[all]" (with pip); ' 'or "u8darts-torch" or "u8darts-all" (with conda).' ) + TFTExplainer = NotImportedModule(module_name="(Py)Torch", warn=False) + +__all__ = [ + "ShapExplainabilityResult", + "TFTExplainabilityResult", + "_ExplainabilityResult", + "ShapExplainer", + "TFTExplainer", +] diff --git a/darts/explainability/explainability.py b/darts/explainability/explainability.py index d1287d749a..b29365e4b7 100644 --- a/darts/explainability/explainability.py +++ b/darts/explainability/explainability.py @@ -3,8 +3,10 @@ A `_ForecastingModelExplainer` takes a fitted forecasting model as input and generates explanations for it. """ + from abc import ABC, abstractmethod -from typing import Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union from darts import TimeSeries from darts.explainability.explainability_result import _ExplainabilityResult @@ -176,8 +178,7 @@ def _process_horizons_and_targets( self, horizons: Optional[Union[int, Sequence[int]]], target_components: Optional[Union[str, Sequence[str]]], - ) -> Tuple[Sequence[int], Sequence[str]]: - + ) -> tuple[Sequence[int], Sequence[str]]: return process_horizons_and_targets( horizons=horizons, fallback_horizon=self.n, diff --git a/darts/explainability/explainability_result.py b/darts/explainability/explainability_result.py index 69515040cf..561d67e96c 100644 --- a/darts/explainability/explainability_result.py +++ b/darts/explainability/explainability_result.py @@ -15,7 +15,7 @@ """ from abc import ABC, abstractmethod -from typing import Any, Dict, List, Optional, Union +from typing import Any, Optional, Union import pandas as pd import shap @@ -51,7 +51,7 @@ class ComponentBasedExplainabilityResult(_ExplainabilityResult): def __init__( self, - explained_components: Union[Dict[str, Any], List[Dict[str, Any]]], + explained_components: Union[dict[str, Any], list[dict[str, Any]]], ): if isinstance(explained_components, list): comps_available = explained_components[0].keys() @@ -67,7 +67,7 @@ def __init__( self.explained_components = explained_components self.available_components = comps_available - def get_explanation(self, component) -> Union[Any, List[Any]]: + def get_explanation(self, component) -> Union[Any, list[Any]]: """ Returns one or several explanations for a given component. @@ -80,7 +80,7 @@ def get_explanation(self, component) -> Union[Any, List[Any]]: def _query_explainability_result( self, - attr: Union[Dict[str, Any], List[Dict[str, Any]]], + attr: Union[dict[str, Any], list[dict[str, Any]]], component: str, ) -> Any: """ @@ -190,8 +190,8 @@ class HorizonBasedExplainabilityResult(_ExplainabilityResult): def __init__( self, explained_forecasts: Union[ - Dict[int, Dict[str, TimeSeries]], - List[Dict[int, Dict[str, TimeSeries]]], + dict[int, dict[str, TimeSeries]], + list[dict[int, dict[str, TimeSeries]]], ], ): self.explained_forecasts = explained_forecasts @@ -231,7 +231,7 @@ def __init__( def get_explanation( self, horizon: int, component: Optional[str] = None - ) -> Union[TimeSeries, List[TimeSeries]]: + ) -> Union[TimeSeries, list[TimeSeries]]: """ Returns one or several `TimeSeries` representing the explanations for a given horizon and component. @@ -250,7 +250,7 @@ def get_explanation( def _query_explainability_result( self, - attr: Union[Dict[int, Dict[str, Any]], List[Dict[int, Dict[str, Any]]]], + attr: Union[dict[int, dict[str, Any]], list[dict[int, dict[str, Any]]]], horizon: int, component: Optional[str] = None, ) -> Any: @@ -350,16 +350,16 @@ class ShapExplainabilityResult(HorizonBasedExplainabilityResult): def __init__( self, explained_forecasts: Union[ - Dict[int, Dict[str, TimeSeries]], - List[Dict[int, Dict[str, TimeSeries]]], + dict[int, dict[str, TimeSeries]], + list[dict[int, dict[str, TimeSeries]]], ], feature_values: Union[ - Dict[int, Dict[str, TimeSeries]], - List[Dict[int, Dict[str, TimeSeries]]], + dict[int, dict[str, TimeSeries]], + list[dict[int, dict[str, TimeSeries]]], ], shap_explanation_object: Union[ - Dict[int, Dict[str, shap.Explanation]], - List[Dict[int, Dict[str, shap.Explanation]]], + dict[int, dict[str, shap.Explanation]], + list[dict[int, dict[str, shap.Explanation]]], ], ): super().__init__(explained_forecasts) @@ -368,7 +368,7 @@ def __init__( def get_feature_values( self, horizon: int, component: Optional[str] = None - ) -> Union[TimeSeries, List[TimeSeries]]: + ) -> Union[TimeSeries, list[TimeSeries]]: """ Returns one or several `TimeSeries` representing the feature values for a given horizon and component. @@ -387,7 +387,7 @@ def get_feature_values( def get_shap_explanation_object( self, horizon: int, component: Optional[str] = None - ) -> Union[shap.Explanation, List[shap.Explanation]]: + ) -> Union[shap.Explanation, list[shap.Explanation]]: """ Returns the underlying `shap.Explanation` object for a given horizon and component. @@ -434,8 +434,8 @@ class TFTExplainabilityResult(ComponentBasedExplainabilityResult): def __init__( self, explanations: Union[ - Dict[str, Any], - List[Dict[str, Any]], + dict[str, Any], + list[dict[str, Any]], ], ): super().__init__(explanations) @@ -445,7 +445,7 @@ def __init__( "static_covariates_importance", ] - def get_attention(self) -> Union[TimeSeries, List[TimeSeries]]: + def get_attention(self) -> Union[TimeSeries, list[TimeSeries]]: """ Returns the time-dependent attention on the encoder and decoder for each `horizon` in (1, `output_chunk_length`). The time index ranges from the prediction series' start time - input_chunk_length and @@ -458,7 +458,7 @@ def get_attention(self) -> Union[TimeSeries, List[TimeSeries]]: def get_feature_importances( self, - ) -> Dict[str, Union[pd.DataFrame, List[pd.DataFrame]]]: + ) -> dict[str, Union[pd.DataFrame, list[pd.DataFrame]]]: """ Returns the feature importances for the encoder, decoder and static covariates as pd.DataFrames. If multiple series were used in :func:`TFTExplainer.explain() @@ -466,7 +466,7 @@ def get_feature_importances( """ return {comp: self.get_explanation(comp) for comp in self.feature_importances} - def get_encoder_importance(self) -> Union[pd.DataFrame, List[pd.DataFrame]]: + def get_encoder_importance(self) -> Union[pd.DataFrame, list[pd.DataFrame]]: """ Returns the time-dependent encoder importances as a pd.DataFrames. If multiple series were used in :func:`TFTExplainer.explain() @@ -474,7 +474,7 @@ def get_encoder_importance(self) -> Union[pd.DataFrame, List[pd.DataFrame]]: """ return self.get_explanation("encoder_importance") - def get_decoder_importance(self) -> Union[pd.DataFrame, List[pd.DataFrame]]: + def get_decoder_importance(self) -> Union[pd.DataFrame, list[pd.DataFrame]]: """ Returns the time-dependent decoder importances as a pd.DataFrames. If multiple series were used in :func:`TFTExplainer.explain() @@ -484,7 +484,7 @@ def get_decoder_importance(self) -> Union[pd.DataFrame, List[pd.DataFrame]]: def get_static_covariates_importance( self, - ) -> Union[pd.DataFrame, List[pd.DataFrame]]: + ) -> Union[pd.DataFrame, list[pd.DataFrame]]: """ Returns the numeric and categorical static covariates importances as a pd.DataFrames. If multiple series were used in :func:`TFTExplainer.explain() diff --git a/darts/explainability/shap_explainer.py b/darts/explainability/shap_explainer.py index 26978bb00a..b931bbdc74 100644 --- a/darts/explainability/shap_explainer.py +++ b/darts/explainability/shap_explainer.py @@ -24,8 +24,9 @@ layout. """ +from collections.abc import Sequence from enum import Enum -from typing import Dict, NewType, Optional, Sequence, Union +from typing import NewType, Optional, Union import matplotlib.pyplot as plt import pandas as pd @@ -162,7 +163,7 @@ def __init__( test_stationarity=True, ) - if model._is_probabilistic: + if model.supports_probabilistic_prediction: logger.warning( "The model is probabilistic, but num_samples=1 will be used for explainability." ) @@ -311,7 +312,6 @@ def explain( feature_values_list = [] shap_explanation_object_list = [] for idx, foreground_ts in enumerate(foreground_series): - foreground_past_cov_ts = None foreground_future_cov_ts = None @@ -375,7 +375,7 @@ def summary_plot( num_samples: Optional[int] = None, plot_type: Optional[str] = "dot", **kwargs, - ) -> Dict[int, Dict[str, shap.Explanation]]: + ) -> dict[int, dict[str, shap.Explanation]]: """ Display a shap plot summary for each horizon and each component dimension of the target. This method reuses the initial background data as foreground (potentially sampled) to give a general importance @@ -399,7 +399,7 @@ def summary_plot( Returns ------- shaps_ - A nested dictionary {horizon : {component : shap.Explaination}} containing the raw Explanations for all + A nested dictionary {horizon : {component : shap.Explanation}} containing the raw Explanations for all the horizons and components. """ @@ -420,7 +420,11 @@ def summary_plot( for t in target_components: for h in horizons: - plt.title("Target: `{}` - Horizon: {}".format(t, "t+" + str(h))) + plt.title( + "Target: `{}` - Horizon: {}".format( + t, "t+" + str(h + self.model.output_chunk_shift) + ) + ) shap.summary_plot( shaps_[h][t], foreground_X_sampled, @@ -573,7 +577,6 @@ def __init__( background_num_samples: Optional[int] = None, **kwargs, ): - self.model = model self.target_dim = self.model.input_dim["target"] self.is_multioutputregressor = isinstance( @@ -623,8 +626,7 @@ def shap_explanations( foreground_X: pd.DataFrame, horizons: Optional[Sequence[int]] = None, target_components: Optional[Sequence[str]] = None, - ) -> Dict[int, Dict[str, shap.Explanation]]: - + ) -> dict[int, dict[str, shap.Explanation]]: """ Return a dictionary of dictionaries of shap.Explanation instances: - the first dimension corresponds to the n forecasts ahead we want to explain (Horizon). @@ -646,7 +648,6 @@ def shap_explanations( # native multiOutput estimators shap_explanations = {} if self.is_multioutputregressor: - for h in horizons: tmp_n = {} for t_idx, t in enumerate(self.target_components): @@ -662,7 +663,7 @@ def shap_explanations( shap_explanation_tmp = self.explainers(foreground_X) for h in horizons: tmp_n = {} - for t_idx, t in enumerate(target_components): + for t_idx, t in enumerate(self.target_components): if t not in target_components: continue if not self.single_output: @@ -693,7 +694,6 @@ def _build_explainer_sklearn( shap_method: Optional[ShapMethod] = None, **kwargs, ): - model_name = type(model_sklearn).__name__ # no shap methods - we need to take the default one @@ -760,14 +760,14 @@ def _create_regression_model_shap_X( X, indexes = create_lagged_prediction_data( target_series=target_series if lags_list else None, past_covariates=past_covariates if lags_past_covariates_list else None, - future_covariates=future_covariates - if lags_future_covariates_list - else None, + future_covariates=( + future_covariates if lags_future_covariates_list else None + ), lags=lags_list, lags_past_covariates=lags_past_covariates_list if past_covariates else None, - lags_future_covariates=lags_future_covariates_list - if future_covariates - else None, + lags_future_covariates=( + lags_future_covariates_list if future_covariates else None + ), uses_static_covariates=self.model.uses_static_covariates, last_static_covariates_shape=self.model._static_covariates_shape, ) diff --git a/darts/explainability/tft_explainer.py b/darts/explainability/tft_explainer.py index 754ea035ad..09d8c91dd2 100644 --- a/darts/explainability/tft_explainer.py +++ b/darts/explainability/tft_explainer.py @@ -22,7 +22,8 @@ `_. """ -from typing import Dict, List, Optional, Sequence, Union +from collections.abc import Sequence +from typing import Literal, Optional, Union import matplotlib.axes import matplotlib.pyplot as plt @@ -35,13 +36,7 @@ from darts.explainability.explainability import _ForecastingModelExplainer from darts.logging import get_logger, raise_log from darts.models import TFTModel -from darts.utils.timeseries_generation import generate_index - -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal - +from darts.utils.utils import generate_index logger = get_logger(__name__) @@ -225,16 +220,14 @@ def explain( times=times, columns=[f"horizon {str(i)}" for i in horizons], ) - results.append( - { - "attention": attention, - "encoder_importance": encoder_importance.iloc[idx : idx + 1], - "decoder_importance": decoder_importance.iloc[idx : idx + 1], - "static_covariates_importance": static_covariates_importance.iloc[ - idx : idx + 1 - ], - } - ) + results.append({ + "attention": attention, + "encoder_importance": encoder_importance.iloc[idx : idx + 1], + "decoder_importance": decoder_importance.iloc[idx : idx + 1], + "static_covariates_importance": static_covariates_importance.iloc[ + idx : idx + 1 + ], + }) return TFTExplainabilityResult( explanations=results[0] if len(results) == 1 else results ) @@ -473,7 +466,7 @@ def _static_covariates_importance(self) -> pd.DataFrame: def _get_importance( self, weight: Tensor, - names: List[str], + names: list[str], n_decimals=3, ) -> pd.DataFrame: """Returns the encoder or decoder variable of the TFT model. @@ -515,7 +508,7 @@ def _get_importance( return importance.transpose().sort_values(0, ascending=True).transpose() @property - def _name_mapping(self) -> Dict[str, str]: + def _name_mapping(self) -> dict[str, str]: """Returns the feature name mapping of the TFT model. Returns diff --git a/darts/explainability/utils.py b/darts/explainability/utils.py index 682c8d6a10..0a99b8845d 100644 --- a/darts/explainability/utils.py +++ b/darts/explainability/utils.py @@ -1,10 +1,11 @@ -from typing import List, Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union from darts import TimeSeries from darts.logging import get_logger, raise_if, raise_if_not, raise_log from darts.models.forecasting.forecasting_model import ForecastingModel from darts.utils.statistics import stationarity_tests -from darts.utils.utils import series2seq +from darts.utils.ts_utils import series2seq logger = get_logger(__name__) @@ -185,7 +186,7 @@ def process_horizons_and_targets( target_components: Optional[Union[str, Sequence[str]]] = None, fallback_target_components: Optional[Sequence[str]] = None, check_component_names: bool = False, -) -> Tuple[Sequence[int], Sequence[str]]: +) -> tuple[Sequence[int], Sequence[str]]: """Processes the input horizons and target component names. horizons @@ -242,7 +243,7 @@ def get_component_names( past_covariates: Optional[Sequence[TimeSeries]] = None, future_covariates: Optional[Sequence[TimeSeries]] = None, idx: int = 0, -) -> Tuple[List[str], Optional[List[str]], Optional[List[str]], Optional[List[str]]]: +) -> tuple[list[str], Optional[list[str]], Optional[list[str]], Optional[list[str]]]: """Extract and return the components of target series, static covariate, past and future covariates series. Parameters @@ -287,9 +288,9 @@ def _check_valid_input( series: Sequence[TimeSeries], past_covariates: Optional[Sequence[TimeSeries]], future_covariates: Optional[Sequence[TimeSeries]], - target_components: Optional[List[str]], - past_covariates_components: Optional[List[str]], - future_covariates_components: Optional[List[str]], + target_components: Optional[list[str]], + past_covariates_components: Optional[list[str]], + future_covariates_components: Optional[list[str]], check_component_names: bool = False, requires_input: bool = False, test_stationarity: bool = False, @@ -342,18 +343,20 @@ def _check_valid_input( # for explained features. for idx in range(len(series)): raise_if_not( - all( - [ - series[idx].columns.to_list() == target_components, + all([ + series[idx].columns.to_list() == target_components, + ( past_covariates[idx].columns.to_list() == past_covariates_components if past_covariates is not None - else True, + else True + ), + ( future_covariates[idx].columns.to_list() == future_covariates_components if future_covariates is not None - else True, - ] - ), + else True + ), + ]), "Columns names must be identical between TimeSeries list components (multi-TimeSeries).", ) diff --git a/darts/logging.py b/darts/logging.py index 301f5436f5..d52ea7e83c 100644 --- a/darts/logging.py +++ b/darts/logging.py @@ -2,6 +2,7 @@ import os import time import warnings +from typing import NoReturn def get_logger(name): @@ -104,7 +105,9 @@ def raise_if( raise_if_not(not condition, message, logger) -def raise_log(exception: Exception, logger: logging.Logger = get_logger("main_logger")): +def raise_log( + exception: Exception, logger: logging.Logger = get_logger("main_logger") +) -> NoReturn: """ Can be used to replace "raise" when throwing an exception to ensure the logging of the exception. After logging it, the exception is raised. diff --git a/darts/metrics/__init__.py b/darts/metrics/__init__.py index adc84a48cb..3efb47f03e 100644 --- a/darts/metrics/__init__.py +++ b/darts/metrics/__init__.py @@ -1,21 +1,217 @@ """ Metrics ------- + +For deterministic forecasts (point predictions with `num_samples == 1`), probabilistic forecasts (`num_samples > 1`), +and quantile forecasts. For probabilistic and quantile forecasts, use parameter `q` to define the quantile(s) to +compute the deterministic metrics on: + +- Aggregated over time: + Absolute metrics: + - :func:`MERR `: Mean Error + - :func:`MAE `: Mean Absolute Error + - :func:`MSE `: Mean Squared Error + - :func:`RMSE `: Root Mean Squared Error + - :func:`RMSLE `: Root Mean Squared Log Error + + Relative metrics: + - :func:`MASE `: Mean Absolute Scaled Error + - :func:`MSSE `: Mean Squared Scaled Error + - :func:`RMSSE `: Root Mean Squared Scaled Error + - :func:`MAPE `: Mean Absolute Percentage Error + - :func:`wMAPE `: weighted Mean Absolute Percentage Error + - :func:`sMAPE `: symmetric Mean Absolute Percentage Error + - :func:`OPE `: Overall Percentage Error + - :func:`MARRE `: Mean Absolute Ranged Relative Error + + Other metrics: + - :func:`R2 `: Coefficient of Determination + - :func:`CV `: Coefficient of Variation + +- Per time step: + Absolute metrics: + - :func:`ERR `: Error + - :func:`AE `: Absolute Error + - :func:`SE `: Squared Error + - :func:`SLE `: Squared Log Error + + Relative metrics: + - :func:`ASE `: Absolute Scaled Error + - :func:`SSE `: Squared Scaled Error + - :func:`APE `: Absolute Percentage Error + - :func:`sAPE `: symmetric Absolute Percentage Error + - :func:`ARRE `: Absolute Ranged Relative Error + +For probabilistic forecasts (storchastic predictions with `num_samples >> 1`) and quantile forecasts: + +- Aggregated over time: + Quantile metrics: + - :func:`MQL `: Mean Quantile Loss + - :func:`QR `: Quantile Risk + + Quantile interval metrics: + - :func:`MIW `: Mean Interval Width + - :func:`MWS `: Mean Interval Winkler Score + - :func:`MIC `: Mean Interval Coverage + - :func:`MINCS_QR `: Mean Interval Non-Conformity Score for Quantile Regression + +- Per time step: + Quantile metrics: + - :func:`QL `: Quantile Loss + + Quantile interval metrics: + - :func:`IW `: Interval Width + - :func:`WS `: Interval Winkler Score + - :func:`IC `: Interval Coverage + - :func:`INCS_QR `: Interval Non-Conformity Score for Quantile Regression + +For Dynamic Time Warping (DTW) (aggregated over time): + +- :func:`DTW `: Dynamic Time Warping Metric """ -from .metrics import ( +from darts.metrics.metrics import ( + ae, + ape, + arre, + ase, coefficient_of_variation, dtw_metric, + err, + ic, + incs_qr, + iw, + iws, mae, mape, marre, mase, + merr, + mic, + mincs_qr, + miw, + miws, + mql, mse, + msse, ope, - quantile_loss, + ql, + qr, r2_score, - rho_risk, rmse, rmsle, + rmsse, + sape, + se, + sle, smape, + sse, + wmape, ) + +ALL_METRICS = { + ae, + ape, + arre, + ase, + coefficient_of_variation, + dtw_metric, + err, + iw, + iws, + mae, + mape, + wmape, + marre, + mase, + merr, + miw, + miws, + mql, + mse, + msse, + ope, + ql, + qr, + r2_score, + rmse, + rmsle, + rmsse, + sape, + se, + sle, + smape, + sse, + ic, + mic, + incs_qr, + mincs_qr, +} + +TIME_DEPENDENT_METRICS = { + ae, + ape, + arre, + ase, + err, + ql, + sape, + se, + sle, + sse, + iw, + iws, + ic, + incs_qr, +} + +Q_INTERVAL_METRICS = { + iw, + iws, + miw, + miws, + ic, + mic, + incs_qr, +} + +NON_Q_METRICS = {dtw_metric} + +__all__ = [ + "ae", + "ape", + "arre", + "ase", + "coefficient_of_variation", + "dtw_metric", + "err", + "mae", + "mape", + "wmape", + "marre", + "mase", + "merr", + "mql", + "mse", + "msse", + "ope", + "ql", + "qr", + "r2_score", + "rmse", + "rmsle", + "rmsse", + "sape", + "se", + "sle", + "smape", + "sse", + "iw", + "miw", + "iws", + "miws", + "ic", + "mic", + "incs_qr", + "mincs_qr", +] diff --git a/darts/metrics/metrics.py b/darts/metrics/metrics.py index d016c4ea51..4463810314 100644 --- a/darts/metrics/metrics.py +++ b/darts/metrics/metrics.py @@ -5,34 +5,95 @@ Some metrics to compare time series. """ +import inspect +from collections.abc import Sequence from functools import wraps from inspect import signature -from typing import Callable, List, Optional, Sequence, Tuple, Union -from warnings import warn +from typing import Any, Callable, Optional, Union import numpy as np +import pandas as pd from darts import TimeSeries from darts.dataprocessing import dtw -from darts.logging import get_logger, raise_if_not, raise_log -from darts.utils import _build_tqdm_iterator, _parallel_apply -from darts.utils.statistics import check_seasonality +from darts.logging import get_logger, raise_log +from darts.utils.ts_utils import SeriesType, get_series_seq_type, series2seq +from darts.utils.utils import ( + _build_tqdm_iterator, + _parallel_apply, + likelihood_component_names, + n_steps_between, + quantile_names, +) logger = get_logger(__name__) - +TIME_AX = 0 +COMP_AX = 1 +SMPL_AX = 2 # Note: for new metrics added to this module to be able to leverage the two decorators, it is required both having # the `actual_series` and `pred_series` parameters, and not having other ``Sequence`` as args (since these decorators # don't "unpack" parameters different from `actual_series` and `pred_series`). In those cases, the new metric must take # care of dealing with Sequence[TimeSeries] and multivariate TimeSeries on its own (See mase() implementation). +METRIC_OUTPUT_TYPE = Union[float, list[float], np.ndarray, list[np.ndarray]] +METRIC_TYPE = Callable[ + ..., + METRIC_OUTPUT_TYPE, +] + + +def interval_support(func) -> Callable[..., METRIC_OUTPUT_TYPE]: + """ + This decorator adds support for quantile interval metrics with sanity checks, processing, and extraction of + quantiles from the intervals. + """ + + @wraps(func) + def wrapper_interval_support(*args, **kwargs): + q = kwargs.get("q") + if q is not None: + raise_log( + ValueError( + "`q` is not supported for quantile interval metrics; use `q_interval` instead." + ) + ) + q_interval = kwargs.get("q_interval") + if q_interval is None: + raise_log( + ValueError("Quantile interval metrics require setting `q_interval`.") + ) + if isinstance(q_interval, tuple): + q_interval = [q_interval] + q_interval = np.array(q_interval) + if not q_interval.ndim == 2 or q_interval.shape[1] != 2: + raise_log( + ValueError( + "`q_interval` must be a tuple (float, float) or a sequence of tuples (float, float)." + ), + logger=logger, + ) + if not np.all(q_interval[:, 1] - q_interval[:, 0] > 0): + raise_log( + ValueError( + "all intervals in `q_interval` must be tuples of (lower q, upper q) with `lower q > upper q`. " + f"Received `q_interval={q_interval}`" + ), + logger=logger, + ) + kwargs["q_interval"] = q_interval + kwargs["q"] = np.sort(np.unique(q_interval)) + return func(*args, **kwargs) + return wrapper_interval_support -def multi_ts_support(func) -> Union[float, List[float]]: + +def multi_ts_support(func) -> Callable[..., METRIC_OUTPUT_TYPE]: """ - This decorator further adapts the metrics that took as input two univariate/multivariate ``TimeSeries`` instances, - adding support for equally-sized sequences of ``TimeSeries`` instances. The decorator computes the pairwise metric - for ``TimeSeries`` with the same indices, and returns a float value that is computed as a function of all the - pairwise metrics using a `inter_reduction` subroutine passed as argument to the metric function. + This decorator further adapts the metrics that took as input two (or three for scaled metrics with `insample`) + univariate/multivariate ``TimeSeries`` instances, adding support for equally-sized sequences of ``TimeSeries`` + instances. The decorator computes the pairwise metric for ``TimeSeries`` with the same indices, and returns a float + value that is computed as a function of all the pairwise metrics using a `series_reduction` subroutine passed as + argument to the metric function. If a 'Sequence[TimeSeries]' is passed as input, this decorator provides also parallelisation of the metric evaluation regarding different ``TimeSeries`` (if the `n_jobs` parameter is not set 1). @@ -51,40 +112,118 @@ def wrapper_multi_ts_support(*args, **kwargs): else args[1] ) - n_jobs = kwargs.pop("n_jobs", signature(func).parameters["n_jobs"].default) - verbose = kwargs.pop("verbose", signature(func).parameters["verbose"].default) - - raise_if_not(isinstance(n_jobs, int), "n_jobs must be an integer") - raise_if_not(isinstance(verbose, bool), "verbose must be a bool") - - actual_series = ( - [actual_series] - if not isinstance(actual_series, Sequence) - else actual_series + params = signature(func).parameters + n_jobs = kwargs.pop("n_jobs", params["n_jobs"].default) + verbose = kwargs.pop("verbose", params["verbose"].default) + + # sanity check reduction functions + _ = _get_reduction( + kwargs=kwargs, + params=params, + red_name="time_reduction", + axis=TIME_AX, + sanity_check=True, ) - pred_series = ( - [pred_series] if not isinstance(pred_series, Sequence) else pred_series + _ = _get_reduction( + kwargs=kwargs, + params=params, + red_name="component_reduction", + axis=COMP_AX, + sanity_check=True, ) - - raise_if_not( - len(actual_series) == len(pred_series), - "The two TimeSeries sequences must have the same length.", - logger, + series_reduction = _get_reduction( + kwargs=kwargs, + params=params, + red_name="series_reduction", + axis=0, + sanity_check=True, ) + series_seq_type = get_series_seq_type(actual_series) + actual_series = series2seq(actual_series) + pred_series = series2seq(pred_series) + + if len(actual_series) != len(pred_series): + raise_log( + ValueError( + f"Mismatch between number of series in `actual_series` (n={len(actual_series)}) and " + f"`pred_series` (n={len(pred_series)})." + ), + logger=logger, + ) num_series_in_args = int("actual_series" not in kwargs) + int( "pred_series" not in kwargs ) + input_series = (actual_series, pred_series) + kwargs.pop("actual_series", 0) kwargs.pop("pred_series", 0) + # handle `insample` parameter for scaled metrics + if "insample" in params: + insample = kwargs.get("insample") + if insample is None: + insample = args[ + 2 - ("actual_series" in kwargs) - ("pred_series" in kwargs) + ] + + insample = [insample] if not isinstance(insample, Sequence) else insample + if len(actual_series) != len(insample): + raise_log( + ValueError( + f"Mismatch between number of series in `actual_series` (n={len(actual_series)}) and " + f"`insample` series (n={len(insample)})." + ), + logger=logger, + ) + input_series += (insample,) + num_series_in_args += int("insample" not in kwargs) + kwargs.pop("insample", 0) + + # handle `q` (quantile) parameter for probabilistic (or quantile) forecasts + if "q" in params: + # convert `q` to tuple of (quantile values, optional quantile component names) + q = kwargs.get("q", params["q"].default) + q_comp_names = None + if q is None: + kwargs["q"] = None + else: + if isinstance(q, tuple): + q, q_comp_names = q + if isinstance(q, float): + q = np.array([q]) + else: + q = np.array(q) + + if not np.all(q[1:] - q[:-1] > 0.0): + raise_log( + ValueError( + "`q` must be of type `float`, or a sequence of increasing order with unique values only. " + f"Received `q={q}`." + ), + logger=logger, + ) + if not np.all(q >= 0.0) & np.all(q <= 1.0): + raise_log( + ValueError( + f"All `q` values must be in the range `(>=0,<=1)`. Received `q={q}`." + ), + logger=logger, + ) + kwargs["q"] = (q, q_comp_names) + iterator = _build_tqdm_iterator( - iterable=zip(actual_series, pred_series), + iterable=zip(*input_series), verbose=verbose, total=len(actual_series), + desc=f"metric `{func.__name__}()`", ) - value_list = _parallel_apply( + # `vals` is a list of series metrics of length `len(actual_series)`. Each metric has shape + # `(n time steps, n components)`; + # - n times step is `1` if `time_reduction` is other than `None` + # - n components: is 1 if `component_reduction` is other than `None` + vals = _parallel_apply( iterator=iterator, fn=func, n_jobs=n_jobs, @@ -92,165 +231,440 @@ def wrapper_multi_ts_support(*args, **kwargs): fn_kwargs=kwargs, ) - # in case the reduction is not reducing the metrics sequence to a single value, e.g., if returning the - # np.ndarray of values with the identity function, we must handle the single TS case, where we should - # return a single value instead of a np.array of len 1 - - if len(value_list) == 1: - value_list = value_list[0] - - if "inter_reduction" in kwargs: - return kwargs["inter_reduction"](value_list) - else: - return signature(func).parameters["inter_reduction"].default(value_list) + # we flatten metrics along the time axis if n time steps == 1, + # and/or along component axis if n components == 1 + vals = [ + val[ + slice(None) if val.shape[TIME_AX] != 1 else 0, + slice(None) if val.shape[COMP_AX] != 1 else 0, + ] + for val in vals + ] + + # reduce metrics along series axis + if series_reduction is not None: + vals = kwargs["series_reduction"](vals, axis=0) + elif series_seq_type == SeriesType.SINGLE: + vals = vals[0] + + # flatten along series axis if n series == 1 + return vals return wrapper_multi_ts_support -def multivariate_support(func) -> Union[float, List[float]]: +def multivariate_support(func) -> Callable[..., METRIC_OUTPUT_TYPE]: """ This decorator transforms a metric function that takes as input two univariate TimeSeries instances into a function that takes two equally-sized multivariate TimeSeries instances, computes the pairwise univariate metrics for components with the same indices, and returns a float value that is computed as a function of all the - univariate metrics using a `reduction` subroutine passed as argument to the metric function. + univariate metrics using a `component_reduction` subroutine passed as argument to the metric function. """ @wraps(func) - def wrapper_multivariate_support(*args, **kwargs): + def wrapper_multivariate_support(*args, **kwargs) -> METRIC_OUTPUT_TYPE: + params = signature(func).parameters # we can avoid checks about args and kwargs since the input is adjusted by the previous decorator actual_series = args[0] pred_series = args[1] + num_series_in_args = 2 + + q, q_comp_names = kwargs.get("q"), None + if q is None: + # without quantiles, the number of components must match + if actual_series.n_components != pred_series.n_components: + raise_log( + ValueError( + f"Mismatch between number of components in `actual_series` " + f"(n={actual_series.width}) and `pred_series` (n={pred_series.width})." + ), + logger=logger, + ) + # compute median for stochastic predictions + if pred_series.is_stochastic: + q = np.array([0.5]) + else: + # `q` is required to be a tuple (handled by `multi_ts_support` wrapper) + if not isinstance(q, tuple) or not len(q) == 2: + raise_log( + ValueError( + "`q` must be of tuple of `(np.ndarray, Optional[pd.Index])` " + "where the (quantile values, optional quantile component names). " + f"Received `q={q}`." + ), + logger=logger, + ) + q, q_comp_names = q + if not pred_series.is_stochastic: + # quantile component names are required if the predictions are not stochastic (as for stochastic + # predictions, the quantiles can be retrieved from the sample dimension for each component) + if q_comp_names is None: + q_comp_names = pd.Index( + likelihood_component_names( + components=actual_series.components, + parameter_names=quantile_names(q=q), + ) + ) + if not q_comp_names.isin(pred_series.components).all(): + raise_log( + ValueError( + f"Computing a metric with quantile(s) `q={q}` is only supported for probabilistic " + f"`pred_series` (num samples > 1) or `pred_series` containing the predicted " + f"quantiles as columns / components. Either pass a probabilistic `pred_series` or " + f"a series containing the expected quantile components: {q_comp_names.tolist()} " + ), + logger=logger, + ) - raise_if_not( - actual_series.width == pred_series.width, - "The two TimeSeries instances must have the same width.", - logger, - ) - - value_list = [] - for i in range(actual_series.width): - value_list.append( - func( - actual_series.univariate_component(i), - pred_series.univariate_component(i), - *args[2:], - **kwargs + if "q" in params: + kwargs["q"] = (q, q_comp_names) + + # handle `insample` parameters for scaled metrics + input_series = (actual_series, pred_series) + if "insample" in params: + insample = args[2] + if actual_series.n_components != insample.n_components: + raise_log( + ValueError( + f"Mismatch between number of components in `actual_series` " + f"(n={actual_series.width}) and `insample` (n={insample.width}." + ), + logger=logger, ) - ) # [2:] since we already know the first two arguments are the series - if "reduction" in kwargs: - return kwargs["reduction"](value_list) + input_series += (insample,) + num_series_in_args += 1 + + vals = func(*input_series, *args[num_series_in_args:], **kwargs) + # bring vals to shape (n_time, n_comp, n_quantile) + if not 2 <= len(vals.shape) <= 3: + raise_log( + ValueError( + "Metric output must have 2 dimensions (n components, n quantiles) " + "for aggregated metrics (e.g. `mae()`, ...), " + "or 3 dimension (n times, n components, n quantiles) " + "for time dependent metrics (e.g. `ae()`, ...)" + ), + logger=logger, + ) + if len(vals.shape) == 2: + vals = np.expand_dims(vals, TIME_AX) + + time_reduction = _get_reduction( + kwargs=kwargs, + params=params, + red_name="time_reduction", + axis=TIME_AX, + sanity_check=False, + ) + if time_reduction is not None: + # -> (1, n_comp, n_quantile) + vals = np.expand_dims(time_reduction(vals, axis=TIME_AX), axis=TIME_AX) + + component_reduction = _get_reduction( + kwargs=kwargs, + params=params, + red_name="component_reduction", + axis=COMP_AX, + sanity_check=False, + ) + if component_reduction is not None: + # -> (*, n_quantile) + vals = component_reduction(vals, axis=COMP_AX) else: - return signature(func).parameters["reduction"].default(value_list) + # -> (*, n_comp * n_quantile), with order [c0_q0, c0_q1, ... c1_q0, c1_q1, ...] + vals = vals.reshape(vals.shape[0], -1) + return vals return wrapper_multivariate_support def _get_values( - series: TimeSeries, stochastic_quantile: Optional[float] = 0.5 + vals: np.ndarray, + vals_components: pd.Index, + actual_components: pd.Index, + q: Optional[tuple[Sequence[float], Union[Optional[pd.Index]]]] = None, ) -> np.ndarray: """ - Returns the numpy values of a time series. - For stochastic series, return either all sample values with (stochastic_quantile=None) or the quantile sample value - with (stochastic_quantile {>=0,<=1}) + Returns a deterministic or probabilistic numpy array from the values of a time series of shape + (times, components, samples / quantiles). + To extract quantile (sample) values from quantile or stachastic `vals`, use `q`. + + Parameters + ---------- + vals + A numpy array with the values of a TimeSeries (actual values or predictions). + vals_components + The components of the `vals` TimeSeries. + actual_components + The components of the actual TimeSeries. + q + Optionally, for stochastic or quantile series/values, return deterministic quantile values. + If not `None`, must a tuple with (quantile values, + `None` if `pred_series` is stochastic else the quantile component names). """ - if series.is_deterministic: - series_values = series.univariate_values() - else: # stochastic - if stochastic_quantile is None: - series_values = series.all_values(copy=False) - else: - series_values = series.quantile_timeseries( - quantile=stochastic_quantile - ).univariate_values() - return series_values + # return values as is (times, components, samples) + if q is None: + return vals + + q, q_names = q + if vals.shape[SMPL_AX] == 1: # deterministic (or quantile components) input + if q_names is not None: + # `q_names` are the component names of the predicted quantile parameters + # we extract the relevant quantile components with shape (times, components * quantiles) + vals = vals[:, vals_components.get_indexer(q_names)] + # rearrange into (times, components, quantiles) + vals = vals.reshape((len(vals), len(actual_components), -1)) + return vals + + # probabilistic input + # compute multiple quantiles for all times and components; with shape: (quantiles, times, components) + out = np.quantile(vals, q, axis=SMPL_AX) + # rearrange into (times, components, quantiles) + return out.transpose((1, 2, 0)) def _get_values_or_raise( series_a: TimeSeries, series_b: TimeSeries, intersect: bool, - stochastic_quantile: Optional[float] = 0.5, + q: Optional[tuple[Sequence[float], Union[Optional[pd.Index]]]] = None, remove_nan_union: bool = False, -) -> Tuple[np.ndarray, np.ndarray]: + is_insample: bool = False, +) -> tuple[np.ndarray, np.ndarray]: """Returns the processed numpy values of two time series. Processing can be customized with arguments - `intersect, stochastic_quantile, remove_nan_union`. - - Raises a ValueError if the two time series (or their intersection) do not have the same time index. + `intersect, q, remove_nan_union`. Parameters ---------- series_a - A univariate deterministic ``TimeSeries`` instance (the actual series). + A deterministic ``TimeSeries`` instance. If `is_insample=False`, it is the `actual_series`. + Otherwise, it is the `insample` series. series_b - A univariate (deterministic or stochastic) ``TimeSeries`` instance (the predicted series). + A deterministic or stochastic ``TimeSeries`` instance (the predictions `pred_series`). intersect A boolean for whether to only consider the time intersection between `series_a` and `series_b` - stochastic_quantile - Optionally, for stochastic predicted series, return either all sample values with (`stochastic_quantile=None`) - or any deterministic quantile sample values by setting `stochastic_quantile=quantile` {>=0,<=1}. + q + Optionally, for predicted stochastic or quantile series, return deterministic quantile values. + If not `None`, must a tuple with (quantile values, + `None` if `pred_series` is stochastic else the quantile component names). remove_nan_union - By setting `remove_non_union` to True, remove all indices from `series_a` and `series_b` which have a NaN value - in either of the two input series. - """ + By setting `remove_non_union` to True, sets all values from `series_a` and `series_b` to `np.nan` at indices + where any of the two series contain a NaN value. Only effective when `is_insample=False`. + is_insample + Whether `series_a` corresponds to the `insample` series for scaled metrics. - raise_if_not( - series_a.width == series_b.width, - "The two time series must have the same number of components", - logger, + Raises + ------ + ValueError + If `is_insample=False` and the two time series do not have at least a partially overlapping time index. + """ + make_copy = False + if not is_insample: + # get the time intersection and values of the two series (corresponds to `actual_series` and `pred_series` + if series_a.has_same_time_as(series_b) or not intersect: + vals_a_common = series_a.all_values(copy=make_copy) + vals_b_common = series_b.all_values(copy=make_copy) + else: + vals_a_common = series_a.slice_intersect_values(series_b, copy=make_copy) + vals_b_common = series_b.slice_intersect_values(series_a, copy=make_copy) + + vals_b = _get_values( + vals=vals_b_common, + vals_components=series_b.components, + actual_components=series_a.components, + q=q, + ) + else: + # for `insample` series we extract only values up until before start of `pred_series` + # find how many steps `insample` overlaps into `series_b` + end = ( + n_steps_between( + end=series_b.start_time(), start=series_a.end_time(), freq=series_a.freq + ) + - 1 + ) + if end > 0 or abs(end) >= len(series_a): + raise_log( + ValueError( + "The `insample` series must start before the `pred_series` and " + "extend at least until one time step before the start of `pred_series`." + ), + logger=logger, + ) + end = end or None + vals_a_common = series_a.all_values(copy=make_copy)[:end] + vals_b = None + vals_a = _get_values( + vals=vals_a_common, + vals_components=series_a.components, + actual_components=series_a.components, + q=([0.5], None), ) - raise_if_not(isinstance(intersect, bool), "The intersect parameter must be a bool") - - series_a_common = series_a.slice_intersect(series_b) if intersect else series_a - series_b_common = series_b.slice_intersect(series_a) if intersect else series_b + if not remove_nan_union or is_insample: + return vals_a, vals_b - raise_if_not( - series_a_common.has_same_time_as(series_b_common), - "The two time series (or their intersection) " - "must have the same time index." - "\nFirst series: {}\nSecond series: {}".format( - series_a.time_index, series_b.time_index - ), - logger, + isnan_mask = np.expand_dims( + np.logical_or(np.isnan(vals_a), np.isnan(vals_b)).any(axis=SMPL_AX), axis=-1 + ) + isnan_mask_pred = np.repeat(isnan_mask, vals_b.shape[SMPL_AX], axis=SMPL_AX) + return np.where(isnan_mask, np.nan, vals_a), np.where( + isnan_mask_pred, np.nan, vals_b ) - series_a_det = _get_values(series_a_common, stochastic_quantile=stochastic_quantile) - series_b_det = _get_values(series_b_common, stochastic_quantile=stochastic_quantile) - if not remove_nan_union: - return series_a_det, series_b_det +def _get_quantile_intervals( + vals: np.ndarray, + q: tuple[Sequence[float], Any], + q_interval: np.ndarray = None, +) -> tuple[np.ndarray, np.ndarray]: + """Returns the lower and upper bound values from `vals` for all quantile intervals in `q_interval`. - b_is_deterministic = bool(len(series_b_det.shape) == 1) - if b_is_deterministic: - isnan_mask = np.logical_or(np.isnan(series_a_det), np.isnan(series_b_det)) + Parameters + ---------- + vals + A numpy array with predicted quantile values of shape (n times, n components, n quantiles). + q + A tuple with (quantile values, any). + q_interval + A numpy array with the lower and upper quantile interval bound of shape (n intervals, 2). + """ + q, _ = q + # find index of every `q_interval` value in `q`; we have guarantees from support wrappers: + # - `q` has increasing order + # - `vals` has same order as `q` in dim 3 (quantile dim) + # - `q_interval` holds (lower q, upper q) in that order + q_idx = np.searchsorted(q, q_interval.flatten()).reshape(q_interval.shape) + return vals[:, :, q_idx[:, 0]], vals[:, :, q_idx[:, 1]] + + +def _get_wrapped_metric( + func: Callable[..., METRIC_OUTPUT_TYPE], n_wrappers: int = 2 +) -> Callable[..., METRIC_OUTPUT_TYPE]: + """Returns the inner metric function `func` which bypasses the decorators `multi_ts_support` and + `multivariate_support`. It significantly decreases process time compared to calling `func` directly. + Only use this to compute a pre-defined metric within the scope of another metric. + """ + if not 2 <= n_wrappers <= 3: + raise_log( + NotImplementedError("Only 2-3 wrappers are currently supported"), + logger=logger, + ) + if n_wrappers == 2: + return func.__wrapped__.__wrapped__ else: - isnan_mask = np.logical_or( - np.isnan(series_a_det), np.isnan(series_b_det).any(axis=2).flatten() + return func.__wrapped__.__wrapped__.__wrapped__ + + +def _get_reduction( + kwargs, params, red_name, axis, sanity_check: bool = True +) -> Optional[Callable[..., np.ndarray]]: + """Returns the reduction function either from user kwargs or metric default. + Optionally performs sanity checks for presence of `axis` parameter, and correct output type and + reduced shape.""" + if red_name not in params: + return None + + red_fn = kwargs[red_name] if red_name in kwargs else params[red_name].default + if not sanity_check: + return red_fn + + if red_fn is not None: + red_params = inspect.signature(red_fn).parameters + if "axis" not in red_params: + raise_log( + ValueError( + f"Invalid `{red_name}` function: Must have a parameter called `axis`." + ), + logger=logger, + ) + # verify `red_fn` reduces to array with correct shape + shape_in = (2, 1) if axis == 0 else (1, 2) + out = red_fn(np.zeros(shape_in), axis=axis) + + if not isinstance(out, np.ndarray): + raise_log( + ValueError( + f"Invalid `{red_name}` function output type: Expected type " + f"`np.ndarray`, received type=`{type(out)}`." + ), + logger=logger, + ) + shape_invalid = out.shape != (1,) + if shape_invalid: + raise_log( + ValueError( + f"Invalid `{red_name}` function output shape: The function must reduce an input " + f"`np.ndarray` of shape (t, c) to a `np.ndarray` of shape `(c,)`. " + f"However, the function reduced a test array of shape `{shape_in}` to " + f"`{out.shape}`." + ), + logger=logger, + ) + return red_fn + + +def _get_error_scale( + insample: TimeSeries, + pred_series: TimeSeries, + m: int, + metric: str, +): + """Computes the error scale based on a naive seasonal forecasts on `insample` values with seasonality `m`.""" + if not isinstance(m, int): + raise_log( + ValueError(f"Seasonality `m` must be of type `int`, received `m={m}`"), + logger=logger, ) - return np.delete(series_a_det, isnan_mask), np.delete( - series_b_det, isnan_mask, axis=0 + + # `x_t` are the true `y` values before the start of `y_pred` + x_t, _ = _get_values_or_raise( + insample, pred_series, intersect=False, remove_nan_union=False, is_insample=True ) + diff = x_t[m:] - x_t[:-m] + if metric == "mae": + scale = np.nanmean(np.abs(diff), axis=TIME_AX) + elif metric == "mse": + scale = np.nanmean(np.power(diff, 2), axis=TIME_AX) + elif metric == "rmse": + scale = np.sqrt(np.nanmean(np.power(diff, 2), axis=TIME_AX)) + else: + raise_log( + ValueError( + f"unknown `metric={metric}`. Must be one of ('mae', 'mse', 'rmse')." + ), + logger=logger, + ) + + if np.isclose(scale, 0.0).any(): + raise_log(ValueError("cannot use MASE with periodical signals"), logger=logger) + return scale @multi_ts_support @multivariate_support -def mae( +def err( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """Mean Absolute Error (MAE). + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Error (ERR). - For two time series :math:`y^1` and :math:`y^2` of length :math:`T`, it is computed as + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column, (optional) quantile, and time step :math:`t` as: - .. math:: \\frac{1}{T}\\sum_{t=1}^T{(|y^1_t - y^2_t|)}. + .. math:: y_t - \\hat{y}_t - If any of the series is stochastic (containing several samples), the median sample value is considered. + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -261,15 +675,24 @@ def mae( intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -279,35 +702,62 @@ def mae( Returns ------- - Union[float, List[float]] - The Mean Absolute Error (MAE) + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. """ - y1, y2 = _get_values_or_raise( - actual_series, pred_series, intersect, remove_nan_union=True + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=False, + q=q, ) - return np.mean(np.abs(y1 - y2)) + return y_true - y_pred @multi_ts_support @multivariate_support -def mse( +def merr( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """Mean Squared Error (MSE). + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Error (MERR). - For two time series :math:`y^1` and :math:`y^2` of length :math:`T`, it is computed as + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column and (optional) quantile as: - .. math:: \\frac{1}{T}\\sum_{t=1}^T{(y^1_t - y^2_t)^2}. + .. math:: \\frac{1}{T}\\sum_{t=1}^T{(y_t - \\hat{y}_t)} - If any of the series is stochastic (containing several samples), the median sample value is considered. + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -318,15 +768,19 @@ def mse( intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -336,35 +790,60 @@ def mse( Returns ------- - Union[float, List[float]] - The Mean Squared Error (MSE) + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. """ - - y_true, y_pred = _get_values_or_raise( - actual_series, pred_series, intersect, remove_nan_union=True + return np.nanmean( + _get_wrapped_metric(err)( + actual_series, + pred_series, + intersect, + q=q, + ), + axis=TIME_AX, ) - return np.mean((y_true - y_pred) ** 2) @multi_ts_support @multivariate_support -def rmse( +def ae( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """Root Mean Squared Error (RMSE). + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Absolute Error (AE). - For two time series :math:`y^1` and :math:`y^2` of length :math:`T`, it is computed as + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column, (optional) quantile, and time step :math:`t` as: - .. math:: \\sqrt{\\frac{1}{T}\\sum_{t=1}^T{(y^1_t - y^2_t)^2}}. + .. math:: |y_t - \\hat{y}_t| - If any of the series is stochastic (containing several samples), the median sample value is considered. + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -375,15 +854,24 @@ def rmse( intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -393,33 +881,62 @@ def rmse( Returns ------- - Union[float, List[float]] - The Root Mean Squared Error (RMSE) + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. """ - return np.sqrt(mse(actual_series, pred_series, intersect)) + + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=False, + q=q, + ) + return np.abs(y_true - y_pred) @multi_ts_support @multivariate_support -def rmsle( +def mae( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """Root Mean Squared Log Error (RMSLE). - - For two time series :math:`y^1` and :math:`y^2` of length :math:`T`, it is computed as + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Absolute Error (MAE). - .. math:: \\sqrt{\\frac{1}{T}\\sum_{t=1}^T{\\left(\\log{(y^1_t + 1)} - \\log{(y^2_t + 1)}\\right)^2}}, + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column and (optional) quantile as: - using the natural logarithm. + .. math:: \\frac{1}{T}\\sum_{t=1}^T{|y_t - \\hat{y}_t|} - If any of the series is stochastic (containing several samples), the median sample value is considered. + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -430,15 +947,19 @@ def rmsle( intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -448,40 +969,70 @@ def rmsle( Returns ------- - Union[float, List[float]] - The Root Mean Squared Log Error (RMSLE) + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. """ - - y1, y2 = _get_values_or_raise( - actual_series, pred_series, intersect, remove_nan_union=True + return np.nanmean( + _get_wrapped_metric(ae)( + actual_series, + pred_series, + intersect, + q=q, + ), + axis=TIME_AX, ) - y1, y2 = np.log(y1 + 1), np.log(y2 + 1) - return np.sqrt(np.mean((y1 - y2) ** 2)) @multi_ts_support @multivariate_support -def coefficient_of_variation( +def ase( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], + insample: Union[TimeSeries, Sequence[TimeSeries]], + m: int = 1, intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """Coefficient of Variation (percentage). + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Absolute Scaled Error (ASE) (see [1]_ for more information on scaled forecasting errors). + + It is the Absolute Error (AE) scaled by the Mean AE (MAE) of the naive m-seasonal forecast. - Given a time series of actual values :math:`y_t` and a time series of predicted values :math:`\\hat{y}_t`, - it is a percentage value, computed as + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column, (optional) quantile, and time step :math:`t` as: - .. math:: 100 \\cdot \\text{RMSE}(y_t, \\hat{y}_t) / \\bar{y_t}, + .. math:: \\frac{AE(y_{t_p+1:t_p+T}, \\hat{y}_{t_p+1:t_p+T})}{E_m}, - where :math:`\\text{RMSE}()` denotes the root mean squared error, and - :math:`\\bar{y_t}` is the average of :math:`y_t`. + where :math:`t_p` is the prediction time (one step before the first forecasted point), :math:`AE` is the Absolute + Error (:func:`~darts.metrics.metrics.ae`), and :math:`E_m` is the Mean AE (MAE) of the naive m-seasonal + forecast on the `insample` series :math:`y_{0:t_p}` (the true series ending at :math:`t_p`): - Currently this only supports deterministic series (made of one sample). + .. math:: E_m = MAE(y_{m:t_p}, y_{0:t_p - m}). + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -489,18 +1040,34 @@ def coefficient_of_variation( The (sequence of) actual series. pred_series The (sequence of) predicted series. + insample + The training series used to forecast `pred_series` . This series serves to compute the scale of the error + obtained by a naive forecaster on the training data. + m + The seasonality to use for differencing to compute the error scale :math:`E_m` (as described in the metric + description). :math:`m=1` corresponds to a non-seasonal :math:`E_m` (e.g. naive repetition of the last observed + value), whereas :math:`m>1` corresponds to a seasonal :math:`E_m`. intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -508,42 +1075,83 @@ def coefficient_of_variation( verbose Optionally, whether to print operations progress + Raises + ------ + ValueError + If the `insample` series is periodic ( :math:`y_t = y_{t-m}` ) or any series in `insample` does not end one + time step before the start of the corresponding forecast in `pred_series`. + Returns ------- - Union[float, List[float]] - The Coefficient of Variation + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://www.pmorgan.com.au/tutorials/mae%2C-mape%2C-mase-and-the-scaled-rmse/ """ - - y_true, y_pred = _get_values_or_raise( - actual_series, pred_series, intersect, remove_nan_union=True + error_scale = _get_error_scale(insample, pred_series, m=m, metric="mae") + errors = _get_wrapped_metric(ae)( + actual_series, + pred_series, + intersect, + q=q, ) - # not calling rmse as y_true and y_pred are np.ndarray - return 100 * np.sqrt(np.mean((y_true - y_pred) ** 2)) / y_true.mean() + return errors / error_scale @multi_ts_support @multivariate_support -def mape( +def mase( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], + insample: Union[TimeSeries, Sequence[TimeSeries]], + m: int = 1, intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """Mean Absolute Percentage Error (MAPE). + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Absolute Scaled Error (MASE) (see [1]_ for more information on scaled forecasting errors). - Given a time series of actual values :math:`y_t` and a time series of predicted values :math:`\\hat{y}_t` - both of length :math:`T`, it is a percentage value computed as + It is the Mean Absolute Error (MAE) scaled by the MAE of the naive m-seasonal forecast. - .. math:: 100 \\cdot \\frac{1}{T} \\sum_{t=1}^{T}{\\left| \\frac{y_t - \\hat{y}_t}{y_t} \\right|}. + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column and (optional) quantile as: - Note that it will raise a `ValueError` if :math:`y_t = 0` for some :math:`t`. Consider using - the Mean Absolute Scaled Error (MASE) in these cases. + .. math:: \\frac{MAE(y_{t_p+1:t_p+T}, \\hat{y}_{t_p+1:t_p+T})}{E_m}, + + where :math:`t_p` is the prediction time (one step before the first forecasted point), :math:`MAE` is the Mean + Absolute Error (:func:`~darts.metrics.metrics.mae`), and :math:`E_m` is the MAE of the naive m-seasonal + forecast on the `insample` series :math:`y_{0:t_p}` (the true series ending at :math:`t_p`): - If any of the series is stochastic (containing several samples), the median sample value is considered. + .. math:: E_m = MAE(y_{m:t_p}, y_{0:t_p - m}). + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -551,18 +1159,29 @@ def mape( The (sequence of) actual series. pred_series The (sequence of) predicted series. + insample + The training series used to forecast `pred_series` . This series serves to compute the scale of the error + obtained by a naive forecaster on the training data. + m + The seasonality to use for differencing to compute the error scale :math:`E_m` (as described in the metric + description). :math:`m=1` corresponds to a non-seasonal :math:`E_m` (e.g. naive repetition of the last observed + value), whereas :math:`m>1` corresponds to a seasonal :math:`E_m`. intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -573,50 +1192,71 @@ def mape( Raises ------ ValueError - If the actual series contains some zeros. + If the `insample` series is periodic ( :math:`y_t = y_{t-m}` ) or any series in `insample` does not end one + time step before the start of the corresponding forecast in `pred_series`. Returns ------- - Union[float, List[float]] - The Mean Absolute Percentage Error (MAPE) + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://www.pmorgan.com.au/tutorials/mae%2C-mape%2C-mase-and-the-scaled-rmse/ """ - - y_true, y_hat = _get_values_or_raise( - actual_series, pred_series, intersect, remove_nan_union=True - ) - raise_if_not( - (y_true != 0).all(), - "The actual series must be strictly positive to compute the MAPE.", - logger, + return np.nanmean( + _get_wrapped_metric(ase)( + actual_series, + pred_series, + insample, + m=m, + intersect=intersect, + q=q, + ), + axis=TIME_AX, ) - return 100.0 * np.mean(np.abs((y_true - y_hat) / y_true)) @multi_ts_support @multivariate_support -def smape( +def se( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """symmetric Mean Absolute Percentage Error (sMAPE). - - Given a time series of actual values :math:`y_t` and a time series of predicted values :math:`\\hat{y}_t` - both of length :math:`T`, it is a percentage value computed as + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Squared Error (SE). - .. math:: - 200 \\cdot \\frac{1}{T} - \\sum_{t=1}^{T}{\\frac{\\left| y_t - \\hat{y}_t \\right|}{\\left| y_t \\right| + \\left| \\hat{y}_t \\right|} }. + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column, (optional) quantile, and time step :math:`t` as: - Note that it will raise a `ValueError` if :math:`\\left| y_t \\right| + \\left| \\hat{y}_t \\right| = 0` - for some :math:`t`. Consider using the Mean Absolute Scaled Error (MASE) in these cases. + .. math:: (y_t - \\hat{y}_t)^2. - If any of the series is stochastic (containing several samples), the median sample value is considered. + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -627,15 +1267,24 @@ def smape( intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -643,47 +1292,64 @@ def smape( verbose Optionally, whether to print operations progress - Raises - ------ - ValueError - If the actual series and the pred series contains some zeros at the same time index. - Returns ------- - Union[float, List[float]] - The symmetric Mean Absolute Percentage Error (sMAPE) + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. """ - y_true, y_hat = _get_values_or_raise( - actual_series, pred_series, intersect, remove_nan_union=True - ) - raise_if_not( - np.logical_or(y_true != 0, y_hat != 0).all(), - "The actual series must be strictly positive to compute the sMAPE.", - logger, + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=False, + q=q, ) - return 200.0 * np.mean(np.abs(y_true - y_hat) / (np.abs(y_true) + np.abs(y_hat))) + return (y_true - y_pred) ** 2 -# mase cannot leverage multivariate and multi_ts with the decorator since also the `insample` is a Sequence[TimeSeries] -def mase( +@multi_ts_support +@multivariate_support +def mse( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], - insample: Union[TimeSeries, Sequence[TimeSeries]], - m: Optional[int] = 1, intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """Mean Absolute Scaled Error (MASE). + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Squared Error (MSE). - See `Mean absolute scaled error wikipedia page `_ - for details about the MASE and how it is computed. + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column and (optional) quantile as: - If any of the series is stochastic (containing several samples), the median sample value is considered. + .. math:: \\frac{1}{T}\\sum_{t=1}^T{(y_t - \\hat{y}_t)^2}. + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -691,26 +1357,22 @@ def mase( The (sequence of) actual series. pred_series The (sequence of) predicted series. - insample - The training series used to forecast `pred_series` . - This series serves to compute the scale of the error obtained by a naive forecaster on the training data. - m - Optionally, the seasonality to use for differencing. - `m=1` corresponds to the non-seasonal MASE, whereas `m>1` corresponds to seasonal MASE. - If `m=None`, it will be tentatively inferred - from the auto-correlation function (ACF). It will fall back to a value of 1 if this fails. intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -718,162 +1380,72 @@ def mase( verbose Optionally, whether to print operations progress - Raises - ------ - ValueError - If the `insample` series is periodic ( :math:`X_t = X_{t-m}` ) - Returns ------- - Union[float, List[float]] - The Mean Absolute Scaled Error (MASE) + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. """ - - def _multivariate_mase( - actual_series: TimeSeries, - pred_series: TimeSeries, - insample: TimeSeries, - m: int, - intersect: bool, - reduction: Callable[[np.ndarray], float], - ): - - raise_if_not( - actual_series.width == pred_series.width, - "The two TimeSeries instances must have the same width.", - logger, - ) - raise_if_not( - actual_series.width == insample.width, - "The insample TimeSeries must have the same width as the other series.", - logger, - ) - raise_if_not( - insample.end_time() + insample.freq == pred_series.start_time(), - "The pred_series must be the forecast of the insample series", - logger, - ) - - insample_ = ( - insample.quantile_timeseries(quantile=0.5) - if insample.is_stochastic - else insample - ) - - value_list = [] - for i in range(actual_series.width): - # old implementation of mase on univariate TimeSeries - if m is None: - test_season, m = check_seasonality(insample) - if not test_season: - warn( - "No seasonality found when computing MASE. Fixing the period to 1.", - UserWarning, - ) - m = 1 - - y_true, y_hat = _get_values_or_raise( - actual_series.univariate_component(i), - pred_series.univariate_component(i), - intersect, - remove_nan_union=False, - ) - - x_t = insample_.univariate_component(i).values() - errors = np.abs(y_true - y_hat) - scale = np.mean(np.abs(x_t[m:] - x_t[:-m])) - raise_if_not( - not np.isclose(scale, 0), - "cannot use MASE with periodical signals", - logger, - ) - value_list.append(np.mean(errors / scale)) - - return reduction(value_list) - - if isinstance(actual_series, TimeSeries): - raise_if_not( - isinstance(pred_series, TimeSeries), - "Expecting pred_series to be TimeSeries", - ) - raise_if_not( - isinstance(insample, TimeSeries), "Expecting insample to be TimeSeries" - ) - return _multivariate_mase( - actual_series=actual_series, - pred_series=pred_series, - insample=insample, - m=m, - intersect=intersect, - reduction=reduction, - ) - - elif isinstance(actual_series, Sequence) and isinstance( - actual_series[0], TimeSeries - ): - - raise_if_not( - isinstance(pred_series, Sequence) - and isinstance(pred_series[0], TimeSeries), - "Expecting pred_series to be a Sequence[TimeSeries]", - ) - raise_if_not( - isinstance(insample, Sequence) and isinstance(insample[0], TimeSeries), - "Expecting insample to be a Sequence[TimeSeries]", - ) - raise_if_not( - len(pred_series) == len(actual_series) - and len(pred_series) == len(insample), - "The TimeSeries sequences must have the same length.", - logger, - ) - - raise_if_not(isinstance(n_jobs, int), "n_jobs must be an integer") - raise_if_not(isinstance(verbose, bool), "verbose must be a bool") - - iterator = _build_tqdm_iterator( - iterable=zip(actual_series, pred_series, insample), - verbose=verbose, - total=len(actual_series), - ) - - value_list = _parallel_apply( - iterator=iterator, - fn=_multivariate_mase, - n_jobs=n_jobs, - fn_args=dict(), - fn_kwargs={"m": m, "intersect": intersect, "reduction": reduction}, - ) - return inter_reduction(value_list) - else: - raise_log( - ValueError( - "Input type not supported, only TimeSeries and Sequence[TimeSeries] are accepted." - ) - ) + return np.nanmean( + _get_wrapped_metric(se)( + actual_series, + pred_series, + intersect, + q=q, + ), + axis=TIME_AX, + ) @multi_ts_support @multivariate_support -def ope( +def sse( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], + insample: Union[TimeSeries, Sequence[TimeSeries]], + m: int = 1, intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """Overall Percentage Error (OPE). + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Squared Scaled Error (SSE) (see [1]_ for more information on scaled forecasting errors). - Given a time series of actual values :math:`y_t` and a time series of predicted values :math:`\\hat{y}_t` - both of length :math:`T`, it is a percentage value computed as + It is the Squared Error (SE) scaled by the Mean SE (MSE) of the naive m-seasonal forecast. - .. math:: 100 \\cdot \\left| \\frac{\\sum_{t=1}^{T}{y_t} - - \\sum_{t=1}^{T}{\\hat{y}_t}}{\\sum_{t=1}^{T}{y_t}} \\right|. + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column, (optional) quantile, and time step :math:`t` as: + + .. math:: \\frac{SE(y_{t_p+1:t_p+T}, \\hat{y}_{t_p+1:t_p+T})}{E_m}, + + where :math:`t_p` is the prediction time (one step before the first forecasted point), :math:`SE` is the Squared + Error (:func:`~darts.metrics.metrics.se`), and :math:`E_m` is the Mean SE (MSE) of the naive m-seasonal + forecast on the `insample` series :math:`y_{0:t_p}` (the true series ending at :math:`t_p`): - If any of the series is stochastic (containing several samples), the median sample value is considered. + .. math:: E_m = MSE(y_{m:t_p}, y_{0:t_p - m}). + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -881,18 +1453,34 @@ def ope( The (sequence of) actual series. pred_series The (sequence of) predicted series. + insample + The training series used to forecast `pred_series` . This series serves to compute the scale of the error + obtained by a naive forecaster on the training data. + m + The seasonality to use for differencing to compute the error scale :math:`E_m` (as described in the metric + description). :math:`m=1` corresponds to a non-seasonal :math:`E_m` (e.g. naive repetition of the last observed + value), whereas :math:`m>1` corresponds to a seasonal :math:`E_m`. intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -903,47 +1491,80 @@ def ope( Raises ------ ValueError - If :math:`\\sum_{t=1}^{T}{y_t} = 0`. + If the `insample` series is periodic ( :math:`y_t = y_{t-m}` ) or any series in `insample` does not end one + time step before the start of the corresponding forecast in `pred_series`. Returns ------- - Union[float, List[float]] - The Overall Percentage Error (OPE) + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://www.pmorgan.com.au/tutorials/mae%2C-mape%2C-mase-and-the-scaled-rmse/ """ - - y_true, y_pred = _get_values_or_raise( - actual_series, pred_series, intersect, remove_nan_union=True - ) - y_true_sum, y_pred_sum = np.sum(y_true), np.sum(y_pred) - raise_if_not( - y_true_sum > 0, - "The series of actual value cannot sum to zero when computing OPE.", - logger, + error_scale = _get_error_scale(insample, pred_series, m=m, metric="mse") + errors = _get_wrapped_metric(se)( + actual_series, + pred_series, + intersect, + q=q, ) - return np.abs((y_true_sum - y_pred_sum) / y_true_sum) * 100.0 + return errors / error_scale @multi_ts_support @multivariate_support -def marre( +def msse( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], + insample: Union[TimeSeries, Sequence[TimeSeries]], + m: int = 1, intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """Mean Absolute Ranged Relative Error (MARRE). + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Squared Scaled Error (MSSE) (see [1]_ for more information on scaled forecasting errors). - Given a time series of actual values :math:`y_t` and a time series of predicted values :math:`\\hat{y}_t` - both of length :math:`T`, it is a percentage value computed as + It is the Mean Squared Error (MSE) scaled by the MSE of the naive m-seasonal forecast. - .. math:: 100 \\cdot \\frac{1}{T} \\sum_{t=1}^{T} {\\left| \\frac{y_t - \\hat{y}_t} {\\max_t{y_t} - - \\min_t{y_t}} \\right|} + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column and (optional) quantile as: + + .. math:: \\frac{MSE(y_{t_p+1:t_p+T}, \\hat{y}_{t_p+1:t_p+T})}{E_m}, - If any of the series is stochastic (containing several samples), the median sample value is considered. + where :math:`t_p` is the prediction time (one step before the first forecasted point), :math:`MSE` is the Mean + Squared Error (:func:`~darts.metrics.metrics.mse`), and :math:`E_m` is the MSE of the naive m-seasonal + forecast on the `insample` series :math:`y_{0:t_p}` (the true series ending at :math:`t_p`): + + .. math:: E_m = MSE(y_{m:t_p}, y_{0:t_p - m}). + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -951,18 +1572,29 @@ def marre( The (sequence of) actual series. pred_series The (sequence of) predicted series. + insample + The training series used to forecast `pred_series` . This series serves to compute the scale of the error + obtained by a naive forecaster on the training data. + m + The seasonality to use for differencing to compute the error scale :math:`E_m` (as described in the metric + description). :math:`m=1` corresponds to a non-seasonal :math:`E_m` (e.g. naive repetition of the last observed + value), whereas :math:`m>1` corresponds to a seasonal :math:`E_m`. intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -973,47 +1605,70 @@ def marre( Raises ------ ValueError - If :math:`\\max_t{y_t} = \\min_t{y_t}`. + If the `insample` series is periodic ( :math:`y_t = y_{t-m}` ) or any series in `insample` does not end one + time step before the start of the corresponding forecast in `pred_series`. Returns ------- - Union[float, List[float]] - The Mean Absolute Ranged Relative Error (MARRE) + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://www.pmorgan.com.au/tutorials/mae%2C-mape%2C-mase-and-the-scaled-rmse/ """ - - y_true, y_hat = _get_values_or_raise( - actual_series, pred_series, intersect, remove_nan_union=True - ) - raise_if_not( - y_true.max() > y_true.min(), - "The difference between the max and min values must be strictly" - "positive to compute the MARRE.", - logger, + return np.nanmean( + _get_wrapped_metric(sse)( + actual_series, + pred_series, + insample, + m=m, + intersect=intersect, + q=q, + ), + axis=TIME_AX, ) - true_range = y_true.max() - y_true.min() - return 100.0 * np.mean(np.abs((y_true - y_hat) / true_range)) @multi_ts_support @multivariate_support -def r2_score( +def rmse( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> Union[float, np.ndarray]: - """Coefficient of Determination :math:`R^2`. + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Root Mean Squared Error (RMSE). - See `Coefficient of determination wikipedia page `_ - for details about the :math:`R^2` score and how it is computed. - Please note that this metric is not symmetric, `actual_series` should correspond to the ground truth series, - whereas `pred_series` should correspond to the predicted series. + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column and (optional) quantile as: - If any of the series is stochastic (containing several samples), the median sample value is considered. + .. math:: \\sqrt{\\frac{1}{T}\\sum_{t=1}^T{(y_t - \\hat{y}_t)^2}} + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -1024,15 +1679,19 @@ def r2_score( intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -1042,44 +1701,68 @@ def r2_score( Returns ------- - Union[float, List[float]] - The Coefficient of Determination :math:`R^2` + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. """ - y1, y2 = _get_values_or_raise( - actual_series, pred_series, intersect, remove_nan_union=True + return np.sqrt( + _get_wrapped_metric(mse)( + actual_series, + pred_series, + intersect, + q=q, + ) ) - ss_errors = np.sum((y1 - y2) ** 2) - y_hat = y1.mean() - ss_tot = np.sum((y1 - y_hat) ** 2) - return 1 - ss_errors / ss_tot -# Dynamic Time Warping @multi_ts_support -def dtw_metric( +@multivariate_support +def rmsse( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], - metric: Callable[ - [ - Union[TimeSeries, Sequence[TimeSeries]], - Union[TimeSeries, Sequence[TimeSeries]], - ], - Union[float, np.ndarray], - ] = mae, + insample: Union[TimeSeries, Sequence[TimeSeries]], + m: int = 1, + intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, verbose: bool = False, - **kwargs -) -> float: - """ - Applies Dynamic Time Warping to actual_series and pred_series before passing it into the metric. - Enables comparison between series of different lengths, phases and time indices. +) -> METRIC_OUTPUT_TYPE: + """Root Mean Squared Scaled Error (RMSSE) (see [1]_ for more information on scaled forecasting errors). + + It is the Root Mean Squared Error (RMSE) scaled by the RMSE of the naive m-seasonal forecast. + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column and (optional) quantile as: + + .. math:: \\frac{RMSE(y_{t_p+1:t_p+T}, \\hat{y}_{t_p+1:t_p+T})}{E_m}, - Defaults to using mae as a metric. + where :math:`t_p` is the prediction time (one step before the first forecasted point), :math:`RMSE` is the Root + Mean Squared Error (:func:`~darts.metrics.metrics.rmse`), and :math:`E_m` is the RMSE of the naive m-seasonal + forecast on the `insample` series :math:`y_{0:t_p}` (the true series ending at :math:`t_p`): - See darts.dataprocessing.dtw.dtw for more supported parameters. + .. math:: E_m = RMSE(y_{m:t_p}, y_{0:t_p - m}). + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -1087,17 +1770,29 @@ def dtw_metric( The (sequence of) actual series. pred_series The (sequence of) predicted series. - metric - The selected metric with signature '[[TimeSeries, TimeSeries], float]' to use. Default: `mae`. - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + insample + The training series used to forecast `pred_series` . This series serves to compute the scale of the error + obtained by a naive forecaster on the training data. + m + The seasonality to use for differencing to compute the error scale :math:`E_m` (as described in the metric + description). :math:`m=1` corresponds to a non-seasonal :math:`E_m` (e.g. naive repetition of the last observed + value), whereas :math:`m>1` corresponds to a seasonal :math:`E_m`. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -1105,56 +1800,260 @@ def dtw_metric( verbose Optionally, whether to print operations progress + Raises + ------ + ValueError + If the `insample` series is periodic ( :math:`y_t = y_{t-m}` ) or any series in `insample` does not end one + time step before the start of the corresponding forecast in `pred_series`. + Returns ------- float - Result of calling metric(warped_series1, warped_series2) + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://www.pmorgan.com.au/tutorials/mae%2C-mape%2C-mase-and-the-scaled-rmse/ """ + error_scale = _get_error_scale(insample, pred_series, m=m, metric="rmse") + errors = _get_wrapped_metric(rmse)( + actual_series, + pred_series, + intersect, + q=q, + ) + return errors / error_scale - alignment = dtw.dtw(actual_series, pred_series, **kwargs) - if metric == mae and "distance" not in kwargs: - return alignment.mean_distance() - warped_actual_series, warped_pred_series = alignment.warped() +@multi_ts_support +@multivariate_support +def sle( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Squared Log Error (SLE). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column, (optional) quantile, and time step :math:`t` as: + + .. math:: \\left(\\log{(y_t + 1)} - \\log{(\\hat{y} + 1)}\\right)^2 + + using the natural logarithm. + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ - return metric(warped_actual_series, warped_pred_series) + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=False, + q=q, + ) + y_true, y_pred = np.log(y_true + 1), np.log(y_pred + 1) + return (y_true - y_pred) ** 2 -# rho-risk (quantile risk) @multi_ts_support @multivariate_support -def rho_risk( +def rmsle( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], - rho: float = 0.5, intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> float: + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Root Mean Squared Log Error (RMSLE). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column and (optional) quantile as: + + .. math:: \\sqrt{\\frac{1}{T}\\sum_{t=1}^T{\\left(\\log{(y_t + 1)} - \\log{(\\hat{y}_t + 1)}\\right)^2}} + + using the natural logarithm. + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). - """:math:`\\rho`-risk (rho-risk or quantile risk). + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress - Given a time series of actual values :math:`y_t` of length :math:`T` and a time series of stochastic predictions - (containing N samples) :math:`\\hat{y}_t` of shape :math:`T \\times N`, rho-risk is a metric that quantifies the - accuracy of a specific quantile :math:`\\rho` from the predicted value distribution. + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + return np.sqrt( + np.nanmean( + _get_wrapped_metric(sle)( + actual_series, + pred_series, + intersect, + q=q, + ), + axis=TIME_AX, + ) + ) - For a univariate stochastic predicted TimeSeries the :math:`\\rho`-risk is given by: - .. math:: \\frac{ L_{\\rho} \\left( Z, \\hat{Z}_{\\rho} \\right) } {Z}, +@multi_ts_support +@multivariate_support +def ape( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Absolute Percentage Error (APE). - where :math:`L_{\\rho} \\left( Z, \\hat{Z}_{\\rho} \\right)` is the :math:`\\rho`-loss function: + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a + percentage value per component/column and time step :math:`t` with: - .. math:: L_{\\rho} \\left( Z, \\hat{Z}_{\\rho} \\right) = 2 \\left( Z - \\hat{Z}_{\\rho} \\right) - \\left( \\rho I_{\\hat{Z}_{\\rho} < Z} - \\left( 1 - \\rho \\right) I_{\\hat{Z}_{\\rho} \\geq Z} \\right), + .. math:: 100 \\cdot \\left| \\frac{y_t - \\hat{y}_t}{y_t} \\right| - where :math:`Z = \\sum_{t=1}^{T} y_t` (1) is the aggregated target value and :math:`\\hat{Z}_{\\rho}` is the - :math:`\\rho`-quantile of the predicted values. For this, each sample realization :math:`i \\in N` is first - aggregated over the time span similar to (1) with :math:`\\hat{Z}_{i} = \\sum_{t=1}^{T} \\hat{y}_{i,t}`. + Note that it will raise a `ValueError` if :math:`y_t = 0` for some :math:`t`. Consider using + the Absolute Scaled Error (:func:`~darts.metrics.metrics.ase`) in these cases. - :math:`I_{cond} = 1` if cond is True else :math:`0`` + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -1162,20 +2061,27 @@ def rho_risk( The (sequence of) actual series. pred_series The (sequence of) predicted series. - rho - The quantile (float [0, 1]) of interest for the risk evaluation. intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -1183,56 +2089,170 @@ def rho_risk( verbose Optionally, whether to print operations progress + Raises + ------ + ValueError + If `actual_series` contains some zeros. + Returns ------- - Union[float, List[float]] - The rho-risk metric + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. """ - raise_if_not( - pred_series.is_stochastic, - "rho (quantile) loss should only be computed for stochastic predicted TimeSeries.", - ) - - z_true, z_hat = _get_values_or_raise( + y_true, y_pred = _get_values_or_raise( actual_series, pred_series, intersect, - stochastic_quantile=None, - remove_nan_union=True, + remove_nan_union=False, + q=q, ) + if not (y_true != 0).all(): + raise_log( + ValueError( + "`actual_series` must be strictly positive to compute the MAPE." + ), + logger=logger, + ) + return 100.0 * np.abs((y_true - y_pred) / y_true) + + +@multi_ts_support +@multivariate_support +def mape( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Absolute Percentage Error (MAPE). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a + percentage value per component/column and (optional) quantile with: + + .. math:: 100 \\cdot \\frac{1}{T} \\sum_{t=1}^{T}{\\left| \\frac{y_t - \\hat{y}_t}{y_t} \\right|} - z_true = z_true.sum(axis=0) - z_hat = z_hat.sum(axis=0) # aggregate all individual sample realizations + Note that it will raise a `ValueError` if :math:`y_t = 0` for some :math:`t`. Consider using + the Mean Absolute Scaled Error (:func:`~darts.metrics.metrics.mase`) in these cases. + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress - z_hat_rho = np.quantile(z_hat, q=rho) # get the quantile from aggregated samples + Raises + ------ + ValueError + If `actual_series` contains some zeros. - pred_above = np.where(z_hat_rho >= z_true, 1, 0) - pred_below = np.where(z_hat_rho < z_true, 1, 0) + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ - rho_loss = 2 * (z_true - z_hat_rho) * (rho * pred_below - (1 - rho) * pred_above) - return rho_loss / z_true + return np.nanmean( + _get_wrapped_metric(ape)( + actual_series, + pred_series, + intersect, + q=q, + ), + axis=TIME_AX, + ) -# Quantile Loss (Pinball Loss) @multi_ts_support @multivariate_support -def quantile_loss( +def wmape( actual_series: Union[TimeSeries, Sequence[TimeSeries]], pred_series: Union[TimeSeries, Sequence[TimeSeries]], - tau: float = 0.5, intersect: bool = True, *, - reduction: Callable[[np.ndarray], float] = np.mean, - inter_reduction: Callable[[np.ndarray], Union[float, np.ndarray]] = lambda x: x, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, n_jobs: int = 1, - verbose: bool = False -) -> float: - """ - Also known as Pinball Loss, given a time series of actual values :math:`y` of length :math:`T` - and a time series of stochastic predictions (containing N samples) :math:`y'` of shape :math:`T x N` - quantile loss is a metric that quantifies the accuracy of a specific quantile :math:`tau` - from the predicted value distribution. + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Weighted Mean Absolute Percentage Error (WMAPE). (see [1]_ for more information). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a + percentage value per component/column and (optional) quantile with: + + .. math:: 100 \\cdot \\frac{\\sum_{t=1}^T |y_t - \\hat{y}_t|}{\\sum_{t=1}^T |y_t|} + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). Parameters ---------- @@ -1240,20 +2260,22 @@ def quantile_loss( The (sequence of) actual series. pred_series The (sequence of) predicted series. - tau - The quantile (float [0, 1]) of interest for the loss. intersect For time series that are overlapping in time without having the same time index, setting `True` will consider the values only over their common time interval (intersection in time). - reduction - Function taking as input a ``np.ndarray`` and returning a scalar value. This function is used to aggregate - the metrics of different components in case of multivariate ``TimeSeries`` instances. - inter_reduction - Function taking as input a ``np.ndarray`` and returning either a scalar value or a ``np.ndarray``. - This function can be used to aggregate the metrics of different series in case the metric is evaluated on a - ``Sequence[TimeSeries]``. Defaults to the identity function, which returns the pairwise metrics for each pair - of ``TimeSeries`` received in input. Example: ``inter_reduction=np.mean``, will return the average of the - pairwise metrics. + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` @@ -1261,31 +2283,1953 @@ def quantile_loss( verbose Optionally, whether to print operations progress + Raises + ------ + ValueError + If `actual_series` contains some zeros. + Returns ------- - Union[float, List[float]] - The quantile loss metric + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://en.wikipedia.org/wiki/Mean_absolute_percentage_error#WMAPE """ - raise_if_not( - pred_series.is_stochastic, - "quantile (pinball) loss should only be computed for stochastic predicted TimeSeries.", + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=False, + q=q, + ) + + return ( + 100.0 + * np.nansum(np.abs(y_true - y_pred), axis=TIME_AX) + / np.nansum(np.abs(y_true), axis=TIME_AX) ) - y, y_hat = _get_values_or_raise( + +@multi_ts_support +@multivariate_support +def sape( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """symmetric Absolute Percentage Error (sAPE). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a + percentage value per component/column, (optional) quantile and time step :math:`t` with: + + .. math:: + 200 \\cdot \\frac{\\left| y_t - \\hat{y}_t \\right|}{\\left| y_t \\right| + \\left| \\hat{y}_t \\right|} + + Note that it will raise a `ValueError` if :math:`\\left| y_t \\right| + \\left| \\hat{y}_t \\right| = 0` for some + :math:`t`. Consider using the Absolute Scaled Error (:func:`~darts.metrics.metrics.ase`) in these cases. + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Raises + ------ + ValueError + If `actual_series` and `pred_series` contain some zeros at the same time index. + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + + y_true, y_pred = _get_values_or_raise( actual_series, pred_series, intersect, - stochastic_quantile=None, remove_nan_union=True, + q=q, ) + if not np.logical_or(y_true != 0, y_pred != 0).all(): + raise_log( + ValueError( + "`actual_series` must be strictly positive to compute the sMAPE." + ), + logger=logger, + ) + return 200.0 * np.abs(y_true - y_pred) / (np.abs(y_true) + np.abs(y_pred)) - ts_length, _, sample_size = y_hat.shape - y = y.reshape(ts_length, -1, 1).repeat(sample_size, axis=2) - y_hat = y_hat.reshape( - ts_length, -1, sample_size - ) # make sure y shape == y_hat shape - errors = y - y_hat - losses = np.maximum((tau - 1) * errors, tau * errors) - return losses.mean() +@multi_ts_support +@multivariate_support +def smape( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """symmetric Mean Absolute Percentage Error (sMAPE). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a + percentage value per component/column and (optional) quantile with: + + .. math:: + 200 \\cdot \\frac{1}{T} + \\sum_{t=1}^{T}{\\frac{\\left| y_t - \\hat{y}_t \\right|}{\\left| y_t \\right| + \\left| \\hat{y}_t \\right|} } + + Note that it will raise a `ValueError` if :math:`\\left| y_t \\right| + \\left| \\hat{y}_t \\right| = 0` + for some :math:`t`. Consider using the Mean Absolute Scaled Error (:func:`~darts.metrics.metrics.mase`) in these + cases. + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Raises + ------ + ValueError + If the `actual_series` and the `pred_series` contain some zeros at the same time index. + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + + return np.nanmean( + _get_wrapped_metric(sape)( + actual_series, + pred_series, + intersect, + q=q, + ), + axis=TIME_AX, + ) + + +@multi_ts_support +@multivariate_support +def ope( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Overall Percentage Error (OPE). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a + percentage value per component/column and (optional) quantile with: + + .. math:: 100 \\cdot \\left| \\frac{\\sum_{t=1}^{T}{y_t} + - \\sum_{t=1}^{T}{\\hat{y}_t}}{\\sum_{t=1}^{T}{y_t}} \\right|. + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Raises + ------ + ValueError + If :math:`\\sum_{t=1}^{T}{y_t} = 0`. + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + y_true_sum, y_pred_sum = ( + np.nansum(y_true, axis=TIME_AX), + np.nansum(y_pred, axis=TIME_AX), + ) + if not (y_true_sum > 0).all(): + raise_log( + ValueError( + "The series of actual value cannot sum to zero when computing OPE." + ), + logger=logger, + ) + return np.abs((y_true_sum - y_pred_sum) / y_true_sum) * 100.0 + + +@multi_ts_support +@multivariate_support +def arre( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Absolute Ranged Relative Error (ARRE). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a + percentage value per component/column, (optional) quantile and time step :math:`t` with: + + .. math:: 100 \\cdot \\left| \\frac{y_t - \\hat{y}_t} {\\max_t{y_t} - \\min_t{y_t}} \\right| + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Raises + ------ + ValueError + If :math:`\\max_t{y_t} = \\min_t{y_t}`. + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + y_max, y_min = np.nanmax(y_true, axis=TIME_AX), np.nanmin(y_true, axis=TIME_AX) + if not (y_max > y_min).all(): + raise_log( + ValueError( + "The difference between the max and min values must " + "be strictly positive to compute the MARRE." + ), + logger=logger, + ) + true_range = y_max - y_min + return 100.0 * np.abs((y_true - y_pred) / true_range) + + +@multi_ts_support +@multivariate_support +def marre( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Absolute Ranged Relative Error (MARRE). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed as a + percentage value per component/column and (optional) quantile with: + + .. math:: 100 \\cdot \\frac{1}{T} \\sum_{t=1}^{T} {\\left| \\frac{y_t - \\hat{y}_t} {\\max_t{y_t} - + \\min_t{y_t}} \\right|} + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Raises + ------ + ValueError + If :math:`\\max_t{y_t} = \\min_t{y_t}`. + + float + A single metric score for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components,) without component reduction. For: + + - a single multivariate series and at least `component_reduction=None`. + - sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + return np.nanmean( + _get_wrapped_metric(arre)( + actual_series, + pred_series, + intersect, + q=q, + ), + axis=TIME_AX, + ) + + +@multi_ts_support +@multivariate_support +def r2_score( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Coefficient of Determination :math:`R^2` (see [1]_ for more details). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column and (optional) quantile as: + + .. math:: 1 - \\frac{\\sum_{t=1}^T{(y_t - \\hat{y}_t)^2}}{\\sum_{t=1}^T{(y_t - \\bar{y})^2}}, + + where :math:`\\bar{y}` is the mean of :math:`y` over all time steps. + + This metric is not symmetric. + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://en.wikipedia.org/wiki/Coefficient_of_determination + """ + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + ss_errors = np.nansum((y_true - y_pred) ** 2, axis=TIME_AX) + y_hat = np.nanmean(y_true, axis=TIME_AX) + ss_tot = np.nansum((y_true - y_hat) ** 2, axis=TIME_AX) + return 1 - ss_errors / ss_tot + + +@multi_ts_support +@multivariate_support +def coefficient_of_variation( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Coefficient of Variation (percentage). + + For the true series :math:`y` and predicted series :math:`\\hat{y}` of length :math:`T`, it is computed per + component/column and (optional) quantile as a percentage value with: + + .. math:: 100 \\cdot \\text{RMSE}(y_t, \\hat{y}_t) / \\bar{y}, + + where :math:`RMSE` is the Root Mean Squared Error (:func:`~darts.metrics.metrics.rmse`), and :math:`\\bar{y}` is + the average of :math:`y` over all time steps. + + If :math:`\\hat{y}_t` are stochastic (contains several samples) or quantile predictions, use parameter `q` to + specify on which quantile(s) to compute the metric on. By default, it uses the median 0.5 quantile + (over all samples, or, if given, the quantile prediction itself). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + Optionally, the quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + # not calling rmse as y_true and y_pred are np.ndarray + return ( + 100 + * np.sqrt(np.nanmean((y_true - y_pred) ** 2, axis=TIME_AX)) + / np.nanmean(y_true, axis=TIME_AX) + ) + + +# Dynamic Time Warping +@multi_ts_support +@multivariate_support +def dtw_metric( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + metric: Callable[ + [ + Union[TimeSeries, Sequence[TimeSeries]], + Union[TimeSeries, Sequence[TimeSeries]], + ], + METRIC_OUTPUT_TYPE, + ] = mae, + *, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, + **kwargs, +) -> METRIC_OUTPUT_TYPE: + """ + Applies Dynamic Time Warping to `actual_series` and `pred_series` before passing it into the metric. + Enables comparison between series of different lengths, phases and time indices. + + Defaults to using :func:`~darts.metrics.metrics.mae` as a metric. + + See :func:`~darts.dataprocessing.dtw.dtw.dtw` for more supported parameters. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + metric + The selected metric with signature '[[TimeSeries, TimeSeries], float]' to use. Default: `mae`. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + + alignment = dtw.dtw(actual_series, pred_series, **kwargs) + warped_actual_series, warped_pred_series = alignment.warped() + return _get_wrapped_metric(metric)( + warped_actual_series, + warped_pred_series, + ) + + +@multi_ts_support +@multivariate_support +def qr( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Union[float, list[float], tuple[np.ndarray, pd.Index]] = 0.5, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Quantile Risk (QR) + + QR is a metric that quantifies the accuracy of a specific quantile :math:`q` from the predicted value + distribution of a stochastic/probabilistic `pred_series` containing N samples. + + The main difference to the Quantile Loss (QL) is that QR computes the quantile and loss on the aggregate of all + sample values summed up along the time axis (QL computes the quantile and loss per time step). + + For the true series :math:`y` and predicted stochastic/probabilistic series (containing N samples) :math:`\\hat{y}` + of of shape :math:`T \\times N`, it is computed per column/component and quantile as: + + .. math:: 2 \\frac{QL(Z, \\hat{Z}_q)}{Z}, + + where :math:`QL` is the Quantile Loss (:func:`~darts.metrics.metrics.ql`), :math:`Z = \\sum_{t=1}^{T} y_t` is + the sum of all target/actual values, :math:`\\hat{Z} = \\sum_{t=1}^{T} \\hat{y}_t` is the sum of all predicted + samples along the time axis, and :math:`\\hat{Z}_q` is the quantile :math:`q` of that sum. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + The quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + if not pred_series.is_stochastic: + raise_log( + ValueError( + "quantile risk (qr) should only be computed for stochastic predicted TimeSeries." + ), + logger=logger, + ) + + z_true, z_hat = _get_values_or_raise( + actual_series, + pred_series, + intersect, + q=None, + remove_nan_union=True, + ) + z_true = np.nansum(z_true, axis=TIME_AX) + z_hat = np.nansum( + z_hat, axis=TIME_AX + ) # aggregate all individual sample realizations + # quantile loss + q, _ = q + z_hat_rho = np.quantile( + z_hat, q=q, axis=1 + ).T # get the quantile from aggregated samples + + errors = z_true - z_hat_rho + losses = 2 * np.maximum((q - 1) * errors, q * errors) + return losses / z_true + + +@multi_ts_support +@multivariate_support +def ql( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Union[float, list[float], tuple[np.ndarray, pd.Index]] = 0.5, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Quantile Loss (QL). + + Also known as Pinball Loss. QL is a metric that quantifies the accuracy of a specific quantile :math:`q` from the + predicted deterministic quantiles or value distribution of a stochastic/probabilistic `pred_series` containing N + samples. + + QL computes the quantile of all sample values and the loss per time step. + + For the true series :math:`y` and predicted stochastic/probabilistic series (containing N samples) :math:`\\hat{y}` + of of shape :math:`T \\times N`, it is computed per column/component, quantile and time step :math:`t` as: + + .. math:: 2 \\max((q - 1) (y_t - \\hat{y}_{t,q}), q (y_t - \\hat{y}_{t,q})), + + where :math:`\\hat{y}_{t,q}` is quantile value :math:`q` (of all predicted quantiles or samples) at time :math:`t`. + The factor `2` makes the loss more interpretable, as for `q=0.5` the loss is identical to the Absolute Error + (:func:`~darts.metrics.metrics.ae`). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + The quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n quantiles) without time + and component reductions, and shape (n time steps, n quantiles) without time but component reduction and + `len(q) > 1`. For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + q=q, + remove_nan_union=True, + ) + q, _ = q + errors = y_true - y_pred + losses = 2.0 * np.maximum((q - 1) * errors, q * errors) + return losses + + +@multi_ts_support +@multivariate_support +def mql( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q: Union[float, list[float], tuple[np.ndarray, pd.Index]] = 0.5, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Quantile Loss (MQL). + + Also known as Pinball Loss. QL is a metric that quantifies the accuracy of a specific quantile :math:`q` from the + predicted deterministic quantiles or value distribution of a stochastic/probabilistic `pred_series` containing N + samples. + + MQL first computes the quantile of all sample values and the loss per time step, and then takes the mean over the + time axis. + + For the true series :math:`y` and predicted stochastic/probabilistic series (containing N samples) :math:`\\hat{y}` + of of shape :math:`T \\times N`, it is computed per column/component and quantile as: + + .. math:: 2 \\frac{1}{T}\\sum_{t=1}^T{\\max((q - 1) (y_t - \\hat{y}_{t,q}), q (y_t - \\hat{y}_{t,q}))}, + + where :math:`\\hat{y}_{t,q}` is quantile value :math:`q` (of all predicted quantiles or samples) at time :math:`t`. + The factor `2` makes the loss more interpretable, as for `q=0.5` the loss is identical to the Mean Absolute Error + (:func:`~darts.metrics.metrics.mae`). + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q + The quantile (float [0, 1]) or list of quantiles of interest to compute the metric on. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score (when `len(q) <= 1`) for: + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n quantiles,) without component reduction, + and shape (n quantiles,) with component reduction and `len(q) > 1`. + For: + + - the same input arguments that result in the `float` return case from above but with `len(q) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + return np.nanmean( + _get_wrapped_metric(ql)( + actual_series, + pred_series, + q=q, + intersect=intersect, + ), + axis=TIME_AX, + ) + + +@interval_support +@multi_ts_support +@multivariate_support +def iw( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Interval Width (IW). + + IL gives the width / length of predicted quantile intervals. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step + :math:`t` as: + + .. math:: U_t - L_t, + + where :math:`U_t` are the predicted upper bound quantile values :math:`\\hat{y}_{q_h,t}` (of all predicted + quantiles or samples) at time :math:`t`, and :math:`L_t` are the predicted lower bound quantile values + :math:`\\hat{y}_{q_l,t}`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n q intervals) without time + and component reductions, and shape (n time steps, n q intervals) without time but component reduction and + `len(q_interval) > 1`. For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + y_pred_lo, y_pred_hi = _get_quantile_intervals(y_pred, q=q, q_interval=q_interval) + return y_pred_hi - y_pred_lo + + +@interval_support +@multi_ts_support +@multivariate_support +def miw( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Interval Width (MIW). + + MIW gives the time-aggregated width / length of predicted quantile intervals. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step + :math:`t` as: + + .. math:: \\frac{1}{T}\\sum_{t=1}^T{U_t - L_t}, + + where :math:`U_t` are the predicted upper bound quantile values :math:`\\hat{y}_{q_h,t}` (of all predicted + quantiles or samples) at time :math:`t`, and :math:`L_t` are the predicted lower bound quantile values + :math:`\\hat{y}_{q_l,t}`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n q intervals,) without component reduction, + and shape (n q intervals,) with component reduction and `len(q_interval) > 1`. + For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + return np.nanmean( + _get_wrapped_metric(iw, n_wrappers=3)( + actual_series, + pred_series, + intersect, + q=q, + q_interval=q_interval, + ), + axis=TIME_AX, + ) + + +@interval_support +@multi_ts_support +@multivariate_support +def iws( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Interval Winkler Score (IWS) [1]_. + + IWS gives the length / width of the quantile intervals plus a penalty if the observation is outside the interval. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: + \\begin{equation} + \\begin{cases} + (U_t - L_t) + \\frac{1}{q_l} (L_t - y_t) & \\text{if } y_t < L_t \\\\ + (U_t - L_t) & \\text{if } L_t \\leq y_t \\leq U_t \\\\ + (U_t - L_t) + \\frac{1}{1 - q_h} (y_t - U_t) & \\text{if } y_t > U_t + \\end{cases} + \\end{equation} + + where :math:`U_t` are the predicted upper bound quantile values :math:`\\hat{y}_{q_h,t}` (of all predicted + quantiles or samples) at time :math:`t`, and :math:`L_t` are the predicted lower bound quantile values + :math:`\\hat{y}_{q_l,t}`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n q intervals) without time + and component reductions, and shape (n time steps, n q intervals) without time but component reduction and + `len(q_interval) > 1`. For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://otexts.com/fpp3/distaccuracy.html + """ + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + y_pred_lo, y_pred_hi = _get_quantile_intervals(y_pred, q=q, q_interval=q_interval) + interval_width = y_pred_hi - y_pred_lo + + # `c_alpha = 2 / alpha` corresponds to: + # - `1 / (1 - q_hi)` for the high quantile + # - `1 / q_lo` for the low quantile + c_alpha_hi = 1 / (1 - q_interval[:, 1]) + c_alpha_lo = 1 / q_interval[:, 0] + + score = np.where( + y_true < y_pred_lo, + interval_width + c_alpha_lo * (y_pred_lo - y_true), + np.where( + y_true > y_pred_hi, + interval_width + c_alpha_hi * (y_true - y_pred_hi), + interval_width, + ), + ) + return score + + +@interval_support +@multi_ts_support +@multivariate_support +def miws( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Interval Winkler Score (IWS) [1]_. + + MIWS gives the time-aggregated length / width of the quantile intervals plus a penalty if the observation is + outside the interval. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: \\frac{1}{T}\\sum_{t=1}^T{W_t(y_t, \\hat{y}_{t}, q_h, q_l)}, + + where :math:`W` is the Winkler Score :func:`~darts.metrics.metrics.iws`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n q intervals,) without component reduction, + and shape (n q intervals,) with component reduction and `len(q_interval) > 1`. + For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + + References + ---------- + .. [1] https://otexts.com/fpp3/distaccuracy.html + """ + return np.nanmean( + _get_wrapped_metric(iws, n_wrappers=3)( + actual_series, + pred_series, + intersect, + q=q, + q_interval=q_interval, + ), + axis=TIME_AX, + ) + + +@interval_support +@multi_ts_support +@multivariate_support +def ic( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Interval Coverage (IC). + + IC gives a binary outcome with `1` if the observation is within the interval, and `0` otherwise. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: + \\begin{equation} + \\begin{cases} + 1 & \\text{if } L_t < y_t < U_t \\\\ + 0 & \\text{otherwise} + \\end{cases} + \\end{equation} + + where :math:`U_t` are the predicted upper bound quantile values :math:`\\hat{y}_{q_h,t}` (of all predicted + quantiles or samples) at time :math:`t`, and :math:`L_t` are the predicted lower bound quantile values + :math:`\\hat{y}_{q_l,t}`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n q intervals) without time + and component reductions, and shape (n time steps, n q intervals) without time but component reduction and + `len(q_interval) > 1`. For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + y_pred_lo, y_pred_hi = _get_quantile_intervals(y_pred, q=q, q_interval=q_interval) + return np.where((y_pred_lo <= y_true) & (y_true <= y_pred_hi), 1.0, 0.0) + + +@interval_support +@multi_ts_support +@multivariate_support +def mic( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Interval Coverage (MIC). + + MIC gives the time-aggregated Interval Coverage :func:`~darts.metrics.metrics.ic` - the ratio of observations + being within the interval. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: \\frac{1}{T}\\sum_{t=1}^T{C(y_t, \\hat{y}_{t}, q_h, q_l)}, + + where :math:`C` is the Interval Coverage :func:`~darts.metrics.metrics.ic`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n q intervals,) without component reduction, + and shape (n q intervals,) with component reduction and `len(q_interval) > 1`. + For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + return np.nanmean( + _get_wrapped_metric(ic, n_wrappers=3)( + actual_series, + pred_series, + intersect, + q=q, + q_interval=q_interval, + ), + axis=TIME_AX, + ) + + +@interval_support +@multi_ts_support +@multivariate_support +def incs_qr( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + symmetric: bool = True, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + time_reduction: Optional[Callable[..., np.ndarray]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Interval Non-Conformity Score for Quantile Regression (INCS_QR). + + INCS_QR gives the absolute error to the closest predicted quantile interval bound when the observation is outside + the interval. Otherwise, it gives the negative absolute error to the closer bound. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: \\max(L_t - y_t, y_t - U_t) + + where :math:`U_t` are the predicted upper bound quantile values :math:`\\hat{y}_{q_h,t}` (of all predicted + quantiles or samples) at time :math:`t`, and :math:`L_t` are the predicted lower bound quantile values + :math:`\\hat{y}_{q_l,t}`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + symmetric + Whether to return symmetric non-conformity scores. If `False`, returns asymmetric scores (individual scores + for lower- and upper quantile interval bounds; returned in the component axis). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + time_reduction + Optionally, a function to aggregate the metrics over the time axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(c,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + time axis. If `None`, will return a metric per time step. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction`, `component_reduction` and + `time_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n time steps, n components * n q intervals) without time + and component reductions, and shape (n time steps, n q intervals) without time but component reduction and + `len(q_interval) > 1`. For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a single uni/multivariate series and at least `time_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + y_true, y_pred = _get_values_or_raise( + actual_series, + pred_series, + intersect, + remove_nan_union=True, + q=q, + ) + y_pred_lo, y_pred_hi = _get_quantile_intervals(y_pred, q=q, q_interval=q_interval) + if symmetric: + return np.maximum(y_pred_lo - y_true, y_true - y_pred_hi) + else: + return np.concatenate([y_pred_lo - y_true, y_true - y_pred_hi], axis=SMPL_AX) + + +@interval_support +@multi_ts_support +@multivariate_support +def mincs_qr( + actual_series: Union[TimeSeries, Sequence[TimeSeries]], + pred_series: Union[TimeSeries, Sequence[TimeSeries]], + intersect: bool = True, + *, + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]] = None, + symmetric: bool = True, + q: Optional[Union[float, list[float], tuple[np.ndarray, pd.Index]]] = None, + component_reduction: Optional[Callable[[np.ndarray], float]] = np.nanmean, + series_reduction: Optional[Callable[[np.ndarray], Union[float, np.ndarray]]] = None, + n_jobs: int = 1, + verbose: bool = False, +) -> METRIC_OUTPUT_TYPE: + """Mean Interval Non-Conformity Score for Quantile Regression (MINCS_QR). + + MINCS_QR gives the time-aggregated INCS_QR :func:`~darts.metrics.metrics.incs_qr`. + + For the true series :math:`y` and predicted stochastic or quantile series :math:`\\hat{y}` of length :math:`T`, + it is computed per component/column, quantile interval :math:`(q_l,q_h)`, and time step :math:`t` as: + + .. math:: \\frac{1}{T}\\sum_{t=1}^T{INCS_QR(y_t, \\hat{y}_{t}, q_h, q_l)}, + + where :math:`INCS_QR` is the Interval Non-Conformity Score for Quantile Regression + :func:`~darts.metrics.metrics.incs_qr`. + + Parameters + ---------- + actual_series + The (sequence of) actual series. + pred_series + The (sequence of) predicted series. + intersect + For time series that are overlapping in time without having the same time index, setting `True` + will consider the values only over their common time interval (intersection in time). + q_interval + The quantile interval(s) to compute the metric on. Must be a tuple (single interval) or sequence of tuples + (multiple intervals) with elements (low quantile, high quantile). + symmetric + Whether to return symmetric non-conformity scores. If `False`, returns asymmetric scores (individual scores + for lower- and upper quantile interval bounds; returned in the component axis). + q + Quantiles `q` not supported by this metric; use `q_interval` instead. + component_reduction + Optionally, a function to aggregate the metrics over the component/column axis. It must reduce a `np.ndarray` + of shape `(t, c)` to a `np.ndarray` of shape `(t,)`. The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `1` corresponding to the + component axis. If `None`, will return a metric per component. + series_reduction + Optionally, a function to aggregate the metrics over multiple series. It must reduce a `np.ndarray` + of shape `(s, t, c)` to a `np.ndarray` of shape `(t, c)` The function takes as input a ``np.ndarray`` and a + parameter named `axis`, and returns the reduced array. The `axis` receives value `0` corresponding to the + series axis. For example with `np.nanmean`, will return the average over all series metrics. If `None`, will + return a metric per component. + n_jobs + The number of jobs to run in parallel. Parallel jobs are created only when a ``Sequence[TimeSeries]`` is + passed as input, parallelising operations regarding different ``TimeSeries``. Defaults to `1` + (sequential). Setting the parameter to `-1` means using all the available processors. + verbose + Optionally, whether to print operations progress + + Returns + ------- + float + A single metric score for (with `len(q_interval) <= 1`): + + - a single univariate series. + - a single multivariate series with `component_reduction`. + - a sequence (list) of uni/multivariate series with `series_reduction` and `component_reduction`. + np.ndarray + A numpy array of metric scores. The array has shape (n components * n q intervals,) without component reduction, + and shape (n q intervals,) with component reduction and `len(q_interval) > 1`. + For: + + - the input from the `float` return case above but with `len(q_interval) > 1`. + - a single multivariate series and at least `component_reduction=None`. + - a sequence of uni/multivariate series including `series_reduction` and `component_reduction=None`. + list[float] + Same as for type `float` but for a sequence of series. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. + """ + return np.nanmean( + _get_wrapped_metric(incs_qr, n_wrappers=3)( + actual_series, + pred_series, + intersect, + q=q, + q_interval=q_interval, + symmetric=symmetric, + ), + axis=TIME_AX, + ) diff --git a/darts/models/__init__.py b/darts/models/__init__.py index 0dac1a280d..bfbe716b54 100644 --- a/darts/models/__init__.py +++ b/darts/models/__init__.py @@ -7,15 +7,29 @@ logger = get_logger(__name__) +from darts.models.utils import NotImportedModule + +try: + # `lightgbm` needs to be imported first to avoid segmentation fault + from darts.models.forecasting.lgbm import LightGBMModel +except ModuleNotFoundError: + LightGBMModel = NotImportedModule(module_name="LightGBM", warn=False) + # Forecasting from darts.models.forecasting.arima import ARIMA from darts.models.forecasting.auto_arima import AutoARIMA from darts.models.forecasting.baselines import ( NaiveDrift, + NaiveEnsembleModel, NaiveMean, NaiveMovingAverage, NaiveSeasonal, ) +from darts.models.forecasting.conformal_models import ( + ConformalNaiveModel, + ConformalQRModel, +) +from darts.models.forecasting.ensemble_model import EnsembleModel from darts.models.forecasting.exponential_smoothing import ExponentialSmoothing from darts.models.forecasting.fft import FFT from darts.models.forecasting.kalman_forecaster import KalmanForecaster @@ -26,11 +40,15 @@ from darts.models.forecasting.tbats_model import BATS, TBATS from darts.models.forecasting.theta import FourTheta, Theta from darts.models.forecasting.varima import VARIMA -from darts.models.utils import NotImportedModule try: from darts.models.forecasting.block_rnn_model import BlockRNNModel from darts.models.forecasting.dlinear import DLinearModel + from darts.models.forecasting.global_baseline_models import ( + GlobalNaiveAggregate, + GlobalNaiveDrift, + GlobalNaiveSeasonal, + ) from darts.models.forecasting.nbeats import NBEATSModel from darts.models.forecasting.nhits import NHiTSModel from darts.models.forecasting.nlinear import NLinearModel @@ -39,17 +57,27 @@ from darts.models.forecasting.tft_model import TFTModel from darts.models.forecasting.tide_model import TiDEModel from darts.models.forecasting.transformer_model import TransformerModel + from darts.models.forecasting.tsmixer_model import TSMixerModel except ModuleNotFoundError: logger.warning( "Support for Torch based models not available. " 'To enable them, install "darts", "u8darts[torch]" or "u8darts[all]" (with pip); ' 'or "u8darts-torch" or "u8darts-all" (with conda).' ) - -try: - from darts.models.forecasting.lgbm import LightGBMModel -except ModuleNotFoundError: - LightGBMModel = NotImportedModule(module_name="LightGBM", warn=False) + BlockRNNModel = NotImportedModule(module_name="(Py)Torch", warn=False) + DLinearModel = NotImportedModule(module_name="(Py)Torch", warn=False) + GlobalNaiveAggregate = NotImportedModule(module_name="(Py)Torch", warn=False) + GlobalNaiveDrift = NotImportedModule(module_name="(Py)Torch", warn=False) + GlobalNaiveSeasonal = NotImportedModule(module_name="(Py)Torch", warn=False) + NBEATSModel = NotImportedModule(module_name="(Py)Torch", warn=False) + NHiTSModel = NotImportedModule(module_name="(Py)Torch", warn=False) + NLinearModel = NotImportedModule(module_name="(Py)Torch", warn=False) + RNNModel = NotImportedModule(module_name="(Py)Torch", warn=False) + TCNModel = NotImportedModule(module_name="(Py)Torch", warn=False) + TFTModel = NotImportedModule(module_name="(Py)Torch", warn=False) + TiDEModel = NotImportedModule(module_name="(Py)Torch", warn=False) + TransformerModel = NotImportedModule(module_name="(Py)Torch", warn=False) + TSMixerModel = NotImportedModule(module_name="(Py)Torch", warn=False) try: from darts.models.forecasting.prophet_model import Prophet @@ -66,6 +94,7 @@ from darts.models.forecasting.sf_auto_arima import StatsForecastAutoARIMA from darts.models.forecasting.sf_auto_ces import StatsForecastAutoCES from darts.models.forecasting.sf_auto_ets import StatsForecastAutoETS + from darts.models.forecasting.sf_auto_tbats import StatsForecastAutoTBATS from darts.models.forecasting.sf_auto_theta import StatsForecastAutoTheta except ImportError: @@ -80,18 +109,66 @@ StatsForecastAutoCES = NotImportedModule(module_name="StatsForecast", warn=False) StatsForecastAutoETS = NotImportedModule(module_name="StatsForecast", warn=False) StatsForecastAutoTheta = NotImportedModule(module_name="StatsForecast", warn=False) + StatsForecastAutoTBATS = NotImportedModule(module_name="StatsForecast", warn=False) try: from darts.models.forecasting.xgboost import XGBModel except ImportError: XGBModel = NotImportedModule(module_name="XGBoost") +# Filtering from darts.models.filtering.gaussian_process_filter import GaussianProcessFilter from darts.models.filtering.kalman_filter import KalmanFilter - -# Filtering from darts.models.filtering.moving_average_filter import MovingAverageFilter -from darts.models.forecasting.baselines import NaiveEnsembleModel -# Ensembling -from darts.models.forecasting.ensemble_model import EnsembleModel +__all__ = [ + "LightGBMModel", + "ARIMA", + "AutoARIMA", + "NaiveDrift", + "NaiveMean", + "NaiveMovingAverage", + "NaiveSeasonal", + "ExponentialSmoothing", + "FFT", + "KalmanForecaster", + "LinearRegressionModel", + "RandomForest", + "RegressionEnsembleModel", + "RegressionModel", + "BATS", + "TBATS", + "FourTheta", + "Theta", + "VARIMA", + "BlockRNNModel", + "DLinearModel", + "GlobalNaiveAggregate", + "GlobalNaiveDrift", + "GlobalNaiveSeasonal", + "NBEATSModel", + "NHiTSModel", + "NLinearModel", + "RNNModel", + "TCNModel", + "TFTModel", + "TiDEModel", + "TransformerModel", + "TSMixerModel", + "Prophet", + "CatBoostModel", + "Croston", + "StatsForecastAutoARIMA", + "StatsForecastAutoCES", + "StatsForecastAutoETS", + "StatsForecastAutoTheta", + "StatsForecastAutoTBATS", + "XGBModel", + "GaussianProcessFilter", + "KalmanFilter", + "MovingAverageFilter", + "NaiveEnsembleModel", + "EnsembleModel", + "ConformalNaiveModel", + "ConformalQRModel", +] diff --git a/darts/models/forecasting/__init__.py b/darts/models/forecasting/__init__.py index 63cb81d09d..85ad3d8730 100644 --- a/darts/models/forecasting/__init__.py +++ b/darts/models/forecasting/__init__.py @@ -3,46 +3,55 @@ ------------------ Baseline Models (`LocalForecastingModel `_) - - :class:`NaiveMean ` - - :class:`NaiveSeasonal ` - - :class:`NaiveDrift ` - - :class:`NaiveMovingAverage ` + - :class:`~darts.models.forecasting.baselines.NaiveMean` + - :class:`~darts.models.forecasting.baselines.NaiveSeasonal` + - :class:`~darts.models.forecasting.baselines.NaiveDrift` + - :class:`~darts.models.forecasting.baselines.NaiveMovingAverage` +Global Baseline Models (`GlobalForecastingModel `_) + - :class:`~darts.models.forecasting.global_baseline_models.GlobalNaiveAggregate` + - :class:`~darts.models.forecasting.global_baseline_models.GlobalNaiveDrift` + - :class:`~darts.models.forecasting.global_baseline_models.GlobalNaiveSeasonal` Statistical Models (`LocalForecastingModel `_) - - :class:`ARIMA ` - - :class:`VARIMA ` - - :class:`AutoARIMA ` - - :class:`StatsForecastAutoARIMA ` - - :class:`ExponentialSmoothing ` - - :class:`StatsForecastAutoETS ` - - :class:`StatsForecastAutoCES ` - - :class:`BATS ` - - :class:`TBATS ` - - :class:`Theta ` - - :class:`FourTheta ` - - :class:`StatsForecastAutoTheta ` - - :class:`Prophet ` - - :class:`FFT (Fast Fourier Transform) ` - - :class:`KalmanForecaster ` - - :class:`Croston ` + - :class:`~darts.models.forecasting.arima.ARIMA` + - :class:`~darts.models.forecasting.varima.VARIMA` + - :class:`~darts.models.forecasting.auto_arima.AutoARIMA` + - :class:`~darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA` + - :class:`~darts.models.forecasting.exponential_smoothing.ExponentialSmoothing` + - :class:`~darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS` + - :class:`~darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES` + - :class:`~darts.models.forecasting.tbats_model.BATS` + - :class:`~darts.models.forecasting.tbats_model.TBATS` + - :class:`~darts.models.forecasting.sf_auto_tbats.StatsForecastAutoTBATS` + - :class:`~darts.models.forecasting.theta.Theta` + - :class:`~darts.models.forecasting.theta.FourTheta` + - :class:`~darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta` + - :class:`~darts.models.forecasting.prophet_model.Prophet` + - :class:`~Fast Fourier Transform) `_) - - :class:`RegressionModel ` - - :class:`LinearRegressionModel ` - - :class:`RandomForest ` - - :class:`LightGBMModel ` - - :class:`XGBModel ` - - :class:`CatBoostModel ` + - :class:`~darts.models.forecasting.regression_model.RegressionModel` + - :class:`~darts.models.forecasting.linear_regression_model.LinearRegressionModel` + - :class:`~darts.models.forecasting.random_forest.RandomForest` + - :class:`~darts.models.forecasting.lgbm.LightGBMModel` + - :class:`~darts.models.forecasting.xgboost.XGBModel` + - :class:`~darts.models.forecasting.catboost_model.CatBoostModel` PyTorch (Lightning)-based Models (`GlobalForecastingModel `_) - - :class:`RNNModel ` - - :class:`BlockRNNModel ` - - :class:`NBEATSModel ` - - :class:`NHiTSModel ` - - :class:`TCNModel ` - - :class:`TransformerModel ` - - :class:`TFTModel ` - - :class:`DLinearModel ` - - :class:`NLinearModel ` - - :class:`TiDEModel ` + - :class:`~darts.models.forecasting.rnn_model.RNNModel` + - :class:`~darts.models.forecasting.block_rnn_model.BlockRNNModel` + - :class:`~darts.models.forecasting.nbeats.NBEATSModel` + - :class:`~darts.models.forecasting.nhits.NHiTSModel` + - :class:`~darts.models.forecasting.tcn_model.TCNModel` + - :class:`~darts.models.forecasting.transformer_model.TransformerModel` + - :class:`~darts.models.forecasting.tft_model.TFTModel` + - :class:`~darts.models.forecasting.dlinear.DLinearModel` + - :class:`~darts.models.forecasting.nlinear.NLinearModel` + - :class:`~darts.models.forecasting.tide_model.TiDEModel` + - :class:`~darts.models.forecasting.tsmixer_model.TSMixerModel` Ensemble Models (`GlobalForecastingModel `_) - - :class:`NaiveEnsembleModel ` - - :class:`RegressionEnsembleModel ` + - :class:`~darts.models.forecasting.baselines.NaiveEnsembleModel` + - :class:`~darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel` +Conformal Models (`GlobalForecastingModel `_) + - :class:`~darts.models.forecasting.conformal_models.ConformalNaiveModel` + - :class:`~darts.models.forecasting.conformal_models.ConformalQRModel` """ diff --git a/darts/models/forecasting/arima.py b/darts/models/forecasting/arima.py index 4891b33719..7c84c2385c 100644 --- a/darts/models/forecasting/arima.py +++ b/darts/models/forecasting/arima.py @@ -10,7 +10,14 @@ .. [1] https://wikipedia.org/wiki/Autoregressive_integrated_moving_average """ -from typing import Optional, Tuple +import sys +from collections.abc import Sequence +from typing import Literal, Optional, Union + +if sys.version_info >= (3, 10): + from typing import TypeAlias +else: + from typing_extensions import TypeAlias import numpy as np from statsmodels import __version_tuple__ as statsmodels_version @@ -28,14 +35,22 @@ statsmodels_above_0135 = statsmodels_version > (0, 13, 5) +IntOrIntSequence: TypeAlias = Union[int, Sequence[int]] + + class ARIMA(TransferableFutureCovariatesLocalForecastingModel): def __init__( self, - p: int = 12, + p: IntOrIntSequence = 12, d: int = 1, - q: int = 0, - seasonal_order: Tuple[int, int, int, int] = (0, 0, 0, 0), - trend: Optional[str] = None, + q: IntOrIntSequence = 0, + seasonal_order: tuple[int, IntOrIntSequence, IntOrIntSequence, int] = ( + 0, + 0, + 0, + 0, + ), + trend: Optional[Union[Literal["n", "c", "t", "ct"], list[int]]] = None, random_state: Optional[int] = None, add_encoders: Optional[dict] = None, ): @@ -45,20 +60,29 @@ def __init__( Parameters ---------- - p : int + p : int | Sequence[int] Order (number of time lags) of the autoregressive model (AR). + If a sequence of integers, specifies the exact lags to include. d : int The order of differentiation; i.e., the number of times the data have had past values subtracted (I). - q : int + q : int | Sequence[int] The size of the moving average window (MA). - seasonal_order: Tuple[int, int, int, int] - The (P,D,Q,s) order of the seasonal component for the AR parameters, - differences, MA parameters and periodicity. - trend: str - Parameter controlling the deterministic trend. 'n' indicates no trend, - 'c' a constant term, 't' linear trend in time, and 'ct' includes both. - Default is 'c' for models without integration, and no trend for models with integration. + If a sequence of integers, specifies the exact lags to include in the window. + seasonal_order: Tuple[int | Sequence[int], int, int | Sequence[int], int] + The (P,D,Q,s) order of the seasonal component for the AR parameters (P), + differences (D), MA parameters (Q) and periodicity (s). D and s are always integers, + while P and Q may either be integers or sequence of positive integers + specifying exactly which lag orders are included. + trend: Literal['n', 'c', 't', 'ct'] | list[int], optional + Parameter controlling the deterministic trend. Either a string or list of integers. + If a string, can be 'n' for no trend, 'c' for a constant term, 't' for a linear trend in time, + and 'ct' for a constant term and linear trend. + If a list of integers, defines a polynomial according to `numpy.poly1d` [1]_. E.g., `[1,1,0,1]` would + translate to :math:`a + bt + ct^3`. + Trend term of lower order than `d + D` cannot be as they would be eliminated due to the differencing + operation. + Default is 'c' for models without integration, and 'n' for models with integration. add_encoders A large number of future covariates can be automatically generated with `add_encoders`. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that @@ -103,6 +127,10 @@ def encode_year(idx): [481.07892911], [502.11286509], [555.50153984]]) + + References + ---------- + .. [1] https://numpy.org/doc/stable/reference/generated/numpy.poly1d.html """ super().__init__(add_encoders=add_encoders) self.order = p, d, q @@ -151,7 +179,6 @@ def _predict( num_samples: int = 1, verbose: bool = False, ) -> TimeSeries: - if num_samples > 1 and self.trend: logger.warning( "Trends are not well supported yet for getting probabilistic forecasts with ARIMA." @@ -167,17 +194,19 @@ def _predict( if series is not None: self.model = self.model.apply( series.values(copy=False), - exog=historic_future_covariates.values(copy=False) - if historic_future_covariates - else None, + exog=( + historic_future_covariates.values(copy=False) + if historic_future_covariates + else None + ), ) if num_samples == 1: forecast = self.model.forecast( steps=n, - exog=future_covariates.values(copy=False) - if future_covariates - else None, + exog=( + future_covariates.values(copy=False) if future_covariates else None + ), ) else: forecast = self.model.simulate( @@ -186,24 +215,26 @@ def _predict( initial_state=self.model.states.predicted[-1, :], random_state=self._random_state, anchor="end", - exog=future_covariates.values(copy=False) - if future_covariates - else None, + exog=( + future_covariates.values(copy=False) if future_covariates else None + ), ) # restoring statsmodels results object state if series is not None: self.model = self.model.apply( self._orig_training_series.values(copy=False), - exog=self.training_historic_future_covariates.values(copy=False) - if self.training_historic_future_covariates - else None, + exog=( + self.training_historic_future_covariates.values(copy=False) + if self.training_historic_future_covariates + else None + ), ) return self._build_forecast_series(forecast) @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return True @property diff --git a/darts/models/forecasting/baselines.py b/darts/models/forecasting/baselines.py index 2b370aad00..8a0e3981c8 100644 --- a/darts/models/forecasting/baselines.py +++ b/darts/models/forecasting/baselines.py @@ -2,10 +2,11 @@ Baseline Models --------------- -A collection of simple benchmark models for univariate series. +A collection of simple benchmark models for single uni- and multivariate series. """ -from typing import List, Optional, Sequence, Union +from collections.abc import Sequence +from typing import Optional, Union import numpy as np @@ -193,7 +194,7 @@ class NaiveMovingAverage(LocalForecastingModel): def __init__(self, input_chunk_length: int = 1): """Naive Moving Average Model - This model forecasts using an auto-regressive moving average (ARMA). + This model forecasts using an autoregressive moving average (ARMA). Parameters ---------- @@ -269,7 +270,7 @@ def predict( class NaiveEnsembleModel(EnsembleModel): def __init__( self, - forecasting_models: List[ForecastingModel], + forecasting_models: list[ForecastingModel], train_forecasting_models: bool = True, show_warnings: bool = True, ): @@ -326,6 +327,7 @@ def fit( series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): super().fit( series=series, @@ -336,14 +338,16 @@ def fit( for model in self.forecasting_models: model._fit_wrapper( series=series, - past_covariates=past_covariates - if model.supports_past_covariates - else None, - future_covariates=future_covariates - if model.supports_future_covariates + past_covariates=( + past_covariates if model.supports_past_covariates else None + ), + future_covariates=( + future_covariates if model.supports_future_covariates else None + ), + sample_weight=sample_weight + if model.supports_sample_weight else None, ) - return self def ensemble( @@ -364,9 +368,11 @@ def ensemble( if isinstance(predictions, Sequence): return [ - self._target_average(p, ts) - if not predict_likelihood_parameters - else self._params_average(p, ts) + ( + self._target_average(p, ts) + if not predict_likelihood_parameters + else self._params_average(p, ts) + ) for p, ts in zip(predictions, series) ] else: @@ -381,9 +387,11 @@ def _target_average(self, prediction: TimeSeries, series: TimeSeries) -> TimeSer n_forecasting_models = len(self.forecasting_models) n_components = series.n_components prediction_values = prediction.all_values(copy=False) - target_values = np.zeros( - (prediction.n_timesteps, n_components, prediction.n_samples) - ) + target_values = np.zeros(( + prediction.n_timesteps, + n_components, + prediction.n_samples, + )) for idx_target in range(n_components): target_values[:, idx_target] = prediction_values[ :, @@ -415,9 +423,10 @@ def _params_average(self, prediction: TimeSeries, series: TimeSeries) -> TimeSer n_components = series.n_components # aggregate across predictions [model1_param0, model1_param1, ..., modeln_param0, modeln_param1] prediction_values = prediction.values(copy=False) - params_values = np.zeros( - (prediction.n_timesteps, likelihood_n_params * n_components) - ) + params_values = np.zeros(( + prediction.n_timesteps, + likelihood_n_params * n_components, + )) for idx_param in range(likelihood_n_params * n_components): params_values[:, idx_param] = prediction_values[ :, diff --git a/darts/models/forecasting/block_rnn_model.py b/darts/models/forecasting/block_rnn_model.py index 36cf6e210d..d8c88e726d 100644 --- a/darts/models/forecasting/block_rnn_model.py +++ b/darts/models/forecasting/block_rnn_model.py @@ -5,7 +5,7 @@ import inspect from abc import ABC, abstractmethod -from typing import List, Optional, Tuple, Type, Union +from typing import Optional, Union import torch import torch.nn as nn @@ -28,8 +28,9 @@ def __init__( num_layers: int, target_size: int, nr_params: int, - num_layers_out_fc: Optional[List] = None, + num_layers_out_fc: Optional[list] = None, dropout: float = 0.0, + activation: str = "ReLU", **kwargs, ): """This class allows to create custom block RNN modules that can later be used with Darts' @@ -63,8 +64,11 @@ def __init__( This network connects the last hidden layer of the PyTorch RNN module to the output. dropout The fraction of neurons that are dropped in all-but-last RNN layers. + activation + The name of the activation function to be applied between the layers of the fully connected network. **kwargs - all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class. + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. """ super().__init__(**kwargs) @@ -76,11 +80,12 @@ def __init__( self.nr_params = nr_params self.num_layers_out_fc = [] if num_layers_out_fc is None else num_layers_out_fc self.dropout = dropout + self.activation = activation self.out_len = self.output_chunk_length @io_processor @abstractmethod - def forward(self, x_in: Tuple) -> torch.Tensor: + def forward(self, x_in: tuple) -> torch.Tensor: """BlockRNN Module forward. Parameters @@ -104,9 +109,9 @@ class _BlockRNNModule(CustomBlockRNNModule): def __init__( self, name: str, + activation: Optional[str] = None, **kwargs, ): - """PyTorch module implementing a block RNN to be used in `BlockRNNModel`. PyTorch module implementing a simple block RNN with the specified `name` layer. @@ -116,6 +121,7 @@ def __init__( This module uses an RNN to encode the input sequence, and subsequently uses a fully connected network as the decoder which takes as input the last hidden state of the encoder RNN. + Optionally, a non-linear activation function can be applied between the layers of the fully connected network. The final output of the decoder is a sequence of length `output_chunk_length`. In this sense, the `_BlockRNNModule` produces 'blocks' of forecasts at a time (which is different from `_RNNModule` used by the `RNNModel`). @@ -124,8 +130,11 @@ def __init__( ---------- name The name of the specific PyTorch RNN module ("RNN", "GRU" or "LSTM"). + activation + The name of the activation function to be applied between the layers of the fully connected network. + Options include "ReLU", "Sigmoid", "Tanh", or None for no activation. Default: None. **kwargs - all parameters required for the :class:`darts.model.forecasting_models.CustomBlockRNNModule` base class. + all parameters required for the :class:`darts.models.forecasting.CustomBlockRNNModule` base class. Inputs ------ @@ -155,15 +164,20 @@ def __init__( # to the output of desired length last = self.hidden_dim feats = [] - for feature in self.num_layers_out_fc + [ - self.out_len * self.target_size * self.nr_params - ]: + for index, feature in enumerate( + self.num_layers_out_fc + [self.out_len * self.target_size * self.nr_params] + ): feats.append(nn.Linear(last, feature)) + + # Add activation only between layers, but not on the final layer + if activation and index < len(self.num_layers_out_fc): + activation_function = getattr(nn, activation)() + feats.append(activation_function) last = feature self.fc = nn.Sequential(*feats) @io_processor - def forward(self, x_in: Tuple): + def forward(self, x_in: tuple): x, _ = x_in # data is of size (batch_size, input_chunk_length, input_size) batch_size = x.size(0) @@ -189,14 +203,15 @@ def __init__( self, input_chunk_length: int, output_chunk_length: int, - model: Union[str, Type[CustomBlockRNNModule]] = "RNN", + output_chunk_shift: int = 0, + model: Union[str, type[CustomBlockRNNModule]] = "RNN", hidden_dim: int = 25, n_rnn_layers: int = 1, - hidden_fc_sizes: Optional[List] = None, + hidden_fc_sizes: Optional[list] = None, dropout: float = 0.0, + activation: str = "ReLU", **kwargs, ): - """Block Recurrent Neural Network Model (RNNs). This is a neural network model that uses an RNN encoder to encode fixed-length input chunks, and @@ -221,10 +236,16 @@ def __init__( Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values from future covariates to use as a model input (if the model supports future covariates). It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated - using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). model Either a string specifying the RNN module type ("RNN", "LSTM" or "GRU"), or a subclass of :class:`CustomBlockRNNModule` (the class itself, not an object of the class) with a custom logic. @@ -236,7 +257,10 @@ def __init__( hidden_fc_sizes Sizes of hidden layers connecting the last hidden layer of the RNN module to the output, if any. dropout - Fraction of neurons afected by Dropout. + Fraction of neurons affected by Dropout. + activation + The name of a torch.nn activation function to be applied between the layers of the fully connected network. + Default: "ReLU". **kwargs Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and Darts' :class:`TorchForecastingModel`. @@ -429,12 +453,13 @@ def encode_year(idx): self.hidden_dim = hidden_dim self.n_rnn_layers = n_rnn_layers self.dropout = dropout + self.activation = activation @property def supports_multivariate(self) -> bool: return True - def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module: + def _create_model(self, train_sample: tuple[torch.Tensor]) -> torch.nn.Module: # samples are made of (past_target, past_covariates, future_target) input_dim = train_sample[0].shape[1] + ( train_sample[1].shape[1] if train_sample[1] is not None else 0 @@ -458,6 +483,15 @@ def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module: num_layers=self.n_rnn_layers, num_layers_out_fc=hidden_fc_sizes, dropout=self.dropout, + activation=self.activation, **self.pl_module_params, **kwargs, ) + + def _check_ckpt_parameters(self, tfm_save): + # new parameters were added that will break loading weights + new_params = ["activation"] + for param in new_params: + if param not in tfm_save.model_params: + tfm_save.model_params[param] = "ReLU" + super()._check_ckpt_parameters(tfm_save) diff --git a/darts/models/forecasting/catboost_model.py b/darts/models/forecasting/catboost_model.py index fbb8e3df7d..c338948bf9 100644 --- a/darts/models/forecasting/catboost_model.py +++ b/darts/models/forecasting/catboost_model.py @@ -7,10 +7,11 @@ This implementation comes with the ability to produce probabilistic forecasts. """ -from typing import List, Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union import numpy as np -from catboost import CatBoostRegressor +from catboost import CatBoostRegressor, Pool from darts.logging import get_logger from darts.models.forecasting.regression_model import RegressionModel, _LikelihoodMixin @@ -23,12 +24,13 @@ class CatBoostModel(RegressionModel, _LikelihoodMixin): def __init__( self, lags: Union[int, list] = None, - lags_past_covariates: Union[int, List[int]] = None, - lags_future_covariates: Union[Tuple[int, int], List[int]] = None, + lags_past_covariates: Union[int, list[int]] = None, + lags_future_covariates: Union[tuple[int, int], list[int]] = None, output_chunk_length: int = 1, + output_chunk_shift: int = 0, add_encoders: Optional[dict] = None, likelihood: str = None, - quantiles: List = None, + quantiles: list = None, random_state: Optional[int] = None, multi_models: Optional[bool] = True, use_static_covariates: bool = True, @@ -39,24 +41,52 @@ def __init__( Parameters ---------- lags - Lagged target values used to predict the next time step. If an integer is given the last `lags` past lags - are used (from -1 backward). Otherwise a list of integers with lags is required (each lag must be < 0). + Lagged target `series` values used to predict the next time step/s. + If an integer, must be > 0. Uses the last `n=lags` past lags; e.g. `(-1, -2, ..., -lags)`, where `0` + corresponds the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. + If a list of integers, each value must be < 0. Uses only the specified values as lags. + If a dictionary, the keys correspond to the `series` component names (of the first series when + using multiple series) and the values correspond to the component lags (integer or list of integers). The + key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some + components are missing and the 'default_lags' key is not provided. lags_past_covariates - Number of lagged past_covariates values used to predict the next time step. If an integer is given the last - `lags_past_covariates` past lags are used (inclusive, starting from lag -1). Otherwise a list of integers - with lags < 0 is required. + Lagged `past_covariates` values used to predict the next time step/s. + If an integer, must be > 0. Uses the last `n=lags_past_covariates` past lags; e.g. `(-1, -2, ..., -lags)`, + where `0` corresponds to the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. + If a list of integers, each value must be < 0. Uses only the specified values as lags. + If a dictionary, the keys correspond to the `past_covariates` component names (of the first series when + using multiple series) and the values correspond to the component lags (integer or list of integers). The + key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some + components are missing and the 'default_lags' key is not provided. lags_future_covariates - Number of lagged future_covariates values used to predict the next time step. If an tuple (past, future) is - given the last `past` lags in the past are used (inclusive, starting from lag -1) along with the first - `future` future lags (starting from 0 - the prediction time - up to `future - 1` included). Otherwise a list - of integers with lags is required. + Lagged `future_covariates` values used to predict the next time step/s. The lags are always relative to the + first step in the output chunk, even when `output_chunk_shift > 0`. + If a tuple of `(past, future)`, both values must be > 0. Uses the last `n=past` past lags and `n=future` + future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` corresponds the first + predicted time step of each sample. If `output_chunk_shift > 0`, the position of negative lags differ from + those of `lags` and `lags_past_covariates`. In this case a future lag `-5` would point at the same + step as a target lag of `-5 + output_chunk_shift`. + If a list of integers, uses only the specified values as lags. + If a dictionary, the keys correspond to the `future_covariates` component names (of the first series when + using multiple series) and the values correspond to the component lags (tuple or list of integers). The key + 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some + components are missing and the 'default_lags' key is not provided. output_chunk_length Number of time steps predicted at once (per chunk) by the internal model. It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated using a - one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is + one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input (history of target and past covariates) and + output. If the model supports `future_covariates`, the `lags_future_covariates` are relative to the first + step in the shifted output chunk. Predictions will start `output_chunk_shift` steps after the end of the + target `series`. If `output_chunk_shift` is set, the model cannot generate autoregressive predictions + (`n > output_chunk_length`). add_encoders A large number of past and future covariates can be automatically generated with `add_encoders`. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that @@ -93,8 +123,9 @@ def encode_year(idx): Control the randomness in the fitting procedure and for sampling. Default: ``None``. multi_models - If True, a separate model will be trained for each future lag to predict. If False, a single model is - trained to predict at step 'output_chunk_length' in the future. Default: True. + If True, a separate model will be trained for each future lag to predict. If False, a single model + is trained to predict all the steps in 'output_chunk_length' (features lags are shifted back by + `output_chunk_length - n` for each step `n`). Default: True. use_static_covariates Whether the model should use static covariate information in case the input `series` passed to ``fit()`` contain static covariates. If ``True``, and static covariates are available at fitting time, will enforce @@ -136,7 +167,7 @@ def encode_year(idx): self._median_idx = None self._model_container = None self._rng = None - self.likelihood = likelihood + self._likelihood = likelihood self.quantiles = None self._output_chunk_length = output_chunk_length @@ -170,6 +201,7 @@ def encode_year(idx): lags_past_covariates=lags_past_covariates, lags_future_covariates=lags_future_covariates, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, add_encoders=add_encoders, multi_models=multi_models, model=CatBoostRegressor(**kwargs), @@ -185,6 +217,11 @@ def fit( val_past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, val_future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, max_samples_per_ts: Optional[int] = None, + n_jobs_multioutput_wrapper: Optional[int] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + val_sample_weight: Optional[ + Union[TimeSeries, Sequence[TimeSeries], str] + ] = None, verbose: Optional[Union[int, bool]] = 0, **kwargs, ): @@ -212,20 +249,26 @@ def fit( creation) to know their sizes, which might be expensive on big datasets. If some series turn out to have a length that would allow more than `max_samples_per_ts`, only the most recent `max_samples_per_ts` samples will be considered. + n_jobs_multioutput_wrapper + Number of jobs of the MultiOutputRegressor wrapper to run in parallel. Only used if the model doesn't + support multi-output regression natively. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. + val_sample_weight + Same as for `sample_weight` but for the evaluation dataset. verbose An integer or a boolean that can be set to 1 to display catboost's default verbose output **kwargs Additional kwargs passed to `catboost.CatboostRegressor.fit()` """ - - if val_series is not None: - kwargs["eval_set"] = self._create_lagged_data( - target_series=val_series, - past_covariates=val_past_covariates, - future_covariates=val_future_covariates, - max_samples_per_ts=max_samples_per_ts, - ) - if self.likelihood == "quantile": # empty model container in case of multiple calls to fit, e.g. when backtesting self._model_container.clear() @@ -234,29 +277,37 @@ def fit( # translating to catboost argument self.kwargs["loss_function"] = f"Quantile:alpha={this_quantile}" self.model = CatBoostRegressor(**self.kwargs) - super().fit( series=series, past_covariates=past_covariates, future_covariates=future_covariates, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, max_samples_per_ts=max_samples_per_ts, + n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper, + sample_weight=sample_weight, + val_sample_weight=val_sample_weight, verbose=verbose, **kwargs, ) - self._model_container[quantile] = self.model - return self super().fit( series=series, past_covariates=past_covariates, future_covariates=future_covariates, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, max_samples_per_ts=max_samples_per_ts, + n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper, + sample_weight=sample_weight, + val_sample_weight=val_sample_weight, verbose=verbose, **kwargs, ) - return self def _predict_and_sample( @@ -282,7 +333,7 @@ def _predict_and_sample( def _likelihood_components_names( self, input_series: TimeSeries - ) -> Optional[List[str]]: + ) -> Optional[list[str]]: """Override of RegressionModel's method to support the gaussian/normal likelihood""" if self.likelihood == "quantile": return self._quantiles_generate_components_names(input_series) @@ -295,17 +346,61 @@ def _likelihood_components_names( else: return None + def _add_val_set_to_kwargs( + self, + kwargs: dict, + val_series: Sequence[TimeSeries], + val_past_covariates: Optional[Sequence[TimeSeries]], + val_future_covariates: Optional[Sequence[TimeSeries]], + val_sample_weight: Optional[Union[Sequence[TimeSeries], str]], + max_samples_per_ts: int, + ) -> dict: + # CatBoostRegressor requires sample weights to be passed with a validation set `Pool` + kwargs = super()._add_val_set_to_kwargs( + kwargs=kwargs, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, + val_sample_weight=val_sample_weight, + max_samples_per_ts=max_samples_per_ts, + ) + val_set_name, val_weight_name = self.val_set_params + val_sets = kwargs[val_set_name] + # CatBoost requires eval set Pool with sample weights -> remove from kwargs + val_weights = kwargs.pop(val_weight_name) + val_pools = [] + for i, val_set in enumerate(val_sets): + val_pools.append( + Pool( + data=val_set[0], + label=val_set[1], + weight=val_weights[i] if val_weights is not None else None, + ) + ) + kwargs[val_set_name] = val_pools + return kwargs + @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return self.likelihood is not None + @property + def supports_val_set(self) -> bool: + return True + + @property + def val_set_params(self) -> tuple[Optional[str], Optional[str]]: + return "eval_set", "eval_sample_weight" + @property def min_train_series_length(self) -> int: # Catboost requires a minimum of 2 train samples, therefore the min_train_series_length should be one more than # for other regression models return max( 3, - -self.lags["target"][0] + self.output_chunk_length + 1 - if "target" in self.lags - else self.output_chunk_length, + ( + -self.lags["target"][0] + self.output_chunk_length + 1 + if "target" in self.lags + else self.output_chunk_length + ), ) diff --git a/darts/models/forecasting/conformal_models.py b/darts/models/forecasting/conformal_models.py new file mode 100644 index 0000000000..d330039738 --- /dev/null +++ b/darts/models/forecasting/conformal_models.py @@ -0,0 +1,1882 @@ +""" +Conformal Models +--------------- + +A collection of conformal prediction models for pre-trained global forecasting models. +""" + +import copy +import math +import os +import sys +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import Any, BinaryIO, Callable, Optional, Union + +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal + +if sys.version_info >= (3, 11): + from typing import Self +else: + from typing_extensions import Self + +import numpy as np +import pandas as pd + +from darts import TimeSeries, metrics +from darts.dataprocessing.pipeline import Pipeline +from darts.dataprocessing.transformers import BaseDataTransformer +from darts.logging import get_logger, raise_log +from darts.metrics.metrics import METRIC_TYPE +from darts.models.forecasting.forecasting_model import GlobalForecastingModel +from darts.models.utils import TORCH_AVAILABLE +from darts.utils import _build_tqdm_iterator, _with_sanity_checks +from darts.utils.historical_forecasts.utils import ( + _adjust_historical_forecasts_time_index, +) +from darts.utils.timeseries_generation import _build_forecast_series +from darts.utils.ts_utils import ( + SeriesType, + get_series_seq_type, + series2seq, +) +from darts.utils.utils import ( + _check_quantiles, + generate_index, + likelihood_component_names, + n_steps_between, + quantile_names, + random_method, + sample_from_quantiles, +) + +if TORCH_AVAILABLE: + from darts.models.forecasting.torch_forecasting_model import TorchForecastingModel +else: + TorchForecastingModel = None + +logger = get_logger(__name__) + + +class ConformalModel(GlobalForecastingModel, ABC): + @random_method + def __init__( + self, + model: GlobalForecastingModel, + quantiles: list[float], + symmetric: bool = True, + cal_length: Optional[int] = None, + cal_stride: int = 1, + cal_num_samples: int = 500, + random_state: Optional[int] = None, + ): + """Base Conformal Prediction Model. + + Base class for any conformal prediction model. A conformal model calibrates the predictions from any + pre-trained global forecasting model. It does not have to be trained, and can generate calibrated forecasts + directly using the underlying trained forecasting model. Since it is a probabilistic model, you can generate + forecasts in two ways (when calling `predict()`, `historical_forecasts()`, ...): + + - Predict the calibrated quantile intervals directly: Pass parameters `predict_likelihood_parameters=True`, and + `num_samples=1` to the forecast method. + - Predict stochastic samples from the calibrated quantile intervals: Pass parameters + `predict_likelihood_parameters=False`, and `num_samples>>1` to the forecast method. + + Conformal models can be applied to any of Darts' global forecasting model, as long as the model has been + fitted before. In general the workflow of the models to produce one calibrated forecast/prediction is as + follows: + + - Extract a calibration set: The calibration set for each conformal forecast is automatically extracted from + the most recent past of your input series relative to the forecast start point. The number of calibration + examples (forecast errors / non-conformity scores) to consider can be defined at model creation with + parameter `cal_length`. Note that when using `cal_stride>1`, a longer history is required since the + calibration examples are generated with stridden historical forecasts. + - Generate historical forecasts on the calibration set (using the forecasting model) with a stride `cal_stride`. + - Compute the errors/non-conformity scores (specific to each conformal model) on these historical forecasts + - Compute the quantile values from the errors / non-conformity scores (using our desired quantiles set at model + creation with parameter `quantiles`). + - Compute the conformal prediction: Using these quantile values, add calibrated intervals to (or adjust the + existing intervals of) the forecasting model's predictions. + + Some notes: + + - When computing `historical_forecasts()`, `backtest()`, `residuals()`, ... the above is applied for each + forecast (the forecasting model's historical forecasts are only generated once for efficiency). + - For multi-horizon forecasts, the above is applied for each step in the horizon separately. + + Parameters + ---------- + model + A pre-trained global forecasting model. See the list of models + `here `_. + quantiles + A list of quantiles centered around the median `q=0.5` to use. For example quantiles + [0.1, 0.2, 0.5, 0.8 0.9] correspond to two intervals with (0.9 - 0.1) = 80%, and (0.8 - 0.2) 60% coverage + around the median (model forecast). + symmetric + Whether to use symmetric non-conformity scores. If `False`, uses asymmetric scores (individual scores + for lower- and upper quantile interval bounds). + cal_length + The number of past forecast errors / non-conformity scores to use as calibration for each conformal + forecast (and each step in the horizon). If `None`, considers all scores. + cal_stride + The stride to apply when computing the historical forecasts and non-conformity scores on the calibration + set. The actual conformal forecasts can have a different stride given with parameter `stride` in downstream + tasks (e.g. historical forecasts, backtest, ...) + cal_num_samples + The number of samples to generate for each calibration forecast (if `model` is a probabilistic forecasting + model). The non-conformity scores are computed on the quantile values of these forecasts (using quantiles + `quantiles`). Uses `1` for deterministic models. The actual conformal forecasts can have a different number + of samples given with parameter `num_samples` in downstream tasks (e.g. predict, historical forecasts, ...). + random_state + Control the randomness of probabilistic conformal forecasts (sample generation) across different runs. + """ + if not isinstance(model, GlobalForecastingModel) or not model._fit_called: + raise_log( + ValueError("`model` must be a pre-trained `GlobalForecastingModel`."), + logger=logger, + ) + _check_quantiles(quantiles) + + if cal_length is not None and cal_length < 1: + raise_log( + ValueError("`cal_length` must be `>=1` or `None`."), logger=logger + ) + if cal_stride < 1: + raise_log(ValueError("`cal_stride` must be `>=1`."), logger=logger) + if cal_num_samples < 1: + raise_log(ValueError("`cal_num_samples` must be `>=1`."), logger=logger) + + super().__init__(add_encoders=None) + + # quantiles and interval setup + self.quantiles = np.array(quantiles) + self.idx_median = quantiles.index(0.5) + self.q_interval = [ + (q_l, q_h) + for q_l, q_h in zip( + quantiles[: self.idx_median], quantiles[self.idx_median + 1 :][::-1] + ) + ] + self.interval_range = np.array([ + q_high - q_low for q_low, q_high in self.q_interval + ]) + + if symmetric: + # symmetric considers both tails together + self.interval_range_sym = copy.deepcopy(self.interval_range) + else: + # asymmetric considers tails separately + self.interval_range_sym = 1 - (1 - self.interval_range) / 2 + self.symmetric = symmetric + + # model setup + self.model = model + self.cal_length = cal_length + self.cal_stride = cal_stride + self.cal_num_samples = ( + cal_num_samples if model.supports_probabilistic_prediction else 1 + ) + self._likelihood = "quantile" + self._fit_called = True + + def fit( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + **kwargs, + ) -> "ConformalModel": + """Fit/train the underlying forecasting model on (potentially multiple) series. + + Optionally, one or multiple past and/or future covariates series can be provided as well, depending on the + forecasting model used. The number of covariates series must match the number of target series. + + Notes + ----- + Conformal Models do not require calling `fit()`, since they use pre-trained global forecasting models. + You can call `predict()` directly. Also, make sure that the input series used in `predict()` corresponds to + a calibration set, and not the same as used during training with `fit()`. + + Parameters + ---------- + series + One or several target time series. The model will be trained to forecast these time series. + The series may or may not be multivariate, but if multiple series are provided + they must have the same number of components. + past_covariates + One or several past-observed covariate time series. These time series will not be forecast, but can + be used by some models as an input. The covariate(s) may or may not be multivariate, but if multiple + covariates are provided they must have the same number of components. If `past_covariates` is provided, + it must contain the same number of series as `series`. + future_covariates + One or several future-known covariate time series. These time series will not be forecast, but can + be used by some models as an input. The covariate(s) may or may not be multivariate, but if multiple + covariates are provided they must have the same number of components. If `future_covariates` is provided, + it must contain the same number of series as `series`. + **kwargs + Optional keyword arguments that will passed to the underlying forecasting model's `fit()` method. + + Returns + ------- + self + Fitted model. + """ + # does not have to be trained, but we allow it for unified API + self.model.fit( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + **kwargs, + ) + return self + + def predict( + self, + n: int, + series: Union[TimeSeries, Sequence[TimeSeries]] = None, + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + num_samples: int = 1, + verbose: bool = False, + predict_likelihood_parameters: bool = False, + show_warnings: bool = True, + **kwargs, + ) -> Union[TimeSeries, Sequence[TimeSeries]]: + """Forecasts calibrated quantile intervals (or samples from calibrated intervals) for `n` time steps after the + end of the `series`. + + It is important that the input series for prediction correspond to a calibration set - a set different to the + series that the underlying forecasting `model` was trained on. + + Since it is a probabilistic model, you can generate forecasts in two ways: + + - Predict the calibrated quantile intervals directly: Pass parameters `predict_likelihood_parameters=True`, and + `num_samples=1` to the forecast method. + - Predict stochastic samples from the calibrated quantile intervals: Pass parameters + `predict_likelihood_parameters=False`, and `num_samples>>1` to the forecast method. + + Under the hood, the simplified workflow to produce one calibrated forecast/prediction for every step in the + horizon `n` is as follows (note: `cal_length` and `cal_stride` can be set at model creation): + + - Extract a calibration set: The calibration set for each conformal forecast is automatically extracted from + the most recent past of your input series relative to the forecast start point. The number of calibration + examples (forecast errors / non-conformity scores) to consider can be defined at model creation + with parameter `cal_length`. Note that when using `cal_stride>1`, a longer history is required since + the calibration examples are generated with stridden historical forecasts. + - Generate historical forecasts on the calibration set (using the forecasting model) with a stride `cal_stride`. + - Compute the errors/non-conformity scores (specific to each conformal model) on these historical forecasts + - Compute the quantile values from the errors / non-conformity scores (using our desired quantiles set at model + creation with parameter `quantiles`). + - Compute the conformal prediction: Using these quantile values, add calibrated intervals to (or adjust the + existing intervals of) the forecasting model's predictions. + + Parameters + ---------- + n + Forecast horizon - the number of time steps after the end of the series for which to produce predictions. + series + A series or sequence of series, representing the history of the target series whose future is to be + predicted. Will use the past of this series for calibration. The series should not have any overlap with + the series used to train the forecasting model. + past_covariates + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + future_covariates + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + num_samples + Number of times a prediction is sampled from the calibrated quantile predictions using linear + interpolation in-between the quantiles. For larger values, the sample distribution approximates the + calibrated quantile predictions. + verbose + Whether to print the progress. + predict_likelihood_parameters + If set to `True`, generates the quantile predictions directly. Only supported with `num_samples = 1`. + show_warnings + Whether to show warnings related auto-regression and past covariates usage. + **kwargs + Optional keyword arguments that will passed to the underlying forecasting model's `predict()` and + `historical_forecasts()` methods. + + Returns + ------- + Union[TimeSeries, Sequence[TimeSeries]] + If `series` is not specified, this function returns a single time series containing the `n` + next points after then end of the training series. + If `series` is given and is a simple ``TimeSeries``, this function returns the `n` next points + after the end of `series`. + If `series` is given and is a sequence of several time series, this function returns + a sequence where each element contains the corresponding `n` points forecasts. + """ + # call predict to verify that all series have required input times + _ = self.model.predict( + n=n, + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + num_samples=self.cal_num_samples, + verbose=verbose, + predict_likelihood_parameters=False, + show_warnings=show_warnings, + **kwargs, + ) + + series = series or self.model.training_series + called_with_single_series = get_series_seq_type(series) == SeriesType.SINGLE + series = series2seq(series) + + # generate only the required forecasts for calibration (including the last forecast which is the output of + # `predict()`) + cal_start, cal_start_format = _get_calibration_hfc_start( + series=series, + horizon=n, + output_chunk_shift=self.output_chunk_shift, + cal_length=self.cal_length, + cal_stride=self.cal_stride, + start="end", + start_format="position", + ) + + cal_hfcs = self.model.historical_forecasts( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=n, + num_samples=self.cal_num_samples, + start=cal_start, + start_format=cal_start_format, + stride=self.cal_stride, + retrain=False, + overlap_end=True, + last_points_only=False, + verbose=verbose, + show_warnings=False, + predict_likelihood_parameters=False, + predict_kwargs=kwargs, + ) + cal_preds = self._calibrate_forecasts( + series=series, + forecasts=cal_hfcs, + num_samples=num_samples, + start="end", # uses last hist fc (output of `predict()`) + start_format="position", + forecast_horizon=n, + stride=self.cal_stride, + overlap_end=True, + last_points_only=False, + verbose=verbose, + show_warnings=show_warnings, + predict_likelihood_parameters=predict_likelihood_parameters, + ) + # convert historical forecasts output to simple forecast / prediction + if called_with_single_series: + return cal_preds[0][0] + else: + return [cp[0] for cp in cal_preds] + + @_with_sanity_checks("_historical_forecasts_sanity_checks") + def historical_forecasts( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + forecast_horizon: int = 1, + num_samples: int = 1, + train_length: Optional[int] = None, + start: Optional[Union[pd.Timestamp, int]] = None, + start_format: Literal["position", "value"] = "value", + stride: int = 1, + retrain: Union[bool, int, Callable[..., bool]] = True, + overlap_end: bool = False, + last_points_only: bool = True, + verbose: bool = False, + show_warnings: bool = True, + predict_likelihood_parameters: bool = False, + enable_optimization: bool = True, + data_transformers: Optional[ + dict[str, Union[BaseDataTransformer, Pipeline]] + ] = None, + fit_kwargs: Optional[dict[str, Any]] = None, + predict_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: + """Generates calibrated historical forecasts by simulating predictions at various points in time throughout the + history of the provided (potentially multiple) `series`. This process involves retrospectively applying the + model to different time steps, as if the forecasts were made in real-time at those specific moments. This + allows for an evaluation of the model's performance over the entire duration of the series, providing insights + into its predictive accuracy and robustness across different historical periods. + + Currently, conformal models only support the pre-trained historical forecasts mode (`retrain=False`). + Parameters `retrain` and `train_length` are ignored. + + **Pre-trained Mode:** First, all historical forecasts are generated using the underlying pre-trained global + forecasting model (see :meth:`ForecastingModel.historical_forecasts() + ` for more info). Then it + repeatedly builds a calibration set by either expanding from the beginning of the historical forecasts or by + using a fixed-length moving window with length `cal_length` (the start point can also be configured with + `start` and `start_format`). + The next forecast of length `forecast_horizon` is then calibrated on this calibration set. Subsequently, the + end of the calibration set is moved forward by `stride` time steps, and the process is repeated. + + By default, with `last_points_only=True`, this method returns a single time series (or a sequence of time + series when `series` is also a sequence of series) composed of the last point from each calibrated historical + forecast. This time series will thus have a frequency of `series.freq * stride`. + If `last_points_only=False`, it will instead return a list (or a sequence of lists) with all calibrated + historical forecasts of length `forecast_horizon` and frequency `series.freq`. + + Parameters + ---------- + series + A (sequence of) target time series used to successively compute the historical forecasts. Will use the past + of this series for calibration. The series should not have any overlap with the series used to train the + forecasting model. + past_covariates + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + future_covariates + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + forecast_horizon + The forecast horizon for the predictions. + num_samples + Number of times a prediction is sampled from the calibrated quantile predictions using linear + interpolation in-between the quantiles. For larger values, the sample distribution approximates the + calibrated quantile predictions. + train_length + Currently ignored by conformal models. + start + Optionally, the first point in time at which a prediction is computed. This parameter supports: + ``int``, ``pandas.Timestamp``, and ``None``. + If an ``int``, it is either the index position of the first prediction point for `series` with a + `pd.DatetimeIndex`, or the index value for `series` with a `pd.RangeIndex`. The latter can be changed to + the index position with `start_format="position"`. + If a ``pandas.Timestamp``, it is the time stamp of the first prediction point. + If ``None``, the first prediction point will automatically be set to: + + - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first + predictable point is earlier than the first trainable point. + - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. + - the first trainable point (given `train_length`) otherwise + + Note: If the model uses a shifted output (`output_chunk_shift > 0`), then the first predicted point is also + shifted by `output_chunk_shift` points into the future. + Note: Raises a ValueError if `start` yields a time outside the time index of `series`. + Note: If `start` is outside the possible historical forecasting times, will ignore the parameter + (default behavior with ``None``) and start at the first trainable/predictable point. + start_format + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. + stride + The number of time steps between two consecutive predictions. Must be a round-multiple of `cal_stride` + (set at model creation) and `>=cal_stride`. + retrain + Currently ignored by conformal models. + overlap_end + Whether the returned forecasts can go beyond the series' end or not. + last_points_only + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. + Otherwise, returns a list of historical ``TimeSeries`` forecasts. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + predict_likelihood_parameters + If set to `True`, generates the quantile predictions directly. Only supported with `num_samples = 1`. + enable_optimization + Whether to use the optimized version of `historical_forecasts` when supported and available. + Default: ``True``. + data_transformers + Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series + (possibles keys; "series", "past_covariates", "future_covariates"). If provided, all input series must be + in the un-transformed space. For fittable transformer / pipeline: + + - if `retrain=True`, the data transformer re-fit on the training data at each historical forecast step + (currently ignored by conformal models). + - if `retrain=False`, the data transformer transforms the series once before all the forecasts. + + The fitted transformer is used to transform the input during both training and prediction. + If the transformation is invertible, the forecasts will be inverse-transformed. + fit_kwargs + Currently ignored by conformal models. + predict_kwargs + Optionally, some additional arguments passed to the model `predict()` method. + sample_weight + Currently ignored by conformal models. + + Returns + ------- + TimeSeries + A single historical forecast for a single `series` and `last_points_only=True`: it contains only the + predictions at step `forecast_horizon` from all historical forecasts. + list[TimeSeries] + A list of historical forecasts for: + + - a sequence (list) of `series` and `last_points_only=True`: for each series, it contains only the + predictions at step `forecast_horizon` from all historical forecasts. + - a single `series` and `last_points_only=False`: for each historical forecast, it contains the entire + horizon `forecast_horizon`. + list[list[TimeSeries]] + A list of lists of historical forecasts for a sequence of `series` and `last_points_only=False`. For each + series, and historical forecast, it contains the entire horizon `forecast_horizon`. The outer list + is over the series provided in the input sequence, and the inner lists contain the historical forecasts for + each series. + """ + called_with_single_series = get_series_seq_type(series) == SeriesType.SINGLE + series = series2seq(series) + past_covariates = series2seq(past_covariates) + future_covariates = series2seq(future_covariates) + + # generate only the required forecasts (if `start` is given, we have to start earlier to satisfy the + # calibration set requirements) + cal_start, cal_start_format = _get_calibration_hfc_start( + series=series, + horizon=forecast_horizon, + output_chunk_shift=self.output_chunk_shift, + cal_length=self.cal_length, + cal_stride=self.cal_stride, + start=start, + start_format=start_format, + ) + hfcs = self.model.historical_forecasts( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=forecast_horizon, + num_samples=self.cal_num_samples, + start=cal_start, + start_format=cal_start_format, + stride=self.cal_stride, + retrain=False, + overlap_end=overlap_end, + last_points_only=last_points_only, + verbose=verbose, + show_warnings=False, + predict_likelihood_parameters=False, + enable_optimization=enable_optimization, + data_transformers=data_transformers, + fit_kwargs=fit_kwargs, + predict_kwargs=predict_kwargs, + ) + calibrated_forecasts = self._calibrate_forecasts( + series=series, + forecasts=hfcs, + num_samples=num_samples, + start=start, + start_format=start_format, + forecast_horizon=forecast_horizon, + stride=stride, + overlap_end=overlap_end, + last_points_only=last_points_only, + verbose=verbose, + show_warnings=show_warnings, + predict_likelihood_parameters=predict_likelihood_parameters, + ) + return ( + calibrated_forecasts[0] + if called_with_single_series + else calibrated_forecasts + ) + + def backtest( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + historical_forecasts: Optional[ + Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] + ] = None, + forecast_horizon: int = 1, + num_samples: int = 1, + train_length: Optional[int] = None, + start: Optional[Union[pd.Timestamp, int]] = None, + start_format: Literal["position", "value"] = "value", + stride: int = 1, + retrain: Union[bool, int, Callable[..., bool]] = True, + overlap_end: bool = False, + last_points_only: bool = False, + metric: Union[METRIC_TYPE, list[METRIC_TYPE]] = metrics.mape, + reduction: Union[Callable[..., float], None] = np.mean, + verbose: bool = False, + show_warnings: bool = True, + predict_likelihood_parameters: bool = False, + enable_optimization: bool = True, + data_transformers: Optional[ + dict[str, Union[BaseDataTransformer, Pipeline]] + ] = None, + metric_kwargs: Optional[Union[dict[str, Any], list[dict[str, Any]]]] = None, + fit_kwargs: Optional[dict[str, Any]] = None, + predict_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + ) -> Union[float, np.ndarray, list[float], list[np.ndarray]]: + """Compute error values that the model produced for historical forecasts on (potentially multiple) `series`. + + If `historical_forecasts` are provided, the metric(s) (given by the `metric` function) is evaluated directly on + all forecasts and actual values. The same `series` and `last_points_only` value must be passed that were used + to generate the historical forecasts. Finally, the method returns an optional `reduction` (the mean by default) + of all these metric scores. + + If `historical_forecasts` is ``None``, it first generates the historical forecasts with the parameters given + below (see :meth:`ConformalModel.historical_forecasts() + ` for more info) and then + evaluates as described above. + + The metric(s) can be further customized `metric_kwargs` (e.g. control the aggregation over components, time + steps, multiple series, other required arguments such as `q` for quantile metrics, ...). + + Notes + ----- + Darts has several metrics to evaluate probabilistic forecasts. For conformal models, we recommend using + quantile interval metrics (see `here `_). + You can specify which intervals to evaluate by setting `metric_kwargs={'q_interval': my_intervals}`. To check + all intervals used by your conformal model `my_model`, you can set ``{'q_interval': my_model.q_interval}``. + + Parameters + ---------- + series + A (sequence of) target time series used to successively compute the historical forecasts. Will use the past + of this series for calibration. The series should not have any overlap with the series used to train the + forecasting model. + past_covariates + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + future_covariates + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + historical_forecasts + Optionally, the (or a sequence of / a sequence of sequences of) historical forecasts time series to be + evaluated. Corresponds to the output of :meth:`historical_forecasts() + `. The same `series` and + `last_points_only` values must be passed that were used to generate the historical forecasts. If provided, + will skip historical forecasting and ignore all parameters except `series`, `last_points_only`, `metric`, + and `reduction`. + forecast_horizon + The forecast horizon for the predictions. + num_samples + Number of times a prediction is sampled from the calibrated quantile predictions using linear + interpolation in-between the quantiles. For larger values, the sample distribution approximates the + calibrated quantile predictions. + train_length + Currently ignored by conformal models. + start + Optionally, the first point in time at which a prediction is computed. This parameter supports: + ``int``, ``pandas.Timestamp``, and ``None``. + If an ``int``, it is either the index position of the first prediction point for `series` with a + `pd.DatetimeIndex`, or the index value for `series` with a `pd.RangeIndex`. The latter can be changed to + the index position with `start_format="position"`. + If a ``pandas.Timestamp``, it is the time stamp of the first prediction point. + If ``None``, the first prediction point will automatically be set to: + + - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first + predictable point is earlier than the first trainable point. + - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. + - the first trainable point (given `train_length`) otherwise + + Note: If the model uses a shifted output (`output_chunk_shift > 0`), then the first predicted point is also + shifted by `output_chunk_shift` points into the future. + Note: Raises a ValueError if `start` yields a time outside the time index of `series`. + Note: If `start` is outside the possible historical forecasting times, will ignore the parameter + (default behavior with ``None``) and start at the first trainable/predictable point. + start_format + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. + stride + The number of time steps between two consecutive predictions. + retrain + Currently ignored by conformal models. + overlap_end + Whether the returned forecasts can go beyond the series' end or not. + last_points_only + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. + Otherwise, returns a list of historical ``TimeSeries`` forecasts. + metric + A metric function or a list of metric functions. Each metric must either be a Darts metric (see `here + `_), or a custom metric that has an + identical signature as Darts' metrics, uses decorators :func:`~darts.metrics.metrics.multi_ts_support` and + :func:`~darts.metrics.metrics.multi_ts_support`, and returns the metric score. + reduction + A function used to combine the individual error scores obtained when `last_points_only` is set to `False`. + When providing several metric functions, the function will receive the argument `axis = 1` to obtain single + value for each metric function. + If explicitly set to `None`, the method will return a list of the individual error scores instead. + Set to ``np.mean`` by default. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + predict_likelihood_parameters + If set to `True`, generates the quantile predictions directly. Only supported with `num_samples = 1`. + enable_optimization + Whether to use the optimized version of `historical_forecasts` when supported and available. + Default: ``True``. + data_transformers + Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series + (possibles keys; "series", "past_covariates", "future_covariates"). If provided, all input series must be + in the un-transformed space. For fittable transformer / pipeline: + + - if `retrain=True`, the data transformer re-fit on the training data at each historical forecast step + (currently ignored by conformal models). + - if `retrain=False`, the data transformer transforms the series once before all the forecasts. + + The fitted transformer is used to transform the input during both training and prediction. + If the transformation is invertible, the forecasts will be inverse-transformed. + Only effective when `historical_forecasts=None`. + metric_kwargs + Additional arguments passed to `metric()`, such as `'n_jobs'` for parallelization, `'component_reduction'` + for reducing the component wise metrics, seasonality `'m'` for scaled metrics, etc. Will pass arguments to + each metric separately and only if they are present in the corresponding metric signature. Parameter + `'insample'` for scaled metrics (e.g. mase`, `rmsse`, ...) is ignored, as it is handled internally. + fit_kwargs + Currently ignored by conformal models. + predict_kwargs + Optionally, some additional arguments passed to the model `predict()` method. + sample_weight + Currently ignored by conformal models. + + Returns + ------- + float + A single backtest score for single uni/multivariate series, a single `metric` function and: + + - `historical_forecasts` generated with `last_points_only=True` + - `historical_forecasts` generated with `last_points_only=False` and using a backtest `reduction` + np.ndarray + An numpy array of backtest scores. For single series and one of: + + - a single `metric` function, `historical_forecasts` generated with `last_points_only=False` + and backtest `reduction=None`. The output has shape (n forecasts, *). + - multiple `metric` functions and `historical_forecasts` generated with `last_points_only=False`. + The output has shape (*, n metrics) when using a backtest `reduction`, and (n forecasts, *, n metrics) + when `reduction=None` + - multiple uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None` for "per time step metrics" + list[float] + Same as for type `float` but for a sequence of series. The returned metric list has length + `len(series)` with the `float` metric for each input `series`. + list[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. The returned metric list has length + `len(series)` with the `np.ndarray` metrics for each input `series`. + """ + return super().backtest( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + historical_forecasts=historical_forecasts, + forecast_horizon=forecast_horizon, + num_samples=num_samples, + train_length=train_length, + start=start, + start_format=start_format, + stride=stride, + retrain=retrain, + overlap_end=overlap_end, + last_points_only=last_points_only, + metric=metric, + reduction=reduction, + verbose=verbose, + show_warnings=show_warnings, + predict_likelihood_parameters=predict_likelihood_parameters, + enable_optimization=enable_optimization, + data_transformers=data_transformers, + metric_kwargs=metric_kwargs, + fit_kwargs=fit_kwargs, + predict_kwargs=predict_kwargs, + sample_weight=sample_weight, + ) + + def residuals( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + historical_forecasts: Optional[ + Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] + ] = None, + forecast_horizon: int = 1, + num_samples: int = 1, + train_length: Optional[int] = None, + start: Optional[Union[pd.Timestamp, int]] = None, + start_format: Literal["position", "value"] = "value", + stride: int = 1, + retrain: Union[bool, int, Callable[..., bool]] = True, + overlap_end: bool = False, + last_points_only: bool = True, + metric: METRIC_TYPE = metrics.err, + verbose: bool = False, + show_warnings: bool = True, + predict_likelihood_parameters: bool = False, + enable_optimization: bool = True, + data_transformers: Optional[ + dict[str, Union[BaseDataTransformer, Pipeline]] + ] = None, + metric_kwargs: Optional[dict[str, Any]] = None, + fit_kwargs: Optional[dict[str, Any]] = None, + predict_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + values_only: bool = False, + ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: + """Compute the residuals that the model produced for historical forecasts on (potentially multiple) `series`. + + This function computes the difference (or one of Darts' "per time step" metrics) between the actual + observations from `series` and the fitted values obtained by training the model on `series` (or using a + pre-trained model with `retrain=False`). Not all models support fitted values, so we use historical forecasts + as an approximation for them. + + In sequence this method performs: + + - use pre-computed `historical_forecasts` or compute historical forecasts for each series (see + :meth:`~darts.models.forecasting.conformal_models.ConformalModel.historical_forecasts` for more details). + How the historical forecasts are generated can be configured with parameters `num_samples`, `train_length`, + `start`, `start_format`, `forecast_horizon`, `stride`, `retrain`, `last_points_only`, `fit_kwargs`, and + `predict_kwargs`. + - compute a backtest using a "per time step" `metric` between the historical forecasts and `series` per + component/column and time step (see + :meth:`~darts.models.forecasting.conformal_models.ConformalModel.backtest` for more details). By default, + uses the residuals :func:`~darts.metrics.metrics.err` (error) as a `metric`. + - create and return `TimeSeries` (or simply a np.ndarray with `values_only=True`) with the time index from + historical forecasts, and values from the metrics per component and time step. + + This method works for single or multiple univariate or multivariate series. + It uses the median prediction (when dealing with stochastic forecasts). + + Notes + ----- + Darts has several metrics to evaluate probabilistic forecasts. For conformal models, we recommend using + "per time step" quantile interval metrics (see `here + `_). You can specify which intervals to + evaluate by setting `metric_kwargs={'q_interval': my_intervals}`. To check all intervals used by your conformal + model `my_model`, you can set ``{'q_interval': my_model.q_interval}``. + + Parameters + ---------- + series + A (sequence of) target time series used to successively compute the historical forecasts. Will use the past + of this series for calibration. The series should not have any overlap with the series used to train the + forecasting model. + past_covariates + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + future_covariates + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + Their dimension must match that of the past covariates used for training. Will use this series for + calibration. + historical_forecasts + Optionally, the (or a sequence of / a sequence of sequences of) historical forecasts time series to be + evaluated. Corresponds to the output of :meth:`historical_forecasts() + `. The same `series` and + `last_points_only` values must be passed that were used to generate the historical forecasts. If provided, + will skip historical forecasting and ignore all parameters except `series`, `last_points_only`, `metric`, + and `reduction`. + forecast_horizon + The forecast horizon for the predictions. + num_samples + Number of times a prediction is sampled from the calibrated quantile predictions using linear + interpolation in-between the quantiles. For larger values, the sample distribution approximates the + calibrated quantile predictions. + train_length + Currently ignored by conformal models. + start + Optionally, the first point in time at which a prediction is computed. This parameter supports: + ``int``, ``pandas.Timestamp``, and ``None``. + If an ``int``, it is either the index position of the first prediction point for `series` with a + `pd.DatetimeIndex`, or the index value for `series` with a `pd.RangeIndex`. The latter can be changed to + the index position with `start_format="position"`. + If a ``pandas.Timestamp``, it is the time stamp of the first prediction point. + If ``None``, the first prediction point will automatically be set to: + + - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first + predictable point is earlier than the first trainable point. + - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. + - the first trainable point (given `train_length`) otherwise + + Note: If the model uses a shifted output (`output_chunk_shift > 0`), then the first predicted point is also + shifted by `output_chunk_shift` points into the future. + Note: Raises a ValueError if `start` yields a time outside the time index of `series`. + Note: If `start` is outside the possible historical forecasting times, will ignore the parameter + (default behavior with ``None``) and start at the first trainable/predictable point. + start_format + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. + stride + The number of time steps between two consecutive predictions. + retrain + Currently ignored by conformal models. + overlap_end + Whether the returned forecasts can go beyond the series' end or not. + last_points_only + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. + Otherwise, returns a list of historical ``TimeSeries`` forecasts. + metric + Either one of Darts' "per time step" metrics (see `here + `_), or a custom metric that has an + identical signature as Darts' "per time step" metrics, uses decorators + :func:`~darts.metrics.metrics.multi_ts_support` and :func:`~darts.metrics.metrics.multi_ts_support`, + and returns one value per time step. + verbose + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + predict_likelihood_parameters + If set to `True`, generates the quantile predictions directly. Only supported with `num_samples = 1`. + enable_optimization + Whether to use the optimized version of `historical_forecasts` when supported and available. + Default: ``True``. + data_transformers + Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series + (possibles keys; "series", "past_covariates", "future_covariates"). If provided, all input series must be + in the un-transformed space. For fittable transformer / pipeline: + + - if `retrain=True`, the data transformer re-fit on the training data at each historical forecast step + (currently ignored by conformal models). + - if `retrain=False`, the data transformer transforms the series once before all the forecasts. + + The fitted transformer is used to transform the input during both training and prediction. + If the transformation is invertible, the forecasts will be inverse-transformed. + Only effective when `historical_forecasts=None`. + metric_kwargs + Additional arguments passed to `metric()`, such as `'n_jobs'` for parallelization, `'m'` for scaled + metrics, etc. Will pass arguments only if they are present in the corresponding metric signature. Ignores + reduction arguments `"series_reduction", "component_reduction", "time_reduction"`, and parameter + `'insample'` for scaled metrics (e.g. mase`, `rmsse`, ...), as they are handled internally. + fit_kwargs + Currently ignored by conformal models. + predict_kwargs + Optionally, some additional arguments passed to the model `predict()` method. + sample_weight + Currently ignored by conformal models. + values_only + Whether to return the residuals as `np.ndarray`. If `False`, returns residuals as `TimeSeries`. + + Returns + ------- + TimeSeries + Residual `TimeSeries` for a single `series` and `historical_forecasts` generated with + `last_points_only=True`. + list[TimeSeries] + A list of residual `TimeSeries` for a sequence (list) of `series` with `last_points_only=True`. + The residual list has length `len(series)`. + list[list[TimeSeries]] + A list of lists of residual `TimeSeries` for a sequence of `series` with `last_points_only=False`. + The outer residual list has length `len(series)`. The inner lists consist of the residuals from + all possible series-specific historical forecasts. + """ + return super().residuals( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + historical_forecasts=historical_forecasts, + forecast_horizon=forecast_horizon, + num_samples=num_samples, + train_length=train_length, + start=start, + start_format=start_format, + stride=stride, + retrain=retrain, + overlap_end=overlap_end, + last_points_only=last_points_only, + metric=metric, + verbose=verbose, + show_warnings=show_warnings, + predict_likelihood_parameters=predict_likelihood_parameters, + enable_optimization=enable_optimization, + data_transformers=data_transformers, + metric_kwargs=metric_kwargs, + fit_kwargs=fit_kwargs, + predict_kwargs=predict_kwargs, + sample_weight=sample_weight, + values_only=values_only, + ) + + @random_method + def _calibrate_forecasts( + self, + series: Sequence[TimeSeries], + forecasts: Union[Sequence[Sequence[TimeSeries]], Sequence[TimeSeries]], + num_samples: int = 1, + start: Optional[Union[pd.Timestamp, int, str]] = None, + start_format: Literal["position", "value"] = "value", + forecast_horizon: int = 1, + stride: int = 1, + overlap_end: bool = False, + last_points_only: bool = True, + verbose: bool = False, + show_warnings: bool = True, + predict_likelihood_parameters: bool = False, + ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: + """Generate calibrated historical forecasts. + + In general the workflow of the models to produce one calibrated forecast/prediction per step in the horizon + is as follows: + + - Generate historical forecasts for `series` with stride `cal_stride` (using the forecasting model) + - Extract a calibration set: The forecasts from the most recent past to use as calibration for one conformal + prediction. The number of examples to use can be defined at model creation with parameter `cal_length`. It + automatically extracts the calibration set from the most recent past of your input series (`series`, + `past_covariates`, ...). + - Compute the errors/non-conformity scores (specific to each conformal model) on these historical forecasts + - Compute the quantile values from the errors / non-conformity scores (using our desired quantiles set at model + creation with parameter `quantiles`). + - Compute the conformal prediction: Using these quantile values, add calibrated intervals to (or adjust the + existing intervals of) the forecasting model's predictions. + """ + cal_stride = self.cal_stride + cal_length = self.cal_length + metric, metric_kwargs = self._residuals_metric + residuals = self.model.residuals( + series=series, + historical_forecasts=forecasts, + overlap_end=overlap_end, + last_points_only=last_points_only, + verbose=verbose, + show_warnings=show_warnings, + values_only=True, + metric=metric, + metric_kwargs=metric_kwargs, + ) + + outer_iterator = enumerate(zip(series, forecasts, residuals)) + if len(series) > 1: + # Use tqdm on the outer loop only if there's more than one series to iterate over + # (otherwise use tqdm on the inner loop). + outer_iterator = _build_tqdm_iterator( + outer_iterator, + verbose, + total=len(series), + desc="conformal forecasts", + ) + + cp_hfcs = [] + for series_idx, (series_, s_hfcs, res) in outer_iterator: + cp_preds = [] + + # no historical forecasts were generated + if not s_hfcs: + cp_hfcs.append(cp_preds) + continue + + last_hfc = s_hfcs if last_points_only else s_hfcs[-1] + + # compute the minimum required number of useful calibration residuals + # at least one or `cal_length` examples + min_n_cal = cal_length or 1 + # `last_points_only=False` requires additional examples to use most recent information + # from all steps in the horizon + if not last_points_only: + min_n_cal += math.ceil(forecast_horizon / cal_stride) - 1 + + # determine first forecast index for conformal prediction + # we need at least one residual per point in the horizon prior to the first conformal forecast + horizon_ocs = forecast_horizon + self.output_chunk_shift + first_idx_train = math.ceil(horizon_ocs / cal_stride) + + # plus some additional examples based on `cal_length` + if cal_length is not None: + first_idx_train += cal_length - 1 + + # check if later we need to drop some residuals without useful information (unknown residuals) + if overlap_end: + delta_end = n_steps_between( + end=last_hfc.end_time(), + start=series_.end_time(), + freq=series_.freq, + ) + else: + delta_end = 0 + + # ignore residuals without useful information + if last_points_only and delta_end > 0: + # useful residual information only up until the forecast *ending* at the last time step in `series` + ignore_n_residuals = delta_end + elif not last_points_only and delta_end >= forecast_horizon: + # useful residual information only up until the forecast *starting* at the last time step in `series` + ignore_n_residuals = delta_end - forecast_horizon + 1 + else: + # ignore at least one forecast residuals from the end, since we can only use prior residuals + ignore_n_residuals = self.output_chunk_shift + 1 + # with last points only, ignore the last `horizon` residuals to avoid look-ahead bias + if last_points_only: + ignore_n_residuals += forecast_horizon - 1 + + # get the last index respecting `cal_stride` + last_res_idx = -math.ceil(ignore_n_residuals / cal_stride) + # get only useful residuals + res = res[:last_res_idx] + + if first_idx_train >= len(s_hfcs) or len(res) < min_n_cal: + raise_log( + ValueError( + "Could not build the minimum required calibration input with the provided " + f"`series` and `*_covariates` at series index: {series_idx}. " + f"Expected to generate at least `{min_n_cal}` calibration forecasts with known residuals " + f"before the first conformal forecast, but could only generate `{len(res)}`." + ), + logger=logger, + ) + + # adjust first index based on `start` + first_idx_start = 0 + if start == "end": + # called from `predict()`; start at the last forecast + first_idx_start = len(s_hfcs) - 1 + elif start is not None: + # called from `historical_forecasts()`: use user-defined start + # the conformal forecastable index ranges from the start of the first valid historical + # forecast until the start of the last historical forecast + historical_forecasts_time_index = ( + s_hfcs[first_idx_train].start_time(), + s_hfcs[-1].start_time(), + ) + # adjust forecast start points in case of output shift or `last_points_only=True` + adjust_idx = ( + self.output_chunk_shift + + int(last_points_only) * (forecast_horizon - 1) + ) * series_.freq + historical_forecasts_time_index = ( + historical_forecasts_time_index[0] - adjust_idx, + historical_forecasts_time_index[1] - adjust_idx, + ) + + # adjust forecastable times based on user start, assuming hfcs were generated with `stride=1` + first_start_time, _ = _adjust_historical_forecasts_time_index( + series=series_, + series_idx=series_idx, + start=start, + start_format=start_format, + stride=stride, + historical_forecasts_time_index=historical_forecasts_time_index, + show_warnings=show_warnings, + ) + # find position relative to start + first_idx_start = n_steps_between( + first_start_time + adjust_idx, + s_hfcs[0].start_time(), + freq=series_.freq, + ) + # adjust by stride + first_idx_start = math.ceil(first_idx_start / cal_stride) + + # get final first index + first_fc_idx = max([first_idx_train, first_idx_start]) + # bring `res` from shape (forecasting steps, n components, n past residuals) into + # shape (forecasting steps, n components, n past residuals) + if last_points_only: + # -> (1, n components, n samples * n past residuals) + res = res.transpose(2, 1, 0) + else: + # rearrange the residuals to avoid look-ahead bias and to have the same number of examples per + # point in the horizon. We want the most recent residuals in the past for each step in the horizon. + res = np.array(res) + + # go through each step in the horizon, use all useful information from the end (most recent values), + # and skip information at beginning (most distant past); + # -> (forecast horizon, n components, n past residuals) + res_ = [] + for idx_horizon in range(forecast_horizon): + n = idx_horizon + 1 + # ignore residuals at beginning + idx_fc_start = math.floor((forecast_horizon - n) / cal_stride) + # keep as many residuals as possible from end + idx_fc_end = -( + math.ceil(forecast_horizon / cal_stride) - (idx_fc_start + 1) + ) + res_.append(res[idx_fc_start : idx_fc_end or None, idx_horizon]) + res = np.concatenate(res_, axis=2).T + + # get the last conformal forecast index (exclusive) based on the residual examples + last_fc_idx = res.shape[2] + math.ceil(horizon_ocs / cal_stride) + + # forecasts are stridden, so stride must be relative + rel_stride = math.ceil(stride / cal_stride) + + def conformal_predict(idx_, pred_vals_): + # get the last residual index for calibration, `cal_end` is exclusive + # to avoid look-ahead bias, use only residuals from before the conformal forecast start point; + # for `last_points_only=True`, the last residual historically available at the forecasting + # point is `horizon_ocs - 1` steps before. The same applies to `last_points_only=False` thanks to + # the residual rearrangement + cal_end = ( + first_fc_idx + + idx_ * rel_stride + - (math.ceil(horizon_ocs / cal_stride) - 1) + ) + # optionally, use only `cal_length` residuals + cal_start = cal_end - cal_length if cal_length is not None else None + + # calibrate and apply interval to the forecasts + q_hat_ = self._calibrate_interval(res[:, :, cal_start:cal_end]) + vals = self._apply_interval(pred_vals_, q_hat_) + + # optionally, generate samples from the intervals + if not predict_likelihood_parameters: + vals = sample_from_quantiles( + vals, self.quantiles, num_samples=num_samples + ) + return vals + + # historical conformal prediction + # for each forecast, compute calibrated quantile intervals based on past residuals + if last_points_only: + inner_iterator = enumerate( + s_hfcs.all_values(copy=False)[first_fc_idx:last_fc_idx:rel_stride] + ) + else: + inner_iterator = enumerate(s_hfcs[first_fc_idx:last_fc_idx:rel_stride]) + + comp_names_out = ( + self._cp_component_names(series_) + if predict_likelihood_parameters + else None + ) + if len(series) == 1: + # only use progress bar if there's no outer loop + inner_iterator = _build_tqdm_iterator( + inner_iterator, + verbose, + total=(last_fc_idx - 1 - first_fc_idx) // rel_stride + 1, + desc="conformal forecasts", + ) + + if last_points_only: + for idx, pred_vals in inner_iterator: + pred_vals = np.expand_dims(pred_vals, 0) + cp_pred = conformal_predict(idx, pred_vals) + cp_preds.append(cp_pred) + cp_preds = _build_forecast_series( + points_preds=np.concatenate(cp_preds, axis=0), + input_series=series_, + custom_columns=comp_names_out, + time_index=generate_index( + start=s_hfcs._time_index[first_fc_idx], + length=len(cp_preds), + freq=series_.freq * stride, + name=series_._time_index.name, + ), + with_static_covs=not predict_likelihood_parameters, + with_hierarchy=False, + ) + else: + for idx, pred in inner_iterator: + pred_vals = pred.all_values(copy=False) + cp_pred = conformal_predict(idx, pred_vals) + cp_pred = _build_forecast_series( + points_preds=cp_pred, + input_series=series_, + custom_columns=comp_names_out, + time_index=pred._time_index, + with_static_covs=not predict_likelihood_parameters, + with_hierarchy=False, + ) + cp_preds.append(cp_pred) + cp_hfcs.append(cp_preds) + return cp_hfcs + + def _clean(self) -> Self: + """Cleans the model and sub-model.""" + cleaned_model = super()._clean() + cleaned_model.model = cleaned_model.model._clean() + return cleaned_model + + def save( + self, + path: Optional[Union[str, os.PathLike, BinaryIO]] = None, + clean: bool = False, + **pkl_kwargs, + ) -> None: + """ + Saves the conformal model under a given path or file handle. + + Additionally, two files are stored if `self.model` is a `TorchForecastingModel`. + + Example for saving and loading a :class:`ConformalNaiveModel`: + + .. highlight:: python + .. code-block:: python + + from darts.datasets import AirPassengersDataset + from darts.models import ConformalNaiveModel, LinearRegressionModel + + series = AirPassengersDataset().load() + forecasting_model = LinearRegressionModel(lags=4).fit(series) + + model = ConformalNaiveModel( + model=forecasting_model, + quantiles=[0.1, 0.5, 0.9], + ) + + model.save("my_model.pkl") + model_loaded = ConformalNaiveModel.load("my_model.pkl") + .. + + Parameters + ---------- + path + Path or file handle under which to save the ensemble model at its current state. If no path is specified, + the ensemble model is automatically saved under ``"{ConformalNaiveModel}_{YYYY-mm-dd_HH_MM_SS}.pkl"``. + If the forecasting model is a `TorchForecastingModel`, two files (model object and checkpoint) are saved + under ``"{path}.{ModelClass}.pt"`` and ``"{path}.{ModelClass}.ckpt"``. + clean + Whether to store a cleaned version of the model. If `True`, the training series and covariates are removed. + If the underlying forecasting `model` is a `TorchForecastingModel`, will additionally remove all Lightning + Trainer-related parameters. + + Note: After loading a model stored with `clean=True`, a `series` must be passed 'predict()', + `historical_forecasts()` and other forecasting methods. + pkl_kwargs + Keyword arguments passed to `pickle.dump()` + """ + + if path is None: + # default path + path = self._default_save_path() + ".pkl" + + super().save(path, clean=clean, **pkl_kwargs) + + if TORCH_AVAILABLE and issubclass(type(self.model), TorchForecastingModel): + path_tfm = f"{path}.{type(self.model).__name__}.pt" + self.model.save(path=path_tfm, clean=clean) + + @staticmethod + def load( + path: Union[str, os.PathLike, BinaryIO], + pl_trainer_kwargs: Optional[dict] = None, + **kwargs, + ) -> "ConformalModel": + """ + Loads a model from a given path or file handle. + + Parameters + ---------- + path + Path or file handle from which to load the model. + pl_trainer_kwargs + Only effective if the underlying forecasting model is a `TorchForecastingModel`. + Optionally, a set of kwargs to create a new Lightning Trainer used to configure the model for downstream + tasks (e.g. prediction). + Some examples include specifying the batch size or moving the model to CPU/GPU(s). Check the + `Lightning Trainer documentation `_ + for more information about the supported kwargs. + **kwargs + Only effective if the underlying forecasting model is a `TorchForecastingModel`. + Additional kwargs for PyTorch Lightning's :func:`LightningModule.load_from_checkpoint()` method, + For more information, read the `official documentation `_. + """ + model: ConformalModel = GlobalForecastingModel.load(path) + + if TORCH_AVAILABLE and issubclass(type(model.model), TorchForecastingModel): + path_tfm = f"{path}.{type(model.model).__name__}.pt" + model.model = TorchForecastingModel.load( + path_tfm, + pl_trainer_kwargs=pl_trainer_kwargs, + **kwargs, + ) + return model + + @abstractmethod + def _calibrate_interval( + self, residuals: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + """Computes the lower and upper calibrated forecast intervals based on residuals. + + Parameters + ---------- + residuals + The residuals are expected to have shape (horizon, n components, n historical forecasts * n samples) + """ + + @abstractmethod + def _apply_interval(self, pred: np.ndarray, q_hat: tuple[np.ndarray, np.ndarray]): + """Applies the calibrated interval to the predicted quantiles. Returns an array with `len(quantiles)` + conformalized quantile predictions (lower quantiles, model forecast, upper quantiles) per component. + + E.g. output is `(target1_q1, target1_pred, target1_q2, target2_q1, ...)` + """ + + @property + @abstractmethod + def _residuals_metric(self) -> tuple[METRIC_TYPE, Optional[dict]]: + """Gives the "per time step" metric and optional metric kwargs used to compute residuals / + non-conformity scores.""" + + def _cp_component_names(self, input_series) -> list[str]: + """Gives the component names for generated forecasts.""" + return likelihood_component_names( + input_series.components, quantile_names(self.quantiles) + ) + + def _historical_forecasts_sanity_checks(self, *args: Any, **kwargs: Any) -> None: + super()._historical_forecasts_sanity_checks(*args, **kwargs, is_conformal=True) + + @property + def output_chunk_length(self) -> Optional[int]: + # conformal models can predict any horizon if the calibration set is large enough + return None + + @property + def output_chunk_shift(self) -> int: + return self.model.output_chunk_shift + + @property + def _model_encoder_settings(self): + raise NotImplementedError(f"not supported by `{self.__class__.__name__}`.") + + @property + def extreme_lags( + self, + ) -> tuple[ + Optional[int], + Optional[int], + Optional[int], + Optional[int], + Optional[int], + Optional[int], + int, + Optional[int], + ]: + raise NotImplementedError(f"not supported by `{self.__class__.__name__}`.") + + @property + def min_train_series_length(self) -> int: + raise NotImplementedError(f"not supported by `{self.__class__.__name__}`.") + + @property + def min_train_samples(self) -> int: + raise NotImplementedError(f"not supported by `{self.__class__.__name__}`.") + + @property + def supports_multivariate(self) -> bool: + return self.model.supports_multivariate + + @property + def supports_past_covariates(self) -> bool: + return self.model.supports_past_covariates + + @property + def supports_future_covariates(self) -> bool: + return self.model.supports_future_covariates + + @property + def supports_static_covariates(self) -> bool: + return self.model.supports_static_covariates + + @property + def supports_sample_weight(self) -> bool: + return self.model.supports_sample_weight + + @property + def supports_likelihood_parameter_prediction(self) -> bool: + return True + + @property + def supports_probabilistic_prediction(self) -> bool: + return True + + @property + def uses_past_covariates(self) -> bool: + return self.model.uses_past_covariates + + @property + def uses_future_covariates(self) -> bool: + return self.model.uses_future_covariates + + @property + def uses_static_covariates(self) -> bool: + return self.model.uses_static_covariates + + @property + def considers_static_covariates(self) -> bool: + return self.model.considers_static_covariates + + @property + def likelihood(self) -> str: + return self._likelihood + + +class ConformalNaiveModel(ConformalModel): + def __init__( + self, + model: GlobalForecastingModel, + quantiles: list[float], + symmetric: bool = True, + cal_length: Optional[int] = None, + cal_stride: int = 1, + cal_num_samples: int = 500, + random_state: Optional[int] = None, + ): + """Naive Conformal Prediction Model. + + A probabilistic model that adds calibrated intervals around the median forecast from a pre-trained + global forecasting model. It does not have to be trained and can generated calibrated forecasts + directly using the underlying trained forecasting model. It supports two symmetry modes: + + - `symmetric=True`: + - The lower and upper interval bounds are calibrated with the same magnitude. + - Non-conformity scores: uses metric `ae()` (see absolute error :func:`~darts.metrics.metrics.ae`) to + compute the non-conformity scores on the calibration set. + - `symmetric=False` + - The lower and upper interval bounds are calibrated separately. + - Non-conformity scores: uses metric `err()` (see error :func:`~darts.metrics.metrics.err`) to compute the + non-conformity scores on the calibration set for the upper bounds, an `-err()` for the lower bounds. + + Since it is a probabilistic model, you can generate forecasts in two ways (when calling `predict()`, + `historical_forecasts()`, ...): + + - Predict the calibrated quantile intervals directly: Pass parameters `predict_likelihood_parameters=True`, and + `num_samples=1` to the forecast method. + - Predict stochastic samples from the calibrated quantile intervals: Pass parameters + `predict_likelihood_parameters=False`, and `num_samples>>1` to the forecast method. + + Conformal models can be applied to any of Darts' global forecasting model, as long as the model has been + fitted before. In general the workflow of the models to produce one calibrated forecast/prediction is as + follows: + + - Extract a calibration set: The calibration set for each conformal forecast is automatically extracted from + the most recent past of your input series relative to the forecast start point. The number of calibration + examples (forecast errors / non-conformity scores) to consider can be defined at model creation + with parameter `cal_length`. Note that when using `cal_stride>1`, a longer history is required since + the calibration examples are generated with stridden historical forecasts. + - Generate historical forecasts on the calibration set (using the forecasting model) with a stride `cal_stride`. + - Compute the errors/non-conformity scores (as defined above) on these historical forecasts + - Compute the quantile values from the errors / non-conformity scores (using our desired quantiles set at model + creation with parameter `quantiles`). + - Compute the conformal prediction: Using these quantile values, add calibrated intervals to the forecasting + model's predictions. + + Some notes: + + - When computing `historical_forecasts()`, `backtest()`, `residuals()`, ... the above is applied for each + forecast (the forecasting model's historical forecasts are only generated once for efficiency). + - For multi-horizon forecasts, the above is applied for each step in the horizon separately. + + Parameters + ---------- + model + A pre-trained global forecasting model. See the list of models + `here `_. + quantiles + A list of quantiles centered around the median `q=0.5` to use. For example quantiles + [0.1, 0.2, 0.5, 0.8 0.9] correspond to two intervals with (0.9 - 0.1) = 80%, and (0.8 - 0.2) 60% coverage + around the median (model forecast). + symmetric + Whether to use symmetric non-conformity scores. If `True`, uses metric `ae()` (see + :func:`~darts.metrics.metrics.ae`) to compute the non-conformity scores. If `False`, uses metric `-err()` + (see :func:`~darts.metrics.metrics.err`) for the lower, and `err()` for the upper quantile interval bound. + cal_length + The number of past forecast errors / non-conformity scores to use as calibration for each conformal + forecast (and each step in the horizon). If `None`, considers all scores. + cal_stride + The stride to apply when computing the historical forecasts and non-conformity scores on the calibration + set. The actual conformal forecasts can have a different stride given with parameter `stride` in downstream + tasks (e.g. historical forecasts, backtest, ...) + cal_num_samples + The number of samples to generate for each calibration forecast (if `model` is a probabilistic forecasting + model). The non-conformity scores are computed on the quantile values of these forecasts (using quantiles + `quantiles`). Uses `1` for deterministic models. The actual conformal forecasts can have a different number + of samples given with parameter `num_samples` in downstream tasks (e.g. predict, historical forecasts, ...). + random_state + Control the randomness of probabilistic conformal forecasts (sample generation) across different runs. + """ + super().__init__( + model=model, + quantiles=quantiles, + symmetric=symmetric, + cal_length=cal_length, + cal_num_samples=cal_num_samples, + random_state=random_state, + cal_stride=cal_stride, + ) + + def _calibrate_interval( + self, residuals: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + def q_hat_from_residuals(residuals_): + # compute quantiles of shape (forecast horizon, n components, n quantile intervals) + return np.quantile( + residuals_, + q=self.interval_range_sym, + method="higher", + axis=2, + ).transpose((1, 2, 0)) + + # residuals shape (horizon, n components, n past forecasts) + if self.symmetric: + # symmetric (from metric `ae()`) + q_hat = q_hat_from_residuals(residuals) + return -q_hat, q_hat[:, :, ::-1] + else: + # asymmetric (from metric `err()`) + q_hat = q_hat_from_residuals( + np.concatenate([-residuals, residuals], axis=1) + ) + n_comps = residuals.shape[1] + return -q_hat[:, :n_comps, :], q_hat[:, n_comps:, ::-1] + + def _apply_interval(self, pred: np.ndarray, q_hat: tuple[np.ndarray, np.ndarray]): + # convert stochastic predictions to median + if pred.shape[2] != 1: + pred = np.expand_dims(np.quantile(pred, 0.5, axis=2), -1) + # shape (forecast horizon, n components, n quantiles) + pred = np.concatenate([pred + q_hat[0], pred, pred + q_hat[1]], axis=2) + # -> (forecast horizon, n components * n quantiles) + return pred.reshape(len(pred), -1) + + @property + def _residuals_metric(self) -> tuple[METRIC_TYPE, Optional[dict]]: + return (metrics.ae if self.symmetric else metrics.err), None + + +class ConformalQRModel(ConformalModel): + def __init__( + self, + model: GlobalForecastingModel, + quantiles: list[float], + symmetric: bool = True, + cal_length: Optional[int] = None, + cal_stride: int = 1, + cal_num_samples: int = 500, + random_state: Optional[int] = None, + ): + """Conformalized Quantile Regression Model. + + A probabilistic model that calibrates the quantile predictions from a pre-trained probabilistic global + forecasting model. It does not have to be trained and can generated calibrated forecasts + directly using the underlying trained forecasting model. It supports two symmetry modes: + + - `symmetric=True`: + - The lower and upper quantile predictions are calibrated with the same magnitude. + - Non-conformity scores: uses metric `incs_qr(symmetric=True)` (see Non-Conformity Score for Quantile + Regression :func:`~darts.metrics.metrics.incs_qr`) to compute the non-conformity scores on the calibration + set. + - `symmetric=False` + - The lower and upper quantile predictions are calibrated separately. + - Non-conformity scores: uses metric `incs_qr(symmetric=False)` (see Non-Conformity Score for Quantile + Regression :func:`~darts.metrics.metrics.incs_qr`) to compute the non-conformity scores for the upper and + lower bound separately. + + Since it is a probabilistic model, you can generate forecasts in two ways (when calling `predict()`, + `historical_forecasts()`, ...): + + - Predict the calibrated quantile intervals directly: Pass parameters `predict_likelihood_parameters=True`, and + `num_samples=1` to the forecast method. + - Predict stochastic samples from the calibrated quantile intervals: Pass parameters + `predict_likelihood_parameters=False`, and `num_samples>>1` to the forecast method. + + Conformal models can be applied to any of Darts' global forecasting model, as long as the model has been + fitted before. In general the workflow of the models to produce one calibrated forecast/prediction is as + follows: + + - Extract a calibration set: The calibration set for each conformal forecast is automatically extracted from + the most recent past of your input series relative to the forecast start point. The number of calibration + examples (forecast errors / non-conformity scores) to consider can be defined at model creation with + parameter `cal_length`. Note that when using `cal_stride>1`, a longer history is required since the + calibration examples are generated with stridden historical forecasts. + - Generate historical forecasts (quantile predictions) on the calibration set (using the forecasting model) + with a stride `cal_stride`. + - Compute the errors/non-conformity scores (as defined above) on these historical quantile predictions + - Compute the quantile values from the errors / non-conformity scores (using our desired quantiles set at model + creation with parameter `quantiles`). + - Compute the conformal prediction: Using these quantile values, calibrate the predicted quantiles from the + forecasting model's predictions. + + Some notes: + + - When computing `historical_forecasts()`, `backtest()`, `residuals()`, ... the above is applied for each + forecast (the forecasting model's historical forecasts are only generated once for efficiency). + - For multi-horizon forecasts, the above is applied for each step in the horizon separately. + + Parameters + ---------- + model + A pre-trained global forecasting model. See the list of models + `here `_. + quantiles + A list of quantiles centered around the median `q=0.5` to use. For example quantiles + [0.1, 0.2, 0.5, 0.8 0.9] correspond to two intervals with (0.9 - 0.1) = 80%, and (0.8 - 0.2) 60% coverage + around the median (model forecast). + symmetric + Whether to use symmetric non-conformity scores. If `True`, uses symmetric metric + `incs_qr(..., symmetric=True)` (see :func:`~darts.metrics.metrics.incs_qr`) to compute the non-conformity + scores. If `False`, uses asymmetric metric `incs_qr(..., symmetric=False)` with individual scores for the + lower- and upper quantile interval bounds. + cal_length + The number of past forecast errors / non-conformity scores to use as calibration for each conformal + forecast (and each step in the horizon). If `None`, considers all scores. + cal_stride + The stride to apply when computing the historical forecasts and non-conformity scores on the calibration + set. The actual conformal forecasts can have a different stride given with parameter `stride` in downstream + tasks (e.g. historical forecasts, backtest, ...) + cal_num_samples + The number of samples to generate for each calibration forecast (if `model` is a probabilistic forecasting + model). The non-conformity scores are computed on the quantile values of these forecasts (using quantiles + `quantiles`). Uses `1` for deterministic models. The actual conformal forecasts can have a different number + of samples given with parameter `num_samples` in downstream tasks (e.g. predict, historical forecasts, ...). + random_state + Control the randomness of probabilistic conformal forecasts (sample generation) across different runs. + """ + if not model.supports_probabilistic_prediction: + raise_log( + ValueError( + "`model` must support probabilistic forecasting. Consider using a `likelihood` at " + "forecasting model creation, or use another conformal model." + ), + logger=logger, + ) + super().__init__( + model=model, + quantiles=quantiles, + symmetric=symmetric, + cal_length=cal_length, + cal_num_samples=cal_num_samples, + random_state=random_state, + cal_stride=cal_stride, + ) + + def _calibrate_interval( + self, residuals: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + n_comps = residuals.shape[1] // ( + len(self.interval_range) * (1 + int(not self.symmetric)) + ) + n_intervals = len(self.interval_range) + + def q_hat_from_residuals(residuals_): + # TODO: is there a more efficient way? + # compute quantiles with shape (horizon, n components, n quantile intervals) + # over all past residuals + q_hat_tmp = np.quantile( + residuals_, q=self.interval_range_sym, method="higher", axis=2 + ).transpose((1, 2, 0)) + q_hat_ = np.empty((len(residuals_), n_comps, n_intervals)) + for i in range(n_intervals): + for c in range(n_comps): + q_hat_[:, c, i] = q_hat_tmp[:, i + c * n_intervals, i] + return q_hat_ + + if self.symmetric: + # symmetric has one nc-score per interval (from metric `incs_qr(symmetric=True)`) + # residuals shape (horizon, n components * n intervals, n past forecasts) + q_hat = q_hat_from_residuals(residuals) + return -q_hat, q_hat[:, :, ::-1] + else: + # asymmetric has two nc-score per interval (for lower and upper quantiles, from metric + # `incs_qr(symmetric=False)`) + # lower and upper residuals are concatenated along axis=1; + # residuals shape (horizon, n components * n intervals * 2, n past forecasts) + half_idx = residuals.shape[1] // 2 + q_hat_lo = q_hat_from_residuals(residuals[:, :half_idx]) + q_hat_hi = q_hat_from_residuals(residuals[:, half_idx:]) + return -q_hat_lo, q_hat_hi[:, :, ::-1] + + def _apply_interval(self, pred: np.ndarray, q_hat: tuple[np.ndarray, np.ndarray]): + # get quantile predictions with shape (n times, n components, n quantiles) + pred = np.quantile(pred, self.quantiles, axis=2).transpose((1, 2, 0)) + # shape (forecast horizon, n components, n quantiles) + pred = np.concatenate( + [ + pred[:, :, : self.idx_median] + q_hat[0], # lower quantiles + pred[:, :, self.idx_median : self.idx_median + 1], # model forecast + pred[:, :, self.idx_median + 1 :] + q_hat[1], # upper quantiles + ], + axis=2, + ) + # -> (forecast horizon, n components * n quantiles) + return pred.reshape(len(pred), -1) + + @property + def _residuals_metric(self) -> tuple[METRIC_TYPE, Optional[dict]]: + return metrics.incs_qr, { + "q_interval": self.q_interval, + "symmetric": self.symmetric, + } + + +def _get_calibration_hfc_start( + series: Sequence[TimeSeries], + horizon: int, + output_chunk_shift: int, + cal_length: Optional[int], + cal_stride: int, + start: Optional[Union[pd.Timestamp, int, Literal["end"]]], + start_format: Literal["position", "value"], +) -> tuple[Optional[Union[int, pd.Timestamp]], Literal["position", "value"]]: + """Find the calibration start point (CSP) (for historical forecasts on calibration set). + + - If `start=None`, the CSP is also `None` (all possible hfcs). + - If `start="end"` (when calling `predict()`), returns the CSP as a positional index relative to the end of the + series (<0). + - Otherwise (when calling `historical_forecasts()`), the CSP is the start value (`start_format="value"`) or start + position (`start_format="position"`) adjusted by the positions computed for the case above. + + If this function is called from `historical_forecasts`, the sanity checks guarantee the following: + + - `start` cannot be a `float` + - when `start_format='value'`, all `series` have the same frequency + """ + if start is None: + return start, start_format + + cal_start_format: Literal["position", "value"] + horizon_ocs = horizon + output_chunk_shift + if cal_length is not None: + # we only need `cal_length` forecasts with stride `cal_stride` before the `predict()` start point; + # the last valid calibration forecast must start at least `horizon_ocs` before `predict()` start + add_steps = math.ceil(horizon_ocs / cal_stride) - 1 + start_idx_rel = -cal_stride * (cal_length + add_steps) + cal_start_format = "position" + elif cal_stride > 1: + # we need all forecasts with stride `cal_stride` before the `predict()` start point + max_len_series = max(len(series_) for series_ in series) + start_idx_rel = -cal_stride * math.ceil(max_len_series / cal_stride) + cal_start_format = "position" + else: + # we need all possible forecasts with `cal_stride=1` + start_idx_rel, cal_start_format = None, "value" + + if start == "end": + # `predict()` is relative to the end + return start_idx_rel, cal_start_format + + # `historical_forecasts()` is relative to `start` + start_is_position = isinstance(start, (int, np.int64)) and ( + start_format == "position" or series[0]._has_datetime_index + ) + cal_start_format = start_format + if start_idx_rel is None: + cal_start = start_idx_rel + elif start_is_position: + cal_start = start + start_idx_rel + # if start switches sign, it would be relative to the end; + # correct it to be positive (relative to beginning) + if cal_start < 0 <= start: + cal_start += math.ceil(abs(cal_start) / cal_stride) * cal_stride + else: + cal_start = start + start_idx_rel * series[0].freq + return cal_start, cal_start_format diff --git a/darts/models/forecasting/croston.py b/darts/models/forecasting/croston.py index d71aaf2b29..0f6a76eadf 100644 --- a/darts/models/forecasting/croston.py +++ b/darts/models/forecasting/croston.py @@ -3,25 +3,22 @@ -------------- """ -from typing import Optional - from statsforecast.models import TSB as CrostonTSB from statsforecast.models import CrostonClassic, CrostonOptimized, CrostonSBA from darts.logging import raise_if, raise_if_not from darts.models.forecasting.forecasting_model import ( - FutureCovariatesLocalForecastingModel, + LocalForecastingModel, ) from darts.timeseries import TimeSeries -class Croston(FutureCovariatesLocalForecastingModel): +class Croston(LocalForecastingModel): def __init__( self, version: str = "classic", alpha_d: float = None, alpha_p: float = None, - add_encoders: Optional[dict] = None, ): """An implementation of the `Croston method `_ for intermittent @@ -46,30 +43,6 @@ def __init__( For the "tsb" version, the alpha smoothing parameter to apply on demand. alpha_p For the "tsb" version, the alpha smoothing parameter to apply on probability. - add_encoders - A large number of future covariates can be automatically generated with `add_encoders`. - This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that - will be used as index encoders. Additionally, a transformer such as Darts' :class:`Scaler` can be added to - transform the generated covariates. This happens all under one hood and only needs to be specified at - model creation. - Read :meth:`SequentialEncoder ` to find out more about - ``add_encoders``. Default: ``None``. An example showing some of ``add_encoders`` features: - - .. highlight:: python - .. code-block:: python - - def encode_year(idx): - return (idx.year - 1950) / 50 - - add_encoders={ - 'cyclic': {'future': ['month']}, - 'datetime_attribute': {'future': ['hour', 'dayofweek']}, - 'position': {'future': ['relative']}, - 'custom': {'future': [encode_year]}, - 'transformer': Scaler(), - 'tz': 'CET' - } - .. References ---------- @@ -96,7 +69,7 @@ def encode_year(idx): [461.7666], [461.7666]]) """ - super().__init__(add_encoders=add_encoders) + super().__init__(add_encoders=None) raise_if_not( version.lower() in ["classic", "optimized", "sba", "tsb"], 'The provided "version" parameter must be set to "classic", "optimized", "sba" or "tsb".', @@ -123,33 +96,30 @@ def encode_year(idx): def supports_multivariate(self) -> bool: return False - def _fit(self, series: TimeSeries, future_covariates: Optional[TimeSeries] = None): - super()._fit(series, future_covariates) + def fit(self, series: TimeSeries): + super().fit(series) self._assert_univariate(series) series = self.training_series self.model.fit( y=series.values(copy=False).flatten(), - X=future_covariates.values(copy=False).flatten() - if future_covariates is not None - else None, + # X can be used to passe future covariates only when conformal prediction is used + X=None, ) return self - def _predict( + def predict( self, n: int, - future_covariates: Optional[TimeSeries] = None, num_samples: int = 1, verbose: bool = False, ): - super()._predict(n, future_covariates, num_samples) + super().predict(n, num_samples) values = self.model.predict( h=n, - X=future_covariates.values(copy=False).flatten() - if future_covariates is not None - else None, + # X can be used to passe future covariates only when conformal prediction is used + X=None, )["mean"] return self._build_forecast_series(values) @@ -160,7 +130,3 @@ def min_train_series_length(self) -> int: @property def _supports_range_index(self) -> bool: return True - - @property - def _is_probabilistic(self) -> bool: - return False diff --git a/darts/models/forecasting/dlinear.py b/darts/models/forecasting/dlinear.py index ed90c55c20..817b0f8088 100644 --- a/darts/models/forecasting/dlinear.py +++ b/darts/models/forecasting/dlinear.py @@ -3,7 +3,7 @@ -------- """ -from typing import Optional, Tuple +from typing import Optional import torch import torch.nn as nn @@ -15,7 +15,7 @@ ) from darts.models.forecasting.torch_forecasting_model import MixedCovariatesTorchModel -MixedCovariatesTrainTensorType = Tuple[ +MixedCovariatesTrainTensorType = tuple[ torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor ] @@ -100,7 +100,8 @@ def __init__( const_init Whether to initialize the weights to 1/in_len **kwargs - all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class. + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. Inputs ------ @@ -155,7 +156,7 @@ def _create_linear_layer(in_dim, out_dim): @io_processor def forward( - self, x_in: Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]] + self, x_in: tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]] ): """ x_in @@ -232,6 +233,7 @@ def __init__( self, input_chunk_length: int, output_chunk_length: int, + output_chunk_shift: int = 0, shared_weights: bool = False, kernel_size: int = 25, const_init: bool = True, @@ -253,10 +255,16 @@ def __init__( Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values from future covariates to use as a model input (if the model supports future covariates). It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated - using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). shared_weights Whether to use shared weights for all components of multivariate series. diff --git a/darts/models/forecasting/ensemble_model.py b/darts/models/forecasting/ensemble_model.py index b772594d1e..899b8f5d11 100644 --- a/darts/models/forecasting/ensemble_model.py +++ b/darts/models/forecasting/ensemble_model.py @@ -2,8 +2,16 @@ Ensemble Model Base Class """ +import os +import sys from abc import abstractmethod -from typing import List, Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import BinaryIO, Optional, Union + +if sys.version_info >= (3, 11): + from typing import Self +else: + from typing_extensions import Self from darts.logging import get_logger, raise_if, raise_if_not, raise_log from darts.models.forecasting.forecasting_model import ( @@ -11,8 +19,14 @@ GlobalForecastingModel, LocalForecastingModel, ) +from darts.models.utils import TORCH_AVAILABLE from darts.timeseries import TimeSeries, concatenate -from darts.utils.utils import series2seq +from darts.utils.ts_utils import series2seq + +if TORCH_AVAILABLE: + from darts.models.forecasting.torch_forecasting_model import TorchForecastingModel +else: + TorchForecastingModel = None logger = get_logger(__name__) @@ -41,7 +55,7 @@ class EnsembleModel(GlobalForecastingModel): If `forecasting_models` are probabilistic and `train_num_samples` > 1, method used to reduce the samples dimension to 1. Possible values: "mean", "median" or float value corresponding to the desired quantile. - retrain_forecasting_models + train_forecasting_models If set to `False`, the `forecasting_models` are not retrained when calling `fit()` (only supported if all the `forecasting_models` are pretrained `GlobalForecastingModels`). Default: ``True``. show_warnings @@ -50,7 +64,7 @@ class EnsembleModel(GlobalForecastingModel): def __init__( self, - forecasting_models: List[ForecastingModel], + forecasting_models: list[ForecastingModel], train_num_samples: int, train_samples_reduction: Optional[Union[str, float]], train_forecasting_models: bool = True, @@ -73,14 +87,10 @@ def __init__( self.is_global_ensemble = all(is_global_model) raise_if_not( - all( - [ - local_model or global_model - for local_model, global_model in zip( - is_local_model, is_global_model - ) - ] - ), + all([ + local_model or global_model + for local_model, global_model in zip(is_local_model, is_global_model) + ]), "All models must be of type `GlobalForecastingModel`, or `LocalForecastingModel`. " "Also, make sure that all `forecasting_models` are instantiated.", logger, @@ -95,7 +105,7 @@ def __init__( or (self.is_global_ensemble and not (self.all_trained or not some_trained)), "Cannot instantiate EnsembleModel with a mixture of unfitted and fitted `forecasting_models`. " "Consider resetting all models with `my_model.untrained_model()` or using only trained " - "GlobalForecastingModels together with `retrain_forecasting_models=False`.", + "GlobalForecastingModels together with `train_forecasting_models=False`.", logger, ) @@ -103,7 +113,7 @@ def __init__( # prevent issues with pytorch-lightning trainer during retraining raise_if( some_trained, - "`retrain_forecasting_models=True` but some `forecasting_models` were already fitted. " + "`train_forecasting_models=True` but some `forecasting_models` were already fitted. " "Consider resetting all the `forecasting_models` with `my_model.untrained_model()` " "before passing them to the `EnsembleModel`.", logger, @@ -111,7 +121,7 @@ def __init__( else: raise_if_not( self.is_global_ensemble and self.all_trained, - "`retrain_forecasting_models=False` is supported only if all the `forecasting_models` are " + "`train_forecasting_models=False` is supported only if all the `forecasting_models` are " "already trained `GlobalForecastingModels`.", logger, ) @@ -119,7 +129,9 @@ def __init__( raise_if( train_num_samples is not None and train_num_samples > 1 - and all([not m._is_probabilistic for m in forecasting_models]), + and all([ + not m.supports_probabilistic_prediction for m in forecasting_models + ]), "`train_num_samples` is greater than 1 but the `RegressionEnsembleModel` " "contains only deterministic `forecasting_models`.", logger, @@ -222,9 +234,7 @@ def fit( ) self._verify_past_future_covariates(past_covariates, future_covariates) - super().fit(series, past_covariates, future_covariates) - return self def _stack_ts_seq(self, predictions): @@ -235,9 +245,10 @@ def _stack_ts_multiseq(self, predictions_list): # stacks multiple sequences of timeseries elementwise return [self._stack_ts_seq(ts_list) for ts_list in zip(*predictions_list)] + @property def _model_encoder_settings(self): raise NotImplementedError( - "Encoders are not supported by EnsembleModels. Instead add encoder to the underlying `forecasting_models`." + "Encoders are not supported by EnsembleModels. Instead add encoders to the underlying `forecasting_models`." ) def _make_multiple_predictions( @@ -255,13 +266,15 @@ def _make_multiple_predictions( model._predict_wrapper( n=n, series=series, - past_covariates=past_covariates - if model.supports_past_covariates - else None, - future_covariates=future_covariates - if model.supports_future_covariates - else None, - num_samples=num_samples if model._is_probabilistic else 1, + past_covariates=( + past_covariates if model.supports_past_covariates else None + ), + future_covariates=( + future_covariates if model.supports_future_covariates else None + ), + num_samples=( + num_samples if model.supports_probabilistic_prediction else 1 + ), predict_likelihood_parameters=predict_likelihood_parameters, ) for model in self.forecasting_models @@ -379,6 +392,109 @@ def _predictions_reduction( ] return predictions[0] if is_single_series else predictions + def _clean(self) -> Self: + """Cleans the model and sub-models.""" + cleaned_model = super()._clean() + cleaned_model.forecasting_models = [ + model._clean() for model in self.forecasting_models + ] + return cleaned_model + + def save( + self, + path: Optional[Union[str, os.PathLike, BinaryIO]] = None, + clean: bool = False, + **pkl_kwargs, + ) -> None: + """ + Saves the ensemble model under a given path or file handle. + + Additionally, two files are stored for each `TorchForecastingModel` under the forecasting models. + + Example for saving and loading a :class:`RegressionEnsembleModel`: + + .. highlight:: python + .. code-block:: python + + from darts.models import RegressionEnsembleModel, LinearRegressionModel, TiDEModel + + model = RegressionEnsembleModel( + forecasting_models = [ + LinearRegressionModel(lags=4), + TiDEModel(input_chunk_length=4, output_chunk_length=4), + ], + regression_train_n_points=10, + ) + + model.save("my_ensemble_model.pkl") + model_loaded = RegressionEnsembleModel.load("my_ensemble_model.pkl") + .. + + Parameters + ---------- + path + Path or file handle under which to save the ensemble model at its current state. If no path is specified, + the ensemble model is automatically saved under ``"{RegressionEnsembleModel}_{YYYY-mm-dd_HH_MM_SS}.pkl"``. + If the i-th model of `forecasting_models` is a TorchForecastingModel, two files (model object and + checkpoint) are saved under ``"{path}.{ithModelClass}_{i}.pt"`` and ``"{path}.{ithModelClass}_{i}.ckpt"``. + clean + Whether to store a cleaned version of the model. If `True`, the training series and covariates are removed. + If the underlying `forecasting_models` contain any `TorchForecastingModel`, will additionally remove all of + their Lightning Trainer-related parameters. + + Note: After loading a model stored with `clean=True`, a `series` must be passed 'predict()', + `historical_forecasts()` and other forecasting methods. + pkl_kwargs + Keyword arguments passed to `pickle.dump()` + """ + + if path is None: + # default path + path = self._default_save_path() + ".pkl" + + super().save(path, clean=clean, **pkl_kwargs) + + for i, m in enumerate(self.forecasting_models): + if TORCH_AVAILABLE and issubclass(type(m), TorchForecastingModel): + path_tfm = f"{path}.{type(m).__name__}_{i}.pt" + m.save(path=path_tfm, clean=clean) + + @staticmethod + def load( + path: Union[str, os.PathLike, BinaryIO], + pl_trainer_kwargs: Optional[dict] = None, + **kwargs, + ) -> "EnsembleModel": + """ + Loads a model from a given path or file handle. + + Parameters + ---------- + path + Path or file handle from which to load the model. + pl_trainer_kwargs + Only effective if the underlying forecasting models contain a `TorchForecastingModel`. + Optionally, a set of kwargs to create a new Lightning Trainer used to configure the model for downstream + tasks (e.g. prediction). + Some examples include specifying the batch size or moving the model to CPU/GPU(s). Check the + `Lightning Trainer documentation `_ + for more information about the supported kwargs. + **kwargs + Only effective if the underlying forecasting models contain a `TorchForecastingModel`. + Additional kwargs for PyTorch Lightning's :func:`LightningModule.load_from_checkpoint()` method, + For more information, read the `official documentation `_. + """ + model: EnsembleModel = GlobalForecastingModel.load(path) + + for i, m in enumerate(model.forecasting_models): + if TORCH_AVAILABLE and issubclass(type(m), TorchForecastingModel): + path_tfm = f"{path}.{type(m).__name__}_{i}.pt" + model.forecasting_models[i] = TorchForecastingModel.load( + path_tfm, pl_trainer_kwargs=pl_trainer_kwargs, **kwargs + ) + return model + @property def min_train_series_length(self) -> int: return max(model.min_train_series_length for model in self.forecasting_models) @@ -390,12 +506,14 @@ def min_train_samples(self) -> int: @property def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ + Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, Optional[int], ]: def find_max_lag_or_none(lag_id, aggregator) -> Optional[int]: @@ -408,7 +526,7 @@ def find_max_lag_or_none(lag_id, aggregator) -> Optional[int]: max_lag = aggregator(max_lag, curr_lag) return max_lag - lag_aggregators = (min, max, min, max, min, max) + lag_aggregators = (min, max, min, max, min, max, max, max) return tuple( find_max_lag_or_none(i, agg) for i, agg in enumerate(lag_aggregators) ) @@ -431,7 +549,9 @@ def output_chunk_length(self) -> Optional[int]: @property def _models_are_probabilistic(self) -> bool: - return all([model._is_probabilistic for model in self.forecasting_models]) + return all([ + model.supports_probabilistic_prediction for model in self.forecasting_models + ]) @property def _models_same_likelihood(self) -> bool: @@ -453,7 +573,7 @@ def _models_same_likelihood(self) -> bool: # check the quantiles if lkl_simplified_name == "quantile": - quantiles: List[str] = ( + quantiles: list[str] = ( likelihood.quantiles if is_obj_lkl else m.quantiles ) if tmp_quantiles is None: @@ -469,17 +589,15 @@ def supports_likelihood_parameter_prediction(self) -> bool: same likelihood. """ return ( - all( - [ - m.supports_likelihood_parameter_prediction - for m in self.forecasting_models - ] - ) + all([ + m.supports_likelihood_parameter_prediction + for m in self.forecasting_models + ]) and self._models_same_likelihood ) @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return self._models_are_probabilistic @property @@ -488,15 +606,15 @@ def supports_multivariate(self) -> bool: @property def supports_past_covariates(self) -> bool: - return any( - [model.supports_past_covariates for model in self.forecasting_models] - ) + return any([ + model.supports_past_covariates for model in self.forecasting_models + ]) @property def supports_future_covariates(self) -> bool: - return any( - [model.supports_future_covariates for model in self.forecasting_models] - ) + return any([ + model.supports_future_covariates for model in self.forecasting_models + ]) @property def supports_optimized_historical_forecasts(self) -> bool: @@ -510,14 +628,14 @@ def _supports_non_retrainable_historical_forecasts(self) -> bool: return self.is_global_ensemble def _full_past_covariates_support(self) -> bool: - return all( - [model.supports_past_covariates for model in self.forecasting_models] - ) + return all([ + model.supports_past_covariates for model in self.forecasting_models + ]) def _full_future_covariates_support(self) -> bool: - return all( - [model.supports_future_covariates for model in self.forecasting_models] - ) + return all([ + model.supports_future_covariates for model in self.forecasting_models + ]) def _verify_past_future_covariates(self, past_covariates, future_covariates): """ diff --git a/darts/models/forecasting/exponential_smoothing.py b/darts/models/forecasting/exponential_smoothing.py index f535eb0d03..381d3bac98 100644 --- a/darts/models/forecasting/exponential_smoothing.py +++ b/darts/models/forecasting/exponential_smoothing.py @@ -3,7 +3,7 @@ --------------------- """ -from typing import Any, Dict, Optional +from typing import Any, Optional import numpy as np import statsmodels.tsa.holtwinters as hw @@ -24,10 +24,9 @@ def __init__( seasonal: Optional[SeasonalityMode] = SeasonalityMode.ADDITIVE, seasonal_periods: Optional[int] = None, random_state: int = 0, - kwargs: Optional[Dict[str, Any]] = None, - **fit_kwargs + kwargs: Optional[dict[str, Any]] = None, + **fit_kwargs, ): - """Exponential Smoothing This is a wrapper around @@ -127,7 +126,7 @@ def fit(self, series: TimeSeries): seasonal_periods=seasonal_periods_param, freq=series.freq if series.has_datetime_index else None, dates=series.time_index if series.has_datetime_index else None, - **self.constructor_kwargs + **self.constructor_kwargs, ) hw_results = hw_model.fit(**self.fit_kwargs) self.model = hw_results @@ -160,7 +159,7 @@ def supports_multivariate(self) -> bool: return False @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return True @property diff --git a/darts/models/forecasting/fft.py b/darts/models/forecasting/fft.py index 490210ac69..2143c917c5 100644 --- a/darts/models/forecasting/fft.py +++ b/darts/models/forecasting/fft.py @@ -105,7 +105,7 @@ def _find_relevant_timestamp_attributes(series: TimeSeries) -> set: # check for yearly seasonality if _check_approximate_seasonality(series, 12, 1, 0): relevant_attributes.add("month") - elif type(series.freq) == pd.tseries.offsets.Day: + elif type(series.freq) is pd.tseries.offsets.Day: # check for yearly seasonality if _check_approximate_seasonality(series, 365, 5, 20): relevant_attributes.update({"month", "day"}) @@ -115,7 +115,7 @@ def _find_relevant_timestamp_attributes(series: TimeSeries) -> set: # check for weekly seasonality elif _check_approximate_seasonality(series, 7, 0, 0): relevant_attributes.add("weekday") - elif type(series.freq) == pd.tseries.offsets.Hour: + elif type(series.freq) is pd.tseries.offsets.Hour: # check for yearly seasonality if _check_approximate_seasonality(series, 8760, 100, 100): relevant_attributes.update({"month", "day", "hour"}) @@ -128,7 +128,7 @@ def _find_relevant_timestamp_attributes(series: TimeSeries) -> set: # check for daily seasonality elif _check_approximate_seasonality(series, 24, 1, 1): relevant_attributes.add("hour") - elif type(series.freq) == pd.tseries.offsets.Minute: + elif type(series.freq) is pd.tseries.offsets.Minute: # check for daily seasonality if _check_approximate_seasonality(series, 1440, 20, 50): relevant_attributes.update({"hour", "minute"}) @@ -356,7 +356,7 @@ def fit(self, series: TimeSeries): ] # set all other values in the frequency domain to 0 - self.fft_values_filtered = np.zeros(len(self.fft_values), dtype=np.complex_) + self.fft_values_filtered = np.zeros(len(self.fft_values), dtype=np.complex128) self.fft_values_filtered[self.filtered_indices] = self.fft_values[ self.filtered_indices ] @@ -374,10 +374,10 @@ def predict( show_warnings: bool = True, ): super().predict(n, num_samples) - trend_forecast = np.array( - [self.trend_function(i + len(self.training_series)) for i in range(n)] - ) - periodic_forecast = np.array( - [self.predicted_values[i % len(self.predicted_values)] for i in range(n)] - ) + trend_forecast = np.array([ + self.trend_function(i + len(self.training_series)) for i in range(n) + ]) + periodic_forecast = np.array([ + self.predicted_values[i % len(self.predicted_values)] for i in range(n) + ]) return self._build_forecast_series(periodic_forecast + trend_forecast) diff --git a/darts/models/forecasting/forecasting_model.py b/darts/models/forecasting/forecasting_model.py index f1ab933b05..f9b56242ca 100644 --- a/darts/models/forecasting/forecasting_model.py +++ b/darts/models/forecasting/forecasting_model.py @@ -11,46 +11,67 @@ one or several time series. The function `predict()` applies `f()` on one or several time series in order to obtain forecasts for a desired number of time stamps into the future. """ + import copy import datetime import inspect import io import os import pickle +import sys import time from abc import ABC, ABCMeta, abstractmethod from collections import OrderedDict +from collections.abc import Sequence from itertools import product from random import sample -from typing import Any, BinaryIO, Callable, Dict, List, Optional, Sequence, Tuple, Union +from typing import Any, BinaryIO, Callable, Literal, Optional, Union -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal +if sys.version_info >= (3, 11): + from typing import Self +else: + from typing_extensions import Self import numpy as np import pandas as pd from darts import metrics from darts.dataprocessing.encoders import SequentialEncoder +from darts.dataprocessing.pipeline import Pipeline +from darts.dataprocessing.transformers import BaseDataTransformer from darts.logging import get_logger, raise_if, raise_if_not, raise_log +from darts.metrics.metrics import METRIC_TYPE from darts.timeseries import TimeSeries from darts.utils import _build_tqdm_iterator, _parallel_apply, _with_sanity_checks from darts.utils.historical_forecasts.utils import ( _adjust_historical_forecasts_time_index, + _apply_data_transformers, + _apply_inverse_data_transformers, + _convert_data_transformers, + _extend_series_for_overlap_end, _get_historical_forecast_predict_index, _get_historical_forecast_train_index, _historical_forecasts_general_checks, _historical_forecasts_sanitize_kwargs, + _process_historical_forecast_for_backtest, _reconciliate_historical_time_indices, ) from darts.utils.timeseries_generation import ( _build_forecast_series, _generate_new_dates, +) +from darts.utils.ts_utils import ( + SeriesType, + get_series_seq_type, + get_single_series, + series2seq, +) +from darts.utils.utils import ( generate_index, + likelihood_component_names, + quantile_interval_names, + quantile_names, ) -from darts.utils.utils import get_single_series, series2seq logger = get_logger(__name__) @@ -77,13 +98,9 @@ class ModelMeta(ABCMeta): def __call__(cls, *args, **kwargs): # 1) get all default values from class' __init__ signature sig = inspect.signature(cls.__init__) - all_params = OrderedDict( - [ - (p.name, p.default) - for p in sig.parameters.values() - if not p.name == "self" - ] - ) + all_params = OrderedDict([ + (p.name, p.default) for p in sig.parameters.values() if not p.name == "self" + ]) # 2) fill params with positional args for param, arg in zip(all_params, args): @@ -144,7 +161,7 @@ def __init__(self, *args, **kwargs): # by default models do not use encoders self.add_encoders = kwargs["add_encoders"] - self.encoders: Optional[SequentialEncoder] = None + self.encoders = self.initialize_encoders(default=True) @abstractmethod def fit(self, series: TimeSeries) -> "ForecastingModel": @@ -160,12 +177,19 @@ def fit(self, series: TimeSeries) -> "ForecastingModel": self Fitted model. """ - raise_if_not( - len(series) >= self.min_train_series_length, - "Train series only contains {} elements but {} model requires at least {} entries".format( - len(series), str(self), self.min_train_series_length - ), - ) + if not isinstance(series, TimeSeries): + raise_log( + ValueError("Train `series` must be a single `TimeSeries`."), + logger=logger, + ) + if not len(series) >= self.min_train_series_length: + raise_log( + ValueError( + f"Train series only contains {len(series)} elements" + f" but {str(self)} model requires at least {self.min_train_series_length} entries" + ), + logger=logger, + ) self.training_series = series self._fit_called = True @@ -183,9 +207,10 @@ def _supports_range_index(self) -> bool: return True @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: """ - Checks if the forecasting model supports probabilistic predictions. + Checks if the forecasting model with this configuration supports probabilistic predictions. + By default, returns False. Needs to be overwritten by models that do support probabilistic predictions. """ @@ -195,7 +220,9 @@ def _is_probabilistic(self) -> bool: def _supports_non_retrainable_historical_forecasts(self) -> bool: """ Checks if the forecasting model supports historical forecasts without retraining - the model. By default, returns False. Needs to be overwritten by models that do + the model. + + By default, returns False. Needs to be overwritten by models that do support historical forecasts without retraining. """ return False @@ -228,6 +255,13 @@ def supports_static_covariates(self) -> bool: """ return False + @property + def supports_sample_weight(self) -> bool: + """ + Whether model supports sample weight for training. + """ + return False + @property def supports_likelihood_parameter_prediction(self) -> bool: """ @@ -241,7 +275,6 @@ def supports_transferrable_series_prediction(self) -> bool: """ Whether the model supports prediction for any input `series`. """ - pass @property def uses_past_covariates(self) -> bool: @@ -285,6 +318,13 @@ def output_chunk_length(self) -> Optional[int]: """ return None + @property + def output_chunk_shift(self) -> int: + """ + Number of time steps that the output/prediction starts after the end of the input. + """ + return 0 + @abstractmethod def predict( self, @@ -300,8 +340,7 @@ def predict( n Forecast horizon - the number of time steps after the end of the series for which to produce predictions. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose Optionally, set the prediction verbosity. Not effective for all models. show_warnings @@ -321,8 +360,21 @@ def predict( ), logger, ) + is_autoregression = ( + False + if self.output_chunk_length is None + else (n > self.output_chunk_length) + ) + if self.output_chunk_shift and is_autoregression: + raise_log( + ValueError( + "Cannot perform auto-regression `(n > output_chunk_length)` with a model that uses a " + "shifted output chunk `(output_chunk_shift > 0)`." + ), + logger=logger, + ) - if not self._is_probabilistic and num_samples > 1: + if not self.supports_probabilistic_prediction and num_samples > 1: raise_log( ValueError( "`num_samples > 1` is only supported for probabilistic models." @@ -332,22 +384,23 @@ def predict( def _fit_wrapper( self, - series: TimeSeries, - past_covariates: Optional[TimeSeries] = None, - future_covariates: Optional[TimeSeries] = None, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, **kwargs, ): add_kwargs = {} # handle past and future covariates based on model support - for covs, covs_name in zip( - [past_covariates, future_covariates], - ["past_covariates", "future_covariates"], + for series_, series_name in zip( + [past_covariates, future_covariates, sample_weight], + ["past_covariates", "future_covariates", "sample_weight"], ): - if getattr(self, f"supports_{covs_name}"): - add_kwargs[covs_name] = covs - elif covs is not None: + if getattr(self, f"supports_{series_name}"): + add_kwargs[series_name] = series_ + elif series_ is not None: raise_log( - ValueError(f"Model cannot be fit/trained with `{covs_name}`."), + ValueError(f"Model cannot be fit/trained with `{series_name}`."), logger, ) self.fit(series=series, **add_kwargs, **kwargs) @@ -405,19 +458,21 @@ def min_train_samples(self) -> int: @abstractmethod def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ + Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, Optional[int], ]: """ - A 6-tuple containing in order: + A 8-tuple containing in order: (min target lag, max target lag, min past covariate lag, max past covariate lag, min future covariate - lag, max future covariate lag). If 0 is the index of the first prediction, then all lags are relative to this - index. + lag, max future covariate lag, output shift, max target lag train (only for RNNModel)). If 0 is the index of the + first prediction, then all lags are relative to this index. See examples below. @@ -434,30 +489,34 @@ def extreme_lags( Notes ----- maximum target lag (second value) cannot be `None` and is always larger than or equal to 0. + Examples -------- >>> model = LinearRegressionModel(lags=3, output_chunk_length=2) >>> model.fit(train_series) >>> model.extreme_lags - (-3, 1, None, None, None, None) + (-3, 1, None, None, None, None, 0, None) + >>> model = LinearRegressionModel(lags=3, output_chunk_length=2, output_chunk_shift=2) + >>> model.fit(train_series) + >>> model.extreme_lags + (-3, 1, None, None, None, None, 2, None) >>> model = LinearRegressionModel(lags=[-3, -5], lags_past_covariates = 4, output_chunk_length=7) >>> model.fit(train_series, past_covariates=past_covariates) >>> model.extreme_lags - (-5, 6, -4, -1, None, None) + (-5, 6, -4, -1, None, None, 0, None) >>> model = LinearRegressionModel(lags=[3, 5], lags_future_covariates = [4, 6], output_chunk_length=7) >>> model.fit(train_series, future_covariates=future_covariates) >>> model.extreme_lags - (-5, 6, None, None, 4, 6) + (-5, 6, None, None, 4, 6, 0, None) >>> model = NBEATSModel(input_chunk_length=10, output_chunk_length=7) >>> model.fit(train_series) >>> model.extreme_lags - (-10, 6, None, None, None, None) + (-10, 6, None, None, None, None, 0, None) >>> model = NBEATSModel(input_chunk_length=10, output_chunk_length=7, lags_future_covariates=[4, 6]) >>> model.fit(train_series, future_covariates) >>> model.extreme_lags - (-10, 6, None, None, 4, 6) + (-10, 6, None, None, 4, 6, 0, None) """ - pass @property def _training_sample_time_index_length(self) -> int: @@ -471,10 +530,14 @@ def _training_sample_time_index_length(self) -> int: max_past_cov_lag, min_future_cov_lag, max_future_cov_lag, + output_chunk_shift, + max_target_lag_train, ) = self.extreme_lags + # some models can have different output chunks for training and prediction (e.g. `RNNModel`) + output_lag = max_target_lag_train or max_target_lag return max( - max_target_lag + 1, + output_lag + 1, max_future_cov_lag + 1 if max_future_cov_lag else 0, ) - min( min_target_lag if min_target_lag else 0, @@ -482,46 +545,6 @@ def _training_sample_time_index_length(self) -> int: min_future_cov_lag if min_future_cov_lag else 0, ) - @property - def _predict_sample_time_index_length(self) -> int: - """ - Required time_index length for one `predict` function call, for any model. - """ - ( - min_target_lag, - max_target_lag, - min_past_cov_lag, - max_past_cov_lag, - min_future_cov_lag, - max_future_cov_lag, - ) = self.extreme_lags - - return (max_future_cov_lag + 1 if max_future_cov_lag else 0) - min( - min_target_lag if min_target_lag else 0, - min_past_cov_lag if min_past_cov_lag else 0, - min_future_cov_lag if min_future_cov_lag else 0, - ) - - @property - def _predict_sample_time_index_past_length(self) -> int: - """ - Required time_index length in the past for one `predict` function call, for any model. - """ - ( - min_target_lag, - max_target_lag, - min_past_cov_lag, - max_past_cov_lag, - min_future_cov_lag, - max_future_cov_lag, - ) = self.extreme_lags - - return -min( - min_target_lag if min_target_lag else 0, - min_past_cov_lag if min_past_cov_lag else 0, - min_future_cov_lag if min_future_cov_lag else 0, - ) - def _generate_new_dates( self, n: int, input_series: Optional[TimeSeries] = None ) -> Union[pd.DatetimeIndex, pd.RangeIndex]: @@ -537,7 +560,7 @@ def _build_forecast_series( self, points_preds: Union[np.ndarray, Sequence[np.ndarray]], input_series: Optional[TimeSeries] = None, - custom_components: Union[List[str], None] = None, + custom_components: Union[list[str], None] = None, with_static_covs: bool = True, with_hierarchy: bool = True, pred_start: Optional[Union[pd.Timestamp, int]] = None, @@ -555,9 +578,9 @@ def _build_forecast_series( custom_components New names for the forecast TimeSeries components, used when the number of components changes with_static_covs - If set to False, do not copy the input_series `static_covariates` attribute + If set to `False`, do not copy the input_series `static_covariates` attribute with_hierarchy - If set to False, do not copy the input_series `hierarchy` attribute + If set to `False`, do not copy the input_series `hierarchy` attribute pred_start Optionally, give a custom prediction start point. @@ -595,7 +618,10 @@ def _historical_forecasts_sanity_checks(self, *args: Any, **kwargs: Any) -> None """ # parse args and kwargs series = args[0] - _historical_forecasts_general_checks(self, series, kwargs) + is_conformal = kwargs.get("is_conformal", False) + _historical_forecasts_general_checks( + self, series, kwargs, is_conformal=is_conformal + ) def _get_last_prediction_time( self, @@ -626,11 +652,11 @@ def historical_forecasts( series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + forecast_horizon: int = 1, num_samples: int = 1, train_length: Optional[int] = None, start: Optional[Union[pd.Timestamp, float, int]] = None, start_format: Literal["position", "value"] = "value", - forecast_horizon: int = 1, stride: int = 1, retrain: Union[bool, int, Callable[..., bool]] = True, overlap_end: bool = False, @@ -639,47 +665,68 @@ def historical_forecasts( show_warnings: bool = True, predict_likelihood_parameters: bool = False, enable_optimization: bool = True, - fit_kwargs: Optional[Dict[str, Any]] = None, - predict_kwargs: Optional[Dict[str, Any]] = None, - ) -> Union[ - TimeSeries, List[TimeSeries], Sequence[TimeSeries], Sequence[List[TimeSeries]] - ]: - """Compute the historical forecasts that would have been obtained by this model on - (potentially multiple) `series`. - - This method repeatedly builds a training set: either expanding from the beginning of `series` or moving with - a fixed length `train_length`. It trains the model on the training set, emits a forecast of length equal to - forecast_horizon, and then moves the end of the training set forward by `stride` time steps. - - By default, this method will return one (or a sequence of) single time series made up of - the last point of each historical forecast. - This time series will thus have a frequency of ``series.freq * stride``. - If `last_points_only` is set to False, it will instead return one (or a sequence of) list of the - historical forecasts series. - - By default, this method always re-trains the models on the entire available history, corresponding to an - expanding window strategy. If `retrain` is set to False, the model must have been fit before. This is not - supported by all models. + data_transformers: Optional[ + dict[str, Union[BaseDataTransformer, Pipeline]] + ] = None, + fit_kwargs: Optional[dict[str, Any]] = None, + predict_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: + """Generates historical forecasts by simulating predictions at various points in time throughout the history of + the provided (potentially multiple) `series`. This process involves retrospectively applying the model to + different time steps, as if the forecasts were made in real-time at those specific moments. This allows for an + evaluation of the model's performance over the entire duration of the series, providing insights into its + predictive accuracy and robustness across different historical periods. + + There are two main modes for this method: + + - Re-training Mode (Default, `retrain=True`): The model is re-trained at each step of the simulation, and + generates a forecast using the updated model. In case of multiple series, the model is re-trained on each + series independently (global training is not yet supported). + - Pre-trained Mode (`retrain=False`): The forecasts are generated at each step of the simulation without + re-training. It is only supported for pre-trained global forecasting models. This mode is significantly + faster as it skips the re-training step. + + By choosing the appropriate mode, you can balance between computational efficiency and the need for up-to-date + model training. + + **Re-training Mode:** This mode repeatedly builds a training set by either expanding from the beginning of + the `series` or by using a fixed-length `train_length` (the start point can also be configured with `start` + and `start_format`). The model is then trained on this training set, and a forecast of length `forecast_horizon` + is generated. Subsequently, the end of the training set is moved forward by `stride` time steps, and the process + is repeated. + + **Pre-trained Mode:** This mode is only supported for pre-trained global forecasting models. It uses the same + simulation steps as in the *Re-training Mode* (ignoring `train_length`), but generates the forecasts directly + without re-training. + + By default, with `last_points_only=True`, this method returns a single time series (or a sequence of time + series) composed of the last point from each historical forecast. This time series will thus have a frequency of + `series.freq * stride`. + If `last_points_only=False`, it will instead return a list (or a sequence of lists) of the full historical + forecast series each with frequency `series.freq`. Parameters ---------- series - The (or a sequence of) target time series used to successively train and compute the historical forecasts. + A (sequence of) target time series used to successively train (if `retrain` is not ``False``) and compute + the historical forecasts. past_covariates - Optionally, one (or a sequence of) past-observed covariate series. This applies only if the model - supports past covariates. + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + This applies only if the model supports past covariates. future_covariates - Optionally, one (or a sequence of) of future-known covariate series. This applies only if the model - supports future covariates. + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + This applies only if the model supports future covariates. + forecast_horizon + The forecast horizon for the predictions. num_samples - Number of times a prediction is sampled from a probabilistic model. Use values `>1` only for probabilistic + Number of times a prediction is sampled from a probabilistic model. Use values ``>1`` only for probabilistic models. train_length - Number of time steps in our training set (size of backtesting window to train on). Only effective when - `retrain` is not ``False``. Default is set to `train_length=None` where it takes all available time steps - up until prediction time, otherwise the moving window strategy is used. If larger than the number of time - steps available, all steps up until prediction time are used, as in default case. Needs to be at least - `min_train_series_length`. + Optionally, use a fixed length / number of time steps for every constructed training set (rolling window + mode). Only effective when `retrain` is not ``False``. The default is ``None``, where it uses all time + steps up until the prediction time (expanding window mode). If larger than the number of available time + steps, uses the expanding mode. Needs to be at least `min_train_series_length`. start Optionally, the first point in time at which a prediction is computed. This parameter supports: ``float``, ``int``, ``pandas.Timestamp``, and ``None``. @@ -693,28 +740,28 @@ def historical_forecasts( - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first predictable point is earlier than the first trainable point. - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), - or `retrain` is a Callable and the first trainable point is earlier than the first predictable point. + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. - the first trainable point (given `train_length`) otherwise - Note: Raises a ValueError if `start` yields a time outside the time index of `series`. + Note: If `start` is not within the trainable / forecastable points, uses the closest valid start point that + is a round multiple of `stride` ahead of `start`. Raises a `ValueError`, if no valid start point exists. + Note: If the model uses a shifted output (`output_chunk_shift > 0`), then the first predicted point is also + shifted by `output_chunk_shift` points into the future. Note: If `start` is outside the possible historical forecasting times, will ignore the parameter - (default behavior with ``None``) and start at the first trainable/predictable point. + (default behavior with ``None``) and start at the first trainable/predictable point. start_format - Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a - `pd.RangeIndex`. - If set to 'position', `start` corresponds to the index position of the first predicted point and can range - from `(-len(series), len(series) - 1)`. - If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise - an error if the value is not in `series`' index. Default: ``'value'`` - forecast_horizon - The forecast horizon for the predictions. + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. stride The number of time steps between two consecutive predictions. retrain Whether and/or on which condition to retrain the model before predicting. - This parameter supports 3 different datatypes: ``bool``, (positive) ``int``, and - ``Callable`` (returning a ``bool``). - In the case of ``bool``: retrain the model at each step (`True`), or never retrains the model (`False`). + This parameter supports 3 different types: ``bool``, (positive) ``int``, and ``Callable`` (returning a + ``bool``). + In the case of ``bool``: retrain the model at each step (`True`), or never retrain the model (`False`). In the case of ``int``: the model is retrained every `retrain` iterations. In the case of ``Callable``: the model is retrained whenever callable returns `True`. The callable must have the following positional arguments: @@ -723,45 +770,74 @@ def historical_forecasts( - `pred_time` (pd.Timestamp or int): timestamp of forecast time (end of the training series) - `train_series` (TimeSeries): train series up to `pred_time` - `past_covariates` (TimeSeries): past_covariates series up to `pred_time` - - `future_covariates` (TimeSeries): future_covariates series up - to `min(pred_time + series.freq * forecast_horizon, series.end_time())` + - `future_covariates` (TimeSeries): future_covariates series up to `min(pred_time + series.freq * + forecast_horizon, series.end_time())` Note: if any optional `*_covariates` are not passed to `historical_forecast`, ``None`` will be passed to the corresponding retrain function argument. - Note: some models do require being retrained every time and do not support anything other - than `retrain=True`. + Note: some models require being retrained every time and do not support anything other than + `retrain=True`. + Note: also controls the retraining of the `data_transformers`. overlap_end Whether the returned forecasts can go beyond the series' end or not. last_points_only - Whether to retain only the last point of each historical forecast. - If set to True, the method returns a single ``TimeSeries`` containing the successive point forecasts. + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. Otherwise, returns a list of historical ``TimeSeries`` forecasts. verbose - Whether to print progress. + Whether to print the progress. show_warnings Whether to show warnings related to historical forecasts optimization, or parameters `start` and `train_length`. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. - Default: ``False`` + Default: ``False``. enable_optimization - Whether to use the optimized version of historical_forecasts when supported and available. + Whether to use the optimized version of `historical_forecasts` when supported and available. + Default: ``True``. + data_transformers + Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series + (possibles keys; "series", "past_covariates", "future_covariates"). If provided, all input series must be + in the un-transformed space. For fittable transformer / pipeline: + + - if `retrain=True`, the data transformer re-fit on the training data at each historical forecast step. + - if `retrain=False`, the data transformer transforms the series once before all the forecasts. + + The fitted transformer is used to transform the input during both training and prediction. + If the transformation is invertible, the forecasts will be inverse-transformed. fit_kwargs - Additional arguments passed to the model `fit()` method. + Optionally, some additional arguments passed to the model `fit()` method. predict_kwargs - Additional arguments passed to the model `predict()` method. + Optionally, some additional arguments passed to the model `predict()` method. + sample_weight + Optionally, some sample weights to apply to the target `series` labels for training. Only effective when + `retrain` is not ``False``. They are applied per observation, per label (each step in + `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed per time `series`. Returns ------- - TimeSeries or List[TimeSeries] or List[List[TimeSeries]] - If `last_points_only` is set to True and a single series is provided in input, a single ``TimeSeries`` - is returned, which contains the historical forecast at the desired horizon. - - A ``List[TimeSeries]`` is returned if either `series` is a ``Sequence`` of ``TimeSeries``, - or if `last_points_only` is set to False. A list of lists is returned if both conditions are met. - In this last case, the outer list is over the series provided in the input sequence, - and the inner lists contain the different historical forecasts. + TimeSeries + A single historical forecast for a single `series` and `last_points_only=True`: it contains only the + predictions at step `forecast_horizon` from all historical forecasts. + List[TimeSeries] + A list of historical forecasts for: + + - a sequence (list) of `series` and `last_points_only=True`: for each series, it contains only the + predictions at step `forecast_horizon` from all historical forecasts. + - a single `series` and `last_points_only=False`: for each historical forecast, it contains the entire + horizon `forecast_horizon`. + List[List[TimeSeries]] + A list of lists of historical forecasts for a sequence of `series` and `last_points_only=False`. For each + series, and historical forecast, it contains the entire horizon `forecast_horizon`. The outer list + is over the series provided in the input sequence, and the inner lists contain the historical forecasts for + each series. """ model: ForecastingModel = self # only GlobalForecastingModels support historical forecasting without retraining the model @@ -861,6 +937,23 @@ def retrain_func( logger, ) + data_transformers = _convert_data_transformers( + data_transformers=data_transformers, copy=True + ) + + using_prefitted_transformers = False + # data transformer already fitted and can be directly applied to all the series + if data_transformers and not retrain: + using_prefitted_transformers = True + series, past_covariates, future_covariates = _apply_data_transformers( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + data_transformers=data_transformers, + max_future_cov_lag=model.extreme_lags[5], + fit_transformers=False, + ) + # remove unsupported arguments, raise exception if interference with historical forecasts logic fit_kwargs, predict_kwargs = _historical_forecasts_sanitize_kwargs( model=model, @@ -870,10 +963,6 @@ def retrain_func( show_warnings=show_warnings, ) - series = series2seq(series) - past_covariates = series2seq(past_covariates) - future_covariates = series2seq(future_covariates) - if ( enable_optimization and model.supports_optimized_historical_forecasts @@ -883,7 +972,7 @@ def retrain_func( show_warnings=show_warnings, ) ): - return model._optimized_historical_forecasts( + forecasts = model._optimized_historical_forecasts( series=series, past_covariates=past_covariates, future_covariates=future_covariates, @@ -900,12 +989,28 @@ def retrain_func( **predict_kwargs, ) + return _apply_inverse_data_transformers( + series=series, forecasts=forecasts, data_transformers=data_transformers + ) + + sequence_type_in = get_series_seq_type(series) + series = series2seq(series) + past_covariates = series2seq(past_covariates) + future_covariates = series2seq(future_covariates) + sample_weight = ( + sample_weight + if isinstance(sample_weight, str) + else series2seq(sample_weight) + ) + if len(series) == 1: # Use tqdm on the outer loop only if there's more than one series to iterate over # (otherwise use tqdm on the inner loop). outer_iterator = series else: - outer_iterator = _build_tqdm_iterator(series, verbose) + outer_iterator = _build_tqdm_iterator( + series, verbose, total=len(series), desc="historical forecasts" + ) # deactivate the warning after displaying it once if show_warnings is True show_predict_warnings = show_warnings @@ -914,6 +1019,10 @@ def retrain_func( for idx, series_ in enumerate(outer_iterator): past_covariates_ = past_covariates[idx] if past_covariates else None future_covariates_ = future_covariates[idx] if future_covariates else None + if isinstance(sample_weight, str): + sample_weight_ = sample_weight + else: + sample_weight_ = sample_weight[idx] if sample_weight else None # predictable time indexes (assuming model is already trained) historical_forecasts_time_index_predict = ( @@ -978,6 +1087,7 @@ def retrain_func( historical_forecasts_time_index=historical_forecasts_time_index, start=start, start_format=start_format, + stride=stride, show_warnings=show_warnings, ) @@ -996,7 +1106,10 @@ def retrain_func( if len(series) == 1: # Only use tqdm if there's no outer loop iterator = _build_tqdm_iterator( - historical_forecasts_time_index[::stride], verbose + historical_forecasts_time_index[::stride], + verbose, + total=(len(historical_forecasts_time_index) - 1) // stride + 1, + desc="historical forecasts", ) else: iterator = historical_forecasts_time_index[::stride] @@ -1019,6 +1132,20 @@ def retrain_func( if train_length_ and len(train_series) > train_length_: train_series = train_series[-train_length_:] + # when `retrain=True`, data transformers are also retrained between iterations to avoid data-leakage + # using a single series + if data_transformers and retrain: + train_series, past_covariates_, future_covariates_ = ( + _apply_data_transformers( + series=train_series, + past_covariates=past_covariates_, + future_covariates=future_covariates_, + data_transformers=data_transformers, + max_future_cov_lag=model.extreme_lags[5], + fit_transformers=True, + ) + ) + # testing `retrain` to exclude `False` and `0` if ( retrain @@ -1041,6 +1168,7 @@ def retrain_func( series=train_series, past_covariates=past_covariates_, future_covariates=future_covariates_, + sample_weight=sample_weight_, **fit_kwargs, ) else: @@ -1081,7 +1209,7 @@ def retrain_func( length=1, freq=series_.freq, ), - values=np.array([np.NaN]), + values=np.array([np.nan]), ) forecast = model._predict_wrapper( @@ -1095,6 +1223,14 @@ def retrain_func( show_warnings=show_predict_warnings, **predict_kwargs, ) + + forecast = _apply_inverse_data_transformers( + series=train_series, + forecasts=forecast, + data_transformers=data_transformers, + series_idx=idx if using_prefitted_transformers else None, + ) + show_predict_warnings = False if forecast_components is None: @@ -1115,88 +1251,102 @@ def retrain_func( freq=series_.freq * stride, ), np.array(last_points_values), - columns=forecast_components - if forecast_components is not None - else series_.columns, - static_covariates=series_.static_covariates - if not predict_likelihood_parameters - else None, - hierarchy=series_.hierarchy - if not predict_likelihood_parameters - else None, + columns=( + forecast_components + if forecast_components is not None + else series_.columns + ), + static_covariates=( + series_.static_covariates + if not predict_likelihood_parameters + else None + ), + hierarchy=( + series_.hierarchy + if not predict_likelihood_parameters + else None + ), ) ) else: forecasts_list.append(forecasts) - return forecasts_list if len(series) > 1 else forecasts_list[0] + return series2seq(forecasts_list, seq_type_out=sequence_type_in) def backtest( self, series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - historical_forecasts: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + historical_forecasts: Optional[ + Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] + ] = None, + forecast_horizon: int = 1, num_samples: int = 1, train_length: Optional[int] = None, start: Optional[Union[pd.Timestamp, float, int]] = None, start_format: Literal["position", "value"] = "value", - forecast_horizon: int = 1, stride: int = 1, retrain: Union[bool, int, Callable[..., bool]] = True, overlap_end: bool = False, last_points_only: bool = False, - metric: Union[ - Callable[[TimeSeries, TimeSeries], float], - List[Callable[[TimeSeries, TimeSeries], float]], - ] = metrics.mape, - reduction: Union[Callable[[np.ndarray], float], None] = np.mean, + metric: Union[METRIC_TYPE, list[METRIC_TYPE]] = metrics.mape, + reduction: Union[Callable[..., float], None] = np.mean, verbose: bool = False, show_warnings: bool = True, - fit_kwargs: Optional[Dict[str, Any]] = None, - predict_kwargs: Optional[Dict[str, Any]] = None, - ) -> Union[float, List[float], Sequence[float], List[Sequence[float]]]: - """Compute error values that the model would have produced when - used on (potentially multiple) `series`. - - If `historical_forecasts` are provided, the metric (given by the `metric` function) is evaluated directly on - the forecast and the actual values. Otherwise, it repeatedly builds a training set: either expanding from the - beginning of `series` or moving with a fixed length `train_length`. It trains the current model on the - training set, emits a forecast of length equal to `forecast_horizon`, and then moves the end of the training - set forward by `stride` time steps. The metric is then evaluated on the forecast and the actual values. - Finally, the method returns a `reduction` (the mean by default) of all these metric scores. - - By default, this method uses each historical forecast (whole) to compute error scores. - If `last_points_only` is set to True, it will use only the last point of each historical - forecast. In this case, no reduction is used. - - By default, this method always re-trains the models on the entire available history, corresponding to an - expanding window strategy. If `retrain` is set to False (useful for models for which training might be - time-consuming, such as deep learning models), the trained model will be used directly to emit the forecasts. + predict_likelihood_parameters: bool = False, + enable_optimization: bool = True, + data_transformers: Optional[ + dict[str, Union[BaseDataTransformer, Pipeline]] + ] = None, + metric_kwargs: Optional[Union[dict[str, Any], list[dict[str, Any]]]] = None, + fit_kwargs: Optional[dict[str, Any]] = None, + predict_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + ) -> Union[float, np.ndarray, list[float], list[np.ndarray]]: + """Compute error values that the model produced for historical forecasts on (potentially multiple) `series`. + + If `historical_forecasts` are provided, the metric(s) (given by the `metric` function) is evaluated directly on + all forecasts and actual values. The same `series` and `last_points_only` value must be passed that were used + to generate the historical forecasts. Finally, the method returns an optional `reduction` (the mean by default) + of all these metric scores. + + If `historical_forecasts` is ``None``, it first generates the historical forecasts with the parameters given + below (see :meth:`ForecastingModel.historical_forecasts() + ` for more info) and then + evaluates as described above. + + The metric(s) can be further customized `metric_kwargs` (e.g. control the aggregation over components, time + steps, multiple series, other required arguments such as `q` for quantile metrics, ...). Parameters ---------- series - The (or a sequence of) target time series used to successively train and evaluate the historical forecasts. + A (sequence of) target time series used to successively train (if `retrain` is not ``False``) and compute + the historical forecasts. past_covariates - Optionally, one (or a sequence of) past-observed covariate series. This applies only if the model - supports past covariates. + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + This applies only if the model supports past covariates. future_covariates - Optionally, one (or a sequence of) future-known covariate series. This applies only if the model - supports future covariates. + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + This applies only if the model supports future covariates. historical_forecasts - Optionally, the (or a sequence of) historical forecasts time series to be evaluated. Corresponds to - the output of :meth:`historical_forecasts() `. If provided, will - skip historical forecasting and ignore all parameters except `series`, `metric`, and `reduction`. + Optionally, the (or a sequence of / a sequence of sequences of) historical forecasts time series to be + evaluated. Corresponds to the output of :meth:`historical_forecasts() + `. The same `series` and + `last_points_only` values must be passed that were used to generate the historical forecasts. If provided, + will skip historical forecasting and ignore all parameters except `series`, `last_points_only`, `metric`, + and `reduction`. + forecast_horizon + The forecast horizon for the predictions. num_samples - Number of times a prediction is sampled from a probabilistic model. Use values `>1` only for probabilistic + Number of times a prediction is sampled from a probabilistic model. Use values ``>1`` only for probabilistic models. train_length - Number of time steps in our training set (size of backtesting window to train on). Only effective when - `retrain` is not ``False``. Default is set to `train_length=None` where it takes all available time steps - up until prediction time, otherwise the moving window strategy is used. If larger than the number of time - steps available, all steps up until prediction time are used, as in default case. Needs to be at least - `min_train_series_length`. + Optionally, use a fixed length / number of time steps for every constructed training set (rolling window + mode). Only effective when `retrain` is not ``False``. The default is ``None``, where it uses all time + steps up until the prediction time (expanding window mode). If larger than the number of available time + steps, uses the expanding mode. Needs to be at least `min_train_series_length`. start Optionally, the first point in time at which a prediction is computed. This parameter supports: ``float``, ``int``, ``pandas.Timestamp``, and ``None``. @@ -1210,121 +1360,255 @@ def backtest( - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first predictable point is earlier than the first trainable point. - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), - or `retrain` is a Callable and the first trainable point is earlier than the first predictable point. + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. - the first trainable point (given `train_length`) otherwise - Note: Raises a ValueError if `start` yields a time outside the time index of `series`. + Note: If `start` is not within the trainable / forecastable points, uses the closest valid start point that + is a round multiple of `stride` ahead of `start`. Raises a `ValueError`, if no valid start point exists. + Note: If the model uses a shifted output (`output_chunk_shift > 0`), then the first predicted point is also + shifted by `output_chunk_shift` points into the future. Note: If `start` is outside the possible historical forecasting times, will ignore the parameter - (default behavior with ``None``) and start at the first trainable/predictable point. + (default behavior with ``None``) and start at the first trainable/predictable point. start_format - Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a - `pd.RangeIndex`. - If set to 'position', `start` corresponds to the index position of the first predicted point and can range - from `(-len(series), len(series) - 1)`. - If set to 'value', `start` corresponds to the index value/label of the first predicted point. Will raise - an error if the value is not in `series`' index. Default: ``'value'`` - forecast_horizon - The forecast horizon for the point predictions. + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. stride The number of time steps between two consecutive predictions. retrain Whether and/or on which condition to retrain the model before predicting. - This parameter supports 3 different datatypes: ``bool``, (positive) ``int``, and - ``Callable`` (returning a ``bool``). - In the case of ``bool``: retrain the model at each step (`True`), or never retrains the model (`False`). + This parameter supports 3 different types: ``bool``, (positive) ``int``, and ``Callable`` (returning a + ``bool``). + In the case of ``bool``: retrain the model at each step (`True`), or never retrain the model (`False`). In the case of ``int``: the model is retrained every `retrain` iterations. In the case of ``Callable``: the model is retrained whenever callable returns `True`. The callable must have the following positional arguments: - - `counter` (int): current `retrain` iteration - - `pred_time` (pd.Timestamp or int): timestamp of forecast time (end of the training series) - - `train_series` (TimeSeries): train series up to `pred_time` - - `past_covariates` (TimeSeries): past_covariates series up to `pred_time` - - `future_covariates` (TimeSeries): future_covariates series up - to `min(pred_time + series.freq * forecast_horizon, series.end_time())` + - `counter` (int): current `retrain` iteration + - `pred_time` (pd.Timestamp or int): timestamp of forecast time (end of the training series) + - `train_series` (TimeSeries): train series up to `pred_time` + - `past_covariates` (TimeSeries): past_covariates series up to `pred_time` + - `future_covariates` (TimeSeries): future_covariates series up to `min(pred_time + series.freq * + forecast_horizon, series.end_time())` Note: if any optional `*_covariates` are not passed to `historical_forecast`, ``None`` will be passed to the corresponding retrain function argument. - Note: some models do require being retrained every time and do not support anything other - than `retrain=True`. + Note: some models require being retrained every time and do not support anything other than + `retrain=True`. + Note: also controls the retraining of the `data_transformers`. overlap_end Whether the returned forecasts can go beyond the series' end or not. last_points_only - Whether to use the whole historical forecasts or only the last point of each forecast to compute the error. + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. + Otherwise, returns a list of historical ``TimeSeries`` forecasts. metric - A function or a list of function that takes two ``TimeSeries`` instances as inputs and returns an - error value. + A metric function or a list of metric functions. Each metric must either be a Darts metric (see `here + `_), or a custom metric that has an + identical signature as Darts' metrics, uses decorators :func:`~darts.metrics.metrics.multi_ts_support` and + :func:`~darts.metrics.metrics.multi_ts_support`, and returns the metric score. reduction - A function used to combine the individual error scores obtained when `last_points_only` is set to False. - When providing several metric functions, the function will receive the argument `axis = 0` to obtain single + A function used to combine the individual error scores obtained when `last_points_only` is set to `False`. + When providing several metric functions, the function will receive the argument `axis = 1` to obtain single value for each metric function. If explicitly set to `None`, the method will return a list of the individual error scores instead. Set to ``np.mean`` by default. verbose - Whether to print progress. + Whether to print the progress. show_warnings - Whether to show warnings related to parameters `start`, and `train_length`. + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + predict_likelihood_parameters + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only + supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. + Default: ``False``. + enable_optimization + Whether to use the optimized version of `historical_forecasts` when supported and available. + Default: ``True``. + data_transformers + Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series + (possibles keys; "series", "past_covariates", "future_covariates"). If provided, all input series must be + in the un-transformed space. For fittable transformer / pipeline: + + - if `retrain=True`, the data transformer re-fit on the training data at each historical forecast step. + - if `retrain=False`, the data transformer transforms the series once before all the forecasts. + + The fitted transformer is used to transform the input during both training and prediction. + If the transformation is invertible, the forecasts will be inverse-transformed. + Only effective when `historical_forecasts=None`. + metric_kwargs + Additional arguments passed to `metric()`, such as `'n_jobs'` for parallelization, `'component_reduction'` + for reducing the component wise metrics, seasonality `'m'` for scaled metrics, etc. Will pass arguments to + each metric separately and only if they are present in the corresponding metric signature. Parameter + `'insample'` for scaled metrics (e.g. mase`, `rmsse`, ...) is ignored, as it is handled internally. fit_kwargs - Additional arguments passed to the model `fit()` method. + Optionally, some additional arguments passed to the model `fit()` method. predict_kwargs - Additional arguments passed to the model `predict()` method. + Optionally, some additional arguments passed to the model `predict()` method. + sample_weight + Optionally, some sample weights to apply to the target `series` labels for training. Only effective when + `retrain` is not ``False``. They are applied per observation, per label (each step in + `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed per time `series`. Returns ------- - float or List[float] or List[List[float]] - The (sequence of) error score on a series, or list of list containing error scores for each - provided series and each sample. - """ - if historical_forecasts is None: - forecasts = self.historical_forecasts( - series=series, - past_covariates=past_covariates, - future_covariates=future_covariates, - num_samples=num_samples, - train_length=train_length, - start=start, - start_format=start_format, - forecast_horizon=forecast_horizon, - stride=stride, - retrain=retrain, - overlap_end=overlap_end, - last_points_only=last_points_only, - verbose=verbose, - show_warnings=show_warnings, - fit_kwargs=fit_kwargs, - predict_kwargs=predict_kwargs, - ) - else: - forecasts = historical_forecasts - - series = series2seq(series) - if len(series) == 1: - forecasts = [forecasts] + float + A single backtest score for single uni/multivariate series, a single `metric` function and: + + - `historical_forecasts` generated with `last_points_only=True` + - `historical_forecasts` generated with `last_points_only=False` and using a backtest `reduction` + np.ndarray + An numpy array of backtest scores. For single series and one of: + + - a single `metric` function, `historical_forecasts` generated with `last_points_only=False` + and backtest `reduction=None`. The output has shape (n forecasts, *). + - multiple `metric` functions and `historical_forecasts` generated with `last_points_only=False`. + The output has shape (*, n metrics) when using a backtest `reduction`, and (n forecasts, *, n metrics) + when `reduction=None` + - multiple uni/multivariate series including `series_reduction` and at least one of + `component_reduction=None` or `time_reduction=None` for "per time step metrics" + List[float] + Same as for type `float` but for a sequence of series. The returned metric list has length + `len(series)` with the `float` metric for each input `series`. + List[np.ndarray] + Same as for type `np.ndarray` but for a sequence of series. The returned metric list has length + `len(series)` with the `np.ndarray` metrics for each input `series`. + """ + metric_kwargs = metric_kwargs or dict() + if not isinstance(metric_kwargs, list): + metric_kwargs = [metric_kwargs] if not isinstance(metric, list): metric = [metric] + if len(metric_kwargs) > 1 and len(metric_kwargs) != len(metric): + raise_log( + ValueError( + f"Mismatch between number of metric-specific `metric_kwargs` " + f"({len(metric_kwargs)}) and number of metrics in `metric` ({len(metric)}). " + f"For `metric_kwargs`, either give a list of dicts of length `{len(metric)}` " + f"with metric-specific kwargs, or a single dict that is applied to all metrics." + ), + logger=logger, + ) + if len(metric_kwargs) != len(metric): + metric_kwargs = [metric_kwargs[0] for _ in range(len(metric))] + + historical_forecasts = historical_forecasts or self.historical_forecasts( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + num_samples=num_samples, + train_length=train_length, + start=start, + start_format=start_format, + forecast_horizon=forecast_horizon, + stride=stride, + retrain=retrain, + overlap_end=overlap_end, + last_points_only=last_points_only, + verbose=verbose, + show_warnings=show_warnings, + predict_likelihood_parameters=predict_likelihood_parameters, + enable_optimization=enable_optimization, + data_transformers=data_transformers, + fit_kwargs=fit_kwargs, + predict_kwargs=predict_kwargs, + sample_weight=sample_weight, + ) + + # remember input series type + series_seq_type = get_series_seq_type(series) + # validate historical forecasts and convert to multiple series with multiple forecasts case + series, historical_forecasts = _process_historical_forecast_for_backtest( + series=series, + historical_forecasts=historical_forecasts, + last_points_only=last_points_only, + ) + + # we have multiple forecasts per series: rearrange forecasts to call each metric only once; + # flatten historical forecasts, get matching target series index, remember cumulative target lengths + # for later reshaping back to original + series_idx = [] + cum_len = [0] + forecasts_list = [] + for idx, fc_list in enumerate(historical_forecasts): + series_idx += [idx] * len(fc_list) + cum_len.append(cum_len[-1] + len(fc_list)) + forecasts_list.extend(fc_list) + + class SeriesGenerator(Sequence): + """Yields the target `series` corresponding the historical forecast at index `i`. + Allows lazy loading of target `series` in case it is a Sequence. + """ + + def __len__(self): + return len(forecasts_list) + + def __getitem__(self, index) -> TimeSeries: + return series[series_idx[index]] + + # extract metrics per metric and series, and optionally reduce + # errors shape `(n metrics, n total historical forecasts)` + series_gen = SeriesGenerator() + errors = [] + for metric_f, metric_f_kwargs in zip(metric, metric_kwargs): + # add user supplied metric kwargs + kwargs = {k: v for k, v in metric_f_kwargs.items()} + metric_params = inspect.signature(metric_f).parameters + + # scaled metrics require `insample` series + if "insample" in metric_params: + kwargs["insample"] = series_gen + + errors.append(metric_f(series_gen, forecasts_list, **kwargs)) + try: + # multiple series can result in different number of forecasts; try if we can run it efficiently + errors = np.array(errors) + is_arr = True + except ValueError: + # otherwise, compute array later + is_arr = False + + # get errors for each input `series` backtest_list = [] - for idx, target_ts in enumerate(series): - if last_points_only: - errors = [metric_f(target_ts, forecasts[idx]) for metric_f in metric] - if len(errors) == 1: - errors = errors[0] - backtest_list.append(errors) + for i in range(len(cum_len) - 1): + # errors_series with shape `(n metrics, n series specific historical forecasts, *)` + if is_arr: + errors_series = errors[:, cum_len[i] : cum_len[i + 1]] else: - errors = [ - [metric_f(target_ts, f) for metric_f in metric] - if len(metric) > 1 - else metric[0](target_ts, f) - for f in forecasts[idx] - ] - - if reduction is None: - backtest_list.append(errors) - else: - backtest_list.append(reduction(np.array(errors), axis=0)) + errors_series = np.array([ + errors_[cum_len[i] : cum_len[i + 1]] for errors_ in errors + ]) + + if reduction is not None: + # shape `(n metrics, n forecasts, *)` -> `(n metrics, *)` + errors_series = reduction(errors_series, axis=1) + elif last_points_only: + # shape `(n metrics, n forecasts = 1, *)` -> `(n metrics, *)` + errors_series = errors_series[:, 0] + + if len(metric) == 1: + # shape `(n metrics, *)` -> `(*,)` + errors_series = errors_series[0] + else: + # shape `(n metrics, *)` -> `(*, n metrics)` + errors_series = errors_series.transpose( + tuple(i for i in range(1, errors_series.ndim)) + (0,) + ) - return backtest_list if len(backtest_list) > 1 else backtest_list[0] + backtest_list.append(errors_series) + return ( + backtest_list if series_seq_type > SeriesType.SINGLE else backtest_list[0] + ) @classmethod def gridsearch( @@ -1335,7 +1619,7 @@ def gridsearch( future_covariates: Optional[TimeSeries] = None, forecast_horizon: Optional[int] = None, stride: int = 1, - start: Union[pd.Timestamp, float, int] = 0.5, + start: Optional[Union[pd.Timestamp, float, int]] = None, start_format: Literal["position", "value"] = "value", last_points_only: bool = False, show_warnings: bool = True, @@ -1346,9 +1630,13 @@ def gridsearch( verbose=False, n_jobs: int = 1, n_random_samples: Optional[Union[int, float]] = None, - fit_kwargs: Optional[Dict[str, Any]] = None, - predict_kwargs: Optional[Dict[str, Any]] = None, - ) -> Tuple["ForecastingModel", Dict[str, Any], float]: + data_transformers: Optional[ + dict[str, Union[BaseDataTransformer, Pipeline]] + ] = None, + fit_kwargs: Optional[dict[str, Any]] = None, + predict_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, str]] = None, + ) -> tuple["ForecastingModel", dict[str, Any], float]: """ Find the best hyper-parameters among a given set using a grid search. @@ -1421,9 +1709,12 @@ def gridsearch( or `retrain` is a Callable and the first trainable point is earlier than the first predictable point. - the first trainable point (given `train_length`) otherwise - Note: Raises a ValueError if `start` yields a time outside the time index of `series`. + Note: If `start` is not within the trainable / forecastable points, uses the closest valid start point that + is a round multiple of `stride` ahead of `start`. Raises a `ValueError`, if no valid start point exists. + Note: If the model uses a shifted output (`output_chunk_shift > 0`), then the first predicted point is also + shifted by `output_chunk_shift` points into the future. Note: If `start` is outside the possible historical forecasting times, will ignore the parameter - (default behavior with ``None``) and start at the first trainable/predictable point. + (default behavior with ``None``) and start at the first trainable/predictable point. start_format Only used in expanding window mode. Defines the `start` format. Only effective when `start` is an integer and `series` is indexed with a `pd.RangeIndex`. @@ -1443,13 +1734,15 @@ def gridsearch( If `True`, uses the comparison with the fitted values. Raises an error if ``fitted_values`` is not an attribute of `model_class`. metric - A function that takes two TimeSeries instances as inputs (actual and prediction, in this order), - and returns a float error value. + A metric function that returns the error between two `TimeSeries` as a float value . Must either be one of + Darts' "aggregated over time" metrics (see `here + `_), or a custom metric that as input two + `TimeSeries` and returns the error reduction A reduction function (mapping array to float) describing how to aggregate the errors obtained on the different validation series when backtesting. By default it'll compute the mean of errors. verbose - Whether to print progress. + Whether to print the progress. n_jobs The number of jobs to run in parallel. Parallel jobs are created only when there are two or more parameters combinations to evaluate. Each job will instantiate, train, and evaluate a different instance of the model. @@ -1461,10 +1754,29 @@ def gridsearch( must be between `0` and the total number of parameter combinations. If a float, `n_random_samples` is the ratio of parameter combinations selected from the full grid and must be between `0` and `1`. Defaults to `None`, for which random selection will be ignored. + data_transformers + Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series + (possibles keys; "series", "past_covariates", "future_covariates"). If provided, all input series must be + in the un-transformed space. For fittable transformer / pipeline: + + - if `retrain=True`, the data transformer re-fit on the training data at each historical forecast step. + - if `retrain=False`, the data transformer transforms the series once before all the forecasts. + + The fitted transformer is used to transform the input during both training and prediction. + If the transformation is invertible, the forecasts will be inverse-transformed. fit_kwargs Additional arguments passed to the model `fit()` method. predict_kwargs Additional arguments passed to the model `predict()` method. + sample_weight + Optionally, some sample weights to apply to the target `series` labels for training. Only effective when + `retrain` is not ``False``. They are applied per observation, per label (each step in + `output_chunk_length`), and per component. + If a series, then those weights are used. If the weight series only have a single component / column, then + the weights are applied globally to all components in `series`. Otherwise, for component-specific weights, + the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. Returns ------- @@ -1479,14 +1791,34 @@ def gridsearch( + use_fitted_values == 1, "Please pass exactly one of the arguments 'forecast_horizon', " - "'val_target_series' or 'use_fitted_values'.", + "'val_series' or 'use_fitted_values'.", logger, ) + if not isinstance(parameters, dict): + raise_log( + ValueError( + f"`parameters` should be a dictionary, received a: {type(parameters)}." + ) + ) + + if not all( + isinstance(params, (list, np.ndarray)) for params in parameters.values() + ): + raise_log( + ValueError( + "Every value in the `parameters` dictionary should be a list or a np.ndarray." + ), + logger, + ) + if use_fitted_values: raise_if_not( - hasattr(model_class(), "fitted_values"), - "The model must have a fitted_values attribute to compare with the train TimeSeries", + hasattr( + model_class(**{k: v[0] for k, v in parameters.items()}), + "fitted_values", + ), + "The model must have a fitted_values attribute to compare with the train TimeSeries (local models)", logger, ) @@ -1497,6 +1829,10 @@ def gridsearch( logger, ) + data_transformers = _convert_data_transformers( + data_transformers=data_transformers, copy=True + ) + if fit_kwargs is None: fit_kwargs = dict() if predict_kwargs is None: @@ -1513,7 +1849,10 @@ def gridsearch( # iterate through all combinations of the provided parameters and choose the best one iterator = _build_tqdm_iterator( - zip(params_cross_product), verbose, total=len(params_cross_product) + zip(params_cross_product), + verbose, + total=len(params_cross_product), + desc="gridsearch", ) def _evaluate_combination(param_combination) -> float: @@ -1522,21 +1861,45 @@ def _evaluate_combination(param_combination) -> float: ) if param_combination_dict.get("model_name", None): current_time = time.strftime("%Y-%m-%d_%H.%M.%S.%f", time.localtime()) - param_combination_dict[ - "model_name" - ] = f"{current_time}_{param_combination_dict['model_name']}" + param_combination_dict["model_name"] = ( + f"{current_time}_{param_combination_dict['model_name']}" + ) model = model_class(**param_combination_dict) if use_fitted_values: # fitted value mode + if data_transformers: + series_, past_covariates_, future_covariates_ = ( + _apply_data_transformers( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + data_transformers=data_transformers, + max_future_cov_lag=model.extreme_lags[5], + fit_transformers=True, + ) + ) + else: + series_ = series + past_covariates_ = past_covariates + future_covariates_ = future_covariates + model._fit_wrapper( - series=series, - past_covariates=past_covariates, - future_covariates=future_covariates, + series=series_, + past_covariates=past_covariates_, + future_covariates=future_covariates_, + sample_weight=sample_weight, **fit_kwargs, ) fitted_values = TimeSeries.from_times_and_values( series.time_index, model.fitted_values ) + if data_transformers and "series" in data_transformers: + fitted_values = _apply_inverse_data_transformers( + series=series_, + forecasts=fitted_values, + data_transformers=data_transformers, + series_idx=None, + ) error = metric(series, fitted_values) elif val_series is None: # expanding window mode error = model.backtest( @@ -1553,30 +1916,54 @@ def _evaluate_combination(param_combination) -> float: last_points_only=last_points_only, verbose=verbose, show_warnings=show_warnings, + data_transformers=data_transformers, fit_kwargs=fit_kwargs, predict_kwargs=predict_kwargs, + sample_weight=sample_weight, ) else: # split mode + if data_transformers: + series_, past_covariates_, future_covariates_ = ( + _apply_data_transformers( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + data_transformers=data_transformers, + max_future_cov_lag=model.extreme_lags[5], + fit_transformers=True, + ) + ) + else: + series_ = series + past_covariates_ = past_covariates + future_covariates_ = future_covariates + model._fit_wrapper( - series=series, - past_covariates=past_covariates, - future_covariates=future_covariates, + series=series_, + past_covariates=past_covariates_, + future_covariates=future_covariates_, + sample_weight=sample_weight, **fit_kwargs, ) pred = model._predict_wrapper( n=len(val_series), - series=series, - past_covariates=past_covariates, - future_covariates=future_covariates, + series=series_, + past_covariates=past_covariates_, + future_covariates=future_covariates_, num_samples=1, verbose=verbose, **predict_kwargs, ) + pred = _apply_inverse_data_transformers( + series=series_, + forecasts=pred, + data_transformers=data_transformers, + ) error = metric(val_series, pred) return float(error) - errors: List[float] = _parallel_apply( + errors: list[float] = _parallel_apply( iterator, _evaluate_combination, n_jobs, {}, {} ) @@ -1595,88 +1982,336 @@ def residuals( series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + historical_forecasts: Optional[ + Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] + ] = None, forecast_horizon: int = 1, - retrain: bool = True, + num_samples: int = 1, + train_length: Optional[int] = None, + start: Optional[Union[pd.Timestamp, float, int]] = None, + start_format: Literal["position", "value"] = "value", + stride: int = 1, + retrain: Union[bool, int, Callable[..., bool]] = True, + overlap_end: bool = False, + last_points_only: bool = True, + metric: METRIC_TYPE = metrics.err, verbose: bool = False, - ) -> Union[TimeSeries, Sequence[TimeSeries]]: - """Compute the residuals produced by this model on a (or sequence of) univariate time series. - - This function computes the difference between the actual observations from `series` and the fitted values - vector `p` obtained by training the model on `series`. For every index `i` in `series`, `p[i]` is computed - by training the model on ``series[:(i - forecast_horizon)]`` and forecasting `forecast_horizon` into the future. - (`p[i]` will be set to the last value of the predicted series.) - The vector of residuals will be shorter than `series` due to the minimum training series length required by the - model and the gap introduced by `forecast_horizon`. Most commonly, the term "residuals" implies a value for - `forecast_horizon` of 1; but this can be configured. - - This method works only on univariate series. It uses the median - prediction (when dealing with stochastic forecasts). + show_warnings: bool = True, + predict_likelihood_parameters: bool = False, + enable_optimization: bool = True, + data_transformers: Optional[ + dict[str, Union[BaseDataTransformer, Pipeline]] + ] = None, + metric_kwargs: Optional[dict[str, Any]] = None, + fit_kwargs: Optional[dict[str, Any]] = None, + predict_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + values_only: bool = False, + ) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: + """Compute the residuals that the model produced for historical forecasts on (potentially multiple) `series`. + + This function computes the difference (or one of Darts' "per time step" metrics) between the actual + observations from `series` and the fitted values obtained by training the model on `series` (or using a + pre-trained model with `retrain=False`). Not all models support fitted values, so we use historical forecasts + as an approximation for them. + + In sequence this method performs: + + - use pre-computed `historical_forecasts` or compute historical forecasts for each series (see + :meth:`~darts.models.forecasting.forecasting_model.ForecastingModel.historical_forecasts` for more details). + How the historical forecasts are generated can be configured with parameters `num_samples`, `train_length`, + `start`, `start_format`, `forecast_horizon`, `stride`, `retrain`, `last_points_only`, `fit_kwargs`, and + `predict_kwargs`. + - compute a backtest using a "per time step" `metric` between the historical forecasts and `series` per + component/column and time step (see + :meth:`~darts.models.forecasting.forecasting_model.ForecastingModel.backtest` for more details). By default, + uses the residuals :func:`~darts.metrics.metrics.err` (error) as a `metric`. + - create and return `TimeSeries` (or simply a np.ndarray with `values_only=True`) with the time index from + historical forecasts, and values from the metrics per component and time step. + + This method works for single or multiple univariate or multivariate series. + It uses the median prediction (when dealing with stochastic forecasts). Parameters ---------- series - The univariate TimeSeries instance which the residuals will be computed for. + A (sequence of) target time series used to successively train (if `retrain` is not ``False``) and compute + the historical forecasts. past_covariates - One or several past-observed covariate time series. + Optionally, a (sequence of) past-observed covariate time series for every input time series in `series`. + This applies only if the model supports past covariates. future_covariates - One or several future-known covariate time series. + Optionally, a (sequence of) future-known covariate time series for every input time series in `series`. + This applies only if the model supports future covariates. + historical_forecasts + Optionally, the (or a sequence of / a sequence of sequences of) historical forecasts time series to be + evaluated. Corresponds to the output of :meth:`historical_forecasts() + `. The same `series` and + `last_points_only` values must be passed that were used to generate the historical forecasts. If provided, + will skip historical forecasting and ignore all parameters except `series`, `last_points_only`, `metric`, + and `reduction`. forecast_horizon - The forecasting horizon used to predict each fitted value. + The forecast horizon for the predictions. + num_samples + Number of times a prediction is sampled from a probabilistic model. Use values ``>1`` only for probabilistic + models. + train_length + Optionally, use a fixed length / number of time steps for every constructed training set (rolling window + mode). Only effective when `retrain` is not ``False``. The default is ``None``, where it uses all time + steps up until the prediction time (expanding window mode). If larger than the number of available time + steps, uses the expanding mode. Needs to be at least `min_train_series_length`. + start + Optionally, the first point in time at which a prediction is computed. This parameter supports: + ``float``, ``int``, ``pandas.Timestamp``, and ``None``. + If a ``float``, it is the proportion of the time series that should lie before the first prediction point. + If an ``int``, it is either the index position of the first prediction point for `series` with a + `pd.DatetimeIndex`, or the index value for `series` with a `pd.RangeIndex`. The latter can be changed to + the index position with `start_format="position"`. + If a ``pandas.Timestamp``, it is the time stamp of the first prediction point. + If ``None``, the first prediction point will automatically be set to: + + - the first predictable point if `retrain` is ``False``, or `retrain` is a Callable and the first + predictable point is earlier than the first trainable point. + - the first trainable point if `retrain` is ``True`` or ``int`` (given `train_length`), + or `retrain` is a ``Callable`` and the first trainable point is earlier than the first predictable point. + - the first trainable point (given `train_length`) otherwise + + Note: If `start` is not within the trainable / forecastable points, uses the closest valid start point that + is a round multiple of `stride` ahead of `start`. Raises a `ValueError`, if no valid start point exists. + Note: If the model uses a shifted output (`output_chunk_shift > 0`), then the first predicted point is also + shifted by `output_chunk_shift` points into the future. + Note: If `start` is outside the possible historical forecasting times, will ignore the parameter + (default behavior with ``None``) and start at the first trainable/predictable point. + start_format + Defines the `start` format. + If set to ``'position'``, `start` corresponds to the index position of the first predicted point and can + range from `(-len(series), len(series) - 1)`. + If set to ``'value'``, `start` corresponds to the index value/label of the first predicted point. Will raise + an error if the value is not in `series`' index. Default: ``'value'``. + stride + The number of time steps between two consecutive predictions. retrain - Whether to train the model at each iteration, for models that support it. - If False, the model is not trained at all. Default: True + Whether and/or on which condition to retrain the model before predicting. + This parameter supports 3 different types: ``bool``, (positive) ``int``, and ``Callable`` (returning a + ``bool``). + In the case of ``bool``: retrain the model at each step (`True`), or never retrain the model (`False`). + In the case of ``int``: the model is retrained every `retrain` iterations. + In the case of ``Callable``: the model is retrained whenever callable returns `True`. + The callable must have the following positional arguments: + + - `counter` (int): current `retrain` iteration + - `pred_time` (pd.Timestamp or int): timestamp of forecast time (end of the training series) + - `train_series` (TimeSeries): train series up to `pred_time` + - `past_covariates` (TimeSeries): past_covariates series up to `pred_time` + - `future_covariates` (TimeSeries): future_covariates series up to `min(pred_time + series.freq * + forecast_horizon, series.end_time())` + + Note: if any optional `*_covariates` are not passed to `historical_forecast`, ``None`` will be passed + to the corresponding retrain function argument. + Note: some models require being retrained every time and do not support anything other than + `retrain=True`. + Note: also controls the retraining of the `data_transformers`. + overlap_end + Whether the returned forecasts can go beyond the series' end or not. + last_points_only + Whether to return only the last point of each historical forecast. If set to ``True``, the method returns a + single ``TimeSeries`` (for each time series in `series`) containing the successive point forecasts. + Otherwise, returns a list of historical ``TimeSeries`` forecasts. + metric + Either one of Darts' "per time step" metrics (see `here + `_), or a custom metric that has an + identical signature as Darts' "per time step" metrics, uses decorators + :func:`~darts.metrics.metrics.multi_ts_support` and :func:`~darts.metrics.metrics.multi_ts_support`, + and returns one value per time step. verbose - Whether to print progress. + Whether to print the progress. + show_warnings + Whether to show warnings related to historical forecasts optimization, or parameters `start` and + `train_length`. + predict_likelihood_parameters + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only + supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. + Default: ``False``. + enable_optimization + Whether to use the optimized version of `historical_forecasts` when supported and available. + Default: ``True``. + data_transformers + Optionally, a dictionary of `BaseDataTransformer` or `Pipeline` to apply to the corresponding series + (possibles keys; "series", "past_covariates", "future_covariates"). If provided, all input series must be + in the un-transformed space. For fittable transformer / pipeline: + + - if `retrain=True`, the data transformer re-fit on the training data at each historical forecast step. + - if `retrain=False`, the data transformer transforms the series once before all the forecasts. + + The fitted transformer is used to transform the input during both training and prediction. + If the transformation is invertible, the forecasts will be inverse-transformed. + Only effective when `historical_forecasts=None`. + metric_kwargs + Additional arguments passed to `metric()`, such as `'n_jobs'` for parallelization, `'m'` for scaled + metrics, etc. Will pass arguments only if they are present in the corresponding metric signature. Ignores + reduction arguments `"series_reduction", "component_reduction", "time_reduction"`, and parameter + `'insample'` for scaled metrics (e.g. mase`, `rmsse`, ...), as they are handled internally. + fit_kwargs + Optionally, some additional arguments passed to the model `fit()` method. + predict_kwargs + Optionally, some additional arguments passed to the model `predict()` method. + sample_weight + Optionally, some sample weights to apply to the target `series` labels for training. Only effective when + `retrain` is not ``False``. They are applied per observation, per label (each step in + `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed per time `series`. + values_only + Whether to return the residuals as `np.ndarray`. If `False`, returns residuals as `TimeSeries`. Returns ------- - TimeSeries (or Sequence[TimeSeries]) - The vector of residuals. - """ + TimeSeries + Residual `TimeSeries` for a single `series` and `historical_forecasts` generated with + `last_points_only=True`. + List[TimeSeries] + A list of residual `TimeSeries` for a sequence (list) of `series` with `last_points_only=True`. + The residual list has length `len(series)`. + List[List[TimeSeries]] + A list of lists of residual `TimeSeries` for a sequence of `series` with `last_points_only=False`. + The outer residual list has length `len(series)`. The inner lists consist of the residuals from + all possible series-specific historical forecasts. + """ + # `residuals()` should return metrics per series, component and time step (no reduction) + metric_kwargs = copy.deepcopy(metric_kwargs) or {} + metric_kwargs["series_reduction"] = None + metric_kwargs["component_reduction"] = None + metric_kwargs["time_reduction"] = None + + historical_forecasts = historical_forecasts or self.historical_forecasts( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + num_samples=num_samples, + train_length=train_length, + start=start, + start_format=start_format, + forecast_horizon=forecast_horizon, + stride=stride, + retrain=retrain, + last_points_only=last_points_only, + verbose=verbose, + show_warnings=show_warnings, + predict_likelihood_parameters=predict_likelihood_parameters, + enable_optimization=enable_optimization, + data_transformers=data_transformers, + fit_kwargs=fit_kwargs, + predict_kwargs=predict_kwargs, + overlap_end=overlap_end, + sample_weight=sample_weight, + ) - series = series2seq(series) - past_covariates = series2seq(past_covariates) - future_covariates = series2seq(future_covariates) + # remember input series type + series_seq_type = get_series_seq_type(series) + # validate historical forecasts and convert to multiple series with multiple forecasts case + series, historical_forecasts = _process_historical_forecast_for_backtest( + series=series, + historical_forecasts=historical_forecasts, + last_points_only=last_points_only, + ) - raise_if_not( - all([serie.is_univariate for serie in series]), - "Each series in the sequence must be univariate.", - logger, + # optionally, add nans to end of series to get residuals of same shape for each forecast + if overlap_end: + series = _extend_series_for_overlap_end( + series=series, historical_forecasts=historical_forecasts + ) + + residuals = self.backtest( + series=series, + historical_forecasts=historical_forecasts, + last_points_only=False, + metric=metric, + reduction=None, + data_transformers=data_transformers, + metric_kwargs=metric_kwargs, ) - residuals_list = [] - # compute residuals - for idx, target_ts in enumerate(series): - # get first index not contained in the first training set - first_index = target_ts.time_index[self.min_train_series_length] - - # compute fitted values - forecasts = self.historical_forecasts( - series=target_ts, - past_covariates=past_covariates[idx] if past_covariates else None, - future_covariates=future_covariates[idx] if future_covariates else None, - start=first_index, - forecast_horizon=forecast_horizon, - stride=1, - retrain=retrain, - last_points_only=True, - verbose=verbose, + # sanity check residual output + q, q_interval = metric_kwargs.get("q"), metric_kwargs.get("q_interval") + try: + series_, res, fc = series[0], residuals[0][0], historical_forecasts[0][0] + _ = np.reshape(res, (len(fc), -1, 1)) + except Exception as err: + raise_log( + ValueError( + f"`metric` function did not yield expected output. Make sure " + f"to use one of Darts 'per time step' metrics, or a similar " + f"custom metric. The following exception was raised: " + f"{type(err).__name__}('{err}')" + ), + logger=logger, ) - series_trimmed = target_ts.slice_intersect(forecasts) - residuals_list.append( - series_trimmed - - ( - forecasts.quantile_timeseries(quantile=0.5) - if forecasts.is_stochastic - else forecasts + + # process residuals + residuals_out = [] + for series_, fc_list, res_list in zip(series, historical_forecasts, residuals): + res_list_out = [] + if q is not None: + q = [q] if isinstance(q, float) else q + # multi-quantile metrics yield more components + comp_names = likelihood_component_names( + components=series_.components, + parameter_names=quantile_names(q), ) - ) + # `q` and `q_interval` are mutually exclusive + elif q_interval is not None: + # multi-quantile metrics yield more components + q_interval = ( + [q_interval] if isinstance(q_interval, tuple) else q_interval + ) + comp_names = likelihood_component_names( + components=series_.components, + parameter_names=quantile_interval_names(q_interval), + ) + else: + comp_names = None + for fc, res in zip(fc_list, res_list): + # make sure all residuals have shape (n time steps, n components * n quantiles, n samples=1) + if len(res.shape) != 3: + res = np.reshape(res, (len(fc), -1, 1)) + if values_only: + res = res + elif (q is None and q_interval is None) and res.shape[ + 1 + ] == fc.n_components: + res = fc.with_values(res) + else: + # quantile (interval) metrics created different number of components; + # create new series with unknown components + res = TimeSeries.from_times_and_values( + times=fc._time_index, + values=res, + columns=comp_names, + ) + res_list_out.append(res) + + residuals_out.append(res_list_out) - return residuals_list if len(residuals_list) > 1 else residuals_list[0] + # if required, reduce to `series` input type + if series_seq_type == SeriesType.SINGLE: + return residuals_out[0][0] if last_points_only else residuals_out[0] - def initialize_encoders(self) -> SequentialEncoder: + return ( + [res for res_list in residuals_out for res in res_list] + if last_points_only + else residuals_out + ) + + def initialize_encoders(self, default=False) -> SequentialEncoder: """instantiates the SequentialEncoder object based on self._model_encoder_settings and parameter ``add_encoders`` used at model creation""" + if default: + return SequentialEncoder(add_encoders={}) + ( input_chunk_length, output_chunk_length, @@ -1701,7 +2336,7 @@ def generate_fit_encodings( series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - ) -> Tuple[ + ) -> tuple[ Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]] ]: """Generates the covariate encodings that were used/generated for fitting the model and returns a tuple of @@ -1742,7 +2377,7 @@ def generate_predict_encodings( series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - ) -> Tuple[ + ) -> tuple[ Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]] ]: """Generates covariate encodings for the inference/prediction set and returns a tuple of past, and future @@ -1788,7 +2423,7 @@ def generate_fit_predict_encodings( series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - ) -> Tuple[ + ) -> tuple[ Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]] ]: """Generates covariate encodings for training and inference/prediction and returns a tuple of past, and future @@ -1828,24 +2463,116 @@ def generate_fit_predict_encodings( future_covariates=future_covariates, ) + def _process_validation_set( + self, + series: Sequence[TimeSeries], + past_covariates: Optional[Sequence[TimeSeries]], + future_covariates: Optional[Sequence[TimeSeries]], + val_series: Optional[Sequence[TimeSeries]], + val_past_covariates: Optional[Sequence[TimeSeries]], + val_future_covariates: Optional[Sequence[TimeSeries]], + ) -> tuple[ + Optional[Sequence[TimeSeries]], + Optional[Sequence[TimeSeries]], + Optional[Sequence[TimeSeries]], + ]: + """Validates the validation set and generates/adds the required encodings.""" + if val_series is None: + return None, None, None + + # generate encodings for the val set covariates + if self.encoders.encoding_available: + ( + val_past_covariates, + val_future_covariates, + ) = self.generate_fit_encodings( + series=val_series, + past_covariates=val_past_covariates, + future_covariates=val_future_covariates, + ) + + for idx in range(len(val_series)): + val_s = val_series[idx] + val_pc = ( + val_past_covariates[idx] if val_past_covariates is not None else None + ) + val_fc = ( + val_future_covariates[idx] + if val_future_covariates is not None + else None + ) + + # check that val set has same number of features as train set + match_series = series[0].width == val_s.width + match_past_covariates = ( + past_covariates[0].width if past_covariates is not None else None + ) == (val_pc.width if val_pc is not None else None) + match_future_covariates = ( + future_covariates[0].width if future_covariates is not None else None + ) == (val_fc.width if val_fc is not None else None) + + if self.uses_static_covariates: + self._verify_static_covariates(val_s.static_covariates) + match_static_covariates = ( + series[0].static_covariates.shape + if series[0].static_covariates is not None + else None + ) == ( + val_s.static_covariates.shape + if val_s.static_covariates is not None + else None + ) + else: + match_static_covariates = True + + matches = [ + match_series, + match_past_covariates, + match_future_covariates, + match_static_covariates, + ] + if not all(matches): + invalid_series = [ + name + for match, name in zip( + matches, + [ + "`series`", + "`past_covariates`", + "`future_covariates`", + "`static_covariates`", + ], + ) + if not match + ] + raise_log( + ValueError( + f"The dimensions of the ({', '.join(invalid_series)}) between the training and " + f"validation set " + f"{'' if len(val_series) == 1 else 'at sequence/list index `' + str(idx) + '` '}" + f"do not match." + ), + logger=logger, + ) + return val_series, val_past_covariates, val_future_covariates + @property @abstractmethod def _model_encoder_settings( self, - ) -> Tuple[ + ) -> tuple[ Optional[int], Optional[int], bool, bool, - Optional[List[int]], - Optional[List[int]], + Optional[list[int]], + Optional[list[int]], ]: """Abstract property that returns model specific encoder settings that are used to initialize the encoders. Must return Tuple (input_chunk_length, output_chunk_length, takes_past_covariates, takes_future_covariates, lags_past_covariates, lags_future_covariates). """ - pass @classmethod def _sample_params(model_class, params, n_random_samples): @@ -1874,7 +2601,7 @@ def _extract_model_creation_params(self): return model_params def untrained_model(self): - """Returns a new (untrained) model instance create with the same parameters.""" + """Returns a new (untrained) model instance created with the same parameters.""" return self.__class__(**copy.deepcopy(self.model_params)) @property @@ -1887,8 +2614,15 @@ def model_params(self) -> dict: def _default_save_path(cls) -> str: return f"{cls.__name__}_{datetime.datetime.now().strftime('%Y-%m-%d_%H_%M_%S')}" + def _clean(self) -> Self: + """Returns a cleaned instance of the model. Has no effect for local forecasting models.""" + return self + def save( - self, path: Optional[Union[str, os.PathLike, BinaryIO]] = None, **pkl_kwargs + self, + path: Optional[Union[str, os.PathLike, BinaryIO]] = None, + clean: bool = False, + **pkl_kwargs, ) -> None: """ Saves the model under a given path or file handle. @@ -1912,6 +2646,12 @@ def save( Path or file handle under which to save the model at its current state. If no path is specified, the model is automatically saved under ``"{ModelClass}_{YYYY-mm-dd_HH_MM_SS}.pkl"``. E.g., ``"RegressionModel_2020-01-01_12_00_00.pkl"``. + clean + Whether to store a cleaned version of the model. Only effective for global forecasting models. + If `True`, the training series and covariates are removed. + + Note: After loading a global forecasting model stored with `clean=True`, a `series` must be passed + 'predict()', `historical_forecasts()` and other forecasting methods. pkl_kwargs Keyword arguments passed to `pickle.dump()` """ @@ -1920,13 +2660,14 @@ def save( # default path path = self._default_save_path() + ".pkl" + model_to_save = self._clean() if clean else self if isinstance(path, (str, os.PathLike)): # save the whole object using pickle with open(path, "wb") as handle: - pickle.dump(obj=self, file=handle, **pkl_kwargs) + pickle.dump(obj=model_to_save, file=handle, **pkl_kwargs) elif isinstance(path, io.BufferedWriter): # save the whole object using pickle - pickle.dump(obj=self, file=path, **pkl_kwargs) + pickle.dump(obj=model_to_save, file=path, **pkl_kwargs) else: raise_log( ValueError( @@ -1939,7 +2680,7 @@ def save( @staticmethod def load(path: Union[str, os.PathLike, BinaryIO]) -> "ForecastingModel": """ - Loads the model from a given path or file handle. + Loads a model from a given path or file handle. Parameters ---------- @@ -2039,7 +2780,7 @@ def _verify_static_covariates(self, static_covariates: Optional[pd.DataFrame]): """ Verify that all static covariates are numeric. """ - if static_covariates is not None and self.uses_static_covariates: + if static_covariates is not None: numeric_mask = static_covariates.columns.isin( static_covariates.select_dtypes(include=np.number) ) @@ -2056,9 +2797,9 @@ def _verify_static_covariates(self, static_covariates: Optional[pd.DataFrame]): def _optimized_historical_forecasts( self, - series: Optional[Sequence[TimeSeries]], - past_covariates: Optional[Sequence[TimeSeries]] = None, - future_covariates: Optional[Sequence[TimeSeries]] = None, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, num_samples: int = 1, start: Optional[Union[pd.Timestamp, float, int]] = None, start_format: Literal["position", "value"] = "value", @@ -2069,9 +2810,9 @@ def _optimized_historical_forecasts( verbose: bool = False, show_warnings: bool = True, predict_likelihood_parameters: bool = False, - ) -> Union[ - TimeSeries, List[TimeSeries], Sequence[TimeSeries], Sequence[List[TimeSeries]] - ]: + data_transformers: Optional[dict[str, BaseDataTransformer]] = None, + **kwargs, + ) -> Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]]: logger.warning( "`optimized historical forecasts is not available for this model, use `historical_forecasts` instead." ) @@ -2095,13 +2836,13 @@ def __init__(self, add_encoders: Optional[dict] = None): @property def _model_encoder_settings( self, - ) -> Tuple[ + ) -> tuple[ Optional[int], Optional[int], bool, bool, - Optional[List[int]], - Optional[List[int]], + Optional[list[int]], + Optional[list[int]], ]: return None, None, False, False, None, None @@ -2113,19 +2854,21 @@ def fit(self, series: TimeSeries) -> "LocalForecastingModel": @property def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, + Optional[int], ]: # TODO: LocalForecastingModels do not yet handle extreme lags properly. Especially # TransferableFutureCovariatesLocalForecastingModel, where there is a difference between fit and predict mode) # do not yet. In general, Local models train on the entire series (input=output), different to Global models # that use an input to predict an output. - return -self.min_train_series_length, -1, None, None, None, None + return -self.min_train_series_length, -1, None, None, None, None, 0, None @property def supports_transferrable_series_prediction(self) -> bool: @@ -2273,12 +3016,11 @@ def predict( One future-known covariate time series for every input time series in `series`. They must match the past covariates that have been used with the :func:`fit()` function for training in terms of dimension. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose - Optionally, whether to print progress. + Whether to print the progress. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. Default: ``False`` show_warnings @@ -2337,6 +3079,17 @@ def predict( "To hide this warning, set `show_warnings=False`." ) + def _clean(self) -> Self: + """Returns a cleaned instance of the model by removing the training series and covariates.""" + + # a shallow copy is enough since we are only interested in removing pointers to the training data + cleaned_model = copy.copy(self) + cleaned_model.training_series = None + cleaned_model.past_covariate_series = None + cleaned_model.future_covariate_series = None + cleaned_model.static_covariates = None + return cleaned_model + @property def _supports_non_retrainable_historical_forecasts(self) -> bool: """GlobalForecastingModel supports historical forecasts without retraining the model""" @@ -2356,6 +3109,13 @@ def supports_transferrable_series_prediction(self) -> bool: """ return True + @property + def supports_sample_weight(self) -> bool: + """ + Whether model supports sample weight for training. + """ + return True + def _sanity_check_predict_likelihood_parameters( self, n: int, output_chunk_length: Union[int, None], num_samples: int ): @@ -2455,7 +3215,6 @@ def _fit(self, series: TimeSeries, future_covariates: Optional[TimeSeries] = Non """Fits/trains the model on the provided series. DualCovariatesModels must implement the fit logic in this method. """ - pass def predict( self, @@ -2479,8 +3238,7 @@ def predict( the covariate time series that has been used with the :func:`fit()` method for training, and it must contain at least the next `n` time steps/indices after the end of the training target series. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose Optionally, set the prediction verbosity. Not effective for all models. show_warnings @@ -2549,18 +3307,17 @@ def _predict( """Forecasts values for a certain number of time steps after the end of the series. DualCovariatesModels must implement the predict logic in this method. """ - pass @property def _model_encoder_settings( self, - ) -> Tuple[ + ) -> tuple[ Optional[int], Optional[int], bool, bool, - Optional[List[int]], - Optional[List[int]], + Optional[list[int]], + Optional[list[int]], ]: return None, None, False, True, None, None @@ -2583,25 +3340,27 @@ def _verify_passed_predict_covariates(self, future_covariates): @property def _supress_generate_predict_encoding(self) -> bool: - """Controls wether encodings should be generated in :func:`FutureCovariatesLocalForecastingModel.predict()``""" + """Controls whether encodings should be generated in :func:`FutureCovariatesLocalForecastingModel.predict()``""" return False @property def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ + Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, Optional[int], ]: # TODO: LocalForecastingModels do not yet handle extreme lags properly. Especially # TransferableFutureCovariatesLocalForecastingModel, where there is a difference between fit and predict mode) # do not yet. In general, Local models train on the entire series (input=output), different to Global models # that use an input to predict an output. - return -self.min_train_series_length, -1, None, None, 0, 0 + return -self.min_train_series_length, -1, None, None, 0, 0, 0, None class TransferableFutureCovariatesLocalForecastingModel( @@ -2653,8 +3412,7 @@ def predict( training target series. If `series` is set, it must contain at least the time steps/indices corresponding to the new target series (historic future covariates), plus the next `n` time steps/indices after the end. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. verbose Optionally, set the prediction verbosity. Not effective for all models. show_warnings @@ -2722,7 +3480,7 @@ def generate_predict_encodings( series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - ) -> Tuple[ + ) -> tuple[ Union[TimeSeries, Sequence[TimeSeries]], Union[TimeSeries, Sequence[TimeSeries]] ]: raise_if( @@ -2751,7 +3509,6 @@ def _predict( """Forecasts values for a certain number of time steps after the end of the series. TransferableFutureCovariatesLocalForecastingModel must implement the predict logic in this method. """ - pass @property def supports_transferrable_series_prediction(self) -> bool: diff --git a/darts/models/forecasting/global_baseline_models.py b/darts/models/forecasting/global_baseline_models.py new file mode 100644 index 0000000000..509ddfe140 --- /dev/null +++ b/darts/models/forecasting/global_baseline_models.py @@ -0,0 +1,667 @@ +""" +Global Baseline Models (Naive) +------------------------------ + +A collection of simple benchmark models working with univariate, multivariate, single, and multiple series. + +- :class:`GlobalNaiveAggregate` +- :class:`GlobalNaiveDrift` +- :class:`GlobalNaiveSeasonal` +""" + +from abc import ABC, abstractmethod +from collections.abc import Sequence +from typing import Callable, Optional, Union + +import torch + +from darts import TimeSeries +from darts.logging import get_logger, raise_log +from darts.models.forecasting.pl_forecasting_module import ( + PLMixedCovariatesModule, + io_processor, +) +from darts.models.forecasting.torch_forecasting_model import ( + MixedCovariatesTorchModel, + TorchForecastingModel, +) +from darts.utils.data.sequential_dataset import MixedCovariatesSequentialDataset +from darts.utils.data.training_dataset import MixedCovariatesTrainingDataset + +MixedCovariatesTrainTensorType = tuple[ + torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor +] + + +logger = get_logger(__name__) + + +def _extract_targets(batch: tuple[torch.Tensor], n_targets: int): + """Extracts and returns the target components from an input batch + + Parameters + ---------- + batch + The input batch tuple for the forward method. Has elements `(x_past, x_future, x_static)`. + n_targets + The number of target components to extract. + """ + return batch[0][:, :, :n_targets] + + +def _repeat_along_output_chunk(x: torch.Tensor, ocl: int) -> torch.Tensor: + """Expands a tensor `x` of shape (batch size, n components) to a tensor of shape + (batch size, `ocl`, n target components, 1 (n samples)), by repeating the values + along the `output_chunk_length` axis. + + Parameters + ---------- + x + An input tensor of shape (batch size, n target components) + ocl + The output_chunk_length. + """ + return x.view(-1, 1, x[0].shape[-1], 1).expand(-1, ocl, -1, -1) + + +class _GlobalNaiveModule(PLMixedCovariatesModule, ABC): + def __init__(self, *args, **kwargs): + """Pytorch module for implementing naive models. + + Implement your own naive module by subclassing from `_GlobalNaiveModule`, and implement the + logic for prediction in the private `_forward` method. + """ + super().__init__(*args, **kwargs) + + @io_processor + def forward( + self, x_in: tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]] + ) -> torch.Tensor: + """Naive model forward pass. + + Parameters + ---------- + x_in + comes as tuple `(x_past, x_future, x_static)` where `x_past` is the input/past chunk and `x_future` + is the output/future chunk. Input dimensions are `(batch_size, time_steps, components)` + + Returns + ------- + torch.Tensor + The output Tensor of shape `(batch_size, output_chunk_length, output_dim, nr_params)` + """ + return self._forward(x_in) + + @abstractmethod + def _forward(self, x_in) -> torch.Tensor: + """Private method to implement the forward method in the subclasses.""" + pass + + +class _GlobalNaiveModel(MixedCovariatesTorchModel, ABC): + def __init__( + self, + input_chunk_length: int, + output_chunk_length: int, + output_chunk_shift: int = 0, + use_static_covariates: bool = True, + **kwargs, + ): + """Base class for global naive models. The naive models inherit from `MixedCovariatesTorchModel` giving access + to past, future, and static covariates in the model `forward()` method. This allows to create custom models + naive models which can make use of the covariates. The built-in naive models will not use this information. + + The naive models do not have to be trained before generating predictions. + + To add a new naive model: + - subclass from `_GlobalNaiveModel` with implementation of private method `_create_model` that creates an + object of: + - subclass from `_GlobalNaiveModule` with implementation of private method `_forward` + + .. note:: + - Model checkpointing with `save_checkpoints=True`, and checkpoint loading with `load_from_checkpoint()` + and `load_weights_from_checkpoint()` are not supported for global naive models. + + Parameters + ---------- + input_chunk_length + The length of the input sequence fed to the model. + output_chunk_length + The length of the emitted forecast and output sequence fed to the model. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). + use_static_covariates + Whether the model should use static covariate information in case the input `series` passed to ``fit()`` + contain static covariates. If ``True``, and static covariates are available at fitting time, will enforce + that all target `series` have the same static covariate dimensionality in ``fit()`` and ``predict()``. + **kwargs + Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and + Darts' :class:`TorchForecastingModel`. + Since naive models are not trained, the following parameters will have no effect: + `loss_fn`, `likelihood`, `optimizer_cls`, `optimizer_kwargs`, `lr_scheduler_cls`, `lr_scheduler_kwargs`, + `n_epochs`, `save_checkpoints`, and some of `pl_trainer_kwargs`. + """ + super().__init__(**self._extract_torch_model_params(**self.model_params)) + + # extract pytorch lightning module kwargs + self.pl_module_params = self._extract_pl_module_params(**self.model_params) + + self._considers_static_covariates = use_static_covariates + + def fit( + self, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + *args, + **kwargs, + ) -> TorchForecastingModel: + """Fit/train the model on a (or potentially multiple) series. + This method is only implemented for naive baseline models to provide a unified fit/predict API with other + forecasting models. + + The model is not really trained on the input, but `fit()` is used to setup the model based on the input series. + Also, it stores the training `series` in case only a single `TimeSeries` was passed. This allows to call + `predict()` without having to pass the single `series`. + + Parameters + ---------- + series + A series or sequence of series serving as target (i.e. what the model will be trained to forecast) + past_covariates + Optionally, a series or sequence of series specifying past-observed covariates + future_covariates + Optionally, a series or sequence of series specifying future-known covariates + **kwargs + Optionally, some keyword arguments. + + Returns + ------- + self + Fitted model. + """ + return super().fit(series, past_covariates, future_covariates, *args, **kwargs) + + @staticmethod + def load_from_checkpoint( + model_name: str, + work_dir: str = None, + file_name: str = None, + best: bool = True, + **kwargs, + ) -> "TorchForecastingModel": + raise_log( + NotImplementedError( + "GlobalNaiveModels do not support loading from checkpoint since they are never trained." + ), + logger=logger, + ) + + def load_weights_from_checkpoint( + self, + model_name: str = None, + work_dir: str = None, + file_name: str = None, + best: bool = True, + strict: bool = True, + load_encoders: bool = True, + skip_checks: bool = False, + **kwargs, + ): + raise_log( + NotImplementedError( + "GlobalNaiveModels do not support weights loading since they do not have any weights/parameters." + ), + logger=logger, + ) + + @abstractmethod + def _create_model( + self, train_sample: MixedCovariatesTrainTensorType + ) -> _GlobalNaiveModule: + pass + + def _verify_predict_sample(self, predict_sample: tuple): + # naive models do not have to be trained, predict sample does not + # have to match the training sample + pass + + @property + def supports_likelihood_parameter_prediction(self) -> bool: + return False + + @property + def supports_probabilistic_prediction(self) -> bool: + return False + + @property + def supports_static_covariates(self) -> bool: + return True + + @property + def supports_multivariate(self) -> bool: + return True + + @property + def _requires_training(self) -> bool: + # naive models do not have to be trained. + return False + + def _build_train_dataset( + self, + target: Sequence[TimeSeries], + past_covariates: Optional[Sequence[TimeSeries]], + future_covariates: Optional[Sequence[TimeSeries]], + sample_weight: Optional[Sequence[TimeSeries]], + max_samples_per_ts: Optional[int], + ) -> MixedCovariatesTrainingDataset: + return MixedCovariatesSequentialDataset( + target_series=target, + past_covariates=past_covariates, + future_covariates=future_covariates, + input_chunk_length=self.input_chunk_length, + output_chunk_length=0, + output_chunk_shift=self.output_chunk_shift, + max_samples_per_ts=max_samples_per_ts, + use_static_covariates=self.uses_static_covariates, + sample_weight=sample_weight, + ) + + +class _NoCovariatesMixin: + @property + def supports_static_covariates(self) -> bool: + return False + + @property + def supports_future_covariates(self) -> bool: + return False + + @property + def supports_past_covariates(self) -> bool: + return False + + +class _GlobalNaiveAggregateModule(_GlobalNaiveModule): + def __init__( + self, agg_fn: Callable[[torch.Tensor, int], torch.Tensor], *args, **kwargs + ): + super().__init__(*args, **kwargs) + self.agg_fn = agg_fn + + def _forward(self, x_in) -> torch.Tensor: + y_target = _extract_targets(x_in, self.n_targets) + aggregate = self.agg_fn(y_target, dim=1) + return _repeat_along_output_chunk(aggregate, self.output_chunk_length) + + +class GlobalNaiveAggregate(_NoCovariatesMixin, _GlobalNaiveModel): + def __init__( + self, + input_chunk_length: int, + output_chunk_length: int, + output_chunk_shift: int = 0, + agg_fn: Union[str, Callable[[torch.Tensor, int], torch.Tensor]] = "mean", + **kwargs, + ): + """Global Naive Aggregate Model. + + The model generates forecasts for each `series` as described below: + + - take an aggregate (computed with `agg_fn`, default: mean) from each target component over the last + `input_chunk_length` points + - the forecast is the component aggregate repeated `output_chunk_length` times + + Depending on the horizon `n` used when calling `model.predict()`, the forecasts are either: + + - a constant aggregate value (default: mean) if `n <= output_chunk_length`, or + - a moving aggregate if `n > output_chunk_length`, as a result of the autoregressive prediction. + + This model is equivalent to: + + - :class:`~darts.models.forecasting.baselines.NaiveMean`, when `input_chunk_length` is equal to the length of + the input target `series`, and `agg_fn='mean'`. + - :class:`~darts.models.forecasting.baselines.NaiveMovingAverage`, with identical `input_chunk_length` + and `output_chunk_length=1`, and `agg_fn='mean'`. + + .. note:: + - Model checkpointing with `save_checkpoints=True`, and checkpoint loading with `load_from_checkpoint()` + and `load_weights_from_checkpoint()` are not supported for global naive models. + + Parameters + ---------- + input_chunk_length + The length of the input sequence fed to the model. + output_chunk_length + The length of the emitted forecast and output sequence fed to the model. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). + agg_fn + The aggregation function to use. If a string, must be the name of `torch` function that can be imported + directly from `torch` (e.g. `"mean"` for `torch.mean`, `"sum"` for `torch.sum`). + The function must have the signature below. If a `Callable`, it must also have the signature below. + + .. highlight:: python + .. code-block:: python + + def agg_fn(x: torch.Tensor, dim: int, *args, **kwargs) -> torch.Tensor: + # x has shape `(batch size, input_chunk_length, n targets)`, `dim` is always `1`. + # function must return a tensor of shape `(batch size, n targets)` + return torch.mean(x, dim=dim) + .. + **kwargs + Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and + Darts' :class:`TorchForecastingModel`. + Since naive models are not trained, the following parameters will have no effect: + `loss_fn`, `likelihood`, `optimizer_cls`, `optimizer_kwargs`, `lr_scheduler_cls`, `lr_scheduler_kwargs`, + `n_epochs`, `save_checkpoints`, and some of `pl_trainer_kwargs`. + + Examples + -------- + >>> from darts.datasets import IceCreamHeaterDataset + >>> from darts.models import GlobalNaiveAggregate + >>> # create list of multivariate series + >>> series_1 = IceCreamHeaterDataset().load() + >>> series_2 = series_1 + 100. + >>> series = [series_1, series_2] + >>> # predict 3 months, take mean over last 60 months + >>> horizon, icl = 3, 60 + >>> # naive mean over last 60 months (with `output_chunk_length = horizon`) + >>> model = GlobalNaiveAggregate(input_chunk_length=icl, output_chunk_length=horizon) + >>> # predict after end of each multivariate series + >>> pred = model.fit(series).predict(n=horizon, series=series) + >>> [p.values() for p in pred] + [array([[29.666668, 50.983337], + [29.666668, 50.983337], + [29.666668, 50.983337]]), array([[129.66667, 150.98334], + [129.66667, 150.98334], + [129.66667, 150.98334]])] + >>> # naive moving mean (with `output_chunk_length < horizon`) + >>> model = GlobalNaiveAggregate(input_chunk_length=icl, output_chunk_length=1, agg_fn="mean") + >>> pred = model.fit(series).predict(n=horizon, series=series) + >>> [p.values() for p in pred] + [array([[29.666668, 50.983337], + [29.894447, 50.88306 ], + [30.109352, 50.98111 ]]), array([[129.66667, 150.98334], + [129.89445, 150.88307], + [130.10936, 150.98111]])] + >>> # naive moving sum (with `output_chunk_length < horizon`) + >>> model = GlobalNaiveAggregate(input_chunk_length=icl, output_chunk_length=1, agg_fn="sum") + >>> pred = model.fit(series).predict(n=horizon, series=series) + >>> [p.values() for p in pred] + [array([[ 1780., 3059.], + [ 3544., 6061.], + [ 7071., 12077.]]), array([[ 7780., 9059.], + [15444., 17961.], + [30771., 35777.]])] + """ + super().__init__( + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, + use_static_covariates=False, + **kwargs, + ) + if isinstance(agg_fn, str): + agg_fn = getattr(torch, agg_fn, None) + if agg_fn is None: + raise_log( + ValueError( + "When `agg_fn` is a string, must be the name of a PyTorch function that " + "can be imported directly from `torch`. E.g., `'mean'` for `torch.mean`" + ), + logger=logger, + ) + if not isinstance(agg_fn, Callable): + raise_log( + ValueError("`agg_fn` must be a string or callable."), + logger=logger, + ) + + # check that `agg_fn` returns the expected output + batch_size, n_targets = 5, 3 + x = torch.ones((batch_size, 4, n_targets)) + try: + agg = agg_fn(x, dim=1) + assert isinstance(agg, torch.Tensor), ( + "`agg_fn` output must be a torch Tensor." + ) + assert agg.shape == ( + batch_size, + n_targets, + ), "Unexpected `agg_fn` output shape." + except Exception as err: + raise_log( + ValueError( + f"`agg_fn` sanity check raised the following error: ({err}) Read the parameter " + f"description to properly define the aggregation function." + ), + logger=logger, + ) + self.agg_fn = agg_fn + + def _create_model( + self, train_sample: MixedCovariatesTrainTensorType + ) -> _GlobalNaiveModule: + return _GlobalNaiveAggregateModule(agg_fn=self.agg_fn, **self.pl_module_params) + + +class _GlobalNaiveSeasonalModule(_GlobalNaiveModule): + def _forward(self, x_in) -> torch.Tensor: + y_target = _extract_targets(x_in, self.n_targets) + season = y_target[:, 0, :] + return _repeat_along_output_chunk(season, self.output_chunk_length) + + +class GlobalNaiveSeasonal(_NoCovariatesMixin, _GlobalNaiveModel): + def __init__( + self, + input_chunk_length: int, + output_chunk_length: int, + output_chunk_shift: int = 0, + **kwargs, + ): + """Global Naive Seasonal Model. + + The model generates forecasts for each `series` as described below: + + - take the value from each target component at the `input_chunk_length`th point before the end of the + target `series`. + - the forecast is the component value repeated `output_chunk_length` times. + + Depending on the horizon `n` used when calling `model.predict()`, the forecasts are either: + + - a constant value if `n <= output_chunk_length`, or + - a moving (seasonal) value if `n > output_chunk_length`, as a result of the autoregressive prediction. + + This model is equivalent to: + + - :class:`~darts.models.forecasting.baselines.NaiveSeasonal`, when `input_chunk_length` is equal to the length + of the input target `series` and `output_chunk_length=1`. + + .. note:: + - Model checkpointing with `save_checkpoints=True`, and checkpoint loading with `load_from_checkpoint()` + and `load_weights_from_checkpoint()` are not supported for global naive models. + + Parameters + ---------- + input_chunk_length + The length of the input sequence fed to the model. + output_chunk_length + The length of the emitted forecast and output sequence fed to the model. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). + **kwargs + Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and + Darts' :class:`TorchForecastingModel`. + Since naive models are not trained, the following parameters will have no effect: + `loss_fn`, `likelihood`, `optimizer_cls`, `optimizer_kwargs`, `lr_scheduler_cls`, `lr_scheduler_kwargs`, + `n_epochs`, `save_checkpoints`, and some of `pl_trainer_kwargs`. + + Examples + -------- + >>> from darts.datasets import IceCreamHeaterDataset + >>> from darts.models import GlobalNaiveSeasonal + >>> # create list of multivariate series + >>> series_1 = IceCreamHeaterDataset().load() + >>> series_2 = series_1 + 100. + >>> series = [series_1, series_2] + >>> # predict 3 months, use value from 12 months ago + >>> horizon, icl = 3, 12 + >>> # repeated seasonal value (with `output_chunk_length = horizon`) + >>> model = GlobalNaiveSeasonal(input_chunk_length=icl, output_chunk_length=horizon) + >>> # predict after end of each multivariate series + >>> pred = model.fit(series).predict(n=horizon, series=series) + >>> [p.values() for p in pred] + [array([[ 21., 100.], + [ 21., 100.], + [ 21., 100.]]), array([[121., 200.], + [121., 200.], + [121., 200.]])] + >>> # moving seasonal value (with `output_chunk_length < horizon`) + >>> model = GlobalNaiveSeasonal(input_chunk_length=icl, output_chunk_length=1) + >>> pred = model.fit(series).predict(n=horizon, series=series) + >>> [p.values() for p in pred] + [array([[ 21., 100.], + [ 21., 68.], + [ 24., 51.]]), array([[121., 200.], + [121., 168.], + [124., 151.]])] + """ + super().__init__( + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, + use_static_covariates=False, + **kwargs, + ) + + def _create_model( + self, train_sample: MixedCovariatesTrainTensorType + ) -> _GlobalNaiveModule: + return _GlobalNaiveSeasonalModule(**self.pl_module_params) + + +class _GlobalNaiveDrift(_GlobalNaiveModule): + def _forward(self, x_in) -> torch.Tensor: + y_target = _extract_targets(x_in, self.n_targets) + slope = _repeat_along_output_chunk( + (y_target[:, -1, :] - y_target[:, 0, :]) / (self.input_chunk_length - 1), + self.output_chunk_length, + ) + + x = torch.arange( + start=self.output_chunk_shift + 1, + end=self.output_chunk_length + self.output_chunk_shift + 1, + device=self.device, + ).view(1, self.output_chunk_length, 1, 1) + + y_0 = y_target[:, -1, :].view(-1, 1, y_target.shape[-1], 1) + return slope * x + y_0 + + +class GlobalNaiveDrift(_NoCovariatesMixin, _GlobalNaiveModel): + def __init__( + self, + input_chunk_length: int, + output_chunk_length: int, + output_chunk_shift: int = 0, + **kwargs, + ): + """Global Naive Drift Model. + + The model generates forecasts for each `series` as described below: + + - take the slope `m` from each target component between the `input_chunk_length`th and last point before the + end of the `series`. + - the forecast is `m * x + c` per component where `x` are the values + `range(1 + output_chunk_shift, 1 + output_chunk_length + output_chunk_shift)`, and `c` are the last values + from each target component. + + Depending on the horizon `n` used when calling `model.predict()`, the forecasts are either: + + - a linear drift if `n <= output_chunk_length`, or + - a moving drift if `n > output_chunk_length`, as a result of the autoregressive prediction. + + This model is equivalent to: + + - :class:`~darts.models.forecasting.baselines.NaiveDrift`, when `input_chunk_length` is equal to the length + of the input target `series` and `output_chunk_length=n`. + + .. note:: + - Model checkpointing with `save_checkpoints=True`, and checkpoint loading with `load_from_checkpoint()` + and `load_weights_from_checkpoint()` are not supported for global naive models. + + Parameters + ---------- + input_chunk_length + The length of the input sequence fed to the model. + output_chunk_length + The length of the emitted forecast and output sequence fed to the model. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). + **kwargs + Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and + Darts' :class:`TorchForecastingModel`. + Since naive models are not trained, the following parameters will have no effect: + `loss_fn`, `likelihood`, `optimizer_cls`, `optimizer_kwargs`, `lr_scheduler_cls`, `lr_scheduler_kwargs`, + `n_epochs`, `save_checkpoints`, and some of `pl_trainer_kwargs`. + + Examples + -------- + >>> from darts.datasets import IceCreamHeaterDataset + >>> from darts.models import GlobalNaiveDrift + >>> # create list of multivariate series + >>> series_1 = IceCreamHeaterDataset().load() + >>> series_2 = series_1 + 100. + >>> series = [series_1, series_2] + >>> # predict 3 months, use drift over the last 60 months + >>> horizon, icl = 3, 60 + >>> # linear drift (with `output_chunk_length = horizon`) + >>> model = GlobalNaiveDrift(input_chunk_length=icl, output_chunk_length=horizon) + >>> # predict after end of each multivariate series + >>> pred = model.fit(series).predict(n=horizon, series=series) + >>> [p.values() for p in pred] + [array([[24.135593, 74.28814 ], + [24.271187, 74.57627 ], + [24.40678 , 74.86441 ]]), array([[124.13559, 174.28813], + [124.27119, 174.57628], + [124.40678, 174.86441]])] + >>> # moving drift (with `output_chunk_length < horizon`) + >>> model = GlobalNaiveDrift(input_chunk_length=icl, output_chunk_length=1) + >>> pred = model.fit(series).predict(n=horizon, series=series) + >>> [p.values() for p in pred] + [array([[24.135593, 74.28814 ], + [24.256536, 74.784546], + [24.34563 , 75.45886 ]]), array([[124.13559, 174.28813], + [124.25653, 174.78455], + [124.34563, 175.45886]])] + """ + super().__init__( + input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, + use_static_covariates=False, + **kwargs, + ) + + def _create_model( + self, train_sample: MixedCovariatesTrainTensorType + ) -> _GlobalNaiveModule: + return _GlobalNaiveDrift(**self.pl_module_params) diff --git a/darts/models/forecasting/kalman_forecaster.py b/darts/models/forecasting/kalman_forecaster.py index 34ef91a0a4..f6c8c665e9 100644 --- a/darts/models/forecasting/kalman_forecaster.py +++ b/darts/models/forecasting/kalman_forecaster.py @@ -111,7 +111,6 @@ def encode_year(idx): self.darts_kf = KalmanFilter(dim_x, kf) def _fit(self, series: TimeSeries, future_covariates: Optional[TimeSeries] = None): - super()._fit(series, future_covariates) if self.kf is None: self.darts_kf.fit(series=series, covariates=future_covariates) @@ -142,7 +141,6 @@ def _predict( num_samples: int = 1, verbose: bool = False, ) -> TimeSeries: - super()._predict( n, series, historic_future_covariates, future_covariates, num_samples ) @@ -171,5 +169,5 @@ def supports_multivariate(self) -> bool: return True @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return True diff --git a/darts/models/forecasting/lgbm.py b/darts/models/forecasting/lgbm.py index 4a3d748719..2d5e60a288 100644 --- a/darts/models/forecasting/lgbm.py +++ b/darts/models/forecasting/lgbm.py @@ -10,7 +10,8 @@ https://github.com/unit8co/darts/blob/master/INSTALL.md """ -from typing import List, Optional, Sequence, Union +from collections.abc import Sequence +from typing import Optional, Union import lightgbm as lgb import numpy as np @@ -34,15 +35,16 @@ def __init__( lags_past_covariates: Optional[LAGS_TYPE] = None, lags_future_covariates: Optional[FUTURE_LAGS_TYPE] = None, output_chunk_length: int = 1, + output_chunk_shift: int = 0, add_encoders: Optional[dict] = None, likelihood: Optional[str] = None, - quantiles: Optional[List[float]] = None, + quantiles: Optional[list[float]] = None, random_state: Optional[int] = None, multi_models: Optional[bool] = True, use_static_covariates: bool = True, - categorical_past_covariates: Optional[Union[str, List[str]]] = None, - categorical_future_covariates: Optional[Union[str, List[str]]] = None, - categorical_static_covariates: Optional[Union[str, List[str]]] = None, + categorical_past_covariates: Optional[Union[str, list[str]]] = None, + categorical_future_covariates: Optional[Union[str, list[str]]] = None, + categorical_static_covariates: Optional[Union[str, list[str]]] = None, **kwargs, ): """LGBM Model @@ -52,7 +54,8 @@ def __init__( lags Lagged target `series` values used to predict the next time step/s. If an integer, must be > 0. Uses the last `n=lags` past lags; e.g. `(-1, -2, ..., -lags)`, where `0` - corresponds the first predicted time step of each sample. + corresponds the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. If a list of integers, each value must be < 0. Uses only the specified values as lags. If a dictionary, the keys correspond to the `series` component names (of the first series when using multiple series) and the values correspond to the component lags (integer or list of integers). The @@ -61,17 +64,21 @@ def __init__( lags_past_covariates Lagged `past_covariates` values used to predict the next time step/s. If an integer, must be > 0. Uses the last `n=lags_past_covariates` past lags; e.g. `(-1, -2, ..., -lags)`, - where `0` corresponds to the first predicted time step of each sample. + where `0` corresponds to the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. If a list of integers, each value must be < 0. Uses only the specified values as lags. If a dictionary, the keys correspond to the `past_covariates` component names (of the first series when using multiple series) and the values correspond to the component lags (integer or list of integers). The key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some components are missing and the 'default_lags' key is not provided. lags_future_covariates - Lagged `future_covariates` values used to predict the next time step/s. + Lagged `future_covariates` values used to predict the next time step/s. The lags are always relative to the + first step in the output chunk, even when `output_chunk_shift > 0`. If a tuple of `(past, future)`, both values must be > 0. Uses the last `n=past` past lags and `n=future` - future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` - corresponds the first predicted time step of each sample. + future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` corresponds the first + predicted time step of each sample. If `output_chunk_shift > 0`, the position of negative lags differ from + those of `lags` and `lags_past_covariates`. In this case a future lag `-5` would point at the same + step as a target lag of `-5 + output_chunk_shift`. If a list of integers, uses only the specified values as lags. If a dictionary, the keys correspond to the `future_covariates` component names (of the first series when using multiple series) and the values correspond to the component lags (tuple or list of integers). The key @@ -80,10 +87,17 @@ def __init__( output_chunk_length Number of time steps predicted at once (per chunk) by the internal model. It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated using a - one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is + one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input (history of target and past covariates) and + output. If the model supports `future_covariates`, the `lags_future_covariates` are relative to the first + step in the shifted output chunk. Predictions will start `output_chunk_shift` steps after the end of the + target `series`. If `output_chunk_shift` is set, the model cannot generate autoregressive predictions + (`n > output_chunk_length`). add_encoders A large number of past and future covariates can be automatically generated with `add_encoders`. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that @@ -117,8 +131,9 @@ def encode_year(idx): Control the randomness in the fitting procedure and for sampling. Default: ``None``. multi_models - If True, a separate model will be trained for each future lag to predict. If False, a single model is - trained to predict at step 'output_chunk_length' in the future. Default: True. + If True, a separate model will be trained for each future lag to predict. If False, a single model + is trained to predict all the steps in 'output_chunk_length' (features lags are shifted back by + `output_chunk_length - n` for each step `n`). Default: True. use_static_covariates Whether the model should use static covariate information in case the input `series` passed to ``fit()`` contain static covariates. If ``True``, and static covariates are available at fitting time, will enforce @@ -175,7 +190,7 @@ def encode_year(idx): self._median_idx = None self._model_container = None self.quantiles = None - self.likelihood = likelihood + self._likelihood = likelihood self._rng = None # parse likelihood @@ -194,6 +209,7 @@ def encode_year(idx): lags_past_covariates=lags_past_covariates, lags_future_covariates=lags_future_covariates, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, add_encoders=add_encoders, multi_models=multi_models, model=lgb.LGBMRegressor(**self.kwargs), @@ -212,6 +228,11 @@ def fit( val_past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, val_future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, max_samples_per_ts: Optional[int] = None, + n_jobs_multioutput_wrapper: Optional[int] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + val_sample_weight: Optional[ + Union[TimeSeries, Sequence[TimeSeries], str] + ] = None, **kwargs, ): """ @@ -238,44 +259,59 @@ def fit( creation) to know their sizes, which might be expensive on big datasets. If some series turn out to have a length that would allow more than `max_samples_per_ts`, only the most recent `max_samples_per_ts` samples will be considered. + n_jobs_multioutput_wrapper + Number of jobs of the MultiOutputRegressor wrapper to run in parallel. Only used if the model doesn't + support multi-output regression natively. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. + val_sample_weight + Same as for `sample_weight` but for the evaluation dataset. **kwargs Additional kwargs passed to `lightgbm.LGBRegressor.fit()` """ - if val_series is not None: - kwargs["eval_set"] = self._create_lagged_data( - target_series=val_series, - past_covariates=val_past_covariates, - future_covariates=val_future_covariates, - max_samples_per_ts=max_samples_per_ts, - ) - if self.likelihood == "quantile": # empty model container in case of multiple calls to fit, e.g. when backtesting self._model_container.clear() for quantile in self.quantiles: self.kwargs["alpha"] = quantile self.model = lgb.LGBMRegressor(**self.kwargs) - super().fit( series=series, past_covariates=past_covariates, future_covariates=future_covariates, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, max_samples_per_ts=max_samples_per_ts, + n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper, + sample_weight=sample_weight, + val_sample_weight=val_sample_weight, **kwargs, ) - self._model_container[quantile] = self.model - return self super().fit( series=series, past_covariates=past_covariates, future_covariates=future_covariates, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, max_samples_per_ts=max_samples_per_ts, + n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper, + sample_weight=sample_weight, + val_sample_weight=val_sample_weight, **kwargs, ) - return self def _predict_and_sample( @@ -296,16 +332,26 @@ def _predict_and_sample( ) @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return self.likelihood is not None + @property + def supports_val_set(self) -> bool: + return True + + @property + def val_set_params(self) -> tuple[Optional[str], Optional[str]]: + return "eval_set", "eval_sample_weight" + @property def min_train_series_length(self) -> int: # LightGBM requires a minimum of 2 train samples, therefore the min_train_series_length should be one more than # for other regression models return max( 3, - -self.lags["target"][0] + self.output_chunk_length + 1 - if "target" in self.lags - else self.output_chunk_length, + ( + -self.lags["target"][0] + self.output_chunk_length + 1 + if "target" in self.lags + else self.output_chunk_length + ), ) diff --git a/darts/models/forecasting/linear_regression_model.py b/darts/models/forecasting/linear_regression_model.py index e599dd017b..54094fc15d 100644 --- a/darts/models/forecasting/linear_regression_model.py +++ b/darts/models/forecasting/linear_regression_model.py @@ -5,7 +5,9 @@ A forecasting model using a linear regression of some of the target series' lags, as well as optionally some covariate series lags in order to obtain a forecast. """ -from typing import List, Optional, Sequence, Union + +from collections.abc import Sequence +from typing import Optional, Union import numpy as np from scipy.optimize import linprog @@ -30,9 +32,10 @@ def __init__( lags_past_covariates: Optional[LAGS_TYPE] = None, lags_future_covariates: Optional[FUTURE_LAGS_TYPE] = None, output_chunk_length: int = 1, + output_chunk_shift: int = 0, add_encoders: Optional[dict] = None, likelihood: Optional[str] = None, - quantiles: Optional[List[float]] = None, + quantiles: Optional[list[float]] = None, random_state: Optional[int] = None, multi_models: Optional[bool] = True, use_static_covariates: bool = True, @@ -45,7 +48,8 @@ def __init__( lags Lagged target `series` values used to predict the next time step/s. If an integer, must be > 0. Uses the last `n=lags` past lags; e.g. `(-1, -2, ..., -lags)`, where `0` - corresponds the first predicted time step of each sample. + corresponds the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. If a list of integers, each value must be < 0. Uses only the specified values as lags. If a dictionary, the keys correspond to the `series` component names (of the first series when using multiple series) and the values correspond to the component lags (integer or list of integers). The @@ -54,17 +58,21 @@ def __init__( lags_past_covariates Lagged `past_covariates` values used to predict the next time step/s. If an integer, must be > 0. Uses the last `n=lags_past_covariates` past lags; e.g. `(-1, -2, ..., -lags)`, - where `0` corresponds to the first predicted time step of each sample. + where `0` corresponds to the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. If a list of integers, each value must be < 0. Uses only the specified values as lags. If a dictionary, the keys correspond to the `past_covariates` component names (of the first series when using multiple series) and the values correspond to the component lags (integer or list of integers). The key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some components are missing and the 'default_lags' key is not provided. lags_future_covariates - Lagged `future_covariates` values used to predict the next time step/s. + Lagged `future_covariates` values used to predict the next time step/s. The lags are always relative to the + first step in the output chunk, even when `output_chunk_shift > 0`. If a tuple of `(past, future)`, both values must be > 0. Uses the last `n=past` past lags and `n=future` - future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` - corresponds the first predicted time step of each sample. + future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` corresponds the first + predicted time step of each sample. If `output_chunk_shift > 0`, the position of negative lags differ from + those of `lags` and `lags_past_covariates`. In this case a future lag `-5` would point at the same + step as a target lag of `-5 + output_chunk_shift`. If a list of integers, uses only the specified values as lags. If a dictionary, the keys correspond to the `future_covariates` component names (of the first series when using multiple series) and the values correspond to the component lags (tuple or list of integers). The key @@ -73,10 +81,17 @@ def __init__( output_chunk_length Number of time steps predicted at once (per chunk) by the internal model. It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated using a - one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is + one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input (history of target and past covariates) and + output. If the model supports `future_covariates`, the `lags_future_covariates` are relative to the first + step in the shifted output chunk. Predictions will start `output_chunk_shift` steps after the end of the + target `series`. If `output_chunk_shift` is set, the model cannot generate autoregressive predictions + (`n > output_chunk_length`). add_encoders A large number of past and future covariates can be automatically generated with `add_encoders`. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that @@ -114,8 +129,9 @@ def encode_year(idx): no `likelihood` is set. Default: ``None``. multi_models - If True, a separate model will be trained for each future lag to predict. If False, a single model is - trained to predict at step 'output_chunk_length' in the future. Default: True. + If True, a separate model will be trained for each future lag to predict. If False, a single model + is trained to predict all the steps in 'output_chunk_length' (features lags are shifted back by + `output_chunk_length - n` for each step `n`). Default: True. use_static_covariates Whether the model should use static covariate information in case the input `series` passed to ``fit()`` contain static covariates. If ``True``, and static covariates are available at fitting time, will enforce @@ -160,7 +176,7 @@ def encode_year(idx): self._median_idx = None self._model_container = None self.quantiles = None - self.likelihood = likelihood + self._likelihood = likelihood self._rng = None # parse likelihood @@ -183,6 +199,7 @@ def encode_year(idx): lags_past_covariates=lags_past_covariates, lags_future_covariates=lags_future_covariates, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, add_encoders=add_encoders, model=model, multi_models=multi_models, @@ -196,33 +213,9 @@ def fit( future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, max_samples_per_ts: Optional[int] = None, n_jobs_multioutput_wrapper: Optional[int] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, **kwargs, ): - """ - Fit/train the model on one or multiple series. - - Parameters - ---------- - series - TimeSeries or Sequence[TimeSeries] object containing the target values. - past_covariates - Optionally, a series or sequence of series specifying past-observed covariates - future_covariates - Optionally, a series or sequence of series specifying future-known covariates - max_samples_per_ts - This is an integer upper bound on the number of tuples that can be produced - per time series. It can be used in order to have an upper bound on the total size of the dataset and - ensure proper sampling. If `None`, it will read all of the individual time series in advance (at dataset - creation) to know their sizes, which might be expensive on big datasets. - If some series turn out to have a length that would allow more than `max_samples_per_ts`, only the - most recent `max_samples_per_ts` samples will be considered. - n_jobs_multioutput_wrapper - Number of jobs of the MultiOutputRegressor wrapper to run in parallel. Only used if the model doesn't - support multi-output regression natively. - **kwargs - Additional keyword arguments passed to the `fit` method of the model. - """ - if self.likelihood == "quantile": # set solver for linear program if "solver" not in self.kwargs: @@ -252,12 +245,14 @@ def fit( past_covariates=past_covariates, future_covariates=future_covariates, max_samples_per_ts=max_samples_per_ts, + n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper, + sample_weight=sample_weight, **kwargs, ) self._model_container[quantile] = self.model - # replace the last trained QuantileRegressor with the dictionnary of Regressors. + # replace the last trained QuantileRegressor with the dictionary of Regressors. self.model = self._model_container return self @@ -268,6 +263,8 @@ def fit( past_covariates=past_covariates, future_covariates=future_covariates, max_samples_per_ts=max_samples_per_ts, + n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper, + sample_weight=sample_weight, **kwargs, ) @@ -290,5 +287,5 @@ def _predict_and_sample( ) @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return self.likelihood is not None diff --git a/darts/models/forecasting/nbeats.py b/darts/models/forecasting/nbeats.py index 7bcb9aa469..4f36a93000 100644 --- a/darts/models/forecasting/nbeats.py +++ b/darts/models/forecasting/nbeats.py @@ -4,7 +4,7 @@ """ from enum import Enum -from typing import List, NewType, Tuple, Union +from typing import NewType, Union import numpy as np import torch @@ -368,7 +368,7 @@ def __init__( num_stacks: int, num_blocks: int, num_layers: int, - layer_widths: List[int], + layer_widths: list[int], expansion_coefficient_dim: int, trend_polynomial_degree: int, batch_norm: bool, @@ -412,7 +412,8 @@ def __init__( activation The activation function of encoder/decoder intermediate layer. **kwargs - all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class. + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. Inputs ------ @@ -494,7 +495,7 @@ def __init__( self.stacks_list[-1].blocks[-1].backcast_g.requires_grad_(False) @io_processor - def forward(self, x_in: Tuple): + def forward(self, x_in: tuple): x, _ = x_in # if x1, x2,... y1, y2... is one multivariate ts containing x and y, and a1, a2... one covariate ts @@ -538,11 +539,12 @@ def __init__( self, input_chunk_length: int, output_chunk_length: int, + output_chunk_shift: int = 0, generic_architecture: bool = True, num_stacks: int = 30, num_blocks: int = 1, num_layers: int = 4, - layer_widths: Union[int, List[int]] = 256, + layer_widths: Union[int, list[int]] = 256, expansion_coefficient_dim: int = 5, trend_polynomial_degree: int = 2, dropout: float = 0.0, @@ -569,10 +571,16 @@ def __init__( Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values from future covariates to use as a model input (if the model supports future covariates). It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated - using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). generic_architecture Boolean value indicating whether the generic architecture of N-BEATS is used. If not, the interpretable architecture outlined in the paper (consisting of one trend @@ -809,7 +817,7 @@ def encode_year(idx): def supports_multivariate(self) -> bool: return True - def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module: + def _create_model(self, train_sample: tuple[torch.Tensor]) -> torch.nn.Module: # samples are made of (past_target, past_covariates, future_target) input_dim = train_sample[0].shape[1] + ( train_sample[1].shape[1] if train_sample[1] is not None else 0 diff --git a/darts/models/forecasting/nhits.py b/darts/models/forecasting/nhits.py index 98d195d6ed..99c9f6f84a 100644 --- a/darts/models/forecasting/nhits.py +++ b/darts/models/forecasting/nhits.py @@ -3,7 +3,7 @@ ------ """ -from typing import List, Optional, Tuple, Union +from typing import Optional, Union import numpy as np import torch @@ -216,8 +216,8 @@ def __init__( num_layers: int, layer_width: int, nr_params: int, - pooling_kernel_sizes: Tuple[int], - n_freq_downsample: Tuple[int], + pooling_kernel_sizes: tuple[int], + n_freq_downsample: tuple[int], batch_norm: bool, dropout: float, activation: str, @@ -327,9 +327,9 @@ def __init__( num_stacks: int, num_blocks: int, num_layers: int, - layer_widths: List[int], - pooling_kernel_sizes: Tuple[Tuple[int]], - n_freq_downsample: Tuple[Tuple[int]], + layer_widths: list[int], + pooling_kernel_sizes: tuple[tuple[int]], + n_freq_downsample: tuple[tuple[int]], batch_norm: bool, dropout: float, activation: str, @@ -370,7 +370,8 @@ def __init__( MaxPool1d Use MaxPool1d pooling. False uses AvgPool1d **kwargs - all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class. + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. Inputs ------ @@ -421,7 +422,7 @@ def __init__( self.stacks_list[-1].blocks[-1].backcast_linear_layer.requires_grad_(False) @io_processor - def forward(self, x_in: Tuple): + def forward(self, x_in: tuple): x, _ = x_in # if x1, x2,... y1, y2... is one multivariate ts containing x and y, and a1, a2... one covariate ts @@ -465,12 +466,13 @@ def __init__( self, input_chunk_length: int, output_chunk_length: int, + output_chunk_shift: int = 0, num_stacks: int = 3, num_blocks: int = 1, num_layers: int = 2, - layer_widths: Union[int, List[int]] = 512, - pooling_kernel_sizes: Optional[Tuple[Tuple[int]]] = None, - n_freq_downsample: Optional[Tuple[Tuple[int]]] = None, + layer_widths: Union[int, list[int]] = 512, + pooling_kernel_sizes: Optional[tuple[tuple[int]]] = None, + n_freq_downsample: Optional[tuple[tuple[int]]] = None, dropout: float = 0.1, activation: str = "ReLU", MaxPool1d: bool = True, @@ -506,10 +508,16 @@ def __init__( Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values from future covariates to use as a model input (if the model supports future covariates). It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated - using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). num_stacks The number of stacks that make up the whole model. num_blocks @@ -799,7 +807,7 @@ def _check_sizes(tup, name): return pooling_kernel_sizes, n_freq_downsample - def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module: + def _create_model(self, train_sample: tuple[torch.Tensor]) -> torch.nn.Module: # samples are made of (past_target, past_covariates, future_target) input_dim = train_sample[0].shape[1] + ( train_sample[1].shape[1] if train_sample[1] is not None else 0 diff --git a/darts/models/forecasting/nlinear.py b/darts/models/forecasting/nlinear.py index 347b3aeecf..b3f4f65b96 100644 --- a/darts/models/forecasting/nlinear.py +++ b/darts/models/forecasting/nlinear.py @@ -3,7 +3,7 @@ ------ """ -from typing import Optional, Tuple +from typing import Optional import torch import torch.nn as nn @@ -56,7 +56,8 @@ def __init__( Whether to apply the "normalization" described in the paper. **kwargs - all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class. + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. Inputs ------ @@ -108,7 +109,7 @@ def _create_linear_layer(in_dim, out_dim): @io_processor def forward( - self, x_in: Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]] + self, x_in: tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]] ): """ x_in @@ -182,6 +183,7 @@ def __init__( self, input_chunk_length: int, output_chunk_length: int, + output_chunk_shift: int = 0, shared_weights: bool = False, const_init: bool = True, normalize: bool = False, @@ -203,10 +205,16 @@ def __init__( Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values from future covariates to use as a model input (if the model supports future covariates). It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated - using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). shared_weights Whether to use shared weights for all components of multivariate series. @@ -416,7 +424,7 @@ def encode_year(idx): "normalize = True cannot be used with probabilistic NLinearModel", ) - def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module: + def _create_model(self, train_sample: tuple[torch.Tensor]) -> torch.nn.Module: # samples are made of # (past_target, past_covariates, historic_future_covariates, # future_covariates, static_covariates, future_target) diff --git a/darts/models/forecasting/pl_forecasting_module.py b/darts/models/forecasting/pl_forecasting_module.py index ab98ee59c2..de5c074dc5 100644 --- a/darts/models/forecasting/pl_forecasting_module.py +++ b/darts/models/forecasting/pl_forecasting_module.py @@ -2,9 +2,11 @@ This file contains abstract classes for deterministic and probabilistic PyTorch Lightning Modules """ +import copy from abc import ABC, abstractmethod +from collections.abc import Sequence from functools import wraps -from typing import Any, Dict, Optional, Sequence, Tuple, Union +from typing import Any, Optional, Union import pytorch_lightning as pl import torch @@ -51,7 +53,7 @@ def forward_wrapper(self, *args, **kwargs): # x is input batch tuple which by definition has the past features in the first element starting with the # first n target features # assuming `args[0][0]` is torch.Tensor we could clone it to prevent target re-normalization - x: Tuple = args[0][0].clone() + x: tuple = args[0][0].clone() # apply reversible instance normalization x[:, :, : self.n_targets] = self.rin(x[:, :, : self.n_targets]) # run the forward pass @@ -71,16 +73,17 @@ def __init__( self, input_chunk_length: int, output_chunk_length: int, - train_sample_shape: Optional[Tuple] = None, + output_chunk_shift: int = 0, + train_sample_shape: Optional[tuple] = None, loss_fn: nn.modules.loss._Loss = nn.MSELoss(), torch_metrics: Optional[ Union[torchmetrics.Metric, torchmetrics.MetricCollection] ] = None, likelihood: Optional[Likelihood] = None, optimizer_cls: torch.optim.Optimizer = torch.optim.Adam, - optimizer_kwargs: Optional[Dict] = None, + optimizer_kwargs: Optional[dict] = None, lr_scheduler_cls: Optional[torch.optim.lr_scheduler._LRScheduler] = None, - lr_scheduler_kwargs: Optional[Dict] = None, + lr_scheduler_kwargs: Optional[dict] = None, use_reversible_instance_norm: bool = False, ) -> None: """ @@ -88,13 +91,14 @@ def __init__( This class is meant to be inherited to create a new PyTorch Lightning-based forecasting module. When subclassing this class, please make sure to add the following methods with the given signatures: - - :func:`PLTorchForecastingModel.__init__()` - - :func:`PLTorchForecastingModel.forward()` - - :func:`PLTorchForecastingModel._produce_train_output()` - - :func:`PLTorchForecastingModel._get_batch_prediction()` + - :func:`PLForecastingModule.__init__()` + - :func:`PLForecastingModule.forward()` + - :func:`PLForecastingModule._process_input_batch()` + - :func:`PLForecastingModule._produce_train_output()` + - :func:`PLForecastingModule._get_batch_prediction()` In subclass `MyModel`'s :func:`__init__` function call ``super(MyModel, self).__init__(**kwargs)`` where - ``kwargs`` are the parameters of :class:`PLTorchForecastingModel`. + ``kwargs`` are the parameters of :class:`PLForecastingModule`. Parameters ---------- @@ -105,7 +109,7 @@ def __init__( Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values from future covariates to use as a model input (if the model supports future covariates). It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated - using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). @@ -156,9 +160,17 @@ def __init__( self.input_chunk_length = input_chunk_length # output_chunk_length is a property self._output_chunk_length = output_chunk_length + self.output_chunk_shift = output_chunk_shift # define the loss function self.criterion = loss_fn + self.train_criterion = copy.deepcopy(loss_fn) + self.val_criterion = copy.deepcopy(loss_fn) + # reduction will be set to `None` when calling `TFM.fit()` with sample weights; + # reset the actual criterion in method `on_fit_end()` + self.train_criterion_reduction: Optional[str] = None + self.val_criterion_reduction: Optional[str] = None + # by default models are deterministic (i.e. not probabilistic) self.likelihood = likelihood @@ -195,6 +207,7 @@ def __init__( self.pred_batch_size: Optional[int] = None self.pred_n_jobs: Optional[int] = None self.predict_likelihood_parameters: Optional[bool] = None + self.pred_mc_dropout: Optional[bool] = None @property def first_prediction_index(self) -> int: @@ -209,11 +222,11 @@ def forward(self, *args, **kwargs) -> Any: def training_step(self, train_batch, batch_idx) -> torch.Tensor: """performs the training step""" - output = self._produce_train_output(train_batch[:-1]) - target = train_batch[ - -1 - ] # By convention target is always the last element returned by datasets - loss = self._compute_loss(output, target) + # by convention, the last two elements are sample weights and future target + output = self._produce_train_output(train_batch[:-2]) + sample_weight = train_batch[-2] + target = train_batch[-1] + loss = self._compute_loss(output, target, self.train_criterion, sample_weight) self.log( "train_loss", loss, @@ -221,14 +234,16 @@ def training_step(self, train_batch, batch_idx) -> torch.Tensor: prog_bar=True, sync_dist=True, ) - self._calculate_metrics(output, target, self.train_metrics) + self._update_metrics(output, target, self.train_metrics) return loss def validation_step(self, val_batch, batch_idx) -> torch.Tensor: """performs the validation step""" - output = self._produce_train_output(val_batch[:-1]) + # the last two elements are sample weights and future target + output = self._produce_train_output(val_batch[:-2]) + sample_weight = val_batch[-2] target = val_batch[-1] - loss = self._compute_loss(output, target) + loss = self._compute_loss(output, target, self.val_criterion, sample_weight) self.log( "val_loss", loss, @@ -236,17 +251,41 @@ def validation_step(self, val_batch, batch_idx) -> torch.Tensor: prog_bar=True, sync_dist=True, ) - self._calculate_metrics(output, target, self.val_metrics) + self._update_metrics(output, target, self.val_metrics) return loss + def on_fit_end(self) -> None: + # revert the loss function reduction change when sample weights were used + if self.train_criterion_reduction is not None: + self.train_criterion.reduction = self.train_criterion_reduction + self.train_criterion_reduction = None + if self.val_criterion_reduction is not None: + self.val_criterion.reduction = self.val_criterion_reduction + self.val_criterion_reduction = None + + def on_train_epoch_end(self): + self._compute_metrics(self.train_metrics) + + def on_validation_epoch_end(self): + self._compute_metrics(self.val_metrics) + + def on_predict_start(self) -> None: + # optionally, activate monte carlo dropout for prediction + self.set_mc_dropout(active=self.pred_mc_dropout) + + def on_predict_end(self) -> None: + # deactivate, monte carlo dropout for any downstream task + self.set_mc_dropout(active=False) + def predict_step( - self, batch: Tuple, batch_idx: int, dataloader_idx: Optional[int] = None + self, batch: tuple, batch_idx: int, dataloader_idx: Optional[int] = None ) -> Sequence[TimeSeries]: """performs the prediction step batch output of Darts' :class:`InferenceDataset` - tuple of ``(past_target, past_covariates, - historic_future_covariates, future_covariates, future_past_covariates, input_timeseries)`` + historic_future_covariates, future_covariates, future_past_covariates, input time series, + prediction start time step)`` batch_idx the batch index of the current batch dataloader_idx @@ -274,7 +313,6 @@ def predict_step( # repeat prediction procedure for every needed sample batch_predictions = [] while sample_count < self.pred_num_samples: - # make sure we don't produce too many samples if sample_count + batch_sample_size > self.pred_num_samples: batch_sample_size = self.pred_num_samples - sample_count @@ -313,9 +351,11 @@ def predict_step( delayed(_build_forecast_series)( [batch_prediction[batch_idx] for batch_prediction in batch_predictions], input_series, - custom_columns=self.likelihood.likelihood_components_names(input_series) - if self.predict_likelihood_parameters - else None, + custom_columns=( + self.likelihood.likelihood_components_names(input_series) + if self.predict_likelihood_parameters + else None + ), with_static_covs=False if self.predict_likelihood_parameters else True, with_hierarchy=False if self.predict_likelihood_parameters else True, pred_start=pred_start, @@ -334,6 +374,7 @@ def set_predict_parameters( batch_size: int, n_jobs: int, predict_likelihood_parameters: bool, + mc_dropout: bool, ) -> None: """to be set from TorchForecastingModel before calling trainer.predict() and reset at self.on_predict_end()""" self.pred_n = n @@ -342,35 +383,52 @@ def set_predict_parameters( self.pred_batch_size = batch_size self.pred_n_jobs = n_jobs self.predict_likelihood_parameters = predict_likelihood_parameters + self.pred_mc_dropout = mc_dropout - def _compute_loss(self, output, target): + def _compute_loss(self, output, target, criterion, sample_weight): # output is of shape (batch_size, n_timesteps, n_components, n_params) if self.likelihood: - return self.likelihood.compute_loss(output, target) + loss = self.likelihood.compute_loss(output, target, sample_weight) else: # If there's no likelihood, nr_params=1, and we need to squeeze out the # last dimension of model output, for properly computing the loss. - return self.criterion(output.squeeze(dim=-1), target) + loss = criterion(output.squeeze(dim=-1), target) + if sample_weight is not None: + loss = (loss * sample_weight).mean() + return loss - def _calculate_metrics(self, output, target, metrics): + def _update_metrics(self, output, target, metrics): if not len(metrics): return if self.likelihood: - _metric = metrics(self.likelihood.sample(output), target) + pred = self.likelihood.sample(output) else: # If there's no likelihood, nr_params=1, and we need to squeeze out the # last dimension of model output, for properly computing the metric. - _metric = metrics(output.squeeze(dim=-1), target) + pred = output.squeeze(dim=-1) + + # torch metrics require 2D targets of shape (batch size * ocl, num targets) + if self.n_targets > 1: + target = target.reshape(-1, self.n_targets) + pred = pred.reshape(-1, self.n_targets) + + metrics.update(pred, target) + + def _compute_metrics(self, metrics): + if not len(metrics): + return + res = metrics.compute() self.log_dict( - _metric, + res, on_epoch=True, on_step=False, logger=True, prog_bar=True, sync_dist=True, ) + metrics.reset() def configure_optimizers(self): """configures optimizers and learning rate schedulers for model optimization.""" @@ -384,9 +442,9 @@ def _create_from_cls_and_kwargs(cls, kws): ValueError( "Error when building the optimizer or learning rate scheduler;" "please check the provided class and arguments" - "\nclass: {}" - "\narguments (kwargs): {}" - "\nerror:\n{}".format(cls, kws, e) + f"\nclass: {cls}" + f"\narguments (kwargs): {kws}" + f"\nerror:\n{e}" ), logger, ) @@ -402,26 +460,35 @@ def _create_from_cls_and_kwargs(cls, kws): lr_sched_kws = {k: v for k, v in self.lr_scheduler_kwargs.items()} lr_sched_kws["optimizer"] = optimizer - # ReduceLROnPlateau requires a metric to "monitor" which must be set separately, most others do not - lr_monitor = lr_sched_kws.pop("monitor", None) + # lr scheduler can be configured with lightning; defaults below + lr_config_params = { + "monitor": "val_loss", + "interval": "epoch", + "frequency": 1, + "strict": True, + "name": None, + } + # update config with user params + lr_config_params = { + k: (v if k not in lr_sched_kws else lr_sched_kws.pop(k)) + for k, v in lr_config_params.items() + } lr_scheduler = _create_from_cls_and_kwargs( self.lr_scheduler_cls, lr_sched_kws ) - return [optimizer], { - "scheduler": lr_scheduler, - "monitor": lr_monitor if lr_monitor is not None else "val_loss", - } + + return [optimizer], dict({"scheduler": lr_scheduler}, **lr_config_params) else: return optimizer @abstractmethod - def _produce_train_output(self, input_batch: Tuple) -> torch.Tensor: + def _produce_train_output(self, input_batch: tuple) -> torch.Tensor: pass @abstractmethod def _get_batch_prediction( - self, n: int, input_batch: Tuple, roll_size: int + self, n: int, input_batch: tuple, roll_size: int ) -> torch.Tensor: """ In charge of applying the recurrent logic for non-recurrent models. @@ -450,14 +517,15 @@ def recurse_children(children, acc): return recurse_children(self.children(), set()) def set_mc_dropout(self, active: bool): + # optionally, activate dropout in all MonteCarloDropout modules for module in self._get_mc_dropout_modules(): - module.mc_dropout_enabled = active + module._mc_dropout_enabled = active @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return self.likelihood is not None or len(self._get_mc_dropout_modules()) > 0 - def _produce_predict_output(self, x: Tuple) -> torch.Tensor: + def _produce_predict_output(self, x: tuple) -> torch.Tensor: if self.likelihood: output = self(x) if self.predict_likelihood_parameters: @@ -467,18 +535,18 @@ def _produce_predict_output(self, x: Tuple) -> torch.Tensor: else: return self(x).squeeze(dim=-1) - def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: + def on_save_checkpoint(self, checkpoint: dict[str, Any]) -> None: # we must save the dtype for correct parameter precision at loading time checkpoint["model_dtype"] = self.dtype - # we must save the shape of the input to be able to instanciate the model without calling fit_from_dataset + # we must save the shape of the input to be able to instantiate the model without calling fit_from_dataset checkpoint["train_sample_shape"] = self.train_sample_shape # we must save the loss to properly restore it when resuming training checkpoint["loss_fn"] = self.criterion - # we must save the metrics to continue outputing them when resuming training + # we must save the metrics to continue logging them when resuming training checkpoint["torch_metrics_train"] = self.train_metrics checkpoint["torch_metrics_val"] = self.val_metrics - def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None: + def on_load_checkpoint(self, checkpoint: dict[str, Any]) -> None: # by default our models are initialized as float32. For other dtypes, we need to cast to the correct precision # before parameters are loaded by PyTorch-Lightning dtype = checkpoint["model_dtype"] @@ -535,7 +603,7 @@ def output_chunk_length(self) -> Optional[int]: @staticmethod def configure_torch_metrics( - torch_metrics: Union[torchmetrics.Metric, torchmetrics.MetricCollection] + torch_metrics: Union[torchmetrics.Metric, torchmetrics.MetricCollection], ) -> torchmetrics.MetricCollection: """process the torch_metrics parameter.""" if torch_metrics is None: @@ -555,7 +623,7 @@ def configure_torch_metrics( class PLPastCovariatesModule(PLForecastingModule, ABC): - def _produce_train_output(self, input_batch: Tuple): + def _produce_train_output(self, input_batch: tuple): """ Feeds PastCovariatesTorchModel with input and output chunks of a PastCovariatesSequentialDataset for training. @@ -565,18 +633,51 @@ def _produce_train_output(self, input_batch: Tuple): input_batch ``(past_target, past_covariates, static_covariates)`` """ - past_target, past_covariates, static_covariates = input_batch + return self(self._process_input_batch(input_batch)) + + def _process_input_batch( + self, input_batch: tuple + ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + Converts output of PastCovariatesDataset (training dataset) into an input/past- and + output/future chunk. + + Parameters + ---------- + input_batch + ``(past_target, past_covariates, historic_future_covariates, future_covariates, static_covariates)``. + + Returns + ------- + tuple + ``(x_past, x_static)`` the input/past and output/future chunks. + """ + # because of future past covariates, the batch shape is different during training and prediction + if len(input_batch) == 3: + ( + past_target, + past_covariates, + static_covariates, + ) = input_batch + else: + ( + past_target, + past_covariates, + future_past_covariates, + static_covariates, + ) = input_batch # Currently all our PastCovariates models require past target and covariates concatenated - inpt = ( - torch.cat([past_target, past_covariates], dim=2) - if past_covariates is not None - else past_target, + return ( + ( + torch.cat([past_target, past_covariates], dim=2) + if past_covariates is not None + else past_target + ), static_covariates, ) - return self(inpt) def _get_batch_prediction( - self, n: int, input_batch: Tuple, roll_size: int + self, n: int, input_batch: tuple, roll_size: int ) -> torch.Tensor: """ Feeds PastCovariatesTorchModel with input and output chunks of a PastCovariatesSequentialDataset to forecast @@ -605,12 +706,9 @@ def _get_batch_prediction( past_covariates.shape[dim_component] if past_covariates is not None else 0 ) - input_past = torch.cat( - [ds for ds in [past_target, past_covariates] if ds is not None], - dim=dim_component, - ) + input_past, input_static = self._process_input_batch(input_batch) - out = self._produce_predict_output(x=(input_past, static_covariates))[ + out = self._produce_predict_output(x=(input_past, input_static))[ :, self.first_prediction_index :, : ] @@ -650,13 +748,13 @@ def _get_batch_prediction( # update past covariates to include next `roll_size` future past covariates elements if n_past_covs and self.input_chunk_length >= roll_size: - input_past[ - :, -roll_size:, n_targets : n_targets + n_past_covs - ] = future_past_covariates[:, left_past:right_past, :] + input_past[:, -roll_size:, n_targets : n_targets + n_past_covs] = ( + future_past_covariates[:, left_past:right_past, :] + ) elif n_past_covs: - input_past[ - :, :, n_targets : n_targets + n_past_covs - ] = future_past_covariates[:, left_past:right_past, :] + input_past[:, :, n_targets : n_targets + n_past_covs] = ( + future_past_covariates[:, left_past:right_past, :] + ) # take only last part of the output sequence where needed out = self._produce_predict_output(x=(input_past, static_covariates))[ @@ -674,14 +772,14 @@ def _get_batch_prediction( class PLFutureCovariatesModule(PLForecastingModule, ABC): def _get_batch_prediction( - self, n: int, input_batch: Tuple, roll_size: int + self, n: int, input_batch: tuple, roll_size: int ) -> torch.Tensor: raise NotImplementedError("TBD: Darts doesn't contain such a model yet.") class PLDualCovariatesModule(PLForecastingModule, ABC): def _get_batch_prediction( - self, n: int, input_batch: Tuple, roll_size: int + self, n: int, input_batch: tuple, roll_size: int ) -> torch.Tensor: raise NotImplementedError( "TBD: The only DualCovariatesModel is an RNN with a specific implementation." @@ -690,8 +788,8 @@ def _get_batch_prediction( class PLMixedCovariatesModule(PLForecastingModule, ABC): def _produce_train_output( - self, input_batch: Tuple - ) -> Tuple[torch.Tensor, torch.Tensor]: + self, input_batch: tuple + ) -> tuple[torch.Tensor, torch.Tensor]: """ Feeds MixedCovariatesTorchModel with input and output chunks of a MixedCovariatesSequentialDataset for training. @@ -705,7 +803,7 @@ def _produce_train_output( def _process_input_batch( self, input_batch - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: """ Converts output of MixedCovariatesDataset (training dataset) into an input/past- and output/future chunk. @@ -727,7 +825,7 @@ def _process_input_batch( future_covariates, static_covariates, ) = input_batch - dim_variable = 2 + dim_comp = 2 x_past = torch.cat( [ @@ -739,12 +837,12 @@ def _process_input_batch( ] if tensor is not None ], - dim=dim_variable, + dim=dim_comp, ) return x_past, future_covariates, static_covariates def _get_batch_prediction( - self, n: int, input_batch: Tuple, roll_size: int + self, n: int, input_batch: tuple, roll_size: int ) -> torch.Tensor: """ Feeds MixedCovariatesModel with input and output chunks of a MixedCovariatesSequentialDataset to forecast @@ -781,17 +879,17 @@ def _get_batch_prediction( else 0 ) - input_past, input_future, input_static = self._process_input_batch( + input_past, input_future, input_static = self._process_input_batch(( + past_target, + past_covariates, + historic_future_covariates, ( - past_target, - past_covariates, - historic_future_covariates, future_covariates[:, :roll_size, :] if future_covariates is not None - else None, - static_covariates, - ) - ) + else None + ), + static_covariates, + )) out = self._produce_predict_output(x=(input_past, input_future, input_static))[ :, self.first_prediction_index :, : @@ -800,13 +898,15 @@ def _get_batch_prediction( batch_prediction = [out[:, :roll_size, :]] prediction_length = roll_size - while prediction_length < n: - # we want the last prediction to end exactly at `n` into the future. + # predict at least `output_chunk_length` points, so that we use the most recent target values + min_n = n if n >= self.output_chunk_length else self.output_chunk_length + while prediction_length < min_n: + # we want the last prediction to end exactly at `min_n` into the future. # this means we may have to truncate the previous prediction and step # back the roll size for the last chunk - if prediction_length + self.output_chunk_length > n: + if prediction_length + self.output_chunk_length > min_n: spillover_prediction_length = ( - prediction_length + self.output_chunk_length - n + prediction_length + self.output_chunk_length - min_n ) roll_size -= spillover_prediction_length prediction_length -= spillover_prediction_length @@ -833,19 +933,19 @@ def _get_batch_prediction( # update past covariates to include next `roll_size` future past covariates elements if n_past_covs and self.input_chunk_length >= roll_size: - input_past[ - :, -roll_size:, n_targets : n_targets + n_past_covs - ] = future_past_covariates[:, left_past:right_past, :] + input_past[:, -roll_size:, n_targets : n_targets + n_past_covs] = ( + future_past_covariates[:, left_past:right_past, :] + ) elif n_past_covs: - input_past[ - :, :, n_targets : n_targets + n_past_covs - ] = future_past_covariates[:, left_past:right_past, :] + input_past[:, :, n_targets : n_targets + n_past_covs] = ( + future_past_covariates[:, left_past:right_past, :] + ) # update historic future covariates to include next `roll_size` future covariates elements if n_future_covs and self.input_chunk_length >= roll_size: - input_past[ - :, -roll_size:, n_targets + n_past_covs : - ] = future_covariates[:, left_past:right_past, :] + input_past[:, -roll_size:, n_targets + n_past_covs :] = ( + future_covariates[:, left_past:right_past, :] + ) elif n_future_covs: input_past[:, :, n_targets + n_past_covs :] = future_covariates[ :, left_past:right_past, : @@ -876,6 +976,6 @@ def _get_batch_prediction( class PLSplitCovariatesModule(PLForecastingModule, ABC): def _get_batch_prediction( - self, n: int, input_batch: Tuple, roll_size: int + self, n: int, input_batch: tuple, roll_size: int ) -> torch.Tensor: raise NotImplementedError("TBD: Darts doesn't contain such a model yet.") diff --git a/darts/models/forecasting/prophet_model.py b/darts/models/forecasting/prophet_model.py index b6674463fa..592728aaab 100644 --- a/darts/models/forecasting/prophet_model.py +++ b/darts/models/forecasting/prophet_model.py @@ -5,7 +5,8 @@ import logging import re -from typing import Callable, List, Optional, Sequence, Union +from collections.abc import Sequence +from typing import Callable, Optional, Union import numpy as np import pandas as pd @@ -24,7 +25,7 @@ class Prophet(FutureCovariatesLocalForecastingModel): def __init__( self, - add_seasonalities: Optional[Union[dict, List[dict]]] = None, + add_seasonalities: Optional[Union[dict, list[dict]]] = None, country_holidays: Optional[str] = None, suppress_stdout_stderror: bool = True, add_encoders: Optional[dict] = None, @@ -111,7 +112,7 @@ def encode_year(idx): } .. cap - Parameter specifiying the maximum carrying capacity when predicting with logistic growth. + Parameter specifying the maximum carrying capacity when predicting with logistic growth. Mandatory when `growth = 'logistic'`, otherwise ignored. See for more information on logistic forecasts. @@ -121,7 +122,7 @@ def encode_year(idx): - a function taking a DatetimeIndex or RangeIndex and returning a corresponding a Sequence of numbers, where each number indicates the carrying capacity at this index. floor - Parameter specifiying the minimum carrying capacity when predicting logistic growth. + Parameter specifying the minimum carrying capacity when predicting logistic growth. Optional when `growth = 'logistic'` (defaults to 0), otherwise ignored. See for more information on logistic forecasts. @@ -204,7 +205,6 @@ def encode_year(idx): self._floor = 0 def _fit(self, series: TimeSeries, future_covariates: Optional[TimeSeries] = None): - super()._fit(series, future_covariates) self._assert_univariate(series) series = self.training_series @@ -264,7 +264,6 @@ def _predict( num_samples: int = 1, verbose: bool = False, ) -> TimeSeries: - _ = self._check_seasonality_conditions(future_covariates=future_covariates) super()._predict(n, future_covariates, num_samples) @@ -316,7 +315,7 @@ def _generate_predict_df( def _check_seasonality_conditions( self, future_covariates: Optional[TimeSeries] = None - ) -> List[str]: + ) -> list[str]: """ Checks if the conditions for custom conditional seasonalities are met. Each custom seasonality that has a `condition_name` other than None is checked. If the `condition_name` is not a column in the `future_covariates` @@ -350,9 +349,11 @@ def _check_seasonality_conditions( condition_name = attributes["condition_name"] if condition_name is not None: if condition_name not in future_covariates_columns: - invalid_conditional_seasonalities.append( - (seasonality_name, condition_name, "column missing") - ) + invalid_conditional_seasonalities.append(( + seasonality_name, + condition_name, + "column missing", + )) continue if ( not future_covariates[condition_name] @@ -360,9 +361,11 @@ def _check_seasonality_conditions( .isin([True, False]) .all() ): - invalid_conditional_seasonalities.append( - (seasonality_name, condition_name, "invalid values") - ) + invalid_conditional_seasonalities.append(( + seasonality_name, + condition_name, + "invalid values", + )) continue conditional_seasonality_covariates.append(condition_name) @@ -386,7 +389,7 @@ def supports_multivariate(self) -> bool: return False @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return True def _stochastic_samples(self, predict_df, n_samples) -> np.ndarray: @@ -527,7 +530,7 @@ def _store_add_seasonality_call( ] raise_if( len(missing_kws) > 0, - f'Seasonality `{add_seasonality_call["name"]}` has missing mandatory keywords or empty arguments: ' + f"Seasonality `{add_seasonality_call['name']}` has missing mandatory keywords or empty arguments: " f"{missing_kws}.", logger, ) @@ -545,7 +548,7 @@ def _store_add_seasonality_call( ] raise_if( len(invalid_kws) > 0, - f'Seasonality `{add_seasonality_call["name"]}` has invalid keywords: {invalid_kws}. Only the ' + f"Seasonality `{add_seasonality_call['name']}` has invalid keywords: {invalid_kws}. Only the " f"following arguments are supported: {list(seasonality_default)}", logger, ) @@ -558,8 +561,8 @@ def _store_add_seasonality_call( ] raise_if( len(invalid_types) > 0, - f'Seasonality `{add_seasonality_call["name"]}` has invalid value dtypes: {invalid_types} must be ' - f'of type {[seasonality_properties[kw]["dtype"] for kw in invalid_types]}.', + f"Seasonality `{add_seasonality_call['name']}` has invalid value dtypes: {invalid_types} must be " + f"of type {[seasonality_properties[kw]['dtype'] for kw in invalid_types]}.", logger, ) @@ -595,19 +598,30 @@ def _freq_to_days(freq: str) -> float: seconds_per_day = 86400 days = 0 - if freq in ["A", "BA", "Y", "BY", "RE"] or freq.startswith( - ("A", "BA", "Y", "BY", "RE") - ): # year + if freq in ["A", "BA", "Y", "BY", "RE"] or freq.startswith(( + "A", + "BA", + "Y", + "BY", + "RE", + )): # year days = 365.25 - elif freq in ["Q", "BQ", "REQ"] or freq.startswith( - ("Q", "BQ", "REQ") - ): # quarter + elif freq in ["Q", "BQ", "REQ"] or freq.startswith(( + "Q", + "BQ", + "REQ", + )): # quarter days = 3 * 30.4375 - elif freq in ["M", "BM", "CBM", "SM"] or freq.startswith( - ("M", "BM", "BS", "CBM", "SM") - ): # month + elif freq in [ + "M", + "BM", + "CBM", + "SM", + "LWOM", + "WOM", + ] or freq.startswith(("M", "BME", "BS", "CBM", "SM", "LWOM", "WOM")): # month days = 30.4375 - elif freq in ["W"]: # week + elif freq == "W" or freq.startswith("W-"): # week days = 7.0 elif freq in ["B", "C"]: # business day days = 1 * 7 / 5 @@ -626,7 +640,7 @@ def _freq_to_days(freq: str) -> float: days = 1 / (seconds_per_day * 10**3) elif freq_lower in ["u", "us"]: # microsecond days = 1 / (seconds_per_day * 10**6) - elif freq_lower in ["n"]: # nanosecond + elif freq_lower in ["n", "ns"]: # nanosecond days = 1 / (seconds_per_day * 10**9) if not days: diff --git a/darts/models/forecasting/random_forest.py b/darts/models/forecasting/random_forest.py index 34cee5f38f..78dbae8f43 100644 --- a/darts/models/forecasting/random_forest.py +++ b/darts/models/forecasting/random_forest.py @@ -14,6 +14,7 @@ ---------- .. [1] https://en.wikipedia.org/wiki/Random_forest """ + from typing import Optional from sklearn.ensemble import RandomForestRegressor @@ -35,6 +36,7 @@ def __init__( lags_past_covariates: Optional[LAGS_TYPE] = None, lags_future_covariates: Optional[FUTURE_LAGS_TYPE] = None, output_chunk_length: int = 1, + output_chunk_shift: int = 0, add_encoders: Optional[dict] = None, n_estimators: Optional[int] = 100, max_depth: Optional[int] = None, @@ -49,7 +51,8 @@ def __init__( lags Lagged target `series` values used to predict the next time step/s. If an integer, must be > 0. Uses the last `n=lags` past lags; e.g. `(-1, -2, ..., -lags)`, where `0` - corresponds the first predicted time step of each sample. + corresponds the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. If a list of integers, each value must be < 0. Uses only the specified values as lags. If a dictionary, the keys correspond to the `series` component names (of the first series when using multiple series) and the values correspond to the component lags (integer or list of integers). The @@ -58,17 +61,21 @@ def __init__( lags_past_covariates Lagged `past_covariates` values used to predict the next time step/s. If an integer, must be > 0. Uses the last `n=lags_past_covariates` past lags; e.g. `(-1, -2, ..., -lags)`, - where `0` corresponds to the first predicted time step of each sample. + where `0` corresponds to the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. If a list of integers, each value must be < 0. Uses only the specified values as lags. If a dictionary, the keys correspond to the `past_covariates` component names (of the first series when using multiple series) and the values correspond to the component lags (integer or list of integers). The key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some components are missing and the 'default_lags' key is not provided. lags_future_covariates - Lagged `future_covariates` values used to predict the next time step/s. + Lagged `future_covariates` values used to predict the next time step/s. The lags are always relative to the + first step in the output chunk, even when `output_chunk_shift > 0`. If a tuple of `(past, future)`, both values must be > 0. Uses the last `n=past` past lags and `n=future` - future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` - corresponds the first predicted time step of each sample. + future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` corresponds the first + predicted time step of each sample. If `output_chunk_shift > 0`, the position of negative lags differ from + those of `lags` and `lags_past_covariates`. In this case a future lag `-5` would point at the same + step as a target lag of `-5 + output_chunk_shift`. If a list of integers, uses only the specified values as lags. If a dictionary, the keys correspond to the `future_covariates` component names (of the first series when using multiple series) and the values correspond to the component lags (tuple or list of integers). The key @@ -77,10 +84,17 @@ def __init__( output_chunk_length Number of time steps predicted at once (per chunk) by the internal model. It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated using a - one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is + one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input (history of target and past covariates) and + output. If the model supports `future_covariates`, the `lags_future_covariates` are relative to the first + step in the shifted output chunk. Predictions will start `output_chunk_shift` steps after the end of the + target `series`. If `output_chunk_shift` is set, the model cannot generate autoregressive predictions + (`n > output_chunk_length`). add_encoders A large number of past and future covariates can be automatically generated with `add_encoders`. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that @@ -111,8 +125,9 @@ def encode_year(idx): The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. multi_models - If True, a separate model will be trained for each future lag to predict. If False, a single model is - trained to predict at step 'output_chunk_length' in the future. Default: True. + If True, a separate model will be trained for each future lag to predict. If False, a single model + is trained to predict all the steps in 'output_chunk_length' (features lags are shifted back by + `output_chunk_length - n` for each step `n`). Default: True. use_static_covariates Whether the model should use static covariate information in case the input `series` passed to ``fit()`` contain static covariates. If ``True``, and static covariates are available at fitting time, will enforce @@ -161,6 +176,7 @@ def encode_year(idx): lags_past_covariates=lags_past_covariates, lags_future_covariates=lags_future_covariates, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, add_encoders=add_encoders, multi_models=multi_models, model=RandomForestRegressor(**kwargs), diff --git a/darts/models/forecasting/regression_ensemble_model.py b/darts/models/forecasting/regression_ensemble_model.py index b55170aede..435ad3e5f8 100644 --- a/darts/models/forecasting/regression_ensemble_model.py +++ b/darts/models/forecasting/regression_ensemble_model.py @@ -4,7 +4,9 @@ An ensemble model which uses a regression model to compute the ensemble forecast. """ -from typing import List, Optional, Sequence, Tuple, Union + +from collections.abc import Sequence +from typing import Optional, Union from darts.logging import get_logger, raise_if, raise_if_not from darts.models.forecasting.ensemble_model import EnsembleModel @@ -12,7 +14,7 @@ from darts.models.forecasting.linear_regression_model import LinearRegressionModel from darts.models.forecasting.regression_model import RegressionModel from darts.timeseries import TimeSeries, concatenate -from darts.utils.utils import seq2series, series2seq +from darts.utils.ts_utils import seq2series, series2seq logger = get_logger(__name__) @@ -20,7 +22,7 @@ class RegressionEnsembleModel(EnsembleModel): def __init__( self, - forecasting_models: List[ForecastingModel], + forecasting_models: list[ForecastingModel], regression_train_n_points: int, regression_model=None, regression_train_num_samples: int = 1, @@ -56,7 +58,7 @@ def __init__( `train_forecasting_models=False`. regression_model Any regression model with ``predict()`` and ``fit()`` methods (e.g. from scikit-learn) - Default: ``darts.model.LinearRegressionModel(fit_intercept=False)`` + Default: ``darts.models.LinearRegressionModel(fit_intercept=False)`` .. note:: if `regression_model` is probabilistic, the `RegressionEnsembleModel` will also be probabilistic. @@ -154,7 +156,7 @@ def __init__( ) # converted to List[int] if regression_train_n_points=-1 and ensemble is trained with multiple series - self.train_n_points: Union[int, List[int]] = regression_train_n_points + self.train_n_points: Union[int, list[int]] = regression_train_n_points raise_if( train_using_historical_forecasts and not self.is_global_ensemble, @@ -166,8 +168,8 @@ def __init__( self.train_using_historical_forecasts = train_using_historical_forecasts def _split_multi_ts_sequence( - self, n: Union[int, List[int]], ts_sequence: Sequence[TimeSeries] - ) -> Tuple[Sequence[TimeSeries], Sequence[TimeSeries]]: + self, n: Union[int, list[int]], ts_sequence: Sequence[TimeSeries] + ) -> tuple[Sequence[TimeSeries], Sequence[TimeSeries]]: if isinstance(n, int): n = [n] * len(ts_sequence) left = [ts[:-n_] for ts, n_ in zip(ts_sequence, n)] @@ -213,15 +215,17 @@ def _make_multiple_historical_forecasts( tmp_pred = model.historical_forecasts( series=series, - past_covariates=past_covariates - if model.supports_past_covariates - else None, - future_covariates=future_covariates - if model.supports_future_covariates - else None, + past_covariates=( + past_covariates if model.supports_past_covariates else None + ), + future_covariates=( + future_covariates if model.supports_future_covariates else None + ), forecast_horizon=model.output_chunk_length, stride=model.output_chunk_length, - num_samples=num_samples if model._is_probabilistic else 1, + num_samples=( + num_samples if model.supports_probabilistic_prediction else 1 + ), start=-start_hist_forecasts, start_format="position", retrain=False, @@ -231,10 +235,7 @@ def _make_multiple_historical_forecasts( predict_likelihood_parameters=False, ) # concatenate the strided predictions of output_chunk_length values each - if is_single_series: - tmp_pred = [concatenate(tmp_pred, axis=0)] - else: - tmp_pred = [concatenate(sub_pred, axis=0) for sub_pred in tmp_pred] + tmp_pred = [concatenate(sub_pred, axis=0) for sub_pred in tmp_pred] # add the missing steps at beginning by taking the first values of precomputed predictions if missing_steps: @@ -281,6 +282,7 @@ def fit( series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ Fits the forecasting models with the entire series except the last `regression_train_n_points` values, which @@ -299,6 +301,16 @@ def fit( future_covariates Optionally, a series or sequence of series specifying future-known covariates passed to the forecasting models + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().fit( series, past_covariates=past_covariates, future_covariates=future_covariates @@ -316,9 +328,9 @@ def fit( # shift by the forecasting models' largest input length all_shifts = [] # when it's not clearly defined, extreme_lags returns - # min_train_serie_length for the LocalForecastingModels + # `min_train_series_length` for the LocalForecastingModels for model in self.forecasting_models: - min_target_lag, _, _, _, _, _ = model.extreme_lags + min_target_lag, _, _, _, _, _, _, _ = model.extreme_lags if min_target_lag is not None: all_shifts.append(-min_target_lag) @@ -350,9 +362,9 @@ def fit( if is_single_series: train_n_points_too_big = len(series) <= self.train_n_points else: - train_n_points_too_big = any( - [len(s) <= self.train_n_points for s in series] - ) + train_n_points_too_big = any([ + len(s) <= self.train_n_points for s in series + ]) raise_if( train_n_points_too_big, @@ -374,11 +386,14 @@ def fit( # maximize covariate usage model._fit_wrapper( series=forecast_training, - past_covariates=past_covariates - if model.supports_past_covariates - else None, - future_covariates=future_covariates - if model.supports_future_covariates + past_covariates=( + past_covariates if model.supports_past_covariates else None + ), + future_covariates=( + future_covariates if model.supports_future_covariates else None + ), + sample_weight=sample_weight + if model.supports_sample_weight else None, ) @@ -404,23 +419,28 @@ def fit( # train the regression model on the individual models' predictions self.regression_model.fit( - series=regression_target, future_covariates=predictions + series=regression_target, + future_covariates=predictions, + sample_weight=sample_weight, ) # prepare the forecasting models for further predicting by fitting them with the entire data if self.train_forecasting_models: # Some models may need to be 'reset' to allow being retrained from scratch, especially torch-based models - self.forecasting_models: List[ForecastingModel] = [ + self.forecasting_models: list[ForecastingModel] = [ model.untrained_model() for model in self.forecasting_models ] for model in self.forecasting_models: model._fit_wrapper( series=series, - past_covariates=past_covariates - if model.supports_past_covariates - else None, - future_covariates=future_covariates - if model.supports_future_covariates + past_covariates=( + past_covariates if model.supports_past_covariates else None + ), + future_covariates=( + future_covariates if model.supports_future_covariates else None + ), + sample_weight=sample_weight + if model.supports_sample_weight else None, ) return self @@ -451,12 +471,14 @@ def ensemble( @property def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ + Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, Optional[int], ]: extreme_lags_ = super().extreme_lags @@ -484,9 +506,9 @@ def supports_multivariate(self) -> bool: ) @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: """ A RegressionEnsembleModel is probabilistic if its regression model is probabilistic (ensembling layer) """ - return self.regression_model._is_probabilistic + return self.regression_model.supports_probabilistic_prediction diff --git a/darts/models/forecasting/regression_model.py b/darts/models/forecasting/regression_model.py index 47fd5d2b92..6c69c48c3e 100644 --- a/darts/models/forecasting/regression_model.py +++ b/darts/models/forecasting/regression_model.py @@ -26,23 +26,21 @@ When static covariates are present, they are appended to the lagged features. When multiple time series are passed, if their static covariates do not have the same size, the shorter ones are padded with 0 valued features. """ -from collections import OrderedDict -from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal +from collections import OrderedDict +from collections.abc import Sequence +from typing import Any, Callable, Literal, Optional, Union import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression +from sklearn.utils.validation import has_fit_parameter from darts.logging import get_logger, raise_if, raise_if_not, raise_log from darts.models.forecasting.forecasting_model import GlobalForecastingModel from darts.timeseries import TimeSeries from darts.utils.data.tabularization import ( - add_static_covariates_to_lagged_data, + _create_lagged_data_autoregression, create_lagged_component_names, create_lagged_training_data, ) @@ -53,18 +51,18 @@ _process_historical_forecast_input, ) from darts.utils.multioutput import MultiOutputRegressor +from darts.utils.ts_utils import get_single_series, seq2series, series2seq from darts.utils.utils import ( _check_quantiles, - get_single_series, - seq2series, - series2seq, + likelihood_component_names, + quantile_names, ) logger = get_logger(__name__) -LAGS_TYPE = Union[int, List[int], Dict[str, Union[int, List[int]]]] +LAGS_TYPE = Union[int, list[int], dict[str, Union[int, list[int]]]] FUTURE_LAGS_TYPE = Union[ - Tuple[int, int], List[int], Dict[str, Union[Tuple[int, int], List[int]]] + tuple[int, int], list[int], dict[str, Union[tuple[int, int], list[int]]] ] @@ -75,6 +73,7 @@ def __init__( lags_past_covariates: Optional[LAGS_TYPE] = None, lags_future_covariates: Optional[FUTURE_LAGS_TYPE] = None, output_chunk_length: int = 1, + output_chunk_shift: int = 0, add_encoders: Optional[dict] = None, model=None, multi_models: Optional[bool] = True, @@ -88,7 +87,8 @@ def __init__( lags Lagged target `series` values used to predict the next time step/s. If an integer, must be > 0. Uses the last `n=lags` past lags; e.g. `(-1, -2, ..., -lags)`, where `0` - corresponds the first predicted time step of each sample. + corresponds the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. If a list of integers, each value must be < 0. Uses only the specified values as lags. If a dictionary, the keys correspond to the `series` component names (of the first series when using multiple series) and the values correspond to the component lags (integer or list of integers). The @@ -97,17 +97,21 @@ def __init__( lags_past_covariates Lagged `past_covariates` values used to predict the next time step/s. If an integer, must be > 0. Uses the last `n=lags_past_covariates` past lags; e.g. `(-1, -2, ..., -lags)`, - where `0` corresponds to the first predicted time step of each sample. + where `0` corresponds to the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. If a list of integers, each value must be < 0. Uses only the specified values as lags. If a dictionary, the keys correspond to the `past_covariates` component names (of the first series when using multiple series) and the values correspond to the component lags (integer or list of integers). The key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some components are missing and the 'default_lags' key is not provided. lags_future_covariates - Lagged `future_covariates` values used to predict the next time step/s. + Lagged `future_covariates` values used to predict the next time step/s. The lags are always relative to the + first step in the output chunk, even when `output_chunk_shift > 0`. If a tuple of `(past, future)`, both values must be > 0. Uses the last `n=past` past lags and `n=future` - future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` - corresponds the first predicted time step of each sample. + future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` corresponds the first + predicted time step of each sample. If `output_chunk_shift > 0`, the position of negative lags differ from + those of `lags` and `lags_past_covariates`. In this case a future lag `-5` would point at the same + step as a target lag of `-5 + output_chunk_shift`. If a list of integers, uses only the specified values as lags. If a dictionary, the keys correspond to the `future_covariates` component names (of the first series when using multiple series) and the values correspond to the component lags (tuple or list of integers). The key @@ -116,10 +120,17 @@ def __init__( output_chunk_length Number of time steps predicted at once (per chunk) by the internal model. It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated using a - one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is + one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input (history of target and past covariates) and + output. If the model supports `future_covariates`, the `lags_future_covariates` are relative to the first + step in the shifted output chunk. Predictions will start `output_chunk_shift` steps after the end of the + target `series`. If `output_chunk_shift` is set, the model cannot generate autoregressive predictions + (`n > output_chunk_length`). add_encoders A large number of past and future covariates can be automatically generated with `add_encoders`. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that @@ -150,8 +161,9 @@ def encode_year(idx): will be used per component in the multivariate series. If None, defaults to: ``sklearn.linear_model.LinearRegression(n_jobs=-1)``. multi_models - If True, a separate model will be trained for each future lag to predict. If False, a single model is - trained to predict at step 'output_chunk_length' in the future. Default: True. + If True, a separate model will be trained for each future lag to predict. If False, a single model + is trained to predict all the steps in 'output_chunk_length' (features lags are shifted back by + `output_chunk_length - n` for each step `n`). Default: True. use_static_covariates Whether the model should use static covariate information in case the input `series` passed to ``fit()`` contain static covariates. If ``True``, and static covariates are available at fitting time, will enforce @@ -191,13 +203,14 @@ def encode_year(idx): super().__init__(add_encoders=add_encoders) self.model = model - self.lags: Dict[str, List[int]] = {} - self.component_lags: Dict[str, Dict[str, List[int]]] = {} + self.lags: dict[str, list[int]] = {} + self.component_lags: dict[str, dict[str, list[int]]] = {} self.input_dim = None self.multi_models = True if multi_models or output_chunk_length == 1 else False self._considers_static_covariates = use_static_covariates - self._static_covariates_shape: Optional[Tuple[int, int]] = None - self._lagged_feature_names: Optional[List[str]] = None + self._static_covariates_shape: Optional[tuple[int, int]] = None + self._lagged_feature_names: Optional[list[str]] = None + self._lagged_label_names: Optional[list[str]] = None # check and set output_chunk_length raise_if_not( @@ -206,6 +219,7 @@ def encode_year(idx): logger=logger, ) self._output_chunk_length = output_chunk_length + self._output_chunk_shift = output_chunk_shift # model checks if self.model is None: @@ -234,16 +248,18 @@ def encode_year(idx): lags=lags, lags_past_covariates=lags_past_covariates, lags_future_covariates=lags_future_covariates, + output_chunk_shift=output_chunk_shift, ) self.pred_dim = self.output_chunk_length if self.multi_models else 1 + @staticmethod def _generate_lags( - self, lags: Optional[LAGS_TYPE], lags_past_covariates: Optional[LAGS_TYPE], lags_future_covariates: Optional[FUTURE_LAGS_TYPE], - ) -> Tuple[Dict[str, List[int]], Dict[str, Dict[str, List[int]]]]: + output_chunk_shift: int, + ) -> tuple[dict[str, list[int]], dict[str, dict[str, list[int]]]]: """ Based on the type of the argument and the nature of the covariates, perform some sanity checks before converting the lags to a list of integer. @@ -252,9 +268,11 @@ def _generate_lags( attributes contain only the extreme values If the lags are provided as integer, list, tuple or dictionary containing only the 'default_lags' keys, the lags values are contained in the self.lags attribute and the self.component_lags is an empty dictionary. + + If `output_chunk_shift > 0`, the `lags_future_covariates` are shifted into the future. """ - processed_lags: Dict[str, List[int]] = dict() - processed_component_lags: Dict[str, Dict[str, List[int]]] = dict() + processed_lags: dict[str, list[int]] = dict() + processed_component_lags: dict[str, dict[str, list[int]]] = dict() for lags_values, lags_name, lags_abbrev in zip( [lags, lags_past_covariates, lags_future_covariates], ["lags", "lags_past_covariates", "lags_future_covariates"], @@ -278,7 +296,7 @@ def _generate_lags( supported_types = "" min_lags = None max_lags = None - tmp_components_lags: Dict[str, List[int]] = dict() + tmp_components_lags: dict[str, list[int]] = dict() for comp_name, comp_lags in lags_values.items(): if lags_name == "lags_future_covariates": if isinstance(comp_lags, tuple): @@ -347,7 +365,7 @@ def _generate_lags( raise_log( ValueError( f"`{lags_name}` - `{comp_name}`: must be either a {supported_types}. " - f"Gived : {type(comp_lags)}." + f"Given : {type(comp_lags)}." ), logger, ) @@ -363,13 +381,28 @@ def _generate_lags( else: max_lags = max(max_lags, tmp_components_lags[comp_name][-1]) + # Check if only default lags are provided + has_default_lags = list(tmp_components_lags.keys()) == ["default_lags"] + # revert to shared lags logic when applicable - if list(tmp_components_lags.keys()) == ["default_lags"]: + if has_default_lags: processed_lags[lags_abbrev] = tmp_components_lags["default_lags"] else: processed_lags[lags_abbrev] = [min_lags, max_lags] processed_component_lags[lags_abbrev] = tmp_components_lags + # if output chunk is shifted, shift future covariates lags with it + if output_chunk_shift and lags_abbrev == "future": + processed_lags[lags_abbrev] = [ + lag_ + output_chunk_shift for lag_ in processed_lags[lags_abbrev] + ] + if processed_component_lags and not has_default_lags: + processed_component_lags[lags_abbrev] = { + comp_: [lag_ + output_chunk_shift for lag_ in lags_] + for comp_, lags_ in processed_component_lags[ + lags_abbrev + ].items() + } return processed_lags, processed_component_lags def _get_lags(self, lags_type: str): @@ -385,7 +418,7 @@ def _get_lags(self, lags_type: str): @property def _model_encoder_settings( self, - ) -> Tuple[int, int, bool, bool, Optional[List[int]], Optional[List[int]]]: + ) -> tuple[int, int, bool, bool, Optional[list[int]], Optional[list[int]]]: target_lags = self.lags.get("target", [0]) lags_past_covariates = self.lags.get("past", None) if lags_past_covariates is not None: @@ -403,7 +436,7 @@ def _model_encoder_settings( ] return ( abs(min(target_lags)), - self.output_chunk_length, + self.output_chunk_length + self.output_chunk_shift, lags_past_covariates is not None, lags_future_covariates is not None, lags_past_covariates, @@ -413,16 +446,18 @@ def _model_encoder_settings( @property def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, + Optional[int], ]: min_target_lag = self.lags["target"][0] if "target" in self.lags else None - max_target_lag = self.output_chunk_length - 1 + max_target_lag = self.output_chunk_length - 1 + self.output_chunk_shift min_past_cov_lag = self.lags["past"][0] if "past" in self.lags else None max_past_cov_lag = self.lags["past"][-1] if "past" in self.lags else None min_future_cov_lag = self.lags["future"][0] if "future" in self.lags else None @@ -434,6 +469,8 @@ def extreme_lags( max_past_cov_lag, min_future_cov_lag, max_future_cov_lag, + self.output_chunk_shift, + None, ) @property @@ -448,9 +485,12 @@ def supports_multivariate(self) -> bool: def min_train_series_length(self) -> int: return max( 3, - -self.lags["target"][0] + self.output_chunk_length - if "target" in self.lags - else self.output_chunk_length, + ( + -self.lags["target"][0] + self.output_chunk_length + if "target" in self.lags + else self.output_chunk_length + ) + + self.output_chunk_shift, ) @property @@ -461,40 +501,132 @@ def min_train_samples(self) -> int: def output_chunk_length(self) -> int: return self._output_chunk_length - def get_multioutput_estimator(self, horizon, target_dim): + @property + def output_chunk_shift(self) -> int: + return self._output_chunk_shift + + def get_multioutput_estimator(self, horizon: int, target_dim: int): + """Returns the estimator that forecasts the `horizon`th step of the `target_dim`th target component. + + Internally, estimators are grouped by `output_chunk_length` position, then by component. + + Parameters + ---------- + horizon + The index of the forecasting point within `output_chunk_length`. + target_dim + The index of the target component. + """ raise_if_not( isinstance(self.model, MultiOutputRegressor), "The sklearn model is not a MultiOutputRegressor object.", + logger, + ) + raise_if_not( + 0 <= horizon < self.output_chunk_length, + f"`horizon` must be `>= 0` and `< output_chunk_length={self.output_chunk_length}`.", + logger, ) + raise_if_not( + 0 <= target_dim < self.input_dim["target"], + f"`target_dim` must be `>= 0`, and `< n_target_components={self.input_dim['target']}`.", + logger, + ) + + # when multi_models=True, one model per horizon and target component + idx_estimator = ( + self.multi_models * self.input_dim["target"] * horizon + target_dim + ) + return self.model.estimators_[idx_estimator] + + def get_estimator(self, horizon: int, target_dim: int): + """Returns the estimator that forecasts the `horizon`th step of the `target_dim`th target component. - return self.model.estimators_[horizon + target_dim] + The model is returned directly if it supports multi-output natively. + + Parameters + ---------- + horizon + The index of the forecasting point within `output_chunk_length`. + target_dim + The index of the target component. + """ + + if isinstance(self.model, MultiOutputRegressor): + return self.get_multioutput_estimator( + horizon=horizon, target_dim=target_dim + ) + else: + logger.info( + "Model supports multi-output; a single estimator forecasts all the horizons and components." + ) + return self.model + + def _add_val_set_to_kwargs( + self, + kwargs: dict, + val_series: Sequence[TimeSeries], + val_past_covariates: Optional[Sequence[TimeSeries]], + val_future_covariates: Optional[Sequence[TimeSeries]], + val_sample_weight: Optional[Union[Sequence[TimeSeries], str]], + max_samples_per_ts: int, + ) -> dict: + """Creates a validation set and returns a new set of kwargs passed to `self.model.fit()` including the + validation set. This method can be overridden if the model requires a different logic to add the eval set.""" + val_samples, val_labels, val_weight = self._create_lagged_data( + series=val_series, + past_covariates=val_past_covariates, + future_covariates=val_future_covariates, + max_samples_per_ts=max_samples_per_ts, + sample_weight=val_sample_weight, + last_static_covariates_shape=self._static_covariates_shape, + ) + # create validation sets for MultiOutputRegressor + if val_labels.ndim == 2 and isinstance(self.model, MultiOutputRegressor): + val_sets, val_weights = [], [] + for i in range(val_labels.shape[1]): + val_sets.append((val_samples, val_labels[:, i])) + if val_weight is not None: + val_weights.append(val_weight[:, i]) + val_weights = val_weights or None + else: + val_sets = [(val_samples, val_labels)] + val_weights = [val_weight] + + val_set_name, val_weight_name = self.val_set_params + return dict(kwargs, **{val_set_name: val_sets, val_weight_name: val_weights}) def _create_lagged_data( self, - target_series: Sequence[TimeSeries], + series: Sequence[TimeSeries], past_covariates: Sequence[TimeSeries], future_covariates: Sequence[TimeSeries], max_samples_per_ts: int, + sample_weight: Optional[Union[TimeSeries, str]] = None, + last_static_covariates_shape: Optional[tuple[int, int]] = None, ): ( features, labels, _, self._static_covariates_shape, + sample_weights, ) = create_lagged_training_data( - target_series=target_series, + target_series=series, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, past_covariates=past_covariates, future_covariates=future_covariates, lags=self._get_lags("target"), lags_past_covariates=self._get_lags("past"), lags_future_covariates=self._get_lags("future"), uses_static_covariates=self.uses_static_covariates, - last_static_covariates_shape=None, + last_static_covariates_shape=last_static_covariates_shape, max_samples_per_ts=max_samples_per_ts, multi_models=self.multi_models, check_inputs=False, concatenate=False, + sample_weight=sample_weight, ) expected_nb_feat = ( @@ -507,7 +639,7 @@ def _create_lagged_data( if expected_nb_feat != X_i.shape[1]: shape_error_msg = [] for ts, cov_name, arg_name in zip( - [target_series, past_covariates, future_covariates], + [series, past_covariates, future_covariates], ["target", "past", "future"], ["series", "past_covariates", "future_covariates"], ): @@ -519,48 +651,89 @@ def _create_lagged_data( raise_log(ValueError("\n".join(shape_error_msg)), logger) features[i] = X_i[:, :, 0] labels[i] = y_i[:, :, 0] + if sample_weights is not None: + sample_weights[i] = sample_weights[i][:, :, 0] - training_samples = np.concatenate(features, axis=0) - training_labels = np.concatenate(labels, axis=0) + features = np.concatenate(features, axis=0) + labels = np.concatenate(labels, axis=0) + if sample_weights is not None: + sample_weights = np.concatenate(sample_weights, axis=0) - return training_samples, training_labels + # if labels are of shape (n_samples, 1) flatten it to shape (n_samples,) + if labels.ndim == 2 and labels.shape[1] == 1: + labels = labels.ravel() + if ( + sample_weights is not None + and sample_weights.ndim == 2 + and sample_weights.shape[1] == 1 + ): + sample_weights = sample_weights.ravel() + + return features, labels, sample_weights def _fit_model( self, - target_series: Sequence[TimeSeries], + series: Sequence[TimeSeries], past_covariates: Sequence[TimeSeries], future_covariates: Sequence[TimeSeries], max_samples_per_ts: int, + sample_weight: Optional[Union[Sequence[TimeSeries], str]], + val_series: Optional[Sequence[TimeSeries]] = None, + val_past_covariates: Optional[Sequence[TimeSeries]] = None, + val_future_covariates: Optional[Sequence[TimeSeries]] = None, + val_sample_weight: Optional[Union[Sequence[TimeSeries], str]] = None, **kwargs, ): """ - Function that fit the model. Deriving classes can override this method for adding additional parameters (e.g., - adding validation data), keeping the sanity checks on series performed by fit(). + Function that fit the model. Deriving classes can override this method for adding additional + parameters (e.g., adding validation data), keeping the sanity checks on series performed by fit(). """ - - training_samples, training_labels = self._create_lagged_data( - target_series, - past_covariates, - future_covariates, - max_samples_per_ts, + training_samples, training_labels, sample_weights = self._create_lagged_data( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + max_samples_per_ts=max_samples_per_ts, + sample_weight=sample_weight, + last_static_covariates_shape=None, ) - # if training_labels is of shape (n_samples, 1) flatten it to shape (n_samples,) - if len(training_labels.shape) == 2 and training_labels.shape[1] == 1: - training_labels = training_labels.ravel() - self.model.fit(training_samples, training_labels, **kwargs) + if self.supports_val_set and val_series is not None: + kwargs = self._add_val_set_to_kwargs( + kwargs=kwargs, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, + val_sample_weight=val_sample_weight, + max_samples_per_ts=max_samples_per_ts, + ) + + # only use `sample_weight` if model supports it + sample_weight_kwargs = dict() + if sample_weights is not None: + if self.supports_sample_weight: + sample_weight_kwargs = {"sample_weight": sample_weights} + else: + logger.warning( + "`sample_weight` was ignored since underlying regression model's " + "`fit()` method does not support it." + ) + self.model.fit( + training_samples, training_labels, **sample_weight_kwargs, **kwargs + ) # generate and store the lagged components names (for feature importance analysis) - self._lagged_feature_names, _ = create_lagged_component_names( - target_series=target_series, - past_covariates=past_covariates, - future_covariates=future_covariates, - lags=self._get_lags("target"), - lags_past_covariates=self._get_lags("past"), - lags_future_covariates=self._get_lags("future"), - output_chunk_length=self.output_chunk_length, - concatenate=False, - use_static_covariates=self.uses_static_covariates, + self._lagged_feature_names, self._lagged_label_names = ( + create_lagged_component_names( + target_series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + lags=self._get_lags("target"), + lags_past_covariates=self._get_lags("past"), + lags_future_covariates=self._get_lags("future"), + output_chunk_length=self.output_chunk_length, + concatenate=False, + use_static_covariates=self.uses_static_covariates, + ) ) def fit( @@ -570,6 +743,7 @@ def fit( future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, max_samples_per_ts: Optional[int] = None, n_jobs_multioutput_wrapper: Optional[int] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, **kwargs, ): """ @@ -593,6 +767,16 @@ def fit( n_jobs_multioutput_wrapper Number of jobs of the MultiOutputRegressor wrapper to run in parallel. Only used if the model doesn't support multi-output regression natively. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. **kwargs Additional keyword arguments passed to the `fit` method of the model. """ @@ -600,8 +784,15 @@ def fit( series = series2seq(series) past_covariates = series2seq(past_covariates) future_covariates = series2seq(future_covariates) + val_series = series2seq(kwargs.pop("val_series", None)) + val_past_covariates = series2seq(kwargs.pop("val_past_covariates", None)) + val_future_covariates = series2seq(kwargs.pop("val_future_covariates", None)) - self._verify_static_covariates(series[0].static_covariates) + if not isinstance(sample_weight, str): + sample_weight = series2seq(sample_weight) + val_sample_weight = kwargs.pop("val_sample_weight", None) + if not isinstance(val_sample_weight, str): + val_sample_weight = series2seq(val_sample_weight) self.encoders = self.initialize_encoders() if self.encoders.encoding_available: @@ -620,6 +811,7 @@ def fit( and self.supports_static_covariates and self.considers_static_covariates ): + self._verify_static_covariates(get_single_series(series).static_covariates) self._uses_static_covariates = True for covs, name in zip([past_covariates, future_covariates], ["past", "future"]): @@ -635,6 +827,18 @@ def fit( "constructor.", ) + if self.supports_val_set: + val_series, val_past_covariates, val_future_covariates = ( + self._process_validation_set( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, + ) + ) + # saving the dims of all input series to check at prediction time self.input_dim = { "target": series[0].width, @@ -643,29 +847,42 @@ def fit( } # if multi-output regression + use_mor = False if not series[0].is_univariate or ( - self.output_chunk_length > 1 and self.multi_models + self.output_chunk_length > 1 + and self.multi_models + and not isinstance(self.model, MultiOutputRegressor) ): - # and model isn't wrapped already - if not isinstance(self.model, MultiOutputRegressor): - # check whether model supports multi-output regression natively - if not ( - callable(getattr(self.model, "_get_tags", None)) - and isinstance(self.model._get_tags(), dict) - and self.model._get_tags().get("multioutput") - ): - # if not, wrap model with MultiOutputRegressor - self.model = MultiOutputRegressor( - self.model, n_jobs=n_jobs_multioutput_wrapper - ) - elif self.model.__class__.__name__ == "CatBoostRegressor": - if ( - self.model.get_params()["loss_function"] - == "RMSEWithUncertainty" - ): - self.model = MultiOutputRegressor( - self.model, n_jobs=n_jobs_multioutput_wrapper - ) + if sample_weight is not None: + # we have 2D sample (and time) weights, only supported in Darts + use_mor = True + elif not ( + callable(getattr(self.model, "_get_tags", None)) + and isinstance(self.model._get_tags(), dict) + and self.model._get_tags().get("multioutput") + ): + # model does not support multi-output regression natively + use_mor = True + elif ( + self.model.__class__.__name__ == "CatBoostRegressor" + and self.model.get_params()["loss_function"] == "RMSEWithUncertainty" + ): + use_mor = True + elif ( + self.model.__class__.__name__ == "XGBRegressor" + and self.likelihood is not None + ): + # since xgboost==2.1.0, likelihoods do not support native multi output regression + use_mor = True + + if use_mor: + val_set_name, val_weight_name = self.val_set_params + mor_kwargs = { + "eval_set_name": val_set_name, + "eval_weight_name": val_weight_name, + "n_jobs": n_jobs_multioutput_wrapper, + } + self.model = MultiOutputRegressor(self.model, **mor_kwargs) # warn if n_jobs_multioutput_wrapper was provided but not used if ( @@ -716,9 +933,11 @@ def fit( else: # reorder the components based on the input series, insert the default when necessary self.component_lags[variate_type] = { - comp_name: self.component_lags[variate_type][comp_name] - if comp_name in self.component_lags[variate_type] - else self.component_lags[variate_type]["default_lags"] + comp_name: ( + self.component_lags[variate_type][comp_name] + if comp_name in self.component_lags[variate_type] + else self.component_lags[variate_type]["default_lags"] + ) for comp_name in variate[0].components } @@ -727,9 +946,17 @@ def fit( raise_log(ValueError("\n".join(component_lags_error_msg)), logger) self._fit_model( - series, past_covariates, future_covariates, max_samples_per_ts, **kwargs + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, + sample_weight=sample_weight, + val_sample_weight=val_sample_weight, + max_samples_per_ts=max_samples_per_ts, + **kwargs, ) - return self def predict( @@ -764,9 +991,9 @@ def predict( Number of times a prediction is sampled from a probabilistic model. Should be set to 1 for deterministic models. verbose - Optionally, whether to print progress. + Whether to print the progress. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. Default: ``False`` **kwargs : dict, optional @@ -781,13 +1008,13 @@ def predict( raise_log( ValueError( "Input `series` must be provided. This is the result either from fitting on multiple series, " - "or from not having fit the model yet." + "from not having fit the model yet, or from loading a model saved with `clean=True`." ), logger, ) series = self.training_series - called_with_single_series = True if isinstance(series, TimeSeries) else False + called_with_single_series = isinstance(series, TimeSeries) # guarantee that all inputs are either list of TimeSeries or None series = series2seq(series) @@ -799,7 +1026,8 @@ def predict( past_covariates = series2seq(past_covariates) future_covariates = series2seq(future_covariates) - self._verify_static_covariates(series[0].static_covariates) + if self.uses_static_covariates: + self._verify_static_covariates(series[0].static_covariates) # encoders are set when calling fit(), but not when calling fit_from_dataset() # when covariates are loaded from model, they already contain the encodings: this is not a problem as the @@ -874,14 +1102,19 @@ def predict( # check for sufficient covariate data if not (cov.start_time() <= start_ts and cov.end_time() >= end_ts): + index_text = ( + " " + if called_with_single_series + else f" at list/sequence index {idx} " + ) raise_log( ValueError( - f"The corresponding {cov_type}_covariate of the series at index {idx} isn't sufficiently " - f"long. Given horizon `n={n}`, `min(lags_{cov_type}_covariates)={lags[0]}`, " + f"The `{cov_type}_covariates`{index_text}are not long enough. " + f"Given horizon `n={n}`, `min(lags_{cov_type}_covariates)={lags[0]}`, " f"`max(lags_{cov_type}_covariates)={lags[-1]}` and " - f"`output_chunk_length={self.output_chunk_length}`, the {cov_type}_covariate has to range " - f"from {start_ts} until {end_ts} (inclusive), but it ranges only from {cov.start_time()} " - f"until {cov.end_time()}." + f"`output_chunk_length={self.output_chunk_length}`, the `{cov_type}_covariates` have to " + f"range from {start_ts} until {end_ts} (inclusive), but they only range from " + f"{cov.start_time()} until {cov.end_time()}." ), logger=logger, ) @@ -897,12 +1130,10 @@ def predict( series_matrix = None if "target" in self.lags: - series_matrix = np.stack( - [ - ts.values(copy=False)[self.lags["target"][0] - shift :, :] - for ts in series - ] - ) + series_matrix = np.stack([ + ts.values(copy=False)[self.lags["target"][0] - shift :, :] + for ts in series + ]) # repeat series_matrix to shape (num_samples * num_series, n_lags, n_components) # [series 0 sample 0, series 0 sample 1, ..., series n sample k] @@ -911,10 +1142,18 @@ def predict( # same for covariate matrices for cov_type, data in covariate_matrices.items(): covariate_matrices[cov_type] = np.repeat(data, num_samples, axis=0) + + # for concatenating target with predictions (or quantile parameters) + if predict_likelihood_parameters and self.likelihood is not None: + # with `multi_models=False`, the predictions are concatenated with the past target, even if `n<=ocl` + # to make things work, we just append the first predicted parameter (it will never be accessed) + sample_slice = slice(0, None, self.num_parameters) + else: + sample_slice = slice(None) + # prediction predictions = [] last_step_shift = 0 - # t_pred indicates the number of time steps after the first prediction for t_pred in range(0, n, step): # in case of autoregressive forecast `(t_pred > 0)` and if `n` is not a round multiple of `step`, @@ -923,83 +1162,27 @@ def predict( last_step_shift = t_pred - (n - step) t_pred = n - step - np_X = [] - # retrieve target lags - if "target" in self.lags: - if predictions: - series_matrix = np.concatenate( - [series_matrix, predictions[-1]], axis=1 - ) - # component-wise lags - if "target" in self.component_lags: - tmp_X = [ - series_matrix[ - :, - [lag - (shift + last_step_shift) for lag in comp_lags], - comp_i, - ] - for comp_i, (comp, comp_lags) in enumerate( - self.component_lags["target"].items() - ) - ] - # values are grouped by component - np_X.append( - np.concatenate(tmp_X, axis=1).reshape( - len(series) * num_samples, -1 - ) - ) - else: - # values are grouped by lags - np_X.append( - series_matrix[ - :, - [ - lag - (shift + last_step_shift) - for lag in self.lags["target"] - ], - ].reshape(len(series) * num_samples, -1) - ) - # retrieve covariate lags, enforce order (dict only preserves insertion order for python 3.6+) - for cov_type in ["past", "future"]: - if cov_type in covariate_matrices: - # component-wise lags - if cov_type in self.component_lags: - tmp_X = [ - covariate_matrices[cov_type][ - :, - np.array(comp_lags) - self.lags[cov_type][0] + t_pred, - comp_i, - ] - for comp_i, (comp, comp_lags) in enumerate( - self.component_lags[cov_type].items() - ) - ] - np_X.append( - np.concatenate(tmp_X, axis=1).reshape( - len(series) * num_samples, -1 - ) - ) - else: - np_X.append( - covariate_matrices[cov_type][ - :, relative_cov_lags[cov_type] + t_pred - ].reshape(len(series) * num_samples, -1) - ) + # concatenate previous iteration forecasts + if "target" in self.lags and predictions: + series_matrix = np.concatenate( + [series_matrix, predictions[-1][:, :, sample_slice]], axis=1 + ) - # concatenate retrieved lags - X = np.concatenate(np_X, axis=1) - # Need to split up `X` into three equally-sized sub-blocks - # corresponding to each timeseries in `series`, so that - # static covariates can be added to each block; valid since - # each block contains same number of observations: - X_blocks = np.split(X, len(series), axis=0) - X_blocks, _ = add_static_covariates_to_lagged_data( - X_blocks, - series, + # extract and concatenate lags from target and covariates series + X = _create_lagged_data_autoregression( + target_series=series, + t_pred=t_pred, + shift=shift, + last_step_shift=last_step_shift, + series_matrix=series_matrix, + covariate_matrices=covariate_matrices, + lags=self.lags, + component_lags=self.component_lags, + relative_cov_lags=relative_cov_lags, + num_samples=num_samples, uses_static_covariates=self.uses_static_covariates, - last_shape=self._static_covariates_shape, + last_static_covariates_shape=self._static_covariates_shape, ) - X = np.concatenate(X_blocks, axis=0) # X has shape (n_series * n_samples, n_regression_features) prediction = self._predict_and_sample( @@ -1022,11 +1205,15 @@ def predict( self._build_forecast_series( points_preds=row, input_series=input_tgt, - custom_components=self._likelihood_components_names(input_tgt) - if predict_likelihood_parameters - else None, + custom_components=( + self._likelihood_components_names(input_tgt) + if predict_likelihood_parameters + else None + ), with_static_covs=False if predict_likelihood_parameters else True, with_hierarchy=False if predict_likelihood_parameters else True, + pred_start=input_tgt.end_time() + + (1 + self.output_chunk_shift) * input_tgt.freq, ) for idx_ts, (row, input_tgt) in enumerate(zip(predictions, series)) ] @@ -1046,7 +1233,7 @@ def _predict_and_sample( return prediction.reshape(k, self.pred_dim, -1) @property - def lagged_feature_names(self) -> Optional[List[str]]: + def lagged_feature_names(self) -> Optional[list[str]]: """The lagged feature names the model has been trained on. The naming convention for target, past and future covariates is: ``"{name}_{type}_lag{i}"``, where: @@ -1063,9 +1250,24 @@ def lagged_feature_names(self) -> Optional[List[str]]: """ return self._lagged_feature_names + @property + def lagged_label_names(self) -> Optional[list[str]]: + """The lagged label name for the model's estimators. + + The naming convention is: ``"{name}_target_hrz{i}"``, where: + + - ``{name}`` the component name of the (first) series + - ``{i}`` is the position in output_chunk_length (label lag) + """ + return self._lagged_label_names + def __str__(self): return self.model.__str__() + @property + def likelihood(self) -> Optional[str]: + return getattr(self, "_likelihood", None) + @property def supports_past_covariates(self) -> bool: return len(self.lags.get("past", [])) > 0 @@ -1078,6 +1280,26 @@ def supports_future_covariates(self) -> bool: def supports_static_covariates(self) -> bool: return True + @property + def supports_val_set(self) -> bool: + """Whether the model supports a validation set during training.""" + return False + + @property + def supports_sample_weight(self) -> bool: + """Whether the model supports a validation set during training.""" + return ( + self.model.supports_sample_weight + if isinstance(self.model, MultiOutputRegressor) + else has_fit_parameter(self.model, "sample_weight") + ) + + @property + def val_set_params(self) -> tuple[Optional[str], Optional[str]]: + """Returns the parameter names for the validation set, and validation sample weights if it supports + a validation set.""" + return None, None + def _check_optimizable_historical_forecasts( self, forecast_horizon: int, @@ -1098,9 +1320,9 @@ def _check_optimizable_historical_forecasts( def _optimized_historical_forecasts( self, - series: Optional[Sequence[TimeSeries]], - past_covariates: Optional[Sequence[TimeSeries]] = None, - future_covariates: Optional[Sequence[TimeSeries]] = None, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, num_samples: int = 1, start: Optional[Union[pd.Timestamp, float, int]] = None, start_format: Literal["position", "value"] = "value", @@ -1112,9 +1334,7 @@ def _optimized_historical_forecasts( show_warnings: bool = True, predict_likelihood_parameters: bool = False, **kwargs, - ) -> Union[ - TimeSeries, List[TimeSeries], Sequence[TimeSeries], Sequence[List[TimeSeries]] - ]: + ) -> Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]]: """ For RegressionModels we create the lagged prediction data once per series using a moving window. With this, we can avoid having to recreate the tabular input data and call `model.predict()` for each @@ -1123,18 +1343,20 @@ def _optimized_historical_forecasts( TODO: support forecast_horizon > output_chunk_length (auto-regression) """ - series, past_covariates, future_covariates = _process_historical_forecast_input( - model=self, - series=series, - past_covariates=past_covariates, - future_covariates=future_covariates, - forecast_horizon=forecast_horizon, - allow_autoregression=False, + series, past_covariates, future_covariates, series_seq_type = ( + _process_historical_forecast_input( + model=self, + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=forecast_horizon, + allow_autoregression=False, + ) ) # TODO: move the loop here instead of duplicated code in each sub-routine? if last_points_only: - return _optimized_historical_forecasts_last_points_only( + hfc = _optimized_historical_forecasts_last_points_only( model=self, series=series, past_covariates=past_covariates, @@ -1146,11 +1368,12 @@ def _optimized_historical_forecasts( stride=stride, overlap_end=overlap_end, show_warnings=show_warnings, + verbose=verbose, predict_likelihood_parameters=predict_likelihood_parameters, **kwargs, ) else: - return _optimized_historical_forecasts_all_points( + hfc = _optimized_historical_forecasts_all_points( model=self, series=series, past_covariates=past_covariates, @@ -1162,9 +1385,11 @@ def _optimized_historical_forecasts( stride=stride, overlap_end=overlap_end, show_warnings=show_warnings, + verbose=verbose, predict_likelihood_parameters=predict_likelihood_parameters, **kwargs, ) + return series2seq(hfc, seq_type_out=series_seq_type) class _LikelihoodMixin: @@ -1207,7 +1432,7 @@ def _prepare_quantiles(quantiles): def _likelihood_components_names( self, input_series: TimeSeries - ) -> Optional[List[str]]: + ) -> Optional[list[str]]: if self.likelihood == "quantile": return self._quantiles_generate_components_names(input_series) elif self.likelihood == "poisson": @@ -1453,20 +1678,18 @@ def num_parameters(self) -> int: def _quantiles_generate_components_names( self, input_series: TimeSeries - ) -> List[str]: + ) -> list[str]: return self._likelihood_generate_components_names( input_series, - [f"q{quantile:.2f}" for quantile in self._model_container.keys()], + quantile_names(q=self._model_container.keys()), ) def _likelihood_generate_components_names( - self, input_series: TimeSeries, parameter_names: List[str] - ) -> List[str]: - return [ - f"{tgt_name}_{param_n}" - for tgt_name in input_series.components - for param_n in parameter_names - ] + self, input_series: TimeSeries, parameter_names: list[str] + ) -> list[str]: + return likelihood_component_names( + components=input_series.components, parameter_names=parameter_names + ) class _QuantileModelContainer(OrderedDict): @@ -1478,16 +1701,17 @@ class RegressionModelWithCategoricalCovariates(RegressionModel): def __init__( self, lags: Union[int, list] = None, - lags_past_covariates: Union[int, List[int]] = None, - lags_future_covariates: Union[Tuple[int, int], List[int]] = None, + lags_past_covariates: Union[int, list[int]] = None, + lags_future_covariates: Union[tuple[int, int], list[int]] = None, output_chunk_length: int = 1, + output_chunk_shift: int = 0, add_encoders: Optional[dict] = None, model=None, multi_models: Optional[bool] = True, use_static_covariates: bool = True, - categorical_past_covariates: Optional[Union[str, List[str]]] = None, - categorical_future_covariates: Optional[Union[str, List[str]]] = None, - categorical_static_covariates: Optional[Union[str, List[str]]] = None, + categorical_past_covariates: Optional[Union[str, list[str]]] = None, + categorical_future_covariates: Optional[Union[str, list[str]]] = None, + categorical_static_covariates: Optional[Union[str, list[str]]] = None, ): """ Extension of `RegressionModel` for regression models that support categorical covariates. @@ -1495,24 +1719,52 @@ def __init__( Parameters ---------- lags - Lagged target values used to predict the next time step. If an integer is given the last `lags` past lags - are used (from -1 backward). Otherwise, a list of integers with lags is required (each lag must be < 0). + Lagged target `series` values used to predict the next time step/s. + If an integer, must be > 0. Uses the last `n=lags` past lags; e.g. `(-1, -2, ..., -lags)`, where `0` + corresponds the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. + If a list of integers, each value must be < 0. Uses only the specified values as lags. + If a dictionary, the keys correspond to the `series` component names (of the first series when + using multiple series) and the values correspond to the component lags (integer or list of integers). The + key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some + components are missing and the 'default_lags' key is not provided. lags_past_covariates - Number of lagged past_covariates values used to predict the next time step. If an integer is given the last - `lags_past_covariates` past lags are used (inclusive, starting from lag -1). Otherwise a list of integers - with lags < 0 is required. + Lagged `past_covariates` values used to predict the next time step/s. + If an integer, must be > 0. Uses the last `n=lags_past_covariates` past lags; e.g. `(-1, -2, ..., -lags)`, + where `0` corresponds to the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. + If a list of integers, each value must be < 0. Uses only the specified values as lags. + If a dictionary, the keys correspond to the `past_covariates` component names (of the first series when + using multiple series) and the values correspond to the component lags (integer or list of integers). The + key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some + components are missing and the 'default_lags' key is not provided. lags_future_covariates - Number of lagged future_covariates values used to predict the next time step. If a tuple (past, future) is - given the last `past` lags in the past are used (inclusive, starting from lag -1) along with the first - `future` future lags (starting from 0 - the prediction time - up to `future - 1` included). Otherwise a list - of integers with lags is required. + Lagged `future_covariates` values used to predict the next time step/s. The lags are always relative to the + first step in the output chunk, even when `output_chunk_shift > 0`. + If a tuple of `(past, future)`, both values must be > 0. Uses the last `n=past` past lags and `n=future` + future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` corresponds the first + predicted time step of each sample. If `output_chunk_shift > 0`, the position of negative lags differ from + those of `lags` and `lags_past_covariates`. In this case a future lag `-5` would point at the same + step as a target lag of `-5 + output_chunk_shift`. + If a list of integers, uses only the specified values as lags. + If a dictionary, the keys correspond to the `future_covariates` component names (of the first series when + using multiple series) and the values correspond to the component lags (tuple or list of integers). The key + 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some + components are missing and the 'default_lags' key is not provided. output_chunk_length Number of time steps predicted at once (per chunk) by the internal model. It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated using a - one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is + one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input (history of target and past covariates) and + output. If the model supports `future_covariates`, the `lags_future_covariates` are relative to the first + step in the shifted output chunk. Predictions will start `output_chunk_shift` steps after the end of the + target `series`. If `output_chunk_shift` is set, the model cannot generate autoregressive predictions + (`n > output_chunk_length`). add_encoders A large number of past and future covariates can be automatically generated with `add_encoders`. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that @@ -1564,6 +1816,7 @@ def encode_year(idx): lags_past_covariates=lags_past_covariates, lags_future_covariates=lags_future_covariates, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, add_encoders=add_encoders, model=model, multi_models=multi_models, @@ -1592,6 +1845,7 @@ def fit( future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, max_samples_per_ts: Optional[int] = None, n_jobs_multioutput_wrapper: Optional[int] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, **kwargs, ): self._validate_categorical_covariates( @@ -1605,11 +1859,12 @@ def fit( future_covariates=future_covariates, max_samples_per_ts=max_samples_per_ts, n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper, + sample_weight=sample_weight, **kwargs, ) @property - def _categorical_fit_param(self) -> Tuple[str, Any]: + def _categorical_fit_param(self) -> tuple[str, Any]: """ Returns the name, and default value of the categorical features parameter from model's `fit` method . Can be overridden in subclasses. @@ -1682,10 +1937,10 @@ def _validate_categorical_covariates( def _get_categorical_features( self, - series: Union[List[TimeSeries], TimeSeries], - past_covariates: Optional[Union[List[TimeSeries], TimeSeries]] = None, - future_covariates: Optional[Union[List[TimeSeries], TimeSeries]] = None, - ) -> Tuple[List[int], List[str]]: + series: Union[Sequence[TimeSeries], TimeSeries], + past_covariates: Optional[Union[Sequence[TimeSeries], TimeSeries]] = None, + future_covariates: Optional[Union[Sequence[TimeSeries], TimeSeries]] = None, + ) -> tuple[list[int], list[str]]: """ Returns the indices and column names of the categorical features in the regression model. @@ -1757,10 +2012,11 @@ def _get_categorical_features( def _fit_model( self, - target_series, + series, past_covariates, future_covariates, max_samples_per_ts, + sample_weight, **kwargs, ): """ @@ -1768,9 +2024,9 @@ def _fit_model( handle categorical features directly. """ cat_col_indices, _ = self._get_categorical_features( - target_series, - past_covariates, - future_covariates, + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, ) cat_param_name, cat_param_default = self._categorical_fit_param @@ -1778,9 +2034,10 @@ def _fit_model( cat_col_indices if cat_col_indices else cat_param_default ) super()._fit_model( - target_series=target_series, + series=series, past_covariates=past_covariates, future_covariates=future_covariates, max_samples_per_ts=max_samples_per_ts, + sample_weight=sample_weight, **kwargs, ) diff --git a/darts/models/forecasting/rnn_model.py b/darts/models/forecasting/rnn_model.py index 16ef18015e..d51b0a3838 100644 --- a/darts/models/forecasting/rnn_model.py +++ b/darts/models/forecasting/rnn_model.py @@ -5,7 +5,8 @@ import inspect from abc import ABC, abstractmethod -from typing import Optional, Sequence, Tuple, Type, Union +from collections.abc import Sequence +from typing import Optional, Union import torch import torch.nn as nn @@ -62,7 +63,8 @@ def __init__( dropout The fraction of neurons that are dropped in all-but-last RNN layers. **kwargs - all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class. + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. """ # RNNModule doesn't really need input and output_chunk_length for PLModule super().__init__(**kwargs) @@ -78,8 +80,8 @@ def __init__( @io_processor @abstractmethod def forward( - self, x_in: Tuple, h: Optional[torch.Tensor] = None - ) -> Tuple[torch.Tensor, torch.Tensor]: + self, x_in: tuple, h: Optional[torch.Tensor] = None + ) -> tuple[torch.Tensor, torch.Tensor]: """RNN Module forward. Parameters @@ -101,7 +103,13 @@ def forward( """ pass - def _produce_train_output(self, input_batch: Tuple) -> torch.Tensor: + def _produce_train_output(self, input_batch: tuple) -> torch.Tensor: + # only return the forecast, not the hidden state + return self(self._process_input_batch(input_batch))[0] + + def _process_input_batch( + self, input_batch: tuple + ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: ( past_target, historic_future_covariates, @@ -110,17 +118,18 @@ def _produce_train_output(self, input_batch: Tuple) -> torch.Tensor: ) = input_batch # For the RNN we concatenate the past_target with the future_covariates # (they have the same length because we enforce a Shift dataset for RNNs) - model_input = ( - torch.cat([past_target, future_covariates], dim=2) - if future_covariates is not None - else past_target, + return ( + ( + torch.cat([past_target, future_covariates], dim=2) + if future_covariates is not None + else past_target + ), static_covariates, ) - return self(model_input)[0] def _produce_predict_output( - self, x: Tuple, last_hidden_state: Optional[torch.Tensor] = None - ) -> Tuple[torch.Tensor, torch.Tensor]: + self, x: tuple, last_hidden_state: Optional[torch.Tensor] = None + ) -> tuple[torch.Tensor, torch.Tensor]: """overwrite parent classes `_produce_predict_output` method""" output, hidden = self(x, last_hidden_state) if self.likelihood: @@ -132,7 +141,7 @@ def _produce_predict_output( return output.squeeze(dim=-1), hidden def _get_batch_prediction( - self, n: int, input_batch: Tuple, roll_size: int + self, n: int, input_batch: tuple, roll_size: int ) -> torch.Tensor: """ This model is recurrent, so we have to write a specific way to @@ -160,14 +169,14 @@ def _get_batch_prediction( cov_future = None batch_prediction = [] - out, last_hidden_state = self._produce_predict_output( - (input_series, static_covariates) - ) + out, last_hidden_state = self._produce_predict_output(( + input_series, + static_covariates, + )) batch_prediction.append(out[:, -1:, :]) prediction_length = 1 while prediction_length < n: - # create new input to model from last prediction and current covariates, if available new_input = ( torch.cat( @@ -215,7 +224,7 @@ def __init__( name The name of the specific PyTorch RNN module ("RNN", "GRU" or "LSTM"). **kwargs - all parameters required for the :class:`darts.model.forecasting_models.CustomRNNModule` base class. + all parameters required for the :class:`darts.models.forecasting.CustomRNNModule` base class. Inputs ------ @@ -248,8 +257,8 @@ def __init__( @io_processor def forward( - self, x_in: Tuple, h: Optional[torch.Tensor] = None - ) -> Tuple[torch.Tensor, torch.Tensor]: + self, x_in: tuple, h: Optional[torch.Tensor] = None + ) -> tuple[torch.Tensor, torch.Tensor]: x, _ = x_in # data is of size (batch_size, input_length, input_size) batch_size = x.shape[0] @@ -271,14 +280,13 @@ class RNNModel(DualCovariatesTorchModel): def __init__( self, input_chunk_length: int, - model: Union[str, Type[CustomRNNModule]] = "RNN", + model: Union[str, type[CustomRNNModule]] = "RNN", hidden_dim: int = 25, n_rnn_layers: int = 1, dropout: float = 0.0, training_length: int = 24, **kwargs, ): - """Recurrent Neural Network Model (RNNs). This class provides three variants of RNNs: @@ -292,7 +300,7 @@ def __init__( RNNModel is fully recurrent in the sense that, at prediction time, an output is computed using these inputs: - previous target value, which will be set to the last known target value for the first prediction, - and for all other predictions it will be set to the previous prediction (in an auto-regressive fashion), + and for all other predictions it will be set to the previous prediction (in an autoregressive fashion), - the previous hidden state, - the covariates at time `t` for forecasting the target at time `t` (if the model was trained with covariates), @@ -316,12 +324,12 @@ def __init__( n_rnn_layers The number of recurrent layers. dropout - Fraction of neurons afected by Dropout. + Fraction of neurons affected by Dropout. training_length The length of both input (target and covariates) and output (target) time series used during - training. Generally speaking, `training_length` should have a higher value than `input_chunk_length` - because otherwise during training the RNN is never run for as many iterations as it will during - inference. For more information on this parameter, please see `darts.utils.data.ShiftedDataset` + training. Must have a larger value than `input_chunk_length`, because otherwise during training + the RNN is never run for as many iterations as it will during inference. For more information on + this parameter, please see `darts.utils.data.ShiftedDataset`. **kwargs Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and Darts' :class:`TorchForecastingModel`. @@ -483,11 +491,23 @@ def encode_year(idx): `RNN example notebook `_ presents techniques that can be used to improve the forecasts quality compared to this simple usage example. """ + if training_length < input_chunk_length: + raise_log( + ValueError( + f"`training_length` ({training_length}) must be `>=input_chunk_length` ({input_chunk_length})." + ), + logger=logger, + ) # create copy of model parameters model_kwargs = {key: val for key, val in self.model_params.items()} for kwarg, default_value in zip( - ["output_chunk_length", "use_reversible_instance_norm"], [1, False] + [ + "output_chunk_length", + "use_reversible_instance_norm", + "output_chunk_shift", + ], + [1, False, 0], ): if model_kwargs.get(kwarg) is not None: logger.warning( @@ -518,7 +538,7 @@ def encode_year(idx): self.n_rnn_layers = n_rnn_layers self.training_length = training_length - def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module: + def _create_model(self, train_sample: tuple[torch.Tensor]) -> torch.nn.Module: # samples are made of (past_target, historic_future_covariates, future_covariates, future_target) # historic_future_covariates and future_covariates have the same width input_dim = train_sample[0].shape[1] + ( @@ -549,9 +569,9 @@ def _build_train_dataset( target: Sequence[TimeSeries], past_covariates: Optional[Sequence[TimeSeries]], future_covariates: Optional[Sequence[TimeSeries]], + sample_weight: Optional[Sequence[TimeSeries]], max_samples_per_ts: Optional[int], ) -> DualCovariatesShiftedDataset: - return DualCovariatesShiftedDataset( target_series=target, covariates=future_covariates, @@ -559,6 +579,7 @@ def _build_train_dataset( shift=1, max_samples_per_ts=max_samples_per_ts, use_static_covariates=self.uses_static_covariates, + sample_weight=sample_weight, ) def _verify_train_dataset_type(self, train_dataset: TrainingDataset): @@ -578,3 +599,27 @@ def supports_multivariate(self) -> bool: @property def min_train_series_length(self) -> int: return self.training_length + 1 + + @property + def extreme_lags( + self, + ) -> tuple[ + Optional[int], + Optional[int], + Optional[int], + Optional[int], + Optional[int], + Optional[int], + int, + Optional[int], + ]: + return ( + -self.input_chunk_length, + self.output_chunk_length - 1, + None, + None, + -self.input_chunk_length, + self.output_chunk_length - 1, + self.output_chunk_shift, + self.training_length - self.input_chunk_length, + ) diff --git a/darts/models/forecasting/sf_auto_arima.py b/darts/models/forecasting/sf_auto_arima.py index c036a80b80..dba6e1c6bc 100644 --- a/darts/models/forecasting/sf_auto_arima.py +++ b/darts/models/forecasting/sf_auto_arima.py @@ -32,7 +32,7 @@ def __init__( It is probabilistic, whereas :class:`AutoARIMA` is not. We refer to the `statsforecast AutoARIMA documentation - `_ + `_ for the exhaustive documentation of the arguments. Parameters @@ -134,5 +134,5 @@ def _supports_range_index(self) -> bool: return True @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return True diff --git a/darts/models/forecasting/sf_auto_ces.py b/darts/models/forecasting/sf_auto_ces.py index 4b79aa111d..8f3a9f8adb 100644 --- a/darts/models/forecasting/sf_auto_ces.py +++ b/darts/models/forecasting/sf_auto_ces.py @@ -18,7 +18,7 @@ def __init__(self, *autoces_args, **autoces_kwargs): We refer to the `statsforecast AutoCES documentation - `_ + `_ for the exhaustive documentation of the arguments. Parameters @@ -84,7 +84,3 @@ def min_train_series_length(self) -> int: @property def _supports_range_index(self) -> bool: return True - - @property - def _is_probabilistic(self) -> bool: - return False diff --git a/darts/models/forecasting/sf_auto_ets.py b/darts/models/forecasting/sf_auto_ets.py index 9636436e0a..d4959db607 100644 --- a/darts/models/forecasting/sf_auto_ets.py +++ b/darts/models/forecasting/sf_auto_ets.py @@ -31,7 +31,7 @@ def __init__( on Numba and jit compilation. We refer to the `statsforecast AutoETS documentation - `_ + `_ for the exhaustive documentation of the arguments. In addition to the StatsForecast implementation, this model can handle future covariates. It does so by first @@ -164,5 +164,5 @@ def _supports_range_index(self) -> bool: return True @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return True diff --git a/darts/models/forecasting/sf_auto_tbats.py b/darts/models/forecasting/sf_auto_tbats.py new file mode 100644 index 0000000000..7e1bc16746 --- /dev/null +++ b/darts/models/forecasting/sf_auto_tbats.py @@ -0,0 +1,104 @@ +""" +StatsForecastAutoTBATS +----------- +""" + +from statsforecast.models import AutoTBATS as SFAutoTBATS + +from darts import TimeSeries +from darts.models.components.statsforecast_utils import ( + create_normal_samples, + one_sigma_rule, + unpack_sf_dict, +) +from darts.models.forecasting.forecasting_model import LocalForecastingModel + + +class StatsForecastAutoTBATS(LocalForecastingModel): + def __init__(self, *autoTBATS_args, **autoTBATS_kwargs): + """Auto-TBATS based on `Statsforecasts package + `_. + + Automatically selects the best TBATS model from all feasible combinations of the parameters `use_boxcox`, + `use_trend`, `use_damped_trend`, and `use_arma_errors`. Selection is made using the AIC. + Default value for `use_arma_errors` is True since this enables the evaluation of models with + and without ARMA errors. + + + + We refer to the `statsforecast AutoTBATS documentation + `_ + for the exhaustive documentation of the arguments. + + Parameters + ---------- + autoTBATS_args + Positional arguments for ``statsforecasts.models.AutoTBATS``. + autoTBATS_kwargs + Keyword arguments for ``statsforecasts.models.AutoTBATS``. + + Examples + -------- + >>> from darts.datasets import AirPassengersDataset + >>> from darts.models import StatsForecastAutoTBATS + >>> series = AirPassengersDataset().load() + >>> # define StatsForecastAutoTBATS parameters + >>> model = StatsForecastAutoTBATS(season_length=12) + >>> model.fit(series) + >>> pred = model.predict(6) + >>> pred.values() + array([[450.79653684], + [472.09265790], + [497.76948306], + [510.74927369], + [520.92224557], + [570.33881522]]) + """ + super().__init__() + self.model = SFAutoTBATS(*autoTBATS_args, **autoTBATS_kwargs) + + def fit(self, series: TimeSeries): + super().fit(series) + self._assert_univariate(series) + series = self.training_series + self.model.fit( + series.values(copy=False).flatten(), + ) + return self + + def predict( + self, + n: int, + num_samples: int = 1, + verbose: bool = False, + show_warnings: bool = True, + ): + super().predict(n, num_samples) + forecast_dict = self.model.predict( + h=n, + level=(one_sigma_rule,), # ask one std for the confidence interval. + ) + + mu, std = unpack_sf_dict(forecast_dict) + if num_samples > 1: + samples = create_normal_samples(mu, std, num_samples, n) + else: + samples = mu + + return self._build_forecast_series(samples) + + @property + def supports_multivariate(self) -> bool: + return False + + @property + def min_train_series_length(self) -> int: + return 10 + + @property + def _supports_range_index(self) -> bool: + return True + + @property + def supports_probabilistic_prediction(self) -> bool: + return True diff --git a/darts/models/forecasting/sf_auto_theta.py b/darts/models/forecasting/sf_auto_theta.py index 53a6400cca..628c1a1f04 100644 --- a/darts/models/forecasting/sf_auto_theta.py +++ b/darts/models/forecasting/sf_auto_theta.py @@ -26,7 +26,7 @@ def __init__(self, *autotheta_args, **autotheta_kwargs): It is probabilistic, whereas :class:`FourTheta` is not. We refer to the `statsforecast AutoTheta documentation - `_ + `_ for the exhaustive documentation of the arguments. Parameters @@ -99,5 +99,5 @@ def _supports_range_index(self) -> bool: return True @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return True diff --git a/darts/models/forecasting/tbats_model.py b/darts/models/forecasting/tbats_model.py index eb251726d3..d8ec825c73 100644 --- a/darts/models/forecasting/tbats_model.py +++ b/darts/models/forecasting/tbats_model.py @@ -21,7 +21,7 @@ """ from abc import ABC, abstractmethod -from typing import List, Optional, Tuple, Union +from typing import Optional, Union import numpy as np from scipy.special import inv_boxcox @@ -49,21 +49,27 @@ def _seasonality_from_freq(series: TimeSeries): return [5] elif freq == "D": return [7] - elif freq == "W": + elif freq == "W" or freq.startswith("W-"): return [52] - elif freq in ["M", "BM", "CBM", "SM"] or freq.startswith( - ("M", "BM", "BS", "CBM", "SM") - ): + elif freq in [ + "M", + "BM", + "CBM", + "SM", + "LWOM", + "WOM", + ] or freq.startswith(("M", "BM", "BS", "CBM", "SM", "LWOM", "WOM")): return [12] # month elif freq in ["Q", "BQ", "REQ"] or freq.startswith(("Q", "BQ", "REQ")): return [4] # quarter - elif freq in ["H", "BH", "CBH"]: - return [24] # hour - elif freq in ["T", "min"]: - return [60] # minute - elif freq == "S": - return [60] # second - + else: + freq_lower = freq.lower() + if freq_lower in ["h", "bh", "cbh"]: + return [24] # hour + elif freq_lower in ["t", "min"]: + return [60] # minute + elif freq_lower == "s": + return [60] # second return None @@ -115,17 +121,16 @@ class _BaseBatsTbatsModel(LocalForecastingModel, ABC): def __init__( self, use_box_cox: Optional[bool] = None, - box_cox_bounds: Tuple = (0, 1), + box_cox_bounds: tuple = (0, 1), use_trend: Optional[bool] = None, use_damped_trend: Optional[bool] = None, - seasonal_periods: Optional[Union[str, List]] = "freq", + seasonal_periods: Optional[Union[str, list]] = "freq", use_arma_errors: Optional[bool] = True, show_warnings: bool = False, n_jobs: Optional[int] = None, multiprocessing_start_method: Optional[str] = "spawn", random_state: int = 0, ): - """ This is a wrapper around `tbats @@ -249,13 +254,13 @@ def supports_multivariate(self) -> bool: return False @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return True @property def min_train_series_length(self) -> int: if ( - isinstance(self.seasonal_periods, List) + isinstance(self.seasonal_periods, list) and len(self.seasonal_periods) > 0 and max(self.seasonal_periods) > 1 ): diff --git a/darts/models/forecasting/tcn_model.py b/darts/models/forecasting/tcn_model.py index e93f5b86cf..3d66b4613d 100644 --- a/darts/models/forecasting/tcn_model.py +++ b/darts/models/forecasting/tcn_model.py @@ -4,7 +4,8 @@ """ import math -from typing import Optional, Sequence, Tuple +from collections.abc import Sequence +from typing import Optional import torch import torch.nn as nn @@ -29,7 +30,7 @@ def __init__( num_filters: int, kernel_size: int, dilation_base: int, - dropout_fn, + dropout: float, weight_norm: bool, nr_blocks_below: int, num_layers: int, @@ -46,8 +47,8 @@ def __init__( The size of every kernel in a convolutional layer. dilation_base The base of the exponent that will determine the dilation on every level. - dropout_fn - The dropout function to be applied to every convolutional layer. + dropout + The dropout to be applied to every convolutional layer. weight_norm Boolean value indicating whether to use weight normalization. nr_blocks_below @@ -77,7 +78,8 @@ def __init__( self.dilation_base = dilation_base self.kernel_size = kernel_size - self.dropout_fn = dropout_fn + self.dropout1 = MonteCarloDropout(dropout) + self.dropout2 = MonteCarloDropout(dropout) self.num_layers = num_layers self.nr_blocks_below = nr_blocks_below @@ -96,9 +98,10 @@ def __init__( dilation=(dilation_base**nr_blocks_below), ) if weight_norm: - self.conv1, self.conv2 = nn.utils.weight_norm( - self.conv1 - ), nn.utils.weight_norm(self.conv2) + self.conv1, self.conv2 = ( + nn.utils.parametrizations.weight_norm(self.conv1), + nn.utils.parametrizations.weight_norm(self.conv2), + ) if input_dim != output_dim: self.conv3 = nn.Conv1d(input_dim, output_dim, 1) @@ -111,14 +114,14 @@ def forward(self, x): self.kernel_size - 1 ) x = F.pad(x, (left_padding, 0)) - x = self.dropout_fn(F.relu(self.conv1(x))) + x = self.dropout1(F.relu(self.conv1(x))) # second step x = F.pad(x, (left_padding, 0)) x = self.conv2(x) if self.nr_blocks_below < self.num_layers - 1: x = F.relu(x) - x = self.dropout_fn(x) + x = self.dropout2(x) # add residual if self.conv1.in_channels != self.conv2.out_channels: @@ -141,9 +144,8 @@ def __init__( nr_params: int, target_length: int, dropout: float, - **kwargs + **kwargs, ): - """PyTorch module implementing a dilated TCN module used in `TCNModel`. @@ -170,7 +172,8 @@ def __init__( dropout The dropout rate for every convolutional layer. **kwargs - all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class. + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. Inputs ------ @@ -195,7 +198,6 @@ def __init__( self.target_size = target_size self.nr_params = nr_params self.dilation_base = dilation_base - self.dropout = MonteCarloDropout(p=dropout) # If num_layers is not passed, compute number of layers needed for full history coverage if num_layers is None and dilation_base > 1: @@ -221,21 +223,21 @@ def __init__( self.res_blocks_list = [] for i in range(num_layers): res_block = _ResidualBlock( - num_filters, - kernel_size, - dilation_base, - self.dropout, - weight_norm, - i, - num_layers, - self.input_size, - target_size * nr_params, + num_filters=num_filters, + kernel_size=kernel_size, + dilation_base=dilation_base, + dropout=dropout, + weight_norm=weight_norm, + nr_blocks_below=i, + num_layers=num_layers, + input_size=self.input_size, + target_size=target_size * nr_params, ) self.res_blocks_list.append(res_block) self.res_blocks = nn.ModuleList(self.res_blocks_list) @io_processor - def forward(self, x_in: Tuple): + def forward(self, x_in: tuple): x, _ = x_in # data is of size (batch_size, input_chunk_length, input_size) batch_size = x.size(0) @@ -261,15 +263,15 @@ def __init__( self, input_chunk_length: int, output_chunk_length: int, + output_chunk_shift: int = 0, kernel_size: int = 3, num_filters: int = 3, num_layers: Optional[int] = None, dilation_base: int = 2, weight_norm: bool = False, dropout: float = 0.2, - **kwargs + **kwargs, ): - """Temporal Convolutional Network Model (TCN). This is an implementation of a dilated TCN used for forecasting, inspired from [1]_. @@ -285,10 +287,16 @@ def __init__( Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values from future covariates to use as a model input (if the model supports future covariates). It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated - using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). kernel_size The size of every kernel in a convolutional layer. num_filters @@ -501,7 +509,7 @@ def encode_year(idx): def supports_multivariate(self) -> bool: return True - def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module: + def _create_model(self, train_sample: tuple[torch.Tensor]) -> torch.nn.Module: # samples are made of (past_target, past_covariates, future_target) input_dim = train_sample[0].shape[1] + ( train_sample[1].shape[1] if train_sample[1] is not None else 0 @@ -528,14 +536,15 @@ def _build_train_dataset( target: Sequence[TimeSeries], past_covariates: Optional[Sequence[TimeSeries]], future_covariates: Optional[Sequence[TimeSeries]], + sample_weight: Optional[Sequence[TimeSeries]], max_samples_per_ts: Optional[int], ) -> PastCovariatesShiftedDataset: - return PastCovariatesShiftedDataset( target_series=target, covariates=past_covariates, length=self.input_chunk_length, - shift=self.output_chunk_length, + shift=self.output_chunk_length + self.output_chunk_shift, max_samples_per_ts=max_samples_per_ts, use_static_covariates=self.uses_static_covariates, + sample_weight=sample_weight, ) diff --git a/darts/models/forecasting/tft_model.py b/darts/models/forecasting/tft_model.py index baca30f71c..2f53e12af5 100644 --- a/darts/models/forecasting/tft_model.py +++ b/darts/models/forecasting/tft_model.py @@ -3,7 +3,8 @@ ------- """ -from typing import Dict, List, Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union import numpy as np import pandas as pd @@ -37,7 +38,7 @@ logger = get_logger(__name__) -MixedCovariatesTrainTensorType = Tuple[ +MixedCovariatesTrainTensorType = tuple[ torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor ] @@ -45,22 +46,21 @@ class _TFTModule(PLMixedCovariatesModule): def __init__( self, - output_dim: Tuple[int, int], - variables_meta: Dict[str, Dict[str, List[str]]], + output_dim: tuple[int, int], + variables_meta: dict[str, dict[str, list[str]]], num_static_components: int, - hidden_size: Union[int, List[int]], + hidden_size: Union[int, list[int]], lstm_layers: int, num_attention_heads: int, full_attention: bool, feed_forward: str, hidden_continuous_size: int, - categorical_embedding_sizes: Dict[str, Tuple[int, int]], + categorical_embedding_sizes: dict[str, tuple[int, int]], dropout: float, add_relative_index: bool, norm_type: Union[str, nn.Module], **kwargs, ): - """PyTorch module implementing the TFT architecture from `this paper `_ The implementation is built upon `pytorch-forecasting's TemporalFusionTransformer `_. @@ -108,7 +108,8 @@ def __init__( norm_type: str | nn.Module The type of LayerNorm variant to use. **kwargs - all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class. + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. """ super().__init__(**kwargs) @@ -158,9 +159,11 @@ def __init__( # continuous variable processing self.prescalers_linear = { name: nn.Linear( - 1 - if name not in self.numeric_static_variables - else self.num_static_components, + ( + 1 + if name not in self.numeric_static_variables + else self.num_static_components + ), self.hidden_continuous_size, ) for name in self.reals @@ -171,12 +174,9 @@ def __init__( name: self.input_embeddings.output_size[name] for name in self.categorical_static_variables } - static_input_sizes.update( - { - name: self.hidden_continuous_size - for name in self.numeric_static_variables - } - ) + static_input_sizes.update({ + name: self.hidden_continuous_size for name in self.numeric_static_variables + }) self.static_covariates_vsn = _VariableSelectionNetwork( input_sizes=static_input_sizes, @@ -333,42 +333,42 @@ def __init__( self._decoder_sparse_weights = None @property - def reals(self) -> List[str]: + def reals(self) -> list[str]: """ List of all continuous variables in model """ return self.variables_meta["model_config"]["reals_input"] @property - def static_variables(self) -> List[str]: + def static_variables(self) -> list[str]: """ List of all static variables in model """ return self.variables_meta["model_config"]["static_input"] @property - def numeric_static_variables(self) -> List[str]: + def numeric_static_variables(self) -> list[str]: """ List of numeric static variables in model """ return self.variables_meta["model_config"]["static_input_numeric"] @property - def categorical_static_variables(self) -> List[str]: + def categorical_static_variables(self) -> list[str]: """ List of categorical static variables in model """ return self.variables_meta["model_config"]["static_input_categorical"] @property - def encoder_variables(self) -> List[str]: + def encoder_variables(self) -> list[str]: """ List of all encoder variables in model (excluding static variables) """ return self.variables_meta["model_config"]["time_varying_encoder_input"] @property - def decoder_variables(self) -> List[str]: + def decoder_variables(self) -> list[str]: """ List of all decoder variables in model (excluding static variables) """ @@ -453,7 +453,7 @@ def get_attention_mask_future( @io_processor def forward( - self, x_in: Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]] + self, x_in: tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]] ) -> torch.Tensor: """TFT model forward pass. @@ -541,13 +541,11 @@ def forward( else: static_embedding = {} # add numerical static covariates - static_embedding.update( - { - name: x_static[:, :, idx] - for idx, name in enumerate(self.static_variables) - if name in self.numeric_static_variables - } - ) + static_embedding.update({ + name: x_static[:, :, idx] + for idx, name in enumerate(self.static_variables) + if name in self.numeric_static_variables + }) static_embedding, static_covariate_var = self.static_covariates_vsn( static_embedding ) @@ -659,7 +657,8 @@ def __init__( self, input_chunk_length: int, output_chunk_length: int, - hidden_size: Union[int, List[int]] = 16, + output_chunk_shift: int = 0, + hidden_size: Union[int, list[int]] = 16, lstm_layers: int = 1, num_attention_heads: int = 4, full_attention: bool = False, @@ -667,7 +666,7 @@ def __init__( dropout: float = 0.1, hidden_continuous_size: int = 8, categorical_embedding_sizes: Optional[ - Dict[str, Union[int, Tuple[int, int]]] + dict[str, Union[int, tuple[int, int]]] ] = None, add_relative_index: bool = False, loss_fn: Optional[nn.Module] = None, @@ -705,11 +704,17 @@ def __init__( Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values from future covariates to use as a model input (if the model supports future covariates). It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated - using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). Also called: Decoder length + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). hidden_size Hidden state size of the TFT. It is the main hyper-parameter and common across the internal TFT architecture. @@ -955,7 +960,7 @@ def encode_year(idx): else {} ) self.add_relative_index = add_relative_index - self.output_dim: Optional[Tuple[int, int]] = None + self.output_dim: Optional[tuple[int, int]] = None self.norm_type = norm_type self._considers_static_covariates = use_static_covariates @@ -1078,9 +1083,9 @@ def _create_model(self, train_sample: MixedCovariatesTrainTensorType) -> nn.Modu if ( self.static_covariates is None ): # when training with fit_from_dataset - static_cols = pd.Index( - [i for i in range(static_covariates.shape[1])] - ) + static_cols = pd.Index([ + i for i in range(static_covariates.shape[1]) + ]) else: static_cols = self.static_covariates.columns numeric_mask = ~static_cols.isin(self.categorical_embedding_sizes) @@ -1155,9 +1160,9 @@ def _build_train_dataset( target: Sequence[TimeSeries], past_covariates: Optional[Sequence[TimeSeries]], future_covariates: Optional[Sequence[TimeSeries]], + sample_weight: Optional[Sequence[TimeSeries]], max_samples_per_ts: Optional[int], ) -> MixedCovariatesSequentialDataset: - raise_if( future_covariates is None and not self.add_relative_index, "TFTModel requires future covariates. The model applies multi-head attention queries on future " @@ -1173,8 +1178,10 @@ def _build_train_dataset( future_covariates=future_covariates, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, max_samples_per_ts=max_samples_per_ts, use_static_covariates=self.uses_static_covariates, + sample_weight=sample_weight, ) def _verify_train_dataset_type(self, train_dataset: TrainingDataset): diff --git a/darts/models/forecasting/tft_submodels.py b/darts/models/forecasting/tft_submodels.py index 32bc2321e0..137e621bc2 100644 --- a/darts/models/forecasting/tft_submodels.py +++ b/darts/models/forecasting/tft_submodels.py @@ -20,7 +20,7 @@ ' """ -from typing import Dict, List, Optional, Tuple, Union +from typing import Optional, Union import torch import torch.nn as nn @@ -31,7 +31,7 @@ logger = get_logger(__name__) -HiddenState = Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor] +HiddenState = Union[tuple[torch.Tensor, torch.Tensor], torch.Tensor] def get_embedding_size(n: int, max_size: int = 100) -> int: @@ -55,7 +55,6 @@ def __init__(self, *args, batch_first: bool = False, **kwargs): self.batch_first = batch_first def forward(self, x): - if len(x.size()) <= 2: return super().forward(x) @@ -79,8 +78,8 @@ def forward(self, x): class _MultiEmbedding(nn.Module): def __init__( self, - embedding_sizes: Dict[str, Tuple[int, int]], - variable_names: List[str], + embedding_sizes: dict[str, tuple[int, int]], + variable_names: list[str], ): """Embedding layer for categorical variables including groups of categorical variables. Enabled for static and dynamic categories (i.e. 3 dimensions for batch x time x categories). @@ -99,19 +98,19 @@ def __init__( self.embedding_sizes = embedding_sizes self.variable_names = variable_names - self.embeddings = nn.ModuleDict( - {name: nn.Embedding(*embedding_sizes[name]) for name in variable_names} - ) + self.embeddings = nn.ModuleDict({ + name: nn.Embedding(*embedding_sizes[name]) for name in variable_names + }) @property def input_size(self) -> int: return len(self.variable_names) @property - def output_size(self) -> Union[Dict[str, int], int]: + def output_size(self) -> Union[dict[str, int], int]: return {name: sizes[1] for name, sizes in self.embedding_sizes.items()} - def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]: + def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]: """ Parameters ---------- @@ -151,7 +150,6 @@ def interpolate(self, x): return upsampled def forward(self, x): - if len(x.size()) <= 2: return self.interpolate(x) @@ -383,13 +381,13 @@ def forward(self, x, context=None, residual=None): class _VariableSelectionNetwork(nn.Module): def __init__( self, - input_sizes: Dict[str, int], + input_sizes: dict[str, int], hidden_size: int, - input_embedding_flags: Optional[Dict[str, bool]] = None, + input_embedding_flags: Optional[dict[str, bool]] = None, dropout: float = 0.1, context_size: int = None, - single_variable_grns: Optional[Dict[str, _GatedResidualNetwork]] = None, - prescalers: Optional[Dict[str, nn.Linear]] = None, + single_variable_grns: Optional[dict[str, _GatedResidualNetwork]] = None, + prescalers: Optional[dict[str, nn.Linear]] = None, layer_norm: nn.Module = nn.LayerNorm, ): """ @@ -466,7 +464,7 @@ def input_size_total(self): def num_inputs(self): return len(self.input_sizes) - def forward(self, x: Dict[str, torch.Tensor], context: torch.Tensor = None): + def forward(self, x: dict[str, torch.Tensor], context: torch.Tensor = None): if self.num_inputs > 1: # transform single variables var_outputs = [] @@ -543,12 +541,12 @@ def __init__(self, n_head: int, d_model: int, dropout: float = 0.0): self.dropout = MonteCarloDropout(p=dropout) self.v_layer = nn.Linear(self.d_model, self.d_v) - self.q_layers = nn.ModuleList( - [nn.Linear(self.d_model, self.d_q) for _ in range(self.n_head)] - ) - self.k_layers = nn.ModuleList( - [nn.Linear(self.d_model, self.d_k) for _ in range(self.n_head)] - ) + self.q_layers = nn.ModuleList([ + nn.Linear(self.d_model, self.d_q) for _ in range(self.n_head) + ]) + self.k_layers = nn.ModuleList([ + nn.Linear(self.d_model, self.d_k) for _ in range(self.n_head) + ]) self.attention = _ScaledDotProductAttention() self.w_h = nn.Linear(self.d_v, self.d_model, bias=False) @@ -561,7 +559,7 @@ def init_weights(self): else: torch.nn.init.zeros_(p) - def forward(self, q, k, v, mask=None) -> Tuple[torch.Tensor, torch.Tensor]: + def forward(self, q, k, v, mask=None) -> tuple[torch.Tensor, torch.Tensor]: heads = [] attns = [] vs = self.v_layer(v) diff --git a/darts/models/forecasting/theta.py b/darts/models/forecasting/theta.py index fcbadd43e8..d63d4976fc 100644 --- a/darts/models/forecasting/theta.py +++ b/darts/models/forecasting/theta.py @@ -4,7 +4,7 @@ """ import math -from typing import List, Optional +from typing import Optional import numpy as np import statsmodels.tsa.holtwinters as hw @@ -165,19 +165,15 @@ def predict( forecast = self.model.forecast(n) # Forecast of the Linear Regression part. - drift = self.coef * np.array( - [ - i + (1 - (1 - self.alpha) ** self.length) / self.alpha - for i in range(0, n) - ] - ) + drift = self.coef * np.array([ + i + (1 - (1 - self.alpha) ** self.length) / self.alpha for i in range(0, n) + ]) # Combining the two forecasts forecast += drift # Re-apply the seasonal trend of the TimeSeries if self.is_seasonal: - replicated_seasonality = np.tile( self.seasonality.pd_series()[-self.season_period :], math.ceil(n / self.season_period), @@ -427,7 +423,6 @@ def predict( # Re-apply the seasonal trend of the TimeSeries if self.is_seasonal: - replicated_seasonality = np.tile( self.seasonality.pd_series()[-self.season_period :], math.ceil(n / self.season_period), @@ -445,7 +440,7 @@ def predict( @staticmethod def select_best_model( ts: TimeSeries, - thetas: Optional[List[int]] = None, + thetas: Optional[list[int]] = None, m: Optional[int] = None, normalization: bool = True, n_jobs: int = 1, diff --git a/darts/models/forecasting/tide_model.py b/darts/models/forecasting/tide_model.py index 14b942c76b..dd4cc4002d 100644 --- a/darts/models/forecasting/tide_model.py +++ b/darts/models/forecasting/tide_model.py @@ -3,7 +3,7 @@ ------ """ -from typing import Optional, Tuple +from typing import Optional import torch import torch.nn as nn @@ -14,8 +14,9 @@ io_processor, ) from darts.models.forecasting.torch_forecasting_model import MixedCovariatesTorchModel +from darts.utils.torch import MonteCarloDropout -MixedCovariatesTrainTensorType = Tuple[ +MixedCovariatesTrainTensorType = tuple[ torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor ] @@ -40,7 +41,7 @@ def __init__( nn.Linear(input_dim, hidden_size), nn.ReLU(), nn.Linear(hidden_size, output_dim), - nn.Dropout(dropout), + MonteCarloDropout(dropout), ) # linear skip connection from input to output of self.dense @@ -53,7 +54,6 @@ def __init__( self.layer_norm = None def forward(self, x: torch.Tensor) -> torch.Tensor: - # residual connection x = self.dense(x) + self.skip(x) @@ -81,6 +81,8 @@ def __init__( temporal_width_future: int, use_layer_norm: bool, dropout: float, + temporal_hidden_size_past: Optional[int] = None, + temporal_hidden_size_future: Optional[int] = None, **kwargs, ): """Pytorch module implementing the TiDE architecture. @@ -111,12 +113,17 @@ def __init__( The width of the past covariate embedding space. temporal_width_future The width of the future covariate embedding space. + temporal_hidden_size_past + The width of the hidden layers in the past covariate projection Residual Block. + temporal_hidden_size_future + The width of the hidden layers in the future covariate projection Residual Block. use_layer_norm Whether to use layer normalization in the Residual Blocks. dropout Dropout probability **kwargs - all parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class. + all parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. Inputs ------ @@ -147,6 +154,8 @@ def __init__( self.dropout = dropout self.temporal_width_past = temporal_width_past self.temporal_width_future = temporal_width_future + self.temporal_hidden_size_past = temporal_hidden_size_past or hidden_size + self.temporal_hidden_size_future = temporal_hidden_size_future or hidden_size # past covariates handling: either feature projection, raw features, or no features self.past_cov_projection = None @@ -155,7 +164,7 @@ def __init__( self.past_cov_projection = _ResidualBlock( input_dim=self.past_cov_dim, output_dim=temporal_width_past, - hidden_size=hidden_size, + hidden_size=temporal_hidden_size_past, use_layer_norm=use_layer_norm, dropout=dropout, ) @@ -173,7 +182,7 @@ def __init__( self.future_cov_projection = _ResidualBlock( input_dim=future_cov_dim, output_dim=temporal_width_future, - hidden_size=hidden_size, + hidden_size=temporal_hidden_size_future, use_layer_norm=use_layer_norm, dropout=dropout, ) @@ -258,7 +267,7 @@ def __init__( @io_processor def forward( - self, x_in: Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]] + self, x_in: tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]] ) -> torch.Tensor: """TiDE model forward pass. Parameters @@ -337,9 +346,11 @@ def forward( # stack and temporally decode with future covariate last output steps temporal_decoder_input = [ decoded, - x_dynamic_future_covariates[:, -self.output_chunk_length :, :] - if self.future_cov_dim > 0 - else None, + ( + x_dynamic_future_covariates[:, -self.output_chunk_length :, :] + if self.future_cov_dim > 0 + else None + ), ] temporal_decoder_input = [t for t in temporal_decoder_input if t is not None] @@ -365,12 +376,15 @@ def __init__( self, input_chunk_length: int, output_chunk_length: int, + output_chunk_shift: int = 0, num_encoder_layers: int = 1, num_decoder_layers: int = 1, decoder_output_dim: int = 16, hidden_size: int = 128, temporal_width_past: int = 4, temporal_width_future: int = 4, + temporal_hidden_size_past: int = None, + temporal_hidden_size_future: int = None, temporal_decoder_hidden: int = 32, use_layer_norm: bool = False, dropout: float = 0.1, @@ -401,10 +415,16 @@ def __init__( Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values from future covariates to use as a model input (if the model supports future covariates). It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated - using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). num_encoder_layers The number of residual blocks in the encoder. num_decoder_layers @@ -414,11 +434,19 @@ def __init__( hidden_size The width of the layers in the residual blocks of the encoder and decoder. temporal_width_past - The width of the layers in the past covariate projection residual block. If `0`, + The width of the output layer in the past covariate projection residual block. If `0`, will bypass feature projection and use the raw feature data. temporal_width_future - The width of the layers in the future covariate projection residual block. If `0`, + The width of the output layer in the future covariate projection residual block. If `0`, will bypass feature projection and use the raw feature data. + temporal_hidden_size_past + The width of the hidden layer in the past covariate projection residual block. If not specified, + defaults to `hidden_size`, which is the width of the hidden layer in the encoder and decoder. + This is likely to be too large in many cases, so it is recommended to set this parameter explicitly. + temporal_hidden_size_future + The width of the hidden layer in the future covariate projection residual block. If not specified, + defaults to `hidden_size`, which is the width of the hidden layer in the encoder and decoder. + This is likely to be too large in many cases, so it is recommended to set this parameter explicitly. temporal_decoder_hidden The width of the layers in the temporal decoder. use_layer_norm @@ -616,6 +644,8 @@ def encode_year(idx): self.hidden_size = hidden_size self.temporal_width_past = temporal_width_past self.temporal_width_future = temporal_width_future + self.temporal_hidden_size_past = temporal_hidden_size_past or hidden_size + self.temporal_hidden_size_future = temporal_hidden_size_future or hidden_size self.temporal_decoder_hidden = temporal_decoder_hidden self._considers_static_covariates = use_static_covariates @@ -683,6 +713,8 @@ def _create_model( hidden_size=self.hidden_size, temporal_width_past=self.temporal_width_past, temporal_width_future=self.temporal_width_future, + temporal_hidden_size_past=self.temporal_hidden_size_past, + temporal_hidden_size_future=self.temporal_hidden_size_future, temporal_decoder_hidden=self.temporal_decoder_hidden, use_layer_norm=self.use_layer_norm, dropout=self.dropout, @@ -696,3 +728,11 @@ def supports_static_covariates(self) -> bool: @property def supports_multivariate(self) -> bool: return True + + def _check_ckpt_parameters(self, tfm_save): + # new parameters were added that will break loading weights + new_params = ["temporal_hidden_size_past", "temporal_hidden_size_future"] + for param in new_params: + if param not in tfm_save.model_params: + tfm_save.model_params[param] = None + super()._check_ckpt_parameters(tfm_save) diff --git a/darts/models/forecasting/torch_forecasting_model.py b/darts/models/forecasting/torch_forecasting_model.py index fe0c67c364..b949efb914 100644 --- a/darts/models/forecasting/torch_forecasting_model.py +++ b/darts/models/forecasting/torch_forecasting_model.py @@ -14,9 +14,6 @@ as well as past and future values of some future covariates. * SplitCovariatesTorchModel(TorchForecastingModel) for torch models consuming past-observed as well as future values of some future covariates. - - * TorchParametricProbabilisticForecastingModel(TorchForecastingModel) is the super-class of all probabilistic torch - forecasting models. """ import copy @@ -27,13 +24,14 @@ import shutil import sys from abc import ABC, abstractmethod +from collections.abc import Sequence from glob import glob -from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union +from typing import Any, Callable, Literal, Optional, Union -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal +if sys.version_info >= (3, 11): + from typing import Self +else: + from typing_extensions import Self import numpy as np import pandas as pd @@ -89,7 +87,7 @@ ) from darts.utils.likelihood_models import Likelihood from darts.utils.torch import random_method -from darts.utils.utils import get_single_series, seq2series, series2seq +from darts.utils.ts_utils import get_single_series, seq2series, series2seq # Check whether we are running pytorch-lightning >= 2.0.0 or not: tokens = pl.__version__.split(".") @@ -300,7 +298,7 @@ def encode_year(idx): # class name will be set in fit_from_dataset() self._module_name: Optional[str] = "" - self.train_sample: Optional[Tuple] = None + self.train_sample: Optional[tuple] = None self.output_dim: Optional[int] = None self.n_epochs = n_epochs @@ -358,7 +356,7 @@ def encode_year(idx): ) # setup trainer parameters from model creation parameters - self.trainer_params: Dict[str, Any] = { + self.trainer_params: dict[str, Any] = { "logger": model_logger, "max_epochs": n_epochs, "check_val_every_n_epoch": nr_epochs_val_period, @@ -530,9 +528,9 @@ def _setup_trainer( return trainer trainer_params = {key: val for key, val in self.trainer_params.items()} - has_progress_bar = any( - [isinstance(cb, ProgressBar) for cb in trainer_params.get("callbacks", [])] - ) + has_progress_bar = any([ + isinstance(cb, ProgressBar) for cb in trainer_params.get("callbacks", []) + ]) # we ignore `verbose` if `trainer` has a progress bar, to avoid errors from lightning if verbose is not None and not has_progress_bar: trainer_params["enable_model_summary"] = ( @@ -559,7 +557,7 @@ def _init_trainer( ) @abstractmethod - def _create_model(self, train_sample: Tuple[Tensor]) -> PLForecastingModule: + def _create_model(self, train_sample: tuple[Tensor]) -> PLForecastingModule: """ This method has to be implemented by all children. It is in charge of instantiating the actual torch model, based on examples input/output tensors (i.e. implement a model with the right input/output sizes). @@ -572,6 +570,7 @@ def _build_train_dataset( target: Sequence[TimeSeries], past_covariates: Optional[Sequence[TimeSeries]], future_covariates: Optional[Sequence[TimeSeries]], + sample_weight: Optional[Union[Sequence[TimeSeries], str]], max_samples_per_ts: Optional[int], ) -> TrainingDataset: """ @@ -609,19 +608,103 @@ def _verify_inference_dataset_type(self, inference_dataset: InferenceDataset): pass @abstractmethod - def _verify_predict_sample(self, predict_sample: Tuple): + def _verify_predict_sample(self, predict_sample: tuple): """ verify that the (first) sample contained in the inference dataset matches the model type and the data the model has been trained on. """ pass - @abstractmethod def _verify_past_future_covariates(self, past_covariates, future_covariates): """ Verify that any non-None covariates comply with the model type. """ - pass + invalid_covs = [] + if past_covariates is not None and not self.supports_past_covariates: + invalid_covs.append("`past_covariates`") + if future_covariates is not None and not self.supports_future_covariates: + invalid_covs.append("`future_covariates`") + if self.uses_static_covariates and not self.supports_static_covariates: + invalid_covs.append("`static_covariates`") + if invalid_covs: + supported_covs = [] + if self.supports_past_covariates: + supported_covs.append("`past_covariates`") + if self.supports_future_covariates: + supported_covs.append("`future_covariates`") + if self.supports_static_covariates: + supported_covs.append("`static_covariates`") + if supported_covs: + add_txt = f"It only supports {', '.join(supported_covs)}." + else: + add_txt = "It does not support any covariates." + + raise_log( + ValueError( + f"The model does not support {', '.join(invalid_covs)}. " + add_txt + ), + logger=logger, + ) + + def to_onnx(self, path: Optional[str] = None, **kwargs): + """Export model to ONNX format for optimized inference, wrapping around PyTorch Lightning's + :func:`torch.onnx.export` method (`official documentation `_). + + Note: requires `onnx` library (optional dependency) to be installed. + + Example for exporting a :class:`DLinearModel`: + + .. highlight:: python + .. code-block:: python + + from darts.datasets import AirPassengersDataset + from darts.models import DLinearModel + + series = AirPassengersDataset().load() + model = DLinearModel(input_chunk_length=4, output_chunk_length=1) + model.fit(series, epochs=1) + model.to_onnx("my_model.onnx") + .. + + Parameters + ---------- + path + Path under which to save the model at its current state. If no path is specified, the model + is automatically saved under ``"{ModelClass}_{YYYY-mm-dd_HH_MM_SS}.onnx"``. + **kwargs + Additional kwargs for PyTorch's :func:`torch.onnx.export` method (except parameters ``file_path``, + ``input_sample``, ``input_name``). For more information, read the `official documentation + `_. + """ + if not self._fit_called: + raise_log( + ValueError("`fit()` needs to be called before `to_onnx()`."), logger + ) + + if path is None: + path = self._default_save_path() + ".onnx" + + # last dimension in train_sample_shape is the expected target + mock_batch = tuple( + torch.rand((1,) + shape, dtype=self.model.dtype) if shape else None + for shape in self.model.train_sample_shape[:-1] + ) + input_sample = self.model._process_input_batch(mock_batch) + + # torch models necessarily use historic target values as features in current implementation + input_names = ["x_past"] + if self.uses_future_covariates: + input_names.append("x_future") + if self.uses_static_covariates: + input_names.append("x_static") + + self.model.to_onnx( + file_path=path, + input_sample=(input_sample,), + input_names=input_names, + **kwargs, + ) @random_method def fit( @@ -636,7 +719,11 @@ def fit( verbose: Optional[bool] = None, epochs: int = 0, max_samples_per_ts: Optional[int] = None, - num_loader_workers: int = 0, + dataloader_kwargs: Optional[dict[str, Any]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + val_sample_weight: Optional[ + Union[TimeSeries, Sequence[TimeSeries], str] + ] = None, ) -> "TorchForecastingModel": """Fit/train the model on one or multiple series. @@ -675,11 +762,13 @@ def fit( Optionally, the past covariates corresponding to the validation series (must match ``covariates``) val_future_covariates Optionally, the future covariates corresponding to the validation series (must match ``covariates``) + val_sample_weight + Same as for `sample_weight` but for the evaluation dataset. trainer Optionally, a custom PyTorch-Lightning Trainer object to perform training. Using a custom ``trainer`` will override Darts' default trainer. verbose - Optionally, whether to print the progress. Ignored if there is a `ProgressBar` callback in + Whether to print the progress. Ignored if there is a `ProgressBar` callback in `pl_trainer_kwargs`. epochs If specified, will train the model for ``epochs`` (additional) epochs, irrespective of what ``n_epochs`` @@ -690,11 +779,24 @@ def fit( large number of training samples. This parameter upper-bounds the number of training samples per time series (taking only the most recent samples in each series). Leaving to None does not apply any upper bound. - num_loader_workers - Optionally, an integer specifying the ``num_workers`` to use in PyTorch ``DataLoader`` instances, - both for the training and validation loaders (if any). - A larger number of workers can sometimes increase performance, but can also incur extra overheads - and increase memory usage, as more batches are loaded in parallel. + dataloader_kwargs + Optionally, a dictionary of keyword arguments used to create the PyTorch `DataLoader` instances for the + training and validation datasets. For more information on `DataLoader`, check out `this link + `_. + By default, Darts configures parameters ("batch_size", "shuffle", "drop_last", "collate_fn", "pin_memory") + for seamless forecasting. Changing them should be done with care to avoid unexpected behavior. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. + val_sample_weight + Same as for `sample_weight` but for the evaluation dataset. Returns ------- @@ -702,21 +804,26 @@ def fit( Fitted model. """ ( - series, - past_covariates, - future_covariates, - ), params = self._setup_for_fit_from_dataset( + ( + series, + past_covariates, + future_covariates, + ), + params, + ) = self._setup_for_fit_from_dataset( series=series, past_covariates=past_covariates, future_covariates=future_covariates, + sample_weight=sample_weight, val_series=val_series, val_past_covariates=val_past_covariates, val_future_covariates=val_future_covariates, + val_sample_weight=val_sample_weight, trainer=trainer, verbose=verbose, epochs=epochs, max_samples_per_ts=max_samples_per_ts, - num_loader_workers=num_loader_workers, + dataloader_kwargs=dataloader_kwargs, ) # call super fit only if user is actually fitting the model super().fit( @@ -731,27 +838,31 @@ def _setup_for_fit_from_dataset( series: Union[TimeSeries, Sequence[TimeSeries]], past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, val_series: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, val_past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, val_future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + val_sample_weight: Optional[ + Union[TimeSeries, Sequence[TimeSeries], str] + ] = None, trainer: Optional[pl.Trainer] = None, verbose: Optional[bool] = None, epochs: int = 0, max_samples_per_ts: Optional[int] = None, - num_loader_workers: int = 0, - ) -> Tuple[ - Tuple[ + dataloader_kwargs: Optional[dict[str, Any]] = None, + ) -> tuple[ + tuple[ Sequence[TimeSeries], Optional[Sequence[TimeSeries]], Optional[Sequence[TimeSeries]], ], - Tuple[ + tuple[ TrainingDataset, Optional[TrainingDataset], Optional[pl.Trainer], Optional[bool], int, - int, + Optional[dict[str, Any]], ], ]: """This method acts on `TimeSeries` inputs. It performs sanity checks, and sets up / returns the datasets and @@ -764,6 +875,10 @@ def _setup_for_fit_from_dataset( val_series = series2seq(val_series) val_past_covariates = series2seq(val_past_covariates) val_future_covariates = series2seq(val_future_covariates) + if not isinstance(sample_weight, str): + sample_weight = series2seq(sample_weight) + if not isinstance(val_sample_weight, str): + val_sample_weight = series2seq(val_sample_weight) self.encoders = self.initialize_encoders() if self.encoders.encoding_available: @@ -772,69 +887,38 @@ def _setup_for_fit_from_dataset( past_covariates=past_covariates, future_covariates=future_covariates, ) - - if past_covariates is not None: - self._uses_past_covariates = True - if future_covariates is not None: - self._uses_future_covariates = True + self._verify_past_future_covariates( + past_covariates=past_covariates, future_covariates=future_covariates + ) if ( get_single_series(series).static_covariates is not None and self.supports_static_covariates and self.considers_static_covariates ): + self._verify_static_covariates(get_single_series(series).static_covariates) self._uses_static_covariates = True - self._verify_past_future_covariates( - past_covariates=past_covariates, future_covariates=future_covariates - ) - self._verify_static_covariates(series[0].static_covariates) + if past_covariates is not None: + self._uses_past_covariates = True + if future_covariates is not None: + self._uses_future_covariates = True - # Check that dimensions of train and val set match; on first series only - if val_series is not None: - if self.encoders.encoding_available: - ( - val_past_covariates, - val_future_covariates, - ) = self.generate_fit_encodings( - series=val_series, - past_covariates=val_past_covariates, - future_covariates=val_future_covariates, - ) - self._verify_past_future_covariates( - past_covariates=val_past_covariates, - future_covariates=val_future_covariates, - ) - self._verify_static_covariates(val_series[0].static_covariates) - - match = ( - series[0].width == val_series[0].width - and (past_covariates[0].width if past_covariates is not None else None) - == ( - val_past_covariates[0].width - if val_past_covariates is not None - else None - ) - and ( - future_covariates[0].width - if future_covariates is not None - else None - ) - == ( - val_future_covariates[0].width - if val_future_covariates is not None - else None - ) - ) - raise_if_not( - match, - "The dimensions of the series in the training set " - "and the validation set do not match.", + val_series, val_past_covariates, val_future_covariates = ( + self._process_validation_set( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, ) + ) train_dataset = self._build_train_dataset( target=series, past_covariates=past_covariates, future_covariates=future_covariates, + sample_weight=sample_weight, max_samples_per_ts=max_samples_per_ts, ) @@ -843,12 +927,13 @@ def _setup_for_fit_from_dataset( target=val_series, past_covariates=val_past_covariates, future_covariates=val_future_covariates, + sample_weight=val_sample_weight, max_samples_per_ts=max_samples_per_ts, ) else: val_dataset = None - # Pro-actively catch length exceptions to display nicer messages + # proactively catch length exceptions to display nicer messages length_ok = True try: len(train_dataset) @@ -858,11 +943,10 @@ def _setup_for_fit_from_dataset( not length_ok or len(train_dataset) == 0, # mind the order "The train dataset does not contain even one training sample. " + "This is likely due to the provided training series being too short. " - + "This model expect series of length at least {}.".format( - self.min_train_series_length - ), + + f"This model expect series of length at least {self.min_train_series_length}.", ) logger.info(f"Train dataset contains {len(train_dataset)} samples.") + series_input = (series, past_covariates, future_covariates) fit_from_ds_params = ( train_dataset, @@ -870,7 +954,7 @@ def _setup_for_fit_from_dataset( trainer, verbose, epochs, - num_loader_workers, + dataloader_kwargs, ) return series_input, fit_from_ds_params @@ -882,7 +966,7 @@ def fit_from_dataset( trainer: Optional[pl.Trainer] = None, verbose: Optional[bool] = None, epochs: int = 0, - num_loader_workers: int = 0, + dataloader_kwargs: Optional[dict[str, Any]] = None, ) -> "TorchForecastingModel": """ Train the model with a specific :class:`darts.utils.data.TrainingDataset` instance. @@ -910,16 +994,17 @@ def fit_from_dataset( Optionally, a custom PyTorch-Lightning Trainer object to perform prediction. Using a custom `trainer` will override Darts' default trainer. verbose - Optionally, whether to print the progress. Ignored if there is a `ProgressBar` callback in + Whether to print the progress. Ignored if there is a `ProgressBar` callback in `pl_trainer_kwargs`. epochs If specified, will train the model for ``epochs`` (additional) epochs, irrespective of what ``n_epochs`` was provided to the model constructor. - num_loader_workers - Optionally, an integer specifying the ``num_workers`` to use in PyTorch ``DataLoader`` instances, - both for the training and validation loaders (if any). - A larger number of workers can sometimes increase performance, but can also incur extra overheads - and increase memory usage, as more batches are loaded in parallel. + dataloader_kwargs + Optionally, a dictionary of keyword arguments used to create the PyTorch `DataLoader` instances for the + training and validation datasets. For more information on `DataLoader`, check out `this link + `_. + By default, Darts configures parameters ("batch_size", "shuffle", "drop_last", "collate_fn", "pin_memory") + for seamless forecasting. Changing them should be done with care to avoid unexpected behavior. Returns ------- @@ -933,7 +1018,7 @@ def fit_from_dataset( trainer=trainer, verbose=verbose, epochs=epochs, - num_loader_workers=num_loader_workers, + dataloader_kwargs=dataloader_kwargs, ) ) return self @@ -945,14 +1030,14 @@ def _setup_for_train( trainer: Optional[pl.Trainer] = None, verbose: Optional[bool] = None, epochs: int = 0, - num_loader_workers: int = 0, - ) -> Tuple[pl.Trainer, PLForecastingModule, DataLoader, Optional[DataLoader]]: + dataloader_kwargs: Optional[dict[str, Any]] = None, + ) -> tuple[pl.Trainer, PLForecastingModule, DataLoader, Optional[DataLoader]]: """This method acts on `TrainingDataset` inputs. It performs sanity checks, and sets up / returns the trainer, model, and dataset loaders required for training the model with `_train()`. """ self._verify_train_dataset_type(train_dataset) - # Pro-actively catch length exceptions to display nicer messages + # proactively catch length exceptions to display nicer messages train_length_ok, val_length_ok = True, True try: len(train_dataset) @@ -976,59 +1061,103 @@ def _setup_for_train( ) train_sample = train_dataset[0] + # ignore sample weights [-2] for model dimensions + train_sample_no_weight = train_sample[:-2] + train_sample[-1:] if self.model is None: - # Build model, based on the dimensions of the first series in the train set. - self.train_sample, self.output_dim = train_sample, train_sample[-1].shape[1] + # build model based on the dimensions of the first series in the train set. + self.train_sample = train_sample_no_weight + self.output_dim = train_sample[-1].shape[1] model = self._init_model(trainer) else: model = self.model - # Check existing model has input/output dims matching what's provided in the training set. + # check existing model has input/output dims matching what's provided in the training set. raise_if_not( - len(train_sample) == len(self.train_sample), - "The size of the training set samples (tuples) does not match what the model has been " - "previously trained on. Trained on tuples of length {}, received tuples of length {}.".format( - len(self.train_sample), len(train_sample) - ), + len(train_sample_no_weight) == len(self.train_sample), + "The size of the training set samples (tuples) does not match what the model has been" + f" previously trained on. Trained on tuples of length {len(self.train_sample)}," + f" received tuples of length {len(train_sample_no_weight)}.", ) - same_dims = tuple( - s.shape[1] if s is not None else None for s in train_sample - ) == tuple(s.shape[1] if s is not None else None for s in self.train_sample) + sample_shapes_last = [ + s.shape[1] if s is not None else None for s in self.train_sample + ] + sample_shapes = [ + s.shape[1] if s is not None else None for s in train_sample_no_weight + ] raise_if_not( - same_dims, + sample_shapes == sample_shapes_last, "The dimensionality of the series in the training set do not match the dimensionality" " of the series the model has previously been trained on. " - "Model input/output dimensions = {}, provided input/output dimensions = {}".format( - tuple( - s.shape[1] if s is not None else None for s in self.train_sample - ), - tuple(s.shape[1] if s is not None else None for s in train_sample), - ), + f"Model input/output dimensions = {sample_shapes_last}," + f" provided input/output dimensions = {sample_shapes}", ) - # Setting drop_last to False makes the model see each sample at least once, and guarantee the presence of at + # loss must not reduce the output when using sample weight + train_sample_weight = train_sample[-2] + val_sample_weight = val_dataset[0][-2] if val_dataset is not None else None + for sample_weight, criterion, set_name in [ + (train_sample_weight, model.train_criterion, "train"), + (val_sample_weight, model.val_criterion, "val"), + ]: + if criterion is None or sample_weight is None: + continue + + # we need to check that loss has a reduction param that we can change when calling + # `fit()` with sample weights + if not hasattr(criterion, "reduction"): + raise_log( + ValueError( + "torch loss function `loss_fn` must have an attribute `reduction` which controls how " + "to reduce the loss over each batch. With `reduction='none'` it must not reduce the loss." + ), + logger=logger, + ) + + # remember the original reduction (reset in `PLForecastingModule.on_fit_end()` + if set_name == "train": + model.train_criterion_reduction = criterion.reduction + else: + model.val_criterion_reduction = criterion.reduction + # overwrite criterion to not reduce the loss for sample weights + criterion.reduction = "none" + + shape_out = (2, 2) + loss = criterion(torch.ones(shape_out), torch.zeros(shape_out)) + if not loss.shape == shape_out: + raise_log( + ValueError( + "Failed to make `loss_fn` not reduce the loss output when using `(val)_sample_weight`. " + "The loss function `loss_fn` must have an attribute `reduction` which when setting it to " + "`'none'`, must not reduce the output." + ), + logger=logger, + ) + + # setting drop_last to False makes the model see each sample at least once, and guarantee the presence of at # least one batch no matter the chosen batch size + dataloader_kwargs = dict( + { + "batch_size": self.batch_size, + "shuffle": True, + "pin_memory": True, + "drop_last": False, + "collate_fn": self._batch_collate_fn, + }, + **(dataloader_kwargs or dict()), + ) + train_loader = DataLoader( train_dataset, - batch_size=self.batch_size, - shuffle=True, - num_workers=num_loader_workers, - pin_memory=True, - drop_last=False, - collate_fn=self._batch_collate_fn, + **dataloader_kwargs, ) - # Prepare validation data + # prepare validation data + dataloader_kwargs["shuffle"] = False val_loader = ( None if val_dataset is None else DataLoader( val_dataset, - batch_size=self.batch_size, - shuffle=False, - num_workers=num_loader_workers, - pin_memory=True, - drop_last=False, - collate_fn=self._batch_collate_fn, + **dataloader_kwargs, ) ) @@ -1070,12 +1199,13 @@ def _train( ckpt_path = self.load_ckpt_path self.load_ckpt_path = None - trainer.fit( - model, - train_dataloaders=train_loader, - val_dataloaders=val_loader, - ckpt_path=ckpt_path, - ) + if self._requires_training: + trainer.fit( + model, + train_dataloaders=train_loader, + val_dataloaders=val_loader, + ckpt_path=ckpt_path, + ) self.model = model self.trainer = trainer @@ -1088,11 +1218,15 @@ def lr_find( val_series: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, val_past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, val_future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + val_sample_weight: Optional[ + Union[TimeSeries, Sequence[TimeSeries], str] + ] = None, trainer: Optional[pl.Trainer] = None, verbose: Optional[bool] = None, epochs: int = 0, max_samples_per_ts: Optional[int] = None, - num_loader_workers: int = 0, + dataloader_kwargs: Optional[dict[str, Any]] = None, min_lr: float = 1e-08, max_lr: float = 1, num_training: int = 100, @@ -1149,11 +1283,23 @@ def lr_find( Optionally, the past covariates corresponding to the validation series (must match ``covariates``) val_future_covariates Optionally, the future covariates corresponding to the validation series (must match ``covariates``) + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. + val_sample_weight + Same as for `sample_weight` but for the evaluation dataset. trainer Optionally, a custom PyTorch-Lightning Trainer object to perform training. Using a custom ``trainer`` will override Darts' default trainer. verbose - Optionally, whether to print the progress. Ignored if there is a `ProgressBar` callback in + Whether to print the progress. Ignored if there is a `ProgressBar` callback in `pl_trainer_kwargs`. epochs If specified, will train the model for ``epochs`` (additional) epochs, irrespective of what ``n_epochs`` @@ -1164,11 +1310,12 @@ def lr_find( large number of training samples. This parameter upper-bounds the number of training samples per time series (taking only the most recent samples in each series). Leaving to None does not apply any upper bound. - num_loader_workers - Optionally, an integer specifying the ``num_workers`` to use in PyTorch ``DataLoader`` instances, - both for the training and validation loaders (if any). - A larger number of workers can sometimes increase performance, but can also incur extra overheads - and increase memory usage, as more batches are loaded in parallel. + dataloader_kwargs + Optionally, a dictionary of keyword arguments used to create the PyTorch `DataLoader` instances for the + training and validation datasets. For more information on `DataLoader`, check out `this link + `_. + By default, Darts configures parameters ("batch_size", "shuffle", "drop_last", "collate_fn", "pin_memory") + for seamless forecasting. Changing them should be done with care to avoid unexpected behavior. min_lr minimum learning rate to investigate max_lr @@ -1193,14 +1340,16 @@ def lr_find( series=series, past_covariates=past_covariates, future_covariates=future_covariates, + sample_weight=sample_weight, val_series=val_series, val_past_covariates=val_past_covariates, val_future_covariates=val_future_covariates, + val_sample_weight=val_sample_weight, trainer=trainer, verbose=verbose, epochs=epochs, max_samples_per_ts=max_samples_per_ts, - num_loader_workers=num_loader_workers, + dataloader_kwargs=dataloader_kwargs, ) trainer, model, train_loader, val_loader = self._setup_for_train(*params) return Tuner(trainer).lr_find( @@ -1229,7 +1378,7 @@ def predict( n_jobs: int = 1, roll_size: Optional[int] = None, num_samples: int = 1, - num_loader_workers: int = 0, + dataloader_kwargs: Optional[dict[str, Any]] = None, mc_dropout: bool = False, predict_likelihood_parameters: bool = False, show_warnings: bool = True, @@ -1279,7 +1428,7 @@ def predict( batch_size Size of batches during prediction. Defaults to the models' training ``batch_size`` value. verbose - Optionally, whether to print the progress. Ignored if there is a `ProgressBar` callback in + Whether to print the progress. Ignored if there is a `ProgressBar` callback in `pl_trainer_kwargs`. n_jobs The number of jobs to run in parallel. ``-1`` means using all processors. Defaults to ``1``. @@ -1289,18 +1438,18 @@ def predict( (and optionally future covariates) back into the model. If this parameter is not provided, it will be set ``output_chunk_length`` by default. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. - num_loader_workers - Optionally, an integer specifying the ``num_workers`` to use in PyTorch ``DataLoader`` instances, - for the inference/prediction dataset loaders (if any). - A larger number of workers can sometimes increase performance, but can also incur extra overheads - and increase memory usage, as more batches are loaded in parallel. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. + dataloader_kwargs + Optionally, a dictionary of keyword arguments used to create the PyTorch `DataLoader` instance for the + inference/prediction dataset. For more information on `DataLoader`, check out `this link + `_. + By default, Darts configures parameters ("batch_size", "shuffle", "drop_last", "collate_fn", "pin_memory") + for seamless forecasting. Changing them should be done with care to avoid unexpected behavior. mc_dropout Optionally, enable monte carlo dropout for predictions using neural network based models. This allows bayesian approximation by specifying an implicit prior over learned models. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. Default: ``False``. show_warnings @@ -1317,13 +1466,13 @@ def predict( raise_log( ValueError( "Input `series` must be provided. This is the result either from fitting on multiple series, " - "or from not having fit the model yet." + "from not having fit the model yet, or from loading a model saved with `clean=True`." ), logger, ) series = self.training_series - called_with_single_series = True if isinstance(series, TimeSeries) else False + called_with_single_series = isinstance(series, TimeSeries) # guarantee that all inputs are either list of TimeSeries or None series = series2seq(series) @@ -1335,7 +1484,11 @@ def predict( past_covariates = series2seq(past_covariates) future_covariates = series2seq(future_covariates) - self._verify_static_covariates(series[0].static_covariates) + self._verify_past_future_covariates( + past_covariates=past_covariates, future_covariates=future_covariates + ) + if self.uses_static_covariates: + self._verify_static_covariates(get_single_series(series).static_covariates) # encoders are set when calling fit(), but not when calling fit_from_dataset() # when covariates are loaded from model, they already contain the encodings: this is not a problem as the @@ -1375,7 +1528,7 @@ def predict( n_jobs=n_jobs, roll_size=roll_size, num_samples=num_samples, - num_loader_workers=num_loader_workers, + dataloader_kwargs=dataloader_kwargs, mc_dropout=mc_dropout, predict_likelihood_parameters=predict_likelihood_parameters, ) @@ -1393,7 +1546,7 @@ def predict_from_dataset( n_jobs: int = 1, roll_size: Optional[int] = None, num_samples: int = 1, - num_loader_workers: int = 0, + dataloader_kwargs: Optional[dict[str, Any]] = None, mc_dropout: bool = False, predict_likelihood_parameters: bool = False, ) -> Sequence[TimeSeries]: @@ -1422,7 +1575,7 @@ def predict_from_dataset( batch_size Size of batches during prediction. Defaults to the models ``batch_size`` value. verbose - Optionally, whether to print the progress. Ignored if there is a `ProgressBar` callback in + Whether to print the progress. Ignored if there is a `ProgressBar` callback in `pl_trainer_kwargs`. n_jobs The number of jobs to run in parallel. ``-1`` means using all processors. Defaults to ``1``. @@ -1432,18 +1585,18 @@ def predict_from_dataset( (and optionally future covariates) back into the model. If this parameter is not provided, it will be set ``output_chunk_length`` by default. num_samples - Number of times a prediction is sampled from a probabilistic model. Should be left set to 1 - for deterministic models. - num_loader_workers - Optionally, an integer specifying the ``num_workers`` to use in PyTorch ``DataLoader`` instances, - for the inference/prediction dataset loaders (if any). - A larger number of workers can sometimes increase performance, but can also incur extra overheads - and increase memory usage, as more batches are loaded in parallel. + Number of times a prediction is sampled from a probabilistic model. Must be `1` for deterministic models. + dataloader_kwargs + Optionally, a dictionary of keyword arguments used to create the PyTorch `DataLoader` instance for the + inference/prediction dataset. For more information on `DataLoader`, check out `this link + `_. + By default, Darts configures parameters ("batch_size", "shuffle", "drop_last", "collate_fn", "pin_memory") + for seamless forecasting. Changing them should be done with care to avoid unexpected behavior. mc_dropout Optionally, enable monte carlo dropout for predictions using neural network based models. This allows bayesian approximation by specifying an implicit prior over learned models. predict_likelihood_parameters - If set to `True`, the model predict the parameters of its Likelihood parameters instead of the target. Only + If set to `True`, the model predicts the parameters of its `likelihood` instead of the target. Only supported for probabilistic models with a likelihood, `num_samples = 1` and `n<=output_chunk_length`. Default: ``False`` @@ -1453,7 +1606,7 @@ def predict_from_dataset( Returns one or more forecasts for time series. """ - # We need to call super's super's method directly, because GlobalForecastingModel expects series: + # we need to call super's super's method directly, because GlobalForecastingModel expects series: ForecastingModel.predict(self, n, num_samples) self._verify_inference_dataset_type(input_series_dataset) @@ -1490,28 +1643,32 @@ def predict_from_dataset( batch_size=batch_size, n_jobs=n_jobs, predict_likelihood_parameters=predict_likelihood_parameters, + mc_dropout=mc_dropout, + ) + + dataloader_kwargs = dict( + { + "batch_size": batch_size, + "pin_memory": True, + "drop_last": False, + "collate_fn": self._batch_collate_fn, + }, + **(dataloader_kwargs or {}), + **{"shuffle": False}, ) pred_loader = DataLoader( input_series_dataset, - batch_size=batch_size, - shuffle=False, - num_workers=num_loader_workers, - pin_memory=True, - drop_last=False, - collate_fn=self._batch_collate_fn, + **dataloader_kwargs, ) - # set mc_dropout rate - self.model.set_mc_dropout(mc_dropout) - # set up trainer. use user supplied trainer or create a new trainer from scratch self.trainer = self._setup_trainer( trainer=trainer, model=self.model, verbose=verbose, epochs=self.n_epochs ) # prediction output comes as nested list: list of predicted `TimeSeries` for each batch. - predictions = self.trainer.predict(self.model, pred_loader) + predictions = self.trainer.predict(model=self.model, dataloaders=pred_loader) # flatten and return return [ts for batch in predictions for ts in batch] @@ -1528,10 +1685,12 @@ def min_train_series_length(self) -> int: Class property defining the minimum required length for the training series; overriding the default value of 3 of ForecastingModel """ - return self.input_chunk_length + self.output_chunk_length + return ( + self.input_chunk_length + self.output_chunk_length + self.output_chunk_shift + ) @staticmethod - def _batch_collate_fn(batch: List[Tuple]) -> Tuple: + def _batch_collate_fn(batch: list[tuple]) -> tuple: """ Returns a batch Tuple from a list of samples """ @@ -1549,12 +1708,30 @@ def _batch_collate_fn(batch: List[Tuple]) -> Tuple: aggregated.append([sample[i] for sample in batch]) return tuple(aggregated) - def save(self, path: Optional[str] = None) -> None: + def _clean(self) -> Self: + """Returns a cleaned model, keeping only the necessary attributes for prediction.""" + model = super()._clean() + # Copy from super()._clean() call __getstate__ which removes model and trainer + # a shallow copy is enough since we are only interested in removing pointers + model.model = copy.copy(self.model) # keep the model for prediction + model._model_params = copy.copy(self._model_params) + model._model_params["pl_trainer_kwargs"] = None + model.trainer_params = {} + return model + + def save( + self, + path: Optional[str] = None, + clean: bool = False, + ) -> None: """ Saves the model under a given path. Creates two files under ``path`` (model object) and ``path``.ckpt (checkpoint). + Note: Pickle errors may occur when saving models with custom classes. In this case, consider using + the `clean` flag to strip the saved model from training related attributes. + Example for saving and loading a :class:`RNNModel`: .. highlight:: python @@ -1575,6 +1752,13 @@ def save(self, path: Optional[str] = None) -> None: "best-" to avoid collision with Pytorch-Ligthning checkpoints. If no path is specified, the model is automatically saved under ``"{ModelClass}_{YYYY-mm-dd_HH_MM_SS}.pt"``. E.g., ``"RNNModel_2020-01-01_12_00_00.pt"``. + clean + Whether to store a cleaned version of the model. If `True`, the training series and covariates are removed. + Additionally, removes all Lightning Trainer-related parameters (passed with `pl_trainer_kwargs` at model + creation). + + Note: After loading a model stored with `clean=True`, a `series` must be passed 'predict()', + `historical_forecasts()` and other forecasting methods. """ if path is None: # default path @@ -1582,12 +1766,13 @@ def save(self, path: Optional[str] = None) -> None: # save the TorchForecastingModel (does not save the PyTorch LightningModule, and Trainer) with open(path, "wb") as f_out: - torch.save(self, f_out) + torch.save(self if not clean else self._clean(), f_out) - # save the LightningModule checkpoint + # save the LightningModule checkpoint (weights only with `clean=True`) path_ptl_ckpt = path + ".ckpt" if self.trainer is not None: - self.trainer.save_checkpoint(path_ptl_ckpt) + self.trainer.save_checkpoint(path_ptl_ckpt, weights_only=clean) + # TODO: keep track of PyTorch Lightning to see if they implement model checkpoint saving # without having to call fit/predict/validate/test before # try to recover original automatic PL checkpoint @@ -1602,7 +1787,9 @@ def save(self, path: Optional[str] = None) -> None: ) @staticmethod - def load(path: str, **kwargs) -> "TorchForecastingModel": + def load( + path: str, pl_trainer_kwargs: Optional[dict] = None, **kwargs + ) -> "TorchForecastingModel": """ Loads a model from a given file path. @@ -1616,6 +1803,16 @@ def load(path: str, **kwargs) -> "TorchForecastingModel": model_loaded = RNNModel.load(path) .. + Example for loading an :class:`RNNModel` to GPU that was trained on CPU: + + .. highlight:: python + .. code-block:: python + + from darts.models import RNNModel + + model_loaded = RNNModel.load(path, pl_trainer_kwargs={"accelerator": "gpu"}) + .. + Example for loading an :class:`RNNModel` to CPU that was saved on GPU: .. highlight:: python @@ -1623,8 +1820,7 @@ def load(path: str, **kwargs) -> "TorchForecastingModel": from darts.models import RNNModel - model_loaded = RNNModel.load(path, map_location="cpu") - model_loaded.to_cpu() + model_loaded = RNNModel.load(path, map_location="cpu", pl_trainer_kwargs={"accelerator": "gpu"}) .. Parameters @@ -1632,17 +1828,22 @@ def load(path: str, **kwargs) -> "TorchForecastingModel": path Path from which to load the model. If no path was specified when saving the model, the automatically generated path ending with ".pt" has to be provided. + pl_trainer_kwargs + Optionally, a set of kwargs to create a new Lightning Trainer used to configure the model for downstream + tasks (e.g. prediction). + Some examples include specifying the batch size or moving the model to CPU/GPU(s). Check the + `Lightning Trainer documentation `_ + for more information about the supported kwargs. **kwargs Additional kwargs for PyTorch Lightning's :func:`LightningModule.load_from_checkpoint()` method, - such as ``map_location`` to load the model onto a different device than the one from which it was saved. + such as ``map_location`` to load the model onto a different device than the one on which it was saved. For more information, read the `official documentation `_. """ - # load the base TorchForecastingModel (does not contain the actual PyTorch LightningModule) with open(path, "rb") as fin: model: TorchForecastingModel = torch.load( - fin, map_location=kwargs.get("map_location", None) + fin, weights_only=False, map_location=kwargs.get("map_location", None) ) # if a checkpoint was saved, we also load the PyTorch LightningModule from checkpoint @@ -1655,6 +1856,11 @@ def load(path: str, **kwargs) -> "TorchForecastingModel": f"Model was loaded without weights since no PyTorch LightningModule checkpoint ('.ckpt') could be " f"found at {path_ptl_ckpt}. Please call `fit()` before calling `predict()`." ) + + if pl_trainer_kwargs is not None: + model.trainer_params = pl_trainer_kwargs + model._model_params["pl_trainer_kwargs"] = copy.deepcopy(pl_trainer_kwargs) + return model @staticmethod @@ -1737,7 +1943,7 @@ def load_from_checkpoint( logger, ) model: TorchForecastingModel = torch.load( - base_model_path, map_location=kwargs.get("map_location") + base_model_path, weights_only=False, map_location=kwargs.get("map_location") ) # load PyTorch LightningModule from checkpoint @@ -1754,6 +1960,8 @@ def load_from_checkpoint( loss_fn = model.model_params.get("loss_fn") if loss_fn is not None: model.model.criterion = loss_fn + model.model.train_criterion = copy.deepcopy(loss_fn) + model.model.val_criterion = copy.deepcopy(loss_fn) # train and val metrics also need to be restored torch_metrics = model.model.configure_torch_metrics( model.model_params.get("torch_metrics") @@ -1869,7 +2077,7 @@ def load_weights_from_checkpoint( tfm_save_file_name = file_name[:-5] ckpt_path = os.path.join(checkpoint_dir, file_name) - ckpt = torch.load(ckpt_path, **kwargs) + ckpt = torch.load(ckpt_path, weights_only=False, **kwargs) # indicate to the user than checkpoints generated with darts <= 0.23.1 are not supported raise_if_not( @@ -1904,7 +2112,9 @@ def load_weights_from_checkpoint( # updating model attributes before self._init_model() which create new tfm ckpt with open(tfm_save_file_path, "rb") as tfm_save_file: tfm_save: TorchForecastingModel = torch.load( - tfm_save_file, map_location=kwargs.get("map_location", None) + tfm_save_file, + weights_only=False, + map_location=kwargs.get("map_location", None), ) # encoders are necessary for direct inference @@ -1915,7 +2125,7 @@ def load_weights_from_checkpoint( # meaningful error message if parameters are incompatible with the ckpt weights self._check_ckpt_parameters(tfm_save) - # instanciate the model without having to call `fit_from_dataset` + # instantiate the model without having to call `fit_from_dataset` self.model = self._init_model() # cast model precision to correct type self.model.to_dtype(ckpt["model_dtype"]) @@ -2009,13 +2219,26 @@ def output_chunk_length(self) -> int: ) @property - def _is_probabilistic(self) -> bool: + def output_chunk_shift(self) -> int: + return ( + self.model.output_chunk_shift + if self.model_created + else self.pl_module_params["output_chunk_shift"] + ) + + @property + def supports_probabilistic_prediction(self) -> bool: return ( - self.model._is_probabilistic + self.model.supports_probabilistic_prediction if self.model_created else True # all torch models can be probabilistic (via Dropout) ) + @property + def _requires_training(self) -> bool: + """Whether the model should be trained when calling a `fit*` method.""" + return True + def _check_optimizable_historical_forecasts( self, forecast_horizon: int, @@ -2036,9 +2259,9 @@ def _check_optimizable_historical_forecasts( def _optimized_historical_forecasts( self, - series: Optional[Sequence[TimeSeries]], - past_covariates: Optional[Sequence[TimeSeries]] = None, - future_covariates: Optional[Sequence[TimeSeries]] = None, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, num_samples: int = 1, start: Optional[Union[pd.Timestamp, float, int]] = None, start_format: Literal["position", "value"] = "value", @@ -2050,20 +2273,20 @@ def _optimized_historical_forecasts( show_warnings: bool = True, predict_likelihood_parameters: bool = False, **kwargs, - ) -> Union[ - TimeSeries, List[TimeSeries], Sequence[TimeSeries], Sequence[List[TimeSeries]] - ]: + ) -> Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]]: """ For TorchForecastingModels we use a strided inference dataset to avoid having to recreate trainers and datasets for each forecastable index and series. """ - series, past_covariates, future_covariates = _process_historical_forecast_input( - model=self, - series=series, - past_covariates=past_covariates, - future_covariates=future_covariates, - forecast_horizon=forecast_horizon, - allow_autoregression=True, + series, past_covariates, future_covariates, series_seq_type = ( + _process_historical_forecast_input( + model=self, + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + forecast_horizon=forecast_horizon, + allow_autoregression=True, + ) ) forecasts_list = _optimized_historical_forecasts( model=self, @@ -2082,11 +2305,11 @@ def _optimized_historical_forecasts( predict_likelihood_parameters=predict_likelihood_parameters, **kwargs, ) - return forecasts_list + return series2seq(forecasts_list, seq_type_out=series_seq_type) def _load_encoders( self, tfm_save: "TorchForecastingModel", load_encoders: bool - ) -> Tuple[SequentialEncoder, Dict]: + ) -> tuple[SequentialEncoder, dict]: """Return the encoders from a model save with several sanity checks.""" if self.add_encoders is None: same_encoders = True @@ -2098,7 +2321,7 @@ def _load_encoders( # transformers are equal if they are instances of the same class self_transformer = self.add_encoders.get("transformer", None) tfm_transformer = tfm_save.add_encoders.get("transformer", None) - same_transformer = type(self_transformer) == type(tfm_transformer) + same_transformer = type(self_transformer) is type(tfm_transformer) # encoders are equal if they have the same entries (transformer excluded) self_encoders = { @@ -2127,7 +2350,7 @@ def _load_encoders( logger, ) - new_add_encoders: Dict = copy.deepcopy(tfm_save.add_encoders) + new_add_encoders: dict = copy.deepcopy(tfm_save.add_encoders) new_encoders: SequentialEncoder = copy.deepcopy(tfm_save.encoders) else: raise_if( @@ -2138,7 +2361,7 @@ def _load_encoders( logger, ) - new_add_encoders: Dict = self.add_encoders + new_add_encoders: dict = self.add_encoders new_encoders: SequentialEncoder = self.initialize_encoders() # compare the dimensions of the new and ckpt encoders @@ -2187,6 +2410,7 @@ def _check_ckpt_parameters(self, tfm_save): "optimizer_kwargs", "lr_scheduler_cls", "lr_scheduler_kwargs", + "output_chunk_shift", ] # model_params can be missing some kwargs params_to_check = set(tfm_save.model_params.keys()).union( @@ -2201,23 +2425,19 @@ def _check_ckpt_parameters(self, tfm_save): missing_params.append((param_key, tfm_save.model_params[param_key])) # new param was used at loading model creation elif param_key not in tfm_save.model_params.keys(): - incorrect_params.append( - ( - param_key, - None, - self.model_params[param_key], - ) - ) + incorrect_params.append(( + param_key, + None, + self.model_params[param_key], + )) # param was different at loading model creation elif self.model_params[param_key] != tfm_save.model_params[param_key]: # NOTE: for TFTModel, default is None but converted to `QuantileRegression()` - incorrect_params.append( - ( - param_key, - tfm_save.model_params[param_key], - self.model_params[param_key], - ) - ) + incorrect_params.append(( + param_key, + tfm_save.model_params[param_key], + self.model_params[param_key], + )) # at least one discrepancy was detected if len(missing_params) + len(incorrect_params) > 0: @@ -2225,7 +2445,7 @@ def _check_ckpt_parameters(self, tfm_save): "The values of the hyper-parameters in the model and loaded checkpoint should be identical." ] - # warning messages formated to facilate copy-pasting + # warning messages formatted to facilitate copy-pasting if len(missing_params) > 0: msg += ["missing :"] msg += [ @@ -2268,7 +2488,7 @@ def _raise_if_wrong_type(obj, exp_type, msg="expected type {}, got: {}"): # TODO: there's a lot of repetition below... is there a cleaner way to do this in Python- Using eg generics or something -def _basic_compare_sample(train_sample: Tuple, predict_sample: Tuple): +def _basic_compare_sample(train_sample: tuple, predict_sample: tuple): """ For all models relying on one type of covariates only (Past, Future, Dual), we can rely on the fact that training/inference datasets have target and covariates in first and second position to do the checks. @@ -2310,16 +2530,17 @@ def _basic_compare_sample(train_sample: Tuple, predict_sample: Tuple): ) -def _mixed_compare_sample(train_sample: Tuple, predict_sample: Tuple): +def _mixed_compare_sample(train_sample: tuple, predict_sample: tuple): """ For models relying on MixedCovariates. Parameters ---------- train_sample - (past_target, past_covariates, historic_future_covariates, future_covariates, future_target) + (past_target, past_covariates, historic_future_covariates, future_covariates, static covariates, future_target) predict_sample - (past_target, past_covariates, historic_future_covariates, future_covariates, future_past_covariates, ts_target) + (past_target, past_covariates, historic_future_covariates, future_covariates, future_past_covariates, + static_covariates, ts_target) """ # datasets; we skip future_target for train and predict, and skip future_past_covariates for predict datasets ds_names = [ @@ -2377,20 +2598,18 @@ def _build_train_dataset( target: Sequence[TimeSeries], past_covariates: Optional[Sequence[TimeSeries]], future_covariates: Optional[Sequence[TimeSeries]], + sample_weight: Optional[Union[Sequence[TimeSeries], str]], max_samples_per_ts: Optional[int], ) -> PastCovariatesTrainingDataset: - raise_if_not( - future_covariates is None, - "Specified future_covariates for a PastCovariatesModel (only past_covariates are expected).", - ) - return PastCovariatesSequentialDataset( target_series=target, covariates=past_covariates, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, max_samples_per_ts=max_samples_per_ts, use_static_covariates=self.uses_static_covariates, + sample_weight=sample_weight, ) def _build_inference_dataset( @@ -2402,11 +2621,6 @@ def _build_inference_dataset( stride: int = 0, bounds: Optional[np.ndarray] = None, ) -> PastCovariatesInferenceDataset: - raise_if_not( - future_covariates is None, - "Specified future_covariates for a PastCovariatesModel (only past_covariates are expected).", - ) - return PastCovariatesInferenceDataset( target_series=target, covariates=past_covariates, @@ -2415,6 +2629,7 @@ def _build_inference_dataset( bounds=bounds, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, use_static_covariates=self.uses_static_covariates, ) @@ -2424,27 +2639,20 @@ def _verify_train_dataset_type(self, train_dataset: TrainingDataset): def _verify_inference_dataset_type(self, inference_dataset: InferenceDataset): _raise_if_wrong_type(inference_dataset, PastCovariatesInferenceDataset) - def _verify_predict_sample(self, predict_sample: Tuple): + def _verify_predict_sample(self, predict_sample: tuple): _basic_compare_sample(self.train_sample, predict_sample) - def _verify_past_future_covariates(self, past_covariates, future_covariates): - raise_if_not( - future_covariates is None, - "Some future_covariates have been provided to a PastCovariates model. These models " - "support only past_covariates.", - ) - @property def _model_encoder_settings( self, - ) -> Tuple[int, int, bool, bool, Optional[List[int]], Optional[List[int]]]: + ) -> tuple[int, int, bool, bool, Optional[list[int]], Optional[list[int]]]: input_chunk_length = self.input_chunk_length output_chunk_length = self.output_chunk_length takes_past_covariates = True takes_future_covariates = False return ( input_chunk_length, - output_chunk_length, + output_chunk_length + self.output_chunk_shift, takes_past_covariates, takes_future_covariates, None, @@ -2454,20 +2662,24 @@ def _model_encoder_settings( @property def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, + Optional[int], ]: return ( -self.input_chunk_length, - self.output_chunk_length - 1, - -self.input_chunk_length if self.uses_past_covariates else None, - -1 if self.uses_past_covariates else None, + self.output_chunk_length - 1 + self.output_chunk_shift, + -self.input_chunk_length, + -1, + None, None, + self.output_chunk_shift, None, ) @@ -2480,20 +2692,18 @@ def _build_train_dataset( target: Sequence[TimeSeries], past_covariates: Optional[Sequence[TimeSeries]], future_covariates: Optional[Sequence[TimeSeries]], + sample_weight: Optional[Union[Sequence[TimeSeries], str]], max_samples_per_ts: Optional[int], ) -> FutureCovariatesTrainingDataset: - raise_if_not( - past_covariates is None, - "Specified past_covariates for a FutureCovariatesModel (only future_covariates are expected).", - ) - return FutureCovariatesSequentialDataset( target_series=target, covariates=future_covariates, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, max_samples_per_ts=max_samples_per_ts, use_static_covariates=self.uses_static_covariates, + sample_weight=sample_weight, ) def _build_inference_dataset( @@ -2505,11 +2715,6 @@ def _build_inference_dataset( stride: int = 0, bounds: Optional[np.ndarray] = None, ) -> FutureCovariatesInferenceDataset: - raise_if_not( - past_covariates is None, - "Specified past_covariates for a FutureCovariatesModel (only future_covariates are expected).", - ) - return FutureCovariatesInferenceDataset( target_series=target, covariates=future_covariates, @@ -2517,6 +2722,7 @@ def _build_inference_dataset( stride=stride, bounds=bounds, input_chunk_length=self.input_chunk_length, + output_chunk_shift=self.output_chunk_shift, use_static_covariates=self.uses_static_covariates, ) @@ -2526,27 +2732,20 @@ def _verify_train_dataset_type(self, train_dataset: TrainingDataset): def _verify_inference_dataset_type(self, inference_dataset: InferenceDataset): _raise_if_wrong_type(inference_dataset, FutureCovariatesInferenceDataset) - def _verify_predict_sample(self, predict_sample: Tuple): + def _verify_predict_sample(self, predict_sample: tuple): _basic_compare_sample(self.train_sample, predict_sample) - def _verify_past_future_covariates(self, past_covariates, future_covariates): - raise_if_not( - past_covariates is None, - "Some past_covariates have been provided to a PastCovariates model. These models " - "support only future_covariates.", - ) - @property def _model_encoder_settings( self, - ) -> Tuple[int, int, bool, bool, Optional[List[int]], Optional[List[int]]]: + ) -> tuple[int, int, bool, bool, Optional[list[int]], Optional[list[int]]]: input_chunk_length = self.input_chunk_length output_chunk_length = self.output_chunk_length takes_past_covariates = False takes_future_covariates = True return ( input_chunk_length, - output_chunk_length, + output_chunk_length + self.output_chunk_shift, takes_past_covariates, takes_future_covariates, None, @@ -2556,21 +2755,25 @@ def _model_encoder_settings( @property def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ + Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, Optional[int], ]: return ( -self.input_chunk_length, - self.output_chunk_length - 1, + self.output_chunk_length - 1 + self.output_chunk_shift, None, None, - 0 if self.uses_future_covariates else None, - self.output_chunk_length - 1 if self.uses_future_covariates else None, + self.output_chunk_shift, + self.output_chunk_length - 1 + self.output_chunk_shift, + self.output_chunk_shift, + None, ) @@ -2582,6 +2785,7 @@ def _build_train_dataset( target: Sequence[TimeSeries], past_covariates: Optional[Sequence[TimeSeries]], future_covariates: Optional[Sequence[TimeSeries]], + sample_weight: Optional[Union[Sequence[TimeSeries], str]], max_samples_per_ts: Optional[int], ) -> DualCovariatesTrainingDataset: return DualCovariatesSequentialDataset( @@ -2589,8 +2793,10 @@ def _build_train_dataset( covariates=future_covariates, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, max_samples_per_ts=max_samples_per_ts, use_static_covariates=self.uses_static_covariates, + sample_weight=sample_weight, ) def _build_inference_dataset( @@ -2610,6 +2816,7 @@ def _build_inference_dataset( bounds=bounds, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, use_static_covariates=self.uses_static_covariates, ) @@ -2619,27 +2826,20 @@ def _verify_train_dataset_type(self, train_dataset: TrainingDataset): def _verify_inference_dataset_type(self, inference_dataset: InferenceDataset): _raise_if_wrong_type(inference_dataset, DualCovariatesInferenceDataset) - def _verify_predict_sample(self, predict_sample: Tuple): + def _verify_predict_sample(self, predict_sample: tuple): _basic_compare_sample(self.train_sample, predict_sample) - def _verify_past_future_covariates(self, past_covariates, future_covariates): - raise_if_not( - past_covariates is None, - "Some past_covariates have been provided to a DualCovariates Torch model. These models " - "support only future_covariates.", - ) - @property def _model_encoder_settings( self, - ) -> Tuple[int, int, bool, bool, Optional[List[int]], Optional[List[int]]]: + ) -> tuple[int, int, bool, bool, Optional[list[int]], Optional[list[int]]]: input_chunk_length = self.input_chunk_length output_chunk_length = self.output_chunk_length takes_past_covariates = False takes_future_covariates = True return ( input_chunk_length, - output_chunk_length, + output_chunk_length + self.output_chunk_shift, takes_past_covariates, takes_future_covariates, None, @@ -2649,21 +2849,25 @@ def _model_encoder_settings( @property def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, + Optional[int], ]: return ( -self.input_chunk_length, - self.output_chunk_length - 1, + self.output_chunk_length - 1 + self.output_chunk_shift, None, None, - -self.input_chunk_length if self.uses_future_covariates else None, - self.output_chunk_length - 1 if self.uses_future_covariates else None, + -self.input_chunk_length, + self.output_chunk_length - 1 + self.output_chunk_shift, + self.output_chunk_shift, + None, ) @@ -2673,6 +2877,7 @@ def _build_train_dataset( target: Sequence[TimeSeries], past_covariates: Optional[Sequence[TimeSeries]], future_covariates: Optional[Sequence[TimeSeries]], + sample_weight: Optional[Union[Sequence[TimeSeries], str]], max_samples_per_ts: Optional[int], ) -> MixedCovariatesTrainingDataset: return MixedCovariatesSequentialDataset( @@ -2681,8 +2886,10 @@ def _build_train_dataset( future_covariates=future_covariates, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, max_samples_per_ts=max_samples_per_ts, use_static_covariates=self.uses_static_covariates, + sample_weight=sample_weight, ) def _build_inference_dataset( @@ -2703,6 +2910,7 @@ def _build_inference_dataset( bounds=bounds, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, use_static_covariates=self.uses_static_covariates, ) @@ -2712,24 +2920,20 @@ def _verify_train_dataset_type(self, train_dataset: TrainingDataset): def _verify_inference_dataset_type(self, inference_dataset: InferenceDataset): _raise_if_wrong_type(inference_dataset, MixedCovariatesInferenceDataset) - def _verify_predict_sample(self, predict_sample: Tuple): + def _verify_predict_sample(self, predict_sample: tuple): _mixed_compare_sample(self.train_sample, predict_sample) - def _verify_past_future_covariates(self, past_covariates, future_covariates): - # both covariates are supported; do nothing - pass - @property def _model_encoder_settings( self, - ) -> Tuple[int, int, bool, bool, Optional[List[int]], Optional[List[int]]]: + ) -> tuple[int, int, bool, bool, Optional[list[int]], Optional[list[int]]]: input_chunk_length = self.input_chunk_length output_chunk_length = self.output_chunk_length takes_past_covariates = True takes_future_covariates = True return ( input_chunk_length, - output_chunk_length, + output_chunk_length + self.output_chunk_shift, takes_past_covariates, takes_future_covariates, None, @@ -2739,79 +2943,27 @@ def _model_encoder_settings( @property def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ + Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, Optional[int], ]: return ( -self.input_chunk_length, - self.output_chunk_length - 1, - -self.input_chunk_length if self.uses_past_covariates else None, - -1 if self.uses_past_covariates else None, - -self.input_chunk_length if self.uses_future_covariates else None, - self.output_chunk_length - 1 if self.uses_future_covariates else None, + self.output_chunk_length - 1 + self.output_chunk_shift, + -self.input_chunk_length, + -1, + -self.input_chunk_length, + self.output_chunk_length - 1 + self.output_chunk_shift, + self.output_chunk_shift, + None, ) - def predict( - self, - n: int, - series: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - trainer: Optional[pl.Trainer] = None, - batch_size: Optional[int] = None, - verbose: Optional[bool] = None, - n_jobs: int = 1, - roll_size: Optional[int] = None, - num_samples: int = 1, - num_loader_workers: int = 0, - mc_dropout: bool = False, - predict_likelihood_parameters: bool = False, - show_warnings: bool = True, - ) -> Union[TimeSeries, Sequence[TimeSeries]]: - # since we have future covariates, the inference dataset for future input must be at least of length - # `output_chunk_length`. If not, we would have to step back which causes past input to be shorter than - # `input_chunk_length`. - - if n >= self.output_chunk_length: - return super().predict( - n=n, - series=series, - past_covariates=past_covariates, - future_covariates=future_covariates, - trainer=trainer, - batch_size=batch_size, - verbose=verbose, - n_jobs=n_jobs, - roll_size=roll_size, - num_samples=num_samples, - num_loader_workers=num_loader_workers, - mc_dropout=mc_dropout, - predict_likelihood_parameters=predict_likelihood_parameters, - show_warnings=show_warnings, - ) - else: - return super().predict( - n=self.output_chunk_length, - series=series, - past_covariates=past_covariates, - future_covariates=future_covariates, - trainer=trainer, - batch_size=batch_size, - verbose=verbose, - n_jobs=n_jobs, - roll_size=roll_size, - num_samples=num_samples, - num_loader_workers=num_loader_workers, - mc_dropout=mc_dropout, - predict_likelihood_parameters=predict_likelihood_parameters, - show_warnings=show_warnings, - )[:n] - class SplitCovariatesTorchModel(TorchForecastingModel, ABC): def _build_train_dataset( @@ -2819,6 +2971,7 @@ def _build_train_dataset( target: Sequence[TimeSeries], past_covariates: Optional[Sequence[TimeSeries]], future_covariates: Optional[Sequence[TimeSeries]], + sample_weight: Optional[Union[Sequence[TimeSeries], str]], max_samples_per_ts: Optional[int], ) -> SplitCovariatesTrainingDataset: return SplitCovariatesSequentialDataset( @@ -2827,8 +2980,10 @@ def _build_train_dataset( future_covariates=future_covariates, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, max_samples_per_ts=max_samples_per_ts, use_static_covariates=self.uses_static_covariates, + sample_weight=sample_weight, ) def _build_inference_dataset( @@ -2849,6 +3004,7 @@ def _build_inference_dataset( bounds=bounds, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, use_static_covariates=self.uses_static_covariates, ) @@ -2858,25 +3014,21 @@ def _verify_train_dataset_type(self, train_dataset: TrainingDataset): def _verify_inference_dataset_type(self, inference_dataset: InferenceDataset): _raise_if_wrong_type(inference_dataset, SplitCovariatesInferenceDataset) - def _verify_past_future_covariates(self, past_covariates, future_covariates): - # both covariates are supported; do nothing - pass - - def _verify_predict_sample(self, predict_sample: Tuple): + def _verify_predict_sample(self, predict_sample: tuple): # TODO: we have to check both past and future covariates raise NotImplementedError() @property def _model_encoder_settings( self, - ) -> Tuple[int, int, bool, bool, Optional[List[int]], Optional[List[int]]]: + ) -> tuple[int, int, bool, bool, Optional[list[int]], Optional[list[int]]]: input_chunk_length = self.input_chunk_length output_chunk_length = self.output_chunk_length takes_past_covariates = True takes_future_covariates = True return ( input_chunk_length, - output_chunk_length, + output_chunk_length + self.output_chunk_shift, takes_past_covariates, takes_future_covariates, None, @@ -2886,19 +3038,23 @@ def _model_encoder_settings( @property def extreme_lags( self, - ) -> Tuple[ + ) -> tuple[ Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], Optional[int], + int, + Optional[int], ]: return ( -self.input_chunk_length, - self.output_chunk_length - 1, - -self.input_chunk_length if self.uses_past_covariates else None, - -1 if self.uses_past_covariates else None, - 0 if self.uses_future_covariates else None, - self.output_chunk_length - 1 if self.uses_future_covariates else None, + self.output_chunk_length - 1 + self.output_chunk_shift, + -self.input_chunk_length, + -1, + self.output_chunk_shift, + self.output_chunk_length - 1 + self.output_chunk_shift, + self.output_chunk_shift, + None, ) diff --git a/darts/models/forecasting/transformer_model.py b/darts/models/forecasting/transformer_model.py index d68f30e2fa..59870e4975 100644 --- a/darts/models/forecasting/transformer_model.py +++ b/darts/models/forecasting/transformer_model.py @@ -4,7 +4,7 @@ """ import math -from typing import Optional, Tuple, Union +from typing import Optional, Union import torch import torch.nn as nn @@ -21,6 +21,7 @@ io_processor, ) from darts.models.forecasting.torch_forecasting_model import PastCovariatesTorchModel +from darts.utils.torch import MonteCarloDropout logger = get_logger(__name__) @@ -99,7 +100,7 @@ def __init__(self, d_model, dropout=0.1, max_len=500): Tensor containing the embedded time series enhanced with positional encoding. """ super().__init__() - self.dropout = nn.Dropout(p=dropout) + self.dropout = MonteCarloDropout(p=dropout) pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) @@ -167,7 +168,8 @@ def __init__( custom_decoder A custom transformer decoder provided by the user (default=None). **kwargs - All parameters required for :class:`darts.model.forecasting_models.PLForecastingModule` base class. + All parameters required for :class:`darts.models.forecasting.pl_forecasting_module.PLForecastingModule` + base class. Inputs ------ @@ -294,7 +296,7 @@ def _create_transformer_inputs(self, data): return src, tgt @io_processor - def forward(self, x_in: Tuple): + def forward(self, x_in: tuple): data, _ = x_in # Here we create 'src' and 'tgt', the inputs for the encoder and decoder # side of the Transformer architecture @@ -327,6 +329,7 @@ def __init__( self, input_chunk_length: int, output_chunk_length: int, + output_chunk_shift: int = 0, d_model: int = 64, nhead: int = 4, num_encoder_layers: int = 3, @@ -339,7 +342,6 @@ def __init__( custom_decoder: Optional[nn.Module] = None, **kwargs, ): - """Transformer model Transformer is a state-of-the-art deep learning model introduced in 2017. It is an encoder-decoder @@ -362,10 +364,16 @@ def __init__( Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values from future covariates to use as a model input (if the model supports future covariates). It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated - using either a one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). d_model The number of expected features in the transformer encoder/decoder inputs (default=64). nhead @@ -594,7 +602,7 @@ def encode_year(idx): def supports_multivariate(self) -> bool: return True - def _create_model(self, train_sample: Tuple[torch.Tensor]) -> torch.nn.Module: + def _create_model(self, train_sample: tuple[torch.Tensor]) -> torch.nn.Module: # samples are made of (past_target, past_covariates, future_target) input_dim = train_sample[0].shape[1] + ( train_sample[1].shape[1] if train_sample[1] is not None else 0 diff --git a/darts/models/forecasting/tsmixer_model.py b/darts/models/forecasting/tsmixer_model.py new file mode 100644 index 0000000000..56100ed4c6 --- /dev/null +++ b/darts/models/forecasting/tsmixer_model.py @@ -0,0 +1,845 @@ +""" +Time-Series Mixer (TSMixer) +--------------------------- +""" + +# The inner layers (``nn.Modules``) and the ``TimeBatchNorm2d`` were provided by a PyTorch implementation +# of TSMixer: https://github.com/ditschuk/pytorch-tsmixer +# +# The License of pytorch-tsmixer v0.2.0 from https://github.com/ditschuk/pytorch-tsmixer/blob/main/LICENSE, +# accessed Thursday, March 21st, 2024: +# 'The MIT License +# +# Copyright 2023 Konstantin Ditschuneit +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +# associated documentation files (the “Software”), to deal in the Software without restriction, +# including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +# and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +# subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all copies or substantial +# portions of the Software. +# ' + +from typing import Callable, Optional, Union + +import torch +from torch import nn + +from darts.logging import get_logger, raise_log +from darts.models.components import layer_norm_variants +from darts.models.forecasting.pl_forecasting_module import ( + PLMixedCovariatesModule, + io_processor, +) +from darts.models.forecasting.torch_forecasting_model import MixedCovariatesTorchModel +from darts.utils.torch import MonteCarloDropout + +MixedCovariatesTrainTensorType = tuple[ + torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor +] + +logger = get_logger(__name__) + +ACTIVATIONS = [ + "ReLU", + "RReLU", + "PReLU", + "ELU", + "Softplus", + "Tanh", + "SELU", + "LeakyReLU", + "Sigmoid", + "GELU", +] + +NORMS = [ + "LayerNorm", + "LayerNormNoBias", + "TimeBatchNorm2d", +] + + +def _time_to_feature(x: torch.Tensor) -> torch.Tensor: + """Converts a time series Tensor to a feature Tensor.""" + return x.permute(0, 2, 1) + + +class TimeBatchNorm2d(nn.BatchNorm2d): + def __init__(self, *args, **kwargs): + """A batch normalization layer that normalizes over the last two dimensions of a Tensor.""" + super().__init__(num_features=1) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # `x` has shape (batch_size, time, features) + if x.ndim != 3: + raise_log( + ValueError( + f"Expected 3D input Tensor, but got {x.ndim}D Tensor instead." + ), + logger=logger, + ) + # apply 2D batch norm over reshape input_data `(batch_size, 1, timepoints, features)` + output = super().forward(x.unsqueeze(1)) + # reshape back to (batch_size, timepoints, features) + return output.squeeze(1) + + +class _FeatureMixing(nn.Module): + def __init__( + self, + sequence_length: int, + input_dim: int, + output_dim: int, + ff_size: int, + activation: Callable[[torch.Tensor], torch.Tensor], + dropout: float, + normalize_before: bool, + norm_type: nn.Module, + ) -> None: + """A module for feature mixing with flexibility in normalization and activation based on the + `PyTorch implementation of TSMixer `_. + + This module provides options for batch normalization before or after mixing + features, uses dropout for regularization, and allows for different activation + functions. + + Parameters + ---------- + sequence_length + The length of the input sequences. + input_dim + The number of input channels to the module. + output_dim + The number of output channels from the module. + ff_size + The dimension of the feed-forward network internal to the module. + activation + The activation function used within the feed-forward network. + dropout + The dropout probability used for regularization. + normalize_before + A boolean indicating whether to apply normalization before + the rest of the operations. + norm_type + The type of normalization to use. + """ + super().__init__() + + self.projection = ( + nn.Linear(input_dim, output_dim) + if input_dim != output_dim + else nn.Identity() + ) + self.norm_before = ( + norm_type((sequence_length, input_dim)) + if normalize_before + else nn.Identity() + ) + self.fc1 = nn.Linear(input_dim, ff_size) + self.activation = activation + self.dropout1 = MonteCarloDropout(dropout) + self.fc2 = nn.Linear(ff_size, output_dim) + self.dropout2 = MonteCarloDropout(dropout) + self.norm_after = ( + norm_type((sequence_length, output_dim)) + if not normalize_before + else nn.Identity() + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x_proj = self.projection(x) + x = self.norm_before(x) + x = self.fc1(x) + x = self.activation(x) + x = self.dropout1(x) + x = self.fc2(x) + x = self.dropout2(x) + x = x_proj + x + x = self.norm_after(x) + return x + + +class _TimeMixing(nn.Module): + def __init__( + self, + sequence_length: int, + input_dim: int, + activation: Callable, + dropout: float, + normalize_before: bool, + norm_type: nn.Module, + ) -> None: + """Applies a transformation over the time dimension of a sequence based on the + `PyTorch implementation of TSMixer `_. + + This module applies a linear transformation followed by an activation function + and dropout over the sequence length of the input feature torch.Tensor after converting + feature maps to the time dimension and then back. + + Parameters + ---------- + sequence_length + The length of the sequences to be transformed. + input_dim + The number of input channels to the module. + activation + The activation function to be used after the linear + transformation. + dropout + The dropout probability to be used after the activation function. + normalize_before + Whether to apply normalization before or after feature mixing. + norm_type + The type of normalization to use. + """ + super().__init__() + self.normalize_before = normalize_before + self.norm_before = ( + norm_type((sequence_length, input_dim)) + if normalize_before + else nn.Identity() + ) + self.activation = activation + self.dropout = MonteCarloDropout(dropout) + self.fc1 = nn.Linear(sequence_length, sequence_length) + self.norm_after = ( + norm_type((sequence_length, input_dim)) + if not normalize_before + else nn.Identity() + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # permute the feature dim with the time dim + x_temp = self.norm_before(x) + x_temp = _time_to_feature(x_temp) + x_temp = self.activation(self.fc1(x_temp)) + x_temp = self.dropout(x_temp) + # permute back the time dim with the feature dim + x_temp = x + _time_to_feature(x_temp) + x_temp = self.norm_after(x_temp) + return x_temp + + +class _ConditionalMixerLayer(nn.Module): + def __init__( + self, + sequence_length: int, + input_dim: int, + output_dim: int, + static_cov_dim: int, + ff_size: int, + activation: Callable, + dropout: float, + normalize_before: bool, + norm_type: nn.Module, + ) -> None: + """Conditional mix layer combining time and feature mixing with static context based on the + `PyTorch implementation of TSMixer `_. + + This module combines time mixing and conditional feature mixing, where the latter + is influenced by static features. This allows the module to learn representations + that are influenced by both dynamic and static features. + + Parameters + ---------- + sequence_length + The length of the input sequences. + input_dim + The number of input channels of the dynamic features. + output_dim + The number of output channels after feature mixing. + static_cov_dim + The number of channels in the static feature input. + ff_size + The inner dimension of the feedforward network used in feature mixing. + activation + The activation function used in both mixing operations. + dropout + The dropout probability used in both mixing operations. + normalize_before + Whether to apply normalization before or after mixing. + norm_type + The type of normalization to use. + """ + super().__init__() + + mixing_input = input_dim + if static_cov_dim != 0: + self.feature_mixing_static = _FeatureMixing( + sequence_length=sequence_length, + input_dim=static_cov_dim, + output_dim=output_dim, + ff_size=ff_size, + activation=activation, + dropout=dropout, + normalize_before=normalize_before, + norm_type=norm_type, + ) + mixing_input += output_dim + else: + self.feature_mixing_static = None + + self.time_mixing = _TimeMixing( + sequence_length=sequence_length, + input_dim=mixing_input, + activation=activation, + dropout=dropout, + normalize_before=normalize_before, + norm_type=norm_type, + ) + self.feature_mixing = _FeatureMixing( + sequence_length=sequence_length, + input_dim=mixing_input, + output_dim=output_dim, + ff_size=ff_size, + activation=activation, + dropout=dropout, + normalize_before=normalize_before, + norm_type=norm_type, + ) + + def forward( + self, x: torch.Tensor, x_static: Optional[torch.Tensor] + ) -> torch.Tensor: + if self.feature_mixing_static is not None: + x_static_mixed = self.feature_mixing_static(x_static) + x = torch.cat([x, x_static_mixed], dim=-1) + x = self.time_mixing(x) + x = self.feature_mixing(x) + return x + + +class _TSMixerModule(PLMixedCovariatesModule): + def __init__( + self, + input_dim: int, + output_dim: int, + past_cov_dim: int, + future_cov_dim: int, + static_cov_dim: int, + nr_params: int, + hidden_size: int, + ff_size: int, + num_blocks: int, + activation: str, + dropout: float, + norm_type: Union[str, nn.Module], + normalize_before: bool, + **kwargs, + ) -> None: + """ + Initializes the TSMixer module for use within a Darts forecasting model. + + Parameters + ---------- + input_dim + Number of input target features. + output_dim + Number of output target features. + past_cov_dim + Number of past covariate features. + future_cov_dim + Number of future covariate features. + static_cov_dim + Number of static covariate features (number of target features + (or 1 if global static covariates) * number of static covariate features). + nr_params + The number of parameters of the likelihood (or 1 if no likelihood is used). + hidden_size + Hidden state size of the TSMixer. + ff_size + Dimension of the feedforward network internal to the module. + num_blocks + Number of mixer blocks. + activation + Activation function to use. + dropout + Dropout rate for regularization. + norm_type + Type of normalization to use. + normalize_before + Whether to apply normalization before or after mixing. + """ + super().__init__(**kwargs) + self.input_dim = input_dim + self.output_dim = output_dim + self.future_cov_dim = future_cov_dim + self.static_cov_dim = static_cov_dim + self.nr_params = nr_params + + if activation not in ACTIVATIONS: + raise_log( + ValueError( + f"Invalid `activation={activation}`. Must be on of {ACTIVATIONS}." + ), + logger=logger, + ) + activation = getattr(nn, activation)() + + if isinstance(norm_type, str): + if norm_type not in NORMS: + raise_log( + ValueError( + f"Invalid `norm_type={norm_type}`. Must be on of {NORMS}." + ), + logger=logger, + ) + if norm_type == "TimeBatchNorm2d": + norm_type = TimeBatchNorm2d + else: + norm_type = getattr(layer_norm_variants, norm_type) + else: + norm_type = norm_type + + mixer_params = { + "ff_size": ff_size, + "activation": activation, + "dropout": dropout, + "norm_type": norm_type, + "normalize_before": normalize_before, + } + + self.fc_hist = nn.Linear(self.input_chunk_length, self.output_chunk_length) + self.feature_mixing_hist = _FeatureMixing( + sequence_length=self.output_chunk_length, + input_dim=input_dim + past_cov_dim + future_cov_dim, + output_dim=hidden_size, + **mixer_params, + ) + if future_cov_dim: + self.feature_mixing_future = _FeatureMixing( + sequence_length=self.output_chunk_length, + input_dim=future_cov_dim, + output_dim=hidden_size, + **mixer_params, + ) + else: + self.feature_mixing_future = None + self.conditional_mixer = self._build_mixer( + prediction_length=self.output_chunk_length, + num_blocks=num_blocks, + hidden_size=hidden_size, + future_cov_dim=future_cov_dim, + static_cov_dim=static_cov_dim, + **mixer_params, + ) + self.fc_out = nn.Linear(hidden_size, output_dim * nr_params) + + @staticmethod + def _build_mixer( + prediction_length: int, + num_blocks: int, + hidden_size: int, + future_cov_dim: int, + static_cov_dim: int, + **kwargs, + ) -> nn.ModuleList: + """Build the mixer blocks for the model.""" + # the first block takes `x` consisting of concatenated features with size `hidden_size`: + # - historic features + # - optional future features + input_dim_block = hidden_size * (1 + int(future_cov_dim > 0)) + + mixer_layers = nn.ModuleList() + for _ in range(num_blocks): + layer = _ConditionalMixerLayer( + input_dim=input_dim_block, + output_dim=hidden_size, + sequence_length=prediction_length, + static_cov_dim=static_cov_dim, + **kwargs, + ) + mixer_layers.append(layer) + # after the first block, `x` consists of previous block output with size `hidden_size` + input_dim_block = hidden_size + return mixer_layers + + @io_processor + def forward( + self, + x_in: tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]], + ) -> torch.Tensor: + # x_hist contains the historical time series data and the historical + """TSMixer model forward pass. + + Parameters + ---------- + x_in + comes as Tuple `(x_past, x_future, x_static)` where `x_past` is the input/past chunk and + `x_future` is the output/future chunk. Input dimensions are `(batch_size, time_steps, + components)`. + + Returns + ------- + torch.torch.Tensor + The output Tensorof shape `(batch_size, output_chunk_length, output_dim, nr_params)`. + """ + # B: batch size + # L: input chunk length + # T: output chunk length + # C: target components + # P: past cov features + # F: future cov features + # S: static cov features + # H = C + P + F: historic features + # H_S: hidden Size + # N_P: likelihood parameters + + # `x`: (B, L, H), `x_future`: (B, T, F), `x_static`: (B, C or 1, S) + x, x_future, x_static = x_in + + # swap feature and time dimensions (B, L, H) -> (B, H, L) + x = _time_to_feature(x) + # linear transformations to horizon (B, H, L) -> (B, H, T) + x = self.fc_hist(x) + # (B, H, T) -> (B, T, H) + x = _time_to_feature(x) + + # feature mixing for historical features (B, T, H) -> (B, T, H_S) + x = self.feature_mixing_hist(x) + if self.future_cov_dim: + # feature mixing for future features (B, T, F) -> (B, T, H_S) + x_future = self.feature_mixing_future(x_future) + # (B, T, H_S) + (B, T, H_S) -> (B, T, 2*H_S) + x = torch.cat([x, x_future], dim=-1) + + if self.static_cov_dim: + # (B, C, S) -> (B, 1, C * S) + x_static = x_static.reshape(x_static.shape[0], 1, -1) + # repeat to match horizon (B, 1, C * S) -> (B, T, C * S) + x_static = x_static.repeat(1, self.output_chunk_length, 1) + + for mixing_layer in self.conditional_mixer: + # conditional mixer layers with static covariates (B, T, 2 * H_S), (B, T, C * S) -> (B, T, H_S) + x = mixing_layer(x, x_static=x_static) + + # linear transformation to generate the forecast (B, T, H_S) -> (B, T, C * N_P) + x = self.fc_out(x) + # (B, T, C * N_P) -> (B, T, C, N_P) + x = x.view(-1, self.output_chunk_length, self.output_dim, self.nr_params) + return x + + +class TSMixerModel(MixedCovariatesTorchModel): + def __init__( + self, + input_chunk_length: int, + output_chunk_length: int, + output_chunk_shift: int = 0, + hidden_size: int = 64, + ff_size: int = 64, + num_blocks: int = 2, + activation: str = "ReLU", + dropout: float = 0.1, + norm_type: Union[str, nn.Module] = "LayerNorm", + normalize_before: bool = False, + use_static_covariates: bool = True, + **kwargs, + ) -> None: + """Time-Series Mixer (TSMixer): An All-MLP Architecture for Time Series. + + This is an implementation of the TSMixer architecture, as outlined in [1]_. A major part of the architecture + was adopted from `this PyTorch implementation `_. Additional + changes were applied to increase model performance and efficiency. + + TSMixer forecasts time series data by integrating historical time series data, future known inputs, and static + contextual information. It uses a combination of conditional feature mixing and mixer layers to process and + combine these different types of data for effective forecasting. + + This model supports past covariates (known for `input_chunk_length` points before prediction time), future + covariates (known for `output_chunk_length` points after prediction time), static covariates, as well as + probabilistic forecasting. + + Parameters + ---------- + input_chunk_length + Number of time steps in the past to take as a model input (per chunk). Applies to the target + series, and past and/or future covariates (if the model supports it). + Also called: Encoder length + output_chunk_length + Number of time steps predicted at once (per chunk) by the internal model. Also, the number of future values + from future covariates to use as a model input (if the model supports future covariates). It is not the same + as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated + using either a one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents + auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit + the model from using future values of past and / or future covariates for prediction (depending on the + model's covariate support). + Also called: Decoder length + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input and output. If the model supports + `future_covariates`, the future values are extracted from the shifted output chunk. Predictions will start + `output_chunk_shift` steps after the end of the target `series`. If `output_chunk_shift` is set, the model + cannot generate autoregressive predictions (`n > output_chunk_length`). + hidden_size + The hidden state size / size of the second feed-forward layer in the feature mixing MLP. + ff_size + The size of the first feed-forward layer in the feature mixing MLP. + num_blocks + The number of mixer blocks in the model. The number includes the first block and all subsequent blocks. + activation + The name of the activation function to use in the mixer layers. Default: `"ReLU"`. Must be one of + `"ReLU", "RReLU", "PReLU", "ELU", "Softplus", "Tanh", "SELU", "LeakyReLU", "Sigmoid", "GELU"`. + dropout + Fraction of neurons affected by dropout. This is compatible with Monte Carlo dropout at inference time + for model uncertainty estimation (enabled with ``mc_dropout=True`` at prediction time). + norm_type + The type of `LayerNorm` variant to use. Default: `"LayerNorm"`. If a string, must be one of + `"LayerNormNoBias", "LayerNorm", "TimeBatchNorm2d"`. Otherwise, must be a custom `nn.Module`. + normalize_before + Whether to apply layer normalization before or after mixer layer. + use_static_covariates + Whether the model should use static covariate information in case the input `series` passed to ``fit()`` + contain static covariates. If ``True``, and static covariates are available at fitting time, will enforce + that all target `series` have the same static covariate dimensionality in ``fit()`` and ``predict()``. + **kwargs + Optional arguments to initialize the pytorch_lightning.Module, pytorch_lightning.Trainer, and + Darts' :class:`TorchForecastingModel`. + + loss_fn + PyTorch loss function used for training. + This parameter will be ignored for probabilistic models if the ``likelihood`` parameter is specified. + Default: ``torch.nn.MSELoss()``. + likelihood + One of Darts' :meth:`Likelihood ` models to be used for + probabilistic forecasts. Default: ``None``. + torch_metrics + A torch metric or a ``MetricCollection`` used for evaluation. A full list of available metrics can be found + at https://torchmetrics.readthedocs.io/en/latest/. Default: ``None``. + optimizer_cls + The PyTorch optimizer class to be used. Default: ``torch.optim.Adam``. + optimizer_kwargs + Optionally, some keyword arguments for the PyTorch optimizer (e.g., ``{'lr': 1e-3}`` + for specifying a learning rate). Otherwise, the default values of the selected ``optimizer_cls`` + will be used. Default: ``None``. + lr_scheduler_cls + Optionally, the PyTorch learning rate scheduler class to be used. Specifying ``None`` corresponds + to using a constant learning rate. Default: ``None``. + lr_scheduler_kwargs + Optionally, some keyword arguments for the PyTorch learning rate scheduler. Default: ``None``. + use_reversible_instance_norm + Whether to use reversible instance normalization `RINorm` against distribution shift as shown in [3]_. + It is only applied to the features of the target series and not the covariates. + batch_size + Number of time series (input and output sequences) used in each training pass. Default: ``32``. + n_epochs + Number of epochs over which to train the model. Default: ``100``. + model_name + Name of the model. Used for creating checkpoints and saving torch.Tensorboard data. If not specified, + defaults to the following string ``"YYYY-mm-dd_HH_MM_SS_torch_model_run_PID"``, where the initial part + of the name is formatted with the local date and time, while PID is the processed ID (preventing models + spawned at the same time by different processes to share the same model_name). E.g., + ``"2021-06-14_09_53_32_torch_model_run_44607"``. + work_dir + Path of the working directory, where to save checkpoints and torch.Tensorboard summaries. + Default: current working directory. + log_torch.Tensorboard + If set, use torch.Tensorboard to log the different parameters. The logs will be located in: + ``"{work_dir}/darts_logs/{model_name}/logs/"``. Default: ``False``. + nr_epochs_val_period + Number of epochs to wait before evaluating the validation loss (if a validation + ``TimeSeries`` is passed to the :func:`fit()` method). Default: ``1``. + force_reset + If set to ``True``, any previously-existing model with the same name will be reset (all checkpoints will + be discarded). Default: ``False``. + save_checkpoints + Whether to automatically save the untrained model and checkpoints from training. + To load the model from checkpoint, call :func:`MyModelClass.load_from_checkpoint()`, where + :class:`MyModelClass` is the :class:`TorchForecastingModel` class that was used (such as :class:`TFTModel`, + :class:`NBEATSModel`, etc.). If set to ``False``, the model can still be manually saved using + :func:`save()` and loaded using :func:`load()`. Default: ``False``. + add_encoders + A large number of past and future covariates can be automatically generated with `add_encoders`. + This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that + will be used as index encoders. Additionally, a transformer such as Darts' :class:`Scaler` can be added to + transform the generated covariates. This happens all under one hood and only needs to be specified at + model creation. + Read :meth:`SequentialEncoder ` to find out more about + ``add_encoders``. Default: ``None``. An example showing some of ``add_encoders`` features: + + .. highlight:: python + .. code-block:: python + + def encode_year(idx): + return (idx.year - 1950) / 50 + + add_encoders={ + 'cyclic': {'future': ['month']}, + 'datetime_attribute': {'future': ['hour', 'dayofweek']}, + 'position': {'past': ['relative'], 'future': ['relative']}, + 'custom': {'past': [encode_year]}, + 'transformer': Scaler(), + 'tz': 'CET' + } + .. + random_state + Control the randomness of the weight's initialization. Check this + `link `_ for more details. + Default: ``None``. + pl_trainer_kwargs + By default :class:`TorchForecastingModel` creates a PyTorch Lightning Trainer with several useful presets + that performs the training, validation and prediction processes. These presets include automatic + checkpointing, torch.Tensorboard logging, setting the torch device and more. + With ``pl_trainer_kwargs`` you can add additional kwargs to instantiate the PyTorch Lightning trainer + object. Check the `PL Trainer documentation + `_ for more information about the + supported kwargs. Default: ``None``. + Running on GPU(s) is also possible using ``pl_trainer_kwargs`` by specifying keys ``"accelerator", + "devices", and "auto_select_gpus"``. Some examples for setting the devices inside the ``pl_trainer_kwargs`` + dict: + + - ``{"accelerator": "cpu"}`` for CPU, + - ``{"accelerator": "gpu", "devices": [i]}`` to use only GPU ``i`` (``i`` must be an integer), + - ``{"accelerator": "gpu", "devices": -1, "auto_select_gpus": True}`` to use all available GPUS. + + For more info, see here: + https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#trainer-flags , and + https://pytorch-lightning.readthedocs.io/en/stable/accelerators/gpu_basic.html#train-on-multiple-gpus + + With parameter ``"callbacks"`` you can add custom or PyTorch-Lightning built-in callbacks to Darts' + :class:`TorchForecastingModel`. Below is an example for adding EarlyStopping to the training process. + The model will stop training early if the validation loss `val_loss` does not improve beyond + specifications. For more information on callbacks, visit: + `PyTorch Lightning Callbacks + `_ + + .. highlight:: python + .. code-block:: python + + from pytorch_lightning.callbacks.early_stopping import EarlyStopping + + # stop training when validation loss does not decrease more than 0.05 (`min_delta`) over + # a period of 5 epochs (`patience`) + my_stopper = EarlyStopping( + monitor="val_loss", + patience=5, + min_delta=0.05, + mode='min', + ) + + pl_trainer_kwargs={"callbacks": [my_stopper]} + .. + + Note that you can also use a custom PyTorch Lightning Trainer for training and prediction with optional + parameter ``trainer`` in :func:`fit()` and :func:`predict()`. + show_warnings + whether to show warnings raised from PyTorch Lightning. Useful to detect potential issues of + your forecasting use case. Default: ``False``. + + References + ---------- + .. [1] https://arxiv.org/abs/2303.06053 + + Examples + -------- + >>> from darts.datasets import WeatherDataset + >>> from darts.models import TSMixerModel + >>> series = WeatherDataset().load() + >>> # predicting temperatures + >>> target = series['T (degC)'][:100] + >>> # optionally, use past observed rainfall (pretending to be unknown beyond index 100) + >>> past_cov = series['rain (mm)'][:100] + >>> # optionally, use future atmospheric pressure (pretending this component is a forecast) + >>> future_cov = series['p (mbar)'][:106] + >>> model = TSMixerModel( + >>> input_chunk_length=6, + >>> output_chunk_length=6, + >>> use_reversible_instance_norm=True, + >>> n_epochs=20 + >>> ) + >>> model.fit(target, past_covariates=past_cov, future_covariates=future_cov) + >>> pred = model.predict(6) + >>> pred.values() + array([[3.92519848], + [4.05650312], + [4.21781987], + [4.29394973], + [4.4122863 ], + [4.42762751]]) + """ + model_kwargs = {key: val for key, val in self.model_params.items()} + super().__init__(**self._extract_torch_model_params(**model_kwargs)) + + # extract pytorch lightning module kwargs + self.pl_module_params = self._extract_pl_module_params(**model_kwargs) + + # Model specific parameters + self.ff_size = ff_size + self.dropout = dropout + self.num_blocks = num_blocks + self.activation = activation + self.normalize_before = normalize_before + self.norm_type = norm_type + self.hidden_size = hidden_size + self._considers_static_covariates = use_static_covariates + + def _create_model(self, train_sample: MixedCovariatesTrainTensorType) -> nn.Module: + """ + Parameters + ---------- + train_sample + contains the following torch.Tensors: `(past_target, past_covariates, historic_future_covariates, + future_covariates, static_covariates, future_target)`: + + - past/historic torch.Tensors have shape (input_chunk_length, n_variables) + - future torch.Tensors have shape (output_chunk_length, n_variables) + - static covariates have shape (component, static variable) + """ + ( + past_target, + past_covariates, + historic_future_covariates, + future_covariates, + static_covariates, + future_target, + ) = train_sample + + input_dim = past_target.shape[1] + output_dim = future_target.shape[1] + + static_cov_dim = ( + static_covariates.shape[0] * static_covariates.shape[1] + if static_covariates is not None + else 0 + ) + future_cov_dim = ( + future_covariates.shape[1] if future_covariates is not None else 0 + ) + past_cov_dim = past_covariates.shape[1] if past_covariates is not None else 0 + nr_params = 1 if self.likelihood is None else self.likelihood.num_parameters + + return _TSMixerModule( + input_dim=input_dim, + output_dim=output_dim, + future_cov_dim=future_cov_dim, + past_cov_dim=past_cov_dim, + static_cov_dim=static_cov_dim, + nr_params=nr_params, + hidden_size=self.hidden_size, + ff_size=self.ff_size, + num_blocks=self.num_blocks, + activation=self.activation, + dropout=self.dropout, + norm_type=self.norm_type, + normalize_before=self.normalize_before, + **self.pl_module_params, + ) + + @property + def supports_multivariate(self) -> bool: + return True + + @property + def supports_static_covariates(self) -> bool: + return True + + @property + def supports_future_covariates(self) -> bool: + return True + + @property + def supports_past_covariates(self) -> bool: + return True diff --git a/darts/models/forecasting/varima.py b/darts/models/forecasting/varima.py index 7e49df4fa7..fa615a2327 100644 --- a/darts/models/forecasting/varima.py +++ b/darts/models/forecasting/varima.py @@ -9,6 +9,7 @@ ---------- .. [1] https://en.wikipedia.org/wiki/Vector_autoregression """ + from typing import Optional import numpy as np @@ -158,7 +159,6 @@ def _predict( num_samples: int = 1, verbose: bool = False, ) -> TimeSeries: - if num_samples > 1 and self.trend: logger.warning( "Trends are not well supported yet for getting probabilistic forecasts with ARIMA." @@ -191,27 +191,29 @@ def _predict( self.model = self.model.apply( series.values(copy=False), - exog=historic_future_covariates.values(copy=False) - if historic_future_covariates - else None, + exog=( + historic_future_covariates.values(copy=False) + if historic_future_covariates + else None + ), ) # forecast before restoring the training state if num_samples == 1: forecast = self.model.forecast( steps=n, - exog=future_covariates.values(copy=False) - if future_covariates - else None, + exog=( + future_covariates.values(copy=False) if future_covariates else None + ), ) else: forecast = self.model.simulate( nsimulations=n, repetitions=num_samples, initial_state=self.model.states.predicted[-1, :], - exog=future_covariates.values(copy=False) - if future_covariates - else None, + exog=( + future_covariates.values(copy=False) if future_covariates else None + ), ) forecast = self._invert_transformation(forecast) @@ -220,9 +222,11 @@ def _predict( if series is not None: self.model = self.model.apply( self._orig_training_series.values(copy=False), - exog=self.training_historic_future_covariates.values(copy=False) - if self.training_historic_future_covariates - else None, + exog=( + self.training_historic_future_covariates.values(copy=False) + if self.training_historic_future_covariates + else None + ), ) self._last_values = self._training_last_values @@ -249,7 +253,7 @@ def min_train_series_length(self) -> int: return 30 @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return True @property diff --git a/darts/models/forecasting/xgboost.py b/darts/models/forecasting/xgboost.py index 246e68c17a..522f68ee18 100644 --- a/darts/models/forecasting/xgboost.py +++ b/darts/models/forecasting/xgboost.py @@ -7,13 +7,14 @@ This implementation comes with the ability to produce probabilistic forecasts. """ +from collections.abc import Sequence from functools import partial -from typing import List, Optional, Sequence, Union +from typing import Optional, Union import numpy as np import xgboost as xgb -from darts.logging import get_logger +from darts.logging import get_logger, raise_if_not from darts.models.forecasting.regression_model import ( FUTURE_LAGS_TYPE, LAGS_TYPE, @@ -21,7 +22,6 @@ _LikelihoodMixin, ) from darts.timeseries import TimeSeries -from darts.utils.utils import raise_if_not logger = get_logger(__name__) @@ -56,9 +56,10 @@ def __init__( lags_past_covariates: Optional[LAGS_TYPE] = None, lags_future_covariates: Optional[FUTURE_LAGS_TYPE] = None, output_chunk_length: int = 1, + output_chunk_shift: int = 0, add_encoders: Optional[dict] = None, likelihood: Optional[str] = None, - quantiles: Optional[List[float]] = None, + quantiles: Optional[list[float]] = None, random_state: Optional[int] = None, multi_models: Optional[bool] = True, use_static_covariates: bool = True, @@ -71,7 +72,8 @@ def __init__( lags Lagged target `series` values used to predict the next time step/s. If an integer, must be > 0. Uses the last `n=lags` past lags; e.g. `(-1, -2, ..., -lags)`, where `0` - corresponds the first predicted time step of each sample. + corresponds the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. If a list of integers, each value must be < 0. Uses only the specified values as lags. If a dictionary, the keys correspond to the `series` component names (of the first series when using multiple series) and the values correspond to the component lags (integer or list of integers). The @@ -80,17 +82,21 @@ def __init__( lags_past_covariates Lagged `past_covariates` values used to predict the next time step/s. If an integer, must be > 0. Uses the last `n=lags_past_covariates` past lags; e.g. `(-1, -2, ..., -lags)`, - where `0` corresponds to the first predicted time step of each sample. + where `0` corresponds to the first predicted time step of each sample. If `output_chunk_shift > 0`, then + lag `-1` translates to `-1 - output_chunk_shift` steps before the first prediction step. If a list of integers, each value must be < 0. Uses only the specified values as lags. If a dictionary, the keys correspond to the `past_covariates` component names (of the first series when using multiple series) and the values correspond to the component lags (integer or list of integers). The key 'default_lags' can be used to provide default lags for un-specified components. Raises and error if some components are missing and the 'default_lags' key is not provided. lags_future_covariates - Lagged `future_covariates` values used to predict the next time step/s. + Lagged `future_covariates` values used to predict the next time step/s. The lags are always relative to the + first step in the output chunk, even when `output_chunk_shift > 0`. If a tuple of `(past, future)`, both values must be > 0. Uses the last `n=past` past lags and `n=future` - future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` - corresponds the first predicted time step of each sample. + future lags; e.g. `(-past, -(past - 1), ..., -1, 0, 1, .... future - 1)`, where `0` corresponds the first + predicted time step of each sample. If `output_chunk_shift > 0`, the position of negative lags differ from + those of `lags` and `lags_past_covariates`. In this case a future lag `-5` would point at the same + step as a target lag of `-5 + output_chunk_shift`. If a list of integers, uses only the specified values as lags. If a dictionary, the keys correspond to the `future_covariates` component names (of the first series when using multiple series) and the values correspond to the component lags (tuple or list of integers). The key @@ -99,10 +105,17 @@ def __init__( output_chunk_length Number of time steps predicted at once (per chunk) by the internal model. It is not the same as forecast horizon `n` used in `predict()`, which is the desired number of prediction points generated using a - one-shot- or auto-regressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is + one-shot- or autoregressive forecast. Setting `n <= output_chunk_length` prevents auto-regression. This is useful when the covariates don't extend far enough into the future, or to prohibit the model from using future values of past and / or future covariates for prediction (depending on the model's covariate support). + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future (relative to the + input chunk end). This will create a gap between the input (history of target and past covariates) and + output. If the model supports `future_covariates`, the `lags_future_covariates` are relative to the first + step in the shifted output chunk. Predictions will start `output_chunk_shift` steps after the end of the + target `series`. If `output_chunk_shift` is set, the model cannot generate autoregressive predictions + (`n > output_chunk_length`). add_encoders A large number of past and future covariates can be automatically generated with `add_encoders`. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that @@ -136,8 +149,9 @@ def encode_year(idx): Control the randomness in the fitting procedure and for sampling. Default: ``None``. multi_models - If True, a separate model will be trained for each future lag to predict. If False, a single model is - trained to predict at step 'output_chunk_length' in the future. Default: True. + If True, a separate model will be trained for each future lag to predict. If False, a single model + is trained to predict all the steps in 'output_chunk_length' (features lags are shifted back by + `output_chunk_length - n` for each step `n`). Default: True. use_static_covariates Whether the model should use static covariate information in case the input `series` passed to ``fit()`` contain static covariates. If ``True``, and static covariates are available at fitting time, will enforce @@ -181,7 +195,7 @@ def encode_year(idx): self._median_idx = None self._model_container = None self.quantiles = None - self.likelihood = likelihood + self._likelihood = likelihood self._rng = None # parse likelihood @@ -204,6 +218,7 @@ def encode_year(idx): lags_past_covariates=lags_past_covariates, lags_future_covariates=lags_future_covariates, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, add_encoders=add_encoders, multi_models=multi_models, model=xgb.XGBRegressor(**self.kwargs), @@ -219,6 +234,11 @@ def fit( val_past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, val_future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, max_samples_per_ts: Optional[int] = None, + n_jobs_multioutput_wrapper: Optional[int] = None, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, + val_sample_weight: Optional[ + Union[TimeSeries, Sequence[TimeSeries], str] + ] = None, **kwargs, ): """ @@ -245,22 +265,24 @@ def fit( creation) to know their sizes, which might be expensive on big datasets. If some series turn out to have a length that would allow more than `max_samples_per_ts`, only the most recent `max_samples_per_ts` samples will be considered. + n_jobs_multioutput_wrapper + Number of jobs of the MultiOutputRegressor wrapper to run in parallel. Only used if the model doesn't + support multi-output regression natively. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. + val_sample_weight + Same as for `sample_weight` but for the evaluation dataset. **kwargs Additional kwargs passed to `xgb.XGBRegressor.fit()` """ - - if val_series is not None: - # Note: we create a list here as it's what's expected by XGBRegressor.fit() - # This is handled as a separate case in multioutput.py - kwargs["eval_set"] = [ - self._create_lagged_data( - target_series=val_series, - past_covariates=val_past_covariates, - future_covariates=val_future_covariates, - max_samples_per_ts=max_samples_per_ts, - ) - ] - # TODO: XGBRegressor supports multi quantile reqression which we could leverage in the future # see https://xgboost.readthedocs.io/en/latest/python/examples/quantile_regression.html if self.likelihood == "quantile": @@ -273,27 +295,35 @@ def fit( objective = partial(xgb_quantile_loss, quantile=quantile) self.kwargs["objective"] = objective self.model = xgb.XGBRegressor(**self.kwargs) - super().fit( series=series, past_covariates=past_covariates, future_covariates=future_covariates, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, max_samples_per_ts=max_samples_per_ts, + n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper, + sample_weight=sample_weight, + val_sample_weight=val_sample_weight, **kwargs, ) - self._model_container[quantile] = self.model - return self super().fit( series=series, past_covariates=past_covariates, future_covariates=future_covariates, + val_series=val_series, + val_past_covariates=val_past_covariates, + val_future_covariates=val_future_covariates, max_samples_per_ts=max_samples_per_ts, + n_jobs_multioutput_wrapper=n_jobs_multioutput_wrapper, + sample_weight=sample_weight, + val_sample_weight=val_sample_weight, **kwargs, ) - return self def _predict_and_sample( @@ -314,9 +344,17 @@ def _predict_and_sample( ) @property - def _is_probabilistic(self) -> bool: + def supports_probabilistic_prediction(self) -> bool: return self.likelihood is not None + @property + def supports_val_set(self) -> bool: + return True + + @property + def val_set_params(self) -> tuple[Optional[str], Optional[str]]: + return "eval_set", "sample_weight_eval_set" + @property def min_train_series_length(self) -> int: # XGBModel requires a minimum of 2 training samples, @@ -324,7 +362,9 @@ def min_train_series_length(self) -> int: # more than for other regression models return max( 3, - -self.lags["target"][0] + self.output_chunk_length + 1 - if "target" in self.lags - else self.output_chunk_length, + ( + -self.lags["target"][0] + self.output_chunk_length + 1 + if "target" in self.lags + else self.output_chunk_length + ), ) diff --git a/darts/models/utils.py b/darts/models/utils.py index 8d0d0d11ea..17520be9bf 100644 --- a/darts/models/utils.py +++ b/darts/models/utils.py @@ -2,6 +2,13 @@ logger = get_logger(__name__) +try: + import torch # noqa: F401 + + TORCH_AVAILABLE = True +except ImportError: + TORCH_AVAILABLE = False + class NotImportedModule: """Helper class for handling import errors of optional dependencies.""" diff --git a/darts/tests/ad/test_aggregators.py b/darts/tests/ad/test_aggregators.py index 52d2e227a7..b07bf390af 100644 --- a/darts/tests/ad/test_aggregators.py +++ b/darts/tests/ad/test_aggregators.py @@ -1,27 +1,78 @@ -from typing import Sequence +from collections.abc import Sequence import numpy as np import pytest from sklearn.ensemble import GradientBoostingClassifier from darts import TimeSeries -from darts.ad.aggregators.and_aggregator import AndAggregator -from darts.ad.aggregators.ensemble_sklearn_aggregator import EnsembleSklearnAggregator -from darts.ad.aggregators.or_aggregator import OrAggregator +from darts.ad.aggregators import ( + AndAggregator, + EnsembleSklearnAggregator, + FittableAggregator, + OrAggregator, +) from darts.models import MovingAverageFilter +# element shape : (model_cls, model_kwargs, expected metrics) list_NonFittableAggregator = [ - OrAggregator(), - AndAggregator(), + ( + OrAggregator, + {}, + { + "only_ones": {"accuracy": 1, "recall": 1, "f1": 1, "precision": 1}, + "multivariate": {"accuracy": 0, "recall": 0, "f1": 0, "precision": 0}, + "synthetic": { + "accuracy": 0.56, + "recall": 0.72549, + "f1": 0.62711, + "precision": 0.55223, + "total": 67, + }, + "multiple_series": { + "accuracy": [0.56, 0.52], + "recall": [0.72549, 0.764706], + "f1": [0.627119, 0.619048], + "precision": [0.552239, 0.52], + "total": [67, 75], + }, + }, + ), + ( + AndAggregator, + {}, + { + "only_ones": {"accuracy": 1, "recall": 1, "f1": 1, "precision": 1}, + "multivariate": {"accuracy": 1, "recall": 0, "f1": 0, "precision": 0}, + "synthetic": { + "accuracy": 0.44, + "recall": 0.21568, + "f1": 0.28205, + "precision": 0.40740, + "total": 27, + }, + "multiple_series": { + "accuracy": [0.44, 0.53], + "recall": [0.215686, 0.27451], + "f1": [0.282051, 0.373333], + "precision": [0.407407, 0.583333], + "total": [27, 24], + }, + }, + ), ] +# expected metrics values are declared in the test list_FittableAggregator = [ - EnsembleSklearnAggregator(model=GradientBoostingClassifier()) + (EnsembleSklearnAggregator, {"model": GradientBoostingClassifier()}, {}) ] -class TestADAggregators: +list_Aggregator = list_NonFittableAggregator + list_FittableAggregator +delta = 1e-05 + + +class TestAnomalyDetectionAggregator: np.random.seed(42) # univariate series @@ -66,6 +117,9 @@ class TestADAggregators: columns=["component 1", "component 2"], ) + # series has 3 components, and real_anomalies_3w is equal to + # - component 1 when component 3 is 1 + # - component 2 when component 3 is 0 np_real_anomalies_3w = [ elem[0] if elem[2] == 1 else elem[1] for elem in np_anomalies_w3 ] @@ -73,705 +127,466 @@ class TestADAggregators: train._time_index, np_real_anomalies_3w ) - def test_DetectNonFittableAggregator(self): - - aggregator = OrAggregator() + @staticmethod + def helper_eval_metric_single_series( + aggregator, + series: TimeSeries, + pred_series: TimeSeries, + expected_vals: dict[str, float], + ): + """Evaluate model on given series, for all 4 supported metric functions""" + for m_func in ["accuracy", "recall", "f1", "precision"]: + assert ( + np.abs( + expected_vals[m_func] + - aggregator.eval_metric( + series, + pred_series, + metric=m_func, + ) + ) + < delta + ) - # Check return types - assert isinstance(aggregator.predict(self.mts_anomalies1), TimeSeries) - assert isinstance( - aggregator.predict([self.mts_anomalies1]), - Sequence, - ) - assert isinstance( - aggregator.predict([self.mts_anomalies1, self.mts_anomalies2]), - Sequence, - ) + @staticmethod + def helper_eval_metric_multiple_series( + aggregator, + series: Sequence[TimeSeries], + pred_series: Sequence[TimeSeries], + expected_vals: dict[str, list[float]], + ): + """Evaluate model on multiple series, for all 4 supported metric functions""" + for m_func in ["accuracy", "recall", "f1", "precision"]: + np.testing.assert_array_almost_equal( + np.array( + aggregator.eval_metric( + series, + pred_series, + metric=m_func, + ) + ), + np.array(expected_vals[m_func]), + decimal=1, + ) - def test_DetectFittableAggregator(self): - aggregator = EnsembleSklearnAggregator(model=GradientBoostingClassifier()) + @pytest.mark.parametrize("config", list_Aggregator) + def test_predict_return_type(self, config): + """Check that predict's output are properly unpacked depending on input type""" + aggregator_cls, cls_kwargs, _ = config + aggregator = aggregator_cls(**cls_kwargs) - # Check return types - aggregator.fit(self.real_anomalies, self.mts_anomalies1) + if isinstance(aggregator, FittableAggregator): + aggregator.fit(self.real_anomalies, self.mts_anomalies1) - # Check return types + # single TimeSeries assert isinstance(aggregator.predict(self.mts_anomalies1), TimeSeries) + + # Sequence of one TimeSeries assert isinstance( aggregator.predict([self.mts_anomalies1]), Sequence, ) + + # Sequence of several TimeSeries assert isinstance( aggregator.predict([self.mts_anomalies1, self.mts_anomalies2]), Sequence, ) - def test_eval_accuracy(self): + @pytest.mark.parametrize("config", list_Aggregator) + def test_eval_metric_return_type(self, config): + """Check that eval_metric's output are properly unpacked depending on input type""" + aggregator_cls, cls_kwargs, _ = config + aggregator = aggregator_cls(**cls_kwargs) - aggregator = AndAggregator() + if isinstance(aggregator, FittableAggregator): + aggregator.fit(self.real_anomalies, self.mts_anomalies1) # Check return types assert isinstance( - aggregator.eval_accuracy(self.real_anomalies, self.mts_anomalies1), + aggregator.eval_metric(self.real_anomalies, self.mts_anomalies1), float, ) + assert isinstance( - aggregator.eval_accuracy([self.real_anomalies], [self.mts_anomalies1]), + aggregator.eval_metric([self.real_anomalies], [self.mts_anomalies1]), Sequence, ) + assert isinstance( - aggregator.eval_accuracy(self.real_anomalies, [self.mts_anomalies1]), + aggregator.eval_metric(self.real_anomalies, [self.mts_anomalies1]), Sequence, ) + assert isinstance( - aggregator.eval_accuracy( + aggregator.eval_metric( [self.real_anomalies, self.real_anomalies], [self.mts_anomalies1, self.mts_anomalies2], ), Sequence, ) - # intersection between 'actual_anomalies' and the series in the sequence 'list_series' + # Check if return type is the same number of series in input + assert ( + len( + aggregator.eval_metric( + [self.real_anomalies, self.real_anomalies], + [self.mts_anomalies1, self.mts_anomalies2], + ) + ) + == 2 + ) + + # intersection between 'anomalies' and the series in the sequence 'list_series' # must be non empty with pytest.raises(ValueError): - aggregator.eval_accuracy(self.real_anomalies[:30], self.mts_anomalies1[40:]) + aggregator.eval_metric(self.real_anomalies[:30], self.mts_anomalies1[40:]) with pytest.raises(ValueError): - aggregator.eval_accuracy( + aggregator.eval_metric( [self.real_anomalies, self.real_anomalies[:30]], [self.mts_anomalies1, self.mts_anomalies1[40:]], ) # window parameter must be smaller than the length of the input (len = 100) with pytest.raises(ValueError): - aggregator.eval_accuracy( - self.real_anomalies, self.mts_anomalies1, window=101 - ) + aggregator.eval_metric(self.real_anomalies, self.mts_anomalies1, window=101) - def test_NonFittableAggregator(self): - - for aggregator in list_NonFittableAggregator: - - # name must be of type str - assert type(aggregator.__str__()) == str - - # Check if trainable is False, being a NonFittableAggregator - assert not aggregator.trainable - - # predict on (sequence of) univariate series - with pytest.raises(ValueError): - aggregator.predict([self.real_anomalies]) - with pytest.raises(ValueError): - aggregator.predict(self.real_anomalies) - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, self.real_anomalies]) - - # input a (sequence of) non binary series - with pytest.raises(ValueError): - aggregator.predict(self.mts_train) - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, self.mts_train]) - - # input a (sequence of) probabilistic series - with pytest.raises(ValueError): - aggregator.predict(self.mts_probabilistic) - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, self.mts_probabilistic]) - - # input an element that is not a series - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, "random"]) - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, 1]) - - # Check width return - # Check if return type is the same number of series in input - assert len( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - ) - ), len([self.mts_anomalies1, self.mts_anomalies2]) - - def test_FittableAggregator(self): + @pytest.mark.parametrize("config", list_Aggregator) + def test_aggregator_predict_wrong_inputs(self, config): + """Check that exception is raised when predict() arguments are incorrects.""" + aggregator_cls, cls_kwargs, _ = config + aggregator = aggregator_cls(**cls_kwargs) - for aggregator in list_FittableAggregator: - - # name must be of type str - assert type(aggregator.__str__()) == str + # fit aggregator on series with 2 components + if isinstance(aggregator, FittableAggregator): + aggregator.fit(self.real_anomalies, self.mts_anomalies1) - # Need to call fit() before calling predict() - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, self.mts_anomalies1]) + # predict on (sequence of) univariate series + with pytest.raises(ValueError): + aggregator.predict([self.real_anomalies]) + with pytest.raises(ValueError): + aggregator.predict(self.real_anomalies) + with pytest.raises(ValueError): + aggregator.predict([self.mts_anomalies1, self.real_anomalies]) + + # input a (sequence of) non binary series + expected_msg = "Input series `series` must have binary values only." + with pytest.raises(ValueError) as err: + aggregator.predict(self.mts_train) + assert str(err.value) == expected_msg + with pytest.raises(ValueError) as err: + aggregator.predict([self.mts_anomalies1, self.mts_train]) + assert str(err.value) == expected_msg + + # input a (sequence of) probabilistic series + with pytest.raises(ValueError): + aggregator.predict(self.mts_probabilistic) + with pytest.raises(ValueError): + aggregator.predict([self.mts_anomalies1, self.mts_probabilistic]) - # Check if trainable is True, being a FittableAggregator - assert aggregator.trainable + # input an element that is not a series + with pytest.raises(ValueError): + aggregator.predict([self.mts_anomalies1, "random"]) + with pytest.raises(ValueError): + aggregator.predict([self.mts_anomalies1, 1]) - # Check if _fit_called is False - assert not aggregator._fit_called + @pytest.mark.parametrize("config", list_NonFittableAggregator) + def test_NonFittableAggregator_predict(self, config): + """Check that predict() works as intended""" + aggregator_cls, cls_kwargs, _ = config + aggregator = aggregator_cls(**cls_kwargs) - # fit on sequence with series that have different width - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies3], - ) + # name must be of type str + assert isinstance(aggregator.__str__(), str) - # fit on a (sequence of) univariate series - with pytest.raises(ValueError): - aggregator.fit(self.real_anomalies, self.real_anomalies) - with pytest.raises(ValueError): - aggregator.fit(self.real_anomalies, [self.real_anomalies]) - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.real_anomalies], - ) + assert not isinstance(aggregator, FittableAggregator) - # fit on a (sequence of) non binary series - with pytest.raises(ValueError): - aggregator.fit(self.real_anomalies, self.mts_train) - with pytest.raises(ValueError): - aggregator.fit(self.real_anomalies, [self.mts_train]) - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_train], - ) + # Check that predict can be called when series is appropriate + pred = aggregator.predict(self.mts_anomalies1) - # fit on a (sequence of) probabilistic series - with pytest.raises(ValueError): - aggregator.fit(self.real_anomalies, self.mts_probabilistic) - with pytest.raises(ValueError): - aggregator.fit(self.real_anomalies, [self.mts_probabilistic]) - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_probabilistic], - ) + # Check that the aggregated result has only one component + assert pred.width == 1 - # input an element that is not a series - with pytest.raises(ValueError): - aggregator.fit(self.real_anomalies, "random") - with pytest.raises(ValueError): - aggregator.fit(self.real_anomalies, [self.mts_anomalies1, "random"]) - with pytest.raises(ValueError): - aggregator.fit(self.real_anomalies, [self.mts_anomalies1, 1]) - - # fit on a (sequence of) multivariate anomalies - with pytest.raises(ValueError): - aggregator.fit(self.mts_anomalies1, self.mts_anomalies1) - with pytest.raises(ValueError): - aggregator.fit([self.mts_anomalies1], [self.mts_anomalies1]) - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, self.mts_anomalies1], - [self.mts_anomalies1, self.mts_anomalies1], - ) + @pytest.mark.parametrize("config", list_FittableAggregator) + def test_FittableAggregator_fit_wrong_inputs(self, config): + """Check that exception is raised when fit() arguments are incorrects""" + aggregator_cls, cls_kwargs, _ = config + aggregator = aggregator_cls(**cls_kwargs) - # fit on a (sequence of) non binary anomalies - with pytest.raises(ValueError): - aggregator.fit(self.train, self.mts_anomalies1) - with pytest.raises(ValueError): - aggregator.fit([self.train], self.mts_anomalies1) - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, self.train], - [self.mts_anomalies1, self.mts_anomalies1], - ) + # fit on sequence with series that have different width + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, self.real_anomalies], + [self.mts_anomalies1, self.mts_anomalies3], + ) - # fit on a (sequence of) probabilistic anomalies - with pytest.raises(ValueError): - aggregator.fit(self.mts_probabilistic, self.mts_anomalies1) - with pytest.raises(ValueError): - aggregator.fit([self.mts_probabilistic], self.mts_anomalies1) - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, self.mts_probabilistic], - [self.mts_anomalies1, self.mts_anomalies1], - ) + # fit on a (sequence of) univariate series + with pytest.raises(ValueError): + aggregator.fit(self.real_anomalies, self.real_anomalies) + with pytest.raises(ValueError): + aggregator.fit(self.real_anomalies, [self.real_anomalies]) + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, self.real_anomalies], + [self.mts_anomalies1, self.real_anomalies], + ) - # input an element that is not a anomalies - with pytest.raises(ValueError): - aggregator.fit("random", self.mts_anomalies1) - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, "random"], - [self.mts_anomalies1, self.mts_anomalies1], - ) - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, 1], [self.mts_anomalies1, self.mts_anomalies1] - ) + # fit on a (sequence of) non binary series + with pytest.raises(ValueError): + aggregator.fit(self.real_anomalies, self.mts_train) + with pytest.raises(ValueError): + aggregator.fit(self.real_anomalies, [self.mts_train]) + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, self.real_anomalies], + [self.mts_anomalies1, self.mts_train], + ) - # nbr of anomalies must match nbr of input series - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, self.real_anomalies], self.mts_anomalies1 - ) - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies, self.real_anomalies], [self.mts_anomalies1] - ) - with pytest.raises(ValueError): - aggregator.fit( - [self.real_anomalies], [self.mts_anomalies1, self.mts_anomalies1] - ) + # fit on a (sequence of) probabilistic series + with pytest.raises(ValueError): + aggregator.fit(self.real_anomalies, self.mts_probabilistic) + with pytest.raises(ValueError): + aggregator.fit(self.real_anomalies, [self.mts_probabilistic]) + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, self.real_anomalies], + [self.mts_anomalies1, self.mts_probabilistic], + ) - # case1: fit - aggregator.fit(self.real_anomalies, self.mts_anomalies1) + # input an element that is not a series + with pytest.raises(ValueError): + aggregator.fit(self.real_anomalies, "random") + with pytest.raises(ValueError): + aggregator.fit(self.real_anomalies, [self.mts_anomalies1, "random"]) + with pytest.raises(ValueError): + aggregator.fit(self.real_anomalies, [self.mts_anomalies1, 1]) - # Check if _fit_called is True after being fitted - assert aggregator._fit_called - - # series must be same width as series used for training - with pytest.raises(ValueError): - aggregator.predict(self.mts_anomalies3) - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies3]) - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, self.mts_anomalies3]) - - # predict on (sequence of) univariate series - with pytest.raises(ValueError): - aggregator.predict([self.real_anomalies]) - with pytest.raises(ValueError): - aggregator.predict(self.real_anomalies) - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, self.real_anomalies]) - - # input a (sequence of) non binary series - with pytest.raises(ValueError): - aggregator.predict(self.mts_train) - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, self.mts_train]) - - # input a (sequence of) probabilistic series - with pytest.raises(ValueError): - aggregator.predict(self.mts_probabilistic) - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, self.mts_probabilistic]) - - # input an element that is not a series - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, "random"]) - with pytest.raises(ValueError): - aggregator.predict([self.mts_anomalies1, 1]) - - # Check width return - # Check if return type is the same number of series in input - assert len( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - ) - ), len([self.mts_anomalies1, self.mts_anomalies2]) + # fit on a (sequence of) multivariate anomalies + with pytest.raises(ValueError): + aggregator.fit(self.mts_anomalies1, self.mts_anomalies1) + with pytest.raises(ValueError): + aggregator.fit([self.mts_anomalies1], [self.mts_anomalies1]) + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, self.mts_anomalies1], + [self.mts_anomalies1, self.mts_anomalies1], + ) - def test_OrAggregator(self): + # fit on a (sequence of) non binary anomalies + with pytest.raises(ValueError): + aggregator.fit(self.train, self.mts_anomalies1) + with pytest.raises(ValueError): + aggregator.fit([self.train], self.mts_anomalies1) + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, self.train], + [self.mts_anomalies1, self.mts_anomalies1], + ) - aggregator = OrAggregator() + # fit on a (sequence of) probabilistic anomalies + with pytest.raises(ValueError): + aggregator.fit(self.mts_probabilistic, self.mts_anomalies1) + with pytest.raises(ValueError): + aggregator.fit([self.mts_probabilistic], self.mts_anomalies1) + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, self.mts_probabilistic], + [self.mts_anomalies1, self.mts_anomalies1], + ) - # simple case - # aggregator must have an accuracy of 0 for input with 2 components - # (only 1 and only 0) and ground truth is only 0 - assert ( - abs( - aggregator.eval_accuracy( - self.onlyzero, - self.series_1_and_0, - metric="accuracy", - ) - - 0 + # input an element that is not a anomalies + with pytest.raises(ValueError): + aggregator.fit("random", self.mts_anomalies1) + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, "random"], + [self.mts_anomalies1, self.mts_anomalies1], ) - < 1e-05 - ) - # aggregator must have an accuracy of 1 for input with 2 components - # (only 1 and only 0) and ground truth is only 1 - assert ( - abs( - aggregator.eval_accuracy( - self.onlyones, - self.series_1_and_0, - metric="accuracy", - ) - - 1 + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, 1], [self.mts_anomalies1, self.mts_anomalies1] ) - < 1e-05 - ) - # aggregator must have an accuracy of 1 for the input containing only 1 - assert ( - abs( - aggregator.eval_accuracy( - self.onlyones, - self.mts_onlyones, - metric="accuracy", - ) - - 1 + # nbr of anomalies must match nbr of input series + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, self.real_anomalies], self.mts_anomalies1 ) - < 1e-05 - ) - # aggregator must have an accuracy of 1 for the input containing only 1 - assert ( - abs( - aggregator.eval_accuracy( - self.onlyones, - self.mts_onlyones, - metric="recall", - ) - - 1 + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies, self.real_anomalies], [self.mts_anomalies1] ) - < 1e-05 - ) - # aggregator must have an accuracy of 1 for the input containing only 1 - assert ( - abs( - aggregator.eval_accuracy( - self.onlyones, - self.mts_onlyones, - metric="precision", - ) - - 1 + with pytest.raises(ValueError): + aggregator.fit( + [self.real_anomalies], [self.mts_anomalies1, self.mts_anomalies1] ) - < 1e-05 - ) - # single series case (random example) - # aggregator must found 67 anomalies in the input mts_anomalies1 - assert ( - aggregator.predict(self.mts_anomalies1) - .sum(axis=0) - .all_values() - .flatten()[0] - == 67 - ) + @pytest.mark.parametrize("config", list_FittableAggregator) + def test_FittableAggregator_predict_wrong_inputs(self, config): + """Check that exception specific to FittableAggregator are properly raised""" + aggregator_cls, cls_kwargs, _ = config + aggregator = aggregator_cls(**cls_kwargs) - # aggregator must have an accuracy of 0.56 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, - self.mts_anomalies1, - metric="accuracy", - ) - - 0.56 - ) - < 1e-05 - ) - # aggregator must have an recall of 0.72549 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, self.mts_anomalies1, metric="recall" - ) - - 0.72549 - ) - < 1e-05 - ) - # aggregator must have an f1 of 0.62711 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, self.mts_anomalies1, metric="f1" - ) - - 0.62711 - ) - < 1e-05 + aggregator.fit(self.real_anomalies, self.mts_anomalies1) + + # series must be same width as series used for training + with pytest.raises(ValueError): + aggregator.predict(self.mts_anomalies3) + with pytest.raises(ValueError): + aggregator.predict([self.mts_anomalies3]) + with pytest.raises(ValueError): + aggregator.predict([self.mts_anomalies1, self.mts_anomalies3]) + + @pytest.mark.parametrize("config", list_FittableAggregator) + def test_FittableAggregator_fit_predict(self, config): + """Check that consecutive calls to fit() and predict() work as intended""" + aggregator_cls, cls_kwargs, _ = config + aggregator = aggregator_cls(**cls_kwargs) + + # name must be of type str + assert isinstance( + aggregator.__str__(), + str, ) - # aggregator must have an precision of 0.55223 for the input mts_anomalies1 + + # Need to call fit() before calling predict() + with pytest.raises(ValueError) as err: + aggregator.predict([self.mts_anomalies1, self.mts_anomalies1]) assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, - self.mts_anomalies1, - metric="precision", - ) - - 0.55223 - ) - < 1e-05 + str(err.value) + == "The `Aggregator` has not been fitted yet. Call `Aggregator.fit()` first." ) - # multiple series case (random example) - # aggregator must found [67,75] anomalies in the input [mts_anomalies1, mts_anomalies2] - values = aggregator.predict([self.mts_anomalies1, self.mts_anomalies2]) - np.testing.assert_array_almost_equal( - [v.sum(axis=0).all_values().flatten()[0] for v in values], - [67, 75], - decimal=1, - ) + # Check if _fit_called is False before calling fit + assert not aggregator._fit_called - # aggregator must have an accuracy of [0.56,0.52] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="accuracy", - ) - ), - np.array([0.56, 0.52]), - decimal=1, - ) - # aggregator must have an recall of [0.72549,0.764706] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="recall", - ) - ), - np.array([0.72549, 0.764706]), - decimal=1, - ) - # aggregator must have an f1 of [0.627119,0.619048] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="f1", - ) - ), - np.array([0.627119, 0.619048]), - decimal=1, - ) - # aggregator must have an precision of [0.552239,0.52] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="precision", - ) - ), - np.array([0.552239, 0.52]), - decimal=1, - ) + aggregator.fit(self.real_anomalies, self.mts_anomalies1) - def test_AndAggregator(self): + # Check if _fit_called is True after calling fit + assert aggregator._fit_called - aggregator = AndAggregator() + # Check that predict can be called when series is appropriate + pred = aggregator.predict(self.mts_anomalies1) - # simple case - # aggregator must have an accuracy of 0 for input with 2 components - # (only 1 and only 0) and ground truth is only 1 - assert ( - abs( - aggregator.eval_accuracy( - self.onlyones, - self.series_1_and_0, - metric="accuracy", - ) - - 0 - ) - < 1e-05 - ) - # aggregator must have an accuracy of 0 for input with 2 components - # (only 1 and only 0) and ground truth is only 0 - assert ( - abs( - aggregator.eval_accuracy( - self.onlyzero, - self.series_1_and_0, - metric="accuracy", - ) - - 1 - ) - < 1e-05 - ) + # Check that the aggregated result has only one component + assert pred.width == 1 - # aggregator must have an accuracy of 1 for the input containing only 1 - assert ( - abs( - aggregator.eval_accuracy( - self.onlyones, - self.mts_onlyones, - metric="accuracy", - ) - - 1 - ) - < 1e-05 + @pytest.mark.parametrize("config", list_NonFittableAggregator) + def test_aggregator_performance_single_series(self, config): + aggregator_cls, cls_kwargs, metrics = config + aggregator = aggregator_cls(**cls_kwargs) + + # both actual and pred contain only 1 + self.helper_eval_metric_single_series( + aggregator=aggregator, + series=self.onlyones, + pred_series=self.mts_onlyones, + expected_vals=metrics["only_ones"], ) - # aggregator must have an accuracy of 1 for the input containing only 1 - assert ( - abs( - aggregator.eval_accuracy( - self.onlyones, - self.mts_onlyones, - metric="recall", - ) - - 1 - ) - < 1e-05 + + # input with 2 components (only 1 and only 0) and ground truth is only 0 + self.helper_eval_metric_single_series( + aggregator=aggregator, + series=self.onlyzero, + pred_series=self.series_1_and_0, + expected_vals=metrics["multivariate"], ) - # aggregator must have an accuracy of 1 for the input containing only 1 - assert ( - abs( - aggregator.eval_accuracy( - self.onlyones, - self.mts_onlyones, - metric="precision", - ) - - 1 - ) - < 1e-05 + + # synthetic example + self.helper_eval_metric_single_series( + aggregator=aggregator, + series=self.real_anomalies, + pred_series=self.mts_anomalies1, + expected_vals=metrics["synthetic"], ) - # single series case (random example) - # aggregator must found 27 anomalies in the input mts_anomalies1 + # number of detected anomalies in synthetic example assert ( aggregator.predict(self.mts_anomalies1) .sum(axis=0) .all_values() .flatten()[0] - == 27 + == metrics["synthetic"]["total"] ) - # aggregator must have an accuracy of 0.44 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, - self.mts_anomalies1, - metric="accuracy", - ) - - 0.44 - ) - < 1e-05 - ) - # aggregator must have an recall of 0.21568 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, self.mts_anomalies1, metric="recall" - ) - - 0.21568 - ) - < 1e-05 - ) - # aggregator must have an f1 of 0.28205 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, self.mts_anomalies1, metric="f1" - ) - - 0.28205 - ) - < 1e-05 - ) - # aggregator must have an precision of 0.40740 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, - self.mts_anomalies1, - metric="precision", - ) - - 0.40740 - ) - < 1e-05 + @pytest.mark.parametrize("config", list_NonFittableAggregator) + def test_aggregator_performance_multiple_series(self, config): + aggregator_cls, cls_kwargs, metrics = config + aggregator = aggregator_cls(**cls_kwargs) + + self.helper_eval_metric_multiple_series( + aggregator=aggregator, + series=[self.real_anomalies, self.real_anomalies], + pred_series=[self.mts_anomalies1, self.mts_anomalies2], + expected_vals=metrics["multiple_series"], ) - # multiple series case (random example) - # aggregator must found [27,24] anomalies in the input [mts_anomalies1, mts_anomalies2] + # number of detected anomalies values = aggregator.predict([self.mts_anomalies1, self.mts_anomalies2]) np.testing.assert_array_almost_equal( [v.sum(axis=0).all_values().flatten()[0] for v in values], - [27, 24], - decimal=1, - ) - - # aggregator must have an accuracy of [0.44,0.53] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="accuracy", - ) - ), - np.array([0.44, 0.53]), - decimal=1, - ) - # aggregator must have an recall of [0.215686,0.27451] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="recall", - ) - ), - np.array([0.215686, 0.27451]), - decimal=1, - ) - # aggregator must have an f1 of [0.282051,0.373333] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="f1", - ) - ), - np.array([0.282051, 0.373333]), - decimal=1, - ) - # aggregator must have an precision of [0.407407, 0.583333] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="precision", - ) - ), - np.array([0.407407, 0.583333]), + metrics["multiple_series"]["total"], decimal=1, ) - def test_EnsembleSklearn(self): - + def test_ensemble_aggregator_constructor(self): # Need to input an EnsembleSklearn model with pytest.raises(ValueError): EnsembleSklearnAggregator(model=MovingAverageFilter(window=10)) - # simple case - # series has 3 components, and real_anomalies_3w is equal to - # - component 1 when component 3 is 1 - # - component 2 when component 3 is 0 - # must have a high accuracy (here 0.92) + @pytest.mark.parametrize( + "config", + [ + ( + real_anomalies_3w, + mts_anomalies3, + { + "accuracy": 0.92, + "recall": 0.86666, + "f1": 0.92857, + "precision": 1.0, + "total": 52, + }, + ), + ( + real_anomalies, + mts_anomalies1, + { + "accuracy": 0.51, + "recall": 1.0, + "f1": 0.67549, + "precision": 0.51, + "total": 100, + }, + ), + ], + ) + def test_ensemble_aggregator_single_series(self, config): + """Check performance of ensemble aggregator on single series cases""" + series, pred_series, expected_metrics = config + aggregator = EnsembleSklearnAggregator( model=GradientBoostingClassifier( n_estimators=50, learning_rate=1.0, max_depth=1 ) ) - aggregator.fit(self.real_anomalies_3w, self.mts_anomalies3) - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies_3w, - self.mts_anomalies3, - metric="accuracy", - ) - - 0.92 - ) - < 1e-05 + aggregator.fit(series, pred_series) + + self.helper_eval_metric_single_series( + aggregator=aggregator, + series=series, + pred_series=pred_series, + expected_vals=expected_metrics, ) - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies_3w, self.real_anomalies_3w], - [self.mts_anomalies3, self.mts_anomalies3], - metric="accuracy", - ) - ), - np.array([0.92, 0.92]), - decimal=1, + assert ( + aggregator.predict(pred_series).sum(axis=0).all_values().flatten()[0] + == expected_metrics["total"] ) - # single series case (random example) + def test_ensemble_aggregator_multiple_series(self): + """Ensemble aggregator is fitted on one series, evaluated on two.""" aggregator = EnsembleSklearnAggregator( model=GradientBoostingClassifier( n_estimators=50, learning_rate=1.0, max_depth=1 @@ -779,114 +594,21 @@ def test_EnsembleSklearn(self): ) aggregator.fit(self.real_anomalies, self.mts_anomalies1) - # aggregator must found 100 anomalies in the input mts_anomalies1 - assert ( - aggregator.predict(self.mts_anomalies1) - .sum(axis=0) - .all_values() - .flatten()[0] - == 100 - ) - - # aggregator must have an accuracy of 0.51 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, - self.mts_anomalies1, - metric="accuracy", - ) - - 0.51 - ) - < 1e-05 - ) - # aggregator must have an recall 1.0 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, self.mts_anomalies1, metric="recall" - ) - - 1.0 - ) - < 1e-05 - ) - # aggregator must have an f1 of 0.67549 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, self.mts_anomalies1, metric="f1" - ) - - 0.67549 - ) - < 1e-05 - ) - # aggregator must have an precision of 0.51 for the input mts_anomalies1 - assert ( - abs( - aggregator.eval_accuracy( - self.real_anomalies, - self.mts_anomalies1, - metric="precision", - ) - - 0.51 - ) - < 1e-05 + self.helper_eval_metric_multiple_series( + aggregator=aggregator, + series=[self.real_anomalies, self.real_anomalies], + pred_series=[self.mts_anomalies1, self.mts_anomalies2], + expected_vals={ + "accuracy": [0.51, 0.51], + "recall": [1, 1], + "f1": [0.68, 0.68], + "precision": [0.51, 0.51], + }, ) - # multiple series case (random example) - # aggregator must found [100,100] anomalies in the input [mts_anomalies1, mts_anomalies2] values = aggregator.predict([self.mts_anomalies1, self.mts_anomalies2]) np.testing.assert_array_almost_equal( [v.sum(axis=0).all_values().flatten()[0] for v in values], - [100, 100.0], - decimal=1, - ) - - # aggregator must have an accuracy of [0.51, 0.51] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="accuracy", - ) - ), - np.array([0.51, 0.51]), - decimal=1, - ) - # aggregator must have an recall of [1,1] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="recall", - ) - ), - np.array([1, 1]), - decimal=1, - ) - # aggregator must have an f1 of [0.675497, 0.675497] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="f1", - ) - ), - np.array([0.675497, 0.675497]), - decimal=1, - ) - # aggregator must have an precision of [0.51, 0.51] for the input [mts_anomalies1, mts_anomalies2] - np.testing.assert_array_almost_equal( - np.array( - aggregator.eval_accuracy( - [self.real_anomalies, self.real_anomalies], - [self.mts_anomalies1, self.mts_anomalies2], - metric="precision", - ) - ), - np.array([0.51, 0.51]), + [100, 100], decimal=1, ) diff --git a/darts/tests/ad/test_anomaly_model.py b/darts/tests/ad/test_anomaly_model.py index 98f0dccb90..c6da7555e8 100644 --- a/darts/tests/ad/test_anomaly_model.py +++ b/darts/tests/ad/test_anomaly_model.py @@ -1,4 +1,5 @@ -from typing import Dict, Sequence, Tuple +from collections.abc import Sequence +from itertools import product import numpy as np import pandas as pd @@ -6,17 +7,10 @@ from pyod.models.knn import KNN from darts import TimeSeries - -# anomaly aggregators -# import everything in darts.ad (also for testing imports) -from darts.ad import AndAggregator # noqa: F401 -from darts.ad import EnsembleSklearnAggregator # noqa: F401 -from darts.ad import OrAggregator # noqa: F401 -from darts.ad import QuantileDetector # noqa: F401 -from darts.ad import ThresholdDetector # noqa: F401 -from darts.ad import CauchyNLLScorer -from darts.ad import DifferenceScorer as Difference from darts.ad import ( + AndAggregator, # noqa: F401 + CauchyNLLScorer, + EnsembleSklearnAggregator, # noqa: F401 ExponentialNLLScorer, FilteringAnomalyModel, ForecastingAnomalyModel, @@ -24,16 +18,47 @@ GaussianNLLScorer, KMeansScorer, LaplaceNLLScorer, - NormScorer, + OrAggregator, # noqa: F401 PoissonNLLScorer, PyODScorer, + QuantileDetector, # noqa: F401 + ThresholdDetector, # noqa: F401 WassersteinScorer, ) -from darts.ad.utils import eval_accuracy_from_scores, show_anomalies_from_scores +from darts.ad import DifferenceScorer as Difference +from darts.ad import NormScorer as Norm +from darts.ad.utils import eval_metric_from_scores, show_anomalies_from_scores from darts.models import MovingAverageFilter, NaiveSeasonal, RegressionModel - -class TestADAnomalyModel: +filtering_am = [ + ( + FilteringAnomalyModel, + {"model": MovingAverageFilter(window=10), "scorer": Norm()}, + ), + ( + FilteringAnomalyModel, + {"model": MovingAverageFilter(window=10), "scorer": [Norm(), KMeansScorer()]}, + ), + ( + FilteringAnomalyModel, + {"model": MovingAverageFilter(window=10), "scorer": KMeansScorer()}, + ), +] + +forecasting_am = [ + (ForecastingAnomalyModel, {"model": RegressionModel(lags=10), "scorer": Norm()}), + ( + ForecastingAnomalyModel, + {"model": RegressionModel(lags=10), "scorer": [Norm(), KMeansScorer()]}, + ), + ( + ForecastingAnomalyModel, + {"model": RegressionModel(lags=10), "scorer": KMeansScorer()}, + ), +] + + +class TestAnomalyDetectionModel: np.random.seed(42) # univariate series @@ -79,178 +104,155 @@ class TestADAnomalyModel: mts_train._time_index, np_mts_anomalies ) - def test_Scorer(self): - - list_NonFittableAnomalyScorer = [ - NormScorer(), - Difference(), - GaussianNLLScorer(), - ExponentialNLLScorer(), - PoissonNLLScorer(), - LaplaceNLLScorer(), - CauchyNLLScorer(), - GammaNLLScorer(), - ] - - for scorers in list_NonFittableAnomalyScorer: - for anomaly_model in [ - ForecastingAnomalyModel(model=RegressionModel(lags=10), scorer=scorers), - FilteringAnomalyModel( - model=MovingAverageFilter(window=20), scorer=scorers - ), - ]: - - # scorer are trainable - assert not anomaly_model.scorers_are_trainable - - list_FittableAnomalyScorer = [ - PyODScorer(model=KNN()), - KMeansScorer(), - WassersteinScorer(), - ] - - for scorers in list_FittableAnomalyScorer: - for anomaly_model in [ - ForecastingAnomalyModel(model=RegressionModel(lags=10), scorer=scorers), + @pytest.mark.parametrize( + "scorer,anomaly_model_config", + product( + [ + Norm(), + Difference(), + GaussianNLLScorer(), + ExponentialNLLScorer(), + PoissonNLLScorer(), + LaplaceNLLScorer(), + CauchyNLLScorer(), + GammaNLLScorer(), + ], + [ + (ForecastingAnomalyModel, {"model": RegressionModel(lags=10)}), + (FilteringAnomalyModel, {"model": MovingAverageFilter(window=20)}), + ], + ), + ) + def test_non_fittable_scorer(self, scorer, anomaly_model_config): + am_cls, am_kwargs = anomaly_model_config + anomaly_model = am_cls(scorer=scorer, **am_kwargs) + assert not anomaly_model.scorers_are_trainable + + @pytest.mark.parametrize( + "scorer,anomaly_model_config", + product( + [ + PyODScorer(model=KNN()), + KMeansScorer(), + WassersteinScorer(window_agg=False), + ], + [ + (ForecastingAnomalyModel, {"model": RegressionModel(lags=10)}), + (FilteringAnomalyModel, {"model": MovingAverageFilter(window=20)}), + ], + ), + ) + def test_fittable_scorer(self, scorer, anomaly_model_config): + am_cls, am_kwargs = anomaly_model_config + anomaly_model = am_cls(scorer=scorer, **am_kwargs) + assert anomaly_model.scorers_are_trainable + + def test_no_local_model(self): + with pytest.raises(ValueError) as err: + _ = ForecastingAnomalyModel(model=NaiveSeasonal(), scorer=KMeansScorer()) + assert str(err.value) == "`model` must be a Darts `GlobalForecastingModel`." + + @pytest.mark.parametrize( + "anomaly_model,fit_model", + [ + ( + ForecastingAnomalyModel(model=RegressionModel(lags=10), scorer=Norm()), + True, + ), + ( FilteringAnomalyModel( - model=MovingAverageFilter(window=20), scorer=scorers + model=MovingAverageFilter(window=20), scorer=Norm() ), - ]: - - # scorer are not trainable - assert anomaly_model.scorers_are_trainable - - def test_Score(self): + False, + ), + ], + ) + def test_score(self, anomaly_model, fit_model): + if fit_model: + anomaly_model.fit(self.train, allow_model_training=True) - am1 = ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=NormScorer() + # if return_model_prediction set to true, output must be tuple + assert isinstance( + anomaly_model.score(self.test, return_model_prediction=True), tuple ) - am1.fit(self.train, allow_model_training=True) - am2 = FilteringAnomalyModel( - model=MovingAverageFilter(window=20), scorer=NormScorer() + # if return_model_prediction set to false output must be + # Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] + assert not isinstance( + anomaly_model.score(self.test, return_model_prediction=False), tuple ) - for am in [am1, am2]: - # Parameter return_model_prediction - # parameter return_model_prediction must be bool - with pytest.raises(ValueError): - am.score(self.test, return_model_prediction=1) - with pytest.raises(ValueError): - am.score(self.test, return_model_prediction="True") - - # if return_model_prediction set to true, output must be tuple - assert isinstance(am.score(self.test, return_model_prediction=True), Tuple) - - # if return_model_prediction set to false output must be - # Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] - assert not isinstance( - am.score(self.test, return_model_prediction=False), Tuple - ) - - def test_FitFilteringAnomalyModelInput(self): - - for anomaly_model in [ - FilteringAnomalyModel( - model=MovingAverageFilter(window=20), scorer=NormScorer() - ), - FilteringAnomalyModel( - model=MovingAverageFilter(window=20), - scorer=[NormScorer(), KMeansScorer()], - ), - FilteringAnomalyModel( - model=MovingAverageFilter(window=20), scorer=KMeansScorer() - ), - ]: - - # filter must be fittable if allow_filter_training is set to True - with pytest.raises(ValueError): - anomaly_model.fit(self.train, allow_model_training=True) - - # input 'series' must be a series or Sequence of series - with pytest.raises(ValueError): - anomaly_model.fit([self.train, "str"], allow_model_training=True) - with pytest.raises(ValueError): - anomaly_model.fit([[self.train, self.train]], allow_model_training=True) - with pytest.raises(ValueError): - anomaly_model.fit("str", allow_model_training=True) - with pytest.raises(ValueError): - anomaly_model.fit([1, 2, 3], allow_model_training=True) - - # allow_model_training must be a bool - with pytest.raises(ValueError): - anomaly_model.fit(self.train, allow_model_training=1) - with pytest.raises(ValueError): - anomaly_model.fit(self.train, allow_model_training="True") + @pytest.mark.parametrize("anomaly_model_config", filtering_am) + def test_FitFilteringAnomalyModelInput(self, anomaly_model_config): + am_cls, am_kwargs = anomaly_model_config + anomaly_model = am_cls(**am_kwargs) + # `allow_model_training=True` has no effect if filter model has no `fit()` method + anomaly_model.fit(self.train, allow_model_training=True) - def test_FitForecastingAnomalyModelInput(self): + # input 'series' must be a series or Sequence of series + with pytest.raises(ValueError): + anomaly_model.fit([self.train, "str"], allow_model_training=True) + with pytest.raises(ValueError): + anomaly_model.fit([[self.train, self.train]], allow_model_training=True) + with pytest.raises(ValueError): + anomaly_model.fit("str", allow_model_training=True) + with pytest.raises(ValueError): + anomaly_model.fit([1, 2, 3], allow_model_training=True) - for anomaly_model in [ - ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=NormScorer() - ), - ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=[NormScorer(), KMeansScorer()] - ), - ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=KMeansScorer() - ), - ]: + @pytest.mark.parametrize("anomaly_model_config", forecasting_am) + def test_FitForecastingAnomalyModelInput(self, anomaly_model_config): + am_cls, am_kwargs = anomaly_model_config + anomaly_model = am_cls(**am_kwargs) - # input 'series' must be a series or Sequence of series - with pytest.raises(ValueError): - anomaly_model.fit([self.train, "str"], allow_model_training=True) - with pytest.raises(ValueError): - anomaly_model.fit([[self.train, self.train]], allow_model_training=True) - with pytest.raises(ValueError): - anomaly_model.fit("str", allow_model_training=True) - with pytest.raises(ValueError): - anomaly_model.fit([1, 2, 3], allow_model_training=True) + # input 'series' must be a series or Sequence of series + with pytest.raises(ValueError): + anomaly_model.fit([self.train, "str"], allow_model_training=True) + with pytest.raises(ValueError): + anomaly_model.fit([[self.train, self.train]], allow_model_training=True) + with pytest.raises(ValueError): + anomaly_model.fit("str", allow_model_training=True) + with pytest.raises(ValueError): + anomaly_model.fit([1, 2, 3], allow_model_training=True) - # allow_model_training must be a bool - with pytest.raises(ValueError): - anomaly_model.fit(self.train, allow_model_training=1) + # 'allow_model_training' must be set to True if forecasting model is not fitted + if anomaly_model.scorers_are_trainable: with pytest.raises(ValueError): - anomaly_model.fit(self.train, allow_model_training="True") - - # 'allow_model_training' must be set to True if forecasting model is not fitted - if anomaly_model.scorers_are_trainable: - with pytest.raises(ValueError): - anomaly_model.fit(self.train, allow_model_training=False) - anomaly_model.score(self.train) + anomaly_model.fit(self.train, allow_model_training=False) + anomaly_model.score(self.train) - with pytest.raises(ValueError): - # number of 'past_covariates' must be the same as the number of Timeseries in 'series' - anomaly_model.fit( - series=[self.train, self.train], - past_covariates=self.covariates, - allow_model_training=True, - ) + with pytest.raises(ValueError): + # number of 'past_covariates' must be the same as the number of Timeseries in 'series' + anomaly_model.fit( + series=[self.train, self.train], + past_covariates=self.covariates, + allow_model_training=True, + ) - with pytest.raises(ValueError): - # number of 'past_covariates' must be the same as the number of Timeseries in 'series' - anomaly_model.fit( - series=self.train, - past_covariates=[self.covariates, self.covariates], - allow_model_training=True, - ) + with pytest.raises(ValueError): + # number of 'past_covariates' must be the same as the number of Timeseries in 'series' + anomaly_model.fit( + series=self.train, + past_covariates=[self.covariates, self.covariates], + allow_model_training=True, + ) - with pytest.raises(ValueError): - # number of 'future_covariates' must be the same as the number of Timeseries in 'series' - anomaly_model.fit( - series=[self.train, self.train], - future_covariates=self.covariates, - allow_model_training=True, - ) + with pytest.raises(ValueError): + # number of 'future_covariates' must be the same as the number of Timeseries in 'series' + anomaly_model.fit( + series=[self.train, self.train], + future_covariates=self.covariates, + allow_model_training=True, + ) - with pytest.raises(ValueError): - # number of 'future_covariates' must be the same as the number of Timeseries in 'series' - anomaly_model.fit( - series=self.train, - future_covariates=[self.covariates, self.covariates], - allow_model_training=True, - ) + with pytest.raises(ValueError): + # number of 'future_covariates' must be the same as the number of Timeseries in 'series' + anomaly_model.fit( + series=self.train, + future_covariates=[self.covariates, self.covariates], + allow_model_training=True, + ) + def test_pretrain_forecasting_model(self): fitted_model = RegressionModel(lags=10).fit(self.train) # Fittable scorer must be fitted before calling .score(), even if forecasting model is fitted with pytest.raises(ValueError): @@ -259,267 +261,249 @@ def test_FitForecastingAnomalyModelInput(self): ) with pytest.raises(ValueError): ForecastingAnomalyModel( - model=fitted_model, scorer=[NormScorer(), KMeansScorer()] + model=fitted_model, scorer=[Norm(), KMeansScorer()] ).score(series=self.test) # forecasting model that do not accept past/future covariates - anomaly_model = ForecastingAnomalyModel( - model=NaiveSeasonal(), scorer=NormScorer() - ) - with pytest.raises(TypeError): - anomaly_model.fit( - series=self.train, - past_covariates=self.covariates, - allow_model_training=True, - ) - anomaly_model = ForecastingAnomalyModel( - model=NaiveSeasonal(), scorer=NormScorer() - ) - with pytest.raises(TypeError): - anomaly_model.fit( - series=self.train, - future_covariates=self.covariates, - allow_model_training=True, - ) + # with pytest.raises(ValueError): + # ForecastingAnomalyModel(model=ExponentialSmoothing(), + # scorer=NormScorer()).fit( + # series=self.train, past_covariates=self.covariates, allow_model_training=True + # ) + # with pytest.raises(ValueError): + # ForecastingAnomalyModel(model=ExponentialSmoothing(), + # scorer=NormScorer()).fit( + # series=self.train, future_covariates=self.covariates, allow_model_training=True + # ) # check window size # max window size is len(series.drop_before(series.get_timestamp_at_point(start))) + 1 with pytest.raises(ValueError): ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=KMeansScorer(window=50) + model=RegressionModel(lags=10), + scorer=KMeansScorer(window=50, window_agg=False), ).fit(series=self.train, start=0.9) # forecasting model that cannot be trained on a list of series with pytest.raises(ValueError): - ForecastingAnomalyModel(model=NaiveSeasonal(), scorer=NormScorer()).fit( + ForecastingAnomalyModel(model=NaiveSeasonal(), scorer=Norm()).fit( series=[self.train, self.train], allow_model_training=True ) - def test_ScoreForecastingAnomalyModelInput(self): - - for anomaly_model in [ - ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=NormScorer() - ), - ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=[NormScorer(), KMeansScorer()] - ), - ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=KMeansScorer() - ), - ]: - - anomaly_model.fit(self.train, allow_model_training=True) + @pytest.mark.parametrize("anomaly_model_config", forecasting_am) + def test_ScoreForecastingAnomalyModelInput(self, anomaly_model_config): + am_cls, am_kwargs = anomaly_model_config + anomaly_model = am_cls(**am_kwargs) + anomaly_model.fit(self.train, allow_model_training=True) - # number of 'past_covariates' must be the same as the number of Timeseries in 'series' - with pytest.raises(ValueError): - anomaly_model.score( - series=[self.train, self.train], past_covariates=self.covariates - ) + # number of 'past_covariates' must be the same as the number of Timeseries in 'series' + with pytest.raises(ValueError): + anomaly_model.score( + series=[self.train, self.train], past_covariates=self.covariates + ) - # number of 'past_covariates' must be the same as the number of Timeseries in 'series' - with pytest.raises(ValueError): - anomaly_model.score( - series=self.train, - past_covariates=[self.covariates, self.covariates], - ) + # number of 'past_covariates' must be the same as the number of Timeseries in 'series' + with pytest.raises(ValueError): + anomaly_model.score( + series=self.train, + past_covariates=[self.covariates, self.covariates], + ) - # number of 'future_covariates' must be the same as the number of Timeseries in 'series' - with pytest.raises(ValueError): - anomaly_model.score( - series=[self.train, self.train], future_covariates=self.covariates - ) + # number of 'future_covariates' must be the same as the number of Timeseries in 'series' + with pytest.raises(ValueError): + anomaly_model.score( + series=[self.train, self.train], future_covariates=self.covariates + ) - # number of 'future_covariates' must be the same as the number of Timeseries in 'series' - with pytest.raises(ValueError): - anomaly_model.score( - series=self.train, - future_covariates=[self.covariates, self.covariates], - ) + # number of 'future_covariates' must be the same as the number of Timeseries in 'series' + with pytest.raises(ValueError): + anomaly_model.score( + series=self.train, + future_covariates=[self.covariates, self.covariates], + ) - # check window size + def test_window_size(self): # max window size is len(series.drop_before(series.get_timestamp_at_point(start))) + 1 for score() anomaly_model = ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=KMeansScorer(window=30) + model=RegressionModel(lags=10), + scorer=KMeansScorer(window=30, window_agg=False), ) anomaly_model.fit(self.train, allow_model_training=True) with pytest.raises(ValueError): anomaly_model.score(series=self.train, start=0.9) - def test_ScoreFilteringAnomalyModelInput(self): + @pytest.mark.parametrize("anomaly_model_config", filtering_am) + def test_ScoreFilteringAnomalyModelInput(self, anomaly_model_config): + am_cls, am_kwargs = anomaly_model_config + anomaly_model = am_cls(**am_kwargs) - for anomaly_model in [ - FilteringAnomalyModel( - model=MovingAverageFilter(window=10), scorer=NormScorer() + if anomaly_model.scorers_are_trainable: + anomaly_model.fit(self.train) + + @pytest.mark.parametrize( + "anomaly_model,fit_kwargs", + [ + ( + ForecastingAnomalyModel(model=RegressionModel(lags=10), scorer=Norm()), + {"series": train, "allow_model_training": True}, ), - FilteringAnomalyModel( - model=MovingAverageFilter(window=10), - scorer=[NormScorer(), KMeansScorer()], + ( + FilteringAnomalyModel( + model=MovingAverageFilter(window=20), scorer=Norm() + ), + False, ), - FilteringAnomalyModel( - model=MovingAverageFilter(window=10), scorer=KMeansScorer() + ( + ForecastingAnomalyModel( + model=RegressionModel(lags=10), + scorer=[Norm(), WassersteinScorer(window_agg=False)], + ), + {"series": train, "allow_model_training": True}, ), - ]: - - if anomaly_model.scorers_are_trainable: - anomaly_model.fit(self.train) - - def test_eval_accuracy(self): - - am1 = ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=NormScorer() - ) - am1.fit(self.train, allow_model_training=True) - - am2 = FilteringAnomalyModel( - model=MovingAverageFilter(window=20), scorer=NormScorer() - ) - - am3 = ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=[NormScorer(), WassersteinScorer()] - ) - am3.fit(self.train, allow_model_training=True) - - am4 = FilteringAnomalyModel( - model=MovingAverageFilter(window=20), - scorer=[NormScorer(), WassersteinScorer()], - ) - am4.fit(self.train) - - for am in [am1, am2, am3, am4]: - - # if the anomaly_model have scorers that have the parameter univariate_scorer set to True, - # 'actual_anomalies' must have widths of 1 - if am.univariate_scoring: - with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=self.mts_anomalies, series=self.test - ) - with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=self.mts_anomalies, series=self.mts_test - ) - with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=[self.anomalies, self.mts_anomalies], - series=[self.test, self.mts_test], - ) + ( + FilteringAnomalyModel( + model=MovingAverageFilter(window=20), + scorer=[Norm(), WassersteinScorer(window_agg=False)], + ), + {"series": train}, + ), + ], + ) + def test_eval_metric(self, anomaly_model, fit_kwargs): + if fit_kwargs: + anomaly_model.fit(**fit_kwargs) - # 'metric' must be str and "AUC_ROC" or "AUC_PR" + # if the anomaly_model have scorers that have the parameter is_univariate set to True, + # 'anomalies' must have widths of 1 + if anomaly_model.scorers_are_univariate: with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=self.anomalies, series=self.test, metric=1 + anomaly_model.eval_metric( + anomalies=self.mts_anomalies, series=self.test ) with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=self.anomalies, series=self.test, metric="auc_roc" + anomaly_model.eval_metric( + anomalies=self.mts_anomalies, series=self.mts_test ) - with pytest.raises(TypeError): - am.eval_accuracy( - actual_anomalies=self.anomalies, - series=self.test, - metric=["AUC_ROC"], - ) - - # 'actual_anomalies' must be binary with pytest.raises(ValueError): - am.eval_accuracy(actual_anomalies=self.test, series=self.test) - - # 'actual_anomalies' must contain anomalies (at least one) - with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=self.only_0_anomalies, series=self.test + anomaly_model.eval_metric( + anomalies=[self.anomalies, self.mts_anomalies], + series=[self.test, self.mts_test], ) - # 'actual_anomalies' cannot contain only anomalies - with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=self.only_1_anomalies, series=self.test - ) + # 'metric' must be str and "AUC_ROC" or "AUC_PR" + with pytest.raises(ValueError): + anomaly_model.eval_metric( + anomalies=self.anomalies, series=self.test, metric=1 + ) + with pytest.raises(ValueError): + anomaly_model.eval_metric( + anomalies=self.anomalies, + series=self.test, + metric="auc_roc", + ) + with pytest.raises(TypeError): + anomaly_model.eval_metric( + anomalies=self.anomalies, + series=self.test, + metric=["AUC_ROC"], + ) - # 'actual_anomalies' must match the number of series - with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=self.anomalies, series=[self.test, self.test] - ) - with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=[self.anomalies, self.anomalies], series=self.test - ) + # 'anomalies' must be binary + with pytest.raises(ValueError): + anomaly_model.eval_metric(anomalies=self.test, series=self.test) - # 'actual_anomalies' must have non empty intersection with 'series' - with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=self.anomalies[:20], series=self.test[30:] - ) - with pytest.raises(ValueError): - am.eval_accuracy( - actual_anomalies=[self.anomalies, self.anomalies[:20]], - series=[self.test, self.test[40:]], - ) + # 'anomalies' must contain anomalies (at least one) + with pytest.raises(ValueError): + anomaly_model.eval_metric(anomalies=self.only_0_anomalies, series=self.test) - # Check input type - # 'actual_anomalies' and 'series' must be of same length - with pytest.raises(ValueError): - am.eval_accuracy([self.anomalies], [self.test, self.test]) - with pytest.raises(ValueError): - am.eval_accuracy(self.anomalies, [self.test, self.test]) - with pytest.raises(ValueError): - am.eval_accuracy([self.anomalies, self.anomalies], [self.test]) - with pytest.raises(ValueError): - am.eval_accuracy([self.anomalies, self.anomalies], self.test) + # 'anomalies' cannot contain only anomalies + with pytest.raises(ValueError): + anomaly_model.eval_metric(anomalies=self.only_1_anomalies, series=self.test) - # 'actual_anomalies' and 'series' must be of type Timeseries - with pytest.raises(ValueError): - am.eval_accuracy([self.anomalies], [2, 3, 4]) - with pytest.raises(ValueError): - am.eval_accuracy([self.anomalies], "str") - with pytest.raises(ValueError): - am.eval_accuracy([2, 3, 4], self.test) - with pytest.raises(ValueError): - am.eval_accuracy("str", self.test) - with pytest.raises(ValueError): - am.eval_accuracy( - [self.anomalies, self.anomalies], [self.test, [3, 2, 1]] - ) - with pytest.raises(ValueError): - am.eval_accuracy([self.anomalies, [3, 2, 1]], [self.test, self.test]) + # 'anomalies' must match the number of series + with pytest.raises(ValueError): + anomaly_model.eval_metric( + anomalies=self.anomalies, series=[self.test, self.test] + ) + with pytest.raises(ValueError): + anomaly_model.eval_metric( + anomalies=[self.anomalies, self.anomalies], + series=self.test, + ) - # Check return types - # Check if return type is float when input is a series - assert isinstance( - am.eval_accuracy(self.anomalies, self.test), - Dict, + # 'anomalies' must have non empty intersection with 'series' + with pytest.raises(ValueError): + anomaly_model.eval_metric( + anomalies=self.anomalies[:20], series=self.test[30:] ) + with pytest.raises(ValueError): + anomaly_model.eval_metric( + anomalies=[self.anomalies, self.anomalies[:20]], + series=[self.test, self.test[40:]], + ) + + # Check input type + # 'anomalies' and 'series' must be of same length + with pytest.raises(ValueError): + anomaly_model.eval_metric([self.anomalies], [self.test, self.test]) + with pytest.raises(ValueError): + anomaly_model.eval_metric(self.anomalies, [self.test, self.test]) + with pytest.raises(ValueError): + anomaly_model.eval_metric([self.anomalies, self.anomalies], [self.test]) + with pytest.raises(ValueError): + anomaly_model.eval_metric([self.anomalies, self.anomalies], self.test) - # Check if return type is Sequence when input is a Sequence of series - assert isinstance( - am.eval_accuracy(self.anomalies, [self.test]), - Sequence, + # 'anomalies' and 'series' must be of type Timeseries + with pytest.raises(ValueError): + anomaly_model.eval_metric([self.anomalies], [2, 3, 4]) + with pytest.raises(ValueError): + anomaly_model.eval_metric([self.anomalies], "str") + with pytest.raises(ValueError): + anomaly_model.eval_metric([2, 3, 4], self.test) + with pytest.raises(ValueError): + anomaly_model.eval_metric("str", self.test) + with pytest.raises(ValueError): + anomaly_model.eval_metric( + [self.anomalies, self.anomalies], [self.test, [3, 2, 1]] ) - assert isinstance( - am.eval_accuracy( - [self.anomalies, self.anomalies], [self.test, self.test] - ), - Sequence, + with pytest.raises(ValueError): + anomaly_model.eval_metric( + [self.anomalies, [3, 2, 1]], [self.test, self.test] ) - def test_ForecastingAnomalyModelInput(self): + # Check return types + # Check if return type is float when input is a series + assert isinstance( + anomaly_model.eval_metric(self.anomalies, self.test), + dict, + ) + # Check if return type is Sequence when input is a Sequence of series + assert isinstance( + anomaly_model.eval_metric(self.anomalies, [self.test]), + Sequence, + ) + + assert isinstance( + anomaly_model.eval_metric( + [self.anomalies, self.anomalies], [self.test, self.test] + ), + Sequence, + ) + + def test_ForecastingAnomalyModelInput(self): # model input # model input must be of type ForecastingModel with pytest.raises(ValueError): - ForecastingAnomalyModel(model="str", scorer=NormScorer()) + ForecastingAnomalyModel(model="str", scorer=Norm()) with pytest.raises(ValueError): - ForecastingAnomalyModel(model=1, scorer=NormScorer()) + ForecastingAnomalyModel(model=1, scorer=Norm()) with pytest.raises(ValueError): - ForecastingAnomalyModel( - model=MovingAverageFilter(window=10), scorer=NormScorer() - ) + ForecastingAnomalyModel(model=MovingAverageFilter(window=10), scorer=Norm()) with pytest.raises(ValueError): ForecastingAnomalyModel( model=[RegressionModel(lags=10), RegressionModel(lags=5)], - scorer=NormScorer(), + scorer=Norm(), ) # scorer input @@ -534,23 +518,22 @@ def test_ForecastingAnomalyModelInput(self): ) with pytest.raises(ValueError): ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=[NormScorer(), "str"] + model=RegressionModel(lags=10), scorer=[Norm(), "str"] ) def test_FilteringAnomalyModelInput(self): - # model input # model input must be of type FilteringModel with pytest.raises(ValueError): - FilteringAnomalyModel(model="str", scorer=NormScorer()) + FilteringAnomalyModel(model="str", scorer=Norm()) with pytest.raises(ValueError): - FilteringAnomalyModel(model=1, scorer=NormScorer()) + FilteringAnomalyModel(model=1, scorer=Norm()) with pytest.raises(ValueError): - FilteringAnomalyModel(model=RegressionModel(lags=10), scorer=NormScorer()) + FilteringAnomalyModel(model=RegressionModel(lags=10), scorer=Norm()) with pytest.raises(ValueError): FilteringAnomalyModel( model=[MovingAverageFilter(window=10), MovingAverageFilter(window=10)], - scorer=NormScorer(), + scorer=Norm(), ) # scorer input @@ -566,11 +549,10 @@ def test_FilteringAnomalyModelInput(self): ) with pytest.raises(ValueError): FilteringAnomalyModel( - model=MovingAverageFilter(window=10), scorer=[NormScorer(), "str"] + model=MovingAverageFilter(window=10), scorer=[Norm(), "str"] ) def test_univariate_ForecastingAnomalyModel(self): - np.random.seed(40) np_train_slope = np.array(range(0, 100, 1)) @@ -594,53 +576,51 @@ def test_univariate_ForecastingAnomalyModel(self): anomaly_model = ForecastingAnomalyModel( model=RegressionModel(lags=5), scorer=[ - NormScorer(), + Norm(), Difference(), - WassersteinScorer(), + WassersteinScorer(window_agg=False), KMeansScorer(k=5), - KMeansScorer(window=10), + KMeansScorer(window=10, window_agg=False), PyODScorer(model=KNN()), - PyODScorer(model=KNN(), window=10), - WassersteinScorer(window=15), + PyODScorer(model=KNN(), window=10, window_agg=False), + WassersteinScorer(window=15, window_agg=False), ], ) anomaly_model.fit(train_series_slope, allow_model_training=True, start=0.1) - score, model_output = anomaly_model.score( + score, pred_series = anomaly_model.score( test_series_slope, return_model_prediction=True, start=0.1 ) - # check that NormScorer is the abs difference of model_output and test_series_slope + # check that NormScorer is the abs difference of pred_series and test_series_slope assert ( - model_output - test_series_slope.slice_intersect(model_output) - ).__abs__() == NormScorer().score_from_prediction( - test_series_slope, model_output - ) + pred_series - test_series_slope.slice_intersect(pred_series) + ).__abs__() == Norm().score_from_prediction(test_series_slope, pred_series) - # check that Difference is the difference of model_output and test_series_slope + # check that Difference is the difference of pred_series and test_series_slope assert test_series_slope.slice_intersect( - model_output - ) - model_output == Difference().score_from_prediction( - test_series_slope, model_output + pred_series + ) - pred_series == Difference().score_from_prediction( + test_series_slope, pred_series ) - dict_auc_roc = anomaly_model.eval_accuracy( + dict_auc_roc = anomaly_model.eval_metric( ts_anomalies, test_series_slope, metric="AUC_ROC", start=0.1 ) - dict_auc_pr = anomaly_model.eval_accuracy( + dict_auc_pr = anomaly_model.eval_metric( ts_anomalies, test_series_slope, metric="AUC_PR", start=0.1 ) - auc_roc_from_scores = eval_accuracy_from_scores( - actual_anomalies=[ts_anomalies] * 8, - anomaly_score=score, + auc_roc_from_scores = eval_metric_from_scores( + anomalies=[ts_anomalies] * 8, + pred_scores=score, window=[1, 1, 10, 1, 10, 1, 10, 15], metric="AUC_ROC", ) - auc_pr_from_scores = eval_accuracy_from_scores( - actual_anomalies=[ts_anomalies] * 8, - anomaly_score=score, + auc_pr_from_scores = eval_metric_from_scores( + anomalies=[ts_anomalies] * 8, + pred_scores=score, window=[1, 1, 10, 1, 10, 1, 10, 15], metric="AUC_PR", ) @@ -686,7 +666,6 @@ def test_univariate_ForecastingAnomalyModel(self): np.testing.assert_array_almost_equal(auc_pr_from_scores, true_auc_pr, decimal=1) def test_univariate_FilteringAnomalyModel(self): - np.random.seed(40) np_series_train = np.array(range(0, 100, 1)) + np.random.normal( @@ -720,52 +699,50 @@ def test_univariate_FilteringAnomalyModel(self): anomaly_model = FilteringAnomalyModel( model=MovingAverageFilter(window=5), scorer=[ - NormScorer(), + Norm(), Difference(), - WassersteinScorer(), + WassersteinScorer(window_agg=False), KMeansScorer(), - KMeansScorer(window=10), + KMeansScorer(window=10, window_agg=False), PyODScorer(model=KNN()), - PyODScorer(model=KNN(), window=10), - WassersteinScorer(window=15), + PyODScorer(model=KNN(), window=10, window_agg=False), + WassersteinScorer(window=15, window_agg=False), ], ) anomaly_model.fit(train_series_noise) - score, model_output = anomaly_model.score( + score, pred_series = anomaly_model.score( test_series_noise, return_model_prediction=True ) - # check that Difference is the difference of model_output and test_series_noise + # check that Difference is the difference of pred_series and test_series_noise assert test_series_noise.slice_intersect( - model_output - ) - model_output == Difference().score_from_prediction( - test_series_noise, model_output + pred_series + ) - pred_series == Difference().score_from_prediction( + test_series_noise, pred_series ) - # check that NormScorer is the abs difference of model_output and test_series_noise + # check that NormScorer is the abs difference of pred_series and test_series_noise assert ( - test_series_noise.slice_intersect(model_output) - model_output - ).__abs__() == NormScorer().score_from_prediction( - test_series_noise, model_output - ) + test_series_noise.slice_intersect(pred_series) - pred_series + ).__abs__() == Norm().score_from_prediction(test_series_noise, pred_series) - dict_auc_roc = anomaly_model.eval_accuracy( + dict_auc_roc = anomaly_model.eval_metric( ts_anomalies, test_series_noise, metric="AUC_ROC" ) - dict_auc_pr = anomaly_model.eval_accuracy( + dict_auc_pr = anomaly_model.eval_metric( ts_anomalies, test_series_noise, metric="AUC_PR" ) - auc_roc_from_scores = eval_accuracy_from_scores( - actual_anomalies=[ts_anomalies] * 8, - anomaly_score=score, + auc_roc_from_scores = eval_metric_from_scores( + anomalies=[ts_anomalies] * 8, + pred_scores=score, window=[1, 1, 10, 1, 10, 1, 10, 15], metric="AUC_ROC", ) - auc_pr_from_scores = eval_accuracy_from_scores( - actual_anomalies=[ts_anomalies] * 8, - anomaly_score=score, + auc_pr_from_scores = eval_metric_from_scores( + anomalies=[ts_anomalies] * 8, + pred_scores=score, window=[1, 1, 10, 1, 10, 1, 10, 15], metric="AUC_PR", ) @@ -811,7 +788,6 @@ def test_univariate_FilteringAnomalyModel(self): np.testing.assert_array_almost_equal(auc_pr_from_scores, true_auc_pr, decimal=1) def test_univariate_covariate_ForecastingAnomalyModel(self): - np.random.seed(40) day_week = [0, 1, 2, 3, 4, 5, 6] @@ -847,14 +823,14 @@ def test_univariate_covariate_ForecastingAnomalyModel(self): anomaly_model = ForecastingAnomalyModel( model=RegressionModel(lags=2, lags_future_covariates=[0]), scorer=[ - NormScorer(), + Norm(), Difference(), - WassersteinScorer(), + WassersteinScorer(window_agg=False), KMeansScorer(k=4), - KMeansScorer(k=7, window=10), + KMeansScorer(k=7, window=10, window_agg=False), PyODScorer(model=KNN()), - PyODScorer(model=KNN(), window=10), - WassersteinScorer(window=15), + PyODScorer(model=KNN(), window=10, window_agg=False), + WassersteinScorer(window=15, window_agg=False), ], ) @@ -865,42 +841,40 @@ def test_univariate_covariate_ForecastingAnomalyModel(self): start=0.2, ) - score, model_output = anomaly_model.score( + score, pred_series = anomaly_model.score( series_test, return_model_prediction=True, future_covariates=covariates, start=0.2, ) - # check that NormScorer is the abs difference of model_output and series_test + # check that NormScorer is the abs difference of pred_series and series_test assert ( - series_test.slice_intersect(model_output) - model_output - ).__abs__() == NormScorer().score_from_prediction(series_test, model_output) + series_test.slice_intersect(pred_series) - pred_series + ).__abs__() == Norm().score_from_prediction(series_test, pred_series) - # check that Difference is the difference of model_output and series_test + # check that Difference is the difference of pred_series and series_test assert series_test.slice_intersect( - model_output - ) - model_output == Difference().score_from_prediction( - series_test, model_output - ) + pred_series + ) - pred_series == Difference().score_from_prediction(series_test, pred_series) - dict_auc_roc = anomaly_model.eval_accuracy( + dict_auc_roc = anomaly_model.eval_metric( ts_anomalies, series_test, metric="AUC_ROC", start=0.2 ) - dict_auc_pr = anomaly_model.eval_accuracy( + dict_auc_pr = anomaly_model.eval_metric( ts_anomalies, series_test, metric="AUC_PR", start=0.2 ) - auc_roc_from_scores = eval_accuracy_from_scores( - actual_anomalies=[ts_anomalies] * 8, - anomaly_score=score, + auc_roc_from_scores = eval_metric_from_scores( + anomalies=[ts_anomalies] * 8, + pred_scores=score, window=[1, 1, 10, 1, 10, 1, 10, 15], metric="AUC_ROC", ) - auc_pr_from_scores = eval_accuracy_from_scores( - actual_anomalies=[ts_anomalies] * 8, - anomaly_score=score, + auc_pr_from_scores = eval_metric_from_scores( + anomalies=[ts_anomalies] * 8, + pred_scores=score, window=[1, 1, 10, 1, 10, 1, 10, 15], metric="AUC_PR", ) @@ -936,8 +910,7 @@ def test_univariate_covariate_ForecastingAnomalyModel(self): ) np.testing.assert_array_almost_equal(auc_pr_from_scores, true_auc_pr, decimal=1) - def test_multivariate__FilteringAnomalyModel(self): - + def test_multivariate_FilteringAnomalyModel(self): np.random.seed(40) data_1 = np.random.normal(0, 0.1, 100) @@ -996,44 +969,44 @@ def test_multivariate__FilteringAnomalyModel(self): anomaly_model = FilteringAnomalyModel( model=MovingAverageFilter(window=10), scorer=[ - NormScorer(component_wise=False), - WassersteinScorer(), - WassersteinScorer(window=12), + Norm(component_wise=False), + WassersteinScorer(window_agg=False), + WassersteinScorer(window=12, window_agg=False), KMeansScorer(), - KMeansScorer(window=5), + KMeansScorer(window=5, window_agg=False), PyODScorer(model=KNN()), - PyODScorer(model=KNN(), window=5), + PyODScorer(model=KNN(), window=5, window_agg=False), ], ) anomaly_model.fit(mts_series_train) - scores, model_output = anomaly_model.score( + scores, pred_series = anomaly_model.score( mts_series_test, return_model_prediction=True ) - # model_output must be multivariate (same width as input) - assert model_output.width == mts_series_test.width + # pred_series must be multivariate (same width as input) + assert pred_series.width == mts_series_test.width # scores must be of the same length as the number of scorers assert len(scores) == len(anomaly_model.scorers) - dict_auc_roc = anomaly_model.eval_accuracy( + dict_auc_roc = anomaly_model.eval_metric( mts_anomalies, mts_series_test, metric="AUC_ROC" ) - dict_auc_pr = anomaly_model.eval_accuracy( + dict_auc_pr = anomaly_model.eval_metric( mts_anomalies, mts_series_test, metric="AUC_PR" ) - auc_roc_from_scores = eval_accuracy_from_scores( - actual_anomalies=[mts_anomalies] * 7, - anomaly_score=scores, + auc_roc_from_scores = eval_metric_from_scores( + anomalies=[mts_anomalies] * 7, + pred_scores=scores, window=[1, 10, 12, 1, 5, 1, 5], metric="AUC_ROC", ) - auc_pr_from_scores = eval_accuracy_from_scores( - actual_anomalies=[mts_anomalies] * 7, - anomaly_score=scores, + auc_pr_from_scores = eval_metric_from_scores( + anomalies=[mts_anomalies] * 7, + pred_scores=scores, window=[1, 10, 12, 1, 5, 1, 5], metric="AUC_PR", ) @@ -1080,45 +1053,47 @@ def test_multivariate__FilteringAnomalyModel(self): anomaly_model = FilteringAnomalyModel( model=MovingAverageFilter(window=10), scorer=[ - NormScorer(component_wise=True), + Norm(component_wise=True), Difference(), - WassersteinScorer(component_wise=True), - WassersteinScorer(window=12, component_wise=True), + WassersteinScorer(component_wise=True, window_agg=False), + WassersteinScorer(window=12, component_wise=True, window_agg=False), KMeansScorer(component_wise=True), - KMeansScorer(window=5, component_wise=True), + KMeansScorer(window=5, component_wise=True, window_agg=False), PyODScorer(model=KNN(), component_wise=True), - PyODScorer(model=KNN(), window=5, component_wise=True), + PyODScorer( + model=KNN(), window=5, component_wise=True, window_agg=False + ), ], ) anomaly_model.fit(mts_series_train) - scores, model_output = anomaly_model.score( + scores, pred_series = anomaly_model.score( mts_series_test, return_model_prediction=True ) - # model_output must be multivariate (same width as input) - assert model_output.width == mts_series_test.width + # pred_series must be multivariate (same width as input) + assert pred_series.width == mts_series_test.width # scores must be of the same length as the number of scorers assert len(scores) == len(anomaly_model.scorers) - dict_auc_roc = anomaly_model.eval_accuracy( + dict_auc_roc = anomaly_model.eval_metric( ts_anomalies, mts_series_test, metric="AUC_ROC" ) - dict_auc_pr = anomaly_model.eval_accuracy( + dict_auc_pr = anomaly_model.eval_metric( ts_anomalies, mts_series_test, metric="AUC_PR" ) - auc_roc_from_scores = eval_accuracy_from_scores( - actual_anomalies=[ts_anomalies] * 8, - anomaly_score=scores, + auc_roc_from_scores = eval_metric_from_scores( + anomalies=[ts_anomalies] * 8, + pred_scores=scores, window=[1, 1, 10, 12, 1, 5, 1, 5], metric="AUC_ROC", ) - auc_pr_from_scores = eval_accuracy_from_scores( - actual_anomalies=[ts_anomalies] * 8, - anomaly_score=scores, + auc_pr_from_scores = eval_metric_from_scores( + anomalies=[ts_anomalies] * 8, + pred_scores=scores, window=[1, 1, 10, 12, 1, 5, 1, 5], metric="AUC_PR", ) @@ -1163,8 +1138,7 @@ def test_multivariate__FilteringAnomalyModel(self): ) np.testing.assert_array_almost_equal(auc_pr_from_scores, true_auc_pr, decimal=1) - def test_multivariate__ForecastingAnomalyModel(self): - + def test_multivariate_ForecastingAnomalyModel(self): np.random.seed(40) data_sin = np.array([np.sin(x) for x in np.arange(0, 20 * np.pi, 0.2)]) @@ -1224,44 +1198,44 @@ def test_multivariate__ForecastingAnomalyModel(self): anomaly_model = ForecastingAnomalyModel( model=RegressionModel(lags=10), scorer=[ - NormScorer(component_wise=False), - WassersteinScorer(), - WassersteinScorer(window=20), + Norm(component_wise=False), + WassersteinScorer(window_agg=False), + WassersteinScorer(window=20, window_agg=False), KMeansScorer(), - KMeansScorer(window=20), + KMeansScorer(window=20, window_agg=False), PyODScorer(model=KNN()), - PyODScorer(model=KNN(), window=10), + PyODScorer(model=KNN(), window=10, window_agg=False), ], ) anomaly_model.fit(mts_series_train, allow_model_training=True, start=0.1) - scores, model_output = anomaly_model.score( + scores, pred_series = anomaly_model.score( mts_series_test, return_model_prediction=True, start=0.1 ) - # model_output must be multivariate (same width as input) - assert model_output.width == mts_series_test.width + # pred_series must be multivariate (same width as input) + assert pred_series.width == mts_series_test.width # scores must be of the same length as the number of scorers assert len(scores) == len(anomaly_model.scorers) - dict_auc_roc = anomaly_model.eval_accuracy( + dict_auc_roc = anomaly_model.eval_metric( mts_anomalies, mts_series_test, start=0.1, metric="AUC_ROC" ) - dict_auc_pr = anomaly_model.eval_accuracy( + dict_auc_pr = anomaly_model.eval_metric( mts_anomalies, mts_series_test, start=0.1, metric="AUC_PR" ) - auc_roc_from_scores = eval_accuracy_from_scores( - actual_anomalies=[mts_anomalies] * 7, - anomaly_score=scores, + auc_roc_from_scores = eval_metric_from_scores( + anomalies=[mts_anomalies] * 7, + pred_scores=scores, window=[1, 10, 20, 1, 20, 1, 10], metric="AUC_ROC", ) - auc_pr_from_scores = eval_accuracy_from_scores( - actual_anomalies=[mts_anomalies] * 7, - anomaly_score=scores, + auc_pr_from_scores = eval_metric_from_scores( + anomalies=[mts_anomalies] * 7, + pred_scores=scores, window=[1, 10, 20, 1, 20, 1, 10], metric="AUC_PR", ) @@ -1308,45 +1282,47 @@ def test_multivariate__ForecastingAnomalyModel(self): anomaly_model = ForecastingAnomalyModel( model=RegressionModel(lags=10), scorer=[ - NormScorer(component_wise=True), + Norm(component_wise=True), Difference(), - WassersteinScorer(component_wise=True), - WassersteinScorer(window=20, component_wise=True), + WassersteinScorer(component_wise=True, window_agg=False), + WassersteinScorer(window=20, component_wise=True, window_agg=False), KMeansScorer(component_wise=True), - KMeansScorer(window=20, component_wise=True), + KMeansScorer(window=20, component_wise=True, window_agg=False), PyODScorer(model=KNN(), component_wise=True), - PyODScorer(model=KNN(), window=10, component_wise=True), + PyODScorer( + model=KNN(), window=10, component_wise=True, window_agg=False + ), ], ) anomaly_model.fit(mts_series_train, allow_model_training=True, start=0.1) - scores, model_output = anomaly_model.score( + scores, pred_series = anomaly_model.score( mts_series_test, return_model_prediction=True, start=0.1 ) - # model_output must be multivariate (same width as input) - assert model_output.width == mts_series_test.width + # pred_series must be multivariate (same width as input) + assert pred_series.width == mts_series_test.width # scores must be of the same length as the number of scorers assert len(scores) == len(anomaly_model.scorers) - dict_auc_roc = anomaly_model.eval_accuracy( + dict_auc_roc = anomaly_model.eval_metric( ts_anomalies, mts_series_test, start=0.1, metric="AUC_ROC" ) - dict_auc_pr = anomaly_model.eval_accuracy( + dict_auc_pr = anomaly_model.eval_metric( ts_anomalies, mts_series_test, start=0.1, metric="AUC_PR" ) - auc_roc_from_scores = eval_accuracy_from_scores( - actual_anomalies=[ts_anomalies] * 8, - anomaly_score=scores, + auc_roc_from_scores = eval_metric_from_scores( + anomalies=[ts_anomalies] * 8, + pred_scores=scores, window=[1, 1, 10, 20, 1, 20, 1, 10], metric="AUC_ROC", ) - auc_pr_from_scores = eval_accuracy_from_scores( - actual_anomalies=[ts_anomalies] * 8, - anomaly_score=scores, + auc_pr_from_scores = eval_metric_from_scores( + anomalies=[ts_anomalies] * 8, + pred_scores=scores, window=[1, 1, 10, 20, 1, 20, 1, 10], metric="AUC_PR", ) @@ -1391,201 +1367,116 @@ def test_multivariate__ForecastingAnomalyModel(self): ) np.testing.assert_array_almost_equal(auc_pr_from_scores, true_auc_pr, decimal=1) - def test_show_anomalies(self): - + def test_visualization(self): + # test function show_anomalies() and show_anomalies_from_scores() forecasting_anomaly_model = ForecastingAnomalyModel( - model=RegressionModel(lags=10), scorer=NormScorer() + model=RegressionModel(lags=10), scorer=Norm() ) forecasting_anomaly_model.fit(self.train, allow_model_training=True) filtering_anomaly_model = FilteringAnomalyModel( - model=MovingAverageFilter(window=10), scorer=NormScorer() + model=MovingAverageFilter(window=10), scorer=Norm() ) - for anomaly_model in [forecasting_anomaly_model, filtering_anomaly_model]: - - # must input only one series - with pytest.raises(ValueError): - anomaly_model.show_anomalies(series=[self.train, self.train]) + self.show_anomalies_function( + visualization_function=forecasting_anomaly_model.show_anomalies + ) + self.show_anomalies_function( + visualization_function=filtering_anomaly_model.show_anomalies + ) + self.show_anomalies_function(visualization_function=show_anomalies_from_scores) - # input must be a series - with pytest.raises(ValueError): - anomaly_model.show_anomalies(series=[1, 2, 4]) + def show_anomalies_function(self, visualization_function): + # must input only one series + with pytest.raises(ValueError) as err: + visualization_function(series=[self.train, self.train]) + assert ( + str(err.value) + == "`series` must be single `TimeSeries` or a sequence of `TimeSeries` of length `1`." + ) + # input must be a series + with pytest.raises(ValueError): + visualization_function(series=[1, 2, 4]) + if visualization_function != show_anomalies_from_scores: # metric must be "AUC_ROC" or "AUC_PR" with pytest.raises(ValueError): - anomaly_model.show_anomalies( - series=self.train, actual_anomalies=self.anomalies, metric="str" + visualization_function( + series=self.train, + anomalies=self.anomalies, + metric="str", ) with pytest.raises(ValueError): - anomaly_model.show_anomalies( - series=self.train, actual_anomalies=self.anomalies, metric="auc_roc" + visualization_function( + series=self.train, + anomalies=self.anomalies, + metric="auc_roc", ) with pytest.raises(ValueError): - anomaly_model.show_anomalies( - series=self.train, actual_anomalies=self.anomalies, metric=1 + visualization_function( + series=self.train, anomalies=self.anomalies, metric=1 ) - # actual_anomalies must be not none if metric is given + # anomalies must be not none if metric is given with pytest.raises(ValueError): - anomaly_model.show_anomalies(series=self.train, metric="AUC_ROC") + visualization_function(series=self.train, metric="AUC_ROC") - # actual_anomalies must be binary + # anomalies must be binary with pytest.raises(ValueError): - anomaly_model.show_anomalies( - series=self.train, actual_anomalies=self.test, metric="AUC_ROC" + visualization_function( + series=self.train, + anomalies=self.test, + metric="AUC_ROC", ) - # actual_anomalies must contain at least 1 anomaly if metric is given + # anomalies must contain at least 1 anomaly if metric is given with pytest.raises(ValueError): - anomaly_model.show_anomalies( + visualization_function( series=self.train, - actual_anomalies=self.only_0_anomalies, + anomalies=self.only_0_anomalies, metric="AUC_ROC", ) - # actual_anomalies must contain at least 1 non-anomoulous timestamp + # anomalies must contain at least 1 non-anomoulous timestamp # if metric is given with pytest.raises(ValueError): - anomaly_model.show_anomalies( + visualization_function( series=self.train, - actual_anomalies=self.only_1_anomalies, + anomalies=self.only_1_anomalies, metric="AUC_ROC", ) - - # names_of_scorers must be str + else: + # window must be a positive int with pytest.raises(ValueError): - anomaly_model.show_anomalies(series=self.train, names_of_scorers=2) - # nbr of names_of_scorers must match the nbr of scores (only 1 here) + show_anomalies_from_scores( + series=self.train, pred_scores=self.test, window=-1 + ) + # window must smaller than the score series with pytest.raises(ValueError): - anomaly_model.show_anomalies( - series=self.train, names_of_scorers=["scorer1", "scorer2"] + show_anomalies_from_scores( + series=self.train, pred_scores=self.test, window=200 ) - - # title must be str + # must have the same nbr of windows than scores with pytest.raises(ValueError): - anomaly_model.show_anomalies(series=self.train, title=1) - - def test_show_anomalies_from_scores(self): - - # must input only one series - with pytest.raises(ValueError): - show_anomalies_from_scores(series=[self.train, self.train]) - - # input must be a series - with pytest.raises(ValueError): - show_anomalies_from_scores(series=[1, 2, 4]) - - # must input only one model_output - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, model_output=[self.test, self.train] - ) - - # metric must be "AUC_ROC" or "AUC_PR" - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, - anomaly_scores=self.test, - actual_anomalies=self.anomalies, - metric="str", - ) - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, - anomaly_scores=self.test, - actual_anomalies=self.anomalies, - metric="auc_roc", - ) - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, - anomaly_scores=self.test, - actual_anomalies=self.anomalies, - metric=1, - ) - - # actual_anomalies must be not none if metric is given - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, anomaly_scores=self.test, metric="AUC_ROC" - ) - - # actual_anomalies must be binary - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, - anomaly_scores=self.test, - actual_anomalies=self.test, - metric="AUC_ROC", - ) - - # actual_anomalies must contain at least 1 anomaly if metric is given - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, - anomaly_scores=self.test, - actual_anomalies=self.only_0_anomalies, - metric="AUC_ROC", - ) - - # actual_anomalies must contain at least 1 non-anomoulous timestamp - # if metric is given - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, - anomaly_scores=self.test, - actual_anomalies=self.only_1_anomalies, - metric="AUC_ROC", - ) - - # window must be int - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, anomaly_scores=self.test, window="1" - ) - # window must be an int positive - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, anomaly_scores=self.test, window=-1 - ) - # window must smaller than the score series - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, anomaly_scores=self.test, window=200 - ) - - # must have the same nbr of windows than scores - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, anomaly_scores=self.test, window=[1, 2] - ) - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, - anomaly_scores=[self.test, self.test], - window=[1, 2, 1], - ) - - # names_of_scorers must be str - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, anomaly_scores=self.test, names_of_scorers=2 - ) - # nbr of names_of_scorers must match the nbr of scores - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, - anomaly_scores=self.test, - names_of_scorers=["scorer1", "scorer2"], - ) - with pytest.raises(ValueError): - show_anomalies_from_scores( - series=self.train, - anomaly_scores=[self.test, self.test], - names_of_scorers=["scorer1", "scorer2", "scorer3"], - ) - - # title must be str - with pytest.raises(ValueError): - show_anomalies_from_scores(series=self.train, title=1) + show_anomalies_from_scores( + series=self.train, pred_scores=self.test, window=[1, 2] + ) + with pytest.raises(ValueError): + show_anomalies_from_scores( + series=self.train, + pred_scores=[self.test, self.test], + window=[1, 2, 1], + ) + # nbr of names_of_scorers must match the nbr of scores + with pytest.raises(ValueError): + show_anomalies_from_scores( + series=self.train, + pred_scores=self.test, + names_of_scorers=["scorer1", "scorer2"], + ) + with pytest.raises(ValueError): + show_anomalies_from_scores( + series=self.train, + pred_scores=[self.test, self.test], + names_of_scorers=["scorer1", "scorer2", "scorer3"], + ) diff --git a/darts/tests/ad/test_detectors.py b/darts/tests/ad/test_detectors.py index 3dc7e5a04f..defada1a8f 100644 --- a/darts/tests/ad/test_detectors.py +++ b/darts/tests/ad/test_detectors.py @@ -1,19 +1,27 @@ -from typing import Sequence +from collections.abc import Sequence +from itertools import product import numpy as np import pytest from darts import TimeSeries +from darts.ad.detectors.detectors import FittableDetector +from darts.ad.detectors.iqr_detector import IQRDetector from darts.ad.detectors.quantile_detector import QuantileDetector from darts.ad.detectors.threshold_detector import ThresholdDetector -list_NonFittableDetector = [ThresholdDetector(low_threshold=0.2)] +list_Detector = [(ThresholdDetector, {"low_threshold": 0.2})] -list_FittableDetector = [QuantileDetector(low_quantile=0.2)] +list_FittableDetector = [(QuantileDetector, {"low_quantile": 0.2})] +list_detectors = list_Detector + list_FittableDetector -class TestADDetectors: +metric_func = ["accuracy", "recall", "f1", "precision"] +delta = 1e-05 + + +class TestAnomalyDetectionDetector: np.random.seed(42) # univariate series @@ -41,118 +49,147 @@ class TestADDetectors: np_probabilistic = np.random.choice(a=[0, 1], p=[0.5, 0.5], size=[100, 1, 5]) probabilistic = TimeSeries.from_values(np_probabilistic) - def test_DetectNonFittableDetector(self): - - detector = ThresholdDetector(low_threshold=0.2) - - # Check return types - # Check if return TimeSeries is float when input is a series - assert isinstance(detector.detect(self.test), TimeSeries) - + @pytest.mark.parametrize( + "detector_config,series", + product(list_detectors, [(train, test), (mts_train, mts_test)]), + ) + def test_detect_return_type(self, detector_config, series): + """Check that detect() behave as expected""" + detector_cls, detector_kwargs = detector_config + ts_train, ts_test = series + detector = detector_cls(**detector_kwargs) + if isinstance(detector, FittableDetector): + detector.fit(ts_train) + + # Check if return type is TimeSeries when input is a single series + assert isinstance(detector.detect(ts_test), TimeSeries) # Check if return type is Sequence when input is a Sequence of series - assert isinstance(detector.detect([self.test]), Sequence) - - # Check if return TimeSeries is Sequence when input is a multivariate series - assert isinstance(detector.detect(self.mts_test), TimeSeries) - - # Check if return type is Sequence when input is a multivariate series - assert isinstance(detector.detect([self.mts_test]), Sequence) - - with pytest.raises(ValueError): - # Input cannot be probabilistic - detector.detect(self.probabilistic) - - def test_DetectFittableDetector(self): - detector = QuantileDetector(low_quantile=0.2) - - # Check return types - - detector.fit(self.train) - # Check if return type is float when input is a series - assert isinstance(detector.detect(self.test), TimeSeries) - - # Check if return type is Sequence when input is a sequence of series - assert isinstance(detector.detect([self.test]), Sequence) - - detector.fit(self.mts_train) - # Check if return type is Sequence when input is a multivariate series - assert isinstance(detector.detect(self.mts_test), TimeSeries) - - # Check if return type is Sequence when input is a sequence of multivariate series - assert isinstance(detector.detect([self.mts_test]), Sequence) + assert isinstance(detector.detect([ts_test]), Sequence) + # Input cannot be probabilistic with pytest.raises(ValueError): - # Input cannot be probabilistic detector.detect(self.probabilistic) - def test_eval_accuracy(self): - - detector = ThresholdDetector(low_threshold=0.2) + @pytest.mark.parametrize("detector_config", list_detectors) + def test_eval_metric_return_type(self, detector_config): + """Check that eval_metric() behave as expected""" + detector_cls, detector_kwargs = detector_config + detector = detector_cls(**detector_kwargs) - # Check return types + # univariate + if isinstance(detector, FittableDetector): + detector.fit(self.train) # Check if return type is float when input is a series - assert isinstance(detector.eval_accuracy(self.anomalies, self.test), float) - + assert isinstance( + detector.eval_metric(anomalies=self.anomalies, pred_scores=self.test), + float, + ) # Check if return type is Sequence when input is a Sequence of series - assert isinstance(detector.eval_accuracy(self.anomalies, [self.test]), Sequence) + assert isinstance( + detector.eval_metric(anomalies=self.anomalies, pred_scores=[self.test]), + Sequence, + ) + # multivariate + if isinstance(detector, FittableDetector): + detector.fit(self.mts_train) # Check if return type is Sequence when input is a multivariate series assert isinstance( - detector.eval_accuracy(self.mts_anomalies, self.mts_test), Sequence + detector.eval_metric( + anomalies=self.mts_anomalies, pred_scores=self.mts_test + ), + Sequence, ) - # Check if return type is Sequence when input is a multivariate series assert isinstance( - detector.eval_accuracy(self.mts_anomalies, [self.mts_test]), Sequence + detector.eval_metric( + anomalies=self.mts_anomalies, pred_scores=[self.mts_test] + ), + Sequence, ) + # Input cannot be probabilistic with pytest.raises(ValueError): - # Input cannot be probabilistic - detector.eval_accuracy(self.anomalies, self.probabilistic) + detector.eval_metric( + anomalies=self.anomalies, pred_scores=self.probabilistic + ) - def test_FittableDetector(self): + @pytest.mark.parametrize( + "config", + [ + ( + ThresholdDetector, + {"low_threshold": 4.8, "high_threshold": 10.5}, + {"low_threshold": [4.8, 4.8], "high_threshold": [10.5, 10.5]}, + ), + ( + QuantileDetector, + {"low_quantile": 0.05, "high_quantile": 0.95}, + {"low_quantile": [0.05, 0.05], "high_quantile": [0.95, 0.95]}, + ), + ], + ) + def test_bounded_detectors_parameters_broadcasting(self, config): + """If two values are given for low and high, and a series of width 2 is given, + then the results must be the same as a detector that was given only one value + for low and high (will duplicate the value for each width)""" + detector_cls, kwargs_1param, kwargs_2params = config + + # detector that should broadcast the parameters to match series' width + detector = detector_cls(**kwargs_1param) + # detector created with a number of parameters matching the series' width + detector_2param = detector_cls(**kwargs_2params) + if isinstance(detector, FittableDetector): + detector.fit(self.mts_train) + detector_2param.fit(self.mts_train) - for detector in list_FittableDetector: + binary_detection = detector.detect(self.mts_test) + binary_detection_2param = detector_2param.detect(self.mts_test) + assert binary_detection == binary_detection_2param - # Need to call fit() before calling detect() - with pytest.raises(ValueError): - detector.detect(self.test) + @pytest.mark.parametrize("detector_config", list_FittableDetector) + def test_fit_detect_series_width(self, detector_config): + detector_cls, detector_kwargs = detector_config + detector = detector_cls(**detector_kwargs) - # Check if _fit_called is False - assert not detector._fit_called + # Need to call fit() before calling detect() + with pytest.raises(ValueError): + detector.detect(self.test) - with pytest.raises(ValueError): - # fit on sequence with series that have different width - detector.fit([self.train, self.mts_train]) + # Check if _fit_called is False + assert not detector._fit_called - with pytest.raises(ValueError): - # Input cannot be probabilistic - detector.fit(self.probabilistic) + with pytest.raises(ValueError): + # fit on sequence with series that have different width + detector.fit([self.train, self.mts_train]) - # case1: fit on UTS - detector1 = detector - detector1.fit(self.train) + with pytest.raises(ValueError): + # Input cannot be probabilistic + detector.fit(self.probabilistic) - # Check if _fit_called is True after being fitted - assert detector1._fit_called + # case1: fit on UTS + detector1 = detector + detector1.fit(self.train) - with pytest.raises(ValueError): - # series must be same width as series used for training - detector1.detect(self.mts_test) + # Check if _fit_called is True after being fitted + assert detector1._fit_called - # case2: fit on MTS - detector2 = detector - detector2.fit(self.mts_test) + with pytest.raises(ValueError): + # series must be same width as series used for training + detector1.detect(self.mts_test) - # Check if _fit_called is True after being fitted - assert detector2._fit_called + # case2: fit on MTS + detector2 = detector + detector2.fit(self.mts_test) - with pytest.raises(ValueError): - # series must be same width as series used for training - detector2.detect(self.train) + # Check if _fit_called is True after being fitted + assert detector2._fit_called - def test_QuantileDetector(self): + with pytest.raises(ValueError): + # series must be same width as series used for training + detector2.detect(self.train) + def test_QuantileDetector_constructor(self): # Need to have at least one parameter (low, high) not None with pytest.raises(ValueError): QuantileDetector() @@ -214,80 +251,164 @@ def test_QuantileDetector(self): detector.fit(self.train) assert detector.low_threshold == detector.high_threshold - # widths of series used for fitting must match the number of values given for high or/and low, - # if high and low have a length higher than 1 + @pytest.mark.parametrize( + "detector_kwargs", + [ + {"low_quantile": 0.1, "high_quantile": [0.8, 0.7]}, + {"low_quantile": [0.1, 0.2], "high_quantile": [0.8, 0.9]}, + {"low_quantile": [0.1, 0.2], "high_quantile": 0.8}, + {"low_quantile": [0.1, 0.2]}, + {"high_quantile": [0.1, 0.2]}, + ], + ) + def test_quantile_detector_fit_detect_matching_width(self, detector_kwargs): + """Widths of series should match the number of values given for high or/and low, + if more than one value is provided for either of them. - detector = QuantileDetector(low_quantile=0.1, high_quantile=[0.8, 0.7]) + `self.train` series has only one component whereas model is created with 2 values for at + least one of the model""" + detector = QuantileDetector(**detector_kwargs) + + # during training with pytest.raises(ValueError): detector.fit(self.train) with pytest.raises(ValueError): detector.fit([self.train, self.mts_train]) - detector = QuantileDetector(low_quantile=[0.1, 0.2], high_quantile=[0.8, 0.9]) + # during detection + detector.fit(self.mts_train) with pytest.raises(ValueError): - detector.fit(self.train) + detector.detect(self.train) with pytest.raises(ValueError): - detector.fit([self.train, self.mts_train]) + detector.detect([self.train, self.mts_train]) - detector = QuantileDetector(low_quantile=[0.1, 0.2], high_quantile=0.8) + def test_ThresholdDetector_constructor(self): + # Need to have at least one parameter (low, high) not None with pytest.raises(ValueError): - detector.fit(self.train) + ThresholdDetector() with pytest.raises(ValueError): - detector.fit([self.train, self.mts_train]) + ThresholdDetector(low_threshold=None, high_threshold=None) - detector = QuantileDetector(low_quantile=[0.1, 0.2]) + # if high and low are both sequences of length>1, they must be of the same size with pytest.raises(ValueError): - detector.fit(self.train) + ThresholdDetector(low_threshold=[0.2, 0.1], high_threshold=[0.95, 0.8, 0.9]) with pytest.raises(ValueError): - detector.fit([self.train, self.mts_train]) + ThresholdDetector(low_threshold=[0.2, 0.1, 0.7], high_threshold=[0.95, 0.8]) - detector = QuantileDetector(high_quantile=[0.1, 0.2]) + # Parameter high must be higher or equal than parameter low with pytest.raises(ValueError): - detector.fit(self.train) + ThresholdDetector(low_threshold=0.7, high_threshold=0.2) with pytest.raises(ValueError): - detector.fit([self.train, self.mts_train]) + ThresholdDetector(low_threshold=[0.2, 0.9], high_threshold=[0.95, 0.1]) + with pytest.raises(ValueError): + ThresholdDetector(low_threshold=0.2, high_threshold=[0.95, 0.1]) + with pytest.raises(ValueError): + ThresholdDetector(low_threshold=[0.2, 0.9], high_threshold=0.8) + with pytest.raises(ValueError): + ThresholdDetector(low_threshold=[0.2, 0.9, None], high_threshold=0.8) + + # Parameter high/low cannot be sequence of only None + with pytest.raises(ValueError): + ThresholdDetector(low_threshold=[None, None, None]) + with pytest.raises(ValueError): + ThresholdDetector(high_threshold=[None, None, None]) + with pytest.raises(ValueError): + ThresholdDetector(low_threshold=[None], high_threshold=[None, None, None]) # widths of series used for scoring must match the number of values given for high or/and low, # if high and low have a length higher than 1 - detector = QuantileDetector(low_quantile=0.1, high_quantile=[0.8, 0.7]) - detector.fit(self.mts_train) + detector = ThresholdDetector(low_threshold=0.1, high_threshold=[0.8, 0.7]) with pytest.raises(ValueError): detector.detect(self.train) with pytest.raises(ValueError): detector.detect([self.train, self.mts_train]) - detector = QuantileDetector(low_quantile=[0.1, 0.2], high_quantile=[0.8, 0.9]) - detector.fit(self.mts_train) + detector = ThresholdDetector( + low_threshold=[0.1, 0.2], high_threshold=[0.8, 0.9] + ) with pytest.raises(ValueError): detector.detect(self.train) with pytest.raises(ValueError): detector.detect([self.train, self.mts_train]) - detector = QuantileDetector(low_quantile=[0.1, 0.2], high_quantile=0.8) - detector.fit(self.mts_train) + detector = ThresholdDetector(low_threshold=[0.1, 0.2], high_threshold=0.8) with pytest.raises(ValueError): detector.detect(self.train) with pytest.raises(ValueError): detector.detect([self.train, self.mts_train]) - detector = QuantileDetector(low_quantile=[0.1, 0.2]) - detector.fit(self.mts_train) + detector = ThresholdDetector(low_threshold=[0.1, 0.2]) with pytest.raises(ValueError): detector.detect(self.train) with pytest.raises(ValueError): detector.detect([self.train, self.mts_train]) - detector = QuantileDetector(high_quantile=[0.1, 0.2]) - detector.fit(self.mts_train) + detector = ThresholdDetector(high_threshold=[0.1, 0.2]) with pytest.raises(ValueError): detector.detect(self.train) with pytest.raises(ValueError): detector.detect([self.train, self.mts_train]) - detector = QuantileDetector(low_quantile=0.05, high_quantile=0.95) - detector.fit(self.train) - + @pytest.mark.parametrize( + "config", + [ + ( + ThresholdDetector, + {"low_threshold": 9.5, "high_threshold": 10.5}, + { + "anomalies": 58, + "accuracy": 0.41, + "recall": 0.40, + "f1": 0.06349, + "precision": 0.03448, + }, + None, + ), + ( + QuantileDetector, + {"low_quantile": 0.05, "high_quantile": 0.95}, + { + "anomalies": 42, + "accuracy": 0.57, + "recall": 0.40, + "f1": 0.08510, + "precision": 0.04761, + }, + (9.13658, 10.74007), + ), + ( + IQRDetector, + {}, + { + "anomalies": 28, + "accuracy": 0.69, + "recall": 0.2, + "f1": 0.060606, + "precision": 0.035714, + }, + (8.9444, 10.95811), + ), + ( + IQRDetector, + {"scale": 1}, + { + "anomalies": 47, + "accuracy": 0.52, + "recall": 0.4, + "f1": 0.07692, + "precision": 0.042553, + }, + (9.19611, 10.70640), + ), + ], + ) + def test_bounded_detector_eval_metric_univariate(self, config): + """Verifying the performance of the bounded detectors on an univariate example""" + detector_cls, detector_kwargs, expected_values, fitted_params = config + detector = detector_cls(**detector_kwargs) + if isinstance(detector, FittableDetector): + detector.fit(self.train) binary_detection = detector.detect(self.test) # Return of .detect() must be binary @@ -299,496 +420,282 @@ def test_QuantileDetector(self): # Return of .detect() must be same len as input assert len(binary_detection) == len(self.test) - # univariate test - # detector parameter 'abs_low_' must be equal to 9.13658 when trained on the series 'train' - assert abs(detector.low_threshold[0] - 9.13658) < 1e-05 - - # detector parameter 'abs_high_' must be equal to 10.74007 when trained on the series 'train' - assert abs(detector.high_threshold[0] - 10.74007) < 1e-05 - - # detector must found 10 anomalies in the series 'train' - assert detector.detect(self.train).sum(axis=0).all_values().flatten()[0] == 10 - - # detector must found 42 anomalies in the series 'test' - assert binary_detection.sum(axis=0).all_values().flatten()[0] == 42 - - # detector must have an accuracy of 0.57 for the series 'test' assert ( - abs( - detector.eval_accuracy(self.anomalies, self.test, metric="accuracy") - - 0.57 - ) - < 1e-05 - ) - # detector must have an recall of 0.4 for the series 'test' - assert ( - abs( - detector.eval_accuracy(self.anomalies, self.test, metric="recall") - 0.4 - ) - < 1e-05 + binary_detection.sum(axis=0).all_values().flatten()[0] + == expected_values["anomalies"] ) - # detector must have an f1 of 0.08510 for the series 'test' - assert ( - abs( - detector.eval_accuracy(self.anomalies, self.test, metric="f1") - 0.08510 - ) - < 1e-05 - ) - # detector must have an precision of 0.04761 for the series 'test' - assert ( - abs( - detector.eval_accuracy(self.anomalies, self.test, metric="precision") - - 0.04761 - ) - < 1e-05 - ) - - # multivariate test - detector_1param = QuantileDetector(low_quantile=0.05, high_quantile=0.95) - detector_1param.fit(self.mts_train) - binary_detection = detector_1param.detect(self.mts_test) - - # if two values are given for low and high, and a series of width 2 is given, then the results must - # be the same as a detector that was given only one value for low and high. - # (will duplicate the value for each component) - detector_2param = QuantileDetector( - low_quantile=[0.05, 0.05], high_quantile=[0.95, 0.95] - ) - detector_2param.fit(self.mts_train) - binary_detection_2param = detector_2param.detect(self.mts_test) - assert binary_detection == binary_detection_2param - # width of output must be equal to 2 (same dimension as input) - assert binary_detection.width == 2 - assert ( - len( - detector_1param.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="accuracy" + for m_func in metric_func: + assert ( + np.abs( + expected_values[m_func] + - detector.eval_metric(self.anomalies, self.test, metric=m_func), ) + < delta ) - == 2 - ) - assert ( - len( - detector_1param.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="recall" - ) - ) - == 2 - ) - assert ( - len( - detector_1param.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="f1" - ) - ) - == 2 - ) - assert ( - len( - detector_1param.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="precision" - ) - ) - == 2 - ) - - abs_low_ = detector_1param.low_threshold - abs_high_ = detector_1param.high_threshold - - # detector_1param parameter 'abs_high_' must be equal to 10.83047 when trained - # on the series 'train' for the 1st component - assert abs(abs_high_[0] - 10.83047) < 1e-05 - # detector_1param parameter 'abs_high_' must be equal to 6.47822 when trained - # on the series 'train' for the 2nd component - assert abs(abs_high_[1] - 6.47822) < 1e-05 - - # detector_1param parameter 'abs_low_' must be equal to 9.20248 when trained - # on the series 'train' for the 1st component - assert abs(abs_low_[0] - 9.20248) < 1e-05 - # detector_1param parameter 'abs_low_' must be equal to 3.61853 when trained - # on the series 'train' for the 2nd component - assert abs(abs_low_[1] - 3.61853) < 1e-05 - - # detector_1param must found 37 anomalies on the first component of the series 'test' - assert binary_detection["0"].sum(axis=0).all_values().flatten()[0] == 37 - # detector_1param must found 38 anomalies on the second component of the series 'test' - assert binary_detection["1"].sum(axis=0).all_values().flatten()[0] == 38 - - acc = detector_1param.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="accuracy" - ) - # detector_1param must have an accuracy of 0.58 on the first component of the series 'mts_test' - assert abs(acc[0] - 0.58) < 1e-05 - # detector_1param must have an accuracy of 0.58 on the second component of the series 'mts_test' - assert abs(acc[1] - 0.58) < 1e-05 - precision = detector_1param.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="precision" - ) - # detector_1param must have an precision of 0.08108 on the first component of the series 'mts_test' - assert abs(precision[0] - 0.08108) < 1e-05 - # detector_1param must have an precision of 0.07894 on the second component of the series 'mts_test' - assert abs(precision[1] - 0.07894) < 1e-05 - - recall = detector_1param.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="recall" - ) - # detector_1param must have an recall of 0.2727 on the first component of the series 'mts_test' - assert abs(recall[0] - 0.27272) < 1e-05 - # detector_1param must have an recall of 0.3 on the second component of the series 'mts_test' - assert abs(recall[1] - 0.3) < 1e-05 - - f1 = detector_1param.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="f1" - ) - # detector_1param must have an f1 of 0.125 on the first component of the series 'mts_test' - assert abs(f1[0] - 0.125) < 1e-05 - # detector_1param must have an f1 of 0.125 on the second component of the series 'mts_test' - assert abs(f1[1] - 0.125) < 1e-05 - - # exemple multivariate with Nones - detector = QuantileDetector( - low_quantile=[0.05, None], high_quantile=[None, 0.95] - ) - detector.fit(self.mts_train) + # check the fitted parameters + if isinstance(detector, QuantileDetector): + assert np.abs(fitted_params[0] - detector.low_threshold[0]) < delta + assert np.abs(fitted_params[1] - detector.high_threshold[0]) < delta + + @pytest.mark.parametrize( + "config", + [ + ( + ThresholdDetector, + {"low_threshold": [4.8, 4.8], "high_threshold": [10.5, 10.5]}, + { + "anomalies": [28, 52], + "accuracy": (0.71, 0.48), + "recall": (0.45454, 0.5), + "f1": (0.25641, 0.16129), + "precision": (0.17857, 0.09615), + }, + ), + ( + ThresholdDetector, + {"low_threshold": [10, None], "high_threshold": [None, 5]}, + { + "anomalies": [48, 43], + "accuracy": (0.51, 0.57), + "recall": (0.45454, 0.5), + "f1": (0.16949, 0.18867), + "precision": (0.10416, 0.11627), + }, + ), + ( + QuantileDetector, + {"low_quantile": [0.05, 0.05], "high_quantile": [0.95, 0.95]}, + { + "anomalies": [37, 38], + "accuracy": (0.58, 0.58), + "recall": (0.27272, 0.3), + "f1": (0.125, 0.125), + "precision": (0.08108, 0.07894), + }, + ), + ( + QuantileDetector, + {"low_quantile": [0.05, None], "high_quantile": [None, 0.95]}, + { + "anomalies": [20, 19], + "accuracy": (0.69, 0.75), + "recall": (0.0, 0.2), + "f1": (0.0, 0.13793), + "precision": (0.0, 0.10526), + }, + ), + ( + IQRDetector, + {"scale": [0.5, np.inf]}, + { + "anomalies": [46, 0], + "accuracy": (0.51, 0.9), + "recall": (0.363636, 0.0), + "f1": (0.14035, 0.0), + "precision": (0.08695, 0.0), + }, + ), + ( + IQRDetector, + {"scale": [np.inf, 0.77]}, + { + "anomalies": [0, 34], + "accuracy": (0.89, 0.62), + "recall": (0.0, 0.3), + "f1": (0.0, 0.136363), + "precision": (0.0, 0.08823), + }, + ), + ( + IQRDetector, + {"scale": [0.5, 0.77]}, + { + "anomalies": [46, 34], + "accuracy": (0.51, 0.62), + "recall": (0.363636, 0.3), + "f1": (0.14035, 0.136363), + "precision": (0.08695, 0.08823), + }, + ), + ], + ) + def test_bounded_detector_performance_multivariate(self, config): + """ + TODO: improve these tests to introduce some correlation between actual and detected anomalies + """ + detector_cls, detector_kwargs, expected_values = config + detector = detector_cls(**detector_kwargs) + + if isinstance(detector, FittableDetector): + detector.fit(self.mts_train) binary_detection = detector.detect(self.mts_test) - # width of output must be equal to 2 (same dimension as input) - assert binary_detection.width == 2 - assert ( - len( - detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="accuracy" - ) - ) - == 2 - ) - assert ( - len( - detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="recall" + # output must have the same width as the input + expected_width = self.mts_test.n_components + assert binary_detection.width == expected_width + for m_func in metric_func: + assert ( + len( + detector.eval_metric( + self.mts_anomalies, self.mts_test, metric=m_func + ) ) + == expected_width ) - == 2 - ) + + # check number of anomalies detected in the first component assert ( - len(detector.eval_accuracy(self.mts_anomalies, self.mts_test, metric="f1")) - == 2 + binary_detection["0"].sum(axis=0).all_values().flatten()[0] + == expected_values["anomalies"][0] ) + # check number of anomalies detected in the second component assert ( - len( - detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="precision" - ) - ) - == 2 + binary_detection["1"].sum(axis=0).all_values().flatten()[0] + == expected_values["anomalies"][1] ) - # TODO: we should improve these tests to introduce some correlation - # between actual and detected anomalies... - - # detector must found 20 anomalies on the first component of the series 'test' - # Note: there are 200 values (100 time step x 2 components) so this matches - # well a detection rate of 10% (bottom 5% on first component and top 5% on second component) - assert binary_detection["0"].sum(axis=0).all_values().flatten()[0] == 20 - # detector must found 19 anomalies on the second component of the series 'test' - assert binary_detection["1"].sum(axis=0).all_values().flatten()[0] == 19 - - acc = detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="accuracy" - ) - assert abs(acc[0] - 0.69) < 1e-05 - assert abs(acc[1] - 0.75) < 1e-05 - - precision = detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="precision" - ) - assert abs(precision[0] - 0.0) < 1e-05 - assert abs(precision[1] - 0.10526) < 1e-05 + # check each metric on each component of the series + for m_func in metric_func: + metric_vals = detector.eval_metric( + self.mts_anomalies, self.mts_test, metric=m_func + ) + assert np.abs(expected_values[m_func][0] - metric_vals[0]) < delta + assert np.abs(expected_values[m_func][1] - metric_vals[1]) < delta - recall = detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="recall" - ) - assert abs(recall[0] - 0.0) < 1e-05 - assert abs(recall[1] - 0.2) < 1e-05 + def test_fit_detect(self): + """Calling fit() then detect() and fit_detect() should yield the same results""" + detector1 = QuantileDetector(low_quantile=0.05, high_quantile=0.95) + detector1.fit(self.train) + prediction1 = detector1.detect(self.train) - f1 = detector.eval_accuracy(self.mts_anomalies, self.mts_test, metric="f1") - assert abs(f1[0] - 0.0) < 1e-05 - assert abs(f1[1] - 0.13793) < 1e-05 + detector2 = QuantileDetector(low_quantile=0.05, high_quantile=0.95) + prediction2 = detector2.fit_detect(self.train) - def test_ThresholdDetector(self): + assert prediction1 == prediction2 - # Parameters - # Need to have at least one parameter (low, high) not None + def test_IQRDetector_constructor(self): + # Numbers in `scale must be non-negative numbers with pytest.raises(ValueError): - ThresholdDetector() + IQRDetector(scale=-1) with pytest.raises(ValueError): - ThresholdDetector(low_threshold=None, high_threshold=None) - - # if high and low are both sequences of length>1, they must be of the same size + IQRDetector(scale=[-2]) with pytest.raises(ValueError): - ThresholdDetector(low_threshold=[0.2, 0.1], high_threshold=[0.95, 0.8, 0.9]) + IQRDetector(scale=[3, -4]) with pytest.raises(ValueError): - ThresholdDetector(low_threshold=[0.2, 0.1, 0.7], high_threshold=[0.95, 0.8]) + IQRDetector(scale="3") - # Parameter high must be higher or equal than parameter low - with pytest.raises(ValueError): - ThresholdDetector(low_threshold=0.7, high_threshold=0.2) - with pytest.raises(ValueError): - ThresholdDetector(low_threshold=[0.2, 0.9], high_threshold=[0.95, 0.1]) - with pytest.raises(ValueError): - ThresholdDetector(low_threshold=0.2, high_threshold=[0.95, 0.1]) - with pytest.raises(ValueError): - ThresholdDetector(low_threshold=[0.2, 0.9], high_threshold=0.8) - with pytest.raises(ValueError): - ThresholdDetector(low_threshold=[0.2, 0.9, None], high_threshold=0.8) + IQRDetector() + IQRDetector(scale=1.2345) + IQRDetector(scale=0) + IQRDetector(scale=[1, 2, np.inf, 3, 0]) - # Parameter high/low cannot be sequence of only None - with pytest.raises(ValueError): - ThresholdDetector(low_threshold=[None, None, None]) - with pytest.raises(ValueError): - ThresholdDetector(high_threshold=[None, None, None]) - with pytest.raises(ValueError): - ThresholdDetector(low_threshold=[None], high_threshold=[None, None, None]) + def test_iqr_detector_fit_detect_matching_width(self): + """Widths of series should match the number of values given for `scale`, + if more than one value is provided. - # widths of series used for scoring must match the number of values given for high or/and low, - # if high and low have a length higher than 1 + `self.train` series has only one component whereas model is created with 2/3 values""" + detector = IQRDetector(scale=[1.5, 1.5]) - detector = ThresholdDetector(low_threshold=0.1, high_threshold=[0.8, 0.7]) + # during training with pytest.raises(ValueError): - detector.detect(self.train) + detector.fit(self.train) with pytest.raises(ValueError): - detector.detect([self.train, self.mts_train]) + detector.fit([self.train, self.mts_train]) - detector = ThresholdDetector( - low_threshold=[0.1, 0.2], high_threshold=[0.8, 0.9] - ) + # during detection + detector.fit(self.mts_train) with pytest.raises(ValueError): detector.detect(self.train) with pytest.raises(ValueError): detector.detect([self.train, self.mts_train]) - detector = ThresholdDetector(low_threshold=[0.1, 0.2], high_threshold=0.8) - with pytest.raises(ValueError): - detector.detect(self.train) + # single `scale` but fit to wrong widths + detector = IQRDetector(scale=1.5) + detector.fit(self.train) with pytest.raises(ValueError): - detector.detect([self.train, self.mts_train]) + detector.detect(self.mts_train) - detector = ThresholdDetector(low_threshold=[0.1, 0.2]) + detector = IQRDetector(scale=1.5) + detector.fit(self.mts_train) with pytest.raises(ValueError): detector.detect(self.train) - with pytest.raises(ValueError): - detector.detect([self.train, self.mts_train]) - detector = ThresholdDetector(high_threshold=[0.1, 0.2]) + detector = IQRDetector(scale=[1.5]) + detector.fit(self.mts_train) with pytest.raises(ValueError): detector.detect(self.train) - with pytest.raises(ValueError): - detector.detect([self.train, self.mts_train]) - detector = ThresholdDetector(low_threshold=9.5, high_threshold=10.5) - binary_detection = detector.detect(self.test) + # Test if the IQR detector is actually using the IQR algorithm + def test_iqr_detector_fit_logic(self): + # concatenate everything along the time axis + np_series = self.train.all_values(copy=False) - # Return of .detect() must be binary - np.testing.assert_array_equal( - binary_detection.values(copy=False), - binary_detection.values(copy=False).astype(bool), - ) + q1 = np.quantile(np_series, q=0.25) + q3 = np.quantile(np_series, q=0.75) - # Return of .detect() must be same len as input - assert len(binary_detection) == len(self.test) + # With scale=0 it should detect only outside the IQR + detector = IQRDetector(scale=0) + detector.fit(self.train) - # univariate test - # detector must found 58 anomalies in the series 'test' - assert binary_detection.sum(axis=0).all_values().flatten()[0] == 58 - # detector must have an accuracy of 0.41 for the series 'test' - assert ( - abs( - detector.eval_accuracy(self.anomalies, self.test, metric="accuracy") - - 0.41 - ) - < 1e-05 - ) - # detector must have an recall of 0.4 for the series 'test' - assert ( - abs( - detector.eval_accuracy(self.anomalies, self.test, metric="recall") - 0.4 - ) - < 1e-05 - ) - # detector must have an f1 of 0.06349 for the series 'test' - assert ( - abs( - detector.eval_accuracy(self.anomalies, self.test, metric="f1") - 0.06349 - ) - < 1e-05 - ) - # detector must have an precision of 0.03448 for the series 'test' - assert ( - abs( - detector.eval_accuracy(self.anomalies, self.test, metric="precision") - - 0.03448 - ) - < 1e-05 - ) + assert np.abs(detector.detector.low_threshold - q1) < delta + assert np.abs(detector.detector.high_threshold - q3) < delta - # multivariate test - detector = ThresholdDetector(low_threshold=4.8, high_threshold=10.5) - binary_detection = detector.detect(self.mts_test) + # With larger scale it should add "padding" accordingly + detector = IQRDetector(scale=0.5) + detector.fit(self.train) - # if two values are given for low and high, and a series of width 2 is given, - # then the results must be the same as a detector that was given only one value - # for low and high. (will duplicate the value for each width) - detector_2param = ThresholdDetector( - low_threshold=[4.8, 4.8], high_threshold=[10.5, 10.5] - ) - binary_detection_2param = detector_2param.detect(self.mts_test) - assert binary_detection == binary_detection_2param + assert detector.detector.low_threshold < q1 + assert detector.detector.high_threshold > q3 - # width of output must be equal to 2 (same dimension as input) - assert binary_detection.width == 2 - assert ( - len( - detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="accuracy" - ) - ) - == 2 - ) - assert ( - len( - detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="recall" - ) - ) - == 2 - ) - assert ( - len(detector.eval_accuracy(self.mts_anomalies, self.mts_test, metric="f1")) - == 2 - ) - assert ( - len( - detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="precision" - ) - ) - == 2 - ) + def test_iqr_detector_detect_logic(self): + np.random.seed(24) - # detector must found 28 anomalies on the first width of the series 'test' - assert binary_detection["0"].sum(axis=0).all_values().flatten()[0] == 28 - # detector must found 52 anomalies on the second width of the series 'test' - assert binary_detection["1"].sum(axis=0).all_values().flatten()[0] == 52 + values = np.random.uniform(low=0, high=10, size=30) + nice_ts = TimeSeries.from_values(values) - acc = detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="accuracy" - ) - # detector must have an accuracy of 0.71 on the first width of the series 'mts_test' - assert abs(acc[0] - 0.71) < 1e-05 - # detector must have an accuracy of 0.48 on the second width of the series 'mts_test' - assert abs(acc[1] - 0.48) < 1e-05 + np_series = nice_ts.all_values(copy=False) + q1 = np.quantile(np_series, q=0.25) + q3 = np.quantile(np_series, q=0.75) - precision = detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="precision" - ) - # detector must have an precision of 0.17857 on the first width of the series 'mts_test' - assert abs(precision[0] - 0.17857) < 1e-05 - # detector must have an precision of 0.09615 on the second width of the series 'mts_test' - assert abs(precision[1] - 0.09615) < 1e-05 + diff = q3 - q1 + scale = 0.5 + expected_low_threshold = q1 - diff * scale + expected_high_threshold = q3 + diff * scale - recall = detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="recall" - ) - # detector must have an recall of 0.45454 on the first width of the series 'mts_test' - assert abs(recall[0] - 0.45454) < 1e-05 - # detector must have an recall of 0.5 on the second width of the series 'mts_test' - assert abs(recall[1] - 0.5) < 1e-05 - - f1 = detector.eval_accuracy(self.mts_anomalies, self.mts_test, metric="f1") - # detector must have an f1 of 0.25641 on the first width of the series 'mts_test' - assert abs(f1[0] - 0.25641) < 1e-05 - # detector must have an f1 of 0.16129 on the second width of the series 'mts_test' - assert abs(f1[1] - 0.16129) < 1e-05 - - # exemple multivariate with Nones - detector = ThresholdDetector(low_threshold=[10, None], high_threshold=[None, 5]) - binary_detection = detector.detect(self.mts_test) + expected_anomalies = 10 + expected_not_anomalies = 20 - # width of output must be equal to 2 (same dimension as input) - assert binary_detection.width == 2 - assert ( - len( - detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="accuracy" - ) - ) - == 2 + not_anomalies = np.random.uniform( + low=expected_low_threshold + delta, + high=expected_high_threshold - delta, + size=expected_not_anomalies, ) - assert ( - len( - detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="recall" - ) - ) - == 2 + anomalies_high = np.random.uniform( + low=expected_high_threshold + delta, + high=expected_high_threshold + 10, + size=expected_anomalies // 2, ) - assert ( - len(detector.eval_accuracy(self.mts_anomalies, self.mts_test, metric="f1")) - == 2 - ) - assert ( - len( - detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="precision" - ) - ) - == 2 + anomalies_low = np.random.uniform( + low=expected_low_threshold - 10, + high=expected_low_threshold - delta, + size=expected_anomalies // 2, ) + anomalous_arr = np.hstack((anomalies_high, not_anomalies, anomalies_low)) + anomalous_ts = TimeSeries.from_values(anomalous_arr) - # detector must found 48 anomalies on the first width of the series 'test' - assert binary_detection["0"].sum(axis=0).all_values().flatten()[0] == 48 - # detector must found 43 anomalies on the second width of the series 'test' - assert binary_detection["1"].sum(axis=0).all_values().flatten()[0] == 43 + detector = IQRDetector(scale=scale) + detector.fit(nice_ts) - acc = detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="accuracy" - ) - # detector must have an accuracy of 0.51 on the first width of the series 'mts_test' - assert abs(acc[0] - 0.51) < 1e-05 - # detector must have an accuracy of 0.57 on the second width of the series 'mts_test' - assert abs(acc[1] - 0.57) < 1e-05 - - precision = detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="precision" + assert ( + np.abs(detector.detector.low_threshold[0] - expected_low_threshold) < delta ) - # detector must have an precision of 0.10416 and on the first width of the series 'mts_test' - assert abs(precision[0] - 0.10416) < 1e-05 - # detector must have an precision of 0.11627 on the second width of the series 'mts_test' - assert abs(precision[1] - 0.11627) < 1e-05 - - recall = detector.eval_accuracy( - self.mts_anomalies, self.mts_test, metric="recall" + assert ( + np.abs(detector.detector.high_threshold[0] - expected_high_threshold) + < delta ) - # detector must have an recall of 0.45454 on the first width of the series 'mts_test' - assert abs(recall[0] - 0.45454) < 1e-05 - # detector must have an recall of 0.5 on the second width of the series 'mts_test' - assert abs(recall[1] - 0.5) < 1e-05 - f1 = detector.eval_accuracy(self.mts_anomalies, self.mts_test, metric="f1") - # detector must have an f1 of 0.16949 on the first width of the series 'mts_test' - assert abs(f1[0] - 0.16949) < 1e-05 - # detector must have an f1 of 0.18867 on the second width of the series 'mts_test' - assert abs(f1[1] - 0.18867) < 1e-05 - - def test_fit_detect(self): + detection = detector.detect(anomalous_ts) - detector1 = QuantileDetector(low_quantile=0.05, high_quantile=0.95) - detector1.fit(self.train) - prediction1 = detector1.detect(self.train) - - detector2 = QuantileDetector(low_quantile=0.05, high_quantile=0.95) - prediction2 = detector2.fit_detect(self.train) - - assert prediction1 == prediction2 + assert detection.sum(axis=0).all_values().flatten()[0] == expected_anomalies diff --git a/darts/tests/ad/test_evaluation.py b/darts/tests/ad/test_evaluation.py new file mode 100644 index 0000000000..42b62b5a13 --- /dev/null +++ b/darts/tests/ad/test_evaluation.py @@ -0,0 +1,171 @@ +import itertools + +import numpy as np +import pandas as pd +import pytest + +from darts import TimeSeries +from darts.ad.utils import eval_metric_from_binary_prediction, eval_metric_from_scores + + +class TestAnomalyDetectionModel: + np.random.seed(42) + + # univariate series + ts_uv = TimeSeries.from_times_and_values( + values=np.array([0.0, 1.0, 0.0, 0.0, 1.0, 1.0]), + times=pd.date_range("2000-01-01", freq="D", periods=6), + ) + # multivariate series + ts_mv = ts_uv.stack( + TimeSeries.from_times_and_values( + values=np.array([1.0, 0.0, 1.0, 1.0, 0.0, 0.0]), + times=pd.date_range("2000-01-01", freq="D", periods=6), + ) + ) + # series with integer index + ts_uv_idx = TimeSeries.from_values(ts_uv.values(copy=False)) + ts_mv_idx = TimeSeries.from_values(ts_mv.values(copy=False)) + + @pytest.mark.parametrize( + "config", + itertools.product( + [ + ("AUC_ROC", (1.0, 0.0, 0.5)), + ("AUC_PR", (1.0, 0.5, 0.5)), + ], + [ + # ts_uv, + ts_mv, + # ts_uv_idx, + ts_mv_idx, + ], + [False, True], + ), + ) + def test_eval_pred_scores(self, config): + (metric, scores_exp), series, series_as_list = config + is_multivariate = series.width > 1 + + # the inverse of the binary anomalies will have 0. accuracy + inv_series = TimeSeries.from_times_and_values( + values=~series.values().astype(bool), times=series.time_index + ) + + # average (0.5) scores + med_vals = inv_series.values(copy=True) + med_vals[:] = 0.5 + med_series = TimeSeries.from_times_and_values( + values=med_vals, times=series.time_index + ) + + series = [series] if series_as_list else series + inv_series = [inv_series] if series_as_list else inv_series + med_series = [med_series] if series_as_list else med_series + + def check_metric(series, pred_series, metric, sc_exp): + score = eval_metric_from_scores( + anomalies=series, pred_scores=pred_series, metric=metric + ) + score = score if series_as_list else [score] + assert isinstance(score, list) and len(score) == 1 + score = score[0] + if not is_multivariate: + assert isinstance(score, float) + assert score == sc_exp + else: + assert isinstance(score, list) and score == [sc_exp] * 2 + + # perfect predictions + check_metric(series, series, metric, scores_exp[0]) + + # worst predictions + check_metric(series, inv_series, metric, scores_exp[1]) + + # 0.5 predictions + check_metric(series, med_series, metric, scores_exp[2]) + + # actual series must be binary + with pytest.raises(ValueError) as err: + check_metric(med_series, series, metric, scores_exp[2]) + assert str(err.value).startswith( + "Input series `anomalies` must have binary values only." + ) + + # wrong metric + with pytest.raises(ValueError) as err: + check_metric(series, med_series, "recall", scores_exp[2]) + assert str(err.value).startswith("Argument `metric` must be one of ") + + @pytest.mark.parametrize( + "config", + itertools.product( + [ + ("precision", (1.0, 0.0, 0.5)), + ("recall", (1.0, 0.0, 0.5)), + ("f1", (1.0, 0.0, 0.5)), + ("accuracy", (1.0, 0.0, 0.5)), + ], + [ts_uv, ts_mv, ts_uv_idx, ts_mv_idx], + [False, True], + ), + ) + def test_eval_pred_binary(self, config): + (metric, scores_exp), series, series_as_list = config + is_multivariate = series.width > 1 + + # the inverse of the binary anomalies will have 0. accuracy + inv_series = TimeSeries.from_times_and_values( + values=~series.values().astype(bool), times=series.time_index + ) + + # average (0.5) scores + med_vals = inv_series.values(copy=True) + med_vals[:] = 0.5 + med_series = TimeSeries.from_times_and_values( + values=med_vals, times=series.time_index + ) + + series = [series] if series_as_list else series + inv_series = [inv_series] if series_as_list else inv_series + med_series = [med_series] if series_as_list else med_series + + def check_metric(series, pred_series, metric, sc_exp): + score = eval_metric_from_binary_prediction( + anomalies=series, + pred_anomalies=pred_series, + metric=metric, + ) + score = score if series_as_list else [score] + assert isinstance(score, list) and len(score) == 1 + score = score[0] + if not is_multivariate: + assert isinstance(score, float) + assert score == sc_exp + else: + assert isinstance(score, list) and score == [sc_exp] * 2 + + # perfect predictions + check_metric(series, series, metric, scores_exp[0]) + + # worst predictions + check_metric(series, inv_series, metric, scores_exp[1]) + + # actual series must be binary + with pytest.raises(ValueError) as err: + check_metric(med_series, series, metric, scores_exp[2]) + assert str(err.value).startswith( + "Input series `anomalies` must have binary values only." + ) + + # pred must be binary + with pytest.raises(ValueError) as err: + check_metric(series, med_series, metric, scores_exp[2]) + assert str(err.value).startswith( + "Input series `pred_anomalies` must have binary values only." + ) + + # wrong metric + with pytest.raises(ValueError) as err: + check_metric(series, med_series, "AUC_ROC", scores_exp[2]) + assert str(err.value).startswith("Argument `metric` must be one of ") diff --git a/darts/tests/ad/test_scorers.py b/darts/tests/ad/test_scorers.py index 50afbe83b4..bc3d79e1d5 100644 --- a/darts/tests/ad/test_scorers.py +++ b/darts/tests/ad/test_scorers.py @@ -1,4 +1,5 @@ -from typing import Sequence +from collections.abc import Sequence +from itertools import product import numpy as np import pytest @@ -6,22 +7,28 @@ from pyod.models.knn import KNN from scipy.stats import cauchy, expon, gamma, laplace, norm, poisson -from darts import TimeSeries -from darts.ad.scorers import CauchyNLLScorer -from darts.ad.scorers import DifferenceScorer as Difference +from darts import TimeSeries, metrics from darts.ad.scorers import ( + CauchyNLLScorer, ExponentialNLLScorer, + FittableAnomalyScorer, GammaNLLScorer, GaussianNLLScorer, KMeansScorer, LaplaceNLLScorer, + PoissonNLLScorer, + PyODScorer, + WassersteinScorer, ) +from darts.ad.scorers import DifferenceScorer as Difference from darts.ad.scorers import NormScorer as Norm -from darts.ad.scorers import PoissonNLLScorer, PyODScorer, WassersteinScorer +from darts.ad.scorers.scorers import NLLScorer from darts.models import MovingAverageFilter +from darts.utils.timeseries_generation import linear_timeseries list_NonFittableAnomalyScorer = [ - Norm(), + Norm(component_wise=False), + Norm(component_wise=True), Difference(), GaussianNLLScorer(), ExponentialNLLScorer(), @@ -32,23 +39,67 @@ ] list_FittableAnomalyScorer = [ - PyODScorer(model=KNN()), - KMeansScorer(), - WassersteinScorer(), + (PyODScorer, {"model": KNN(), "component_wise": False}), + (KMeansScorer, {"component_wise": False}), + (WassersteinScorer, {"window_agg": False, "component_wise": False}), ] +# (scorer_cls, values, distribution, distribution_kwargs, prob_density_func, prob_density_func) list_NLLScorer = [ - GaussianNLLScorer(), - ExponentialNLLScorer(), - PoissonNLLScorer(), - LaplaceNLLScorer(), - CauchyNLLScorer(), - GammaNLLScorer(), + ( + CauchyNLLScorer, + [3, 2, 0.5, 0.9], + np.random.standard_cauchy, + {}, + cauchy.pdf, + None, + ), + ( + ExponentialNLLScorer, + [3, 0.1, 2, 0.01], + np.random.exponential, + {"scale": 2.0}, + expon.pdf, + None, + ), + ( + GammaNLLScorer, + [3, 0.1, 2, 0.5], + np.random.gamma, + {"shape": 2, "scale": 2}, + gamma.pdf, + {"a": 2, "scale": 2}, + ), + ( + GaussianNLLScorer, + [3, 0.1, -2, 0.01], + np.random.normal, + {"loc": 0, "scale": 2}, + norm.pdf, + None, + ), + ( + LaplaceNLLScorer, + [3, 10, -2, 0.01], + np.random.laplace, + {"loc": 0, "scale": 2}, + laplace.pdf, + None, + ), + ( + PoissonNLLScorer, + [3, 2, 10, 1], + np.random.poisson, + {"lam": 1}, + poisson.pmf, + {"mu": 1}, + ), ] +delta = 1e-05 -class TestADAnomalyScorer: +class TestAnomalyDetectionScorer: np.random.seed(42) # univariate series @@ -101,37 +152,53 @@ class TestADAnomalyScorer: mts_train._time_index, np_mts_probabilistic ) - def test_ScoreNonFittableAnomalyScorer(self): - scorer = Norm() + @pytest.mark.parametrize("scorer", list_NonFittableAnomalyScorer) + def test_score_from_pred_non_fittable_scorer(self, scorer): + # NLLScorer require deterministic `series` + if isinstance(scorer, NLLScorer): + # series and pred_series are both deterministic + with pytest.raises(ValueError): + scorer.score_from_prediction(series=self.test, pred_series=self.test) + # series is probabilistic, pred_series is deterministic + with pytest.raises(ValueError): + scorer.score_from_prediction( + series=self.probabilistic, pred_series=self.train + ) - # Check return types for score_from_prediction() - # Check if return type is float when input is a series - assert isinstance( - scorer.score_from_prediction(self.test, self.modified_test), TimeSeries - ) + score = scorer.score_from_prediction( + series=self.train, pred_series=self.probabilistic + ) + assert isinstance(score, TimeSeries) + assert score.all_values().shape == (len(self.train), 1, 1) + else: + # Check if return type is float when input is a series + assert isinstance( + scorer.score_from_prediction(self.test, self.modified_test), TimeSeries + ) - # Check if return type is Sequence when input is a Sequence of series - assert isinstance( - scorer.score_from_prediction([self.test], [self.modified_test]), - Sequence, - ) + # Check if return type is Sequence when input is a Sequence of series + assert isinstance( + scorer.score_from_prediction([self.test], [self.modified_test]), + Sequence, + ) - # Check if return type is Sequence when input is a multivariate series - assert isinstance( - scorer.score_from_prediction(self.mts_test, self.modified_mts_test), - TimeSeries, - ) + # Check if return type is Sequence when input is a multivariate series + assert isinstance( + scorer.score_from_prediction(self.mts_test, self.modified_mts_test), + TimeSeries, + ) - # Check if return type is Sequence when input is a multivariate series - assert isinstance( - scorer.score_from_prediction([self.mts_test], [self.modified_mts_test]), - Sequence, - ) + # Check if return type is Sequence when input is a multivariate series + assert isinstance( + scorer.score_from_prediction([self.mts_test], [self.modified_mts_test]), + Sequence, + ) - def test_ScoreFittableAnomalyScorer(self): - scorer = KMeansScorer() + @pytest.mark.parametrize("scorer_config", list_FittableAnomalyScorer) + def test_score_return_type(self, scorer_config): + scorer_cls, scorer_kwargs = scorer_config + scorer = scorer_cls(**scorer_kwargs) - # Check return types for score() scorer.fit(self.train) # Check if return type is float when input is a series assert isinstance(scorer.score(self.test), TimeSeries) @@ -172,414 +239,401 @@ def test_ScoreFittableAnomalyScorer(self): Sequence, ) - def test_eval_accuracy_from_prediction(self): - + def test_eval_metric_from_prediction_return_type(self): scorer = Norm(component_wise=False) - # Check return types # Check if return type is float when input is a series assert isinstance( - scorer.eval_accuracy_from_prediction( + scorer.eval_metric_from_prediction( self.anomalies, self.test, self.modified_test ), float, ) - # Check if return type is Sequence when input is a Sequence of series assert isinstance( - scorer.eval_accuracy_from_prediction( + scorer.eval_metric_from_prediction( self.anomalies, [self.test], self.modified_test ), Sequence, ) - - # Check if return type is a float when input is a multivariate series and component_wise is set to False + # Check if return type is a float when input is a multivariate series and component_wise is set to `False` assert isinstance( - scorer.eval_accuracy_from_prediction( + scorer.eval_metric_from_prediction( self.anomalies, self.mts_test, self.modified_mts_test ), float, ) - - # Check if return type is Sequence when input is a multivariate series and component_wise is set to False + # Check if return type is Sequence when input is a multivariate series and component_wise is set to `False` assert isinstance( - scorer.eval_accuracy_from_prediction( + scorer.eval_metric_from_prediction( self.anomalies, [self.mts_test], self.modified_mts_test ), Sequence, ) scorer = Norm(component_wise=True) - # Check return types - # Check if return type is float when input is a series - assert isinstance( - scorer.eval_accuracy_from_prediction( - self.anomalies, self.test, self.modified_test - ), - float, - ) - - # Check if return type is Sequence when input is a Sequence of series - assert isinstance( - scorer.eval_accuracy_from_prediction( - self.anomalies, [self.test], self.modified_test - ), - Sequence, - ) - - # Check if return type is a float when input is a multivariate series and component_wise is set to True + # Check if return type is a float when input is a multivariate series and component_wise is set to `True` assert isinstance( - scorer.eval_accuracy_from_prediction( + scorer.eval_metric_from_prediction( self.mts_anomalies, self.mts_test, self.modified_mts_test ), Sequence, ) - - # Check if return type is Sequence when input is a multivariate series and component_wise is set to True + # Check if return type is Sequence when input is a multivariate series and component_wise is set to `True` assert isinstance( - scorer.eval_accuracy_from_prediction( + scorer.eval_metric_from_prediction( self.mts_anomalies, [self.mts_test], self.modified_mts_test ), Sequence, ) - non_fittable_scorer = Norm(component_wise=False) - fittable_scorer = KMeansScorer(component_wise=False) + @pytest.mark.parametrize("scorer_config", list_FittableAnomalyScorer) + def test_eval_metric_fittable_scorer(self, scorer_config): + scorer_cls, scorer_kwargs = scorer_config + fittable_scorer = scorer_cls(**scorer_kwargs) fittable_scorer.fit(self.train) - # if component_wise set to False, 'actual_anomalies' must have widths of 1 + # if component_wise set to False, 'anomalies' must have widths of 1 with pytest.raises(ValueError): - fittable_scorer.eval_accuracy( - actual_anomalies=self.mts_anomalies, series=self.test - ) + fittable_scorer.eval_metric(anomalies=self.mts_anomalies, series=self.test) with pytest.raises(ValueError): - fittable_scorer.eval_accuracy( - actual_anomalies=[self.anomalies, self.mts_anomalies], + fittable_scorer.eval_metric( + anomalies=[self.anomalies, self.mts_anomalies], series=[self.test, self.test], ) # 'metric' must be str and "AUC_ROC" or "AUC_PR" with pytest.raises(ValueError): - fittable_scorer.eval_accuracy( - actual_anomalies=self.anomalies, series=self.test, metric=1 + fittable_scorer.eval_metric( + anomalies=self.anomalies, series=self.test, metric=1 ) with pytest.raises(ValueError): - fittable_scorer.eval_accuracy( - actual_anomalies=self.anomalies, series=self.test, metric="auc_roc" + fittable_scorer.eval_metric( + anomalies=self.anomalies, + series=self.test, + metric="auc_roc", ) with pytest.raises(TypeError): - fittable_scorer.eval_accuracy( - actual_anomalies=self.anomalies, series=self.test, metric=["AUC_ROC"] + fittable_scorer.eval_metric( + anomalies=self.anomalies, + series=self.test, + metric=["AUC_ROC"], ) - # 'actual_anomalies' must be binary + # 'anomalies' must be binary with pytest.raises(ValueError): - fittable_scorer.eval_accuracy(actual_anomalies=self.test, series=self.test) + fittable_scorer.eval_metric(anomalies=self.test, series=self.test) - # 'actual_anomalies' must contain anomalies (at least one) + # 'anomalies' must contain anomalies (at least one) with pytest.raises(ValueError): - fittable_scorer.eval_accuracy( - actual_anomalies=self.only_0_anomalies, series=self.test + fittable_scorer.eval_metric( + anomalies=self.only_0_anomalies, series=self.test ) - # 'actual_anomalies' cannot contain only anomalies + # 'anomalies' cannot contain only anomalies with pytest.raises(ValueError): - fittable_scorer.eval_accuracy( - actual_anomalies=self.only_1_anomalies, series=self.test + fittable_scorer.eval_metric( + anomalies=self.only_1_anomalies, series=self.test ) - # 'actual_anomalies' must match the number of series if length higher than 1 + # 'anomalies' must match the number of series if length higher than 1 with pytest.raises(ValueError): - fittable_scorer.eval_accuracy( - actual_anomalies=[self.anomalies, self.anomalies], series=self.test + fittable_scorer.eval_metric( + anomalies=[self.anomalies, self.anomalies], + series=self.test, ) with pytest.raises(ValueError): - fittable_scorer.eval_accuracy( - actual_anomalies=[self.anomalies, self.anomalies], + fittable_scorer.eval_metric( + anomalies=[self.anomalies, self.anomalies], series=[self.test, self.test, self.test], ) - # 'actual_anomalies' must have non empty intersection with 'series' + # 'anomalies' must have non empty intersection with 'series' with pytest.raises(ValueError): - fittable_scorer.eval_accuracy( - actual_anomalies=self.anomalies[:20], series=self.test[30:] + fittable_scorer.eval_metric( + anomalies=self.anomalies[:20], series=self.test[30:] ) with pytest.raises(ValueError): - fittable_scorer.eval_accuracy( - actual_anomalies=[self.anomalies, self.anomalies[:20]], + fittable_scorer.eval_metric( + anomalies=[self.anomalies, self.anomalies[:20]], series=[self.test, self.test[40:]], ) - for scorer in [non_fittable_scorer, fittable_scorer]: - - # name must be of type str - assert type(scorer.__str__()) == str - - # 'metric' must be str and "AUC_ROC" or "AUC_PR" - with pytest.raises(ValueError): - fittable_scorer.eval_accuracy_from_prediction( - actual_anomalies=self.anomalies, - actual_series=self.test, - pred_series=self.modified_test, - metric=1, - ) - with pytest.raises(ValueError): - fittable_scorer.eval_accuracy_from_prediction( - actual_anomalies=self.anomalies, - actual_series=self.test, - pred_series=self.modified_test, - metric="auc_roc", - ) - with pytest.raises(TypeError): - fittable_scorer.eval_accuracy_from_prediction( - actual_anomalies=self.anomalies, - actual_series=self.test, - pred_series=self.modified_test, - metric=["AUC_ROC"], - ) - - # 'actual_anomalies' must be binary - with pytest.raises(ValueError): - scorer.eval_accuracy_from_prediction( - actual_anomalies=self.test, - actual_series=self.test, - pred_series=self.modified_test, - ) + @pytest.mark.parametrize( + "scorer", [Norm(component_wise=False), KMeansScorer(component_wise=False)] + ) + def test_eval_metric_from_prediction(self, scorer): + if isinstance(scorer, FittableAnomalyScorer): + scorer.fit(self.train) - # 'actual_anomalies' must contain anomalies (at least one) - with pytest.raises(ValueError): - scorer.eval_accuracy_from_prediction( - actual_anomalies=self.only_0_anomalies, - actual_series=self.test, - pred_series=self.modified_test, - ) + # name must be of type str + assert isinstance(scorer.__str__(), str) - # 'actual_anomalies' cannot contain only anomalies - with pytest.raises(ValueError): - scorer.eval_accuracy_from_prediction( - actual_anomalies=self.only_1_anomalies, - actual_series=self.test, - pred_series=self.modified_test, - ) + # 'metric' must be str and "AUC_ROC" or "AUC_PR" + with pytest.raises(ValueError): + scorer.eval_metric_from_prediction( + anomalies=self.anomalies, + series=self.test, + pred_series=self.modified_test, + metric=1, + ) + with pytest.raises(ValueError): + scorer.eval_metric_from_prediction( + anomalies=self.anomalies, + series=self.test, + pred_series=self.modified_test, + metric="auc_roc", + ) + with pytest.raises(TypeError): + scorer.eval_metric_from_prediction( + anomalies=self.anomalies, + series=self.test, + pred_series=self.modified_test, + metric=["AUC_ROC"], + ) - # 'actual_anomalies' must match the number of series if length higher than 1 - with pytest.raises(ValueError): - scorer.eval_accuracy_from_prediction( - actual_anomalies=[self.anomalies, self.anomalies], - actual_series=[self.test, self.test, self.test], - pred_series=[ - self.modified_test, - self.modified_test, - self.modified_test, - ], - ) - with pytest.raises(ValueError): - scorer.eval_accuracy_from_prediction( - actual_anomalies=[self.anomalies, self.anomalies], - actual_series=self.test, - pred_series=self.modified_test, - ) + # 'anomalies' must be binary + with pytest.raises(ValueError): + scorer.eval_metric_from_prediction( + anomalies=self.test, + series=self.test, + pred_series=self.modified_test, + ) - # 'actual_anomalies' must have non empty intersection with 'actual_series' and 'pred_series' - with pytest.raises(ValueError): - scorer.eval_accuracy_from_prediction( - actual_anomalies=self.anomalies[:20], - actual_series=self.test[30:], - pred_series=self.modified_test[30:], - ) - with pytest.raises(ValueError): - scorer.eval_accuracy_from_prediction( - actual_anomalies=[self.anomalies, self.anomalies[:20]], - actual_series=[self.test, self.test[40:]], - pred_series=[self.modified_test, self.modified_test[40:]], - ) + # 'anomalies' must contain anomalies (at least one) + with pytest.raises(ValueError): + scorer.eval_metric_from_prediction( + anomalies=self.only_0_anomalies, + series=self.test, + pred_series=self.modified_test, + ) - def test_NonFittableAnomalyScorer(self): + # 'anomalies' cannot contain only anomalies + with pytest.raises(ValueError): + scorer.eval_metric_from_prediction( + anomalies=self.only_1_anomalies, + series=self.test, + pred_series=self.modified_test, + ) - for scorer in list_NonFittableAnomalyScorer: - # Check if trainable is False, being a NonFittableAnomalyScorer - assert not scorer.trainable + # 'anomalies' must match the number of series if length higher than 1 + with pytest.raises(ValueError): + scorer.eval_metric_from_prediction( + anomalies=[self.anomalies, self.anomalies], + series=[self.test, self.test, self.test], + pred_series=[ + self.modified_test, + self.modified_test, + self.modified_test, + ], + ) + with pytest.raises(ValueError): + scorer.eval_metric_from_prediction( + anomalies=[self.anomalies, self.anomalies], + series=self.test, + pred_series=self.modified_test, + ) - # checks for score_from_prediction() - # input must be Timeseries or sequence of Timeseries - with pytest.raises(ValueError): - scorer.score_from_prediction(self.train, "str") - with pytest.raises(ValueError): - scorer.score_from_prediction( - [self.train, self.train], [self.modified_train, "str"] - ) - # score on sequence with series that have different width - with pytest.raises(ValueError): - scorer.score_from_prediction(self.train, self.modified_mts_train) - # input sequences have different length - with pytest.raises(ValueError): - scorer.score_from_prediction( - [self.train, self.train], [self.modified_train] - ) - # two inputs must have a non zero intersection - with pytest.raises(ValueError): - scorer.score_from_prediction(self.train[:50], self.train[55:]) - # every pairwise element must have a non zero intersection - with pytest.raises(ValueError): - scorer.score_from_prediction( - [self.train, self.train[:50]], [self.train, self.train[55:]] - ) + # 'anomalies' must have non empty intersection with 'series' and 'pred_series' + with pytest.raises(ValueError): + scorer.eval_metric_from_prediction( + anomalies=self.anomalies[:20], + series=self.test[30:], + pred_series=self.modified_test[30:], + ) + with pytest.raises(ValueError): + scorer.eval_metric_from_prediction( + anomalies=[self.anomalies, self.anomalies[:20]], + series=[self.test, self.test[40:]], + pred_series=[self.modified_test, self.modified_test[40:]], + ) - def test_FittableAnomalyScorer(self): + @pytest.mark.parametrize("scorer", list_NonFittableAnomalyScorer) + def test_NonFittableAnomalyScorer(self, scorer): + # Check if trainable is False, being a NonFittableAnomalyScorer + assert not scorer.is_trainable - for scorer in list_FittableAnomalyScorer: + # checks for score_from_prediction() + # input must be Timeseries or sequence of Timeseries + with pytest.raises(ValueError): + scorer.score_from_prediction(self.train, "str") + with pytest.raises(ValueError): + scorer.score_from_prediction( + [self.train, self.train], [self.modified_train, "str"] + ) + # score on sequence with series that have different width + with pytest.raises(ValueError): + scorer.score_from_prediction(self.train, self.modified_mts_train) + # input sequences have different length + with pytest.raises(ValueError): + scorer.score_from_prediction( + [self.train, self.train], [self.modified_train] + ) + # two inputs must have a non zero intersection + with pytest.raises(ValueError): + scorer.score_from_prediction(self.train[:50], self.train[55:]) + # every pairwise element must have a non zero intersection + with pytest.raises(ValueError): + scorer.score_from_prediction( + [self.train, self.train[:50]], [self.train, self.train[55:]] + ) - # Need to call fit() before calling score() - with pytest.raises(ValueError): - scorer.score(self.test) + @pytest.mark.parametrize("scorer_config", list_FittableAnomalyScorer) + def test_FittableAnomalyScorer(self, scorer_config): + scorer_cls, scorer_kwargs = scorer_config + fittable_scorer = scorer_cls(**scorer_kwargs) - # Need to call fit() before calling score_from_prediction() - with pytest.raises(ValueError): - scorer.score_from_prediction(self.test, self.modified_test) + # Need to call fit() before calling score() + with pytest.raises(ValueError): + fittable_scorer.score(self.test) - # Check if trainable is True, being a FittableAnomalyScorer - assert scorer.trainable + # Need to call fit() before calling score_from_prediction() + with pytest.raises(ValueError): + fittable_scorer.score_from_prediction(self.test, self.modified_test) - # Check if _fit_called is False - assert not scorer._fit_called + # Check if _fit_called is False + assert not fittable_scorer._fit_called - # fit on sequence with series that have different width - with pytest.raises(ValueError): - scorer.fit([self.train, self.mts_train]) + # fit on sequence with series that have different width + with pytest.raises(ValueError): + fittable_scorer.fit([self.train, self.mts_train]) - # fit on sequence with series that have different width - with pytest.raises(ValueError): - scorer.fit_from_prediction( - [self.train, self.mts_train], - [self.modified_train, self.modified_mts_train], - ) + # fit on sequence with series that have different width + with pytest.raises(ValueError): + fittable_scorer.fit_from_prediction( + [self.train, self.mts_train], + [self.modified_train, self.modified_mts_train], + ) - # checks for fit_from_prediction() - # input must be Timeseries or sequence of Timeseries - with pytest.raises(ValueError): - scorer.score_from_prediction(self.train, "str") - with pytest.raises(ValueError): - scorer.score_from_prediction( - [self.train, self.train], [self.modified_train, "str"] - ) - # two inputs must have the same length - with pytest.raises(ValueError): - scorer.fit_from_prediction( - [self.train, self.train], [self.modified_train] - ) - # two inputs must have the same width - with pytest.raises(ValueError): - scorer.fit_from_prediction([self.train], [self.modified_mts_train]) - # every element must have the same width - with pytest.raises(ValueError): - scorer.fit_from_prediction( - [self.train, self.mts_train], - [self.modified_train, self.modified_mts_train], - ) - # two inputs must have a non zero intersection - with pytest.raises(ValueError): - scorer.fit_from_prediction(self.train[:50], self.train[55:]) - # every pairwise element must have a non zero intersection - with pytest.raises(ValueError): - scorer.fit_from_prediction( - [self.train, self.train[:50]], [self.train, self.train[55:]] - ) + # checks for fit_from_prediction() + # input must be Timeseries or sequence of Timeseries + with pytest.raises(ValueError): + fittable_scorer.score_from_prediction(self.train, "str") + with pytest.raises(ValueError): + fittable_scorer.score_from_prediction( + [self.train, self.train], [self.modified_train, "str"] + ) + # two inputs must have the same length + with pytest.raises(ValueError): + fittable_scorer.fit_from_prediction( + [self.train, self.train], [self.modified_train] + ) + # two inputs must have the same width + with pytest.raises(ValueError): + fittable_scorer.fit_from_prediction([self.train], [self.modified_mts_train]) + # every element must have the same width + with pytest.raises(ValueError): + fittable_scorer.fit_from_prediction( + [self.train, self.mts_train], + [self.modified_train, self.modified_mts_train], + ) + # two inputs must have a non zero intersection + with pytest.raises(ValueError): + fittable_scorer.fit_from_prediction(self.train[:50], self.train[55:]) + # every pairwise element must have a non zero intersection + with pytest.raises(ValueError): + fittable_scorer.fit_from_prediction( + [self.train, self.train[:50]], [self.train, self.train[55:]] + ) - # checks for fit() - # input must be Timeseries or sequence of Timeseries - with pytest.raises(ValueError): - scorer.fit("str") - with pytest.raises(ValueError): - scorer.fit([self.modified_train, "str"]) + # checks for fit() + # input must be Timeseries or sequence of Timeseries + with pytest.raises(ValueError): + fittable_scorer.fit("str") + with pytest.raises(ValueError): + fittable_scorer.fit([self.modified_train, "str"]) - # checks for score_from_prediction() - scorer.fit_from_prediction(self.train, self.modified_train) - # input must be Timeseries or sequence of Timeseries - with pytest.raises(ValueError): - scorer.score_from_prediction(self.train, "str") - with pytest.raises(ValueError): - scorer.score_from_prediction( - [self.train, self.train], [self.modified_train, "str"] - ) - # two inputs must have the same length - with pytest.raises(ValueError): - scorer.score_from_prediction( - [self.train, self.train], [self.modified_train] - ) - # two inputs must have the same width - with pytest.raises(ValueError): - scorer.score_from_prediction([self.train], [self.modified_mts_train]) - # every element must have the same width - with pytest.raises(ValueError): - scorer.score_from_prediction( - [self.train, self.mts_train], - [self.modified_train, self.modified_mts_train], - ) - # two inputs must have a non zero intersection - with pytest.raises(ValueError): - scorer.score_from_prediction(self.train[:50], self.train[55:]) - # every pairwise element must have a non zero intersection - with pytest.raises(ValueError): - scorer.score_from_prediction( - [self.train, self.train[:50]], [self.train, self.train[55:]] - ) + # checks for score_from_prediction() + fittable_scorer.fit_from_prediction(self.train, self.modified_train) + # input must be Timeseries or sequence of Timeseries + with pytest.raises(ValueError): + fittable_scorer.score_from_prediction(self.train, "str") + with pytest.raises(ValueError): + fittable_scorer.score_from_prediction( + [self.train, self.train], [self.modified_train, "str"] + ) + # two inputs must have the same length + with pytest.raises(ValueError): + fittable_scorer.score_from_prediction( + [self.train, self.train], [self.modified_train] + ) + # two inputs must have the same width + with pytest.raises(ValueError): + fittable_scorer.score_from_prediction( + [self.train], [self.modified_mts_train] + ) + # every element must have the same width + with pytest.raises(ValueError): + fittable_scorer.score_from_prediction( + [self.train, self.mts_train], + [self.modified_train, self.modified_mts_train], + ) + # two inputs must have a non zero intersection + with pytest.raises(ValueError): + fittable_scorer.score_from_prediction(self.train[:50], self.train[55:]) + # every pairwise element must have a non zero intersection + with pytest.raises(ValueError): + fittable_scorer.score_from_prediction( + [self.train, self.train[:50]], [self.train, self.train[55:]] + ) - # checks for score() - # input must be Timeseries or sequence of Timeseries - with pytest.raises(ValueError): - scorer.score("str") - with pytest.raises(ValueError): - scorer.score([self.modified_train, "str"]) - - # caseA: fit with fit() - # case1: fit on UTS - scorerA1 = scorer - scorerA1.fit(self.train) - # Check if _fit_called is True after being fitted - assert scorerA1._fit_called - with pytest.raises(ValueError): - # series must be same width as series used for training - scorerA1.score(self.mts_test) - # case2: fit on MTS - scorerA2 = scorer - scorerA2.fit(self.mts_train) - # Check if _fit_called is True after being fitted - assert scorerA2._fit_called - with pytest.raises(ValueError): - # series must be same width as series used for training - scorerA2.score(self.test) - - # caseB: fit with fit_from_prediction() - # case1: fit on UTS - scorerB1 = scorer - scorerB1.fit_from_prediction(self.train, self.modified_train) - # Check if _fit_called is True after being fitted - assert scorerB1._fit_called - with pytest.raises(ValueError): - # series must be same width as series used for training - scorerB1.score_from_prediction(self.mts_test, self.modified_mts_test) - # case2: fit on MTS - scorerB2 = scorer - scorerB2.fit_from_prediction(self.mts_train, self.modified_mts_train) - # Check if _fit_called is True after being fitted - assert scorerB2._fit_called - with pytest.raises(ValueError): - # series must be same width as series used for training - scorerB2.score_from_prediction(self.test, self.modified_test) + # checks for score() + # input must be Timeseries or sequence of Timeseries + with pytest.raises(ValueError): + fittable_scorer.score("str") + with pytest.raises(ValueError): + fittable_scorer.score([self.modified_train, "str"]) + + # caseA: fit with fit() + # case1: fit on UTS + fittable_scorerA1 = fittable_scorer + fittable_scorerA1.fit(self.train) + # Check if _fit_called is True after being fitted + assert fittable_scorerA1._fit_called + with pytest.raises(ValueError): + # series must be same width as series used for training + fittable_scorerA1.score(self.mts_test) + # case2: fit on MTS + fittable_scorerA2 = fittable_scorer + fittable_scorerA2.fit(self.mts_train) + # Check if _fit_called is True after being fitted + assert fittable_scorerA2._fit_called + with pytest.raises(ValueError): + # series must be same width as series used for training + fittable_scorerA2.score(self.test) + + # caseB: fit with fit_from_prediction() + # case1: fit on UTS + fittable_scorerB1 = fittable_scorer + fittable_scorerB1.fit_from_prediction(self.train, self.modified_train) + # Check if _fit_called is True after being fitted + assert fittable_scorerB1._fit_called + with pytest.raises(ValueError): + # series must be same width as series used for training + fittable_scorerB1.score_from_prediction( + self.mts_test, self.modified_mts_test + ) + # case2: fit on MTS + fittable_scorerB2 = fittable_scorer + fittable_scorerB2.fit_from_prediction(self.mts_train, self.modified_mts_train) + # Check if _fit_called is True after being fitted + assert fittable_scorerB2._fit_called + with pytest.raises(ValueError): + # series must be same width as series used for training + fittable_scorerB2.score_from_prediction(self.test, self.modified_test) def test_Norm(self): + # Check parameters + self.expects_deterministic_input(Norm) - # component_wise must be bool - with pytest.raises(ValueError): - Norm(component_wise=1) - with pytest.raises(ValueError): - Norm(component_wise="string") # if component_wise=False must always return a univariate anomaly score scorer = Norm(component_wise=False) assert scorer.score_from_prediction(self.test, self.modified_test).width == 1 + assert ( scorer.score_from_prediction(self.mts_test, self.modified_mts_test).width == 1 ) + # if component_wise=True must always return the same width as the input scorer = Norm(component_wise=True) assert scorer.score_from_prediction(self.test, self.modified_test).width == 1 @@ -589,12 +643,6 @@ def test_Norm(self): ) scorer = Norm(component_wise=True) - # always expects a deterministic input - with pytest.raises(ValueError): - scorer.score_from_prediction(self.train, self.probabilistic) - with pytest.raises(ValueError): - scorer.score_from_prediction(self.probabilistic, self.train) - # univariate case (equivalent to abs diff) assert scorer.score_from_prediction(self.test, self.test + 1).sum( axis=0 @@ -613,6 +661,7 @@ def test_Norm(self): scorer.score_from_prediction(self.mts_test, self.mts_test * 2)["1"] == self.mts_test["1"] ) + # abs(2a - a) = a assert ( scorer.score_from_prediction(self.mts_test * 2, self.mts_test)["0"] @@ -625,8 +674,6 @@ def test_Norm(self): scorer = Norm(component_wise=False) - # always expects a deterministic input - # univariate case (equivalent to abs diff) assert scorer.score_from_prediction(self.test, self.test + 1).sum( axis=0 @@ -638,42 +685,42 @@ def test_Norm(self): # multivariate case with component_wise set to False # norm(a - a + sqrt(2)) = 2 * len(a) with a being series of dim=2 assert ( - abs( - scorer.score_from_prediction(self.mts_test, self.mts_test + np.sqrt(2)) + np.abs( + 2 * len(self.mts_test) + - scorer.score_from_prediction( + self.mts_test, self.mts_test + np.sqrt(2) + ) .sum(axis=0) .all_values() .flatten()[0] - - 2 * len(self.mts_test) ) - < 1e-05 + < delta ) assert not scorer.is_probabilistic def test_Difference(self): + self.expects_deterministic_input(Difference) scorer = Difference() - # always expects a deterministic input - with pytest.raises(ValueError): - scorer.score_from_prediction(self.train, self.probabilistic) - with pytest.raises(ValueError): - scorer.score_from_prediction(self.probabilistic, self.train) - # univariate case assert scorer.score_from_prediction(self.test, self.test + 1).sum( axis=0 ).all_values().flatten()[0] == -len(self.test) - assert scorer.score_from_prediction(self.test + 1, self.test).sum( - axis=0 - ).all_values().flatten()[0] == len(self.test) + + assert ( + scorer.score_from_prediction(self.test + 1, self.test) + .sum(axis=0) + .all_values() + .flatten()[0] + ) == len(self.test) # multivariate case # output of score() must be the same width as the width of the input assert ( scorer.score_from_prediction(self.mts_test, self.mts_test).width - == self.mts_test.width - ) + ) == self.mts_test.width # a - 2a = - a assert ( @@ -696,106 +743,125 @@ def test_Difference(self): assert not scorer.is_probabilistic - def test_WassersteinScorer(self): - - # component_wise parameter - # component_wise must be bool + @staticmethod + def helper_check_type_window(scorer, **kwargs): + # window must be non-negative with pytest.raises(ValueError): - WassersteinScorer(component_wise=1) + scorer(window=-1, **kwargs) + # window must be different from 0 with pytest.raises(ValueError): - WassersteinScorer(component_wise="string") + scorer(window=0, **kwargs) + + def helper_window_parameter(self, scorer_to_test, **kwargs): + self.helper_check_type_window(scorer_to_test, **kwargs) + + if scorer_to_test(**kwargs).is_trainable: + # window must be smaller than the input of score() + scorer = scorer_to_test(window=len(self.train) + 1, **kwargs) + with pytest.raises(ValueError): + scorer.fit(self.train) + + scorer = scorer_to_test(window=len(self.train) - 20, **kwargs) + scorer.fit(self.train) + with pytest.raises(ValueError): + scorer.score(self.test[: len(self.train) // 2]) + + else: + # case only NLL scorers for now + + scorer = scorer_to_test(window=101) + # window must be smaller than the input of score_from_prediction() + with pytest.raises(ValueError): + scorer.score_from_prediction( + series=self.test, pred_series=self.probabilistic + ) # len(self.test)=100 + + def diff_fn_parameter(self, scorer, **kwargs): + # must be one of Darts per time step metrics (e.g. ae, err, ...) + with pytest.raises(ValueError): + scorer(diff_fn="abs_diff", **kwargs) + # absolute error / absolute difference + s_tmp = scorer(diff_fn=metrics.ae, **kwargs) + diffs = s_tmp._diff_series([self.train], [self.test]) + assert diffs == [abs(self.train - self.test)] + # error / difference + s_tmp = scorer(diff_fn=metrics.err, **kwargs) + diffs = s_tmp._diff_series([self.train], [self.test]) + assert diffs == [self.train - self.test] + + def component_wise_parameter(self, scorer_to_test, **kwargs): # if component_wise=False must always return a univariate anomaly score - scorer = WassersteinScorer(component_wise=False) + scorer = scorer_to_test(component_wise=False, **kwargs) scorer.fit(self.train) assert scorer.score(self.test).width == 1 scorer.fit(self.mts_train) assert scorer.score(self.mts_test).width == 1 + # if component_wise=True must always return the same width as the input - scorer = WassersteinScorer(component_wise=True) + scorer = scorer_to_test(component_wise=True, **kwargs) scorer.fit(self.train) assert scorer.score(self.test).width == 1 scorer.fit(self.mts_train) assert scorer.score(self.mts_test).width == self.mts_test.width - # window parameter - # window must be int - with pytest.raises(ValueError): - WassersteinScorer(window=True) - with pytest.raises(ValueError): - WassersteinScorer(window="string") - # window must be non negative - with pytest.raises(ValueError): - WassersteinScorer(window=-1) - # window must be different from 0 - with pytest.raises(ValueError): - WassersteinScorer(window=0) - - # diff_fn paramter - # must be None, 'diff' or 'abs_diff' - with pytest.raises(ValueError): - WassersteinScorer(diff_fn="random") - with pytest.raises(ValueError): - WassersteinScorer(diff_fn=1) - - # test _diff_series() directly + def check_diff_series(self, scorer, **kwargs): + # test _diff_series() directly: parameter must by "abs_diff" or "diff" with pytest.raises(ValueError): - s_tmp = WassersteinScorer() + s_tmp = scorer(**kwargs) s_tmp.diff_fn = "random" s_tmp._diff_series(self.train, self.test) - WassersteinScorer(diff_fn="diff")._diff_series(self.train, self.test) - WassersteinScorer()._diff_series(self.train, self.test) - scorer = WassersteinScorer() + def expects_deterministic_input(self, scorer, **kwargs): + scorer = scorer(**kwargs) + if scorer.is_trainable: + scorer.fit(self.train) + np.testing.assert_warns(scorer.score(self.probabilistic)) # always expects a deterministic input - with pytest.raises(ValueError): + np.testing.assert_warns( scorer.score_from_prediction(self.train, self.probabilistic) - with pytest.raises(ValueError): + ) + np.testing.assert_warns( scorer.score_from_prediction(self.probabilistic, self.train) - with pytest.raises(ValueError): - scorer.score(self.probabilistic) - - # window must be smaller than the input of score() - scorer = WassersteinScorer(window=101) - with pytest.raises(ValueError): - scorer.fit(self.train) # len(self.train)=100 + ) - scorer = WassersteinScorer(window=80) - scorer.fit(self.train) - with pytest.raises(ValueError): - scorer.score(self.test[:50]) # len(self.test)=100 + def test_WassersteinScorer(self): + # Check parameters and inputs + self.component_wise_parameter(WassersteinScorer) + self.helper_window_parameter(WassersteinScorer) + self.diff_fn_parameter(WassersteinScorer) + self.expects_deterministic_input(WassersteinScorer) # test plotting (just call the functions) - scorer = WassersteinScorer(window=2) + scorer = WassersteinScorer(window=2, window_agg=False) scorer.fit(self.train) scorer.show_anomalies(self.test, self.anomalies) with pytest.raises(ValueError): # should fail for a sequence of series scorer.show_anomalies([self.test, self.test], self.anomalies) scorer.show_anomalies_from_prediction( - actual_series=self.test, + series=self.test, pred_series=self.test + 1, - actual_anomalies=self.anomalies, + anomalies=self.anomalies, ) with pytest.raises(ValueError): # should fail for a sequence of series scorer.show_anomalies_from_prediction( - actual_series=[self.test, self.test], + series=[self.test, self.test], pred_series=self.test + 1, - actual_anomalies=self.anomalies, + anomalies=self.anomalies, ) with pytest.raises(ValueError): # should fail for a sequence of series scorer.show_anomalies_from_prediction( - actual_series=self.test, + series=self.test, pred_series=[self.test + 1, self.test + 2], - actual_anomalies=self.anomalies, + anomalies=self.anomalies, ) assert not scorer.is_probabilistic def test_univariate_Wasserstein(self): - # univariate example np.random.seed(42) @@ -826,32 +892,31 @@ def test_univariate_Wasserstein(self): ) # test model with window of 10 - scorer_10 = WassersteinScorer(window=10) + scorer_10 = WassersteinScorer(window=10, window_agg=False) scorer_10.fit(train_wasserstein) - auc_roc_w10 = scorer_10.eval_accuracy( + auc_roc_w10 = scorer_10.eval_metric( anomalies_wasserstein, test_wasserstein, metric="AUC_ROC" ) - auc_pr_w10 = scorer_10.eval_accuracy( + auc_pr_w10 = scorer_10.eval_metric( anomalies_wasserstein, test_wasserstein, metric="AUC_PR" ) # test model with window of 20 - scorer_20 = WassersteinScorer(window=20) + scorer_20 = WassersteinScorer(window=20, window_agg=False) scorer_20.fit(train_wasserstein) - auc_roc_w20 = scorer_20.eval_accuracy( + auc_roc_w20 = scorer_20.eval_metric( anomalies_wasserstein, test_wasserstein, metric="AUC_ROC" ) - auc_pr_w20 = scorer_20.eval_accuracy( + auc_pr_w20 = scorer_20.eval_metric( anomalies_wasserstein, test_wasserstein, metric="AUC_PR" ) - assert abs(auc_roc_w10 - 0.80637) < 1e-05 - assert abs(auc_pr_w10 - 0.83390) < 1e-05 - assert abs(auc_roc_w20 - 0.77828) < 1e-05 - assert abs(auc_pr_w20 - 0.93934) < 1e-05 + assert np.abs(0.80637 - auc_roc_w10) < delta + assert np.abs(0.83390 - auc_pr_w10) < delta + assert np.abs(0.77828 - auc_roc_w20) < delta + assert np.abs(0.93934 - auc_pr_w20) < delta def test_multivariate_componentwise_Wasserstein(self): - # example multivariate WassersteinScorer component wise (True and False) np.random.seed(3) np_mts_train_wasserstein = np.abs( @@ -904,90 +969,37 @@ def test_multivariate_componentwise_Wasserstein(self): ) # test scorer with component_wise=False - scorer_w10_cwfalse = WassersteinScorer(window=10, component_wise=False) + scorer_w10_cwfalse = WassersteinScorer( + window=10, component_wise=False, window_agg=False + ) scorer_w10_cwfalse.fit(mts_train_wasserstein) - auc_roc_cwfalse = scorer_w10_cwfalse.eval_accuracy( + auc_roc_cwfalse = scorer_w10_cwfalse.eval_metric( anomalies_common_wasserstein, mts_test_wasserstein, metric="AUC_ROC" ) # test scorer with component_wise=True - scorer_w10_cwtrue = WassersteinScorer(window=10, component_wise=True) + scorer_w10_cwtrue = WassersteinScorer( + window=10, component_wise=True, window_agg=False + ) scorer_w10_cwtrue.fit(mts_train_wasserstein) - auc_roc_cwtrue = scorer_w10_cwtrue.eval_accuracy( + auc_roc_cwtrue = scorer_w10_cwtrue.eval_metric( anomalies_wasserstein_per_width, mts_test_wasserstein, metric="AUC_ROC" ) - assert abs(auc_roc_cwfalse - 0.94637) < 1e-05 - assert abs(auc_roc_cwtrue[0] - 0.98606) < 1e-05 - assert abs(auc_roc_cwtrue[1] - 0.96722) < 1e-05 + assert np.abs(0.94637 - auc_roc_cwfalse) < delta + assert np.abs(0.98606 - auc_roc_cwtrue[0]) < delta + assert np.abs(0.96722 - auc_roc_cwtrue[1]) < delta def test_kmeansScorer(self): - - # component_wise parameter - # component_wise must be bool - with pytest.raises(ValueError): - KMeansScorer(component_wise=1) - with pytest.raises(ValueError): - KMeansScorer(component_wise="string") - # if component_wise=False must always return a univariate anomaly score - scorer = KMeansScorer(component_wise=False) - scorer.fit(self.train) - assert scorer.score(self.test).width == 1 - scorer.fit(self.mts_train) - assert scorer.score(self.mts_test).width == 1 - # if component_wise=True must always return the same width as the input - scorer = KMeansScorer(component_wise=True) - scorer.fit(self.train) - assert scorer.score(self.test).width == 1 - scorer.fit(self.mts_train) - assert scorer.score(self.mts_test).width == self.mts_test.width - - # window parameter - # window must be int - with pytest.raises(ValueError): - KMeansScorer(window=True) - with pytest.raises(ValueError): - KMeansScorer(window="string") - # window must be non negative - with pytest.raises(ValueError): - KMeansScorer(window=-1) - # window must be different from 0 - with pytest.raises(ValueError): - KMeansScorer(window=0) - - # diff_fn paramter - # must be None, 'diff' or 'abs_diff' - with pytest.raises(ValueError): - KMeansScorer(diff_fn="random") - with pytest.raises(ValueError): - KMeansScorer(diff_fn=1) - - scorer = KMeansScorer() - - # always expects a deterministic input - with pytest.raises(ValueError): - scorer.score_from_prediction(self.train, self.probabilistic) - with pytest.raises(ValueError): - scorer.score_from_prediction(self.probabilistic, self.train) - with pytest.raises(ValueError): - scorer.score(self.probabilistic) - - # window must be smaller than the input of score() - scorer = KMeansScorer(window=101) - with pytest.raises(ValueError): - scorer.fit(self.train) # len(self.train)=100 - - scorer = KMeansScorer(window=80) - scorer.fit(self.train) - with pytest.raises(ValueError): - scorer.score(self.test[:50]) # len(self.test)=100 - - assert not scorer.is_probabilistic + # Check parameters and inputs + self.component_wise_parameter(KMeansScorer) + self.helper_window_parameter(KMeansScorer) + self.diff_fn_parameter(KMeansScorer) + self.expects_deterministic_input(KMeansScorer) + assert not KMeansScorer().is_probabilistic def test_univariate_kmeans(self): - # univariate example - np.random.seed(40) # create the train set @@ -1056,10 +1068,10 @@ def test_univariate_kmeans(self): kmeans_scorer = KMeansScorer(k=2, window=1, component_wise=False) kmeans_scorer.fit(KMeans_mts_train) - metric_AUC_ROC = kmeans_scorer.eval_accuracy( + metric_AUC_ROC = kmeans_scorer.eval_metric( KMeans_mts_anomalies, KMeans_mts_test, metric="AUC_ROC" ) - metric_AUC_PR = kmeans_scorer.eval_accuracy( + metric_AUC_PR = kmeans_scorer.eval_metric( KMeans_mts_anomalies, KMeans_mts_test, metric="AUC_PR" ) @@ -1067,9 +1079,7 @@ def test_univariate_kmeans(self): assert metric_AUC_PR == 1.0 def test_multivariate_window_kmeans(self): - # multivariate example with different windows - np.random.seed(1) # create the train set @@ -1120,30 +1130,25 @@ def test_multivariate_window_kmeans(self): kmeans_scorer_w1 = KMeansScorer(k=4, window=1) kmeans_scorer_w1.fit(ts_train) - kmeans_scorer_w2 = KMeansScorer(k=8, window=2) + kmeans_scorer_w2 = KMeansScorer(k=8, window=2, window_agg=False) kmeans_scorer_w2.fit(ts_train) - auc_roc_w1 = kmeans_scorer_w1.eval_accuracy( + auc_roc_w1 = kmeans_scorer_w1.eval_metric( ts_anomalies, ts_test, metric="AUC_ROC" ) - auc_pr_w1 = kmeans_scorer_w1.eval_accuracy( - ts_anomalies, ts_test, metric="AUC_PR" - ) + auc_pr_w1 = kmeans_scorer_w1.eval_metric(ts_anomalies, ts_test, metric="AUC_PR") - auc_roc_w2 = kmeans_scorer_w2.eval_accuracy( + auc_roc_w2 = kmeans_scorer_w2.eval_metric( ts_anomalies, ts_test, metric="AUC_ROC" ) - auc_pr_w2 = kmeans_scorer_w2.eval_accuracy( - ts_anomalies, ts_test, metric="AUC_PR" - ) + auc_pr_w2 = kmeans_scorer_w2.eval_metric(ts_anomalies, ts_test, metric="AUC_PR") - assert abs(auc_roc_w1 - 0.41551) < 1e-05 - assert abs(auc_pr_w1 - 0.064761) < 1e-05 - assert abs(auc_roc_w2 - 0.957513) < 1e-05 - assert abs(auc_pr_w2 - 0.88584) < 1e-05 + assert np.abs(0.41551 - auc_roc_w1) < delta + assert np.abs(0.064761 - auc_pr_w1) < delta + assert np.abs(0.957513 - auc_roc_w2) < delta + assert np.abs(0.88584 - auc_pr_w2) < delta def test_multivariate_componentwise_kmeans(self): - # example multivariate KMeans component wise (True and False) np.random.seed(1) @@ -1197,40 +1202,45 @@ def test_multivariate_componentwise_kmeans(self): ) # test scorer with component_wise=False - scorer_w10_cwfalse = KMeansScorer(window=10, component_wise=False, n_init=10) + scorer_w10_cwfalse = KMeansScorer( + window=10, component_wise=False, n_init=10, window_agg=False + ) scorer_w10_cwfalse.fit(mts_train_kmeans) - auc_roc_cwfalse = scorer_w10_cwfalse.eval_accuracy( + auc_roc_cwfalse = scorer_w10_cwfalse.eval_metric( anomalies_common_kmeans, mts_test_kmeans, metric="AUC_ROC" ) # test scorer with component_wise=True - scorer_w10_cwtrue = KMeansScorer(window=10, component_wise=True, n_init=10) + scorer_w10_cwtrue = KMeansScorer( + window=10, component_wise=True, n_init=10, window_agg=False + ) scorer_w10_cwtrue.fit(mts_train_kmeans) - auc_roc_cwtrue = scorer_w10_cwtrue.eval_accuracy( + auc_roc_cwtrue = scorer_w10_cwtrue.eval_metric( anomalies_kmeans_per_width, mts_test_kmeans, metric="AUC_ROC" ) - assert abs(auc_roc_cwtrue[0] - 1.0) < 1e-05 - assert abs(auc_roc_cwtrue[1] - 0.97666) < 1e-05 + assert np.abs(1.0 - auc_roc_cwtrue[0]) < delta + assert np.abs(0.97666 - auc_roc_cwtrue[1]) < delta # sklearn changed the centroid initialization in version 1.3.0 # so the results are slightly different for older versions if sklearn.__version__ < "1.3.0": - assert abs(auc_roc_cwfalse - 0.9851) < 1e-05 + assert np.abs(0.9851 - auc_roc_cwfalse) < delta else: - assert abs(auc_roc_cwfalse - 0.99007) < 1e-05 + assert np.abs(0.99007 - auc_roc_cwfalse) < delta def test_PyODScorer(self): + # Check parameters and inputs + self.component_wise_parameter(PyODScorer, model=KNN()) + self.helper_window_parameter(PyODScorer, model=KNN()) + self.diff_fn_parameter(PyODScorer, model=KNN()) + self.expects_deterministic_input(PyODScorer, model=KNN()) + assert not PyODScorer(model=KNN()).is_probabilistic - # model parameter must be pyod.models typy BaseDetector + # model parameter must be pyod.models type BaseDetector with pytest.raises(ValueError): PyODScorer(model=MovingAverageFilter(window=10)) # component_wise parameter - # component_wise must be bool - with pytest.raises(ValueError): - PyODScorer(model=KNN(), component_wise=1) - with pytest.raises(ValueError): - PyODScorer(model=KNN(), component_wise="string") # if component_wise=False must always return a univariate anomaly score scorer = PyODScorer(model=KNN(), component_wise=False) scorer.fit(self.train) @@ -1245,19 +1255,14 @@ def test_PyODScorer(self): assert scorer.score(self.mts_test).width == self.mts_test.width # window parameter - # window must be int - with pytest.raises(ValueError): - PyODScorer(model=KNN(), window=True) - with pytest.raises(ValueError): - PyODScorer(model=KNN(), window="string") - # window must be non negative + # window must be non-negative with pytest.raises(ValueError): PyODScorer(model=KNN(), window=-1) # window must be different from 0 with pytest.raises(ValueError): PyODScorer(model=KNN(), window=0) - # diff_fn paramter + # diff_fn parameter # must be None, 'diff' or 'abs_diff' with pytest.raises(ValueError): PyODScorer(model=KNN(), diff_fn="random") @@ -1266,28 +1271,11 @@ def test_PyODScorer(self): scorer = PyODScorer(model=KNN()) - # always expects a deterministic input - with pytest.raises(ValueError): - scorer.score_from_prediction(self.train, self.probabilistic) - with pytest.raises(ValueError): - scorer.score_from_prediction(self.probabilistic, self.train) - with pytest.raises(ValueError): - scorer.score(self.probabilistic) - - # window must be smaller than the input of score() - scorer = PyODScorer(model=KNN(), window=101) - with pytest.raises(ValueError): - scorer.fit(self.train) # len(self.train)=100 - - scorer = PyODScorer(model=KNN(), window=80) - scorer.fit(self.train) + # model parameter must be pyod.models type BaseDetector with pytest.raises(ValueError): - scorer.score(self.test[:50]) # len(self.test)=100 - - assert not scorer.is_probabilistic + PyODScorer(model=MovingAverageFilter(window=10)) def test_univariate_PyODScorer(self): - # univariate test np.random.seed(40) @@ -1359,10 +1347,10 @@ def test_univariate_PyODScorer(self): ) pyod_scorer.fit(pyod_mts_train) - metric_AUC_ROC = pyod_scorer.eval_accuracy( + metric_AUC_ROC = pyod_scorer.eval_metric( pyod_mts_anomalies, pyod_mts_test, metric="AUC_ROC" ) - metric_AUC_PR = pyod_scorer.eval_accuracy( + metric_AUC_PR = pyod_scorer.eval_metric( pyod_mts_anomalies, pyod_mts_test, metric="AUC_PR" ) @@ -1370,9 +1358,7 @@ def test_univariate_PyODScorer(self): assert metric_AUC_PR == 1.0 def test_multivariate_window_PyODScorer(self): - # multivariate example (with different window) - np.random.seed(1) # create the train set @@ -1426,29 +1412,26 @@ def test_multivariate_window_PyODScorer(self): pyod_scorer_w1.fit(ts_train) pyod_scorer_w2 = PyODScorer( - model=KNN(n_neighbors=10), component_wise=False, window=2 + model=KNN(n_neighbors=10), + component_wise=False, + window=2, + window_agg=False, ) pyod_scorer_w2.fit(ts_train) - auc_roc_w1 = pyod_scorer_w1.eval_accuracy( - ts_anomalies, ts_test, metric="AUC_ROC" - ) - auc_pr_w1 = pyod_scorer_w1.eval_accuracy(ts_anomalies, ts_test, metric="AUC_PR") + auc_roc_w1 = pyod_scorer_w1.eval_metric(ts_anomalies, ts_test, metric="AUC_ROC") + auc_pr_w1 = pyod_scorer_w1.eval_metric(ts_anomalies, ts_test, metric="AUC_PR") - auc_roc_w2 = pyod_scorer_w2.eval_accuracy( - ts_anomalies, ts_test, metric="AUC_ROC" - ) - auc_pr_w2 = pyod_scorer_w2.eval_accuracy(ts_anomalies, ts_test, metric="AUC_PR") + auc_roc_w2 = pyod_scorer_w2.eval_metric(ts_anomalies, ts_test, metric="AUC_ROC") + auc_pr_w2 = pyod_scorer_w2.eval_metric(ts_anomalies, ts_test, metric="AUC_PR") - assert abs(auc_roc_w1 - 0.5) < 1e-05 - assert abs(auc_pr_w1 - 0.07) < 1e-05 - assert abs(auc_roc_w2 - 0.957513) < 1e-05 - assert abs(auc_pr_w2 - 0.88584) < 1e-05 + assert np.abs(0.5 - auc_roc_w1) < delta + assert np.abs(0.07 - auc_pr_w1) < delta + assert np.abs(0.957513 - auc_roc_w2) < delta + assert np.abs(0.88584 - auc_pr_w2) < delta def test_multivariate_componentwise_PyODScorer(self): - # multivariate example with component wise (True and False) - np.random.seed(1) np_mts_train_PyOD = np.abs( @@ -1502,1071 +1485,249 @@ def test_multivariate_componentwise_PyODScorer(self): # test scorer with component_wise=False scorer_w10_cwfalse = PyODScorer( - model=KNN(n_neighbors=10), component_wise=False, window=10 + model=KNN(n_neighbors=10), + component_wise=False, + window=10, + window_agg=False, ) scorer_w10_cwfalse.fit(mts_train_PyOD) - auc_roc_cwfalse = scorer_w10_cwfalse.eval_accuracy( + auc_roc_cwfalse = scorer_w10_cwfalse.eval_metric( anomalies_common_PyOD, mts_test_PyOD, metric="AUC_ROC" ) # test scorer with component_wise=True scorer_w10_cwtrue = PyODScorer( - model=KNN(n_neighbors=10), component_wise=True, window=10 + model=KNN(n_neighbors=10), + component_wise=True, + window=10, + window_agg=False, ) scorer_w10_cwtrue.fit(mts_train_PyOD) - auc_roc_cwtrue = scorer_w10_cwtrue.eval_accuracy( + auc_roc_cwtrue = scorer_w10_cwtrue.eval_metric( anomalies_pyod_per_width, mts_test_PyOD, metric="AUC_ROC" ) - assert abs(auc_roc_cwfalse - 0.990566) < 1e-05 - assert abs(auc_roc_cwtrue[0] - 1.0) < 1e-05 - assert abs(auc_roc_cwtrue[1] - 0.98311) < 1e-05 - - def test_NLLScorer(self): - - for s in list_NLLScorer: - # expects for 'actual_series' a deterministic input and for 'pred_series' a probabilistic input - with pytest.raises(ValueError): - s.score_from_prediction(actual_series=self.test, pred_series=self.test) - with pytest.raises(ValueError): - s.score_from_prediction( - actual_series=self.probabilistic, pred_series=self.train - ) - - def test_GaussianNLLScorer(self): - - # window parameter - # window must be int - with pytest.raises(ValueError): - GaussianNLLScorer(window=True) - with pytest.raises(ValueError): - GaussianNLLScorer(window="string") - # window must be non negative - with pytest.raises(ValueError): - GaussianNLLScorer(window=-1) - # window must be different from 0 - with pytest.raises(ValueError): - GaussianNLLScorer(window=0) - - scorer = GaussianNLLScorer(window=101) - # window must be smaller than the input of score_from_prediction() - with pytest.raises(ValueError): - scorer.score_from_prediction( - actual_series=self.test, pred_series=self.probabilistic - ) # len(self.test)=100 - - np.random.seed(4) - scorer = GaussianNLLScorer() - - # test 1 univariate (len=1 and window=1) - gaussian_samples_1 = np.random.normal(loc=0, scale=2, size=10000) - distribution_series = TimeSeries.from_values( - gaussian_samples_1.reshape(1, 1, -1) - ) - actual_series = TimeSeries.from_values(np.array([3])) - value_test1 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - ) - - # check if value_test1 is the - log likelihood - assert abs(value_test1 + np.log(norm.pdf(3, loc=0, scale=2))) < 1e-01 - - # test 2 univariate (len=1 and window=1) - gaussian_samples_2 = np.random.normal(loc=0, scale=2, size=10000) - distribution_series = TimeSeries.from_values( - gaussian_samples_2.reshape(1, 1, -1) - ) - actual_series = TimeSeries.from_values(np.array([-2])) - value_test2 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - ) - - # check if value_test2 is the - log likelihood - assert abs(value_test2 + np.log(norm.pdf(-2, loc=0, scale=2))) < 1e-01 - - # test window univariate (len=2 and window=2) - distribution_series = TimeSeries.from_values( - np.array( - [gaussian_samples_1.reshape(1, -1), gaussian_samples_2.reshape(1, -1)] - ) - ) - actual_series = TimeSeries.from_values(np.array([3, -2])) - value_window = scorer.score_from_prediction(actual_series, distribution_series) - - # check length - assert len(value_window) == 2 - # check width - assert value_window.width == 1 - - # check equal value_test1 and value_test2 - assert value_window.all_values().flatten()[0] == value_test1 - assert value_window.all_values().flatten()[1] == value_test2 - - scorer = GaussianNLLScorer(window=2) - # check avg of two values - assert ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - == (value_test1 + value_test2) / 2 - ) - - # test window multivariate (n_samples=2, len=1, window=1) - scorer = GaussianNLLScorer(window=1) - distribution_series = TimeSeries.from_values( - np.array([gaussian_samples_1, gaussian_samples_2]).reshape(1, 2, -1) - ) - actual_series = TimeSeries.from_values(np.array([3, -2]).reshape(1, -1)) - value_multivariate = scorer.score_from_prediction( - actual_series, distribution_series - ) - - # check length - assert len(value_multivariate) == 1 - # check width - assert value_multivariate.width == 2 - - # check equal value_test1 and value_test2 - assert value_multivariate.all_values().flatten()[0] == value_test1 - assert value_multivariate.all_values().flatten()[1] == value_test2 + assert np.abs(0.990566 - auc_roc_cwfalse) < delta + assert np.abs(1.0 - auc_roc_cwtrue[0]) < delta + assert np.abs(0.98311 - auc_roc_cwtrue[1]) < delta - # test window multivariate (n_samples=2, len=2, window=1 and 2) - scorer_w1 = GaussianNLLScorer(window=1) - scorer_w2 = GaussianNLLScorer(window=2) + @staticmethod + def helper_evaluate_nll_scorer( + NLLscorer_to_test, + distribution_arrays, + deterministic_values, + real_NLL_values, + ): + NLLscorer_w1 = NLLscorer_to_test(window=1) + NLLscorer_w2 = NLLscorer_to_test(window=2) - gaussian_samples_3 = np.random.normal(loc=0, scale=2, size=10000) - gaussian_samples_4 = np.random.normal(loc=0, scale=2, size=10000) + assert NLLscorer_w1.is_probabilistic + # create timeseries distribution_series = TimeSeries.from_values( - np.array( - [ - gaussian_samples_1, - gaussian_samples_2, - gaussian_samples_3, - gaussian_samples_4, - ] - ).reshape(2, 2, -1) - ) - - actual_series = TimeSeries.from_values( - np.array([1.5, 2.1, 0.1, 0.001]).reshape(2, -1) - ) - - score_w1 = scorer_w1.score_from_prediction(actual_series, distribution_series) - score_w2 = scorer_w2.score_from_prediction(actual_series, distribution_series) - - # check length - assert len(score_w1) == 2 - assert len(score_w2) == 1 - # check width - assert score_w1.width == 2 - assert score_w2.width == 2 - - # check values for window=1 - assert ( - abs( - score_w1.all_values().flatten()[0] - + np.log(norm.pdf(1.5, loc=0, scale=2)) - ) - < 1e-01 - ) - assert ( - abs( - score_w1.all_values().flatten()[1] - + np.log(norm.pdf(2.1, loc=0, scale=2)) - ) - < 1e-01 - ) - assert ( - abs( - score_w1.all_values().flatten()[2] - + np.log(norm.pdf(0.1, loc=0, scale=2)) - ) - < 1e-01 + distribution_arrays.reshape(2, 2, -1) ) - assert ( - abs( - score_w1.all_values().flatten()[3] - + np.log(norm.pdf(0.001, loc=0, scale=2)) - ) - < 1e-01 - ) - - # check values for window=2 (must be equal to the mean of the past 2 values) - assert ( - abs( - score_w2.all_values().flatten()[0] - - ( - -np.log(norm.pdf(1.5, loc=0, scale=2)) - - np.log(norm.pdf(0.1, loc=0, scale=2)) - ) - / 2 - ) - < 1e-01 - ) - assert ( - abs( - score_w2.all_values().flatten()[1] - - ( - -np.log(norm.pdf(2.1, loc=0, scale=2)) - - np.log(norm.pdf(0.001, loc=0, scale=2)) - ) - / 2 - ) - < 1e-01 + series = TimeSeries.from_values( + np.array(deterministic_values).reshape(2, 2, -1) ) - assert scorer.is_probabilistic - - def test_LaplaceNLLScorer(self): - - # window parameter - # window must be int - with pytest.raises(ValueError): - LaplaceNLLScorer(window=True) - with pytest.raises(ValueError): - LaplaceNLLScorer(window="string") - # window must be non negative - with pytest.raises(ValueError): - LaplaceNLLScorer(window=-1) - # window must be different from 0 - with pytest.raises(ValueError): - LaplaceNLLScorer(window=0) - - scorer = LaplaceNLLScorer(window=101) - # window must be smaller than the input of score_from_prediction() - with pytest.raises(ValueError): - scorer.score_from_prediction( - actual_series=self.test, pred_series=self.probabilistic - ) # len(self.test)=100 - - np.random.seed(4) - - scorer = LaplaceNLLScorer() - - # test 1 univariate (len=1 and window=1) - laplace_samples_1 = np.random.laplace(loc=0, scale=2, size=1000) - distribution_series = TimeSeries.from_values( - laplace_samples_1.reshape(1, 1, -1) - ) - actual_series = TimeSeries.from_values(np.array([3])) - value_test1 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] + # compute the NLL values with score_from_prediction for scorer with window=1 and 2 + # t -> timestamp, c -> component and w -> window used in scorer + value_t1_c1_w1 = NLLscorer_w1.score_from_prediction( + series[0]["0"], distribution_series[0]["0"] ) - - # check if value_test1 is the - log likelihood - assert abs(value_test1 + np.log(laplace.pdf(3, loc=0, scale=2))) < 1e-01 - - # test 2 univariate (len=1 and window=1) - laplace_samples_2 = np.random.laplace(loc=0, scale=2, size=1000) - distribution_series = TimeSeries.from_values( - laplace_samples_2.reshape(1, 1, -1) + value_t2_c1_w1 = NLLscorer_w1.score_from_prediction( + series[1]["0"], distribution_series[1]["0"] ) - actual_series = TimeSeries.from_values(np.array([-2])) - value_test2 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] + value_t1_2_c1_w1 = NLLscorer_w1.score_from_prediction( + series["0"], distribution_series["0"] ) - - # check if value_test2 is the - log likelihood - assert abs(value_test2 + np.log(laplace.pdf(-2, loc=0, scale=2))) < 1e-01 - - # test window univariate (len=2 and window=2) - distribution_series = TimeSeries.from_values( - np.array( - [laplace_samples_1.reshape(1, -1), laplace_samples_2.reshape(1, -1)] - ) + value_t1_2_c1_w2 = NLLscorer_w2.score_from_prediction( + series["0"], distribution_series["0"] ) - actual_series = TimeSeries.from_values(np.array([3, -2])) - value_window = scorer.score_from_prediction(actual_series, distribution_series) # check length - assert len(value_window) == 2 + assert len(value_t1_2_c1_w1) == 2 # check width - assert value_window.width == 1 + assert value_t1_2_c1_w1.width == 1 # check equal value_test1 and value_test2 - assert round(abs(value_window.all_values().flatten()[0] - value_test1), 7) == 0 - assert round(abs(value_window.all_values().flatten()[1] - value_test2), 7) == 0 - - scorer = LaplaceNLLScorer(window=2) - # check avg of two values - assert ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - == (value_test1 + value_test2) / 2 - ) + assert value_t1_2_c1_w1[0] == value_t1_c1_w1 + assert value_t1_2_c1_w1[1] == value_t2_c1_w1 - # test window multivariate (n_samples=2, len=1, window=1) - scorer = LaplaceNLLScorer(window=1) - distribution_series = TimeSeries.from_values( - np.array([laplace_samples_1, laplace_samples_2]).reshape(1, 2, -1) - ) - actual_series = TimeSeries.from_values(np.array([3, -2]).reshape(1, -1)) - value_multivariate = scorer.score_from_prediction( - actual_series, distribution_series + # check if value_t1_2_c1_w1 is the - log likelihood + np.testing.assert_array_almost_equal( + # This is approximate because our NLL scorer is fit from samples + value_t1_2_c1_w1.all_values().reshape(-1), + real_NLL_values[::2], + decimal=1, ) - # check length - assert len(value_multivariate) == 1 - # check width - assert value_multivariate.width == 2 - - # check equal value_test1 and value_test2 - assert ( - round(abs(value_multivariate.all_values().flatten()[0] - value_test1), 7) - == 0 - ) + # check if result is equal to avg of two values when window is equal to 2 assert ( - round(abs(value_multivariate.all_values().flatten()[1] - value_test2), 7) - == 0 + value_t1_2_c1_w2.all_values().reshape(-1)[0] + == value_t1_2_c1_w1.mean(axis=0).all_values().reshape(-1)[0] ) - # test window multivariate (n_samples=2, len=2, window=1 and 2) - scorer_w1 = LaplaceNLLScorer(window=1) - scorer_w2 = LaplaceNLLScorer(window=2) - - laplace_samples_3 = np.random.laplace(loc=0, scale=2, size=1000) - laplace_samples_4 = np.random.laplace(loc=0, scale=2, size=1000) - - distribution_series = TimeSeries.from_values( - np.array( - [ - laplace_samples_1, - laplace_samples_2, - laplace_samples_3, - laplace_samples_4, - ] - ).reshape(2, 2, -1) + # multivariate case + # compute the NLL values with score_from_prediction for scorer with window=1 and window=2 + value_t1_2_c1_2_w1 = NLLscorer_w1.score_from_prediction( + series, distribution_series ) - - actual_series = TimeSeries.from_values( - np.array([1.5, 2, 0.1, 0.001]).reshape(2, -1) + value_t1_2_c1_2_w2 = NLLscorer_w2.score_from_prediction( + series, distribution_series ) - score_w1 = scorer_w1.score_from_prediction(actual_series, distribution_series) - score_w2 = scorer_w2.score_from_prediction(actual_series, distribution_series) - # check length - assert len(score_w1) == 2 - assert len(score_w2) == 1 + assert len(value_t1_2_c1_2_w1) == 2 + assert len(value_t1_2_c1_2_w2) == 1 # check width - assert score_w1.width == 2 - assert score_w2.width == 2 - - # check values for window=1 - assert ( - abs( - score_w1.all_values().flatten()[0] - + np.log(laplace.pdf(1.5, loc=0, scale=2)) - ) - < 1e-01 - ) - assert ( - abs( - score_w1.all_values().flatten()[1] - + np.log(laplace.pdf(2, loc=0, scale=2)) - ) - < 0.5 - ) - assert ( - abs( - score_w1.all_values().flatten()[2] - + np.log(laplace.pdf(0.1, loc=0, scale=2)) - ) - < 1e-01 - ) - assert ( - abs( - score_w1.all_values().flatten()[3] - + np.log(laplace.pdf(0.001, loc=0, scale=2)) - ) - < 1e-01 - ) + assert value_t1_2_c1_2_w1.width == 2 + assert value_t1_2_c1_2_w2.width == 2 - # check values for window=2 (must be equal to the mean of the past 2 values) - assert ( - abs( - score_w2.all_values().flatten()[0] - - ( - -np.log(laplace.pdf(1.5, loc=0, scale=2)) - - np.log(laplace.pdf(0.1, loc=0, scale=2)) - ) - / 2 - ) - < 1e-01 - ) - assert ( - abs( - score_w2.all_values().flatten()[1] - - ( - -np.log(laplace.pdf(2, loc=0, scale=2)) - - np.log(laplace.pdf(0.001, loc=0, scale=2)) - ) - / 2 - ) - < 0.5 + # check if value_t1_2_c1_2_w1 is the - log likelihood + np.testing.assert_array_almost_equal( + # This is approximate because our NLL scorer is fit from samples + value_t1_2_c1_2_w1.all_values().reshape(-1), + real_NLL_values, + decimal=1, ) - assert scorer.is_probabilistic - - def test_ExponentialNLLScorer(self): - - # window parameter - # window must be int - with pytest.raises(ValueError): - ExponentialNLLScorer(window=True) - with pytest.raises(ValueError): - ExponentialNLLScorer(window="string") - # window must be non negative - with pytest.raises(ValueError): - ExponentialNLLScorer(window=-1) - # window must be different from 0 - with pytest.raises(ValueError): - ExponentialNLLScorer(window=0) - - scorer = ExponentialNLLScorer(window=101) - # window must be smaller than the input of score_from_prediction() - with pytest.raises(ValueError): - scorer.score_from_prediction( - actual_series=self.test, pred_series=self.probabilistic - ) # len(self.test)=100 + # check if result is equal to avg of two values when window is equal to 2 + assert value_t1_2_c1_w2.all_values().reshape(-1) == value_t1_2_c1_w1.mean( + axis=0 + ).all_values().reshape(-1) + @pytest.mark.parametrize("config", list_NLLScorer) + def test_nll_scorer(self, config): np.random.seed(4) - scorer = ExponentialNLLScorer() - - # test 1 univariate (len=1 and window=1) - exponential_samples_1 = np.random.exponential(scale=2.0, size=1000) - distribution_series = TimeSeries.from_values( - exponential_samples_1.reshape(1, 1, -1) - ) - actual_series = TimeSeries.from_values(np.array([3])) - value_test1 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - ) - # check if value_test1 is the - log likelihood - assert abs(value_test1 + np.log(expon.pdf(3, scale=2.0))) < 1e-01 - - # test 2 univariate (len=1 and window=1) - exponential_samples_2 = np.random.exponential(scale=2.0, size=1000) - distribution_series = TimeSeries.from_values( - exponential_samples_2.reshape(1, 1, -1) - ) - actual_series = TimeSeries.from_values(np.array([10])) - value_test2 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - ) - - # check if value_test2 is the - log likelihood - assert abs(value_test2 + np.log(expon.pdf(10, scale=2))) < 1e-01 - - # test window univariate (len=2 and window=2) - distribution_series = TimeSeries.from_values( - np.array( - [ - exponential_samples_1.reshape(1, -1), - exponential_samples_2.reshape(1, -1), - ] - ) - ) - actual_series = TimeSeries.from_values(np.array([3, 10])) - value_window = scorer.score_from_prediction(actual_series, distribution_series) - - # check length - assert len(value_window) == 2 - # check width - assert value_window.width == 1 - - # check equal value_test1 and value_test2 - assert value_window.all_values().flatten()[0] == value_test1 - assert value_window.all_values().flatten()[1] == value_test2 - - scorer = ExponentialNLLScorer(window=2) - # check avg of two values - assert ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - == (value_test1 + value_test2) / 2 - ) - - # test window multivariate (n_samples=2, len=1, window=1) - scorer = ExponentialNLLScorer(window=1) - distribution_series = TimeSeries.from_values( - np.array([exponential_samples_1, exponential_samples_2]).reshape(1, 2, -1) - ) - actual_series = TimeSeries.from_values(np.array([3, 10]).reshape(1, -1)) - value_multivariate = scorer.score_from_prediction( - actual_series, distribution_series - ) - - # check length - assert len(value_multivariate) == 1 - # check width - assert value_multivariate.width == 2 - - # check equal value_test1 and value_test2 - assert value_multivariate.all_values().flatten()[0] == value_test1 - assert value_multivariate.all_values().flatten()[1] == value_test2 - - # test window multivariate (n_samples=2, len=2, window=1 and 2) - scorer_w1 = ExponentialNLLScorer(window=1) - scorer_w2 = ExponentialNLLScorer(window=2) - - exponential_samples_3 = np.random.exponential(scale=2, size=1000) - exponential_samples_4 = np.random.exponential(scale=2, size=1000) - - distribution_series = TimeSeries.from_values( - np.array( - [ - exponential_samples_1, - exponential_samples_2, - exponential_samples_3, - exponential_samples_4, - ] - ).reshape(2, 2, -1) - ) - - actual_series = TimeSeries.from_values( - np.array([1.5, 2, 0.1, 0.001]).reshape(2, -1) - ) - - score_w1 = scorer_w1.score_from_prediction(actual_series, distribution_series) - score_w2 = scorer_w2.score_from_prediction(actual_series, distribution_series) - - # check length - assert len(score_w1) == 2 - assert len(score_w2) == 1 - # check width - assert score_w1.width == 2 - assert score_w2.width == 2 - - # check values for window=1 - assert ( - abs(score_w1.all_values().flatten()[0] + np.log(expon.pdf(1.5, scale=2))) - < 1e-01 - ) - assert ( - abs(score_w1.all_values().flatten()[1] + np.log(expon.pdf(2, scale=2))) - < 1e-01 - ) - assert ( - abs(score_w1.all_values().flatten()[2] + np.log(expon.pdf(0.1, scale=2))) - < 1e-01 - ) - assert ( - abs(score_w1.all_values().flatten()[3] + np.log(expon.pdf(0.001, scale=2))) - < 1e-01 - ) - - # check values for window=2 (must be equal to the mean of the past 2 values) - assert ( - abs( - score_w2.all_values().flatten()[0] - - (-np.log(expon.pdf(1.5, scale=2)) - np.log(expon.pdf(0.1, scale=2))) - / 2 - ) - < 1e-01 - ) - assert ( - abs( - score_w2.all_values().flatten()[1] - - (-np.log(expon.pdf(2, scale=2)) - np.log(expon.pdf(0.001, scale=2))) - / 2 - ) - < 1e-01 - ) - - assert scorer.is_probabilistic - - def test_GammaNLLScorer(self): - - # window parameter - # window must be int - with pytest.raises(ValueError): - GammaNLLScorer(window=True) - with pytest.raises(ValueError): - GammaNLLScorer(window="string") - # window must be non negative - with pytest.raises(ValueError): - GammaNLLScorer(window=-1) - # window must be different from 0 - with pytest.raises(ValueError): - GammaNLLScorer(window=0) - - scorer = GammaNLLScorer(window=101) - # window must be smaller than the input of score_from_prediction() - with pytest.raises(ValueError): - scorer.score_from_prediction( - actual_series=self.test, pred_series=self.probabilistic - ) # len(self.test)=100 - - np.random.seed(4) - scorer = GammaNLLScorer() - - # test 1 univariate (len=1 and window=1) - gamma_samples_1 = np.random.gamma(shape=2, scale=2, size=10000) - distribution_series = TimeSeries.from_values(gamma_samples_1.reshape(1, 1, -1)) - actual_series = TimeSeries.from_values(np.array([3])) - value_test1 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - ) - - # check if value_test1 is the - log likelihood - assert abs(value_test1 + np.log(gamma.pdf(3, 2, scale=2))) < 1e-01 - - # test 2 univariate (len=1 and window=1) - gamma_samples_2 = np.random.gamma(2, scale=2, size=10000) - distribution_series = TimeSeries.from_values(gamma_samples_2.reshape(1, 1, -1)) - actual_series = TimeSeries.from_values(np.array([10])) - value_test2 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - ) - - # check if value_test2 is the - log likelihood - assert abs(value_test2 + np.log(gamma.pdf(10, 2, scale=2))) < 1e-01 - - # test window univariate (len=2 and window=2) - distribution_series = TimeSeries.from_values( - np.array([gamma_samples_1.reshape(1, -1), gamma_samples_2.reshape(1, -1)]) - ) - actual_series = TimeSeries.from_values(np.array([3, 10])) - value_window = scorer.score_from_prediction(actual_series, distribution_series) - - # check length - assert len(value_window) == 2 - # check width - assert value_window.width == 1 - - # check equal value_test1 and value_test2 - assert value_window.all_values().flatten()[0] == value_test1 - assert value_window.all_values().flatten()[1] == value_test2 - - scorer = GammaNLLScorer(window=2) - # check avg of two values - assert ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - == (value_test1 + value_test2) / 2 - ) - - # test window multivariate (n_samples=2, len=1, window=1) - scorer = GammaNLLScorer(window=1) - distribution_series = TimeSeries.from_values( - np.array([gamma_samples_1, gamma_samples_2]).reshape(1, 2, -1) - ) - actual_series = TimeSeries.from_values(np.array([3, 10]).reshape(1, -1)) - value_multivariate = scorer.score_from_prediction( - actual_series, distribution_series - ) - - # check length - assert len(value_multivariate) == 1 - # check width - assert value_multivariate.width == 2 - - # check equal value_test1 and value_test2 - assert value_multivariate.all_values().flatten()[0] == value_test1 - assert value_multivariate.all_values().flatten()[1] == value_test2 - - # test window multivariate (n_samples=2, len=2, window=1 and 2) - scorer_w1 = GammaNLLScorer(window=1) - scorer_w2 = GammaNLLScorer(window=2) - - gamma_samples_3 = np.random.gamma(2, scale=2, size=10000) - gamma_samples_4 = np.random.gamma(2, scale=2, size=10000) - - distribution_series = TimeSeries.from_values( - np.array( - [gamma_samples_1, gamma_samples_2, gamma_samples_3, gamma_samples_4] - ).reshape(2, 2, -1) - ) - - actual_series = TimeSeries.from_values( - np.array([1.5, 2, 0.5, 0.9]).reshape(2, -1) - ) - - score_w1 = scorer_w1.score_from_prediction(actual_series, distribution_series) - score_w2 = scorer_w2.score_from_prediction(actual_series, distribution_series) - - # check length - assert len(score_w1) == 2 - assert len(score_w2) == 1 - # check width - assert score_w1.width == 2 - assert score_w2.width == 2 - - # check values for window=1 - assert ( - abs(score_w1.all_values().flatten()[0] + np.log(gamma.pdf(1.5, 2, scale=2))) - < 1e-01 - ) - assert ( - abs(score_w1.all_values().flatten()[1] + np.log(gamma.pdf(2, 2, scale=2))) - < 1e-01 - ) - assert ( - abs(score_w1.all_values().flatten()[2] + np.log(gamma.pdf(0.5, 2, scale=2))) - < 1e-01 - ) - assert ( - abs(score_w1.all_values().flatten()[3] + np.log(gamma.pdf(0.9, 2, scale=2))) - < 1e-01 - ) - - # check values for window=2 (must be equal to the mean of the past 2 values) - assert ( - abs( - score_w2.all_values().flatten()[0] - - ( - -np.log(gamma.pdf(1.5, 2, scale=2)) - - np.log(gamma.pdf(0.5, 2, scale=2)) - ) - / 2 - ) - < 1e-01 - ) - assert ( - abs( - score_w2.all_values().flatten()[1] - - ( - -np.log(gamma.pdf(2, 2, scale=2)) - - np.log(gamma.pdf(0.9, 2, scale=2)) - ) - / 2 - ) - < 1e-01 - ) - - assert scorer.is_probabilistic - - def test_CauchyNLLScorer(self): - - # window parameter - # window must be int - with pytest.raises(ValueError): - CauchyNLLScorer(window=True) - with pytest.raises(ValueError): - CauchyNLLScorer(window="string") - # window must be non negative - with pytest.raises(ValueError): - CauchyNLLScorer(window=-1) - # window must be different from 0 - with pytest.raises(ValueError): - CauchyNLLScorer(window=0) - - scorer = CauchyNLLScorer(window=101) - # window must be smaller than the input of score_from_prediction() - with pytest.raises(ValueError): - scorer.score_from_prediction( - actual_series=self.test, pred_series=self.probabilistic - ) # len(self.test)=100 - - np.random.seed(4) - scorer = CauchyNLLScorer() - - # test 1 univariate (len=1 and window=1) - cauchy_samples_1 = np.random.standard_cauchy(size=10000) - distribution_series = TimeSeries.from_values(cauchy_samples_1.reshape(1, 1, -1)) - actual_series = TimeSeries.from_values(np.array([3])) - value_test1 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - ) - - # check if value_test1 is the - log likelihood - assert abs(value_test1 + np.log(cauchy.pdf(3))) < 1e-01 - - # test 2 univariate (len=1 and window=1) - cauchy_samples_2 = np.random.standard_cauchy(size=10000) - distribution_series = TimeSeries.from_values(cauchy_samples_2.reshape(1, 1, -1)) - actual_series = TimeSeries.from_values(np.array([-2])) - value_test2 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - ) - - # check if value_test2 is the - log likelihood - assert abs(value_test2 + np.log(cauchy.pdf(-2))) < 1e-01 - - # test window univariate (len=2 and window=2) - distribution_series = TimeSeries.from_values( - np.array([cauchy_samples_1.reshape(1, -1), cauchy_samples_2.reshape(1, -1)]) - ) - actual_series = TimeSeries.from_values(np.array([3, -2])) - value_window = scorer.score_from_prediction(actual_series, distribution_series) - - # check length - assert len(value_window) == 2 - # check width - assert value_window.width == 1 - - # check equal value_test1 and value_test2 - assert value_window.all_values().flatten()[0] == value_test1 - assert value_window.all_values().flatten()[1] == value_test2 - - scorer = CauchyNLLScorer(window=2) - # check avg of two values - assert ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - == (value_test1 + value_test2) / 2 - ) - - # test window multivariate (n_samples=2, len=1, window=1) - scorer = CauchyNLLScorer(window=1) - distribution_series = TimeSeries.from_values( - np.array([cauchy_samples_1, cauchy_samples_2]).reshape(1, 2, -1) - ) - actual_series = TimeSeries.from_values(np.array([3, -2]).reshape(1, -1)) - value_multivariate = scorer.score_from_prediction( - actual_series, distribution_series - ) - - # check length - assert len(value_multivariate) == 1 - # check width - assert value_multivariate.width == 2 - - # check equal value_test1 and value_test2 - assert value_multivariate.all_values().flatten()[0] == value_test1 - assert value_multivariate.all_values().flatten()[1] == value_test2 - - # test window multivariate (n_samples=2, len=2, window=1 and 2) - scorer_w1 = CauchyNLLScorer(window=1) - scorer_w2 = CauchyNLLScorer(window=2) - - cauchy_samples_3 = np.random.standard_cauchy(size=10000) - cauchy_samples_4 = np.random.standard_cauchy(size=10000) - - distribution_series = TimeSeries.from_values( - np.array( - [cauchy_samples_1, cauchy_samples_2, cauchy_samples_3, cauchy_samples_4] - ).reshape(2, 2, -1) - ) - - actual_series = TimeSeries.from_values( - np.array([1.5, 2, 0.5, 0.9]).reshape(2, -1) - ) - - score_w1 = scorer_w1.score_from_prediction(actual_series, distribution_series) - score_w2 = scorer_w2.score_from_prediction(actual_series, distribution_series) - - # check length - assert len(score_w1) == 2 - assert len(score_w2) == 1 - # check width - assert score_w1.width == 2 - assert score_w2.width == 2 - - # check values for window=1 - assert abs(score_w1.all_values().flatten()[0] + np.log(cauchy.pdf(1.5))) < 1e-01 - assert abs(score_w1.all_values().flatten()[1] + np.log(cauchy.pdf(2))) < 1e-01 - assert abs(score_w1.all_values().flatten()[2] + np.log(cauchy.pdf(0.5))) < 1e-01 - assert abs(score_w1.all_values().flatten()[3] + np.log(cauchy.pdf(0.9))) < 1e-01 - - # check values for window=2 (must be equal to the mean of the past 2 values) - assert ( - abs( - score_w2.all_values().flatten()[0] - - (-np.log(cauchy.pdf(1.5)) - np.log(cauchy.pdf(0.5))) / 2 - ) - < 1e-01 - ) - assert ( - abs( - score_w2.all_values().flatten()[1] - - (-np.log(cauchy.pdf(2)) - np.log(cauchy.pdf(0.9))) / 2 - ) - < 1e-01 - ) - - assert scorer.is_probabilistic - - def test_PoissonNLLScorer(self): - - # window parameter - # window must be int - with pytest.raises(ValueError): - PoissonNLLScorer(window=True) - with pytest.raises(ValueError): - PoissonNLLScorer(window="string") - # window must be non negative - with pytest.raises(ValueError): - PoissonNLLScorer(window=-1) - # window must be different from 0 - with pytest.raises(ValueError): - PoissonNLLScorer(window=0) - - scorer = PoissonNLLScorer(window=101) - # window must be smaller than the input of score_from_prediction() - with pytest.raises(ValueError): - scorer.score_from_prediction( - actual_series=self.test, pred_series=self.probabilistic - ) # len(self.test)=100 - - np.random.seed(4) - scorer = PoissonNLLScorer() - - # test 1 univariate (len=1 and window=1) - poisson_samples_1 = np.random.poisson(size=10000, lam=1) - distribution_series = TimeSeries.from_values( - poisson_samples_1.reshape(1, 1, -1) - ) - actual_series = TimeSeries.from_values(np.array([3])) - value_test1 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - ) - - # check if value_test1 is the - log likelihood - assert abs(value_test1 + np.log(poisson.pmf(3, mu=1))) < 1e-02 - - # test 2 univariate (len=1 and window=1) - poisson_samples_2 = np.random.poisson(size=10000, lam=1) - distribution_series = TimeSeries.from_values( - poisson_samples_2.reshape(1, 1, -1) - ) - actual_series = TimeSeries.from_values(np.array([10])) - value_test2 = ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - ) - - # check if value_test2 is the - log likelihood - assert abs(value_test2 + np.log(poisson.pmf(10, mu=1))) < 1e-01 - - # test window univariate (len=2 and window=2) - distribution_series = TimeSeries.from_values( - np.array( - [poisson_samples_1.reshape(1, -1), poisson_samples_2.reshape(1, -1)] - ) - ) - actual_series = TimeSeries.from_values(np.array([3, 10])) - value_window = scorer.score_from_prediction(actual_series, distribution_series) - - # check length - assert len(value_window) == 2 - # check width - assert value_window.width == 1 - - # check equal value_test1 and value_test2 - assert value_window.all_values().flatten()[0] == value_test1 - assert value_window.all_values().flatten()[1] == value_test2 - - scorer = PoissonNLLScorer(window=2) - # check avg of two values - assert ( - scorer.score_from_prediction(actual_series, distribution_series) - .all_values() - .flatten()[0] - == (value_test1 + value_test2) / 2 - ) - - # test window multivariate (n_samples=2, len=1, window=1) - scorer = PoissonNLLScorer(window=1) - distribution_series = TimeSeries.from_values( - np.array([poisson_samples_1, poisson_samples_2]).reshape(1, 2, -1) - ) - actual_series = TimeSeries.from_values(np.array([3, 10]).reshape(1, -1)) - value_multivariate = scorer.score_from_prediction( - actual_series, distribution_series - ) - - # check length - assert len(value_multivariate) == 1 - # check width - assert value_multivariate.width == 2 - - # check equal value_test1 and value_test2 - assert value_multivariate.all_values().flatten()[0] == value_test1 - assert value_multivariate.all_values().flatten()[1] == value_test2 - - # test window multivariate (n_samples=2, len=2, window=1 and 2) - scorer_w1 = PoissonNLLScorer(window=1) - scorer_w2 = PoissonNLLScorer(window=2) - - poisson_samples_3 = np.random.poisson(size=10000, lam=1) - poisson_samples_4 = np.random.poisson(size=10000, lam=1) - - distribution_series = TimeSeries.from_values( - np.array( - [ - poisson_samples_1, - poisson_samples_2, - poisson_samples_3, - poisson_samples_4, - ] - ).reshape(2, 2, -1) - ) - - actual_series = TimeSeries.from_values(np.array([1, 2, 3, 4]).reshape(2, -1)) - - score_w1 = scorer_w1.score_from_prediction(actual_series, distribution_series) - score_w2 = scorer_w2.score_from_prediction(actual_series, distribution_series) - - # check length - assert len(score_w1) == 2 - assert len(score_w2) == 1 - # check width - assert score_w1.width == 2 - assert score_w2.width == 2 - - # check values for window=1 - assert ( - abs(score_w1.all_values().flatten()[0] + np.log(poisson.pmf(1, mu=1))) - < 1e-01 - ) - assert ( - abs(score_w1.all_values().flatten()[1] + np.log(poisson.pmf(2, mu=1))) - < 1e-01 - ) - assert ( - abs(score_w1.all_values().flatten()[2] + np.log(poisson.pmf(3, mu=1))) - < 1e-01 - ) - assert ( - abs(score_w1.all_values().flatten()[3] + np.log(poisson.pmf(4, mu=1))) - < 1e-01 - ) - - # check values for window=2 (must be equal to the mean of the past 2 values) - assert ( - abs( - score_w2.all_values().flatten()[0] - - (-np.log(poisson.pmf(1, mu=1)) - np.log(poisson.pmf(3, mu=1))) / 2 - ) - < 1e-01 - ) - assert ( - abs( - score_w2.all_values().flatten()[1] - - (-np.log(poisson.pmf(2, mu=1)) - np.log(poisson.pmf(4, mu=1))) / 2 - ) - < 1e-01 - ) - - assert scorer.is_probabilistic + ( + scorer_cls, + values, + distribution, + dist_kwargs, + prob_dens_func, + pdf_kwargs, + ) = config + # some pdf don't have the same parameters as the corresponding distribution + if pdf_kwargs is None: + pdf_kwargs = dist_kwargs + self.helper_window_parameter(scorer_cls) + + distribution = np.array([ + distribution(size=10000, **dist_kwargs) for _ in range(len(values)) + ]) + real_values = [-np.log(prob_dens_func(value, **pdf_kwargs)) for value in values] + + self.helper_evaluate_nll_scorer(scorer_cls, distribution, values, real_values) + + @pytest.mark.parametrize( + "model,series", + product( + [(KMeansScorer, {"random_state": 42}), (PyODScorer, {"model": KNN()})], + [(train, test), (mts_train, mts_test)], + ), + ) + def test_window_equal_one(self, model, series): + """Check that model, created with window=1 generate the same score regardless of window_agg value.""" + ts_train, ts_test = series + model_cls, model_kwargs = model + + scorer_T = model_cls(window=1, window_agg=True, **model_kwargs) + scorer_F = model_cls(window=1, window_agg=False, **model_kwargs) + + scorer_T.fit(ts_train) + scorer_F.fit(ts_train) + + auc_roc_T = scorer_T.eval_metric(anomalies=self.anomalies, series=ts_test) + auc_roc_F = scorer_F.eval_metric(anomalies=self.anomalies, series=ts_test) + + assert auc_roc_T == auc_roc_F + + @pytest.mark.parametrize( + "window,model,series", + product( + [2, 10, 39], + [ + (KMeansScorer, {"random_state": 42}), + (WassersteinScorer, {}), + (PyODScorer, {"model": KNN()}), + ], + [(train, test), (mts_train, mts_test)], + ), + ) + def test_window_greater_than_one(self, window, model, series): + """Check scorer with same window greater than 1 and different values of window_agg produce correct scores""" + ts_train, ts_test = series + model_cls, model_kwargs = model + scorer_T = model_cls(window=window, window_agg=True, **model_kwargs) + scorer_F = model_cls(window=window, window_agg=False, **model_kwargs) + + scorer_T.fit(ts_train) + scorer_F.fit(ts_train) + + score_T = scorer_T.score(ts_test) + score_F = scorer_F.score(ts_test) + + # same length + assert len(score_T) == len(score_F) + + # same width + assert score_T.width == score_F.width + + # same first time index + assert score_T.time_index[0] == score_F.time_index[0] + + # same last time index + assert score_T.time_index[-1] == score_F.time_index[-1] + + # same last value (by definition) + assert score_T[-1] == score_F[-1] + + def test_fun_window_agg(self): + """Verify that the anomaly score aggregation works as intended""" + # window = 2, alternating anomaly scores + window = 2 + scorer = KMeansScorer(window=window) + anomaly_scores = TimeSeries.from_values(np.resize([1, -1], 10)) + aggreg_scores = scorer._fun_window_agg([anomaly_scores], window=window)[0] + # in the last window, the score is not zeroed + np.testing.assert_array_almost_equal( + aggreg_scores.values(), np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, -1]]).T + ) + assert aggreg_scores.time_index.equals(anomaly_scores.time_index) + + # window = 3, increment of 2 anomaly scores + window = 3 + scorer = KMeansScorer(window=window) + anomaly_scores = linear_timeseries(length=10, start_value=2, end_value=20) + aggreg_scores = scorer._fun_window_agg([anomaly_scores], window=window)[0] + # on the last "window" values, difference of only 1 between consecutive aggregated scores + np.testing.assert_array_almost_equal( + aggreg_scores.values(), np.array([[4, 6, 8, 10, 12, 14, 16, 18, 19, 20]]).T + ) + assert aggreg_scores.time_index.equals(anomaly_scores.time_index) + + # window = 6, increment of 2 anomaly scores + window = 6 + scorer = KMeansScorer(window=window) + anomaly_scores = linear_timeseries(length=10, start_value=2, end_value=20) + aggreg_scores = scorer._fun_window_agg([anomaly_scores], window=window)[0] + # on the last "window" values, difference of only 1 between consecutive aggregated scores + np.testing.assert_array_almost_equal( + aggreg_scores.values(), np.array([[7, 9, 11, 13, 15, 16, 17, 18, 19, 20]]).T + ) + assert aggreg_scores.time_index.equals(anomaly_scores.time_index) + + # window = 7, increment of 2 anomaly scores + window = 7 + scorer = KMeansScorer(window=window) + anomaly_scores = linear_timeseries(length=10, start_value=2, end_value=20) + aggreg_scores = scorer._fun_window_agg([anomaly_scores], window=window)[0] + # on the last "window" values, difference of only 1 between consecutive aggregated scores + np.testing.assert_array_almost_equal( + aggreg_scores.values(), + np.array([[8, 10, 12, 14, 15, 16, 17, 18, 19, 20]]).T, + ) + assert aggreg_scores.time_index.equals(anomaly_scores.time_index) diff --git a/darts/tests/conftest.py b/darts/tests/conftest.py index c4304bb392..90bf29e20b 100644 --- a/darts/tests/conftest.py +++ b/darts/tests/conftest.py @@ -1,9 +1,22 @@ import logging +import os import shutil import tempfile import pytest +from darts.logging import get_logger + +logger = get_logger(__name__) + +try: + import torch # noqa: F401 + + TORCH_AVAILABLE = True +except ImportError: + logger.warning("Torch not installed - Some tests will be skipped.") + TORCH_AVAILABLE = False + tfm_kwargs = { "pl_trainer_kwargs": { "accelerator": "cpu", @@ -28,15 +41,31 @@ def tear_down_tests(): @pytest.fixture(scope="module") def tmpdir_module(): - """Sets up a temporary directory that will be deleted after the test module (script) finished.""" + """Sets up and moves into a temporary directory that will be deleted after the test module (script) finished.""" temp_work_dir = tempfile.mkdtemp(prefix="darts") + # remember origin + cwd = os.getcwd() + # move to temp dir + os.chdir(temp_work_dir) + # go into test with temp dir as input yield temp_work_dir + # move back to origin shutil.rmtree(temp_work_dir) + # remove temp dir + os.chdir(cwd) @pytest.fixture(scope="function") def tmpdir_fn(): - """Sets up a temporary directory that will be deleted after the test function finished.""" + """Sets up and moves into a temporary directory that will be deleted after the test function finished.""" temp_work_dir = tempfile.mkdtemp(prefix="darts") + # remember origin + cwd = os.getcwd() + # move to temp dir + os.chdir(temp_work_dir) + # go into test with temp dir as input yield temp_work_dir + # move back to origin + os.chdir(cwd) + # remove temp dir shutil.rmtree(temp_work_dir) diff --git a/darts/tests/dataprocessing/dtw/test_dtw.py b/darts/tests/dataprocessing/dtw/test_dtw.py index 458fcec6ed..ef98dcf576 100644 --- a/darts/tests/dataprocessing/dtw/test_dtw.py +++ b/darts/tests/dataprocessing/dtw/test_dtw.py @@ -63,12 +63,13 @@ def test_shift(self): exact_alignment = dtw.dtw(series1, series2, multi_grid_radius=-1) - assert ( - exact_alignment.distance() == 0 - ), "Minimum cost between two shifted series should be 0" - np.testing.assert_array_equal( - exact_alignment.path(), expected_path - ), "Incorrect path" + assert exact_alignment.distance() == 0, ( + "Minimum cost between two shifted series should be 0" + ) + ( + np.testing.assert_array_equal(exact_alignment.path(), expected_path), + "Incorrect path", + ) def test_multi_grid(self): size = 2**5 - 1 # test odd size diff --git a/darts/tests/dataprocessing/encoders/test_covariate_index_generators.py b/darts/tests/dataprocessing/encoders/test_covariate_index_generators.py index 72c0d3fa22..01bf290b0c 100644 --- a/darts/tests/dataprocessing/encoders/test_covariate_index_generators.py +++ b/darts/tests/dataprocessing/encoders/test_covariate_index_generators.py @@ -10,6 +10,7 @@ ) from darts.logging import get_logger from darts.utils import timeseries_generation as tg +from darts.utils.utils import generate_index logger = get_logger(__name__) @@ -34,7 +35,7 @@ class TestCovariatesIndexGenerator: # pd.DatetimeIndex # expected covariates for inference dataset for n <= output_chunk_length cov_time_inf_short = TimeSeries.from_times_and_values( - tg.generate_index( + generate_index( start=target_time.start_time(), length=n_target + n_short, freq=target_time.freq, @@ -43,7 +44,7 @@ class TestCovariatesIndexGenerator: ) # expected covariates for inference dataset for n > output_chunk_length cov_time_inf_long = TimeSeries.from_times_and_values( - tg.generate_index( + generate_index( start=target_time.start_time(), length=n_target + n_long, freq=target_time.freq, @@ -52,18 +53,18 @@ class TestCovariatesIndexGenerator: ) # integer index - # excpected covariates for inference dataset for n <= output_chunk_length + # expected covariates for inference dataset for n <= output_chunk_length cov_int_inf_short = TimeSeries.from_times_and_values( - tg.generate_index( + generate_index( start=target_int.start_time(), length=n_target + n_short, freq=target_int.freq, ), np.arange(n_target + n_short), ) - # excpected covariates for inference dataset for n > output_chunk_length + # expected covariates for inference dataset for n > output_chunk_length cov_int_inf_long = TimeSeries.from_times_and_values( - tg.generate_index( + generate_index( start=target_int.start_time(), length=n_target + n_long, freq=target_int.freq, diff --git a/darts/tests/dataprocessing/encoders/test_encoders.py b/darts/tests/dataprocessing/encoders/test_encoders.py index 41bc8c29dc..fa235479a3 100644 --- a/darts/tests/dataprocessing/encoders/test_encoders.py +++ b/darts/tests/dataprocessing/encoders/test_encoders.py @@ -1,5 +1,6 @@ import copy -from typing import Optional, Sequence +from collections.abc import Sequence +from typing import Optional import numpy as np import pandas as pd @@ -29,18 +30,15 @@ ) from darts.dataprocessing.transformers import Scaler from darts.logging import get_logger, raise_log +from darts.tests.conftest import TORCH_AVAILABLE from darts.utils import timeseries_generation as tg +from darts.utils.utils import freqs, generate_index logger = get_logger(__name__) -try: +if TORCH_AVAILABLE: from darts.models import TFTModel - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not installed - will be skipping Torch models tests") - TORCH_AVAILABLE = False - class TestEncoder: encoders_cls = [ @@ -79,7 +77,7 @@ class TestEncoder: # multi-TS at prediction should be as follows inf_ts_short_future = [ TimeSeries.from_times_and_values( - tg.generate_index( + generate_index( start=ts.end_time() + (1 - 12) * ts.freq, length=12 + 6, freq=ts.freq ), np.arange(12 + 6), @@ -89,7 +87,7 @@ class TestEncoder: inf_ts_long_future = [ TimeSeries.from_times_and_values( - tg.generate_index( + generate_index( start=ts.end_time() + (1 - 12) * ts.freq, length=12 + 8, freq=ts.freq ), np.arange(12 + 8), @@ -99,7 +97,7 @@ class TestEncoder: inf_ts_short_past = [ TimeSeries.from_times_and_values( - tg.generate_index( + generate_index( start=ts.end_time() + (1 - 12) * ts.freq, length=12, freq=ts.freq ), np.arange(12), @@ -109,7 +107,7 @@ class TestEncoder: inf_ts_long_past = [ TimeSeries.from_times_and_values( - tg.generate_index( + generate_index( start=ts.end_time() + (1 - 12) * ts.freq, length=12 + (8 - 6), freq=ts.freq, @@ -354,9 +352,9 @@ def some_f(idx): base_comp_name = "darts_enc_pc_" else: base_comp_name = "darts_enc_fc_" - comps_expected = pd.Index( - [base_comp_name + comp_name for comp_name in comps_expected] - ) + comps_expected = pd.Index([ + base_comp_name + comp_name for comp_name in comps_expected + ]) assert not enc.fit_called # initially, no components @@ -493,32 +491,28 @@ def extract_year(index): "tz": "CET", } # given `add_encoders` dict, we expect encoders to generate the following components - comps_expected_past = pd.Index( - [ - "darts_enc_pc_cyc_month_sin", - "darts_enc_pc_cyc_month_cos", - "darts_enc_pc_cyc_day_sin", - "darts_enc_pc_cyc_day_cos", - "darts_enc_pc_dta_month", - "darts_enc_pc_dta_year", - "darts_enc_pc_pos_relative", - "darts_enc_pc_cus_custom", - "darts_enc_pc_cus_custom_1", - ] - ) - comps_expected_future = pd.Index( - [ - "darts_enc_fc_cyc_day_sin", - "darts_enc_fc_cyc_day_cos", - "darts_enc_fc_cyc_month_sin", - "darts_enc_fc_cyc_month_cos", - "darts_enc_fc_dta_year", - "darts_enc_fc_dta_month", - "darts_enc_fc_pos_relative", - "darts_enc_fc_cus_custom", - "darts_enc_fc_cus_custom_1", - ] - ) + comps_expected_past = pd.Index([ + "darts_enc_pc_cyc_month_sin", + "darts_enc_pc_cyc_month_cos", + "darts_enc_pc_cyc_day_sin", + "darts_enc_pc_cyc_day_cos", + "darts_enc_pc_dta_month", + "darts_enc_pc_dta_year", + "darts_enc_pc_pos_relative", + "darts_enc_pc_cus_custom", + "darts_enc_pc_cus_custom_1", + ]) + comps_expected_future = pd.Index([ + "darts_enc_fc_cyc_day_sin", + "darts_enc_fc_cyc_day_cos", + "darts_enc_fc_cyc_month_sin", + "darts_enc_fc_cyc_month_cos", + "darts_enc_fc_dta_year", + "darts_enc_fc_dta_month", + "darts_enc_fc_pos_relative", + "darts_enc_fc_cus_custom", + "darts_enc_fc_cus_custom_1", + ]) kwargs = { "add_encoders": add_encoders, "input_chunk_length": input_chunk_length, @@ -667,7 +661,7 @@ def test_cyclic_encoder(self): attribute = "month" month_series = TimeSeries.from_times_and_values( - times=tg.generate_index( + times=generate_index( start=pd.to_datetime("2000-01-01"), length=24, freq="MS" ), values=np.arange(24), @@ -724,7 +718,7 @@ def test_datetime_attribute_encoder(self): attribute = "month" month_series = TimeSeries.from_times_and_values( - times=tg.generate_index( + times=generate_index( start=pd.to_datetime("2000-01-01"), length=24, freq="MS" ), values=np.arange(24), @@ -890,7 +884,7 @@ def test_integer_positional_encoder(self): def test_callable_encoder(self): """Test `CallableIndexEncoder`""" - ts = tg.linear_timeseries(length=24, freq="A") + ts = tg.linear_timeseries(length=24, freq=freqs["YE"]) input_chunk_length = 12 output_chunk_length = 6 @@ -930,7 +924,7 @@ def index_year_shifted(index): # inference set pc, fc = encs.encode_inference(n=12, target=ts) - year_index = tg.generate_index( + year_index = generate_index( start=ts.end_time() - ts.freq * (input_chunk_length - 1), length=24, freq=ts.freq, @@ -968,7 +962,11 @@ def test_routine_cyclic(past_covs): ) ts1 = tg.linear_timeseries( - start_value=1, end_value=2, length=60, freq="T", column_name="cov_in" + start_value=1, + end_value=2, + length=60, + freq=freqs["min"], + column_name="cov_in", ) encoder_params = { "position": {"future": ["relative"]}, @@ -1020,7 +1018,11 @@ def test_routine_cyclic(past_covs): ) fc_inf = tg.linear_timeseries( - start_value=1, end_value=3, length=80, freq="T", column_name="cov_in" + start_value=1, + end_value=3, + length=80, + freq=freqs["min"], + column_name="cov_in", ) pc3, fc3 = encs.encode_inference(n=60, target=ts1, future_covariates=fc_inf) @@ -1048,7 +1050,7 @@ def test_routine_cyclic(past_covs): def test_transformer_multi_series(self): ts1 = tg.linear_timeseries( - start_value=1, end_value=2, length=21, freq="T", column_name="cov" + start_value=1, end_value=2, length=21, freq=freqs["min"], column_name="cov" ) ts2 = tg.linear_timeseries( start=None, @@ -1056,7 +1058,7 @@ def test_transformer_multi_series(self): start_value=1.5, end_value=2, length=11, - freq="T", + freq=freqs["min"], column_name="cov", ) ts1_inf = ts1.drop_before(ts2.start_time() - ts1.freq) diff --git a/darts/tests/dataprocessing/test_pipeline.py b/darts/tests/dataprocessing/test_pipeline.py index cd056007c1..2ff55cd19c 100644 --- a/darts/tests/dataprocessing/test_pipeline.py +++ b/darts/tests/dataprocessing/test_pipeline.py @@ -1,5 +1,7 @@ -from typing import Any, Mapping +from collections.abc import Mapping, Sequence +from typing import Any, Union +import numpy as np import pytest from darts import TimeSeries @@ -97,6 +99,36 @@ def ts_inverse_transform( ) -> TimeSeries: return data.map(lambda x: x / 2) + class ExtendTransformer(FittableDataTransformer, InvertibleDataTransformer): + def __init__(self, global_fit: bool, coef: int): + self.coef = coef + super().__init__( + name="fittable extending transformer", global_fit=global_fit + ) + + @staticmethod + def ts_fit( + series: Union[TimeSeries, Sequence[TimeSeries]], + params: Mapping[str, Any], + *args, + **kwargs, + ): + coef = params["fixed"]["coef"] + if isinstance(series, Sequence): + return sum(ts.values()[0] for ts in series) + coef + else: + return series.values()[0] + coef + + @staticmethod + def ts_transform(data: TimeSeries, params: Mapping[str, Any]) -> TimeSeries: + return data + params["fitted"] + + @staticmethod + def ts_inverse_transform( + data: TimeSeries, params: Mapping[str, Any] + ) -> TimeSeries: + return data - params["fitted"] + def test_transform(self): # given mock1 = self.DataTransformerMock1() @@ -115,6 +147,91 @@ def test_transform(self): assert t.transform_called assert not t.inverse_transform_called + def test_transform_prefitted(self): + """Check that when multiple series are passed to fit transformers with global_fit=False, + transform behave as expected when series_idx is specified. + + Note: the transformers are fitted independently to make the expected results more intuitive + """ + data = [ + constant_timeseries(value=0, length=2), + constant_timeseries(value=10, length=2), + ] + + def get_transf(global_fit: bool, fit: bool, coef: int): + transf = self.ExtendTransformer(global_fit=global_fit, coef=coef) + if fit: + transf.fit(data) + return transf + + # multiple series, global_fit=False + p = Pipeline([ + get_transf(global_fit=False, fit=True, coef=1), + get_transf(global_fit=False, fit=True, coef=5), + ]) + transformed = p.transform(data) + + # ts + (data[0][0] + 1) + (data[0][0] + 5) = 6 + np.testing.assert_array_almost_equal( + transformed[0].values(), np.array([[6, 6]]).T + ) + # ts + (data[1][0] + 1) + (data[1][0] + 5) = 10 + 11 + 15 = 36 + np.testing.assert_array_almost_equal( + transformed[1].values(), np.array([[36, 36]]).T + ) + # implicitly use the first params of each transformer + np.testing.assert_array_almost_equal( + transformed[0].values(), p.transform(data[0]).values() + ) + # explicitly use the first params of each transformer + np.testing.assert_array_almost_equal( + transformed[0].values(), p.transform(data[0], series_idx=[0]).values() + ) + # implicitly use the first params of each transformer + # ts + (data[0][0] + 1) + (data[0][0] + 5) = 10 + 1 + 5 = 16 + np.testing.assert_array_almost_equal( + np.array([[16, 16]]).T, p.transform(data[1]).values() + ) + # explicitly use the second params of each transformer + np.testing.assert_array_almost_equal( + transformed[1].values(), p.transform(data[1], series_idx=[1]).values() + ) + + # multiple series, mixture of local and global transformers + p = Pipeline([ + get_transf(global_fit=False, fit=True, coef=1), + get_transf(global_fit=True, fit=True, coef=90), + ]) + transformed = p.transform(data) + # ts + (data[0][0] + 1) + (sum(data[;, 0]) + 90) = 0 + 1 + 100 + np.testing.assert_array_almost_equal( + transformed[0].values(), np.array([[101, 101]]).T + ) + # ts + (data[1][0] + 1) + (sum(data[;, 0]) + 90) = 10 + 11 + 100 + np.testing.assert_array_almost_equal( + transformed[1].values(), np.array([[121, 121]]).T + ) + # implicitly use the first params of first transformer, the second is global + np.testing.assert_array_almost_equal( + transformed[0].values(), p.transform(data[0]).values() + ) + # explicitly use the first params of first transformer, the second is global + np.testing.assert_array_almost_equal( + transformed[0].values(), p.transform(data[0], series_idx=[0]).values() + ) + # implicitly use the first params of first transformer, the second is global + # ts + (data[0][0] + 1) + (sum(data[;, 0]) + 90) = 10 + 1 + 100 + np.testing.assert_array_almost_equal( + np.array([[111, 111]]).T, p.transform(data[1]).values() + ) + # explicitly use the second params of first transformer, the second is global + np.testing.assert_array_almost_equal( + transformed[1].values(), p.transform(data[1], series_idx=[1]).values() + ) + + # reversing input, and explicitly selecting reversed indexes + assert transformed[::-1] == p.transform(data[::-1], series_idx=[1, 0]) + def test_inverse_raise_exception(self): # given mock = self.DataTransformerMock1() @@ -206,6 +323,95 @@ def test_inverse_transform(self): # then assert data == back + def test_inverse_transform_prefitted(self): + """Check that when multiple series are passed to fit transformers with global_fit=False, + inverse_transform behave as expected when series_idx is specified. + + Note: the transformers are fitted independently to make the expected results more intuitive + """ + data = [ + constant_timeseries(value=0, length=2), + constant_timeseries(value=10, length=2), + ] + + def get_transf(global_fit: bool, fit: bool, coef: int): + transf = self.ExtendTransformer(global_fit=global_fit, coef=coef) + if fit: + transf.fit(data) + return transf + + # multiple series, global_fit=False + p = Pipeline([ + get_transf(global_fit=False, fit=True, coef=1), + get_transf(global_fit=False, fit=True, coef=5), + ]) + transformed = p.transform(data) + + # implicitly use the first params of each transformer + np.testing.assert_array_almost_equal( + data[0].values(), p.inverse_transform(transformed[0]).values() + ) + # explicitly use the first params of each transformer + np.testing.assert_array_almost_equal( + data[0].values(), + p.inverse_transform(transformed[0], series_idx=[0]).values(), + ) + + # 10 + 11 + 15 + np.testing.assert_array_almost_equal( + np.array([[36, 36]]).T, transformed[1].values() + ) + # implicitly use the first params of each transformer + # inverse_transform[0][0] = lambda x: x - 1, inverse_transform[1][0] = lambda x: x - 5 + np.testing.assert_array_almost_equal( + np.array([[30, 30]]).T, p.inverse_transform(transformed[1]).values() + ) + np.testing.assert_array_almost_equal( + np.array([[30, 30]]).T, + p.inverse_transform(transformed[1], series_idx=0).values(), + ) + # explicitly use the second params of each transformer + # inverse_transform[0][0] = lambda x: x - 11, inverse_transform[1][0] = lambda x: x - 15 + np.testing.assert_array_almost_equal( + data[1].values(), p.inverse_transform(transformed[1], series_idx=1).values() + ) + + # multiple series, mixture of local and global transformers + p = Pipeline([ + get_transf(global_fit=False, fit=True, coef=1), + get_transf(global_fit=True, fit=True, coef=90), + ]) + transformed = p.transform(data) + + # implicitly use the first params of each transformer + np.testing.assert_array_almost_equal( + data[0].values(), p.inverse_transform(transformed[0]).values() + ) + # explicitly use the first params of each transformer + np.testing.assert_array_almost_equal( + data[0].values(), + p.inverse_transform(transformed[0], series_idx=[0]).values(), + ) + # 10 + 11 + 100 + np.testing.assert_array_almost_equal( + np.array([[121, 121]]).T, transformed[1].values() + ) + + # implicitly use the first params of each transformer + # inverse_transform[0][0] = lambda x: x - 1, inverse_transform = lambda x: x - 100 + np.testing.assert_array_almost_equal( + np.array([[20, 20]]).T, p.inverse_transform(transformed[1]).values() + ) + # explicitly use the second params of each transformer + # inverse_transform[0][1] = lambda x: x - 11, inverse_transform = lambda x: x - 100 + np.testing.assert_array_almost_equal( + data[1].values(), + p.inverse_transform(transformed[1], series_idx=[1]).values(), + ) + + # reversing input, and explicitly selecting reversed indexes + assert transformed[::-1] == p.transform(data[::-1], series_idx=[1, 0]) + def test_getitem(self): # given @@ -246,7 +452,6 @@ def test_raises_on_bad_key(self): assert str(err.value) == "key must be either an int or a slice" def test_multi_ts(self): - series1 = constant_timeseries(value=0.0, length=3) series2 = constant_timeseries(value=1.0, length=3) diff --git a/darts/tests/dataprocessing/transformers/test_base_data_transformer.py b/darts/tests/dataprocessing/transformers/test_base_data_transformer.py index 35c2d7cee3..35f17e7295 100644 --- a/darts/tests/dataprocessing/transformers/test_base_data_transformer.py +++ b/darts/tests/dataprocessing/transformers/test_base_data_transformer.py @@ -1,4 +1,5 @@ -from typing import Any, Mapping, Sequence, Union +from collections.abc import Mapping, Sequence +from typing import Any, Union import numpy as np diff --git a/darts/tests/dataprocessing/transformers/test_boxcox.py b/darts/tests/dataprocessing/transformers/test_boxcox.py index fddf9e4f84..862655081b 100644 --- a/darts/tests/dataprocessing/transformers/test_boxcox.py +++ b/darts/tests/dataprocessing/transformers/test_boxcox.py @@ -10,7 +10,6 @@ class TestBoxCox: - sine_series = sine_timeseries(length=50, value_y_offset=5, value_frequency=0.05) lin_series = linear_timeseries(start_value=1, end_value=10, length=50) multi_series = sine_series.stack(lin_series) @@ -61,7 +60,6 @@ def test_boxcox_inverse(self): ) def test_boxcox_multi_ts(self): - test_cases = [ ([[0.2, 0.4], [0.3, 0.6]]), # full lambda (0.4), # single value @@ -96,9 +94,9 @@ def test_boxcox_multiple_calls_to_fit(self): box_cox.fit(self.lin_series) lambda2 = deepcopy(box_cox._fitted_params)[0].tolist() - assert ( - lambda1 != lambda2 - ), "Lambdas should change when the transformer is retrained" + assert lambda1 != lambda2, ( + "Lambdas should change when the transformer is retrained" + ) def test_multivariate_stochastic_series(self): transformer = BoxCox() diff --git a/darts/tests/dataprocessing/transformers/test_data_transformer.py b/darts/tests/dataprocessing/transformers/test_data_transformer.py index 3d31b3ba46..b47c731cfd 100644 --- a/darts/tests/dataprocessing/transformers/test_data_transformer.py +++ b/darts/tests/dataprocessing/transformers/test_data_transformer.py @@ -95,16 +95,16 @@ def test_multivariate_stochastic_series(self): # Test that the transform is done per component (i.e max value over each component should be 1 and min 0) np.testing.assert_allclose( - np.array( - [ss.all_values(copy=False)[:, i, :].max() for i in range(ss.width)] - ), + np.array([ + ss.all_values(copy=False)[:, i, :].max() for i in range(ss.width) + ]), np.array([1.0] * ss.width), ) np.testing.assert_allclose( - np.array( - [ss.all_values(copy=False)[:, i, :].min() for i in range(ss.width)] - ), + np.array([ + ss.all_values(copy=False)[:, i, :].min() for i in range(ss.width) + ]), np.array([0.0] * ss.width), ) diff --git a/darts/tests/dataprocessing/transformers/test_diff.py b/darts/tests/dataprocessing/transformers/test_diff.py index 83634ab511..a1090db5ba 100644 --- a/darts/tests/dataprocessing/transformers/test_diff.py +++ b/darts/tests/dataprocessing/transformers/test_diff.py @@ -1,5 +1,6 @@ +from collections.abc import Sequence from copy import deepcopy -from typing import Optional, Sequence +from typing import Optional import numpy as np import pandas as pd @@ -9,6 +10,7 @@ from darts.timeseries import TimeSeries from darts.timeseries import concatenate as darts_concat from darts.utils.timeseries_generation import linear_timeseries, sine_timeseries +from darts.utils.utils import freqs class TestDiff: @@ -27,7 +29,6 @@ def assert_series_equal( equal_nan: bool, to_compare: Optional[np.ndarray] = None, ): - """ Helper to compare series differenced by `Diff`. @@ -97,7 +98,7 @@ def test_diff_inverse_transform_beyond_fit_data(self): # Artifically truncate series: short_sine = self.sine_series.copy().drop_after(10) - for (lags, dropna) in test_cases: + for lags, dropna in test_cases: # Fit Diff to truncated series: diff = Diff(lags=lags, dropna=dropna) diff.fit(short_sine) @@ -133,7 +134,7 @@ def test_diff_multi_ts(self): (1, False, component_mask), ([1, 2, 3, 2, 1], False, component_mask), ] - for (lags, dropna, mask) in test_cases: + for lags, dropna, mask in test_cases: diff = Diff(lags=lags, dropna=dropna) transformed = diff.fit_transform( [self.sine_series, self.sine_series], component_mask=mask @@ -172,7 +173,7 @@ def test_diff_stochastic_series(self): vals = np.random.rand(10, 5, 10) series = TimeSeries.from_values(vals) - for (lags, dropna) in test_cases: + for lags, dropna in test_cases: transformer = Diff(lags=lags, dropna=dropna) new_series = transformer.fit_transform(series) series_back = transformer.inverse_transform(new_series) @@ -247,7 +248,8 @@ def test_diff_incompatible_inverse_transform_freq(self): values=vals, times=pd.date_range(start="1/1/2018", freq="W", periods=10) ) series2 = TimeSeries.from_times_and_values( - values=vals, times=pd.date_range(start="1/1/2018", freq="M", periods=10) + values=vals, + times=pd.date_range(start="1/1/2018", freq=freqs["ME"], periods=10), ) diff = Diff(lags=1, dropna=True) diff.fit(series1) @@ -275,7 +277,7 @@ def test_diff_incompatible_inverse_transform_shape(self): diff.inverse_transform(series_rm_comp.diff(n=1, periods=1, dropna=True)) assert ( f"Expected series to have {series.n_components} components; " - f"instead, it has {series.n_components-1}." == str(e.value) + f"instead, it has {series.n_components - 1}." == str(e.value) ) series_rm_samp = TimeSeries.from_times_and_values( values=vals[:, :, 1:], times=dates @@ -284,7 +286,7 @@ def test_diff_incompatible_inverse_transform_shape(self): diff.inverse_transform(series_rm_samp.diff(n=1, periods=1, dropna=True)) assert ( f"Expected series to have {series.n_samples} samples; " - f"instead, it has {series.n_samples-1}." == str(e.value) + f"instead, it has {series.n_samples - 1}." == str(e.value) ) def test_diff_multiple_calls_to_fit(self): diff --git a/darts/tests/dataprocessing/transformers/test_fittable_data_transformer.py b/darts/tests/dataprocessing/transformers/test_fittable_data_transformer.py index 465134cae5..cb00e70885 100644 --- a/darts/tests/dataprocessing/transformers/test_fittable_data_transformer.py +++ b/darts/tests/dataprocessing/transformers/test_fittable_data_transformer.py @@ -1,4 +1,5 @@ -from typing import Any, Mapping, Sequence, Union +from collections.abc import Mapping, Sequence +from typing import Any, Union import numpy as np @@ -151,9 +152,10 @@ def test_input_transformed_multiple_series(self): # Don't have different params for different jobs: mock = self.DataTransformerMock(scale=2, translation=10, parallel_params=False) - (transformed_1, transformed_2) = mock.fit_transform( - (test_input_1, test_input_2) - ) + (transformed_1, transformed_2) = mock.fit_transform(( + test_input_1, + test_input_2, + )) # 2 * 1 + 10 = 12 assert transformed_1 == constant_timeseries(value=12, length=10) # 2 * 2 + 10 = 14 @@ -163,9 +165,10 @@ def test_input_transformed_multiple_series(self): mock = self.DataTransformerMock( scale=(2, 3), translation=10, parallel_params=["_scale"] ) - (transformed_1, transformed_2) = mock.fit_transform( - (test_input_1, test_input_2) - ) + (transformed_1, transformed_2) = mock.fit_transform(( + test_input_1, + test_input_2, + )) # 2 * 1 + 10 = 12 assert transformed_1 == constant_timeseries(value=12, length=10) # 3 * 2 + 10 = 16 @@ -173,8 +176,19 @@ def test_input_transformed_multiple_series(self): # If only one timeseries provided, should apply parameters defined for # for the first to that series: - transformed_1 = mock.transform(test_input_1) - assert transformed_1 == constant_timeseries(value=12, length=10) + assert mock.transform(test_input_1) == constant_timeseries(value=12, length=10) + # 2 * 2 + 10 = 14 + assert mock.transform(test_input_2) == constant_timeseries(value=14, length=11) + + # If the index of another set of parameters is provided, the output changes accordingly: + # 3 * 1 + 10 = 13 + assert mock.transform(test_input_1, series_idx=1) == constant_timeseries( + value=13, length=10 + ) + # 3 * 2 + 10 = 16 + assert mock.transform(test_input_2, series_idx=1) == constant_timeseries( + value=16, length=11 + ) # Have different `scale`, `translation`, and `stack_samples` params for different jobs: mock = self.DataTransformerMock( @@ -184,9 +198,10 @@ def test_input_transformed_multiple_series(self): mask_components=(False, False), parallel_params=True, ) - (transformed_1, transformed_2) = mock.fit_transform( - (test_input_1, test_input_2) - ) + (transformed_1, transformed_2) = mock.fit_transform(( + test_input_1, + test_input_2, + )) # 2 * 1 + 10 = 12 assert transformed_1 == constant_timeseries(value=12, length=10) # 3 * 2 + 11 = 17 @@ -194,8 +209,26 @@ def test_input_transformed_multiple_series(self): # If only one timeseries provided, should apply parameters defined for # for the first to that series: - transformed_1 = mock.transform(test_input_1) - assert transformed_1 == constant_timeseries(value=12, length=10) + assert mock.transform(test_input_1) == constant_timeseries(value=12, length=10) + # 2 * 2 + 10 = 14 + assert mock.transform(test_input_2) == constant_timeseries(value=14, length=11) + + # If the index of another set of parameters is provided, the output changes accordingly: + assert mock.transform(test_input_1, series_idx=0) == constant_timeseries( + value=12, length=10 + ) + # 3 * 1 + 11 = 14 + assert mock.transform(test_input_1, series_idx=1) == constant_timeseries( + value=14, length=10 + ) + # 2 * 2 + 10 = 14 + assert mock.transform(test_input_2, series_idx=0) == constant_timeseries( + value=14, length=11 + ) + # 3 * 2 + 11 = 17 + assert mock.transform(test_input_2, series_idx=1) == constant_timeseries( + value=17, length=11 + ) # Train on three series with three different fixed param values, # but pass only one or two series as inputs to `transform`; @@ -297,13 +330,15 @@ def __init__(self, global_fit: bool): global_fit Whether global fitting should be performed. """ - super().__init__(name="DataTransformerMock", global_fit=global_fit) + super().__init__( + name="DataTransformerMock", global_fit=global_fit, mask_components=True + ) @staticmethod def ts_fit( series: Union[TimeSeries, Sequence[TimeSeries]], params: Mapping[str, Any], - **kwargs + **kwargs, ): """ 'Fits' transform by computing time-average of each sample and @@ -356,3 +391,47 @@ def test_global_fitting(self): ).fit_transform([series_1, series_2]) assert transformed_1 == TimeSeries.from_values(-0.5 * np.ones((3, 2, 1))) assert transformed_2 == TimeSeries.from_values(0.5 * np.ones((3, 2, 1))) + + def test_global_fitting_component_masking(self): + cols_1 = ["A", "B"] + cols_2 = ["C", "D"] + series_1_ = TimeSeries.from_values(np.ones((3, 2, 1)), columns=cols_1) + series_2_ = TimeSeries.from_values(2 * np.ones((3, 2, 1)), columns=cols_2) + series_1 = series_1_.stack(series_2_) + series_2 = series_2_.stack(series_1_) + + component_mask = np.array([True] * len(cols_1) + [False] * len(cols_2)) + # Local fitting - subtracting mean of each series from itself should return + # zero-valued series: + transformed_1, transformed_2 = self.DataTransformerMock( + global_fit=False + ).fit_transform([series_1, series_2], component_mask=component_mask) + # transformed components + assert transformed_1[cols_1] == TimeSeries.from_values( + np.zeros((3, 2, 1)), columns=cols_1 + ) + assert transformed_2[cols_2] == TimeSeries.from_values( + np.zeros((3, 2, 1)), columns=cols_2 + ) + + # non-transformed components + assert transformed_1[cols_2] == series_2_ + assert transformed_2[cols_1] == series_1_ + + # Global fitting - mean of `series_1` and `series_2` should be `1.5`, so + # `series_1` values should be transformed to `-0.5` and `series_2` values + # should be transformed to `1.5`: + transformed_1, transformed_2 = self.DataTransformerMock( + global_fit=True + ).fit_transform([series_1, series_2], component_mask=component_mask) + # transformed components + assert transformed_1[cols_1] == TimeSeries.from_values( + -0.5 * np.ones((3, 2, 1)), columns=cols_1 + ) + assert transformed_2[cols_2] == TimeSeries.from_values( + 0.5 * np.ones((3, 2, 1)), columns=cols_2 + ) + + # non-transformed components + assert transformed_1[cols_2] == series_2_ + assert transformed_2[cols_1] == series_1_ diff --git a/darts/tests/dataprocessing/transformers/test_invertible_data_transformer.py b/darts/tests/dataprocessing/transformers/test_invertible_data_transformer.py index 71163eb928..fd9bf13402 100644 --- a/darts/tests/dataprocessing/transformers/test_invertible_data_transformer.py +++ b/darts/tests/dataprocessing/transformers/test_invertible_data_transformer.py @@ -1,4 +1,5 @@ -from typing import Any, Mapping, Sequence, Union +from collections.abc import Mapping, Sequence +from typing import Any, Union import numpy as np @@ -262,6 +263,98 @@ def test_input_transformed_multiple_series(self): assert inv_1 == test_input_1 assert inv_2 == test_input_2 + def test_input_transformed_list_of_lists_of_series(self): + """ + Tests for correct transformation of multiple series when + different param values are used for different parallel + jobs (i.e. test that `parallel_params` argument is treated + correctly). Also tests that transformer correctly handles + being provided with fewer input series than fixed parameter + value sets. + """ + test_input_1 = constant_timeseries(value=1, length=10) + test_input_2 = constant_timeseries(value=2, length=11) + + # Don't have different params for different jobs: + mock = self.DataTransformerMock(scale=2, translation=10, parallel_params=False) + (transformed_1, transformed_2) = mock.transform((test_input_1, test_input_2)) + # 2 * 1 + 10 = 12 + assert transformed_1 == constant_timeseries(value=12, length=10) + # 2 * 2 + 10 = 14 + assert transformed_2 == constant_timeseries(value=14, length=11) + + # list of lists of series must get input back + inv = mock.inverse_transform([[transformed_1], [transformed_2]]) + assert len(inv) == 2 + assert all( + isinstance(series_list, list) and len(series_list) == 1 + for series_list in inv + ) + assert all( + isinstance(series, TimeSeries) + for series_list in inv + for series in series_list + ) + assert inv[0][0] == test_input_1 + assert inv[1][0] == test_input_2 + + # one list of lists of is longer than others, must get input back + inv = mock.inverse_transform([[transformed_1, transformed_1], [transformed_2]]) + assert len(inv) == 2 + assert len(inv[0]) == 2 and len(inv[1]) == 1 + assert all(isinstance(series_list, list) for series_list in inv) + assert all( + isinstance(series, TimeSeries) + for series_list in inv + for series in series_list + ) + assert inv[0][0] == test_input_1 + assert inv[0][1] == test_input_1 + assert inv[1][0] == test_input_2 + + # different types of Sequences, must get input back + inv = mock.inverse_transform(((transformed_1, transformed_1), (transformed_2,))) + assert len(inv) == 2 + assert len(inv[0]) == 2 and len(inv[1]) == 1 + assert all(isinstance(series_list, list) for series_list in inv) + assert all( + isinstance(series, TimeSeries) + for series_list in inv + for series in series_list + ) + assert inv[0][0] == test_input_1 + assert inv[0][1] == test_input_1 + assert inv[1][0] == test_input_2 + + # one list of lists is empty, returns empty list as well + inv = mock.inverse_transform([[], [transformed_2, transformed_2]]) + assert len(inv) == 2 + assert len(inv[0]) == 0 and len(inv[1]) == 2 + assert all(isinstance(series_list, list) for series_list in inv) + assert all(isinstance(series, TimeSeries) for series in inv[1]) + assert inv[1][0] == test_input_2 + assert inv[1][1] == test_input_2 + + # more list of lists than used during transform works + inv = mock.inverse_transform([ + [transformed_1], + [transformed_2], + [transformed_2], + ]) + assert len(inv) == 3 + assert all( + isinstance(series_list, list) and len(series_list) == 1 + for series_list in inv + ) + assert all( + isinstance(series, TimeSeries) + for series_list in inv + for series in series_list + ) + assert inv[0][0] == test_input_1 + assert inv[1][0] == test_input_2 + assert inv[2][0] == test_input_2 + def test_input_transformed_multiple_samples(self): """ Tests that `stack_samples` and `unstack_samples` correctly diff --git a/darts/tests/dataprocessing/transformers/test_invertible_fittable_data_transformer.py b/darts/tests/dataprocessing/transformers/test_invertible_fittable_data_transformer.py index b699dd47bb..2ab7179196 100644 --- a/darts/tests/dataprocessing/transformers/test_invertible_fittable_data_transformer.py +++ b/darts/tests/dataprocessing/transformers/test_invertible_fittable_data_transformer.py @@ -1,6 +1,8 @@ -from typing import Any, Mapping, Sequence, Union +from collections.abc import Mapping, Sequence +from typing import Any, Union import numpy as np +import pytest from darts import TimeSeries from darts.dataprocessing.transformers.fittable_data_transformer import ( @@ -208,9 +210,10 @@ def test_input_transformed_multiple_series(self): # Don't have different params for different jobs: mock = self.DataTransformerMock(scale=2, translation=10, parallel_params=False) - (transformed_1, transformed_2) = mock.fit_transform( - (test_input_1, test_input_2) - ) + (transformed_1, transformed_2) = mock.fit_transform(( + test_input_1, + test_input_2, + )) # 2 * 1 + 10 = 12 assert transformed_1 == constant_timeseries(value=12, length=10) # 2 * 2 + 10 = 14 @@ -231,9 +234,10 @@ def test_input_transformed_multiple_series(self): mock = self.DataTransformerMock( scale=(2, 3), translation=10, parallel_params=["_scale"] ) - (transformed_1, transformed_2) = mock.fit_transform( - (test_input_1, test_input_2) - ) + (transformed_1, transformed_2) = mock.fit_transform(( + test_input_1, + test_input_2, + )) # 2 * 1 + 10 = 12 assert transformed_1 == constant_timeseries(value=12, length=10) # 3 * 2 + 10 = 16 @@ -251,9 +255,10 @@ def test_input_transformed_multiple_series(self): mask_components=(False, False), parallel_params=True, ) - (transformed_1, transformed_2) = mock.fit_transform( - (test_input_1, test_input_2) - ) + (transformed_1, transformed_2) = mock.fit_transform(( + test_input_1, + test_input_2, + )) # 2 * 1 + 10 = 12 assert transformed_1 == constant_timeseries(value=12, length=10) # 3 * 2 + 11 = 17 @@ -293,6 +298,92 @@ def test_input_transformed_multiple_series(self): assert inv_1 == test_input_1 assert inv_2 == test_input_2 + def test_input_transformed_list_of_lists_of_series(self): + """ + Tests for correct transformation of multiple series when + different param values are used for different parallel + jobs (i.e. test that `parallel_params` argument is treated + correctly). Also tests that transformer correctly handles + being provided with fewer input series than fixed parameter + value sets. + """ + test_input_1 = constant_timeseries(value=1, length=10) + test_input_2 = constant_timeseries(value=2, length=11) + + # Don't have different params for different jobs: + mock = self.DataTransformerMock(scale=2, translation=10, parallel_params=False) + (transformed_1, transformed_2) = mock.fit_transform(( + test_input_1, + test_input_2, + )) + # 2 * 1 + 10 = 12 + assert transformed_1 == constant_timeseries(value=12, length=10) + # 2 * 2 + 10 = 14 + assert transformed_2 == constant_timeseries(value=14, length=11) + + # list of lists of series must get input back + inv = mock.inverse_transform([[transformed_1], [transformed_2]]) + assert len(inv) == 2 + assert all( + isinstance(series_list, list) and len(series_list) == 1 + for series_list in inv + ) + assert all( + isinstance(series, TimeSeries) + for series_list in inv + for series in series_list + ) + assert inv[0][0] == test_input_1 + assert inv[1][0] == test_input_2 + + # one list of lists of is longer than others, must get input back + inv = mock.inverse_transform([[transformed_1, transformed_1], [transformed_2]]) + assert len(inv) == 2 + assert len(inv[0]) == 2 and len(inv[1]) == 1 + assert all(isinstance(series_list, list) for series_list in inv) + assert all( + isinstance(series, TimeSeries) + for series_list in inv + for series in series_list + ) + assert inv[0][0] == test_input_1 + assert inv[0][1] == test_input_1 + assert inv[1][0] == test_input_2 + + # different types of Sequences, must get input back + inv = mock.inverse_transform(((transformed_1, transformed_1), (transformed_2,))) + assert len(inv) == 2 + assert len(inv[0]) == 2 and len(inv[1]) == 1 + assert all(isinstance(series_list, list) for series_list in inv) + assert all( + isinstance(series, TimeSeries) + for series_list in inv + for series in series_list + ) + assert inv[0][0] == test_input_1 + assert inv[0][1] == test_input_1 + assert inv[1][0] == test_input_2 + + # one list of lists is empty, returns empty list as well + inv = mock.inverse_transform([[], [transformed_2, transformed_2]]) + assert len(inv) == 2 + assert len(inv[0]) == 0 and len(inv[1]) == 2 + assert all(isinstance(series_list, list) for series_list in inv) + assert all(isinstance(series, TimeSeries) for series in inv[1]) + assert inv[1][0] == test_input_2 + assert inv[1][1] == test_input_2 + + # more list of lists than used during transform, raises error + with pytest.raises(ValueError) as err: + _ = mock.inverse_transform([ + [transformed_1], + [transformed_2], + [transformed_2], + ]) + assert str(err.value).startswith( + "3 TimeSeries were provided but only 2 TimeSeries were specified" + ) + def test_input_transformed_multiple_samples(self): """ Tests that `stack_samples` and `unstack_samples` correctly @@ -391,7 +482,7 @@ def __init__(self, global_fit: bool): def ts_fit( series: Union[TimeSeries, Sequence[TimeSeries]], params: Mapping[str, Any], - **kwargs + **kwargs, ): """ 'Fits' transform by computing time-average of each sample and @@ -447,9 +538,10 @@ def test_global_fitting(self): assert transformed_1 == TimeSeries.from_values(np.zeros((3, 2, 1))) assert transformed_2 == TimeSeries.from_values(np.zeros((3, 2, 1))) # Inverting transform should return input: - untransformed_1, untransformed_2 = transformer.inverse_transform( - [transformed_1, transformed_2] - ) + untransformed_1, untransformed_2 = transformer.inverse_transform([ + transformed_1, + transformed_2, + ]) assert untransformed_1 == series_1 assert untransformed_2 == series_2 @@ -461,8 +553,9 @@ def test_global_fitting(self): assert transformed_1 == TimeSeries.from_values(-0.5 * np.ones((3, 2, 1))) assert transformed_2 == TimeSeries.from_values(0.5 * np.ones((3, 2, 1))) # Inverting transform should return input: - untransformed_1, untransformed_2 = transformer.inverse_transform( - [transformed_1, transformed_2] - ) + untransformed_1, untransformed_2 = transformer.inverse_transform([ + transformed_1, + transformed_2, + ]) assert untransformed_1 == series_1 assert untransformed_2 == series_2 diff --git a/darts/tests/dataprocessing/transformers/test_mappers.py b/darts/tests/dataprocessing/transformers/test_mappers.py index 569855aa08..778e092a53 100644 --- a/darts/tests/dataprocessing/transformers/test_mappers.py +++ b/darts/tests/dataprocessing/transformers/test_mappers.py @@ -49,7 +49,6 @@ def inverse_ts_func(ts, x): ) def test_mapper(self): - test_cases = [ (self.zeroes, self.tens), ([self.zeroes, self.tens], [self.tens, self.twenties]), @@ -68,7 +67,6 @@ def test_invertible_mapper(self): assert back == data def test_mapper_with_timestamp(self): - test_cases = [ (self.lin_series, self.zeroes), ([self.lin_series, self.lin_series], [self.zeroes, self.zeroes]), @@ -88,7 +86,6 @@ def test_mapper_with_timestamp(self): assert transformed == expected_output def test_invertible_mapper_with_timestamp(self): - test_cases = [(self.lin_series), ([self.lin_series, self.lin_series])] for data in test_cases: diff --git a/darts/tests/dataprocessing/transformers/test_midas.py b/darts/tests/dataprocessing/transformers/test_midas.py index 70b88eff9d..f853472d23 100644 --- a/darts/tests/dataprocessing/transformers/test_midas.py +++ b/darts/tests/dataprocessing/transformers/test_midas.py @@ -5,13 +5,8 @@ from darts import TimeSeries from darts.dataprocessing.transformers import MIDAS from darts.models import LinearRegressionModel -from darts.utils.timeseries_generation import generate_index, linear_timeseries - -# TODO: remove this once bumping min python version from 3.8 to 3.9 (pandas v2.2.0 not available for p38) -pd_above_v22 = pd.__version__ >= "2.2" -freq_quarter_end = "QE" if pd_above_v22 else "Q" -freq_month_end = "ME" if pd_above_v22 else "M" -freq_minute = "min" if pd_above_v22 else "T" +from darts.utils.timeseries_generation import linear_timeseries +from darts.utils.utils import freqs, generate_index class TestMIDAS: @@ -20,7 +15,7 @@ class TestMIDAS: end_value=9, start=pd.Timestamp("01-2020"), length=9, - freq="M", + freq=freqs["ME"], column_name="values", ) @@ -34,16 +29,18 @@ class TestMIDAS: columns=["values_midas_0", "values_midas_1", "values_midas_2"], ) - quarterly_end_times = pd.date_range(start="01-2020", periods=3, freq="Q") + quarterly_end_times = pd.date_range(start="01-2020", periods=3, freq=freqs["QE"]) quarterly_with_quarter_end_index_ts = TimeSeries.from_times_and_values( times=quarterly_end_times, values=quarterly_values, columns=["values_midas_0", "values_midas_1", "values_midas_2"], ) - quarterly_not_complete_values = np.array( - [[np.nan, np.nan, 3], [4, 5, 6], [7, 8, np.nan]] - ) + quarterly_not_complete_values = np.array([ + [np.nan, np.nan, 3], + [4, 5, 6], + [7, 8, np.nan], + ]) quarterly_not_complete_ts = TimeSeries.from_times_and_values( times=quarterly_times, values=quarterly_not_complete_values, @@ -63,7 +60,7 @@ def test_complete_monthly_to_quarterly(self): assert self.monthly_ts == inversed_quarterly_ts_midas # to quarter end - midas_2 = MIDAS(low_freq=freq_quarter_end) + midas_2 = MIDAS(low_freq=freqs["QE"]) quarterly_ts_midas = midas_2.fit_transform(self.monthly_ts) assert quarterly_ts_midas == self.quarterly_with_quarter_end_index_ts @@ -197,9 +194,11 @@ def test_multivariate_monthly_to_quarterly(self): # component components are alternating expected_quarterly_ts = TimeSeries.from_times_and_values( times=self.quarterly_ts.time_index, - values=np.array( - [[1, 10, 2, 11, 3, 12], [4, 13, 5, 14, 6, 15], [7, 16, 8, 17, 9, 18]] - ), + values=np.array([ + [1, 10, 2, 11, 3, 12], + [4, 13, 5, 14, 6, 15], + [7, 16, 8, 17, 9, 18], + ]), columns=[ "values_midas_0", "other_midas_0", @@ -244,9 +243,11 @@ def test_probabilistic_multivariate_monthly_to_quarterly(self): # component components are alternating quarterly_ts = TimeSeries.from_times_and_values( times=self.quarterly_ts.time_index, - values=np.array( - [[1, 10, 2, 11, 3, 12], [4, 13, 5, 14, 6, 15], [7, 16, 8, 17, 9, 18]] - ), + values=np.array([ + [1, 10, 2, 11, 3, 12], + [4, 13, 5, 14, 6, 15], + [7, 16, 8, 17, 9, 18], + ]), columns=[ "values_midas_0", "other_midas_0", @@ -291,13 +292,11 @@ def test_ts_with_missing_data(self): # components are interleaved expected_quarterly_ts = TimeSeries.from_times_and_values( times=self.quarterly_ts.time_index, - values=np.array( - [ - [1, 10, 2, 11, 3, 12], - [4, np.nan, 5, np.nan, 6, 15], - [7, 16, 8, 17, 9, 18], - ] - ), + values=np.array([ + [1, 10, 2, 11, 3, 12], + [4, np.nan, 5, np.nan, 6, 15], + [7, 16, 8, 17, 9, 18], + ]), columns=[ "values_midas_0", "other_midas_0", @@ -322,23 +321,24 @@ def test_from_second_to_minute(self): Test to see if other frequencies transforms like second to minute work as well. """ - second_times = pd.date_range(start="01-2020", periods=120, freq="S") + second_times = pd.date_range(start="01-2020", periods=120, freq=freqs["s"]) second_values = np.arange(1, len(second_times) + 1) second_ts = TimeSeries.from_times_and_values( times=second_times, values=second_values, columns=["values"] ) - minute_times = pd.date_range(start="01-2020", periods=2, freq="T") - minute_values = np.array( - [[i for i in range(1, 61)], [i for i in range(61, 121)]] - ) + minute_times = pd.date_range(start="01-2020", periods=2, freq=freqs["min"]) + minute_values = np.array([ + [i for i in range(1, 61)], + [i for i in range(61, 121)], + ]) minute_ts = TimeSeries.from_times_and_values( times=minute_times, values=minute_values, columns=[f"values_midas_{i}" for i in range(60)], ) - midas = MIDAS(low_freq=freq_minute) + midas = MIDAS(low_freq=freqs["min"]) minute_ts_midas = midas.fit_transform(second_ts) assert minute_ts_midas == minute_ts second_ts_midas = midas.inverse_transform(minute_ts_midas) @@ -354,12 +354,12 @@ def test_error_when_from_low_to_high(self): Tests if the transformer raises an error when the user asks for a transform in the wrong direction. """ # wrong direction : low to high freq - midas_1 = MIDAS(low_freq=freq_month_end) + midas_1 = MIDAS(low_freq=freqs["ME"]) with pytest.raises(ValueError): midas_1.fit_transform(self.quarterly_ts) # transform to same index requested - midas_2 = MIDAS(low_freq=freq_quarter_end) + midas_2 = MIDAS(low_freq=freqs["QE"]) with pytest.raises(ValueError): midas_2.fit_transform(self.quarterly_ts) @@ -376,7 +376,7 @@ def test_error_when_frequency_not_suitable_for_midas(self): times=daily_times, values=daily_values, columns=["values"] ) - midas = MIDAS(low_freq=freq_month_end) + midas = MIDAS(low_freq=freqs["ME"]) with pytest.raises(ValueError) as msg: midas.fit_transform(daily_ts) assert str(msg.value).startswith( @@ -390,7 +390,7 @@ def test_inverse_transform_prediction(self): """ # low frequency : QuarterStart monthly_ts = TimeSeries.from_times_and_values( - times=pd.date_range(start="01-2020", periods=24, freq="M"), + times=pd.date_range(start="01-2020", periods=24, freq=freqs["ME"]), values=np.arange(0, 24), columns=["values"], ) @@ -413,8 +413,8 @@ def test_inverse_transform_prediction(self): assert pred_quarterly.time_index.equals(quarterly_test_ts.time_index) assert pred_monthly.time_index.equals(monthly_test_ts.time_index) - # "Q" = QuarterEnd, the 2 "hidden" months must be retrieved - midas_quarterly = MIDAS(low_freq=freq_quarter_end) + # freqs["QE"] = QuarterEnd, the 2 "hidden" months must be retrieved + midas_quarterly = MIDAS(low_freq=freqs["QE"]) quarterly_train_ts = midas_quarterly.fit_transform(monthly_train_ts) quarterly_test_ts = midas_quarterly.transform(monthly_test_ts) @@ -440,11 +440,11 @@ def test_multiple_ts(self): to yearly). """ quarterly_univariate_ts = TimeSeries.from_times_and_values( - times=pd.date_range(start="2000-01-01", periods=12, freq="Q"), + times=pd.date_range(start="2000-01-01", periods=12, freq=freqs["QE"]), values=np.arange(0, 12), ) quarterly_multivariate_ts = TimeSeries.from_times_and_values( - times=pd.date_range(start="2020-01-01", periods=12, freq="Q"), + times=pd.date_range(start="2020-01-01", periods=12, freq=freqs["QE"]), values=np.arange(0, 24).reshape(-1, 2), ) @@ -467,7 +467,7 @@ def test_multiple_ts(self): inverse_transformed = midas_yearly.inverse_transform(list_yearly_ts) assert len(inverse_transformed) == 2 assert len(inverse_transformed[0]) == 0 - assert inverse_transformed[0].freq == freq_month_end + assert inverse_transformed[0].freq == freqs["ME"] assert inverse_transformed[0].n_components == 1 assert ts_to_transform[1:] == inverse_transformed[1:] @@ -514,7 +514,9 @@ def test_ts_with_static_covariates(self): columns=["static_2", "static_3", "static_4"], ) monthly_multivar_with_static_covs = TimeSeries.from_times_and_values( - times=generate_index(start=pd.Timestamp("2000-01"), length=8, freq="M"), + times=generate_index( + start=pd.Timestamp("2000-01"), length=8, freq=freqs["ME"] + ), values=np.stack([np.arange(2)] * 8), static_covariates=components_static_covs, ) diff --git a/darts/tests/dataprocessing/transformers/test_missing_values_filler.py b/darts/tests/dataprocessing/transformers/test_missing_values_filler.py index f8b75ef4ab..e4f7c205ad 100644 --- a/darts/tests/dataprocessing/transformers/test_missing_values_filler.py +++ b/darts/tests/dataprocessing/transformers/test_missing_values_filler.py @@ -6,7 +6,6 @@ class TestMissingValuesFiller: - time = pd.date_range("20130101", "20130130") static_covariate = pd.DataFrame({"0": [1]}) diff --git a/darts/tests/dataprocessing/transformers/test_reconciliation.py b/darts/tests/dataprocessing/transformers/test_reconciliation.py index 5181972c04..2b59da479a 100644 --- a/darts/tests/dataprocessing/transformers/test_reconciliation.py +++ b/darts/tests/dataprocessing/transformers/test_reconciliation.py @@ -134,19 +134,17 @@ def test_mint(self): def test_summation_matrix(self): np.testing.assert_equal( _get_summation_matrix(self.series_complex), - np.array( - [ - [1, 1, 1, 1], - [1, 1, 0, 0], - [0, 0, 1, 1], - [1, 0, 1, 0], - [0, 1, 0, 1], - [1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 1, 0], - [0, 0, 0, 1], - ] - ), + np.array([ + [1, 1, 1, 1], + [1, 1, 0, 0], + [0, 0, 1, 1], + [1, 0, 1, 0], + [0, 1, 0, 1], + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1], + ]), ) def test_hierarchy_preserved_after_predict(self): diff --git a/darts/tests/dataprocessing/transformers/test_static_covariates_transformer.py b/darts/tests/dataprocessing/transformers/test_static_covariates_transformer.py index 9f0db5346b..13f412a329 100644 --- a/darts/tests/dataprocessing/transformers/test_static_covariates_transformer.py +++ b/darts/tests/dataprocessing/transformers/test_static_covariates_transformer.py @@ -43,29 +43,31 @@ class TestStaticCovariatesTransformer: def test_scaling_single_series(self): # 3 categories for each categorical static covariate column (column idx 1 and 3) - test_values = np.array( - [[0.0, 0.0, 0.0, 0.0], [0.5, 1.0, 0.5, 1.0], [1.0, 2.0, 1.0, 2.0]] - ) + test_values = np.array([ + [0.0, 0.0, 0.0, 0.0], + [0.5, 1.0, 0.5, 1.0], + [1.0, 2.0, 1.0, 2.0], + ]) for series in [self.series1, self.series2]: scaler = StaticCovariatesTransformer() self.helper_test_scaling(series, scaler, test_values) - test_values = np.array( - [[-1.0, 0.0, -1.0, 0.0], [0.0, 1.0, 0.0, 1.0], [1.0, 2.0, 1.0, 2.0]] - ) + test_values = np.array([ + [-1.0, 0.0, -1.0, 0.0], + [0.0, 1.0, 0.0, 1.0], + [1.0, 2.0, 1.0, 2.0], + ]) for series in [self.series1, self.series2]: scaler = StaticCovariatesTransformer( transformer_num=MinMaxScaler(feature_range=(-1, 1)) ) self.helper_test_scaling(series, scaler, test_values) - test_values = np.array( - [ - [0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], - [0.5, 0.0, 1.0, 0.0, 0.5, 0.0, 1.0, 0.0], - [1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0], - ] - ) + test_values = np.array([ + [0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], + [0.5, 0.0, 1.0, 0.0, 0.5, 0.0, 1.0, 0.0], + [1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0], + ]) for series in [self.series1, self.series2]: scaler = StaticCovariatesTransformer(transformer_cat=OneHotEncoder()) self.helper_test_scaling(series, scaler, test_values) @@ -150,9 +152,11 @@ def test_scaling_multi_series(self): np.testing.assert_almost_equal( series_tr2[0].static_covariates_values(), - np.array( - [[0.0, 0.0, 0.0, 0.0], [0.25, 1.0, 0.25, 1.0], [0.5, 2.0, 0.5, 2.0]] - ), + np.array([ + [0.0, 0.0, 0.0, 0.0], + [0.25, 1.0, 0.25, 1.0], + [0.5, 2.0, 0.5, 2.0], + ]), ) series_recovered2 = scaler.inverse_transform(series_tr2[0]) assert self.series1.static_covariates.equals( @@ -161,9 +165,11 @@ def test_scaling_multi_series(self): np.testing.assert_almost_equal( series_tr2[1].static_covariates_values(), - np.array( - [[0.5, 2.0, 0.5, 2.0], [0.75, 3.0, 0.75, 3.0], [1.0, 4.0, 1.0, 4.0]] - ), + np.array([ + [0.5, 2.0, 0.5, 2.0], + [0.75, 3.0, 0.75, 3.0], + [1.0, 4.0, 1.0, 4.0], + ]), ) series_recovered3 = scaler.inverse_transform(series_tr2[1]) assert self.series2.static_covariates.equals( @@ -180,15 +186,13 @@ def test_scaling_multi_series(self): def helper_test_scaling(self, series, scaler, test_values): series_tr = scaler.fit_transform(series) - assert all( - [ - a == b - for a, b in zip( - series_tr.static_covariates_values().flatten(), - test_values.flatten(), - ) - ] - ) + assert all([ + a == b + for a, b in zip( + series_tr.static_covariates_values().flatten(), + test_values.flatten(), + ) + ]) series_recovered = scaler.inverse_transform(series_tr) assert series.static_covariates.equals(series_recovered.static_covariates) diff --git a/darts/tests/dataprocessing/transformers/test_window_transformations.py b/darts/tests/dataprocessing/transformers/test_window_transformations.py index 65bc70d001..3d036af43c 100644 --- a/darts/tests/dataprocessing/transformers/test_window_transformations.py +++ b/darts/tests/dataprocessing/transformers/test_window_transformations.py @@ -7,10 +7,34 @@ from darts import TimeSeries from darts.dataprocessing.pipeline import Pipeline from darts.dataprocessing.transformers import Mapper, WindowTransformer +from darts.utils.utils import freqs -class TestTimeSeriesWindowTransform: +def helper_generate_ts_hierarchy(length: int): + values = np.stack( + [ + np.ones( + length, + ) + * 5, + np.ones( + length, + ) + * 3, + np.ones( + length, + ) + * 2, + ], + axis=1, + ) + hierarchy = {"B": "A", "C": "A"} + return TimeSeries.from_values( + values=values, columns=["A", "B", "C"], hierarchy=hierarchy + ) + +class TestTimeSeriesWindowTransform: times = pd.date_range("20130101", "20130110") series_from_values = TimeSeries.from_values( np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) @@ -128,6 +152,49 @@ def test_ts_windowtransf_input_dictionary(self): } # forecating_safe=True vs center=True self.series_univ_det.window_transform(transforms=window_transformations) + # keep_names and overlapping transforms + with pytest.raises(ValueError) as err: + window_transformations = [ + { + "function": "mean", + "mode": "rolling", + "window": 3, + "components": self.series_multi_det.components[:1], + }, + { + "function": "median", + "mode": "rolling", + "window": 3, + "components": self.series_multi_det.components, + }, + ] + self.series_multi_det.window_transform( + transforms=window_transformations, keep_names=True + ) + assert str(err.value) == ( + "Cannot keep the original component names as some transforms are overlapping " + "(applied to the same components). Set `keep_names` to `False`." + ) + + # keep_names and keep_non_transformed + with pytest.raises(ValueError) as err: + window_transformations = [ + { + "function": "mean", + "mode": "rolling", + "window": 3, + "components": self.series_multi_det.components[:1], + }, + ] + self.series_multi_det.window_transform( + transforms=window_transformations, + keep_names=True, + keep_non_transformed=True, + ) + assert str(err.value) == ( + "`keep_names = True` and `keep_non_transformed = True` cannot be used together." + ) + def test_ts_windowtransf_output_series(self): # univariate deterministic input transforms = {"function": "sum", "mode": "rolling", "window": 1} @@ -323,7 +390,6 @@ def test_ts_windowtransf_output_nabehavior(self): self.target.window_transform(window_transformations, treat_na="bfill") def test_tranformed_ts_index(self): - # DateTimeIndex transformed_series = self.target.window_transform({"function": "sum"}) assert ( @@ -333,9 +399,9 @@ def test_tranformed_ts_index(self): # length index should not change for default transformation configurations assert len(self.target._time_index) == len(transformed_series._time_index) # RangeIndex - transformed_series = self.series_from_values.window_transform( - {"function": "sum"} - ) + transformed_series = self.series_from_values.window_transform({ + "function": "sum" + }) assert ( self.series_from_values._time_index.__class__ == transformed_series._time_index.__class__ @@ -380,20 +446,18 @@ def test_include_current(self): ] expected_transformed_series = TimeSeries.from_times_and_values( self.times, - np.array( - [ - ["NaN", "NaN"], - [1, 1], - [2, 2], - [3, 3], - [4, 4], - [5, 5], - [6, 6], - [7, 7], - [8, 8], - [9, 9], - ] - ), + np.array([ + ["NaN", "NaN"], + [1, 1], + [2, 2], + [3, 3], + [4, 4], + [5, 5], + [6, 6], + [7, 7], + [8, 8], + [9, 9], + ]), columns=["rolling_sum_1_0", "ewm_sum_0"], ) transformed_ts = self.target.window_transform( @@ -403,20 +467,18 @@ def test_include_current(self): expected_transformed_series = TimeSeries.from_times_and_values( self.times, - np.array( - [ - [1, 1], - [1, 1], - [2, 2], - [3, 3], - [4, 4], - [5, 5], - [6, 6], - [7, 7], - [8, 8], - [9, 9], - ] - ), + np.array([ + [1, 1], + [1, 1], + [2, 2], + [3, 3], + [4, 4], + [5, 5], + [6, 6], + [7, 7], + [8, 8], + [9, 9], + ]), columns=["rolling_sum_1_0", "ewm_sum_0"], ) transformed_ts = self.target.window_transform( @@ -440,20 +502,18 @@ def test_include_current(self): expected_transformed_series = TimeSeries.from_times_and_values( self.times, - np.array( - [ - ["NaN", "NaN"], - ["NaN", "NaN"], - [3, 2], - [5, 3], - [7, 4], - [9, 5], - [11, 6], - [13, 7], - [15, 8], - [17, 9], - ] - ), + np.array([ + ["NaN", "NaN"], + ["NaN", "NaN"], + [3, 2], + [5, 3], + [7, 4], + [9, 5], + [11, 6], + [13, 7], + [15, 8], + [17, 9], + ]), columns=["rolling_sum_2_2_0", "ewm_sum_2_0"], ) @@ -462,12 +522,103 @@ def test_include_current(self): ) assert transformed_ts == expected_transformed_series + @pytest.mark.parametrize( + "transforms", + [ + { + "function": "median", + "mode": "rolling", + "window": 3, + }, + { + "function": "mean", + "mode": "expanding", + "window": 2, + "components": ["A", "B", "C"], + }, + ], + ) + def test_ts_windowtransf_hierarchy(self, transforms): + """Checking that supported transforms behave as expected: + - implicitly applied to all components + - passing explicitly all components + """ + ts = helper_generate_ts_hierarchy(10) + + # renaming components based on transform parameters + ts_tr = ts.window_transform(transforms=transforms) + tr_prefix = ( + f"{transforms['mode']}_{transforms['function']}_{transforms['window']}_" + ) + assert ts_tr.hierarchy == { + tr_prefix + comp: [tr_prefix + "A"] for comp in ["B", "C"] + } + + # keeping original components name + ts_tr = ts.window_transform(transforms=transforms, keep_names=True) + assert ts_tr.hierarchy == ts.hierarchy == {"C": ["A"], "B": ["A"]} + + @pytest.mark.parametrize( + "transforms", + [ + {"function": "median", "mode": "rolling", "window": 3, "components": ["B"]}, + [ + { + "function": "mean", + "mode": "expanding", + "window": 2, + }, + { + "function": "median", + "mode": "rolling", + "window": 3, + }, + ], + [ + { + "function": "median", + "mode": "rolling", + "window": 3, + "components": ["B", "C"], + }, + { + "function": "sum", + "mode": "rolling", + "window": 5, + "components": ["A", "C"], + }, + ], + ], + ) + def test_ts_windowtransf_drop_hierarchy(self, transforms): + """Checking that hierarchy is correctly removed when + - transform is not applied to all the components + - several transforms applied to all the components + - two transforms with overlapping components + """ + ts = helper_generate_ts_hierarchy(10) + ts_tr = ts.window_transform(transforms=transforms) + assert ts_tr.hierarchy is None + + def test_ts_windowtransf_hierarchy_wrong_args(self): + ts = helper_generate_ts_hierarchy(10) + + # hierarchy + keep_non_transformed = ambiguity for hierarchy + with pytest.raises(ValueError): + ts.window_transform( + transforms={ + "function": "sum", + "mode": "rolling", + "window": 3, + }, + keep_non_transformed=True, + ) -class TestWindowTransformer: +class TestWindowTransformer: times = pd.date_range("20130101", "20130110") target = TimeSeries.from_times_and_values(times, np.array(range(1, 11))) - times_hourly = pd.date_range(start="20130101", freq="1H", periods=10) + times_hourly = pd.date_range(start="20130101", freq="1" + freqs["h"], periods=10) target_hourly = TimeSeries.from_times_and_values( times_hourly, np.array(range(1, 11)) ) @@ -579,3 +730,30 @@ def times_five(x): transformed_series = pipeline.fit_transform(series_1) assert transformed_series == expected_transformed_series + + def test_transformer_hierarchy(self): + ts = helper_generate_ts_hierarchy(10) + transform = { + "function": "median", + "mode": "rolling", + "window": 3, + } + + # renaming components + window_transformer = WindowTransformer( + transforms=[transform], + ) + ts_tr = window_transformer.transform(ts) + tr_prefix = ( + f"{transform['mode']}_{transform['function']}_{transform['window']}_" + ) + assert ts_tr.hierarchy == { + tr_prefix + comp: [tr_prefix + "A"] for comp in ["B", "C"] + } + # keeping old components + window_transformer = WindowTransformer( + transforms=transform, + keep_names=True, + ) + ts_tr = window_transformer.transform(ts) + assert ts_tr.hierarchy == ts.hierarchy == {"C": ["A"], "B": ["A"]} diff --git a/darts/tests/datasets/test_dataset_loaders.py b/darts/tests/datasets/test_dataset_loaders.py index 865331923b..eadef3507d 100644 --- a/darts/tests/datasets/test_dataset_loaders.py +++ b/darts/tests/datasets/test_dataset_loaders.py @@ -25,6 +25,7 @@ MonthlyMilkDataset, MonthlyMilkIncompleteDataset, SunspotsDataset, + TaxiNewYorkDataset, TaylorDataset, TemperatureDataset, TrafficDataset, @@ -70,6 +71,7 @@ (TrafficDataset, 862), (WeatherDataset, 21), (ElectricityConsumptionZurichDataset, 10), + (TaxiNewYorkDataset, 1), ] wrong_hash_dataset = DatasetLoaderCSV( diff --git a/darts/tests/datasets/test_datasets.py b/darts/tests/datasets/test_datasets.py index 24260f337d..05a517a75c 100644 --- a/darts/tests/datasets/test_datasets.py +++ b/darts/tests/datasets/test_datasets.py @@ -1,1323 +1,1736 @@ +import inspect +import itertools + import numpy as np import pandas as pd import pytest from darts import TimeSeries -from darts.logging import get_logger +from darts.tests.conftest import TORCH_AVAILABLE from darts.utils.timeseries_generation import gaussian_timeseries +from darts.utils.utils import freqs -logger = get_logger(__name__) - -try: - from darts.utils.data import ( # noqa: F401 - DualCovariatesInferenceDataset, - DualCovariatesSequentialDataset, - DualCovariatesShiftedDataset, - FutureCovariatesInferenceDataset, - FutureCovariatesSequentialDataset, - FutureCovariatesShiftedDataset, - HorizonBasedDataset, - MixedCovariatesInferenceDataset, - MixedCovariatesSequentialDataset, - MixedCovariatesShiftedDataset, - PastCovariatesInferenceDataset, - PastCovariatesSequentialDataset, - PastCovariatesShiftedDataset, - SplitCovariatesInferenceDataset, - SplitCovariatesSequentialDataset, - SplitCovariatesShiftedDataset, +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, ) - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not installed - dataset tests will be skipped.") - TORCH_AVAILABLE = False - -if TORCH_AVAILABLE: - - class TestDataset: - target1 = gaussian_timeseries(length=100).with_static_covariates( - pd.Series([0, 1], index=["st1", "st2"]) - ) - target2 = gaussian_timeseries(length=150).with_static_covariates( - pd.Series([2, 3], index=["st1", "st2"]) - ) - cov_st1 = target1.static_covariates.values - cov_st2 = target2.static_covariates.values - cov_st2_df = pd.Series([2, 3], index=["st1", "st2"]) - vals1, vals2 = target1.values(), target2.values() - cov1, cov2 = gaussian_timeseries(length=100), gaussian_timeseries(length=150) - - def _assert_eq(self, lefts: tuple, rights: tuple): - for left, right in zip(lefts, rights): - left = left.values() if isinstance(left, TimeSeries) else left - right = right.values() if isinstance(right, TimeSeries) else right - assert type(left) == type(right) - assert ( - isinstance( - left, (TimeSeries, pd.Series, pd.DataFrame, np.ndarray, list) - ) - or left is None +from darts.utils.data import ( # noqa: F401 + DualCovariatesInferenceDataset, + DualCovariatesSequentialDataset, + DualCovariatesShiftedDataset, + FutureCovariatesInferenceDataset, + FutureCovariatesSequentialDataset, + FutureCovariatesShiftedDataset, + HorizonBasedDataset, + MixedCovariatesInferenceDataset, + MixedCovariatesSequentialDataset, + MixedCovariatesShiftedDataset, + PastCovariatesInferenceDataset, + PastCovariatesSequentialDataset, + PastCovariatesShiftedDataset, + SplitCovariatesInferenceDataset, + SplitCovariatesSequentialDataset, + SplitCovariatesShiftedDataset, +) + + +class TestDataset: + target1 = gaussian_timeseries(length=100).with_static_covariates( + pd.Series([0, 1], index=["st1", "st2"]) + ) + target2 = gaussian_timeseries(length=150).with_static_covariates( + pd.Series([2, 3], index=["st1", "st2"]) + ) + cov_st1 = target1.static_covariates.values + cov_st2 = target2.static_covariates.values + cov_st2_df = pd.Series([2, 3], index=["st1", "st2"]) + vals1, vals2 = target1.values(), target2.values() + cov1, cov2 = gaussian_timeseries(length=100), gaussian_timeseries(length=150) + + def _assert_eq(self, lefts: tuple, rights: tuple): + for left, right in zip(lefts, rights): + left = left.values() if isinstance(left, TimeSeries) else left + right = right.values() if isinstance(right, TimeSeries) else right + assert type(left) is type(right) + assert ( + isinstance( + left, (TimeSeries, pd.Series, pd.DataFrame, np.ndarray, list) ) - if isinstance(left, (pd.Series, pd.DataFrame)): - assert left.equals(right) - elif isinstance(left, np.ndarray): - np.testing.assert_array_equal(left, right) - elif isinstance(left, (list, TimeSeries)): - assert left == right - else: - assert right is None - - def test_past_covariates_inference_dataset(self): - # one target series - ds = PastCovariatesInferenceDataset( - target_series=self.target1, input_chunk_length=len(self.target1) - ) - np.testing.assert_almost_equal(ds[0][0], self.vals1) - self._assert_eq(ds[0][1:], (None, None, self.cov_st1, self.target1)) + or left is None + ) + if isinstance(left, (pd.Series, pd.DataFrame)): + assert left.equals(right) + elif isinstance(left, np.ndarray): + np.testing.assert_array_equal(left, right) + elif isinstance(left, (list, TimeSeries)): + assert left == right + else: + assert right is None + + def test_past_covariates_inference_dataset(self): + # one target series + ds = PastCovariatesInferenceDataset( + target_series=self.target1, input_chunk_length=len(self.target1) + ) + np.testing.assert_almost_equal(ds[0][0], self.vals1) + self._assert_eq(ds[0][1:], (None, None, self.cov_st1, self.target1)) - # two target series + # two target series + ds = PastCovariatesInferenceDataset( + target_series=[self.target1, self.target2], + input_chunk_length=max(len(self.target1), len(self.target2)), + ) + np.testing.assert_almost_equal(ds[1][0], self.vals2) + self._assert_eq(ds[1][1:], (None, None, self.cov_st2, self.target2)) + + # fail if covariates do not have same size + with pytest.raises(ValueError): ds = PastCovariatesInferenceDataset( - target_series=[self.target1, self.target2], - input_chunk_length=max(len(self.target1), len(self.target2)), + target_series=[self.target1, self.target2], covariates=[self.cov1] ) - np.testing.assert_almost_equal(ds[1][0], self.vals2) - self._assert_eq(ds[1][1:], (None, None, self.cov_st2, self.target2)) - # fail if covariates do not have same size - with pytest.raises(ValueError): - ds = PastCovariatesInferenceDataset( - target_series=[self.target1, self.target2], covariates=[self.cov1] - ) + # with covariates + ds = PastCovariatesInferenceDataset( + target_series=[self.target1, self.target2], + covariates=[self.cov1, self.cov2], + input_chunk_length=max(len(self.target1), len(self.target2)), + ) + np.testing.assert_almost_equal(ds[1][0], self.vals2) + np.testing.assert_almost_equal(ds[1][1], self.cov2.values()) + self._assert_eq( + ds[1][2:], (None, self.cov_st2, self.target2) + ) # no "future past" covariate here + + # more complex case with future past covariates: + times1 = pd.date_range(start="20100101", end="20100701", freq="D") + times2 = pd.date_range( + start="20100101", end="20100820", freq="D" + ) # 50 days longer than times1 + + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + short_cov = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ) + long_cov = TimeSeries.from_times_and_values( + times2, np.random.randn(len(times2)) + ) - # with covariates - ds = PastCovariatesInferenceDataset( - target_series=[self.target1, self.target2], - covariates=[self.cov1, self.cov2], - input_chunk_length=max(len(self.target1), len(self.target2)), - ) - np.testing.assert_almost_equal(ds[1][0], self.vals2) - np.testing.assert_almost_equal(ds[1][1], self.cov2.values()) - self._assert_eq( - ds[1][2:], (None, self.cov_st2, self.target2) - ) # no "future past" covariate here - - # more complex case with future past covariates: - times1 = pd.date_range(start="20100101", end="20100701", freq="D") - times2 = pd.date_range( - start="20100101", end="20100820", freq="D" - ) # 50 days longer than times1 - - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - short_cov = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ) - long_cov = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) + ds = PastCovariatesInferenceDataset( + target_series=target, + covariates=short_cov, + input_chunk_length=10, + output_chunk_length=10, + n=30, + ) - ds = PastCovariatesInferenceDataset( - target_series=target, - covariates=short_cov, - input_chunk_length=10, - output_chunk_length=10, - n=30, - ) + # should fail if covariates are too short + with pytest.raises(ValueError): + _ = ds[0] + + # Should return correct values when covariates is long enough + ds = PastCovariatesInferenceDataset( + target_series=target, + covariates=long_cov, + input_chunk_length=10, + output_chunk_length=10, + n=30, + ) - # should fail if covariates are too short - with pytest.raises(ValueError): - _ = ds[0] + np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) + np.testing.assert_almost_equal(ds[0][1], long_cov.values()[-60:-50]) + np.testing.assert_almost_equal(ds[0][2], long_cov.values()[-50:-30]) + np.testing.assert_almost_equal(ds[0][3], self.cov_st2) + assert ds[0][4] == target + + # Should also work for integer-indexed series + target = TimeSeries.from_times_and_values( + pd.RangeIndex(start=10, stop=50, step=1), np.random.randn(40) + ).with_static_covariates(self.cov_st2_df) + covariate = TimeSeries.from_times_and_values( + pd.RangeIndex(start=20, stop=80, step=1), np.random.randn(60) + ) - # Should return correct values when covariates is long enough - ds = PastCovariatesInferenceDataset( - target_series=target, - covariates=long_cov, - input_chunk_length=10, - output_chunk_length=10, - n=30, - ) + ds = PastCovariatesInferenceDataset( + target_series=target, + covariates=covariate, + input_chunk_length=10, + output_chunk_length=10, + n=20, + ) - np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) - np.testing.assert_almost_equal(ds[0][1], long_cov.values()[-60:-50]) - np.testing.assert_almost_equal(ds[0][2], long_cov.values()[-50:-30]) - np.testing.assert_almost_equal(ds[0][3], self.cov_st2) - assert ds[0][4] == target - - # Should also work for integer-indexed series - target = TimeSeries.from_times_and_values( - pd.RangeIndex(start=10, stop=50, step=1), np.random.randn(40) - ).with_static_covariates(self.cov_st2_df) - covariate = TimeSeries.from_times_and_values( - pd.RangeIndex(start=20, stop=80, step=1), np.random.randn(60) - ) + np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) + np.testing.assert_almost_equal(ds[0][1], covariate.values()[20:30]) + np.testing.assert_almost_equal(ds[0][2], covariate.values()[30:40]) + np.testing.assert_almost_equal(ds[0][3], self.cov_st2) + assert ds[0][4] == target - ds = PastCovariatesInferenceDataset( - target_series=target, - covariates=covariate, - input_chunk_length=10, - output_chunk_length=10, - n=20, - ) + def test_future_covariates_inference_dataset(self): + # one target series + ds = FutureCovariatesInferenceDataset( + target_series=self.target1, input_chunk_length=len(self.target1) + ) + np.testing.assert_almost_equal(ds[0][0], self.vals1) + self._assert_eq(ds[0][1:], (None, self.cov_st1, self.target1)) - np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) - np.testing.assert_almost_equal(ds[0][1], covariate.values()[20:30]) - np.testing.assert_almost_equal(ds[0][2], covariate.values()[30:40]) - np.testing.assert_almost_equal(ds[0][3], self.cov_st2) - assert ds[0][4] == target + # two target series + ds = FutureCovariatesInferenceDataset( + target_series=[self.target1, self.target2], + input_chunk_length=max(len(self.target1), len(self.target2)), + ) + np.testing.assert_almost_equal(ds[1][0], self.vals2) + self._assert_eq(ds[1][1:], (None, self.cov_st2, self.target2)) - def test_future_covariates_inference_dataset(self): - # one target series + # fail if covariates do not have same size + with pytest.raises(ValueError): ds = FutureCovariatesInferenceDataset( - target_series=self.target1, input_chunk_length=len(self.target1) + target_series=[self.target1, self.target2], covariates=[self.cov1] ) - np.testing.assert_almost_equal(ds[0][0], self.vals1) - self._assert_eq(ds[0][1:], (None, self.cov_st1, self.target1)) - # two target series - ds = FutureCovariatesInferenceDataset( - target_series=[self.target1, self.target2], - input_chunk_length=max(len(self.target1), len(self.target2)), - ) - np.testing.assert_almost_equal(ds[1][0], self.vals2) - self._assert_eq(ds[1][1:], (None, self.cov_st2, self.target2)) + # With future past covariates: + times1 = pd.date_range(start="20100101", end="20100701", freq="D") + times2 = pd.date_range( + start="20100101", end="20100820", freq="D" + ) # 50 days longer than times1 - # fail if covariates do not have same size - with pytest.raises(ValueError): - ds = FutureCovariatesInferenceDataset( - target_series=[self.target1, self.target2], covariates=[self.cov1] - ) + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + short_cov = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ) + long_cov = TimeSeries.from_times_and_values( + times2, np.random.randn(len(times2)) + ) - # With future past covariates: - times1 = pd.date_range(start="20100101", end="20100701", freq="D") - times2 = pd.date_range( - start="20100101", end="20100820", freq="D" - ) # 50 days longer than times1 - - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - short_cov = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ) - long_cov = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) + ds = FutureCovariatesInferenceDataset( + target_series=target, covariates=short_cov, input_chunk_length=10, n=30 + ) - ds = FutureCovariatesInferenceDataset( - target_series=target, covariates=short_cov, input_chunk_length=10, n=30 - ) + # should fail if covariates are too short + with pytest.raises(ValueError): + _ = ds[0] - # should fail if covariates are too short - with pytest.raises(ValueError): - _ = ds[0] + # Should return correct values when covariates is long enough + ds = FutureCovariatesInferenceDataset( + target_series=target, covariates=long_cov, input_chunk_length=10, n=30 + ) - # Should return correct values when covariates is long enough - ds = FutureCovariatesInferenceDataset( - target_series=target, covariates=long_cov, input_chunk_length=10, n=30 - ) + np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) + np.testing.assert_almost_equal(ds[0][1], long_cov.values()[-50:-20]) + np.testing.assert_almost_equal(ds[0][2], self.cov_st2) + assert ds[0][3] == target + + # Should also work for integer-indexed series + target = TimeSeries.from_times_and_values( + pd.RangeIndex(start=10, stop=50, step=1), np.random.randn(40) + ).with_static_covariates(self.cov_st2_df) + covariate = TimeSeries.from_times_and_values( + pd.RangeIndex(start=20, stop=80, step=1), np.random.randn(60) + ) - np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) - np.testing.assert_almost_equal(ds[0][1], long_cov.values()[-50:-20]) - np.testing.assert_almost_equal(ds[0][2], self.cov_st2) - assert ds[0][3] == target - - # Should also work for integer-indexed series - target = TimeSeries.from_times_and_values( - pd.RangeIndex(start=10, stop=50, step=1), np.random.randn(40) - ).with_static_covariates(self.cov_st2_df) - covariate = TimeSeries.from_times_and_values( - pd.RangeIndex(start=20, stop=80, step=1), np.random.randn(60) - ) + ds = FutureCovariatesInferenceDataset( + target_series=target, covariates=covariate, input_chunk_length=10, n=20 + ) - ds = FutureCovariatesInferenceDataset( - target_series=target, covariates=covariate, input_chunk_length=10, n=20 - ) + np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) + np.testing.assert_almost_equal(ds[0][1], covariate.values()[30:50]) + np.testing.assert_almost_equal(ds[0][2], self.cov_st2) + assert ds[0][3] == target - np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) - np.testing.assert_almost_equal(ds[0][1], covariate.values()[30:50]) - np.testing.assert_almost_equal(ds[0][2], self.cov_st2) - assert ds[0][3] == target + def test_dual_covariates_inference_dataset(self): + # one target series + ds = DualCovariatesInferenceDataset( + target_series=self.target1, input_chunk_length=len(self.target1) + ) + np.testing.assert_almost_equal(ds[0][0], self.vals1) + self._assert_eq(ds[0][1:], (None, None, self.cov_st1, self.target1)) - def test_dual_covariates_inference_dataset(self): - # one target series - ds = DualCovariatesInferenceDataset( - target_series=self.target1, input_chunk_length=len(self.target1) - ) - np.testing.assert_almost_equal(ds[0][0], self.vals1) - self._assert_eq(ds[0][1:], (None, None, self.cov_st1, self.target1)) + # two target series + ds = DualCovariatesInferenceDataset( + target_series=[self.target1, self.target2], + input_chunk_length=max(len(self.target1), len(self.target2)), + ) + np.testing.assert_almost_equal(ds[1][0], self.vals2) + self._assert_eq(ds[1][1:], (None, None, self.cov_st2, self.target2)) - # two target series + # fail if covariates do not have same size + with pytest.raises(ValueError): ds = DualCovariatesInferenceDataset( - target_series=[self.target1, self.target2], - input_chunk_length=max(len(self.target1), len(self.target2)), + target_series=[self.target1, self.target2], covariates=[self.cov1] ) - np.testing.assert_almost_equal(ds[1][0], self.vals2) - self._assert_eq(ds[1][1:], (None, None, self.cov_st2, self.target2)) - # fail if covariates do not have same size - with pytest.raises(ValueError): - ds = DualCovariatesInferenceDataset( - target_series=[self.target1, self.target2], covariates=[self.cov1] - ) + # With future past covariates: + times1 = pd.date_range(start="20100101", end="20100701", freq="D") + times2 = pd.date_range( + start="20100101", end="20100820", freq="D" + ) # 50 days longer than times1 - # With future past covariates: - times1 = pd.date_range(start="20100101", end="20100701", freq="D") - times2 = pd.date_range( - start="20100101", end="20100820", freq="D" - ) # 50 days longer than times1 - - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - short_cov = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ) - long_cov = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + short_cov = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ) + long_cov = TimeSeries.from_times_and_values( + times2, np.random.randn(len(times2)) + ) - ds = DualCovariatesInferenceDataset( - target_series=target, - covariates=short_cov, - input_chunk_length=10, - output_chunk_length=10, - n=30, - ) + ds = DualCovariatesInferenceDataset( + target_series=target, + covariates=short_cov, + input_chunk_length=10, + output_chunk_length=10, + n=30, + ) - # should fail if covariates are too short - with pytest.raises(ValueError): - _ = ds[0] + # should fail if covariates are too short + with pytest.raises(ValueError): + _ = ds[0] + + # Should return correct values when covariates is long enough + ds = DualCovariatesInferenceDataset( + target_series=target, + covariates=long_cov, + input_chunk_length=10, + output_chunk_length=10, + n=30, + ) - # Should return correct values when covariates is long enough - ds = DualCovariatesInferenceDataset( - target_series=target, - covariates=long_cov, - input_chunk_length=10, - output_chunk_length=10, - n=30, - ) + np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) + np.testing.assert_almost_equal(ds[0][1], long_cov.values()[-60:-50]) + np.testing.assert_almost_equal(ds[0][2], long_cov.values()[-50:-20]) + np.testing.assert_almost_equal(ds[0][3], self.cov_st2) + assert ds[0][4] == target + + # Should also work for integer-indexed series + target = TimeSeries.from_times_and_values( + pd.RangeIndex(start=10, stop=50, step=1), np.random.randn(40) + ).with_static_covariates(self.cov_st2_df) + covariate = TimeSeries.from_times_and_values( + pd.RangeIndex(start=20, stop=80, step=1), np.random.randn(60) + ) - np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) - np.testing.assert_almost_equal(ds[0][1], long_cov.values()[-60:-50]) - np.testing.assert_almost_equal(ds[0][2], long_cov.values()[-50:-20]) - np.testing.assert_almost_equal(ds[0][3], self.cov_st2) - assert ds[0][4] == target - - # Should also work for integer-indexed series - target = TimeSeries.from_times_and_values( - pd.RangeIndex(start=10, stop=50, step=1), np.random.randn(40) - ).with_static_covariates(self.cov_st2_df) - covariate = TimeSeries.from_times_and_values( - pd.RangeIndex(start=20, stop=80, step=1), np.random.randn(60) - ) + ds = DualCovariatesInferenceDataset( + target_series=target, + covariates=covariate, + input_chunk_length=10, + output_chunk_length=10, + n=20, + ) - ds = DualCovariatesInferenceDataset( - target_series=target, - covariates=covariate, - input_chunk_length=10, - output_chunk_length=10, - n=20, - ) + np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) + np.testing.assert_almost_equal(ds[0][1], covariate.values()[20:30]) + np.testing.assert_almost_equal(ds[0][2], covariate.values()[30:50]) + np.testing.assert_almost_equal(ds[0][3], self.cov_st2) + assert ds[0][4] == target + + def test_mixed_covariates_inference_dataset(self): + # With future past covariates: + times1 = pd.date_range(start="20100101", end="20100701", freq="D") + times2 = pd.date_range( + start="20100201", end="20100820", freq="D" + ) # ends 50 days after times1 + + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + past_cov = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ) + long_past_cov = TimeSeries.from_times_and_values( + times2, np.random.randn(len(times2)) + ) + future_cov = TimeSeries.from_times_and_values( + times2, np.random.randn(len(times2)) + ) - np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) - np.testing.assert_almost_equal(ds[0][1], covariate.values()[20:30]) - np.testing.assert_almost_equal(ds[0][2], covariate.values()[30:50]) - np.testing.assert_almost_equal(ds[0][3], self.cov_st2) - assert ds[0][4] == target - - def test_mixed_covariates_inference_dataset(self): - # With future past covariates: - times1 = pd.date_range(start="20100101", end="20100701", freq="D") - times2 = pd.date_range( - start="20100201", end="20100820", freq="D" - ) # ends 50 days after times1 - - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - past_cov = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ) - long_past_cov = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) - future_cov = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) + ds = MixedCovariatesInferenceDataset( + target_series=target, + past_covariates=past_cov, + future_covariates=past_cov, + input_chunk_length=10, + output_chunk_length=10, + n=30, + ) - ds = MixedCovariatesInferenceDataset( - target_series=target, - past_covariates=past_cov, - future_covariates=past_cov, - input_chunk_length=10, - output_chunk_length=10, - n=30, - ) + # should fail if future covariates are too short + with pytest.raises(ValueError): + _ = ds[0] + + # Should return correct values when covariates is long enough + ds = MixedCovariatesInferenceDataset( + target_series=target, + past_covariates=long_past_cov, + future_covariates=future_cov, + input_chunk_length=10, + output_chunk_length=10, + n=30, + ) - # should fail if future covariates are too short - with pytest.raises(ValueError): - _ = ds[0] + # It should contain: + # past_target, past_covariates, historic_future_covariates, future_covariates, future_past_covariates + np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) + np.testing.assert_almost_equal(ds[0][1], long_past_cov.values()[-60:-50]) + np.testing.assert_almost_equal(ds[0][2], future_cov.values()[-60:-50]) + np.testing.assert_almost_equal(ds[0][3], future_cov.values()[-50:-20]) + np.testing.assert_almost_equal(ds[0][4], long_past_cov.values()[-50:-30]) + np.testing.assert_almost_equal(ds[0][5], self.cov_st2) + assert ds[0][6] == target + + # Should also work for integer-indexed series + target = TimeSeries.from_times_and_values( + pd.RangeIndex(start=10, stop=50, step=1), np.random.randn(40) + ).with_static_covariates(self.cov_st2_df) + past_cov = TimeSeries.from_times_and_values( + pd.RangeIndex(start=20, stop=80, step=1), np.random.randn(60) + ) + future_cov = TimeSeries.from_times_and_values( + pd.RangeIndex(start=30, stop=100, step=1), np.random.randn(70) + ) - # Should return correct values when covariates is long enough - ds = MixedCovariatesInferenceDataset( - target_series=target, - past_covariates=long_past_cov, - future_covariates=future_cov, - input_chunk_length=10, - output_chunk_length=10, - n=30, - ) + ds = MixedCovariatesInferenceDataset( + target_series=target, + past_covariates=past_cov, + future_covariates=future_cov, + input_chunk_length=10, + output_chunk_length=10, + n=20, + ) - # It should contain: - # past_target, past_covariates, historic_future_covariates, future_covariates, future_past_covariates - np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) - np.testing.assert_almost_equal(ds[0][1], long_past_cov.values()[-60:-50]) - np.testing.assert_almost_equal(ds[0][2], future_cov.values()[-60:-50]) - np.testing.assert_almost_equal(ds[0][3], future_cov.values()[-50:-20]) - np.testing.assert_almost_equal(ds[0][4], long_past_cov.values()[-50:-30]) - np.testing.assert_almost_equal(ds[0][5], self.cov_st2) - assert ds[0][6] == target - - # Should also work for integer-indexed series - target = TimeSeries.from_times_and_values( - pd.RangeIndex(start=10, stop=50, step=1), np.random.randn(40) - ).with_static_covariates(self.cov_st2_df) - past_cov = TimeSeries.from_times_and_values( - pd.RangeIndex(start=20, stop=80, step=1), np.random.randn(60) - ) - future_cov = TimeSeries.from_times_and_values( - pd.RangeIndex(start=30, stop=100, step=1), np.random.randn(70) - ) + np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) + np.testing.assert_almost_equal(ds[0][1], past_cov.values()[20:30]) + np.testing.assert_almost_equal(ds[0][2], future_cov.values()[10:20]) + np.testing.assert_almost_equal(ds[0][3], future_cov.values()[20:40]) + np.testing.assert_almost_equal(ds[0][4], past_cov.values()[30:40]) + np.testing.assert_almost_equal(ds[0][5], self.cov_st2) + assert ds[0][6] == target + + def test_split_covariates_inference_dataset(self): + # With future past covariates: + times1 = pd.date_range(start="20100101", end="20100701", freq="D") + times2 = pd.date_range( + start="20100201", end="20100820", freq="D" + ) # ends 50 days after times1 + + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + past_cov = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ) + long_past_cov = TimeSeries.from_times_and_values( + times2, np.random.randn(len(times2)) + ) + future_cov = TimeSeries.from_times_and_values( + times2, np.random.randn(len(times2)) + ) - ds = MixedCovariatesInferenceDataset( - target_series=target, - past_covariates=past_cov, - future_covariates=future_cov, - input_chunk_length=10, - output_chunk_length=10, - n=20, - ) + ds = SplitCovariatesInferenceDataset( + target_series=target, + past_covariates=past_cov, + future_covariates=past_cov, + input_chunk_length=10, + output_chunk_length=10, + n=30, + ) - np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) - np.testing.assert_almost_equal(ds[0][1], past_cov.values()[20:30]) - np.testing.assert_almost_equal(ds[0][2], future_cov.values()[10:20]) - np.testing.assert_almost_equal(ds[0][3], future_cov.values()[20:40]) - np.testing.assert_almost_equal(ds[0][4], past_cov.values()[30:40]) - np.testing.assert_almost_equal(ds[0][5], self.cov_st2) - assert ds[0][6] == target - - def test_split_covariates_inference_dataset(self): - # With future past covariates: - times1 = pd.date_range(start="20100101", end="20100701", freq="D") - times2 = pd.date_range( - start="20100201", end="20100820", freq="D" - ) # ends 50 days after times1 - - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - past_cov = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ) - long_past_cov = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) - future_cov = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) + # should fail if future covariates are too short + with pytest.raises(ValueError): + _ = ds[0] + + # Should return correct values when covariates is long enough + ds = SplitCovariatesInferenceDataset( + target_series=target, + past_covariates=long_past_cov, + future_covariates=future_cov, + input_chunk_length=10, + output_chunk_length=10, + n=30, + ) - ds = SplitCovariatesInferenceDataset( - target_series=target, - past_covariates=past_cov, - future_covariates=past_cov, - input_chunk_length=10, - output_chunk_length=10, - n=30, - ) + # It should contain: + # past_target, past_covariates, future_covariates, future_past_covariates + np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) + np.testing.assert_almost_equal(ds[0][1], long_past_cov.values()[-60:-50]) + np.testing.assert_almost_equal(ds[0][2], future_cov.values()[-50:-20]) + np.testing.assert_almost_equal(ds[0][3], long_past_cov.values()[-50:-30]) + np.testing.assert_almost_equal(ds[0][4], self.cov_st2) + assert ds[0][5] == target + + # Should also work for integer-indexed series + target = TimeSeries.from_times_and_values( + pd.RangeIndex(start=10, stop=50, step=1), np.random.randn(40) + ).with_static_covariates(self.cov_st2_df) + past_cov = TimeSeries.from_times_and_values( + pd.RangeIndex(start=20, stop=80, step=1), np.random.randn(60) + ) + future_cov = TimeSeries.from_times_and_values( + pd.RangeIndex(start=30, stop=100, step=1), np.random.randn(70) + ) - # should fail if future covariates are too short - with pytest.raises(ValueError): - _ = ds[0] + ds = SplitCovariatesInferenceDataset( + target_series=target, + past_covariates=past_cov, + future_covariates=future_cov, + input_chunk_length=10, + output_chunk_length=10, + n=20, + ) - # Should return correct values when covariates is long enough - ds = SplitCovariatesInferenceDataset( + np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) + np.testing.assert_almost_equal(ds[0][1], past_cov.values()[20:30]) + np.testing.assert_almost_equal(ds[0][2], future_cov.values()[20:40]) + np.testing.assert_almost_equal(ds[0][3], past_cov.values()[30:40]) + np.testing.assert_almost_equal(ds[0][4], self.cov_st2) + assert ds[0][5] == target + + @pytest.mark.parametrize( + "config", + [ + # (dataset class, whether contains future, future batch index) + (PastCovariatesInferenceDataset, None), + (FutureCovariatesInferenceDataset, 1), + (DualCovariatesInferenceDataset, 2), + (MixedCovariatesInferenceDataset, 3), + (SplitCovariatesInferenceDataset, 2), + ], + ) + def test_inference_dataset_output_chunk_shift(self, config): + ds_cls, future_idx = config + ocl = 1 + ocs = 2 + target = self.target1[: -(ocl + ocs)] + + ds_covs = {} + ds_init_params = set(inspect.signature(ds_cls.__init__).parameters) + for cov_type in ["covariates", "past_covariates", "future_covariates"]: + if cov_type in ds_init_params: + ds_covs[cov_type] = self.cov1 + + with pytest.raises(ValueError) as err: + _ = ds_cls( target_series=target, - past_covariates=long_past_cov, - future_covariates=future_cov, - input_chunk_length=10, - output_chunk_length=10, - n=30, - ) + input_chunk_length=1, + output_chunk_length=1, + output_chunk_shift=1, + n=2, + **ds_covs, + ) + assert str(err.value).startswith("Cannot perform auto-regression") + + # regular dataset with output shift=0 and ocl=3: the 3rd future values should be identical to the 1st future + # values of a dataset with output shift=2 and ocl=1 + ds_reg = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=3, + output_chunk_shift=0, + n=1, + **ds_covs, + ) - # It should contain: - # past_target, past_covariates, future_covariates, future_past_covariates - np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) - np.testing.assert_almost_equal(ds[0][1], long_past_cov.values()[-60:-50]) - np.testing.assert_almost_equal(ds[0][2], future_cov.values()[-50:-20]) - np.testing.assert_almost_equal(ds[0][3], long_past_cov.values()[-50:-30]) - np.testing.assert_almost_equal(ds[0][4], self.cov_st2) - assert ds[0][5] == target - - # Should also work for integer-indexed series - target = TimeSeries.from_times_and_values( - pd.RangeIndex(start=10, stop=50, step=1), np.random.randn(40) - ).with_static_covariates(self.cov_st2_df) - past_cov = TimeSeries.from_times_and_values( - pd.RangeIndex(start=20, stop=80, step=1), np.random.randn(60) - ) - future_cov = TimeSeries.from_times_and_values( - pd.RangeIndex(start=30, stop=100, step=1), np.random.randn(70) - ) + ds_shift = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=1, + output_chunk_shift=ocs, + n=1, + **ds_covs, + ) - ds = SplitCovariatesInferenceDataset( - target_series=target, - past_covariates=past_cov, - future_covariates=future_cov, - input_chunk_length=10, - output_chunk_length=10, - n=20, - ) + batch_reg, batch_shift = ds_reg[0], ds_shift[0] - np.testing.assert_almost_equal(ds[0][0], target.values()[-10:]) - np.testing.assert_almost_equal(ds[0][1], past_cov.values()[20:30]) - np.testing.assert_almost_equal(ds[0][2], future_cov.values()[20:40]) - np.testing.assert_almost_equal(ds[0][3], past_cov.values()[30:40]) - np.testing.assert_almost_equal(ds[0][4], self.cov_st2) - assert ds[0][5] == target + # shifted prediction starts 2 steps after regular prediction + assert batch_reg[-1] == batch_shift[-1] - ocs * target.freq - def test_past_covariates_sequential_dataset(self): - # one target series - ds = PastCovariatesSequentialDataset( - target_series=self.target1, - input_chunk_length=10, - output_chunk_length=10, - ) - assert len(ds) == 81 - self._assert_eq( - ds[5], (self.target1[75:85], None, self.cov_st1, self.target1[85:95]) + if future_idx is not None: + # 3rd future values of regular ds must be identical to the 1st future values of shifted dataset + np.testing.assert_array_equal( + batch_reg[future_idx][ocs:], batch_shift[future_idx] ) + batch_reg = batch_reg[:future_idx] + batch_reg[future_idx + 1 :] + batch_shift = batch_shift[:future_idx] + batch_shift[future_idx + 1 :] - # two target series - ds = PastCovariatesSequentialDataset( - target_series=[self.target1, self.target2], - input_chunk_length=10, - output_chunk_length=10, - ) - assert len(ds) == 262 - self._assert_eq( - ds[5], (self.target1[75:85], None, self.cov_st1, self.target1[85:95]) - ) - self._assert_eq( - ds[136], - (self.target2[125:135], None, self.cov_st2, self.target2[135:145]), - ) + # without future part, the input will be identical between regular, and shifted dataset + assert all([ + np.all(el_reg == el_shift) + for el_reg, el_shift in zip(batch_reg[:-1], batch_shift[:-1]) + ]) - # two target series with custom max_nr_samples - ds = PastCovariatesSequentialDataset( - target_series=[self.target1, self.target2], - input_chunk_length=10, - output_chunk_length=10, - max_samples_per_ts=50, - ) - assert len(ds) == 100 - self._assert_eq( - ds[5], (self.target1[75:85], None, self.cov_st1, self.target1[85:95]) - ) - self._assert_eq( - ds[55], - (self.target2[125:135], None, self.cov_st2, self.target2[135:145]), - ) + def test_past_covariates_sequential_dataset(self): + # one target series + ds = PastCovariatesSequentialDataset( + target_series=self.target1, + input_chunk_length=10, + output_chunk_length=10, + ) + assert len(ds) == 81 + self._assert_eq( + ds[5], (self.target1[75:85], None, self.cov_st1, None, self.target1[85:95]) + ) - # two targets and one covariate - with pytest.raises(ValueError): - ds = PastCovariatesSequentialDataset( - target_series=[self.target1, self.target2], covariates=[self.cov1] - ) + # two target series + ds = PastCovariatesSequentialDataset( + target_series=[self.target1, self.target2], + input_chunk_length=10, + output_chunk_length=10, + ) + assert len(ds) == 262 + self._assert_eq( + ds[5], (self.target1[75:85], None, self.cov_st1, None, self.target1[85:95]) + ) + self._assert_eq( + ds[136], + (self.target2[125:135], None, self.cov_st2, None, self.target2[135:145]), + ) - # two targets and two covariates - ds = PastCovariatesSequentialDataset( - target_series=[self.target1, self.target2], - covariates=[self.cov1, self.cov2], - input_chunk_length=10, - output_chunk_length=10, - ) - self._assert_eq( - ds[5], - ( - self.target1[75:85], - self.cov1[75:85], - self.cov_st1, - self.target1[85:95], - ), - ) - self._assert_eq( - ds[136], - ( - self.target2[125:135], - self.cov2[125:135], - self.cov_st2, - self.target2[135:145], - ), - ) + # two target series with custom max_nr_samples + ds = PastCovariatesSequentialDataset( + target_series=[self.target1, self.target2], + input_chunk_length=10, + output_chunk_length=10, + max_samples_per_ts=50, + ) + assert len(ds) == 100 + self._assert_eq( + ds[5], (self.target1[75:85], None, self.cov_st1, None, self.target1[85:95]) + ) + self._assert_eq( + ds[55], + (self.target2[125:135], None, self.cov_st2, None, self.target2[135:145]), + ) - # should fail if covariates do not have the required time span, even though covariates are longer - times1 = pd.date_range(start="20100101", end="20110101", freq="D") - times2 = pd.date_range(start="20120101", end="20150101", freq="D") - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) - ds = PastCovariatesSequentialDataset( - target_series=target, - covariates=cov, - input_chunk_length=10, - output_chunk_length=10, - ) - with pytest.raises(ValueError): - _ = ds[5] - - # the same should fail when series are integer-indexed - times1 = pd.RangeIndex(start=0, stop=100, step=1) - times2 = pd.RangeIndex(start=200, stop=400, step=1) - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) - ds = PastCovariatesSequentialDataset( - target_series=target, - covariates=cov, - input_chunk_length=10, - output_chunk_length=10, - ) - with pytest.raises(ValueError): - _ = ds[5] - - # we should get the correct covariate slice even when target and covariates are not aligned - times1 = pd.date_range(start="20100101", end="20110101", freq="D") - times2 = pd.date_range(start="20090101", end="20110106", freq="D") - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + # two targets and one covariate + with pytest.raises(ValueError): ds = PastCovariatesSequentialDataset( - target_series=target, - covariates=cov, - input_chunk_length=10, - output_chunk_length=10, + target_series=[self.target1, self.target2], covariates=[self.cov1] ) - np.testing.assert_almost_equal(ds[0][1], cov.values()[-25:-15]) - np.testing.assert_almost_equal(ds[5][1], cov.values()[-30:-20]) - - # This should also be the case when series are integer indexed - times1 = pd.RangeIndex(start=100, stop=200, step=1) - times2 = pd.RangeIndex(start=50, stop=250, step=1) - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) - ds = PastCovariatesSequentialDataset( - target_series=target, - covariates=cov, - input_chunk_length=10, - output_chunk_length=10, - ) + # two targets and two covariates + ds = PastCovariatesSequentialDataset( + target_series=[self.target1, self.target2], + covariates=[self.cov1, self.cov2], + input_chunk_length=10, + output_chunk_length=10, + ) + self._assert_eq( + ds[5], + ( + self.target1[75:85], + self.cov1[75:85], + self.cov_st1, + None, + self.target1[85:95], + ), + ) + self._assert_eq( + ds[136], + ( + self.target2[125:135], + self.cov2[125:135], + self.cov_st2, + None, + self.target2[135:145], + ), + ) - np.testing.assert_almost_equal(ds[0][1], cov.values()[-70:-60]) - np.testing.assert_almost_equal(ds[5][1], cov.values()[-75:-65]) + # should fail if covariates do not have the required time span, even though covariates are longer + times1 = pd.date_range(start="20100101", end="20110101", freq="D") + times2 = pd.date_range(start="20120101", end="20150101", freq="D") + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + ds = PastCovariatesSequentialDataset( + target_series=target, + covariates=cov, + input_chunk_length=10, + output_chunk_length=10, + ) + with pytest.raises(ValueError): + _ = ds[5] + + # the same should fail when series are integer-indexed + times1 = pd.RangeIndex(start=0, stop=100, step=1) + times2 = pd.RangeIndex(start=200, stop=400, step=1) + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + ds = PastCovariatesSequentialDataset( + target_series=target, + covariates=cov, + input_chunk_length=10, + output_chunk_length=10, + ) + with pytest.raises(ValueError): + _ = ds[5] + + # we should get the correct covariate slice even when target and covariates are not aligned + times1 = pd.date_range(start="20100101", end="20110101", freq="D") + times2 = pd.date_range(start="20090101", end="20110106", freq="D") + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + ds = PastCovariatesSequentialDataset( + target_series=target, + covariates=cov, + input_chunk_length=10, + output_chunk_length=10, + ) - def test_future_covariates_sequential_dataset(self): - # one target series - ds = FutureCovariatesSequentialDataset( - target_series=self.target1, - input_chunk_length=10, - output_chunk_length=10, - ) - assert len(ds) == 81 - self._assert_eq( - ds[5], (self.target1[75:85], None, self.cov_st1, self.target1[85:95]) - ) + np.testing.assert_almost_equal(ds[0][1], cov.values()[-25:-15]) + np.testing.assert_almost_equal(ds[5][1], cov.values()[-30:-20]) + + # This should also be the case when series are integer indexed + times1 = pd.RangeIndex(start=100, stop=200, step=1) + times2 = pd.RangeIndex(start=50, stop=250, step=1) + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + ds = PastCovariatesSequentialDataset( + target_series=target, + covariates=cov, + input_chunk_length=10, + output_chunk_length=10, + ) - # two target series - ds = FutureCovariatesSequentialDataset( - target_series=[self.target1, self.target2], - input_chunk_length=10, - output_chunk_length=10, - ) - assert len(ds) == 262 - self._assert_eq( - ds[5], (self.target1[75:85], None, self.cov_st1, self.target1[85:95]) - ) - self._assert_eq( - ds[136], - (self.target2[125:135], None, self.cov_st2, self.target2[135:145]), - ) + np.testing.assert_almost_equal(ds[0][1], cov.values()[-70:-60]) + np.testing.assert_almost_equal(ds[5][1], cov.values()[-75:-65]) - # two target series with custom max_nr_samples - ds = FutureCovariatesSequentialDataset( - target_series=[self.target1, self.target2], - input_chunk_length=10, - output_chunk_length=10, - max_samples_per_ts=50, - ) - assert len(ds) == 100 - self._assert_eq( - ds[5], (self.target1[75:85], None, self.cov_st1, self.target1[85:95]) - ) - self._assert_eq( - ds[55], - (self.target2[125:135], None, self.cov_st2, self.target2[135:145]), - ) + def test_future_covariates_sequential_dataset(self): + # one target series + ds = FutureCovariatesSequentialDataset( + target_series=self.target1, + input_chunk_length=10, + output_chunk_length=10, + ) + assert len(ds) == 81 + self._assert_eq( + ds[5], (self.target1[75:85], None, self.cov_st1, None, self.target1[85:95]) + ) - # two targets and one covariate - with pytest.raises(ValueError): - ds = FutureCovariatesSequentialDataset( - target_series=[self.target1, self.target2], covariates=[self.cov1] - ) + # two target series + ds = FutureCovariatesSequentialDataset( + target_series=[self.target1, self.target2], + input_chunk_length=10, + output_chunk_length=10, + ) + assert len(ds) == 262 + self._assert_eq( + ds[5], (self.target1[75:85], None, self.cov_st1, None, self.target1[85:95]) + ) + self._assert_eq( + ds[136], + (self.target2[125:135], None, self.cov_st2, None, self.target2[135:145]), + ) - # two targets and two covariates; covariates not aligned, must contain correct values - target1 = TimeSeries.from_values( - np.random.randn(100) - ).with_static_covariates(self.cov_st2_df) - target2 = TimeSeries.from_values( - np.random.randn(50) - ).with_static_covariates(self.cov_st2_df) - cov1 = TimeSeries.from_values(np.random.randn(120)) - cov2 = TimeSeries.from_values(np.random.randn(80)) + # two target series with custom max_nr_samples + ds = FutureCovariatesSequentialDataset( + target_series=[self.target1, self.target2], + input_chunk_length=10, + output_chunk_length=10, + max_samples_per_ts=50, + ) + assert len(ds) == 100 + self._assert_eq( + ds[5], (self.target1[75:85], None, self.cov_st1, None, self.target1[85:95]) + ) + self._assert_eq( + ds[55], + (self.target2[125:135], None, self.cov_st2, None, self.target2[135:145]), + ) + # two targets and one covariate + with pytest.raises(ValueError): ds = FutureCovariatesSequentialDataset( - target_series=[target1, target2], - covariates=[cov1, cov2], - input_chunk_length=10, - output_chunk_length=10, + target_series=[self.target1, self.target2], covariates=[self.cov1] ) - np.testing.assert_almost_equal(ds[0][0], target1.values()[-20:-10]) - np.testing.assert_almost_equal(ds[0][1], cov1.values()[-30:-20]) - np.testing.assert_almost_equal(ds[0][2], self.cov_st2) - np.testing.assert_almost_equal(ds[0][3], target1.values()[-10:]) - - np.testing.assert_almost_equal(ds[101][0], target2.values()[-40:-30]) - np.testing.assert_almost_equal(ds[101][1], cov2.values()[-60:-50]) - np.testing.assert_almost_equal(ds[101][2], self.cov_st2) - np.testing.assert_almost_equal(ds[101][3], target2.values()[-30:-20]) - - # Should also contain correct values when time-indexed with covariates not aligned - times1 = pd.date_range(start="20090201", end="20090220", freq="D") - times2 = pd.date_range(start="20090201", end="20090222", freq="D") - target1 = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov1 = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) + # two targets and two covariates; covariates not aligned, must contain correct values + target1 = TimeSeries.from_values(np.random.randn(100)).with_static_covariates( + self.cov_st2_df + ) + target2 = TimeSeries.from_values(np.random.randn(50)).with_static_covariates( + self.cov_st2_df + ) + cov1 = TimeSeries.from_values(np.random.randn(120)) + cov2 = TimeSeries.from_values(np.random.randn(80)) + + ds = FutureCovariatesSequentialDataset( + target_series=[target1, target2], + covariates=[cov1, cov2], + input_chunk_length=10, + output_chunk_length=10, + ) - ds = FutureCovariatesSequentialDataset( - target_series=[target1], - covariates=[cov1], - input_chunk_length=2, - output_chunk_length=2, - ) + np.testing.assert_almost_equal(ds[0][0], target1.values()[-20:-10]) + np.testing.assert_almost_equal(ds[0][1], cov1.values()[-30:-20]) + np.testing.assert_almost_equal(ds[0][2], self.cov_st2) + assert ds[0][3] is None + np.testing.assert_almost_equal(ds[0][4], target1.values()[-10:]) + + np.testing.assert_almost_equal(ds[101][0], target2.values()[-40:-30]) + np.testing.assert_almost_equal(ds[101][1], cov2.values()[-60:-50]) + np.testing.assert_almost_equal(ds[101][2], self.cov_st2) + assert ds[0][3] is None + np.testing.assert_almost_equal(ds[101][4], target2.values()[-30:-20]) + + # Should also contain correct values when time-indexed with covariates not aligned + times1 = pd.date_range(start="20090201", end="20090220", freq="D") + times2 = pd.date_range(start="20090201", end="20090222", freq="D") + target1 = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov1 = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + + ds = FutureCovariatesSequentialDataset( + target_series=[target1], + covariates=[cov1], + input_chunk_length=2, + output_chunk_length=2, + ) - np.testing.assert_almost_equal(ds[0][0], target1.values()[-4:-2]) - np.testing.assert_almost_equal(ds[0][1], cov1.values()[-4:-2]) - np.testing.assert_almost_equal(ds[0][2], self.cov_st2) - np.testing.assert_almost_equal(ds[0][3], target1.values()[-2:]) + np.testing.assert_almost_equal(ds[0][0], target1.values()[-4:-2]) + np.testing.assert_almost_equal(ds[0][1], cov1.values()[-4:-2]) + np.testing.assert_almost_equal(ds[0][2], self.cov_st2) + assert ds[0][3] is None + np.testing.assert_almost_equal(ds[0][4], target1.values()[-2:]) - # Should fail if covariates are not long enough - target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( - self.cov_st2_df - ) - cov1 = TimeSeries.from_values(np.random.randn(7)) + # Should fail if covariates are not long enough + target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( + self.cov_st2_df + ) + cov1 = TimeSeries.from_values(np.random.randn(7)) - ds = FutureCovariatesSequentialDataset( - target_series=[target1], - covariates=[cov1], - input_chunk_length=2, - output_chunk_length=2, - ) + ds = FutureCovariatesSequentialDataset( + target_series=[target1], + covariates=[cov1], + input_chunk_length=2, + output_chunk_length=2, + ) - with pytest.raises(ValueError): - _ = ds[0] + with pytest.raises(ValueError): + _ = ds[0] - def test_dual_covariates_sequential_dataset(self): - # Must contain (past_target, historic_future_covariates, future_covariates, future_target) + def test_dual_covariates_sequential_dataset(self): + # Must contain (past_target, historic_future_covariates, future_covariates, static covariates, + # sample weight, future_target) - # one target series - ds = DualCovariatesSequentialDataset( - target_series=self.target1, - input_chunk_length=10, - output_chunk_length=10, - ) - assert len(ds) == 81 - self._assert_eq( - ds[5], - (self.target1[75:85], None, None, self.cov_st1, self.target1[85:95]), - ) + # one target series + ds = DualCovariatesSequentialDataset( + target_series=self.target1, + input_chunk_length=10, + output_chunk_length=10, + ) + assert len(ds) == 81 + self._assert_eq( + ds[5], + (self.target1[75:85], None, None, self.cov_st1, None, self.target1[85:95]), + ) - # two target series - ds = DualCovariatesSequentialDataset( - target_series=[self.target1, self.target2], - input_chunk_length=10, - output_chunk_length=10, - ) - assert len(ds) == 262 - self._assert_eq( - ds[5], - (self.target1[75:85], None, None, self.cov_st1, self.target1[85:95]), - ) - self._assert_eq( - ds[136], - ( - self.target2[125:135], - None, - None, - self.cov_st2, - self.target2[135:145], - ), - ) + # two target series + ds = DualCovariatesSequentialDataset( + target_series=[self.target1, self.target2], + input_chunk_length=10, + output_chunk_length=10, + ) + assert len(ds) == 262 + self._assert_eq( + ds[5], + (self.target1[75:85], None, None, self.cov_st1, None, self.target1[85:95]), + ) + self._assert_eq( + ds[136], + ( + self.target2[125:135], + None, + None, + self.cov_st2, + None, + self.target2[135:145], + ), + ) - # two target series with custom max_nr_samples + # two target series with custom max_nr_samples + ds = DualCovariatesSequentialDataset( + target_series=[self.target1, self.target2], + input_chunk_length=10, + output_chunk_length=10, + max_samples_per_ts=50, + ) + assert len(ds) == 100 + self._assert_eq( + ds[5], + (self.target1[75:85], None, None, self.cov_st1, None, self.target1[85:95]), + ) + self._assert_eq( + ds[55], + ( + self.target2[125:135], + None, + None, + self.cov_st2, + None, + self.target2[135:145], + ), + ) + + # two targets and one covariate + with pytest.raises(ValueError): ds = DualCovariatesSequentialDataset( - target_series=[self.target1, self.target2], - input_chunk_length=10, - output_chunk_length=10, - max_samples_per_ts=50, - ) - assert len(ds) == 100 - self._assert_eq( - ds[5], - (self.target1[75:85], None, None, self.cov_st1, self.target1[85:95]), - ) - self._assert_eq( - ds[55], - ( - self.target2[125:135], - None, - None, - self.cov_st2, - self.target2[135:145], - ), + target_series=[self.target1, self.target2], covariates=[self.cov1] ) - # two targets and one covariate - with pytest.raises(ValueError): - ds = DualCovariatesSequentialDataset( - target_series=[self.target1, self.target2], covariates=[self.cov1] - ) + # two targets and two covariates; covariates not aligned, must contain correct values + target1 = TimeSeries.from_values(np.random.randn(100)).with_static_covariates( + self.cov_st2_df + ) + target2 = TimeSeries.from_values(np.random.randn(50)).with_static_covariates( + self.cov_st2_df + ) + cov1 = TimeSeries.from_values(np.random.randn(120)) + cov2 = TimeSeries.from_values(np.random.randn(80)) + + ds = DualCovariatesSequentialDataset( + target_series=[target1, target2], + covariates=[cov1, cov2], + input_chunk_length=10, + output_chunk_length=10, + ) - # two targets and two covariates; covariates not aligned, must contain correct values - target1 = TimeSeries.from_values( - np.random.randn(100) - ).with_static_covariates(self.cov_st2_df) - target2 = TimeSeries.from_values( - np.random.randn(50) - ).with_static_covariates(self.cov_st2_df) - cov1 = TimeSeries.from_values(np.random.randn(120)) - cov2 = TimeSeries.from_values(np.random.randn(80)) + np.testing.assert_almost_equal(ds[0][0], target1.values()[-20:-10]) + np.testing.assert_almost_equal(ds[0][1], cov1.values()[-40:-30]) + np.testing.assert_almost_equal(ds[0][2], cov1.values()[-30:-20]) + np.testing.assert_almost_equal(ds[0][3], self.cov_st2) + assert ds[0][4] is None + np.testing.assert_almost_equal(ds[0][5], target1.values()[-10:]) + + np.testing.assert_almost_equal(ds[101][0], target2.values()[-40:-30]) + np.testing.assert_almost_equal(ds[101][1], cov2.values()[-70:-60]) + np.testing.assert_almost_equal(ds[101][2], cov2.values()[-60:-50]) + np.testing.assert_almost_equal(ds[101][3], self.cov_st2) + assert ds[101][4] is None + np.testing.assert_almost_equal(ds[101][5], target2.values()[-30:-20]) + + # Should also contain correct values when time-indexed with covariates not aligned + times1 = pd.date_range(start="20090201", end="20090220", freq="D") + times2 = pd.date_range(start="20090201", end="20090222", freq="D") + target1 = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov1 = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + + ds = DualCovariatesSequentialDataset( + target_series=[target1], + covariates=[cov1], + input_chunk_length=2, + output_chunk_length=2, + ) - ds = DualCovariatesSequentialDataset( - target_series=[target1, target2], - covariates=[cov1, cov2], - input_chunk_length=10, - output_chunk_length=10, - ) + np.testing.assert_almost_equal(ds[0][0], target1.values()[-4:-2]) + np.testing.assert_almost_equal(ds[0][1], cov1.values()[-6:-4]) + np.testing.assert_almost_equal(ds[0][2], cov1.values()[-4:-2]) + np.testing.assert_almost_equal(ds[0][3], self.cov_st2) + assert ds[0][4] is None + np.testing.assert_almost_equal(ds[0][5], target1.values()[-2:]) - np.testing.assert_almost_equal(ds[0][0], target1.values()[-20:-10]) - np.testing.assert_almost_equal(ds[0][1], cov1.values()[-40:-30]) - np.testing.assert_almost_equal(ds[0][2], cov1.values()[-30:-20]) - np.testing.assert_almost_equal(ds[0][3], self.cov_st2) - np.testing.assert_almost_equal(ds[0][4], target1.values()[-10:]) - - np.testing.assert_almost_equal(ds[101][0], target2.values()[-40:-30]) - np.testing.assert_almost_equal(ds[101][1], cov2.values()[-70:-60]) - np.testing.assert_almost_equal(ds[101][2], cov2.values()[-60:-50]) - np.testing.assert_almost_equal(ds[101][3], self.cov_st2) - np.testing.assert_almost_equal(ds[101][4], target2.values()[-30:-20]) - - # Should also contain correct values when time-indexed with covariates not aligned - times1 = pd.date_range(start="20090201", end="20090220", freq="D") - times2 = pd.date_range(start="20090201", end="20090222", freq="D") - target1 = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov1 = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) + # Should fail if covariates are not long enough + target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( + self.cov_st2_df + ) + cov1 = TimeSeries.from_values(np.random.randn(7)) - ds = DualCovariatesSequentialDataset( - target_series=[target1], - covariates=[cov1], - input_chunk_length=2, - output_chunk_length=2, - ) + ds = DualCovariatesSequentialDataset( + target_series=[target1], + covariates=[cov1], + input_chunk_length=2, + output_chunk_length=2, + ) - np.testing.assert_almost_equal(ds[0][0], target1.values()[-4:-2]) - np.testing.assert_almost_equal(ds[0][1], cov1.values()[-6:-4]) - np.testing.assert_almost_equal(ds[0][2], cov1.values()[-4:-2]) - np.testing.assert_almost_equal(ds[0][3], self.cov_st2) - np.testing.assert_almost_equal(ds[0][4], target1.values()[-2:]) + with pytest.raises(ValueError): + _ = ds[0] - # Should fail if covariates are not long enough - target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( - self.cov_st2_df - ) - cov1 = TimeSeries.from_values(np.random.randn(7)) + def test_past_covariates_shifted_dataset(self): + # one target series + ds = PastCovariatesShiftedDataset( + target_series=self.target1, length=10, shift=5 + ) + assert len(ds) == 86 + self._assert_eq( + ds[5], (self.target1[80:90], None, self.cov_st1, None, self.target1[85:95]) + ) - ds = DualCovariatesSequentialDataset( - target_series=[target1], - covariates=[cov1], - input_chunk_length=2, - output_chunk_length=2, - ) + # two target series + ds = PastCovariatesShiftedDataset( + target_series=[self.target1, self.target2], length=10, shift=5 + ) + assert len(ds) == 272 + self._assert_eq( + ds[5], (self.target1[80:90], None, self.cov_st1, None, self.target1[85:95]) + ) + self._assert_eq( + ds[141], + (self.target2[130:140], None, self.cov_st2, None, self.target2[135:145]), + ) - with pytest.raises(ValueError): - _ = ds[0] + # two target series with custom max_nr_samples + ds = PastCovariatesShiftedDataset( + target_series=[self.target1, self.target2], + length=10, + shift=5, + max_samples_per_ts=50, + ) + assert len(ds) == 100 + self._assert_eq( + ds[5], (self.target1[80:90], None, self.cov_st1, None, self.target1[85:95]) + ) + self._assert_eq( + ds[55], + (self.target2[130:140], None, self.cov_st2, None, self.target2[135:145]), + ) - def test_past_covariates_shifted_dataset(self): - # one target series + # two targets and one covariate + with pytest.raises(ValueError): ds = PastCovariatesShiftedDataset( - target_series=self.target1, length=10, shift=5 - ) - assert len(ds) == 86 - self._assert_eq( - ds[5], (self.target1[80:90], None, self.cov_st1, self.target1[85:95]) + target_series=[self.target1, self.target2], covariates=[self.cov1] ) - # two target series - ds = PastCovariatesShiftedDataset( - target_series=[self.target1, self.target2], length=10, shift=5 - ) - assert len(ds) == 272 - self._assert_eq( - ds[5], (self.target1[80:90], None, self.cov_st1, self.target1[85:95]) - ) - self._assert_eq( - ds[141], - (self.target2[130:140], None, self.cov_st2, self.target2[135:145]), - ) + # two targets and two covariates + ds = PastCovariatesShiftedDataset( + target_series=[self.target1, self.target2], + covariates=[self.cov1, self.cov2], + length=10, + shift=5, + ) + self._assert_eq( + ds[5], + ( + self.target1[80:90], + self.cov1[80:90], + self.cov_st1, + None, + self.target1[85:95], + ), + ) + self._assert_eq( + ds[141], + ( + self.target2[130:140], + self.cov2[130:140], + self.cov_st2, + None, + self.target2[135:145], + ), + ) - # two target series with custom max_nr_samples - ds = PastCovariatesShiftedDataset( - target_series=[self.target1, self.target2], - length=10, - shift=5, - max_samples_per_ts=50, - ) - assert len(ds) == 100 - self._assert_eq( - ds[5], (self.target1[80:90], None, self.cov_st1, self.target1[85:95]) - ) - self._assert_eq( - ds[55], - (self.target2[130:140], None, self.cov_st2, self.target2[135:145]), - ) + # Should contain correct values even when covariates are not aligned + target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( + self.cov_st2_df + ) + cov1 = TimeSeries.from_values(np.random.randn(10)) + ds = PastCovariatesShiftedDataset( + target_series=[target1], covariates=[cov1], length=3, shift=2 + ) + np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) + np.testing.assert_almost_equal(ds[0][1], cov1.values()[-7:-4]) + np.testing.assert_almost_equal(ds[0][2], self.cov_st2) + assert ds[0][3] is None + np.testing.assert_almost_equal(ds[0][4], target1.values()[-3:]) + + # Should also contain correct values when time-indexed with covariates not aligned + times1 = pd.date_range(start="20090201", end="20090220", freq="D") + times2 = pd.date_range(start="20090201", end="20090222", freq="D") + target1 = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov1 = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + ds = PastCovariatesShiftedDataset( + target_series=[target1], covariates=[cov1], length=3, shift=2 + ) + np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) + np.testing.assert_almost_equal(ds[0][1], cov1.values()[-7:-4]) + np.testing.assert_almost_equal(ds[0][2], self.cov_st2) + assert ds[0][3] is None + np.testing.assert_almost_equal(ds[0][4], target1.values()[-3:]) + + # Should fail if covariates are too short + target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( + self.cov_st2_df + ) + cov1 = TimeSeries.from_values(np.random.randn(5)) + ds = PastCovariatesShiftedDataset( + target_series=[target1], covariates=[cov1], length=3, shift=2 + ) + with pytest.raises(ValueError): + _ = ds[0] - # two targets and one covariate - with pytest.raises(ValueError): - ds = PastCovariatesShiftedDataset( - target_series=[self.target1, self.target2], covariates=[self.cov1] - ) + def test_future_covariates_shifted_dataset(self): + # one target series + ds = FutureCovariatesShiftedDataset( + target_series=self.target1, length=10, shift=5 + ) + assert len(ds) == 86 + self._assert_eq( + ds[5], (self.target1[80:90], None, self.cov_st1, None, self.target1[85:95]) + ) - # two targets and two covariates - ds = PastCovariatesShiftedDataset( - target_series=[self.target1, self.target2], - covariates=[self.cov1, self.cov2], - length=10, - shift=5, - ) - self._assert_eq( - ds[5], - ( - self.target1[80:90], - self.cov1[80:90], - self.cov_st1, - self.target1[85:95], - ), - ) - self._assert_eq( - ds[141], - ( - self.target2[130:140], - self.cov2[130:140], - self.cov_st2, - self.target2[135:145], - ), - ) + # two target series + ds = FutureCovariatesShiftedDataset( + target_series=[self.target1, self.target2], length=10, shift=5 + ) + assert len(ds) == 272 + self._assert_eq( + ds[5], (self.target1[80:90], None, self.cov_st1, None, self.target1[85:95]) + ) + self._assert_eq( + ds[141], + (self.target2[130:140], None, self.cov_st2, None, self.target2[135:145]), + ) - # Should contain correct values even when covariates are not aligned - target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( - self.cov_st2_df - ) - cov1 = TimeSeries.from_values(np.random.randn(10)) - ds = PastCovariatesShiftedDataset( - target_series=[target1], covariates=[cov1], length=3, shift=2 - ) - np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) - np.testing.assert_almost_equal(ds[0][1], cov1.values()[-7:-4]) - np.testing.assert_almost_equal(ds[0][2], self.cov_st2) - np.testing.assert_almost_equal(ds[0][3], target1.values()[-3:]) - - # Should also contain correct values when time-indexed with covariates not aligned - times1 = pd.date_range(start="20090201", end="20090220", freq="D") - times2 = pd.date_range(start="20090201", end="20090222", freq="D") - target1 = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov1 = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) - ds = PastCovariatesShiftedDataset( - target_series=[target1], covariates=[cov1], length=3, shift=2 - ) - np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) - np.testing.assert_almost_equal(ds[0][1], cov1.values()[-7:-4]) - np.testing.assert_almost_equal(ds[0][2], self.cov_st2) - np.testing.assert_almost_equal(ds[0][3], target1.values()[-3:]) - - # Should fail if covariates are too short - target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( - self.cov_st2_df - ) - cov1 = TimeSeries.from_values(np.random.randn(5)) - ds = PastCovariatesShiftedDataset( - target_series=[target1], covariates=[cov1], length=3, shift=2 - ) - with pytest.raises(ValueError): - _ = ds[0] + # two target series with custom max_nr_samples + ds = FutureCovariatesShiftedDataset( + target_series=[self.target1, self.target2], + length=10, + shift=5, + max_samples_per_ts=50, + ) + assert len(ds) == 100 + self._assert_eq( + ds[5], (self.target1[80:90], None, self.cov_st1, None, self.target1[85:95]) + ) + self._assert_eq( + ds[55], + (self.target2[130:140], None, self.cov_st2, None, self.target2[135:145]), + ) - def test_future_covariates_shifted_dataset(self): - # one target series + # two targets and one covariate + with pytest.raises(ValueError): ds = FutureCovariatesShiftedDataset( - target_series=self.target1, length=10, shift=5 - ) - assert len(ds) == 86 - self._assert_eq( - ds[5], (self.target1[80:90], None, self.cov_st1, self.target1[85:95]) + target_series=[self.target1, self.target2], covariates=[self.cov1] ) - # two target series - ds = FutureCovariatesShiftedDataset( - target_series=[self.target1, self.target2], length=10, shift=5 - ) - assert len(ds) == 272 - self._assert_eq( - ds[5], (self.target1[80:90], None, self.cov_st1, self.target1[85:95]) - ) - self._assert_eq( - ds[141], - (self.target2[130:140], None, self.cov_st2, self.target2[135:145]), - ) + # two targets and two covariates + ds = FutureCovariatesShiftedDataset( + target_series=[self.target1, self.target2], + covariates=[self.cov1, self.cov2], + length=10, + shift=5, + ) + self._assert_eq( + ds[5], + ( + self.target1[80:90], + self.cov1[85:95], + self.cov_st1, + None, + self.target1[85:95], + ), + ) + self._assert_eq( + ds[141], + ( + self.target2[130:140], + self.cov2[135:145], + self.cov_st2, + None, + self.target2[135:145], + ), + ) - # two target series with custom max_nr_samples - ds = FutureCovariatesShiftedDataset( - target_series=[self.target1, self.target2], - length=10, - shift=5, - max_samples_per_ts=50, - ) - assert len(ds) == 100 - self._assert_eq( - ds[5], (self.target1[80:90], None, self.cov_st1, self.target1[85:95]) - ) - self._assert_eq( - ds[55], - (self.target2[130:140], None, self.cov_st2, self.target2[135:145]), - ) + # Should contain correct values even when covariates are not aligned + target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( + self.cov_st2_df + ) + cov1 = TimeSeries.from_values(np.random.randn(10)) + ds = FutureCovariatesShiftedDataset( + target_series=[target1], covariates=[cov1], length=3, shift=2 + ) + np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) + np.testing.assert_almost_equal(ds[0][1], cov1.values()[-5:-2]) + np.testing.assert_almost_equal(ds[0][2], self.cov_st2) + assert ds[0][3] is None + np.testing.assert_almost_equal(ds[0][4], target1.values()[-3:]) + + # Should also contain correct values when time-indexed with covariates not aligned + times1 = pd.date_range(start="20090201", end="20090220", freq="D") + times2 = pd.date_range(start="20090201", end="20090222", freq="D") + target1 = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov1 = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + ds = FutureCovariatesShiftedDataset( + target_series=[target1], covariates=[cov1], length=3, shift=2 + ) + np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) + np.testing.assert_almost_equal(ds[0][1], cov1.values()[-5:-2]) + np.testing.assert_almost_equal(ds[0][2], self.cov_st2) + assert ds[0][3] is None + np.testing.assert_almost_equal(ds[0][4], target1.values()[-3:]) + + # Should fail if covariates are too short + target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( + self.cov_st2_df + ) + cov1 = TimeSeries.from_values(np.random.randn(7)) + ds = FutureCovariatesShiftedDataset( + target_series=[target1], covariates=[cov1], length=3, shift=2 + ) + with pytest.raises(ValueError): + _ = ds[0] - # two targets and one covariate - with pytest.raises(ValueError): - ds = FutureCovariatesShiftedDataset( - target_series=[self.target1, self.target2], covariates=[self.cov1] - ) + def test_dual_covariates_shifted_dataset(self): + # one target series + ds = DualCovariatesShiftedDataset( + target_series=self.target1, length=10, shift=5 + ) + assert len(ds) == 86 + self._assert_eq( + ds[5], + (self.target1[80:90], None, None, self.cov_st1, None, self.target1[85:95]), + ) - # two targets and two covariates - ds = FutureCovariatesShiftedDataset( - target_series=[self.target1, self.target2], - covariates=[self.cov1, self.cov2], - length=10, - shift=5, - ) - self._assert_eq( - ds[5], - ( - self.target1[80:90], - self.cov1[85:95], - self.cov_st1, - self.target1[85:95], - ), - ) - self._assert_eq( - ds[141], - ( - self.target2[130:140], - self.cov2[135:145], - self.cov_st2, - self.target2[135:145], - ), - ) + # two target series + ds = DualCovariatesShiftedDataset( + target_series=[self.target1, self.target2], length=10, shift=5 + ) + assert len(ds) == 272 + self._assert_eq( + ds[5], + (self.target1[80:90], None, None, self.cov_st1, None, self.target1[85:95]), + ) + self._assert_eq( + ds[141], + ( + self.target2[130:140], + None, + None, + self.cov_st2, + None, + self.target2[135:145], + ), + ) - # Should contain correct values even when covariates are not aligned - target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( - self.cov_st2_df - ) - cov1 = TimeSeries.from_values(np.random.randn(10)) - ds = FutureCovariatesShiftedDataset( - target_series=[target1], covariates=[cov1], length=3, shift=2 - ) - np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) - np.testing.assert_almost_equal(ds[0][1], cov1.values()[-5:-2]) - np.testing.assert_almost_equal(ds[0][2], self.cov_st2) - np.testing.assert_almost_equal(ds[0][3], target1.values()[-3:]) - - # Should also contain correct values when time-indexed with covariates not aligned - times1 = pd.date_range(start="20090201", end="20090220", freq="D") - times2 = pd.date_range(start="20090201", end="20090222", freq="D") - target1 = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov1 = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) - ds = FutureCovariatesShiftedDataset( - target_series=[target1], covariates=[cov1], length=3, shift=2 - ) - np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) - np.testing.assert_almost_equal(ds[0][1], cov1.values()[-5:-2]) - np.testing.assert_almost_equal(ds[0][2], self.cov_st2) - np.testing.assert_almost_equal(ds[0][3], target1.values()[-3:]) - - # Should fail if covariates are too short - target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( - self.cov_st2_df - ) - cov1 = TimeSeries.from_values(np.random.randn(7)) - ds = FutureCovariatesShiftedDataset( - target_series=[target1], covariates=[cov1], length=3, shift=2 - ) - with pytest.raises(ValueError): - _ = ds[0] + # two target series with custom max_nr_samples + ds = DualCovariatesShiftedDataset( + target_series=[self.target1, self.target2], + length=10, + shift=5, + max_samples_per_ts=50, + ) + assert len(ds) == 100 + self._assert_eq( + ds[5], + (self.target1[80:90], None, None, self.cov_st1, None, self.target1[85:95]), + ) + self._assert_eq( + ds[55], + ( + self.target2[130:140], + None, + None, + self.cov_st2, + None, + self.target2[135:145], + ), + ) - def test_dual_covariates_shifted_dataset(self): - # one target series + # two targets and one covariate + with pytest.raises(ValueError): ds = DualCovariatesShiftedDataset( - target_series=self.target1, length=10, shift=5 - ) - assert len(ds) == 86 - self._assert_eq( - ds[5], - (self.target1[80:90], None, None, self.cov_st1, self.target1[85:95]), + target_series=[self.target1, self.target2], covariates=[self.cov1] ) - # two target series - ds = DualCovariatesShiftedDataset( - target_series=[self.target1, self.target2], length=10, shift=5 - ) - assert len(ds) == 272 - self._assert_eq( - ds[5], - (self.target1[80:90], None, None, self.cov_st1, self.target1[85:95]), - ) - self._assert_eq( - ds[141], - ( - self.target2[130:140], - None, - None, - self.cov_st2, - self.target2[135:145], - ), - ) + # two targets and two covariates + ds = DualCovariatesShiftedDataset( + target_series=[self.target1, self.target2], + covariates=[self.cov1, self.cov2], + length=10, + shift=5, + ) + self._assert_eq( + ds[5], + ( + self.target1[80:90], + self.cov1[80:90], + self.cov1[85:95], + self.cov_st1, + None, + self.target1[85:95], + ), + ) + self._assert_eq( + ds[141], + ( + self.target2[130:140], + self.cov2[130:140], + self.cov2[135:145], + self.cov_st2, + None, + self.target2[135:145], + ), + ) - # two target series with custom max_nr_samples - ds = DualCovariatesShiftedDataset( - target_series=[self.target1, self.target2], - length=10, - shift=5, - max_samples_per_ts=50, - ) - assert len(ds) == 100 - self._assert_eq( - ds[5], - (self.target1[80:90], None, None, self.cov_st1, self.target1[85:95]), - ) - self._assert_eq( - ds[55], - ( - self.target2[130:140], - None, - None, - self.cov_st2, - self.target2[135:145], - ), - ) + # Should contain correct values even when covariates are not aligned + target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( + self.cov_st2_df + ) + cov1 = TimeSeries.from_values(np.random.randn(10)) + ds = DualCovariatesShiftedDataset( + target_series=[target1], covariates=[cov1], length=3, shift=2 + ) + np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) + np.testing.assert_almost_equal(ds[0][1], cov1.values()[-7:-4]) + np.testing.assert_almost_equal(ds[0][2], cov1.values()[-5:-2]) + np.testing.assert_almost_equal(ds[0][3], self.cov_st2) + assert ds[0][4] is None + np.testing.assert_almost_equal(ds[0][5], target1.values()[-3:]) + + # Should also contain correct values when time-indexed with covariates not aligned + times1 = pd.date_range(start="20090201", end="20090220", freq="D") + times2 = pd.date_range(start="20090201", end="20090222", freq="D") + target1 = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov1 = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + ds = DualCovariatesShiftedDataset( + target_series=[target1], covariates=[cov1], length=3, shift=2 + ) + np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) + np.testing.assert_almost_equal(ds[0][1], cov1.values()[-7:-4]) + np.testing.assert_almost_equal(ds[0][2], cov1.values()[-5:-2]) + np.testing.assert_almost_equal(ds[0][3], self.cov_st2) + assert ds[0][4] is None + np.testing.assert_almost_equal(ds[0][5], target1.values()[-3:]) + + # Should fail if covariates are too short + target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( + self.cov_st2_df + ) + cov1 = TimeSeries.from_values(np.random.randn(7)) + ds = DualCovariatesShiftedDataset( + target_series=[target1], covariates=[cov1], length=3, shift=2 + ) + with pytest.raises(ValueError): + _ = ds[0] + + @pytest.mark.parametrize("use_weight", [False, True]) + def test_horizon_based_dataset(self, use_weight): + weight1 = self.target1 + 1 + weight2 = self.target2 + 1 + + weight = weight1 if use_weight else None + weight_exp = weight1[85:95] if use_weight else None + # one target series + ds = HorizonBasedDataset( + target_series=self.target1, + output_chunk_length=10, + lh=(1, 3), + lookback=2, + sample_weight=weight, + ) + assert len(ds) == 20 + self._assert_eq( + ds[5], + (self.target1[65:85], None, self.cov_st1, weight_exp, self.target1[85:95]), + ) - # two targets and one covariate - with pytest.raises(ValueError): - ds = DualCovariatesShiftedDataset( - target_series=[self.target1, self.target2], covariates=[self.cov1] - ) + # two target series + weight = [weight1, weight2] if use_weight else None + weight_exp1 = weight1[85:95] if use_weight else None + weight_exp2 = weight2[135:145] if use_weight else None + ds = HorizonBasedDataset( + target_series=[self.target1, self.target2], + output_chunk_length=10, + lh=(1, 3), + lookback=2, + sample_weight=weight, + ) + assert len(ds) == 40 + self._assert_eq( + ds[5], + (self.target1[65:85], None, self.cov_st1, weight_exp1, self.target1[85:95]), + ) + self._assert_eq( + ds[25], + ( + self.target2[115:135], + None, + self.cov_st2, + weight_exp2, + self.target2[135:145], + ), + ) - # two targets and two covariates - ds = DualCovariatesShiftedDataset( - target_series=[self.target1, self.target2], - covariates=[self.cov1, self.cov2], - length=10, - shift=5, - ) - self._assert_eq( - ds[5], - ( - self.target1[80:90], - self.cov1[80:90], - self.cov1[85:95], - self.cov_st1, - self.target1[85:95], - ), - ) - self._assert_eq( - ds[141], - ( - self.target2[130:140], - self.cov2[130:140], - self.cov2[135:145], - self.cov_st2, - self.target2[135:145], - ), - ) + # two targets and one covariate + with pytest.raises(ValueError): + ds = HorizonBasedDataset( + target_series=[self.target1, self.target2], covariates=[self.cov1] + ) + + # two targets and two covariates + weight = [weight1, weight2] if use_weight else None + weight_exp1 = weight1[85:95] if use_weight else None + weight_exp2 = weight2[135:145] if use_weight else None + ds = HorizonBasedDataset( + target_series=[self.target1, self.target2], + covariates=[self.cov1, self.cov2], + output_chunk_length=10, + lh=(1, 3), + lookback=2, + sample_weight=weight, + ) + self._assert_eq( + ds[5], + ( + self.target1[65:85], + self.cov1[65:85], + self.cov_st1, + weight_exp1, + self.target1[85:95], + ), + ) + self._assert_eq( + ds[25], + ( + self.target2[115:135], + self.cov2[115:135], + self.cov_st2, + weight_exp2, + self.target2[135:145], + ), + ) - # Should contain correct values even when covariates are not aligned - target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( - self.cov_st2_df - ) - cov1 = TimeSeries.from_values(np.random.randn(10)) - ds = DualCovariatesShiftedDataset( - target_series=[target1], covariates=[cov1], length=3, shift=2 - ) - np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) - np.testing.assert_almost_equal(ds[0][1], cov1.values()[-7:-4]) - np.testing.assert_almost_equal(ds[0][2], cov1.values()[-5:-2]) - np.testing.assert_almost_equal(ds[0][3], self.cov_st2) - np.testing.assert_almost_equal(ds[0][4], target1.values()[-3:]) - - # Should also contain correct values when time-indexed with covariates not aligned - times1 = pd.date_range(start="20090201", end="20090220", freq="D") - times2 = pd.date_range(start="20090201", end="20090222", freq="D") - target1 = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov1 = TimeSeries.from_times_and_values( - times2, np.random.randn(len(times2)) - ) - ds = DualCovariatesShiftedDataset( - target_series=[target1], covariates=[cov1], length=3, shift=2 - ) - np.testing.assert_almost_equal(ds[0][0], target1.values()[-5:-2]) - np.testing.assert_almost_equal(ds[0][1], cov1.values()[-7:-4]) - np.testing.assert_almost_equal(ds[0][2], cov1.values()[-5:-2]) - np.testing.assert_almost_equal(ds[0][3], self.cov_st2) - np.testing.assert_almost_equal(ds[0][4], target1.values()[-3:]) - - # Should fail if covariates are too short - target1 = TimeSeries.from_values(np.random.randn(8)).with_static_covariates( - self.cov_st2_df - ) - cov1 = TimeSeries.from_values(np.random.randn(7)) - ds = DualCovariatesShiftedDataset( - target_series=[target1], covariates=[cov1], length=3, shift=2 - ) - with pytest.raises(ValueError): - _ = ds[0] + @pytest.mark.parametrize( + "config", + [ + # (dataset class, whether contains future, future batch index) + (PastCovariatesSequentialDataset, None), + (FutureCovariatesSequentialDataset, 1), + (DualCovariatesSequentialDataset, 2), + (MixedCovariatesSequentialDataset, 3), + (SplitCovariatesSequentialDataset, 2), + ], + ) + def test_sequential_training_dataset_output_chunk_shift(self, config): + ds_cls, future_idx = config + ocl = 1 + ocs = 2 + target = self.target1[: -(ocl + ocs)] + sample_weight = target + 1 + + ds_covs = {} + ds_init_params = set(inspect.signature(ds_cls.__init__).parameters) + for cov_type in ["covariates", "past_covariates", "future_covariates"]: + if cov_type in ds_init_params: + ds_covs[cov_type] = self.cov1 + + # regular dataset with output shift=0 and ocl=3: the 3rd future values should be identical to the 1st future + # values of a dataset with output shift=2 and ocl=1 + ds_reg = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=3, + output_chunk_shift=0, + sample_weight=sample_weight, + **ds_covs, + ) - def test_horizon_based_dataset(self): - # one target series - ds = HorizonBasedDataset( - target_series=self.target1, - output_chunk_length=10, - lh=(1, 3), - lookback=2, - ) - assert len(ds) == 20 - self._assert_eq( - ds[5], (self.target1[65:85], None, self.cov_st1, self.target1[85:95]) - ) + ds_shift = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=1, + output_chunk_shift=ocs, + sample_weight=sample_weight, + **ds_covs, + ) - # two target series - ds = HorizonBasedDataset( - target_series=[self.target1, self.target2], - output_chunk_length=10, - lh=(1, 3), - lookback=2, - ) - assert len(ds) == 40 - self._assert_eq( - ds[5], (self.target1[65:85], None, self.cov_st1, self.target1[85:95]) - ) - self._assert_eq( - ds[25], - (self.target2[115:135], None, self.cov_st2, self.target2[135:145]), - ) + batch_reg, batch_shift = ds_reg[0], ds_shift[0] + + if future_idx is not None: + # 3rd future values of regular ds must be identical to the 1st future values of shifted dataset + np.testing.assert_array_equal( + batch_reg[future_idx][-1:], batch_shift[future_idx] + ) + batch_reg = batch_reg[:future_idx] + batch_reg[future_idx + 1 :] + batch_shift = batch_shift[:future_idx] + batch_shift[future_idx + 1 :] + + # last two elements are (sample weight, output chunk of the target series). + # 3rd future values of regular ds must be identical to the 1st future values of shifted dataset + batch_reg = batch_reg[:-2] + (batch_reg[-2][ocs:], batch_reg[-1][ocs:]) + + # without future part, the input will be identical between regular, and shifted dataset + assert all([ + np.all(el_reg == el_shift) + for el_reg, el_shift in zip(batch_reg[:-1], batch_shift[:-1]) + ]) + + @pytest.mark.parametrize( + "config", + itertools.product( + [ + PastCovariatesSequentialDataset, + FutureCovariatesSequentialDataset, + DualCovariatesSequentialDataset, + MixedCovariatesSequentialDataset, + SplitCovariatesSequentialDataset, + ], + [True, False], + ), + ) + def test_sequential_training_dataset_weight(self, config): + ds_cls, manual_weight = config + + def get_built_in_weigths(targets): + if isinstance(targets, list): + max_steps = max([len(ts) for ts in targets]) + else: + max_steps = len(targets) + weight_expected = np.linspace(0, 1, max_steps)[-3:] + return np.expand_dims(weight_expected, -1) + + target1 = self.target1 + target2 = self.target2 + weight1 = target1 + 1 + weight2 = target2 + 1 + built_in_weight = "linear" + + ds_covs = {} + ds_init_params = set(inspect.signature(ds_cls.__init__).parameters) + for cov_type in ["covariates", "past_covariates", "future_covariates"]: + if cov_type in ds_init_params: + ds_covs[cov_type] = self.cov1 + + # no sample weight + ds = ds_cls( + target_series=target1, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=None, + **ds_covs, + ) + assert ds[0][-2] is None + + # whenever we use sample weight, the weight are extracted from the same time frame as the target labels + # since we set the weight to be `target + 1`, the returned batch weight must also be `batch_target_label + 1` + + # single univariate + target = target1 + weight = weight1 if manual_weight else built_in_weight + ds = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=weight, + **ds_covs, + ) + weight_exp = ds[0][-1] + 1 if manual_weight else get_built_in_weigths(target) + assert np.all(ds[0][-2] == weight_exp) + + # single univariate with longer weight + target = target1 + weight = ( + weight1.prepend_values([0.0]).append_values([0.0]) + if manual_weight + else built_in_weight + ) + ds = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=weight, + **ds_covs, + ) + weight_exp = ds[0][-1] + 1 if manual_weight else get_built_in_weigths(target) + assert np.all(ds[0][-2] == weight_exp) + + # single multivariate with multivariate weight + target = target1.stack(target1 + 1) + weight = weight1.stack(weight1 + 1) if manual_weight else built_in_weight + ds = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=weight, + **ds_covs, + ) + weight_exp = ds[0][-1] + 1 if manual_weight else get_built_in_weigths(target) + assert np.all(ds[0][-2] == weight_exp) + + # single multivariate with univariate (global) weight + target = target1.stack(target1 + 1) + weight = weight1 if manual_weight else built_in_weight + ds = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=weight, + **ds_covs, + ) + # output weight corresponds to first target component + 1 (e.g. weight1) + weight_exp = ( + ds[0][-1][:, 0:1] + 1 if manual_weight else get_built_in_weigths(target) + ) + assert np.all(ds[0][-2] == weight_exp) + + # single univariate and list of single weight + target = target1 + weight = [weight1] if manual_weight else built_in_weight + ds = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=weight, + **ds_covs, + ) + weight_exp = ds[0][-1] + 1 if manual_weight else get_built_in_weigths(target) + assert np.all(ds[0][-2] == weight_exp) + + # multiple univariate + target = [target1, target2] + weight = [weight1, weight2] if manual_weight else built_in_weight + ds = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=weight, + **{k: [v] * 2 for k, v in ds_covs.items()}, + ) + weight_exp = ds[0][-1] + 1 if manual_weight else get_built_in_weigths(target) + assert np.all(ds[0][-2] == weight_exp) + + # multiple multivariate + target = [target1.stack(target1 + 1), target2.stack(target2 + 1)] + weight = ( + [weight1.stack(weight1 + 1), weight2.stack(weight2 + 1)] + if manual_weight + else built_in_weight + ) + ds = ds_cls( + target_series=target, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=weight, + **{k: [v] * 2 for k, v in ds_covs.items()}, + ) + weight_exp = ds[0][-1] + 1 if manual_weight else get_built_in_weigths(target) + assert np.all(ds[0][-2] == weight_exp) + + @pytest.mark.parametrize( + "ds_cls", + [ + PastCovariatesSequentialDataset, + FutureCovariatesSequentialDataset, + DualCovariatesSequentialDataset, + MixedCovariatesSequentialDataset, + SplitCovariatesSequentialDataset, + ], + ) + def test_sequential_training_dataset_invalid_weight(self, ds_cls): + ts = self.target1 + + # invalid built-in weight + with pytest.raises(ValueError) as err: + _ = ds_cls( + target_series=[ts, ts], + input_chunk_length=1, + output_chunk_length=3, + sample_weight="invalid", + ) + assert str(err.value).startswith( + "Invalid `sample_weight` value: `'invalid'`. If a string, must be one of: " + ) - # two targets and one covariate - with pytest.raises(ValueError): - ds = HorizonBasedDataset( - target_series=[self.target1, self.target2], covariates=[self.cov1] - ) + # mismatch number of target and weight series + with pytest.raises(ValueError) as err: + _ = ds_cls( + target_series=[ts, ts], + input_chunk_length=1, + output_chunk_length=3, + sample_weight=[ts], + ) + assert ( + str(err.value) + == "The provided sequence of target `series` must have the same " + "length as the provided sequence of `sample_weight`." + ) - # two targets and two covariates - ds = HorizonBasedDataset( - target_series=[self.target1, self.target2], - covariates=[self.cov1, self.cov2], - output_chunk_length=10, - lh=(1, 3), - lookback=2, - ) - self._assert_eq( - ds[5], - ( - self.target1[65:85], - self.cov1[65:85], - self.cov_st1, - self.target1[85:95], - ), - ) - self._assert_eq( - ds[25], - ( - self.target2[115:135], - self.cov2[115:135], - self.cov_st2, - self.target2[135:145], - ), - ) + # too many weight components + ds = ds_cls( + target_series=ts, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=ts.stack(ts + 1), + ) + with pytest.raises(ValueError) as err: + _ = ds[0] + assert ( + str(err.value) + == "The number of components in `sample_weight` must either be `1` or match " + "the number of target series components `1`. (0-th series)" + ) + + # weight too short end + ds = ds_cls( + target_series=ts, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=ts[:-1], + ) + with pytest.raises(ValueError) as err: + _ = ds[0] + assert ( + str(err.value) + == "Missing sample weights; could not find sample weights in index value range: " + "2000-04-07 00:00:00 - 2000-04-09 00:00:00." + ) + + # weight too short start + ds = ds_cls( + target_series=ts, + input_chunk_length=1, + output_chunk_length=3, + sample_weight=ts[2:], + ) + with pytest.raises(ValueError) as err: + _ = ds[len(ds) - 1] + assert ( + str(err.value) + == "Missing sample weights; could not find sample weights in index value range: " + "2000-01-02 00:00:00 - 2000-01-04 00:00:00." + ) - def test_get_matching_index(self): - from darts.utils.data.utils import _get_matching_index - - # Check dividable freq - times1 = pd.date_range(start="20100101", end="20100330", freq="D") - times2 = pd.date_range(start="20100101", end="20100320", freq="D") - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) - assert _get_matching_index(target, cov, idx=15) == 5 - - # check non-dividable freq - times1 = pd.date_range(start="20100101", end="20120101", freq="M") - times2 = pd.date_range(start="20090101", end="20110601", freq="M") - target = TimeSeries.from_times_and_values( - times1, np.random.randn(len(times1)) - ).with_static_covariates(self.cov_st2_df) - cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) - assert _get_matching_index(target, cov, idx=15) == 15 - 7 - - # check integer-indexed series - times2 = pd.RangeIndex(start=10, stop=90) - target = TimeSeries.from_values( - np.random.randn(100) - ).with_static_covariates(self.cov_st2_df) - cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) - assert _get_matching_index(target, cov, idx=15) == 5 + def test_get_matching_index(self): + from darts.utils.data.utils import _get_matching_index + + # Check dividable freq + times1 = pd.date_range(start="20100101", end="20100330", freq="D") + times2 = pd.date_range(start="20100101", end="20100320", freq="D") + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + assert _get_matching_index(target, cov, idx=15) == 5 + + # check non-dividable freq + times1 = pd.date_range(start="20100101", end="20120101", freq=freqs["ME"]) + times2 = pd.date_range(start="20090101", end="20110601", freq=freqs["ME"]) + target = TimeSeries.from_times_and_values( + times1, np.random.randn(len(times1)) + ).with_static_covariates(self.cov_st2_df) + cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + assert _get_matching_index(target, cov, idx=15) == 15 - 7 + + # check integer-indexed series + times2 = pd.RangeIndex(start=10, stop=90) + target = TimeSeries.from_values(np.random.randn(100)).with_static_covariates( + self.cov_st2_df + ) + cov = TimeSeries.from_times_and_values(times2, np.random.randn(len(times2))) + assert _get_matching_index(target, cov, idx=15) == 5 diff --git a/darts/tests/explainability/test_shap_explainer.py b/darts/tests/explainability/test_shap_explainer.py index dfc3773af2..d5c36fba0f 100644 --- a/darts/tests/explainability/test_shap_explainer.py +++ b/darts/tests/explainability/test_shap_explainer.py @@ -205,9 +205,13 @@ def test_creation(self): # Good type of explainers shap_explain = ShapExplainer(m) - assert isinstance( - shap_explain.explainers.explainers[0][0], shap.explainers.Tree - ) + if isinstance(m, XGBModel): + # since xgboost > 2.1.0, model supports native multi output regression + assert isinstance(shap_explain.explainers.explainers, shap.explainers.Tree) + else: + assert isinstance( + shap_explain.explainers.explainers[0][0], shap.explainers.Tree + ) # Linear model - also not a MultiOutputRegressor m = LinearRegressionModel( @@ -266,9 +270,12 @@ def test_creation(self): future_covariates=self.fut_cov_ts, ) shap_explain = ShapExplainer(m) - assert isinstance( - shap_explain.explainers.explainers[0][0], shap.explainers.Tree - ) + if isinstance(m, XGBModel): + assert isinstance(shap_explain.explainers.explainers, shap.explainers.Tree) + else: + assert isinstance( + shap_explain.explainers.explainers[0][0], shap.explainers.Tree + ) # Bad choice of shap explainer with pytest.raises(ValueError): @@ -709,13 +716,26 @@ def test_shap_explanation_object_validity(self): shap.Explanation, ) - def test_shap_selected_components(self): - model_cls = LightGBMModel if lgbm_available else XGBModel + @pytest.mark.parametrize( + "config", + [ + (XGBModel, {}), + ( + LightGBMModel if lgbm_available else XGBModel, + {"likelihood": "quantile", "quantiles": [0.5]}, + ), + ], + ) + def test_shap_selected_components(self, config): + """Test selected components with and without Darts' MultiOutputRegressor""" + model_cls, model_kwargs = config + # model_cls = XGBModel model = model_cls( lags=4, lags_past_covariates=2, lags_future_covariates=[1], output_chunk_length=1, + **model_kwargs, ) model.fit( series=self.target_ts, diff --git a/darts/tests/explainability/test_tft_explainer.py b/darts/tests/explainability/test_tft_explainer.py index 7b16e88bd5..02544bd35f 100644 --- a/darts/tests/explainability/test_tft_explainer.py +++ b/darts/tests/explainability/test_tft_explainer.py @@ -7,490 +7,432 @@ import pytest from darts import TimeSeries -from darts.logging import get_logger -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils import timeseries_generation as tg -logger = get_logger(__name__) - -try: - from darts.explainability import TFTExplainabilityResult, TFTExplainer - from darts.models import TFTModel - - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. RNN tests will be skipped.") - TORCH_AVAILABLE = False - - -if TORCH_AVAILABLE: - - class TestTFTExplainer: - freq = "MS" - series_lin_pos = tg.linear_timeseries( - length=10, freq=freq - ).with_static_covariates(pd.Series([0.0, 0.5], index=["cat", "num"])) - series_sine = tg.sine_timeseries(length=10, freq=freq) - series_mv1 = series_lin_pos.stack(series_sine) - - series_lin_neg = tg.linear_timeseries( - start_value=1, end_value=0, length=10, freq=freq - ).with_static_covariates(pd.Series([1.0, 0.5], index=["cat", "num"])) - series_cos = tg.sine_timeseries(length=10, value_phase=90, freq=freq) - series_mv2 = series_lin_neg.stack(series_cos) - - series_multi = [series_mv1, series_mv2] - pc = tg.constant_timeseries(length=10, freq=freq) - pc_multi = [pc] * 2 - fc = tg.constant_timeseries(length=13, freq=freq) - fc_multi = [fc] * 2 - - def helper_get_input(self, series_option: str): - if series_option == "univariate": - return self.series_lin_pos, self.pc, self.fc - elif series_option == "multivariate": - return self.series_mv1, self.pc, self.fc - else: # multiple - return self.series_multi, self.pc_multi, self.fc_multi - - def helper_create_test_cases(self, series_options: list): - covariates_options = [ - {}, - {"past_covariates"}, - {"future_covariates"}, - {"past_covariates", "future_covariates"}, - ] - relative_index_options = [False, True] - use_encoders_options = [False, True] - return itertools.product( - *[ - series_options, - covariates_options, - relative_index_options, - use_encoders_options, - ] - ) - - def test_explainer_single_univariate_multivariate_series(self): - """Test TFTExplainer with single univariate and multivariate series and a combination of - encoders, covariates, and addition of relative index.""" - series_option: str - cov_option: set - add_relative_idx: bool - use_encoders: bool - - series_options = [ - "univariate", - "multivariate", - # "multiple", - ] - test_cases = self.helper_create_test_cases(series_options) - for series_option, cov_option, add_relative_idx, use_encoders in test_cases: - series, pc, fc = self.helper_get_input(series_option) - cov_test_case = dict() - use_pc, use_fc = False, False - if "past_covariates" in cov_option: - cov_test_case["past_covariates"] = pc - use_pc = True - if "future_covariates" in cov_option: - cov_test_case["future_covariates"] = fc - use_fc = True - - # expected number of features for past covs, future covs, and static covs, and encoder/decoder - n_target_expected = series.n_components - n_pc_expected = 1 if "past_covariates" in cov_test_case else 0 - n_fc_expected = 1 if "future_covariates" in cov_test_case else 0 - n_sc_expected = 2 - # encoder is number of past and future covs plus 4 optional encodings (future and past) - # plus 1 univariate target plus 1 optional relative index - n_enc_expected = ( - n_pc_expected - + n_fc_expected - + n_target_expected - + (4 if use_encoders else 0) - + (1 if add_relative_idx else 0) - ) - # encoder is number of future covs plus 2 optional encodings (future) - # plus 1 optional relative index - n_dec_expected = ( - n_fc_expected - + (2 if use_encoders else 0) - + (1 if add_relative_idx else 0) - ) - model = self.helper_create_model( - use_encoders=use_encoders, add_relative_idx=add_relative_idx - ) - # TFTModel requires future covariates - if ( - not add_relative_idx - and "future_covariates" not in cov_test_case - and not use_encoders - ): - with pytest.raises(ValueError): - model.fit(series=series, **cov_test_case) - continue - +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +from darts.explainability import TFTExplainabilityResult, TFTExplainer +from darts.models import TFTModel + + +def helper_create_test_cases(series_options: list): + covariates_options = [ + {}, + {"past_covariates"}, + {"future_covariates"}, + {"past_covariates", "future_covariates"}, + ] + relative_index_options = [False, True] + use_encoders_options = [False, True] + return itertools.product(*[ + series_options, + covariates_options, + relative_index_options, + use_encoders_options, + ]) + + +class TestTFTExplainer: + freq = "MS" + series_lin_pos = tg.linear_timeseries(length=10, freq=freq).with_static_covariates( + pd.Series([0.0, 0.5], index=["cat", "num"]) + ) + series_sine = tg.sine_timeseries(length=10, freq=freq) + series_mv1 = series_lin_pos.stack(series_sine) + + series_lin_neg = tg.linear_timeseries( + start_value=1, end_value=0, length=10, freq=freq + ).with_static_covariates(pd.Series([1.0, 0.5], index=["cat", "num"])) + series_cos = tg.sine_timeseries(length=10, value_phase=90, freq=freq) + series_mv2 = series_lin_neg.stack(series_cos) + + series_multi = [series_mv1, series_mv2] + pc = tg.constant_timeseries(length=10, freq=freq) + pc_multi = [pc] * 2 + fc = tg.constant_timeseries(length=13, freq=freq) + fc_multi = [fc] * 2 + + def helper_get_input(self, series_option: str): + if series_option == "univariate": + return self.series_lin_pos, self.pc, self.fc + elif series_option == "multivariate": + return self.series_mv1, self.pc, self.fc + else: # multiple + return self.series_multi, self.pc_multi, self.fc_multi + + @pytest.mark.parametrize( + "test_case", helper_create_test_cases(["univariate", "multivariate"]) + ) + def test_explainer_single_univariate_multivariate_series(self, test_case): + """Test TFTExplainer with single univariate and multivariate series and a combination of + encoders, covariates, and addition of relative index.""" + series_option, cov_option, add_relative_idx, use_encoders = test_case + series, pc, fc = self.helper_get_input(series_option) + cov_test_case = dict() + use_pc, use_fc = False, False + if "past_covariates" in cov_option: + cov_test_case["past_covariates"] = pc + use_pc = True + if "future_covariates" in cov_option: + cov_test_case["future_covariates"] = fc + use_fc = True + + # expected number of features for past covs, future covs, and static covs, and encoder/decoder + n_target_expected = series.n_components + n_pc_expected = 1 if "past_covariates" in cov_test_case else 0 + n_fc_expected = 1 if "future_covariates" in cov_test_case else 0 + n_sc_expected = 2 + # encoder is number of past and future covs plus 4 optional encodings (future and past) + # plus 1 univariate target plus 1 optional relative index + n_enc_expected = ( + n_pc_expected + + n_fc_expected + + n_target_expected + + (4 if use_encoders else 0) + + (1 if add_relative_idx else 0) + ) + # encoder is number of future covs plus 2 optional encodings (future) + # plus 1 optional relative index + n_dec_expected = ( + n_fc_expected + (2 if use_encoders else 0) + (1 if add_relative_idx else 0) + ) + model = self.helper_create_model( + use_encoders=use_encoders, add_relative_idx=add_relative_idx + ) + # TFTModel requires future covariates + if ( + not add_relative_idx + and "future_covariates" not in cov_test_case + and not use_encoders + ): + with pytest.raises(ValueError): model.fit(series=series, **cov_test_case) - explainer = TFTExplainer(model) - explainer2 = TFTExplainer( - model, - background_series=series, - background_past_covariates=pc if use_pc else None, - background_future_covariates=fc if use_fc else None, - ) - assert explainer.background_series == explainer2.background_series - assert ( - explainer.background_past_covariates - == explainer2.background_past_covariates - ) - assert ( - explainer.background_future_covariates - == explainer2.background_future_covariates - ) - - assert hasattr(explainer, "model") - assert explainer.background_series[0] == series - if use_pc: - assert explainer.background_past_covariates[0] == pc - assert ( - explainer.background_past_covariates[0].n_components - == n_pc_expected - ) - else: - assert explainer.background_past_covariates is None - if use_fc: - assert explainer.background_future_covariates[0] == fc - assert ( - explainer.background_future_covariates[0].n_components - == n_fc_expected - ) - else: - assert explainer.background_future_covariates is None - result = explainer.explain() - assert isinstance(result, TFTExplainabilityResult) - - enc_imp = result.get_encoder_importance() - dec_imp = result.get_decoder_importance() - stc_imp = result.get_static_covariates_importance() - imps = [enc_imp, dec_imp, stc_imp] - assert all([isinstance(imp, pd.DataFrame) for imp in imps]) - # importances must sum up to 100 percent - assert all( - [ - imp.squeeze().sum() == pytest.approx(100.0, rel=0.2) - for imp in imps - ] - ) - # importances must have the expected number of columns - assert all( - [ - len(imp.columns) == n - for imp, n in zip( - imps, [n_enc_expected, n_dec_expected, n_sc_expected] - ) - ] - ) - - attention = result.get_attention() - assert isinstance(attention, TimeSeries) - # input chunk length + output chunk length = 5 + 2 = 7 - icl, ocl = 5, 2 - freq = series.freq - assert len(attention) == icl + ocl - assert attention.start_time() == series.end_time() - (icl - 1) * freq - assert attention.end_time() == series.end_time() + ocl * freq - assert attention.n_components == ocl - - def test_explainer_multiple_multivariate_series(self): - """Test TFTExplainer with multiple multivaraites series and a combination of encoders, covariates, - and addition of relative index.""" - series_option: str - cov_option: set - add_relative_idx: bool - use_encoders: bool - - series_options = ["multiple"] - test_cases = self.helper_create_test_cases(series_options) - for series_option, cov_option, add_relative_idx, use_encoders in test_cases: - series, pc, fc = self.helper_get_input(series_option) - cov_test_case = dict() - use_pc, use_fc = False, False - if "past_covariates" in cov_option: - cov_test_case["past_covariates"] = pc - use_pc = True - if "future_covariates" in cov_option: - cov_test_case["future_covariates"] = fc - use_fc = True - - # expected number of features for past covs, future covs, and static covs, and encoder/decoder - n_target_expected = series[0].n_components - n_pc_expected = 1 if "past_covariates" in cov_test_case else 0 - n_fc_expected = 1 if "future_covariates" in cov_test_case else 0 - n_sc_expected = 2 - # encoder is number of past and future covs plus 4 optional encodings (future and past) - # plus 1 univariate target plus 1 optional relative index - n_enc_expected = ( - n_pc_expected - + n_fc_expected - + n_target_expected - + (4 if use_encoders else 0) - + (1 if add_relative_idx else 0) - ) - # encoder is number of future covs plus 2 optional encodings (future) - # plus 1 optional relative index - n_dec_expected = ( - n_fc_expected - + (2 if use_encoders else 0) - + (1 if add_relative_idx else 0) - ) - model = self.helper_create_model( - use_encoders=use_encoders, add_relative_idx=add_relative_idx - ) - # TFTModel requires future covariates - if ( - not add_relative_idx - and "future_covariates" not in cov_test_case - and not use_encoders - ): - with pytest.raises(ValueError): - model.fit(series=series, **cov_test_case) - continue - + return + + model.fit(series=series, **cov_test_case) + explainer = TFTExplainer(model) + explainer2 = TFTExplainer( + model, + background_series=series, + background_past_covariates=pc if use_pc else None, + background_future_covariates=fc if use_fc else None, + ) + assert explainer.background_series == explainer2.background_series + assert ( + explainer.background_past_covariates + == explainer2.background_past_covariates + ) + assert ( + explainer.background_future_covariates + == explainer2.background_future_covariates + ) + + assert hasattr(explainer, "model") + assert explainer.background_series[0] == series + if use_pc: + assert explainer.background_past_covariates[0] == pc + assert explainer.background_past_covariates[0].n_components == n_pc_expected + else: + assert explainer.background_past_covariates is None + if use_fc: + assert explainer.background_future_covariates[0] == fc + assert ( + explainer.background_future_covariates[0].n_components == n_fc_expected + ) + else: + assert explainer.background_future_covariates is None + result = explainer.explain() + assert isinstance(result, TFTExplainabilityResult) + + enc_imp = result.get_encoder_importance() + dec_imp = result.get_decoder_importance() + stc_imp = result.get_static_covariates_importance() + imps = [enc_imp, dec_imp, stc_imp] + assert all([isinstance(imp, pd.DataFrame) for imp in imps]) + # importances must sum up to 100 percent + assert all([ + imp.squeeze().sum() == pytest.approx(100.0, rel=0.2) for imp in imps + ]) + # importances must have the expected number of columns + assert all([ + len(imp.columns) == n + for imp, n in zip(imps, [n_enc_expected, n_dec_expected, n_sc_expected]) + ]) + + attention = result.get_attention() + assert isinstance(attention, TimeSeries) + # input chunk length + output chunk length = 5 + 2 = 7 + icl, ocl = 5, 2 + freq = series.freq + assert len(attention) == icl + ocl + assert attention.start_time() == series.end_time() - (icl - 1) * freq + assert attention.end_time() == series.end_time() + ocl * freq + assert attention.n_components == ocl + + @pytest.mark.parametrize("test_case", helper_create_test_cases(["multiple"])) + def test_explainer_multiple_multivariate_series(self, test_case): + """Test TFTExplainer with multiple multivaraites series and a combination of encoders, covariates, + and addition of relative index.""" + series_option, cov_option, add_relative_idx, use_encoders = test_case + series, pc, fc = self.helper_get_input(series_option) + cov_test_case = dict() + use_pc, use_fc = False, False + if "past_covariates" in cov_option: + cov_test_case["past_covariates"] = pc + use_pc = True + if "future_covariates" in cov_option: + cov_test_case["future_covariates"] = fc + use_fc = True + + # expected number of features for past covs, future covs, and static covs, and encoder/decoder + n_target_expected = series[0].n_components + n_pc_expected = 1 if "past_covariates" in cov_test_case else 0 + n_fc_expected = 1 if "future_covariates" in cov_test_case else 0 + n_sc_expected = 2 + # encoder is number of past and future covs plus 4 optional encodings (future and past) + # plus 1 univariate target plus 1 optional relative index + n_enc_expected = ( + n_pc_expected + + n_fc_expected + + n_target_expected + + (4 if use_encoders else 0) + + (1 if add_relative_idx else 0) + ) + # encoder is number of future covs plus 2 optional encodings (future) + # plus 1 optional relative index + n_dec_expected = ( + n_fc_expected + (2 if use_encoders else 0) + (1 if add_relative_idx else 0) + ) + model = self.helper_create_model( + use_encoders=use_encoders, add_relative_idx=add_relative_idx + ) + # TFTModel requires future covariates + if ( + not add_relative_idx + and "future_covariates" not in cov_test_case + and not use_encoders + ): + with pytest.raises(ValueError): model.fit(series=series, **cov_test_case) - # explainer requires background if model trained on multiple time series - with pytest.raises(ValueError): - explainer = TFTExplainer(model) - explainer = TFTExplainer( - model, - background_series=series, - background_past_covariates=pc if use_pc else None, - background_future_covariates=fc if use_fc else None, - ) - assert hasattr(explainer, "model") - assert explainer.background_series, series - if use_pc: - assert explainer.background_past_covariates == pc - assert ( - explainer.background_past_covariates[0].n_components - == n_pc_expected - ) - else: - assert explainer.background_past_covariates is None - if use_fc: - assert explainer.background_future_covariates == fc - assert ( - explainer.background_future_covariates[0].n_components - == n_fc_expected - ) - else: - assert explainer.background_future_covariates is None - result = explainer.explain() - assert isinstance(result, TFTExplainabilityResult) - - enc_imp = result.get_encoder_importance() - dec_imp = result.get_decoder_importance() - stc_imp = result.get_static_covariates_importance() - imps = [enc_imp, dec_imp, stc_imp] - assert all([isinstance(imp, list) for imp in imps]) - assert all([len(imp) == len(series) for imp in imps]) - assert all( - [isinstance(imp_, pd.DataFrame) for imp in imps for imp_ in imp] - ) - # importances must sum up to 100 percent - assert all( - [ - imp_.squeeze().sum() == pytest.approx(100.0, abs=0.11) - for imp in imps - for imp_ in imp - ] - ) - # importances must have the expected number of columns - assert all( - [ - len(imp_.columns) == n - for imp, n in zip( - imps, [n_enc_expected, n_dec_expected, n_sc_expected] - ) - for imp_ in imp - ] - ) - - attention = result.get_attention() - assert isinstance(attention, list) - assert len(attention) == len(series) - assert all([isinstance(att, TimeSeries) for att in attention]) - # input chunk length + output chunk length = 5 + 2 = 7 - icl, ocl = 5, 2 - freq = series[0].freq - assert all([len(att) == icl + ocl for att in attention]) - assert all( - [ - att.start_time() == series_.end_time() - (icl - 1) * freq - for att, series_ in zip(attention, series) - ] - ) - assert all( - [ - att.end_time() == series_.end_time() + ocl * freq - for att, series_ in zip(attention, series) - ] - ) - assert all([att.n_components == ocl for att in attention]) + return - def test_variable_selection_explanation(self): - """Test variable selection (feature importance) explanation results and plotting.""" - model = self.helper_create_model(use_encoders=True, add_relative_idx=True) + model.fit(series=series, **cov_test_case) + # explainer requires background if model trained on multiple time series + with pytest.raises(ValueError): + explainer = TFTExplainer(model) + explainer = TFTExplainer( + model, + background_series=series, + background_past_covariates=pc if use_pc else None, + background_future_covariates=fc if use_fc else None, + ) + assert hasattr(explainer, "model") + assert explainer.background_series, series + if use_pc: + assert explainer.background_past_covariates == pc + assert explainer.background_past_covariates[0].n_components == n_pc_expected + else: + assert explainer.background_past_covariates is None + if use_fc: + assert explainer.background_future_covariates == fc + assert ( + explainer.background_future_covariates[0].n_components == n_fc_expected + ) + else: + assert explainer.background_future_covariates is None + result = explainer.explain() + assert isinstance(result, TFTExplainabilityResult) + + enc_imp = result.get_encoder_importance() + dec_imp = result.get_decoder_importance() + stc_imp = result.get_static_covariates_importance() + imps = [enc_imp, dec_imp, stc_imp] + assert all([isinstance(imp, list) for imp in imps]) + assert all([len(imp) == len(series) for imp in imps]) + assert all([isinstance(imp_, pd.DataFrame) for imp in imps for imp_ in imp]) + # importances must sum up to 100 percent + assert all([ + imp_.squeeze().sum() == pytest.approx(100.0, abs=0.21) + for imp in imps + for imp_ in imp + ]) + # importances must have the expected number of columns + assert all([ + len(imp_.columns) == n + for imp, n in zip(imps, [n_enc_expected, n_dec_expected, n_sc_expected]) + for imp_ in imp + ]) + + attention = result.get_attention() + assert isinstance(attention, list) + assert len(attention) == len(series) + assert all([isinstance(att, TimeSeries) for att in attention]) + # input chunk length + output chunk length = 5 + 2 = 7 + icl, ocl = 5, 2 + freq = series[0].freq + assert all([len(att) == icl + ocl for att in attention]) + assert all([ + att.start_time() == series_.end_time() - (icl - 1) * freq + for att, series_ in zip(attention, series) + ]) + assert all([ + att.end_time() == series_.end_time() + ocl * freq + for att, series_ in zip(attention, series) + ]) + assert all([att.n_components == ocl for att in attention]) + + def test_variable_selection_explanation(self): + """Test variable selection (feature importance) explanation results and plotting.""" + model = self.helper_create_model(use_encoders=True, add_relative_idx=True) + series, pc, fc = self.helper_get_input(series_option="multivariate") + model.fit(series, past_covariates=pc, future_covariates=fc) + explainer = TFTExplainer(model) + results = explainer.explain() + + imps = results.get_feature_importances() + enc_imp = results.get_encoder_importance() + dec_imp = results.get_decoder_importance() + stc_imp = results.get_static_covariates_importance() + imps_direct = [enc_imp, dec_imp, stc_imp] + + imp_names = [ + "encoder_importance", + "decoder_importance", + "static_covariates_importance", + ] + assert list(imps.keys()) == imp_names + for imp, imp_name in zip(imps_direct, imp_names): + assert imps[imp_name].equals(imp) + + enc_expected = pd.DataFrame( + { + "linear_target": 1.7, + "sine_target": 3.1, + "add_relative_index_futcov": 3.6, + "constant_pastcov": 3.9, + "darts_enc_fc_cyc_month_sin_futcov": 5.0, + "darts_enc_pc_cyc_month_sin_pastcov": 10.1, + "darts_enc_pc_cyc_month_cos_pastcov": 19.9, + "constant_futcov": 21.8, + "darts_enc_fc_cyc_month_cos_futcov": 31.0, + }, + index=[0], + ) + # relaxed comparison because M1 chip gives slightly different results than intel chip + assert ((enc_imp.round(decimals=1) - enc_expected).abs() <= 3).all().all() + + dec_expected = pd.DataFrame( + { + "darts_enc_fc_cyc_month_sin_futcov": 5.3, + "darts_enc_fc_cyc_month_cos_futcov": 7.4, + "constant_futcov": 24.5, + "add_relative_index_futcov": 62.9, + }, + index=[0], + ) + # relaxed comparison because M1 chip gives slightly different results than intel chip + assert ((dec_imp.round(decimals=1) - dec_expected).abs() <= 0.6).all().all() + + stc_expected = pd.DataFrame( + {"num_statcov": 11.9, "cat_statcov": 88.1}, index=[0] + ) + # relaxed comparison because M1 chip gives slightly different results than intel chip + assert ((stc_imp.round(decimals=1) - stc_expected).abs() <= 0.1).all().all() + + with patch("matplotlib.pyplot.show") as _: + _ = explainer.plot_variable_selection(results) + + def test_attention_explanation(self): + """Test attention (feature importance) explanation results and plotting.""" + # past attention (full_attention=False) on attends to values in the past relative to each horizon + # (look at the last 0 values in the array) + att_exp_past_att = np.array([ + [1.0, 0.8], + [0.8, 0.7], + [0.6, 0.4], + [0.7, 0.3], + [0.9, 0.4], + [0.0, 1.3], + [0.0, 0.0], + ]) + # full attention (full_attention=True) attends to all values in past, present, and future + # see the that all values are non-0 + att_exp_full_att = np.array([ + [0.8, 0.8], + [0.7, 0.6], + [0.4, 0.4], + [0.3, 0.3], + [0.3, 0.3], + [0.7, 0.8], + [0.8, 0.8], + ]) + for full_attention, att_exp in zip( + [False, True], [att_exp_past_att, att_exp_full_att] + ): + model = self.helper_create_model( + use_encoders=True, + add_relative_idx=True, + full_attention=full_attention, + ) series, pc, fc = self.helper_get_input(series_option="multivariate") model.fit(series, past_covariates=pc, future_covariates=fc) explainer = TFTExplainer(model) results = explainer.explain() - imps = results.get_feature_importances() - enc_imp = results.get_encoder_importance() - dec_imp = results.get_decoder_importance() - stc_imp = results.get_static_covariates_importance() - imps_direct = [enc_imp, dec_imp, stc_imp] - - imp_names = [ - "encoder_importance", - "decoder_importance", - "static_covariates_importance", - ] - assert list(imps.keys()) == imp_names - for imp, imp_name in zip(imps_direct, imp_names): - assert imps[imp_name].equals(imp) - - enc_expected = pd.DataFrame( - { - "linear_target": 1.6, - "sine_target": 3.0, - "add_relative_index_futcov": 3.0, - "constant_pastcov": 4.0, - "darts_enc_fc_cyc_month_sin_futcov": 6.2, - "darts_enc_pc_cyc_month_sin_pastcov": 8.6, - "darts_enc_pc_cyc_month_cos_pastcov": 20.0, - "constant_futcov": 20.2, - "darts_enc_fc_cyc_month_cos_futcov": 33.3, - }, - index=[0], - ) - # relaxed comparison because M1 chip gives slightly different results than intel chip - assert ((enc_imp.round(decimals=1) - enc_expected).abs() <= 3).all().all() - - dec_expected = pd.DataFrame( - { - "darts_enc_fc_cyc_month_cos_futcov": 4.3, - "darts_enc_fc_cyc_month_sin_futcov": 17.1, - "constant_futcov": 19.3, - "add_relative_index_futcov": 59.3, - }, - index=[0], - ) - # relaxed comparison because M1 chip gives slightly different results than intel chip - assert ((dec_imp.round(decimals=1) - dec_expected).abs() <= 0.6).all().all() - - stc_expected = pd.DataFrame( - {"num_statcov": 11.9, "cat_statcov": 88.1}, index=[0] - ) + att = results.get_attention() # relaxed comparison because M1 chip gives slightly different results than intel chip - assert ((stc_imp.round(decimals=1) - stc_expected).abs() <= 0.1).all().all() - + assert np.all(np.abs(np.round(att.values(), decimals=1) - att_exp) <= 0.2) + assert att.columns.tolist() == ["horizon 1", "horizon 2"] with patch("matplotlib.pyplot.show") as _: - _ = explainer.plot_variable_selection(results) - - def test_attention_explanation(self): - """Test attention (feature importance) explanation results and plotting.""" - # past attention (full_attention=False) on attends to values in the past relative to each horizon - # (look at the last 0 values in the array) - att_exp_past_att = np.array( - [ - [1.1, 1.1], - [0.7, 0.7], - [0.6, 0.5], - [0.7, 0.5], - [0.8, 0.5], - [0.0, 0.7], - [0.0, 0.0], - ] - ) - # full attention (full_attention=True) attends to all values in past, present, and future - # see the that all values are non-0 - att_exp_full_att = np.array( - [ - [0.9, 1.0], - [0.6, 0.6], - [0.3, 0.4], - [0.3, 0.4], - [0.4, 0.4], - [0.6, 0.5], - [0.9, 0.8], - ] - ) - for full_attention, att_exp in zip( - [False, True], [att_exp_past_att, att_exp_full_att] - ): - model = self.helper_create_model( - use_encoders=True, - add_relative_idx=True, - full_attention=full_attention, + _ = explainer.plot_attention( + results, plot_type="all", show_index_as="relative" ) - series, pc, fc = self.helper_get_input(series_option="multivariate") - model.fit(series, past_covariates=pc, future_covariates=fc) - explainer = TFTExplainer(model) - results = explainer.explain() - - att = results.get_attention() - # relaxed comparison because M1 chip gives slightly different results than intel chip - assert np.all( - np.abs(np.round(att.values(), decimals=1) - att_exp) <= 0.2 + plt.close() + with patch("matplotlib.pyplot.show") as _: + _ = explainer.plot_attention( + results, plot_type="all", show_index_as="time" ) - assert att.columns.tolist() == ["horizon 1", "horizon 2"] - with patch("matplotlib.pyplot.show") as _: - _ = explainer.plot_attention( - results, plot_type="all", show_index_as="relative" - ) - plt.close() - with patch("matplotlib.pyplot.show") as _: - _ = explainer.plot_attention( - results, plot_type="all", show_index_as="time" - ) - plt.close() - with patch("matplotlib.pyplot.show") as _: - _ = explainer.plot_attention( - results, plot_type="time", show_index_as="relative" - ) - plt.close() - with patch("matplotlib.pyplot.show") as _: - _ = explainer.plot_attention( - results, plot_type="time", show_index_as="time" - ) - plt.close() - with patch("matplotlib.pyplot.show") as _: - _ = explainer.plot_attention( - results, plot_type="heatmap", show_index_as="relative" - ) - plt.close() - with patch("matplotlib.pyplot.show") as _: - _ = explainer.plot_attention( - results, plot_type="heatmap", show_index_as="time" - ) - plt.close() - - def helper_create_model( - self, use_encoders=True, add_relative_idx=True, full_attention=False - ): - add_encoders = ( - {"cyclic": {"past": ["month"], "future": ["month"]}} - if use_encoders - else None - ) - return TFTModel( - input_chunk_length=5, - output_chunk_length=2, - n_epochs=1, - add_encoders=add_encoders, - add_relative_index=add_relative_idx, - full_attention=full_attention, - random_state=42, - **tfm_kwargs - ) + plt.close() + with patch("matplotlib.pyplot.show") as _: + _ = explainer.plot_attention( + results, plot_type="time", show_index_as="relative" + ) + plt.close() + with patch("matplotlib.pyplot.show") as _: + _ = explainer.plot_attention( + results, plot_type="time", show_index_as="time" + ) + plt.close() + with patch("matplotlib.pyplot.show") as _: + _ = explainer.plot_attention( + results, plot_type="heatmap", show_index_as="relative" + ) + plt.close() + with patch("matplotlib.pyplot.show") as _: + _ = explainer.plot_attention( + results, plot_type="heatmap", show_index_as="time" + ) + plt.close() + + def helper_create_model( + self, use_encoders=True, add_relative_idx=True, full_attention=False + ): + add_encoders = ( + {"cyclic": {"past": ["month"], "future": ["month"]}} + if use_encoders + else None + ) + return TFTModel( + input_chunk_length=5, + output_chunk_length=2, + n_epochs=1, + add_encoders=add_encoders, + add_relative_index=add_relative_idx, + full_attention=full_attention, + random_state=42, + **tfm_kwargs, + ) diff --git a/darts/tests/metrics/test_metrics.py b/darts/tests/metrics/test_metrics.py index 18b23138f4..4010ce9792 100644 --- a/darts/tests/metrics/test_metrics.py +++ b/darts/tests/metrics/test_metrics.py @@ -1,9 +1,135 @@ +import copy +import inspect +import itertools + import numpy as np import pandas as pd import pytest +import sklearn.metrics -from darts import TimeSeries +from darts import TimeSeries, concatenate from darts.metrics import metrics +from darts.utils.utils import likelihood_component_names, quantile_names + + +def sklearn_mape(*args, **kwargs): + return sklearn.metrics.mean_absolute_percentage_error(*args, **kwargs) * 100.0 + + +def metric_residuals(y_true, y_pred, **kwargs): + y_true = y_true[:, 0] + y_pred = y_pred[:, 0] + return np.mean(y_true - y_pred) + + +def metric_wmape(y_true, y_pred, **kwargs): + y_true = y_true[:, 0] + y_pred = y_pred[:, 0] + return 100.0 * np.sum(np.abs(y_true - y_pred)) / np.sum(np.abs(y_true)) + + +def metric_smape(y_true, y_pred, **kwargs): + y_true = y_true[:, 0] + y_pred = y_pred[:, 0] + return ( + 100.0 + / len(y_true) + * np.sum(2 * np.abs(y_pred - y_true) / (np.abs(y_true) + np.abs(y_pred))) + ) + + +def metric_ope(y_true, y_pred, **kwargs): + y_true = y_true[:, 0] + y_pred = y_pred[:, 0] + return 100.0 * np.abs((np.sum(y_true) - np.sum(y_pred)) / np.sum(y_true)) + + +def metric_cov(y_true, y_pred, **kwargs): + y_true = y_true[:, 0] + y_pred = y_pred[:, 0] + return ( + 100.0 + * sklearn.metrics.root_mean_squared_error(y_true, y_pred) + / np.mean(y_true) + ) + + +def metric_marre(y_true, y_pred, **kwargs): + y_true = y_true[:, 0] + y_pred = y_pred[:, 0] + return ( + 100.0 + / len(y_true) + * np.sum(np.abs((y_true - y_pred) / (np.max(y_true) - np.min(y_true)))) + ) + + +def metric_rmsle(y_true, y_pred, **kwargs): + y_true = y_true[:, 0] + y_pred = y_pred[:, 0] + return np.sqrt( + 1 / len(y_true) * np.sum((np.log(y_true + 1) - np.log(y_pred + 1)) ** 2) + ) + + +def metric_iw(y_true, y_pred, q_interval=None, **kwargs): + # this tests assumes `y_pred` are stochastic values + if isinstance(q_interval, tuple): + q_interval = [q_interval] + q_interval = np.array(q_interval) + q_lo = q_interval[:, 0] + q_hi = q_interval[:, 1] + y_pred_lo = np.quantile(y_pred, q_lo, axis=2).transpose(1, 2, 0) + y_pred_hi = np.quantile(y_pred, q_hi, axis=2).transpose(1, 2, 0) + res = y_pred_hi - y_pred_lo + return res.reshape(len(y_pred), -1) + + +def metric_iws(y_true, y_pred, q_interval=None, **kwargs): + # this tests assumes `y_pred` are stochastic values + if isinstance(q_interval, tuple): + q_interval = [q_interval] + q_interval = np.array(q_interval) + q_lo = q_interval[:, 0] + q_hi = q_interval[:, 1] + y_pred_lo = np.quantile(y_pred, q_lo, axis=2).transpose(1, 2, 0) + y_pred_hi = np.quantile(y_pred, q_hi, axis=2).transpose(1, 2, 0) + interval_width = y_pred_hi - y_pred_lo + res = np.where( + y_true < y_pred_lo, + interval_width + 1 / q_lo * (y_pred_lo - y_true), + interval_width, + ) + res = np.where( + y_true > y_pred_hi, interval_width + 1 / (1 - q_hi) * (y_true - y_pred_hi), res + ) + return res.reshape(len(y_pred), -1) + + +def metric_ic(y_true, y_pred, q_interval=None, **kwargs): + # this tests assumes `y_pred` are stochastic values + if isinstance(q_interval, tuple): + q_interval = [q_interval] + q_interval = np.array(q_interval) + q_lo = q_interval[:, 0] + q_hi = q_interval[:, 1] + y_pred_lo = np.quantile(y_pred, q_lo, axis=2).transpose(1, 2, 0) + y_pred_hi = np.quantile(y_pred, q_hi, axis=2).transpose(1, 2, 0) + res = np.where((y_pred_lo <= y_true) & (y_true <= y_pred_hi), 1, 0) + return res.reshape(len(y_pred), -1) + + +def metric_incs_qr(y_true, y_pred, q_interval=None, **kwargs): + # this tests assumes `y_pred` are stochastic values + if isinstance(q_interval, tuple): + q_interval = [q_interval] + q_interval = np.array(q_interval) + q_lo = q_interval[:, 0] + q_hi = q_interval[:, 1] + y_pred_lo = np.quantile(y_pred, q_lo, axis=2).transpose(1, 2, 0) + y_pred_hi = np.quantile(y_pred, q_hi, axis=2).transpose(1, 2, 0) + res = np.maximum(y_pred_lo - y_true, y_true - y_pred_hi) + return res.reshape(len(y_pred), -1) class TestMetrics: @@ -15,7 +141,9 @@ class TestMetrics: pd_train_not_periodic = pd.Series( range(31), index=pd.date_range("20121201", "20121231") ) - pd_series1 = pd.Series(range(10), index=pd.date_range("20130101", "20130110")) + pd_series1 = pd.Series( + range(10), index=pd.date_range("20130101", "20130110") + ).astype("float64") pd_series2 = pd.Series( np.random.rand(10) * 10 + 1, index=pd.date_range("20130101", "20130110") ) @@ -52,143 +180,771 @@ class TestMetrics: series1.time_index, np.stack([series1.values(), series2.values()], axis=2) ) - def test_zero(self): - with pytest.raises(ValueError): - metrics.mape(self.series1, self.series1) - - with pytest.raises(ValueError): - metrics.smape(self.series1, self.series1) - + @pytest.mark.parametrize( + "metric", + [ + metrics.ape, + metrics.sape, + metrics.mape, + metrics.smape, + ], + ) + def test_ape_zero(self, metric): with pytest.raises(ValueError): - metrics.mape(self.series12, self.series12) + metric(self.series1, self.series1) with pytest.raises(ValueError): - metrics.smape(self.series12, self.series12) + metric(self.series1, self.series1) + def test_ope_zero(self): with pytest.raises(ValueError): metrics.ope( self.series1 - self.series1.pd_series().mean(), self.series1 - self.series1.pd_series().mean(), ) - def test_same(self): - assert metrics.mape(self.series1 + 1, self.series1 + 1) == 0 - assert metrics.smape(self.series1 + 1, self.series1 + 1) == 0 - assert ( - metrics.mase(self.series1 + 1, self.series1 + 1, self.series_train, 1) == 0 + @pytest.mark.parametrize( + "config", + [ + # time dependent but with time reduction + (metrics.err, False, {"time_reduction": np.mean}), + (metrics.ae, False, {"time_reduction": np.mean}), + (metrics.se, False, {"time_reduction": np.mean}), + (metrics.sle, False, {"time_reduction": np.mean}), + (metrics.ase, False, {"time_reduction": np.mean}), + (metrics.sse, False, {"time_reduction": np.mean}), + (metrics.ape, False, {"time_reduction": np.mean}), + (metrics.sape, False, {"time_reduction": np.mean}), + (metrics.arre, False, {"time_reduction": np.mean}), + (metrics.ql, True, {"time_reduction": np.mean}), + # time aggregates + (metrics.merr, False, {}), + (metrics.mae, False, {}), + (metrics.mse, False, {}), + (metrics.rmse, False, {}), + (metrics.rmsle, False, {}), + (metrics.mase, False, {}), + (metrics.msse, False, {}), + (metrics.rmsse, False, {}), + (metrics.mape, False, {}), + (metrics.wmape, False, {}), + (metrics.smape, False, {}), + (metrics.ope, False, {}), + (metrics.marre, False, {}), + (metrics.r2_score, False, {}), + (metrics.coefficient_of_variation, False, {}), + (metrics.qr, True, {}), + (metrics.mql, True, {}), + (metrics.dtw_metric, False, {}), + ], + ) + def test_output_type_time_aggregated(self, config): + """Test output types and shapes for time aggregated metrics: + for single and multiple univariate or multivariate series, in combination + with different component and series reduction functions.""" + metric, is_probabilistic, kwargs = config + params = inspect.signature(metric).parameters + + # y true + y_t_mv = self.series12 + 1 + y_t_uv = y_t_mv.univariate_component(0) + y_t_multi_mv = [y_t_mv] * 2 + y_t_multi_uv = [y_t_uv] * 2 + + # y pred + y_p_mv = ( + self.series12 + if not is_probabilistic + else self.series12_stochastic.stack(self.series12_stochastic) + ) + 1 + y_p_uv = y_p_mv.univariate_component(0) + y_p_multi_mv = [y_p_mv] * 2 + y_p_multi_uv = [y_p_uv] * 2 + + # insample + kwargs_uv = copy.deepcopy(kwargs) + kwargs_mv = copy.deepcopy(kwargs) + kwargs_list_single_uv = copy.deepcopy(kwargs) + kwargs_list_single_mv = copy.deepcopy(kwargs) + kwargs_multi_uv = copy.deepcopy(kwargs) + kwargs_multi_mv = copy.deepcopy(kwargs) + if "insample" in params: + insample = self.series_train.stack(self.series_train) + 1 + kwargs_uv["insample"] = insample.univariate_component(0) + kwargs_mv["insample"] = insample + kwargs_list_single_uv["insample"] = [kwargs_uv["insample"]] + kwargs_list_single_mv["insample"] = [kwargs_mv["insample"]] + kwargs_multi_uv["insample"] = [kwargs_uv["insample"]] * 2 + kwargs_multi_mv["insample"] = [kwargs_mv["insample"]] * 2 + + # SINGLE UNIVARIATE SERIES + # no reduction + res = metric( + y_t_uv, y_p_uv, **kwargs_uv, series_reduction=None, component_reduction=None + ) + assert isinstance(res, float) + # series reduction + res = metric( + y_t_uv, + y_p_uv, + **kwargs_uv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, float) + # comp reduction + res = metric( + y_t_uv, + y_p_uv, + **kwargs_uv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, float) + # series and comp reduction + res = metric( + y_t_uv, + y_p_uv, + **kwargs_uv, + series_reduction=np.mean, + component_reduction=np.mean, ) - assert metrics.marre(self.series1 + 1, self.series1 + 1) == 0 - assert metrics.r2_score(self.series1 + 1, self.series1 + 1) == 1 - assert metrics.ope(self.series1 + 1, self.series1 + 1) == 0 - assert metrics.rho_risk(self.series1 + 1, self.series11_stochastic + 1) == 0 + assert isinstance(res, float) - def helper_test_shape_equality(self, metric): - assert ( - round( - abs( - metric(self.series12, self.series21) - - metric( - self.series1.append(self.series2b), - self.series2.append(self.series1b), - ) - ), - 7, - ) - == 0 + # LIST OF SINGLE UNIVARIATE SERIES + # no reduction + res = metric( + [y_t_uv], + [y_p_uv], + **kwargs_list_single_uv, + series_reduction=None, + component_reduction=None, + ) + assert isinstance(res, list) and len(res) == 1 + assert isinstance(res[0], float) + # series reduction + res = metric( + [y_t_uv], + [y_p_uv], + **kwargs_list_single_uv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, float) + # comp reduction + res = metric( + [y_t_uv], + [y_p_uv], + **kwargs_list_single_uv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, list) and len(res) == 1 + assert isinstance(res[0], float) + # series and comp reduction + res = metric( + [y_t_uv], + [y_p_uv], + **kwargs_list_single_uv, + series_reduction=np.mean, + component_reduction=np.mean, ) + assert isinstance(res, float) - def get_test_cases(self, **kwargs): - # stochastic metrics (rho-risk) behave similar to deterministic metrics if all samples have equal values - if "is_stochastic" in kwargs and kwargs["is_stochastic"]: - test_cases = [ - (self.series1 + 1, self.series22_stochastic), - (self.series1 + 1, self.series33_stochastic), - (self.series2, self.series33_stochastic), - ] - kwargs.pop("is_stochastic", 0) - else: - test_cases = [ - (self.series1 + 1, self.series2), - (self.series1 + 1, self.series3), - (self.series2, self.series3), - ] - return test_cases, kwargs + # SINGLE MULTIVARIATE SERIES + # no reduction + res = metric( + y_t_mv, y_p_mv, **kwargs_mv, series_reduction=None, component_reduction=None + ) + assert isinstance(res, np.ndarray) + assert res.shape == (2,) + # series reduction + res = metric( + y_t_mv, + y_p_mv, + **kwargs_mv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, np.ndarray) + assert res.shape == (2,) + # comp reduction + res = metric( + y_t_mv, + y_p_mv, + **kwargs_mv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, float) + # series and comp reduction + res = metric( + y_t_mv, + y_p_mv, + **kwargs_mv, + series_reduction=np.mean, + component_reduction=np.mean, + ) + assert isinstance(res, float) - def helper_test_multivariate_duplication_equality(self, metric, **kwargs): - test_cases, kwargs = self.get_test_cases(**kwargs) + # LIST OF SINGLE MULTIVARIATE SERIES + # no reduction + res = metric( + [y_t_mv], + [y_p_mv], + **kwargs_list_single_mv, + series_reduction=None, + component_reduction=None, + ) + assert isinstance(res, list) and len(res) == 1 + assert isinstance(res[0], np.ndarray) and res[0].shape == (2,) + # series reduction + res = metric( + [y_t_mv], + [y_p_mv], + **kwargs_list_single_mv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, np.ndarray) and res.shape == (2,) + # comp reduction + res = metric( + [y_t_mv], + [y_p_mv], + **kwargs_list_single_mv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, list) and len(res) == 1 + assert isinstance(res[0], float) + # series and comp reduction + res = metric( + [y_t_mv], + [y_p_mv], + **kwargs_list_single_mv, + series_reduction=np.mean, + component_reduction=np.mean, + ) + assert isinstance(res, float) - for s1, s2 in test_cases: - s11 = s1.stack(s1) - s22 = s2.stack(s2) - # default intra - assert ( - round(abs(metric(s1, s2, **kwargs) - metric(s11, s22, **kwargs)), 7) - == 0 - ) - # custom intra - assert ( - round( - abs( - metric(s1, s2, **kwargs, reduction=(lambda x: x[0])) - - metric(s11, s22, **kwargs, reduction=(lambda x: x[0])) - ), - 7, - ) - == 0 - ) + # MULTIPLE UNIVARIATE SERIES + # no reduction + res = metric( + y_t_multi_uv, + y_p_multi_uv, + **kwargs_multi_uv, + series_reduction=None, + component_reduction=None, + ) + assert isinstance(res, list) + assert len(res) == 2 + # series reduction + res = metric( + y_t_multi_uv, + y_p_multi_uv, + **kwargs_multi_uv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, float) + # comp reduction + res = metric( + y_t_multi_uv, + y_p_multi_uv, + **kwargs_multi_uv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, list) + assert len(res) == 2 + # series and comp reduction + res = metric( + y_t_multi_uv, + y_p_multi_uv, + **kwargs_multi_uv, + series_reduction=np.mean, + component_reduction=np.mean, + ) + assert isinstance(res, float) - def helper_test_multiple_ts_duplication_equality(self, metric, **kwargs): - test_cases, kwargs = self.get_test_cases(**kwargs) + # MULTIPLE MULTIVARIATE SERIES + # no reduction + res = metric( + y_t_multi_mv, + y_p_multi_mv, + **kwargs_multi_mv, + series_reduction=None, + component_reduction=None, + ) + assert isinstance(res, list) + assert len(res) == 2 + assert all(isinstance(el, np.ndarray) for el in res) + assert all(el.shape == (2,) for el in res) + # series reduction + res = metric( + y_t_multi_mv, + y_p_multi_mv, + **kwargs_multi_mv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, np.ndarray) + assert res.shape == (2,) + # comp reduction + res = metric( + y_t_multi_mv, + y_p_multi_mv, + **kwargs_multi_mv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, list) + assert len(res) == 2 + assert all(isinstance(el, float) for el in res) + # series and comp reduction + res = metric( + y_t_multi_mv, + y_p_multi_mv, + **kwargs_multi_mv, + series_reduction=np.mean, + component_reduction=np.mean, + ) + assert isinstance(res, float) - for s1, s2 in test_cases: - s11 = [s1.stack(s1)] * 2 - s22 = [s2.stack(s2)] * 2 - # default intra and inter - np.testing.assert_almost_equal( - actual=np.array([metric(s1, s2, **kwargs)] * 2), - desired=np.array(metric(s11, s22, **kwargs)), - ) + @pytest.mark.parametrize( + "config", + [ + # time dependent + (metrics.err, False), + (metrics.ae, False), + (metrics.se, False), + (metrics.sle, False), + (metrics.ase, False), + (metrics.sse, False), + (metrics.ape, False), + (metrics.sape, False), + (metrics.arre, False), + (metrics.ql, True), + ], + ) + def test_output_type_time_dependent(self, config): + """Test output types and shapes for time dependent metrics: + for single and multiple univariate or multivariate series, in combination + with different component and series reduction functions.""" + metric, is_probabilistic = config + params = inspect.signature(metric).parameters - # custom intra and inter - assert ( - round( - abs( - metric( - s1, s2, **kwargs, reduction=np.mean, inter_reduction=np.max - ) - - metric( - s11, - s22, - **kwargs, - reduction=np.mean, - inter_reduction=np.max - ) - ), - 7, - ) - == 0 + # y true + y_t_mv = self.series12 + 1 + y_t_uv = y_t_mv.univariate_component(0) + y_t_multi_mv = [y_t_mv] * 2 + y_t_multi_uv = [y_t_uv] * 2 + + # y pred + y_p_mv = ( + self.series12 + if not is_probabilistic + else self.series12_stochastic.stack(self.series12_stochastic) + ) + 1 + y_p_uv = y_p_mv.univariate_component(0) + y_p_multi_mv = [y_p_mv] * 2 + y_p_multi_uv = [y_p_uv] * 2 + + # insample + kwargs_uv = {} + kwargs_mv = {} + kwargs_list_single_uv = {} + kwargs_list_single_mv = {} + kwargs_multi_uv = {} + kwargs_multi_mv = {} + if "insample" in params: + insample = self.series_train.stack(self.series_train) + 1 + kwargs_uv["insample"] = insample.univariate_component(0) + kwargs_mv["insample"] = insample + kwargs_list_single_uv["insample"] = [kwargs_uv["insample"]] + kwargs_list_single_mv["insample"] = [kwargs_mv["insample"]] + kwargs_multi_uv["insample"] = [kwargs_uv["insample"]] * 2 + kwargs_multi_mv["insample"] = [kwargs_mv["insample"]] * 2 + + # SINGLE UNIVARIATE SERIES + # no reduction + res = metric( + y_t_uv, y_p_uv, **kwargs_uv, series_reduction=None, component_reduction=None + ) + assert isinstance(res, np.ndarray) and res.shape == (len(y_p_uv),) + # series reduction + res = metric( + y_t_uv, + y_p_uv, + **kwargs_uv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, np.ndarray) and res.shape == (len(y_p_uv),) + # comp reduction + res = metric( + y_t_uv, + y_p_uv, + **kwargs_uv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, np.ndarray) and res.shape == (len(y_p_uv),) + # series and comp reduction + res = metric( + y_t_uv, + y_p_uv, + **kwargs_uv, + series_reduction=np.mean, + component_reduction=np.mean, + ) + assert isinstance(res, np.ndarray) and res.shape == (len(y_p_uv),) + + # LIST OF SINGLE UNIVARIATE SERIES + # no reduction + res = metric( + [y_t_uv], + [y_p_uv], + **kwargs_list_single_uv, + series_reduction=None, + component_reduction=None, + ) + assert isinstance(res, list) and len(res) == 1 + assert isinstance(res[0], np.ndarray) and res[0].shape == (len(y_p_uv),) + # series reduction + res = metric( + [y_t_uv], + [y_p_uv], + **kwargs_list_single_uv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, np.ndarray) and res.shape == (len(y_p_uv),) + # comp reduction + res = metric( + [y_t_uv], + [y_p_uv], + **kwargs_list_single_uv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, list) and len(res) == 1 + assert isinstance(res[0], np.ndarray) and res[0].shape == (len(y_p_uv),) + + # series and comp reduction + res = metric( + [y_t_uv], + [y_p_uv], + **kwargs_list_single_uv, + series_reduction=np.mean, + component_reduction=np.mean, + ) + assert isinstance(res, np.ndarray) and res.shape == (len(y_p_uv),) + + # SINGLE MULTIVARIATE SERIES + # no reduction + res = metric( + y_t_mv, y_p_mv, **kwargs_mv, series_reduction=None, component_reduction=None + ) + assert isinstance(res, np.ndarray) and res.shape == (len(y_t_mv), 2) + # series reduction + res = metric( + y_t_mv, + y_p_mv, + **kwargs_mv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, np.ndarray) and res.shape == (len(y_t_mv), 2) + # comp reduction + res = metric( + y_t_mv, + y_p_mv, + **kwargs_mv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, np.ndarray) and res.shape == (len(y_t_mv),) + # series and comp reduction + res = metric( + y_t_mv, + y_p_mv, + **kwargs_mv, + series_reduction=np.mean, + component_reduction=np.mean, + ) + assert isinstance(res, np.ndarray) and res.shape == (len(y_t_mv),) + + # LIST OF SINGLE MULTIVARIATE SERIES + # no reduction + res = metric( + [y_t_mv], + [y_p_mv], + **kwargs_list_single_mv, + series_reduction=None, + component_reduction=None, + ) + assert isinstance(res, list) and len(res) == 1 + assert isinstance(res[0], np.ndarray) and res[0].shape == (10, 2) + # series reduction + res = metric( + [y_t_mv], + [y_p_mv], + **kwargs_list_single_mv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, np.ndarray) and res.shape == (10, 2) + # comp reduction + res = metric( + [y_t_mv], + [y_p_mv], + **kwargs_list_single_mv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, list) and len(res) == 1 + assert isinstance(res[0], np.ndarray) and res[0].shape == (10,) + # series and comp reduction + res = metric( + [y_t_mv], + [y_p_mv], + **kwargs_list_single_mv, + series_reduction=np.mean, + component_reduction=np.mean, + ) + assert isinstance(res, np.ndarray) and res.shape == (10,) + + # MULTIPLE UNIVARIATE SERIES + # no reduction + res = metric( + y_t_multi_uv, + y_p_multi_uv, + **kwargs_multi_uv, + series_reduction=None, + component_reduction=None, + ) + assert isinstance(res, list) and len(res) == 2 + assert all(el.shape == (10,) for el in res) + # series reduction + res = metric( + y_t_multi_uv, + y_p_multi_uv, + **kwargs_multi_uv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, np.ndarray) and res.shape == (10,) + # comp reduction + res = metric( + y_t_multi_uv, + y_p_multi_uv, + **kwargs_multi_uv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, list) and len(res) == 2 + assert all(el.shape == (10,) for el in res) + # series and comp reduction + res = metric( + y_t_multi_uv, + y_p_multi_uv, + **kwargs_multi_uv, + series_reduction=np.mean, + component_reduction=np.mean, + ) + assert isinstance(res, np.ndarray) and res.shape == (10,) + + # MULTIPLE MULTIVARIATE SERIES + # no reduction + res = metric( + y_t_multi_mv, + y_p_multi_mv, + **kwargs_multi_mv, + series_reduction=None, + component_reduction=None, + ) + assert isinstance(res, list) and len(res) == 2 + assert all(el.shape == (10, 2) for el in res) + # series reduction + res = metric( + y_t_multi_mv, + y_p_multi_mv, + **kwargs_multi_mv, + series_reduction=np.mean, + component_reduction=None, + ) + assert isinstance(res, np.ndarray) and res.shape == (10, 2) + # comp reduction + res = metric( + y_t_multi_mv, + y_p_multi_mv, + **kwargs_multi_mv, + series_reduction=None, + component_reduction=np.mean, + ) + assert isinstance(res, list) and len(res) == 2 + assert all(el.shape == (10,) for el in res) + # series and comp reduction + res = metric( + y_t_multi_mv, + y_p_multi_mv, + **kwargs_multi_mv, + series_reduction=np.mean, + component_reduction=np.mean, + ) + assert isinstance(res, np.ndarray) and res.shape == (10,) + + @pytest.mark.parametrize( + "config", + itertools.product( + [ + # time dependent + (metrics.err, False), + (metrics.ae, False), + (metrics.se, False), + (metrics.sle, False), + (metrics.ase, False), + (metrics.sse, False), + (metrics.ape, False), + (metrics.sape, False), + (metrics.arre, False), + (metrics.ql, True), + # time aggregates + (metrics.merr, False), + (metrics.mae, False), + (metrics.mse, False), + (metrics.rmse, False), + (metrics.rmsle, False), + (metrics.mase, False), + (metrics.msse, False), + (metrics.rmsse, False), + (metrics.mape, False), + (metrics.wmape, False), + (metrics.smape, False), + (metrics.ope, False), + (metrics.marre, False), + (metrics.r2_score, False), + (metrics.coefficient_of_variation, False), + (metrics.qr, True), + (metrics.mql, True), + (metrics.dtw_metric, False), + ], + ["time", "component", "series"], + ), + ) + def test_reduction_fn_validity(self, config): + """Tests reduction functions sanity checks.""" + (metric, is_probabilistic), red_name = config + params = inspect.signature(metric).parameters + has_time_red = "time_reduction" in params + + # y true + y_t = self.series12 + 1 + + # y pred + y_p = ( + self.series12 + if not is_probabilistic + else self.series12_stochastic.stack(self.series12_stochastic) + ) + 1 + + # insample + kwargs = {} + if "insample" in params: + kwargs["insample"] = self.series_train.stack(self.series_train) + 1 + + red_param = red_name + "_reduction" + if red_name == "time" and not has_time_red: + # time_reduction not an argument + with pytest.raises(TypeError): + _ = metric(y_t, y_p, **kwargs, **{red_param: np.nanmean}) + return + + # check that valid fn works + _ = metric(y_t, y_p, **kwargs, **{red_param: np.nanmean}) + + # no axis in fn + with pytest.raises(ValueError) as err: + _ = metric(y_t, y_p, **kwargs, **{red_param: lambda x: np.nanmean(x)}) + assert str(err.value).endswith("Must have a parameter called `axis`.") + # with axis it works + _ = metric( + y_t, y_p, **kwargs, **{red_param: lambda x, axis: np.nanmean(x, axis)} + ) + + # invalid output type: list + with pytest.raises(ValueError) as err: + _ = metric( + y_t, + y_p, + **kwargs, + **{red_param: lambda x, axis: np.nanmean(x, axis).tolist()}, ) + assert str(err.value).endswith( + "Expected type `np.ndarray`, received type=``." + ) - def helper_test_nan(self, metric, **kwargs): - test_cases, kwargs = self.get_test_cases(**kwargs) + # invalid output type: reduced to float + with pytest.raises(ValueError) as err: + _ = metric(y_t, y_p, **kwargs, **{red_param: lambda x, axis: x[0, 0]}) + assert str(err.value).endswith( + "Expected type `np.ndarray`, received type=``." + ) - for s1, s2 in test_cases: - # univariate - non_nan_metric = metric(s1[:9] + 1, s2[:9]) - nan_s1 = s1.copy() - nan_s1._xa.values[-1, :, :] = np.nan - nan_metric = metric(nan_s1 + 1, s2) - assert non_nan_metric == nan_metric + # invalid output shape: did not reduce correctly + with pytest.raises(ValueError) as err: + _ = metric(y_t, y_p, **kwargs, **{red_param: lambda x, axis: x[:2, :2]}) + assert str(err.value).startswith( + f"Invalid `{red_param}` function output shape:" + ) - # multivariate + multi-TS - s11 = [s1.stack(s1)] * 2 - s22 = [s2.stack(s2)] * 2 - non_nan_metric = metric([s[:9] + 1 for s in s11], [s[:9] for s in s22]) - nan_s11 = s11.copy() - for s in nan_s11: - s._xa.values[-1, :, :] = np.nan - nan_metric = metric([s + 1 for s in nan_s11], s22) - assert non_nan_metric == nan_metric + @pytest.mark.parametrize( + "config", + [ + # time dependent + (metrics.err, 0, False, {"time_reduction": np.mean}), + (metrics.ae, 0, False, {"time_reduction": np.mean}), + (metrics.se, 0, False, {"time_reduction": np.mean}), + (metrics.sle, 0, False, {"time_reduction": np.mean}), + (metrics.ase, 0, False, {"time_reduction": np.mean}), + (metrics.sse, 0, False, {"time_reduction": np.mean}), + (metrics.ape, 0, False, {"time_reduction": np.mean}), + (metrics.sape, 0, False, {"time_reduction": np.mean}), + (metrics.arre, 0, False, {"time_reduction": np.mean}), + (metrics.ql, 0, True, {"time_reduction": np.mean}), + # time aggregates + (metrics.merr, 0, False, {}), + (metrics.mae, 0, False, {}), + (metrics.mse, 0, False, {}), + (metrics.rmse, 0, False, {}), + (metrics.rmsle, 0, False, {}), + (metrics.mase, 0, False, {}), + (metrics.msse, 0, False, {}), + (metrics.rmsse, 0, False, {}), + (metrics.mape, 0, False, {}), + (metrics.wmape, 0, False, {}), + (metrics.smape, 0, False, {}), + (metrics.ope, 0, False, {}), + (metrics.marre, 0, False, {}), + (metrics.r2_score, 1, False, {}), + (metrics.coefficient_of_variation, 0, False, {}), + (metrics.qr, 0, True, {}), + (metrics.mql, 0, True, {}), + (metrics.dtw_metric, 0, False, {}), + ], + ) + def test_same(self, config): + metric, score_exp, is_probabilistic, kwargs = config + params = inspect.signature(metric).parameters + y_true = self.series1 + 1 + y_pred = ( + self.series1 + 1 if not is_probabilistic else self.series11_stochastic + 1 + ) + if "insample" in params: + assert metric(y_true, y_pred, self.series_train + 1, **kwargs) == score_exp + else: + assert metric(y_true, y_pred, **kwargs) == score_exp def test_r2(self): from sklearn.metrics import r2_score @@ -202,60 +958,119 @@ def test_r2(self): self.helper_test_multiple_ts_duplication_equality(metrics.r2_score) self.helper_test_nan(metrics.r2_score) - def test_marre(self): - assert ( - round( - abs( - metrics.marre(self.series1, self.series2) - - metrics.marre(self.series1 + 100, self.series2 + 100) - ), - 7, - ) - == 0 - ) - self.helper_test_multivariate_duplication_equality(metrics.marre) - self.helper_test_multiple_ts_duplication_equality(metrics.marre) - self.helper_test_nan(metrics.marre) - - def test_season(self): - with pytest.raises(ValueError): - metrics.mase(self.series3, self.series3 * 1.3, self.series_train, 8) - - def test_mse(self): - self.helper_test_shape_equality(metrics.mse) - self.helper_test_nan(metrics.mse) + @pytest.mark.parametrize( + "config", + [ + (metrics.se, False, {"time_reduction": np.nanmean}), + (metrics.mse, True, {}), + ], + ) + def test_se(self, config): + metric, is_aggregate, kwargs = config + self.helper_test_shape_equality(metric, **kwargs) + self.helper_test_nan(metric, **kwargs) + self.helper_test_non_aggregate(metric, is_aggregate) - def test_mae(self): - self.helper_test_shape_equality(metrics.mae) - self.helper_test_nan(metrics.mae) + @pytest.mark.parametrize( + "config", + [ + (metrics.ae, False, {"time_reduction": np.nanmean}), + (metrics.mae, True, {}), + ], + ) + def test_ae(self, config): + metric, is_aggregate, kwargs = config + self.helper_test_shape_equality(metric, **kwargs) + self.helper_test_nan(metric, **kwargs) + self.helper_test_non_aggregate(metric, is_aggregate) def test_rmse(self): self.helper_test_multivariate_duplication_equality(metrics.rmse) self.helper_test_multiple_ts_duplication_equality(metrics.rmse) - assert ( - round( - abs( - metrics.rmse( - self.series1.append(self.series2b), - self.series2.append(self.series1b), - ) - - metrics.mse( - self.series12, - self.series21, - reduction=(lambda x: np.sqrt(np.mean(x))), - ) - ), - 7, - ) - == 0 + np.testing.assert_array_almost_equal( + metrics.rmse( + self.series1.append(self.series2b), + self.series2.append(self.series1b), + ), + metrics.mse( + self.series12, + self.series21, + component_reduction=lambda x, axis: np.sqrt(np.mean(x, axis=axis)), + ), ) self.helper_test_nan(metrics.rmse) - def test_rmsle(self): - self.helper_test_multivariate_duplication_equality(metrics.rmsle) - self.helper_test_multiple_ts_duplication_equality(metrics.rmsle) - self.helper_test_nan(metrics.rmsle) + @pytest.mark.parametrize( + "config", + [ + (metrics.sle, False, {"time_reduction": np.nanmean}), + (metrics.rmsle, True, {}), + ], + ) + def test_sle(self, config): + metric, is_aggregate, kwargs = config + self.helper_test_multivariate_duplication_equality(metric, **kwargs) + self.helper_test_multiple_ts_duplication_equality(metric, **kwargs) + self.helper_test_nan(metric, **kwargs) + self.helper_test_non_aggregate(metric, is_aggregate) + + @pytest.mark.parametrize( + "config", + [ + (metrics.arre, False, {"time_reduction": np.nanmean}), + (metrics.marre, True, {}), + ], + ) + def test_arre(self, config): + metric, is_aggregate, kwargs = config + np.testing.assert_array_almost_equal( + metric(self.series1, self.series2, **kwargs), + metric(self.series1 + 100, self.series2 + 100, **kwargs), + ) + self.helper_test_multivariate_duplication_equality(metric, **kwargs) + self.helper_test_multiple_ts_duplication_equality(metric, **kwargs) + self.helper_test_nan(metric, **kwargs) + self.helper_test_non_aggregate(metric, is_aggregate) + + with pytest.raises(ValueError) as exc: + _ = metric( + TimeSeries.from_values(np.ones((3, 1, 1))), + TimeSeries.from_values(np.ones((3, 1, 1))), + ) + assert str(exc.value).startswith( + "The difference between the max and min values must " + ) + + @pytest.mark.parametrize( + "metric", + [ + metrics.ase, + metrics.sse, + metrics.mase, + metrics.msse, + metrics.rmsse, + ], + ) + def test_season(self, metric): + with pytest.raises(ValueError): + metric(self.series3, self.series3 * 1.3, self.series_train, 8) + + @pytest.mark.parametrize( + "config", + [ + (metrics.err, False, {"time_reduction": np.nanmean}), + (metrics.merr, True, {}), + ], + ) + def test_res(self, config): + metric, is_aggregate, kwargs = config + self.helper_test_shape_equality(metric, **kwargs) + self.helper_test_nan(metric, **kwargs) + + assert metric(self.series1, self.series1 + 1, **kwargs) == -1.0 + assert metric(self.series1, self.series1 - 1, **kwargs) == 1.0 + self.helper_test_non_aggregate(metric, is_aggregate, val_exp=-1.0) def test_coefficient_of_variation(self): self.helper_test_multivariate_duplication_equality( @@ -266,118 +1081,126 @@ def test_coefficient_of_variation(self): ) self.helper_test_nan(metrics.coefficient_of_variation) - def test_mape(self): - self.helper_test_multivariate_duplication_equality(metrics.mape) - self.helper_test_multiple_ts_duplication_equality(metrics.mape) - self.helper_test_nan(metrics.mape) - - def test_smape(self): - self.helper_test_multivariate_duplication_equality(metrics.smape) - self.helper_test_multiple_ts_duplication_equality(metrics.smape) - self.helper_test_nan(metrics.smape) + @pytest.mark.parametrize( + "config", + [ + (metrics.ape, False, {"time_reduction": np.nanmean}), + (metrics.mape, True, {}), + ], + ) + def test_ape(self, config): + metric, is_aggregate, kwargs = config + self.helper_test_multivariate_duplication_equality(metric, **kwargs) + self.helper_test_multiple_ts_duplication_equality(metric, **kwargs) + self.helper_test_nan(metric, **kwargs) + self.helper_test_non_aggregate(metric, is_aggregate) - def test_mase(self): + @pytest.mark.parametrize( + "config", + [ + (metrics.ape, False, {"time_reduction": np.nanmean}), + (metrics.mape, True, {}), + ], + ) + def test_sape(self, config): + metric, is_aggregate, kwargs = config + self.helper_test_multivariate_duplication_equality(metric, **kwargs) + self.helper_test_multiple_ts_duplication_equality(metric, **kwargs) + self.helper_test_nan(metric, **kwargs) + self.helper_test_non_aggregate(metric, is_aggregate) + @pytest.mark.parametrize( + "config", + [ + (metrics.ase, False, {"time_reduction": np.nanmean}), + (metrics.sse, False, {"time_reduction": np.nanmean}), + (metrics.mase, True, {}), + (metrics.msse, True, {}), + (metrics.rmsse, True, {}), + ], + ) + def test_scaled_errors(self, config): + metric, is_aggregate, kwargs = config insample = self.series_train test_cases, _ = self.get_test_cases() for s1, s2 in test_cases: - # multivariate, series as args - assert ( - round( - abs( - metrics.mase( - s1.stack(s1), - s2.stack(s2), - insample.stack(insample), - reduction=(lambda x: x[0]), - ) - - metrics.mase(s1, s2, insample) - ), - 7, - ) - == 0 + np.testing.assert_array_almost_equal( + metric(s1.stack(s1), s2.stack(s2), insample.stack(insample), **kwargs), + metric(s1, s2, insample, **kwargs), ) + + # test that internal slicing gives identical results with longer `insample` series + np.testing.assert_array_almost_equal( + metric(s1, s2, insample, **kwargs), + metric( + s1, + s2, + insample.append_values(np.array([100.0, 200.0, 300.0])), + **kwargs, + ), + ) + # multi-ts, series as kwargs - assert ( - round( - abs( - metrics.mase( - actual_series=[s1] * 2, - pred_series=[s2] * 2, - insample=[insample] * 2, - reduction=(lambda x: x[0]), - inter_reduction=(lambda x: x[0]), - ) - - metrics.mase(s1, s2, insample) - ), - 7, - ) - == 0 + np.testing.assert_array_almost_equal( + metric( + actual_series=[s1] * 2, + pred_series=[s2] * 2, + insample=[insample] * 2, + **kwargs, + ), + metric(s1, s2, insample, **kwargs), ) + # checking with n_jobs and verbose - assert ( - round( - abs( - metrics.mase( - [s1] * 5, - pred_series=[s2] * 5, - insample=[insample] * 5, - reduction=(lambda x: x[0]), - inter_reduction=(lambda x: x[0]), - ) - - metrics.mase( - [s1] * 5, - [s2] * 5, - insample=[insample] * 5, - reduction=(lambda x: x[0]), - inter_reduction=(lambda x: x[0]), - n_jobs=-1, - verbose=True, - ) - ), - 7, - ) - == 0 - ) - # checking with m=None - assert ( - round( - abs( - metrics.mase( - self.series2, - self.series2, - self.series_train_not_periodic, - m=None, - ) - - metrics.mase( - [self.series2] * 2, - [self.series2] * 2, - [self.series_train_not_periodic] * 2, - m=None, - inter_reduction=np.mean, - ) + np.testing.assert_array_almost_equal( + metric( + [s1] * 5, pred_series=[s2] * 5, insample=[insample] * 5, **kwargs + ), + metric( + [s1] * 5, + [s2] * 5, + insample=[insample] * 5, + n_jobs=-1, + verbose=True, + **kwargs, ), - 7, ) - == 0 - ) - # fails because of wrong indexes (series1/2 indexes should be the continuation of series3) + # fails with type `m` different from `int` + with pytest.raises(ValueError) as err: + metric(self.series2, self.series2, insample, m=None) + assert str(err.value).startswith("Seasonality `m` must be of type `int`") + # fails if `insample` ends more than one time step before start of `pred_series` + with pytest.raises(ValueError) as err: + metric(self.series1, self.series2, insample[:-1], m=1) + assert str(err.value).startswith( + "The `insample` series must start before the `pred_series`" + ) + # fails if `insample` starts at the beginning of `pred_series` + with pytest.raises(ValueError) as err: + metric(self.series1, self.series2, self.series2, m=1) + assert str(err.value).startswith( + "The `insample` series must start before the `pred_series`" + ) + # fails if `insample` starts after the beginning of `pred_series` + with pytest.raises(ValueError) as err: + metric(self.series1, self.series2, self.series2[1:], m=1) + assert str(err.value).startswith( + "The `insample` series must start before the `pred_series`" + ) + # wrong number of components with pytest.raises(ValueError): - metrics.mase(self.series1, self.series2, self.series3, 1) + metric(self.series1, self.series2, insample.stack(insample)) # multi-ts, second series is not a TimeSeries with pytest.raises(ValueError): - metrics.mase([self.series1] * 2, self.series2, [insample] * 2) + metric([self.series1] * 2, self.series2, [insample] * 2) # multi-ts, insample series is not a TimeSeries with pytest.raises(ValueError): - metrics.mase([self.series1] * 2, [self.series2] * 2, insample) + metric([self.series1] * 2, [self.series2] * 2, insample) # multi-ts one array has different length with pytest.raises(ValueError): - metrics.mase([self.series1] * 2, [self.series2] * 2, [insample] * 3) - # not supported input - with pytest.raises(ValueError): - metrics.mase(1, 2, 3) + metric([self.series1] * 2, [self.series2] * 2, [insample] * 3) def test_ope(self): self.helper_test_multivariate_duplication_equality(metrics.ope) @@ -387,42 +1210,24 @@ def test_ope(self): def test_rho_risk(self): # deterministic not supported with pytest.raises(ValueError): - metrics.rho_risk(self.series1, self.series1) + metrics.qr(self.series1, self.series1) # general univariate, multivariate and multi-ts tests self.helper_test_multivariate_duplication_equality( - metrics.rho_risk, is_stochastic=True + metrics.qr, is_stochastic=True ) self.helper_test_multiple_ts_duplication_equality( - metrics.rho_risk, is_stochastic=True + metrics.qr, is_stochastic=True ) - self.helper_test_nan(metrics.rho_risk, is_stochastic=True) + self.helper_test_nan(metrics.qr, is_stochastic=True) # test perfect predictions -> risk = 0 - for rho in [0.25, 0.5]: - assert ( - round( - abs( - metrics.rho_risk( - self.series1, self.series11_stochastic, rho=rho - ) - - 0.0 - ), - 7, - ) - == 0 - ) - assert ( - round( - abs( - metrics.rho_risk( - self.series12_mean, self.series12_stochastic, rho=0.5 - ) - - 0.0 - ), - 7, + for q in [0.25, 0.5]: + np.testing.assert_array_almost_equal( + metrics.qr(self.series1, self.series11_stochastic, q=q), 0.0 ) - == 0 + np.testing.assert_array_almost_equal( + metrics.qr(self.series12_mean, self.series12_stochastic, q=0.5), 0.0 ) # test whether stochastic sample from two TimeSeries (ts) represents the individual ts at 0. and 1. quantiles @@ -431,36 +1236,51 @@ def test_rho_risk(self): s12_stochastic = TimeSeries.from_times_and_values( s1.time_index, np.stack([s1.values(), s2.values()], axis=2) ) - assert round(abs(metrics.rho_risk(s1, s12_stochastic, rho=0.0) - 0.0), 7) == 0 - assert round(abs(metrics.rho_risk(s2, s12_stochastic, rho=1.0) - 0.0), 7) == 0 + np.testing.assert_array_almost_equal(metrics.qr(s1, s12_stochastic, q=0.0), 0.0) + np.testing.assert_array_almost_equal(metrics.qr(s2, s12_stochastic, q=1.0), 0.0) - def test_quantile_loss(self): + # preds must be probabilistic + q_names = likelihood_component_names( + self.series1.components, + quantile_names([0.5]), + ) + with pytest.raises(ValueError) as exc: + metrics.qr( + self.series1, + self.series1.with_columns_renamed(self.series1.components, q_names), + q=0.5, + ) + assert ( + str(exc.value) + == "quantile risk (qr) should only be computed for stochastic predicted TimeSeries." + ) + + @pytest.mark.parametrize( + "config", + [ + (metrics.ql, False, {"time_reduction": np.nanmean}), + (metrics.mql, True, {}), + ], + ) + def test_quantile_loss(self, config): + metric, is_aggregate, kwargs = config # deterministic not supported with pytest.raises(ValueError): - metrics.quantile_loss(self.series1, self.series1) + metric(self.series1, self.series1, **kwargs) # general univariate, multivariate and multi-ts tests self.helper_test_multivariate_duplication_equality( - metrics.quantile_loss, is_stochastic=True + metric, is_stochastic=True, **kwargs ) self.helper_test_multiple_ts_duplication_equality( - metrics.quantile_loss, is_stochastic=True + metric, is_stochastic=True, **kwargs ) - self.helper_test_nan(metrics.quantile_loss, is_stochastic=True) + self.helper_test_nan(metric, is_stochastic=True, **kwargs) # test perfect predictions -> risk = 0 - for tau in [0.25, 0.5]: - assert ( - round( - abs( - metrics.quantile_loss( - self.series1, self.series11_stochastic, tau=tau - ) - - 0.0 - ), - 7, - ) - == 0 + for q in [0.25, 0.5]: + np.testing.assert_array_almost_equal( + metric(self.series1, self.series11_stochastic, q=q, **kwargs), 0.0 ) # test whether stochastic sample from two TimeSeries (ts) represents the individual ts at 0. and 1. quantiles @@ -469,100 +1289,909 @@ def test_quantile_loss(self): s12_stochastic = TimeSeries.from_times_and_values( s1.time_index, np.stack([s1.values(), s2.values()], axis=2) ) - assert round(metrics.quantile_loss(s1, s12_stochastic, tau=1.0), 7) == 0 - assert round(metrics.quantile_loss(s2, s12_stochastic, tau=0.0), 7) == 0 + np.testing.assert_array_almost_equal( + metric(s1, s12_stochastic, q=1.0, **kwargs), 0.0 + ) + np.testing.assert_array_almost_equal( + metric(s2, s12_stochastic, q=0.0, **kwargs), 0.0 + ) def test_metrics_arguments(self): series00 = self.series0.stack(self.series0) series11 = self.series1.stack(self.series1) - assert metrics.r2_score(series11, series00, True, reduction=np.mean) == 0 - assert metrics.r2_score(series11, series00, reduction=np.mean) == 0 - assert metrics.r2_score(series11, pred_series=series00, reduction=np.mean) == 0 assert ( - metrics.r2_score(series00, actual_series=series11, reduction=np.mean) == 0 + metrics.r2_score(series11, series00, True, component_reduction=np.mean) == 0 + ) + assert metrics.r2_score(series11, series00, component_reduction=np.mean) == 0 + assert ( + metrics.r2_score( + series11, pred_series=series00, component_reduction=np.mean + ) + == 0 + ) + assert ( + metrics.r2_score( + series00, actual_series=series11, component_reduction=np.mean + ) + == 0 ) assert ( metrics.r2_score( - True, reduction=np.mean, pred_series=series00, actual_series=series11 + True, + component_reduction=np.mean, + pred_series=series00, + actual_series=series11, ) == 0 ) assert ( - metrics.r2_score(series00, True, reduction=np.mean, actual_series=series11) + metrics.r2_score( + series00, True, component_reduction=np.mean, actual_series=series11 + ) == 0 ) assert ( - metrics.r2_score(series11, True, reduction=np.mean, pred_series=series00) + metrics.r2_score( + series11, True, component_reduction=np.mean, pred_series=series00 + ) == 0 ) # should fail if kwargs are passed as args, because of the "*" with pytest.raises(TypeError): - metrics.r2_score(series00, series11, False, np.mean) - - def test_multiple_ts(self): - - dim = 2 + metrics.r2_score(series00, series11, False, 0.5, np.mean) + def test_multiple_ts_rmse(self): # simple test multi_ts_1 = [self.series1 + 1, self.series1 + 1] multi_ts_2 = [self.series1 + 2, self.series1 + 1] assert ( metrics.rmse( - multi_ts_1, multi_ts_2, reduction=np.mean, inter_reduction=np.mean + multi_ts_1, + multi_ts_2, + component_reduction=np.mean, + series_reduction=np.mean, ) == 0.5 ) - # checking univariate, multivariate and multi-ts gives same metrics with same values + @pytest.mark.parametrize( + "config", + [ + (metrics.err, "min", {"time_reduction": np.nanmean}), + (metrics.ae, "max", {"time_reduction": np.nanmean}), + (metrics.se, "max", {"time_reduction": np.nanmean}), + (metrics.sle, "max", {"time_reduction": np.nanmean}), + (metrics.ape, "max", {"time_reduction": np.nanmean}), + (metrics.sape, "max", {"time_reduction": np.nanmean}), + (metrics.arre, "max", {"time_reduction": np.nanmean}), + (metrics.merr, "min", {}), + (metrics.mae, "max", {}), + (metrics.mse, "max", {}), + (metrics.rmse, "max", {}), + (metrics.rmsle, "max", {}), + (metrics.mape, "max", {}), + (metrics.wmape, "max", {}), + (metrics.smape, "max", {}), + (metrics.ope, "max", {}), + (metrics.marre, "max", {}), + (metrics.r2_score, "min", {}), + (metrics.coefficient_of_variation, "max", {}), + ], + ) + def test_multiple_ts(self, config): + """Tests that univariate, multivariate and multi-ts give same metrics with same values.""" + metric, series_reduction, kwargs = config + series_reduction = getattr(np, series_reduction) + + dim = 2 series11 = self.series1.stack(self.series1) + 1 series22 = self.series2.stack(self.series2) multi_1 = [series11] * dim multi_2 = [series22] * dim - test_metric = [ - metrics.r2_score, - metrics.rmse, - metrics.mape, - metrics.smape, - metrics.mae, - metrics.coefficient_of_variation, - metrics.ope, - metrics.marre, - metrics.mse, - metrics.rmsle, - ] - - for metric in test_metric: - assert metric(self.series1 + 1, self.series2) == metric(series11, series22) - np.testing.assert_array_almost_equal( - np.array([metric(series11, series22)] * 2), - np.array(metric(multi_1, multi_2)), - ) + np.testing.assert_array_almost_equal( + metric(self.series1 + 1, self.series2, **kwargs), + metric(series11, series22, **kwargs), + ) + np.testing.assert_array_almost_equal( + np.array([metric(series11, series22, **kwargs)] * 2), + np.array(metric(multi_1, multi_2, **kwargs)), + ) # trying different functions shifted_1 = self.series1 + 1 shifted_2 = self.series1 + 2 shifted_3 = self.series1 + 3 - assert metrics.rmse( + assert metric( [shifted_1, shifted_1], [shifted_2, shifted_3], - reduction=np.mean, - inter_reduction=np.max, - ) == metrics.rmse(shifted_1, shifted_3) + component_reduction=np.mean, + series_reduction=series_reduction, + **kwargs, + ) == metric(shifted_1, shifted_3, **kwargs) # checking if the result is the same with different n_jobs and verbose True - assert metrics.rmse( + assert metric( [shifted_1, shifted_1], [shifted_2, shifted_3], - reduction=np.mean, - inter_reduction=np.max, - ) == metrics.rmse( + component_reduction=np.mean, + series_reduction=np.max, + **kwargs, + ) == metric( [shifted_1, shifted_1], [shifted_2, shifted_3], - reduction=np.mean, - inter_reduction=np.max, + component_reduction=np.mean, + series_reduction=np.max, n_jobs=-1, verbose=True, + **kwargs, + ) + + @pytest.mark.parametrize( + "config", + [ + (metrics.err, metric_residuals, {}, {"time_reduction": np.nanmean}), + ( + metrics.ae, + sklearn.metrics.mean_absolute_error, + {}, + {"time_reduction": np.nanmean}, + ), + ( + metrics.se, + sklearn.metrics.mean_squared_error, + {}, + {"time_reduction": np.nanmean}, + ), + ( + lambda *args: np.sqrt(metrics.sle(*args, time_reduction=np.nanmean)), + metric_rmsle, + {}, + {}, + ), + (metrics.ape, sklearn_mape, {}, {"time_reduction": np.nanmean}), + (metrics.sape, metric_smape, {}, {"time_reduction": np.nanmean}), + (metrics.arre, metric_marre, {}, {"time_reduction": np.nanmean}), + (metrics.merr, metric_residuals, {}, {}), + (metrics.mae, sklearn.metrics.mean_absolute_error, {}, {}), + (metrics.mse, sklearn.metrics.mean_squared_error, {}, {}), + (metrics.rmse, sklearn.metrics.root_mean_squared_error, {}, {}), + (metrics.rmsle, metric_rmsle, {}, {}), + (metrics.mape, sklearn_mape, {}, {}), + (metrics.wmape, metric_wmape, {}, {}), + (metrics.smape, metric_smape, {}, {}), + (metrics.ope, metric_ope, {}, {}), + (metrics.marre, metric_marre, {}, {}), + (metrics.r2_score, sklearn.metrics.r2_score, {}, {}), + (metrics.coefficient_of_variation, metric_cov, {}, {}), + ], + ) + def test_metrics_deterministic(self, config): + """Tests deterministic metrics against a reference metric""" + metric, metric_ref, ref_kwargs, kwargs = config + y_true = self.series1.stack(self.series1) + 1 + y_pred = y_true + 1 + + y_true = [y_true] * 2 + y_pred = [y_pred] * 2 + + score = metric(y_true, y_pred, **kwargs) + score_ref = metric_ref(y_true[0].values(), y_pred[0].values(), **ref_kwargs) + np.testing.assert_array_almost_equal(score, np.array(score_ref)) + + @pytest.mark.parametrize( + "config", + [ + ( + metrics.ql, + [(0.30, 0.30), (0.030, 0.030), (0.30, 0.30)], + "q", + {"time_reduction": np.nanmean}, + ), + (metrics.mql, [(0.30, 0.30), (0.030, 0.030), (0.30, 0.30)], "q", {}), + ( + metrics.qr, + [(0.30, 0.025), (0.030, 0.0025), (0.30, 0.025)], + "q", + {}, + ), + ], + ) + def test_metrics_probabilistic(self, config): + """Tests probabilistic metrics against reference scores""" + metric, scores_exp, q_param, kwargs = config + np.random.seed(0) + x = np.random.normal(loc=0.0, scale=1.0, size=10000) + y = np.array([ + [0.0, 10.0], + [1.0, 11.0], + [2.0, 12.0], + ]).reshape(3, 2, 1) + + y_true = [TimeSeries.from_values(y)] * 2 + y_pred = [TimeSeries.from_values(y + x)] * 2 + + for quantile, score_exp in zip([0.1, 0.5, 0.9], scores_exp): + scores = metric( + y_true, + y_pred, + **{q_param: quantile}, + component_reduction=None, + **kwargs, + ) + assert (scores < np.array(score_exp).reshape(1, -1)).all() + + def helper_test_shape_equality(self, metric, **kwargs): + np.testing.assert_array_almost_equal( + metric(self.series12, self.series21, **kwargs), + metric( + self.series1.append(self.series2b), + self.series2.append(self.series1b), + **kwargs, + ), + ) + + def get_test_cases(self, **kwargs): + # stochastic metrics (q-risk) behave similar to deterministic metrics if all samples have equal values + if "is_stochastic" in kwargs and kwargs["is_stochastic"]: + test_cases = [ + (self.series1 + 1, self.series22_stochastic), + (self.series1 + 1, self.series33_stochastic), + (self.series2, self.series33_stochastic), + ] + kwargs.pop("is_stochastic", 0) + else: + test_cases = [ + (self.series1 + 1, self.series2), + (self.series1 + 1, self.series3), + (self.series2, self.series3), + ] + return test_cases, kwargs + + def helper_test_multivariate_duplication_equality(self, metric, **kwargs): + test_cases, kwargs = self.get_test_cases(**kwargs) + + for s1, s2 in test_cases: + s11 = s1.stack(s1) + s22 = s2.stack(s2) + # default intra + np.testing.assert_array_almost_equal( + metric(s1, s2, **kwargs), metric(s11, s22, **kwargs) + ) + # custom intra + np.testing.assert_array_almost_equal( + metric( + s1, + s2, + **kwargs, + component_reduction=(lambda x, axis: x[0, 0:1]), + ), + metric( + s11, + s22, + **kwargs, + component_reduction=(lambda x, axis: x[0, 0:1]), + ), + ) + + def helper_test_multiple_ts_duplication_equality(self, metric, **kwargs): + test_cases, kwargs = self.get_test_cases(**kwargs) + + for s1, s2 in test_cases: + s11 = [s1.stack(s1)] * 2 + s22 = [s2.stack(s2)] * 2 + # default intra and inter + np.testing.assert_almost_equal( + actual=np.array([metric(s1, s2, **kwargs)] * 2), + desired=np.array(metric(s11, s22, **kwargs)), + ) + + # custom intra and inter + np.testing.assert_almost_equal( + metric( + s1, + s2, + **kwargs, + component_reduction=np.mean, + series_reduction=np.max, + ), + metric( + s11, + s22, + **kwargs, + component_reduction=np.mean, + series_reduction=np.max, + ), + ) + + def helper_test_nan(self, metric, **kwargs): + test_cases, kwargs = self.get_test_cases(**kwargs) + + for s1, s2 in test_cases: + # univariate + non_nan_metric = metric(s1[:9] + 1, s2[:9], **kwargs) + nan_s1 = s1.copy() + nan_s1._xa.values[-1, :, :] = np.nan + nan_metric = metric(nan_s1 + 1, s2, **kwargs) + assert non_nan_metric == nan_metric + + # multivariate + multi-TS + s11 = [s1.stack(s1)] * 2 + s22 = [s2.stack(s2)] * 2 + non_nan_metric = metric( + [s[:9] + 1 for s in s11], [s[:9] for s in s22], **kwargs + ) + nan_s11 = s11.copy() + for s in nan_s11: + s._xa.values[-1, :, :] = np.nan + nan_metric = metric([s + 1 for s in nan_s11], s22, **kwargs) + np.testing.assert_array_equal(non_nan_metric, nan_metric) + + def helper_test_non_aggregate(self, metric, is_aggregate, val_exp=None): + if is_aggregate: + return + + # do not aggregate over time + res = metric(self.series1 + 1, self.series1 + 2) + assert len(res) == len(self.series1) + + if val_exp is not None: + assert (res == -1.0).all() + + @pytest.mark.parametrize( + "config", + list( + itertools.product( + [ + # time dependent but with time reduction + metrics.err, + metrics.ae, + metrics.se, + metrics.sle, + metrics.ase, + metrics.sse, + metrics.ape, + metrics.sape, + metrics.arre, + metrics.ql, + # time aggregates + metrics.merr, + metrics.mae, + metrics.mse, + metrics.rmse, + metrics.rmsle, + metrics.mase, + metrics.msse, + metrics.rmsse, + metrics.mape, + metrics.wmape, + metrics.smape, + metrics.ope, + metrics.marre, + metrics.r2_score, + metrics.coefficient_of_variation, + metrics.mql, + ], + [True, False], # univariate series + [True, False], # single series + ) + ), + ) + def test_metric_quantiles(self, config): + """Test output types and shapes for time aggregated metrics with quantiles: + for single and multiple univariate or multivariate series, in combination + with different component and series reduction functions.""" + np.random.seed(42) + metric, is_univar, is_single = config + params = inspect.signature(metric).parameters + + n_comp = 1 if is_univar else 2 + + qs_all = [0.1, 0.5, 0.8] + components = [str(i) for i in range(n_comp)] + + series_vals = np.random.random((10, n_comp, 1)) + + pred_prob_vals = np.random.random((10, n_comp, 100)) + + pred_vals_qs = [] + for i in range(n_comp): + pred_vals_qs.append( + np.quantile(pred_prob_vals[:, [i]], qs_all, axis=2).transpose(1, 0, 2) + ) + pred_vals_qs = np.concatenate(pred_vals_qs, axis=1) + pred_components = likelihood_component_names( + components=components, parameter_names=quantile_names(q=qs_all) + ) + + series = TimeSeries.from_values(series_vals, columns=components) + series_q_exp = concatenate( + [series[comp] for comp in components for _ in qs_all], axis=1 + ) + pred_prob = TimeSeries.from_values(pred_prob_vals, columns=components) + pred_qs = TimeSeries.from_values(pred_vals_qs, columns=pred_components) + insample = series.shift(-len(series)) + insample_q_exp = concatenate( + [insample[comp] for comp in components for _ in qs_all], axis=1 + ) + shape_time = (len(pred_qs),) if "time_reduction" in params else tuple() + + if not is_single: + series = [series] * 2 + series_q_exp = [series_q_exp] * 2 + pred_prob = [pred_prob] * 2 + pred_qs = [pred_qs] * 2 + insample = [insample] * 2 + insample_q_exp = [insample_q_exp] * 2 + + kwargs = {"actual_series": series} + if "insample" in params: + kwargs["insample"] = insample + + def check_res( + pred_prob_, pred_qs_, shape_exp, series_reduction=None, **test_kwargs + ): + res_prob = metric( + pred_series=pred_prob_, + series_reduction=series_reduction, + **kwargs, + **test_kwargs, + ) + res_qs = metric( + pred_series=pred_qs_, + series_reduction=series_reduction, + **kwargs, + **test_kwargs, + ) + if is_single or series_reduction is not None: + res_prob = [res_prob] + res_qs = [res_qs] + if series_reduction is None and not is_single: + assert len(res_prob) == len(res_qs) == len(pred_prob_) + + for res_p, res_q in zip(res_prob, res_qs): + assert res_p.shape == res_q.shape == shape_exp + np.testing.assert_array_almost_equal(res_p, res_q) + + check_res(pred_prob, pred_qs, shape_time, q=0.1) + # one quantile as list + check_res(pred_prob, pred_qs, shape_time, q=[0.1]) + # multiple quantiles + check_res(pred_prob, pred_qs, shape_time + (2,), q=[0.1, 0.8]) + # all quantiles + check_res(pred_prob, pred_qs, shape_time + (3,), q=[0.1, 0.5, 0.8]) + qs = [0.1, 0.8] + # component and series reduction + check_res( + pred_prob, + pred_qs, + shape_time + (len(qs),), + q=qs, + component_reduction=np.mean, + series_reduction=np.mean, + ) + # no component reduction + check_res( + pred_prob, + pred_qs, + shape_time + (len(qs) * n_comp,), + q=qs, + component_reduction=None, + series_reduction=np.mean, + ) + # no series reduction + check_res( + pred_prob, + pred_qs, + shape_time + (len(qs),), + q=qs, + component_reduction=np.mean, + series_reduction=None, + ) + # no series and component reduction + check_res( + pred_prob, + pred_qs, + shape_time + (len(qs) * n_comp,), + q=qs, + component_reduction=None, + series_reduction=None, + ) + + # check that we get identical results as when computing each quantile component against the actual + # target component directly + kwargs_direct = copy.deepcopy(kwargs) + q_direct = {} + if metric.__name__ not in ["ql", "mql"]: + kwargs_direct["actual_series"] = series_q_exp + if "insample" in params: + kwargs_direct["insample"] = insample_q_exp + else: + q_direct["q"] = qs_all + kwargs_direct["actual_series"] = series + + res_direct = metric( + pred_series=pred_qs, component_reduction=None, **kwargs_direct, **q_direct + ) + res_qs = metric( + pred_series=pred_qs, + component_reduction=None, + q=qs_all, + **kwargs, + ) + np.testing.assert_array_almost_equal(res_direct, res_qs) + + def test_invalid_quantiles(self): + np.random.seed(42) + series_a = TimeSeries.from_values(np.random.random((10, 2, 1))) + series_b = TimeSeries.from_values(np.random.random((10, 2, 10))) + + # unsorted quantiles + with pytest.raises(ValueError) as exc: + _ = metrics.mae(series_a, series_b, q=[0.2, 0.1]) + assert "a sequence of increasing order" in str(exc.value) + + # non-unique values metrics + with pytest.raises(ValueError) as exc: + _ = metrics.mae(series_a, series_b, q=[0.2, 0.2]) + assert "with unique values only" in str(exc.value) + + # q > 1 + with pytest.raises(ValueError) as exc: + _ = metrics.mae(series_a, series_b, q=[0.2, 1.01]) + assert "must be in the range `(>=0,<=1)`" in str(exc.value) + + # q < 0 + with pytest.raises(ValueError) as exc: + _ = metrics.mae(series_a, series_b, q=[-0.01, 0.2]) + assert "must be in the range `(>=0,<=1)`" in str(exc.value) + + # but sorted, unique, and valid quantiles work + _ = metrics.mae(series_a, series_b, q=[0.0, 0.5, 1.0]) + + def test_quantile_as_tuple(self): + """Test that `q` as tuple (list of quantiles, quantile component names) gives same results as `q` + as quantile values list.""" + np.random.seed(42) + q = [0.25, 0.75] + + series_a = TimeSeries.from_values(np.random.random((10, 2, 1))) + q_names = pd.Index( + likelihood_component_names(series_a.components, quantile_names(q)) + ) + series_b = TimeSeries.from_values(np.random.random((10, 4, 1)), columns=q_names) + + np.testing.assert_array_almost_equal( + metrics.mae(series_a, series_b, q=(q, q_names)), + metrics.mae(series_a, series_b, q=q), + ) + + def test_custom_metric_wrong_output_shape(self): + """Test that custom metrics must have correct output dim.""" + + @metrics.multi_ts_support + @metrics.multivariate_support + def custom_metric( + actual_series, + pred_series, + intersect=True, + *, + q=None, + time_reduction=None, + component_reduction=np.nanmean, + series_reduction=None, + n_jobs=1, + verbose=False, + out_ndim=1, + ): + return np.ones(tuple(1 for _ in range(out_ndim))) + + for ndim in [1, 4]: + with pytest.raises(ValueError) as exc: + custom_metric(self.series1, self.series2, out_ndim=ndim) + assert str(exc.value).startswith( + "Metric output must have 2 dimensions (n components, n quantiles) for aggregated metrics" + ) + for ndim in [2, 3]: + _ = custom_metric(self.series1, self.series2, out_ndim=ndim) + + def test_wrong_error_scale(self): + with pytest.raises(ValueError) as exc: + _ = metrics._get_error_scale( + self.series1.shift(-len(self.series1)), + self.series1, + m=1, + metric="wrong_metric", + ) + assert str(exc.value).startswith("unknown `metric=wrong_metric`") + + @pytest.mark.parametrize( + "config", + [ + # only time dependent quantile interval metrics + (metrics.iw, metric_iw), + (metrics.iws, metric_iws), + (metrics.ic, metric_ic), + (metrics.incs_qr, metric_incs_qr), + ], + ) + def test_metric_quantile_interval_accuracy(self, config): + """Test output types and shapes for time dependent metrics with quantile intervals: + for single and multiple univariate or multivariate series, in combination + with different component and series reduction functions.""" + np.random.seed(42) + metric, metric_ref = config + n_comp = 2 + components = [str(i) for i in range(n_comp)] + series_vals = np.random.random((10, n_comp, 1)) + pred_prob_vals = np.random.random((10, n_comp, 100)) + series = TimeSeries.from_values(series_vals, columns=components) + pred_prob = TimeSeries.from_values(pred_prob_vals, columns=components) + + def check_ref(**test_kwargs): + res_prob = metric( + actual_series=series, + pred_series=pred_prob, + series_reduction=None, + component_reduction=None, + time_reduction=None, + **test_kwargs, + ) + res_ref = metric_ref( + y_true=series.all_values(), + y_pred=pred_prob.all_values(), + **test_kwargs, + ) + np.testing.assert_array_almost_equal(res_prob, res_ref) + + # one interval as tuple + check_ref(q_interval=(0.1, 0.5)) + # one interval in list + check_ref(q_interval=[(0.1, 0.5)]) + # multiple intervals + check_ref(q_interval=[(0.1, 0.5), (0.5, 0.8)]) + + @pytest.mark.parametrize( + "config", + list( + itertools.product( + [ + # time dependent but with time reduction + metrics.iw, + metrics.miw, + metrics.iws, + metrics.miws, + metrics.ic, + metrics.mic, + metrics.incs_qr, + metrics.mincs_qr, + ], + [True, False], # univariate series + [True, False], # single series + ) + ), + ) + def test_metric_quantile_interval(self, config): + """Test output types and shapes for time aggregated metrics with quantile intervals: + for single and multiple univariate or multivariate series, in combination + with different component and series reduction functions.""" + np.random.seed(42) + metric, is_univar, is_single = config + params = inspect.signature(metric).parameters + + n_comp = 1 if is_univar else 2 + + qs_all = [0.1, 0.5, 0.8] + components = [str(i) for i in range(n_comp)] + + series_vals = np.random.random((10, n_comp, 1)) + pred_prob_vals = np.random.random((10, n_comp, 100)) + + pred_vals_qs = [] + for i in range(n_comp): + pred_vals_qs.append( + np.quantile(pred_prob_vals[:, [i]], qs_all, axis=2).transpose(1, 0, 2) + ) + pred_vals_qs = np.concatenate(pred_vals_qs, axis=1) + pred_components = likelihood_component_names( + components=components, parameter_names=quantile_names(q=qs_all) + ) + + series = TimeSeries.from_values(series_vals, columns=components) + pred_prob = TimeSeries.from_values(pred_prob_vals, columns=components) + pred_qs = TimeSeries.from_values(pred_vals_qs, columns=pred_components) + shape_time = (len(pred_qs),) if "time_reduction" in params else tuple() + + if not is_single: + series = [series] * 2 + pred_prob = [pred_prob] * 2 + pred_qs = [pred_qs] * 2 + + kwargs = {"actual_series": series} + + def check_res( + pred_prob_, pred_qs_, shape_exp, series_reduction=None, **test_kwargs + ): + res_prob = metric( + actual_series=series, + pred_series=pred_prob_, + series_reduction=series_reduction, + **test_kwargs, + ) + res_qs = metric( + actual_series=series, + pred_series=pred_qs_, + series_reduction=series_reduction, + **test_kwargs, + ) + if is_single or series_reduction is not None: + res_prob = [res_prob] + res_qs = [res_qs] + if series_reduction is None and not is_single: + assert len(res_prob) == len(res_qs) == len(pred_prob_) + + for res_p, res_q in zip(res_prob, res_qs): + assert res_p.shape == res_q.shape == shape_exp + np.testing.assert_array_almost_equal(res_p, res_q) + return res_qs + + # one interval as tuple + res = check_res(pred_prob, pred_qs, shape_time, q_interval=(0.1, 0.5)) + # one interval in list + res2 = check_res(pred_prob, pred_qs, shape_time, q_interval=[(0.1, 0.5)]) + np.testing.assert_array_almost_equal(res, res2) + # multiple intervals + check_res( + pred_prob, pred_qs, shape_time + (2,), q_interval=[(0.1, 0.5), (0.5, 0.8)] + ) + # all intervals + check_res( + pred_prob, + pred_qs, + shape_time + (3,), + q_interval=[(0.1, 0.5), (0.5, 0.8), (0.1, 0.8)], + ) + q_intervals = [(0.1, 0.5), (0.5, 0.8)] + # component and series reduction + check_res( + pred_prob, + pred_qs, + shape_time + (len(q_intervals),), + q_interval=q_intervals, + component_reduction=np.mean, + series_reduction=np.mean, + ) + # no component reduction + check_res( + pred_prob, + pred_qs, + shape_time + (len(q_intervals) * n_comp,), + q_interval=q_intervals, + component_reduction=None, + series_reduction=np.mean, + ) + # no series reduction + check_res( + pred_prob, + pred_qs, + shape_time + (len(q_intervals),), + q_interval=q_intervals, + component_reduction=np.mean, + series_reduction=None, + ) + # no series and component reduction + check_res( + pred_prob, + pred_qs, + shape_time + (len(q_intervals) * n_comp,), + q_interval=q_intervals, + component_reduction=None, + series_reduction=None, + ) + + # check that we get identical results as when computing intervals separately (on the time aggregated case) + if "time_reduction" in params: + kwargs["time_reduction"] = np.mean + res_lo = metric( + pred_series=pred_qs, + component_reduction=None, + q_interval=(0.1, 0.5), + **kwargs, + ) + res_hi = metric( + pred_series=pred_qs, + component_reduction=None, + q_interval=(0.5, 0.8), + **kwargs, + ) + res_multi = metric( + pred_series=pred_qs, + component_reduction=None, + q_interval=[(0.1, 0.5), (0.5, 0.8)], + **kwargs, + ) + if is_single: + res_lo = [res_lo] + res_hi = [res_hi] + res_multi = [res_multi] + res_lo_hi = [] + for res_lo_, res_hi_ in zip(res_lo, res_hi): + if res_lo_.ndim == 0: + res_lo_ = np.expand_dims(res_lo_, -1) + res_hi_ = np.expand_dims(res_hi_, -1) + res_lo_hi_ = np.concatenate([res_lo_, res_hi_]) + else: + res_lo_hi_ = np.concatenate( + [(res_lo_[i], res_hi_[i]) for i in range(n_comp)], + ) + res_lo_hi.append(res_lo_hi_) + np.testing.assert_array_almost_equal(res_lo_hi, res_multi) + + def test_invalid_quantile_intervals(self): + np.random.seed(42) + series_a = TimeSeries.from_values(np.random.random((10, 2, 1))) + series_b = TimeSeries.from_values(np.random.random((10, 2, 10))) + + # q not supported + with pytest.raises(ValueError) as exc: + _ = metrics.iw(series_a, series_b, q=[0.2]) + assert str(exc.value).startswith( + "`q` is not supported for quantile interval metrics" + ) + + # no quantile interval + with pytest.raises(ValueError) as exc: + _ = metrics.iw(series_a, series_b, q_interval=None) + assert str(exc.value).startswith( + "Quantile interval metrics require setting `q_interval`." + ) + + # invalid interval type + with pytest.raises(ValueError) as exc: + _ = metrics.iw(series_a, series_b, q_interval=0.6) + assert ( + str(exc.value) + == "`q_interval` must be a tuple (float, float) or a sequence of tuples (float, float)." + ) + + # invalid tuple length + with pytest.raises(ValueError) as exc: + _ = metrics.iw(series_a, series_b, q_interval=(0.1, 0.2, 0.3)) + assert ( + str(exc.value) + == "`q_interval` must be a tuple (float, float) or a sequence of tuples (float, float)." + ) + + # one tuple has invalid length invalid tuple length (raises a numpy error) + with pytest.raises(ValueError): + _ = metrics.iw(series_a, series_b, q_interval=[(0.1, 0.2), (0.2, 0.3, 0.4)]) + + # interval upper bound too high + with pytest.raises(ValueError) as exc: + _ = metrics.iw(series_a, series_b, q_interval=(0.1, 1.1)) + assert str(exc.value).startswith( + "All `q` values must be in the range `(>=0,<=1)`." + ) + + # interval lower bound too low + with pytest.raises(ValueError) as exc: + _ = metrics.iw(series_a, series_b, q_interval=(-0.01, 0.1)) + assert str(exc.value).startswith( + "All `q` values must be in the range `(>=0,<=1)`." + ) + + # lower interval equal to higher interval + with pytest.raises(ValueError) as exc: + _ = metrics.iw(series_a, series_b, q_interval=(0.2, 0.2)) + assert str(exc.value).startswith( + "all intervals in `q_interval` must be tuples of (lower q, upper q)" + ) + + # lower interval higher than higher interval + with pytest.raises(ValueError) as exc: + _ = metrics.iw(series_a, series_b, q_interval=(0.3, 0.2)) + assert str(exc.value).startswith( + "all intervals in `q_interval` must be tuples of (lower q, upper q)" ) diff --git a/darts/tests/models/components/glu_variants.py b/darts/tests/models/components/glu_variants.py index e012c7ebe9..909c44daea 100644 --- a/darts/tests/models/components/glu_variants.py +++ b/darts/tests/models/components/glu_variants.py @@ -1,26 +1,24 @@ -from darts.logging import get_logger +import pytest -logger = get_logger(__name__) +from darts.tests.conftest import TORCH_AVAILABLE -try: - import torch +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import torch - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. Loss tests will be skipped.") - TORCH_AVAILABLE = False +from darts.models.components import glu_variants +from darts.models.components.glu_variants import GLU_FFN -if TORCH_AVAILABLE: - from darts.models.components import glu_variants - from darts.models.components.glu_variants import GLU_FFN +class TestFFN: + def test_ffn(self): + for FeedForward_network in GLU_FFN: + self.feed_forward_block = getattr(glu_variants, FeedForward_network)( + d_model=4, d_ff=16, dropout=0.1 + ) - class TestFFN: - def test_ffn(self): - for FeedForward_network in GLU_FFN: - self.feed_forward_block = getattr(glu_variants, FeedForward_network)( - d_model=4, d_ff=16, dropout=0.1 - ) - - inputs = torch.zeros(1, 4, 4) - self.feed_forward_block(x=inputs) + inputs = torch.zeros(1, 4, 4) + self.feed_forward_block(x=inputs) diff --git a/darts/tests/models/components/test_layer_norm_variants.py b/darts/tests/models/components/test_layer_norm_variants.py index 374fa8deb3..204fee7210 100644 --- a/darts/tests/models/components/test_layer_norm_variants.py +++ b/darts/tests/models/components/test_layer_norm_variants.py @@ -1,53 +1,47 @@ import numpy as np import pytest -from darts.logging import get_logger +from darts.tests.conftest import TORCH_AVAILABLE -logger = get_logger(__name__) - -try: - import torch - - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. Loss tests will be skipped.") - TORCH_AVAILABLE = False - - -if TORCH_AVAILABLE: - from darts.models.components.layer_norm_variants import ( - LayerNorm, - LayerNormNoBias, - RINorm, - RMSNorm, +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, ) - - class TestLayerNormVariants: - def test_lnv(self): - for layer_norm in [RMSNorm, LayerNorm, LayerNormNoBias]: - ln = layer_norm(4) - inputs = torch.zeros(1, 4, 4) - ln(inputs) - - def test_rin(self): - - np.random.seed(42) - torch.manual_seed(42) - - x = torch.randn(3, 4, 7) - affine_options = [True, False] - - # test with and without affine and correct input dim - for affine in affine_options: - - rin = RINorm(input_dim=7, affine=affine) - x_norm = rin(x) - - # expand dims to simulate probablistic forecasting - x_denorm = rin.inverse(x_norm.view(x_norm.shape + (1,))).squeeze(-1) - assert torch.all(torch.isclose(x, x_denorm)).item() - - # try invalid input_dim - rin = RINorm(input_dim=3, affine=True) - with pytest.raises(RuntimeError): - x_norm = rin(x) +import torch + +from darts.models.components.layer_norm_variants import ( + LayerNorm, + LayerNormNoBias, + RINorm, + RMSNorm, +) + + +class TestLayerNormVariants: + def test_lnv(self): + for layer_norm in [RMSNorm, LayerNorm, LayerNormNoBias]: + ln = layer_norm(4) + inputs = torch.zeros(1, 4, 4) + ln(inputs) + + def test_rin(self): + np.random.seed(42) + torch.manual_seed(42) + + x = torch.randn(3, 4, 7) + affine_options = [True, False] + + # test with and without affine and correct input dim + for affine in affine_options: + rin = RINorm(input_dim=7, affine=affine) + x_norm = rin(x) + + # expand dims to simulate probabilistic forecasting + x_denorm = rin.inverse(x_norm.view(x_norm.shape + (1,))).squeeze(-1) + assert torch.all(torch.isclose(x, x_denorm)).item() + + # try invalid input_dim + rin = RINorm(input_dim=3, affine=True) + with pytest.raises(RuntimeError): + x_norm = rin(x) diff --git a/darts/tests/models/forecasting/test_RNN.py b/darts/tests/models/forecasting/test_RNN.py index 3508cb9e3d..61ae91fa1b 100644 --- a/darts/tests/models/forecasting/test_RNN.py +++ b/darts/tests/models/forecasting/test_RNN.py @@ -3,178 +3,173 @@ import pytest from darts import TimeSeries -from darts.logging import get_logger -from darts.tests.conftest import tfm_kwargs - -logger = get_logger(__name__) - -try: - import torch.nn as nn - - from darts.models.forecasting.rnn_model import CustomRNNModule, RNNModel, _RNNModule - - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. RNN tests will be skipped.") - TORCH_AVAILABLE = False - - -if TORCH_AVAILABLE: - - class ModuleValid1(_RNNModule): - """Wrapper around the _RNNModule""" - - def __init__(self, **kwargs): - super().__init__(name="RNN", **kwargs) - - class ModuleValid2(CustomRNNModule): - """Just a linear layer.""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.linear = nn.Linear(self.input_size, self.target_size) - - def forward(self, x_in, h=None): - x = self.linear(x_in[0]) - return x.view(len(x), -1, self.target_size, self.nr_params) +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs + +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import torch.nn as nn + +from darts.models.forecasting.rnn_model import CustomRNNModule, RNNModel, _RNNModule + + +class ModuleValid1(_RNNModule): + """Wrapper around the _RNNModule""" + + def __init__(self, **kwargs): + super().__init__(name="RNN", **kwargs) + + +class ModuleValid2(CustomRNNModule): + """Just a linear layer.""" + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.linear = nn.Linear(self.input_size, self.target_size) + + def forward(self, x_in, h=None): + x = self.linear(x_in[0]) + return x.view(len(x), -1, self.target_size, self.nr_params) + + +class TestRNNModel: + times = pd.date_range("20130101", "20130410") + pd_series = pd.Series(range(100), index=times) + series: TimeSeries = TimeSeries.from_series(pd_series) + module_invalid = _RNNModule( + name="RNN", + input_chunk_length=1, + output_chunk_length=1, + output_chunk_shift=0, + input_size=1, + hidden_dim=25, + num_layers=1, + target_size=1, + nr_params=1, + dropout=0, + ) + + def test_training_length_input(self): + # too small training length + with pytest.raises(ValueError) as msg: + RNNModel(input_chunk_length=2, training_length=1) + assert ( + str(msg.value) + == "`training_length` (1) must be `>=input_chunk_length` (2)." + ) - class TestRNNModel: - times = pd.date_range("20130101", "20130410") - pd_series = pd.Series(range(100), index=times) - series: TimeSeries = TimeSeries.from_series(pd_series) - module_invalid = _RNNModule( - name="RNN", - input_chunk_length=1, - output_chunk_length=1, - input_size=1, - hidden_dim=25, - num_layers=1, - target_size=1, - nr_params=1, - dropout=0, + # training_length >= input_chunk_length works + model = RNNModel( + input_chunk_length=2, + training_length=2, + n_epochs=1, + random_state=42, + **tfm_kwargs, ) + model.fit(self.series[:3]) - def test_creation(self): - # cannot choose any string - with pytest.raises(ValueError) as msg: - RNNModel( - input_chunk_length=1, output_chunk_length=1, model="UnknownRNN?" - ) - assert str(msg.value).startswith("`model` is not a valid RNN model.") - - # cannot create from a class instance - with pytest.raises(ValueError) as msg: - _ = RNNModel( - input_chunk_length=1, - output_chunk_length=1, - model=self.module_invalid, - ) - assert str(msg.value).startswith("`model` is not a valid RNN model.") - - # can create from valid module name - model1 = RNNModel( - input_chunk_length=1, - output_chunk_length=1, - model="RNN", - n_epochs=1, - random_state=42, - **tfm_kwargs - ) - model1.fit(self.series) - preds1 = model1.predict(n=3) + def test_creation(self): + # cannot choose any string + with pytest.raises(ValueError) as msg: + RNNModel(input_chunk_length=1, model="UnknownRNN?") + assert str(msg.value).startswith("`model` is not a valid RNN model.") - # can create from a custom class itself - model2 = RNNModel( + # cannot create from a class instance + with pytest.raises(ValueError) as msg: + _ = RNNModel( input_chunk_length=1, - output_chunk_length=1, - model=ModuleValid1, - n_epochs=1, - random_state=42, - **tfm_kwargs + model=self.module_invalid, ) - model2.fit(self.series) - preds2 = model2.predict(n=3) - np.testing.assert_array_equal(preds1.all_values(), preds2.all_values()) + assert str(msg.value).startswith("`model` is not a valid RNN model.") - model3 = RNNModel( - input_chunk_length=1, - output_chunk_length=1, - model=ModuleValid2, - n_epochs=1, - random_state=42, - **tfm_kwargs - ) - model3.fit(self.series) - preds3 = model2.predict(n=3) - assert preds3.all_values().shape == preds2.all_values().shape - assert preds3.time_index.equals(preds2.time_index) - - def test_fit(self, tmpdir_module): - # Test basic fit() - model = RNNModel( - input_chunk_length=1, output_chunk_length=1, n_epochs=2, **tfm_kwargs - ) - model.fit(self.series) - - # Test fit-save-load cycle - model2 = RNNModel( - input_chunk_length=1, - output_chunk_length=1, - model="LSTM", - n_epochs=1, - model_name="unittest-model-lstm", - work_dir=tmpdir_module, - save_checkpoints=True, - force_reset=True, - **tfm_kwargs - ) - model2.fit(self.series) - model_loaded = model2.load_from_checkpoint( - model_name="unittest-model-lstm", - work_dir=tmpdir_module, - best=False, - map_location="cpu", - ) - pred1 = model2.predict(n=6) - pred2 = model_loaded.predict(n=6) + # can create from valid module name + model1 = RNNModel( + input_chunk_length=1, model="RNN", n_epochs=1, random_state=42, **tfm_kwargs + ) + model1.fit(self.series) + preds1 = model1.predict(n=3) - # Two models with the same parameters should deterministically yield the same output - np.testing.assert_array_equal(pred1.values(), pred2.values()) + # can create from a custom class itself + model2 = RNNModel( + input_chunk_length=1, + model=ModuleValid1, + n_epochs=1, + random_state=42, + **tfm_kwargs, + ) + model2.fit(self.series) + preds2 = model2.predict(n=3) + np.testing.assert_array_equal(preds1.all_values(), preds2.all_values()) - # Another random model should not - model3 = RNNModel( - input_chunk_length=1, - output_chunk_length=1, - model="RNN", - n_epochs=2, - **tfm_kwargs - ) - model3.fit(self.series) - pred3 = model3.predict(n=6) - assert not np.array_equal(pred1.values(), pred3.values()) - - # test short predict - pred4 = model3.predict(n=1) - assert len(pred4) == 1 - - # test validation series input - model3.fit(self.series[:60], val_series=self.series[60:]) - pred4 = model3.predict(n=6) - assert len(pred4) == 6 - - def helper_test_pred_length(self, pytorch_model, series): - model = pytorch_model( - input_chunk_length=1, output_chunk_length=3, n_epochs=1, **tfm_kwargs - ) - model.fit(series) - pred = model.predict(7) - assert len(pred) == 7 - pred = model.predict(2) - assert len(pred) == 2 - assert pred.width == 1 - pred = model.predict(4) - assert len(pred) == 4 - assert pred.width == 1 - - def test_pred_length(self): - self.helper_test_pred_length(RNNModel, self.series) + model3 = RNNModel( + input_chunk_length=1, + model=ModuleValid2, + n_epochs=1, + random_state=42, + **tfm_kwargs, + ) + model3.fit(self.series) + preds3 = model2.predict(n=3) + assert preds3.all_values().shape == preds2.all_values().shape + assert preds3.time_index.equals(preds2.time_index) + + def test_fit(self, tmpdir_module): + # Test basic fit() + model = RNNModel(input_chunk_length=1, n_epochs=2, **tfm_kwargs) + model.fit(self.series) + + # Test fit-save-load cycle + model2 = RNNModel( + input_chunk_length=1, + model="LSTM", + n_epochs=1, + model_name="unittest-model-lstm", + work_dir=tmpdir_module, + save_checkpoints=True, + force_reset=True, + **tfm_kwargs, + ) + model2.fit(self.series) + model_loaded = model2.load_from_checkpoint( + model_name="unittest-model-lstm", + work_dir=tmpdir_module, + best=False, + map_location="cpu", + ) + pred1 = model2.predict(n=6) + pred2 = model_loaded.predict(n=6) + + # Two models with the same parameters should deterministically yield the same output + np.testing.assert_array_equal(pred1.values(), pred2.values()) + + # Another random model should not + model3 = RNNModel(input_chunk_length=1, model="RNN", n_epochs=2, **tfm_kwargs) + model3.fit(self.series) + pred3 = model3.predict(n=6) + assert not np.array_equal(pred1.values(), pred3.values()) + + # test short predict + pred4 = model3.predict(n=1) + assert len(pred4) == 1 + + # test validation series input + model3.fit(self.series[:60], val_series=self.series[60:]) + pred4 = model3.predict(n=6) + assert len(pred4) == 6 + + def helper_test_pred_length(self, pytorch_model, series): + model = pytorch_model(input_chunk_length=1, n_epochs=1, **tfm_kwargs) + model.fit(series) + pred = model.predict(7) + assert len(pred) == 7 + pred = model.predict(2) + assert len(pred) == 2 + assert pred.width == 1 + pred = model.predict(4) + assert len(pred) == 4 + assert pred.width == 1 + + def test_pred_length(self): + self.helper_test_pred_length(RNNModel, self.series) diff --git a/darts/tests/models/forecasting/test_TCN.py b/darts/tests/models/forecasting/test_TCN.py index 0b17f8fc43..f94a8fa439 100644 --- a/darts/tests/models/forecasting/test_TCN.py +++ b/darts/tests/models/forecasting/test_TCN.py @@ -1,209 +1,198 @@ import pytest -from darts.logging import get_logger from darts.metrics import mae -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils import timeseries_generation as tg -logger = get_logger(__name__) - -try: - import torch - - from darts.models.forecasting.tcn_model import TCNModel - - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. TCN tests will be skipped.") - TORCH_AVAILABLE = False - - -if TORCH_AVAILABLE: - - class TestTCNModel: - def test_creation(self): - with pytest.raises(ValueError): - # cannot choose a kernel size larger than the input length - TCNModel(input_chunk_length=20, output_chunk_length=1, kernel_size=100) - TCNModel(input_chunk_length=12, output_chunk_length=1) - - def test_fit(self): - large_ts = tg.constant_timeseries(length=100, value=1000) - small_ts = tg.constant_timeseries(length=100, value=10) - - # Test basic fit and predict - model = TCNModel( - input_chunk_length=12, - output_chunk_length=1, - n_epochs=10, - num_layers=1, - **tfm_kwargs - ) - model.fit(large_ts[:98]) - pred = model.predict(n=2).values()[0] - - # Test whether model trained on one series is better than one trained on another - model2 = TCNModel( - input_chunk_length=12, - output_chunk_length=1, - n_epochs=10, - num_layers=1, - **tfm_kwargs - ) - model2.fit(small_ts[:98]) - pred2 = model2.predict(n=2).values()[0] - assert abs(pred2 - 10) < abs(pred - 10) - - # test short predict - pred3 = model2.predict(n=1) - assert len(pred3) == 1 - - def test_performance(self): - # test TCN performance on dummy time series - ts = tg.sine_timeseries(length=100) + tg.linear_timeseries( - length=100, end_value=2 - ) - train, test = ts[:90], ts[90:] - model = TCNModel( - input_chunk_length=12, - output_chunk_length=10, - n_epochs=300, - random_state=0, - **tfm_kwargs - ) - model.fit(train) - pred = model.predict(n=10) - - assert mae(pred, test) < 0.3 - - @pytest.mark.slow - def test_coverage(self): - torch.manual_seed(0) - input_chunk_lengths = range(20, 50) - kernel_sizes = range(2, 5) - dilation_bases = range(2, 5) - - for kernel_size in kernel_sizes: - for dilation_base in dilation_bases: - if dilation_base > kernel_size: - continue - for input_chunk_length in input_chunk_lengths: - - # create model with all weights set to one - model = TCNModel( - input_chunk_length=input_chunk_length, - output_chunk_length=1, - kernel_size=kernel_size, - dilation_base=dilation_base, - weight_norm=False, - n_epochs=1, - **tfm_kwargs - ) - - # we have to fit the model on a dummy series in order to create the internal nn.Module - model.fit(tg.gaussian_timeseries(length=100)) - - for res_block in model.model.res_blocks: - res_block.conv1.weight = torch.nn.Parameter( - torch.ones( - res_block.conv1.weight.shape, dtype=torch.float64 - ) +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import torch + +from darts.models.forecasting.tcn_model import TCNModel + + +class TestTCNModel: + def test_creation(self): + with pytest.raises(ValueError): + # cannot choose a kernel size larger than the input length + TCNModel(input_chunk_length=20, output_chunk_length=1, kernel_size=100) + TCNModel(input_chunk_length=12, output_chunk_length=1) + + def test_fit(self): + large_ts = tg.constant_timeseries(length=100, value=1000) + small_ts = tg.constant_timeseries(length=100, value=10) + + # Test basic fit and predict + model = TCNModel( + input_chunk_length=12, + output_chunk_length=1, + n_epochs=10, + num_layers=1, + **tfm_kwargs, + ) + model.fit(large_ts[:98]) + pred = model.predict(n=2).values()[0] + + # Test whether model trained on one series is better than one trained on another + model2 = TCNModel( + input_chunk_length=12, + output_chunk_length=1, + n_epochs=10, + num_layers=1, + **tfm_kwargs, + ) + model2.fit(small_ts[:98]) + pred2 = model2.predict(n=2).values()[0] + assert abs(pred2 - 10) < abs(pred - 10) + + # test short predict + pred3 = model2.predict(n=1) + assert len(pred3) == 1 + + def test_performance(self): + # test TCN performance on dummy time series + ts = tg.sine_timeseries(length=100) + tg.linear_timeseries( + length=100, end_value=2 + ) + train, test = ts[:90], ts[90:] + model = TCNModel( + input_chunk_length=12, + output_chunk_length=10, + n_epochs=300, + random_state=0, + **tfm_kwargs, + ) + model.fit(train) + pred = model.predict(n=10) + + assert mae(pred, test) < 0.3 + + @pytest.mark.slow + def test_coverage(self): + torch.manual_seed(0) + input_chunk_lengths = range(20, 50) + kernel_sizes = range(2, 5) + dilation_bases = range(2, 5) + + for kernel_size in kernel_sizes: + for dilation_base in dilation_bases: + if dilation_base > kernel_size: + continue + for input_chunk_length in input_chunk_lengths: + # create model with all weights set to one + model = TCNModel( + input_chunk_length=input_chunk_length, + output_chunk_length=1, + kernel_size=kernel_size, + dilation_base=dilation_base, + weight_norm=False, + n_epochs=1, + **tfm_kwargs, + ) + + # we have to fit the model on a dummy series in order to create the internal nn.Module + model.fit(tg.gaussian_timeseries(length=100)) + + for res_block in model.model.res_blocks: + res_block.conv1.weight = torch.nn.Parameter( + torch.ones( + res_block.conv1.weight.shape, dtype=torch.float64 ) - res_block.conv2.weight = torch.nn.Parameter( - torch.ones( - res_block.conv2.weight.shape, dtype=torch.float64 - ) + ) + res_block.conv2.weight = torch.nn.Parameter( + torch.ones( + res_block.conv2.weight.shape, dtype=torch.float64 ) + ) - model.model.eval() + model.model.eval() - # also disable MC Dropout: - model.model.set_mc_dropout(False) + # also disable MC Dropout: + model.model.set_mc_dropout(False) - input_tensor = torch.zeros( - [1, input_chunk_length, 1], dtype=torch.float64 - ) - zero_output = model.model.forward((input_tensor, None))[ + input_tensor = torch.zeros( + [1, input_chunk_length, 1], dtype=torch.float64 + ) + zero_output = model.model.forward((input_tensor, None))[0, -1, 0] + + # test for full coverage + for i in range(input_chunk_length): + input_tensor[0, i, 0] = 1 + curr_output = model.model.forward((input_tensor, None))[ 0, -1, 0 ] - - # test for full coverage - for i in range(input_chunk_length): - input_tensor[0, i, 0] = 1 - curr_output = model.model.forward((input_tensor, None))[ - 0, -1, 0 - ] - assert zero_output != curr_output - input_tensor[0, i, 0] = 0 - - # create model with all weights set to one and one layer less than is automatically detected - model_2 = TCNModel( - input_chunk_length=input_chunk_length, - output_chunk_length=1, - kernel_size=kernel_size, - dilation_base=dilation_base, - weight_norm=False, - num_layers=model.model.num_layers - 1, - n_epochs=1, - **tfm_kwargs - ) - - # we have to fit the model on a dummy series in order to create the internal nn.Module - model_2.fit(tg.gaussian_timeseries(length=100)) - - for res_block in model_2.model.res_blocks: - res_block.conv1.weight = torch.nn.Parameter( - torch.ones( - res_block.conv1.weight.shape, dtype=torch.float64 - ) + assert zero_output != curr_output + input_tensor[0, i, 0] = 0 + + # create model with all weights set to one and one layer less than is automatically detected + model_2 = TCNModel( + input_chunk_length=input_chunk_length, + output_chunk_length=1, + kernel_size=kernel_size, + dilation_base=dilation_base, + weight_norm=False, + num_layers=model.model.num_layers - 1, + n_epochs=1, + **tfm_kwargs, + ) + + # we have to fit the model on a dummy series in order to create the internal nn.Module + model_2.fit(tg.gaussian_timeseries(length=100)) + + for res_block in model_2.model.res_blocks: + res_block.conv1.weight = torch.nn.Parameter( + torch.ones( + res_block.conv1.weight.shape, dtype=torch.float64 ) - res_block.conv2.weight = torch.nn.Parameter( - torch.ones( - res_block.conv2.weight.shape, dtype=torch.float64 - ) + ) + res_block.conv2.weight = torch.nn.Parameter( + torch.ones( + res_block.conv2.weight.shape, dtype=torch.float64 ) + ) - model_2.model.eval() + model_2.model.eval() - # also disable MC Dropout: - model_2.model.set_mc_dropout(False) + # also disable MC Dropout: + model_2.model.set_mc_dropout(False) - input_tensor = torch.zeros( - [1, input_chunk_length, 1], dtype=torch.float64 - ) - zero_output = model_2.model.forward((input_tensor, None))[ + input_tensor = torch.zeros( + [1, input_chunk_length, 1], dtype=torch.float64 + ) + zero_output = model_2.model.forward((input_tensor, None))[0, -1, 0] + + # test for incomplete coverage + uncovered_input_found = False + if model_2.model.num_layers == 1: + continue + for i in range(input_chunk_length): + input_tensor[0, i, 0] = 1 + curr_output = model_2.model.forward((input_tensor, None))[ 0, -1, 0 ] - - # test for incomplete coverage - uncovered_input_found = False - if model_2.model.num_layers == 1: - continue - for i in range(input_chunk_length): - input_tensor[0, i, 0] = 1 - curr_output = model_2.model.forward((input_tensor, None))[ - 0, -1, 0 - ] - if zero_output == curr_output: - uncovered_input_found = True - break - input_tensor[0, i, 0] = 0 - assert uncovered_input_found - - def helper_test_pred_length(self, pytorch_model, series): - model = pytorch_model( - input_chunk_length=12, output_chunk_length=3, n_epochs=1, **tfm_kwargs - ) - model.fit(series) - pred = model.predict(7) - assert len(pred) == 7 - pred = model.predict(2) - assert len(pred) == 2 - assert pred.width == 1 - pred = model.predict(4) - assert len(pred) == 4 - assert pred.width == 1 - - def test_pred_length(self): - series = tg.linear_timeseries(length=100) - self.helper_test_pred_length(TCNModel, series) + if zero_output == curr_output: + uncovered_input_found = True + break + input_tensor[0, i, 0] = 0 + assert uncovered_input_found + + def helper_test_pred_length(self, pytorch_model, series): + model = pytorch_model( + input_chunk_length=12, output_chunk_length=3, n_epochs=1, **tfm_kwargs + ) + model.fit(series) + pred = model.predict(7) + assert len(pred) == 7 + pred = model.predict(2) + assert len(pred) == 2 + assert pred.width == 1 + pred = model.predict(4) + assert len(pred) == 4 + assert pred.width == 1 + + def test_pred_length(self): + series = tg.linear_timeseries(length=100) + self.helper_test_pred_length(TCNModel, series) diff --git a/darts/tests/models/forecasting/test_TFT.py b/darts/tests/models/forecasting/test_TFT.py index a79d43b095..1eaca05255 100644 --- a/darts/tests/models/forecasting/test_TFT.py +++ b/darts/tests/models/forecasting/test_TFT.py @@ -4,425 +4,415 @@ from darts import TimeSeries, concatenate from darts.dataprocessing.transformers import Scaler -from darts.logging import get_logger -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils import timeseries_generation as tg -logger = get_logger(__name__) +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import torch.nn as nn +from torch.nn import MSELoss -try: - import torch.nn as nn - from torch.nn import MSELoss +from darts.models.forecasting.tft_model import TFTModel +from darts.models.forecasting.tft_submodels import get_embedding_size +from darts.utils.likelihood_models import QuantileRegression - from darts.models.forecasting.tft_model import TFTModel - from darts.models.forecasting.tft_submodels import get_embedding_size - from darts.utils.likelihood_models import QuantileRegression - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. TFT tests will be skipped.") - TORCH_AVAILABLE = False - TFTModel, QuantileRegression, MSELoss = None, None, None +class TestTFTModel: + def test_quantile_regression(self): + q_no_50 = [0.1, 0.4, 0.9] + q_non_symmetric = [0.2, 0.5, 0.9] + # if a QuantileLoss is used, it must have to q=0.5 quantile + with pytest.raises(ValueError): + QuantileRegression(q_no_50) -if TORCH_AVAILABLE: + # if a QuantileLoss is used, it must be symmetric around q=0.5 quantile (i.e. [0.1, 0.5, 0.9]) + with pytest.raises(ValueError): + QuantileRegression(q_non_symmetric) - class TestTFTModel: - def test_quantile_regression(self): - q_no_50 = [0.1, 0.4, 0.9] - q_non_symmetric = [0.2, 0.5, 0.9] + def test_future_covariate_handling(self): + ts_time_index = tg.sine_timeseries(length=2, freq="h") + ts_integer_index = TimeSeries.from_values(values=ts_time_index.values()) - # if a QuantileLoss is used, it must have to q=0.5 quantile - with pytest.raises(ValueError): - QuantileRegression(q_no_50) - - # if a QuantileLoss is used, it must be symmetric around q=0.5 quantile (i.e. [0.1, 0.5, 0.9]) - with pytest.raises(ValueError): - QuantileRegression(q_non_symmetric) - - def test_future_covariate_handling(self): - ts_time_index = tg.sine_timeseries(length=2, freq="h") - ts_integer_index = TimeSeries.from_values(values=ts_time_index.values()) - - # model requires future covariates without cyclic encoding - model = TFTModel(input_chunk_length=1, output_chunk_length=1, **tfm_kwargs) - with pytest.raises(ValueError): - model.fit(ts_time_index, verbose=False) - - # should work with cyclic encoding for time index - model = TFTModel( - input_chunk_length=1, - output_chunk_length=1, - add_encoders={"cyclic": {"future": "hour"}}, - **tfm_kwargs - ) + # model requires future covariates without cyclic encoding + model = TFTModel(input_chunk_length=1, output_chunk_length=1, **tfm_kwargs) + with pytest.raises(ValueError): model.fit(ts_time_index, verbose=False) - # should work with relative index both with time index and integer index - model = TFTModel( - input_chunk_length=1, - output_chunk_length=1, - add_relative_index=True, - **tfm_kwargs - ) - model.fit(ts_time_index, verbose=False) - model.fit(ts_integer_index, verbose=False) - - def test_prediction_shape(self): - """checks whether prediction has same number of variable as input series and - whether prediction has correct length. - Test cases: - - univariate - - multivariate - - multi-TS - """ - season_length = 1 - n_repeat = 20 - - # data comes as multivariate - ( - ts, - ts_train, - ts_val, - covariates, - ) = self.helper_generate_multivariate_case_data(season_length, n_repeat) - - kwargs_TFT_quick_test = { - "input_chunk_length": 1, - "output_chunk_length": 1, - "n_epochs": 1, - "lstm_layers": 1, - "hidden_size": 8, - "loss_fn": MSELoss(), - "random_state": 42, - } - kwargs_TFT_quick_test = dict(kwargs_TFT_quick_test, **tfm_kwargs) - - # univariate - first_var = ts.columns[0] - self.helper_test_prediction_shape( - season_length, - ts[first_var], - ts_train[first_var], - ts_val[first_var], - future_covariates=covariates, - kwargs_tft=kwargs_TFT_quick_test, + # should work with cyclic encoding for time index + model = TFTModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"future": "hour"}}, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False) + + # should work with relative index both with time index and integer index + model = TFTModel( + input_chunk_length=1, + output_chunk_length=1, + add_relative_index=True, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False) + model.fit(ts_integer_index, verbose=False) + + def test_prediction_shape(self): + """checks whether prediction has same number of variable as input series and + whether prediction has correct length. + Test cases: + - univariate + - multivariate + - multi-TS + """ + season_length = 1 + n_repeat = 20 + + # data comes as multivariate + ( + ts, + ts_train, + ts_val, + covariates, + ) = self.helper_generate_multivariate_case_data(season_length, n_repeat) + + kwargs_TFT_quick_test = { + "input_chunk_length": 1, + "output_chunk_length": 1, + "n_epochs": 1, + "lstm_layers": 1, + "hidden_size": 8, + "loss_fn": MSELoss(), + "random_state": 42, + } + kwargs_TFT_quick_test = dict(kwargs_TFT_quick_test, **tfm_kwargs) + + # univariate + first_var = ts.columns[0] + self.helper_test_prediction_shape( + season_length, + ts[first_var], + ts_train[first_var], + ts_val[first_var], + future_covariates=covariates, + kwargs_tft=kwargs_TFT_quick_test, + ) + # univariate and short prediction length + self.helper_test_prediction_shape( + 2, + ts[first_var], + ts_train[first_var], + ts_val[first_var], + future_covariates=covariates, + kwargs_tft=kwargs_TFT_quick_test, + ) + # multivariate + self.helper_test_prediction_shape( + season_length, + ts, + ts_train, + ts_val, + future_covariates=covariates, + kwargs_tft=kwargs_TFT_quick_test, + ) + # multi-TS + kwargs_TFT_quick_test["add_encoders"] = {"cyclic": {"future": "hour"}} + second_var = ts.columns[-1] + self.helper_test_prediction_shape( + season_length, + [ts[first_var], ts[second_var]], + [ts_train[first_var], ts_train[second_var]], + [ts_val[first_var], ts_val[second_var]], + future_covariates=None, + kwargs_tft=kwargs_TFT_quick_test, + ) + + def test_mixed_covariates_and_accuracy(self): + """Performs tests usingpast and future covariates for a multivariate prediction of a + sine wave together with a repeating linear curve. Both curves have the seasonal length. + """ + season_length = 24 + n_repeat = 30 + ( + ts, + ts_train, + ts_val, + covariates, + ) = self.helper_generate_multivariate_case_data(season_length, n_repeat) + + kwargs_TFT_full_coverage = { + "input_chunk_length": 12, + "output_chunk_length": 12, + "n_epochs": 10, + "lstm_layers": 2, + "hidden_size": 32, + "likelihood": QuantileRegression(quantiles=[0.1, 0.5, 0.9]), + "random_state": 42, + "add_encoders": {"cyclic": {"future": "hour"}}, + } + kwargs_TFT_full_coverage = dict(kwargs_TFT_full_coverage, **tfm_kwargs) + + self.helper_test_prediction_accuracy( + season_length, + ts, + ts_train, + ts_val, + past_covariates=covariates, + future_covariates=covariates, + kwargs_tft=kwargs_TFT_full_coverage, + ) + + def test_static_covariates_support(self): + target_multi = concatenate( + [tg.sine_timeseries(length=10, freq="h")] * 2, axis=1 + ) + + target_multi = target_multi.with_static_covariates( + pd.DataFrame( + [[0.0, 1.0, 0, 2], [2.0, 3.0, 1, 3]], + columns=["st1", "st2", "cat1", "cat2"], ) - # univariate and short prediction length - self.helper_test_prediction_shape( + ) + + # should work with cyclic encoding for time index + # set categorical embedding sizes once with automatic embedding size with an `int` and once by + # manually setting it with `tuple(int, int)` + model = TFTModel( + input_chunk_length=3, + output_chunk_length=4, + add_encoders={"cyclic": {"future": "hour"}}, + categorical_embedding_sizes={"cat1": 2, "cat2": (2, 2)}, + pl_trainer_kwargs={ + "fast_dev_run": True, + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) + model.fit(target_multi, verbose=False) + + assert len(model.model.static_variables) == len( + target_multi.static_covariates.columns + ) + + # check model embeddings + target_embedding = { + "static_covariate_2": ( 2, - ts[first_var], - ts_train[first_var], - ts_val[first_var], - future_covariates=covariates, - kwargs_tft=kwargs_TFT_quick_test, - ) - # multivariate - self.helper_test_prediction_shape( - season_length, - ts, - ts_train, - ts_val, - future_covariates=covariates, - kwargs_tft=kwargs_TFT_quick_test, + get_embedding_size(2), + ), # automatic embedding size + "static_covariate_3": (2, 2), # manual embedding size + } + assert model.categorical_embedding_sizes == target_embedding + for cat_var, embedding_dims in target_embedding.items(): + assert ( + model.model.input_embeddings.embeddings[cat_var].num_embeddings + == embedding_dims[0] ) - # multi-TS - kwargs_TFT_quick_test["add_encoders"] = {"cyclic": {"future": "hour"}} - second_var = ts.columns[-1] - self.helper_test_prediction_shape( - season_length, - [ts[first_var], ts[second_var]], - [ts_train[first_var], ts_train[second_var]], - [ts_val[first_var], ts_val[second_var]], - future_covariates=None, - kwargs_tft=kwargs_TFT_quick_test, + assert ( + model.model.input_embeddings.embeddings[cat_var].embedding_dim + == embedding_dims[1] ) - def test_mixed_covariates_and_accuracy(self): - """Performs tests usingpast and future covariates for a multivariate prediction of a - sine wave together with a repeating linear curve. Both curves have the seasonal length. - """ - season_length = 24 - n_repeat = 30 - ( - ts, - ts_train, - ts_val, - covariates, - ) = self.helper_generate_multivariate_case_data(season_length, n_repeat) - - kwargs_TFT_full_coverage = { - "input_chunk_length": 12, - "output_chunk_length": 12, - "n_epochs": 10, - "lstm_layers": 2, - "hidden_size": 32, - "likelihood": QuantileRegression(quantiles=[0.1, 0.5, 0.9]), - "random_state": 42, - "add_encoders": {"cyclic": {"future": "hour"}}, - } - kwargs_TFT_full_coverage = dict(kwargs_TFT_full_coverage, **tfm_kwargs) - - self.helper_test_prediction_accuracy( - season_length, - ts, - ts_train, - ts_val, - past_covariates=covariates, - future_covariates=covariates, - kwargs_tft=kwargs_TFT_full_coverage, - ) - - def test_static_covariates_support(self): - target_multi = concatenate( - [tg.sine_timeseries(length=10, freq="h")] * 2, axis=1 - ) - - target_multi = target_multi.with_static_covariates( - pd.DataFrame( - [[0.0, 1.0, 0, 2], [2.0, 3.0, 1, 3]], - columns=["st1", "st2", "cat1", "cat2"], - ) - ) + preds = model.predict(n=1, series=target_multi, verbose=False) + assert preds.static_covariates.equals(target_multi.static_covariates) - # should work with cyclic encoding for time index - # set categorical embedding sizes once with automatic embedding size with an `int` and once by - # manually setting it with `tuple(int, int)` - model = TFTModel( - input_chunk_length=3, - output_chunk_length=4, - add_encoders={"cyclic": {"future": "hour"}}, - categorical_embedding_sizes={"cat1": 2, "cat2": (2, 2)}, - pl_trainer_kwargs={ - "fast_dev_run": True, - **tfm_kwargs["pl_trainer_kwargs"], - }, - ) - model.fit(target_multi, verbose=False) + # raise an error when trained with static covariates of wrong dimensionality + target_multi = target_multi.with_static_covariates( + pd.concat([target_multi.static_covariates] * 2, axis=1) + ) + with pytest.raises(ValueError): + model.predict(n=1, series=target_multi, verbose=False) - assert len(model.model.static_variables) == len( - target_multi.static_covariates.columns + # raise an error when trained with static covariates and trying to predict without + with pytest.raises(ValueError): + model.predict( + n=1, series=target_multi.with_static_covariates(None), verbose=False ) - # check model embeddings - target_embedding = { - "static_covariate_2": ( - 2, - get_embedding_size(2), - ), # automatic embedding size - "static_covariate_3": (2, 2), # manual embedding size - } - assert model.categorical_embedding_sizes == target_embedding - for cat_var, embedding_dims in target_embedding.items(): - assert ( - model.model.input_embeddings.embeddings[cat_var].num_embeddings - == embedding_dims[0] - ) - assert ( - model.model.input_embeddings.embeddings[cat_var].embedding_dim - == embedding_dims[1] - ) - - preds = model.predict(n=1, series=target_multi, verbose=False) - assert preds.static_covariates.equals(target_multi.static_covariates) - - # raise an error when trained with static covariates of wrong dimensionality - target_multi = target_multi.with_static_covariates( - pd.concat([target_multi.static_covariates] * 2, axis=1) - ) - with pytest.raises(ValueError): - model.predict(n=1, series=target_multi, verbose=False) - - # raise an error when trained with static covariates and trying to predict without - with pytest.raises(ValueError): - model.predict( - n=1, series=target_multi.with_static_covariates(None), verbose=False - ) - - # with `use_static_covariates=False`, we can predict without static covs - model = TFTModel( - input_chunk_length=3, - output_chunk_length=4, - use_static_covariates=False, - add_relative_index=True, - n_epochs=1, - **tfm_kwargs - ) - model.fit(target_multi) - preds = model.predict(n=2, series=target_multi.with_static_covariates(None)) - assert preds.static_covariates is None - - model = TFTModel( - input_chunk_length=3, - output_chunk_length=4, - use_static_covariates=False, - add_relative_index=True, - n_epochs=1, - **tfm_kwargs - ) - model.fit(target_multi.with_static_covariates(None)) - preds = model.predict(n=2, series=target_multi) - assert preds.static_covariates.equals(target_multi.static_covariates) - - def helper_generate_multivariate_case_data(self, season_length, n_repeat): - """generates multivariate test case data. Target series is a sine wave stacked with a repeating - linear curve of equal seasonal length. Covariates are datetime attributes for 'hours'. - """ - - # generate sine wave - ts_sine = tg.sine_timeseries( - value_frequency=1 / season_length, - length=n_repeat * season_length, - freq="h", - ) - - # generate repeating linear curve - ts_linear = tg.linear_timeseries( - 0, 1, length=season_length, start=ts_sine.end_time() + ts_sine.freq - ) - for i in range(n_repeat - 1): - start = ts_linear.end_time() + ts_linear.freq - new_ts = tg.linear_timeseries(0, 1, length=season_length, start=start) - ts_linear = ts_linear.append(new_ts) - ts_linear = TimeSeries.from_times_and_values( - times=ts_sine.time_index, values=ts_linear.values() - ) - - # create multivariate TimeSeries by stacking sine and linear curves - ts = ts_sine.stack(ts_linear) - - # create train/test sets - val_length = 10 * season_length - ts_train, ts_val = ts[:-val_length], ts[-val_length:] - - # scale data - scaler_ts = Scaler() - ts_train_scaled = scaler_ts.fit_transform(ts_train) - ts_val_scaled = scaler_ts.transform(ts_val) - ts_scaled = scaler_ts.transform(ts) - - # generate long enough covariates (past and future covariates will be the same for simplicity) - long_enough_ts = tg.sine_timeseries( - value_frequency=1 / season_length, length=1000, freq=ts.freq - ) - covariates = tg.datetime_attribute_timeseries( - long_enough_ts, attribute="hour" - ) - scaler_covs = Scaler() - covariates_scaled = scaler_covs.fit_transform(covariates) - return ts_scaled, ts_train_scaled, ts_val_scaled, covariates_scaled - - def helper_test_prediction_shape( - self, predict_n, ts, ts_train, ts_val, future_covariates, kwargs_tft - ): - """checks whether prediction has same number of variable as input series and - whether prediction has correct length""" - y_hat = self.helper_fit_predict( - predict_n, ts_train, ts_val, None, future_covariates, kwargs_tft - ) - - y_hat_list = [y_hat] if isinstance(y_hat, TimeSeries) else y_hat - ts_list = [ts] if isinstance(ts, TimeSeries) else ts - - for y_hat_i, ts_i in zip(y_hat_list, ts_list): - assert len(y_hat_i) == predict_n - assert y_hat_i.n_components == ts_i.n_components - - def helper_test_prediction_accuracy( - self, + # with `use_static_covariates=False`, we can predict without static covs + model = TFTModel( + input_chunk_length=3, + output_chunk_length=4, + use_static_covariates=False, + add_relative_index=True, + n_epochs=1, + **tfm_kwargs, + ) + model.fit(target_multi) + preds = model.predict(n=2, series=target_multi.with_static_covariates(None)) + assert preds.static_covariates is None + + model = TFTModel( + input_chunk_length=3, + output_chunk_length=4, + use_static_covariates=False, + add_relative_index=True, + n_epochs=1, + **tfm_kwargs, + ) + model.fit(target_multi.with_static_covariates(None)) + preds = model.predict(n=2, series=target_multi) + assert preds.static_covariates.equals(target_multi.static_covariates) + + def helper_generate_multivariate_case_data(self, season_length, n_repeat): + """generates multivariate test case data. Target series is a sine wave stacked with a repeating + linear curve of equal seasonal length. Covariates are datetime attributes for 'hours'. + """ + + # generate sine wave + ts_sine = tg.sine_timeseries( + value_frequency=1 / season_length, + length=n_repeat * season_length, + freq="h", + ) + + # generate repeating linear curve + ts_linear = tg.linear_timeseries( + 0, 1, length=season_length, start=ts_sine.end_time() + ts_sine.freq + ) + for i in range(n_repeat - 1): + start = ts_linear.end_time() + ts_linear.freq + new_ts = tg.linear_timeseries(0, 1, length=season_length, start=start) + ts_linear = ts_linear.append(new_ts) + ts_linear = TimeSeries.from_times_and_values( + times=ts_sine.time_index, values=ts_linear.values() + ) + + # create multivariate TimeSeries by stacking sine and linear curves + ts = ts_sine.stack(ts_linear) + + # create train/test sets + val_length = 10 * season_length + ts_train, ts_val = ts[:-val_length], ts[-val_length:] + + # scale data + scaler_ts = Scaler() + ts_train_scaled = scaler_ts.fit_transform(ts_train) + ts_val_scaled = scaler_ts.transform(ts_val) + ts_scaled = scaler_ts.transform(ts) + + # generate long enough covariates (past and future covariates will be the same for simplicity) + long_enough_ts = tg.sine_timeseries( + value_frequency=1 / season_length, length=1000, freq=ts.freq + ) + covariates = tg.datetime_attribute_timeseries(long_enough_ts, attribute="hour") + scaler_covs = Scaler() + covariates_scaled = scaler_covs.fit_transform(covariates) + return ts_scaled, ts_train_scaled, ts_val_scaled, covariates_scaled + + def helper_test_prediction_shape( + self, predict_n, ts, ts_train, ts_val, future_covariates, kwargs_tft + ): + """checks whether prediction has same number of variable as input series and + whether prediction has correct length""" + y_hat = self.helper_fit_predict( + predict_n, ts_train, ts_val, None, future_covariates, kwargs_tft + ) + + y_hat_list = [y_hat] if isinstance(y_hat, TimeSeries) else y_hat + ts_list = [ts] if isinstance(ts, TimeSeries) else ts + + for y_hat_i, ts_i in zip(y_hat_list, ts_list): + assert len(y_hat_i) == predict_n + assert y_hat_i.n_components == ts_i.n_components + + def helper_test_prediction_accuracy( + self, + predict_n, + ts, + ts_train, + ts_val, + past_covariates, + future_covariates, + kwargs_tft, + ): + """prediction should be almost equal to y_true. Absolute tolarance is set + to 0.2 to give some flexibility""" + + absolute_tolarance = 0.2 + y_hat = self.helper_fit_predict( predict_n, - ts, ts_train, ts_val, past_covariates, future_covariates, kwargs_tft, - ): - """prediction should be almost equal to y_true. Absolute tolarance is set - to 0.2 to give some flexibility""" - - absolute_tolarance = 0.2 - y_hat = self.helper_fit_predict( - predict_n, - ts_train, - ts_val, - past_covariates, - future_covariates, - kwargs_tft, - ) - - y_true = ts[y_hat.start_time() : y_hat.end_time()] - assert np.allclose( - y_true[1:-1].all_values(), - y_hat[1:-1].all_values(), - atol=absolute_tolarance, - ) - - @staticmethod - def helper_fit_predict( - predict_n, ts_train, ts_val, past_covariates, future_covariates, kwargs_tft - ): - """simple helper that returns prediction for the individual test cases""" - model = TFTModel(**kwargs_tft) - - model.fit( - ts_train, - past_covariates=past_covariates, - future_covariates=future_covariates, - val_series=ts_val, - val_past_covariates=past_covariates, - val_future_covariates=future_covariates, - verbose=False, - ) - - series = None if isinstance(ts_train, TimeSeries) else ts_train - y_hat = model.predict( - n=predict_n, - series=series, - past_covariates=past_covariates, - future_covariates=future_covariates, - num_samples=(100 if model._is_probabilistic else 1), - ) - - if isinstance(y_hat, TimeSeries): - y_hat = y_hat.quantile_timeseries(0.5) if y_hat.n_samples > 1 else y_hat - else: - y_hat = [ - ts.quantile_timeseries(0.5) if ts.n_samples > 1 else ts - for ts in y_hat - ] - return y_hat - - def test_layer_norm(self): - times = pd.date_range("20130101", "20130410") - pd_series = pd.Series(range(100), index=times) - series: TimeSeries = TimeSeries.from_series(pd_series) - base_model = TFTModel - - model1 = base_model( - input_chunk_length=1, - output_chunk_length=1, - add_relative_index=True, - norm_type="RMSNorm", - **tfm_kwargs - ) - model1.fit(series, epochs=1) - - model2 = base_model( + ) + + y_true = ts[y_hat.start_time() : y_hat.end_time()] + assert np.allclose( + y_true[1:-1].all_values(), + y_hat[1:-1].all_values(), + atol=absolute_tolarance, + ) + + @staticmethod + def helper_fit_predict( + predict_n, ts_train, ts_val, past_covariates, future_covariates, kwargs_tft + ): + """simple helper that returns prediction for the individual test cases""" + model = TFTModel(**kwargs_tft) + + model.fit( + ts_train, + past_covariates=past_covariates, + future_covariates=future_covariates, + val_series=ts_val, + val_past_covariates=past_covariates, + val_future_covariates=future_covariates, + verbose=False, + ) + + series = None if isinstance(ts_train, TimeSeries) else ts_train + y_hat = model.predict( + n=predict_n, + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + num_samples=(100 if model.supports_probabilistic_prediction else 1), + ) + + if isinstance(y_hat, TimeSeries): + y_hat = y_hat.quantile_timeseries(0.5) if y_hat.n_samples > 1 else y_hat + else: + y_hat = [ + ts.quantile_timeseries(0.5) if ts.n_samples > 1 else ts for ts in y_hat + ] + return y_hat + + def test_layer_norm(self): + times = pd.date_range("20130101", "20130410") + pd_series = pd.Series(range(100), index=times) + series: TimeSeries = TimeSeries.from_series(pd_series) + base_model = TFTModel + + model1 = base_model( + input_chunk_length=1, + output_chunk_length=1, + add_relative_index=True, + norm_type="RMSNorm", + **tfm_kwargs, + ) + model1.fit(series, epochs=1) + + model2 = base_model( + input_chunk_length=1, + output_chunk_length=1, + add_relative_index=True, + norm_type=nn.LayerNorm, + **tfm_kwargs, + ) + model2.fit(series, epochs=1) + + with pytest.raises(AttributeError): + model4 = base_model( input_chunk_length=1, output_chunk_length=1, add_relative_index=True, - norm_type=nn.LayerNorm, - **tfm_kwargs + norm_type="invalid", + **tfm_kwargs, ) - model2.fit(series, epochs=1) - - with pytest.raises(AttributeError): - model4 = base_model( - input_chunk_length=1, - output_chunk_length=1, - add_relative_index=True, - norm_type="invalid", - **tfm_kwargs - ) - model4.fit(series, epochs=1) + model4.fit(series, epochs=1) diff --git a/darts/tests/models/forecasting/test_backtesting.py b/darts/tests/models/forecasting/test_backtesting.py index ffea1b2ba5..b66d2d166f 100644 --- a/darts/tests/models/forecasting/test_backtesting.py +++ b/darts/tests/models/forecasting/test_backtesting.py @@ -1,3 +1,5 @@ +import itertools +import logging import random from itertools import product @@ -5,41 +7,33 @@ import pandas as pd import pytest +import darts.metrics as metrics from darts import TimeSeries from darts.datasets import AirPassengersDataset, MonthlyMilkDataset from darts.logging import get_logger -from darts.metrics import mape, r2_score from darts.models import ( ARIMA, FFT, ExponentialSmoothing, + LinearRegressionModel, NaiveDrift, NaiveSeasonal, + RandomForest, Theta, ) -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs +from darts.utils.timeseries_generation import constant_timeseries as ct from darts.utils.timeseries_generation import gaussian_timeseries as gt from darts.utils.timeseries_generation import linear_timeseries as lt from darts.utils.timeseries_generation import random_walk_timeseries as rt from darts.utils.timeseries_generation import sine_timeseries as st +from darts.utils.utils import generate_index logger = get_logger(__name__) -try: - from darts.models import ( - BlockRNNModel, - LinearRegressionModel, - RandomForest, - TCNModel, - ) - - TORCH_AVAILABLE = True -except ImportError: - logger.warning( - "Torch models are not installed - will not be tested for backtesting" - ) - TORCH_AVAILABLE = False +if TORCH_AVAILABLE: + from darts.models import BlockRNNModel, TCNModel def get_dummy_series( @@ -53,7 +47,6 @@ def get_dummy_series( def compare_best_against_random(model_class, params, series, stride=1): - # instantiate best model in expanding window mode np.random.seed(1) best_model_1, _, _ = model_class.gridsearch( @@ -61,14 +54,14 @@ def compare_best_against_random(model_class, params, series, stride=1): series, forecast_horizon=10, stride=stride, - metric=mape, + metric=metrics.mape, start=series.time_index[-21], ) # instantiate best model in split mode train, val = series.split_before(series.time_index[-10]) best_model_2, _, _ = model_class.gridsearch( - params, train, val_series=val, metric=mape + params, train, val_series=val, metric=metrics.mape ) # instantiate model with random parameters from 'params' @@ -88,10 +81,10 @@ def compare_best_against_random(model_class, params, series, stride=1): # perform train/val evaluation on both models best_model_2.fit(train) - best_score_2 = mape(best_model_2.predict(len(val)), series) + best_score_2 = metrics.mape(best_model_2.predict(len(val)), series) random_model = model_class(**random_param_choice) random_model.fit(train) - random_score_2 = mape(random_model.predict(len(val)), series) + random_score_2 = metrics.mape(random_model.predict(len(val)), series) # check whether best models are at least as good as random models expanding_window_ok = best_score_1 <= random_score_1 @@ -101,6 +94,426 @@ def compare_best_against_random(model_class, params, series, stride=1): class TestBacktesting: + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], + [False, True], + [[metrics.mape], [metrics.mape, metrics.mape]], + ), + ) + def test_output_single_series_hfc_lpo_true(self, config): + """Tests backtest based on historical forecasts generated on a single `series` (or list of one `series`) + with last_points_only=True""" + is_univariate, series_as_list, metric = config + is_multi_metric = len(metric) > 1 + y = ct(value=1.0, length=10) + hfc = ct(value=2.0, length=10) + if not is_univariate: + y = y.stack(y + 1.0) + hfc = hfc.stack(hfc + 2.0) + y = y if not series_as_list else [y] + hfc = hfc if not series_as_list else [hfc] + + model = NaiveDrift() + + # check that input does not work with `last_points_only=False`` + with pytest.raises(ValueError) as err: + _ = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=None, + metric=metric, + last_points_only=False, + ) + if series_as_list: + error_msg = "Expected `historical_forecasts` of type `Sequence[Sequence[TimeSeries]]`" + else: + error_msg = "Expected `historical_forecasts` of type `Sequence[TimeSeries]`" + assert str(err.value).startswith(error_msg) + + # number of forecasts do not match number of `series` + if series_as_list: + with pytest.raises(ValueError) as err: + _ = model.backtest( + series=y, + historical_forecasts=hfc + y, + reduction=None, + metric=metric, + last_points_only=True, + ) + error_msg = f"expected `historical_forecasts` of type `Sequence[TimeSeries]` with length n={len(y)}." + assert str(err.value).endswith(error_msg) + + # no reduction + bt = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=None, + metric=metric, + last_points_only=True, + ) + bt = bt if series_as_list else [bt] + assert isinstance(bt, list) and len(bt) == 1 + bt = bt[0] + if not is_multi_metric: + # inner type expected: 1 float + assert isinstance(bt, float) and bt == 100.0 + else: + # inner shape expected: (n metrics = 2,) + assert isinstance(bt, np.ndarray) + np.testing.assert_array_almost_equal(bt, np.array([100.0, 100.0])) + + # with reduction + bt = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=np.mean, + metric=metric, + last_points_only=True, + ) + bt = bt if series_as_list else [bt] + assert isinstance(bt, list) and len(bt) == 1 + bt = bt[0] + if not is_multi_metric: + # inner type expected: 1 float + assert isinstance(bt, float) and bt == 100.0 + else: + # inner shape expected: (n metrics = 2,) + assert isinstance(bt, np.ndarray) + np.testing.assert_array_almost_equal(bt, np.array([100.0, 100.0])) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], + [False, True], + [[metrics.mape], [metrics.mape, metrics.mape]], + [1, 2], + ), + ) + def test_output_single_series_hfc_lpo_false(self, config): + """Tests backtest based on historical forecasts generated on a single `series` (or list of one `series`) + with last_points_only=False""" + is_univariate, series_as_list, metric, n_forecasts = config + is_multi_metric = len(metric) > 1 + y = ct(value=1.0, length=10) + hfc = ct(value=2.0, length=10) + if not is_univariate: + y = y.stack(y + 1.0) + hfc = hfc.stack(hfc + 2.0) + hfc = [y, hfc] + hfc = hfc[:n_forecasts] + + y = y if not series_as_list else [y] + hfc = hfc if not series_as_list else [hfc] + + model = NaiveDrift() + + # check that input does not work with `last_points_only=True`` + with pytest.raises(ValueError) as err: + _ = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=None, + metric=metric, + last_points_only=True, + ) + if series_as_list: + error_msg = "Expected `historical_forecasts` of type `Sequence[TimeSeries]`" + else: + error_msg = "Expected `historical_forecasts` of type `TimeSeries`" + assert str(err.value).startswith(error_msg) + + # number of forecasts do not match number of `series` + if series_as_list: + with pytest.raises(ValueError) as err: + _ = model.backtest( + series=y, + historical_forecasts=hfc + [y], + reduction=None, + metric=metric, + last_points_only=False, + ) + error_msg = ( + f"expected `historical_forecasts` of type `Sequence[Sequence[TimeSeries]]`" + f" with length n={len(y)}." + ) + assert str(err.value).endswith(error_msg) + + # no reduction + bt = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=None, + metric=metric, + last_points_only=False, + ) + bt = bt if series_as_list else [bt] + assert isinstance(bt, list) and len(bt) == 1 + bt = bt[0] + assert isinstance(bt, np.ndarray) + if not is_multi_metric: + # inner shape expected: (n hist forecasts = 2,) + np.testing.assert_array_almost_equal( + bt, np.array([0.0, 100.0])[:n_forecasts] + ) + else: + # inner shape expected: (n hist forecasts = 2, n metrics = 2) + np.testing.assert_array_almost_equal( + bt, np.array([[0.0, 0.0], [100.0, 100.0]])[:n_forecasts] + ) + + # with reduction + bt = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=np.mean, + metric=metric, + last_points_only=False, + ) + bt = bt if series_as_list else [bt] + assert isinstance(bt, list) and len(bt) == 1 + bt = bt[0] + score_exp = 0.0 if n_forecasts == 1 else 50.0 + if not is_multi_metric: + # inner shape expected: 1 float + assert isinstance(bt, float) and bt == score_exp + else: + # inner shape expected: (n metrics = 2,) + assert isinstance(bt, np.ndarray) + np.testing.assert_array_almost_equal(bt, np.array([score_exp, score_exp])) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], + [[metrics.mape], [metrics.mape, metrics.mape]], + ), + ) + def test_output_multi_series_hfc_lpo_true(self, config): + """Tests backtest based on historical forecasts generated on multiple `series` with last_points_only=True""" + is_univariate, metric = config + is_multi_metric = len(metric) > 1 + y = ct(value=1.0, length=10) + hfc = ct(value=2.0, length=10) + if not is_univariate: + y = y.stack(y + 1.0) + hfc = hfc.stack(hfc + 2.0) + hfc = [y, hfc] + y = [y, y] + + model = NaiveDrift() + + # check that input does not work with `last_points_only=False`` + with pytest.raises(ValueError) as err: + _ = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=None, + metric=metric, + last_points_only=False, + ) + error_msg = ( + "Expected `historical_forecasts` of type `Sequence[Sequence[TimeSeries]]`" + ) + assert str(err.value).startswith(error_msg) + + # number of forecasts do not match number of `series` + with pytest.raises(ValueError) as err: + _ = model.backtest( + series=y, + historical_forecasts=hfc + [y[0]], + reduction=None, + metric=metric, + last_points_only=True, + ) + error_msg = f"expected `historical_forecasts` of type `Sequence[TimeSeries]` with length n={len(y)}." + assert str(err.value).endswith(error_msg) + + # no reduction + bt = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=None, + last_points_only=True, + metric=metric, + ) + assert isinstance(bt, list) and len(bt) == 2 + if not is_multi_metric: + # per series, inner type expected: 1 float + assert bt == [0.0, 100.0] + else: + # per series, inner shape expected: (n metrics = 2,) + assert all(isinstance(bt_, np.ndarray) for bt_ in bt) + np.testing.assert_array_almost_equal(bt[0], np.array([0.0, 0.0])) + np.testing.assert_array_almost_equal(bt[1], np.array([100.0, 100.0])) + + # with reduction + bt = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=np.mean, + last_points_only=True, + metric=metric, + ) + assert isinstance(bt, list) and len(bt) == 2 + if not is_multi_metric: + # per series, inner type expected: 1 float + assert bt == [0.0, 100.0] + else: + # per series, inner shape expected: (n metrics = 2,) + assert all(isinstance(bt_, np.ndarray) for bt_ in bt) + np.testing.assert_array_almost_equal(bt[0], np.array([0.0, 0.0])) + np.testing.assert_array_almost_equal(bt[1], np.array([100.0, 100.0])) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], + [[metrics.mape], [metrics.mape, metrics.mape]], + ), + ) + def test_output_multi_series_hfc_lpo_false(self, config): + """Tests backtest based on historical forecasts generated on multiple `series` with + last_points_only=False. + """ + is_univariate, metric = config + is_multi_metric = len(metric) > 1 + y = ct(value=1.0, length=10) + hfc = ct(value=2.0, length=10) + if not is_univariate: + y = y.stack(y + 1.0) + hfc = hfc.stack(hfc + 2.0) + hfc = [[y], [hfc]] + y = [y, y] + + model = NaiveDrift() + + # check that input does not work with `last_points_only=False`` + with pytest.raises(ValueError) as err: + _ = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=None, + metric=metric, + last_points_only=True, + ) + error_msg = "Expected `historical_forecasts` of type `Sequence[TimeSeries]`" + assert str(err.value).startswith(error_msg) + + # number of forecasts do not match number of `series` + with pytest.raises(ValueError) as err: + _ = model.backtest( + series=y, + historical_forecasts=hfc + [[y[0]]], + reduction=None, + metric=metric, + last_points_only=False, + ) + error_msg = f"expected `historical_forecasts` of type `Sequence[Sequence[TimeSeries]]` with length n={len(y)}." + assert str(err.value).endswith(error_msg) + + # no reduction + bt = model.backtest( + series=y, historical_forecasts=hfc, reduction=None, metric=metric + ) + assert isinstance(bt, list) and len(bt) == 2 + assert isinstance(bt[0], np.ndarray) + assert isinstance(bt[1], np.ndarray) + if not is_multi_metric: + # inner shape expected: (n hist forecasts = 1,) + np.testing.assert_array_almost_equal(bt[0], np.array([0.0])) + np.testing.assert_array_almost_equal(bt[1], np.array([100.0])) + else: + # inner shape expected: (n metrics = 2, n hist forecasts = 1) + np.testing.assert_array_almost_equal(bt[0], np.array([[0.0, 0.0]])) + np.testing.assert_array_almost_equal(bt[1], np.array([[100.0, 100.0]])) + + # with reduction + bt = model.backtest( + series=y, historical_forecasts=hfc, reduction=np.mean, metric=metric + ) + assert isinstance(bt, list) and len(bt) == 2 + if not is_multi_metric: + # inner type expected: 1 float + assert bt == [0.0, 100.0] + else: + # inner shape expected: (n metrics = 2,) + assert isinstance(bt[0], np.ndarray) + np.testing.assert_array_almost_equal(bt[0], np.array([0.0, 0.0])) + assert isinstance(bt[1], np.ndarray) + np.testing.assert_array_almost_equal(bt[1], np.array([100.0, 100.0])) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], + [[metrics.mape], [metrics.mape, metrics.mape]], + ), + ) + def test_output_multi_series_hfc_lpo_false_different_n_fcs(self, config): + """Tests backtest based on historical forecasts generated on multiple `series` with + last_points_only=False, and the historical forecasts have different lengths + """ + is_univariate, metric = config + is_multi_metric = len(metric) > 1 + y = ct(value=1.0, length=10) + hfc = ct(value=2.0, length=10) + if not is_univariate: + y = y.stack(y + 1.0) + hfc = hfc.stack(hfc + 2.0) + hfc = [[y], [hfc, hfc]] + y = [y, y] + + model = NaiveDrift() + + # check that input does not work with `last_points_only=False`` + with pytest.raises(ValueError) as err: + _ = model.backtest( + series=y, + historical_forecasts=hfc, + reduction=None, + metric=metric, + last_points_only=True, + ) + error_msg = "Expected `historical_forecasts` of type `Sequence[TimeSeries]`" + assert str(err.value).startswith(error_msg) + + # no reduction + bt = model.backtest( + series=y, historical_forecasts=hfc, reduction=None, metric=metric + ) + assert isinstance(bt, list) and len(bt) == 2 + assert isinstance(bt[0], np.ndarray) + assert isinstance(bt[1], np.ndarray) + if not is_multi_metric: + # inner shape expected: (n hist forecasts = 1,) + np.testing.assert_array_almost_equal(bt[0], np.array([0.0])) + # inner shape expected: (n hist forecasts = 2,) + np.testing.assert_array_almost_equal(bt[1], np.array([100.0, 100.0])) + else: + # inner shape expected: (n metrics = 2, n hist forecasts = 1) + np.testing.assert_array_almost_equal(bt[0], np.array([[0.0, 0.0]])) + # inner shape expected: (n metrics = 2, n hist forecasts = 2) + np.testing.assert_array_almost_equal( + bt[1], np.array([[100.0, 100.0], [100.0, 100.0]]) + ) + + # with reduction + bt = model.backtest( + series=y, historical_forecasts=hfc, reduction=np.mean, metric=metric + ) + assert isinstance(bt, list) and len(bt) == 2 + if not is_multi_metric: + # inner type expected: 1 float + assert bt == [0.0, 100.0] + else: + # inner shape expected: (n metrics = 2,) + assert isinstance(bt[0], np.ndarray) + np.testing.assert_array_almost_equal(bt[0], np.array([0.0, 0.0])) + assert isinstance(bt[1], np.ndarray) + def test_backtest_forecasting(self): linear_series = lt(length=50) linear_series_int = TimeSeries.from_values(linear_series.values()) @@ -111,7 +524,7 @@ def test_backtest_forecasting(self): linear_series, start=pd.Timestamp("20000201"), forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, ) assert score == 1.0 @@ -127,7 +540,7 @@ def test_backtest_forecasting(self): historical_forecasts=forecasts, start=pd.Timestamp("20000201"), forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, ) assert score == precalculated_forecasts_score @@ -137,7 +550,7 @@ def test_backtest_forecasting(self): train_length=10000, start=pd.Timestamp("20000201"), forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, ) assert score == 1.0 @@ -147,7 +560,7 @@ def test_backtest_forecasting(self): train_length=10000, start=pd.Timestamp("20000201"), forecast_horizon=3, - metric=[r2_score, mape], + metric=[metrics.r2_score, metrics.mape], ) np.testing.assert_almost_equal(score, np.array([1.0, 0.0])) @@ -158,12 +571,12 @@ def test_backtest_forecasting(self): train_length=2, start=pd.Timestamp("20000201"), forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, ) # test that it also works for time series that are not Datetime-indexed score = NaiveDrift().backtest( - linear_series_int, start=0.7, forecast_horizon=3, metric=r2_score + linear_series_int, start=0.7, forecast_horizon=3, metric=metrics.r2_score ) assert score == 1.0 @@ -234,7 +647,7 @@ def test_backtest_forecasting(self): output_chunk_length=1, batch_size=1, n_epochs=1, - **tfm_kwargs + **tfm_kwargs, ) # cannot perform historical forecasts with `retrain=False` and untrained model with pytest.raises(ValueError): @@ -272,7 +685,7 @@ def test_backtest_forecasting(self): output_chunk_length=1, batch_size=1, n_epochs=1, - **tfm_kwargs + **tfm_kwargs, ) tcn_model.fit(linear_series, verbose=False) # univariate fitted model + multivariate series @@ -290,7 +703,7 @@ def test_backtest_forecasting(self): output_chunk_length=3, batch_size=1, n_epochs=1, - **tfm_kwargs + **tfm_kwargs, ) pred = tcn_model.historical_forecasts( linear_series_multi, @@ -321,8 +734,7 @@ def test_backtest_multiple_series(self): assert round(abs(error[0] - expected[0]), 4) == 0 assert round(abs(error[1] - expected[1]), 4) == 0 - @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") - def test_backtest_regression(self): + def test_backtest_regression(self, caplog): np.random.seed(4) gaussian_series = gt(mean=2, length=50) @@ -349,7 +761,7 @@ def test_backtest_regression(self): future_covariates=features, start=pd.Timestamp("20000201"), forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, last_points_only=True, ) assert score > 0.9 @@ -363,7 +775,7 @@ def test_backtest_regression(self): start=pd.Timestamp("20000201"), train_length=20, forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, last_points_only=True, ) assert score > 0.9 @@ -376,7 +788,7 @@ def test_backtest_regression(self): future_covariates=features, start=30, forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, ) assert score > 0.9 @@ -387,22 +799,35 @@ def test_backtest_regression(self): future_covariates=features, start=0.5, forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, ) assert score > 0.9 # Using a too small start value - with pytest.raises(ValueError): - RandomForest(lags=12).backtest(series=target, start=0, forecast_horizon=3) + warning_expected = ( + "`start` position `{0}` corresponding to time `{1}` is before the first " + "predictable/trainable historical forecasting point for series at index: 0. Using the first historical " + "forecasting point `2000-01-15 00:00:00` that lies a round-multiple of `stride=1` ahead of `start`. " + "To hide these warnings, set `show_warnings=False`." + ) + caplog.clear() + with caplog.at_level(logging.WARNING): + _ = RandomForest(lags=12).backtest( + series=target, start=0, forecast_horizon=3 + ) + assert warning_expected.format(0, target.start_time()) in caplog.text + caplog.clear() - with pytest.raises(ValueError): - RandomForest(lags=12).backtest( + with caplog.at_level(logging.WARNING): + _ = RandomForest(lags=12).backtest( series=target, start=0.01, forecast_horizon=3 ) + assert warning_expected.format(0.01, target.start_time()) in caplog.text + caplog.clear() # Using RandomForest's start default value score = RandomForest(lags=12, random_state=0).backtest( - series=target, forecast_horizon=3, start=0.5, metric=r2_score + series=target, forecast_horizon=3, start=0.5, metric=metrics.r2_score ) assert score > 0.95 @@ -414,7 +839,7 @@ def test_backtest_regression(self): future_covariates=features_multivariate, start=pd.Timestamp("20000201"), forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, ) assert score > 0.94 @@ -427,7 +852,7 @@ def test_backtest_regression(self): future_covariates=features_multivariate, start=pd.Timestamp("20000201"), forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, ) logger.info( "Score for multivariate feature test with train window 35 is: ", score_35 @@ -443,7 +868,7 @@ def test_backtest_regression(self): future_covariates=features_multivariate, start=pd.Timestamp("20000201"), forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, ) logger.info( "Score for multivariate feature test with train window 45 is: ", score_45 @@ -459,7 +884,7 @@ def test_backtest_regression(self): future_covariates=features_multivariate, start=pd.Timestamp("20000201"), forecast_horizon=3, - metric=r2_score, + metric=metrics.r2_score, last_points_only=True, stride=3, ) @@ -527,7 +952,6 @@ def test_gridsearch_metric_score(self): assert score == recalculated_score, "The metric scores should match" - @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") def test_gridsearch_random_search(self): np.random.seed(1) @@ -546,7 +970,6 @@ def test_gridsearch_random_search(self): assert isinstance(result[2], float) assert min(param_range) <= result[1]["lags"] <= max(param_range) - @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") def test_gridsearch_n_random_samples_bad_arguments(self): dummy_series = get_dummy_series(ts_length=50) @@ -569,7 +992,6 @@ def test_gridsearch_n_random_samples_bad_arguments(self): params, dummy_series, forecast_horizon=1, n_random_samples=1.5 ) - @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") def test_gridsearch_n_random_samples(self): np.random.seed(1) @@ -621,7 +1043,6 @@ def test_gridsearch_n_jobs(self): ] for test in test_cases: - model = test["model"] parameters = test["parameters"] @@ -650,7 +1071,9 @@ def test_gridsearch_multi(self): "kernel_size": [2, 3, 4], "pl_trainer_kwargs": [tfm_kwargs["pl_trainer_kwargs"]], } - TCNModel.gridsearch(tcn_params, dummy_series, forecast_horizon=3, metric=mape) + TCNModel.gridsearch( + tcn_params, dummy_series, forecast_horizon=3, metric=metrics.mape + ) @pytest.mark.parametrize( "model_cls,parameters", @@ -677,7 +1100,7 @@ def test_gridsearch_bad_covariates(self, model_cls, parameters): series=ts_train, past_covariates=dummy_series, val_series=ts_val, - **bt_kwargs + **bt_kwargs, ) assert str(msg.value).startswith( "Model cannot be fit/trained with `past_covariates`." @@ -689,8 +1112,361 @@ def test_gridsearch_bad_covariates(self, model_cls, parameters): series=ts_train, future_covariates=dummy_series, val_series=ts_val, - **bt_kwargs + **bt_kwargs, ) assert str(msg.value).startswith( "Model cannot be fit/trained with `future_covariates`." ) + + @pytest.mark.parametrize( + "config", + itertools.product([True, False], [True, False]), + ) + def test_gridsearch_sample_weight(self, config): + """check that passing sample weights work and that it yields different results than without sample weights.""" + manual_weight, use_val_series = config + ts = AirPassengersDataset().load() + if manual_weight: + sample_weight = np.linspace(0, 1, len(ts)) + sample_weight = ts.with_values(np.expand_dims(sample_weight, -1)) + else: + sample_weight = "linear" + + parameters = {"lags": [3], "output_chunk_length": [1]} + start_kwargs = {"start": -1, "start_format": "position"} + gs_kwargs = {"val_series": ts} if use_val_series else {"forecast_horizon": 1} + gs_non_weighted = LinearRegressionModel.gridsearch( + parameters, series=ts[:-1], **start_kwargs, **gs_kwargs + )[-1] + + gs_weighted = LinearRegressionModel.gridsearch( + parameters, + series=ts[:-1], + sample_weight=sample_weight, + **start_kwargs, + **gs_kwargs, + )[-1] + + # check that the predictions are different + assert gs_weighted != gs_non_weighted + + @pytest.mark.parametrize( + "config", + itertools.product( + [ + metrics.ase, + metrics.mase, + ], + [1, 2], + ), + ) + def test_scaled_metrics(self, config): + """Tests backtest for scaled metrics based on historical forecasts generated on a sequence + `series` with last_points_only=False""" + metric, m = config + y = lt(length=20) + hfc = lt(length=10, start=y.start_time() + 10 * y.freq) + y = [y, y] + hfc = [[hfc, hfc], [hfc]] + + model = NaiveDrift() + bts = model.backtest( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=False, + reduction=None, + metric_kwargs={"m": m}, + ) + assert isinstance(bts, list) and len(bts) == 2 + + bt_expected = metric(y[0], hfc[0][0], insample=y[0], m=m) + for bt_list in bts: + for bt in bt_list: + np.testing.assert_array_almost_equal(bt, bt_expected) + + @pytest.mark.parametrize( + "metric", + [ + [metrics.mae], # mae does not support time_reduction + [metrics.mae, metrics.ae], # ae supports time_reduction + ], + ) + def test_metric_kwargs(self, metric): + """Tests backtest with different metric_kwargs based on historical forecasts generated on a sequence + `series` with last_points_only=False""" + y = lt(length=20) + y = y.stack(y + 1.0) + hfc = lt(length=10, start=y.start_time() + 10 * y.freq) + hfc = hfc.stack(hfc + 1.0) + y = [y, y] + hfc = [[hfc, hfc], [hfc]] + + metric_kwargs = [{"component_reduction": np.median}] + if len(metric) > 1: + # give metric specific kwargs + metric_kwargs.append({ + "component_reduction": np.median, + "time_reduction": np.mean, + }) + + model = NaiveDrift() + # backtest should fail with invalid metric kwargs (mae does not support time reduction) + with pytest.raises(TypeError) as err: + _ = model.backtest( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=False, + reduction=None, + metric_kwargs={ + "component_reduction": np.median, + "time_reduction": np.mean, + }, + ) + assert str(err.value).endswith("unexpected keyword argument 'time_reduction'") + + bts = model.backtest( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=False, + reduction=None, + metric_kwargs=metric_kwargs, + ) + assert isinstance(bts, list) and len(bts) == 2 + + # `ae` with time and component reduction is equal to `mae` with component reduction + bt_expected = metrics.mae(y[0], hfc[0][0], component_reduction=np.median) + for bt_list in bts: + for bt in bt_list: + np.testing.assert_array_almost_equal(bt, bt_expected) + + def time_reduced_metric(*args, **kwargs): + return metrics.ae(*args, **kwargs, time_reduction=np.mean) + + # check that single kwargs can be used for all metrics if params are supported + metric = [metric[0], time_reduced_metric] + bts = model.backtest( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=False, + reduction=None, + metric_kwargs=metric_kwargs[0], + ) + assert isinstance(bts, list) and len(bts) == 2 + + # `ae` with time and component reduction is equal to `mae` with component reduction + bt_expected = metrics.mae(y[0], hfc[0][0], component_reduction=np.median) + for bt_list in bts: + for bt in bt_list: + np.testing.assert_array_almost_equal(bt, bt_expected) + + @pytest.mark.parametrize( + "config", + itertools.product( + [ + [metrics.mae], # mae does not support time_reduction + [metrics.mae, metrics.ae], # ae supports time_reduction + [metrics.miw], # quantile interval metric + [metrics.miw, metrics.iw], + ], + [True, False], # last_points_only + ), + ) + def test_metric_quantiles_lpo(self, config): + """Tests backtest with quantile and quantile interval metrics from expected probabilistic or quantile + historical forecasts.""" + metric, lpo = config + is_interval_metric = metric[0].__name__ == "miw" + + q = [0.05, 0.5, 0.60, 0.95] + q_interval = [(0.05, 0.50), (0.50, 0.60), (0.60, 0.95), (0.05, 0.60)] + + y = lt(length=20) + y = y.stack(y + 1.0) + hfc = TimeSeries.from_times_and_values( + times=generate_index(start=y.start_time() + 10 * y.freq, length=10), + values=np.random.random((10, 1, 100)), + ) + hfc = hfc.stack(hfc + 1.0) + y = [y, y] + if lpo: + hfc = [hfc, hfc] + else: + hfc = [[hfc, hfc], [hfc]] + + metric_kwargs = [{"component_reduction": np.median}] + if not is_interval_metric: + metric_kwargs[0]["q"] = q + else: + metric_kwargs[0]["q_interval"] = q_interval + if len(metric) > 1: + # give metric specific kwargs + metric_kwargs2 = { + "component_reduction": np.median, + "time_reduction": np.mean, + } + if not is_interval_metric: + metric_kwargs2["q"] = q + else: + metric_kwargs2["q_interval"] = q_interval + metric_kwargs.append(metric_kwargs2) + + model = NaiveDrift() + + bts = model.backtest( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=lpo, + reduction=None, + metric_kwargs=metric_kwargs, + ) + assert isinstance(bts, list) and len(bts) == 2 + if lpo: + bts = [[bt] for bt in bts] + # `ae` with time and component reduction is equal to `mae` with component reduction + hfc_single = hfc[0][0] if not lpo else hfc[0] + q_kwargs = {"q": q} if not is_interval_metric else {"q_interval": q_interval} + bt_expected = metric[0]( + y[0], hfc_single, component_reduction=np.median, **q_kwargs + ) + shape_expected = (len(q),) + if len(metric) > 1: + bt_expected = np.concatenate([bt_expected[:, None]] * 2, axis=1) + shape_expected += (len(metric),) + for bt_list in bts: + for bt in bt_list: + assert bt.shape == shape_expected + np.testing.assert_array_almost_equal(bt, bt_expected) + + bts = model.backtest( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=lpo, + reduction=np.mean, + metric_kwargs=metric_kwargs, + ) + assert isinstance(bts, list) and len(bts) == 2 + for bt in bts: + assert bt.shape == shape_expected + np.testing.assert_array_almost_equal(bt, bt_expected) + + def time_reduced_metric(*args, **kwargs): + metric_f = metrics.iw if is_interval_metric else metrics.ae + return metric_f(*args, **kwargs, time_reduction=np.mean) + + # check that single kwargs can be used for all metrics if params are supported + metric = [metric[0], time_reduced_metric] + bts = model.backtest( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=lpo, + reduction=None, + metric_kwargs=metric_kwargs[0], + ) + assert isinstance(bts, list) and len(bts) == 2 + if lpo: + bts = [[bt] for bt in bts] + # `ae` / `miw` with time and component reduction is equal to `mae` / `miw` with component reduction + bt_expected = metric[0]( + y[0], hfc_single, component_reduction=np.median, **q_kwargs + ) + bt_expected = np.concatenate([bt_expected[:, None]] * 2, axis=1) + shape_expected = (len(q), len(metric)) + for bt_list in bts: + for bt in bt_list: + assert bt.shape == shape_expected + np.testing.assert_array_almost_equal(bt, bt_expected) + + bts = model.backtest( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=lpo, + reduction=np.mean, + metric_kwargs=metric_kwargs[0], + ) + assert isinstance(bts, list) and len(bts) == 2 + for bt in bts: + assert bt.shape == shape_expected + np.testing.assert_array_almost_equal(bt, bt_expected) + + # without component reduction + metric_kwargs = {"component_reduction": None} + if not is_interval_metric: + metric_kwargs["q"] = q + else: + metric_kwargs["q_interval"] = q_interval + bts = model.backtest( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=lpo, + reduction=None, + metric_kwargs=metric_kwargs, + ) + assert isinstance(bts, list) and len(bts) == 2 + if lpo: + bts = [[bt] for bt in bts] + + # `ae` / `iw` with time and no component reduction is equal to `mae` / `miw` without component reduction + bt_expected = metric[0](y[0], hfc_single, **metric_kwargs) + bt_expected = np.concatenate([bt_expected[:, None]] * 2, axis=1) + shape_expected = (len(q) * y[0].width, len(metric)) + for bt_list in bts: + for bt in bt_list: + assert bt.shape == shape_expected + np.testing.assert_array_almost_equal(bt, bt_expected) + + bts = model.backtest( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=lpo, + reduction=np.mean, + metric_kwargs=metric_kwargs, + ) + assert isinstance(bts, list) and len(bts) == 2 + for bt in bts: + assert bt.shape == shape_expected + np.testing.assert_array_almost_equal(bt, bt_expected) + + @pytest.mark.parametrize( + "config", + product([True, False], [True, False]), + ) + def test_backtest_sample_weight(self, config): + """check that passing sample weights work and that it yields different results than without sample weights.""" + manual_weight, multi_series = config + ts = AirPassengersDataset().load() + if manual_weight: + sample_weight = np.linspace(0, 1, len(ts)) + sample_weight = ts.with_values(np.expand_dims(sample_weight, -1)) + else: + sample_weight = "linear" + + if multi_series: + ts = [ts] * 2 + sample_weight = [sample_weight] * 2 if manual_weight else sample_weight + + model = LinearRegressionModel(lags=3, output_chunk_length=1) + start_kwargs = {"start": -1, "start_format": "position"} + bt_non_weighted = model.backtest(series=ts, **start_kwargs) + + model = LinearRegressionModel(lags=3, output_chunk_length=1) + bt_weighted = model.backtest( + series=ts, sample_weight=sample_weight, **start_kwargs + ) + + if not multi_series: + bt_weighted = [bt_weighted] + bt_non_weighted = [bt_non_weighted] + + # check that the predictions are different + for bt_nw, bt_w in zip(bt_non_weighted, bt_weighted): + assert bt_w != bt_nw diff --git a/darts/tests/models/forecasting/test_baseline_models.py b/darts/tests/models/forecasting/test_baseline_models.py new file mode 100644 index 0000000000..af66478c0e --- /dev/null +++ b/darts/tests/models/forecasting/test_baseline_models.py @@ -0,0 +1,423 @@ +import itertools + +import numpy as np +import pytest + +from darts import TimeSeries +from darts.logging import get_logger +from darts.models import NaiveDrift, NaiveMean, NaiveMovingAverage, NaiveSeasonal +from darts.models.forecasting.forecasting_model import ( + GlobalForecastingModel, + LocalForecastingModel, +) +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs +from darts.utils import timeseries_generation as tg + +logger = get_logger(__name__) + + +icl = 5 +local_models = [ + (NaiveDrift, {}), + (NaiveMean, {}), + (NaiveMovingAverage, {}), + (NaiveSeasonal, {}), +] +global_models = [] + + +if TORCH_AVAILABLE: + import torch + + from darts.models import GlobalNaiveAggregate, GlobalNaiveDrift, GlobalNaiveSeasonal + + global_models += [ + ( + GlobalNaiveAggregate, + {"input_chunk_length": icl, "output_chunk_length": 3, **tfm_kwargs}, + ), + ( + GlobalNaiveAggregate, + {"input_chunk_length": icl, "output_chunk_length": 1, **tfm_kwargs}, + ), + ( + GlobalNaiveDrift, + {"input_chunk_length": icl, "output_chunk_length": 3, **tfm_kwargs}, + ), + ( + GlobalNaiveDrift, + {"input_chunk_length": icl, "output_chunk_length": 1, **tfm_kwargs}, + ), + ( + GlobalNaiveSeasonal, + {"input_chunk_length": icl, "output_chunk_length": 3, **tfm_kwargs}, + ), + ( + GlobalNaiveSeasonal, + {"input_chunk_length": icl, "output_chunk_length": 1, **tfm_kwargs}, + ), + ] + + def custom_mean_valid(x, dim): + return torch.mean(x, dim) + + def custom_mean_invalid_out_shape(x, dim): + return x[:1] + + def custom_mean_invalid_signature(x): + return torch.mean(x, dim=1) + + def custom_mean_invalid_output_type(x, dim): + return torch.mean(x, dim=1).detach().numpy() + +else: + custom_mean_valid = None + custom_mean_invalid_out_shape = None + custom_mean_invalid_signature = None + custom_mean_invalid_output_type = None + + +class TestBaselineModels: + np.random.seed(42) + if TORCH_AVAILABLE: + torch.manual_seed(42) + + @pytest.mark.parametrize( + "config", itertools.product(local_models + global_models, [False, True]) + ) + def test_fit_predict(self, config): + """Tests fit and predict for univariate and multivariate time series.""" + (model_cls, model_kwargs), is_multivariate = config + + # min train series length for global naive models + series = tg.linear_timeseries(length=icl) + if is_multivariate: + series.stack(series + 100) + + model = model_cls(**model_kwargs) + assert not model.supports_probabilistic_prediction + assert not model.supports_likelihood_parameter_prediction + + # calling predict before fit + with pytest.raises(ValueError): + model.predict(n=10) + + # calling fit with covariates + if isinstance(model, GlobalForecastingModel): + err_type = ValueError + err_msg_content = "The model does not support" + else: # for local models, covariates are not part of signature + err_type = TypeError + err_msg_content = "got an unexpected keyword argument" + with pytest.raises(err_type) as err: + model.fit(series=series, past_covariates=series) + assert err_msg_content in str(err.value) + with pytest.raises(err_type) as err: + model.fit(series=series, future_covariates=series) + assert err_msg_content in str(err.value) + + model.fit(series=series) + # calling predict with covariates + with pytest.raises(err_type) as err: + model.predict(n=10, past_covariates=series) + assert err_msg_content in str(err.value) + with pytest.raises(err_type) as err: + model.predict(n=10, future_covariates=series) + assert err_msg_content in str(err.value) + + # single series predict works with all models + preds = model.predict(n=10) + preds_start = series.end_time() + series.freq + assert isinstance(preds, TimeSeries) + assert len(preds) == 10 + assert preds.start_time() == preds_start + assert preds.components.equals(series.components) + + if isinstance(model, LocalForecastingModel): + # no series at prediction time + with pytest.raises(err_type) as err: + _ = model.predict(n=10, series=series) + assert err_msg_content in str(err.value) + # no multiple series prediction + with pytest.raises(err_type) as err: + _ = model.predict(n=10, series=[series, series]) + assert err_msg_content in str(err.value) + else: + preds = model.predict(n=10, series=series) + assert isinstance(preds, TimeSeries) + assert len(preds) == 10 + assert preds.start_time() == preds_start + assert preds.components.equals(series.components) + preds = model.predict(n=10, series=[series, series]) + assert isinstance(preds, list) + assert len(preds) == 2 + assert all([isinstance(p, TimeSeries) for p in preds]) + assert all([len(p) == 10 for p in preds]) + assert all([p.start_time() == preds_start for p in preds]) + assert all([p.components.equals(series.components) for p in preds]) + + # multiple series training only with global baselines + if isinstance(model, LocalForecastingModel): + with pytest.raises(ValueError) as err: + model.fit(series=[series, series]) + assert "Train `series` must be a single `TimeSeries`." == str(err.value) + else: + model.fit(series=[series, series]) + + def test_naive_seasonal(self): + # min train series length for global naive models + series = tg.linear_timeseries(length=icl) + series = series.stack(series + 25.0) + + vals_exp = series.values(copy=False) + + # local naive seasonal + local_model = NaiveSeasonal(K=icl) + preds = local_model.fit(series).predict(n=icl) + np.testing.assert_array_almost_equal(preds.values(copy=False), vals_exp) + + if not TORCH_AVAILABLE: + return + + # equivalent global naive seasonal + global_model = GlobalNaiveSeasonal( + input_chunk_length=icl, output_chunk_length=1, **tfm_kwargs + ) + preds = global_model.fit(series).predict(n=icl) + np.testing.assert_array_almost_equal(preds.values(copy=False), vals_exp) + + preds_multi = global_model.predict(n=icl, series=[series, series + 100.0]) + np.testing.assert_array_almost_equal( + preds_multi[0].values(copy=False), vals_exp + ) + np.testing.assert_array_almost_equal( + preds_multi[1].values(copy=False), vals_exp + 100.0 + ) + + # global naive seasonal that repeats values `output_chunk_length` times + global_model = GlobalNaiveSeasonal( + input_chunk_length=icl, output_chunk_length=icl, **tfm_kwargs + ) + preds = global_model.fit(series).predict(n=icl) + np.testing.assert_array_almost_equal( + preds.values(copy=False), np.repeat(vals_exp[0:1, :], icl, axis=0) + ) + + preds_multi = global_model.predict(n=icl, series=[series, series + 100.0]) + np.testing.assert_array_almost_equal( + preds_multi[0].values(copy=False), np.repeat(vals_exp[0:1, :], icl, axis=0) + ) + np.testing.assert_array_almost_equal( + preds_multi[1].values(copy=False), + np.repeat(vals_exp[0:1, :] + 100.0, icl, axis=0), + ) + + def test_naive_drift(self): + # min train series length for global naive models + series_total = tg.linear_timeseries(length=2 * icl) + series_total = series_total.stack(series_total + 25.0) + series = series_total[:icl] + series_drift = series_total[icl:] + + vals_exp = series_drift.values(copy=False) + + # local naive drift + local_model = NaiveDrift() + preds = local_model.fit(series).predict(n=icl) + np.testing.assert_array_almost_equal(preds.values(copy=False), vals_exp) + + if not TORCH_AVAILABLE: + return + + # identical global naive drift + global_model = GlobalNaiveDrift( + input_chunk_length=icl, output_chunk_length=icl, **tfm_kwargs + ) + preds = global_model.fit(series).predict(n=icl) + np.testing.assert_array_almost_equal(preds.values(copy=False), vals_exp) + + preds_multi = global_model.predict(n=icl, series=[series, series + 100.0]) + np.testing.assert_array_almost_equal( + preds_multi[0].values(copy=False), vals_exp + ) + np.testing.assert_array_almost_equal( + preds_multi[1].values(copy=False), vals_exp + 100.0 + ) + + # global naive moving drift + global_model = GlobalNaiveDrift( + input_chunk_length=icl, output_chunk_length=1, **tfm_kwargs + ) + preds = global_model.fit(series).predict(n=icl) + + # manually compute the moving/autoregressive drift + series_vals = series.values(copy=False) + preds_vals = preds.values(copy=False) + preds_exp = [] + x, y = 1, None + for i in range(0, icl): + y_0 = y if y is not None else series_vals[-1] + m = (y_0 - series_vals[i]) / (icl - 1) + y = m * x + y_0 + preds_exp.append(np.expand_dims(y, 0)) + preds_exp = np.concatenate(preds_exp) + np.testing.assert_array_almost_equal(preds_vals, preds_exp) + + preds_multi = global_model.predict(n=icl, series=[series, series + 100.0]) + np.testing.assert_array_almost_equal( + preds_multi[0].values(copy=False), preds_exp + ) + np.testing.assert_array_almost_equal( + preds_multi[1].values(copy=False), preds_exp + 100.0 + ) + + def test_naive_mean(self): + # min train series length for global naive models + series = tg.linear_timeseries(length=icl) + series = series.stack(series + 25.0) + + # mean repeated n times + vals_exp = np.repeat( + np.expand_dims(series.values(copy=False).mean(axis=0), 0), icl, axis=0 + ) + + # local naive mean + local_model = NaiveMean() + preds = local_model.fit(series).predict(n=icl) + np.testing.assert_array_almost_equal(preds.values(copy=False), vals_exp) + + if not TORCH_AVAILABLE: + return + + # identical global naive mean + global_model = GlobalNaiveAggregate( + input_chunk_length=icl, output_chunk_length=icl, agg_fn="mean", **tfm_kwargs + ) + preds = global_model.fit(series).predict(n=icl) + np.testing.assert_array_almost_equal(preds.values(copy=False), vals_exp) + + preds_multi = global_model.predict(n=icl, series=[series, series + 100.0]) + np.testing.assert_array_almost_equal( + preds_multi[0].values(copy=False), vals_exp + ) + np.testing.assert_array_almost_equal( + preds_multi[1].values(copy=False), vals_exp + 100.0 + ) + + def test_naive_moving_average(self): + # min train series length for global naive models + series = tg.linear_timeseries(length=icl) + series = series.stack(series + 25.0) + + # manually compute the moving/autoregressive average/mean + series_vals = series.values(copy=False) + vals_exp = [] + y = None + for i in range(0, icl): + if y is None: + y_moving = series_vals + else: + y_moving = np.concatenate( + [series_vals[i:], np.concatenate(vals_exp)], axis=0 + ) + y = np.expand_dims(y_moving.mean(axis=0), 0) + vals_exp.append(y) + vals_exp = np.concatenate(vals_exp) + + # local naive mean + local_model = NaiveMovingAverage(input_chunk_length=icl) + preds = local_model.fit(series).predict(n=icl) + np.testing.assert_array_almost_equal(preds.values(copy=False), vals_exp) + + if not TORCH_AVAILABLE: + return + + # identical global naive moving average + global_model = GlobalNaiveAggregate( + input_chunk_length=icl, output_chunk_length=1, agg_fn="mean", **tfm_kwargs + ) + preds = global_model.fit(series).predict(n=icl) + np.testing.assert_array_almost_equal(preds.values(copy=False), vals_exp) + + preds_multi = global_model.predict(n=icl, series=[series, series + 100.0]) + np.testing.assert_array_almost_equal( + preds_multi[0].values(copy=False), vals_exp + ) + np.testing.assert_array_almost_equal( + preds_multi[1].values(copy=False), vals_exp + 100.0 + ) + + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") + @pytest.mark.parametrize( + "agg_fn_config", + [ + ("nanmean", "nanmean"), + ("mean", "mean"), + (custom_mean_valid, "mean"), + ], + ) + def test_global_naive_aggregate(self, agg_fn_config): + agg_fn, agg_name = agg_fn_config + + # min train series length for global naive models + series = tg.linear_timeseries(length=icl) + series = series.stack(series + 25.0) + + # manually compute the moving/autoregressive average/mean + series_vals = series.values(copy=False) + vals_exp = [] + + agg_fn_np = getattr(np, agg_name) + y = None + for i in range(0, icl): + if y is None: + y_moving = series_vals + else: + y_moving = np.concatenate( + [series_vals[i:], np.concatenate(vals_exp)], axis=0 + ) + + y = np.expand_dims(agg_fn_np(y_moving, axis=0), 0) + vals_exp.append(y) + vals_exp = np.concatenate(vals_exp) + + # identical global naive moving average + global_model = GlobalNaiveAggregate( + input_chunk_length=icl, output_chunk_length=1, agg_fn=agg_fn, **tfm_kwargs + ) + preds = global_model.fit(series).predict(n=icl) + np.testing.assert_array_almost_equal(preds.values(copy=False), vals_exp) + + preds_multi = global_model.predict(n=icl, series=[series, series + 100.0]) + np.testing.assert_array_almost_equal( + preds_multi[0].values(copy=False), vals_exp + ) + np.testing.assert_array_almost_equal( + preds_multi[1].values(copy=False), vals_exp + 100.0 + ) + + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") + @pytest.mark.parametrize( + "agg_fn_config", + [ + ("mmean", "When `agg_fn` is a string"), + (1, "`agg_fn` must be a string or callable"), + ( + custom_mean_invalid_output_type, + "`agg_fn` output must be a torch Tensor.", + ), + (custom_mean_invalid_signature, "got an unexpected keyword argument 'dim'"), + (custom_mean_invalid_out_shape, "Unexpected `agg_fn` output shape."), + ], + ) + def test_global_naive_aggregate_invalid_agg_fn(self, agg_fn_config): + agg_fn, err_msg_content = agg_fn_config + # identical global naive moving average + with pytest.raises(ValueError) as err: + _ = GlobalNaiveAggregate( + input_chunk_length=icl, + output_chunk_length=1, + agg_fn=agg_fn, + **tfm_kwargs, + ) + assert err_msg_content in str(err.value) diff --git a/darts/tests/models/forecasting/test_block_RNN.py b/darts/tests/models/forecasting/test_block_RNN.py index 1aa8a6ff2d..23a213394a 100644 --- a/darts/tests/models/forecasting/test_block_RNN.py +++ b/darts/tests/models/forecasting/test_block_RNN.py @@ -3,183 +3,195 @@ import pytest from darts import TimeSeries -from darts.logging import get_logger -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs -logger = get_logger(__name__) - -try: - import torch.nn as nn - - from darts.models.forecasting.block_rnn_model import ( - BlockRNNModel, - CustomBlockRNNModule, - _BlockRNNModule, +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import torch.nn as nn + +from darts.models.forecasting.block_rnn_model import ( + BlockRNNModel, + CustomBlockRNNModule, + _BlockRNNModule, +) + + +class ModuleValid1(_BlockRNNModule): + """Wrapper around the _BlockRNNModule""" + + def __init__(self, **kwargs): + super().__init__(name="RNN", **kwargs) + + +class ModuleValid2(CustomBlockRNNModule): + """Just a linear layer.""" + + def __init__(self, **kwargs): + super().__init__(**kwargs) + self.linear = nn.Linear(self.input_size, self.target_size) + + def forward(self, x_in): + x = self.linear(x_in[0]) + return x.view(len(x), -1, self.target_size, self.nr_params) + + +class TestBlockRNNModel: + times = pd.date_range("20130101", "20130410") + pd_series = pd.Series(range(100), index=times) + series: TimeSeries = TimeSeries.from_series(pd_series) + module_invalid = _BlockRNNModule( + "RNN", + input_size=1, + input_chunk_length=1, + output_chunk_length=1, + output_chunk_shift=0, + hidden_dim=25, + target_size=1, + nr_params=1, + num_layers=1, + num_layers_out_fc=[], + dropout=0, ) - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. RNN tests will be skipped.") - TORCH_AVAILABLE = False - - -if TORCH_AVAILABLE: - - class ModuleValid1(_BlockRNNModule): - """Wrapper around the _BlockRNNModule""" - - def __init__(self, **kwargs): - super().__init__(name="RNN", **kwargs) - - class ModuleValid2(CustomBlockRNNModule): - """Just a linear layer.""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.linear = nn.Linear(self.input_size, self.target_size) + def test_creation(self): + # cannot choose any string + with pytest.raises(ValueError) as msg: + BlockRNNModel( + input_chunk_length=1, output_chunk_length=1, model="UnknownRNN?" + ) + assert str(msg.value).startswith("`model` is not a valid RNN model.") - def forward(self, x_in): - x = self.linear(x_in[0]) - return x.view(len(x), -1, self.target_size, self.nr_params) + # cannot create from a class instance + with pytest.raises(ValueError) as msg: + _ = BlockRNNModel( + input_chunk_length=1, + output_chunk_length=1, + model=self.module_invalid, + ) + assert str(msg.value).startswith("`model` is not a valid RNN model.") - class TestBlockRNNModel: - times = pd.date_range("20130101", "20130410") - pd_series = pd.Series(range(100), index=times) - series: TimeSeries = TimeSeries.from_series(pd_series) - module_invalid = _BlockRNNModule( - "RNN", - input_size=1, + # can create from valid module name + model1 = BlockRNNModel( input_chunk_length=1, output_chunk_length=1, - hidden_dim=25, - target_size=1, - nr_params=1, - num_layers=1, - num_layers_out_fc=[], - dropout=0, + model="RNN", + n_epochs=1, + random_state=42, + **tfm_kwargs, ) + model1.fit(self.series) + preds1 = model1.predict(n=3) - def test_creation(self): - # cannot choose any string - with pytest.raises(ValueError) as msg: - BlockRNNModel( - input_chunk_length=1, output_chunk_length=1, model="UnknownRNN?" - ) - assert str(msg.value).startswith("`model` is not a valid RNN model.") - - # cannot create from a class instance - with pytest.raises(ValueError) as msg: - _ = BlockRNNModel( - input_chunk_length=1, - output_chunk_length=1, - model=self.module_invalid, - ) - assert str(msg.value).startswith("`model` is not a valid RNN model.") - - # can create from valid module name - model1 = BlockRNNModel( - input_chunk_length=1, - output_chunk_length=1, - model="RNN", - n_epochs=1, - random_state=42, - **tfm_kwargs - ) - model1.fit(self.series) - preds1 = model1.predict(n=3) + # can create from valid module name with ReLU activation + model2 = BlockRNNModel( + input_chunk_length=1, + output_chunk_length=1, + model="RNN", + activation="ReLU", + hidden_fc_sizes=[10], + n_epochs=1, + random_state=42, + **tfm_kwargs, + ) + model2.fit(self.series) + preds2 = model2.predict(n=3) + assert preds1.values().shape == preds2.values().shape - # can create from a custom class itself - model2 = BlockRNNModel( - input_chunk_length=1, - output_chunk_length=1, - model=ModuleValid1, - n_epochs=1, - random_state=42, - **tfm_kwargs - ) - model2.fit(self.series) - preds2 = model2.predict(n=3) - np.testing.assert_array_equal(preds1.all_values(), preds2.all_values()) + # can create from a custom class itself + model3 = BlockRNNModel( + input_chunk_length=1, + output_chunk_length=1, + model=ModuleValid1, + n_epochs=1, + random_state=42, + **tfm_kwargs, + ) + model3.fit(self.series) + preds3 = model3.predict(n=3) + np.testing.assert_array_equal(preds1.all_values(), preds3.all_values()) - model3 = BlockRNNModel( - input_chunk_length=1, - output_chunk_length=1, - model=ModuleValid2, - n_epochs=1, - random_state=42, - **tfm_kwargs - ) - model3.fit(self.series) - preds3 = model2.predict(n=3) - assert preds3.all_values().shape == preds2.all_values().shape - assert preds3.time_index.equals(preds2.time_index) - - def test_fit(self, tmpdir_module): - # Test basic fit() - model = BlockRNNModel( - input_chunk_length=1, output_chunk_length=1, n_epochs=2, **tfm_kwargs - ) - model.fit(self.series) + model4 = BlockRNNModel( + input_chunk_length=1, + output_chunk_length=1, + model=ModuleValid2, + n_epochs=1, + random_state=42, + **tfm_kwargs, + ) + model4.fit(self.series) + preds4 = model4.predict(n=3) + assert preds4.all_values().shape == preds3.all_values().shape + assert preds4.time_index.equals(preds3.time_index) + + def test_fit(self, tmpdir_module): + # Test basic fit() + model = BlockRNNModel( + input_chunk_length=1, output_chunk_length=1, n_epochs=2, **tfm_kwargs + ) + model.fit(self.series) - # Test fit-save-load cycle - model2 = BlockRNNModel( - input_chunk_length=1, - output_chunk_length=1, - model="LSTM", - n_epochs=1, - model_name="unittest-model-lstm", - work_dir=tmpdir_module, - save_checkpoints=True, - force_reset=True, - **tfm_kwargs - ) - model2.fit(self.series) - model_loaded = model2.load_from_checkpoint( - model_name="unittest-model-lstm", - work_dir=tmpdir_module, - best=False, - map_location="cpu", - ) - pred1 = model2.predict(n=6) - pred2 = model_loaded.predict(n=6) + # Test fit-save-load cycle + model2 = BlockRNNModel( + input_chunk_length=1, + output_chunk_length=1, + model="LSTM", + n_epochs=1, + model_name="unittest-model-lstm", + work_dir=tmpdir_module, + save_checkpoints=True, + force_reset=True, + **tfm_kwargs, + ) + model2.fit(self.series) + model_loaded = model2.load_from_checkpoint( + model_name="unittest-model-lstm", + work_dir=tmpdir_module, + best=False, + map_location="cpu", + ) + pred1 = model2.predict(n=6) + pred2 = model_loaded.predict(n=6) - # Two models with the same parameters should deterministically yield the same output - np.testing.assert_array_equal(pred1.values(), pred2.values()) + # Two models with the same parameters should deterministically yield the same output + np.testing.assert_array_equal(pred1.values(), pred2.values()) - # Another random model should not - model3 = BlockRNNModel( - input_chunk_length=1, - output_chunk_length=1, - model="RNN", - n_epochs=2, - **tfm_kwargs - ) - model3.fit(self.series) - pred3 = model3.predict(n=6) - assert not np.array_equal(pred1.values(), pred3.values()) - - # test short predict - pred4 = model3.predict(n=1) - assert len(pred4) == 1 - - # test validation series input - model3.fit(self.series[:60], val_series=self.series[60:]) - pred4 = model3.predict(n=6) - assert len(pred4) == 6 - - def helper_test_pred_length(self, pytorch_model, series): - model = pytorch_model( - input_chunk_length=1, output_chunk_length=3, n_epochs=1, **tfm_kwargs - ) - model.fit(series) - pred = model.predict(7) - assert len(pred) == 7 - pred = model.predict(2) - assert len(pred) == 2 - assert pred.width == 1 - pred = model.predict(4) - assert len(pred) == 4 - assert pred.width == 1 - - def test_pred_length(self): - self.helper_test_pred_length(BlockRNNModel, self.series) + # Another random model should not + model3 = BlockRNNModel( + input_chunk_length=1, + output_chunk_length=1, + model="RNN", + n_epochs=2, + **tfm_kwargs, + ) + model3.fit(self.series) + pred3 = model3.predict(n=6) + assert not np.array_equal(pred1.values(), pred3.values()) + + # test short predict + pred4 = model3.predict(n=1) + assert len(pred4) == 1 + + # test validation series input + model3.fit(self.series[:60], val_series=self.series[60:]) + pred4 = model3.predict(n=6) + assert len(pred4) == 6 + + def helper_test_pred_length(self, pytorch_model, series): + model = pytorch_model( + input_chunk_length=1, output_chunk_length=3, n_epochs=1, **tfm_kwargs + ) + model.fit(series) + pred = model.predict(7) + assert len(pred) == 7 + pred = model.predict(2) + assert len(pred) == 2 + assert pred.width == 1 + pred = model.predict(4) + assert len(pred) == 4 + assert pred.width == 1 + + def test_pred_length(self): + self.helper_test_pred_length(BlockRNNModel, self.series) diff --git a/darts/tests/models/forecasting/test_conformal_model.py b/darts/tests/models/forecasting/test_conformal_model.py new file mode 100644 index 0000000000..fa27baf50c --- /dev/null +++ b/darts/tests/models/forecasting/test_conformal_model.py @@ -0,0 +1,1720 @@ +import copy +import itertools +import math +import os + +import numpy as np +import pandas as pd +import pytest + +from darts import TimeSeries, concatenate +from darts.datasets import AirPassengersDataset +from darts.metrics import ae, err, ic, incs_qr, mic +from darts.models import ( + ConformalNaiveModel, + ConformalQRModel, + LinearRegressionModel, + NaiveSeasonal, + NLinearModel, +) +from darts.models.forecasting.conformal_models import _get_calibration_hfc_start +from darts.models.forecasting.forecasting_model import ForecastingModel +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs +from darts.utils import n_steps_between +from darts.utils import timeseries_generation as tg +from darts.utils.timeseries_generation import linear_timeseries +from darts.utils.utils import ( + likelihood_component_names, + quantile_interval_names, + quantile_names, +) + +IN_LEN = 3 +OUT_LEN = 3 +regr_kwargs = {"lags": IN_LEN, "output_chunk_length": OUT_LEN} +tfm_kwargs = copy.deepcopy(tfm_kwargs) +tfm_kwargs["pl_trainer_kwargs"]["fast_dev_run"] = True +torch_kwargs = dict( + {"input_chunk_length": IN_LEN, "output_chunk_length": OUT_LEN, "random_state": 0}, + **tfm_kwargs, +) +pred_lklp = {"num_samples": 1, "predict_likelihood_parameters": True} +q = [0.1, 0.5, 0.9] + + +def train_model( + *args, model_type="regression", model_params=None, quantiles=None, **kwargs +): + model_params = model_params or {} + if model_type == "regression": + return LinearRegressionModel( + **regr_kwargs, + **model_params, + random_state=42, + ).fit(*args, **kwargs) + elif model_type in ["regression_prob", "regression_qr"]: + return LinearRegressionModel( + likelihood="quantile", + quantiles=quantiles, + **regr_kwargs, + **model_params, + random_state=42, + ).fit(*args, **kwargs) + else: + return NLinearModel(**torch_kwargs, **model_params).fit(*args, **kwargs) + + +# pre-trained global model for conformal models +models_cls_kwargs_errs = [ + ( + ConformalNaiveModel, + {"quantiles": q}, + "regression", + ), +] + +if TORCH_AVAILABLE: + models_cls_kwargs_errs.append(( + ConformalNaiveModel, + {"quantiles": q}, + "torch", + )) + + +class TestConformalModel: + """ + Tests all general model behavior for Naive Conformal Model with symmetric non-conformity score. + Additionally, checks correctness of predictions for: + - ConformalNaiveModel with symmetric & asymmetric non-conformity scores + - ConformalQRModel with symmetric & asymmetric non-conformity scores + """ + + np.random.seed(42) + + # forecasting horizon used in runnability tests + horizon = OUT_LEN + 1 + + # some arbitrary static covariates + static_covariates = pd.DataFrame([[0.0, 1.0]], columns=["st1", "st2"]) + + # real timeseries for functionality tests + ts_length = 13 + horizon + ts_passengers = ( + AirPassengersDataset() + .load()[:ts_length] + .with_static_covariates(static_covariates) + ) + ts_pass_train, ts_pass_val = ( + ts_passengers[:-horizon], + ts_passengers[-horizon:], + ) + + # an additional noisy series + ts_pass_train_1 = ts_pass_train + 0.01 * tg.gaussian_timeseries( + length=len(ts_pass_train), + freq=ts_pass_train.freq_str, + start=ts_pass_train.start_time(), + ) + + # an additional time series serving as covariates + year_series = tg.datetime_attribute_timeseries(ts_passengers, attribute="year") + month_series = tg.datetime_attribute_timeseries(ts_passengers, attribute="month") + time_covariates = year_series.stack(month_series) + time_covariates_train = time_covariates[:-horizon] + + # various ts with different static covariates representations + ts_w_static_cov = tg.linear_timeseries(length=ts_length).with_static_covariates( + pd.Series([1, 2]) + ) + ts_shared_static_cov = ts_w_static_cov.stack(tg.sine_timeseries(length=ts_length)) + ts_comps_static_cov = ts_shared_static_cov.with_static_covariates( + pd.DataFrame([[0, 1], [2, 3]], columns=["st1", "st2"]) + ) + + def test_model_construction_naive(self): + local_model = NaiveSeasonal(K=5) + global_model = LinearRegressionModel(**regr_kwargs) + series = self.ts_pass_train + + model_err_msg = "`model` must be a pre-trained `GlobalForecastingModel`." + # un-trained local model + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=local_model, quantiles=q) + assert str(exc.value) == model_err_msg + + # pre-trained local model + local_model.fit(series) + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=local_model, quantiles=q) + assert str(exc.value) == model_err_msg + + # un-trained global model + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=q) + assert str(exc.value) == model_err_msg + + # pre-trained local model should work + global_model.fit(series) + model = ConformalNaiveModel(model=global_model, quantiles=q) + assert model.likelihood == "quantile" + + # non-centered quantiles + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=[0.2, 0.5, 0.6]) + assert str(exc.value) == ( + "quantiles lower than `q=0.5` need to share same difference to `0.5` as quantiles higher than `q=0.5`" + ) + + # quantiles missing median + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=[0.1, 0.9]) + assert str(exc.value) == "median quantile `q=0.5` must be in `quantiles`" + + # too low and high quantiles + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=[-0.1, 0.5, 1.1]) + assert str(exc.value) == "All provided quantiles must be between 0 and 1." + + # `cal_length` must be `>=1` or `None` + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=q, cal_length=0) + assert str(exc.value) == "`cal_length` must be `>=1` or `None`." + + # `cal_stride` must be `>=1` + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=q, cal_stride=0) + assert str(exc.value) == "`cal_stride` must be `>=1`." + + # `num_samples` must be `>=1` + with pytest.raises(ValueError) as exc: + ConformalNaiveModel(model=global_model, quantiles=q, cal_num_samples=0) + assert str(exc.value) == "`cal_num_samples` must be `>=1`." + + def test_model_hfc_stride_checks(self): + series = self.ts_pass_train + model = LinearRegressionModel(**regr_kwargs).fit(series) + cp_model = ConformalNaiveModel(model=model, quantiles=q, cal_stride=2) + + expected_error_start = ( + "The provided `stride` parameter must be a round-multiple of " + "`cal_stride=2` and `>=cal_stride`." + ) + # `stride` must be >= `cal_stride` + with pytest.raises(ValueError) as exc: + cp_model.historical_forecasts(series=series, stride=1) + assert str(exc.value).startswith(expected_error_start) + + # `stride` must be a round multiple of `cal_stride` + with pytest.raises(ValueError) as exc: + cp_model.historical_forecasts(series=series, stride=3) + assert str(exc.value).startswith(expected_error_start) + + # valid stride + _ = cp_model.historical_forecasts(series=series, stride=4) + + def test_model_construction_cqr(self): + model_det = train_model(self.ts_pass_train, model_type="regression") + model_prob_q = train_model( + self.ts_pass_train, model_type="regression_prob", quantiles=q + ) + model_prob_poisson = train_model( + self.ts_pass_train, + model_type="regression", + model_params={"likelihood": "poisson"}, + ) + + # deterministic global model + with pytest.raises(ValueError) as exc: + ConformalQRModel(model=model_det, quantiles=q) + assert str(exc.value).startswith( + "`model` must support probabilistic forecasting." + ) + # probabilistic model works + _ = ConformalQRModel(model=model_prob_q, quantiles=q) + # works also with different likelihood + _ = ConformalQRModel(model=model_prob_poisson, quantiles=q) + + def test_unsupported_properties(self): + """Tests only here for coverage, maybe at some point we support these properties.""" + model = ConformalNaiveModel(train_model(self.ts_pass_train), quantiles=q) + unsupported_properties = [ + "_model_encoder_settings", + "extreme_lags", + "min_train_series_length", + "min_train_samples", + ] + for prop in unsupported_properties: + with pytest.raises(NotImplementedError): + getattr(model, prop) + + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_save_model_parameters(self, config): + # model creation parameters were saved before. check if re-created model has same params as original + model_cls, kwargs, model_type = config + model = model_cls( + model=train_model( + self.ts_pass_train, model_type=model_type, quantiles=kwargs["quantiles"] + ), + **kwargs, + ) + model_fresh = model.untrained_model() + assert model._model_params.keys() == model_fresh._model_params.keys() + for param, val in model._model_params.items(): + if isinstance(val, ForecastingModel): + # Conformal Models require a forecasting model as input, which has no equality + continue + assert val == model_fresh._model_params[param] + + @pytest.mark.parametrize( + "config", itertools.product(models_cls_kwargs_errs, [{}, pred_lklp]) + ) + def test_save_load_model(self, tmpdir_fn, config): + # check if save and load methods work and if loaded model creates same forecasts as original model + (model_cls, kwargs, model_type), pred_kwargs = config + model = model_cls( + train_model( + self.ts_pass_train, model_type=model_type, quantiles=kwargs["quantiles"] + ), + **kwargs, + ) + + # check if save and load methods work and + # if loaded conformal model creates same forecasts as original ensemble models + expected_suffixes = [ + ".pkl", + ".pkl.NLinearModel.pt", + ".pkl.NLinearModel.pt.ckpt", + ] + + # test save + model.save() + model.save(os.path.join(tmpdir_fn, f"{model_cls.__name__}.pkl")) + + model_prediction = model.predict(5, **pred_kwargs) + + assert os.path.exists(tmpdir_fn) + files = os.listdir(tmpdir_fn) + if model_type == "torch": + # 1 from conformal model, 2 from torch, * 2 as `save()` was called twice + assert len(files) == 6 + for f in files: + assert f.startswith(model_cls.__name__) + suffix_counts = { + suffix: sum(1 for p in os.listdir(tmpdir_fn) if p.endswith(suffix)) + for suffix in expected_suffixes + } + assert all(count == 2 for count in suffix_counts.values()) + else: + assert len(files) == 2 + for f in files: + assert f.startswith(model_cls.__name__) and f.endswith(".pkl") + + # test load + pkl_files = [] + for filename in os.listdir(tmpdir_fn): + if filename.endswith(".pkl"): + pkl_files.append(os.path.join(tmpdir_fn, filename)) + for p in pkl_files: + loaded_model = model_cls.load(p) + assert model_prediction == loaded_model.predict(5, **pred_kwargs) + + # test pl_trainer_kwargs (only for torch models) + loaded_model = model_cls.load(p, pl_trainer_kwargs={"accelerator": "cuda"}) + if model_type == "torch": + assert loaded_model.model.trainer_params["accelerator"] == "cuda" + + # test clean save + clean_model_path = os.path.join(tmpdir_fn, f"clean_{model_cls.__name__}.pkl") + model.save(clean_model_path, clean=True) + clean_model = model_cls.load( + clean_model_path, pl_trainer_kwargs={"accelerator": "cpu"} + ) + assert clean_model.model.training_series is None + assert clean_model.model.past_covariate_series is None + assert clean_model.model.future_covariate_series is None + + clean_model_prediction = clean_model.predict( + 5, self.ts_pass_train, **pred_kwargs + ) + # Need the same number of previous call to predict (for random state) + assert model.predict(5, **pred_kwargs) == clean_model_prediction + + def test_fit(self): + model = ConformalNaiveModel(train_model(self.ts_pass_train), quantiles=q) + assert model.model._fit_called + + # check kwargs will be passed to `model.model.fit()` + assert model.supports_sample_weight + model.model._fit_called = False + model.fit(self.ts_pass_train, sample_weight="linear") + assert model.model._fit_called + + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_single_ts(self, config): + model_cls, kwargs, model_type = config + model = model_cls( + train_model( + self.ts_pass_train, model_type=model_type, quantiles=kwargs["quantiles"] + ), + **kwargs, + ) + pred = model.predict(n=self.horizon, **pred_lklp) + assert pred.n_components == self.ts_pass_train.n_components * len( + kwargs["quantiles"] + ) + assert not np.isnan(pred.all_values()).any().any() + + pred_fc = model.model.predict(n=self.horizon) + assert pred_fc.time_index.equals(pred.time_index) + # the center forecasts must be equal to the forecasting model forecast + fc_columns = likelihood_component_names( + self.ts_pass_val.columns, quantile_names([0.5]) + ) + np.testing.assert_array_almost_equal( + pred[fc_columns].all_values(), pred_fc.all_values() + ) + assert pred.static_covariates is None + + # using a different `n`, gives different results, since we can generate more residuals for the horizon + pred1 = model.predict(n=self.horizon - 1, **pred_lklp) + assert not pred1 == pred[: len(pred1)] + + # wrong dimension + with pytest.raises(ValueError): + model.predict( + n=self.horizon, + series=self.ts_pass_train.stack(self.ts_pass_train), + **pred_lklp, + ) + + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_multi_ts(self, config): + model_cls, kwargs, model_type = config + model = model_cls( + train_model( + [self.ts_pass_train, self.ts_pass_train_1], + model_type=model_type, + quantiles=kwargs["quantiles"], + ), + **kwargs, + ) + with pytest.raises(ValueError): + # when model is fit from >1 series, one must provide a series in argument + model.predict(n=1) + + pred = model.predict(n=self.horizon, series=self.ts_pass_train, **pred_lklp) + assert pred.n_components == self.ts_pass_train.n_components * len( + kwargs["quantiles"] + ) + assert not np.isnan(pred.all_values()).any().any() + + # the center forecasts must be equal to the forecasting model forecast + fc_columns = likelihood_component_names( + self.ts_pass_val.columns, quantile_names([0.5]) + ) + pred_fc = model.model.predict(n=self.horizon, series=self.ts_pass_train) + assert pred_fc.time_index.equals(pred.time_index) + np.testing.assert_array_almost_equal( + pred[fc_columns].all_values(), pred_fc.all_values() + ) + + # check prediction for several time series + pred_list = model.predict( + n=self.horizon, + series=[self.ts_pass_train, self.ts_pass_train_1], + **pred_lklp, + ) + pred_fc_list = model.model.predict( + n=self.horizon, + series=[self.ts_pass_train, self.ts_pass_train_1], + ) + assert len(pred_list) == 2, ( + f"Model {model_cls} did not return a list of prediction" + ) + for pred, pred_fc in zip(pred_list, pred_fc_list): + assert pred.n_components == self.ts_pass_train.n_components * len( + kwargs["quantiles"] + ) + assert pred_fc.time_index.equals(pred.time_index) + assert not np.isnan(pred.all_values()).any().any() + np.testing.assert_array_almost_equal( + pred_fc.all_values(), + pred[fc_columns].all_values(), + ) + + # wrong dimension + with pytest.raises(ValueError): + model.predict( + n=self.horizon, + series=[ + self.ts_pass_train, + self.ts_pass_train.stack(self.ts_pass_train), + ], + **pred_lklp, + ) + + @pytest.mark.parametrize( + "config", + itertools.product( + [(ConformalNaiveModel, {"quantiles": [0.1, 0.5, 0.9]}, "regression")], + [ + {"lags_past_covariates": IN_LEN}, + {"lags_future_covariates": (IN_LEN, OUT_LEN)}, + {}, + ], + ), + ) + def test_covariates(self, config): + (model_cls, kwargs, model_type), covs_kwargs = config + model_fc = LinearRegressionModel(**regr_kwargs, **covs_kwargs) + # Here we rely on the fact that all non-Dual models currently are Past models + if model_fc.supports_future_covariates: + cov_name = "future_covariates" + is_past = False + elif model_fc.supports_past_covariates: + cov_name = "past_covariates" + is_past = True + else: + cov_name = None + is_past = None + + covariates = [self.time_covariates_train, self.time_covariates_train] + if cov_name is not None: + cov_kwargs = {cov_name: covariates} + cov_kwargs_train = {cov_name: self.time_covariates_train} + cov_kwargs_notrain = {cov_name: self.time_covariates} + else: + cov_kwargs = {} + cov_kwargs_train = {} + cov_kwargs_notrain = {} + + model_fc.fit(series=[self.ts_pass_train, self.ts_pass_train_1], **cov_kwargs) + + model = model_cls(model=model_fc, **kwargs) + if cov_name == "future_covariates": + assert model.supports_future_covariates + assert not model.supports_past_covariates + assert model.uses_future_covariates + assert not model.uses_past_covariates + elif cov_name == "past_covariates": + assert not model.supports_future_covariates + assert model.supports_past_covariates + assert not model.uses_future_covariates + assert model.uses_past_covariates + else: + assert not model.supports_future_covariates + assert not model.supports_past_covariates + assert not model.uses_future_covariates + assert not model.uses_past_covariates + + with pytest.raises(ValueError): + # when model is fit from >1 series, one must provide a series in argument + model.predict(n=1) + + if cov_name is not None: + with pytest.raises(ValueError): + # when model is fit using multiple covariates, covariates are required at prediction time + model.predict(n=1, series=self.ts_pass_train) + + with pytest.raises(ValueError): + # when model is fit using covariates, n cannot be greater than output_chunk_length... + # (for short covariates) + # past covariates model can predict up until output_chunk_length + # with train future covariates we cannot predict at all after end of series + model.predict( + n=OUT_LEN + 1 if is_past else 1, + series=self.ts_pass_train, + **cov_kwargs_train, + ) + else: + # model does not support covariates + with pytest.raises(ValueError): + model.predict( + n=1, + series=self.ts_pass_train, + past_covariates=self.time_covariates, + ) + with pytest.raises(ValueError): + model.predict( + n=1, + series=self.ts_pass_train, + future_covariates=self.time_covariates, + ) + + # ... unless future covariates are provided + _ = model.predict( + n=self.horizon, series=self.ts_pass_train, **cov_kwargs_notrain + ) + + pred = model.predict( + n=self.horizon, series=self.ts_pass_train, **cov_kwargs_notrain, **pred_lklp + ) + pred_fc = model_fc.predict( + n=self.horizon, + series=self.ts_pass_train, + **cov_kwargs_notrain, + ) + fc_columns = likelihood_component_names( + self.ts_pass_val.columns, quantile_names([0.5]) + ) + np.testing.assert_array_almost_equal( + pred[fc_columns].all_values(), + pred_fc.all_values(), + ) + + if cov_name is None: + return + + # when model is fit using 1 training and 1 covariate series, time series args are optional + model_fc = LinearRegressionModel(**regr_kwargs, **covs_kwargs) + model_fc.fit(series=self.ts_pass_train, **cov_kwargs_train) + model = model_cls(model_fc, **kwargs) + + if is_past: + # can only predict up until ocl + with pytest.raises(ValueError): + _ = model.predict(n=OUT_LEN + 1, **pred_lklp) + # wrong covariates dimension + with pytest.raises(ValueError): + covs = cov_kwargs_train[cov_name] + covs = {cov_name: covs.stack(covs)} + _ = model.predict(n=OUT_LEN, **covs, **pred_lklp) + # with past covariates from train we can predict up until output_chunk_length + pred1 = model.predict(n=OUT_LEN, **pred_lklp) + pred2 = model.predict(n=OUT_LEN, series=self.ts_pass_train, **pred_lklp) + pred3 = model.predict(n=OUT_LEN, **cov_kwargs_train, **pred_lklp) + pred4 = model.predict( + n=OUT_LEN, **cov_kwargs_train, series=self.ts_pass_train, **pred_lklp + ) + else: + # with future covariates we need additional time steps to predict + with pytest.raises(ValueError): + _ = model.predict(n=1, **pred_lklp) + with pytest.raises(ValueError): + _ = model.predict(n=1, series=self.ts_pass_train, **pred_lklp) + with pytest.raises(ValueError): + _ = model.predict(n=1, **cov_kwargs_train, **pred_lklp) + with pytest.raises(ValueError): + _ = model.predict( + n=1, **cov_kwargs_train, series=self.ts_pass_train, **pred_lklp + ) + # wrong covariates dimension + with pytest.raises(ValueError): + covs = cov_kwargs_notrain[cov_name] + covs = {cov_name: covs.stack(covs)} + _ = model.predict(n=OUT_LEN, **covs, **pred_lklp) + pred1 = model.predict(n=OUT_LEN, **cov_kwargs_notrain, **pred_lklp) + pred2 = model.predict( + n=OUT_LEN, series=self.ts_pass_train, **cov_kwargs_notrain, **pred_lklp + ) + pred3 = model.predict(n=OUT_LEN, **cov_kwargs_notrain, **pred_lklp) + pred4 = model.predict( + n=OUT_LEN, **cov_kwargs_notrain, series=self.ts_pass_train, **pred_lklp + ) + + assert pred1 == pred2 + assert pred1 == pred3 + assert pred1 == pred4 + + @pytest.mark.parametrize( + "config,ts", + itertools.product( + models_cls_kwargs_errs, + [ts_w_static_cov, ts_shared_static_cov, ts_comps_static_cov], + ), + ) + def test_use_static_covariates(self, config, ts): + """ + Check that both static covariates representations are supported (component-specific and shared) + for both uni- and multivariate series when fitting the model. + Also check that the static covariates are present in the forecasted series + """ + model_cls, kwargs, model_type = config + model = model_cls( + train_model(ts, model_type=model_type, quantiles=kwargs["quantiles"]), + **kwargs, + ) + assert model.considers_static_covariates + assert model.supports_static_covariates + assert model.uses_static_covariates + pred = model.predict(OUT_LEN) + assert pred.static_covariates.equals(ts.static_covariates) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], # univariate series + [True, False], # single series + [True, False], # use covariates + [True, False], # datetime index + [1, 3, 5], # different horizons + ), + ) + def test_predict(self, config): + (is_univar, is_single, use_covs, is_datetime, horizon) = config + series = self.ts_pass_train + if not is_univar: + series = series.stack(series) + if not is_datetime: + series = TimeSeries.from_values(series.all_values(), columns=series.columns) + if use_covs: + pc, fc = series, series + fc = fc.append_values(fc.values()[: max(horizon, OUT_LEN)]) + if horizon > OUT_LEN: + pc = pc.append_values(pc.values()[: horizon - OUT_LEN]) + model_kwargs = { + "lags_past_covariates": IN_LEN, + "lags_future_covariates": (IN_LEN, OUT_LEN), + } + else: + pc, fc = None, None + model_kwargs = {} + if not is_single: + series = [ + series, + series.with_columns_renamed( + col_names=series.columns.tolist(), + col_names_new=(series.columns + "_s2").tolist(), + ), + ] + if use_covs: + pc = [pc] * 2 + fc = [fc] * 2 + + # testing lags_past_covariates None but past_covariates during prediction + model_instance = LinearRegressionModel( + lags=IN_LEN, output_chunk_length=OUT_LEN, **model_kwargs + ) + model_instance.fit(series=series, past_covariates=pc, future_covariates=fc) + model = ConformalNaiveModel(model_instance, quantiles=q) + + preds = model.predict( + n=horizon, + series=series, + past_covariates=pc, + future_covariates=fc, + **pred_lklp, + ) + + if is_single: + series = [series] + preds = [preds] + + for s_, preds_ in zip(series, preds): + cols_expected = likelihood_component_names(s_.columns, quantile_names(q)) + assert preds_.columns.tolist() == cols_expected + assert len(preds_) == horizon + assert preds_.start_time() == s_.end_time() + s_.freq + assert preds_.freq == s_.freq + + def test_output_chunk_shift(self): + model_params = {"output_chunk_shift": 1} + model = ConformalNaiveModel( + train_model(self.ts_pass_train, model_params=model_params, quantiles=q), + quantiles=q, + ) + pred = model.predict(n=1, **pred_lklp) + pred_fc = model.model.predict(n=1) + + assert pred_fc.time_index.equals(pred.time_index) + # the center forecasts must be equal to the forecasting model forecast + fc_columns = likelihood_component_names( + self.ts_pass_train.columns, quantile_names([0.5]) + ) + + np.testing.assert_array_almost_equal( + pred[fc_columns].all_values(), pred_fc.all_values() + ) + + @pytest.mark.parametrize( + "config", + itertools.product( + [1, 3, 5], # horizon + [True, False], # univariate series + [True, False], # single series + [q, [0.2, 0.3, 0.5, 0.7, 0.8]], + [ + (ConformalNaiveModel, "regression"), + (ConformalNaiveModel, "regression_prob"), + (ConformalQRModel, "regression_qr"), + ], # model type + [True, False], # symmetric non-conformity score + [None, 1], # train length + ), + ) + def test_conformal_model_predict_accuracy(self, config): + """Verifies that naive conformal model computes the correct intervals for: + - different horizons (smaller, equal, larger than ocl) + - uni/multivariate series + - single/multi series + - single/multi quantile intervals + - deterministic/probabilistic forecasting model + - naive conformal and conformalized quantile regression + - symmetric/asymmetric non-conformity scores + + The naive approach computes it as follows: + + - pred_upper = pred + q_interval(absolute error, past) + - pred_middle = pred + - pred_lower = pred - q_interval(absolute error, past) + + Where q_interval(absolute error) is the `q_hi - q_hi` quantile value of all historic absolute errors + between `pred`, and the target series. + """ + ( + n, + is_univar, + is_single, + quantiles, + (model_cls, model_type), + symmetric, + cal_length, + ) = config + idx_med = quantiles.index(0.5) + q_intervals = [ + (q_hi, q_lo) + for q_hi, q_lo in zip(quantiles[:idx_med], quantiles[idx_med + 1 :][::-1]) + ] + series = self.helper_prepare_series(is_univar, is_single) + pred_kwargs = ( + {"num_samples": 1000} + if model_type in ["regression_prob", "regression_qr"] + else {} + ) + + model_fc = train_model(series, model_type=model_type, quantiles=q) + model = model_cls( + model=model_fc, + quantiles=quantiles, + symmetric=symmetric, + cal_length=cal_length, + ) + pred_fc_list = model.model.predict(n, series=series, **pred_kwargs) + pred_cal_list = model.predict(n, series=series, **pred_lklp) + + if issubclass(model_cls, ConformalNaiveModel): + metric = ae if symmetric else err + metric_kwargs = {} + else: + metric = incs_qr + metric_kwargs = {"q_interval": q_intervals, "symmetric": symmetric} + # compute the expected intervals + residuals_list = model.model.residuals( + series, + retrain=False, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + stride=1, + values_only=True, + metric=metric, + metric_kwargs=metric_kwargs, + **pred_kwargs, + ) + if is_single: + pred_fc_list = [pred_fc_list] + pred_cal_list = [pred_cal_list] + residuals_list = [residuals_list] + + for pred_fc, pred_cal, residuals in zip( + pred_fc_list, pred_cal_list, residuals_list + ): + residuals = np.concatenate(residuals[:-1], axis=2) + + pred_vals = pred_fc.all_values() + pred_vals_expected = self.helper_compute_pred_cal( + residuals, + pred_vals, + n, + quantiles, + model_type, + symmetric, + cal_length=cal_length, + ) + self.helper_compare_preds(pred_cal, pred_vals_expected, model_type) + + @pytest.mark.parametrize( + "config", + itertools.product( + [1, 3, 5], # horizon + [True, False], # univariate series + [True, False], # single series, + [0, 1], # output chunk shift + [None, 1], # train length + [False, True], # use covariates + [q, [0.2, 0.3, 0.5, 0.7, 0.8]], # quantiles + ), + ) + def test_naive_conformal_model_historical_forecasts(self, config): + """Checks correctness of naive conformal model historical forecasts for: + - different horizons (smaller, equal and larger the OCL) + - uni and multivariate series + - single and multiple series + - with and without output shift + - with and without training length + - with and without covariates + """ + n, is_univar, is_single, ocs, cal_length, use_covs, quantiles = config + if ocs and n > OUT_LEN: + # auto-regression not allowed with ocs + return + + series = self.helper_prepare_series(is_univar, is_single) + model_params = {"output_chunk_shift": ocs} + + # for covariates, we check that shorter & longer covariates in the calibration set give expected results + covs_kwargs = {} + if use_covs: + model_params["lags_past_covariates"] = regr_kwargs["lags"] + past_covs = series + if n > OUT_LEN: + append_vals = [[[1.0]] * (1 if is_univar else 2)] * (n - OUT_LEN) + if is_single: + past_covs = past_covs.append_values(append_vals) + else: + past_covs = [pc.append_values(append_vals) for pc in past_covs] + covs_kwargs["past_covariates"] = past_covs + + # forecasts from forecasting model + model_fc = train_model(series, model_params=model_params, **covs_kwargs) + hfc_fc_list = model_fc.historical_forecasts( + series, + retrain=False, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + stride=1, + **covs_kwargs, + ) + # residuals to compute the conformal intervals + residuals_list = model_fc.residuals( + series, + historical_forecasts=hfc_fc_list, + overlap_end=True, + last_points_only=False, + values_only=True, + metric=ae, # absolute error + **covs_kwargs, + ) + + # conformal forecasts + model = ConformalNaiveModel( + model=model_fc, quantiles=quantiles, cal_length=cal_length + ) + hfc_conf_list = model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + stride=1, + **covs_kwargs, + **pred_lklp, + ) + + if is_single: + hfc_conf_list = [hfc_conf_list] + residuals_list = [residuals_list] + hfc_fc_list = [hfc_fc_list] + + # validate computed conformal intervals; conformal models start later since they need past residuals as input + first_fc_idx = len(hfc_fc_list[0]) - len(hfc_conf_list[0]) + for hfc_fc, hfc_conf, hfc_residuals in zip( + hfc_fc_list, hfc_conf_list, residuals_list + ): + for idx, (pred_fc, pred_cal) in enumerate( + zip(hfc_fc[first_fc_idx:], hfc_conf) + ): + # need to ignore additional `ocs` (output shift) residuals + residuals = np.concatenate( + hfc_residuals[: first_fc_idx - ocs + idx], axis=2 + ) + + pred_vals = pred_fc.all_values() + pred_vals_expected = self.helper_compute_pred_cal( + residuals, + pred_vals, + n, + quantiles, + cal_length=cal_length, + model_type="regression", + symmetric=True, + ) + np.testing.assert_array_almost_equal( + pred_cal.all_values(), pred_vals_expected + ) + + # checking that last points only is equal to the last forecasted point + hfc_lpo_list = model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=True, + stride=1, + **covs_kwargs, + **pred_lklp, + ) + if is_single: + hfc_lpo_list = [hfc_lpo_list] + + for hfc_lpo, hfc_conf in zip(hfc_lpo_list, hfc_conf_list): + hfc_conf_lpo = concatenate([hfc[-1:] for hfc in hfc_conf], axis=0) + assert hfc_lpo == hfc_conf_lpo + + @pytest.mark.parametrize( + "config", + itertools.product( + [1, 3, 5], # horizon + [0, 1], # output chunk shift + [None, 1], # cal length, + [1, 2], # cal stride + [False, True], # use start + ), + ) + def test_stridden_conformal_model(self, config): + """Checks correctness of naive conformal model historical forecasts for: + - different horizons (smaller, equal and larger the OCL) + - uni and multivariate series + - single and multiple series + - with and without output shift + - with and without training length + - with and without covariates + """ + is_univar, is_single = True, False + n, ocs, cal_length, cal_stride, use_start = config + if ocs and n > OUT_LEN: + # auto-regression not allowed with ocs + return + + series = self.helper_prepare_series(is_univar, is_single) + # shift second series ahead to cover the non overlapping multi series case + series = [series[0], series[1].shift(120)] + model_params = {"output_chunk_shift": ocs} + + # forecasts from forecasting model + model_fc = train_model(series, model_params=model_params) + hfc_fc_list = model_fc.historical_forecasts( + series, + retrain=False, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + stride=cal_stride, + ) + # residuals to compute the conformal intervals + residuals_list = model_fc.residuals( + series, + historical_forecasts=hfc_fc_list, + overlap_end=True, + last_points_only=False, + values_only=True, + metric=ae, # absolute error + ) + + # conformal forecasts + model = ConformalNaiveModel( + model=model_fc, + quantiles=q, + cal_length=cal_length, + cal_stride=cal_stride, + ) + # the expected positional index of the first conformal forecast + # index = (skip n + ocs points (relative to cal_stride) to avoid look-ahead bias) + (number of cal examples) + first_fc_idx = math.ceil((n + ocs) / cal_stride) + ( + cal_length - 1 if cal_length else 0 + ) + first_start = n_steps_between( + hfc_fc_list[0][first_fc_idx].start_time() - ocs * series[0].freq, + series[0].start_time(), + freq=series[0].freq, + ) + + hfc_conf_list = model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + start=first_start if use_start else None, + start_format="position" if use_start else "value", + stride=cal_stride, + **pred_lklp, + ) + + # also, skip some residuals from output chunk shift + ignore_ocs = math.ceil(ocs / cal_stride) if ocs >= cal_stride else 0 + for hfc_fc, hfc_conf, hfc_residuals in zip( + hfc_fc_list, hfc_conf_list, residuals_list + ): + for idx, (pred_fc, pred_cal) in enumerate( + zip(hfc_fc[first_fc_idx:], hfc_conf) + ): + residuals = np.concatenate( + hfc_residuals[: first_fc_idx - ignore_ocs + idx], axis=2 + ) + pred_vals = pred_fc.all_values() + pred_vals_expected = self.helper_compute_pred_cal( + residuals, + pred_vals, + n, + q, + cal_length=cal_length, + model_type="regression", + symmetric=True, + cal_stride=cal_stride, + ) + assert pred_fc.time_index.equals(pred_cal.time_index) + np.testing.assert_array_almost_equal( + pred_cal.all_values(), pred_vals_expected + ) + + # check that with a round-multiple of `cal_stride` we get identical forecasts + assert model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + start=first_start if use_start else None, + start_format="position" if use_start else "value", + stride=2 * cal_stride, + **pred_lklp, + ) == [hfc[::2] for hfc in hfc_conf_list] + + # checking that last points only is equal to the last forecasted point + hfc_lpo_list = model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=True, + stride=cal_stride, + **pred_lklp, + ) + for hfc_lpo, hfc_conf in zip(hfc_lpo_list, hfc_conf_list): + hfc_conf_lpo = concatenate( + [hfc[-1::cal_stride] for hfc in hfc_conf], axis=0 + ) + assert hfc_lpo == hfc_conf_lpo + + # checking that predict gives the same results as last historical forecast + preds = model.predict( + series=series, + n=n, + **pred_lklp, + ) + hfcs_conf_end = model.historical_forecasts( + series=series, + forecast_horizon=n, + overlap_end=True, + last_points_only=False, + start=-cal_stride, + start_format="position", + stride=cal_stride, + **pred_lklp, + ) + hfcs_conf_end = [hfc[-1] for hfc in hfcs_conf_end] + for pred, last_hfc in zip(preds, hfcs_conf_end): + assert pred == last_hfc + + def test_probabilistic_historical_forecast(self): + """Checks correctness of naive conformal historical forecast from probabilistic fc model compared to + deterministic one, + """ + series = self.helper_prepare_series(False, False) + # forecasts from forecasting model + model_det = ConformalNaiveModel( + train_model(series, model_type="regression", quantiles=q), + quantiles=q, + ) + model_prob = ConformalNaiveModel( + train_model(series, model_type="regression_prob", quantiles=q), + quantiles=q, + ) + hfcs_det = model_det.historical_forecasts( + series, + forecast_horizon=2, + last_points_only=True, + stride=1, + **pred_lklp, + ) + hfcs_prob = model_prob.historical_forecasts( + series, + forecast_horizon=2, + last_points_only=True, + stride=1, + **pred_lklp, + ) + assert isinstance(hfcs_det, list) and len(hfcs_det) == 2 + assert isinstance(hfcs_prob, list) and len(hfcs_prob) == 2 + for hfc_det, hfc_prob in zip(hfcs_det, hfcs_prob): + assert hfc_det.columns.equals(hfc_prob.columns) + assert hfc_det.time_index.equals(hfc_prob.time_index) + self.helper_compare_preds( + hfc_prob, hfc_det.all_values(), model_type="regression_prob" + ) + + def helper_prepare_series(self, is_univar, is_single): + series = self.ts_pass_train + if not is_univar: + series = series.stack(series + 3.0) + if not is_single: + series = [series, series + 5] + return series + + @staticmethod + def helper_compare_preds(cp_pred, pred_expected, model_type, tol_rel=0.1): + if isinstance(cp_pred, TimeSeries): + cp_pred = cp_pred.all_values(copy=False) + if model_type == "regression": + # deterministic fc model should give almost identical results + np.testing.assert_array_almost_equal(cp_pred, pred_expected) + else: + # probabilistic fc models have some randomness + diffs_rel = np.abs((cp_pred - pred_expected) / pred_expected) + assert (diffs_rel < tol_rel).all().all() + + @staticmethod + def helper_compute_pred_cal( + residuals, + pred_vals, + horizon, + quantiles, + model_type, + symmetric, + cal_length=None, + cal_stride=1, + ): + """Generates expected prediction results for naive conformal model from: + + - residuals and predictions from deterministic/probabilistic model + - any forecast horizon + - any quantile intervals + - symmetric/ asymmetric non-conformity scores + - any train length + """ + cal_length = cal_length or 0 + n_comps = pred_vals.shape[1] + half_idx = len(quantiles) // 2 + + # get alphas from quantiles (alpha = q_hi - q_lo) per interval + alphas = np.array(quantiles[half_idx + 1 :][::-1]) - np.array( + quantiles[:half_idx] + ) + if not symmetric: + # asymmetric non-conformity scores look only on one tail -> alpha/2 + alphas = 1 - (1 - alphas) / 2 + if model_type == "regression_prob": + # naive conformal model converts probabilistic forecasts to median (deterministic) + pred_vals = np.expand_dims(np.quantile(pred_vals, 0.5, axis=2), -1) + elif model_type == "regression_qr": + # conformalized quantile regression consumes quantile forecasts + pred_vals = np.quantile(pred_vals, quantiles, axis=2).transpose(1, 2, 0) + + is_naive = model_type in ["regression", "regression_prob"] + pred_expected = [] + for alpha_idx, alpha in enumerate(alphas): + q_hats = [] + # compute the quantile `alpha` of all past residuals (absolute "per time step" errors between historical + # forecasts and the target series) + for idx_horizon in range(horizon): + n = idx_horizon + 1 + # ignore residuals at beginning + idx_fc_start = math.floor((horizon - n) / cal_stride) + # keep as many residuals as possible from end + idx_fc_end = -(math.ceil(horizon / cal_stride) - (idx_fc_start + 1)) + res_n = residuals[idx_horizon, :, idx_fc_start : idx_fc_end or None] + if cal_length is not None: + res_n = res_n[:, -cal_length:] + if is_naive and symmetric: + # identical correction for upper and lower bounds + # metric is `ae()` + q_hat_n = np.quantile(res_n, q=alpha, method="higher", axis=1) + q_hats.append((-q_hat_n, q_hat_n)) + elif is_naive: + # correction separately for upper and lower bounds + # metric is `err()` + q_hat_hi = np.quantile(res_n, q=alpha, method="higher", axis=1) + q_hat_lo = np.quantile(-res_n, q=alpha, method="higher", axis=1) + q_hats.append((-q_hat_lo, q_hat_hi)) + elif symmetric: # CQR symmetric + # identical correction for upper and lower bounds + # metric is `incs_qr(symmetric=True)` + q_hat_n = np.quantile(res_n, q=alpha, method="higher", axis=1) + q_hats.append((-q_hat_n, q_hat_n)) + else: # CQR asymmetric + # correction separately for upper and lower bounds + # metric is `incs_qr(symmetric=False)` + half_idx = len(res_n) // 2 + + # residuals have shape (n components * n intervals * 2) + # the factor 2 comes from the metric being computed for lower, and upper bounds separately + # (comp_1_qlow_1, comp_1_qlow_2, ... comp_n_qlow_m, comp_1_qhigh_1, ...) + q_hat_lo = np.quantile( + res_n[:half_idx], q=alpha, method="higher", axis=1 + ) + q_hat_hi = np.quantile( + res_n[half_idx:], q=alpha, method="higher", axis=1 + ) + q_hats.append(( + -q_hat_lo[alpha_idx :: len(alphas)], + q_hat_hi[alpha_idx :: len(alphas)], + )) + # bring to shape (horizon, n components, 2) + q_hats = np.array(q_hats).transpose((0, 2, 1)) + # the prediction interval is given by pred +/- q_hat + pred_vals_expected = [] + for col_idx in range(n_comps): + q_col = q_hats[:, col_idx] + pred_col = pred_vals[:, col_idx] + if is_naive: + # conformal model corrects deterministic predictions + idx_q_lo = slice(0, None) + idx_q_med = slice(0, None) + idx_q_hi = slice(0, None) + else: + # conformal model corrects quantile predictions + idx_q_lo = slice(alpha_idx, alpha_idx + 1) + idx_q_med = slice(len(alphas), len(alphas) + 1) + idx_q_hi = slice( + pred_col.shape[1] - (alpha_idx + 1), + pred_col.shape[1] - alpha_idx, + ) + # correct lower and upper bounds + pred_col_expected = np.concatenate( + [ + pred_col[:, idx_q_lo] + q_col[:, :1], # lower quantile + pred_col[:, idx_q_med], # median forecast + pred_col[:, idx_q_hi] + q_col[:, 1:], + ], # upper quantile + axis=1, + ) + pred_col_expected = np.expand_dims(pred_col_expected, 1) + pred_vals_expected.append(pred_col_expected) + pred_vals_expected = np.concatenate(pred_vals_expected, axis=1) + pred_expected.append(pred_vals_expected) + + # reorder to have columns going from lowest quantiles to highest per component + pred_expected_reshaped = [] + for comp_idx in range(n_comps): + for q_idx in [0, 1, 2]: + for pred_idx in range(len(pred_expected)): + # upper quantiles will have reversed order + if q_idx == 2: + pred_idx = len(pred_expected) - 1 - pred_idx + pred_ = pred_expected[pred_idx][:, comp_idx, q_idx] + pred_ = pred_.reshape(-1, 1, 1) + + # q_hat_idx = q_idx + comp_idx * 3 + alpha_idx * 3 * n_comps + pred_expected_reshaped.append(pred_) + # only add median quantile once + if q_idx == 1: + break + return np.concatenate(pred_expected_reshaped, axis=1) + + @pytest.mark.parametrize( + "config", + itertools.product( + [1, 3, 5], # horizon + [0, 1], # output chunk shift + [False, True], # use covariates + ), + ) + def test_too_short_input_predict(self, config): + """Checks conformal model predict with minimum required input and too short input.""" + n, ocs, use_covs = config + if ocs and n > OUT_LEN: + return + icl = IN_LEN + min_len = icl + ocs + n + series = tg.linear_timeseries(length=min_len) + series_train = [tg.linear_timeseries(length=IN_LEN + OUT_LEN + ocs)] * 2 + + model_params = {"output_chunk_shift": ocs} + covs_kwargs = {} + covs_kwargs_train = {} + covs_kwargs_too_short = {} + if use_covs: + model_params["lags_past_covariates"] = regr_kwargs["lags"] + covs_kwargs_train["past_covariates"] = series_train + # use shorter covariates, to test whether residuals are still properly extracted + past_covs = series + # for auto-regression, we require longer past covariates + if n > OUT_LEN: + past_covs = past_covs.append_values([1.0] * (n - OUT_LEN)) + covs_kwargs["past_covariates"] = past_covs + covs_kwargs_too_short["past_covariates"] = past_covs[:-1] + + model = ConformalNaiveModel( + train_model( + series=series_train, + model_params=model_params, + **covs_kwargs_train, + ), + quantiles=q, + ) + + # prediction works with long enough input + preds1 = model.predict(n=n, series=series, **covs_kwargs) + assert not np.isnan(preds1.all_values()).any().any() + + # series too short: without covariates, make `series` shorter. Otherwise, use the shorter covariates + series_ = series[:-1] if not use_covs else series + with pytest.raises(ValueError) as exc: + _ = model.predict(n=n, series=series_, **covs_kwargs_too_short) + if not use_covs: + assert str(exc.value).startswith( + "Could not build the minimum required calibration input with the provided `series`" + ) + else: + # if `past_covariates` are too short, then it raises error from the forecasting_model.predict() + assert str(exc.value).startswith( + "The `past_covariates` are not long enough." + ) + + @pytest.mark.parametrize( + "config", + itertools.product( + [False, True], # last points only + [False, True], # overlap end + [None, 2], # train length + [0, 1], # output chunk shift + [1, 3, 5], # horizon + [True, False], # use covs + ), + ) + def test_too_short_input_hfc(self, config): + """Checks conformal model historical forecasts with minimum required input and too short input.""" + ( + last_points_only, + overlap_end, + cal_length, + ocs, + n, + use_covs, + ) = config + if ocs and n > OUT_LEN: + return + + icl = IN_LEN + ocl = OUT_LEN + horizon_ocs = n + ocs + add_cal_length = cal_length - 1 if cal_length is not None else 0 + # min length to generate 1 conformal forecast + min_len_val_series = ( + icl + horizon_ocs * (1 + int(not overlap_end)) + add_cal_length + ) + + series_train = [tg.linear_timeseries(length=icl + ocl + ocs)] * 2 + series = tg.linear_timeseries(length=min_len_val_series) + + model_params = {"output_chunk_shift": ocs} + covs_kwargs_train = {} + covs_kwargs = {} + covs_kwargs_short = {} + if use_covs: + model_params["lags_past_covariates"] = regr_kwargs["lags"] + covs_kwargs_train["past_covariates"] = series_train + + # `- horizon_ocs` to generate forecasts extending up until end of target series + if not overlap_end: + past_covs = series[:-horizon_ocs] + else: + past_covs = series + + # for auto-regression, we require longer past covariates + if n > OUT_LEN: + past_covs = past_covs.append_values([1.0] * (n - OUT_LEN)) + + # covariates lengths to generate exactly one forecast + covs_kwargs["past_covariates"] = past_covs + + # use too short covariates to check that errors are raised + covs_kwargs_short["past_covariates"] = covs_kwargs["past_covariates"][:-1] + + model = ConformalNaiveModel( + train_model( + series=series_train, + model_params=model_params, + **covs_kwargs_train, + ), + quantiles=q, + cal_length=cal_length, + ) + + hfc_kwargs = { + "last_points_only": last_points_only, + "overlap_end": overlap_end, + "forecast_horizon": n, + } + # prediction works with long enough input + hfcs = model.historical_forecasts( + series=series, + **covs_kwargs, + **hfc_kwargs, + ) + if last_points_only: + hfcs = [hfcs] + + assert len(hfcs) == 1 + for hfc in hfcs: + assert not np.isnan(hfc.all_values()).any().any() + + # input too short: without covariates, make `series` shorter. Otherwise, use the shorter covariates + series_ = series[:-1] if not use_covs else series + with pytest.raises(ValueError) as exc: + _ = model.historical_forecasts( + series=series_, + **covs_kwargs_short, + **hfc_kwargs, + ) + assert str(exc.value).startswith( + "Could not build the minimum required calibration input with the provided `series` and `*_covariates`" + ) + + @pytest.mark.parametrize("quantiles", [[0.1, 0.5, 0.9], [0.1, 0.3, 0.5, 0.7, 0.9]]) + def test_backtest_and_residuals(self, quantiles): + """Residuals and backtest are already tested for quantile, and interval metrics based on stochastic or quantile + forecasts. So, a simple check that they give expected results should be enough. + """ + n_q = len(quantiles) + half_idx = n_q // 2 + q_interval = [ + (q_lo, q_hi) + for q_lo, q_hi in zip(quantiles[:half_idx], quantiles[half_idx + 1 :][::-1]) + ] + lpo = False + + # series long enough for 2 hfcs + series = self.helper_prepare_series(True, True).append_values([0.1]) + # conformal model + model = ConformalNaiveModel(model=train_model(series), quantiles=quantiles) + + hfc = model.historical_forecasts( + series=series, forecast_horizon=5, last_points_only=lpo, **pred_lklp + ) + bt = model.backtest( + series=series, + historical_forecasts=hfc, + last_points_only=lpo, + metric=mic, + metric_kwargs={"q_interval": model.q_interval}, + ) + # default backtest is equal to backtest with metric kwargs + np.testing.assert_array_almost_equal( + bt, + model.backtest( + series=series, + historical_forecasts=hfc, + last_points_only=lpo, + metric=mic, + metric_kwargs={"q_interval": q_interval}, + ), + ) + np.testing.assert_array_almost_equal( + mic( + [series] * len(hfc), + hfc, + q_interval=q_interval, + series_reduction=np.mean, + ), + bt, + ) + + residuals = model.residuals( + series=series, + historical_forecasts=hfc, + last_points_only=lpo, + metric=ic, + metric_kwargs={"q_interval": q_interval}, + ) + # default residuals is equal to residuals with metric kwargs + assert residuals == model.residuals( + series=series, + historical_forecasts=hfc, + last_points_only=lpo, + metric=ic, + metric_kwargs={"q_interval": q_interval}, + ) + expected_vals = ic([series] * len(hfc), hfc, q_interval=q_interval) + expected_residuals = [] + for vals, hfc_ in zip(expected_vals, hfc): + expected_residuals.append( + TimeSeries.from_times_and_values( + times=hfc_.time_index, + values=vals, + columns=likelihood_component_names( + series.components, quantile_interval_names(q_interval) + ), + ) + ) + assert residuals == expected_residuals + + def test_predict_probabilistic_equals_quantile(self): + """Tests that sampled quantiles predictions have approx. the same quantiles as direct quantile predictions.""" + quantiles = [0.1, 0.3, 0.5, 0.7, 0.9] + + # multiple multivariate series + series = self.helper_prepare_series(False, False) + + # conformal model + model = ConformalNaiveModel(model=train_model(series), quantiles=quantiles) + # direct quantile predictions + pred_quantiles = model.predict(n=3, series=series, **pred_lklp) + # sampled predictions + pred_samples = model.predict(n=3, series=series, num_samples=500) + for pred_q, pred_s in zip(pred_quantiles, pred_samples): + assert pred_q.n_samples == 1 + assert pred_q.n_components == series[0].n_components * len(quantiles) + assert pred_s.n_samples == 500 + assert pred_s.n_components == series[0].n_components + + vals_q = pred_q.all_values() + vals_s = pred_s.all_values() + vals_s_q = np.quantile(vals_s, quantiles, axis=2).transpose((1, 2, 0)) + vals_s_q = vals_s_q.reshape(vals_q.shape) + self.helper_compare_preds( + vals_s_q, + vals_q, + model_type="regression_prob", + ) + + @pytest.mark.parametrize( + "config", + [ + # (cal_length, cal_stride, (start_expected, start_format_expected)) + (None, 1, (None, "value")), + (None, 2, (-4, "position")), + (None, 3, (-6, "position")), + (None, 4, (-4, "position")), + (1, 1, (-3, "position")), + (1, 2, (-4, "position")), + (1, 3, (-3, "position")), + (1, 4, (-4, "position")), + ], + ) + def test_calibration_hfc_start_predict(self, config): + """Test calibration historical forecast start point when calling `predict()` ("end" position).""" + cal_length, cal_stride, start_expected = config + series = linear_timeseries(length=4) + horizon = 2 + output_chunk_shift = 1 + assert ( + _get_calibration_hfc_start( + series=[series], + horizon=horizon, + output_chunk_shift=output_chunk_shift, + cal_length=cal_length, + cal_stride=cal_stride, + start="end", + start_format="position", + ) + == start_expected + ) + + @pytest.mark.parametrize( + "config", + [ + # (cal_length, cal_stride, start, start_expected) + (None, 1, None, None), + (None, 1, 1, None), + (1, 1, -1, -4), + (1, 1, 0, 0), + (1, 2, 0, 0), + (1, 3, 0, 0), + (1, 1, 1, 0), + (1, 2, 1, 1), + (1, 3, 1, 1), + (1, 1, -1, -4), + (1, 2, -1, -5), + (1, 3, -1, -4), + ], + ) + def test_calibration_hfc_start_position_hist_fc(self, config): + """Test calibration historical forecast start point when calling `historical_forecasts()` + with start format "position".""" + cal_length, cal_stride, start, start_expected = config + series = linear_timeseries(length=4) + horizon = 2 + output_chunk_shift = 1 + assert _get_calibration_hfc_start( + series=[series], + horizon=horizon, + output_chunk_shift=output_chunk_shift, + cal_length=cal_length, + cal_stride=cal_stride, + start=start, + start_format="position", + ) == (start_expected, "position") + + @pytest.mark.parametrize( + "config", + [ + # (cal_length, cal_stride, start, start_expected) + (None, 1, None, None), + (None, 1, "2020-01-11", None), + (1, 1, "2020-01-09", "2020-01-06"), # start before series start + (1, 1, "2020-01-10", "2020-01-07"), + (1, 2, "2020-01-10", "2020-01-06"), + (1, 3, "2020-01-10", "2020-01-07"), + (2, 1, "2020-01-09", "2020-01-05"), + (2, 1, "2020-01-10", "2020-01-06"), + (2, 2, "2020-01-10", "2020-01-04"), + (2, 3, "2020-01-10", "2020-01-04"), + ], + ) + def test_calibration_hfc_start_value_hist_fc(self, config): + """Test calibration historical forecast start point when calling `historical_forecasts()` + with start format "value".""" + cal_length, cal_stride, start, start_expected = config + if start is not None: + start = pd.Timestamp(start) + if start_expected is not None: + start_expected = pd.Timestamp(start_expected) + series = linear_timeseries(length=4, start=pd.Timestamp("2020-01-10"), freq="d") + horizon = 2 + output_chunk_shift = 1 + assert _get_calibration_hfc_start( + series=[series], + horizon=horizon, + output_chunk_shift=output_chunk_shift, + cal_length=cal_length, + cal_stride=cal_stride, + start=start, + start_format="value", + ) == (start_expected, "value") + + def test_encoders(self): + """Tests support of covariates encoders.""" + n = OUT_LEN + 1 + min_length = IN_LEN + n + + # create non-overlapping train and val series + series = tg.linear_timeseries(length=min_length) + val_series = tg.linear_timeseries( + start=series.end_time() + series.freq, length=min_length + ) + + model = train_model( + series, + model_params={ + "lags_future_covariates": (IN_LEN, OUT_LEN), + "add_encoders": {"datetime_attribute": {"future": ["hour"]}}, + }, + ) + + cp_model = ConformalNaiveModel(model, quantiles=q) + assert ( + cp_model.model.encoders is not None + and cp_model.model.encoders.encoding_available + ) + assert model.uses_future_covariates + + # predict: encoders using stored train series must work + _ = cp_model.predict(n=n) + # predict: encoding of new series without train overlap must work + _ = cp_model.predict(n=n, series=val_series) + + # check the same for hfc + _ = cp_model.historical_forecasts( + forecast_horizon=n, series=series, overlap_end=True + ) + _ = cp_model.historical_forecasts( + forecast_horizon=n, series=val_series, overlap_end=True + ) diff --git a/darts/tests/models/forecasting/test_dlinear_nlinear.py b/darts/tests/models/forecasting/test_dlinear_nlinear.py index 7348603626..3482271f28 100644 --- a/darts/tests/models/forecasting/test_dlinear_nlinear.py +++ b/darts/tests/models/forecasting/test_dlinear_nlinear.py @@ -5,341 +5,328 @@ import pytest from darts import concatenate -from darts.logging import get_logger from darts.metrics import rmse -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils import timeseries_generation as tg -logger = get_logger(__name__) +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import torch -try: - import torch +from darts.models.forecasting.dlinear import DLinearModel +from darts.models.forecasting.nlinear import NLinearModel +from darts.utils.likelihood_models import GaussianLikelihood - from darts.models.forecasting.dlinear import DLinearModel - from darts.models.forecasting.nlinear import NLinearModel - from darts.utils.likelihood_models import GaussianLikelihood - TORCH_AVAILABLE = True +class TestDlinearNlinearModels: + np.random.seed(42) + torch.manual_seed(42) -except ImportError: - logger.warning("Torch not available. Dlinear and NLinear tests will be skipped.") - TORCH_AVAILABLE = False + def test_creation(self): + with pytest.raises(ValueError): + DLinearModel( + input_chunk_length=1, + output_chunk_length=1, + normalize=True, + likelihood=GaussianLikelihood(), + ) -if TORCH_AVAILABLE: + with pytest.raises(ValueError): + NLinearModel( + input_chunk_length=1, + output_chunk_length=1, + normalize=True, + likelihood=GaussianLikelihood(), + ) - class TestDlinearNlinearModels: + def test_fit(self): + large_ts = tg.constant_timeseries(length=100, value=1000) + small_ts = tg.constant_timeseries(length=100, value=10) + + for model_cls, kwargs in [ + (DLinearModel, {"kernel_size": 5}), + (DLinearModel, {"kernel_size": 6}), + (NLinearModel, {}), + ]: + # Test basic fit and predict + model = model_cls( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=10, + random_state=42, + **kwargs, + **tfm_kwargs, + ) + model.fit(large_ts[:98]) + pred = model.predict(n=2).values()[0] + + # Test whether model trained on one series is better than one trained on another + model2 = model_cls( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=10, + random_state=42, + **tfm_kwargs, + ) + model2.fit(small_ts[:98]) + pred2 = model2.predict(n=2).values()[0] + assert abs(pred2 - 10) < abs(pred - 10) + + # test short predict + pred3 = model2.predict(n=1) + assert len(pred3) == 1 + + def test_logtensorboard(self, tmpdir_module): + ts = tg.constant_timeseries(length=50, value=10) + + for model_cls in [DLinearModel, NLinearModel]: + # Test basic fit and predict + model = model_cls( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=1, + log_tensorboard=True, + work_dir=tmpdir_module, + pl_trainer_kwargs={ + "log_every_n_steps": 1, + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) + model.fit(ts) + model.predict(n=2) + + def test_shared_weights(self): + ts = tg.constant_timeseries(length=50, value=10).stack( + tg.gaussian_timeseries(length=50) + ) + + for model_cls in [DLinearModel, NLinearModel]: + # Test basic fit and predict + model_shared = model_cls( + input_chunk_length=5, + output_chunk_length=1, + n_epochs=2, + const_init=False, + shared_weights=True, + random_state=42, + **tfm_kwargs, + ) + model_not_shared = model_cls( + input_chunk_length=5, + output_chunk_length=1, + n_epochs=2, + const_init=False, + shared_weights=False, + random_state=42, + **tfm_kwargs, + ) + model_shared.fit(ts) + model_not_shared.fit(ts) + pred_shared = model_shared.predict(n=2) + pred_not_shared = model_not_shared.predict(n=2) + assert np.any(np.not_equal(pred_shared.values(), pred_not_shared.values())) + + def test_multivariate_and_covariates(self): np.random.seed(42) torch.manual_seed(42) + # test on multiple multivariate series with future and static covariates + + def _create_multiv_series(f1, f2, n1, n2, nf1, nf2): + bases = [ + tg.sine_timeseries(length=400, value_frequency=f, value_amplitude=1.0) + for f in (f1, f2) + ] + noises = [tg.gaussian_timeseries(length=400, std=n) for n in (n1, n2)] + noise_modulators = [ + tg.sine_timeseries(length=400, value_frequency=nf) + + tg.constant_timeseries(length=400, value=1) / 2 + for nf in (nf1, nf2) + ] + noises = [noises[i] * noise_modulators[i] for i in range(len(noises))] + + target = concatenate( + [bases[i] + noises[i] for i in range(len(bases))], axis="component" + ) - def test_creation(self): - with pytest.raises(ValueError): - DLinearModel( - input_chunk_length=1, - output_chunk_length=1, - normalize=True, - likelihood=GaussianLikelihood(), - ) + target = target.with_static_covariates( + pd.DataFrame([[f1, n1, nf1], [f2, n2, nf2]]) + ) - with pytest.raises(ValueError): - NLinearModel( - input_chunk_length=1, - output_chunk_length=1, - normalize=True, - likelihood=GaussianLikelihood(), - ) + return target, concatenate(noise_modulators, axis="component") + + def _eval_model( + train1, + train2, + val1, + val2, + fut_cov1, + fut_cov2, + past_cov1=None, + past_cov2=None, + val_past_cov1=None, + val_past_cov2=None, + cls=DLinearModel, + lkl=None, + **kwargs, + ): + model = cls( + input_chunk_length=50, + output_chunk_length=10, + shared_weights=False, + const_init=True, + likelihood=lkl, + random_state=42, + **tfm_kwargs, + ) - def test_fit(self): - large_ts = tg.constant_timeseries(length=100, value=1000) - small_ts = tg.constant_timeseries(length=100, value=10) - - for (model_cls, kwargs) in [ - (DLinearModel, {"kernel_size": 5}), - (DLinearModel, {"kernel_size": 6}), - (NLinearModel, {}), - ]: - # Test basic fit and predict - model = model_cls( - input_chunk_length=1, - output_chunk_length=1, - n_epochs=10, - random_state=42, - **kwargs, - **tfm_kwargs - ) - model.fit(large_ts[:98]) - pred = model.predict(n=2).values()[0] - - # Test whether model trained on one series is better than one trained on another - model2 = model_cls( - input_chunk_length=1, - output_chunk_length=1, - n_epochs=10, - random_state=42, - **tfm_kwargs - ) - model2.fit(small_ts[:98]) - pred2 = model2.predict(n=2).values()[0] - assert abs(pred2 - 10) < abs(pred - 10) - - # test short predict - pred3 = model2.predict(n=1) - assert len(pred3) == 1 - - def test_logtensorboard(self, tmpdir_module): - ts = tg.constant_timeseries(length=50, value=10) - - for model_cls in [DLinearModel, NLinearModel]: - # Test basic fit and predict - model = model_cls( - input_chunk_length=1, - output_chunk_length=1, - n_epochs=1, - log_tensorboard=True, - work_dir=tmpdir_module, - pl_trainer_kwargs={ - "log_every_n_steps": 1, - **tfm_kwargs["pl_trainer_kwargs"], - }, - ) - model.fit(ts) - model.predict(n=2) + model.fit( + [train1, train2], + past_covariates=( + [past_cov1, past_cov2] if past_cov1 is not None else None + ), + val_past_covariates=( + [val_past_cov1, val_past_cov2] + if val_past_cov1 is not None + else None + ), + future_covariates=( + [fut_cov1, fut_cov2] if fut_cov1 is not None else None + ), + epochs=10, + ) - def test_shared_weights(self): - ts = tg.constant_timeseries(length=50, value=10).stack( - tg.gaussian_timeseries(length=50) + pred1, pred2 = model.predict( + series=[train1, train2], + future_covariates=( + [fut_cov1, fut_cov2] if fut_cov1 is not None else None + ), + past_covariates=( + [fut_cov1, fut_cov2] if past_cov1 is not None else None + ), + n=len(val1), + num_samples=500 if lkl is not None else 1, ) - for model_cls in [DLinearModel, NLinearModel]: - # Test basic fit and predict - model_shared = model_cls( - input_chunk_length=5, - output_chunk_length=1, - n_epochs=2, - const_init=False, - shared_weights=True, - random_state=42, - **tfm_kwargs - ) - model_not_shared = model_cls( - input_chunk_length=5, - output_chunk_length=1, - n_epochs=2, - const_init=False, - shared_weights=False, - random_state=42, - **tfm_kwargs - ) - model_shared.fit(ts) - model_not_shared.fit(ts) - pred_shared = model_shared.predict(n=2) - pred_not_shared = model_not_shared.predict(n=2) - assert np.any( - np.not_equal(pred_shared.values(), pred_not_shared.values()) - ) + return rmse(val1, pred1), rmse(val2, pred2) - def test_multivariate_and_covariates(self): - np.random.seed(42) - torch.manual_seed(42) - # test on multiple multivariate series with future and static covariates - - def _create_multiv_series(f1, f2, n1, n2, nf1, nf2): - bases = [ - tg.sine_timeseries( - length=400, value_frequency=f, value_amplitude=1.0 - ) - for f in (f1, f2) - ] - noises = [tg.gaussian_timeseries(length=400, std=n) for n in (n1, n2)] - noise_modulators = [ - tg.sine_timeseries(length=400, value_frequency=nf) - + tg.constant_timeseries(length=400, value=1) / 2 - for nf in (nf1, nf2) - ] - noises = [noises[i] * noise_modulators[i] for i in range(len(noises))] - - target = concatenate( - [bases[i] + noises[i] for i in range(len(bases))], axis="component" - ) + series1, fut_cov1 = _create_multiv_series(0.05, 0.07, 0.2, 0.4, 0.02, 0.03) + series2, fut_cov2 = _create_multiv_series(0.04, 0.03, 0.4, 0.1, 0.02, 0.04) - target = target.with_static_covariates( - pd.DataFrame([[f1, n1, nf1], [f2, n2, nf2]]) - ) + train1, val1 = series1.split_after(0.7) + train2, val2 = series2.split_after(0.7) + past_cov1 = train1.copy() + past_cov2 = train2.copy() + val_past_cov1 = val1.copy() + val_past_cov2 = val2.copy() - return target, concatenate(noise_modulators, axis="component") + for model, lkl in product( + [DLinearModel, NLinearModel], [None, GaussianLikelihood()] + ): + e1, e2 = _eval_model( + train1, train2, val1, val2, fut_cov1, fut_cov2, cls=model, lkl=lkl + ) + assert e1 <= 0.34 + assert e2 <= 0.28 - def _eval_model( - train1, - train2, + e1, e2 = _eval_model( + train1.with_static_covariates(None), + train2.with_static_covariates(None), val1, val2, fut_cov1, fut_cov2, - past_cov1=None, - past_cov2=None, - val_past_cov1=None, - val_past_cov2=None, - cls=DLinearModel, - lkl=None, - **kwargs - ): - model = cls( - input_chunk_length=50, - output_chunk_length=10, - shared_weights=False, - const_init=True, - likelihood=lkl, - random_state=42, - **tfm_kwargs - ) - - model.fit( - [train1, train2], - past_covariates=[past_cov1, past_cov2] - if past_cov1 is not None - else None, - val_past_covariates=[val_past_cov1, val_past_cov2] - if val_past_cov1 is not None - else None, - future_covariates=[fut_cov1, fut_cov2] - if fut_cov1 is not None - else None, - epochs=10, - ) - - pred1, pred2 = model.predict( - series=[train1, train2], - future_covariates=[fut_cov1, fut_cov2] - if fut_cov1 is not None - else None, - past_covariates=[fut_cov1, fut_cov2] - if past_cov1 is not None - else None, - n=len(val1), - num_samples=500 if lkl is not None else 1, - ) - - return rmse(val1, pred1), rmse(val2, pred2) - - series1, fut_cov1 = _create_multiv_series(0.05, 0.07, 0.2, 0.4, 0.02, 0.03) - series2, fut_cov2 = _create_multiv_series(0.04, 0.03, 0.4, 0.1, 0.02, 0.04) - - train1, val1 = series1.split_after(0.7) - train2, val2 = series2.split_after(0.7) - past_cov1 = train1.copy() - past_cov2 = train2.copy() - val_past_cov1 = val1.copy() - val_past_cov2 = val2.copy() - - for model, lkl in product( - [DLinearModel, NLinearModel], [None, GaussianLikelihood()] - ): - - e1, e2 = _eval_model( - train1, train2, val1, val2, fut_cov1, fut_cov2, cls=model, lkl=lkl - ) - assert e1 <= 0.34 - assert e2 <= 0.28 - - e1, e2 = _eval_model( - train1.with_static_covariates(None), - train2.with_static_covariates(None), - val1, - val2, - fut_cov1, - fut_cov2, - cls=model, - lkl=lkl, - ) - assert e1 <= 0.32 - assert e2 <= 0.28 + cls=model, + lkl=lkl, + ) + assert e1 <= 0.32 + assert e2 <= 0.28 - e1, e2 = _eval_model( - train1, train2, val1, val2, None, None, cls=model, lkl=lkl - ) - assert e1 <= 0.40 - assert e2 <= 0.34 - - e1, e2 = _eval_model( - train1.with_static_covariates(None), - train2.with_static_covariates(None), - val1, - val2, - None, - None, - cls=model, - lkl=lkl, - ) - assert e1 <= 0.40 - assert e2 <= 0.34 + e1, e2 = _eval_model( + train1, train2, val1, val2, None, None, cls=model, lkl=lkl + ) + assert e1 <= 0.40 + assert e2 <= 0.34 e1, e2 = _eval_model( - train1, - train2, + train1.with_static_covariates(None), + train2.with_static_covariates(None), val1, val2, - fut_cov1, - fut_cov2, - past_cov1=past_cov1, - past_cov2=past_cov2, - val_past_cov1=val_past_cov1, - val_past_cov2=val_past_cov2, - cls=NLinearModel, - lkl=None, - normalize=True, + None, + None, + cls=model, + lkl=lkl, ) - # can only fit models with past/future covariates when shared_weights=False - for model in [DLinearModel, NLinearModel]: - for shared_weights in [True, False]: - model_instance = model( - 5, 5, shared_weights=shared_weights, **tfm_kwargs - ) - assert model_instance.supports_past_covariates == ( - not shared_weights - ) - assert model_instance.supports_future_covariates == ( - not shared_weights - ) - if shared_weights: - with pytest.raises(ValueError): - model_instance.fit(series1, future_covariates=fut_cov1) - - def test_optional_static_covariates(self): - series = tg.sine_timeseries(length=20).with_static_covariates( - pd.DataFrame({"a": [1]}) - ) - for model_cls in [NLinearModel, DLinearModel]: - # training model with static covs and predicting without will raise an error - model = model_cls( - input_chunk_length=12, - output_chunk_length=6, - use_static_covariates=True, - n_epochs=1, - **tfm_kwargs - ) - model.fit(series) - with pytest.raises(ValueError): - model.predict(n=2, series=series.with_static_covariates(None)) - - # with `use_static_covariates=False`, static covariates are ignored and prediction works - model = model_cls( - input_chunk_length=12, - output_chunk_length=6, - use_static_covariates=False, - n_epochs=1, - **tfm_kwargs + assert e1 <= 0.40 + assert e2 <= 0.34 + + e1, e2 = _eval_model( + train1, + train2, + val1, + val2, + fut_cov1, + fut_cov2, + past_cov1=past_cov1, + past_cov2=past_cov2, + val_past_cov1=val_past_cov1, + val_past_cov2=val_past_cov2, + cls=NLinearModel, + lkl=None, + normalize=True, + ) + # can only fit models with past/future covariates when shared_weights=False + for model in [DLinearModel, NLinearModel]: + for shared_weights in [True, False]: + model_instance = model( + 5, 5, shared_weights=shared_weights, **tfm_kwargs ) - model.fit(series) - preds = model.predict(n=2, series=series.with_static_covariates(None)) - assert preds.static_covariates is None - - # with `use_static_covariates=False`, static covariates are ignored and prediction works - model = model_cls( - input_chunk_length=12, - output_chunk_length=6, - use_static_covariates=False, - n_epochs=1, - **tfm_kwargs - ) - model.fit(series.with_static_covariates(None)) - preds = model.predict(n=2, series=series) - assert preds.static_covariates.equals(series.static_covariates) + assert model_instance.supports_past_covariates == (not shared_weights) + assert model_instance.supports_future_covariates == (not shared_weights) + if shared_weights: + with pytest.raises(ValueError): + model_instance.fit(series1, future_covariates=fut_cov1) + + def test_optional_static_covariates(self): + series = tg.sine_timeseries(length=20).with_static_covariates( + pd.DataFrame({"a": [1]}) + ) + for model_cls in [NLinearModel, DLinearModel]: + # training model with static covs and predicting without will raise an error + model = model_cls( + input_chunk_length=12, + output_chunk_length=6, + use_static_covariates=True, + n_epochs=1, + **tfm_kwargs, + ) + model.fit(series) + with pytest.raises(ValueError): + model.predict(n=2, series=series.with_static_covariates(None)) + + # with `use_static_covariates=False`, static covariates are ignored and prediction works + model = model_cls( + input_chunk_length=12, + output_chunk_length=6, + use_static_covariates=False, + n_epochs=1, + **tfm_kwargs, + ) + model.fit(series) + preds = model.predict(n=2, series=series.with_static_covariates(None)) + assert preds.static_covariates is None + + # with `use_static_covariates=False`, static covariates are ignored and prediction works + model = model_cls( + input_chunk_length=12, + output_chunk_length=6, + use_static_covariates=False, + n_epochs=1, + **tfm_kwargs, + ) + model.fit(series.with_static_covariates(None)) + preds = model.predict(n=2, series=series) + assert preds.static_covariates.equals(series.static_covariates) diff --git a/darts/tests/models/forecasting/test_ensemble_models.py b/darts/tests/models/forecasting/test_ensemble_models.py index b8dc2f0c3d..2648a0bf8a 100644 --- a/darts/tests/models/forecasting/test_ensemble_models.py +++ b/darts/tests/models/forecasting/test_ensemble_models.py @@ -1,3 +1,7 @@ +import copy +import itertools +import os + import numpy as np import pandas as pd import pytest @@ -10,23 +14,25 @@ NaiveDrift, NaiveEnsembleModel, NaiveSeasonal, + RegressionEnsembleModel, StatsForecastAutoARIMA, Theta, ) -from darts.tests.conftest import tfm_kwargs +from darts.models.forecasting.forecasting_model import LocalForecastingModel +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils import timeseries_generation as tg +if TORCH_AVAILABLE: + from darts.models.forecasting.torch_forecasting_model import TorchForecastingModel +else: + TorchForecastingModel = None + logger = get_logger(__name__) -try: +if TORCH_AVAILABLE: from darts.models import DLinearModel, NBEATSModel, RNNModel, TCNModel from darts.utils.likelihood_models import QuantileRegression - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not installed - Some ensemble models tests will be skipped.") - TORCH_AVAILABLE = False - def _make_ts(start_value=0, n=100): times = pd.date_range(start="1/1/2013", periods=n, freq="D") @@ -82,7 +88,7 @@ def test_trained_models(self): # both global trained, retrain = True with pytest.raises(ValueError): - # models need to be explicitely reset before retraining them + # models need to be explicitly reset before retraining them NaiveEnsembleModel( [global_model, global_model], train_forecasting_models=True ) @@ -110,6 +116,8 @@ def test_extreme_lag_inference(self): None, None, None, + 0, + None, ) # test if default is okay model1 = LinearRegressionModel( @@ -119,10 +127,23 @@ def test_extreme_lag_inference(self): lags=5, lags_past_covariates=6, lags_future_covariates=[6, 9] ) - ensemble = NaiveEnsembleModel( - [model1, model2] - ) # test if infers extreme lags is okay - expected = (-5, 0, -6, -1, 6, 9) + ensemble = NaiveEnsembleModel([ + model1, + model2, + ]) # test if infers extreme lags is okay + expected = (-5, 0, -6, -1, 6, 9, 0, None) + assert expected == ensemble.extreme_lags + + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") + def test_extreme_lags_rnn(self): + # RNNModel has the 8th element in `extreme_lags` for the `max_target_lag_train`. + # it is given by `training_length - input_chunk_length`. + # for the ensemble model we want the max lag of all forecasting models. + model1 = RNNModel(input_chunk_length=14, training_length=24) + model2 = RNNModel(input_chunk_length=12, training_length=37) + + ensemble = NaiveEnsembleModel([model1, model2]) + expected = (-14, 0, None, None, -14, 0, 0, 37 - 12) assert expected == ensemble.extreme_lags def test_input_models_local_models(self): @@ -133,12 +154,18 @@ def test_input_models_local_models(self): NaiveEnsembleModel([NaiveDrift, NaiveSeasonal, Theta, ExponentialSmoothing]) # one model is not instantiated with pytest.raises(ValueError): - NaiveEnsembleModel( - [NaiveDrift(), NaiveSeasonal, Theta(), ExponentialSmoothing()] - ) - NaiveEnsembleModel( - [NaiveDrift(), NaiveSeasonal(), Theta(), ExponentialSmoothing()] - ) + NaiveEnsembleModel([ + NaiveDrift(), + NaiveSeasonal, + Theta(), + ExponentialSmoothing(), + ]) + NaiveEnsembleModel([ + NaiveDrift(), + NaiveSeasonal(), + Theta(), + ExponentialSmoothing(), + ]) def test_call_predict_local_models(self): naive_ensemble = NaiveEnsembleModel([NaiveSeasonal(), Theta()]) @@ -151,7 +178,7 @@ def test_call_predict_local_models(self): def test_call_backtest_naive_ensemble_local_models(self): ensemble = NaiveEnsembleModel([NaiveSeasonal(5), Theta(2, 5)]) ensemble.fit(self.series1) - assert ensemble.extreme_lags == (-10, -1, None, None, None, None) + assert ensemble.extreme_lags == (-10, -1, None, None, None, None, 0, None) ensemble.backtest(self.series1) def test_predict_univariate_ensemble_local_models(self): @@ -198,7 +225,7 @@ def test_stochastic_naive_ensemble(self): # only probabilistic forecasting models naive_ensemble_proba = NaiveEnsembleModel([model_proba_1, model_proba_2]) - assert naive_ensemble_proba._is_probabilistic + assert naive_ensemble_proba.supports_probabilistic_prediction naive_ensemble_proba.fit(self.series1 + self.series2) # by default, only 1 sample @@ -254,17 +281,19 @@ def test_predict_likelihood_parameters_wrong_args(self): naive_ensemble.predict(n=1, predict_likelihood_parameters=True) # one model has a different likelihood - naive_ensemble = NaiveEnsembleModel( - [m_proba_quantile1.untrained_model(), m_proba_poisson] - ) + naive_ensemble = NaiveEnsembleModel([ + m_proba_quantile1.untrained_model(), + m_proba_poisson, + ]) naive_ensemble.fit(self.series1 + self.series2) with pytest.raises(ValueError): naive_ensemble.predict(n=1, predict_likelihood_parameters=True) # n > shortest output_chunk_length - naive_ensemble = NaiveEnsembleModel( - [m_proba_quantile1.untrained_model(), m_proba_quantile2] - ) + naive_ensemble = NaiveEnsembleModel([ + m_proba_quantile1.untrained_model(), + m_proba_quantile2, + ]) naive_ensemble.fit(self.series1 + self.series2) with pytest.raises(ValueError): naive_ensemble.predict(n=4, predict_likelihood_parameters=True) @@ -287,15 +316,16 @@ def test_predict_likelihood_parameters_univariate_naive_ensemble(self): input_chunk_length=4, output_chunk_length=2, likelihood=QuantileRegression([0.05, 0.5, 0.95]), - **tfm_kwargs + **tfm_kwargs, ) naive_ensemble = NaiveEnsembleModel([m_proba_quantile1, m_proba_quantile2]) naive_ensemble.fit(self.series1) pred_ens = naive_ensemble.predict(n=1, predict_likelihood_parameters=True) - naive_ensemble = NaiveEnsembleModel( - [m_proba_quantile2.untrained_model(), m_proba_quantile3.untrained_model()] - ) + naive_ensemble = NaiveEnsembleModel([ + m_proba_quantile2.untrained_model(), + m_proba_quantile3.untrained_model(), + ]) naive_ensemble.fit(self.series1) pred_mix_ens = naive_ensemble.predict(n=1, predict_likelihood_parameters=True) assert pred_ens.time_index == pred_mix_ens.time_index @@ -324,7 +354,7 @@ def test_predict_likelihood_parameters_multivariate_naive_ensemble(self): input_chunk_length=4, output_chunk_length=2, likelihood=QuantileRegression([0.05, 0.5, 0.95]), - **tfm_kwargs + **tfm_kwargs, ) multivariate_series = self.series1.stack(self.series2) @@ -332,9 +362,10 @@ def test_predict_likelihood_parameters_multivariate_naive_ensemble(self): naive_ensemble = NaiveEnsembleModel([m_proba_quantile1, m_proba_quantile2]) naive_ensemble.fit(multivariate_series) pred_ens = naive_ensemble.predict(n=1, predict_likelihood_parameters=True) - naive_ensemble = NaiveEnsembleModel( - [m_proba_quantile2.untrained_model(), m_proba_quantile3.untrained_model()] - ) + naive_ensemble = NaiveEnsembleModel([ + m_proba_quantile2.untrained_model(), + m_proba_quantile3.untrained_model(), + ]) naive_ensemble.fit(multivariate_series) pred_mix_ens = naive_ensemble.predict(n=1, predict_likelihood_parameters=True) assert pred_ens.time_index == pred_mix_ens.time_index @@ -370,13 +401,11 @@ def test_input_models_global_models(self): @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") def test_call_predict_global_models_univariate_input_no_covariates(self): - naive_ensemble = NaiveEnsembleModel( - [ - RNNModel(12, n_epochs=1, **tfm_kwargs), - TCNModel(10, 2, n_epochs=1, **tfm_kwargs), - NBEATSModel(10, 2, n_epochs=1, **tfm_kwargs), - ] - ) + naive_ensemble = NaiveEnsembleModel([ + RNNModel(12, n_epochs=1, **tfm_kwargs), + TCNModel(10, 2, n_epochs=1, **tfm_kwargs), + NBEATSModel(10, 2, n_epochs=1, **tfm_kwargs), + ]) with pytest.raises(Exception): naive_ensemble.predict(5) @@ -385,25 +414,21 @@ def test_call_predict_global_models_univariate_input_no_covariates(self): @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") def test_call_predict_global_models_multivariate_input_no_covariates(self): - naive_ensemble = NaiveEnsembleModel( - [ - RNNModel(12, n_epochs=1, **tfm_kwargs), - TCNModel(10, 2, n_epochs=1, **tfm_kwargs), - NBEATSModel(10, 2, n_epochs=1, **tfm_kwargs), - ] - ) + naive_ensemble = NaiveEnsembleModel([ + RNNModel(12, n_epochs=1, **tfm_kwargs), + TCNModel(10, 2, n_epochs=1, **tfm_kwargs), + NBEATSModel(10, 2, n_epochs=1, **tfm_kwargs), + ]) naive_ensemble.fit(self.seq1) naive_ensemble.predict(n=5, series=self.seq1) @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") def test_call_predict_global_models_multivariate_input_with_covariates(self): - naive_ensemble = NaiveEnsembleModel( - [ - RNNModel(12, n_epochs=1, **tfm_kwargs), - TCNModel(10, 2, n_epochs=1, **tfm_kwargs), - NBEATSModel(10, 2, n_epochs=1, **tfm_kwargs), - ] - ) + naive_ensemble = NaiveEnsembleModel([ + RNNModel(12, n_epochs=1, **tfm_kwargs), + TCNModel(10, 2, n_epochs=1, **tfm_kwargs), + NBEATSModel(10, 2, n_epochs=1, **tfm_kwargs), + ]) naive_ensemble.fit(self.seq1, self.cov1) predict_series = [s[:12] for s in self.seq1] predict_covariates = [c[:14] for c in self.cov1] @@ -414,9 +439,10 @@ def test_call_predict_global_models_multivariate_input_with_covariates(self): @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") def test_input_models_mixed(self): # NaiveDrift is local, RNNModel is global - naive_ensemble = NaiveEnsembleModel( - [NaiveDrift(), RNNModel(12, n_epochs=1, **tfm_kwargs)] - ) + naive_ensemble = NaiveEnsembleModel([ + NaiveDrift(), + RNNModel(12, n_epochs=1, **tfm_kwargs), + ]) # ensemble is neither local, nor global assert not naive_ensemble.is_local_ensemble assert not naive_ensemble.is_global_ensemble @@ -428,39 +454,37 @@ def test_input_models_mixed(self): @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") def test_call_predict_different_covariates_support(self): # AutoARIMA support future covariates only - local_ensemble_one_covs = NaiveEnsembleModel( - [NaiveDrift(), StatsForecastAutoARIMA()] - ) + local_ensemble_one_covs = NaiveEnsembleModel([ + NaiveDrift(), + StatsForecastAutoARIMA(), + ]) with pytest.raises(ValueError): local_ensemble_one_covs.fit(self.series1, past_covariates=self.series2) local_ensemble_one_covs.fit(self.series1, future_covariates=self.series2) # RNN support future covariates only - mixed_ensemble_one_covs = NaiveEnsembleModel( - [NaiveDrift(), RNNModel(12, n_epochs=1, **tfm_kwargs)] - ) + mixed_ensemble_one_covs = NaiveEnsembleModel([ + NaiveDrift(), + RNNModel(12, n_epochs=1, **tfm_kwargs), + ]) with pytest.raises(ValueError): mixed_ensemble_one_covs.fit(self.series1, past_covariates=self.series2) mixed_ensemble_one_covs.fit(self.series1, future_covariates=self.series2) # both models support future covariates only - mixed_ensemble_future_covs = NaiveEnsembleModel( - [ - StatsForecastAutoARIMA(), - RNNModel(12, n_epochs=1, **tfm_kwargs), - ] - ) + mixed_ensemble_future_covs = NaiveEnsembleModel([ + StatsForecastAutoARIMA(), + RNNModel(12, n_epochs=1, **tfm_kwargs), + ]) mixed_ensemble_future_covs.fit(self.series1, future_covariates=self.series2) with pytest.raises(ValueError): mixed_ensemble_future_covs.fit(self.series1, past_covariates=self.series2) # RegressionModels with different covariates - global_ensemble_both_covs = NaiveEnsembleModel( - [ - LinearRegressionModel(lags=1, lags_past_covariates=[-1]), - LinearRegressionModel(lags=1, lags_future_covariates=[1]), - ] - ) + global_ensemble_both_covs = NaiveEnsembleModel([ + LinearRegressionModel(lags=1, lags_past_covariates=[-1]), + LinearRegressionModel(lags=1, lags_future_covariates=[1]), + ]) # missing future covariates with pytest.raises(ValueError): global_ensemble_both_covs.fit(self.series1, past_covariates=self.series2) @@ -472,19 +496,215 @@ def test_call_predict_different_covariates_support(self): ) def test_fit_multivar_ts_with_local_models(self): - naive = NaiveEnsembleModel( - [NaiveDrift(), NaiveSeasonal(), Theta(), ExponentialSmoothing()] - ) + naive = NaiveEnsembleModel([ + NaiveDrift(), + NaiveSeasonal(), + Theta(), + ExponentialSmoothing(), + ]) with pytest.raises(ValueError): naive.fit(self.seq1) def test_fit_univar_ts_with_covariates_for_local_models(self): - naive = NaiveEnsembleModel( - [NaiveDrift(), NaiveSeasonal(), Theta(), ExponentialSmoothing()] - ) + naive = NaiveEnsembleModel([ + NaiveDrift(), + NaiveSeasonal(), + Theta(), + ExponentialSmoothing(), + ]) with pytest.raises(ValueError): naive.fit(self.series1, self.series2) + @pytest.mark.parametrize("model_cls", [NaiveEnsembleModel, RegressionEnsembleModel]) + def test_sample_weight_mixed_models(self, model_cls): + """Check sample weights for ensemble models with mixed forecasting models. + + NaiveEnsembleModel + Sample weights will only be passed to global models. + A weighted linear model that ignores `1000` should learn that y_t = y_(t-1) + 1. When calling predict(): + - linear model should predict y_(t,lin) = 1000 + 1 = 1001 + - naive seasonal should predict y_(t,ns) = y_(t-1) = 1000 + + The ensemble takes the average: + - y_t = 0.5 * y_(t,lin) + 0.5 * y_(t,ns) = 1001 + 1000 = 1000.5 + + RegressionEnsembleModel + Sample weights will be passed to global forecasting models and regression ensemble model. + A weighted linear model that ignores `1000` should learn that y_t = y_(t-1) + 1. When calling predict(): + - linear model should predict y_(t,lin) = y_(t-1) + 1 + - naive seasonal should predict y_(t,ns) = y_(t-1) + + The training set for regression ensemble covers the forecasts for and labels of last 5 points, where labels + 10000 and 1002 are ignored (0 weight): + - the linear forecasting model generates forecasts: [1001, 1002, 1003, 1004, 1005] + - the naive seasonal model generates forecasts: [1000, 1000, 1000, 1000, 1000] + + The ensemble model should then learn a perfect fit based only on the output of the linear model: + - y_t = 1.0 * y_(t,lin) + 0.0 * y_(t,ns) = 1.0 * (y_(t-1) + 1) + - for y_(t-1) = 1005 -> y_t = 1006 + """ + if issubclass(model_cls, NaiveEnsembleModel): + series = TimeSeries.from_values(np.array([0.0, 1.0, 2.0, 3.0, 1000])) + weights = TimeSeries.from_values(np.array([1.0, 1.0, 1.0, 1.0, 0.0])) + pred_expected = np.array([[1000.5]]) + kwargs = {} + else: + series = TimeSeries.from_values( + np.array([0.0, 1.0, 2.0, 3.0, 4.0, 1000, 10000, 1002, 1003, 1004, 1005]) + ) + weights = TimeSeries.from_values( + np.array([1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]) + ) + pred_expected = np.array([[1006.0]]) + kwargs = {"regression_train_n_points": 5} + + model = model_cls( + [LinearRegressionModel(lags=[-1]), NaiveSeasonal(K=1)], **kwargs + ) + model.fit(series, sample_weight=weights) + preds_weighted = model.predict(n=1) + np.testing.assert_array_almost_equal(preds_weighted.values(), pred_expected) + + # make sure that without weights we get different results + model = model_cls( + [LinearRegressionModel(lags=[-1]), NaiveSeasonal(K=1)], **kwargs + ) + model.fit(series) + preds = model.predict(n=1) + with pytest.raises(AssertionError): + np.testing.assert_array_almost_equal( + preds_weighted.values(), preds.values() + ) + + @pytest.mark.parametrize( + "config", + itertools.product([NaiveEnsembleModel, RegressionEnsembleModel], [True, False]), + ) + def test_sample_weight_global(self, config): + """Check sample weights for ensemble models with global forecasting models. + + NaiveEnsembleModel + Sample weights will only be passed to global forecasting models. + A weighted linear model that ignores `1000` should learn that y_t = y_(t-1) + 1. When calling predict(): + - linear model should predict y_(t,lin) = 1000 + 1 = 1001 + + The ensemble takes the average: + - y_t = 0.5 * y_(t,lin) + 0.5 * y_(t,lin) = 1001 + 1001 = 1001 + + RegressionEnsembleModel + Sample weights will be passed to global forecasting models and regression ensemble model. + A weighted linear model that ignores `1000` should learn that y_t = y_(t-1) + 1. When calling predict(): + - linear model should predict y_(t,lin) = y_(t-1) + 1 + + The training set for regression ensemble covers the forecasts for and labels of last 5 points, where labels + 10000 and 1002 are ignored (0 weight): + - the linear forecasting model generates forecasts: [1001, 1002, 1003, 1004, 1005] + + The ensemble model should then learn a perfect fit based on the output of the linear model: + - y_t = 1.0 * y_(t,lin) + 0.0 * y_(t,ns) = 1.0 * (y_(t-1) + 1) + """ + model_cls, single_series = config + if issubclass(model_cls, NaiveEnsembleModel): + series = TimeSeries.from_values(np.array([0.0, 1.0, 2.0, 3.0, 1000])) + weights = TimeSeries.from_values(np.array([1.0, 1.0, 1.0, 1.0, 0.0])) + pred_expected = np.array([[1001.0]]) + kwargs = {} + else: + series = TimeSeries.from_values( + np.array([0.0, 1.0, 2.0, 3.0, 4.0, 1000, 10000, 1002, 1003, 1004, 1005]) + ) + weights = TimeSeries.from_values( + np.array([1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]) + ) + pred_expected = np.array([[1006.0]]) + kwargs = {"regression_train_n_points": 5} + + if not single_series: + series = [series] * 2 + weights = [weights] * 2 + + model = model_cls( + [LinearRegressionModel(lags=[-1]), LinearRegressionModel(lags=[-1])], + **kwargs, + ) + model.fit(series, sample_weight=weights) + preds_weighted = model.predict(n=1, series=series) + if single_series: + preds_weighted = [preds_weighted] + + for preds in preds_weighted: + np.testing.assert_array_almost_equal(preds.values(), pred_expected) + + # make sure that without weights we get different results + model = model_cls( + [LinearRegressionModel(lags=[-1]), LinearRegressionModel(lags=[-1])], + **kwargs, + ) + model.fit(series) + preds = model.predict(n=1, series=series) + if single_series: + preds = [preds] + + for pred_w, pred_nw in zip(preds_weighted, preds): + with pytest.raises(AssertionError): + np.testing.assert_array_almost_equal(pred_w.values(), pred_nw.values()) + + @pytest.mark.parametrize("model_cls", [NaiveEnsembleModel, RegressionEnsembleModel]) + def test_invalid_sample_weight(self, model_cls): + kwargs = { + "forecasting_models": [ + LinearRegressionModel(lags=[-1]), + NaiveSeasonal(K=1), + ], + } + if issubclass(model_cls, RegressionEnsembleModel): + kwargs["regression_train_n_points"] = 3 + + ts = TimeSeries.from_values(np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])) + # weights too short + model = model_cls(**copy.deepcopy(kwargs)) + with pytest.raises(ValueError) as err: + model.fit(ts, sample_weight=ts[:-1]) + assert ( + str(err.value) + == "The `sample_weight` series must have at least the same times as the target `series`." + ) + + # same number of series + model = model_cls(**copy.deepcopy(kwargs)) + with pytest.raises(ValueError) as err: + model.fit(ts, sample_weight=[ts, ts]) + assert ( + str(err.value) + == "The provided sequence of target `series` must have the same length as the " + "provided sequence of `sample_weight`." + ) + + # same number of components + model = model_cls(**copy.deepcopy(kwargs)) + with pytest.raises(ValueError) as err: + model.fit(ts, sample_weight=ts.stack(ts)) + assert ( + str(err.value) + == "The number of components in `sample_weight` must either be `1` or match the " + "number of target series components `1`." + ) + # with correct number it works + model = model_cls(**copy.deepcopy(kwargs)) + model.fit(ts.stack(ts), sample_weight=ts.stack(ts)) + # or with multivar ts and single component weights (globally applied) + model = model_cls(**copy.deepcopy(kwargs)) + model.fit(ts.stack(ts), sample_weight=ts) + + # invalid string + model = model_cls(**copy.deepcopy(kwargs)) + with pytest.raises(ValueError) as err: + model.fit(ts, sample_weight="invalid") + assert str(err.value).startswith("Invalid `sample_weight` value: `'invalid'`. ") + + # but with valid string it works + model.fit(ts, sample_weight="linear") + def test_predict_with_target(self): series_long = self.series1 series_short = series_long[:25] @@ -550,3 +770,79 @@ def get_global_ensemble_model(output_chunk_length=5): ), ], ) + + @pytest.mark.parametrize("model_cls", [NaiveEnsembleModel, RegressionEnsembleModel]) + def test_save_load_ensemble_models(self, tmpdir_fn, model_cls): + # check if save and load methods work and + # if loaded ensemble model creates same forecasts as original ensemble models + full_model_path_str = os.getcwd() + kwargs = {} + expected_suffixes = [".pkl", ".pkl.RNNModel_2.pt", ".pkl.RNNModel_2.pt.ckpt"] + + if issubclass(model_cls, RegressionEnsembleModel): + kwargs["regression_train_n_points"] = 5 + + if TORCH_AVAILABLE: + model = model_cls( + [ + LinearRegressionModel(lags=[-1]), + NaiveSeasonal(K=1), + RNNModel(10, n_epochs=1, **tfm_kwargs), + ], + **kwargs, + ) + else: + model = model_cls( + [LinearRegressionModel(lags=[-1]), NaiveSeasonal(K=1)], **kwargs + ) + + model.fit(self.series1 + self.series2) + model_prediction = model.predict(5) + + # test save + model.save() + model.save(os.path.join(full_model_path_str, f"{model_cls.__name__}.pkl")) + + assert os.path.exists(full_model_path_str) + files = os.listdir(full_model_path_str) + if TORCH_AVAILABLE: + assert len(files) == 6 + for f in files: + assert f.startswith(model_cls.__name__) + suffix_counts = { + suffix: sum( + 1 for p in os.listdir(full_model_path_str) if p.endswith(suffix) + ) + for suffix in expected_suffixes + } + assert all(count == 2 for count in suffix_counts.values()) + else: + assert len(files) == 2 + for f in files: + assert f.startswith(model_cls.__name__) and f.endswith(".pkl") + + # test load + pkl_files = [] + for filename in os.listdir(full_model_path_str): + if filename.endswith(".pkl"): + pkl_files.append(os.path.join(full_model_path_str, filename)) + for p in pkl_files: + loaded_model = model_cls.load(p) + assert model_prediction == loaded_model.predict(5) + + # test pl_trainer_kwargs (only for torch models) + loaded_model = model_cls.load(p, pl_trainer_kwargs={"accelerator": "cuda"}) + for i, m in enumerate(loaded_model.forecasting_models): + if TORCH_AVAILABLE and issubclass(type(m), TorchForecastingModel): + assert m.trainer_params["accelerator"] == "cuda" + + # test clean save + path = os.path.join(full_model_path_str, f"clean_{model_cls.__name__}.pkl") + model.save(path, clean=True) + clean_model = model_cls.load(path, pl_trainer_kwargs={"accelerator": "cpu"}) + for i, m in enumerate(clean_model.forecasting_models): + if not issubclass(type(m), LocalForecastingModel): + assert m.training_series is None + assert m.past_covariate_series is None + assert m.future_covariate_series is None + assert model.predict(5) == clean_model.predict(5, self.series1 + self.series2) diff --git a/darts/tests/models/forecasting/test_exponential_smoothing.py b/darts/tests/models/forecasting/test_exponential_smoothing.py index 63b494ae44..45903fa548 100644 --- a/darts/tests/models/forecasting/test_exponential_smoothing.py +++ b/darts/tests/models/forecasting/test_exponential_smoothing.py @@ -4,19 +4,20 @@ from darts import TimeSeries from darts.models import ExponentialSmoothing from darts.utils import timeseries_generation as tg +from darts.utils.utils import freqs class TestExponentialSmoothing: - series = tg.sine_timeseries(length=100, freq="H") + series = tg.sine_timeseries(length=100, freq=freqs["h"]) @pytest.mark.parametrize( "freq_string,expected_seasonal_periods", [ ("D", 7), - ("H", 24), - ("M", 12), + (freqs["h"], 24), + (freqs["ME"], 12), ("W", 52), - ("Q", 4), + (freqs["QE"], 4), ("B", 5), ], ) @@ -37,7 +38,7 @@ def test_default_parameters(self): def test_multiple_fit(self): """Test whether a model that inferred a seasonality period before will do it again for a new series""" - series1 = tg.sine_timeseries(length=100, freq="M") + series1 = tg.sine_timeseries(length=100, freq=freqs["ME"]) series2 = tg.sine_timeseries(length=100, freq="D") model = ExponentialSmoothing() model.fit(series1) diff --git a/darts/tests/models/forecasting/test_fft.py b/darts/tests/models/forecasting/test_fft.py index 17632b1538..b105d082d2 100644 --- a/darts/tests/models/forecasting/test_fft.py +++ b/darts/tests/models/forecasting/test_fft.py @@ -2,17 +2,16 @@ from darts.models.forecasting.fft import _find_relevant_timestamp_attributes from darts.utils import timeseries_generation as tg +from darts.utils.utils import freqs class TestFFT: def helper_relevant_attributes(self, freq, length, period_attributes_tuples): - # test random walk random_walk_ts = tg.random_walk_timeseries(freq=freq, length=length) assert _find_relevant_timestamp_attributes(random_walk_ts) == set() for period, relevant_attributes in period_attributes_tuples: - # test seasonal period with no noise seasonal_ts = tg.sine_timeseries( freq=freq, value_frequency=1 / period, length=length @@ -31,11 +30,10 @@ def helper_relevant_attributes(self, freq, length, period_attributes_tuples): ), "failed to recognize season in noisy timeseries" def test_find_relevant_timestamp_attributes(self): - np.random.seed(0) # monthly frequency - self.helper_relevant_attributes("M", 150, [(12, {"month"})]) + self.helper_relevant_attributes(freqs["ME"], 150, [(12, {"month"})]) # daily frequency self.helper_relevant_attributes( @@ -44,7 +42,7 @@ def test_find_relevant_timestamp_attributes(self): # hourly frequency self.helper_relevant_attributes( - "H", + freqs["h"], 3000, [(730, {"day", "hour"}), (168, {"weekday", "hour"}), (24, {"hour"})], ) diff --git a/darts/tests/models/forecasting/test_global_forecasting_models.py b/darts/tests/models/forecasting/test_global_forecasting_models.py index cec70efb4e..40ea3ce8cb 100644 --- a/darts/tests/models/forecasting/test_global_forecasting_models.py +++ b/darts/tests/models/forecasting/test_global_forecasting_models.py @@ -1,3 +1,4 @@ +import copy import os from copy import deepcopy from itertools import product @@ -9,358 +10,459 @@ from darts.dataprocessing.transformers import Scaler from darts.datasets import AirPassengersDataset -from darts.logging import get_logger from darts.metrics import mape -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils import timeseries_generation as tg from darts.utils.timeseries_generation import linear_timeseries -logger = get_logger(__name__) - -try: - import torch - - from darts.models import ( +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import torch + +from darts.models import ( + BlockRNNModel, + DLinearModel, + GlobalNaiveAggregate, + GlobalNaiveDrift, + GlobalNaiveSeasonal, + LinearRegressionModel, + NBEATSModel, + NLinearModel, + RNNModel, + TCNModel, + TFTModel, + TiDEModel, + TransformerModel, + TSMixerModel, +) +from darts.models.forecasting.torch_forecasting_model import ( + DualCovariatesTorchModel, + MixedCovariatesTorchModel, + PastCovariatesTorchModel, + TorchForecastingModel, +) +from darts.utils.likelihood_models import GaussianLikelihood + +IN_LEN = 24 +OUT_LEN = 12 +models_cls_kwargs_errs = [ + ( BlockRNNModel, - DLinearModel, - NBEATSModel, - NLinearModel, + { + "model": "RNN", + "hidden_dim": 10, + "n_rnn_layers": 1, + "batch_size": 32, + "n_epochs": 10, + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 110.0, + ), + ( RNNModel, + { + "model": "RNN", + "training_length": IN_LEN + OUT_LEN, + "hidden_dim": 10, + "batch_size": 32, + "n_epochs": 10, + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 150.0, + ), + ( + RNNModel, + { + "training_length": IN_LEN + OUT_LEN, + "n_epochs": 10, + "likelihood": GaussianLikelihood(), + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 80.0, + ), + ( TCNModel, + { + "n_epochs": 10, + "batch_size": 32, + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 60.0, + ), + ( + TransformerModel, + { + "d_model": 16, + "nhead": 2, + "num_encoder_layers": 2, + "num_decoder_layers": 2, + "dim_feedforward": 16, + "batch_size": 32, + "n_epochs": 10, + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 60.0, + ), + ( + NBEATSModel, + { + "num_stacks": 4, + "num_blocks": 1, + "num_layers": 2, + "layer_widths": 12, + "n_epochs": 10, + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 140.0, + ), + ( TFTModel, + { + "hidden_size": 16, + "lstm_layers": 1, + "num_attention_heads": 4, + "add_relative_index": True, + "n_epochs": 10, + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 70.0, + ), + ( + NLinearModel, + { + "n_epochs": 10, + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 50.0, + ), + ( + DLinearModel, + { + "n_epochs": 10, + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 55.0, + ), + ( TiDEModel, - TransformerModel, + { + "n_epochs": 10, + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 40.0, + ), + ( + TSMixerModel, + { + "n_epochs": 10, + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 60.0, + ), + ( + GlobalNaiveAggregate, + { + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 22, + ), + ( + GlobalNaiveDrift, + { + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 17, + ), + ( + GlobalNaiveSeasonal, + { + "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], + }, + 39, + ), +] + + +class TestGlobalForecastingModels: + # forecasting horizon used in runnability tests + forecasting_horizon = 12 + + np.random.seed(42) + torch.manual_seed(42) + + # some arbitrary static covariates + static_covariates = pd.DataFrame([[0.0, 1.0]], columns=["st1", "st2"]) + + # real timeseries for functionality tests + ts_passengers = ( + AirPassengersDataset().load().with_static_covariates(static_covariates) ) - from darts.models.forecasting.torch_forecasting_model import ( - DualCovariatesTorchModel, - MixedCovariatesTorchModel, - PastCovariatesTorchModel, + scaler = Scaler() + ts_passengers = scaler.fit_transform(ts_passengers) + ts_pass_train, ts_pass_val = ts_passengers[:-36], ts_passengers[-36:] + ts_passangers_mock_cov = linear_timeseries( + length=2 * len(ts_passengers), + start=ts_passengers.start_time(), + freq=ts_passengers.freq_str, + ) + # an additional noisy series + ts_pass_train_1 = ts_pass_train + 0.01 * tg.gaussian_timeseries( + length=len(ts_pass_train), + freq=ts_pass_train.freq_str, + start=ts_pass_train.start_time(), ) - from darts.utils.likelihood_models import GaussianLikelihood - - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not installed - will be skipping Torch models tests") - TORCH_AVAILABLE = False - -if TORCH_AVAILABLE: - IN_LEN = 24 - OUT_LEN = 12 - models_cls_kwargs_errs = [ - ( - BlockRNNModel, - { - "model": "RNN", - "hidden_dim": 10, - "n_rnn_layers": 1, - "batch_size": 32, - "n_epochs": 10, - "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], - }, - 180.0, - ), - ( - RNNModel, - { - "model": "RNN", - "hidden_dim": 10, - "batch_size": 32, - "n_epochs": 10, - "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], - }, - 180.0, - ), - ( - RNNModel, - { - "training_length": 12, - "n_epochs": 10, - "likelihood": GaussianLikelihood(), - "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], - }, - 80.0, - ), - ( - TCNModel, - { - "n_epochs": 10, - "batch_size": 32, - "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], - }, - 240.0, - ), - ( - TransformerModel, - { - "d_model": 16, - "nhead": 2, - "num_encoder_layers": 2, - "num_decoder_layers": 2, - "dim_feedforward": 16, - "batch_size": 32, - "n_epochs": 10, - "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], - }, - 180.0, - ), - ( - NBEATSModel, - { - "num_stacks": 4, - "num_blocks": 1, - "num_layers": 2, - "layer_widths": 12, - "n_epochs": 10, - "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], - }, - 180.0, - ), - ( - TFTModel, - { - "hidden_size": 16, - "lstm_layers": 1, - "num_attention_heads": 4, - "add_relative_index": True, - "n_epochs": 10, - "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], - }, - 100.0, - ), - ( - NLinearModel, - { - "n_epochs": 10, - "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], - }, - 100, - ), - ( - DLinearModel, - { - "n_epochs": 10, - "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], - }, - 100, - ), - ( - TiDEModel, - { - "n_epochs": 10, - "pl_trainer_kwargs": tfm_kwargs["pl_trainer_kwargs"], - }, - 100, - ), - ] + # an additional time series serving as covariates + year_series = tg.datetime_attribute_timeseries(ts_passengers, attribute="year") + month_series = tg.datetime_attribute_timeseries(ts_passengers, attribute="month") + scaler_dt = Scaler() + time_covariates = scaler_dt.fit_transform(year_series.stack(month_series)) + time_covariates_train, time_covariates_val = ( + time_covariates[:-36], + time_covariates[-36:], + ) - class TestGlobalForecastingModels: - # forecasting horizon used in runnability tests - forecasting_horizon = 12 + # an artificial time series that is highly dependent on covariates + ts_length = 400 + split_ratio = 0.6 + sine_1_ts = tg.sine_timeseries(length=ts_length) + sine_2_ts = tg.sine_timeseries(length=ts_length, value_frequency=0.05) + sine_3_ts = tg.sine_timeseries( + length=ts_length, value_frequency=0.003, value_amplitude=5 + ) + linear_ts = tg.linear_timeseries(length=ts_length, start_value=3, end_value=8) - np.random.seed(42) - torch.manual_seed(42) + covariates = sine_3_ts.stack(sine_2_ts).stack(linear_ts) + covariates_past, _ = covariates.split_after(split_ratio) - # some arbitrary static covariates - static_covariates = pd.DataFrame([[0.0, 1.0]], columns=["st1", "st2"]) + target = sine_1_ts + sine_2_ts + linear_ts + sine_3_ts + target_past, target_future = target.split_after(split_ratio) - # real timeseries for functionality tests - ts_passengers = ( - AirPassengersDataset().load().with_static_covariates(static_covariates) - ) - scaler = Scaler() - ts_passengers = scaler.fit_transform(ts_passengers) - ts_pass_train, ts_pass_val = ts_passengers[:-36], ts_passengers[-36:] + # various ts with different static covariates representations + ts_w_static_cov = tg.linear_timeseries(length=80).with_static_covariates( + pd.Series([1, 2]) + ) + ts_shared_static_cov = ts_w_static_cov.stack(tg.sine_timeseries(length=80)) + ts_comps_static_cov = ts_shared_static_cov.with_static_covariates( + pd.DataFrame([[0, 1], [2, 3]], columns=["st1", "st2"]) + ) - # an additional noisy series - ts_pass_train_1 = ts_pass_train + 0.01 * tg.gaussian_timeseries( - length=len(ts_pass_train), - freq=ts_pass_train.freq_str, - start=ts_pass_train.start_time(), + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_save_model_parameters(self, config): + # model creation parameters were saved before. check if re-created model has same params as original + model_cls, kwargs, err = config + model = model_cls( + input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs ) + assert model._model_params, model.untrained_model()._model_params + + @pytest.mark.parametrize( + "model", + [ + RNNModel( + input_chunk_length=4, + hidden_dim=10, + batch_size=32, + n_epochs=10, + **tfm_kwargs, + ), + TCNModel( + input_chunk_length=4, + output_chunk_length=3, + n_epochs=10, + batch_size=32, + **tfm_kwargs, + ), + GlobalNaiveSeasonal( + input_chunk_length=4, + output_chunk_length=3, + **tfm_kwargs, + ), + LinearRegressionModel( + lags=12, + lags_past_covariates=[-1, -2, -3], + lags_future_covariates=[1, 2, 3], + ), + ], + ) + def test_save_load_model(self, tmpdir_fn, model): + # check if save and load methods work and if loaded model creates same forecasts as original model + model_path_str = type(model).__name__ + model_clean_path_str = type(model).__name__ + "_clean" - # an additional time series serving as covariates - year_series = tg.datetime_attribute_timeseries(ts_passengers, attribute="year") - month_series = tg.datetime_attribute_timeseries( - ts_passengers, attribute="month" - ) - scaler_dt = Scaler() - time_covariates = scaler_dt.fit_transform(year_series.stack(month_series)) - time_covariates_train, time_covariates_val = ( - time_covariates[:-36], - time_covariates[-36:], - ) + full_model_path_str = os.path.join(tmpdir_fn, model_path_str) + full_model_clean_path_str = os.path.join(tmpdir_fn, model_clean_path_str) - # an artificial time series that is highly dependent on covariates - ts_length = 400 - split_ratio = 0.6 - sine_1_ts = tg.sine_timeseries(length=ts_length) - sine_2_ts = tg.sine_timeseries(length=ts_length, value_frequency=0.05) - sine_3_ts = tg.sine_timeseries( - length=ts_length, value_frequency=0.003, value_amplitude=5 + cov_kwargs = ( + { + "past_covariates": self.ts_passangers_mock_cov, + "future_covariates": self.ts_passangers_mock_cov, + } + if model.supports_future_covariates and model.supports_past_covariates + else {} ) - linear_ts = tg.linear_timeseries(length=ts_length, start_value=3, end_value=8) - covariates = sine_3_ts.stack(sine_2_ts).stack(linear_ts) - covariates_past, _ = covariates.split_after(split_ratio) + model.fit(series=self.ts_pass_train, **cov_kwargs) - target = sine_1_ts + sine_2_ts + linear_ts + sine_3_ts - target_past, target_future = target.split_after(split_ratio) - - # various ts with different static covariates representations - ts_w_static_cov = tg.linear_timeseries(length=80).with_static_covariates( - pd.Series([1, 2]) + model_prediction = model.predict( + self.forecasting_horizon, self.ts_pass_train, **cov_kwargs ) - ts_shared_static_cov = ts_w_static_cov.stack(tg.sine_timeseries(length=80)) - ts_comps_static_cov = ts_shared_static_cov.with_static_covariates( - pd.DataFrame([[0, 1], [2, 3]], columns=["st1", "st2"]) + + # test save + model.save() + model.save(full_model_path_str) + + temp_training_series = model.training_series.copy() + temp_future_cov = copy.copy(model.future_covariate_series) + temp_past_cov = copy.copy(model.past_covariate_series) + + model.save(full_model_clean_path_str, clean=True) + # No side effect to drop the training series + assert temp_training_series == model.training_series + assert temp_future_cov == model.future_covariate_series + assert temp_past_cov == model.past_covariate_series + + # test load + loaded_model = type(model).load(full_model_path_str) + if isinstance(model, TorchForecastingModel): + load_kwargs = {"pl_trainer_kwargs": {"accelerator": "cpu"}} + else: + load_kwargs = {} + loaded_model_clean_str = type(model).load( + full_model_clean_path_str, **load_kwargs ) - @pytest.mark.parametrize("config", models_cls_kwargs_errs) - def test_save_model_parameters(self, config): - # model creation parameters were saved before. check if re-created model has same params as original - model_cls, kwargs, err = config - model = model_cls( - input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs - ) - assert model._model_params, model.untrained_model()._model_params - - @pytest.mark.parametrize( - "model", - [ - RNNModel( - input_chunk_length=4, - hidden_dim=10, - batch_size=32, - n_epochs=10, - **tfm_kwargs, - ), - TCNModel( - input_chunk_length=4, - output_chunk_length=3, - n_epochs=10, - batch_size=32, - **tfm_kwargs, - ), - ], - ) - def test_save_load_model(self, tmpdir_module, model): - # check if save and load methods work and if loaded model creates same forecasts as original model - cwd = os.getcwd() - os.chdir(tmpdir_module) - model_path_str = type(model).__name__ - full_model_path_str = os.path.join(tmpdir_module, model_path_str) - - model.fit(self.ts_pass_train) - model_prediction = model.predict(self.forecasting_horizon) - - # test save - model.save() - model.save(model_path_str) - - assert os.path.exists(full_model_path_str) - assert ( - len( - [ - p - for p in os.listdir(tmpdir_module) - if p.startswith(type(model).__name__) - ] - ) - == 4 + assert ( + loaded_model.predict( + self.forecasting_horizon, self.ts_pass_train, **cov_kwargs ) + == model_prediction + ) - # test load - loaded_model = type(model).load(model_path_str) + # Training data is not stored in the clean model + assert loaded_model_clean_str.training_series is None + + # The serie to predict need to be provided at prediction time + with pytest.raises(ValueError) as err: + loaded_model_clean_str.predict(self.forecasting_horizon) + assert str(err.value) == ( + "Input `series` must be provided. This is the result either from fitting on multiple series, " + "from not having fit the model yet, or from loading a model saved with `clean=True`." + ) - assert model_prediction == loaded_model.predict(self.forecasting_horizon) + # When the serie to predict is provided, the prediction is the same + assert model_prediction == loaded_model_clean_str.predict( + self.forecasting_horizon, series=self.ts_pass_train, **cov_kwargs + ) - os.chdir(cwd) + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_single_ts(self, config): + model_cls, kwargs, err = config + model = model_cls( + input_chunk_length=IN_LEN, + output_chunk_length=OUT_LEN, + random_state=0, + **kwargs, + ) + model.fit(self.ts_pass_train) + pred = model.predict(n=36) + mape_err = mape(self.ts_pass_val, pred) + assert mape_err < err, ( + f"Model {model_cls} produces errors too high (one time " + f"series). Error = {mape_err}" + ) + assert pred.static_covariates.equals(self.ts_passengers.static_covariates) + + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_multi_ts(self, config): + model_cls, kwargs, err = config + model = model_cls( + input_chunk_length=IN_LEN, + output_chunk_length=OUT_LEN, + random_state=0, + **kwargs, + ) + model.fit([self.ts_pass_train, self.ts_pass_train_1]) + with pytest.raises(ValueError): + # when model is fit from >1 series, one must provide a series in argument + model.predict(n=1) + pred = model.predict(n=36, series=self.ts_pass_train) + mape_err = mape(self.ts_pass_val, pred) + assert mape_err < err, ( + f"Model {model_cls} produces errors too high (several time " + f"series). Error = {mape_err}" + ) - @pytest.mark.parametrize("config", models_cls_kwargs_errs) - def test_single_ts(self, config): - model_cls, kwargs, err = config - model = model_cls( - input_chunk_length=IN_LEN, - output_chunk_length=OUT_LEN, - random_state=0, - **kwargs, - ) - model.fit(self.ts_pass_train) - pred = model.predict(n=36) - mape_err = mape(self.ts_pass_val, pred) - assert mape_err < err, ( - "Model {} produces errors too high (one time " - "series). Error = {}".format(model_cls, mape_err) - ) - assert pred.static_covariates.equals(self.ts_passengers.static_covariates) - - @pytest.mark.parametrize("config", models_cls_kwargs_errs) - def test_multi_ts(self, config): - model_cls, kwargs, err = config - model = model_cls( - input_chunk_length=IN_LEN, - output_chunk_length=OUT_LEN, - random_state=0, - **kwargs, - ) - model.fit([self.ts_pass_train, self.ts_pass_train_1]) - with pytest.raises(ValueError): - # when model is fit from >1 series, one must provide a series in argument - model.predict(n=1) - pred = model.predict(n=36, series=self.ts_pass_train) + # check prediction for several time series + pred_list = model.predict( + n=36, series=[self.ts_pass_train, self.ts_pass_train_1] + ) + assert len(pred_list) == 2, ( + f"Model {model_cls} did not return a list of prediction" + ) + for pred in pred_list: mape_err = mape(self.ts_pass_val, pred) assert mape_err < err, ( - "Model {} produces errors too high (several time " - "series). Error = {}".format(model_cls, mape_err) + f"Model {model_cls} produces errors too high (several time series 2). " + f"Error = {mape_err}" ) - # check prediction for several time series - pred_list = model.predict( - n=36, series=[self.ts_pass_train, self.ts_pass_train_1] - ) - assert ( - len(pred_list) == 2 - ), f"Model {model_cls} did not return a list of prediction" - for pred in pred_list: - mape_err = mape(self.ts_pass_val, pred) - assert mape_err < err, ( - "Model {} produces errors too high (several time series 2). " - "Error = {}".format(model_cls, mape_err) - ) + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_covariates(self, config): + model_cls, kwargs, err = config + model = model_cls( + input_chunk_length=IN_LEN, + output_chunk_length=OUT_LEN, + random_state=0, + **kwargs, + ) - @pytest.mark.parametrize("config", models_cls_kwargs_errs) - def test_covariates(self, config): - model_cls, kwargs, err = config - model = model_cls( - input_chunk_length=IN_LEN, - output_chunk_length=OUT_LEN, - random_state=0, - **kwargs, - ) + # Here we rely on the fact that all non-Dual models currently are Past models + if model.supports_future_covariates: + cov_name = "future_covariates" + is_past = False + elif model.supports_past_covariates: + cov_name = "past_covariates" + is_past = True + else: + cov_name = None + is_past = None + + covariates = [self.time_covariates_train, self.time_covariates_train] + if cov_name is not None: + cov_kwargs = {cov_name: covariates} + cov_kwargs_train = {cov_name: self.time_covariates_train} + cov_kwargs_notrain = {cov_name: self.time_covariates} + else: + cov_kwargs = {} + cov_kwargs_train = {} + cov_kwargs_notrain = {} - # Here we rely on the fact that all non-Dual models currently are Past models - if isinstance(model, DualCovariatesTorchModel): - cov_name = "future_covariates" - is_past = False - else: - cov_name = "past_covariates" - is_past = True + model.fit(series=[self.ts_pass_train, self.ts_pass_train_1], **cov_kwargs) - cov_kwargs = { - cov_name: [self.time_covariates_train, self.time_covariates_train] - } - model.fit(series=[self.ts_pass_train, self.ts_pass_train_1], **cov_kwargs) + if cov_name is None: + with pytest.raises(ValueError): + model.untrained_model().fit( + series=[self.ts_pass_train, self.ts_pass_train_1], + past_covariates=covariates, + ) with pytest.raises(ValueError): - # when model is fit from >1 series, one must provide a series in argument - model.predict(n=1) + model.untrained_model().fit( + series=[self.ts_pass_train, self.ts_pass_train_1], + future_covariates=covariates, + ) + with pytest.raises(ValueError): + # when model is fit from >1 series, one must provide a series in argument + model.predict(n=1) + if cov_name is not None: with pytest.raises(ValueError): # when model is fit using multiple covariates, covariates are required at prediction time model.predict(n=1, series=self.ts_pass_train) - cov_kwargs_train = {cov_name: self.time_covariates_train} - cov_kwargs_notrain = {cov_name: self.time_covariates} with pytest.raises(ValueError): # when model is fit using covariates, n cannot be greater than output_chunk_length... # (for short covariates) @@ -371,379 +473,406 @@ def test_covariates(self, config): series=self.ts_pass_train, **cov_kwargs_train, ) + else: + # model does not support covariates + with pytest.raises(ValueError): + model.predict( + n=1, + series=self.ts_pass_train, + past_covariates=self.time_covariates, + ) + with pytest.raises(ValueError): + model.predict( + n=1, + series=self.ts_pass_train, + future_covariates=self.time_covariates, + ) - # ... unless future covariates are provided - _ = model.predict(n=13, series=self.ts_pass_train, **cov_kwargs_notrain) + # ... unless future covariates are provided + _ = model.predict(n=13, series=self.ts_pass_train, **cov_kwargs_notrain) - pred = model.predict(n=12, series=self.ts_pass_train, **cov_kwargs_notrain) - mape_err = mape(self.ts_pass_val, pred) - assert mape_err < err, ( - "Model {} produces errors too high (several time " - "series with covariates). Error = {}".format(model_cls, mape_err) - ) + pred = model.predict(n=12, series=self.ts_pass_train, **cov_kwargs_notrain) + mape_err = mape(self.ts_pass_val, pred) + assert mape_err < err, ( + f"Model {model_cls} produces errors too high (several time " + f"series with covariates). Error = {mape_err}" + ) - # when model is fit using 1 training and 1 covariate series, time series args are optional - if model._is_probabilistic: - return - model = model_cls( - input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs + # when model is fit using 1 training and 1 covariate series, time series args are optional + if model.supports_probabilistic_prediction: + return + model = model_cls( + input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs + ) + model.fit(series=self.ts_pass_train, **cov_kwargs_train) + if is_past or is_past is None: + # without covariates or with past covariates from train we can predict up until output_chunk_length + pred1 = model.predict(OUT_LEN) + pred2 = model.predict(OUT_LEN, series=self.ts_pass_train) + pred3 = model.predict(OUT_LEN, **cov_kwargs_train) + pred4 = model.predict( + OUT_LEN, **cov_kwargs_train, series=self.ts_pass_train ) - model.fit(series=self.ts_pass_train, **cov_kwargs_train) - if is_past: - # with past covariates from train we can predict up until output_chunk_length - pred1 = model.predict(1) - pred2 = model.predict(1, series=self.ts_pass_train) - pred3 = model.predict(1, **cov_kwargs_train) - pred4 = model.predict(1, **cov_kwargs_train, series=self.ts_pass_train) - else: - # with future covariates we need additional time steps to predict - with pytest.raises(ValueError): - _ = model.predict(1) - with pytest.raises(ValueError): - _ = model.predict(1, series=self.ts_pass_train) - with pytest.raises(ValueError): - _ = model.predict(1, **cov_kwargs_train) - with pytest.raises(ValueError): - _ = model.predict(1, **cov_kwargs_train, series=self.ts_pass_train) - - pred1 = model.predict(1, **cov_kwargs_notrain) - pred2 = model.predict( - 1, series=self.ts_pass_train, **cov_kwargs_notrain - ) - pred3 = model.predict(1, **cov_kwargs_notrain) - pred4 = model.predict( - 1, **cov_kwargs_notrain, series=self.ts_pass_train - ) - assert pred1 == pred2 - assert pred1 == pred3 - assert pred1 == pred4 - - def test_future_covariates(self): - # models with future covariates should produce better predictions over a long forecasting horizon - # than a model trained with no covariates + if is_past is None: + # without covariates we can predict any horizon + _ = model.predict(OUT_LEN + 1) + else: + # with future covariates we need additional time steps to predict + with pytest.raises(ValueError): + _ = model.predict(1) + with pytest.raises(ValueError): + _ = model.predict(1, series=self.ts_pass_train) + with pytest.raises(ValueError): + _ = model.predict(1, **cov_kwargs_train) + with pytest.raises(ValueError): + _ = model.predict(1, **cov_kwargs_train, series=self.ts_pass_train) - model = TCNModel( - input_chunk_length=50, - output_chunk_length=5, - n_epochs=20, - random_state=0, - **tfm_kwargs, + pred1 = model.predict(OUT_LEN, **cov_kwargs_notrain) + pred2 = model.predict( + OUT_LEN, series=self.ts_pass_train, **cov_kwargs_notrain ) - model.fit(series=self.target_past) - long_pred_no_cov = model.predict(n=160) - - model = TCNModel( - input_chunk_length=50, - output_chunk_length=5, - n_epochs=20, - random_state=0, - **tfm_kwargs, + pred3 = model.predict(OUT_LEN, **cov_kwargs_notrain) + pred4 = model.predict( + OUT_LEN, **cov_kwargs_notrain, series=self.ts_pass_train ) - model.fit(series=self.target_past, past_covariates=self.covariates_past) - long_pred_with_cov = model.predict(n=160, past_covariates=self.covariates) - assert mape(self.target_future, long_pred_no_cov) > mape( - self.target_future, long_pred_with_cov - ), "Models with future covariates should produce better predictions." - # block models can predict up to self.output_chunk_length points beyond the last future covariate... - model.predict(n=165, past_covariates=self.covariates) + assert pred1 == pred2 + assert pred1 == pred3 + assert pred1 == pred4 - # ... not more - with pytest.raises(ValueError): - model.predict(n=166, series=self.ts_pass_train) - - # recurrent models can only predict data points for time steps where future covariates are available - model = RNNModel(12, n_epochs=1, **tfm_kwargs) - model.fit(series=self.target_past, future_covariates=self.covariates_past) - model.predict(n=160, future_covariates=self.covariates) - with pytest.raises(ValueError): - model.predict(n=161, future_covariates=self.covariates) + def test_future_covariates(self): + # models with future covariates should produce better predictions over a long forecasting horizon + # than a model trained with no covariates - @pytest.mark.parametrize( - "model_cls,ts", - product( - [TFTModel, DLinearModel, NLinearModel, TiDEModel], - [ts_w_static_cov, ts_shared_static_cov, ts_comps_static_cov], + model = TCNModel( + input_chunk_length=50, + output_chunk_length=5, + n_epochs=20, + random_state=0, + **tfm_kwargs, + ) + model.fit(series=self.target_past) + long_pred_no_cov = model.predict(n=160) + + model = TCNModel( + input_chunk_length=50, + output_chunk_length=5, + n_epochs=20, + random_state=0, + **tfm_kwargs, + ) + model.fit(series=self.target_past, past_covariates=self.covariates_past) + long_pred_with_cov = model.predict(n=160, past_covariates=self.covariates) + assert mape(self.target_future, long_pred_no_cov) > mape( + self.target_future, long_pred_with_cov + ), "Models with future covariates should produce better predictions." + + # block models can predict up to self.output_chunk_length points beyond the last future covariate... + model.predict(n=165, past_covariates=self.covariates) + + # ... not more + with pytest.raises(ValueError): + model.predict(n=166, series=self.ts_pass_train) + + # recurrent models can only predict data points for time steps where future covariates are available + model = RNNModel(12, n_epochs=1, **tfm_kwargs) + model.fit(series=self.target_past, future_covariates=self.covariates_past) + model.predict(n=160, future_covariates=self.covariates) + with pytest.raises(ValueError): + model.predict(n=161, future_covariates=self.covariates) + + @pytest.mark.parametrize( + "model_cls,ts", + product( + [TFTModel, DLinearModel, NLinearModel, TiDEModel, TSMixerModel], + [ts_w_static_cov, ts_shared_static_cov, ts_comps_static_cov], + ), + ) + def test_use_static_covariates(self, model_cls, ts): + """ + Check that both static covariates representations are supported (component-specific and shared) + for both uni- and multivariate series when fitting the model. + Also check that the static covariates are present in the forecasted series + """ + model = model_cls( + input_chunk_length=IN_LEN, + output_chunk_length=OUT_LEN, + random_state=0, + use_static_covariates=True, + n_epochs=1, + **tfm_kwargs, + ) + # must provide mandatory future_covariates to TFTModel + model.fit( + series=ts, + future_covariates=( + self.sine_1_ts if model.supports_future_covariates else None ), ) - def test_use_static_covariates(self, model_cls, ts): - """ - Check that both static covariates representations are supported (component-specific and shared) - for both uni- and multivariate series when fitting the model. - Also check that the static covariates are present in the forecasted series - """ - model = model_cls( - input_chunk_length=IN_LEN, - output_chunk_length=OUT_LEN, - random_state=0, - use_static_covariates=True, - n_epochs=1, - **tfm_kwargs, - ) - # must provide mandatory future_covariates to TFTModel - model.fit( - series=ts, - future_covariates=self.sine_1_ts - if model.supports_future_covariates - else None, - ) - pred = model.predict(OUT_LEN) - assert pred.static_covariates.equals(ts.static_covariates) - - def test_batch_predictions(self): - # predicting multiple time series at once needs to work for arbitrary batch sizes - # univariate case - targets_univar = [ - self.target_past, - self.target_past[:60], - self.target_past[:80], - ] - self._batch_prediction_test_helper_function(targets_univar) - - # multivariate case - targets_multivar = [tgt.stack(tgt) for tgt in targets_univar] - self._batch_prediction_test_helper_function(targets_multivar) - - def _batch_prediction_test_helper_function(self, targets): - epsilon = 1e-4 - model = TCNModel( - input_chunk_length=50, - output_chunk_length=10, - n_epochs=10, - random_state=0, - **tfm_kwargs, - ) - model.fit(series=targets[0], past_covariates=self.covariates_past) - preds_default = model.predict( + pred = model.predict(OUT_LEN) + assert pred.static_covariates.equals(ts.static_covariates) + + def test_batch_predictions(self): + # predicting multiple time series at once needs to work for arbitrary batch sizes + # univariate case + targets_univar = [ + self.target_past, + self.target_past[:60], + self.target_past[:80], + ] + self._batch_prediction_test_helper_function(targets_univar) + + # multivariate case + targets_multivar = [tgt.stack(tgt) for tgt in targets_univar] + self._batch_prediction_test_helper_function(targets_multivar) + + def _batch_prediction_test_helper_function(self, targets): + epsilon = 1e-4 + model = TCNModel( + input_chunk_length=50, + output_chunk_length=10, + n_epochs=10, + random_state=0, + **tfm_kwargs, + ) + model.fit(series=targets[0], past_covariates=self.covariates_past) + preds_default = model.predict( + n=160, + series=targets, + past_covariates=[self.covariates] * len(targets), + batch_size=None, + ) + + # make batch size large enough to test stacking samples + for batch_size in range(1, 4 * len(targets)): + preds = model.predict( n=160, series=targets, past_covariates=[self.covariates] * len(targets), - batch_size=None, + batch_size=batch_size, ) + for i in range(len(targets)): + assert sum(sum((preds[i] - preds_default[i]).values())) < epsilon + + def test_predict_from_dataset_unsupported_input(self): + # an exception should be thrown if an unsupported type is passed + unsupported_type = "unsupported_type" + # just need to test this with one model + model_cls, kwargs, err = models_cls_kwargs_errs[0] + model = model_cls( + input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs + ) + model.fit([self.ts_pass_train, self.ts_pass_train_1]) - # make batch size large enough to test stacking samples - for batch_size in range(1, 4 * len(targets)): - preds = model.predict( - n=160, - series=targets, - past_covariates=[self.covariates] * len(targets), - batch_size=batch_size, - ) - for i in range(len(targets)): - assert sum(sum((preds[i] - preds_default[i]).values())) < epsilon - - def test_predict_from_dataset_unsupported_input(self): - # an exception should be thrown if an unsupported type is passed - unsupported_type = "unsupported_type" - # just need to test this with one model - model_cls, kwargs, err = models_cls_kwargs_errs[0] - model = model_cls( - input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs - ) - model.fit([self.ts_pass_train, self.ts_pass_train_1]) + with pytest.raises(ValueError): + model.predict_from_dataset(n=1, input_series_dataset=unsupported_type) - with pytest.raises(ValueError): - model.predict_from_dataset(n=1, input_series_dataset=unsupported_type) - - @pytest.mark.parametrize("config", models_cls_kwargs_errs) - def test_prediction_with_different_n(self, config): - # test model predictions for n < out_len, n == out_len and n > out_len - model_cls, kwargs, err = config - model = model_cls( - input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs - ) - assert isinstance( - model, - ( - PastCovariatesTorchModel, - DualCovariatesTorchModel, - MixedCovariatesTorchModel, - ), - ), "unit test not yet defined for the given {X}CovariatesTorchModel." - - if isinstance(model, PastCovariatesTorchModel): - past_covs, future_covs = self.covariates, None - elif isinstance(model, DualCovariatesTorchModel): - past_covs, future_covs = None, self.covariates - else: - past_covs, future_covs = self.covariates, self.covariates - - model.fit( - self.target_past, - past_covariates=past_covs, - future_covariates=future_covs, - epochs=1, + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_prediction_with_different_n(self, config): + # test model predictions for n < out_len, n == out_len and n > out_len + model_cls, kwargs, err = config + model = model_cls( + input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs + ) + assert isinstance( + model, + ( + PastCovariatesTorchModel, + DualCovariatesTorchModel, + MixedCovariatesTorchModel, + ), + ), "unit test not yet defined for the given {X}CovariatesTorchModel." + + if model.supports_past_covariates and model.supports_future_covariates: + past_covs, future_covs = None, self.covariates + elif model.supports_past_covariates: + past_covs, future_covs = self.covariates, None + elif model.supports_future_covariates: + past_covs, future_covs = None, self.covariates + else: + past_covs, future_covs = None, None + + model.fit( + self.target_past, + past_covariates=past_covs, + future_covariates=future_covs, + epochs=1, + ) + + # test prediction for n < out_len, n == out_len and n > out_len + for n in [OUT_LEN - 1, OUT_LEN, 2 * OUT_LEN - 1]: + pred = model.predict( + n=n, past_covariates=past_covs, future_covariates=future_covs ) + assert len(pred) == n - # test prediction for n < out_len, n == out_len and n > out_len - for n in [OUT_LEN - 1, OUT_LEN, 2 * OUT_LEN - 1]: - pred = model.predict( - n=n, past_covariates=past_covs, future_covariates=future_covs - ) - assert len(pred) == n + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_same_result_with_different_n_jobs(self, config): + model_cls, kwargs, err = config + model = model_cls( + input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs + ) - @pytest.mark.parametrize("config", models_cls_kwargs_errs) - def test_same_result_with_different_n_jobs(self, config): - model_cls, kwargs, err = config - model = model_cls( - input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs - ) + multiple_ts = [self.ts_pass_train] * 10 - multiple_ts = [self.ts_pass_train] * 10 + model.fit(multiple_ts) - model.fit(multiple_ts) + # safe random state for two successive identical predictions + if model.supports_probabilistic_prediction: + random_state = deepcopy(model._random_instance) + else: + random_state = None - # safe random state for two successive identical predictions - if model._is_probabilistic: - random_state = deepcopy(model._random_instance) - else: - random_state = None + pred1 = model.predict(n=36, series=multiple_ts, n_jobs=1) - pred1 = model.predict(n=36, series=multiple_ts, n_jobs=1) + if random_state is not None: + model._random_instance = random_state - if random_state is not None: - model._random_instance = random_state + pred2 = model.predict( + n=36, series=multiple_ts, n_jobs=-1 + ) # assuming > 1 core available in the machine + assert pred1 == pred2, ( + "Model {} produces different predictions with different number of jobs" + ) - pred2 = model.predict( - n=36, series=multiple_ts, n_jobs=-1 - ) # assuming > 1 core available in the machine - assert ( - pred1 == pred2 - ), "Model {} produces different predictions with different number of jobs" - - @patch( - "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel._init_trainer" - ) - @pytest.mark.parametrize("config", models_cls_kwargs_errs) - def test_fit_with_constr_epochs(self, init_trainer, config): - model_cls, kwargs, err = config - model = model_cls( - input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs - ) - multiple_ts = [self.ts_pass_train] * 10 - model.fit(multiple_ts) + @patch( + "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel._init_trainer" + ) + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_fit_with_constr_epochs(self, init_trainer, config): + model_cls, kwargs, err = config + model = model_cls( + input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs + ) + if not model._requires_training: + return + multiple_ts = [self.ts_pass_train] * 10 + model.fit(multiple_ts) - init_trainer.assert_called_with( - max_epochs=kwargs["n_epochs"], trainer_params=ANY - ) + init_trainer.assert_called_with( + max_epochs=kwargs["n_epochs"], trainer_params=ANY + ) - @patch( - "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel._init_trainer" + @patch( + "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel._init_trainer" + ) + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_fit_with_fit_epochs(self, init_trainer, config): + model_cls, kwargs, err = config + model = model_cls( + input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs ) - @pytest.mark.parametrize("config", models_cls_kwargs_errs) - def test_fit_with_fit_epochs(self, init_trainer, config): - model_cls, kwargs, err = config - model = model_cls( - input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs - ) - multiple_ts = [self.ts_pass_train] * 10 - epochs = 3 + multiple_ts = [self.ts_pass_train] * 10 + epochs = 3 - model.fit(multiple_ts, epochs=epochs) - init_trainer.assert_called_with(max_epochs=epochs, trainer_params=ANY) + model.fit(multiple_ts, epochs=epochs) + init_trainer.assert_called_with(max_epochs=epochs, trainer_params=ANY) - model.total_epochs = epochs - # continue training - model.fit(multiple_ts, epochs=epochs) - init_trainer.assert_called_with(max_epochs=epochs, trainer_params=ANY) + model.total_epochs = epochs + # continue training + model.fit(multiple_ts, epochs=epochs) + init_trainer.assert_called_with(max_epochs=epochs, trainer_params=ANY) - @patch( - "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel._init_trainer" + @patch( + "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel._init_trainer" + ) + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_fit_from_dataset_with_epochs(self, init_trainer, config): + model_cls, kwargs, err = config + model = model_cls( + input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs ) - @pytest.mark.parametrize("config", models_cls_kwargs_errs) - def test_fit_from_dataset_with_epochs(self, init_trainer, config): - model_cls, kwargs, err = config - model = model_cls( - input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs - ) - multiple_ts = [self.ts_pass_train] * 10 - train_dataset = model._build_train_dataset( - multiple_ts, - past_covariates=None, - future_covariates=None, - max_samples_per_ts=None, - ) - epochs = 3 + multiple_ts = [self.ts_pass_train] * 10 + train_dataset = model._build_train_dataset( + multiple_ts, + past_covariates=None, + future_covariates=None, + sample_weight=None, + max_samples_per_ts=None, + ) + epochs = 3 - model.fit_from_dataset(train_dataset, epochs=epochs) - init_trainer.assert_called_with(max_epochs=epochs, trainer_params=ANY) + model.fit_from_dataset(train_dataset, epochs=epochs) + init_trainer.assert_called_with(max_epochs=epochs, trainer_params=ANY) - # continue training - model.fit_from_dataset(train_dataset, epochs=epochs) - init_trainer.assert_called_with(max_epochs=epochs, trainer_params=ANY) + # continue training + model.fit_from_dataset(train_dataset, epochs=epochs) + init_trainer.assert_called_with(max_epochs=epochs, trainer_params=ANY) - def test_predit_after_fit_from_dataset(self): - model_cls, kwargs, _ = models_cls_kwargs_errs[0] - model = model_cls( - input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs - ) + @pytest.mark.parametrize("config", models_cls_kwargs_errs) + def test_predit_after_fit_from_dataset(self, config): + model_cls, kwargs, _ = config + model = model_cls( + input_chunk_length=IN_LEN, output_chunk_length=OUT_LEN, **kwargs + ) - multiple_ts = [self.ts_pass_train] * 10 - train_dataset = model._build_train_dataset( - multiple_ts, - past_covariates=None, - future_covariates=None, - max_samples_per_ts=None, - ) - model.fit_from_dataset(train_dataset, epochs=3) - - # test predict() works after fit_from_dataset() - model.predict(n=1, series=multiple_ts[0]) - - def test_sample_smaller_than_batch_size(self): - """ - Checking that the TorchForecastingModels do not crash even if the number of available samples for training - is strictly lower than the selected batch_size - """ - # TS with 50 timestamps. TorchForecastingModels will use the SequentialDataset for producing training - # samples, which means we will have 50 - 22 - 2 + 1 = 27 samples, which is < 32 (batch_size). The model - # should still train on those samples and not crash in any way - ts = linear_timeseries(start_value=0, end_value=1, length=50) - - model = RNNModel( - input_chunk_length=20, - output_chunk_length=2, - n_epochs=2, - batch_size=32, - **tfm_kwargs, - ) - model.fit(ts) + multiple_ts = [self.ts_pass_train] * 2 + train_dataset = model._build_train_dataset( + multiple_ts, + past_covariates=None, + future_covariates=None, + sample_weight=None, + max_samples_per_ts=None, + ) + model.fit_from_dataset(train_dataset, epochs=1) + + # test predict() works after fit_from_dataset() + model.predict(n=1, series=multiple_ts[0]) + + def test_sample_smaller_than_batch_size(self): + """ + Checking that the TorchForecastingModels do not crash even if the number of available samples for training + is strictly lower than the selected batch_size + """ + # TS with 50 timestamps. TorchForecastingModels will use the SequentialDataset for producing training + # samples, which means we will have 50 - 22 - 2 + 1 = 27 samples, which is < 32 (batch_size). The model + # should still train on those samples and not crash in any way + ts = linear_timeseries(start_value=0, end_value=1, length=50) + + model = RNNModel( + input_chunk_length=20, + output_chunk_length=2, + n_epochs=2, + batch_size=32, + **tfm_kwargs, + ) + model.fit(ts) - def test_max_samples_per_ts(self): - """ - Checking that we can fit TorchForecastingModels with max_samples_per_ts, without crash - """ + def test_max_samples_per_ts(self): + """ + Checking that we can fit TorchForecastingModels with max_samples_per_ts, without crash + """ - ts = linear_timeseries(start_value=0, end_value=1, length=50) + ts = linear_timeseries(start_value=0, end_value=1, length=50) - model = RNNModel( - input_chunk_length=20, - output_chunk_length=2, - n_epochs=2, - batch_size=32, - **tfm_kwargs, - ) + model = RNNModel( + input_chunk_length=20, + output_chunk_length=2, + n_epochs=2, + batch_size=32, + **tfm_kwargs, + ) - model.fit(ts, max_samples_per_ts=5) - - def test_residuals(self): - """ - Torch models should not fail when computing residuals on a series - long enough to accommodate at least one training sample. - """ - ts = linear_timeseries(start_value=0, end_value=1, length=38) - - model = NBEATSModel( - input_chunk_length=24, - output_chunk_length=12, - num_stacks=2, - num_blocks=1, - num_layers=1, - layer_widths=2, - n_epochs=2, - **tfm_kwargs, - ) + model.fit(ts, max_samples_per_ts=5) + + def test_residuals(self): + """ + Torch models should not fail when computing residuals on a series + long enough to accommodate at least one training sample. + """ + ts = linear_timeseries(start_value=0, end_value=1, length=38) + + model = NBEATSModel( + input_chunk_length=24, + output_chunk_length=12, + num_stacks=2, + num_blocks=1, + num_layers=1, + layer_widths=2, + n_epochs=2, + **tfm_kwargs, + ) - res = model.residuals(ts) - assert len(res) == 38 - (24 + 12) + res = model.residuals(ts) + assert len(res) == 38 - (24 + 12) diff --git a/darts/tests/models/forecasting/test_historical_forecasts.py b/darts/tests/models/forecasting/test_historical_forecasts.py deleted file mode 100644 index fe9042d170..0000000000 --- a/darts/tests/models/forecasting/test_historical_forecasts.py +++ /dev/null @@ -1,2138 +0,0 @@ -import itertools -from itertools import product - -import numpy as np -import pandas as pd -import pytest - -import darts -from darts import TimeSeries, concatenate -from darts.dataprocessing.transformers import Scaler -from darts.datasets import AirPassengersDataset -from darts.logging import get_logger -from darts.models import ( - ARIMA, - AutoARIMA, - CatBoostModel, - LightGBMModel, - LinearRegressionModel, - NaiveSeasonal, - NotImportedModule, -) -from darts.tests.conftest import tfm_kwargs -from darts.utils import timeseries_generation as tg - -try: - import torch - - from darts.models import ( - BlockRNNModel, - NBEATSModel, - NLinearModel, - RNNModel, - TCNModel, - TFTModel, - TiDEModel, - TransformerModel, - ) - from darts.utils.likelihood_models import GaussianLikelihood, QuantileRegression - - TORCH_AVAILABLE = True -except ImportError: - logger = get_logger(__name__) - logger.warning( - "Torch not installed - will be skipping historical forecasts tests for torch models" - ) - TORCH_AVAILABLE = False - -models_reg_no_cov_cls_kwargs = [(LinearRegressionModel, {"lags": 8}, {}, (8, 1))] -if not isinstance(CatBoostModel, NotImportedModule): - models_reg_no_cov_cls_kwargs.append( - (CatBoostModel, {"lags": 6}, {"iterations": 1}, (6, 1)) - ) -if not isinstance(LightGBMModel, NotImportedModule): - models_reg_no_cov_cls_kwargs.append( - (LightGBMModel, {"lags": 4}, {"n_estimators": 1}, (4, 1)) - ) - -models_reg_cov_cls_kwargs = [ - # target + past covariates - (LinearRegressionModel, {"lags": 4, "lags_past_covariates": 6}, {}, (6, 1)), - # target + past covariates + outputchunk > 3, 6 > 3 - ( - LinearRegressionModel, - {"lags": 3, "lags_past_covariates": 6, "output_chunk_length": 5}, - {}, - (6, 5), - ), - # target + future covariates, 2 because to predict x, require x and x+1 - (LinearRegressionModel, {"lags": 4, "lags_future_covariates": [0, 1]}, {}, (4, 2)), - # target + fut cov + output_chunk_length > 3, - ( - LinearRegressionModel, - {"lags": 2, "lags_future_covariates": [1, 2], "output_chunk_length": 5}, - {}, - (2, 5), - ), - # fut cov + output_chunk_length > 3, 5 > 2 - ( - LinearRegressionModel, - {"lags_future_covariates": [0, 1], "output_chunk_length": 5}, - {}, - (0, 5), - ), - # past cov only - (LinearRegressionModel, {"lags_past_covariates": 6}, {}, (6, 1)), - # fut cov only - (LinearRegressionModel, {"lags_future_covariates": [0, 1]}, {}, (0, 2)), - # fut + past cov only - ( - LinearRegressionModel, - {"lags_past_covariates": 6, "lags_future_covariates": [0, 1]}, - {}, - (6, 2), - ), - # all - ( - LinearRegressionModel, - {"lags": 3, "lags_past_covariates": 6, "lags_future_covariates": [0, 1]}, - {}, - (6, 2), - ), -] - -if TORCH_AVAILABLE: - IN_LEN = 24 - OUT_LEN = 12 - - NB_EPOCH = 1 - - models_torch_cls_kwargs = [ - ( - BlockRNNModel, - { - "input_chunk_length": IN_LEN, - "output_chunk_length": OUT_LEN, - "model": "RNN", - "hidden_dim": 10, - "n_rnn_layers": 1, - "batch_size": 32, - "n_epochs": NB_EPOCH, - **tfm_kwargs, - }, - # Min of lags needed and max of lags needed - (IN_LEN, OUT_LEN), - "PastCovariates", - ), - ( - RNNModel, - { - "input_chunk_length": IN_LEN, - "model": "RNN", - "hidden_dim": 10, - "batch_size": 32, - "n_epochs": NB_EPOCH, - **tfm_kwargs, - }, - # autoregressive model - (IN_LEN, 1), - "DualCovariates", - ), - ( - RNNModel, - { - "input_chunk_length": IN_LEN, - "training_length": 12, - "n_epochs": NB_EPOCH, - "likelihood": GaussianLikelihood(), - **tfm_kwargs, - }, - (IN_LEN, 1), - "DualCovariates", - ), - ( - TCNModel, - { - "input_chunk_length": IN_LEN, - "output_chunk_length": OUT_LEN, - "n_epochs": NB_EPOCH, - "batch_size": 32, - **tfm_kwargs, - }, - (IN_LEN, OUT_LEN), - "PastCovariates", - ), - ( - TransformerModel, - { - "input_chunk_length": IN_LEN, - "output_chunk_length": OUT_LEN, - "d_model": 16, - "nhead": 2, - "num_encoder_layers": 2, - "num_decoder_layers": 2, - "dim_feedforward": 16, - "batch_size": 32, - "n_epochs": NB_EPOCH, - **tfm_kwargs, - }, - (IN_LEN, OUT_LEN), - "PastCovariates", - ), - ( - NBEATSModel, - { - "input_chunk_length": IN_LEN, - "output_chunk_length": OUT_LEN, - "num_stacks": 4, - "num_blocks": 1, - "num_layers": 2, - "layer_widths": 12, - "n_epochs": NB_EPOCH, - **tfm_kwargs, - }, - (IN_LEN, OUT_LEN), - "PastCovariates", - ), - ( - TFTModel, - { - "input_chunk_length": IN_LEN, - "output_chunk_length": OUT_LEN, - "hidden_size": 16, - "lstm_layers": 1, - "num_attention_heads": 4, - "add_relative_index": True, - "n_epochs": NB_EPOCH, - **tfm_kwargs, - }, - (IN_LEN, OUT_LEN), - "MixedCovariates", - ), - ( - NLinearModel, - { - "input_chunk_length": IN_LEN, - "output_chunk_length": OUT_LEN, - "n_epochs": NB_EPOCH, - **tfm_kwargs, - }, - (IN_LEN, OUT_LEN), - "MixedCovariates", - ), - ( - TiDEModel, - { - "input_chunk_length": IN_LEN, - "output_chunk_length": OUT_LEN, - "n_epochs": NB_EPOCH, - **tfm_kwargs, - }, - (IN_LEN, OUT_LEN), - "MixedCovariates", - ), - ] -else: - models_torch_cls_kwargs = [] - - -class TestHistoricalforecast: - np.random.seed(42) - if TORCH_AVAILABLE: - torch.manual_seed(42) - - # real timeseries for functionality tests - ts_val_length = 72 - ts_passengers = AirPassengersDataset().load() - scaler = Scaler() - ts_passengers = scaler.fit_transform(ts_passengers) - ts_pass_train, ts_pass_val = ( - ts_passengers[:-ts_val_length], - ts_passengers[-ts_val_length:], - ) - - # an additional noisy series - ts_pass_train_1 = ts_pass_train + 0.01 * tg.gaussian_timeseries( - length=len(ts_pass_train), - freq=ts_pass_train.freq_str, - start=ts_pass_train.start_time(), - ) - - ts_past_cov_train = tg.gaussian_timeseries( - length=len(ts_pass_train), - freq=ts_pass_train.freq_str, - start=ts_pass_train.start_time(), - ) - - ts_fut_cov_train = tg.gaussian_timeseries( - length=len(ts_pass_train), - freq=ts_pass_train.freq_str, - start=ts_pass_train.start_time(), - ) - - ts_past_cov_valid_same_start = tg.gaussian_timeseries( - length=len(ts_pass_val), - freq=ts_pass_val.freq_str, - start=ts_pass_val.start_time(), - ) - - ts_past_cov_valid_10_bef_start = tg.gaussian_timeseries( - length=len(ts_pass_val) + 10, - freq=ts_pass_val.freq_str, - start=ts_pass_val.start_time() - 10 * ts_pass_val.freq, - ) - ts_past_cov_valid_5_aft_start = tg.gaussian_timeseries( - length=len(ts_pass_val) - 5, - freq=ts_pass_val.freq_str, - start=ts_pass_val.start_time() + 5 * ts_pass_val.freq, - ) - - ts_fut_cov_valid_same_start = tg.gaussian_timeseries( - length=len(ts_pass_val), - freq=ts_pass_val.freq_str, - start=ts_pass_val.start_time(), - ) - - ts_fut_cov_valid_16_bef_start = tg.gaussian_timeseries( - length=len(ts_pass_val) + 16, - freq=ts_pass_val.freq_str, - start=ts_pass_val.start_time() - 16 * ts_pass_val.freq, - ) - ts_fut_cov_valid_7_aft_start = tg.gaussian_timeseries( - length=len(ts_pass_val) - 7, - freq=ts_pass_val.freq_str, - start=ts_pass_val.start_time() + 7 * ts_pass_val.freq, - ) - - # RangeIndex timeseries - ts_passengers_range = TimeSeries.from_values(ts_passengers.values()) - ts_pass_train_range, ts_pass_val_range = ( - ts_passengers_range[:-ts_val_length], - ts_passengers_range[-ts_val_length:], - ) - - ts_past_cov_train_range = tg.gaussian_timeseries( - length=len(ts_pass_train_range), - freq=ts_pass_train_range.freq_str, - start=ts_pass_train_range.start_time(), - ) - - # same starting point - ts_past_cov_valid_range_same_start = tg.gaussian_timeseries( - length=len(ts_pass_val_range), - freq=ts_pass_val_range.freq_str, - start=ts_pass_val_range.start_time(), - ) - - # optimized historical forecasts - start_ts = pd.Timestamp("2000-01-01") - ts_univariate = tg.linear_timeseries( - start_value=1, end_value=100, length=20, start=start_ts - ) - ts_multivariate = ts_univariate.stack(tg.sine_timeseries(length=20, start=start_ts)) - - # slightly longer to not affect the last predictable timestamp - ts_covs = tg.gaussian_timeseries(length=30, start=start_ts) - - @staticmethod - def create_model(ocl, use_ll=True, model_type="regression"): - if model_type == "regression": - return LinearRegressionModel( - lags=3, - likelihood="quantile" if use_ll else None, - quantiles=[0.05, 0.4, 0.5, 0.6, 0.95] if use_ll else None, - output_chunk_length=ocl, - ) - else: # model_type == "torch" - if not TORCH_AVAILABLE: - return None - return NLinearModel( - input_chunk_length=3, - likelihood=QuantileRegression([0.05, 0.4, 0.5, 0.6, 0.95]) - if use_ll - else None, - output_chunk_length=ocl, - n_epochs=1, - random_state=42, - **tfm_kwargs, - ) - - def test_historical_forecasts_transferrable_future_cov_local_models(self): - model = ARIMA() - assert model.min_train_series_length == 30 - series = tg.sine_timeseries(length=31) - res = model.historical_forecasts( - series, future_covariates=series, retrain=True, forecast_horizon=1 - ) - # ARIMA has a minimum train length of 30, with horizon=1, we expect one forecast at last point - # (series has length 31) - assert len(res) == 1 - assert series.end_time() == res.time_index[0] - - model.fit(series, future_covariates=series) - res = model.historical_forecasts( - series, future_covariates=series, retrain=False, forecast_horizon=1 - ) - # currently even though transferrable local models would allow , the models currently still take the - # min_train_length as input for historical forecast predictions (due to extreme_lags not differentiating - # between fit and predict) - # (series has length 31) - assert len(res) == 1 - assert series.end_time() == res.time_index[0] - - # passing non-supported covariates - with pytest.raises(ValueError) as msg: - model.historical_forecasts( - series, - past_covariates=series, - retrain=False, - ) - assert str(msg.value).startswith( - "Model prediction does not support `past_covariates`" - ) - - def test_historical_forecasts_future_cov_local_models(self): - model = AutoARIMA() - assert model.min_train_series_length == 10 - series = tg.sine_timeseries(length=11) - res = model.historical_forecasts( - series, future_covariates=series, retrain=True, forecast_horizon=1 - ) - # AutoARIMA has a minimum train length of 10, with horizon=1, we expect one forecast at last point - # (series has length 11) - assert len(res) == 1 - assert series.end_time() == res.time_index[0] - - model.fit(series, future_covariates=series) - with pytest.raises(ValueError) as msg: - model.historical_forecasts( - series, future_covariates=series, retrain=False, forecast_horizon=1 - ) - assert str(msg.value).startswith( - "FutureCovariatesLocalForecastingModel does not support historical forecasting " - "with `retrain` set to `False`" - ) - - # passing non-supported covariates - with pytest.raises(ValueError) as msg: - model.historical_forecasts( - series, - past_covariates=series, - retrain=True, - ) - assert str(msg.value).startswith( - "Model cannot be fit/trained with `past_covariates`." - ) - - def test_historical_forecasts_local_models(self): - model = NaiveSeasonal() - assert model.min_train_series_length == 3 - series = tg.sine_timeseries(length=4) - res = model.historical_forecasts(series, retrain=True, forecast_horizon=1) - # NaiveSeasonal has a minimum train length of 3, with horizon=1, we expect one forecast at last point - # (series has length 4) - assert len(res) == 1 - assert series.end_time() == res.time_index[0] - - model.fit(series) - with pytest.raises(ValueError) as msg: - model.historical_forecasts(series, retrain=False, forecast_horizon=1) - assert str(msg.value).startswith( - "LocalForecastingModel does not support historical forecasting with `retrain` set to `False`" - ) - - def test_historical_forecasts_position_start(self): - series = tg.sine_timeseries(length=10) - - model = LinearRegressionModel(lags=2) - model.fit(series[:8]) - - # negative index - forecasts_neg = model.historical_forecasts( - series=series, start=-2, start_format="position", retrain=False - ) - assert len(forecasts_neg) == 2 - assert (series.time_index[-2:] == forecasts_neg.time_index).all() - - # positive index - forecasts_pos = model.historical_forecasts( - series=series, start=8, start_format="position", retrain=False - ) - assert forecasts_pos == forecasts_neg - - def test_historical_forecasts_negative_rangeindex(self): - series = TimeSeries.from_times_and_values( - times=pd.RangeIndex(start=-5, stop=5, step=1), values=np.arange(10) - ) - - model = LinearRegressionModel(lags=2) - model.fit(series[:8]) - - # start as point - forecasts = model.historical_forecasts( - series=series, start=-2, start_format="value", retrain=False - ) - assert len(forecasts) == 7 - assert (series.time_index[-7:] == forecasts.time_index).all() - - # start as index - forecasts = model.historical_forecasts( - series=series, start=-2, start_format="position", retrain=False - ) - assert len(forecasts) == 2 - assert (series.time_index[-2:] == forecasts.time_index).all() - - @pytest.mark.parametrize("config", models_reg_no_cov_cls_kwargs) - def test_historical_forecasts(self, config): - train_length = 10 - forecast_horizon = 8 - # if no fit and retrain=false, should fit at fist iteration - model_cls, kwargs, model_kwarg, bounds = config - model = model_cls(**kwargs, **model_kwarg) - - # time index - forecasts = model.historical_forecasts( - series=self.ts_pass_val, - forecast_horizon=forecast_horizon, - stride=1, - train_length=train_length, - retrain=True, - overlap_end=False, - ) - - theorical_forecast_length = ( - self.ts_val_length - - max( - [ - ( - bounds[0] + bounds[1] + 1 - ), # +1 as sklearn models require min 2 train samples - train_length, - ] - ) # because we train - - forecast_horizon # because we have overlap_end = False - + 1 # because we include the first element - ) - - assert len(forecasts) == theorical_forecast_length, ( - f"Model {model_cls.__name__} does not return the right number of historical forecasts in the case " - f"of retrain=True and overlap_end=False, and a time index of type DateTimeIndex. " - f"Expected {theorical_forecast_length}, got {len(forecasts)}" - ) - - # range index - forecasts = model.historical_forecasts( - series=self.ts_pass_val_range, - forecast_horizon=forecast_horizon, - train_length=train_length, - stride=1, - retrain=True, - overlap_end=False, - ) - - assert len(forecasts) == theorical_forecast_length, ( - f"Model {model_cls.__name__} does not return the right number of historical forecasts in the case " - f"of retrain=True, overlap_end=False, and a time index of type RangeIndex." - f"Expected {theorical_forecast_length}, got {len(forecasts)}" - ) - - # stride 2 - forecasts = model.historical_forecasts( - series=self.ts_pass_val_range, - forecast_horizon=forecast_horizon, - train_length=train_length, - stride=2, - retrain=True, - overlap_end=False, - ) - - theorical_forecast_length = np.floor( - ( - ( - self.ts_val_length - - max( - [ - ( - bounds[0] + bounds[1] + 1 - ), # +1 as sklearn models require min 2 train samples - train_length, - ] - ) # because we train - - forecast_horizon # because we have overlap_end = False - + 1 # because we include the first element - ) - - 1 - ) - / 2 - + 1 # because of stride - ) # if odd number of elements, we keep the floor - - assert len(forecasts) == theorical_forecast_length, ( - f"Model {model_cls.__name__} does not return the right number of historical forecasts in the case " - f"of retrain=True and overlap_end=False and stride=2. " - f"Expected {theorical_forecast_length}, got {len(forecasts)}" - ) - - # stride 3 - forecasts = model.historical_forecasts( - series=self.ts_pass_val_range, - forecast_horizon=forecast_horizon, - train_length=train_length, - stride=3, - retrain=True, - overlap_end=False, - ) - - theorical_forecast_length = np.floor( - ( - ( - self.ts_val_length - - max( - [ - ( - bounds[0] + bounds[1] + 1 - ), # +1 as sklearn models require min 2 train samples - train_length, - ] - ) # because we train - - forecast_horizon # because we have overlap_end = False - + 1 # because we include the first element - ) - - 1 - ) # the first is always included, so we calculate a modulo on the rest - / 3 # because of stride - + 1 # and we readd the first - ) # if odd number of elements, we keep the floor - - # Here to adapt if forecast_horizon or train_length change - assert len(forecasts) == theorical_forecast_length, ( - f"Model {model_cls.__name__} does not return the right number of historical forecasts in the case " - f"of retrain=True and overlap_end=False and stride=3. " - f"Expected {theorical_forecast_length}, got {len(forecasts)}" - ) - - # last points only False - forecasts = model.historical_forecasts( - series=self.ts_pass_val_range, - forecast_horizon=forecast_horizon, - train_length=train_length, - stride=1, - retrain=True, - overlap_end=False, - last_points_only=False, - ) - - theorical_forecast_length = ( - self.ts_val_length - - max( - [ - ( - bounds[0] + bounds[1] + 1 - ), # +1 as sklearn models require min 2 train samples - train_length, - ] - ) # because we train - - forecast_horizon # because we have overlap_end = False - + 1 # because we include the first element - ) - - assert len(forecasts) == theorical_forecast_length, ( - f"Model {model_cls} does not return the right number of historical forecasts in the case of " - f"retrain=True and overlap_end=False, and last_points_only=False. " - f"expected {theorical_forecast_length}, got {len(forecasts)}" - ) - - assert len(forecasts[0]) == forecast_horizon, ( - f"Model {model_cls} does not return forecast_horizon points per historical forecast in the case of " - f"retrain=True and overlap_end=False, and last_points_only=False" - ) - - if not model.supports_past_covariates: - with pytest.raises(ValueError) as msg: - model.historical_forecasts( - series=self.ts_pass_val_range, - past_covariates=self.ts_passengers, - retrain=True, - ) - assert str(msg.value).startswith( - "Model cannot be fit/trained with `past_covariates`." - ) - - if not model.supports_future_covariates: - with pytest.raises(ValueError) as msg: - model.historical_forecasts( - series=self.ts_pass_val_range, - future_covariates=self.ts_passengers, - last_points_only=False, - ) - assert str(msg.value).startswith( - "Model cannot be fit/trained with `future_covariates`." - ) - - def test_sanity_check_invalid_start(self): - timeidx_ = tg.linear_timeseries(length=10) - rangeidx_step1 = tg.linear_timeseries(start=0, length=10, freq=1) - rangeidx_step2 = tg.linear_timeseries(start=0, length=10, freq=2) - - # label_index (int), too large - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts(timeidx_, start=11) - assert str(msg.value).startswith("`start` index `11` is out of bounds") - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts( - rangeidx_step1, start=rangeidx_step1.end_time() + rangeidx_step1.freq - ) - assert str(msg.value).startswith( - "`start` index `10` is larger than the last index" - ) - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts( - rangeidx_step2, start=rangeidx_step2.end_time() + rangeidx_step2.freq - ) - assert str(msg.value).startswith( - "`start` index `20` is larger than the last index" - ) - - # label_index (timestamp) too high - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts( - timeidx_, start=timeidx_.end_time() + timeidx_.freq - ) - assert str(msg.value).startswith( - "`start` time `2000-01-11 00:00:00` is after the last timestamp `2000-01-10 00:00:00`" - ) - - # label_index, invalid - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts(rangeidx_step2, start=11) - assert str(msg.value).startswith("The provided point is not a valid index") - - # label_index, too low - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts( - timeidx_, start=timeidx_.start_time() - timeidx_.freq - ) - assert str(msg.value).startswith( - "`start` time `1999-12-31 00:00:00` is before the first timestamp `2000-01-01 00:00:00`" - ) - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts( - rangeidx_step1, start=rangeidx_step1.start_time() - rangeidx_step1.freq - ) - assert str(msg.value).startswith( - "`start` index `-1` is smaller than the first index `0`" - ) - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts( - rangeidx_step2, start=rangeidx_step2.start_time() - rangeidx_step2.freq - ) - assert str(msg.value).startswith( - "`start` index `-2` is smaller than the first index `0`" - ) - - # positional_index, predicting only the last position - LinearRegressionModel(lags=1).historical_forecasts( - timeidx_, start=9, start_format="position" - ) - - # positional_index, predicting from the first position with retrain=True - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts( - timeidx_, start=-10, start_format="position" - ) - assert str(msg.value).endswith(", resulting in an empty training set.") - - # positional_index, beyond boundaries - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts( - timeidx_, start=10, start_format="position" - ) - assert str(msg.value).startswith( - "`start` index `10` is out of bounds for series of length 10" - ) - with pytest.raises(ValueError) as msg: - LinearRegressionModel(lags=1).historical_forecasts( - timeidx_, start=-11, start_format="position" - ) - assert str(msg.value).startswith( - "`start` index `-11` is out of bounds for series of length 10" - ) - - @pytest.mark.parametrize("config", models_reg_no_cov_cls_kwargs) - def test_regression_auto_start_multiple_no_cov(self, config): - train_length = 15 - forecast_horizon = 10 - model_cls, kwargs, model_kwargs, bounds = config - model = model_cls( - **kwargs, - **model_kwargs, - ) - model.fit(self.ts_pass_train) - - forecasts = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - forecast_horizon=forecast_horizon, - train_length=train_length, - stride=1, - retrain=True, - overlap_end=False, - ) - - assert ( - len(forecasts) == 2 - ), f"Model {model_cls} did not return a list of historical forecasts" - - theorical_forecast_length = ( - self.ts_val_length - - max( - [ - ( - bounds[0] + bounds[1] + 1 - ), # +1 as sklearn models require min 2 train samples - train_length, - ] - ) # because we train - - forecast_horizon # because we have overlap_end = False - + 1 # because we include the first element - ) - - assert len(forecasts[0]) == len(forecasts[1]) == theorical_forecast_length, ( - f"Model {model_cls.__name__} does not return the right number of historical forecasts in the case " - f"of retrain=True and overlap_end=False, and a time index of type DateTimeIndex. " - f"Expected {theorical_forecast_length}, got {len(forecasts[0])} and {len(forecasts[1])}" - ) - - @pytest.mark.slow - @pytest.mark.parametrize( - "config", - itertools.product( - [ts_univariate, ts_multivariate], - models_reg_no_cov_cls_kwargs + models_reg_cov_cls_kwargs, - [True, False], - [1, 5], - ), - ) - def test_optimized_historical_forecasts_regression(self, config): - ts, model_config, multi_models, forecast_horizon = config - # slightly longer to not affect the last predictable timestamp - ts_covs = self.ts_covs - start = 14 - - model_cls = LinearRegressionModel - _, model_kwargs, _, _ = model_config - # cover several covariates combinations and several regression models - # ocl == forecast horizon - model_kwargs_same = model_kwargs.copy() - model_kwargs_same["output_chunk_length"] = forecast_horizon - model_kwargs_same["multi_models"] = multi_models - model_same = model_cls(**model_kwargs_same) - model_same.fit( - series=ts[:start], - past_covariates=ts_covs if model_same.supports_past_covariates else None, - future_covariates=ts_covs - if model_same.supports_future_covariates - else None, - ) - # ocl >= forecast horizon - model_kwargs_diff = model_kwargs.copy() - model_kwargs_diff["output_chunk_length"] = 5 - model_kwargs_diff["multi_models"] = multi_models - model_diff = model_cls(**model_kwargs_same) - model_diff.fit( - series=ts[:start], - past_covariates=ts_covs if model_diff.supports_past_covariates else None, - future_covariates=ts_covs - if model_diff.supports_future_covariates - else None, - ) - # no parametrization to save time on model training at the cost of test granularity - for model in [model_same, model_diff]: - for last_points_only in [True, False]: - for stride in [1, 2]: - hist_fct = model.historical_forecasts( - series=ts, - past_covariates=ts_covs - if model.supports_past_covariates - else None, - future_covariates=ts_covs - if model.supports_future_covariates - else None, - start=start, - retrain=False, - last_points_only=last_points_only, - stride=stride, - forecast_horizon=forecast_horizon, - enable_optimization=False, - ) - - # manually packing the series in list to match expected inputs - opti_hist_fct = model._optimized_historical_forecasts( - series=[ts], - past_covariates=[ts_covs] - if model.supports_past_covariates - else None, - future_covariates=[ts_covs] - if model.supports_future_covariates - else None, - start=start, - last_points_only=last_points_only, - stride=stride, - forecast_horizon=forecast_horizon, - ) - # pack the output to generalize the tests - if last_points_only: - hist_fct = [hist_fct] - opti_hist_fct = [opti_hist_fct] - - for fct, opti_fct in zip(hist_fct, opti_hist_fct): - assert (fct.time_index == opti_fct.time_index).all() - np.testing.assert_array_almost_equal( - fct.all_values(), opti_fct.all_values() - ) - - @pytest.mark.parametrize( - "config", - list( - itertools.product( - [False, True], # use covariates - [True, False], # last points only - [False, True], # overlap end - [1, 3], # stride - [ - 3, # horizon < ocl - 5, # horizon == ocl - ], - [True, False], # multi models - ) - ), - ) - def test_optimized_historical_forecasts_regression_with_encoders(self, config): - use_covs, last_points_only, overlap_end, stride, horizon, multi_models = config - lags = 3 - ocl = 5 - len_val_series = 10 if multi_models else 10 + (ocl - 1) - series_train, series_val = ( - self.ts_pass_train[:10], - self.ts_pass_val[:len_val_series], - ) - model = LinearRegressionModel( - lags=lags, - lags_past_covariates=2, - lags_future_covariates=[2, 3], - add_encoders={ - "cyclic": {"future": ["month"]}, - "datetime_attribute": {"past": ["dayofweek"]}, - }, - output_chunk_length=ocl, - multi_models=multi_models, - ) - if use_covs: - pc = tg.gaussian_timeseries( - start=series_train.start_time() - 2 * series_train.freq, - end=series_val.end_time(), - freq=series_train.freq, - ) - fc = tg.gaussian_timeseries( - start=series_train.start_time() + 3 * series_train.freq, - end=series_val.end_time() + 4 * series_train.freq, - freq=series_train.freq, - ) - else: - pc, fc = None, None - - model.fit(self.ts_pass_train, past_covariates=pc, future_covariates=fc) - - hist_fct = model.historical_forecasts( - series=series_val, - past_covariates=pc, - future_covariates=fc, - retrain=False, - last_points_only=last_points_only, - overlap_end=overlap_end, - stride=stride, - forecast_horizon=horizon, - enable_optimization=False, - ) - - opti_hist_fct = model._optimized_historical_forecasts( - series=[series_val], - past_covariates=[pc], - future_covariates=[fc], - last_points_only=last_points_only, - overlap_end=overlap_end, - stride=stride, - forecast_horizon=horizon, - ) - - if not isinstance(hist_fct, list): - hist_fct = [hist_fct] - opti_hist_fct = [opti_hist_fct] - - if not last_points_only and overlap_end: - n_pred_series_expected = 8 - n_pred_points_expected = horizon - first_ts_expected = series_val.time_index[lags] - last_ts_expected = series_val.end_time() + series_val.freq * horizon - elif not last_points_only: # overlap_end = False - n_pred_series_expected = len(series_val) - lags - horizon + 1 - n_pred_points_expected = horizon - first_ts_expected = series_val.time_index[lags] - last_ts_expected = series_val.end_time() - elif overlap_end: # last_points_only = True - n_pred_series_expected = 1 - n_pred_points_expected = 8 - first_ts_expected = ( - series_val.time_index[lags] + (horizon - 1) * series_val.freq - ) - last_ts_expected = series_val.end_time() + series_val.freq * horizon - else: # last_points_only = True, overlap_end = False - n_pred_series_expected = 1 - n_pred_points_expected = len(series_val) - lags - horizon + 1 - first_ts_expected = ( - series_val.time_index[lags] + (horizon - 1) * series_val.freq - ) - last_ts_expected = series_val.end_time() - - if not multi_models: - first_ts_expected += series_val.freq * (ocl - 1) - if not overlap_end: - if not last_points_only: - n_pred_series_expected -= ocl - 1 - else: - n_pred_points_expected -= ocl - 1 - - # to make it simple in case of stride, we assume that non-optimized hist fc returns correct results - if stride > 1: - n_pred_series_expected = len(hist_fct) - n_pred_points_expected = len(hist_fct[0]) - first_ts_expected = hist_fct[0].start_time() - last_ts_expected = hist_fct[-1].end_time() - - # check length match between optimized and default hist fc - assert len(opti_hist_fct) == n_pred_series_expected - assert len(hist_fct) == len(opti_hist_fct) - # check hist fc start - assert opti_hist_fct[0].start_time() == first_ts_expected - # check hist fc end - assert opti_hist_fct[-1].end_time() == last_ts_expected - for hfc, ohfc in zip(hist_fct, opti_hist_fct): - assert len(ohfc) == n_pred_points_expected - assert (hfc.time_index == ohfc.time_index).all() - np.testing.assert_array_almost_equal(hfc.all_values(), ohfc.all_values()) - - def test_optimized_historical_forecasts_regression_with_component_specific_lags( - self, - ): - horizon = 1 - lags = 3 - len_val_series = 10 - series_train, series_val = ( - self.ts_pass_train[:10], - self.ts_pass_val[:len_val_series], - ) - model = LinearRegressionModel( - lags=lags, - lags_past_covariates={"default_lags": 2, "darts_enc_pc_dta_dayofweek": 1}, - lags_future_covariates=[2, 3], - add_encoders={ - "cyclic": {"future": ["month"]}, - "datetime_attribute": {"past": ["dayofweek"]}, - }, - ) - model.fit(series_train) - hist_fct = model.historical_forecasts( - series=series_val, - retrain=False, - enable_optimization=False, - ) - - opti_hist_fct = model._optimized_historical_forecasts(series=[series_val]) - - if not isinstance(hist_fct, list): - hist_fct = [hist_fct] - opti_hist_fct = [opti_hist_fct] - - n_pred_series_expected = 1 - n_pred_points_expected = len(series_val) - lags - horizon + 1 - first_ts_expected = ( - series_val.time_index[lags] + (horizon - 1) * series_val.freq - ) - last_ts_expected = series_val.end_time() - - # check length match between optimized and default hist fc - assert len(opti_hist_fct) == n_pred_series_expected - assert len(hist_fct) == len(opti_hist_fct) - # check hist fc start - assert opti_hist_fct[0].start_time() == first_ts_expected - # check hist fc end - assert opti_hist_fct[-1].end_time() == last_ts_expected - for hfc, ohfc in zip(hist_fct, opti_hist_fct): - assert len(ohfc) == n_pred_points_expected - assert (hfc.time_index == ohfc.time_index).all() - np.testing.assert_array_almost_equal(hfc.all_values(), ohfc.all_values()) - - @pytest.mark.slow - @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") - @pytest.mark.parametrize( - "config", - list( - itertools.product( - [False, True], # use covariates - [True, False], # last points only - [False, True], # overlap end - [1, 3], # stride - [ - 3, # horizon < ocl - 5, # horizon == ocl - 7, # horizon > ocl -> autoregression - ], - [False, True], # use integer indexed series - [False, True], # use multi-series - ) - ), - ) - def test_optimized_historical_forecasts_torch_with_encoders(self, config): - ( - use_covs, - last_points_only, - overlap_end, - stride, - horizon, - use_int_idx, - use_multi_series, - ) = config - icl = 3 - ocl = 5 - len_val_series = 10 - series_train, series_val = ( - self.ts_pass_train[:10], - self.ts_pass_val[:len_val_series], - ) - if use_int_idx: - series_train = TimeSeries.from_values( - series_train.all_values(), columns=series_train.columns - ) - series_val = TimeSeries.from_times_and_values( - values=series_val.all_values(), - times=pd.RangeIndex( - start=series_train.end_time() + series_train.freq, - stop=series_train.end_time() - + (len(series_val) + 1) * series_train.freq, - step=series_train.freq, - ), - columns=series_train.columns, - ) - - def f_encoder(idx): - return idx.month if not use_int_idx else idx - - model = NLinearModel( - input_chunk_length=icl, - add_encoders={ - "custom": {"past": [f_encoder], "future": [f_encoder]}, - }, - output_chunk_length=ocl, - n_epochs=1, - **tfm_kwargs, - ) - if use_covs: - pc = tg.gaussian_timeseries( - start=series_train.start_time(), - end=series_val.end_time() + max(0, horizon - ocl) * series_train.freq, - freq=series_train.freq, - ) - fc = tg.gaussian_timeseries( - start=series_train.start_time(), - end=series_val.end_time() + max(ocl, horizon) * series_train.freq, - freq=series_train.freq, - ) - else: - pc, fc = None, None - - model.fit(series_train, past_covariates=pc, future_covariates=fc) - - if use_multi_series: - series_val = [ - series_val, - (series_val + 10) - .shift(1) - .with_columns_renamed(series_val.columns, "test_col"), - ] - pc = [pc, pc.shift(1)] if pc is not None else None - fc = [fc, fc.shift(1)] if fc is not None else None - - hist_fct = model.historical_forecasts( - series=series_val, - past_covariates=pc, - future_covariates=fc, - retrain=False, - last_points_only=last_points_only, - overlap_end=overlap_end, - stride=stride, - forecast_horizon=horizon, - enable_optimization=False, - ) - - opti_hist_fct = model._optimized_historical_forecasts( - series=series_val if isinstance(series_val, list) else [series_val], - past_covariates=pc if (isinstance(pc, list) or pc is None) else [pc], - future_covariates=fc if (isinstance(fc, list) or fc is None) else [fc], - last_points_only=last_points_only, - overlap_end=overlap_end, - stride=stride, - forecast_horizon=horizon, - ) - - if not isinstance(series_val, list): - series_val = [series_val] - hist_fct = [hist_fct] - opti_hist_fct = [opti_hist_fct] - - for series, hfc, ohfc in zip(series_val, hist_fct, opti_hist_fct): - if not isinstance(hfc, list): - hfc = [hfc] - ohfc = [ohfc] - - if not last_points_only and overlap_end: - n_pred_series_expected = 8 - n_pred_points_expected = horizon - first_ts_expected = series.time_index[icl] - last_ts_expected = series.end_time() + series.freq * horizon - elif not last_points_only: # overlap_end = False - n_pred_series_expected = len(series) - icl - horizon + 1 - n_pred_points_expected = horizon - first_ts_expected = series.time_index[icl] - last_ts_expected = series.end_time() - elif overlap_end: # last_points_only = True - n_pred_series_expected = 1 - n_pred_points_expected = 8 - first_ts_expected = series.time_index[icl] + (horizon - 1) * series.freq - last_ts_expected = series.end_time() + series.freq * horizon - else: # last_points_only = True, overlap_end = False - n_pred_series_expected = 1 - n_pred_points_expected = len(series) - icl - horizon + 1 - first_ts_expected = series.time_index[icl] + (horizon - 1) * series.freq - last_ts_expected = series.end_time() - - # to make it simple in case of stride, we assume that non-optimized hist fc returns correct results - if stride > 1: - n_pred_series_expected = len(hfc) - n_pred_points_expected = len(hfc[0]) - first_ts_expected = hfc[0].start_time() - last_ts_expected = hfc[-1].end_time() - - # check length match between optimized and default hist fc - assert len(ohfc) == n_pred_series_expected - assert len(hfc) == len(ohfc) - # check hist fc start - assert ohfc[0].start_time() == first_ts_expected - # check hist fc end - assert ohfc[-1].end_time() == last_ts_expected - for hfc, ohfc in zip(hfc, ohfc): - assert hfc.columns.equals(series.columns) - assert ohfc.columns.equals(series.columns) - assert len(ohfc) == n_pred_points_expected - assert (hfc.time_index == ohfc.time_index).all() - np.testing.assert_array_almost_equal( - hfc.all_values(), ohfc.all_values() - ) - - @pytest.mark.slow - @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") - @pytest.mark.parametrize("model_config", models_torch_cls_kwargs) - def test_torch_auto_start_multiple_no_cov(self, model_config): - forecast_hrz = 10 - model_cls, kwargs, bounds, _ = model_config - model = model_cls( - random_state=0, - **kwargs, - ) - model.fit(self.ts_pass_train) - - # check historical forecasts for several time series, - # retrain True and overlap_end False - forecasts = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - forecast_horizon=forecast_hrz, - stride=1, - retrain=True, - overlap_end=False, - ) - assert ( - len(forecasts) == 2 - ), f"Model {model_cls} did not return a list of historical forecasts" - # If retrain=True and overlap_end=False, as ts has 72 values, we can only forecast - # (target length)-(training length=input_chunk_length+output_chunk_length) - (horizon - 1) - # indeed we start to predict after the first trainable point (input_chunk_length+output_chunk_length) - # and we stop in this case (overlap_end=False) at the end_time: - # target.end_time() - (horizon - 1) * target.freq - - # explanation: - # (bounds): train sample length - # (horizon - 1): with overlap_end=False, if entire horizon is available (overlap_end=False), - # we can predict 1 - theorical_forecast_length = ( - self.ts_val_length - (bounds[0] + bounds[1]) - (forecast_hrz - 1) - ) - assert len(forecasts[0]) == len(forecasts[1]) == theorical_forecast_length, ( - f"Model {model_cls} does not return the right number of historical forecasts in the case of " - f"retrain=True and overlap_end=False. " - f"Expected {theorical_forecast_length}, got {len(forecasts[0])} and {len(forecasts[1])}" - ) - - model = model_cls( - random_state=0, - **kwargs, - ) - - model.fit(self.ts_pass_train) - # check historical forecasts for several time series, - # retrain True and overlap_end True - forecasts = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - forecast_horizon=forecast_hrz, - stride=1, - retrain=True, - overlap_end=True, - ) - - assert ( - len(forecasts) == 2 - ), f"Model {model_cls} did not return a list of historical forecasts" - theorical_forecast_length = ( - self.ts_val_length - - (bounds[0] + bounds[1]) # train sample length - + 1 # with overlap_end=True, we are not restricted by the end of the series or horizon - ) - assert len(forecasts[0]) == len(forecasts[1]) == theorical_forecast_length - - model = model_cls( - random_state=0, - **kwargs, - ) - model.fit(self.ts_pass_train) - # check historical forecasts for several time series, - # retrain False and overlap_end False - forecasts = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - forecast_horizon=forecast_hrz, - stride=1, - retrain=False, - overlap_end=False, - ) - - assert ( - len(forecasts) == 2 - ), f"Model {model_cls} did not return a list of historical forecasts" - theorical_forecast_length = ( - self.ts_val_length - - bounds[0] # prediction input sample length - - ( - forecast_hrz - 1 - ) # overlap_end=False -> if entire horizon is available, we can predict 1 - ) - assert len(forecasts[0]) == len(forecasts[1]) == theorical_forecast_length - assert ( - forecasts[0].end_time() - == forecasts[1].end_time() - == self.ts_pass_val.end_time() - ) - - model = model_cls( - random_state=0, - **kwargs, - ) - model.fit(self.ts_pass_train) - # check historical forecasts for several time series, - # retrain False and overlap_end True - forecasts = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - forecast_horizon=forecast_hrz, - stride=1, - retrain=False, - overlap_end=True, - ) - - assert ( - len(forecasts) == 2 - ), f"Model {model_cls} did not return a list of historical forecasts" - theorical_forecast_length = ( - self.ts_val_length - - bounds[0] # prediction input sample length - + 1 # overlap_end=True -> last possible prediction start is one step after end of target - ) - assert len(forecasts[0]) == len(forecasts[1]) == theorical_forecast_length - assert ( - forecasts[0].end_time() - == forecasts[1].end_time() - == self.ts_pass_val.end_time() + forecast_hrz * self.ts_pass_val.freq - ) - - def test_hist_fc_end_exact_with_covs(self): - model = LinearRegressionModel( - lags=2, - lags_past_covariates=2, - lags_future_covariates=(2, 1), - output_chunk_length=2, - ) - series = tg.sine_timeseries(length=10) - model.fit(series, past_covariates=series, future_covariates=series) - fc = model.historical_forecasts( - series, - past_covariates=series[:-2], - future_covariates=series, - forecast_horizon=2, - stride=2, - overlap_end=False, - last_points_only=True, - retrain=False, - ) - assert len(fc) == 4 - assert fc.end_time() == series.end_time() - - fc = model.historical_forecasts( - series, - past_covariates=series[:-2], - future_covariates=series, - forecast_horizon=2, - stride=2, - overlap_end=False, - last_points_only=False, - retrain=False, - ) - fc = concatenate(fc) - assert len(fc) == 8 - assert fc.end_time() == series.end_time() - - @pytest.mark.parametrize("model_config", models_reg_cov_cls_kwargs) - def test_regression_auto_start_multiple_with_cov_retrain(self, model_config): - forecast_hrz = 10 - model_cls, kwargs, _, bounds = model_config - model = model_cls( - random_state=0, - **kwargs, - ) - - forecasts_retrain = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - past_covariates=[ - self.ts_past_cov_valid_same_start, - self.ts_past_cov_valid_same_start, - ] - if "lags_past_covariates" in kwargs - else None, - future_covariates=[ - self.ts_past_cov_valid_same_start, - self.ts_past_cov_valid_same_start, - ] - if "lags_future_covariates" in kwargs - else None, - last_points_only=True, - forecast_horizon=forecast_hrz, - stride=1, - retrain=True, - overlap_end=False, - ) - - assert ( - len(forecasts_retrain) == 2 - ), f"Model {model_cls} did not return a list of historical forecasts" - - ( - min_target_lag, - max_target_lag, - min_past_cov_lag, - max_past_cov_lag, - min_future_cov_lag, - max_future_cov_lag, - ) = model.extreme_lags - - past_lag = min( - min_target_lag if min_target_lag else 0, - min_past_cov_lag if min_past_cov_lag else 0, - min_future_cov_lag - if min_future_cov_lag is not None and min_future_cov_lag < 0 - else 0, - ) - - future_lag = ( - max_future_cov_lag - if max_future_cov_lag is not None and max_future_cov_lag > 0 - else 0 - ) - # length input - largest past lag - forecast horizon - max(largest future lag, output_chunk_length) - theorical_retrain_forecast_length = len(self.ts_pass_val) - ( - -past_lag - + forecast_hrz - + max(future_lag + 1, kwargs.get("output_chunk_length", 1)) - ) - assert ( - len(forecasts_retrain[0]) - == len(forecasts_retrain[1]) - == theorical_retrain_forecast_length - ), ( - f"Model {model_cls} does not return the right number of historical forecasts in the case of " - f"retrain=True and overlap_end=False. " - f"Expected {theorical_retrain_forecast_length}, got {len(forecasts_retrain[0])} " - f"and {len(forecasts_retrain[1])}" - ) - - # with last_points_only=True: start is shifted by biggest past lag + training timestamps - # (forecast horizon + output_chunk_length) - expected_start = ( - self.ts_pass_val.start_time() - + (-past_lag + forecast_hrz + kwargs.get("output_chunk_length", 1)) - * self.ts_pass_val.freq - ) - assert forecasts_retrain[0].start_time() == expected_start - - # end is shifted back by the biggest future lag - if model.output_chunk_length - 1 > future_lag: - shift = 0 - else: - shift = future_lag - expected_end = self.ts_pass_val.end_time() - shift * self.ts_pass_val.freq - assert forecasts_retrain[0].end_time() == expected_end - - @pytest.mark.parametrize("model_config", models_reg_cov_cls_kwargs) - def test_regression_auto_start_multiple_with_cov_no_retrain(self, model_config): - forecast_hrz = 10 - model_cls, kwargs, _, bounds = model_config - model = model_cls( - random_state=0, - **kwargs, - ) - - model.fit( - series=[self.ts_pass_val, self.ts_pass_val], - past_covariates=[ - self.ts_past_cov_valid_same_start, - self.ts_past_cov_valid_same_start, - ] - if "lags_past_covariates" in kwargs - else None, - future_covariates=[ - self.ts_past_cov_valid_same_start, - self.ts_past_cov_valid_same_start, - ] - if "lags_future_covariates" in kwargs - else None, - ) - forecasts_no_retrain = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - past_covariates=[ - self.ts_past_cov_valid_same_start, - self.ts_past_cov_valid_same_start, - ] - if "lags_past_covariates" in kwargs - else None, - future_covariates=[ - self.ts_past_cov_valid_same_start, - self.ts_past_cov_valid_same_start, - ] - if "lags_future_covariates" in kwargs - else None, - last_points_only=True, - forecast_horizon=forecast_hrz, - stride=1, - retrain=False, - overlap_end=False, - ) - - ( - min_target_lag, - max_target_lag, - min_past_cov_lag, - max_past_cov_lag, - min_future_cov_lag, - max_future_cov_lag, - ) = model.extreme_lags - - past_lag = min( - min_target_lag if min_target_lag else 0, - min_past_cov_lag if min_past_cov_lag else 0, - min_future_cov_lag if min_future_cov_lag else 0, - ) - - future_lag = ( - max_future_cov_lag - if max_future_cov_lag is not None and max_future_cov_lag > 0 - else 0 - ) - - # with last_points_only=True: start is shifted by the biggest past lag plus the forecast horizon - expected_start = ( - self.ts_pass_val.start_time() - + (-past_lag + forecast_hrz - 1) * self.ts_pass_val.freq - ) - assert forecasts_no_retrain[0].start_time() == expected_start - - # end is shifted by the biggest future lag if future lag > output_chunk_length - shift_back = future_lag if future_lag + 1 > model.output_chunk_length else 0 - expected_end = self.ts_pass_val.end_time() - shift_back * self.ts_pass_val.freq - assert forecasts_no_retrain[0].end_time() == expected_end - - @pytest.mark.slow - @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") - @pytest.mark.parametrize("model_config", models_torch_cls_kwargs) - def test_torch_auto_start_with_past_cov(self, model_config): - forecast_hrz = 10 - # Past covariates only - model_cls, kwargs, bounds, type = model_config - if type == "DualCovariates": - return - - model = model_cls( - random_state=0, - **kwargs, - ) - model.fit(self.ts_pass_train, self.ts_past_cov_train) - - # same start - forecasts = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - past_covariates=[ - self.ts_past_cov_valid_same_start, - self.ts_past_cov_valid_same_start, - ], - forecast_horizon=forecast_hrz, - stride=1, - retrain=True, - overlap_end=False, - ) - - assert ( - len(forecasts) == 2 - ), f"Model {model_cls} did not return a list of historical forecasts" - - theorical_forecast_length = ( - self.ts_val_length - - (bounds[0] + bounds[1]) # train sample length - - (forecast_hrz - 1) # if entire horizon is available, we can predict 1 - - 0 # past covs have same start as target -> no shift - - 0 # we don't have future covs in output chunk -> no shift - ) - assert len(forecasts[0]) == len(forecasts[1]) == theorical_forecast_length, ( - f"Model {model_cls} does not return the right number of historical forecasts in case " - f"of retrain=True and overlap_end=False and past_covariates with same start. " - f"Expected {theorical_forecast_length}, got {len(forecasts[0])} and {len(forecasts[1])}" - ) - - model = model_cls( - random_state=0, - **kwargs, - ) - model.fit(self.ts_pass_train, past_covariates=self.ts_past_cov_train) - - # start before, after - forecasts = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - past_covariates=[ - self.ts_past_cov_valid_5_aft_start, - self.ts_past_cov_valid_10_bef_start, - ], - forecast_horizon=forecast_hrz, - stride=1, - retrain=True, - overlap_end=False, - ) - theorical_forecast_length = ( - self.ts_val_length - - (bounds[0] + bounds[1]) # train sample length - - (forecast_hrz - 1) # if entire horizon is available, we can predict 1 - - 5 # past covs start 5 later -> shift - - 0 # we don't have future covs in output chunk -> no shift - ) - assert len(forecasts[0]) == theorical_forecast_length, ( - f"Model {model_cls} does not return the right number of historical forecasts in case " - f"of retrain=True and overlap_end=False and past_covariates starting after. " - f"Expected {theorical_forecast_length}, got {len(forecasts[0])}" - ) - theorical_forecast_length = ( - self.ts_val_length - - (bounds[0] + bounds[1]) # train sample length - - (forecast_hrz - 1) # if entire horizon is available, we can predict 1 - - 0 # past covs have same start as target -> no shift - - 0 # we don't have future covs in output chunk -> no shift - ) - assert len(forecasts[1]) == theorical_forecast_length, ( - f"Model {model_cls} does not return the right number of historical forecasts in case " - f"of retrain=True and overlap_end=False and past_covariates starting before. " - f"Expected {theorical_forecast_length}, got {len(forecasts[1])}" - ) - - @pytest.mark.slow - @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") - @pytest.mark.parametrize("model_config", models_torch_cls_kwargs) - def test_torch_auto_start_with_past_future_cov(self, model_config): - forecast_hrz = 10 - # Past and future covariates - for model_cls, kwargs, bounds, type in models_torch_cls_kwargs: - if not type == "MixedCovariates": - return - - model = model_cls( - random_state=0, - **kwargs, - ) - model.fit( - self.ts_pass_train, - past_covariates=self.ts_past_cov_train, - future_covariates=self.ts_fut_cov_train, - ) - - forecasts = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - past_covariates=[ - self.ts_past_cov_valid_5_aft_start, - self.ts_past_cov_valid_same_start, - ], - future_covariates=[ - self.ts_fut_cov_valid_7_aft_start, - self.ts_fut_cov_valid_16_bef_start, - ], - forecast_horizon=forecast_hrz, - stride=1, - retrain=True, - overlap_end=False, - ) - theorical_forecast_length = ( - self.ts_val_length - - (bounds[0] + bounds[1]) # train sample length - - (forecast_hrz - 1) # if entire horizon is available, we can predict 1 - - 7 # future covs start 7 after target (more than past covs) -> shift - - 2 # future covs in output chunk -> difference between horizon=10 and output_chunk_length=12 - ) - assert len(forecasts[0]) == theorical_forecast_length, ( - f"Model {model_cls} does not return the right number of historical forecasts in case " - f"of retrain=True and overlap_end=False and past_covariates and future_covariates with " - f"different start. " - f"Expected {theorical_forecast_length}, got {len(forecasts[0])}" - ) - theorical_forecast_length = ( - self.ts_val_length - - (bounds[0] + bounds[1]) # train sample length - - ( - forecast_hrz - 1 - ) # if entire horizon is available, we can predict 1, - - 0 # all covs start at the same time as target -> no shift, - - 2 # future covs in output chunk -> difference between horizon=10 and output_chunk_length=12 - ) - assert len(forecasts[1]) == theorical_forecast_length, ( - f"Model {model_cls} does not return the right number of historical forecasts in case " - f"of retrain=True and overlap_end=False and past_covariates with different start. " - f"Expected {theorical_forecast_length}, got {len(forecasts[1])}" - ) - - @pytest.mark.slow - @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") - @pytest.mark.parametrize("model_config", models_torch_cls_kwargs) - def test_torch_auto_start_with_future_cov(self, model_config): - forecast_hrz = 10 - # Future covariates only - for model_cls, kwargs, bounds, type in models_torch_cls_kwargs: - # todo case of DualCovariates (RNN) - if type == "PastCovariates" or type == "DualCovariates": - return - - model = model_cls( - random_state=0, - **kwargs, - ) - model.fit(self.ts_pass_train, future_covariates=self.ts_fut_cov_train) - - # Only fut covariate - forecasts = model.historical_forecasts( - series=[self.ts_pass_val, self.ts_pass_val], - future_covariates=[ - self.ts_fut_cov_valid_7_aft_start, - self.ts_fut_cov_valid_16_bef_start, - ], - forecast_horizon=forecast_hrz, - stride=1, - retrain=True, - overlap_end=False, - ) - - assert ( - len(forecasts) == 2 - ), f"Model {model_cls} did not return a list of historical forecasts" - theorical_forecast_length = ( - self.ts_val_length - - (bounds[0] + bounds[1]) # train sample length - - ( - forecast_hrz - 1 - ) # (horizon - 1): if entire horizon is available, we can predict 1, - - 7 # future covs start 7 after target (more than past covs) -> shift - - 2 # future covs in output chunk -> difference between horizon=10 and output_chunk_length=12 - ) - assert len(forecasts[0]) == theorical_forecast_length, ( - f"Model {model_cls} does not return the right number of historical forecasts in case " - f"of retrain=True and overlap_end=False and no past_covariates and future_covariates " - f"with different start. " - f"Expected {theorical_forecast_length}, got {len(forecasts[0])}" - ) - theorical_forecast_length = ( - self.ts_val_length - - (bounds[0] + bounds[1]) # train sample length - - (forecast_hrz - 1) # if entire horizon is available, we can predict 1 - - 0 # all covs start at the same time as target -> no shift - - 2 # future covs in output chunk -> difference between horizon=10 and output_chunk_length=12 - ) - assert len(forecasts[1]) == theorical_forecast_length, ( - f"Model {model_cls} does not return the right number of historical forecasts in case " - f"of retrain=True and overlap_end=False and no past_covariates and future_covariates " - f"with different start. " - f"Expected {theorical_forecast_length}, got {len(forecasts[1])}" - ) - - def test_retrain(self): - """test historical_forecasts for an untrained model with different retrain values.""" - - def helper_hist_forecasts(retrain_val, start): - model = LinearRegressionModel(lags=4, output_chunk_length=4) - return model.historical_forecasts( - self.ts_passengers, start=start, retrain=retrain_val, verbose=False - ) - - def retrain_f_invalid( - counter, pred_time, train_series, past_covariates, future_covariates - ): - return False - - def retrain_f_missing_arg( - counter, train_series, past_covariates, future_covariates - ): - if len(train_series) % 2 == 0: - return True - else: - return False - - def retrain_f_invalid_ouput_int( - counter, pred_time, train_series, past_covariates, future_covariates - ): - return 1 - - def retrain_f_invalid_ouput_str( - counter, pred_time, train_series, past_covariates, future_covariates - ): - return "True" - - def retrain_f_valid( - counter, pred_time, train_series, past_covariates, future_covariates - ): - # only retrain once in first iteration - if pred_time == pd.Timestamp("1959-09-01 00:00:00"): - return True - else: - return False - - def retrain_f_delayed_true( - counter, pred_time, train_series, past_covariates, future_covariates - ): - if counter > 1: - return True - else: - return False - - # test callable - helper_hist_forecasts(retrain_f_valid, 0.9) - # missing the `pred_time` positional argument - expected_msg = "the Callable `retrain` must have a signature/arguments matching the following positional" - with pytest.raises(ValueError) as error_msg: - helper_hist_forecasts(retrain_f_missing_arg, 0.9) - assert str(error_msg.value).startswith(expected_msg) - # returning a non-bool value (int) - expected_msg = "Return value of `retrain` must be bool, received " - with pytest.raises(ValueError) as error_msg: - helper_hist_forecasts(retrain_f_invalid_ouput_int, 0.9) - assert str(error_msg.value).startswith(expected_msg) - # returning a non-bool value (str) - expected_msg = "Return value of `retrain` must be bool, received " - with pytest.raises(ValueError) as error_msg: - helper_hist_forecasts(retrain_f_invalid_ouput_str, 0.9) - assert str(error_msg.value).startswith(expected_msg) - # predict fails but model could have been trained before the predict round - expected_msg = "`retrain` is `False` in the first train iteration at prediction point (in time)" - with pytest.raises(ValueError) as error_msg: - helper_hist_forecasts(retrain_f_delayed_true, 0.9) - assert str(error_msg.value).startswith(expected_msg) - # always returns False, treated slightly different than `retrain=False` and `retrain=0` - with pytest.raises(ValueError) as error_msg: - helper_hist_forecasts(retrain_f_invalid, 0.9) - assert str(error_msg.value).startswith(expected_msg) - - # test int - helper_hist_forecasts(10, 0.9) - expected_msg = "Model has not been fit yet." - # `retrain=0` with not-trained model, encountering directly a predictable time index - with pytest.raises(ValueError) as error_msg: - helper_hist_forecasts(0, 0.9) - assert str(error_msg.value).startswith(expected_msg), str(error_msg.value) - - # test bool - helper_hist_forecasts(True, 0.9) - # `retrain=False` with not-trained model, encountering directly a predictable time index - expected_msg = "The model has not been fitted yet, and `retrain` is ``False``." - with pytest.raises(ValueError) as error_msg: - helper_hist_forecasts(False, 0.9) - assert str(error_msg.value).startswith(expected_msg) - - expected_start = pd.Timestamp("1949-10-01 00:00:00") - # start before first trainable time index should still work - res = helper_hist_forecasts(True, pd.Timestamp("1949-09-01 00:00:00")) - assert res.time_index[0] == expected_start - # start at first trainable time index should still work - res = helper_hist_forecasts(True, expected_start) - assert res.time_index[0] == expected_start - # start at last trainable time index should still work - expected_end = pd.Timestamp("1960-12-01 00:00:00") - res = helper_hist_forecasts(True, expected_end) - assert res.time_index[0] == expected_end - - @pytest.mark.parametrize("model_type", ["regression", "torch"]) - def test_predict_likelihood_parameters(self, model_type): - """standard checks that historical forecasts work with direct likelihood parameter predictions - with regression and torch models.""" - - model = self.create_model(1, False, model_type=model_type) - # skip torch models if not installed - if model is None: - return - # model doesn't use likelihood - with pytest.raises(ValueError): - model.historical_forecasts( - self.ts_pass_train, - predict_likelihood_parameters=True, - ) - - model = self.create_model(1, model_type=model_type) - # forecast_horizon > output_chunk_length doesn't work - with pytest.raises(ValueError): - model.historical_forecasts( - self.ts_pass_train, - predict_likelihood_parameters=True, - forecast_horizon=2, - ) - - model = self.create_model(1, model_type=model_type) - # num_samples != 1 doesn't work - with pytest.raises(ValueError): - model.historical_forecasts( - self.ts_pass_train, - predict_likelihood_parameters=True, - forecast_horizon=1, - num_samples=2, - ) - - n = 3 - target_name = self.ts_pass_train.components[0] - qs_expected = ["q0.05", "q0.40", "q0.50", "q0.60", "q0.95"] - qs_expected = pd.Index([target_name + "_" + q for q in qs_expected]) - # check that it works with retrain - model = self.create_model(1, model_type=model_type) - hist_fc = model.historical_forecasts( - self.ts_pass_train, - predict_likelihood_parameters=True, - forecast_horizon=1, - num_samples=1, - start=len(self.ts_pass_train) - n, # predict on last 10 steps - retrain=True, - ) - assert hist_fc.components.equals(qs_expected) - assert len(hist_fc) == n - - # check for equal results between predict and hist fc without retraining - model = self.create_model(1, model_type=model_type) - model.fit(series=self.ts_pass_train[:-n]) - hist_fc = model.historical_forecasts( - self.ts_pass_train, - predict_likelihood_parameters=True, - forecast_horizon=1, - num_samples=1, - start=len(self.ts_pass_train) - n, # predict on last 10 steps - retrain=False, - ) - assert hist_fc.components.equals(qs_expected) - assert len(hist_fc) == n - - preds = [] - for n_i in range(n): - preds.append( - model.predict( - n=1, - series=self.ts_pass_train[: -(n - n_i)], - predict_likelihood_parameters=True, - ) - ) - preds = darts.concatenate(preds) - np.testing.assert_array_almost_equal( - preds.all_values(copy=False), hist_fc.all_values(copy=False) - ) - - # check equal results between predict and hist fc with higher output_chunk_length and horizon, - # and last_points_only=False - model = self.create_model(2, model_type=model_type) - # we take one more training step so that model trained on ocl=1 has the same training samples - # as model above - model.fit(series=self.ts_pass_train[: -(n - 1)]) - hist_fc = model.historical_forecasts( - self.ts_pass_train, - predict_likelihood_parameters=True, - forecast_horizon=2, - num_samples=1, - start=len(self.ts_pass_train) - n, # predict on last 10 steps - retrain=False, - last_points_only=False, - overlap_end=True, - ) - # because of overlap_end, we get an additional prediction - # generate the same predictions manually - preds = [] - for n_i in range(n + 1): - right = -(n - n_i) if n_i < 3 else len(self.ts_pass_train) - preds.append( - model.predict( - n=2, - series=self.ts_pass_train[:right], - predict_likelihood_parameters=True, - ) - ) - for p, hfc in zip(preds, hist_fc): - assert p.columns.equals(hfc.columns) - assert p.time_index.equals(hfc.time_index) - np.testing.assert_array_almost_equal( - p.all_values(copy=False), hfc.all_values(copy=False) - ) - assert len(hist_fc) == n + 1 - - @pytest.mark.parametrize( - "model_type,enable_optimization", - product(["regression", "torch"], [True, False]), - ) - def test_fit_kwargs(self, model_type, enable_optimization): - """check that the parameters provided in fit_kwargs are correctly processed""" - valid_fit_kwargs = {"max_samples_per_ts": 3} - invalid_fit_kwargs = {"series": self.ts_pass_train} - unsupported_fit_kwargs = {"unsupported": "unsupported"} - - n = 2 - model = self.create_model(1, use_ll=False, model_type=model_type) - - # torch not available - if model is None: - return - - model.fit(series=self.ts_pass_train[:-n]) - - # supported argument - hist_fc = model.historical_forecasts( - self.ts_pass_train, - forecast_horizon=1, - num_samples=1, - start=len(self.ts_pass_train) - n, - retrain=True, - enable_optimization=enable_optimization, - fit_kwargs=valid_fit_kwargs, - ) - - assert hist_fc.components.equals(self.ts_pass_train.components) - assert len(hist_fc) == n - - # passing unsupported argument - with pytest.raises(TypeError): - hist_fc = model.historical_forecasts( - self.ts_pass_train, - forecast_horizon=1, - start=len(self.ts_pass_train) - n, - retrain=True, - enable_optimization=enable_optimization, - fit_kwargs=unsupported_fit_kwargs, - ) - - # passing hist_fc parameters in fit_kwargs, with retrain=False - hist_fc = model.historical_forecasts( - self.ts_pass_train, - forecast_horizon=1, - start=len(self.ts_pass_train) - n, - retrain=False, - enable_optimization=enable_optimization, - fit_kwargs=invalid_fit_kwargs, - ) - - assert hist_fc.components.equals(self.ts_pass_train.components) - assert len(hist_fc) == n - - # passing hist_fc parameters in fit_kwargs, interfering with the logic - with pytest.raises(ValueError) as msg: - model.historical_forecasts( - self.ts_pass_train, - forecast_horizon=1, - start=len(self.ts_pass_train) - n, - retrain=True, - enable_optimization=enable_optimization, - fit_kwargs=invalid_fit_kwargs, - ) - assert str(msg.value).startswith( - "The following parameters cannot be passed in `fit_kwargs`" - ) - - @pytest.mark.parametrize( - "model_type,enable_optimization", - product(["regression", "torch"], [True, False]), - ) - def test_predict_kwargs(self, model_type, enable_optimization): - """check that the parameters provided in predict_kwargs are correctly processed""" - invalid_predict_kwargs = {"predict_likelihood_parameters": False} - unsupported_predict_kwargs = {"unsupported": "unsupported"} - if model_type == "regression": - valid_predict_kwargs = {} - else: - valid_predict_kwargs = {"batch_size": 10} - - n = 2 - model = self.create_model(1, use_ll=False, model_type=model_type) - - # torch not available - if model is None: - return - - model.fit(series=self.ts_pass_train[:-n]) - - # supported argument - hist_fc = model.historical_forecasts( - self.ts_pass_train, - forecast_horizon=1, - start=len(self.ts_pass_train) - n, - retrain=False, - enable_optimization=enable_optimization, - predict_kwargs=valid_predict_kwargs, - ) - - assert hist_fc.components.equals(self.ts_pass_train.components) - assert len(hist_fc) == n - - # passing unsupported prediction argument - with pytest.raises(TypeError): - hist_fc = model.historical_forecasts( - self.ts_pass_train, - forecast_horizon=1, - start=len(self.ts_pass_train) - n, - retrain=False, - enable_optimization=enable_optimization, - predict_kwargs=unsupported_predict_kwargs, - ) - - # passing hist_fc parameters in predict_kwargs, interfering with the logic - with pytest.raises(ValueError) as msg: - hist_fc = model.historical_forecasts( - self.ts_pass_train, - forecast_horizon=1, - start=len(self.ts_pass_train) - n, - retrain=False, - enable_optimization=enable_optimization, - predict_kwargs=invalid_predict_kwargs, - ) - assert str(msg.value).startswith( - "The following parameters cannot be passed in `predict_kwargs`" - ) diff --git a/darts/tests/models/forecasting/test_local_forecasting_models.py b/darts/tests/models/forecasting/test_local_forecasting_models.py index f3ac21d40d..9e05f65710 100644 --- a/darts/tests/models/forecasting/test_local_forecasting_models.py +++ b/darts/tests/models/forecasting/test_local_forecasting_models.py @@ -35,6 +35,7 @@ StatsForecastAutoARIMA, StatsForecastAutoCES, StatsForecastAutoETS, + StatsForecastAutoTBATS, StatsForecastAutoTheta, Theta, ) @@ -44,7 +45,7 @@ ) from darts.timeseries import TimeSeries from darts.utils import timeseries_generation as tg -from darts.utils.utils import ModelMode, SeasonalityMode, TrendMode +from darts.utils.utils import ModelMode, SeasonalityMode, TrendMode, generate_index logger = get_logger(__name__) @@ -57,6 +58,7 @@ (StatsForecastAutoTheta(season_length=12), 5.5), (StatsForecastAutoCES(season_length=12, model="Z"), 7.3), (StatsForecastAutoETS(season_length=12, model="AAZ"), 7.3), + (StatsForecastAutoTBATS(season_length=12), 10), (Croston(version="classic"), 23), (Croston(version="tsb", alpha_d=0.1, alpha_p=0.1), 23), (Theta(), 11), @@ -139,11 +141,9 @@ def test_save_model_parameters(self): for model, _ in models: assert model._model_params == model.untrained_model()._model_params - @pytest.mark.parametrize("model", [ARIMA(1, 1, 1), LinearRegressionModel(lags=12)]) + @pytest.mark.parametrize("model", [ARIMA(1, 1, 1)]) def test_save_load_model(self, tmpdir_module, model): # check if save and load methods work and if loaded model creates same forecasts as original model - cwd = os.getcwd() - os.chdir(tmpdir_module) model_path_str = type(model).__name__ model_path_pathlike = pathlib.Path(model_path_str + "_pathlike") model_path_binary = model_path_str + "_binary" @@ -164,13 +164,11 @@ def test_save_load_model(self, tmpdir_module, model): assert os.path.exists(p) assert ( - len( - [ - p - for p in os.listdir(tmpdir_module) - if p.startswith(type(model).__name__) - ] - ) + len([ + p + for p in os.listdir(tmpdir_module) + if p.startswith(type(model).__name__) + ]) == len(full_model_paths) + 1 ) @@ -188,8 +186,6 @@ def test_save_load_model(self, tmpdir_module, model): for loaded_model in loaded_models: assert model_prediction == loaded_model.predict(self.forecasting_horizon) - os.chdir(cwd) - def test_save_load_model_invalid_path(self): # check if save and load methods raise an error when given an invalid path model = ARIMA(1, 1, 1) @@ -222,10 +218,9 @@ def test_models_performance(self, config): model.fit(self.ts_pass_train) prediction = model.predict(len(self.ts_pass_val)) current_mape = mape(self.ts_pass_val, prediction) - assert ( - current_mape < max_mape - ), "{} model exceeded the maximum MAPE of {}. " "with a MAPE of {}".format( - str(model), max_mape, current_mape + assert current_mape < max_mape, ( + f"{str(model)} model exceeded the maximum MAPE of {max_mape}. " + f"with a MAPE of {current_mape}" ) @pytest.mark.parametrize("config", multivariate_models) @@ -236,10 +231,9 @@ def test_multivariate_models_performance(self, config): model.fit(self.ts_ice_heater_train) prediction = model.predict(len(self.ts_ice_heater_val)) current_mape = mape(self.ts_ice_heater_val, prediction) - assert ( - current_mape < max_mape - ), "{} model exceeded the maximum MAPE of {}. " "with a MAPE of {}".format( - str(model), max_mape, current_mape + assert current_mape < max_mape, ( + f"{str(model)} model exceeded the maximum MAPE of {max_mape}. " + f"with a MAPE of {current_mape}" ) def test_multivariate_input(self): @@ -259,11 +253,11 @@ def test_exogenous_variables_support(self, model): # test case with numerical pd.RangeIndex target_num_idx = TimeSeries.from_times_and_values( - times=tg.generate_index(start=0, length=len(self.ts_gaussian)), + times=generate_index(start=0, length=len(self.ts_gaussian)), values=self.ts_gaussian.all_values(copy=False), ) fc_num_idx = TimeSeries.from_times_and_values( - times=tg.generate_index(start=0, length=len(self.ts_gaussian_long)), + times=generate_index(start=0, length=len(self.ts_gaussian_long)), values=self.ts_gaussian_long.all_values(copy=False), ) diff --git a/darts/tests/models/forecasting/test_nbeats_nhits.py b/darts/tests/models/forecasting/test_nbeats_nhits.py index 378d027379..fe58241f3a 100644 --- a/darts/tests/models/forecasting/test_nbeats_nhits.py +++ b/darts/tests/models/forecasting/test_nbeats_nhits.py @@ -1,193 +1,198 @@ import numpy as np import pytest -from darts.logging import get_logger -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils import timeseries_generation as tg -logger = get_logger(__name__) +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +from darts.models.forecasting.nbeats import NBEATSModel +from darts.models.forecasting.nhits import NHiTSModel -try: - from darts.models.forecasting.nbeats import NBEATSModel - from darts.models.forecasting.nhits import NHiTSModel - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. Nbeats and NHiTs tests will be skipped.") - TORCH_AVAILABLE = False - - -if TORCH_AVAILABLE: - - class TestNbeatsNhitsModel: - def test_creation(self): - with pytest.raises(ValueError): - # if a list is passed to the `layer_widths` argument, it must have a length equal to `num_stacks` - NBEATSModel( - input_chunk_length=1, - output_chunk_length=1, - num_stacks=3, - layer_widths=[1, 2], - ) - - with pytest.raises(ValueError): - NHiTSModel( - input_chunk_length=1, - output_chunk_length=1, - num_stacks=3, - layer_widths=[1, 2], - ) - - def test_fit(self): - large_ts = tg.constant_timeseries(length=100, value=1000) - small_ts = tg.constant_timeseries(length=100, value=10) - - for model_cls in [NBEATSModel, NHiTSModel]: - # Test basic fit and predict - model = model_cls( - input_chunk_length=1, - output_chunk_length=1, - n_epochs=10, - num_stacks=1, - num_blocks=1, - layer_widths=20, - random_state=42, - **tfm_kwargs - ) - model.fit(large_ts[:98]) - pred = model.predict(n=2).values()[0] +class TestNbeatsNhitsModel: + def test_creation(self): + with pytest.raises(ValueError): + # if a list is passed to the `layer_widths` argument, it must have a length equal to `num_stacks` + NBEATSModel( + input_chunk_length=1, + output_chunk_length=1, + num_stacks=3, + layer_widths=[1, 2], + ) - # Test whether model trained on one series is better than one trained on another - model2 = model_cls( - input_chunk_length=1, - output_chunk_length=1, - n_epochs=10, - num_stacks=1, - num_blocks=1, - layer_widths=20, - random_state=42, - **tfm_kwargs - ) - model2.fit(small_ts[:98]) - pred2 = model2.predict(n=2).values()[0] - assert abs(pred2 - 10) < abs(pred - 10) - - # test short predict - pred3 = model2.predict(n=1) - assert len(pred3) == 1 - - def test_multivariate(self): - # testing a 2-variate linear ts, first one from 0 to 1, second one from 0 to 0.5, length 100 - series_multivariate = tg.linear_timeseries(length=100).stack( - tg.linear_timeseries(length=100, start_value=0, end_value=0.5) + with pytest.raises(ValueError): + NHiTSModel( + input_chunk_length=1, + output_chunk_length=1, + num_stacks=3, + layer_widths=[1, 2], ) - for model_cls in [NBEATSModel, NHiTSModel]: - model = model_cls( - input_chunk_length=3, - output_chunk_length=1, - n_epochs=20, - random_state=42, - **tfm_kwargs - ) + def test_fit(self): + large_ts = tg.constant_timeseries(length=100, value=1000) + small_ts = tg.constant_timeseries(length=100, value=10) - model.fit(series_multivariate) - res = model.predict(n=2).values() + for model_cls in [NBEATSModel, NHiTSModel]: + # Test basic fit and predict + model = model_cls( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=10, + num_stacks=1, + num_blocks=1, + layer_widths=20, + random_state=42, + **tfm_kwargs, + ) + model.fit(large_ts[:98]) + pred = model.predict(n=2).values()[0] - # the theoretical result should be [[1.01, 1.02], [0.505, 0.51]]. - # We just test if the given result is not too far on average. - assert abs( - np.average(res - np.array([[1.01, 1.02], [0.505, 0.51]])) < 0.03 - ) + # Test whether model trained on one series is better than one trained on another + model2 = model_cls( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=10, + num_stacks=1, + num_blocks=1, + layer_widths=20, + random_state=42, + **tfm_kwargs, + ) + model2.fit(small_ts[:98]) + pred2 = model2.predict(n=2).values()[0] + assert abs(pred2 - 10) < abs(pred - 10) + + # test short predict + pred3 = model2.predict(n=1) + assert len(pred3) == 1 + + def test_multivariate(self): + # testing a 2-variate linear ts, first one from 0 to 1, second one from 0 to 0.5, length 100 + series_multivariate = tg.linear_timeseries(length=100).stack( + tg.linear_timeseries(length=100, start_value=0, end_value=0.5) + ) + + for model_cls in [NBEATSModel, NHiTSModel]: + model = model_cls( + input_chunk_length=3, + output_chunk_length=1, + n_epochs=20, + random_state=42, + **tfm_kwargs, + ) - # Test Covariates - series_covariates = tg.linear_timeseries(length=100).stack( - tg.linear_timeseries(length=100, start_value=0, end_value=0.1) - ) - model = model_cls( - input_chunk_length=3, - output_chunk_length=4, - n_epochs=5, - random_state=42, - **tfm_kwargs - ) - model.fit(series_multivariate, past_covariates=series_covariates) + model.fit(series_multivariate) + res = model.predict(n=2).values() - res = model.predict( - n=3, series=series_multivariate, past_covariates=series_covariates - ).values() + # the theoretical result should be [[1.01, 1.02], [0.505, 0.51]]. + # We just test if the given result is not too far on average. + assert abs(np.average(res - np.array([[1.01, 1.02], [0.505, 0.51]])) < 0.03) - assert len(res) == 3 - assert abs(np.average(res)) < 5 + # Test Covariates + series_covariates = tg.linear_timeseries(length=100).stack( + tg.linear_timeseries(length=100, start_value=0, end_value=0.1) + ) + model = model_cls( + input_chunk_length=3, + output_chunk_length=4, + n_epochs=5, + random_state=42, + **tfm_kwargs, + ) + model.fit(series_multivariate, past_covariates=series_covariates) - def test_nhits_sampling_sizes(self): - # providing bad sizes or shapes should fail - with pytest.raises(ValueError): + res = model.predict( + n=3, series=series_multivariate, past_covariates=series_covariates + ).values() - # wrong number of coeffs for stacks and blocks - NHiTSModel( - input_chunk_length=1, - output_chunk_length=1, - num_stacks=1, - num_blocks=2, - pooling_kernel_sizes=((1,), (1,)), - n_freq_downsample=((1,), (1,)), - ) - with pytest.raises(ValueError): - NHiTSModel( - input_chunk_length=1, - output_chunk_length=1, - num_stacks=2, - num_blocks=2, - pooling_kernel_sizes=((1, 1), (1, 1)), - n_freq_downsample=((2, 1), (2, 2)), - ) + assert len(res) == 3 + assert abs(np.average(res)) < 5 - # it shouldn't fail with the right number of coeffs - _ = NHiTSModel( + def test_nhits_sampling_sizes(self): + # providing bad sizes or shapes should fail + with pytest.raises(ValueError): + # wrong number of coeffs for stacks and blocks + NHiTSModel( input_chunk_length=1, output_chunk_length=1, - num_stacks=2, + num_stacks=1, num_blocks=2, - pooling_kernel_sizes=((2, 1), (2, 1)), - n_freq_downsample=((2, 1), (2, 1)), + pooling_kernel_sizes=((1,), (1,)), + n_freq_downsample=((1,), (1,)), ) - - # default freqs should be such that last one is 1 - model = NHiTSModel( + with pytest.raises(ValueError): + NHiTSModel( input_chunk_length=1, output_chunk_length=1, num_stacks=2, num_blocks=2, + pooling_kernel_sizes=((1, 1), (1, 1)), + n_freq_downsample=((2, 1), (2, 2)), ) - assert model.n_freq_downsample[-1][-1] == 1 - def test_logtensorboard(self, tmpdir_module): - ts = tg.constant_timeseries(length=50, value=10) + # it shouldn't fail with the right number of coeffs + _ = NHiTSModel( + input_chunk_length=1, + output_chunk_length=1, + num_stacks=2, + num_blocks=2, + pooling_kernel_sizes=((2, 1), (2, 1)), + n_freq_downsample=((2, 1), (2, 1)), + ) + + # default freqs should be such that last one is 1 + model = NHiTSModel( + input_chunk_length=1, + output_chunk_length=1, + num_stacks=2, + num_blocks=2, + ) + assert model.n_freq_downsample[-1][-1] == 1 + + def test_logtensorboard(self, tmpdir_module): + ts = tg.constant_timeseries(length=50, value=10) + + # testing if both the modes (generic and interpretable) runs with tensorboard + architectures = [True, False] + for architecture in architectures: + # Test basic fit and predict + model = NBEATSModel( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=1, + log_tensorboard=True, + work_dir=tmpdir_module, + generic_architecture=architecture, + pl_trainer_kwargs={ + "log_every_n_steps": 1, + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) + model.fit(ts) + model.predict(n=2) - # testing if both the modes (generic and interpretable) runs with tensorboard - architectures = [True, False] - for architecture in architectures: - # Test basic fit and predict - model = NBEATSModel( - input_chunk_length=1, - output_chunk_length=1, - n_epochs=1, - log_tensorboard=True, - work_dir=tmpdir_module, - generic_architecture=architecture, - pl_trainer_kwargs={ - "log_every_n_steps": 1, - **tfm_kwargs["pl_trainer_kwargs"], - }, - ) - model.fit(ts) - model.predict(n=2) + def test_activation_fns(self): + ts = tg.constant_timeseries(length=50, value=10) - def test_activation_fns(self): - ts = tg.constant_timeseries(length=50, value=10) + for model_cls in [NBEATSModel, NHiTSModel]: + model = model_cls( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=10, + num_stacks=1, + num_blocks=1, + layer_widths=20, + random_state=42, + activation="LeakyReLU", + **tfm_kwargs, + ) + model.fit(ts) - for model_cls in [NBEATSModel, NHiTSModel]: + with pytest.raises(ValueError): model = model_cls( input_chunk_length=1, output_chunk_length=1, @@ -196,21 +201,7 @@ def test_activation_fns(self): num_blocks=1, layer_widths=20, random_state=42, - activation="LeakyReLU", - **tfm_kwargs + activation="invalid", + **tfm_kwargs, ) model.fit(ts) - - with pytest.raises(ValueError): - model = model_cls( - input_chunk_length=1, - output_chunk_length=1, - n_epochs=10, - num_stacks=1, - num_blocks=1, - layer_widths=20, - random_state=42, - activation="invalid", - **tfm_kwargs - ) - model.fit(ts) diff --git a/darts/tests/models/forecasting/test_probabilistic_models.py b/darts/tests/models/forecasting/test_probabilistic_models.py index a854775690..fd63793463 100644 --- a/darts/tests/models/forecasting/test_probabilistic_models.py +++ b/darts/tests/models/forecasting/test_probabilistic_models.py @@ -1,3 +1,4 @@ +import itertools import platform import numpy as np @@ -11,18 +12,19 @@ BATS, TBATS, CatBoostModel, + ConformalNaiveModel, ExponentialSmoothing, LightGBMModel, LinearRegressionModel, NotImportedModule, XGBModel, ) -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils import timeseries_generation as tg logger = get_logger(__name__) -try: +if TORCH_AVAILABLE: import torch from darts.models import ( @@ -34,6 +36,7 @@ TFTModel, TiDEModel, TransformerModel, + TSMixerModel, ) from darts.models.forecasting.torch_forecasting_model import TorchForecastingModel from darts.utils.likelihood_models import ( @@ -56,23 +59,19 @@ WeibullLikelihood, ) - TORCH_AVAILABLE = True -except ImportError: - logger.warning( - "Torch not available. Tests related to torch-based models will be skipped." - ) - TORCH_AVAILABLE = False - lgbm_available = not isinstance(LightGBMModel, NotImportedModule) cb_available = not isinstance(CatBoostModel, NotImportedModule) +# conformal models require a fitted base model +# in tests below, the model is re-trained for new input series. +# using a fake trained model should allow the same API with conformal models +conformal_forecaster = LinearRegressionModel(lags=10, output_chunk_length=5) +conformal_forecaster._fit_called = True + # model_cls, model_kwargs, err_univariate, err_multivariate models_cls_kwargs_errs = [ (ExponentialSmoothing, {}, 0.3, None), (ARIMA, {"p": 1, "d": 0, "q": 1, "random_state": 42}, 0.03, None), -] - -models_cls_kwargs_errs += [ ( BATS, { @@ -97,8 +96,36 @@ 0.04, 0.04, ), + ( + ConformalNaiveModel, + { + "model": conformal_forecaster, + "cal_length": 1, + "random_state": 42, + "quantiles": [0.1, 0.5, 0.9], + }, + 0.04, + 0.04, + ), ] +xgb_test_params = { + "n_estimators": 1, + "max_depth": 1, + "max_leaves": 1, +} +lgbm_test_params = { + "n_estimators": 1, + "max_depth": 1, + "num_leaves": 2, + "verbosity": -1, +} +cb_test_params = { + "iterations": 1, + "depth": 1, + "verbose": -1, +} + if TORCH_AVAILABLE: models_cls_kwargs_errs += [ ( @@ -125,7 +152,7 @@ **tfm_kwargs, }, 0.06, - 0.05, + 0.06, ), ( BlockRNNModel, @@ -193,6 +220,24 @@ 0.06, 0.1, ), + ( + TSMixerModel, + { + "input_chunk_length": 10, + "output_chunk_length": 5, + "n_epochs": 100, + "random_state": 0, + "num_blocks": 1, + "hidden_size": 32, + "dropout": 0.2, + "ff_size": 32, + "batch_size": 8, + "likelihood": GaussianLikelihood(), + **tfm_kwargs, + }, + 0.06, + 0.1, + ), ] @@ -255,7 +300,7 @@ def test_probabilistic_forecast_accuracy_multivariate(self, config): def helper_test_probabilistic_forecast_accuracy(self, model, err, ts, noisy_ts): model.fit(noisy_ts[:100]) - pred = model.predict(n=100, num_samples=100) + pred = model.predict(n=50, num_samples=100) # test accuracy of the median prediction compared to the noiseless ts mae_err_median = mae(ts[100:], pred) @@ -278,81 +323,89 @@ def helper_test_probabilistic_forecast_accuracy(self, model, err, ts, noisy_ts): mae_err = new_mae @pytest.mark.slow - def test_predict_likelihood_parameters_regression_models(self): + @pytest.mark.parametrize( + "config", + itertools.product( + [(LinearRegressionModel, False, {}), (XGBModel, False, xgb_test_params)] + + ([(LightGBMModel, False, lgbm_test_params)] if lgbm_available else []) + + ([(CatBoostModel, True, cb_test_params)] if cb_available else []), + [1, 3], # n components + [ + "quantile", + "poisson", + "gaussian", + ], # likelihood + [True, False], # multi models + [1, 2], # horizon + ), + ) + def test_predict_likelihood_parameters_regression_models(self, config): """ Check that the shape of the predicted likelihood parameters match expectations, for both univariate and multivariate series. Note: values are not tested as it would be too time consuming """ + ( + (model_cls, supports_gaussian, model_kwargs), + n_comp, + likelihood, + multi_models, + horizon, + ) = config + seed = 142857 n_times, n_samples = 100, 1 - model_classes = [LinearRegressionModel, XGBModel] - if lgbm_available: - model_classes.append(LightGBMModel) - if cb_available: - model_classes.append(CatBoostModel) - - for n_comp in [1, 3]: - list_lkl = [ - { - "kwargs": { - "likelihood": "quantile", - "quantiles": [0.05, 0.50, 0.95], - }, - "ts": TimeSeries.from_values( - np.random.normal( - loc=0, scale=1, size=(n_times, n_comp, n_samples) - ) - ), - "expected": np.array([-1.67, 0, 1.67]), - }, - { - "kwargs": {"likelihood": "poisson"}, - "ts": TimeSeries.from_values( - np.random.poisson(lam=4, size=(n_times, n_comp, n_samples)) - ), - "expected": np.array([4]), - }, - ] + lkl = {"kwargs": {"likelihood": likelihood}} + + if likelihood == "quantile": + lkl["kwargs"]["quantiles"] = [0.05, 0.50, 0.95] + lkl["ts"] = TimeSeries.from_values( + np.random.normal(loc=0, scale=1, size=(n_times, n_comp, n_samples)) + ) + lkl["expected"] = np.array([-1.67, 0, 1.67]) + elif likelihood == "poisson": + lkl["ts"] = TimeSeries.from_values( + np.random.poisson(lam=4, size=(n_times, n_comp, n_samples)) + ) + lkl["expected"] = np.array([4]) + elif likelihood == "gaussian": + if not supports_gaussian: + return - for model_cls in model_classes: - # Catboost is the only regression model supporting the GaussianLikelihood - if cb_available and issubclass(model_cls, CatBoostModel): - list_lkl.append( - { - "kwargs": {"likelihood": "gaussian"}, - "ts": TimeSeries.from_values( - np.random.normal( - loc=10, scale=3, size=(n_times, n_comp, n_samples) - ) - ), - "expected": np.array([10, 3]), - } - ) - - for lkl in list_lkl: - model = model_cls(lags=3, random_state=seed, **lkl["kwargs"]) - model.fit(lkl["ts"]) - pred_lkl_params = model.predict( - n=1, num_samples=1, predict_likelihood_parameters=True - ) - if n_comp == 1: - assert ( - lkl["expected"].shape == pred_lkl_params.values()[0].shape - ), ( - "The shape of the predicted likelihood parameters do not match expectation " - "for univariate series." - ) - else: - assert ( - 1, - len(lkl["expected"]) * n_comp, - 1, - ) == pred_lkl_params.all_values().shape, ( - "The shape of the predicted likelihood parameters do not match expectation " - "for multivariate series." - ) + lkl["ts"] = TimeSeries.from_values( + np.random.normal(loc=10, scale=3, size=(n_times, n_comp, n_samples)) + ) + lkl["expected"] = np.array([10, 3]) + else: + assert False, f"unknown likelihood {likelihood}" + + model = model_cls( + lags=3, + output_chunk_length=2, + random_state=seed, + **lkl["kwargs"], + multi_models=multi_models, + **model_kwargs, + ) + model.fit(lkl["ts"]) + pred_lkl_params = model.predict( + n=horizon, num_samples=1, predict_likelihood_parameters=True + ) + if n_comp == 1: + assert lkl["expected"].shape == pred_lkl_params.values()[0].shape, ( + "The shape of the predicted likelihood parameters do not match expectation " + "for univariate series." + ) + else: + assert ( + horizon, + len(lkl["expected"]) * n_comp, + 1, + ) == pred_lkl_params.all_values().shape, ( + "The shape of the predicted likelihood parameters do not match expectation " + "for multivariate series." + ) """ More likelihood tests """ @@ -418,11 +471,11 @@ def _get_avgs(series): avgs_orig, avgs_pred = _get_avgs(series), _get_avgs(pred) assert abs(avgs_orig[0] - avgs_pred[0]) < diff1, ( "The difference between the mean forecast and the mean series is larger " - "than expected on component 0 for distribution {}".format(lkl) + f"than expected on component 0 for distribution {lkl}" ) assert abs(avgs_orig[1] - avgs_pred[1]) < diff2, ( "The difference between the mean forecast and the mean series is larger " - "than expected on component 1 for distribution {}".format(lkl) + f"than expected on component 1 for distribution {lkl}" ) @pytest.mark.parametrize( @@ -476,11 +529,13 @@ def test_predict_likelihood_parameters_univariate_torch_models( loc=0, scale=1, size=(n_times, n_comp, n_samples) ) else: - values = lkl._distr_from_params(lkl_params).sample( - (n_times, n_comp, n_samples) - ) + values = lkl._distr_from_params(lkl_params).sample(( + n_times, + n_comp, + n_samples, + )) - # Dirichlet must be handled sligthly differently since its multivariate + # Dirichlet must be handled slightly differently since its multivariate if isinstance(lkl, DirichletLikelihood): values = torch.swapaxes(values, 1, 3) values = torch.squeeze(values, 3) @@ -557,9 +612,11 @@ def test_predict_likelihood_parameters_multivariate_torch_models( loc=0, scale=1, size=(n_times, n_comp, n_samples) ) else: - values = lkl._distr_from_params(lkl_params).sample( - (n_times, n_comp, n_samples) - ) + values = lkl._distr_from_params(lkl_params).sample(( + n_times, + n_comp, + n_samples, + )) ts = TimeSeries.from_values( values, columns=[f"dummy_{i}" for i in range(values.shape[1])] ) diff --git a/darts/tests/models/forecasting/test_prophet.py b/darts/tests/models/forecasting/test_prophet.py index 21ec5b2b60..8aea5e1e04 100644 --- a/darts/tests/models/forecasting/test_prophet.py +++ b/darts/tests/models/forecasting/test_prophet.py @@ -8,6 +8,7 @@ from darts.logging import get_logger from darts.models import NotImportedModule, Prophet from darts.utils import timeseries_generation as tg +from darts.utils.utils import freqs, generate_index logger = get_logger(__name__) @@ -32,7 +33,7 @@ def test_add_seasonality_calls(self): "prior_scale": 1.0, "mode": "additive", "condition_name": "custom_condition", - } + }, ) model1 = Prophet(add_seasonalities=kwargs_all) model2 = Prophet() @@ -72,24 +73,24 @@ def test_prophet_model(self): perform_full_test = False test_cases_all = { - "A": 12, + freqs["YE"]: 12, "W": 7, - "BM": 12, + freqs["BME"]: 12, "C": 5, "D": 7, "MS": 12, "B": 5, - "H": 24, - "BH": 8, - "Q": 4, - "min": 60, - "S": 60, - "30S": 60, - "24T": 60, + freqs["h"]: 24, + freqs["bh"]: 8, + freqs["QE"]: 4, + freqs["min"]: 60, + freqs["s"]: 60, + "30" + freqs["s"]: 60, + "24" + freqs["min"]: 60, } test_cases_fast = { - key: test_cases_all[key] for key in ["MS", "D", "H"] + key: test_cases_all[key] for key in ["MS", "D", freqs["h"]] } # monthly, daily, hourly self.helper_test_freq_coversion(test_cases_all) @@ -108,32 +109,34 @@ def test_prophet_model_without_stdout_suppression(self): model = Prophet(suppress_stdout_stderror=False) model._execute_and_suppress_output = Mock(return_value=True) model._model_builder = Mock(return_value=Mock(fit=Mock(return_value=True))) - df = pd.DataFrame( - { - "ds": pd.date_range(start="2022-01-01", periods=30, freq="D"), - "y": np.linspace(0, 10, 30), - } - ) + df = pd.DataFrame({ + "ds": pd.date_range(start="2022-01-01", periods=30, freq="D"), + "y": np.linspace(0, 10, 30), + }) ts = TimeSeries.from_dataframe(df, time_col="ds", value_cols="y") model.fit(ts) - model._execute_and_suppress_output.assert_not_called(), "Suppression should not be called" + ( + model._execute_and_suppress_output.assert_not_called(), + "Suppression should not be called", + ) model.model.fit.assert_called_once(), "Model should still be fitted" def test_prophet_model_with_stdout_suppression(self): model = Prophet(suppress_stdout_stderror=True) model._execute_and_suppress_output = Mock(return_value=True) model._model_builder = Mock(return_value=Mock(fit=Mock(return_value=True))) - df = pd.DataFrame( - { - "ds": pd.date_range(start="2022-01-01", periods=30, freq="D"), - "y": np.linspace(0, 10, 30), - } - ) + df = pd.DataFrame({ + "ds": pd.date_range(start="2022-01-01", periods=30, freq="D"), + "y": np.linspace(0, 10, 30), + }) ts = TimeSeries.from_dataframe(df, time_col="ds", value_cols="y") model.fit(ts) - model._execute_and_suppress_output.assert_called_once(), "Suppression should be called once" + ( + model._execute_and_suppress_output.assert_called_once(), + "Suppression should be called once", + ) def test_prophet_model_default_with_prophet_constructor(self): from prophet import Prophet as FBProphet @@ -145,7 +148,7 @@ def test_prophet_model_with_logistic_growth(self): model = Prophet(growth="logistic", cap=1) # Create timeseries with logistic function - times = tg.generate_index( + times = generate_index( pd.Timestamp("20200101"), pd.Timestamp("20210101"), freq="D" ) values = np.linspace(-10, 10, len(times)) @@ -172,7 +175,8 @@ def helper_test_freq_coversion(self, test_cases): assert ( abs( - Prophet._freq_to_days(freq="30S") - 30 * Prophet._freq_to_days(freq="S") + Prophet._freq_to_days(freq="30" + freqs["s"]) + - 30 * Prophet._freq_to_days(freq=freqs["s"]) ) < 10e-9 ) @@ -217,7 +221,7 @@ def helper_test_prophet_model(self, period, freq, compare_all_models=False): model = Prophet( add_seasonalities=custom_seasonality, seasonality_mode="additive", - **supress_auto_seasonality + **supress_auto_seasonality, ) model.fit(train, future_covariates=train_cov) diff --git a/darts/tests/models/forecasting/test_ptl_trainer.py b/darts/tests/models/forecasting/test_ptl_trainer.py index d9449fa58d..42100fcccd 100644 --- a/darts/tests/models/forecasting/test_ptl_trainer.py +++ b/darts/tests/models/forecasting/test_ptl_trainer.py @@ -1,283 +1,277 @@ import numpy as np import pytest -from darts.logging import get_logger -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils.timeseries_generation import linear_timeseries -logger = get_logger(__name__) - -try: - import pytorch_lightning as pl - - from darts.models.forecasting.rnn_model import RNNModel - - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. RNN tests will be skipped.") - TORCH_AVAILABLE = False - - -if TORCH_AVAILABLE: - - class TestTorchForecastingModel: - trainer_params = { - "max_epochs": 1, - "logger": False, - "enable_checkpointing": False, - } - series = linear_timeseries(length=100).astype(np.float32) - pl_200_or_above = int(pl.__version__.split(".")[0]) >= 2 - precisions = { - 32: "32" if not pl_200_or_above else "32-true", - 64: "64" if not pl_200_or_above else "64-true", - } - - def test_prediction_loaded_custom_trainer(self, tmpdir_module): - """validate manual save with automatic save files by comparing output between the two""" - auto_name = "test_save_automatic" - model = RNNModel( - 12, - "RNN", - 10, - 10, - model_name=auto_name, - work_dir=tmpdir_module, - save_checkpoints=True, - random_state=42, - **tfm_kwargs, - ) - - # fit model with custom trainer - trainer = pl.Trainer( - max_epochs=1, - enable_checkpointing=True, - logger=False, - callbacks=model.trainer_params["callbacks"], - **tfm_kwargs["pl_trainer_kwargs"], - ) +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import pytorch_lightning as pl + +from darts.models.forecasting.rnn_model import RNNModel + + +class TestPTLTrainer: + trainer_params = { + "max_epochs": 1, + "logger": False, + "enable_checkpointing": False, + } + series = linear_timeseries(length=100).astype(np.float32) + pl_200_or_above = int(pl.__version__.split(".")[0]) >= 2 + precisions = { + 32: "32" if not pl_200_or_above else "32-true", + 64: "64" if not pl_200_or_above else "64-true", + } + + def test_prediction_loaded_custom_trainer(self, tmpdir_module): + """validate manual save with automatic save files by comparing output between the two""" + auto_name = "test_save_automatic" + model = RNNModel( + 12, + "RNN", + 10, + 10, + model_name=auto_name, + work_dir=tmpdir_module, + save_checkpoints=True, + random_state=42, + **tfm_kwargs, + ) + + # fit model with custom trainer + trainer = pl.Trainer( + max_epochs=1, + enable_checkpointing=True, + logger=False, + callbacks=model.trainer_params["callbacks"], + **tfm_kwargs["pl_trainer_kwargs"], + ) + model.fit(self.series, trainer=trainer) + + # load automatically saved model with manual load_model() and load_from_checkpoint() + model_loaded = RNNModel.load_from_checkpoint( + model_name=auto_name, + work_dir=tmpdir_module, + best=False, + map_location="cpu", + ) + + # compare prediction of loaded model with original model + assert model.predict(n=4) == model_loaded.predict(n=4) + + def test_prediction_custom_trainer(self): + model = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) + model2 = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) + + # fit model with custom trainer + trainer = pl.Trainer( + **self.trainer_params, + precision=self.precisions[32], + **tfm_kwargs["pl_trainer_kwargs"], + ) + model.fit(self.series, trainer=trainer) + + # fit model with built-in trainer + model2.fit(self.series, epochs=1) + + # both should produce identical prediction + assert model.predict(n=4) == model2.predict(n=4) + + def test_custom_trainer_setup(self): + model = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) + + # trainer with wrong precision should raise ValueError + trainer = pl.Trainer( + **self.trainer_params, + precision=self.precisions[64], + **tfm_kwargs["pl_trainer_kwargs"], + ) + with pytest.raises(ValueError): model.fit(self.series, trainer=trainer) - # load automatically saved model with manual load_model() and load_from_checkpoint() - model_loaded = RNNModel.load_from_checkpoint( - model_name=auto_name, - work_dir=tmpdir_module, - best=False, - map_location="cpu", - ) - - # compare prediction of loaded model with original model - assert model.predict(n=4) == model_loaded.predict(n=4) - - def test_prediction_custom_trainer(self): - model = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) - model2 = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) - - # fit model with custom trainer - trainer = pl.Trainer( - **self.trainer_params, - precision=self.precisions[32], + # no error with correct precision + trainer = pl.Trainer( + **self.trainer_params, + precision=self.precisions[32], + **tfm_kwargs["pl_trainer_kwargs"], + ) + model.fit(self.series, trainer=trainer) + + # check if number of epochs trained is same as trainer.max_epochs + assert trainer.max_epochs == model.epochs_trained + + def test_builtin_extended_trainer(self): + # wrong precision parameter name + with pytest.raises(TypeError): + invalid_trainer_kwarg = { + "precisionn": self.precisions[32], **tfm_kwargs["pl_trainer_kwargs"], - ) - model.fit(self.series, trainer=trainer) - - # fit model with built-in trainer - model2.fit(self.series, epochs=1) - - # both should produce identical prediction - assert model.predict(n=4) == model2.predict(n=4) - - def test_custom_trainer_setup(self): - model = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) - - # trainer with wrong precision should raise ValueError - trainer = pl.Trainer( - **self.trainer_params, - precision=self.precisions[64], - **tfm_kwargs["pl_trainer_kwargs"], - ) - with pytest.raises(ValueError): - model.fit(self.series, trainer=trainer) - - # no error with correct precision - trainer = pl.Trainer( - **self.trainer_params, - precision=self.precisions[32], - **tfm_kwargs["pl_trainer_kwargs"], - ) - model.fit(self.series, trainer=trainer) - - # check if number of epochs trained is same as trainer.max_epochs - assert trainer.max_epochs == model.epochs_trained - - def test_builtin_extended_trainer(self): - # wrong precision parameter name - with pytest.raises(TypeError): - invalid_trainer_kwarg = { - "precisionn": self.precisions[32], - **tfm_kwargs["pl_trainer_kwargs"], - } - model = RNNModel( - 12, - "RNN", - 10, - 10, - random_state=42, - pl_trainer_kwargs=invalid_trainer_kwarg, - ) - model.fit(self.series, epochs=1) - - # flaot 16 not supported - with pytest.raises(ValueError): - invalid_trainer_kwarg = { - "precision": "16-mixed", - **tfm_kwargs["pl_trainer_kwargs"], - } - model = RNNModel( - 12, - "RNN", - 10, - 10, - random_state=42, - pl_trainer_kwargs=invalid_trainer_kwarg, - ) - model.fit(self.series.astype(np.float16), epochs=1) - - # precision value doesn't match `series` dtype - with pytest.raises(ValueError): - invalid_trainer_kwarg = { - "precision": self.precisions[64], - **tfm_kwargs["pl_trainer_kwargs"], - } - model = RNNModel( - 12, - "RNN", - 10, - 10, - random_state=42, - pl_trainer_kwargs=invalid_trainer_kwarg, - ) - model.fit(self.series.astype(np.float32), epochs=1) - - for precision in [64, 32]: - valid_trainer_kwargs = { - "precision": self.precisions[precision], - **tfm_kwargs["pl_trainer_kwargs"], - } - - # valid parameters shouldn't raise error - model = RNNModel( - 12, - "RNN", - 10, - 10, - random_state=42, - pl_trainer_kwargs=valid_trainer_kwargs, - ) - ts_dtype = getattr(np, f"float{precision}") - model.fit(self.series.astype(ts_dtype), epochs=1) - preds = model.predict(n=3) - assert model.trainer.precision == self.precisions[precision] - assert preds.dtype == ts_dtype - - def test_custom_callback(self, tmpdir_module): - class CounterCallback(pl.callbacks.Callback): - # counts the number of trained epochs starting from count_default - def __init__(self, count_default): - self.counter = count_default - - def on_train_epoch_end(self, *args, **kwargs): - self.counter += 1 - - my_counter_0 = CounterCallback(count_default=0) - my_counter_2 = CounterCallback(count_default=2) - + } model = RNNModel( 12, "RNN", 10, 10, random_state=42, - pl_trainer_kwargs={ - "callbacks": [my_counter_0, my_counter_2], - **tfm_kwargs["pl_trainer_kwargs"], - }, + pl_trainer_kwargs=invalid_trainer_kwarg, ) + model.fit(self.series, epochs=1) - # check if callbacks were added - assert len(model.trainer_params["callbacks"]) == 2 - model.fit(self.series, epochs=2, verbose=True) - # check that lightning did not mutate callbacks (verbosity adds a progress bar callback) - assert len(model.trainer_params["callbacks"]) == 2 - - assert my_counter_0.counter == model.epochs_trained - assert my_counter_2.counter == model.epochs_trained + 2 - - # check that callbacks don't overwrite Darts' built-in checkpointer + # float 16 not supported + with pytest.raises(ValueError): + invalid_trainer_kwarg = { + "precision": "16-mixed", + **tfm_kwargs["pl_trainer_kwargs"], + } model = RNNModel( 12, "RNN", 10, 10, random_state=42, - work_dir=tmpdir_module, - save_checkpoints=True, - pl_trainer_kwargs={ - "callbacks": [CounterCallback(0), CounterCallback(2)], - **tfm_kwargs["pl_trainer_kwargs"], - }, + pl_trainer_kwargs=invalid_trainer_kwarg, ) - # we expect 3 callbacks - assert len(model.trainer_params["callbacks"]) == 3 + model.fit(self.series.astype(np.float16), epochs=1) - # first one is our Checkpointer - assert isinstance( - model.trainer_params["callbacks"][0], pl.callbacks.ModelCheckpoint - ) - - # second and third are CounterCallbacks - for i in range(1, 3): - assert isinstance(model.trainer_params["callbacks"][i], CounterCallback) - - def test_early_stopping(self): - my_stopper = pl.callbacks.early_stopping.EarlyStopping( - monitor="val_loss", - stopping_threshold=1e9, - ) + # precision value doesn't match `series` dtype + with pytest.raises(ValueError): + invalid_trainer_kwarg = { + "precision": self.precisions[64], + **tfm_kwargs["pl_trainer_kwargs"], + } model = RNNModel( 12, "RNN", 10, 10, - nr_epochs_val_period=1, random_state=42, - pl_trainer_kwargs={ - "callbacks": [my_stopper], - **tfm_kwargs["pl_trainer_kwargs"], - }, + pl_trainer_kwargs=invalid_trainer_kwarg, ) + model.fit(self.series.astype(np.float32), epochs=1) - # training should stop immediately with high stopping_threshold - model.fit(self.series, val_series=self.series, epochs=100, verbose=True) - assert model.epochs_trained == 1 + for precision in [64, 32]: + valid_trainer_kwargs = { + "precision": self.precisions[precision], + **tfm_kwargs["pl_trainer_kwargs"], + } - # check that early stopping only takes valid monitor variables - my_stopper = pl.callbacks.early_stopping.EarlyStopping( - monitor="invalid_variable", - stopping_threshold=1e9, - ) + # valid parameters shouldn't raise error model = RNNModel( 12, "RNN", 10, 10, - nr_epochs_val_period=1, random_state=42, - pl_trainer_kwargs={ - "callbacks": [my_stopper], - **tfm_kwargs["pl_trainer_kwargs"], - }, + pl_trainer_kwargs=valid_trainer_kwargs, ) + ts_dtype = getattr(np, f"float{precision}") + model.fit(self.series.astype(ts_dtype), epochs=1) + preds = model.predict(n=3) + assert model.trainer.precision == self.precisions[precision] + assert preds.dtype == ts_dtype + + def test_custom_callback(self, tmpdir_module): + class CounterCallback(pl.callbacks.Callback): + # counts the number of trained epochs starting from count_default + def __init__(self, count_default): + self.counter = count_default + + def on_train_epoch_end(self, *args, **kwargs): + self.counter += 1 + + my_counter_0 = CounterCallback(count_default=0) + my_counter_2 = CounterCallback(count_default=2) + + model = RNNModel( + 12, + "RNN", + 10, + 10, + random_state=42, + pl_trainer_kwargs={ + "callbacks": [my_counter_0, my_counter_2], + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) + + # check if callbacks were added + assert len(model.trainer_params["callbacks"]) == 2 + model.fit(self.series, epochs=2, verbose=True) + # check that lightning did not mutate callbacks (verbosity adds a progress bar callback) + assert len(model.trainer_params["callbacks"]) == 2 + + assert my_counter_0.counter == model.epochs_trained + assert my_counter_2.counter == model.epochs_trained + 2 + + # check that callbacks don't overwrite Darts' built-in checkpointer + model = RNNModel( + 12, + "RNN", + 10, + 10, + random_state=42, + work_dir=tmpdir_module, + save_checkpoints=True, + pl_trainer_kwargs={ + "callbacks": [CounterCallback(0), CounterCallback(2)], + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) + # we expect 3 callbacks + assert len(model.trainer_params["callbacks"]) == 3 + + # first one is our Checkpointer + assert isinstance( + model.trainer_params["callbacks"][0], pl.callbacks.ModelCheckpoint + ) + + # second and third are CounterCallbacks + for i in range(1, 3): + assert isinstance(model.trainer_params["callbacks"][i], CounterCallback) + + def test_early_stopping(self): + my_stopper = pl.callbacks.early_stopping.EarlyStopping( + monitor="val_loss", + stopping_threshold=1e9, + ) + model = RNNModel( + 12, + "RNN", + 10, + 10, + nr_epochs_val_period=1, + random_state=42, + pl_trainer_kwargs={ + "callbacks": [my_stopper], + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) + + # training should stop immediately with high stopping_threshold + model.fit(self.series, val_series=self.series, epochs=100, verbose=True) + assert model.epochs_trained == 1 + + # check that early stopping only takes valid monitor variables + my_stopper = pl.callbacks.early_stopping.EarlyStopping( + monitor="invalid_variable", + stopping_threshold=1e9, + ) + model = RNNModel( + 12, + "RNN", + 10, + 10, + nr_epochs_val_period=1, + random_state=42, + pl_trainer_kwargs={ + "callbacks": [my_stopper], + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) - with pytest.raises(RuntimeError): - model.fit(self.series, val_series=self.series, epochs=100, verbose=True) + with pytest.raises(RuntimeError): + model.fit(self.series, val_series=self.series, epochs=100, verbose=True) diff --git a/darts/tests/models/forecasting/test_regression_ensemble_model.py b/darts/tests/models/forecasting/test_regression_ensemble_model.py index 258b1a1507..0a5862997e 100644 --- a/darts/tests/models/forecasting/test_regression_ensemble_model.py +++ b/darts/tests/models/forecasting/test_regression_ensemble_model.py @@ -1,4 +1,4 @@ -from typing import List, Union +from typing import Union import numpy as np import pandas as pd @@ -7,7 +7,6 @@ from sklearn.linear_model import LinearRegression from darts import TimeSeries -from darts.logging import get_logger from darts.metrics import mape, rmse from darts.models import ( LinearRegressionModel, @@ -18,23 +17,16 @@ RegressionModel, Theta, ) -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.tests.models.forecasting.test_ensemble_models import _make_ts from darts.tests.models.forecasting.test_regression_models import train_test_split from darts.utils import timeseries_generation as tg -logger = get_logger(__name__) - -try: +if TORCH_AVAILABLE: import torch from darts.models import BlockRNNModel, RNNModel - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. Some tests will be skipped.") - TORCH_AVAILABLE = False - class TestRegressionEnsembleModels: RANDOM_SEED = 111 @@ -70,17 +62,19 @@ def get_local_models(self): return [NaiveDrift(), NaiveSeasonal(5), NaiveSeasonal(10)] @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") - def get_global_models(self, output_chunk_length=5): + def get_global_models( + self, output_chunk_length=5, input_chunk_length=20, training_length=24 + ): return [ RNNModel( - input_chunk_length=20, - output_chunk_length=output_chunk_length, + input_chunk_length=input_chunk_length, + training_length=training_length, n_epochs=1, random_state=42, **tfm_kwargs, ), BlockRNNModel( - input_chunk_length=20, + input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, n_epochs=1, random_state=42, @@ -150,7 +144,7 @@ def test_accept_pretrain_global_models(self): model_ens.fit(self.sine_series[:45]) model_ens.predict(5) - # retrain_forecasting_models=True requires all the model to be reset + # train_forecasting_models=True requires all the model to be reset with pytest.raises(ValueError): RegressionEnsembleModel( forecasting_models=[linreg1, linreg2], @@ -460,9 +454,9 @@ def helper_test_models_accuracy( prediction = model_instance.predict(n=n, past_covariates=past_covariates) current_rmse = rmse(test_series, prediction) - assert ( - current_rmse <= min_rmse - ), f"Model was not able to denoise data. A rmse score of {current_rmse} was recorded." + assert current_rmse <= min_rmse, ( + f"Model was not able to denoise data. A rmse score of {current_rmse} was recorded." + ) def denoising_input(self): np.random.seed(self.RANDOM_SEED) @@ -551,7 +545,16 @@ def test_call_backtest_regression_ensemble_local_models(self): max(m_.min_train_series_length for m_ in ensemble.forecasting_models) == 10 ) # -10 comes from the maximum minimum train series length of all models - assert ensemble.extreme_lags == (-10 - regr_train_n, -1, None, None, None, None) + assert ensemble.extreme_lags == ( + -10 - regr_train_n, + -1, + None, + None, + None, + None, + 0, + None, + ) ensemble.backtest(self.sine_series) def test_extreme_lags(self): @@ -566,7 +569,7 @@ def test_extreme_lags(self): regression_train_n_points=train_n_points, ) - assert model.extreme_lags == (-train_n_points, 0, -3, -1, 0, 0) + assert model.extreme_lags == (-train_n_points, 0, -3, -1, 0, 0, 0, None) # mix of all the lags model3 = RandomForest( @@ -578,7 +581,29 @@ def test_extreme_lags(self): regression_train_n_points=train_n_points, ) - assert model.extreme_lags == (-7 - train_n_points, 0, -3, -1, -2, 5) + assert model.extreme_lags == (-7 - train_n_points, 0, -3, -1, -2, 5, 0, None) + + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") + def test_extreme_lags_torch(self): + # test RNN case which has the 8th extreme lags element (max_target_lag_train) + train_n_points = 10 + icl = 20 + ocl = 5 + training_length = 24 + model = RegressionEnsembleModel( + forecasting_models=self.get_global_models(ocl, icl, training_length), + regression_train_n_points=train_n_points, + ) + assert model.extreme_lags == ( + -icl - train_n_points, + ocl - 1, + -icl, # past covs from BlockRNN + -1, # past covs from BlockRNN + -icl, # future covs from RNN + 0, # future covs from RNN + 0, + training_length - icl, # training length from RNN + ) def test_stochastic_regression_ensemble_model(self): quantiles = [0.25, 0.5, 0.75] @@ -602,7 +627,7 @@ def test_stochastic_regression_ensemble_model(self): ) assert ensemble_allproba._models_are_probabilistic - assert ensemble_allproba._is_probabilistic + assert ensemble_allproba.supports_probabilistic_prediction ensemble_allproba.fit(self.ts_random_walk[:100]) # probabilistic forecasting is supported pred = ensemble_allproba.predict(5, num_samples=10) @@ -619,7 +644,7 @@ def test_stochastic_regression_ensemble_model(self): ) assert not ensemble_mixproba._models_are_probabilistic - assert ensemble_mixproba._is_probabilistic + assert ensemble_mixproba.supports_probabilistic_prediction ensemble_mixproba.fit(self.ts_random_walk[:100]) # probabilistic forecasting is supported pred = ensemble_mixproba.predict(5, num_samples=10) @@ -639,7 +664,7 @@ def test_stochastic_regression_ensemble_model(self): ) assert not ensemble_mixproba2._models_are_probabilistic - assert ensemble_mixproba2._is_probabilistic + assert ensemble_mixproba2.supports_probabilistic_prediction ensemble_mixproba2.fit(self.ts_random_walk[:100]) pred = ensemble_mixproba2.predict(5, num_samples=10) assert pred.n_samples == 10 @@ -655,7 +680,7 @@ def test_stochastic_regression_ensemble_model(self): ) assert not ensemble_proba_reg._models_are_probabilistic - assert ensemble_proba_reg._is_probabilistic + assert ensemble_proba_reg.supports_probabilistic_prediction ensemble_proba_reg.fit(self.ts_random_walk[:100]) # probabilistic forecasting is supported pred = ensemble_proba_reg.predict(5, num_samples=10) @@ -672,7 +697,7 @@ def test_stochastic_regression_ensemble_model(self): ) assert ensemble_dete_reg._models_are_probabilistic - assert not ensemble_dete_reg._is_probabilistic + assert not ensemble_dete_reg.supports_probabilistic_prediction ensemble_dete_reg.fit(self.ts_random_walk[:100]) # deterministic forecasting is supported ensemble_dete_reg.predict(5, num_samples=1) @@ -691,7 +716,7 @@ def test_stochastic_regression_ensemble_model(self): ) assert not ensemble_alldete._models_are_probabilistic - assert not ensemble_alldete._is_probabilistic + assert not ensemble_alldete.supports_probabilistic_prediction ensemble_alldete.fit(self.ts_random_walk[:100]) # deterministic forecasting is supported ensemble_alldete.predict(5, num_samples=1) @@ -729,7 +754,7 @@ def test_stochastic_training_regression_ensemble_model(self): regression_train_num_samples=500, ) - # must use apprioriate reduction method + # must use appropriate reduction method with pytest.raises(ValueError): RegressionEnsembleModel( forecasting_models=[ @@ -885,8 +910,8 @@ def test_predict_likelihood_parameters_multivariate_regression_ensemble(self): ) and all(pred_ens["linear_q0.50"].values() < pred_ens["linear_q0.95"].values()) def test_wrong_model_creation_params(self): - """Since `multi_models=False` requires to shift the regression model lags in the past (outside of the forecasting - model predictions), it is not supported.""" + """Since `multi_models=False` requires to shift the regression model lags in the past (outside of the + forecasting model predictions), it is not supported.""" forcasting_models = [ self.get_deterministic_global_model(2), self.get_deterministic_global_model([-5, -7]), @@ -911,10 +936,10 @@ def test_wrong_model_creation_params(self): @staticmethod def get_probabilistic_global_model( - lags: Union[int, List[int]], + lags: Union[int, list[int]], output_chunk_length: int = 1, likelihood: str = "quantile", - quantiles: Union[None, List[float]] = [0.05, 0.5, 0.95], + quantiles: Union[None, list[float]] = [0.05, 0.5, 0.95], random_state: int = 42, ) -> LinearRegressionModel: return LinearRegressionModel( @@ -926,6 +951,6 @@ def get_probabilistic_global_model( @staticmethod def get_deterministic_global_model( - lags: Union[int, List[int]], random_state: int = 13 + lags: Union[int, list[int]], random_state: int = 13 ) -> LinearRegressionModel: return LinearRegressionModel(lags=lags, random_state=random_state) diff --git a/darts/tests/models/forecasting/test_regression_models.py b/darts/tests/models/forecasting/test_regression_models.py index 9d5c369526..cc9a514b51 100644 --- a/darts/tests/models/forecasting/test_regression_models.py +++ b/darts/tests/models/forecasting/test_regression_models.py @@ -1,7 +1,9 @@ -import copy import functools -import itertools +import importlib +import inspect import math +from copy import deepcopy +from itertools import product from unittest.mock import patch import numpy as np @@ -9,6 +11,7 @@ import pytest from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor from sklearn.linear_model import LinearRegression +from sklearn.neighbors import KNeighborsRegressor import darts from darts import TimeSeries @@ -29,6 +32,7 @@ ) from darts.utils import timeseries_generation as tg from darts.utils.multioutput import MultiOutputRegressor +from darts.utils.utils import generate_index logger = get_logger(__name__) @@ -78,10 +82,8 @@ def dummy_timeseries( freq="D", integer_index=False, ): - targets, pcovs, fcovs = [], [], [] for series_idx in range(n_series): - target_start_date = ( series_idx * multiseries_offset if integer_index @@ -156,10 +158,29 @@ class NewCls(cls): return NewCls -class TestRegressionModels: +xgb_test_params = { + "n_estimators": 1, + "max_depth": 1, + "max_leaves": 1, + "random_state": 42, +} +lgbm_test_params = { + "n_estimators": 1, + "max_depth": 1, + "num_leaves": 2, + "verbosity": -1, + "random_state": 42, +} +cb_test_params = { + "iterations": 1, + "depth": 1, + "verbose": -1, + "random_state": 42, +} - np.random.seed(42) +class TestRegressionModels: + np.random.seed(42) # default regression models models = [ RandomForest, @@ -178,20 +199,28 @@ class TestRegressionModels: LinearRegressionModel, likelihood="poisson", random_state=42 ) PoissonXGBModel = partialclass( - XGBModel, likelihood="poisson", random_state=42, tree_method="exact" + XGBModel, + likelihood="poisson", + tree_method="exact", + **xgb_test_params, ) QuantileXGBModel = partialclass( - XGBModel, likelihood="quantile", random_state=42, tree_method="exact" + XGBModel, + likelihood="quantile", + tree_method="exact", + **xgb_test_params, ) - # targets for poisson regression must be positive, so we exclude them for some tests - models.extend( - [ - QuantileLinearRegressionModel, - PoissonLinearRegressionModel, - PoissonXGBModel, - QuantileXGBModel, - ] + KNeighborsRegressorModel = partialclass( + RegressionModel, + model=KNeighborsRegressor(n_neighbors=1), ) + # targets for poisson regression must be positive, so we exclude them for some tests + models.extend([ + QuantileLinearRegressionModel, + PoissonLinearRegressionModel, + PoissonXGBModel, + QuantileXGBModel, + ]) univariate_accuracies = [ 0.03, # RandomForest @@ -199,8 +228,8 @@ class TestRegressionModels: 1e-13, # RegressionModel 0.8, # QuantileLinearRegressionModel 0.4, # PoissonLinearRegressionModel - 1e-01, # PoissonXGBModel - 0.5, # QuantileXGBModel + 0.75, # PoissonXGBModel + 0.75, # QuantileXGBModel ] multivariate_accuracies = [ 0.3, # RandomForest @@ -208,8 +237,8 @@ class TestRegressionModels: 1e-13, # RegressionModel 0.8, # QuantileLinearRegressionModel 0.4, # PoissonLinearRegressionModel - 0.15, # PoissonXGBModel - 0.4, # QuantileXGBModel + 0.75, # PoissonXGBModel + 0.75, # QuantileXGBModel ] multivariate_multiseries_accuracies = [ 0.05, # RandomForest @@ -217,23 +246,26 @@ class TestRegressionModels: 1e-13, # RegressionModel 0.8, # QuantileLinearRegressionModel 0.4, # PoissonLinearRegressionModel - 1e-01, # PoissonXGBModel - 0.4, # QuantileXGBModel + 0.85, # PoissonXGBModel + 0.65, # QuantileXGBModel ] lgbm_w_categorical_covariates = NotImportedModule if lgbm_available: + RegularLightGBMModel = partialclass(LightGBMModel, **lgbm_test_params) QuantileLightGBMModel = partialclass( LightGBMModel, likelihood="quantile", quantiles=[0.05, 0.5, 0.95], - random_state=42, + **lgbm_test_params, ) PoissonLightGBMModel = partialclass( - LightGBMModel, likelihood="poisson", random_state=42 + LightGBMModel, + likelihood="poisson", + **lgbm_test_params, ) models += [ - LightGBMModel, + RegularLightGBMModel, QuantileLightGBMModel, PoissonLightGBMModel, ] @@ -246,62 +278,67 @@ class TestRegressionModels: categorical_future_covariates=["fut_cov_promo_mechanism"], categorical_past_covariates=["past_cov_cat_dummy"], categorical_static_covariates=["product_id"], + **lgbm_test_params, ) univariate_accuracies += [ - 0.3, # LightGBMModel - 0.5, # QuantileLightGBMModel - 0.4, # PoissonLightGBMModel + 0.75, # LightGBMModel + 0.75, # QuantileLightGBMModel + 0.75, # PoissonLightGBMModel ] multivariate_accuracies += [ - 0.4, # LightGBMModel - 0.4, # QuantileLightGBMModel - 0.4, # PoissonLightGBMModel + 0.7, # LightGBMModel + 0.75, # QuantileLightGBMModel + 0.75, # PoissonLightGBMModel ] multivariate_multiseries_accuracies += [ - 0.05, # LightGBMModel - 0.4, # QuantileLightGBMModel - 0.4, # PoissonLightGBMModel + 0.7, # LightGBMModel + 0.7, # QuantileLightGBMModel + 0.75, # PoissonLightGBMModel ] if cb_available: + RegularCatBoostModel = partialclass( + CatBoostModel, + **cb_test_params, + ) QuantileCatBoostModel = partialclass( CatBoostModel, likelihood="quantile", quantiles=[0.05, 0.5, 0.95], - random_state=42, + **cb_test_params, ) PoissonCatBoostModel = partialclass( CatBoostModel, likelihood="poisson", - random_state=42, + **cb_test_params, ) NormalCatBoostModel = partialclass( CatBoostModel, likelihood="gaussian", - random_state=42, + **cb_test_params, ) models += [ - CatBoostModel, + RegularCatBoostModel, QuantileCatBoostModel, PoissonCatBoostModel, NormalCatBoostModel, ] univariate_accuracies += [ 0.75, # CatBoostModel - 1e-03, # QuantileCatBoostModel - 1e-01, # PoissonCatBoostModel - 1e-05, # NormalCatBoostModel + 0.75, # QuantileCatBoostModel + 0.9, # PoissonCatBoostModel + 0.75, # NormalCatBoostModel ] multivariate_accuracies += [ 0.75, # CatBoostModel - 1e-03, # QuantileCatBoostModel - 0.15, # PoissonCatBoostModel - 1e-05, # NormalCatBoostModel + 0.75, # QuantileCatBoostModel + 0.86, # PoissonCatBoostModel + 0.75, # NormalCatBoostModel ] multivariate_multiseries_accuracies += [ 0.75, # CatBoostModel - 1e-03, # QuantileCatBoostModel - 1e-01, # PoissonCatBoostModel - 1e-03, # NormalCatBoostModel + 0.75, # QuantileCatBoostModel + 1.2, # PoissonCatBoostModel + 0.75, # NormalCatBoostModel ] # dummy feature and target TimeSeries instances @@ -338,8 +375,8 @@ def inputs_for_tests_categorical_covariates(self): - series is a univariate TimeSeries with daily frequency. - future_covariates are a TimeSeries with 2 components. The first component represents a "promotion" mechanism and has an impact on the target quantiy according to 'apply_promo_mechanism'. The second - component contains random data that should have no impact on the target quantity. Note that altough the - intention is to model the "promotion_mechnism" as a categorical variable, it is encoded as integers. + component contains random data that should have no impact on the target quantity. Note that although the + intention is to model the "promotion_mechanism" as a categorical variable, it is encoded as integers. This is required by LightGBM. - past_covariates are a TimeSeries with 2 components. It only contains dummy data and does not have any impact on the target series. @@ -371,18 +408,14 @@ def _apply_promo_mechanism(promo_mechanism): date_range = pd.date_range(start="2020-01-01", end="2023-01-01", freq="D") df = ( - pd.DataFrame( - { - "date": date_range, - "baseline": np.random.normal(100, 10, len(date_range)), - "fut_cov_promo_mechanism": np.random.randint( - 0, 11, len(date_range) - ), - "fut_cov_dummy": np.random.normal(10, 2, len(date_range)), - "past_cov_dummy": np.random.normal(10, 2, len(date_range)), - "past_cov_cat_dummy": np.random.normal(10, 2, len(date_range)), - } - ) + pd.DataFrame({ + "date": date_range, + "baseline": np.random.normal(100, 10, len(date_range)), + "fut_cov_promo_mechanism": np.random.randint(0, 11, len(date_range)), + "fut_cov_dummy": np.random.normal(10, 2, len(date_range)), + "past_cov_dummy": np.random.normal(10, 2, len(date_range)), + "past_cov_cat_dummy": np.random.normal(10, 2, len(date_range)), + }) .assign( target_qty=lambda _df: _df.baseline + _df.fut_cov_promo_mechanism.apply(_apply_promo_mechanism) @@ -405,7 +438,7 @@ def _apply_promo_mechanism(promo_mechanism): return series, past_covariates, future_covariates - @pytest.mark.parametrize("config", itertools.product(models, [True, False])) + @pytest.mark.parametrize("config", product(models, [True, False])) def test_model_construction(self, config): model, mode = config # TESTING SINGLE INT @@ -498,8 +531,8 @@ def test_training_data_creation(self, mode): max_samples_per_ts = 17 - training_samples, training_labels = model_instance._create_lagged_data( - target_series=self.target_series, + training_samples, training_labels, _ = model_instance._create_lagged_data( + series=self.target_series, past_covariates=self.past_covariates, future_covariates=self.future_covariates, max_samples_per_ts=max_samples_per_ts, @@ -549,8 +582,8 @@ def test_training_data_creation(self, mode): max_samples_per_ts = 3 # using only one series of each - training_samples, training_labels = model_instance._create_lagged_data( - target_series=self.target_series[0], + training_samples, training_labels, _ = model_instance._create_lagged_data( + series=self.target_series[0], past_covariates=self.past_covariates[0], future_covariates=self.future_covariates[0], max_samples_per_ts=max_samples_per_ts, @@ -665,12 +698,10 @@ def test_prediction_data_creation(self, mode): series_matrix = None if "target" in self.lags_1: - series_matrix = np.stack( - [ - ts.values(copy=False)[self.lags_1["target"][0] - shift :, :] - for ts in series - ] - ) + series_matrix = np.stack([ + ts.values(copy=False)[self.lags_1["target"][0] - shift :, :] + for ts in series + ]) # prediction preprocessing end assert all([lag >= 0 for lags in relative_cov_lags.values() for lag in lags]) @@ -827,12 +858,10 @@ def test_optional_static_covariates(self, model_cls): # with `use_static_covariates=True`, all static covs must have same shape model = model_cls(lags=4, use_static_covariates=True) with pytest.raises(ValueError): - model.fit( - [ - series, - series.with_static_covariates(pd.DataFrame({"a": [1], "b": [2]})), - ] - ) + model.fit([ + series, + series.with_static_covariates(pd.DataFrame({"a": [1], "b": [2]})), + ]) # with `use_static_covariates=False`, static covariates are ignored and prediction works model = model_cls(lags=4, use_static_covariates=False) @@ -869,7 +898,7 @@ def test_static_cov_accuracy(self): """ Tests that `RandomForest` regression model reproduces same behaviour as `examples/15-static-covariates.ipynb` notebook; see this notebook for - futher details. Notebook is also hosted online at: + further details. Notebook is also hosted online at: https://unit8co.github.io/darts/examples/15-static-covariates.html """ @@ -923,9 +952,6 @@ def test_static_cov_accuracy(self): assert rmses[1] < rmses[0] # given series of different sizes in input - train_series_no_cov = [sine_series[period:], irregular_series] - train_series_static_cov = [sine_series_st_cat[period:], irregular_series_st_cat] - fitting_series = [ train_series_no_cov[0][: (60 - period)], train_series_no_cov[1][:60], @@ -971,38 +997,36 @@ def test_static_cov_accuracy(self): rmses = [rmse(series, ps) for ps in [ps_no_st, ps_st_cat]] assert rmses[1] < rmses[0] - @pytest.mark.parametrize("config", itertools.product(models, [True, False])) + @pytest.mark.parametrize("config", product(models, [True, False])) def test_models_runnability(self, config): model, mode = config train_y, test_y = self.sine_univariate1.split_before(0.7) # testing past covariates + model_instance = model(lags=4, lags_past_covariates=None, multi_models=mode) with pytest.raises(ValueError): # testing lags_past_covariates None but past_covariates during training - model_instance = model(lags=4, lags_past_covariates=None, multi_models=mode) model_instance.fit( series=self.sine_univariate1, past_covariates=self.sine_multivariate1, ) + model_instance = model(lags=4, lags_past_covariates=3, multi_models=mode) with pytest.raises(ValueError): # testing lags_past_covariates but no past_covariates during fit - model_instance = model(lags=4, lags_past_covariates=3, multi_models=mode) model_instance.fit(series=self.sine_univariate1) # testing future_covariates + model_instance = model(lags=4, lags_future_covariates=None, multi_models=mode) with pytest.raises(ValueError): # testing lags_future_covariates None but future_covariates during training - model_instance = model( - lags=4, lags_future_covariates=None, multi_models=mode - ) model_instance.fit( series=self.sine_univariate1, future_covariates=self.sine_multivariate1, ) + model_instance = model(lags=4, lags_future_covariates=(0, 3), multi_models=mode) with pytest.raises(ValueError): # testing lags_covariate but no covariate during fit - model_instance = model(lags=4, lags_future_covariates=3, multi_models=mode) model_instance.fit(series=self.sine_univariate1) # testing input_dim @@ -1025,20 +1049,21 @@ def test_models_runnability(self, config): prediction = model_instance.predict(n=1) assert len(prediction) == 1 - @pytest.mark.slow @pytest.mark.parametrize( "config", - itertools.product( - models, [True, False], [sine_univariate1, sine_multivariate1] - ), + product(models, [True, False], [sine_univariate1, sine_multivariate1]), ) def test_fit(self, config): # test fitting both on univariate and multivariate timeseries model, mode, series = config + + series = series[:15] + sine_multivariate1 = self.sine_multivariate1[:15] + # auto-regression but past_covariates does not extend enough in the future with pytest.raises(ValueError): model_instance = model(lags=4, lags_past_covariates=4, multi_models=mode) - model_instance.fit(series=series, past_covariates=self.sine_multivariate1) + model_instance.fit(series=series, past_covariates=sine_multivariate1) model_instance.predict(n=10) # inconsistent number of components in series Sequence[TimeSeries] @@ -1071,19 +1096,19 @@ def test_fit(self, config): assert model_instance.lags.get("past") is None model_instance = model(lags=12, lags_past_covariates=12, multi_models=mode) - model_instance.fit(series=series, past_covariates=self.sine_multivariate1) + model_instance.fit(series=series, past_covariates=sine_multivariate1) assert len(model_instance.lags.get("past")) == 12 model_instance = model( lags=12, lags_future_covariates=(0, 1), multi_models=mode ) - model_instance.fit(series=series, future_covariates=self.sine_multivariate1) + model_instance.fit(series=series, future_covariates=sine_multivariate1) assert len(model_instance.lags.get("future")) == 1 model_instance = model( lags=12, lags_past_covariates=[-1, -4, -6], multi_models=mode ) - model_instance.fit(series=series, past_covariates=self.sine_multivariate1) + model_instance.fit(series=series, past_covariates=sine_multivariate1) assert len(model_instance.lags.get("past")) == 3 model_instance = model( @@ -1094,8 +1119,8 @@ def test_fit(self, config): ) model_instance.fit( series=series, - past_covariates=self.sine_multivariate1, - future_covariates=self.sine_multivariate1, + past_covariates=sine_multivariate1, + future_covariates=sine_multivariate1, ) assert len(model_instance.lags.get("past")) == 3 @@ -1134,7 +1159,7 @@ def helper_test_models_accuracy( @pytest.mark.parametrize( "config", - itertools.product(zip(models, range(len(models))), [True, False], [1, 5]), + product(zip(models, range(len(models))), [True, False], [1, 5]), ) def test_models_accuracy_univariate(self, config): (model, idx), mode, ocl = config @@ -1152,7 +1177,7 @@ def test_models_accuracy_univariate(self, config): @pytest.mark.parametrize( "config", - itertools.product(zip(models, range(len(models))), [True, False], [1, 5]), + product(zip(models, range(len(models))), [True, False], [1, 5]), ) def test_models_accuracy_multivariate(self, config): (model, idx), mode, ocl = config @@ -1170,7 +1195,7 @@ def test_models_accuracy_multivariate(self, config): @pytest.mark.parametrize( "config", - itertools.product(zip(models, range(len(models))), [True, False], [1, 5]), + product(zip(models, range(len(models))), [True, False], [1, 5]), ) def test_models_accuracy_multiseries_multivariate(self, config): (model, idx), mode, ocl = config @@ -1257,6 +1282,48 @@ def test_historical_forecast(self, mode): ) assert len(result) == 21 + def test_opti_historical_forecast_predict_checks(self): + """ + Verify that the sanity check implemented in ForecastingModel.predict are also defined for optimized historical + forecasts as it does not call this method + """ + model = self.models[1](lags=5) + + msg_expected = ( + "The model has not been fitted yet, and `retrain` is ``False``. Either call `fit()` before " + "`historical_forecasts()`, or set `retrain` to something different than ``False``." + ) + # untrained model, optimized + with pytest.raises(ValueError) as err: + model.historical_forecasts( + series=self.sine_univariate1, + start=0.9, + forecast_horizon=1, + retrain=False, + enable_optimization=True, + verbose=False, + ) + assert str(err.value) == msg_expected + + model.fit( + series=self.sine_univariate1, + ) + # deterministic model, num_samples > 1, optimized + with pytest.raises(ValueError) as err: + model.historical_forecasts( + series=self.sine_univariate1, + start=0.9, + forecast_horizon=1, + retrain=False, + enable_optimization=True, + num_samples=10, + verbose=False, + ) + assert ( + str(err.value) + == "`num_samples > 1` is only supported for probabilistic models." + ) + @pytest.mark.parametrize( "config", [ @@ -1270,57 +1337,137 @@ def test_historical_forecast(self, mode): ], ) def test_multioutput_wrapper(self, config): + """Check that with input_chunk_length=1, wrapping in MultiOutputRegressor is not happening""" model, supports_multioutput_natively = config model.fit(series=self.sine_multivariate1) if supports_multioutput_natively: assert not isinstance(model.model, MultiOutputRegressor) + # single estimator is responsible for both components + assert ( + model.model + == model.get_estimator(horizon=0, target_dim=0) + == model.get_estimator(horizon=0, target_dim=1) + ) else: assert isinstance(model.model, MultiOutputRegressor) + # one estimator (sub-model) per component + assert model.get_estimator(horizon=0, target_dim=0) != model.get_estimator( + horizon=0, target_dim=1 + ) - def test_multioutput_validation(self): - - lags = 4 + model_configs = [(XGBModel, dict({"likelihood": "poisson"}, **xgb_test_params))] + if lgbm_available: + model_configs += [(LightGBMModel, lgbm_test_params)] + if cb_available: + model_configs += [(CatBoostModel, cb_test_params)] - models = [ - XGBModel( - lags=lags, output_chunk_length=1, multi_models=True, tree_method="exact" - ), - XGBModel( - lags=lags, - output_chunk_length=1, - multi_models=False, - tree_method="exact", - ), - XGBModel( - lags=lags, output_chunk_length=2, multi_models=True, tree_method="exact" - ), - XGBModel( - lags=lags, - output_chunk_length=2, - multi_models=False, - tree_method="exact", - ), - ] - if lgbm_available: - models += [ - LightGBMModel(lags=lags, output_chunk_length=1, multi_models=True), - LightGBMModel(lags=lags, output_chunk_length=1, multi_models=False), - LightGBMModel(lags=lags, output_chunk_length=2, multi_models=True), - LightGBMModel(lags=lags, output_chunk_length=2, multi_models=False), - ] - if cb_available: - models += [ - CatBoostModel(lags=lags, output_chunk_length=1, multi_models=True), - CatBoostModel(lags=lags, output_chunk_length=1, multi_models=False), - CatBoostModel(lags=lags, output_chunk_length=2, multi_models=True), - CatBoostModel(lags=lags, output_chunk_length=2, multi_models=False), - ] + @pytest.mark.parametrize("config", product(model_configs, [1, 2], [True, False])) + def test_multioutput_validation(self, config): + """Check that models not supporting multi-output are properly wrapped when ocl>1""" + (model_cls, model_kwargs), ocl, multi_models = config train, val = self.sine_univariate1.split_after(0.6) + model = model_cls( + **model_kwargs, lags=4, output_chunk_length=ocl, multi_models=multi_models + ) + model.fit(series=train, val_series=val) + if model.output_chunk_length > 1 and model.multi_models: + assert isinstance(model.model, MultiOutputRegressor) + else: + assert not isinstance(model.model, MultiOutputRegressor) - for model in models: - model.fit(series=train, val_series=val) - if model.output_chunk_length > 1 and model.multi_models: - assert isinstance(model.model, MultiOutputRegressor) + def test_get_multioutput_estimator_multi_models(self): + """Craft training data so that estimator_[i].predict(X) == i + 1""" + + def helper_check_overfitted_estimators(ts: TimeSeries, ocl: int): + # since xgboost==2.1.0, the regular deterministic models have native multi output regression + # -> we use a quantile likelihood to activate Darts' MultiOutputRegressor + m = XGBModel( + lags=3, + output_chunk_length=ocl, + multi_models=True, + likelihood="quantile", + quantiles=[0.5], + ) + m.fit(ts) + + assert len(m.model.estimators_) == ocl * ts.width + + dummy_feats = np.array([[0, 0, 0] * ts.width]) + estimator_counter = 0 + for i in range(ocl): + for j in range(ts.width): + sub_model = m.get_multioutput_estimator(horizon=i, target_dim=j) + pred = sub_model.predict(dummy_feats)[0] + # sub-model is overfitted on the training series + assert np.abs(estimator_counter - pred) < 1e-2 + estimator_counter += 1 + + # univariate, one-sub model per step in output_chunk_length + ocl = 3 + ts = TimeSeries.from_values(np.array([0, 0, 0, 0, 1, 2]).T) + # estimators_[0] labels : [0] + # estimators_[1] labels : [1] + # estimators_[2] labels : [2] + helper_check_overfitted_estimators(ts, ocl) + + # multivariate, one sub-model per component + ocl = 1 + ts = TimeSeries.from_values( + np.array([[0, 0, 0, 0], [0, 0, 0, 1], [0, 0, 0, 2]]).T + ) + # estimators_[0] labels : [0] + # estimators_[1] labels : [1] + # estimators_[2] labels : [2] + helper_check_overfitted_estimators(ts, ocl) + + # multivariate, one sub-model per position, per component + ocl = 2 + ts = TimeSeries.from_values( + np.array([ + [0, 0, 0, 0, 2], + [0, 0, 0, 1, 3], + ]).T + ) + # estimators_[0] labels : [0] + # estimators_[1] labels : [1] + # estimators_[2] labels : [2] + # estimators_[3] labels : [3] + helper_check_overfitted_estimators(ts, ocl) + + def test_get_multioutput_estimator_single_model(self): + """Check estimator getter when multi_models=False""" + # multivariate, one sub-model per component + ocl = 2 + ts = TimeSeries.from_values( + np.array([ + [0, 0, 0, 0, 1], + [0, 0, 0, 0, 2], + ]).T + ) + # estimators_[0] labels : [1] + # estimators_[1] labels : [2] + + # since xgboost==2.1.0, the regular deterministic models have native multi output regression + # -> we use a quantile likelihood to activate Darts' MultiOutputRegressor + m = XGBModel( + lags=3, + output_chunk_length=ocl, + multi_models=False, + likelihood="quantile", + quantiles=[0.5], + ) + m.fit(ts) + + # one estimator is reused for all the horizon of a given component + assert len(m.model.estimators_) == ts.width + + dummy_feats = np.array([[0, 0, 0] * ts.width]) + for i in range(ocl): + for j in range(ts.width): + sub_model = m.get_multioutput_estimator(horizon=i, target_dim=j) + pred = sub_model.predict(dummy_feats)[0] + # sub-model forecast only depend on the target_dim + assert np.abs(j + 1 - pred) < 1e-2 @pytest.mark.parametrize("mode", [True, False]) def test_regression_model(self, mode): @@ -1440,12 +1587,12 @@ def test_multiple_ts(self, mode): error_past_only = rmse( [target_test_1, target_test_2], prediction_past_only, - inter_reduction=np.mean, + series_reduction=np.mean, ) error_both = rmse( [target_test_1, target_test_2], prediction_past_and_future, - inter_reduction=np.mean, + series_reduction=np.mean, ) assert error_past_only > error_both @@ -1470,11 +1617,153 @@ def test_multiple_ts(self, mode): error_both_multi_ts = rmse( [target_test_1, target_test_2], prediction_past_and_future_multi_ts, - inter_reduction=np.mean, + series_reduction=np.mean, ) assert error_both > error_both_multi_ts + @pytest.mark.parametrize( + "config", + product( + [ + (LinearRegressionModel, {}), + (RandomForest, {"bootstrap": False}), + (XGBModel, xgb_test_params), + (KNeighborsRegressorModel, {}), # no weights support + ] + + ( + [(CatBoostModel, dict({"allow_const_label": True}, **cb_test_params))] + if cb_available + else [] + ) + + ([(LightGBMModel, lgbm_test_params)] if lgbm_available else []), + [True, False], + ), + ) + def test_weights_built_in(self, config): + (model_cls, model_kwargs), single_series = config + + ts = TimeSeries.from_values(values=np.array([0, 0, 0, 0, 1, 0, 0])) + + model = model_cls(lags=3, output_chunk_length=1, **model_kwargs) + model.fit( + ts if single_series else [ts] * 2, + sample_weight="linear", + ) + preds = model.predict(n=3, series=ts if single_series else [ts] * 2) + + model_no_weight = model_cls(lags=3, output_chunk_length=1, **model_kwargs) + model_no_weight.fit( + ts if single_series else [ts] * 2, + sample_weight=None, + ) + preds_no_weight = model_no_weight.predict( + n=3, series=ts if single_series else [ts] * 2 + ) + + if single_series: + preds = [preds] + preds_no_weight = [preds_no_weight] + + for pred, pred_no_weight in zip(preds, preds_no_weight): + if model.supports_sample_weight: + with pytest.raises(AssertionError): + np.testing.assert_array_almost_equal( + pred.all_values(), pred_no_weight.all_values() + ) + else: + np.testing.assert_array_almost_equal( + pred.all_values(), pred_no_weight.all_values() + ) + + @pytest.mark.parametrize( + "config", + product( + [ + (LinearRegressionModel, {}), + (RandomForest, {"bootstrap": False}), + (XGBModel, xgb_test_params), + (KNeighborsRegressorModel, {}), # no weights support + ] + + ( + [(CatBoostModel, dict({"allow_const_label": True}, **cb_test_params))] + if cb_available + else [] + ) + + ([(LightGBMModel, lgbm_test_params)] if lgbm_available else []), + [True, False], + ), + ) + def test_weights_single_step_horizon(self, config): + (model_cls, model_kwargs), single_series = config + model = model_cls(lags=3, output_chunk_length=1, **model_kwargs) + + weights = TimeSeries.from_values(np.array([0, 0, 0, 0, 1, 0, 0])) + + ts = TimeSeries.from_values(values=np.array([0, 0, 0, 0, 1, 0, 0])) + + model.fit( + ts if single_series else [ts] * 2, + sample_weight=weights if single_series else [weights] * 2, + ) + + preds = model.predict(n=3, series=ts if single_series else [ts] * 2) + + preds = [preds] if single_series else preds + for pred in preds: + if model.supports_sample_weight: + np.testing.assert_array_almost_equal(pred.values()[:, 0], [1, 1, 1]) + else: + with pytest.raises(AssertionError): + np.testing.assert_array_almost_equal(pred.values()[:, 0], [1, 1, 1]) + + @pytest.mark.parametrize( + "config", + [ + (LinearRegressionModel, {}), + (RandomForest, {"bootstrap": False}), + (XGBModel, xgb_test_params), + (KNeighborsRegressorModel, {}), # no weights support + ] + + ( + [(CatBoostModel, dict({"allow_const_label": True}, **cb_test_params))] + if cb_available + else [] + ) + + ([(LightGBMModel, lgbm_test_params)] if lgbm_available else []), + ) + def test_weights_multi_horizon(self, config): + (model_cls, model_kwargs) = config + model = model_cls(lags=3, output_chunk_length=3, **model_kwargs) + + weights = TimeSeries.from_values(np.array([0, 0, 0, 1, 1, 1, 0, 0, 0])) + + # model should only fit on ones in the middle + ts = TimeSeries.from_values(values=np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])) + + model.fit(ts, sample_weight=weights) + + pred = model.predict(n=3) + + if model.supports_sample_weight: + np.testing.assert_array_almost_equal(pred.values()[:, 0], [1, 1, 1]) + else: + with pytest.raises(AssertionError): + np.testing.assert_array_almost_equal(pred.values()[:, 0], [1, 1, 1]) + + def test_weights_multimodel_false_multi_horizon(self): + model = LinearRegressionModel(lags=3, output_chunk_length=3, multi_models=False) + + weights = TimeSeries.from_values(np.array([0, 0, 0, 0, 0, 1, 0, 0])) + + ts = TimeSeries.from_values(values=np.array([0, 0, 0, 0, 0, 1, 0, 0])) + + model.fit(ts, sample_weight=weights) + + pred = model.predict(n=3) + + np.testing.assert_array_almost_equal(pred.values()[:, 0], [1, 1, 1]) + @pytest.mark.parametrize("mode", [True, False]) def test_only_future_covariates(self, mode): model = RegressionModel(lags_future_covariates=[-2], multi_models=mode) @@ -1502,7 +1791,7 @@ def test_only_future_covariates(self, mode): @pytest.mark.parametrize( "config", - itertools.product( + product( [True, False], [ (1, 0, 13), @@ -1578,42 +1867,212 @@ def test_not_enough_covariates(self, config): future_covariates=future_covariates[: -26 + req_future_offset], ) - @pytest.mark.skipif(not lgbm_available, reason="requires lightgbm") - @patch.object( - darts.models.forecasting.lgbm.lgb.LGBMRegressor - if lgbm_available - else darts.models.utils.NotImportedModule, - "fit", + @pytest.mark.parametrize( + "config", + product( + [(XGBModel, xgb_test_params)] + + ([(LightGBMModel, lgbm_test_params)] if lgbm_available else []) + + ([(CatBoostModel, cb_test_params)] if cb_available else []), + [True, False], + ), ) - def test_gradient_boosted_model_with_eval_set(self, lgb_fit_patch): - """Test whether these evaluation set parameters are passed to LGBRegressor""" - model = LightGBMModel(lags=4, lags_past_covariates=2) + def test_val_set_weights_runnability_trees(self, config): + """Tests using weights in val set for single and multi series.""" + (model_cls, model_kwargs), single_series = config + model = model_cls(lags=10, **model_kwargs) + + series = tg.sine_timeseries(length=20) + weights = tg.linear_timeseries(length=20) + if not single_series: + series = [series] * 2 + weights = [weights] * 2 + model.fit( - series=self.sine_univariate1, - past_covariates=self.sine_multivariate1, - val_series=self.sine_univariate1, - val_past_covariates=self.sine_multivariate1, - early_stopping_rounds=2, + series=series, + val_series=series, + sample_weight=weights, + val_sample_weight=weights, ) + _ = model.predict(1, series=series) - lgb_fit_patch.assert_called_once() - assert lgb_fit_patch.call_args[1]["eval_set"] is not None - assert lgb_fit_patch.call_args[1]["early_stopping_rounds"] == 2 + @pytest.mark.parametrize( + "config", + product( + [ + ( + XGBModel, + xgb_test_params, + "xgboost.xgb.XGBRegressor", + "xgboost.XGBRegressor", + ) + ] + + ( + [ + ( + LightGBMModel, + lgbm_test_params, + "lgbm.lgb.LGBMRegressor", + "lightgbm.LGBMRegressor", + ) + ] + if lgbm_available + else [] + ) + + ( + [ + ( + CatBoostModel, + cb_test_params, + "catboost_model.CatBoostRegressor", + "catboost.CatBoostRegressor", + ) + ] + if cb_available + else [] + ), + [False, True], + ), + ) + def test_val_set(self, config): + """Test whether the evaluation set parameters are passed to the wrapped regression model.""" + (model_cls, model_kwargs, model_loc, model_import), use_weights = config + module_name, model_name = model_import.split(".") + # mocking `fit` loses function signature. MultiOutputRegressor checks the function signature + # internally, so we have to overwrite the mocked function signature with the original one. + fit_sig = inspect.signature( + getattr(importlib.import_module(module_name), model_name).fit + ) + with patch(f"darts.models.forecasting.{model_loc}.fit") as fit_patch: + fit_patch.__signature__ = fit_sig + self.helper_check_val_set( + model_cls, model_kwargs, fit_patch, use_weights=use_weights + ) + + def helper_check_val_set(self, model_cls, model_kwargs, fit_patch, use_weights): + series1 = tg.sine_timeseries(length=10, column_name="tg_1") + series2 = tg.sine_timeseries(length=10, column_name="tg_2") / 2 + 10 + series = series1.stack(series2) + series = series.with_static_covariates( + pd.DataFrame({"sc1": [0, 1], "sc2": [3, 4]}) + ) + pc = series1 * 10 - 3 + fc = TimeSeries.from_times_and_values( + times=series.time_index, values=series.values() * -1, columns=["fc1", "fc2"] + ) + + weights_kwargs = ( + { + "sample_weight": tg.linear_timeseries(length=10), + "val_sample_weight": tg.linear_timeseries(length=10), + } + if use_weights + else {} + ) + + model = model_cls( + lags={"default_lags": [-4, -3, -2, -1]}, + lags_past_covariates=3, + lags_future_covariates={ + "default_lags": [-1, 0], + "fc1": [0], + }, + likelihood="quantile", + add_encoders={"cyclic": {"future": ["month"]}}, + quantiles=[0.1, 0.5, 0.9], + **model_kwargs, + ) + + # check that an error is raised with an invalid validation series + with pytest.raises(ValueError) as err: + model.fit( + series=series, + past_covariates=pc, + future_covariates=fc, + val_series=series["tg_1"], + val_past_covariates=pc, + val_future_covariates=fc["fc1"], + early_stopping_rounds=2, + **weights_kwargs, + ) + msg_expected = ( + "The dimensions of the (`series`, `future_covariates`, `static_covariates`) between " + "the training and validation set do not match." + ) + assert str(err.value) == msg_expected + + # check that an error is raised if only second validation series are invalid + with pytest.raises(ValueError) as err: + model.fit( + series=series, + past_covariates=pc, + future_covariates=fc, + val_series=[series, series["tg_1"]], + val_past_covariates=[pc, pc], + val_future_covariates=[fc, fc["fc1"]], + early_stopping_rounds=2, + **weights_kwargs, + ) + msg_expected = ( + "The dimensions of the (`series`, `future_covariates`, `static_covariates`) between " + "the training and validation set at sequence/list index `1` do not match." + ) + assert str(err.value) == msg_expected - @patch.object(darts.models.forecasting.xgboost.xgb.XGBRegressor, "fit") - def test_xgboost_with_eval_set(self, xgb_fit_patch): - model = XGBModel(lags=4, lags_past_covariates=2) model.fit( - series=self.sine_univariate1, - past_covariates=self.sine_multivariate1, - val_series=self.sine_univariate1, - val_past_covariates=self.sine_multivariate1, + series=series, + past_covariates=pc, + future_covariates=fc, + val_series=series, + val_past_covariates=pc, + val_future_covariates=fc, early_stopping_rounds=2, + **weights_kwargs, ) + # fit called 6 times (3 quantiles * 2 target features) + assert fit_patch.call_count == 6 + + X_train, y_train = fit_patch.call_args[0] + + # check weights in training set + weight_train = None + if use_weights: + assert "sample_weight" in fit_patch.call_args[1] + weight_train = fit_patch.call_args[1]["sample_weight"] + + # check eval set + eval_set_name, eval_weight_name = model.val_set_params + assert eval_set_name in fit_patch.call_args[1] + eval_set = fit_patch.call_args[1]["eval_set"] + assert eval_set is not None + assert isinstance(eval_set, list) + eval_set = eval_set[0] + + weight = None + if cb_available and isinstance(model, CatBoostModel): + # CatBoost requires eval set as `Pool` + from catboost import Pool - xgb_fit_patch.assert_called_once() - assert xgb_fit_patch.call_args[1]["eval_set"] is not None - assert xgb_fit_patch.call_args[1]["early_stopping_rounds"] == 2 + assert isinstance(eval_set, Pool) + X, y = eval_set.get_features(), eval_set.get_label() + if use_weights: + weight = np.array(eval_set.get_weight()) + + else: + assert isinstance(eval_set, tuple) and len(eval_set) == 2 + X, y = eval_set + if use_weights: + assert eval_weight_name in fit_patch.call_args[1] + weight = fit_patch.call_args[1][eval_weight_name] + assert isinstance(weight, list) + weight = weight[0] + + # check same number of features for each dataset + assert X.shape[1:] == X_train.shape[1:] + assert y.shape[1:] == y_train.shape[1:] + assert fit_patch.call_args[1]["early_stopping_rounds"] == 2 + if use_weights: + assert weight_train.shape == y_train.shape + assert weight.shape == y.shape @pytest.mark.parametrize("mode", [True, False]) def test_integer_indexed_series(self, mode): @@ -1652,7 +2111,7 @@ def test_integer_indexed_series(self, mode): @pytest.mark.parametrize( "config", - itertools.product( + product( [ ({"lags": [-3, -2, -1]}, {"lags": {"gaussian": 3}}), ({"lags": 3}, {"lags": {"gaussian": 3, "sine": 3}}), @@ -1660,6 +2119,14 @@ def test_integer_indexed_series(self, mode): {"lags_past_covariates": 2}, {"lags_past_covariates": {"lin_past": 2}}, ), + ( + {"lags_future_covariates": [-2, -1]}, + {"lags_future_covariates": {"lin_future": [-2, -1]}}, + ), + ( + {"lags_future_covariates": [1, 2]}, + {"lags_future_covariates": {"lin_future": [1, 2]}}, + ), ( {"lags": 5, "lags_future_covariates": [-2, 3]}, { @@ -1687,111 +2154,145 @@ def test_integer_indexed_series(self, mode): }, ), ], + [0, 5], [True, False], ), ) def test_component_specific_lags_forecasts(self, config): - """Verify that the same lags, defined using int/list or dictionnaries yield the same results""" - (list_lags, dict_lags), multiple_series = config - multivar_target = "lags" in dict_lags and len(dict_lags["lags"]) > 1 - multivar_future_cov = ( - "lags_future_covariates" in dict_lags - and len(dict_lags["lags_future_covariates"]) > 1 + """Verify that the same lags, defined using int/list or dictionaries yield the same results, + including output_chunk_shift.""" + (list_lags, dict_lags), output_chunk_shift, multiple_series = config + max_forecast = 3 + series, past_cov, future_cov = self.helper_generate_input_series_from_lags( + list_lags, + dict_lags, + multiple_series, + output_chunk_shift, + max_forecast, ) - # create series based on the model parameters - series = tg.gaussian_timeseries(length=20, column_name="gaussian") - if multivar_target: - series = series.stack(tg.sine_timeseries(length=20, column_name="sine")) - - future_cov = tg.linear_timeseries(length=30, column_name="lin_future") - if multivar_future_cov: - future_cov = future_cov.stack( - tg.sine_timeseries(length=30, column_name="sine_future") - ) - - past_cov = tg.linear_timeseries(length=30, column_name="lin_past") - - if multiple_series: - # second series have different component names - series = [ - series, - series.with_columns_renamed( - ["gaussian", "sine"][: series.width], - ["other", "names"][: series.width], - ) - + 10, - ] - past_cov = [past_cov, past_cov] - future_cov = [future_cov, future_cov] - - # the lags are identical across the components for each series - model = LinearRegressionModel(**list_lags) + model = LinearRegressionModel( + **list_lags, output_chunk_shift=output_chunk_shift + ) model.fit( series=series, - past_covariates=past_cov if model.supports_past_covariates else None, - future_covariates=future_cov if model.supports_future_covariates else None, + past_covariates=past_cov, + future_covariates=future_cov, ) # the lags are specified for each component, individually - model2 = LinearRegressionModel(**dict_lags) + model2 = LinearRegressionModel( + **dict_lags, output_chunk_shift=output_chunk_shift + ) model2.fit( series=series, - past_covariates=past_cov if model2.supports_past_covariates else None, - future_covariates=future_cov if model2.supports_future_covariates else None, + past_covariates=past_cov, + future_covariates=future_cov, ) + if "lags_future_covariates" in list_lags: + assert model.lags["future"] == [ + lag_ + output_chunk_shift + for lag_ in list_lags["lags_future_covariates"] + ] + + if "default_lags" in dict_lags["lags_future_covariates"]: + # check that default lags + default_components = ( + model2.component_lags["future"].keys() + - dict_lags["lags_future_covariates"].keys() + ) + else: + default_components = dict() + + lags_specific = { + comp_: ( + dict_lags["lags_future_covariates"]["default_lags"] + if comp_ in default_components + else dict_lags["lags_future_covariates"][comp_] + ) + for comp_ in model2.component_lags["future"] + } + assert model2.component_lags["future"] == { + comp_: [lag_ + output_chunk_shift for lag_ in lags_] + for comp_, lags_ in lags_specific.items() + } + # n == output_chunk_length + s_ = series[0] if multiple_series else series + pred_start_expected = s_.end_time() + (1 + output_chunk_shift) * s_.freq pred = model.predict( 1, - series=series[0] if multiple_series else None, - past_covariates=past_cov[0] - if multiple_series and model.supports_past_covariates - else None, - future_covariates=future_cov[0] - if multiple_series and model.supports_future_covariates - else None, + series=s_, + past_covariates=( + past_cov[0] + if multiple_series and model.supports_past_covariates + else None + ), + future_covariates=( + future_cov[0] + if multiple_series and model.supports_future_covariates + else None + ), ) + assert pred.start_time() == pred_start_expected pred2 = model2.predict( 1, - series=series[0] if multiple_series else None, - past_covariates=past_cov[0] - if multiple_series and model2.supports_past_covariates - else None, - future_covariates=future_cov[0] - if multiple_series and model2.supports_future_covariates - else None, + series=s_, + past_covariates=( + past_cov[0] + if multiple_series and model2.supports_past_covariates + else None + ), + future_covariates=( + future_cov[0] + if multiple_series and model2.supports_future_covariates + else None + ), ) + assert pred2.start_time() == pred_start_expected np.testing.assert_array_almost_equal(pred.values(), pred2.values()) assert pred.time_index.equals(pred2.time_index) + # auto-regression not supported for shifted output (tested in `test_output_shift`) + if output_chunk_shift: + return + # n > output_chunk_length pred = model.predict( - 3, + max_forecast, series=series[0] if multiple_series else None, - past_covariates=past_cov[0] - if multiple_series and model.supports_past_covariates - else None, - future_covariates=future_cov[0] - if multiple_series and model.supports_future_covariates - else None, + past_covariates=( + past_cov[0] + if multiple_series and model.supports_past_covariates + else None + ), + future_covariates=( + future_cov[0] + if multiple_series and model.supports_future_covariates + else None + ), ) pred2 = model2.predict( - 3, + max_forecast, series=series[0] if multiple_series else None, - past_covariates=past_cov[0] - if multiple_series and model2.supports_past_covariates - else None, - future_covariates=future_cov[0] - if multiple_series and model2.supports_future_covariates - else None, + past_covariates=( + past_cov[0] + if multiple_series and model2.supports_past_covariates + else None + ), + future_covariates=( + future_cov[0] + if multiple_series and model2.supports_future_covariates + else None + ), ) np.testing.assert_array_almost_equal(pred.values(), pred2.values()) assert pred.time_index.equals(pred2.time_index) @pytest.mark.parametrize( "config", - itertools.product( + product( [ {"lags": {"gaussian": [-1, -3], "sine": [-2, -4, -6]}}, {"lags_past_covariates": {"default_lags": 2}}, @@ -1863,37 +2364,408 @@ def test_component_specific_lags(self, config): model.predict( 1, series=series[0] if multiple_series else None, - past_covariates=past_cov[0] - if multiple_series and model.supports_past_covariates - else None, - future_covariates=future_cov[0] - if multiple_series and model.supports_future_covariates - else None, + past_covariates=( + past_cov[0] + if multiple_series and model.supports_past_covariates + else None + ), + future_covariates=( + future_cov[0] + if multiple_series and model.supports_future_covariates + else None + ), ) # n > output_chunk_length - model.predict( + pred = model.predict( 7, series=series[0] if multiple_series else None, - past_covariates=past_cov[0] - if multiple_series and model.supports_past_covariates - else None, - future_covariates=future_cov[0] - if multiple_series and model.supports_future_covariates - else None, + past_covariates=( + past_cov[0] + if multiple_series and model.supports_past_covariates + else None + ), + future_covariates=( + future_cov[0] + if multiple_series and model.supports_future_covariates + else None + ), ) + # check that lagged features are properly extracted during auto-regression + if multivar_target: + np.testing.assert_array_almost_equal( + tg.sine_timeseries(length=27)[-7:].values(), pred["sine"].values() + ) @pytest.mark.parametrize( "config", - itertools.product( - [RegressionModel, LinearRegressionModel, XGBModel] - + ([LightGBMModel] if lgbm_available else []), + product( + [ + {"lags": [-1, -3]}, + {"lags_past_covariates": 2}, + {"lags_future_covariates": [-2, -1]}, + {"lags_future_covariates": [1, 2]}, + { + "lags": 5, + "lags_past_covariates": [-3, -1], + }, + {"lags": [-5, -4], "lags_future_covariates": [-2, 0, 1, 2]}, + { + "lags": 5, + "lags_past_covariates": 4, + "lags_future_covariates": [-3, 1], + }, + # check that component-specific lags with output_chunk_shift works + { + "lags_past_covariates": {"lin_past": [-3, -1]}, + "lags_future_covariates": [1, 2], + }, + { + "lags_past_covariates": [-3, -1], + "lags_future_covariates": {"lin_future": [1, 2]}, + }, + { + "lags": {"gaussian": 5}, + "lags_past_covariates": [-3, -1], + "lags_future_covariates": [1, 2], + }, + ], + [True, False], + [3, 5], + [1, 4], + ), + ) + def test_same_result_output_chunk_shift(self, config): + """Tests that a model with that uses an output shift gets identical results for a multi-model + without a shift. This only applies to the regressors that overlap. + + Example models: + * non-shifted model with ocl=5, shift=0, multi_models=True + * shifted model with ocl=2, shift=3, multi_models=True + + The 4th and 5th regressors from the non-shifted models should generate identical results as the 1st + and 2nd regressor of the shifted model. + """ + list_lags, multiple_series, output_chunk_shift, ocl_shifted = config + ocl = output_chunk_shift + ocl_shifted + max_forecast = ocl + series, past_cov, future_cov = self.helper_generate_input_series_from_lags( + list_lags, + {}, + multiple_series, + output_chunk_shift, + max_forecast, + output_chunk_length=ocl, + ) + + model = LinearRegressionModel( + **list_lags, output_chunk_shift=0, output_chunk_length=ocl + ) + + # with output shift, future lags are shifted + model_shift = LinearRegressionModel( + **list_lags, + output_chunk_shift=output_chunk_shift, + output_chunk_length=ocl_shifted, + ) + # adjusting the future lags should give identical models to non-shifted + list_lags_adj = deepcopy(list_lags) + # this loop works for both component-specific and non-component-specific future lags + if "lags_future_covariates" in list_lags_adj: + if isinstance(list_lags_adj["lags_future_covariates"], dict): + for key in list_lags_adj["lags_future_covariates"]: + list_lags_adj["lags_future_covariates"][key] = [ + lag_ - output_chunk_shift + for lag_ in list_lags_adj["lags_future_covariates"][key] + ] + else: + list_lags_adj["lags_future_covariates"] = [ + lag_ - output_chunk_shift + for lag_ in list_lags_adj["lags_future_covariates"] + ] + model_shift_adj = LinearRegressionModel( + **list_lags_adj, + output_chunk_shift=output_chunk_shift, + output_chunk_length=ocl_shifted, + ) + + if not multiple_series: + series = [series] + past_cov = [past_cov] if past_cov is not None else past_cov + future_cov = [future_cov] if future_cov is not None else future_cov + + for m_ in [model, model_shift, model_shift_adj]: + m_.fit( + series=series, + past_covariates=past_cov, + future_covariates=future_cov, + ) + + pred = model.predict( + ocl, + series=series, + past_covariates=past_cov, + future_covariates=future_cov, + ) + pred_shift = model_shift.predict( + ocl_shifted, + series=series, + past_covariates=past_cov, + future_covariates=future_cov, + ) + pred_shift_adj = model_shift_adj.predict( + ocl_shifted, + series=series, + past_covariates=past_cov, + future_covariates=future_cov, + ) + # expected shifted start is `output_chunk_shift` steps after non-shifted pred start + for s_, pred_, pred_shift_, pred_shift_adj_ in zip( + series, pred, pred_shift, pred_shift_adj + ): + pred_shift_start_expected = ( + s_.end_time() + (1 + output_chunk_shift) * s_.freq + ) + assert pred_.start_time() == s_.end_time() + pred_.freq + assert ( + pred_.end_time() + == pred_shift_start_expected + (ocl_shifted - 1) * pred_.freq + ) + assert pred_shift_.start_time() == pred_shift_start_expected + assert ( + pred_shift_.end_time() + == pred_shift_start_expected + (ocl_shifted - 1) * pred_shift_.freq + ) + assert pred_shift_.time_index.equals(pred_shift_adj_.time_index) + + if "lags_future_covariates" not in list_lags: + # without future lags, all lags should be identical between shift and non-shifted model + np.testing.assert_almost_equal( + pred_[-ocl_shifted:].all_values(copy=False), + pred_shift_.all_values(copy=False), + ) + else: + # without future lags, the shifted model also shifts future lags + with pytest.raises(AssertionError): + np.testing.assert_almost_equal( + pred_[-ocl_shifted:].all_values(copy=False), + pred_shift_.all_values(copy=False), + ) + + # with adjusted future lags, the models should be identical + np.testing.assert_almost_equal( + pred_[-ocl_shifted:].all_values(copy=False), + pred_shift_adj_.all_values(copy=False), + ) + + @pytest.mark.parametrize( + "config", + product( + [ + {"lags": [-1, -3]}, + {"lags_past_covariates": 2}, + {"lags_future_covariates": [-2, -1]}, + {"lags_future_covariates": [1, 2]}, + { + "lags": 5, + "lags_past_covariates": [-3, -1], + }, + {"lags": [-5, -4], "lags_future_covariates": [-2, 0, 1, 2]}, + { + "lags": 5, + "lags_past_covariates": 4, + "lags_future_covariates": [-3, 1], + }, + ], + [3, 7, 10], + ), + ) + def test_output_shift(self, config): + """Tests shifted output for shift smaller than, equal to, and larger than output_chunk_length.""" + np.random.seed(0) + lags, shift = config + ocl = 7 + series = tg.gaussian_timeseries( + length=28, start=pd.Timestamp("2000-01-01"), freq="d" + ) + + model_target_only = LinearRegressionModel( + lags=3, + output_chunk_length=ocl, + output_chunk_shift=shift, + ) + model_target_only.fit(series) + + # no auto-regression with shifted output + with pytest.raises(ValueError) as err: + _ = model_target_only.predict(n=ocl + 1) + assert str(err.value).startswith("Cannot perform auto-regression") + + # pred starts with a shift + for ocl_test in [ocl - 1, ocl]: + pred = model_target_only.predict(n=ocl_test) + assert pred.start_time() == series.end_time() + (shift + 1) * series.freq + assert len(pred) == ocl_test + assert pred.freq == series.freq + + series, past_cov, future_cov = self.helper_generate_input_series_from_lags( + lags, + {}, + multiple_series=False, + output_chunk_shift=shift, + max_forecast=ocl, + output_chunk_length=ocl, + add_length=2, # add length for hist fc that don't use target lags + ) + + # model trained on encoders + cov_support = [] + covs = {} + if "lags_past_covariates" in lags: + cov_support.append("past") + covs["past_covariates"] = tg.datetime_attribute_timeseries( + past_cov, + attribute="dayofweek", + add_length=0, + ) + if "lags_future_covariates" in lags: + cov_support.append("future") + covs["future_covariates"] = tg.datetime_attribute_timeseries( + future_cov, + attribute="dayofweek", + add_length=0, + ) + + if not cov_support: + return + + add_encoders = { + "datetime_attribute": {cov: ["dayofweek"] for cov in cov_support} + } + model_enc_shift = LinearRegressionModel( + **lags, + output_chunk_length=ocl, + output_chunk_shift=shift, + add_encoders=add_encoders, + ) + model_enc_shift.fit(series) + + # model trained with identical covariates + model_fc_shift = LinearRegressionModel( + **lags, + output_chunk_length=ocl, + output_chunk_shift=shift, + ) + model_fc_shift.fit(series, **covs) + + pred_enc = model_enc_shift.predict(n=ocl) + pred_fc = model_fc_shift.predict(n=ocl) + assert pred_enc == pred_fc + + # check that historical forecasts works properly + hist_fc_start = -(ocl + shift) + pred_last_hist_fc = model_fc_shift.predict(n=ocl, series=series[:hist_fc_start]) + # non-optimized hist fc + hist_fc = model_fc_shift.historical_forecasts( + series=series, + start=hist_fc_start, + start_format="position", + retrain=False, + forecast_horizon=ocl, + last_points_only=False, + enable_optimization=False, + **covs, + ) + assert len(hist_fc) == 1 + assert hist_fc[0] == pred_last_hist_fc + # optimized hist fc, routine: last_points_only=False + hist_fc_opt = model_fc_shift.historical_forecasts( + series=series, + start=hist_fc_start, + start_format="position", + retrain=False, + forecast_horizon=ocl, + last_points_only=False, + enable_optimization=True, + **covs, + ) + assert len(hist_fc_opt) == 1 + assert hist_fc_opt[0].time_index.equals(pred_last_hist_fc.time_index) + np.testing.assert_array_almost_equal( + hist_fc_opt[0].values(copy=False), pred_last_hist_fc.values(copy=False) + ) + + # optimized hist fc, routine: last_points_only=True + hist_fc_opt = model_fc_shift.historical_forecasts( + series=series, + start=hist_fc_start, + start_format="position", + retrain=False, + forecast_horizon=ocl, + last_points_only=True, + enable_optimization=True, + **covs, + ) + assert isinstance(hist_fc_opt, TimeSeries) + assert len(hist_fc_opt) == 1 + assert hist_fc_opt.start_time() == pred_last_hist_fc.end_time() + np.testing.assert_array_almost_equal( + hist_fc_opt.values(copy=False), pred_last_hist_fc[-1].values(copy=False) + ) + + @pytest.mark.parametrize("lpo", [True, False]) + def test_historical_forecasts_no_target_lags_with_static_covs(self, lpo): + """Tests that historical forecasts work without target lags but with static covariates. + For last_points_only `True` and `False`.""" + ocl = 7 + series = tg.linear_timeseries( + length=28, start=pd.Timestamp("2000-01-01"), freq="d" + ).with_static_covariates(pd.Series([1.0])) + + model = LinearRegressionModel( + lags=None, + lags_future_covariates=(3, 0), + output_chunk_length=ocl, + use_static_covariates=True, + ) + model.fit(series, future_covariates=series) + + preds1 = model.historical_forecasts( + series, + future_covariates=series, + retrain=False, + enable_optimization=True, + last_points_only=lpo, + ) + preds2 = model.historical_forecasts( + series, + future_covariates=series, + retrain=False, + enable_optimization=False, + last_points_only=lpo, + ) + if lpo: + preds1 = [preds1] + preds2 = [preds2] + + for p1, p2 in zip(preds1, preds2): + np.testing.assert_array_almost_equal(p1.values(), p2.values()) + + @pytest.mark.parametrize( + "config", + product( + [ + (RegressionModel, {}), + (LinearRegressionModel, {}), + (XGBModel, xgb_test_params), + ] + + ([(LightGBMModel, lgbm_test_params)] if lgbm_available else []), [True, False], [1, 2], ), ) def test_encoders(self, config): - model_cls, mode, ocl = config + (model_cls, model_kwargs), mode, ocl = config max_past_lag = -4 max_future_lag = 4 # target @@ -1908,7 +2780,7 @@ def test_encoders(self, config): # past and future covariates longer than target n_comp = 2 covs = TimeSeries.from_times_and_values( - tg.generate_index( + generate_index( start=pd.Timestamp("1999-01-01"), end=pd.Timestamp("2002-12-01"), freq="MS", @@ -1936,18 +2808,21 @@ def test_encoders(self, config): add_encoders=encoder_examples["past"], multi_models=mode, output_chunk_length=ocl, + **model_kwargs, ) model_fc_valid0 = model_cls( lags=2, add_encoders=encoder_examples["future"], multi_models=mode, output_chunk_length=ocl, + **model_kwargs, ) model_mixed_valid0 = model_cls( lags=2, add_encoders=encoder_examples["mixed"], multi_models=mode, output_chunk_length=ocl, + **model_kwargs, ) # encoders will not generate covariates without lags @@ -1962,12 +2837,14 @@ def test_encoders(self, config): add_encoders=encoder_examples["past"], multi_models=mode, output_chunk_length=ocl, + **model_kwargs, ) model_fc_valid0 = model_cls( lags_future_covariates=[-1, 0], add_encoders=encoder_examples["future"], multi_models=mode, output_chunk_length=ocl, + **model_kwargs, ) model_mixed_valid0 = model_cls( lags_past_covariates=[-2, -1], @@ -1975,6 +2852,7 @@ def test_encoders(self, config): add_encoders=encoder_examples["mixed"], multi_models=mode, output_chunk_length=ocl, + **model_kwargs, ) # check that fit/predict works with model internal covariate requirement checks for model in [model_pc_valid0, model_fc_valid0, model_mixed_valid0]: @@ -1989,6 +2867,7 @@ def test_encoders(self, config): add_encoders=encoder_examples["past"], multi_models=mode, output_chunk_length=ocl, + **model_kwargs, ) model_fc_valid1 = model_cls( lags=2, @@ -1996,6 +2875,7 @@ def test_encoders(self, config): add_encoders=encoder_examples["future"], multi_models=mode, output_chunk_length=ocl, + **model_kwargs, ) model_mixed_valid1 = model_cls( lags=2, @@ -2004,6 +2884,7 @@ def test_encoders(self, config): add_encoders=encoder_examples["mixed"], multi_models=mode, output_chunk_length=ocl, + **model_kwargs, ) for model, ex in zip( @@ -2011,7 +2892,7 @@ def test_encoders(self, config): ): covariates = covariates_examples[ex] # don't pass covariates, let them be generated by encoders. Test single target series input - model_copy = copy.deepcopy(model) + model_copy = deepcopy(model) model_copy.fit(ts[0]) assert model_copy.encoders.encoding_available self.helper_test_encoders_settings(model_copy, ex) @@ -2038,7 +2919,7 @@ def test_encoders(self, config): _ = model.predict(n=3, series=ts, **covariates) _ = model.predict(n=8, series=ts, **covariates) - @pytest.mark.parametrize("config", itertools.product([True, False], [True, False])) + @pytest.mark.parametrize("config", product([True, False], [True, False])) def test_encoders_from_covariates_input(self, config): multi_models, extreme_lags = config series = tg.linear_timeseries(length=10, freq="MS") @@ -2158,69 +3039,51 @@ def train_start_end(start_base, end_base): if train_past is None: assert infer_past is None and refer_past is None else: - assert all( - [isinstance(el, list) for el in [train_past, infer_past, refer_past]] - ) + assert all([ + isinstance(el, list) for el in [train_past, infer_past, refer_past] + ]) assert len(train_past) == len(infer_past) == len(refer_past) - assert all( - [ - t_p.start_time() == tp_s - for t_p, tp_s in zip(train_past, t_train["pc_start"]) - ] - ) - assert all( - [ - t_p.end_time() == tp_e - for t_p, tp_e in zip(train_past, t_train["pc_end"]) - ] - ) - assert all( - [ - i_p.start_time() == ip_s - for i_p, ip_s in zip(infer_past, t_infer["pc_start"]) - ] - ) - assert all( - [ - i_p.end_time() == ip_e - for i_p, ip_e in zip(infer_past, t_infer["pc_end"]) - ] - ) + assert all([ + t_p.start_time() == tp_s + for t_p, tp_s in zip(train_past, t_train["pc_start"]) + ]) + assert all([ + t_p.end_time() == tp_e + for t_p, tp_e in zip(train_past, t_train["pc_end"]) + ]) + assert all([ + i_p.start_time() == ip_s + for i_p, ip_s in zip(infer_past, t_infer["pc_start"]) + ]) + assert all([ + i_p.end_time() == ip_e + for i_p, ip_e in zip(infer_past, t_infer["pc_end"]) + ]) if train_future is None: assert infer_future is None and refer_future is None else: - assert all( - [ - isinstance(el, list) - for el in [train_future, infer_future, refer_future] - ] - ) + assert all([ + isinstance(el, list) + for el in [train_future, infer_future, refer_future] + ]) assert len(train_future) == len(infer_future) == len(refer_future) - assert all( - [ - t_f.start_time() == tf_s - for t_f, tf_s in zip(train_future, t_train["fc_start"]) - ] - ) - assert all( - [ - t_f.end_time() == tf_e - for t_f, tf_e in zip(train_future, t_train["fc_end"]) - ] - ) - assert all( - [ - i_f.start_time() == if_s - for i_f, if_s in zip(infer_future, t_infer["fc_start"]) - ] - ) - assert all( - [ - i_f.end_time() == if_e - for i_f, if_e in zip(infer_future, t_infer["fc_end"]) - ] - ) + assert all([ + t_f.start_time() == tf_s + for t_f, tf_s in zip(train_future, t_train["fc_start"]) + ]) + assert all([ + t_f.end_time() == tf_e + for t_f, tf_e in zip(train_future, t_train["fc_end"]) + ]) + assert all([ + i_f.start_time() == if_s + for i_f, if_s in zip(infer_future, t_infer["fc_start"]) + ]) + assert all([ + i_f.end_time() == if_e + for i_f, if_e in zip(infer_future, t_infer["fc_end"]) + ]) @staticmethod def helper_test_encoders_settings(model, example: str): @@ -2248,29 +3111,6 @@ def helper_test_encoders_settings(model, example: str): assert len(model.encoders.future_encoders) == 1 assert isinstance(model.encoders.future_encoders[0], FutureCyclicEncoder) - @pytest.mark.skipif(not cb_available, reason="requires catboost") - @patch.object( - darts.models.forecasting.catboost_model.CatBoostRegressor - if cb_available - else darts.models.utils.NotImportedModule, - "fit", - ) - def test_catboost_model_with_eval_set(self, lgb_fit_patch): - """Test whether these evaluation set parameters are passed to CatBoostRegressor""" - model = CatBoostModel(lags=4, lags_past_covariates=2) - model.fit( - series=self.sine_univariate1, - past_covariates=self.sine_multivariate1, - val_series=self.sine_univariate1, - val_past_covariates=self.sine_multivariate1, - early_stopping_rounds=2, - ) - - lgb_fit_patch.assert_called_once() - - assert lgb_fit_patch.call_args[1]["eval_set"] is not None - assert lgb_fit_patch.call_args[1]["early_stopping_rounds"] == 2 - @pytest.mark.skipif(not lgbm_available, reason="requires lightgbm") def test_quality_forecast_with_categorical_covariates(self): """Test case: two time series, a full sine wave series and a sine wave series @@ -2309,6 +3149,7 @@ def get_model_params(): return { "lags": int(period / 2), "output_chunk_length": int(period / 2), + "verbose": -1, } # test case without using categorical static covariates @@ -2340,42 +3181,48 @@ def get_model_params(): # categorical covariates make model aware of the underlying curve type -> improves rmse rmses_no_cat = rmse(train_series_cat, preds_no_cat) rmses_cat = rmse(train_series_cat, preds_cat) - assert all( - [ - rmse_no_cat > rmse_cat - for rmse_no_cat, rmse_cat in zip(rmses_no_cat, rmses_cat) - ] - ) + assert all([ + rmse_no_cat > rmse_cat + for rmse_no_cat, rmse_cat in zip(rmses_no_cat, rmses_cat) + ]) @pytest.mark.skipif(not lgbm_available, reason="requires lightgbm") @pytest.mark.parametrize( "model", - [ - LightGBMModel( - lags=1, - lags_past_covariates=1, - output_chunk_length=1, - categorical_past_covariates=["does_not_exist", "past_cov_cat_dummy"], - categorical_static_covariates=["product_id"], - ), - LightGBMModel( - lags=1, - lags_past_covariates=1, - output_chunk_length=1, - categorical_past_covariates=[ - "past_cov_cat_dummy", - ], - categorical_static_covariates=["does_not_exist"], - ), - LightGBMModel( - lags=1, - lags_past_covariates=1, - output_chunk_length=1, - categorical_future_covariates=["does_not_exist"], - ), - ] - if lgbm_available - else [], + ( + [ + LightGBMModel( + lags=1, + lags_past_covariates=1, + output_chunk_length=1, + categorical_past_covariates=[ + "does_not_exist", + "past_cov_cat_dummy", + ], + categorical_static_covariates=["product_id"], + **lgbm_test_params, + ), + LightGBMModel( + lags=1, + lags_past_covariates=1, + output_chunk_length=1, + categorical_past_covariates=[ + "past_cov_cat_dummy", + ], + categorical_static_covariates=["does_not_exist"], + **lgbm_test_params, + ), + LightGBMModel( + lags=1, + lags_past_covariates=1, + output_chunk_length=1, + categorical_future_covariates=["does_not_exist"], + **lgbm_test_params, + ), + ] + if lgbm_available + else [] + ), ) def test_fit_with_categorical_features_raises_error(self, model): ( @@ -2415,9 +3262,11 @@ def test_get_categorical_features_helper(self): @pytest.mark.skipif(not lgbm_available, reason="requires lightgbm") @patch.object( - darts.models.forecasting.lgbm.lgb.LGBMRegressor - if lgbm_available - else darts.models.utils.NotImportedModule, + ( + darts.models.forecasting.lgbm.lgb.LGBMRegressor + if lgbm_available + else darts.models.utils.NotImportedModule + ), "fit", ) def test_lgbm_categorical_features_passed_to_fit_correctly(self, lgb_fit_patch): @@ -2460,6 +3309,94 @@ def helper_create_LinearModel(self, multi_models=True, extreme_lags=False): }, ) + def helper_generate_input_series_from_lags( + self, + list_lags, + dict_lags, + multiple_series, + output_chunk_shift, + max_forecast, + output_chunk_length: int = 1, + add_length: int = 0, + ): + np.random.seed(0) + if dict_lags: + multivar_target = "lags" in dict_lags and len(dict_lags["lags"]) > 1 + multivar_future_cov = ( + "lags_future_covariates" in dict_lags + and len(dict_lags["lags_future_covariates"]) > 1 + ) + else: + multivar_target = False + multivar_future_cov = False + + # the lags are identical across the components for each series + model = LinearRegressionModel( + **list_lags, + output_chunk_shift=output_chunk_shift, + output_chunk_length=output_chunk_length, + ) + autoreg_add_steps = max(max_forecast - model.output_chunk_length, 0) + + # create series based on the model parameters + n_s = model.min_train_series_length + add_length + series = tg.gaussian_timeseries(length=n_s, column_name="gaussian") + if multivar_target: + series = series.stack(tg.sine_timeseries(length=n_s, column_name="sine")) + + if model.supports_future_covariates: + # prepend values if not target lags are used + if "target" not in model.lags and min(model.lags["future"]) < 0: + prep = abs(min(model.lags["future"])) + else: + prep = 0 + + # minimum future covariates length + n_fc = n_s + max(model.lags["future"]) + 1 + autoreg_add_steps + future_cov = tg.gaussian_timeseries( + start=series.start_time() - prep * series.freq, + length=n_fc + prep, + column_name="lin_future", + ) + if multivar_future_cov: + future_cov = future_cov.stack( + tg.gaussian_timeseries(length=n_fc, column_name="sine_future") + ) + else: + future_cov = None + + if model.supports_past_covariates: + # prepend values if not target lags are used + if "target" not in model.lags: + prep = abs(min(model.lags["past"])) + else: + prep = 0 + + # minimum past covariates length + n_pc = n_s + autoreg_add_steps + + past_cov = tg.gaussian_timeseries( + start=series.start_time() - prep * series.freq, + length=n_pc + prep, + column_name="lin_past", + ) + else: + past_cov = None + + if multiple_series: + # second series have different component names + series = [ + series, + series.with_columns_renamed( + ["gaussian", "sine"][: series.width], + ["other", "names"][: series.width], + ) + + 10, + ] + past_cov = [past_cov, past_cov] if past_cov else None + future_cov = [future_cov, future_cov] if future_cov else None + return series, past_cov, future_cov + class TestProbabilisticRegressionModels: models_cls_kwargs_errs = [ @@ -2488,8 +3425,8 @@ class TestProbabilisticRegressionModels: { "lags": 2, "likelihood": "poisson", - "random_state": 42, "multi_models": True, + **xgb_test_params, }, 0.6, ), @@ -2499,8 +3436,8 @@ class TestProbabilisticRegressionModels: "lags": 2, "likelihood": "quantile", "quantiles": [0.1, 0.3, 0.5, 0.7, 0.9], - "random_state": 42, "multi_models": True, + **xgb_test_params, }, 0.4, ), @@ -2512,8 +3449,8 @@ class TestProbabilisticRegressionModels: { "lags": 2, "likelihood": "quantile", - "random_state": 42, "multi_models": True, + **lgbm_test_params, }, 0.4, ), @@ -2523,8 +3460,8 @@ class TestProbabilisticRegressionModels: "lags": 2, "likelihood": "quantile", "quantiles": [0.1, 0.3, 0.5, 0.7, 0.9], - "random_state": 42, "multi_models": True, + **lgbm_test_params, }, 0.4, ), @@ -2533,8 +3470,8 @@ class TestProbabilisticRegressionModels: { "lags": 2, "likelihood": "poisson", - "random_state": 42, "multi_models": True, + **lgbm_test_params, }, 0.6, ), @@ -2546,8 +3483,8 @@ class TestProbabilisticRegressionModels: { "lags": 2, "likelihood": "quantile", - "random_state": 42, "multi_models": True, + **cb_test_params, }, 0.05, ), @@ -2557,8 +3494,8 @@ class TestProbabilisticRegressionModels: "lags": 2, "likelihood": "quantile", "quantiles": [0.1, 0.3, 0.5, 0.7, 0.9], - "random_state": 42, "multi_models": True, + **cb_test_params, }, 0.05, ), @@ -2567,8 +3504,8 @@ class TestProbabilisticRegressionModels: { "lags": 2, "likelihood": "poisson", - "random_state": 42, "multi_models": True, + **cb_test_params, }, 0.6, ), @@ -2577,8 +3514,8 @@ class TestProbabilisticRegressionModels: { "lags": 2, "likelihood": "gaussian", - "random_state": 42, "multi_models": True, + **cb_test_params, }, 0.05, ), @@ -2590,10 +3527,7 @@ class TestProbabilisticRegressionModels: constant_noisy_multivar_ts = constant_noisy_ts.stack(constant_noisy_ts) num_samples = 5 - @pytest.mark.slow - @pytest.mark.parametrize( - "config", itertools.product(models_cls_kwargs_errs, [True, False]) - ) + @pytest.mark.parametrize("config", product(models_cls_kwargs_errs, [True, False])) def test_fit_predict_determinism(self, config): (model_cls, model_kwargs, _), mode = config # whether the first predictions of two models initiated with the same random state are the same @@ -2612,10 +3546,7 @@ def test_fit_predict_determinism(self, config): pred3 = model.predict(n=10, num_samples=2).values() assert (pred2 != pred3).any() - @pytest.mark.slow - @pytest.mark.parametrize( - "config", itertools.product(models_cls_kwargs_errs, [True, False]) - ) + @pytest.mark.parametrize("config", product(models_cls_kwargs_errs, [True, False])) def test_probabilistic_forecast_accuracy_univariate(self, config): (model_cls, model_kwargs, err), mode = config model_kwargs["multi_models"] = mode @@ -2627,10 +3558,7 @@ def test_probabilistic_forecast_accuracy_univariate(self, config): self.constant_noisy_ts, ) - @pytest.mark.slow - @pytest.mark.parametrize( - "config", itertools.product(models_cls_kwargs_errs, [True, False]) - ) + @pytest.mark.parametrize("config", product(models_cls_kwargs_errs, [True, False])) def test_probabilistic_forecast_accuracy_multivariate(self, config): (model_cls, model_kwargs, err), mode = config model_kwargs["multi_models"] = mode diff --git a/darts/tests/models/forecasting/test_residuals.py b/darts/tests/models/forecasting/test_residuals.py new file mode 100644 index 0000000000..47515a0756 --- /dev/null +++ b/darts/tests/models/forecasting/test_residuals.py @@ -0,0 +1,887 @@ +import itertools + +import numpy as np +import pandas as pd +import pytest + +import darts.metrics as metrics +from darts import TimeSeries, concatenate +from darts.datasets import AirPassengersDataset +from darts.logging import get_logger +from darts.models import LinearRegressionModel, NaiveDrift, NaiveSeasonal +from darts.tests.models.forecasting.test_regression_models import dummy_timeseries +from darts.utils.timeseries_generation import constant_timeseries as ct +from darts.utils.timeseries_generation import linear_timeseries as lt +from darts.utils.utils import ( + generate_index, + likelihood_component_names, + quantile_interval_names, + quantile_names, +) + +logger = get_logger(__name__) + + +class TestResiduals: + np.random.seed(42) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], + [False, True], + [(metrics.err, (-1.0, -2.0)), (metrics.ape, (100.0, 100.0))], + ), + ) + def test_output_single_series_hfc_lpo_true(self, config): + """Tests backtest based on historical forecasts generated on a single `series` (or list of one `series`) + with last_points_only=True""" + is_univariate, series_as_list, (metric, score_exp) = config + n_ts = 10 + y = ct(value=1.0, length=n_ts) + hfc = ct(value=2.0, length=n_ts) + if not is_univariate: + y = y.stack(y + 1.0) + hfc = hfc.stack(hfc + 2.0) + n_comps = y.n_components + y = y if not series_as_list else [y] + hfc = hfc if not series_as_list else [hfc] + + # expected residuals values of shape (n time steps, n components, n samples=1) + score_exp = np.array([score_exp[:n_comps]] * 10).reshape(n_ts, -1, 1) + model = NaiveDrift() + + # check that input does not work with `last_points_only=False`` + with pytest.raises(ValueError) as err: + _ = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=False, + ) + if series_as_list: + error_msg = "Expected `historical_forecasts` of type `Sequence[Sequence[TimeSeries]]`" + else: + error_msg = "Expected `historical_forecasts` of type `Sequence[TimeSeries]`" + assert str(err.value).startswith(error_msg) + + for vals_only in [False, True]: + res = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=True, + values_only=vals_only, + ) + res = res if series_as_list else [res] + assert isinstance(res, list) and len(res) == 1 + res = res[0] + vals = res if vals_only else res.all_values() + np.testing.assert_array_almost_equal(vals, score_exp) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], + [False, True], + [ + (metrics.err, ((0.0, 0.0), (-1.0, -2.0))), + (metrics.ape, ((0.0, 0.0), (100.0, 100.0))), + ], + [1, 2], + ), + ) + def test_output_single_series_hfc_lpo_false(self, config): + """Tests residuals based on historical forecasts generated on a single `series` (or list of one `series`) + with last_points_only=False""" + is_univariate, series_as_list, (metric, score_exp), n_forecasts = config + n_ts = 10 + y = ct(value=1.0, length=n_ts) + hfc = ct(value=2.0, length=n_ts) + if not is_univariate: + y = y.stack(y + 1.0) + hfc = hfc.stack(hfc + 2.0) + n_comps = y.n_components + + hfc = [y, hfc] + hfc = hfc[:n_forecasts] + y = y if not series_as_list else [y] + hfc = hfc if not series_as_list else [hfc] + + # expected residuals values of shape (n time steps, n components, n samples=1) per forecast + scores_exp = [] + for i in range(n_forecasts): + scores_exp.append( + np.array([score_exp[i][:n_comps]] * 10).reshape(n_ts, -1, 1) + ) + + model = NaiveDrift() + + # check that input does not work with `last_points_only=True`` + with pytest.raises(ValueError) as err: + _ = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=True, + ) + if series_as_list: + error_msg = "Expected `historical_forecasts` of type `Sequence[TimeSeries]`" + else: + error_msg = "Expected `historical_forecasts` of type `TimeSeries`" + assert str(err.value).startswith(error_msg) + + for vals_only in [False, True]: + res = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=False, + values_only=vals_only, + ) + res = res if series_as_list else [res] + assert isinstance(res, list) and len(res) == 1 + res = res[0] + assert isinstance(res, list) and len(res) == n_forecasts + for res_, score_exp_ in zip(res, scores_exp): + vals = res_ if vals_only else res_.all_values() + np.testing.assert_array_almost_equal(vals, score_exp_) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], # is univariate + [True, False], # same lengths + [ + (metrics.err, ((0.0, 0.0), (-1.0, -2.0))), + (metrics.ape, ((0.0, 0.0), (100.0, 100.0))), + ], + ), + ) + def test_output_multi_series_hfc_lpo_true(self, config): + """Tests residuals based on historical forecasts generated on multiple `series` with last_points_only=True""" + is_univariate, same_lengths, (metric, score_exp) = config + n_ts = 10 + y = ct(value=1.0, length=n_ts) + hfc = ct(value=2.0, length=n_ts) + if not same_lengths: + y = y.append_values([1.0]) + if not is_univariate: + y = y.stack(y + 1.0) + hfc = hfc.stack(hfc + 2.0) + n_comps = y.n_components + hfc = [y, hfc] + y = [y, y] + + # expected residuals values of shape (n time steps, n components, n samples=1) per forecast + scores_exp = [] + for i in range(len(hfc)): + num_fcs = len(hfc[i]) + scores_exp.append( + np.array([score_exp[i][:n_comps]] * num_fcs).reshape(num_fcs, -1, 1) + ) + + model = NaiveDrift() + + # check that input does not work with `last_points_only=False`` + with pytest.raises(ValueError) as err: + _ = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=False, + ) + error_msg = ( + "Expected `historical_forecasts` of type `Sequence[Sequence[TimeSeries]]`" + ) + assert str(err.value).startswith(error_msg) + + for vals_only in [False, True]: + res = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=True, + values_only=vals_only, + ) + assert isinstance(res, list) and len(res) == len(y) + for res_, score_exp_ in zip(res, scores_exp): + vals = res_ if vals_only else res_.all_values() + np.testing.assert_array_almost_equal(vals, score_exp_) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], # is univariate + [True, False], # same lengths + [ + (metrics.err, ((0.0, 0.0), (-1.0, -2.0))), + (metrics.ape, ((0.0, 0.0), (100.0, 100.0))), + ], + ), + ) + def test_output_multi_series_hfc_lpo_false(self, config): + """Tests residuals based on historical forecasts generated on multiple `series` with + last_points_only=False. + """ + is_univariate, same_lengths, (metric, score_exp) = config + n_ts = 10 + y = ct(value=1.0, length=n_ts) + hfc = ct(value=2.0, length=n_ts) + if not same_lengths: + y = y.append_values([1.0]) + if not is_univariate: + y = y.stack(y + 1.0) + hfc = hfc.stack(hfc + 2.0) + n_comps = y.n_components + hfc = [[y], [hfc]] + y = [y, y] + + # expected residuals values of shape (n time steps, n components, n samples=1) per forecast + scores_exp = [] + for i in range(len(hfc)): + num_fcs = len(hfc[i][0]) + scores_exp.append( + np.array([score_exp[i][:n_comps]] * num_fcs).reshape(num_fcs, -1, 1) + ) + + model = NaiveDrift() + + # check that input does not work with `last_points_only=False`` + with pytest.raises(ValueError) as err: + _ = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=True, + ) + error_msg = "Expected `historical_forecasts` of type `Sequence[TimeSeries]`" + assert str(err.value).startswith(error_msg) + + for vals_only in [False, True]: + res = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=False, + values_only=vals_only, + ) + assert isinstance(res, list) and len(res) == len(y) + for res_list, score_exp_ in zip(res, scores_exp): + assert isinstance(res_list, list) and len(res_list) == 1 + res_ = res_list[0] + vals = res_ if vals_only else res_.all_values() + np.testing.assert_array_almost_equal(vals, score_exp_) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], + [ + (metrics.err, ((0.0, 0.0), (-1.0, -2.0))), + (metrics.ape, ((0.0, 0.0), (100.0, 100.0))), + ], + ), + ) + def test_output_multi_series_hfc_lpo_false_different_n_fcs(self, config): + """Tests residuals based on historical forecasts generated on multiple `series` with + last_points_only=False, and the historical forecasts have different lengths + """ + is_univariate, (metric, score_exp) = config + n_ts = 10 + y = ct(value=1.0, length=n_ts) + hfc = ct(value=2.0, length=n_ts) + if not is_univariate: + y = y.stack(y + 1.0) + hfc = hfc.stack(hfc + 2.0) + n_comps = y.n_components + hfc = [[y], [hfc, hfc]] + y = [y, y] + + # expected residuals values of shape (n time steps, n components, n samples=1) per forecast + scores_exp = [] + for i in range(len(hfc)): + scores_exp.append( + np.array([score_exp[i][:n_comps]] * 10).reshape(n_ts, -1, 1) + ) + # repeat following `hfc` + scores_exp = [[scores_exp[0]], [scores_exp[1]] * 2] + + model = NaiveDrift() + + # check that input does not work with `last_points_only=False`` + with pytest.raises(ValueError) as err: + _ = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=True, + ) + error_msg = "Expected `historical_forecasts` of type `Sequence[TimeSeries]`" + assert str(err.value).startswith(error_msg) + + for vals_only in [False, True]: + res = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=False, + values_only=vals_only, + ) + assert isinstance(res, list) and len(res) == len(y) + for res_list, hfc_list, score_exp_list in zip(res, hfc, scores_exp): + assert isinstance(res_list, list) and len(res_list) == len(hfc_list) + for res_, score_exp_ in zip(res_list, score_exp_list): + vals = res_ if vals_only else res_.all_values() + np.testing.assert_array_almost_equal(vals, score_exp_) + + def test_wrong_metric(self): + y = ct(value=1.0, length=10) + hfc = ct(value=2.0, length=10) + + model = NaiveDrift() + + with pytest.raises(TypeError) as err: + _ = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metrics.mape, + last_points_only=True, + ) + assert str(err.value).endswith( + "got an unexpected keyword argument 'time_reduction'" + ) + + def test_forecasting_residuals_nocov_output(self): + model = NaiveSeasonal(K=1) + + # test zero residuals + constant_ts = ct(length=20) + residuals = model.residuals(constant_ts) + np.testing.assert_almost_equal( + residuals.univariate_values(), np.zeros(len(residuals)) + ) + residuals_vals = model.residuals(constant_ts, values_only=True) + np.testing.assert_almost_equal(residuals.all_values(), residuals_vals) + + # test constant, positive residuals + linear_ts = lt(length=20) + residuals = model.residuals(linear_ts) + np.testing.assert_almost_equal( + np.diff(residuals.univariate_values()), np.zeros(len(residuals) - 1) + ) + np.testing.assert_array_less( + np.zeros(len(residuals)), residuals.univariate_values() + ) + residuals_vals = model.residuals(linear_ts, values_only=True) + np.testing.assert_almost_equal(residuals.all_values(), residuals_vals) + + def test_forecasting_residuals_multiple_series(self): + # test input types past and/or future covariates + + # dummy covariates and target TimeSeries instances + series, past_covariates, future_covariates = dummy_timeseries( + length=10, + n_series=1, + comps_target=1, + comps_pcov=1, + comps_fcov=1, + ) # outputs Sequences[TimeSeries] and not TimeSeries + + model = LinearRegressionModel( + lags=1, lags_past_covariates=1, lags_future_covariates=(1, 1) + ) + model.fit( + series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + # residuals TimeSeries zero + res = model.residuals( + series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + assert isinstance(res, list) and len(res) == len(series) == 1 + res_vals = res[0].all_values(copy=False) + np.testing.assert_almost_equal(res_vals, np.zeros((len(res[0]), 1, 1))) + + # return values only + res_vals_direct = model.residuals( + series, + past_covariates=past_covariates, + future_covariates=future_covariates, + values_only=True, + ) + assert ( + isinstance(res_vals_direct, list) + and len(res_vals_direct) == len(series) == 1 + ) + np.testing.assert_almost_equal(res_vals_direct[0], res_vals) + + # with precomputed historical forecasts + hfc = model.historical_forecasts( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + res_hfc = model.residuals(series, historical_forecasts=hfc) + assert res == res_hfc + + # with pretrained model + res_pretrained = model.residuals( + series, + start=model.min_train_series_length, + past_covariates=past_covariates, + future_covariates=future_covariates, + retrain=False, + values_only=True, + ) + np.testing.assert_almost_equal(res_pretrained[0], res_vals) + + # if model is trained with covariates, should raise error when covariates are missing in residuals() + with pytest.raises(ValueError): + model.residuals(series) + + with pytest.raises(ValueError): + model.residuals(series, past_covariates=past_covariates) + + with pytest.raises(ValueError): + model.residuals(series, future_covariates=future_covariates) + + @pytest.mark.parametrize( + "series", + [ + ct(value=0.5, length=10), + lt(length=10), + ], + ) + def test_forecasting_residuals_cov_output(self, series): + # if covariates are constant and the target is constant/linear, + # residuals should be zero (for a LinearRegression model) + past_covariates = ct(value=0.2, length=10) + future_covariates = ct(value=0.1, length=10) + + model = LinearRegressionModel( + lags=1, lags_past_covariates=1, lags_future_covariates=(1, 1) + ) + model.fit( + series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + + # residuals TimeSeries zero + res = model.residuals( + series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + np.testing.assert_almost_equal(res.univariate_values(), np.zeros(len(res))) + + # return values only + res_vals = model.residuals( + series, + past_covariates=past_covariates, + future_covariates=future_covariates, + values_only=True, + ) + np.testing.assert_almost_equal(res.all_values(), res_vals) + + # with precomputed historical forecasts + hfc = model.historical_forecasts( + series=series, + past_covariates=past_covariates, + future_covariates=future_covariates, + ) + res_hfc = model.residuals(series, historical_forecasts=hfc) + assert res == res_hfc + + # with pretrained model + res_pretrained = model.residuals( + series, + start=model.min_train_series_length, + past_covariates=past_covariates, + future_covariates=future_covariates, + retrain=False, + values_only=True, + ) + np.testing.assert_almost_equal(res_vals, res_pretrained) + + # if model is trained with covariates, should raise error when covariates are missing in residuals() + with pytest.raises(ValueError): + model.residuals(series) + + with pytest.raises(ValueError): + model.residuals(series, past_covariates=past_covariates) + + with pytest.raises(ValueError): + model.residuals(series, future_covariates=future_covariates) + + @pytest.mark.parametrize( + "config", + itertools.product( + [ + metrics.ase, + metrics.sse, + ], + [1, 2], + ), + ) + def test_scaled_metrics(self, config): + """Tests residuals for scaled metrics based on historical forecasts generated on a sequence + `series` with last_points_only=False""" + metric, m = config + y = lt(length=20) + hfc = lt(length=10, start=y.start_time() + 10 * y.freq) + y = [y, y] + hfc = [[hfc, hfc], [hfc]] + + model = NaiveDrift() + bts = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=False, + metric_kwargs={"m": m}, + values_only=True, + ) + assert isinstance(bts, list) and len(bts) == 2 + + bt_expected = metric(y[0], hfc[0][0], insample=y[0], m=m) + bt_expected = np.reshape(bt_expected, (len(hfc[0][0]), y[0].n_components, 1)) + for bt_list in bts: + for bt in bt_list: + np.testing.assert_array_almost_equal(bt, bt_expected) + + def test_metric_kwargs(self): + """Tests residuals with different metric_kwargs based on historical forecasts generated on a sequence + `series` with last_points_only=False""" + y = lt(length=20) + y = y.stack(y + 1.0) + hfc = lt(length=10, start=y.start_time() + 10 * y.freq) + hfc = hfc.stack(hfc + 1.0) + y = [y, y] + hfc = [[hfc, hfc], [hfc]] + + model = NaiveDrift() + # reduction `metric_kwargs` are bypassed, n_jobs not + bts = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metrics.ae, + last_points_only=False, + metric_kwargs={ + "component_reduction": np.median, + "time_reduction": np.mean, + "n_jobs": -1, + }, + values_only=True, + ) + assert isinstance(bts, list) and len(bts) == 2 + + # `ae` with time and component reduction is equal to `mae` with component reduction + bt_expected = metrics.ae( + y[0], + hfc[0][0], + series_reduction=None, + time_reduction=None, + component_reduction=None, + )[:, :, None] + for bt_list in bts: + for bt in bt_list: + np.testing.assert_array_almost_equal(bt, bt_expected) + + @pytest.mark.parametrize( + "config", + itertools.product([True, False], [True, False]), + ) + def test_sample_weight(self, config): + """check that passing sample weights work and that it yields different results than without sample weights.""" + manual_weight, multi_series = config + ts = AirPassengersDataset().load() + if manual_weight: + sample_weight = np.linspace(0, 1, len(ts)) + sample_weight = ts.with_values(np.expand_dims(sample_weight, -1)) + else: + sample_weight = "linear" + + if multi_series: + ts = [ts] * 2 + sample_weight = [sample_weight] * 2 if manual_weight else sample_weight + + model = LinearRegressionModel(lags=3, output_chunk_length=1) + start_kwargs = {"start": -1, "start_format": "position"} + res_non_weighted = model.residuals(series=ts, values_only=True, **start_kwargs) + + model = LinearRegressionModel(lags=3, output_chunk_length=1) + res_weighted = model.residuals( + series=ts, sample_weight=sample_weight, values_only=True, **start_kwargs + ) + + if not multi_series: + res_weighted = [res_weighted] + res_non_weighted = [res_non_weighted] + + # check that the predictions are different + for res_nw, res_w in zip(res_non_weighted, res_weighted): + with pytest.raises(AssertionError): + np.testing.assert_array_almost_equal(res_w, res_nw) + + @pytest.mark.parametrize( + "config", + itertools.product( + [metrics.ae, metrics.iw], # quantile (interval) metrics + [True, False], # last_points_only + [False, True], # from stochastic predictions (or predicted quantiles) + ), + ) + def test_residuals_with_quantiles_metrics(self, config): + """Tests residuals with quantile metrics from expected probabilistic or quantile historical forecasts.""" + metric, lpo, stochastic_pred = config + is_interval_metric = metric.__name__ == "iw" + + # multi-quantile metrics yield more components + q = [0.05, 0.50, 0.60, 0.95] + q_interval = [(0.05, 0.50), (0.50, 0.60), (0.60, 0.95), (0.05, 0.60)] + + y = lt(length=20) + y = y.stack(y + 1.0) + if not is_interval_metric: + q_comp_names_expected = pd.Index( + likelihood_component_names( + components=y.components, + parameter_names=quantile_names(q), + ) + ) + else: + q_comp_names_expected = pd.Index( + likelihood_component_names( + components=y.components, + parameter_names=quantile_interval_names(q_interval), + ) + ) + # historical forecasts + vals = np.random.random((10, 1, 100)) + if not stochastic_pred: + vals = np.quantile(vals, q, axis=2).transpose((1, 0, 2)) + comp_names = pd.Index( + likelihood_component_names( + components=y.components, + parameter_names=quantile_names(q=q), + ) + ) + else: + comp_names = y.components + vals = np.concatenate([vals, vals + 1], axis=1) + hfc = TimeSeries.from_times_and_values( + times=generate_index(start=y.start_time() + 10 * y.freq, length=10), + values=vals, + columns=comp_names, + ) + + y = [y, y] + if lpo: + hfc = [hfc, hfc] + else: + hfc = [[hfc, hfc], [hfc]] + + metric_kwargs = {"component_reduction": None} + if not is_interval_metric: + metric_kwargs["q"] = q + else: + metric_kwargs["q_interval"] = q_interval + + model = NaiveDrift() + + # return TimeSeries + bts = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=lpo, + metric_kwargs=metric_kwargs, + ) + assert isinstance(bts, list) and len(bts) == 2 + if lpo: + bts = [[bt] for bt in bts] + + # `ae` with time and component reduction is equal to `mae` with component reduction + hfc_single = hfc[0][0] if not lpo else hfc[0] + bt_expected = metric(y[0], hfc_single, **metric_kwargs) + shape_expected = (len(hfc_single), len(q) * y[0].n_components) + for bt_list in bts: + for bt in bt_list: + assert bt.shape[:2] == shape_expected + assert bt.components.equals(q_comp_names_expected) + np.testing.assert_array_almost_equal(bt.values(), bt_expected) + + # values only + bts = model.residuals( + series=y, + historical_forecasts=hfc, + metric=metric, + last_points_only=lpo, + metric_kwargs=metric_kwargs, + values_only=True, + ) + assert isinstance(bts, list) and len(bts) == 2 + if lpo: + bts = [[bt] for bt in bts] + + # `ae` with time and component reduction is equal to `mae` with component reduction + for bt_list in bts: + for bt in bt_list: + assert bt.shape[:2] == shape_expected + np.testing.assert_array_almost_equal(bt[:, :, 0], bt_expected) + + @pytest.mark.parametrize( + "config", + list( + itertools.product( + [metrics.ae, metrics.iw], # quantile (interval) metrics + [True, False], # last_points_only + ) + ), + ) + def test_quantiles_from_model(self, config): + """Tests residuals from quantile regression model works for both direct likelihood parameter prediction or + sampled prediction by giving the correct metrics kwargs.""" + metric, lpo = config + + is_interval_metric = metric.__name__ == "iw" + + # multi-quantile metrics yield more components + q = [0.05, 0.50, 0.95] + q_interval = [(0.05, 0.50), (0.50, 0.95), (0.05, 0.95)] + + y = lt(length=20) + y = y.stack(y + 1.0) + if not is_interval_metric: + q_comp_names_expected = pd.Index( + likelihood_component_names( + components=y.components, + parameter_names=quantile_names(q), + ) + ) + else: + q_comp_names_expected = pd.Index( + likelihood_component_names( + components=y.components, + parameter_names=quantile_interval_names(q_interval), + ) + ) + y = [y, y] + metric_kwargs = {"component_reduction": None} + if not is_interval_metric: + metric_kwargs["q"] = q + else: + metric_kwargs["q_interval"] = q_interval + + icl = 3 + model = LinearRegressionModel( + lags=icl, output_chunk_length=1, likelihood="quantile", quantiles=q + ) + model.fit(y) + + # quantile forecasts + bts = model.residuals( + series=y, + forecast_horizon=1, + metric=metric, + last_points_only=lpo, + metric_kwargs=metric_kwargs, + predict_likelihood_parameters=True, + retrain=False, + ) + assert isinstance(bts, list) and len(bts) == 2 + if not lpo: + bts = [concatenate(bt, axis=0) for bt in bts] + + # `ae` with time and component reduction is equal to `mae` with component reduction + shape_expected = (len(y[0]) - icl, len(q) * y[0].n_components) + for bt in bts: + assert bt.shape[:2] == shape_expected + assert bt.components.equals(q_comp_names_expected) + + # probabilistic forecasts + bts_prob = model.residuals( + series=y, + forecast_horizon=1, + metric=metric, + last_points_only=lpo, + metric_kwargs=metric_kwargs, + predict_likelihood_parameters=False, + num_samples=1000, + retrain=False, + ) + assert isinstance(bts_prob, list) and len(bts_prob) == 2 + if not lpo: + bts_prob = [concatenate(bt, axis=0) for bt in bts_prob] + for bt_p, bt_q in zip(bts_prob, bts): + assert bt_p.shape == bt_q.shape + assert bt_p.components.equals(bt_q.components) + # check that the results are similar + assert np.abs(bt_p.all_values() - bt_q.all_values()).max() < 0.1 + + # single quantile + q_single = 0.05 + q_interval_single = (0.05, 0.50) + metric_kwargs = {"component_reduction": None} + if not is_interval_metric: + metric_kwargs["q"] = q_single + else: + metric_kwargs["q_interval"] = q_interval_single + bts = model.residuals( + series=y, + forecast_horizon=1, + metric=metric, + last_points_only=lpo, + metric_kwargs=metric_kwargs, + predict_likelihood_parameters=True, + retrain=False, + ) + assert isinstance(bts, list) and len(bts) == 2 + if not lpo: + bts = [concatenate(bt, axis=0) for bt in bts] + + # `ae` with time and component reduction is equal to `mae` with component reduction + shape_expected = (len(y[0]) - icl, y[0].n_components) + for bt in bts: + assert bt.shape[:2] == shape_expected + assert bt.components.equals( + pd.Index( + likelihood_component_names( + y[0].components, + parameter_names=( + quantile_names([q_single]) + if not is_interval_metric + else quantile_interval_names([q_interval_single]) + ), + ) + ) + ) + + # wrong quantile + q_wrong = [0.99] + q_interval_wrong = (0.05, 0.99) + metric_kwargs = {"component_reduction": None} + if not is_interval_metric: + metric_kwargs["q"] = q_wrong + else: + metric_kwargs["q_interval"] = q_interval_wrong + with pytest.raises(ValueError) as exc: + _ = model.residuals( + series=y, + forecast_horizon=1, + metric=metric, + last_points_only=lpo, + metric_kwargs=metric_kwargs, + predict_likelihood_parameters=True, + retrain=False, + ) + assert str(exc.value).startswith( + f"Computing a metric with quantile(s) " + f"`q={'[0.99]' if not is_interval_metric else '[0.05 0.99]'}` is only supported" + ) diff --git a/darts/tests/models/forecasting/test_tide_model.py b/darts/tests/models/forecasting/test_tide_model.py index 3a86c0285e..4d6d66e461 100644 --- a/darts/tests/models/forecasting/test_tide_model.py +++ b/darts/tests/models/forecasting/test_tide_model.py @@ -3,276 +3,269 @@ import pytest from darts import concatenate -from darts.logging import get_logger -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils import timeseries_generation as tg -logger = get_logger(__name__) +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import torch -try: - import torch +from darts.models.forecasting.tide_model import TiDEModel +from darts.utils.likelihood_models import GaussianLikelihood - from darts.models.forecasting.tide_model import TiDEModel - from darts.utils.likelihood_models import GaussianLikelihood - TORCH_AVAILABLE = True +class TestTiDEModel: + np.random.seed(42) + torch.manual_seed(42) -except ImportError: - logger.warning("Torch not available. TiDEModel tests will be skipped.") - TORCH_AVAILABLE = False - -if TORCH_AVAILABLE: - - class TestTiDEModel: - np.random.seed(42) - torch.manual_seed(42) - - def test_creation(self): - model = TiDEModel( - input_chunk_length=1, - output_chunk_length=1, - likelihood=GaussianLikelihood(), - ) - - assert model.input_chunk_length == 1 + def test_creation(self): + model = TiDEModel( + input_chunk_length=1, + output_chunk_length=1, + likelihood=GaussianLikelihood(), + ) - def test_fit(self): - large_ts = tg.constant_timeseries(length=100, value=1000) - small_ts = tg.constant_timeseries(length=100, value=10) + assert model.input_chunk_length == 1 - model = TiDEModel( - input_chunk_length=1, - output_chunk_length=1, - n_epochs=10, - random_state=42, - **tfm_kwargs - ) + def test_fit(self): + large_ts = tg.constant_timeseries(length=100, value=1000) + small_ts = tg.constant_timeseries(length=100, value=10) - model.fit(large_ts[:98]) - pred = model.predict(n=2).values()[0] + model = TiDEModel( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=10, + random_state=42, + **tfm_kwargs, + ) - # Test whether model trained on one series is better than one trained on another - model2 = TiDEModel( - input_chunk_length=1, - output_chunk_length=1, - n_epochs=10, - random_state=42, - **tfm_kwargs - ) + model.fit(large_ts[:98]) + pred = model.predict(n=2).values()[0] - model2.fit(small_ts[:98]) - pred2 = model2.predict(n=2).values()[0] - assert abs(pred2 - 10) < abs(pred - 10) + # Test whether model trained on one series is better than one trained on another + model2 = TiDEModel( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=10, + random_state=42, + **tfm_kwargs, + ) - # test short predict - pred3 = model2.predict(n=1) - assert len(pred3) == 1 + model2.fit(small_ts[:98]) + pred2 = model2.predict(n=2).values()[0] + assert abs(pred2 - 10) < abs(pred - 10) + + # test short predict + pred3 = model2.predict(n=1) + assert len(pred3) == 1 + + def test_logtensorboard(self, tmpdir_module): + ts = tg.constant_timeseries(length=50, value=10) + + # Test basic fit and predict + model = TiDEModel( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=1, + log_tensorboard=True, + work_dir=tmpdir_module, + pl_trainer_kwargs={ + "log_every_n_steps": 1, + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) + model.fit(ts) + model.predict(n=2) + + def test_future_covariate_handling(self): + ts_time_index = tg.sine_timeseries(length=2, freq="h") + + model = TiDEModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"future": "hour"}}, + use_reversible_instance_norm=False, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False, epochs=1) + + model = TiDEModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"future": "hour"}}, + use_reversible_instance_norm=True, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False, epochs=1) - def test_logtensorboard(self, tmpdir_module): - ts = tg.constant_timeseries(length=50, value=10) + def test_future_and_past_covariate_handling(self): + ts_time_index = tg.sine_timeseries(length=2, freq="h") - # Test basic fit and predict - model = TiDEModel( - input_chunk_length=1, - output_chunk_length=1, - n_epochs=1, - log_tensorboard=True, - work_dir=tmpdir_module, - pl_trainer_kwargs={ - "log_every_n_steps": 1, - **tfm_kwargs["pl_trainer_kwargs"], - }, - ) - model.fit(ts) - model.predict(n=2) + model = TiDEModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False, epochs=1) - def test_future_covariate_handling(self): - ts_time_index = tg.sine_timeseries(length=2, freq="h") + model = TiDEModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False, epochs=1) - model = TiDEModel( + @pytest.mark.parametrize("temporal_widths", [(-1, 1), (1, -1)]) + def test_failing_future_and_past_temporal_widths(self, temporal_widths): + # invalid temporal widths + with pytest.raises(ValueError): + TiDEModel( input_chunk_length=1, output_chunk_length=1, - add_encoders={"cyclic": {"future": "hour"}}, - use_reversible_instance_norm=False, - **tfm_kwargs + temporal_width_past=temporal_widths[0], + temporal_width_future=temporal_widths[1], + **tfm_kwargs, ) - model.fit(ts_time_index, verbose=False, epochs=1) - model = TiDEModel( - input_chunk_length=1, - output_chunk_length=1, - add_encoders={"cyclic": {"future": "hour"}}, - use_reversible_instance_norm=True, - **tfm_kwargs - ) - model.fit(ts_time_index, verbose=False, epochs=1) + @pytest.mark.parametrize( + "temporal_widths", + [ + (2, 2), # feature projection to same amount of features + (1, 2), # past: feature reduction, future: same amount of features + (2, 1), # past: same amount of features, future: feature reduction + (3, 3), # feature expansion + (0, 2), # bypass past feature projection + (2, 0), # bypass future feature projection + (0, 0), # bypass all feature projection + ], + ) + def test_future_and_past_temporal_widths(self, temporal_widths): + ts_time_index = tg.sine_timeseries(length=2, freq="h") + + # feature projection to 2 features (same amount as input features) + model = TiDEModel( + input_chunk_length=1, + output_chunk_length=1, + temporal_width_past=temporal_widths[0], + temporal_width_future=temporal_widths[1], + add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False, epochs=1) + assert model.model.temporal_width_past == temporal_widths[0] + assert model.model.temporal_width_future == temporal_widths[1] + + def test_past_covariate_handling(self): + ts_time_index = tg.sine_timeseries(length=2, freq="h") + + model = TiDEModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"past": "hour"}}, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False, epochs=1) - def test_future_and_past_covariate_handling(self): - ts_time_index = tg.sine_timeseries(length=2, freq="h") + def test_future_and_past_covariate_as_timeseries_handling(self): + ts_time_index = tg.sine_timeseries(length=2, freq="h") + for enable_rin in [True, False]: + # test with past_covariates timeseries model = TiDEModel( input_chunk_length=1, output_chunk_length=1, add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, - **tfm_kwargs + use_reversible_instance_norm=enable_rin, + **tfm_kwargs, ) - model.fit(ts_time_index, verbose=False, epochs=1) - - model = TiDEModel( - input_chunk_length=1, - output_chunk_length=1, - add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, - **tfm_kwargs + model.fit( + ts_time_index, + past_covariates=ts_time_index, + verbose=False, + epochs=1, ) - model.fit(ts_time_index, verbose=False, epochs=1) - - @pytest.mark.parametrize("temporal_widths", [(-1, 1), (1, -1)]) - def test_failing_future_and_past_temporal_widths(self, temporal_widths): - # invalid temporal widths - with pytest.raises(ValueError): - TiDEModel( - input_chunk_length=1, - output_chunk_length=1, - temporal_width_past=temporal_widths[0], - temporal_width_future=temporal_widths[1], - **tfm_kwargs - ) - - @pytest.mark.parametrize( - "temporal_widths", - [ - (2, 2), # feature projection to same amount of features - (1, 2), # past: feature reduction, future: same amount of features - (2, 1), # past: same amount of features, future: feature reduction - (3, 3), # feature expansion - (0, 2), # bypass past feature projection - (2, 0), # bypass future feature projection - (0, 0), # bypass all feature projection - ], - ) - def test_future_and_past_temporal_widths(self, temporal_widths): - ts_time_index = tg.sine_timeseries(length=2, freq="h") - # feature projection to 2 features (same amount as input features) + # test with past_covariates and future_covariates timeseries model = TiDEModel( input_chunk_length=1, output_chunk_length=1, - temporal_width_past=temporal_widths[0], - temporal_width_future=temporal_widths[1], add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, - **tfm_kwargs - ) - model.fit(ts_time_index, verbose=False, epochs=1) - assert model.model.temporal_width_past == temporal_widths[0] - assert model.model.temporal_width_future == temporal_widths[1] - - def test_past_covariate_handling(self): - ts_time_index = tg.sine_timeseries(length=2, freq="h") - - model = TiDEModel( - input_chunk_length=1, - output_chunk_length=1, - add_encoders={"cyclic": {"past": "hour"}}, - **tfm_kwargs + use_reversible_instance_norm=enable_rin, + **tfm_kwargs, ) - model.fit(ts_time_index, verbose=False, epochs=1) - - def test_future_and_past_covariate_as_timeseries_handling(self): - ts_time_index = tg.sine_timeseries(length=2, freq="h") - - for enable_rin in [True, False]: - - # test with past_covariates timeseries - model = TiDEModel( - input_chunk_length=1, - output_chunk_length=1, - add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, - use_reversible_instance_norm=enable_rin, - **tfm_kwargs - ) - model.fit( - ts_time_index, - past_covariates=ts_time_index, - verbose=False, - epochs=1, - ) - - # test with past_covariates and future_covariates timeseries - model = TiDEModel( - input_chunk_length=1, - output_chunk_length=1, - add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, - use_reversible_instance_norm=enable_rin, - **tfm_kwargs - ) - model.fit( - ts_time_index, - past_covariates=ts_time_index, - future_covariates=ts_time_index, - verbose=False, - epochs=1, - ) - - def test_static_covariates_support(self): - target_multi = concatenate( - [tg.sine_timeseries(length=10, freq="h")] * 2, axis=1 + model.fit( + ts_time_index, + past_covariates=ts_time_index, + future_covariates=ts_time_index, + verbose=False, + epochs=1, ) - target_multi = target_multi.with_static_covariates( - pd.DataFrame( - [[0.0, 1.0, 0, 2], [2.0, 3.0, 1, 3]], - columns=["st1", "st2", "cat1", "cat2"], - ) - ) + def test_static_covariates_support(self): + target_multi = concatenate( + [tg.sine_timeseries(length=10, freq="h")] * 2, axis=1 + ) - # test with static covariates in the timeseries - model = TiDEModel( - input_chunk_length=3, - output_chunk_length=4, - add_encoders={"cyclic": {"future": "hour"}}, - pl_trainer_kwargs={ - "fast_dev_run": True, - **tfm_kwargs["pl_trainer_kwargs"], - }, + target_multi = target_multi.with_static_covariates( + pd.DataFrame( + [[0.0, 1.0, 0, 2], [2.0, 3.0, 1, 3]], + columns=["st1", "st2", "cat1", "cat2"], ) - model.fit(target_multi, verbose=False) + ) - assert model.model.static_cov_dim == np.prod( - target_multi.static_covariates.values.shape - ) + # test with static covariates in the timeseries + model = TiDEModel( + input_chunk_length=3, + output_chunk_length=4, + add_encoders={"cyclic": {"future": "hour"}}, + pl_trainer_kwargs={ + "fast_dev_run": True, + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) + model.fit(target_multi, verbose=False) - # raise an error when trained with static covariates of wrong dimensionality - target_multi = target_multi.with_static_covariates( - pd.concat([target_multi.static_covariates] * 2, axis=1) - ) - with pytest.raises(ValueError): - model.predict(n=1, series=target_multi, verbose=False) + assert model.model.static_cov_dim == np.prod( + target_multi.static_covariates.values.shape + ) - # raise an error when trained with static covariates and trying to predict without - with pytest.raises(ValueError): - model.predict( - n=1, series=target_multi.with_static_covariates(None), verbose=False - ) + # raise an error when trained with static covariates of wrong dimensionality + target_multi = target_multi.with_static_covariates( + pd.concat([target_multi.static_covariates] * 2, axis=1) + ) + with pytest.raises(ValueError): + model.predict(n=1, series=target_multi, verbose=False) - # with `use_static_covariates=False`, we can predict without static covs - model = TiDEModel( - input_chunk_length=3, - output_chunk_length=4, - use_static_covariates=False, - n_epochs=1, - **tfm_kwargs + # raise an error when trained with static covariates and trying to predict without + with pytest.raises(ValueError): + model.predict( + n=1, series=target_multi.with_static_covariates(None), verbose=False ) - model.fit(target_multi) - preds = model.predict(n=2, series=target_multi.with_static_covariates(None)) - assert preds.static_covariates is None - model = TiDEModel( - input_chunk_length=3, - output_chunk_length=4, - use_static_covariates=False, - n_epochs=1, - **tfm_kwargs - ) - model.fit(target_multi.with_static_covariates(None)) - preds = model.predict(n=2, series=target_multi) - assert preds.static_covariates.equals(target_multi.static_covariates) + # with `use_static_covariates=False`, we can predict without static covs + model = TiDEModel( + input_chunk_length=3, + output_chunk_length=4, + use_static_covariates=False, + n_epochs=1, + **tfm_kwargs, + ) + model.fit(target_multi) + preds = model.predict(n=2, series=target_multi.with_static_covariates(None)) + assert preds.static_covariates is None + + model = TiDEModel( + input_chunk_length=3, + output_chunk_length=4, + use_static_covariates=False, + n_epochs=1, + **tfm_kwargs, + ) + model.fit(target_multi.with_static_covariates(None)) + preds = model.predict(n=2, series=target_multi) + assert preds.static_covariates.equals(target_multi.static_covariates) diff --git a/darts/tests/models/forecasting/test_torch_forecasting_model.py b/darts/tests/models/forecasting/test_torch_forecasting_model.py index 24b8fd501e..4a52eb4128 100644 --- a/darts/tests/models/forecasting/test_torch_forecasting_model.py +++ b/darts/tests/models/forecasting/test_torch_forecasting_model.py @@ -1,956 +1,1121 @@ +import copy +import itertools import os -from typing import Any, Dict, Optional +from typing import Any, Optional from unittest.mock import patch import numpy as np import pandas as pd import pytest +import darts.utils.timeseries_generation as tg from darts import TimeSeries from darts.dataprocessing.encoders import SequentialEncoder from darts.dataprocessing.transformers import BoxCox, Scaler -from darts.logging import get_logger from darts.metrics import mape -from darts.tests.conftest import tfm_kwargs -from darts.utils.timeseries_generation import linear_timeseries - -logger = get_logger(__name__) - -try: - import torch - from pytorch_lightning.loggers.logger import DummyLogger - from pytorch_lightning.tuner.lr_finder import _LRFinder - from torchmetrics import ( - MeanAbsoluteError, - MeanAbsolutePercentageError, - MetricCollection, - ) +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs - from darts.models import ( - BlockRNNModel, - DLinearModel, - NBEATSModel, - NHiTSModel, - NLinearModel, - RNNModel, - TCNModel, - TFTModel, - TiDEModel, - TransformerModel, +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, ) - from darts.models.components.layer_norm_variants import RINorm - from darts.utils.likelihood_models import ( - GaussianLikelihood, - LaplaceLikelihood, - Likelihood, +import torch +from pytorch_lightning.callbacks import Callback +from pytorch_lightning.loggers.logger import DummyLogger +from pytorch_lightning.tuner.lr_finder import _LRFinder +from torch.utils.data import DataLoader, RandomSampler, SequentialSampler +from torchmetrics import ( + MeanAbsoluteError, + MeanAbsolutePercentageError, + Metric, + MetricCollection, +) + +from darts.models import ( + BlockRNNModel, + DLinearModel, + GlobalNaiveAggregate, + GlobalNaiveDrift, + GlobalNaiveSeasonal, + NBEATSModel, + NHiTSModel, + NLinearModel, + RNNModel, + TCNModel, + TFTModel, + TiDEModel, + TransformerModel, + TSMixerModel, +) +from darts.models.components.layer_norm_variants import RINorm +from darts.models.forecasting.global_baseline_models import _GlobalNaiveModel +from darts.utils.likelihood_models import ( + CauchyLikelihood, + GaussianLikelihood, + LaplaceLikelihood, + Likelihood, + QuantileRegression, +) + +kwargs = { + "input_chunk_length": 10, + "output_chunk_length": 1, + "n_epochs": 1, + "random_state": 42, + "pl_trainer_kwargs": {"fast_dev_run": True, **tfm_kwargs["pl_trainer_kwargs"]}, +} +# make models light weight +dlinear_light_kwargs = {"kernel_size": 2} +nbeats_light_kwargs = { + "num_stacks": 1, + "num_blocks": 1, + "num_layers": 1, + "layer_widths": 2, +} +tcn_light_kwargs = { + "kernel_size": 2, + "num_filters": 1, + "dilation_base": 1, +} +trafo_light_kwargs = { + "d_model": 2, + "nhead": 1, + "num_encoder_layers": 1, + "num_decoder_layers": 1, + "dim_feedforward": 2, +} +tft_light_kwargs = { + "hidden_size": 2, + "lstm_layers": 1, + "num_attention_heads": 1, + "hidden_continuous_size": 2, +} +models = [ + (BlockRNNModel, kwargs), + (DLinearModel, dict(kwargs, **dlinear_light_kwargs)), + (NBEATSModel, dict(kwargs, **nbeats_light_kwargs)), + (NHiTSModel, dict(kwargs, **nbeats_light_kwargs)), + (NLinearModel, kwargs), + (RNNModel, {"training_length": 10, **kwargs}), + (TCNModel, dict(kwargs, **tcn_light_kwargs)), + (TFTModel, {"add_relative_index": 2, **kwargs, **tft_light_kwargs}), + (TiDEModel, kwargs), + (TransformerModel, dict(kwargs, **trafo_light_kwargs)), + (TSMixerModel, kwargs), + (GlobalNaiveSeasonal, kwargs), + (GlobalNaiveAggregate, kwargs), + (GlobalNaiveDrift, kwargs), +] + + +class NumsCalled(Metric): + def __init__(self): + super().__init__() + self.add_state("preds", default=[], dist_reduce_fx="cat") + + def update(self, preds, target) -> None: + self.preds.append(preds) + + def compute(self): + return len(self.preds) + + +class TestTorchForecastingModel: + times = pd.date_range("20130101", "20130410") + pd_series = pd.Series(range(100), index=times) + series = TimeSeries.from_series(pd_series) + + df = pd.DataFrame({"var1": range(100), "var2": range(100)}, index=times) + multivariate_series = TimeSeries.from_dataframe(df) + + def test_save_model_parameters(self): + # check if re-created model has same params as original + model = RNNModel(12, "RNN", 10, 10, **tfm_kwargs) + params_old = model.model_params + params_new = model.untrained_model().model_params + + assert params_old.keys() == params_new.keys() + assert all([params_old[k] == params_new[k] for k in params_old]) + + @patch( + "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel.save" ) + def test_suppress_automatic_save(self, patch_save_model, tmpdir_fn): + model_name = "test_model" + model1 = RNNModel( + 12, + "RNN", + 10, + 10, + model_name=model_name, + work_dir=tmpdir_fn, + save_checkpoints=False, + **tfm_kwargs, + ) + model2 = RNNModel( + 12, + "RNN", + 10, + 10, + model_name=model_name, + work_dir=tmpdir_fn, + force_reset=True, + save_checkpoints=False, + **tfm_kwargs, + ) - kwargs = { - "input_chunk_length": 10, - "output_chunk_length": 1, - "n_epochs": 1, - "pl_trainer_kwargs": {"fast_dev_run": True, **tfm_kwargs["pl_trainer_kwargs"]}, - } - models = [ - (BlockRNNModel, kwargs), - (DLinearModel, kwargs), - (NBEATSModel, kwargs), - (NHiTSModel, kwargs), - (NLinearModel, kwargs), - (RNNModel, {"training_length": 2, **kwargs}), - (TCNModel, kwargs), - (TFTModel, {"add_relative_index": 2, **kwargs}), - (TiDEModel, kwargs), - (TransformerModel, kwargs), - ] - - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. Tests will be skipped.") - TORCH_AVAILABLE = False - -if TORCH_AVAILABLE: - - class TestTorchForecastingModel: - times = pd.date_range("20130101", "20130410") - pd_series = pd.Series(range(100), index=times) - series = TimeSeries.from_series(pd_series) - - df = pd.DataFrame({"var1": range(100), "var2": range(100)}, index=times) - multivariate_series = TimeSeries.from_dataframe(df) - - def test_save_model_parameters(self): - # check if re-created model has same params as original - model = RNNModel(12, "RNN", 10, 10, **tfm_kwargs) - assert model._model_params, model.untrained_model()._model_params - - @patch( - "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel.save" - ) - def test_suppress_automatic_save(self, patch_save_model, tmpdir_fn): - model_name = "test_model" - model1 = RNNModel( - 12, - "RNN", - 10, - 10, - model_name=model_name, - work_dir=tmpdir_fn, - save_checkpoints=False, - **tfm_kwargs, - ) - model2 = RNNModel( - 12, - "RNN", - 10, - 10, - model_name=model_name, - work_dir=tmpdir_fn, - force_reset=True, - save_checkpoints=False, - **tfm_kwargs, - ) + model1.fit(self.series, epochs=1) + model2.fit(self.series, epochs=1) + + model1.predict(n=1) + model2.predict(n=2) + + patch_save_model.assert_not_called() + + model1.save(path=os.path.join(tmpdir_fn, model_name)) + patch_save_model.assert_called() + + @pytest.mark.parametrize("clean", [False, True]) + def test_manual_save_and_load(self, tmpdir_fn, clean): + """validate manual save with automatic save files by comparing output between the two""" + + class CustomCallback(Callback): + def on_train_epoch_end(self, trainer, pl_module): + pass + + custom_callback = CustomCallback() + kwargs = copy.deepcopy(tfm_kwargs) + if clean: + kwargs["pl_trainer_kwargs"]["callbacks"] = [custom_callback] + + model_dir = os.path.join(tmpdir_fn) + manual_name = "test_save_manual" + auto_name = "test_save_automatic" + model_manual_save = RNNModel( + 12, + "RNN", + 10, + 10, + model_name=manual_name, + work_dir=tmpdir_fn, + save_checkpoints=False, + random_state=42, + **kwargs, + ) + model_auto_save = RNNModel( + 12, + "RNN", + 10, + 10, + model_name=auto_name, + work_dir=tmpdir_fn, + save_checkpoints=True, + random_state=42, + **tfm_kwargs, + ) - model1.fit(self.series, epochs=1) - model2.fit(self.series, epochs=1) + # save model without training + no_training_ckpt_path = os.path.join(model_dir, "no_training.pth.tar") + + model_manual_save.save(no_training_ckpt_path, clean=clean) + + # check that model object file was created + assert os.path.exists(no_training_ckpt_path) + # check that the PyTorch Ligthning ckpt does not exist + assert not os.path.exists(no_training_ckpt_path + ".ckpt") + # informative exception about `fit()` not called + with pytest.raises(ValueError) as err: + no_train_model = RNNModel.load(no_training_ckpt_path) + no_train_model.predict(n=4) + assert str(err.value) == ( + "Input `series` must be provided. This is the result either from fitting on multiple series, " + "from not having fit the model yet, or from loading a model saved with `clean=True`." + ) - model1.predict(n=1) - model2.predict(n=2) + model_manual_save.fit(self.series, epochs=1) + model_auto_save.fit(self.series, epochs=1) - patch_save_model.assert_not_called() + # check that file was not created with manual save + assert not os.path.exists(os.path.join(model_dir, manual_name, "checkpoints")) + # check that file was created with automatic save + assert os.path.exists(os.path.join(model_dir, auto_name, "checkpoints")) - model1.save(path=os.path.join(tmpdir_fn, model_name)) - patch_save_model.assert_called() + # create manually saved model checkpoints folder + checkpoint_path_manual = os.path.join(model_dir, manual_name) + os.mkdir(checkpoint_path_manual) - def test_manual_save_and_load(self, tmpdir_fn): - """validate manual save with automatic save files by comparing output between the two""" + model_path_manual = os.path.join(checkpoint_path_manual, "checkpoint_0.pth.tar") + model_path_manual_ckpt = os.path.join( + checkpoint_path_manual, "checkpoint_0.pth.tar.ckpt" + ) - model_dir = os.path.join(tmpdir_fn) - manual_name = "test_save_manual" - auto_name = "test_save_automatic" - model_manual_save = RNNModel( - 12, - "RNN", - 10, - 10, - model_name=manual_name, - work_dir=tmpdir_fn, - save_checkpoints=False, - random_state=42, - **tfm_kwargs, - ) - model_auto_save = RNNModel( - 12, - "RNN", - 10, - 10, - model_name=auto_name, - work_dir=tmpdir_fn, - save_checkpoints=True, - random_state=42, - **tfm_kwargs, + # save manually clean model + training_series = model_manual_save.training_series.copy() + model_manual_save.save(model_path_manual, clean=clean) + assert model_manual_save.training_series == training_series + + assert os.path.exists(model_path_manual) + + # check that the PTL checkpoint path is also there + assert os.path.exists(model_path_manual_ckpt) + + # load manual save model and compare with automatic model results + pl_kwargs_load = {"accelerator": "cpu"} + model_manual_save = RNNModel.load( + model_path_manual, pl_trainer_kwargs=pl_kwargs_load + ) + + if clean: + # Training params are not saved with `clean=True` + assert model_manual_save.trainer is None + assert model_manual_save.training_series is None + assert model_manual_save.past_covariate_series is None + assert model_manual_save.future_covariate_series is None + assert model_manual_save.trainer_params == pl_kwargs_load + assert ( + model_manual_save._model_params["pl_trainer_kwargs"] == pl_kwargs_load ) - # save model without training - no_training_ckpt = "no_training.pth.tar" - no_training_ckpt_path = os.path.join(model_dir, no_training_ckpt) - model_manual_save.save(no_training_ckpt_path) - # check that model object file was created - assert os.path.exists(no_training_ckpt_path) - # check that the PyTorch Ligthning ckpt does not exist - assert not os.path.exists(no_training_ckpt_path + ".ckpt") - # informative exception about `fit()` not called + # Predicting without giving the series in args with pytest.raises(ValueError) as err: - no_train_model = RNNModel.load(no_training_ckpt_path) - no_train_model.predict(n=4) + model_manual_save.predict(n=4) assert str(err.value) == ( - "Input `series` must be provided. This is the result either from " - "fitting on multiple series, or from not having fit the model yet." + "Input `series` must be provided. This is the result either from fitting on multiple series, " + "from not having fit the model yet, or from loading a model saved with `clean=True`." ) + # Predicting while giving the training series in args should yield same prediction + assert model_manual_save.predict( + n=4, series=self.series + ) == model_auto_save.predict(n=4) - model_manual_save.fit(self.series, epochs=1) - model_auto_save.fit(self.series, epochs=1) - - # check that file was not created with manual save - assert not os.path.exists( - os.path.join(model_dir, manual_name, "checkpoints") + model_manual_save_custom_trainer = RNNModel.load( + model_path_manual, + pl_trainer_kwargs={"accelerator": "gpu", "enable_progress_bar": False}, ) - # check that file was created with automatic save - assert os.path.exists(os.path.join(model_dir, auto_name, "checkpoints")) - # create manually saved model checkpoints folder - checkpoint_path_manual = os.path.join(model_dir, manual_name) - os.mkdir(checkpoint_path_manual) + assert model_manual_save_custom_trainer.trainer_params == { + "accelerator": "gpu", + "enable_progress_bar": False, + } + assert model_manual_save_custom_trainer.model_params[ + "pl_trainer_kwargs" + ] == {"accelerator": "gpu", "enable_progress_bar": False} - checkpoint_file_name = "checkpoint_0.pth.tar" - model_path_manual = os.path.join( - checkpoint_path_manual, checkpoint_file_name - ) - checkpoint_file_name_cpkt = "checkpoint_0.pth.tar.ckpt" - model_path_manual_ckpt = os.path.join( - checkpoint_path_manual, checkpoint_file_name_cpkt - ) + else: + assert model_manual_save.predict(n=4) == model_auto_save.predict(n=4) - # save manually saved model - model_manual_save.save(model_path_manual) - assert os.path.exists(model_path_manual) + # load automatically saved model with manual load() and load_from_checkpoint() + model_auto_save1 = RNNModel.load_from_checkpoint( + model_name=auto_name, + work_dir=tmpdir_fn, + best=False, + map_location="cpu", + ) + model_auto_save1.to_cpu() + # compare loaded checkpoint with manual save + assert model_manual_save.predict( + n=4, series=self.series + ) == model_auto_save.predict(n=4) + + # save() model directly after load_from_checkpoint() + checkpoint_file_name_2 = "checkpoint_1.pth.tar" + checkpoint_file_name_cpkt_2 = checkpoint_file_name_2 + ".ckpt" + + model_path_manual_2 = os.path.join( + checkpoint_path_manual, checkpoint_file_name_2 + ) + model_path_manual_ckpt_2 = os.path.join( + checkpoint_path_manual, checkpoint_file_name_cpkt_2 + ) + model_auto_save2 = RNNModel.load_from_checkpoint( + model_name=auto_name, + work_dir=tmpdir_fn, + best=False, + map_location="cpu", + ) + # save model directly after loading, model has no trainer + model_auto_save2.save(model_path_manual_2, clean=clean) - # check that the PTL checkpoint path is also there - assert os.path.exists(model_path_manual_ckpt) + # assert original .ckpt checkpoint was correctly copied + assert os.path.exists(model_path_manual_ckpt_2) - # load manual save model and compare with automatic model results - model_manual_save = RNNModel.load(model_path_manual, map_location="cpu") - model_manual_save.to_cpu() - assert model_manual_save.predict(n=4) == model_auto_save.predict(n=4) + model_chained_load_save = RNNModel.load( + model_path_manual_2, pl_trainer_kwargs=pl_kwargs_load + ) - # load automatically saved model with manual load() and load_from_checkpoint() - model_auto_save1 = RNNModel.load_from_checkpoint( - model_name=auto_name, - work_dir=tmpdir_fn, - best=False, - map_location="cpu", - ) - model_auto_save1.to_cpu() - # compare loaded checkpoint with manual save - assert model_manual_save.predict(n=4) == model_auto_save1.predict(n=4) + # compare chained load_from_checkpoint() save() with manual save + assert model_chained_load_save.predict( + n=4, series=self.series + ) == model_manual_save.predict(n=4, series=self.series) + + @pytest.mark.parametrize("clean", [False, True]) + def test_manual_save_and_load_precision(self, tmpdir_fn, clean): + # test precision (type) of the model + + tfm_kwargs_32 = copy.deepcopy(tfm_kwargs) + tfm_kwargs_32["pl_trainer_kwargs"]["precision"] = "32-true" + + model_32_name = "test_save_32" + model_32 = RNNModel( + 12, + "RNN", + 10, + 10, + model_name=model_32_name, + work_dir=tmpdir_fn, + save_checkpoints=False, + random_state=42, + **tfm_kwargs_32, + ) - # save() model directly after load_from_checkpoint() - checkpoint_file_name_2 = "checkpoint_1.pth.tar" - checkpoint_file_name_cpkt_2 = checkpoint_file_name_2 + ".ckpt" + series_32 = self.series.astype(np.float32) + series_64 = self.series.astype(np.float64) - model_path_manual_2 = os.path.join( - checkpoint_path_manual, checkpoint_file_name_2 - ) - model_path_manual_ckpt_2 = os.path.join( - checkpoint_path_manual, checkpoint_file_name_cpkt_2 - ) - model_auto_save2 = RNNModel.load_from_checkpoint( - model_name=auto_name, - work_dir=tmpdir_fn, - best=False, - map_location="cpu", - ) - # save model directly after loading, model has no trainer - model_auto_save2.save(model_path_manual_2) + model_32.fit(series_32, epochs=1) - # assert original .ckpt checkpoint was correctly copied - assert os.path.exists(model_path_manual_ckpt_2) + model_32_path = os.path.join(tmpdir_fn, f"{model_32_name}.pth.tar") - model_chained_load_save = RNNModel.load( - model_path_manual_2, map_location="cpu" - ) + model_32.save(model_32_path, clean=clean) - # compare chained load_from_checkpoint() save() with manual save - assert model_chained_load_save.predict(n=4) == model_manual_save.predict( - n=4 - ) + model_32_loaded = RNNModel.load( + model_32_path, pl_trainer_kwargs={"accelerator": "cpu"} + ) - def test_valid_save_and_load_weights_with_different_params(self, tmpdir_fn): - """ - Verify that save/load does not break encoders. - - Note: since load_weights() calls load_weights_from_checkpoint(), it will be used - for all but one test. - Note: Using DLinear since it supports both past and future covariates - """ - - def create_model(**kwargs): - return DLinearModel( - input_chunk_length=4, - output_chunk_length=1, - **kwargs, - **tfm_kwargs, - ) + assert model_32_loaded.predict(n=4, series=series_32) == model_32.predict(n=4) + with pytest.raises(ValueError) as err: + model_32_loaded.predict(n=4, series=series_64) + assert str(err.value) == ( + "input must have the type torch.float32, got type torch.float64" + ) - model_dir = os.path.join(tmpdir_fn) - manual_name = "save_manual" - # create manually saved model checkpoints folder - checkpoint_path_manual = os.path.join(model_dir, manual_name) - os.mkdir(checkpoint_path_manual) - checkpoint_file_name = "checkpoint_0.pth.tar" - model_path_manual = os.path.join( - checkpoint_path_manual, checkpoint_file_name - ) - model = create_model() - model.fit(self.series, epochs=1) - model.save(model_path_manual) - - kwargs_valid = [ - {"optimizer_cls": torch.optim.SGD}, - {"optimizer_kwargs": {"lr": 0.1}}, - ] - # check that all models can be created with different valid kwargs - for kwargs_ in kwargs_valid: - model_new = create_model(**kwargs_) - model_new.load_weights(model_path_manual) - - def test_save_and_load_weights_w_encoders(self, tmpdir_fn): - """ - Verify that save/load does not break encoders. - - Note: since load_weights() calls load_weights_from_checkpoint(), it will be used - for all but one test. - Note: Using DLinear since it supports both past and future covariates - """ - model_dir = os.path.join(tmpdir_fn) - manual_name = "save_manual" - auto_name = "save_auto" - auto_name_other = "save_auto_other" - # create manually saved model checkpoints folder - checkpoint_path_manual = os.path.join(model_dir, manual_name) - os.mkdir(checkpoint_path_manual) - checkpoint_file_name = "checkpoint_0.pth.tar" - model_path_manual = os.path.join( - checkpoint_path_manual, checkpoint_file_name - ) + def test_load_accelerator(self, tmpdir_fn): + pass - # define encoders sets - encoders_past = { - "datetime_attribute": {"past": ["day"]}, - "transformer": Scaler(), - } - encoders_other_past = { - "datetime_attribute": {"past": ["hour"]}, - "transformer": Scaler(), - } - encoders_past_noscaler = { - "datetime_attribute": {"past": ["day"]}, - } - encoders_past_other_transformer = { - "datetime_attribute": {"past": ["day"]}, - "transformer": BoxCox(), - } - encoders_2_past = { - "datetime_attribute": {"past": ["hour", "day"]}, - "transformer": Scaler(), - } - encoders_past_n_future = { - "datetime_attribute": {"past": ["day"], "future": ["dayofweek"]}, - "transformer": Scaler(), - } + def test_valid_save_and_load_weights_with_different_params(self, tmpdir_fn): + """ + Verify that save/load does not break encoders. - model_auto_save = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name=auto_name, - save_checkpoints=True, - add_encoders=encoders_past, - ) - model_auto_save.fit(self.series, epochs=1) + Note: since load_weights() calls load_weights_from_checkpoint(), it will be used + for all but one test. + Note: Using DLinear since it supports both past and future covariates + """ - model_manual_save = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name=manual_name, - save_checkpoints=False, - add_encoders=encoders_past, + def create_model(**kwargs): + return DLinearModel( + input_chunk_length=4, + output_chunk_length=1, + **kwargs, + **tfm_kwargs, ) - model_manual_save.fit(self.series, epochs=1) - model_manual_save.save(model_path_manual) - model_auto_save_other = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name=auto_name_other, - save_checkpoints=True, - add_encoders=encoders_other_past, - ) - model_auto_save_other.fit(self.series, epochs=1) + model_dir = os.path.join(tmpdir_fn) + manual_name = "save_manual" + # create manually saved model checkpoints folder + checkpoint_path_manual = os.path.join(model_dir, manual_name) + os.mkdir(checkpoint_path_manual) + checkpoint_file_name = "checkpoint_0.pth.tar" + model_path_manual = os.path.join(checkpoint_path_manual, checkpoint_file_name) + model = create_model() + model.fit(self.series, epochs=1) + model.save(model_path_manual) + + kwargs_valid = [ + {"optimizer_cls": torch.optim.SGD}, + {"optimizer_kwargs": {"lr": 0.1}}, + ] + # check that all models can be created with different valid kwargs + for kwargs_ in kwargs_valid: + model_new = create_model(**kwargs_) + model_new.load_weights(model_path_manual) + + def test_save_and_load_weights_w_encoders(self, tmpdir_fn): + """ + Verify that save/load does not break encoders. + + Note: since load_weights() calls load_weights_from_checkpoint(), it will be used + for all but one test. + Note: Using DLinear since it supports both past and future covariates + """ + model_dir = os.path.join(tmpdir_fn) + manual_name = "save_manual" + auto_name = "save_auto" + auto_name_other = "save_auto_other" + # create manually saved model checkpoints folder + checkpoint_path_manual = os.path.join(model_dir, manual_name) + os.mkdir(checkpoint_path_manual) + checkpoint_file_name = "checkpoint_0.pth.tar" + model_path_manual = os.path.join(checkpoint_path_manual, checkpoint_file_name) + + # define encoders sets + encoders_past = { + "datetime_attribute": {"past": ["day"]}, + "transformer": Scaler(), + } + encoders_other_past = { + "datetime_attribute": {"past": ["hour"]}, + "transformer": Scaler(), + } + encoders_past_noscaler = { + "datetime_attribute": {"past": ["day"]}, + } + encoders_past_other_transformer = { + "datetime_attribute": {"past": ["day"]}, + "transformer": BoxCox(lmbda=-0.7), + } + encoders_2_past = { + "datetime_attribute": {"past": ["hour", "day"]}, + "transformer": Scaler(), + } + encoders_past_n_future = { + "datetime_attribute": {"past": ["day"], "future": ["dayofweek"]}, + "transformer": Scaler(), + } + + model_auto_save = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name=auto_name, + save_checkpoints=True, + add_encoders=encoders_past, + ) + model_auto_save.fit(self.series, epochs=1) + + model_manual_save = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name=manual_name, + save_checkpoints=False, + add_encoders=encoders_past, + ) + model_manual_save.fit(self.series, epochs=1) + model_manual_save.save(model_path_manual) + + model_auto_save_other = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name=auto_name_other, + save_checkpoints=True, + add_encoders=encoders_other_past, + ) + model_auto_save_other.fit(self.series, epochs=1) - # prediction are different when using different encoders - assert model_auto_save.predict(n=4) != model_auto_save_other.predict(n=4) + # prediction are different when using different encoders + assert model_auto_save.predict(n=4) != model_auto_save_other.predict(n=4) - # model with undeclared encoders - model_no_enc = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, model_name="no_encoder", add_encoders=None - ) - # weights were trained with encoders, new model must be instantiated with encoders - with pytest.raises(ValueError): - model_no_enc.load_weights_from_checkpoint( - auto_name, - work_dir=tmpdir_fn, - best=False, - load_encoders=False, - map_location="cpu", - ) - # overwritte undeclared encoders + # model with undeclared encoders + model_no_enc = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, model_name="no_encoder", add_encoders=None + ) + # weights were trained with encoders, new model must be instantiated with encoders + with pytest.raises(ValueError): model_no_enc.load_weights_from_checkpoint( auto_name, work_dir=tmpdir_fn, best=False, - load_encoders=True, + load_encoders=False, map_location="cpu", ) - self.helper_equality_encoders( - model_auto_save.add_encoders, model_no_enc.add_encoders - ) - self.helper_equality_encoders_transfo( - model_auto_save.add_encoders, model_no_enc.add_encoders - ) - # cannot directly verify equality between encoders, using predict as proxy - assert model_auto_save.predict(n=4) == model_no_enc.predict( - n=4, series=self.series - ) + # overwrite undeclared encoders + model_no_enc.load_weights_from_checkpoint( + auto_name, + work_dir=tmpdir_fn, + best=False, + load_encoders=True, + map_location="cpu", + ) + self.helper_equality_encoders( + model_auto_save.add_encoders, model_no_enc.add_encoders + ) + self.helper_equality_encoders_transfo( + model_auto_save.add_encoders, model_no_enc.add_encoders + ) + # cannot directly verify equality between encoders, using predict as proxy + assert model_auto_save.predict(n=4) == model_no_enc.predict( + n=4, series=self.series + ) - # model with identical encoders (fittable) - model_same_enc_noload = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="same_encoder_noload", - add_encoders=encoders_past, - ) - model_same_enc_noload.load_weights( + # model with identical encoders (fittable) + model_same_enc_noload = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="same_encoder_noload", + add_encoders=encoders_past, + ) + model_same_enc_noload.load_weights( + model_path_manual, + load_encoders=False, + map_location="cpu", + ) + # cannot predict because of un-fitted encoder + with pytest.raises(ValueError): + model_same_enc_noload.predict(n=4, series=self.series) + + model_same_enc_load = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="same_encoder_load", + add_encoders=encoders_past, + ) + model_same_enc_load.load_weights( + model_path_manual, + load_encoders=True, + map_location="cpu", + ) + assert model_manual_save.predict(n=4) == model_same_enc_load.predict( + n=4, series=self.series + ) + + # model with different encoders (fittable) + model_other_enc_load = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="other_encoder_load", + add_encoders=encoders_other_past, + ) + # cannot overwrite different declared encoders + with pytest.raises(ValueError): + model_other_enc_load.load_weights( model_path_manual, - load_encoders=False, + load_encoders=True, map_location="cpu", ) - # cannot predict because of un-fitted encoder - with pytest.raises(ValueError): - model_same_enc_noload.predict(n=4, series=self.series) - model_same_enc_load = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="same_encoder_load", - add_encoders=encoders_past, - ) - model_same_enc_load.load_weights( + # model with different encoders but same dimensions (fittable) + model_other_enc_noload = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="other_encoder_noload", + add_encoders=encoders_other_past, + ) + model_other_enc_noload.load_weights( + model_path_manual, + load_encoders=False, + map_location="cpu", + ) + self.helper_equality_encoders( + model_other_enc_noload.add_encoders, encoders_other_past + ) + self.helper_equality_encoders_transfo( + model_other_enc_noload.add_encoders, encoders_other_past + ) + # new encoders were instantiated + assert isinstance(model_other_enc_noload.encoders, SequentialEncoder) + # since fit() was not called, new fittable encoders were not trained + with pytest.raises(ValueError): + model_other_enc_noload.predict(n=4, series=self.series) + + # predict() can be called after fit() + model_other_enc_noload.fit(self.series, epochs=1) + model_other_enc_noload.predict(n=4, series=self.series) + + # model with same encoders but no scaler (non-fittable) + model_new_enc_noscaler_noload = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="same_encoder_noscaler", + add_encoders=encoders_past_noscaler, + ) + model_new_enc_noscaler_noload.load_weights( + model_path_manual, + load_encoders=False, + map_location="cpu", + ) + + self.helper_equality_encoders( + model_new_enc_noscaler_noload.add_encoders, encoders_past_noscaler + ) + self.helper_equality_encoders_transfo( + model_new_enc_noscaler_noload.add_encoders, encoders_past_noscaler + ) + # predict() can be called directly since new encoders don't contain scaler + model_new_enc_noscaler_noload.predict(n=4, series=self.series) + + # model with same encoders but different transformer (fittable) + model_new_enc_other_transformer = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="same_encoder_other_transform", + add_encoders=encoders_past_other_transformer, + ) + # cannot overwrite different declared encoders + with pytest.raises(ValueError): + model_new_enc_other_transformer.load_weights( model_path_manual, load_encoders=True, map_location="cpu", ) - assert model_manual_save.predict(n=4) == model_same_enc_load.predict( - n=4, series=self.series - ) - # model with different encoders (fittable) - model_other_enc_load = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="other_encoder_load", - add_encoders=encoders_other_past, - ) - # cannot overwritte different declared encoders - with pytest.raises(ValueError): - model_other_enc_load.load_weights( - model_path_manual, - load_encoders=True, - map_location="cpu", - ) + model_new_enc_other_transformer.load_weights( + model_path_manual, + load_encoders=False, + map_location="cpu", + ) + # since fit() was not called, new fittable encoders were not trained + with pytest.raises(ValueError): + model_new_enc_other_transformer.predict(n=4, series=self.series) - # model with different encoders but same dimensions (fittable) - model_other_enc_noload = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="other_encoder_noload", - add_encoders=encoders_other_past, + # predict() can be called after fit() + model_new_enc_other_transformer.fit(self.series, epochs=1) + model_new_enc_other_transformer.predict(n=4, series=self.series) + + # model with encoders containing more components (fittable) + model_new_enc_2_past = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="encoder_2_components_past", + add_encoders=encoders_2_past, + ) + # cannot overwrite different declared encoders + with pytest.raises(ValueError): + model_new_enc_2_past.load_weights( + model_path_manual, + load_encoders=True, + map_location="cpu", ) - model_other_enc_noload.load_weights( + # new encoders have one additional past component + with pytest.raises(ValueError): + model_new_enc_2_past.load_weights( model_path_manual, load_encoders=False, map_location="cpu", ) - self.helper_equality_encoders( - model_other_enc_noload.add_encoders, encoders_other_past - ) - self.helper_equality_encoders_transfo( - model_other_enc_noload.add_encoders, encoders_other_past - ) - # new encoders were instantiated - assert isinstance(model_other_enc_noload.encoders, SequentialEncoder) - # since fit() was not called, new fittable encoders were not trained - with pytest.raises(ValueError): - model_other_enc_noload.predict(n=4, series=self.series) - # predict() can be called after fit() - model_other_enc_noload.fit(self.series, epochs=1) - model_other_enc_noload.predict(n=4, series=self.series) - - # model with same encoders but no scaler (non-fittable) - model_new_enc_noscaler_noload = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="same_encoder_noscaler", - add_encoders=encoders_past_noscaler, + # model with encoders containing past and future covs (fittable) + model_new_enc_past_n_future = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="encoder_past_n_future", + add_encoders=encoders_past_n_future, + ) + # cannot overwrite different declared encoders + with pytest.raises(ValueError): + model_new_enc_past_n_future.load_weights( + model_path_manual, + load_encoders=True, + map_location="cpu", ) - model_new_enc_noscaler_noload.load_weights( + # identical past components, but different future components + with pytest.raises(ValueError): + model_new_enc_past_n_future.load_weights( model_path_manual, load_encoders=False, map_location="cpu", ) - self.helper_equality_encoders( - model_new_enc_noscaler_noload.add_encoders, encoders_past_noscaler - ) - self.helper_equality_encoders_transfo( - model_new_enc_noscaler_noload.add_encoders, encoders_past_noscaler - ) - # predict() can be called directly since new encoders don't contain scaler - model_new_enc_noscaler_noload.predict(n=4, series=self.series) + def test_save_and_load_weights_w_likelihood(self, tmpdir_fn): + """ + Verify that save/load does not break likelihood. + + Note: since load_weights() calls load_weights_from_checkpoint(), it will be used + for all but one test. + Note: Using DLinear since it supports both past and future covariates + """ + model_dir = os.path.join(tmpdir_fn) + manual_name = "save_manual" + auto_name = "save_auto" + # create manually saved model checkpoints folder + checkpoint_path_manual = os.path.join(model_dir, manual_name) + os.mkdir(checkpoint_path_manual) + checkpoint_file_name = "checkpoint_0.pth.tar" + model_path_manual = os.path.join(checkpoint_path_manual, checkpoint_file_name) + + model_auto_save = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name=auto_name, + save_checkpoints=True, + likelihood=GaussianLikelihood(prior_mu=0.5), + ) + model_auto_save.fit(self.series, epochs=1) + pred_auto = model_auto_save.predict(n=4, series=self.series) + + model_manual_save = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name=manual_name, + save_checkpoints=False, + likelihood=GaussianLikelihood(prior_mu=0.5), + ) + model_manual_save.fit(self.series, epochs=1) + model_manual_save.save(model_path_manual) + pred_manual = model_manual_save.predict(n=4, series=self.series) + + # predictions are identical when using the same likelihood + assert np.array_equal(pred_auto.values(), pred_manual.values()) + + # model with identical likelihood + model_same_likelihood = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="same_likelihood", + likelihood=GaussianLikelihood(prior_mu=0.5), + ) + model_same_likelihood.load_weights(model_path_manual, map_location="cpu") + model_same_likelihood.predict(n=4, series=self.series) + # cannot check predictions since this model is not fitted, random state is different + + # loading models weights with respective methods + model_manual_same_likelihood = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="same_likelihood", + likelihood=GaussianLikelihood(prior_mu=0.5), + ) + model_manual_same_likelihood.load_weights(model_path_manual, map_location="cpu") + preds_manual_from_weights = model_manual_same_likelihood.predict( + n=4, series=self.series + ) - # model with same encoders but different transformer (fittable) - model_new_enc_other_transformer = self.helper_create_DLinearModel( + model_auto_same_likelihood = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="same_likelihood", + likelihood=GaussianLikelihood(prior_mu=0.5), + ) + model_auto_same_likelihood.load_weights_from_checkpoint( + auto_name, work_dir=tmpdir_fn, best=False, map_location="cpu" + ) + preds_auto_from_weights = model_auto_same_likelihood.predict( + n=4, series=self.series + ) + # check that weights from checkpoint give identical predictions as weights from manual save + assert preds_manual_from_weights == preds_auto_from_weights + # model with explicitly no likelihood + model_no_likelihood = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, model_name="no_likelihood", likelihood=None + ) + with pytest.raises(ValueError) as error_msg: + model_no_likelihood.load_weights_from_checkpoint( + auto_name, work_dir=tmpdir_fn, - model_name="same_encoder_other_transform", - add_encoders=encoders_past_other_transformer, + best=False, + map_location="cpu", ) - # cannot overwritte different declared encoders - with pytest.raises(ValueError): - model_new_enc_other_transformer.load_weights( - model_path_manual, - load_encoders=True, - map_location="cpu", - ) + assert str(error_msg.value).startswith( + "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" + "incorrect" + ) - model_new_enc_other_transformer.load_weights( - model_path_manual, - load_encoders=False, + # model with missing likelihood (as if user forgot them) + model_no_likelihood_bis = DLinearModel( + input_chunk_length=4, + output_chunk_length=1, + model_name="no_likelihood_bis", + add_encoders=None, + work_dir=tmpdir_fn, + save_checkpoints=False, + random_state=42, + force_reset=True, + n_epochs=1, + # likelihood=likelihood, + **tfm_kwargs, + ) + with pytest.raises(ValueError) as error_msg: + model_no_likelihood_bis.load_weights_from_checkpoint( + auto_name, + work_dir=tmpdir_fn, + best=False, map_location="cpu", ) - # since fit() was not called, new fittable encoders were not trained - with pytest.raises(ValueError): - model_new_enc_other_transformer.predict(n=4, series=self.series) + assert str(error_msg.value).startswith( + "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" + "missing" + ) - # predict() can be called after fit() - model_new_enc_other_transformer.fit(self.series, epochs=1) - model_new_enc_other_transformer.predict(n=4, series=self.series) + # model with a different likelihood + model_other_likelihood = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="other_likelihood", + likelihood=LaplaceLikelihood(), + ) + with pytest.raises(ValueError) as error_msg: + model_other_likelihood.load_weights(model_path_manual, map_location="cpu") + assert str(error_msg.value).startswith( + "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" + "incorrect" + ) - # model with encoders containing more components (fittable) - model_new_enc_2_past = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="encoder_2_components_past", - add_encoders=encoders_2_past, + # model with the same likelihood but different parameters + model_same_likelihood_other_prior = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + model_name="same_likelihood_other_prior", + likelihood=GaussianLikelihood(), + ) + with pytest.raises(ValueError) as error_msg: + model_same_likelihood_other_prior.load_weights( + model_path_manual, map_location="cpu" ) - # cannot overwritte different declared encoders - with pytest.raises(ValueError): - model_new_enc_2_past.load_weights( - model_path_manual, - load_encoders=True, - map_location="cpu", - ) - # new encoders have one additional past component - with pytest.raises(ValueError): - model_new_enc_2_past.load_weights( - model_path_manual, - load_encoders=False, - map_location="cpu", - ) + assert str(error_msg.value).startswith( + "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" + "incorrect" + ) - # model with encoders containing past and future covs (fittable) - model_new_enc_past_n_future = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="encoder_past_n_future", - add_encoders=encoders_past_n_future, - ) - # cannot overwritte different declared encoders - with pytest.raises(ValueError): - model_new_enc_past_n_future.load_weights( - model_path_manual, - load_encoders=True, - map_location="cpu", - ) - # identical past components, but different future components - with pytest.raises(ValueError): - model_new_enc_past_n_future.load_weights( - model_path_manual, - load_encoders=False, - map_location="cpu", - ) + def test_load_weights_params_check(self, tmpdir_fn): + """ + Verify that the method comparing the parameters between the saved model and the loading model + behave as expected, used to return meaningful error message instead of the torch.load ones. + """ + model_name = "params_check" + ckpt_path = os.path.join(tmpdir_fn, f"{model_name}.pt") + # barebone model + model = DLinearModel( + input_chunk_length=4, output_chunk_length=1, n_epochs=1, **tfm_kwargs + ) + model.fit(self.series[:10]) + model.save(ckpt_path) - def test_save_and_load_weights_w_likelihood(self, tmpdir_fn): - """ - Verify that save/load does not break likelihood. - - Note: since load_weights() calls load_weights_from_checkpoint(), it will be used - for all but one test. - Note: Using DLinear since it supports both past and future covariates - """ - model_dir = os.path.join(tmpdir_fn) - manual_name = "save_manual" - auto_name = "save_auto" - # create manually saved model checkpoints folder - checkpoint_path_manual = os.path.join(model_dir, manual_name) - os.mkdir(checkpoint_path_manual) - checkpoint_file_name = "checkpoint_0.pth.tar" - model_path_manual = os.path.join( - checkpoint_path_manual, checkpoint_file_name - ) + # identical model + loading_model = DLinearModel( + input_chunk_length=4, output_chunk_length=1, **tfm_kwargs + ) + loading_model.load_weights(ckpt_path) + + # different optimizer + loading_model = DLinearModel( + input_chunk_length=4, + output_chunk_length=1, + optimizer_cls=torch.optim.AdamW, + **tfm_kwargs, + ) + loading_model.load_weights(ckpt_path) + + model_summary_kwargs = { + "pl_trainer_kwargs": dict( + {"enable_model_sumamry": False}, **tfm_kwargs["pl_trainer_kwargs"] + ) + } + # different pl_trainer_kwargs + loading_model = DLinearModel( + input_chunk_length=4, + output_chunk_length=1, + **model_summary_kwargs, + ) + loading_model.load_weights(ckpt_path) - model_auto_save = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name=auto_name, - save_checkpoints=True, - likelihood=GaussianLikelihood(prior_mu=0.5), - ) - model_auto_save.fit(self.series, epochs=1) - pred_auto = model_auto_save.predict(n=4, series=self.series) + # different input_chunk_length (tfm parameter) + loading_model = DLinearModel( + input_chunk_length=4 + 1, output_chunk_length=1, **tfm_kwargs + ) + with pytest.raises(ValueError) as error_msg: + loading_model.load_weights(ckpt_path) + assert str(error_msg.value).startswith( + "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" + "incorrect" + ) - model_manual_save = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name=manual_name, - save_checkpoints=False, - likelihood=GaussianLikelihood(prior_mu=0.5), - ) - model_manual_save.fit(self.series, epochs=1) - model_manual_save.save(model_path_manual) - pred_manual = model_manual_save.predict(n=4, series=self.series) + # different kernel size (cls specific parameter) + loading_model = DLinearModel( + input_chunk_length=4, + output_chunk_length=1, + kernel_size=10, + **tfm_kwargs, + ) + with pytest.raises(ValueError) as error_msg: + loading_model.load_weights(ckpt_path) + assert str(error_msg.value).startswith( + "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" + "incorrect" + ) - # predictions are identical when using the same likelihood - assert np.array_equal(pred_auto.values(), pred_manual.values()) + def test_create_instance_new_model_no_name_set(self, tmpdir_fn): + RNNModel(12, "RNN", 10, 10, work_dir=tmpdir_fn, **tfm_kwargs) + # no exception is raised + + def test_create_instance_existing_model_with_name_no_fit(self, tmpdir_fn): + model_name = "test_model" + RNNModel( + 12, + "RNN", + 10, + 10, + work_dir=tmpdir_fn, + model_name=model_name, + **tfm_kwargs, + ) + # no exception is raised - # model with identical likelihood - model_same_likelihood = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="same_likelihood", - likelihood=GaussianLikelihood(prior_mu=0.5), - ) - model_same_likelihood.load_weights(model_path_manual, map_location="cpu") - model_same_likelihood.predict(n=4, series=self.series) - # cannot check predictions since this model is not fitted, random state is different + @patch( + "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel.reset_model" + ) + def test_create_instance_existing_model_with_name_force( + self, patch_reset_model, tmpdir_fn + ): + model_name = "test_model" + RNNModel( + 12, + "RNN", + 10, + 10, + work_dir=tmpdir_fn, + model_name=model_name, + **tfm_kwargs, + ) + # no exception is raised + # since no fit, there is no data stored for the model, hence `force_reset` does noting + + RNNModel( + 12, + "RNN", + 10, + 10, + work_dir=tmpdir_fn, + model_name=model_name, + force_reset=True, + **tfm_kwargs, + ) + patch_reset_model.assert_not_called() - # loading models weights with respective methods - model_manual_same_likelihood = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="same_likelihood", - likelihood=GaussianLikelihood(prior_mu=0.5), - ) - model_manual_same_likelihood.load_weights( - model_path_manual, map_location="cpu" - ) - preds_manual_from_weights = model_manual_same_likelihood.predict( - n=4, series=self.series - ) + @patch( + "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel.reset_model" + ) + def test_create_instance_existing_model_with_name_force_fit_with_reset( + self, patch_reset_model, tmpdir_fn + ): + model_name = "test_model" + model1 = RNNModel( + 12, + "RNN", + 10, + 10, + work_dir=tmpdir_fn, + model_name=model_name, + save_checkpoints=True, + **tfm_kwargs, + ) + # no exception is raised + + model1.fit(self.series, epochs=1) + + RNNModel( + 12, + "RNN", + 10, + 10, + work_dir=tmpdir_fn, + model_name=model_name, + save_checkpoints=True, + force_reset=True, + **tfm_kwargs, + ) + patch_reset_model.assert_called_once() + + # TODO for PTL: currently we (have to (?)) create a mew PTL trainer object every time fit() is called which + # resets some of the model's attributes such as epoch and step counts. We have check whether there is another + # way of doing this. + + # n_epochs=20, fit|epochs=None, epochs_trained=0 - train for 20 epochs + def test_train_from_0_n_epochs_20_no_fit_epochs(self): + model1 = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=20, + **tfm_kwargs, + ) - model_auto_same_likelihood = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="same_likelihood", - likelihood=GaussianLikelihood(prior_mu=0.5), - ) - model_auto_same_likelihood.load_weights_from_checkpoint( - auto_name, work_dir=tmpdir_fn, best=False, map_location="cpu" - ) - preds_auto_from_weights = model_auto_same_likelihood.predict( - n=4, series=self.series - ) - # check that weights from checkpoint give identical predictions as weights from manual save - assert preds_manual_from_weights == preds_auto_from_weights - # model with explicitely no likelihood - model_no_likelihood = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, model_name="no_likelihood", likelihood=None - ) - with pytest.raises(ValueError) as error_msg: - model_no_likelihood.load_weights_from_checkpoint( - auto_name, - work_dir=tmpdir_fn, - best=False, - map_location="cpu", - ) - assert str(error_msg.value).startswith( - "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" - "incorrect" - ) + model1.fit(self.series) - # model with missing likelihood (as if user forgot them) - model_no_likelihood_bis = DLinearModel( - input_chunk_length=4, - output_chunk_length=1, - model_name="no_likelihood_bis", - add_encoders=None, - work_dir=tmpdir_fn, - save_checkpoints=False, - random_state=42, - force_reset=True, - n_epochs=1, - # likelihood=likelihood, - **tfm_kwargs, - ) - with pytest.raises(ValueError) as error_msg: - model_no_likelihood_bis.load_weights_from_checkpoint( - auto_name, - work_dir=tmpdir_fn, - best=False, - map_location="cpu", - ) - assert str(error_msg.value).startswith( - "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" - "missing" - ) + assert 20 == model1.epochs_trained - # model with a different likelihood - model_other_likelihood = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="other_likelihood", - likelihood=LaplaceLikelihood(), - ) - with pytest.raises(ValueError) as error_msg: - model_other_likelihood.load_weights( - model_path_manual, map_location="cpu" - ) - assert str(error_msg.value).startswith( - "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" - "incorrect" - ) - - # model with the same likelihood but different parameters - model_same_likelihood_other_prior = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - model_name="same_likelihood_other_prior", - likelihood=GaussianLikelihood(), - ) - with pytest.raises(ValueError) as error_msg: - model_same_likelihood_other_prior.load_weights( - model_path_manual, map_location="cpu" - ) - assert str(error_msg.value).startswith( - "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" - "incorrect" - ) - - def test_load_weights_params_check(self, tmpdir_fn): - """ - Verify that the method comparing the parameters between the saved model and the loading model - behave as expected, used to return meaningful error message instead of the torch.load ones. - """ - model_name = "params_check" - ckpt_path = os.path.join(tmpdir_fn, f"{model_name}.pt") - # barebone model - model = DLinearModel( - input_chunk_length=4, output_chunk_length=1, n_epochs=1, **tfm_kwargs - ) - model.fit(self.series[:10]) - model.save(ckpt_path) - - # identical model - loading_model = DLinearModel( - input_chunk_length=4, output_chunk_length=1, **tfm_kwargs - ) - loading_model.load_weights(ckpt_path) - - # different optimizer - loading_model = DLinearModel( - input_chunk_length=4, - output_chunk_length=1, - optimizer_cls=torch.optim.AdamW, - **tfm_kwargs, - ) - loading_model.load_weights(ckpt_path) - - model_summary_kwargs = { - "pl_trainer_kwargs": dict( - {"enable_model_sumamry": False}, **tfm_kwargs["pl_trainer_kwargs"] - ) - } - # different pl_trainer_kwargs - loading_model = DLinearModel( - input_chunk_length=4, - output_chunk_length=1, - **model_summary_kwargs, - ) - loading_model.load_weights(ckpt_path) - - # different input_chunk_length (tfm parameter) - loading_model = DLinearModel( - input_chunk_length=4 + 1, output_chunk_length=1, **tfm_kwargs - ) - with pytest.raises(ValueError) as error_msg: - loading_model.load_weights(ckpt_path) - assert str(error_msg.value).startswith( - "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" - "incorrect" - ) - - # different kernel size (cls specific parameter) - loading_model = DLinearModel( - input_chunk_length=4, - output_chunk_length=1, - kernel_size=10, - **tfm_kwargs, - ) - with pytest.raises(ValueError) as error_msg: - loading_model.load_weights(ckpt_path) - assert str(error_msg.value).startswith( - "The values of the hyper-parameters in the model and loaded checkpoint should be identical.\n" - "incorrect" - ) - - def test_create_instance_new_model_no_name_set(self, tmpdir_fn): - RNNModel(12, "RNN", 10, 10, work_dir=tmpdir_fn, **tfm_kwargs) - # no exception is raised - - def test_create_instance_existing_model_with_name_no_fit(self, tmpdir_fn): - model_name = "test_model" - RNNModel( - 12, - "RNN", - 10, - 10, - work_dir=tmpdir_fn, - model_name=model_name, - **tfm_kwargs, - ) - # no exception is raised - - @patch( - "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel.reset_model" + # n_epochs = 20, fit|epochs=None, epochs_trained=20 - train for another 20 epochs + def test_train_from_20_n_epochs_40_no_fit_epochs(self): + model1 = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=20, + **tfm_kwargs, ) - def test_create_instance_existing_model_with_name_force( - self, patch_reset_model, tmpdir_fn - ): - model_name = "test_model" - RNNModel( - 12, - "RNN", - 10, - 10, - work_dir=tmpdir_fn, - model_name=model_name, - **tfm_kwargs, - ) - # no exception is raised - # since no fit, there is no data stored for the model, hence `force_reset` does noting - - RNNModel( - 12, - "RNN", - 10, - 10, - work_dir=tmpdir_fn, - model_name=model_name, - force_reset=True, - **tfm_kwargs, - ) - patch_reset_model.assert_not_called() - @patch( - "darts.models.forecasting.torch_forecasting_model.TorchForecastingModel.reset_model" + model1.fit(self.series) + assert 20 == model1.epochs_trained + + model1.fit(self.series) + assert 20 == model1.epochs_trained + + # n_epochs = 20, fit|epochs=None, epochs_trained=10 - train for another 20 epochs + def test_train_from_10_n_epochs_20_no_fit_epochs(self): + model1 = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=20, + **tfm_kwargs, ) - def test_create_instance_existing_model_with_name_force_fit_with_reset( - self, patch_reset_model, tmpdir_fn - ): - model_name = "test_model" - model1 = RNNModel( - 12, - "RNN", - 10, - 10, - work_dir=tmpdir_fn, - model_name=model_name, - save_checkpoints=True, - **tfm_kwargs, - ) - # no exception is raised - model1.fit(self.series, epochs=1) - - RNNModel( - 12, - "RNN", - 10, - 10, - work_dir=tmpdir_fn, - model_name=model_name, - save_checkpoints=True, - force_reset=True, - **tfm_kwargs, - ) - patch_reset_model.assert_called_once() - - # TODO for PTL: currently we (have to (?)) create a mew PTL trainer object every time fit() is called which - # resets some of the model's attributes such as epoch and step counts. We have check whether there is another - # way of doing this. - - # n_epochs=20, fit|epochs=None, epochs_trained=0 - train for 20 epochs - def test_train_from_0_n_epochs_20_no_fit_epochs(self): - model1 = RNNModel( - 12, - "RNN", - 10, - 10, - n_epochs=20, - **tfm_kwargs, - ) - - model1.fit(self.series) - - assert 20 == model1.epochs_trained - - # n_epochs = 20, fit|epochs=None, epochs_trained=20 - train for another 20 epochs - def test_train_from_20_n_epochs_40_no_fit_epochs(self): - model1 = RNNModel( - 12, - "RNN", - 10, - 10, - n_epochs=20, - **tfm_kwargs, - ) - - model1.fit(self.series) - assert 20 == model1.epochs_trained - - model1.fit(self.series) - assert 20 == model1.epochs_trained - - # n_epochs = 20, fit|epochs=None, epochs_trained=10 - train for another 20 epochs - def test_train_from_10_n_epochs_20_no_fit_epochs(self): - model1 = RNNModel( - 12, - "RNN", - 10, - 10, - n_epochs=20, - **tfm_kwargs, - ) - - # simulate the case that user interrupted training with Ctrl-C after 10 epochs - model1.fit(self.series, epochs=10) - assert 10 == model1.epochs_trained - - model1.fit(self.series) - assert 20 == model1.epochs_trained - - # n_epochs = 20, fit|epochs=15, epochs_trained=10 - train for 15 epochs - def test_train_from_10_n_epochs_20_fit_15_epochs(self): - model1 = RNNModel( - 12, - "RNN", - 10, - 10, - n_epochs=20, - **tfm_kwargs, - ) - - # simulate the case that user interrupted training with Ctrl-C after 10 epochs - model1.fit(self.series, epochs=10) - assert 10 == model1.epochs_trained + # simulate the case that user interrupted training with Ctrl-C after 10 epochs + model1.fit(self.series, epochs=10) + assert 10 == model1.epochs_trained + + model1.fit(self.series) + assert 20 == model1.epochs_trained + + # n_epochs = 20, fit|epochs=15, epochs_trained=10 - train for 15 epochs + def test_train_from_10_n_epochs_20_fit_15_epochs(self): + model1 = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=20, + **tfm_kwargs, + ) - model1.fit(self.series, epochs=15) - assert 15 == model1.epochs_trained + # simulate the case that user interrupted training with Ctrl-C after 10 epochs + model1.fit(self.series, epochs=10) + assert 10 == model1.epochs_trained + + model1.fit(self.series, epochs=15) + assert 15 == model1.epochs_trained + + def test_load_weights_from_checkpoint(self, tmpdir_fn): + ts_training, ts_test = self.series.split_before(90) + original_model_name = "original" + retrained_model_name = "retrained" + # original model, checkpoints are saved + model = RNNModel( + 12, + "RNN", + 5, + 1, + n_epochs=5, + work_dir=tmpdir_fn, + save_checkpoints=True, + model_name=original_model_name, + random_state=1, + **tfm_kwargs, + ) + model.fit(ts_training) + original_preds = model.predict(10) + original_mape = mape(original_preds, ts_test) + + # load last checkpoint of original model, train it for 2 additional epochs + model_rt = RNNModel( + 12, + "RNN", + 5, + 1, + n_epochs=5, + work_dir=tmpdir_fn, + model_name=retrained_model_name, + random_state=1, + **tfm_kwargs, + ) + model_rt.load_weights_from_checkpoint( + model_name=original_model_name, + work_dir=tmpdir_fn, + best=False, + map_location="cpu", + ) - def test_load_weights_from_checkpoint(self, tmpdir_fn): - ts_training, ts_test = self.series.split_before(90) - original_model_name = "original" - retrained_model_name = "retrained" - # original model, checkpoints are saved - model = RNNModel( - 12, - "RNN", - 5, - 1, - n_epochs=5, - work_dir=tmpdir_fn, - save_checkpoints=True, - model_name=original_model_name, - random_state=1, - **tfm_kwargs, - ) - model.fit(ts_training) - original_preds = model.predict(10) - original_mape = mape(original_preds, ts_test) + # must indicate series otherwise self.training_series must be saved in checkpoint + loaded_preds = model_rt.predict(10, ts_training) + # save/load checkpoint should produce identical predictions + assert original_preds == loaded_preds + + model_rt.fit(ts_training) + retrained_preds = model_rt.predict(10) + retrained_mape = mape(retrained_preds, ts_test) + assert retrained_mape < original_mape, ( + f"Retrained model has a greater error (mape) than the original model, " + f"respectively {retrained_mape} and {original_mape}" + ) - # load last checkpoint of original model, train it for 2 additional epochs + # raise Exception when trying to load ckpt weights in different architecture + with pytest.raises(ValueError): model_rt = RNNModel( 12, "RNN", + 10, # loaded model has only 5 hidden_layers 5, - 1, - n_epochs=5, - work_dir=tmpdir_fn, - model_name=retrained_model_name, - random_state=1, - **tfm_kwargs, ) model_rt.load_weights_from_checkpoint( model_name=original_model_name, @@ -959,580 +1124,1153 @@ def test_load_weights_from_checkpoint(self, tmpdir_fn): map_location="cpu", ) - # must indicate series otherwise self.training_series must be saved in checkpoint - loaded_preds = model_rt.predict(10, ts_training) - # save/load checkpoint should produce identical predictions - assert original_preds == loaded_preds - - model_rt.fit(ts_training) - retrained_preds = model_rt.predict(10) - retrained_mape = mape(retrained_preds, ts_test) - assert retrained_mape < original_mape, ( - f"Retrained model has a greater error (mape) than the original model, " - f"respectively {retrained_mape} and {original_mape}" - ) - - # raise Exception when trying to load ckpt weights in different architecture - with pytest.raises(ValueError): - model_rt = RNNModel( - 12, - "RNN", - 10, # loaded model has only 5 hidden_layers - 5, - ) - model_rt.load_weights_from_checkpoint( - model_name=original_model_name, - work_dir=tmpdir_fn, - best=False, - map_location="cpu", - ) - - # raise Exception when trying to pass `weights_only`=True to `torch.load()` - with pytest.raises(ValueError): - model_rt = RNNModel(12, "RNN", 5, 5, **tfm_kwargs) - model_rt.load_weights_from_checkpoint( - model_name=original_model_name, - work_dir=tmpdir_fn, - best=False, - weights_only=True, - map_location="cpu", - ) - - def test_load_weights(self, tmpdir_fn): - ts_training, ts_test = self.series.split_before(90) - original_model_name = "original" - retrained_model_name = "retrained" - # original model, checkpoints are saved - model = RNNModel( - 12, - "RNN", - 5, - 1, - n_epochs=5, - work_dir=tmpdir_fn, - save_checkpoints=False, + # raise Exception when trying to pass `weights_only`=True to `torch.load()` + with pytest.raises(ValueError): + model_rt = RNNModel(12, "RNN", 5, 5, **tfm_kwargs) + model_rt.load_weights_from_checkpoint( model_name=original_model_name, - random_state=1, - **tfm_kwargs, - ) - model.fit(ts_training) - path_manual_save = os.path.join(tmpdir_fn, "RNN_manual_save.pt") - model.save(path_manual_save) - original_preds = model.predict(10) - original_mape = mape(original_preds, ts_test) - - # load last checkpoint of original model, train it for 2 additional epochs - model_rt = RNNModel( - 12, - "RNN", - 5, - 1, - n_epochs=5, work_dir=tmpdir_fn, - model_name=retrained_model_name, - random_state=1, - **tfm_kwargs, - ) - model_rt.load_weights(path=path_manual_save, map_location="cpu") - - # must indicate series otherwise self.training_series must be saved in checkpoint - loaded_preds = model_rt.predict(10, ts_training) - # save/load checkpoint should produce identical predictions - assert original_preds == loaded_preds - - model_rt.fit(ts_training) - retrained_preds = model_rt.predict(10) - retrained_mape = mape(retrained_preds, ts_test) - assert retrained_mape < original_mape, ( - f"Retrained model has a greater mape error than the original model, " - f"respectively {retrained_mape} and {original_mape}" - ) - - def test_load_weights_with_float32_dtype(self, tmpdir_fn): - ts_float32 = self.series.astype("float32") - model_name = "test_model" - ckpt_path = os.path.join(tmpdir_fn, f"{model_name}.pt") - # barebone model - model = DLinearModel( - input_chunk_length=4, - output_chunk_length=1, - n_epochs=1, - ) - model.fit(ts_float32) - model.save(ckpt_path) - assert model.model._dtype == torch.float32 # type: ignore - - # identical model - loading_model = DLinearModel( - input_chunk_length=4, - output_chunk_length=1, - ) - loading_model.load_weights(ckpt_path) - loading_model.fit(ts_float32) - assert loading_model.model._dtype == torch.float32 # type: ignore - - def test_multi_steps_pipeline(self, tmpdir_fn): - ts_training, ts_val = self.series.split_before(75) - pretrain_model_name = "pre-train" - retrained_model_name = "re-train" - - # pretraining - model = self.helper_create_RNNModel(pretrain_model_name, tmpdir_fn) - model.fit( - ts_training, - val_series=ts_val, - ) - - # finetuning - model = self.helper_create_RNNModel(retrained_model_name, tmpdir_fn) - model.load_weights_from_checkpoint( - model_name=pretrain_model_name, - work_dir=tmpdir_fn, - best=True, - map_location="cpu", - ) - model.fit( - ts_training, - val_series=ts_val, - ) - - # prediction - model = model.load_from_checkpoint( - model_name=retrained_model_name, - work_dir=tmpdir_fn, - best=True, - map_location="cpu", - ) - model.predict(4, series=ts_training) - - def test_load_from_checkpoint_w_custom_loss(self, tmpdir_fn): - model_name = "pretraining_custom_loss" - # model with a custom loss - model = RNNModel( - 12, - "RNN", - 5, - 1, - n_epochs=1, - work_dir=tmpdir_fn, - model_name=model_name, - save_checkpoints=True, - force_reset=True, - loss_fn=torch.nn.L1Loss(), - **tfm_kwargs, - ) - model.fit(self.series) - - loaded_model = RNNModel.load_from_checkpoint( - model_name, tmpdir_fn, best=False, map_location="cpu" - ) - # custom loss function should be properly restored from ckpt - assert isinstance(loaded_model.model.criterion, torch.nn.L1Loss) - - loaded_model.fit(self.series, epochs=2) - # calling fit() should not impact the loss function - assert isinstance(loaded_model.model.criterion, torch.nn.L1Loss) - - def test_load_from_checkpoint_w_metrics(self, tmpdir_fn): - model_name = "pretraining_metrics" - # model with one torch_metrics - pl_trainer_kwargs = dict( - {"logger": DummyLogger(), "log_every_n_steps": 1}, - **tfm_kwargs["pl_trainer_kwargs"], - ) - model = RNNModel( - 12, - "RNN", - 5, - 1, - n_epochs=1, - work_dir=tmpdir_fn, - model_name=model_name, - save_checkpoints=True, - force_reset=True, - torch_metrics=MeanAbsolutePercentageError(), - pl_trainer_kwargs=pl_trainer_kwargs, - ) - model.fit(self.series) - # check train_metrics before loading - assert isinstance(model.model.train_metrics, MetricCollection) - assert len(model.model.train_metrics) == 1 - - loaded_model = RNNModel.load_from_checkpoint( - model_name, - tmpdir_fn, best=False, + weights_only=True, map_location="cpu", ) - # custom loss function should be properly restored from ckpt torchmetrics.Metric - assert isinstance(loaded_model.model.train_metrics, MetricCollection) - assert len(loaded_model.model.train_metrics) == 1 - - def test_optimizers(self): - - optimizers = [ - (torch.optim.Adam, {"lr": 0.001}), - (torch.optim.SGD, {"lr": 0.001}), - ] - - for optim_cls, optim_kwargs in optimizers: - model = RNNModel( - 12, - "RNN", - 10, - 10, - optimizer_cls=optim_cls, - optimizer_kwargs=optim_kwargs, - **tfm_kwargs, - ) - # should not raise an error - model.fit(self.series, epochs=1) - - def test_lr_schedulers(self): - - lr_schedulers = [ - (torch.optim.lr_scheduler.StepLR, {"step_size": 10}), - ( - torch.optim.lr_scheduler.ReduceLROnPlateau, - {"threshold": 0.001, "monitor": "train_loss"}, - ), - (torch.optim.lr_scheduler.ExponentialLR, {"gamma": 0.09}), - ] - - for lr_scheduler_cls, lr_scheduler_kwargs in lr_schedulers: - model = RNNModel( - 12, - "RNN", - 10, - 10, - lr_scheduler_cls=lr_scheduler_cls, - lr_scheduler_kwargs=lr_scheduler_kwargs, - **tfm_kwargs, - ) - # should not raise an error - model.fit(self.series, epochs=1) - def test_wrong_model_creation_params(self): - valid_kwarg = {"pl_trainer_kwargs": {}} - invalid_kwarg = {"some_invalid_kwarg": None} + def test_load_weights(self, tmpdir_fn): + ts_training, ts_test = self.series.split_before(90) + original_model_name = "original" + retrained_model_name = "retrained" + # original model, checkpoints are saved + model = RNNModel( + 12, + "RNN", + 5, + 1, + n_epochs=5, + work_dir=tmpdir_fn, + save_checkpoints=False, + model_name=original_model_name, + random_state=1, + **tfm_kwargs, + ) + model.fit(ts_training) + path_manual_save = os.path.join(tmpdir_fn, "RNN_manual_save.pt") + model.save(path_manual_save) + original_preds = model.predict(10) + original_mape = mape(original_preds, ts_test) + + # load last checkpoint of original model, train it for 2 additional epochs + model_rt = RNNModel( + 12, + "RNN", + 5, + 1, + n_epochs=5, + work_dir=tmpdir_fn, + model_name=retrained_model_name, + random_state=1, + **tfm_kwargs, + ) + model_rt.load_weights(path=path_manual_save, map_location="cpu") + + # must indicate series otherwise self.training_series must be saved in checkpoint + loaded_preds = model_rt.predict(10, ts_training) + # save/load checkpoint should produce identical predictions + assert original_preds == loaded_preds + + model_rt.fit(ts_training) + retrained_preds = model_rt.predict(10) + retrained_mape = mape(retrained_preds, ts_test) + assert retrained_mape < original_mape, ( + f"Retrained model has a greater mape error than the original model, " + f"respectively {retrained_mape} and {original_mape}" + ) - # valid params should not raise an error - _ = RNNModel(12, "RNN", 10, 10, **valid_kwarg) + def test_load_weights_with_float32_dtype(self, tmpdir_fn): + ts_float32 = self.series.astype("float32") + model_name = "test_model" + ckpt_path = os.path.join(tmpdir_fn, f"{model_name}.pt") + # barebone model + model = DLinearModel( + input_chunk_length=4, + output_chunk_length=1, + n_epochs=1, + ) + model.fit(ts_float32) + model.save(ckpt_path) + assert model.model._dtype == torch.float32 # type: ignore + + # identical model + loading_model = DLinearModel( + input_chunk_length=4, + output_chunk_length=1, + ) + loading_model.load_weights(ckpt_path) + loading_model.fit(ts_float32) + assert loading_model.model._dtype == torch.float32 # type: ignore + + def test_multi_steps_pipeline(self, tmpdir_fn): + ts_training, ts_val = self.series.split_before(75) + pretrain_model_name = "pre-train" + retrained_model_name = "re-train" + + # pretraining + model = self.helper_create_RNNModel(pretrain_model_name, tmpdir_fn) + model.fit( + ts_training, + val_series=ts_val, + ) - # invalid params should raise an error - with pytest.raises(ValueError): - _ = RNNModel(12, "RNN", 10, 10, **invalid_kwarg) + # finetuning + model = self.helper_create_RNNModel(retrained_model_name, tmpdir_fn) + model.load_weights_from_checkpoint( + model_name=pretrain_model_name, + work_dir=tmpdir_fn, + best=True, + map_location="cpu", + ) + model.fit( + ts_training, + val_series=ts_val, + ) - def test_metrics(self): - metric = MeanAbsolutePercentageError() - metric_collection = MetricCollection( - [MeanAbsolutePercentageError(), MeanAbsoluteError()] - ) + # prediction + model = model.load_from_checkpoint( + model_name=retrained_model_name, + work_dir=tmpdir_fn, + best=True, + map_location="cpu", + ) + model.predict(4, series=ts_training) + + def test_load_from_checkpoint_w_custom_loss(self, tmpdir_fn): + model_name = "pretraining_custom_loss" + # model with a custom loss + model = RNNModel( + 12, + "RNN", + 5, + 1, + n_epochs=1, + work_dir=tmpdir_fn, + model_name=model_name, + save_checkpoints=True, + force_reset=True, + loss_fn=torch.nn.L1Loss(), + **tfm_kwargs, + ) + model.fit(self.series) - model_kwargs = { - "logger": DummyLogger(), - "log_every_n_steps": 1, - **tfm_kwargs["pl_trainer_kwargs"], - } - # test single metric - model = RNNModel( - 12, - "RNN", - 10, - 10, - n_epochs=1, - torch_metrics=metric, - pl_trainer_kwargs=model_kwargs, - ) - model.fit(self.series) + loaded_model = RNNModel.load_from_checkpoint( + model_name, tmpdir_fn, best=False, map_location="cpu" + ) + # custom loss function should be properly restored from ckpt + loss_fn_attrs = ["criterion", "train_criterion", "val_criterion"] + for attr in loss_fn_attrs: + assert isinstance(getattr(loaded_model.model, attr), torch.nn.L1Loss) + + loaded_model.fit(self.series, epochs=2) + # calling fit() should not impact the loss function + for attr in loss_fn_attrs: + assert isinstance(getattr(loaded_model.model, attr), torch.nn.L1Loss) + + def test_load_from_checkpoint_w_metrics(self, tmpdir_fn): + model_name = "pretraining_metrics" + # model with one torch_metrics + pl_trainer_kwargs = dict( + {"logger": DummyLogger(), "log_every_n_steps": 1}, + **tfm_kwargs["pl_trainer_kwargs"], + ) + model = RNNModel( + 12, + "RNN", + 5, + 1, + n_epochs=1, + work_dir=tmpdir_fn, + model_name=model_name, + save_checkpoints=True, + force_reset=True, + torch_metrics=MeanAbsolutePercentageError(), + pl_trainer_kwargs=pl_trainer_kwargs, + ) + model.fit(self.series) + # check train_metrics before loading + assert isinstance(model.model.train_metrics, MetricCollection) + assert len(model.model.train_metrics) == 1 + + loaded_model = RNNModel.load_from_checkpoint( + model_name, + tmpdir_fn, + best=False, + map_location="cpu", + ) + # custom loss function should be properly restored from ckpt torchmetrics.Metric + assert isinstance(loaded_model.model.train_metrics, MetricCollection) + assert len(loaded_model.model.train_metrics) == 1 - # test metric collection - model = RNNModel( - 12, - "RNN", - 10, - 10, - n_epochs=1, - torch_metrics=metric_collection, - pl_trainer_kwargs=model_kwargs, - ) - model.fit(self.series) + def test_optimizers(self): + optimizers = [ + (torch.optim.Adam, {"lr": 0.001}), + (torch.optim.SGD, {"lr": 0.001}), + ] - # test multivariate series + for optim_cls, optim_kwargs in optimizers: model = RNNModel( 12, "RNN", 10, 10, - n_epochs=1, - torch_metrics=metric, - pl_trainer_kwargs=model_kwargs, + optimizer_cls=optim_cls, + optimizer_kwargs=optim_kwargs, + **tfm_kwargs, ) - model.fit(self.multivariate_series) + # should not raise an error + model.fit(self.series, epochs=1) - def test_metrics_w_likelihood(self): - metric = MeanAbsolutePercentageError() - metric_collection = MetricCollection( - [MeanAbsolutePercentageError(), MeanAbsoluteError()] - ) - model_kwargs = { - "logger": DummyLogger(), - "log_every_n_steps": 1, - **tfm_kwargs["pl_trainer_kwargs"], - } - # test single metric - model = RNNModel( - 12, - "RNN", - 10, - 10, - n_epochs=1, - likelihood=GaussianLikelihood(), - torch_metrics=metric, - pl_trainer_kwargs=model_kwargs, - ) - model.fit(self.series) + @pytest.mark.parametrize( + "lr_scheduler", + [ + (torch.optim.lr_scheduler.StepLR, {"step_size": 10}), + ( + torch.optim.lr_scheduler.ReduceLROnPlateau, + { + "threshold": 0.001, + "monitor": "train_loss", + "interval": "step", + "frequency": 2, + }, + ), + (torch.optim.lr_scheduler.ExponentialLR, {"gamma": 0.09}), + ], + ) + def test_lr_schedulers(self, lr_scheduler): + lr_scheduler_cls, lr_scheduler_kwargs = lr_scheduler + model = RNNModel( + 12, + "RNN", + 10, + 10, + lr_scheduler_cls=lr_scheduler_cls, + lr_scheduler_kwargs=lr_scheduler_kwargs, + **tfm_kwargs, + ) + # should not raise an error + model.fit(self.series, epochs=1) + + def test_wrong_model_creation_params(self): + valid_kwarg = {"pl_trainer_kwargs": {}} + invalid_kwarg = {"some_invalid_kwarg": None} + + # valid params should not raise an error + _ = RNNModel(12, "RNN", 10, 10, **valid_kwarg) + + # invalid params should raise an error + with pytest.raises(ValueError): + _ = RNNModel(12, "RNN", 10, 10, **invalid_kwarg) + + def test_metrics(self): + metric = MeanAbsolutePercentageError() + metric_collection = MetricCollection([ + MeanAbsolutePercentageError(), + MeanAbsoluteError(), + ]) + + model_kwargs = { + "logger": DummyLogger(), + "log_every_n_steps": 1, + **tfm_kwargs["pl_trainer_kwargs"], + } + # test single metric + model = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=1, + torch_metrics=metric, + pl_trainer_kwargs=model_kwargs, + ) + model.fit(self.series) + + # test metric collection + model = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=1, + torch_metrics=metric_collection, + pl_trainer_kwargs=model_kwargs, + ) + model.fit(self.series) + + # test multivariate series + model = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=1, + torch_metrics=metric_collection, + pl_trainer_kwargs=model_kwargs, + ) + model.fit(self.multivariate_series) + + def test_metrics_w_likelihood(self): + metric = MeanAbsolutePercentageError() + metric_collection = MetricCollection([ + MeanAbsolutePercentageError(), + MeanAbsoluteError(), + ]) + model_kwargs = { + "logger": DummyLogger(), + "log_every_n_steps": 1, + **tfm_kwargs["pl_trainer_kwargs"], + } + # test single metric + model = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=1, + likelihood=GaussianLikelihood(), + torch_metrics=metric, + pl_trainer_kwargs=model_kwargs, + ) + model.fit(self.series) + + # test metric collection + model = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=1, + likelihood=GaussianLikelihood(), + torch_metrics=metric_collection, + pl_trainer_kwargs=model_kwargs, + ) + model.fit(self.series) + + # test multivariate series + model = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=1, + likelihood=GaussianLikelihood(), + torch_metrics=metric_collection, + pl_trainer_kwargs=model_kwargs, + ) + model.fit(self.multivariate_series) - # test metric collection + def test_invalid_metrics(self): + torch_metrics = ["invalid"] + with pytest.raises(AttributeError): model = RNNModel( 12, "RNN", 10, 10, n_epochs=1, - likelihood=GaussianLikelihood(), - torch_metrics=metric_collection, - pl_trainer_kwargs=model_kwargs, + torch_metrics=torch_metrics, + **tfm_kwargs, ) model.fit(self.series) - # test multivariate series + def test_stateful_metrics(self): + torch_metrics = NumsCalled() + model = RNNModel( + 12, + "RNN", + 10, + 10, + n_epochs=1, + torch_metrics=torch_metrics, + **tfm_kwargs, + ) + model.fit(self.series) + assert model.model.trainer.logged_metrics["train_NumsCalled"] > 1 + + @pytest.mark.slow + def test_lr_find(self): + train_series, val_series = self.series[:-40], self.series[-40:] + model = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) + # find the learning rate + res = model.lr_find(series=train_series, val_series=val_series, epochs=50) + assert isinstance(res, _LRFinder) + assert res.suggestion() is not None + # verify that learning rate finder bypasses the `fit` logic + assert model.model is None + assert not model._fit_called + # cannot predict with an untrained model + with pytest.raises(ValueError): + model.predict(n=3, series=self.series) + + # check that results are reproducible + model = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) + res2 = model.lr_find(series=train_series, val_series=val_series, epochs=50) + assert res.suggestion() == res2.suggestion() + + # check that suggested learning rate is better than the worst + lr_worst = res.results["lr"][np.argmax(res.results["loss"])] + lr_suggested = res.suggestion() + scores = {} + for lr, lr_name in zip([lr_worst, lr_suggested], ["worst", "suggested"]): model = RNNModel( 12, "RNN", 10, 10, - n_epochs=1, - likelihood=GaussianLikelihood(), - torch_metrics=metric_collection, - pl_trainer_kwargs=model_kwargs, + n_epochs=10, + random_state=42, + optimizer_cls=torch.optim.Adam, + optimizer_kwargs={"lr": lr}, + **tfm_kwargs, ) - model.fit(self.multivariate_series) - - def test_invalid_metrics(self): - torch_metrics = ["invalid"] - with pytest.raises(AttributeError): - model = RNNModel( - 12, - "RNN", - 10, - 10, - n_epochs=1, - torch_metrics=torch_metrics, - **tfm_kwargs, - ) - model.fit(self.series) - - @pytest.mark.slow - def test_lr_find(self): - train_series, val_series = self.series[:-40], self.series[-40:] - model = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) - # find the learning rate - res = model.lr_find(series=train_series, val_series=val_series, epochs=50) - assert isinstance(res, _LRFinder) - assert res.suggestion() is not None - # verify that learning rate finder bypasses the `fit` logic - assert model.model is None - assert not model._fit_called - # cannot predict with an untrained model - with pytest.raises(ValueError): - model.predict(n=3, series=self.series) - - # check that results are reproducible - model = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) - res2 = model.lr_find(series=train_series, val_series=val_series, epochs=50) - assert res.suggestion() == res2.suggestion() - - # check that suggested learning rate is better than the worst - lr_worst = res.results["lr"][np.argmax(res.results["loss"])] - lr_suggested = res.suggestion() - scores = {} - for lr, lr_name in zip([lr_worst, lr_suggested], ["worst", "suggested"]): - model = RNNModel( - 12, - "RNN", - 10, - 10, - n_epochs=10, - random_state=42, - optimizer_cls=torch.optim.Adam, - optimizer_kwargs={"lr": lr}, - **tfm_kwargs, - ) - model.fit(train_series) - scores[lr_name] = mape( - val_series, model.predict(len(val_series), series=train_series) - ) - assert scores["worst"] > scores["suggested"] + model.fit(train_series) + scores[lr_name] = mape( + val_series, model.predict(len(val_series), series=train_series) + ) + assert scores["worst"] > scores["suggested"] - def test_encoders(self, tmpdir_fn): - series = linear_timeseries(length=10) - pc = linear_timeseries(length=12) - fc = linear_timeseries(length=13) - # 1 == output_chunk_length, 3 > output_chunk_length - ns = [1, 3] + def test_encoders(self, tmpdir_fn): + series = tg.linear_timeseries(length=10) + pc = tg.linear_timeseries(length=12) + fc = tg.linear_timeseries(length=13) + # 1 == output_chunk_length, 3 > output_chunk_length + ns = [1, 3] - model = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - add_encoders={ - "datetime_attribute": {"past": ["hour"], "future": ["month"]} - }, - ) - model.fit(series) - for n in ns: - _ = model.predict(n=n) - with pytest.raises(ValueError): - _ = model.predict(n=n, past_covariates=pc) - with pytest.raises(ValueError): - _ = model.predict(n=n, future_covariates=fc) - with pytest.raises(ValueError): - _ = model.predict(n=n, past_covariates=pc, future_covariates=fc) - - model = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - add_encoders={ - "datetime_attribute": {"past": ["hour"], "future": ["month"]} - }, - ) - for n in ns: - model.fit(series, past_covariates=pc) - _ = model.predict(n=n) + model = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + add_encoders={ + "datetime_attribute": {"past": ["hour"], "future": ["month"]} + }, + ) + model.fit(series) + for n in ns: + _ = model.predict(n=n) + with pytest.raises(ValueError): _ = model.predict(n=n, past_covariates=pc) - with pytest.raises(ValueError): - _ = model.predict(n=n, future_covariates=fc) - with pytest.raises(ValueError): - _ = model.predict(n=n, past_covariates=pc, future_covariates=fc) + with pytest.raises(ValueError): + _ = model.predict(n=n, future_covariates=fc) + with pytest.raises(ValueError): + _ = model.predict(n=n, past_covariates=pc, future_covariates=fc) - model = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - add_encoders={ - "datetime_attribute": {"past": ["hour"], "future": ["month"]} - }, - ) - for n in ns: - model.fit(series, future_covariates=fc) - _ = model.predict(n=n) - with pytest.raises(ValueError): - _ = model.predict(n=n, past_covariates=pc) + model = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + add_encoders={ + "datetime_attribute": {"past": ["hour"], "future": ["month"]} + }, + ) + for n in ns: + model.fit(series, past_covariates=pc) + _ = model.predict(n=n) + _ = model.predict(n=n, past_covariates=pc) + with pytest.raises(ValueError): _ = model.predict(n=n, future_covariates=fc) - with pytest.raises(ValueError): - _ = model.predict(n=n, past_covariates=pc, future_covariates=fc) + with pytest.raises(ValueError): + _ = model.predict(n=n, past_covariates=pc, future_covariates=fc) - model = self.helper_create_DLinearModel( - work_dir=tmpdir_fn, - add_encoders={ - "datetime_attribute": {"past": ["hour"], "future": ["month"]} - }, - ) - for n in ns: - model.fit(series, past_covariates=pc, future_covariates=fc) - _ = model.predict(n=n) + model = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + add_encoders={ + "datetime_attribute": {"past": ["hour"], "future": ["month"]} + }, + ) + for n in ns: + model.fit(series, future_covariates=fc) + _ = model.predict(n=n) + with pytest.raises(ValueError): _ = model.predict(n=n, past_covariates=pc) - _ = model.predict(n=n, future_covariates=fc) + _ = model.predict(n=n, future_covariates=fc) + with pytest.raises(ValueError): _ = model.predict(n=n, past_covariates=pc, future_covariates=fc) - @pytest.mark.parametrize("model_config", models) - def test_rin(self, model_config): - model_cls, kwargs = model_config - model_no_rin = model_cls(use_reversible_instance_norm=False, **kwargs) - model_rin = model_cls(use_reversible_instance_norm=True, **kwargs) - - # univariate no RIN - model_no_rin.fit(self.series) - assert not model_no_rin.model.use_reversible_instance_norm - assert model_no_rin.model.rin is None - - # univariate with RIN - model_rin.fit(self.series) - if issubclass(model_cls, RNNModel): - # RNNModel will not use RIN - assert not model_rin.model.use_reversible_instance_norm - assert model_rin.model.rin is None - return + model = self.helper_create_DLinearModel( + work_dir=tmpdir_fn, + add_encoders={ + "datetime_attribute": {"past": ["hour"], "future": ["month"]} + }, + ) + for n in ns: + model.fit(series, past_covariates=pc, future_covariates=fc) + _ = model.predict(n=n) + _ = model.predict(n=n, past_covariates=pc) + _ = model.predict(n=n, future_covariates=fc) + _ = model.predict(n=n, past_covariates=pc, future_covariates=fc) + + @pytest.mark.parametrize("model_config", models) + def test_val_set(self, model_config): + """Test whether these evaluation set parameters are passed to the PyTorch Lightning Trainer""" + with patch("pytorch_lightning.Trainer.fit") as fit_patch: + self.helper_check_val_set(*model_config, fit_patch) + + def test_dataloader_kwargs_setup(self): + train_series, val_series = self.series[:-40], self.series[-40:] + model = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) + + with patch("pytorch_lightning.Trainer.fit") as fit_patch: + model.fit(train_series, val_series=val_series) + assert "train_dataloaders" in fit_patch.call_args.kwargs + assert "val_dataloaders" in fit_patch.call_args.kwargs + + train_dl = fit_patch.call_args.kwargs["train_dataloaders"] + assert isinstance(train_dl, DataLoader) + val_dl = fit_patch.call_args.kwargs["val_dataloaders"] + assert isinstance(val_dl, DataLoader) + + dl_defaults = { + "batch_size": model.batch_size, + "pin_memory": True, + "drop_last": False, + "collate_fn": model._batch_collate_fn, + } + assert all([getattr(train_dl, k) == v for k, v in dl_defaults.items()]) + # shuffle=True gives random sampler + assert isinstance(train_dl.sampler, RandomSampler) + + assert all([getattr(val_dl, k) == v for k, v in dl_defaults.items()]) + # shuffle=False gives sequential sampler + assert isinstance(val_dl.sampler, SequentialSampler) + + # check that overwriting the dataloader kwargs works + dl_custom = dict(dl_defaults, **{"batch_size": 50, "drop_last": True}) + model.fit(train_series, val_series=val_series, dataloader_kwargs=dl_custom) + train_dl = fit_patch.call_args.kwargs["train_dataloaders"] + val_dl = fit_patch.call_args.kwargs["val_dataloaders"] + assert all([getattr(train_dl, k) == v for k, v in dl_custom.items()]) + assert all([getattr(val_dl, k) == v for k, v in dl_custom.items()]) + + with patch("pytorch_lightning.Trainer.predict") as pred_patch: + # calling predict with the patch will raise an error, but we only need to + # check the dataloader setup + with pytest.raises(Exception): + model.predict(n=1) + assert "dataloaders" in pred_patch.call_args.kwargs + pred_dl = pred_patch.call_args.kwargs["dataloaders"] + assert isinstance(pred_dl, DataLoader) + assert all([getattr(pred_dl, k) == v for k, v in dl_defaults.items()]) + # shuffle=False gives sequential sampler + assert isinstance(val_dl.sampler, SequentialSampler) + + # check that overwriting the dataloader kwargs works + with pytest.raises(Exception): + model.predict(n=1, dataloader_kwargs=dl_custom) + pred_dl = pred_patch.call_args.kwargs["dataloaders"] + assert all([getattr(pred_dl, k) == v for k, v in dl_custom.items()]) + + def test_dataloader_kwargs_fit_predict(self): + train_series, val_series = self.series[:-40], self.series[-40:] + model = RNNModel(12, "RNN", 10, 10, random_state=42, **tfm_kwargs) + + model.fit( + train_series, + val_series=val_series, + dataloader_kwargs={"batch_size": 100, "shuffle": False}, + ) + + # check same results with default batch size (32) and custom batch size + preds_default = model.predict( + n=2, + series=[train_series, val_series], + ) + preds_custom = model.predict( + n=2, + series=[train_series, val_series], + dataloader_kwargs={"batch_size": 100}, + ) + assert preds_default == preds_custom + + def helper_check_val_set(self, model_cls, model_kwargs, fit_patch): + # naive models don't call the Trainer + if issubclass(model_cls, _GlobalNaiveModel): + return + + series1 = tg.sine_timeseries(length=11, column_name="tg_1") + series2 = tg.sine_timeseries(length=11, column_name="tg_2") / 2 + 10 + series = series1.stack(series2) + series = series.with_static_covariates( + pd.DataFrame({"sc1": [0, 1], "sc2": [3, 4]}) + ) + pc = series1 * 10 - 3 + fc = TimeSeries.from_times_and_values( + times=series.time_index, values=series.values() * -1, columns=["fc1", "fc2"] + ) + model = model_cls(**model_kwargs) + + # check that an error is raised with an invalid validation series + fit_kwargs = { + "series": series, + "val_series": series["tg_1"], + } + invalid_series_txt = "`series`" + if model.supports_past_covariates: + fit_kwargs["past_covariates"] = pc + fit_kwargs["val_past_covariates"] = pc + if model.supports_future_covariates: + fit_kwargs["future_covariates"] = fc + fit_kwargs["val_future_covariates"] = fc["fc1"] + invalid_series_txt += ", `future_covariates`" + if model.supports_static_covariates: + invalid_series_txt += ", `static_covariates`" + + with pytest.raises(ValueError) as err: + model.fit(**fit_kwargs) + msg_expected = ( + f"The dimensions of the ({invalid_series_txt}) between " + "the training and validation set do not match." + ) + assert str(err.value) == msg_expected + + # check that an error is raised if only second validation series are invalid + fit_kwargs = { + "series": series, + "val_series": [series, series["tg_1"]], + } + invalid_series_txt = "`series`" + if model.supports_past_covariates: + fit_kwargs["past_covariates"] = pc + fit_kwargs["val_past_covariates"] = [pc, pc] + if model.supports_future_covariates: + fit_kwargs["future_covariates"] = fc + fit_kwargs["val_future_covariates"] = [fc, fc["fc1"]] + invalid_series_txt += ", `future_covariates`" + if model.supports_static_covariates: + invalid_series_txt += ", `static_covariates`" + + with pytest.raises(ValueError) as err: + model.fit(**fit_kwargs) + msg_expected = ( + f"The dimensions of the ({invalid_series_txt}) between " + "the training and validation set at sequence/list index `1` do not match." + ) + assert str(err.value) == msg_expected + + fit_kwargs = {"series": series, "val_series": series} + if model.supports_past_covariates: + fit_kwargs["past_covariates"] = pc + fit_kwargs["val_past_covariates"] = pc + if model.supports_future_covariates: + fit_kwargs["future_covariates"] = fc + fit_kwargs["val_future_covariates"] = fc + + model.fit(**fit_kwargs) + # fit called only once + assert fit_patch.call_count == 1 + + train_ds = fit_patch.call_args[1]["train_dataloaders"].dataset + val_dl = fit_patch.call_args[1]["val_dataloaders"] + assert val_dl is not None + val_ds = val_dl.dataset + + # check same dataset type + assert isinstance(val_ds, train_ds.__class__) + + # check that input in first batch have same dimensions + train_sample = train_ds[0] + val_sample = val_ds[0] + assert len(val_sample) == len(train_sample) + for x_train, x_val in zip(train_sample, val_sample): + if x_train is None: + assert x_val is None else: - assert model_rin.model.use_reversible_instance_norm - assert isinstance(model_rin.model.rin, RINorm) - assert model_rin.model.rin.input_dim == self.series.n_components - # multivariate with RIN - model_rin_mv = model_rin.untrained_model() - model_rin_mv.fit(self.multivariate_series) - assert model_rin_mv.model.use_reversible_instance_norm - assert isinstance(model_rin_mv.model.rin, RINorm) - assert ( - model_rin_mv.model.rin.input_dim - == self.multivariate_series.n_components - ) + assert x_val.shape[1:] == x_train.shape[1:] + + @pytest.mark.parametrize("model_config", models) + def test_rin(self, model_config): + model_cls, kwargs = model_config + model_no_rin = model_cls(use_reversible_instance_norm=False, **kwargs) + model_rin = model_cls(use_reversible_instance_norm=True, **kwargs) + + # univariate no RIN + model_no_rin.fit(self.series) + assert not model_no_rin.model.use_reversible_instance_norm + assert model_no_rin.model.rin is None + + # univariate with RIN + model_rin.fit(self.series) + if issubclass(model_cls, RNNModel): + # RNNModel will not use RIN + assert not model_rin.model.use_reversible_instance_norm + assert model_rin.model.rin is None + return + else: + assert model_rin.model.use_reversible_instance_norm + assert isinstance(model_rin.model.rin, RINorm) + assert model_rin.model.rin.input_dim == self.series.n_components + # multivariate with RIN + model_rin_mv = model_rin.untrained_model() + model_rin_mv.fit(self.multivariate_series) + assert model_rin_mv.model.use_reversible_instance_norm + assert isinstance(model_rin_mv.model.rin, RINorm) + assert model_rin_mv.model.rin.input_dim == self.multivariate_series.n_components + + @pytest.mark.parametrize("use_mc_dropout", [False, True]) + def test_mc_dropout_active(self, use_mc_dropout): + """Test that model activates dropout .""" + + class CheckMCDropout(Callback): + def __init__(self, activate_mc_dropout): + self.use_mc_dropout = activate_mc_dropout + + @staticmethod + def _check_dropout_activity(pl_module, expected_active: bool): + dropouts = pl_module._get_mc_dropout_modules() + assert all([ + dropout.mc_dropout_enabled is expected_active + for dropout in dropouts + ]) + + def on_train_batch_start(self, *args, **kwargs) -> None: + self._check_dropout_activity(args[1], expected_active=True) + + def on_validation_batch_start(self, *args, **kwargs) -> None: + self._check_dropout_activity(args[1], expected_active=False) + + def on_predict_batch_start(self, *args, **kwargs) -> None: + self._check_dropout_activity( + args[1], expected_active=self.use_mc_dropout + ) - def helper_equality_encoders( - self, first_encoders: Dict[str, Any], second_encoders: Dict[str, Any] - ): - if first_encoders is None: - first_encoders = {} - if second_encoders is None: - second_encoders = {} - assert {k: v for k, v in first_encoders.items() if k != "transformer"} == { - k: v for k, v in second_encoders.items() if k != "transformer" - } + series = self.series[:20] + pl_trainer_kwargs = copy.deepcopy(tfm_kwargs) + pl_trainer_kwargs["pl_trainer_kwargs"]["callbacks"] = [ + CheckMCDropout(activate_mc_dropout=use_mc_dropout) + ] + model = TiDEModel(10, 10, dropout=0.1, random_state=42, **pl_trainer_kwargs) + model.fit(series, val_series=series, epochs=1) + + num_samples = 1 if not use_mc_dropout else 10 + preds = model.predict( + n=10, series=series, mc_dropout=use_mc_dropout, num_samples=num_samples + ) + assert preds.n_samples == num_samples + + @pytest.mark.parametrize("use_mc_dropout", [False, True]) + def test_dropout_output(self, use_mc_dropout): + """Test that model without dropout generates different results than one which uses near-full dropout.""" + series = self.series[:20] + num_samples = 1 if not use_mc_dropout else 10 + + # dropouts for overfit and underfit + preds = [] + for dropout in [0.0, 0.99]: + model = TiDEModel(10, 10, dropout=dropout, random_state=42, **tfm_kwargs) + model.fit(series, val_series=series, epochs=1) + preds.append( + model.predict( + n=10, + series=series, + mc_dropout=use_mc_dropout, + num_samples=num_samples, + ).all_values() + ) + assert not np.array_equal(preds[0], preds[1]) + + @pytest.mark.parametrize( + "config", + itertools.product( + models, + [3, 7, 10], + ), + ) + def test_output_shift(self, config): + """Tests shifted output for shift smaller than, equal to, and larger than output_chunk_length. + RNNModel does not support shift output chunk. + """ + np.random.seed(0) + (model_cls, model_kwargs), shift = config + if issubclass(model_cls, RNNModel): + return + + model_kwargs = copy.deepcopy(model_kwargs) + model_kwargs.pop("input_chunk_length") + model_kwargs.pop("output_chunk_length") + + if issubclass(model_cls, TFTModel): + model_kwargs.update({"likelihood": None, "loss_fn": torch.nn.MSELoss()}) + + icl = 8 + ocl = 7 + series = tg.gaussian_timeseries( + length=28, start=pd.Timestamp("2000-01-01"), freq="d" + ) - def helper_equality_encoders_transfo( - self, first_encoders: Dict[str, Any], second_encoders: Dict[str, Any] - ): - if first_encoders is None: - first_encoders = {} - if second_encoders is None: - second_encoders = {} - assert ( - first_encoders.get("transformer", None).__class__ - == second_encoders.get("transformer", None).__class__ - ) + model = self.helper_create_torch_model( + model_cls, icl, ocl, shift, **model_kwargs + ) + model.fit(series) + + # no auto-regression with shifted output + with pytest.raises(ValueError) as err: + _ = model.predict(n=ocl + 1) + assert str(err.value).startswith("Cannot perform auto-regression") + + # pred starts with a shift + for ocl_test in [ocl - 1, ocl]: + pred = model.predict(n=ocl_test) + assert pred.start_time() == series.end_time() + (shift + 1) * series.freq + assert len(pred) == ocl_test + assert pred.freq == series.freq + + # check that shifted output chunk results with encoders are the + # same as using identical covariates + + # model trained on encoders + cov_support = [] + covs = {} + if model.supports_past_covariates: + cov_support.append("past") + covs["past_covariates"] = tg.datetime_attribute_timeseries( + series, + attribute="dayofweek", + add_length=0, + ) + if model.supports_future_covariates: + cov_support.append("future") + covs["future_covariates"] = tg.datetime_attribute_timeseries( + series, + attribute="dayofweek", + add_length=ocl + shift, + ) + + if not cov_support: + return + + add_encoders = { + "datetime_attribute": {cov: ["dayofweek"] for cov in cov_support} + } + model_enc_shift = self.helper_create_torch_model( + model_cls, icl, ocl, shift, add_encoders=add_encoders, **model_kwargs + ) + model_enc_shift.fit(series) - def helper_create_RNNModel(self, model_name: str, tmpdir_fn): - return RNNModel( - input_chunk_length=4, - hidden_dim=3, - add_encoders={ - "cyclic": {"past": ["month"]}, - "datetime_attribute": { - "past": ["hour"], - }, - "transformer": Scaler(), + # model trained with identical covariates + model_fc_shift = self.helper_create_torch_model( + model_cls, icl, ocl, shift, **model_kwargs + ) + + model_fc_shift.fit(series, **covs) + + pred_enc = model_enc_shift.predict(n=ocl) + pred_fc = model_fc_shift.predict(n=ocl) + assert pred_enc == pred_fc + + # check that historical forecasts works properly + hist_fc_start = -(ocl + shift) + pred_last_hist_fc = model_fc_shift.predict(n=ocl, series=series[:hist_fc_start]) + # non-optimized hist fc + hist_fc = model_fc_shift.historical_forecasts( + series=series, + start=hist_fc_start, + start_format="position", + retrain=False, + forecast_horizon=ocl, + last_points_only=False, + enable_optimization=False, + **covs, + ) + assert len(hist_fc) == 1 + assert hist_fc[0] == pred_last_hist_fc + # optimized hist fc, due to batch predictions, slight deviations in values + hist_fc_opt = model_fc_shift.historical_forecasts( + series=series, + start=hist_fc_start, + start_format="position", + retrain=False, + forecast_horizon=ocl, + last_points_only=False, + enable_optimization=True, + **covs, + ) + assert len(hist_fc_opt) == 1 + assert hist_fc_opt[0].time_index.equals(pred_last_hist_fc.time_index) + np.testing.assert_array_almost_equal( + hist_fc_opt[0].values(copy=False), pred_last_hist_fc.values(copy=False) + ) + + # covs too short + for cov_name in cov_support: + with pytest.raises(ValueError) as err: + add_covs = { + cov_name + "_covariates": covs[cov_name + "_covariates"][:-1] + } + _ = model_fc_shift.predict(n=ocl, **add_covs) + assert f"provided {cov_name} covariates at dataset index" in str(err.value) + + @pytest.mark.parametrize("config", itertools.product(models, [2, 3, 4])) + def test_multi_ts_prediction(self, config): + (model_cls, model_kwargs), n = config + model_kwargs = copy.deepcopy(model_kwargs) + model_kwargs["output_chunk_length"] = 3 + series = tg.linear_timeseries( + length=model_kwargs["input_chunk_length"] + + model_kwargs["output_chunk_length"] + ) + model = model_cls(**model_kwargs) + model.fit(series) + # test with more series that `n` + n_series_more = 5 + pred = model.predict(n=n, series=[series] * n_series_more) + assert len(pred) == n_series_more + assert all(len(p) == n for p in pred) + + # test with less series that `n` + n_series_less = 1 + pred = model.predict(n=n, series=[series] * n_series_less) + assert len(pred) == n_series_less + assert all(len(p) == n for p in pred) + + @pytest.mark.parametrize( + "config", + itertools.product(models, [True, False], [True, False], [True, False]), + ) + def test_weights(self, config): + (model_cls, model_kwargs), built_in_weight, single_series, univ_series = config + model_kwargs = copy.deepcopy(model_kwargs) + # take larger learning rate to make network weights updates more pronounced + model_kwargs["optimizer_kwargs"] = {"lr": 0.1} + model_kwargs["pl_trainer_kwargs"]["max_epochs"] = 2 + model_kwargs["pl_trainer_kwargs"]["fast_dev_run"] = False + # create more than one batch sample as otherwise linear sample weight would always be `1.` + ts = tg.linear_timeseries( + length=model_kwargs["input_chunk_length"] + + model_kwargs["output_chunk_length"] + + 1 + ) + if not univ_series: + ts = ts.stack(ts) + + if built_in_weight: + weights = "linear" + else: + weights = np.expand_dims(np.linspace(0, 1, len(ts)), -1) + if not univ_series: + weights = np.concatenate([weights] * ts.n_components, axis=1) + weights = ts.with_values(weights) + + if not single_series: + ts = [ts] * 2 + weights = weights if built_in_weight else [weights] * 2 + + model = model_cls(**model_kwargs) + model.fit(ts, sample_weight=weights) + preds = model.predict(n=3, series=ts) + + # check deterministic results + model_identical = model_cls(**model_kwargs) + model_identical.fit(ts, sample_weight=weights) + preds_identical = model_identical.predict(n=3, series=ts) + + if single_series: + preds = [preds] + preds_identical = [preds_identical] + + for pred, preds_identical in zip(preds, preds_identical): + np.testing.assert_array_almost_equal( + pred.all_values(), preds_identical.all_values() + ) + + model_no_weight = model_cls(**model_kwargs) + model_no_weight.fit(ts, sample_weight=None) + preds_no_weight = model_no_weight.predict(n=3, series=ts) + + if single_series: + preds_no_weight = [preds_no_weight] + + for pred, pred_no_weight in zip(preds, preds_no_weight): + if isinstance(model, _GlobalNaiveModel): + # naive models don't learn, so output should be the same + np.testing.assert_array_almost_equal( + pred.all_values(), pred_no_weight.all_values() + ) + else: + # all other models should have different results from sample weights + with pytest.raises(AssertionError): + np.testing.assert_array_almost_equal( + pred.all_values(), pred_no_weight.all_values() + ) + + model_kwargs["pl_trainer_kwargs"]["max_epochs"] = 1 + model_kwargs["pl_trainer_kwargs"]["fast_dev_run"] = True + model = model_cls(**model_kwargs) + # try with validation series and only train weights + model.fit(ts, val_series=ts, sample_weight=weights) + + # try with validation series and only val weights + model.fit(ts, val_series=ts, val_sample_weight=weights) + + # try with validation series and train and val weights + model.fit(ts, val_series=ts, sample_weight=weights, val_sample_weight=weights) + + def test_invalid_weights(self): + model_cls, model_kwargs = models[0] + ts = tg.linear_timeseries( + length=model_kwargs["input_chunk_length"] + + model_kwargs["output_chunk_length"] + ) + + # weights too short + model = model_cls(**model_kwargs) + with pytest.raises(ValueError) as err: + model.fit(ts, sample_weight=ts[:-1]) + assert ( + str(err.value) + == "Missing sample weights; could not find sample weights in index value range: " + "2000-01-11 00:00:00 - 2000-01-11 00:00:00." + ) + + # same number of series + model = model_cls(**model_kwargs) + with pytest.raises(ValueError) as err: + model.fit(ts, sample_weight=[ts, ts]) + assert ( + str(err.value) + == "The provided sequence of target `series` must have the same length as the " + "provided sequence of `sample_weight`." + ) + + # same number of components + model = model_cls(**model_kwargs) + with pytest.raises(ValueError) as err: + model.fit(ts, sample_weight=ts.stack(ts)) + assert ( + str(err.value) + == "The number of components in `sample_weight` must either be `1` or match the " + "number of target series components `1`. (0-th series)" + ) + # with correct number it works + model = model_cls(**model_kwargs) + model.fit(ts.stack(ts), sample_weight=ts.stack(ts)) + # or with multivar ts and single component weights (globally applied) + model = model_cls(**model_kwargs) + model.fit(ts.stack(ts), sample_weight=ts) + + # invalid string + model = model_cls(**model_kwargs) + with pytest.raises(ValueError) as err: + model.fit(ts, sample_weight="invalid") + assert str(err.value).startswith("Invalid `sample_weight` value: `'invalid'`. ") + + @pytest.mark.parametrize( + "likelihood", + [ + QuantileRegression([0.1, 0.5, 0.9]), + LaplaceLikelihood(), + GaussianLikelihood(), + CauchyLikelihood(), + ], + ) + def test_weights_probabilistic(self, likelihood): + model_cls, model_kwargs = models[0] + ts = tg.linear_timeseries( + length=model_kwargs["input_chunk_length"] + + model_kwargs["output_chunk_length"] + ) + + model_kwargs = copy.deepcopy(model_kwargs) + model_kwargs["likelihood"] = likelihood + model_kwargs["loss_fn"] = None + + model = model_cls(**model_kwargs) + model.fit(ts, sample_weight=ts) + pred = model.predict(n=3, num_samples=10) + + # check results are deterministic with same sample weights + model_same = model_cls(**model_kwargs) + model_same.fit(ts, sample_weight=ts) + pred_same = model_same.predict(n=3, num_samples=10) + np.testing.assert_array_almost_equal(pred.all_values(), pred_same.all_values()) + + # check different results without sample weights + model_no_weight = model_cls(**model_kwargs) + model_no_weight.fit(ts, sample_weight=ts) + pred_no_weight = model.predict(n=3, num_samples=10) + + # all other models should have different results from sample weights + with pytest.raises(AssertionError): + np.testing.assert_array_almost_equal( + pred.all_values(), pred_no_weight.all_values() + ) + + def helper_equality_encoders( + self, first_encoders: dict[str, Any], second_encoders: dict[str, Any] + ): + if first_encoders is None: + first_encoders = {} + if second_encoders is None: + second_encoders = {} + assert {k: v for k, v in first_encoders.items() if k != "transformer"} == { + k: v for k, v in second_encoders.items() if k != "transformer" + } + + def helper_equality_encoders_transfo( + self, first_encoders: dict[str, Any], second_encoders: dict[str, Any] + ): + if first_encoders is None: + first_encoders = {} + if second_encoders is None: + second_encoders = {} + assert ( + first_encoders.get("transformer", None).__class__ + == second_encoders.get("transformer", None).__class__ + ) + + def helper_create_RNNModel(self, model_name: str, tmpdir_fn): + return RNNModel( + input_chunk_length=4, + hidden_dim=3, + add_encoders={ + "cyclic": {"past": ["month"]}, + "datetime_attribute": { + "past": ["hour"], }, - n_epochs=2, - model_name=model_name, - work_dir=tmpdir_fn, - force_reset=True, - save_checkpoints=True, - **tfm_kwargs, - ) + "transformer": Scaler(), + }, + n_epochs=2, + model_name=model_name, + work_dir=tmpdir_fn, + force_reset=True, + save_checkpoints=True, + **tfm_kwargs, + ) - def helper_create_DLinearModel( - self, - work_dir: Optional[str] = None, - model_name: str = "unitest_model", - add_encoders: Optional[Dict] = None, - save_checkpoints: bool = False, - likelihood: Optional[Likelihood] = None, - ): - return DLinearModel( - input_chunk_length=4, - output_chunk_length=1, - model_name=model_name, - add_encoders=add_encoders, - work_dir=work_dir, - save_checkpoints=save_checkpoints, - random_state=42, - force_reset=True, - n_epochs=1, - likelihood=likelihood, - **tfm_kwargs, - ) + def helper_create_DLinearModel( + self, + work_dir: Optional[str] = None, + model_name: str = "unitest_model", + add_encoders: Optional[dict] = None, + save_checkpoints: bool = False, + likelihood: Optional[Likelihood] = None, + output_chunk_length: int = 1, + **kwargs, + ): + return DLinearModel( + input_chunk_length=4, + output_chunk_length=output_chunk_length, + model_name=model_name, + add_encoders=add_encoders, + work_dir=work_dir, + save_checkpoints=save_checkpoints, + random_state=42, + force_reset=True, + n_epochs=1, + likelihood=likelihood, + **tfm_kwargs, + **kwargs, + ) + + def helper_create_torch_model(self, model_cls, icl, ocl, shift, **kwargs): + params = { + "input_chunk_length": icl, + "output_chunk_length": ocl, + "output_chunk_shift": shift, + "n_epochs": 1, + "random_state": 42, + } + params.update(tfm_kwargs) + params.update(kwargs) + return model_cls(**params) diff --git a/darts/tests/models/forecasting/test_transformer_model.py b/darts/tests/models/forecasting/test_transformer_model.py index 8ece59c09d..adc02819fc 100644 --- a/darts/tests/models/forecasting/test_transformer_model.py +++ b/darts/tests/models/forecasting/test_transformer_model.py @@ -3,202 +3,195 @@ import pytest from darts import TimeSeries -from darts.logging import get_logger -from darts.tests.conftest import tfm_kwargs +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs from darts.utils import timeseries_generation as tg -logger = get_logger(__name__) - -try: - import torch.nn as nn - - from darts.models.components.transformer import ( - CustomFeedForwardDecoderLayer, - CustomFeedForwardEncoderLayer, +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, ) - from darts.models.forecasting.transformer_model import ( - TransformerModel, - _TransformerModule, +import torch.nn as nn + +from darts.models.components.transformer import ( + CustomFeedForwardDecoderLayer, + CustomFeedForwardEncoderLayer, +) +from darts.models.forecasting.transformer_model import ( + TransformerModel, + _TransformerModule, +) + + +class TestTransformerModel: + times = pd.date_range("20130101", "20130410") + pd_series = pd.Series(range(100), index=times) + series: TimeSeries = TimeSeries.from_series(pd_series) + series_multivariate = series.stack(series * 2) + module = _TransformerModule( + input_size=1, + input_chunk_length=1, + output_chunk_length=1, + output_chunk_shift=0, + train_sample_shape=((1, 1),), + output_size=1, + nr_params=1, + d_model=512, + nhead=8, + num_encoder_layers=6, + num_decoder_layers=6, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + norm_type=None, + custom_encoder=None, + custom_decoder=None, ) - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. Transformer tests will be skipped.") - TORCH_AVAILABLE = False - - -if TORCH_AVAILABLE: - - class TestTransformerModel: - times = pd.date_range("20130101", "20130410") - pd_series = pd.Series(range(100), index=times) - series: TimeSeries = TimeSeries.from_series(pd_series) - series_multivariate = series.stack(series * 2) - module = _TransformerModule( - input_size=1, + def test_fit(self, tmpdir_module): + # Test fit-save-load cycle + model2 = TransformerModel( input_chunk_length=1, output_chunk_length=1, - train_sample_shape=((1, 1),), - output_size=1, - nr_params=1, - d_model=512, - nhead=8, - num_encoder_layers=6, - num_decoder_layers=6, - dim_feedforward=2048, - dropout=0.1, - activation="relu", - norm_type=None, - custom_encoder=None, - custom_decoder=None, + n_epochs=2, + model_name="unittest-model-transformer", + work_dir=tmpdir_module, + save_checkpoints=True, + force_reset=True, + **tfm_kwargs, + ) + model2.fit(self.series) + model_loaded = model2.load_from_checkpoint( + model_name="unittest-model-transformer", + work_dir=tmpdir_module, + best=False, + map_location="cpu", ) + pred1 = model2.predict(n=6) + pred2 = model_loaded.predict(n=6) + + # Two models with the same parameters should deterministically yield the same output + np.testing.assert_array_equal(pred1.values(), pred2.values()) - def test_fit(self, tmpdir_module): - # Test fit-save-load cycle - model2 = TransformerModel( + # Another random model should not + model3 = TransformerModel( + input_chunk_length=1, output_chunk_length=1, n_epochs=1, **tfm_kwargs + ) + model3.fit(self.series) + pred3 = model3.predict(n=6) + assert not np.array_equal(pred1.values(), pred3.values()) + + # test short predict + pred4 = model3.predict(n=1) + assert len(pred4) == 1 + + # test validation series input + model3.fit(self.series[:60], val_series=self.series[60:]) + pred4 = model3.predict(n=6) + assert len(pred4) == 6 + + def helper_test_pred_length(self, pytorch_model, series): + model = pytorch_model( + input_chunk_length=1, output_chunk_length=3, n_epochs=1, **tfm_kwargs + ) + model.fit(series) + pred = model.predict(7) + assert len(pred) == 7 + pred = model.predict(2) + assert len(pred) == 2 + assert pred.width == 1 + pred = model.predict(4) + assert len(pred) == 4 + assert pred.width == 1 + + def test_pred_length(self): + series = tg.linear_timeseries(length=100) + self.helper_test_pred_length(TransformerModel, series) + + def test_activations(self): + with pytest.raises(ValueError): + model1 = TransformerModel( input_chunk_length=1, output_chunk_length=1, - n_epochs=2, - model_name="unittest-model-transformer", - work_dir=tmpdir_module, - save_checkpoints=True, - force_reset=True, - **tfm_kwargs - ) - model2.fit(self.series) - model_loaded = model2.load_from_checkpoint( - model_name="unittest-model-transformer", - work_dir=tmpdir_module, - best=False, - map_location="cpu", + activation="invalid", + **tfm_kwargs, ) - pred1 = model2.predict(n=6) - pred2 = model_loaded.predict(n=6) + model1.fit(self.series, epochs=1) - # Two models with the same parameters should deterministically yield the same output - np.testing.assert_array_equal(pred1.values(), pred2.values()) + # internal activation function uses PyTorch TransformerEncoderLayer + model2 = TransformerModel( + input_chunk_length=1, + output_chunk_length=1, + activation="gelu", + **tfm_kwargs, + ) + model2.fit(self.series, epochs=1) + assert isinstance( + model2.model.transformer.encoder.layers[0], nn.TransformerEncoderLayer + ) + assert isinstance( + model2.model.transformer.decoder.layers[0], nn.TransformerDecoderLayer + ) - # Another random model should not - model3 = TransformerModel( - input_chunk_length=1, output_chunk_length=1, n_epochs=1, **tfm_kwargs - ) - model3.fit(self.series) - pred3 = model3.predict(n=6) - assert not np.array_equal(pred1.values(), pred3.values()) - - # test short predict - pred4 = model3.predict(n=1) - assert len(pred4) == 1 - - # test validation series input - model3.fit(self.series[:60], val_series=self.series[60:]) - pred4 = model3.predict(n=6) - assert len(pred4) == 6 - - def helper_test_pred_length(self, pytorch_model, series): - model = pytorch_model( - input_chunk_length=1, output_chunk_length=3, n_epochs=1, **tfm_kwargs - ) - model.fit(series) - pred = model.predict(7) - assert len(pred) == 7 - pred = model.predict(2) - assert len(pred) == 2 - assert pred.width == 1 - pred = model.predict(4) - assert len(pred) == 4 - assert pred.width == 1 - - def test_pred_length(self): - series = tg.linear_timeseries(length=100) - self.helper_test_pred_length(TransformerModel, series) - - def test_activations(self): - with pytest.raises(ValueError): - model1 = TransformerModel( - input_chunk_length=1, - output_chunk_length=1, - activation="invalid", - **tfm_kwargs - ) - model1.fit(self.series, epochs=1) - - # internal activation function uses PyTorch TransformerEncoderLayer - model2 = TransformerModel( - input_chunk_length=1, - output_chunk_length=1, - activation="gelu", - **tfm_kwargs - ) - model2.fit(self.series, epochs=1) - assert isinstance( - model2.model.transformer.encoder.layers[0], nn.TransformerEncoderLayer - ) - assert isinstance( - model2.model.transformer.decoder.layers[0], nn.TransformerDecoderLayer - ) + # glue variant FFN uses our custom _FeedForwardEncoderLayer + model3 = TransformerModel( + input_chunk_length=1, + output_chunk_length=1, + activation="SwiGLU", + **tfm_kwargs, + ) + model3.fit(self.series, epochs=1) + assert isinstance( + model3.model.transformer.encoder.layers[0], + CustomFeedForwardEncoderLayer, + ) + assert isinstance( + model3.model.transformer.decoder.layers[0], + CustomFeedForwardDecoderLayer, + ) - # glue variant FFN uses our custom _FeedForwardEncoderLayer - model3 = TransformerModel( - input_chunk_length=1, - output_chunk_length=1, - activation="SwiGLU", - **tfm_kwargs - ) - model3.fit(self.series, epochs=1) - assert isinstance( - model3.model.transformer.encoder.layers[0], - CustomFeedForwardEncoderLayer, - ) - assert isinstance( - model3.model.transformer.decoder.layers[0], - CustomFeedForwardDecoderLayer, - ) + def test_layer_norm(self): + base_model = TransformerModel - def test_layer_norm(self): - base_model = TransformerModel + # default norm_type is None + model0 = base_model(input_chunk_length=1, output_chunk_length=1, **tfm_kwargs) + y0 = model0.fit(self.series, epochs=1) - # default norm_type is None - model0 = base_model( - input_chunk_length=1, output_chunk_length=1, **tfm_kwargs - ) - y0 = model0.fit(self.series, epochs=1) + model1 = base_model( + input_chunk_length=1, + output_chunk_length=1, + norm_type="RMSNorm", + **tfm_kwargs, + ) + y1 = model1.fit(self.series, epochs=1) - model1 = base_model( - input_chunk_length=1, - output_chunk_length=1, - norm_type="RMSNorm", - **tfm_kwargs - ) - y1 = model1.fit(self.series, epochs=1) + model2 = base_model( + input_chunk_length=1, + output_chunk_length=1, + norm_type=nn.LayerNorm, + **tfm_kwargs, + ) + y2 = model2.fit(self.series, epochs=1) - model2 = base_model( - input_chunk_length=1, - output_chunk_length=1, - norm_type=nn.LayerNorm, - **tfm_kwargs - ) - y2 = model2.fit(self.series, epochs=1) + model3 = base_model( + input_chunk_length=1, + output_chunk_length=1, + activation="gelu", + norm_type="RMSNorm", + **tfm_kwargs, + ) + y3 = model3.fit(self.series, epochs=1) + + assert y0 != y1 + assert y0 != y2 + assert y0 != y3 + assert y1 != y3 - model3 = base_model( + with pytest.raises(AttributeError): + model4 = base_model( input_chunk_length=1, output_chunk_length=1, - activation="gelu", - norm_type="RMSNorm", - **tfm_kwargs + norm_type="invalid", + **tfm_kwargs, ) - y3 = model3.fit(self.series, epochs=1) - - assert y0 != y1 - assert y0 != y2 - assert y0 != y3 - assert y1 != y3 - - with pytest.raises(AttributeError): - model4 = base_model( - input_chunk_length=1, - output_chunk_length=1, - norm_type="invalid", - **tfm_kwargs - ) - model4.fit(self.series, epochs=1) + model4.fit(self.series, epochs=1) diff --git a/darts/tests/models/forecasting/test_tsmixer.py b/darts/tests/models/forecasting/test_tsmixer.py new file mode 100644 index 0000000000..5eb7f80d57 --- /dev/null +++ b/darts/tests/models/forecasting/test_tsmixer.py @@ -0,0 +1,364 @@ +import pytest + +from darts.tests.conftest import TORCH_AVAILABLE + +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import numpy as np +import pandas as pd +import torch +from torch import nn + +from darts import concatenate +from darts.models.forecasting.tsmixer_model import TimeBatchNorm2d, TSMixerModel +from darts.tests.conftest import tfm_kwargs +from darts.utils import timeseries_generation as tg +from darts.utils.likelihood_models import GaussianLikelihood + + +class TestTSMixerModel: + np.random.seed(42) + torch.manual_seed(42) + + def test_creation(self): + model = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + likelihood=GaussianLikelihood(), + ) + + assert model.input_chunk_length == 1 + + def test_fit(self): + large_ts = tg.constant_timeseries(length=10, value=1.0) + small_ts = tg.constant_timeseries(length=10, value=0.1) + + model = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=10, + random_state=42, + **tfm_kwargs, + ) + + model.fit(large_ts) + pred = model.predict(n=2).values()[0] + + # Test whether model trained on one series is better + # than one trained on another + model2 = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=10, + random_state=42, + **tfm_kwargs, + ) + + model2.fit(small_ts) + pred2 = model2.predict(n=2).values()[0] + assert abs(pred2 - 0.1) < abs(pred - 0.1) + + # test short predict + pred3 = model2.predict(n=1) + assert len(pred3) == 1 + + def test_likelihood_fit(self): + ts = tg.constant_timeseries(length=3) + + model = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=1, + random_state=42, + likelihood=GaussianLikelihood(), + **tfm_kwargs, + ) + model.fit(ts) + # sampled from distribution + pred = model.predict(n=1, num_samples=20) + assert pred.n_samples == 20 + + # direct distribution parameter prediction + pred = model.predict(n=1, num_samples=1, predict_likelihood_parameters=True) + assert pred.n_components == 2 + assert pred.n_samples == 1 + + model = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=1, + random_state=42, + **tfm_kwargs, + ) + model.fit(ts) + # mc dropout + pred = model.predict(n=1, mc_dropout=True, num_samples=10) + assert pred.n_samples == 10 + + def test_logtensorboard(self, tmpdir_module): + ts = tg.constant_timeseries(length=4) + + # Test basic fit and predict + model = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + n_epochs=1, + log_tensorboard=True, + batch_size=2, + work_dir=tmpdir_module, + pl_trainer_kwargs={ + "log_every_n_steps": 1, + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) + model.fit(ts) + _ = model.predict(n=2) + + def test_static_covariates_support(self): + target_multi = concatenate( + [tg.sine_timeseries(length=10, freq="h")] * 2, axis=1 + ) + + target_multi = target_multi.with_static_covariates( + pd.DataFrame( + [[0.0, 1.0, 0, 2], [2.0, 3.0, 1, 3]], + columns=["st1", "st2", "cat1", "cat2"], + ) + ) + + # should work with cyclic encoding for time index + model = TSMixerModel( + input_chunk_length=3, + output_chunk_length=4, + add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, + pl_trainer_kwargs={ + "fast_dev_run": True, + **tfm_kwargs["pl_trainer_kwargs"], + }, + ) + model.fit(target_multi, verbose=False) + + assert model.model.static_cov_dim == np.prod( + target_multi.static_covariates.values.shape + ) + + # raise an error when trained with static covariates of wrong dimensionality + target_multi = target_multi.with_static_covariates( + pd.concat([target_multi.static_covariates] * 2, axis=1) + ) + with pytest.raises(ValueError): + model.predict(n=1, series=target_multi, verbose=False) + + # raise an error when trained with static covariates and trying to predict without + with pytest.raises(ValueError): + model.predict( + n=1, series=target_multi.with_static_covariates(None), verbose=False + ) + + # with `use_static_covariates=False`, we can predict without static covs + model = TSMixerModel( + input_chunk_length=3, + output_chunk_length=4, + use_static_covariates=False, + n_epochs=1, + **tfm_kwargs, + ) + model.fit(target_multi) + preds = model.predict(n=2, series=target_multi.with_static_covariates(None)) + assert preds.static_covariates is None + + model = TSMixerModel( + input_chunk_length=3, + output_chunk_length=4, + use_static_covariates=False, + n_epochs=1, + **tfm_kwargs, + ) + model.fit(target_multi.with_static_covariates(None)) + preds = model.predict(n=2, series=target_multi) + assert preds.static_covariates.equals(target_multi.static_covariates) + + @pytest.mark.parametrize("enable_rin", [True, False]) + def test_future_covariate_handling(self, enable_rin): + ts_time_index = tg.sine_timeseries(length=2, freq="h") + + model = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"future": "hour"}}, + use_reversible_instance_norm=enable_rin, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False, epochs=1) + + def test_past_covariate_handling(self): + ts_time_index = tg.sine_timeseries(length=2, freq="h") + + model = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"past": "hour"}}, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False, epochs=1) + + def test_future_and_past_covariate_handling(self): + ts_time_index = tg.sine_timeseries(length=2, freq="h") + + model = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, + **tfm_kwargs, + ) + model.fit(ts_time_index, verbose=False, epochs=1) + + def test_future_past_and_static_covariate_as_timeseries_handling(self): + ts_time_index = tg.sine_timeseries(length=2, freq="h") + ts_time_index = ts_time_index.with_static_covariates( + pd.DataFrame( + [ + [ + 0.0, + ] + ], + columns=["st1"], + ) + ) + for enable_rin in [True, False]: + # test with past_covariates timeseries + model = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"future": "hour"}}, + use_reversible_instance_norm=enable_rin, + **tfm_kwargs, + ) + model.fit( + ts_time_index, + past_covariates=ts_time_index, + verbose=False, + epochs=1, + ) + + # test with past_covariates and future_covariates timeseries + model = TSMixerModel( + input_chunk_length=1, + output_chunk_length=1, + add_encoders={"cyclic": {"future": "hour", "past": "hour"}}, + use_reversible_instance_norm=enable_rin, + **tfm_kwargs, + ) + model.fit( + ts_time_index, + past_covariates=ts_time_index, + future_covariates=ts_time_index, + verbose=False, + epochs=1, + ) + + @pytest.mark.parametrize( + "norm_type, expect_exception", + [ + ("LayerNorm", False), + ("LayerNormNoBias", False), + (nn.LayerNorm, False), + ("TimeBatchNorm2d", False), + ("invalid", True), + ], + ) + def test_layer_norms_with_parametrization(self, norm_type, expect_exception): + series = tg.sine_timeseries(length=3) + base_model = TSMixerModel + + if expect_exception: + with pytest.raises(ValueError): + model = base_model( + input_chunk_length=1, + output_chunk_length=1, + norm_type=norm_type, + **tfm_kwargs, + ) + model.fit(series, epochs=1) + else: + model = base_model( + input_chunk_length=1, + output_chunk_length=1, + norm_type=norm_type, + **tfm_kwargs, + ) + model.fit(series, epochs=1) + + @pytest.mark.parametrize( + "activation, expect_error", + [ + ("ReLU", False), + ("RReLU", False), + ("PReLU", False), + ("ELU", False), + ("Softplus", False), + ("Tanh", False), + ("SELU", False), + ("LeakyReLU", False), + ("Sigmoid", False), + ("invalid", True), + ], + ) + def test_activation_functions(self, activation, expect_error): + series = tg.sine_timeseries(length=3) + base_model = TSMixerModel + + if expect_error: + with pytest.raises(ValueError): + model = base_model( + input_chunk_length=1, + output_chunk_length=1, + activation=activation, + **tfm_kwargs, + ) + model.fit(series, epochs=1) + else: + model = base_model( + input_chunk_length=1, + output_chunk_length=1, + activation=activation, + **tfm_kwargs, + ) + model.fit(series, epochs=1) + + def test_time_batch_norm_3d(self): + torch.manual_seed(0) + + layer = TimeBatchNorm2d() + # 4D does not work + with pytest.raises(ValueError): + layer.forward(torch.randn(3, 3, 3, 3)) + + # 2D does not work + with pytest.raises(ValueError): + layer.forward(torch.randn(3, 3)) + + # 3D works + norm = layer.forward(torch.randn(3, 3, 3)).detach() + assert norm.mean().numpy() == pytest.approx(0.0, abs=0.1) + assert norm.std().numpy() == pytest.approx(1.0, abs=0.1) + + @pytest.mark.parametrize("batch_size", [1, 2, 5, 10]) + def test_time_batch_norm_2d_different_batch_sizes(self, batch_size): + layer = TimeBatchNorm2d() + input_tensor = torch.randn(batch_size, 3, 3) + output = layer.forward(input_tensor) + assert output.shape == input_tensor.shape + + def test_time_batch_norm_2d_gradients(self): + normalized_shape = (10, 32) + layer = TimeBatchNorm2d(normalized_shape) + input_tensor = torch.randn(5, 10, 32, requires_grad=True) + + output = layer.forward(input_tensor) + output.mean().backward() + + assert input_tensor.grad is not None diff --git a/darts/tests/test_logging.py b/darts/tests/test_logging.py index b980dc9c0e..8c73fe7318 100644 --- a/darts/tests/test_logging.py +++ b/darts/tests/test_logging.py @@ -61,13 +61,11 @@ def test_timeseries_constructor_error_log(): except Exception: pass - lc.check( - ( - "darts.timeseries", - "ERROR", - "ValueError: TimeSeries require DataArray of dimensionality 3 (('time', 'component', 'sample')).", - ) - ) + lc.check(( + "darts.timeseries", + "ERROR", + "ValueError: TimeSeries require DataArray of dimensionality 3 (('time', 'component', 'sample')).", + )) def test_timeseries_split_error_log(): @@ -82,13 +80,11 @@ def test_timeseries_split_error_log(): except Exception: pass - lc.check( - ( - "darts.timeseries", - "ERROR", - "ValueError: Timestamp must be between 2000-01-01 00:00:00 and 2000-01-03 00:00:00", - ) - ) + lc.check(( + "darts.timeseries", + "ERROR", + "ValueError: Timestamp must be between 2000-01-01 00:00:00 and 2000-01-03 00:00:00", + )) def test_time_log(): diff --git a/darts/tests/test_timeseries.py b/darts/tests/test_timeseries.py index 31f2a5fa02..41b04aebd4 100644 --- a/darts/tests/test_timeseries.py +++ b/darts/tests/test_timeseries.py @@ -1,3 +1,4 @@ +import itertools import math from tempfile import NamedTemporaryFile from unittest.mock import patch @@ -8,12 +9,9 @@ import xarray as xr from scipy.stats import kurtosis, skew -from darts import TimeSeries, concatenate -from darts.utils.timeseries_generation import ( - constant_timeseries, - generate_index, - linear_timeseries, -) +from darts import TimeSeries, concatenate, slice_intersect +from darts.utils.timeseries_generation import constant_timeseries, linear_timeseries +from darts.utils.utils import expand_arr, freqs, generate_index class TestTimeSeries: @@ -23,13 +21,13 @@ class TestTimeSeries: pd_series3 = pd.Series(range(15, 25), index=times) series1: TimeSeries = TimeSeries.from_series(pd_series1) series2: TimeSeries = TimeSeries.from_series(pd_series2) - series3: TimeSeries = TimeSeries.from_series(pd_series2) + series3: TimeSeries = TimeSeries.from_series(pd_series3) def test_creation(self): series_test = TimeSeries.from_series(self.pd_series1) assert series_test.pd_series().equals(self.pd_series1.astype(float)) - # Creation with a well formed array: + # Creation with a well-formed array: ar = xr.DataArray( np.random.randn(10, 2, 3), dims=("time", "component", "sample"), @@ -246,13 +244,16 @@ def test_univariate_component(self): "0", "component" ) mseries = concatenate([series] * 3, axis="component") - mseries = mseries.with_hierarchy( - {"component_1": ["component"], "component_2": ["component"]} - ) + mseries = mseries.with_hierarchy({ + "component_1": ["component"], + "component_2": ["component"], + }) - static_cov = pd.DataFrame( - {"dim0": [1, 2, 3], "dim1": [-2, -1, 0], "dim2": [0.0, 0.1, 0.2]} - ) + static_cov = pd.DataFrame({ + "dim0": [1, 2, 3], + "dim1": [-2, -1, 0], + "dim2": [0.0, 0.1, 0.2], + }) mseries = mseries.with_static_covariates(static_cov) @@ -506,7 +507,7 @@ def helper_test_split(test_case, test_series: TimeSeries): with pytest.raises(ValueError): test_series.split_before(value) - # Test split points between series indeces + # Test split points between series indices times = pd.date_range("20130101", "20130120", freq="2D") pd_series = pd.Series(range(10), index=times) test_series2: TimeSeries = TimeSeries.from_series(pd_series) @@ -531,58 +532,153 @@ def helper_test_drop(test_case, test_series: TimeSeries): assert test_series.freq_str == seriesA.freq_str assert test_series.freq_str == seriesB.freq_str + def test_rescale(self): + with pytest.raises(ValueError): + self.series1.rescale_with_value(1) + + seriesA = self.series2.rescale_with_value(0) + assert np.all(seriesA.values() == 0) + + seriesB = self.series2.rescale_with_value(-5) + assert self.series2 * -1.0 == seriesB + + seriesC = self.series2.rescale_with_value(1) + assert self.series2 * 0.2 == seriesC + + seriesD = self.series2.rescale_with_value( + 1e20 + ) # TODO: test will fail if value > 1e24 due to num imprecision + assert self.series2 * 0.2e20 == seriesD + @staticmethod - def helper_test_intersect(test_case, test_series: TimeSeries): - seriesA = TimeSeries.from_series( - pd.Series(range(2, 8), index=pd.date_range("20130102", "20130107")) + def helper_test_intersect(freq, is_mixed_freq: bool, is_univariate: bool): + start = pd.Timestamp("20130101") if isinstance(freq, str) else 0 + freq = pd.tseries.frequencies.to_offset(freq) if isinstance(freq, str) else freq + + # handle identical and mixed frequency setup + if not is_mixed_freq: + freq_other = freq + n_steps = 11 + elif "2" not in str(freq): # 1 or "1D" + freq_other = freq * 2 + n_steps = 21 + else: # 2 or "2D" + freq_other = freq / 2 + n_steps = 11 + freq_other = int(freq_other) if isinstance(freq_other, float) else freq_other + # if freq_other has a higher freq, we expect the slice to have the higher freq + freq_expected = freq if freq > freq_other else freq_other + idx = generate_index(start=start, freq=freq, length=n_steps) + end = idx[-1] + + n_cols = 1 if is_univariate else 2 + series = TimeSeries.from_times_and_values( + values=np.random.randn(n_steps, n_cols), times=idx ) - seriesB = test_series.slice_intersect(seriesA) - assert seriesB.start_time() == pd.Timestamp("20130102") - assert seriesB.end_time() == pd.Timestamp("20130107") + def check_intersect(other, start_, end_, freq_): + s_int = series.slice_intersect(other) + assert s_int.components.equals(series.components) + assert s_int.freq == freq_ - # Outside of range - seriesD = test_series.slice_intersect( - TimeSeries.from_series( - pd.Series(range(6, 13), index=pd.date_range("20130106", "20130112")) - ) - ) - assert seriesD.start_time() == pd.Timestamp("20130106") - assert seriesD.end_time() == pd.Timestamp("20130110") + if start_ is None: # empty slice + assert len(s_int) == 0 + return - # Small intersect - seriesE = test_series.slice_intersect( - TimeSeries.from_series( - pd.Series(range(9, 13), index=pd.date_range("20130109", "20130112")) - ) - ) - assert len(seriesE) == 2 + assert s_int.start_time() == start_ + assert s_int.end_time() == end_ - # No intersect - with pytest.raises(ValueError): - test_series.slice_intersect( - TimeSeries( - pd.Series(range(6, 13), index=pd.date_range("20130116", "20130122")) - ) + s_int_vals = series.slice_intersect_values(other, copy=False) + np.testing.assert_array_equal(s_int.all_values(), s_int_vals) + # check that first and last values are as expected + start_ = series.get_index_at_point(start_) + end_ = series.get_index_at_point(end_) + np.testing.assert_array_equal( + series[start_].all_values(), s_int_vals[0:1, :, :] ) + np.testing.assert_array_equal( + series[end_].all_values(), s_int_vals[-1:, :, :] + ) + # check that the time index is the same with `slice_intersect_times` + s_int_idx = series.slice_intersect_times(other, copy=False) + assert s_int.time_index.equals(s_int_idx) - def test_rescale(self): - with pytest.raises(ValueError): - self.series1.rescale_with_value(1) - - seriesA = self.series3.rescale_with_value(0) - assert np.all(seriesA.values() == 0) + assert slice_intersect([series, other]) == [ + series.slice_intersect(other), + other.slice_intersect(series), + ] - seriesB = self.series3.rescale_with_value(-5) - assert self.series3 * -1.0 == seriesB + # slice with exact range + startA = start + endA = end + idxA = generate_index(startA, endA, freq=freq_other) + seriesA = TimeSeries.from_series(pd.Series(range(len(idxA)), index=idxA)) + check_intersect(seriesA, startA, endA, freq_expected) + + # entire slice within the range + startB = start + freq + endB = startB + 6 * freq_other + idxB = generate_index(startB, endB, freq=freq_other) + seriesB = TimeSeries.from_series(pd.Series(range(len(idxB)), index=idxB)) + check_intersect(seriesB, startB, endB, freq_expected) + + # start outside of range + startC = start - 4 * freq + endC = start + 4 * freq_other + idxC = generate_index(startC, endC, freq=freq_other) + seriesC = TimeSeries.from_series(pd.Series(range(len(idxC)), index=idxC)) + check_intersect(seriesC, start, endC, freq_expected) + + # end outside of range + startD = start + 4 * freq + endD = end + 4 * freq_other + idxD = generate_index(startD, endD, freq=freq_other) + seriesD = TimeSeries.from_series(pd.Series(range(len(idxD)), index=idxD)) + check_intersect(seriesD, startD, end, freq_expected) + + # small intersect + startE = start + (n_steps - 1) * freq + endE = startE + 2 * freq_other + idxE = generate_index(startE, endE, freq=freq_other) + seriesE = TimeSeries.from_series(pd.Series(range(len(idxE)), index=idxE)) + check_intersect(seriesE, startE, end, freq_expected) - seriesC = self.series3.rescale_with_value(1) - assert self.series3 * 0.2 == seriesC + # No intersect + startF = end + 3 * freq + endF = startF + 6 * freq_other + idxF = generate_index(startF, endF, freq=freq_other) + seriesF = TimeSeries.from_series(pd.Series(range(len(idxF)), index=idxF)) + # for empty slices, we expect the original freq + check_intersect(seriesF, None, None, freq) + + # sequence with zero or one element + assert slice_intersect([]) == [] + assert slice_intersect([series]) == [series] + + # sequence with more than 2 elements + intersected_series = slice_intersect([series, seriesA, seriesE]) + s1_int = intersected_series[0] + s2_int = intersected_series[1] + s3_int = intersected_series[2] + + assert s1_int.time_index.equals(s2_int.time_index) and s1_int.time_index.equals( + s3_int.time_index + ) + assert s1_int.start_time() == startE + assert s1_int.end_time() == endA + + # check treatment different time index types + if series.has_datetime_index: + seriesF = TimeSeries.from_series( + pd.Series(range(len(idxF)), index=pd.to_numeric(idxF)) + ) + else: + seriesF = TimeSeries.from_series( + pd.Series(range(len(idxF)), index=pd.to_datetime(idxF)) + ) - seriesD = self.series3.rescale_with_value( - 1e20 - ) # TODO: test will fail if value > 1e24 due to num imprecision - assert self.series3 * 0.2e20 == seriesD + with pytest.raises(IndexError): + slice_intersect([series, seriesF]) @staticmethod def helper_test_shift(test_case, test_series: TimeSeries): @@ -607,7 +703,7 @@ def helper_test_shift(test_case, test_series: TimeSeries): test_series.shift(1e6) seriesM = TimeSeries.from_times_and_values( - pd.date_range("20130101", "20130601", freq="m"), range(5) + pd.date_range("20130101", "20130601", freq=freqs["ME"]), range(5) ) with pytest.raises(OverflowError): seriesM.shift(1e4) @@ -627,9 +723,11 @@ def helper_test_shift(test_case, test_series: TimeSeries): def helper_test_append(test_case, test_series: TimeSeries): # reconstruct series seriesA, seriesB = test_series.split_after(pd.Timestamp("20130106")) - assert seriesA.append(seriesB) == test_series - assert seriesA.append(seriesB).freq == test_series.freq - assert test_series.time_index.equals(seriesA.append(seriesB).time_index) + appended = seriesA.append(seriesB) + assert appended == test_series + assert appended.freq == test_series.freq + assert test_series.time_index.equals(appended.time_index) + assert appended.components.equals(seriesA.components) # Creating a gap is not allowed seriesC = test_series.drop_before(pd.Timestamp("20130108")) @@ -648,23 +746,26 @@ def helper_test_append_values(test_case, test_series: TimeSeries): # reconstruct series seriesA, seriesB = test_series.split_after(pd.Timestamp("20130106")) arrayB = seriesB.all_values() - assert seriesA.append_values(arrayB) == test_series - assert test_series.time_index.equals(seriesA.append_values(arrayB).time_index) + appended = seriesA.append_values(arrayB) + assert appended == test_series + assert test_series.time_index.equals(appended.time_index) # arrayB shape shouldn't affect append_values output: squeezed_arrayB = arrayB.squeeze() - assert seriesA.append_values(squeezed_arrayB) == test_series - assert test_series.time_index.equals( - seriesA.append_values(squeezed_arrayB).time_index - ) + appended_sq = seriesA.append_values(squeezed_arrayB) + assert appended_sq == test_series + assert test_series.time_index.equals(appended_sq.time_index) + assert appended_sq.components.equals(seriesA.components) @staticmethod def helper_test_prepend(test_case, test_series: TimeSeries): # reconstruct series seriesA, seriesB = test_series.split_after(pd.Timestamp("20130106")) - assert seriesB.prepend(seriesA) == test_series - assert seriesB.prepend(seriesA).freq == test_series.freq - assert test_series.time_index.equals(seriesB.prepend(seriesA).time_index) + prepended = seriesB.prepend(seriesA) + assert prepended == test_series + assert prepended.freq == test_series.freq + assert test_series.time_index.equals(prepended.time_index) + assert prepended.components.equals(seriesB.components) # Creating a gap is not allowed seriesC = test_series.drop_before(pd.Timestamp("20130108")) @@ -683,15 +784,20 @@ def helper_test_prepend_values(test_case, test_series: TimeSeries): # reconstruct series seriesA, seriesB = test_series.split_after(pd.Timestamp("20130106")) arrayA = seriesA.data_array().values - assert seriesB.prepend_values(arrayA) == test_series - assert test_series.time_index.equals(seriesB.prepend_values(arrayA).time_index) + prepended = seriesB.prepend_values(arrayA) + assert prepended == test_series + assert test_series.time_index.equals(prepended.time_index) + assert prepended.components.equals(test_series.components) # arrayB shape shouldn't affect append_values output: squeezed_arrayA = arrayA.squeeze() - assert seriesB.prepend_values(squeezed_arrayA) == test_series - assert test_series.time_index.equals( - seriesB.prepend_values(squeezed_arrayA).time_index - ) + prepended_sq = seriesB.prepend_values(squeezed_arrayA) + assert prepended_sq == test_series + assert test_series.time_index.equals(prepended_sq.time_index) + assert prepended_sq.components.equals(test_series.components) + + # component and sample dimension should match + assert prepended._xa.shape[1:] == test_series._xa.shape[1:] def test_slice(self): TestTimeSeries.helper_test_slice(self, self.series1) @@ -702,8 +808,14 @@ def test_split(self): def test_drop(self): TestTimeSeries.helper_test_drop(self, self.series1) - def test_intersect(self): - TestTimeSeries.helper_test_intersect(self, self.series1) + @pytest.mark.parametrize( + "config", itertools.product(["D", "2D", 1, 2], [False, True]) + ) + def test_intersect(self, config): + """Tests slice intersection between two series with datetime or range index with identical and + mixed frequencies.""" + freq, mixed_freq = config + self.helper_test_intersect(freq, mixed_freq, is_univariate=True) def test_shift(self): TestTimeSeries.helper_test_shift(self, self.series1) @@ -711,8 +823,8 @@ def test_shift(self): def test_append(self): TestTimeSeries.helper_test_append(self, self.series1) # Check `append` deals with `RangeIndex` series correctly: - series_1 = linear_timeseries(start=1, length=5, freq=2) - series_2 = linear_timeseries(start=11, length=2, freq=2) + series_1 = linear_timeseries(start=1, length=5, freq=2, column_name=freqs["YE"]) + series_2 = linear_timeseries(start=11, length=2, freq=2, column_name="B") appended = series_1.append(series_2) expected_vals = np.concatenate( [series_1.all_values(), series_2.all_values()], axis=0 @@ -720,24 +832,120 @@ def test_append(self): expected_idx = pd.RangeIndex(start=1, stop=15, step=2) assert np.allclose(appended.all_values(), expected_vals) assert appended.time_index.equals(expected_idx) + assert appended.components.equals(series_1.components) - def test_append_values(self): - TestTimeSeries.helper_test_append_values(self, self.series1) - # Check `append_values` deals with `RangeIndex` series correctly: - series = linear_timeseries(start=1, length=5, freq=2) - appended = series.append_values(np.ones((2, 1, 1))) - expected_vals = np.concatenate( - [series.all_values(), np.ones((2, 1, 1))], axis=0 + @pytest.mark.parametrize( + "config", + itertools.product( + [ + ( # univariate array + np.array([0, 1, 2]).reshape((3, 1, 1)), + np.array([0, 1]).reshape((2, 1, 1)), + ), + ( # multivariate array + np.array([0, 1, 2, 3, 4, 5]).reshape((3, 2, 1)), + np.array([0, 1, 2, 3]).reshape((2, 2, 1)), + ), + ( # empty array + np.array([0, 1, 2]).reshape((3, 1, 1)), + np.array([]).reshape((0, 1, 1)), + ), + ( + # wrong number of components + np.array([0, 1, 2]).reshape((3, 1, 1)), + np.array([0, 1, 2, 3]).reshape((2, 2, 1)), + ), + ( + # wrong number of samples + np.array([0, 1, 2]).reshape((3, 1, 1)), + np.array([0, 1, 2, 3]).reshape((2, 1, 2)), + ), + ( # univariate list with times + np.array([0, 1, 2]).reshape((3, 1, 1)), + [0, 1], + ), + ( # univariate list with times and components + np.array([0, 1, 2]).reshape((3, 1, 1)), + [[0], [1]], + ), + ( # univariate list with times, components and samples + np.array([0, 1, 2]).reshape((3, 1, 1)), + [[[0]], [[1]]], + ), + ( # multivar with list has wrong shape + np.array([0, 1, 2, 3]).reshape((2, 2, 1)), + [[1, 2], [3, 4]], + ), + ( # list with wrong number of components + np.array([0, 1, 2]).reshape((3, 1, 1)), + [[1, 2], [3, 4]], + ), + ( # list with wrong number of samples + np.array([0, 1, 2]).reshape((3, 1, 1)), + [[[0, 1]], [[1, 2]]], + ), + ( # multivar input but list has wrong shape + np.array([0, 1, 2, 3]).reshape((2, 2, 1)), + [1, 2], + ), + ], + [True, False], + ["append_values", "prepend_values"], + ), + ) + def test_append_and_prepend_values(self, config): + (series_vals, vals), is_datetime, method = config + start = "20240101" if is_datetime else 1 + series_idx = generate_index( + start=start, length=len(series_vals), name="some_name" ) - expected_idx = pd.RangeIndex(start=1, stop=15, step=2) + series = TimeSeries.from_times_and_values( + times=series_idx, + values=series_vals, + ) + + # expand if it's a list + vals_arr = np.array(vals) if isinstance(vals, list) else vals + vals_arr = expand_arr(vals_arr, ndim=3) + + ts_method = getattr(TimeSeries, method) + + if vals_arr.shape[1:] != series_vals.shape[1:]: + with pytest.raises(ValueError) as exc: + _ = ts_method(series, vals) + assert str(exc.value).startswith( + "The (expanded) values must have the same number of components and samples" + ) + return + + appended = ts_method(series, vals) + + if method == "append_values": + expected_vals = np.concatenate([series_vals, vals_arr], axis=0) + expected_idx = generate_index( + start=series.start_time(), + length=len(series_vals) + len(vals), + freq=series.freq, + ) + else: + expected_vals = np.concatenate([vals_arr, series_vals], axis=0) + expected_idx = generate_index( + end=series.end_time(), + length=len(series_vals) + len(vals), + freq=series.freq, + ) + assert np.allclose(appended.all_values(), expected_vals) assert appended.time_index.equals(expected_idx) + assert appended.components.equals(series.components) + assert appended._xa.shape[1:] == series._xa.shape[1:] + assert appended.time_index.name == series.time_index.name def test_prepend(self): TestTimeSeries.helper_test_prepend(self, self.series1) # Check `prepend` deals with `RangeIndex` series correctly: - series_1 = linear_timeseries(start=1, length=5, freq=2) - series_2 = linear_timeseries(start=11, length=2, freq=2) + series_1 = linear_timeseries(start=1, length=5, freq=2, column_name=freqs["YE"]) + series_2 = linear_timeseries(start=11, length=2, freq=2, column_name="B") prepended = series_2.prepend(series_1) expected_vals = np.concatenate( [series_1.all_values(), series_2.all_values()], axis=0 @@ -745,35 +953,52 @@ def test_prepend(self): expected_idx = pd.RangeIndex(start=1, stop=15, step=2) assert np.allclose(prepended.all_values(), expected_vals) assert prepended.time_index.equals(expected_idx) + assert prepended.components.equals(series_1.components) + + @pytest.mark.parametrize( + "config", + [ + ("with_values", True), + ("with_times_and_values", True), + ("with_times_and_values", False), + ], + ) + def test_with_x_values(self, config): + """Test `with_values`, and `with_times_and_values`, where the latter can have identical or different times.""" + method, use_entire_index = config + mask = slice(None) if use_entire_index else slice(1, 4) - def test_prepend_values(self): - TestTimeSeries.helper_test_prepend_values(self, self.series1) - # Check `prepend_values` deals with `RangeIndex` series correctly: - series = linear_timeseries(start=1, length=5, freq=2) - prepended = series.prepend_values(np.ones((2, 1, 1))) - expected_vals = np.concatenate( - [np.ones((2, 1, 1)), series.all_values()], axis=0 - ) - expected_idx = pd.RangeIndex(start=-3, stop=11, step=2) - assert np.allclose(prepended.all_values(), expected_vals) - assert prepended.time_index.equals(expected_idx) - - def test_with_values(self): vals = np.random.rand(5, 10, 3) series = TimeSeries.from_values(vals) - series2 = series.with_values(vals + 1) - series3 = series2.with_values(series2.all_values() - 1) + + vals = vals[mask] + series[::2] + kwargs = ( + {"times": series.time_index[mask]} + if method == "with_times_and_values" + else dict() + ) + series2 = getattr(series, method)(values=vals + 1, **kwargs) + series3 = getattr(series2, method)(values=series2.all_values() - 1, **kwargs) # values should work - np.testing.assert_allclose(series3.all_values(), series.all_values()) + np.testing.assert_allclose(series3.all_values(), series[mask].all_values()) np.testing.assert_allclose(series2.all_values(), vals + 1) # should fail if nr components is not the same: with pytest.raises(ValueError): - series.with_values(np.random.rand(5, 11, 3)) + getattr(series, method)(values=np.random.rand(len(vals), 11, 3), **kwargs) + + # should not fail if nr samples is not the same: + getattr(series, method)(values=np.random.rand(len(vals), 10, 2), **kwargs) # should not fail if nr samples is not the same: - series.with_values(np.random.rand(5, 10, 2)) + getattr(series, method)(values=np.random.rand(len(vals), 10, 2), **kwargs) + + # should not fail for univariate deterministic series if values is a 1D array + getattr(series[series.columns[0]], method)( + values=np.random.rand(len(vals)), **kwargs + ) def test_cumsum(self): cumsum_expected = TimeSeries.from_dataframe( @@ -868,24 +1093,86 @@ def test_ops(self): # Cannot divide by 0. self.series1 / 0 + def test_ops_array(self): + # can work with xarray directly + series2_x = self.series2.data_array(copy=False) + assert self.series1 + self.series2 == self.series1 + series2_x + assert self.series1 - self.series2 == self.series1 - series2_x + assert self.series1 * self.series2 == self.series1 * series2_x + assert self.series1 / self.series2 == self.series1 / series2_x + assert self.series1**self.series2 == self.series1**series2_x + # can work with ndarray directly + series2_nd = self.series2.all_values(copy=False) + assert self.series1 + self.series2 == self.series1 + series2_nd + assert self.series1 - self.series2 == self.series1 - series2_nd + assert self.series1 * self.series2 == self.series1 * series2_nd + assert self.series1 / self.series2 == self.series1 / series2_nd + assert self.series1**self.series2 == self.series1**series2_nd + + @pytest.mark.parametrize( + "broadcast_components,broadcast_samples", + itertools.product([True, False], [True, False]), + ) + def test_ops_broadcasting(self, broadcast_components, broadcast_samples): + # generate random time-series + t, c, s = 10, 5, 3 + arrayA = np.random.rand(t, c, s) + arrayB = np.random.rand( + t, 1 if broadcast_components else c, 1 if broadcast_samples else s + ) + + seriesA = TimeSeries.from_times_and_values(self.times, arrayA) + seriesB = TimeSeries.from_times_and_values(self.times, arrayB) + + seriesAdd = TimeSeries.from_times_and_values(self.times, arrayA + arrayB) + seriesSub = TimeSeries.from_times_and_values(self.times, arrayA - arrayB) + seriesMul = TimeSeries.from_times_and_values(self.times, arrayA * arrayB) + seriesDiv = TimeSeries.from_times_and_values(self.times, arrayA / arrayB) + seriesPow = TimeSeries.from_times_and_values(self.times, arrayA**arrayB) + + # assert different operations; must be equivalent to operations with scalar + assert seriesA + seriesB == seriesAdd + assert seriesA - seriesB == seriesSub + assert seriesA * seriesB == seriesMul + assert seriesA / seriesB == seriesDiv + assert seriesA**seriesB == seriesPow + + # it also works with numpy arrays directly + assert seriesA + arrayB == seriesAdd + assert seriesA - arrayB == seriesSub + assert seriesA * arrayB == seriesMul + assert seriesA / arrayB == seriesDiv + assert seriesA**arrayB == seriesPow + def test_getitem_datetime_index(self): - seriesA: TimeSeries = self.series1.drop_after(pd.Timestamp("20130105")) - assert self.series1[pd.date_range("20130101", " 20130104")] == seriesA - assert self.series1[:4] == seriesA + series_short: TimeSeries = self.series1.drop_after(pd.Timestamp("20130105")) + series_stride_2: TimeSeries = self.series1.with_times_and_values( + times=self.series1.time_index[::2], + values=self.series1.all_values()[::2], + ) + # getitem from slice + assert self.series1[:] == self.series1[::] == self.series1[::1] == self.series1 + assert self.series1[::2] == series_stride_2 + assert self.series1[::2].freq == self.series1.freq * 2 + assert self.series1[:4] == series_short + # getitem from dates + assert self.series1[pd.date_range("20130101", " 20130104")] == series_short assert self.series1[pd.Timestamp("20130101")] == TimeSeries.from_dataframe( self.series1.pd_dataframe()[:1], freq=self.series1.freq ) assert ( - self.series1[pd.Timestamp("20130101") : pd.Timestamp("20130104")] == seriesA + self.series1[pd.Timestamp("20130101") : pd.Timestamp("20130104")] + == series_short ) + # not all dates in index with pytest.raises(KeyError): self.series1[pd.date_range("19990101", "19990201")] - + # date not in index with pytest.raises(KeyError): self.series1["19990101"] - - with pytest.raises(IndexError): + # cannot reverse series + with pytest.raises(ValueError): self.series1[::-1] def test_getitem_integer_index(self): @@ -901,6 +1188,15 @@ def test_getitem_integer_index(self): assert series.end_time() == end assert series[idx_int] == series == series[0 : len(series)] + # getitem from slice + series_stride_2 = self.series1.with_times_and_values( + times=series.time_index[::2], + values=series.all_values()[::2], + ) + assert series[:] == series[::] == series[::1] == series + assert series[::2] == series_stride_2 + assert series[::2].freq == series.freq * 2 + series_single = series.drop_after(start + 2 * freq) assert ( series[pd.RangeIndex(start=start, stop=start + 2 * freq, step=freq)] @@ -935,10 +1231,8 @@ def test_getitem_integer_index(self): def test_getitem_frequency_inferrence(self): ts = self.series1 assert ts.freq == "D" - ts_got = ts[1::2] - assert ts_got.freq == "2D" - ts_got = ts[pd.Timestamp("20130103") :: 2] - assert ts_got.freq == "2D" + assert ts[::2].freq == ts[1::2].freq == ts[:-1:2].freq == "2D" + assert ts[pd.Timestamp("20130103") :: 2].freq == "2D" idx = pd.DatetimeIndex(["20130102", "20130105", "20130108"]) ts_idx = ts[idx] @@ -970,9 +1264,8 @@ def test_getitem_frequency_inferrence_integer_index(self): ) assert ts.freq == freq - ts_got = ts[1::2] - assert ts_got.start_time() == start + freq - assert ts_got.freq == 2 * freq + assert ts[::2].freq == ts[1::2].freq == ts[:-1:2].freq == 2 * freq + assert ts[1::2].start_time() == start + freq idx = pd.RangeIndex( start=start + 2 * freq, stop=start + 4 * freq, step=2 * freq @@ -1026,56 +1319,51 @@ def test_fill_missing_dates(self): "C", "D", "W", - "M", - "SM", - "BM", - "CBM", + freqs["ME"], + freqs["SME"], + freqs["BME"], + freqs["CBME"], "MS", "SMS", "BMS", "CBMS", - "Q", - "BQ", + freqs["QE"], + freqs["BQE"], "QS", "BQS", - "A", - "Y", - "BA", - "BY", - "AS", + freqs["YE"], + freqs["BYE"], + freqs["YS"], "YS", - "BAS", + freqs["BYS"], "BYS", - "BH", - "H", - "T", - "min", - "S", - "L", - "U", - "us", - "N", + freqs["bh"], + freqs["h"], + freqs["min"], + freqs["s"], + freqs["ms"], + freqs["us"], + freqs["ns"], ] # fill_missing_dates will find multiple inferred frequencies (i.e. for 'B' it finds {'B', 'D'}) -> good offset_aliases_raise = [ "B", "C", - "SM", - "BM", - "CBM", + freqs["SME"], + freqs["BME"], + freqs["CBME"], "SMS", "BMS", "CBMS", - "BQ", - "BA", - "BY", - "BAS", + freqs["BQE"], + freqs["BYE"], + freqs["BYS"], "BYS", - "BH", + freqs["bh"], "BQS", ] # frequency cannot be inferred for these types (finds '15D' instead of 'SM') - offset_not_supported = ["SM", "SMS"] + offset_not_supported = [freqs["SME"], "SMS"] ts_length = 25 for offset_alias in offset_aliases: @@ -1151,32 +1439,214 @@ def test_fillna_value(self): assert series_1 == series_no_nan def test_resample_timeseries(self): + # 01/01/2013 -> 10/01/2013, one value per day: 0 1 2 3 … 9 times = pd.date_range("20130101", "20130110") pd_series = pd.Series(range(10), index=times) timeseries = TimeSeries.from_series(pd_series) - resampled_timeseries = timeseries.resample("h") - assert resampled_timeseries.freq_str.lower() == "h" + # up-sample with pad + # one value per hour -> same value for the whole day + resampled_timeseries = timeseries.resample(freqs["h"]) + assert resampled_timeseries.freq_str == freqs["h"] + # day 1: -> 0 assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101020000")] == 0 + # day 2: -> 1 assert resampled_timeseries.pd_series().at[pd.Timestamp("20130102020000")] == 1 + # day 9: -> 8 assert resampled_timeseries.pd_series().at[pd.Timestamp("20130109090000")] == 8 + # down-sample with pad + # one value per 2 days -> entries for every other days do not exist, value of the first day is kept resampled_timeseries = timeseries.resample("2D") assert resampled_timeseries.freq_str == "2D" + # day 1: -> 0 assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 0 + # day 2: -> does not exist with pytest.raises(KeyError): resampled_timeseries.pd_series().at[pd.Timestamp("20130102")] - + # day 9: -> 8 assert resampled_timeseries.pd_series().at[pd.Timestamp("20130109")] == 8 + # down-sample with all + # one value per 2 days -> if all scalar in group are > 0 then 1 else 0 + resampled_timeseries = timeseries.resample("2D", "all") + # group: [0,1] -> 0 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 0 + # group: [2,3] -> 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 1 + + # down-sample with any + # one value per 2 days -> if any scalar in group is > 0 then 1 else 0 + resampled_timeseries = timeseries.resample("2D", "any") + # group: [0,1] -> 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 1 + # group: [2,3] -> 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 1 + + # up-sample with asfreq + # two values per day -> holes are filled with nan + resampled_timeseries = timeseries.resample("12h", "asfreq") + # day 1, 0h -> 0 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101000000")] == 0 + # day 1, 12h -> nan + assert pd.isna( + resampled_timeseries.pd_series().at[pd.Timestamp("20130101120000")] + ) + + # up-sample with backfill + # two values per day -> holes are filled with next value + resampled_timeseries = timeseries.resample("12h", "backfill") + # hole in day 1 -> 1, from day 2 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101120000")] == 1 + # day 2 -> 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130102000000")] == 1 + + # up-sample with bfill (same as backfill) + # two values per day -> holes are filled with next value + resampled_timeseries = timeseries.resample("12h", "bfill") + # hole in day 1 -> 1, from day 2 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101120000")] == 1 + # day 2 -> 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130102000000")] == 1 + + # down-sample with count + # two values per day -> count number of values per group + resampled_timeseries = timeseries.resample("2D", "count") + # days 1,2 grouped -> 2 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 2 + # days 3,4 grouped -> 2 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 2 + + # up-sample with ffill + # two values per day -> holes are filled with previous value + resampled_timeseries = timeseries.resample("12h", "ffill") + # day 1 -> 0 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101000000")] == 0 + # hole in day 1 -> 0, from day 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101120000")] == 0 + + # down-sample with first + # one value per 2 days -> keep first value of the group + resampled_timeseries = timeseries.resample("2D", "first") + # days 1,2 grouped -> 0, from day 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 0 + # days 3,4 grouped -> 2, from day 3 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 2 + + # up-sample with interpolate + # two values per day -> holes are filled with linearly interpolated values + resampled_timeseries = timeseries.resample("12h", "interpolate") + # day 1, 0h -> 0 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101000000")] == 0 + # between [0,1] -> 0.5 + assert ( + resampled_timeseries.pd_series().at[pd.Timestamp("20130101120000")] == 0.5 + ) + + # down-sample with last + # one value per 2 days -> keep last value of the group + resampled_timeseries = timeseries.resample("2D", "last") + # days 1,2 grouped -> 1, from day 2 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 1 + # days 3,4 grouped -> 3, from day 4 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 3 + + # down-sample with max + # one value per 2 days -> keep the max value of the group + resampled_timeseries = timeseries.resample("2D", "max") + # days 1,2 group: [0,1] -> 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 1 + # days 3,4 group: [2,3] -> 3 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 3 + + # down-sample with mean + # one value per 2 days -> keep the mean of the values of the group + resampled_timeseries = timeseries.resample("2D", "mean") + # days 1,2 group: [0,1] -> 0.5 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 0.5 + # days 3,4 group: [2,3] -> 2.5 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 2.5 + + # down-sample with median + # one value per 3 days -> keep the median of the values of the group + resampled_timeseries = timeseries.resample("3D", "median") + # days 1,2,3 group: [0,1,2] -> 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 1 + # days 4,5,6 group: [3,4,5] -> 4 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130104")] == 4 + + # down-sample with min + # one value per 2 days -> keep the min value of the group + resampled_timeseries = timeseries.resample("2D", "min") + # days 1,2 group: [0,1] -> 0 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 0 + # days 3,4 group: [2,3] -> 2 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 2 + + # up-sample with nearest (next is the nearest if equals) + # two values per day -> holes are filled with nearest value + resampled_timeseries = timeseries.resample("12h", "nearest") + # days 1.5 -> 1 from day 2 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101120000")] == 1 + # days 2 -> 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130102000000")] == 1 + + # down-sample with quantile + # one value per 2 days -> keep the quantile of the values of the group + resampled_timeseries = timeseries.resample( + "2D", "quantile", method_kwargs={"q": 0.05} + ) + # days 1,2 group: [0,1] -> 0.05 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 0.05 + # days 3,4 group: [2,3] -> 2.05 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 2.05 + + # down-sample with std + # one value per 2 days -> keep the std of the values of the group + resampled_timeseries = timeseries.resample("2D", "std") + # days 1,2 group: [0,1] -> 0.5 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 0.5 + # days 3,4 group: [2,3] -> 0.5 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 0.5 + + # down-sample with sum using reduce + # one value per 2 days -> keep the sum of the values of the group + resampled_timeseries = timeseries.resample( + "2D", "reduce", method_kwargs={"func": np.sum} + ) + # days 1,2 group: [0,1] -> 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 1 + # days 3,4 group: [2,3] -> 5 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 5 + + # down-sample with sum + # one value per 2 days -> keep the sum of the values of the group + resampled_timeseries = timeseries.resample("2D", "sum") + # days 1,2 group: [0,1] -> 1 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 1 + # days 3,4 group: [2,3] -> 5 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 5 + + # down-sample with var + # one value per 2 days -> keep the sum of the values of the group + resampled_timeseries = timeseries.resample("2D", "var") + # days 1,2 group: [0,1] -> 0.25 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130101")] == 0.25 + # days 3,4 group: [2,4] -> 0.25 + assert resampled_timeseries.pd_series().at[pd.Timestamp("20130103")] == 0.25 + + # unsupported method: apply + with pytest.raises(ValueError): + _ = timeseries.resample("2D", "apply") + # using offset to avoid nan in the first value times = pd.date_range( - start=pd.Timestamp("20200101233000"), periods=10, freq="15T" + start=pd.Timestamp("20200101233000"), periods=10, freq="15" + freqs["min"] ) pd_series = pd.Series(range(10), index=times) timeseries = TimeSeries.from_series(pd_series) resampled_timeseries = timeseries.resample( - freq="1h", offset=pd.Timedelta("30T") + freq="1" + freqs["h"], offset=pd.Timedelta("30" + freqs["min"]) ) assert resampled_timeseries.pd_series().at[pd.Timestamp("20200101233000")] == 0 @@ -1226,7 +1696,7 @@ def test_short_series_creation(self): pd.date_range("20130101", "20130105"), range(5), fill_missing_dates=False, - freq="M", + freq=freqs["ME"], ) assert seriesA.freq == "D" # test successful instantiation of TimeSeries with length 2 @@ -1327,10 +1797,15 @@ def test_map(self): df_01 = series.pd_dataframe() df_012 = series.pd_dataframe() - df_0[["0"]] = df_0[["0"]].applymap(fn) - df_2[["2"]] = df_2[["2"]].applymap(fn) - df_01[["0", "1"]] = df_01[["0", "1"]].applymap(fn) - df_012 = df_012.applymap(fn) + PANDAS_210 = pd.__version__ >= "2.1.0" + select_map = "map" + if not PANDAS_210: + select_map = "applymap" + + df_0[["0"]] = getattr(df_0[["0"]], select_map)(fn) + df_2[["2"]] = getattr(df_2[["2"]], select_map)(fn) + df_01[["0", "1"]] = getattr(df_01[["0", "1"]], select_map)(fn) + df_012 = getattr(df_012, select_map)(fn) series_0 = TimeSeries.from_dataframe(df_0, freq="D") series_2 = TimeSeries.from_dataframe(df_2, freq="D") @@ -1386,8 +1861,8 @@ def add(x, y, z): def test_gaps(self): times1 = pd.date_range("20130101", "20130110") - times2 = pd.date_range("20120101", "20210301", freq="Q") - times3 = pd.date_range("20120101", "20210301", freq="AS") + times2 = pd.date_range("20120101", "20210301", freq=freqs["QE"]) + times3 = pd.date_range("20120101", "20210301", freq=freqs["YS"]) times4 = pd.date_range("20120101", "20210301", freq="2MS") pd_series1 = pd.Series( @@ -1451,23 +1926,19 @@ def test_gaps(self): assert gaps6["gap_size"].values.tolist() == [1, 5, 9] assert ( gaps6["gap_start"] - == pd.DatetimeIndex( - [ - pd.Timestamp("20130901"), - pd.Timestamp("20160101"), - pd.Timestamp("20191101"), - ] - ) + == pd.DatetimeIndex([ + pd.Timestamp("20130901"), + pd.Timestamp("20160101"), + pd.Timestamp("20191101"), + ]) ).all() assert ( gaps6["gap_end"] - == pd.DatetimeIndex( - [ - pd.Timestamp("20130901"), - pd.Timestamp("20160901"), - pd.Timestamp("20210301"), - ] - ) + == pd.DatetimeIndex([ + pd.Timestamp("20130901"), + pd.Timestamp("20160901"), + pd.Timestamp("20210301"), + ]) ).all() gaps7 = series7.gaps() assert gaps7.empty @@ -2103,7 +2574,7 @@ def test_time_col_convert_rangeindex(self): ts = TimeSeries.from_dataframe(df=df, time_col="Time") # check type (should convert to RangeIndex): - assert type(ts.time_index) == pd.RangeIndex + assert type(ts.time_index) is pd.RangeIndex # check values inside the index (should be sorted correctly): assert list(ts.time_index) == sorted(expected) @@ -2167,7 +2638,7 @@ def test_time_col_with_tz(self): assert ts.time_index.tz is None time_range_H = pd.date_range( - start="20200518", end="20200521", freq="H", tz="CET" + start="20200518", end="20200521", freq=freqs["h"], tz="CET" ) values = np.random.uniform(low=-10, high=10, size=len(time_range_H)) @@ -2177,8 +2648,8 @@ def test_time_col_with_tz(self): assert list(ts.time_index.tz_localize("CET")) == list(time_range_H) assert ts.time_index.tz is None - serie = pd.Series(data=values, index=time_range_H) - ts = TimeSeries.from_series(pd_series=serie) + series = pd.Series(data=values, index=time_range_H) + ts = TimeSeries.from_series(pd_series=series) assert list(ts.time_index) == list(time_range_H.tz_localize(None)) assert list(ts.time_index.tz_localize("CET")) == list(time_range_H) assert ts.time_index.tz is None diff --git a/darts/tests/test_timeseries_multivariate.py b/darts/tests/test_timeseries_multivariate.py index bf56251194..70266424a5 100644 --- a/darts/tests/test_timeseries_multivariate.py +++ b/darts/tests/test_timeseries_multivariate.py @@ -1,13 +1,15 @@ +import itertools + import numpy as np import pandas as pd import pytest from darts import TimeSeries from darts.tests.test_timeseries import TestTimeSeries +from darts.utils.utils import freqs class TestTimeSeriesMultivariate: - times1 = pd.date_range("20130101", "20130110") times2 = pd.date_range("20130206", "20130215") dataframe1 = pd.DataFrame( @@ -91,8 +93,12 @@ def test_split(self): def test_drop(self): TestTimeSeries.helper_test_drop(self, self.series1) - def test_intersect(self): - TestTimeSeries.helper_test_intersect(self, self.series1) + @pytest.mark.parametrize( + "config", itertools.product(["D", "2D", 1, 2], [False, True]) + ) + def test_intersect(self, config): + freq, mixed_freq = config + TestTimeSeries.helper_test_intersect(freq, mixed_freq, is_univariate=False) def test_shift(self): TestTimeSeries.helper_test_shift(self, self.series1) @@ -162,11 +168,12 @@ def test_univariate_component(self): assert self.series1 == seriesB def test_add_datetime_attribute(self): + """datetime_attributes are 0-indexed (shift is applied when necessary)""" seriesA = self.series1.add_datetime_attribute("day") assert seriesA.width == self.series1.width + 1 assert set( seriesA.pd_dataframe().iloc[:, seriesA.width - 1].values.flatten() - ) == set(range(1, 11)) + ) == set(range(0, 10)) seriesB = self.series3.add_datetime_attribute("day", True) assert seriesB.width == self.series3.width + 31 assert set( @@ -197,7 +204,13 @@ def test_add_datetime_attribute(self): assert np.allclose(np.add(np.square(values_sin), np.square(values_cos)), 1) df = seriesF.pd_dataframe() + # first day is equivalent to t=0 df = df[df.index.day == 1] + assert np.allclose(df["day_sin"].values, 0, atol=0.03) + assert np.allclose(df["day_cos"].values, 1, atol=0.03) + + # second day is equivalent to t=1 + df = df[df.index.day == 2] assert np.allclose(df["day_sin"].values, 0.2, atol=0.03) assert np.allclose(df["day_cos"].values, 0.97, atol=0.03) @@ -221,7 +234,9 @@ def test_add_holidays(self): assert seriesA.width == 3 # testing hourly time series - times = pd.date_range(start=pd.Timestamp("20201224"), periods=50, freq="H") + times = pd.date_range( + start=pd.Timestamp("20201224"), periods=50, freq=freqs["h"] + ) seriesB = TimeSeries.from_times_and_values(times, range(len(times))) seriesB = seriesB.add_holidays("US") last_column = seriesB.pd_dataframe().iloc[:, seriesB.width - 1] diff --git a/darts/tests/test_timeseries_static_covariates.py b/darts/tests/test_timeseries_static_covariates.py index fa188dbb4a..0c34b22bdf 100644 --- a/darts/tests/test_timeseries_static_covariates.py +++ b/darts/tests/test_timeseries_static_covariates.py @@ -8,7 +8,8 @@ from darts import TimeSeries, concatenate from darts.dataprocessing.transformers import BoxCox, Scaler from darts.timeseries import DEFAULT_GLOBAL_STATIC_COV_NAME, STATIC_COV_TAG -from darts.utils.timeseries_generation import generate_index, linear_timeseries +from darts.utils.timeseries_generation import linear_timeseries +from darts.utils.utils import generate_index def setup_test_case(): @@ -103,6 +104,37 @@ def test_ts_from_x(self, tmpdir_module): ts, TimeSeries.from_json(ts_json, static_covariates=ts.static_covariates) ) + @pytest.mark.parametrize("index_type", ["int", "dt", "str"]) + def test_from_group_dataframe(self, index_type): + """Tests correct extract of TimeSeries groups from a long DataFrame with unsorted (time/integer) index""" + group = ["a", "a", "a", "b", "b", "b"] + values = np.arange(len(group)) + + if index_type == "int": + index_expected = pd.RangeIndex(3) + time = [2, 1, 0, 0, 1, 2] + else: + index_expected = pd.date_range("2024-01-01", periods=3) + time = index_expected[::-1].append(index_expected) + if index_type == "str": + time = time.astype(str) + + # create a df with unsorted time + df = pd.DataFrame({ + "group": group, + "time": time, + "x": values, + }) + ts = TimeSeries.from_group_dataframe(df, group_cols="group", time_col="time") + + # check the time index + assert ts[0].time_index.equals(index_expected) + assert ts[1].time_index.equals(index_expected) + + # check the values + assert (ts[0].values().flatten() == [values[2], values[1], values[0]]).all() + assert (ts[1].values().flatten() == [values[3], values[4], values[5]]).all() + def test_timeseries_from_longitudinal_df(self): # univariate static covs: only group by "st1", keep static covs "st1" value_cols = ["a", "b", "c"] @@ -214,6 +246,16 @@ def test_timeseries_from_longitudinal_df(self): for ts in ts_groups7: assert ts.static_covariates is None + ts_groups7_parallel = TimeSeries.from_group_dataframe( + df=self.df_long_multi, + group_cols=["st1", "st2"], + time_col="times", + value_cols=value_cols, + drop_group_cols=["st1", "st2"], + n_jobs=-1, + ) + assert ts_groups7_parallel == ts_groups7 + def test_from_group_dataframe_invalid_drop_cols(self): # drop col is not part of `group_cols` with pytest.raises(ValueError) as err: @@ -516,11 +558,11 @@ def test_non_numerical_static_covariates(self): values=np.random.random((10, 2)) ).with_static_covariates(static_covs) assert ts.static_covariates.dtypes["num"] == ts.dtype == "float64" - assert ts.static_covariates.dtypes["cat"] == object + assert isinstance(ts.static_covariates.dtypes["cat"], object) ts = ts.astype(np.float32) assert ts.static_covariates.dtypes["num"] == ts.dtype == "float32" - assert ts.static_covariates.dtypes["cat"] == object + assert isinstance(ts.static_covariates.dtypes["cat"], object) def test_get_item(self): # multi component static covariates @@ -636,9 +678,9 @@ def test_ts_methods_with_static_covariates(self): static_covs = pd.Series([0, 1], index=["st1", "st2"]).astype(int) ts = ts.with_static_covariates(static_covs) - assert ts.static_covariates.dtypes[0] == "float64" + assert ts.static_covariates.dtypes.iloc[0] == "float64" ts = ts.astype("float32") - assert ts.static_covariates.dtypes[0] == "float32" + assert ts.static_covariates.dtypes.iloc[0] == "float32" ts_stoch = ts.from_times_and_values( times=ts.time_index, diff --git a/darts/tests/utils/historical_forecasts/test_historical_forecasts.py b/darts/tests/utils/historical_forecasts/test_historical_forecasts.py new file mode 100644 index 0000000000..f696724cb2 --- /dev/null +++ b/darts/tests/utils/historical_forecasts/test_historical_forecasts.py @@ -0,0 +1,3799 @@ +import itertools +import logging +import math +from copy import deepcopy +from itertools import product +from typing import Optional + +import numpy as np +import pandas as pd +import pytest +from sklearn.preprocessing import MaxAbsScaler + +import darts +from darts import TimeSeries, concatenate +from darts.dataprocessing.pipeline import Pipeline +from darts.dataprocessing.transformers import ( + FittableDataTransformer, + InvertibleDataTransformer, + Scaler, +) +from darts.datasets import AirPassengersDataset +from darts.models import ( + ARIMA, + AutoARIMA, + CatBoostModel, + ConformalNaiveModel, + LightGBMModel, + LinearRegressionModel, + NaiveDrift, + NaiveSeasonal, + NotImportedModule, +) +from darts.models.forecasting.forecasting_model import ( + LocalForecastingModel, +) +from darts.tests.conftest import TORCH_AVAILABLE, tfm_kwargs +from darts.utils import n_steps_between +from darts.utils import timeseries_generation as tg +from darts.utils.ts_utils import SeriesType, get_series_seq_type +from darts.utils.utils import likelihood_component_names, quantile_names + +if TORCH_AVAILABLE: + import torch + + from darts.models import ( + BlockRNNModel, + GlobalNaiveAggregate, + GlobalNaiveDrift, + GlobalNaiveSeasonal, + NBEATSModel, + NLinearModel, + RNNModel, + TCNModel, + TFTModel, + TiDEModel, + TransformerModel, + TSMixerModel, + ) + from darts.utils.likelihood_models import GaussianLikelihood, QuantileRegression + +models = [LinearRegressionModel, NaiveDrift] +models_reg_no_cov_cls_kwargs = [ + (LinearRegressionModel, {"lags": 8}, {}, (8, 1)), + # output_chunk_length only + (LinearRegressionModel, {"lags": 5, "output_chunk_length": 2}, {}, (5, 2)), + # output_chunk_shift only + (LinearRegressionModel, {"lags": 5, "output_chunk_shift": 1}, {}, (5, 2)), + # output_chunk_shift + output_chunk_length only + ( + LinearRegressionModel, + {"lags": 5, "output_chunk_shift": 1, "output_chunk_length": 2}, + {}, + (5, 3), + ), +] +if not isinstance(CatBoostModel, NotImportedModule): + models_reg_no_cov_cls_kwargs.append(( + CatBoostModel, + {"lags": 6}, + {"iterations": 1}, + (6, 1), + )) +if not isinstance(LightGBMModel, NotImportedModule): + models_reg_no_cov_cls_kwargs.append(( + LightGBMModel, + {"lags": 4}, + {"n_estimators": 1}, + (4, 1), + )) + +models_reg_cov_cls_kwargs = [ + # target + past covariates + (LinearRegressionModel, {"lags": 4, "lags_past_covariates": 6}, {}, (6, 1)), + # target + past covariates + outputchunk > 3, 6 > 3 + ( + LinearRegressionModel, + {"lags": 3, "lags_past_covariates": 6, "output_chunk_length": 5}, + {}, + (6, 5), + ), + # target + future covariates, 2 because to predict x, require x and x+1 + (LinearRegressionModel, {"lags": 4, "lags_future_covariates": [0, 1]}, {}, (4, 2)), + # target + fut cov + output_chunk_length > 3, + ( + LinearRegressionModel, + {"lags": 2, "lags_future_covariates": [1, 2], "output_chunk_length": 5}, + {}, + (2, 5), + ), + # fut cov + output_chunk_length > 3, 5 > 2 + ( + LinearRegressionModel, + {"lags_future_covariates": [0, 1], "output_chunk_length": 5}, + {}, + (0, 5), + ), + # past cov only + (LinearRegressionModel, {"lags_past_covariates": 6}, {}, (6, 1)), + # fut cov only + (LinearRegressionModel, {"lags_future_covariates": [0, 1]}, {}, (0, 2)), + # fut + past cov only + ( + LinearRegressionModel, + {"lags_past_covariates": 6, "lags_future_covariates": [0, 1]}, + {}, + (6, 2), + ), + # all + ( + LinearRegressionModel, + {"lags": 3, "lags_past_covariates": 6, "lags_future_covariates": [0, 1]}, + {}, + (6, 2), + ), +] + +if TORCH_AVAILABLE: + IN_LEN = 24 + OUT_LEN = 12 + + NB_EPOCH = 1 + + models += [NLinearModel] + + models_torch_cls_kwargs = [ + ( + BlockRNNModel, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + "model": "RNN", + "hidden_dim": 10, + "n_rnn_layers": 1, + "batch_size": 32, + "n_epochs": NB_EPOCH, + **tfm_kwargs, + }, + # Min of lags needed and max of lags needed + (IN_LEN, OUT_LEN), + "PastCovariates", + ), + ( + RNNModel, + { + "input_chunk_length": IN_LEN, + "training_length": IN_LEN + OUT_LEN - 1, + "model": "RNN", + "hidden_dim": 10, + "batch_size": 32, + "n_epochs": NB_EPOCH, + **tfm_kwargs, + }, + # autoregressive model + (IN_LEN, 1), + "DualCovariates", + ), + ( + RNNModel, + { + "input_chunk_length": IN_LEN, + "training_length": IN_LEN + OUT_LEN - 1, + "n_epochs": NB_EPOCH, + "likelihood": GaussianLikelihood(), + **tfm_kwargs, + }, + (IN_LEN, 1), + "DualCovariates", + ), + ( + TCNModel, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + "n_epochs": NB_EPOCH, + "batch_size": 32, + **tfm_kwargs, + }, + (IN_LEN, OUT_LEN), + "PastCovariates", + ), + ( + TransformerModel, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + "d_model": 16, + "nhead": 2, + "num_encoder_layers": 2, + "num_decoder_layers": 2, + "dim_feedforward": 16, + "batch_size": 32, + "n_epochs": NB_EPOCH, + **tfm_kwargs, + }, + (IN_LEN, OUT_LEN), + "PastCovariates", + ), + ( + NBEATSModel, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + "num_stacks": 4, + "num_blocks": 1, + "num_layers": 2, + "layer_widths": 12, + "n_epochs": NB_EPOCH, + **tfm_kwargs, + }, + (IN_LEN, OUT_LEN), + "PastCovariates", + ), + ( + TFTModel, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + "hidden_size": 16, + "lstm_layers": 1, + "num_attention_heads": 4, + "add_relative_index": True, + "n_epochs": NB_EPOCH, + **tfm_kwargs, + }, + (IN_LEN, OUT_LEN), + "MixedCovariates", + ), + ( + NLinearModel, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + "n_epochs": NB_EPOCH, + **tfm_kwargs, + }, + (IN_LEN, OUT_LEN), + "MixedCovariates", + ), + ( + TiDEModel, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + "n_epochs": NB_EPOCH, + **tfm_kwargs, + }, + (IN_LEN, OUT_LEN), + "MixedCovariates", + ), + ( + TSMixerModel, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + "n_epochs": NB_EPOCH, + **tfm_kwargs, + }, + (IN_LEN, OUT_LEN), + "MixedCovariates", + ), + ( + GlobalNaiveAggregate, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + **tfm_kwargs, + }, + (IN_LEN, OUT_LEN), + "MixedCovariates", + ), + ( + GlobalNaiveDrift, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + **tfm_kwargs, + }, + (IN_LEN, OUT_LEN), + "MixedCovariates", + ), + ( + GlobalNaiveSeasonal, + { + "input_chunk_length": IN_LEN, + "output_chunk_length": OUT_LEN, + **tfm_kwargs, + }, + (IN_LEN, OUT_LEN), + "MixedCovariates", + ), + ] +else: + models_torch_cls_kwargs = [] + + +class TestHistoricalforecast: + np.random.seed(42) + if TORCH_AVAILABLE: + torch.manual_seed(42) + + # real timeseries for functionality tests + ts_val_length = 72 + ts_passengers = AirPassengersDataset().load() + scaler = Scaler() + ts_passengers = scaler.fit_transform(ts_passengers) + ts_pass_train, ts_pass_val = ( + ts_passengers[:-ts_val_length], + ts_passengers[-ts_val_length:], + ) + + # an additional noisy series + ts_pass_train_1 = ts_pass_train + 0.01 * tg.gaussian_timeseries( + length=len(ts_pass_train), + freq=ts_pass_train.freq_str, + start=ts_pass_train.start_time(), + ) + + ts_past_cov_train = tg.gaussian_timeseries( + length=len(ts_pass_train), + freq=ts_pass_train.freq_str, + start=ts_pass_train.start_time(), + ) + + ts_fut_cov_train = tg.gaussian_timeseries( + length=len(ts_pass_train), + freq=ts_pass_train.freq_str, + start=ts_pass_train.start_time(), + ) + + ts_past_cov_valid_same_start = tg.gaussian_timeseries( + length=len(ts_pass_val), + freq=ts_pass_val.freq_str, + start=ts_pass_val.start_time(), + ) + + ts_past_cov_valid_10_bef_start = tg.gaussian_timeseries( + length=len(ts_pass_val) + 10, + freq=ts_pass_val.freq_str, + start=ts_pass_val.start_time() - 10 * ts_pass_val.freq, + ) + ts_past_cov_valid_5_aft_start = tg.gaussian_timeseries( + length=len(ts_pass_val) - 5, + freq=ts_pass_val.freq_str, + start=ts_pass_val.start_time() + 5 * ts_pass_val.freq, + ) + + ts_fut_cov_valid_same_start = tg.gaussian_timeseries( + length=len(ts_pass_val), + freq=ts_pass_val.freq_str, + start=ts_pass_val.start_time(), + ) + + ts_fut_cov_valid_16_bef_start = tg.gaussian_timeseries( + length=len(ts_pass_val) + 16, + freq=ts_pass_val.freq_str, + start=ts_pass_val.start_time() - 16 * ts_pass_val.freq, + ) + ts_fut_cov_valid_7_aft_start = tg.gaussian_timeseries( + length=len(ts_pass_val) - 7, + freq=ts_pass_val.freq_str, + start=ts_pass_val.start_time() + 7 * ts_pass_val.freq, + ) + + # RangeIndex timeseries + ts_passengers_range = TimeSeries.from_values(ts_passengers.values()) + ts_pass_train_range, ts_pass_val_range = ( + ts_passengers_range[:-ts_val_length], + ts_passengers_range[-ts_val_length:], + ) + + ts_past_cov_train_range = tg.gaussian_timeseries( + length=len(ts_pass_train_range), + freq=ts_pass_train_range.freq_str, + start=ts_pass_train_range.start_time(), + ) + + # same starting point + ts_past_cov_valid_range_same_start = tg.gaussian_timeseries( + length=len(ts_pass_val_range), + freq=ts_pass_val_range.freq_str, + start=ts_pass_val_range.start_time(), + ) + + # optimized historical forecasts + start_ts = pd.Timestamp("2000-01-01") + ts_univariate = tg.linear_timeseries( + start_value=1, end_value=100, length=20, start=start_ts + ) + ts_multivariate = ts_univariate.stack(tg.sine_timeseries(length=20, start=start_ts)) + + # slightly longer to not affect the last predictable timestamp + ts_covs = tg.gaussian_timeseries(length=30, start=start_ts) + + # + sine_univariate1 = tg.sine_timeseries(length=50) * 2 + 1.5 + sine_univariate2 = tg.sine_timeseries(length=50, value_phase=1.5705) * 5 + 1.5 + sine_univariate3 = tg.sine_timeseries(length=50, value_phase=0.1963125) * -9 + 1.5 + + @staticmethod + def create_model(ocl, use_ll=True, model_type="regression", n_epochs=1, **kwargs): + if model_type == "regression": + return LinearRegressionModel( + lags=3, + likelihood="quantile" if use_ll else None, + quantiles=[0.05, 0.4, 0.5, 0.6, 0.95] if use_ll else None, + output_chunk_length=ocl, + **kwargs, + ) + else: # model_type == "torch" + if not TORCH_AVAILABLE: + return None + return NLinearModel( + input_chunk_length=3, + likelihood=( + QuantileRegression([0.05, 0.4, 0.5, 0.6, 0.95]) if use_ll else None + ), + loss_fn=torch.nn.MSELoss() if not use_ll else None, + output_chunk_length=ocl, + n_epochs=n_epochs, + random_state=42, + **tfm_kwargs, + **kwargs, + ) + + @pytest.mark.parametrize( + "config", + list( + itertools.product( + [True, False], + [0, 1, 3], + [0, 1, 2], + ) + ), + ) + def test_historical_forecasts_output(self, config): + """Tests historical forecasts output type and values for all combinations of: + + - uni or multivariate `series` + - different number of `series`, `0` represents a single `TimeSeries`, + `1` a list of one `TimeSeries`, and so on. + - different number of expected forecasts. + """ + is_univariate, series_list_length, n_fc_expected = config + + model = NaiveDrift() + horizon = 7 + ts_length = horizon + model.min_train_series_length + (n_fc_expected - 1) + + y = tg.constant_timeseries(value=1.0, length=ts_length) + if not is_univariate: + y = y.stack(y + 1.0) + # remember `y` for expected output + y_ref = y + + if series_list_length: + y = [y] * series_list_length + + if not n_fc_expected: + # cannot generate a single forecast + with pytest.raises(ValueError) as err: + _ = model.historical_forecasts( + series=y, forecast_horizon=horizon, last_points_only=True + ) + assert str(err.value).startswith( + "Cannot build a single input for prediction" + ) + return + + # last_points_only = True: gives a list with a single forecasts per series, + # where each forecast contains only the last points of all possible historical + # forecasts + hfcs = model.historical_forecasts( + series=y, forecast_horizon=horizon, last_points_only=True + ) + if not series_list_length: + # make output the same as if a list of `series` was used + hfcs = [hfcs] + + n_series = len(y) if series_list_length else 1 + assert isinstance(hfcs, list) and len(hfcs) == n_series + for hfc in hfcs: + assert isinstance(hfc, TimeSeries) and len(hfc) == n_fc_expected + np.testing.assert_array_almost_equal( + hfc.values(), y_ref.values()[-n_fc_expected:] + ) + + # last_points_only = False: gives a list of lists, where each inner list + # contains the forecasts (with the entire forecast horizon) of one series + hfcs = model.historical_forecasts( + series=y, forecast_horizon=horizon, last_points_only=False + ) + if not series_list_length: + # make output the same as if a list of `series` was used + hfcs = [hfcs] + + assert isinstance(hfcs, list) and len(hfcs) == n_series + for hfc_series in hfcs: # list of forecasts per series + assert isinstance(hfc_series, list) and len(hfc_series) == n_fc_expected + for hfc in hfc_series: # each individual forecast + assert isinstance(hfc, TimeSeries) and len(hfc) == horizon + np.testing.assert_array_almost_equal( + hfc.values(), y_ref.values()[-horizon:] + ) + + @pytest.mark.parametrize( + "arima_args", + [ + {}, + { + "p": np.array([1, 2, 3, 4]), + "q": (2, 3), + "seasonal_order": ([1, 5], 1, (1, 2, 3), 6), + "trend": [0, 0, 2, 1], + }, + ], + ) + def test_historical_forecasts_transferrable_future_cov_local_models( + self, arima_args: dict + ): + model = ARIMA(**arima_args) + assert model.min_train_series_length == 30 + series = tg.sine_timeseries(length=31) + res = model.historical_forecasts( + series, future_covariates=series, retrain=True, forecast_horizon=1 + ) + # ARIMA has a minimum train length of 30, with horizon=1, we expect one forecast at last point + # (series has length 31) + assert len(res) == 1 + assert series.end_time() == res.time_index[0] + + model.fit(series, future_covariates=series) + res = model.historical_forecasts( + series, future_covariates=series, retrain=False, forecast_horizon=1 + ) + # currently even though transferrable local models would allow , the models currently still take the + # min_train_length as input for historical forecast predictions (due to extreme_lags not differentiating + # between fit and predict) + # (series has length 31) + assert len(res) == 1 + assert series.end_time() == res.time_index[0] + + # passing non-supported covariates + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + series, + past_covariates=series, + retrain=False, + ) + assert str(msg.value).startswith( + "Model prediction does not support `past_covariates`" + ) + + def test_historical_forecasts_future_cov_local_models(self): + model = AutoARIMA() + assert model.min_train_series_length == 10 + series = tg.sine_timeseries(length=11) + res = model.historical_forecasts( + series, future_covariates=series, retrain=True, forecast_horizon=1 + ) + # AutoARIMA has a minimum train length of 10, with horizon=1, we expect one forecast at last point + # (series has length 11) + assert len(res) == 1 + assert series.end_time() == res.time_index[0] + + model.fit(series, future_covariates=series) + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + series, future_covariates=series, retrain=False, forecast_horizon=1 + ) + assert str(msg.value).startswith( + "FutureCovariatesLocalForecastingModel does not support historical forecasting " + "with `retrain` set to `False`" + ) + + # passing non-supported covariates + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + series, + past_covariates=series, + retrain=True, + ) + assert str(msg.value).startswith( + "Model cannot be fit/trained with `past_covariates`." + ) + + def test_historical_forecasts_local_models(self): + model = NaiveSeasonal() + assert model.min_train_series_length == 3 + series = tg.sine_timeseries(length=4) + res = model.historical_forecasts(series, retrain=True, forecast_horizon=1) + # NaiveSeasonal has a minimum train length of 3, with horizon=1, we expect one forecast at last point + # (series has length 4) + assert len(res) == 1 + assert series.end_time() == res.time_index[0] + + model.fit(series) + with pytest.raises(ValueError) as msg: + model.historical_forecasts(series, retrain=False, forecast_horizon=1) + assert str(msg.value).startswith( + "LocalForecastingModel does not support historical forecasting with `retrain` set to `False`" + ) + + def test_historical_forecasts_position_start(self): + series = tg.sine_timeseries(length=10) + + model = LinearRegressionModel(lags=2) + model.fit(series[:8]) + + # negative index + forecasts_neg = model.historical_forecasts( + series=series, start=-2, start_format="position", retrain=False + ) + assert len(forecasts_neg) == 2 + assert (series.time_index[-2:] == forecasts_neg.time_index).all() + + # positive index + forecasts_pos = model.historical_forecasts( + series=series, start=8, start_format="position", retrain=False + ) + assert forecasts_pos == forecasts_neg + + def test_historical_forecasts_negative_rangeindex(self): + series = TimeSeries.from_times_and_values( + times=pd.RangeIndex(start=-5, stop=5, step=1), values=np.arange(10) + ) + + model = LinearRegressionModel(lags=2) + model.fit(series[:8]) + + # start as point + forecasts = model.historical_forecasts( + series=series, start=-2, start_format="value", retrain=False + ) + assert len(forecasts) == 7 + assert (series.time_index[-7:] == forecasts.time_index).all() + + # start as index + forecasts = model.historical_forecasts( + series=series, start=-2, start_format="position", retrain=False + ) + assert len(forecasts) == 2 + assert (series.time_index[-2:] == forecasts.time_index).all() + + @pytest.mark.parametrize("config", models_reg_no_cov_cls_kwargs) + def test_historical_forecasts(self, config): + """Tests historical forecasts with retraining for expected forecast lengths and times""" + forecast_horizon = 8 + # if no fit and retrain=false, should fit at fist iteration + model_cls, kwargs, model_kwarg, bounds = config + model = model_cls(**kwargs, **model_kwarg) + # set train length to be the minimum required training length + # +1 as sklearn models require min 2 train samples + train_length = bounds[0] + bounds[1] + 1 + + if model.output_chunk_shift > 0: + with pytest.raises(ValueError) as err: + forecasts = model.historical_forecasts( + series=self.ts_pass_val, + forecast_horizon=forecast_horizon, + stride=1, + train_length=train_length, + retrain=True, + overlap_end=False, + ) + assert str(err.value).startswith( + "Cannot perform auto-regression `(n > output_chunk_length)`" + ) + # continue the test without auto-regression if we are using shifts + forecast_horizon = model.output_chunk_length + + # time index without train length + forecasts_no_train_length = model.historical_forecasts( + series=self.ts_pass_val, + forecast_horizon=forecast_horizon, + stride=1, + train_length=None, + retrain=True, + overlap_end=False, + ) + + # time index with minimum train length + forecasts = model.historical_forecasts( + series=self.ts_pass_val, + forecast_horizon=forecast_horizon, + stride=1, + train_length=train_length, + retrain=True, + overlap_end=False, + ) + + assert len(forecasts_no_train_length) == len(forecasts) + theorical_forecast_length = ( + self.ts_val_length + - train_length # because we train + - forecast_horizon # because we have overlap_end = False + + 1 # because we include the first element + ) + assert len(forecasts) == theorical_forecast_length, ( + f"Model {model_cls.__name__} does not return the right number of historical forecasts in the case " + f"of retrain=True and overlap_end=False, and a time index of type DateTimeIndex. " + f"Expected {theorical_forecast_length}, got {len(forecasts)}" + ) + assert forecasts.time_index.equals( + self.ts_pass_val.time_index[-theorical_forecast_length:] + ) + + # range index + forecasts = model.historical_forecasts( + series=self.ts_pass_val_range, + forecast_horizon=forecast_horizon, + train_length=train_length, + stride=1, + retrain=True, + overlap_end=False, + ) + + assert len(forecasts) == theorical_forecast_length, ( + f"Model {model_cls.__name__} does not return the right number of historical forecasts in the case " + f"of retrain=True, overlap_end=False, and a time index of type RangeIndex." + f"Expected {theorical_forecast_length}, got {len(forecasts)}" + ) + assert forecasts.time_index.equals( + self.ts_pass_val_range.time_index[-theorical_forecast_length:] + ) + start_idx = self.ts_pass_val_range.get_index_at_point(forecasts.start_time()) + + # stride 2 + forecasts = model.historical_forecasts( + series=self.ts_pass_val_range, + forecast_horizon=forecast_horizon, + train_length=train_length, + stride=2, + retrain=True, + overlap_end=False, + ) + + theorical_forecast_length = int( + np.floor( + ( + ( + self.ts_val_length + - train_length # because we train + - forecast_horizon # because we have overlap_end = False + + 1 # because we include the first element + ) + - 1 + ) + / 2 + + 1 # because of stride + ) # if odd number of elements, we keep the floor + ) + + assert len(forecasts) == theorical_forecast_length, ( + f"Model {model_cls.__name__} does not return the right number of historical forecasts in the case " + f"of retrain=True and overlap_end=False and stride=2. " + f"Expected {theorical_forecast_length}, got {len(forecasts)}" + ) + assert forecasts.time_index.equals( + self.ts_pass_val_range.time_index[start_idx::2] + ) + + # stride 3 + forecasts = model.historical_forecasts( + series=self.ts_pass_val_range, + forecast_horizon=forecast_horizon, + train_length=train_length, + stride=3, + retrain=True, + overlap_end=False, + ) + + theorical_forecast_length = np.floor( + ( + ( + self.ts_val_length + - train_length # because we train + - forecast_horizon # because we have overlap_end = False + + 1 # because we include the first element + ) + - 1 + ) # the first is always included, so we calculate a modulo on the rest + / 3 # because of stride + + 1 # and we readd the first + ) # if odd number of elements, we keep the floor + + # Here to adapt if forecast_horizon or train_length change + assert len(forecasts) == theorical_forecast_length, ( + f"Model {model_cls.__name__} does not return the right number of historical forecasts in the case " + f"of retrain=True and overlap_end=False and stride=3. " + f"Expected {theorical_forecast_length}, got {len(forecasts)}" + ) + assert forecasts.time_index.equals( + self.ts_pass_val_range.time_index[start_idx::3] + ) + + # last points only False + forecasts = model.historical_forecasts( + series=self.ts_pass_val_range, + forecast_horizon=forecast_horizon, + train_length=train_length, + stride=1, + retrain=True, + overlap_end=False, + last_points_only=False, + ) + + theorical_forecast_length = ( + self.ts_val_length + - train_length # because we train + - forecast_horizon # because we have overlap_end = False + + 1 # because we include the first element + ) + + assert len(forecasts) == theorical_forecast_length, ( + f"Model {model_cls} does not return the right number of historical forecasts in the case of " + f"retrain=True and overlap_end=False, and last_points_only=False. " + f"expected {theorical_forecast_length}, got {len(forecasts)}" + ) + + assert len(forecasts[0]) == forecast_horizon, ( + f"Model {model_cls} does not return forecast_horizon points per historical forecast in the case of " + f"retrain=True and overlap_end=False, and last_points_only=False" + ) + last_points_times = np.array([fc.end_time() for fc in forecasts]) + np.testing.assert_equal( + last_points_times, + self.ts_pass_val_range.time_index[-theorical_forecast_length:].values, + ) + + if not model.supports_past_covariates: + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + series=self.ts_pass_val_range, + past_covariates=self.ts_passengers, + retrain=True, + ) + assert str(msg.value).startswith( + "Model cannot be fit/trained with `past_covariates`." + ) + + if not model.supports_future_covariates: + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + series=self.ts_pass_val_range, + future_covariates=self.ts_passengers, + last_points_only=False, + ) + assert str(msg.value).startswith( + "Model cannot be fit/trained with `future_covariates`." + ) + + def test_sanity_check_start(self): + timeidx_ = tg.linear_timeseries(length=10) + rangeidx_step1 = tg.linear_timeseries(start=0, length=10, freq=1) + rangeidx_step2 = tg.linear_timeseries(start=0, length=10, freq=2) + + # invalid start float + model = LinearRegressionModel(lags=1) + with pytest.raises(ValueError) as msg: + model.historical_forecasts(rangeidx_step1, start=1.1) + assert str(msg.value).startswith( + "if `start` is a float, must be between 0.0 and 1.0." + ) + with pytest.raises(ValueError) as msg: + model.historical_forecasts(rangeidx_step1, start=-0.1) + assert str(msg.value).startswith( + "if `start` is a float, must be between 0.0 and 1.0." + ) + + # invalid start type + with pytest.raises(TypeError) as msg: + model.historical_forecasts(rangeidx_step1, start=[0.1]) + assert str(msg.value).startswith( + "`start` must be either `float`, `int`, `pd.Timestamp` or `None`." + ) + + # label_index (timestamp) with range index series + model = LinearRegressionModel(lags=1) + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + rangeidx_step1, start=timeidx_.end_time() + timeidx_.freq + ) + assert str(msg.value).startswith( + "if `start` is a `pd.Timestamp`, all series must be indexed with a `pd.DatetimeIndex`" + ) + + # label_index (int), too large + with pytest.raises(ValueError) as msg: + model.historical_forecasts(timeidx_, start=11) + assert str(msg.value).startswith("`start` position `11` is out of bounds") + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + rangeidx_step1, start=rangeidx_step1.end_time() + rangeidx_step1.freq + ) + assert str(msg.value).startswith( + "`start` time `10` is larger than the last index" + ) + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + rangeidx_step2, start=rangeidx_step2.end_time() + rangeidx_step2.freq + ) + assert str(msg.value).startswith( + "`start` time `20` is larger than the last index" + ) + + # label_index (timestamp) too high + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + timeidx_, start=timeidx_.end_time() + timeidx_.freq + ) + assert str(msg.value).startswith( + "`start` time `2000-01-11 00:00:00` is after the last timestamp `2000-01-10 00:00:00`" + ) + + # label_index (timestamp), before series start and stride does not allow to find valid start point in series + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + timeidx_, + start=timeidx_.start_time() - timeidx_.freq, + stride=len(timeidx_) + 1, + ) + assert str(msg.value) == ( + "`start` time `1999-12-31 00:00:00` is smaller than the first time index `2000-01-01 00:00:00` " + "for series at index: 0, and could not find a valid start point within the time index that lies a " + "round-multiple of `stride=11` ahead of `start` (first inferred start is `2000-01-11 00:00:00`, " + "but last time index is `2000-01-10 00:00:00`." + ) + + # label_index (timestamp), before trainable/predictable index and stride does not allow to find valid start + # point in series + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + timeidx_, start=timeidx_.start_time(), stride=len(timeidx_) + ) + assert str(msg.value) == ( + "`start` time `2000-01-01 00:00:00` is smaller than the first historical forecastable time index " + "`2000-01-04 00:00:00` for series at index: 0, and could not find a valid start point within the " + "historical forecastable time index that lies a round-multiple of `stride=10` ahead of `start` " + "(first inferred start is `2000-01-11 00:00:00`, but last historical forecastable time index is " + "`2000-01-10 00:00:00`." + ) + + # label_index (int), too low and stride does not allow to find valid start point in series + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + rangeidx_step1, + start=rangeidx_step1.start_time() - rangeidx_step1.freq, + stride=len(rangeidx_step1) + 1, + ) + assert str(msg.value) == ( + "`start` time `-1` is smaller than the first time index `0` for series at index: 0, and could not " + "find a valid start point within the time index that lies a round-multiple of `stride=11` ahead of " + "`start` (first inferred start is `10`, but last time index is `9`." + ) + + # label_index (int), before trainable/predictable index and stride does not allow to find valid start + # point in series + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + rangeidx_step1, + start=rangeidx_step1.start_time(), + stride=len(rangeidx_step1), + ) + assert str(msg.value) == ( + "`start` time `0` is smaller than the first historical forecastable time index `3` for series at " + "index: 0, and could not find a valid start point within the historical forecastable time index " + "that lies a round-multiple of `stride=10` ahead of `start` (first inferred start is `10`, but last " + "historical forecastable time index is `9`." + ) + + # positional_index with time index, predicting only the last position + preds = model.historical_forecasts(timeidx_, start=9, start_format="position") + assert len(preds) == 1 + assert preds.start_time() == timeidx_.time_index[9] + + # positional_index, predicting from the first position with retrain=True + preds1 = model.historical_forecasts( + timeidx_, start=-10, start_format="position" + ) + # positional_index, before start of series gives same results + preds2 = model.historical_forecasts( + timeidx_, start=-11, start_format="position" + ) + assert ( + len(preds1) == len(preds2) == len(timeidx_) - model.min_train_series_length + ) + assert ( + preds1.start_time() + == preds2.start_time() + == timeidx_.time_index[model.min_train_series_length] + ) + + # positional_index, beyond boundaries + with pytest.raises(ValueError) as msg: + model.historical_forecasts(timeidx_, start=10, start_format="position") + assert str(msg.value).startswith( + "`start` position `10` is out of bounds for series of length 10" + ) + + # positional_index with range index, predicting only the last position + preds = model.historical_forecasts( + rangeidx_step2, start=9, start_format="position" + ) + assert len(preds) == 1 + assert preds.start_time() == rangeidx_step2.time_index[9] + + # positional_index, predicting from the first position with retrain=True + preds1 = model.historical_forecasts( + rangeidx_step2, start=-10, start_format="position" + ) + # positional_index, before start of series gives same results + preds2 = model.historical_forecasts( + rangeidx_step2, start=-11, start_format="position" + ) + assert ( + len(preds1) + == len(preds2) + == len(rangeidx_step2) - model.min_train_series_length + ) + assert ( + preds1.start_time() + == preds2.start_time() + == rangeidx_step2.time_index[model.min_train_series_length] + ) + + # positional_index, beyond boundaries + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + rangeidx_step2, start=10, start_format="position" + ) + assert str(msg.value).startswith( + "`start` position `10` is out of bounds for series of length 10" + ) + + @pytest.mark.parametrize( + "config", + list( + itertools.product( + [ + ( + "2000-01-01 00:00:00", # start + 1, # stride + "2000-01-01 03:00:00", # expected start + "h", # freq + ), + ("2000-01-01 00:00:00", 2, "2000-01-01 04:00:00", "h"), + ("1999-01-01 00:00:00", 6, "2000-01-01 06:00:00", "h"), + ("2000-01-01 00:00:00", 2, "2000-01-01 08:00:00", "2h"), + # special case where start is not in the frequency -> start will be converted + # to "2000-01-01 00:00:00", and then it's adjusted to be within the historical fc index + ("1999-12-31 23:00:00", 2, "2000-01-01 08:00:00", "2h"), + # integer index + (0, 1, 3, 1), + (0, 2, 4, 1), + (-24, 6, 6, 1), + (0, 2, 8, 2), + # special case where start is not in the frequency -> start will be converted + # to 0, and then it's adjusted to be within the historical fc index + (-1, 2, 8, 2), + ], + ["value", "position"], # start format + [True, False], # retrain + [True, False] if TORCH_AVAILABLE else [False], # use torch model + ) + ), + ) + def test_historical_forecasts_start_too_early(self, caplog, config): + """If start is not within the trainable/forecastable index, it should start a round-multiple of `stride` ahead + of `start`. Checks for: + - correct warnings + - datetime / integer index + - different frequencies + - different strides + - start "value" and "position" + - retrain / no-retrain (optimized and non-optimized) + - torch and regression model + """ + # the configuration is defined for `retrain = True` and `start_format = "value"` + ( + (start, stride, start_expected, freq), + start_format, + retrain, + use_torch_model, + ) = config + if isinstance(freq, str): + start, start_expected = pd.Timestamp(start), pd.Timestamp(start_expected) + start_series = pd.Timestamp("2000-01-01 00:00:00") + else: + start_series = 0 + + series = tg.linear_timeseries( + start=start_series, + length=7, + freq=freq, + ) + # when hist fc `start` is not in the valid frequency range, it is converted to a time that is valid. + # e.g. `start="1999-12-31 23:00:00:` with `freq="2h"` is converted to `"2000-01-01 00:00:00"` + start_position = n_steps_between(end=start_series, start=start, freq=freq) + start_time_expected = series.start_time() - start_position * series.freq + + if start_format == "position": + start = -start_position + if start < 0: + # negative position is relative to the end of the series + start -= len(series) + start_format_msg = f"position `{start}` corresponding to time " + else: + start_format_msg = "time " + + if use_torch_model: + kwargs = deepcopy(tfm_kwargs) + kwargs["pl_trainer_kwargs"]["fast_dev_run"] = True + # use ocl=2 to have same `min_train_length` as the regression model + model = BlockRNNModel( + input_chunk_length=1, output_chunk_length=2, n_epochs=1, **kwargs + ) + else: + model = LinearRegressionModel(lags=1) + + model.fit(series) + # if the stride is shorter than the train series length, retrain=False can start earlier + if not retrain and stride <= model.min_train_series_length: + start_expected -= ( + model.min_train_series_length + model.extreme_lags[0] + ) * series.freq + + # label index + warning_expected = ( + f"`start` {start_format_msg}`{start_time_expected}` is before the first predictable/trainable historical " + f"forecasting point for series at index: 0. Using the first historical forecasting point " + f"`{start_expected}` that lies a round-multiple of `stride={stride}` ahead of `start`. To hide these " + f"warnings, set `show_warnings=False`." + ) + + # check that warning is raised when too early + enable_optimizations = [False] if retrain else [False, True] + for enable_optimization in enable_optimizations: + with caplog.at_level(logging.WARNING): + pred = model.historical_forecasts( + series, + start=start, + stride=stride, + retrain=retrain, + start_format=start_format, + enable_optimization=enable_optimization, + ) + assert warning_expected in caplog.text + assert pred.start_time() == start_expected + caplog.clear() + # but no warning when start is at the right time + warning_short = ( + f"Using the first historical forecasting point `{start_expected}` that lies a round-multiple " + f"of `stride={stride}` ahead of `start`. To hide these warnings, set `show_warnings=False`." + ) + with caplog.at_level(logging.WARNING): + pred = model.historical_forecasts( + series, + start=start_expected, + stride=stride, + retrain=False, + start_format="value", + enable_optimization=True, + ) + assert warning_short not in caplog.text + assert pred.start_time() == start_expected + + @pytest.mark.parametrize("config", models_reg_no_cov_cls_kwargs) + def test_regression_auto_start_multiple_no_cov(self, config): + # minimum required train length (+1 since sklearn models require 2 samples) + forecast_horizon = 10 + model_cls, kwargs, model_kwargs, bounds = config + train_length = bounds[0] + bounds[1] + 1 + model = model_cls( + **kwargs, + **model_kwargs, + ) + model.fit(self.ts_pass_train) + + if model.output_chunk_shift > 0: + with pytest.raises(ValueError) as err: + forecasts = model.historical_forecasts( + series=[self.ts_pass_val, self.ts_pass_val], + forecast_horizon=forecast_horizon, + train_length=train_length, + stride=1, + retrain=True, + overlap_end=False, + ) + assert str(err.value).startswith( + "Cannot perform auto-regression `(n > output_chunk_length)`" + ) + # continue the test without autogregression if we are using shifts + forecast_horizon = model.output_chunk_length + + forecasts = model.historical_forecasts( + series=[self.ts_pass_val, self.ts_pass_val], + forecast_horizon=forecast_horizon, + train_length=train_length, + stride=1, + retrain=True, + overlap_end=False, + ) + + assert len(forecasts) == 2, ( + f"Model {model_cls} did not return a list of historical forecasts" + ) + + theorical_forecast_length = ( + self.ts_val_length + - train_length + - forecast_horizon # because we have overlap_end = False + + 1 # because we include the first element + ) + + assert len(forecasts[0]) == len(forecasts[1]) == theorical_forecast_length, ( + f"Model {model_cls.__name__} does not return the right number of historical forecasts in the case " + f"of retrain=True and overlap_end=False, and a time index of type DateTimeIndex. " + f"Expected {theorical_forecast_length}, got {len(forecasts[0])} and {len(forecasts[1])}" + ) + assert forecasts[0].time_index.equals(forecasts[1].time_index) and forecasts[ + 0 + ].time_index.equals(self.ts_pass_val.time_index[-theorical_forecast_length:]) + + @pytest.mark.slow + @pytest.mark.parametrize( + "config", + itertools.product( + [ts_univariate, ts_multivariate], + models_reg_no_cov_cls_kwargs + models_reg_cov_cls_kwargs, + [True, False], + [1, 5], + ), + ) + def test_optimized_historical_forecasts_regression(self, config): + ts, model_config, multi_models, forecast_horizon = config + # slightly longer to not affect the last predictable timestamp + ts_covs = self.ts_covs + start = 14 + + model_cls = LinearRegressionModel + _, model_kwargs, _, _ = model_config + # cover several covariates combinations and several regression models + # ocl == forecast horizon + model_kwargs_same = model_kwargs.copy() + model_kwargs_same["output_chunk_length"] = forecast_horizon + model_kwargs_same["multi_models"] = multi_models + model_same = model_cls(**model_kwargs_same) + model_same.fit( + series=ts[:start], + past_covariates=ts_covs if model_same.supports_past_covariates else None, + future_covariates=( + ts_covs if model_same.supports_future_covariates else None + ), + ) + # ocl >= forecast horizon + model_kwargs_diff = model_kwargs.copy() + model_kwargs_diff["output_chunk_length"] = 5 + model_kwargs_diff["multi_models"] = multi_models + model_diff = model_cls(**model_kwargs_same) + model_diff.fit( + series=ts[:start], + past_covariates=ts_covs if model_diff.supports_past_covariates else None, + future_covariates=( + ts_covs if model_diff.supports_future_covariates else None + ), + ) + # no parametrization to save time on model training at the cost of test granularity + for model in [model_same, model_diff]: + for last_points_only in [True, False]: + for stride in [1, 2]: + hist_fct = model.historical_forecasts( + series=ts, + past_covariates=( + ts_covs if model.supports_past_covariates else None + ), + future_covariates=( + ts_covs if model.supports_future_covariates else None + ), + start=start, + retrain=False, + last_points_only=last_points_only, + stride=stride, + forecast_horizon=forecast_horizon, + enable_optimization=False, + ) + + # manually packing the series in list to match expected inputs + opti_hist_fct = model._optimized_historical_forecasts( + series=ts, + past_covariates=( + ts_covs if model.supports_past_covariates else None + ), + future_covariates=( + ts_covs if model.supports_future_covariates else None + ), + start=start, + last_points_only=last_points_only, + stride=stride, + forecast_horizon=forecast_horizon, + ) + + self.helper_compare_hf(hist_fct, opti_hist_fct) + + @pytest.mark.parametrize( + "config", + list( + itertools.product( + [False, True], # use covariates + [True, False], # last points only + [False, True], # overlap end + [1, 3], # stride + [ + 3, # horizon < ocl + 5, # horizon == ocl + ], + [True, False], # multi models + ) + ), + ) + def test_optimized_historical_forecasts_regression_with_encoders(self, config): + np.random.seed(0) + use_covs, last_points_only, overlap_end, stride, horizon, multi_models = config + lags = 3 + ocl = 5 + len_val_series = 10 if multi_models else 10 + (ocl - 1) + series_train, series_val = ( + self.ts_pass_train[:10], + self.ts_pass_val[:len_val_series], + ) + model = LinearRegressionModel( + lags=lags, + lags_past_covariates=2, + lags_future_covariates=[2, 3], + add_encoders={ + "cyclic": {"future": ["month"]}, + "datetime_attribute": {"past": ["dayofweek"]}, + }, + output_chunk_length=ocl, + multi_models=multi_models, + ) + if use_covs: + pc = tg.gaussian_timeseries( + start=series_train.start_time() - 2 * series_train.freq, + end=series_val.end_time(), + freq=series_train.freq, + ) + fc = tg.gaussian_timeseries( + start=series_train.start_time() + 3 * series_train.freq, + end=series_val.end_time() + 4 * series_train.freq, + freq=series_train.freq, + ) + else: + pc, fc = None, None + + model.fit(self.ts_pass_train, past_covariates=pc, future_covariates=fc) + + hist_fct = model.historical_forecasts( + series=series_val, + past_covariates=pc, + future_covariates=fc, + retrain=False, + last_points_only=last_points_only, + overlap_end=overlap_end, + stride=stride, + forecast_horizon=horizon, + enable_optimization=False, + ) + + opti_hist_fct = model._optimized_historical_forecasts( + series=series_val, + past_covariates=pc, + future_covariates=fc, + last_points_only=last_points_only, + overlap_end=overlap_end, + stride=stride, + forecast_horizon=horizon, + ) + + if not isinstance(hist_fct, list): + hist_fct = [hist_fct] + opti_hist_fct = [opti_hist_fct] + + if not last_points_only and overlap_end: + n_pred_series_expected = 8 + n_pred_points_expected = horizon + first_ts_expected = series_val.time_index[lags] + last_ts_expected = series_val.end_time() + series_val.freq * horizon + elif not last_points_only: # overlap_end = False + n_pred_series_expected = len(series_val) - lags - horizon + 1 + n_pred_points_expected = horizon + first_ts_expected = series_val.time_index[lags] + last_ts_expected = series_val.end_time() + elif overlap_end: # last_points_only = True + n_pred_series_expected = 1 + n_pred_points_expected = 8 + first_ts_expected = ( + series_val.time_index[lags] + (horizon - 1) * series_val.freq + ) + last_ts_expected = series_val.end_time() + series_val.freq * horizon + else: # last_points_only = True, overlap_end = False + n_pred_series_expected = 1 + n_pred_points_expected = len(series_val) - lags - horizon + 1 + first_ts_expected = ( + series_val.time_index[lags] + (horizon - 1) * series_val.freq + ) + last_ts_expected = series_val.end_time() + + if not multi_models: + first_ts_expected += series_val.freq * (ocl - 1) + if not overlap_end: + if not last_points_only: + n_pred_series_expected -= ocl - 1 + else: + n_pred_points_expected -= ocl - 1 + + # to make it simple in case of stride, we assume that non-optimized hist fc returns correct results + if stride > 1: + n_pred_series_expected = len(hist_fct) + n_pred_points_expected = len(hist_fct[0]) + first_ts_expected = hist_fct[0].start_time() + last_ts_expected = hist_fct[-1].end_time() + + # check length match between optimized and default hist fc + assert len(opti_hist_fct) == n_pred_series_expected + assert len(hist_fct) == len(opti_hist_fct) + # check hist fc start + assert opti_hist_fct[0].start_time() == first_ts_expected + # check hist fc end + assert opti_hist_fct[-1].end_time() == last_ts_expected + for hfc, ohfc in zip(hist_fct, opti_hist_fct): + assert len(ohfc) == n_pred_points_expected + assert (hfc.time_index == ohfc.time_index).all() + np.testing.assert_array_almost_equal(hfc.all_values(), ohfc.all_values()) + + def test_optimized_historical_forecasts_regression_with_component_specific_lags( + self, + ): + horizon = 1 + lags = 3 + len_val_series = 10 + series_train, series_val = ( + self.ts_pass_train[:10], + self.ts_pass_val[:len_val_series], + ) + model = LinearRegressionModel( + lags=lags, + lags_past_covariates={"default_lags": 2, "darts_enc_pc_dta_dayofweek": 1}, + lags_future_covariates=[2, 3], + add_encoders={ + "cyclic": {"future": ["month"]}, + "datetime_attribute": {"past": ["dayofweek"]}, + }, + ) + model.fit(series_train) + hist_fct = model.historical_forecasts( + series=series_val, + retrain=False, + enable_optimization=False, + ) + + opti_hist_fct = model._optimized_historical_forecasts(series=series_val) + + if not isinstance(hist_fct, list): + hist_fct = [hist_fct] + opti_hist_fct = [opti_hist_fct] + + n_pred_series_expected = 1 + n_pred_points_expected = len(series_val) - lags - horizon + 1 + first_ts_expected = ( + series_val.time_index[lags] + (horizon - 1) * series_val.freq + ) + last_ts_expected = series_val.end_time() + + # check length match between optimized and default hist fc + assert len(opti_hist_fct) == n_pred_series_expected + assert len(hist_fct) == len(opti_hist_fct) + # check hist fc start + assert opti_hist_fct[0].start_time() == first_ts_expected + # check hist fc end + assert opti_hist_fct[-1].end_time() == last_ts_expected + for hfc, ohfc in zip(hist_fct, opti_hist_fct): + assert len(ohfc) == n_pred_points_expected + assert (hfc.time_index == ohfc.time_index).all() + np.testing.assert_array_almost_equal(hfc.all_values(), ohfc.all_values()) + + @pytest.mark.slow + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") + @pytest.mark.parametrize( + "config", + list( + itertools.product( + [False, True], # use covariates + [True, False], # last points only + [False, True], # overlap end + [1, 3], # stride + [ + 3, # horizon < ocl + 5, # horizon == ocl + 7, # horizon > ocl -> autoregression + ], + [False, True], # use integer indexed series + [False, True], # use multi-series + ) + ), + ) + def test_optimized_historical_forecasts_torch_with_encoders(self, config): + ( + use_covs, + last_points_only, + overlap_end, + stride, + horizon, + use_int_idx, + use_multi_series, + ) = config + icl = 3 + ocl = 5 + len_val_series = 10 + series_train, series_val = ( + self.ts_pass_train[:10], + self.ts_pass_val[:len_val_series], + ) + if use_int_idx: + series_train = TimeSeries.from_values( + series_train.all_values(), columns=series_train.columns + ) + series_val = TimeSeries.from_times_and_values( + values=series_val.all_values(), + times=pd.RangeIndex( + start=series_train.end_time() + series_train.freq, + stop=series_train.end_time() + + (len(series_val) + 1) * series_train.freq, + step=series_train.freq, + ), + columns=series_train.columns, + ) + + def f_encoder(idx): + return idx.month if not use_int_idx else idx + + model = NLinearModel( + input_chunk_length=icl, + add_encoders={ + "custom": {"past": [f_encoder], "future": [f_encoder]}, + }, + output_chunk_length=ocl, + n_epochs=1, + **tfm_kwargs, + ) + if use_covs: + pc = tg.gaussian_timeseries( + start=series_train.start_time(), + end=series_val.end_time() + max(0, horizon - ocl) * series_train.freq, + freq=series_train.freq, + ) + fc = tg.gaussian_timeseries( + start=series_train.start_time(), + end=series_val.end_time() + max(ocl, horizon) * series_train.freq, + freq=series_train.freq, + ) + else: + pc, fc = None, None + + model.fit(series_train, past_covariates=pc, future_covariates=fc) + + if use_multi_series: + series_val = [ + series_val, + (series_val + 10) + .shift(1) + .with_columns_renamed(series_val.columns, "test_col"), + ] + pc = [pc, pc.shift(1)] if pc is not None else None + fc = [fc, fc.shift(1)] if fc is not None else None + + hist_fct = model.historical_forecasts( + series=series_val, + past_covariates=pc, + future_covariates=fc, + retrain=False, + last_points_only=last_points_only, + overlap_end=overlap_end, + stride=stride, + forecast_horizon=horizon, + enable_optimization=False, + ) + + opti_hist_fct = model._optimized_historical_forecasts( + series=series_val, + past_covariates=pc, + future_covariates=fc, + last_points_only=last_points_only, + overlap_end=overlap_end, + stride=stride, + forecast_horizon=horizon, + ) + + if not isinstance(series_val, list): + series_val = [series_val] + hist_fct = [hist_fct] + opti_hist_fct = [opti_hist_fct] + + for series, hfc, ohfc in zip(series_val, hist_fct, opti_hist_fct): + if not isinstance(hfc, list): + hfc = [hfc] + ohfc = [ohfc] + + if not last_points_only and overlap_end: + n_pred_series_expected = 8 + n_pred_points_expected = horizon + first_ts_expected = series.time_index[icl] + last_ts_expected = series.end_time() + series.freq * horizon + elif not last_points_only: # overlap_end = False + n_pred_series_expected = len(series) - icl - horizon + 1 + n_pred_points_expected = horizon + first_ts_expected = series.time_index[icl] + last_ts_expected = series.end_time() + elif overlap_end: # last_points_only = True + n_pred_series_expected = 1 + n_pred_points_expected = 8 + first_ts_expected = series.time_index[icl] + (horizon - 1) * series.freq + last_ts_expected = series.end_time() + series.freq * horizon + else: # last_points_only = True, overlap_end = False + n_pred_series_expected = 1 + n_pred_points_expected = len(series) - icl - horizon + 1 + first_ts_expected = series.time_index[icl] + (horizon - 1) * series.freq + last_ts_expected = series.end_time() + + # to make it simple in case of stride, we assume that non-optimized hist fc returns correct results + if stride > 1: + n_pred_series_expected = len(hfc) + n_pred_points_expected = len(hfc[0]) + first_ts_expected = hfc[0].start_time() + last_ts_expected = hfc[-1].end_time() + + # check length match between optimized and default hist fc + assert len(ohfc) == n_pred_series_expected + assert len(hfc) == len(ohfc) + # check hist fc start + assert ohfc[0].start_time() == first_ts_expected + # check hist fc end + assert ohfc[-1].end_time() == last_ts_expected + for hfc_, ohfc_ in zip(hfc, ohfc): + assert hfc_.columns.equals(series.columns) + assert ohfc_.columns.equals(series.columns) + assert len(ohfc_) == n_pred_points_expected + assert (hfc_.time_index == ohfc_.time_index).all() + np.testing.assert_array_almost_equal( + hfc_.all_values(), ohfc_.all_values() + ) + + def test_hist_fc_end_exact_with_covs(self): + model = LinearRegressionModel( + lags=2, + lags_past_covariates=2, + lags_future_covariates=(2, 1), + output_chunk_length=2, + ) + series = tg.sine_timeseries(length=10) + model.fit(series, past_covariates=series, future_covariates=series) + fc = model.historical_forecasts( + series, + past_covariates=series[:-2], + future_covariates=series, + forecast_horizon=2, + stride=2, + overlap_end=False, + last_points_only=True, + retrain=False, + ) + assert len(fc) == 4 + assert fc.end_time() == series.end_time() + + fc = model.historical_forecasts( + series, + past_covariates=series[:-2], + future_covariates=series, + forecast_horizon=2, + stride=2, + overlap_end=False, + last_points_only=False, + retrain=False, + ) + fc = concatenate(fc) + assert len(fc) == 8 + assert fc.end_time() == series.end_time() + + @pytest.mark.parametrize("model_config", models_reg_cov_cls_kwargs) + def test_regression_auto_start_multiple_with_cov_retrain(self, model_config): + forecast_hrz = 10 + model_cls, kwargs, _, bounds = model_config + model = model_cls( + random_state=0, + **kwargs, + ) + + forecasts_retrain = model.historical_forecasts( + series=[self.ts_pass_val, self.ts_pass_val], + past_covariates=( + [ + self.ts_past_cov_valid_same_start, + self.ts_past_cov_valid_same_start, + ] + if "lags_past_covariates" in kwargs + else None + ), + future_covariates=( + [ + self.ts_past_cov_valid_same_start, + self.ts_past_cov_valid_same_start, + ] + if "lags_future_covariates" in kwargs + else None + ), + last_points_only=True, + forecast_horizon=forecast_hrz, + stride=1, + retrain=True, + overlap_end=False, + ) + + assert len(forecasts_retrain) == 2, ( + f"Model {model_cls} did not return a list of historical forecasts" + ) + + ( + min_target_lag, + max_target_lag, + min_past_cov_lag, + max_past_cov_lag, + min_future_cov_lag, + max_future_cov_lag, + output_chunk_shift, + _, + ) = model.extreme_lags + + past_lag = min( + min_target_lag if min_target_lag else 0, + min_past_cov_lag if min_past_cov_lag else 0, + ( + min_future_cov_lag + if min_future_cov_lag is not None and min_future_cov_lag < 0 + else 0 + ), + ) + + future_lag = ( + max_future_cov_lag + if max_future_cov_lag is not None and max_future_cov_lag > 0 + else 0 + ) + # length input - largest past lag - forecast horizon - max(largest future lag, output_chunk_length) + theorical_retrain_forecast_length = len(self.ts_pass_val) - ( + -past_lag + + forecast_hrz + + max(future_lag + 1, kwargs.get("output_chunk_length", 1)) + ) + assert ( + len(forecasts_retrain[0]) + == len(forecasts_retrain[1]) + == theorical_retrain_forecast_length + ), ( + f"Model {model_cls} does not return the right number of historical forecasts in the case of " + f"retrain=True and overlap_end=False. " + f"Expected {theorical_retrain_forecast_length}, got {len(forecasts_retrain[0])} " + f"and {len(forecasts_retrain[1])}" + ) + + # with last_points_only=True: start is shifted by biggest past lag + training timestamps + # (forecast horizon + output_chunk_length) + expected_start = ( + self.ts_pass_val.start_time() + + (-past_lag + forecast_hrz + kwargs.get("output_chunk_length", 1)) + * self.ts_pass_val.freq + ) + assert forecasts_retrain[0].start_time() == expected_start + + # end is shifted back by the biggest future lag + if model.output_chunk_length - 1 > future_lag: + shift = 0 + else: + shift = future_lag + expected_end = self.ts_pass_val.end_time() - shift * self.ts_pass_val.freq + assert forecasts_retrain[0].end_time() == expected_end + + @pytest.mark.parametrize("model_config", models_reg_cov_cls_kwargs) + def test_regression_auto_start_multiple_with_cov_no_retrain(self, model_config): + forecast_hrz = 10 + model_cls, kwargs, _, bounds = model_config + model = model_cls( + random_state=0, + **kwargs, + ) + + model.fit( + series=[self.ts_pass_val, self.ts_pass_val], + past_covariates=( + [ + self.ts_past_cov_valid_same_start, + self.ts_past_cov_valid_same_start, + ] + if "lags_past_covariates" in kwargs + else None + ), + future_covariates=( + [ + self.ts_past_cov_valid_same_start, + self.ts_past_cov_valid_same_start, + ] + if "lags_future_covariates" in kwargs + else None + ), + ) + forecasts_no_retrain = model.historical_forecasts( + series=[self.ts_pass_val, self.ts_pass_val], + past_covariates=( + [ + self.ts_past_cov_valid_same_start, + self.ts_past_cov_valid_same_start, + ] + if "lags_past_covariates" in kwargs + else None + ), + future_covariates=( + [ + self.ts_past_cov_valid_same_start, + self.ts_past_cov_valid_same_start, + ] + if "lags_future_covariates" in kwargs + else None + ), + last_points_only=True, + forecast_horizon=forecast_hrz, + stride=1, + retrain=False, + overlap_end=False, + ) + + ( + min_target_lag, + max_target_lag, + min_past_cov_lag, + max_past_cov_lag, + min_future_cov_lag, + max_future_cov_lag, + output_chunk_shift, + _, + ) = model.extreme_lags + + past_lag = min( + min_target_lag if min_target_lag else 0, + min_past_cov_lag if min_past_cov_lag else 0, + min_future_cov_lag if min_future_cov_lag else 0, + ) + + future_lag = ( + max_future_cov_lag + if max_future_cov_lag is not None and max_future_cov_lag > 0 + else 0 + ) + + # with last_points_only=True: start is shifted by the biggest past lag plus the forecast horizon + expected_start = ( + self.ts_pass_val.start_time() + + (-past_lag + forecast_hrz - 1) * self.ts_pass_val.freq + ) + assert forecasts_no_retrain[0].start_time() == expected_start + + # end is shifted by the biggest future lag if future lag > output_chunk_length + shift_back = future_lag if future_lag + 1 > model.output_chunk_length else 0 + expected_end = self.ts_pass_val.end_time() - shift_back * self.ts_pass_val.freq + assert forecasts_no_retrain[0].end_time() == expected_end + + @pytest.mark.slow + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") + @pytest.mark.parametrize( + "model_config,retrain", + itertools.product(models_torch_cls_kwargs, [True, False]), + ) + def test_torch_auto_start_multiple_no_cov(self, model_config, retrain): + n_fcs = 3 + forecast_hrz = 10 + model_cls, kwargs, bounds, _ = model_config + model = model_cls( + random_state=0, + **kwargs, + ) + + # we expect first predicted point after `min_train_series_length` + # model is expected to generate `n_fcs` historical forecasts with `n=forecast_hrz` and + # `series` of length `length_series_history` + length_series_history = model.min_train_series_length + forecast_hrz + n_fcs - 1 + series = self.ts_pass_train[:length_series_history] + if not retrain: + model.fit(series) + + # check historical forecasts for several time series, + # retrain True and overlap_end False + forecasts = model.historical_forecasts( + series=[series] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=False, + ) + assert len(forecasts) == 2, ( + f"Model {model_cls} did not return a list of historical forecasts" + ) + + # with the required time spans we expect to get `n_fcs` forecasts + if not retrain: + # with retrain=False, we can start `output_chunk_length` steps earlier for non-RNNModels + # and `training_length - input_chunk_length` steps for RNNModels + if not isinstance(model, RNNModel): + add_fcs = model.extreme_lags[1] + 1 + else: + add_fcs = model.extreme_lags[7] + 1 + else: + add_fcs = 0 + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + add_fcs + assert forecasts[0].end_time() == forecasts[1].end_time() == series.end_time() + + # check historical forecasts for several time series, + # retrain True and overlap_end True + forecasts = model.historical_forecasts( + series=[series] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=True, + ) + + assert len(forecasts) == 2, ( + f"Model {model_cls} did not return a list of historical forecasts" + ) + # with overlap_end=True, we can generate additional `forecast_hrz` + # with retrain=False, we can start `add_fcs` steps earlier + # forecasts after the end of `series` + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + forecast_hrz + add_fcs + assert ( + forecasts[0].end_time() + == forecasts[1].end_time() + == series.end_time() + forecast_hrz * series.freq + ) + + @pytest.mark.slow + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") + @pytest.mark.parametrize( + "model_config,retrain", + itertools.product(models_torch_cls_kwargs, [True, False]), + ) + def test_torch_auto_start_with_past_cov(self, model_config, retrain): + n_fcs = 3 + forecast_hrz = 10 + # past covariates only + model_cls, kwargs, bounds, cov_type = model_config + + model = model_cls( + random_state=0, + **kwargs, + ) + + if not model.supports_past_covariates: + with pytest.raises(ValueError) as err: + model.fit( + series=self.ts_pass_train, past_covariates=self.ts_past_cov_train + ) + assert str(err.value).startswith( + "The model does not support `past_covariates`." + ) + return + + # we expect first predicted point after `min_train_series_length` + # model is expected to generate `n_fcs` historical forecasts with `n=forecast_hrz`, + # `series` of length `length_series_history`, and covariates that cover the required time range + length_series_history = model.min_train_series_length + forecast_hrz + n_fcs - 1 + series = self.ts_pass_train[:length_series_history] + + # for historical forecasts, minimum required past covariates should end + # `forecast_hrz` before the end of `series` + pc = series[:-forecast_hrz] + + if not retrain: + model.fit(series, past_covariates=pc) + + # same start, overlap_end=False + forecasts = model.historical_forecasts( + series=[series] * 2, + past_covariates=[pc] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=False, + ) + assert len(forecasts) == 2, ( + f"Model {model_cls} did not return a list of historical forecasts" + ) + + # with the required time spans we expect to get `n_fcs` forecasts + if not retrain: + # with retrain=False, we can start `output_chunk_length` steps earlier for non-RNNModels + # and `training_length - input_chunk_length` steps for RNNModels + add_fcs = model.extreme_lags[1] + 1 + else: + add_fcs = 0 + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + add_fcs + assert forecasts[0].end_time() == forecasts[1].end_time() == series.end_time() + + # check the same for `overlap_end=True` + forecasts = model.historical_forecasts( + series=[series] * 2, + past_covariates=[pc] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=True, + ) + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + add_fcs + assert forecasts[0].end_time() == forecasts[1].end_time() == series.end_time() + + # same time index, `overlap_end=True` + forecasts = model.historical_forecasts( + series=[series] * 2, + past_covariates=[series] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=True, + ) + assert len(forecasts) == 2, ( + f"Model {model_cls} did not return a list of historical forecasts" + ) + # with overlap_end=True, we can generate additional `forecast_hrz` + # with retrain=False, we can start `add_fcs` steps earlier + # forecasts after the end of `series` + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + forecast_hrz + add_fcs + assert ( + forecasts[0].end_time() + == forecasts[1].end_time() + == series.end_time() + forecast_hrz * series.freq + ) + + # `pc_longer` has more than required length + pc_longer = pc.prepend_values([0.0]).append_values([0.0]) + # `pc_before` starts before and has required times + pc_longer_start = pc.prepend_values([0.0]) + # `pc_after` has required length but starts one step after `pc` + pc_start_after = pc[1:].append_values([0.0]) + # `pc_end_before` has required length but end one step before `pc` + pc_end_before = pc[:-1].prepend_values([0.0]) + + # checks for long enough and shorter covariates + forecasts = model.historical_forecasts( + series=[series] * 4, + past_covariates=[ + pc_longer, + pc_longer_start, + pc_start_after, + pc_end_before, + ], + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=False, + ) + + # for long enough past covariates (but too short for overlapping after the end), we expect `n_fcs` forecast + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + add_fcs + # `pc_start_after` and `pc_end_before` are one step too short for all `n_fcs` + assert len(forecasts[2]) == len(forecasts[3]) == n_fcs + add_fcs - 1 + assert all([fc.end_time() == series.end_time() for fc in forecasts[:3]]) + assert forecasts[3].end_time() == series.end_time() - series.freq + + @pytest.mark.slow + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") + @pytest.mark.parametrize( + "model_config,retrain", + list(itertools.product(models_torch_cls_kwargs, [True, False]))[2:], + ) + def test_torch_auto_start_with_future_cov(self, model_config, retrain): + n_fcs = 3 + forecast_hrz = 10 + # future covariates only + model_cls, kwargs, bounds, cov_type = model_config + + model = model_cls( + random_state=0, + **kwargs, + ) + if not model.supports_future_covariates: + with pytest.raises(ValueError) as err: + model.fit( + series=self.ts_pass_train, future_covariates=self.ts_fut_cov_train + ) + assert str(err.value).startswith( + "The model does not support `future_covariates`." + ) + return + + # we expect first predicted point after `min_train_series_length` + # model is expected to generate `n_fcs` historical forecasts with `n=forecast_hrz`, + # `series` of length `length_series_history`, and covariates that cover the required time range + length_series_history = model.min_train_series_length + forecast_hrz + n_fcs - 1 + series = self.ts_pass_train[:length_series_history] + + # to generate `n_fcs` historical forecasts, and since `forecast_horizon > output_chunk_length`, + # we need additional `output_chunk_length - horizon` future covariates steps + add_n = max(model.extreme_lags[1] + 1 - forecast_hrz, 0) + fc = series.append_values([0.0] * add_n) if add_n else series + + if not retrain: + model.fit(series, future_covariates=fc) + + # same start, overlap_end=False + forecasts = model.historical_forecasts( + series=[series] * 2, + future_covariates=[fc] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=False, + ) + assert len(forecasts) == 2, ( + f"Model {model_cls} did not return a list of historical forecasts" + ) + + # with the required time spans we expect to get `n_fcs` forecasts + if not retrain: + # with retrain=False, we can start `output_chunk_length` steps earlier for non-RNNModels + # and `training_length - input_chunk_length` steps for RNNModels + if not isinstance(model, RNNModel): + add_fcs = model.extreme_lags[1] + 1 + else: + add_fcs = model.extreme_lags[7] + 1 + else: + add_fcs = 0 + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + add_fcs + assert forecasts[0].end_time() == forecasts[1].end_time() == series.end_time() + + # check the same for `overlap_end=True` + forecasts = model.historical_forecasts( + series=[series] * 2, + future_covariates=[fc] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=True, + ) + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + add_fcs + assert forecasts[0].end_time() == forecasts[1].end_time() == series.end_time() + + # `overlap_end=True`, with long enough future covariates + if not isinstance(model, RNNModel): + add_n = model.output_chunk_length + else: + # RNNModel is a special case with always `output_chunk_length=1` + add_n = forecast_hrz + fc_long = fc.append_values([0.0] * add_n) + forecasts = model.historical_forecasts( + series=[series] * 2, + future_covariates=[fc_long] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=True, + ) + assert len(forecasts) == 2, ( + f"Model {model_cls} did not return a list of historical forecasts" + ) + # with overlap_end=True, we can generate additional `forecast_hrz` + # with retrain=False, we can start `add_fcs` steps earlier + # forecasts after the end of `series` + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + forecast_hrz + add_fcs + assert ( + forecasts[0].end_time() + == forecasts[1].end_time() + == series.end_time() + forecast_hrz * series.freq + ) + + # `fc_longer` has more than required length + fc_longer = fc.prepend_values([0.0]).append_values([0.0]) + # `fc_before` starts before and has required times + fc_longer_start = fc.prepend_values([0.0]) + # `fc_after` has required length but starts one step after `fc` + fc_start_after = fc[1:].append_values([0.0]) + # `fc_end_before` has required length but end one step before `fc` + fc_end_before = fc[:-1].prepend_values([0.0]) + + # checks for long enough and shorter covariates + forecasts = model.historical_forecasts( + series=[series] * 4, + future_covariates=[ + fc_longer, + fc_longer_start, + fc_start_after, + fc_end_before, + ], + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=False, + ) + + # for long enough future covariates (but too short for overlapping after the end), we expect `n_fcs` forecast + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + add_fcs + # `fc_start_after` and `fc_end_before` are one step too short for all `n_fcs` + assert len(forecasts[2]) == len(forecasts[3]) == n_fcs + add_fcs - 1 + assert all([fc.end_time() == series.end_time() for fc in forecasts[:3]]) + assert forecasts[3].end_time() == series.end_time() - series.freq + + @pytest.mark.slow + @pytest.mark.skipif(not TORCH_AVAILABLE, reason="requires torch") + @pytest.mark.parametrize( + "model_config,retrain", + itertools.product(models_torch_cls_kwargs, [True, False]), + ) + def test_torch_auto_start_with_past_and_future_cov(self, model_config, retrain): + n_fcs = 3 + forecast_hrz = 10 + # past and future covariates + model_cls, kwargs, bounds, cov_type = model_config + + model = model_cls( + random_state=0, + **kwargs, + ) + if not (model.supports_past_covariates and model.supports_future_covariates): + with pytest.raises(ValueError) as err: + model.fit( + self.ts_pass_train, + past_covariates=self.ts_past_cov_train, + future_covariates=self.ts_fut_cov_train, + ) + invalid_covs = [] + if not model.supports_past_covariates: + invalid_covs.append("`past_covariates`") + if not model.supports_future_covariates: + invalid_covs.append("`future_covariates`") + assert str(err.value).startswith( + f"The model does not support {', '.join(invalid_covs)}" + ) + return + + # we expect first predicted point after `min_train_series_length` + # model is expected to generate `n_fcs` historical forecasts with `n=forecast_hrz`, + # `series` of length `length_series_history`, and covariates that cover the required time range + length_series_history = model.min_train_series_length + forecast_hrz + n_fcs - 1 + series = self.ts_pass_train[:length_series_history] + + # for historical forecasts, minimum required past covariates should end + # `forecast_hrz` before the end of `series` + pc = series[:-forecast_hrz] + + # to generate `n_fcs` historical forecasts, and since `forecast_horizon > output_chunk_length`, + # we need additional `output_chunk_length - horizon` future covariates steps + add_n = max(model.extreme_lags[1] + 1 - forecast_hrz, 0) + fc = series.append_values([0.0] * add_n) if add_n else series + + if not retrain: + model.fit(series, past_covariates=pc, future_covariates=fc) + + # same start, overlap_end=False + forecasts = model.historical_forecasts( + series=[series] * 2, + past_covariates=[pc] * 2, + future_covariates=[fc] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=False, + ) + assert len(forecasts) == 2, ( + f"Model {model_cls} did not return a list of historical forecasts" + ) + + # with the required time spans we expect to get `n_fcs` forecasts + if not retrain: + # with retrain=False, we can start `output_chunk_length` steps earlier for non-RNNModels + # and `training_length - input_chunk_length` steps for RNNModels + if not isinstance(model, RNNModel): + add_fcs = model.extreme_lags[1] + 1 + else: + add_fcs = model.extreme_lags[7] + 1 + else: + add_fcs = 0 + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + add_fcs + assert forecasts[0].end_time() == forecasts[1].end_time() == series.end_time() + + # check the same for `overlap_end=True` + forecasts = model.historical_forecasts( + series=[series] * 2, + past_covariates=[pc] * 2, + future_covariates=[fc] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=True, + ) + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + add_fcs + assert forecasts[0].end_time() == forecasts[1].end_time() == series.end_time() + + # `overlap_end=True`, with long enough past and future covariates + if not isinstance(model, RNNModel): + add_n = model.output_chunk_length + else: + # RNNModel is a special case with always `output_chunk_length=1` + add_n = forecast_hrz + fc_long = fc.append_values([0.0] * add_n) + forecasts = model.historical_forecasts( + series=[series] * 2, + past_covariates=[series] * 2, + future_covariates=[fc_long] * 2, + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=True, + ) + assert len(forecasts) == 2, ( + f"Model {model_cls} did not return a list of historical forecasts" + ) + # with overlap_end=True, we can generate additional `forecast_hrz` + # with retrain=False, we can start `add_fcs` steps earlier + # forecasts after the end of `series` + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + forecast_hrz + add_fcs + assert ( + forecasts[0].end_time() + == forecasts[1].end_time() + == series.end_time() + forecast_hrz * series.freq + ) + + # `pc_longer` has more than required length + pc_longer = pc.prepend_values([0.0]).append_values([0.0]) + # `pc_before` starts before and has required times + pc_longer_start = pc.prepend_values([0.0]) + # `pc_after` has required length but starts one step after `pc` + pc_start_after = pc[1:].append_values([0.0]) + # `pc_end_before` has required length but end one step before `pc` + pc_end_before = pc[:-1].prepend_values([0.0]) + + # `fc_longer` has more than required length + fc_longer = fc.prepend_values([0.0]).append_values([0.0]) + # `fc_before` starts before and has required times + fc_longer_start = fc.prepend_values([0.0]) + # `fc_after` has required length but starts one step after `fc` + fc_start_after = fc[1:].append_values([0.0]) + # `fc_end_before` has required length but end one step before `fc` + fc_end_before = fc[:-1].prepend_values([0.0]) + + # checks for long enough and shorter covariates + forecasts = model.historical_forecasts( + series=[series] * 4, + past_covariates=[ + pc_longer, + pc_longer_start, + pc_start_after, + pc_end_before, + ], + future_covariates=[ + fc_longer, + fc_longer_start, + fc_start_after, + fc_end_before, + ], + forecast_horizon=forecast_hrz, + stride=1, + retrain=retrain, + overlap_end=False, + ) + + # for long enough future covariates (but too short for overlapping after the end), we expect `n_fcs` forecast + assert len(forecasts[0]) == len(forecasts[1]) == n_fcs + add_fcs + # `*_start_after` and `*_end_bore` are one step too short for all `n_fcs` + assert len(forecasts[2]) == len(forecasts[3]) == n_fcs + add_fcs - 1 + assert all([fc.end_time() == series.end_time() for fc in forecasts[:3]]) + assert forecasts[3].end_time() == series.end_time() - series.freq + + def test_retrain(self): + """test historical_forecasts for an untrained model with different retrain values.""" + + def helper_hist_forecasts(retrain_val, start): + model = LinearRegressionModel(lags=4, output_chunk_length=4) + return model.historical_forecasts( + self.ts_passengers, start=start, retrain=retrain_val, verbose=False + ) + + def retrain_f_invalid( + counter, pred_time, train_series, past_covariates, future_covariates + ): + return False + + def retrain_f_missing_arg( + counter, train_series, past_covariates, future_covariates + ): + if len(train_series) % 2 == 0: + return True + else: + return False + + def retrain_f_invalid_ouput_int( + counter, pred_time, train_series, past_covariates, future_covariates + ): + return 1 + + def retrain_f_invalid_ouput_str( + counter, pred_time, train_series, past_covariates, future_covariates + ): + return "True" + + def retrain_f_valid( + counter, pred_time, train_series, past_covariates, future_covariates + ): + # only retrain once in first iteration + if pred_time == pd.Timestamp("1959-09-01 00:00:00"): + return True + else: + return False + + def retrain_f_delayed_true( + counter, pred_time, train_series, past_covariates, future_covariates + ): + if counter > 1: + return True + else: + return False + + # test callable + helper_hist_forecasts(retrain_f_valid, 0.9) + # missing the `pred_time` positional argument + expected_msg = "the Callable `retrain` must have a signature/arguments matching the following positional" + with pytest.raises(ValueError) as error_msg: + helper_hist_forecasts(retrain_f_missing_arg, 0.9) + assert str(error_msg.value).startswith(expected_msg) + # returning a non-bool value (int) + expected_msg = "Return value of `retrain` must be bool, received " + with pytest.raises(ValueError) as error_msg: + helper_hist_forecasts(retrain_f_invalid_ouput_int, 0.9) + assert str(error_msg.value).startswith(expected_msg) + # returning a non-bool value (str) + expected_msg = "Return value of `retrain` must be bool, received " + with pytest.raises(ValueError) as error_msg: + helper_hist_forecasts(retrain_f_invalid_ouput_str, 0.9) + assert str(error_msg.value).startswith(expected_msg) + # predict fails but model could have been trained before the predict round + expected_msg = "`retrain` is `False` in the first train iteration at prediction point (in time)" + with pytest.raises(ValueError) as error_msg: + helper_hist_forecasts(retrain_f_delayed_true, 0.9) + assert str(error_msg.value).startswith(expected_msg) + # always returns False, treated slightly different than `retrain=False` and `retrain=0` + with pytest.raises(ValueError) as error_msg: + helper_hist_forecasts(retrain_f_invalid, 0.9) + assert str(error_msg.value).startswith(expected_msg) + + # test int + helper_hist_forecasts(10, 0.9) + expected_msg = "Model has not been fit yet." + # `retrain=0` with not-trained model, encountering directly a predictable time index + with pytest.raises(ValueError) as error_msg: + helper_hist_forecasts(0, 0.9) + assert str(error_msg.value).startswith(expected_msg), str(error_msg.value) + + # test bool + helper_hist_forecasts(True, 0.9) + # `retrain=False` with not-trained model, encountering directly a predictable time index + expected_msg = "The model has not been fitted yet, and `retrain` is ``False``." + with pytest.raises(ValueError) as error_msg: + helper_hist_forecasts(False, 0.9) + assert str(error_msg.value).startswith(expected_msg) + + expected_start = pd.Timestamp("1949-10-01 00:00:00") + # start before first trainable time index should still work + res = helper_hist_forecasts(True, pd.Timestamp("1949-09-01 00:00:00")) + assert res.time_index[0] == expected_start + # start at first trainable time index should still work + res = helper_hist_forecasts(True, expected_start) + assert res.time_index[0] == expected_start + # start at last trainable time index should still work + expected_end = pd.Timestamp("1960-12-01 00:00:00") + res = helper_hist_forecasts(True, expected_end) + assert res.time_index[0] == expected_end + + @pytest.mark.parametrize("model_type", ["regression", "torch"]) + def test_predict_likelihood_parameters(self, model_type): + """standard checks that historical forecasts work with direct likelihood parameter predictions + with regression and torch models.""" + + model = self.create_model(1, False, model_type=model_type) + # skip torch models if not installed + if model is None: + return + # model doesn't use likelihood + with pytest.raises(ValueError): + model.historical_forecasts( + self.ts_pass_train, + predict_likelihood_parameters=True, + ) + + model = self.create_model(1, model_type=model_type) + # forecast_horizon > output_chunk_length doesn't work + with pytest.raises(ValueError): + model.historical_forecasts( + self.ts_pass_train, + predict_likelihood_parameters=True, + forecast_horizon=2, + ) + + model = self.create_model(1, model_type=model_type) + # num_samples != 1 doesn't work + with pytest.raises(ValueError): + model.historical_forecasts( + self.ts_pass_train, + predict_likelihood_parameters=True, + forecast_horizon=1, + num_samples=2, + ) + + n = 3 + target_name = self.ts_pass_train.components[0] + qs_expected = ["q0.05", "q0.40", "q0.50", "q0.60", "q0.95"] + qs_expected = pd.Index([target_name + "_" + q for q in qs_expected]) + # check that it works with retrain + model = self.create_model(1, model_type=model_type) + hist_fc = model.historical_forecasts( + self.ts_pass_train, + predict_likelihood_parameters=True, + forecast_horizon=1, + num_samples=1, + start=len(self.ts_pass_train) - n, # predict on last 10 steps + retrain=True, + ) + assert hist_fc.components.equals(qs_expected) + assert len(hist_fc) == n + + # check for equal results between predict and hist fc without retraining + model = self.create_model(1, model_type=model_type) + model.fit(series=self.ts_pass_train[:-n]) + hist_fc = model.historical_forecasts( + self.ts_pass_train, + predict_likelihood_parameters=True, + forecast_horizon=1, + num_samples=1, + start=len(self.ts_pass_train) - n, # predict on last 10 steps + retrain=False, + ) + assert hist_fc.components.equals(qs_expected) + assert len(hist_fc) == n + + preds = [] + for n_i in range(n): + preds.append( + model.predict( + n=1, + series=self.ts_pass_train[: -(n - n_i)], + predict_likelihood_parameters=True, + ) + ) + preds = darts.concatenate(preds) + np.testing.assert_array_almost_equal( + preds.all_values(copy=False), hist_fc.all_values(copy=False) + ) + + # check equal results between predict and hist fc with higher output_chunk_length and horizon, + # and last_points_only=False + model = self.create_model(2, model_type=model_type) + # we take one more training step so that model trained on ocl=1 has the same training samples + # as model above + model.fit(series=self.ts_pass_train[: -(n - 1)]) + hist_fc = model.historical_forecasts( + self.ts_pass_train, + predict_likelihood_parameters=True, + forecast_horizon=2, + num_samples=1, + start=len(self.ts_pass_train) - n, # predict on last 10 steps + retrain=False, + last_points_only=False, + overlap_end=True, + ) + # because of overlap_end, we get an additional prediction + # generate the same predictions manually + preds = [] + for n_i in range(n + 1): + right = -(n - n_i) if n_i < 3 else len(self.ts_pass_train) + preds.append( + model.predict( + n=2, + series=self.ts_pass_train[:right], + predict_likelihood_parameters=True, + ) + ) + for p, hfc in zip(preds, hist_fc): + assert p.columns.equals(hfc.columns) + assert p.time_index.equals(hfc.time_index) + np.testing.assert_array_almost_equal( + p.all_values(copy=False), hfc.all_values(copy=False) + ) + assert len(hist_fc) == n + 1 + + @pytest.mark.parametrize( + "config", + product( + [False, True], # last_points_only + [True, False], # multi_models + [1, 2, 3], # horizon + ), + ) + def test_probabilistic_optimized_hist_fc_regression(self, config): + """Tests optimized probabilistic historical forecasts for regression models.""" + np.random.seed(42) + lpo, multi_models, n = config + ocl = 2 + q = [0.05, 0.50, 0.95] + + y = tg.linear_timeseries(length=20) + y = y.stack(y + 1.0) + y = [y, y] + + icl = 3 + model = LinearRegressionModel( + lags=icl, + output_chunk_length=ocl, + likelihood="quantile", + quantiles=q, + multi_models=multi_models, + ) + model.fit(y) + # probabilistic forecasts non-optimized + hfcs_no_opt = model.historical_forecasts( + series=y, + forecast_horizon=n, + last_points_only=lpo, + retrain=False, + enable_optimization=False, + num_samples=1000, + stride=n, + ) + # probabilistic forecasts optimized + hfcs_opt = model.historical_forecasts( + series=y, + forecast_horizon=n, + last_points_only=lpo, + retrain=False, + enable_optimization=True, + num_samples=1000, + stride=n, + ) + if n <= ocl: + # quantile forecasts optimized + hfcs_opt_q = model.historical_forecasts( + series=y, + forecast_horizon=n, + last_points_only=lpo, + retrain=False, + enable_optimization=True, + predict_likelihood_parameters=True, + stride=n, + ) + if lpo: + q_med = hfcs_opt_q[0].components[1::3].tolist() + else: + q_med = hfcs_opt_q[0][0].components[1::3].tolist() + hfcs_opt_q = ( + [concatenate(hfc) for hfc in hfcs_opt_q] + if hfcs_opt_q is not None + else hfcs_opt_q + ) + hfcs_opt_q = ( + [hfc[q_med] for hfc in hfcs_opt_q] + if hfcs_opt_q is not None + else hfcs_opt_q + ) + else: + hfcs_opt_q = [None] * len(hfcs_opt) + + if not lpo: + hfcs_opt = [concatenate(hfc) for hfc in hfcs_opt] + hfcs_no_opt = [concatenate(hfc) for hfc in hfcs_no_opt] + + for hfc_opt, mean_opt_q, hfc_no_opt in zip(hfcs_opt, hfcs_opt_q, hfcs_no_opt): + mean_opt = hfc_opt.all_values().mean(axis=2) + mean_no_opt = hfc_no_opt.all_values().mean(axis=2) + assert np.abs(mean_opt - mean_no_opt).max() < 0.1 + if mean_opt_q is not None: + assert np.abs(mean_opt - mean_opt_q.values()).max() < 0.1 + + def helper_manual_scaling_prediction( + self, + model, + ts: dict[str, TimeSeries], + hf_scaler: dict[str, Scaler], + retrain: bool, + end_idx: int, + ocl: int, + series_idx: Optional[int] = None, + ): + ts_copy = deepcopy(ts) + hf_scaler_copy = deepcopy(hf_scaler) + for ts_name in hf_scaler_copy: + # train the fittable scaler without leaking data + if isinstance(hf_scaler_copy[ts_name], FittableDataTransformer): + if ts_name == "series" or ts_name == "past_covariates": + tmp_ts = ts_copy[ts_name][:end_idx] + else: + # for future covariates, the scaler may access future information + tmp_ts = ts_copy[ts_name][: end_idx + max(0, model.extreme_lags[5])] + if retrain: + hf_scaler_copy[ts_name].fit(tmp_ts) + # apply the scaler on the whole series + ts_copy[ts_name] = hf_scaler_copy[ts_name].transform( + ts_copy[ts_name], series_idx=series_idx + ) + + series = ts_copy.pop("series")[:end_idx] + if retrain: + # completly reset model for reproducibility of the predict() + model = model.untrained_model() + model.fit(series=series, **ts_copy) + + # local model does not support the "series" argument in predict() + if isinstance(model, LocalForecastingModel): + pred = model.predict(n=ocl, **ts_copy) + else: + pred = model.predict(n=ocl, series=series, **ts_copy) + + # scale back the forecasts + if isinstance(hf_scaler_copy.get("series"), InvertibleDataTransformer): + return hf_scaler_copy["series"].inverse_transform( + pred, series_idx=series_idx + ) + else: + return pred + + def helper_compare_hf(self, ts_A, ts_B): + """Helper method to compare all the entries between two historical forecasts""" + type_ts_a = get_series_seq_type(ts_A) + type_ts_b = get_series_seq_type(ts_B) + + assert type_ts_a == type_ts_b + assert len(ts_A) == len(ts_B) + + if type_ts_a == SeriesType.SINGLE: + ts_A = [[ts_A]] + ts_B = [[ts_B]] + elif type_ts_a == SeriesType.SEQ: + ts_A = [ts_A] + ts_B = [ts_B] + + for ts_a, ts_b in zip(ts_A, ts_B): + for ts_a_, ts_b_ in zip(ts_a, ts_b): + assert ts_a_.time_index.equals(ts_b_.time_index) + np.testing.assert_almost_equal( + ts_a_.all_values(), + ts_b_.all_values(), + ) + + def helper_get_model_params( + self, model_cls, series: dict, output_chunk_length: int + ) -> dict: + model_params = {} + if TORCH_AVAILABLE and issubclass(model_cls, NLinearModel): + model_params["input_chunk_length"] = 5 + model_params["output_chunk_length"] = output_chunk_length + model_params["n_epochs"] = 1 + model_params["random_state"] = 123 + model_params = { + **model_params, + **tfm_kwargs, + } + elif issubclass(model_cls, LinearRegressionModel): + model_params["lags"] = 5 + model_params["output_chunk_length"] = output_chunk_length + if "past_covariates" in series: + model_params["lags_past_covariates"] = 4 + if "future_covariates" in series: + model_params["lags_future_covariates"] = [-3, -2] + + return model_params + + @pytest.mark.parametrize( + "params", + product( + [ + ( + { + "series": sine_univariate1 - 11, + }, + {"series": Scaler(scaler=MaxAbsScaler())}, + ), + ( + { + "series": sine_univariate3 + 2, + "past_covariates": sine_univariate1 * 3 + 3, + }, + {"past_covariates": Scaler()}, + ), + ( + { + "series": sine_univariate3 + 5, + "future_covariates": sine_univariate1 * (-4) + 3, + }, + {"future_covariates": Scaler(scaler=MaxAbsScaler())}, + ), + ( + { + "series": sine_univariate3 * 2 + 7, + "past_covariates": sine_univariate1 + 2, + "future_covariates": sine_univariate2 + 3, + }, + {"series": Scaler(), "past_covariates": Scaler()}, + ), + ], + [True, False], # retrain + [True, False], # last point only + models, + ), + ) + def test_historical_forecasts_with_scaler(self, params): + """Apply manually the scaler on the target and covariates to compare with automatic scaling for both + optimized and un-optimized historical forecasts + """ + + (ts, hf_scaler), retrain, last_points_only, model_cls = params + ocl = 6 + model_params = self.helper_get_model_params(model_cls, ts, ocl) + model = model_cls(**model_params) + + # local models do not support historical forecast with retrain=False + if isinstance(model, LocalForecastingModel) and not retrain: + return + # skip test when model does not support the covariate + if ("past_covariates" in ts and not model.supports_past_covariates) or ( + "future_covariates" in ts and not model.supports_future_covariates + ): + return + + # pre-train on the entire unscaled target, overfitting/accuracy is not important + if not retrain: + model.fit(**ts) + for ts_name in hf_scaler.keys(): + hf_scaler[ts_name].fit(ts[ts_name]) + + hf_args = { + "start": -ocl - 1, # in order to get 2 forecasts since stride=1 + "start_format": "position", + "forecast_horizon": ocl, + "stride": 1, + "retrain": retrain, + "overlap_end": False, + "last_points_only": last_points_only, + "verbose": False, + "enable_optimization": False, + } + # un-transformed series, scaler applied within the method + hf_auto = model.historical_forecasts( + **ts, + **hf_args, + data_transformers=hf_scaler, + ) + + hf_auto_pipeline = model.historical_forecasts( + **ts, + **hf_args, + data_transformers={ + key_: Pipeline([val_]) for key_, val_ in hf_scaler.items() + }, + ) + + # verify that the results are identical when using single Scaler or a Pipeline + assert len(hf_auto) == len(hf_auto_pipeline) == 2 + self.helper_compare_hf(hf_auto, hf_auto_pipeline) + + # optimized historical forecast since horizon_length <= ocl and retrain=False + if not retrain: + opti_hf_args = {**hf_args, **{"enable_optimization": True}} + assert opti_hf_args["enable_optimization"] + + opti_hf_auto = model.historical_forecasts( + **ts, + **opti_hf_args, + data_transformers=hf_scaler, + ) + assert len(opti_hf_auto) == len(hf_auto) == 2 + self.helper_compare_hf(hf_auto, opti_hf_auto) + + # for 2nd to last historical forecast + manual_hf_0 = self.helper_manual_scaling_prediction( + model, ts, hf_scaler, retrain, -ocl - 1, ocl + ) + # for last historical forecast + manual_hf_1 = self.helper_manual_scaling_prediction( + model, ts, hf_scaler, retrain, -ocl, ocl + ) + + # verify that automatic and manual pre-scaling produce identical forecasts + if last_points_only: + tmp_ts = TimeSeries.from_times_and_values( + times=manual_hf_1.time_index[-2:], + values=np.array([manual_hf_0.values()[-1], manual_hf_1.values()[-1]]), + columns=manual_hf_0.components, + ) + self.helper_compare_hf(tmp_ts, hf_auto) + else: + self.helper_compare_hf(hf_auto, [manual_hf_0, manual_hf_1]) + + def test_historical_forecasts_with_scaler_errors(self, caplog): + """Check that the appropriate exception is raised when providing incorrect parameters or the expected + warning is display in the corner cases.""" + ocl = 2 + hf_args = { + "start": -ocl - 1, + "start_format": "position", + "forecast_horizon": ocl, + "verbose": False, + } + model = LinearRegressionModel(lags=5, output_chunk_length=ocl) + model.fit(self.sine_univariate1) + + # retrain=False and unfitted data transformers + with pytest.raises(ValueError) as err: + model.historical_forecasts( + **hf_args, + series=self.sine_univariate1, + data_transformers={"series": Scaler()}, + retrain=False, + ) + assert str(err.value).startswith( + "All the fittable entries in `data_transformers` must already be fitted when `retrain=False`, the " + ) + + # retrain=False, multiple series not matching the fitted data transformers dimensions + with pytest.raises(ValueError) as err: + model.historical_forecasts( + **hf_args, + series=[self.sine_univariate1] * 2, + data_transformers={ + "series": Scaler(global_fit=False).fit([self.sine_univariate1] * 3) + }, + retrain=False, + ) + assert str(err.value).startswith( + "When multiple series are provided, their number should match the number of " + "`TimeSeries` used to fit the data transformers `n=3`" + ) + + # retrain=True, multiple series and unfitted data transformers with global_fit=True + expected_warning = ( + "When `retrain=True` and multiple series are provided, the fittable `data_transformers` " + "are trained on each series independently (`global_fit=True` will be ignored)." + ) + with caplog.at_level(logging.WARNING): + model.historical_forecasts( + **hf_args, + series=[self.sine_univariate1, self.sine_univariate2], + data_transformers={"series": Scaler(global_fit=True)}, + retrain=True, + ) + assert expected_warning in caplog.text + + # data transformer (global_fit=False) prefitted on several series but only series is forecasted + expected_warning = ( + "Provided only a single series, but at least one of the `data_transformers` " + "that use `global_fit=False` was fitted on multiple `TimeSeries`." + ) + with caplog.at_level(logging.WARNING): + model.historical_forecasts( + **hf_args, + series=[self.sine_univariate2], + data_transformers={ + "series": Scaler(global_fit=False).fit([ + self.sine_univariate1, + self.sine_univariate2, + ]) + }, + retrain=False, + ) + assert expected_warning in caplog.text + + @pytest.mark.parametrize("params", product([True, False], [True, False])) + def test_historical_forecasts_with_scaler_multiple_series(self, params): + """Verify that the scaling in historical forecasts behave as expected when multiple series are used. + + The difference in behavior is caused by the difference in number of parameters when a scaler is fitted on + a single series/multiple series with global_fit=True or with multplie series with global_fit=False. + """ + retrain, global_fit = params + # due to either of the argument, the scaler will have only one set of parameters + unique_param_entry = retrain or global_fit + ocl = 2 + hf_args = { + "start": -ocl, + "start_format": "position", + "forecast_horizon": ocl, + "last_points_only": False, + "retrain": retrain, + "verbose": False, + } + series = [self.sine_univariate1, self.sine_univariate2, self.sine_univariate3] + + model = LinearRegressionModel(lags=5, output_chunk_length=ocl) + model.fit(series) + + def get_scaler(fit: bool): + if fit: + return Scaler(global_fit=global_fit).fit(series) + else: + return Scaler(global_fit=global_fit) + + # using all the series used to fit the scaler + hf = model.historical_forecasts( + **hf_args, + series=series, + data_transformers={"series": get_scaler(fit=True)}, + ) + manual_hf_0 = self.helper_manual_scaling_prediction( + model, + {"series": series[0]}, + {"series": get_scaler(fit=True)}, + retrain, + -ocl, + ocl, + series_idx=None if unique_param_entry else 0, + ) + manual_hf_1 = self.helper_manual_scaling_prediction( + model, + {"series": series[1]}, + {"series": get_scaler(fit=True)}, + retrain, + -ocl, + ocl, + series_idx=None if unique_param_entry else 1, + ) + manual_hf_2 = self.helper_manual_scaling_prediction( + model, + {"series": series[2]}, + {"series": get_scaler(fit=True)}, + retrain, + -ocl, + ocl, + series_idx=None if unique_param_entry else 2, + ) + self.helper_compare_hf(hf, [[manual_hf_0], [manual_hf_1], [manual_hf_2]]) + + # scaler fit on 3 series, historical forecast only over the first one + hf = model.historical_forecasts( + **hf_args, + series=series[0], + data_transformers={"series": get_scaler(fit=True)}, + ) + manual_hf_0 = self.helper_manual_scaling_prediction( + model, + {"series": series[0]}, + {"series": get_scaler(fit=True)}, + retrain, + -ocl, + ocl, + ) + self.helper_compare_hf(hf, [manual_hf_0]) + + # scaler fit on 3 series, historical forecast only over the last one, causing a mismatch + hf = model.historical_forecasts( + **hf_args, + series=series[2], + data_transformers={"series": get_scaler(fit=True)}, + ) + # note that the series_idx is not specified, only the first transformer is used (instead of the 3rd) + manual_hf_2 = self.helper_manual_scaling_prediction( + model, + {"series": series[2]}, + {"series": get_scaler(fit=True)}, + retrain, + -ocl, + ocl, + ) + self.helper_compare_hf(hf, [manual_hf_2]) + + # data_transformers are not pre-fitted + if retrain: + hf = model.historical_forecasts( + **hf_args, + series=series, + data_transformers={"series": get_scaler(fit=False)}, + ) + manual_hf_0 = self.helper_manual_scaling_prediction( + model, + {"series": series[0]}, + {"series": get_scaler(fit=False)}, + retrain, + -ocl, + ocl, + ) + manual_hf_1 = self.helper_manual_scaling_prediction( + model, + {"series": series[1]}, + {"series": get_scaler(fit=False)}, + retrain, + -ocl, + ocl, + ) + manual_hf_2 = self.helper_manual_scaling_prediction( + model, + {"series": series[2]}, + {"series": get_scaler(fit=False)}, + retrain, + -ocl, + ocl, + ) + self.helper_compare_hf(hf, [[manual_hf_0], [manual_hf_1], [manual_hf_2]]) + + @pytest.mark.parametrize( + "model_type,enable_optimization", + product(["regression", "torch"], [True, False]), + ) + def test_fit_kwargs(self, model_type, enable_optimization): + """check that the parameters provided in fit_kwargs are correctly processed""" + valid_fit_kwargs = {"max_samples_per_ts": 3} + invalid_fit_kwargs = {"series": self.ts_pass_train} + unsupported_fit_kwargs = {"unsupported": "unsupported"} + + n = 2 + model = self.create_model(1, use_ll=False, model_type=model_type) + + # torch not available + if model is None: + return + + model.fit(series=self.ts_pass_train[:-n]) + + # supported argument + hist_fc = model.historical_forecasts( + self.ts_pass_train, + forecast_horizon=1, + num_samples=1, + start=len(self.ts_pass_train) - n, + retrain=True, + enable_optimization=enable_optimization, + fit_kwargs=valid_fit_kwargs, + ) + + assert hist_fc.components.equals(self.ts_pass_train.components) + assert len(hist_fc) == n + + # passing unsupported argument + with pytest.raises(TypeError): + hist_fc = model.historical_forecasts( + self.ts_pass_train, + forecast_horizon=1, + start=len(self.ts_pass_train) - n, + retrain=True, + enable_optimization=enable_optimization, + fit_kwargs=unsupported_fit_kwargs, + ) + + # passing hist_fc parameters in fit_kwargs, with retrain=False + hist_fc = model.historical_forecasts( + self.ts_pass_train, + forecast_horizon=1, + start=len(self.ts_pass_train) - n, + retrain=False, + enable_optimization=enable_optimization, + fit_kwargs=invalid_fit_kwargs, + ) + + assert hist_fc.components.equals(self.ts_pass_train.components) + assert len(hist_fc) == n + + # passing hist_fc parameters in fit_kwargs, interfering with the logic + with pytest.raises(ValueError) as msg: + model.historical_forecasts( + self.ts_pass_train, + forecast_horizon=1, + start=len(self.ts_pass_train) - n, + retrain=True, + enable_optimization=enable_optimization, + fit_kwargs=invalid_fit_kwargs, + ) + assert str(msg.value).startswith( + "The following parameters cannot be passed in `fit_kwargs`" + ) + + @pytest.mark.parametrize( + "model_type,enable_optimization", + product(["regression", "torch"], [True, False]), + ) + def test_predict_kwargs(self, model_type, enable_optimization): + """check that the parameters provided in predict_kwargs are correctly processed""" + invalid_predict_kwargs = {"predict_likelihood_parameters": False} + unsupported_predict_kwargs = {"unsupported": "unsupported"} + if model_type == "regression": + valid_predict_kwargs = {} + else: + valid_predict_kwargs = {"batch_size": 10} + + n = 2 + model = self.create_model(1, use_ll=False, model_type=model_type) + + # torch not available + if model is None: + return + + model.fit(series=self.ts_pass_train[:-n]) + + # supported argument + hist_fc = model.historical_forecasts( + self.ts_pass_train, + forecast_horizon=1, + start=len(self.ts_pass_train) - n, + retrain=False, + enable_optimization=enable_optimization, + predict_kwargs=valid_predict_kwargs, + ) + + assert hist_fc.components.equals(self.ts_pass_train.components) + assert len(hist_fc) == n + + # passing unsupported prediction argument + with pytest.raises(TypeError): + hist_fc = model.historical_forecasts( + self.ts_pass_train, + forecast_horizon=1, + start=len(self.ts_pass_train) - n, + retrain=False, + enable_optimization=enable_optimization, + predict_kwargs=unsupported_predict_kwargs, + ) + + # passing hist_fc parameters in predict_kwargs, interfering with the logic + with pytest.raises(ValueError) as msg: + hist_fc = model.historical_forecasts( + self.ts_pass_train, + forecast_horizon=1, + start=len(self.ts_pass_train) - n, + retrain=False, + enable_optimization=enable_optimization, + predict_kwargs=invalid_predict_kwargs, + ) + assert str(msg.value).startswith( + "The following parameters cannot be passed in `predict_kwargs`" + ) + + @pytest.mark.parametrize( + "config", + product(["regression", "torch"], [True, False], [True, False]), + ) + def test_sample_weight(self, config): + """check that passing sample weights work and that it yields different results than without sample weights.""" + model_type, manual_weight, multi_series = config + ts = self.ts_pass_train + if manual_weight: + sample_weight = np.linspace(0, 1, len(ts)) + sample_weight = ts.with_values(np.expand_dims(sample_weight, -1)) + else: + sample_weight = "linear" + + if multi_series: + ts = [ts] * 2 + sample_weight = [sample_weight] * 2 if manual_weight else sample_weight + + model_kwargs = ( + {"n_epochs": 3, "optimizer_kwargs": {"lr": 0.1}} + if model_type == "torch" + else {} + ) + model = self.create_model( + 1, use_ll=False, model_type=model_type, **model_kwargs + ) + + # torch not available + if model is None: + return + + start_kwargs = {"start": -1, "start_format": "position"} + hfc_non_weighted = model.historical_forecasts(series=ts, **start_kwargs) + + model = self.create_model(1, use_ll=False, model_type=model_type) + hfc_weighted = model.historical_forecasts( + series=ts, sample_weight=sample_weight, **start_kwargs + ) + + if not multi_series: + hfc_weighted = [hfc_weighted] + hfc_non_weighted = [hfc_non_weighted] + + # check that the predictions are different + for hfc_nw, hfc_w in zip(hfc_non_weighted, hfc_weighted): + with pytest.raises(AssertionError): + np.testing.assert_array_almost_equal( + hfc_w.all_values(), hfc_nw.all_values() + ) + + if manual_weight: + if multi_series: + sample_weight[1] = sample_weight[1][1:] + invalid_idx = 1 + else: + sample_weight = sample_weight[:-1] + invalid_idx = 0 + + with pytest.raises(ValueError) as err: + _ = model.historical_forecasts( + series=ts, sample_weight=sample_weight, **start_kwargs + ) + assert ( + str(err.value) + == f"`sample_weight` at series index {invalid_idx} must contain " + f"at least all times of the corresponding target `series`." + ) + + def test_historical_forecast_additional_sanity_checks(self): + model = LinearRegressionModel(lags=1) + + # `stride <= 0` + with pytest.raises(ValueError) as err: + _ = model.historical_forecasts( + series=self.ts_pass_train, + stride=0, + ) + assert ( + str(err.value) + == "The provided stride parameter must be a positive integer." + ) + + # start_format="position" but `start` is not `int` + with pytest.raises(ValueError) as err: + _ = model.historical_forecasts( + series=self.ts_pass_train, + start=pd.Timestamp("01-01-2020"), + start_format="position", + ) + assert str(err.value).startswith( + "Since `start_format='position'`, `start` must be an integer, received" + ) + + @pytest.mark.parametrize( + "config", + itertools.product( + [False, True], # use covariates + [True, False], # last points only + [True, False], # overlap end + [1, 3], # stride + [ + 3, # horizon < ocl + 5, # horizon == ocl + 7, # horizon > ocl -> autoregression + ], + [False, True], # use integer indexed series + [False, True], # use multi-series + [0, 1], # output chunk shift + ), + ) + def test_conformal_historical_forecasts(self, config): + """Tests historical forecasts output naive conformal model with last points only, covariates, stride, + different horizons and overlap end. + Tests that the returned dimensions, lengths and start / end times are correct. + """ + ( + use_covs, + last_points_only, + overlap_end, + stride, + horizon, + use_int_idx, + use_multi_series, + ocs, + ) = config + q = [0.1, 0.5, 0.9] + pred_lklp = {"num_samples": 1, "predict_likelihood_parameters": True} + # compute minimum series length to generate n forecasts + icl = 3 + ocl = 5 + horizon_ocs = horizon + ocs + min_len_val_series = icl + horizon_ocs + int(not overlap_end) * horizon_ocs + n_forecasts = 3 + # get train and val series of that length + series = self.ts_pass_val[: min_len_val_series + n_forecasts - 1] + if use_int_idx: + series = TimeSeries.from_values( + values=series.all_values(), + columns=series.columns, + ) + # check that too short input raises error + series_too_short = series[:-n_forecasts] + + # optionally, generate covariates + if use_covs: + pc = tg.gaussian_timeseries( + start=series.start_time(), + end=series.end_time() + max(0, horizon - ocl) * series.freq, + freq=series.freq, + ) + fc = tg.gaussian_timeseries( + start=series.start_time(), + end=series.end_time() + (max(ocl, horizon) + ocs) * series.freq, + freq=series.freq, + ) + else: + pc, fc = None, None + + # first train the ForecastingModel + model_kwargs = ( + {} + if not use_covs + else {"lags_past_covariates": icl, "lags_future_covariates": (icl, ocl)} + ) + forecasting_model = LinearRegressionModel( + lags=icl, output_chunk_length=ocl, output_chunk_shift=ocs, **model_kwargs + ) + forecasting_model.fit(series, past_covariates=pc, future_covariates=fc) + + # add an offset and rename columns in second series to make sure that conformal hist fc works as expected + if use_multi_series: + series = [ + series, + (series + 10).shift(1).with_columns_renamed(series.columns, "test_col"), + ] + pc = [pc, pc.shift(1)] if pc is not None else None + fc = [fc, fc.shift(1)] if fc is not None else None + + # conformal model + model = ConformalNaiveModel(forecasting_model, quantiles=q) + + hfc_kwargs = dict( + { + "retrain": False, + "last_points_only": last_points_only, + "overlap_end": overlap_end, + "stride": stride, + "forecast_horizon": horizon, + }, + **pred_lklp, + ) + # cannot perform auto regression with output chunk shift + if ocs and horizon > ocl: + with pytest.raises(ValueError) as exc: + _ = model.historical_forecasts( + series=series, + past_covariates=pc, + future_covariates=fc, + **hfc_kwargs, + ) + assert str(exc.value).startswith("Cannot perform auto-regression") + return + + # compute conformal historical forecasts + hist_fct = model.historical_forecasts( + series=series, past_covariates=pc, future_covariates=fc, **hfc_kwargs + ) + # raises error with too short target series + with pytest.raises(ValueError) as exc: + _ = model.historical_forecasts( + series=series_too_short, + past_covariates=pc, + future_covariates=fc, + **hfc_kwargs, + ) + assert str(exc.value).startswith( + "Could not build the minimum required calibration input with the provided `series`" + ) + + if not isinstance(series, list): + series = [series] + hist_fct = [hist_fct] + + for ( + series_, + hfc, + ) in zip(series, hist_fct): + if not isinstance(hfc, list): + hfc = [hfc] + + n_preds_with_overlap = ( + len(series_) + - icl # input for first prediction + - horizon_ocs # skip first forecasts to avoid look-ahead bias + + 1 # minimum one forecast + ) + if not last_points_only: + # last points only = False gives a list of forecasts per input series + # where each forecast contains the predictions over the entire horizon + n_pred_series_expected = n_preds_with_overlap + n_pred_points_expected = horizon + first_ts_expected = series_.time_index[icl] + series_.freq * ( + horizon_ocs + ocs + ) + last_ts_expected = series_.end_time() + series_.freq * horizon_ocs + # no overlapping means less predictions + if not overlap_end: + n_pred_series_expected -= horizon_ocs + else: + # last points only = True gives one contiguous time series per input series + # with only predictions from the last point in the horizon + n_pred_series_expected = 1 + n_pred_points_expected = n_preds_with_overlap + first_ts_expected = series_.time_index[icl] + series_.freq * ( + horizon_ocs + ocs + horizon - 1 + ) + last_ts_expected = series_.end_time() + series_.freq * horizon_ocs + # no overlapping means less predictions + if not overlap_end: + n_pred_points_expected -= horizon_ocs + + # no overlapping means less predictions + if not overlap_end: + last_ts_expected -= series_.freq * horizon_ocs + + # adapt based on stride + if stride > 1: + if not last_points_only: + n_pred_series_expected = n_pred_series_expected // stride + int( + n_pred_series_expected % stride + ) + else: + n_pred_points_expected = n_pred_points_expected // stride + int( + n_pred_points_expected % stride + ) + first_ts_expected = hfc[0].start_time() + last_ts_expected = hfc[-1].end_time() + + cols_excpected = likelihood_component_names( + series_.columns, quantile_names(q) + ) + # check length match between optimized and default hist fc + assert len(hfc) == n_pred_series_expected + # check hist fc start + assert hfc[0].start_time() == first_ts_expected + # check hist fc end + assert hfc[-1].end_time() == last_ts_expected + for hfc_ in hfc: + assert hfc_.columns.tolist() == cols_excpected + assert len(hfc_) == n_pred_points_expected + + @pytest.mark.parametrize( + "config", + itertools.product( + [False, True], # last points only + [None, 1, 2], # cal length + [False, True], # use start + ["value", "position"], # start format + [False, True], # use integer indexed series + [False, True], # use multi-series + [0, 1], # output chunk shift + ), + ) + def test_conformal_historical_start_cal_length(self, config): + """Tests naive conformal model historical forecasts without `cal_stride`.""" + ( + last_points_only, + cal_length, + use_start, + start_format, + use_int_idx, + use_multi_series, + ocs, + ) = config + q = [0.1, 0.5, 0.9] + pred_lklp = {"num_samples": 1, "predict_likelihood_parameters": True} + # compute minimum series length to generate n forecasts + icl = 3 + ocl = 5 + horizon = 5 + horizon_ocs = horizon + ocs + add_cal_length = cal_length - 1 if cal_length is not None else 0 + add_start = 2 * int(use_start) + min_len_val_series = icl + 2 * horizon_ocs + add_cal_length + add_start + n_forecasts = 3 + # get train and val series of that length + series = self.ts_pass_val[: min_len_val_series + n_forecasts - 1] + + if use_int_idx: + series = TimeSeries.from_values( + values=series.all_values(), + columns=series.columns, + ) + + # first train the ForecastingModel + forecasting_model = LinearRegressionModel( + lags=icl, + output_chunk_length=ocl, + output_chunk_shift=ocs, + ) + forecasting_model.fit(series) + + # optionally compute the start as a positional index + start_position = icl + horizon_ocs + add_cal_length + add_start + start = None + if use_start: + if start_format == "value": + start = series.time_index[start_position] + else: + start = start_position + + # add an offset and rename columns in second series to make sure that conformal hist fc works as expected + if use_multi_series: + series = [ + series, + (series + 10).shift(1).with_columns_renamed(series.columns, "test_col"), + ] + + # compute conformal historical forecasts (skips some of the first forecasts to get minimum required cal set) + model = ConformalNaiveModel( + forecasting_model, quantiles=q, cal_length=cal_length + ) + hist_fct = model.historical_forecasts( + series=series, + retrain=False, + start=start, + start_format=start_format, + last_points_only=last_points_only, + forecast_horizon=horizon, + overlap_end=False, + **pred_lklp, + ) + + if not isinstance(series, list): + series = [series] + hist_fct = [hist_fct] + + for idx, ( + series_, + hfc, + ) in enumerate(zip(series, hist_fct)): + if not isinstance(hfc, list): + hfc = [hfc] + + # multi series: second series is shifted by one time step (+/- idx); + # start_format = "value" requires a shift + add_start_series_2 = idx * int(use_start) * int(start_format == "value") + + n_preds_without_overlap = ( + len(series_) + - icl # input for first prediction + - horizon_ocs # skip first forecasts to avoid look-ahead bias + - horizon_ocs # cannot compute with `overlap_end=False` + + 1 # minimum one forecast + - add_cal_length # skip based on train length + - add_start # skip based on start + + add_start_series_2 # skip based on start if second series + ) + if not last_points_only: + n_pred_series_expected = n_preds_without_overlap + n_pred_points_expected = horizon + # seconds series is shifted by one time step (- idx) + first_ts_expected = series_.time_index[ + start_position - add_start_series_2 + ocs + ] + last_ts_expected = series_.end_time() + else: + n_pred_series_expected = 1 + n_pred_points_expected = n_preds_without_overlap + # seconds series is shifted by one time step (- idx) + first_ts_expected = ( + series_.time_index[start_position - add_start_series_2] + + (horizon_ocs - 1) * series_.freq + ) + last_ts_expected = series_.end_time() + + cols_excpected = likelihood_component_names( + series_.columns, quantile_names(q) + ) + # check historical forecasts dimensions + assert len(hfc) == n_pred_series_expected + # check hist fc start + assert hfc[0].start_time() == first_ts_expected + # check hist fc end + assert hfc[-1].end_time() == last_ts_expected + for hfc_ in hfc: + assert hfc_.columns.tolist() == cols_excpected + assert len(hfc_) == n_pred_points_expected + + @pytest.mark.parametrize( + "config", + itertools.product( + [False, True], # last points only + [None, 2], # cal length + ["value", "position"], # start format + [2, 4], # stride + [1, 2], # cal stride + [0, 1], # output chunk shift + ), + ) + def test_conformal_historical_forecast_start_stride(self, caplog, config): + """Tests naive conformal model with `start` being the first forecastable index is identical to a start + before forecastable index (including stride, cal stride). + """ + ( + last_points_only, + cal_length, + start_format, + stride, + cal_stride, + ocs, + ) = config + q = [0.1, 0.5, 0.9] + pred_lklp = {"num_samples": 1, "predict_likelihood_parameters": True} + # compute minimum series length to generate n forecasts + icl = 3 + ocl = 5 + horizon = 2 + + # the position of the first conformal forecast start point without look-ahead bias; assuming min cal_length=1 + horizon_ocs = math.ceil((horizon + ocs) / cal_stride) * cal_stride + # adjust by the number of calibration examples + add_cal_length = cal_stride * (cal_length - 1) if cal_length is not None else 0 + # the minimum series length is the sum of the above, plus the length of one forecast (horizon + ocs) + min_len_val_series = icl + horizon_ocs + add_cal_length + horizon + ocs + n_forecasts = 3 + # to get `n_forecasts` with `stride`, we need more points + n_forecasts_stride = stride * n_forecasts - int(1 % stride > 0) + # get train and val series of that length + series = tg.linear_timeseries( + length=min_len_val_series + n_forecasts_stride - 1 + ) + + # first train the ForecastingModel + forecasting_model = LinearRegressionModel( + lags=icl, + output_chunk_length=ocl, + output_chunk_shift=ocs, + ) + forecasting_model.fit(series) + + # optionally compute the start as a positional index + start_position = icl + horizon_ocs + add_cal_length + if start_format == "value": + start = series.time_index[start_position] + start_too_early = series.time_index[start_position - 1] + start_too_early_stride = series.time_index[start_position - stride] + else: + start = start_position + start_too_early = start_position - 1 + start_too_early_stride = start_position - stride + start_first_fc = series.time_index[start_position] + series.freq * ( + horizon + ocs - 1 if last_points_only else ocs + ) + too_early_warn_exp = "is before the first predictable/trainable historical" + + hfc_params = { + "series": series, + "retrain": False, + "start_format": start_format, + "stride": stride, + "last_points_only": last_points_only, + "forecast_horizon": horizon, + } + # compute regular historical forecasts + hist_fct_all = forecasting_model.historical_forecasts(start=start, **hfc_params) + assert len(hist_fct_all) == n_forecasts + assert hist_fct_all[0].start_time() == start_first_fc + assert ( + hist_fct_all[1].start_time() - stride * series.freq + == hist_fct_all[0].start_time() + ) + + # compute conformal historical forecasts (starting at first possible conformal forecast) + model = ConformalNaiveModel( + forecasting_model, quantiles=q, cal_length=cal_length, cal_stride=cal_stride + ) + with caplog.at_level(logging.WARNING): + hist_fct = model.historical_forecasts( + start=start, **hfc_params, **pred_lklp + ) + assert too_early_warn_exp not in caplog.text + caplog.clear() + assert len(hist_fct) == len(hist_fct_all) + assert hist_fct_all[0].start_time() == hist_fct[0].start_time() + assert ( + hist_fct[1].start_time() - stride * series.freq == hist_fct[0].start_time() + ) + + # start one earlier gives warning + with caplog.at_level(logging.WARNING): + _ = model.historical_forecasts( + start=start_too_early, **hfc_params, **pred_lklp + ) + assert too_early_warn_exp in caplog.text + caplog.clear() + + # starting stride before first valid start, gives identical results + hist_fct_too_early = model.historical_forecasts( + start=start_too_early_stride, **hfc_params, **pred_lklp + ) + assert hist_fct_too_early == hist_fct diff --git a/darts/tests/utils/historical_forecasts/test_utils.py b/darts/tests/utils/historical_forecasts/test_utils.py new file mode 100644 index 0000000000..7554d807e7 --- /dev/null +++ b/darts/tests/utils/historical_forecasts/test_utils.py @@ -0,0 +1,156 @@ +import itertools + +import pandas as pd +import pytest + +import darts.utils.historical_forecasts.utils as hfc_utils +from darts.models import LinearRegressionModel +from darts.utils.timeseries_generation import linear_timeseries + + +class TestHistoricalForecastsUtils: + model = LinearRegressionModel(lags=1) + + def test_historical_forecasts_check_kwargs(self): + # `hfc_args` not part of `dict_kwargs` works + hfc_args = {"a", "b"} + dict_kwargs = {"c": 0, "d": 0} + out = hfc_utils._historical_forecasts_check_kwargs( + hfc_args=hfc_args, + name_kwargs="some_name", + dict_kwargs=dict_kwargs, + ) + assert out == dict_kwargs + + # `hfc_args` is part of `dict_kwargs` fails + with pytest.raises(ValueError): + _ = hfc_utils._historical_forecasts_check_kwargs( + hfc_args={"c"}, + name_kwargs="some_name", + dict_kwargs=dict_kwargs, + ) + + @pytest.mark.parametrize( + "config", + itertools.product( + [True, False], # retrain + [True, False], # show warnings + [{}, {"some_fit_param": 0}], # fit kwargs + [{}, {"some_predict_param": 0}], # predict kwargs + ), + ) + def test_historical_forecasts_sanitize_kwargs(self, config): + retrain, show_warnings, fit_kwargs, pred_kwargs = config + fit_kwargs_out, pred_kwargs_out = ( + hfc_utils._historical_forecasts_sanitize_kwargs( + self.model, + fit_kwargs=fit_kwargs, + predict_kwargs=pred_kwargs, + retrain=retrain, + show_warnings=show_warnings, + ) + ) + assert fit_kwargs_out == fit_kwargs + assert pred_kwargs_out == pred_kwargs + + @pytest.mark.parametrize( + "kwargs", + [ + { + "fit_kwargs": {"series": 0}, + "predict_kwargs": None, + "retrain": True, + "show_warnings": False, + }, + { + "fit_kwargs": None, + "predict_kwargs": {"series": 0}, + "retrain": True, + "show_warnings": False, + }, + ], + ) + def test_historical_forecasts_sanitize_kwargs_invalid(self, kwargs): + with pytest.raises(ValueError): + _ = hfc_utils._historical_forecasts_sanitize_kwargs(self.model, **kwargs) + + def test_historical_forecasts_check_start(self): + """""" + series = linear_timeseries(start=0, length=1) + kwargs = { + "start": 0, + "start_format": "value", + "series_start": 0, + "ref_start": 0, + "ref_end": 0, + "stride": 0, + "series_idx": 0, + "is_historical_forecast": False, + } + # low enough start idx works with any kwargs + hfc_utils._check_start(series, start_idx=0, **kwargs) + + # start idx >= len(series) raises error + with pytest.raises(ValueError): + hfc_utils._check_start(series, start_idx=1, **kwargs) + + @pytest.mark.parametrize( + "config", + [ + (True, pd.Timestamp("2000-01-01"), "value"), + (True, 0.9, "value"), + (True, 0.9, "position"), + (True, 0, "position"), + (True, 0, "value"), + (True, -1, "position"), + (False, pd.Timestamp("2000-01-01"), "value"), + (False, 0.9, "value"), + (False, 0.9, "position"), + (False, 0, "position"), + (False, -1, "position"), + ], + ) + def test_historical_forecasts_check_start_invalid(self, config): + """""" + is_dt, start, start_format = config + series = linear_timeseries(start="2000-01-01" if is_dt else 0, length=1) + series_start = series.start_time() + kwargs = { + "start": start, + "start_format": start_format, + "series_start": series_start, + "ref_start": 0, + "ref_end": 0, + "stride": 0, + "series_idx": 0, + "is_historical_forecast": False, + } + + # low enough start idx works with any kwargs + with pytest.raises(ValueError) as err: + hfc_utils._check_start(series, start_idx=1, **kwargs) + + # make sure we reach the expected error message and message is specific to input + position_msg = f"position `{start}` corresponding to time " + if start_format == "position" or is_dt and not isinstance(start, pd.Timestamp): + assert position_msg in str(err.value) + else: + assert position_msg not in str(err.value) + + @pytest.mark.parametrize( + "config", + [ + (0, 0, 0), + (1, 1, 1), + (1, 10, 1), + (-1, 1, 0), + (-3, 1, 0), + (-1, 2, 1), + (-2, 2, 0), + (-3, 2, 1), + ], + ) + def test_adjust_start(self, config): + """Check relative start position adjustment.""" + start_rel, stride, start_expected = config + assert hfc_utils._adjust_start(start_rel, stride) == start_expected diff --git a/darts/tests/utils/tabularization/test_create_lagged_prediction_data.py b/darts/tests/utils/tabularization/test_create_lagged_prediction_data.py index 4bff71fbe9..b1c4ef6069 100644 --- a/darts/tests/utils/tabularization/test_create_lagged_prediction_data.py +++ b/darts/tests/utils/tabularization/test_create_lagged_prediction_data.py @@ -1,6 +1,8 @@ +import itertools import warnings +from collections.abc import Sequence from itertools import product -from typing import Optional, Sequence +from typing import Optional import numpy as np import pandas as pd @@ -137,9 +139,9 @@ def get_feature_times_target_or_past( """ times = series.time_index min_lag = -max(lags) - times = times.union( - [times[-1] + i * series.freq for i in range(1, min_lag + 1)] - ) + times = times.union([ + times[-1] + i * series.freq for i in range(1, min_lag + 1) + ]) max_lag = -min(lags) times = times[max_lag:] return times @@ -194,20 +196,18 @@ def get_feature_times_future( # Case 1: if (min_lag > 0) and (max_lag > 0): # Can create features for times extending after the end of `future_covariates`: - times = times.union( - [times[-1] + i * future_covariates.freq for i in range(1, min_lag + 1)] - ) + times = times.union([ + times[-1] + i * future_covariates.freq for i in range(1, min_lag + 1) + ]) # Can't create features for first `max_lag` times in series: times = times[max_lag:] # Case 2: elif (min_lag <= 0) and (max_lag <= 0): # Can create features for times before the start of `future_covariates`: - times = times.union( - [ - times[0] - i * future_covariates.freq - for i in range(1, abs(max_lag) + 1) - ] - ) + times = times.union([ + times[0] - i * future_covariates.freq + for i in range(1, abs(max_lag) + 1) + ]) # Can't create features for last `abs(min_lag)` times in series: times = times[:min_lag] if min_lag != 0 else times # Case 3: @@ -304,7 +304,7 @@ def construct_X_block( time_idx = np.searchsorted(series_times, time) X_row = [] for lag in lags: - # Offet by particular lag value: + # Offset by particular lag value: idx_to_get = time_idx + lag # Account for prepended values: idx_to_get -= num_prepended @@ -330,7 +330,11 @@ def construct_X_block( target_lag_combos = past_lag_combos = (None, [-1, -3], [-3, -1]) future_lag_combos = (*target_lag_combos, [0], [2, 1], [-1, 1], [0, 2]) - def test_lagged_prediction_data_equal_freq_range_index(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_lagged_prediction_data_equal_freq(self, series_type): """ Tests that `create_lagged_prediction_data` produces `X` and `times` outputs that are consistent with those generated by using the helper @@ -339,112 +343,50 @@ def test_lagged_prediction_data_equal_freq_range_index(self): `self.target_lag_combos`, `self.covariates_lag_combos`, and `self.max_samples_per_ts_combos`. - This particular test uses timeseries with range time indices of equal + This particular test uses timeseries with time indices of equal frequencies. Since all of the timeseries are of the same frequency, the implementation of the 'moving window' method is being tested here. """ # Define range index timeseries - each has different number of components, # different start times, different lengths, and different values, but # they're all of the same frequency: - target = self.create_multivariate_linear_timeseries( - n_components=2, start_value=0, end_value=10, start=2, end=20, freq=1 - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, start_value=10, end_value=20, start=4, end=23, freq=2 - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, start_value=20, end_value=30, start=6, end=26, freq=3 - ) - # Conduct test for each input parameter combo: - for (lags, lags_past, lags_future, max_samples_per_ts) in product( - self.target_lag_combos, - self.past_lag_combos, - self.future_lag_combos, - self.max_samples_per_ts_combos, - ): - all_lags = (lags, lags_past, lags_future) - # Skip test where all lags are `None` - can't assemble features - # for this single combo of input params: - lags_is_none = [x is None for x in all_lags] - if all(lags_is_none): - continue - X, times = create_lagged_prediction_data( - target_series=target if lags else None, - past_covariates=past if lags_past else None, - future_covariates=future if lags_future else None, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=True, + if series_type == "integer": + target = self.create_multivariate_linear_timeseries( + n_components=2, start_value=0, end_value=10, start=2, end=20, freq=2 ) - feats_times = self.get_feature_times( - target, - past, - future, - lags, - lags_past, - lags_future, - max_samples_per_ts, + past = self.create_multivariate_linear_timeseries( + n_components=3, start_value=10, end_value=20, start=4, end=23, freq=2 + ) + future = self.create_multivariate_linear_timeseries( + n_components=4, start_value=20, end_value=30, start=6, end=26, freq=2 + ) + else: + target = self.create_multivariate_linear_timeseries( + n_components=2, + start_value=0, + end_value=10, + start=pd.Timestamp("1/2/2000"), + end=pd.Timestamp("1/16/2000"), + freq="2d", + ) + past = self.create_multivariate_linear_timeseries( + n_components=3, + start_value=10, + end_value=20, + start=pd.Timestamp("1/4/2000"), + end=pd.Timestamp("1/18/2000"), + freq="2d", + ) + future = self.create_multivariate_linear_timeseries( + n_components=4, + start_value=20, + end_value=30, + start=pd.Timestamp("1/6/2000"), + end=pd.Timestamp("1/20/2000"), + freq="2d", ) - # Construct `X` by constructing each block, then concatenate these - # blocks together along component axis: - X_target = self.construct_X_block(target, feats_times, lags) - X_past = self.construct_X_block(past, feats_times, lags_past) - X_future = self.construct_X_block(future, feats_times, lags_future) - all_X = (X_target, X_past, X_future) - to_concat = [X for X in all_X if X is not None] - expected_X = np.concatenate(to_concat, axis=1) - # Number of observations should match number of feature times: - assert X.shape[0] == len(feats_times) - assert X.shape[0] == len(times[0]) - # Check that outputs match: - assert np.allclose(expected_X, X[:, :, 0]) - assert feats_times.equals(times[0]) - - def test_lagged_prediction_data_equal_freq_datetime_index(self): - """ - Tests that `create_lagged_prediction_data` produces `X` and `times` - outputs that are consistent with those generated by using the helper - functions `get_feature_times` and `construct_X_block`. Consistency is - checked over all of the combinations of parameter values specified by - `self.target_lag_combos`, `self.covariates_lag_combos`, and - `self.max_samples_per_ts_combos`. - - This particular test uses timeseries with datetime time indices of equal - frequencies. Since all of the timeseries are of the same frequency, - the implementation of the 'moving window' method is being tested here. - """ - # Define datetime index timeseries - each has different number of components, - # different start times, different lengths, and different values, but - # they're all of the same frequency: - target = self.create_multivariate_linear_timeseries( - n_components=2, - start_value=0, - end_value=10, - start=pd.Timestamp("1/2/2000"), - end=pd.Timestamp("1/16/2000"), - freq="2d", - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, - start_value=10, - end_value=20, - start=pd.Timestamp("1/4/2000"), - end=pd.Timestamp("1/18/2000"), - freq="2d", - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, - start_value=20, - end_value=30, - start=pd.Timestamp("1/6/2000"), - end=pd.Timestamp("1/20/2000"), - freq="2d", - ) # Conduct test for each input parameter combo: - for (lags, lags_past, lags_future, max_samples_per_ts) in product( + for lags, lags_past, lags_future, max_samples_per_ts in product( self.target_lag_combos, self.past_lag_combos, self.future_lag_combos, @@ -491,7 +433,11 @@ def test_lagged_prediction_data_equal_freq_datetime_index(self): assert np.allclose(expected_X, X[:, :, 0]) assert feats_times.equals(times[0]) - def test_lagged_prediction_data_unequal_freq_range_index(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_lagged_prediction_data_unequal_freq(self, series_type): """ Tests that `create_lagged_prediction_data` produces `X` and `times` outputs that are consistent with those generated by using the helper @@ -500,97 +446,50 @@ def test_lagged_prediction_data_unequal_freq_range_index(self): `self.target_lag_combos`, `self.covariates_lag_combos`, and `self.max_samples_per_ts_combos`. - This particular test uses timeseries with range time indices of unequal + This particular test uses timeseries with time indices of unequal frequencies. Since all of the timeseries are *not* of the same frequency, the implementation of the 'time intersection' method is being tested here. """ # Define range index timeseries - each has different number of components, # different start times, different lengths, different values, and of # different frequencies: - target = self.create_multivariate_linear_timeseries( - n_components=2, start_value=0, end_value=10, start=2, end=20, freq=1 - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, start_value=10, end_value=20, start=4, end=23, freq=2 - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, start_value=20, end_value=30, start=6, end=26, freq=3 - ) - # Conduct test for each input parameter combo: - for (lags, lags_past, lags_future, max_samples_per_ts) in product( - self.target_lag_combos, - self.past_lag_combos, - self.future_lag_combos, - self.max_samples_per_ts_combos, - ): - all_lags = (lags, lags_past, lags_future) - # Skip test where all lags are `None` - can't assemble features - # for this single combo of input params: - lags_is_none = [x is None for x in all_lags] - if all(lags_is_none): - continue - X, times = create_lagged_prediction_data( - target_series=target if lags else None, - past_covariates=past if lags_past else None, - future_covariates=future if lags_future else None, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=True, + if series_type == "integer": + target = self.create_multivariate_linear_timeseries( + n_components=2, start_value=0, end_value=10, start=2, end=20, freq=1 ) - feats_times = self.get_feature_times( - target, - past, - future, - lags, - lags_past, - lags_future, - max_samples_per_ts, + past = self.create_multivariate_linear_timeseries( + n_components=3, start_value=10, end_value=20, start=4, end=23, freq=2 + ) + future = self.create_multivariate_linear_timeseries( + n_components=4, start_value=20, end_value=30, start=6, end=26, freq=3 + ) + else: + target = self.create_multivariate_linear_timeseries( + n_components=2, + start_value=0, + end_value=10, + start=pd.Timestamp("1/2/2000"), + end=pd.Timestamp("1/20/2000"), + freq="1d", + ) + past = self.create_multivariate_linear_timeseries( + n_components=3, + start_value=10, + end_value=20, + start=pd.Timestamp("1/4/2000"), + end=pd.Timestamp("1/23/2000"), + freq="2d", + ) + future = self.create_multivariate_linear_timeseries( + n_components=4, + start_value=20, + end_value=30, + start=pd.Timestamp("1/6/2000"), + end=pd.Timestamp("1/26/2000"), + freq="3d", ) - # Construct `X` by constructing each block, then concatenate these - # blocks together along component axis: - X_target = self.construct_X_block(target, feats_times, lags) - X_past = self.construct_X_block(past, feats_times, lags_past) - X_future = self.construct_X_block(future, feats_times, lags_future) - all_X = (X_target, X_past, X_future) - to_concat = [X for X in all_X if X is not None] - expected_X = np.concatenate(to_concat, axis=1) - # Number of observations should match number of feature times: - assert X.shape[0] == len(feats_times) - assert X.shape[0] == len(times[0]) - # Check that outputs match: - assert np.allclose(expected_X, X[:, :, 0]) - assert feats_times.equals(times[0]) - - def test_lagged_prediction_data_unequal_freq_datetime_index(self): - """ - Tests that `create_lagged_prediction_data` produces `X` and `times` - outputs that are consistent with those generated by using the helper - functions `get_feature_times` and `construct_X_block`. Consistency is - checked over all of the combinations of parameter values specified by - `self.target_lag_combos`, `self.covariates_lag_combos`, and - `self.max_samples_per_ts_combos`. - - This particular test uses timeseries with datetime time indices of unequal - frequencies. Since all of the timeseries are *not* of the same frequency, - the implementation of the 'time intersection' method is being tested here. - """ - # Define datetime index timeseries - each has different number of components, - # different start times, different lengths, different values, and of - # different frequencies: - target = self.create_multivariate_linear_timeseries( - n_components=2, start_value=0, end_value=10, start=2, end=20, freq=1 - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, start_value=10, end_value=20, start=4, end=23, freq=2 - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, start_value=20, end_value=30, start=6, end=26, freq=3 - ) # Conduct test for each input parameter combo: - for (lags, lags_past, lags_future, max_samples_per_ts) in product( + for lags, lags_past, lags_future, max_samples_per_ts in product( self.target_lag_combos, self.past_lag_combos, self.future_lag_combos, @@ -637,7 +536,11 @@ def test_lagged_prediction_data_unequal_freq_datetime_index(self): assert np.allclose(expected_X, X[:, :, 0]) assert feats_times.equals(times[0]) - def test_lagged_prediction_data_method_consistency_range_index(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_lagged_prediction_data_method_consistency_range_index(self, series_type): """ Tests that `create_lagged_prediction_data` produces the same result when `use_moving_windows = False` and when `use_moving_windows = True` @@ -647,118 +550,47 @@ def test_lagged_prediction_data_method_consistency_range_index(self): are both wrong in the same way, this test won't reveal any bugs. With this being said, if this test fails, something is definitely wrong in either one or both of the implemented methods. - - This particular test uses range index timeseries. """ # Define datetime index timeseries - each has different number of components, # different start times, different lengths, different values, and of # different frequencies: - target = self.create_multivariate_linear_timeseries( - n_components=2, - start_value=0, - end_value=10, - start=pd.Timestamp("1/2/2000"), - end=pd.Timestamp("1/16/2000"), - freq="2d", - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, - start_value=10, - end_value=20, - start=pd.Timestamp("1/4/2000"), - end=pd.Timestamp("1/18/2000"), - freq="2d", - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, - start_value=20, - end_value=30, - start=pd.Timestamp("1/6/2000"), - end=pd.Timestamp("1/20/2000"), - freq="2d", - ) - # Conduct test for each input parameter combo: - for (lags, lags_past, lags_future, max_samples_per_ts) in product( - self.target_lag_combos, - self.past_lag_combos, - self.future_lag_combos, - self.max_samples_per_ts_combos, - ): - all_lags = (lags, lags_past, lags_future) - # Skip test where all lags are `None` - can't assemble features - # for this single combo of input params: - lags_is_none = [x is None for x in all_lags] - if all(lags_is_none): - continue - # Using moving window method: - X_mw, times_mw = create_lagged_prediction_data( - target_series=target if lags else None, - past_covariates=past if lags_past else None, - future_covariates=future if lags_future else None, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=True, + if series_type == "integer": + target = self.create_multivariate_linear_timeseries( + n_components=2, start_value=0, end_value=10, start=2, end=16, freq=2 ) - # Using time intersection method: - X_ti, times_ti = create_lagged_prediction_data( - target_series=target if lags else None, - past_covariates=past if lags_past else None, - future_covariates=future if lags_future else None, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=False, + past = self.create_multivariate_linear_timeseries( + n_components=3, start_value=10, end_value=20, start=4, end=18, freq=2 + ) + future = self.create_multivariate_linear_timeseries( + n_components=4, start_value=20, end_value=30, start=6, end=20, freq=2 + ) + else: + target = self.create_multivariate_linear_timeseries( + n_components=2, + start_value=0, + end_value=10, + start=pd.Timestamp("1/2/2000"), + end=pd.Timestamp("1/16/2000"), + freq="2d", + ) + past = self.create_multivariate_linear_timeseries( + n_components=3, + start_value=10, + end_value=20, + start=pd.Timestamp("1/4/2000"), + end=pd.Timestamp("1/18/2000"), + freq="2d", + ) + future = self.create_multivariate_linear_timeseries( + n_components=4, + start_value=20, + end_value=30, + start=pd.Timestamp("1/6/2000"), + end=pd.Timestamp("1/20/2000"), + freq="2d", ) - assert np.allclose(X_mw, X_ti) - assert times_mw[0].equals(times_ti[0]) - - def test_lagged_prediction_data_method_consistency_datetime_index(self): - """ - Tests that `create_lagged_prediction_data` produces the same result - when `use_moving_windows = False` and when `use_moving_windows = True` - for all of the parameter combinations used in the 'generated' test cases. - - Obviously, if both the 'Moving Window Method' and the 'Time Intersection' - are both wrong in the same way, this test won't reveal any bugs. With this - being said, if this test fails, something is definitely wrong in either - one or both of the implemented methods. - - This particular test uses datetime index timeseries. - """ - # Define datetime index timeseries - each has different number of components, - # different start times, different lengths, different values, and of - # different frequencies: - target = self.create_multivariate_linear_timeseries( - n_components=2, - start_value=0, - end_value=10, - start=pd.Timestamp("1/2/2000"), - end=pd.Timestamp("1/16/2000"), - freq="2d", - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, - start_value=10, - end_value=20, - start=pd.Timestamp("1/4/2000"), - end=pd.Timestamp("1/18/2000"), - freq="2d", - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, - start_value=20, - end_value=30, - start=pd.Timestamp("1/6/2000"), - end=pd.Timestamp("1/20/2000"), - freq="2d", - ) # Conduct test for each input parameter combo: - for (lags, lags_past, lags_future, max_samples_per_ts) in product( + for lags, lags_past, lags_future, max_samples_per_ts in product( self.target_lag_combos, self.past_lag_combos, self.future_lag_combos, @@ -801,17 +633,24 @@ def test_lagged_prediction_data_method_consistency_datetime_index(self): # Specified Cases Tests # - def test_lagged_prediction_data_single_lag_single_component_same_series_range_idx( - self, + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [False, True]), + ) + def test_lagged_prediction_data_single_lag_single_component_same_series( + self, config ): """ Tests that `create_lagged_prediction_data` correctly produces `X` and `times` when all the `series` inputs are identical, and all the `lags` inputs consist of a single value. In this situation, the expected `X` value can be found by - concatenating three different slices of the same time series. This particular - test uses a time series with a range index. + concatenating three different slices of the same time series. """ - series = linear_timeseries(start=0, length=15) + series_type, use_moving_windows = config + if series_type == "integer": + series = linear_timeseries(start=0, length=15) + else: + series = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=15) lags = [-1] past_lags = [-3] future_lags = [2] @@ -827,294 +666,172 @@ def test_lagged_prediction_data_single_lag_single_component_same_series_range_id expected_X = np.concatenate( [expected_X_target, expected_X_past, expected_X_future], axis=1 ) - for use_moving_windows in (False, True): - X, times = create_lagged_prediction_data( - target_series=series, - past_covariates=series, - future_covariates=series, - lags=lags, - lags_past_covariates=past_lags, - lags_future_covariates=future_lags, - uses_static_covariates=False, - use_moving_windows=use_moving_windows, - ) - # Number of observations should match number of feature times: - assert X.shape[0] == len(expected_times) - assert X.shape[0] == len(times[0]) - # Check that outputs match: - assert np.allclose(expected_X, X[:, :, 0]) - assert expected_times.equals(times[0]) - - def test_lagged_prediction_data_single_lag_single_component_same_series_datetime_idx( - self, - ): - """ - Tests that `create_lagged_prediction_data` correctly produces `X` and `times` - when all the `series` inputs are identical, and all the `lags` inputs consist - of a single value. In this situation, the expected `X` value can be found by - concatenating three different slices of the same time series. This particular - test uses a time series with a datetime index. - """ - series = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=15) - lags = [-1] - past_lags = [-3] - future_lags = [2] - # Can't create features for first 3 times (because `past_lags`) and last - # two times (because `future_lags`): - expected_times = series.time_index[3:-2] - # Offset `3:-2` by `-1` lag: - expected_X_target = series.all_values(copy=False)[2:-3, :, 0] - # Offset `3:-2` by `-3` lag -> gives `0:-5` - expected_X_past = series.all_values(copy=False)[:-5, :, 0] - # Offset `3:-2` by `+2` lag -> gives `5:None`: - expected_X_future = series.all_values(copy=False)[5:, :, 0] - expected_X = np.concatenate( - [expected_X_target, expected_X_past, expected_X_future], axis=1 + X, times = create_lagged_prediction_data( + target_series=series, + past_covariates=series, + future_covariates=series, + lags=lags, + lags_past_covariates=past_lags, + lags_future_covariates=future_lags, + uses_static_covariates=False, + use_moving_windows=use_moving_windows, ) - for use_moving_windows in (False, True): - X, times = create_lagged_prediction_data( - target_series=series, - past_covariates=series, - future_covariates=series, - lags=lags, - lags_past_covariates=past_lags, - lags_future_covariates=future_lags, - uses_static_covariates=False, - use_moving_windows=use_moving_windows, - ) - # Number of observations should match number of feature times: - assert X.shape[0] == len(expected_times) - assert X.shape[0] == len(times[0]) - # Check that outputs match: - assert np.allclose(expected_X, X[:, :, 0]) - assert expected_times.equals(times[0]) + # Number of observations should match number of feature times: + assert X.shape[0] == len(expected_times) + assert X.shape[0] == len(times[0]) + # Check that outputs match: + assert np.allclose(expected_X, X[:, :, 0]) + assert expected_times.equals(times[0]) - def test_lagged_prediction_data_extend_past_and_future_covariates_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [False, True]), + ) + def test_lagged_prediction_data_extend_past_and_future_covariates(self, config): """ Tests that `create_lagged_prediction_data` correctly handles case where features can be created for a time that is *not* contained in `target_series`, `past_covariates` - and/or `future_covariates`. This particular test checks this behaviour by using - range index timeseries. + and/or `future_covariates`. More specifically, we define the series and lags such that a prediction feature can be generated for time `target.end_time() + target.freq`, even though this time isn't contained in any of the define series. """ - # Can create feature for time `t = 9`, but this time isn't in any of the three series: - target = linear_timeseries(start=0, end=9, start_value=1, end_value=2) - lags = [-1] - past = linear_timeseries(start=0, end=8, start_value=2, end_value=3) - lags_past = [-2] - future = linear_timeseries(start=0, end=6, start_value=3, end_value=4) - lags_future = [-4] - # Only want to check very last generated observation: - max_samples_per_ts = 1 - # Expect `X` to be constructed from the very last values of each series: - expected_X = np.concatenate( - [ - target.all_values(copy=False)[-1, :, 0], - past.all_values(copy=False)[-1, :, 0], - future.all_values(copy=False)[-1, :, 0], - ] - ).reshape(1, -1) - # Check correctness for both 'moving window' method - # and 'time intersection' method: - for use_moving_windows in (False, True): - X, times = create_lagged_prediction_data( - target, - past_covariates=past, - future_covariates=future, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=use_moving_windows, + series_type, use_moving_windows = config + if series_type == "integer": + # Can create feature for time `t = 9`, but this time isn't in any of the three series: + target = linear_timeseries(start=0, end=9, start_value=1, end_value=2) + past = linear_timeseries(start=0, end=8, start_value=2, end_value=3) + future = linear_timeseries(start=0, end=6, start_value=3, end_value=4) + else: + # Can create feature for time `t = '1/10/2000'`, but this time isn't in any of the three series: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + end=pd.Timestamp("1/10/2000"), + start_value=1, + end_value=2, + ) + past = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + end=pd.Timestamp("1/9/2000"), + start_value=2, + end_value=3, + ) + future = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + end=pd.Timestamp("1/7/2000"), + start_value=3, + end_value=4, ) - assert times[0][0] == target.end_time() + target.freq - assert np.allclose(expected_X, X[:, :, 0]) - - def test_lagged_prediction_data_extend_past_and_future_covariates_datetime_idx( - self, - ): - """ - Tests that `create_lagged_prediction_data` correctly handles case where features - can be created for a time that is *not* contained in `target_series`, `past_covariates` - and/or `future_covariates`. This particular test checks this behaviour by using - datetime index timeseries. - More specifically, we define the series and lags such that a prediction feature - can be generated for time `target.end_time() + target.freq`, even though this time - isn't contained in any of the define series. - """ - # Can create feature for time `t = '1/10/2000'`, but this time isn't in any of the three series: - target = linear_timeseries( - start=pd.Timestamp("1/1/2000"), - end=pd.Timestamp("1/10/2000"), - start_value=1, - end_value=2, - ) lags = [-1] - past = linear_timeseries( - start=pd.Timestamp("1/1/2000"), - end=pd.Timestamp("1/9/2000"), - start_value=2, - end_value=3, - ) lags_past = [-2] - future = linear_timeseries( - start=pd.Timestamp("1/1/2000"), - end=pd.Timestamp("1/7/2000"), - start_value=3, - end_value=4, - ) lags_future = [-4] # Only want to check very last generated observation: max_samples_per_ts = 1 # Expect `X` to be constructed from the very last values of each series: - expected_X = np.concatenate( - [ - target.all_values(copy=False)[-1, :, 0], - past.all_values(copy=False)[-1, :, 0], - future.all_values(copy=False)[-1, :, 0], - ] - ).reshape(1, -1) + expected_X = np.concatenate([ + target.all_values(copy=False)[-1, :, 0], + past.all_values(copy=False)[-1, :, 0], + future.all_values(copy=False)[-1, :, 0], + ]).reshape(1, -1) # Check correctness for both 'moving window' method # and 'time intersection' method: - for use_moving_windows in (False, True): - X, times = create_lagged_prediction_data( - target, - past_covariates=past, - future_covariates=future, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=use_moving_windows, - ) - assert times[0][0] == target.end_time() + target.freq - assert np.allclose(expected_X, X[:, :, 0]) + X, times = create_lagged_prediction_data( + target, + past_covariates=past, + future_covariates=future, + lags=lags, + lags_past_covariates=lags_past, + lags_future_covariates=lags_future, + uses_static_covariates=False, + max_samples_per_ts=max_samples_per_ts, + use_moving_windows=use_moving_windows, + ) + assert times[0][0] == target.end_time() + target.freq + assert np.allclose(expected_X, X[:, :, 0]) - def test_lagged_prediction_data_single_point_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [False, True]), + ) + def test_lagged_prediction_data_single_point(self, config): """ Tests that `create_lagged_prediction_data` correctly handles case - where only one possible training point can be generated. This - particular test checks this behaviour by using range index timeseries. + where only one possible training point can be generated. """ # Can only create feature using first value of target (i.e. `0`): - target = linear_timeseries(start=0, length=1, start_value=0, end_value=1) - expected_X = np.zeros((1, 1, 1)) - # Prediction time extend beyond end of series: - lag = 5 - for use_moving_windows in (False, True): - X, times = create_lagged_prediction_data( - target, - lags=[-lag], - use_moving_windows=use_moving_windows, - uses_static_covariates=False, + series_type, use_moving_windows = config + if series_type == "integer": + target = linear_timeseries(start=0, length=1, start_value=0, end_value=1) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=1, start_value=0, end_value=1 ) - assert np.allclose(expected_X, X) - # Should only have one sample, generated for - # `t = target.end_time() + lag * target.freq`: - assert len(times) == 1 - assert times[0] == target.end_time() + lag * target.freq - def test_lagged_prediction_data_single_point_datetime_idx(self): - """ - Tests that `create_lagged_prediction_data` correctly handles case - where only one possible training point can be generated. This - particular test checks this behaviour by using datetime index timeseries. - """ - # Can only create feature using first value of target (i.e. `0`): - target = linear_timeseries( - start=pd.Timestamp("1/1/2000"), length=1, start_value=0, end_value=1 - ) expected_X = np.zeros((1, 1, 1)) # Prediction time extend beyond end of series: lag = 5 - for use_moving_windows in (False, True): - X, times = create_lagged_prediction_data( - target, - lags=[-lag], - use_moving_windows=use_moving_windows, - uses_static_covariates=False, - ) - assert np.allclose(expected_X, X) - # Should only have one sample, generated for - # `t = target.end_time() + lag * target.freq`: - assert len(times[0]) == 1 - assert times[0][0] == target.end_time() + lag * target.freq + X, times = create_lagged_prediction_data( + target, + lags=[-lag], + use_moving_windows=use_moving_windows, + uses_static_covariates=False, + ) + assert np.allclose(expected_X, X) + # Should only have one sample, generated for + # `t = target.end_time() + lag * target.freq`: + assert len(times) == 1 + assert times[0] == target.end_time() + lag * target.freq - def test_lagged_prediction_data_zero_lags_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [False, True]), + ) + def test_lagged_prediction_data_zero_lags(self, config): """ Tests that `create_lagged_prediction_data` correctly handles case when `0` is included in `lags_future_covariates` (i.e. when we're using the values `future_covariates` at time `t` to predict the value of `target_series` at - that same time point). This particular test checks this behaviour by using - range index timeseries. + that same time point). """ # Define `future` so that only value occurs at the same time as # the only possible label that can be extracted from `target_series`; the # only possible feature that can be created using these series utilises # the value of `future` at the same time as the label (i.e. a lag # of `0` away from the only feature time): - target = linear_timeseries(start=0, length=1, start_value=0, end_value=1) - future = linear_timeseries(start=1, length=1, start_value=1, end_value=2) - # X comprises of first value of `target` (i.e. 0) and only value in `future`: - expected_X = np.array([0.0, 1.0]).reshape(1, 2, 1) - # Check correctness for 'moving windows' and 'time intersection' methods, as - # well as for different `multi_models` values: - for use_moving_windows in (False, True): - X, times = create_lagged_prediction_data( - target, - future_covariates=future, - lags=[-1], - lags_future_covariates=[0], - uses_static_covariates=False, - use_moving_windows=use_moving_windows, + series_type, use_moving_windows = config + if series_type == "integer": + target = linear_timeseries(start=0, length=1, start_value=0, end_value=1) + future = linear_timeseries(start=1, length=1, start_value=1, end_value=2) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=1, start_value=0, end_value=1 + ) + future = linear_timeseries( + start=pd.Timestamp("1/2/2000"), length=1, start_value=1, end_value=2 ) - assert np.allclose(expected_X, X) - assert len(times[0]) == 1 - assert times[0][0] == future.start_time() - - def test_lagged_prediction_data_zero_lags_datetime_idx(self): - """ - Tests that `create_lagged_prediction_data` correctly handles case when - `0` is included in `lags_future_covariates` (i.e. when we're using the values - `future_covariates` at time `t` to predict the value of `target_series` at - that same time point). This particular test checks this behaviour by using - datetime index timeseries. - """ - # Define `future` so that only value occurs at the same time as - # the only possible label that can be extracted from `target_series`; the - # only possible feature that can be created using these series utilises - # the value of `future` at the same time as the label (i.e. a lag - # of `0` away from the only feature time): - target = linear_timeseries( - start=pd.Timestamp("1/1/2000"), length=1, start_value=0, end_value=1 - ) - future = linear_timeseries( - start=pd.Timestamp("1/2/2000"), length=1, start_value=1, end_value=2 - ) # X comprises of first value of `target` (i.e. 0) and only value in `future`: expected_X = np.array([0.0, 1.0]).reshape(1, 2, 1) # Check correctness for 'moving windows' and 'time intersection' methods, as # well as for different `multi_models` values: - for use_moving_windows in (False, True): - X, times = create_lagged_prediction_data( - target, - future_covariates=future, - lags=[-1], - lags_future_covariates=[0], - uses_static_covariates=False, - use_moving_windows=use_moving_windows, - ) - assert np.allclose(expected_X, X) - assert len(times[0]) == 1 - assert times[0][0] == future.start_time() + X, times = create_lagged_prediction_data( + target, + future_covariates=future, + lags=[-1], + lags_future_covariates=[0], + uses_static_covariates=False, + use_moving_windows=use_moving_windows, + ) + assert np.allclose(expected_X, X) + assert len(times[0]) == 1 + assert times[0][0] == future.start_time() - def test_lagged_prediction_data_positive_lags_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [False, True]), + ) + def test_lagged_prediction_data_positive_lags(self, config): """ Tests that `create_lagged_prediction_data` correctly handles case when `0` is included in `lags_future_covariates` (i.e. when we're using the values @@ -1127,60 +844,32 @@ def test_lagged_prediction_data_positive_lags_range_idx(self): # only possible feature that can be created using these series utilises # the value of `future` one timestep after the time of the label (i.e. a lag # of `1` away from the only feature time): - target = linear_timeseries(start=0, length=1, start_value=0, end_value=1) - future = linear_timeseries(start=2, length=1, start_value=1, end_value=2) - # X comprises of first value of `target` (i.e. 0) and only value in `future`: - expected_X = np.array([0.0, 1.0]).reshape(1, 2, 1) - # Check correctness for 'moving windows' and 'time intersection' methods, as - # well as for different `multi_models` values: - for use_moving_windows in (False, True): - X, times = create_lagged_prediction_data( - target, - future_covariates=future, - lags=[-1], - lags_future_covariates=[1], - uses_static_covariates=False, - use_moving_windows=use_moving_windows, + series_type, use_moving_windows = config + if series_type == "integer": + target = linear_timeseries(start=0, length=1, start_value=0, end_value=1) + future = linear_timeseries(start=2, length=1, start_value=1, end_value=2) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=1, start_value=0, end_value=1 + ) + future = linear_timeseries( + start=pd.Timestamp("1/3/2000"), length=1, start_value=1, end_value=2 ) - assert np.allclose(expected_X, X) - assert len(times[0]) == 1 - assert times[0][0] == target.end_time() + target.freq - - def test_lagged_prediction_data_positive_lags_datetime_idx(self): - """ - Tests that `create_lagged_prediction_data` correctly handles case when - `0` is included in `lags_future_covariates` (i.e. when we're using the values - `future_covariates` at time `t` to predict the value of `target_series` at - that same time point). This particular test checks this behaviour by using - datetime index timeseries. - """ - # Define `past` and `future` so their only value occurs at the same time as - # the only possible label that can be extracted from `target_series`; the - # only possible feature that can be created using these series utilises - # the values of `past` and `future` at the same time as the label (i.e. a lag - # of `0` away from the only feature time): - target = linear_timeseries( - start=pd.Timestamp("1/1/2000"), length=1, start_value=0, end_value=1 - ) - future = linear_timeseries( - start=pd.Timestamp("1/3/2000"), length=1, start_value=1, end_value=2 - ) # X comprises of first value of `target` (i.e. 0) and only value in `future`: expected_X = np.array([0.0, 1.0]).reshape(1, 2, 1) # Check correctness for 'moving windows' and 'time intersection' methods, as # well as for different `multi_models` values: - for use_moving_windows in (False, True): - X, times = create_lagged_prediction_data( - target, - future_covariates=future, - lags=[-1], - lags_future_covariates=[1], - uses_static_covariates=False, - use_moving_windows=use_moving_windows, - ) - assert np.allclose(expected_X, X) - assert len(times[0]) == 1 - assert times[0][0] == target.end_time() + target.freq + X, times = create_lagged_prediction_data( + target, + future_covariates=future, + lags=[-1], + lags_future_covariates=[1], + uses_static_covariates=False, + use_moving_windows=use_moving_windows, + ) + assert np.allclose(expected_X, X) + assert len(times[0]) == 1 + assert times[0][0] == target.end_time() + target.freq def test_lagged_prediction_data_sequence_inputs(self): """ @@ -1359,7 +1048,7 @@ def test_lagged_prediction_data_series_too_short_error(self): assert ( "`target_series` must have at least " "`-min(lags) + max(lags) + 1` = 20 " - "timesteps; instead, it only has 2." + "time steps; instead, it only has 2." ) == str(err.value) with pytest.raises(ValueError) as err: create_lagged_prediction_data( @@ -1371,7 +1060,7 @@ def test_lagged_prediction_data_series_too_short_error(self): assert ( "`past_covariates` must have at least " "`-min(lags_past_covariates) + max(lags_past_covariates) + 1` = 20 " - "timesteps; instead, it only has 2." + "time steps; instead, it only has 2." ) == str(err.value) def test_lagged_prediction_data_invalid_lag_values_error(self): diff --git a/darts/tests/utils/tabularization/test_create_lagged_training_data.py b/darts/tests/utils/tabularization/test_create_lagged_training_data.py index 9afe53d3f1..cd4f32f1e9 100644 --- a/darts/tests/utils/tabularization/test_create_lagged_training_data.py +++ b/darts/tests/utils/tabularization/test_create_lagged_training_data.py @@ -1,6 +1,8 @@ +import itertools import warnings +from collections.abc import Sequence from itertools import product -from typing import Optional, Sequence +from typing import Any, Optional, Union import numpy as np import pandas as pd @@ -14,6 +16,26 @@ create_lagged_training_data, ) from darts.utils.timeseries_generation import linear_timeseries +from darts.utils.utils import freqs, generate_index + + +def helper_create_multivariate_linear_timeseries( + n_components: int, components_names: Sequence[str] = None, **kwargs +) -> TimeSeries: + """ + Helper function that creates a `linear_timeseries` with a specified number of + components. To help distinguish each component from one another, `i` is added on + to each value of the `i`th component. Any additional keyword arguments are passed + to `linear_timeseries` (`start_value`, `end_value`, `start`, `end`, `length`, etc). + """ + if components_names is None or len(components_names) < n_components: + components_names = [f"lin_ts_{i}" for i in range(n_components)] + timeseries = [] + for i in range(n_components): + # Values of each component is 1 larger than the last: + timeseries_i = linear_timeseries(column_name=components_names[i], **kwargs) + i + timeseries.append(timeseries_i) + return darts_concatenate(timeseries, axis=1) class TestCreateLaggedTrainingData: @@ -39,27 +61,6 @@ class TestCreateLaggedTrainingData: # Helper Functions for Generated Test Cases # - @staticmethod - def create_multivariate_linear_timeseries( - n_components: int, components_names: Sequence[str] = None, **kwargs - ) -> TimeSeries: - """ - Helper function that creates a `linear_timeseries` with a specified number of - components. To help distinguish each component from one another, `i` is added on - to each value of the `i`th component. Any additional keyword arguments are passed - to `linear_timeseries` (`start_value`, `end_value`, `start`, `end`, `length`, etc). - """ - timeseries = [] - if components_names is None or len(components_names) < n_components: - components_names = [f"lin_ts_{i}" for i in range(n_components)] - for i in range(n_components): - # Values of each component is 1 larger than the last: - timeseries_i = ( - linear_timeseries(column_name=components_names[i], **kwargs) + i - ) - timeseries.append(timeseries_i) - return darts_concatenate(timeseries, axis=1) - @staticmethod def get_feature_times( target: TimeSeries, @@ -70,9 +71,10 @@ def get_feature_times( lags_future: Optional[Sequence[int]], output_chunk_length: Optional[int], max_samples_per_ts: Optional[int], + output_chunk_shift: int, ): """ - Helper function that returns the times shared by all of the specified series that can be used + Helper function that returns the times shared by all specified series that can be used to create features and labels. This is performed by using the helper functions `get_feature_times_target`, `get_feature_times_past`, and `get_feature_times_future` (all defined below) to extract the feature times from the target series, past covariates, and future @@ -85,7 +87,7 @@ def get_feature_times( """ # Get feature times for `target_series`: times = TestCreateLaggedTrainingData.get_feature_times_target( - target, lags, output_chunk_length + target, lags, output_chunk_length, output_chunk_shift ) # Intersect `times` with `past_covariates` feature times if past covariates to be added to `X`: if lags_past is not None: @@ -109,16 +111,17 @@ def get_feature_times_target( target_series: TimeSeries, lags: Optional[Sequence[int]], output_chunk_length: int, + output_chunk_shift: int, ) -> pd.Index: """ - Helper function called by `get_feature_times` that extracts all of the times within a + Helper function called by `get_feature_times` that extracts all times within a `target_series` that can be used to create a feature and label. More specifically, we can create features and labels for times within `target_series` that have *both*: - 1. At least `max_lag = -min(lags)` values preceeding them, since these preceeding + 1. At least `max_lag = -min(lags)` values preceding them, since these preceding values are required to construct a feature vector for that time. Since the first `max_lag` - times do not fulfill this condition, they are exluded *if* values from `target_series` are + times do not fulfill this condition, they are excluded *if* values from `target_series` are to be added to `X`. - 2. At least `(output_chunk_length - 1)` values after them, because the all of the times from + 2. At least `(output_chunk_length - 1)` values after them, because the all times from time `t` to time `t + output_chunk_length - 1` will be used as labels. Since the last `(output_chunk_length - 1)` times do not fulfil this condition, they are excluded. """ @@ -128,6 +131,8 @@ def get_feature_times_target( times = times[max_lag:] if output_chunk_length > 1: times = times[: -output_chunk_length + 1] + if output_chunk_shift: + times = times[:-output_chunk_shift] return times @staticmethod @@ -136,7 +141,7 @@ def get_feature_times_past( past_covariates_lags: Sequence[int], ) -> pd.Index: """ - Helper function called by `get_feature_times` that extracts all of the times within + Helper function called by `get_feature_times` that extracts all times within `past_covariates` that can be used to create features. More specifically, we can create features for times within `past_covariates` that have at least `max_lag = -min(past_covariates_lags)` values preceeding them, since these preceeding values are required to construct a feature vector for @@ -156,9 +161,9 @@ def get_feature_times_past( times = past_covariates.time_index min_lag = -max(past_covariates_lags) # Add times after end of series for which we can create features: - times = times.union( - [times[-1] + i * past_covariates.freq for i in range(1, min_lag + 1)] - ) + times = times.union([ + times[-1] + i * past_covariates.freq for i in range(1, min_lag + 1) + ]) max_lag = -min(past_covariates_lags) times = times[max_lag:] return times @@ -169,7 +174,7 @@ def get_feature_times_future( future_covariates_lags: Sequence[int], ) -> pd.Index: """ - Helper function called by `get_feature_times` that extracts all of the times within + Helper function called by `get_feature_times` that extracts all times within `future_covariates` that can be used to create features. Unlike the lag values for `target_series` and `past_covariates`, the values in @@ -213,20 +218,18 @@ def get_feature_times_future( # Case 1: if (min_lag > 0) and (max_lag > 0): # Can create features for times extending after the end of `future_covariates`: - times = times.union( - [times[-1] + i * future_covariates.freq for i in range(1, min_lag + 1)] - ) + times = times.union([ + times[-1] + i * future_covariates.freq for i in range(1, min_lag + 1) + ]) # Can't create features for first `max_lag` times in series: times = times[max_lag:] # Case 2: elif (min_lag <= 0) and (max_lag <= 0): # Can create features for times before the start of `future_covariates`: - times = times.union( - [ - times[0] - i * future_covariates.freq - for i in range(1, abs(max_lag) + 1) - ] - ) + times = times.union([ + times[0] - i * future_covariates.freq + for i in range(1, abs(max_lag) + 1) + ]) # Can't create features for last `abs(min_lag)` times in series: times = times[:min_lag] if min_lag != 0 else times # Case 3: @@ -253,7 +256,7 @@ def construct_X_block( """ Helper function that creates the lagged features 'block' of a specific `series` (i.e. either `target_series`, `past_covariates`, or `future_covariates`); - the feature matrix `X` is formed by concatenating the blocks of all of the specified + the feature matrix `X` is formed by concatenating the blocks of all specified series along the components axis. If `lags` is `None`, then `None` will be returned in lieu of an array. Please refer to the `create_lagged_features` docstring for further details about the structure of the `X` feature matrix. @@ -261,7 +264,7 @@ def construct_X_block( The returned `X_block` is constructed by looping over each time in `feature_times`, finding the index position of that time in the series, and then for each lag value in `lags`, offset this index position by a particular lag value; this offset index is then - used to extract all of the components at a single lagged time. + used to extract all components at a single lagged time. Unlike the implementation found in `darts.utils.data.tabularization`, this function doesn't use any 'vectorisation' tricks, which makes it slower to run, but more easily interpretable. @@ -272,7 +275,7 @@ def construct_X_block( before searching for the index of each time in the series. Even though the integer indices of the 'extended times' won't be contained within the original `series`, offsetting these found indices by the requested lag value should 'bring us back' to a time within the original, unextended `series`. - However, if we've prepended times to `series.time_index`, we have to note that all of the indices will + However, if we've prepended times to `series.time_index`, we have to note that all indices will be 'bumped up' by the number of values we've prepended, even after offsetting by a lag value. For example, if we extended `series.time_index` by prepending two values to the start, the integer index of the first actual value in `series` will occur at an index of `2` instead of `0`. To 'undo' this, we must subtract off @@ -323,7 +326,7 @@ def construct_X_block( time_idx = np.searchsorted(series_times, time) X_row = [] for lag in lags: - # Offet by particular lag value: + # Offset by particular lag value: idx_to_get = time_idx + lag # Account for prepended values: idx_to_get -= num_prepended @@ -346,6 +349,7 @@ def create_y( feature_times: pd.Index, output_chunk_length: int, multi_models: bool, + output_chunk_shift: int, ) -> np.ndarray: """ Helper function that constructs the labels array `y` from the target series. @@ -372,13 +376,13 @@ def create_y( f"Unexpected label time at {time}, but `series` ends at {target.end_time()}.", ) time_idx = np.searchsorted(target.time_index, time) - # If `multi_models = True`, want to predict all of the values from time `t` to + # If `multi_models = True`, want to predict all values from time `t` to # time `t + output_chunk_lenth - 1`; if `multi_models = False`, only want to # predict time `t + output_chunk_length - 1`: timesteps_ahead = ( - range(output_chunk_length) + range(output_chunk_shift, output_chunk_length + output_chunk_shift) if multi_models - else (output_chunk_length - 1,) + else [output_chunk_length + output_chunk_shift - 1] ) y_row = [] for i in timesteps_ahead: @@ -393,150 +397,321 @@ def create_y( y = np.stack(y, axis=0) return y - # - # Generated Test Cases - # - - # Input parameter combinations used to generate test cases: - output_chunk_length_combos = (1, 3) - multi_models_combos = (False, True) - max_samples_per_ts_combos = (1, 2, None) - target_lag_combos = past_lag_combos = (None, [-1, -3], [-3, -1]) - future_lag_combos = (*target_lag_combos, [0], [2, 1], [-1, 1], [0, 2]) - - def test_lagged_training_data_equal_freq_range_index(self): - """ - Tests that `create_lagged_training_data` produces `X`, `y`, and `times` - outputs that are consistent with those generated by using the helper - functions `get_feature_times`, `construct_X_block`, and `construct_labels`. - Consistency is checked over all of the combinations of parameter values - specified by `self.target_lag_combos`, `self.covariates_lag_combos`, - `self.output_chunk_length_combos`, `self.multi_models_combos`, and - `self.max_samples_per_ts_combos`. + @staticmethod + def convert_lags_to_dict(ts_tg, ts_pc, ts_fc, lags_tg, lags_pc, lags_fc): + """Convert lags to the dictionary format, assuming the lags are shared across the components""" + lags_as_dict = dict() + for ts_, lags_, name_ in zip( + [ts_tg, ts_pc, ts_fc], + [lags_tg, lags_pc, lags_fc], + ["target", "past", "future"], + ): + single_ts = ts_[0] if isinstance(ts_, Sequence) else ts_ + if single_ts is None or lags_ is None: + lags_as_dict[name_] = None + # already in dict format + elif isinstance(lags_, dict): + lags_as_dict[name_] = lags_ + # from list + elif isinstance(lags_, list): + lags_as_dict[name_] = {c_name: lags_ for c_name in single_ts.components} + else: + raise ValueError( + f"Lags should be `None`, a list or a dictionary. Received {type(lags_)}." + ) + return lags_as_dict - This particular test uses timeseries with range time indices of equal - frequencies. Since all of the timeseries are of the same frequency, - the implementation of the 'moving window' method is being tested here. - """ - # Define datetime index timeseries - each has different number of components, - # different start times, different lengths, and different values, but - # they're all of the same frequency: - target = self.create_multivariate_linear_timeseries( - n_components=2, start_value=0, end_value=10, start=2, length=8, freq=2 - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, start_value=10, end_value=20, start=4, length=9, freq=2 - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, start_value=20, end_value=30, start=6, length=10, freq=2 - ) - # Conduct test for each input parameter combo: - for ( + def helper_create_expected_lagged_data( + self, + target: Optional[Union[TimeSeries, list[TimeSeries]]], + past: Optional[Union[TimeSeries, list[TimeSeries]]], + future: Optional[Union[TimeSeries, list[TimeSeries]]], + lags: Optional[Union[list[int], dict[str, list[int]]]], + lags_past: Optional[Union[list[int], dict[str, list[int]]]], + lags_future: Optional[Union[list[int], dict[str, list[int]]]], + output_chunk_length: int, + output_chunk_shift: int, + multi_models: bool, + max_samples_per_ts: Optional[int], + ) -> tuple[np.ndarray, np.ndarray, Any]: + """Helper function to create the X and y arrays by building them block by block (one per covariates).""" + feats_times = self.get_feature_times( + target, + past, + future, lags, lags_past, lags_future, output_chunk_length, - multi_models, max_samples_per_ts, - ) in product( - self.target_lag_combos, - self.past_lag_combos, - self.future_lag_combos, - self.output_chunk_length_combos, - self.multi_models_combos, - self.max_samples_per_ts_combos, - ): - all_lags = (lags, lags_past, lags_future) - # Skip test where all lags are `None` - can't assemble features and - # labels for this single combo of input params: - lags_is_none = [x is None for x in all_lags] - if all(lags_is_none): - continue - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length, - past_covariates=past if lags_past else None, - future_covariates=future if lags_future else None, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - multi_models=multi_models, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=True, - ) - feats_times = self.get_feature_times( + output_chunk_shift, + ) + # Construct `X` by constructing each block, then concatenate these + # blocks together along component axis: + X_target = self.construct_X_block(target, feats_times, lags) + X_past = self.construct_X_block(past, feats_times, lags_past) + X_future = self.construct_X_block(future, feats_times, lags_future) + all_X = (X_target, X_past, X_future) + to_concat = [X for X in all_X if X is not None] + expected_X = np.concatenate(to_concat, axis=1) + expected_y = self.create_y( + target, + feats_times, + output_chunk_length, + multi_models, + output_chunk_shift, + ) + if len(expected_X.shape) == 2: + expected_X = expected_X[:, :, np.newaxis] + if len(expected_y.shape) == 2: + expected_y = expected_y[:, :, np.newaxis] + return expected_X, expected_y, feats_times + + def helper_check_lagged_data( + self, + convert_lags_to_dict: bool, + expected_X: np.ndarray, + expected_y: np.ndarray, + expected_times_x, + expected_times_y, + target: Optional[Union[TimeSeries, list[TimeSeries]]], + past_cov: Optional[Union[TimeSeries, list[TimeSeries]]], + future_cov: Optional[Union[TimeSeries, list[TimeSeries]]], + lags: Optional[Union[list[int], dict[str, list[int]]]], + lags_past: Optional[Union[list[int], dict[str, list[int]]]], + lags_future: Optional[Union[list[int], dict[str, list[int]]]], + output_chunk_length: int, + output_chunk_shift: int, + use_static_covariates: bool, + multi_models: bool, + max_samples_per_ts: Optional[int], + use_moving_windows: bool, + concatenate: bool, + **kwargs, + ): + """Helper function to call the `create_lagged_training_data()` method with lags argument either in the list + format or the dictionary format (automatically convert them when they are identical across components). + + Assertions are different depending on the value of `concatenate` to account for the output shape. + """ + if convert_lags_to_dict: + lags_as_dict = self.convert_lags_to_dict( target, - past, - future, + past_cov if lags_past else None, + future_cov if lags_future else None, lags, lags_past, lags_future, - output_chunk_length, - max_samples_per_ts, - ) - # Construct `X` by constructing each block, then concatenate these - # blocks together along component axis: - X_target = self.construct_X_block(target, feats_times, lags) - X_past = self.construct_X_block(past, feats_times, lags_past) - X_future = self.construct_X_block(future, feats_times, lags_future) - all_X = (X_target, X_past, X_future) - to_concat = [X for X in all_X if X is not None] - expected_X = np.concatenate(to_concat, axis=1) - expected_y = self.create_y( - target, feats_times, output_chunk_length, multi_models ) + lags_ = lags_as_dict["target"] + lags_past_ = lags_as_dict["past"] + lags_future_ = lags_as_dict["future"] + else: + lags_ = lags + lags_past_ = lags_past + lags_future_ = lags_future + + # convert indexes to list of tuples to simplify processing + expected_times_x = ( + expected_times_x + if isinstance(expected_times_x, Sequence) + else [expected_times_x] + ) + expected_times_y = ( + expected_times_y + if isinstance(expected_times_y, Sequence) + else [expected_times_y] + ) + + X, y, times, _, _ = create_lagged_training_data( + target_series=target, + output_chunk_length=output_chunk_length, + past_covariates=past_cov if lags_past_ else None, + future_covariates=future_cov if lags_future_ else None, + lags=lags_, + lags_past_covariates=lags_past_, + lags_future_covariates=lags_future_, + uses_static_covariates=use_static_covariates, + multi_models=multi_models, + max_samples_per_ts=max_samples_per_ts, + use_moving_windows=use_moving_windows, + output_chunk_shift=output_chunk_shift, + concatenate=concatenate, + ) + # should have the exact same number of indexes + assert len(times) == len(expected_times_x) == len(expected_times_y) + + # Check that time index(es) match: + for time, exp_time in zip(times, expected_times_x): + assert exp_time.equals(time) + + if concatenate: # Number of observations should match number of feature times: - assert X.shape[0] == len(feats_times) - assert y.shape[0] == len(feats_times) - assert X.shape[0] == len(times[0]) - assert y.shape[0] == len(times[0]) + data_length = sum(len(time) for time in times) + exp_length_x = sum(len(exp_time) for exp_time in expected_times_x) + exp_length_y = sum(len(exp_time) for exp_time in expected_times_y) + assert exp_length_x == exp_length_y + assert X.shape[0] == exp_length_x == data_length + assert y.shape[0] == exp_length_y == data_length + + # Check that outputs match: + assert X.shape == expected_X.shape + assert np.allclose(expected_X, X) + assert y.shape == expected_y.shape + assert np.allclose(expected_y, y) + else: + # Check the number of observation for each series + for x_, exp_time_x, y_, exp_time_y, time in zip( + X, expected_times_x, y, expected_times_y, times + ): + assert x_.shape[0] == len(time) == len(exp_time_x) + assert y_.shape[0] == len(time) == len(exp_time_y) + # Check that outputs match: - assert np.allclose(expected_X, X[:, :, 0]) - assert np.allclose(expected_y, y[:, :, 0]) - assert feats_times.equals(times[0]) + for x_, y_ in zip(X, y): + assert np.allclose(X, x_) + assert np.allclose(y, y_) + + # + # Generated Test Cases + # + + target_with_no_cov = helper_create_multivariate_linear_timeseries( + n_components=1, + components_names=["no_static"], + start_value=0, + end_value=10, + start=2, + length=10, + freq=2, + ) + n_comp = 2 + target_with_static_cov = helper_create_multivariate_linear_timeseries( + n_components=n_comp, + components_names=["static_0", "static_1"], + start_value=0, + end_value=10, + start=2, + length=10, + freq=2, + ) + target_with_static_cov = target_with_static_cov.with_static_covariates( + pd.DataFrame({"dummy": [1]}) # leads to "global" static cov component name + ) + target_with_static_cov2 = target_with_static_cov.with_static_covariates( + pd.DataFrame({ + "dummy": [i for i in range(n_comp)] + }) # leads to sharing target component names + ) + target_with_static_cov3 = target_with_static_cov.with_static_covariates( + pd.DataFrame({ + "dummy": [i for i in range(n_comp)], + "dummy1": [i for i in range(n_comp)], + }) # leads to sharing target component names + ) + + past = helper_create_multivariate_linear_timeseries( + n_components=3, + components_names=["past_0", "past_1", "past_2"], + start_value=10, + end_value=20, + start=2, + length=10, + freq=2, + ) + future = helper_create_multivariate_linear_timeseries( + n_components=4, + components_names=["future_0", "future_1", "future_2", "future_3"], + start_value=20, + end_value=30, + start=2, + length=10, + freq=2, + ) + + # Input parameter combinations used to generate test cases: + output_chunk_length_combos = (1, 3) + output_chunk_shift_combos = (0, 1) + multi_models_combos = (False, True) + max_samples_per_ts_combos = (1, 2, None) + # lags are sorted ascending as done by the models internally + target_lag_combos = past_lag_combos = (None, [-3, -1], [-2, -1]) + future_lag_combos = (*target_lag_combos, [0], [1, 2], [-1, 1], [0, 2]) - def test_lagged_training_data_equal_freq_datetime_index(self): + # minimum series length + min_n_ts = 8 + max(output_chunk_shift_combos) + + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_lagged_training_data_equal_freq(self, series_type: str): """ Tests that `create_lagged_training_data` produces `X`, `y`, and `times` outputs that are consistent with those generated by using the helper functions `get_feature_times`, `construct_X_block`, and `construct_labels`. - Consistency is checked over all of the combinations of parameter values + Consistency is checked over all combinations of parameter values specified by `self.target_lag_combos`, `self.covariates_lag_combos`, `self.output_chunk_length_combos`, `self.multi_models_combos`, and `self.max_samples_per_ts_combos`. - This particular test uses timeseries with datetime time indices of equal - frequencies. Since all of the timeseries are of the same frequency, - the implementation of the 'moving window' method is being tested here. + This particular test uses timeseries with equal frequencies. Since all timeseries + are of the same frequency, the implementation of the 'moving window' method is + being tested here. """ # Define datetime index timeseries - each has different number of components, # different start times, different lengths, and different values, but # they're all of the same frequency: - target = self.create_multivariate_linear_timeseries( - n_components=2, - start_value=0, - end_value=10, - start=pd.Timestamp("1/2/2000"), - length=8, - freq="2d", - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, - start_value=10, - end_value=20, - start=pd.Timestamp("1/4/2000"), - length=9, - freq="2d", - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, - start_value=20, - end_value=30, - start=pd.Timestamp("1/6/2000"), - length=10, - freq="2d", - ) + if series_type == "integer": + target = helper_create_multivariate_linear_timeseries( + n_components=2, + start_value=0, + end_value=10, + start=2, + length=self.min_n_ts, + freq=2, + ) + past = helper_create_multivariate_linear_timeseries( + n_components=3, + start_value=10, + end_value=20, + start=4, + length=self.min_n_ts + 1, + freq=2, + ) + future = helper_create_multivariate_linear_timeseries( + n_components=4, + start_value=20, + end_value=30, + start=6, + length=self.min_n_ts + 2, + freq=2, + ) + else: + target = helper_create_multivariate_linear_timeseries( + n_components=2, + start_value=0, + end_value=10, + start=pd.Timestamp("1/2/2000"), + length=self.min_n_ts, + freq="2D", + ) + past = helper_create_multivariate_linear_timeseries( + n_components=3, + start_value=10, + end_value=20, + start=pd.Timestamp("1/4/2000"), + length=self.min_n_ts + 1, + freq="2D", + ) + future = helper_create_multivariate_linear_timeseries( + n_components=4, + start_value=20, + end_value=30, + start=pd.Timestamp("1/6/2000"), + length=self.min_n_ts + 1, + freq="2D", + ) # Conduct test for each input parameter combo: for ( lags, @@ -545,6 +720,7 @@ def test_lagged_training_data_equal_freq_datetime_index(self): output_chunk_length, multi_models, max_samples_per_ts, + output_chunk_shift, ) in product( self.target_lag_combos, self.past_lag_combos, @@ -552,6 +728,7 @@ def test_lagged_training_data_equal_freq_datetime_index(self): self.output_chunk_length_combos, self.multi_models_combos, self.max_samples_per_ts_combos, + self.output_chunk_shift_combos, ): all_lags = (lags, lags_past, lags_future) # Skip test where all lags are `None` - can't assemble features and @@ -559,183 +736,102 @@ def test_lagged_training_data_equal_freq_datetime_index(self): lags_is_none = [x is None for x in all_lags] if all(lags_is_none): continue - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length, - past_covariates=past if lags_past else None, - future_covariates=future if lags_future else None, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - multi_models=multi_models, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=True, - ) - feats_times = self.get_feature_times( - target, - past, - future, - lags, - lags_past, - lags_future, - output_chunk_length, - max_samples_per_ts, - ) - # Construct `X` by constructing each block, then concatenate these - # blocks together along component axis: - X_target = self.construct_X_block(target, feats_times, lags) - X_past = self.construct_X_block(past, feats_times, lags_past) - X_future = self.construct_X_block(future, feats_times, lags_future) - all_X = (X_target, X_past, X_future) - to_concat = [x for x in all_X if x is not None] - expected_X = np.concatenate(to_concat, axis=1) - expected_y = self.create_y( - target, feats_times, output_chunk_length, multi_models + + expected_X, expected_y, expected_times = ( + self.helper_create_expected_lagged_data( + target, + past, + future, + lags, + lags_past, + lags_future, + output_chunk_length, + output_chunk_shift, + multi_models, + max_samples_per_ts, + ) ) - # Number of observations should match number of feature times: - assert X.shape[0] == len(feats_times) - assert y.shape[0] == len(feats_times) - assert X.shape[0] == len(times[0]) - assert y.shape[0] == len(times[0]) - # Check that outputs match: - assert np.allclose(expected_X, X[:, :, 0]) - assert np.allclose(expected_y, y[:, :, 0]) - assert feats_times.equals(times[0]) - def test_lagged_training_data_unequal_freq_range_index(self): + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": expected_times, + "expected_times_y": expected_times, + "target": target, + "past_cov": past, + "future_cov": future, + "lags": lags, + "lags_past": lags_past, + "lags_future": lags_future, + "output_chunk_length": output_chunk_length, + "output_chunk_shift": output_chunk_shift, + "use_static_covariates": False, + "multi_models": multi_models, + "max_samples_per_ts": max_samples_per_ts, + "use_moving_windows": True, + "concatenate": True, + } + + self.helper_check_lagged_data(convert_lags_to_dict=False, **kwargs) + + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_lagged_training_data_unequal_freq(self, series_type): """ Tests that `create_lagged_training_data` produces `X`, `y`, and `times` outputs that are consistent with those generated by using the helper functions `get_feature_times`, `construct_X_block`, and `construct_labels`. - Consistency is checked over all of the combinations of parameter values + Consistency is checked over all combinations of parameter values specified by `self.target_lag_combos`, `self.covariates_lag_combos`, `self.output_chunk_length_combos`, `self.multi_models_combos`, and `self.max_samples_per_ts_combos`. - This particular test uses timeseries with range time indices of unequal - frequencies. Since all of the timeseries are *not* of the same frequency, - the implementation of the 'time intersection' method is being tested here. + This particular test uses timeseries of unequal frequencies. Since all timeseries + are *not* of the same frequency, the implementation of the 'time intersection' method + is being tested here. """ # Define range index timeseries - each has different number of components, # different start times, different lengths, different values, and different # frequencies: - target = self.create_multivariate_linear_timeseries( - n_components=2, start_value=0, end_value=10, start=2, length=20, freq=1 - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, start_value=10, end_value=20, start=4, length=10, freq=2 - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, start_value=20, end_value=30, start=6, length=7, freq=3 - ) - # Conduct test for each input parameter combo: - for ( - lags, - lags_past, - lags_future, - output_chunk_length, - multi_models, - max_samples_per_ts, - ) in product( - self.target_lag_combos, - self.past_lag_combos, - self.future_lag_combos, - self.output_chunk_length_combos, - self.multi_models_combos, - self.max_samples_per_ts_combos, - ): - all_lags = (lags, lags_past, lags_future) - # Skip test where all lags are `None` - can't assemble features and - # labels for this single combo of input params: - lags_is_none = [x is None for x in all_lags] - if all(lags_is_none): - continue - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length, - past_covariates=past if lags_past else None, - future_covariates=future if lags_future else None, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - multi_models=multi_models, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=False, + if series_type == "integer": + target = helper_create_multivariate_linear_timeseries( + n_components=2, start_value=0, end_value=10, start=2, length=20, freq=1 ) - feats_times = self.get_feature_times( - target, - past, - future, - lags, - lags_past, - lags_future, - output_chunk_length, - max_samples_per_ts, - ) - # Construct `X` by constructing each block, then concatenate these - # blocks together along component axis: - X_target = self.construct_X_block(target, feats_times, lags) - X_past = self.construct_X_block(past, feats_times, lags_past) - X_future = self.construct_X_block(future, feats_times, lags_future) - all_X = (X_target, X_past, X_future) - to_concat = [x for x in all_X if x is not None] - expected_X = np.concatenate(to_concat, axis=1) - expected_y = self.create_y( - target, feats_times, output_chunk_length, multi_models + past = helper_create_multivariate_linear_timeseries( + n_components=3, start_value=10, end_value=20, start=4, length=10, freq=2 + ) + future = helper_create_multivariate_linear_timeseries( + n_components=4, start_value=20, end_value=30, start=6, length=7, freq=3 + ) + else: + target = helper_create_multivariate_linear_timeseries( + n_components=2, + start_value=0, + end_value=10, + start=pd.Timestamp("1/1/2000"), + length=20, + freq="D", + ) + past = helper_create_multivariate_linear_timeseries( + n_components=3, + start_value=10, + end_value=20, + start=pd.Timestamp("1/2/2000"), + length=10, + freq="2D", + ) + future = helper_create_multivariate_linear_timeseries( + n_components=4, + start_value=20, + end_value=30, + start=pd.Timestamp("1/3/2000"), + length=7, + freq="3D", ) - # Number of observations should match number of feature times: - assert X.shape[0] == len(feats_times) - assert y.shape[0] == len(feats_times) - assert X.shape[0] == len(times[0]) - assert y.shape[0] == len(times[0]) - # Check that outputs match: - assert np.allclose(expected_X, X[:, :, 0]) - assert np.allclose(expected_y, y[:, :, 0]) - assert feats_times.equals(times[0]) - - def test_lagged_training_data_unequal_freq_datetime_index(self): - """ - Tests that `create_lagged_training_data` produces `X`, `y`, and `times` - outputs that are consistent with those generated by using the helper - functions `get_feature_times`, `construct_X_block`, and `construct_labels`. - Consistency is checked over all of the combinations of parameter values - specified by `self.target_lag_combos`, `self.covariates_lag_combos`, - `self.output_chunk_length_combos`, `self.multi_models_combos`, and - `self.max_samples_per_ts_combos`. - - This particular test uses timeseries with datetime time indices of unequal - frequencies. Since all of the timeseries are *not* of the same frequency, - the implementation of the 'time intersection' method is being tested here. - """ - # Define datetime index timeseries - each has different number of components, - # different start times, different lengths, different values, and different - # frequencies: - target = self.create_multivariate_linear_timeseries( - n_components=2, - start_value=0, - end_value=10, - start=pd.Timestamp("1/1/2000"), - length=20, - freq="d", - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, - start_value=10, - end_value=20, - start=pd.Timestamp("1/2/2000"), - length=10, - freq="2d", - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, - start_value=20, - end_value=30, - start=pd.Timestamp("1/3/2000"), - length=7, - freq="3d", - ) # Conduct test for each input parameter combo: for ( lags, @@ -744,6 +840,7 @@ def test_lagged_training_data_unequal_freq_datetime_index(self): output_chunk_length, multi_models, max_samples_per_ts, + output_chunk_shift, ) in product( self.target_lag_combos, self.past_lag_combos, @@ -751,6 +848,7 @@ def test_lagged_training_data_unequal_freq_datetime_index(self): self.output_chunk_length_combos, self.multi_models_combos, self.max_samples_per_ts_combos, + self.output_chunk_shift_combos, ): all_lags = (lags, lags_past, lags_future) # Skip test where all lags are `None` - can't assemble features and @@ -758,75 +856,103 @@ def test_lagged_training_data_unequal_freq_datetime_index(self): lags_is_none = [x is None for x in all_lags] if all(lags_is_none): continue - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length, - past_covariates=past if lags_past else None, - future_covariates=future if lags_future else None, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - multi_models=multi_models, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=False, + + expected_X, expected_y, expected_times = ( + self.helper_create_expected_lagged_data( + target, + past, + future, + lags, + lags_past, + lags_future, + output_chunk_length, + output_chunk_shift, + multi_models, + max_samples_per_ts, + ) ) - feats_times = self.get_feature_times( - target, - past, - future, - lags, - lags_past, - lags_future, - output_chunk_length, - max_samples_per_ts, - ) - # Construct `X` by constructing each block, then concatenate these - # blocks together along component axis: - X_target = self.construct_X_block(target, feats_times, lags) - X_past = self.construct_X_block(past, feats_times, lags_past) - X_future = self.construct_X_block(future, feats_times, lags_future) - all_X = (X_target, X_past, X_future) - to_concat = [x for x in all_X if x is not None] - expected_X = np.concatenate(to_concat, axis=1) - expected_y = self.create_y( - target, feats_times, output_chunk_length, multi_models + + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": expected_times, + "expected_times_y": expected_times, + "target": target, + "past_cov": past, + "future_cov": future, + "lags": lags, + "lags_past": lags_past, + "lags_future": lags_future, + "output_chunk_length": output_chunk_length, + "output_chunk_shift": output_chunk_shift, + "use_static_covariates": False, + "multi_models": multi_models, + "max_samples_per_ts": max_samples_per_ts, + "use_moving_windows": False, + "concatenate": True, + } + + self.helper_check_lagged_data(convert_lags_to_dict=False, **kwargs) + + with pytest.raises(ValueError) as err: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + assert str(err.value).startswith( + "`use_moving_windows=False` is not supported when any of the lags" ) - # Number of observations should match number of feature times: - assert X.shape[0] == len(feats_times) - assert y.shape[0] == len(feats_times) - assert X.shape[0] == len(times[0]) - assert y.shape[0] == len(times[0]) - # Check that outputs match: - assert np.allclose(expected_X, X[:, :, 0]) - assert np.allclose(expected_y, y[:, :, 0]) - assert feats_times.equals(times[0]) - def test_lagged_training_data_method_consistency_range_index(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_lagged_training_data_method_consistency(self, series_type): """ Tests that `create_lagged_training_data` produces the same result when `use_moving_windows = False` and when `use_moving_windows = True` - for all of the parameter combinations used in the 'generated' test cases. + for all parameter combinations used in the 'generated' test cases. Obviously, if both the 'Moving Window Method' and the 'Time Intersection' are both wrong in the same way, this test won't reveal any bugs. With this being said, if this test fails, something is definitely wrong in either one or both of the implemented methods. - - This particular test uses range index timeseries. """ # Define datetime index timeseries - each has different number of components, # different start times, different lengths, different values, and of # different frequencies: - target = self.create_multivariate_linear_timeseries( - n_components=2, start_value=0, end_value=10, start=2, length=20, freq=1 - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, start_value=10, end_value=20, start=4, length=10, freq=2 - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, start_value=20, end_value=30, start=6, length=7, freq=3 - ) + if series_type == "integer": + target = helper_create_multivariate_linear_timeseries( + n_components=2, start_value=0, end_value=10, start=2, length=20, freq=1 + ) + past = helper_create_multivariate_linear_timeseries( + n_components=3, start_value=10, end_value=20, start=4, length=10, freq=2 + ) + future = helper_create_multivariate_linear_timeseries( + n_components=4, start_value=20, end_value=30, start=6, length=7, freq=3 + ) + else: + target = helper_create_multivariate_linear_timeseries( + n_components=2, + start_value=0, + end_value=10, + start=pd.Timestamp("1/2/2000"), + end=pd.Timestamp("1/18/2000"), + freq="2D", + ) + past = helper_create_multivariate_linear_timeseries( + n_components=3, + start_value=10, + end_value=20, + start=pd.Timestamp("1/4/2000"), + end=pd.Timestamp("1/20/2000"), + freq="2D", + ) + future = helper_create_multivariate_linear_timeseries( + n_components=4, + start_value=20, + end_value=30, + start=pd.Timestamp("1/6/2000"), + end=pd.Timestamp("1/22/2000"), + freq="2D", + ) # Conduct test for each input parameter combo: for ( lags, @@ -835,6 +961,7 @@ def test_lagged_training_data_method_consistency_range_index(self): output_chunk_length, multi_models, max_samples_per_ts, + output_chunk_shift, ) in product( self.target_lag_combos, self.past_lag_combos, @@ -842,6 +969,7 @@ def test_lagged_training_data_method_consistency_range_index(self): self.output_chunk_length_combos, self.multi_models_combos, self.max_samples_per_ts_combos, + self.output_chunk_shift_combos, ): all_lags = (lags, lags_past, lags_future) # Skip test where all lags are `None` - can't assemble features @@ -850,7 +978,7 @@ def test_lagged_training_data_method_consistency_range_index(self): if all(lags_is_none): continue # Using moving window method: - X_mw, y_mw, times_mw, _ = create_lagged_training_data( + X_mw, y_mw, times_mw, _, _ = create_lagged_training_data( target_series=target, output_chunk_length=output_chunk_length, past_covariates=past if lags_past else None, @@ -862,9 +990,10 @@ def test_lagged_training_data_method_consistency_range_index(self): max_samples_per_ts=max_samples_per_ts, multi_models=multi_models, use_moving_windows=True, + output_chunk_shift=output_chunk_shift, ) # Using time intersection method: - X_ti, y_ti, times_ti, _ = create_lagged_training_data( + X_ti, y_ti, times_ti, _, _ = create_lagged_training_data( target_series=target, output_chunk_length=output_chunk_length, past_covariates=past if lags_past else None, @@ -876,476 +1005,569 @@ def test_lagged_training_data_method_consistency_range_index(self): max_samples_per_ts=max_samples_per_ts, multi_models=multi_models, use_moving_windows=False, + output_chunk_shift=output_chunk_shift, ) assert np.allclose(X_mw, X_ti) assert np.allclose(y_mw, y_ti) assert times_mw[0].equals(times_ti[0]) - def test_lagged_training_data_method_consistency_datetime_index(self): - """ - Tests that `create_lagged_training_data` produces the same result - when `use_moving_windows = False` and when `use_moving_windows = True` - for all of the parameter combinations used in the 'generated' test cases. - - Obviously, if both the 'Moving Window Method' and the 'Time Intersection' - are both wrong in the same way, this test won't reveal any bugs. With this - being said, if this test fails, something is definitely wrong in either - one or both of the implemented methods. + # + # Specified Cases Tests + # - This particular test uses datetime index timeseries. - """ - # Define datetime index timeseries - each has different number of components, - # different start times, different lengths, different values, and of - # different frequencies: - target = self.create_multivariate_linear_timeseries( - n_components=2, - start_value=0, - end_value=10, - start=pd.Timestamp("1/2/2000"), - end=pd.Timestamp("1/16/2000"), - freq="2d", - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, - start_value=10, - end_value=20, - start=pd.Timestamp("1/4/2000"), - end=pd.Timestamp("1/18/2000"), - freq="2d", - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, - start_value=20, - end_value=30, - start=pd.Timestamp("1/6/2000"), - end=pd.Timestamp("1/20/2000"), - freq="2d", - ) - # Conduct test for each input parameter combo: - for ( - lags, - lags_past, - lags_future, - output_chunk_length, - multi_models, - max_samples_per_ts, - ) in product( - self.target_lag_combos, - self.past_lag_combos, - self.future_lag_combos, - self.output_chunk_length_combos, - self.multi_models_combos, - self.max_samples_per_ts_combos, - ): - all_lags = (lags, lags_past, lags_future) - # Skip test where all lags are `None` - can't assemble features - # for this single combo of input params: - lags_is_none = [x is None for x in all_lags] - if all(lags_is_none): - continue - # Using moving window method: - X_mw, y_mw, times_mw, _ = create_lagged_training_data( - target_series=target, - output_chunk_length=output_chunk_length, - past_covariates=past if lags_past else None, - future_covariates=future if lags_future else None, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - max_samples_per_ts=max_samples_per_ts, - multi_models=multi_models, - use_moving_windows=True, - ) - # Using time intersection method: - X_ti, y_ti, times_ti, _ = create_lagged_training_data( - target_series=target, - output_chunk_length=output_chunk_length, - past_covariates=past if lags_past else None, - future_covariates=future if lags_future else None, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - max_samples_per_ts=max_samples_per_ts, - multi_models=multi_models, - use_moving_windows=False, - ) - assert np.allclose(X_mw, X_ti) - assert np.allclose(y_mw, y_ti) - assert times_mw[0].equals(times_ti[0]) - - # - # Specified Cases Tests - # - - def test_lagged_training_data_single_lag_single_component_same_series_range_idx( - self, - ): + @pytest.mark.parametrize( + "config", + itertools.product( + [0, 1, 3], + [False, True], + ["datetime", "integer"], + ), + ) + def test_lagged_training_data_single_lag_single_component_same_series(self, config): """ Tests that `create_lagged_training_data` correctly produces `X`, `y` and `times` when all the `series` inputs are identical, all the `lags` inputs consist of a single value, and `output_chunk_length` is `1`. In this situation, the expected `X` values can be found by concatenating three different slices of the same time series, and the expected `y` can be formed by taking a single slice - from the `target`. This particular test uses a time series with a range index. + from the `target`. """ - series = linear_timeseries(start=0, length=15) - lags = [-1] - output_chunk_length = 1 - past_lags = [-3] - future_lags = [2] - # Can't create features for first 3 times (because `past_lags`) and last - # two times (because `future_lags`): - expected_times = series.time_index[3:-2] - expected_y = series.all_values(copy=False)[3:-2, :, 0] - # Offset `3:-2` by `-1` lag: - expected_X_target = series.all_values(copy=False)[2:-3, :, 0] - # Offset `3:-2` by `-3` lag -> gives `0:-5`: - expected_X_past = series.all_values(copy=False)[:-5, :, 0] - # Offset `3:-2` by `+2` lag -> gives `5:None`: - expected_X_future = series.all_values(copy=False)[5:, :, 0] - expected_X = np.concatenate( - [expected_X_target, expected_X_past, expected_X_future], axis=1 - ) - for use_moving_windows in (False, True): - X, y, times, _ = create_lagged_training_data( - target_series=series, - output_chunk_length=output_chunk_length, - past_covariates=series, - future_covariates=series, - lags=lags, - lags_past_covariates=past_lags, - lags_future_covariates=future_lags, - uses_static_covariates=False, - use_moving_windows=use_moving_windows, - ) - # Number of observations should match number of feature times: - assert X.shape[0] == len(expected_times) - assert X.shape[0] == len(times[0]) - assert y.shape[0] == len(expected_times) - assert y.shape[0] == len(times[0]) - # Check that outputs match: - assert np.allclose(expected_X, X[:, :, 0]) - assert np.allclose(expected_y, y[:, :, 0]) - assert expected_times.equals(times[0]) + output_chunk_shift, use_moving_windows, series_type = config + if series_type == "integer": + series = linear_timeseries(start=0, length=15) + else: + series = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=15) - def test_lagged_training_data_single_lag_single_component_same_series_datetime_idx( - self, - ): - """ - Tests that `create_lagged_training_data` correctly produces `X`, `y` and `times` - when all the `series` inputs are identical, all the `lags` inputs consist - of a single value, and `output_chunk_length` is `1`. In this situation, the - expected `X` values can be found by concatenating three different slices of the - same time series, and the expected `y` can be formed by taking a single slice - from the `target`. This particular test uses a time series with a datetime index. - """ - series = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=15) lags = [-1] output_chunk_length = 1 past_lags = [-3] future_lags = [2] # Can't create features for first 3 times (because `past_lags`) and last # two times (because `future_lags`): - expected_times = series.time_index[3:-2] - expected_y = series.all_values(copy=False)[3:-2, :, 0] + # also up until output_chunk_shift>=2, the future_lags are the reason for pushing back the end time + # of expected X; after that the output shift pushes back additionally. + step_back = max(0, output_chunk_shift - 2) + expected_times_x = series.time_index[3 : -2 - step_back] + expected_times_y = expected_times_x + output_chunk_shift * series.freq + expected_y = series.all_values(copy=False)[ + 3 + output_chunk_shift : 3 + output_chunk_shift + len(expected_times_y), + :, + :, + ] # Offset `3:-2` by `-1` lag: - expected_X_target = series.all_values(copy=False)[2:-3, :, 0] + expected_X_target = series.all_values(copy=False)[ + 2 : 2 + len(expected_times_x), :, 0 + ] # Offset `3:-2` by `-3` lag -> gives `0:-5`: - expected_X_past = series.all_values(copy=False)[:-5, :, 0] + expected_X_past = series.all_values(copy=False)[: len(expected_times_x), :, 0] # Offset `3:-2` by `+2` lag -> gives `5:None`: - expected_X_future = series.all_values(copy=False)[5:, :, 0] + expected_X_future = series.all_values(copy=False)[ + 5 : 5 + len(expected_times_x), :, 0 + ] expected_X = np.concatenate( [expected_X_target, expected_X_past, expected_X_future], axis=1 ) - for use_moving_windows in (False, True): - X, y, times, _ = create_lagged_training_data( - target_series=series, - output_chunk_length=output_chunk_length, - past_covariates=series, - future_covariates=series, - lags=lags, - lags_past_covariates=past_lags, - lags_future_covariates=future_lags, - uses_static_covariates=False, - use_moving_windows=use_moving_windows, + expected_X = np.expand_dims(expected_X, axis=-1) + + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": expected_times_x, + "expected_times_y": expected_times_y, + "target": series, + "past_cov": series, + "future_cov": series, + "lags": lags, + "lags_past": past_lags, + "lags_future": future_lags, + "output_chunk_length": output_chunk_length, + "output_chunk_shift": output_chunk_shift, + "use_static_covariates": False, + "multi_models": True, + "max_samples_per_ts": None, + "use_moving_windows": use_moving_windows, + "concatenate": True, + } + + self.helper_check_lagged_data(convert_lags_to_dict=False, **kwargs) + + if use_moving_windows: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + else: + with pytest.raises(ValueError) as err: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + assert str(err.value).startswith( + "`use_moving_windows=False` is not supported when any of the lags" ) - # Number of observations should match number of feature times: - assert X.shape[0] == len(expected_times) - assert X.shape[0] == len(times[0]) - assert y.shape[0] == len(expected_times) - assert y.shape[0] == len(times[0]) - # Check that outputs match: - assert np.allclose(expected_X, X[:, :, 0]) - assert np.allclose(expected_y, y[:, :, 0]) - assert expected_times.equals(times[0]) - def test_lagged_training_data_extend_past_and_future_covariates_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product( + [0, 1, 3], + [False, True], + list(itertools.product(["datetime"], ["D", "2D", freqs["ms"], freqs["YE"]])) + + list(itertools.product(["integer"], [1, 2])), + ), + ) + def test_lagged_training_data_extend_past_and_future_covariates(self, config): """ Tests that `create_lagged_training_data` correctly handles case where features and labels can be created for a time that is *not* contained in `past_covariates` - and/or `future_covariates`. This particular test checks this behaviour by using - range index timeseries. + and/or `future_covariates`. More specifically, we define the series and lags such that a training example can be generated for time `target.end_time()`, even though this time isn't contained in neither `past` nor `future`. """ + output_chunk_shift, use_moving_windows, (series_type, freq) = config + if series_type == "integer": + target = linear_timeseries( + start=0, length=10, start_value=1, end_value=2, freq=freq + ) + past = linear_timeseries( + start=0, length=8, start_value=2, end_value=3, freq=freq + ) + future = linear_timeseries( + start=0, length=6, start_value=3, end_value=4, freq=freq + ) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + start_value=1, + end_value=2, + length=11, + freq=freq, + ) + past = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + start_value=2, + end_value=3, + length=9, + freq=freq, + ) + future = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + start_value=3, + end_value=4, + length=7, + freq=freq, + ) + # Can create feature for time `t = 10`, but this time isn't in `past` or `future`: - target = linear_timeseries(start=0, end=10, start_value=1, end_value=2) lags = [-1] - past = linear_timeseries(start=0, end=8, start_value=2, end_value=3) lags_past = [-2] - future = linear_timeseries(start=0, end=6, start_value=3, end_value=4) lags_future = [-4] # Only want to check very last generated observation: max_samples_per_ts = 1 # Expect `X` to be constructed from second-to-last value of `target` (i.e. # the value immediately prior to the label), and the very last values of # `past` and `future`: - expected_X = np.concatenate( - [ - target.all_values(copy=False)[-2, :, 0], - past.all_values(copy=False)[-1, :, 0], - future.all_values(copy=False)[-1, :, 0], - ] - ).reshape(1, -1) + expected_X = np.concatenate([ + target.all_values(copy=False)[-2 - output_chunk_shift, :, 0], + past.all_values(copy=False)[-1 - output_chunk_shift, :, 0], + future.all_values(copy=False)[-1 - output_chunk_shift, :, 0], + ]).reshape(1, -1, 1) # Label is very last value of `target`: - expected_y = target.all_values(copy=False)[-1, :, 0] - # Check correctness for both 'moving window' method - # and 'time intersection' method: - for use_moving_windows in (False, True): - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length=1, - past_covariates=past, - future_covariates=future, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=use_moving_windows, - ) - assert times[0][0] == target.end_time() - assert np.allclose(expected_X, X[:, :, 0]) - assert np.allclose(expected_y, y[:, :, 0]) + expected_y = target.all_values(copy=False)[-1:, :, :] - @pytest.mark.parametrize("freq", ["D", "MS", "Y"]) - def test_lagged_training_data_extend_past_and_future_covariates_datetime_idx( - self, freq - ): - """ - Tests that `create_lagged_training_data` correctly handles case where features - and labels can be created for a time that is *not* contained in `past_covariates` - and/or `future_covariates`. This particular test checks this behaviour by using - datetime index timeseries and three different frequencies: daily, month start and - year end. - - More specifically, we define the series and lags such that a training example can - be generated for time `target.end_time()`, even though this time isn't contained in - neither `past` nor `future`. - """ - # Can create feature for time `t = '1/1/2000'+11*freq`, but this time isn't in `past` or `future`: - target = linear_timeseries( - start=pd.Timestamp("1/1/2000"), - start_value=1, - end_value=2, - length=11, - freq=freq, - ) - lags = [-1] - past = linear_timeseries( - start=pd.Timestamp("1/1/2000"), - start_value=2, - end_value=3, - length=9, - freq=freq, + expected_times = generate_index( + start=target.end_time() - output_chunk_shift * target.freq, + length=1, + freq=target.freq, ) - lags_past = [-2] - future = linear_timeseries( - start=pd.Timestamp("1/1/2000"), - start_value=3, - end_value=4, - length=7, - freq=freq, - ) - lags_future = [-4] - # Only want to check very last generated observation: - max_samples_per_ts = 1 - # Expect `X` to be constructed from second-to-last value of `target` (i.e. - # the value immediately prior to the label), and the very last values of - # `past` and `future`: - expected_X = np.concatenate( - [ - target.all_values(copy=False)[-2, :, 0], - past.all_values(copy=False)[-1, :, 0], - future.all_values(copy=False)[-1, :, 0], - ] - ).reshape(1, -1) - # Label is very last value of `target`: - expected_y = target.all_values(copy=False)[-1, :, 0] + # Check correctness for both 'moving window' method # and 'time intersection' method: - for use_moving_windows in (False, True): - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length=1, - past_covariates=past, - future_covariates=future, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - max_samples_per_ts=max_samples_per_ts, - use_moving_windows=use_moving_windows, + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": expected_times, + "expected_times_y": expected_times, + "target": target, + "past_cov": past, + "future_cov": future, + "lags": lags, + "lags_past": lags_past, + "lags_future": lags_future, + "output_chunk_length": 1, + "output_chunk_shift": output_chunk_shift, + "use_static_covariates": False, + "multi_models": True, + "max_samples_per_ts": max_samples_per_ts, + "use_moving_windows": use_moving_windows, + "concatenate": True, + } + + self.helper_check_lagged_data(convert_lags_to_dict=False, **kwargs) + + if use_moving_windows: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + else: + with pytest.raises(ValueError) as err: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + assert str(err.value).startswith( + "`use_moving_windows=False` is not supported when any of the lags" ) - assert times[0][0] == target.end_time() - assert np.allclose(expected_X, X[:, :, 0]) - assert np.allclose(expected_y, y[:, :, 0]) - def test_lagged_training_data_single_point_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product( + [0, 1, 3], [False, True], ["datetime", "integer"], [False, True] + ), + ) + def test_lagged_training_data_single_point(self, config): """ Tests that `create_lagged_training_data` correctly handles case - where only one possible training point can be generated. This - particular test checks this behaviour by using range index timeseries. + where only one possible training point can be generated. """ + output_chunk_shift, use_moving_windows, series_type, multi_models = config # Can only create feature using first value of series (i.e. `0`) # and can only create label using last value of series (i.e. `1`) - target = linear_timeseries(start=0, length=2, start_value=0, end_value=1) - output_chunk_length = 1 - lags = [-1] - expected_X = np.zeros((1, 1, 1)) - expected_y = np.ones((1, 1, 1)) - # Test correctness for 'moving window' and for 'time intersection' methods, as well - # as for different `multi_models` values: - for (use_moving_windows, multi_models) in product([False, True], [False, True]): - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length, - lags=lags, - uses_static_covariates=False, - multi_models=multi_models, - use_moving_windows=use_moving_windows, + if series_type == "integer": + target = linear_timeseries( + start=0, length=2 + output_chunk_shift, start_value=0, end_value=1 + ) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + length=2 + output_chunk_shift, + start_value=0, + end_value=1, ) - assert np.allclose(expected_X, X) - assert np.allclose(expected_y, y) - # Should only have one sample, generated for `t = target.end_time()`: - assert len(times[0]) == 1 - assert times[0][0] == target.end_time() - def test_lagged_training_data_single_point_datetime_idx(self): - """ - Tests that `create_lagged_training_data` correctly handles case - where only one possible training point can be generated. This - particular test checks this behaviour by using datetime index timeseries. - """ - # Can only create feature using first value of series (i.e. `0`) - # and can only create label using last value of series (i.e. `1`) - target = linear_timeseries( - start=pd.Timestamp("1/1/2000"), length=2, start_value=0, end_value=1 - ) output_chunk_length = 1 lags = [-1] expected_X = np.zeros((1, 1, 1)) expected_y = np.ones((1, 1, 1)) + expected_times = generate_index( + start=target.end_time() - output_chunk_shift * target.freq, + length=1, + freq=target.freq, + ) # Test correctness for 'moving window' and for 'time intersection' methods, as well # as for different `multi_models` values: - for (use_moving_windows, multi_models) in product([False, True], [False, True]): - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length, - lags=lags, - uses_static_covariates=False, - multi_models=multi_models, - use_moving_windows=use_moving_windows, + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": expected_times, + "expected_times_y": expected_times, + "target": target, + "past_cov": None, + "future_cov": None, + "lags": lags, + "lags_past": None, + "lags_future": None, + "output_chunk_length": output_chunk_length, + "output_chunk_shift": output_chunk_shift, + "use_static_covariates": False, + "multi_models": multi_models, + "max_samples_per_ts": None, + "use_moving_windows": use_moving_windows, + "concatenate": True, + } + + self.helper_check_lagged_data(convert_lags_to_dict=False, **kwargs) + + if use_moving_windows: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + else: + with pytest.raises(ValueError) as err: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + assert str(err.value).startswith( + "`use_moving_windows=False` is not supported when any of the lags" ) - assert np.allclose(expected_X, X) - assert np.allclose(expected_y, y) - # Should only have one sample, generated for `t = target.end_time()`: - assert len(times[0]) == 1 - assert times[0][0] == target.end_time() - def test_lagged_training_data_zero_lags_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product( + [0, 1, 3], [False, True], ["datetime", "integer"], [False, True] + ), + ) + def test_lagged_training_data_zero_lags(self, config): """ Tests that `create_lagged_training_data` correctly handles case when `0` is included in `lags_future_covariates` (i.e. when we're using the values `future_covariates` at time `t` to predict the value of `target_series` at - that same time point). This particular test checks this behaviour by using - range index timeseries. + that same time point). """ # Define `future` so that only value occurs at the same time as # the only possible label that can be extracted from `target_series`; the # only possible feature that can be created using these series utilises # the value of `future` at the same time as the label (i.e. a lag # of `0` away from the only feature time): - target = linear_timeseries(start=0, length=2, start_value=0, end_value=1) - future = linear_timeseries( - start=target.end_time(), length=1, start_value=1, end_value=2 - ) + output_chunk_shift, use_moving_windows, series_type, multi_models = config + + if series_type == "integer": + target = linear_timeseries( + start=0, length=2 + output_chunk_shift, start_value=0, end_value=1 + ) + future = linear_timeseries( + start=target.end_time() - output_chunk_shift * target.freq, + length=1, + start_value=1, + end_value=2, + ) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + length=2 + output_chunk_shift, + start_value=0, + end_value=1, + ) + future = linear_timeseries( + start=target.end_time() - output_chunk_shift * target.freq, + length=1, + start_value=1, + end_value=2, + ) + # X comprises of first value of `target` (i.e. 0) and only value in `future`: - expected_X = np.array([0.0, 1.0]).reshape(1, 2, 1) + expected_X = np.array([[[0.0], [1.0]]]) expected_y = np.ones((1, 1, 1)) + expected_times = generate_index( + start=target.end_time() - output_chunk_shift * target.freq, + length=1, + freq=target.freq, + ) # Check correctness for 'moving windows' and 'time intersection' methods, as # well as for different `multi_models` values: - for (use_moving_windows, multi_models) in product([False, True], [False, True]): - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length=1, - future_covariates=future, - lags=[-1], - lags_future_covariates=[0], - uses_static_covariates=False, - multi_models=multi_models, - use_moving_windows=use_moving_windows, + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": expected_times, + "expected_times_y": expected_times, + "target": target, + "past_cov": None, + "future_cov": future, + "lags": [-1], + "lags_past": None, + "lags_future": [0], + "output_chunk_length": 1, + "output_chunk_shift": output_chunk_shift, + "use_static_covariates": False, + "multi_models": multi_models, + "max_samples_per_ts": None, + "use_moving_windows": use_moving_windows, + "concatenate": True, + } + + self.helper_check_lagged_data(convert_lags_to_dict=False, **kwargs) + + if use_moving_windows: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + else: + with pytest.raises(ValueError) as err: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + assert str(err.value).startswith( + "`use_moving_windows=False` is not supported when any of the lags" ) - assert np.allclose(expected_X, X) - assert np.allclose(expected_y, y) - assert len(times[0]) == 1 - assert times[0][0] == target.end_time() - def test_lagged_training_data_zero_lags_datetime_idx(self): - """ - Tests that `create_lagged_training_data` correctly handles case when - `0` is included in `lags_future_covariates` (i.e. when we're using the values - `future_covariates` at time `t` to predict the value of `target_series` at - that same time point). This particular test checks this behaviour by using - datetime index timeseries. - """ - # Define `future` so that only value occurs at the same time as - # the only possible label that can be extracted from `target_series`; the - # only possible feature that can be created using these series utilises - # the value of `future` at the same time as the label (i.e. a lag - # of `0` away from the only feature time): - target = linear_timeseries( - start=pd.Timestamp("1/1/2000"), length=2, start_value=0, end_value=1 - ) - future = linear_timeseries( - start=target.end_time(), length=1, start_value=1, end_value=2 + @pytest.mark.parametrize( + "config", + itertools.product( + [0, 1, 3], + [False, True], + ["datetime", "integer"], + [False, True], + [-1, 0, 1], + [-2, 0, 2], + ), + ) + def test_lagged_training_data_no_target_lags_future_covariates(self, config): + """ + Tests that `create_lagged_training_data` correctly handles case without target lags and different + future covariates lags. + This test should always result in one training sample. + Additionally, we test that: + - future starts before the target but extends far enough to create one training sample + - future shares same time as target + - future starts after target but target extends far enough to create one training sample. + """ + ( + output_chunk_shift, + use_moving_windows, + series_type, + multi_models, + cov_start_shift, + cov_lag, + ) = config + + # adapt covariate start, length, and target length so that only 1 sample can be extracted + target_length = 1 + output_chunk_shift + max(cov_start_shift, 0) + cov_length = 1 - min(cov_start_shift, 0) + if series_type == "integer": + cov_start = 0 + cov_start_shift + cov_lag + target = linear_timeseries( + start=0, length=target_length, start_value=0, end_value=1 + ) + future = linear_timeseries( + start=cov_start, length=cov_length, start_value=2, end_value=3 + ) + else: + freq = pd.tseries.frequencies.to_offset("D") + cov_start = pd.Timestamp("1/1/2000") + (cov_start_shift + cov_lag) * freq + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + length=target_length, + start_value=0, + end_value=1, + freq=freq, + ) + future = linear_timeseries( + start=cov_start, + length=cov_length, + start_value=2, + end_value=3, + freq=freq, + ) + + # X comprises of first value of `target` (i.e. 0) and only value in `future`: + expected_X = future[-1].all_values(copy=False) + expected_y = target[-1].all_values(copy=False) + expected_times = generate_index( + start=target.end_time() - output_chunk_shift * target.freq, + length=1, + freq=target.freq, ) + # Check correctness for 'moving windows' and 'time intersection' methods, as + # well as for different `multi_models` values: + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": expected_times, + "expected_times_y": expected_times, + "target": target, + "past_cov": None, + "future_cov": future, + "lags": None, + "lags_past": None, + "lags_future": [cov_lag], + "output_chunk_length": 1, + "output_chunk_shift": output_chunk_shift, + "use_static_covariates": False, + "multi_models": multi_models, + "max_samples_per_ts": None, + "use_moving_windows": use_moving_windows, + "concatenate": True, + } + + self.helper_check_lagged_data(convert_lags_to_dict=False, **kwargs) + + if use_moving_windows: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + else: + with pytest.raises(ValueError) as err: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + assert str(err.value).startswith( + "`use_moving_windows=False` is not supported when any of the lags" + ) + + @pytest.mark.parametrize( + "config", + itertools.product( + [0, 1, 3], + [False, True], + ["datetime", "integer"], + [False, True], + [-1, 0], + [-2, -1], + ), + ) + def test_lagged_training_data_no_target_lags_past_covariates(self, config): + """ + Tests that `create_lagged_training_data` correctly handles case without target lags and different + past covariates lags. + This test should always result in one training sample. + Additionally, we test that: + - past starts before the target but extends far enough to create one training sample + - past shares same time as target + """ + ( + output_chunk_shift, + use_moving_windows, + series_type, + multi_models, + cov_start_shift, + cov_lag, + ) = config + + # adapt covariate start, length, and target length so that only 1 sample can be extracted + target_length = 1 + output_chunk_shift + max(cov_start_shift, 0) + cov_length = 1 - min(cov_start_shift, 0) + if series_type == "integer": + cov_start = 0 + cov_start_shift + cov_lag + target = linear_timeseries( + start=0, length=target_length, start_value=0, end_value=1 + ) + past = linear_timeseries( + start=cov_start, length=cov_length, start_value=2, end_value=3 + ) + else: + freq = pd.tseries.frequencies.to_offset("D") + cov_start = pd.Timestamp("1/1/2000") + (cov_start_shift + cov_lag) * freq + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + length=target_length, + start_value=0, + end_value=1, + freq=freq, + ) + past = linear_timeseries( + start=cov_start, + length=cov_length, + start_value=2, + end_value=3, + freq=freq, + ) + # X comprises of first value of `target` (i.e. 0) and only value in `future`: - expected_X = np.array([0.0, 1.0]).reshape(1, 2, 1) - expected_y = np.ones((1, 1, 1)) + expected_X = past[-1].all_values(copy=False) + expected_y = target[-1].all_values(copy=False) + expected_times = generate_index( + start=target.end_time() - output_chunk_shift * target.freq, + length=1, + freq=target.freq, + ) # Check correctness for 'moving windows' and 'time intersection' methods, as # well as for different `multi_models` values: - for (use_moving_windows, multi_models) in product([False, True], [False, True]): - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length=1, - future_covariates=future, - lags=[-1], - lags_future_covariates=[0], - uses_static_covariates=False, - multi_models=multi_models, - use_moving_windows=use_moving_windows, + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": expected_times, + "expected_times_y": expected_times, + "target": target, + "past_cov": past, + "future_cov": None, + "lags": None, + "lags_past": [cov_lag], + "lags_future": None, + "output_chunk_length": 1, + "output_chunk_shift": output_chunk_shift, + "use_static_covariates": False, + "multi_models": multi_models, + "max_samples_per_ts": None, + "use_moving_windows": use_moving_windows, + "concatenate": True, + } + + self.helper_check_lagged_data(convert_lags_to_dict=False, **kwargs) + + if use_moving_windows: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + else: + with pytest.raises(ValueError) as err: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + assert str(err.value).startswith( + "`use_moving_windows=False` is not supported when any of the lags" ) - assert np.allclose(expected_X, X) - assert np.allclose(expected_y, y) - assert len(times[0]) == 1 - assert times[0][0] == target.end_time() - def test_lagged_training_data_positive_lags_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product( + [0, 1, 3], [False, True], ["datetime", "integer"], [False, True] + ), + ) + def test_lagged_training_data_positive_lags(self, config): """ Tests that `create_lagged_training_data` correctly handles case when `0` is included in `lags_future_covariates` (i.e. when we're using the values @@ -1358,70 +1580,210 @@ def test_lagged_training_data_positive_lags_range_idx(self): # only possible feature that can be created using these series utilises # the value of `future` one timestep after the time of the label (i.e. a lag # of `1` away from the only feature time): - target = linear_timeseries(start=0, length=2, start_value=0, end_value=1) - future = linear_timeseries( - start=target.end_time() + target.freq, length=1, start_value=1, end_value=2 - ) + output_chunk_shift, use_moving_windows, series_type, multi_models = config + + if series_type == "integer": + target = linear_timeseries( + start=0, length=2 + output_chunk_shift, start_value=0, end_value=1 + ) + future = linear_timeseries( + start=target.end_time() - (output_chunk_shift - 1) * target.freq, + length=1, + start_value=1, + end_value=2, + ) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), + length=2 + output_chunk_shift, + start_value=0, + end_value=1, + ) + future = linear_timeseries( + start=target.end_time() - (output_chunk_shift - 1) * target.freq, + length=1, + start_value=1, + end_value=2, + ) # X comprises of first value of `target` (i.e. 0) and only value in `future`: - expected_X = np.array([0.0, 1.0]).reshape(1, 2, 1) + expected_X = np.array([[[0.0], [1.0]]]) expected_y = np.ones((1, 1, 1)) + expected_times = generate_index( + start=target.end_time() - output_chunk_shift * target.freq, + length=1, + freq=target.freq, + ) # Check correctness for 'moving windows' and 'time intersection' methods, as # well as for different `multi_models` values: - for (use_moving_windows, multi_models) in product([False, True], [False, True]): - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length=1, - future_covariates=future, - lags=[-1], - lags_future_covariates=[1], - uses_static_covariates=False, - multi_models=multi_models, - use_moving_windows=use_moving_windows, + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": expected_times, + "expected_times_y": expected_times, + "target": target, + "past_cov": None, + "future_cov": future, + "lags": [-1], + "lags_past": None, + "lags_future": [1], + "output_chunk_length": 1, + "output_chunk_shift": output_chunk_shift, + "use_static_covariates": False, + "multi_models": multi_models, + "max_samples_per_ts": None, + "use_moving_windows": use_moving_windows, + "concatenate": True, + } + + self.helper_check_lagged_data(convert_lags_to_dict=False, **kwargs) + + if use_moving_windows: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + else: + with pytest.raises(ValueError) as err: + self.helper_check_lagged_data(convert_lags_to_dict=True, **kwargs) + assert str(err.value).startswith( + "`use_moving_windows=False` is not supported when any of the lags" ) - assert np.allclose(expected_X, X) - assert np.allclose(expected_y, y) - assert len(times[0]) == 1 - assert times[0][0] == target.end_time() - def test_lagged_training_data_positive_lags_datetime_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product( + [0, 1, 3], + [1, 2], + [True, False], + ["datetime", "integer"], + ), + ) + def test_lagged_training_data_comp_wise_lags(self, config): """ - Tests that `create_lagged_training_data` correctly handles case when - `0` is included in `lags_future_covariates` (i.e. when we're using the values - `future_covariates` at time `t` to predict the value of `target_series` at - that same time point). This particular test checks this behaviour by using - datetime index timeseries. + Tests that `create_lagged_training_data` generate the expected values when the + lags are component-specific over multivariate series. + + Note that this is supported only when use_moving_window=True. """ - # Define `past` and `future` so their only value occurs at the same time as - # the only possible label that can be extracted from `target_series`; the - # only possible feature that can be created using these series utilises - # the values of `past` and `future` at the same time as the label (i.e. a lag - # of `0` away from the only feature time): - target = linear_timeseries( - start=pd.Timestamp("1/1/2000"), length=2, start_value=0, end_value=1 + output_chunk_shift, output_chunk_length, multi_models, series_type = config + + lags_tg = {"target_0": [-4, -1], "target_1": [-4, -1]} + lags_pc = [-3] + lags_fc = {"future_0": [-1, 0], "future_1": [-2, 1]} + + if series_type == "integer": + start_tg = 0 + start_pc = start_tg + 1 + start_fc = start_tg + 2 + else: + start_tg = pd.Timestamp("2000-01-15") + start_pc = pd.Timestamp("2000-01-16") + start_fc = pd.Timestamp("2000-01-17") + + # length = max lag - min lag + 1 = -1 + 4 + 1 = 4 + target = helper_create_multivariate_linear_timeseries( + n_components=2, + components_names=["target_0", "target_1"], + length=4 + output_chunk_shift + output_chunk_length, + start=start_tg, ) - future = linear_timeseries( - start=target.end_time() + target.freq, length=1, start_value=1, end_value=2 + # length = max lag - min lag + 1 = -3 + 3 + 1 = 1 + past = ( + helper_create_multivariate_linear_timeseries( + n_components=2, + components_names=["past_0", "past_1"], + length=1, + start=start_pc, + ) + + 100 ) - # X comprises of first value of `target` (i.e. 0) and only value in `future`: - expected_X = np.array([0.0, 1.0]).reshape(1, 2, 1) - expected_y = np.ones((1, 1, 1)) - # Check correctness for 'moving windows' and 'time intersection' methods, as - # well as for different `multi_models` values: - for (use_moving_windows, multi_models) in product([False, True], [False, True]): - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length=1, - future_covariates=future, - lags=[-1], - lags_future_covariates=[1], - uses_static_covariates=False, - multi_models=multi_models, - use_moving_windows=use_moving_windows, + # length = max lag - min lag + 1 = 1 + 2 + 1 = 4 + future = ( + helper_create_multivariate_linear_timeseries( + n_components=2, + components_names=["future_0", "future_1"], + length=4 + output_chunk_shift + output_chunk_length, + start=start_fc, ) - assert np.allclose(expected_X, X) - assert np.allclose(expected_y, y) - assert len(times[0]) == 1 - assert times[0][0] == target.end_time() + + 200 + ) + + # extremes lags are manually computed, similarly to the model.lags attribute + feats_times = self.get_feature_times( + target, + past, + future, + [-4, -1], # min, max target lag + [-3], # unique past lag + [-2, 1], # min, max future lag + output_chunk_length, + None, + output_chunk_shift, + ) + + # reorder the features to obtain target_0_lag-4, target_1_lag-4, target_0_lag-1, target_1_lag-1 + X_target = [ + self.construct_X_block( + target["target_0"], feats_times, lags_tg["target_0"][0:1] + ), + self.construct_X_block( + target["target_1"], feats_times, lags_tg["target_1"][0:1] + ), + self.construct_X_block( + target["target_0"], feats_times, lags_tg["target_0"][1:2] + ), + self.construct_X_block( + target["target_1"], feats_times, lags_tg["target_1"][1:2] + ), + ] + # single lag for all the components, can be kept as is + X_past = [ + self.construct_X_block(past[name], feats_times, lags_pc) + for name in ["past_0", "past_1"] + ] + # reorder the features to obtain future_1_lag-2, future_0_lag-1, future_0_lag0, future_1_lag1 + X_future = [ + self.construct_X_block( + future["future_1"], feats_times, lags_fc["future_1"][0:1] + ), + self.construct_X_block( + future["future_0"], feats_times, lags_fc["future_0"][0:1] + ), + self.construct_X_block( + future["future_0"], feats_times, lags_fc["future_0"][1:2] + ), + self.construct_X_block( + future["future_1"], feats_times, lags_fc["future_1"][1:2] + ), + ] + all_X = X_target + X_past + X_future + expected_X = np.concatenate(all_X, axis=1)[:, :, np.newaxis] + expected_y = self.create_y( + target, + feats_times, + output_chunk_length, + multi_models, + output_chunk_shift, + )[:, :, np.newaxis] + + # lags are already in dict format + self.helper_check_lagged_data( + convert_lags_to_dict=True, + expected_X=expected_X, + expected_y=expected_y, + expected_times_x=feats_times, + expected_times_y=feats_times, + target=target, + past_cov=past, + future_cov=future, + lags=lags_tg, + lags_past=lags_pc, + lags_future=lags_fc, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, + use_static_covariates=False, + multi_models=multi_models, + max_samples_per_ts=None, + use_moving_windows=True, + concatenate=True, + ) def test_lagged_training_data_sequence_inputs(self): """ @@ -1432,6 +1794,9 @@ def test_lagged_training_data_sequence_inputs(self): # Define two simple tabularization problems: target_1 = past_1 = future_1 = linear_timeseries(start=0, end=5) target_2 = past_2 = future_2 = linear_timeseries(start=6, end=11) + ts_tg = (target_1, target_2) + ts_pc = (past_1, past_2) + ts_fc = (future_1, future_2) lags = lags_past = lags_future = [-1] output_chunk_length = 1 # Expected solution: @@ -1447,43 +1812,41 @@ def test_lagged_training_data_sequence_inputs(self): expected_y = np.concatenate([expected_y_1, expected_y_2], axis=0) expected_times_1 = target_1.time_index[1:] expected_times_2 = target_2.time_index[1:] - # Check when `concatenate = True`: - X, y, times, _ = create_lagged_training_data( - (target_1, target_2), - output_chunk_length=output_chunk_length, - past_covariates=(past_1, past_2), - future_covariates=(future_1, future_2), - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, + + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": [expected_times_1, expected_times_2], + "expected_times_y": [expected_times_1, expected_times_2], + "target": ts_tg, + "past_cov": ts_pc, + "future_cov": ts_fc, + "lags": lags, + "lags_past": lags_past, + "lags_future": lags_future, + "output_chunk_length": output_chunk_length, + "output_chunk_shift": 0, + "use_static_covariates": False, + "multi_models": True, + "max_samples_per_ts": None, + "use_moving_windows": True, + } + + # concatenate=True + self.helper_check_lagged_data( + convert_lags_to_dict=False, concatenate=True, **kwargs ) - assert np.allclose(X, expected_X) - assert np.allclose(y, expected_y) - assert len(times) == 2 - assert times[0].equals(expected_times_1) - assert times[1].equals(expected_times_2) - # Check when `concatenate = False`: - X, y, times, _ = create_lagged_training_data( - (target_1, target_2), - output_chunk_length=output_chunk_length, - past_covariates=(past_1, past_2), - future_covariates=(future_1, future_2), - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, - concatenate=False, + self.helper_check_lagged_data( + convert_lags_to_dict=True, concatenate=True, **kwargs + ) + + # concatenate=False + self.helper_check_lagged_data( + convert_lags_to_dict=False, concatenate=False, **kwargs + ) + self.helper_check_lagged_data( + convert_lags_to_dict=True, concatenate=False, **kwargs ) - assert len(X) == 2 - assert len(y) == 2 - assert np.allclose(X[0], expected_X_1) - assert np.allclose(X[1], expected_X_2) - assert np.allclose(y[0], expected_y_1) - assert np.allclose(y[1], expected_y_2) - assert len(times) == 2 - assert times[0].equals(expected_times_1) - assert times[1].equals(expected_times_2) def test_lagged_training_data_stochastic_series(self): """ @@ -1504,19 +1867,32 @@ def test_lagged_training_data_stochastic_series(self): ) expected_y = target.all_values(copy=False)[1:, :, :] expected_times = target.time_index[1:] - X, y, times, _ = create_lagged_training_data( - target, - output_chunk_length=output_chunk_length, - past_covariates=past, - future_covariates=future, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - uses_static_covariates=False, + + kwargs = { + "expected_X": expected_X, + "expected_y": expected_y, + "expected_times_x": expected_times, + "expected_times_y": expected_times, + "target": target, + "past_cov": past, + "future_cov": future, + "lags": lags, + "lags_past": lags_past, + "lags_future": lags_future, + "output_chunk_length": output_chunk_length, + "output_chunk_shift": 0, + "use_static_covariates": False, + "multi_models": True, + "max_samples_per_ts": None, + "use_moving_windows": True, + } + + self.helper_check_lagged_data( + convert_lags_to_dict=False, concatenate=True, **kwargs + ) + self.helper_check_lagged_data( + convert_lags_to_dict=True, concatenate=True, **kwargs ) - assert np.allclose(X, expected_X) - assert np.allclose(y, expected_y) - assert times[0].equals(expected_times) def test_lagged_training_data_no_shared_times_error(self): """ @@ -1539,6 +1915,7 @@ def test_lagged_training_data_no_shared_times_error(self): lags_past_covariates=lags, uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert ( "Specified series do not share any common times for which features can be created." @@ -1567,6 +1944,7 @@ def test_lagged_training_data_no_specified_series_lags_pairs_error(self): lags_past_covariates=lags, uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert "Must specify at least one series-lags pair." == str(err.value) # Warnings will be thrown indicating that `past_covariates` @@ -1583,6 +1961,7 @@ def test_lagged_training_data_no_specified_series_lags_pairs_error(self): past_covariates=series_2, uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert "Must specify at least one series-lags pair." == str(err.value) @@ -1603,6 +1982,7 @@ def test_lagged_training_data_invalid_output_chunk_length_error(self): lags=lags, uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert "`output_chunk_length` must be a positive `int`." == str(err.value) with pytest.raises(ValueError) as err: @@ -1612,6 +1992,7 @@ def test_lagged_training_data_invalid_output_chunk_length_error(self): lags=lags, uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert "`output_chunk_length` must be a positive `int`." == str(err.value) @@ -1629,6 +2010,7 @@ def test_lagged_training_data_no_lags_specified_error(self): output_chunk_length=1, uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert ( "Must specify at least one of: `lags`, `lags_past_covariates`, `lags_future_covariates`." @@ -1656,11 +2038,12 @@ def test_lagged_training_data_series_too_short_error(self): lags=[-20, -10], uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert ( "`target_series` must have at least " - "`-min(lags) + output_chunk_length` = 25 " - "timesteps; instead, it only has 2." + "`-min(lags) + output_chunk_length + output_chunk_shift` = 25 " + "time steps; instead, it only has 2." ) == str(err.value) # `lags_past_covariates` too large test: with pytest.raises(ValueError) as err: @@ -1671,11 +2054,12 @@ def test_lagged_training_data_series_too_short_error(self): lags_past_covariates=[-5, -3], uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert ( "`past_covariates` must have at least " "`-min(lags_past_covariates) + max(lags_past_covariates) + 1` = 3 " - "timesteps; instead, it only has 2." + "time steps; instead, it only has 2." ) == str(err.value) def test_lagged_training_data_invalid_lag_values_error(self): @@ -1700,6 +2084,7 @@ def test_lagged_training_data_invalid_lag_values_error(self): lags=[0], uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert ( "`lags` must be a `Sequence` or `Dict` containing only `int` values less than 0." @@ -1713,6 +2098,7 @@ def test_lagged_training_data_invalid_lag_values_error(self): lags_past_covariates=[0], uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert ( "`lags_past_covariates` must be a `Sequence` or `Dict` containing only `int` values less than 0." @@ -1725,8 +2111,49 @@ def test_lagged_training_data_invalid_lag_values_error(self): lags_future_covariates=[-1, 0, 1], uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) + def test_lagged_training_data_dict_lags_no_moving_window_error(self): + """ + Tests that `create_lagged_training_data` throws correct error + when `use_moving_window` is set to `False` and lags are provided + as a dict for a multivariate series. + """ + ts = linear_timeseries(start=1, length=20, freq=1, column_name="lin1") + lags = [-1] + lags_dict = {"lin1": [-1]} + # one series, one set of lags are dict + with pytest.raises(ValueError) as err: + create_lagged_training_data( + target_series=ts, + output_chunk_length=1, + lags=lags_dict, + uses_static_covariates=False, + use_moving_windows=False, + output_chunk_shift=0, + ) + assert str(err.value).startswith( + "`use_moving_windows=False` is not supported when any of the lags is provided as a dictionary." + ) + # all the series are provided, only one passed as dict + with pytest.raises(ValueError) as err: + create_lagged_training_data( + target_series=ts, + past_covariates=ts, + future_covariates=ts, + output_chunk_length=1, + lags=lags, + lags_past_covariates=lags_dict, + lags_future_covariates=lags, + uses_static_covariates=False, + use_moving_windows=False, + output_chunk_shift=0, + ) + assert str(err.value).startswith( + "`use_moving_windows=False` is not supported when any of the lags is provided as a dictionary." + ) + def test_lagged_training_data_unspecified_lag_or_series_warning(self): """ Tests that `create_lagged_training_data` throws correct @@ -1751,6 +2178,7 @@ def test_lagged_training_data_unspecified_lag_or_series_warning(self): future_covariates=series, uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert len(w) == 1 assert issubclass(w[0].category, UserWarning) @@ -1767,6 +2195,7 @@ def test_lagged_training_data_unspecified_lag_or_series_warning(self): lags_future_covariates=lags, uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert len(w) == 1 assert issubclass(w[0].category, UserWarning) @@ -1785,6 +2214,7 @@ def test_lagged_training_data_unspecified_lag_or_series_warning(self): lags_future_covariates=lags, uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert len(w) == 2 assert issubclass(w[0].category, UserWarning) @@ -1807,298 +2237,643 @@ def test_lagged_training_data_unspecified_lag_or_series_warning(self): lags_past_covariates=lags, uses_static_covariates=False, use_moving_windows=use_moving_windows, + output_chunk_shift=0, ) assert len(w) == 0 - def test_create_lagged_component_names(self): + @pytest.mark.parametrize( + "config", + [ + # target no static covariate + ( + target_with_no_cov, + None, + None, + [-2, -1], + None, + None, + False, + 1, + ["no_static_target_lag-2", "no_static_target_lag-1"], + ["no_static_target_hrz0"], + ), + # target with static covariate (but don't use them in feature names) + ( + target_with_static_cov, + None, + None, + [-4, -1], + None, + None, + False, + 2, + [ + "static_0_target_lag-4", + "static_1_target_lag-4", + "static_0_target_lag-1", + "static_1_target_lag-1", + ], + [ + "static_0_target_hrz0", + "static_1_target_hrz0", + "static_0_target_hrz1", + "static_1_target_hrz1", + ], + ), + # target with static covariate (acting on global target components) + ( + target_with_static_cov, + None, + None, + [-4, -1], + None, + None, + True, + 1, + [ + "static_0_target_lag-4", + "static_1_target_lag-4", + "static_0_target_lag-1", + "static_1_target_lag-1", + "dummy_statcov_target_global_components", + ], + [ + "static_0_target_hrz0", + "static_1_target_hrz0", + ], + ), + # target with static covariate (component specific) + ( + target_with_static_cov2, + None, + None, + [-4, -1], + None, + None, + True, + 1, + [ + "static_0_target_lag-4", + "static_1_target_lag-4", + "static_0_target_lag-1", + "static_1_target_lag-1", + "dummy_statcov_target_static_0", + "dummy_statcov_target_static_1", + ], + [ + "static_0_target_hrz0", + "static_1_target_hrz0", + ], + ), + # target with static covariate (component specific & multivariate) + ( + target_with_static_cov3, + None, + None, + [-4, -1], + None, + None, + True, + 1, + [ + "static_0_target_lag-4", + "static_1_target_lag-4", + "static_0_target_lag-1", + "static_1_target_lag-1", + "dummy_statcov_target_static_0", + "dummy_statcov_target_static_1", + "dummy1_statcov_target_static_0", + "dummy1_statcov_target_static_1", + ], + [ + "static_0_target_hrz0", + "static_1_target_hrz0", + ], + ), + # target + past + ( + target_with_no_cov, + past, + None, + [-4, -3], + [-1], + None, + False, + 1, + [ + "no_static_target_lag-4", + "no_static_target_lag-3", + "past_0_pastcov_lag-1", + "past_1_pastcov_lag-1", + "past_2_pastcov_lag-1", + ], + ["no_static_target_hrz0"], + ), + # target + future + ( + target_with_no_cov, + None, + future, + [-2, -1], + None, + [3], + False, + 1, + [ + "no_static_target_lag-2", + "no_static_target_lag-1", + "future_0_futcov_lag3", + "future_1_futcov_lag3", + "future_2_futcov_lag3", + "future_3_futcov_lag3", + ], + ["no_static_target_hrz0"], + ), + # past + future + ( + target_with_no_cov, + past, + future, + None, + [-1], + [2], + False, + 1, + [ + "past_0_pastcov_lag-1", + "past_1_pastcov_lag-1", + "past_2_pastcov_lag-1", + "future_0_futcov_lag2", + "future_1_futcov_lag2", + "future_2_futcov_lag2", + "future_3_futcov_lag2", + ], + ["no_static_target_hrz0"], + ), + # target with static (not used) + past + future + ( + target_with_static_cov, + past, + future, + [-2, -1], + [-1], + [2], + False, + 1, + [ + "static_0_target_lag-2", + "static_1_target_lag-2", + "static_0_target_lag-1", + "static_1_target_lag-1", + "past_0_pastcov_lag-1", + "past_1_pastcov_lag-1", + "past_2_pastcov_lag-1", + "future_0_futcov_lag2", + "future_1_futcov_lag2", + "future_2_futcov_lag2", + "future_3_futcov_lag2", + ], + [ + "static_0_target_hrz0", + "static_1_target_hrz0", + ], + ), + # multiple series with same components names, including past/future covariates + ( + [target_with_static_cov, target_with_static_cov], + [past, past], + [future, future], + [-3], + [-1], + [2], + False, + 1, + [ + "static_0_target_lag-3", + "static_1_target_lag-3", + "past_0_pastcov_lag-1", + "past_1_pastcov_lag-1", + "past_2_pastcov_lag-1", + "future_0_futcov_lag2", + "future_1_futcov_lag2", + "future_2_futcov_lag2", + "future_3_futcov_lag2", + ], + [ + "static_0_target_hrz0", + "static_1_target_hrz0", + ], + ), + # multiple series with different components will use the first series as reference + ( + [ + target_with_static_cov, + target_with_no_cov.stack(target_with_no_cov), + ], + [past, past], + [future, past.stack(target_with_no_cov)], + [-2, -1], + [-1], + [2], + False, + 1, + [ + "static_0_target_lag-2", + "static_1_target_lag-2", + "static_0_target_lag-1", + "static_1_target_lag-1", + "past_0_pastcov_lag-1", + "past_1_pastcov_lag-1", + "past_2_pastcov_lag-1", + "future_0_futcov_lag2", + "future_1_futcov_lag2", + "future_2_futcov_lag2", + "future_3_futcov_lag2", + ], + [ + "static_0_target_hrz0", + "static_1_target_hrz0", + ], + ), + ], + ) + def test_create_lagged_component_names(self, config): """ Tests that `create_lagged_component_names` produces the expected features name depending on the lags, output_chunk_length and covariates. - """ - target_with_no_cov = self.create_multivariate_linear_timeseries( - n_components=1, - components_names=["no_static"], - start_value=0, - end_value=10, - start=2, - length=10, - freq=2, - ) - n_comp = 2 - target_with_static_cov = self.create_multivariate_linear_timeseries( - n_components=n_comp, - components_names=["static_0", "static_1"], - start_value=0, - end_value=10, - start=2, - length=10, - freq=2, - ) - target_with_static_cov = target_with_static_cov.with_static_covariates( - pd.DataFrame({"dummy": [1]}) # leads to "global" static cov component name - ) - target_with_static_cov2 = target_with_static_cov.with_static_covariates( - pd.DataFrame( - {"dummy": [i for i in range(n_comp)]} - ) # leads to sharing target component names - ) - target_with_static_cov3 = target_with_static_cov.with_static_covariates( - pd.DataFrame( - { - "dummy": [i for i in range(n_comp)], - "dummy1": [i for i in range(n_comp)], - } - ) # leads to sharing target component names - ) - past = self.create_multivariate_linear_timeseries( - n_components=3, - components_names=["past_0", "past_1", "past_2"], - start_value=10, - end_value=20, - start=2, - length=10, - freq=2, - ) - future = self.create_multivariate_linear_timeseries( - n_components=4, - components_names=["future_0", "future_1", "future_2", "future_3"], - start_value=20, - end_value=30, - start=2, - length=10, - freq=2, - ) - - # target no static covariate - expected_lagged_features = ["no_static_target_lag-2", "no_static_target_lag-1"] - created_lagged_features, _ = create_lagged_component_names( - target_series=target_with_no_cov, - past_covariates=None, - future_covariates=None, - lags=[-2, -1], - lags_past_covariates=None, - lags_future_covariates=None, + When lags are component-specific, they are identical across all the components. + """ + ( + ts_tg, + ts_pc, + ts_fc, + lags_tg, + lags_pc, + lags_fc, + use_static_cov, + ocl, + expected_lagged_features, + expected_lagged_labels, + ) = config + # lags as list + created_lagged_features, created_lagged_labels = create_lagged_component_names( + target_series=ts_tg, + past_covariates=ts_pc, + future_covariates=ts_fc, + lags=lags_tg, + lags_past_covariates=lags_pc, + lags_future_covariates=lags_fc, concatenate=False, - use_static_covariates=False, + use_static_covariates=use_static_cov, + output_chunk_length=ocl, ) - assert expected_lagged_features == created_lagged_features - # target with static covariate (but don't use them in feature names) - expected_lagged_features = [ - "static_0_target_lag-4", - "static_1_target_lag-4", - "static_0_target_lag-1", - "static_1_target_lag-1", - ] - created_lagged_features, _ = create_lagged_component_names( - target_series=target_with_static_cov, - past_covariates=None, - future_covariates=None, - lags=[-4, -1], - lags_past_covariates=None, - lags_future_covariates=None, - concatenate=False, - use_static_covariates=False, + # converts lags to dictionary format + lags_as_dict = self.convert_lags_to_dict( + ts_tg, + ts_pc, + ts_fc, + lags_tg, + lags_pc, + lags_fc, ) - assert expected_lagged_features == created_lagged_features - # target with static covariate (acting on global target components) - expected_lagged_features = [ - "static_0_target_lag-4", - "static_1_target_lag-4", - "static_0_target_lag-1", - "static_1_target_lag-1", - "dummy_statcov_target_global_components", - ] - created_lagged_features, _ = create_lagged_component_names( - target_series=target_with_static_cov, - past_covariates=None, - future_covariates=None, - lags=[-4, -1], - lags_past_covariates=None, - lags_future_covariates=None, - concatenate=False, - use_static_covariates=True, + created_lagged_features_dict_lags, created_lagged_labels_dict_lags = ( + create_lagged_component_names( + target_series=ts_tg, + past_covariates=ts_pc, + future_covariates=ts_fc, + lags=lags_as_dict["target"], + lags_past_covariates=lags_as_dict["past"], + lags_future_covariates=lags_as_dict["future"], + concatenate=False, + use_static_covariates=use_static_cov, + output_chunk_length=ocl, + ) ) assert expected_lagged_features == created_lagged_features + assert expected_lagged_features == created_lagged_features_dict_lags + assert expected_lagged_labels == created_lagged_labels + assert expected_lagged_labels == created_lagged_labels_dict_lags + + @pytest.mark.parametrize( + "config", + [ + # lags have the same minimum + ( + target_with_static_cov, + None, + None, + {"static_0": [-4, -2], "static_1": [-4, -3]}, + None, + None, + False, + [ + "static_0_target_lag-4", + "static_1_target_lag-4", + "static_1_target_lag-3", + "static_0_target_lag-2", + ], + ), + # lags are not overlapping + ( + target_with_static_cov, + None, + None, + {"static_0": [-4, -1], "static_1": [-3, -2]}, + None, + None, + False, + [ + "static_0_target_lag-4", + "static_1_target_lag-3", + "static_1_target_lag-2", + "static_0_target_lag-1", + ], + ), + # default lags for target, overlapping lags for past covariates + ( + target_with_static_cov, + past, + None, + {"static_0": [-3], "static_1": [-3]}, + {"past_0": [-4, -3], "past_1": [-3, -2], "past_2": [-2]}, + None, + False, + [ + "static_0_target_lag-3", + "static_1_target_lag-3", + "past_0_pastcov_lag-4", + "past_0_pastcov_lag-3", + "past_1_pastcov_lag-3", + "past_1_pastcov_lag-2", + "past_2_pastcov_lag-2", + ], + ), + # no lags for target, future covariates lags are not in the components order + ( + target_with_static_cov, + None, + future, + None, + None, + { + "future_3": [-2, 0, 2], + "future_0": [-4, 1], + "future_2": [1], + "future_1": [-2, 2], + }, + False, + [ + "future_0_futcov_lag-4", + "future_1_futcov_lag-2", + "future_3_futcov_lag-2", + "future_3_futcov_lag0", + "future_0_futcov_lag1", + "future_2_futcov_lag1", + "future_1_futcov_lag2", + "future_3_futcov_lag2", + ], + ), + ], + ) + def test_create_lagged_component_names_different_lags(self, config): + """ + Tests that `create_lagged_component_names` when lags are different across components. + + The lagged features should be sorted by lags, then by components. + """ + ( + ts_tg, + ts_pc, + ts_fc, + lags_tg, + lags_pc, + lags_fc, + use_static_cov, + expected_lagged_features, + ) = config - # target with static covariate (component specific) - expected_lagged_features = [ - "static_0_target_lag-4", - "static_1_target_lag-4", - "static_0_target_lag-1", - "static_1_target_lag-1", - "dummy_statcov_target_static_0", - "dummy_statcov_target_static_1", - ] created_lagged_features, _ = create_lagged_component_names( - target_series=target_with_static_cov2, - past_covariates=None, - future_covariates=None, - lags=[-4, -1], - lags_past_covariates=None, - lags_future_covariates=None, + target_series=ts_tg, + past_covariates=ts_pc, + future_covariates=ts_fc, + lags=lags_tg, + lags_past_covariates=lags_pc, + lags_future_covariates=lags_fc, concatenate=False, - use_static_covariates=True, + use_static_covariates=use_static_cov, ) assert expected_lagged_features == created_lagged_features - # target with static covariate (component specific & multivariate) - expected_lagged_features = [ - "static_0_target_lag-4", - "static_1_target_lag-4", - "static_0_target_lag-1", - "static_1_target_lag-1", - "dummy_statcov_target_static_0", - "dummy_statcov_target_static_1", - "dummy1_statcov_target_static_0", - "dummy1_statcov_target_static_1", - ] - created_lagged_features, _ = create_lagged_component_names( - target_series=target_with_static_cov3, - past_covariates=None, - future_covariates=None, + @pytest.mark.parametrize( + "config", + itertools.product( + [10, 50], + [True, False], + ["linear", "exponential"], + ["D", "2D", 2], + [True, False], + ), + ) + def test_correct_generated_weights_exponential(self, config): + """Tests built in weights generation for: + - varying target series sizes + - with and without moving window tabularization + - different weight functions + - datetime and integer index + - single and multiple series + """ + training_size, use_moving_windows, sample_weight, freq, single_series = config + + if not isinstance(freq, int): + freq = pd.tseries.frequencies.to_offset(freq) + start = pd.Timestamp("2000-01-01") + else: + start = 1 + + train_y = linear_timeseries(start=start, length=training_size, freq=freq) + + _, y, _, _, weights = create_lagged_training_data( lags=[-4, -1], - lags_past_covariates=None, - lags_future_covariates=None, - concatenate=False, - use_static_covariates=True, + target_series=train_y if single_series else [train_y] * 2, + output_chunk_length=1, + uses_static_covariates=False, + sample_weight=sample_weight, + output_chunk_shift=0, + use_moving_windows=use_moving_windows, ) - assert expected_lagged_features == created_lagged_features - # target + past - expected_lagged_features = [ - "no_static_target_lag-4", - "no_static_target_lag-3", - "past_0_pastcov_lag-1", - "past_1_pastcov_lag-1", - "past_2_pastcov_lag-1", - ] - created_lagged_features, _ = create_lagged_component_names( - target_series=target_with_no_cov, - past_covariates=past, - future_covariates=None, - lags=[-4, -3], - lags_past_covariates=[-1], - lags_future_covariates=None, - concatenate=False, + len_y = len(y) if single_series else int(len(y) / 2) + if sample_weight == "linear": + expected_weights = np.linspace(0, 1, len(train_y))[-len_y:, None, None] + else: # exponential decay + time_steps = np.linspace(0, 1, len(train_y)) + expected_weights = np.exp(-10 * (1 - time_steps))[-len_y:, None, None] + + if not single_series: + expected_weights = np.concatenate([expected_weights] * 2, axis=0) + + assert weights.shape == y.shape + np.testing.assert_array_almost_equal(weights, expected_weights) + + @pytest.mark.parametrize( + "config", + itertools.product( + [10, 20], + [True, False], + [True, False], + [1, 2], + [0, 1], + ["D", "2D", 2], + [True, False], + [True, False], + ), + ) + def test_correct_user_weights(self, config): + """Checks correct weights extraction for: + - varying target series sizes + - with and without moving window tabularization + - weights with exact matching index and longer weights + - single and multi horizon + - with and without output chunk shift + - datetime and integer index + - single and multiple series + - uni- and multivariate series + """ + ( + training_size, + use_moving_windows, + weights_longer, + ocl, + ocs, + freq, + single_series, + univar_series, + ) = config + if not isinstance(freq, int): + freq = pd.tseries.frequencies.to_offset(freq) + start = pd.Timestamp("2000-01-01") + else: + start = 1 + + train_y = linear_timeseries(start=start, length=training_size, freq=freq) + if not univar_series: + train_y.stack(train_y) + + # weights are either longer or have the exact time index as the target series + n_weights = len(train_y) + 2 * int(weights_longer) + ts_weights = TimeSeries.from_times_and_values( + times=generate_index( + start=train_y.start_time() - int(weights_longer) * freq, + length=n_weights, + freq=freq, + ), + values=np.linspace(0, 1, n_weights), ) - assert expected_lagged_features == created_lagged_features + if not univar_series: + ts_weights.stack(ts_weights + 1.0) - # target + future - expected_lagged_features = [ - "no_static_target_lag-2", - "no_static_target_lag-1", - "future_0_futcov_lag3", - "future_1_futcov_lag3", - "future_2_futcov_lag3", - "future_3_futcov_lag3", - ] - created_lagged_features, _ = create_lagged_component_names( - target_series=target_with_no_cov, - past_covariates=None, - future_covariates=future, - lags=[-2, -1], - lags_past_covariates=None, - lags_future_covariates=[3], - concatenate=False, + _, y, _, _, weights = create_lagged_training_data( + lags=[-4, -1], + target_series=train_y if single_series else [train_y] * 2, + output_chunk_length=ocl, + uses_static_covariates=False, + sample_weight=ts_weights if single_series else [ts_weights] * 2, + output_chunk_shift=ocs, + use_moving_windows=use_moving_windows, ) - assert expected_lagged_features == created_lagged_features - # past + future - expected_lagged_features = [ - "past_0_pastcov_lag-1", - "past_1_pastcov_lag-1", - "past_2_pastcov_lag-1", - "future_0_futcov_lag2", - "future_1_futcov_lag2", - "future_2_futcov_lag2", - "future_3_futcov_lag2", - ] - created_lagged_features, _ = create_lagged_component_names( - target_series=target_with_no_cov, - past_covariates=past, - future_covariates=future, - lags=None, - lags_past_covariates=[-1], - lags_future_covariates=[2], - concatenate=False, + # weights shape must match label shape, since we have one + # weight per sample and predict step + assert weights.shape == y.shape + + # get the weights matching the index of the target series + weights_exact = ts_weights.values() + if weights_longer: + weights_exact = weights_exact[1:-1] + + # the weights correspond to the same sample and time index as the `y` labels + expected_weights = [] + len_y_single = len(y) if single_series else int(len(y) / 2) + for i in range(ocl): + mask = slice(-(i + len_y_single), -i if i else None) + expected_weights.append(weights_exact[mask]) + expected_weights = np.concatenate(expected_weights, axis=1)[:, ::-1] + if not single_series: + expected_weights = np.concatenate([expected_weights] * 2, axis=0) + np.testing.assert_array_almost_equal(weights[:, :, 0], expected_weights) + + @pytest.mark.parametrize( + "use_moving_windows", + [True, False], + ) + def test_invalid_sample_weights(self, use_moving_windows): + """Checks invalid weights raise error with and without moving window tabularization + - too short series + - not enough series + - invalid string + - weights shape does not match number of `series` components + """ + training_size = 10 + + train_y = linear_timeseries(length=training_size) + weights_too_short = train_y[:-2] + with pytest.raises(ValueError) as err: + _ = create_lagged_training_data( + lags=[-4, -1], + target_series=train_y, + output_chunk_length=1, + uses_static_covariates=False, + sample_weight=weights_too_short, + output_chunk_shift=0, + use_moving_windows=use_moving_windows, + ) + assert ( + str(err.value) + == "The `sample_weight` series must have at least the same times as the target `series`." ) - assert expected_lagged_features == created_lagged_features - # target with static + past + future - expected_lagged_features = [ - "static_0_target_lag-2", - "static_1_target_lag-2", - "static_0_target_lag-1", - "static_1_target_lag-1", - "past_0_pastcov_lag-1", - "past_1_pastcov_lag-1", - "past_2_pastcov_lag-1", - "future_0_futcov_lag2", - "future_1_futcov_lag2", - "future_2_futcov_lag2", - "future_3_futcov_lag2", - ] - created_lagged_features, _ = create_lagged_component_names( - target_series=target_with_static_cov, - past_covariates=past, - future_covariates=future, - lags=[-2, -1], - lags_past_covariates=[-1], - lags_future_covariates=[2], - concatenate=False, + with pytest.raises(ValueError) as err: + _ = create_lagged_training_data( + lags=[-4, -1], + target_series=[train_y] * 2, + output_chunk_length=1, + uses_static_covariates=False, + sample_weight=[train_y], + output_chunk_shift=0, + use_moving_windows=use_moving_windows, + ) + assert ( + str(err.value) + == "The provided sequence of target `series` must have the same length as the provided sequence " + "of `sample_weight`." ) - assert expected_lagged_features == created_lagged_features - # multiple series with same components, including past/future covariates - expected_lagged_features = [ - "static_0_target_lag-3", - "static_1_target_lag-3", - "past_0_pastcov_lag-1", - "past_1_pastcov_lag-1", - "past_2_pastcov_lag-1", - "future_0_futcov_lag2", - "future_1_futcov_lag2", - "future_2_futcov_lag2", - "future_3_futcov_lag2", - ] - created_lagged_features, _ = create_lagged_component_names( - target_series=[target_with_static_cov, target_with_static_cov], - past_covariates=[past, past], - future_covariates=[future, future], - lags=[-3], - lags_past_covariates=[-1], - lags_future_covariates=[2], - concatenate=False, - ) - assert expected_lagged_features == created_lagged_features + with pytest.raises(ValueError) as err: + _ = create_lagged_training_data( + lags=[-4, -1], + target_series=[train_y] * 2, + output_chunk_length=1, + uses_static_covariates=False, + sample_weight="invalid", + output_chunk_shift=0, + use_moving_windows=use_moving_windows, + ) + assert str(err.value).startswith("Invalid `sample_weight` value: `'invalid'`. ") - # multiple series with different components will use the first series as reference - expected_lagged_features = [ - "static_0_target_lag-2", - "static_1_target_lag-2", - "static_0_target_lag-1", - "static_1_target_lag-1", - "past_0_pastcov_lag-1", - "past_1_pastcov_lag-1", - "past_2_pastcov_lag-1", - "future_0_futcov_lag2", - "future_1_futcov_lag2", - "future_2_futcov_lag2", - "future_3_futcov_lag2", - ] - created_lagged_features, _ = create_lagged_component_names( - target_series=[ - target_with_static_cov, - target_with_no_cov.stack(target_with_no_cov), - ], - past_covariates=[past, past], - future_covariates=[future, past.stack(target_with_no_cov)], - lags=[-2, -1], - lags_past_covariates=[-1], - lags_future_covariates=[2], - concatenate=False, + with pytest.raises(ValueError) as err: + _ = create_lagged_training_data( + lags=[-4, -1], + target_series=train_y, + output_chunk_length=1, + uses_static_covariates=False, + sample_weight=train_y.stack(train_y), + output_chunk_shift=0, + use_moving_windows=use_moving_windows, + ) + assert str(err.value) == ( + "The number of components in `sample_weight` must either be `1` or " + "match the number of target series components `1`." ) - assert expected_lagged_features == created_lagged_features diff --git a/darts/tests/utils/tabularization/test_get_feature_times.py b/darts/tests/utils/tabularization/test_get_feature_times.py index e63a8e4057..cb0c895522 100644 --- a/darts/tests/utils/tabularization/test_get_feature_times.py +++ b/darts/tests/utils/tabularization/test_get_feature_times.py @@ -1,6 +1,7 @@ +import itertools import warnings +from collections.abc import Sequence from itertools import product -from typing import Sequence import pandas as pd import pytest @@ -38,6 +39,7 @@ def get_feature_times_target_training( target_series: TimeSeries, lags: Sequence[int], output_chunk_length: int, + output_chunk_shift: int, ): """ Helper function that returns all the times within `target_series` that can be used to @@ -58,6 +60,8 @@ def get_feature_times_target_training( # Exclude last `output_chunk_length - 1` times: if output_chunk_length > 1: times = times[: -output_chunk_length + 1] + if output_chunk_shift: + times = times[:-output_chunk_shift] return times @staticmethod @@ -83,9 +87,9 @@ def get_feature_times_past( times = past_covariates.time_index min_lag = -max(past_covariates_lags) # Add times after end of series for which we can create features: - times = times.union( - [times[-1] + i * past_covariates.freq for i in range(1, min_lag + 1)] - ) + times = times.union([ + times[-1] + i * past_covariates.freq for i in range(1, min_lag + 1) + ]) max_lag = -min(past_covariates_lags) times = times[max_lag:] return times @@ -154,20 +158,18 @@ def get_feature_times_future( # Case 1: if (min_lag > 0) and (max_lag > 0): # Can create features for times extending after the end of `future_covariates`: - times = times.union( - [times[-1] + i * future_covariates.freq for i in range(1, min_lag + 1)] - ) + times = times.union([ + times[-1] + i * future_covariates.freq for i in range(1, min_lag + 1) + ]) # Can't create features for first `max_lag` times in series: times = times[max_lag:] # Case 2: elif (min_lag <= 0) and (max_lag <= 0): # Can create features for times before the start of `future_covariates`: - times = times.union( - [ - times[0] - i * future_covariates.freq - for i in range(1, abs(max_lag) + 1) - ] - ) + times = times.union([ + times[0] - i * future_covariates.freq + for i in range(1, abs(max_lag) + 1) + ]) # Can't create features for last `abs(min_lag)` times in series: times = times[:min_lag] if min_lag != 0 else times # Case 3: @@ -201,7 +203,14 @@ def get_feature_times_future( lags_future_combos = (*target_lag_combos, [0], [0, 1], [1, 3], [-2, 2]) ocl_combos = (1, 2, 5, 10) - def test_feature_times_training_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product( + ["datetime", "integer"], + [0, 1, 3], + ), + ) + def test_feature_times_training(self, config): """ Tests that `_get_feature_times` produces the same `times` output as that generated by using the various `get_feature_times_*` helper @@ -212,47 +221,22 @@ def test_feature_times_training_range_idx(self): with range time indices. """ # Define timeseries with different starting points, lengths, and frequencies: - target = linear_timeseries(start=1, length=20, freq=1) - past = linear_timeseries(start=2, length=25, freq=2) - future = linear_timeseries(start=3, length=30, freq=3) - for (lags, lags_past, lags_future, ocl) in product( - self.target_lag_combos, - self.lags_past_combos, - self.lags_future_combos, - self.ocl_combos, - ): - feature_times = _get_feature_times( - target_series=target, - past_covariates=past, - future_covariates=future, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - output_chunk_length=ocl, - is_training=True, + series_type, output_chunk_shift = config + if series_type == "integer": + target = linear_timeseries(start=1, length=20, freq=1) + past = linear_timeseries(start=2, length=25, freq=2) + future = linear_timeseries(start=3, length=30, freq=3) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=20, freq="1d" ) - target_expected = self.get_feature_times_target_training(target, lags, ocl) - past_expected = self.get_feature_times_past(past, lags_past) - future_expected = self.get_feature_times_future(future, lags_future) - assert target_expected.equals(feature_times[0]) - assert past_expected.equals(feature_times[1]) - assert future_expected.equals(feature_times[2]) - - def test_feature_times_training_datetime_idx(self): - """ - Tests that `_get_feature_times` produces the same `times` output as - that generated by using the various `get_feature_times_*` helper - functions defined in this module when `is_training = True`. Consistency - is checked over all of the combinations of parameter values specified by - `self.target_lag_combos`, `self.lags_past_combos`, `self.lags_future_combos` - and `self.max_samples_per_ts_combos`. This particular test uses timeseries - with datetime time indices. - """ - # Define timeseries with different starting points, lengths, and frequencies: - target = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=20, freq="1d") - past = linear_timeseries(start=pd.Timestamp("1/2/2000"), length=25, freq="2d") - future = linear_timeseries(start=pd.Timestamp("1/3/2000"), length=30, freq="3d") - for (lags, lags_past, lags_future, ocl) in product( + past = linear_timeseries( + start=pd.Timestamp("1/2/2000"), length=25, freq="2d" + ) + future = linear_timeseries( + start=pd.Timestamp("1/3/2000"), length=30, freq="3d" + ) + for lags, lags_past, lags_future, ocl in product( self.target_lag_combos, self.lags_past_combos, self.lags_future_combos, @@ -267,15 +251,25 @@ def test_feature_times_training_datetime_idx(self): lags_future_covariates=lags_future, output_chunk_length=ocl, is_training=True, + output_chunk_shift=output_chunk_shift, + ) + target_expected = self.get_feature_times_target_training( + target, lags, ocl, output_chunk_shift=output_chunk_shift ) - target_expected = self.get_feature_times_target_training(target, lags, ocl) past_expected = self.get_feature_times_past(past, lags_past) future_expected = self.get_feature_times_future(future, lags_future) assert target_expected.equals(feature_times[0]) assert past_expected.equals(feature_times[1]) assert future_expected.equals(feature_times[2]) - def test_feature_times_prediction_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product( + ["datetime", "integer"], + [0, 1, 3], + ), + ) + def test_feature_times_prediction(self, config): """ Tests that `_get_feature_times` produces the same `times` output as that generated by using the various `get_feature_times_*` helper @@ -286,43 +280,23 @@ def test_feature_times_prediction_range_idx(self): uses timeseries with range time indices. """ # Define timeseries with different starting points, lengths, and frequencies: - target = linear_timeseries(start=1, length=20, freq=1) - past = linear_timeseries(start=2, length=25, freq=2) - future = linear_timeseries(start=3, length=30, freq=3) - for (lags, lags_past, lags_future) in product( - self.target_lag_combos, self.lags_past_combos, self.lags_future_combos - ): - feature_times = _get_feature_times( - target_series=target, - past_covariates=past, - future_covariates=future, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future, - is_training=False, + series_type, output_chunk_shift = config + if series_type == "integer": + target = linear_timeseries(start=1, length=20, freq=1) + past = linear_timeseries(start=2, length=25, freq=2) + future = linear_timeseries(start=3, length=30, freq=3) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=20, freq="1d" + ) + past = linear_timeseries( + start=pd.Timestamp("1/2/2000"), length=25, freq="2d" + ) + future = linear_timeseries( + start=pd.Timestamp("1/3/2000"), length=30, freq="3d" ) - target_expected = self.get_feature_times_target_prediction(target, lags) - past_expected = self.get_feature_times_past(past, lags_past) - future_expected = self.get_feature_times_future(future, lags_future) - assert target_expected.equals(feature_times[0]) - assert past_expected.equals(feature_times[1]) - assert future_expected.equals(feature_times[2]) - def test_feature_times_prediction_datetime_idx(self): - """ - Tests that `_get_feature_times` produces the same `times` output as - that generated by using the various `get_feature_times_*` helper - functions defined in this module when `is_training = False` (i.e. when creaiting - prediction data). Consistency is checked over all of the combinations of parameter - values specified by `self.target_lag_combos`, `self.lags_past_combos`, - `self.lags_future_combos` and `self.max_samples_per_ts_combos`. This particular test - uses timeseries with datetime time indices. - """ - # Define timeseries with different starting points, lengths, and frequencies: - target = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=20, freq="1d") - past = linear_timeseries(start=pd.Timestamp("1/2/2000"), length=25, freq="2d") - future = linear_timeseries(start=pd.Timestamp("1/3/2000"), length=30, freq="3d") - for (lags, lags_past, lags_future) in product( + for lags, lags_past, lags_future in product( self.target_lag_combos, self.lags_past_combos, self.lags_future_combos ): feature_times = _get_feature_times( @@ -333,6 +307,7 @@ def test_feature_times_prediction_datetime_idx(self): lags_past_covariates=lags_past, lags_future_covariates=lags_future, is_training=False, + output_chunk_shift=output_chunk_shift, ) target_expected = self.get_feature_times_target_prediction(target, lags) past_expected = self.get_feature_times_past(past, lags_past) @@ -345,50 +320,45 @@ def test_feature_times_prediction_datetime_idx(self): # Specified Test Cases # - def test_feature_times_output_chunk_length_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [0, 1, 3]), + ) + def test_feature_times_output_chunk_length_output_chunk_shift(self, config): """ Tests that the last feature time for the `target_series` returned by `_get_feature_times` corresponds to - `output_chunk_length - 1` timesteps *before* the end of + `output_chunk_length - output_chunk_shift - 1` timesteps *before* the end of the target series; this is the last time point in `target_series` which has enough values in front of it to create a label. This particular test uses range time index series to check this behaviour. """ - target = linear_timeseries(start=0, length=20, freq=2) - # Test multiple `output_chunk_length` values: - for ocl in (1, 2, 3, 4, 5): - feature_times = _get_feature_times( - target_series=target, - lags=[-2, -3, -5], - output_chunk_length=ocl, - is_training=True, + series_type, output_chunk_shift = config + if series_type == "integer": + target = linear_timeseries(start=0, length=20, freq=2) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=20, freq="2d" ) - assert feature_times[0][-1] == target.end_time() - target.freq * (ocl - 1) - - def test_feature_times_output_chunk_length_datetime_idx(self): - """ - Tests that the last feature time for the `target_series` - returned by `_get_feature_times` when `is_training = True` - corresponds to the time that is `(output_chunk_length - 1)` - timesteps *before* the end of the target series; this is the - last time point in `target_series` which has enough values - in front of it to create a label. This particular test uses - datetime time index series to check this behaviour. - """ - target = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=20, freq="2d") # Test multiple `output_chunk_length` values: for ocl in (1, 2, 3, 4, 5): - # `is_training = True` feature_times = _get_feature_times( target_series=target, lags=[-2, -3, -5], output_chunk_length=ocl, is_training=True, + output_chunk_shift=output_chunk_shift, + ) + assert feature_times[0][-1] == target.end_time() - target.freq * ( + ocl + output_chunk_shift - 1 ) - assert feature_times[0][-1] == target.end_time() - target.freq * (ocl - 1) - def test_feature_times_lags_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [0, 1, 3]), + ) + def test_feature_times_lags(self, config): """ Tests that the first feature time for the `target_series` returned by `_get_feature_times` corresponds to the time @@ -398,30 +368,13 @@ def test_feature_times_lags_range_idx(self): to create a feature. This particular test uses range time index series to check this behaviour. """ - target = linear_timeseries(start=0, length=20, freq=2) - # Expect same behaviour when training and predicting: - for is_training in (False, True): - for max_lags in (-1, -2, -3, -4, -5): - feature_times = _get_feature_times( - target_series=target, - lags=[-1, max_lags], - is_training=is_training, - ) - assert feature_times[0][0] == target.start_time() + target.freq * abs( - max_lags - ) - - def test_feature_times_lags_datetime_idx(self): - """ - Tests that the first feature time for the `target_series` - returned by `_get_feature_times` corresponds to the time - that is `max_lags` timesteps *after* the start of - the target series; this is the first time point in - `target_series` which has enough values in preceeding it - to create a feature. This particular test uses datetime time - index series to check this behaviour. - """ - target = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=20, freq="2d") + series_type, output_chunk_shift = config + if series_type == "integer": + target = linear_timeseries(start=0, length=20, freq=2) + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=20, freq="2d" + ) # Expect same behaviour when training and predicting: for is_training in (False, True): for max_lags in (-1, -2, -3, -4, -5): @@ -429,65 +382,53 @@ def test_feature_times_lags_datetime_idx(self): target_series=target, lags=[-1, max_lags], is_training=is_training, + output_chunk_shift=output_chunk_shift, ) assert feature_times[0][0] == target.start_time() + target.freq * abs( max_lags ) - def test_feature_times_training_single_time_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [0, 1, 3]), + ) + def test_feature_times_training_single_time(self, config): """ Tests that `_get_feature_times` correctly handles case where only a single time can be used to create training features and labels. This particular test uses range index timeseries. """ # Can only create feature and label for time `1` (`-1` lag behind is time `0`): - target = linear_timeseries(start=0, length=2, freq=1) - lags = [-1] - feature_times = _get_feature_times( - target_series=target, - output_chunk_length=1, - lags=lags, - is_training=True, - ) - assert len(feature_times[0]) == 1 - assert feature_times[0][0] == 1 - - # Can only create feature for time `6` (`-2` lags behind is time `2`): - future = linear_timeseries(start=2, length=1, freq=2) - future_lags = [-2] - feature_times = _get_feature_times( - target_series=target, - future_covariates=future, - output_chunk_length=1, - lags=lags, - lags_future_covariates=future_lags, - is_training=True, - ) - assert len(feature_times[0]) == 1 - assert feature_times[0][0] == 1 - assert len(feature_times[2]) == 1 - assert feature_times[2][0] == 6 + series_type, output_chunk_shift = config + if series_type == "integer": + target = linear_timeseries(start=0, length=2 + output_chunk_shift, freq=1) + # Can only create feature for time `6` (`-2` lags behind is time `2`): + future = linear_timeseries(start=2, length=1, freq=2) + exp_start_target, exp_start_future = 1, 6 + else: + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=2 + output_chunk_shift, freq="d" + ) + # Can only create feature for "1/6/2000" (`-2` lags behind is "1/2/2000"): + future = linear_timeseries( + start=pd.Timestamp("1/2/2000"), length=1, freq="2d" + ) + exp_start_target, exp_start_future = ( + pd.Timestamp("1/2/2000"), + pd.Timestamp("1/6/2000"), + ) - def test_feature_times_training_single_time_datetime_idx(self): - """ - Tests that `_get_feature_times` correctly handles case where only - a single time can be used to create training features and labels. - This particular test uses datetime index timeseries. - """ - # Can only create feature and label for "1/2/2000" (`-1` lag behind is "1/1/2000"): - target = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=2, freq="d") lags = [-1] feature_times = _get_feature_times( target_series=target, output_chunk_length=1, lags=lags, is_training=True, + output_chunk_shift=output_chunk_shift, ) assert len(feature_times[0]) == 1 - assert feature_times[0][0] == pd.Timestamp("1/2/2000") + assert feature_times[0][0] == exp_start_target - # Can only create feature for "1/6/2000" (`-2` lags behind is "1/2/2000"): - future = linear_timeseries(start=pd.Timestamp("1/2/2000"), length=1, freq="2d") future_lags = [-2] feature_times = _get_feature_times( target_series=target, @@ -496,52 +437,45 @@ def test_feature_times_training_single_time_datetime_idx(self): lags=lags, lags_future_covariates=future_lags, is_training=True, + output_chunk_shift=output_chunk_shift, ) assert len(feature_times[0]) == 1 - assert feature_times[0][0] == pd.Timestamp("1/2/2000") + assert feature_times[0][0] == exp_start_target assert len(feature_times[2]) == 1 - assert feature_times[2][0] == pd.Timestamp("1/6/2000") + assert feature_times[2][0] == exp_start_future - def test_feature_times_prediction_single_time_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [0, 1, 3]), + ) + def test_feature_times_prediction_single_time(self, config): """ Tests that `_get_feature_times` correctly handles case where only a single time can be used to create prediction features. This particular test uses range index timeseries. """ - # Can only create feature for time `1` (`-1` lag behind is time `0`): - target = linear_timeseries(start=0, length=1, freq=1) - lags = [-1] - feature_times = _get_feature_times( - target_series=target, - lags=lags, - is_training=False, - ) - assert len(feature_times[0]) == 1 - assert feature_times[0][0] == 1 + series_type, output_chunk_shift = config + if series_type == "integer": + # Can only create feature for time `1` (`-1` lag behind is time `0`): + target = linear_timeseries(start=0, length=1, freq=1) + # Can only create feature for time `6` (`-2` lags behind is time `2`): + future = linear_timeseries(start=2, length=1, freq=2) + exp_start_target, exp_start_future = 1, 6 - # Can only create feature for time `6` (`-2` lags behind is time `2`): - future = linear_timeseries(start=2, length=1, freq=2) - lags_future = [-2] - feature_times = _get_feature_times( - target_series=target, - future_covariates=future, - lags=lags, - lags_future_covariates=lags_future, - is_training=False, - ) - assert len(feature_times[0]) == 1 - assert feature_times[0][0] == 1 - assert len(feature_times[2]) == 1 - assert feature_times[2][0] == 6 + else: + # Can only create feature for "1/2/2000" (`-1` lag behind is time "1/1/2000"): + target = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=1, freq="d" + ) + # Can only create feature for "1/6/2000" (`-2` lag behind is time "1/2/2000"): + future = linear_timeseries( + start=pd.Timestamp("1/2/2000"), length=1, freq="2d" + ) + exp_start_target, exp_start_future = ( + pd.Timestamp("1/2/2000"), + pd.Timestamp("1/6/2000"), + ) - def test_feature_times_prediction_single_time_datetime_idx(self): - """ - Tests that `_get_feature_times` correctly handles case where only - a single time can be used to create prediction features. - This particular test uses datetime index timeseries. - """ - # Can only create feature for "1/2/2000" (`-1` lag behind is time "1/1/2000"): - target = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=1, freq="d") lags = [-1] feature_times = _get_feature_times( target_series=target, @@ -549,10 +483,8 @@ def test_feature_times_prediction_single_time_datetime_idx(self): is_training=False, ) assert len(feature_times[0]) == 1 - assert feature_times[0][0] == pd.Timestamp("1/2/2000") + assert feature_times[0][0] == exp_start_target - # Can only create feature for "1/6/2000" (`-2` lag behind is time "1/2/2000"): - future = linear_timeseries(start=pd.Timestamp("1/2/2000"), length=1, freq="2d") lags_future = [-2] feature_times = _get_feature_times( target_series=target, @@ -560,13 +492,18 @@ def test_feature_times_prediction_single_time_datetime_idx(self): lags=lags, lags_future_covariates=lags_future, is_training=False, + output_chunk_shift=output_chunk_shift, ) assert len(feature_times[0]) == 1 - assert feature_times[0][0] == pd.Timestamp("1/2/2000") + assert feature_times[0][0] == exp_start_target assert len(feature_times[2]) == 1 - assert feature_times[2][0] == pd.Timestamp("1/6/2000") + assert feature_times[2][0] == exp_start_future - def test_feature_times_extend_time_index_range_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [0, 1, 3]), + ) + def test_feature_times_extend_time_index_range_idx(self, config): """ Tests that `_get_feature_times` is able to return feature times that occur after the end of a series or occur before @@ -574,50 +511,21 @@ def test_feature_times_extend_time_index_range_idx(self): index time series. """ # Feature times occur after end of series: - target = linear_timeseries(start=10, length=1, freq=3) - past = linear_timeseries(start=2, length=1, freq=2) - future = linear_timeseries(start=3, length=1, freq=1) - lags = lags_past = lags_future_1 = [-4] - feature_times = _get_feature_times( - target_series=target, - past_covariates=past, - future_covariates=future, - lags=lags, - lags_past_covariates=lags_past, - lags_future_covariates=lags_future_1, - is_training=False, - ) - assert len(feature_times[0]) == 1 - assert feature_times[0][0] == target.start_time() - lags[0] * target.freq - assert len(feature_times[1]) == 1 - assert feature_times[1][0] == past.start_time() - lags_past[0] * past.freq - assert len(feature_times[2]) == 1 - assert ( - feature_times[2][0] == future.start_time() - lags_future_1[0] * future.freq - ) - # Feature time occurs before start of series: - lags_future_2 = [4] - feature_times = _get_feature_times( - future_covariates=future, - lags_future_covariates=lags_future_2, - is_training=False, - ) - assert len(feature_times[2]) == 1 - assert ( - feature_times[2][0] == future.start_time() - lags_future_2[0] * future.freq - ) - - def test_feature_times_extend_time_index_datetime_idx(self): - """ - Tests that `_get_feature_times` is able to return feature - times that occur after the end of a series or occur before - the beginning of a series. This particular test uses datetime - index time series. - """ - # Feature times occur after end of series: - target = linear_timeseries(start=pd.Timestamp("1/10/2000"), length=1, freq="3d") - past = linear_timeseries(start=pd.Timestamp("1/2/2000"), length=1, freq="2d") - future = linear_timeseries(start=pd.Timestamp("1/3/2000"), length=1, freq="1d") + series_type, output_chunk_shift = config + if series_type == "integer": + target = linear_timeseries(start=10, length=1, freq=3) + past = linear_timeseries(start=2, length=1, freq=2) + future = linear_timeseries(start=3, length=1, freq=1) + else: + target = linear_timeseries( + start=pd.Timestamp("1/10/2000"), length=1, freq="3d" + ) + past = linear_timeseries( + start=pd.Timestamp("1/2/2000"), length=1, freq="2d" + ) + future = linear_timeseries( + start=pd.Timestamp("1/3/2000"), length=1, freq="1d" + ) lags = lags_past = lags_future_1 = [-4] feature_times = _get_feature_times( target_series=target, @@ -627,6 +535,7 @@ def test_feature_times_extend_time_index_datetime_idx(self): lags_past_covariates=lags_past, lags_future_covariates=lags_future_1, is_training=False, + output_chunk_shift=output_chunk_shift, ) assert len(feature_times[0]) == 1 assert feature_times[0][0] == target.start_time() - lags[0] * target.freq @@ -642,58 +551,18 @@ def test_feature_times_extend_time_index_datetime_idx(self): future_covariates=future, lags_future_covariates=lags_future_2, is_training=False, + output_chunk_shift=output_chunk_shift, ) assert len(feature_times[2]) == 1 assert ( feature_times[2][0] == future.start_time() - lags_future_2[0] * future.freq ) - def test_feature_times_future_lags_range_idx(self): - """ - Tests that `_get_feature_times` correctly handles the `lags_future_covariates` - argument for the following three cases: - 1. `lags_future_covariates` contains only `0` - 2. `lags_future_covariates` contains only a positive lag - 3. `lags_future_covariates` contains a combination of positive, - zero, and negative lags - This particular test uses range index timeseries. - """ - future = linear_timeseries(start=0, length=10, freq=2) - # Case 1 - Zero lag: - lags_future = [0] - feature_times = _get_feature_times( - future_covariates=future, - lags_future_covariates=lags_future, - is_training=False, - ) - # All times will be feature times: - assert len(feature_times[2]) == future.n_timesteps - assert feature_times[2].equals(future.time_index) - - # Case 2 - Positive lag: - lags_future = [1] - feature_times = _get_feature_times( - future_covariates=future, - lags_future_covariates=lags_future, - is_training=False, - ) - # Need to include new time at start of series; only last time will be excluded: - extended_future = future.prepend_values([0]) - assert len(feature_times[2]) == extended_future.n_timesteps - 1 - assert feature_times[2].equals(extended_future.time_index[:-1]) - - # Case 3 - Combo of negative, zero, and positive lags: - lags_future = [-1, 0, 1] - feature_times = _get_feature_times( - future_covariates=future, - lags_future_covariates=lags_future, - is_training=False, - ) - # Only first and last times will be excluded: - assert len(feature_times[2]) == future.n_timesteps - 2 - assert feature_times[2].equals(future.time_index[1:-1]) - - def test_feature_times_future_lags_datetime_idx(self): + @pytest.mark.parametrize( + "config", + itertools.product(["datetime", "integer"], [0, 1, 3]), + ) + def test_feature_times_future_lags(self, config): """ Tests that `_get_feature_times` correctly handles the `lags_future_covariates` argument for the following three cases: @@ -701,15 +570,21 @@ def test_feature_times_future_lags_datetime_idx(self): 2. `lags_future_covariates` contains only a positive lag 3. `lags_future_covariates` contains a combination of positive, zero, and negative lags - This particular test uses datetime index timeseries. """ - future = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=10, freq="2d") + series_type, output_chunk_shift = config + if series_type == "integer": + future = linear_timeseries(start=0, length=10, freq=2) + else: + future = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=10, freq="2d" + ) # Case 1 - Zero lag: lags_future = [0] feature_times = _get_feature_times( future_covariates=future, lags_future_covariates=lags_future, is_training=False, + output_chunk_shift=output_chunk_shift, ) # All times will be feature times: assert len(feature_times[2]) == future.n_timesteps @@ -721,6 +596,7 @@ def test_feature_times_future_lags_datetime_idx(self): future_covariates=future, lags_future_covariates=lags_future, is_training=False, + output_chunk_shift=output_chunk_shift, ) # Need to include new time at start of series; only last time will be excluded: extended_future = future.prepend_values([0]) @@ -827,7 +703,7 @@ def test_feature_times_unspecified_lag_or_series_warning(self): vice versa. The only circumstance under which a warning should *not* be issued is when `target_series` is specified, but `lags` is not when `is_training = True`; this is because - the user may not want to add auto-regressive features to `X`, + the user may not want to add autoregressive features to `X`, but they still need to specify `target_series` to create labels. """ # Define some arbitrary input values: @@ -1014,7 +890,7 @@ def test_feature_times_series_too_short_error(self): _get_feature_times(target_series=series, lags=[-20, -1], is_training=False) assert ( "`target_series` must have at least `-min(lags) + max(lags) + 1` = 20 " - "timesteps; instead, it only has 2." + "time steps; instead, it only has 2." ) == str(err.value) # `target_series` too short when training: with pytest.raises(ValueError) as err: @@ -1025,8 +901,8 @@ def test_feature_times_series_too_short_error(self): is_training=True, ) assert ( - "`target_series` must have at least `-min(lags) + output_chunk_length` = 25 " - "timesteps; instead, it only has 2." + "`target_series` must have at least `-min(lags) + output_chunk_length + output_chunk_shift` = 25 " + "time steps; instead, it only has 2." ) == str(err.value) # `past_covariates` too short when training: with pytest.raises(ValueError) as err: @@ -1039,7 +915,7 @@ def test_feature_times_series_too_short_error(self): ) assert ( "`past_covariates` must have at least " - "`-min(lags_past_covariates) + max(lags_past_covariates) + 1` = 20 timesteps; " + "`-min(lags_past_covariates) + max(lags_past_covariates) + 1` = 20 time steps; " "instead, it only has 2." ) == str(err.value) diff --git a/darts/tests/utils/tabularization/test_get_shared_times.py b/darts/tests/utils/tabularization/test_get_shared_times.py index 3f2b399734..dab53a8092 100644 --- a/darts/tests/utils/tabularization/test_get_shared_times.py +++ b/darts/tests/utils/tabularization/test_get_shared_times.py @@ -1,4 +1,4 @@ -from math import gcd +from math import lcm import pandas as pd import pytest @@ -7,31 +7,36 @@ from darts.utils.timeseries_generation import linear_timeseries -# math.lcm is not available in Python <= 3.8, so we define it here -def lcm(*integers): - a = integers[0] - for b in integers[1:]: - a = (a * b) // gcd(a, b) - return a - - class TestGetSharedTimes: - """ Tests `get_shared_times` function defined in `darts.utils.data.tabularization`. """ - def test_shared_times_equal_freq_range_idx(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_shared_times_equal_freq(self, series_type): """ - Tests that `get_shared_times` correctly handles range time - index series that are of equal frequency. + Tests that `get_shared_times` correctly handles time index series that are of equal frequency. """ # `series_1` begins before `series_2` does and ends # before `series_2` does, and `series_2` begins before # `series_3` does and ends before `series_3` does: - series_1 = linear_timeseries(start=1, end=11, freq=2) - series_2 = linear_timeseries(start=3, end=13, freq=2) - series_3 = linear_timeseries(start=5, end=15, freq=2) + if series_type == "integer": + series_1 = linear_timeseries(start=1, end=11, freq=2) + series_2 = linear_timeseries(start=3, end=13, freq=2) + series_3 = linear_timeseries(start=5, end=15, freq=2) + else: + series_1 = linear_timeseries( + start=pd.Timestamp("1/1/2000"), end=pd.Timestamp("1/11/2000"), freq="2d" + ) + series_2 = linear_timeseries( + start=pd.Timestamp("1/3/2000"), end=pd.Timestamp("1/13/2000"), freq="2d" + ) + series_3 = linear_timeseries( + start=pd.Timestamp("1/5/2000"), end=pd.Timestamp("1/15/2000"), freq="2d" + ) # Intersection of a single time index is just the original time index: assert series_1.time_index.equals(get_shared_times(series_1)) @@ -66,70 +71,40 @@ def test_shared_times_equal_freq_range_idx(self): get_shared_times(series_1, series_2, series_3) ) - def test_shared_times_equal_freq_datetime_idx(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_shared_times_unequal_freq(self, series_type): """ - Tests that `get_shared_times` correctly handles datetime time - index series that are of equal frequency. - """ - # `series_1` begins before `series_2` does and ends - # before `series_2` does, and `series_2` begins before - # `series_3` does and ends before `series_3` does: - series_1 = linear_timeseries( - start=pd.Timestamp("1/1/2000"), end=pd.Timestamp("1/11/2000"), freq="2d" - ) - series_2 = linear_timeseries( - start=pd.Timestamp("1/3/2000"), end=pd.Timestamp("1/13/2000"), freq="2d" - ) - series_3 = linear_timeseries( - start=pd.Timestamp("1/5/2000"), end=pd.Timestamp("1/15/2000"), freq="2d" - ) - - # Intersection of a single time index is just the original time index: - assert series_1.time_index.equals(get_shared_times(series_1)) - assert series_2.time_index.equals(get_shared_times(series_2)) - assert series_3.time_index.equals(get_shared_times(series_3)) - - # Intersection of two time indices begins at start time of later series - # and stops at end time of earlier series. - # Since `series_1` is before `series_2`: - expected_12 = linear_timeseries( - start=series_2.start_time(), end=series_1.end_time(), freq=series_1.freq - ) - assert expected_12.time_index.equals(get_shared_times(series_1, series_2)) - # Since `series_2` is before `series_3`: - expected_23 = linear_timeseries( - start=series_3.start_time(), end=series_2.end_time(), freq=series_2.freq - ) - assert expected_23.time_index.equals(get_shared_times(series_2, series_3)) - # Since `series_1` is before `series_3`: - expected_13 = linear_timeseries( - start=series_3.start_time(), end=series_1.end_time(), freq=series_1.freq - ) - assert expected_13.time_index.equals(get_shared_times(series_1, series_3)) - - # Intersection of all three time series should begin at start of series_3 (i.e. - # the last series to begin) and end at the end of series_1 (i.e. the first series - # to end): - expected_123 = linear_timeseries( - start=series_3.start_time(), end=series_1.end_time(), freq=series_1.freq - ) - assert expected_123.time_index.equals( - get_shared_times(series_1, series_2, series_3) - ) - - def test_shared_times_unequal_freq_range_idx(self): - """ - Tests that `get_shared_times` correctly handles range time - index series that are of different frequencies. + Tests that `get_shared_times` correctly handles time index series that are of different frequencies. """ # `series_1` begins before `series_2` does and ends # before `series_2` does, and `series_2` begins before # `series_3` does and ends before `series_3` does. Each # series is of a different frequency: - series_1 = linear_timeseries(start=1, end=11, freq=1) - series_2 = linear_timeseries(start=3, end=13, freq=2) - series_3 = linear_timeseries(start=5, end=17, freq=3) - + if series_type == "integer": + series_1 = linear_timeseries(start=1, end=11, freq=1) + series_2 = linear_timeseries(start=3, end=13, freq=2) + series_3 = linear_timeseries(start=5, end=17, freq=3) + freq_12 = lcm(series_1.freq, series_2.freq) + freq_23 = lcm(series_2.freq, series_3.freq) + freq_13 = lcm(series_1.freq, series_3.freq) + freq_123 = lcm(series_1.freq, series_2.freq, series_3.freq) + else: + series_1 = linear_timeseries( + start=pd.Timestamp("1/1/2000"), end=pd.Timestamp("1/11/2000"), freq="2d" + ) + series_2 = linear_timeseries( + start=pd.Timestamp("1/3/2000"), end=pd.Timestamp("1/13/2000"), freq="2d" + ) + series_3 = linear_timeseries( + start=pd.Timestamp("1/5/2000"), end=pd.Timestamp("1/15/2000"), freq="2d" + ) + freq_12 = f"{lcm(series_1.freq.n, series_2.freq.n)}d" + freq_23 = f"{lcm(series_2.freq.n, series_3.freq.n)}d" + freq_13 = f"{lcm(series_1.freq.n, series_3.freq.n)}d" + freq_123 = f"{lcm(series_1.freq.n, series_2.freq.n, series_3.freq.n)}d" # Intersection of a single time index is just the original time index: assert series_1.time_index.equals(get_shared_times(series_1)) assert series_2.time_index.equals(get_shared_times(series_2)) @@ -140,84 +115,6 @@ def test_shared_times_unequal_freq_range_idx(self): # is the lowest common multiple between the frequencies of the two series: # `series_1` is before `series_2`: - expected_12 = linear_timeseries( - start=series_2.start_time(), - end=series_1.end_time(), - freq=lcm(series_1.freq, series_2.freq), - ) - # `linear_timeseries` may have added point beyond specified `end`; - # remove this point if present: - if expected_12.time_index[-1] > series_1.end_time(): - expected_12 = expected_12.drop_after(expected_12.time_index[-1]) - assert expected_12.time_index.equals(get_shared_times(series_1, series_2)) - # `series_2` is before `series_3`: - expected_23 = linear_timeseries( - start=series_3.start_time(), - end=series_2.end_time(), - freq=lcm(series_2.freq, series_3.freq), - ) - # `linear_timeseries` may have added point beyond specified `end`; - # remove this point if present: - if expected_23.time_index[-1] > series_2.end_time(): - expected_23 = expected_23.drop_after(expected_23.time_index[-1]) - assert expected_23.time_index.equals(get_shared_times(series_2, series_3)) - # `series_1` is before `series_3`: - expected_13 = linear_timeseries( - start=series_3.start_time(), - end=series_1.end_time(), - freq=lcm(series_1.freq, series_3.freq), - ) - # `linear_timeseries` may have added point beyond specified `end`; - # remove this point if present: - if expected_13.time_index[-1] > series_1.end_time(): - expected_13 = expected_13.drop_after(expected_13.time_index[-1]) - assert expected_13.time_index.equals(get_shared_times(series_1, series_3)) - - # Intersection of all three time series should begin at start of series_3 (i.e. - # the last series to begin) and end at the end of series_1 (i.e. the first series - # to end). The frequency of the intersection should be the lowest common multiple - # shared by all three frequencies: - expected_123 = linear_timeseries( - start=series_3.start_time(), - end=series_1.end_time(), - freq=lcm(series_1.freq, series_2.freq, series_3.freq), - ) - if expected_123.time_index[-1] > series_1.end_time(): - expected_123 = expected_123.drop_after(expected_123.time_index[-1]) - assert expected_123.time_index.equals( - get_shared_times(series_1, series_2, series_3) - ) - - def test_shared_times_unequal_freq_datetime_idx(self): - """ - Tests that `get_shared_times` correctly handles range time - index series that are of different frequencies. - """ - # `series_1` begins before `series_2` does and ends - # before `series_2` does, and `series_2` begins before - # `series_3` does and ends before `series_3` does. Each - # series is of a different frequency: - series_1 = linear_timeseries( - start=pd.Timestamp("1/1/2000"), end=pd.Timestamp("1/11/2000"), freq="2d" - ) - series_2 = linear_timeseries( - start=pd.Timestamp("1/3/2000"), end=pd.Timestamp("1/13/2000"), freq="2d" - ) - series_3 = linear_timeseries( - start=pd.Timestamp("1/5/2000"), end=pd.Timestamp("1/15/2000"), freq="2d" - ) - - # Intersection of a single time index is just the original time index: - assert series_1.time_index.equals(get_shared_times(series_1)) - assert series_2.time_index.equals(get_shared_times(series_2)) - assert series_3.time_index.equals(get_shared_times(series_3)) - - # Intersection of two time indices begins at start time of later series - # and stops at end time of earlier series. The frequency of the intersection - # is the lowest common multiple between the frequencies of the two series: - - # `series_1` is before `series_2`: - freq_12 = f"{lcm(series_1.freq.n, series_2.freq.n)}d" expected_12 = linear_timeseries( start=series_2.start_time(), end=series_1.end_time(), @@ -229,7 +126,6 @@ def test_shared_times_unequal_freq_datetime_idx(self): expected_12 = expected_12.drop_after(expected_12.time_index[-1]) assert expected_12.time_index.equals(get_shared_times(series_1, series_2)) # `series_2` is before `series_3`: - freq_23 = f"{lcm(series_2.freq.n, series_3.freq.n)}d" expected_23 = linear_timeseries( start=series_3.start_time(), end=series_2.end_time(), @@ -241,7 +137,6 @@ def test_shared_times_unequal_freq_datetime_idx(self): expected_23 = expected_23.drop_after(expected_23.time_index[-1]) assert expected_23.time_index.equals(get_shared_times(series_2, series_3)) # `series_1` is before `series_3`: - freq_13 = f"{lcm(series_1.freq.n, series_3.freq.n)}d" expected_13 = linear_timeseries( start=series_3.start_time(), end=series_1.end_time(), @@ -257,7 +152,6 @@ def test_shared_times_unequal_freq_datetime_idx(self): # the last series to begin) and end at the end of series_1 (i.e. the first series # to end). The frequency of the intersection should be the lowest common multiple # shared by all three frequencies: - freq_123 = f"{lcm(series_1.freq.n, series_2.freq.n, series_3.freq.n)}d" expected_123 = linear_timeseries( start=series_3.start_time(), end=series_1.end_time(), @@ -269,84 +163,72 @@ def test_shared_times_unequal_freq_datetime_idx(self): get_shared_times(series_1, series_2, series_3) ) - def test_shared_times_no_overlap_range_idx(self): - """ - Tests that `get_shared_times` returns `None` when - supplied range time index series share no temporal overlap. - """ - # Define `series_2` so that it starts after `series_1` ends: - series_1 = linear_timeseries(start=1, end=11, freq=2) - series_2 = linear_timeseries(start=series_1.end_time() + 1, length=5, freq=3) - assert get_shared_times(series_1, series_2) is None - assert get_shared_times(series_1, series_1, series_2) is None - assert get_shared_times(series_1, series_2, series_2) is None - assert get_shared_times(series_1, series_1, series_2, series_2) is None - - def test_shared_times_no_overlap_datetime_idx(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_shared_times_no_overlap(self, series_type): """ - Tests that `get_shared_times` returns `None` when - supplied datetime time index series share no temporal overlap. + Tests that `get_shared_times` returns `None` when supplied time index series share no temporal overlap. """ # Define `series_2` so that it starts after `series_1` ends: - series_1 = linear_timeseries( - start=pd.Timestamp("1/1/2000"), end=pd.Timestamp("1/11/2000"), freq="2d" - ) - series_2 = linear_timeseries( - start=series_1.end_time() + pd.Timedelta(1, "d"), length=5, freq="3d" - ) + if series_type == "integer": + series_1 = linear_timeseries(start=1, end=11, freq=2) + series_2 = linear_timeseries( + start=series_1.end_time() + 1, length=5, freq=3 + ) + else: + series_1 = linear_timeseries( + start=pd.Timestamp("1/1/2000"), end=pd.Timestamp("1/11/2000"), freq="2d" + ) + series_2 = linear_timeseries( + start=series_1.end_time() + pd.Timedelta(1, "d"), length=5, freq="3d" + ) assert get_shared_times(series_1, series_2) is None assert get_shared_times(series_1, series_1, series_2) is None assert get_shared_times(series_1, series_2, series_2) is None assert get_shared_times(series_1, series_1, series_2, series_2) is None - def test_shared_times_single_time_point_overlap_range_idx(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_shared_times_single_time_point_overlap(self, series_type): """ - Tests that `get_shared_times` returns correct bounds when - given range index series that overlap at a single time point. + Tests that `get_shared_times` returns correct bounds when given time index series that overlap + at a single time point. """ # `series_1` and `series_2` only overlap at `series_1.end_time()`: - series_1 = linear_timeseries(start=1, end=11, freq=2) - series_2 = linear_timeseries(start=series_1.end_time(), length=5, freq=3) + if series_type == "integer": + series_1 = linear_timeseries(start=1, end=11, freq=2) + series_2 = linear_timeseries(start=series_1.end_time(), length=5, freq=3) + else: + series_1 = linear_timeseries( + start=pd.Timestamp("1/1/2000"), end=pd.Timestamp("1/11/2000"), freq="2d" + ) + series_2 = linear_timeseries(start=series_1.end_time(), length=5, freq="3d") overlap_val = series_1.end_time() assert get_shared_times(series_1, series_2) == overlap_val assert get_shared_times(series_1, series_1, series_2) == overlap_val assert get_shared_times(series_1, series_2, series_2) == overlap_val assert get_shared_times(series_1, series_1, series_2, series_2) == overlap_val - def test_shared_times_single_time_point_overlap_datetime_idx(self): - """ - Tests that `get_shared_times` returns correct bounds when - given datetime index series that overlap at a single time point. - """ - # `series_1` and `series_2` only overlap at `series_1.end_time()`: - series_1 = linear_timeseries( - start=pd.Timestamp("1/1/2000"), end=pd.Timestamp("1/11/2000"), freq="2d" - ) - series_2 = linear_timeseries(start=series_1.end_time(), length=5, freq="3d") - overlap_val = series_1.end_time() - assert get_shared_times(series_1, series_2) == overlap_val - assert get_shared_times(series_1, series_1, series_2) == overlap_val - assert get_shared_times(series_1, series_2, series_2) == overlap_val - assert get_shared_times(series_1, series_1, series_2, series_2) == overlap_val - - def test_shared_times_identical_inputs_range_idx(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_shared_times_identical_inputs(self, series_type): """ Tests that `get_shared_times` correctly handles case where - multiple copies of same range index timeseries is passed; + multiple copies of same time index timeseries is passed; we expect that the unaltered time index of the series is returned. """ - series = linear_timeseries(start=0, length=5, freq=1) - assert series.time_index.equals(get_shared_times(series)) - assert series.time_index.equals(get_shared_times(series, series)) - assert series.time_index.equals(get_shared_times(series, series, series)) - - def test_shared_times_identical_inputs_datetime_idx(self): - """ - Tests that `get_shared_times` correctly handles case where - multiple copies of same datetime index timeseries is passed; - we expect that the unaltered time index of the series is returned. - """ - series = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=5, freq="d") + if series_type == "integer": + series = linear_timeseries(start=0, length=5, freq=1) + else: + series = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=5, freq="d" + ) assert series.time_index.equals(get_shared_times(series)) assert series.time_index.equals(get_shared_times(series, series)) assert series.time_index.equals(get_shared_times(series, series, series)) diff --git a/darts/tests/utils/tabularization/test_get_shared_times_bounds.py b/darts/tests/utils/tabularization/test_get_shared_times_bounds.py index 7435457021..c56ecdec85 100644 --- a/darts/tests/utils/tabularization/test_get_shared_times_bounds.py +++ b/darts/tests/utils/tabularization/test_get_shared_times_bounds.py @@ -10,29 +10,26 @@ class TestGetSharedTimesBounds: Tests `get_shared_times_bounds` function defined in `darts.utils.data.tabularization`. """ - def test_shared_times_bounds_overlapping_range_idx_series(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_shared_times_bounds_overlapping_range_idx_series(self, series_type): """ Tests that `get_shared_times_bounds` correctly computes bounds - of two overlapping range index timeseries. + of two overlapping time index timeseries. """ # Defined so `series_1` starts and ends before `series_2` does: - series_1 = linear_timeseries(start=1, end=15, freq=3) - series_2 = linear_timeseries(start=2, end=20, freq=2) - expected_bounds = (series_2.start_time(), series_1.end_time()) - assert get_shared_times_bounds(series_1, series_2) == expected_bounds - - def test_shared_times_bounds_overlapping_datetime_idx_series(self): - """ - Tests that `get_shared_times_bounds` correctly computes bounds - of two overlapping datetime index timeseries. - """ - # Defined so `series_1` starts and ends before `series_2` does: - series_1 = linear_timeseries( - start=pd.Timestamp("1/1/2000"), end=pd.Timestamp("1/15/2000"), freq="3d" - ) - series_2 = linear_timeseries( - start=pd.Timestamp("1/2/2000"), end=pd.Timestamp("1/20/2000"), freq="2d" - ) + if series_type == "integer": + series_1 = linear_timeseries(start=1, end=15, freq=3) + series_2 = linear_timeseries(start=2, end=20, freq=2) + else: + series_1 = linear_timeseries( + start=pd.Timestamp("1/1/2000"), end=pd.Timestamp("1/15/2000"), freq="3d" + ) + series_2 = linear_timeseries( + start=pd.Timestamp("1/2/2000"), end=pd.Timestamp("1/20/2000"), freq="2d" + ) expected_bounds = (series_2.start_time(), series_1.end_time()) assert get_shared_times_bounds(series_1, series_2) == expected_bounds @@ -66,43 +63,25 @@ def test_shared_times_bounds_time_idx_inputs(self): == expected_bounds ) - def test_shared_times_bounds_subset_series_range_idx(self): - """ - Tests that `get_shared_times_bounds` correctly handles case where - the provided series are formed by taking successive subsets of an - initial series (i.e. `series_2` is formed by taking a subset of - `series_1`, and `series_3` is formed by taking a subset of `series_2`). - In such cases, the bounds are simply the start and end times of the - shortest series. This particular test uses range index series to - check this behaviour. - """ - series = linear_timeseries(start=0, length=10, freq=3) - subseries = ( - series.copy() - .drop_after(series.time_index[-1]) - .drop_before(series.time_index[1]) - ) - subsubseries = ( - subseries.copy() - .drop_after(subseries.time_index[-1]) - .drop_before(subseries.time_index[1]) - ) - expected_bounds = (subsubseries.start_time(), subsubseries.end_time()) - assert ( - get_shared_times_bounds(series, subseries, subsubseries) == expected_bounds - ) - - def test_shared_times_bounds_subset_series_datetime_idx(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_shared_times_bounds_subset_series(self, series_type): """ Tests that `get_shared_times_bounds` correctly handles case where the provided series are formed by taking successive subsets of an initial series (i.e. `series_2` is formed by taking a subset of `series_1`, and `series_3` is formed by taking a subset of `series_2`). In such cases, the bounds are simply the start and end times of the - shortest series. This particular test uses datetime index series to - check this behaviour. - """ - series = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=10, freq="3d") + shortest series. + """ + if series_type == "integer": + series = linear_timeseries(start=0, length=10, freq=3) + else: + series = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=10, freq="3d" + ) subseries = ( series.copy() .drop_after(series.time_index[-1]) @@ -118,28 +97,23 @@ def test_shared_times_bounds_subset_series_datetime_idx(self): get_shared_times_bounds(series, subseries, subsubseries) == expected_bounds ) - def test_shared_times_bounds_identical_inputs_range_idx(self): - """ - Tests that `get_shared_times_bounds` correctly handles case where - multiple copies of the same series is passed as an input; we expect - the return bounds to just be the start and end times of that repeated - series. This particular test uses range index series to - check this behaviour. - """ - series = linear_timeseries(start=0, length=5, freq=1) - expected = (series.start_time(), series.end_time()) - assert get_shared_times_bounds(series, series) == expected - assert get_shared_times_bounds(series, series, series) == expected - - def test_shared_times_bounds_identical_inputs_datetime_idx(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_shared_times_bounds_identical_inputs(self, series_type): """ Tests that `get_shared_times_bounds` correctly handles case where multiple copies of the same series is passed as an input; we expect the return bounds to just be the start and end times of that repeated - series. This particular test uses datetime index series to - check this behaviour. - """ - series = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=5, freq="d") + series. + """ + if series_type == "integer": + series = linear_timeseries(start=0, length=5, freq=1) + else: + series = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=5, freq="d" + ) expected = (series.start_time(), series.end_time()) assert get_shared_times_bounds(series) == expected assert get_shared_times_bounds(series, series) == expected @@ -164,77 +138,63 @@ def test_shared_times_bounds_unspecified_inputs(self): assert get_shared_times_bounds(None) is None assert get_shared_times_bounds(None, None, None) is None - def test_shared_times_bounds_single_idx_overlap_range_idx(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_shared_times_bounds_single_idx_overlap(self, series_type): """ Tests that `get_shared_times_bounds` correctly handles cases - where the bounds contains a single time index value. This - particular test uses range time index series to check this - behaviour. + where the bounds contains a single time index value. """ # Pass multiple copies of timeseries with single time # value - bounds should be start time and end time of # this single-valued series: - series = linear_timeseries(start=0, length=1, freq=1) - assert get_shared_times_bounds(series, series) == ( - series.start_time(), - series.end_time(), - ) # `series_1` and `series_2` share only a single overlap point # at the end of `series_1`: - series_1 = linear_timeseries(start=0, length=3, freq=1) - series_2 = linear_timeseries(start=series_1.end_time(), length=2, freq=2) - assert get_shared_times_bounds(series_1, series_2) == ( - series_1.end_time(), - series_2.start_time(), - ) - - def test_shared_times_bounds_single_idx_overlap_datetime_idx(self): - """ - Tests that `get_shared_times_bounds` correctly handles cases - where the bounds contains a single time index value. This - particular test uses range time index series to check this - behaviour. - """ - # Pass multiple copies of timeseries with single time - # value - bounds should be start time and end time of - # this single-valued series: - series = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=1, freq="d") + if series_type == "integer": + series = linear_timeseries(start=0, length=1, freq=1) + series_1 = linear_timeseries(start=0, length=3, freq=1) + series_2 = linear_timeseries(start=series_1.end_time(), length=2, freq=2) + else: + series = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=1, freq="d" + ) + series_1 = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=3, freq="d" + ) + series_2 = linear_timeseries(start=series_1.end_time(), length=2, freq="2d") assert get_shared_times_bounds(series, series) == ( series.start_time(), series.end_time(), ) - # `series_1` and `series_2` share only a single overlap point - # at the end of `series_1`: - series_1 = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=3, freq="d") - series_2 = linear_timeseries(start=series_1.end_time(), length=2, freq="2d") assert get_shared_times_bounds(series_1, series_2) == ( series_1.end_time(), series_2.start_time(), ) - def test_shared_times_bounds_no_overlap_range_idx(self): + @pytest.mark.parametrize( + "series_type", + ["datetime", "integer"], + ) + def test_shared_times_bounds_no_overlap(self, series_type): """ Tests that `get_shared_times_bounds` returns `None` when provided - with two series that share no overlap. This particular test uses - range index series to check this behaviour. + with two series that share no overlap. """ # Have `series_2` begin after the end of `series_1`: - series_1 = linear_timeseries(start=0, length=5, freq=1) - series_2 = linear_timeseries(start=series_1.end_time() + 1, length=6, freq=2) - assert get_shared_times_bounds(series_1, series_2) is None - assert get_shared_times_bounds(series_2, series_1, series_2) is None - - def test_shared_times_bounds_no_overlap_datetime_idx(self): - """ - Tests that `get_shared_times_bounds` returns `None` when provided - with two series that share no overlap. This particular test uses - datetime index series to check this behaviour. - """ - # Have `series_2` begin after the end of `series_1`: - series_1 = linear_timeseries(start=pd.Timestamp("1/1/2000"), length=5, freq="d") - series_2 = linear_timeseries( - start=series_1.end_time() + pd.Timedelta("1d"), length=6, freq="2d" - ) + if series_type == "integer": + series_1 = linear_timeseries(start=0, length=5, freq=1) + series_2 = linear_timeseries( + start=series_1.end_time() + 1, length=6, freq=2 + ) + else: + series_1 = linear_timeseries( + start=pd.Timestamp("1/1/2000"), length=5, freq="d" + ) + series_2 = linear_timeseries( + start=series_1.end_time() + pd.Timedelta("1d"), length=6, freq="2d" + ) assert get_shared_times_bounds(series_1, series_2) is None assert get_shared_times_bounds(series_2, series_1, series_2) is None diff --git a/darts/tests/utils/tabularization/test_strided_moving_window.py b/darts/tests/utils/tabularization/test_strided_moving_window.py index 164e9bea94..9bad422d7f 100644 --- a/darts/tests/utils/tabularization/test_strided_moving_window.py +++ b/darts/tests/utils/tabularization/test_strided_moving_window.py @@ -7,7 +7,6 @@ class TestStridedMovingWindow: - """ Tests `strided_moving_window` function defined in `darts.utils.data.tabularization`. """ @@ -28,10 +27,12 @@ def test_strided_moving_windows_extracted_windows(self): # Create a 'dummy input' with linearly increasing values: x_shape = (10, 8, 12) x = np.arange(np.prod(x_shape)).reshape(*x_shape) - for (axis, stride, window_len) in product( + for axis, stride, window_len in product( axis_combos, stride_combos, window_len_combos ): - windows = strided_moving_window(x, window_len, stride, axis) + windows = strided_moving_window( + x=x, window_len=window_len, stride=stride, axis=axis + ) # Iterate over extracted windows: for i in range(windows.shape[axis]): # All of the extract windows are found along the `axis` dimension; shift diff --git a/darts/tests/utils/test_likelihood_models.py b/darts/tests/utils/test_likelihood_models.py index 3c0dd67bc9..cd6e4ee4ec 100644 --- a/darts/tests/utils/test_likelihood_models.py +++ b/darts/tests/utils/test_likelihood_models.py @@ -1,66 +1,64 @@ from itertools import combinations -from darts.logging import get_logger +import pytest -logger = get_logger(__name__) +from darts.tests.conftest import TORCH_AVAILABLE -try: - from darts.utils.likelihood_models import ( - BetaLikelihood, - CauchyLikelihood, - ExponentialLikelihood, - GaussianLikelihood, - PoissonLikelihood, - QuantileRegression, - WeibullLikelihood, +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, ) +from darts.utils.likelihood_models import ( + BetaLikelihood, + CauchyLikelihood, + ExponentialLikelihood, + GaussianLikelihood, + PoissonLikelihood, + QuantileRegression, + WeibullLikelihood, +) - likelihood_models = { - "quantile": [QuantileRegression(), QuantileRegression([0.25, 0.5, 0.75])], - "gaussian": [ - GaussianLikelihood(prior_mu=0, prior_sigma=1), - GaussianLikelihood(prior_mu=10, prior_sigma=1), - ], - "exponential": [ - ExponentialLikelihood(prior_lambda=0.1), - ExponentialLikelihood(prior_lambda=0.5), - ], - "poisson": [ - PoissonLikelihood(prior_lambda=2), - PoissonLikelihood(prior_lambda=5), - ], - "cauchy": [ - CauchyLikelihood(prior_xzero=-0.4, prior_gamma=2), - CauchyLikelihood(prior_xzero=3, prior_gamma=2), - ], - "weibull": [ - WeibullLikelihood(prior_strength=1.0), - WeibullLikelihood(prior_strength=0.8), - ], - "beta": [ - BetaLikelihood(prior_alpha=0.2, prior_beta=0.4, prior_strength=0.3), - BetaLikelihood(prior_alpha=0.2, prior_beta=0.4, prior_strength=0.6), - ], - } +likelihood_models = { + "quantile": [QuantileRegression(), QuantileRegression([0.25, 0.5, 0.75])], + "gaussian": [ + GaussianLikelihood(prior_mu=0, prior_sigma=1), + GaussianLikelihood(prior_mu=10, prior_sigma=1), + ], + "exponential": [ + ExponentialLikelihood(prior_lambda=0.1), + ExponentialLikelihood(prior_lambda=0.5), + ], + "poisson": [ + PoissonLikelihood(prior_lambda=2), + PoissonLikelihood(prior_lambda=5), + ], + "cauchy": [ + CauchyLikelihood(prior_xzero=-0.4, prior_gamma=2), + CauchyLikelihood(prior_xzero=3, prior_gamma=2), + ], + "weibull": [ + WeibullLikelihood(prior_strength=1.0), + WeibullLikelihood(prior_strength=0.8), + ], + "beta": [ + BetaLikelihood(prior_alpha=0.2, prior_beta=0.4, prior_strength=0.3), + BetaLikelihood(prior_alpha=0.2, prior_beta=0.4, prior_strength=0.6), + ], +} - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. LikelihoodModels tests will be skipped.") - TORCH_AVAILABLE = False -if TORCH_AVAILABLE: +class TestLikelihoodModel: + def test_intra_class_equality(self): + for _, model_pair in likelihood_models.items(): + assert model_pair[0] == model_pair[0] + assert model_pair[1] == model_pair[1] + assert model_pair[0] != model_pair[1] - class TestLikelihoodModel: - def test_intra_class_equality(self): - for _, model_pair in likelihood_models.items(): - assert model_pair[0] == model_pair[0] - assert model_pair[1] == model_pair[1] - assert model_pair[0] != model_pair[1] - - def test_inter_class_equality(self): - model_combinations = combinations(likelihood_models.keys(), 2) - for (first_model_name, second_model_name) in model_combinations: - assert ( - likelihood_models[first_model_name][0] - != likelihood_models[second_model_name][0] - ) + def test_inter_class_equality(self): + model_combinations = combinations(likelihood_models.keys(), 2) + for first_model_name, second_model_name in model_combinations: + assert ( + likelihood_models[first_model_name][0] + != likelihood_models[second_model_name][0] + ) diff --git a/darts/tests/utils/test_losses.py b/darts/tests/utils/test_losses.py index 329ae45dbc..dff6398dc4 100644 --- a/darts/tests/utils/test_losses.py +++ b/darts/tests/utils/test_losses.py @@ -1,59 +1,52 @@ -from darts.logging import get_logger - -logger = get_logger(__name__) - -try: - import torch - - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. Loss tests will be skipped.") - TORCH_AVAILABLE = False - - -if TORCH_AVAILABLE: - from darts.utils.losses import MAELoss, MapeLoss, SmapeLoss - - class TestLosses: - x = torch.tensor([1.1, 2.2, 0.6345, -1.436]) - y = torch.tensor([1.5, 0.5]) - - def helper_test_loss(self, exp_loss_val, exp_w_grad, loss_fn): - W = torch.tensor([[0.1, -0.2, 0.3, -0.4], [-0.8, 0.7, -0.6, 0.5]]) - W.requires_grad = True - y_hat = W @ self.x - lval = loss_fn(y_hat, self.y) - lval.backward() - - assert torch.allclose(lval, exp_loss_val, atol=1e-3) - assert torch.allclose(W.grad, exp_w_grad, atol=1e-3) - - def test_smape_loss(self): - exp_val = torch.tensor(0.7753) - exp_grad = torch.tensor( - [ - [-0.2843, -0.5685, -0.1640, 0.3711], - [-0.5859, -1.1718, -0.3380, 0.7649], - ] - ) - self.helper_test_loss(exp_val, exp_grad, SmapeLoss()) - - def test_mape_loss(self): - exp_val = torch.tensor(1.2937) - exp_grad = torch.tensor( - [ - [-0.3667, -0.7333, -0.2115, 0.4787], - [-1.1000, -2.2000, -0.6345, 1.4360], - ] - ) - self.helper_test_loss(exp_val, exp_grad, MapeLoss()) - - def test_mae_loss(self): - exp_val = torch.tensor(1.0020) - exp_grad = torch.tensor( - [ - [-0.5500, -1.1000, -0.3173, 0.7180], - [-0.5500, -1.1000, -0.3173, 0.7180], - ] - ) - self.helper_test_loss(exp_val, exp_grad, MAELoss()) +import pytest + +from darts.tests.conftest import TORCH_AVAILABLE + +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) + +import torch + +from darts.utils.losses import MAELoss, MapeLoss, SmapeLoss + + +class TestLosses: + x = torch.tensor([1.1, 2.2, 0.6345, -1.436]) + y = torch.tensor([1.5, 0.5]) + + def helper_test_loss(self, exp_loss_val, exp_w_grad, loss_fn): + W = torch.tensor([[0.1, -0.2, 0.3, -0.4], [-0.8, 0.7, -0.6, 0.5]]) + W.requires_grad = True + y_hat = W @ self.x + lval = loss_fn(y_hat, self.y) + lval.backward() + + assert torch.allclose(lval, exp_loss_val, atol=1e-3) + assert torch.allclose(W.grad, exp_w_grad, atol=1e-3) + + def test_smape_loss(self): + exp_val = torch.tensor(0.7753) + exp_grad = torch.tensor([ + [-0.2843, -0.5685, -0.1640, 0.3711], + [-0.5859, -1.1718, -0.3380, 0.7649], + ]) + self.helper_test_loss(exp_val, exp_grad, SmapeLoss()) + + def test_mape_loss(self): + exp_val = torch.tensor(1.2937) + exp_grad = torch.tensor([ + [-0.3667, -0.7333, -0.2115, 0.4787], + [-1.1000, -2.2000, -0.6345, 1.4360], + ]) + self.helper_test_loss(exp_val, exp_grad, MapeLoss()) + + def test_mae_loss(self): + exp_val = torch.tensor(1.0020) + exp_grad = torch.tensor([ + [-0.5500, -1.1000, -0.3173, 0.7180], + [-0.5500, -1.1000, -0.3173, 0.7180], + ]) + self.helper_test_loss(exp_val, exp_grad, MAELoss()) diff --git a/darts/tests/utils/test_missing_values.py b/darts/tests/utils/test_missing_values.py index 5df8a1cf2e..246c64bcd6 100644 --- a/darts/tests/utils/test_missing_values.py +++ b/darts/tests/utils/test_missing_values.py @@ -6,7 +6,6 @@ class TestMissingValues: - time = pd.date_range("20130101", "20130130") lin = [float(i) for i in range(len(time))] cub = [float(i - 4) ** 2 for i in range(len(time))] diff --git a/darts/tests/utils/test_model_selection.py b/darts/tests/utils/test_model_selection.py index 7b33836b75..75410b9ac0 100644 --- a/darts/tests/utils/test_model_selection.py +++ b/darts/tests/utils/test_model_selection.py @@ -59,19 +59,17 @@ def test_horiz_number_of_samples_too_small(self): def test_sunny_day_horiz_split(self): train_set, test_set = train_test_split(make_dataset(8, 10)) - assert verify_shape(train_set, 6, 10) and verify_shape( - test_set, 2, 10 - ), "Wrong shapes: training set shape: ({}, {}); test set shape ({}, {})".format( - len(train_set), len(train_set[0]), len(test_set), len(test_set[0]) + assert verify_shape(train_set, 6, 10) and verify_shape(test_set, 2, 10), ( + f"Wrong shapes: training set shape: ({len(train_set)}, {len(train_set[0])});" + f" test set shape ({len(test_set)}, {len(test_set[0])})" ) def test_sunny_day_horiz_split_absolute(self): train_set, test_set = train_test_split(make_dataset(8, 10), test_size=2) - assert verify_shape(train_set, 6, 10) and verify_shape( - test_set, 2, 10 - ), "Wrong shapes: training set shape: ({}, {}); test set shape ({}, {})".format( - len(train_set), len(train_set[0]), len(test_set), len(test_set[0]) + assert verify_shape(train_set, 6, 10) and verify_shape(test_set, 2, 10), ( + f"Wrong shapes: training set shape: ({len(train_set)}, {len(train_set[0])});" + f" test set shape ({len(test_set)}, {len(test_set[0])})" ) def test_horiz_split_overindexing_train_set(self): @@ -106,10 +104,9 @@ def test_sunny_day_vertical_split(self): vertical_split_type=MODEL_AWARE, ) - assert verify_shape(train_set, 2, 151) and verify_shape( - test_set, 2, 169 - ), "Wrong shapes: training set shape: ({}, {}); test set shape ({}, {})".format( - len(train_set), len(train_set[0]), len(test_set), len(test_set[0]) + assert verify_shape(train_set, 2, 151) and verify_shape(test_set, 2, 169), ( + f"Wrong shapes: training set shape: ({len(train_set)}, {len(train_set[0])});" + f" test set shape ({len(test_set)}, {len(test_set[0])})" ) # test 7 @@ -129,10 +126,9 @@ def test_test_split_absolute_number_vertical(self): vertical_split_type=MODEL_AWARE, ) - assert verify_shape(train_set, 4, 7) and verify_shape( - test_set, 4, 4 - ), "Wrong shapes: training set shape: ({}, {}); test set shape ({}, {})".format( - len(train_set), len(train_set[0]), len(test_set), len(test_set[0]) + assert verify_shape(train_set, 4, 7) and verify_shape(test_set, 4, 4), ( + f"Wrong shapes: training set shape: ({len(train_set)}, {len(train_set[0])});" + f" test set shape ({len(test_set)}, {len(test_set[0])})" ) def test_negative_test_start_index(self): @@ -177,10 +173,8 @@ def test_single_timeseries_sunny_day(self): vertical_split_type=MODEL_AWARE, ) - assert ( - len(train_set) == 7 and len(test_set) == 4 - ), "Wrong shapes: training set shape: {}; test set shape {}".format( - len(train_set), len(test_set) + assert len(train_set) == 7 and len(test_set) == 4, ( + f"Wrong shapes: training set shape: {len(train_set)}; test set shape {len(test_set)}" ) def test_multi_timeseries_variable_ts_length_sunny_day(self): @@ -204,8 +198,8 @@ def test_multi_timeseries_variable_ts_length_sunny_day(self): 4, 4, 4, - ], "Wrong shapes: training set shape: {}; test set shape {}".format( - train_lengths, test_lengths + ], ( + f"Wrong shapes: training set shape: {train_lengths}; test set shape {test_lengths}" ) def test_multi_timeseries_variable_ts_length_one_ts_too_small(self): @@ -230,10 +224,9 @@ def test_simple_vertical_split_sunny_day(self): make_dataset(4, 10), axis=1, vertical_split_type=SIMPLE, test_size=0.2 ) - assert verify_shape(train_set, 4, 8) and verify_shape( - test_set, 4, 2 - ), "Wrong shapes: training set shape: ({}, {}); test set shape ({}, {})".format( - len(train_set), len(train_set[0]), len(test_set), len(test_set[0]) + assert verify_shape(train_set, 4, 8) and verify_shape(test_set, 4, 2), ( + f"Wrong shapes: training set shape: ({len(train_set)}, {len(train_set[0])});" + f" test set shape ({len(test_set)}, {len(test_set[0])})" ) def test_simple_vertical_split_sunny_day_absolute_split(self): @@ -241,10 +234,9 @@ def test_simple_vertical_split_sunny_day_absolute_split(self): make_dataset(4, 10), axis=1, vertical_split_type=SIMPLE, test_size=2 ) - assert verify_shape(train_set, 4, 8) and verify_shape( - test_set, 4, 2 - ), "Wrong shapes: training set shape: ({}, {}); test set shape ({}, {})".format( - len(train_set), len(train_set[0]), len(test_set), len(test_set[0]) + assert verify_shape(train_set, 4, 8) and verify_shape(test_set, 4, 2), ( + f"Wrong shapes: training set shape: ({len(train_set)}, {len(train_set[0])});" + f" test set shape ({len(test_set)}, {len(test_set[0])})" ) def test_simple_vertical_split_exception_on_bad_param(self): diff --git a/darts/tests/utils/test_residuals.py b/darts/tests/utils/test_residuals.py deleted file mode 100644 index 664e49a1e5..0000000000 --- a/darts/tests/utils/test_residuals.py +++ /dev/null @@ -1,113 +0,0 @@ -import numpy as np -import pytest - -from darts.logging import get_logger -from darts.models import LinearRegressionModel, NaiveSeasonal -from darts.tests.models.forecasting.test_regression_models import dummy_timeseries -from darts.utils.timeseries_generation import constant_timeseries as ct -from darts.utils.timeseries_generation import linear_timeseries as lt - -logger = get_logger(__name__) - - -class TestResiduals: - - np.random.seed(42) - - def test_forecasting_residuals_nocov_output(self): - model = NaiveSeasonal(K=1) - - # test zero residuals - constant_ts = ct(length=20) - residuals = model.residuals(constant_ts) - np.testing.assert_almost_equal( - residuals.univariate_values(), np.zeros(len(residuals)) - ) - - # test constant, positive residuals - linear_ts = lt(length=20) - residuals = model.residuals(linear_ts) - np.testing.assert_almost_equal( - np.diff(residuals.univariate_values()), np.zeros(len(residuals) - 1) - ) - np.testing.assert_array_less( - np.zeros(len(residuals)), residuals.univariate_values() - ) - - def test_forecasting_residuals_inputs(self): - # test input types past and/or future covariates - - # dummy covariates and target TimeSeries instances - - target_series, past_covariates, future_covariates = dummy_timeseries( - length=10, - n_series=1, - comps_target=1, - comps_pcov=1, - comps_fcov=1, - ) # outputs Sequences[TimeSeries] and not TimeSeries - - model = LinearRegressionModel( - lags=4, lags_past_covariates=4, lags_future_covariates=(4, 1) - ) - model.fit( - series=target_series, - past_covariates=past_covariates, - future_covariates=future_covariates, - ) - - def test_forecasting_residuals_cov_output(self): - # if covariates are constant and the target is constant/linear, - # residuals should be zero (for a LinearRegression model) - - target_series_1 = ct(value=0.5, length=10) - target_series_2 = lt(length=10) - past_covariates = ct(value=0.2, length=10) - future_covariates = ct(value=0.1, length=10) - - model_1 = LinearRegressionModel( - lags=1, lags_past_covariates=1, lags_future_covariates=(1, 1) - ) - model_2 = LinearRegressionModel( - lags=1, lags_past_covariates=1, lags_future_covariates=(1, 1) - ) - model_1.fit( - target_series_1, - past_covariates=past_covariates, - future_covariates=future_covariates, - ) - residuals_1 = model_1.residuals( - target_series_1, - past_covariates=past_covariates, - future_covariates=future_covariates, - ) - - model_2.fit( - target_series_2, - past_covariates=past_covariates, - future_covariates=future_covariates, - ) - residuals_2 = model_2.residuals( - target_series_2, - past_covariates=past_covariates, - future_covariates=future_covariates, - ) - - # residuals zero - np.testing.assert_almost_equal( - residuals_1.univariate_values(), np.zeros(len(residuals_1)) - ) - - np.testing.assert_almost_equal( - residuals_2.univariate_values(), np.zeros(len(residuals_2)) - ) - - # if model is trained with covariates, should raise error when covariates are missing in residuals() - with pytest.raises(ValueError): - model_1.residuals(target_series_1) - - with pytest.raises(ValueError): - model_1.residuals(target_series_1, past_covariates=past_covariates) - - with pytest.raises(ValueError): - model_1.residuals(target_series_1, future_covariates=future_covariates) diff --git a/darts/tests/utils/test_timeseries_generation.py b/darts/tests/utils/test_timeseries_generation.py index 606e36d311..39d395b3ff 100644 --- a/darts/tests/utils/test_timeseries_generation.py +++ b/darts/tests/utils/test_timeseries_generation.py @@ -6,6 +6,7 @@ from darts import TimeSeries from darts.utils.timeseries_generation import ( + ONE_INDEXED_FREQS, autoregressive_timeseries, constant_timeseries, datetime_attribute_timeseries, @@ -16,6 +17,7 @@ random_walk_timeseries, sine_timeseries, ) +from darts.utils.utils import freqs class TestTimeSeriesGeneration: @@ -42,7 +44,6 @@ def test_routine(start, end=None, length=None): test_routine(start=pd.Timestamp("2000-01-01"), end=end_date) def test_linear_timeseries(self): - # testing parameters start_value = 5 end_value = 12 @@ -81,7 +82,6 @@ def test_routine(start, end=None, length=None): test_routine(start=pd.Timestamp("2000-01-01"), end=end_date) def test_sine_timeseries(self): - # testing parameters value_amplitude = 5 value_y_offset = -3 @@ -109,7 +109,6 @@ def test_routine(start, end=None, length=None): test_routine(start=pd.Timestamp("2000-01-01"), end=end_date) def test_gaussian_timeseries(self): - # testing for correct length def test_routine(start, end=None, length=None): gaussian_ts = gaussian_timeseries(start=start, end=end, length=length) @@ -125,7 +124,6 @@ def test_routine(start, end=None, length=None): test_routine(start=pd.Timestamp("2000-01-01"), end=end_date) def test_random_walk_timeseries(self): - # testing for correct length def test_routine(start, end=None, length=None): random_walk_ts = random_walk_timeseries(start=start, end=end, length=length) @@ -148,7 +146,7 @@ def test_holidays_timeseries(self): periods=365 * 3, freq="D", start=pd.Timestamp("2014-12-24") ) time_index_3 = pd.date_range( - periods=10, freq="Y", start=pd.Timestamp("1950-01-01") + periods=10, freq=freqs["YE"], start=pd.Timestamp("1950-01-01") ) + pd.Timedelta(days=1) # testing we have at least one holiday flag in each year @@ -161,7 +159,9 @@ def test_routine( ts = holidays_timeseries( time_index, country_code, until=until, add_length=add_length ) - assert all(ts.pd_dataframe().groupby(pd.Grouper(freq="y")).sum().values) + assert all( + ts.pd_dataframe().groupby(pd.Grouper(freq=freqs["YE"])).sum().values + ) for time_index in [time_index_1, time_index_2, time_index_3]: for country_code in ["US", "CH", "AR"]: @@ -192,7 +192,7 @@ def test_routine( # test holiday with and without time zone, 1st of August is national holiday in Switzerland # time zone naive (e.g. in UTC) idx = generate_index( - start=pd.Timestamp("2000-07-31 22:00:00"), length=3, freq="h" + start=pd.Timestamp("2000-07-31 22:00:00"), length=3, freq=freqs["h"] ) ts = holidays_timeseries(idx, country_code="CH") np.testing.assert_array_almost_equal(ts.values()[:, 0], np.array([0, 0, 1])) @@ -223,7 +223,6 @@ def test_routine( for length in [1, 2, 5, 50]: for start in [0, 1, 9]: - # test pd.RangeIndex with varying step sizes for step in [1, 2, 4]: expected_start = start @@ -345,22 +344,26 @@ def test_calculation(coef): for coef_assert in [[-1], [-1, 1.618], [1, 2, 3], list(range(10))]: test_calculation(coef=coef_assert) - def test_datetime_attribute_timeseries(self): - idx = generate_index(start=pd.Timestamp("2000-01-01"), length=48, freq="h") - - def helper_routine(idx, attr, vals_exp, **kwargs): - ts = datetime_attribute_timeseries(idx, attribute=attr, **kwargs) - vals_exp = np.array(vals_exp, dtype=ts.dtype) - if len(vals_exp.shape) == 1: - vals_act = ts.values()[:, 0] - else: - vals_act = ts.values() - np.testing.assert_array_almost_equal(vals_act, vals_exp) - + @staticmethod + def helper_routine(idx, attr, vals_exp, **kwargs): + ts = datetime_attribute_timeseries(idx, attribute=attr, **kwargs) + vals_exp = np.array(vals_exp, dtype=ts.dtype) + if len(vals_exp.shape) == 1: + vals_act = ts.values()[:, 0] + else: + vals_act = ts.values() + np.testing.assert_array_almost_equal(vals_act, vals_exp) + + def test_datetime_attribute_timeseries_wrong_args(self): + idx = generate_index( + start=pd.Timestamp("2000-01-01"), length=48, freq=freqs["h"] + ) # no pd.DatetimeIndex with pytest.raises(ValueError) as err: - helper_routine( - pd.RangeIndex(start=0, stop=len(idx)), "h", vals_exp=np.arange(len(idx)) + self.helper_routine( + pd.RangeIndex(start=0, stop=len(idx)), + freqs["h"], + vals_exp=np.arange(len(idx)), ) assert str(err.value).startswith( "`time_index` must be a pandas `DatetimeIndex`" @@ -368,23 +371,29 @@ def helper_routine(idx, attr, vals_exp, **kwargs): # invalid attribute with pytest.raises(ValueError) as err: - helper_routine(idx, "h", vals_exp=np.arange(len(idx))) + self.helper_routine(idx, freqs["h"], vals_exp=np.arange(len(idx))) assert str(err.value).startswith( - "attribute `h` needs to be an attribute of pd.DatetimeIndex." + f"attribute `{freqs['h']}` needs to be an attribute of pd.DatetimeIndex." ) # no time zone aware index with pytest.raises(ValueError) as err: - helper_routine(idx.tz_localize("UTC"), "h", vals_exp=np.arange(len(idx))) + self.helper_routine( + idx.tz_localize("UTC"), freqs["h"], vals_exp=np.arange(len(idx)) + ) assert "`time_index` must be time zone naive." == str(err.value) + def test_datetime_attribute_timeseries(self): + idx = generate_index( + start=pd.Timestamp("2000-01-01"), length=48, freq=freqs["h"] + ) # ===> datetime attribute # hour vals = [i for i in range(24)] * 2 - helper_routine(idx, "hour", vals_exp=vals) + self.helper_routine(idx, "hour", vals_exp=vals) # hour from TimeSeries - helper_routine( + self.helper_routine( TimeSeries.from_times_and_values(times=idx, values=np.arange(len(idx))), "hour", vals_exp=vals, @@ -392,45 +401,235 @@ def helper_routine(idx, attr, vals_exp, **kwargs): # tz=CET is +1 hour to UTC vals = vals[1:] + [0] - helper_routine(idx, "hour", vals_exp=vals, tz="CET") + self.helper_routine(idx, "hour", vals_exp=vals, tz="CET") - # day - vals = [1] * 24 + [2] * 24 - helper_routine(idx, "day", vals_exp=vals) + # day, 0-indexed + vals = [0] * 24 + [1] * 24 + self.helper_routine(idx, "day", vals_exp=vals) # dayofweek vals = [5] * 24 + [6] * 24 - helper_routine(idx, "dayofweek", vals_exp=vals) - - # month - vals = [1] * 48 - helper_routine(idx, "month", vals_exp=vals) - - # ===> one hot encoded - # month - vals = [1] + [0] * 11 - vals = [vals for _ in range(48)] - helper_routine(idx, "month", vals_exp=vals, one_hot=True) - - # tz=CET, month - vals = [1] + [0] * 11 - vals = [vals for _ in range(48)] - helper_routine(idx, "month", vals_exp=vals, tz="CET", one_hot=True) - - # ===> sine/cosine cyclic encoding - # hour (period = 24 hours in one day) - period = 24 + self.helper_routine(idx, "dayofweek", vals_exp=vals) + + # month, 0-indexed + vals = [0] * 48 + self.helper_routine(idx, "month", vals_exp=vals) + + @pytest.mark.parametrize( + "config", + [ + (freqs["ME"], "month", 12), + (freqs["h"], "hour", 24), + ("D", "weekday", 7), + (freqs["s"], "second", 60), + ("W", "weekofyear", 52), + ("D", "dayofyear", 365), + (freqs["QE"], "quarter", 4), + ], + ) + def test_datetime_attribute_timeseries_indexing_shift(self, config): + """Check that the original indexing of the attribute is properly shifted to obtain 0-indexing when + the start timestamp of the index is the first possible value of the attribute + + Note: 2001 is neither leap year nor a year with 53 weeks + """ + ( + base_freq, + attribute_freq, + period, + ) = config + start_timestamp = "2001-01-01 00:00:00" + + idx = generate_index( + start=pd.Timestamp(start_timestamp), length=1, freq=base_freq + ) + + # default encoding should be 0 + vals_exp = np.zeros((1, 1)) + self.helper_routine( + idx, attribute_freq, vals_exp=vals_exp, one_hot=False, cyclic=False + ) + + # one-hot encoding must be 1 in the first column + vals_exp = np.zeros((1, period)) + vals_exp[0, 0] = 1 + self.helper_routine(idx, attribute_freq, vals_exp=vals_exp, one_hot=True) + + # cyclic encoding must start at t=0 + vals_exp = np.array([[np.sin(0), np.cos(0)]]) + self.helper_routine(idx, attribute_freq, vals_exp=vals_exp, cyclic=True) + + @pytest.mark.parametrize( + "config", + [ + (freqs["ME"], "month", 12), + (freqs["h"], "hour", 24), + ("D", "weekday", 7), + (freqs["s"], "second", 60), + ("W", "weekofyear", 52), + (freqs["QE"], "quarter", 4), + ("D", "dayofyear", 365), + ], + ) + def test_datetime_attribute_timeseries_one_hot(self, config): + """Verifying that proper one hot encoding is generated (not leap year)""" + base_freq, attribute_freq, period = config + # first quarter/year, month/year, week/year, day/year, day/week, hour/day, second/hour + simple_start = pd.Timestamp("2001-01-01 00:00:00") + idx = generate_index(start=simple_start, length=period, freq=base_freq) + vals = np.eye(period) + + # simple start + self.helper_routine(idx, attribute_freq, vals_exp=vals, one_hot=True) + # with time-zone + if attribute_freq == "hour": + # shift to mimic conversion from UTC to CET + vals = np.roll(vals, shift=-1, axis=0) + self.helper_routine(idx, attribute_freq, vals_exp=vals, tz="CET", one_hot=True) + + # missing values + cut_period = period // 3 + idx = generate_index(start=simple_start, length=cut_period, freq=base_freq) + vals = np.eye(period) + # removing missing rows + vals = vals[:cut_period] + # mask missing attribute values + vals[:, cut_period:] = 0 + + self.helper_routine(idx, attribute_freq, vals_exp=vals, one_hot=True) + + # shifted time index + shifted_start = pd.Timestamp("2001-05-05 05:00:05") + # 5th month/year, day/week, hour/day, second/hour + shift = 5 + # 125th day of year + if attribute_freq == "dayofyear": + shift = 125 + # 18th week of year + if attribute_freq == "weekofyear": + shift = 18 + # 2nd quarter of the year + elif attribute_freq == "quarter": + shift = 2 + + # account for 1-indexing of the attribute + if attribute_freq in ONE_INDEXED_FREQS: + shift -= 1 + + idx = generate_index(start=shifted_start, length=period, freq=base_freq) + vals = np.eye(period) + # shift values + vals = np.roll(vals, shift=-shift, axis=0) + + self.helper_routine(idx, attribute_freq, vals_exp=vals, one_hot=True) + + @pytest.mark.parametrize( + "config", [(freqs["h"], "hour", 24), (freqs["ME"], "month", 12)] + ) + def test_datetime_attribute_timeseries_cyclic(self, config): + base_freq, attribute_freq, period = config + idx = generate_index( + start=pd.Timestamp("2000-01-01"), length=2 * period, freq=base_freq + ) + freq = 2 * np.pi / period - vals_dta = [i for i in range(24)] * 2 + vals_dta = [i for i in range(period)] * 2 vals = np.array(vals_dta) sin_vals = np.sin(freq * vals)[:, None] cos_vals = np.cos(freq * vals)[:, None] - vals = np.concatenate([sin_vals, cos_vals], axis=1) - helper_routine(idx, "hour", vals_exp=vals, cyclic=True) + vals_exp = np.concatenate([sin_vals, cos_vals], axis=1) + self.helper_routine(idx, attribute_freq, vals_exp=vals_exp, cyclic=True) - # tz=CET, hour - vals = np.array(vals_dta[1:] + [0]) + # with time-zone conversion + if attribute_freq == "hour": + # UTC to CET shift by 1 hour + vals = np.array(vals_dta[1:] + vals_dta[0:1]) sin_vals = np.sin(freq * vals)[:, None] cos_vals = np.cos(freq * vals)[:, None] - vals = np.concatenate([sin_vals, cos_vals], axis=1) - helper_routine(idx, "hour", vals_exp=vals, tz="CET", cyclic=True) + vals_exp = np.concatenate([sin_vals, cos_vals], axis=1) + self.helper_routine( + idx, attribute_freq, vals_exp=vals_exp, tz="CET", cyclic=True + ) + + def test_datetime_attribute_timeseries_leap_years(self): + """Check that the additional day of leap years is properly handled""" + days_leap_year = 366 + # 2000 is a leap year, contains 366 days + index = pd.date_range( + start=pd.Timestamp("2000-01-01"), end=pd.Timestamp("2000-12-31"), freq="D" + ) + assert len(index) == days_leap_year + vals_exp = np.arange(days_leap_year) + self.helper_routine(index, "day_of_year", vals_exp=vals_exp) + # full leap year, the encoding is a diagonal matrix + vals_exp = np.eye(days_leap_year) + self.helper_routine(index, "day_of_year", vals_exp=vals_exp, one_hot=True) + + # partial leap year, the encoding should still contain 366 columns + index_partial = index[30:72] + # remove the missing rows + vals_exp = vals_exp[30:72] + # mask the missing dates + vals_exp[:, :30] = 0 + vals_exp[:, 73:] = 0 + self.helper_routine( + index_partial, "day_of_year", vals_exp=vals_exp, one_hot=True + ) + + # index containing both a regular year and leap year, for a total of 731 days + index_long = pd.date_range( + start=pd.Timestamp("1999-01-01"), end=pd.Timestamp("2000-12-31"), freq="D" + ) + assert len(index_long) == 731 + # leap year encoding is a diagonal matrix + leap_year_oh = np.eye(days_leap_year) + # regular year drops the last day row + regular_year_oh = np.eye(days_leap_year) + regular_year_oh = regular_year_oh[:-1] + vals_exp = np.concatenate([regular_year_oh, leap_year_oh]) + self.helper_routine(index_long, "day_of_year", vals_exp=vals_exp, one_hot=True) + + @pytest.mark.parametrize("year", [1998, 2020]) + def test_datetime_attribute_timeseries_special_years(self, year): + """Check that years with 53 weeks are is properly handled: + - 1998 is a regular year starting on a thursday + - 2020 is a leap year starting on a wednesday + """ + + start_date = pd.Timestamp(f"{year}-01-01") + end_date = pd.Timestamp(f"{year}-12-31") + + # the 53th week appear when created with freq="D" + weeks_special_year = 53 + index = pd.date_range(start=start_date, end=end_date, freq="D") + assert index[-1].week == weeks_special_year + vals_exp = np.zeros((len(index), weeks_special_year)) + # first week is incomplete, its length depend on the first day of the year + week_shift = index[0].weekday() + for week_index in range(weeks_special_year): + week_start = max(7 * week_index - week_shift, 0) + week_end = 7 * (week_index + 1) - week_shift + vals_exp[week_start:week_end, week_index] = 1 + self.helper_routine(index, "week_of_year", vals_exp=vals_exp, one_hot=True) + + # the 53th week is omitted from index when created with freq="W" + index_weeks = pd.date_range(start=start_date, end=end_date, freq="W") + assert len(index_weeks) == weeks_special_year - 1 + # and 53th week properly excluded from the encoding + vals_exp = np.eye(weeks_special_year - 1)[: len(index_weeks)] + assert vals_exp.shape[1] == weeks_special_year - 1 + self.helper_routine( + index_weeks, "week_of_year", vals_exp=vals_exp, one_hot=True + ) + + # extending the time index with the days missing from the incomplete first week + index_weeks_ext = pd.date_range( + start=start_date, end=end_date + pd.Timedelta(days=6 - week_shift), freq="W" + ) + assert len(index_weeks_ext) == weeks_special_year + # the 53th week is properly appearing in the encoding + vals_exp = np.eye(weeks_special_year) + assert vals_exp.shape[1] == weeks_special_year + self.helper_routine( + index_weeks_ext, "week_of_year", vals_exp=vals_exp, one_hot=True + ) diff --git a/darts/tests/utils/test_ts_utils.py b/darts/tests/utils/test_ts_utils.py new file mode 100644 index 0000000000..3374c44068 --- /dev/null +++ b/darts/tests/utils/test_ts_utils.py @@ -0,0 +1,106 @@ +import pytest + +from darts.utils.timeseries_generation import linear_timeseries +from darts.utils.ts_utils import ( + SeriesType, + get_series_seq_type, + get_single_series, + series2seq, +) + + +class TestTsUtils: + def test_series_type(self): + assert SeriesType.NONE.value == -1 + assert SeriesType.SINGLE.value == 0 + assert SeriesType.SEQ.value == 1 + assert SeriesType.SEQ_SEQ.value == 2 + + # equality works with members + assert SeriesType.NONE == SeriesType.NONE + assert SeriesType.SINGLE == SeriesType.SINGLE + assert SeriesType.SEQ == SeriesType.SEQ + assert SeriesType.SEQ_SEQ == SeriesType.SEQ_SEQ + + # inequality works with members + assert SeriesType.SINGLE != SeriesType.SEQ + assert SeriesType.SEQ != SeriesType.SEQ_SEQ + + # equality does not work with non-members + with pytest.raises(ValueError) as err: + _ = SeriesType.SINGLE == 0 + assert str(err.value).startswith("`other` must be a `SeriesType` enum.") + + # single series order is < sequence of series order < sequence of sequences of series order + assert SeriesType.NONE < SeriesType.SINGLE < SeriesType.SEQ < SeriesType.SEQ_SEQ + assert SeriesType.SEQ_SEQ > SeriesType.SEQ > SeriesType.SINGLE > SeriesType.NONE + + def test_get_series_seq_type(self): + ts = linear_timeseries(length=3) + assert get_series_seq_type(None) == SeriesType.NONE + assert get_series_seq_type(ts) == SeriesType.SINGLE + assert get_series_seq_type([ts]) == SeriesType.SEQ + assert get_series_seq_type([[ts]]) == SeriesType.SEQ_SEQ + + # unknown sequence type + with pytest.raises(ValueError) as err: + _ = get_series_seq_type([[[ts]]]) + assert str(err.value).startswith( + "input series must be of type `TimeSeries`, `Sequence[TimeSeries]`" + ) + + # sequence with elements different from `TimeSeries` + with pytest.raises(ValueError) as err: + _ = get_series_seq_type([[0.0, 1.0, 2]]) + assert str(err.value).startswith( + "input series must be of type `TimeSeries`, `Sequence[TimeSeries]`" + ) + + def test_series2seq(self): + ts = linear_timeseries(length=3) + + # `None` to different sequence types + assert series2seq(None, seq_type_out=SeriesType.SINGLE) is None + assert series2seq(None, seq_type_out=SeriesType.SEQ) is None + assert series2seq(None, seq_type_out=SeriesType.SEQ_SEQ) is None + + # `TimeSeries` to different sequence types + assert series2seq(ts, seq_type_out=SeriesType.SINGLE) == ts + assert series2seq(ts, seq_type_out=SeriesType.SEQ) == [ts] + assert series2seq(ts, seq_type_out=SeriesType.SEQ_SEQ) == [[ts]] + + # Sequence[`TimeSeries`] to different sequence types + assert series2seq([ts], seq_type_out=SeriesType.SINGLE) == ts + assert series2seq([ts], seq_type_out=SeriesType.SEQ) == [ts] + assert series2seq([ts], seq_type_out=SeriesType.SEQ_SEQ) == [[ts]] + + # Sequence[`TimeSeries`, `TimeSeries`] to different sequence types + # cannot reduce dimension since there is more than one element in SEQ + assert series2seq([ts, ts], seq_type_out=SeriesType.SINGLE) == [ts, ts] + assert series2seq([ts, ts], seq_type_out=SeriesType.SEQ) == [ts, ts] + assert series2seq([ts, ts], seq_type_out=SeriesType.SEQ_SEQ) == [[ts, ts]] + assert series2seq([ts, ts], seq_type_out=SeriesType.SEQ_SEQ, nested=True) == [ + [ts], + [ts], + ] + + # Sequence[Sequence[`TimeSeries`]] to different sequence types + # SEQ_SEQ represents historical forecasts (and downstream tasks) output + # the outer sequence represents the series axis, therefore reducing to SINGLE + # actually returns a Sequence[`TimeSeries`] + assert series2seq([[ts]], seq_type_out=SeriesType.SINGLE) == [ts] + assert series2seq([[ts]], seq_type_out=SeriesType.SEQ) == [[ts]] + assert series2seq([[ts]], seq_type_out=SeriesType.SEQ_SEQ) == [[ts]] + + # Sequence[`TimeSeries`, `TimeSeries`] to different sequence types + # cannot reduce dimension since there is more than one element in SEQ_SEQ + assert series2seq([[ts], [ts]], seq_type_out=SeriesType.SINGLE) == [[ts], [ts]] + assert series2seq([[ts], [ts]], seq_type_out=SeriesType.SEQ) == [[ts], [ts]] + assert series2seq([[ts], [ts]], seq_type_out=SeriesType.SEQ_SEQ) == [[ts], [ts]] + + def test_get_single_series(self): + ts = linear_timeseries(length=3) + assert get_single_series(None) is None + assert get_single_series(ts) == ts + assert get_single_series([ts]) == ts + assert get_single_series([ts, ts]) == ts diff --git a/darts/tests/utils/test_utils.py b/darts/tests/utils/test_utils.py index c8c7f8351c..003d2253aa 100644 --- a/darts/tests/utils/test_utils.py +++ b/darts/tests/utils/test_utils.py @@ -1,10 +1,24 @@ +import itertools + import numpy as np import pandas as pd import pytest +from pandas.tseries.offsets import CustomBusinessDay from darts import TimeSeries -from darts.utils import _with_sanity_checks, retain_period_common_to_all +from darts.utils import _with_sanity_checks from darts.utils.missing_values import extract_subseries +from darts.utils.ts_utils import retain_period_common_to_all +from darts.utils.utils import ( + expand_arr, + freqs, + generate_index, + likelihood_component_names, + n_steps_between, + quantile_interval_names, + quantile_names, + sample_from_quantiles, +) class TestUtils: @@ -93,3 +107,621 @@ def test_extract_subseries(self): assert subseries_any[0] == series[:2] assert subseries_any[1] == series[3:5] assert subseries_any[2] == series[-1] + + @pytest.mark.parametrize( + "config", + [ + # regular date offset frequencies + # day + ("2000-01-02", "2000-01-01", None, None, "D", 0), # empty time index + ("2000-01-01", "2000-01-01", None, None, "D", 1), # increasing time index + ("2000-01-01", "2000-01-02", None, None, "D", 2), + ("2000-01-01 01:00:00", "2000-01-02 02:00:00", None, None, "D", 2), + # 2 * day + ("2000-01-01", "1999-12-31", None, None, "2D", 0), + ("2000-01-01", "2000-01-02", None, None, "2D", 1), + ("2000-01-01", "2000-01-03", None, None, "2D", 2), + # hour + ("2000-01-01", "2000-01-01", None, None, "h", 1), + ("2000-01-01", "2000-01-02", None, None, "h", 25), + ("2000-01-01 01:00:00", "2000-01-02 02:00:00", None, None, "h", 26), + ("2000-01-01 01:30:00", "2000-01-02 02:00:00", None, None, "h", 25), + # 2 * hour + ("2000-01-01", "2000-01-01", None, None, "2h", 1), + ("2000-01-01", "2000-01-02", None, None, "2h", 13), + ("2000-01-01 01:00:00", "2000-01-02 02:00:00", None, None, "2h", 13), + ("2000-01-01 01:30:00", "2000-01-02 02:00:00", None, None, "2h", 13), + # ambiguous frequencies + # week-monday + ( + "2000-01-01", # saturday + "2000-01-03", # first monday + "2000-01-03", # first monday + None, # first wednesday + "W-MON", + 1, + ), + # week-monday, start and end are not part of freq (two mondays) + ( + "2000-01-01", # saturday + "2000-01-12", # second wednesday + "2000-01-03", # first monday + "2000-01-10", # second monday + "W-MON", + 2, + ), + # week-monday, start is part of freq (two mondays) + ( + "2000-01-03", # saturday + "2000-01-12", # second wednesday + "2000-01-03", # first monday + "2000-01-10", # second monday + "W-MON", + 2, + ), + # week-monday, end is part of freq (one monday, end exclusive) + ( + "2000-01-01", # saturday + "2000-01-10", # second monday + "2000-01-03", # first monday + None, # second wednesday + "W-MON", + 2, + ), + # week-monday, start and end are part of freq (one monday, end exclusive) + ( + "2000-01-03", # saturday + "2000-01-10", # second monday + "2000-01-03", # first monday + None, # second wednesday + "W-MON", + 2, + ), + # month start + ("2000-01-31", "2000-01-31", None, None, "MS", 0), + ("2000-01-01", "2000-01-02", None, "2000-01-01", "MS", 1), + ("2000-01-01", "2000-01-01", None, None, "MS", 1), + ("2000-01-01", "2000-02-01", None, None, "MS", 2), + ("2000-01-01", "2000-03-01", None, None, "MS", 3), + # month end + ("2000-01-01", "2000-01-02", None, None, freqs["ME"], 0), + ("2000-01-31", "2000-02-29", None, None, freqs["ME"], 2), + # 2 * months + ("2000-01-01", "2000-01-01", None, None, "2MS", 1), + ("2000-01-01", "2000-02-11", None, "2000-01-01", "2MS", 1), + ("2000-01-01", "2000-03-01", None, None, "2MS", 2), + ("2000-01-01", "2000-05-01", None, None, "2MS", 3), + # quarter + ("2000-01-01", "2000-04-01", None, None, "QS", 2), + # year + ("2000-01-01", "2001-04-01", None, "2001-01-01", "YS", 2), + # 2*year + ("2001-01-01", "2010-04-01", None, "2009-01-01", "2YS", 5), + (0, -1, None, None, 1, 0), # empty int index + (0, -1, None, None, -1, 2), # decreasing int index + (0, 0, None, None, 1, 1), # increasing int index + (0, 0, None, None, 2, 1), + (0, 1, None, None, 1, 2), + (0, 1, None, None, 2, 1), + (0, 2, None, None, 1, 3), + (0, 2, None, None, 2, 2), + ], + ) + def test_generate_index_with_start_end(self, config): + """Test that generate index returns the expected length, start, and end points + using `start`, `end`, and `freq` as input. + Also tests the reverse index generation with a negative frequency. + """ + start, end, expected_start, expected_start_rev, freq, expected_n_steps = config + if isinstance(start, str): + start = pd.Timestamp(start) + end = pd.Timestamp(end) + expected_start = ( + pd.Timestamp(expected_start) if expected_start is not None else start + ) + expected_start_rev = ( + pd.Timestamp(expected_start_rev) + if expected_start_rev is not None + else end + ) + freq = pd.tseries.frequencies.to_offset(freq) + else: + expected_start = expected_start if expected_start is not None else start + expected_start_rev = ( + expected_start_rev if expected_start_rev is not None else end + ) + + idx = generate_index(start=start, end=end, freq=freq) + + if isinstance(freq, int): + assert idx.step == freq + else: + assert idx.freq == freq + + # idx has expected length + assert len(idx) == expected_n_steps + + if expected_n_steps == 0: + return + + # start and end are as expected + assert idx[0] == expected_start + assert idx[-1] == expected_start + freq * (expected_n_steps - 1) + + # reversed operations generates expected index + idx_rev = generate_index(start=end, end=start, freq=-freq) + assert idx_rev[0] == expected_start_rev + assert idx_rev[-1] == expected_start_rev - freq * (expected_n_steps - 1) + + @pytest.mark.parametrize( + "config", + [ + # regular date offset frequencies + # day + ("2000-01-02", None, "D", 0), # empty time index + ("2000-01-01", "2000-01-01", "D", 1), # increasing time index + ("2000-01-01", "2000-01-02", "D", 2), + ("2000-01-01 01:00:00", "2000-01-02 01:00:00", "D", 2), + # 2 * day + ("2000-01-01", None, "2D", 0), + ("2000-01-01", "2000-01-01", "2D", 1), + ("2000-01-01", "2000-01-03", "2D", 2), + # hour + ("2000-01-01", "2000-01-01", "h", 1), + ("2000-01-01", "2000-01-02", "h", 25), + ("2000-01-01 01:00:00", "2000-01-02 02:00:00", "h", 26), + ("2000-01-01 01:30:00", "2000-01-02 01:30:00", "h", 25), + # 2 * hour + ("2000-01-01", "2000-01-01", "2h", 1), + ("2000-01-01", "2000-01-02", "2h", 13), + ("2000-01-01 01:00:00", "2000-01-02 01:00:00", "2h", 13), + ("2000-01-01 01:30:00", "2000-01-02 01:30:00", "2h", 13), + # ambiguous frequencies + # week-monday + ( + "2000-01-01", # saturday + "2000-01-03", # first monday + "W-MON", + 1, + ), + # week-monday, start is not part of freq (two mondays) + ( + "2000-01-01", # saturday + "2000-01-10", # second monday + "W-MON", + 2, + ), + # week-monday, start and end are part of freq (two mondays) + ( + "2000-01-03", # saturday + "2000-01-10", # second monday + "W-MON", + 2, + ), + # month start + ("2000-01-31", None, "MS", 0), + ("2000-01-01", "2000-01-01", "MS", 1), + ("2000-01-01", "2000-02-01", "MS", 2), + ("2000-01-01", "2000-03-01", "MS", 3), + # month end + ("2000-01-01", None, freqs["ME"], 0), + ("2000-01-31", "2000-02-29", freqs["ME"], 2), + # 2 * months + ("2000-01-01", "2000-01-01", "2MS", 1), + ("2000-01-01", "2000-03-01", "2MS", 2), + ("2000-01-01", "2000-05-01", "2MS", 3), + # quarter + ("2000-01-01", "2000-04-01", "QS", 2), + # year + ("2000-01-01", "2001-01-01", "YS", 2), + # 2*year + ("2001-01-01", "2009-01-01", "2YS", 5), + (0, None, 1, 0), # empty int index + (0, -1, -1, 2), # decreasing int index + (0, 0, 1, 1), # increasing int index + (0, 0, 2, 1), + (0, 1, 1, 2), + (0, 2, 1, 3), + (0, 2, 2, 2), + ], + ) + def test_generate_index_with_start_length(self, config): + """Test that generate index returns the expected length, start, and end points + using `start`, `length`, and `freq` as input. + """ + start, expected_end, freq, n_steps = config + if isinstance(start, str): + freq = pd.tseries.frequencies.to_offset(freq) + start = pd.Timestamp(start) + expected_end = ( + pd.Timestamp(expected_end) if expected_end is not None else None + ) + idx = generate_index(start=start, length=n_steps, freq=freq) + assert len(idx) == n_steps + if n_steps == 0: + return + + assert idx[-1] == expected_end + assert idx[0] == expected_end - (n_steps - 1) * freq + + @pytest.mark.parametrize( + "config", + [ + # regular date offset frequencies + # day + (None, "2000-01-02", "D", 0), # empty time index + ("2000-01-01", "2000-01-01", "D", 1), # increasing time index + ("2000-01-01", "2000-01-02", "D", 2), + ("2000-01-01 01:00:00", "2000-01-02 01:00:00", "D", 2), + # 2 * day + (None, "2000-01-01", "2D", 0), + ("2000-01-01", "2000-01-01", "2D", 1), + ("2000-01-01", "2000-01-03", "2D", 2), + # hour + ("2000-01-01", "2000-01-01", "h", 1), + ("2000-01-01", "2000-01-02", "h", 25), + ("2000-01-01 01:00:00", "2000-01-02 02:00:00", "h", 26), + ("2000-01-01 01:30:00", "2000-01-02 01:30:00", "h", 25), + # 2 * hour + ("2000-01-01", "2000-01-01", "2h", 1), + ("2000-01-01", "2000-01-02", "2h", 13), + ("2000-01-01 01:00:00", "2000-01-02 01:00:00", "2h", 13), + ("2000-01-01 01:30:00", "2000-01-02 01:30:00", "2h", 13), + # ambiguous frequencies + # week-monday, end is not part of freq + ( + "1999-12-27", # saturday + "2000-01-02", # first monday + "W-MON", + 1, + ), + # week-monday, end is part of freq + ( + "2000-01-03", # saturday + "2000-01-10", # second monday + "W-MON", + 2, + ), + # month start + (None, "2000-01-31", "MS", 0), + ("2000-01-01", "2000-01-01", "MS", 1), + ("2000-01-01", "2000-02-01", "MS", 2), + ("2000-01-01", "2000-03-01", "MS", 3), + # month end + (None, "2000-01-01", freqs["ME"], 0), + ("2000-01-31", "2000-02-29", freqs["ME"], 2), + # 2 * months + ("2000-01-01", "2000-01-01", "2MS", 1), + ("2000-01-01", "2000-03-01", "2MS", 2), + ("2000-01-01", "2000-05-01", "2MS", 3), + # quarter + ("2000-01-01", "2000-04-01", "QS", 2), + # year + ("2000-01-01", "2001-01-01", "YS", 2), + # 2*year + ("2001-01-01", "2009-01-01", "2YS", 5), + (None, 0, 1, 0), # empty int index + (0, -1, -1, 2), # decreasing int index + (0, 0, 1, 1), # increasing int index + (0, 0, 2, 1), + (0, 1, 1, 2), + (0, 2, 1, 3), + (0, 2, 2, 2), + ], + ) + def test_generate_index_with_end_length(self, config): + """Test that generate index returns the expected length, start, and end points + using `end`, `length`, and `freq` as input. + """ + expected_start, end, freq, n_steps = config + + if isinstance(end, str): + freq = pd.tseries.frequencies.to_offset(freq) + expected_start = ( + pd.Timestamp(expected_start) if expected_start is not None else None + ) + end = pd.Timestamp(end) + idx = generate_index(end=end, length=n_steps, freq=freq) + assert len(idx) == n_steps + if n_steps == 0: + return + + assert idx[0] == expected_start + assert idx[-1] == expected_start + (n_steps - 1) * freq + + @pytest.mark.parametrize( + "config", + [ + ("2000-01-01", None), + (None, "2000-01-03"), + ("2000-01-01", "2000-01-03"), + ], + ) + def test_generate_index_with_string(self, config): + """Test that index generation with strings as start or end gives same results as with pandas TimeStamps.""" + start, end = config + length = 3 if (start is None or end is None) else None + idx = generate_index(start=start, end=end, length=length) + + start_ts = pd.Timestamp(start) if start is not None else start + end_ts = pd.Timestamp(end) if end is not None else end + idx_expected = generate_index(start=start_ts, end=end_ts, length=length) + assert idx.equals(idx_expected) + + @pytest.mark.parametrize( + "config", + [ + # regular date offset frequencies + # day + ("2000-01-01", "2000-01-01", "D", 0), + ("2000-01-01", "2000-01-02", "D", 1), + ("2000-01-01", "2005-02-05", "D", 1862), + # 2*days + ("2000-01-01", "2000-01-01", "2D", 0), + ("2000-01-01", "2000-01-02", "2D", 0), + ("2000-01-01", "2000-01-03", "2D", 1), + # hour + ("2000-01-01", "2000-01-01", "h", 0), + ("2000-01-01", "2000-01-01 06:00:00", "h", 6), + ("2000-01-01", "2000-01-02", "h", 24), + # ambiguous frequencies + # week-monday, start and end are not part of freq (two mondays) + ( + "2000-01-01", # saturday + "2000-01-12", # second wednesday + "W-MON", + 2, + ), + # week-monday, start is part of freq (two mondays) + ( + "2000-01-03", # monday + "2000-01-12", # second wednesday + "W-MON", + 2, + ), + # week-monday, end is part of freq (one monday, end exclusive) + ( + "2000-01-01", # saturday + "2000-01-10", # second monday + "W-MON", + 1, + ), + # week-monday, start and end are part of freq (one monday, end exclusive) + ( + "2000-01-03", # saturday + "2000-01-10", # second monday + "W-MON", + 1, + ), + # month + ("2000-01-01", "2000-01-02", freqs["ME"], 0), + ("2000-01-01", "2000-01-01", freqs["ME"], 0), + ("2000-01-01", "2000-02-01", freqs["ME"], 1), + ("2000-01-01", "2000-03-01", freqs["ME"], 2), + # 2 * months + ("2000-01-01", "2000-01-01", "2" + freqs["ME"], 0), + ("2000-01-01", "2000-02-11", "2" + freqs["ME"], 0), + ("2000-01-01", "2000-03-01", "2" + freqs["ME"], 1), + ("2000-01-01", "2000-05-01", "2" + freqs["ME"], 2), + # quarter + ("2000-01-01", "2000-04-01", freqs["QE"], 1), + # year + ("2000-01-01", "2001-04-01", freqs["YE"], 1), + # 2*year + ("2000-01-01", "2010-04-01", "2" + freqs["YE"], 5), + # custom frequencies + # business day + ( + "2000-01-01", # saturday (no business) + "2000-01-01", + CustomBusinessDay(weekmask="Mon Tue Wed Thu Fri"), + 0, + ), + ( + "2000-01-01", # saturday (no business) + "2000-01-02", # sunday (no business) + CustomBusinessDay(weekmask="Mon Tue Wed Thu Fri"), + 0, + ), + ( + "2000-01-01", # saturday (no business) + "2000-01-03", # monday (first business day) + CustomBusinessDay(weekmask="Mon Tue Wed Thu Fri"), + 0, + ), + ( + "2000-01-01", # saturday (no business) + "2000-01-08", # second saturday (first business day) + CustomBusinessDay(weekmask="Mon Tue Wed Thu Fri"), + 4, + ), + ( + "2000-01-03", # monday + "2000-01-07", # friday + CustomBusinessDay(weekmask="Mon Tue Wed Thu Fri"), + 4, + ), + # 2 * business days + ( + "2000-01-01", # saturday (no business) + "2000-01-08", # second saturday (first business day) + 2 * CustomBusinessDay(weekmask="Mon Tue Wed Thu Fri"), + 2, + ), + # integer steps/frequencies + (0, -1, 1, -1), + (0, 0, 1, 0), + (0, 0, 2, 0), + (0, 1, 1, 1), + (0, 1, 2, 0), + (0, 2, 1, 2), + (0, 2, 2, 1), + ], + ) + def test_n_steps_between(self, config): + """Test the number of frequency steps/periods between two time steps.""" + start, end, freq, expected_n_steps = config + if isinstance(start, str): + start = pd.Timestamp(start) + end = pd.Timestamp(end) + freq = pd.tseries.frequencies.to_offset(freq) + n_steps = n_steps_between(end=end, start=start, freq=freq) + assert n_steps == expected_n_steps + n_steps_reversed = n_steps_between(end=start, start=end, freq=freq) + assert n_steps_reversed == -expected_n_steps + + @pytest.mark.parametrize( + "config", + [ + (np.array([0, 1, 2]), (3, 1, 1)), + (np.array([[0], [1], [2]]), (3, 1, 1)), + (np.array([[[0]], [[1]], [[2]]]), (3, 1, 1)), + (np.array([[0, 1], [2, 3], [3, 4]]), (3, 2, 1)), + (np.array([[[0], [1]], [[1], [2]], [[3], [4]]]), (3, 2, 1)), + ( + np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]], [[8, 9], [10, 11]]]), + (3, 2, 2), + ), + ], + ) + def test_expand_arr(self, config): + """tests array expansion to 3D.""" + arr, shape_expected = config + + if len(arr.shape) == 1: + arr_expected = arr[:, None, None] + elif len(arr.shape) == 2: + arr_expected = arr[:, :, None] + else: + arr_expected = arr + + arr = expand_arr(arr, ndim=3) + assert arr.shape == shape_expected + np.testing.assert_array_almost_equal(arr, arr_expected) + + def test_likelihood_component_names(self): + names = likelihood_component_names(["a", "b"], ["1", "2", "3"]) + assert names == ["a_1", "a_2", "a_3", "b_1", "b_2", "b_3"] + + assert ( + likelihood_component_names(pd.Index(["a", "b"]), ["1", "2", "3"]) == names + ) + + @pytest.mark.parametrize( + "config", + [ + (0.25, "a_q0.25"), + (0.2501, "a_q0.25"), + ([0.25], ["a_q0.25"]), + ([0.25, 0.75], ["a_q0.25", "a_q0.75"]), + ], + ) + def test_quantile_names(self, config): + q, names_expected = config + names = quantile_names(q, "a") + assert names == names_expected + + @pytest.mark.parametrize( + "config", + [ + ((0.25, 0.5), "a_q0.25_q0.50"), + ((0.2501, 0.4999), "a_q0.25_q0.50"), + ([(0.25, 0.5)], ["a_q0.25_q0.50"]), + ([(0.25, 0.50), (0.6, 0.75)], ["a_q0.25_q0.50", "a_q0.60_q0.75"]), + ], + ) + def test_quantile_interval_names(self, config): + q, names_expected = config + names = quantile_interval_names(q, "a") + assert names == names_expected + + @pytest.mark.parametrize("ndim", [2, 3]) + def test_generate_samples_shape(self, ndim): + """Checks that the output shape of generated samples from quantiles and quantile predictions + is as expected.""" + n_time_steps = 10 + n_columns = 5 + n_quantiles = 20 + num_samples = 50 + + q = np.linspace(0, 1, n_quantiles) + q_pred = np.random.rand(n_time_steps, n_columns, n_quantiles) + if ndim == 2: + q_pred = q_pred.reshape((n_time_steps, n_columns * n_quantiles)) + y_pred = sample_from_quantiles(q_pred, q, num_samples) + assert y_pred.shape == (n_time_steps, n_columns, num_samples) + + @pytest.mark.parametrize( + "config", + itertools.product( + [1, 2], # n times + [2, 3], # ndim + [1, 2], # n components + ), + ) + def test_generate_samples_output(self, config): + """Tests sample generation from quantiles and quantile predictions for: + + - single/multiple time steps + - from 2 or 3 dimensions + - uni/multivariate + """ + np.random.seed(42) + n_times, ndim, n_comps = config + num_samples = 100000 + + q = np.array([0.2, 0.5, 0.75]) + q_pred = np.array([[[1.0, 2.0, 3.0]]]) + if n_times == 2: + q_pred = np.concatenate([q_pred, np.array([[[5.0, 7.0, 9.0]]])], axis=0) + if n_comps == 2: + q_pred = np.concatenate([q_pred, q_pred + 1.0], axis=1) + if ndim == 2: + q_pred = q_pred.reshape((len(q_pred), -1)) + y_pred = sample_from_quantiles(q_pred, q, num_samples) + + q_pred = q_pred.reshape((q_pred.shape[0], n_comps, len(q))) + for i in range(n_comps): + # edges must be identical to min/max predicted quantiles + assert y_pred[:, i].min() == q_pred[:, i].min() + assert y_pred[:, i].max() == q_pred[:, i].max() + + # check that sampled quantiles values equal to the predicted quantiles + assert np.quantile(y_pred[:, i], q[0], axis=1) == pytest.approx( + q_pred[:, i, 0], abs=0.02 + ) + assert np.quantile(y_pred[:, i], q[1], axis=1) == pytest.approx( + q_pred[:, i, 1], abs=0.02 + ) + assert np.quantile(y_pred[:, i], q[2], axis=1) == pytest.approx( + q_pred[:, i, 2], abs=0.02 + ) + + # for each component and quantile, check that the expected ratio of sampled values is approximately + # equal to the quantile + assert (y_pred[:, i] == q_pred[:, i, 0:1]).mean(axis=1) == pytest.approx( + 0.2, abs=0.02 + ) + assert ( + (q_pred[:, i, 0:1] < y_pred[:, i]) & (y_pred[:, i] <= q_pred[:, i, 1:2]) + ).mean(axis=1) == pytest.approx(0.3, abs=0.02) + assert ( + (q_pred[:, i, 1:2] < y_pred[:, i]) & (y_pred[:, i] < q_pred[:, i, 2:3]) + ).mean(axis=1) == pytest.approx(0.25, abs=0.02) + assert (y_pred[:, i] == q_pred[:, i, 2:3]).mean(axis=1) == pytest.approx( + 0.25, abs=0.02 + ) + + # between the quantiles, the values must be linearly interpolated + # check that number of unique values is approximately equal to the difference between two adjacent quantiles + mask1 = (q_pred[:, i, 0:1] < y_pred[:, i]) & ( + y_pred[:, i] < q_pred[:, i, 1:2] + ) + share_unique1 = len(np.unique(y_pred[:, i][mask1])) / num_samples + assert share_unique1 == pytest.approx(n_times * (q[1] - q[0]), abs=0.05) + + mask2 = (q_pred[:, i, 1:2] < y_pred[:, i]) & ( + y_pred[:, i] < q_pred[:, i, 2:3] + ) + share_unique2 = len(np.unique(y_pred[:, i][mask2])) / num_samples + assert share_unique2 == pytest.approx(n_times * (q[2] - q[1]), abs=0.05) diff --git a/darts/tests/utils/test_utils_torch.py b/darts/tests/utils/test_utils_torch.py index 05cc92dc64..11c69845ca 100644 --- a/darts/tests/utils/test_utils_torch.py +++ b/darts/tests/utils/test_utils_torch.py @@ -1,118 +1,115 @@ import pytest from numpy.random import RandomState -from darts.logging import get_logger - -logger = get_logger(__name__) - -try: - import torch - - from darts.utils.torch import random_method - - TORCH_AVAILABLE = True -except ImportError: - logger.warning("Torch not available. Torch utils will not be tested.") - TORCH_AVAILABLE = False - - -if TORCH_AVAILABLE: - # use a simple torch model mock - class TorchModelMock: - @random_method - def __init__(self, some_params=None, **kwargs): - self.model = torch.randn(5) - # super().__init__() - - @random_method - def fit(self, some_params=None): - self.fit_value = torch.randn(5) - - class TestRandomMethod: - def test_it_raises_error_if_used_on_function(self): - with pytest.raises(ValueError): - - @random_method - def a_random_function(): - pass - - def test_model_is_random_by_default(self): - model1 = TorchModelMock() - model2 = TorchModelMock() - assert not torch.equal(model1.model, model2.model) - - def test_model_is_random_when_None_random_state_specified(self): - model1 = TorchModelMock(random_state=None) - model2 = TorchModelMock(random_state=None) - assert not torch.equal(model1.model, model2.model) - - def helper_test_reproducibility(self, model1, model2): - assert torch.equal(model1.model, model2.model) - - model1.fit() - model2.fit() - assert torch.equal(model1.fit_value, model2.fit_value) - - def test_model_is_reproducible_when_seed_specified(self): - model1 = TorchModelMock(random_state=42) - model2 = TorchModelMock(random_state=42) - self.helper_test_reproducibility(model1, model2) - - def test_model_is_reproducible_when_random_instance_specified(self): - model1 = TorchModelMock(random_state=RandomState(42)) - model2 = TorchModelMock(random_state=RandomState(42)) - self.helper_test_reproducibility(model1, model2) - - def test_model_is_different_for_different_seeds(self): - model1 = TorchModelMock(random_state=42) - model2 = TorchModelMock(random_state=43) - assert not torch.equal(model1.model, model2.model) - - def test_model_is_different_for_different_random_instance(self): - model1 = TorchModelMock(random_state=RandomState(42)) - model2 = TorchModelMock(random_state=RandomState(43)) - assert not torch.equal(model1.model, model2.model) - - def helper_test_successive_call_are_different(self, model): - # different between init and fit - model.fit() - assert not torch.equal(model.model, model.fit_value) - - # different between 2 fit - old_fit_value = model.fit_value.clone() - model.fit() - assert not torch.equal(model.fit_value, old_fit_value) - - def test_successive_call_to_rng_are_different_when_seed_specified(self): - model = TorchModelMock(random_state=42) - self.helper_test_successive_call_are_different(model) - - def test_successive_call_to_rng_are_different_when_random_instance_specified( - self, - ): - model = TorchModelMock(random_state=RandomState(42)) - self.helper_test_successive_call_are_different(model) - - def test_no_side_effect_between_rng_with_seeds(self): - model = TorchModelMock(random_state=42) - model.fit() - fit_value = model.fit_value.clone() - - model = TorchModelMock(random_state=42) - model2 = TorchModelMock(random_state=42) - model2.fit() - model.fit() - - assert torch.equal(model.fit_value, fit_value) - - def test_no_side_effect_between_rng_with_random_instance(self): - model = TorchModelMock(random_state=RandomState(42)) - model.fit() - fit_value = model.fit_value.clone() - - model = TorchModelMock(random_state=RandomState(42)) - model2 = TorchModelMock(random_state=RandomState(42)) - model2.fit() - model.fit() - - assert torch.equal(model.fit_value, fit_value) +from darts.tests.conftest import TORCH_AVAILABLE + +if not TORCH_AVAILABLE: + pytest.skip( + f"Torch not available. {__name__} tests will be skipped.", + allow_module_level=True, + ) +import torch + +from darts.utils.torch import random_method + + +# use a simple torch model mock +class TorchModelMock: + @random_method + def __init__(self, some_params=None, **kwargs): + self.model = torch.randn(5) + # super().__init__() + + @random_method + def fit(self, some_params=None): + self.fit_value = torch.randn(5) + + +class TestRandomMethod: + def test_it_raises_error_if_used_on_function(self): + with pytest.raises(ValueError): + + @random_method + def a_random_function(): + pass + + def test_model_is_random_by_default(self): + model1 = TorchModelMock() + model2 = TorchModelMock() + assert not torch.equal(model1.model, model2.model) + + def test_model_is_random_when_None_random_state_specified(self): + model1 = TorchModelMock(random_state=None) + model2 = TorchModelMock(random_state=None) + assert not torch.equal(model1.model, model2.model) + + def helper_test_reproducibility(self, model1, model2): + assert torch.equal(model1.model, model2.model) + + model1.fit() + model2.fit() + assert torch.equal(model1.fit_value, model2.fit_value) + + def test_model_is_reproducible_when_seed_specified(self): + model1 = TorchModelMock(random_state=42) + model2 = TorchModelMock(random_state=42) + self.helper_test_reproducibility(model1, model2) + + def test_model_is_reproducible_when_random_instance_specified(self): + model1 = TorchModelMock(random_state=RandomState(42)) + model2 = TorchModelMock(random_state=RandomState(42)) + self.helper_test_reproducibility(model1, model2) + + def test_model_is_different_for_different_seeds(self): + model1 = TorchModelMock(random_state=42) + model2 = TorchModelMock(random_state=43) + assert not torch.equal(model1.model, model2.model) + + def test_model_is_different_for_different_random_instance(self): + model1 = TorchModelMock(random_state=RandomState(42)) + model2 = TorchModelMock(random_state=RandomState(43)) + assert not torch.equal(model1.model, model2.model) + + def helper_test_successive_call_are_different(self, model): + # different between init and fit + model.fit() + assert not torch.equal(model.model, model.fit_value) + + # different between 2 fit + old_fit_value = model.fit_value.clone() + model.fit() + assert not torch.equal(model.fit_value, old_fit_value) + + def test_successive_call_to_rng_are_different_when_seed_specified(self): + model = TorchModelMock(random_state=42) + self.helper_test_successive_call_are_different(model) + + def test_successive_call_to_rng_are_different_when_random_instance_specified( + self, + ): + model = TorchModelMock(random_state=RandomState(42)) + self.helper_test_successive_call_are_different(model) + + def test_no_side_effect_between_rng_with_seeds(self): + model = TorchModelMock(random_state=42) + model.fit() + fit_value = model.fit_value.clone() + + model = TorchModelMock(random_state=42) + model2 = TorchModelMock(random_state=42) + model2.fit() + model.fit() + + assert torch.equal(model.fit_value, fit_value) + + def test_no_side_effect_between_rng_with_random_instance(self): + model = TorchModelMock(random_state=RandomState(42)) + model.fit() + fit_value = model.fit_value.clone() + + model = TorchModelMock(random_state=RandomState(42)) + model2 = TorchModelMock(random_state=RandomState(42)) + model2.fit() + model.fit() + + assert torch.equal(model.fit_value, fit_value) diff --git a/darts/timeseries.py b/darts/timeseries.py index 30d5aac716..0804c40133 100644 --- a/darts/timeseries.py +++ b/darts/timeseries.py @@ -21,7 +21,7 @@ - Have a monotonically increasing time index, without holes (without missing dates) - Contain numeric types only - Have distinct components/columns names - - Have a well defined frequency (`date offset aliases + - Have a well-defined frequency (`date offset aliases `_ for ``DateTimeIndex``, or step size for ``RangeIndex``) - Have static covariates consistent with their components, or no static covariates @@ -38,9 +38,11 @@ import re import sys from collections import defaultdict +from collections.abc import Sequence from copy import deepcopy from inspect import signature -from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union +from io import StringIO +from typing import Any, Callable, Literal, Optional, Union import matplotlib.axes import matplotlib.pyplot as plt @@ -50,12 +52,14 @@ from pandas.tseries.frequencies import to_offset from scipy.stats import kurtosis, skew -from .logging import get_logger, raise_if, raise_if_not, raise_log - -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal +from darts.logging import get_logger, raise_if, raise_if_not, raise_log +from darts.utils import _build_tqdm_iterator, _parallel_apply +from darts.utils.utils import ( + SUPPORTED_RESAMPLE_METHODS, + expand_arr, + generate_index, + n_steps_between, +) if sys.version_info >= (3, 11): from typing import Self @@ -67,6 +71,7 @@ # dimension names in the DataArray # the "time" one can be different, if it has a name in the underlying Series/DataFrame. DIMS = ("time", "component", "sample") +AXES = {"time": 0, "component": 1, "sample": 2} VALID_INDEX_TYPES = (pd.DatetimeIndex, pd.RangeIndex) STATIC_COV_TAG = "static_covariates" @@ -132,9 +137,7 @@ def __init__(self, xa: xr.DataArray, copy=True): # The first dimension represents the time and may be named differently. raise_log( ValueError( - "The last two dimensions of the DataArray must be named {}".format( - DIMS[-2:] - ) + f"The last two dimensions of the DataArray must be named {DIMS[-2:]}" ), logger, ) @@ -267,7 +270,7 @@ def __init__(self, xa: xr.DataArray, copy=True): ), logger, ) - # pre-compute grouping informations + # pre-compute grouping information components_set = set(self.components) children = set(hierarchy.keys()) @@ -402,11 +405,11 @@ def from_xarray( # clean components (columns) names if needed (if names are not unique, or not strings) components = xa_.get_index(DIMS[1]) - if len(set(components)) != len(components) or any( - [not isinstance(s, str) for s in components] - ): + if len(set(components)) != len(components) or any([ + not isinstance(s, str) for s in components + ]): - def _clean_component_list(columns) -> List[str]: + def _clean_component_list(columns) -> list[str]: # return a list of string containing column names # make each column name unique in case some columns have the same names clist = columns.to_list() @@ -425,9 +428,7 @@ def _clean_component_list(columns) -> List[str]: name_to_occurence[clist[i]] += 1 if name_to_occurence[clist[i]] > 1: - clist[i] = clist[i] + "_{}".format( - name_to_occurence[clist[i]] - 1 - ) + clist[i] = clist[i] + f"_{name_to_occurence[clist[i]] - 1}" has_duplicate = len(set(clist)) != len(clist) @@ -474,12 +475,12 @@ def from_csv( cls, filepath_or_buffer, time_col: Optional[str] = None, - value_cols: Optional[Union[List[str], str]] = None, + value_cols: Optional[Union[list[str], str]] = None, fill_missing_dates: Optional[bool] = False, freq: Optional[Union[str, int]] = None, fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, - hierarchy: Optional[Dict] = None, + hierarchy: Optional[dict] = None, **kwargs, ) -> Self: """ @@ -570,12 +571,12 @@ def from_dataframe( cls, df: pd.DataFrame, time_col: Optional[str] = None, - value_cols: Optional[Union[List[str], str]] = None, + value_cols: Optional[Union[list[str], str]] = None, fill_missing_dates: Optional[bool] = False, freq: Optional[Union[str, int]] = None, fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, - hierarchy: Optional[Dict] = None, + hierarchy: Optional[dict] = None, ) -> Self: """ Build a deterministic TimeSeries instance built from a selection of columns of a DataFrame. @@ -749,15 +750,17 @@ def from_dataframe( def from_group_dataframe( cls, df: pd.DataFrame, - group_cols: Union[List[str], str], + group_cols: Union[list[str], str], time_col: Optional[str] = None, - value_cols: Optional[Union[List[str], str]] = None, - static_cols: Optional[Union[List[str], str]] = None, + value_cols: Optional[Union[list[str], str]] = None, + static_cols: Optional[Union[list[str], str]] = None, fill_missing_dates: Optional[bool] = False, freq: Optional[Union[str, int]] = None, fillna_value: Optional[float] = None, - drop_group_cols: Optional[Union[List[str], str]] = None, - ) -> List[Self]: + drop_group_cols: Optional[Union[list[str], str]] = None, + n_jobs: Optional[int] = 1, + verbose: Optional[bool] = False, + ) -> list[Self]: """ Build a list of TimeSeries instances grouped by a selection of columns from a DataFrame. One column (or the DataFrame index) has to represent the time, @@ -806,6 +809,11 @@ def from_group_dataframe( Optionally, a numeric value to fill missing values (NaNs) with. drop_group_cols Optionally, a string or list of strings with `group_cols` column(s) to exclude from the static covariates. + n_jobs + Optionally, an integer representing the number of parallel jobs to run. Behavior is the same as in the + `joblib.Parallel` class. + verbose + Optionally, a boolean value indicating whether to display a progress bar. Returns ------- @@ -859,18 +867,38 @@ def from_group_dataframe( df = df[static_cov_cols + extract_value_cols + extract_time_col] - # sort on entire `df` to avoid having to sort individually later on if time_col: - df.index = pd.DatetimeIndex(df[time_col]) - df = df.drop(columns=time_col) - df = df.sort_index() - - # split df by groups, and store group values and static values (static covariates) - # single elements group columns must be unpacked for same groupby() behavior across different pandas versions - splits = [] - for static_cov_vals, group in df.groupby( - group_cols[0] if len(group_cols) == 1 else group_cols - ): + if np.issubdtype(df[time_col].dtype, object) or np.issubdtype( + df[time_col].dtype, np.datetime64 + ): + df.index = pd.DatetimeIndex(df[time_col]) + df = df.drop(columns=time_col) + else: + df = df.set_index(time_col) + + if df.index.is_monotonic_increasing: + logger.warning( + "UserWarning: The (time) index from `df` is monotonically increasing. This " + "results in time series groups with non-overlapping (time) index. You can ignore this warning if the " + "index represents the actual index of each individual time series group." + ) + + # sort on entire `df` to avoid having to sort individually later on + else: + df = df.sort_index() + + groups = df.groupby(group_cols[0] if len(group_cols) == 1 else group_cols) + + iterator = _build_tqdm_iterator( + groups, + verbose=verbose, + total=len(groups), + desc="Creating TimeSeries", + ) + + def from_group(static_cov_vals, group): + split = group[extract_value_cols] + static_cov_vals = ( (static_cov_vals,) if not isinstance(static_cov_vals, tuple) @@ -908,27 +936,26 @@ def from_group_dataframe( ) # add the static covariates to the group values static_cov_vals += tuple(group[static_cols].values[0]) - # store static covariate Series and group DataFrame (without static cov columns) - splits.append( - ( - pd.DataFrame([static_cov_vals], columns=extract_static_cov_cols) - if extract_static_cov_cols - else None, - group[extract_value_cols], - ) - ) - # create a list with multiple TimeSeries and add static covariates - return [ - cls.from_dataframe( + return cls.from_dataframe( df=split, fill_missing_dates=fill_missing_dates, freq=freq, fillna_value=fillna_value, - static_covariates=static_covs, + static_covariates=( + pd.DataFrame([static_cov_vals], columns=extract_static_cov_cols) + if extract_static_cov_cols + else None + ), ) - for static_covs, split in splits - ] + + return _parallel_apply( + iterator, + from_group, + n_jobs, + fn_args=dict(), + fn_kwargs=dict(), + ) @classmethod def from_series( @@ -995,7 +1022,7 @@ def from_times_and_values( columns: Optional[pd._typing.Axes] = None, fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, - hierarchy: Optional[Dict] = None, + hierarchy: Optional[dict] = None, ) -> Self: """ Build a series from a time index and value array. @@ -1087,12 +1114,7 @@ def from_times_and_values( # avoid copying if data is already np.ndarray: values = np.array(values) if not isinstance(values, np.ndarray) else values - - if len(values.shape) == 1: - values = np.expand_dims(values, 1) - if len(values.shape) == 2: - values = np.expand_dims(values, 2) - + values = expand_arr(values, ndim=len(DIMS)) coords = {times_name: times} if columns is not None: coords[DIMS[1]] = columns @@ -1103,7 +1125,6 @@ def from_times_and_values( coords=coords, attrs={STATIC_COV_TAG: static_covariates, HIERARCHY_TAG: hierarchy}, ) - return cls.from_xarray( xa=xa, fill_missing_dates=fill_missing_dates, @@ -1118,7 +1139,7 @@ def from_values( columns: Optional[pd._typing.Axes] = None, fillna_value: Optional[float] = None, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, - hierarchy: Optional[Dict] = None, + hierarchy: Optional[dict] = None, ) -> Self: """ Build an integer-indexed series from an array of values. @@ -1193,7 +1214,7 @@ def from_json( cls, json_str: str, static_covariates: Optional[Union[pd.Series, pd.DataFrame]] = None, - hierarchy: Optional[Dict] = None, + hierarchy: Optional[dict] = None, ) -> Self: """ Build a series from the JSON String representation of a ``TimeSeries`` @@ -1243,7 +1264,7 @@ def from_json( TimeSeries The time series object converted from the JSON String """ - df = pd.read_json(json_str, orient="split") + df = pd.read_json(StringIO(json_str), orient="split") return cls.from_dataframe( df, static_covariates=static_covariates, hierarchy=hierarchy ) @@ -1289,7 +1310,7 @@ def static_covariates(self) -> Optional[pd.DataFrame]: return self._xa.attrs.get(STATIC_COV_TAG, None) @property - def hierarchy(self) -> Optional[Dict]: + def hierarchy(self) -> Optional[dict]: """ The hierarchy of this TimeSeries, if any. If set, the hierarchy is encoded as a dictionary, whose keys are individual components @@ -1310,7 +1331,7 @@ def top_level_component(self) -> Optional[str]: return self._top_level_component @property - def bottom_level_components(self) -> Optional[List[str]]: + def bottom_level_components(self) -> Optional[list[str]]: """ The bottom level component names of this series, or None if the series has no hierarchy. """ @@ -1325,7 +1346,7 @@ def top_level_series(self) -> Optional[Self]: return self[self.top_level_component] if self.has_hierarchy else None @property - def bottom_level_series(self) -> Optional[List[Self]]: + def bottom_level_series(self) -> Optional[list[Self]]: """ The series containing the bottom-level components of this series in the same order as they appear in the series, or None if the series has no hierarchy. @@ -1338,15 +1359,20 @@ def bottom_level_series(self) -> Optional[List[Self]]: else None ) + @property + def shape(self) -> tuple[int]: + """The shape of the series (n_timesteps, n_components, n_samples).""" + return self._xa.shape + @property def n_samples(self) -> int: """Number of samples contained in the series.""" - return len(self._xa.sample) + return self.shape[AXES["sample"]] @property def n_components(self) -> int: """Number of components (dimensions) contained in the series.""" - return len(self._xa.component) + return self.shape[AXES["component"]] @property def width(self) -> int: @@ -1356,12 +1382,12 @@ def width(self) -> int: @property def n_timesteps(self) -> int: """Number of time steps in the series.""" - return len(self._time_index) + return self.shape[AXES["time"]] @property def is_deterministic(self) -> bool: """Whether this series is deterministic.""" - return self.n_samples == 1 + return self.shape[AXES["sample"]] == 1 @property def is_stochastic(self) -> bool: @@ -1376,7 +1402,7 @@ def is_probabilistic(self) -> bool: @property def is_univariate(self) -> bool: """Whether this series is univariate.""" - return self.n_components == 1 + return self.shape[AXES["component"]] == 1 @property def freq(self) -> Union[pd.DateOffset, int]: @@ -1488,9 +1514,7 @@ def _raise_if_not_within(self, ts: Union[pd.Timestamp, int]): raise_if_not( is_inside, - "Timestamp must be between {} and {}".format( - self.start_time(), self.end_time() - ), + f"Timestamp must be between {self.start_time()} and {self.end_time()}", logger, ) @@ -1704,7 +1728,7 @@ def quantile_timeseries(self, quantile=0.5, **kwargs) -> Self: return self.__class__(new_xa) - def quantiles_df(self, quantiles: Tuple[float] = (0.1, 0.5, 0.9)) -> pd.DataFrame: + def quantiles_df(self, quantiles: tuple[float] = (0.1, 0.5, 0.9)) -> pd.DataFrame: """ Return a Pandas DataFrame containing the desired quantiles of each component (over the samples). @@ -2143,7 +2167,7 @@ def get_index_at_point( ``pd.Timestamp`` work only on series that are indexed with a ``pd.DatetimeIndex``. In such cases, the returned point will be the index of this timestamp if it is present in the series time index. - It it's not present in the time index, the index of the next timestamp is returned if `after=True` + If it's not present in the time index, the index of the next timestamp is returned if `after=True` (if it exists in the series), otherwise the index of the previous timestamp is returned (if it exists in the series). @@ -2156,7 +2180,6 @@ def get_index_at_point( after If the provided pandas Timestamp is not in the time series index, whether to return the index of the next timestamp or the index of the previous one. - """ point_index = -1 if isinstance(point, float): @@ -2227,7 +2250,7 @@ def get_timestamp_at_point( def _split_at( self, split_point: Union[pd.Timestamp, float, int], after: bool = True - ) -> Tuple[Self, Self]: + ) -> tuple[Self, Self]: # Get index with not after in order to avoid moving twice if split_point is not in self point_index = self.get_index_at_point(split_point, not after) return ( @@ -2237,7 +2260,7 @@ def _split_at( def split_after( self, split_point: Union[pd.Timestamp, float, int] - ) -> Tuple[Self, Self]: + ) -> tuple[Self, Self]: """ Splits the series in two, after a provided `split_point`. @@ -2260,7 +2283,7 @@ def split_after( def split_before( self, split_point: Union[pd.Timestamp, float, int] - ) -> Tuple[Self, Self]: + ) -> tuple[Self, Self]: """ Splits the series in two, before a provided `split_point`. @@ -2338,7 +2361,7 @@ def slice( A new series, with indices greater or equal than `start_ts` and smaller or equal than `end_ts`. """ raise_if_not( - type(start_ts) == type(end_ts), + type(start_ts) is type(end_ts), "The two timestamps provided to slice() have to be of the same type.", logger, ) @@ -2472,8 +2495,87 @@ def slice_intersect(self, other: Self) -> Self: TimeSeries a new series, containing the values of this series, over the time-span common to both time series. """ - time_index = self.time_index.intersection(other.time_index) - return self[time_index] + if other.has_same_time_as(self): + return self.__class__(self._xa) + elif other.freq == self.freq and len(self) and len(other): + start, end = self._slice_intersect_bounds(other) + return self[start:end] + else: + time_index = self.time_index.intersection(other.time_index) + return self[time_index] + + def slice_intersect_values(self, other: Self, copy: bool = False) -> np.ndarray: + """ + Return the sliced values of this series, where the time index has been intersected with the one + of the `other` series. + + This method is in general *not* symmetric. + + Parameters + ---------- + other + The other time series + copy + Whether to return a copy of the values, otherwise returns a view. + Leave it to True unless you know what you are doing. + + Returns + ------- + np.ndarray + The values of this series, over the time-span common to both time series. + """ + vals = self.all_values(copy=copy) + if other.has_same_time_as(self): + return vals + if other.freq == self.freq: + start, end = self._slice_intersect_bounds(other) + return vals[start:end] + else: + return vals[self._time_index.isin(other._time_index)] + + def slice_intersect_times( + self, other: Self, copy: bool = True + ) -> Union[pd.DatetimeIndex, pd.RangeIndex]: + """ + Return time index of this series, where the time index has been intersected with the one + of the `other` series. + + This method is in general *not* symmetric. + + Parameters + ---------- + other + The other time series + copy + Whether to return a copy of the time index, otherwise returns a view. Leave it to True unless you know + what you are doing. + + Returns + ------- + Union[pd.DatetimeIndex, pd.RangeIndex] + The time index of this series, over the time-span common to both time series. + """ + + time_index = self.time_index if copy else self._time_index + if other.has_same_time_as(self): + return time_index + if other.freq == self.freq: + start, end = self._slice_intersect_bounds(other) + return time_index[start:end] + else: + return time_index[time_index.isin(other._time_index)] + + def _slice_intersect_bounds(self, other: Self) -> tuple[int, int]: + """Find the start (absolute index) and end (index relative to the end) indices that represent the time + intersection from `self` and `other`.""" + shift_start = n_steps_between( + other.start_time(), self.start_time(), freq=self.freq + ) + shift_end = len(other) - (len(self) - shift_start) + + shift_start = shift_start if shift_start >= 0 else 0 + shift_end = shift_end if shift_end < 0 else None + return shift_start, shift_end def strip(self, how: str = "all") -> Self: """ @@ -2621,8 +2723,8 @@ def shift(self, n: int) -> Self: except pd.errors.OutOfBoundsDatetime: raise_log( OverflowError( - "the add operation between {} and {} will " - "overflow".format(n * self.freq, self.time_index[-1]) + f"the add operation between {n * self.freq} and {self.time_index[-1]} will " + "overflow" ), logger, ) @@ -2654,7 +2756,7 @@ def diff( Optionally, periods to shift for calculating difference. For instance, periods=12 computes the difference between values at time `t` and times `t-12`. dropna - Whether to drop the missing values after each differencing steps. If set to False, the corresponding + Whether to drop the missing values after each differencing steps. If set to `False`, the corresponding first `periods` time steps will be filled with NaNs. Returns @@ -2714,7 +2816,12 @@ def has_same_time_as(self, other: Self) -> bool: """ if len(other) != len(self): return False - return (other.time_index == self.time_index).all() + elif other.freq != self.freq: + return False + elif other.start_time() != self.start_time(): + return False + else: + return True def append(self, other: Self) -> Self: """ @@ -2742,7 +2849,7 @@ def append(self, other: Self) -> Self: ) raise_if_not( other.freq == self.freq, - "Appended TimeSeries must have the same frequency as the current one", + "Both series must have the same frequency.", logger, ) raise_if_not( @@ -2755,10 +2862,10 @@ def append(self, other: Self) -> Self: "Both series must have the same number of components.", logger, ) - if self._has_datetime_index: + if len(self) > 0 and len(other) > 0: raise_if_not( other.start_time() == self.end_time() + self.freq, - "Appended TimeSeries must start one time step after current one.", + "Appended TimeSeries must start one (time) step after current one.", logger, ) @@ -2792,17 +2899,28 @@ def append_values(self, values: np.ndarray) -> Self: TimeSeries A new TimeSeries with the new values appended """ - if self._has_datetime_index: - idx = pd.DatetimeIndex( - [self.end_time() + i * self._freq for i in range(1, len(values) + 1)], - freq=self._freq, - ) - else: - idx = pd.RangeIndex( - start=self.end_time() + self._freq, - stop=self.end_time() + (len(values) + 1) * self._freq, - step=self._freq, + if len(values) == 0: + return self.copy() + + values = np.array(values) if not isinstance(values, np.ndarray) else values + values = expand_arr(values, ndim=len(DIMS)) + if not values.shape[1:] == self._xa.values.shape[1:]: + raise_log( + ValueError( + f"The (expanded) values must have the same number of components and samples " + f"(second and third dims) as the series to append to. " + f"Received shape: {values.shape}, expected: {self._xa.values.shape}" + ), + logger=logger, ) + + idx = generate_index( + start=self.end_time() + self.freq, + length=len(values), + freq=self.freq, + name=self._time_index.name, + ) + return self.append( self.__class__.from_times_and_values( values=values, @@ -2851,31 +2969,97 @@ def prepend_values(self, values: np.ndarray) -> Self: TimeSeries A new TimeSeries with the new values prepended. """ + if len(values) == 0: + return self.copy() - if self._has_datetime_index: - idx = pd.DatetimeIndex( - [ - self.start_time() - i * self._freq - for i in reversed(range(1, len(values) + 1)) - ], - freq=self._freq, - ) - else: - idx = pd.RangeIndex( - self.start_time() - self.freq * len(values), - self.start_time(), - step=self.freq, + values = np.array(values) if not isinstance(values, np.ndarray) else values + values = expand_arr(values, ndim=len(DIMS)) + if not values.shape[1:] == self._xa.values.shape[1:]: + raise_log( + ValueError( + f"The (expanded) values must have the same number of components and samples " + f"(second and third dims) as the series to prepend to. " + f"Received shape: {values.shape}, expected: {self._xa.values.shape}" + ), + logger=logger, ) + idx = generate_index( + end=self.start_time() - self.freq, + length=len(values), + freq=self.freq, + name=self._time_index.name, + ) + return self.prepend( self.__class__.from_times_and_values( values=values, times=idx, fill_missing_dates=False, static_covariates=self.static_covariates, + columns=self.columns, + hierarchy=self.hierarchy, ) ) + def with_times_and_values( + self, + times: Union[pd.DatetimeIndex, pd.RangeIndex, pd.Index], + values: np.ndarray, + fill_missing_dates: Optional[bool] = False, + freq: Optional[Union[str, int]] = None, + fillna_value: Optional[float] = None, + ) -> Self: + """ + Return a new ``TimeSeries`` similar to this one but with new specified values. + + Parameters + ---------- + times + A pandas DateTimeIndex, RangeIndex (or Index that can be converted to a RangeIndex) representing the new + time axis for the time series. It is better if the index has no holes; alternatively setting + `fill_missing_dates` can in some cases solve these issues (filling holes with NaN, or with the provided + `fillna_value` numeric value, if any). + values + A Numpy array with new values. It must have the dimensions for `times` and components, but may contain a + different number of samples. + fill_missing_dates + Optionally, a boolean value indicating whether to fill missing dates (or indices in case of integer index) + with NaN values. This requires either a provided `freq` or the possibility to infer the frequency from the + provided timestamps. See :meth:`_fill_missing_dates() ` for more info. + freq + Optionally, a string or integer representing the frequency of the underlying index. This is useful in order + to fill in missing values if some dates are missing and `fill_missing_dates` is set to `True`. + If a string, represents the frequency of the pandas DatetimeIndex (see `offset aliases + `_ for more info on + supported frequencies). + If an integer, represents the step size of the pandas Index or pandas RangeIndex. + fillna_value + Optionally, a numeric value to fill missing values (NaNs) with. + + Returns + ------- + TimeSeries + A new TimeSeries with the new values and same index, static covariates and hierarchy + """ + values = np.array(values) if not isinstance(values, np.ndarray) else values + values = expand_arr(values, ndim=len(DIMS)) + raise_if_not( + values.shape[1] == self._xa.values.shape[1], + "The new values must have the same number of components as the present series. " + f"Received: {values.shape[1]}, expected: {self._xa.values.shape[1]}", + ) + return self.from_times_and_values( + times=times, + values=values, + fill_missing_dates=fill_missing_dates, + freq=freq, + columns=self.columns, + fillna_value=fillna_value, + static_covariates=self.static_covariates, + hierarchy=self.hierarchy, + ) + def with_values(self, values: np.ndarray) -> Self: """ Return a new ``TimeSeries`` similar to this one but with new specified values. @@ -2891,12 +3075,12 @@ def with_values(self, values: np.ndarray) -> Self: TimeSeries A new TimeSeries with the new values and same index, static covariates and hierarchy """ + values = np.array(values) if not isinstance(values, np.ndarray) else values + values = expand_arr(values, ndim=len(DIMS)) raise_if_not( values.shape[:2] == self._xa.values.shape[:2], "The new values must have the same shape (time, components) as the present series. " - "Received: {}, expected: {}".format( - values.shape[:2], self._xa.values.shape[:2] - ), + f"Received: {values.shape[:2]}, expected: {self._xa.values.shape[:2]}", ) new_xa = xr.DataArray( @@ -2968,7 +3152,7 @@ def with_static_covariates( ) ) - def with_hierarchy(self, hierarchy: Dict[str, Union[str, List[str]]]): + def with_hierarchy(self, hierarchy: dict[str, Union[str, list[str]]]): """ Adds a hierarchy to the TimeSeries. @@ -3032,7 +3216,7 @@ def stack(self, other: Self) -> Self: """ return concatenate([self, other], axis=1) - def drop_columns(self, col_names: Union[List[str], str]) -> Self: + def drop_columns(self, col_names: Union[list[str], str]) -> Self: """ Return a new ``TimeSeries`` instance with dropped columns/components. @@ -3095,6 +3279,12 @@ def add_datetime_attribute( This works only for deterministic time series (i.e., made of 1 sample). + Notes + ----- + 0-indexing is enforced across all the encodings, see + :meth:`datetime_attribute_timeseries() ` + for more information. + Parameters ---------- attribute @@ -3115,7 +3305,7 @@ def add_datetime_attribute( New TimeSeries instance enhanced by `attribute`. """ self._assert_deterministic() - from .utils import timeseries_generation as tg + from darts.utils import timeseries_generation as tg return self.stack( tg.datetime_attribute_timeseries( @@ -3161,7 +3351,7 @@ def add_holidays( A new TimeSeries instance, enhanced with binary holiday component. """ self._assert_deterministic() - from .utils import timeseries_generation as tg + from darts.utils import timeseries_generation as tg return self.stack( tg.holidays_timeseries( @@ -3173,31 +3363,41 @@ def add_holidays( ) ) - def resample(self, freq: str, method: str = "pad", **kwargs) -> Self: + def resample( + self, + freq: Union[str, pd.DateOffset], + method: str = "pad", + method_kwargs: Optional[dict[str, Any]] = None, + **kwargs, + ) -> Self: """ Build a reindexed ``TimeSeries`` with a given frequency. - Provided method is used to fill holes in reindexed TimeSeries, by default 'pad'. + Provided method is used to aggregate/fill holes in the reindexed TimeSeries, by default 'pad'. Parameters ---------- freq The new time difference between two adjacent entries in the returned TimeSeries. - A DateOffset alias is expected. - method: - Method to fill holes in reindexed TimeSeries (note this does not fill NaNs that already were present): - - 'pad': propagate last valid observation forward to next valid - - 'backfill': use NEXT valid observation to fill. + Expects a `pandas.DateOffset` or `DateOffset` alias. + method + Method to either aggregate grouped values (for down-sampling) or fill holes (for up-sampling) + in the reindexed TimeSeries. For more information, see the `xarray DataArrayResample documentation + `_. + Supported methods: ["all", "any", "asfreq", "backfill", "bfill", "count", "ffill", "first", "interpolate", + "last", "max", "mean", "median", "min", "nearest", "pad", "prod", "quantile", "reduce", "std", "sum", + "var"]. + method_kwargs + Additional keyword arguments for the specified `method`. Some methods require additional arguments. + Xarray's errors will be raised on invalid keyword arguments. kwargs some keyword arguments for the `xarray.resample` method, notably `offset` or `base` to indicate where to start the resampling and avoid nan at the first value of the resampled TimeSeries - For more informations, see the `xarray resample() documentation + For more information, see the `xarray resample() documentation `_. Examples -------- - >>> times = pd.date_range(start=pd.Timestamp("20200101233000"), periods=6, freq="15T") + >>> times = pd.date_range(start=pd.Timestamp("20200101233000"), periods=6, freq="15min") >>> pd_series = pd.Series(range(6), index=times) >>> ts = TimeSeries.from_series(pd_series) >>> print(ts.time_index) @@ -3212,30 +3412,47 @@ def resample(self, freq: str, method: str = "pad", **kwargs) -> Self: >>> print(resampled_nokwargs_ts.values()) [[nan] [ 2.]] - >>> resampled_ts = ts.resample(freq="1h", offset=pd.Timedelta("30T")) + >>> resampled_ts = ts.resample(freq="1h", offset=pd.Timedelta("30min")) >>> print(resampled_ts.time_index) DatetimeIndex(['2020-01-01 23:30:00', '2020-01-02 00:30:00'], dtype='datetime64[ns]', name='time', freq='H') >>> print(resampled_ts.values()) [[0.] [4.]] + >>> resampled_ts = ts.resample(freq="1h", offset=pd.Timedelta("30min")) + >>> downsampled_mean_ts = ts.resample(freq="30min", method="mean") + >>> print(downsampled_mean_ts.values()) + [[0.5] + [2.5] + [4.5]] + >>> downsampled_reduce_ts = ts.resample(freq="30min", method="reduce", method_args={"func":np.mean}) + >>> print(downsampled_reduce_ts.values()) + [[0.5] + [2.5] + [4.5]] Returns ------- TimeSeries A reindexed TimeSeries with given frequency. """ + method_kwargs = method_kwargs or {} + if isinstance(freq, pd.DateOffset): + freq = freq.freqstr resample = self._xa.resample( indexer={self._time_dim: freq}, **kwargs, ) - # TODO: check - if method == "pad": - new_xa = resample.pad() - elif method == "bfill": - new_xa = resample.backfill() + if method in SUPPORTED_RESAMPLE_METHODS: + applied_method = getattr(xr.core.resample.DataArrayResample, method) + new_xa = applied_method(resample, **method_kwargs) + + # Convert boolean to int as Timeseries must contain numeric values only + # method: "all", "any" + if new_xa.dtype == "bool": + new_xa = new_xa.astype(int) else: raise_log(ValueError(f"Unknown method: {method}"), logger) return self.__class__(new_xa) @@ -3321,23 +3538,17 @@ def map( elif num_args == 2: # map function uses timestamp f(timestamp, x) # go over shortest amount of iterations, either over time steps or components and samples if self.n_timesteps <= self.n_components * self.n_samples: - new_vals = np.vstack( - [ - np.expand_dims( - fn(self.time_index[i], self._xa[i, :, :]), axis=0 - ) - for i in range(self.n_timesteps) - ] - ) + new_vals = np.vstack([ + np.expand_dims(fn(self.time_index[i], self._xa[i, :, :]), axis=0) + for i in range(self.n_timesteps) + ]) else: new_vals = np.stack( [ - np.column_stack( - [ - fn(self.time_index, self._xa[:, i, j]) - for j in range(self.n_samples) - ] - ) + np.column_stack([ + fn(self.time_index, self._xa[:, i, j]) + for j in range(self.n_samples) + ]) for i in range(self.n_components) ], axis=1, @@ -3351,11 +3562,12 @@ def map( def window_transform( self, - transforms: Union[Dict, Sequence[Dict]], + transforms: Union[dict, Sequence[dict]], treat_na: Optional[Union[str, Union[int, float]]] = None, forecasting_safe: Optional[bool] = True, keep_non_transformed: Optional[bool] = False, include_current: Optional[bool] = True, + keep_names: Optional[bool] = False, ) -> Self: """ Applies a moving/rolling, expanding or exponentially weighted window transformation over this ``TimeSeries``. @@ -3454,11 +3666,15 @@ def window_transform( keep_non_transformed ``False`` to return the transformed components only, ``True`` to return all original components along - the transformed ones. Default is ``False``. + the transformed ones. Default is ``False``. If the series has a hierarchy, must be set to ``False``. include_current ``True`` to include the current time step in the window, ``False`` to exclude it. Default is ``True``. + keep_names + Whether the transformed components should keep the original component names or. Must be set to ``False`` + if `keep_non_transformed = True` or the number of transformation is greater than 1. + Returns ------- TimeSeries @@ -3607,6 +3823,53 @@ def _get_kwargs(transformation, forecasting_safe): if isinstance(transforms, dict): transforms = [transforms] + # check if some transformations are applied to the same components + overlapping_transforms = False + transformed_components = set() + for tr in transforms: + if not isinstance(tr, dict): + raise_log( + ValueError("Every entry in `transforms` must be a dictionary"), + logger, + ) + tr_comps = set(tr["components"] if "components" in tr else self.components) + if len(transformed_components.intersection(tr_comps)) > 0: + overlapping_transforms = True + transformed_components = transformed_components.union(tr_comps) + + if keep_names and overlapping_transforms: + raise_log( + ValueError( + "Cannot keep the original component names as some transforms are overlapping " + "(applied to the same components). Set `keep_names` to `False`." + ), + logger, + ) + + # actually, this could be allowed to allow transformation "in place"? + # keep_non_transformed can be changed to False/ignored if the transforms are not partial + if keep_names and keep_non_transformed: + raise_log( + ValueError( + "`keep_names = True` and `keep_non_transformed = True` cannot be used together." + ), + logger, + ) + + partial_transforms = transformed_components != set(self.components) + new_hierarchy = None + convert_hierarchy = False + comp_names_map = dict() + if self.hierarchy: + # the partial_transform covers for scenario keep_non_transformed = True + if len(transforms) > 1 or partial_transforms: + logger.warning( + "The hierarchy cannot be retained, either because there is more than one transform or " + "because the transform is not applied to all the components of the series." + ) + else: + convert_hierarchy = True + raise_if_not( all([isinstance(tr, dict) for tr in transforms]), "`transforms` must be a non-empty dictionary or a non-empty list of dictionaries.", @@ -3680,24 +3943,32 @@ def _get_kwargs(transformation, forecasting_safe): function_name = fn if fn != "apply" else "udf" name_prefix = ( f"{window_mode}_{function_name}" - f"{'_'+str(transformation['window']) if 'window' in transformation else ''}" - f"{'_'+str(min_periods) if min_periods>1 else ''}" + f"{'_' + str(transformation['window']) if 'window' in transformation else ''}" + f"{'_' + str(min_periods) if min_periods > 1 else ''}" ) - new_columns.extend( - [f"{name_prefix}_{comp_name}" for comp_name in comps_to_transform] - ) + if keep_names: + new_columns.extend(comps_to_transform) + else: + names_w_prefix = [ + f"{name_prefix}_{comp_name}" for comp_name in comps_to_transform + ] + new_columns.extend(names_w_prefix) + if convert_hierarchy: + comp_names_map.update({ + c_name: new_c_name + for c_name, new_c_name in zip( + comps_to_transform, names_w_prefix + ) + }) # track how many NaN rows are added by each transformation on each transformed column # NaNs would appear only if user changes "min_periods" to else than 1, if not, # by default there should be no NaNs unless the original series starts with NaNs (those would be maintained) total_na = min_periods + shifts + (closed == "left") - added_na.extend( - [ - total_na - 1 if min_periods > 0 else total_na - for _ in filter_df_columns - ] - ) + added_na.extend([ + total_na - 1 if min_periods > 0 else total_na for _ in filter_df_columns + ]) # keep all original components if keep_non_transformed: @@ -3741,6 +4012,15 @@ def _get_kwargs(transformation, forecasting_safe): # revert dataframe to TimeSeries new_index = original_index.__class__(resulting_transformations.index) + if convert_hierarchy: + if keep_names: + new_hierarchy = self.hierarchy + else: + new_hierarchy = { + comp_names_map[k]: [comp_names_map[old_name] for old_name in v] + for k, v in self.hierarchy.items() + } + transformed_time_series = TimeSeries.from_times_and_values( times=new_index, values=resulting_transformations.values.reshape( @@ -3748,7 +4028,7 @@ def _get_kwargs(transformation, forecasting_safe): ), columns=new_columns, static_covariates=self.static_covariates, - hierarchy=self.hierarchy, + hierarchy=new_hierarchy, ) return transformed_time_series @@ -3821,9 +4101,13 @@ def plot( low_quantile: Optional[float] = 0.05, high_quantile: Optional[float] = 0.95, default_formatting: bool = True, + title: Optional[str] = None, label: Optional[Union[str, Sequence[str]]] = "", max_nr_components: int = 10, ax: Optional[matplotlib.axes.Axes] = None, + alpha: Optional[float] = None, + color: Optional[Union[str, tuple, Sequence[str, tuple]]] = None, + c: Optional[Union[str, tuple, Sequence[str, tuple]]] = None, *args, **kwargs, ) -> matplotlib.axes.Axes: @@ -3850,6 +4134,8 @@ def plot( interval is shown if `high_quantile` is None (default 0.95). default_formatting Whether to use the darts default scheme. + title + Optionally, a custom plot title. If `None`, will use the name of the underlying `xarray.DataArray`. label Can either be a string or list of strings. If a string and the series only has a single component, it is used as the label for that component. If a string and the series has multiple components, it is used as @@ -3861,8 +4147,16 @@ def plot( Optionally, an axis to plot on. If `None`, and `new_plot=False`, will use the current axis. If `new_plot=True`, will create a new axis. alpha - Optionally, set the line alpha for deterministic series, or the confidence interval alpha for + Optionally, set the line alpha for deterministic series, or the confidence interval alpha for probabilistic series. + color + Can either be a single color or list of colors. Any matplotlib color is accepted (string, hex string, + RGB/RGBA tuple). If a single color and the series only has a single component, it is used as the color + for that component. If a single color and the series has multiple components, it is used as the color + for each component. If a list of colors with length equal to the number of components in the series, the + colors will be mapped to the components in order. + c + An alias for `color`. args some positional arguments for the `plot()` method kwargs @@ -3889,40 +4183,63 @@ def plot( logger, ) - if new_plot: - fig, ax = plt.subplots() + if max_nr_components == -1: + n_components_to_plot = self.n_components else: - if ax is None: - ax = plt.gca() + n_components_to_plot = min(self.n_components, max_nr_components) - if not any(lw in kwargs for lw in ["lw", "linewidth"]): - kwargs["lw"] = 2 - - n_components_to_plot = max_nr_components - if n_components_to_plot == -1: - n_components_to_plot = self.n_components - elif self.n_components > max_nr_components: + if self.n_components > n_components_to_plot: logger.warning( - f"Number of components is larger than {max_nr_components} ({self.n_components}). " - f"Plotting only the first {max_nr_components} components." - f"You can overwrite this in the using the `plot_all_components` argument in plot()" - f"Beware that plotting a large number of components may cause performance issues." + f"Number of series components ({self.n_components}) is larger than the maximum number of " + f"components to plot ({max_nr_components}). Plotting only the first `{max_nr_components}` " + f"components. You can adjust the number of components to plot using `max_nr_components`." ) if not isinstance(label, str) and isinstance(label, Sequence): - raise_if_not( - len(label) == self.n_components - or ( - self.n_components > n_components_to_plot - and len(label) >= n_components_to_plot + if len(label) != self.n_components and len(label) != n_components_to_plot: + raise_log( + ValueError( + f"The `label` sequence must have the same length as the number of series components " + f"({self.n_components}) or as the number of plotted components ({n_components_to_plot}). " + f"Received length `{len(label)}`." + ), + logger, + ) + custom_labels = True + else: + custom_labels = False + + if color and c: + raise_log( + ValueError( + "`color` and `c` must not be used simultaneously, use one or the other." ), - "The label argument should have the same length as the number of plotted components " - f"({min(self.n_components, n_components_to_plot)}), only {len(label)} labels were provided", logger, ) - custom_labels = True + color = color or c + if not isinstance(color, (str, tuple)) and isinstance(color, Sequence): + if len(color) != self.n_components and len(color) != n_components_to_plot: + raise_log( + ValueError( + f"The `color` sequence must have the same length as the number of series components " + f"({self.n_components}) or as the number of plotted components ({n_components_to_plot}). " + f"Received length `{len(label)}`." + ), + logger, + ) + custom_colors = True else: - custom_labels = False + custom_colors = False + + kwargs["alpha"] = alpha + if not any(lw in kwargs for lw in ["lw", "linewidth"]): + kwargs["lw"] = 2 + + if new_plot: + fig, ax = plt.subplots() + else: + if ax is None: + ax = plt.gca() for i, c in enumerate(self._xa.component[:n_components_to_plot]): comp_name = str(c.values) @@ -3936,9 +4253,6 @@ def plot( else: central_series = comp.mean(dim=DIMS[2]) - alpha = kwargs["alpha"] if "alpha" in kwargs else None - if not self.is_deterministic: - kwargs["alpha"] = 1 if custom_labels: label_to_use = label[i] else: @@ -3949,16 +4263,20 @@ def plot( else: label_to_use = f"{label}_{comp_name}" kwargs["label"] = label_to_use + kwargs["c"] = color[i] if custom_colors else color + kwargs_central = deepcopy(kwargs) + if not self.is_deterministic: + kwargs_central["alpha"] = 1 if central_series.shape[0] > 1: - p = central_series.plot(*args, ax=ax, **kwargs) + p = central_series.plot(*args, ax=ax, **kwargs_central) # empty TimeSeries elif central_series.shape[0] == 0: p = ax.plot( [], [], *args, - **kwargs, + **kwargs_central, ) ax.set_xlabel(self.time_index.name) else: @@ -3967,7 +4285,7 @@ def plot( central_series.values[0], "o", *args, - **kwargs, + **kwargs_central, ) color_used = p[0].get_color() if default_formatting else None @@ -3997,11 +4315,11 @@ def plot( ) ax.legend() - ax.set_title(self._xa.name) + ax.set_title(title if title is not None else self._xa.name) return ax def with_columns_renamed( - self, col_names: Union[List[str], str], col_names_new: Union[List[str], str] + self, col_names: Union[list[str], str], col_names_new: Union[list[str], str] ) -> Self: """ Return a new ``TimeSeries`` instance with new columns/components names. It also @@ -4391,11 +4709,24 @@ def _combine_arrays( else: other_vals = other - raise_if_not( - self._xa.values.shape == other_vals.shape, - "Attempted to perform operation on two TimeSeries of unequal shapes.", - logger, - ) + t, c, s = self._xa.shape + other_shape = other_vals.shape + if not ( + # can combine arrays if shapes are equal (t, c, s) + other_shape == (t, c, s) + # or broadcast [t, 1, 1] onto [t, c, s] + or other_shape == (t, 1, 1) + # or broadcast [t, c, 1] onto [t, c, s] + or other_shape == (t, c, 1) + # or broadcast [t, 1, s] onto [t, c, s] + or other_shape == (t, 1, s), + ): + raise_log( + ValueError( + "Attempted to perform operation on two TimeSeries of unequal shapes." + ), + logger=logger, + ) new_xa = self._xa.copy() new_xa.values = combine_fn(new_xa.values, other_vals) return self.__class__(new_xa) @@ -4443,9 +4774,9 @@ def _fill_missing_dates( time_dim = xa.dims[0] sorted_xa = cls._sort_index(xa, copy=False) - time_index: Union[ - pd.Index, pd.RangeIndex, pd.DatetimeIndex - ] = sorted_xa.get_index(time_dim) + time_index: Union[pd.Index, pd.RangeIndex, pd.DatetimeIndex] = ( + sorted_xa.get_index(time_dim) + ) if isinstance(time_index, pd.DatetimeIndex): has_datetime_index = True @@ -4659,9 +4990,7 @@ def _get_dim_name(self, axis: Union[int, str]) -> str: known_dims = (self._time_dim,) + DIMS[1:] raise_if_not( axis in known_dims, - "`axis` must be a known dimension of this series: {}".format( - known_dims - ), + f"`axis` must be a known dimension of this series: {known_dims}", ) return axis @@ -4675,9 +5004,7 @@ def _get_dim(self, axis: Union[int, str]) -> int: known_dims = (self._time_dim,) + DIMS[1:] raise_if_not( axis in known_dims, - "`axis` must be a known dimension of this series: {}".format( - known_dims - ), + f"`axis` must be a known dimension of this series: {known_dims}", ) return known_dims.index(axis) @@ -4703,9 +5030,7 @@ def __add__(self, other): else: raise_log( TypeError( - "unsupported operand type(s) for + or add(): '{}' and '{}'.".format( - type(self).__name__, type(other).__name__ - ) + f"unsupported operand type(s) for + or add(): '{type(self).__name__}' and '{type(other).__name__}'." ), logger, ) @@ -4724,9 +5049,7 @@ def __sub__(self, other): else: raise_log( TypeError( - "unsupported operand type(s) for - or sub(): '{}' and '{}'.".format( - type(self).__name__, type(other).__name__ - ) + f"unsupported operand type(s) for - or sub(): '{type(self).__name__}' and '{type(other).__name__}'." ), logger, ) @@ -4745,9 +5068,7 @@ def __mul__(self, other): else: raise_log( TypeError( - "unsupported operand type(s) for * or mul(): '{}' and '{}'.".format( - type(self).__name__, type(other).__name__ - ) + f"unsupported operand type(s) for * or mul(): '{type(self).__name__}' and '{type(other).__name__}'." ), logger, ) @@ -4767,9 +5088,7 @@ def __pow__(self, n): else: raise_log( TypeError( - "unsupported operand type(s) for ** or pow(): '{}' and '{}'.".format( - type(self).__name__, type(n).__name__ - ) + f"unsupported operand type(s) for ** or pow(): '{type(self).__name__}' and '{type(n).__name__}'." ), logger, ) @@ -4783,18 +5102,23 @@ def __truediv__(self, other): ) return self.__class__(xa_) elif isinstance(other, (TimeSeries, xr.DataArray, np.ndarray)): - if not (other.all_values(copy=False) != 0).all(): + if isinstance(other, TimeSeries): + other_vals = other.data_array(copy=False).values + elif isinstance(other, xr.DataArray): + other_vals = other.values + else: + other_vals = other + if not (other_vals != 0).all(): raise_log( ZeroDivisionError("Cannot divide by a TimeSeries with a value 0."), logger, ) - return self._combine_arrays(other, lambda s1, s2: s1 / s2) + return self._combine_arrays(other_vals, lambda s1, s2: s1 / s2) else: raise_log( TypeError( - "unsupported operand type(s) for / or truediv(): '{}' and '{}'.".format( - type(self).__name__, type(other).__name__ - ) + "unsupported operand type(s) for / or truediv():" + f" '{type(self).__name__}' and '{type(other).__name__}'." ), logger, ) @@ -4828,9 +5152,7 @@ def __lt__(self, other) -> xr.DataArray: else: raise_log( TypeError( - "unsupported operand type(s) for < : '{}' and '{}'.".format( - type(self).__name__, type(other).__name__ - ) + f"unsupported operand type(s) for < : '{type(self).__name__}' and '{type(other).__name__}'." ), logger, ) @@ -4849,9 +5171,7 @@ def __gt__(self, other) -> xr.DataArray: else: raise_log( TypeError( - "unsupported operand type(s) for < : '{}' and '{}'.".format( - type(self).__name__, type(other).__name__ - ) + f"unsupported operand type(s) for < : '{type(self).__name__}' and '{type(other).__name__}'." ), logger, ) @@ -4870,9 +5190,7 @@ def __le__(self, other) -> xr.DataArray: else: raise_log( TypeError( - "unsupported operand type(s) for < : '{}' and '{}'.".format( - type(self).__name__, type(other).__name__ - ) + f"unsupported operand type(s) for < : '{type(self).__name__}' and '{type(other).__name__}'." ), logger, ) @@ -4891,9 +5209,7 @@ def __ge__(self, other) -> xr.DataArray: else: raise_log( TypeError( - "unsupported operand type(s) for < : '{}' and '{}'.".format( - type(self).__name__, type(other).__name__ - ) + f"unsupported operand type(s) for < : '{type(self).__name__}' and '{type(other).__name__}'." ), logger, ) @@ -4920,9 +5236,9 @@ def __getitem__( key: Union[ pd.DatetimeIndex, pd.RangeIndex, - List[str], - List[int], - List[pd.Timestamp], + list[str], + list[int], + list[pd.Timestamp], str, int, pd.Timestamp, @@ -5007,24 +5323,47 @@ def _get_freq(xa_in: xr.DataArray): return self.__class__(xa_) elif isinstance(key, pd.RangeIndex): _check_range() - xa_ = self._xa.sel({self._time_dim: key}) + idx_ = key + if not len(key) and self.freq != key.step: + # keep original step size in case of empty range index + idx_ = pd.RangeIndex(step=self.freq) + + xa_ = self._xa.sel({self._time_dim: idx_}) # sel() gives us an Int64Index. We have to set the RangeIndex. # see: https://github.com/pydata/xarray/issues/6256 - xa_ = xa_.assign_coords({self.time_dim: key}) + xa_ = xa_.assign_coords({self.time_dim: idx_}) return self.__class__(xa_) # handle slices: elif isinstance(key, slice): - if isinstance(key.start, str) or isinstance(key.stop, str): + if key.start is None and key.stop is None: + if key.step is not None and key.step <= 0: + raise_log( + ValueError( + "Indexing a `TimeSeries` with a `slice` of `step<=0` (reverse) is not " + "possible since `TimeSeries` must have a monotonically increasing time index." + ), + logger=logger, + ) + else: + xa_ = self._xa.isel({self._time_dim: key}) + if _get_freq(xa_) is None: + # indexing discarded the freq; we restore it + freq = key.step * self.freq if key.step else self.freq + _set_freq_in_xa(xa_, freq) + return self.__class__(xa_) + elif isinstance(key.start, str) or isinstance(key.stop, str): xa_ = self._xa.sel({DIMS[1]: key}) # selecting components discards the hierarchy, if any xa_ = _xarray_with_attrs( xa_, - xa_.attrs[STATIC_COV_TAG][key.start : key.stop] - if adapt_covs_on_component - else xa_.attrs[STATIC_COV_TAG], + ( + xa_.attrs[STATIC_COV_TAG][key.start : key.stop] + if adapt_covs_on_component + else xa_.attrs[STATIC_COV_TAG] + ), None, ) return self.__class__(xa_) @@ -5055,9 +5394,11 @@ def _get_freq(xa_in: xr.DataArray): # selecting components discards the hierarchy, if any xa_ = _xarray_with_attrs( xa_, - xa_.attrs[STATIC_COV_TAG].loc[[key]] - if adapt_covs_on_component - else xa_.attrs[STATIC_COV_TAG], + ( + xa_.attrs[STATIC_COV_TAG].loc[[key]] + if adapt_covs_on_component + else xa_.attrs[STATIC_COV_TAG] + ), None, ) return self.__class__(xa_) @@ -5069,15 +5410,13 @@ def _get_freq(xa_in: xr.DataArray): if pd.api.types.is_integer_dtype(time_idx) and not isinstance( time_idx, pd.RangeIndex ): - xa_ = xa_.assign_coords( - { - self._time_dim: pd.RangeIndex( - start=time_idx[0], - stop=time_idx[0] + self.freq, - step=self.freq, - ) - } - ) + xa_ = xa_.assign_coords({ + self._time_dim: pd.RangeIndex( + start=time_idx[0], + stop=time_idx[0] + self.freq, + step=self.freq, + ) + }) # indexing may discard the freq, so we restore it... _set_freq_in_xa(xa_, freq=self.freq) return self.__class__(xa_) @@ -5096,9 +5435,11 @@ def _get_freq(xa_in: xr.DataArray): xa_ = self._xa.sel({DIMS[1]: key}) xa_ = _xarray_with_attrs( xa_, - xa_.attrs[STATIC_COV_TAG].loc[key] - if adapt_covs_on_component - else xa_.attrs[STATIC_COV_TAG], + ( + xa_.attrs[STATIC_COV_TAG].loc[key] + if adapt_covs_on_component + else xa_.attrs[STATIC_COV_TAG] + ), None, ) return self.__class__(xa_) @@ -5176,9 +5517,9 @@ def _concat_static_covs(series: Sequence[TimeSeries]) -> Optional[pd.DataFrame]: if not any([ts.has_static_covariates for ts in series]): return None - only_first = series[0].has_static_covariates and not any( - [ts.has_static_covariates for ts in series[1:]] - ) + only_first = series[0].has_static_covariates and not any([ + ts.has_static_covariates for ts in series[1:] + ]) all_have = all([ts.has_static_covariates for ts in series]) raise_if_not( @@ -5192,12 +5533,10 @@ def _concat_static_covs(series: Sequence[TimeSeries]) -> Optional[pd.DataFrame]: raise_if_not( all([len(ts.static_covariates) == ts.n_components for ts in series]) - and all( - [ - ts.static_covariates.columns.equals(series[0].static_covariates.columns) - for ts in series - ] - ), + and all([ + ts.static_covariates.columns.equals(series[0].static_covariates.columns) + for ts in series + ]), "Concatenation of multiple TimeSeries with static covariates requires all `static_covariates` " "DataFrames to have identical columns (static variable names), and the number of each TimeSeries' " "components must match the number of corresponding static covariate components (the number of rows " @@ -5311,8 +5650,6 @@ def concatenate( "of the first series.", ) - from darts.utils.timeseries_generation import generate_index - tindex = generate_index( start=series[0].start_time(), freq=series[0].freq_str, @@ -5359,9 +5696,9 @@ def concatenate( if axis == 1: # When concatenating along component dimension, we have to re-create a component index # we rely on the factory method of TimeSeries to disambiguate names later on if needed. - component_index = pd.Index( - [c for cl in [ts.components for ts in series] for c in cl] - ) + component_index = pd.Index([ + c for cl in [ts.components for ts in series] for c in cl + ]) static_covariates = ( _concat_static_covs(series) if not ignore_static_covariates @@ -5383,9 +5720,38 @@ def concatenate( return TimeSeries.from_xarray(da_concat, fill_missing_dates=False) +def slice_intersect(series: Sequence[TimeSeries]) -> list[TimeSeries]: + """Returns a list of ``TimeSeries``, where all `series` have been intersected along the time index. + + Parameters + ---------- + series : Sequence[TimeSeries] + sequence of ``TimeSeries`` to intersect + + Returns + ------- + Sequence[TimeSeries] + Intersected series. + """ + if not series: + return [] + + # find global intersection on first series + intersection = series[0] + for series_ in series[1:]: + intersection = intersection.slice_intersect(series_) + + # intersect all other series + series_intersected = [intersection] + for series_ in series[1:]: + series_intersected.append(series_.slice_intersect(intersection)) + + return series_intersected + + def _finite_rows_boundaries( values: np.ndarray, how: str = "all" -) -> Tuple[Optional[int], Optional[int]]: +) -> tuple[Optional[int], Optional[int]]: """ Return the indices of the first rows containing finite values starting from the start and the end of the first dimension of the ndarray. diff --git a/darts/utils/__init__.py b/darts/utils/__init__.py index a13d1d8b69..ec10bf202b 100644 --- a/darts/utils/__init__.py +++ b/darts/utils/__init__.py @@ -2,9 +2,17 @@ Utils ----- """ -from .utils import ( + +from darts.utils.utils import ( _build_tqdm_iterator, _parallel_apply, _with_sanity_checks, - retain_period_common_to_all, + n_steps_between, ) + +__all__ = [ + "_build_tqdm_iterator", + "_parallel_apply", + "_with_sanity_checks", + "n_steps_between", +] diff --git a/darts/utils/callbacks.py b/darts/utils/callbacks.py index d3d8db339b..0b70db72b0 100644 --- a/darts/utils/callbacks.py +++ b/darts/utils/callbacks.py @@ -12,7 +12,7 @@ def __init__( enable_validation_bar: bool = True, enable_prediction_bar: bool = True, enable_train_bar_only: bool = False, - **kwargs + **kwargs, ): """Darts' Progress Bar for `TorchForecastingModels`. diff --git a/darts/utils/data/__init__.py b/darts/utils/data/__init__.py index 2474189aab..5107f4b9d7 100644 --- a/darts/utils/data/__init__.py +++ b/darts/utils/data/__init__.py @@ -6,10 +6,10 @@ try: # Base classes for training datasets: # Implementation (horizon-based) - from .horizon_based_dataset import HorizonBasedDataset + from darts.utils.data.horizon_based_dataset import HorizonBasedDataset # Base class and implementations for inference datasets: - from .inference_dataset import ( + from darts.utils.data.inference_dataset import ( DualCovariatesInferenceDataset, FutureCovariatesInferenceDataset, InferenceDataset, @@ -19,7 +19,7 @@ ) # Implementations (sequential) - from .sequential_dataset import ( + from darts.utils.data.sequential_dataset import ( DualCovariatesSequentialDataset, FutureCovariatesSequentialDataset, MixedCovariatesSequentialDataset, @@ -28,14 +28,14 @@ ) # Implementations (shifted) - from .shifted_dataset import ( + from darts.utils.data.shifted_dataset import ( DualCovariatesShiftedDataset, FutureCovariatesShiftedDataset, MixedCovariatesShiftedDataset, PastCovariatesShiftedDataset, SplitCovariatesShiftedDataset, ) - from .training_dataset import ( + from darts.utils.data.training_dataset import ( DualCovariatesTrainingDataset, FutureCovariatesTrainingDataset, MixedCovariatesTrainingDataset, @@ -43,7 +43,95 @@ SplitCovariatesTrainingDataset, TrainingDataset, ) +except ImportError: # Torch is not available + from darts.models.utils import NotImportedModule -except ImportError: - # Torch is not available - pass + HorizonBasedDataset = NotImportedModule(module_name="(Py)Torch", warn=False) + DualCovariatesInferenceDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + FutureCovariatesInferenceDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + InferenceDataset = NotImportedModule(module_name="(Py)Torch", warn=False) + MixedCovariatesInferenceDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + PastCovariatesInferenceDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + SplitCovariatesInferenceDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + DualCovariatesSequentialDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + FutureCovariatesSequentialDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + MixedCovariatesSequentialDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + PastCovariatesSequentialDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + SplitCovariatesSequentialDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + DualCovariatesShiftedDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + FutureCovariatesShiftedDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + MixedCovariatesShiftedDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + PastCovariatesShiftedDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + SplitCovariatesShiftedDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + DualCovariatesTrainingDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + FutureCovariatesTrainingDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + MixedCovariatesTrainingDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + PastCovariatesTrainingDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + SplitCovariatesTrainingDataset = NotImportedModule( + module_name="(Py)Torch", warn=False + ) + TrainingDataset = NotImportedModule(module_name="(Py)Torch", warn=False) + +__all__ = [ + "HorizonBasedDataset", + "DualCovariatesInferenceDataset", + "FutureCovariatesInferenceDataset", + "InferenceDataset", + "MixedCovariatesInferenceDataset", + "PastCovariatesInferenceDataset", + "SplitCovariatesInferenceDataset", + "DualCovariatesSequentialDataset", + "FutureCovariatesSequentialDataset", + "MixedCovariatesSequentialDataset", + "PastCovariatesSequentialDataset", + "SplitCovariatesSequentialDataset", + "DualCovariatesShiftedDataset", + "FutureCovariatesShiftedDataset", + "MixedCovariatesShiftedDataset", + "PastCovariatesShiftedDataset", + "SplitCovariatesShiftedDataset", + "DualCovariatesTrainingDataset", + "FutureCovariatesTrainingDataset", + "MixedCovariatesTrainingDataset", + "PastCovariatesTrainingDataset", + "SplitCovariatesTrainingDataset", + "TrainingDataset", +] diff --git a/darts/utils/data/horizon_based_dataset.py b/darts/utils/data/horizon_based_dataset.py index 2b3c05610c..962e910df0 100644 --- a/darts/utils/data/horizon_based_dataset.py +++ b/darts/utils/data/horizon_based_dataset.py @@ -3,15 +3,16 @@ ------------------------------ """ -from typing import Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union import numpy as np from darts import TimeSeries -from darts.logging import get_logger, raise_if_not - -from .training_dataset import PastCovariatesTrainingDataset -from .utils import CovariateType +from darts.logging import get_logger, raise_log +from darts.utils.data.training_dataset import PastCovariatesTrainingDataset +from darts.utils.data.utils import CovariateType, _process_sample_weight +from darts.utils.ts_utils import series2seq logger = get_logger(__name__) @@ -22,12 +23,14 @@ def __init__( target_series: Union[TimeSeries, Sequence[TimeSeries]], covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, output_chunk_length: int = 12, - lh: Tuple[int, int] = (1, 3), + lh: tuple[int, int] = (1, 3), lookback: int = 3, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ) -> None: """ - A time series dataset containing tuples of (past_target, past_covariates, static_covariates, future_target) + A time series dataset containing tuples of (past_target, past_covariates, static_covariates, sample weights, + future_target) arrays, in a way inspired by the N-BEATS way of training on the M4 dataset: https://arxiv.org/abs/1905.10437. @@ -54,7 +57,7 @@ def __init__( ---------- target_series One or a sequence of target `TimeSeries`. - covariates: + covariates Optionally, one or a sequence of `TimeSeries` containing past-observed covariates. If this parameter is set, the provided sequence must have the same length as that of `target_series`. Moreover, all covariates in the sequence must have a time span large enough to contain all the required slices. @@ -71,33 +74,46 @@ def __init__( `3 * output_chunk_length`. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() - self.target_series = ( - [target_series] if isinstance(target_series, TimeSeries) else target_series - ) - self.covariates = ( - [covariates] if isinstance(covariates, TimeSeries) else covariates - ) + self.target_series = series2seq(target_series) + self.covariates = series2seq(covariates) self.covariate_type = CovariateType.PAST + if covariates is not None and len(self.target_series) != len(self.covariates): + raise_log( + ValueError( + "The provided sequence of target series must have the same length as " + "the provided sequence of covariate series." + ), + logger=logger, + ) + self.sample_weight = _process_sample_weight(sample_weight, self.target_series) + self.output_chunk_length = output_chunk_length self.min_lh, self.max_lh = lh self.lookback = lookback # Checks - raise_if_not( - self.max_lh >= self.min_lh >= 1, - "The lh parameter should be an int tuple (min_lh, max_lh), " - "with 1 <= min_lh <= max_lh", - ) - raise_if_not( - covariates is None or len(self.target_series) == len(self.covariates), - "The provided sequence of target series must have the same length as " - "the provided sequence of covariate series.", - ) - + if not (self.max_lh >= self.min_lh >= 1): + raise_log( + ValueError( + "The lh parameter should be an int tuple (min_lh, max_lh), " + "with 1 <= min_lh <= max_lh" + ), + logger=logger, + ) self.nr_samples_per_ts = (self.max_lh - self.min_lh) * self.output_chunk_length self.total_nr_samples = len(self.target_series) * self.nr_samples_per_ts self.use_static_covariates = use_static_covariates @@ -110,18 +126,26 @@ def __len__(self): def __getitem__( self, idx: int - ) -> Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], np.ndarray]: + ) -> tuple[ + np.ndarray, + Optional[np.ndarray], + Optional[np.ndarray], + Optional[np.ndarray], + np.ndarray, + ]: # determine the index of the time series. target_idx = idx // self.nr_samples_per_ts target_series = self.target_series[target_idx] target_vals = target_series.random_component_values(copy=False) - raise_if_not( - len(target_vals) - >= (self.lookback + self.max_lh) * self.output_chunk_length, - "The dataset contains some input/target series that are shorter than " - "`(lookback + max_lh) * H` ({}-th series)".format(target_idx), - ) + if len(target_vals) < (self.lookback + self.max_lh) * self.output_chunk_length: + raise_log( + ValueError( + "The dataset contains some input/target series that are shorter than " + f"`(lookback + max_lh) * H` ({target_idx}-th series)" + ), + logger=logger, + ) # determine the index lh_idx of the forecasting point (the last point of the input series, before the target) # lh_idx should be in [0, self.nr_samples_per_ts) @@ -140,6 +164,11 @@ def __getitem__( CovariateType.NONE if self.covariates is None else CovariateType.PAST ) + # optionally, load sample weight + sample_weight_series = ( + self.sample_weight[target_idx] if self.sample_weight is not None else None + ) + shift = self.lookback * self.output_chunk_length input_chunk_length = shift @@ -151,6 +180,8 @@ def __getitem__( future_end, cov_start, cov_end, + sample_weight_start, + sample_weight_end, ) = self._memory_indexer( target_idx=target_idx, target_series=target_series, @@ -160,33 +191,64 @@ def __getitem__( end_of_output_idx=end_of_output_idx, covariate_series=covariate_series, covariate_type=main_covariate_type, + sample_weight_series=sample_weight_series, ) # extract sample target future_target = target_vals[future_start:future_end] past_target = target_vals[past_start:past_end] - # optionally, extract sample covariates + # extract sample covariates covariate = None if self.covariates is not None: - raise_if_not( - cov_end <= len(covariate_series), - f"The dataset contains 'past' covariates that don't extend far enough into the future. " - f"({idx}-th sample)", - ) - + if cov_end > len(covariate_series): + raise_log( + ValueError( + f"The dataset contains past covariates that don't extend far enough into the future. " + f"({idx}-th sample)" + ), + logger=logger, + ) covariate = covariate_series.random_component_values(copy=False)[ cov_start:cov_end ] + if len(covariate) != len(past_target): + raise_log( + ValueError( + "The dataset contains past covariates whose time axis doesn't allow to obtain the " + "input (or output) chunk relative to the target series." + ), + logger=logger, + ) + + # extract sample weights + sample_weight = None + if self.sample_weight is not None: + if sample_weight_end > len(sample_weight_series): + raise_log( + ValueError( + f"The dataset contains sample weights " + f"that don't extend far enough into the future. ({idx}-th sample)" + ), + logger=logger, + ) + + sample_weight = sample_weight_series.random_component_values(copy=False)[ + sample_weight_start:sample_weight_end + ] - raise_if_not( - len(covariate) == len(past_target), - "The dataset contains 'past' covariates whose time axis doesn't allow to obtain the " - "input (or output) chunk relative to the target series.", - ) + if len(sample_weight) != self.output_chunk_length: + raise_log( + ValueError( + "The dataset contains sample weights whose time axis doesn't allow to obtain " + "the input (or output) chunk relative to the target series." + ), + logger=logger, + ) + # extract sample static covariates if self.use_static_covariates: static_covariate = target_series.static_covariates_values(copy=False) else: static_covariate = None - return past_target, covariate, static_covariate, future_target + return past_target, covariate, static_covariate, sample_weight, future_target diff --git a/darts/utils/data/inference_dataset.py b/darts/utils/data/inference_dataset.py index c60f1f22c0..f648b4ff27 100644 --- a/darts/utils/data/inference_dataset.py +++ b/darts/utils/data/inference_dataset.py @@ -5,7 +5,8 @@ import bisect from abc import ABC, abstractmethod -from typing import Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union import numpy as np import pandas as pd @@ -13,10 +14,9 @@ from darts import TimeSeries from darts.logging import get_logger, raise_log +from darts.utils.data.utils import CovariateType from darts.utils.historical_forecasts.utils import _process_predict_start_points_bounds -from .utils import CovariateType - logger = get_logger(__name__) @@ -50,6 +50,7 @@ def _covariate_indexer( covariate_type: CovariateType, input_chunk_length: int, output_chunk_length: int, + output_chunk_shift: int, n: int, ): """returns tuple of (past_start, past_end, future_start, future_end)""" @@ -74,10 +75,17 @@ def _covariate_indexer( past_end + max(0, n - output_chunk_length) * covariate_series.freq ) else: # CovariateType.FUTURE - future_end = past_end + max(n, output_chunk_length) * covariate_series.freq + # optionally, for future part of future covariates shift start and end by `output_chunk_shift` + future_end = ( + past_end + + (max(n, output_chunk_length) + output_chunk_shift) + * covariate_series.freq + ) future_start = ( - past_end + covariate_series.freq if future_end != past_end else future_end + past_end + covariate_series.freq * (1 + output_chunk_shift) + if future_end != past_end + else future_end ) if input_chunk_length == 0: # for regression ensemble models @@ -109,7 +117,7 @@ def _covariate_indexer( logger=logger, ) - # extract the index position (index) from time_index value + # extract the index position (integer index) from time_index value covariate_start = covariate_series.time_index.get_loc(past_start) covariate_end = covariate_series.time_index.get_loc(future_end) + 1 return covariate_start, covariate_end @@ -125,6 +133,7 @@ def __init__( bounds: Optional[np.ndarray] = None, input_chunk_length: int = 12, output_chunk_length: int = 1, + output_chunk_shift: int = 0, covariate_type: CovariateType = CovariateType.PAST, use_static_covariates: bool = True, ): @@ -158,6 +167,8 @@ def __init__( The length of the target series the model takes as input. output_chunk_length The length of the target series the model emits in output. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. use_static_covariates Whether to use/include static covariate data from input series. """ @@ -170,13 +181,6 @@ def __init__( [covariates] if isinstance(covariates, TimeSeries) else covariates ) - self.covariate_type = covariate_type - - self.n = n - self.input_chunk_length = input_chunk_length - self.output_chunk_length = output_chunk_length - self.use_static_covariates = use_static_covariates - if not (covariates is None or len(self.target_series) == len(self.covariates)): raise_log( ValueError( @@ -193,6 +197,23 @@ def __init__( logger=logger, ) + if output_chunk_shift and n > output_chunk_length: + raise_log( + ValueError( + "Cannot perform auto-regression `(n > output_chunk_length)` with a model that uses a " + "shifted output chunk `(output_chunk_shift > 0)`." + ), + logger=logger, + ) + + self.covariate_type = covariate_type + + self.n = n + self.input_chunk_length = input_chunk_length + self.output_chunk_length = output_chunk_length + self.output_chunk_shift = output_chunk_shift + self.use_static_covariates = use_static_covariates + self.stride = stride if bounds is None: self.bounds = bounds @@ -221,7 +242,7 @@ def find_list_index(index, cumulative_lengths, bounds, stride): def __getitem__( self, idx: int - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], @@ -276,6 +297,7 @@ def __getitem__( covariate_type=self.covariate_type, input_chunk_length=self.input_chunk_length, output_chunk_length=self.output_chunk_length, + output_chunk_shift=self.output_chunk_shift, n=self.n, ) @@ -286,7 +308,7 @@ def __getitem__( if self.input_chunk_length != 0: # regular models past_covariate, future_covariate = ( covariate[: self.input_chunk_length], - covariate[self.input_chunk_length :], + covariate[self.input_chunk_length + self.output_chunk_shift :], ) else: # regression ensemble models have a input_chunk_length == 0 part for using predictions as input past_covariate, future_covariate = covariate, covariate @@ -314,7 +336,7 @@ def __getitem__( future_covariate, static_covariate, target_series, - past_end + target_series.freq, + past_end + target_series.freq * (1 + self.output_chunk_shift), ) @@ -328,6 +350,7 @@ def __init__( bounds: Optional[np.ndarray] = None, input_chunk_length: int = 12, output_chunk_length: int = 1, + output_chunk_shift: int = 0, covariate_type: CovariateType = CovariateType.PAST, use_static_covariates: bool = True, ): @@ -359,6 +382,8 @@ def __init__( The length of the target series the model takes as input. output_chunk_length The length of the target series the model emits in output. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. use_static_covariates Whether to use/include static covariate data from input series. """ @@ -373,6 +398,7 @@ def __init__( bounds=bounds, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, covariate_type=covariate_type, use_static_covariates=use_static_covariates, ) @@ -382,7 +408,7 @@ def __len__(self): def __getitem__( self, idx: int - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], @@ -402,6 +428,8 @@ def __init__( stride: int = 0, bounds: Optional[np.ndarray] = None, input_chunk_length: int = 12, + output_chunk_length: Optional[int] = None, + output_chunk_shift: int = 0, covariate_type: CovariateType = CovariateType.FUTURE, use_static_covariates: bool = True, ): @@ -426,6 +454,11 @@ def __init__( If provided, `stride` must be `>=1`. input_chunk_length The length of the target series the model takes as input. + output_chunk_length + Optionally, the length of the target series the model emits in output. If `None`, will use the same value + as `n`. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. use_static_covariates Whether to use/include static covariate data from input series. """ @@ -438,7 +471,8 @@ def __init__( stride=stride, bounds=bounds, input_chunk_length=input_chunk_length, - output_chunk_length=n, + output_chunk_length=output_chunk_length or n, + output_chunk_shift=output_chunk_shift, covariate_type=covariate_type, use_static_covariates=use_static_covariates, ) @@ -448,7 +482,7 @@ def __len__(self): def __getitem__( self, idx: int - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], @@ -482,6 +516,7 @@ def __init__( bounds: Optional[np.ndarray] = None, input_chunk_length: int = 12, output_chunk_length: int = 1, + output_chunk_shift: int = 0, use_static_covariates: bool = True, ): """ @@ -507,6 +542,8 @@ def __init__( The length of the target series the model takes as input. output_chunk_length The length of the target series the model emits in output. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. use_static_covariates Whether to use/include static covariate data from input series. """ @@ -521,6 +558,7 @@ def __init__( bounds=bounds, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, covariate_type=CovariateType.HISTORIC_FUTURE, use_static_covariates=use_static_covariates, ) @@ -533,6 +571,8 @@ def __init__( stride=stride, bounds=bounds, input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, covariate_type=CovariateType.FUTURE, use_static_covariates=use_static_covariates, ) @@ -542,7 +582,7 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], @@ -580,6 +620,7 @@ def __init__( bounds: Optional[np.ndarray] = None, input_chunk_length: int = 12, output_chunk_length: int = 1, + output_chunk_shift: int = 0, use_static_covariates: bool = True, ): """ @@ -611,6 +652,8 @@ def __init__( The length of the target series the model takes as input. output_chunk_length The length of the target series the model emits in output. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. use_static_covariates Whether to use/include static covariate data from input series. """ @@ -625,6 +668,7 @@ def __init__( bounds=bounds, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, covariate_type=CovariateType.PAST, use_static_covariates=use_static_covariates, ) @@ -638,6 +682,7 @@ def __init__( bounds=bounds, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, use_static_covariates=use_static_covariates, ) @@ -646,7 +691,7 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], @@ -656,7 +701,6 @@ def __getitem__( TimeSeries, Union[pd.Timestamp, int], ]: - ( past_target, past_covariate, @@ -689,6 +733,7 @@ def __init__( bounds: Optional[np.ndarray] = None, input_chunk_length: int = 12, output_chunk_length: int = 1, + output_chunk_shift: int = 0, use_static_covariates: bool = True, ): """ @@ -719,6 +764,8 @@ def __init__( The length of the target series the model takes as input. output_chunk_length The length of the target series the model emits in output. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. use_static_covariates Whether to use/include static covariate data from input series. """ @@ -733,6 +780,7 @@ def __init__( bounds=bounds, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, covariate_type=CovariateType.PAST, use_static_covariates=use_static_covariates, ) @@ -745,6 +793,8 @@ def __init__( stride=stride, bounds=bounds, input_chunk_length=input_chunk_length, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, covariate_type=CovariateType.FUTURE, use_static_covariates=use_static_covariates, ) @@ -754,7 +804,7 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], @@ -763,7 +813,6 @@ def __getitem__( TimeSeries, Union[pd.Timestamp, int], ]: - ( past_target, past_covariate, diff --git a/darts/utils/data/sequential_dataset.py b/darts/utils/data/sequential_dataset.py index 881dc344fe..0ebd68a1cb 100644 --- a/darts/utils/data/sequential_dataset.py +++ b/darts/utils/data/sequential_dataset.py @@ -3,21 +3,21 @@ --------------------------- """ -from typing import Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union import numpy as np from darts import TimeSeries - -from .shifted_dataset import GenericShiftedDataset -from .training_dataset import ( +from darts.utils.data.shifted_dataset import GenericShiftedDataset +from darts.utils.data.training_dataset import ( DualCovariatesTrainingDataset, FutureCovariatesTrainingDataset, MixedCovariatesTrainingDataset, PastCovariatesTrainingDataset, SplitCovariatesTrainingDataset, ) -from .utils import CovariateType +from darts.utils.data.utils import CovariateType class PastCovariatesSequentialDataset(PastCovariatesTrainingDataset): @@ -27,11 +27,14 @@ def __init__( covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, input_chunk_length: int = 12, output_chunk_length: int = 1, + output_chunk_shift: int = 0, max_samples_per_ts: Optional[int] = None, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ - A time series dataset containing tuples of (past_target, past_covariates, static_covariates, future_target). + A time series dataset containing tuples of (past_target, past_covariates, static_covariates, sample weights, + future_target). The "past" series have length `input_chunk_length` and the "future" series have length `output_chunk_length`. The "future" series are immediately consecutive to the "past" series. The slicing of past and future covariates matches that of past and future targets, respectively. The slicing @@ -59,6 +62,8 @@ def __init__( The length of the emitted past series. output_chunk_length The length of the emitted future series. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. max_samples_per_ts This is an upper bound on the number of tuples that can be produced per time series. It can be used in order to have an upper bound on the total size of the dataset and @@ -68,20 +73,31 @@ def __init__( most recent `max_samples_per_ts` samples will be considered. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() - + shift = input_chunk_length + output_chunk_shift self.ds = GenericShiftedDataset( target_series=target_series, covariates=covariates, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, - shift=input_chunk_length, + shift=shift, shift_covariates=False, max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.PAST, use_static_covariates=use_static_covariates, + sample_weight=sample_weight, ) def __len__(self): @@ -89,7 +105,13 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], np.ndarray]: + ) -> tuple[ + np.ndarray, + Optional[np.ndarray], + Optional[np.ndarray], + Optional[np.ndarray], + np.ndarray, + ]: return self.ds[idx] @@ -100,11 +122,14 @@ def __init__( covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, input_chunk_length: int = 12, output_chunk_length: int = 1, + output_chunk_shift: int = 0, max_samples_per_ts: Optional[int] = None, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ - A time series dataset containing tuples of (past_target, future_covariates, static_covariates, future_target). + A time series dataset containing tuples of (past_target, future_covariates, static_covariates, sample weights, + future_target). The "past" series have length `input_chunk_length` and the "future" series have length `output_chunk_length`. The "future" series are immediately consecutive to the "past" series. The slicing of past and future covariates matches that of past and future targets, respectively. The slicing @@ -132,6 +157,8 @@ def __init__( The length of the emitted past series. output_chunk_length The length of the emitted future series. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. max_samples_per_ts This is an upper bound on the number of tuples that can be produced per time series. It can be used in order to have an upper bound on the total size of the dataset and @@ -141,20 +168,31 @@ def __init__( most recent `max_samples_per_ts` samples will be considered. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() - + shift = input_chunk_length + output_chunk_shift self.ds = GenericShiftedDataset( target_series=target_series, covariates=covariates, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, - shift=input_chunk_length, + shift=shift, shift_covariates=True, max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.FUTURE, use_static_covariates=use_static_covariates, + sample_weight=sample_weight, ) def __len__(self): @@ -162,7 +200,13 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], np.ndarray]: + ) -> tuple[ + np.ndarray, + Optional[np.ndarray], + Optional[np.ndarray], + Optional[np.ndarray], + np.ndarray, + ]: return self.ds[idx] @@ -173,12 +217,15 @@ def __init__( covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, input_chunk_length: int = 12, output_chunk_length: int = 1, + output_chunk_shift: int = 0, max_samples_per_ts: Optional[int] = None, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ A time series dataset containing tuples of - (past_target, historic_future_covariates, future_covariates, static_covariates, future_target). + (past_target, historic_future_covariates, future_covariates, static_covariates, sample weights, + future_target). The "past" series (incl `historic_future_covariates`) have length `input_chunk_length` and the "future" series have length `output_chunk_length`. The "future" series are immediately consecutive to the "past" series. The slicing of past and future covariates matches that of past and future targets, @@ -206,6 +253,8 @@ def __init__( The length of the emitted past series. output_chunk_length The length of the emitted future series. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. max_samples_per_ts This is an upper bound on the number of tuples that can be produced per time series. It can be used in order to have an upper bound on the total size of the dataset and @@ -215,21 +264,32 @@ def __init__( most recent `max_samples_per_ts` samples will be considered. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() - + shift = input_chunk_length + output_chunk_shift # This dataset is in charge of historical future covariates self.ds_past = GenericShiftedDataset( target_series=target_series, covariates=covariates, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, - shift=input_chunk_length, + shift=shift, shift_covariates=False, max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.HISTORIC_FUTURE, use_static_covariates=use_static_covariates, + sample_weight=sample_weight, ) # This dataset is in charge of serving future covariates @@ -238,7 +298,7 @@ def __init__( covariates=covariates, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, - shift=input_chunk_length, + shift=shift, shift_covariates=True, max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.FUTURE, @@ -250,20 +310,24 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], + Optional[np.ndarray], np.ndarray, ]: - past_target, past_covariate, static_covariate, future_target = self.ds_past[idx] - _, future_covariate, _, _ = self.ds_future[idx] + past_target, past_covariate, static_covariate, sample_weight, future_target = ( + self.ds_past[idx] + ) + _, future_covariate, _, _, _ = self.ds_future[idx] return ( past_target, past_covariate, future_covariate, static_covariate, + sample_weight, future_target, ) @@ -276,12 +340,15 @@ def __init__( future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, input_chunk_length: int = 12, output_chunk_length: int = 1, + output_chunk_shift: int = 0, max_samples_per_ts: Optional[int] = None, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ A time series dataset containing tuples of - (past_target, past_covariates, historic_future_covariates, future_covariates, static_covariates, future_target). + (past_target, past_covariates, historic_future_covariates, future_covariates, static_covariates, + sample weights, future_target). The "past" series (incl `historic_future_covariates`) have length `input_chunk_length` and the "future" series have length `output_chunk_length`. The "future" series are immediately consecutive to the "past" series. The slicing of past and future covariates matches that of past and future targets, @@ -312,6 +379,8 @@ def __init__( The length of the emitted past series. output_chunk_length The length of the emitted future series. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. max_samples_per_ts This is an upper bound on the number of tuples that can be produced per time series. It can be used in order to have an upper bound on the total size of the dataset and @@ -321,21 +390,32 @@ def __init__( most recent `max_samples_per_ts` samples will be considered. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() - + shift = input_chunk_length + output_chunk_shift # This dataset is in charge of serving past covariates self.ds_past = GenericShiftedDataset( target_series=target_series, covariates=past_covariates, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, - shift=input_chunk_length, + shift=shift, shift_covariates=False, max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.PAST, use_static_covariates=use_static_covariates, + sample_weight=sample_weight, ) # This dataset is in charge of serving historical and future future covariates @@ -344,6 +424,7 @@ def __init__( covariates=future_covariates, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, max_samples_per_ts=max_samples_per_ts, use_static_covariates=use_static_covariates, ) @@ -353,23 +434,26 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], + Optional[np.ndarray], np.ndarray, ]: - - past_target, past_covariate, static_covariate, future_target = self.ds_past[idx] - _, historic_future_covariate, future_covariate, _, _ = self.ds_dual[idx] + past_target, past_covariate, static_covariate, sample_weight, future_target = ( + self.ds_past[idx] + ) + _, historic_future_covariate, future_covariate, _, _, _ = self.ds_dual[idx] return ( past_target, past_covariate, historic_future_covariate, future_covariate, static_covariate, + sample_weight, future_target, ) @@ -382,12 +466,14 @@ def __init__( future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, input_chunk_length: int = 12, output_chunk_length: int = 1, + output_chunk_shift: int = 0, max_samples_per_ts: Optional[int] = None, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ A time series dataset containing tuples of (past_target, past_covariates, future_covariates, static_covariates, - future_target). + sample weights, future_target). The "past" series have length `input_chunk_length` and the "future" series have length `output_chunk_length`. The "future" series are immediately consecutive to the "past" series. The slicing of past and future covariates matches that of past and future targets, respectively. The slicing @@ -418,6 +504,8 @@ def __init__( The length of the emitted past series. output_chunk_length The length of the emitted future series. + output_chunk_shift + Optionally, the number of steps to shift the start of the output chunk into the future. max_samples_per_ts This is an upper bound on the number of tuples that can be produced per time series. It can be used in order to have an upper bound on the total size of the dataset and @@ -427,20 +515,31 @@ def __init__( most recent `max_samples_per_ts` samples will be considered. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() - + shift = input_chunk_length + output_chunk_shift # This dataset is in charge of serving past covariates self.ds_past = GenericShiftedDataset( target_series=target_series, covariates=past_covariates, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, - shift=input_chunk_length, + shift=shift, shift_covariates=False, max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.PAST, use_static_covariates=use_static_covariates, + sample_weight=sample_weight, ) # This dataset is in charge of serving future covariates @@ -449,7 +548,7 @@ def __init__( covariates=future_covariates, input_chunk_length=input_chunk_length, output_chunk_length=output_chunk_length, - shift=input_chunk_length, + shift=shift, shift_covariates=True, max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.FUTURE, @@ -461,19 +560,23 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], + Optional[np.ndarray], np.ndarray, ]: - past_target, past_covariate, static_covariate, future_target = self.ds_past[idx] - _, future_covariate, _, _ = self.ds_future[idx] + past_target, past_covariate, static_covariate, sample_weight, future_target = ( + self.ds_past[idx] + ) + _, future_covariate, _, _, _ = self.ds_future[idx] return ( past_target, past_covariate, future_covariate, static_covariate, + sample_weight, future_target, ) diff --git a/darts/utils/data/shifted_dataset.py b/darts/utils/data/shifted_dataset.py index 1a82ff5583..d7e537dc4b 100644 --- a/darts/utils/data/shifted_dataset.py +++ b/darts/utils/data/shifted_dataset.py @@ -3,14 +3,14 @@ ------------------------ """ -from typing import Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union import numpy as np from darts import TimeSeries -from darts.logging import raise_if_not - -from .training_dataset import ( +from darts.logging import get_logger, raise_log +from darts.utils.data.training_dataset import ( DualCovariatesTrainingDataset, FutureCovariatesTrainingDataset, MixedCovariatesTrainingDataset, @@ -18,7 +18,10 @@ SplitCovariatesTrainingDataset, TrainingDataset, ) -from .utils import CovariateType +from darts.utils.data.utils import CovariateType, _process_sample_weight +from darts.utils.ts_utils import series2seq + +logger = get_logger(__name__) class PastCovariatesShiftedDataset(PastCovariatesTrainingDataset): @@ -30,9 +33,11 @@ def __init__( shift: int = 1, max_samples_per_ts: Optional[int] = None, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ - A time series dataset containing tuples of (past_target, past_covariates, static_covariates, future_target) + A time series dataset containing tuples of (past_target, past_covariates, static_covariates, sample weights, + future_target) arrays, which all have length `length`. The "future_target" is the "past_target" target shifted by `shift` time steps forward. So if an emitted "past_target" (and "past_covariates") goes from position `i` to `i+length`, @@ -59,7 +64,7 @@ def __init__( length The length of the emitted past and future series. shift - The number of time steps by which to shift the output relative to the input. + The number of time steps by which to shift the output chunks relative to the start of the input chunks. max_samples_per_ts This is an upper bound on the number of tuples that can be produced per time series. It can be used in order to have an upper bound on the total size of the dataset and @@ -69,6 +74,16 @@ def __init__( most recent `max_samples_per_ts` samples will be considered. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() @@ -82,6 +97,7 @@ def __init__( max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.PAST, use_static_covariates=use_static_covariates, + sample_weight=sample_weight, ) def __len__(self): @@ -89,7 +105,13 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], np.ndarray]: + ) -> tuple[ + np.ndarray, + Optional[np.ndarray], + Optional[np.ndarray], + Optional[np.ndarray], + np.ndarray, + ]: return self.ds[idx] @@ -102,9 +124,11 @@ def __init__( shift: int = 1, max_samples_per_ts: Optional[int] = None, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ - A time series dataset containing tuples of (past_target, future_covariates, static_covariates, future_target) + A time series dataset containing tuples of (past_target, future_covariates, static_covariates, sample weights, + future_target) arrays, which all have length `length`. The "future_target" is the "past_target" target shifted by `shift` time steps forward. So if an emitted "past_target" goes from position `i` to `i+length`, @@ -133,7 +157,7 @@ def __init__( length The length of the emitted past and future series. shift - The number of time steps by which to shift the output relative to the input. + The number of time steps by which to shift the output chunks relative to the start of the input chunks. max_samples_per_ts This is an upper bound on the number of tuples that can be produced per time series. It can be used in order to have an upper bound on the total size of the dataset and @@ -143,6 +167,16 @@ def __init__( most recent `max_samples_per_ts` samples will be considered. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() @@ -157,6 +191,7 @@ def __init__( max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.FUTURE, use_static_covariates=use_static_covariates, + sample_weight=sample_weight, ) def __len__(self): @@ -164,7 +199,13 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], np.ndarray]: + ) -> tuple[ + np.ndarray, + Optional[np.ndarray], + Optional[np.ndarray], + Optional[np.ndarray], + np.ndarray, + ]: return self.ds[idx] @@ -177,10 +218,12 @@ def __init__( shift: int = 1, max_samples_per_ts: Optional[int] = None, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ A time series dataset containing tuples of - (past_target, historic_future_covariates, future_covariates, static_covariates, future_target) + (past_target, historic_future_covariates, future_covariates, static_covariates, sample weights, + future_target) arrays, which all have length `length`. The "future_target" is the "past_target" target shifted by `shift` time steps forward. So if an emitted "past_target" goes from position `i` to `i+length`, @@ -210,7 +253,7 @@ def __init__( length The length of the emitted past and future series. shift - The number of time steps by which to shift the output relative to the input. + The number of time steps by which to shift the output chunks relative to the start of the input chunks. max_samples_per_ts This is an upper bound on the number of tuples that can be produced per time series. It can be used in order to have an upper bound on the total size of the dataset and @@ -220,6 +263,16 @@ def __init__( most recent `max_samples_per_ts` samples will be considered. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() @@ -235,6 +288,7 @@ def __init__( max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.HISTORIC_FUTURE, use_static_covariates=use_static_covariates, + sample_weight=sample_weight, ) # This dataset is in charge of serving future covariates @@ -255,20 +309,24 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], + Optional[np.ndarray], np.ndarray, ]: - past_target, past_covariate, static_covariate, future_target = self.ds_past[idx] - _, future_covariate, _, _ = self.ds_future[idx] + past_target, past_covariate, static_covariate, sample_weight, future_target = ( + self.ds_past[idx] + ) + _, future_covariate, _, _, _ = self.ds_future[idx] return ( past_target, past_covariate, future_covariate, static_covariate, + sample_weight, future_target, ) @@ -283,10 +341,11 @@ def __init__( shift: int = 1, max_samples_per_ts: Optional[int] = None, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ A time series dataset containing tuples of (past_target, past_covariates, historic_future_covariates, - future_covariates, static_covariates, future_target) arrays, which all have length `length`. + future_covariates, static_covariates, sample weights, future_target) arrays, which all have length `length`. The "future_target" is the "past_target" target shifted by `shift` time steps forward. So if an emitted "past_target" goes from position `i` to `i+length`, the emitted "future_target" will go from position `i+shift` to `i+shift+length`. @@ -317,7 +376,7 @@ def __init__( length The length of the emitted past and future series. shift - The number of time steps by which to shift the output relative to the input. + The number of time steps by which to shift the output chunks relative to the start of the input chunks. max_samples_per_ts This is an upper bound on the number of tuples that can be produced per time series. It can be used in order to have an upper bound on the total size of the dataset and @@ -327,6 +386,16 @@ def __init__( most recent `max_samples_per_ts` samples will be considered. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() @@ -341,6 +410,7 @@ def __init__( max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.PAST, use_static_covariates=use_static_covariates, + sample_weight=sample_weight, ) # The dual dataset serves both historical and future future covariates @@ -358,23 +428,26 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], + Optional[np.ndarray], np.ndarray, ]: - - past_target, past_covariate, static_covariate, future_target = self.ds_past[idx] - _, historic_future_covariate, future_covariate, _, _ = self.ds_dual[idx] + past_target, past_covariate, static_covariate, sample_weight, future_target = ( + self.ds_past[idx] + ) + _, historic_future_covariate, future_covariate, _, _, _ = self.ds_dual[idx] return ( past_target, past_covariate, historic_future_covariate, future_covariate, static_covariate, + sample_weight, future_target, ) @@ -389,10 +462,11 @@ def __init__( shift: int = 1, max_samples_per_ts: Optional[int] = None, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ A time series dataset containing tuples of (past_target, past_covariates, future_covariates, static_covariates, - future_target) arrays, which all have length `length`. + sample weights, future_target) arrays, which all have length `length`. The "future_target" is the "past_target" target shifted by `shift` time steps forward. So if an emitted "past_target" goes from position `i` to `i+length`, the emitted "future_target" will go from position `i+shift` to `i+shift+length`. @@ -423,7 +497,7 @@ def __init__( length The length of the emitted past and future series. shift - The number of time steps by which to shift the output relative to the input. + The number of time steps by which to shift the output chunks relative to the start of the input chunks. max_samples_per_ts This is an upper bound on the number of tuples that can be produced per time series. It can be used in order to have an upper bound on the total size of the dataset and @@ -433,6 +507,16 @@ def __init__( most recent `max_samples_per_ts` samples will be considered. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() @@ -448,6 +532,7 @@ def __init__( max_samples_per_ts=max_samples_per_ts, covariate_type=CovariateType.PAST, use_static_covariates=use_static_covariates, + sample_weight=sample_weight, ) # This dataset is in charge of serving future covariates @@ -468,20 +553,24 @@ def __len__(self): def __getitem__( self, idx - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], + Optional[np.ndarray], np.ndarray, ]: - past_target, past_covariate, static_covariate, future_target = self.ds_past[idx] - _, future_covariate, _, _ = self.ds_future[idx] + past_target, past_covariate, static_covariate, sample_weight, future_target = ( + self.ds_past[idx] + ) + _, future_covariate, _, _, _ = self.ds_future[idx] return ( past_target, past_covariate, future_covariate, static_covariate, + sample_weight, future_target, ) @@ -498,10 +587,11 @@ def __init__( max_samples_per_ts: Optional[int] = None, covariate_type: CovariateType = CovariateType.NONE, use_static_covariates: bool = True, + sample_weight: Optional[Union[TimeSeries, Sequence[TimeSeries], str]] = None, ): """ - Contains (past_target, _covariates, static_covariates, future_target), where "" is past if - `shift_covariates = False` and future otherwise. + Contains (past_target, _covariates, static_covariates, sample weights, future_target), where "" is past + if `shift_covariates = False` and future otherwise. The past chunks have length `input_chunk_length` and the future chunks have length `output_chunk_length`. The future chunks start `shift` after the past chunks' start. @@ -519,7 +609,7 @@ def __init__( output_chunk_length The length of the emitted future series. shift - The number of time steps by which to shift the output chunks relative to the input chunks. + The number of time steps by which to shift the output chunks relative to the start of the input chunks. shift_covariates Whether to shift the covariates forward the same way as the target. FutureCovariatesModel's require this set to True, while PastCovariatesModel's require this set to False. @@ -534,50 +624,90 @@ def __init__( An instance of `CovariateType` describing the type of `covariates`. use_static_covariates Whether to use/include static covariate data from input series. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. """ super().__init__() - self.target_series = ( - [target_series] if isinstance(target_series, TimeSeries) else target_series - ) - self.covariates = ( - [covariates] if isinstance(covariates, TimeSeries) else covariates - ) - self.covariate_type = covariate_type - - raise_if_not( - covariates is None or len(self.target_series) == len(self.covariates), - "The provided sequence of target series must have the same length as " - "the provided sequence of covariate series.", - ) - - self.input_chunk_length, self.output_chunk_length = ( - input_chunk_length, - output_chunk_length, - ) - self.shift, self.shift_covariates = shift, shift_covariates + # setup target and sequence + self.target_series = series2seq(target_series) + self.input_chunk_length = input_chunk_length + self.output_chunk_length = output_chunk_length + self.shift = shift self.max_samples_per_ts = max_samples_per_ts - self.size_of_both_chunks = max( self.input_chunk_length, self.shift + self.output_chunk_length ) + # setup covariates; ignore past/historic covariates when `icl==0` and future covariates when `ocl==0` + main_covariate_type = CovariateType.NONE + if covariates is not None: + if shift_covariates and output_chunk_length > 0: + main_covariate_type = CovariateType.FUTURE + elif not shift_covariates and input_chunk_length > 0: + main_covariate_type = CovariateType.PAST + else: + main_covariate_type = CovariateType.NONE + + self.main_covariate_type = main_covariate_type + if main_covariate_type is not CovariateType.NONE: + self.covariates = series2seq(covariates) + self.covariate_type = covariate_type + self.shift_covariates = shift_covariates + else: + self.covariates = None + self.covariate_type = CovariateType.NONE + self.shift_covariates = 0 + self.use_static_covariates = use_static_covariates + + if self.covariates is not None and len(self.target_series) != len( + self.covariates + ): + raise_log( + ValueError( + "The provided sequence of target series must have the same length as " + "the provided sequence of covariate series." + ), + logger=logger, + ) + + # setup sample weights; ignore weights when `ocl==0` + self.sample_weight = None + if sample_weight is not None: + if output_chunk_length > 0: + self.sample_weight = _process_sample_weight( + sample_weight, self.target_series + ) + else: + self.sample_weight = None + + # setup samples if self.max_samples_per_ts is None: # read all time series to get the maximum size self.max_samples_per_ts = ( max(len(ts) for ts in self.target_series) - self.size_of_both_chunks + 1 ) - self.ideal_nr_samples = len(self.target_series) * self.max_samples_per_ts - self.use_static_covariates = use_static_covariates def __len__(self): return self.ideal_nr_samples def __getitem__( self, idx - ) -> Tuple[ - np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray] + ) -> tuple[ + np.ndarray, + Optional[np.ndarray], + Optional[np.ndarray], + Optional[np.ndarray], + np.ndarray, ]: # determine the index of the time series. target_idx = idx // self.max_samples_per_ts @@ -587,12 +717,15 @@ def __getitem__( # determine the actual number of possible samples in this time series n_samples_in_ts = len(target_vals) - self.size_of_both_chunks + 1 - raise_if_not( - n_samples_in_ts >= 1, - "The dataset contains some time series that are too short to contain " - "`max(self.input_chunk_length, self.shift + self.output_chunk_length)` " - "({}-th series)".format(target_idx), - ) + if n_samples_in_ts < 1: + raise_log( + ValueError( + "The dataset contains some time series that are too short to contain " + "`max(self.input_chunk_length, self.shift + self.output_chunk_length)` " + f"({target_idx}-th series)" + ), + logger=logger, + ) # determine the index at the end of the output chunk # it is originally in [0, self.max_samples_per_ts), so we use a modulo to have it in [0, n_samples_in_ts) @@ -606,11 +739,21 @@ def __getitem__( self.covariates[target_idx] if self.covariates is not None else None ) - main_covariate_type = CovariateType.NONE - if self.covariates is not None: - main_covariate_type = ( - CovariateType.FUTURE if self.shift_covariates else CovariateType.PAST - ) + # optionally, load sample weight + if self.sample_weight is not None: + sample_weight_series = self.sample_weight[target_idx] + weight_n_comp = sample_weight_series.n_components + if weight_n_comp > 1 and weight_n_comp != target_series.n_components: + raise_log( + ValueError( + "The number of components in `sample_weight` must either be `1` or match " + f"the number of target series components `{target_series.n_components}`. " + f"({target_idx}-th series)" + ), + logger=logger, + ) + else: + sample_weight_series = None # get all indices for the current sample ( @@ -620,6 +763,8 @@ def __getitem__( future_end, covariate_start, covariate_end, + sample_weight_start, + sample_weight_end, ) = self._memory_indexer( target_idx=target_idx, target_series=target_series, @@ -628,40 +773,72 @@ def __getitem__( output_chunk_length=self.output_chunk_length, end_of_output_idx=end_of_output_idx, covariate_series=covariate_series, - covariate_type=main_covariate_type, + covariate_type=self.main_covariate_type, + sample_weight_series=sample_weight_series, ) # extract sample target future_target = target_vals[future_start:future_end] past_target = target_vals[past_start:past_end] - # optionally, extract sample covariates + # extract sample covariates covariate = None if self.covariates is not None: - raise_if_not( - covariate_end <= len(covariate_series), - f"The dataset contains {main_covariate_type.value} covariates " - f"that don't extend far enough into the future. ({idx}-th sample)", - ) + if covariate_end > len(covariate_series): + raise_log( + ValueError( + f"The dataset contains {self.main_covariate_type.value} covariates " + f"that don't extend far enough into the future. ({idx}-th sample)" + ), + logger=logger, + ) covariate = covariate_series.random_component_values(copy=False)[ covariate_start:covariate_end ] - raise_if_not( - len(covariate) - == ( - self.output_chunk_length - if self.shift_covariates - else self.input_chunk_length - ), - f"The dataset contains {main_covariate_type.value} covariates " - f"whose time axis doesn't allow to obtain the input (or output) chunk relative to the " - f"target series.", - ) + if len(covariate) != ( + self.output_chunk_length + if self.shift_covariates + else self.input_chunk_length + ): + raise_log( + ValueError( + f"The dataset contains {self.main_covariate_type.value} covariates " + f"whose time axis doesn't allow to obtain the input (or output) chunk relative to the " + f"target series." + ), + logger=logger, + ) + + # extract sample weights + sample_weight = None + if self.sample_weight is not None: + if sample_weight_end > len(sample_weight_series): + raise_log( + ValueError( + f"The dataset contains sample weights " + f"that don't extend far enough into the future. ({idx}-th sample)" + ), + logger=logger, + ) + + sample_weight = sample_weight_series.random_component_values(copy=False)[ + sample_weight_start:sample_weight_end + ] + + if len(sample_weight) != self.output_chunk_length: + raise_log( + ValueError( + "The dataset contains sample weights whose time axis doesn't allow to obtain " + "the input (or output) chunk relative to the target series." + ), + logger=logger, + ) + # extract sample static covariates if self.use_static_covariates: static_covariate = target_series.static_covariates_values(copy=False) else: static_covariate = None - return past_target, covariate, static_covariate, future_target + return past_target, covariate, static_covariate, sample_weight, future_target diff --git a/darts/utils/data/tabularization.py b/darts/utils/data/tabularization.py index be28af04f1..1742f7ccd1 100644 --- a/darts/utils/data/tabularization.py +++ b/darts/utils/data/tabularization.py @@ -1,22 +1,19 @@ import warnings +from collections.abc import Sequence from functools import reduce -from math import inf -from typing import Dict, List, Optional, Sequence, Tuple, Union - -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal - from itertools import chain +from math import inf +from typing import Literal, Optional, Union import numpy as np import pandas as pd from numpy.lib.stride_tricks import as_strided -from darts.logging import get_logger, raise_if, raise_if_not, raise_log +from darts.logging import get_logger, raise_log from darts.timeseries import TimeSeries -from darts.utils.utils import get_single_series, series2seq +from darts.utils.data.utils import _process_sample_weight +from darts.utils.ts_utils import get_single_series, series2seq +from darts.utils.utils import n_steps_between logger = get_logger(__name__) @@ -27,23 +24,27 @@ def create_lagged_data( target_series: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - lags: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, - lags_past_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, - lags_future_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, + lags: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, + lags_past_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, + lags_future_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, output_chunk_length: int = 1, + output_chunk_shift: int = 0, uses_static_covariates: bool = True, - last_static_covariates_shape: Optional[Tuple[int, int]] = None, + last_static_covariates_shape: Optional[tuple[int, int]] = None, max_samples_per_ts: Optional[int] = None, multi_models: bool = True, check_inputs: bool = True, use_moving_windows: bool = True, is_training: bool = True, concatenate: bool = True, -) -> Tuple[ + sample_weight: Optional[Union[str, TimeSeries, Sequence[TimeSeries]]] = None, + show_warnings: bool = True, +) -> tuple[ ArrayOrArraySequence, Union[None, ArrayOrArraySequence], Sequence[pd.Index], - Optional[Tuple[int, int]], + Optional[tuple[int, int]], + Optional[ArrayOrArraySequence], ]: """ Creates the features array `X` and labels array `y` to train a lagged-variables regression model (e.g. an @@ -66,8 +67,8 @@ def create_lagged_data( The `X` array is constructed from the lagged values of up to three separate timeseries: 1. The `target_series`, which contains the values we're trying to predict. A regression model that - uses previous values of the target its predicting is referred to as *auto-regressive*; please refer to - [1]_ for further details about auto-regressive timeseries models. + uses previous values of the target its predicting is referred to as *autoregressive*; please refer to + [1]_ for further details about autoregressive timeseries models. 2. The past covariates series, which contains values that are *not* known into the future. Unlike the target series, however, past covariates are *not* to be predicted by the regression model. 3. The future covariates (AKA 'exogenous' covariates) series, which contains values that are known @@ -101,7 +102,7 @@ def create_lagged_data( `lags_future_covariates` can contain negative, positive, and/or zero lag values (i.e. we *can* use the values of `future_covariates` at time `t` or beyond to predict the value of `target_series` at time `t`). - The exact method used to construct `X` and `y` depends on whether all of the specified timeseries are + The exact method used to construct `X` and `y` depends on whether all specified timeseries are of the same frequency or not: - If all specified timeseries are of the same frequency, `strided_moving_window` is used to extract contiguous time blocks from each timeseries; the lagged variables are then extracted from each window. @@ -111,7 +112,7 @@ def create_lagged_data( In cases where it can be validly applied, the 'moving window' method is expected to be faster than the 'intersecting time' method. However, in exceptional cases where only a small number of lags are being extracted, but the difference between the lag values is large (e.g. `lags = [-1, -1000]`), the 'moving - window' method is expected to consume significantly more memory, since it extracts all of the series values + window' method is expected to consume significantly more memory, since it extracts all series values between the maximum and minimum lags as 'windows', before actually extracting the specific requested lag values. In order for the lagged features of a series to be added to `X`, *both* that series and the corresponding lags @@ -120,8 +121,8 @@ def create_lagged_data( of each series. If the provided series are stochastic (i.e. `series.n_components > 1`), then an `X` and `y` array will be - constructed for each sample; the arrays corresponding to each sample are concatenated togather along the `2`nd - axis of `X` and `y`. In other words, `create_lagged_data` is vectorised over the sample axis of the `target_series`, + constructed for each sample; the arrays corresponding to each sample are concatenated together along the `2`nd + axis of `X` and `y`. In other words, `create_lagged_data` is vectorized over the sample axis of the `target_series`, `past_covariates`, and `future_covariates` inputs. Importantly, if stochastic series are provided, each series must have the same number of samples, otherwise an error will be thrown. @@ -140,9 +141,6 @@ def create_lagged_data( target_series Optionally, the series for the regression model to predict. Must be specified if `is_training = True`. Can be specified as either a `TimeSeries` or as a `Sequence[TimeSeries]`. - output_chunk_length - Optionally, the number of timesteps ahead into the future the regression model is to predict. Must - best specified if `is_training = True`. past_covariates Optionally, the past covariates series that the regression model will use as inputs. Unlike the `target_series`, `past_covariates` are *not* to be predicted by the regression model. Can be @@ -151,9 +149,9 @@ def create_lagged_data( Optionally, the future covariates (i.e. exogenous covariates) series that the regression model will use as inputs. Can be specified as either a `TimeSeries` or as a `Sequence[TimeSeries]`. lags - Optionally, the lags of the target series to be used as (auto-regressive) features. If not specified, - auto-regressive features will *not* be added to `X`. Each lag value is assumed to be negative (e.g. - `lags = [-3, -1]` will extract `target_series` values which are 3 timesteps and 1 timestep away from + Optionally, the lags of the target series to be used as (autoregressive) features. If not specified, + autoregressive features will *not* be added to `X`. Each lag value is assumed to be negative (e.g. + `lags = [-3, -1]` will extract `target_series` values which are 3 time steps and 1 time step away from the current value). If the lags are provided as a dictionary, the lags values are specific to each component in the target series. lags_past_covariates @@ -164,8 +162,14 @@ def create_lagged_data( Optionally, the lags of `future_covariates` to be used as features. Unlike `lags` and `lags_past_covariates`, `lags_future_covariates` values can be positive (i.e. use values *after* time `t` to predict target at time `t`), zero (i.e. use values *at* time `t` to predict target at time `t`), and/or - negative (i.e. use values *before* time `t` to predict target at time `t`). If the lags are provided as + negative (i.e. use values *before* time `t` to predict target at time `t`). If `output_chunk_shift > 0`, the + lags are relative to the first time step of the shifted output chunk. If the lags are provided as a dictionary, the lags values are specific to each component in the future covariates series. + output_chunk_length + Optionally, the number of time steps ahead into the future the regression model is to predict. Must + best specified if `is_training = True`. + output_chunk_shift + Optionally, the number of time steps to shift the output chunk ahead into the future. uses_static_covariates Whether the model uses/expects static covariates. If `True`, it enforces that static covariates must have identical shapes across all target series. @@ -177,18 +181,18 @@ def create_lagged_data( samples are kept. In theory, specifying a smaller `max_samples_per_ts` should reduce computation time, especially in cases where many observations could be generated. multi_models - Optionally, specifies whether the regression model predicts multiple timesteps into the future. If `True`, - then the regression model is assumed to predict all of the timesteps from time `t` to `t+output_chunk_length`. - If `False`, then the regression model is assumed to predict *only* the timestep at `t+output_chunk_length`. + Optionally, specifies whether the regression model predicts multiple time steps into the future. If `True`, + then the regression model is assumed to predict all time steps from time `t` to `t+output_chunk_length`. + If `False`, then the regression model is assumed to predict *only* the time step at `t+output_chunk_length`. This input is ignored if `is_training = False`. check_inputs Optionally, specifies that the `lags_*` and `series_*` inputs should be checked for validity. Should be set to `False` if inputs have already been checked for validity (e.g. inside the `__init__` of a class), otherwise should be set to `True`. use_moving_windows - Optionally, specifies that the 'moving window' method should be used to construct `X` and `y` if all of the + Optionally, specifies that the 'moving window' method should be used to construct `X` and `y` if all provided series are of the same frequency. If `use_moving_windows = False`, the 'time intersection' method - will always be used, even when all of the provided series are of the same frequency. In general, setting + will always be used, even when all provided series are of the same frequency. In general, setting to `True` results in faster tabularization at the potential cost of higher memory usage. See Notes for further details. is_training @@ -203,9 +207,21 @@ def create_lagged_data( a `Sequence[np.ndarray]`. If each series input is specified as a `Sequence[TimeSeries]` and `concatenate = False`, `X` and `y` will be lists whose `i`th element corresponds to the feature matrix or label array formed by the `i`th `TimeSeries` in each `Sequence[TimeSeries]` input. Conversely, if `concatenate = True` - when `Sequence[TimeSeries]` are provided, then `X` and `y` will be arrays created by concatenating all of the + when `Sequence[TimeSeries]` are provided, then `X` and `y` will be arrays created by concatenating all feature/label arrays formed by each `TimeSeries` along the `0`th axis. Note that `times` is still returned as `Sequence[pd.Index]`, even when `concatenate = True`. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. + show_warnings + Whether to show warnings. Returns ------- @@ -228,7 +244,8 @@ def create_lagged_data( last_static_covariates_shape The last observed shape of the static covariates. This is ``None`` when `uses_static_covariates` is ``False``. - + sample_weight + The weights to apply to each observation in `X` and output step `y`, returned as a `Sequence` of `np.ndarray`. Raises ------ @@ -256,59 +273,108 @@ def create_lagged_data( tabularization.create_lagged_component_names : return the lagged features names as a list of strings. """ - raise_if( - is_training and (target_series is None), - "Must specify `target_series` if `is_training = True`.", - ) + if is_training and (target_series is None): + raise_log( + ValueError("Must specify `target_series` if `is_training = True`."), + logger=logger, + ) # ensure list of TimeSeries format target_series = series2seq(target_series) past_covariates = series2seq(past_covariates) future_covariates = series2seq(future_covariates) + seq_ts_lens = [ len(seq_ts) for seq_ts in (target_series, past_covariates, future_covariates) if seq_ts is not None ] seq_ts_lens = set(seq_ts_lens) - raise_if( - len(seq_ts_lens) > 1, - "Must specify the same number of `TimeSeries` for each series input.", + if len(seq_ts_lens) > 1: + raise_log( + ValueError( + "Must specify the same number of `TimeSeries` for each series input." + ), + logger, + ) + + # process / check sample weight and generate series in case of built-in weight generator + sample_weight = _process_sample_weight(sample_weight, target_series) + + lags_passed_as_dict = any( + isinstance(lags_, dict) + for lags_ in [lags, lags_past_covariates, lags_future_covariates] ) + if (not use_moving_windows) and lags_passed_as_dict: + raise_log( + ValueError( + "`use_moving_windows=False` is not supported when any of the lags is provided as a dictionary. " + f"Received: {[lags, lags_past_covariates, lags_future_covariates]}." + ), + logger, + ) + if max_samples_per_ts is None: max_samples_per_ts = inf - X, y, times = [], [], [] + + # lags are identical for multiple series: pre-compute lagged features and reordered lagged features + lags_extract, lags_order = _get_lagged_indices( + lags, + lags_past_covariates, + lags_future_covariates, + ) + X, y, times, sample_weights = [], [], [], [] for i in range(max(seq_ts_lens)): target_i = target_series[i] if target_series else None past_i = past_covariates[i] if past_covariates else None future_i = future_covariates[i] if future_covariates else None - if use_moving_windows and _all_equal_freq(target_i, past_i, future_i): - X_i, y_i, times_i = _create_lagged_data_by_moving_window( - target_i, - output_chunk_length, - past_i, - future_i, - lags, - lags_past_covariates, - lags_future_covariates, - max_samples_per_ts, - multi_models, - check_inputs, - is_training, + sample_weight_i = sample_weight[i] if sample_weight else None + series_equal_freq = _all_equal_freq(target_i, past_i, future_i) + # component-wise lags extraction is not support with times intersection at the moment + if use_moving_windows and lags_passed_as_dict and (not series_equal_freq): + raise_log( + ValueError( + f"Cannot create tabularized data for the {i}th series because target and covariates don't have " + "the same frequency and some of the lags are provided as a dictionary. Either resample the " + "series or change the lags definition." + ), + logger, + ) + if use_moving_windows and series_equal_freq: + X_i, y_i, times_i, weights_i = _create_lagged_data_by_moving_window( + target_series=target_i, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, + past_covariates=past_i, + future_covariates=future_i, + sample_weight=sample_weight_i, + lags=lags, + lags_past_covariates=lags_past_covariates, + lags_future_covariates=lags_future_covariates, + lags_extract=lags_extract, + lags_order=lags_order, + max_samples_per_ts=max_samples_per_ts, + multi_models=multi_models, + check_inputs=check_inputs, + is_training=is_training, + show_warnings=show_warnings, ) else: - X_i, y_i, times_i = _create_lagged_data_by_intersecting_times( - target_i, - output_chunk_length, - past_i, - future_i, - lags, - lags_past_covariates, - lags_future_covariates, - max_samples_per_ts, - multi_models, - check_inputs, - is_training, + X_i, y_i, times_i, weights_i = _create_lagged_data_by_intersecting_times( + target_series=target_i, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, + past_covariates=past_i, + future_covariates=future_i, + sample_weight=sample_weight_i, + lags=lags, + lags_past_covariates=lags_past_covariates, + lags_future_covariates=lags_future_covariates, + max_samples_per_ts=max_samples_per_ts, + multi_models=multi_models, + check_inputs=check_inputs, + is_training=is_training, + show_warnings=show_warnings, ) X_i, last_static_covariates_shape = add_static_covariates_to_lagged_data( features=X_i, @@ -319,6 +385,8 @@ def create_lagged_data( X.append(X_i) y.append(y_i) times.append(times_i) + if weights_i is not None: + sample_weights.append(weights_i) if concatenate: X = np.concatenate(X, axis=0) @@ -326,29 +394,37 @@ def create_lagged_data( y = None elif concatenate: y = np.concatenate(y, axis=0) - return X, y, times, last_static_covariates_shape + + if sample_weights and concatenate: + sample_weights = np.concatenate(sample_weights, axis=0) + elif not sample_weights: + sample_weights = None + return X, y, times, last_static_covariates_shape, sample_weights def create_lagged_training_data( target_series: Union[TimeSeries, Sequence[TimeSeries]], output_chunk_length: int, + output_chunk_shift: int, past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - lags: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, - lags_past_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, - lags_future_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, + lags: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, + lags_past_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, + lags_future_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, uses_static_covariates: bool = True, - last_static_covariates_shape: Optional[Tuple[int, int]] = None, + last_static_covariates_shape: Optional[tuple[int, int]] = None, max_samples_per_ts: Optional[int] = None, multi_models: bool = True, check_inputs: bool = True, use_moving_windows: bool = True, concatenate: bool = True, -) -> Tuple[ + sample_weight: Optional[Union[TimeSeries, str]] = None, +) -> tuple[ ArrayOrArraySequence, Union[None, ArrayOrArraySequence], Sequence[pd.Index], - Optional[Tuple[int, int]], + Optional[tuple[int, int]], + Optional[ArrayOrArraySequence], ]: """ Creates the features array `X` and labels array `y` to train a lagged-variables regression model (e.g. an @@ -364,7 +440,9 @@ def create_lagged_training_data( target_series The series for the regression model to predict. output_chunk_length - The number of timesteps ahead into the future the regression model is to predict. + The number of time steps ahead into the future the regression model is to predict. + output_chunk_shift + Optionally, the number of time steps to shift the output chunk ahead into the future. past_covariates Optionally, the past covariates series that the regression model will use as inputs. Unlike the `target_series`, `past_covariates` are *not* to be predicted by the regression model. @@ -372,9 +450,9 @@ def create_lagged_training_data( Optionally, the future covariates (i.e. exogenous covariates) series that the regression model will use as inputs. lags - Optionally, the lags of the target series to be used as (auto-regressive) features. If not specified, - auto-regressive features will *not* be added to `X`. Each lag value is assumed to be negative (e.g. - `lags = [-3, -1]` will extract `target_series` values which are 3 timesteps and 1 timestep away from + Optionally, the lags of the target series to be used as (autoregressive) features. If not specified, + autoregressive features will *not* be added to `X`. Each lag value is assumed to be negative (e.g. + `lags = [-3, -1]` will extract `target_series` values which are 3 time steps and 1 time step away from the current value). If the lags are provided as a dictionary, the lags values are specific to each component in the target series. lags_past_covariates @@ -398,17 +476,17 @@ def create_lagged_training_data( samples are kept. In theory, specifying a smaller `max_samples_per_ts` should reduce computation time, especially in cases where many observations could be generated. multi_models - Optionally, specifies whether the regression model predicts multiple timesteps into the future. If `True`, - then the regression model is assumed to predict all of the timesteps from time `t` to `t+output_chunk_length`. - If `False`, then the regression model is assumed to predict *only* the timestep at `t+output_chunk_length`. + Optionally, specifies whether the regression model predicts multiple time steps into the future. If `True`, + then the regression model is assumed to predict all time steps from time `t` to `t+output_chunk_length`. + If `False`, then the regression model is assumed to predict *only* the time step at `t+output_chunk_length`. check_inputs Optionally, specifies that the `lags_*` and `series_*` inputs should be checked for validity. Should be set to `False` if inputs have already been checked for validity (e.g. inside the `__init__` of a class), otherwise should be set to `True`. use_moving_windows - Optionally, specifies that the 'moving window' method should be used to construct `X` and `y` if all of the + Optionally, specifies that the 'moving window' method should be used to construct `X` and `y` if all provided series are of the same frequency. If `use_moving_windows = False`, the 'time intersection' method - will always be used, even when all of the provided series are of the same frequency. In general, setting + will always be used, even when all provided series are of the same frequency. In general, setting to `True` results in faster tabularization at the potential cost of higher memory usage. See Notes for further details. concatenate @@ -416,9 +494,19 @@ def create_lagged_training_data( a `Sequence[np.ndarray]`. If each series input is specified as a `Sequence[TimeSeries]` and `concatenate = False`, `X` and `y` will be lists whose `i`th element corresponds to the feature matrix or label array formed by the `i`th `TimeSeries` in each `Sequence[TimeSeries]` input. Conversely, if `concatenate = True` - when `Sequence[TimeSeries]` are provided, then `X` and `y` will be arrays created by concatenating all of the + when `Sequence[TimeSeries]` are provided, then `X` and `y` will be arrays created by concatenating all feature/label arrays formed by each `TimeSeries` along the `0`th axis. Note that `times` is still returned as `Sequence[pd.Index]`, even when `concatenate = True`. + sample_weight + Optionally, some sample weights to apply to the target `series` labels. They are applied per observation, + per label (each step in `output_chunk_length`), and per component. + If a series or sequence of series, then those weights are used. If the weight series only have a single + component / column, then the weights are applied globally to all components in `series`. Otherwise, for + component-specific weights, the number of components must match those of `series`. + If a string, then the weights are generated using built-in weighting functions. The available options are + `"linear"` or `"exponential"` decay - the further in the past, the lower the weight. The weights are + computed globally based on the length of the longest series in `series`. Then for each series, the weights + are extracted from the end of the global weights. This gives a common time weighting across all series. Returns ------- @@ -438,6 +526,8 @@ def create_lagged_training_data( gives the times of those observations formed using the `i`th `TimeSeries` object in each `Sequence`. Otherwise, if the series inputs were specified as `TimeSeries`, the only element is the times of those observations formed from the lone `TimeSeries` inputs. + sample_weight + The weights to apply to each observation in `X` and output step `y`, returned as a `Sequence` of `np.ndarray`. Raises ------ @@ -460,6 +550,7 @@ def create_lagged_training_data( lags_past_covariates=lags_past_covariates, lags_future_covariates=lags_future_covariates, output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, uses_static_covariates=uses_static_covariates, last_static_covariates_shape=last_static_covariates_shape, max_samples_per_ts=max_samples_per_ts, @@ -468,6 +559,7 @@ def create_lagged_training_data( use_moving_windows=use_moving_windows, is_training=True, concatenate=concatenate, + sample_weight=sample_weight, ) @@ -475,16 +567,17 @@ def create_lagged_prediction_data( target_series: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - lags: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, - lags_past_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, - lags_future_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, + lags: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, + lags_past_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, + lags_future_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, uses_static_covariates: bool = True, - last_static_covariates_shape: Optional[Tuple[int, int]] = None, + last_static_covariates_shape: Optional[tuple[int, int]] = None, max_samples_per_ts: Optional[int] = None, check_inputs: bool = True, use_moving_windows: bool = True, concatenate: bool = True, -) -> Tuple[ArrayOrArraySequence, Sequence[pd.Index]]: + show_warnings: bool = True, +) -> tuple[ArrayOrArraySequence, Sequence[pd.Index]]: """ Creates the features array `X` to produce a series of prediction from an already-trained regression model; the time index values of each observation is also returned. @@ -505,9 +598,9 @@ def create_lagged_prediction_data( Optionally, the future covariates (i.e. exogenous covariates) series that the regression model will use as inputs. lags - Optionally, the lags of the target series to be used as (auto-regressive) features. If not specified, - auto-regressive features will *not* be added to `X`. Each lag value is assumed to be negative (e.g. - `lags = [-3, -1]` will extract `target_series` values which are 3 timesteps and 1 timestep away from + Optionally, the lags of the target series to be used as (autoregressive) features. If not specified, + autoregressive features will *not* be added to `X`. Each lag value is assumed to be negative (e.g. + `lags = [-3, -1]` will extract `target_series` values which are 3 time steps and 1 time step away from the current value). If the lags are provided as a dictionary, the lags values are specific to each component in the target series. lags_past_covariates @@ -535,9 +628,9 @@ def create_lagged_prediction_data( to `False` if inputs have already been checked for validity (e.g. inside the `__init__` of a class), otherwise should be set to `True`. use_moving_windows - Optionally, specifies that the 'moving window' method should be used to construct `X` and `y` if all of the + Optionally, specifies that the 'moving window' method should be used to construct `X` and `y` if all provided series are of the same frequency. If `use_moving_windows = False`, the 'time intersection' method - will always be used, even when all of the provided series are of the same frequency. In general, setting + will always be used, even when all provided series are of the same frequency. In general, setting to `True` results in faster tabularization at the potential cost of higher memory usage. See Notes for further details. concatenate @@ -545,9 +638,11 @@ def create_lagged_prediction_data( `Sequence[np.ndarray]`. If each series input is specified as a `Sequence[TimeSeries]` and `concatenate = False`, `X` will be a list whose `i`th element corresponds to the feature matrix or label array formed by the `i`th `TimeSeries` in each `Sequence[TimeSeries]` input. Conversely, if `concatenate = True` when - `Sequence[TimeSeries]` are provided, then `X` will be an array created by concatenating all of the feature + `Sequence[TimeSeries]` are provided, then `X` will be an array created by concatenating all feature arrays formed by each `TimeSeries` along the `0`th axis. Note that `times` is still returned as `Sequence[pd.Index]`, even when `concatenate = True`. + show_warnings + Whether to show warnings. Returns ------- @@ -574,7 +669,7 @@ def create_lagged_prediction_data( If the provided series do not share the same type of `time_index` (e.g. `target_series` uses a pd.RangeIndex, but `future_covariates` uses a `pd.DatetimeIndex`). """ - X, _, times, _ = create_lagged_data( + X, _, times, _, _ = create_lagged_data( target_series=target_series, past_covariates=past_covariates, future_covariates=future_covariates, @@ -588,6 +683,7 @@ def create_lagged_prediction_data( use_moving_windows=use_moving_windows, is_training=False, concatenate=concatenate, + show_warnings=show_warnings, ) return X, times @@ -596,7 +692,7 @@ def add_static_covariates_to_lagged_data( features: Union[np.ndarray, Sequence[np.ndarray]], target_series: Union[TimeSeries, Sequence[TimeSeries]], uses_static_covariates: bool = True, - last_shape: Optional[Tuple[int, int]] = None, + last_shape: Optional[tuple[int, int]] = None, ) -> Union[np.ndarray, Sequence[np.ndarray]]: """ Add static covariates to the features' table for RegressionModels. @@ -671,12 +767,10 @@ def add_static_covariates_to_lagged_data( if len(features[idx].shape) == 2 else (len(features[idx]), len(static_covs), 1) ) - features[idx] = np.hstack( - [ - features[idx], - np.broadcast_to(static_covs, shape_out[:2]).reshape(shape_out), - ] - ) + features[idx] = np.hstack([ + features[idx], + np.broadcast_to(static_covs, shape_out[:2]).reshape(shape_out), + ]) if input_not_list: features = features[0] @@ -687,13 +781,13 @@ def create_lagged_component_names( target_series: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, - lags: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, - lags_past_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, - lags_future_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, + lags: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, + lags_past_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, + lags_future_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, output_chunk_length: int = 1, concatenate: bool = True, use_static_covariates: bool = False, -) -> Tuple[List[List[str]], List[List[str]]]: +) -> tuple[list[list[str]], list[list[str]]]: """ Helper function called to retrieve the name of the features and labels arrays created with `create_lagged_data()`. The order of the features is the following: @@ -704,9 +798,9 @@ def create_lagged_component_names( For `*_lags=[-2,-1]` and `*_series.n_components = 2` (lags shared across all the components), each `lagged_*` has the following structure (grouped by lags): comp0_*_lag-2 | comp1_*_lag-2 | comp0_*_lag_-1 | comp1_*_lag-1 - For `*_lags={'comp0':[-2, -1], 'comp1':[-5, -3]}` and `*_series.n_components = 2` (component- - specific lags), each `lagged_*` has the following structure (grouped by components): - comp0_*_lag-2 | comp0_*_lag-1 | comp1_*_lag_-5 | comp1_*_lag-3 + For `*_lags={'comp0':[-3, -1], 'comp1':[-5, -3]}` and `*_series.n_components = 2` (component- + specific lags), each `lagged_*` has the following structure (sorted by lags, then by components): + comp1_*_lag-5 | comp0_*_lag-3 | comp1_*_lag_-3 | comp0_*_lag-1 and for static covariates (2 static covariates acting on 2 target components): cov0_*_target_comp0 | cov0_*_target_comp1 | cov1_*_target_comp0 | cov1_*_target_comp1 @@ -718,7 +812,7 @@ def create_lagged_component_names( Note : will only use the component names of the first series from `target_series`, `past_covariates`, `future_covariates`, and static_covariates. - The naming convention for target, past and future covariates is: ``"{name}_{type}_lag{i}"``, where: + The naming convention for target, past and future covariates lags is: ``"{name}_{type}_lag{i}"``, where: - ``{name}`` the component name of the (first) series - ``{type}`` is the feature type, one of "target", "pastcov", and "futcov" @@ -730,6 +824,11 @@ def create_lagged_component_names( - ``{comp}`` the target component name of the (first) that the static covariate act on. If the static covariate acts globally on a multivariate target series, will show "global". + The naming convention for labels is: ``"{name}_target_hrz{i}"``, where: + + - ``{name}`` the component name of the (first) series + - ``{i}`` is the step in the forecast horizon + Returns ------- features_cols_name @@ -755,15 +854,45 @@ def create_lagged_component_names( [lags, lags_past_covariates, lags_future_covariates], ["target", "pastcov", "futcov"], ): - if variate is None or variate_lags is None: + if variate is None: continue components = get_single_series(variate).components.tolist() + # target labels + if variate_type == "target": + label_feature_names = [ + f"{name}_target_hrz{lag}" + for lag in range(output_chunk_length) + for name in components + ] + + if variate_lags is None: + continue + if isinstance(variate_lags, dict): + if "default_lags" in variate_lags: + raise_log( + ValueError( + "All the lags must be explicitly defined, 'default_lags' is not allowed in the " + "lags dictionary." + ), + logger, + ) + + # combine all the lags and sort them in ascending order across all the components + comp_lags_reordered = np.concatenate([ + np.array(variate_lags[comp_name], dtype=int) for comp_name in components + ]).argsort() + tmp_lagged_feats_names = [] for name in components: - lagged_feature_names += [ + tmp_lagged_feats_names += [ f"{name}_{variate_type}_lag{lag}" for lag in variate_lags[name] ] + + # adding feats names reordered across components + lagged_feature_names += [ + tmp_lagged_feats_names[idx] for idx in comp_lags_reordered + ] else: lagged_feature_names += [ f"{name}_{variate_type}_lag{lag}" @@ -771,13 +900,6 @@ def create_lagged_component_names( for name in components ] - if variate_type == "target" and lags: - label_feature_names = [ - f"{name}_target_lag{lag}" - for lag in range(output_chunk_length) - for name in components - ] - # static covariates if use_static_covariates: static_covs = get_single_series(target_series).static_covariates @@ -795,19 +917,62 @@ def create_lagged_component_names( return lagged_feature_names, label_feature_names +def _get_lagged_indices( + lags, + lags_past_covariates, + lags_future_covariates, +): + """Computes and returns: + + - the lagged feature indices for extraction from windows + - the reordered indices to apply after the window extraction (in case of component specific lags) + + Assumes that all input series share identical component order. + """ + lags_extract = [] + lags_order = [] + for lags_i in [lags, lags_past_covariates, lags_future_covariates]: + if lags_i is None: + lags_extract.append(None) + lags_order.append(None) + continue + + # Within each window, the `-1` indexed value (i.e. the value at the very end of + # the window) corresponds to time `t - min_lag_i`. The negative index of the time + # `t + lag_i` within this window is, therefore, `-1 + lag_i + min_lag_i`: + if isinstance(lags_i, list): + lags_extract_i = np.array(lags_i, dtype=int) + # Feats are already grouped by lags and ordered + lags_order_i = slice(None) + else: + # Assume keys are in the same order as the series components + # Lags are grouped by component, extracted from the same window + lags_extract_i = [np.array(c_lags, dtype=int) for c_lags in lags_i.values()] + # Sort the lags across the components in ascending order + lags_order_i = np.concatenate(lags_extract_i).argsort() + lags_extract.append(lags_extract_i) + lags_order.append(lags_order_i) + return lags_extract, lags_order + + def _create_lagged_data_by_moving_window( target_series: Optional[TimeSeries], output_chunk_length: int, + output_chunk_shift: int, past_covariates: Optional[TimeSeries], future_covariates: Optional[TimeSeries], - lags: Optional[Union[Sequence[int], Dict[str, List[int]]]], - lags_past_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]], - lags_future_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]], + sample_weight: Optional[TimeSeries], + lags: Optional[Union[Sequence[int], dict[str, list[int]]]], + lags_past_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]], + lags_future_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]], + lags_extract: list[Optional[np.ndarray]], + lags_order: list[Optional[np.ndarray]], max_samples_per_ts: Optional[int], multi_models: bool, check_inputs: bool, is_training: bool, -) -> Tuple[np.ndarray, np.ndarray, pd.Index]: + show_warnings: bool = True, +) -> tuple[np.ndarray, Optional[np.ndarray], pd.Index, Optional[np.ndarray]]: """ Helper function called by `create_lagged_data` that computes `X`, `y`, and `times` by extracting 'moving windows' from each series using the `strided_moving_window` @@ -820,30 +985,39 @@ def _create_lagged_data_by_moving_window( and `t + output_chunk_length - 1` from the target series. In both cases, the extracted windows can then be reshaped into the correct shape. This approach can only be used if we *can* assume that the specified series are all of the same frequency. + + Assumes that all the lags are sorted in ascending order. """ feature_times, min_lags, max_lags = _get_feature_times( - target_series, - past_covariates, - future_covariates, - lags, - lags_past_covariates, - lags_future_covariates, - output_chunk_length, + target_series=target_series, + past_covariates=past_covariates, + future_covariates=future_covariates, + lags=lags, + lags_past_covariates=lags_past_covariates, + lags_future_covariates=lags_future_covariates, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, is_training=is_training, return_min_and_max_lags=True, check_inputs=check_inputs, + show_warnings=show_warnings, ) if check_inputs: series_and_lags_not_specified = [max_lag is None for max_lag in max_lags] - raise_if( - all(series_and_lags_not_specified), - "Must specify at least one series-lags pair.", - ) + if all(series_and_lags_not_specified): + raise_log( + ValueError("Must specify at least one series-lags pair."), logger=logger + ) + sample_weight_vals = _extract_sample_weight(sample_weight, target_series) + time_bounds = get_shared_times_bounds(*feature_times) - raise_if( - time_bounds is None, - "Specified series do not share any common times for which features can be created.", - ) + if time_bounds is None: + raise_log( + ValueError( + "Specified series do not share any common times for which features can be created." + ), + logger=logger, + ) freq = _get_freqs(target_series, past_covariates, future_covariates)[0] if isinstance(time_bounds[0], int): # `stop` is exclusive, so need `+ freq` to include end-point: @@ -862,10 +1036,11 @@ def _create_lagged_data_by_moving_window( X = [] start_time_idx = None target_start_time_idx = None - for i, (series_i, lags_i, min_lag_i, max_lag_i) in enumerate( + for i, (series_i, lags_extract_i, lags_order_i, min_lag_i, max_lag_i) in enumerate( zip( [target_series, past_covariates, future_covariates], - [lags, lags_past_covariates, lags_future_covariates], + lags_extract, + lags_order, min_lags, max_lags, ) @@ -874,6 +1049,9 @@ def _create_lagged_data_by_moving_window( is_target_series = is_training and (i == 0) if is_target_series or series_and_lags_specified: time_index_i = series_i.time_index + + if time_index_i[0] == start_time: + start_time_idx = 0 # If lags are sufficiently large, `series_i` may not contain all # feature times. For example, if `lags_past_covariates = [-50]`, # then we can construct features for time `51` using the value @@ -883,29 +1061,19 @@ def _create_lagged_data_by_moving_window( # for all feature times - these values will become labels. # If `start_time` not included in `time_index_i`, can 'manually' calculate # what its index *would* be if `time_index_i` were extended to include that time: - if not is_target_series and (time_index_i[-1] < start_time): - # Series frequency represents a non-ambiguous timedelta value (not ‘M’, ‘Y’ or ‘y’) - if pd.to_timedelta(series_i.freq, errors="coerce") is not pd.NaT: - start_time_idx = ( - len(time_index_i) - - 1 - + (start_time - time_index_i[-1]) // series_i.freq - ) - else: - # Create a temporary DatetimeIndex to extract the actual start index. - start_time_idx = ( - len(time_index_i) - - 1 - + len( - pd.date_range( - start=time_index_i[-1] + series_i.freq, - end=start_time, - freq=series_i.freq, - ) - ) + elif not is_target_series and (time_index_i[-1] < start_time): + start_time_idx = ( + len(time_index_i) + - 1 + + n_steps_between( + end=start_time, start=time_index_i[-1], freq=series_i.freq ) - elif not is_target_series and (time_index_i[0] >= start_time): - start_time_idx = max_lag_i + ) + # future covariates can start after `start_time` if all lags are > 0 + elif not is_target_series and (time_index_i[0] > start_time): + start_time_idx = -n_steps_between( + end=time_index_i[0], start=start_time, freq=series_i.freq + ) # If `start_time` *is* included in `time_index_i`, need to binary search `time_index_i` # for its position: else: @@ -923,57 +1091,68 @@ def _create_lagged_data_by_moving_window( first_window_start_idx : first_window_end_idx + num_samples - 1, :, : ] windows = strided_moving_window( - vals, window_len, stride=1, axis=0, check_inputs=False + x=vals, window_len=window_len, stride=1, axis=0, check_inputs=False ) + # Within each window, the `-1` indexed value (i.e. the value at the very end of # the window) corresponds to time `t - min_lag_i`. The negative index of the time # `t + lag_i` within this window is, therefore, `-1 + lag_i + min_lag_i`: - if isinstance(lags_i, list): - lags_to_extract = np.array(lags_i, dtype=int) + min_lag_i - 1 - else: - # Lags are grouped by component, extracted from the same window - lags_to_extract = [ - np.array(comp_lags, dtype=int) + min_lag_i - 1 - for comp_lags in lags_i.values() - ] - lagged_vals = _extract_lagged_vals_from_windows(windows, lags_to_extract) - X.append(lagged_vals) + # extract lagged values + lagged_vals = _extract_lagged_vals_from_windows( + windows, lags_extract_i, lags_shift=min_lag_i - 1 + ) + # extract and append the reordered lagged values + X.append(lagged_vals[:, lags_order_i]) # Cache `start_time_idx` for label creation: if is_target_series: target_start_time_idx = start_time_idx X = np.concatenate(X, axis=1) # Construct labels array `y`: if is_training: - # All values between times `t` and `t + output_chunk_length` used as labels: + # All values between times `t` and `t + output_chunk_length` used as labels / weights: # Window taken between times `t` and `t + output_chunk_length - 1`: - first_window_start_idx = target_start_time_idx + first_window_start_idx = target_start_time_idx + output_chunk_shift # Add `+ 1` since end index is exclusive in Python: - first_window_end_idx = target_start_time_idx + output_chunk_length - # To create `(num_samples - 1)` other windows in addition to first window, - # must take `(num_samples - 1)` values ahead of `first_window_end_idx` - vals = target_series.all_values(copy=False)[ - first_window_start_idx : first_window_end_idx + num_samples - 1, - :, - :, - ] - windows = strided_moving_window( - vals, - window_len=output_chunk_length, - stride=1, - axis=0, - check_inputs=False, + first_window_end_idx = ( + target_start_time_idx + output_chunk_length + output_chunk_shift ) lags_to_extract = None if multi_models else -np.ones((1,), dtype=int) - y = _extract_lagged_vals_from_windows(windows, lags_to_extract) - # Only values at times `t + output_chunk_length - 1` used as labels: + + # extract target labels and sample weights + y_and_weights = [] + for vals in [target_series.all_values(copy=False), sample_weight_vals]: + if vals is None: + y_and_weights.append(None) + continue + + # To create `(num_samples - 1)` other windows in addition to first window, + # must take `(num_samples - 1)` values ahead of `first_window_end_idx` + vals = vals[ + first_window_start_idx : first_window_end_idx + num_samples - 1, + :, + :, + ] + windows = strided_moving_window( + x=vals, + window_len=output_chunk_length, + stride=1, + axis=0, + check_inputs=False, + ) + # Only values at times `t + output_chunk_length - 1` used as labels: + vals = _extract_lagged_vals_from_windows(windows, lags_to_extract) + y_and_weights.append(vals) + + y, weights = y_and_weights else: - y = None - return X, y, times + y, weights = None, None + return X, y, times, weights def _extract_lagged_vals_from_windows( windows: np.ndarray, - lags_to_extract: Optional[Union[np.ndarray, List[np.ndarray]]] = None, + lags_to_extract: Optional[Union[np.ndarray, list[np.ndarray]]] = None, + lags_shift: int = 0, ) -> np.ndarray: """ Helper function called by `_create_lagged_data_by_moving_window` that @@ -983,7 +1162,7 @@ def _extract_lagged_vals_from_windows( is done such that the order of elements along axis 1 matches the pattern described in the docstring of `create_lagged_data`. - If `lags_to_extract` is not specified, all of the values within each window is extracted. + If `lags_to_extract` is not specified, all values within each window is extracted. If `lags_to_extract` is specified as an np.ndarray, then only those values within each window that are indexed by `lags_to_extract` will be returned. In such cases, the shape of the returned lagged values is `(num_windows, num_components * lags_to_extract.size, num_series)`. For example, @@ -998,7 +1177,7 @@ def _extract_lagged_vals_from_windows( if isinstance(lags_to_extract, list): # iterate over the components-specific lags comp_windows = [ - windows[:, i, :, comp_lags_to_extract] + windows[:, i, :, comp_lags_to_extract + lags_shift] for i, comp_lags_to_extract in enumerate(lags_to_extract) ] # windows.shape = (sum(lags_len) across components, num_windows, num_samples): @@ -1006,7 +1185,7 @@ def _extract_lagged_vals_from_windows( lagged_vals = np.moveaxis(windows, (1, 0, 2), (0, 1, 2)) else: if lags_to_extract is not None: - windows = windows[:, :, :, lags_to_extract] + windows = windows[:, :, :, lags_to_extract + lags_shift] # windows.shape = (num_windows, window_len, num_components, num_samples): windows = np.moveaxis(windows, (0, 3, 1, 2), (0, 1, 2, 3)) # lagged_vals.shape = (num_windows, num_components*window_len, num_samples): @@ -1017,8 +1196,10 @@ def _extract_lagged_vals_from_windows( def _create_lagged_data_by_intersecting_times( target_series: TimeSeries, output_chunk_length: int, + output_chunk_shift: int, past_covariates: Optional[TimeSeries], future_covariates: Optional[TimeSeries], + sample_weight: Optional[TimeSeries], lags: Optional[Sequence[int]], lags_past_covariates: Optional[Sequence[int]], lags_future_covariates: Optional[Sequence[int]], @@ -1026,7 +1207,13 @@ def _create_lagged_data_by_intersecting_times( multi_models: bool, check_inputs: bool, is_training: bool, -) -> Tuple[np.ndarray, np.ndarray, Union[pd.RangeIndex, pd.DatetimeIndex]]: + show_warnings: bool = True, +) -> tuple[ + np.ndarray, + Optional[np.ndarray], + Union[pd.RangeIndex, pd.DatetimeIndex], + Optional[np.ndarray], +]: """ Helper function called by `_create_lagged_data` that computes `X`, `y`, and `times` by first finding the time points in each series that *could* be used to create features/labels, @@ -1037,28 +1224,34 @@ def _create_lagged_data_by_intersecting_times( specified series are of the same frequency. """ feature_times, min_lags, _ = _get_feature_times( - target_series, - past_covariates, - future_covariates, - lags, - lags_past_covariates, - lags_future_covariates, - output_chunk_length, + target_series=target_series, + past_covariates=past_covariates, + future_covariates=future_covariates, + lags=lags, + lags_past_covariates=lags_past_covariates, + lags_future_covariates=lags_future_covariates, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, is_training=is_training, return_min_and_max_lags=True, check_inputs=check_inputs, + show_warnings=show_warnings, ) if check_inputs: series_and_lags_not_specified = [min_lag is None for min_lag in min_lags] - raise_if( - all(series_and_lags_not_specified), - "Must specify at least one series-lags pair.", - ) + if all(series_and_lags_not_specified): + raise_log( + ValueError("Must specify at least one series-lags pair."), logger=logger + ) + sample_weight_vals = _extract_sample_weight(sample_weight, target_series) shared_times = get_shared_times(*feature_times, sort=True) - raise_if( - shared_times is None, - "Specified series do not share any common times for which features can be created.", - ) + if shared_times is None: + raise_log( + ValueError( + "Specified series do not share any common times for which features can be created." + ), + logger=logger, + ) if len(shared_times) > max_samples_per_ts: shared_times = shared_times[-max_samples_per_ts:] X = [] @@ -1114,45 +1307,177 @@ def _create_lagged_data_by_intersecting_times( if is_training: if multi_models: # All points between time `t` and `t + output_chunk_length - 1` are labels: - idx_to_get = label_shared_time_idx + np.arange(output_chunk_length) + idx_to_get = ( + label_shared_time_idx + + np.arange(output_chunk_length) + + output_chunk_shift + ) else: # Only point at time `t + output_chunk_length - 1` is a label: - idx_to_get = label_shared_time_idx + output_chunk_length - 1 - # Before reshaping: lagged_vals.shape = (n_observations, num_lags, n_components, n_samples) - lagged_vals = target_series.all_values(copy=False)[idx_to_get, :, :] - # After reshaping: lagged_vals.shape = (n_observations, num_lags*n_components, n_samples) - y = lagged_vals.reshape(lagged_vals.shape[0], -1, lagged_vals.shape[-1]) + idx_to_get = ( + label_shared_time_idx + output_chunk_length + output_chunk_shift - 1 + ) + + # extract target labels and sample weights + y_and_weights = [] + for vals in [target_series.all_values(copy=False), sample_weight_vals]: + if vals is None: + y_and_weights.append(None) + continue + + # Before reshaping: lagged_vals.shape = (n_observations, num_lags, n_components, n_samples) + vals = vals[idx_to_get, :, :] + # After reshaping: lagged_vals.shape = (n_observations, num_lags*n_components, n_samples) + vals = vals.reshape(vals.shape[0], -1, vals.shape[-1]) + y_and_weights.append(vals) + y, weights = y_and_weights else: - y = None - return X, y, shared_times + y, weights = None, None + return X, y, shared_times, weights + + +def _create_lagged_data_autoregression( + target_series: Union[TimeSeries, Sequence[TimeSeries]], + t_pred: int, + shift: int, + last_step_shift: int, + series_matrix: np.ndarray, + covariate_matrices: dict[str, np.ndarray], + lags: dict[str, list[int]], + component_lags: dict[str, dict[str, list[int]]], + relative_cov_lags: dict[str, np.ndarray], + uses_static_covariates: bool, + last_static_covariates_shape: Optional[tuple[int, int]], + num_samples: int, +) -> np.ndarray: + """Extract lagged data from target, past covariates and future covariates for auto-regression + with RegressionModels. + """ + series_length = len(target_series) + X = [] + for series_type in ["target", "past", "future"]: + if series_type not in lags: + continue + + # extract series specific data + values_matrix = ( + series_matrix + if series_type == "target" + else covariate_matrices[series_type] + ) + + if series_type not in component_lags: + # for global lags over all components, directly extract lagged values from the data + if series_type == "target": + relative_lags = [ + lag - (shift + last_step_shift) for lag in lags[series_type] + ] + else: + relative_lags = relative_cov_lags[series_type] + t_pred + + lagged_data = values_matrix[:, relative_lags].reshape( + series_length * num_samples, -1 + ) + else: + # for component-specific lags, sort by lags and components and then extract + tmp_X = _extract_component_lags_autoregression( + series_type=series_type, + values_matrix=values_matrix, + shift=shift, + last_step_shift=last_step_shift, + t_pred=t_pred, + lags=lags, + component_lags=component_lags, + ) + lagged_data = tmp_X.reshape(series_length * num_samples, -1) + X.append(lagged_data) + # concatenate retrieved lags + X = np.concatenate(X, axis=1) + + if not uses_static_covariates: + return X + + # Need to split up `X` into three equally-sized sub-blocks + # corresponding to each timeseries in `series`, so that + # static covariates can be added to each block; valid since + # each block contains same number of observations: + X = np.split(X, series_length, axis=0) + X, _ = add_static_covariates_to_lagged_data( + features=X, + target_series=target_series, + uses_static_covariates=uses_static_covariates, + last_shape=last_static_covariates_shape, + ) + + # concatenate retrieved lags + return np.concatenate(X, axis=0) + + +def _extract_component_lags_autoregression( + series_type: str, + values_matrix: np.ndarray, + shift: int, + last_step_shift: int, + t_pred: int, + lags: dict[str, list[int]], + component_lags: dict[str, dict[str, list[int]]], +) -> np.ndarray: + """Extract, concatenate and reorder component-wise lags to obtain a feature order + identical to tabularization. + """ + # prepare index to reorder features by lags across components + comp_lags_reordered = np.concatenate([ + comp_lags for comp_lags in component_lags[series_type].values() + ]).argsort() + + # convert relative lags to absolute + if series_type == "target": + lags_shift = -shift - last_step_shift + else: + lags_shift = -lags[series_type][0] + t_pred + + # extract features + tmp_X = [ + values_matrix[ + :, + [lag + lags_shift for lag in comp_lags], + comp_i, + ] + for comp_i, comp_lags in enumerate(component_lags[series_type].values()) + ] + + # concatenate on features dimension and reorder + return np.concatenate(tmp_X, axis=1)[:, comp_lags_reordered] # For convenience, define following types for `_get_feature_times`: -FeatureTimes = Tuple[ +FeatureTimes = tuple[ Optional[Union[pd.Index, pd.DatetimeIndex, pd.RangeIndex]], Optional[Union[pd.Index, pd.DatetimeIndex, pd.RangeIndex]], Optional[Union[pd.Index, pd.DatetimeIndex, pd.RangeIndex]], ] -MinLags = Tuple[Optional[int], Optional[int], Optional[int]] -MaxLags = Tuple[Optional[int], Optional[int], Optional[int]] +MinLags = tuple[Optional[int], Optional[int], Optional[int]] +MaxLags = tuple[Optional[int], Optional[int], Optional[int]] def _get_feature_times( target_series: Optional[TimeSeries] = None, past_covariates: Optional[TimeSeries] = None, future_covariates: Optional[TimeSeries] = None, - lags: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, - lags_past_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, - lags_future_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]] = None, + lags: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, + lags_past_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, + lags_future_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]] = None, output_chunk_length: int = 1, + output_chunk_shift: int = 0, is_training: bool = True, return_min_and_max_lags: bool = False, check_inputs: bool = True, -) -> Union[FeatureTimes, Tuple[FeatureTimes, MinLags, MaxLags]]: + show_warnings: bool = True, +) -> Union[FeatureTimes, tuple[FeatureTimes, MinLags, MaxLags]]: """ Returns a tuple containing the times in `target_series`, the times in `past_covariates`, and the times in `future_covariates` that *could* be used to create features. The returned tuple of times can then be passed - to `get_shared_times` to compute the 'eligible time points' shared by all of the specified series. + to `get_shared_times` to compute the 'eligible time points' shared by all specified series. Notes ----- @@ -1170,7 +1495,7 @@ def _get_feature_times( The values contained in `lags_future_covariates`, on the other hand, can be negative, zero, or positive; this means that there are three cases to consider: 1. Both `min_lag` and `max_lag` are positive, which means that all the values in `lags_future_covariates` - are negative. In this case, `min_lag` and `max_lag` correspond to the to the smallest and largest + are negative. In this case, `min_lag` and `max_lag` correspond to the smallest and largest lag magnitudes respectively. For example: `lags_future_covariates = [-3, -2, -1] -> min_lag = 1, max_lag = 3` 2. `min_lag` is non-positive (i.e. zero or negative), but `max_lag` is positive, which means that @@ -1188,16 +1513,16 @@ def _get_feature_times( 2. `max_lag <= 0` is a sufficient condition for `min_lag` and `max_lag` both being non-positive (i.e. Case 2). To extract feature times from a `target_series` when `is_training = True`, the following steps are performed: - 1. The first `max_lag` times of the series are excluded; these times have too few preceeding values to + 1. The first `max_lag` times of the series are excluded; these times have too few preceding values to construct features from. - 2. The last `output_chunk_length - 1` times are excluded; these times have too few succeeding times - to construct labels from. + 2. The last `output_chunk_length - output_chunk_shift - 1` times are excluded; these times have too few + succeeding times to construct labels from. To extract feature times from a `target_series` when `is_training = False`, the following steps are performed: 1. An additional `min_lag` times are appended to the end of the series; although these times are not contained in the original series, we're able to construct features for them since we only need the values of the series from time `t - max_lag` to `t - min_lag` to construct a feature for time `t`. - 2. The first `max_lag` times of the series are then excluded; these times have too few preceeding values to + 2. The first `max_lag` times of the series are then excluded; these times have too few preceding values to construct features from. The exact same procedure is performed to extract the feature times from a `past_covariates` series. @@ -1238,15 +1563,17 @@ def _get_feature_times( Optionally, the future covariates (i.e. exogenous covariates) series that the regression model will use as inputs. lags - Optionally, the lags of the target series to be used as (auto-regressive) features. If not specified, - auto-regressive features will *not* be added to `X`. + Optionally, the lags of the target series to be used as (autoregressive) features. If not specified, + autoregressive features will *not* be added to `X`. lags_past_covariates Optionally, the lags of `past_covariates` to be used as features. lags_future_covariates Optionally, the lags of `future_covariates` to be used as features. output_chunk_length - Optionally, the number of timesteps ahead into the future the regression model is to predict. This is ignored + Optionally, the number of time steps ahead into the future the regression model is to predict. This is ignored if `is_training = False`. + output_chunk_shift + Optionally, the number of time steps to shift the output chunk ahead into the future. is_training Optionally, specifies that training data is to be generated from the specified series. If `True`, `target_series`, `output_chunk_length`, and `multi_models` must all be specified. @@ -1257,6 +1584,8 @@ def _get_feature_times( return_min_and_max_lags Optionally, specifies whether the largest magnitude lag value for each series should also be returned along with the 'eligible' feature times + show_warnings + Whether to show warnings. Note: if the lags are provided as a dictionary for the target series or any of the covariates series, the component-specific lags are grouped into a single list to compute the corresponding feature time. @@ -1287,18 +1616,20 @@ def _get_feature_times( UserWarning If a `lags_*` input is specified without the accompanying time series or vice versa. The only expection to this is when `lags` isn't specified alongside `target_series` when `is_training = True`, since one may wish to fit - a regression model without using auto-regressive features. + a regression model without using autoregressive features. """ - raise_if( - is_training and (target_series is None), - "Must specify `target_series` when `is_training = True`.", - ) - if check_inputs: - raise_if( - not isinstance(output_chunk_length, int) or output_chunk_length < 1, - "`output_chunk_length` must be a positive `int`.", + if is_training and (target_series is None): + raise_log( + ValueError("Must specify `target_series` when `is_training = True`."), + logger=logger, ) + if check_inputs: + if not isinstance(output_chunk_length, int) or output_chunk_length < 1: + raise_log( + ValueError("`output_chunk_length` must be a positive `int`."), + logger=logger, + ) _check_lags(lags, lags_past_covariates, lags_future_covariates) feature_times, min_lags, max_lags = [], [], [] for name_i, series_i, lags_i in zip( @@ -1312,11 +1643,12 @@ def _get_feature_times( if check_inputs and (series_i is not None): _check_series_length( - series_i, - lags_i, - output_chunk_length, - is_training, - name_i, + series=series_i, + lags=lags_i, + output_chunk_length=output_chunk_length, + output_chunk_shift=output_chunk_shift, + is_training=is_training, + name=name_i, ) series_specified = series_i is not None lags_specified = lags_i is not None @@ -1326,7 +1658,10 @@ def _get_feature_times( min_lag_i = -max(lags_i) if lags_specified else None if is_label_series: # Exclude last `output_chunk_length - 1` times: - end_idx = -output_chunk_length + 1 if output_chunk_length > 1 else None + if not output_chunk_shift: + end_idx = -output_chunk_length + 1 if output_chunk_length > 1 else None + else: + end_idx = -output_chunk_length - output_chunk_shift + 1 times_i = times_i[:end_idx] elif series_specified and lags_specified: # Prepend times to start of series - see Step 1a for extracting @@ -1337,7 +1672,9 @@ def _get_feature_times( # Append times to end of series - see Step 1b for extracting features # times from `future_covariates`, or Step 1 for extracting features # from `target_series`/`past_covariates` in `Notes`: - new_end = times_i[-1] + series_i.freq * min_lag_i if min_lag_i > 0 else None + new_end = ( + times_i[-1] + series_i.freq * (min_lag_i) if min_lag_i > 0 else None + ) times_i = _extend_time_index( times_i, series_i.freq, new_start=new_start, new_end=new_end ) @@ -1351,7 +1688,11 @@ def _get_feature_times( # `target_series`/`past_covariates` in `Notes`: if max_lag_i > 0: times_i = times_i[max_lag_i:] - elif (not is_label_series) and (series_specified ^ lags_specified): + elif ( + show_warnings + and (not is_label_series) + and (series_specified ^ lags_specified) + ): # Warn user that series/lags input will be ignored: times_i = max_lag_i = None lags_name = "lags" if name_i == "target_series" else f"lags_{name_i}" @@ -1360,6 +1701,7 @@ def _get_feature_times( warnings.warn( f"`{specified}` was specified without accompanying `{unspecified}` and, thus, will be ignored." ) + feature_times.append(times_i) # Note `max_lag_i` and `min_lag_i` if requested: if series_specified and lags_specified: @@ -1379,7 +1721,7 @@ def get_shared_times( *series_or_times: Union[TimeSeries, pd.Index, None], sort: bool = True ) -> pd.Index: """ - Returns the times shared by all of the specified `TimeSeries` or time indexes (i.e. the intersection of all + Returns the times shared by all specified `TimeSeries` or time indexes (i.e. the intersection of all these times). If `sort = True`, then these shared times are sorted from earliest to latest. Any `TimeSeries` or time indices in `series_or_times` that aren't specified (i.e. are `None`) are simply ignored. @@ -1393,7 +1735,7 @@ def get_shared_times( Returns ------- shared_times - The time indices present in all of the specified `TimeSeries` and/or time indices. + The time indices present in all specified `TimeSeries` and/or time indices. Raises ------ @@ -1438,28 +1780,28 @@ def intersection_func(series_or_times_1, series_or_times_2): type(ts.time_index if isinstance(ts, TimeSeries) else ts) for ts in specified_inputs ] - raise_if_not( - len(set(times_types)) == 1, - ( - "Specified series and/or times must all " - "have the same type of `time_index` (i.e. all " - "`pd.RangeIndex` or all `pd.DatetimeIndex`)." - ), - ) + if not len(set(times_types)) == 1: + raise_log( + ValueError( + "Specified series and/or times must all have the same type of " + "`time_index` (i.e. all `pd.RangeIndex` or all `pd.DatetimeIndex`)." + ), + logger=logger, + ) return shared_times def get_shared_times_bounds( - *series_or_times: Sequence[Union[TimeSeries, pd.Index, None]] -) -> Union[Tuple[pd.Index, pd.Index], None]: + *series_or_times: Sequence[Union[TimeSeries, pd.Index, None]], +) -> Union[tuple[pd.Index, pd.Index], None]: """ - Returns the latest `start_time` and the earliest `end_time` among all of the non-`None` `series_or_times`; + Returns the latest `start_time` and the earliest `end_time` among all non-`None` `series_or_times`; these are (non-tight) lower and upper `bounds` on the intersection of all these `series_or_times` respectively. - If no potential overlap exists between all of the specified series, `None` is returned instead. + If no potential overlap exists between all specified series, `None` is returned instead. Notes ----- - If all of the specified `series_or_times` are of the same frequency, then `get_shared_times_bounds` + If all specified `series_or_times` are of the same frequency, then `get_shared_times_bounds` returns tight `bounds` (i.e. the earliest and latest time within the intersection of all the timeseries is returned). To see this, suppose we have three equal-frequency series with observations made at different times: @@ -1473,7 +1815,7 @@ def get_shared_times_bounds( Series 2: |---|--- Series 3: --|---|- UB - If the specified timeseries are *not* all of the same frequency, then the returned `bounds` is potentially non-tight + If the specified timeseries are *not* of the same frequency, then the returned `bounds` is potentially non-tight (i.e. `LB <= intersection.start_time() < intersection.end_time() <= UB`, where `intersection` are the times shared by all specified timeseries) @@ -1508,14 +1850,14 @@ def get_shared_times_bounds( bounds = None else: times_types = [type(time) for time in start_times] - raise_if_not( - len(set(times_types)) == 1, - ( - "Specified series and/or times must all " - "have the same type of `time_index` " - "(i.e. all `pd.RangeIndex` or all `pd.DatetimeIndex`)." - ), - ) + if not len(set(times_types)) == 1: + raise_log( + ValueError( + "Specified series and/or times must all have the same type of " + "`time_index` (i.e. all `pd.RangeIndex` or all `pd.DatetimeIndex`)." + ), + logger=logger, + ) # If `start_times` empty, no series were specified -> `bounds = (1, -1)` will # be 'converted' to `None` in next line: bounds = (max(start_times), min(end_times)) if start_times else (1, -1) @@ -1585,22 +1927,22 @@ def strided_moving_window( .. [1] https://numpy.org/doc/stable/reference/generated/numpy.lib.stride_tricks.as_strided.html """ if check_inputs: - raise_if( - not isinstance(stride, int) or stride < 1, - "`stride` must be a positive `int`.", - ) - raise_if( - not isinstance(window_len, int) or window_len < 1, - "`window_len` must be a positive `int`.", - ) - raise_if( - not isinstance(axis, int) or axis > x.ndim - 1 or axis < -x.ndim, - "`axis` must be an `int` that is less than `x.ndim`.", - ) - raise_if( - window_len > x.shape[axis], - "`window_len` must be less than or equal to x.shape[axis].", - ) + if not isinstance(stride, int) or stride < 1: + raise_log(ValueError("`stride` must be a positive `int`."), logger=logger) + if not isinstance(window_len, int) or window_len < 1: + raise_log( + ValueError("`window_len` must be a positive `int`."), logger=logger + ) + if not isinstance(axis, int) or axis > x.ndim - 1 or axis < -x.ndim: + raise_log( + ValueError("`axis` must be an `int` that is less than `x.ndim`."), + logger=logger, + ) + if window_len > x.shape[axis]: + raise_log( + ValueError("`window_len` must be less than or equal to x.shape[axis]."), + logger=logger, + ) num_windows = (x.shape[axis] - window_len) // stride + 1 new_shape = list(x.shape) new_shape[axis] = num_windows @@ -1640,7 +1982,7 @@ def _extend_time_index( def _get_freqs(*series: Union[TimeSeries, None]): """ - Returns list with the frequency of all of the specified (i.e. non-`None`) `series`. + Returns list with the frequency of all specified (i.e. non-`None`) `series`. """ freqs = [] for ts in series: @@ -1651,16 +1993,16 @@ def _get_freqs(*series: Union[TimeSeries, None]): def _all_equal_freq(*series: Union[TimeSeries, None]) -> bool: """ - Returns `True` is all of the specified (i.e. non-`None`) `series` have the same frequency. + Returns `True` if all specified (i.e. non-`None`) `series` have the same frequency. """ freqs = _get_freqs(*series) return len(set(freqs)) == 1 def _check_lags( - lags: Optional[Union[Sequence[int], Dict[str, List[int]]]], - lags_past_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]], - lags_future_covariates: Optional[Union[Sequence[int], Dict[str, List[int]]]], + lags: Optional[Union[Sequence[int], dict[str, list[int]]]], + lags_past_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]], + lags_future_covariates: Optional[Union[Sequence[int], dict[str, list[int]]]], ) -> None: """ Throws `ValueError` if any `lag` values aren't negative OR if no lags have been specified. @@ -1677,14 +2019,22 @@ def _check_lags( if isinstance(lags_i, dict): lags_i = list(set(chain(*lags_i.values()))) - raise_if( - any((lag > max_lag or not isinstance(lag, int)) for lag in lags_i), - f"`lags{suffix}` must be a `Sequence` or `Dict` containing only `int` values less than {max_lag + 1}.", - ) - raise_if( - all(lags_is_none), - "Must specify at least one of: `lags`, `lags_past_covariates`, `lags_future_covariates`.", - ) + if any((lag > max_lag or not isinstance(lag, int)) for lag in lags_i): + raise_log( + ValueError( + f"`lags{suffix}` must be a `Sequence` or `Dict` containing only `int` " + f"values less than {max_lag + 1}." + ), + logger=logger, + ) + + if all(lags_is_none): + raise_log( + ValueError( + "Must specify at least one of: `lags`, `lags_past_covariates`, `lags_future_covariates`." + ), + logger=logger, + ) return None @@ -1692,6 +2042,7 @@ def _check_series_length( series: TimeSeries, lags: Union[None, Sequence[int]], output_chunk_length: int, + output_chunk_shift: int, is_training: bool, name: Literal["target_series", "past_covariates", "future_covariates"], ) -> None: @@ -1707,21 +2058,52 @@ def _check_series_length( "-min(lags) + output_chunk_length" if lags_specified else "output_chunk_length" - ) + ) + " + output_chunk_shift" minimum_len = ( - -min(lags) + output_chunk_length if lags_specified else output_chunk_length + output_chunk_length + + output_chunk_shift + + (-min(lags) if lags_specified else 0) ) elif lags_specified: lags_name = "lags" if name == "target_series" else f"lags_{name}" minimum_len_str = f"-min({lags_name}) + max({lags_name}) + 1" minimum_len = -min(lags) + max(lags) + 1 if lags_specified: - raise_if( - series.n_timesteps < minimum_len, - ( - f"`{name}` must have at least " - f"`{minimum_len_str}` = {minimum_len} timesteps; " - f"instead, it only has {series.n_timesteps}." + if series.n_timesteps < minimum_len: + raise_log( + ValueError( + f"`{name}` must have at least `{minimum_len_str}` = {minimum_len} time " + f"steps; instead, it only has {series.n_timesteps}." + ), + logger=logger, + ) + return None + + +def _extract_sample_weight(sample_weight, target_series): + """Extracts sample weights values from the time intersection with the target labels.""" + if sample_weight is None: + return None + + sample_weight_vals = sample_weight.slice_intersect_values(target_series, copy=False) + if len(sample_weight_vals) != len(target_series): + raise_log( + ValueError( + "The `sample_weight` series must have at least the same times as the target `series`." ), + logger=logger, ) - return None + + weight_n_comp = sample_weight_vals.shape[1] + series_n_comp = target_series.n_components + if weight_n_comp > 1 and weight_n_comp != series_n_comp: + raise_log( + ValueError( + "The number of components in `sample_weight` must either be `1` or match " + f"the number of target series components `{series_n_comp}`." + ), + logger=logger, + ) + elif weight_n_comp != series_n_comp: + sample_weight_vals = sample_weight_vals.repeat(series_n_comp, axis=1) + return sample_weight_vals diff --git a/darts/utils/data/training_dataset.py b/darts/utils/data/training_dataset.py index d485ee6159..09dc516c95 100644 --- a/darts/utils/data/training_dataset.py +++ b/darts/utils/data/training_dataset.py @@ -4,18 +4,19 @@ """ from abc import ABC, abstractmethod -from typing import Dict, Optional, Tuple +from typing import Optional import numpy as np from torch.utils.data import Dataset from darts import TimeSeries -from darts.logging import get_logger, raise_if_not - -from .utils import CovariateType +from darts.logging import get_logger, raise_log +from darts.utils.data.utils import CovariateType logger = get_logger(__name__) -SampleIndexType = Tuple[int, int, int, int, int, int] +SampleIndexType = tuple[ + int, int, int, int, Optional[int], Optional[int], Optional[int], Optional[int] +] class TrainingDataset(ABC, Dataset): @@ -64,7 +65,7 @@ def __init__(self): underlying the `TimeSeries`. """ - self._index_memory: Dict = {} + self._index_memory: dict = {} @abstractmethod def __len__(self) -> int: @@ -82,8 +83,9 @@ def _memory_indexer( input_chunk_length: int, output_chunk_length: int, end_of_output_idx: int, - covariate_series: TimeSeries, + covariate_series: Optional[TimeSeries] = None, covariate_type: CovariateType = CovariateType.NONE, + sample_weight_series: Optional[TimeSeries] = None, ) -> SampleIndexType: """Returns the (start, end) indices for past target, future target and covariates (sub sets) of the current sample `i` from `target_idx`. @@ -111,12 +113,15 @@ def _memory_indexer( the index where the output chunk of the current sample ends in `target_series`. covariate_series current covariate TimeSeries. - covariate_type: + covariate_type the type of covariate to extract. Instance of `CovariateType`: One of (`CovariateType.PAST`, `CovariateType.FUTURE`, `CovariateType.NONE`). + sample_weight_series + current sample weight TimeSeries. """ covariate_start, covariate_end = None, None + sample_weight_start, sample_weight_end = None, None # the first time target_idx is observed if target_idx not in self._index_memory: @@ -136,7 +141,7 @@ def _memory_indexer( ) if covariate_type is not CovariateType.NONE: - # not CovariateType.Future -> both CovariateType.PAST and CovariateType.HISTORIC_FUTURE + # not CovariateType.FUTURE -> both CovariateType.PAST and CovariateType.HISTORIC_FUTURE start = ( future_start if covariate_type is CovariateType.FUTURE @@ -146,21 +151,52 @@ def _memory_indexer( # we need to be careful with getting ranges and indexes: # to get entire range, full_range = ts[:len(ts)]; to get last index: last_idx = ts[len(ts) - 1] + # extract actual index value (respects datetime- and integer-based indexes; also from non-zero + # start) + target_time_index = target_series._time_index + covariate_time_index = covariate_series._time_index + start_time = target_time_index[start] + end_time = target_time_index[end - 1] + + if ( + start_time not in covariate_time_index + or end_time not in covariate_time_index + ): + raise_log( + ValueError( + f"Missing covariates; could not find {covariate_type.value} covariates in index " + f"value range: {start_time} - {end_time}." + ), + logger=logger, + ) - # extract actual index value (respects datetime- and integer-based indexes; also from non-zero start) - start_time = target_series.time_index[start] - end_time = target_series.time_index[end - 1] - - raise_if_not( - start_time in covariate_series.time_index - and end_time in covariate_series.time_index, - f"Missing covariates; could not find {covariate_type.value} covariates in index value range: " - f"{start_time} - {end_time}.", - ) + # extract the index position (index) from index value + covariate_start = covariate_time_index.get_loc(start_time) + covariate_end = covariate_time_index.get_loc(end_time) + 1 + # sample weight + if sample_weight_series is not None: # extract the index position (index) from index value - covariate_start = covariate_series.time_index.get_loc(start_time) - covariate_end = covariate_series.time_index.get_loc(end_time) + 1 + target_time_index = target_series._time_index + sample_weight_time_index = sample_weight_series._time_index + + start_time = target_time_index[future_start] + end_time = target_time_index[future_end - 1] + + if ( + start_time not in sample_weight_time_index + or end_time not in sample_weight_time_index + ): + raise_log( + ValueError( + f"Missing sample weights; could not find sample weights in index " + f"value range: {start_time} - {end_time}." + ), + logger=logger, + ) + + sample_weight_start = sample_weight_time_index.get_loc(start_time) + sample_weight_end = sample_weight_time_index.get_loc(end_time) + 1 # store position of initial sample and all relevant sub set indices self._index_memory[target_idx] = { @@ -168,6 +204,7 @@ def _memory_indexer( "past_target": (past_start, past_end), "future_target": (future_start, future_end), "covariate": (covariate_start, covariate_end), + "sample_weight": (sample_weight_start, sample_weight_end), } else: # load position of initial sample and its sub set indices @@ -175,6 +212,9 @@ def _memory_indexer( past_start, past_end = self._index_memory[target_idx]["past_target"] future_start, future_end = self._index_memory[target_idx]["future_target"] covariate_start, covariate_end = self._index_memory[target_idx]["covariate"] + sample_weight_start, sample_weight_end = self._index_memory[target_idx][ + "sample_weight" + ] # evaluate how much the new sample needs to be shifted, and shift all indexes idx_shift = end_of_output_idx - end_of_output_idx_last @@ -188,6 +228,14 @@ def _memory_indexer( covariate_end = ( covariate_end + idx_shift if covariate_end is not None else None ) + sample_weight_start = ( + sample_weight_start + idx_shift + if sample_weight_start is not None + else None + ) + sample_weight_end = ( + sample_weight_end + idx_shift if sample_weight_end is not None else None + ) return ( past_start, @@ -196,6 +244,8 @@ def _memory_indexer( future_end, covariate_start, covariate_end, + sample_weight_start, + sample_weight_end, ) @@ -211,7 +261,13 @@ def __init__(self): @abstractmethod def __getitem__( self, idx: int - ) -> Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], np.ndarray]: + ) -> tuple[ + np.ndarray, + Optional[np.ndarray], + Optional[np.ndarray], + Optional[np.ndarray], + np.ndarray, + ]: pass @@ -227,7 +283,13 @@ def __init__(self): @abstractmethod def __getitem__( self, idx: int - ) -> Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], np.ndarray]: + ) -> tuple[ + np.ndarray, + Optional[np.ndarray], + Optional[np.ndarray], + Optional[np.ndarray], + np.ndarray, + ]: pass @@ -243,11 +305,12 @@ def __init__(self): @abstractmethod def __getitem__( self, idx: int - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], + Optional[np.ndarray], np.ndarray, ]: pass @@ -266,12 +329,13 @@ def __init__(self): @abstractmethod def __getitem__( self, idx: int - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], + Optional[np.ndarray], np.ndarray, ]: pass @@ -289,11 +353,12 @@ def __init__(self): @abstractmethod def __getitem__( self, idx: int - ) -> Tuple[ + ) -> tuple[ np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], + Optional[np.ndarray], np.ndarray, ]: pass diff --git a/darts/utils/data/utils.py b/darts/utils/data/utils.py index d639cadcac..ac8a0ec622 100644 --- a/darts/utils/data/utils.py +++ b/darts/utils/data/utils.py @@ -1,13 +1,19 @@ from enum import Enum from typing import Union +import numpy as np import pandas as pd from darts import TimeSeries -from darts.logging import raise_if_not +from darts.logging import get_logger, raise_log +from darts.utils.ts_utils import series2seq + +logger = get_logger(__name__) # Those freqs can be used to divide Time deltas (the others can't): -DIVISIBLE_FREQS = {"D", "H", "T", "min", "S", "L", "ms", "U", "us", "N"} +DIVISIBLE_FREQS = {"D", "h", "H", "T", "min", "s", "S", "L", "ms", "U", "us", "N", "ns"} +# supported built-in sample weight generators for regression and torch models +SUPPORTED_SAMPLE_WEIGHT = {"linear", "exponential"} class CovariateType(Enum): @@ -29,11 +35,14 @@ def _get_matching_index(ts_target: TimeSeries, ts_covariate: TimeSeries, idx: in Note: this function does not check if the matching index value is in `ts_covariate` or not. """ - raise_if_not( - ts_target.freq == ts_covariate.freq, - "The dataset contains some target/covariates series pair that have incompatible " - 'time axes (not the same "freq") and thus cannot be matched', - ) + if ts_target.freq != ts_covariate.freq: + raise_log( + ValueError( + "The dataset contains some target/covariates series pair that have incompatible " + 'time axes (not the same "freq") and thus cannot be matched' + ), + logger=logger, + ) freq = ts_target.freq @@ -59,3 +68,53 @@ def _index_diff( return -1 + len(pd.date_range(start=self, end=other, freq=freq)) else: return 1 - len(pd.date_range(start=other, end=self, freq=freq)) + + +def _process_sample_weight(sample_weight, target_series): + if sample_weight is None: + return None + + if target_series is None: + raise_log( + ValueError("Must supply target `series` when using `sample_weight`."), + logger=logger, + ) + + # get sample weights + if isinstance(sample_weight, str): + if sample_weight not in SUPPORTED_SAMPLE_WEIGHT: + raise_log( + ValueError( + f"Invalid `sample_weight` value: `'{sample_weight}'`. " + f"If a string, must be one of: {SUPPORTED_SAMPLE_WEIGHT}." + ), + logger=logger, + ) + # create global time weights based on the longest target series + max_len = max(len(target_i) for target_i in target_series) + if sample_weight == "linear": + weights = np.linspace(0, 1, max_len) + else: # "exponential" + time_steps = np.linspace(0, 1, max_len) + weights = np.exp(-10 * (1 - time_steps)) + weights = np.expand_dims(weights, -1).astype(target_series[0].dtype) + + # create sequence of series for tabularization + sample_weight = [ + TimeSeries.from_times_and_values( + times=target_i.time_index, + values=weights[-len(target_i) :], + ) + for target_i in target_series + ] + + sample_weight = series2seq(sample_weight) + if len(target_series) != len(sample_weight): + raise_log( + ValueError( + "The provided sequence of target `series` must have the same length as " + "the provided sequence of `sample_weight`." + ), + logger=logger, + ) + return sample_weight diff --git a/darts/utils/historical_forecasts/__init__.py b/darts/utils/historical_forecasts/__init__.py index 2edf85ebd4..51145458b1 100644 --- a/darts/utils/historical_forecasts/__init__.py +++ b/darts/utils/historical_forecasts/__init__.py @@ -1,11 +1,19 @@ -from .optimized_historical_forecasts_regression import ( +from darts.utils.historical_forecasts.optimized_historical_forecasts_regression import ( _optimized_historical_forecasts_all_points, _optimized_historical_forecasts_last_points_only, ) -from .utils import ( +from darts.utils.historical_forecasts.utils import ( _check_optimizable_historical_forecasts_global_models, _get_historical_forecast_boundaries, _historical_forecasts_general_checks, - _historical_forecasts_start_warnings, _process_historical_forecast_input, ) + +__all__ = [ + "_optimized_historical_forecasts_all_points", + "_optimized_historical_forecasts_last_points_only", + "_check_optimizable_historical_forecasts_global_models", + "_get_historical_forecast_boundaries", + "_historical_forecasts_general_checks", + "_process_historical_forecast_input", +] diff --git a/darts/utils/historical_forecasts/optimized_historical_forecasts_regression.py b/darts/utils/historical_forecasts/optimized_historical_forecasts_regression.py index 2876d716eb..a8a1444731 100644 --- a/darts/utils/historical_forecasts/optimized_historical_forecasts_regression.py +++ b/darts/utils/historical_forecasts/optimized_historical_forecasts_regression.py @@ -1,9 +1,5 @@ -from typing import List, Optional, Sequence, Union - -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal +from collections.abc import Sequence +from typing import Literal, Optional, Union import numpy as np import pandas as pd @@ -11,9 +7,12 @@ from darts.logging import get_logger from darts.timeseries import TimeSeries +from darts.utils import _build_tqdm_iterator from darts.utils.data.tabularization import create_lagged_prediction_data -from darts.utils.historical_forecasts.utils import _get_historical_forecast_boundaries -from darts.utils.timeseries_generation import generate_index +from darts.utils.historical_forecasts.utils import ( + _get_historical_forecast_boundaries, +) +from darts.utils.utils import generate_index logger = get_logger(__name__) @@ -30,18 +29,22 @@ def _optimized_historical_forecasts_last_points_only( stride: int = 1, overlap_end: bool = False, show_warnings: bool = True, + verbose: bool = False, predict_likelihood_parameters: bool = False, **kwargs, -) -> Union[ - TimeSeries, List[TimeSeries], Sequence[TimeSeries], Sequence[List[TimeSeries]] -]: +) -> Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]]: """ Optimized historical forecasts for RegressionModel with last_points_only = True Rely on _check_optimizable_historical_forecasts() to check that the assumptions are verified. + + The data_transformers are applied in historical_forecasts (input and predictions) """ forecasts_list = [] - for idx, series_ in enumerate(series): + iterator = _build_tqdm_iterator( + series, verbose, total=len(series), desc="historical forecasts" + ) + for idx, series_ in enumerate(iterator): past_covariates_ = past_covariates[idx] if past_covariates is not None else None future_covariates_ = ( future_covariates[idx] if future_covariates is not None else None @@ -73,6 +76,7 @@ def _optimized_historical_forecasts_last_points_only( start_format=start_format, forecast_horizon=forecast_horizon, overlap_end=overlap_end, + stride=stride, freq=freq, show_warnings=show_warnings, ) @@ -101,15 +105,22 @@ def _optimized_historical_forecasts_last_points_only( ) X, times = create_lagged_prediction_data( - target_series=None - if model._get_lags("target") is None - else series_[hist_fct_tgt_start:hist_fct_tgt_end], - past_covariates=None - if past_covariates_ is None - else past_covariates_[hist_fct_pc_start:hist_fct_pc_end], - future_covariates=None - if future_covariates_ is None - else future_covariates_[hist_fct_fc_start:hist_fct_fc_end], + target_series=( + None + if model._get_lags("target") is None + and not model.uses_static_covariates + else series_[hist_fct_tgt_start:hist_fct_tgt_end] + ), + past_covariates=( + None + if past_covariates_ is None + else past_covariates_[hist_fct_pc_start:hist_fct_pc_end] + ), + future_covariates=( + None + if future_covariates_ is None + else future_covariates_[hist_fct_fc_start:hist_fct_fc_end] + ), lags=model._get_lags("target"), lags_past_covariates=model._get_lags("past"), lags_future_covariates=model._get_lags("future"), @@ -133,37 +144,49 @@ def _optimized_historical_forecasts_last_points_only( ) # forecast has shape ((forecastable_index_length-1)*num_samples, k, n_component) # where k = output_chunk length if multi_models, 1 otherwise - - # reshape into (forecasted indexes, n_components, n_samples), components are interleaved - forecast = forecast.reshape(X.shape[0], -1, num_samples) + # reshape into (forecasted indexes, output_chunk_length, n_components, n_samples) + forecast = np.moveaxis( + forecast.reshape( + X.shape[0], + num_samples, + model.output_chunk_length if model.multi_models else 1, + -1, + ), + 1, + -1, + ) # extract the last sub-model forecast for each component if model.multi_models: - forecast = forecast[ - :, - (forecast_horizon - 1) - * len(forecast_components) : (forecast_horizon) - * len(forecast_components), - :, - ] + forecast = forecast[:, forecast_horizon - 1] + else: + forecast = forecast[:, 0] + + if ( + stride == 1 + and model.output_chunk_length == 1 + and model.output_chunk_shift == 0 + ): + times = times[0] + else: + times = generate_index( + start=hist_fct_start + + (forecast_horizon + model.output_chunk_shift - 1) * freq, + length=forecast.shape[0], + freq=freq * stride, + name=series_.time_index.name, + ) forecasts_list.append( TimeSeries.from_times_and_values( - times=times[0] - if stride == 1 and model.output_chunk_length == 1 - else generate_index( - start=hist_fct_start + (forecast_horizon - 1) * freq, - length=forecast.shape[0], - freq=freq * stride, - name=series_.time_index.name, - ), + times=times, values=forecast, columns=forecast_components, static_covariates=series_.static_covariates, hierarchy=series_.hierarchy, ) ) - return forecasts_list if len(series) > 1 else forecasts_list[0] + return forecasts_list def _optimized_historical_forecasts_all_points( @@ -178,18 +201,20 @@ def _optimized_historical_forecasts_all_points( stride: int = 1, overlap_end: bool = False, show_warnings: bool = True, + verbose: bool = False, predict_likelihood_parameters: bool = False, **kwargs, -) -> Union[ - TimeSeries, List[TimeSeries], Sequence[TimeSeries], Sequence[List[TimeSeries]] -]: +) -> Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]]: """ Optimized historical forecasts for RegressionModel with last_points_only = False. Rely on _check_optimizable_historical_forecasts() to check that the assumptions are verified. """ forecasts_list = [] - for idx, series_ in enumerate(series): + iterator = _build_tqdm_iterator( + series, verbose, total=len(series), desc="historical forecasts" + ) + for idx, series_ in enumerate(iterator): past_covariates_ = past_covariates[idx] if past_covariates is not None else None future_covariates_ = ( future_covariates[idx] if future_covariates is not None else None @@ -221,13 +246,13 @@ def _optimized_historical_forecasts_all_points( start_format=start_format, forecast_horizon=forecast_horizon, overlap_end=overlap_end, + stride=stride, freq=freq, show_warnings=show_warnings, ) # Additional shift, to account for the model output_chunk_length shift_start = 0 - # shift_end = 0 if model.output_chunk_length > 1: # used to convert the shift into the appropriate unit unit = freq if series_.has_datetime_index else 1 @@ -248,15 +273,22 @@ def _optimized_historical_forecasts_all_points( ) X, _ = create_lagged_prediction_data( - target_series=None - if model._get_lags("target") is None - else series_[hist_fct_tgt_start:hist_fct_tgt_end], - past_covariates=None - if past_covariates_ is None - else past_covariates_[hist_fct_pc_start:hist_fct_pc_end], - future_covariates=None - if future_covariates_ is None - else future_covariates_[hist_fct_fc_start:hist_fct_fc_end], + target_series=( + None + if model._get_lags("target") is None + and not model.uses_static_covariates + else series_[hist_fct_tgt_start:hist_fct_tgt_end] + ), + past_covariates=( + None + if past_covariates_ is None + else past_covariates_[hist_fct_pc_start:hist_fct_pc_end] + ), + future_covariates=( + None + if future_covariates_ is None + else future_covariates_[hist_fct_fc_start:hist_fct_fc_end] + ), lags=model._get_lags("target"), lags_past_covariates=model._get_lags("past"), lags_future_covariates=model._get_lags("future"), @@ -266,6 +298,7 @@ def _optimized_historical_forecasts_all_points( check_inputs=True, use_moving_windows=True, concatenate=False, + show_warnings=False, ) # stride must be applied post-hoc to avoid missing values @@ -278,26 +311,27 @@ def _optimized_historical_forecasts_all_points( predict_likelihood_parameters=predict_likelihood_parameters, **kwargs, ) - - # reshape and stride the forecast into (forecastable_index, forecast_horizon, n_components, num_samples) - if model.multi_models: - # forecast has shape ((forecastable_index_length-1)*num_samples, output_chunk_length, n_component) - # and the components are interleaved - forecast = forecast.reshape( + # forecast has shape ((forecastable_index_length-1)*num_samples, k, n_component) + # where k = output_chunk length if multi_models, 1 otherwise + # reshape into (forecasted indexes, output_chunk_length, n_components, n_samples) + forecast = np.moveaxis( + forecast.reshape( X.shape[0], - model.output_chunk_length, - len(forecast_components), num_samples, - ) + model.output_chunk_length if model.multi_models else 1, + -1, + ), + 1, + -1, + ) + + if model.multi_models: forecast = forecast[::stride, :forecast_horizon] else: - # forecast has shape ((forecastable_index_length-1)*num_samples, 1, n_component) - # and the components are interleaved - forecast = forecast.reshape(X.shape[0], -1, num_samples) - - # forecasts depend on lagged data only, output_chunk_length is reconstitued by applying a sliding window + # entire forecast horizon is given by multiple (previous) forecasts -> apply sliding window forecast = sliding_window_view( - forecast, (forecast_horizon, len(forecast_components), num_samples) + forecast[:, 0], + (forecast_horizon, len(forecast_components), num_samples), ) # apply stride, remove the last windows, slice output_chunk_length to keep forecast_horizon values @@ -316,8 +350,8 @@ def _optimized_historical_forecasts_all_points( # TODO: check if faster to create in the loop new_times = generate_index( - start=hist_fct_start, - length=forecast_horizon * stride * forecast.shape[0], + start=hist_fct_start + model.output_chunk_shift * series_.freq, + length=forecast_horizon + (forecast.shape[0] - 1) * stride, freq=freq, name=series_.time_index.name, ) @@ -337,4 +371,4 @@ def _optimized_historical_forecasts_all_points( ) forecasts_list.append(forecasts_) - return forecasts_list if len(series) > 1 else forecasts_list[0] + return forecasts_list diff --git a/darts/utils/historical_forecasts/optimized_historical_forecasts_torch.py b/darts/utils/historical_forecasts/optimized_historical_forecasts_torch.py index 0aa41d4eab..4a849f976a 100644 --- a/darts/utils/historical_forecasts/optimized_historical_forecasts_torch.py +++ b/darts/utils/historical_forecasts/optimized_historical_forecasts_torch.py @@ -1,11 +1,6 @@ -from typing import List, Optional, Sequence, Union - -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal - import inspect +from collections.abc import Sequence +from typing import Literal, Optional, Union import numpy as np import pandas as pd @@ -16,7 +11,7 @@ _get_historical_forecast_boundaries, _process_predict_start_points_bounds, ) -from darts.utils.timeseries_generation import generate_index +from darts.utils.utils import generate_index logger = get_logger(__name__) @@ -37,13 +32,13 @@ def _optimized_historical_forecasts( verbose: bool = False, predict_likelihood_parameters: bool = False, **kwargs, -) -> Union[ - TimeSeries, List[TimeSeries], Sequence[TimeSeries], Sequence[List[TimeSeries]] -]: +) -> Union[Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]]: """ Optimized historical forecasts for TorchForecastingModels Rely on _check_optimizable_historical_forecasts() to check that the assumptions are verified. + + The data_transformers are applied in historical_forecasts (input and predictions) """ bounds = [] for idx, series_ in enumerate(series): @@ -71,6 +66,7 @@ def _optimized_historical_forecasts( start_format=start_format, forecast_horizon=forecast_horizon, overlap_end=overlap_end, + stride=stride, freq=series_.freq, show_warnings=show_warnings, ) @@ -147,4 +143,4 @@ def _optimized_historical_forecasts( hierarchy=preds[0].hierarchy, ) forecasts_list.append(preds) - return forecasts_list if len(forecasts_list) > 1 else forecasts_list[0] + return forecasts_list diff --git a/darts/utils/historical_forecasts/utils.py b/darts/utils/historical_forecasts/utils.py index db71e40d82..c8502cd7c4 100644 --- a/darts/utils/historical_forecasts/utils.py +++ b/darts/utils/historical_forecasts/utils.py @@ -1,33 +1,35 @@ -from types import SimpleNamespace -from typing import Any, Callable, Dict, Optional, Sequence, Set, Tuple, Union - -try: - from typing import Literal -except ImportError: - from typing_extensions import Literal - import inspect +from collections.abc import Sequence +from types import SimpleNamespace +from typing import Any, Callable, Literal, Optional, Union import numpy as np import pandas as pd from numpy.typing import ArrayLike -from darts.logging import get_logger, raise_if_not, raise_log +from darts.dataprocessing.pipeline import Pipeline +from darts.dataprocessing.transformers import ( + BaseDataTransformer, + FittableDataTransformer, +) +from darts.logging import get_logger, raise_log from darts.timeseries import TimeSeries -from darts.utils.timeseries_generation import generate_index -from darts.utils.utils import series2seq +from darts.utils.ts_utils import SeriesType, get_series_seq_type, series2seq +from darts.utils.utils import generate_index, n_steps_between logger = get_logger(__name__) TimeIndex = Union[ pd.DatetimeIndex, pd.RangeIndex, - Tuple[int, int], - Tuple[pd.Timestamp, pd.Timestamp], + tuple[int, int], + tuple[pd.Timestamp, pd.Timestamp], ] -def _historical_forecasts_general_checks(model, series, kwargs): +def _historical_forecasts_general_checks( + model, series, kwargs, is_conformal: bool = False +): """ Performs checks common to ForecastingModel and RegressionModel backtest() methods @@ -37,9 +39,6 @@ def _historical_forecasts_general_checks(model, series, kwargs): The forecasting model. series Either series when called from ForecastingModel, or target_series if called from RegressionModel - signature_params - A dictionary of the signature parameters of the calling method, to get the default values - Typically would be signature(self.backtest).parameters kwargs Params specified by the caller of backtest(), they take precedence over the arguments' default values """ @@ -47,18 +46,30 @@ def _historical_forecasts_general_checks(model, series, kwargs): n = SimpleNamespace(**kwargs) # check forecast horizon - raise_if_not( - n.forecast_horizon > 0, - "The provided forecasting horizon must be a positive integer.", - logger, - ) + if not n.forecast_horizon > 0: + raise_log( + ValueError("The provided forecasting horizon must be a positive integer."), + logger, + ) # check stride - raise_if_not( - n.stride > 0, - "The provided stride parameter must be a positive integer.", - logger, - ) + if not n.stride > 0: + raise_log( + ValueError("The provided stride parameter must be a positive integer."), + logger, + ) + + # check stride for ConformalModel + if is_conformal and ( + n.stride < model.cal_stride or n.stride % model.cal_stride > 0 + ): + raise_log( + ValueError( + f"The provided `stride` parameter must be a round-multiple of `cal_stride={model.cal_stride}` " + f"and `>=cal_stride`. Received `stride={n.stride}`" + ), + logger, + ) series = series2seq(series) @@ -78,99 +89,109 @@ def _historical_forecasts_general_checks(model, series, kwargs): f"`start_format` must be on of ['position', 'value']. Received '{n.start_format}'." ) ) - if n.start_format == "position": - raise_if_not( - isinstance(n.start, (int, np.int64)), - f"Since `start_format='position'`, `start` must be an integer, received {type(n.start)}.", + if n.start_format == "position" and not isinstance(n.start, (int, np.int64)): + raise_log( + ValueError( + f"Since `start_format='position'`, `start` must be an integer, received {type(n.start)}." + ), logger, ) - if isinstance(n.start, float): - raise_if_not( - 0.0 <= n.start <= 1.0, - "if `start` is a float, must be between 0.0 and 1.0.", - logger, - ) + if is_conformal: + raise_log( + ValueError( + "`start` of type float is not supported for `ConformalModel`." + ), + logger, + ) + if not 0.0 <= n.start <= 1.0: + raise_log( + ValueError("if `start` is a float, must be between 0.0 and 1.0."), + logger, + ) - # verbose error messages - if not isinstance(n.start, pd.Timestamp): - start_value_msg = f"`start` value `{n.start}` corresponding to timestamp" - else: - start_value_msg = "`start` time" + series_freq = None for idx, series_ in enumerate(series): + start_is_value = False # check specifically for int and Timestamp as error by `get_timestamp_at_point` is too generic if isinstance(n.start, pd.Timestamp): - if n.start > series_.end_time(): + if not series_._has_datetime_index: raise_log( ValueError( - f"`start` time `{n.start}` is after the last timestamp `{series_.end_time()}` of the " - f"series at index: {idx}." + "if `start` is a `pd.Timestamp`, all series must be indexed with a `pd.DatetimeIndex`" ), logger, ) - elif n.start < series_.start_time(): + if n.start > series_.end_time(): raise_log( ValueError( - f"`start` time `{n.start}` is before the first timestamp `{series_.start_time()}` of the " + f"`start` time `{n.start}` is after the last timestamp `{series_.end_time()}` of the " f"series at index: {idx}." ), logger, ) + start_is_value = True elif isinstance(n.start, (int, np.int64)): - out_of_bound_error = False - if n.start_format == "position": - if (n.start > 0 and n.start >= len(series_)) or ( - n.start < 0 and np.abs(n.start) > len(series_) - ): - out_of_bound_error = True - elif series_.has_datetime_index: + if n.start_format == "position" or series_.has_datetime_index: if n.start >= len(series_): - out_of_bound_error = True - elif n.start < series_.time_index[0]: + raise_log( + ValueError( + f"`start` position `{n.start}` is out of bounds for series of length {len(series_)} " + f"at index: {idx}." + ), + logger, + ) + else: + if ( + n.start > series_.time_index[-1] + ): # format "value" and range index + raise_log( + ValueError( + f"`start` time `{n.start}` is larger than the last index `{series_.time_index[-1]}` " + f"for series at index: {idx}." + ), + logger, + ) + start_is_value = True + + # `ConformalModel` with `start_format='value'` requires all series to have the same frequency + if is_conformal and start_is_value: + if series_freq is None: + series_freq = series_.freq + + if series_freq != series_.freq: raise_log( ValueError( - f"`start` index `{n.start}` is smaller than the first index `{series_.time_index[0]}` " - f"for series at index: {idx}." + f"Found mismatching `series` time index frequencies `{series_freq}` and `{series_.freq}`. " + f"`start_format='value'` with `ConformalModel` is only supported if all series in " + f"`series` have the same frequency." ), - logger, - ) - elif n.start > series_.time_index[-1]: - raise_log( - ValueError( - f"`start` index `{n.start}` is larger than the last index `{series_.time_index[-1]}` " - f"for series at index: {idx}." - ), - logger, - ) - - if out_of_bound_error: - raise_log( - ValueError( - f"`start` index `{n.start}` is out of bounds for series of length {len(series_)} " - f"at index: {idx}." - ), - logger, + logger=logger, ) - if n.start_format == "value": - start = series_.get_timestamp_at_point(n.start) - else: - start = series_.time_index[n.start] - - if n.retrain is not False and start == series_.start_time(): - raise_log( - ValueError( - f"{start_value_msg} `{start}` is the first timestamp of the series {idx}, resulting in an " - f"empty training set." - ), - logger, - ) + # find valid start position relative to the series start time, otherwise raise an error + start_idx, _ = _get_start_index( + series_, idx, n.start, n.start_format, n.stride + ) - # check that overlap_end and start together form a valid combination + # check that `overlap_end` and `start` are a valid combination overlap_end = n.overlap_end - if not overlap_end and not ( - start + (series_.freq * (n.forecast_horizon - 1)) in series_ + if ( + not overlap_end + and start_idx + n.forecast_horizon + model.output_chunk_shift + > len(series_) ): + # verbose error messages + if n.start_format == "position" or ( + not isinstance(n.start, pd.Timestamp) + and series_._has_datetime_index + ): + start_value_msg = ( + f"`start` position `{n.start}` corresponding to time" + ) + else: + start_value_msg = "`start` time" + start = series_._time_index[start_idx] raise_log( ValueError( f"{start_value_msg} `{start}` is too late in the series {idx} to make any predictions with " @@ -179,6 +200,13 @@ def _historical_forecasts_general_checks(model, series, kwargs): logger, ) + # duplication of ForecastingModel.predict() check for the optimized historical forecasts implementations + if not model.supports_probabilistic_prediction and n.num_samples > 1: + raise_log( + ValueError("`num_samples > 1` is only supported for probabilistic models."), + logger, + ) + # check direct likelihood parameter prediction before fitting a model if n.predict_likelihood_parameters: if not model.supports_likelihood_parameter_prediction: @@ -210,14 +238,123 @@ def _historical_forecasts_general_checks(model, series, kwargs): logger, ) + if n.data_transformers is not None: + # check the type + if not isinstance(n.data_transformers, dict): + raise_log( + ValueError( + "`data_transformers` should either `None` or a dictionary.", logger + ) + ) + # check the keys + supported_keys = {"series", "past_covariates", "future_covariates"} + incorrect_keys = set(n.data_transformers.keys()) - supported_keys + if len(incorrect_keys) > 0: + raise_log( + ValueError( + f"The keys supported by `data_transformers` are {supported_keys}, received the following " + f"incorrect keys: {incorrect_keys}." + ), + logger, + ) + + # convert to Pipelines + data_pipelines = _convert_data_transformers( + data_transformers=n.data_transformers, copy=False + ) + # extract pipelines containing at least one fittable element + fittable_pipelines = [ + transf_ for transf_ in data_pipelines.values() if transf_.fittable + ] + # extract pipelines where all the fittable transformer are fitted globally + global_fit_pipelines = [ + transf_ for transf_ in fittable_pipelines if transf_._global_fit + ] + + if n.retrain: + # if more than one series is passed and the pipelines are retrained, they cannot be global + if n.show_warnings and len(series) > 1 and len(global_fit_pipelines) > 0: + logger.warning( + "When `retrain=True` and multiple series are provided, the fittable `data_transformers` " + "are trained on each series independently (`global_fit=True` will be ignored)." + ) + else: + # must already be fitted without retraining + not_fitted_pipelines = [ + name_ + for name_, transf_ in data_pipelines.items() + if transf_.fittable and not transf_._fit_called + ] + if len(not_fitted_pipelines) > 0: + raise_log( + ValueError( + "All the fittable entries in `data_transformers` must already be fitted when " + f"`retrain=False`, the following entries were not fitted: {', '.join(not_fitted_pipelines)}." + ), + logger, + ) + # extract the number of fitted params in each pipeline (already fitted) + fitted_params_pipelines = [ + max( + len(t._fitted_params) + for t in pipeline + if isinstance(t, FittableDataTransformer) + ) + for pipeline in data_pipelines.values() + ] + + if len(series) > 1: + # if multiple series are passed and the pipelines are not all globally fitted, the number of series must + # match the number of fitted params in the pipelines + if len(global_fit_pipelines) != len(fittable_pipelines) and len( + series + ) != max(fitted_params_pipelines): + raise_log( + ValueError( + f"When multiple series are provided, their number should match the number of " + f"`TimeSeries` used to fit the data transformers `n={max(fitted_params_pipelines)}` " + f"(only relevant for fittable transformers that use `global_fit=False`)." + ), + logger, + ) + else: + # at least one pipeline was fitted on several series with `global_fit=False` but only + # one series was passed + if n.show_warnings and max(fitted_params_pipelines) > 1: + logger.warning( + "Provided only a single series, but at least one of the `data_transformers` " + "that use `global_fit=False` was fitted on multiple `TimeSeries`." + ) + + if ( + n.sample_weight is not None + and not isinstance(n.sample_weight, str) + and model.supports_sample_weight + ): + sample_weight = series2seq(n.sample_weight) + for idx, (series_, sample_weight_) in enumerate(zip(series, sample_weight)): + is_valid = ( + sample_weight_.freq == series_.freq + and sample_weight_.start_time() <= series_.start_time() + and len(sample_weight_) >= len(series_) + ) + if not is_valid: + raise_log( + ValueError( + f"`sample_weight` at series index {idx} must contain at least all times " + f"of the corresponding target `series`." + ), + logger=logger, + ) + def _historical_forecasts_sanitize_kwargs( model, - fit_kwargs: Optional[Dict[str, Any]], - predict_kwargs: Optional[Dict[str, Any]], + fit_kwargs: Optional[dict[str, Any]], + predict_kwargs: Optional[dict[str, Any]], retrain: bool, show_warnings: bool, -) -> Tuple[Dict[str, Any], Dict[str, Any]]: +) -> tuple[dict[str, Any], dict[str, Any]]: """Convert kwargs to dictionary, check that their content is compatible with called methods.""" hfc_args = set(inspect.signature(model.historical_forecasts).parameters) # replace `forecast_horizon` with `n` @@ -249,10 +386,10 @@ def _historical_forecasts_sanitize_kwargs( def _historical_forecasts_check_kwargs( - hfc_args: Set[str], + hfc_args: set[str], name_kwargs: str, - dict_kwargs: Dict[str, Any], -) -> Dict[str, Any]: + dict_kwargs: dict[str, Any], +) -> dict[str, Any]: """ Return the kwargs dict without the arguments unsupported by the model method. @@ -271,35 +408,156 @@ def _historical_forecasts_check_kwargs( return dict_kwargs -def _historical_forecasts_start_warnings( - idx: int, - start: Union[pd.Timestamp, int], - start_time_: Union[int, pd.Timestamp], - historical_forecasts_time_index: TimeIndex, +def _get_start_index( + series: TimeSeries, + series_idx: int, + start: Union[pd.Timestamp, int, float], + start_format: Literal["value", "position"], + stride: int, + historical_forecasts_time_index: Optional[TimeIndex] = None, ): - """Warnings when start value provided by user is not within the forecastable indexes boundaries""" - if not isinstance(start, pd.Timestamp): - start_value_msg = f"value `{start}` corresponding to timestamp `{start_time_}`" + """Finds a valid historical forecast start point within either `series` or `historical_forecasts_time_index` + (depending on whether `historical_forecasts_time_index` is passed, denoted as `ref`). + + - If `start` is larger or equal to the first index of `ref`, uses `start` directly. + - If `start` is before the first index of `ref`, tries to find a start point within `ref` that lies a + round-multiple `stride` time steps ahead of `start`. + + Raises an error if the new start index from above is larger than the last index in `ref`. + + Parameters + ---------- + series + A time series. If `historical_forecasts_time_index` is `None`, will use this series' time index as a reference + index. + series_idx + The sequence index of the `series`. + start + The start point for historical forecasts. + start_format + The start format for historical forecasts. + stride + The stride for historical forecasts. + historical_forecasts_time_index + Optionally, the historical forecast index (or the boundaries only) to use as the reference index. + """ + series_start, series_end = series._time_index[0], series._time_index[-1] + has_dti = series._has_datetime_index + # find start position relative to the series start time + if isinstance(start, float): + # fraction of series + rel_start = series.get_index_at_point(start) + elif start_format == "value" and not (isinstance(start, int) and has_dti): + # start is a time stamp for DatetimeIndex, and integer for RangeIndex + rel_start = n_steps_between(start, series_start, freq=series.freq) else: - start_value_msg = f"time `{start_time_}`" + # start is a positional index + start: int + rel_start = start if start >= 0 else len(series) - abs(start) - if start_time_ < historical_forecasts_time_index[0]: - logger.warning( - f"`start` {start_value_msg} is before the first predictable/trainable historical " - f"forecasting point for series at index: {idx}. Ignoring `start` for this series and " - f"beginning at first trainable/predictable time: {historical_forecasts_time_index[0]}. " - f"To hide these warnings, set `show_warnings=False`." + # find actual start time + start_idx = _adjust_start(rel_start, stride) + _check_start( + series=series, + start_idx=start_idx, + start=start, + start_format=start_format, + series_start=series_start, + ref_start=series_start, + ref_end=series_end, + stride=stride, + series_idx=series_idx, + is_historical_forecast=False, + ) + if historical_forecasts_time_index is not None: + hfc_start, hfc_end = ( + historical_forecasts_time_index[0], + historical_forecasts_time_index[-1], ) + # at this point, we know that `start_idx` is within `series`. Now, find the position of that time step + # relative to the first forecastable point + rel_start_hfc = n_steps_between( + series._time_index[start_idx], hfc_start, freq=series.freq + ) + # get the positional index of `hfc_start` in `series` + hfc_start_idx = start_idx - rel_start_hfc + # potentially, adjust the position to be inside the forecastable points + hfc_start_idx += _adjust_start(rel_start_hfc, stride) + _check_start( + series=series, + start_idx=hfc_start_idx, + start=start, + start_format=start_format, + series_start=series_start, + ref_start=hfc_start, + ref_end=hfc_end, + stride=stride, + series_idx=series_idx, + is_historical_forecast=True, + ) + start_idx = hfc_start_idx + return start_idx, rel_start + + +def _adjust_start(rel_start, stride): + """If relative start position `rel_start` is negative, then adjust it to the first non-negative index that lies a + round-multiple of `stride` ahead of `rel_start` + """ + if rel_start >= 0: + start_idx = rel_start else: - logger.warning( - f"`start` {start_value_msg} is after the last trainable/predictable historical " - f"forecasting point for series at index: {idx}. This would results in empty historical " - f"forecasts. Ignoring `start` for this series and beginning at first trainable/" - f"predictable time: {historical_forecasts_time_index[0]}. Non-empty forecasts can be " - f"generated by setting `start` value to times between (including): " - f"{historical_forecasts_time_index[0], historical_forecasts_time_index[-1]}. " - f"To hide these warnings, set `show_warnings=False`." + # if `start` lies before the start time of `series` -> check if there is a valid start point in + # `series` that is a round-multiple of `stride` ahead of `start` + start_idx = ( + rel_start + + (abs(rel_start) // stride + int(abs(rel_start) % stride > 0)) * stride ) + return start_idx + + +def _check_start( + series: TimeSeries, + start_idx: int, + start: Union[pd.Timestamp, int, float], + start_format: Literal["value", "position"], + series_start: Union[pd.Timestamp, int], + ref_start: Union[pd.Timestamp, int], + ref_end: Union[pd.Timestamp, int], + stride: int, + series_idx: int, + is_historical_forecast: bool, +): + """Raises an error if the start index (position) is not within the series.""" + if start_idx < len(series): + return + + if start_format == "position" or ( + not isinstance(start, pd.Timestamp) and series._has_datetime_index + ): + start_format_msg = f"position `{start}` corresponding to time " + if isinstance(start, float): + # fraction of series + start = series.get_index_at_point(start) + elif start >= 0: + # start >= 0 is relative to the start + start = series.start_time() + start * series.freq + else: + # start < 0 is relative to the end + start = series.end_time() + (start + 1) * series.freq + else: + start_format_msg = "time " + ref_msg = "" if not is_historical_forecast else "historical forecastable " + start_new = series_start + start_idx * series.freq + raise_log( + ValueError( + f"`start` {start_format_msg}`{start}` is smaller than the first {ref_msg}time index " + f"`{ref_start}` for series at index: {series_idx}, and could not find a valid start " + f"point within the {ref_msg}time index that lies a round-multiple of `stride={stride}` " + f"ahead of `start` (first inferred start is `{start_new}`, but last {ref_msg}time index " + f"is `{ref_end}`." + ), + logger=logger, + ) def _get_historical_forecastable_time_index( @@ -314,8 +572,8 @@ def _get_historical_forecastable_time_index( ) -> Union[ pd.DatetimeIndex, pd.RangeIndex, - Tuple[int, int], - Tuple[pd.Timestamp, pd.Timestamp], + tuple[int, int], + tuple[pd.Timestamp, pd.Timestamp], None, ]: """ @@ -356,7 +614,7 @@ def _get_historical_forecastable_time_index( Returns ------- - Union[pd.DatetimeIndex, pd.RangeIndex, Tuple[int, int], Tuple[pd.Timestamp, pd.Timestamp], None] + Union[pd.DatetimeIndex, pd.RangeIndex, tuple[int, int], tuple[pd.Timestamp, pd.Timestamp], None] The longest time_index that can be used for historical forecasting, either as a range or a tuple. Examples @@ -402,18 +660,30 @@ def _get_historical_forecastable_time_index( max_past_cov_lag, min_future_cov_lag, max_future_cov_lag, + output_chunk_shift, + max_target_lag_train, ) = model.extreme_lags # max_target_lag < 0 are local models which can predict for n (horizon) -> infinity (no auto-regression) - is_autoregression = max_target_lag >= 0 and forecast_horizon > max_target_lag + 1 + is_autoregression = ( + max_target_lag >= 0 + and forecast_horizon > max_target_lag - output_chunk_shift + 1 + ) if min_target_lag is None: min_target_lag = 0 + if is_training and max_target_lag_train is not None: + # the output lag/window can be different for train and predict modes + output_lag = max_target_lag_train + else: + output_lag = max_target_lag + # longest possible time index for target if is_training: start = ( - series.start_time() + (max_target_lag - min_target_lag + 1) * series.freq + series.start_time() + + (output_lag - output_chunk_shift - min_target_lag + 1) * series.freq ) else: start = series.start_time() - min_target_lag * series.freq @@ -426,7 +696,8 @@ def _get_historical_forecastable_time_index( if is_training: start_pc = ( past_covariates.start_time() - - (min_past_cov_lag - max_target_lag - 1) * past_covariates.freq + + (output_lag - output_chunk_shift - min_past_cov_lag + 1) + * past_covariates.freq ) else: start_pc = ( @@ -436,7 +707,7 @@ def _get_historical_forecastable_time_index( shift_pc_end = max_past_cov_lag if is_autoregression: # we step back in case of auto-regression - shift_pc_end += forecast_horizon - (max_target_lag + 1) + shift_pc_end += forecast_horizon - (max_target_lag - output_chunk_shift + 1) end_pc = past_covariates.end_time() - shift_pc_end * past_covariates.freq intersect_ = ( @@ -449,7 +720,8 @@ def _get_historical_forecastable_time_index( if is_training: start_fc = ( future_covariates.start_time() - - (min_future_cov_lag - max_target_lag - 1) * future_covariates.freq + + (output_lag - output_chunk_shift - min_future_cov_lag + 1) + * future_covariates.freq ) else: start_fc = ( @@ -460,7 +732,7 @@ def _get_historical_forecastable_time_index( shift_fc_end = max_future_cov_lag if is_autoregression: # we step back in case of auto-regression - shift_fc_end += forecast_horizon - (max_target_lag + 1) + shift_fc_end += forecast_horizon - (max_target_lag - output_chunk_shift + 1) end_fc = future_covariates.end_time() - shift_fc_end * future_covariates.freq intersect_ = ( @@ -468,12 +740,16 @@ def _get_historical_forecastable_time_index( min([intersect_[1], end_fc]), ) - # overlap_end = True -> predictions must not go beyond end of target series + # overlap_end = False -> predictions must not go beyond end of target series if ( not overlap_end - and intersect_[1] + (forecast_horizon - 1) * series.freq > series.end_time() + and intersect_[1] + (forecast_horizon + output_chunk_shift - 1) * series.freq + > series.end_time() ): - intersect_ = (intersect_[0], end - forecast_horizon * series.freq) + intersect_ = ( + intersect_[0], + end - (forecast_horizon + output_chunk_shift) * series.freq, + ) # end comes before the start if intersect_[1] < intersect_[0]: @@ -502,37 +778,51 @@ def _adjust_historical_forecasts_time_index( historical_forecasts_time_index: TimeIndex, start: Optional[Union[pd.Timestamp, float, int]], start_format: Literal["position", "value"], + stride: int, show_warnings: bool, ) -> TimeIndex: """ Shrink the beginning and end of the historical forecasts time index based on the values of `start`, `forecast_horizon` and `overlap_end`. """ + # retrieve actual start # when applicable, shift the start of the forecastable index based on `start` if start is not None: - if start_format == "value": - start_time_ = series.get_timestamp_at_point(start) - else: - start_time_ = series.time_index[start] - # ignore user-defined `start` - if ( - not historical_forecasts_time_index[0] - <= start_time_ - <= historical_forecasts_time_index[-1] - ): - if show_warnings: - _historical_forecasts_start_warnings( - idx=series_idx, - start=start, - start_time_=start_time_, - historical_forecasts_time_index=historical_forecasts_time_index, + # find valid start position relative to the hfc start time, otherwise raise an error + start_idx, start_idx_orig = _get_start_index( + series=series, + series_idx=series_idx, + start=start, + start_format=start_format, + stride=stride, + historical_forecasts_time_index=historical_forecasts_time_index, + ) + start_time = series._time_index[start_idx] + + if start_idx != start_idx_orig and show_warnings: + if start_idx_orig >= 0: + start_time_orig = series._time_index[start_idx_orig] + else: + start_time_orig = series.start_time() + start_idx_orig * series.freq + + if start_format == "position" or ( + not isinstance(start, pd.Timestamp) and series._has_datetime_index + ): + start_value_msg = ( + f"position `{start}` corresponding to time `{start_time_orig}`" ) - else: - historical_forecasts_time_index = ( - max(historical_forecasts_time_index[0], start_time_), - historical_forecasts_time_index[1], + else: + start_value_msg = f"time `{start_time_orig}`" + logger.warning( + f"`start` {start_value_msg} is before the first predictable/trainable historical " + f"forecasting point for series at index: {series_idx}. Using the first historical forecasting " + f"point `{start_time}` that lies a round-multiple of `stride={stride}` " + f"ahead of `start`. To hide these warnings, set `show_warnings=False`." ) - + historical_forecasts_time_index = ( + max(historical_forecasts_time_index[0], start_time), + historical_forecasts_time_index[1], + ) return historical_forecasts_time_index @@ -617,7 +907,7 @@ def _reconciliate_historical_time_indices( retrain: Union[bool, int, Callable[..., bool]], train_length: Optional[int], show_warnings: bool, -) -> Tuple[TimeIndex, Optional[int]]: +) -> tuple[TimeIndex, Optional[int]]: """Depending on the value of retrain, select which time indices will be used during the historical forecasts.""" train_length_ = None if isinstance(retrain, Callable): @@ -679,9 +969,10 @@ def _get_historical_forecast_boundaries( start_format: Literal["position", "value"], forecast_horizon: int, overlap_end: bool, + stride: int, freq: pd.DateOffset, show_warnings: bool = True, -) -> Tuple[Any, ...]: +) -> tuple[Any, ...]: """ Based on the boundaries of the forecastable time index, generates the boundaries of each covariates using the lags. @@ -708,10 +999,12 @@ def _get_historical_forecast_boundaries( historical_forecasts_time_index=historical_forecasts_time_index, start=start, start_format=start_format, + stride=stride, show_warnings=show_warnings, ) # re-adjust the slicing indexes to account for the lags + # `max_target_lag_train` is redundant, since optimized hist fc is running in predict mode only ( min_target_lag, _, @@ -719,6 +1012,8 @@ def _get_historical_forecast_boundaries( max_past_cov_lag, min_future_cov_lag, max_future_cov_lag, + output_chunk_shift, + max_target_lag_train, ) = model.extreme_lags # target lags are <= 0 @@ -788,12 +1083,12 @@ def _check_optimizable_historical_forecasts_global_models( if show_warnings: if not retrain_off: logger.warning( - "`enable_optimization=True` is ignored because `retrain` is not `False` or `0`." + "`enable_optimization=True` is ignored because `retrain` is not `False` or `0`. " "To hide this warning, set `show_warnings=False` or `enable_optimization=False`." ) if is_autoregressive: logger.warning( - "`enable_optimization=True` is ignored because `forecast_horizon > model.output_chunk_length`." + "`enable_optimization=True` is ignored because `forecast_horizon > model.output_chunk_length`. " "To hide this warning, set `show_warnings=False` or `enable_optimization=False`." ) @@ -802,12 +1097,17 @@ def _check_optimizable_historical_forecasts_global_models( def _process_historical_forecast_input( model, - series: Optional[Sequence[TimeSeries]], - past_covariates: Optional[Sequence[TimeSeries]] = None, - future_covariates: Optional[Sequence[TimeSeries]] = None, + series: Union[TimeSeries, Sequence[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, + future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]] = None, forecast_horizon: int = 1, allow_autoregression: bool = False, -): +) -> Union[ + Sequence[TimeSeries], + Optional[Sequence[TimeSeries]], + Optional[Sequence[TimeSeries]], + int, +]: if not model._fit_called: raise_log( ValueError("Model has not been fit yet."), @@ -822,6 +1122,10 @@ def _process_historical_forecast_input( ), logger, ) + series_seq_type = get_series_seq_type(series) + series = series2seq(series) + past_covariates = series2seq(past_covariates) + future_covariates = series2seq(future_covariates) # manage covariates, usually handled by RegressionModel.predict() if past_covariates is None and model.past_covariate_series is not None: @@ -829,7 +1133,8 @@ def _process_historical_forecast_input( if future_covariates is None and model.future_covariate_series is not None: future_covariates = [model.future_covariate_series] * len(series) - model._verify_static_covariates(series[0].static_covariates) + if model.uses_static_covariates: + model._verify_static_covariates(series[0].static_covariates) if model.encoders.encoding_available: past_covariates, future_covariates = model.generate_fit_predict_encodings( @@ -838,12 +1143,12 @@ def _process_historical_forecast_input( past_covariates=past_covariates, future_covariates=future_covariates, ) - return series, past_covariates, future_covariates + return series, past_covariates, future_covariates, series_seq_type def _process_predict_start_points_bounds( series: Sequence[TimeSeries], bounds: ArrayLike, stride: int -) -> Tuple[np.ndarray, np.ndarray]: +) -> tuple[np.ndarray, np.ndarray]: """Processes the historical forecastable time index bounds (earliest, and latest possible prediction start points). @@ -874,3 +1179,188 @@ def _process_predict_start_points_bounds( bounds[:, 1] -= steps_too_long cum_lengths = np.cumsum(np.diff(bounds) // stride + 1) return bounds, cum_lengths + + +def _convert_data_transformers( + data_transformers: Optional[dict[str, Union[BaseDataTransformer, Pipeline]]], + copy: bool, +) -> dict[str, Pipeline]: + if data_transformers is None: + return dict() + else: + return { + key_: val_ + if isinstance(val_, Pipeline) + else Pipeline(transformers=[val_], copy=copy) + for key_, val_ in data_transformers.items() + } + + +def _apply_data_transformers( + series: Union[TimeSeries, list[TimeSeries]], + past_covariates: Optional[Union[TimeSeries, list[TimeSeries]]], + future_covariates: Optional[Union[TimeSeries, list[TimeSeries]]], + data_transformers: dict[str, Pipeline], + max_future_cov_lag: int, + fit_transformers: bool, +) -> tuple[ + Union[TimeSeries, list[TimeSeries]], + Union[TimeSeries, list[TimeSeries]], + Union[TimeSeries, list[TimeSeries]], +]: + """Transform each series using the corresponding Pipeline. + + If the Pipeline is fittable and `fit_transformers=True`, the series are sliced to correspond + to the information available at model training time + """ + # `global_fit`` is not supported, requires too complex time indexes manipulation across series (slice and align) + if fit_transformers and any( + not (isinstance(ts, TimeSeries) or ts is None) + for ts in [series, past_covariates, future_covariates] + ): + raise_log( + ValueError( + "Fitting the data transformers on multiple series is not supported, either provide trained " + "`data_transformers` or a single series (including for the covariates).", + logger, + ) + ) + transformed_ts = [] + for ts_type, ts in zip( + ["series", "past_covariates", "future_covariates"], + [series, past_covariates, future_covariates], + ): + if ts is None or data_transformers.get(ts_type) is None: + transformed_ts.append(ts) + else: + if fit_transformers and data_transformers[ts_type].fittable: + # must slice the ts to distinguish accessible information from future information + if ts_type == "past_covariates": + # known information is aligned with the target series + tmp_ts = ts.drop_after(series.end_time()) + elif ts_type == "future_covariates": + # known information goes up to the first forecasts iteration (in case of autoregression) + tmp_ts = ts.drop_after( + series.end_time() + max(0, max_future_cov_lag + 1) * series.freq + ) + else: + # nothing to do, the target series is already sliced appropriately + tmp_ts = ts + data_transformers[ts_type].fit(tmp_ts) + # transforming the series + transformed_ts.append(data_transformers[ts_type].transform(ts)) + return tuple(transformed_ts) + + +def _apply_inverse_data_transformers( + series: Union[TimeSeries, Sequence[TimeSeries]], + forecasts: Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]], + data_transformers: dict[str, Pipeline], + series_idx: Optional[int] = None, +) -> Union[TimeSeries, list[TimeSeries], list[list[TimeSeries]]]: + """ + Apply the inverse transform to the forecasts when defined. + + `series_idx` is used to retrieve the appropriate transformer when the data transformer was + fitted with several series and global_fit=False. + """ + if "series" in data_transformers and data_transformers["series"].invertible: + called_with_single_series = get_series_seq_type(series) == SeriesType.SINGLE + if called_with_single_series: + forecasts = [forecasts] + forecasts = data_transformers["series"].inverse_transform( + forecasts, series_idx=series_idx + ) + return forecasts[0] if called_with_single_series else forecasts + else: + return forecasts + + +def _process_historical_forecast_for_backtest( + series: Union[TimeSeries, Sequence[TimeSeries]], + historical_forecasts: Union[ + TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]] + ], + last_points_only: bool, +): + """Checks that the `historical_forecasts` have the correct format based on the input `series` and + `last_points_only`. If all checks have passed, it converts `series` and `historical_forecasts` format into a + multiple series case with `last_points_only=False`. + """ + # remember input series type + series_seq_type = get_series_seq_type(series) + series = series2seq(series) + + # check that `historical_forecasts` have correct type + expected_seq_type = None + forecast_seq_type = get_series_seq_type(historical_forecasts) + if last_points_only and not series_seq_type == forecast_seq_type: + # lpo=True -> fc sequence type must be the same + expected_seq_type = series_seq_type + elif not last_points_only and forecast_seq_type != series_seq_type + 1: + # lpo=False -> fc sequence type must be one order higher + expected_seq_type = series_seq_type + 1 + + if expected_seq_type is not None: + raise_log( + ValueError( + f"Expected `historical_forecasts` of type {expected_seq_type} " + f"with `last_points_only={last_points_only}` and `series` of type " + f"{series_seq_type}. However, received `historical_forecasts` of type " + f"{forecast_seq_type}. Make sure to pass the same `last_points_only` " + f"value that was used to generate the historical forecasts." + ), + logger=logger, + ) + + # we must wrap each fc in a list if `last_points_only=True` + nested = last_points_only and forecast_seq_type == SeriesType.SEQ + historical_forecasts = series2seq( + historical_forecasts, seq_type_out=SeriesType.SEQ_SEQ, nested=nested + ) + + # check that the number of series-specific forecasts corresponds to the + # number of series in `series` + if len(series) != len(historical_forecasts): + error_msg = ( + f"Mismatch between the number of series-specific `historical_forecasts` " + f"(n={len(historical_forecasts)}) and the number of `TimeSeries` in `series` " + f"(n={len(series)}). For `last_points_only={last_points_only}`, expected " + ) + expected_seq_type = series_seq_type if last_points_only else series_seq_type + 1 + if expected_seq_type == SeriesType.SINGLE: + error_msg += f"a single `historical_forecasts` of type {expected_seq_type}." + else: + error_msg += f"`historical_forecasts` of type {expected_seq_type} with length n={len(series)}." + raise_log( + ValueError(error_msg), + logger=logger, + ) + return series, historical_forecasts + + +def _extend_series_for_overlap_end( + series: Sequence[TimeSeries], + historical_forecasts: Sequence[Sequence[TimeSeries]], +): + """Extends each target `series` to the end of the last historical forecast for that series. + Fills the values all missing dates with `np.nan`. + + Assumes the input meets the multiple `series` case with `last_points_only=False` (e.g. the output of + `darts.utils.historical_forecasts.utils_process_historical_forecast_for_backtest()`). + """ + series_extended = [] + append_vals = [np.nan] * series[0].n_components + for series_, hfcs_ in zip(series, historical_forecasts): + # find number of missing target time steps based on the last forecast + missing_steps = n_steps_between( + hfcs_[-1].end_time(), series[0].end_time(), freq=series[0].freq + ) + # extend the target if it is too short + if missing_steps > 0: + series_extended.append( + series_.append_values(np.array([append_vals] * missing_steps)) + ) + else: + series_extended.append(series_) + return series_extended diff --git a/darts/utils/likelihood_models.py b/darts/utils/likelihood_models.py index 900c47687d..5f12895226 100644 --- a/darts/utils/likelihood_models.py +++ b/darts/utils/likelihood_models.py @@ -33,7 +33,7 @@ import collections.abc import inspect from abc import ABC, abstractmethod -from typing import List, Optional, Tuple, Union +from typing import Optional, Union import numpy as np import torch @@ -57,9 +57,14 @@ from torch.distributions.kl import kl_divergence from darts import TimeSeries +from darts.logging import raise_if_not # TODO: Table on README listing distribution, possible priors and wiki article -from darts.utils.utils import _check_quantiles, raise_if_not +from darts.utils.utils import ( + _check_quantiles, + likelihood_component_names, + quantile_names, +) MIN_CAUCHY_GAMMA_SAMPLING = 1e-100 @@ -97,13 +102,18 @@ def __init__(self, prior_strength=1.0): # used for equality operator between likelihood objects self.ignore_attrs_equality = [] - def compute_loss(self, model_output: torch.Tensor, target: torch.Tensor): + def compute_loss( + self, + model_output: torch.Tensor, + target: torch.Tensor, + sample_weight: torch.Tensor, + ): """ Computes a loss from a `model_output`, which represents the parameters of a given probability distribution for every ground truth value in `target`, and the `target` itself. """ params_out = self._params_from_output(model_output) - loss = self._nllloss(params_out, target) + loss = self._nllloss(params_out, target, sample_weight) prior_params = self._prior_params use_prior = prior_params is not None and any( @@ -114,9 +124,11 @@ def compute_loss(self, model_output: torch.Tensor, target: torch.Tensor): device = params_out[0].device prior_params = tuple( # use model output as "prior" for parameters not specified as prior - torch.tensor(prior_params[i]).to(device) - if prior_params[i] is not None - else params_out[i] + ( + torch.tensor(prior_params[i]).to(device) + if prior_params[i] is not None + else params_out[i] + ) for i in range(len(prior_params)) ) prior_distr = self._distr_from_params(prior_params) @@ -128,13 +140,16 @@ def compute_loss(self, model_output: torch.Tensor, target: torch.Tensor): return loss - def _nllloss(self, params_out, target): + def _nllloss(self, params_out, target, sample_weight): """ This is the basic way to compute the NLL loss. It can be overwritten by likelihoods for which PyTorch proposes a numerically better NLL loss. """ out_distr = self._distr_from_params(params_out) - return -out_distr.log_prob(target).mean() + loss = -out_distr.log_prob(target) + if sample_weight is not None: + loss = loss * sample_weight + return loss.mean() @property def _prior_params(self): @@ -145,7 +160,7 @@ def _prior_params(self): return None @abstractmethod - def _distr_from_params(self, params: Tuple) -> torch.distributions.Distribution: + def _distr_from_params(self, params: tuple) -> torch.distributions.Distribution: """ Returns a torch distribution built with the specified params """ @@ -154,7 +169,7 @@ def _distr_from_params(self, params: Tuple) -> torch.distributions.Distribution: @abstractmethod def _params_from_output( self, model_output: torch.Tensor - ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]: + ) -> Union[tuple[torch.Tensor, ...], torch.Tensor]: """ Returns the distribution parameters, obtained from the raw model outputs (e.g. applies softplus or sigmoids to get parameters in the expected domains). @@ -179,20 +194,22 @@ def predict_likelihood_parameters(self, model_output: torch.Tensor) -> torch.Ten else: # interleave the predicted parameters to group them by input series component num_samples, n_times, n_components, n_params = model_output.shape - return torch.stack(params, dim=3).reshape( - (num_samples, n_times, n_components * n_params) - ) + return torch.stack(params, dim=3).reshape(( + num_samples, + n_times, + n_components * n_params, + )) @abstractmethod - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: """ Generates names for the parameters of the Likelihood. """ pass def _likelihood_generate_components_names( - self, input_series: TimeSeries, parameter_names: List[str] - ) -> List[str]: + self, input_series: TimeSeries, parameter_names: list[str] + ) -> list[str]: return [ f"{tgt_name}_{param_n}" for tgt_name in input_series.components @@ -238,13 +255,11 @@ def __repr__(self) -> str: cls_name = self.__class__.__name__ # only display the constructor parameters as user cannot change the other attributes init_signature = inspect.signature(self.__class__.__init__) - params_string = ", ".join( - [ - f"{str(v)}" - for _, v in init_signature.parameters.items() - if str(v) != "self" - ] - ) + params_string = ", ".join([ + f"{str(v)}" + for _, v in init_signature.parameters.items() + if str(v) != "self" + ]) return f"{cls_name}({params_string})" @@ -290,14 +305,12 @@ def __init__( self.beta_nll = beta_nll _check_strict_positive(self.prior_sigma, "sigma") - self.nllloss = nn.GaussianNLLLoss( - reduction="none" if self.beta_nll > 0.0 else "mean", full=True - ) + self.nllloss = nn.GaussianNLLLoss(full=True, reduction="none") self.softplus = nn.Softplus() super().__init__(prior_strength) - def _nllloss(self, params_out, target): + def _nllloss(self, params_out, target, sample_weight): means_out, sigmas_out = params_out # Note: GaussianNLLLoss expects variance (and not stdev) cont_var = sigmas_out.contiguous() ** 2 @@ -305,8 +318,10 @@ def _nllloss(self, params_out, target): # apply Beta-NLL if self.beta_nll > 0.0: # Note: there is no mean reduction if beta_nll > 0, so we compute it here - loss = (loss * (cont_var.detach() ** self.beta_nll)).mean() - return loss + loss = loss * (cont_var.detach() ** self.beta_nll) + if sample_weight is not None: + loss = loss * sample_weight + return loss.mean() @property def _prior_params(self): @@ -329,7 +344,7 @@ def _params_from_output(self, model_output): sigma = self.softplus(model_output[:, :, :, 1]) return mu, sigma - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["mu", "sigma"]) def simplified_name(self) -> str: @@ -358,13 +373,16 @@ def __init__(self, prior_lambda=None, prior_strength=1.0): self.prior_lambda = prior_lambda _check_strict_positive(self.prior_lambda, "lambda") - self.nllloss = nn.PoissonNLLLoss(log_input=False, full=True) + self.nllloss = nn.PoissonNLLLoss(log_input=False, full=True, reduction="none") self.softplus = nn.Softplus() super().__init__(prior_strength) - def _nllloss(self, params_out, target): + def _nllloss(self, params_out, target, sample_weight): lambda_out = params_out - return self.nllloss(lambda_out, target) + loss = self.nllloss(lambda_out, target) + if sample_weight is not None: + loss = loss * sample_weight + return loss.mean() @property def _prior_params(self): @@ -386,7 +404,7 @@ def _params_from_output(self, model_output): lmbda = self.softplus(model_output.squeeze(dim=-1)) return lmbda - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["lambda"]) def simplified_name(self) -> str: @@ -445,7 +463,7 @@ def _params_from_output(self, model_output): alpha = self.softplus(model_output[:, :, :, 1]) return mu, alpha - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["r", "p"]) @property @@ -500,7 +518,7 @@ def _params_from_output(self, model_output: torch.Tensor): p = self.sigmoid(model_output.squeeze(dim=-1)) return p - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["p"]) def simplified_name(self) -> str: @@ -557,7 +575,7 @@ def _params_from_output(self, model_output): beta = self.softplus(model_output[:, :, :, 1]) return alpha, beta - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names( input_series, ["alpha", "beta"] ) @@ -621,7 +639,7 @@ def _params_from_output(self, model_output): gamma[gamma < MIN_CAUCHY_GAMMA_SAMPLING] = MIN_CAUCHY_GAMMA_SAMPLING return xzero, gamma - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names( input_series, ["xzero", "gamma"] ) @@ -675,7 +693,7 @@ def _params_from_output(self, model_output: torch.Tensor): lmbda = self.sigmoid(model_output.squeeze(dim=-1)) return lmbda - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["lambda"]) def simplified_name(self) -> str: @@ -710,7 +728,7 @@ def __init__(self, prior_alphas=None, prior_strength=1.0): def _prior_params(self): return (self.prior_alphas,) - def _distr_from_params(self, params: Tuple): + def _distr_from_params(self, params: tuple): alphas = params[0] return _Dirichlet(alphas) @@ -733,7 +751,7 @@ def _params_from_output(self, model_output): ) # take softmax over components return alphas - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: # one alpha per component return self._likelihood_generate_components_names(input_series, ["alpha"]) @@ -768,7 +786,7 @@ def __init__(self, prior_lambda=None, prior_strength=1.0): def _prior_params(self): return (self.prior_lambda,) - def _distr_from_params(self, params: Tuple): + def _distr_from_params(self, params: tuple): lmbda = params[0] return _Exponential(lmbda) @@ -785,7 +803,7 @@ def _params_from_output(self, model_output: torch.Tensor): lmbda = self.softplus(model_output.squeeze(dim=-1)) return lmbda - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["lambda"]) def simplified_name(self) -> str: @@ -823,7 +841,7 @@ def __init__(self, prior_alpha=None, prior_beta=None, prior_strength=1.0): def _prior_params(self): return self.prior_alpha, self.prior_beta - def _distr_from_params(self, params: Tuple): + def _distr_from_params(self, params: tuple): alpha, beta = params return _Gamma(alpha, beta) @@ -841,7 +859,7 @@ def _params_from_output(self, model_output: torch.Tensor): beta = self.softplus(model_output[:, :, :, 1]) return alpha, beta - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names( input_series, ["alpha", "beta"] ) @@ -877,7 +895,7 @@ def __init__(self, prior_p=None, prior_strength=1.0): def _prior_params(self): return (self.prior_p,) - def _distr_from_params(self, params: Tuple): + def _distr_from_params(self, params: tuple): p = params[0] return _Geometric(p) @@ -894,7 +912,7 @@ def _params_from_output(self, model_output: torch.Tensor): p = self.sigmoid(model_output.squeeze(dim=-1)) return p - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["p"]) def simplified_name(self) -> str: @@ -931,7 +949,7 @@ def __init__(self, prior_mu=None, prior_beta=None, prior_strength=1.0): def _prior_params(self): return self.prior_mu, self.prior_beta - def _distr_from_params(self, params: Tuple): + def _distr_from_params(self, params: tuple): mu, beta = params return _Gumbel(mu, beta) @@ -949,7 +967,7 @@ def _params_from_output(self, model_output: torch.Tensor): beta = self.softplus(model_output[:, :, :, 1]) return mu, beta - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["mu", "beta"]) def simplified_name(self) -> str: @@ -983,7 +1001,7 @@ def __init__(self, prior_sigma=None, prior_strength=1.0): def _prior_params(self): return (self.prior_sigma,) - def _distr_from_params(self, params: Tuple): + def _distr_from_params(self, params: tuple): sigma = params[0] return _HalfNormal(sigma) @@ -1000,7 +1018,7 @@ def _params_from_output(self, model_output: torch.Tensor): sigma = self.softplus(model_output.squeeze(dim=-1)) return sigma - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["sigma"]) def simplified_name(self) -> str: @@ -1037,7 +1055,7 @@ def __init__(self, prior_mu=None, prior_b=None, prior_strength=1.0): def _prior_params(self): return self.prior_mu, self.prior_b - def _distr_from_params(self, params: Tuple): + def _distr_from_params(self, params: tuple): mu, b = params return _Laplace(mu, b) @@ -1055,7 +1073,7 @@ def _params_from_output(self, model_output: torch.Tensor): b = self.softplus(model_output[:, :, :, 1]) return mu, b - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["mu", "b"]) def simplified_name(self) -> str: @@ -1110,7 +1128,7 @@ def _params_from_output(self, model_output): sigma = self.softplus(model_output[:, :, :, 1]) return mu, sigma - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["mu", "sigma"]) def simplified_name(self) -> str: @@ -1142,7 +1160,7 @@ def __init__(self, prior_strength=1.0): def _prior_params(self): return None - def _distr_from_params(self, params: Tuple): + def _distr_from_params(self, params: tuple): lmba, k = params return _Weibull(lmba, k) @@ -1160,7 +1178,7 @@ def _params_from_output(self, model_output: torch.Tensor): k = self.softplus(model_output[:, :, :, 1]) return lmbda, k - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: return self._likelihood_generate_components_names(input_series, ["lambda", "k"]) def simplified_name(self) -> str: @@ -1168,7 +1186,7 @@ def simplified_name(self) -> str: class QuantileRegression(Likelihood): - def __init__(self, quantiles: Optional[List[float]] = None): + def __init__(self, quantiles: Optional[list[float]] = None): """ The "likelihood" corresponding to quantile regression. It uses the Quantile Loss Metric for custom quantiles centered around q=0.5. @@ -1285,7 +1303,12 @@ def predict_likelihood_parameters(self, model_output: torch.Tensor) -> torch.Ten def num_parameters(self) -> int: return len(self.quantiles) - def compute_loss(self, model_output: torch.Tensor, target: torch.Tensor): + def compute_loss( + self, + model_output: torch.Tensor, + target: torch.Tensor, + sample_weight: torch.Tensor, + ): """ We are re-defining a custom loss (which is not a likelihood loss) compared to Likelihood @@ -1295,11 +1318,10 @@ def compute_loss(self, model_output: torch.Tensor, target: torch.Tensor): must be of shape (batch_size, n_timesteps, n_target_variables, n_quantiles) target must be of shape (n_samples, n_timesteps, n_target_variables) + sample_weight + must be of shape (n_samples, n_timesteps, n_target_variables) """ - dim_q = 3 - - batch_size, length = model_output.shape[:2] device = model_output.device # test if torch model forward produces correct output and store quantiles tensor @@ -1320,11 +1342,13 @@ def compute_loss(self, model_output: torch.Tensor, target: torch.Tensor): errors = target.unsqueeze(-1) - model_output losses = torch.max( (self.quantiles_tensor - 1) * errors, self.quantiles_tensor * errors - ) + ).sum(dim=dim_q) - return losses.sum(dim=dim_q).mean() + if sample_weight is not None: + losses = losses * sample_weight + return losses.mean() - def _distr_from_params(self, params: Tuple) -> None: + def _distr_from_params(self, params: tuple) -> None: # This should not be called in this class (we are abusing Likelihood) return None @@ -1332,13 +1356,12 @@ def _params_from_output(self, model_output: torch.Tensor) -> None: # This should not be called in this class (we are abusing Likelihood) return None - def likelihood_components_names(self, input_series: TimeSeries) -> List[str]: + def likelihood_components_names(self, input_series: TimeSeries) -> list[str]: """Each component have their own quantiles""" - return [ - f"{tgt_name}_q{quantile:.2f}" - for tgt_name in input_series.components - for quantile in self.quantiles - ] + return likelihood_component_names( + components=input_series.components, + parameter_names=quantile_names(self.quantiles), + ) def simplified_name(self) -> str: return "quantile" diff --git a/darts/utils/losses.py b/darts/utils/losses.py index a2eb251337..948660e791 100644 --- a/darts/utils/losses.py +++ b/darts/utils/losses.py @@ -2,6 +2,7 @@ PyTorch Loss Functions ---------------------- """ + # Inspiration: https://github.com/ElementAI/N-BEATS/blob/master/common/torch/losses.py import numpy as np @@ -16,7 +17,7 @@ def _divide_no_nan(a, b): result = a / b result[result != result] = 0.0 result[result == np.inf] = 0.0 - result[result == np.NINF] = 0.0 + result[result == -np.inf] = 0.0 return result diff --git a/darts/utils/missing_values.py b/darts/utils/missing_values.py index 2de3a2511e..f91cd48b9d 100644 --- a/darts/utils/missing_values.py +++ b/darts/utils/missing_values.py @@ -3,7 +3,7 @@ -------------------------------- """ -from typing import List, Optional, Union +from typing import Optional, Union from darts.logging import get_logger, raise_if, raise_if_not from darts.timeseries import TimeSeries @@ -71,7 +71,7 @@ def fill_missing_values( def extract_subseries( series: TimeSeries, min_gap_size: Optional[int] = 1, mode: str = "all" -) -> List[TimeSeries]: +) -> list[TimeSeries]: """ Partitions the series into a sequence of sub-series by using significant gaps of missing values diff --git a/darts/utils/model_selection.py b/darts/utils/model_selection.py index fa8816f84c..8b53a92a4b 100644 --- a/darts/utils/model_selection.py +++ b/darts/utils/model_selection.py @@ -4,7 +4,8 @@ Utilities that help in model selection e.g. by splitting a dataset. """ -from typing import Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union from darts import TimeSeries @@ -27,7 +28,6 @@ def __init__( horizon: Optional[int] = None, vertical_split_type: Optional[str] = SIMPLE, ): - if type not in ["train", "test"]: raise AttributeError( "Value for type parameter should be either `train` or `test`" @@ -74,7 +74,6 @@ def _get_horizontal_split_index(self): return self._horizontal_split_index def _get_vertical_split_indices(self, ts_length): - if self.vertical_split_type == SIMPLE: if 0 < self.test_size < 1: test_size = int(ts_length * self.test_size) @@ -167,9 +166,8 @@ def make_splitter( vertical_split_type: Optional[str] = SIMPLE, lazy: bool = False, ) -> Union[ - Tuple[TimeSeries, TimeSeries], Tuple[Sequence[TimeSeries], Sequence[TimeSeries]] + tuple[TimeSeries, TimeSeries], tuple[Sequence[TimeSeries], Sequence[TimeSeries]] ]: - if not isinstance(data, Sequence): axis = 1 data = [data] # convert to sequence for unified processing later @@ -215,7 +213,7 @@ def train_test_split( vertical_split_type: Optional[str] = SIMPLE, lazy: bool = False, ) -> Union[ - Tuple[TimeSeries, TimeSeries], Tuple[Sequence[TimeSeries], Sequence[TimeSeries]] + tuple[TimeSeries, TimeSeries], tuple[Sequence[TimeSeries], Sequence[TimeSeries]] ]: """ Splits the provided series into training and test series. diff --git a/darts/utils/multioutput.py b/darts/utils/multioutput.py index 84e4f04523..f7ff24dadd 100644 --- a/darts/utils/multioutput.py +++ b/darts/utils/multioutput.py @@ -1,3 +1,5 @@ +from typing import Optional + from sklearn import __version__ as sklearn_version from sklearn.base import is_classifier from sklearn.multioutput import MultiOutputRegressor as sk_MultiOutputRegressor @@ -5,6 +7,8 @@ from sklearn.utils.multiclass import check_classification_targets from sklearn.utils.validation import has_fit_parameter +from darts.logging import get_logger, raise_log + if sklearn_version >= "1.4": # sklearn renamed `_check_fit_params` to `_check_method_params` in v1.4 from sklearn.utils.validation import _check_method_params @@ -18,6 +22,8 @@ from joblib import Parallel from sklearn.utils.fixes import delayed +logger = get_logger(__name__) + class MultiOutputRegressor(sk_MultiOutputRegressor): """ @@ -25,6 +31,20 @@ class MultiOutputRegressor(sk_MultiOutputRegressor): validation data correctly. The validation data has to be passed as parameter ``eval_set`` in ``**fit_params``. """ + def __init__( + self, + *args, + eval_set_name: Optional[str] = None, + eval_weight_name: Optional[str] = None, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.eval_set_name_ = eval_set_name + self.eval_weight_name_ = eval_weight_name + self.estimators_ = None + self.n_features_in_ = None + self.feature_names_in_ = None + def fit(self, X, y, sample_weight=None, **fit_params): """Fit the model to data, separately for each output variable. @@ -37,7 +57,7 @@ def fit(self, X, y, sample_weight=None, **fit_params): Multi-output targets. An indicator matrix turns on multilabel estimation. - sample_weight : array-like of shape (n_samples,), default=None + sample_weight : array-like of shape (n_samples, n_outputs), default=None Sample weights. If `None`, then samples are equally weighted. Only supported if the underlying regressor supports sample weights. @@ -54,7 +74,10 @@ def fit(self, X, y, sample_weight=None, **fit_params): """ if not hasattr(self.estimator, "fit"): - raise ValueError("The base estimator should implement a fit method") + raise_log( + ValueError("The base estimator should implement a fit method"), + logger=logger, + ) y = self._validate_data(X="no_validation", y=y, multi_output=True) @@ -62,43 +85,50 @@ def fit(self, X, y, sample_weight=None, **fit_params): check_classification_targets(y) if y.ndim == 1: - raise ValueError( - "y must have at least two dimensions for " - "multi-output regression but has only one." + raise_log( + ValueError( + "`y` must have at least two dimensions for multi-output regression but has only one." + ), + logger=logger, ) - - if sample_weight is not None and not has_fit_parameter( - self.estimator, "sample_weight" + if sample_weight is not None and ( + sample_weight.ndim == 1 or sample_weight.shape[1] != y.shape[1] ): - raise ValueError("Underlying estimator does not support sample weights.") - - fit_params_validated = _check_method_params(X, fit_params) + raise_log( + ValueError("`sample_weight` must have the same dimensions as `y`."), + logger=logger, + ) - if "eval_set" in fit_params_validated.keys(): - # with validation set - eval_set = fit_params_validated.pop("eval_set") - self.estimators_ = Parallel(n_jobs=self.n_jobs)( - delayed(_fit_estimator)( - self.estimator, - X, - y[:, i], - sample_weight, - # eval set may be a list (for XGBRegressor), in which case we have to keep it as a list - eval_set=[(eval_set[0][0], eval_set[0][1][:, i])] - if isinstance(eval_set, list) - else (eval_set[0], eval_set[1][:, i]), - **fit_params_validated - ) - for i in range(y.shape[1]) + if sample_weight is not None and not self.supports_sample_weight: + raise_log( + ValueError("Underlying estimator does not support sample weights."), + logger=logger, ) - else: - # without validation set - self.estimators_ = Parallel(n_jobs=self.n_jobs)( - delayed(_fit_estimator)( - self.estimator, X, y[:, i], sample_weight, **fit_params_validated - ) - for i in range(y.shape[1]) + + fit_params_validated = _check_method_params(X, fit_params) + eval_set = fit_params_validated.pop(self.eval_set_name_, None) + eval_weight = fit_params_validated.pop(self.eval_weight_name_, None) + + self.estimators_ = Parallel(n_jobs=self.n_jobs)( + delayed(_fit_estimator)( + self.estimator, + X, + y[:, i], + sample_weight=sample_weight[:, i] + if sample_weight is not None + else None, + **( + {self.eval_set_name_: [eval_set[i]]} if eval_set is not None else {} + ), + **( + {self.eval_weight_name_: [eval_weight[i]]} + if eval_weight is not None + else {} + ), + **fit_params_validated, ) + for i in range(y.shape[1]) + ) if hasattr(self.estimators_[0], "n_features_in_"): self.n_features_in_ = self.estimators_[0].n_features_in_ @@ -106,3 +136,10 @@ def fit(self, X, y, sample_weight=None, **fit_params): self.feature_names_in_ = self.estimators_[0].feature_names_in_ return self + + @property + def supports_sample_weight(self) -> bool: + """ + Whether model supports sample weight for training. + """ + return has_fit_parameter(self.estimator, "sample_weight") diff --git a/darts/utils/statistics.py b/darts/utils/statistics.py index faf4d1304c..125f06ef0c 100644 --- a/darts/utils/statistics.py +++ b/darts/utils/statistics.py @@ -4,7 +4,8 @@ """ import math -from typing import List, Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union import matplotlib.pyplot as plt import numpy as np @@ -23,9 +24,8 @@ from darts import TimeSeries from darts.logging import get_logger, raise_if, raise_if_not, raise_log - -from .missing_values import fill_missing_values -from .utils import ModelMode, SeasonalityMode +from darts.utils.missing_values import fill_missing_values +from darts.utils.utils import ModelMode, SeasonalityMode logger = get_logger(__name__) @@ -135,7 +135,7 @@ def extract_trend_and_seasonality( model: Union[SeasonalityMode, ModelMode] = ModelMode.MULTIPLICATIVE, method: str = "naive", **kwargs, -) -> Tuple[TimeSeries, TimeSeries]: +) -> tuple[TimeSeries, TimeSeries]: """ Extracts trend and seasonality from a TimeSeries instance using `statsmodels.tsa`. @@ -189,7 +189,6 @@ def extract_trend_and_seasonality( ) if method == "naive": - decomp = seasonal_decompose( ts.pd_series(), period=freq, model=model.value, extrapolate_trend="freq" ) @@ -278,9 +277,7 @@ def remove_from_series( else: raise_log( ValueError( - "Invalid parameter; must be either ADDITIVE or MULTIPLICATIVE. Was: {}".format( - model - ) + f"Invalid parameter; must be either ADDITIVE or MULTIPLICATIVE. Was: {model}" ) ) return new_ts @@ -390,7 +387,6 @@ def stationarity_tests( p_value_threshold_adfuller: float = 0.05, p_value_threshold_kpss: float = 0.05, ) -> bool: - """ Double test on stationarity using both Kwiatkowski-Phillips-Schmidt-Shin and Augmented Dickey-Fuller statistical tests. @@ -602,7 +598,7 @@ def plot_acf( max_lag: int = 24, alpha: float = 0.05, bartlett_confint: bool = True, - fig_size: Tuple[int, int] = (10, 5), + fig_size: tuple[int, int] = (10, 5), axis: Optional[plt.axis] = None, default_formatting: bool = True, ) -> None: @@ -622,9 +618,9 @@ def plot_acf( The confidence interval to display. bartlett_confint The boolean value indicating whether the confidence interval should be - calculated using Bartlett's formula. If set to True, the confidence interval + calculated using Bartlett's formula. If set to `True`, the confidence interval can be used in the model identification stage for fitting ARIMA models. - If set to False, the confidence interval can be used to test for randomness + If set to `False`, the confidence interval can be used to test for randomness (i.e. there is no time dependence in the data) of the data. fig_size The size of the figure to be displayed. @@ -668,9 +664,11 @@ def plot_acf( axis.plot( (i, i), (0, r[i]), - color=("#b512b8" if m is not None and i == m else "black") - if default_formatting - else None, + color=( + ("#b512b8" if m is not None and i == m else "black") + if default_formatting + else None + ), lw=(1 if m is not None and i == m else 0.5), ) @@ -698,7 +696,7 @@ def plot_pacf( max_lag: int = 24, method: str = "ywadjusted", alpha: float = 0.05, - fig_size: Tuple[int, int] = (10, 5), + fig_size: tuple[int, int] = (10, 5), axis: Optional[plt.axis] = None, default_formatting: bool = True, ) -> None: @@ -800,7 +798,7 @@ def plot_ccf( max_lag: int = 24, alpha: float = 0.05, bartlett_confint: bool = True, - fig_size: Tuple[int, int] = (10, 5), + fig_size: tuple[int, int] = (10, 5), axis: Optional[plt.axis] = None, default_formatting: bool = True, ) -> None: @@ -887,9 +885,11 @@ def plot_ccf( axis.plot( (i, i), (0, ccf[i]), - color=("#b512b8" if m is not None and i == m else "black") - if default_formatting - else None, + color=( + ("#b512b8" if m is not None and i == m else "black") + if default_formatting + else None + ), lw=(1 if m is not None and i == m else 0.5), ) @@ -912,11 +912,11 @@ def plot_ccf( def plot_hist( - data: Union[TimeSeries, List[float], np.ndarray], - bins: Optional[Union[int, np.ndarray, List[float]]] = None, + data: Union[TimeSeries, list[float], np.ndarray], + bins: Optional[Union[int, np.ndarray, list[float]]] = None, density: bool = False, title: Optional[str] = None, - fig_size: Optional[Tuple[int, int]] = None, + fig_size: Optional[tuple[int, int]] = None, ax: Optional[plt.axis] = None, ) -> None: """This function plots the histogram of values in a TimeSeries instance or an array-like. @@ -935,7 +935,7 @@ def plot_hist( Optionally, either an integer value for the number of bins to be displayed or an array-like of floats determining the position of bins. density - bool, if `density` is set to True, the bin counts will be converted to probability density + bool, if `density` is set to `True`, the bin counts will be converted to probability density title The title of the figure to be displayed fig_size @@ -1008,7 +1008,7 @@ def plot_residuals_analysis( This function takes a univariate TimeSeries instance of residuals and plots their values, their distribution and their ACF. Please note that if the residual TimeSeries instance contains NaN values, the plots - might be displayed incorrectly. If `fill_nan` is set to True, the missing values will + might be displayed incorrectly. If `fill_nan` is set to `True`, the missing values will be interpolated. Parameters @@ -1040,11 +1040,13 @@ def plot_residuals_analysis( ax1.set_title("Residual values") # plot histogram and distribution - res_mean, res_std = np.mean(residuals.univariate_values()), np.std( - residuals.univariate_values() + res_mean, res_std = ( + np.mean(residuals.univariate_values()), + np.std(residuals.univariate_values()), ) - res_min, res_max = min(residuals.univariate_values()), max( - residuals.univariate_values() + res_min, res_max = ( + min(residuals.univariate_values()), + max(residuals.univariate_values()), ) x = np.linspace(res_min, res_max, 100) ax2 = fig.add_subplot(gs[1:, 1:]) diff --git a/darts/utils/timeseries_generation.py b/darts/utils/timeseries_generation.py index da1d2a524c..1094303736 100644 --- a/darts/utils/timeseries_generation.py +++ b/darts/utils/timeseries_generation.py @@ -4,7 +4,8 @@ """ import math -from typing import List, Optional, Sequence, Tuple, Union +from collections.abc import Sequence +from typing import Optional, Union import holidays import numpy as np @@ -12,76 +13,20 @@ from darts import TimeSeries from darts.logging import get_logger, raise_if, raise_if_not, raise_log +from darts.utils.utils import generate_index logger = get_logger(__name__) - -def generate_index( - start: Optional[Union[pd.Timestamp, int]] = None, - end: Optional[Union[pd.Timestamp, int]] = None, - length: Optional[int] = None, - freq: Union[str, int, pd.DateOffset] = None, - name: str = None, -) -> Union[pd.DatetimeIndex, pd.RangeIndex]: - """Returns an index with a given start point and length. Either a pandas DatetimeIndex with given frequency - or a pandas RangeIndex. The index starts at - - Parameters - ---------- - start - The start of the returned index. If a pandas Timestamp is passed, the index will be a pandas - DatetimeIndex. If an integer is passed, the index will be a pandas RangeIndex index. Works only with - either `length` or `end`. - end - Optionally, the end of the returned index. Works only with either `start` or `length`. If `start` is - set, `end` must be of same type as `start`. Else, it can be either a pandas Timestamp or an integer. - length - Optionally, the length of the returned index. Works only with either `start` or `end`. - freq - The time difference between two adjacent entries in the returned index. In case `start` is a timestamp, - a DateOffset alias is expected; see - `docs `_. - By default, "D" (daily) is used. - If `start` is an integer, `freq` will be interpreted as the step size in the underlying RangeIndex. - The freq is optional for generating an integer index (if not specified, 1 is used). - name - Optionally, an index name. - """ - constructors = [ - arg_name - for arg, arg_name in zip([start, end, length], ["start", "end", "length"]) - if arg is not None - ] - raise_if( - len(constructors) != 2, - "index can only be generated with exactly two of the following parameters: [`start`, `end`, `length`]. " - f"Observed parameters: {constructors}. For generating an index with `end` and `length` consider setting " - f"`start` to None.", - logger, - ) - raise_if( - end is not None and start is not None and type(start) != type(end), - "index generation with `start` and `end` requires equal object types of `start` and `end`", - logger, - ) - - if isinstance(start, pd.Timestamp) or isinstance(end, pd.Timestamp): - index = pd.date_range( - start=start, - end=end, - periods=length, - freq="D" if freq is None else freq, - name=name, - ) - else: # int - step = 1 if freq is None else freq - index = pd.RangeIndex( - start=start if start is not None else end - step * length + step, - stop=end + step if end is not None else start + step * length, - step=step, - name=name, - ) - return index +ONE_INDEXED_FREQS = { + "day", + "month", + "quarter", + "dayofyear", + "day_of_year", + "week", + "weekofyear", + "week_of_year", +} def constant_timeseries( @@ -311,14 +256,14 @@ def gaussian_timeseries( A white noise TimeSeries created as indicated above. """ - if type(mean) == np.ndarray: + if isinstance(mean, np.ndarray): raise_if_not( mean.shape == (length,), "If a vector of means is provided, " "it requires the same length as the TimeSeries.", logger, ) - if type(std) == np.ndarray: + if isinstance(std, np.ndarray): raise_if_not( std.shape == (length, length), "If a matrix of standard deviations is provided, " @@ -463,7 +408,6 @@ def _extend_time_index_until( until: Optional[Union[int, str, pd.Timestamp]], add_length: int, ) -> pd.DatetimeIndex: - if not add_length and not until: return time_index @@ -598,13 +542,14 @@ def datetime_attribute_timeseries( until: Optional[Union[int, str, pd.Timestamp]] = None, add_length: int = 0, dtype=np.float64, - with_columns: Optional[Union[List[str], str]] = None, + with_columns: Optional[Union[list[str], str]] = None, tz: Optional[str] = None, ) -> TimeSeries: """ Returns a new TimeSeries with index `time_index` and one or more dimensions containing (optionally one-hot encoded or cyclic encoded) pd.DatatimeIndex attribute information derived from the index. + 1-indexed attributes are shifted to enforce 0-indexing across all the encodings. Parameters ---------- @@ -693,6 +638,33 @@ def datetime_attribute_timeseries( .rename("time") ) + # shift 1-indexed datetime attributes + if attribute in ONE_INDEXED_FREQS: + values -= 1 + + # leap years insert an additional day on the 29th of February + if attribute in {"dayofyear", "day_of_year"} and any(time_index.is_leap_year): + num_values_dict[attribute] += 1 + + # years contain an additional week if they are : + # - a regular year starting on a thursday + # - a leap year starting on a wednesday + if attribute in {"week", "weekofyear", "week_of_year"}: + years = time_index.year.unique() + # check if year respect properties + additional_week_year = any( + ((not first_day.is_leap_year) and first_day.day_name() == "Thursday") + or (first_day.is_leap_year and first_day.day_name() == "Wednesday") + for first_day in [pd.Timestamp(f"{year}-01-01") for year in years] + ) + # check if time index actually include the additional week + additional_week_in_index = time_index[-1] - time_index[0] + pd.Timedelta( + days=1 + ) >= pd.Timedelta(days=365) + + if additional_week_year and additional_week_in_index: + num_values_dict[attribute] += 1 + if one_hot or cyclic: raise_if_not( attribute in num_values_dict, @@ -704,10 +676,19 @@ def datetime_attribute_timeseries( if one_hot: values_df = pd.get_dummies(values) # fill missing columns (in case not all values appear in time_index) - for i in range(1, num_values_dict[attribute] + 1): - if not (i in values_df.columns): - values_df[i] = 0 - values_df = values_df[range(1, num_values_dict[attribute] + 1)] + attribute_range = np.arange(num_values_dict[attribute]) + is_missing = np.isin(attribute_range, values_df.columns.values, invert=True) + # if there are attribute_range columns that are + # not in values_df.columns.values + if is_missing.any(): + dict_0 = {i: False for i in attribute_range[is_missing]} + # Make a dataframe from the dictionary and concatenate it + # to the values values_df in which the existing columns + values_df = pd.concat( + [values_df, pd.DataFrame(dict_0, index=values_df.index)], axis=1 + ).sort_index(axis=1) + else: + values_df = values_df[attribute_range] if with_columns is None: with_columns = [ @@ -740,12 +721,10 @@ def datetime_attribute_timeseries( "The first string for the sine component name, the second for the cosine component name.", logger=logger, ) - values_df = pd.DataFrame( - { - with_columns[0]: np.sin(freq * values), - with_columns[1]: np.cos(freq * values), - } - ) + values_df = pd.DataFrame({ + with_columns[0]: np.sin(freq * values), + with_columns[1]: np.cos(freq * values), + }) else: if with_columns is None: with_columns = attribute @@ -763,10 +742,11 @@ def datetime_attribute_timeseries( def _build_forecast_series( points_preds: Union[np.ndarray, Sequence[np.ndarray]], input_series: TimeSeries, - custom_columns: List[str] = None, + custom_columns: list[str] = None, with_static_covs: bool = True, with_hierarchy: bool = True, pred_start: Optional[Union[pd.Timestamp, int]] = None, + time_index: Union[pd.DatetimeIndex, pd.RangeIndex] = None, ) -> TimeSeries: """ Builds a forecast time series starting after the end of an input time series, with the @@ -781,28 +761,30 @@ def _build_forecast_series( custom_columns New names for the forecast TimeSeries, used when the number of components changes with_static_covs - If set to False, do not copy the input_series `static_covariates` attribute + If set to `False`, do not copy the input_series `static_covariates` attribute with_hierarchy - If set to False, do not copy the input_series `hierarchy` attribute + If set to `False`, do not copy the input_series `hierarchy` attribute pred_start - Optionally, give a custom prediction start point. + Optionally, give a custom prediction start point. Only effective if `time_index` is `None`. + time_index + Optionally, the index to use for the forecast time series. Returns ------- TimeSeries New TimeSeries instance starting after the input series """ - time_index_length = ( - len(points_preds) - if isinstance(points_preds, np.ndarray) - else len(points_preds[0]) - ) - - time_index = _generate_new_dates( - time_index_length, - input_series=input_series, - start=pred_start, - ) + if time_index is None: + time_index_length = ( + len(points_preds) + if isinstance(points_preds, np.ndarray) + else len(points_preds[0]) + ) + time_index = _generate_new_dates( + time_index_length, + input_series=input_series, + start=pred_start, + ) values = ( points_preds if isinstance(points_preds, np.ndarray) @@ -838,7 +820,7 @@ def _process_time_index( tz: Optional[str] = None, until: Optional[Union[int, str, pd.Timestamp]] = None, add_length: int = 0, -) -> Tuple[pd.DatetimeIndex, pd.DatetimeIndex]: +) -> tuple[pd.DatetimeIndex, pd.DatetimeIndex]: """ Extracts the time index, and optionally adds some time steps after the end of the index, and/or converts the index to another time zone. diff --git a/darts/utils/torch.py b/darts/utils/torch.py index 552f285384..81edf78d01 100644 --- a/darts/utils/torch.py +++ b/darts/utils/torch.py @@ -4,24 +4,21 @@ """ from functools import wraps -from inspect import signature -from typing import Any, Callable, TypeVar +from typing import Callable, TypeVar +import numpy as np import torch.nn as nn import torch.nn.functional as F -from numpy.random import randint from sklearn.utils import check_random_state from torch import Tensor from torch.random import fork_rng, manual_seed -from darts.logging import get_logger, raise_if_not +from darts.logging import get_logger, raise_log +from darts.utils.utils import MAX_NUMPY_SEED_VALUE, MAX_TORCH_SEED_VALUE, _is_method T = TypeVar("T") logger = get_logger(__name__) -MAX_TORCH_SEED_VALUE = (1 << 31) - 1 # to accommodate 32-bit architectures -MAX_NUMPY_SEED_VALUE = (1 << 31) - 1 - class MonteCarloDropout(nn.Dropout): """ @@ -37,49 +34,20 @@ class MonteCarloDropout(nn.Dropout): often improves its performance. """ - # We need to init it to False as some models may start by - # a validation round, in which case MC dropout is disabled. - mc_dropout_enabled: bool = False - - def train(self, mode: bool = True): - # NOTE: we could use the line below if self.mc_dropout_rate represented - # a rate to be applied at inference time, and self.applied_rate the - # actual rate to be used in self.forward(). However, the original paper - # considers the same rate for training and inference; we also stick to this. - - # self.applied_rate = self.p if mode else self.mc_dropout_rate - - if mode: # in train mode, keep dropout as is - self.mc_dropout_enabled = True - # in eval mode, bank on the mc_dropout_enabled flag - # mc_dropout_enabled is set equal to "mc_dropout" param given to predict() + # mc dropout is deactivated at init; see `MonteCarloDropout.mc_dropout_enabled` for more info + _mc_dropout_enabled = False def forward(self, input: Tensor) -> Tensor: # NOTE: we could use the following line in case a different rate # is used for inference: # return F.dropout(input, self.applied_rate, True, self.inplace) - return F.dropout(input, self.p, self.mc_dropout_enabled, self.inplace) - -def _is_method(func: Callable[..., Any]) -> bool: - """Check if the specified function is a method. - - Parameters - ---------- - func - the function to inspect. - - Returns - ------- - bool - true if `func` is a method, false otherwise. - """ - spec = signature(func) - if len(spec.parameters) > 0: - if list(spec.parameters.keys())[0] == "self": - return True - return False + @property + def mc_dropout_enabled(self) -> bool: + # mc dropout is only activated on `PLForecastingModule.on_predict_start()` + # otherwise, it is activated based on the `model.training` flag. + return self._mc_dropout_enabled or self.training def random_method(decorated: Callable[..., T]) -> Callable[..., T]: @@ -91,22 +59,22 @@ def random_method(decorated: Callable[..., T]) -> Callable[..., T]: ---------- decorated A method to be run in an isolated torch random context. - """ # check that @random_method has been applied to a method. - raise_if_not( - _is_method(decorated), "@random_method can only be used on methods.", logger - ) + if not _is_method(decorated): + raise_log(ValueError("@random_method can only be used on methods."), logger) @wraps(decorated) def decorator(self, *args, **kwargs) -> T: if "random_state" in kwargs.keys(): + # get random state for first time from model constructor self._random_instance = check_random_state(kwargs["random_state"]) elif not hasattr(self, "_random_instance"): + # get random state for first time from other method self._random_instance = check_random_state( - randint(0, high=MAX_NUMPY_SEED_VALUE) + np.random.randint(0, high=MAX_NUMPY_SEED_VALUE) ) - + # handle the randomness with fork_rng(): manual_seed(self._random_instance.randint(0, high=MAX_TORCH_SEED_VALUE)) return decorated(self, *args, **kwargs) diff --git a/darts/utils/ts_utils.py b/darts/utils/ts_utils.py new file mode 100644 index 0000000000..b029db3091 --- /dev/null +++ b/darts/utils/ts_utils.py @@ -0,0 +1,274 @@ +""" +Additional util functions +------------------------- +""" + +from collections.abc import Sequence +from enum import Enum +from functools import total_ordering +from typing import Optional, Union + +from darts import TimeSeries +from darts.logging import get_logger, raise_log + +try: + from IPython import get_ipython +except ModuleNotFoundError: + get_ipython = None + +logger = get_logger(__name__) + +_SEQ_TYPE_NAMES = { + 0: "`TimeSeries`", + 1: "`Sequence[TimeSeries]`", + 2: "`Sequence[Sequence[TimeSeries]]`", +} + + +@total_ordering +class SeriesType(Enum): + """An Enum for different `TimeSeries` sequence types.""" + + NONE = -1 # `None` + SINGLE = 0 # `TimeSeries` + SEQ = 1 # `Sequence[TimeSeries]` + SEQ_SEQ = 2 # `Sequence[Sequence[TimeSeries]]` + + def _check_member(self, other): + if self.__class__ is not other.__class__: + raise_log(ValueError("`other` must be a `SeriesType` enum."), logger=logger) + + def __eq__(self, other): + self._check_member(other) + return super().__eq__(other) + + def __lt__(self, other): + self._check_member(other) + return self.value < other.value + + def __add__(self, other: int): + if not isinstance(other, int): + raise_log(ValueError("`other` must be of type `int`."), logger=logger) + new_val = self.value + other + if new_val > 2: + raise_log( + ValueError("Cannot go higher than `SeriesType.SEQ_SEQ`."), logger=logger + ) + return SeriesType(new_val) + + def __str__(self): + return _SEQ_TYPE_NAMES[self.value] + + +def series2seq( + ts: Optional[ + Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] + ], + seq_type_out: SeriesType = SeriesType.SEQ, + nested: bool = False, +) -> Optional[Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]]]: + """If possible, converts `ts` into the desired sequence type `seq_type_out`. Otherwise, returns the + original `ts`. + + Parameters + ---------- + ts + None, a single TimeSeries, a sequence of TimeSeries, or a sequence of sequences of TimeSeries. + seq_type_out + The output sequence type: + + - SeriesType.SINGLE: `TimeSeries` (e.g. a single series) + - SeriesType.SEQ: sequence of `TimeSeries` (e.g. multiple series) + - SeriesType.SEQ_SEQ: sequence of sequences of `TimeSeries` (e.g. historical forecasts output) + nested + Only applies with `seq_type_out=SeriesType.SEQ_SEQ` and `ts` having a sequence type `SeriesType.SEQ`. + In this case, wrap each element in `ts` in a list ([ts1, ts2] -> [[ts1], [ts2]]). + + Raises + ------ + ValueError + If there is an invalid `seq_type_out` value. + """ + if ts is None: + return ts + + if not isinstance(seq_type_out, SeriesType): + raise_log( + ValueError( + f"Invalid parameter `seq_type_out={seq_type_out}`. Must be one of `(0, 1, 2)`" + ), + logger=logger, + ) + + seq_type_in = get_series_seq_type(ts) + + if seq_type_out == seq_type_in: + return ts + + n_series = 1 if seq_type_in == SeriesType.SINGLE else len(ts) + + if seq_type_in == SeriesType.SINGLE and seq_type_out == SeriesType.SEQ: + # ts -> [ts] + return [ts] + elif seq_type_in == SeriesType.SINGLE and seq_type_out == SeriesType.SEQ_SEQ: + # ts -> [[ts]] + return [[ts]] + elif ( + seq_type_in == SeriesType.SEQ + and seq_type_out == SeriesType.SINGLE + and n_series == 1 + ): + # [ts] -> ts + return ts[0] + elif seq_type_in == SeriesType.SEQ and seq_type_out == SeriesType.SEQ_SEQ: + if not nested: + # [ts1, ts2] -> [[ts1, ts2]] + return [ts] + else: + # [ts1, ts2] -> [[ts1], [ts2]] + return [[ts_] for ts_ in ts] + elif ( + seq_type_in == SeriesType.SEQ_SEQ + and seq_type_out == SeriesType.SINGLE + and n_series == 1 + ): + # [[ts]] -> [ts] + return ts[0] + elif ( + seq_type_in == SeriesType.SEQ_SEQ + and seq_type_out == SeriesType.SEQ + and n_series == 1 + ): + # [[ts1, ts2]] -> [[ts1, ts2]] + return ts + else: + # ts -> ts + return ts + + +def seq2series( + ts: Optional[Union[TimeSeries, Sequence[TimeSeries]]], +) -> Optional[TimeSeries]: + """If `ts` is a Sequence with only a single series, return the single series as TimeSeries. + + Parameters + ---------- + ts + None, a single TimeSeries, or a sequence of TimeSeries + + Returns + ------- + `ts` if `ts` if is not a single element TimeSeries sequence, else `ts[0]` + + """ + return series2seq(ts, seq_type_out=SeriesType.SINGLE) + + +def get_single_series( + ts: Optional[ + Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]] + ], +) -> Optional[TimeSeries]: + """Returns a single (first) TimeSeries or `None` from `ts`. Returns `ts` if `ts` is a TimeSeries, `ts[0]` if + `ts` is a `Sequence[TimeSeries]`, and `ts[0][0]` if `ts` is a `Sequence[Sequence[TimeSeries]]`. + Otherwise, returns `None`. + + Parameters + ---------- + ts + None, a single `TimeSeries`, a sequence of `TimeSeries`, or a sequence of sequences of `TimeSeries`. + + Returns + ------- + TimeSeries + `ts` if `ts` is a TimeSeries, `ts[0]` if `ts` is a Sequence of TimeSeries. Otherwise, returns `None` + + """ + seq_type = get_series_seq_type(ts) + if seq_type <= SeriesType.SINGLE: + # `None` and `TimeSeries` + return ts + elif seq_type == SeriesType.SEQ: + return ts[0] + else: + return ts[0][0] + + +def get_series_seq_type( + ts: Union[TimeSeries, Sequence[TimeSeries], Sequence[Sequence[TimeSeries]]], +) -> SeriesType: + """Returns the sequence type of `ts`. + + - SeriesType.SINGLE: `TimeSeries` (e.g. a single series) + - SeriesType.SEQ: sequence of `TimeSeries` (e.g. multiple series) + - SeriesType.SEQ_SEQ: sequence of sequences of `TimeSeries` (e.g. historical forecasts output) + + Parameters + ---------- + ts + The input series to get the sequence type from. + + Raises + ------ + ValueError + If `ts` does not have one of the expected sequence types. + """ + if ts is None: + return SeriesType.NONE + elif isinstance(ts, TimeSeries): + return SeriesType.SINGLE + elif isinstance(ts[0], TimeSeries): + return SeriesType.SEQ + else: + try: + if isinstance(ts[0][0], TimeSeries): + return SeriesType.SEQ_SEQ + else: + raise_log( + ValueError( + "input series must be of type `TimeSeries`, `Sequence[TimeSeries]`, or " + "`Sequence[Sequence[TimeSeries]]`." + ), + logger=logger, + ) + except Exception as err: + raise_log( + ValueError( + "input series must be of type `TimeSeries`, `Sequence[TimeSeries]`, or " + f"`Sequence[Sequence[TimeSeries]]`. Raised: `{type(err).__name__}('{str(err)}')`" + ), + logger=logger, + ) + + +# TODO: we do not check the time index here +def retain_period_common_to_all(series: list[TimeSeries]) -> list[TimeSeries]: + """ + Trims all series in the provided list, if necessary, so that the returned time series have + a common span (corresponding to largest time sub-interval common to all series). + + Parameters + ---------- + series + The list of series to consider. + + Raises + ------ + ValueError + If no common time sub-interval exists + + Returns + ------- + List[TimeSeries] + A list of series, where each series have the same span + """ + + last_first = max(map(lambda s: s.start_time(), series)) + first_last = min(map(lambda s: s.end_time(), series)) + + if last_first >= first_last: + raise_log( + ValueError("The provided time series must have nonzero overlap"), logger + ) + + return list(map(lambda s: s.slice(last_first, first_last), series)) diff --git a/darts/utils/utils.py b/darts/utils/utils.py index 7a7adc7c59..7d7cea2fa1 100644 --- a/darts/utils/utils.py +++ b/darts/utils/utils.py @@ -2,19 +2,22 @@ Additional util functions ------------------------- """ + +from collections.abc import Iterator, Sequence from enum import Enum from functools import wraps from inspect import Parameter, getcallargs, signature -from typing import Callable, Iterator, List, Optional, Sequence, Tuple, TypeVar, Union +from typing import Any, Callable, Optional, TypeVar, Union +import numpy as np import pandas as pd from joblib import Parallel, delayed +from pandas._libs.tslibs.offsets import BusinessMixin +from sklearn.utils import check_random_state from tqdm import tqdm from tqdm.notebook import tqdm as tqdm_notebook -from darts import TimeSeries -from darts.logging import get_logger, raise_if_not, raise_log -from darts.utils.timeseries_generation import generate_index +from darts.logging import get_logger, raise_if, raise_if_not, raise_log try: from IPython import get_ipython @@ -23,6 +26,34 @@ logger = get_logger(__name__) +MAX_TORCH_SEED_VALUE = (1 << 31) - 1 # to accommodate 32-bit architectures +MAX_NUMPY_SEED_VALUE = (1 << 31) - 1 + +SUPPORTED_RESAMPLE_METHODS = [ + "all", + "any", + "asfreq", + "backfill", + "bfill", + "count", + "ffill", + "first", + "interpolate", + "last", + "max", + "mean", + "median", + "min", + "nearest", + "pad", + "prod", + "quantile", + "reduce", + "std", + "sum", + "var", +] + # Enums class SeasonalityMode(Enum): @@ -42,37 +73,88 @@ class ModelMode(Enum): NONE = None -# TODO: we do not check the time index here -def retain_period_common_to_all(series: List[TimeSeries]) -> List[TimeSeries]: - """ - Trims all series in the provided list, if necessary, so that the returned time series have - a common span (corresponding to largest time sub-interval common to all series). +# TODO: remove this at some point when we set a lower cap on pandas v2.2.0 +pd_above_v22 = pd.__version__ >= "2.2" +freqs = { + "YE": "YE" if pd_above_v22 else "A", + "YS": "YS" if pd_above_v22 else "AS", + "BYS": "BYS" if pd_above_v22 else "BAS", + "BYE": "BYE" if pd_above_v22 else "BA", + "QE": "QE" if pd_above_v22 else "Q", + "BQE": "BQE" if pd_above_v22 else "BQ", + "ME": "ME" if pd_above_v22 else "M", + "SME": "SME" if pd_above_v22 else "SM", + "BME": "BME" if pd_above_v22 else "BM", + "CBME": "CBME" if pd_above_v22 else "CBM", + "h": "h" if pd_above_v22 else "H", + "bh": "bh" if pd_above_v22 else "BH", + "cbh": "cbh" if pd_above_v22 else "CBH", + "min": "min" if pd_above_v22 else "T", + "s": "s" if pd_above_v22 else "S", + "ms": "ms" if pd_above_v22 else "L", + "us": "us" if pd_above_v22 else "U", + "ns": "ns" if pd_above_v22 else "N", +} + + +def likelihood_component_names( + components: Union[pd.Index, list[str]], parameter_names: list[str] +): + """Generates formatted likelihood parameter names for components and parameter names. + + The order of the returned names is: `[comp1_param_1, ... comp1_param_n, ..., comp_n_param_n]`. Parameters ---------- - series - The list of series to consider. + components + A sequence of component names to add to the beginning of the returned names. + parameter_names + A sequence of likelihood parameter names to add to the end of the returned names. + """ + return [ + f"{tgt_name}_{param_n}" + for tgt_name in components + for param_n in parameter_names + ] - Raises - ------ - ValueError - If no common time sub-interval exists - Returns - ------- - List[TimeSeries] - A list of series, where each series have the same span +def quantile_names(q: Union[float, list[float]], component: Optional[str] = None): + """Generates formatted quantile names, optionally added to a component name. + + Parameters + ---------- + q + A float or list of floats with the quantiles to generate the names for. + component + Optionally, a component name to add to the beginning of the quantile names. """ + # predicted quantile text format + comp = f"{component}_" if component is not None else "" + if isinstance(q, float): + return f"{comp}q{q:.2f}" + else: + return [f"{comp}q{q_i:.2f}" for q_i in q] - last_first = max(map(lambda s: s.start_time(), series)) - first_last = min(map(lambda s: s.end_time(), series)) - if last_first >= first_last: - raise_log( - ValueError("The provided time series must have nonzero overlap"), logger - ) +def quantile_interval_names( + q_interval: Union[tuple[float, float], Sequence[tuple[float, float]]], + component: Optional[str] = None, +): + """Generates formatted quantile interval names, optionally added to a component name. - return list(map(lambda s: s.slice(last_first, first_last), series)) + Parameters + ---------- + q_interval + A tuple or multiple tuples with the (lower bound, upper bound) of the quantile intervals. + component + Optionally, a component name to add to the beginning of the quantile names. + """ + # predicted quantile text format + comp = f"{component}_" if component is not None else "" + if isinstance(q_interval, tuple): + return f"{comp}q{q_interval[0]:.2f}_q{q_interval[1]:.2f}" + else: + return [f"{comp}q{q_lo:.2f}_q{q_hi:.2f}" for q_lo, q_hi in q_interval] def _build_tqdm_iterator(iterable, verbose, **kwargs): @@ -115,19 +197,18 @@ def _isnotebook(): return iterator -# Types for sanity checks decorator -A = TypeVar("A") -B = TypeVar("B") +# Types for sanity checks decorator: T is the output of the method to sanitize T = TypeVar("T") def _with_sanity_checks( *sanity_check_methods: str, -) -> Callable[[Callable[[A, B], T]], Callable[[A, B], T]]: +) -> Callable[[Callable[..., T]], Callable[..., T]]: """ Decorator allowing to specify some sanity check method(s) to be used on a class method. The decorator guarantees that args and kwargs from the method to sanitize will be available in the sanity check methods as specified in the sanitized method's signature, irrespective of how it was called. + TypeVar `T` corresponds to the output of the method that the sanity checks are performed for. Parameters ---------- @@ -149,9 +230,10 @@ def fit(self, a, b=0, c=0): ... """ - def decorator(method_to_sanitize: Callable[[A, B], T]) -> Callable[[A, B], T]: + def decorator(method_to_sanitize: Callable[..., T]) -> Callable[..., T]: @wraps(method_to_sanitize) - def sanitized_method(self, *args: A, **kwargs: B) -> T: + def sanitized_method(self, *args, **kwargs) -> T: + only_args, only_kwargs = {}, {} for sanity_check_method in sanity_check_methods: # Convert all arguments into keyword arguments all_as_kwargs = getcallargs(method_to_sanitize, self, *args, **kwargs) @@ -182,8 +264,8 @@ def sanitized_method(self, *args: A, **kwargs: B) -> T: def _parallel_apply( - iterator: Iterator[Tuple], fn: Callable, n_jobs: int, fn_args, fn_kwargs -) -> List: + iterator: Iterator[tuple], fn: Callable, n_jobs: int, fn_args, fn_kwargs +) -> list: """ Utility function that parallelise the execution of a function over an Iterator @@ -212,6 +294,23 @@ def _parallel_apply( return returned_data +def _is_method(func: Callable[..., Any]) -> bool: + """Check if the specified function is a method. + + Parameters + ---------- + func + the function to inspect. + + Returns + ------- + bool + true if `func` is a method, false otherwise. + """ + spec = signature(func) + return len(spec.parameters) > 0 and list(spec.parameters.keys())[0] == "self" + + def _check_quantiles(quantiles): raise_if_not( all([0 < q < 1 for q in quantiles]), @@ -235,43 +334,6 @@ def _check_quantiles(quantiles): ) -def series2seq( - ts: Optional[Union[TimeSeries, Sequence[TimeSeries]]] -) -> Optional[Sequence[TimeSeries]]: - """If `ts` is a single TimeSeries, return it as a list of a single TimeSeries. - - Parameters - ---------- - ts - None, a single TimeSeries, or a sequence of TimeSeries - - Returns - ------- - `ts` if `ts` is not a TimeSeries, else `[ts]` - - """ - return [ts] if isinstance(ts, TimeSeries) else ts - - -def seq2series( - ts: Optional[Union[TimeSeries, Sequence[TimeSeries]]] -) -> Optional[TimeSeries]: - """If `ts` is a Sequence with only a single series, return the single series as TimeSeries. - - Parameters - ---------- - ts - None, a single TimeSeries, or a sequence of TimeSeries - - Returns - ------- - `ts` if `ts` if is not a single element TimeSeries sequence, else `ts[0]` - - """ - - return ts[0] if isinstance(ts, Sequence) and len(ts) == 1 else ts - - def slice_index( index: Union[pd.RangeIndex, pd.DatetimeIndex], start: Union[int, pd.Timestamp], @@ -300,7 +362,7 @@ def slice_index( included. """ - if type(start) != type(end): + if type(start) is not type(end): raise_log( ValueError( "start and end values must be of the same type (either both integers or both pd.Timestamps)" @@ -376,23 +438,309 @@ def drop_after_index( return slice_index(index, index[0], split_point) -def get_single_series( - ts: Optional[Union[TimeSeries, Sequence[TimeSeries]]] -) -> Optional[TimeSeries]: - """Returns a single (first) TimeSeries or `None` from `ts`. Returns `ts` if `ts` is a TimeSeries, `ts[0]` if - `ts` is a Sequence of TimeSeries. Otherwise, returns `None`. +def n_steps_between( + end: Union[pd.Timestamp, int], + start: Union[pd.Timestamp, int], + freq: Union[pd.DateOffset, int, str], +) -> int: + """Get the number of time steps with a given frequency `freq` between `end` and `start`. + Works for both integers and time stamps. + + * if `end`, `start`, `freq` are all integers, we can simple divide the difference by the frequency. + * if `freq` is a pandas Dateoffset with non-ambiguous timedelate (e.g. "d", "h", ..., and not "ME", "YE", ...), + we can simply divide by the frequency + * otherwise, we take the period difference between the two time stamps. Parameters ---------- - ts - None, a single TimeSeries, or a sequence of TimeSeries. + end + The end pandas Timestamp / integer. + start + The start pandas Timestamp / integer. + freq + The frequency / step size. Returns ------- - `ts` if `ts` is a TimeSeries, `ts[0]` if `ts` is a Sequence of TimeSeries. Otherwise, returns `None` + int + The number of steps/periods between `end` and `start` with a given frequency `freq`. + Examples + -------- + >>> n_steps_between(start=pd.Timestamp("2000-01-01"), end=pd.Timestamp("2000-03-01"), freq="ME") + 2 + >>> n_steps_between(start=0, end=2, freq=1) + 2 + >>> n_steps_between(start=0, end=2, freq=2) + 1 """ - if isinstance(ts, TimeSeries) or ts is None: - return ts + freq = pd.tseries.frequencies.to_offset(freq) if isinstance(freq, str) else freq + valid_freq = freq >= 0 if isinstance(freq, int) else freq.n >= 0 + if not valid_freq: + raise_log( + ValueError(f"`freq` must be positive/increasing, received freq={freq}."), + logger=logger, + ) + valid_int = ( + isinstance(start, int) and isinstance(end, int) and isinstance(freq, int) + ) + valid_time = ( + isinstance(start, pd.Timestamp) + and isinstance(end, pd.Timestamp) + and isinstance(freq, pd.DateOffset) + ) + if not (valid_int or valid_time): + raise_log( + ValueError( + "Either `start` and `end` must be pandas Timestamps and `freq` a pandas Dateoffset, " + "or all `start`, `end`, `freq` must be integers." + ), + logger=logger, + ) + # Series frequency represents a non-ambiguous timedelta value (not ‘M’, ‘Y’ or ‘y’, 'W') + if pd.to_timedelta(freq, errors="coerce") is not pd.NaT: + diff = end - start + if abs(diff) != diff: + # (A) when diff is negative, not perfectly divisible by freq, and freq is a multiple of a base frequency + # (e.g., "2D" or step=2), then computing `diff // freq` can be one off + # Example: `end=1, start=2, freq=2` -> then `diff // freq` gives `-1`, but should be `0`. + diff += diff % freq + n_steps = diff // freq else: - return ts[0] + period_alias = pd.tseries.frequencies.get_period_alias(freq.name) + if isinstance(freq, BusinessMixin) or period_alias is None: + # for lower pandas versions ~1.5.0, business frequencies wrongly have a period alias. + # taking the period difference as computed in `else` gives wrong results. + # in this (worst) case for special frequencies (e.g "C*"), we must generate the index + is_reversed = end < start + if is_reversed: + # always generate an increasing index, since pandas (v2.2.1) gives inconsistent result for + # negative/decreasing frequencies. Then reverse the index in case of negative/decreasing + # input frequency + start, end = end, start + n_steps = len(generate_index(start=start, end=end, freq=freq)) + if n_steps: + # index includes end, take away for difference + n_steps -= 1 + if is_reversed: + n_steps *= -1 + else: + # get the number of base periods ("2MS" has base freq "MS") between the two time steps + diff = (end.to_period(period_alias) - start.to_period(period_alias)).n + if abs(diff) != diff: + # similar case as with (A) + diff += diff % freq.n + # floor division by the frequency multiplier ("2MS" has multiplier 2) + n_steps = diff // freq.n + return n_steps + + +def generate_index( + start: Optional[Union[pd.Timestamp, str, int]] = None, + end: Optional[Union[pd.Timestamp, str, int]] = None, + length: Optional[int] = None, + freq: Union[str, int, pd.DateOffset] = None, + name: str = None, +) -> Union[pd.DatetimeIndex, pd.RangeIndex]: + """Returns an index with a given start point and length. Either a pandas DatetimeIndex with given frequency + or a pandas RangeIndex. The index starts at + + Parameters + ---------- + start + The start of the returned index. If a pandas Timestamp or a date string is passed, the index will be a pandas + DatetimeIndex. If an integer is passed, the index will be a pandas RangeIndex index. Works only with + either `length` or `end`. + end + Optionally, the end of the returned index. Works only with either `start` or `length`. If `start` is + set, `end` must be of same type as `start`. Else, it can be either a pandas Timestamp or an integer. + length + Optionally, the length of the returned index. Works only with either `start` or `end`. + freq + The time difference between two adjacent entries in the returned index. In case `start` is a timestamp, + a DateOffset alias is expected; see + `docs `_. + By default, "D" (daily) is used. + If `start` is an integer, `freq` will be interpreted as the step size in the underlying RangeIndex. + The freq is optional for generating an integer index (if not specified, 1 is used). + name + Optionally, an index name. + """ + constructors = [ + arg_name + for arg, arg_name in zip([start, end, length], ["start", "end", "length"]) + if arg is not None + ] + raise_if( + len(constructors) != 2, + "index can only be generated with exactly two of the following parameters: [`start`, `end`, `length`]. " + f"Observed parameters: {constructors}. For generating an index with `end` and `length` consider setting " + f"`start` to None.", + logger, + ) + raise_if( + end is not None and start is not None and type(start) is not type(end), + "index generation with `start` and `end` requires equal object types of `start` and `end`", + logger, + ) + + start = pd.Timestamp(start) if isinstance(start, str) else start + end = pd.Timestamp(end) if isinstance(end, str) else end + + if isinstance(start, pd.Timestamp) or isinstance(end, pd.Timestamp): + freq = "D" if freq is None else freq + freq = pd.tseries.frequencies.to_offset(freq) if isinstance(freq, str) else freq + index = pd.date_range( + start=start, + end=end, + periods=length, + freq=freq, + name=name, + ) + if freq.n < 0: + if start is not None and not freq.is_on_offset(start): + # for anchored negative frequencies, and `start` does not intersect with `freq`: + # pandas (v2.2.1) generates an index that starts one step before `start` -> remove this step + index = index[1:] + elif end is not None and not freq.is_on_offset(end): + # if `start` intersects with `freq`, then the same can happen for `end` -> remove this step + index = index[:-1] + else: # int + step = 1 if freq is None else freq + if start is None: + start_ = end - step * length + step + else: + start_ = start + + if end is None: + end_ = start + step * length + else: + # make end inclusive + end_ = end + 1 if step >= 0 else end - 1 + + index = pd.RangeIndex( + start=start_, + stop=end_, + step=step, + name=name, + ) + return index + + +def expand_arr(arr: np.ndarray, ndim: int): + """Expands a np.ndarray to `ndim` dimensions (if not already satisfied).""" + shape = arr.shape + if len(shape) != ndim: + arr = arr.reshape(shape + tuple(1 for _ in range(ndim - len(shape)))) + return arr + + +def sample_from_quantiles( + vals: np.ndarray, + quantiles: np.ndarray, + num_samples: int, +): + """Generates `num_samples` samples from quantile predictions using linear interpolation. The generated samples + should have quantile values close to the quantile predictions. For the lowest and highest quantiles, the lowest + and highest quantile predictions are repeated. + + Parameters + ---------- + vals + A numpy array of quantile predictions/values. Either an array with two dimensions + (n times, n components * n quantiles), or with three dimensions (n times, n components, n quantiles). + In the two-dimensional case, the order is first by ascending column, then by ascending quantile value + `(comp_0_q_0, comp_0_q_1, ... comp_n_q_m)` + quantiles + A numpy array of quantiles. + num_samples + The number of samples to generate. + """ + if not 2 <= vals.ndim <= 3: + raise_log( + ValueError( + "`vals` must have either two dimensions with `(n times, n components * n quantiles)` or three " + "dimensions with shape `(n times, n components, n quantiles)`" + ) + ) + n_time_steps = len(vals) + n_quantiles = len(quantiles) + if vals.ndim == 2: + if vals.shape[1] % n_quantiles > 0: + raise_log( + ValueError( + "`vals` with two dimension must have shape `(n times, n components * n quantiles)`." + ) + ) + vals = vals.reshape((n_time_steps, -1, n_quantiles)) + elif vals.ndim == 3 and vals.shape[2] != n_quantiles: + raise_log( + ValueError( + "`vals` with three dimension must have shape `(n times, n components, n quantiles)`." + ) + ) + n_columns = vals.shape[1] + + # Generate uniform random samples + random_samples = np.random.uniform(0, 1, (n_time_steps, n_columns, num_samples)) + # Find the indices of the quantiles just below and above the random samples + lower_indices = np.searchsorted(quantiles, random_samples, side="right") - 1 + upper_indices = lower_indices + 1 + + # Handle edge cases + lower_indices = np.clip(lower_indices, 0, n_quantiles - 1) + upper_indices = np.clip(upper_indices, 0, n_quantiles - 1) + + # Gather the corresponding quantile values and vals values + q_lower = quantiles[lower_indices] + q_upper = quantiles[upper_indices] + z_lower = np.take_along_axis(vals, lower_indices, axis=2) + z_upper = np.take_along_axis(vals, upper_indices, axis=2) + + y = z_lower + # Linear interpolation + mask = q_lower != q_upper + y[mask] = z_lower[mask] + (z_upper[mask] - z_lower[mask]) * ( + random_samples[mask] - q_lower[mask] + ) / (q_upper[mask] - q_lower[mask]) + return y + + +def random_method(decorated: Callable[..., T]) -> Callable[..., T]: + """Decorator usable on any method within a class that will provide a random context. + + The decorator will store a `_random_instance` property on the object in order to persist successive calls to the + RNG. + + This is the equivalent to `darts.utils.torch.random_method` but for non-torch models. + + Parameters + ---------- + decorated + A method to be run in an isolated torch random context. + """ + # check that @random_method has been applied to a method. + if not _is_method(decorated): + raise_log(ValueError("@random_method can only be used on methods."), logger) + + @wraps(decorated) + def decorator(self, *args, **kwargs): + if "random_state" in kwargs.keys(): + # get random state for first time from model constructor + self._random_instance = check_random_state( + kwargs["random_state"] + ).get_state() + elif not hasattr(self, "_random_instance"): + # get random state for first time from other method + self._random_instance = check_random_state( + np.random.randint(0, high=MAX_NUMPY_SEED_VALUE) + ).get_state() + + # handle the randomness + np.random.set_state(self._random_instance) + result = decorated(self, *args, **kwargs) + # update the random state after the function call + self._random_instance = np.random.get_state() + return result + + return decorator diff --git a/datasets/taxi_new_york_passengers.csv b/datasets/taxi_new_york_passengers.csv new file mode 100644 index 0000000000..68c58f2de5 --- /dev/null +++ b/datasets/taxi_new_york_passengers.csv @@ -0,0 +1,10321 @@ +time,#Passengers +2014-07-01 00:00:00,10844 +2014-07-01 00:30:00,8127 +2014-07-01 01:00:00,6210 +2014-07-01 01:30:00,4656 +2014-07-01 02:00:00,3820 +2014-07-01 02:30:00,2873 +2014-07-01 03:00:00,2369 +2014-07-01 03:30:00,2064 +2014-07-01 04:00:00,2221 +2014-07-01 04:30:00,2158 +2014-07-01 05:00:00,2515 +2014-07-01 05:30:00,4364 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13:30:00,23298 +2015-01-31 14:00:00,21817 +2015-01-31 14:30:00,21565 +2015-01-31 15:00:00,21729 +2015-01-31 15:30:00,22838 +2015-01-31 16:00:00,21068 +2015-01-31 16:30:00,19920 +2015-01-31 17:00:00,20715 +2015-01-31 17:30:00,23595 +2015-01-31 18:00:00,26044 +2015-01-31 18:30:00,27286 +2015-01-31 19:00:00,28804 +2015-01-31 19:30:00,27773 +2015-01-31 20:00:00,24985 +2015-01-31 20:30:00,23291 +2015-01-31 21:00:00,23719 +2015-01-31 21:30:00,24670 +2015-01-31 22:00:00,25721 +2015-01-31 22:30:00,27309 +2015-01-31 23:00:00,26591 +2015-01-31 23:30:00,26288 diff --git a/docs/Makefile b/docs/Makefile index a155c7f7df..06f603e79a 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -23,6 +23,7 @@ clean: @rm -rf "./$(SOURCEDIR)/generated_api" @rm -rf "./$(SOURCEDIR)/quickstart" @rm -rf "./$(SOURCEDIR)/userguide" + @rm -rf "./$(SOURCEDIR)/release_notes" @rm -rf "./$(SOURCEDIR)/README.rst" copy-examples: @@ -41,6 +42,12 @@ generate-readme: @m2r2 ../README.md @mv ../README.rst "$(SOURCEDIR)" +generate-release_notes: + @echo "[Makefile] generating RELEASE_NOTES rst file..." + @mkdir -p "$(SOURCEDIR)/release_notes" + @m2r2 ../CHANGELOG.md + @mv ../CHANGELOG.rst "$(SOURCEDIR)/release_notes/RELEASE_NOTES.rst" + generate-userguide: @echo "[Makefile] generating userguide rst files..." @find $(USERGUIDEDIR)/*.md -exec m2r2 {} \; @@ -58,7 +65,8 @@ html: @echo "[Makefile] generating HTML pages using sphinx-build..." @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -build-all-docs: clean copy-examples copy-quickstart generate-readme generate-userguide generate-api html +build-all-docs: clean copy-examples copy-quickstart generate-readme generate-release_notes generate-userguide generate-api html +build-api: clean generate-api html # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). diff --git a/docs/source/conf.py b/docs/source/conf.py index b260072f18..ad96924d78 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -22,7 +22,7 @@ project = "darts" copyright = f"2020 - {datetime.now().year}, Unit8 SA (Apache 2.0 License)" author = "Unit8 SA" -version = "0.27.2" +version = "0.33.0" # -- General configuration --------------------------------------------------- @@ -48,12 +48,13 @@ autodoc_default_options = { "inherited-members": None, "show-inheritance": None, - "exclude-members": "ForecastingModel,LocalForecastingModel,FutureCovariatesLocalForecastingModel," + "ignore-module-all": True, + "exclude-members": "LocalForecastingModel,FutureCovariatesLocalForecastingModel," + "TransferableFutureCovariatesLocalForecastingModel,GlobalForecastingModel,TorchForecastingModel," + "PastCovariatesTorchModel,FutureCovariatesTorchModel,DualCovariatesTorchModel,MixedCovariatesTorchModel," - + "SplitCovariatesTorchModel,TorchParametricProbabilisticForecastingModel," + + "SplitCovariatesTorchModel," + "min_train_series_length," - + "untrained_model,first_prediction_index,future_covariate_series,past_covariate_series," + + "first_prediction_index,future_covariate_series,past_covariate_series," + "initialize_encoders,register_datapipe_as_function,register_function,functions," + "SplitTimeSeriesSequence,randint,AnomalyModel", } diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 72b2557920..4efe4c1b53 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -86,6 +86,15 @@ Regression models example notebook: examples/20-RegressionModel-examples.ipynb +Conformal Prediction +================= + +Conformal prediction example notebook: + +.. toctree:: + :maxdepth: 1 + + examples/23-Conformal-Prediction-examples.ipynb Fast Fourier Transform ====================== @@ -177,6 +186,16 @@ TiDE model example notebook: examples/18-TiDE-examples.ipynb +TimeSeries Mixer (TSMixer) Model +======================================= + +TSMixer model example notebook: + +.. toctree:: + :maxdepth: 1 + + examples/21-TSMixer-examples.ipynb + Ensemble Models ============================= @@ -207,6 +226,16 @@ Gaussian process filter model example notebook: examples/11-GP-filter-examples.ipynb +Anomaly Detection +======================================= + +Anomaly detection example notebook: + +.. toctree:: + :maxdepth: 1 + + examples/22-anomaly-detection-examples.ipynb + Dynamic Time Warping (DTW) ============================= diff --git a/docs/source/index.rst b/docs/source/index.rst index dee0bd55b4..7f74692989 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -20,12 +20,16 @@ API Reference - .. toctree:: :hidden: Examples +.. toctree:: + :hidden: + + Release Notes + Indices and tables ================== diff --git a/docs/source/userguide.rst b/docs/source/userguide.rst index a1f81fe61c..e25d17922e 100644 --- a/docs/source/userguide.rst +++ b/docs/source/userguide.rst @@ -25,7 +25,7 @@ You will find here some more detailed information about Darts. .. userguide/probabilistic_forecasting.md .. userguide/ensembling.md - + .. userguide/filtering_models.md .. userguide/preprocessing_and_pipelines.md diff --git a/docs/userguide/covariates.md b/docs/userguide/covariates.md index 27bdaa3310..c393594360 100644 --- a/docs/userguide/covariates.md +++ b/docs/userguide/covariates.md @@ -90,13 +90,13 @@ Let's have a look at some examples of past, future, and static covariates: - daily average **forecasted** temperatures (known in the future) - day of week, month, year, ... - `static_covariates`: time independent/constant/static `target` characteristics - - categorical: + - categorical: - location of `target` (country, city, .. name) - `target` identifier: (product ID, store ID, ...) - numerical: - population of `target`'s country/market area (assuming it stays constant over the forecasting horizon) - average temperature of `target`'s region (assuming it stays constant over the forecasting horizon) - + Temporal attributes are powerful because they are known in advance and can help models capture trends and / or seasonal patterns of the `target` series. Static attributes are powerful when working with multiple `targets` (either multiple `TimeSeries`, or multivariate series containing multiple dimensions each). The time independent information can help models identify the nature/environment of the underlying series and improve forecasts across different `targets`. @@ -117,7 +117,7 @@ Darts' forecasting models accept optional `past_covariates` and / or `future_cov LFMs are models that can be trained on a single target series only. In Darts most models in this category tend to be simpler statistical models (such as ETS or ARIMA). LFMs accept only a single `target` (and covariate) time series and usually train on the entire series you supplied when calling `fit()` at once. They can also predict in one go for any number of predictions `n` after the end of the training series. ### Global Forecasting Models (GFMs) -GFMs are broadly speaking "machine learning based" models, which denote PyTorch-based (deep learning) models, RegressionModels, as well EnsembleModels (depending on their ensemble model and / or the forecasting models they ensemble). Global models can all be trained on multiple `target` (and covariate) time series. Different to LFMs, the GFMs train and predict on fixed-length sub-samples (chunks) of the input data. +GFMs are models that can be trained on multiple target (and covariate) time series. Different to LFMs, the GFMs train and predict on fixed-length sub-samples (chunks) of the input data. In Darts, these are the global (naive) baseline models, regression models, PyTorch (Lightning)-based models (neural networks), as well ensemble models (depending on their ensemble model and / or the forecasting models they ensemble). ---- @@ -133,6 +133,7 @@ GFMs are broadly speaking "machine learning based" models, which denote PyTorch- | [StatsforecastAutoETS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS) | | ✅ | | | [StatsforecastAutoCES](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES) | | | | | [BATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.BATS) and [TBATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.TBATS) | | | | +| [StatsForecastAutoTBATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_tbats.html#darts.models.forecasting.sf_auto_tbats.StatsForecastAutoTBATS) | | | | | [Theta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta) and [FourTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta) | | | | | [StatsForecastAutoTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta) | | | | | [Prophet](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet) | | ✅ | | @@ -140,31 +141,38 @@ GFMs are broadly speaking "machine learning based" models, which denote PyTorch- | [KalmanForecaster](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster) | | ✅ | | | [Croston](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.croston.html#darts.models.forecasting.croston.Croston) method | | | | | **Global Forecasting Models (GFMs)** | | | | -| Regression Models (b) | ✅ | ✅ | ✅ | -| [RNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel) (c) | | ✅ | | -| [BlockRNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel) (d) | ✅ | | | -| [NBEATSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel) | ✅ | | | -| [NHiTSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel) | ✅ | | | -| [TCNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel) | ✅ | | | -| [TransformerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel) | ✅ | | | -| [TFTModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel) | ✅ | ✅ | ✅ | -| [DLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel) | ✅ | ✅ | ✅ | -| [NLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel) | ✅ | ✅ | ✅ | -| [TiDEModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel) | ✅ | ✅ | ✅ | -| Ensemble Models (e) | ✅ | ✅ | ✅ | +| Global Naive Baselines (b) | | | | +| Regression Models (c) | ✅ | ✅ | ✅ | +| [RNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel) (d) | | ✅ | | +| [BlockRNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel) (e) | ✅ | | | +| [NBEATSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel) | ✅ | | | +| [NHiTSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel) | ✅ | | | +| [TCNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel) | ✅ | | | +| [TransformerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel) | ✅ | | | +| [TFTModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel) | ✅ | ✅ | ✅ | +| [DLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel) | ✅ | ✅ | ✅ | +| [NLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel) | ✅ | ✅ | ✅ | +| [TiDEModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel) | ✅ | ✅ | ✅ | +| [TSMixerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tsmixer_model.html#darts.models.forecasting.tsmixer_model.TSMixerModel) | ✅ | ✅ | ✅ | +| Ensemble Models (f) | ✅ | ✅ | ✅ | +| Conformal Prediction Models (g) | ✅ | ✅ | ✅ | **Table 1: Darts' forecasting models and their covariate support** -(a) Naive Baselines including [NaiveMean](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean), [NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal), [NaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift), and [NaiveMovingAverage](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage). +(a) Naive Baselines including [NaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift), [NaiveMean](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean), [NaiveMovingAverage](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage), and [NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal). + +(b) Global Naive Baselines including [GlobalNaiveAggregate](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveAggregate), [GlobalNaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveDrift), and [GlobalNaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.global_baseline_models.html#darts.models.forecasting.global_baseline_models.GlobalNaiveSeasonal). + +(c) Regression Models including [RegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html#regression-model), [LinearRegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel), [RandomForest](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest), [LightGBMModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel), [XGBModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel), and [CatBoostModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel). RegressionModel is a special kind of GFM which can use arbitrary lags on covariates (past and/or future) and past targets to do predictions. -(b) Regression Models including [RegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html#regression-model), [LinearRegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel), [RandomForest](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest), [LightGBMModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel), [XGBModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel), and [CatBoostModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel). RegressionModel is a special kind of GFM which can use arbitrary lags on covariates (past and/or future) and past targets to do predictions. +(d) [RNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel) including `LSTM` and `GRU`; equivalent to DeepAR in its probabilistic version -(c) [RNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel) including `LSTM` and `GRU`; equivalent to DeepAR in its probabilistic version +(e) [BlockRNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel) including `LSTM` and `GRU` -(d) [BlockRNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel) including `LSTM` and `GRU` +(f) Ensemble Model including [RegressionEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel), and [NaiveEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel). The covariate support is given by the covariate support of the ensembled forecasting models. -(e) Ensemble Model including [RegressionEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel), and [NaiveEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel). The covariate support is given by the covariate support of the ensembled forecasting models. +(g) Conformal Prediction Model including [ConformalNaiveModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalNaiveModel), and [ConformalQRModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.conformal_models.html#darts.models.forecasting.conformal_models.ConformalQRModel). The covariate support is given by the covariate support of the underlying forecasting model. ---- diff --git a/docs/userguide/forecasting_overview.md b/docs/userguide/forecasting_overview.md index b56ad4b568..16aba9a448 100644 --- a/docs/userguide/forecasting_overview.md +++ b/docs/userguide/forecasting_overview.md @@ -15,7 +15,7 @@ by calling the `fit()` function, and finally they are used to obtain one or seve from darts.models import NaiveSeasonal naive_model = NaiveSeasonal(K=1) # init -naive_model.fit(train) # fit +naive_model.fit(train) # fit naive_forecast = naive_model.predict(n=36) # predict ``` @@ -111,14 +111,14 @@ These models are shown with a "✅" under the `Multivariate` column on the [mode ## Handling multiple series Some models support being fit on multiple time series. To do this, it is enough to simply provide a Python `Sequence` of `TimeSeries` (for instance a list of `TimeSeries`) to `fit()`. When a model is fit this way, the `predict()` function will expect the argument `series` to be set, containing -one or several `TimeSeries` (i.e., a single or a `Sequence` of `TimeSeries`) that need to be forecasted. +one or several `TimeSeries` (i.e., a single or a `Sequence` of `TimeSeries`) that need to be forecasted. The advantage of training on multiple series is that a single model can be exposed to more patterns occurring across all series in the training dataset. That can often be beneficial, especially for larger models with more capacity. In turn, the advantage of having `predict()` providing forecasts for potentially several series at once is that the computation can often be batched and vectorized across the multiple series, which is computationally faster than calling `predict()` multiple times on isolated series. These models are shown with a "✅" under the `Multiple-series training` column on the [model list](https://github.com/unit8co/darts#forecasting-models). -You can also find out programatically, whether a model supports multiple series. +You can also find out programmatically, whether a model supports multiple series. ```python from darts.models import RegressionModel from darts.models.forecasting.forecasting_model import GlobalForecastingModel @@ -178,9 +178,9 @@ pred.plot(label='forecast') ![Exponential Smoothing](./images/probabilistic/example_ets.png) ### Probabilistic neural networks -All neural networks (torch-based models) in Darts have a rich support to estimate different kinds of probability distributions. -When creating the model, it is possible to provide one of the *likelihood models* available in [darts.utils.likelihood_models](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html), which determine the distribution that will be estimated by the model. -In such cases, the model will output the parameters of the distribution, and it will be trained by minimising the negative log-likelihood of the training samples. +All neural networks (torch-based models) in Darts have a rich support to estimate different kinds of probability distributions. +When creating the model, it is possible to provide one of the *likelihood models* available in [darts.utils.likelihood_models](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html), which determine the distribution that will be estimated by the model. +In such cases, the model will output the parameters of the distribution, and it will be trained by minimising the negative log-likelihood of the training samples. Most of the likelihood models also support prior values for the distribution's parameters, in which case the training loss is regularized by a Kullback-Leibler divergence term pushing the resulting distribution in the direction of the distribution specified by the prior parameters. The strength of this regularization term can also be specified when creating the likelihood model object. @@ -201,7 +201,7 @@ train = scaler.fit_transform(train) val = scaler.transform(val) series = scaler.transform(series) -model = TCNModel(input_chunk_length=30, +model = TCNModel(input_chunk_length=30, output_chunk_length=12, likelihood=LaplaceLikelihood(prior_b=0.1)) model.fit(train, epochs=400) @@ -232,7 +232,7 @@ train = scaler.fit_transform(train) val = scaler.transform(val) series = scaler.transform(series) -model = TCNModel(input_chunk_length=30, +model = TCNModel(input_chunk_length=30, output_chunk_length=12, likelihood=QuantileRegression(quantiles=[0.01, 0.05, 0.2, 0.5, 0.8, 0.95, 0.99])) model.fit(train, epochs=400) @@ -291,8 +291,8 @@ from darts.models import LinearRegressionModel series = AirPassengersDataset().load() train, val = series[:-36], series[-36:] -model = LinearRegressionModel(lags=30, - likelihood="quantile", +model = LinearRegressionModel(lags=30, + likelihood="quantile", quantiles=[0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95]) model.fit(train) pred = model.predict(n=36, num_samples=500) @@ -304,4 +304,4 @@ pred.plot(label='forecast') ![quantile linear regression](./images/probabilistic/example_linreg_quantile.png) -[1] Yarin Gal, Zoubin Ghahramani, ["Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"](https://arxiv.org/abs/1506.02142) \ No newline at end of file +[1] Yarin Gal, Zoubin Ghahramani, ["Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"](https://arxiv.org/abs/1506.02142) diff --git a/docs/userguide/gpu_and_tpu_usage.md b/docs/userguide/gpu_and_tpu_usage.md index 5585a84534..02f646d49f 100644 --- a/docs/userguide/gpu_and_tpu_usage.md +++ b/docs/userguide/gpu_and_tpu_usage.md @@ -66,9 +66,9 @@ IPU available: False, using: 0 IPUs | Name | Type | Params -------------------------------------- -0 | criterion | MSELoss | 0 -1 | rnn | RNN | 460 -2 | V | Linear | 21 +0 | criterion | MSELoss | 0 +1 | rnn | RNN | 460 +2 | V | Linear | 21 -------------------------------------- 481 Trainable params 0 Non-trainable params @@ -105,9 +105,9 @@ LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] | Name | Type | Params -------------------------------------- -0 | criterion | MSELoss | 0 -1 | rnn | RNN | 460 -2 | V | Linear | 21 +0 | criterion | MSELoss | 0 +1 | rnn | RNN | 460 +2 | V | Linear | 21 -------------------------------------- 481 Trainable params 0 Non-trainable params @@ -122,11 +122,11 @@ From the output we can see that the GPU is both available and used. The rest of ### Multi GPU support -Darts utilizes [Lightning's multi GPU capabilities](https://pytorch-lightning.readthedocs.io/en/stable/accelerators/gpu_intermediate.html) to be able to capitalize on scalable hardware. +Darts utilizes [Lightning's multi GPU capabilities](https://pytorch-lightning.readthedocs.io/en/stable/accelerators/gpu_intermediate.html) to be able to capitalize on scalable hardware. -Multiple parallelization strategies exist for multiple GPU training, which - because of different strategies for multiprocessing and data handling - interact strongly with the execution environment. +Multiple parallelization strategies exist for multiple GPU training, which - because of different strategies for multiprocessing and data handling - interact strongly with the execution environment. -Currently in Darts the `ddp_spawn` distribution strategy is tested. +Currently in Darts the `ddp_spawn` distribution strategy is tested. As per the description of the [Lightning documentation](https://pytorch-lightning.readthedocs.io/en/stable/accelerators/gpu_intermediate.html#distributed-data-parallel-spawn) has some noteworthy limitations, eg. it __can not run__ in: @@ -152,11 +152,11 @@ Beyond this, no other major modification to your models is necessary other than This method automatically selects all available GPUs for training. Manual setting of the number of devices is also possible. -The `ddp` family of strategies creates indiviual subprocesses for each GPU, so contents of the memory (notably the `Dataloder`) gets copied over. Thus, as per the [description of lightning docs](https://pytorch-lightning.readthedocs.io/en/stable/accelerators/gpu_intermediate.html#distributed-data-parallel) caution is advised in setting the `Dataloader(num_workers=N)` too high, since according to it: +The `ddp` family of strategies creates individual subprocesses for each GPU, so contents of the memory (notably the `Dataloder`) gets copied over. Thus, as per the [description of lightning docs](https://pytorch-lightning.readthedocs.io/en/stable/accelerators/gpu_intermediate.html#distributed-data-parallel) caution is advised in setting the `Dataloader(num_workers=N)` too high, since according to it: "Dataloader(num_workers=N), where N is large, bottlenecks training with DDP… ie: it will be VERY slow or won’t work at all. This is a PyTorch limitation." -Usage of other distribution strategies with Darts currently _might_ very well work, but are yet untested and subject to individual setup / experimentation. +Usage of other distribution strategies with Darts currently _might_ very well work, but are yet untested and subject to individual setup / experimentation. ## Use a TPU @@ -197,9 +197,9 @@ IPU available: False, using: 0 IPUs | Name | Type | Params -------------------------------------- -0 | criterion | MSELoss | 0 -1 | rnn | RNN | 460 -2 | V | Linear | 21 +0 | criterion | MSELoss | 0 +1 | rnn | RNN | 460 +2 | V | Linear | 21 -------------------------------------- 481 Trainable params 0 Non-trainable params diff --git a/docs/userguide/hyperparameter_optimization.md b/docs/userguide/hyperparameter_optimization.md index bfd659000b..5097532424 100644 --- a/docs/userguide/hyperparameter_optimization.md +++ b/docs/userguide/hyperparameter_optimization.md @@ -1,18 +1,19 @@ # Hyperparameter Optimization in Darts + There is nothing special in Darts when it comes to hyperparameter optimization. The main thing to be aware of is probably the existence of PyTorch Lightning callbacks for early stopping and pruning of experiments with Darts' deep learning based TorchForecastingModels. Below, we show examples of hyperparameter optimization done with [Optuna](https://optuna.org/) and [Ray Tune](https://docs.ray.io/en/latest/tune/examples/tune-pytorch-lightning.html). - ## Hyperparameter optimization with Optuna + [Optuna](https://optuna.org/) is a great option for hyperparameter optimization with Darts. Below, we show a minimal example using PyTorch Lightning callbacks for pruning experiments. For the sake of the example, we train a `TCNModel` on a single series, and optimize (probably overfitting) its hyperparameters by minimizing the prediction error on a validation set. You can also have a look at [this notebook](https://github.com/unit8co/darts/blob/master/examples/17-hyperparameter-optimization.ipynb) for a more complete example. ->**NOTE** (2023-19-02): Optuna's `PyTorchLightningPruningCallback` raises an error with pytorch-lightning>=1.8. Until this fixed, a workaround is proposed [here](https://github.com/optuna/optuna-examples/issues/166#issuecomment-1403112861). +> **NOTE** (2023-19-02): Optuna's `PyTorchLightningPruningCallback` raises an error with pytorch-lightning>=1.8. Until this fixed, a workaround is proposed [here](https://github.com/optuna/optuna-examples/issues/166#issuecomment-1403112861). ```python import numpy as np @@ -65,7 +66,7 @@ def objective(trial): num_workers = 4 else: num_workers = 0 - + pl_trainer_kwargs = { "accelerator": "auto", "callbacks": callbacks, @@ -80,7 +81,7 @@ def objective(trial): # reproducibility torch.manual_seed(42) - + # build the TCN model model = TCNModel( input_chunk_length=in_len, @@ -101,8 +102,8 @@ def objective(trial): force_reset=True, save_checkpoints=True, ) - - + + # when validating during training, we can use a slightly longer validation # set which also contains the first input_chunk_length time steps model_val_set = scaler.transform(series[-(VAL_LEN + in_len) :]) @@ -116,7 +117,7 @@ def objective(trial): # reload best model over course of training model = TCNModel.load_from_checkpoint("tcn_model") - + # Evaluate how good it is on the validation set, using sMAPE preds = model.predict(series=train, n=VAL_LEN) smapes = smape(val, preds, n_jobs=-1, verbose=True) @@ -138,41 +139,55 @@ if __name__ == "__main__": ``` ## Hyperparameter optimization with Ray Tune + [Ray Tune](https://docs.ray.io/en/latest/tune/examples/tune-pytorch-lightning.html) is another option for hyperparameter optimization with automatic pruning. -Here is an example of how to use Ray Tune to with the `NBEATSModel` model using the [Asynchronous Hyperband scheduler](https://blog.ml.cmu.edu/2018/12/12/massively-parallel-hyperparameter-optimization/). +Here is an example of how to use Ray Tune to with the `NBEATSModel` model using the [Asynchronous Hyperband scheduler](https://blog.ml.cmu.edu/2018/12/12/massively-parallel-hyperparameter-optimization/). The example was tested with ray version `ray==2.32.0`. ```python +import numpy as np import pandas as pd +import pytorch_lightning as pl from pytorch_lightning.callbacks import EarlyStopping from ray import tune +from ray.train import RunConfig from ray.tune import CLIReporter -from ray.tune.integration.pytorch_lightning import TuneReportCallback +from ray.tune.integration.pytorch_lightning import TuneReportCheckpointCallback from ray.tune.schedulers import ASHAScheduler -from torchmetrics import MeanAbsoluteError, MeanAbsolutePercentageError, MetricCollection +from ray.tune.tuner import Tuner +from torchmetrics import ( + MeanAbsoluteError, + MeanAbsolutePercentageError, + MetricCollection, +) from darts.dataprocessing.transformers import Scaler from darts.datasets import AirPassengersDataset from darts.models import NBEATSModel + def train_model(model_args, callbacks, train, val): - torch_metrics = MetricCollection([MeanAbsolutePercentageError(), MeanAbsoluteError()]) + torch_metrics = MetricCollection( + [MeanAbsolutePercentageError(), MeanAbsoluteError()] + ) # Create the model using model_args from Ray Tune model = NBEATSModel( input_chunk_length=24, output_chunk_length=12, - n_epochs=500, + n_epochs=100, torch_metrics=torch_metrics, pl_trainer_kwargs={"callbacks": callbacks, "enable_progress_bar": False}, - **model_args) + **model_args, + ) model.fit( series=train, val_series=val, ) + # Read data: -series = AirPassengersDataset().load() +series = AirPassengersDataset().load().astype(np.float32) # Create training and validation sets: train, val = series.split_after(pd.Timestamp(year=1957, month=12, day=1)) @@ -188,10 +203,16 @@ my_stopper = EarlyStopping( monitor="val_MeanAbsolutePercentageError", patience=5, min_delta=0.05, - mode='min', + mode="min", ) + # set up ray tune callback +class TuneReportCallback(TuneReportCheckpointCallback, pl.Callback): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + tune_callback = TuneReportCallback( { "loss": "val_loss", @@ -200,6 +221,17 @@ tune_callback = TuneReportCallback( on="validation_end", ) +# Define the trainable function that will be tuned by Ray Tune +train_fn_with_parameters = tune.with_parameters( + train_model, + callbacks=[tune_callback, my_stopper], + train=train, + val=val, +) + +# Set the resources to be used for each trial (disable GPU, if you don't have one) +resources_per_trial = {"cpu": 8, "gpu": 1} + # define the hyperparameter space config = { "batch_size": tune.choice([16, 32, 64, 128]), @@ -208,40 +240,36 @@ config = { "dropout": tune.uniform(0, 0.2), } -reporter = CLIReporter( - parameter_columns=list(config.keys()), - metric_columns=["loss", "MAPE", "training_iteration"], -) - -resources_per_trial = {"cpu": 8, "gpu": 1} - # the number of combinations to try num_samples = 10 +# Configure the ASHA scheduler scheduler = ASHAScheduler(max_t=1000, grace_period=3, reduction_factor=2) -train_fn_with_parameters = tune.with_parameters( - train_model, callbacks=[my_stopper, tune_callback], train=train, val=val, +# Configure the CLI reporter to display the progress +reporter = CLIReporter( + parameter_columns=list(config.keys()), + metric_columns=["loss", "MAPE", "training_iteration"], ) -# optimize hyperparameters by minimizing the MAPE on the validation set -analysis = tune.run( - train_fn_with_parameters, - resources_per_trial=resources_per_trial, - # Using a metric instead of loss allows for - # comparison between different likelihood or loss functions. - metric="MAPE", # any value in TuneReportCallback. - mode="min", - config=config, - num_samples=num_samples, - scheduler=scheduler, - progress_reporter=reporter, - name="tune_darts", +# Create the Tuner object and run the hyperparameter search +tuner = Tuner( + trainable=tune.with_resources( + train_fn_with_parameters, resources=resources_per_trial + ), + param_space=config, + tune_config=tune.TuneConfig( + metric="MAPE", mode="min", num_samples=num_samples, scheduler=scheduler + ), + run_config=RunConfig(name="tune_darts", progress_reporter=reporter), ) +results = tuner.fit() -print("Best hyperparameters found were: ", analysis.best_config) +# Print the best hyperparameters found +print("Best hyperparameters found were: ", results.get_best_result().config) ``` ## Hyperparameter optimization using `gridsearch()` + Each forecasting models in Darts offer a `gridsearch()` method for basic hyperparameter search. This method is limited to very simple cases, with very few hyperparameters, and working with a single time series only. diff --git a/docs/userguide/timeseries.md b/docs/userguide/timeseries.md index 7faeb66234..0027290b82 100644 --- a/docs/userguide/timeseries.md +++ b/docs/userguide/timeseries.md @@ -19,7 +19,7 @@ We distinguish univariate from multivariate series: Sometimes the dimensions are called *components*. A single `TimeSeries` object can be either univariate (if it has a single component), or multivariate (if it has multiple components). In a multivariate series, all components share the same time axis. I.e., they all share the same time stamps. -Some models in Darts (and all machine learning models) support multivariate series. This means that they can take multivariate series in inputs (either as targets or as covariates), and the forecasts they produce will have a dimensionality matching that of the targets. +Some models in Darts (and all machine learning models) support multivariate series. This means that they can take multivariate series in inputs (either as targets or as covariates), and the forecasts they produce will have a dimensionality matching that of the targets. In addition, some models can work on *multiple time series*, meaning that they can be trained on multiple `TimeSeries` objects, and used to forecasts multiple `TimeSeries` objects in one go. This is sometimes referred to as panel data. In such cases, the different `TimeSeries` need not share the same time index -- for instance, some series might be in 1990 and others in 2000. In fact, the series need not even have the same frequency. The models handling multiple series expect Python `Sequence`s of `TimeSeries` in inputs (for example, a simple list of `TimeSeries`). diff --git a/docs/userguide/torch_forecasting_models.md b/docs/userguide/torch_forecasting_models.md index 0c2ba84fde..928612e7f5 100644 --- a/docs/userguide/torch_forecasting_models.md +++ b/docs/userguide/torch_forecasting_models.md @@ -16,17 +16,18 @@ We assume that you already know about covariates in Darts. If you're new to the - [Training with validation set](#training-with-a-validation-dataset) - [Forecast / Prediction](#forecastprediction) -3. Advanced functionnalities section provides some example of TFMs advanced features: +3. Advanced functionalities section provides some example of TFMs advanced features: - [Model saving and loading](#saving-and-loading-model-states) - [Checkpoint saving / loading](#automatic-checkpointing) - [Manual saving / loading](#manual-saving--loading) - [Train & save on GPU, load on CPU](#trainingsaving-on-gpu-and-loading-on-cpu) - [Load pre-trained model for fine-tuning](#re-training-or-fine-tuning-a-pre-trained-model) + - [Exporting model to ONNX format for inference](#exporting-model-to-ONNX-format-for-inference) - [Callbacks](#callbacks) - [Early Stopping](#example-with-early-stopping) - [Custom Callback](#example-of-custom-callback-to-store-losses) -4. [Performance optimisation section](#performance-recommendations) lists tricks to speed up the computation during training. +4. [Performance optimization section](#performance-recommendations) lists tricks to speed up the computation during training. ## Introduction In Darts, **Torch Forecasting Models (TFMs)** are broadly speaking "machine learning based" models, which denote PyTorch-based (deep learning) models. @@ -116,6 +117,7 @@ Each Torch Forecasting Model inherits from one `{X}CovariatesModel` (covariate c | `NLinearModel` | | | | | ✅ | | `DLinearModel` | | | | | ✅ | | `TiDEModel` | | | | | ✅ | +| `TSMixerModel` | | | | | ✅ | **Table 2: Darts' Torch Forecasting Model covariate support** @@ -326,7 +328,7 @@ loaded_model.to_cpu() To re-train or fine-tune a model using a different optimizer and/or learning rate scheduler, you can load the weights from the automatic checkpoints into a new model: ```python -# model with identical architecture but different optimizer (default: torch.optim.Adam) +# model with identical architecture but different optimizer (default: torch.optim.Adam) model_finetune = SomeTorchForecastingModel(..., # use identical parameters & values as in original model optimizer_cls=torch.optim.SGD, optimizer_kwargs={"lr": 0.001}) @@ -349,6 +351,93 @@ model_finetune = SomeTorchForecastingModel(..., # use identical parameters & va model_finetune.load_weights("/your/path/to/save/model.pt") ``` +#### Exporting model to ONNX format for inference + +It is also possible to export the model weights to the ONNX format to run inference in a lightweight environment. The example below works for any `TorchForecastingModel` except `RNNModel` and for optional usage of past, future and / or static covariates. Note that all series and covariates must extend far enough into the past (`input_chunk_length)` and future (`output_chunk_length`) relative to the end of the target `series`. It will not be possible to forecast a horizon `n > output_chunk_length` without implementing the auto-regression logic. + +```python +model = SomeTorchForecastingModel(...) +model.fit(...) + +# make sure to have `onnx` and `onnxruntime` installed +onnx_filename = "example_onnx.onnx" +model.to_onnx(onnx_filename, export_params=True) +``` + +Now, to load the model and predict steps after the end of the series: + +```python +from typing import Optional +import onnx +import onnxruntime as ort +import numpy as np +from darts import TimeSeries + +def prepare_onnx_inputs( + model, + series: TimeSeries, + past_covariates : Optional[TimeSeries] = None, + future_covariates : Optional[TimeSeries] = None, +) -> tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]: + """Helper function to slice and concatenate the input features""" + past_feats, future_feats, static_feats = None, None, None + # get input & output windows + past_start = series.end_time() - (model.input_chunk_length - 1) * series.freq + past_end = series.end_time() + future_start = past_end + 1 * series.freq + future_end = past_end + model.output_chunk_length * series.freq + # extract all historic and future features from target, past and future covariates + past_feats = series[past_start:past_end].values() + if past_covariates and model.uses_past_covariates: + # extract past covariates + past_feats = np.concatenate( + [ + past_feats, + past_covariates[past_start:past_end].values() + ], + axis=1 + ) + if future_covariates and model.uses_future_covariates: + # extract past part of future covariates + past_feats = np.concatenate( + [ + past_feats, + future_covariates[past_start:past_end].values() + ], + axis=1 + ) + # extract future part of future covariates + future_feats = future_covariates[future_start:future_end].values() + # add batch dimension -> (batch, n time steps, n components) + past_feats = np.expand_dims(past_feats, axis=0).astype(series.dtype) + future_feats = np.expand_dims(future_feats, axis=0).astype(series.dtype) + # extract static covariates + if series.has_static_covariates and model.uses_static_covariates: + static_feats = np.expand_dims(series.static_covariates_values(), axis=0).astype(series.dtype) + return past_feats, future_feats, static_feats + +onnx_model = onnx.load(onnx_filename) +onnx.checker.check_model(onnx_model) +ort_session = ort.InferenceSession(onnx_filename) + +# use helper function to extract the features from the series +past_feats, future_feats, static_feats = prepare_onnx_inputs( + model=model, + series=series, + past_covariates=ts_past, + future_covariates=ts_future, +) + +# extract only the features expected by the model +ort_inputs = {} +for name, arr in zip(['x_past', 'x_future', 'x_static'], [past_feats, future_feats, static_feats]): + if name in [inp.name for inp in list(ort_session.get_inputs())]: + ort_inputs[name] = arr + +# output has shape (batch, output_chunk_length, n components, 1 or n likelihood params) +ort_out = ort_session.run(None, ort_inputs) +``` + ### Callbacks Callbacks are a powerful way to monitor or control the behavior of the model during the training process. Some examples: @@ -365,8 +454,8 @@ The code is triggered once the process execution reaches the corresponding hooks Some useful predefined PyTorch Lightning callbacks can be found [here](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html#built-in-callbacks). #### Example with Early Stopping -Early stopping is an efficient way to avoid overfitting and reduce training time. -It will exit the training process once the validation loss has not significantly improved over some epochs. +Early stopping is an efficient way to avoid overfitting and reduce training time. +It will exit the training process once the validation loss has not significantly improved over some epochs. You can use Early Stopping with any `TorchForecastingModel`, leveraging PyTorch Lightning's [EarlyStopping](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html#lightning.pytorch.callbacks.EarlyStopping) callback: ```python @@ -483,9 +572,8 @@ A larger batch size tends to speed up the training because it reduces the number of backward passes per epoch and has the potential to better parallelize computation. However it also changes the training dynamics (e.g. you might need more epochs, and the convergence dynamics is affected). Furthermore larger batch sizes increase memory consumption. So here too some testing is required. ### Tune `num_loader_workers` -All deep learning models in Darts have a parameter `num_loader_workers` in their `fit()` and `predict()` functions, which -configures the `num_workers` parameter in the PyTorch `DataLoaders`. By default -it is set to 0, which means that the main process will also take care of loading the data. Setting `num_workers > 0` will use additional workers to load the data. This typically incurs some overhead (notably increasing memory consumption), but in some cases it can also substantially improve performance. +All deep learning models in Darts have a parameter `dataloader_kwargs` in their `fit()` and `predict()` functions, which configures the PyTorch DataLoaders. The `num_workers` parameter for PyTorch DataLoaders can be set using the `num_workers` key in the `dataloader_kwargs` dictionary. +Setting `num_workers > 0` will use additional workers to load the data. This typically incurs some overhead (notably increasing memory consumption), but in some cases it can also substantially improve performance. The ideal value depends on many factors such as the batch size, whether you are using a GPU, the number of CPU cores available, and whether loading the data involved I/O operations (if the series are stored on disk). @@ -567,5 +655,3 @@ We train two models; `NBEATSModel` and `TFTModel`, with default parameters and ` | `TFTModel` | Energy | 32 | yes | 1024 | 0 | 41s | | `TFTModel` | Energy | 32 | yes | 1024 | 2 | 31s | | `TFTModel` | Energy | 32 | yes | 1024 | 4 | 31s | - - diff --git a/examples/00-quickstart.ipynb b/examples/00-quickstart.ipynb index c4bf8a58f6..64388433e3 100644 --- a/examples/00-quickstart.ipynb +++ b/examples/00-quickstart.ipynb @@ -15,9 +15,12 @@ "* [Machine learning and global models](#Machine-learning-and-global-models)\n", "* [Covariates: using external data](#Covariates:-using-external-data)\n", "* [Regression forecasting models](#Regression-forecasting-models)\n", + "* [Sample weights for training](#Sample-Weights)\n", + "* [Forecast Start Shifting](#Forecast-Start-Shifting)\n", "* [Probabilistic forecasts](#Probabilistic-forecasts)\n", "* [Ensembling models](#Ensembling-models)\n", "* [Filtering models](#Filtering-models)\n", + "* [Anomaly Detection](#Anomaly-Detection)\n", "\n", "We will only show some minimal \"get started\" examples here. For more in depth information, you can refer to our [user guide](https://unit8co.github.io/darts/userguide.html) and [example notebooks](https://unit8co.github.io/darts/examples.html)." ] @@ -40,7 +43,7 @@ "conda install -c conda-forge -c pytorch u8darts-all\n", "```\n", "\n", - "Consult the [detailed install guide](https://github.com/unit8co/darts#installation-guide) if you run into issues or want to install a different flavour (avoiding certain dependencies)." + "Consult the [detailed install guide](https://github.com/unit8co/darts/blob/master/INSTALL.md) if you run into issues or want to install a different flavour (avoiding certain dependencies)." ] }, { @@ -59,12 +62,24 @@ "tags": [] }, "outputs": [], + "source": [ + "# fix python path if working locally\n", + "from utils import fix_pythonpath_if_working_locally\n", + "\n", + "fix_pythonpath_if_working_locally()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%matplotlib inline\n", "\n", - "import pandas as pd\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", "from darts import TimeSeries\n", "from darts.datasets import AirPassengersDataset" @@ -76,7 +91,7 @@ "metadata": {}, "source": [ "# Building and manipulating `TimeSeries`\n", - "`TimeSeries` is the main data class in Darts. A `TimeSeries` represents a univariate or multivariate time series, with a proper time index. The time index can either be of type `pandas.DatetimeIndex` (containing datetimes), or of type `pandas.RangeIndex` (containing integers; useful for representing sequential data without specific timestamps). In some cases, `TimeSeries` can even represent *probabilistic* series, in order for instance to obtain confidence intervals. All models in Darts consume `TimeSeries` and produce `TimeSeries`.\n", + "`TimeSeries` is the main data class in Darts. A `TimeSeries` represents a univariate or multivariate time series, with a proper time index. The time index can either be of type `pandas.DatetimeIndex` (containing datetimes), or of type `pandas.RangeIndex` (containing integers useful for representing sequential data without specific timestamps). In some cases, `TimeSeries` can even represent *probabilistic* series, in order for instance to obtain confidence intervals. All models in Darts consume `TimeSeries` and produce `TimeSeries`.\n", "\n", "## Read data and build a `TimeSeries`\n", "`TimeSeries` can be built easily using a few factory methods:\n", @@ -87,6 +102,7 @@ "* From a Pandas `Series`, using `TimeSeries.from_series()` ([docs](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.from_series)).\n", "* From an `xarray.DataArray`, using `TimeSeries.from_xarray()` ([docs](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.from_xarray)).\n", "* From a CSV file, using `TimeSeries.from_csv()` ([docs](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.from_csv)).\n", + "* Create multiple `TimeSeries` by groups from a Pandas `DataFrame`, using `TimeSeries.from_group_dataframe()` ([docs](https://unit8co.github.io/darts/generated_api/darts.timeseries.html#darts.timeseries.TimeSeries.from_group_dataframe)).\n", "\n", "Below, we get a `TimeSeries` by directly loading the air passengers series from one of the datasets available in Darts:" ] @@ -98,20 +114,18 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "series = AirPassengersDataset().load()\n", - "series.plot()" + "series.plot();" ] }, { @@ -122,7 +136,7 @@ "## Some `TimeSeries` Operations\n", "`TimeSeries` support different kinds of operations - here are a few examples.\n", "\n", - "**splitting**\n", + "**Splitting**\n", "\n", "We can also split at a fraction of the series, at a pandas `Timestamp` or at an integer index value." ] @@ -134,21 +148,19 @@ "outputs": [ { "data": { - "image/png": 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QoUMBGDIdS5cuxZdffonHH38cZ86cwaJFi6z+ZWqqY8eOoWPHjvDy8sLp06cREBBQJlD75z//iTt37uDMmTPo1q0bxowZg6NHjzqoxY5XusCZiKguEqf2+noaRiqUwYhj2mQJ1cGIj48Pli5dil9++QW7du3Cc889J52bPHky9u/fj4MHD+LPf/6zYjinbdu22LRpE44cOYLVq1er3p22Njt69ChiYmIAAIcPH0avXr3KvMbHxwfBwcGIiorChx9+CA8PD+zevRs6nQ5TpkxBZGQkPDw80LJlS6xYsULx3p9++gndu3eHl5cX/Pz8EBMTg4SEBABAXFwcHn/8cfj4+MDX1xddunTByZMnpfcePnwYvXv3hoeHBxo3boxZs2Yp6n8iIiKwePFivPjii/Dx8UGTJk3KLPV+9OhRdOrUCe7u7ujatSt27NgBjUaDs2fPSq/5448/MHjwYHh7eyMoKAh/+tOfkJaWJp3v27cvZs6cidmzZyMwMBCDBg2CIAh46623EBERgVatWiEsLAyzZs2y/H8EEVENIwUjD+eIKIKRGjS1l4N6DpKYmAg/Pz/4+flh+fLl+Pjjj+Hn54c33ngDO3fuRMeOHTFjxgyT73V2doaLiwuKioqg1+sRFhaGLVu24MKFC1iwYAHeeOMNbN68GQBQUlKCYcOGoU+fPjh37hyOHTuGqVOnSoHi+PHjERYWhhMnTuDUqVN4/fXX4eJi2G0pPj4eTz75JEaMGIFz587hq6++wuHDhzFz5kxFe5YtW4auXbvizJkzmD59Ol599VVcvnwZAJCdnY1nnnkG7du3x+nTp7Fo0SLMmzdP8f7MzEz069cP0dHROHnyJPbu3Yu7d++WmT312WefwdXVFUeOHMFHH32Er7/+Gu+++y5WrVqF/fv3Y9u2bWjfvn3V/+cQEdUQ4jCN78MgpKZmRiCQQxQXFws3btwQ4uLiBBcXFyEuLk64du2a4O3tLRw8eFD45ZdfhLt37wqCIAjh4eHCu+++KwiCIBQWFgqLFy8WAAjffPONyc+eMWOGMGLECEEQBCE9PV0AIPz0008mX+vj4yPExsaaPDdlyhRh6tSpiucOHTokaLVaIT8/X2rbhAkTpPN6vV5o2LChsGrVKkEQBGHVqlVC/fr1pdcLgiCsXr1aACCcOXNGEARBWLRokTBw4EDFdZKSkgQAwuXLlwVBEIQ+ffoI0dHRitcsW7ZMiIqKEgoKCoTr168LOp3O5PcgJZ1Ox/4yE/tKHfaXOlXtr6JivYDeOgG9dULMdMNn5BUYn+s7q+b8f6iVG+V1fVmPlPv2v25wAHBytXnJJmdnZ0RERGDz5s3o1q0bOnTogCNHjiAoKAiPPfYYEhISEBgYKL1+3rx5mD9/PgoKCuDt7Y3//Oc/eOqppwAAH374IdauXYvExETk5+ejqKgInTp1AgAEBARg0qRJGDRoEJ544gkMGDAAo0ePlobJ/vKXv+Cll17C+vXrMWDAAIwaNUqaBRUXF4dz585hw4YNUjsEQYBer8eNGzfQunVrAECHDh2k8xqNBsHBwbh37x4A4PLly+jQoQPc3d2l13Tv3l3RF3FxcTh48CC8vb3L9FN8fDyioqIAGAqh5UaNGoX33nsPzZs3R0xMDEaNGoWhQ4fC2blW/lgTESnIMx9iZsTdFXByAnS6mpUZqZX/aqfcB26lOroVFWvbti0SEhJQXFwMvV4Pb29vlJSUoKSkBL6+vggJCcGlS5ek18+ZMweTJk2SairEYZZNmzbhr3/9K5YtW4aePXtKNT3Hjx+X3rtu3TrMmjULe/fuxVdffYX58+fjxx9/RI8ePfDWW2/h+eefx7fffovvvvsO//jHP7Bp0yYMHz4cOTk5mDZtmsk6jCZNmkjH4rCOSKPRqFpRMCcnB8888wzeeeedMufktUWlF85r3LgxLl++jB9++AHbt2/HzJkzsWzZMvz8889l2kREVNvI96URa0Y0Gg28PQRk5TAYcbjggOp/3T179qC4uBj9+/fHkiVL0KVLF4wdOxaTJk3CwIEDpcyCKDAwEM2bNy/zOUeOHEGvXr0wffp06TlxGX656OhoREdH429/+xt69uyJjRs3okePHgCAqKgoREVF4bXXXsO4ceOwbt06DB8+HJ07d8aFCxdMXtdcLVu2xBdffIHCwkJp0bwTJ04oXtO5c2d8/fXXiIiIUJ3V8PDwwDPPPIMOHTpg3rx5aNOmDX7//Xd07tzZ4jYTEdUE2fLMiOx3NR8PMBipDswdKnGk8PBwpKSk4O7duxg6dCg0Gg3Onz+PESNGICgoyOzf7Fu0aIHPP/8c33//PSIjI7F+/XqcOHFCWn73xo0b+OSTT/Dss88iJCQEly9fxtWrV/HCCy8gPz8fc+bMwciRIxEZGYnk5GScOHECI0aMAGAYGurRowdmzpyJl156CV5eXrhw4QJ+/PFHfPDBB2a17/nnn8ff//53TJ06Fa+//joSExPx3//+F4BxwbwZM2Zg9erVGDduHObOnYuAgABcu3YNmzZtwpo1a+Dk5GTys2NjY6HT6dCtWzdkZGTgxx9/hIeHB8LDw81qGxFRTabIjMgKV8Ui1poUjFT/u3Yt9tNPP6Fbt25wd3fHb7/9hrCwMNVTnqdNm4bnnnsOY8aMwSOPPIL09HRFlsTT0xOXLl3CiBEjEBUVhalTp2LGjBmYNm0anJyckJ6ejhdeeAFRUVEYPXo0Bg8ejIULFwIw1IL8/PPPuHLlCnr37o3o6GgsWLAAISEhZrfP19cXu3fvxtmzZ9GpUyf8/e9/x4IFCwBAqiMJCQnBkSNHoNPpMHDgQLRv3x6zZ8+Gn59fhasS+vn5YfXq1ejduzeGDBmC/fv3Y/fu3ahfv2Ysf0xEVBXKYRrjUhpiMJKTj2qxUZ45NIIg1IyW1iF6vR4JCQkIDw+vlUsqb9iwAZMnT0ZWVpa0f1FV1Pb+sjb2l/nYV+qwv9Span9t2i9g3ELDLfzdmRrMHm0ISAa8psf+U4bXZO/VwMez+i8JXyuHaah6+fzzz9G0aVOEhoYiLi4O8+bNw+jRo60SiBAR1VWmCliBsmuN+Jjek7ZaYTBCNpeSkoIFCxYgJSUFjRo1wqhRo/Dvf//b0c0iIqrRzA1GagIGI2Rzc+fOxdy5cx3dDCKiWiVbthGeooBVlnSuKcEIB/WIiIhqIHlmpJ5szciamBlhMEJERFQDZZU7tddYsMpghIiIiGymNtWMMBghIiKqgcpdgVUejOTbrz1VwWCEiIioBhIzIxoN4GXci5SZESIiIrIPMRjx8QS02rIrsALAg7yasa4pgxEiIqIaSBym8S21qBmn9hIREZFdiJkReb0IoMyM5LBmhIiIqHbKyhEwdake//pMgCO2eNPpBCnQqCgYqSmZEa7ASkREpNLrHwtYvRsABDzVU4PoKPteX57xKD1M481hGiIiototNVNA7HfGx8mp9m9DeauvAjUzM8JghIiISIVVO4CCIuNjR9zwFWuMlMqMuLlq4PJw3IPrjBAREdUy+YUCPtimrBFxRDCSlWM8Ll0zAhizI8yMEBER1TJf/ACkZiqfy65mmRGAwQgREVGtpNcLWP5V2ZkzjlhYTLkvjabMeXGtEQYjREREtch3x4FLiYZjfx/j8w6vGalgmCa/ECgpqf6rsDIYISIiMsO6Pcab+l/HGrMRDglG5JmRCoZpgJqx8BmDESIiIjMk3TP8qdEAY/oZn3dMMGIMjCrKjAA1Y6iGwQgREZEZMh4Y/qznBfjJ1vaozgWsQM2Y3stghIiIyAyZD6fT+nk7PvOgLGAte97R7VOLwQgREVElBEEwBiM+gKuLBm6uhseOLmCtx2CEiIio9ssvBIpLDMfiEI0jp89WmhnxcGyBrVoMRoiIiCqRKVvxVApGHLiwWJYsGJFvjCdiZoSIiKiWqSgYcUgB68NgxNsDcHIysegZp/YSERHVLpkPjMelg5GiYqCo2L4Li4nBiKkhGoCZESIiolpHnhnx9zFkInwdeMMX22OqeBUoHYxwBVYiIqIaL6OCzAhg32CkoFCQhl4C65l+DTMjREREtUxFNSOAfetG0rONx+UFI/KiVi56RkREVAtUFozYM/uQlmU8buBn+jXMjBAREdUymTnGugsxGJEXj9rzhp+aaTwO9DP9GgYjREREtYwiM+Jj+NNRC4vJMyOB9cpO6wWMC7IBDEaIiIhqhWo1TJNpPC6vZsTZWQN3By5XrxaDESIiokoop/Ya/lQUsObCbtKyjENG5QUjgGNXiFWLwQgREVElxKm9Wq1xpooiM2LHGSvKYZryXycFI5xNQ0REVPNJO/Z6AxrNw0XPFAWs9ltYTHUwwswIERFRzScPRkSOKhJVBCN+5b/OkcvVq8VghIiIqAKCIJgORhxUwCpO7XV1Mb1jr6gmzahhMEJERFSB3HxApzMclxeM2LeA1fBnYD3jkJEpNWmtEQYjREREFTA1rRdwTAGrIAhSMFLe6qsiBiNERES1hKlpvQDg6W6YXQPY72afk2+oAQEqLl4FlMFITjWfUcNghIiIqAKmduwFDEMk9p6xYs6CZyJmRoiIiGoJ5TCNskZDLBK1WzBi5rReAPDxdMxy9ZZgMEJERFSB8mpGAGP2IbtaBiPGY3u1z1IMRoiIqNrS6wUs+kzAnJV65Bc6Zq0MU5vkicQbfm6+oa22ptixt5xN8kQN/YzHd+/bpDlWozoYmTp1Knr16oXevXujd+/emDVrlnQuNjYWAwYMQL9+/bBixQoIgvF/zPnz5zF27FjExMRg6tSpuHPnjnW+ARER1Vqb9gMLPhXw303A1z87pg0VZUbkq7Dao0jU3AXPACAk0Hh8O70WLno2f/58HDp0CIcOHcL7778PADh8+DC2bNmC2NhYbN68GUePHsXOnTsBAEVFRZg7dy7Gjh2LAwcOoGPHjnjzzTet9y2IiKhW+vx740008a5j2pD5wNiGMsM0dl5YzNxN8gBlMHIr1UYNshKrDdPs2bMHw4cPR1hYGAIDAzFhwgTs2bMHAHDq1Cm4uLhg2LBhcHNzw5QpU3Dx4kXcunXLWpcnIqJa5u59AftOGR/n5Dt+mMa/nGEawF7BiPG4smAkOMB4fDvdNu2xFmdL3rR8+XIsX74cUVFReO2119CiRQvcuHEDgwYNkl7TvHlzxMfHAwCuX7+OFi1aSOfc3d0RFhaG69evIzQ0tMznFxUVoaioSNlQZ2e4urpa0twaR6/XK/6kirG/1GF/mY99pY61++urA8aVTwHDKqeO+H+RIQtGfD0FRW2IfDn2zBxBVd2IJf0lrxmp71vx9ZydDAujpWYCt9Mc93Os1Vae91AdjMyaNQtNmzaFVqvFV199hVmzZmHr1q3Iy8uDl5dx8MzLywv5+YYBtPz8fMU58Xxenukwct26dVi9erXiuVGjRmH06NFqm1ujJSUlOboJNQr7Sx32l/nYV+pYq79ivw0G4CY9TknNQUKC/X/FT0kNAuAOAMjOSESJbIdefbEfAEOK4tqNuwjyLFD9+Wr669ZdY1tyMxOQUEk2JtC3EVIzXXEnTcCNG4kwIy6wusjIyEpfozoYadeunXQ8ceJE7Nq1C7///js8PT2Rm2tcnD83NxceHoaQ0cPDQ3FOPO/p6QlTJk+ejPHjxysbWscyI0lJSWjcuLFZEWVdx/5Sh/1lPvaVOtbsr/hbwJl45XOC1hvh4d6m32BDBSWGP52dgFYtmkC+HUzjEOOxp08QwsPN/1xL+iun0PCntwcQ1aLyi4U3Ai4mAsU6Dbz9wytdQt5RLBqmkRM7MDIyEteuXUOfPn0AAPHx8WjWrBkAoGnTpti6dav0noKCAiQnJ6Np06YmP9PV1bXOBB4V0Wq1/AdQBfaXOuwv87Gv1LFGf206IABQDkHk5JuX8re2zBzD8IafN+DkpLy+r6exnbkFGmi1FU+3NUVNf6VmGtoSWM+8vggNNA7NpNzXIChAffvsQdX/1QcPHuDXX39FUVERiouLsWHDBmRnZ6Ndu3YYMmQItm3bhuTkZKSnp2PDhg0YMmQIAKBLly4oLCzEzp07UVRUhLVr16J169Ym60WIiKhuEwQBG34sWwvhqP1VxALW0jNpAPsWsOp0Au4/XJq+smm9IsX03jSrN8lqVGVGSkpK8OGHHyIhIQHOzs6IiorCihUr4O3tjUcffRQjR47ExIkTodfrMWzYMAwdOhSAIdOxdOlSLFq0CEuWLEGbNm2waNEim3whIiKq2c5cAS4nGo4f6wj8dhEoKHLMkuZ6vYCsh1UGpRc8A0qtcppb9rw1ZeYAYg1qZTNpRKGBGoiZm1oTjPj7+2P9+vXlnp88eTImT55s8lzbtm2xadMmda0jIqI6Z+cRY1Zk/BMaXEwQUFDkmMxITr4xAPA3kRmRL3r2IF8AYLthEDXTekWKtUaqcTDCQVAiIqpW5IubxbQ3Tp91RGakoqXgAfsO01Q1GLmdVn1XYWUwQkRE1cq9DONxkL/xhu+IzEjGA+Oxo2tG0jKNx5XtSyMKqW88rs7DNAxGiIioWrmXafhTqwUCfI2ZkYIioKTEvr/dV7QvDWDf5eDlmRFzp+g29Ie0tkh1XoWVwQgREVUrYmakgR+g1WqU2Qc7Z0cyFZmRstkIexawWjJM4+SkkZaFZ2aEiIjIDIIgSMFIQz/Dn/Il13PsXDdSaWbEjoFSaqb5m+TJiXUjdzPsn1kyF4MRIiKqNnLyDcMxgGGIAbDvDb+0yoIRJycNPA2rs9u3gNXP/PeJdSN6vXEIrLphMEJERNWGvHhVrIuQByPVLTMCGNtXHWfTADVj4TMGI0REVG3IgxFTwzT2z4wYhzX8TUztBYxFrPasGQkopy2mhAQaa11upVqxQVbEYISIiKoNRTDib7iJ+ngab6bVOjOSb6h5sRUxGPH3AZydzV9cLVSeGammM2oYjBARUbWRKvvtX6wZcWRmpLJ1RgDjKqw6nbHexRbEYETNEA1QMxY+YzBCRETVhqlhGkXNiCMLWMsbprHDwmfFJQKyHralasGI9dpkTQxGiIio2riXYfzN3WRmxEHDNK4ugLur6dfYIxi5e994rDoYka/CymEaIiKiismnnpqc2ptn32EGcWgkwAfQaEzXachXYbVVEevmg8bjFmHq3lu/HuDycFtcZkaIiKjas2UBpjmUBayGP+U3e3sO0+j1gpSRaFS//NfZOjOi0wn439fG/y8vP6NuZ2CNRiMN1TAYISKiaunu3bt4++230bx5c/j6+uLAgQMOa4sYjLi7GodnvO24GZ1cejZQojMcVxSM+HoZgwNbFNjuOgLcTDEcP/kI0CpcXTACGIdq0rKAwqLqV8Tq7OgGEBGRYxQUFGD69OlYv349SkpKpOc/++wz9OvXzyFtEodpGvgZh0UclRm5I6uvEPd3McXWmZEVW43Bw59Hqg9EAGURa8p9IDy4qq2yLmZGiIjqqF27dmHdunWKQAQAUlMdszKWXi8gNdNwLA7RAI7LjKTIgxEHDdOcvSrg57OG41ZNgIHdLPsceTBSHRc+YzBCRFRHXb16VTqeOHEitA/3mndUMHI/27B/CmCc1gs4LjOSIpvB0iig/IyELQtYFVmRURpotRZmRuob31cdZ9QwGCEiqqOSkpKk4xkzZiAgwDAWkZbmmCpHUzNpAMDNFXByMhzbMzNyx8zMSICv8fj+A+vVY6RlCti4z3Ds5w38aaDlnxXawHhcHYtYGYwQEdVRycnJ0nFYWBgCAw25fIcFIyZm0gCG2hEx+2DfzIgxsKioZkTeVvl3qKrfLgJFxYbjFwYBXh6WZUUAZfvl36u6YDBCRFRHiZkRZ2dnBAUFScFITk4OCgoK7N4esV4EABr6KW+84swau9aMyIdpKsiMyIeUrBmMJN4zHrdvZnkgAiizN/Il7qsLBiNERHWUmBkJDQ2FVquVghEASE+3f2FBeZkRQLYZnaOGaSrIjAT6GY/lQ01VlXjXmMFo0rBqnyXf5ZfBCBERVQt5eXm4f9/wq3/jxo0BAA0aGAsLHDFUcy+z7FLwIjEYyS0wzLqxBzEz4uNZ8RCJi7NGyjxYNTNy13jcJKhqn6Woa8mu2mfZAoMRIqI6qHS9CABFZsQhwYiJTfJE4jCNIAB5dhpBEjMjFWVFRGJ7rRmMJFgxGPH1MhYB32dmhIiIqgP5TBoxM1KtgpFyMiOAfYpY8woEaZquOcFIAz/Dnzn5QH6hdTI3YmYksB7g6V61mhGNRgN/b8MxMyNERFQtyDMjpoIRR6w1Iq+3EG/uInvv3HvXzOJVkTx4khfiWqqkRMCth/FgVbMiIv+HdSPMjBARUbUgz4xUt2EaXy/AzVWZCbB3ZsTc4lWRtaf33kkHdA/3xbFWMCLWjWTlGDbfq04YjBAR1UGVZUYcGYyUrhcB7J8ZkU/rDa5f+RCJtaf3yqf1VnUmjUg+oyYzxzqfaS0MRoiI6qDqlhkpKhakG2TpehEA8PE0BgT2yIyYu8aIqKG/sX3WmN6rnElTtXoRUXWeUcNghIioDhIzIy4uLmjY0PCrtyOn9ioWPDMZjBiP7ZEZuZNu3uqrImsP0ySkGI+ttcOuctl663ymtTAYISKqg8TMiLjgGQB4e3vD1dUVgIODEb+y5xXDNNUxM+JnPL6XUfV6jMR71lvwTCQfpmFmhIiIHCo3NxcZGYZf38V6EcAw/dNR+9NUNK0XKFXAapfMiPFYdWYks+rXt+aCZ6IAX+NwD4MRIiJyKFMLnonEYCQ1NRWCYL8ZF8ode8vWSDgqM6LVGtb5qIy1h2nEYMTVxXRwZgl/eWaEwzRERORIpmbSiMRgpKioCDk59ptyUdHqq0DpzIjtgyQxGAnyB5ycKi8g9fMGnB+ucGqNdUbE2TSNGwJarfULWKvb/jQMRoiI6hhTq6+KHDWjRl5nUXrBM8C+mRG9XpAWPTNniAYwBAxiu6uaGcnKEZD1MA60Vr0IULpmhOuMEBGRA5kzTAPYORjJNB5XWjNi42AkPRsoebjgmDnFqyKx3fcyUaUhLnm9iLVm0gDVezaNs6MbQERE9lVRZsRR03vTMo3Hpmo07LnomdriVZE4vFRUDGTnAvW8Lbu+YsEzKxWvAuWvM7L2WwGuLkB4ENC7o3WGhNRiMEJEVMdUx8xIhqw8RX7TFNkzM5IiD0ZUZEbkw0v3MqoQjMhn0jS0XnDgL2uPPBh5/WMBqZlAWAMg6WvHBCMcpiEiqmPEzIirq6siEwI4LhgRb45eHoCrS9kbope78djWmRHFGiMB5t+crTW9N/GubI0RK2ZGnJ018PUyHIvDNPmFglRwa81rqcVghIiojhEzI/IFz0SO2rlXDEbkRZZyWq0GXg+Hauw6TKOqZkS2JHwVilhtscaISNq592F/J8uGhBpbsVhWLQYjRER1SHkLnokckRkRBEEapvEvJxgBAJ+HwYjNh2nuq1sKXmStzfISbBiMiMFexgNDvycxGCEiInurqF4EcEwwkl8IFBYZjk3Vi4i87ZQZUbsUvMh6wzSGPxv4AR5u1q3hEPu3RGcI6pTBiGPqRQAGI0REdUpFM2kAoH59493XXsGIvJjSv4KiT7GI1daZEYtn08iCkdRMy6b2lpQIuPWw221Rw1F6Rg0zI0REZHeVZUY8PDzg5WWocrRXMCJfDdSczEhxCVBYZLtFu8TMiI8n4OWhooDVz3hs6TDN7XRArzccW3PBM1HpzfKS7tmmWFYtBiNERHXIrVu3pGNTwQhgXGvEbpkRM4MRe03vFTMjarIigHX2p7Fl8SpQduGzRGZGiIjI3uQBRulpvSKxbiQ9PR168dd0G5JnRvx9ys9EyIMRW9WN5OYLyM41HKsNRrw8NPB8OAXZ0poRZTBi/RqOAB/lzr3iMI2bq+ll+O2FwQgRUR2Snm4siJDXh8iJwYher5dm3tiSvGakvKm9gHIVVltlRs7FG49bmE4cVUgcqqksM5KaKeD1j4GDce6K56/fNh6H2yAzIp+tlPHAGIyENQA0GhawEhGRHagJRgD7DNUoClgrmtprh8zIqcvG4y4t1d+cxaGatCxApyu/rmX6cgFLvwSm/6+B4vtfSDC+p1W46stXSj5MczPFmAVy5BANwGCEiKhOkQcj/v4mdqSD/YORjBzjDdicAlbAdsHIycvGtnRpqf79YjAiCIYN90xJvidg2y+G48JiLU5dMZ67cNPwp7OTZZmZysj7N+6a8dgWxbJqMBghIrIxnU6Hfv36ITAwED/99JND2yIGI35+fnB2Nr09mSMzIxUN0/h4GjMVthqmETMjzk5Ah2bq3196fxpTVn8jQF6Kc/aq4U+dTsClRMNxizDTy+JXlbx/42RDUsyMEBHVcqdOncLBgweRnp6OF154ATk5OZW/yUbEYKS8IRrAAZkRRQFr+a+zdWYkr0DAhQTDcdtIyxYck0/vFfd8kSsuEfDJLuVzZx4GI9dvGxd/axOh+tJmkWdGbslW+3fkgmcAgxEiIpu7ceOGdJyUlISFCxc6pB06nQ6ZmZkAKg5G5LNs7J4ZceDU3rhrxjU+ukRZ9hmV7U+z45ByhVfAmBkRh2gA+wQjcsyMEBHVcjdv3lQ8fvfdd3Hu3Dm7tyMzMxOCYKiJqE6ZEXGdEa1WGXCUZuvMSFWLV4HKl4RftcNYkyLuRHwl2TCl+PxN4+vaRtgmU+HhpoG7a9nnGYwQEdVyCQkJisc6nQ6vvPKKXdbwkDNnJg1g/517xWEafx/D7rzlUWZGqrYCqyAI2HVYwM5Dxs85daVqxatA6VVYlW28eFPAwTOG46jGwJh+YlsMU4ov3DS+3laZEcD0UJgjV18FGIwQEdmcPDMirnp67NgxfPrpp3ZthzwYCQgof0UvRxWwVlS8Clh3au+hOGDoGwKG/V3Al/sMQYCYGXGysHgVAIJk3Srf4wYAPtppDDZeHapBZ9lQ0JmrxmEaJydDsGIrpYdqfDyBet41tGbk3Llz6NatG9asWSM9FxsbiwEDBqBfv35YsWKFlA4EgPPnz2Ps2LGIiYnB1KlTcefOnaq1nIiohhAzI+7u7vjss8+k5x0ZjFSUGZEHKrYORnQ6AZkP63krKl4FrLvomXxxs3+sFfAgT1a8GmH5brlhskVtk0sllXYcNvzp7gpMHAx0amE8d+qygIsPr988FHBztV1wUDroc/QQDWBhMKLX67F8+XK0adNGeu7w4cPYsmULYmNjsXnzZhw9ehQ7d+4EABQVFWHu3LkYO3YsDhw4gI4dO+LNN9+0zjcgIqrGBEGQgpHw8HD069cPoaGhAIDExES7tsXcYMTZ2Vlag8TWwzRZucbjiopXAWWwUt4aHuaS74dzNRl44xMBOp3hsaVDNIDhO4g1GfJgpKREkB63a2pY9r59JKDRGH5p330UKBBn0thgsbPSbZSrscHItm3b0K5dO0RGRkrP7dmzB8OHD0dYWBgCAwMxYcIE7NmzB4BhWpuLiwuGDRsGNzc3TJkyBRcvXlRs2EREVBulp6cjN9dwxw0PN9xlGjVqBAC4e/cudOId0E5tEVUUjABAw4aGO5StgxFzV18FDPUY2od3rVtVTNjcz1bWc3ywzXjc1cLiVcCwpLqYHZEHI3dku/GK5709gcjgEgDKacC2rBcBqmcwYnrFmwpkZmbiyy+/RGxsLJYtWyY9f+PGDQwaNEh63Lx5c8THG/Jg169fR4sWxnyUu7s7wsLCcP36dek3BLmioiIUFRUpG+rsDFdXEyXAtZBY1Gbv4raaiv2lDvvLfNboK/m03vDwcOj1egQHB0ufm5KSIgUntiYfcvH396/wezVo0ACXL1/GgwcPkJeXB3d393JfK7Kkv+QZDn/vit+r1Ro2r7udBiTfq9r/l4oyK9EtBOj1lhfIhjUArt0CsnKArBw9fDyVu+OGNTC0Xa/Xo02TIly/46J4f6tw2/799PdWPm7c0LbX02orz3uoDkZWrlyJcePGwcdHGcLm5eXBy8tLeuzl5YX8fMOgXn5+vuKceD4vz3QF0rp167B69WrFc6NGjcLo0aPVNrdGS0pKcnQTahT2lzrsL/NVpa9OnDghHfv6+iIhIUHx7+epU6fQvn37KrXPXPJC2qKiojKzfOTk/2afOXMGISEhZl9HTX9duuYOwDCVw0nIREJCVoWvD/QJxu00N9zNEHAtPhEuqu9iBrfuNgBQdh6xk1aAn0sSEhIsD0b8POsDMNzxT8TdQrOQEpw57wnAkBLxcs5AQoIhGmob7otvjivvjwFut5GQUGzx9Suj0fsCMM5B9tCmISEht/w3VJF8FKU8qv43Xrp0CRcuXMC8efPKnPP09JRSkQCQm5sLDw9DtZGHh4finHje09P0hPLJkydj/PjxyobWscxIUlISGjdubFZEWdexv9Rhf5nPGn0l/lIGAJ06dUJ4eDiiopQraonDN7Ymzzi3a9euwuvKz7m6uprVRkv6y1m2P0pEmB/Cw/0qfH3TMODcDUAQNHDzCbd4iKGgxHjcow3w6wXDcdtIDVq2aGLZhz7UKhLAUcOxziUU4eFA4XHj+fYt/REe7v8wM6IcBtNqgT7dQ+DhVqUmVCiy1EydTq0DER4eaPrFdqIqGDl9+jQSEhIwZMgQAEBOTg6cnJxw69YtREZG4tq1a+jTpw8AID4+Hs2aGeZGNW3aFFu3bpU+p6CgAMnJyWjatKnJ67i6utaZwKMiWq2WNwsV2F/qsL/MV5W+kmcfIiMjodVqFcPTd+/etdv/h/v3jUt/NmjQoMLrBgUZF55IT09X1UY1/ZWVIwB4uBCbr6bCdUYAIKyBcTjhdpoG4cGW1XdkPDB8jo8n8M8pGgz8f4Y29Ghj3rBCRRo3NH6n22mG75Scamx3k4bG79kmXFmS0LQR4OVh25+HQF9j+wAgPLjyfrc1VcHIc889h4EDB0qPly1bhpCQEEyaNAlxcXF4++23MWjQIHh4eGDDhg0YM2YMAKBLly4oLCzEzp07MXjwYKxduxatW7c2WS9CRFSbyIORiIgIAFDUiNy+fdtubRELWF1dXcsMnZcmXxL+3r17FbyyatQUsAJAaKAG4o20KkWs4myaAF9gQFfgnVc0OHlZwBt/qvpN2dT03mTFPjDG4/q+eoQGGr9L28pHNKqsdAGrvL2OoioYcXd3VxQxubm5wcPDAz4+Pnj00UcxcuRITJw4EXq9HsOGDcPQoUMBGH7wly5dikWLFmHJkiVo06YNFi1aZN1vQkRUDYl1Gi4uLlIQIg9G7LnmknyTPI2m4puuOJsGsO2MmowHxt/QK5vaCwChshvnLQubJQiCYqE1jUaDuc8DgHWyA4pg5J4AQIOkh/GcRgOElBoR6dTCGIzYeiYNoOznwHqAp7tjsyKABQWscm+99Zbi8eTJkzF58mSTr23bti02bdpUlcsREdU4YmakSZMmUvpfXgzqqGCkMnbLjMjW+zAnGJHf6G+lGW70auXkAyU686+pVpgs8yFmRMRgJMgfcHVRtjm6BfDtMcOxrfakkZMvelYdpvUCXA6eiMhmMjMzkZVlmB0iLwBt2LChFJjYa5gmPz8fBQUFAMwLRuyVGblfampvZUIrWOHUkmvaIhgJrAe4Ppytm3QPKC4RpKXhw0zc/Kc8BTQLBbq1Aob1tn57Sguub1xa39Jl762tSpkRIiIqn6l6EQBwcnJCw4YNkZKSYrfMiJoFzwBlMGLLzEiGLDNiXs2I8djSYRpFMGLGNdXSajUIDRRw444hYLqTbtgMDwAam6jPaBIEXN2oqXTozFo83DT46i3gx5MCXhvl+CEagJkRIiKbka/rUXpqrDhUk5KSYpdVWNUGI/K6EptmRh4GIx5ugLsZ+8F4eWjg9zCDYmkBq9qhIUuIwx8ZD4DLslX/TWVGANgtEBEN7qHB8plaNA5iMEJEVKvJMyOlgxGxiFWn09llZ1xzd+wVOTk5SUGLPWbTqAkKxKGaW6lQbMiq9poAEOBjm5uxvLZFXMMEABo3rB43/+qGwQgRkY3IMyPyYRrA/jNq1GZGAGMRq21n0xj+VDNcIg7VFBQpAwtz2bpmBCgVjJwXTD5PRgxGiIhspKLMiHxGjT2KWC0JRsS6kdzc3DKraFtDfqEg7VRrTr2ISDG914Kkkj2GacJkGRBlZsQ216vpGIwQEdmImBlxcnJCWFiY4lxNyowAtsmOZFgYFIRVca0R+Y69tihgBZRtlGdimBkxjcEIEZGNiJmR0NBQODsrJy/WhGDE1tN7LZ3VYliF1cCSzIjaGTyWMBV0mFrwjAwYjBAR2UBOTo4UAJSuFwFqxjCNrRc+U7sUvCi0zAqnll/XHjUjIlMLnpEBgxEiIhuoqF4EYGYEKD1MY/5NWrHWSDWtGQkKAJydlM+xXqR8DEaIiGygsmAkKChIWlvC3pkRc6b2AnbIjFg4XCJfq8OymhHDn+6uhgXAbEGr1SgyOADrRSrCYISIyAbu3r0rHcuHZEQuLi7Szd6emZF69eqVqV8pT3WtGZEvt27JkvDyHXttqXTwwcxI+RiMEBHZgDwYCQoKMvkacagmJSUFer3epu1Rs0meyNaZEbU79oo0Gg1CHn4Ni4ZpZDv22lLZYIT1IuVhMEJEZAPyYESeYZATg5Hi4mLFMIq16XQ6ZGRkAFAXjNg8M1KFWS3iUM39bMN6JebKLxSQX2g4tndmpLyl4InBCBGRTcgzCeVlRuTDN7YcqsnKypKWTVcTjAQEBEi7C9smMyK7lsrAQF7EeltFdqQq11QrrIEyE8JhmvIxGCGiWiU/Px9XrlyxaM8Sa1KTGQFsG4xYMpMGALRaLQIDDXd9W9eMqM2MyIMRNXUjtt6xV650JoQFrOVjMEJEtYZOp0OXLl3QsmVLrFy50qFtETMJbm5u8PU1/Su4vdYasWQmjUisG7l3757VAzxxmEajAep5qXtvqCzroGZGjT3WGBHJMyFc8KxiDEaIqNa4du0aLl68CAAOD0bEzIh8Cm9p1T0zAhizOgUFBRbtTyMIAsYt1KPNn/T4PV5QPJ/ysFl+3oapsGqEWbg/zX0L1zaxhLyNwQGAizMLWMvDYISIag35Df3ChQuKXXPtSafTIS3NcIcsb4gGUAYj9sqMqA1Gqjqj5swVYNN+4GICMH+NMRg5f8M4vNKxueqPVW6Wl1p+xqagUMCba/RYtcPwGnssBS8KDgC8PAzHLcIqfm1dx2CEiGqN0jf0PXv2OKQd6enp0lTd8opXAfsVsFojMwJYVjeSKItfvjtu3KRu2y/G5597TH3GwNyakY93Af/6HJi+XMBPZwS71ow4OWmw6i8aDOgKLJ7KrEhFGIwQUa1R+obuqGDEnDVGACA4OFg6tlYwYqquw5GZEflMl+ISYOtPhuNtvxjbOexR1R+rqL+oaJjm4BnjdXYfEXDfwrVNLPWnQRr8uFyLmPYMRirCYISIao3SN/QDBw4gPz/f7u0wZyYNALi6ukrBgTWGaaZOnQpXV1esWrVK8bwjMyO305TB0cZ9Aq7fFhB3zfC4WyugcZD6G7WriwYN/AzHd8pZokUQBBy/YHz8wwn7zqYh8zEYIaJao3Qwkp+fj59++snu7TBnjRGROFRz586dKs1Wyc/Px5o1a1BSUoLZs2fjypUrAAw3ZPEYcEBmpFSg8PNZ4P2txu9pyRCNqNHDr3I7zXRGKPEukHLf+PiPG4b/RPbIjJB5GIwQUa1haqjj22+/tXs7zM2MAMYi1qKiImmVVEukpqZKN+SioiJMnz4dgiBg06ZNOHDggNSW0NBQVZ9b9cxI2efe/9p4/Fwf1R8pEYdqikuA9Kyy5+VZEdHhc8ZjBiPVB4MRIqo1xGDEzc0NLi6GndS+/fZbuy+AZm7NCGC96b3i7B3R/v378d///hczZsyQnnv//felfjGXtWpGtLK7jfi/o20kENXY8sxIiCzJUzoDAwDHL5T//93JCfDxtPjSZGUMRoio1hBv5mFhYXjssccAADdv3sSlS5fs2g41wzTWCkZMZS3mzp0rZVvGjh2LMWPGqP7cKmdGHgYJTRoC3Vsrzz33mOqPUwipZEn44xeNx57uynMBPih3/ReyPwYjRFQr5OfnIyvLkKtv1KgRhgwZIp2z91CNJcM0QNWKWOWBgoeHR5lrfPjhhxZ9rp+fH5ycnACoz4wUFgnS8ElIIDD+CeXNf3jvqgUDIYHG95cuYi0uEXDqsuE4shEwqJvyPIdoqhcGI0RUK8izCo0aNcJTTz0lPbb3FF8xGNFqtZUWjNoiM7Jw4UJ4e3tLj9esWaN6GXiRVquVhmrUZkbkAUJIIDCmn3G4JiIY6NTCoiYZP1M+TFMqM/J7PFBQZDju0RYY2F0Z+HAmTfXCYISIaoXSwUhUVBQaN24MADh16pRd60bEDEJgYKCUVSiPLWpGOnXqhPXr16Ndu3ZYsmSJIktkCXGoKSUlBTqdzuz3yQOEkPpAUIAGS1/VoF0ksGKWpsrDJMphGuX/X/kQzSOtNcyMVHMMRoioVigdjGg0GrRq1QoAkJ2dXabA01YEQVDsS1MZW2RGAgMDMWzYMPz++++YM2eOxZ8patKkCQCgpKQEKSkpZr/vtiIzYgg8/jJGg98/0+LZR6ter6EIRkoN0/x63hicPNIGiAzRoLlsIpGtl4IndRiMEFGtUDoYAYDmzY2bnly7ds0u7cjOzkZRkWF8wFHBiHwGjDWEh4dLx4mJiWa/T5EZscGOtUH+ht1wS18LMGZGXJyBTg9/DAbKsiMcpqleGIwQUa0gv5GLC4m1aGEsSrBXMKKmeBUAPD094etrGDOorsGImBkByg9Gjv4uYPpy4Kk3G+HoH4bn5EMntghGnJ01aOgvXsv4fMYDAZcfNrNTc8DdzRCxDOlpzMY0bsiZNNUJgxEiqhUqy4xcvXrVLu1Qs8aISGyvNYIRHx8fuLm5Wfw5psiDkYSEBJOvuZBg2JTuYqKrMRiRD9OoW/jVbOLnptwH9HpD8HNCXi/Sxng8pAcwZxwwbgAwabBt2kOWYTBCRLVCdRmmUbPGiEhsb25uLh48eFDJq00Ta2KsnRUBys+MpKam4sKFCygqKkKvdsbXGzMjxudskRmRf26JDkh7OI1YXrzao40xA6LRaLDkVS02LtAi0I+ZkeqEwQgR1QpiMOLq6ipNY42MjJRmbFTXYRqg6nUjJSUluH/fsAmLPYORLVu2oG3btvD09MTpI1/C7+Fs4mPnDYW8YjDi6Q74elm9WQBML3x24qKyeJWqPwYjRFQriDfx4OBgKQBxd3eXbqSOCEbUZkYAy4IR+a68tghGgoOD4ezsDEAZjFy+bFhVTKfToXFYqHTjv5cB3LhjHKYJqW+71U5NrTVy7rrhTx9PoJm6rXjIQRiMEFGNV1xcLNVMyG/sgHGoJiMjQ3HTthX5MI29MiOlp/Vam5OTk7RmizwYkS+z36pVK/Rsa3zPvpNAVo7h2FZDNIbPNgY5t9OBrBwBCQ9nH7dvyiXfawoGI0RU48mzEeUFI4B9siOOyIzYciaNSMwwZWRkSHUtYmakXr16aNiwoSIY+fpn286kETUqlRn544bxcfumtrsuWReDESKq8UwVr4ocGYyYmxkRpyIDlgUj8gXdbB2MAIbsSF5enjSzplWrVtBoNOjeGtBqDEHIgdPG99pqJg1QdhXWc/HGxx2aMStSUzg7ugFERFVVUTBi77VGxGGaevXqmT3FtiZlRgBDMFJSUiI9btmyJQBDkWpUWDEuJbmiRLZqvHwoxdpK14xotcaMDDMjNQeDESKq8eS73VaUGbHHWiNqloIX1cRgJDs7W3osLrsPAJ2bF+JSkqvivbYcpmnob9h8T68H7twH7stmRrdjMFJjcJiGiGq8ijIjTZsa70i2zozk5+dL9RRqghFfX194eHgAqL7BSOkl4cV6EaBsMFKaLYMRJycNgh9uSHwr1Vgz0rgh4O/DYZqagsEIEdV4FQUjHh4eCAsLA2D7YMSSmTSAYcZHVVZhtfVsGqBsZkQ+k0YcpgGAzi1MBCM2rBkBjMHOnXTjDB4O0dQsDEaIqMarKBgBjHUj6enpyMjIsFk7LJlJIxLbnZGRgYKCAlXvtUcBqzi1FzAsCS8GI05OTmjWrJl0LrxhCRr4Kd/byNbBiInP79Cs7HNUfTEYIaIaTwxGtFqtyYyEvG4kPj6+zHlrsTQzAiiDqJSUFFXvFTMjbm5u8Pb2VvVec3l7e0sr2968eVMapomMjFQU6mo0QA/ZFF9fL8Db07bDJaaCnfZNOURTkzAYIaIaTwxGGjZsCCcnpzLn7VXEao3MCKB+qEYMRho0aGDTRb7EoZqkpCTk5eUBUNaLiHrJghFbD9EApmfrcJimZmEwQkQ1mk6nk4IAU0M0gPXXGjl27BhGjhyJDRs2SM8JgoADBw5Ij+0VjAiCYNNN8uTkRawieb2ISJ4ZsWXxannXcHEGWjYx/VqqnhiMEFGNlpaWBp3OsKhFecGItdcaee211/D1119jwoQJeOWVV1BUVIQ5c+Zg48aNAAAXFxd0795d1WeqCUYuXryITZs2oaSkBJmZmdKaH7YORuRFrCJTmZHurYDwYMNx32jbD5eUzr60agK4unCYpibhOiNEVKNVVrwKWH9678WLxj3qP/74Y+zdu1dajVSj0SA2Nhahoep2aDM3GLl58ya6d++OnJwcnD17FlOmTJHOOSIYMZUZcXcDjq3U4GIC0KeTTZsEoGxmhMWrNQ8zI0RUo926dUs6li+rLufl5SWdq2rNSFZWlmLBLwBSIAIAn3zyCZ5//nnVn2tuMDJ//nzk5Bjmr65Zs0ax4JutpvWKzM2MAECjQA36ddHAyckOmZFSX5vFqzUPgxEiqtGSk5OlY/n009LEupHU1FRkZWVZfD35rrU9e/ZUZEBWrFiBl156yaLPNScYOXXqlKJOJT09XfHY3pmRgIAAmwdA5gisBzjL6pZZvFrzMBghohpNHoyIi5uZIl8L4+bNmxZfLykpSToeMGAATp06hUWLFmH37t2YNWuWxZ9bv359ODsbRs5NBSOCIGDOnDllnl+/fr10bO9gpGXLljadvWMurVajmN7LYZqah8EIEdVo5gYjERER0nFVghF5ZqRJkyYICgrC/Pnz8fTTT1v8mYBhjZTgYEPVp6lgZM+ePTh48CAAQw2MuOaHfIE0WwcjwcHBcHFxkR6XN0TjCNEPa5SbhQKhtu0GsgEGI0RUo8kzFfYIRuTXq2hYyBLiUM29e/cUu+KWlJRg7ty50uP//Oc/GDVqVJn32zoY0Wq1iu9sqnjVUVb+RYN3XtFg52JNtcjWkDoMRoioRhMzI15eXvDz8yv3dZGRkdLxjRs3LL5e6cyINYnBiCAIilVY9+3bhwsXLgAAHnnkEYwcORLjx48v835bByOA8jtXp8xIaAMN5j6vQdtIBiI1kepg5N///jcGDRqEPn36YMyYMfjll1+kc7GxsRgwYAD69euHFStWQBAE6dz58+cxduxYxMTEYOrUqRZtBkVE1Yv877ijri8GI2FhYRX+RmyLYRprZ0bkbZQHTH/88Yd0PGPGDGg0GsTExJS5vj2CEXk2pH379ja/HtUNqoOR8ePHY/fu3fj555+xYMECvPnmm8jMzMThw4exZcsWxMbGYvPmzTh69Ch27twJACgqKsLcuXMxduxYHDhwAB07dsSbb75p9S9DRPazcOFCeHl5YenSpQ5rQ1ZWFnJzcwFUPEQDGKb9igWi1him8ff3t/o+MOWtFCvfT0dcwE2r1SqmEDs5OVWYGbKWuXPnYtiwYVi6dKli/RaiqlAdjERERMDV1RWAYXGfkpISpKamYs+ePRg+fDjCwsIQGBiICRMmYM+ePQAM09FcXFwwbNgwuLm5YcqUKbh48aJifQAiqjkEQcB///tf5Ofn44033sCVK1cc0g5zi1cBw81aHGKwNBjR6XTSNa09RAMoV4qVr4ciD0bks4LkwUj9+vWh1dp+5L1p06bYvn07/vrXv9r8WlR3WLQC63/+8x/s3r0bhYWFiImJQfPmzXHjxg0MGjRIek3z5s2lv0DXr19X/CVzd3dHWFgYrl+/bnKVwqKiIhQVFSkb6uwsBUG1nV6vV/xJFWN/qWON/kpNTZUW3iopKcHrr7+OrVu3WqV9asiHTEJDQyv9TpGRkbh+/TqysrKQnp4Of3//Cl9fuq/u3LmD4uJiAIYhGmv/zMkzDVevXpU+X8yS+Pj4ICAgQHq+Xbt26N27Nw4dOoQePXo4/O8A/y6qU1f6y5wg2aJg5PXXX8ecOXNw6tQpxMfHQ6PRIC8vD15eXtJrvLy8kJ+fDwDIz89XnBPPi7s+lrZu3TqsXr1a8dyoUaMwevRoS5pbY8mr9qly7C91qtJfcXFxisfbt2/H119/ja5du1a1WRa3w8PDQ7ESqin16xsXozh27Bjatm1bwauNxL46e/as9Jyfn1+l17OEk5MTdDodLl68iISEBBQXF0tBV5MmTRQBGGBYaO3EiROIiYmxSXsswb+L6tT2/pIXj5fH4r1pnJyc0L17d3z55Zdo3LgxPD09pbFbAMjNzYWHhwcAwz8S8nPieU9PT5OfPXny5DKV4nUtM5KUlITGjRvbJe1a07G/1LFGfx0/frzMc++++y4OHTpk12mV4i88ANCxY0eTu8rKtWvXDps3bwYAFBYWVvr60n3122+/Sefatm1b6fstERERgfj4eCQmJqJJkyaIj4+XNgJs1apVmWuGh4ejY8eOVm+HJfh3UR32l1GVN8oTx1AjIyNx7do19OnTB4BhjFMc22zatKkihVtQUIDk5ORyi59cXV3rTOBREa1WW+d/QNVgf6lTlf6S/3Yu/iZ/7NgxbN++HSNHjrRWEyslrztr0qRJpd9H/htaYmKi2d9f7Cv59cLDw23y8yYOcT948ADp6emKWTXNmzevET/j/LuoDvtLZQFrTk4O9u7di7y8PJSUlGDfvn04efIkoqOjMWTIEGzbtg3JycnSfglDhgwBAHTp0gWFhYXYuXMnioqKsHbtWrRu3Vr1rpZEVD3IC0DfeOMNxbE9p/uqKWAFqr7WiC2n9YpKF7GWV7xKVJuoDsW2b9+OIUOGoH///oiNjcW//vUvtGzZEo8++ihGjhyJiRMnYuTIkejRoweGDh0KwJDpWLp0Kb788ks8/vjjOHPmDBYtWmT1L0NE9iG/kc+YMQO9evUCYLh5lq5psCUxGHF3d5eWR69IVdcaseWCZ6LS03sZjFBdoGqYxtvbGx9//HG55ydPnozJkyebPNe2bVts2rRJXeuIqFoSb+Senp5o2LAhevXqhaNHjwIwBCS2qKUwxdwFz0SNGjWCi4sLiouLLQpGxEJDrVaLkJAQ1e83B4MRqovq9iAVEakmCIJ0I4+IiIBGoyl3fQxbys7ORnZ2NgDzh0y0Wq0UKN28eVP1kJKYGZEvoGZt5QUjLi4uZg1FEdVEDEaISJV79+5JO8WKwx5RUVHSeXstgKa2XkQk1o08ePAA9+/fN/t9BQUFuHfvHgDbDdEAhvaJxYxXr17F9evXpeednJxsdl0iR2IwQkSqyOtFxGDEEZkRS4MRNXUjqamp+OWXXxQrrwK2K14FDDV2YvYmLi5OWo+JQzRUmzEYISJV5Ddw8cYeEhIirRtUW4KRpKQkREdHY9KkSfjTn/6kWFDMlpkRwDhUU1JSIj3HYIRqMwYjRKSK/AYuDnloNBrpBnr9+nXFTdRWbBmM5ObmYujQodLu4l999RXefvtt6by9ghE5BiNUmzEYISJVTA3TAMa6kZKSkirtimsu+RLaaoZNKltrRK/XY9KkSThz5ozi+f3791t0PUswGKG6hsEIEaliapgGsH/diK0yI4sWLZJWjPbx8cHjjz9e5jXMjBBZF4MRIlJFvIF7eXkpNp5zVDDi6uqKwMBAs98XFBQENzc3AGWDkfj4eLz11lsADENPGzduxIoVK8oEArYORuR9KTJnszGimorBCBGZTa/XS4WckZGRioXG5DdQe0zvVbvgmaiitUZOnTolHb/22msYMmQIvL298fnnn0vTan18fMxa7bUqSvdtaGiotPEoUW3EYISIzJaSkoLCwkIAyuEOQLnWiK0zIzk5OcjMzASgbohGJGYZcnNzkZaWJj0vX+69W7du0nGPHj3w6aefonnz5li8eLHNdyZ2d3dX1KWYGrYhqk0YjBDVICUlJVi5ciX27NnjkOuXVy8CAA0aNICvry8A2wcj8t1zLQlG5G2XF7FWtPfMxIkTcfXqVcycOVP19SwhD0BYL0K1HYMRohpk9uzZmDFjBp599llcu3bN7tevKBiRLwufkJAgZVBswdKZNKKmTZtKx+YGI/bGYITqEgYjRDXEb7/9hpUrVwIAdDodfvvtN7u3QX7jNlVQKQ7V6PV6aRlzazl9+jT+/Oc/o2vXrhgyZIj0vCWZEXkwIt+ITgxynJyc0KhRoyq0turatm0rHbdu3dqBLSGyPdvs9EREVlVSUoJp06Ypii3ttdKpXEWZEaDsjJqq3kRLSkrw2Wef4aOPPsLJkydNvqZLly6qP1cejMiDJjEzEhoaCicnJ+j1etWfbS0TJ07EgQMH4Ovri6efftph7SCyBwYjRDXA//73P5w9e1bxXHUbpgGsP713zpw5eO+99xTPaTQatGrVCt27d8fQoUPRo0cP1Z8rH/YQg5G8vDypmNXRQzQAUK9ePezYscPRzSCyCwYjRNVcUlIS3nzzTQCGG7GYHXFEMCIO0/j4+MDf37/MeWtP7923b590HB0djWnTpmHMmDHw8/Or0ufWq1cPAQEBuH//vjRMI69DqQ7BCFFdwmCEqJr7xz/+gdzcXADAtGnT8M033yA5OdnuwYhOp5OGMUqvgyGyZmZEEAQp+GnRogVOnTpl1Sm1TZs2xf3795GUlISioqJqVbxKVNewgJWoGhMEAXv37gVgWPF08eLF0g0/LS1NWmvDHm7evIni4mIAypoLuYCAAGlV1qoGI6mpqVIQ1rRpU6uv7SEO1QiCgISEBAYjRA7EYISoGktISJB2ju3Zsyf8/f0VUz7tmR25cOGCdCyf6VGaGCwlJycjLy/P4uvJC0vLC36qonQRK4MRIsdhMEJUjR05ckQ6jomJAQCHBSPnz5+Xjtu0aVPu6+RDNVVpn3wasa2Dkfj4eAYjRA7EYISoGjt69Kh03KtXLwCOC0bMzYzIl4W/fPmyxdeTZ0ZssUkcMyNE1QeDEaJqTAxGNBqNNIXV3rvjisRgRKvVKgKO0uRri1y8eNHi69l6mKb09F4xGPH19UW9evWsfj0iKh+DEaJq6sGDBzh37hwAoH379tK+L/Ibs70yI3q9XgosmjZtWuEOstYKRmw9TBMWFgZnZ8OEwmvXrklTe5kVIbI/BiNE1dTx48elFUDFehHAMKsmJCQEgP2CkYSEBKkYtaJ6EcAwjOTk5ATAOpkRf39/m2QqnJycpIXbLly4IO2lw2CEyP4YjBBVU/LiVbFeRCTWjdy7dw/Z2dk2b4u59SIA4OrqKg2BXL582aIl1YuLi6VMhS2yIiKxnTqdTnqOwQiR/TEYIaqm5MWr8swIYL0ZK+aSByOVZUYA41BNQUEBEhISVF8vMTFRCmJsGYyY+mwGI0T2x2CEqBrS6XT49ddfAQDBwcFl9oGx94wac6f1iqpaN2LrmTQiBiNE1QODEaJq6Pz589LwS69evcqsPmrvYETMjIib1FWmqsGIrYtXK/psBiNE9sdghKgaqmiIBrBvMCIIghSMREZGwtPTs9L31JTMiHx6r6hx48Y2ux4RmcZghKgcOp0OBQUFDrl2RcWrgPImauu1RhITE6U9YswZogGgyJ6YG4wUFxdLOxLbeo0RUelAR6PRIDQ01GbXIyLTGIwQmZCUlIQmTZogLCzM7rvj6vV6HDx4EADg5uaGzp07l3mNj48PgoODAdg+M6JmJo3Ix8cHYWFhAAzBiBhklOcf//gHXF1d8dprrwEwDtNotVqbDpv4+voiMDBQehwSEgIXFxebXY+ITGMwQmTC8uXLcfv2baSnp2Pz5s12vfYvv/yCW7duAQAGDBgAV1dXk68Th2pSUlKQk5Njs/aonUkjEodqMjIycO/evXJfl5aWhrfffhsAsGLFChw6dEjKjISFhZX7/a1FnmVivQiRYzAYISolNzcX69atkx5XZX8VS2zYsEE6Hj9+fLmvk9eNxMfH26w9lgYj5g7VbNy4EcXFxdLjGTNm4P79+wBsO0Qjkl+DwQiRYzAYISpl06ZNyMrKkh5fuXLFqp9/9+5dXLt2zeRiYIWFhdi6dSsAw0qrzz77bLmfIw9GbFk3Ip/Wa85MGpG5Raxr165VPP7999+lYwYjRHUDgxEiGUEQsHLlSsVz1gxGLly4gMjISLRo0QJ+fn7o27cvFi5cKA2zfPfdd8jMzAQADBs2DF5eXuV+lnzhs6pmb/Ly8vDf//4Xu3fvVjwvn0kTEREBb29vsz/TnGDkzJkziIuLAwBF7YbIljNpRC1btpSOTc2uISLbYzBCJHPixAmcPn1a8dz9+/eRlpZmlc//8ccfkZ+fD8CwEd7PP/+Mt956C8OHD0dJSQk2btwovbaiIRoAaNeunXQs3tAt9f7772POnDl49tlnsX//fun55ORkPHjwAIC6IRrAvGBEnhVZtGgR+vXrpzhvj8zIyJEj8fTTT+OJJ57AuHHjbH49IiqLwQiRjDwr0qBBA+nYWtmR5ORk6TggIEA63rdvH2bNmiVlJgIDAzFgwIAKPysqKgpubm4Aqh6MyIdGXn31VWlK89KlS6Xn5cGPORo2bAh/f38AwKVLl8qcLygokOpj3N3dMW7cOMX1APsEIx4eHti9ezd++OEHaWdkIrIvBiNED6Wnp+Orr74CAPj5+UnTTAHrFbGKm78Bhl15Dxw4IG1jv2rVKikIGDNmTKVTTJ2dnaUA4erVq9JaIJZISUmRjq9evYp33nkH+/fvx//+9z8AhmBhypQpqj5To9FI2RF5hkW0a9cuZGRkAABGjBiBevXqoXPnzpg4cSIAwNvbW3U2hohqJgYjRA/FxsZKwcCkSZPQqVMn6Zy1MiPyYCQsLAyPP/443n333TKvq2yIRtSxY0cAhtqOP/74w+J2yYMRAFi8eDFeeOEF6fF//vMfREVFqf5c+VBN6eyIfMbSiy++KB1/9NFHWLlyJQ4ePMhMBVEdwWCECIab+SeffCI9fuWVVxSFjdYepmnQoAHc3d0BGKayTpo0SXpNZGQkevToYdbnyQOmqgzV3LlzR/G4qKgIt2/fBgA8/vjj+L//+z+LPlcejMinCGdmZuKHH34AYCiM7du3r3TO3d0dr776Krp27WrRNYmo5mEwQgTDQmNiwNG3b1+0bNkS4eHh0oJb1him0el00mJm4uqkgGE4Y9WqVXjyySfh7OyMRYsWldkYrzxiZgQAzp49a1G7CgsLpeGSjh07Ijw8XDrn4+ODdevWQau17J+K9u3bm2xfXFycNLV5yJAhFn8+EdUO/BeACFBkRaZOnQoAcHJyktbyuHbtGnQ6XZWukZKSIn1G6c3Y3N3dsWfPHuTl5Zk9RAMAHTp0kI4tzYzcvXtXOm7WrBlWrlwpBQcffPCBIjhRKzo6Wjo+c+aMdCxvqzy7Q0R1E4MRqvPS0tKkhcbq16+P5557Tjon1kkUFhYiMTGxSteRz6SRZ0ZEGo1G9b4ofn5+UrBw7tw5kwupVUY+RNOoUSMMGTIEv/32G44dO6aoG7FEgwYNpI3nzp49K+1RI8+SyLM7RFQ3MRihOu/zzz9HUVERAEPhqjhdFoBV60bkxavW3KZevJnn5ORIG8ypIS9eFTff69Kli9l1K5URMx9ZWVm4efMmAGNmRKvVqp4yTES1D4MRqtNKF66+/PLLivPyGSTVPRgBLBuqMRWMWFPpoZqSkhJpifmoqCh4enpa/ZpEVLMwGKE67dChQ1Jxap8+fRSZEEAZjFS1iLWyYRpLVXVGTelhGmsrHYxcvnwZhYWFADhEQ0QGzo5uAJEjrV+/XjqeNm1amfM1aZgGsGxGjb0zI/LpvgxGiAhgZoTqOPlCYaZ2yA0MDISfnx+AqmdG5MGIWNRpDZGRkdIGdtVxmCYiIgL16tUDYAiWWLxKRKUxGKE67erVqwAMwyamdsjVaDRSdiQxMVHa5M4S4jBNw4YNFUWyVaXVaqUpvgkJCdKuv+YSh2k0Gg0aNmxotXaJNBqNNJR069Yt7Nu3TzrHYISIAAYjVIdlZGQgPT0dANCiRYtyXyevG7l27ZpF1yopKZFWNLXmEI1IflM/d+6cqveKmZHAwEDVU4vNZWq9kfr16yMkJMQm1yOimoXBCNVZ8fHx0rG4uJkp8roRS4dq7ty5I60BYs3iVZGlM2oEQZCCEVsM0YjkwYioU6dOZq80S0S1G4MRqrPkWY6KghFrTO+Vz6SxdWbk999/N/t9GRkZ0hortphJIzIVjHCIhohEDEaozhLrRQDzgxE1N3q50rv1Wpt8hsrFixfNfp+ti1dFrVq1KlMnw2CEiEQMRqjOMjcz0qZNG2m2ysGDB6UlzdWw1bReUb169aT6C0uDEVtmRlxcXMqstMpghIhEDEaozpIHI82aNSv3dS4uLujTpw8Aw6Zy4uqhath6mAYwZkfS09ORmppq8jWZmZlYt26dNNwkX/DMlpkRQDlU4+LiosjmEFHdpioYKSoqwsKFC/HUU0+hT58+mDRpkqJyPzY2FgMGDEC/fv2wYsUKxW+Q58+fx9ixYxETE4OpU6cq/hEkcgQxGAkJCTE5rVduwIAB0rF8aqq5bD1MA1Q8VCMIAj7//HO0bNkSL730EsaMGYPs7Gy7DdMAymCkTZs2cHV1ten1iKjmUBWM6HQ6hISE4NNPP8XBgwcxbtw4vPbaa8jLy8Phw4exZcsWxMbGYvPmzTh69Ch27twJwBDEzJ07F2PHjsWBAwfQsWNHvPnmmzb5QkTmyM7Oxr179wBUPEQjqmowIs+MWHPBM7nygpGbN2+ib9++mDhxovSds7Ky8N1339ltmAYwbL4nki9hT0SkKhjx8PDAyy+/jODgYGi1WgwaNAguLi5ISEjAnj17MHz4cISFhSEwMBATJkzAnj17AACnTp2Ci4sLhg0bBjc3N0yZMgUXL17ErVu3bPKlqPpLSUnBsWPHpP/s/bNgbr2IqG3btggKCgIA/PTTTyguLlZ1PTEzEhQUZLOMQKtWraTjS5cuSccTJkzAL7/8Uub1u3btsuswTffu3TFhwgS0bNkSr732mk2vRUQ1S5X2pklMTER2djYaN26MGzduYNCgQdK55s2bS+s4XL9+XbGolLu7O8LCwnD9+nWTvyUWFRVJ0w2lhjo715m0rrgehfhnbXPkyBH07dtX8f00Gg2+//579O/fX/XnWdJf8im6zZo1M+u9/fv3x8aNG5Gbm4tjx47h0UcfNetaxcXF0k2/cePGNvv/Kl8P5cKFC9Dr9UhLS8ORI0cAGIKNjz76CJMmTUJmZib27NmjKCJt2LChzX/mPvvsM+m4Jvx81/a/i9bG/lKnrvSXVlt53sPiYKSgoABvvvkmJk2aBG9vb+Tl5SnG3b28vKSls/Pz88uMyXt5eSEvL8/kZ69btw6rV69WPDdq1CiMHj3a0ubWSPI6g9pk1apVZf7yCYKAJUuWmJWlKI+a/jp58qR0XK9ePSQkJFT6nujoaGzcuBEAsHXr1goLUQVBwMWLF5GWloaMjAypfiogIMCsa1lCEAT4+PjgwYMH+OOPP5CQkID9+/dL559++ml06NABjz32GHbt2oXs7GwpUHF3d0dGRobqpeTritr6d9FW2F/q1Pb+ioyMrPQ1FgUjJSUleP3119G4cWO8/PLLAABPT0/k5uZKr8nNzYWHhwcAw/CO/Jx43tPT0+TnT548GePHj1c2tI5lRpKSktC4cWOzIsqaRix6dnJywsyZM7FhwwakpaXh0KFD8Pf3h6+vr6rPs6S/xGXgAaBnz54IDw+v9D2jR4/GnDlzABiGHk29JzMzE1988QU++eQTk7NuoqKizLqWpdq2bYtff/0Vt2/fRv369RVrqQwaNAjh4eEYN24cdu3aBcD4G1mjRo0QERFhs3bVVLX976K1sb/UYX8ZqQ5G9Ho93nzzTWg0Grz11lvScs6RkZG4du2aNAUyPj5emi7ZtGlTbN26VfqMgoICJCcno2nTpiav4erqWmcCj4potdpa9wOamZkp3aQ7deqE9957DyUlJfjwww9RWFiIPXv24Pnnn7fos0v3161bt5CammqyWFJeM9KiRQuz+rlJkyZo1aoVLl26hOPHjyMnJ0cROO3evRtjx44tN+MHAL169bLp/9PWrVvj119/BWBY1O3YsWPSuUcffRRarRaDBw+Gq6urYihUrAMj02rj30VbYn+pw/6yYJ2RxYsXIz09Hf/5z3/g7GyMZYYMGYJt27YhOTkZ6enp2LBhA4YMGQLAUEVfWFiInTt3oqioCGvXrkXr1q1tNquAqi/xRgkYbsyAYQhOJA9aq+Lu3bvo0qULoqOjERsbW+a8GIwEBQXBx8fH7M8VZ9XodDr8/PPPinNLlixRBCK9evXC/PnzsWDBAixYsAAbNmyw+VCjfEZNXFwcTpw4AcDwC4FYoOrj44OePXsq3mfrmTRERBVRlRm5c+cOduzYATc3N8VUx/fffx+PPvooRo4ciYkTJ0Kv12PYsGEYOnQoAEOmY+nSpVi0aBGWLFmCNm3aYNGiRdb9JlQjiDUKgDEYefTRRxEUFIS7d+/iu+++Q05OjrTiqaW++OIL3L17FwCwaNEivPDCC9JvHjk5OdKU1op26zVlwIAB+OCDDwAYpvg+88wzAAxFqmIdSmhoKPbu3VtmxVF7kAcjGzduREFBAQBjX4sGDBigCKZsPZOGiKgiqoKRRo0aKQr/Sps8eTImT55s8lzbtm2xadMmda2jWufo0aPScUxMDABD7chzzz2HVatWoaCgAN9++y3GjBlTpets2LBBOr5+/Tp++OEHPPnkkwDM363XlL59+8LJyQk6nU6x3si5c+ekG3+fPn0cEogAyum98uJVsa9FAwYMUKz1w2CEiBypbg9SkV2VlJTg+PHjAAyrkMpno4wcOVI63rJlS5Wuc/HiRZw5c0bx3MqVK6VjtWuMyNWrVw9du3YFYJg+K07ZlQ8/9ejRQ3WbrSUyMrLMhnRA2cxIUFAQunfvLj3mMA0RORKDEbKbc+fOSbOqSv+m/thjj6FBgwYAgD179pSZfaWGPCsi+uabb6Qptebu1lse+VooBw8eBAApyAIcG4w4OTkpdhkGAF9fX7Rt27bMa0eMGCEdy9coISKyNwYjZDfyIZrSv6k7Oztj+PDhAAzr0oir96olCIIUjGi1WsyYMUN6/pNPPgFQtcwIAPTr1086FodCxMyIm5ubw3ejLb0BXY8ePeDk5FTmdf/3f/+HuXPn4u233zZ7ATciIltgMEJ2Iy9eLZ0ZAZSzarZt22bRNY4ePYqbN28CMNRFzJ8/X5r1tWbNGly6dEkxpGJJMNKrVy9pKOTAgQNIT0+Xsi2dO3d2+LT00sGIqb4GDIHTO++8g9dff12aok9E5AgMRshuxMyIp6cnOnToUOZ83759pVk08sBFDfkQzfjx4xEcHIznnnsOAHDv3j20bt1aWuckKCgI9erVU30NDw8P6QZ/8+ZNfPnll9I5Rw7RiEoHI6WzUERE1Q2DEbKL5ORkJCYmAjBsmObi4lLmNc7OzlJxaFJSEm7fvq3qGkVFRdi8eTMAQ8AgDvu8+uqrZV7r6uqKBQsWqPp8OflQzZIlS6Tj6haMaLVaPPLIIw5sDRFR5RiMUJX98MMPeOutt5CRkVHua0xN6TVFfjOXF4Wa49NPP5WWeX/22Welxcz69OkjbeLYpEkTvP3220hKSsL06dNVfb6cvIhVvq9EdbjxR0VFSUNFHTt2VLWoGxGRI1Rp116i9PR0DB06VFrif82aNWVeU1RUJC0UBlQ8bCAPRn799Vcpu1GZnJwc/POf/5Qey7eo12g02LlzJ+7cuYPGjRubLOZUq2vXrvD19UV2drb0XHBwMJo0aVLlz64qd3d3LFu2DOvXr8fixYsd3RwiokoxM0JVEhcXJy32tWXLFhQWFirOC4KAWbNm4dChQwCAkJAQ9O3bt9zPk2cW5IWmlfnkk09w7949AIYN7UpnKNzc3BAREWGVQAQwDCmJ+zCJevToUW0KQWfOnInjx48rMjhERNUVgxGqkosXL0rH2dnZilVJAcNiYx9//DEAQ0Cwbdu2cndrBgzZBXFX25MnT6KkpKTSNty6dUvKyLi4uNgtGyCvGwGqxxANEVFNxGCEqkQejADKje4OHDiAP//5z9LjNWvWmHXDFodq8vLy8Mcff1T6+n/84x9Sdmb69OnSbtG2VjrrUB2KV4mIaiLWjNRyp0+fVhSCBgQEYNiwYSaXDLdE6WBkx44d+PjjjyEIAqZMmQKdTgcAmDt3LiZMmGDWZ/bo0QNfffUVAMNQTadOncp97ZEjR/DZZ58BMCzVLt9vxdbatWuHBg0aIDU1FVqtVpoJRERE6jAYqcUuXryI7t27SwGBaNasWVixYoXVriGXmZmJAwcO4OrVq9LiY3379lU1dFK6iPWVV14x+bpff/0VgwcPhl6vBwD87W9/Q/369VV+A8tpNBrMnz8fc+fOxfTp06u80zARUV3FYZpa7JtvvikTiADA6tWrkZmZWeXPz8rKkjaKk686um7dOixatEh6vGzZMlWFo506dZLWISlveu9vv/2GQYMG4cGDBwAMM3TkQ0L2MmvWLOTk5GD58uV2vzYRUW3BYKQWk69iumzZMgwbNgyAYe+Xzz//vMqff+nSJel4zJgxUmZg8+bNSE1NlZ7v3Lmzqs91d3dHdHS0dI3S65ecP38eAwcOlKbV9uvXD6tXr3bYMuzicvNERGQZBiO1lCAI0kJjfn5+mD17Nv71r39J51euXAlBEKp0DfkQTXR0NJ5++mnFeScnJ0WGRA35UM1vv/2mOLd48WJkZWUBMAwB7dy5Ex4eHhZdh4iIHI/BSC117do1KTvRq1cvaLVatG3bVlob4/Llyzh48GCVriEPRlq3bo2RI0cqzr/00kto0aKFRZ9d3kqsgiDgp59+AgB4eXnhm2++qXCqMBERVX8MRmop+fLr8hVP5fu0rFq1qkrXKB2MDB48GF5eXgAMe8NUZe8XeTBy7Ngx6fj69evSnjW9evWSrkdERDUXg5FaSl4vIt8LZvjw4QgKCgIAbN++XfVmdHJiMOLp6YnGjRvD09MTq1atQnR0NGJjYxESEmLxZ0dERCA4OBgA8PPPPyMvLw8ApJVcAeCxxx6z+POJiKj6YDBSS4mZEScnJ3Tr1k163tXVFS+//DIAQKfTYfXq1RZ9fkFBAa5fvw4AaNWqFbRaw4/Sn/70J5w+fRqjR4+uSvOh0WikGpT8/HxpZVd5MNK7d+8qXYOIiKoHBiO1UGZmJs6fPw/AUFhaeihj6tSpUvCwfv16i65x9epVaX0P+Zb11jR06FDpeOfOnQCAX375BYAhqOrevbtNrktERPbFYKQWktdYmNoht3HjxujZsycAID4+Hvfv31d9jdL1IrbQv39/qTh19+7duHXrFq5duwYA6NatG2fQEBHVEgxGaiF58aq8XkROvvbH2bNnVV/DHsGIh4cHnnzySQBAamoqli5dKp3jEA0RUe3BYKQWkhevmsqMAJAWFQOAM2fOqL6GPYIRQDlUs3LlSumYxatERLUHg5FapqSkRFqXo3HjxggLCzP5OmsFI87OzmjevLkFLTXPU089JS0lX1xcDMBQ3FpekEVERDUPgxEr0ev1uH79Oq5du4Zr165VacpsVcTFxUnTYMsbogGANm3aSPu/qA1GTpw4IQUjzZs3lz7HFurXr49HH31U8VzHjh1Rr149m12TiIjsi8GIFRQXF6Nnz55o1qwZWrRogRYtWiA0NBSzZ8+2e1sqK14Vubq6ol27dgAM+7+IAUxlTp48iSeeeELKUgwaNKgKrTWPfKgG4BANEVFtw2DECr777rsy+6cAwAcffIBbt27ZtS0nTpyQjh955JEKXysO1ej1epw7d67Szz59+jSeeOIJxb4w//73v6vQWvOUDkZYvEpEVLswGLEC+Q64Tz/9tDRttiqLillKDEZcXFzQsWPHCl+rpm7kzJkzGDBgADIzMwEYshPffPONXZZjb9q0Kdq3by89Lj1sQ0RENRuDkSq6f/8+du/eDQAICgrC9u3bsXnzZqno8pNPPpGGNGztwYMHuHTpEgCgQ4cOcHNzq/D15gYjcXFxGDBgADIyMgAYgoFvv/3WrvvCLF26FC1btsSCBQukZeKJiKh2YDBSRV999RWKiooAAOPHj4ezszPCwsLw7LPPAgDu3LmDXbt2WeVa586dw4IFC6Rl2Es7ffo0BEEAAHTt2rXSz+vYsSM0Gg2A8oORc+fOoX///tLCaDExMdizZw+8vb0t+QoWGzRoEC5duoSFCxfa9bpERGR7DEaqSD5E88ILL0jH8t1x5etjWEqv1+PZZ5/FokWLEBMTg+Tk5DKvkdeLyPejKY+3tzdatGgBAPj999/LZHBu3bqF/v37Iz09HQDQs2dPfPfdd/Dx8anKVyEiIlJgMFIFly9fxq+//grAkGWQ12j0799futEfOHBAsUiYJU6cOIGEhAQAQEpKCoYOHVpmBozaYAQwDtUUFhZKQzyi2NhYpKWlAQB69OiBvXv3MhAhIiKrYzBSBfJN5uRZEQDQarV45ZVXpMcfffRRla4l1qWITp8+jRdffFEalgEM024BwzLqbdq0MetzK6obkT/+9NNP4evrq7rdRERElWEwYiG9Xi8FI05OTnj++efLvGbSpElwd3cHAHz22WcoKCiw+HrffPONdCwWjn711VfS1Nr09HSplqRz585wdnY263MrCkbi4uIAGIKbli1bWtx2IiKiijAYsdAvv/yCxMREAIbiSlMzPAICAjBy5EgAQFZWFg4dOmTRtZKSkqTAoFu3bti4caNUePrWW2/h+vXrUlYEMK94VVReMJKTk4P4+HgAQLt27aTZQURERNbGYMSEM2fOYPjw4fjvf/+LwsJCk6/ZsGGDdDxhwoRyP+vpp5+Wjvfu3WtRe+RZkaeffhrPPvssXn/9dQCGtUyWLFliUb0IADRo0EDav+bMmTPQ6/UADAWt4hBQZeuVEBERVQWDERNeffVV7NixA3PmzEHHjh2xf/9+xfnCwkJs3boVgGHIRJzGa8oTTzwBrdbQzdYKRgBgzpw5UjHpunXrFDUlaoIR+euzs7OlDIz4J8BghIiIbIvBSClJSUnSrreAYcbMgAEDMGXKFOh0OgCG5d/FlUiHDx9e4eJfAQEB0rLsFy5ckIZ2zJWbmysFQyEhIdKwir+/P6ZPnw4AKCoqkpajr1evnupddPv16ycdi9diMEJERPbCYKSUHTt2SMf+/v7S8dq1a6UZMRs3bpSeN1W4WtqTTz4pHX///feq2rN//35pqOjpp5+WakUA4LXXXpMKZEVdunSRMjHm6t+/v+J6AHD27FnpuQ4dOqj6PCIiIjUYjJSybds26fjnn3/Gxx9/LD2eP38+4uPjpSGRBg0aYMCAAZV+pjwY+e6771S1x9QQjSgoKAgvvfSS4jm1QzQA0KpVKzRq1AgAcOjQIRQWFuL3338HAERGRqJevXqqP5OIiMhcDEZkUlNT8csvvwAAoqKi0K5dO0ydOhUTJ04EAGRmZqJ///7SFN3Ro0fDxcWl0s/t2rUrAgMDAQD79u0ze6+a3NxcqTbF3d1dkcEQzZkzRzGN15JgRKPRSEM1ubm5+PLLL5GbmwuAQzRERGR7DEZkdu3aJc0mGT58uDQk8s4770jZAXEVVMCwF405tFotBg4cCMCwmd2xY8fMel9sbKy0Od3o0aPh6elZ5jVNmjTB5MmTAQCurq6IiYkx67NLkwc6y5cvl44ZjBARka0xGJGRD9E899xz0nFQUBD++c9/Kl4bGRmJHj16mP3Z8qEac2bV6HQ6vPfee9Lj//f//l+5r33vvffwzjvvYM+ePRbvaCsvYhWHaAAGI0REZHsMRh7Kzs7Gvn37AABhYWFlFg6bPn26opDz+eefVxSTVkbMjADmBSM//PCDtKLqE088UWERqaenJ+bOnWtyGMdc4eHhaNasWZnnO3XqZPFnEhERmYPByEN79uxBUVERAMMQTekZKc7OzlizZg0CAgIQHBys2HfGHEFBQejSpQsAw+Jid+7ckc4JgoD33nsPs2fPxs2bNwEY9oIRVZQVsabSwYyvry8iIiLscm0iIqq7GIzAUJj61ltvSY/lQzRy3bp1Q1JSEpKSkqRVS9UYMmSIdCyfQrx//3689tprWLFiBdq0aYNXXnkFp0+fBmBYil2eVbGl0sFIhw4dVGV/iIiILFHng5GSkhKMHTsWly9fBmC4Affu3bvc13t6epq9CV1pI0aMkI63bNkiHcuXls/Pz8fq1aulx//v//0/uwUEjz/+uOIx60WIiMge6nwwMnfuXGkhsvr162P79u022xSuQ4cO0uqoP//8M+7du4fCwkJs374dgGE2jPzawcHBGDdunE3aYkqDBg0UtSkMRoiIyB7qdDCydu1avPvuuwAMNSFff/01mjZtarPraTQajBo1CgCg1+uxfft2fP/998jKygIAjBkzBqdOnULfvn3h4eGBJUuWwM3NzWbtMWXw4MHSsZrZQkRERJaybLyhFoiLi1MUoX744Yfo06ePza87cuRIvP322wCArVu3omHDhtK5MWPGSBvz3bhxA5GRkTZvT2l/+9vfkJeXhzZt2qB9+/Z2vz4REdU9dTYYadu2LWbMmIH33nsPM2fOxNSpU+1y3ejoaERGRuLGjRs4ePCglPnw9/fHE088Ib1O7f4y1lKvXj28//77Drk2ERHVTXV2mMbZ2Rnvvvsudu3aJQ3V2IN8qEan0yEvLw+AobjV1dXVbu0gIiKqLupsMCJ65plnLJ4dY6mRI0eWeW7MmDF2bQMREVF1oSoY2bp1K8aPH49HHnlEsZstAOzevRtDhgxBnz59sHDhQsVmcMnJyXjxxRcRExOD8ePH48qVK9ZpfQ3VtWtXhIeHS48bNmyIvn37Oq5BREREDqQqGAkMDMTUqVMV+5gAwLVr17B8+XIsXboU3377Le7evYs1a9ZI59944w088sgjOHDgAIYPH445c+agpKTEOt+gBtJoNIrsyKhRo+yenSEiIqouVAUjffv2RZ8+feDj46N4fu/evejXrx/atm0Lb29vvPjii/j2228BADdv3sSNGzcwefJkuLm5YeTIkdDr9Th79qzVvkRN9Morr8DHxweenp6YMWOGo5tDRETkMFb5dfz69evo3r279Lh58+ZISUlBXl4ebty4gSZNmiiKM5s3b474+Pgym9GJioqKpH1ipIY6O9eqAs+mTZsiKSkJgiDA19cXer1eOicey5+j8rG/1GF/mY99pQ77S5260l/mzA61SjCSn58PLy8v6bG3tzcAIC8vD3l5eYpzAODl5YX8/PxyP2/dunWKJdEBw1DG6NGjrdHcaicjI8Pk80lJSXZuSc3G/lKH/WU+9pU67C91ant/mbNmllWCEQ8PD+Tm5kqPc3JyABj2cfH09FScA4Dc3Fx4eHiU+3mTJ0/G+PHjlQ2tZZmRiuj1eiQlJaFx48YOW2+kJmF/qcP+Mh/7Sh32lzrsLyOrBCNNmzbFtWvXpMfx8fEIDg6Gp6cnIiMjkZSUhKKiIimYiI+PLxNsyLm6utaZwKMiWq22zv+AqsH+Uof9ZT72lTrsL3XYXyoLWEtKSlBYWAi9Xg+dTofCwkLodDo8+eSTOHDgAC5evIicnBysXbsWTz31FAAgIiICERERiI2NRVFREbZt2waNRoNOnTrZ4vsQERFRDaMqGPn0008RExODHTt2YO3atYiJicGePXvQvHlzvPbaa/jLX/6CIUOGoEGDBpgyZYr0vn//+9/49ddf8fjjj2Pr1q1YsmQJp7ISERERAEAjCILg6EaQkl6vR0JCAsLDw+t86s4c7C912F/mY1+pw/5Sh/1lVLe/PRERETkcgxEiIiJyKAYjRERE5FAMRoiIiMihGIwQERGRQzEYISIiIodiMEJEREQOxWCEiIiIHIrBCBERETkUV2AlIiIih2JmhIiIiByKwQgRERE5FIMRIiIicigGI0RERORQDEaIiIjIoRiMEBERkUMxGCEiIiKHYjBCREREDsVghIiIiByKwQgRERE5FIMRB+JK/GRL/PkyD/uJbI0/Y5VjMGJnmZmZuHXrFgBAo9E4uDXVX3Z2NtLS0hzdjBojLS0N+/fvB8B/ACuTkpKCTz75BJcvX3Z0U2qE+/fv48KFC9DpdI5uSo3Af+vVcXZ0A+qSZcuWYe/evQgJCUHXrl0xePBgNG/eHHq9Hlot48LSli1bhl9++QVBQUHo0qULnnrqKYSFhUEQBP7lNqG4uBhTp05FUlIStmzZgoiICOh0Ojg5OTm6adXO2rVrERsbi0GDBsHb2xslJSVwduY/h+VZtmwZvv/+ewQHByM8PByjR49G+/bt+XexHPy3Xj32ip0cPXoU58+fx5YtWzBjxgzk5uZi8eLFAMAfzlLu3buHv/71r7h+/To+/fRTPP/880hOTsbevXsB8LcMU/R6PVxcXNCxY0d069YNK1asAAAGIiZkZWXhwoULWLNmDf7+978jLCyMgUgFtmzZgvPnz2PXrl2YP38+fH19+XexAvy33jLsGRsqKCiQjpOSkuDk5AQ/Pz90794dL730EkpKSqSbhl6vd1Qzqw2xvx48eIAWLVrgnXfeQWBgIPr27YsGDRogPT0dAPtKJPaX+NtWVlYWrly5gpdeegmpqan48ccfAQAlJSWObGa1IP+7eOXKFSQnJyMqKgpxcXFYunQpvvvuO1y5cgUAf74AZX+lpKQgNDQU7u7uiIqKgre3N3x8fBzYuuonLy9POua/9ZZhMGIDGRkZeP311/HRRx9Jzzk5OSEiIkKqfwgMDMSMGTOwbds2pKWlQavV1tkxfrG/Vq1aBQBo1qwZnnrqKXh7e6O4uBgAEBAQII2/1vXfLkr/fGm1Wuh0OtSrVw9t2rSBn58f+vbti/Xr1wNAnf6t39TfRY1Gg+joaGzcuBFvvPEGXFxc8MMPP2DBggX8u2iivzw8PODs7IwjR46guLgYJ06cwN27d/Hrr79KN+G63F9z5szBokWLpKCf/9Zbpm7/q24DH3/8MYYNGwYPDw/MmDFDer558+Y4f/48kpOTpec6deqEXr164auvvgJQN1Oe8v6aOXOm9HxYWBgA44300qVL6N69u0PaWJ2U9/Pl5OQkFRg2aNAAkydPhpubG1588UW8/fbbDmyx45TXV15eXoiPj8fx48fx9ttvY/bs2fjXv/6F1q1bY/ny5QD4d1HeX8OGDUNMTAw+++wzPPbYYwgNDUVoaCi++OILfPjhhwDqZn9duHABEydOhI+PD6ZMmSJlPPhvvWXq7q9MNvDpp59i48aNWLx4MWJiYgBAKvDq1KkTwsLCsH37doSFhSEwMBBarRaNGjWCXq+vk4WGpvpLTuy7kpISpKeno1OnTtK5goICuLu716kCuop+vgDAx8cHHTp0gKenJ/bt24dbt24hNzcXEyZMAIA69TNWUV+1bt0aTZs2xfbt2zFy5EgAgKenJ3r27IkdO3YgOzsbvr6+jmy+3VXUX4GBgRgwYAASEhLQpUsXTJs2DQDw3XffYfv27cjMzISfn58DW+8YcXFxiImJwbx58wAAOTk5cHV1RadOnRAeHo5t27bx33oVGIxUkfxm8Nhjj+HkyZPw9PTE77//jk2bNiE0NBT169fHmDFj8Oc//xnz5s3Djz/+iIEDB6J+/fp48OABQkJC6swPpzn91bBhQ4wcOVJ6XXp6OrKystCuXTtcunQJH374IR5//HE899xztT4QUdNfDx48wIEDB3D+/Hnk5eXhhRdewIkTJ7Bv3z7069ev1v+Mqfm7OGnSJPz++++Ij49HmzZtUL9+fSQmJqJZs2Z1JhBR87OVn5+P48ePY9CgQdL7kpOTER4eXmcCEfF7C4KA4uJiJCYmIiYmBklJSViwYAHq168Pf39//N///R/+8pe/YPbs2fjhhx8waNCgOvlvvVoMRiyUl5eHlStXwsXFBb169UK7du3QokULtGvXDm+++SaKi4vxzDPPwM3NDatXr4YgCBg7diwmTZqEPXv24Mcff0SDBg1w+vRpLF261NFfx+bU9NdHH30EQRDw1FNPwdPTE3/88QcKCwuxcOFCHDhwAOPHj8dzzz3n6K9kU2r7q6SkBGPHjsWQIUOg1+vxyiuvwMPDAy1btsTt27cd/XVsytK/iy+++CL27t2LQ4cOoWHDhjh9+jTefPNNR38dm7P0Z6t79+7Ys2cPUlJSkJaWhiNHjuBvf/ubo7+Ozcn7KyYmBm3btoWHhwfS0tLw7bffokGDBnj00UfRq1cvrFixAu+99x5mzZqFadOmYfv27di3b1+d+rfeUhqBlTSqXb16Fa+//jratGmDwMBAXLx4EY0aNcI//vEPZGdn47PPPsOIESMQEhICANi3bx+++OIL/O9//4OPjw/S09Nx5MgR3Lt3D2PHjoW3t7eDv5FtWdJfGzduxLJly+Dv7481a9bg448/xjPPPIO//OUv7K9yfr7ef/99eHt716kC36r+XczMzMTx48dx584djBw5kj9bJvpr/fr1WLVqFVxdXXHw4EGcPXsWAPDqq6/W6f46f/48pk+fjrZt22LlypXS6z///HMMHDgQvXv3rnP/1leJQKpt3bpVmDt3rvT4xo0bQs+ePYX9+/cLgiAImZmZgiAIQkFBgSAIgvDgwQOhb9++wunTp+3f2GrA0v46efKkIAiCEBcXJ1y/ft3OrXYcS/qrT58+wpkzZ+zeVkfj30V1qvp3URAEoaSkxI4tdqzy+uvgwYOCIAjCvHnzhJEjRwqCYOyXcePGCdu3b7d3U2u8uvMrVBWkpaUhMTERgKEI8MGDB/Dy8pKmtdWvXx+BgYFYvXo1AKBevXoAADc3NwCGQqdOnTqhRYsWDmi9/Vmrv1q2bAkA6NChAyIjI+39NezGGv0VHR2N5s2bO6D19sW/i+pY++8iULsX0jOnv+rXr49PPvkEADB9+nTcvn0bX3zxBXJycpCamgofH5868XfR2hiMVEAQBKxatQrPPvssNm/ejOzsbDg5OcHHxwf5+fk4ffo0AMOiUo888ghSU1OxY8cOAIY9VX777Tf8+9//xoIFC9CrV69an6Jjf6nD/jIf+0od9pc6avqrR48euHv3Lnbs2IEmTZrgX//6F06dOoW//vWvGD16NKKjo9GuXTsHf6OahwWsFTh+/Dhu3bqFoUOHIicnB8ePH8cTTzyBZ555BklJSXj//ffxww8/4NChQ5g2bRqaN2+OhIQEAIbfLL7//nvk5uZi8+bNqF+/voO/je2xv9Rhf5mPfaUO+0udqvTX448/jt69e+PKlSsIDQ2VskukDgtYK1BQUIBr164hIiJCWh103LhxCAsLw4MHD5CQkIALFy4gKioKnTp1wr/+9S9ERERI6zqIa2HUFewvddhf5mNfqcP+Uqcq/cXN76yDPVgBd3d3tGvXDt7e3ujTpw/u37+P48ePAzAsMNWuXTuMHj0anTp1wt27d3H79m20adNG8f66hP2lDvvLfOwrddhf6lSlvxiIWAd70Uzdu3dHREQEzp8/j/Pnz0vP379/H4sXL8Zzzz2HqKgodO7c2YGtrD7YX+qwv8zHvlKH/aUO+8sxGIyYQRzJ6t+/P4qKivDHH38AAC5evIji4mJ06NABmzdvxuzZsx3YyuqD/aUO+8t87Ct12F/qsL8chzUjZhIeLgX83XffYdu2bbhw4QJatmyJ5cuX15nlkNVgf6nD/jIf+0od9pc67C/H4GwaM2k0GhQUFGDLli24fv06Zs2ahTFjxji6WdUW+0sd9pf52FfqsL/UYX85BoMRFY4cOYKWLVti1apV0qJAVD72lzrsL/Oxr9Rhf6nD/rI/DtOoIAh1Z7t6a2B/qcP+Mh/7Sh32lzrsL/tjMEJEREQOxdk0RERE5FAMRoiIiMihGIwQERGRQzEYISIiIodiMEJEREQOxWCEiIiIHIrBCBHVaF27dkXXrl2xe/duRzeFiCzEYISIKjV16lTppj9u3DjFuczMTMTExEjn//e//1n9+rt375Y+n4hqHwYjRKTK1atXcfr0aenxjh07UFhY6MAWEVFNx2CEiMzm7GzYzuqrr74CAOh0OmzdulV6Xi4rKwvvvPMOnnrqKTzyyCMYOHAg3nzzTaSkpEiv+fjjj9G1a1c888wz2LdvH0aMGIFHH30UL7/8Mm7evAkAeOutt7Bw4ULpPWKG5OOPP1ZcLycnBwsXLkSfPn0wePBgrFmzxtpfn4hshMEIEZktKioKoaGh+Omnn3D37l388ssvSElJQf/+/RWvKywsxNSpU7FlyxakpaUhPDwcubm5+O677zB58mRkZGQoXn/v3j3Mnz8fGo0GhYWFOHPmDP75z38CAMLCwhAaGiq9tl27dmjXrh2CgoIUn/HBBx/g2LFjcHFxQWpqKj766CP8+uuvNuoJIrImBiNEZDatVotRo0ZJGRExQ1J6i/Xvv/8e8fHxAIB33nkHmzdvxqeffgqtVovU1FRs3rxZ8XqdToclS5Zg69atUk3KuXPnUFBQgJdeegkvvfSS9NrY2FjExsZi2LBhis9o2bIldu/ercjUnDhxwqrfn4hsg8EIEakydOhQeHh4YPPmzTh58iRat26NDh06KF5z4cIFAIC7uzv69u0LAGjVqhXCw8MV50Xe3t547LHHAABNmzaVni+dQanIgAED4OLiAj8/PwQEBAAA7t+/r+7LEZFDMBghIlV8fHwwePBg5ObmAiibFbH0M0VOTk7SsZpNxU19BjclJ6oZGIwQkWqjR48GAPj7+2PgwIFlzrdp0wYAUFBQgJ9++gkAcOnSJSQkJCjOm8vd3V06zs/Pt6TJRFSNlS2BJyKqRPPmzbF//344OTnB1dW1zPlBgwZhw4YNiI+Px7x58xAeHo5bt25Br9ejQYMGUjBjroiICOl41KhRCAwMxOzZs9GpU6cqfhMiqg6YGSEii9SrVw/e3t4mz7m5ueGTTz6RAoeEhAR4enpi8ODBWLduHfz9/VVdq0WLFnjppZdQv359pKSk4I8//sCDBw+s8TWIqBrQCBxUJSIiIgdiZoSIiIgcisEIERERORSDESIiInIoBiNERETkUAxGiIiIyKEYjBAREZFDMRghIiIih2IwQkRERA7FYISIiIgcisEIERERORSDESIiInIoBiNERETkUP8fwRyVeTleX98AAAAASUVORK5CYII=", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "series1, series2 = series.split_after(0.75)\n", "series1.plot()\n", - "series2.plot()" + "series2.plot();" ] }, { @@ -156,7 +168,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**slicing:**" + "**Slicing:**" ] }, { @@ -166,21 +178,19 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "series1, series2 = series[:-36], series[-36:]\n", "series1.plot()\n", - "series2.plot()" + "series2.plot();" ] }, { @@ -188,7 +198,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**arithmetic operations:**" + "**Arithmetic operations:**" ] }, { @@ -198,14 +208,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -213,7 +221,7 @@ "series_noise = TimeSeries.from_times_and_values(\n", " series.time_index, np.random.randn(len(series))\n", ")\n", - "(series / 2 + 20 * series_noise - 10).plot()" + "(series / 2 + 20 * series_noise - 10).plot();" ] }, { @@ -221,7 +229,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**stacking**\n", + "**Stacking**\n", "\n", "Concatenating a new dimension to produce a new single multivariate series." ] @@ -233,19 +241,17 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "(series / 50).stack(series_noise).plot()" + "(series / 50).stack(series_noise).plot();" ] }, { @@ -253,7 +259,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**mapping:**" + "**Mapping:**" ] }, { @@ -263,19 +269,17 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "series.map(np.log).plot()" + "series.map(np.log).plot();" ] }, { @@ -283,7 +287,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**mapping on both timestamps and values:**" + "**Mapping on both timestamps and values:**" ] }, { @@ -293,19 +297,17 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "series.map(lambda ts, x: x / ts.days_in_month).plot()" + "series.map(lambda ts, x: x / ts.days_in_month).plot();" ] }, { @@ -323,19 +325,17 @@ "outputs": [ { "data": { - "image/png": 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", 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czz77LCIiIhAXF4d58+bxr3OJ2W666SYYDAZRojYA+O6775CamopWrVrhtttuQ1lZmZJdQBAEQXhAYx4OEhwEzzfffIOoqCjs3r0bjz32GB566CFMmzYNgwYNwrJlyzBu3DjceeedqKysRG5uLiZOnIh+/frh0KFD+PTTT/HVV1/h1VdftfnMoKAg7Nq1C2+//TZeeeUVrFu3DgCwZ88eAGxytUuXLvGPAeDs2bNYunQpVqxYgRUrVmDz5s1488031esMgiAIQhKNeTia0pKK23k4tCb9ATPyrzR+nNzERQB7v3Bdp/Xs2ZMv1f7CCy/gzTffRFRUFB544AFkZ2dj7ty5+Oyzz3D48GEsX74cycnJ+Pjjj2EwGNC5c2fk5eXhueeewz//+U9+H3ePHj3w8ssvAwA6dOiAjz/+GBs2bMDYsWMRHR0NgFXAcXFxIlvMZjMWLlyIkJAQAMCdd96JDRs24LXXXvO4XwiCIAj54QSHr68vn2+jqXo4mqzgyL8C5BZpbUXj9OjRg297eXkhMjIS3bt355+LjY0FABQWFuLEiRMYNGiQKGtcRkYGysvLcfHiRbRu3drmMwEgPj4ehYWFjdqSmprKiw0p7yMIgiC0QVgplpsbmmrQaJMVHHERTeN7fXx8RI8NBoPoOe4EMpvNYBjGJkUtwzCi4xx9pisFfNx9H0EQBKENnAeDW04Bmm7QaJMVHFKWNZoKXbt2xeLFi0XCY/v27QgJCUFiYqLLn+Pj4wOTyaSUmQRBEIQK1NfX84H9QsHRVD0czW/WbsI8/PDDyMnJwWOPPYaTJ0/i999/x8svv4ynnnpKUh7+1NRUbNiwAfn5+aKAI4IgCKLpIPReNAcPBwkOHZGYmIiVK1di9+7d6NmzJx588EHcd999fNCpq7z77rtYt24dkpOT0bt3b4WsJQiCIJTE3g4VgPVic8VRm5KHw8BwQQKEqpjNZmRnZyMlJYUqLjYC9ZU0qL+kQf0lDeov1/G0r3bv3o0BAwYAAB555BF8/PHH/GsJCQm4dOkSkpKSkJOTI5vNSkJnC0EQBEHoEEceDsASx9GUPBwkOAiCIAhChzgTHFwcR1lZWZPZJECCgyAIgiB0iCseDgC4du2aWiZ5BAkOgiAIgrBDdnY2fvjhB1RVVWny/a54OICms1OFBAdBEARBWHHt2jUMHjwYt99+O2bPnq2JDa56OEhwEARBEEQT5Z133kFeXh4A4ODBg5rY4KqHo6kEjpLgIAiCIAgBly5dwnvvvcc/1mpCJw8HQRAEQTRj5s+fj8rKSv6xVhM6eTgIgiAIoply6tQpfPnll6LnSktLoUWOTE7oeHt7IygoSPQaeTgIgiAIognz4osv2uS1qK2tRXV1teq22CtNz0EeDoIgCIJoomRmZuK3334DAMTFxWHMmDH8a1p4EYSCwxrycBAEQRBEE+X06dN8e+bMmUhMTOQfqz2pm0wm3nNhT3A0RQ+Ht9YGEARBEIQeuHz5Mt+Oj4/XNHBUKCKE3gx7z5GHgyAIgiCaEELBERUVJZrU1fYiONuhAjRNDwcJDoIgCIIAUFxczLcjIyM19SI0JjiCg4P5kvfk4SAIgiCIJoQzD4feBIfBYOC9HOThIAiCIIgmhFBwREZGalogrTHBAVjiOMjDQRAEQRBNiKa0pAJY4ji0SkwmFRIcBEEQBAGLh8PPzw9BQUGaCo6ioiK+HRUVZfcYzr76+npUVVWpYZZHkOAgCIIgCFg8HJGRkTAYDJoKjvz8fL4dHx9v95imtjWWBAdBEATR4mEYhvdwREZGAoCm22IvXbrEt+Pi4uwe09S2xpLgIAiCIFo8lZWVqKmpAWBZwggNDeVf19LDERsba/cY8nAQBEEQRBPDOmAUALy8vHjRoZXgCA8Ph5+fn91jyMNBEARBEE0M6xwcHMKdIGrBMAwvOBzFbwDk4SAIgiCIJod1Dg4OLXJdlJeX83VcHMVvAOThIAiCIIgmh70lFcAiOKqrq1FdXa2KLcL4DWeCgzwcBEEQBNHEcLSkosVOFVd2qADk4SAIgiCIJkdjHg5APS8CeTgIgiAIopnSWAwHoJ4XwVXBQR4OgiAIgmhiuLKkooWHg3apEARBEEQzwtGSihYVY93xcJDgIAiCIIgmAOfh8PLyEk3kWns4nAkOHx8fBAYGAqAlFYIgCIJolLq6Ohw+fBj19fWa2SCso2IwGPjntRQc3t7eiIiIcHosZ59wSUivkOAgCIIgNOXvf/87evbsienTp2tmg7BSrBAtBAe3LTY2NhZGo/NpOjk5GQArUrhaMHpFkuCora3F/PnzMXHiRAwfPhyzZs1CZmYmAGD58uUYMGAAhg4dyv8TuoUIgiAIwppz585h4cKFAIDVq1drYkN1dTUqKioAiANGAfUFh8lkQmFhIQDnyykcKSkpfDsnJ0cxu+RAkuAwmUxITEzEggULsHHjRgwbNgxz5szhX+/fvz/++usv/p8rnUUQBEG0XD755BMwDAMAqKqqQl1dneo2ONoSC6i/Lba4uBhmsxmA8x0qHELBcf78eaXMkgVvKQcHBATg/vvv5x/feuut+OCDDySrvtraWtTW1ooN8faGr6+vpM9pynAnFPc/4RjqK2lQf0mD+ksacvZXRUUFvvrqK9FzJSUlNl4GpSkqKuLbERERor9NWKK+pKRE0t/tTl/l5eXx7djY2EbfKxQcWVlZmpzHjS37cEgSHNYcPnwYERERvAI8dOgQRo8ejYiICNx666245ZZb7L5vwYIF+OKLL0TPTZs2TdP1O63QuwtMT1BfSYP6SxrUX9KQo79++OEHmxvW48ePiyZRNTh+/Djf9vHxQXZ2Nv9Y6HEpLCwUveYqUvrq8OHDfDsgIKDR7/P39+fbR44cccs+T2nTpo1Lx7ktOMrLy/H666/j4YcfBgD06dMHP/74I+Li4nD8+HE8/fTTiIyMxMiRI23eO3PmTMyYMUNsSAv0cOTk5CA5OdllddhSob6SBvWXNKi/pCFXfzEMgx9//NHm+eDgYNUFx+7du/l227Ztbb4/KCgIFRUVqKqqkmSbO30l9FB06tSp0e/r168f3y4pKVG976TgluCoqanBnDlzMGTIEEyZMgUAkJiYyL+elpaG2267DZs2bbIrOHx9fVuUuHCG0Wiki5yLUF9Jg/pLGtRf0vC0vzZv3owjR47YPF9WVqb671BSUsK3o6KibL4/LCwMFRUVKC0tdcs2KX1VUFDAt+Pj4xt9n9C7cOHCBV2fw5Itq6+vx4svvojo6Gg88cQTDo8T7mMmCIIgCCEfffQR3+7Rowff1iKBlaO05hxc2IAau1RcTfrFERISwufq0GI5RQqSBcdrr72GmpoazJs3TyQqtm/fzqvEkydP4qeffsLQoUPls5QgCIJoFlRWVmLp0qUA2En13nvv5V/TQnA4SmvOwQmOyspKxXfRuFpHRQi3jHLx4kVNk6c1hqQllUuXLmH58uXw8/MTLZV8+OGH2LVrF15++WVUV1cjOjoad911F8aOHSu7wQRBEETT5uLFizCZTACAkSNHirwKevZwAKx9Su6iEQqO2NhYl96TmpqKAwcOwGQyITc3V7dxHJIER3x8PPbu3Wv3td69e+PJJ5+UxSiCIAii+SLc+pmYmKh5ETJXPRwAa58agiMkJARBQUEuvUcoMLKzs3UrOPQbXUIQBEE0S4SCIyEhQSQ4tPRwGAwGhIeH27yupiDiBIeUxJmpqal8W8/Jv0hwEARBEKqSm5vLt/UkOMLDw+Hl5WXzulrpzauqqvi/X4rgsPZw6BUSHARBEISqOFtS0TJo1N5yCqCe4JC6Q4WDBAdBEARB2EFPSyr19fX8d+pJcLi6QwWgJRWCIAiCsItwSSU+Ph4hISH8Y7UFx5UrV/i2o2BQvXs4wsLC+D4kDwdBEARBNMB5OCIiIhAQEAAvLy9+wlRbcDS2QwVQr2Ksu4LDYDDwyyoXLlzQbSFCEhwEQRCEajAMwwuOhIQE/nluWUVtwdFYDg5A/x4OwLKsUltbK/ocPUGCgyAIglCNkpIS1NTUABALDm5S16OHQ61tsZmZmXxbquBoCoGjJDgIgiAI1RDGbwiLfnKTelVVleLpw4UIA1gdZfZUw8ORlZWFX375BQBbnbZjx46S3i8MHCXBQRAEQWhGUVERPv/8c9x666347LPPNLPDeocKh1Y7VXJycvh269at7R6jhodj/vz5vNB66qmnXM4yyiH0cOh1p4pb5ekJgiCIpsG+ffvwwgsvYOPGjXz9kl9//RU333wzoqOjVbfHVcGhZPpwIULBkZycbPcYPz8/BAQEoKqqShHBcfz4cXz33XcA2ORjc+bMkfwZtKRCEARBaMr999+PdevW8WIDAMxms2aTknWWUQ6t6qlcuHCBbzsSHICyJer/+c9/8jtLnnvuOVFfuAotqRAEQRCaYTKZcPToUQBAaGgo0tPT+de02slgnWWUQ+sllYiICAQGBjo8TinBsXfvXixevBgAGyj66KOPuvU50dHRCAgIAKDfJRUSHARBEM2Uixcvor6+HgAwatQoPPDAA/xrehAcWsdwcOXcAcfxGxzc8lN5eTkqKipks+Hdd9/l2//4xz8kx25wGAwG/m/Izs4GwzCy2CcnJDgIgiCaKWfPnuXb7dq1E221vHTpkhYm8RO80WgU7QrRQnDk5+fzgszZcgog9sYIRZOn7N+/HwAQEBCA+++/36PP4mysrKxEeXm5x7bJDQkOgiCIZsq5c+f4dtu2bUWCQ2sPR2xsLLy9LfsWtBAcrgSMcgi9MXIJjvr6emRlZQEA2rdvDz8/P48+LyYmhm8XFRV59FlKQIKDIAiimaI3wWEymfjvFU7ggPaCo7ElFaG9wsBXT7+f2wrbvn17jz9PuOuosLDQ48+TGxIcBEEQzRRrwSFcwtBCcBQWFvK7MfQgOFzdoQIos6QizCwqt+AgDwdBEAShGlwMh8FgQGpqKvz8/BAREQFAG8HhKMsooL2HQ4sllTNnzvDtDh06ePx5wiUV8nAQBEEQqsF5OJKTk+Hr6wvAUqMjPz9f9Z0MjnaoAE1LcMi1pEIeDoIgCKLJU1paiitXrgBgl1M4OMFRWVmJsrIyVW1yJjjUKgEvhBMcBoPBxuNijRIeDrkFB3k4CIIgCNXhdj8A7JZYDi0DR50tqYSEhMBgMABQP4YjPj4ePj4+To8NCAhAeHg4APkFh7+/f6OCxxXIw0EQBEGojnXAKIeWgsOZh8NoNCIkJASAOoKjpqYGBQUFABpfTuHgbM7NzfV4OcpkMvExNu3atYPR6Pl0TNtiCYIgCNURJv1qCoIDsMRxqCE4hN4WqYKjpqYGJSUlHn3/xYsXUVtbC0Ce5RSAXZbicpvQkgpBEAShCo48HPHx8XxbqyUVX19fREZG2rzOCQ41ircJt8Q2loODQ86tsXLHbwBsLApXZZc8HARBEIQqCAWHXmI4uEk6ISGBj9cQwgmO6upq/u5fKaTsUOGQc6eKEoIDsCyrFBYW6q6eCgkOgiCIZggnOEJDQ/ncGwA0q6dSU1ODy5cvA7C/nAKouzXWU8HhqYdD7hwcHFzgaG1treq7kBqDBAdBEEQzo76+HtnZ2QDY5RShN0ErD4dQ3OhBcDTHJRXA/tZYvXg6SHAQBEE0M3JycvgqqMLlFACIiIjgAwvVFBzC7xKKHiFNycMh15KKr68vkpKSPPosIfa2xv7666+Ij4/HkCFDsHz5ctm+SyrejR9CEARBNCUcBYwClrLwubm5qgoOYRCjcFIUooXg8PHxEXkFnCHXkorZbOZ3EbVt2xZeXl5uf5Y19gq4ZWZmIj8/H/n5+aiqqpLtu6RCHg6CIIhmhjPBAVh2qhQWFsJkMqlik1BwOJrgtVhSSUpKcjkHRlxcHL885YngyM3NRXV1NQB54zcA+7k4hFukrT1eakKCgyAIopnRmODgljTMZjOKi4tVsUlPHo6ysjL+812N3wAAb29vvuKuJ0sqSsVvAPaXVEhwEARBEIrQ2ASjReCongSHO/EbHNyySn5+vtveISUFh72gUe58iIiIENWsURsSHARBEM0MzsNhNBrt3sFrsTXWFcGhVgE3TwQHt1PFbDa7nc1TTQ9HTU0NLl68CEBb7wZAgoMgCKLZwQmO1q1b2y1K1tI9HMItse56OAD34ziUysEB2Ho4srKy+G2xJDgIgiAI2aiqquLrfDiKT9BScBiNRlEiMiFaCA4pMRyAPFtjOQ+Hj4+PZMHTGK1ateJFZlFRkW7iNwASHARBEM0KoZufC3C0RgvBwdkVGRnpcFeIUHAoWU9l3759fFuqh0GKh+M///kPnnnmGdFx9fX1vOBo06YNnxNFLoT1VAoLC3UlOCgPB0EQRDOCK7kOON5+qkUBN87D4SznhRoeDpPJhG3btvG2SBUcrmYb3bRpEx5//HH+ez755BMAwMmTJ/lcGD169JD03a4SExODS5cuoaioSBQvorXgIA8HQRCEDJSUlODdd9/Fnj17NLVDKDgceTiEz6shOCoqKvhJ1lH8BgCEhITweS6UEhxHjhzBtWvXAABDhw61W0TOGa4uqcyfP59vb968mW/v37+fb/ft21fSd7sK18d1dXU4cOAA/zwJDoIgiGbAyy+/jKeffhqDBw/Grl27NLPDFcERFBSEkJAQAOrsUnElYBRg4zs4u5QSHFu2bOHbw4YNk/x+V5ZUNm/eLBIZx48f5+NqhMs5SgkOoRdp7969AAB/f3+RZ0sLSHAQBEHIwNatWwGwa/S33norP8GojTCGw9nyBRfHoYaHw1XBAViWVZQSHH/99RffHjp0qOT3R0VF8UGZjgSH0LvBsWPHDgBiwdGnTx/J3+8Kwj7mMpq2bdvW5YyqSkGCgyAIwkMYhsHp06f5x9nZ2bj//vs1qdLpiocDsAiOa9euobKyUlGb9CI4GIbhBUdoaKhbMRQGg4H3cthbUvnrr7+wadMmABDVSNm2bRtMJhO/xJGSkoLIyEjJ3+8K9oSm3Pk+3IEEB0EQhIfk5+ejoqJC9Nxvv/3GBwqqiVTBYf0eJXBHcFRXV6O2ttaj7127di0mTpyIFStWAGDzX3B/a0ZGhttF0zjBcfnyZd6DwCH0brzyyit8e/v27Th16hQv7pRaTgHs97HW8RsACQ6CIAiPEXo3evbsybefeuopUVZLNXBVcKi5U0W4zOOq4AA893I88cQTWLVqFW699Vbk5OR4vJzCIcydIfx99+zZgw0bNgBgJ/inn36a39Wya9cuUWwPCY5GqK2txfz58zFx4kQMHz4cs2bNEm25WbhwIcaMGYNRo0bhgw8+0MSdSBAEoTZCwTFr1iw8+OCDANhrpnCSUwNucg8MDERQUJDD49RMb+5KpVgOYXpzT+NgsrOzAQCVlZV4+umnRb+FOwGjHG3atOHbwkJ5ws9/6qmn4O3tzcdpVFVV4auvvuJfV1Jw2OvjJic4TCYTEhMTsWDBAmzcuBHDhg3DnDlzALABU7/++isWLlyIn3/+GVu3bsWyZcsUMZogCEJPCAVHx44dMXr0aP4xN+mpBefhcObdAMQeDjUFR2MeDmGeC2FGUKlUVFSIYlN+/vlnLF68GADg5+eH9PR0tz9bWIE3KyuLbwvFR+/evQGIhQWX/8P6ebnRq4dDUuKvgIAA3H///fzjW2+9FR988AFKS0uxcuVK3HLLLUhKSgIA3HHHHVi1ahWmTJli8zm1tbU2a3Pe3t7w9fV1529okpjNZtH/hGOor6RB/SUNOfrr1KlTfLt9+/aiZY3s7GzVfou6ujpcvnwZACs4nH2v0MORm5vrso3u9JdwSSUyMtLpe1NSUvj22bNnMWrUKJe/R4i9uJTy8nIAwMCBA+Hj4+P275Kamiqykfscocc/NTUVZrPZrrBo3bo1IiIiFDsvuEyjHEajEcnJyYp9n6u7XzzKNHr48GG+3G1WVhYmTpzIv9axY0f85z//sfu+BQsW4IsvvhA9N23aNEyfPt0Tc5okaq/vNmWor6RB/SUNT/rrxIkTANg7Z5PJJEomderUKdW8HMKJPTg42OXvPX36tGQbpfQXt5vDYDCgvLycTwJmj8DAQL59+PBht/vu8OHDDl/r3r27R7+Jn58f3z569Cj/WZynKzAwEFVVVcjJyUGnTp0QFBQkCiru3LmzoucEwzDw8fFBXV0dADbIVUkvlnCJyRluC47y8nK8/vrrePjhhwGwa2TBwcH860FBQQ63Ws2cORMzZswQG9ICPRw5OTlITk7WfG+03qG+kgb1lzQ87S+TycRPHh06dECbNm2QkpICPz8/1NTUoLi4WHTXriTCmIfU1FSn38sl2ALYrbGu2uhOf3GZPSMiIkTLEfYYMGAA375y5YrbfXf06FG+PWnSJKxcuZKPK5w0aZJHv0lCQgK8vLxgMplQUFCAlJQUmEwmXli1a9eO93Dk5ORgwIAB2LhxI//+IUOGKH5OREdH83lCOnXqpNo56Ay3BEdNTQ3mzJmDIUOG8EsmgYGBvLsKYNfPhEpViK+vb4sSF84wGo00KbgI9ZU0qL+k4W5/nT9/nr+T7NixI/85ycnJyMzMRHZ2NgwGg+QU2u4gjJWIi4tz+vdERkbyoig/P1/y3y6lvzi7oqOjG32P8G75/Pnzbp/DxcXFfHvSpElo164dPvzwQyQnJ2Po0KEejQ0/Pz+0bt0aWVlZOHfuHIxGI3Jzc/lQAeskWxkZGSLBkZ6ervjYjImJ4QVHu3btdHEtkGxBfX09XnzxRURHR+OJJ57gn2/Tpo1o/er06dONKlmCIIimjnXAKAd3R1lWVqZoqXUhrhRu4zAYDHzgaGNVTz2hqqqKvxltLGAUYGMFufiS8+fPu/291oGq77//PtauXYtt27Y53b3jKpwwunr1KkpKSkRVWa3nvkGDBokeKxkwyiHsaz0EjAJuCI7XXnsNNTU1mDdvnkixT5w4EYsXL0Zubi6Ki4uxaNEiTJgwQVZjCYIg9MaZM2f4tlBwtG7dmm97sttCCq6UphciTGBVU1OjiE1StsRycJN5fn6+03gPZ1ineDcYDBg7dqwoh4YnCEXFuXPnRDtUrAXHwIED+fkyKSnJ5X7wBOF3NEnBcenSJSxfvhwHDhzAyJEjMXToUAwdOhQHDhzAkCFDMHXqVNx1112YNm0aMjIycMMNNyhlN0EQhC5w5OHQQnC4mvSLQ1iITKmgQilbYjmsl1XU+l4pOBMc1hN8q1at8PDDD8Pb2xtPPvmk7LbYo2vXrgDYpS9ui67WSIrhiI+P5yvP2WPmzJmYOXOmx0YRBEE0FYSCo0OHDnxbKDjU2qXiieDIy8sTbfeUC3cmfqEdWVlZ6NKli+TvdbWInbtI8XAAwMcff4x3331XtMNFSR555BHU1NSgW7duuglv8GhbLEEQREuHExytWrUSTajCXQFaeDhcmWRdKbXuKVp7OLy8vBAeHu7WZzjDWnBwMRwGg8HhjhC1xAbAno/2qtZqifZhqwRBEE2U6upq3nvRsWNHUVybljEcPj4+Lk2yTUFwCDN5SoHri6ioKEV2aFhnG+U8HImJifD395f9+5oDJDgIgiDc5Ny5c3xuB2H8BgA+6zKgvoeDC5JsDDXSm2shOBiGEW3FVYKIiAg+l8mhQ4f4bbh6CdDUIyQ4CIIg3MRRwCjAbu/kljXUiOEwm838Xb2rMQt69XAIk4q5s6RSXl7Ol41XakeIwWDgvRzCeBG9xEvoERIcBEEQbuIoYJSDW8vPy8vjk4MpxZUrV2AymQC4FjAKqC84XJ38fXx8eA+ROx4OpXeocNgTFyQ4HEOCgyAIwk2ceTgASxwHwzB82mulkJqDA2ADCwMCAgAoJzisC7e5CrescuXKFT41ujvfSYJDP5DgIAiCcBNh0i97Hg41A0el7lAB2GUBzsuhtIcjPDwcPj4+Lr/PkzgOd7wq7mBPXFAMh2NIcBAEQbgJJyIiIyMRGhpq87qauTik5uDg4ARHaWmpw4KbnuBu8KYwF4fUOA7ycOgTEhwEQRBuYDabea9AYmKi3WPUzMXhqeAA5N+pUlNTwy+HSJ34m4KHw7ose3BwMKKiohT7vqYOCQ6CIAg3KC4u5quDOhIcai6puBPDASi7NVZYsVVNwaGWhyMlJUW0/bht27aqVAVuqpDgIAiiyVFTU8Pnv9AKYRCoHgSHOzEcgLI7VTzZLeLJkopaHg5/f3/Rb0/xG84hwUEQRJNi3bp1iIiIwJAhQxSrcOoKQsEhTPIlJCoqit8FovcYDsAzwWFPAHoy8SckJPBBpnr1cADimA2K33AOCQ6CIJoUCxcuRGVlJbZv344VK1ZoZocrHg6DwcB7OS5cuKCoV4YTHAaDQVIcgRyC448//kBMTAzuvPNO0d8o9OpInfi9vLz4GJisrCxJfccJHW9vb4SFhUn6XqmQ4HAdEhwEQTQphJ6CH374QTM7XBEcgGVZpaKiAiUlJYrZw93VR0ZGwtvb9bqccgiOjz/+GMXFxfj++++xadMm/vkff/yRb/fp00fy53JxHOXl5bh8+bLD495880107NgRS5YsAaB8HRUhaWlpdtuELSQ4CIJoUgjvmlesWIGrV69qYodUwQEoF8fBMAzv4ZCynALIIziESx4fffQRADbuYsOGDQDY2IYhQ4ZI/lzrMvX2KC0txUsvvYQzZ87giSeeENVRUTJ+g+P+++/Hfffdh7lz52Lo0KGKf19ThgQHQRBNhvr6etFEX1NTg6VLl2piy8WLF/m2q4LD0ziOq1ev4v7778fLL7+M+vp6/vmysjK+dohUwRESEoLg4GAA7u1SYRhG9HctW7YM2dnZ+Oabb/hlkHvvvdet3RtCweGo7zZv3gyz2QyAFXQbN27kdw8pHb8BsNlav/zyS7zyyiu0Q6URXPe7EQRBaExeXh4/uXD873//w9133626LZzw8fPzQ0REhMPj5MzF8d///hdfffUVAKCurg6vv/46AHHGU6mCA2C3xp45c8YtD0dhYSEvdgA2P8nHH3+MX375BQBgNBpx1113Sf5cwDXBsXHjRtHjTz75hG+r4eEgXIc8HARBNBnsTdgbNmwQ7dBQC05wJCUlOb2zlXNJRVi75Y033sCaNWtw9epV3Hnnnfzz3bp1k/y53LLKtWvXUF5eLum99oTA+++/zz8/fvx4h7t4GkMo1lwVHL///jvfVsPDQbgOCQ6CIJoMwgmb8yqYTCb+blotKisrUVpaCsD5cgogr+CwXvK48847ccstt+DEiRMAgC5duuCxxx6T/LmeZBsVCgFuG6twuefee++VbA9HY4KjoKAAR48eFT3HVcwFyMOhN0hwEATRZBBO2I8++ijfVnu3iqsBo4DYA+JpDIe1GCgqKsL69esBsMXRli1bZremS2N4EjgqTMr1yCOPiF6LiorCDTfcINkejvj4eH7Hjb2+E+6IsedFIQ+HviDBQRBEk0EoOCZNmsRvQ9y+fbvkbJSe4GrAKMDGeMTFxQGQz8MREREh+l4vLy/88ssvaN++vVuf64ngEAqB6dOno1+/fvzjO+64A76+vm7ZBLB/V3Jyss33cAiXU9566y2bLbDk4dAXJDgIgmgyCCfs1q1b429/+xv/eM2aNarZIcXDAViWVS5duuR2dlSTycTHqrRp0wY//PAD/Pz8AAAffPABRo8e7dbnAmLBkZOTI+m9QiGQkpKCl19+GUajEaGhoXjooYfctkn4mQC7/dV6CzQnOHx9fXHjjTdixIgRotfJw6EvSHAQBNFk4ASHr68vYmJikJGRwb927Ngx1exwV3AAYu+IFIqKivgdOvHx8Rg6dCiOHTuGgwcP2ixlSEVYA+Ts2bOS3ssJDl9fX8TFxWHSpEk4cuQIDh8+jI4dO3pkF+A4jiM7O5u3ddCgQQgMDMS0adNE7yUPh74gwUEQRJOBExzJyckwGo2iHRnWwYNK4kodFSFybI0Vxm9wFV7btWuHnj17uvV5QoRLMZmZmU6P5crNA2wODm4pi/tNAKBr166iv9kTHAkOYfzGqFGjAAA33XSTaFmFPBz6ggQHQRBNgqtXr/Iudc5jEBUVxeedaCoeDjkFh1yEh4fzu34cCY78/HzcdNNNCA8Px5w5cwCwyxxlZWUAxDkz5MSR4BDGb3CCIzY2FuPGjQPACqBWrVopYhPhHiQ4CIJoEghjC4QTOOflKCwsFFUnVRJOcBgMBpcmf70LDsDi5cjJyREl8mIYBj/88AO6devGZ3VdsmQJCgoKbOI3lMCe4GAYhk+bHhgYiP79+/PHfP3113j99dexfPlyyvypM0hwEATRJLAOGOUQFsxSy8vBxWHExMTwuSecIUd6c7UEB8MwOHfuHP/8888/j9tvvx1XrlwRHb9+/XrNBIcwK+rQoUNFO2Hi4+PxwgsvyLLURMgLCQ6CIJoEjgSH2nEcJpMJ+fn5AFxbTgGUi+GQkw4dOvBtblmltLQU77zzDv/8oEGD+Pb69etFW5GVEhzctljAIjh27tzJPzds2DBFvpeQHxIcBEE0CfTi4SgoKOCzWboqOMLDwxEUFARAv4LDXuDooUOH+AJs99xzDzZt2oTAwEAAwLp160QeDqViOPz8/Pi/l/u+Xbt28a8PGDBAke8l5IcEB0EQTQJHgqNr1658Ww0Ph9SAUYCN9eBsvnDhAj+JS0EoONwp0NYY9gTHwYMH+ecGDx4MPz8/vgT7pUuXsGrVKv51pTwcws8uKChAdXU1du/eDYDtV2GiMULfkOAgCKJJIBQcQjd7WFgYvzX12LFjbk3mUpC6JZaDExxVVVUoLi6W/L2c4IiKivIoe6cjHHk4OLiYiLFjx/LPnTx5EgBbEdZV8eUOQjFz+vRp3q4uXbq4lcqd0AYSHARBNAk4d3pkZCS/PMHBxXGUlJRILj4mFXc8HIBncRwMw/B/lxLLKQDbr2FhYQAs5e45D4fRaOSXroSCgyMpKcml4Fl3Efbd77//jrq6OgAQ7U4h9A8JDoIgdE99fT0/0QuXUziEcRxKL6tIqaMixJOtsSUlJaitrQWgnOAwGAy8l+PChQuoqKjgY2I6duzIx25069bNJoOnkssp1p8vrAxM8RtNCxIcBEHonkuXLvGBmvYEh3CnitKBo+56ODwRHEoHjHJwgsNsNmPVqlW8yOnVqxd/jMFgEKWUB5QXHMKA1CNHjvBt8nA0LUhwEAShexwFjHKo6eGQQ3BIzcWhtuAAgMWLF/Nt65wWQ4YMET1W08PB4e/vj+7duyv6vYS8kOAgCEL3NCY4unTpwrfV8nAEBQVJClj0JIZDC8GxYsUKvi30cACw8XAotSWWw57g6NOnj6JxI4T8kOAgCEL3NCY4goOD0aZNGwDy7FQxm82YP38+nnzySdFkf/r0ad47kZSUJCl1dmJiIn+8XgWHMPlXeXk537b2cMTExIi8C0p7OIKDg/laLxwUv9H0IMFBEITuaUxwAJY4jvLycreTa3GsW7cO8+bNw/vvv49u3bph0aJF2Lp1KwYNGsTXGZGa/8HHxwcJCQkAGhccV69exT//+U8sX74cgDYeDo6YmBjExcXZPD916lQAbFn6Hj16KGYTh7WoofiNpoe31gYQBEE0hiuCIy0tjV8GOHr0qEd33cL8EyUlJbjjjjtgMBh4z0n37t3x1ltvSf7c1q1bIzc3l09g5e/vb3MMwzCYNm0a1q1bB6PRiCNHjqgmOKKjoxESEsJXgAVY74Y9T86zzz6LmJgYpKWlKZKIzJqUlBQcOHCAf0wejqYHeTgIgtA9WVlZANg01/butgF5d6qcPXvW5jlObIwdOxZ//fUX762QglAECavfCvnqq6+wbt06AOzSzscff6ya4BBujeWwjt/g8Pf3x8MPP6xaLRNh30VFRSkeN0LIDwkOgiB0jbB6aWpqKoxG+5ctoeA4fvy4R9/JZdoEgC+++AJRUVEAgPvuuw9//PEHWrVq5dbnNrY19uLFi5gzZ47ouW+//Za3JzQ0lM+HoRTCOA7ANn5DK4SCY8CAAVR6vglCSyoEQeiagoICVFVVAQDatm3r8LiOHTvy7VOnTnn0ndwEHx4ejvvvvx8zZsxAfn4+H5jqLs4EB8MwmDVrFq5duwaADZQsLy9HRUUFKioqACjr3eBw1cOhNsLfd/DgwRpaQrgLeTgIgtA1nHcDcC44goKC+Borp06dcnunSk1NDb/cwU2+AQEBHosNwHkuju+++44vhpaQkIA1a9bYvF9tweHn54dOnTop/p2uMH78eNxzzz2YPHkyHnroIa3NIdyABAdBELrGVcEBgJ8cS0pKcPnyZbe+Lysrixcr7dq1c+szHCFcFuDiUjj+7//+j29//vnnGDx4MEaPHi06Rm3BkZaWBm9vfTjCvb29sWDBAixfvhzh4eFam0O4gSTB8fnnn2PatGno16+fSH0vX74cAwYMwNChQ/l/+fn5shtLEIR6MAyDTz/9FAsXLlS8AqszpAgOOZZVhAGj9raJekL79u352IMTJ07wz9fW1vKPu3fvjsmTJwMAHnvsMdH71RAc3bt354vjjRgxQvHvI1oOkqRrcnIy5syZg88++8zmtf79++Ojjz6SzTCCILTlt99+w8MPPwwAKC0txRNPPKGJHUJPgKseDoAVHNYZMV1BGDAqt4cjMDAQbdu2xdmzZ3Hs2DGYzWYYjUZkZmaivr4egDhN++TJk5GSksIvv6ghOMLCwrBmzRrs3r0b9913n+LfR7QcJAmOiRMnAgC+/vprj760traWLwrEG+LtDV9fX48+tylhNptF/xOOob6Shlz9tXXrVr794osvYuLEibLf8buC0MORkpLi9O8S7rA4efKkS31g3V9caXaAFThyn3fdunXD2bNnUVlZiaysLLRp00ZU/6VLly78dxoMBjz88MN47rnnALAeEjXGwaBBgzBo0CAAtucRjUfXaSl95WjnmDWyLc4dOnQIo0ePRkREBG699VbccsstDo9dsGABvvjiC9Fz06ZNw/Tp0+Uyp8ngaC8+YQv1lTQ87a+DBw/y7aqqKtx555343//+5/LFRS44ARAREYErV67gypUrDo8NDg7m24cOHZJUJI3rL+Hk7+/vL7nQWmMkJSXx7U2bNsFoNGLHjh38c1FRUaLvvPHGG3Hu3Dl4eXmhW7dustvjLjQeXae595WrAdWyCI4+ffrgxx9/RFxcHI4fP46nn34akZGRGDlypN3jZ86ciRkzZogNaYEejpycHCQnJ6t+AW9qUF9JQ67+unjxoujx7t27sXLlSjzyyCOemugy1dXVfDxY+/btG80empSUBD8/P36niSvZRq37Ky8vDwC766Vv376y53sYPHgwPvnkEwBAUVERUlJS+O8EgOHDh9vYzR2vB2g8ug71lRhZBIewRHNaWhpuu+02bNq0yaHg8PX1bVHiwhlGo5FORBehvpKGJ/1VW1vLx06Eh4ejpKQEAPDCCy9g8uTJsmwRdYWcnBw+YLVt27aN/j1GoxEdOnTA0aNHkZmZCbPZ7PIuC6PRCLPZjPPnzwNg4ze8vLw8st8ewqJnx44dg9Fo5BOV+fj4oEOHDk3iPKfx6DrUVyyK9ABlgCOIps3Zs2dhMpkAsPkPHnzwQQBARUWFqnfbUnaocHCBo3V1dbx4cJWcnBzU1dUBkH+HCkenTp14IXPs2DHU19fzO2o6duxIJdeJZoskwVFfX4+amhowDMO3zWYztm/fzt8BnTx5Ej/99BOGDh2qiMEEQSiPcEtpp06d8M9//pN/LCygpTRSdqhwCHeqnD59WtL3KblDhcPPz48Pbj1x4gROnz7Ni5yuXbsq8p0EoQckCY5XX30VGRkZOHDgAF5++WVkZGRg//792LVrF6ZPn46hQ4fixRdfxF133YWxY8cqZTNBEApjLTji4+MRHR0NADhy5Ihqdnji4QCk5+JQMgeHEK7uS01NDV+CHiDBQTRvJMVwzJs3D/PmzbN5Pj09HU8++aRcNhEEoTFCzwA3gXfv3h0bN25EYWEhCgsLERMTo7gdQsHhatyIJ8m/1PBwAGys2+LFiwEAv/zyC/88CQ6iOUNRLARB2CCcqDn3vzAhlXDrqJJwgsPb21u0ndQZUjwchw4dwrBhw/DUU0+hqqpKdQ8HAOzbt49vk+AgmjP6SJJPEISu4CbqhIQEhISEABDvrjhy5AhGjRqlqA3CsvQpKSku7zYJDw9HdHQ0ioqKnMZwHDp0CKNGjeLzetx777388b6+vi4LHHcQijcOLy8vm9LwBNGcIMFBEISIK1euoLi4GIDYW2AtOJTm8uXLKCsrA+B6/AZHp06dUFRUhLy8PJSVlfGiiePw4cMYPXq0KInYzz//zLfbtGmjyJZYjvbt28PHx4cPFuWe8/PzU+w7CUJraEmFIAgR9uI3APEygBqCw52AUQ5nO1VOnDiB0aNH89Vku3btarOVX+kU7j4+PujcubPoOVpOIZo7JDgIghAhjHsQBmAGBwfzgZtc4TElcWdLLIezwNEXX3yR9+AMHDgQ27Ztw0svvSQ6RsmAUQ6hgLP3mCCaGyQ4CIIQYb0lVgi3rFJRUSE5qZZU3NmhwuEscHT//v0AgFatWmH16tUIDQ3FzJkz8fe//50/pk+fPu6YLAnrOA7ycBDNHYrhIAidcfnyZezcuROjRo1CQECA6t/fmOBYtmwZAHZZRarnQQpyLakI/x6uxgp3TKtWrWA2m2EwGPDBBx+gQ4cOKC8vt6n1pATWHg0SHERzhzwcBKEj8vLykJaWhsmTJ+Mf//iHJjZwE7SPjw9SU1NFr6kVOFpbW4uTJ0/yj6UKjnbt2vG7WoSfk5WVxddmsY7T8PHxwZw5c/Dyyy+7vCPGE4SCw2g0ipaBCKI5QoKDIHSCyWTCjBkz+OqowgyUatrAJb9q3769zU4NpXJxmM1mbN26FY8//jjS0tIQFBSErVu3AgDCwsIQHh4u6fN8fHx4QXHq1Cm+LowwsZfSgaGN0bZtWwQFBfG2aOHNIgg1IcFBEDrh1VdfxZ9//sk/zszMRFVVlao2XLhwATU1NQBsl1MAcXExOTwcDMPg9ddfR3JyMoYOHYqPPvqIL2jGMXjwYLc+u0uXLgDYEvfZ2dkA9CU4vLy88H//939IS0vDG2+8oaktBKEGJDgIQgds2rQJ8+fPFz3HMIxoOUANnMVvAKzngJvIT506xYsTd9m7dy9eeukl5OXl8c95e3uje/fuuP322/H222/j22+/deuzOTsBdissoC/BAQAPPvggjhw5gqlTp2ptCkEoDgkOgtCYsrIyzJgxw25sgVopxDkcbYkVwsVxmEwmjwWRsPLs4MGD8d1336G4uBiHDx/GokWL8MwzzyAyMtKtzxYKjuPHjwPQn+AgiJYECQ6C0JgVK1bg0qVLAIAxY8bg3//+N//asWPHVLVFKHCsE1NxCOM4PF1W4TwPAPCvf/0Ld9xxB1q1auXRZ3I483CEhIQgKipKlu8hCMI1aFssQWjMmjVr+PYLL7wgSjqltoeDy1FhMBjQo0cPu8cId6p4ap/QQyIUCHIgFEwnTpxAXV0dnzukffv2NtlFCYJQFvJwEISGMAyDtWvXAgACAwORkZGB1q1bIzg4GIC6Ho6amhreY9G5c2feBmvk3BrLCY7Q0FDExcV59FnWBAUFoXXr1gBYwXH+/Hl+twotpxCE+pDgIAgNOXbsGL+cMmLECPj5+cFgMPA5Gs6fP4/y8nJVbDl69ChfTKxv374Oj0tOTkZYWBgA4ODBg25/X2VlJb97pHPnzop4HDivydWrV/lttgAJDoLQAhIcBKEhnHcDAMaNG8e3hXESXMCj0nDLKYBzwWEwGNCrVy8AbKKywsJCt77v9OnTfKCs3MspHMLsncK8JiQ4CEJ9SHAQhIY4EhzCLJRqxXHs27ePbzdWS6R37958W7jTRArC+A1HAaqeIhQywr4mwUEQ6kOCgyA0orq6Gps3bwYAJCUliSZdoYdDrTgOTnAYDAaRoLCHHIJDuENFKQ+H8HMrKir4NgkOglAfEhwEoRFbt25FdXU1ANa7IYxhUNvDUVtbi8OHDwNg82+EhIQ4PV4oOIRLMVJQ28PBERAQgPj4eEW+jyAIx5DgIAiNcLScAgDx8fF8/RA1PBzHjx9HbW0tAOfxGxydO3eGv78/AM89HD4+PopVnY2MjER0dLToOdoSSxDaQIKDIDSCExwGgwFjxowRvSbcqZKbm4vS0lJFbZESvwGw6ce5PB2ZmZm4du2apO8zmUw4ffo0AFYAcPVZlMDay0HLKQShDSQ4iBaL2WzGhQsX+J0SapKfn49Dhw4BANLT0+2m7xYuqyjt5RAKDlc8HIB4WYX7W5xx7do1lJSUAACys7P5OixKLadwkOAgCH1AgoNosTzyyCNISUnBXXfdpfp3L1myhG9bL6dwKFUK3h7COIzGAkbtHdfYssrJkyeRmpqK1NRUbN++XZWAUUefT4KDILSBBAfRIjl27Bg+++wzAMCPP/7ocdVTKTAMg48//ph/fMstt9g9Ti0PR319Pe+h6NChg8u1TKQIjn/9618oKSnBtWvXcPfdd4sEjtoeDmHqeIIg1IMEB9Eiee211/h2fX29qmXgN23axCfzGjJkCJ9Eyxq1PBzHjx/nd8u4Er/B0b17d3h5eQFwLjiys7Px008/8Y8zMzPx+uuv84+V9nAIk38B5OEgCK0gwUG0OE6fPi2aAAHwW0Ll4JtvvkHbtm3Ru3dv3HHHHXjjjTeQlZXFv/7RRx/x7UcffdTh50RHR/M7LI4ePepxrMnFixexYsUKvp4IhzvxGwC7vZTzThw7dsyhl+j999+3+U5O4ABAp06dXP5Od0hMTOS3+fr6+iIpKUnR7yMIwj4kOIgWxxtvvAGz2Sx6Tk7B8eKLLyIrKwsHDx7EokWL8OKLLyItLQ3btm1DdnY2li1bBgBISEjA1KlTnX4W5/0oKiri6464Q11dHUaMGIHrr78eN998s0i8uCs4AMuySn19vd1ln5KSEnzxxRcAAH9/fzzzzDOi15OSkhrN+eEpBoMBN910EwBg8uTJvFeGIAh1IcFBtCiysrLw3XffAWCriXLIJTjKy8uRl5dn83xlZSUmTpyIJ554ghc7Dz74YKPbQQcNGsS3d+zY4bZd2dnZOHv2LADg999/x/vvvw+ALb62YMECAK5lGLWmsTiOzz77jM/wOXPmTLz++uvo168f/7rS8RscX3/9Nfbv32/j2SIIQj1IcBAtijfffJN37z/99NOIiooC4Nq2TlcQLp3cdtttOHToEL8L5dq1a1i6dCkA1rU/a9asRj9PKDh27tzptl05OTmix8899xxWrFiBG264AZWVlQCA++67j0825irCmA/rjKPV1dX48MMPAbBi5qmnnoK3tzcWLFiAgIAAAMD48eMl/y3u4OXlhd69e8Pb21uV7yMIwhYSHESLIScnh7+bDwkJwezZs/nkVQUFBSgoKPD4OzgvAsDevffo0QNLlizBsGHDRMdNnz4dsbGxjX7egAED+LYnHg5rwVFXV4frr7+ef37gwIGi2BJXEQa8Wns4fvjhB+Tn5wMApk6dygdrduvWDYcOHcLSpUsxe/Zsyd9JEETThAQH0WJ4++23UVdXB4AN1gwPD+cFBwAcOXLE4+84d+4c3+a2XwYGBmLFihXo378//9rjjz/u0ueFh4fzyw4HDhxAVVWVW3ZduHCBb4eGhopeS0xMxG+//canKpdCWFgYn5b8wIEDfHp0AFixYgXffvLJJ0Xv69ChA6ZMmaJohlGCIPQFCQ6iRZCfn88HLwYGBvIToFBwyBHHIRQcwvogISEhWLt2LV599VX8+uuvojiGxuCWVerr690ulCb0cHz99df80om/vz+WLl3qUTGzwYMHA2CXUA4ePAiAzTXCeWRCQkIwcOBAtz+fIIjmAQkOokXwf//3f/y2zYceeojfbiq34BAuqVgnmGrVqhVeeukl3HzzzZI+UzhZu7usIhQcQ4YMwdq1a3HPPfdgzZo1SE9Pd+szOTjBAQDbtm3jv+/SpUsAgP79+9POEIIgSHCoRV09g13HGVTVqF+3wxkVVQx2HmNgMunLrstXGew/xchS56SoqAiffvopAMDPzw9z5szhX+vatSuMRnYYuCI48i8zOHzWsU2chyMwMBAxMTGemM3jSuDouTwG5/Ic28UJDl9fX0RHRyM9PR0LFiywiS1xh4yMDL69fft2AAJhFNQDPfqO9vg75KS+YSxWVuvrnK+pZcdibZ2+7LpWwWD3cXnGopwUXHE+FrXiXB6Ds7n6squ4lMErCxnsPcnAbNbONhIcKvH8ZwwGPshg1BP6OhGnvMhg0EMMHvm3fuyqqGLQ9wH236dLPf+8f//73/xOjAceeEC0fBAQEICOHTsCYJNX1dfXO/yc4lIG3e9h0HMmg5832vaXyWTid6m0bdtWthLoXbt25XNV7Nixw+bCv/8Ug04zGHScweDoOfu/IxfDkZyczAssuejWrRtv37Zt28AwDCuMomcAfQ7gywNPoqJKP+fXC/9lx+LI2fqxCQAefJcdize9pB+7zGa2nwY8yOClL/Rj1+WrlrH4w3r92HXgNDsWO93B4IiOxNCa3cDLXzPoN4vBvAUkOJo19fUMvl7Jtnceg27uYM7lMdjQkPNp80FNTRGxbi+QzW5uwOaDnvVVWVkZX7fEx8cHzz77rM0x3LJKbW0tXzLdHku3AsVX2faWQ7Z25ebm8kGpctbr8PLy4gNO8/LycPHiRdHrC1czqDcBJhOw3U4G9GvXrvHl45OTk2WzS2gft+xz6dIlZGdnsx6O+PsAAGXVvjia5ewT1MNkYvDVH2x79wmgWicex/JKBv9bz7Y3y7NDWxYOnAH2NwwJPV0jft8KFJWybXtjUSu+EYzFbcrWW5TEql2WPhrVR54bIXcgwaECO48DpeWWxzV12tkiZJXAO68XmwBg5U7L4PDUrr1796KsrAwAcPvtt9udcHv27Mm3nS2rrNzh3C5HAaNy4CwB2MpGfkdh/IYSggMQL6ts3LgR+w+fA0Itz9XU2nuX+uw6DpSUWR7r5bzfuB+obbBFL30FNH5uaYWc1wg5EfWXTn5Hk4nB6t1sOyQQyOiunS0kOFRAqC4B/ZyIQrv0MmgZhpFVCAnTbQuDG4UIA0cdJQCrrWOw3pIB3K5dwoBRtQTHmRwGZ3MFdtk5t9QQHMK+/fjjj1EXNAwwWJJs6eX8shmLOrSr3gRN19mFrBJO7Dq5btXVM1i31/JYL3ZlXmRwRuB81Mu5teckcLnBMzs2HfDxJg9Hs2alVZyfHk7E6hoGGwU7LPVgEwAcPQdcLLI89vRiIqyyKiz3LsSVnSrbjgBllc7tspeDQy6ECcCEgaOunFvCHBytW7eW1S6OAQMG8LEhBw4cAMInNGqXFtj0lw4mK4ZhdHmNuHyVwc7jlsd6sAlglw2vVVge68WuVbvEj/Vjl0U0ThyondgASHAoTl4xg4NnxM/p4UTcfAioEhT31MOFF5B/0Ao9HI4ER3JyMlq1agXAseBw5c5YSQ9HZGQkH9y6f/9+fouv0LXM2mV7Z6yGhyM0NBTdu3O+WgMQcZ3YLh2cX/mXGT4egUMPY/H4eeCCVZJbPfTX2j2AMD5ZD30FiL0ugH7sshmLtfrwUgnF7ASN0+GQ4FCY1btsn9PDxUQYjwDoeNB6YBfDMLyHIzExEWFhYXaPMxgMvJfj4sWLKC4utmOX+LGzGA6DwYDU1FS37XYE5+Wora3FsWPHUFHF2AQYarWkAgjiOIJ6Ar7iRGJ6OL+4dWwherBrlZ2dznqwS86xKCd69FJVVjP406p2oR76q+AKg70n2XbP9kBCFHk4mjXWgxbQx4loPWjr6rVfN75azmCrVXZxTy4mly5dQmlpKQAgLS3N6bHCzJ+rV68WvXahgMExq10WzpZUkpKS4OfnJ93gRrCuzLrpgK0dWgWNAoI4jogJNq/p45y3MxZ1MFnp8RphNjM2N0t66KucAgZHzomf07qvAODPA0C1C2NRbdYIRPZEHST7JcGhIHX1DNbusX1e6xPxTA6DzFzb52s1tmv9XnY7mRBP+sqV+A2OKVOm8O0lS5aIXnPlDvTq1au4fPkyAPmXUziEgmP//v12J6ryyjq89957eOWVV3h7uBiOkJAQfulICXgPR7j+BEe9TsfitQoGf9lZxdN6ct970rIFnEPrvgL066VqCmJW6/gNAJBUq/nzzz/H+vXrcf78ebz66qui0tILFy7E999/D7PZjClTpuDxxx+XLfFRU8U60JBD64ndOk6Co7Ye8Jf/xtxl7A3aWsd5uBpFGL/RmIcjIyMD0dHRKCoqwurVq1FVVcWXUHfFLiUDRjmElVn3HziAS0W2x3y36GfUHn0GAJt35PPPP+fzdiQnJys6JlNSUhCb2AUFoba3Ulqf8zuOAVfLbZ/35PySg/V72V0p1mhtl9xjUS7s2qXxuWUv6BfQvr/q6xnewxEWDAzsqq09gEQPR3JyMubMmWNzt7h161b8+uuvWLhwIX7++Wds3boVy5Ytk9XQpogwuCk1zvK81opcGACpF7sYhuGFUIAfEBPeYJMHdwlSPBxeXl644YYbAACVlZVYu3Ztw/cz2NCwmycmnLXNnl1KBoxyhIWFoU2bNgCAg6eq+eRoSVGWK5vw4vvjjz8iJycH1dXVAJRdTgHY2JUO/R8BDGzdlNYxlplUt+e8xneh+rXL0ubsMpmgaQmE2joG6xu2w0a1AoLY+wHNz63TOUAWWzZIN9dTANh1wpL/aVw/wFvD7bAckgTHxIkTMXDgQPj6+oqeX7lyJW655RYkJSUhKioKd9xxB1atWuXwc2pra1FeXi76V11dDbPZ3Kz+carXYACut+RAQlUNA7PZDACq21Reacamhgk0MQro01FgVzUj2/e88sorCAsLQ0BAAAICAhAaGornn3/e4fEHTjO4xK4AYERvoFUQ266pg9t9JfRwdO7cudHjhcsqv/32G8xmMzYfZFDRUBF+fH/A31dsF/dPKDhSU1MV+/04L0d1wAj++5IDBUkJjH781teysjJ88MEHluOSkxU/v2I7381/39ThloJt1TXynVvu/FspyJV2g3As1mo3Fk0mM79c5+/LTgrWdmnxr+CKGXsaAg27twXaJgjs0vDateUQg3LhWPRpGIu16tsi/PeHIAD/xqGWvqqudf/aJbdd4/sra4OrSFpScURWVhYmTpzIP+7YsSP+85//ODx+wYIFfKlwjmnTpmH69OlymKMLcou9cDQrCQDQs20NAr0qAbC37RfzCpGTw44cYUCfGmw4GICaOrao2NBuZaisNQJgZ/dz53NhqvLcD1haWor58+eLTsTq6mq89dZbGDt2rF0PwP9Wh4LrnwEdLuPsxRAAvqiuMfN9JKWvhDtUkpKScPnyZT6mwREdOnRAUFAQKioqsGzZMpw9exY/ro0GEAoA6NeuCKt3hgPwRkVVPbZu3YnDhw/DbDZj5cqV/OcEBQUhOzvbZVulwHk4EGEZb1fOLAD82GWMfgOG4Knxb+Fvf/sbAPBF6wA2hkMpuwDAZAY2HWLP+SB/M9KSigGw51phcSmys686ebdyXLrihUNnWbt6tKlBkLdwLBYhJ4dd91R7LB7P9kFuMTubD+hcBYO5FgAbY3MhJx8JwTVO3q0cv20NAsNEAQAGd7mKkzm+AFh3Qua5HLQKYse12v3149pwCMfi+j3sWKysrkd2tp2gNJVYvCkGXP8MbJ8PgHVzlF6tRE4Ou+6pdl8BwNIt8QDYO6S0hBxkZ7suDKTCX5caQRbBUVlZieDgYP5xUFAQXyzLHjNnzsSMGTPEhnh723hOmjIrBVukbhruh9BAS3BEaFgMkpPZiVSJYlrO2POrpX3ruBD8vtXyOComESkpnn/H7t27ebGRkJAAPz8/vqjZ6tWr8dZbb9m8Z9sJS/vOiZFY3uDSra03Ijk5WXJfZWdno6KCzQ7Us2dPpLj4h02YMAG//vorSktLkZ2djb+OsfEYXkZgxoRovP0zu0xQWFSK4cOH2/2MwYMHIyoqyqXvk8qIESPw3gdfAqHsrVSbODPO/L4YGPA5ACAsIg5Tp05FUlISLl68iKqqKv69aWlpLveDO+w4BlxpSBs+tp8R7VIt1XL9A8OQkhKm2Hc7Y7Vg6/BNw/0QESoYi62iNRuL32+2tG8ZGYDC0gD+cVhEnCxj0R12LrC0bx/fCu/9bHkcE5uMmHBt+uuvBoellxGYMTEaH/wOoBSoN3srel4741oFsPsU206NA64bGge8wT42ege6de2Sg5xC4ERDvr/0TkB6T2WXU11FFsERGBiI8nJLRFZFRQUCAwMdHu/r69usxIU9Vu60qMnrBxuw6wQAsC6uunoDf/IZjUbVTkQ2uIm1wc8XGJtuwJrdFrcba5fn63zr16/n2wsWLEDv3r2RmJiIuro6fPPNN3jttddEv39RKYNdJ1g7urUB2iYa4efL9p/JDDCM/b7KycnBvHnzMH78eBvv2PHjlhSJaWlpLvfx1KlT8euvrCr75ucdyMwdBQAY0gOAqRR5ucWAX3vU1dv/vLS0NERHRysWnNm3b18gfBxgZP3J3eLPI6vOIu5r61jxPnXqVHz44Yei96akpCh6rq2yOufZeBeGt0vNC65DuzIM2HfKYpdWYxEAVu6y2DV5sKGheJvQLvXX3NmddawN4SHA4DQDPlkquEaYtOmvzIsMTuewdgxOA6LCjPDzYfuvRsNza8M+BvUNcS2TBwMBfgbYO+fVPrdW72J4OyYP1uZcsocsPdCmTRtkZmbyj0+fPq1Y4FxToLLakjY8IQro1QHwFUg7rQLCjgjSho/oBQQFGODnY3ldjiAnhmH4gEs/Pz8MHToU0dHRuOmmmwAARUVF+P3330XvWb3LktFwUsMGB18X7Lrjjjvw9ddf47bbbhMJDEDaDhUhEydOhI8P++UrtlsmhEmDDFi5ciXM9Q0eA6MfevXqhZdffhnvv/8+3n//fXz66adYs2aNojtB4uPj4Z8w1fLElZUAYzmhuL6aOnUqrFEqrTnHH4JI/YkDrc55jQLoqmosQb9xEUDvDpD9nHeHolIGuxpO2W5tgNR4gy76a9sRS9rw6wawgYaujEWl+UMQgzNpEDu+OLu0DM78Q7AxYNIgA3x08BsCEMVvTBrk5ECVkSQ46uvrUVNTA4Zh+LbZbMbEiROxePFi5Obmori4GIsWLcKECbZ78VsKG/dbksBMHMhG7+vhIrdiu6VtPWgBeew6ffo0n/dh2LBh/NbSWbNm8cdYx++s2C4etEDjk8LBgwexZcsWAKzIefXVV0WvS9mhIqRVq1YYPXo0AKDUaClINmkQsGHDBsDMrqsbvQNw4MABzJs3D7Nnz8bs2bPx4IMPIiEhwe7nygXDAOZWDdvRTeXYuvJ1AAxgZjuJE7OtW7fG0KFDRe9NSkpSzK6LhZYU/n07AXGRBvgJnJhanfN/HgAq2U06mDgQMBqtJlCNxP+qnbYiWw/9JRqLA+2MRY36a4WdCdRPEDTKCHOwq4TZzPBCKNCfvYkzGAyaC6GqGkuhydgI8cYArZEkOF599VVkZGTgwIEDePnll5GRkYH9+/djyJAhmDp1Ku666y5MmzYNGRkZ/BbDlohQXU7mJlAdXEzsqV65LyacdwMAxo0bx7dHjhzJe73WrVvH562oq2ewpiEhU1gw6y51xa6PPvpI9PjHH3/EyZMn+cech8NoNKJz586S/oabbroJ8AoBQocBANrEA51bM9i4cSMvOMyMUZMtgntOArUIYx+UrEfpFbYIh9HIBvsKz60777yTb0dFRfHiTwmEeQgm2zu3dHDOTx7smphVg0bt0mhi5yZQo5H1cADa91dZJYPNB9l2ShzrEQLE19Q6DXJe7DsFFJaw7TF9AX8/8e+o1W/45wFLnaxJDSJbL0gSHPPmzcPevXtF/9LT0wGwgaAbNmzApk2bMHv27Bab9IthLKrX1wcY3Zdta30xEVZ+7JICtE3gBofld5LjYuJIcBiNRjzwwAP846+++goAW/mRS8jEuXAB5wKtuLgY//vf/0TPCb0cJpOJX2Jp166d5Il2ypQpQPhYPk5i0iA2sdeFCxcAxrJzQIuLr3CiwhXLzhg/O3dV06ZN42OppIouT+yy66XS4JwXjkUfb2AMe6nSXPwLRXZ4CDCowQGn9cR+Lo/ByYZAw0HdgMhW+hBo6/daBMWkBo+xHuyyd84D9seimjiySw9QanOZOXqOjRAGWBdbcKC9waH+nfHqXQC3S1W4pifnxbe2thabNm0CAMTGxgqqh7Lcc8898PZmFzm//vpr1NXVNTpo7dn15Zdf8smsHnjgAURGRgIAfvjhB5w+fRpZWVn861LiNzhiY2MR0/ke/nHP5Fx2OQUAzLbxEmoiXMtGiWArbgDbr8KJPTQ0FD/88ANuueUWvPvuu4rZVG3lwu3biW1rPbGfyAbONyRHG94LCNHJWBSK7PH9XRPZamAvTgKwsksD4ejSNUILu6xilji4/tLiN7QW2WP7OT9ebUhwyMwKVwatBifiCjsuXEDeQbt9+3Z+K+q4ceNsvFxxcXG4/vrrAQD5+fn466+/+LgSoxG4rr/lWEeCo76+Hp988gkA9k7nueeew5w5cwCwiW1uvfVWPgYDkBa/wWE2M6j0H8E+MFXg0skfBYJD4OFQ+SKXV2wpr26sPAjUsukNIyMjERzIdpj1uXXDDTfgl19+Qf/+/aEUfx60xElMGGBx4Wp9ByqKWRqon4nKXswSoC+7JgtvSmT2gkpBGCcR4AeM7CO0y9JW265LxeIqrEkx+vBwHD9vEdnDelpEtl4gwSEzjqKDtbyY1NdbKj+2EsRJ2Njl4QARLqcI6+wIufnmm/n24j/24URDHqqBXYGosMYvvr///jufRGfSpElo164dHn30UURERABgg0m5oFWADVyVyr5TQHlNQ6rT0g1Y/vsvbPwGAB9vy84VtS8owjiJxEBLcomxY8dqeldlL2YJ0H4CFcdJWJ7XWgiJ4iRcENlqUF7J4M+DbDs5BkgTbDLU0q4DZ4D8K2x7dF9u22mDXRrexAlTv0+22gWiZQyH0Es1WWfLKQAJDlm5fJXBjobdmJ1bA+0S7V981S7qs+OYJaf++H6Aj7f9QevIritXrmDu3Lk221mtEQqOMWPG2D1m1KhRfNuRC9eRXdXV1Zg7dy7//GOPPQaAzaD5zDPP8M97eXnhuuuuw+LFix3a4QzrOIk9e/bwWUrjuCIvUL9olNCuQR2v8O3x48fz55faNjlz4fpqeM6XlDHY1rBRqWMy0D7JwTmvcn+dy2Mci2zROa/uUs+GfZa+mDQIIu+klv3l9BohPL80HIuOrl1aFG/T63ZYDlkSfxEsjuIkAG3VuMhVOtjxoHWkyF977TW89957MBqNOHbsmN0AxP/+97/Yt49dyO/VqxdiY2PtflZ8fDy6du2K48ePI7ssjcswLboDtbGrob/+9a9/4cQJNiVpenq6SEw8++yziIuLg8lkwg033IDo6Gj7f4wLCJfFcOUP0WtJiTHIyRLbpQY1tQzWNZRMiQ4DXnxsHDYsjUR8fDxuvvlmfLLdYpOaOwRPZFsKVw3tAYQGWc4vdosgg9o69c/5NbvZYmOAnbGo4R27qxOo2nfHjpZcAW37S7xNV/yaVnbV1DJY2xD0G9UK6N/Fvl21Ko9FocjukAR0SCYPR7NGmATGnYldKbjgJoPBstWNw5VBy+W7MJvNNjk0ADavxt///nf+sXA3ij1Gjx4NGAOBVmxq8KRotkiUQ7tqgSNHjuCdd94BwJZdX7BggShzn9FoxD333IP77rvPI7FxqZhpyEQJdEqqAmrzRK+3SYm32KXiRW7zQfBF5CYOBHr27I78/HwcPnwYISEhvKBlGPvlzpVC5MIdbHuB08q97GiZB9BacNhf5gG0s0tYXt3fFxjZWx92FVxh+CJyPdoBybGOvaBqnl9/HQZfRG7CQMDLSx+eF6HItj639AIJDpmwjpPIEG/Q0GzQZuczONZwRz6gKxAd5mTQ2rGrrq4OR44c4R9/8803qKmxBE5+9dVXoqRezz77LB566CGnNo0ePRoIGw0Y/QHYunBZuyyPK6rq8Oyzz8LUMJrmzp3r1u4TVxCuzd48MkBUlCg+Ph6xgiUVNS9y9ly43t7e9rcIamaX7etaBNCZTAz/O4YENqSlF9qk0URVUWWJk7ArsjXygh48A+QVs+1RfYBAf31M7MKx6OzcAtTtL9E5P9CxyAY0tEuH8RsALanIxs7jQElD4apxVnESgHIXk/r6emzfvh1Xr7KVOA0GA9LT0xEXx1YsbCyISDxRMQDEx5w8eVIkMC5fvowlS5bgtttuw+bNm0XejDlz5uDNN99sNAfL8OHDgch8i11O7owB4NvvfsKpU6zboVevXnj++eedfr4nCAft9YMNqL7pJrz33nsA2PgTfw0mBYZh+GUeby9xGXMOLS5ypWUMtjZo0faJQEc7Llwtgll3nwAuNxSmHdcP8PXRh4djwz7LhG1XZGsmGi3txsaiVhNoo9cuVe1i//fyYrc1W6OFcLQW2UN7OD9eK8jDIRP2UgILUaqWyuOPP47hw4fjhhtuwA033IDrr78eaWlpuHTpkq1dbtwlHDhwwOa5L774AtXV1XjggQf4lMJPPPEE3nnnHZcSvrVq1Qo+sVPYB6YqdE0ssDlG2F8rVq4DwN7RL1iwgK91IjfWa7P9OgN33XUXvLy8AAC33XabJvUuTl0AzjWs7AztAbQKtnN+aXDxdRYnwcH1l5oT6IpG7vS0mqgas0urWir20oYL0cKu2joGa3az7chWrHfWxi4NtuuezmFw5iLbzkgDwkLUu9Y7Y9dxi8gem24rsvUCCQ6Z4FSvwcCu61mjxEWurq4O3333nc3zly9fxjvvvIPKagabGvRCYjS7X1yqXULBwU30GzduxH333YczZ9jiGYMGDcK7777rcnbZQ5lAnaGhdPnVP7Fz20bbv63WUn0YBrac+Ntvv41evXq59B3uYG9ttmfPnti9ezc2bdqEyZMni5Z61LqYOAs05NBiEnUWs8ShxZKKsL8m2huLGnmphHESXAZire0qKmWwm43DRloboHWsYy8VwHlBlWfrYaCsoQjydf1t4yQAbTxCjXmDAG1+R1fGoh4gwSED2fkMjjbESfTvAsSEq3MHunPnTpSXsxPzgAED8Oqrr8Lfn42L+Oyzz7B4Y6lNETmndtkZtELB8cQTT/BtLrW4j48PvvjiC0mll/+w2gXCJ9VqoK6uDv9+703+sbdfED744APMnj3b5e9wB0drs3369MGIESMAaDSxu7DVTe2Lr8nEYFXDBBocwCYZsmuXyksqFwsZHGooXN2vMxAbYXvOe3tZ2mpNVIcygdyGSs0je9vGSQDaTKCiInKunFuanPM6EtlSx6JqdlnaEwY4Pk5rSHDIgCt3oEajpXSxXCehMO/Fww8/jJdeegkPPvggAKCqqgr/99UJ/nVHSWCcDQ6GYXDw4EEAbKXRp59+2mY548UXX5SczXOFVZ6LDRs2iKo9btmyBTnnM/nHDz38BJ+hVEm4zJSO1mYB9S8mV8sZ/HWYbbdLBDo5qDCv9l3V7hNAsQsuXPEWQeXvjl0ZiwaDQXUhZK9SszVaTFTOtsNyaGMX+7/TsajyOX+twlJELjWOrUllD7X7K6fAIrLTO7OVmvUKCQ4ZEN8ZOz5O7i2Ca9as4dtjx44FwO4S4bwcR3LZcuR+Dly43Gsc1oMjKyuLD0bt3bs3YmJicOONN/Kvd+nSBS+88IIkm4tKGexqKCIXhCygJhvZ2dl89VgAWLlypahIWlyCg5EtI6dzGGTmsu0h3e2vzQLqX+TW7rFsc53kwEsFqH+RczUiXu0tgq4mPlJ7qUfo8nZ4Z6zyuVVXb4mTCA9hE5E1apcKnpfMiwxOs8mEMbgbEBHqwjmvgl3rhGPRTtCvVnYJMxA7m3/0AAkOD6msZrBxP9tOiAJ6d3R8rJx3VZcvX8bevWwmqO7duyM+ns0PER8fz25TDewOxpcVHCN6AUEB0gfH/v37+Xbv3uzmfE7QBAUF4auvvoKfn58ku1fvsrhw+7SxBIsuWbKEb69cuVL1miWu3BkD6l9M3JnYVREcDgpXWaPmJFpdw2BDwykbFwH0cTYWVcwPIhTZ3doAqfH2f0dvLzYGDFDnN9x2BLjGlj4SVWq2Rn0xa2nr65x3LU5CbeH4hwteKr1AgsNDNh1Ao3ESHHLeVQmXIazrljz77LPwirEsQYzoXtGoTfbsEsZvcIIjPT0dZ8+exZkzZzBokPTcucLB8eD0ZL79zTffgGEYZGVl4eTJk2LBofagdeHOGFDeLrPZEmgYFMBWPHWEmnehuUUMDrLxwujbCYiP0sekICwiN3GgpYicM7vUOLeEItvZHajBYFBVCDW27ZRDS++ZswRWak7swrEY6M/exDlCzf6qEojs2EZEth4gweEhjio/2kPOehfC+I1x48aJXktMTER0p5n845r8JXCEs1oq9gQHACQkJPAeFSnU1TNYLXDhTh+fhMGD2SvK0aNHceDAAaxatYo9QLCkonRNAuHabJt4oLOTFRw1lwj2nASKStn22HRxMjRbuyyvKd1f4jtQ58eq2V+SxqKK9S70axf7v9Fom4HYnk2A8r9hmaCIXEoc0DXV8bFqnlv7TgEFXBG5PoC/n4tjUWmRfcB1ka0HSHB4gLBwla8PMMZBnASHXHdVDMPwgsPf3x9DhgwRvX75KoPCqoYMmZXHcfLgqkZtAmzvqjjBER4ejtatHUQrSmD7UeAqV0SuP+vCvfvuu/nXv/nmG3Y5BVB1ScXVtVlA3bsqKZkD1byrcvXOGFCvv5wVkbNrl0qehLp6BmsacruEWVVq1tKuc3kMTjYUVR7kJE5CaBOg/Lm1fi9Q1yC2nMUs2dqlbECylGULra4ReqwOaw0JDg84eg7IKWTbI3oBwYGunYienoSnTp3iS7QPGzYMAQEBotdX7wLMTIMtl//Atm3bHH6Wj4OkPvn5+cjPZ7OB9u7d2+UcG86wNzimT5/Ox4EsWrSILwMfFRlq1y4lkLKHXd2LnKXtLE4CUC+2pLqGwXq2Rh9iI9glFT3YdSIbON+QvHZ4LyCksbGo0pKKUGQ7i5NQ2y4pZcw1E7NSJnaFBZqrMUuAev0lVWTrARIcHrDCxeAmDuHdiyc7BIW7U6yXU1i7BB9eshI5OTm8QLHG0RZBR8spnmDPhRsWFsbvfLl8+TKqqtjMW8OGWHy8Sg5as9kyaAP9geEO8klwqDWB5hUz2H+abffpCCQ4iZMA1LurEsZJTBjQuAtXrYuvaNupnUy/1nD9VVdvqfCsBFKWUwD1BEdjGYiFqDWxC8digB8wopHLjlrn1qViBnsbisj1bA8kxejjnD9+3iKyh/VsXGTrAaql4iL19fVYunQpCgsL+ed+2P43AK0AND5oAfGJWOfBGq2z+A1hETk/ryrUXGOvxNu2bcNtt93m0K6aWsvFpLi4GK+99hr/uhyCIyuPwYlstj2wKxDZyjI47r77bvz000+i48eMGoLfFrJtJQftvlNAYUnDd/Z1vjYLqDexi7a6STy3lLRLaoEobexq/Hi11v+FGYivc5BPQoga+UHKBXESyTFAWlunh6uW2vzAGSCfi5PoCwQ0NhZVOrcaKyJnjVoCzdXdPHqCBIeLvPLKK/jXv/5lecI7Ahj4AGAAOrcG2iU2/oPLkW20rq4OmzdvBgDExcXZVE3dcQwobXDhDuh4FVv+ZJXN9u3bnQoOzqYTJ05g8uTJfF6M0NBQPseHJzgbHGPHjkVcXBy/hOPl5YVRI4cCCxvsUnTQSptA1br4SrZLhQlU6MJ1VETOmV1K/Y4lZQy2HWXbHZOBDnaKyFmjxmQlFNmDugFRYa6fX0qe8xv2Wc6RxmKWADatuJcXA5NJ6XPe0pZ6zqs1Fl2JkxBeI5QVs67trNMTtKTiAmVlZfjwww/FT4aPBwxsjuSJLv7Ycrjj9+3bh4oKdpvrqFGjbC4WwpPwb9e14l93FsfB2XWtvBqDBg3ixUZcXBzWrVuHmJgY94wV2iWKkxC/5u3tjTvuuIN/nJGRgejIEP6xohcTqZ4EFTwcNbUM1rEpVhAdxqbobgw1lnpOZgNZbE1ADOsJhAbpY2Jf60IROWvUsMudO1Du/Ko3sUsMSuBO3Q01glldTaDIocY5X1snLujYv4tEuxQ6t4Qiu0OSayJbD5DgcIEFCxbwGTcnTZqEb7/9Fq3a3s6/HmXc7dLnyHEict4NoKHMuxUrBC7cm0cG8mnHDx06xNddsbGr4SJ3+Uo5/3dyRcv693fBD9wIFVWWInJJ0UB3Oy7c+++/H76+rCF/+9vfVHF5C9dme3UAEqMlTqAKXeQ2HwQqGorIubrVTY2LnNSYJUC8lVc5u6R5gwB1hGNjVVjtofTvKPRS+fuydV2k2KVUXxVcsRSR69EOSLZTRM6RTUrateWQpaDjdQPsF5GzsUuFc8uVSs16hARHI5hMJnzwwQf84zfeeAN/+9sdMIWMYZ+ov4rfFj7lUp2Ixk5Ek8mEN998Ex9++CHq6+0Hefz5559821pwZOczONZQRG5AVyA6zICMjAz+s3ft2gVr6uvrUVLccNvaUJV1ypQp2Lp1K5KTk22Od4cN+yyTsyMXbqdOnbBt2zYsXrwYDzzwgCoXE9HarIspgdW5M9bnBCo1TgJQXqCZTAz/O4YEAkN76MOuiipLnERSNDuJuoLSdh08A+QVs+1RfewXkXNmlypj0dVzS/VzXlpfAcrdlLhjlx4gwdEIy5cv55cYxowZg+7du2PncaC8puFsL1mLvbt3iAI5HdHYZPXpp5/ihRdewOzZs3HPPffAxEnYBurr67F161YA7HJHx47itHL2trpxggOwv6zy8MMP48rlhlBnox+effZZ/PbbbwgODm7073EVV7e6paenY+rUqfDy8oK3twFcAVo1Bq3LrmWFL3IMw/DLPK7GSQDKC6HSMgZbj7Dt9olsrIQe7Np9ArjcUERuXD/HReSsUXr93xWRbQ+l+8uV8ur2UDqY1Z18EupM7Oz/zorIWaP0bygU2c4qNesREhyN8O9//5tvP/XUUwDEgwNX2ERV8+fPb9TL0dgAWbhwId9etGgRZs6cKRIdBw4c4JdFhg8f7jR+g7tLcCY4Kioq8NVXX1mSbBl98cYbb0oqNd8YwgnUz5e9q3IVJe+qrNdmXYmTENoEKHORO50DnG0oIje0B9AqWB8X37V7xC5clydQhQWau3d6yk/sbtqldH8J4jcayychRMkYDmERuYhQ1jsrxSZAmb46k8PgzEW2nZEGhDso6Ghjl8K/obsiWw+Q4HDCvn37sGXLFgBA586d+Zolwq1unWPPAwB27NiB9evXO/08ZyfiyZMnsW/fPtFz3333He69915edAiXU0aMGCE6VlhELjGa3S8OAG3atEFcXBxvo1DA7NmzB2azWZxGXOYBcvgskFvEtkf1dt2FCygrOP46bFmbnTDQtbVZoU1K2eXuVjfdTqBK2yUI+p3gJD23NUraxTCWuhvuimxA/sldWEQurQ2QEqePsbj1MFBWybYnuBgnASjvpWpuY1EPkOBwwvvvv8+3n3zySRiNRlwoYHCkoZJ6v87A/H88wh/z2WefOf08USCkVYjGokWL+PYNN9wAb292b9W3336Ld955B4DzgFFHReQMBkscR1lZGY4ePcq/Z8eOhhFltlzZ5K7h4Mlecb6uhNKD1oVEURzsFkG2rUS9C3fiJADnNXE8xWy2uHCDJLpwlQz+FRaRS+8MxEW6NykoIbIvNojskb0dV2pu1C6Zf0dRETmJgYZK1nhxdwI1Gg18pmRFrhE73RyLCge8S8l6qjdapOAwm8147rnnMH36dJHXQEhBQQGfjCoiIoLftilOyGTAzTffzG8bXblyJcrKyhx+r6O7F4Zh8P333wMAjEYjPv/8c/z888/80sarr76KnJwc/PXXXwCAmJgYdO4sXgNwNoEKl1W4GBBAKDiUq8zq7gQKKHtX5c7aLIdS7uVrFQy2HGLbbROAThLK1yh5ZyyliJxTu2T+HUVjUeKFV0m3t0hkSxCzgLJJozy5M+Z+R5PJsrQmF1x/GY0ejEWZf8OySktBx8aKyNnYpOC5ZV2pWYrI1gMtUnAsW7YMb7/9Nn755ReMHDkSI0eO5JdOOP773/+iro49Wx544AEEBgYCsJ1Avby8cMsttwAAqqursXz5coff62iL4Pbt23H+/HkAbGBqXFwcbrrpJjz00EMA2FiLqVOn4tq1awBs4zesi8iNtioiN2zYML7NFUdjGAY7d7JXbB9vS25nOS9yl68y2Nngwu2aCqTGu3eRk3vQZl5kcLoh03tGGhDm4tqs0nYJi8gJvVRSbFLCLo8mKt1OoMpt1xXa5WqOHg6lfsd6QZxEWDCbiEwPdgmLyA1Oc15Ezq5dCgWzSikiZ2OTWiK7CW2H5WiRguOrr74SPf7zzz8xfPhwvPrqqwDYbJ7c8ojRaOQn/qoaBhsEhat6d2Dbt956K/9Z1im6hTg6ETnvBgBRAqz58+cjPDwcALB3717+eevllGNZwIWChtd62haR6927NxITEwEA69evR1lZGc6dO4eiItbvq1ShtNW7LHUq3HH9KTWxi4uiSb9DUMyunTJN7Ar2l5Q4CUC5Ync1tZYicjHhjReRc2qXQiK7c2ugbYJ7YhaQ93fcftSSgZir1KwHu2QbiwqK2YlueoMAhe1yo7+0psUJjry8PP4uPyYmBh06dOBf++c//4k///wTS5YsQV5eHgA2niIlJQUA8OcBoKph9UGYkCkjIwPx8fEAgNWrV/PJs6yxN2hra2vx888/AwACAgL4QmYAEBkZifnz59t8jnXAaGNxEkajEVOmTOG/b/Xq1ZblFABxMeE2dsmBJxMoILh7UfTOWPr7lbDLbLYEGgb6s9WHJdmk0IRwSVBEztXkaGrYJUyO5koRORu7FBJoa3ZbRLYn5xYg98Quz1gE5D3vPR6LCoh/YdCvlORovE0K/YZCke1qBmK90eIEx3fffcfuzAAwa9YsHD9+HC+99BIA9kS744478Pbbb/PHP/bYY3zbUZyEl5cXpk2bBoCd0H///Xe7321v0P7666+4coWtWHTjjTciJCRE9J4HH3wQXbpY8ulGRUWha1fxvjFXBu1NN93Et5cuXcovpwBAYnykjV2eIiwi1yoYyOgu/TP4dWMz+08OyisZbG6Ik2gdC3Rr475dcl5M9p8GCrjCVX0aLyLnyCYAqJVxQvAkTgJQa6Jy/84Y0NnELlzqkbO/Gn5Hg8FSqVkKSvSXdXI0exmIG0OJsXjgNHDpMtuWkhzN2iZA3qBRT0W2HmhRgoNhGHz99df843vuuQfe3t545ZVXMGrUKABAbm4uvz21a9euGDlyJP9ezpPg4w2MtUrIJFxW4TwW1lgX/qqtrcXLL7/MP3fffffZvMfHx0eUC2T06NGi9cSSMgbbj7HtjslA+yT7J+Hw4cPRqhVb2faPP/7gY1YMBgOSEy21UuQauDuPAyUN8bPj+gE+El24gHVBMnkG13qJhavs2sUV2FLItezORKXUFkFPJ1Alit25mxxNiBITqMnEYHVDnERoEDDExaynQpToL3sZiCXbpUB/uZsczZ5d8npmLW13znkfhQo8NuXtsBwtSnBs374dp0+z/uERI0agXTs237DRaMS3336LyMhI0fGPPvooPwhOZgPnGxJyDu1hW7hq4MCBSEpKAsCWjy8pKbH5fmvl++OPP/JZTEeNGsWLHmvGjx+Pjz/+GNOnTxd5XwDXC1f5+Phg8uTJAICrV6/iyBE2bWS3bt0QEmS5DZVrgKzc6XjXjKuItwjKM8BWerjMAygTqLbSzYRMHEajAd4N23Xlsqu2zlJEztXCVdYo4V4+c9GSHG2IhORojuySyyO08zhwhY3rdltkK9FfYi+V52NRkWuEu2NREMPhSnkJV1jp5nZYDoPBYBFCCngbvdwU2XqgRQkOoXfD2puQmJgoCiYNDQ3FnXfeyT92JU5i+vTpANig0yVLloheZxgGpSUF/OOrZdX46KOP+MdvvfWWU4X/yCOP4KeffkLr1uK9klLiJITxIRwDBw5UxO0tTI42wc294nK7Jj1dm7W2i90i6PlFrrCEwZ6GInLd27pWuMquXTILIWFyNFcLV9nYpMBE5cm2Uw5l7JJvAgXkHIueTaCA/HYJPcZSk6OJ7BJcu+pkyBEiTI7WTWJyNJFdMnteTucwyOREdnfpO+v0QosRHKdOneJ3kISGhmLq1Kk2x0yZMgVvv/022rVrh//85z+ieiKuJIERLqsId54AwNy5c/Howw/wj9dt2ILLly/z70tPT5f8N5lMDFY1TKCuFK667rrr4OfnJ3pu0KBBsm8RzClgcPgs2+7XGYgJ92wCBeTxcBzKFGQ9dWNtlrdL5slq1U73EzIJkTtiv8VMoAoIIam7eTjktquqxpKBOCGKDfzVg11HzrmfHE1Ju0TJ0TxIqiW34PB0yVUvtAjBcfr0aYwcORIVFRUA2K2nXF4Na5555hlkZmaKtqdeLWew9TDbbuekcFW/fv34gmqbNm1CZmYm+/6rV9k4DEGCra3b2MVeb29vfjuuVPacBIobNsSMTW88p35wcDDGjh0reo4VHJbHcgwQ6+Ro7iKySwbBIdegldvt7eluHg6lLnLuJEfjbZK5r8oqLcnR2sQDnVP0YdfFQrHIjo3wXGTLYdem/eKdde7ESShhlxxeKkB+QStXnITc3kY5RLYeaPaC48yZMxg5ciQuXWJLsPfq1UvyBL9WkJDJWRIYg8GA+++/n3/85ZdfAmB3xlRWVopqlnCl4GfNmoX27dtLsofDncEhXFYJCwtDp06dxAFhsg9a9z9HvKQih+DwLE6CQ86LnLBwVXgIMNDFwlXO7JLjIidMjja4m+uFqxzZJJdd6/YIEjK5GWgIWAVnynDOKyKy9TSByuwF9SQ5mhA5zy9hcrRWwWwiMk/tkuM3FIrs1Digi5siWw80a8GRmZmJkSNH8jk1evbsifXr1/PJtFxFShKYu+++Gz4+7Nm2YMEC1NbW4pNPPmFfFHg4YPRDSkoK5s6dK8kWsV2Wtqsu3Ouvv563b+jQoTAajbIO2uoaBhsaXLjC5GjuIGfQaHGpZ1lPHdnlaX9tOwJcYx1vuG6A9IRMIrtkvKvyNCETh5ITqGx2yT2ByiVmPbRLuJvHx9s2A7Hbdnn4O16+ymBHw846d5KjieyS0fMiSo7Wz8OxKKP4F4psT7xUeqBZC445c+YgN5eNtOnevTvWr19vsxOlMYSFqwL92UyezoiJieG9CIWFhXj66adx4sQJAEDvXpbb15n3/R0rV67k67BIJa+YwYGGnPp9OgLxUa6dhDExMfjuu+9w55138kXh5Cz89edBoLKabQuTo7mDOCDMs0G2erc8cRKAVX95eEFxt4icPbiLnBwBtnJ5qURbmz08t4TJ0QL8gBFuBv0C8p7z1TWWhEyxEdKznjq2y7OA5OPngeyGnXXDewEhgfKMRU/7y9PkaELkDCyXc9upnMXumsN2WI5mLTgWLFiAPn36IC0tDRs2bEBUVJTkz9h7Eihs2OE6pq9rCZlmzZrFt4U7Uf526818mzH4ISAgQLI9HJ7k1L/11lvx7bffolMn9soo592LEhMo4PnAlfViIuNdqHA3jzsJmYQI76o82SEoTI6WHAOkuZGQiUO0RdDDvjpwBsjnkqP1BQIkJkcTIuc5v/mQRWR7mpBJ3rFoacs5Fj0/53U6FgXJ0dzdWcchFP+ejEVrkT3Szd08eqFZC46IiAisX78eGzZsQHR0tFuf4U5A36hRo9CmjTiFZXR0NCZNHMM/bo6DtrHkaJ7Y5UkMh3XWU0/WZq3t8qS/svIYnMhm2wO7ApGt5LmrYhhLzJE7yJEcTWSXTOvZckbqt4gJVMZAQ7mEkBzJ0UR2ybSlX5gcrX8X95KjieyS6WZJKLJH9fFMZOuBZi04ACA8PNztZQtA7ElwdW3WaDSKgkcBNu9HqDDBlgeDo6aWwfqGhExy5NSXax301AUgi43NtZscTbpdlvd7EsOx87h4bdadhEwiu2S6+MoVaMghV8yLHAmZhMi1nu1pcjQhcp3zQpHt7cXuFtODXaVlDLYdZdsdkoAOyfKIWcCzpYtdMiRHE9klk0DT71i0tJv6cgogs+CYNWsWBg8ejKFDh2Lo0KF4/PHH5fx41cm/zGBvQ0Kmnu2BpBjXf/CZM2fCy4tN/WgwGPD3v/9dtouJMCGTHDn1xROo+/4/ufeKy+XhkHsNVK7fUe6tbnJc5KyTo7mbkElklwzBrEWlDHazoVDo3hZo7WZyNN4mmSaq0znAOTYmHUPdzHrq0C4PxOzaPa5lIHYVufpLri3gHLLZJVoK9sCgBsQCTa5rlycW6QPvxg+Rxssvv4zx48fL/bGawAWLAtJ/7Pj4eDz33HN4/fXX8eSTTyI1NRVXyy0nj3wTlU4HrQyDQ7h10ZO7BDmyngqRo95FZTWDTQfYdmI0K2g9tksGgSZMjjayt/vJ0UR2cbVnPJhA5UqOxiFXsTu5RbZctVTkvkbIZ5el7W5yNCFs7iH2b3XXLmFytPhIoHdHGeyS4dolFNlpbTwX2XpAdsHhCrW1tai1GuXe3t7w9fV18A5tWLHd0p4wAHyVWVd55ZVXMHfuXPj4+MBsNsPHy/Ia50mQ+pmAICGTERjTl4HZ7Fk0u7DYUHWtezZdq2A9LwDQLgFonyiDXVYTqDt25RSyWQ0BoH9nIDLUc7uEE3tVjXuft34v29cAe24xDONxLQjri5w7/SU85ycOdO9csEa4pOLu562wmqg8tUt0ztd5PhZZu/QxFs1my81ScAAwpLvMdtW4118XC1lBC7DLwNFhMoxFgV3ujsUN+yzJ0WQbizJcu/7YYRHZco1FpTAaXVsskV1wvPPOO3jnnXfQsWNHPPnkk+jQwTYRw4IFC/DFF1+Inps2bRpfi0QP1NYDa3cnAzAiLMiEuKCLyM727DPZ84XN2lJWwc42OTk5kj7jXL43zlxMBACkd6zG1SsFuHrFM7uuFPsASAAAXL5Shuxs6R+4ck8g6k1sYO7Qbtdw4YJt8TqplF8NAsDuLKqpl95XALBoYzAAdiv04C6lyM6+6rFdFeUhACIAALl5RcjOrpT8GT+tiwAQAgDo164Q2dlVHttVXxsJgE3HX1vnXn8t2RwLwB8A0CM5F9nZnu/rMyAegC9qahlkZ1+Q/P66emD1LnYstgoyIT7Y87HIMIDB0BoMY0BZuXtjsazKgC2HkgEY0Dq6Dv5Mnsd2lZQbAbCpjEuvVSE7u1DyZxw864ui0ngAwOCulbiUV+SZUQBKS/wAxAEAii6zQRhS++uHTYKx2Ll5j8W6GsFYrDe4NRZ/3RAFIAgA0KdNPrKza5y/QUOsN0k4QlbB8fjjj6Nt27YwGo346aefMHv2bPz66682acRnzpyJGTNmiA3RmYdjwz6gnMsnMcgLbdvIk97Nx5u9gDIG9m9NTk52WR0CwJLdlvbUEf5ISfHcLuHw8vELQUpKiOTP2P0/S/vWcaFISQn12K6Es5Z2bb1Bcl8BwI5Tlvbt14UhJSXMY7viBTHIIWHRkPoTMAywpSGgz9cHuG18DILtZ9qXRIQgn507/VVUChxo6POuqcCQ9ETPjQIQEmSxqXXrFEjd9PLnAaCsYR6ZMNAL7drKMxb9fFgvgrtj8dc/LbuBbhjqg9RUz+2KFMyXXt4Bbo3vr9Zb2reMCpTlGpFbZmn7BYQCKJXcXztOW9pyjcW4WEs7pJVnY9HHG/jbhBiEyDAWI0VjUfq5VVcP/NWQHC08BLhpVBy8NVmPkBdZ/4S0NMt+w7vvvhvLli3DsWPH0K+feH+kr6+vrsSFPVbtsrivJg82eByYyeHnY0ZdvWWN3Wg0SjoRV+6U364APwbcOmhtvevuMQ6zmcHq3ez7gwKAkb0VsKvOILmvqmoYbDrAvj8+EujbySBLlj5/gV119dL/1sNnGVwsYt8/ohcQGixP7La/r+XccKe/1u6xuJInDZJ+HjjCz8diV73JINp95AqisThIxrHoa0Z1rftjUYlrRIC/5dyqqXPvNxBdI2TqL/E1Qnp/Vdcw2LiffX9sBJDeWSa7fD0bi0fPMbhQwL5/eC+glU7G4o5jDK5VsHaN7w/4+jaPDaWK/hVyXbC0QI7CVfbwJGL/WoW4cJVcOfU93csuTI42Nh2SJxSHdnm460JYuEqOfBL27HKnv4Tr/pMHyxcI5mmxO2Gg4WQZt+B5uquH6y+j0fPkaEI82a4rTMgUFNB4BmJX8fYC7wFyx67cIksG4r6dXM9A3BienvPCDMSTPMxA7NAuD84tQLlz3p1rl1JjUWtkUwRlZWXYuXMnamtrUVdXh0WLFuHatWvo0qWLXF+hGmdyxIWrIkLlnxTcGbRyFa6yxtMIdLm3unF4ui1WqZTAnl/klNnq5slFTlhELkyG5Ggiuzzor3OC5GiDunmeHE0Iv3vGjd9w3ynpGYhdwWAweHSN8CQDsTPkPef1JGYVGoseX7vY/+UW2Voj25JKfX09/vOf/+D8+fPw8fFBx44d8cEHHyA4OFiur1ANube6CeFT3roRi6fYxO5hnQTrnQ1yIaqlYpL29zIMw+9s8PVhJwXZ7PKgfoOchats7bJsEayT+DuKClf196xwla1dlrbU/lJ0LHL1LtyYqFYoWN/Cz5eNLXHrGiFjaQFrmzik2sUwDH+NkCMDscguD86tK9csydE6JgPtk5QZi7USr13nZM5ArCdkExzh4eH47rvv5Po4TRFP7PJ+trtuXKELN9CfXfuX2yZA+l3VpWIG+xuCwXp3ABJkcuFa2yX1YnIsC7hQwLaH9wSCPShcZWOXB3dVq3dZCldNHiybSQA8u6tSskCUJ/2lZOIjT5ZUxNV05bGHw10PR02tpYhcTDiQ7mEGYns2uWPXiWzgfEMRuWE9PSsi59SuOgaA658tZxE5G7s8SPyl1JKrHmi6QRYKUVbJYPNBtp0SB3RzbbePy/AxHBIH7f7TQEHDblU5XbiAeI+91Iuv0IWr6AQqcYlAyUFre5FzHaW8VIA8/WUwyJOQSYi7k1V5pSU5WnIMm2FUCbuknvOXihnsa9j91LsDkBitzO8o1a7NB4GKhi1nnlZqdmSTO3YpFScBeBZ/pmSchCe1VJpbdlEhJDisWL9XECcxUL44CQ53K3qu2K7cRGUwGNwOZlXrzljqXcIKFe6MAWkXOesichndZbbLzRiOrDwGx8+z7YFdgSgPC1fZ2OXmZLVB5iJyNnY19FddveVO1xWUipPgcFdwKL3MwyF9WUylsSjBLpOJ4ZOjhQR6XkTOmV1Srl1CkZ0ULb/I1hoSHFYoObED4hOxTkJFzz8EFzm5XbiAe27cmloGa2UsIufIJkDaBHrlGoPtDWuznVoD7RL1sUSw4xhQ0pDPQI4icjZ2uXmRUzJOAnB/UlDSGwRYjUUJd6FKimzAvZ1scheRs8bdwPKSMgZbj7BtOYrIWePuubXTqogcmyJdRrvcFP9Ki2ytIcEhQBgnEeAHjJShcJU17tS7EBaR69VBWhE5V3HnrmrLIeVcuID79QhEa7MKiDN3L75KT1S+bgo04cQu97IYILbLVUErnEDlKiJnjTvbiGtqGaxTUGQD7tWeOXXBUkRuWE/Pi8hZ4+VlQEMtSkl2rd1tKSKn+Lnl5lhUIk7C3WuXeCw2L7EBkOAQceAMkN8QJzG6LxAgY5wEhzt37SIXrgITKOCe4FDrTg+Qeseu7KB118MhdxE5a9zxcFRUiV24Pdopa5er/XXwDJBXzLZH9ZGniJwzu1wdi9aVmr28lDu/6k1wuTaIcKeYUmXM3bpGqOilkiKE5C4iZ407Y1ENka01JDgEqDloAddPRKUndkC6G1e47dTbi3VLym6TGxOCcG02NEj+tVlru1y9yGXnMziaxbb7dwGiZY6TsLbL1UC1Dfssf8NEBWKWAHEiOFfPL6WXeQD3BK3SS66AewJNyZ11HFIFh8lk8RiHBAJDlR6LLtp1oYDB4YYU/v06A7ERCt8suXjtEopsuSo16w0SHAKEE7sScRKA9BOxto7B2j1sO6oVO1kpYpfEGI7TOcDZXLY9pIf8LlyhTYDrgWrWa7Nyx0lY2+XOBKpU5kB3LnKqiFk3BJqaEyggpb/Y/5US2YD0/iotY7CVq9ScyOaUUNIuV8/53SeAy1fZ9th0+eMkAPe8jeKgXz3dWFrazXE5BSDBwVNwhcGehjiJHu2A1rEqnIgu3IVuOSRw4Q5UxoUrtEtPE6i3twFcdnxX19jVSAnszkVOja1uYiHU+N/OMJY7UD9fdhlRebsaP76olMGu42w7rQ2QEqePSeF0DoNMgcgOC9GHh2PtHksRuckKBhpK3dKv9NIm4KaY3aFszBLgnrdRDZGtNSQ4GuDc8ICyP7bUi5xSmQOtEW8RbHzdWK294pbMrK66vNn/lYqTENoEuHaRq6xmsHE/206IYgN/lbbLlf46fBa42FC5fGRvIChAHwJt1U7LlnFFzy2JHiHRkqsKYxFwrb/U8FIBbtyUCDwJSsRJANLFWVUNgw0NydHiItg8KorYJfHcEorsbgqKbK0hwdGAGmuzgPQTUakictZIWb64Wm4pItc2gd16qrRdroizCwUMjpxj2/06AzHh+rgD3bifTVUNKLvVTaqYVW0Cldhfak+ggGt3oWrcGQPSBK0wZikogN2horRdrvyGFwsZHGwoIpfeGYiLVOZ3lLpLRVjQUYmddRxSx6JQZE9upt4NgAQHAHGcREQom/xIKaTchZ7OYXDmItse0l05F66tXc6PXbdXHRcuIPC8uFCPQI21WUC8RVDqRKXWnbEr/aW2lwpoXMzW1TNY3VBELjyELdimhl11jYxFYaVmtUQ20Pj5teckUFTKtsfJWKnZrl0Sas+IMhArOBaNRgOfKdkVu9RY5gGkexvVEtlaQ4IDwNbDQFkl21ZqqxuHlBNRjUh9Dil3CmoODouHo/FjxfEbChnUgKtBtsKtbr4+ysVJCG0CGu+v4lIGOxtcuF1TgTYyFpFzZldj59a2I8C1CrZ93QB5i8hZIyUnzjpBnITSCZmkpOvWYiyazOw/Z6iZnttVzwvDMPwyj483MEbm5GgimyR4suvqGaxpuOFVWmRrDQkOKJsS2BrhHUhjFzktBi3g/CInTI4WFAAM76WoWS7HcAjXZuMjgd4d1bGrsYvckXNATiHbHtlb3iJyjmwCGu+vVbvUiZMAPJhAFfQGAWio6MnSWH+tUCEYmUOKQFOyiJw1ri4TVNdYisjFRgB9lB6LLm7pP5YFZDcUkRveS94icjY2SfBSbTsCXG2o1Ky0yNYaEhywDFqjUdk4CcD1E1Howk2NA7qkqGeXs4G79yRQWMK2x/RV1oUrtKsxcWa9Nqt0SmBXBYeaXiopeSW0uDMGGi92JxyL1ykUaMjhaqZRrUQ24Pz8yi1icKAhTqJPRyBexkrNjdnlTKD9eRCorGbbSsZJWNvVuJi1tJUXs5a2pLGosF1a0+IFR+ZFBqdz2HZGGhARqo8Tcd0eS32HyYNVmEBdjIxXO/Uuv27c6PKTumugrm4R1MpL5ay/6usZrGmIkwgLBganqWeXs3PrXB6DE9lse1A3ILKVPgTavlMqi2wXx6KSlZrt4WoBNzW2pgtxXfyrE/QLSFtSUVNka02LFxxq3oECrte7UDolsDWuLqmo6cIFLDUJTGYDX5PBGmHWU18fZddmre1ydpG7fJXBjmNsu0sK0FbBOAnA9XNr+1GgtMGFO76/MsnRHNnl6rml9jnvzNuo5pIrAPgKfg/n/aXunbEr9UGEMUs+3sBYhZKjiexyQXBcucZge8NY7JgMtE9S+JwX9pUTMSsU2QO7Ki+ytabFCw4ly5jbw5W7UKELN9AfGNFLXbscDdxLxQz2nWLbvTsACQq7cAHX7vaOZQEXCtj28J7Krs3ydrlwkVu9S1BETu1zy8lFTu0J1FUPh5reIMD1/lJbZLvSX8I4iZhwduup4nYJx6KDa9eJbCDrEtsepqOxuEZQRE6Nc8vlG8sWkF1USIsWHGWVDDYfZNutY9mEK0rjykVu3ymggCsi1wfwV6CInDO7HA1c8bZTZe3hcMUute+MAdcC1dT2UhmNBni7sF1XWERODReuK6KxvFJcRK57W3XtcjQpWIvsxGh9iOzNhyyVmicMUD5OAnDt2qXJWBTEcDCM/RghtZdcDQYDLzqcC47mn11USIsWHOv3CuIkFN7qxuHKRU6tveJCXNlJoIldLrjj1Vyb5eC3CJrY5EvW1NczWN2QkKlVMJDRXSW7+FwJ9n+frDwGx8+z7YFdlSkiZ2OTC6Jxwz5LXIAaMUvWdjkai2plIBYi/ZxXfyy6dO1Sq7+E+WfsCG1hcjSlisjZtauRgHctRLbWtGjBoVZ2USEu3SUIPAlquHAB8RZBe5NCTS2DtXvZdnQYm8lTFbsaudu7co3BtqNsu1NroF2iBkLIjl07jgElZWx7vEJF5JzZ5XhCsLS1OOcdTqAqe4MA18ai8Bqhici2c24xDMNnifX2YgujqW2Xvf4qKWOw9Qjb7pAEdEjWR39ZF3RUooicM7sceRu1ENla02IFhzBOIsAPGNlHne9tLFDtUjGDvQ1F5Hq2B5Ji9DFot2jgwnXFrjW7BXESKokzoHEhpFXmwEYFhwYFohrrK2Ggob8vMEqtsdiIt7GmlsE6HYrsk4I4iaEKVWpuzC57/bVW5TgJjsYErWZjsRFvoxYiW2tarOA4cAbI5+Ik+gIBKsRJAI0PWqELV82c+o0tqWjhwgUaFxxaT+yAo/5i/1eyiJw9nLlxK6osLtzEaFbQqmkTYP83PHgGyCtm26P6AIH+Wng4bF//67CgUrNWItvJuQWoPRaFSQttX1d7yzxH49cIS1stjzHgXPxrJbK1psUKDi1cy0DjbkldTKBWg1a47dTbi3VLamKX1cVXuDYbGsSWDNfELqv+ys5ncDSLbQ9QKU6Ct4sLZrVzkdu439KHk1RIjsbbJGEC1Wws2ukvLZZTABcmUI3KmDvrL5OJwaoGj7GacRKAc49QTgGDw2fZdr/OQGyEPryNWolsrWmxgkN4MdFC9QK2J2JNraWIXFQroH8XrewSv3Y6Bziby7aHqOjCBaw9QuLXtFqbtbHL6iKnZkZDa7jf0V7wnBYxS4D1FkHb19Xems4hDja0J/7Z/1UX2aJzXhyQXFrG4K/DbLtdIptTQgu7rPtr9wmg+CrbHpuu8lh04qn6Q7SzTuWxyBWedHJuAS1nOQVooYKj4AqDPQ1xEt3bAq1jtZpAxd8rdOFep3AROWucJWfSwwQK2N69aFlh0XW7VDKoAeFdlXCHIMNYYpb8fJUtImeNcIugdV8VlTLYfYJtd2sDpMSpeM47SWR1OodBplYi28lYXLtHECehopfK2i7r/qKxaIujsQho56XSmhYpOLhAMEC7kxCwHbRrdutz0K7epf2gBWwvvsJ4lwkqpwR21F/VNZY4iYQooFcHle1qELQMY+CrmwLA8fPAxSK2PaIXEBSgzfll/Ruu2yMoIqeip1FoEwDUWC1vrhLeGetIZOvlGmG9HLx6t6WtpscYcBx/VlPLYON+tq1GETkbuxx4Xi5fZbCroVKz2iJba1qm4NhjGbTj+2s5aMWvcULIYGBrNqiJo3XQ6hqLCzc5BuiscBE5W7vsb9fNv8zgoKBwlZprs4BjIbTjmKWI3Lh+6m91c2TXuj2WttrnPOA4G+S6vRqORSfeRuHErnZ9C0djkWEsu2b8fdlMnprZJVgau3yVwf7TbLtneyAuUh8CbedxSxG5cf3UC/ptzK71ey0i+zqFi4XqjRYnOISDNtCfLRKlJo5S3hZcYXAok2337gBEqRhoCFhPVJaL7bajQHXDxDUmXf0JVOj2Fg7atYIJVIuCR74OLibCCXRMX/Undkf9JbZLRYMasLekYj2BKl1EzhpHcUvVNQz+PMi2E6PVyUAsRPQbCkTj6Rwgp5BtD+mhTgZiR3YJr10b91smUC3PLcDqnN+j8Vh0YNfq3dqJbK1pcYLjRDZw6TLbHtZT+cqP1jhyS3KuP0C9RD5CHKlx4aAdm67dnTEgtkt4Bzq+n37sEi7XqVFEzhp7d8e1dQw2H2LbcRFAmgYZDe0tqZy6AOQ2LPMM66n+BOpoLP512OKlGq+1l8rqzphD67Eo7C+hmB2ryVi07wXVfCza8TYyjGVjQICfurt59ECLExxC17Lmg7ZeT4PW0nY0aNUMNOSwtz5rNlsGbUggMEjlO2PWLsFFrsGuy1ctdTd6tFN/mQew/zvuPGZJ2qaFl0pol6NzS5Ox6GBJZY3Gd6COllRE1wiNxSzXXwzD8NdUXx9tJlB7E3tJGYO9DWOxWxt1Ck3a2GXndzx6zrIddkQv9UW21rQ8waHxoLW3pCIctP6+QIYmE6ilzQ3a4lIGBxriJHq2B2LCtb6rYv8/cMayBW9UH/XShjuyi7uYbDqgrWsZsH/xFZ/z2lzg7BW7E3nPVNx2yuHtxcZLAeI79jUNAZBGo37ujOvrLcHI0WHqJW0TYs/DcS4POJ/PPpeRpk0+Cbtjcb8lA7EW13nAvl1rBMG1LW05BWhhgqO2zrI2G6uRa9loNMDHm7OHPeFOXbDsINDCtQzYHxzCtVm9DtrrBmg0gdqxSzyBajuxAwK7NHYtA8IMqKzArqu3jMWYcG0KVxkMBpvkTLlFlqRt/ToDEaH6GIu7TwDXKtj26L7qB0Ba28X1l8hLpatzXgci2961S7RhQWWDdECLEhy7jgtcy321K5ZjXdRnvWhC0M/g0MWgtXMxEW7THa/BnTHgfGLXyrUM2P6OpWWWnDNdU7VxLVvbVVvHTqBllexjrSZQQFDvomECFd+BamAQ7J9b6/dZntMiABKwv6QijvFS2yIW+1499n8fb/V383BY21VRxWBLQyxV61i22GRLo0UJDj1MoIBtUR+tl3kA2yUV4Q4CXx9104aL7LIatNcqGOw4xj7ukAS0SdB+Aq2pBc7mMnxBrcFp6ue5sGtXHbvMo7VrGbCdRLUORuawrj2jdTAyIF7qsSv+tRLZVqLRZLIEu4eHsLvrtLarpg7IymP4zMiDugHBgdoLtJo6YPNBy7Lw+P4tozqsNS1McFjaWrmWAXEGurp6iNZme7TT1iaAHRxnc4HshrXZId21y/VvbdfG/eATWmnpkrS2S+sdBBzWwlHrYGQO6/4SB4yqbw+H0NtoMlnsCgtWt7SAEOFST00tUFbJYGeDyO6YrG5mZCHWSyr7TgOl5ezj0X3VzYwssstqYhd6g/QgZgHWLlFulxYYvwEA3o0f0jy4Wm5Jodw1FUiM1v5ErKs3iFzLY9I1dC1b5STQegcBh3UtFXFCJu1/Q4C9a9l+VHsvFcBtEWRtqa237Mry9gKGa+RaZu2ytItLgV0NY7FzayApRvvfsa7egL2ngJIy9vGYdMBbg2Bk3i5fNv9NbT3w5wGLyNaLl6qu3qAfkW01FvXgDQKsxmIdsKZhLHp5abPjTw+0GA/H2j36cC0Dgi2C9QasFqTn1svEXlMLrNypl0FradfUWuq6+Pqw28q0Qthf5VWW5aeIUPVTKAsR9tfx8+DrgQxO0861DIj7a8UOSz0QLc8tQBzDsVKQzlzrHQRCD8eqXfpafgLY/lolunapbw+H0K6KasuW+bBgoK+WY1Fwzp/IZjcHAMDArurW5tETLcbDoVWpaXsIL3LCwmhq1yAQwq0bMwzrJuUCDeMjtVubBcQXk32nLJkWR/bWLk4CENu1fp9lB8EElYvuWSO067fNlrbm57zArsWbBWNR40qZwhgOvYxFQJy3ZMX2hudULrrnyCYAyC/xwtHzbLtrqnaxVIB4Yt+4H7jasMwzYaDGXipBfy3ZYmlrfc5rSYsQHCaTpVJmcIB2UcscwovcobNsu38XbRJFcbAVPRnU1AJHzlmenzRIu2UeQHwx4dzwgPaDVngx4QoxATqY2B32l/q2CLHXX0EBwPBempjDw+XFqTMZ+JwzfTtpt5uHg+svLisywIpsvXipjmT58W09nluAzq4RwrE4WH1b9EKLWFLZddySKGp8f8DXR9sTUZj8i0PriQoQDxAOrQetrwNJrPWgtfcbenlpv7feXn+1TVC/6J419vprXLr6pQWssX/Oq2+HNXavEbodi/qzy2jUpsaSEHu/YUqc+rV59ESLEBwrduhnOQXQ70XO2i6tXbiA/b5KawOkxutvohrSHQgP0Y+Hg2PyIO234Nk953U6Fq/P0KddkzS+Rnh5GeDlJX4uPET9ApjW2DvnM9K0SdomxO65NVj7saglLUNwNKyBGgzsGrvWWJ+ICVFALw3jJDis7dLahQs4mEB14JK0P7FrfyFpShO71nESgK1dWscscVifX3oQ2YBtf00YoG2cBNC0znk92KUlzV5wZOczfEyC1nESHNYnoh7uQAHbi9z1Ohgceh209u+M1bfDGmu7gnUQJwHYLp306wzERergd7Q657WOWeKwuUboQGQDtnbp1Rt0vQ76y/rcCgrQdmu6Hmj2goPzbgD6mEAB2xNRDxMoYDtwtXbhArY2RYSy28q0xtqu9olsUiatsT639BCzBNibQLW3CbAn/nVqlw77y8uofcwSYHvO6yFmCbD9Dcemt7zqsNbIKjhKSkowe/ZsZGRkYOrUqdi9e3fjb1IYcfyGhoYIEJ6I/jqIk+AQ2tW9LZASp/3g8PY2wCg4SycO1HbbKYetaNSJl6oJTFSAPmKWALFdfj76HIt6EdmA+LzXQ8wSoGOPsU7FrJbIKjjeeustREdHY8OGDXj88cfx/PPP49q1a3J+hSTKKxk+139StHZpw60Rnogj+2iXNtwa4cVEL+IMEPeXXgZtU5jY9RKzBIjPrYQooLeGCZmECO0aoYOYJQ6hXXoR2YD4/NKDBxTQ8Vi0uinRQ8yS1siWh6OyshKbN2/G8uXL4e/vjxEjRmDRokXYsmULJk+eLDq2trYWtbW1YkO8veHraycSzwPW7bUUy5k0iC1IxjCM8zepgHC71MQBZj4Dqtb4Cc6GiQMBs04M8/NhUFVjgJeRwdh0wGzW/jf0EUTrhwQCGWmMPuwS/Ib9OjOIDtNJf1mdW7oZiyK79DMWfXU7Fi3tCTrpL2/BWAwOAIZ018lYFNjVtxOD2Ah9jEUlMBpd813IJjguXLiA4OBgREVF8c916NAB586dszl2wYIF+OKLL0TPTZs2DdOnT5fLHADA75vDAYQCAPq1K0R2dpWsn+8uXRICAUQjPNiE/u3ykJ2tg1ELoEdqK2w+FIbOybWIC7qE7GytLWJJ7xCN9QcCMWlAJa5eKcbVK1pbxNK3fSz2Zfrj5oxruJRXorU5AIAggwERIYm4UuaFqYMvIzu7QmuTAACp4d7w9koAwwAT++QjO7u28TepQOf4AAAxCAsyYaCOxmJacjCASMRH1KNbQh6ys/UxUfVqE44j50LRv1M1AlGgn2tEx1jsPe2PqRnXkH9JH2MxEEBkaBIuX/PCLYOvIDu7XGuTFKNNG9eSixgYmW4zDhw4gPnz52Pp0qX8c//5z39QXl6O5557TnSsWh4Ok4lN0b1yJ/D8DCDQX9aP94iDZ8yor8xFn+6JLqtDpWEYtr86JQOtgrW2xkJ1jRmrthZgfEYsAv310VcAUFUDHMpkM1P66Chnb2GJGXsOF+C6IbHw8tJPf53LY+sZtU/S2hIxhzLNqK3IQ9/uCboZiwBw4AyQHA1EhWltiQWzGdh53Iwwn4vo3CFJN/1FY1FbVPdwBAQEoKJCfDdVUVGBgIAAm2N9fX1lFxf2MBqBwd3Zf3qjVwcgO9sMo9Gom0ELAAM1TuJjD38/oE/7WgT666uvggL0eW7FhANpqbXw8tJXf+lNaHD0bA9kZ5t0Nxb7dtLaAluMRrYQYHY2o6v+orHYNJCtB1q3bo3y8nIUFxfzz505cwZt27aV6ysIgiAIgmiiyCY4AgMDMWzYMHz++eeorq7G5s2bcfbsWQwbNkyuryAIgiAIookiq4/n+eefR0FBAUaPHo0PPvgAb7zxBkJDQ+X8CoIgCIIgmiCyhteEh4fjww8/lPMjCYIgCIJoBlAUC0EQBEEQikOCgyAIgiAIxSHBQRAEQRCE4pDgIAiCIAhCcUhwEARBEAShOCQ4CIIgCIJQHBIcBEEQBEEoDgkOgiAIgiAUhwQHQRAEQRCKQ4KDIAiCIAjFMTAMw2htBEEQBEEQzRvycBAEQRAEoTgkOAiCIAiCUBwSHARBEARBKA4JDoIgCIIgFIcEB0EQBEEQikOCgyAIgiAIxSHBQRAEQRCE4pDgIAiCIAhCcUhwEARBEAShOCQ4CIIgCIJQHBIcMvD5559j2rRp6NevH9asWcM/X11djddeew1jx47FuHHj8N1334nel56ejiFDhmDo0KEYOnQovv76a9F7586di2HDhmHSpElYvXq1an+P0ijRX++99x6mTJmCYcOG4c4778T+/ftV+3uURon+4sjLy0NGRgZef/11xf8ONVCqr5YtW4abbroJQ4YMwS233ILs7GxV/h6lUaK/cnNz8cgjj2DEiBGYMGECFixYoNrfozTu9ld5eTleeeUVjBo1CiNGjMBLL70kem9zvdZb4621Ac2B5ORkzJkzB5999pno+a+++gp5eXlYsmQJysvL8dBDD6F9+/YYNGgQf8zSpUsRFRVl85mff/45rl69ipUrV+Ls2bOYPXs2unTpgpSUFMX/HqVRor+Cg4Px8ccfIzExERs3bsTTTz+N5cuXIygoSPG/R2mU6C+O9957D506dVLMdrVRoq+2bNmC77//Hv/3f/+Htm3bIjc3FyEhIYr/LWqgRH+98847SExMxAcffICCggLcd9996NatG/r376/436M07vbX/PnzERsbi2XLlsHf3x+ZmZn8e5vztd4a8nDIwMSJEzFw4ED4+vqKnt+xYwduv/12BAcHIy4uDjfccAP++OMPlz5z5cqVmDVrFoKDg9GzZ08MGzYMa9euVcJ81VGiv2bNmoXk5GQYjUaMGTMGfn5+uHDhghLmq44S/cW9n2EYDBgwQG6TNUOJvvryyy/x1FNPoV27djAYDEhKSkKrVq2UMF91lOivS5cuYdy4cfD29kZiYiJ69eqFc+fOKWG+6rjTX2fPnsXJkyfx5JNPIjg4GN7e3ujcuTP/3uZ8rbeGBIfCCIvxMgxjM/DuuOMOTJgwAfPmzUNpaSkA4Nq1a7h8+TLat2/PH9exY8dmM2id4U5/WZOXl4dr164hOTlZSVN1gbv9VVdXhw8++ABPPPGESpZqjzt9ZTKZcOrUKWRmZmLixIm44YYb8MUXX6AlFNl299yaNm0a1qxZg9raWly4cAFHjhxBenq6WmZrhqP+OnHiBFq3bo25c+di9OjRuOuuu3DgwAEALe9aT4JDQQYOHIgffvgBZWVlyMvLw4oVK1BdXc2//sUXX2DFihX43//+h+rqarzyyisAgMrKSnh5ecHf358/NigoCJWVlar/DWribn8Jqa+vx7x583DnnXciODhYTfNVx5P+WrRoETIyMlqEKAPc76srV67AZDJhz549+Omnn/Df//4X69atw/Lly7X6U1TBk3OrZ8+eOHLkCIYOHYqpU6diypQpogm1OeKsvwoLC7Fr1y70798fa9aswT333IOnn34aV69ebXHXehIcCnLfffchISEBt9xyCx5//HGMHj0a0dHR/Ou9e/eGt7c3wsPD8fTTT2Pbtm2oq6tDYGAgTCaTaIBXVFQgMDBQiz9DNdztLw6GYTBv3jyEh4dj1qxZWvwJquJufxUWFmLZsmW49957NbReXdztKz8/PwDA3XffjZCQEMTFxWHatGnYtm2bVn+KKrjbXyaTCbNnz8aNN96Ibdu2YdmyZVi/fj3Wr1+v4V+jPM76y8/PD4mJibjxxhvh7e2NUaNGITExEUeOHGlx13oSHAoSEBCAl156CWvWrMGvv/4Kg8GArl272j3WaGR/CoZhEBoaisjISFFg0enTp9G2bVtV7NYKd/uL4+2330ZRURH+9a9/8a83Z9ztr+PHj6OgoABTp07F+PHj8f333+OPP/7AY489pqb5quLJWBROtNzzzR13++vatWsoKirCLbfcAm9vbyQkJGDEiBHYt2+fmuarjrP+ateuncP3tbRrffO/KqtAfX09ampqwDAM3zabzSgoKEBxcTFMJhN27tyJ5cuX4/bbbwfABhKdPn0aJpMJ165dw7vvvosBAwbwwUgTJ07El19+iYqKChw5cgRbtmzB2LFjtfwzZUOJ/vr8889x6NAhvPvuuzYBXU0duftr8ODB+P3337Fo0SIsWrQIN998M8aMGYN//etfGv+lnqPEuTV58mR8++23qKioQFFRERYvXowhQ4Zo+WfKhtz9FR4ejtjYWCxdupT/nM2bNzuddJsS7vRXeno6GIbBihUrYDKZsHnzZuTm5qJ79+4Amve13hoD0xLkusLMmzcPK1asED3HbZt6+eWXUVpaitTUVDz99NPo3bs3AGDPnj144403UFhYiKCgIPTv3x9PPvkkIiIiALB7s1999VVs3rwZoaGheOyxx3Ddddep+4cphBL9lZ6eDl9fX3h5efGf+eKLL2LChAkq/VXKoUR/Cfn8889x+fJlvPjii8r/MQqjRF/V1dXhrbfewrp16xAYGIgbb7wRs2bNgsFgUPePUwAl+uvYsWN49913cfbsWfj7+2PcuHF44oknRGOzqeJOfwHAmTNn8K9//QtZWVlITk7G008/jT59+gBo3td6a0hwEARBEAShOLSkQhAEQRCE4pDgIAiCIAhCcUhwEARBEAShOCQ4CIIgCIJQHBIcBEEQBEEoDgkOgiAIgiAUhwQHQRAEQRCKQ4KDIAiCIAjFIcFBEITuSU9PR3p6erOv0koQzRkSHARBAABmzZrFT+x/+9vfRK+VlpYiIyODf/2jjz6S/fuXL1/Ofz5BEM0PEhwEQdhw5swZ7N+/n3+8dOlS1NTUaGgRQRBNHRIcBEGI8Pb2BgD89NNPAACTyYRff/2Vf17I1atX8dZbb2HSpEkYMGAAxo0bh7lz5yI/P58/5vPPP0d6ejquv/56rFu3DjfffDOGDBmCBx54AOfPnwfAFsWaP38+/x7O0/H555+Lvq+8vBzz5s3D8OHDMWHCBHz55Zdy//kEQSgECQ6CIER07NgRiYmJ+PPPP1FQUIAtW7YgPz8fo0ePFh1XU1ODWbNm4ZdffkFxcTFSUlJQUVGBVatWYebMmSgpKREdX1hYiLlz58JgMKCmpgYHDhzAK6+8AgBISkpCYmIif2xaWhrS0tIQGxsr+oyPP/4YO3fuhI+PD4qKivDZZ59h586dCvUEQRByQoKDIAgRRqMR06ZN4z0bnKfj1ltvFR23Zs0anD17FgDw1ltv4eeff8ZXX30Fo9GIoqIi/Pzzz//frt2rphJFYRj+DoOJouJfUo8EE9RCvIJgo8FKG72CuYB0uQdvwUILK0lnLdhrJUGshCmEVDZBtFFTZcAzOYeYZJMU71NtZu3ZM6v7WDNH+3e7nVqtlh4fH71/RKbTqbbbrRzHkeM43t5ut6tut6t6vX50xs3NjQaDwdHEZTwef2v/AMwgcADwqdVqCoVC6vf7mkwmyuVyKhQKR3tms5kkKRgMqlQqSZKy2axs2z6qv4lEIrq9vZUkXV1dedf/noT8T7lcViAQUDweVzKZlCStVqvTmgPwIwgcAHyi0aiq1arW67Uk/3Tjs2e+sSzLWx8Ohy+dccr9AH4OgQPAu5rNpiQpHo+rUqn46vl8XpK03W41Go0kSfP5XK7rHtU/KhgMeuvNZvOZVwbwi/l/OwcASZlMRsPhUJZl6ezszFe/u7tTr9fTYrHQw8ODbNvWcrnUfr/X5eWlF1g+Kp1Oe+tGo6GLiwvd39+rWCx+sRMAvwETDgD/FIvFFIlE3q2dn5+r3W574cB1XYXDYVWrVXU6HSUSiZOedX19LcdxlEql9Pz8rKenJ728vHxHGwB+gT8HPoACAADDmHAAAADjCBwAAMA4AgcAADCOwAEAAIwjcAAAAOMIHAAAwDgCBwAAMI7AAQAAjCNwAAAA4wgcAADAOAIHAAAw7hUnKqzeuNhTMwAAAABJRU5ErkJggg==", "text/plain": [ - "
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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "(series / 200).add_holidays(\"US\").plot()" + "(series / 200).add_holidays(\"US\").plot();" ] }, { @@ -373,7 +371,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**differencing:**" + "**Differencing:**" ] }, { @@ -383,19 +381,17 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -435,7 +429,7 @@ "series_ = TimeSeries.from_values(values)\n", "\n", "(series_ - 10).plot(label=\"with missing values (shifted below)\")\n", - "fill_missing_values(series_).plot(label=\"without missing values\")" + "fill_missing_values(series_).plot(label=\"without missing values\");" ] }, { @@ -456,21 +450,19 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "train, val = series.split_before(pd.Timestamp(\"19580101\"))\n", "train.plot(label=\"training\")\n", - "val.plot(label=\"validation\")" + "val.plot(label=\"validation\");" ] }, { @@ -493,14 +485,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -512,7 +502,7 @@ "naive_forecast = naive_model.predict(36)\n", "\n", "series.plot(label=\"actual\")\n", - "naive_forecast.plot(label=\"naive forecast (K=1)\")" + "naive_forecast.plot(label=\"naive forecast (K=1)\");" ] }, { @@ -520,7 +510,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It's very easy to fit models and produce predictions on `TimeSeries`. All the models have a `fit()` and a `predict()` function. This is similar to [Scikit-learn](https://scikit-learn.org/), except that it is specific to time series. The `fit()` function takes in argument the training time series on which to fit the model, and the `predict()` function takes in argument the number of time steps (after the end of the training series) over which to forecast.\n", + "It's very easy to fit models and produce predictions on `TimeSeries`. All the models have a `fit()` and a `predict()` function. This is similar to [Scikit-learn](https://scikit-learn.org/), except that it is specific to time series. The `fit()` function takes as argument the training time series on which to fit the model, and the `predict()` function takes as argument the number of time steps (after the end of the training series) over which to forecast.\n", "\n", "### Inspect Seasonality\n", "Our model above is perhaps a bit too naive. We can already improve by exploiting the seasonality in the data. It seems quite obvious that the data has a yearly seasonality, which we can confirm by looking at the auto-correlation function (ACF), and highlighting the lag `m=12`:" @@ -533,9 +523,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -543,9 +533,9 @@ } ], "source": [ - "from darts.utils.statistics import plot_acf, check_seasonality\n", + "from darts.utils.statistics import check_seasonality, plot_acf\n", "\n", - "plot_acf(train, m=12, alpha=0.05)" + "plot_acf(train, m=12, alpha=0.05, max_lag=24)" ] }, { @@ -575,7 +565,7 @@ "for m in range(2, 25):\n", " is_seasonal, period = check_seasonality(train, m=m, alpha=0.05)\n", " if is_seasonal:\n", - " print(\"There is seasonality of order {}.\".format(period))" + " print(f\"There is seasonality of order {period}.\")" ] }, { @@ -594,14 +584,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -611,7 +599,7 @@ "seasonal_forecast = seasonal_model.predict(36)\n", "\n", "series.plot(label=\"actual\")\n", - "seasonal_forecast.plot(label=\"naive forecast (K=12)\")" + "seasonal_forecast.plot(label=\"naive forecast (K=12)\");" ] }, { @@ -629,14 +617,12 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -651,7 +637,7 @@ "\n", "series.plot()\n", "combined_forecast.plot(label=\"combined\")\n", - "drift_forecast.plot(label=\"drift\")" + "drift_forecast.plot(label=\"drift\");" ] }, { @@ -687,9 +673,7 @@ "from darts.metrics import mape\n", "\n", "print(\n", - " \"Mean absolute percentage error for the combined naive drift + seasonal: {:.2f}%.\".format(\n", - " mape(series, combined_forecast)\n", - " )\n", + " f\"Mean absolute percentage error for the combined naive drift + seasonal: {mape(series, combined_forecast):.2f}%.\"\n", ")" ] }, @@ -698,7 +682,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`darts.metrics` contains many more metrics to compare time series. The metrics will compare only common slices of series when the two series are not aligned, and parallelize computation over a large number of pairs of series - but let's not get ahead of ourselves." + "`darts.metrics` contains many more metrics to compare time series. The metrics will compare only common slices of series when the two series are not aligned, and parallelize computation over a large number of series pairs - but let's not get ahead of ourselves." ] }, { @@ -721,21 +705,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "model ExponentialSmoothing(trend=ModelMode.ADDITIVE, damped=False, seasonal=SeasonalityMode.ADDITIVE, seasonal_periods=12 obtains MAPE: 5.11%\n", - "model (T)BATS obtains MAPE: 5.87%\n", - "model Auto-ARIMA obtains MAPE: 11.65%\n", - "model Theta(2) obtains MAPE: 8.15%\n" + "model ExponentialSmoothing() obtains MAPE: 5.11%\n", + "model TBATS() obtains MAPE: 5.87%\n", + "model AutoARIMA() obtains MAPE: 11.65%\n", + "model Theta() obtains MAPE: 8.15%\n" ] } ], "source": [ - "from darts.models import ExponentialSmoothing, TBATS, AutoARIMA, Theta\n", + "from darts.models import TBATS, AutoARIMA, ExponentialSmoothing, Theta\n", "\n", "\n", "def eval_model(model):\n", " model.fit(train)\n", " forecast = model.predict(len(val))\n", - " print(\"model {} obtains MAPE: {:.2f}%\".format(model, mape(val, forecast)))\n", + " print(f\"model {model} obtains MAPE: {mape(val, forecast):.2f}%\")\n", "\n", "\n", "eval_model(ExponentialSmoothing())\n", @@ -749,7 +733,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here, we did only built these models with their default parameters. We can probably do better if we fine-tune to our problem. Let's try with the Theta method." + "Here, we only created these models with their default parameters. We can probably do better if we fine-tune to our problem. Let's try with the Theta method." ] }, { @@ -804,7 +788,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "The MAPE is: 4.40, with theta = -3.5102040816326543.\n" + "Lowest MAPE is: 4.40, with theta = -3.5102040816326543.\n" ] } ], @@ -813,11 +797,7 @@ "best_theta_model.fit(train)\n", "pred_best_theta = best_theta_model.predict(len(val))\n", "\n", - "print(\n", - " \"The MAPE is: {:.2f}, with theta = {}.\".format(\n", - " mape(val, pred_best_theta), best_theta\n", - " )\n", - ")" + "print(f\"Lowest MAPE is: {mape(val, pred_best_theta):.2f}, with theta = {best_theta}.\")" ] }, { @@ -827,21 +807,19 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "train.plot(label=\"train\")\n", "val.plot(label=\"true\")\n", - "pred_best_theta.plot(label=\"prediction\")" + "pred_best_theta.plot(label=\"prediction\");" ] }, { @@ -862,7 +840,7 @@ "\n", "Backtesting simulates predictions that would have been obtained historically with a given model. It can take a while to produce, since the model is (by default) re-trained every time the simulated prediction time advances.\n", "\n", - "Such simulated forecasts are always defined with respect to a *forecast horizon*, which is the number of time steps that separate the prediction time from the forecast time. In the example below, we simulate forecasts done for 3 months in the future (compared to prediction time). The result of calling `historical_forecasts()` is (by default) a `TimeSeries` that contains those 3-months ahead forecasts:" + "Such simulated forecasts are always defined with respect to a *forecast horizon*, which is the number of time steps that separate the prediction time from the forecast time. In the example below, we simulate forecasts done for 3 months into the future (compared to prediction time). The result of calling `historical_forecasts()` is (by default) a `TimeSeries` which contains only the last predicted value of each of those 3-months ahead forecasts:" ] }, { @@ -873,12 +851,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0eff2362782e48238db87ae206484ad9", + "model_id": "63d737212b284b639cca604f784a67dd", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/57 [00:00" + "
" ] }, "metadata": {}, @@ -903,13 +881,80 @@ } ], "source": [ + "hfc_params = {\n", + " \"series\": series,\n", + " \"start\": pd.Timestamp(\n", + " \"1956-01-01\"\n", + " ), # can also be a float for the fraction of the series to start at\n", + " \"forecast_horizon\": 3,\n", + " \"verbose\": True,\n", + "}\n", "historical_fcast_theta = best_theta_model.historical_forecasts(\n", - " series, start=0.6, forecast_horizon=3, verbose=True\n", + " last_points_only=True, **hfc_params\n", ")\n", "\n", "series.plot(label=\"data\")\n", "historical_fcast_theta.plot(label=\"backtest 3-months ahead forecast (Theta)\")\n", - "print(\"MAPE = {:.2f}%\".format(mape(historical_fcast_theta, series)))" + "print(f\"MAPE = {mape(series, historical_fcast_theta):.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also retrieve all predicted values from each historical forecast, by setting `last_points_only=False`. With the `stride` parameter we define how many steps to move between two consecutative forecasts. We set it to 3 months, so that we can then concatenate the forecasts into a single `TimeSeries`." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2f491b35917c42c0a7df2c0cad4f187d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/20 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "historical_fcast_theta_all = best_theta_model.historical_forecasts(\n", + " last_points_only=False, stride=3, **hfc_params\n", + ")\n", + "\n", + "series.plot(label=\"data\")\n", + "for idx, hfc in enumerate(historical_fcast_theta_all):\n", + " hfc.plot(label=f\"forecast {idx}\")\n", + "\n", + "from darts import concatenate\n", + "\n", + "historical_fcast_theta_all = concatenate(historical_fcast_theta_all, axis=0)\n", + "print(f\"MAPE = {mape(series, historical_fcast_theta_all):.2f}%\")" ] }, { @@ -924,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": { "tags": [] }, @@ -932,12 +977,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bf0c34fd03b549c9859261eef2fffe9a", + "model_id": "fab8f9b568944fbaa5696af67742f3f9", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/57 [00:00" + "
" ] }, "metadata": {}, @@ -958,7 +1003,7 @@ "best_theta_model = Theta(best_theta)\n", "\n", "raw_errors = best_theta_model.backtest(\n", - " series, start=0.6, forecast_horizon=3, metric=mape, reduction=None, verbose=True\n", + " metric=mape, reduction=None, last_points_only=False, stride=1, **hfc_params\n", ")\n", "\n", "from darts.utils.statistics import plot_hist\n", @@ -967,7 +1012,7 @@ " raw_errors,\n", " bins=np.arange(0, max(raw_errors), 1),\n", " title=\"Individual backtest error scores (histogram)\",\n", - ")" + ");" ] }, { @@ -980,18 +1025,18 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38076a207e3c48f8a3fd842dd11f9780", + "model_id": "4b1dd960b406441599d7a99c5fbcd673", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/57 [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1066,13 +1146,74 @@ "source": [ "We can see that the distribution is not centered at 0, which means that our `Theta` model is biased. We can also make out a large ACF value at lag equal to 12, which indicates that the residuals contain information that was not used by the model.\n", "\n", + "Our `residuals` method is actually much more powerful! It can be used to compute any *per-time step metric* from Darts (see a list [here](https://unit8co.github.io/darts/generated_api/darts.metrics.html)) even for multi-step forecasts. It also supports pre-computed historical forecasts similar to backtest.\n", + "\n", + "Now let's check the distribution of absolute errors we get for each step in the 3 months forecasts." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Absolute errors per forecast step')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from darts.metrics import ae\n", + "\n", + "residuals = best_theta_model.residuals(\n", + " historical_forecasts=hfc_precomputed,\n", + " metric=ae, # the absolute error per time step\n", + " last_points_only=False,\n", + " values_only=True, # return a list of numpy arrays\n", + " **hfc_params,\n", + ")\n", + "residuals = np.concatenate(residuals, axis=1)[:, :, 0]\n", + "\n", + "fig, ax = plt.subplots()\n", + "for forecast_step in range(len(residuals)):\n", + " ax.hist(residuals[forecast_step], label=f\"step {forecast_step}\", alpha=0.5)\n", + "ax.legend()\n", + "ax.set_title(\"Absolute errors per forecast step\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can clearly see that the errors increase the further ahead into the future we predict." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "### A better model\n", "Could we maybe do better with a simple `ExponentialSmoothing` model?" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": { "tags": [] }, @@ -1080,12 +1221,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7ffad74cde1b46f0af21c2634b51af36", + "model_id": "b7e428038e914511bcc1826e6cd49193", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/57 [00:00" + "
" ] }, "metadata": {}, @@ -1111,13 +1252,11 @@ ], "source": [ "model_es = ExponentialSmoothing(seasonal_periods=12)\n", - "historical_fcast_es = model_es.historical_forecasts(\n", - " series, start=0.6, forecast_horizon=3, verbose=True\n", - ")\n", + "historical_fcast_es = model_es.historical_forecasts(**hfc_params)\n", "\n", "series.plot(label=\"data\")\n", "historical_fcast_es.plot(label=\"backtest 3-months ahead forecast (Exp. Smoothing)\")\n", - "print(\"MAPE = {:.2f}%\".format(mape(historical_fcast_es, series)))" + "print(f\"MAPE = {mape(historical_fcast_es, series):.2f}%\")" ] }, { @@ -1125,21 +1264,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This much better! We get a mean absolute percentage error of about 4-5% when backtesting with a 3-months forecast horizon in this case. " + "This is much better! We get a mean absolute percentage error of about 4-5% when backtesting with a 3-months forecast horizon in this case. " ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 32, "metadata": { "tags": [] }, "outputs": [ { "data": { - "image/png": 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", + "application/vnd.jupyter.widget-view+json": { + "model_id": "3d14b6efe7484c9e81bec7eb998856ca", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/120 [00:00" + "
" ] }, "metadata": {}, @@ -1147,7 +1300,7 @@ } ], "source": [ - "plot_residuals_analysis(model_es.residuals(series))" + "plot_residuals_analysis(model_es.residuals(series, verbose=True))" ] }, { @@ -1177,27 +1330,25 @@ "\n", "This is a key point of using ML-based models for forecasting: more often than not, ML models (especially deep learning models) need to be trained on large amounts of data, which often means a large amount of separate yet related time series. \n", "\n", - "In Darts, the basic way to specify multiple `TimeSeries` is using a `Sequence` of `TimeSeries` (for instance, a simple list of `TimeSeries`).\n", + "In Darts, the basic way to specify multiple `TimeSeries` is using a `Sequence` of `TimeSeries` (for example, a simple list of `TimeSeries`).\n", "\n", "## A toy example with two series\n", - "These models can be trained on thousands of series. Here, for the sake of illustration, we will load two distinct series - the air traffic passenger count and another series containing the number of pounds of milk produced per cow monthly. We also cast our series to `np.float32` as that will slightly speedup the training:" + "These models can be trained on thousands of series. Here, for the sake of illustration, we will load two distinct series - the air traffic passenger count and another series with the number of pounds of milk produced per cow monthly. We also cast our series to `np.float32` as that will slightly speedup the training:" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1214,7 +1365,7 @@ "train_air.plot()\n", "val_air.plot()\n", "train_milk.plot()\n", - "val_milk.plot()" + "val_milk.plot();" ] }, { @@ -1227,19 +1378,17 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1250,7 +1399,7 @@ "train_air_scaled, train_milk_scaled = scaler.fit_transform([train_air, train_milk])\n", "\n", "train_air_scaled.plot()\n", - "train_milk_scaled.plot()" + "train_milk_scaled.plot();" ] }, { @@ -1267,6 +1416,9 @@ "metadata": {}, "source": [ "## Using deep learning: example with N-BEATS\n", + "\n", + "> Note: You can find a detailed user guide for our Neural Network Models (TorchForecastingModels) [here](https://unit8co.github.io/darts/userguide/torch_forecasting_models.html).\n", + "\n", "Next, we will build an [N-BEATS model](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html). This model can be tuned with many hyper-parameters (such as number of stacks, layers, etc). Here, for simplicity, we will use it with default hyper-parameters. The only two hyper-parameters that we have to provide are:\n", "\n", "* `input_chunk_length`: this is the \"lookback window\" of the model - i.e., how many time steps of history the neural network takes as input to produce its output in a forward pass.\n", @@ -1279,7 +1431,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1288,16 +1440,17 @@ "text": [ "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", "\n", - " | Name | Type | Params\n", - "---------------------------------------------------\n", - "0 | criterion | MSELoss | 0 \n", - "1 | train_metrics | MetricCollection | 0 \n", - "2 | val_metrics | MetricCollection | 0 \n", - "3 | stacks | ModuleList | 6.2 M \n", - "---------------------------------------------------\n", + " | Name | Type | Params | Mode \n", + "-------------------------------------------------------------\n", + "0 | criterion | MSELoss | 0 | train\n", + "1 | train_criterion | MSELoss | 0 | train\n", + "2 | val_criterion | MSELoss | 0 | train\n", + "3 | train_metrics | MetricCollection | 0 | train\n", + "4 | val_metrics | MetricCollection | 0 | train\n", + "5 | stacks | ModuleList | 6.2 M | train\n", + "-------------------------------------------------------------\n", "6.2 M Trainable params\n", "1.4 K Non-trainable params\n", "6.2 M Total params\n", @@ -1307,12 +1460,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f57c5a9cfc7e4f209cfb642f790cb81f", + "model_id": "213ff85594ba4650b6d56d88959d31f0", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Training: 0it [00:00, ?it/s]" + "Training: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "pred_air = model.predict(series=train_air_scaled, n=36)\n", - "pred_milk = model.predict(series=train_milk_scaled, n=36)\n", + "pred_air, pred_milk = model.predict(series=[train_air_scaled, train_milk_scaled], n=36)\n", "\n", "# scale back:\n", "pred_air, pred_milk = scaler.inverse_transform([pred_air, pred_milk])\n", @@ -1419,7 +1544,7 @@ "series_air.plot(label=\"actual (air)\")\n", "series_milk.plot(label=\"actual (milk)\")\n", "pred_air.plot(label=\"forecast (air)\")\n", - "pred_milk.plot(label=\"forecast (milk)\")" + "pred_milk.plot(label=\"forecast (milk)\");" ] }, { @@ -1442,30 +1567,42 @@ "\n", "There are two kinds of covariate time series in Darts:\n", "\n", - "* `past_covariates` are series not necessarily known ahead of the forecast time. Those can for instance represent things that have to be measured and are not known upfront. Models do not use the future values of `past_covariates` when making forecasts.\n", - "* `future_covariates` are series which are known in advance, up to the forecast horizon. This can represent things such as calendar information, holidays, weather forecasts, etc. Models that accept `future_covariates` will look at the future values (up to the forecast horizon) when making forecasts.\n", + "* `past_covariates` are series not necessarily known ahead of the forecast time. They can for instance represent things that have to be measured and are not known upfront. Models do not use future values of `past_covariates` when making forecasts (only for global models when predicting with `n > output_chunk_length` due to auto-regression). For more info on past/future covariates, check out this [user guide](https://unit8co.github.io/darts/userguide/covariates.html).\n", + "* `future_covariates` are series which are known in advance, up to the forecast horizon. They can represent things such as calendar information, holidays, weather forecasts, etc. Models that accept `future_covariates` will look at the future values (up to the forecast horizon) when making forecasts.\n", + "* `static_covariates` are characteristics of the target series which do not change over time. They are embedded directly into the target series. They can represent things such as the category of product, country information, etc. For more info on static covariates, check out this [user guide](https://unit8co.github.io/darts/examples/15-static-covariates.html).\n", "\n", "![covariates](static/images/covariates-highlevel.png)\n", "\n", - "\n", "Each covariate can potentially be multivariate. If you have several covariate series (such as month and year values), you should `stack()` or `concatenate()` them to obtain a multivariate series.\n", "\n", - "The covariates you provide can be longer than necessary. Darts will try to be smart and slice them in the right way for forecasting the target, based on the time indexes of the different series. You will receive an error if your covariates do not have a sufficient time span, though.\n", + "The covariates you provide can be longer than necessary. **Darts's model will automatically handle the extraction of relevant time frames for you!** You will receive an error if your covariates do not have a sufficient time span, though.\n", + "\n", + "Not all models support every covariate type. You can find a model list [here](https://unit8co.github.io/darts/index.html#forecasting-models) stating which types they support.\n", "\n", "Let's now build some external covariates containing both monthly and yearly values for our air and milk series.\n", - "In the cell below, we use the `darts.utils.timeseries_generation.datetime_attribute_timeseries()` function to generate series containing the month and year values, and we `concatenate()` these series along the `\"component\"` axis in order to obtain one covariate series with two components (month and year), per target series. For simplicity, we directly scale the month and year values to have them between (roughly) 0 and 1:" + "In the cell below, we use the `darts.utils.timeseries_generation.datetime_attribute_timeseries()` function to generate series containing the month and year values, and we `concatenate()` these series along the `\"component\"` axis (same as `axis=1`) in order to obtain one covariate series with two components (month and year), per target series. For simplicity, we directly scale the month and year values to have them between 0 and 1:" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "Text(0.5, 1.0, 'one multivariate time series of 2 dimensions, containing covariates for the air series:')" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -1478,21 +1615,23 @@ "\n", "air_covs = concatenate(\n", " [\n", - " dt_attr(series_air.time_index, \"month\", dtype=np.float32) / 12,\n", - " (dt_attr(series_air.time_index, \"year\", dtype=np.float32) - 1948) / 12,\n", + " dt_attr(series_air, \"month\", dtype=np.float32),\n", + " dt_attr(series_air, \"year\", dtype=np.float32),\n", " ],\n", " axis=\"component\",\n", ")\n", "\n", "milk_covs = concatenate(\n", " [\n", - " dt_attr(series_milk.time_index, \"month\", dtype=np.float32) / 12,\n", - " (dt_attr(series_milk.time_index, \"year\", dtype=np.float32) - 1962) / 13,\n", + " dt_attr(series_milk, \"month\", dtype=np.float32),\n", + " dt_attr(series_milk, \"year\", dtype=np.float32),\n", " ],\n", " axis=\"component\",\n", ")\n", "\n", - "air_covs.plot()\n", + "air_covs_scaled, milk_covs_scaled = Scaler().fit_transform([air_covs, milk_covs])\n", + "air_covs_scaled.plot()\n", + "milk_covs_scaled.plot()\n", "plt.title(\n", " \"one multivariate time series of 2 dimensions, containing covariates for the air series:\"\n", ");" @@ -1503,12 +1642,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Not all models support all types of covariates. `NBEATSModel` supports only `past_covariates`. Therefore, even though our covariates represent calendar information and are known in advance, we will use them as `past_covariates` with N-BEATS. To train, all we have to do is give them as `past_covariates` to the `fit()` function, in the same order as the targets:" + "`NBEATSModel` supports only `past_covariates`. Therefore, even though our covariates represent calendar information and are known in advance, we will use them as `past_covariates` with N-BEATS. To train, all we have to do is give them as `past_covariates` to the `fit()` function, in the same order as the targets:" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1517,16 +1656,17 @@ "text": [ "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", "\n", - " | Name | Type | Params\n", - "---------------------------------------------------\n", - "0 | criterion | MSELoss | 0 \n", - "1 | train_metrics | MetricCollection | 0 \n", - "2 | val_metrics | MetricCollection | 0 \n", - "3 | stacks | ModuleList | 6.6 M \n", - "---------------------------------------------------\n", + " | Name | Type | Params | Mode \n", + "-------------------------------------------------------------\n", + "0 | criterion | MSELoss | 0 | train\n", + "1 | train_criterion | MSELoss | 0 | train\n", + "2 | val_criterion | MSELoss | 0 | train\n", + "3 | train_metrics | MetricCollection | 0 | train\n", + "4 | val_metrics | MetricCollection | 0 | train\n", + "5 | stacks | ModuleList | 6.6 M | train\n", + "-------------------------------------------------------------\n", "6.6 M Trainable params\n", "1.7 K Non-trainable params\n", "6.6 M Total params\n", @@ -1536,12 +1676,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "131580853d4d4680b3b001edb5135e5d", + "model_id": "6b6d4fe1a7b34f16a62b70f21dfa28c6", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Training: 0it [00:00, ?it/s]" + "Training: | | 0/? [00:00 output_chunk_length`: using auto-regression to forecast the values after `output_chunk_length` points. The model will access `(n - output_chunk_length)` future values of your `past_covariates` (relative to the first predicted time step). To hide this warning, set `show_warnings=False`.\n", "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "01b945b214d14f7cbf0d211d407729a9", + "model_id": "bac56a6b9236484c98590e66927f76bc", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Predicting: 0it [00:00, ?it/s]" + "Predicting: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "pred_air = model.predict(series=train_air_scaled, past_covariates=air_covs, n=36)\n", - "pred_milk = model.predict(series=train_milk_scaled, past_covariates=milk_covs, n=36)\n", + "preds = model.predict(\n", + " series=[train_air_scaled, train_milk_scaled],\n", + " past_covariates=[air_covs_scaled, milk_covs_scaled],\n", + " n=36,\n", + ")\n", "\n", "# scale back:\n", - "pred_air, pred_milk = scaler.inverse_transform([pred_air, pred_milk])\n", + "pred_air, pred_milk = scaler.inverse_transform(preds)\n", "\n", "plt.figure(figsize=(10, 6))\n", "series_air.plot(label=\"actual (air)\")\n", "series_milk.plot(label=\"actual (milk)\")\n", "pred_air.plot(label=\"forecast (air)\")\n", - "pred_milk.plot(label=\"forecast (milk)\")" + "pred_milk.plot(label=\"forecast (milk)\");" ] }, { @@ -1659,7 +1776,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "It seems that now the model captures better the trend of the air series (which also perturbs a bit the forecasts of the milk series)." + "It seems that now the model captures the trend of the air series better (which also perturbs a bit the forecasts of the milk series). \n", + "\n", + "The model warning is pretty important. It tells us that future values of our `past_covariates` were used for prediction because we picked a hoizon `n > output_chunk_length` which activates auto-regression. The model will then cosume it's own predictions as input for the next prediction. Since the prediction point moves ahead, the model requires new values from the past covariates. These will however only be extracted from the past relative to the prediciton point (never from the forecast horizon itself)!\n", + "\n", + "In our case it's fine to do this since we only use calendar information that we know in advance. If instead we used some features that we only know in the past, then we should only forecast with `n <= output_chunk_length`." ] }, { @@ -1669,14 +1790,14 @@ "source": [ "## Encoders: using covariates for free\n", "\n", - "Using covariates related to the calendar or time axis (such as months and years as in our example above) is so frequent that deep learning models in Darts have a built-in functionality to use such covariates out of the box.\n", + "Using covariates related to the calendar or time axis (such as months and years as in our example above) is so frequent that all of Darts' models with covariates support have a built-in functionality to generate them out of the box.\n", "\n", "To easily integrate such covariates to your model, you can simply specify the `add_encoders` parameter at model creation. This parameter has to be a dictionary containing informations about what should be encoded as extra covariates. Here is an example of what such a dictionary could look like, for a model supporting both past and future covariates:" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1708,14 +1829,14 @@ "* An additional custom function of the year should be used as past covariates.\n", "* All the above covariates should be scaled using a `Scaler`, which will be fit upon calling the model `fit()` function and used afterwards to transform the covariates.\n", "\n", - "We refer to [the API doc](https://unit8co.github.io/darts/generated_api/darts.utils.data.encoders.html#darts.utils.data.encoders.SequentialEncoder) for more informations about how to use encoders. Note that lambda functions cannot be used as they are not pickable.\n", + "We refer to [the API doc](https://unit8co.github.io/darts/generated_api/darts.dataprocessing.encoders.encoders.html#sequentialencoder) for more informations about how to use encoders. Note that lambda functions cannot be used as they are not pickable.\n", "\n", "To replicate our example with month and year used as past covariates with N-BEATS, we can use some encoders as follows:" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1732,7 +1853,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1741,16 +1862,17 @@ "text": [ "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", "\n", - " | Name | Type | Params\n", - "---------------------------------------------------\n", - "0 | criterion | MSELoss | 0 \n", - "1 | train_metrics | MetricCollection | 0 \n", - "2 | val_metrics | MetricCollection | 0 \n", - "3 | stacks | ModuleList | 6.6 M \n", - "---------------------------------------------------\n", + " | Name | Type | Params | Mode \n", + "-------------------------------------------------------------\n", + "0 | criterion | MSELoss | 0 | train\n", + "1 | train_criterion | MSELoss | 0 | train\n", + "2 | val_criterion | MSELoss | 0 | train\n", + "3 | train_metrics | MetricCollection | 0 | train\n", + "4 | val_metrics | MetricCollection | 0 | train\n", + "5 | stacks | ModuleList | 6.6 M | train\n", + "-------------------------------------------------------------\n", "6.6 M Trainable params\n", "1.7 K Non-trainable params\n", "6.6 M Total params\n", @@ -1760,12 +1882,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "75598345902c4f84a87bdf52830754e4", + "model_id": "c04aaaca0dd14941b5dc8c09ed02ce73", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Training: 0it [00:00, ?it/s]" + "Training: | | 0/? [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "pred_air = model.predict(series=train_air_scaled, n=36)\n", + "preds = model.predict(\n", + " series=[train_air_scaled, train_milk_scaled], n=36, show_warnings=False\n", + ")\n", "\n", "# scale back:\n", - "pred_air = scaler.inverse_transform(pred_air)\n", + "pred_air, pred_milk = scaler.inverse_transform(preds)\n", "\n", "plt.figure(figsize=(10, 6))\n", "series_air.plot(label=\"actual (air)\")\n", - "pred_air.plot(label=\"forecast (air)\")" + "series_milk.plot(label=\"actual (milk)\")\n", + "pred_air.plot(label=\"forecast (air)\")\n", + "pred_milk.plot(label=\"forecast (milk)\");" ] }, { @@ -1858,30 +1981,36 @@ "source": [ "# Regression forecasting models\n", "\n", - "[RegressionModel's](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html) are forecasting models which wrap around sklearn-compatible regression models. The inner regression model is used to predict future values of the target series, as a function of certain lags of the target, past and future covariates. Behind the scenes, the time series are tabularized in order to build a training dataset in the right format. \n", + "> Note: You can find a detailed example notebook for our RegressionModel [here](https://unit8co.github.io/darts/examples/20-RegressionModel-examples.html).\n", + "\n", + "[RegressionModels](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html) are forecasting models which wrap around sklearn-compatible regression models. The inner regression model is used to predict future values of the target series, as a function of certain lags of the target, past and future covariates. Behind the scenes, the time series are tabularized in order to build a training dataset in the right format. \n", "\n", "By default, the `RegressionModel` will do a linear regression. It is very easy to use any desired sklearn-compatible regression model by specifying the `model` parameter, but for convenience Darts also provides a couple of ready-made models out of the box:\n", "\n", + "* [LinearRegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html) wraps around `sklearn.linear_model.LinearRegression` (accepting the same kwargs).\n", "* [RandomForest](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html) wraps around `sklearn.ensemble.RandomForestRegressor`.\n", "* [LightGBMModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html) wraps around `lightbm`.\n", - "* [LinearRegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html) wraps around `sklearn.linear_model.LinearRegression` (accepting the same kwargs).\n", + "* [XGBModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html) wraps around `xgboost`.\n", + "* [CatBoostModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html) wraps around `catboost`.\n", "\n", "For example, this is what fitting a Bayesian ridge regression to our toy two-series problem looks like:" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ - "from darts.models import RegressionModel\n", "from sklearn.linear_model import BayesianRidge\n", "\n", + "from darts.models import RegressionModel\n", + "\n", "model = RegressionModel(lags=72, lags_future_covariates=[-6, 0], model=BayesianRidge())\n", "\n", "model.fit(\n", - " [train_air_scaled, train_milk_scaled], future_covariates=[air_covs, milk_covs]\n", + " [train_air_scaled, train_milk_scaled],\n", + " future_covariates=[air_covs_scaled, milk_covs_scaled],\n", ");" ] }, @@ -1892,8 +2021,8 @@ "source": [ "Several things happened above:\n", "\n", - "* `lags=72` is telling the `RegressionModel` to look at the past 72 lags of the target.\n", - "* In addition, `lags_future_covariates=[-6, 0]` means that the model will also look at lags of the `future_covariates` we provide. Here we enumerate the precise lags we want the models to take into account; the \"-6th\" and the \"0th\" lags. The \"0th\" lag means the \"current\" lag (i.e., at the time step being forecasted); obviously, knowning this lag requires knowing the data in advance (hence the fact we are using `future_covariates`). Similarly, `-6` means we also look at the value of the covariates 6 months before the forecasted time step (which also requires to know the covariates in advance if we are forecasting at a horizon more than 6 steps ahead).\n", + "* `lags=72` is telling the `RegressionModel` to look at the past 72 lags / months of the target.\n", + "* In addition, `lags_future_covariates=[-6, 0]` means that the model will also look at lags of the `future_covariates` we provide. Here we specify the exact lags we want the models to take into account; the \"-6th\" and the \"0th\" lag. The \"0th\" lag means the \"current\" lag (i.e., at the time step being forecasted); obviously, knowning this lag requires knowing the data in advance (hence the fact we are using `future_covariates`). Similarly, `-6` means we also look at the value of the covariates 6 months before the forecasted time step (which also requires to know the covariates in advance if we are forecasting at a horizon more than 6 steps ahead).\n", "* `model=BayesianRidge()` provides the actual inner regression model.\n", "\n", "Now let's get some forecasts:" @@ -1901,37 +2030,35 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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EPvvsM7z22mv497//ja997Wshj7Hb7ejrG///oAkhhBAyPgglhI7JSjd2jn59aZzdYkah3YZD7gFUszQuMg4cOKCIJACYNm0aHnroIQBAVVWVzqmw2+0oLi5GVVUVHA4H2tradPunTZuGHTt2hJ3L4/HA49H/xbBYLLBawytkWUTIf37+aBQfzmDkaxiJCy64AMXFxXj00UdRVFQEv9+PuXPnor+/H36/H8nJycOeT64jDbXf5/Mp2wcGBpTj/H4/nnvuOdx888245557cMIJJyA1NRX33HMPPv/8c+U9oihCFMWwc2dlZaGvrw/9/f1B98VisQS9z2w2684NAIODg8o1aT9H4OcSRRGTJ09GdnY2Hn/8cZx33nkh/y60t7cjOzs74u+fBBN4L0hiwfuT+PAeJT68R4nPRLpHTX39utc1vW7DP3dL/4AyzrZKz2hlKck45B5Ay4AHXQMepCZFLhcS5f6YTJG1ohgihPr6+uB0OpXXDodD+e272+2Gw+HQHe9wOOB2u9HX1wez2aw81Ae+NxRPPvmkUlYns2zZsojKturq6iL6PEeajo4O7Nq1C7/5zW8Ukbh+/XoAQEtLC2pqalBUVIQVK1Zg69atIcMEXC4X3G43ampqlG39/dI/qK1btypO0tq1awEATU1NqKmpwZtvvon58+frhO2OHTvg8XiUc4U6t5bs7GwAwHvvvYeZM2cq2wcHB9He3h70vpaWFuXeHDp0SPkzMzMTbW1t8Pv9yns6OzuDriUlJQX33XcfrrjiClx00UX4y1/+gqSkJOX8NTU16O/vR15eXthrJpEzVv4dTVR4fxIf3qPEh/co8ZkI92h3a7fu9Y7GZtQkhzk4Rg60tinjwY421HhcyIEqYj7bV4WpKbaoz3uk709FRUVExxkihFJSUtDb26u8lh9OAckBcrlcuuNdLhfsdjtSUlLg8/nQ39+viCHte0Nx7bXX4sorr9R/iAgcobq6OpSUlESsEI8kJSUlyM7Oxquvvor58+ejtrYW99xzDwAgNzcXZWVl+MEPfoAVK1bghz/8IX73u9+hsLAQmzdvRlFREU488UQcffTReOSRR9DR0YHi4mKkpqbCZrPhhBNOwMqVK3H88cejtbVVce7y8/NRVlaG+fPn4+WXX8bu3btRUVGBf/zjH9i+fTsqKiqUvi2HwwGv1xu2j6usrAzHHHMMqqqqcN555ynbLRYLsrKygt6Xm5uLkpIS1NXVKSEahYWFKCsrQ3Z2Nkwmk/KejIwMWK3WoGs59thj8cEHH+CMM87A//7v/+LZZ5+FxSL99X7//fdRWVmJU045xahbNCEZa/+OJhq8P4kP71Hiw3uU+EykeyT21wBoVl53mCyG99APHOxSxnPKy1CckozZLj9ea+uR9qdloGxS5MuYjLX7Y4gQqqiowL59+7Bo0SIAwJ49e1BZWQkAqKysxEsvvaQc63a7UV9fj8rKSqSlpSE7Oxv79u3D7Nmzg94bCqvVOqzoGQ6TyTQmborJZMKqVatw0003Ye7cuZg+fToefPBBnHrqqcpnSE5Oxn//+1/85Cc/wVe/+lUMDg5i5syZeOihh2AymbBs2TK8/PLLOOOMM9DZ2Yknn3wS11xzDZ544glcd911OP744zF9+nTcfffdOPvss5Xzfu9738PWrVtx+eWXQxAEXH755bjhhhvw5ptvKt+dHMk93He5fPlyrFy5MmhB1VDv094X7Z+htst9R6GupaioCKtXr8app56Kq666Cv/85z9hNpvxr3/9C9dff/2YuPdjgbHy72iiwvuT+PAeJT68R4nPRLhHbR6v7nVdX7/hn1k7R26yDSaTCZWpqiFR7YptzrFyfwQxilDywcFB+Hw+/OAHP8DSpUtx5plnIikpCR9//DH++Mc/4pFHHoHNZsMNN9yAK6+8UkmN+9rXvobvfve7OOecc/C3v/0NW7duVcrbHnjgARw4cAC/+93vUFVVhRtvvBErV65EeXm5YR9SLq0qKysbEzdlPNDf34/p06dj1apVOPHEE0c8frTu0fbt23HGGWdgz549SE83tsFwosF/R4kN70/iw3uU+PAeJT4T6R7d8Nl2rKo5pLy2mUxo+PrpMEW5vMhwHP/mR9jX0wenxYzai08HAKxv68Q570ktGddOLsafjz0q4vONtfsT1RXeeeedWLhwITZv3ozf/OY3WLhwITZt2oRFixbh4osvxtVXX41ly5Zh4cKFymKaVqsVd999N5555hmcdtpp2Lp1K+644w7lnN/5znfgdDpx7rnn4uc//zl+/vOfGyqCyJEhOTkZf//739Ha2npEr+PgwYP4+9//ThFECCGEkDFFc0Bq3IDfjyZNuEG81Lnc2Ncj9eWXO9XY7AqHxhEa58lxUZXG3XbbbbjttttC7rv22mtx7bXXhtw3a9YsrFq1KuS+5ORk3HnnndFcBhkjJMKaPWefffaRvgRCCCGEkKjRRlvL1Lr6UWg3JjHh+dpGZXxRcb4yzrYlwWkxo3fQh2rX+F56JPE9K0IIIYQQQhKIPd0u/L2qHt0BfTxG0jIQSggZ49CIooh/a8ruLiktVMaCIKDCKblCda5+DI7jqHIKIUIIIYQQQiLE6/fjax9sxI827MId2/aNyhyiKAYtqApIi6oawY6uXuzullKdj89OR5mmNA4AyhzS60FRRH3AekbjCQohQgghhBBCIqTJPYBDbqlX56OWjlGZo8s7CK9fyjPLsalrIxrlCGndoGVlhUH7KzTCqNqgORMRCiFCCCGEEEIiRBticKC3Dz5/xAHMEdOi6Q86NksNfKp1xe/O+EURLwz1B1kEAUtL8oOOkUvjgPEdmEAhRAghhBBCSIRoRYrHPzqlY9qyuMmpKUhLkvLNjCiNO+QewMEhR2tRXiaybcHrc5Y6VEfIqHK8RIRCiBBCCCGEkAhpDkhz29vjMnwObVBCjs2q9Ow09MUfXtA36FPGhXZbyGPKHGoynVHleIkIhRAhhBBCCCER0hIghPb3GB8xrY3Ovv3hJHg6JGEyKIpKf1Ks9PlUIWQ3m0MeU5xih7xsKx0hctgRRRHLly9HVlYWBEHAli1bjvQlHRZWr16NGTNmwB/FbzvKy8tx//33D3vMtm3bUFxcDJfL+N/aEEIIIWTi0DygFyL7e40XQlrXqbfDil07jCtVcw8GC6H9DSJuuNeP1z+R+p1sZhMKhtwiOkLksPPWW29h5cqVeO2113Do0CHMnj37SF9SzEQiVGR+/vOf45e//CVMpsj/aq5fvx7Lly8f9pg5c+bg+OOPx3333RfxeQkhhBBCAjkcpXHaHiHRbUV/m1qqFm94gUvjCKVYJCH0q8dEPPwycNltIlxuSQzJ5XitA164NOJpPEEhlKDs378fhYWFOOmkk1BQUACLxRL1OURRxODg4Chc3eiwceNG7N27F8uWLYvqfbm5uUhJSQm73+uVFju79tpr8fDDD8PnG5//mAkhhJCJzqYvRfz7fRGDg8YnuckcjtI4bY+Qv88KX7eRjpBadZNilqTA3nrpda8b2FUjjbWBCePVFaIQSkCuueYa3HjjjaitrYUgCCgvLwcADAwM4KabbkJeXh6Sk5OxaNEirF+/XnnfmjVrIAgC3n77bSxYsAA2mw1r166FKIq4++67UVlZCbvdjnnz5uH555/Xzbljxw6cf/75SEtLQ2pqKk4++WTs378fgOS4nHXWWcjJyUF6ejoWL16MTZs26d5/2223obS0FDabDUVFRbjpppsAAKeeeipqampw8803QxAECIKAcLz22ms466yzkJys/tZj//79uOiii5Cfnw+n04njjjsO7777ru59gY6TIAh45JFHcNFFF8HhcODOO+8EAJxzzjloa2vDBx98EOGdIIQQQshYoaldxIk3iLj0NyIefWX05gl0hOr7+nXlZkag7RES3UnwGymEQjhCrV3q/u0HpD9LJ0BgQvQ2wzhg3emfwNMcX6NZLFjzbFi0+sQRj3vggQcwefJk/O1vf8P69ethHqrfvOWWW/DCCy/gqaeeQllZGe6++26cc8452LdvH7KyspT333LLLbjnnntQWVmJjIwM/OpXv8KLL76Ihx9+GFOnTsWHH36Ib37zm8jNzcXixYvR0NCAU045BaeeeipWr16NtLQ0fPTRR4qb1NPTg29961t48MEHAQB//vOfsWTJEuzduxepqal4/vnncd9992HVqlWYNWsWGhsbsXXrVgDAiy++iHnz5mH58uW4/vrrh/3cn3/+Oa6++mrdtt7eXixZsgR33nknkpOT8dRTT+GCCy7Al19+idLS0rDn+s1vfoM//OEPuO+++5Tvz2q1Yt68eVi7di1OP/30Ee8DIYQQQsYO26oAj1QEgrc+F/H9i8P/8jUeWgaCnyGrevswKyPVsDnk0jjRYwZ8Zvi7VVESrxByhegR0gmhKhGAoJTGARRC4wpP8wD6Dx1+IRQp6enpSE1NhdlsRkFBAQDA5XLh4YcfxsqVK3HeeecBAFasWIF33nkHjz/+OH72s58p77/jjjtw1llnKe+79957sXr1apx4oiTCKisrsW7dOjz66KNYvHgxHnroIaSnp2PVqlVISpJWL542bZpyvkDR8OijjyIzMxMffPABvvrVr6K2thYFBQU488wzkZSUhNLSUhx//PEAgKysLJjNZqSmpiqfJRz19fUoLNSvbjxv3jzMmzdPeX3nnXfipZdewiuvvIIf/OAHYc91xRVX4LrrrgvaPmnSJFRXVw97HYQQQggZe7R0quPNe0dnDo/Pj05PcNvBvh5jhZDsOvn7htb48Znhd1lhcngMdYTsFjP6B0RoT6k6QuN/LaEJKYSseaEz0xN53v3798Pr9WLhwoXKtqSkJBx//PHYtWuX7tgFCxYo4507d6K/v18RRjIejwfz588HAGzZsgUnn3yyIoICaW5uxq233orVq1ejqakJPp8PfX19qK2tBQAsW7YM999/PyorK3HuuediyZIluOCCC6LuaxoYGNCVxQGSkLv99tvx2muv4eDBgxgcHITb7VbmDof2O9Bit9vR12d8LS8hhBBCjixaV6OhBWjpFJGbYawrpO3dEX0CBLPUi2Rkn5Br0IcuryS2RLe62Km/yw6Tw4Pmfg/6Bn1KWVu0aNcRcljMaOvW75eFkN4RMn7R2ERgQgqhSMrTEg1RlP6hBfbYiKIYtM3hcChjOYb69ddfx6RJk3TH2WySMLPb7RiOa665Bi0tLbj//vtRVlYGm82GE088ER6P9MOgpKQEX375Jd555x28++67uOGGG/CnP/0JH3zwQVhxFYrMzEx0dHTotv3sZz/D22+/jXvuuQdTpkyB3W7HJZdcoswdDu13oKW9vR2TJ0+O+JoIIYQQMjZo7dIHJGzeA5x9vLFzaIMSfM1psBRK6mufgclx7x5qVedoc6rjbjssRdJ8NS43jkp3Br03Etw+NSzBbjbpBCQgiciOHhFFDhvMggCfKI7b0jiGJYwRpkyZAqvVinXr1inbvF4vNmzYgKOOOirs+2bOnAmbzYba2lpMmTJF919JSQkAYO7cuVi7dq2SrhbI2rVrcdNNN2HJkiWYNWsWbDYbWltbdcfY7XZceOGFePDBB7FmzRp88skn2LZtGwCpNyeSpLaZM2cGuVtr167FNddcg6997WuYM2cOCgoK4ipt2759u+KEEUIIIWT80Nqpfz0a5XHN/WprxeChdGW8z8C1hF6pb1LGnv25ylgbmBCPMNE6QikWM9q6go/ZcQCwmEyYlCL90ny8lsZRCI0RHA4Hvve97+FnP/sZ3nrrLezcuRPXX389+vr68O1vfzvs+1JTU/HTn/4UN998M5566ins378fmzdvxkMPPYSnnnoKAPCDH/wA3d3duOyyy7Bhwwbs3bsXTz/9NL788ksAkgh7+umnsWvXLnz22We48sordS7SypUr8fjjj2P79u2oqqrC008/DbvdjrKyMgBSqtuHH36IhoaGIAGl5ZRTTsFHH32k2zZlyhS8+OKL2LJlC7Zu3YorrrgiqsVWtVRXV6OhoQFnnnlmTO8nhBBCSOLSEvBAv3mv8RHa2sQ4X7cd/h5JKBhVGuce9OG/Q46QzZ+EwYZMZZ82MCGetYT6tKlxZnOQIwQEl8d1eQfR5Qn9C/OxDIXQGOKuu+7C17/+dVx11VU45phjsG/fPrz99tvIzMwc9n2//e1vceutt+IPf/gDjjrqKJxzzjl49dVXUVFRAQDIzs7G6tWr0dvbi8WLF+PYY4/FihUrlLK2J554Ah0dHZg/fz6uuuoqJcJbJiMjAytWrMDChQsxd+5cvPfee3j11VeRnZ0NQApvqK6uxuTJk5Gbmxt8gUMsXboUO3fuVAQYANx3333IzMzESSedhAsuuADnnHMOjjnmmJi+v2effRZnn322ItAIIYQQMn4IdIQ27TF+Dl2PkGZ9nw6PF93e+NdufK+xTUl1y+/JBfzSo/rRU2FYhLY7IDUusEcIkJPjAtcSGn99QhOyR2gs8KMf/Qg/+tGPdNuSk5Px4IMPKjHWgZx66qlKL5EWQRBw0003KWv7hGLu3Ll4++23Q+6bP3++br0iALjkkkuU8dKlS7F06dKw5z7hhBOUOO3hSE9Px/e//33ce++9ePTRRwFIbtLq1at1x33/+9/XvQ4slQv1HQwMDODhhx/Gs88+O+J1EEIIIcQ4GlpEnP0TEekO4J17BTjsoxNrHehs7K0HevpEpKYYN5/WEfL3WeHvUV2aepcbM+NMjtOWxTma1V86L5gObF1jjBDqC1hHaDhHqDRFP+ecTOOS8RIBOkIkofjFL36BsrKyiHqKoqGmpga//OUvdal7hBBCCBl9/rUa2FkNfLIDeGEU1zTXxmfLbN1n8ByaHiHRbdW5NHV98Tkm/T4f3jrYAgBIT7LArymLmz9VgOiyQfRJoi4uITSothikmE1o7Qz+BfL2A9Ivlsuc4ztCm0KIJBTp6en4xS9+oSyCahTTpk3Dd77zHUPPSQghhJCRaWpXH7S3VRnftwNID+2hnA2jAxO0pXGBjlC8yWqb27vRO1S2tmRSLtq7pMf0NAcwZRIAUVDmq3G5Q1bAREJfwDpC2tK4iqHlHNu6gKb2wNI4CiFCCCGEEEIiRitQ5JIro+l2AXLrS4YmVdrowISmodI40WsCvGZdgEG8jlCnJoxgstOB9iGBkpUKlA2tSS87UK5BH9oGYgsvkHuEkkwCkkz6+OzFR6vjndVAcYr6+Q66x1+PEIUQIYQQQggZNbSOw/aq0ZlD+zB/yjzANPSEu83g+eR1hPxuKwAB/h7jHBOdU2M2ob1HGmenA6X50tjfpc5XHeN87qF57EPVN/J3ZzIBx0xT+6lqmoA8m7qga1P/8Gs4jkUohAghhBBCyKihFUL1LUBnj/Hlcdr+oNJ8oEgKrkVtU8jDY8Lr96NjyLUR+ySB4O+1Qa5Qq4/TEXJp0txMPjPk1UKyUgG7TUB+FuDThjPEOJ88j8MiCSF5HaHsNKC8QD2utgmwmk3Itkkpwk3uAYw3KIQIIYQQQsioERhrvaN6FObQOEI56YLioDR3AO4BY4RXS0BinCAA8Jsg9kprCcXtCGlCDHxetVc6e2jd1rJ8QOxVhVBDjELI7ZPmsZslGdCqEULy9wYAtU3S95aXLH2+5n5PzH1JiQqFECGEEEIIGTUC16nZMQp9QlohlJuhf6CvbzZmDq0DI7qtmC0tx6gEGLQOeNE3GHvqrfa93n71ET1rKLG6vADKAq5A7EJInifFYsaAR4S8NmtOhiS2ZGqG3LT8ZMn9GvD70WXAWkmJBIUQIYQQQggZFURRVHpdZLYfMN5V0LpOOelAqboED2oNEkLrmjuU8WBzKo6bASRZAF+PNkI7joVOfVohFMIRKgD8vfGVxvn8Igb8siOkT4zLSQfSnUBqivS6VhFCqvhq6h9f5XEUQoQQQgghZFTo6gUClwYcjcCEFs1aODnpQGm+2vRvVJ/Q+01tyniwNhsF2cDkIgREaMfeJ6TtERroU4VQVqr0WcryBfhdak9SLCluWrGVYtYvppqdBgiCWlZY2ywJWZ0Qco+vwAQKoQRFFEUsX74cWVlZEAQBW7ZsOdKXdFhYvXo1ZsyYAb/fP/LBwyAIAl5++WUAQHV1te47XLNmDQRBQGdnZ8j3Njc3Izc3Fw0NDXFdAyGEEDLRCbW2z2hEaA9XGlfTGL8D1eMdxOdtnQAAX6cd/h47cod6kbQR2vXxLHSqESluV7AjVF4IqSdpKKghltI47RwpFrMSlABIAhJQ3bQBjxRCkZesJsc10xEih4O33noLK1euxGuvvYZDhw5h9uzZR/qSYqa8vBz3339/RMf+/Oc/xy9/+UuYTPH91Tx06BDOO++8mN6bl5eHq666Cr/5zW/iugZCCCFkohPYHwRID9fNHcaWx7UGPNCXadPPDCiN+7ilA16/dM3e2iwAkuAqyQtwhOJIjtP2CPVphFBWmvSn3L8jz9fc74HHF90vjt2aQAa72RQUMgEEBiYA+XbVEWqkECKHg/3796OwsBAnnXQSCgoKYLFYoj6HKIoYHBw7TW0bN27E3r17sWzZsrjPVVBQAJvNNvKBYbj22mvxzDPPoKOjY+SDCSGEEBISreMgqNVqhpfH6Uq8AnuEDCiNe79RUxZXJ2VzS0JIv5ZQXTyOkEYI9Xarj+jZshCSF1UdSqkTARyKMtLaFeAIBX5vQHBZYb7GEWJpHBl1rrnmGtx4442ora2FIAgoLy8HAAwMDOCmm25CXl4ekpOTsWjRIqxfv155n1zy9fbbb2PBggWw2WxYu3YtRFHE3XffjcrKStjtdsybNw/PP/+8bs4dO3bg/PPPR1paGlJTU3HyySdj//79AID169fjrLPOQk5ODtLT07F48WJs2rRJ9/7bbrsNpaWlsNlsKCoqwk033QQAOPXUU1FTU4Obb74ZgiBA0P4UDOC1117DWWedheRk9Tcrt912G44++mg88cQTKC0thdPpxPe+9z34fD7cfffdKCgoQF5eHn73u9/pzqUtjRsJt9uN888/HyeccALa29sBAHPmzEFBQQFeeumliM5BCCGEkGC0D9rzpqhjo8vj5HWE0hyANUkI2fQfD+83Sc8HgijAW58JQOsIqb94jccR0oqU7p5gRyg1RUBWmj4woSHKPiH3oL5HKGRpXKAjpOkRGm+lcdHbDOOA09/59IisjpufbMXqs04Y8bgHHngAkydPxt/+9jesX78e5qGVf2+55Ra88MILeOqpp1BWVoa7774b55xzDvbt24esrCzl/bfccgvuueceVFZWIiMjA7/61a/w4osv4uGHH8bUqVPx4Ycf4pvf/CZyc3OxePFiNDQ04JRTTsGpp56K1atXIy0tDR999JHiJvX09OBb3/oWHnzwQQDAn//8ZyxZsgR79+5Famoqnn/+edx3331YtWoVZs2ahcbGRmzduhUA8OKLL2LevHlYvnw5rr/++mE/9+eff46rr746aPv+/fvx5ptv4q233sL+/ftxySWX4MCBA5g2bRo++OADfPzxx7juuutwxhln4IQTRv5+tXR1deGrX/0qkpOT8d5778HhcCj7jj/+eKxduxbXXXddVOckhBBCiIS2NO6UucCWvdL4y1oRQPhfjkaLLLhyM6Q/paZ/ETsOqE3/w/0ydjjqXW7s7XEBANJcaWj3WpS5SvIA+Mzwu6wwOTzx9QhpREpXhzYsQT2mvADY0RP7WkLasAS7xYzWLrVEMTugBA8AaptFXWnckXh+Hk0mpBBq6vdEbSUeTtLT05Gamgqz2YyCAskHdblcePjhh7Fy5Uql92XFihV455138Pjjj+NnP/uZ8v477rgDZ511lvK+e++9F6tXr8aJJ54IAKisrMS6devw6KOPYvHixXjooYeQnp6OVatWISlJWj142rRpyvlOP/103fU9+uijyMzMxAcffICvfvWrqK2tRUFBAc4880wkJSWhtLQUxx9/PAAgKysLZrMZqampymcJR319PQoLC4O2+/1+PPHEE0hNTcXMmTNx2mmn4csvv8Qbb7wBk8mE6dOn449//CPWrFkTlRBqamrCN77xDUyePBnPPvssrFarbv+kSZOwefPmiM9HCCGEED1tmgftY6YJkAq6gIZW4+YYHBTRMRTRLbsagFQet+OApuk/M7bzr21Ry+StreovnnMzoKzB4+9JhsnhQWO/B/0+H5LNZkSLLFJsJhM6elTRlqkRQmX5wLaDqjCJNkK7L8AR2qeNz86Q/gx0hFItZtjNJrh9/nEXnz0hhZC21nGszLt//354vV4sXLhQ2ZaUlITjjz8eu3bt0h27YMECZbxz50709/crwkjG4/Fg/vz5AIAtW7bg5JNPVkRQIM3Nzbj11luxevVqNDU1wefzoa+vD7W1tQCAZcuW4f7770dlZSXOPfdcLFmyBBdccEHUfU0DAwO6sjiZ8vJypKaqPwXy8/NhNpt1gQr5+flobo6uG/LMM8/Ecccdh+eee05x3bTY7Xb09fVFdU5CCCGEqGhL42aWA2azFKdd32LcHNp1inRCKOCBPlYh1KJxQQZanACksjubVUBxriTs/N3JQIGkKhr6+jE51RF8ohHQLnTaPiRQMpyA2ayKorICwL8ndkeoTxOuYLcEhiVIfxblACYT4PdLi6oKgoD8ZBuqXW4KofFAJOVpiYY4FBofaOuGsnq15V1yDPXrr7+OSZMm6Y6TwwTsdjuG45prrkFLSwvuv/9+lJWVwWaz4cQTT4THI/1gKCkpwZdffol33nkH7777Lm644Qb86U9/wgcffBBWXIUiMzMzZDhB4DkEQQi5LdrI7fPPPx8vvPACdu7ciTlz5gTtb29vR25ublTnJIQQQoiKtjQuPwsoygbqmqX/jELuDwIChZDqQNU0AgtmxHb+Xk3wVHeX9ItTuQTPmSIgwyliIGCh03iF0MGh7y07XX9MeYGg7xGKwxFyaNYRMpkk0QUASRYBRdki6lvU/qq8ZCuqXW50egYx4PPDZh4fMQPj41NMAKZMmQKr1Yp169Yp27xeLzZs2ICjjjoq7PtmzpwJm82G2tpaTJkyRfdfSUkJAGDu3LlYu3YtvF5vyHOsXbsWN910E5YsWYJZs2bBZrOhtVXvadvtdlx44YV48MEHsWbNGnzyySfYtm0bAMBqtcIXuJpamGsNdLdGk7vuugvf+ta3cMYZZ2Dnzp1B+7dv3664ZoQQQgiJHm0zfnYaUDz0+8XmDmDAY0yEduAaQjL6XpfYz69d6LS7Uy+EgOAI7WjL1ZR5htyaFLNJKfXT9gcBkiMk9lkh+qRfgkcdlqB5HrMKJtQ0qvOYTOov1mU3rbkDcA/o+4QCAxO6vYM40NuHbu+g8ov7sQKF0BjB4XDge9/7Hn72s5/hrbfews6dO3H99dejr68P3/72t8O+LzU1FT/96U9x880346mnnsL+/fuxefNmPPTQQ3jqqacAAD/4wQ/Q3d2Nyy67DBs2bMDevXvx9NNP48svvwQgibCnn34au3btwmeffYYrr7xS5yKtXLkSjz/+OLZv346qqio8/fTTsNvtKCsrAyCVtn344YdoaGgIElBaTjnlFHz00UdGfF0Rc8899+DKK6/E6aefjt27dyvb+/r6sHHjRpx99tmH9XoIIYSQ8YQsUmxWICUZKNbEWh80qE9Ik2ytrIUDBJbGxf6ArnVR4DUPzaNuChRCda7ohZAoiso8fo8Zsp4oydMfV14AQBTgd0nCpKEvulI17WfZvMusuGknBSxXqf3u6pv1yXGBgQnvNbbi2Dc+QvlL7+P/9tZGdT1HGgqhMcRdd92Fr3/967jqqqtwzDHHYN++fXj77beRmTl80etvf/tb3HrrrfjDH/6Ao446Cueccw5effVVVFRUAACys7OxevVq9Pb2YvHixTj22GOxYsUKpfzsiSeeQEdHB+bPn4+rrrpKifCWycjIwIoVK7Bw4ULMnTsX7733Hl599VVkZ0s5+3fccQeqq6sxefLkYUvNli5dip07dyoC7HBx33334dJLL8Xpp5+OPXv2AAD+85//oLS0FCeffPJhvRZCCCFkPCGXxmWnSWXsxZrHAKP6hB57XRU5U4vV7YE9QrGidYTEwdFxhDx+Eb4h9dOhSYy79DR9+0PgWkIdHq/u+kIhiiKe2FeHB3dXo0dT5vfaWlUG/PgbAfMEuGm6tYQCHKG2frWiKNt6ZPrwY2VC9giNBX70ox/hRz/6kW5bcnIyHnzwQSXGOpBTTz01pCUpCAJuuukmZW2fUMydOxdvv/12yH3z58/XrVcEAJdccokyXrp0KZYuXRr23CeccIISpz0c6enp+P73v497770Xjz76KABpHaHbbrtNd9zKlSuD3rtmzRrda+33UF5ernsd6nsK/F7vu+8+3HrrrSNeMyGEEEJCI4qiIoRkB6U4V+3bMUIIfbRNxLsbpHFlEXCBmimla/qPpzRO66KIQ47Q9BJVOEiLqsYnhLQlay2t0hxZacDSgN/HZjiltZJ8vckAJLutoa8f09LC9yR92tqJn26Sql4KNSVu+2ukeY6bAZwyT/+ewP6qvKM0jlBA8nKbR3WIsm1JgGfsRGzTESIJxS9+8QuUlZVF1FM0WjQ3N+OSSy7B5ZdffsSugRBCCBnruNxSdDWgrlFjtCN0+5PqLzZ/eZWAJIsqUKSmf2ks98LEgiuEELp4sbq/JA8QPRaIHmlffV/0awlp5/B5pMfzK8+Skum0CIKAsny9AzVSYMKurl5lrF0+Rna3fnpZ8IL3svMEANWNIgrsqtPzfG0jLnh/A/5eVQ8AaBvQOEK2yEOyEgEKIZJQpKen4xe/+EXIOOvDRV5eHm655ZaYF14jhBBCiD4xLltxhNRt9c3xNdZ/vE3EO0NuUEUhcNU5wcfID/QtnYDLHdt8vQE9QnMnA9N0jhAAqK5QQ98A/FGGBvSFKL+7bkno55CyArU0TppveCHUHGYRVNFrQnkBcPEpwfsqNcs6Vh3U9wh92tqJj1o68L+bv4TX70fbgNYRGlulcRRChBBCCCETFL9/9FK+2kKsUaMNS4jXEfrX6vBukEyF5oG+OkZXqG+oSkX0mgAIWHaqfh450EAWQgN+v27toWjmkOYxY/5U4OipoYVQeYHeETo4QnJcy0CYaxk0Y9FcwBLieyvXfG8HDgF5GiEk4/ZJn1PrCGVZ6QgRQgghhJAEZ90XIvIuEnHez/yjIoh0jtBQaVxhNiAXXMQrhLTnD+xxkakIeKCPBddQwIBcFrfsNP3+SUMuly+OPqHAZLrzTwx/bFm+AH9f+BS3QMKJMtFrRkqwvgEA2G0CCofKCqsOArlhnJ7mfo/iCNlMJjgtR66iJxYohAghhBBCJiBPvCGirQt46zPg0x3Gn1+7vk/2UKx1kkVAQZa0LV4h1NOnjlNTQh9TUai6HdUxCqEej+wISWVx00v1DordJiAnPb7kuMBkunRH+PL88kLA79KkuLmHj9AOXPdHO4/DHnIXAFVENrZLvV7TQwQyNPcPKEIoy5Y05toKKIQIIYQQQiYgzR3qeOt+488fqjQOUPuEDrUB3sHYnajuCIRQuabp/8ChGHuEvLIQsuCSU0M/6JfkAf5ezVpCUQYmBJbGpSSHP7YsHxDd2jjrERyhsKVxprCOECCl8MlUNwIPHT8Ly6eU4NuT1Yzyxv4BtHmk0ricMdYfBFAIEUIIIYRMSLSOzdZ9h6c0DlB7akRRvxhqtMiOkMmEsMIh3tI4n1+EF37pxaAJs8pDH1eSB/i7Y3eE3NrSuEETHMMJoQIAfhP8bqkfJ3Bdn0BChSXI/U4Oe3gHpyIgMOGYrHTcdcwMnFaQrWzf19MH71BZZdYYS4wDKIQIIYQQQiYkOiE0Co5QU4cqrrJDOEIAUBfH+j6yEHLaEbYkqzgXkINoD8QQluDSOjUeS9hSskBHqN4Ve4/QSI5QbgZgtwF+l2TnNPcPhFxHEpBK7kItuCon0w3vCKnfadVBdbs2OEEbzU1HiBBCCCGEjAm0QuiL/YDPZ6wr9NE26U9BAKZMUrcX56kP2PH0CclCKFxZHCAlopUOOVCx9AgFCZQwwqEkT4DoskL0S58t6h6hgNK44RwheS0hsU8SHh6/iA6PN+SxLeH6g4aCH4YTXJU6N039u5GfrAqenRohlD3GEuMACiFCCCGEkITCOyjiv5+LaOsavWhr76AIzTMs+vqB/QfDHx8th1pFfDHkMh07XQ1LAIxbVFURQsM0/ANqn1BnL9DZE913KifGAUPhAmGEQ0keANGkrO8TT2qcODi8IwTIgQkjJ8eFW0MIg5IEGE5wBZbGyeRqhJB2gVaWxhFCCCGEkLj4yUMizvmpiDNuFsOWPMWLNshAZus+487/3/Xq+Jzj9Pv0Qii2z+f3A71DeQQhwsx0xNMn1DfoV18MU7IWuJZQu8cbsiQt4nmGKVkDoHOEgPB9QuGCEpTSuGGEUFEOIJs82u8t2WxGepIl6HiWxhFCCCGEkLj4cKv059Z9QE2Mi4CORGsIIbTFwMCEt9er5zrneH3/jhE9QtoWnOFK4wB9hHa0QkjnCA1TsiZ/Jn2EduTJcbrUuBFirQGgvECIKEJbu4ZQsUb1yKVxwzlCZrNUggcAVYegE+WhFljNohAihBBCCCHx0K5JW9u0Z3Tm0Ca6yRjlCPn9It4ZcoRSU4ATZun3T9IEGFTFWI4XyRpCMlpHqDpKYdkbYYjBpFypF0oXoT1CYILPL+KF2kasbW7XryPkHT7WGpCS4yJZVFW7htDC3Ex1x1Bp3EgleHKEtssNtHSq27V9QjI5LI0jhBBCCCHxoBUpm/eOTmlca2fwNqOE0KY9quN0xrHSIqparEmq07CvATGV/0UjhMrDNP1HQuD6PuEcFGuSgPxMKD1CANA4wkKnL9U14vpPt+FrH2zEFx2amx6BI1ScC4iuCErjNAJJK4QicYSA8GWFeSGEEB0hQgghhBASM/0DIrR99pv3js48oUrj6ltgSEDD25+r48CyOBk5Ra7bpXcaIkW3mOoIoiGeHiFdn4/XjOGe9UvyAFETYNA4wvo+G4YatfwisEOTXDFcOp1MQVaAIxRGdDVreoROyc/CV3IyIIgCPPskJTqyIxQ6QjvfHnyBdIQIIYQQQsYpu2tE3PmUiKqDo5fmFliydjiEUGm+OjbCFXpvo6Y/6LjQx0wtVsf76qOfIxpHqCALioCJWgh5VSFkFcxh1ysChtYSiqBvR+ZgmP2i14wQOkNHYTbg14UlhC6N0zpCeclWvHHaAizaeTK8Q0IoVkcoP0SPUCbjswkhhBBCxieX3S7i14+L+PYfR08ItQcIoYOtQHOH8fO1apyf049Rt++sjv/cDa3Sn2kOoKIonCOkbt8btxAKL04AwGQSlAjt6sboSvG0pXE2wTzssZIQitwROugO7iESRSDZbILZPPxncqYIcFgsED3SNYUrjZN7hNKSLEg2S0LO06cKlhEdIV2EtjYsQW+NZVgtSDKNPVkx9q6YEEIIIeQw4/eL2H5AGn+6Q3o9GoQKMRgNV0jrCB1/lPrQXdsc/+eSMwKcw5SsTS1Rx/saRrdHCFDXEurrj64Ur1eTGpc8ohASILqtkHVWozvMGj5DHAy11pDXjJTk4UWQTGGW6gqNtI6QVrhopx3JESoPEzQRmBqXbR17/UEAhRAhhBBCyIh0uQDZHOj3ALVNozNPqPV9No9CcpxWCB0zTR0bEdftGkqNHu4hW+4RAmJ0hDTJ1NEIISC6e6ftEUo2j+wIwW+C6JYcl+EcIa/fH1K8iIOmEcWJTEG26kD1eAd1i7ICgHvQp6Te5Wqam2ShajYDIZYD0pGZqgpa7d+NwNS47DHYHwRQCBFCCCGEjEhgytru2tGZp70neNumUUiOk4WQyQTMnaxurzFA4MkP2sMln1UUSnMDUnJctPRqHKG0CIRQca7qstS3RD6PVlw4RhBCylpCQ+KkuX8A/jBleE3uAYTaM1xEdyCF2cMnx2kXU80N4Qg5kjFszxMg7S8bEpG1zaoTGuQIjcHEOIBCiBBCCCFkRAJT1nbXjM48h80R6pT+zE4D7DYBBVnS63gdIY9XhHeommw4Z0Mbob23PvoI7WhL44rz1HFDFEKo26MKoRRLBI4Q1OQ4r19E+4A35LEN4YIUBkdOjJMZKTmuWReUoB4nC9VI55HdNI8XaGqXxlnWJJg1IoqOECGEEELIOCVICNWOVo+Qel75uXtfA9DtMnY++fPkpEt/yr/1b2yXIrxjxRVF/4mcHNftCh3nPRxRC6FcdVzfMvzne3hPDa7+aCvOeOczdA2oPULOEYRQYbbkcunT3EILnlBBCcDQWkUjxIGr8wnDJse1aObWlsb1ReDYaSnTpArKjqHZJCBXI36yxmBiHEAhRAghhBAyIsFCaHTm0YYlfGWmOt5xwLg5+gdE9A712ChCSPOwW9cc+7ldmt6d4cISgIA+obro5umOSwgNf+yHTe14raEZmzu6UaWpwXNahxdCFouAomx9ctyhMM7Pwb7w0dnROELadYsCRZfWEdKWxkXrCJUVqM5PuMCEe/+ehGlX+PHUW5GdM1GgECKEEEIIGYHDJYS08dnHzVDH8YiTQLRiK9ARAuLrE9I5QiMIoanF6gN2tH1C0TpCk6IQQpWaE9a41YnSRhBCQHCEdtSO0KA54rCEwLWEnqs5hKs/2opPWzoA6HuE5HADv1+ErM1icoR0Qkide6DHir31UMoixwoUQoQQQggZ0/QPiKg3IPZ5OFo79edvagc6eoyfUytS5k2JrcF/JLSiLidD+rMsXxOhHY8Q0jhCIz3QT9Esqrq3fnR7hFJTBKQ5pPFIPUKTneoJ5asSvSY4bCPHWpfk6V2axggcIYe25M5riiosQSu6NrV347WGZvzvli8BBDpC0nHay4ncEVLHNU3atYTUE/iHkvK0gnMsQCFECCGEkDGLxytizjUiSi4Rseq90RNDoXpYvhwFV0gOS3DYgclF6vY6A4We9rNkp0l/hnvYjZZYeoSA6B2h3ijjswG1PK6+ZfhwhskhThhp747kCIXv25Fp0DhCJ8pqFJIjFKkQKsgC/L3JEP16gVY19OVo3SjZEdJW5EXqPIVzhOZlpgIABFGAv11SmZNyIjtnokAhRAghhJAxy7Yq9SF6xauHVwiNRnmcHJ+dnRZdX0s0aKPAc9Klh+hwD7vREk1pnDZCO9q1hGRHyGYFkiyRLUAqP6S7B4COEDHlMlpHSCbS3p1JuYIuyS28IyR9UXnJVszOSNXNE6lAyUkHzD4L3Gunwt6ajSK7uqaQa9CnS5H78Z+ScM5P/KjTuH2RCq78LOl7BvRlk1dVTsI9x8zA1D3z4O+RbjaFECGEEELIYaKxXR1/vB0Y8IyOGAophGqMnUsURcURykoDijQPlaNWGmd0j5CuNG54gWJNElA6FDm9P8YeoUjWEJLRRmgP930WpSQj2ax/RBa9FjjsIwuuwmxA1PTthFpUdVCzmGqR3Yapcs0eonOEzGYBeRnAwLYS4N2jcYLGWWpyDyilcSmw4PnVJvx3PbDiNfXvbKSCy2RS71NNo+qmJZvNuG5KCVz7sgFIYikrLbJzJgoUQoQQQggZszRphFC/B/h81+jMo+3dkfkyyqSzkejpA+T1O7PTAJtVQF6m9LrewLCEUD1CaQ4BGU5pbJgjFMGDtizAOnuB3r7IhaUshCItiwP0DttwfUImQUBFoCsUoSNUkAXAb4K/T+qZCeUINfd74BsSE9mWZByTmgnL0CO5ryltRAGppVDSIGjqAPJs2rS6fqU0zuxRt+/SrH8VqeAC1PvU69YHegBAQ6v056QcYIT1WRMOCiFCCCGEjFmaOvSv12wZnXlk8VBeAMhhWUYvqtoWondHfng/1A4MDhrjQLV2qeeRHSFAfditawZ8vtjmiqY0DlAXIZXnHY7+AREfbQMGvEDPkPMUnRCKPHwisDwu0h4heWFaOcSgqX8gqB/poEYcvbHahoXX2HCD5SvoefEYeKtyIw4x0M7n8wGpgvrGvT19cPv8AID+LtWh0vZiReoIAfrSyZfWAkt/4ceq90S4B0SlzLBojJXFARRChBBCCBnDNLbpHzLXbDa+NM7nE5XfgudlAtNKpPG+BsBrkDgB1P4gQC0xkoWQzxcs+mIlVHw2AKX8adAHHGqL7dy9UaTGAdEJoSt/K+KUG4Eb/pILj1faFo0QmhTFoqqVIYRQxI4QAHGoT8jrF9EuX+wQ2uhsX48NbV3A+k9TMHgwE4AQlVMjO0IAYPWqF7itU/3L1Nuubtc6YSlROE/atYS+c4+I/6wDvv1HEQcOqceMtf4ggEKIEEIIIWOYQHEwGn1CHT2A/Ev97DQ17WzQZ2zJ2nCOEGBcn5C2nDCUIwTE3ifkckfXg1KSpz5gDyeEPF4Rr34sjd/fqoqU1AjXwgECvssR7lt5iv7EkTpC6U7JMdQmxwWWx2mjs8VeSaTE6tTIwgsABLc659YOVe1qe5a0xOoI+SWjCX39wPub1O0UQoQQQgghh5FAIdTvAdbvNnaOwJ6aUs1D4WgtdJo9lOZWrBEKRoiuAY+Iz4b6qHIzAE2fviFrCcVXGhdewO44EHqxzph7hFrDH/fKOhE33B5w8YOROUKCIEix1tq1hAICE7SOkN8lqRGtsxKdI6Tes8Fedc4dnb3qHGGEUDTzlBeE3v7OBvWeTcodYw1CoBAihBBCyBimMUQJ15rNxs4RmLIWqYsRLdom9NFyhD7Yoia7nfcV6cFdRucIxRiYoBVCTgN7hDbvDb09GiGUlab2dw33Xf7uaRH9rSFK4yJd3ydbv6jqndv24WtrNmLbUDONHJ0NAP4h8aIVebE6Qu4Odc4B2baB/lq0ROUIhRFCq+kIEUIIIYQcGWRHSOs+rNlibGmcXggJuof32lFyhAJ7hICR+1oi4fVP1HN89ST9b/C1v/WvOhRjWMIo9Qht3hv6eqIRQoIgKH1C4YTQwVYRn++SyslEj1nZHs36PgVZehdma0cPPmhux727DgAADmlK5fwhREqsPUJtbSY4LOagY4xwhCblAObgUyvpfYC+B2usQCFECCGEkDGJx6smVs2tlEq9AOBLgxc6DXaE1NfDlXNFS5smzU12hEoiXPsmEkRRxOufSmOLGTj7OP3+yZPUcbTr+shEG5+dmao+kI+2IwSowrKrF+gJEdf9ykfySICvUz15VI5QFuBrcwZtrx1SiXKstX/AAgwGq4uoUuM0Qujf7wtwisFv1vYraYnGEbJYBBw3QxofN0N11rQwNY4QQgghBNJD919eEPH7p0XDYp8Dadb0BxVkqQ9iTR2A32/cnK2d6jhYCBk2TUCPkPTnJANL4/bUqQJn0Vwg3al3hLLSBGSmSuP9B2ObI9oeIUFQHba6FgRFTQPSvdy6L/T701Ki60vROmyb9wSnDr68Vn3t71I/QHSOkAB/Vwp635iDr6dVIss6tKbQkACSF1MVwwmUKAIgSvPUvyON7UB9bfA5xT4bZlcEvzcaRwgAXvytgBW3CHjtjwJmlAXvL8oO3pboUAgRQgghxHA+2gbc9ICIX64Q8eALozNHoyb9LD9LLRPyDgYv+hgPgevuFGQBSRbp9Wj1CGUNCRK7TVBEUbxhCa9/oo7PPyG0gJBdodqm2NL3oi2NA1Rh6XJLC6sGsq9BH8utJVZHCAAW3ySi5BIR7w41/Hf1irqeF60jhCgdIQDwVuXhK4OVKBtSNk39A+jyeOEaWjXX3xfa+onGEbJYBKz9i6C4e4H9QKJPgDhgwXknBL83GkcIAApzBPy/rwrIyxQwM0AIZacDyTaGJRBCCCGE6FawX/GqGPI3/fGiTYzLz9T3S8S6Dk4oAlPjTCZBaQwfDUdIEKA4M4D68N7QGp/Tpe0POv/E0MdMGRJCoghUxxCYIDtCZjMwZISMyEgO2+Y96tgWcM5ohZA26AKQItBf+Uj6Xt78TB9a4D0g3WTRL2DwUEbEAkUbYNDYBhTYpTf6Rf36PmFL1qJwhACgokjAW/cIOGZacD+Q1IMk4CtHBYuUaB0hLbMq9Ocbi0EJgMFCaPfu3bjuuuuwePFiXHTRRXjllVeUfStXrsSZZ56J008/HQ888IDuB+KOHTtw+eWXY+HChVi+fDkOHToU6vSEEEIIGSNoy9Z21wKf7jB+jiadIyTohJDWLYqXwB4hQH14b+/Wr50TD7IQynACZrP6oCkLIe8g0NIZ27lFUcTH26VxWQFCljYBwOQidbwvwj6h7VUiHnpRREePqAghR7I+kW44RhRCmqCEpSfr90UrhC5aJLleWhEgz6ktiwMAX3M6up4+Ed1Pn4ikfrvungxHge7voagIIQDY3K5d3yd+R0hGEARMmRQcviD2WZGbAVQWBb8nWkdIy6xy/WsKIQC33norFi5ciPfffx9//OMfcc8996Cmpgbr1q3D888/j5UrV+K5557DunXrFJHk8Xhwyy234LLLLsPq1asxe/Zs3HrrrUZeFiGEEEIOM80d+ofKJ94w3hHSip2CLKk3Q8ZIR6gtRMma0X1CHq+IhqEeIDn0QcaICG2PV1pjCZAWxwwnUqYUq9sjCUwYHBRxzk9F/OB+Ef/7qKiUsEXzkD1SHLk2KOGa8/T7oi6NyxOw71kTut4QYDLp5/xwq/RnulNN7fN3pcDfY4/q8wQK8kKNENqiWejU77LimGn69woCYAttFI1IeUFwaZy/z4qyAr1LJROPIzSzXP96LCbGAQYLocbGRpx77rkwmUyYMWMGysvLUVNTgzfeeAOXXHIJiouLkZOTg29+85t48803AQAbN26E3W7HRRddBJvNhuuvvx47d+6kK0QIIYSMYZo79a9XvQf0hkjpioemdvV8o1oa1yn9mZkq9WQAxi+qun63KlS+MlO/r1izUGWsfUKRprlpHaH9DSPfr0NtwMGhxUnX71Z7hKITQup4V7WI7/3Zj1+t8EMUpZJKWQjlZgCnzQesSep1RSuEZCwWQWnur2sG+gdE5e/MUWX6awKic2nyM9VxYztQkKy+eatWCPXZsCSgdycaJy2QiiIhqNxO7LOhLB/Iy4Qi/LRzxUplkV6wjcXEOACwGHmySy+9FG+88QauvfZa7N69G01NTZg9ezYefvhhLFmyRDlu2rRpeOihhwAAVVVVmDJlirLPbrejuLgYVVVVKCwsDJrD4/HA4/HoP4TFAqs1vHz2Dy0q5dcsLkUSC96jxIf3KLHh/Ul8Jto90pbGAVKz+3Pvi7jmPOPEkNYRys0Q4dN8tYfaxKj7acLdI7k0Lidd3ad1aWqaop8rkPc1TfqnzNNfg/a37QcOxTZXt0sdO+zh/x5qS6j2Noz89/WgRnA2tEBXGhfp33VtWZU2WOO0Y0TMKFXLAY+eCphNfkwr8mJ7jW1onti/+5I8yWFr7gC+rFPPUZYPdPdBl1QXzedJskiiuaNH6hHK12RNV2lSH8z9Vpw4S//elCjmCaQsP7g0zu+yorQCEAQRuen6vjqrJfbvThCAGSXA1v3S68Js6boT5eecKVD1hcFQIXTiiSfiN7/5DR577DEAwC9+8QtkZWWhr68PTqeap+5wONDXJ63A5Ha74XA4dOdxOBxwu0PHgzz55JNYsWKFbtuyZctw6aWXjnh9dXV1UX0ecvjhPUp8eI8SG96fxGei3KP65kIA+l9SPr/ahdNmtho2R82hfADSr7UHemvhc5sBSN3+++tcqKmJbS7tPfIOAp29UkNNanI/amqaAAA22AFItsG2PZ2omdUVdJ5oeOvTPABSl/zU3AbU1Khd+ykmGwBptdOtX3ajpqYjxBmGZ99BC+TvRhzsRU1NaMtMFAG7tQRujwlf1nhRUzN8jva23er3oH3ItgjqdzUSYr8AoDRo+9qN7Whr9UD+7EUZ3air68Ci2RnYXmNDhsMHsb8BNTWxPcxnOXIASM+gr37QBkCyiDLsXYDPBEBNrLCYBlBTE3l6RHZqITp6rDjU5ofYEfq7zjJbIHgPAlDVp80y8nceDqtogd+Vr9vm77MiNakdNTU9yE4tRFOH9G8y2eqP+2dRWV4Otu6Xvr8kfzNqatRn9yP9c66iIkReeAgME0KdnZ348Y9/jNtuuw2nnHIKDhw4gJtuugmTJ09GSkoKenvVPESXy4WUFMnLtNvtcLlcunO5XC7Y7aEjM6699lpceeWV+g8RgSNUV1eHkpKSiBUiObzwHiU+vEeJDe9P4jPR7lHn0P/aczPU3+j3DjhQVuYI95ao6Rpa1d6RDMycXoo+TflXd3/0c4W6R/vq1f3F+ckoK5NE0TGa4pQeTwbKyjJi+QgApP6dTUPuQ0kesGjBJGiro5I0H6O5Jw1lZWlRz9Gs+f1yQa4TZWXBC37KTCkGtlUBdS1JKC4ugzl4zU8F37bQ27My1e8qEtIdQJf+cRCd/VnQ5lDMm5aGkhInbryoHgtmpeK4GWZMrwwWUJEyvRx4/XNpvLNBraucOy1dCuL4QD02M80W1ecpyQf2HQT6BkyYVlgB7AgWBhWZKVgwR38v0xxJUc2jJb8AgFeE6DFDsEoR3aLLhnkzslBWloXSQmDn0GLDTrsp5nlkLjkdeOUTwG4Dzj8lD3mZY+/nnGFCqKGhAU6nE6eddhoAYMqUKTj22GOxadMmVFRUYN++fVi0aBEAYM+ePaisrAQAVFZW4qWXXlLO43a7UV9fr+wPxGq1Dit6hsNkMo2JmzKR4T1KfHiPEhven8RnItwjn09U1t4pKwD6BqTekbbuyEtWIqGpQyq/yc+SzutMAdIcfnS7pN6VWOfS3qOPd4gApM9y3AwBJpOkUMoK1O31zfF9rk17RLgHpHMtPhowm/XnKsoRkWwV0e8BDhyKbS7p/NIcTvvw55g8yY9tVZIbdrBNQFlB+J4VKRQj2JFxJkd3nc4Uf5AQqjooLfIqn7+ySIDJZILdJuLaJfH/OyrNV6993Rfq9soiARYzoP1cKVF+noJstTTs768mwWQzwW9St4leE8qyzcjJEGBNEuHxxjaPlhQ7UJTjR2+fFWarpHz9fVZUFEp/bws11xTPPDLfPFtEWT5QnAcUZOv/joyVn3OGXWFZWRlcLhc+/PBDiKKI6upqrF+/HlOmTMGSJUvwwgsvoKGhAa2trXjmmWdw3nlS7Mexxx4Lt9uNV199FR6PB48//jhmzpwZsj+IEEIIIYlPew8gtwjkZQDZQ7/0bo2vekzHgEdEx9CSLNpELDkwwaj47HVfqA/DJ89Tt2elqalb8YYlrNmijk89Olh0mEyC0rtTdSi2tYS0C5I67cM348trCQEjR2gfagt9LdGuhTPgCd5WdQioOqievyJEBHQ8hAu8KMsPTkGLNlhA+3fyN48D3p7g9X1K8wQIgj72PdrvLZCKQn2fkL/PhjKpslA/TxxBCTKCIOCUowVUFo29hVRlDBNCTqcTf/jDH/DII49g8eLF+P73v49LL70UJ510EhYtWoSLL74YV199NZYtW4aFCxfiwgsvBCA5PHfffTeeeeYZnHbaadi6dSvuuOMOoy6LEEIIIYcZbVBCXqa69k5bNwxbWFU7hzalS34A7ekzZn2ftUNOQZIFOP4odbsgCEqyWF1LfJ9rzWb1vafOD32MLIQGPLEl4rl0Qmj4YydPijxCuzHMtUT7oP39r0l/JlvV+PCqg9J/MhUG/448MBlOpqwASqKcTLRR09oodyB0rHVpvnSMVqDEsoaQlvICdSHYwRYnHD6bsjiv9priic4eTxgelnDiiaGXKr722mtx7bXXhtw3a9YsrFq1yshLIYQQQsgRQieEMlRHyOcDunqBjNSQb4sKbWN+fghHCJAEw5TiOOZoF7FnqLXjuBmA3aZ/uC3JA76slURGR4+69kw0eAdFfDS00GlxbuiFL4HAWOvo123ROkIjuQ56R0gEEP43/uGct2iF0P9cKQnLeVOA3zwh4o1PAfcAsHGPtD8nHUhNEeJO59MSSgjlZQIpyQIm5erniccRAkIsdOqyKfMXGewIDbxTCm9NNvzddswqFZQ4bqMdofFA4hfvEUIIIcQwevtE/OhBP+76h2iYOxOI3hESkJOhvtYuThoPDZqFRbWOUOBilvGg7RtZNCd4vxGLqlYfghLycNLs8GvIaMuP9scQKqZdR2hERyhAdA1HWCEU5QO93Sbg218VsGCGoBOD8ndjtBsESM6TNUm/rWyoXC4nXXIBZaJ1UAoDHKWgWOs+q1KaZ6QjVFEoABDg73ACPrPyeQC9OKMjJEEhRAghhEwg/v428MDzwP/+TcQHW0ZnjkC3Ri6NA4zrE9rwpSrijipTRYK2/CfeRVXX6vqDggVKqQFCqLtPHWu/p0Ama1wabd9MpOgcoREegkvyMBQWIPXphEMUxWEcodj7RiaH6DkJ55TFg8kk6NaDAoDyQnVfPA7KyXMlZ81hl8bB6/toHKEc40rWAgWj3B8E6NdrSotxIdrxBoUQIYQQMoGQSp0kPt4+OnNISWISUmmc+qBnlBDSXvtCjVsTWBoXD+uGoqEFQT+HTEme+rliFUK9Efbu6FyaGByhXk2/1EiOkMUiKKV3w32unj6pfC0U8ZRehRI9o+EIAcHlceVhhENKlMIu2Sbgy2cENLwg4MavC0FCyOq1IWMowdzIkjXt9QNAWb563eWFwKWnARlO4Lrzx27AgZEY2iNECCGEkMRGXtMHkGKbh+v/iJVmzRzasAQAaDNACA0OivhslzQuyQOKNYJEXxoX++fr6ROxea80nl0BZKYGn0f7EF3bFNtcOiGUEv795QWSIBNFfYBApLii6BECJLerplG6X339YkghMFzpYTy9Llr3S2a0kskChZBWOBRphFAsAsVkEpDuBGaWi0FhCbk2q1IGqRW5eZnxfc6SPMBslvrxAL0jJAgC/nW7AJ9PhNlMIQTQESKEEEImFFpHRn7QN5rA1Lhsg0vjtlWpD/Ynzdbv0/ZBxOMIbditRoAvmhv6GCN6hHo1pXGpw4iHZJugOBQj9e2EnCeK1Dggss8WLjEOiM/ZCOX+HDZHSDOP3hGKfY6pxYDg1guhIk0z0MnzgB9cDFy0CPjWubHPA0huXomm3K80RCAERZAKhRAhhBCSAPj9It7fJKKhZXQCDGS0jlDVQaCr1/j5tEIoJz3AEeqOfz5tWdxJs/UPdUaVxmndjmnFoR8cDRFCOkdo+GPlkrHWLqDbFd33qA1LiESkaNfYqW0KfcxoOUIpyUJQ2MDoCSH9vdWWlmmdxnh6aqxJAsrT9OsIVWSorwVBwF9+ZMLLvzfF7QgB+tLC8lH63sYLFEKEEEJIAvDA88DpPxKx4HoRff2jJ4a0QggAtuwzfg65NC4zVXoI1IUldIZ6R3R8vF39fgIdoaw0NQksHiHU5VLH6c7QxzhTBGWNltHuEQICAxNGbx4Ayho3QPjPpv1+A4VLJHMMh/Zh3mTSCzMjCS6NU8eXnyHtn1YCnPuV+OaZWWyBOCAlUIg+AZNzrCO8I3a+/zUBTjtw5Vn6IAYSDIUQIYQQkgB8uFV6uG9sB77YP3rzBJambd5j/ByyI5Q3FGudrVlfx4jSONkRstukdWe0CIKglMfFE5/d1auO0x3hj5MfpOtbENMaN9EIlMpC9aE2WiGkc4SiLI2T+p+CkXqwJI6drt8Xb9O/tm+mJA9Isox+j1B2ur5PqzhPQPVzAnY9LSDdGd/8R5UBg03SbwR8zWm6XiSjuXixgI7XBfzj13zMHwl+Q4QQQkgCoA0RkBfxNJq+flFZl0Vm815j3Sf3gIieob6XvAzpz2xdaVx85z/YKqK6URoff1ToB2TZnWjpBDze2D5fl6b0LJwjBKgP0t5BoCkG4RVNmpvWEYo2OU4WXCYTkByBGaErjQvXI6T5vMdO0++LVwhpwxEqR7G8SyuEykK4TiaTAJMpftEys1yA692ZcK2Zjt63Z4dczNVILKMkHMcbFEKEEEJIAqAVCHvrR6c0LrAsDjA+MEE7h+wIpSQLsA/1hsfrCOn7g0IfI68NI4qxl8dF6wgBsZXH9WjCEkZ0hHQLnUbZIzQkhBzJ4Rdt1RLJGkk6ITRdf854eoQAvegbrf4gAMhKE3DeUNnbVWePnng4qgwQ+2zwbC+G2JscMsSAHH4ohAghhJAEQCuERssRCiVCdtZILo5RBCbGych9QvHGZ2sdrBNmhn5w1S6SGWvvTmeEQiiSXprhiKo0TiOEZFcs2nki7d1Jd6rHjhSWkGQB5lTq98XrCH3lKMm9AoATZ4+uu/H63QIaXxbwo0tHb54ZpfrXxRRCCQGFECGEEHKEEUVRXxpXPzrzhHKEfD5ge5Vxc+iEUIY6loVQa5f0eWNF69Ro13nRok0Cq2+JcZ4IwhKkudRxvEIodYRkspx0Ncb5wKHo5pF7hCIVQoIgKOVxtU2h75kcn52fGXwv4hVCU0sEvPUnAU/8XIg7UnokBEFAftboii1niqAI2Um5gN3G0rVEgEKIEEIIOcL09AGDPvX13vr4xEI4tI6Q1l0wsjxOv5iq+rAn9wkN+oBuF2ImklIyIxwhnRCKuDRudMMSBEFQysSqG6MLZ5DniaZkTRZC/Z5gJ8/nE5V7XZAtpQPmZkivTSbAZkAo2lnHCbh2iTBqQQmHm/t+IGDhHODe74+PzzMeoBAihBBCjjCBD5kud3zRz+HQOkKnzVfHRvYkaYVHqNI4IL7AhEgcFG3ZUX0M4gRQnadkK2Czhn9w1aWrxekIReKiyELI4408Fc87KMLjlcbRxFrrk+P0+1q71AVn5ZS+uZOlPycXRdaHNNG4cJGAdQ+ZcOnp/G4SBQohQggh5AgTShjsHYXyuJZOVRTMm6I+jDV1hDo6Nj7YopljsrpdF6HdGfv5I3FQtA/w8ZbGDVcWBwCTNCVh8ZTGJVsjS/rSBgeMVB63YbeIf70n6vqdoilZK9WUGAaKPK0Ik4XQQzcL+OllwDO/5oM+GRtYjvQFEEIIIROdUEJoTx2w+Ghj59GWxs0qV8exxD6Hon9AxEfbpHFJHjClWN03Go5QuIf6wmxAEKTUuHhL44YriwMkt6ggS0Rje3xCKFKnprxAACCJzQOHgIVzQh9XfUjEyT8Q0e8B/ucKdXs0jpA2QruuWQrVkHtbDraq+2QhNL1UwJ9uoAgiYwc6QoQQQsgRJlSS2mhEaGtL46YWS2lfgHGO0MfbpX4SADhzgb48KiddHccToR2Jg5JkURdVjcUR8vvVPqaRhBCgOlCH2qQytGiQe54iFShaR6h6GEfo+TXqvXh3o7o9GkdI66z94R8iss4XcfoP/fD7RdRoSuVKR3FxUEJGEwohQggh5AgTzhEyGq0AyctUe3iMcoTe26SKgDOO0T8c6xZVNUAIjSQc5If4xvboF1Xt6ZPcJGDk0jjtXKIINEQpvKJ1hPSlceE/18vr1H27atTtsTpCh9okYfX+ZmBbFVDTqJ4/1EKkhIwFKIQIIYSQYXhutYhjvu3HM/8dnUVOAaCtK/jco9MjJP2ZmiKVdOUPCaGWrugSyMLxnsZ5OP0Y/T5taVxriM8bKZEKh3gWVY00MU4m1ghtn09EnxxrPUJ0tkx5gToO1yPU1C7qFp6V5wCiS43Tpu9p2d8AnSNUVhD6OEISHQohQgghZBh+9ZiIzXuBH/1FHJVIa0DvCMmLSO5rkB6UjUQWQrIokYWQzxdf3w4AdPaIWL9bGs8sBwpz9I6QXgjFPk+0QgiIvjwuWiEU66KqfQPqODVCgZKRKiBjyKUKt6jqqx+rjlYgTnvkZWzh0vL2NehT5ErpCJExCoUQIYQQMgyym9DaFRwhbBRaETK7QvrT44290T8Ug4MiOnqksbzeS36Wur85zj6hD7aoccpnHBu8P9uAsIRoHBTtoqrRfo/aRVujKY0D9E7JSPRGsCZSKOTyuNpm6b4G8vLa8AI62oVOtTHrMvsbRNQMibC8TC4OSsYuFEKEEEJIGDxeUZdSZuTCo1q0PTMnzlLHRvYJtfeoLoEihDTr/MTbJ7R6mP4gwBhHyKUp8RrJQdGvJRTdPFpHKMM58kO+tlytepi+HZk9dSJ2Vev/bkVaGqedz+cLdrt6+0RdOEIg0QguALjnBgGXngY8/j/q97CrBjg49AsCukFkLEMhRAghhIRBdlBkNu0Z3dI4sxk4emr4tVviQZsYp5TGZRm3lpC2p+mEWcH7U5IF2G3SOFb3KZI1hGS0pXF1US6qGm1pXDRr+3yxX8TMq6X/3t+sbo/FEQo133/XAwNDaXE2a/B7o+kRAoBjpgv41+0mXHe+WpL3+W7V/WNQAhnLUAgRQgghYWgPKOEabUcoK1Xv0mjFS7xoXRjZEcrLULfF6whpRUpGmHIyuYSsuhEx9VtF46DEs6iqTghFUBqXna4KmXB9OzIvfiDC55PG/9Eku0UnhFQBGzjfjmp1fNHC4PdG6whpkdeFkoUWQCFExjYUQoQQQsYkPp+I2qbRS3IDpHIyLaMmhIYEV3aaKlIAoKXTuM+nd4SkB2ltj1BTR3xzyWVrZjNgTQp9TGWR9Gdff2wiL5qeGnlRVSB6IdSt7RGKwBESBEFxaWqahk/g+2yXOt5ZrY6jCTEoHyZCu17jfp0yL/ic0fYIaZlcFLytrID9QWTsQiFECCFkTHLOT0WULRNxz7OjJ4YCHaGGFmPFCSD1IcmLamanq2v7AKPvCOl6hOIsjdOmuWkXUtVSqXmArzoY+xzyPMNhTVLjwaMNS+iMUggBat+OxwscbA19jCiK+Gyn+lobrGBUaZxW9J0YokQxLkdoUvA2OkJkLEMhRAghZMzR2SMqa9b8453DJ4QAYPMeY+fQBiUEOkLxJrlp0YqqUKlx8ZbGyY7QcI6DtqRrpF6aUOiF0MhOhHZRVW+IdLVwRFsaB+jFSbjyuL31+r4zbXVgagxhCQDw4ofADff6UTfkjsqiz5oEzJ0sOXRa4hFCkycFf+dcQ4iMZSiECCGEjDm0JWu7a41fbyfUPDJGl8dpo6Sz06X+GsvQw6uRjpDWyZLDErLT1HWLjHSEwlGpKa0abUcI0C+qGs6lCUW0YQlAZCLv0x3h3x+NQHHYBSVmva8fePhl4KyfSPdXdoQm5QAWixC0KGq0YQlaQjpCFEJkDEMhRAghZMyh/a36gCe2h+pIaO8OFlib9xorunRCKE0qK5MdGyOF0Ibd6rgwW/rTbFbnitd9cg2JlOEdIXVcdTD677FH0yMUiYOiDUyIpjwuFkeoPILkuE93hv/M0To1b9wt4LsXASlD3/eXtUBdk7pWlCyAtN9BLPNomRwghFJTwgdjEDIWoBAihBAy5giMtdYmZY3mPMAoOEK60jjJVVDESWds6WqB7KoW8cmQGzG7Qv9bfLmPpqkj9rk8XhGDQ0lokTpC8ZfGjXx8ab7q0tSMkObW0CLiyt8Cj72VGnVYAhDYtxP6e9T2BwUSrUApyRfw8E9M+Na56rb3NqljeR2l0gAhFE9YQmE2lAh0QFpDKFw/GCFjAQohQgghY47A3p0dB0ZpHo0Qyh4qJ9tTB/T0GecKBZbGAaoQ8nj1LkisPPmmer3fPl/QPbzKQsjjBbp6A98ZGVqBMlzpVbpTQGaqNK46DEJIK/i0wQShuH2liFXvAb9/Ngtb9knbkq1S6EIkjNQj1NcvYuv+8O+P1amZXqJen3ZRW9kR0i54Kgh6IRMtgiDokuMYlEDGOhRChBBCxhyBTs3O6lHqEdKIlEVz1HHtCA/V0RAYlgAYmxznHRTx97elcZIFuPJs/X59hHZsc7i0QmgEx0F2heqaIwsweHmtiNJL/Pj1Y370uqNbd0cbKlDTGH4uURTx9ufqazn4IZqyrzSHgKyh+xfK7dq0B8r6QUmW4P2xCqFpJepYDhABgOJcSSCV5KlCKSUZMJnic3DktYQA9geRsQ+FECGEkDFHYIjBaJXGyUJIEICpmgdAI9Pc2jR9SIojlG7cXG9+qibCXbgQyM3QPwjrIrRjTI6LxqmRhZDfH5mgvGOliLpm4Pf/0ItCIx2h/Q2hryXS/iAZWXiFEnnaoISzjwt+b8yOUKk61gZChHKE4ukPktE7QiyLI2MbCiFCCCFjjo4e/UPmaCXHyYIrMxUoyFIf+owVQupYdoS0YiVeR2hlQFlcIPmazxWzI9SvjkdyhCo04mSkkIv+ARHbqqSx3w98Wafui+ShPiddLQULF2kNAO9uDL090v4gGbk8zu8H6gPCGbbuV+/DxacE34do4rO1lOWHXsA2lBCKpz9IZv5U9drnVMZ/PkKOJCHMWUIIISSxCSyNk5PjppaEPj5WZEcoK1VfrtbcadwcoUrjtGsJxSuE5HCHdGdoJ+LwO0ICAEkUjBSY8MV+KCEMALC7JvJ5AKmnpSxfxO5ayfERRTFkc/97G0OL6FiFEAD89P9ENHWI+M01As46TsC+BnXfGccGvzfWWGuzWcCUSSJ2Vuu3y2EJ2tQ4IxyhS0+XfvFgSxJw7lfiPx8hRxI6QoQQQsYcoRY6DXwQjBefT0TnUHhAVlqAEOowzn3SCh25NM7IHqHuobCF3HTpoTkQfY9QbJ9L5wiNsNBpNBHaGwMWr23ViEZnhA6KXB7nHgj9Xfr9It7fHPq90ZfGqZ/9xQ+Bj7ZJggiQFlMFgEm5kktjs6rvs1mBJEvsZWbTA34BYDGrAjfDCcwsl8ZHT4l5CoUki4Df/j8TfvUtIe5+I0KONBRChBBCxhyhYq2N7hPqckkLcQJDQihD3WdUaZzLLWLTkGNTkqcmlGkdoebO2EWXKIpK6ly40isjPlcsYQnAyMlxG7+Mf90dbbJZqAjtrftUV05eX0kmHkdIZme1JJzlOaYWS05ViWah03idGm2fEAAU5aiiVxAEvHG3gCf/V8ADN1G4EKKFQogQQsiYIzAsATA+OU7rOo1WadzqTVJZHwCcpykzMqo0bsADeAelcTghVKBxhA61xTZPNKVxpfmAaejpY6TSuEBHSCYaB6VM49KECkzQJq3dvAxIMqt/j4wQQoM+4I1P1ddThhYlNbJkbXqp/rsoztXvLysQcM15AjJSKYQI0UIhRAghZMzRoQkxMJulsdGOkE4IpQW4NAY5Qq9/oj50n3+i+pBqVGlcj0aghHWEMlVhcijGHiF9adzwx1qTBOVBfbiwhP4BEdurQu+LRjjoI7SD97+nWXtnyYnAnIoB5XW6MzrhMKMMWHy0FI89d7K6/aUP1TmmTJJjrdX98QqhacX614FCiBASGgohQgghYw5ZpORnqnG+X9ZKpWBG0aFZXDQrTXqAl9eVMUIIiaKI1z+RxjarvoE+wyn1ecQ7l3Yx1rQw7obZLCiukDZ+ORqiXehULo9r7wY6e0Lfs21V+qAELdEIB32EdvBccipdVhowoxRYME0jhKJ0hARBwPsPCGh9VcCd/08VUf9drx4jx7Ab6wjpX1MIERIZFEKEEELGFN5BUXnwzkwFJuVIY/eAvlclXvSlcdJDrezUGFEat60KqG+RxqfN14cMCIKAnKHghLgcIY0QSh3mYbto6DtsbI8thtylWeg0kohm7Vo04VyhcGVxQJRCaJgeoQGPqIi/yUXSelFLju9THLKTZkc+j4wgCEhzCJhdoW7r96jjKYoQUu93vEIoO11QgjYAoDiPJXCERAKFECGEkDFFZ4BTM1qx1lohlJkq/SnP1e2SSrfi4bWP1fH5JwQ/uMpztXTF7nR1u9TxcOvUFA2FBPj9sTlQ0TpCkyepn1cbK61FG5SgTViLdA6ZwmzVXQvsEaprVgMx5P6euRUebFwBbHpMwHFHxS4oygpClwnKIlDrCMW6hpAWbXkcHSFCIoNCiBBCyJgiUKDoY60NnEcTyJA1tL6PNmEt3lhrfX9Q8H65J2nAo3d2okHnCKWEf6iXHSEAOBhDYEI0PUKAGhgAAPvDOUJfSn+aTMCpR+v3RSOEzGZBER3VjcCBgyLe2yjC7xd1YQ3aXqK5k4H50+JzVUwmAbPK9duKclTnT1uyF20JXihmlKljrcgihISHQogQQsiYQhudnZUK5GWoD6zxihMt7d2qUMkKcISA+Nwn76CIT3dK4xmlQEVR8EO3EclxkfQIAUBRjjp/LH1COiEUZWncvvpgt8vnE7H9gDSeXhK8Tk60DoosOrp6gcrLRJx5s4jHX5eEkUyoexAv2vI4QC8AZ5YDXzsZyEkHrl0S/9zfvVBAbobUa3bcjLhPR8iEwHKkL4AQQgiJhkBHaDTS3ALnURwhg9ynjh6pDA0AJk8KfUygEAp33HBEkhoHqKVxQGxCqFcjuCIrjVPHoRyhpg419nvyJGBSrgBAFUzR9tRo+4RkXvlIxJxK9XWo6Ot4mV2pv+6pmvI1QRDw4u8E+P2iIQuTHj9TQOPL4CKnhEQBHSFCCCFjCq0jlJkqjF6PUMjSOPUhM14hJCP3HwVixFy6HqEIwhIA4GBbDGEJUTpC6U41DCJUj1BdszouyQvueYlaCBUEb9u6T+8IlYc4Jl6CHaFgkWKkcKEIIiQ6KIQIIYSMKToCBIrepTEuPnu4sARAci1iJRIhZHRp3LCOkFYIxeIIaZynlAiEEKCmpzW0AO6A4Am9EBLiFkIVhcECoa5Z7UMCQrtG8RIkhIpDH0cIOTJQCBFCCBlTtAeICCMEQ8h5hoRQagqQZNHHZwPxia5ohVCsTldPn3qNw/cIqeNIhdCj/xEx/Uo//vmOqDhCKcmRuxLaPiFtaAEA1Ac4QpOChFB0zsfFp0iiZPIk/XpNe+qkPwuzgWSb8W5KQbbqJgL60jhCyJGHQogQQsiYoqNHH2KgTXIzskdIXlBV+yCbb1AZnk4IOUM/gGvFSX1zbKIrUkcoOw1IGuoajjQ17hcrROypA37zhLquUyRlcTLa4IB99fp9dZrPW5yrrhUlE60jlOYQsO0pE/Y8I+DyM4K/79HoDwKkPqD5U6VxkkUv/gghRx4KIUIIIWOKwJK1dKf6EG9Uj5Aoiso8WRrHxsiwBJlwjpC2ZyXQMYmU7giFkMkkoHAoMKGhZeTzdvao3091o9qLFI1A0a4lFBiYUKe5hpI8ya3J0SwYGusCpCaTgHlTgrePRn+QzO+vF3D6McCDPxTgHCbCnBBy+GFqHCGEkDFFoIgQBAG5GSIOthpXGld1UE0tK8hSt2c4pcU5B32jL4TyswC7DXAPxC6EInWEAMmBqm2SvkOPV4Q1KfxDe62mdG3Qp36eaByh4AhtdT5tj5DcH1ScB7R2SeNYhRAAzKqQ1iaSU/uA0XOEACnN7b37KYAISUToCBFCCBlTyCVrQHCIQXOH5ObEy5rN6njRXPUh1mQSlN6d+Erj1GsMJ4QEQVCciurG2D6Xbh2hkYSQJkK7sX34Y2ubQm93RrG+jzY4YP9B4J31Il77WPqMco9QXiZgs0rfv7Y8Ltp1hLTYbULQukTlIcIUCCHjHwohQgghYwq5JCslWX1IlvuEBn1AZ2/o90XDmi2q6Dj1aP0+I0RXKDEXCtmp6PcATSOIk1DIQijJon5X4YgmMKGmMfT2aByh3AzV2Xn7c+Dsn4i44OciXv1IVPqUtGlx2rEjDkcIAI6eqn89mo4QISRxoRAihBAyppDLsLS9O0YuqiqKIj7YIo3tNmDBDP1+WXR5B4GuGEVXYAR4OOLtE5J7hCJxUIpyVKE0khCqbQotAKMpWRMEIWSc9KOviErZWkmeuv2rJ0nXl+EEFkyPfJ5QzJusF4Wj2SNECElcKIQIIYSMKeT47MwwIQbx9gkdOKT2qCycg6BeGSMWcI2kRwjQr38TixCSHaGRyuIAfenZiI5QmNK4aByhwDll/rteHQcKod3/ELDvWQHpYZL2IkUbmGAyAaWjsIYQISTxoRAihBAyZnAPiBjwSGOdEMpQH4zjdYS0/UGnHh38wK1bVDWGcjVAFUI2q9SzEg5tyVZ1mHK0QHZVi1j5pgiXW1SEUGSOkDo+2DZ8yV+4HqFoS9ZCJbjJIRWAtJiqlumlArLT4+/nOVozb3Guuk4UIWRiwdQ4QgghYwZtdLa2pMyIxUdldP1B84P3F2QJAKRjIl18NBBZCGU6hz+uXCOEDhzSJ6uFoq9fxKk/FNHcAWzeIyXOATEIoZFK45pDb482ze27Fwp481MRRTlSydsz7+j3F+eGfl+8FGQLOGaaiE17gnvACCETBwohQgghYwatAzMapXGiKCqOkN0GHDcj+BhtGVU4QTASocr7QlGhE0Ijn3fjl6oj9san6vaIhJAmNW44IeTximH3R1saV5IvYNPjkrh79l0Rz7yjd6K0pXFG8+afBHy0DTj7uNGbgxCS2LA0jhBCyJjhbU3/yJxK1R3RL3Qae3y2tj/opNnB/UEAUKp5OK9rjn4u7yDgckvjkYRQZiqQ5lCvbSTW71bH+xrUsXyO4Uh3SuIPABqGEUINLUC4sDynPfYSs5NmB28bTSGUlynga6cIcMRxzYSQsQ2FECGEkDHDf9apT+AXLVK3G5Uat18jHkK5QUCAIxSmV2Y4Ig1KAKRkNdkVqm0CfL7hhdfnu0LvT42gZE0QBCW8oK45fDT4cC5YtI6QltJ8fXmeIOhfE0KI0VAIEUIIGRMcbBXx2U5pPHcyUFmkcYQy1OPi6RHqdqnjjDDJZIXZgNksjUdbCAFqtPOgb3inBtA7QloiXYC0skj6s6cPaOsKfYx2DaHA649nfR9BEHSuUH5maEeOEEKMgkKIEELImOCVj9Sx1g0CpAdwuawrnh6hHrc6DldOZjbrnZNoiVYIRdon1NYloupg6H2RCqFI5tKKv4Vz9PuiDUsI5KTZqvAZzbI4QggBKIQIIYSMEV5eq5ZqLV2kdwoEQVD6hOIpjZPjpoHhxYNcHtfaJSW1RUOHZhHWyIRQZGsJbfgy/L40R2TOitZlCyeqajSLqZ48V3/eeErjAH2fEIUQIWS0oRAihBCS8HS7RKzeJI1L8oD504KPkfuEWrtG7qUJP486Hq6vpkQXmBDdHDpHKIKFQXVrCR0K/7k+3xX+HNGWxgFAVQSO0Mlz9fvidYQWTAfOXACkJAPfPp9lcYSQ0YXx2YQQQhKed9arC20uPVlygAKR+4REEWjr1ifJRUpPnyo0hnWEAoTQ9NLI5+iMsUcIGN4RWh8mKAGILCwBCCyNC71ukSyEUpKBo6fq98XTIwRIZYfv3CvA4xXZH0QIGXXoCBFCCEl4ajQuxImzwocYyNTHuL6PtjRuuMjp0nz1GqINTIinR2h/mHI1URSVoIR0pz5FD4jREQoxlyiKyr0ozQPsNkEnOON1hGQoggghhwMKIUIIIQlPl0t1OzKcoY8pK1AfnmtiSHMD9GEJw4kHbWlcbdPo9gg5UwTkZ0ljbby3loYWoHFosdkF04Epk/T7I1lHSLoeAelD328oIdTWBbgHpHHZkFOljROPt0eIEEIOJxRChBBCEh5t7056mId6bQmZNuI51nkiCUsA4uwRikAIAcDUYunPxnZ9+Z7M5r3q+LgZwJRi/f5IHSEAqJTXLWoGBgf1c2nFUdnQd/CVo6Q/s9ODnShCCElkKIQIIYQkPF1aIRTOEdKIk5ooXRqZaFPjgOEXGNXi8Up9TrEIIa3DE8oV0vYOTS8VMLlIX1oWlRAaKo/z+YJF3p56dTytRJrjjm8LuO8HAt6+R4DNypI2QsjYgWEJhBBCEp4uTTlZWpiH+jKNI1QdoyMkCyFBGL7MK8MpBQO43JH1CLV1iTjm/wHt3SUwa34FGbkjJACQxN3e+uCQAm15Xlk+kGTW7480LAHQ9yRVHQIqNH1De+vVeWSXKitNwI8ujfz8hBCSKFAIEUIISXi6NU5NOEdoUg5gNktORqylcbIQctoBkym8uyEIAkrzROyqkVwTURRDJtnJvPqxLJhUFWRNUheBHQmtI7QvhCOk7YkqKwg+b6Q9QoC8lpAkeAJT6vbUqeOpAeV3hBAy1mBpHCGETFAOHTyEVX/9F7q6uo70pYyI7AgJQvhkMotFwKQcaRxrWIIsuCIpJZPL49wDUojAcOysDi7Vy0wNHQMeCq3o0LoyMrLwEwSgOFcvnEwmKeo6UvTJcfq59tar59QeRwghYxEKIUIImaA8c9yzSPtNBh4+89EjfSkjohUowzk1cp9QezfQGyJUYCR6ohBC+uS44Y/dcSB4W2YYZysU2vCDffXB+2XhV5gtRU9np6vOmdMeueAC9KVxz70PHPVNP879qR8DHlERQmX5YD8QIWTMQyFECCETkAH3AGb2zwIAFO0rQnd39xG+ouGRHaFwiXEy2j6haF0hURTROxSfHa4PSYt2LaGRkuN2Vgdv210b+bWlaiK09wYIIfeAiOYOaSwLQUEQcP4J0viUeZHPI59D1k37G6TrfPtzYMWraqretJLozkkIIYkIhRAhhExAOho6lXGOKRdvPfvWkbuYCJBT40bqddElx0XZJ+RyA+KQiRRRaZzGERpOdPX2iSHDG46bEd31yeVuje16t6s2oD9I5vH/EbDmQQH/vj0658ZmFULGYD/8n+CgBEIIGctQCBFCyASk62Cn7vWGf208MhcSAd5BUVnEcyRHqLww9kVVuyOMzpbRio4Dh8KX4e2qUccXL+zFKfMAixn48TeiEyha8bFfs56PVvBphWCyTcDiowUk26IvYQslhLSulhydTQghYxmmxhFCyASku7FH97prUzcGBgZgs0UYY3YY0S5yGp0jJAKI/IE90jWEZPQBBuGP0wqIGaUe3LEc6PcIcNijExNTJqlpblv3AWu3ivjKzMDEOGMEyiWLpb6mZKsUvhCYVEdHiBAyHqAQIoSQCUhvU6/udbm/HGvWrME555xzhK4oPFohFE2PULRrCWmFUCQ9QkU5UhpbX78+VjqQHZrEuKlFXmmNoihFEKAXH9/6vXTONAfwzbPU7VohGA//c6WAmeXAsdOBf74L3Pq43vGiECKEjAdYGkcIIRMQV4tL93qqZSr+89J/jtDVDE9XFI5QpH07oYjWERIEQREEBw5JJXyh0DpCUyd5o7soDdpIbJluF/CPd9TXWiEYD3abgEtPFzB5koAzjtHvS7IYJ7gIIeRIYrgQWrlyJc4//3yccsopuOKKK9DT06NsP/PMM3H66afjgQcegCiq/8PYsWMHLr/8cixcuBDLly/HoUOHwp2eEEKIAfS39etepwgOrP/Pet3P5kShKwpHKNmmpqtFG5agdZ5SUyJzbOT0tEEfUB3mf11ydHZqClCY5YvuojRMCePCaK97NATKcUfp126qLJLWbCKEkLGOoUJo1apV+Pjjj/HYY4/hgw8+wB133AGr1Yp169bh+eefx8qVK/Hcc89h3bp1eOWVVwAAHo8Ht9xyCy677DKsXr0as2fPxq233mrkZRFCCAlgoMMTtC2jPRP9/f0hjj6y6EvjRn4Al8XAoTZgwBO5sOtxq+ORnCeZkfqEXG41MW5mmRpLHQtpI3z2rDTAGaGAi4Yki4DFR6uvp7EsjhAyTjBMCPl8Pjz55JP41a9+hcLCQgiCgClTpsBms+GNN97AJZdcguLiYuTk5OCb3/wm3nzzTQDAxo0bYbfbcdFFF8Fms+H666/Hzp076QoRQsgoMtgVXKI13TJDcfETiWhK4wB9edhI6/t4B0Xc+ZSIR/4jRl0aBwDTilXhEapPSJsYN7MisnMOx7fPl/6cUwmcf6J+32iWq51xrPo52R9ECBkvGBaW0NzcjIGBAbz77rtYtWoVnE4nrrjiClxyySU4cOAAlixZohw7bdo0PPTQQwCAqqoqTJkyRdlnt9tRXFyMqqoqFBYWBs3j8Xjg8eh/k2mxWGC1WsNem9/v1/1JEg/eo8SH9yixifb+DHYHl2hNs0xHV1cXcnJyDL22eOnS5Dqkpojw+4d3ebR9QgcOiagsCn/8/70E/PpxaXzhQnW7I3nkeQB9udqe+uD3bD+gjo8qjf/f0AM3AZefCZwwE/jXauD1T9R9pfmj9+/zksXAbU8CvW7gwkXj9+cAf84lPrxHiU2i3B+TKTKvx1Ah1Nvbi/r6erzyyitoaGjADTfcgPLycvT19cHpdCrHOhwO9PVJv3pzu91wOPS/4nM4HHC73QjFk08+iRUrVui2LVu2DJdeeumI11hXN0ysD0kIeI8SH96jxCbS++PpVH+h5BbcsIt2VJgr8OWuL2GxJFagaHV9GoBMAMCAqxk1NaH//yCTbnMCyAYAfL6tDVNyesMe+6/38gBIDTDvb/ZDLpRw9zShpmbkMsFk0QRAahT6Yq8bNTV6C+rTLzIApAMAclJaAMT/b6gyC2huBGZPUucGgEx7N2pqOuI693B88CcTXP0CitJ9qKkZ+fixDH/OJT68R4nNkb4/FRWRWfCG/d9OXnti+fLlSE5OxuTJk7FkyRJ89NFHSElJQW+v+j8il8uFlBSp7sBut8Pl0qcXuVwu2O12hOLaa6/FlVdeqf8QEThCdXV1KCkpiVghksML71Hiw3uU2ER7f6xe9Wdmt6ML9l47zIIZdrMdZWVlo3mpUWNKUsdTKvIw0uWdNB/A36Vxc082ysqyQx7ncgMb96qve/rU721KRf6I8wBAqQhkpgIdPUBda/B319StjhcdmwsMGvdvqAzAcTOA9bul17OnpqGsLC3u805k+HMu8eE9SmzG2v0xTAiVlZUhKSkp5L6Kigrs27cPixYtAgDs2bMHlZWVAIDKykq89NJLyrFutxv19fXK/kCsVuuwomc4TCbTmLgpExneo8SH9yixifT+mPrNytiX4QOGflfV19qXcPe3x62WWGSmCjCZhg8EmFUuQl54dFdN+BKJD78Q4fGGLn9Ld448j8zUYj8+3yX1Iw14Bdht6vv21UvXbk0CygtMqK839t/QBQtFrN8tfYYZZZFfMxke/pxLfHiPEpuxcn8Mu0K73Y4zzjgDjz/+ODweD6qrq/Hmm29i4cKFWLJkCV544QU0NDSgtbUVzzzzDM477zwAwLHHHgu3241XX30VHo8Hjz/+OGbOnBmyP4gQQogxJHmkX1z1+nthzVB/ueRqdYV7y2HlnmdFnPczP6Zf6UenprItkoVO87OAjKFq7F3DlHC9/Xn4HqBIwxIANUJbFIH9Dep2v1/EvqHXk4sAszn4vfHyo2XAZWcAyy8Azj3e+PMTQsh4xtBC8P/5n//BHXfcgTPPPBPp6en4f//v/2HBggUAgL179+Lqq6+G3+/H0qVLceGFFwKQHJ67774bv/3tb3HXXXdh5syZuOOOO4y8LEIIIQHIpXEuuJCSoZYiu9uH7785XKzbJuKtz6SxRSMg0p2hj9ciCAKOKhPxyQ6gvgXo6RNDrgv09ufhzxGdEBIgO1A3/1XEgEfEzy4XMG8K0D/UijVaSWupKQKe/Q1dIEIIiQVDhVBqair+9Kc/hdx37bXX4tprrw25b9asWVi1apWRl0IIISQMoigi2Z8MAOgX3MhIV/tK+jsGjtRl6ZhTCfxnnTTeWa1uj8QRAoCZ5cAnO6Tx7hppUVAt1YfEkHHXMqmh21RDohU5726Q/jxwSMST/6sKlGklIIQQkmAkfvEeIYQQQ/G7/bAM/R6s3zIAW7pN2efpCl5o9Ugwd3Kwy2FNApJtkbkfR5Wpx4Uqj9O6QYFrE9ltgMUSucsSaoHR+hbg3Q1q6d3UYro2hBCSaFAIEULIBMPbqS6mOpjkRXJWsrqvO3ih1SPBnBB5OekRLKYqc5QmvG1XTXAv0Hub1G3XnqffF01ZHCC5PXZb8Pan/6uOuQgpIYQkHhRChBAywXC3qX1Ag7ZB2LPUOrDBnuCFVo8EUyYByQEBoYHOzXDohRDQ1C7i3Q1qSty2/dI+mxX4xul6tyZaIeRMEfD0LwVctwT443fVcx1sVY9haRwhhCQeFEKEEDLB6DrYpYxFux+OHFVh+F2JsVq7xSJgVsB6eNE4QmUFqkuz8UtgwfUizvqxiN8+JYUZ7B1Kc5tRqhdNQOR9SFq+fqqAx39uwvILACGgCi4lGSjKif6chBBCRhcKIUIImWB0N/UoY9EBOHM1UWzu8JHSh5vA8rhoHCGTScD0Umlc3yL9BwD/XgPsqQN8Q8bXrHIgI1VAbob63mgdIS0ZqQJmleu3TS2WkuwIIYQkFhRChBAywXA1qWsFmVNNSCtQU+OE/sT530JgYEI0jhAAzCwL3vZlrZomBwAzy6U5tD088QghADhptv41+4MIISQxSZz/4xFCCDks9LX2KWNLmgWOHPXJ3zSQOP9bCHSEohVC2uQ4LU//V3W95PI7bQ9PvEJo4Rz9vBRChBCSmCTO//EIIYQcFtxt/crYmmFFUmqS8triNXR5ubiYO1n/OprSOCC490dm3RfqWC5j08Zbx9IjpCXQEZIWXCWEEJJoUAgRQsgEw9OprhVky7LB7DDDDykkIWkwKdzbDjt5mQLyMtXX0TpCC+eoyXPXnBe832YFKouksZGO0ORJ0PUc0REihJDEhEKIEEImGINdg8rYnmOHYBIwIAwAAGy+5HBvOyJoXaE0R3TOSkG2gI2PCXjrHgGP3SLAYdfvn1EKmM3SOU89Gkgfyow467j4HBxBEHDqfGmcZAnvTBFCCDmyJE4NBCGEEB0tLS3YsGEDzjzzTCQlGefU+HvUiGxnvvT07zEPwD5oRzKS4ff7YTIlxu/J5lYC726QxtE6QoAUhjCzXBovmC7igy3qPm26W06GgP3PAu3dwFQDStnu+o4Au1XEWccJyEpjaRwhhCQiifF/OkIIITq8Xi9OPfVULFmyBDfeeKOh5xZdalhA6pAQ8loklyhFSEFvb6+h88XDGceqIiKwZyhajpuhfy0nxslkpwuGiCAAqCwS8NQvTfjm2RRBhBCSqFAIEUJIAvLvf/8bO3fuBAA8+uijhp5b6JMezr2iB2k5UnS2zyoJIbtgR3dnt6HzxcN5JwDP3S7gpd8JOGFWfOc6boZelAQu2EoIIWRiwdI4QghJMERRxD333DNq5zcPSD/6e0UX0tPTAQB+m1ou19XYheLSxOjwFwQBy04z5lyBjlDgwqeEEEImFnSECCEkwVi9ejW2bN6ivM7NzTX0/EkeWQj1Ii1NcoREu1ou19PcY+h8iUJ5IZAj6T5dYhwhhJCJCYUQIYQkEP5BPw5cV4N/ZvwLU83TAAA9PcYJE9EnwuqzAQBcYq/iCAkpatlYb4vLsPkSCUEQcMsVAqxJwM3L1MQ4QgghExMKIUIISSB2v/4linonIc2UhouTvw4A6O/vh9frNeT83m71PC7RhZQUadEcs8OsbO9rG59CCAB+drmA3rcF/OE7/N8fIYRMdPh/AkIISSAadjco42OSjoFp6Me0Ua6Qdg0hj8UDQZBcEUuqVgi5DZkrUUmy0AkihBBCIUQIIQmFq1l1Y5ymVEy3TAdgnBDyaoSQN8mjjJPSrMp4oGPAkLkIIYSQRIZCiBBCEoj+1n7d62OTjgNgpBBSS+N8yT5lbMvQCKEuDwghhJDxDoUQIYQkEJ52vQg5NmkBAKC725i1ffrbVaEl2tXtyVnJyniwx5h+JEIIISSRoRAihJAEQtvDAwDTLNOQLqQb5gj1NPUqY8Ghbk/JSlHGvl4fCCGEkPEOhRAhhCQQ/h4xaNsxSccaJoRcLaoQMjnV/wU4clRV5HcFXwMhhBAy3qAQIoSQBEJwBSeaHZu0wDAh1KsJY9CWw6XmOdWD+gyZihBCCEloKIQIISSBMPdLMdY+0QfRJDkzpeZSw3qE+lpUlePMU12gtPw0ZSwMMF6aEELI+IdCiBBCEogkj5Te1iv2AENtOw7BaZgj1N+hhiWk5qcq4/TCdGVsGjCDEEIIGe9QCBFCSAKRPCiVq7nggilV+hHtNFAIadcRypiUoc6bngy/6AcAJHkthsxFCCGEJDIUQoQQkiD4vX7YhzKt3eZ+WNIlZyZFSEF3lzGlcf4eNREuuyRbGQsmAW7BDQBI8lmD3kcIIYSMNyiECCEkQdC6NR7rAJLSkwAAJsGE/o4BYyYZahHyiB7kFuXqdg0I0hw2v82YuQghhJAEhkKIEEIShL5mNchg0DYIW5YqSLydxixyahoKY3CJLuTk5Oj2ec3SYq7JYnLQ+wghhJDxBoUQIYQkCB11HcpYdIiwZ6uCJHCh1VhJ8kouU0ghlCSJLbtgR39ff9B7CSGEkPEEhRAhhCQIXQe7lLHgBJKz7cprbW9PrIh+EVa/1P/jFtxITtY7Pz6rOkdXYxcIIYSQ8QyFECGEJAg9jWoynCnNjKSMJHVniIVWo2WwdxCmoR/73iRP0H5/sl8ZdzUZE85ACCGEJCoUQoQQkiC4mtQeIWtWEpLS1RhrwR2/EBpoVwMXtKJHQWMQ9TQbE9dNCCGEJCoUQoQQEiXNzc3YtGmT4eftb1P7cpKzk3WOkBxyEA/t9WoPkrxYqxaTQxVbvc29cc9HCCGEJDIUQoQQEgWdnZ2YP38+jj32WPztb38z9NyedrVcLSXXrsRnA4DVmwS/P4SLEwXt9e3K2JwaLKxsGWpKXUdDR9B+QgghZDxBIUQIIVFw77334uDBgwCA3/3ud4ae26dJhnMWOGHRlMY5BSd6e+NzabTiRlt2p8yR61SPPUghRAghZHxDIUQIIRHStbUbn/zzU+X14KAxkdYy/h5RGacXpetK45xCKnp64uvb6WlS32/LCl4rKL0wXRl3N7FHiBBCyPiGQogQQiKga2s3Pjr9E/yw/WbMsswGAEybNs3QOYQ+qUfHL/qRUZSBpAzVtXEIDnR3x5fk5mpxKeOUHHvQ/qxJWcq4r9UVtJ8QQggZT1AIEUJIBHR8JpWKmQQTzrWdBwBxl6oFYnZLwsclupCZnYmkNI0jZHLG7Qi5NWEMafmpQftzy3KVcX8HF1QlhBAyvqEQIoSQCBhoUYMMvpJ0AixIiluYBJLkkYRPj9iDjIwMCGYBg1ap/M4hxC+EPB1qfHZ6UXrQ/vQCddtgt7Flf4QQQkiiQSFECCER0LinURk7TA7MT5pvqCMk+kUk+6TUtl6xB+npkijxJ/sASGEJ8ZbGDfb4lHFmUWbQfm1Knd8lBu0nhBBCxhMUQoQQEgGtVa2614usJxvqCA12D0IY+pHsNrthMkljcWi9H6fgRE93fPOJGnGTU5YTtD8pVe1JShpMgsvFPiFCCCHjFwohQgiJAF+Hfg2fE5JORH9vP0TRGOfE0+FVx0lqGZ7JKQUoJAlJ6G2Pz4ES3OqP/JzS3KD9ljRVCKUIDiUmnBBCCBmPUAgRQkgEmHoE3WunyYm55nlwu92GnF+7mOpgstqfY05TFz7ta41vLsuAJHQGMQhralLQflOyCX6TJPgcQgqFECGEkHENhRAhhERAktsatG1B0nGGlce5mtQyNDFFdZm0Lk1/W3xCyOaTPkO/0A9BEIL2C4IAv00SQikUQoQQQsY5FEKEEDICvj4fknySg3LQp4qDbFO2YYEJXQe7lLHgVEWKLcumjD0dHsTKwMAA7EMNRx7LQNjjTA5p7hTBgYaGhpjnI4QQQhIdCiFCCBkBT5sqQA5BFUJOIdUwR6inUT2POV390ZycrQohb1fskdatra1wCA4AgM/mC3uc7EA5BAcONtARIoQQMn6hECKEkBEYaFYdlN7kXvjMkpBINVAI9TX3KeOkLLV/JyUnRRn7esILmJFoqW+BWZD6jUR7+IAHW4YkvCyCBU31TTHPRwghhCQ6FEKEEDIC7qZ+ZexP9cM3tLZPqslpWGmcu0WdIzk7WRk7853KWOyNPaGutUaN/zY5w//oT8lWhVd7fXvM8xFCCCGJDoUQIYSMQHu1KghMGQL8KVKggJGlcdr+H3uOXRk78xzKWOiL/Ud27e46ZWzRJNEFkpypirDOQ50xz0cIIYQkOhRChJBxgd/vx7p16/DRRx8Zfu7O2k5lbMuxQRgyaZKFZPS0GyOEvJp1hLJKMpWxNVNNqzP1x/YjWxRFvLrqFeV1fmVB2GO1KXW9LS7D1kkihBBCEg0KIULImGZwcBD33nsvpk2bhpNPPhmLFi3CG2+8YegcvQ1q+VtKgR2mNPVHp7akLR583Wr/T/7kfGWclK72C8nrAEXL2rVr0bBPDT4onVEa9lhLqjqHxWNBZ2dnTHMSQgghiQ6FECFkTPPwww/jJz/5Cfbv369s++yzzwydw92sip3U4lSY09XSsv42Y4SQ4JJiq3v9vSicVKhsT8pQhZB1MHgto0i4//77kS6kq+dMDy+otI5QipDCCG1CCCHjFgohQsiYZtOmTUHbWltbQxwZO4Ntamx1ZnmWrlxNW9IWD7Lb0yv2oLBQFUIWjWhJ9iVHXapWVVWFl19+GSXmEmWbozIl7PFJGiHkEBxcVJUQQsi4hUKIEDKmqa+vD9pmtBASuyTx4RU9yC3NQXKOurbPYEfsa/so5xdF2AalkIJeuJCWlqbsMyebMShIczjgjLpUbdWqVRBFEaXmMmWbc4Yz7PHa0rgUIYVCiBBCyLiFQogQMqapq6sL2tbS0mLoHGaXJA46/V3Iz8/Xpbppe3tiZbBnEGZI5XZemweCIOj2e61SopxDcKC5uTmqc+/btw8AFCFkcZqRXJQc9nh9aZwD7e2M0CaEEDI+oRAihIxZRFFUhND06dNhs0lOjZGOkOgXYR2QSuE6xQ7k5eXBka9GWsOAZYRcTS5l7Lf7g/b7k4fiuk1ONDVFt8hpbW0tkpGMArOUFOec7gwSWlq0QshhohAihBAyfqEQIoSMWTo6OtDX1wcAKCkpQU5ODgBjhZC3wwvT0I/KTn8ncnJykFqQquwX+sKLikhp2q+KGyE1xPmc0rYUIQXNh6JzhGpqalCs6Q8ariwOCCiNQwqFECGEkHELhRAhZMyi7Q8qKSlBbm4uAEkIGbX+zUCLutBpv7UfFosFaUWqEDK5wy9OGikt1apwS8pMCtqvXQC1tTZykef3+1FXV4cybX/Q9OGFUBIdIUIIIRMECiFCyJhF2x+kdYS8Xi+6u7sNmcPTMqCMB1Ok0AJrlhqWYPXEFmmtpb1WFRv2nOD+HZsmpa6joTPi87a0tGBgYEAXlJA6wzHMO4LjsymECCGEjFcohAghY5ZwQggwrjyuu75HfZEmuUxWjWtj9doC3xL9HAdV0ebMD3Zs7NlqOEN3Y+QCr6amBgBQalYXUB2xNM5BIUQIIWRiQCFECBmzaEvjiouLldI4wDgh1FHToYzNWZJIMNlN8EJaPyh5MHwCW6T0tfQp4/RJ6UH7teLI1Rx5OkNtbS2AyBPjAEAwC7A4pVI8B1PjCCGEjGMohAghY5bhHCGjIrS76lQHJjlXcn8EQYDb7AYAOMTwi5NGSn+bWn6XXZYdtD+9UBVH7rb+iM9bU1MTVWKcjCVNcrwYn00IIWQ8QyFECBmzHI7SuL5GNdraUaT21/RbJEHiFFIxMDAQ9L5o8HWpaxHlVeQG7deWxnk7vRGft7a2Vp8YN0JQgozcJ5QipKCrqwuDg/EvGksIIYQkGhRChJAxiyyEUlNTkZaWNipCSJsal1GiOjNe61BpnJCM7tY4gxk01W75kwuCdlvS1b4dX09kC7iKoojBrT58w36Zsm2k/iBlvqEIbbtghwkmdHZ2RvQ+QgghZCxBIUQIGZOIoqj0CJWUSK6HtkfIqNI4X4cqPLLL1bI1X7LqknQe7IprDku/KnRCpcYlpavhDKZ+EzweT9Axgez/cxXO33YBTrIuVLalzUod5h2a+ZgcRwghZAJAIUQIGZO0tbWhv18qT5OF0Gg4QkKv1FPT6+9FbpEqtPwpfmWsTX2LFlEUleS5fqEfpqTgH8tJGkfIITgj+mzN76hC0AsvJl1ehOyTsyK6Jn2EttQnNG/ePBx//PH47ne/G9E5CCGEkETHMvIhhBCSeAT2BwGjI4QsfZIb0yV2YlbWDHWHU12w1dXsCnxbxLS3t8MpSCVrA0mhe40sGkfIITjQ3NyMoqKiYc/rbpDCHLr8nXh85gq8+9d3I74muTROmi8Fhw4dwhdffAEAMJn4+zNCCCHjA/4fjRAyJtEKoeLiYgBAdrZaumaEEPIN+GEdlBYz7fJ3IStLdVRMqeqPz77mvqD3RsrBgweRKkgla3576P4frSPkFJxoamoa9pyiT8RAs1Q+1+xvRmFlYVTXFOgIbdu2TXldWloa6i2EEELImINCiBAyJtGuISQ7QlarFenpUqCBET1C3ja1F6dT7ERmZqby2pxuVsb9rZFHWgfSWN0IizAkPJyho621wsQpONHc3DzsOQeaB4AhTdXmb4tavCTpHCEHtmzZoryWv2tCCCFkrEMhRAgZk4QqjQPU8jgjHKGBVlUIuUwuJCerQQbWTKsy9rSPHF4Qjub9qqixZJhDHmOymCAmS6V4DpNjRCHUf1AVZq3+1qiFkCVACG3dulV5TUeIEELIeIFCiBAyJhlJCHV0dMS9/o1HI4Q8yXqxY8uxKWNvZ+zztNd1KOPk7ODEOBnTkFvkiMAR6j+o9hq1+ltRVlYW1TUlZak9SWlCGqqqqpTXdIQIIYSMFyiECCFjEm2fTGGh2gOjjdCON/ZZK4R8Dr3Y0cZc+7v9iJXO+k5l7MhPCXucJU0SJ3JYQijkFL3+Q6oj1BaDI2TLV0VelkmfNEchRAghZLxAIUQIGZO0tbUBACwWC9LS0pTt2uS4ePuEeg9p0uDS9PsceQ5lLPaKiJXG/Y3KOKcsJ+xxtixJnCQLyWhtbNPtE0URF198MebMmYO//e1vcZfGJRdohZAUQDHdPANFpiIU5xdHdS5CCCEkUaEQIoSMCn6/H+vWrTNsYdNAZCGUlZUFQVBDBoyM0O6uV9cHsmTqVxtwFjiVseAKHXIwEqIooqlKdXeGE0J2TdlcT6N+3aJ169bhP//5D3w+Hx5++GG4NUIoKT8JTqcT0WArUOeSHaHfpv4OKzKewJcX743qXIQQQkiiQiFECBkV7rjjDpx88sk49thj0dHRMfIbokQWQtrIbMBYIdTXqMZiJ2Un6fal5qTCK0qlc2Z3bEuyHThwABa3GpCQlJkU9lhrhhrO0Neqj+v+v//7P2W8c+dOdFZ1Ka8LZxZEfV2WVDPMKdJ1ZZmy4BAccJgkB8xebI/6fIQQQkgiQiFECBkV3n1XWsCzrq4OP//5zw0998DAAFwuqWwtUAhpe4TidaP6m1VnxV6gFwBpaWnoEXsBAJaB2ITQhg0b4BxaQwjQhxQEol1LqL+9H6IoleM1NjbihRdeUPYNDg6i80AnAKDX34Npc6ZFfV2CIMA2VB6XJWQjz5Sn7LOXUAgRQggZH1AIEUJGherqamX8t7/9DR999JFh55bdIGB0HSFvuxqQkFqoLy9zOp3oFXsAAFavFZHi8/lQVVUFv9+PjRs3osCsOja2PFvY91nSVZGU5LWit1cSYY899hi8Xq/uWFOn9KO91d+KWbNmRXxtWuQ+IafJiWKzGpCQXBw+2Y4QQggZS4yKEPriiy9w3HHHYeXKlcq2lStX4swzz8Tpp5+OBx54QPltJgDs2LEDl19+ORYuXIjly5fj0KFDo3FZhJDDhMfjwcGDB3XbvvOd7wQ9sMfK4RJC/k4pDa7L34WsHH16WmpqKnr8khix+q3weyNLjvvGN76ByZMn48c//jE2bNiACnMFAMBkNyGlLLzbonWEHIIDjY2NGBwcxKOPPqo7Lk1IQxIk0RSPENImx82wzFDGLI0jhBAyXjBcCPn9ftx7772YOXOmsm3dunV4/vnnsXLlSjz33HNYt24dXnnlFQDSA9Mtt9yCyy67DKtXr8bs2bNx6623Gn1ZhJDDSF1dne6XHYD0C48PPvjAkPMPJ4SMLI0z9UghCF1iJzIzM3X7LBYL3Ca1V8fbObLIE0VRKWN74IEHsOWTLSg0FwEA0malQjCFD12Q47MBwCk4cfDgQXz22Weor68HACxevBgAkGNShWCbvw1HHXXUiNcVCpsmOW66RT2HvYSOECGEkPGB4ULoxRdfxOzZs1FRUaFse+ONN3DJJZeguLgYOTk5+OY3v4k333wTALBx40bY7XZcdNFFsNlsuP7667Fz5066QoSMYbRlcRkZGSG3x8PhcIR8fT6YvFJgQJe/C1lZWUHHeJLUdYa8HSMLoe5ufdpbvjdfGafOSg08XEdShuoIOQUn6urqdAudXnTRRZhcMhnZGiHkTfXoosWjQRuhPcU8WRnbWRpHCCFknBBbh28Yurq68Oyzz+LJJ5/Evffeq2w/cOAAlixZoryeNm0aHnroIQBAVVUVpkyZouyz2+0oLi5GVVWVbpFEGY/HA49Hv8K7xWKB1Rq+Rt/v9+v+JIkH71HiE809OnDggDL+yle+grfffhsA0NDQYMg91jo9WVlZunOmpaXBYrFgcHAQzc3NMc/X36IGJXSJXcjIyAg6l+AUgCFt09fSh5Qp4RdEBYKFWYW5UhmnznQOe63mNDVdziE4gly3yrem4L6+B9GYov4SyT7JHvPnt+apP1OTBHVsK7Tx32mM8Odc4sN7lPjwHiU2iXJ/TKbIvB5DhdBDDz2Eyy+/POg3kH19fbp1LBwOB/r6pJISt9sNh8OhO97hcMDtdoec48knn8SKFSt025YtW4ZLL710xOurq6uL6HOQIwfvUeITyT364osvlPFRRx2lCKE9e/agpqYm7mvYv3+/Mvb7/UHnzM7ORlNTEw4dOhTzfO4dqhDq9HfC7XYHnUtwQhFCOzfsQlnR8AuX7tixQ/e63Kw6565s17DX2u9Sr8chOLFr1y7lfzTZQjaSNkhiZZJZXfA0uTA55s/vEvqCtpmyTKhr5L/ReOHPucSH9yjx4T1KbI70/dFWpg2HYUJo9+7d2LFjB/7nf/4naF9KSoqScAQALpcLKSnSb07tdrsSg6vdb7eHbsi99tprceWVV+q2ReII1dXVoaSkJGKFSA4vvEeJTzT3SLtu0Lnnnov7778fANDT04OysrK4r8Xn8ynjGTNmBJ2zoKAATU1NaG9vR2lpqW7B1Uhp2dOCatQCkByh2bNnIz09XXdMakEqIGdC9GLEz7Znzx7d60qNIzTltMlISgsfn90nuHFg6HocggO1ndWKEJpimRryPVO/MjXm77vX60It6nXbUitSDbl/ExX+nEt8eI8SH96jxGas3R/DhNCmTZtQW1urlMD19vbCbDajvr4eFRUV2LdvHxYtWgRAehiorJQeACorK/HSSy8p53G73aivr1f2B2K1WocVPcNhMpnGxE2ZyPAeJT6R3KPa2lplfMIJJ0AQBIiiiMbGRkPub3t7uzLOzc0NOmdenrTujdfrRU9Pj65PKVK8bWp0dje6kZGRESSoUgvUvp7Ohs4RP1tnZ6cyPu7Y41BeJf3Gyl5qhy0jfHQ2ANgy1Z97TlOqrsxwunV6yPdMO2FazN934LpJgNQfxH+f8cOfc4kP71Hiw3uU2IyV+2OYELr44otx9tlnK6///Oc/o6SkBFdddRW2bt2KP/7xjzjrrLNgs9nwzDPPKK7OscceC7fbjVdffRXnnHMOHn/8ccycOTNkfxAhZGwghyJkZ2cjPT0d+fn5aGxsNCwEZbiwBEAVQgDQ3NwckxAaaFV7EQdTvCFdpYxJ6nl7GntGPGd7ezvKzOU4IelEXHjOBbA/IomNtBGCEgDAkmoBBAAi4BQcqK+vV5yxWSmzAZ/++HZ/OxYef8KI5w0/nxlIBqBW5P3/9u40sKkybxv4lTRtuqR7aUsXukGhgKxls2WRVWBYhVGR0UGh84jOuKCjo8PDooMjIs/rDC5YHXBhUASVTaiAAoqiFCoglKUtUGgLpZRSurdJ3g8xd0+aNE3SpFuu3xdPTnLunHigzcX/vv+HN1MlIqIOxW5ByN3dHe7u9d2ElEolPD094e3tjeTkZJw/fx4PPvggNBoNpk+fjqlTpwLQVXhWrlyJl156Cf/85z/Rs2dPLF++3F6nRUQtrLa2VrR0jo6OBgB07twZV69exdWrV6HRaJr9r0TSIGSqm5u0hXZhYSHi4+Otfo+aG/VBSOZjempdUHQQaqCrHFUWmV7XKFVcXIwXVH9HhEsEtGvrGx1491KZOeq3c5DLoPBRoO5WHbxkKhQWFornYqCrLLn4ynFoxPco21yOsj63Mdf3/ibHbfT9ZDK4BbuhJrf+/4NHODvGERFRx2HXZglSS5cuNXg8b948zJs3z+Rre/XqhU8++cRRp0JELUg6ZUsahDIyMlBXV4eioiKDio0t9EHIx8cHrq7G62qk49t6L6HqwmqxLfczHYRCY0OQizzd62/WmHyN1K1rt9DfJREAINPWj9lU62w9V1/X34JQfYOZIHkQvNS6IOWe4I4l7/8vzj13Dt26mV43ZA1VhBeKpUGIFSEiIupA2v7kPSJqV6T3CtIvrA8LCxP78vPzGx5iNX0QMjUtDjCeGmeJC+9cRPoDx1B+QdctrTy/vomLW6DpdYnh8eFiW3O76VahlQVVJvdbMjUOAFx9df92pZIEoa4u9YHHPcEdMpkMCQkJUCia/+9c0puqAryZKhERdSwMQkRkV9IgJK0I6TV3nZBGoxFd6RoLQg2nxjWl+lo1Mv9+FoW7r+P03zKhrlKj9Jhuzc8tzS14Bpu+P5BvWH0XOXlF0z9O6wqNb7qq6qGCZ4z5+w/pKX4LQq4yNyihCyndFPXT/twTzDdcsJYypEEQimBFiIiIOg6HTY0jIuckvW+NPghJK0LNDUIlJSX198+xoCJkydS4iksVwG9Ldoq+uYH8LQXQVuh2/FR7GAGBxuuQAEDuJke1rBpKrRKutW7QarVmW3Vr65vdoduSOAT094d3L2/I5Ja195a21/aSeaFaW41ukoqQR0/7VmzcJRUhhcpFBDEiIqKOgBUhIrIrU1PjpBWh5k6Na6pjHGD91Liqgvr1QFq1Fpl/Pyse/1jzA/z9/Rs9tkahW0PjBS+DczNFXuoitlVRKgQOD4RbgOW3A3D1rw9CvnJdNaqroisAwC3IFYoQ+wYV6dQ49wgPm+7HRERE1FYxCBGRXTUVhJpbEbIkCFk7Na7qarXB47rS3zrBaSuRUXvMZGc6PbWHrm+1t0x3bx9zlBWSYNHZ+mls7pKubUHyTugkD4av3A8A4NPHx+5BxV0yNY7rg4iIqKNhECIiu7p8+TIAwNfXF76+uqqFPZslWBKEVCqVaOdvydS46kaaGByp+Rm1qDUbhOQqXfhwk7nhSs6VRl+n1WrhVV3f5MC9s/XBwiOi/phgeTCiXaLFY587LGu4YA2vrl6QKXSfz9KGDkRERO0FJ3wTkV1dvXoVgGEVKCQkBDKZDFqttkUqQjKZDMHBwcjNzbV6apzUj7U/ADB9ryI9V39X4LdsV5B1tdHXlZeXw19WP07DRgSWkDYrCJYHQ3eHVR3PGE+of7unkb24d3ZHv9Q+KD1eipjHo+06NhERUWtjRYiI7Ka8vBzl5bq20yEhIWK/q6srgoKCALTM1DigfnpcUVGRaK7QmKqrkorQbz8VNXINjtQcAQCza4Q8AuurNNcvNl59Ki4uRqBMd76VrpWQu1n/41daEeokD0aovP7/sUeUYzq6dZ4aiu6L4+Hmb/laJiIiovaAQYjIyeTm5uLtt98WlRt7klZfpEEIqJ8eV1BQAK1Wa/N7WBqE9A0TNBoNiouLG30dUF8RUngrEDUvEgBwPvosKqG7p5C5ipAqRCW2b1652fh5F91AgFw3TrWX6QpUU6RrhDrJOyHEJVQ85s1OiYiIrMMgRORkZs6ciYULF2Lu3Ll2H/vatWtiW9q5DaifKldbW9tkdzVzrA1CgPmGCVqtFtW/BaHbilKE/a0zxl0YjW9C94nXmAtCfhF+Yvt2we1GX1d8sRgKmW42stpH3ejrzHFxd4FbsK4yE+wSjBC5LgjJXGRwD7fvPYSIiIg6OgYhIidSV1eHY8eOAQD27dtnEFzsQTpeYxUhoHnT46ydGgeYD0J1pXVQV+iCydnCs1i0aBEU3gqcPHkSAKBQKMxOjfPpXH9T1YqiSqPn3377baSkpOBixkWxT9Z4rmqSx29VoQBZIMJcdOHSPdwdcgV/nBMREVmDvzmJnEhhYaHBtLRdu3bZdXxzQche9xKypSJkrnOctFFCkaYImzZtwnfffSduDDt69Gi4uro2djjcJPf2qblpOOUtMzMTCxcuRGpqKjas+a/Y79rJ9j41+ilwcpkcnjJdFzqPLmxtTUREZC0GISIn0rASs3PnTruOb2kQsrQiVFNTg9/97ncYNGgQsrKyANQHIVdXV6hUqkaPtXRqXJWkdfYNzQ2Ul5fjT3/6k9g3c+ZMs+covcmpotpVNIsAgIMHD4pt98r6sGJL62w9acMEPc8unjaPR0RE5KwYhIicSMMA8vXXX6OmpsZu41vSLAGwvCK0f/9+7Ny5E+np6Rg4cCBKSkpEpSYwMNDsDUQtnRpXLakIFWt0IevMmTMAdG24p0+fbvYcXX3rg5BKpjL4f3z48GHdOJAhUF5fvVJ1aTzANcU9wrgpgqM6xhEREXVkvI8QkRNpGEBKS0vx/fffY/To0XYZ35JmCYDlFaG8vDyxXVpaiqFDh6KkpAQAkJSUZPbYpqbGVVyqQMGXV6Gtq58qeENj2MQhOTnZKNA15OpX/2NUJVMhPz8fXbt2BQCkH07HP7xfQaxLHDSob+HtF+NndkxzTFeEGISIiIisxSBE5ERMBZCdO3c6JAjZY2pcUVGRweOzZ88CAHx8fLB69Wqzx5qbGnd+ZRayVuVAq9ZClVBfnbmhMXy/pqbFAYZT47xl3iJslpSUwDPbC/28+xsdE9St8bVNTfEwVRFiECIiIrIap8YROZHGgpC96IOQSqWCp6fhupXQ0Pp73lg6Na6xJgf/+te/0KVLF7PHmpsa5xXnBa1aVwkqyywT+29oDO83ZFEQ8pFMjZOrxGc7cuQIEl0TTZ9bTCeT+y3BihAREZF9MAgRORFpEIqNjQWgq7KYW0NjDX0QMjWdTKlUii5vllaETAWhe+65Bw8++GCTx3p4eMDb2xsAjG4eGzotBJ4xhuFBrVWj3LVMTLkbM2ZMk2EL0N3DB79lPpWkInT4x8NIdB1s9PoKbYVRSLSGa4ArXDxdxGO5mwzKUN5DiIiIyFoMQkRORB9AZDIZxo0bJ/afP3++2WNXV1eL9TsN1wfp6Rsm5OfnG7Txbow0CB05cgSff/45Nm7caLZJglRkZCQA4PLlywbvJ1fIEfuXGIPXlmhLEBoWiq1bt+Kzzz7Dpk2bLHoPAFD8tk7IW1ZfETq7/yxCXHSBsDy4DFfVuv/36YojFo9rikwmg3u4pANdpAdkcsv+fxAREVE9BiEiJ6L/kh4cHIyEhASxX9+aujmkoaWxBgP6dUI1NTW4efOmVWP269cPM2bMMHtPn4b0FZ2qqiqDsQoLC/HSN8tRoawQ+25obiAsLAyBgYGYNWsWAgIsv+upe6CuIqOSeSM/77eQd7w+nHSf1x2P3XoUfy99AXtC0ywetzHS6XGcFkdERGQbBiEiJ6HRaMTUtc6dO4vOZoB9KkLmGiXoWdswQR9eAgICoFBY39tFOrUtNzcXAHD06FEkJibivQ/ew4bij8Tzxb8FIVt4huumurnIXFCWV4bs7Gz0rO0lno+dFo35j8/HKfmvePhPD9v0HlL6m6oCbJ1NRERkKwYhIidRVFSEuro6AMZByB4VIUuCkLX3EtIHIWnjA2tERUWJ7dzcXGRkZCA5ORmXL18GAOyq/gpXZbpA9mPNDzYHIQ/JVLW6a2r8dPAn9FL0BgBU+1TDK94L//73v1FWVobHHnvMpvdo7P08IxmEiIiIbMH22UROQlqB6dy5M2JiYiCXy6HRaFosCFlTEaqqqkJZma6jm61BSFoRunTpEnbt2oWqqiqxrxrVWHjjf+Aj98V1TSHGhY+16X2ka3a8q71x7svzGCIbBgBQDnITa5qsmdZnjt8gP7HtP9TfLmMSERE5G1aEiJyENHiEhYXBzc1NVEzOnz9vUfMCc8zdTFX6vnpNVYSka3rsVRHKzMwUj4cMGQJAF4auawqNzs8a0nv7dHLphMIj9V34oiY23XnOWoEjAjDgw34Y+N/+CGAQIiIisgmDEJGTkAYPfWWmW7duAIDS0lKjm5day94VIXsEoYZrhM6cOQMAiIiIwN133230eluDkLukeUEneTA8bte3x45IjLBpTHNkMhlCJ4cgZILpwElERERNYxAichINp8YBsGvDBOm9iNpKEAoLC4Ncrvsxd/ToUdy4cQMA0KNHD9x5550mX28LD4Mg1Amh8vrP6RVt+z2DiIiIyHEYhIicRMOpcUB9RQhofsMEaytCLTE1ztXVFeHh4QB0a4T0unfvjiFDhhjdj0j/WmspQ5TQynVTC4Plwejsovuc1e7VUHhzKSYREVFbxCBE5CQcXRHSByGlUgkfHx+Tr3F3d4e/v7/R+ZhijyAEGE6P0+vRowd8fX3Ru3dvsU+lUsHb29um95Ar5JAH6n6chrmEIVAeCACQmc6DRERE1AYwCBE5CWnwCA0NBeCYilBwcLBRpUVKH8IKCgrMNmiwVxCSNkzQ69GjBwAYTI+zdVqcnr5znKfMS+zz7mpbsCIiIiLHYxAichL6qWiBgYFwc3MDAERHR4s1NNZUhNRqNVJTU7FhwwZoNBpUVlaKZguNTYvT0weOyspK3Lp1q9HXOboiBNg3CPlEG4ee0D6hzRqTiIiIHIeT14mcgFarFRUh6Rd+pVKJLl264OLFi8jKyoJWqzVbzdH74osvkJKSAgD45JNPoNFoRHUnNjbW7LENGyb4+fmJx5WVlXjllVfQpUsXh1WEvLy8xFqg4cOHQyaTQavVGlTHbOHVxcton1+8b7PGJCIiIsdhECJyAjdv3kRNTQ0AwyAC6KbHXbx4Ebdu3UJRUZFFoSM9PV1s79ixQ2x7eXnh+eefN3tsw3sJJSQkiMdPP/003nnnHQC6NTt6QUFBTZ5TYxpWhHr06CHCXkxMDNasWYMDBw40ed5NkXaO0/OMYcc4IiKitopT44icwJUrV8R2w85otjRMyM3NNdrn4eGBnTt3on///maPbayF9unTp5Gamioel5WVAQB8fHygVCotOi9TGgah7t27GzxeuHAhPv300yYrWU1xZxAiIiJqVxiEiJyANAhFRkYaPCcNQhcuXLBoPGkQGjZsGOLi4rBt2zaMHDmyyWMbC0LPPfcc1Gq10eubMy0OMF0RcgSPCA+Dx1p3LdwCXR3yXkRERNR8nBpH5AQuX74stiMiIgyei46OFtvWBqFOnTrhhx9+sOpcGk6NA4Bvv/3WYIqdVHODkI+PD/z8/FBSUgLAkUHIsCLkFeNp0XorIiIiah2sCBE5AWlFqGEQiomJEdsXL15scqy6ujrk5eUBMN2RrSnSipB+nDfeeEPs++Mf/2jw+uYGIcCwYYKjgpDCRwEXlYt47NPV9L2UiIiIqG1gECJqI5YsWYLBgwfjp59+svvY5qbGWVsRys/Ph0ajAWBbEIqMjBQtu3NycgAAp06dAqBrkJCammqwjskeQSgxMRGArulCc7vDNUYmkxlMj/OM9jDzaiIiImptDEJEbcDVq1exfPlyHDlyBHPnzhUd3uzF3NQ4f39/+Prq2jxbUhGSrg+yJQi5ubmJCk1WVhbq6urE+8bFxUGhUGDOnDni9fYIQq+88gpeffVVfPXVV3B3N25qYC8e4fVje8ayUQIREVFbxiBE1AZkZWUZbOtbSNuLviKkUqlE6JHSV4Vyc3NNNiyQkoYqW4IQUN+g4datWzh27Bjq6uoA6IIQAMyfPx8eHrqKyogRI2x6D6lOnTrhr3/9KwYNGtTsscxxj5QEoWgGISIioraMQYioDWhYiVm2bJlY3N9cWq1WBKGIiAiTC/j164Sk638aIw1CDafZWUo6PW337t1iWx+E4uPjceLECaSnp2PSpEk2vUdriLgvHAqVC3z6+iBgqH9rnw4RERGZwSBE1AY0XJtTXFyMFStW2GXskpISlJeXAzCeFqdnzTqh5k6NAwxbdqelpZnc37VrVwwcONCm8VuL/yA/jDl7F5L2DYXcjT9eiYiI2jL+piZqA0ytzVm/fr1dxjbXKEFP2jmuJYKQtCJ0+PBhsa2vCLVnLu4ubJtNRETUDjAIEbUB0iDUr18/AMD169dRXFzc7LHNNUrQs6aFtn48V1dXhISE2HRO0sqPvgMd0DGCEBEREbUPDEJEbYC+CuPr64vBgweL/efPn2/22ObuIaRny9Q4aRtsa8XExBgd6+rqavOaIyIiIiJrMQgRtbK6ujpRZYmJiTGYNmbvINRY0JAGIVMVoX379iEpKQmvv/66aOJg67Q4AFAqlUbHx8TEwMXFpZEjiIiIiOxL0donQOTs8vLyRPvo6OhoxMfHi+fOnTvX7PEtmRrn7e2NwMBA3Lhxw6gidPz4cUybNg3l5eUG63maE4QA3TohaejitDgiIiJqSawIEbUyaRhwdEWosSCkf29AF8z0N3S9du0apkyZIrrOSTV3Gpt0nRDAIEREREQti0GIqJVJKzAxMTGIjY0V62fsURHSByEvLy/4+fk1+jr99DiNRiOqSA8//LDYdnNzM3i9PSpCUg2DEREREZEjMQgRtTJpRSg6OhpKpVKEkvPnz0Or1do8tlarFUGmsZup6jXsHFdTU4Ndu3YBAEJCQnDs2DGD10dFRdl8XgArQkRERNS6GISILJSTk4PU1FTcvHnTruM2rAgB9dWS27dv49q1azaPfevWLTGtrampbA07x+Xl5YkQNnz4cCQkJGDLli2IiorCqFGjMHr0aJvPCzCuCDEIERERUUtiswQiC+Tn52PgwIEoKSnBkSNH8O6779pt7IYVIQCIj49HWloaAF1VKDQ01OpxCwoK8MUXX4jH5tYHAUBsbKzYzsnJMajY6KfB9e/fH9nZ2Xbp7qZvoa3RaCCTyQwqUkRERESOxooQkQWeeOIJ0Tb6wIEDdh1bXxEKCgqCSqUCYFgtsWadkFarxfbt2zFp0iSEh4fjscceE89ZG4T09wsCDNcDmZteZw2lUomEhAQAQPfu3eHu7m6XcYmIiIgswYoQURN27NiBzZs3i8c5OTmoq6uDQtH8vz41NTXIy8sDYDg1TdpC25rOcatWrcJf//pXo/1eXl6YMWOG2WOjo6Mhk8mg1WqNglBz1wM1JjU1FampqZg/f75DxiciIiJqDIMQkRmVlZV4/PHHDfbV1dUhNzfXoIJiq8uXL0Oj0QAwbFZga0VI39wA0FVx5syZg6FDhyIpKQlBQUFmj3Vzc0NkZCRyc3ORnZ3daEXInoYNG4Zhw4Y5ZGwiIiIicxiEiMzYs2cPLl26BACiWgLoqjT2CELZ2dliWxqEoqKi4OrqitraWqsqQjk5OQAAPz8/5OTkWL2WJy4uDrm5uSguLsaJEyfEfkcFISIiIqLWwjVCRGYcOnRIbE+cOFFsZ2Vl2WX8U6dOiW39ehkAcHFxEV3UsrKyRNXInJqaGtEqOy4uzqaGBtJwl56eDgDw8PBAYGCg1WMRERERtWUMQkRmSIPQQw89JLatqdKYc/r0abHds2dPg+f064SqqqpEwDEnNzdXBCZbq1XSFtZqtRqArhpkrwYJRERERG0FgxBRI6qrq0VVJC4uDklJSeI5R1eEAKBHjx5iOzMzs8mx9NPiANuDkKnjOC2OiIiIOiIGIaJGHD16FNXV1QCApKQkhIWFwcPDA4B9gpBWqxUVoS5dusDb29vgeWkwaqkgZOqmpgxCRERE1BExCBE14ocffhDbSUlJkMlk4iaj+hbazZGfn49bt24BAHr16mX0vLVBSH8/IsC+FSFHtc4mIiIiak0MQkSNkK4P0k+L07e1rq2ttWjdjjnm1gcBrTM1LiAgAH5+fgb7WBEiIiKijohBiMgErVYrgpCfn5+ozugrQkDzGyZI1weZqgj5+voiLCwMgC406Vt3N0YfhFxcXBAZGWnzeTUMUQxCRERE1BExCFG7p1arxRQze8nKysL169cB6G76KZfr/qpIb3Ta3HVC0iBkqiIk3V9cXCzOR0qr1eL27dvQarUiCHXp0gWurq42n1fDdUIMQkRERNQRMQhRu1ZWVoY+ffogODgYaWlpdht3//79YlvaLc6eFaGmpsYBTa8TeuKJJ+Dj44NHHnkEJSUlAGyfFqfX8PiIiIhmjUdERETUFjEIUbv20Ucf4fTp06ipqcFHH31kt3E3btwotsePHy+27VUR0mq1oiIUGRlp1DFOz1wQys3NxZo1awAA69atE/ubG4SkFaHQ0FAolcpmjUdERETUFila+wSIbKXVavH222+Lx2fPnrV6jKysLKSlpSEuLg4DBw5Ep06dkJeXJypC3bp1Q2Jioni9voV2ZWVlsypCBQUFZjvG6ZkLQh988IHJdUP2rAhxWhwRERF1VKwIUbv1448/4uTJk+LxuXPnmmwoIKXVajFu3Dg8/vjjmDhxIoKDg5GSkoINGzaIcR544AHIZDJxjEwmE1WhnJwc1NTUWPReb731FlQqFZ599lkAlq0PAhoPQhqNxqAKJNXcINS9e3exLZ0KSERERNSRMAhRuyWtBgFAaWkprl27ZvHxxcXFuHjxosG+1NRULF68WDx+4IEHjI674447AOhaaJ85c8ai9/p//+//oby8HKtWrUJaWhq2bdsmnjNXEQoODkZAQAAAwzVF+/fvF/cN0neW02tuEIqIiMDLL7+MUaNG4fnnn2/WWERERERtFYMQtUtFRUXYtGmT0X5rpsdJ7wPUrVs30RlOX+UZPHiwyYpI3759xfYvv/xi0XtJp9E99NBDYm2PUqnE2LFjGz1OJpOJqlBeXh5KS0sBAP/5z3/Ea1avXo1Ro0YBANzd3REfH2/ROZnz4osv4ttvvxWhj4iIiKijYRCidmndunUisAQHB4v9586ds3gMaRB64IEHsHLlSoPnTVWDAKBfv35i+/jx402+z+3btw0eS6tWK1asaHIdjnR63JkzZ1BWVoYtW7YA0N0Adfr06fj000/x1FNPYdOmTfDx8WnynIiIiIicHYMQtTsajQZr164Vj5ctWya2ba0IRUZG4umnnxbhx9/fH/fdd5/J46ytCF29etXk/pEjR+LJJ59s8njp1Lnjx48jIyMDVVVVAIAZM2ZAqVQiODgYq1evxpQpU5ocj4iIiIjYNY7aob179yI7OxsAMHbsWEyePFk8Z00QunLlitiOiIiATCbDhx9+iHvvvRfx8fEGlSap4OBgdO7cGQUFBTh+/Di0Wq1BQ4WGCgoKjPYFBQVh/fr1YjqeOf379xfb0hAEAIMGDWryeCIiIiIyxiBE7Y60ScKjjz6K8PBweHp6oqKiwuapcZGRkQAAuVxuUVWlb9++KCgowI0bN5CXl2f2pqPSitDKlSsxevRohIeHIzQ01KLzlE7Fy8jIMOhUJ61OEREREZHlODWO2pUrV66IjmudO3fGlClTIJfLRYOAnJwc1NbWWjSWqSBkKWk4aWp6nLQi1LlzZwwcONDiEAQAvr6+ohPciRMncPToUQC6RgpsZkBERERkGwYhalc++ugjaDQaAMCCBQvg6uoKACII1dXVIScnx6Kx9EHIz88PKpXKqvOQVmKaapggrQhZE4CkBgwYAACoqKgQwatbt27w8vKyaTwiIiIiZ8cgRO1KRkaG2JY2M5DeBNSSdUIajUasEbK2GgQ0ryJkC+k6IVPnQERERETWYRCidiUrKwsA4OLigri4OLFfGoQsWSd0/fp1sdbG3PqexnTr1g0eHh4Amq4ISYOQrRUhU0GI64OIiIiIbMcgRO2GVqsVQSgqKgpubm7iOelNRC2pCEk7xtlSEXJxcRHrc7KyslBWVtboa/VT41xdXREQEGD1ewGsCBERERHZG4MQtRuFhYXi5qTdunUzeE5aEcrMzGxyrOY0StDr06cPAF1AM/ee+opQaGio2Tbb5oSGhhpVk1gRIiIiIrKd3YJQTU0Nli1bhkmTJmHkyJFISUkR/3oPAOvXr8fYsWMxevRovPHGG9BqteK5U6dO4f7770dSUhJSUlJM3neFSPrnqWvXrgbP+fj4ICYmBgCQnp6OyspKs2PZIwglJCSI7YZBqLq6WnSwKyoqAmD7+iA9aVUoKCgIYWFhzRqPiIiIyJnZLQip1WqEh4dj3bp1+OabbzBixAgsWrQIAPD9999j8+bNWL9+PTZt2oTvv/9etECuqanBX//6V9x333345ptv0Lt3b/zv//6vvU6LOpDz58+L7YYVIQAYM2YMAF0IOXTokNmxHBWEtFotNmzYgOjoaMTFxeHZZ58Vod+eQahv3742V5eIiIiIyI5ByMPDA/Pnz0dISAhcXFxw7733Ij8/HyUlJfjqq68wa9YsREREICgoCHPnzsWuXbsAAEePHoWHhwemTZsGpVKJBQsW4PTp06wKkRFzFSEAGDt2rNjeu3ev2bGkQciWZgmAYRA6ffo01Go1pk2bhrlz54p1QWvWrBGvsbVRgp6+hTbA9UFEREREzaVw1MAnTpxAQEAA/Pz8cOHCBUyaNEk8Fx8fjzfffBOA7gaY0i+1Hh4eiIiIQE5Ojsl/Qa+pqRHdvvQUCoXBwvmG9Ped0f+XHOe7777DnDlzRBAYMGAAdu3a1WSTAEuukbQbXFxcnNFrR40aJbb37t1rdixps4SwsDCb/mxERETA09MTFRUVyMzMxNatW7F9+3aD16jVarEdGhrarD+D+mmnly9fRkpKSov/eebfo7aN16ft4zVq+3iN2j5eo7atrVwfudyyWo9DglBZWRlWrFiBhQsXAtDdBFJ6w0ovLy9UVFQAACorK41uCunl5dXoGo9169YhNTXVYN/s2bPx+9//vsnzklYByDGWLVuG/Px88Tg9PR2vvfYa/ud//sei481dI/30M7lcDplMhkuXLhm9JiEhAZmZmTh27Bh++eUX+Pv7i+fy8/Px+eef49y5c+LeP/7+/rh+/bpF52ZKbGwsfv31V2RnZ+PLL78U+4cOHYrDhw8bvNbV1dXkOVtj3bp10Gq1jX7+lsC/R20br0/bx2vU9vEatX28Rm1ba18f/brxptg9CFVXV2PRokVITk7GtGnTAACenp4G7YXLy8vh6ekJQFcBKi8vNxijvLxc3KOloXnz5uGBBx4w2GdJRejy5cuIjIy0OCGS9TQajbjhqbu7O6qqqgAA+/btwyuvvNLksdJrVFhYiIKCAtEZTavVii/+0dHRJtcIAcDEiRORmZkpWm3PmjULt27dwoIFC/DFF18Y/QtFVFQUoqKibP7Mffr0wa+//gqNRoOdO3eK/W+//bZRy+tevXo1671aG/8etW28Pm0fr1Hbx2vU9vEatW3t7frYNQjV1dXhhRdeQKdOnfDkk0+K/TExMcjKykJycjIA3RSn2NhYALp/Uf/iiy/EaysrK3HlyhXxfENubm5mQ485crm8XVyU9iozMxMlJSUAgLvvvhu5ubk4duwYjh49iosXLzZ6TaXkcjlKS0sxZMgQ5Obm4s0338TChQsNWmd37dq10es4btw4rF69GgDwzTff4Pe//z3Wr1+PLVu2GL3WxcUFCxYsaNafiZ49e4rt4uJiAECPHj3Qr18/DB48GD///LN4PiwsrEP8+ePfo7aN16ft4zVq+3iN2j5eo7atvVwfu57hP/7xD1RXV2Pp0qUGHa0mTZqELVu2IC8vD0VFRdiwYQMmTpwIABg4cCAqKyuxfft21NTU4P3330fPnj2b3WGLWp60U1tSUhJmz54tHm/evNnicTZt2oTc3FwAwMsvv4za2lqDjnGmGiXoDR8+HK6urgDqGyZIz+u5557DqVOncOHCBRQWForpm7aSNkzQS0pKAgBMnz7dYD//TBMRERG1HXYLQgUFBdi+fTsyMjJw1113Yfjw4Rg+fDgyMjKQnJyMmTNn4sEHH8Ts2bORlJSEqVOnAtBVeFauXIkNGzbgrrvuwvHjx7F8+XJ7nRa1IHNB6LPPPrN4nI8//lhsFxQUYOvWrQYd4xqbFgfo1pfdeeedAIDs7GxcuHABP/74IwDdvYZWrFiBnj17Ijo6uskGDpYwF4T0U0P1QkJCmv1+RERERGQfdpsa17lzZ6Snpzf6/Lx58zBv3jyTz/Xq1QuffPKJvU6FWok+CCmVSgwYMABKpRL9+/dHRkYG0tPTceHChSYXr128eBHfffedwb633npLhBvAfEUI0LXRPnDgAADdjXz1zRsGDx5s9zJt165doVAoUFdXJ/bpg1BCQgJ69uyJ06dPIzY21uYpnURERERkf21/8h61C9euXUN2djYAIDExEUqlEgAMqkKm1uk0tHHjRqN93377rUFHNnMVIcDwfkL/+te/xPbQoUObfH9rubq6GpxPUFCQeCyTybBhwwYsWLAAH3zwgd3fm4iIiIhsxyBEdvHDDz+IbX1FBABmzJghtvVVmsZotVqDaXHShhunTp0CoAse0dHRZsdJTEyEj48PAIjmDYBjghBgOD3uzjvvNFgf169fP7z77ruiUQgRERERtQ0MQmQxjUaDVatWYfXq1dBqtQbPNVwfpNe9e3exFufw4cNGx0n9/PPPOHPmDABd04MlS5aINuuArk36kiVLRLWpMQqFAnfddZfR/iFDhpg9zlbSICT97ERERETUdjEIkcW2bt2KZ599FosWLcJ///tfsT8vLw8bNmwQj6XreWQymajEFBUVIScnx+TYGo3G4F5DjzzyCPz8/LBx40ZMnjwZy5cvx+XLl/Hiiy9adK5jxowxeNy1a1cEBQVZdKy1Zs6cCblcDg8PD8yaNcsh70FERERE9sUgRBb76aefxLY++FRUVGDatGm4evUqAN0NTRsGjmHDhontw4cPmxx706ZNOHHiBACgd+/emDt3LgBg6tSp2LFjBxYvXozQ0FCLz1W6Tghw3LQ4ABgwYAByc3ORm5tr0b2SiIiIiKj1MQg5kYqKChQXF+PmzZtmp6g1JjMzU2zv2bMHN2/exIIFC3D06FEAQHR0tMmmANIQYioIVVdXG1R6Vq1aBRcXF6vPT6pHjx4ICwsTj6VhzBHCw8MdVnEiIiIiIvtjEHISb775Jvz8/BAYGIiAgADceeedqKystGoMaRCqq6vD008/LabIqVQqbN++HZ06dTI6btCgQaKBQMMgVFdXh4cffhgXL14EAIwbNw4TJkyw6rxMkclkGD9+vHjMtTtEREREJMUg5CT++c9/ora2Vjw+fPiwyVbVjamurhbtsfXWr18vtv/1r3+hd+/eJo/19fVFz549AQC//PKLCGB1dXV46KGHRJhyc3PDa6+9ZvE5NWXJkiWYPHkylixZgr59+9ptXCIiIiJq/xiEnEBubi6uXLkCAAbTt958802Lp8idO3cOGo3G5HO9e/fGgw8+aPZ4/fS4uro6MZVu5cqVIgS5urrizTffxB133GHR+VgiOjoaO3bswNKlS+02JhERERF1DAxCTkDa2nrhwoVITEwEABw7dgw///yzRWNIp8VJW1oDwCuvvNLkmh5T64Q+/fRTALppbJs3bzbq9EZERERE5CgMQk6g4T1+Fi5cKB6/9dZbFo0hDUKPPfaY2E5OTsbkyZObPF4ahA4dOoTi4mKcPHkSANC/f3/87ne/s+g8iIiIiIjsgUHICeiDkEwmw5AhQ3DffffB398fgK4qU1RU1OQY0iA0b948vPzyy5g6dSo+/PBD0QjBnISEBDEtb8+ePdi3b5+YljdixAirPxMRERERUXMwCHVwt2/fFvfnueOOO+Dr6wsPDw88/PDDAHRNELZs2dLkOPogpFAo0LVrV7z44ovYunUrYmJiLDoPFxcXTJkyBQBQXl6O5cuXi+cYhIiIiIiopTEIdXA//fSTaHIgbSE9c+ZMsa1vXtAYtVqNs2fPAgC6desGV1dXm85l+vTpYvvXX38V28nJyTaNR0RERERkKwahDq7h+iC9Pn36iCltGRkZZse4cOECqqurAeimuNlq3LhxRo0WEhISTN57iIiIiIjIkRiEWllVVRUOHjyIPXv24Ntvv0V5ebldx28sCKlUKsTHxwMATpw4YXCPoYa+++47sd2cIOTh4WF0s1ROiyMiIiKi1sAg1Iq0Wi3uvvtujBw5EuPHj8fo0aORnJwMtVptl/HVarVoVd25c2dERUUZPD9gwAAAQE1NjUEzBKm0tDQ8+uij4vHgwYObdU7S6XEAgxARERERtQ4GoVZ07NgxHDhwwGDfL7/8gp07d9pl/LNnz+L27dsAdO2rG3Z369+/v8G5NPT1119j2rRpYlrcPffc0+w215MnTza459Dw4cObNR4RERERkS0YhFrRRx99JLZHjhwpti29t49URUUFampqDPYdOXJEbA8aNMjoGGkQarhO6Ouvv8bUqVMNQtDGjRshlzfvj0xgYCCmTp0KQFddioyMbNZ4RERERES2YBBqJXV1ddi4cSMAwM3NDVu2bBGtqNPS0nD+/HmLxzp48CB8fX3Rq1cvXLp0SeyXBiFTU9oaC0L79+83qgRt3LjR5m5xDa1fvx6ff/653SpfRERERETWYhBqJV9//TUKCwsBAFOmTEFgYKDBWpx33nnH4rFWrVqFuro6ZGVlYerUqSgrKwNgGIQGDhxodFxgYCC6dOkCQBeE9G22Fy1ahKqqKgC6Ntv2DEEA4OPjgxkzZogbrBIRERERtTQGoVYinRb3hz/8AQAwb948KJVKAMB//vMfVFRUNDlOZWUl9u7dKx6fOHECc+fORVVVFX755RcAQHx8PPz8/Ewer68KlZWVITs7G5WVleK47t2745NPPrFrCCIiIiIiagsYhByspKQEWq3WYF9paSm+/PJLALqqzMSJEwEAQUFBuPfee8Vxu3fvbnL8b775BpWVlQb7tm7diieeeEKsGTK1PkhP3zkO0DVMOHXqlKgMDR06lCGIiIiIiDokBiEHWrt2LQICAtCjRw+kpaWJ/Vu2bBFTz+699164ubmJ5/RBCAB27drV5Hts375dbC9cuFBsv/vuu2LbXBCSrhNKT0/H8ePHxeN+/fo1+f5ERERERO0Rg5CDaLVarFy5ElqtFufOncPdd9+NOXPmoKamxuS0OL1Ro0aJ6XG7d+82qiY1fI8dO3YAAJRKJVauXGl0w1LA/L1/hgwZIra//fZbMS0OAPr27Wv+QxIRERERtVMMQg5y/Phx5OTkGOzbuHEjnnrqKezfvx8A0K1bN4MgAgCenp6ilfaVK1dw6tSpRt8jIyMDeXl5AIDRo0fDy8sLf//73w1eo1AozFZ2goOD0adPHwC6qXH6cwMYhIiIiIio42IQcpDPP/9cbN9zzz3i/jtvvfWWqPLMnTvX6CanAMSaIcD89LgtW7aI7SlTpgAAkpOTDe5J1Lt3b3h4eJg91zFjxgDQVZh+/fVXAEBkZCQCAgLMHkdERERE1F4xCDmIPqTIZDL8+9//xuOPP270mrlz55o8VhqEGmuYUFFRgbVr1wIAXFxcMG3aNPHciy++KLbvvPPOJs9VH4SkuD6IiIiIiDoyBiEHOHPmDE6fPg0AGDZsGDp37oxly5YhJCREvCYpKQmxsbEmj4+Pj0d0dDQA4LvvvsPt27eNXvPBBx/gxo0bAID77rsPYWFh4rmxY8di1apVmDNnDv72t781eb4jRoyAQqEw2MdpcURERETUkTEIOcDmzZvF9j333AMA8PPzw6pVq8T+lJSURo+XyWSiKlRbW4t9+/YBAFauXInevXvj1VdfxerVq8XrFy1aZHT8okWLsGHDBkRERDR5vt7e3kYNFVgRIiIiIqKOTNH0S8gax44dw4oVK8TjGTNmiO25c+dCpVKhoqIC999/v9lxJk+ejLfffhuAbr3R4MGD8fzzz0Or1eL5558XrxszZoxBC2xbjR07Fj/88IN4zIoQEREREXVkrAjZUUFBAaZNmyZucPrwww8jJibG4DXTp0/HnDlzTDZJkBo7dix8fHwA6G6Q+vHHH5tspf3ss8/a5dyl64RUKlWj0/aIiIiIiDoCBiE7qaqqwowZM3DlyhUAurVBb775ps3jKZVK0QChtLQUL730knhOXwEaOXIkxo8f34yzrjd06FDRJW7YsGGiyx0RERERUUfEb7t2kp6eLm5G2qVLF3zxxRdwd3dv1pizZ88W22VlZQCAPn364OjRo8jKysLevXubrCxZys3NDZ9//jn+8pe/YM2aNXYZk4iIiIioreIaITtJTk7GwYMH8cADD2Dz5s0GHeJsNX78ePj4+KC0tFTsu//++yGTyRAXF9fs8RsaOXKkwT2IiIiIiIg6KlaE7Gjw4MHIzMy0W6MB6fQ4vXvvvdcuYxMREREROTMGITtreD+e5pJOjxsyZIhR8wUiIiIiIrIeg1AbN2HCBAwdOhRubm544YUXWvt0iIiIiIg6BK4RauPc3Nzwww8/oLKyEp6enq19OkREREREHQIrQu2ATCZjCCIiIiIisiMGISIiIiIicjoMQkRERERE5HQYhIiIiIiIyOkwCBERERERkdNhECIiIiIiIqfDIERERERERE6HQYiIiIiIiJwOgxARERERETkdBiEiIiIiInI6DEJEREREROR0GISIiIiIiMjpMAgREREREZHTYRAiIiIiIiKnwyBEREREREROh0GIiIiIiIicDoMQERERERE5HQYhIiIiIiJyOgxCRERERETkdGRarVbb2idBRERERETUklgRIiIiIiIip8MgRERERERETodBiIiIiIiInA6DEBEREREROR0GISIiIiIicjoMQkRERERE5HQYhIiIiIiIyOkwCBERERERkdNhECIiIiIiIqfDIERERERERE6nXQahtWvXYvbs2Rg0aBDS0tLE/qqqKvzjH//AuHHjMH78eHz00Ucmj1+/fj0SExNx8uRJsS8vLw+PPfYYRo0ahYkTJ2LdunUO/xwdla3XJzExEcnJyRg+fDiGDx+O//znP+K51atXY9q0aRgxYgT+8Ic/4NixYy32eToiR1wjANi2bRtmzJiB5ORkzJo1C5cuXWqRz9MR2XqNysrKsHz5cowePRqjRo3Ciy++aHDs4sWLMWLECEyePBm7d+9usc/TETniGunl5+cjKSkJK1ascPjn6KgccX34XcG+bLlGGRkZ4nfQ8OHDkZSUhEGDBuHmzZsA+H3B3hxxjYC2831B0Srv2kyRkZFYtGgR3nnnHYP977//PvLz8/HFF1+grKwMjz76KLp27Yphw4aJ1xQWFmL37t0IDAw0OPa1115DeHg43njjDVy7dg2PPPIIevXqhcGDB7fIZ+pImnN9vvzySwQFBRmNqVKpsGbNGoSHh+Obb77BM888g+3bt8PLy8vhn6cjcsQ1OnjwID7++GOsWrUKsbGxyMvLg7e3t8M/S0dl6zVatmwZQkJCsG3bNri7uyMrK0scu3btWty6dQtfffUVsrOz8cQTTyAhIQFRUVEt+tk6CkdcI73Vq1eje/fuLfI5OipHXB9+V7AvW65R//798d1334nXfvLJJ9i7dy/8/f0B8PuCvTniGrWl7wvtsiI0adIkDB06FG5ubgb7f/zxR8yZMwcqlQqhoaGYOnUqdu7cafCa//u//8Of/vQno2MLCgowfvx4KBQKhIeHo1+/fsjJyXH4Z+mImnN9GpOSkoLIyEjI5XKMHTsWSqUSubm5jjh9p+CIa/Tee+/h6aefRlxcHGQyGSIiIuDr6+uI03cKtlyj7OxsnDlzBk899RRUKhUUCgV69Oghjv3qq6+QkpIClUqFvn37YsSIEfj6669b9HN1JI64RvrjtVothgwZ0mKfpSNyxPXhdwX7ssfvol27dmHixIniMb8v2JcjrlFb+r7QLoOQOVqt1mBb+gMqPT0dt27dwl133WV03OzZs5GWloaamhrk5ubi5MmTSExMbJFzdibmrg8AzJ07FxMnTsTSpUtRUlJicoz8/HyUlpYiMjLSkafqtGy5Rmq1GmfPnkVWVhYmTZqEqVOnIjU11WAssp/GrlFmZia6dOmCxYsXY8yYMXjwwQeRkZEBACgtLcWNGzfQtWtXcWx8fDy/xDmILdcIAGpra/HGG2/gySefbOlTdiq2Xh9+V2g5Tf0uAoDLly/j3LlzGDt2rMkx+H3BsWy5Rm3t+0KHCkJDhw7Fxo0bcfv2beTn52PHjh2oqqoCANTV1WH16tV4+umnTR7bt29fnDx5EsOHD8fMmTMxbdo0gy8M1Hzmrg8ApKamYseOHfjvf/+LqqoqLF++3GiMuro6LF26FH/4wx+gUqla8vSdgq3XqLi4GGq1GkeOHMGnn36Kd999F3v27MH27dtb66N0WOauUWFhIX766ScMHjwYaWlp+OMf/4hnnnkGt27dQkVFBVxcXODu7i7G8vLyQkVFRWt9lA7L1msEABs2bEBSUhK/uDlQc64Pvyu0jKZ+F+nt2rULw4YNM1lN4PcFx7L1GrW17wsdKgg98sgjCAsLw6xZs/CXv/wFY8aMQadOnQAAn332Gfr162fyB5ZarcYTTzyB6dOn49ChQ9i2bRv27t2LvXv3tvRH6NDMXR8A6N+/PxQKBfz9/fHMM8/g0KFDqK2tFc9rtVosXboU/v7+SElJaY2P0OHZeo2USiUA4KGHHoK3tzdCQ0Mxe/ZsHDp0qLU+Sodl7hoplUqEh4dj+vTpUCgUGD16NMLDw3Hy5El4enpCrVYb/KIqLy+Hp6dna32UDsvWa1RYWIht27bh4YcfbuVP0LHZen34XaHlNPW7SG/37t0GU670+H3B8Wy9Rm3t+0KHCkIeHh548cUXkZaWhs2bN0Mmk6Fnz54AdNPidu/ejQkTJmDChAm4du0annzySWzbtg2lpaW4fv06Zs2aBYVCgbCwMIwaNQpHjx5t5U/UsZi7Pg3J5bo/mtJS6cqVK3H9+nW89NJL4nmyL1uvkY+Pj9EPQE6Lcwxz1yguLq7R43x8fBAYGGiw8PvcuXOIjY11+Dk7G1uv0enTp3Ht2jXMnDkTEyZMwMcff4ydO3fiz3/+c0udulOw9frwu0LLseR30alTp3Djxg0MHz7c6Hh+X3A8W69RW/u+0C7/dNTV1aG6uhparVZsazQaXLt2DUVFRVCr1Th8+DC2b9+OOXPmAACWLl2KTZs2YcOGDdiwYQM6deqEZcuWYfz48fD390dISAi+/PJLMc6BAwfM/kCkxtlyfbKzs3Hu3Dmo1WqUlpbi9ddfx5AhQ8TivLVr1+L48eN4/fXXjRbskfUccY1+97vf4cMPP0R5eTmuX7+OLVu2IDk5uTU/ZrtmyzVKTEyEVqvFjh07oFarceDAAeTl5eGOO+4AoFv0+t5776G8vBwnT57EwYMHMW7cuNb8mO2ava/RnXfeia1bt4rfU/fccw/Gjh2Ll156qZU/aftk7+vD7wr2Z8s10tu9ezfuuusug+m+AL8v2JsjrlFb+r4g07bDf7ZdunQpduzYYbBP39ZvyZIlKCkpQXR0NJ555hn079/f5BhTpkzBihUrxBeEU6dO4fXXX0d2djbc3d0xfvx4PPnkk3BxcXHsh+mAbLk+R44cwSuvvILCwkJ4eXlh8ODBeOqppxAQEABA98vJzc3N4Hq88MILJkvi1DRHXKPa2lq8+uqr2LNnDzw9PTF9+nSkpKRAJpO17IfrIGz9OXf+/Hm89NJLuHDhAiIjI/HMM89gwIABAHT3fXj55Zdx4MAB+Pj44M9//jPuvvvulvtQHYwjrpHU2rVrcePGDbzwwguO/SAdlCOuD78r2Jet10itVmPSpElYtmwZhg4danA8vy/YlyOuUVv6vtAugxAREREREVFztMupcURERERERM3BIERERERERE6HQYiIiIiIiJwOgxARERERETkdBiEiIiIiInI6DEJEREREROR0GISIiIiIiMjpMAgRERFBdyPGxMREbN++vbVPhYiIWgCDEBERtZiUlBQROO6//36D50pKSpCUlCSe//e//23399++fbsYn4iInBuDEBERtYrz58/j2LFj4vGXX36J6urqVjwjIiJyJgxCRETU4hQKBQDg008/BQCo1Wps3rxZ7Je6desWXn31VUyePBlDhgzB+PHjsXjxYly9elW8Zu3atUhMTMSUKVOwZ88e3HPPPUhOTsaCBQtw8eJFAMDSpUuxbNkycYy+MrR27VqD9ysrK8PSpUsxcuRITJw4Ee+99569Pz4REbUBDEJERNTi4uPjER4ejv379+PatWs4ePAgrl69ijFjxhi8rrq6GikpKfjss89QVFSEqKgolJeXY9euXZg3bx5u3rxp8PrCwkIsXrwYMpkM1dXVyMjIwPLlywEAERERCA8PF6/t3bs3evfujZCQEIMx1qxZg8OHD8PV1RXXr1/HO++8g8OHDzvo/wQREbUWBiEiImpxcrkcs2fPFpUgfWXo3nvvNXhdWloasrOzAQCvvvoqNm3ahPfffx9yuRzXr1/Hpk2bDF6vVquxcuVKbN68WaxBOnHiBKqqqjB//nzMnz9fvHb9+vVYv349pk+fbjBGfHw8tm/fblChOnLkiF0/PxERtT4GISIiahXTpk2Dh4cHNm3ahPT0dCQkJKBPnz4Grzl9+jQAwN3dHaNGjQIA9OjRA1FRUQbP66lUKowYMQIAEBsbK/Y3rByZM27cOLi6usLPzw8BAQEAgOLiYus+HBERtXkMQkRE1Cq8vb0xceJElJeXAzCuBtk6pp6Li4vY1mq1zRrDmuOJiKh9YBAiIqJW8/vf/x4A4Ofnh/Hjxxs937NnTwBAVVUV9u/fDwA4c+YMLl26ZPC8pdzd3cV2ZWWlLadMREQdhHF7HiIiohbStWtX7Nu3Dy4uLnBzczN6fsKECfj444+Rk5OD5557DlFRUcjLy4NGo0GnTp1EkLJUdHS02J49ezaCgoLw5JNPol+/fs38JERE1N6wIkRERK3K19cXKpXK5HNKpRKpqakitFy6dAleXl6YOHEi1q1bB39/f6veq1u3bpg/fz4CAwNx9epV/Prrr7h9+7Y9PgYREbUzMi0nPhMRERERkZNhRYiIiIiIiJwOgxARERERETkdBiEiIiIiInI6DEJEREREROR0GISIiIiIiMjpMAgREREREZHTYRAiIiIiIiKnwyBEREREREROh0GIiIiIiIicDoMQERERERE5HQYhIiIiIiJyOgxCRERERETkdP4/cJHjhI0i9CkAAAAASUVORK5CYII=", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "pred_air, pred_milk = model.predict(\n", + "preds = model.predict(\n", " series=[train_air_scaled, train_milk_scaled],\n", - " future_covariates=[air_covs, milk_covs],\n", + " future_covariates=[air_covs_scaled, milk_covs_scaled],\n", " n=36,\n", ")\n", "\n", "# scale back:\n", - "pred_air, pred_milk = scaler.inverse_transform([pred_air, pred_milk])\n", + "pred_air, pred_milk = scaler.inverse_transform(preds)\n", "\n", "plt.figure(figsize=(10, 6))\n", "series_air.plot(label=\"actual (air)\")\n", "series_milk.plot(label=\"actual (milk)\")\n", "pred_air.plot(label=\"forecast (air)\")\n", - "pred_milk.plot(label=\"forecast (milk)\")" + "pred_milk.plot(label=\"forecast (milk)\");" ] }, { @@ -1944,16 +2071,16 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[3.41736301779747, 5.282935127615929]" + "[3.4355457, 5.1290045]" ] }, - "execution_count": 43, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1972,22 +2099,22 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4.350149072706699" + "4.282275" ] }, - "execution_count": 44, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "mape([series_air, series_milk], [pred_air, pred_milk], inter_reduction=np.mean)" + "mape([series_air, series_milk], [pred_air, pred_milk], series_reduction=np.mean)" ] }, { @@ -1995,25 +2122,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "By the way: similarly to transformers such as `Scaler`, computing metrics can be parallelized over `N` processors when executed over many series pairs by specifying `n_jobs=N`.\n", - "\n", "It seems that this model performs well on the Air traffic series, how does it do when we backtest it on this one series?" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 48, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`enable_optimization=True` is ignored because `retrain` is not `False` or `0`. To hide this warning, set `show_warnings=False` or `enable_optimization=False`.\n", + "`enable_optimization=True` is ignored because `forecast_horizon > model.output_chunk_length`. To hide this warning, set `show_warnings=False` or `enable_optimization=False`.\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "264b99afcd6b48b194151ca067be63d5", + "model_id": "0ff0a15633aa484a88ee59ec46bec1e7", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/57 [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2045,61 +2176,82 @@ ")\n", "\n", "backtest = bayes_ridge_model.historical_forecasts(\n", - " series_air, future_covariates=air_covs, start=0.6, forecast_horizon=3, verbose=True\n", + " future_covariates=[air_covs_scaled, milk_covs_scaled],\n", + " **hfc_params,\n", ")\n", "\n", - "print(\"MAPE = %.2f\" % (mape(backtest, series_air)))\n", + "print(f\"MAPE = {mape(series_air, backtest):.2f}%\")\n", "series_air.plot()\n", - "backtest.plot()" + "backtest.plot();" ] }, { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our best model so far!" - ] - }, - { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# Probabilistic forecasts\n", + "Our best model so far!\n", "\n", - "Some models can produce probabilistic forecasts. This is the case for all deep learning models (such as `RNNModel`, `NBEATSModel`, etc ...), as well as for `ARIMA` and `ExponentialSmoothing`. The full list is [available on the Darts README page](https://github.com/unit8co/darts#forecasting-models).\n", + "### Applying scaler in backtesting\n", "\n", - "For `ARIMA` and `ExponentialSmoothing`, one can simply specify a `num_samples` parameter to the `predict()` function. The returned `TimeSeries` will then be composed of `num_samples` Monte Carlo samples describing the distribution of the time series' values. The advantage of relying on Monte Carlo samples (in contrast to, say, explicit confidence intervals) is that they can be used to describe any parametric or non-parametric joint distribution over components, and compute arbitrary quantiles." + "To avoid data-leakage through the target and future covariate scaler, we will pass it to `historical_forecasts` so that the scaler is fitted and applied to both the target and the future covariates at each iteration." ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 51, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "`enable_optimization=True` is ignored because `retrain` is not `False` or `0`. To hide this warning, set `show_warnings=False` or `enable_optimization=False`.\n", + "`enable_optimization=True` is ignored because `forecast_horizon > model.output_chunk_length`. To hide this warning, set `show_warnings=False` or `enable_optimization=False`.\n" + ] + }, { "data": { - "image/png": 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", + "application/vnd.jupyter.widget-view+json": { + "model_id": "1142a2caabb54caea8957d4e1c1c0449", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "
" + " 0%| | 0/58 [00:00" + ] }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "model_es = ExponentialSmoothing()\n", - "model_es.fit(train)\n", - "probabilistic_forecast = model_es.predict(len(val), num_samples=500)\n", + "backtest_auto_scaling = bayes_ridge_model.historical_forecasts(\n", + " future_covariates=air_covs,\n", + " data_transformers={\"series\": Scaler(), \"future_covariates\": Scaler()},\n", + " retrain=True, # ensure that the scalers are fitted at each iterations\n", + " **hfc_params,\n", + ")\n", "\n", - "series.plot(label=\"actual\")\n", - "probabilistic_forecast.plot(label=\"probabilistic forecast\")\n", - "plt.legend()\n", - "plt.show()" + "print(f\"MAPE = {mape(series_air, backtest_auto_scaling):.2f}%\")\n", + "series_air.plot()\n", + "backtest_auto_scaling.plot();" ] }, { @@ -2107,289 +2259,240 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## With neural networks\n", + "The MAPE score is slightly worse, which is expected since information cannot leak from the future anymore but the backtesting conditions are more reliable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sample Weights\n", + "\n", + "All our global models (regression-, ensemble-, and neural network models) support sample weights for training. They are applied per observation, label (each step in `output_chunk_length`), and component. This can be very useful to:\n", "\n", - "With neural networks, one has to give a `Likelihood` object to the model. The likelihoods specify which distribution the model will try to fit, along with potential prior values for the distributions' parameters. The full list of available likelihoods is [available in the docs](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html).\n", + "- weigh certain time frames more than others (e.g. decaying weights the further the points in the past, higher weights for times with more business value, ...) \n", + "- reduce / remove the effect of outliers or missing values on the model\n", "\n", - "Using likelihoods is easy. For instance, here is what training an `NBEATSModel` to fit a Laplace likelihood looks like:" + "Let's just introduce some outliers to the air passengers series and see how a linear regression model with a 12 months lookback window (`lags=12`) performs on this data." ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 49, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "----------------------------------------------------\n", - "0 | criterion | MSELoss | 0 \n", - "1 | train_metrics | MetricCollection | 0 \n", - "2 | val_metrics | MetricCollection | 0 \n", - "3 | dropout | MonteCarloDropout | 0 \n", - "4 | res_blocks | ModuleList | 166 \n", - "----------------------------------------------------\n", - "166 Trainable params\n", - "0 Non-trainable params\n", - "166 Total params\n", - "0.001 Total estimated model params size (MB)\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c2d818ed170a450e8e9a8aed016b2b86", + "model_id": "60b8b30f303c46e98b35b3eabcc8ef42", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Training: 0it [00:00, ?it/s]" + " 0%| | 0/57 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from darts.models import TCNModel\n", - "from darts.utils.likelihood_models import LaplaceLikelihood\n", + "# convert to pandas DataFrame and add some outliers\n", + "air_df = series_air.pd_dataframe()\n", + "outlier_mask = (air_df.index.year >= 1950) & (air_df.index.year <= 1951)\n", + "air_df.loc[outlier_mask] = 600.0\n", + "air_outlier = TimeSeries.from_dataframe(air_df)\n", "\n", - "model = TCNModel(\n", - " input_chunk_length=24,\n", - " output_chunk_length=12,\n", - " random_state=42,\n", - " likelihood=LaplaceLikelihood(),\n", - ")\n", + "from darts.models import LinearRegressionModel\n", "\n", - "model.fit(train_air_scaled, epochs=400, verbose=True);" + "model_lin = LinearRegressionModel(lags=12, lags_future_covariates=[0])\n", + "hist_fc_kwargs = {\n", + " \"series\": air_outlier,\n", + " \"future_covariates\": air_covs_scaled,\n", + " \"start\": 0.6,\n", + " \"forecast_horizon\": 3,\n", + " \"verbose\": True,\n", + " \"show_warnings\": False,\n", + "}\n", + "backtest = model_lin.historical_forecasts(**hist_fc_kwargs)\n", + "\n", + "print(f\"MAPE = {mape(air_outlier, backtest):.2f}%\")\n", + "air_outlier.plot(label=\"air series with outliers\")\n", + "backtest.plot(label=\"non-weighted predictions\");" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Then to get probabilistic forecasts, we again only need to specify some `num_samples >> 1`:" + "This doesn't look very good. It would be great if we could ignore the outliers somehow. And this is where sample weights come in!\n", + "\n", + "In Darts, sample weights are treated like covariates - they are defined as `TimeSeries` themselves. Thanks to that our models can automatically extract the relevant time frames for us!\n", + "\n", + "Let's create a weights series in which we set all outlier times to `0.` and the remaining times to `1.`. We also set the 12 months after the last outlier to `0.`, since these times fall into the lookback window of our model. Otherwise we would still have some outliers in the model input." ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 50, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" - ] - }, { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "652bfdddfb864b9b9e859fd67adfd3a4", - "version_major": 2, - "version_minor": 0 - }, + "image/png": 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", "text/plain": [ - "Predicting: 0it [00:00, ?it/s]" + "
" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "pred = model.predict(n=36, num_samples=500)\n", + "weight_mask = (air_df.index.year >= 1950) & (air_df.index.year <= 1952)\n", + "sample_weight = np.ones((len(air_df), 1))\n", + "sample_weight[weight_mask, 0] = 0.0\n", + "sample_weight = air_outlier.with_values(sample_weight)\n", "\n", - "# scale back:\n", - "pred = scaler.inverse_transform(pred)\n", - "\n", - "series_air.plot()\n", - "pred.plot()" + "# and plot the results\n", + "air_outlier.plot(label=\"air series with outliers\")\n", + "(sample_weight * 300).plot(label=\"sample weight * 300\");" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "Furthermore, we could also for instance specify that we have some prior belief that the scale of the distribution is about $0.1$ (in the transformed domain), while still capturing some time dependency of the distribution, by specifying `prior_b=.1`.\n", - "\n", - "Behind the scenes this will regularize the training loss with a Kullback-Leibler divergence term." + "Now we can train the models with the sample weights using methods `fit()`, `historical_forecasts`, `backtest()`, ..." ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 51, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "----------------------------------------------------\n", - "0 | criterion | MSELoss | 0 \n", - "1 | train_metrics | MetricCollection | 0 \n", - "2 | val_metrics | MetricCollection | 0 \n", - "3 | dropout | MonteCarloDropout | 0 \n", - "4 | res_blocks | ModuleList | 166 \n", - "----------------------------------------------------\n", - "166 Trainable params\n", - "0 Non-trainable params\n", - "166 Total params\n", - "0.001 Total estimated model params size (MB)\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f496f739ecc945428729b72519bb5a76", + "model_id": "777ac0ab0c40466f9d1dab234dc7042c", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Training: 0it [00:00, ?it/s]" + " 0%| | 0/57 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "model = TCNModel(\n", - " input_chunk_length=24,\n", - " output_chunk_length=12,\n", - " random_state=42,\n", - " likelihood=LaplaceLikelihood(prior_b=0.1),\n", + "model_lin = LinearRegressionModel(lags=12, lags_future_covariates=[0])\n", + "backtest_weighted = model_lin.historical_forecasts(\n", + " sample_weight=sample_weight, **hist_fc_kwargs\n", ")\n", "\n", - "model.fit(train_air_scaled, epochs=400, verbose=True);" + "print(f\"MAPE = {mape(series_air, backtest_weighted):.2f}%\")\n", + "air_outlier.plot(label=\"air series with outliers\")\n", + "backtest_weighted.plot(label=\"weighted predictions\");" ] }, { - "cell_type": "code", - "execution_count": 50, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cba279282c064f62b654037198473a03", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Predicting: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], "source": [ - "pred = model.predict(n=36, num_samples=500)\n", - "\n", - "# scale back:\n", - "pred = scaler.inverse_transform(pred)\n", + "Wow, this looks great! We managed to completely remove the negative impact of the outliers on our model!\n", "\n", - "series_air.plot()\n", - "pred.plot()" + "**Note:** our sample weights also support multi-horizon forecasts, multiple time series, multivariate series, and weights for evaluation sets (if the model support it) " ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "By default `TimeSeries.plot()` shows the median as well as the 5th and 95th percentiles (of the marginal distributions, if the `TimeSeries` is multivariate). It is possible to control this:" + "# Forecast Start Shifting\n", + "\n", + "We might also be interested in forecasts starting with an offset after the end of our target series. This can be useful for example:\n", + "\n", + "- In (Day-) Ahead Markets, where (each day) we need to make some biddings for a future point in time (next day) - we are not really interested in what happens between now and that future point in time. We can reduce model complexity by only focusing on times of interest.\n", + "- When are covariates (or target series) are reported with a delay\n", + "\n", + "All our global models support such predictions with an offset through model creation parameter `output_chunk_shift` - the number of steps to shift the first predicted step into the future.\n", + "\n", + "With an output shift, the model cannot perform auto-regression anymore. So let's create a linear model that directly predicts the next 12 months with an offset of 12 months." ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "pred.plot(low_quantile=0.01, high_quantile=0.99, label=\"1-99th percentiles\")\n", - "pred.plot(low_quantile=0.2, high_quantile=0.8, label=\"20-80th percentiles\")" + "model_shifted = LinearRegressionModel(\n", + " lags=12,\n", + " lags_future_covariates=(0, 12),\n", + " output_chunk_length=12,\n", + " output_chunk_shift=12,\n", + ")\n", + "\n", + "model_shifted.fit(series_air[:-24], future_covariates=air_covs)\n", + "preds = model_shifted.predict(n=12)\n", + "\n", + "series_air[:-24].plot(label=\"train series\")\n", + "series_air[-24:].plot(label=\"val_series\")\n", + "preds.plot(label=\"shifted prediction\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the prediction starts 12 months after the end of the training series." ] }, { @@ -2397,40 +2500,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Types of distributions\n", - "The likelihood has to be compatible with the domain of your time series' values. For instance [PoissonLikelihood](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html#darts.utils.likelihood_models.PoissonLikelihood) can be used on discrete positive values, [ExponentialLikelihood](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html#darts.utils.likelihood_models.ExponentialLikelihood) can be used on real positive values, and [BetaLikelihood](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html#darts.utils.likelihood_models.BetaLikelihood) on real values in $(0,1)$.\n", + "# Probabilistic forecasts\n", "\n", - "It is also possible to use [QuantileRegression](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html#darts.utils.likelihood_models.QuantileRegression) to apply a quantile loss and fit some desired quantiles directly.\n", + "Most models in Darts support probabilistic forecasts (some local models and all regression-, ensemble- and neural network models). The full support list is [available on the Darts README page](https://github.com/unit8co/darts#forecasting-models).\n", "\n", - "### Evaluating Probabilistic Forecasts\n", - "How can we evaluate the quality of probabilistic forecasts? By default, most metrics functions (such as `mape()`) will keep working but look only at the median forecast. It is also possible to use the $\\rho$-risk metric (or quantile loss), which quantifies the error for each predicted quantiles:" + "\n", + "## With Local Models\n", + "\n", + "For local models (`ARIMA`, `ExponentialSmoothing`, ...), we can simply specify a `num_samples` parameter when calling `predict()`. The returned `TimeSeries` will then contain `num_samples` Monte Carlo samples describing the distribution of the time series' values. The advantage of relying on Monte Carlo samples (in contrast to, say, explicit confidence intervals) is that they can be used to describe any parametric or non-parametric joint distribution over components, and compute arbitrary quantiles." ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 53, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "MAPE of median forecast: 11.80\n", - "rho-risk at quantile 0.05: 0.14\n", - "rho-risk at quantile 0.10: 0.15\n", - "rho-risk at quantile 0.50: 0.11\n", - "rho-risk at quantile 0.90: 0.03\n", - "rho-risk at quantile 0.95: 0.02\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from darts.metrics import rho_risk\n", + "model_es = ExponentialSmoothing()\n", + "model_es.fit(train)\n", + "probabilistic_forecast = model_es.predict(len(val), num_samples=500)\n", "\n", - "print(\"MAPE of median forecast: %.2f\" % mape(series_air, pred))\n", - "for rho in [0.05, 0.1, 0.5, 0.9, 0.95]:\n", - " rr = rho_risk(series_air, pred, rho=rho)\n", - " print(\"rho-risk at quantile %.2f: %.2f\" % (rho, rr))" + "series.plot(label=\"actual\")\n", + "probabilistic_forecast.plot(label=\"probabilistic forecast\")\n", + "plt.legend()\n", + "plt.show()" ] }, { @@ -2438,14 +2542,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Using Quantile Loss\n", + "## With Neural Network Models (TorchForecastingModel)\n", "\n", - "Could we do better by fitting these quantiles directly? We can just use a `QuantileRegression` likelihood:" + "With neural networks, we have to use a `Likelihood` object at model creation. The likelihoods specify which distribution the model will try to fit, along with potential prior values for the distributions' parameters. The full list of available likelihoods is [available in the docs](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html).\n", + "\n", + "Next to the distributions, we also support `QuantileRegression` as likelihood, which estimates future quantile values of the target series.\n", + "\n", + "Using likelihoods is easy. For instance, here is what training an `TCNModel` to fit a Laplace likelihood looks like:" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -2454,32 +2562,32 @@ "text": [ "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n", "\n", - " | Name | Type | Params\n", - "----------------------------------------------------\n", - "0 | criterion | MSELoss | 0 \n", - "1 | train_metrics | MetricCollection | 0 \n", - "2 | val_metrics | MetricCollection | 0 \n", - "3 | dropout | MonteCarloDropout | 0 \n", - "4 | res_blocks | ModuleList | 208 \n", - "----------------------------------------------------\n", - "208 Trainable params\n", + " | Name | Type | Params | Mode \n", + "-------------------------------------------------------------\n", + "0 | criterion | MSELoss | 0 | train\n", + "1 | train_criterion | MSELoss | 0 | train\n", + "2 | val_criterion | MSELoss | 0 | train\n", + "3 | train_metrics | MetricCollection | 0 | train\n", + "4 | val_metrics | MetricCollection | 0 | train\n", + "5 | res_blocks | ModuleList | 166 | train\n", + "-------------------------------------------------------------\n", + "166 Trainable params\n", "0 Non-trainable params\n", - "208 Total params\n", + "166 Total params\n", "0.001 Total estimated model params size (MB)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b104ef44f5b45d5a7fc248f84768f70", + "model_id": "28d472e309914ebda9180bbd03709ba3", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Training: 0it [00:00, ?it/s]" + "Training: | | 0/? [00:00> 1`. This will sample from predicted distribution." + ] + }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -2517,46 +2635,38 @@ "text": [ "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", "HPU available: False, using: 0 HPUs\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dfbbd21dd8be480faa682ea5ada5d239", + "model_id": "6545f7dc627e4dcfbe70b994d89b9499", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Predicting: 0it [00:00, ?it/s]" + "Predicting: | | 0/? [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -2567,90 +2677,65 @@ "pred = scaler.inverse_transform(pred)\n", "\n", "series_air.plot()\n", - "pred.plot()\n", - "\n", - "print(\"MAPE of median forecast: %.2f\" % mape(series_air, pred))\n", - "for rho in [0.05, 0.1, 0.5, 0.9, 0.95]:\n", - " rr = rho_risk(series_air, pred, rho=rho)\n", - " print(\"rho-risk at quantile %.2f: %.2f\" % (rho, rr))" + "pred.plot();" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# Ensembling models\n", - "*Ensembling* is about combining the forecasts produced by several models, in order to obtain a final - and hopefully better forecast.\n", + "### Direct Parameter Predicitons\n", "\n", - "For instance, in our example of a [less naive model above](#A-less-naive-model), we manually combined a naive seasonal model with a naive drift model. Here, we will show how models forecasts can be automatically combined, naively using a `NaiveEnsembleModel`, or learned using `RegressionEnsembleModel`.\n", - "\n", - "It is of course also possible to use `past` and/or `future_covariates` with ensemble model but they will be passed only to the models supporting them when calling `fit()` and `predict()`." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Naive Ensembling\n", + "The great thing about our likelihoods is that instead of sampling from the distribution/quantiles, we can directly predict the distribution/quantile parameters.\n", + "For this, we only have to set `predict_likelihood_parameters=True`.\n", "\n", - "Naive ensembling just takes the average of the forecasts of several models. Darts provides a `NaiveEnsembleModel`, which allows to do this while still manipulating only one forecasting model (which, for instance, allows for easier backtesting):" + "Below we get the predicted location (mu) and scale (b) of the laplace distribution (in the scaled space between 0 and 1)." ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 56, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f09961b31d4340d68f34d345e875a784", + "model_id": "81eae0ce6d8a45b48f55369050b08a19", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/57 [00:00" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "from darts.models import NaiveEnsembleModel\n", - "\n", - "models = [NaiveDrift(), NaiveSeasonal(12)]\n", - "\n", - "ensemble_model = NaiveEnsembleModel(forecasting_models=models)\n", - "\n", - "backtest = ensemble_model.historical_forecasts(\n", - " series_air, start=0.6, forecast_horizon=3, verbose=True\n", - ")\n", + "pred = model.predict(n=12, predict_likelihood_parameters=True)\n", "\n", - "print(\"MAPE = %.2f\" % (mape(backtest, series_air)))\n", - "series_air.plot()\n", - "backtest.plot()" + "train_air_scaled.plot()\n", + "pred.plot(label=\"laplace_dist\");" ] }, { @@ -2658,75 +2743,126 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Learned Ensembling\n", - "\n", - "As expected in this case, the naive ensemble doesn't give great results (although in some cases it could!)\n", - "\n", - "We can sometimes do better if we see the ensembling as a supervised regression problem: given a set of forecasts (features), find a model that combines them in order to minimise errors on the target.\n", - "This is what the `RegressionEnsembleModel` does. It accepts three parameters:\n", - "\n", - "* `forecasting_models` is a list of forecasting models whose predictions we want to ensemble.\n", - "* `regression_train_n_points` is the number of time steps to use for fitting the \"ensemble regression\" model (i.e., the inner model that combines the forecasts).\n", - "* `regression_model` is, optionally, a sklearn-compatible regression model or a Darts `RegressionModel` to be used for the ensemble regression. If not specified, a linear regression is used. Using a sklearn model is easy out-of-the-box, but using a `RegressionModel` allows to potentially take arbitrary lags of the individual forecasts as inputs of the regression model.\n", + "Furthermore, we could also for instance specify that we have some prior belief that the scale of the distribution is about $0.1$ (in the transformed domain), while still capturing some time dependency of the distribution, by specifying `prior_b=.1`.\n", "\n", - "Once these elements are in place, a `RegressionEnsembleModel` can be used like a regular forecasting model:" + "Behind the scenes this will regularize the training loss with a Kullback-Leibler divergence term." ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 57, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params | Mode \n", + "-------------------------------------------------------------\n", + "0 | criterion | MSELoss | 0 | train\n", + "1 | train_criterion | MSELoss | 0 | train\n", + "2 | val_criterion | MSELoss | 0 | train\n", + "3 | train_metrics | MetricCollection | 0 | train\n", + "4 | val_metrics | MetricCollection | 0 | train\n", + "5 | res_blocks | ModuleList | 166 | train\n", + "-------------------------------------------------------------\n", + "166 Trainable params\n", + "0 Non-trainable params\n", + "166 Total params\n", + "0.001 Total estimated model params size (MB)\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d5cd2271e8a74512a947a55d8007e7dc", + "model_id": "7af02fcb5e23483a919191a5a74e28cb", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/57 [00:00" + "Predicting: | | 0/? [00:00" + ] }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "from darts.models import RegressionEnsembleModel\n", - "\n", - "models = [NaiveDrift(), NaiveSeasonal(12)]\n", - "\n", - "ensemble_model = RegressionEnsembleModel(\n", - " forecasting_models=models, regression_train_n_points=12\n", - ")\n", + "pred = model.predict(n=36, num_samples=500)\n", "\n", - "backtest = ensemble_model.historical_forecasts(\n", - " series_air, start=0.6, forecast_horizon=3, verbose=True\n", - ")\n", + "# scale back:\n", + "pred = scaler.inverse_transform(pred)\n", "\n", - "print(\"MAPE = %.2f\" % (mape(backtest, series_air)))\n", "series_air.plot()\n", - "backtest.plot()" + "pred.plot();" ] }, { @@ -2734,28 +2870,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also inspect the coefficients used to weigh the two inner models in the linear combination:" + "By default `TimeSeries.plot()` shows the median as well as the 5th and 95th percentiles (of the marginal distributions, if the `TimeSeries` is multivariate). It is possible to control this:" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "array([0.01368849, 1.0980105 ], dtype=float32)" + "
" ] }, - "execution_count": 57, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "ensemble_model.fit(series_air)\n", - "ensemble_model.regression_model.model.coef_" + "pred.plot(low_quantile=0.01, high_quantile=0.99, label=\"1-99th percentiles\")\n", + "pred.plot(low_quantile=0.2, high_quantile=0.8, label=\"20-80th percentiles\");" ] }, { @@ -2763,38 +2899,167 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "By using a probabilistic regression model, the `RegressionEnsembleModel` can also generate probabilistic forecasts:" - ] - }, + "### Types of distributions\n", + "The likelihood has to be compatible with the domain of your time series' values. For instance [PoissonLikelihood](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html#darts.utils.likelihood_models.PoissonLikelihood) can be used on discrete positive values, [ExponentialLikelihood](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html#darts.utils.likelihood_models.ExponentialLikelihood) can be used on real positive values, and [BetaLikelihood](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html#darts.utils.likelihood_models.BetaLikelihood) on real values in $(0,1)$.\n", + "\n", + "It is also possible to use [QuantileRegression](https://unit8co.github.io/darts/generated_api/darts.utils.likelihood_models.html#darts.utils.likelihood_models.QuantileRegression) to apply a quantile loss and fit some desired quantiles directly.\n", + "\n", + "### Evaluating Probabilistic Forecasts\n", + "How can we evaluate the quality of probabilistic forecasts? By default, most metrics functions (such as `mape()`) will keep working but look only at the median forecast. It is also possible to use the Mean Quantile Loss metric `mql()`, which quantifies the error for each predicted quantiles. For quantile=0.5 (the median), it is identical to the Mean Absolute Error (MAE):" + ] + }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE of median forecast: 12.16\n", + "MAE of median forecast: 51.73\n", + "quantile loss at quantile 0.05: 5.36\n", + "quantile loss at quantile 0.10: 13.24\n", + "quantile loss at quantile 0.50: 51.73\n", + "quantile loss at quantile 0.90: 20.74\n", + "quantile loss at quantile 0.95: 12.20\n" + ] + } + ], + "source": [ + "from darts.metrics import mae, mql\n", + "\n", + "print(f\"MAPE of median forecast: {mape(series_air, pred):.2f}\")\n", + "print(f\"MAE of median forecast: {mae(series_air, pred):.2f}\")\n", + "for q in [0.05, 0.1, 0.5, 0.9, 0.95]:\n", + " q_loss = mql(series_air, pred, q=q)\n", + " print(f\"quantile loss at quantile {q:.2f}: {q_loss:.2f}\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using Quantile Loss\n", + "\n", + "Could we do better by fitting these quantiles directly? We can just use a `QuantileRegression` likelihood:" + ] + }, + { + "cell_type": "code", + "execution_count": 61, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "\n", + " | Name | Type | Params | Mode \n", + "-------------------------------------------------------------\n", + "0 | criterion | MSELoss | 0 | train\n", + "1 | train_criterion | MSELoss | 0 | train\n", + "2 | val_criterion | MSELoss | 0 | train\n", + "3 | train_metrics | MetricCollection | 0 | train\n", + "4 | val_metrics | MetricCollection | 0 | train\n", + "5 | res_blocks | ModuleList | 208 | train\n", + "-------------------------------------------------------------\n", + "208 Trainable params\n", + "0 Non-trainable params\n", + "208 Total params\n", + "0.001 Total estimated model params size (MB)\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8ca7a08507a741f1bb55d4c2136fb1ba", + "model_id": "5dbb2e6852fe42f0874679c1c69e752b", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/57 [00:00" ] @@ -2804,32 +3069,75 @@ } ], "source": [ - "from darts.models import LinearRegressionModel\n", + "pred = model.predict(n=36, num_samples=500)\n", + "\n", + "# scale back:\n", + "pred = scaler.inverse_transform(pred)\n", "\n", - "quantiles = [0.25, 0.5, 0.75]\n", + "series_air.plot()\n", + "pred.plot()\n", "\n", - "models = [NaiveDrift(), NaiveSeasonal(12)]\n", + "print(f\"MAPE of median forecast: {mape(series_air, pred):.2f}\")\n", + "for q in [0.05, 0.1, 0.5, 0.9, 0.95]:\n", + " q_loss = mql(series_air, pred, q=q)\n", + " print(f\"quantile loss at quantile {q:.2f}: {q_loss:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## With Regression Models\n", "\n", - "regression_model = LinearRegressionModel(\n", - " quantiles=quantiles,\n", + "Probailistic support for our `RegressionModels` is similiar to that of the neural networks. We have to specify a `likelihood` at model creation.\n", + "Instead of giving a likelihood object, we can simply pick one of `\"quantile\"` (with some `quantiles`) and `\"poisson\"`." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE of median forecast: 8.48\n", + "quantile loss at quantile 0.05: 20.20\n", + "quantile loss at quantile 0.10: 25.45\n", + "quantile loss at quantile 0.50: 35.19\n", + "quantile loss at quantile 0.90: 11.30\n", + "quantile loss at quantile 0.95: 6.25\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = LinearRegressionModel(\n", + " lags=24,\n", " lags_future_covariates=[0],\n", " likelihood=\"quantile\",\n", - " fit_intercept=False,\n", - ")\n", - "\n", - "ensemble_model = RegressionEnsembleModel(\n", - " forecasting_models=models,\n", - " regression_train_n_points=12,\n", - " regression_model=regression_model,\n", - ")\n", - "\n", - "backtest = ensemble_model.historical_forecasts(\n", - " series_air, start=0.6, forecast_horizon=3, num_samples=500, verbose=True\n", + " quantiles=[0.05, 0.1, 0.5, 0.9, 0.95],\n", ")\n", + "model.fit(train_air, future_covariates=air_covs)\n", + "pred = model.predict(n=36, num_samples=500)\n", "\n", - "print(\"MAPE = %.2f\" % (mape(backtest, series_air)))\n", "series_air.plot()\n", - "backtest.plot()" + "pred.plot()\n", + "\n", + "print(f\"MAPE of median forecast: {mape(series_air, pred):.2f}\")\n", + "for q in [0.05, 0.1, 0.5, 0.9, 0.95]:\n", + " q_loss = mql(series_air, pred, q=q)\n", + " print(f\"quantile loss at quantile {q:.2f}: {q_loss:.2f}\")" ] }, { @@ -2837,7 +3145,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`RegressionEnsembleModel` uses the *stacking* technique to train and combine the `forecasting_models`: each one of them is trained independently and the `regression_model` is then trained using their predictions as `future_covariates`." + "# Ensembling models\n", + "*Ensembling* is about combining the forecasts produced by several models, in order to obtain a final - and hopefully better forecast.\n", + "\n", + "For instance, in our example of a [less naive model above](#A-less-naive-model), we manually combined a naive seasonal model with a naive drift model. Here, we will show how model forecasts can be automatically combined - naively using a `NaiveEnsembleModel`, or learned using `RegressionEnsembleModel`.\n", + "\n", + "It is of course also possible to use `past` and/or `future_covariates` with ensemble model but they will be passed only to the forecasting models supporting them." ] }, { @@ -2845,42 +3158,60 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Filtering models\n", - "In addition to *forecasting* models, which are able to predict future values of series, Darts also contains a couple of helpful *filtering* models, which can model \"in sample\" series' values distributions.\n", - "\n", - "## Fitting a Kalman Filter\n", - "`KalmanFilter` implements a [Kalman Filter](https://unit8co.github.io/darts/generated_api/darts.models.filtering.kalman_filter.html). The implementation relies on [nfoursid](https://nfoursid.readthedocs.io/en/latest/source/kalman.html), so it is for instance possible to provide a `nfoursid.kalman.Kalman` object containing a transition matrix, process noise covariance, observation noise covariance etc.\n", + "## Naive Ensembling\n", "\n", - "It is also possible to do system identification by calling `fit()` to \"train\" the Kalman Filter using the N4SID system identification algorithm:" + "Naive ensembling just takes the average of the forecasts from several models. Darts `NaiveEnsembleModel` does exactly that and all through the same API as the forecasting models (fit, predict, historical forecasts, backtesting, ...)." ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 64, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "application/vnd.jupyter.widget-view+json": { + "model_id": "53085097667f407eb640bf1f00a3d423", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "
" + " 0%| | 0/58 [00:00" + ] }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "from darts.models import KalmanFilter\n", + "from darts.models import NaiveEnsembleModel\n", "\n", - "kf = KalmanFilter(dim_x=3)\n", - "kf.fit(train_air_scaled)\n", - "filtered_series = kf.filter(train_air_scaled, num_samples=100)\n", + "models = [NaiveDrift(), NaiveSeasonal(12)]\n", "\n", - "train_air_scaled.plot()\n", - "filtered_series.plot()" + "ensemble_model = NaiveEnsembleModel(forecasting_models=models)\n", + "\n", + "backtest = ensemble_model.historical_forecasts(**hfc_params)\n", + "\n", + "print(f\"MAPE = {mape(backtest, series_air):.2f}%\")\n", + "series_air.plot()\n", + "backtest.plot();" ] }, { @@ -2888,48 +3219,70 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Inferring missing values with Gaussian Processes\n", + "## Learned Ensembling\n", "\n", - "Darts also contains a `GaussianProcessFilter` which can be used for probabilistic modeling of series:" + "As expected in this case, the naive ensemble doesn't give great results (although in some cases it could!)\n", + "\n", + "We can sometimes do better if we see the ensembling as a supervised regression problem: given a set of forecasts (features), find a model that combines them in order to minimise errors on the target.\n", + "This is what the `RegressionEnsembleModel` does. It accepts parameters:\n", + "\n", + "* `forecasting_models` is a list of forecasting models whose predictions we want to ensemble.\n", + "* `regression_train_n_points` is the number of time steps to use for fitting the \"ensemble regression\" model (i.e., the inner model that combines the forecasts).\n", + "* `regression_model` is, optionally, a sklearn-compatible regression model or a Darts `RegressionModel` to be used for the ensemble regression. If not specified, a linear regression is used. Using a sklearn model is easy out-of-the-box, but using a `RegressionModel` allows to potentially take arbitrary lags of the individual forecasts as inputs of the regression model.\n", + "* and for more, read our this [user guide on ensembling models](https://unit8co.github.io/darts/examples/19-EnsembleModel-examples.html).\n", + "\n", + "Once these elements are in place, a `RegressionEnsembleModel` can be used like a regular forecasting model:" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 65, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" + " 0%| | 0/58 [00:00" + ] }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "from darts.models import GaussianProcessFilter\n", - "from sklearn.gaussian_process.kernels import RBF\n", - "\n", - "# create a series with holes:\n", - "values = train_air_scaled.values()\n", - "values[20:22] = np.nan\n", - "values[28:32] = np.nan\n", - "values[55:59] = np.nan\n", - "values[72:80] = np.nan\n", - "series_holes = TimeSeries.from_times_and_values(train_air_scaled.time_index, values)\n", - "series_holes.plot()\n", + "from darts.models import RegressionEnsembleModel\n", "\n", - "kernel = RBF()\n", + "ensemble_model = RegressionEnsembleModel(\n", + " forecasting_models=models, regression_train_n_points=12\n", + ")\n", "\n", - "gpf = GaussianProcessFilter(kernel=kernel, alpha=0.1, normalize_y=True)\n", - "filtered_series = gpf.filter(series_holes, num_samples=100)\n", + "backtest = ensemble_model.historical_forecasts(**hfc_params)\n", "\n", - "filtered_series.plot()" + "print(f\"MAPE = {mape(backtest, series_air):.2f}\")\n", + "series_air.plot()\n", + "backtest.plot();" ] }, { @@ -2937,26 +3290,168 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# A Word of Caution\n", - "So is N-BEATS, exponential smoothing, or a Bayesian ridge regression trained on milk production the best approach for predicting the future number of airline passengers? Well, at this point it's actually hard to say exactly which one is best. Our time series is small, and our validation set is even smaller. In such cases, it's very easy to overfit the whole forecasting exercise to such a small validation set. That's especially true if the number of available models and their degrees of freedom is high (such as for deep learning models), or if we played with many models on a single test set (as done in this notebook). \n", - "\n", - "As data scientists, it is our responsibility to understand the extent to which our models can be trusted. So always take results with a grain of salt, especially on small datasets, and apply the scientific method before making any kind of forecast :) Happy modeling!" + "We can also inspect the coefficients used to weigh the two inner models in the linear combination:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "interpreter": { - "hash": "f4869ab21c96bf74d997d96f31e1fe34ff80d6a0fad585152839d8f90c7b1199" - }, - "kernelspec": { - "display_name": "Python 3", + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.01368849, 1.0980105 ], dtype=float32)" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ensemble_model.fit(series_air)\n", + "ensemble_model.regression_model.model.coef_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ensemble models can also be probabilistic themselves! You can read about it in [our guide on ensembling models](https://unit8co.github.io/darts/examples/19-EnsembleModel-examples.html#)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`RegressionEnsembleModel` uses the *stacking* technique to train and combine the `forecasting_models`: each one of them is trained independently and the `regression_model` is then trained using their predictions as `future_covariates`." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Filtering models\n", + "In addition to *forecasting* models, which are able to predict future values of series, Darts also contains a couple of helpful *filtering* models, which can model \"in sample\" series' values distributions.\n", + "\n", + "## Fitting a Kalman Filter\n", + "`KalmanFilter` implements a [Kalman Filter](https://unit8co.github.io/darts/generated_api/darts.models.filtering.kalman_filter.html). The implementation relies on [nfoursid](https://nfoursid.readthedocs.io/en/latest/source/kalman.html), so it is for instance possible to provide a `nfoursid.kalman.Kalman` object containing a transition matrix, process noise covariance, observation noise covariance etc.\n", + "\n", + "It is also possible to do system identification by calling `fit()` to \"train\" the Kalman Filter using the N4SID system identification algorithm:" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from darts.models import KalmanFilter\n", + "\n", + "kf = KalmanFilter(dim_x=3)\n", + "kf.fit(train_air_scaled)\n", + "filtered_series = kf.filter(train_air_scaled, num_samples=100)\n", + "\n", + "train_air_scaled.plot()\n", + "filtered_series.plot();" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inferring missing values with Gaussian Processes\n", + "\n", + "Darts also contains a `GaussianProcessFilter` which can be used for probabilistic modeling of series:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.gaussian_process.kernels import RBF\n", + "\n", + "from darts.models import GaussianProcessFilter\n", + "\n", + "# create a series with holes:\n", + "values = train_air_scaled.values()\n", + "values[20:22] = np.nan\n", + "values[28:32] = np.nan\n", + "values[55:59] = np.nan\n", + "values[72:80] = np.nan\n", + "series_holes = TimeSeries.from_times_and_values(train_air_scaled.time_index, values)\n", + "series_holes.plot()\n", + "\n", + "kernel = RBF()\n", + "\n", + "gpf = GaussianProcessFilter(kernel=kernel, alpha=0.1, normalize_y=True)\n", + "filtered_series = gpf.filter(series_holes, num_samples=100)\n", + "\n", + "filtered_series.plot();" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A Word of Caution\n", + "So is N-BEATS, exponential smoothing, or a Bayesian ridge regression trained on milk production the best approach for predicting the future number of airline passengers? Well, at this point it's actually hard to say exactly which one is best. Our time series is small, and our validation set is even smaller. In such cases, it's very easy to overfit the whole forecasting exercise to such a small validation set. That's especially true if the number of available models and their degrees of freedom is high (such as for deep learning models), or if we played with many models on a single test set (as done in this notebook). \n", + "\n", + "As data scientists, it is our responsibility to understand the extent to which our models can be trusted. So always take results with a grain of salt, especially on small datasets, and apply the scientific method before making any kind of forecast :) Happy modeling!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Anomaly Detection\n", + "\n", + "Darts also has an entire module on Time Series Anomaly Detection. For more info, read our [user guide on anomaly detection](https://unit8co.github.io/darts/examples/22-anomaly-detection-examples.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "f4869ab21c96bf74d997d96f31e1fe34ff80d6a0fad585152839d8f90c7b1199" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2970,7 +3465,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.8" }, "pycharm": { "stem_cell": { @@ -2980,6 +3475,2517 @@ }, "source": [] } + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "007e3908d7544d63b12d435b5ddd8cee": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "bar_style": "success", + "layout": 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} }, "nbformat": 4, diff --git a/examples/01-multi-time-series-and-covariates.ipynb b/examples/01-multi-time-series-and-covariates.ipynb index 8744bb69a7..2456a78561 100644 --- a/examples/01-multi-time-series-and-covariates.ipynb +++ b/examples/01-multi-time-series-and-covariates.ipynb @@ -27,32 +27,27 @@ "\n", "fix_pythonpath_if_working_locally()\n", "\n", - "import pandas as pd\n", + "import logging\n", + "\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch\n", - "import matplotlib.pyplot as plt\n", "\n", - "from darts import TimeSeries, concatenate\n", + "from darts import concatenate\n", + "from darts.dataprocessing.transformers import Scaler\n", + "from darts.datasets import AirPassengersDataset, ElectricityDataset, MonthlyMilkDataset\n", + "from darts.metrics import mae, mape\n", + "from darts.models import (\n", + " VARIMA,\n", + " BlockRNNModel,\n", + " NBEATSModel,\n", + " RNNModel,\n", + ")\n", "from darts.utils.callbacks import TFMProgressBar\n", "from darts.utils.timeseries_generation import (\n", - " gaussian_timeseries,\n", - " linear_timeseries,\n", + " datetime_attribute_timeseries,\n", " sine_timeseries,\n", ")\n", - "from darts.models import (\n", - " RNNModel,\n", - " TCNModel,\n", - " TransformerModel,\n", - " NBEATSModel,\n", - " BlockRNNModel,\n", - " VARIMA,\n", - ")\n", - "from darts.metrics import mape, smape, mae\n", - "from darts.dataprocessing.transformers import Scaler\n", - "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n", - "from darts.datasets import AirPassengersDataset, MonthlyMilkDataset, ElectricityDataset\n", - "\n", - "import logging\n", "\n", "logging.disable(logging.CRITICAL)\n", "\n", @@ -217,7 +212,7 @@ " output_chunk_length=12,\n", " n_epochs=200,\n", " random_state=0,\n", - " **generate_torch_kwargs()\n", + " **generate_torch_kwargs(),\n", ")" ] }, @@ -298,7 +293,7 @@ "series_air_scaled.plot(label=\"actual\")\n", "pred.plot(label=\"forecast\")\n", "plt.legend()\n", - "print(\"MAPE = {:.2f}%\".format(mape(series_air_scaled, pred)))" + "print(f\"MAPE = {mape(series_air_scaled, pred):.2f}%\")" ] }, { @@ -348,7 +343,7 @@ " output_chunk_length=12,\n", " n_epochs=100,\n", " random_state=0,\n", - " **generate_torch_kwargs()\n", + " **generate_torch_kwargs(),\n", ")" ] }, @@ -440,7 +435,7 @@ "series_air_scaled.plot(label=\"actual\")\n", "pred.plot(label=\"forecast\")\n", "plt.legend()\n", - "print(\"MAPE = {:.2f}%\".format(mape(series_air_scaled, pred)))" + "print(f\"MAPE = {mape(series_air_scaled, pred):.2f}%\")" ] }, { @@ -607,14 +602,17 @@ "\n", "# scale them between 0 and 1:\n", "scaler_covariates = Scaler()\n", - "air_train_covariates, milk_train_covariates = scaler_covariates.fit_transform(\n", - " [air_train_covariates, milk_train_covariates]\n", - ")\n", - "air_val_covariates, milk_val_covariates = scaler_covariates.transform(\n", - " [air_val_covariates, milk_val_covariates]\n", - ")\n", - "\n", - "# concatenate for the full scaled series; we can feed this to model.fit()/predict() as Darts will extract the required covariates for you\n", + "air_train_covariates, milk_train_covariates = scaler_covariates.fit_transform([\n", + " air_train_covariates,\n", + " milk_train_covariates,\n", + "])\n", + "air_val_covariates, milk_val_covariates = scaler_covariates.transform([\n", + " air_val_covariates,\n", + " milk_val_covariates,\n", + "])\n", + "\n", + "# concatenate for the full scaled series; we can feed this to model.fit()/predict() as Darts will extract the required\n", + "# covariates for you\n", "air_covariates = concatenate([air_train_covariates, air_val_covariates])\n", "milk_covariates = concatenate([milk_train_covariates, milk_val_covariates])\n", "\n", @@ -657,7 +655,7 @@ " model_name=model_name,\n", " save_checkpoints=True, # store model states: latest and best performing of validation set\n", " force_reset=True,\n", - " **generate_torch_kwargs()\n", + " **generate_torch_kwargs(),\n", ")" ] }, @@ -776,7 +774,7 @@ " model_name=model_name,\n", " save_checkpoints=True, # store model states: latest and best performing of validation set\n", " force_reset=True,\n", - " **generate_torch_kwargs()\n", + " **generate_torch_kwargs(),\n", ")\n", "\n", "model_futcov.fit(\n", @@ -967,9 +965,7 @@ ")\n", "backtest_pastcov = concatenate(backtest_pastcov)\n", "print(\n", - " \"MAPE (BlockRNNModel with past covariates) = {:.2f}%\".format(\n", - " mape(series_air_scaled, backtest_pastcov)\n", - " )\n", + " f\"MAPE (BlockRNNModel with past covariates) = {mape(series_air_scaled, backtest_pastcov):.2f}%\"\n", ")\n", "\n", "backtest_futcov = model_futcov.historical_forecasts(\n", @@ -984,9 +980,7 @@ ")\n", "backtest_futcov = concatenate(backtest_futcov)\n", "print(\n", - " \"MAPE (RNNModel with future covariates) = {:.2f}%\".format(\n", - " mape(series_air_scaled, backtest_futcov)\n", - " )\n", + " f\"MAPE (RNNModel with future covariates) = {mape(series_air_scaled, backtest_futcov):.2f}%\"\n", ")" ] }, @@ -1180,16 +1174,16 @@ " n_rnn_layers=3,\n", " training_length=36,\n", " n_epochs=200,\n", - " **generate_torch_kwargs()\n", + " **generate_torch_kwargs(),\n", ")\n", "\n", "# training and prediction with the VARIMA model\n", "forecast_VARIMA = fit_and_pred(model_VARIMA, training_scaled, validation_scaled)\n", - "print(\"MAE (VARIMA) = {:.2f}\".format(mae(validation_scaled, forecast_VARIMA)))\n", + "print(f\"MAE (VARIMA) = {mae(validation_scaled, forecast_VARIMA):.2f}\")\n", "\n", "# training and prediction with the RNN model\n", "forecast_RNN = fit_and_pred(model_GRU, training_scaled, validation_scaled)\n", - "print(\"MAE (RNN) = {:.2f}\".format(mae(validation_scaled, forecast_RNN)))" + "print(f\"MAE (RNN) = {mae(validation_scaled, forecast_RNN):.2f}\")" ] }, { diff --git a/examples/02-data-processing.ipynb b/examples/02-data-processing.ipynb index 47616ad17e..01d0a44330 100644 --- a/examples/02-data-processing.ipynb +++ b/examples/02-data-processing.ipynb @@ -19,7 +19,7 @@ "\n", "`DataTransformer` aims to provide a unified way of dealing with transformations of `TimeSeries`:\n", "\n", - "- `transform()` is implemented by all transformers. This method takes in either a `TimeSeries` of a sequence of `TimeSeries`, applies the transformation and returns it as a new `TimeSeries`/sequence of `TimeSeries.\n", + "- `transform()` is implemented by all transformers. This method takes in either a `TimeSeries` or a sequence of `TimeSeries`, applies the transformation and returns it as a new `TimeSeries`/sequence of `TimeSeries.\n", "- `inverse_transform()` is implemented by transformers for which an inverse transformation function exists. It works in a similar way as `transform()`\n", "- `fit()` allows transformers to extract some information from the time series first before calling `transform()` or `inverse_transform()`\n" ] @@ -53,25 +53,22 @@ "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", + "import warnings\n", + "\n", "import matplotlib.pyplot as plt\n", - "import numpy as np\n", + "import pandas as pd\n", "\n", - "from darts import TimeSeries\n", - "from darts.models import ExponentialSmoothing\n", + "from darts.dataprocessing import Pipeline\n", "from darts.dataprocessing.transformers import (\n", - " Scaler,\n", - " MissingValuesFiller,\n", - " Mapper,\n", " InvertibleMapper,\n", + " Mapper,\n", + " MissingValuesFiller,\n", + " Scaler,\n", ")\n", - "from darts.dataprocessing import Pipeline\n", + "from darts.datasets import MonthlyMilkDataset, MonthlyMilkIncompleteDataset\n", "from darts.metrics import mape\n", - "from darts.utils.statistics import check_seasonality, plot_acf, plot_residuals_analysis\n", + "from darts.models import ExponentialSmoothing\n", "from darts.utils.timeseries_generation import linear_timeseries\n", - "from darts.datasets import MonthlyMilkDataset, MonthlyMilkIncompleteDataset\n", - "\n", - "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", @@ -494,7 +491,7 @@ "model.fit(training)\n", "forecast = model.predict(36)\n", "\n", - "plt.title(\"MAPE = {:.2f}%\".format(mape(forecast, validation)))\n", + "plt.title(f\"MAPE = {mape(forecast, validation):.2f}%\")\n", "series.plot(label=\"actual\")\n", "forecast.plot(label=\"forecast\")\n", "plt.legend()" @@ -590,7 +587,7 @@ "model.fit(dailyavg_train)\n", "dailyavg_forecast = model.predict(36)\n", "\n", - "plt.title(\"MAPE = {:.2f}%\".format(mape(dailyavg_forecast, dailyavg_val)))\n", + "plt.title(f\"MAPE = {mape(dailyavg_forecast, dailyavg_val):.2f}%\")\n", "dailyAverage.plot()\n", "dailyavg_forecast.plot()\n", "plt.legend()" @@ -636,7 +633,7 @@ } ], "source": [ - "plt.title(\"MAPE = {:.2f}%\".format(mape(forecast, validation)))\n", + "plt.title(f\"MAPE = {mape(forecast, validation):.2f}%\")\n", "series.plot(label=\"actual\")\n", "forecast.plot(label=\"forecast\")\n", "plt.legend()" diff --git a/examples/03-FFT-examples.ipynb b/examples/03-FFT-examples.ipynb index c173d726cd..3220ebb4d9 100644 --- a/examples/03-FFT-examples.ipynb +++ b/examples/03-FFT-examples.ipynb @@ -37,18 +37,14 @@ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", + "import warnings\n", "\n", + "import pandas as pd\n", "\n", - "from darts import TimeSeries\n", - "from darts.models import FFT, AutoARIMA, ExponentialSmoothing, Theta\n", + "from darts.datasets import AirPassengersDataset, EnergyDataset, TemperatureDataset\n", "from darts.metrics import mae\n", + "from darts.models import FFT, AutoARIMA, ExponentialSmoothing, Theta\n", "from darts.utils.missing_values import fill_missing_values\n", - "from darts.datasets import TemperatureDataset, AirPassengersDataset, EnergyDataset\n", - "\n", - "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", diff --git a/examples/04-RNN-examples.ipynb b/examples/04-RNN-examples.ipynb index 77155a4d10..b8197aacbd 100644 --- a/examples/04-RNN-examples.ipynb +++ b/examples/04-RNN-examples.ipynb @@ -40,26 +40,18 @@ }, "outputs": [], "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import numpy as np\n", - "import pandas as pd\n", - "import shutil\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "from tqdm import tqdm_notebook as tqdm\n", + "import warnings\n", + "\n", "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", "\n", - "from darts import TimeSeries\n", "from darts.dataprocessing.transformers import Scaler\n", - "from darts.models import RNNModel, ExponentialSmoothing, BlockRNNModel\n", + "from darts.datasets import AirPassengersDataset, SunspotsDataset\n", "from darts.metrics import mape\n", + "from darts.models import BlockRNNModel, ExponentialSmoothing, RNNModel\n", "from darts.utils.statistics import check_seasonality, plot_acf\n", - "from darts.datasets import AirPassengersDataset, SunspotsDataset\n", "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n", "\n", - "import warnings\n", - "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", "\n", @@ -4450,7 +4442,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -4467,7 +4459,7 @@ " plt.figure(figsize=(8, 5))\n", " series_transformed.plot(label=\"actual\")\n", " pred_series.plot(label=\"forecast\")\n", - " plt.title(\"MAPE: {:.2f}%\".format(mape(pred_series, val_transformed)))\n", + " plt.title(f\"MAPE: {mape(pred_series, val_transformed):.2f}%\")\n", " plt.legend()\n", "\n", "\n", @@ -4504,7 +4496,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", + "image/png": 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z+enIg5z+R52YJGOth/Ly8uAWrAUhtWKWEEII0R5lZWUQMxKAL3+EmLBnIOVAyAZhyYSFEEJ0GqWlpRCZCcDw/lBnTYbBH7H3QH2QS+aaBGFg9+7dHH/88e06x6pVqzjrrLOaPd6eXZd2797NW2+91a5yCSHEsaS0tAyisgBY/oxCZvwBCIth94HQDHehWapORIKwEEIETlFpLYTFEB1hITFOISWuFoBD5Q1BLplrEoRtGhoamDlzJoMGDeLCCy+kpqaGhx9+mJEjR3L88ccza9Ys+zyz7du3M3nyZIYMGcLw4cPZsWOH07nWrFnDsGHD2LFjR7OtD4uLi7ngggsYOXIkI0eO5NtvvwXg66+/ZujQoQwdOpRhw4ZRVVXFnDlzWLFiBUOHDuWpp54KeJ0IIURHc7DU+DudEmes458UZ9wvqwrNecJtDsxSVTUZ+B9wHDBa07SNDsfOBu4DLMBaTdN+357CKBOs7Xl5i/Rv2v6usWXLFl555RXGjRvHNddcw/PPP8/s2bP585//DMAVV1zBokWLOPvss5k5cyZz5szhvPPOo66uzr6BAxjN0rfccgsfffQReXl5zbY+/M1vfsPtt9/O+PHj2bt3L1OnTuXXX3/liSee4LnnnmPcuHFUV1cTExPDX//6V5544gkWLVrkl3oRQojOpqQiDBRIS2oEIDnBWKyqvDo0g7A7mXANMB1438Wx9cA4TdPGA5mqqqq+LFwg5ebmMm7cOAAuv/xyVq5cyVdffcWoUaM44YQT+PLLL9m0aRNVVVUUFBRw3nnnAca+wXFxcQD8+uuvzJo1i08++YS8vDyX1/niiy+YPXs2Q4cO5ZxzzqGyspLq6mrGjRvHH/7wB5555hnKy8uJiJCB60II4amy6kgAMlON+12SjCBcVROaDb9t/qXXNM0CFLuKr5qm7XW4ewRoVyrrTsbqL4qiNLt/0003oWkaubm5PPjgg21uRditWzfq6ur46aefyM7Odvkcq9XK6tWriYmJcXp8zpw5TJ8+nSVLljBu3DiWLl3avjckhBDHoMq6WEiErmnhAKQlG0G5ui48mMVqkU/SLVVVRwKZmqb96OLYLGAWwOzZs5kyZYovLulTBw4cYO/evXz88ceMGDGCl156iRNOOIGVK1dSX1/P1q1b+c9//sOZZ55JZWUlmZmZvPLKK0ybNo36+nqsViuNjY3ExMTw7LPPctlll1FTU8PYsWOxWq0UFBRQUFAAwPjx43n00Uf53e9+B8CmTZsYPHgwu3fvpmfPnlx++eV88803fPvtt2RnZ1NSUmJ/bUdisVg6ZLn9RerDmdSHM6kPZ+2pj8NHjJbJlPh6CgoqiY4wkqfD9RFBq+PWNqNodxBWVbU7MBc4z9VxTdPmAfNsd0OyUd5isTBgwADeffdd7rrrLo477jjmzJmD1Wpl6tSpdO3alTFjxpCUlEROTg7vvPMON9xwA08//TSRkZG89957hIeHExMTw9ChQ1m6dClnnHEGr776KpdffjkXXnghX331Ff/85z95+eWXufnmmznjjDNoaGhgwoQJnH766fz1r3/lq6++IiwsjMGDB3P55ZcTFhbG3//+d84880yuuuoqbr/99mBXldtkVxhnUh/OpD6cSX0487Y+6urqaFDSABjYuws5OWH061UBwJHG2JCsY7e3MlRVdT7wRJOBWYnAp8CNjo+3IiSDsC/IL5EzqQ9nUh/OpD6cSX0487Y+9u/fT/ZpKyDjQt5+QOHSSQofLj/CBX+OQClbQuOG6c26HgOkfVsZqqq6BDgdeElV1atUVX3Rdug2oBfwrKqqy1VVPaW9JRVCCCG8UVZWBlEZAGTZBmalpxh9wnpYIrW1tcEqWovcao7WNO3MJg/Ntz3+CPCIj8skhBBCeMzYQclYLcscHZ2cYDsYkUJ5ebl9NkuoCM0x20IIIYSHHNeNNjPh5HjbwfDkkNzEQYKwEEKITqG4pAIiu6BgpUuS8ViKPROWICyEEEL4Tf5BYzpSbEQ1YWHGWKhEs/U5PJHSsvLgFKwVEoSFEEJ0CoXFxiYNCdE19sfCwxUilFpQwjhQXNPSS4NGgrAQQohO4cAhY9FGc/MGU3S4kSEfKAm90dEShIUQQnQKJRVGSEtLct62MDaqHoCiQ/UBL1NbJAgLIYTwKV3XmTlzJueeey7uLgjlC6W2zRsyUpwfj482gnJJuSVgZXGXbNUjhBDCp959913eeustAEpKSsjIyAjIdStrYyAMuqY555eJsVYog9KKxoCUwxOSCQshhPCZ+vp67r77bvv9QE4LqrZt3tA9K9Lp8aR4Ixsvrwq9lZMlCAshhPCZ559/nl27dtnvBzII1zUak4N7dHNeFSslwQh1FaE3OFqCsBBCCN8oKyvjkUeMlYyTk5OBwAVhXdexkAJA79x4p2NpKcZewtW1obensARhIYQQPvF///d/lJWVMXHiRCZPngzY1nMOgNraWvQIY8nKvK7RTsfMTRwO10c2e12wSRAWQgjhEwsXLgTgkUceITXVWLw5UJnwoUNH143OTHE+ltnFCMp1lqiAlMUTEoSFEEL4RHFxMQCDBg0iJSUFCFwQ3ltYAWGRhFmriIl23r63a3osAPWNMQEpiyckCAshhGg3i8VCRUUFYWFhpKSkBDwI79xnjLqKUiqaHcvoYjRDW5VE6urqAlIed0kQFkII0W6lpaUQO4C4ntdzsCws4M3Re/cbS1LGRlQ1O5aSYMuMI5JCbiclWaxDCCFEu5WUlMCA+VQnnkTO+Tq9My6CrLUBC3oFRebmDYebHbPvKRyRQkVFBV27dg1ImdwhmbAQQoh2KykpgZh+AERHwc7iNOj3IsWlgWn+PVBqbN6Q3GTzBnAIwuGht6ewBGEhhBDttv9AKUSmotDIoU8UspLrQQmjuCIwDa7F5UaTc5eEhmbHUhJtNyIkCAshhOiEdhcaA6NiwyuJj1Xo2sXITMuqAzM3t7TSWJIyK6150E+IBbBCeAKHSpsP3AomCcJCCCHabe8BY5vAxBgjGHdNMzLTytroFl/jS+XVRjjLzoxtdkxRFKLCjIFb+4tDa+1KCcJCCCHaraDYyHxT440+2ewMIwM2N1Xwt+o643p53RJcHo+JMMp18JBMURJCCNHJFNlWp0xPNvpku6Ub6zQ3KKkBmZtb22BkwL1yU1wej4s2gnBJWWjtKSxBWAghRLuVVhmZaFYXoxk6K9U2Nzcy0++DoRobG7HoxhDo3nmpLp+TEGPsJXyovPnArWCSICyEEKLdymuNJSG7ZxoDozLNWBiAIFxSUgIRXQDomuZ6IFhinG2gWJXVr2XxlARhIYQQ7Xb4iNEX26Ob0Sxs30QhMsPvQbiwcD9EGFE/NdH1c8xVs8qrdb+WxVMShIUQQrRbXaOxf3Af216+9kw4yv+Z8M49RaBEEE4tUZGKy+d0STLCXVVNaIW90CqNEEKIDsdisdAYlg5AnzwjIw5kc/TOfOP80eEtTz9KS7aN1q4L92tZPCVBWAghRLscOnQIooz1mLPTjUw0LQnACpHpfl8gY0+BsWlDfHTzJStNGalGEK61BGbesrskCAshhGiX/MJDEJGEoh8hxTZNNzxcIS7CyEz3Haz36/ULDhrXSYprbPE5menGwLG6htAKwm0u6qmqajLwP+A4YLSmaRsdjoUDLwH9gLWapt3mp3IKIYQIUTv2GplolFKGonSzP54YU0tNdQIHDrUcHH3hQIkR5Lu0MCgLIKuLEXwb9XiOHDlCVFSUX8vkLncy4RpgOvC+i2NnAYWapp0MxKuqOsaXhRNCCOGZqqoqamoCuzTjrn1H1412lGxbPctcyMNfisuMIJ/RpeW8MiXR3FM42ZjSFCLaDMKaplk0TStu4fBY4HPb7c+Acb4qmBBCCM/U1dUxYsQITjzxRKzWwM2H3XvACLaJMbVOj6cl2hbIqPLvYKiyauPfrmktNzUf3VM4mb179/q1PJ5o7x5TqYD51acC6NL0CaqqzgJmAcyePZspU6a085KhyWKxUFBQEOxihAypD2dSH86kPpz5qj7effddtm3bBsCWLVtISkpq9zndsXPfYQASo6ud3kdCtBGUSyrCPHp/ntSHrutUHg6HJEiOb2jxdXWHI4AMCE/mxx+/Ijc31+3ytFdOTk6Lx9obhMsB8385GSht+gRN0+YB82x3Q2uWtA8VFBS0WtHHGqkPZ1IfzqQ+nPmiPnRd54033rDfj4uLC1gdV9YZmzR0Sw93umav7o2wCWosCR6VxZP6qKqqogGjM7hfrwxyclw38IZF64AOEclUVlaGzOevvaOjVwGTbbenAt+283xCCCG8sHLlSn766Sf7/YqKwO2bW1pl5HNdm7SFds80pgUd9uNOSvv374dI48JpSa4X6gBINjdXCk9m9+7dfiuPp9wKwqqqLgFOB15SVfUqVVVftB1aBOSpqroCqNM07Ts/lVMIIUQrnn76aaf7/l4gw1FFjbFUpRl0TXndbNOCrP5rFt+/fz9EpAAtL1kJEBsN4WFWCI9l5659fiuPp9xqjtY07cwmD823Pd4AXOXbIgkhhPDEnj17WLhwIZGRkRx//PH89NNPAc2ED1sSIAx65sQ4Pd4j28iALaSg6zqK0nKm6q0DBw5ARF8AurQS6xVFITneSmlVGDvyK1t+YoDJYh1CCNHBPffcc1itVi6++GIGDhwIBLY5us6aAkCfXOdUNDfLmIurR2RQW1vb9GU+YWTCrW/eYOqRZXwJ2Fdk9KGHAgnCQgjRwb333nsA3HzzzaSkpACBbY5uUNIAGNA72enxQKwfbWTCRp9wa4t1APTKNqZKHVG6cvDgQb+Ux1MShIUQogOzWq3s22f0cQ4bNozkZCMQBioTLq04AuHx0FhL967O7cEJsaDodRAeT+FB/5SnoLDIWDJTsZIU3/pz87JsN6LzQmZwlgRhIYTowIqKimhoaCAtLY2YmBh7JhyoILxll3GdsMZiwsKcQ4qiKERSDsAu21xiX8vfbyyZmRDdQFhY633OebbmaGIkCAshhPCBwsJC4OiCEGYmHKjm6O17jEFO0YrrtSnNpSzzD9T55fr7i43zpiS23cfbQzJhIYQQvmSuENU0CAcqE965zxhwFRdZ5fJ4fJSxrnRBkcUv1z94yDhvWlLb4Uyao4UQQvhU0yAc6ObofQfNdaNdbxqRGGNkqgdKfb+WtcVioeKwEcYyW9m8wSRBWAghhE+ZQTg7OxsIfHN0YYkRXLvYdkxqKtX2eHG57+cIHzx40D49qUsrq2WZMlIgOtIKkV3YuafI5+XxhgRhIYTowILdHF1kC64ZKa4z3bRk4/FSP+ykZCxZaQvCbUxPAmOgWG6mcXvPgdCYKyxBWAghOrCmA7MCPU+4ptYCVgtd01xnopmpxuPlh6N8fm1jjrB7C3WYenUzwt4RJSsk5gpLEBZCiA4s2JnwsPhn4dtYJh7veinIbmlGBlxVF+vza69evfroQh1uNEeDY79wj5DoF5YgLIQQPhCsps2mQTg+Pp7w8HBqa2s5csR1P60v5efnAzp5ea63BszNMjZ1qGlIcHncWwcPHjQ2rfAwE7bPFY7OlSAshBAdWX19PR9//DG//e1vSU1NZfLkyW2/yIdqa2spKysjMjKS9PR0wLZRQQCz4b179wKQl5fn8nheNyMDrrMmuzzurb/85S8cPnyYrJxBgHt9wuAwVzhEFuxwaxclIYQQzU2bNo3ly5fb7y9btoyamhri4vy3f64jx5HRjjsUJScnU1paSkVFBRkZGX67fmNjo33JzO7du7t8Ts8cYy3JBlJobNQJD2//KOmdO3fywgsvoCgK3fIGc3Bn6zsoOXKcprRnz6p2l6W9JBMWQggvWCwWVqxYgaIoPPbYY3Tt2hWwjdgNkKaDskyBmit88OBBGhoayMzMJCYmxuVzMjNSwXIIlHAO+WgHwQceeACLxcIVV1xBfaPxhcfd5ugeXW03QmSusARhIYTwwp49e2hsbCQ3N5e7776bHj16ALYRuwHStD/YFKi5wm01RQOkpqaCxZiTm3+w/X3Ue/bsYcGCBURFRfHQQw9Raluoy91MOCcdFEWH6Bx27c5vd3naS4KwEEJ4YceOHQD06dMHgG7dugGBzYTbCsL+zoTdCcLh4eFEKaUA/Lq9tN3XXLduHbquc+qpp9KjRw/KbEHY3Uw4OkohM0UHJYL9h9pdnHaTICyEEF4I5SAcqLnC7gRhgIQoYwelbXtcry/tiW3btgHQv39/aurgiAVioiA22v2+ZnOucGV9SkBGkLdGgrAQQnihaRA2+4SD0RxtLllpCqVMGI4uXblrn+v1pT1hBuF+/fp5nAWbHAdnFRcXt7tM7SFBWAghvBAKmXCwB2aZQTg3N7fV52WkGHOo9/lgJyXHIOxpf7DJMQgXFQV3DWkJwkII4QUzCPfu3Rs4GoSPpYFZxkIdbWfCORnGqlkHXW857BFfZMI9zAU7YiQICyFEh6PrOjt37gSaN0cHKhPWdb3FTDjUmqN7ZBvTl0qrItt1vZqaGvbt20dERAQ9evSg1Dblyd2FOkyOS1dKc7QQQnQwBw8epKamhi5dutibfgPdHF1SUsKRI0dITU0lNtZ5XeZANEfX1NRQUlJCVFQUmZmZrT63b54RJavq27eIiWPrQ0REhD2z9jgTdpgrLJmwEEJ0ME37gwEyMzNRFIXi4mIaGxv9XoaWsmAITHO02RSdm5tLWFjroWRQ3zQA6ho97LxtwrEpGuCz742+5pEDPVuFy9zOkOgcCcJCCNHRuArC5vrNVqs1IH/YWxoZDYFpjnZ3UBbACQOM9t/GsC40NLjed9gdjkG4ukZn6Q/G4+dN8Ow8qYkQGd4AESkUHAjMblMtkSAshBAechWEIbBN0i0NyoLAzBN2tz8YIK1LIjSUghLBznzv1650DMKffg91R2Ds8ZCd7lkmrCgK6Yl1AOQf9P5LgS9IEBZCCA+1FIQDOVe4tSAcyEzYnSAMEIXRgbtxq/cDocwg3LdvXz742miKPn+CdxtCdOtiBN8DZe0bLNZeEoSFEMJDoZ4JOwZhf+1z7O70JFNcpDGfaOsu778YmEE4r0c/Fn9nPHa+h03RptwsI/wdqor2ujy+IEFYCCE81HSOsCmQc4XNa5jXdBQdHU1MTAwNDQ3U1LR/lSpXPM2EU+KM5t+d+6q9ul51dTX79+8nKiqKLcV5VNfCsH7QK9u7TLh3ThQAFXUJ9sd+/fVXXn75ZdavX+/VOb0hQVgIITxQVVVFcXEx0dHRzQZFBXKusDn4q6XpQf5ukvZkYBZAepIxYjz/gHdrNf/t9VLofhe5fcfx3xVG4L3gFO/3Ju5lC8KWsCwOHzbWtv7888+5/vrrefHFF70+r6ci3HmSqqqPA2OB3cA1mqZZbI/HAu8CSUAD8BtN0w76p6hCCBF8jllw06k5gWyONheZaCkIp6SkcPDgQSoqKlyOoG4PXdc9DsLd0sNhP+w/5Hnz+IFDOo/8pzv0eowdwJ7Pjce9bYoGyM1UAB2iulNUVESvXr3Yvn07YPQ5B0qbmbCqqkOAHE3TTgY2Axc6HD4D2Khp2inAfOBafxRSCCFCRUv9wRDY5mgzE87IyHB53J9zhYuLi6mvryc1NZXERPdWysjtamSehyrDPb5evjnjq7GacMVCQyOc0BsG9fQ+E+5uVpvDXOFgBGF3MuGxgO17B58BVwNv2+5vBybabqcCJb4snBBChJrWgnCgmqNramo4fPgw0dHRLQZBfzZHe9ofDNA31+h7rayLbeOZzRWakaV8OX+/oZic469hxACPT+MkJ912IyqH4mINOBqEXf3f+os7QTgVMD9RFUAXh2PbgONUVd0EKMBJTV+squosYBbA7NmzmTJlSrsKHKosFot9tKKQ+mhK6sNZR66PDRs2AJCWltbsPZgjkQsLC9m3bx+K4l6m5ml97Nu3D4AuXbrYV85qKjraGPW7a9cun9f1Tz/9BBhN4e6eO83WJ1xjSWzzNU3rY9P2OCAZjhSSnZXKuAHGe27P22q0gkIGelQmG3/Zzgkn7GHXgQjIupr8Qwmk+LDOXI1gN7kThMsx+nwBkoFSh2NXAis1TXtQVdULgfuBuxxfrGnaPGCe7a5/xsqHgIKCglYr+lgj9eFM6sNZR66PkhIjLRs2bJjL95CQkEB1dTWJiYn2bLQtntaHGXi7devW4uvMrDwsLMzndW2OuO7fv7/b554wJgvmQkNYGpmZmURGtjw/t2l91JirbB0pZMyYM332fhKjDlF5JJXS6mgaGxtpTJoMfZ7mw+9h2oTAjFt25yqrgMm221OBbx2OKRxtgi7BCNJCCNFptTY1CALTJG0OymqpPxj8u4nDr7/+CkCvXr3cfk12uq0vODKDwsKW+8wbGhqaZfd79hv7EEdYi+jevbuHpW1ZanwtALv3NxpN0fFDARja1/u+Zk+1GYQ1TVsHHFRVdQUwGPhAVVVz/PZbwFmqqi4HHgGe9FM5hRAiJBw8aEwAycrKcnk8ECOk25qeBP4dmPX1118DMHbsWLdfExWpEK5XgBLBL9tankRz/fXXM2rUKH744Qf7Y1v3GHOLe3SLbHOzCE9kphjBvaBEMYJwwhAAhgZuXJZ7U5Q0TbujyUM32B6vAKb5ulBCCNGawsJC6uvrPcrEfMFqtbY5NSgQI6TdyYT9NTCrqKiIzZs3ExcXx4gRIzx6bWx4JdXWZDbvLOcMF8c3btzIa6+9hq7rfPLJJ5x0kjHMaO9Boz95yKDWt0z0VPcMnTW7oLgiki1bt0Hc1SjonNAnhDJhIYQIJbquc/LJJzNo0CB+/PHHgF770KFDNDY2kpqaSlRUlMvnBKI52p1M2F/N0d988w0AY8aMabEOWpIUazT/7thb5fL4Qw89ZB/ctnLlSvvjpdXGILOxI3p6WtxW9ehm5KFlNfGs31YPYdF0TakmMU6CsBBCuFRSUsLOnTupr6/noosu8usmBU2Z2a0ZaF0JRCbc1hxh8F9ztBmETznlFI9fm5bYAMCe/XXNjm3YsIH333/fHti///57LBYLdfU6R6yJYLUwcdzx7Sh5c31z4wCoPpLMtkLj9vE9/b8XtCMJwkKIDmXr1q322zt37uSaa67x2yYFTbXVHwyBHZjlTp+wr7+kmP3BEyZ4vlxVV9sE18KS5oHuoYceAuDGG2+kT58+1NbW8tNPP7Flty1rthzghBMGe1foFgzqY8yxrtPTOXjY+H8bfaLn85jbQ4KwEKJD2bJlCwDjx48nKSmJDz/8kGeeeSYg13YnCIfKwCxfNEdXVlYyZMgQ7r77bgBKS0v5+eefiYqKYtSoUR6fr3uWkeUWlzuvmrVu3To+/PBDYmJimDNnDiNHjgSMJulvVhs7J8VHlnvc/N2WPrb1o4nKpjHGyLJHD47x6TXaIkFYCNGhmJnwlClTePXVVwHnvkR/CpUg7MnArPY0R69Zs4YNGzbw17/+la+//pqVK1ei6zqjRo0iJsbzYNWvp1Gm0irnMcHm/+MNN9xAt27d7AOyVq5cyffrjIVJMlN830zcLc12I6obxA8DYGg/n1+mVRKEhRAdipkJ9+/fn/PPP5/ExETKysooKyvz+7VDIQjruu5WJmweO3DgAI2N3gUw8zoAN998M8uWLQO86w8GGNTHaI8+bEnkyJGjuymZX6xOO+00AFRVBYwgvGmb8f/aK9v3+/5GRSpE6odACYfIVKLDKo8G5gCRICyE6FDMP9gDBgxAURT7+sV79uzx+7XNINzawKz09HQiIyMpKyujtrbW52U4fPgwdXV1xMbGEh8f3+Lz4uLiyMrKatcSoWbGDbBp0yaef/55wLv+YIDeObYMODrP6f/LcT3uqhqd4voBZGZmUlxcbA/Cx/dL9eqabYmPOPrlLSelxO2lRn1FgrAQosNobGy0L7Lfr5/RbtijRw8gsEG4tUxYURT71oEtrevcHu5kwabevXsDxvrR7bnWuHHjAGM1q4iICI8W6XDUy1xkLLoH27fvsJ9z9+7d9vJe8oDOBQ+n0++kmwCwKMZOC0OO8+0cYVNybLX99qDcer9cozUShIUQHcaePXs4cuQIOTk5JCQYu/IEMgib045aC8JAQIJwa/3BJnMxk/YG4ZkzZ3LeeecBRlNxaxl4axLjFKLDqyE8lp82Gc31+fn5NDQ0kJ2dzbodMXz6ve3JXc42/o0y6jIvy/MtEN2Rnng08I48zrcDv9zh1opZQggRChz7g02hlgmDf4OwO9OTTGYmvHPnTq+u5Zh1P/vsswBcd911Xp3LlJ5QRUFFAj9vNaYeOTZFPzT/6OC6whrbXoW2IJydjl90S7Pa9wmcPNpPF2mFBGEhRIfh2B9sMoOwucetv1itVrebgs1dfvyxXWMwMuHMzEyys7P58MMPvTqPo9yMRgoqYGu+sXCH+QUhMWcKS36AhFhotFrZdTCWmKSe1JlB2E8DpvKywmAjYK3jpOMDvweRNEcLITqMYGbCpaWlNDY2kpKSYt+rtyWhkgn7Mgj7Sv88YwvDghIjBzQz4W11lwJw64Vw0gBj5PQJE++FiERioqwkJ/isCE6O6238X8azjcjIwIdECcJCiA6jtUzY30HYnZHRplDpE/Zlc7SvDOlvbE9/6HASuq4bQTjxJLYd6kNCLPzhYoXxxxtBODL7agByMsL8Nmp51qUncnrvD3l6duAHZYE0RwshOhBXmXDXrl2JjIykuLiYmpoa4uLi/HJtd/uDwb/N0Z5kwt27dyciIoL9+/dTW1tLbKz7SzLW1dVRVVVFRESEffUtXziuTxyg0xCRQ1FRkRGEs64H4KYZkJasMG6wERBXbTICb44fu2ojIyNYOv9C/12gDZIJCyE6hMOHD7Nv3z4iIyPp2bOn/fGwsDByc3MB//YLexKEQyUTDg8P93oetWOw92UWenSaUi927NhhBGHbalVnjDauM7hnA6mJR1/jr0FZoUCCsBCiQ9i2zVhDuE+fPkREODfiBaJJ2t3pSeAchH29nKanTcTeNkn7oykaoIdZfTF5rPruB6qqaiDhRACG9DUOhYfBxKFHX+OvQVmhQIKwEKJDcNUfbApEEPYkE05KSiIhIYGamhqf72LkzrrRjrwdnOWvIBwTrZAQVQlKBIv+tw5i+0FYLD26Qmri0Yx70oijt7PTA7uKVSBJEBZCdAiu+oNNgZim5EkQBv80STuuG91RgzBA12RjOc/VPx2ABKMpeliTjRMmjTh6W5qjhRAiyEIlE3ZndDT4JwhXVlZisViIj493ewBaqDVHA/TsZjTR1yvZED8EgKF9nbPdAXlHdzny58CsYJMgLIToEMz5pH379m12LBCbOHiaCftjhLQ3gTEUM+FBvWyjtGN6QsJQAIY2+W9VFIW5tyjccA6MPd7nRQgZMkVJCNEh7Ntn7CtrjoR2FGp9wuCfTNjT/mA4GoR37tyJrutuj3T2tNnbEyfa5goT0+toJuxiH9+LT1O4+LTO2x8MkgkLITqAxsZG+/68ZnBzZAbmgoICGhoafH59T5asNJnlDHYmnJ6eTkJCApWVlR7tuezPTLhPji30JI6BqEyS4qzkuffdptORICyECHlFRUU0NDSQnp5OTExMs+PR0dF069aNxsZGvyyQUVZWhsViITk52eX1XTGbo32ZCXuTnSqK4lWTtCeLgnjKPlc41uivHtZfCfg+vqFCgrAQwi1Wq5Xzzz+fm266KeDXNpuiu3fv3uJz/DlC2tOmaPBvc7SngdGbwVn+zIS7Z4BCo/3+8P7HZgAGCcJCCDft2rWLhQsX8q9//cu+cEagmNmtO0HYH/3Cno6MhvY1Rx86dIjBgwfz6KOPOj2+efNmj8sBng/O8mYqlCciIhRS46rt95uOjD6WSBAWQrjFMcN86623AnptdzJhf46Q9iYT7tbNaHPdv38/VqvVo+t9/fXX/PLLLzz66KP27LeiosK+leD06dM9Op87Qfixxx4jNzeXnTt3UlVVRX19PfHx8cTHx3t0LXf16R5uv+1qUNaxQoKwEMItjsHtzTff9PlyjK0xg7DZz+qKrzLh2traZu/NmyAcExNDWloajY2N9kDqrt27dwNQX1/PvHnzAPjPf/5DTU0NEydOpF8/z6KW4whpV3Rd5/nnn2ffvn28+uqrfm2KNp3Q19ibMCoSBub57TIhT4KwEMItjsFt+/btrFmzJmDXDlRz9I4dO0hNTeWKK65wCsTeBGHwvkna8T08//zzWCwWXn75ZQCuu+46j84FR4NwS3WzZcsWexnfffdd+/v1ZxDu1c3crAGiIqU5WgghWmU2R6enG8sXLViwIGDX9mRgVnuC8OrVq6mvr2fBggU8/fTTgPG+X331VcD1QiGt8XZwlpkJh4WFUVhYyP3334+maaSkpHD++ed7dC5wrhtXLRjLli2z3962bRuff/454N8gPOo441/H5SmPRRKEhRBuMYPbbbfdBhjNo/6Yk+uKJ83Re/fu9bqp3JyLDHDHHXewbNkyzj77bA4ePMikSZO44IILPDqft9OUzCB8zTXXAPD4448DcPnll3u0J7ApMTGRLl26UFdXZ29qdvTFF18ARwdhvfjii4B/g/CUkQo/vaLwyLXHbhYMbgZhVVUfV1V1haqqb6iqGtnk2KWqqn6pqupyVVXH+KeYQohgM4Pweeedx4ABAygqKrL/8fYnXdfdao5OSkoiJSWF2tpaSkpKvLqWGSy7du1KQ0MDkydPZsOGDfTv35/33nuPyMjINs7gzJvmaF3X7UH4/vvvJyUlxX7Mm6Zok7kHs3luU2NjI1999RUAf/vb3wAC0hwNMLSfQky0BOFWqao6BMjRNO1kYDNwocOxbOBcYJKmaRM1TfvObyUVQgSN1WolPz8fMDLOmTNnAoFpki4rK6O2tpakpCQSExNbfW57m6TNTPixxx5j1KhRAKSmprJo0SJSU1M9Pp83zdHl5eVUVlaSkJBAbm4u119/PQCqqjJkyBCPy2BqqW7Wrl1LRUUFvXv35oorrnCa/uTvICzcy4THAp/bbn8GjHM4Ng2oB/5ny5ITfFw+IUQIKCoqor6+nrS0NOLj47nwQuO7uJlB+ZM7TdGm9k5TMoNljx49+OCDD5g9ezZLly71eDSyyVxOs2n22Rqz7D179kRRFO6++25uvPFGnn/+ea/KYGopEzb7gydPnkx4eLj9/xb8M0dYOHNnA4dUwOwoqQC6OBzLAtKBKcDvgNnAXx1frKrqLGAWwOzZs5kyZUo7ixyaLBaLX5bL66ikPpx19Pr48ccfAWPua0FBAfHx8cTExFBQUMCmTZucmkzd4Ul9rF+/HjACQluvSUsz9r7bsGEDo0eP9qhMgD3bDwsz8pM5c+YA3q//bGbujqOPXXGsj7Vr1wLGSGzzsfvuu69d5QBITk4GYNOmTU7nWbJkCQDDhg2joKCAU089lWeffRYw6iEYn9uO/vvSVGtfIN0JwuWAbcsLkoHSJse+0jRNV1V1GXBf0xdrmjYPmGe7G7iJhQFWUFDg1jf1Y4XUh7OOXh+rVq0CjNHB5vsYPHgwa9eupaysjMGDB3t0Pk/qo66uDoA+ffq0+RqzHOXl5V7VtzloadiwYfag1R4ZGRn2QJaenk50dHSz56xfv541a9bY+3urqqoAGDhwoE8/M0OHDgWgpKTEft7a2lr7VLMLLriAjIwMZsyYQU5ODgUFBQwbNiwon9uO/vviCXeao1cBk223pwLfOhz7Fhhquz0U8GzXaCFEh2A2kZrNvQDHH29s8rpx40a/XtuT5uj29AlXVVVx+PBhYmNjSUpKavsFboiKiqJHjx5YrdZmC2XU1dUxZ84chg8fzvXXX28f5GY2F5vNx77iqjl61apV1NfXM3ToUHvTc1hYGP/973957bXXGDhwoE/LIJprMwhrmrYOOKiq6gpgMPCBqqov2o5tAPJVVV0OXAP8039FFUIEizlH2AxyACeccAIAP//8s1+v7c7IaFN7grDZH5ydne3THX3M/mTH9ba3bt3KsGHDePzxx+1LWi5evBjwXxA262b37t32KVxmf/CkSZOcnquqKr/97W99en3hmjvN0WiadkeTh25wOHaPT0skhAg5ZlBzDMKBzoQ9CcLe7KRkjow213z2lX79+vH55587BeH77ruPzZs3M3DgQK699lruuOMO+wIZ/grCKSkpJCcnU1FRwaFDh0hPT+fbb42GzVNOOcWn1xLuk8U6hBBtaqs52p/rSHvSHJ2RkUF0dDSlpaVUV1e3+XxHZhA2pxX5iqtMWNM0AN577z1uvfVW4uPj+eWXX8jPz3caHe1rjk3SDQ0N9nJ4M4hN+IYEYSFEm1w1R2dnZ5OamkppaanTSlO+5klzdFhYmNfTlMzmaH9kwnA0CJeXl7Nr1y6io6MZOHAgUVFRjBtnzPx87733KC8vJy4uzj7S25ccm6Q3btxITU0NvXv3lqlIQSRBWAjRqqqqKsrKyoiJiXH6Y60oit+bpKurqykvLyc6OtrtoORtv3CgMuENGzYARp96RITRIzhhwgQAXnrpJeDoHGFfMzPhPXv28P333wOSBQebBGEhRKscm6KbBgZ/B2HHLNjdoORtEPZXJtyzZ0/Cw8PJz8+ntraWn376CTCmQZnMPtnNmzfbX+MPjs3Rq1evBiQIB5sEYSFEq1w1RZv8PULak/5gU6hlwpGRkfatBHfs2MG6deuAo/N2wQiO5nPM+/7gWDdmEDaX5xTBIUFYCNEqVyOjTYHMhN3l7Qhpf2XC4Nwk7SoTVhSFqVOn2u/7OxNev349mzdvJjo62unLgAg8CcJCiFa5GhltMoPwpk2b7PNdfcmT6Ukmbwdm+SsThqNBeNOmTfzyyy8oimJvRTCdfvrp9tuuvvD4ghmEzS8ow4YNIyoqyi/XEu6RICyEaFVrzdGpqank5ORQW1vLrl27vL6GxWLh+eeftw9aAmhoaLAvl+nv5ujq6mqqqqqIiYnxyXKVTZlB+KOPPsJisdCvXz8SEpz3uznttNMIDw8H/JcJp6amOl1X+oODT4KwEKJVrTVHw9FsuD39wosXL+bmm29mxIgR3H///Rw6dIhzzz2XxYsXExUV1WxFp9Z0796dsLAwCgsLOXLkSIvP+/7773nuueewWq1OWbA/RiWbQdicl+vYFG1KTk5m1qxZqKraLEv2FUVRnAK8BOHgkyAshGhVa83R4Jt+4U2bNgFG9vvoo4+SnZ3NkiVLSEtL44svvvBog4jIyEiys7PRdd3enN1UQUEB06ZNY/bs2XzwwQd+Wy3L1HQrxJb6YZ9//nnWrFlDbGysX8oBzlm2DMoKPgnCQogW1dbWUlBQQEREhH1v3KZ8MUJ6+/btAFx77bX069ePI0eO0L9/f1avXs3JJ5/s8flaa5LWdZ1Zs2ZRXl4OwDPPPOPXQVlgfIGJjIy033eVCQeKWTdZWVl+63sW7pMgLIRokbmOcY8ePewLSzR13HHHAUfnuHrDDMKXXXYZ69evZ+HChfzwww/07dvXq/O1FoT//e9/s2TJElJSUkhKSmLlypX2zRP8MSgLICIigt69e9vvB3NEspkJjx492i9N78IzEoSFEC3asWMHgFMAaWrAgAGAsTOQtyOkzSDct29fYmNjmTFjRrsGSLU0TWnv3r3cfvvtAPzzn//k2muvBWDBggWA/zJhONok3a1bN7Kysvx2nbbMnDmTM888kzvvvDNoZRBHSRAWQrTI3AO3T58+LT4nKSmJbt26UVdX59XuRVVVVRw4cICoqCiPpiK1xuy/bjpi+6GHHqKyspJzzz2XmTNnMnv2bBRFsW9A4a9MGI4G4WDPy+3WrRuLFy9m7NixQS2HMEgQFqIDWL9+vVcBrr3cyYThaDa8ZcsWr6/Rp08f+xSd9jI3o//111+dHv/hhx8AuOeee1AUhd69e3POOefYj/szEzZHeE+fPt1v1xAdjwRhIUJccXExo0aNQlVVioqKAnptdzJhOBr0vOkXdmyK9hVXi4hYLBb7lwSzHxvg97//vf22PzPh6dOnU1hYyO9+9zu/XUN0PBKEhQhxP//8M/X19RQXF3PTTTf5de/epgKRCfsjCKenp9O1a1eqq6vtg7O2b9+OxWKhR48eTgtWTJw4kdNOO428vLw232d7devWjbAw+bMrjpJPgxAhzjGwffDBB7zzzjsBua7VarX3qbYVnNqTCZtb/DWdS9teTecvm3ORm845VhSFpUuXsmvXLr/OzxXCFQnCQoQ4M7ANGTIEgJtvvpkDBw74/br79++nrq6O9PR0kpKSWn1uqGXC4H4QBmMKkWSoIhjkUydEiDMD20MPPcS0adMoLS3ljjvu8Pt13e0PBmM0ckxMDIWFhVRWVnp0nVAIwkIEiwRhIUKcmQkPHDiQJ598EoBly5b5/bru9gcDhIeH25uTt27d6vY1Dh8+TGFhIZGRkS0ui+mtpit5/fLLL4AEYRFaJAgLEcJqamrYu3evfcWlAQMGEB8fz/79+ykpKfHrtT3JhMG7fmHHQO+r6Ukmx5W8ampq7F8OBg0a5NPrCNEeEoSFCGHbtm1D13X69OlDZGQkYWFhPlmr2R2eZMLgXb+wv5qiARISEujVqxcWi4UlS5ZgsVjo1asX8fHxPr+WEN6SICxECDMDmpllwtFmVse9d/3BzITdDcLuZMJWq5VHHnmEe++9l/r6ensQ9vXIaJNZV+aIcmmKFqHG9YrsQoiQYAY0M8sEOPHEE4HAZcLuNke3lQlbrVZmzZrFK6+8AhjbFppTgvyRCYMxOOvjjz9m0aJFgARhEXokCAsRwoKVCVdVVVFcXEx0dLTbq0g5buTQ2Njo1Mfb2NjIddddx/z584mNjUVRFBYsWEB0dDTg3yAMUFdXB0gQFqFHmqOFCGGuMmEzCG/atInGxka/XNdsiu7Vq5fb82cTExPJzs6mvr6+2TrXd999N/PnzycuLo7Fixfzr3/9i7CwMOrr6wH/N0ebHJerFCIUSBAWIkTpum7PhB2DcJcuXejevTs1NTX2YOlrnvYHm1rqFzb7ZN9//31OPfVUJk2axHPPPQdAbGysz6cnmfr372/fB1lRFBkZLUKOBGEh3OTtXrneKigo4PDhw6Snp5OWluZ0zN9N0p72B5vMLwuOQbiuro78/HzCw8OZPHmy/fEbb7yRt956i3fffdceKH0tKirKXqZevXoRFxfnl+sI4S0JwkK4Ye3atXTr1o177703YNd01R9s8vfgLG8zYTPTdAzCO3fuRNd1evbsSWRkpNPzL7vsMs4666x2lrZ15hcW6Q8WoUiCsBBtKC8v56KLLqKoqIh33303YNd11R9s8nUmXFFRwRtvvMHll1/OkCFDePnllwHPM2Gzz9VcnQr8t0GDu0aPHg3AqFGjgnJ9IVrjVhuQqqqPA2OB3cA1mqZZmhyfA1yoaZrq8xIKEUS6rnP11VfbdxPasWMHNTU1AWnWdCcTbm8QLigo4KabbuLTTz/FYnH6tWbIkCGMHz/eo/OZQXjTpk3ouo6iKPYg7K8R0G256aabyMvL44wzzgjK9YVoTZuZsKqqQ4AcTdNOBjYDFzY5ngic4Oq1QnR0c+fO5b///S/Jycnk5OSg6zq//vprQK7dWiY8YMAAIiIi2LlzJ9XV1V5f49VXX+Xjjz+msbGRU089lWeeeYbVq1dTWVnJunXrSE1N9eh8Xbt2JSUlhbKyMoqKigD8viBHWyIjIznvvPOIiYkJyvWFaI07zdFjgc9ttz8DxjU5/nvgWV8WSohQUFBQwJ133gnA/Pnz7VmhvxfJMLUWhKOiohg0aBC6rtt3B/KG2Wz84osv8uWXX3LLLbcwatQoEhMTvTqfoijNmqSD3RwtRChzpzk6Fdhvu10BdDEPqKqaDJygadqjquq6JVpV1VnALIDZs2czZcqUdhU4VFksFgoKCoJdjJDRGerjrbfeoqGhgcmTJzNy5EhWrVoFwOrVqz3+HHtaH6WlpeTn5xMXF0dMTIzL1/bt25eff/6Zr7/+mu7du3tUHtP69esByMrK8tn/V48ePVi1ahWrVq2if//+9mb1hIQE+zU6w+fDl6Q+nHW2+sjJyWnxmDtBuBwwd/ROBkodjt0G/LO1F2uaNg+YZ7uru3G9DqmgoKDVij7WdIb6+P777wG44IILyMnJYezYsQDs3r3b4/fmaX2Ye+AOHz68xTm0o0aNYuHCheTn53tV142NjfZR0BMmTCA5Odnjc7iiqipvv/02hYWFpKWlUVhYSHh4OKNHj7aPju4Mnw9fkvpwdizVhzvN0asAc3LfVOBbh2N9gftUVf0M6KeqauDmbwjhRw0NDXzxxRcATJ06FWi+P60/aZoGGAGtJUOGDAGOZrOe2r17N/X19eTk5PgsAIPzCOkdO3a0OD1JCOFGENY0bR1wUFXVFcBg4ANVVV+0HbtC07RpmqZNA7ZpmvYXv5ZWiAD5/vvvqaiooH///vTq1QswFnuIjY2lsLCQ0tLSNs7QPmYQHjFiRIvPGTZsGAA//fSTVwuJmAPMfL2KlGMQDvagLCFCnVtTlDRNu6PJQze4eI5MTxKdxtKlS4GjWTBAWFgYgwcPRtM0Nm3axMknn+y3669duxZoPRPOysoiJyeHgoICtm/fTv/+/T26hr+CcG5uLgkJCRQVFbF69WpAgrAQLZHFOoRw4bPPPgNg2rRpTo8Hokn64MGD5Ofnk5CQ0GZgHT58OAA//vijx9cxRy/7Ogg7rtH88ccfA8GbIyxEqJMgLEQTJSUlaJpGVFQUp5xyitMxc2s8c+CUP5hZ8PDhw9vcwag9QdhfmTA0XzlLMmEhXJMgLEQTX3zxBbquM2HCBOLj452OBSIIuzMoy+RtEHZcdMQf2/s1PacEYSFckyAsRBNmU7Rjf7DJsTla1/0z486d/mCTYxD2pDz79++nsrKSLl26kJGR4V1BW+EYhMPDw+nRo4fPryFEZyBBWIS0iooKZs2axbJlywJyPYvFwqeffgo07w8GY1nGLl26UF5eTmFhoV/K4M7IaFNOTg4ZGRmUlZWxZ8+eVp/7j3/8g7vvvhur1erUFK0oSvsL3YRjEO7Vq5dMTxKiBf7ZxFMIH/nrX//KSy+9xMaNG5k0aZLfr/fBBx9QVFTEoEGDXG59pygKxx9/PN988w0bN270+YIChYWFFBYWkpSU5NZgJkVRGD58OEuXLmXt2rX07NnT5fN+/vln/vSnPwHG6GVzSpO/Nrnv0aMHsbGx1NbWyqAsIVohmbAIWYcOHeLZZ41lyTds2ODVXFhPPf300wDceuutLWaI/hwhbTZFjxgxos1BWSZ3+oX/9re/2W/fcccdLF68GPBPfzAYTdDm7k/SHyxEyyQIi5D15JNP2ncIOnz4sH2JxfZYvXo1w4cPZ+rUqdx+++0sWLCAhoYGAH744QdWr15NSkoKV1xxRYvnMAdnebtSFUBdXR2ffPIJdXV1To87BmF3mc9tKQjv2bOHt99+m/DwcCZPnkxNTY2939tfmTAcrSdXWzEKIWx0XQ/kT6e1b9++YBchpLS3PkpKSvSEhAQd0PPy8nRAf//999tdrt/97nc6xhrm9p9LLrlEt1gs+m9+8xsd0O+4445Wz6Fpmg7o/fr1c/u6Tetj7ty5OqCfc845utVq1XVd161Wqz5x4kQd0P/zn/+4fe6dO3fqgJ6RkWE/l6NbbrlFB/SZM2fqJSUlelZWlv2979692+3reGr79u363XffrR8+fLjZMfl9cSb14awT1keLcVEyYRGSnnrqKaqrq5k6dSqXXXYZ0L7M07Rjxw4A7rnnHv7yl7+QmJjIO++8w8UXX8y7775LWFgYN998c6vnOPHEE4mLi2Pbtm0UFxd7VQ5z+8GPP/6YJ598EoAnnniC5cuXExcX12x+cmt69uxJSkoKxcXFzQaLlZSU8PLLLwNw5513kpaWxosvvghAcnIyubm5XpXfHX369OGxxx4jLi7Ob9cQoqOTICxCTmlpKc888wwADzzwQLs3KnBkBuGZM2dyzz338Nlnn5GQkMDChQtpaGhgxowZbU6niYyM5KSTTgLgu+++86oce/futd++6667eOCBB7jrrrsAeOONN+jatavb5zIHZ0HzJulnn32W2tpazjjjDE488UQAzj33XN5++23ef/99t/udhRD+Ib+BIuTMnTuXqqoqpkyZwpgxY3wWhBsaGuzTeMxNGcaOHcvixYvt2dptt93m1rnMbQ3NPYY9lZ+fDxjToBobG3n44YfRdZ2//OUvnH/++R6fzwzCa9ascXp8/vz5gJEFO7r00kuZPHkyQojgkiAsQkpZWZl9hPIDDzwAQP/+/YmOjmbPnj2Ul5d7fe69e/fS0NBATk4OsbGx9scnTJjAd999x8KFC93elGHMmDGAd5mwruv2TPi1116zB/TLL7+cu+++2+PzwdEvBd9+e3Sn0YKCAvbs2UNSUhITJkzw6rxCCP+SICxCyty5c6msrGTSpEmMGzcOgIiICPtI2w0bNnh9brMpuk+fPs2OnXjiicyYMcPtc40ePRowRlRbLBaPylFeXk51dTUJCQlkZGSwdOlSFi1axKuvvur1whlmEF69erW9POYXhNGjR0uzsxAhSn4zRcgoLy9vlgWbfNEk3VoQ9lR6ejoDBgygrq6OdevWefRaMwvOy8tDURQSEhKYPn16u1aVysrKol+/ftTU1NjryGwqNwO0ECL0SBAWIePpp5+moqKCU089tVmzcKgFYfC+X9jsD87Ly/NJOUzjx48HYOXKlU7lkiAsROiSICxCgtVqdRoR3VQoB2FP+4UdM2FfMpvvv/32W+rq6vjxxx9RFIVRo0b59DpCCN+RICxCwu7duyktLaVr164u58ia02s2btxoX+HKU74OwubgLE8zYTMI+3qOrmMmrGkaFouF448/nqSkJJ9eRwjhOxKERUgw12E212VuKjU1lby8POrq6ti2bZvH59d13edBeNCgQSQnJ5Ofn29vYnaHvzLh/v37k56ezoEDB3jzzTcBaYoWItRJEBYhYePGjcDR9YZdaU+TdFFREYcPHyYlJYUuXbp4V8gmwsLCvJqq5K8+YUVR7E3S5vxgCcJChDYJwiIkmEG4pUwYjgZhT0cjg++bok1mf6u5B7A7/JUJw9Em6fr6euBok7kQIjRJEBYhwWyObi0TNgOKuQOQJ/wVhNvawaiphoYGCgoKAHy+FzEcDcJgTKOSvXyFCG0ShEXQHTlyhC1btqAoSqv7206aNInExETWr19vD6ru8lcQdlyzWdf1ZscrKyt5+umnWbhwIQD79++nsbGRrl27Eh0d7dOymOWJiYkBjKZobxf/EEIEhgRhEXRbt26loaGB3r17Ex8f3+LzoqOjOeusswD48MMPPbqGv4JwdnY2mZmZlJWV2delBiP4PvLII/Ts2ZPbbruNW265hZ07d/q1KRogKirKvrmE9AcLEfokCAsnW7duZdWqVaxatQpN07yeDuQJd5qiTRdccAHgfRD2dfNsSzsYnXvuufz5z3+mrKzMPkXotdde89ugLEd33XUXp556KldccYXfriGE8A0JwsJu1apVDBgwgHHjxjFu3DhGjhzpcuEMX3NnZLRp2rRpxMbGsnr1avbt2+f2NfyVCQPNgnB+fj7Lly8nPj6er776yv6F4bXXXmP37t2Af4PwmWeeyZdffkl2drbfriGE8A0JwsLu/fffB4xN4s0mzRdeeIG6ujq/XtedkdGm+Ph4pk2bBsB///tfl88pLy/n9ddfZ8aMGaiqyogRIygqKiI6OtovgalpEP78888BmDx5MhMnTuTUU0+le/fu7NmzhzfeeAPw/UIdQoiOSYKwsFu6dClgzDFdvXo1w4YNo7S0lA8++KDd566rq2Pp0qVYrdZmxzzJhAH7fruuynXnnXeSmZnJlVdeyUcffcTatWvtwXHMmDF+2U3IDMJr165F13X76O2pU6cCxnziCy+8EIBffvkF8G8mLIToOCQIC8BoQv3ll19ITExkzJgxKIrCjTfeCBjZcHvdcccdTJs2jccee8zp8erqanbu3ElkZCT9+/d361xnnXUWkZGRfPPNNxQXF9sfr6io4Mknn8RisXDqqafy3HPP8cMPP7BmzRrWrFnDp59+2u734UrPnj1JSUmhqKiI/Px8vvjiC+BoEAa4+OKLnV4jQVgIARKEhY2ZBZ922mlERUUBcNlll5GQkMDKlSvZtGmT1+cuLS3llVdeAeCJJ56gvLzcfszMDAcOHOj2Vn4pKSlMmjQJq9XKRx99ZH98+fLlNDY2Mn78eL788ktuuukmRo4ciaqqqKpqn7rja46Ds1544QXKy8vp168fvXv3tj8nLy+PiRMnOt0XQgi3grCqqo+rqrpCVdU3VFWNdHj8bFVVv1dVdaWqqk/7r5jC38wmVLO/FSAxMZHLL78cgHnz5nl97pdeeona2lrAyFafeuop+zFPm6JNrkZJmxno5MmTvS6rt8wg/NxzzwHOWbDp6quvBoypVhkZGYErnBAiZLUZhFVVHQLkaJp2MrAZuNDh8HpgnKZp44FMVVVV/xRT+FNDQ4PLJlTA3iT92muvUVNT4/G5LRYLzz77LAD33nsvAE899RSlpaWAZ9OTHJ177rmEhYXxxRdf2DPrUAjClZWVgPOXGdMFF1yAqqpceumlsoiGEAJwLxMeC3xuu/0ZMM48oGnaXk3TzImkR4Dmo25EyPv++++pqKigf//+9OrVy+nYkCFDGDVqFBUVFfbR055YuHAh+/btY8CAATz88MNMnjyZqqoqnnzySQoKClixYgXg3shoRxkZGUyYMAGLxcLixYvZt28fmzdvJjEx0T6yO5DMIAzGghmOTc+m+Ph41qxZY99cQQghItx4Tiqw33a7Ami2BY2qqiOBTE3Tmi2gq6rqLGAWwOzZs5kyZYr3pQ1hFovFviZwR/Pee+8Bxqbwrt7DOeecw/fff88HH3zApEmT3DqnWR9/+9vfALjyyivZv38/s2fP5osvvuDxxx/nscceQ9d1FEWha9euHtffaaedxvLly3nzzTcpKioCYPTo0fbbgRQXF0d8fDyHDx/mpJNOory83KnvuyN/PvxB6sOZ1IezzlYfra4Tr+t6qz8jRoy4acSIEb+13R4xYsSIZ5sc7z5ixIhvR4wYkdnWufRObN++fcEugktHjhzRCwsLW33OyJEjdUBfvHixy+Nbt27VAT09PV1vbGx067r79u3Tly1bpgN6SkqKXl1dbT925pln6oAeExOjn3/++fqSJUvcf0MO8vPzdUCPjY3VZ8yYoQP6008/7dW5fGH8+PE6oP/tb39rdixUPx/BIvXhTOrDWSesjxbjojvN0asAs5NtKvCteUBV1UTgP8ANmqYFPv0QbbrzzjvJzc3lm2++cXn8888/R9M0oqKiOOWUU1w+p2/fvuTl5VFSUuL2Xr7V1dVce+21APzhD39wWhP6rbfe4tNPP6WoqIgPPviAM844w8N3ZejevTujRo2itrbWvnBHMPqDTXfddRdnnXUWV111VdDKIIToWNoMwpqmrQMOqqq6AhgMfKCq6ou2w7cBvYBnVVVdrqqq67/iImiWLFlCY2Mjc+fObXbss88+45xzzkHXdW655ZYWN09QFMUe3MzBT235y1/+wu7duxk2bBhz5sxxOpacnMy0adNITEz07M24YI6SBmMzhUGDBrX7nN4666yz+OSTT2TksxDCfa2lyX746bRCsfmkoqJCB3RADw8P1wsKCuzHFi9erEdFRemAftNNN+lWq7XVc7399ts6oJ9++ultXvfzzz/XAT0yMlLfsGFDu99Ha7Zv325/j7/97W/9eq32CMXPRzBJfTiT+nDWCeujxbjozsAs4WdWq5WFCxfap+1ER0dz/vnnk5CQ0K7zOjYdNzY28sorr3D//fezY8cOLrzwQo4cOcLs2bN55pln2pwyc9pppwGwYsUK6urqWlz4YseOHfZm6AcffNDjUc+e6tOnD0OGDGH9+vVBbYoWQgivtBah/fDTabXnm9s777xjz+bMn2uvvbbdZZo7d64O6P369dMBPTc3V7dYLPrEiRN1QL/kkkvazIAdDRkyRAf0ZcuWuTz+5Zdf6l26dNEBffjw4brFYmn3e3DHDz/8oN933336kSNHAnI9b3TCb/btIvXhTOrDWSesj3YNzBJ+9sknnwBwyimncM011xAeHs78+fPt2+95y9y44Pe//z29e/cmPz+fSy+9lOXLl5ORkcGzzz7r0aIRrfULv/zyy5x++umUlpYyffp0FixYQEREYBpaRo4cySOPPOL2spdCCBEqJAgHmdVqtW999/zzz/PKK69wxRVX0NjYyKOPPtquc5tBWFVVbrjhBuDozkP//Oc/SU9P9+h85hzvpkH44MGD3HjjjTQ0NHDnnXfy0Ucf+WTQlRBCdHYShINs3bp1FBUVkZubax/Ze9999xEeHs4bb7zBtm3bvDpvTU0Nv/zyC+Hh4Zx44olcddVV9kzx3HPPbbarjzvGjx9PVFQUmqbZ+6/h6AjsadOm8fjjjxMeHu5VmYUQ4lgjQTjIzN2Lpk6dam8a7tOnD1deeSWNjY088sgjXp33559/xmq1MmjQIGJjY8nMzGTOnDmMGDGC559/3qu1i+Pj4xk3bhy6rtub0AEWL14MGFN0hBBCuE+CcJC52r0IjGw4IiKCBQsWsGXLFo/PazZFO65p/PDDD6NpGtnZ2V6X97LLLgOwr3985MgRe3P69OnTvT6vEEIciyQIB1FlZSWrVq0iPDy82ZrMvXr14oorrsBqtfLWW295fG5XQdgXLrnkEmJjY1m+fDk7d+5k5cqVVFVVcdxxx9GzZ0+fXksIITo7CcJB9OWXX9LQ0MDo0aNJSUlpdtxs3l25cqXH5/ZXEE5KSrKvUvXaa6/Zm6IlCxZCCM9JEA4ix/5gV8aNM3aNXL16NRaLxe3zHjlyxL5P79ChQ9tXSBfMzennz5/PokWLAAnCQgjhDQnCQaLreov9waasrCz69etHTU0N69atc/vcy5cvx2Kx0L9/f79MFZo4cSI9e/Zk7969bN26leTkZMaOHevz6wghRGd3TAfhX375hYEDB5KdnU12dja9e/dm2bJlAbn29u3b2b17N2lpaa02GY8fPx5wv0n6nXfe4dxzzwVaDu7tFRYWxpVXXmm/P3XqVFkoQwghvHBMB+GHH36YLVu2sH//fvbv38+uXbu47bbbsFqtfr/2V199BcCpp57a6rxad4Ow1Wrl/vvv59JLL6Wuro5rr72Wv//9774rcBOOQViaooUQwjvHbBDeu3cv77//PuHh4axbt469e/eSm5vLxo0bef/99/1+fccg3BrHIKzrusvnVFdXc+GFF/Loo48SFhbG3Llzeemll4iKivJtoR2Yo7d79+7N2Wef7bfrCCFEZ3bMBuFnn32WxsZGLrroIoYMGUJubi733nsvAA899BCNjY3tOv+GDRv4xz/+QV1dXbNjuq6zfPlyoO0g3K9fPzIzMykqKmL79u3Nju/evZtx48axcOFCkpOT+fTTT/n973/v1WIcnnr99dfZsWMHqampfr+WEEJ0RsdkEK6urmbevHkA3H777fbHr776anr06MEvv/zCu+++265rXHnllfzpT3/inHPOoba21unYli1bOHDgAFlZWQwcOLDV8yiK0mKTdF1dHRMmTGDDhg3079+fH374gdNPP71d5RZCCBE4x2QQnj9/PhUVFYwdO5aTTjrJ/nhUVJQ9G3744Ye9zoZ//vln+2jm//3vf5x99tnU1NTYj5tN0RMnTnQrY20pCK9Zs4b8/Hz69OnD999/T//+/b0qrxBCiOA45oKw1Wrl6aefBuAPf/hDs+NXXXUVvXr1YvPmzXz44YdeXeONN94A4MwzzyQrK4tly5Zx9tln2+f6mk3REydOdOt8LQXhVatWAXD66ae7XOxDCCFEaOt0Qbi6upotW7a0OIhp0aJFbN++nZ49ezJjxoxmxyMjI7ntttsAWLBggcfXb2xstL/u3nvvZfny5XTt2pUvv/ySZ5991qP+YNPQoUOJi4tj69atHDx40P64GYTHjBnjcTmFEEIEX6cLwpdddhkDBw5kwoQJ/O9//2sWjJ966ikAbr311hanBl188cWEhYXx6aefUl5e7tH1v/zySwoLC+nTpw9jxoxh4MCBvPzyywA8+OCDLF++nKKiIrp16+Z283FkZKR9MYz//e9/gDG467vvvgOQhTKEEKKD6lRBePv27fZlFFeuXMnpp5/OpEmTqK6uBoy9e5cvX05iYiLXXntti+fp2rUrEydO5MiRIyxcuNCjMrz++usAXHHFFfb+3unTpzN9+nQqKyu55JJLAPf7g03mAhxmE/mOHTsoLi4mMzOT3r17e1RGIYQQoaFTBeFXX30VgIsuuojHHnuMtLQ0vvrqK2655RbgaBZ87bXXkpSU1Oq5zC37/vOf/7h9/erqanuQvOKKK5yOPfXUU0RGRlJcXAy43xRtMpvOP/vsM2pqapyaogMxHUkIIYTvdZog3NDQwL///W/AaGq+++67+frrr4mNjWX+/Pk88cQTvP3224SFhXHrrbe2eb7zzz+fiIgIli1bRlFRkVtlePXVV6mpqWHcuHHNstN+/fo5DQRzd1CWqXv37px00knU1taydOlSexCWpmghhOi4Ok0QXrJkCQcOHGDgwIH23YcGDx5sHwl9xx13YLFYmDFjBr169WrzfF26dGHq1Kk0Nja6tYJWZWUlDz/8sP1artx3330cd9xxjB8/nr59+7r71uzOP/98wGiSlv5gIYTo+DpNEH7ppZcAuO6665yaZ6+77jouuugi+33HxTna4kmT9HPPPcehQ4c4+eSTOeecc1w+JyEhgZ9//pkVK1Z41YR83nnnAfDxxx/z888/ExERwYgRIzw+jxBCiNAQEewC+EJ+fj5LliwhMjKyWV+soijMmzePvXv3kpeXZ8+S3XHOOecQExPDihUr7K8HY2Tyq6++SlhYGL/5zW8oKiqyj4B+4oknWg2wYWHef+/p378/gwcPZtOmTQAMHz6c2NhYr88nhBAiuDp8JlxSUsLZZ5+N1WplxowZZGZmNntOSkoKq1ev5t133/UoA01MTLQPiHrllVfsj69cuZLrrruOa665hn79+nHJJZdQX1/PJZdc4rQClz+YTdIgTdFCCNHRdeggXFxczKRJk1i/fj39+/dn7ty5Pr/GDTfcAMDLL79MQ0MDYDQ9gxGk8/Pz+e6774iMjOSxxx7z+fWbcgzCskiHEEJ0bB02CBcVFXHaaaexYcMGBgwYwFdffUV2drbPr3PKKacwYMAACgsLWbRoEfv37+eDDz4gLCzMvu3h5MmTefDBBwMyX3fIkCEcf/zxxMbGcvLJJ/v9ekIIIfynw/YJL1myhI0bNzJw4EC+/PJLunXr5pfrKIrCrFmz+OMf/8iLL77I+vXraWho4PzzzycvL4+8vDwuuOACCgoK/HJ9V+X54osvqKys9Nt7FkIIERhuBWFVVR8HxgK7gWs0TbPYHg8HXgL6AWs1TbvNP8Vs7qqrrsJqtTJ9+nSysrL8eq0rr7ySe+65h6VLl7JmzRoAbr75Zr9eszVZWVl+f89CCCH8r83maFVVhwA5mqadDGwGLnQ4fBZQaDsWr6pqQDspr7nmmoAEo7S0NC666CJ0XefQoUMMGjTI4xWvhBBCiKbc6RMeC3xuu/0ZMM7NY52KOUAL4KabbpKlIoUQQrSbO83RqcB+2+0KoEuTY5UtHANAVdVZwCyA2bNnM2XKFK8LG0w9e/Zk7Nix7Nu3j8mTJzfrA7ZYLAHrF+4IpD6cSX04k/pwJvXhrLPVR05OTovH3AnC5YC520EyUOrmMQA0TZsHzLPddb3JbwexcuVKdF13ueBGQUFBqxV9rJH6cCb14Uzqw5nUh7NjqT7caY5eBUy23Z4KfOvmsU5HUZR2rXglhBBCOGozomiatg44qKrqCmAw8IGqqi/aDi8C8mzH6jRN+85vJRVCCCE6GbemKGma1nRboBtsjzcAV/m4TEIIIcQxQdpWhRBCiCCRICyEEEIEiQRhIYQQIkgkCAshhBBBIkFYCCGECBIJwkIIIUSQSBAWQgghgkTR9Q69kqQQQgjRYUkmLIQQQgSJBGEhhBAiSCQICyGEEEEiQVgIIYQIEgnCQgghRJBIEBZCCCGCRIKwEEIIESQShIUQQoggkSDsAVVV423/KsEuS6hQVTXO9q/UCaCqag/bv1IfgKqqo6QujlJVNS/YZQglqqqmBrsMwSYrZrlBVdXTgeuBQuBxTdMKg1ykoFNVdQZwOZAP/P1YrxPbl5G/AbnAhZqmWYJcpKBSVXUI8DSwGvizpmlHglykoFJVdRowG6gH3gY+0zStOrilCh5VVU8B/giUAM8BmzRNqwtuqYJDMmH3/AZ4GdgI3Kiq6slBLk9Qqap6FnA18DhQDtxle/yYzXg0TasBjgCJGHVzTNcHcDLwmKZpc4DewS5MMKmqGg7cCMwDHgJUIP4Y/3xcAvwb4wvJmcAFwS1O8EQEuwChyJbVXAKsBA4Ce4EfgK9sj49QVXXHsZT92erkMuBT4EfgOk3TilVV3Qr8R1XVTE3TioJayABy+Ix8o2naDtsf1O3Ah8Ctqqp+pmna3qAWMoAcf2c0TdsG1ADTVFWdA+xXVXUN8ImmaTuCWc5AsdXHpcDXQDXwM0ZL2h5gKBALRGJ8cev0VFWNBf6M0QLwNbAL2I/xd7UOmK6q6kBN0zYHsZhBIZlwE6qqXgYsB+KAnZqmVQJdgTG2JrWfgBggOWiFDDCHOokBijRNK7QF4DCMzG/XMRaAzfqIxfiChqZpOnAcxufiQ+AGVVVzg1XGQGpSH7ttD8cB3YA/ATdhNMNOD0LxAq5pfWiadhBYhtF98xNGE+z1wM3BKmMg2X4P3sZIaL6zPawAvQAd+AXj96hvUAoYZBKEHaiqmgRcDDyC8UszWVXVdOBfwHWqqsZrmrYR6AH0DFpBA8hFnUxUVXUggKZpVow/tg225+Z19ia2JvXxJXCKqqqDbYe/xmglOIzxB/dW22s67e+Zi/o4VVXVbOADjEwvV9O0CozgbH5OOu1nxMXvyyRVVftpmrYc+AJ4TtO0y4FFQJSqqmGduT5sIoCPMVoSb1FVdSywFBgLDNY07RDGF/xY6NyfD1eO+YFZttGKfwIWA98CE4DbgSjgE+C3wCnALIwP0wqMPr8PNE1bFIwy+1sbdfIxRp2cq2nablVVr8X4ZaoA0oCbO9uAEzfr43TgBmAiUITR9HhY07T7g1Bkv3Lzd2YSRl2ciJHpnAls1zTtoSAU2a/c/HycgdES0A0jAM0GyjRNuzUYZfYnh/r4GGMcTXfb/QKML6lXAX8FTgCSgM3AWRhdOy8HochB1Wm/obtDVdXuwD8w+mm6Aq9rmrYE+DtwqqZpTwCvA3/TNO1xjF+wG4ANnTgAt1Un/8AYUPG47SV5GEF4m6ZpV3bCAOxOfbwOPAg8Abyqadqlmqb9oZMGYHd+Z17DmEXwHkYz5ChgVScNwJ58Pl4Fttlu/9BJA7BjfeQAz2uapmF8QT+iadoC2/HTgTcwum5OAdYciwEYjtEgrKrqBIcmjxRN0/6hadprQKKqqndrmvY5Rp8FwFwgTlXVRFuT0pWapj0V+FL7l4d18iy2pkWMJrYxmqb9K8BF9isP6+NpjG/0aJr2pu31nep3y4v6iFJVNUnTtF+AP8rng3ggRtO0tzFakf4ZhGL7TSv1kayq6nXAX4CTADRN+wwYaHveRuDWzlYfnuhUfyjaoqpqgqqq/8PoqzkTY4DESlVVb7A9ZQVwjqqqKZqmNaqqOgH4L8ao12oATdMamp+542pHnewE0DRthaZp5YEvuX+05zNim6YE2PvLO7x21McO26BGNE1rDELR/aKdn4/DAJ1pzrQb9fENcI3t35Wqqj5ge36h7bmd6vPhjWOuT1hV1REYCyqchDFJPMX2726MQHsYI7vbBLyE0bz4QTDKGihSJ86kPpxJfTiT+nDmRn3UY3wR+Q7IwhiM9XkQihqSjrkgbFJV9RmMfpk3VVXthtGcuB24DVigadqBYJYvGKROnEl9OJP6cCb14ayN+njjWJrG6IljqjkanIa/L8CYTpGpadp+jPmd72FMP6rqbH16rZE6cSb14Uzqw5nUhzM366P6WJt65K5jNhMGUFX1FqAPUAbsALZqmvZDcEsVXFInzqQ+nEl9OJP6cCb14blj4ptaUw7fUE/EmL+3U9O0N4/lD4vUiTOpD2dSH86kPpxJfXjvWM+ELwAWaZpWH+yyhAqpE2dSH86kPpxJfTiT+vDcMR2EhRBCiGA6JpujhRBCiFAgQVgIIYQIEgnCQgghRJBIEBZCCCGCJCLYBRBCeE5V1TjgToxN4+erqnoVxu5Wd9h2MhJCdACSCQvRMcUBD2DszQrwNXAZxn6+QogOQjJhITomzfbvKaqq6hj7t/YA7gC2qKq6G0jH2Nv3cmAlxhaU8zB+76/WNO0zVVWjgMcwAng88D/gJk3TigP4XoQ4ZkkmLETHdI/t318xAqirJuh427/fYWwz9y+MzeYzgb/ajt0N/BEjg56LsdrRC34psRCiGQnCQnRM5lZwRZqm/QfbftdNWIHbAXMbvTc0TXsGYy/XXrbHzrL9ewNG83Y8MMUvJRZCNCPN0UJ0TO4sdVeradoRVVUttvsVtn8bgXCH5zVgBGNzc3X5ci5EgMgvmxAdUyVGpttXVdWZGP3B3liE8WX8SiAPmIaRFQshAkCCsBAdkKZpFoz+3RTgTY5msZ76P9t5TsYYuHUGxkhrIUQAyAYOQgghRJBIJiyEEEIEiQRhIYQQIkgkCAshhBBBIkFYCCGECBIJwkIIIUSQSBAWQgghgkSCsBBCCBEkEoSFEEKIIPl/Kz2+hB8cJ1cAAAAASUVORK5CYII=", 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\n", 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", 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\n", + "image/png": 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", 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\n", 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", 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" ] diff --git a/examples/05-TCN-examples.ipynb b/examples/05-TCN-examples.ipynb index dcf1459d42..06f7dd87e8 100644 --- a/examples/05-TCN-examples.ipynb +++ b/examples/05-TCN-examples.ipynb @@ -28,21 +28,18 @@ "source": [ "%matplotlib inline\n", "\n", - "import numpy as np\n", - "import pandas as pd\n", + "import warnings\n", + "\n", "import matplotlib.pyplot as plt\n", - "from pytorch_lightning.callbacks import TQDMProgressBar\n", + "import pandas as pd\n", "\n", "from darts import TimeSeries, concatenate\n", - "from darts.utils.callbacks import TFMProgressBar\n", - "from darts.models import TCNModel, RNNModel\n", "from darts.dataprocessing.transformers import Scaler\n", - "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n", - "from darts.metrics import mape, r2_score\n", + "from darts.datasets import AirPassengersDataset, EnergyDataset, SunspotsDataset\n", + "from darts.models import TCNModel\n", + "from darts.utils.callbacks import TFMProgressBar\n", "from darts.utils.missing_values import fill_missing_values\n", - "from darts.datasets import AirPassengersDataset, SunspotsDataset, EnergyDataset\n", - "\n", - "import warnings\n", + "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", @@ -120,7 +117,7 @@ " save_checkpoints=True,\n", " model_name=model_name,\n", " force_reset=True,\n", - " **generate_torch_kwargs()\n", + " **generate_torch_kwargs(),\n", ")" ] }, @@ -294,7 +291,7 @@ " save_checkpoints=True,\n", " model_name=model_name,\n", " force_reset=True,\n", - " **generate_torch_kwargs()\n", + " **generate_torch_kwargs(),\n", ")" ] }, @@ -491,7 +488,7 @@ " save_checkpoints=True,\n", " model_name=model_name,\n", " force_reset=True,\n", - " **generate_torch_kwargs()\n", + " **generate_torch_kwargs(),\n", ")" ] }, diff --git a/examples/06-Transformer-examples.ipynb b/examples/06-Transformer-examples.ipynb index dcee081139..771df38afd 100644 --- a/examples/06-Transformer-examples.ipynb +++ b/examples/06-Transformer-examples.ipynb @@ -40,26 +40,17 @@ }, "outputs": [], "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import numpy as np\n", - "import pandas as pd\n", - "import shutil\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "from tqdm import tqdm_notebook as tqdm\n", + "import warnings\n", "\n", - "from tensorboardX import SummaryWriter\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", - "from darts import TimeSeries\n", "from darts.dataprocessing.transformers import Scaler\n", - "from darts.models import TransformerModel, ExponentialSmoothing\n", - "from darts.metrics import mape\n", - "from darts.utils.statistics import check_seasonality, plot_acf\n", "from darts.datasets import AirPassengersDataset, SunspotsDataset\n", - "\n", - "import warnings\n", + "from darts.metrics import mape\n", + "from darts.models import ExponentialSmoothing, TransformerModel\n", + "from darts.utils.statistics import check_seasonality\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", @@ -118,7 +109,7 @@ } ], "source": [ - "\"the 'air passengers' dataset has {} data points\".format(len(series))" + "f\"the 'air passengers' dataset has {len(series)} data points\"" ] }, { @@ -234,7 +225,7 @@ " plt.figure(figsize=(8, 5))\n", " series.plot(label=\"actual\")\n", " pred_series.plot(label=\"forecast\")\n", - " plt.title(\"MAPE: {:.2f}%\".format(mape(pred_series, val_series)))\n", + " plt.title(f\"MAPE: {mape(pred_series, val_series):.2f}%\")\n", " plt.legend()\n", "\n", "\n", @@ -421,7 +412,7 @@ } ], "source": [ - "\"the 'monthly sun spots' dataset has {} data points\".format(len(series_sunspot))" + "f\"the 'monthly sun spots' dataset has {len(series_sunspot)} data points\"" ] }, { diff --git a/examples/07-NBEATS-examples.ipynb b/examples/07-NBEATS-examples.ipynb index 7178bb568f..c495f7b91c 100644 --- a/examples/07-NBEATS-examples.ipynb +++ b/examples/07-NBEATS-examples.ipynb @@ -29,19 +29,18 @@ "metadata": {}, "outputs": [], "source": [ + "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "\n", "from darts import TimeSeries, concatenate\n", - "from darts.utils.callbacks import TFMProgressBar\n", - "from darts.models import NBEATSModel\n", - "from darts.dataprocessing.transformers import Scaler, MissingValuesFiller\n", - "from darts.metrics import mape, r2_score\n", + "from darts.dataprocessing.transformers import MissingValuesFiller, Scaler\n", "from darts.datasets import EnergyDataset\n", - "from darts import concatenate\n", - "\n", - "import warnings\n", + "from darts.metrics import r2_score\n", + "from darts.models import NBEATSModel\n", + "from darts.utils.callbacks import TFMProgressBar\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", @@ -71,9 +70,7 @@ " ts_transformed = ts_transformed.drop_before(start_date)\n", " ts_transformed.univariate_component(0).plot(label=\"actual\")\n", " pred_series.plot(label=(\"historic \" + forecast_type + \" forecasts\"))\n", - " plt.title(\n", - " \"R2: {}\".format(r2_score(ts_transformed.univariate_component(0), pred_series))\n", - " )\n", + " plt.title(f\"R2: {r2_score(ts_transformed.univariate_component(0), pred_series)}\")\n", " plt.legend()" ] }, diff --git a/examples/08-DeepAR-examples.ipynb b/examples/08-DeepAR-examples.ipynb index dd957040fa..c6e82d1dda 100644 --- a/examples/08-DeepAR-examples.ipynb +++ b/examples/08-DeepAR-examples.ipynb @@ -40,31 +40,22 @@ }, "outputs": [], "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import numpy as np\n", - "import pandas as pd\n", - "import shutil\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "from tqdm import tqdm_notebook as tqdm\n", + "import warnings\n", "\n", - "from tensorboardX import SummaryWriter\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", + "import darts.utils.timeseries_generation as tg\n", "from darts import TimeSeries\n", - "from darts.utils.callbacks import TFMProgressBar\n", "from darts.dataprocessing.transformers import Scaler\n", - "from darts.models import RNNModel, ExponentialSmoothing, BlockRNNModel\n", - "from darts.metrics import mape\n", - "from darts.utils.statistics import check_seasonality, plot_acf\n", - "import darts.utils.timeseries_generation as tg\n", - "from darts.datasets import AirPassengersDataset, EnergyDataset\n", - "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n", - "from darts.utils.missing_values import fill_missing_values\n", - "from darts.utils.likelihood_models import GaussianLikelihood\n", + "from darts.datasets import EnergyDataset\n", + "from darts.models import RNNModel\n", "from darts.timeseries import concatenate\n", - "import warnings\n", + "from darts.utils.callbacks import TFMProgressBar\n", + "from darts.utils.likelihood_models import GaussianLikelihood\n", + "from darts.utils.missing_values import fill_missing_values\n", + "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", diff --git a/examples/09-DeepTCN-examples.ipynb b/examples/09-DeepTCN-examples.ipynb index f6e721b0ae..a8aa9f0e77 100644 --- a/examples/09-DeepTCN-examples.ipynb +++ b/examples/09-DeepTCN-examples.ipynb @@ -34,19 +34,19 @@ }, "outputs": [], "source": [ + "import warnings\n", + "\n", "import pandas as pd\n", "\n", + "import darts.utils.timeseries_generation as tg\n", + "from darts import TimeSeries, concatenate\n", + "from darts.dataprocessing.transformers import Scaler\n", + "from darts.datasets import EnergyDataset\n", "from darts.models import TCNModel\n", "from darts.utils.callbacks import TFMProgressBar\n", - "import darts.utils.timeseries_generation as tg\n", "from darts.utils.likelihood_models import GaussianLikelihood, QuantileRegression\n", - "from darts.datasets import EnergyDataset\n", "from darts.utils.missing_values import fill_missing_values\n", - "from darts import TimeSeries\n", - "from darts.dataprocessing.transformers import Scaler\n", "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n", - "from darts import concatenate\n", - "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", diff --git a/examples/10-Kalman-filter-examples.ipynb b/examples/10-Kalman-filter-examples.ipynb index cc6544909f..b284fcc7da 100644 --- a/examples/10-Kalman-filter-examples.ipynb +++ b/examples/10-Kalman-filter-examples.ipynb @@ -22,8 +22,8 @@ "%autoreload 2\n", "%matplotlib inline\n", "\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "\n", "from darts import TimeSeries\n", "from darts.models import KalmanFilter\n", diff --git a/examples/11-GP-filter-examples.ipynb b/examples/11-GP-filter-examples.ipynb index e3dee0adf2..37fdc1d95e 100644 --- a/examples/11-GP-filter-examples.ipynb +++ b/examples/11-GP-filter-examples.ipynb @@ -24,7 +24,7 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "from sklearn.gaussian_process.kernels import ExpSineSquared, RBF\n", + "from sklearn.gaussian_process.kernels import ExpSineSquared\n", "\n", "from darts import TimeSeries\n", "from darts.models import GaussianProcessFilter\n", diff --git a/examples/12-Dynamic-Time-Warping-example.ipynb b/examples/12-Dynamic-Time-Warping-example.ipynb index d37bcc924d..1ee2679f5c 100644 --- a/examples/12-Dynamic-Time-Warping-example.ipynb +++ b/examples/12-Dynamic-Time-Warping-example.ipynb @@ -34,17 +34,16 @@ "%autoreload 2\n", "%matplotlib inline\n", "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "from scipy.signal import argrelextrema\n", + "\n", "from darts.dataprocessing import dtw\n", - "from darts.utils import timeseries_generation as tg\n", - "from darts.utils.missing_values import fill_missing_values\n", "from darts.datasets import SunspotsDataset\n", - "from darts.timeseries import TimeSeries\n", - "from darts.metrics import dtw_metric, mae, mape\n", + "from darts.metrics import dtw_metric, mae\n", "from darts.models import MovingAverageFilter\n", - "from scipy.signal import argrelextrema\n", - "import numpy as np\n", - "import pandas as pd\n", - "from matplotlib import pyplot as plt" + "from darts.timeseries import TimeSeries" ] }, { diff --git a/examples/13-TFT-examples.ipynb b/examples/13-TFT-examples.ipynb index 8187bca483..37b10c812e 100644 --- a/examples/13-TFT-examples.ipynb +++ b/examples/13-TFT-examples.ipynb @@ -40,22 +40,20 @@ }, "outputs": [], "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "from tqdm import tqdm_notebook as tqdm\n", + "import warnings\n", "\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", "from darts import TimeSeries, concatenate\n", "from darts.dataprocessing.transformers import Scaler\n", - "from darts.models import TFTModel\n", + "from darts.datasets import AirPassengersDataset, IceCreamHeaterDataset\n", "from darts.metrics import mape\n", + "from darts.models import TFTModel\n", + "from darts.utils.likelihood_models import QuantileRegression\n", "from darts.utils.statistics import check_seasonality, plot_acf\n", - "from darts.datasets import AirPassengersDataset, IceCreamHeaterDataset\n", "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n", - "from darts.utils.likelihood_models import QuantileRegression\n", - "\n", - "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", @@ -341,7 +339,7 @@ " )\n", " pred_series.plot(low_quantile=low_q, high_quantile=high_q, label=label_q_inner)\n", "\n", - " plt.title(\"MAPE: {:.2f}%\".format(mape(val_series, pred_series)))\n", + " plt.title(f\"MAPE: {mape(val_series, pred_series):.2f}%\")\n", " plt.legend()\n", "\n", "\n", diff --git a/examples/14-transfer-learning.ipynb b/examples/14-transfer-learning.ipynb index 1e43ae785b..dd6109e020 100644 --- a/examples/14-transfer-learning.ipynb +++ b/examples/14-transfer-learning.ipynb @@ -33,6 +33,7 @@ { "cell_type": "code", "execution_count": null, + "id": "7fb27b941602401d91542211134fc71a", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +70,8 @@ "# Execute this cell once to download all three datasets\n", "!curl -L https://forecasters.org/data/m3comp/M3C.xls -o m3_dataset.xls\n", "!curl -L https://data.transportation.gov/api/views/xgub-n9bw/rows.csv -o carrier_passengers.csv\n", - "!curl -L https://raw.githubusercontent.com/Mcompetitions/M4-methods/master/Dataset/Train/Monthly-train.csv -o m4_monthly.csv\n", + "!curl -L https://raw.githubusercontent.com/Mcompetitions/M4-methods/master/Dataset/Train/Monthly-train.csv \\\n", + " -o m4_monthly.csv\n", "!curl -L https://raw.githubusercontent.com/Mcompetitions/M4-methods/master/Dataset/M4-info.csv -o m4_metadata.csv" ] }, @@ -94,28 +96,35 @@ "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", - "import os\n", - "import time\n", + "\n", "import random\n", - "import pandas as pd\n", - "import pickle\n", - "import numpy as np\n", - "from tqdm.auto import tqdm\n", + "import time\n", "from datetime import datetime\n", "from itertools import product\n", + "from typing import Optional\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", "import torch\n", - "from torch import nn\n", - "from typing import List, Tuple, Dict, Optional\n", "from sklearn.preprocessing import MaxAbsScaler\n", - "from sklearn.linear_model import Ridge\n", - "import matplotlib.pyplot as plt\n", + "from tqdm.auto import tqdm\n", "\n", "from darts import TimeSeries\n", - "from darts.utils.losses import SmapeLoss\n", "from darts.dataprocessing.transformers import Scaler\n", "from darts.metrics import smape\n", - "from darts.utils.utils import SeasonalityMode, TrendMode, ModelMode\n", - "from darts.models import *" + "from darts.models import (\n", + " ARIMA,\n", + " ExponentialSmoothing,\n", + " KalmanForecaster,\n", + " LightGBMModel,\n", + " LinearRegressionModel,\n", + " NaiveSeasonal,\n", + " NBEATSModel,\n", + " RandomForest,\n", + " Theta,\n", + ")\n", + "from darts.utils.losses import SmapeLoss" ] }, { @@ -158,7 +167,7 @@ "metadata": {}, "outputs": [], "source": [ - "def load_m3() -> Tuple[List[TimeSeries], List[TimeSeries]]:\n", + "def load_m3() -> tuple[list[TimeSeries], list[TimeSeries]]:\n", " print(\"building M3 TimeSeries...\")\n", "\n", " # Read DataFrame\n", @@ -181,7 +190,7 @@ " ).astype(np.float32)\n", " m3_series.append(series)\n", "\n", - " print(\"\\nThere are {} monthly series in the M3 dataset\".format(len(m3_series)))\n", + " print(f\"\\nThere are {len(m3_series)} monthly series in the M3 dataset\")\n", "\n", " # Split train/test\n", " print(\"splitting train/test...\")\n", @@ -191,18 +200,16 @@ " # Scale so that the largest value is 1\n", " print(\"scaling...\")\n", " scaler_m3 = Scaler(scaler=MaxAbsScaler())\n", - " m3_train_scaled: List[TimeSeries] = scaler_m3.fit_transform(m3_train)\n", - " m3_test_scaled: List[TimeSeries] = scaler_m3.transform(m3_test)\n", + " m3_train_scaled: list[TimeSeries] = scaler_m3.fit_transform(m3_train)\n", + " m3_test_scaled: list[TimeSeries] = scaler_m3.transform(m3_test)\n", "\n", " print(\n", - " \"done. There are {} series, with average training length {}\".format(\n", - " len(m3_train_scaled), np.mean([len(s) for s in m3_train_scaled])\n", - " )\n", + " f\"done. There are {len(m3_train_scaled)} series, with average training length {np.mean([len(s) for s in m3_train_scaled])}\" # noqa: E501\n", " )\n", " return m3_train_scaled, m3_test_scaled\n", "\n", "\n", - "def load_air() -> Tuple[List[TimeSeries], List[TimeSeries]]:\n", + "def load_air() -> tuple[list[TimeSeries], list[TimeSeries]]:\n", " # download csv file\n", " df = pd.read_csv(\"carrier_passengers.csv\")\n", " # extract relevant columns\n", @@ -212,7 +219,7 @@ " # move indexes to columns\n", " df = df.reset_index()\n", "\n", - " # group bt carrier, specificy time index and target variable\n", + " # group bt carrier, specify time index and target variable\n", " all_air_series = TimeSeries.from_group_dataframe(\n", " df, group_cols=\"carrier\", time_col=\"data_dte\", value_cols=\"Total\", freq=\"MS\"\n", " )\n", @@ -229,7 +236,7 @@ " # extract longest contiguous slice\n", " try:\n", " series = series.longest_contiguous_slice()\n", - " except:\n", + " except Exception:\n", " continue\n", " # remove static covariates\n", " series = series.with_static_covariates(None)\n", @@ -241,20 +248,18 @@ " # Scale so that the largest value is 1\n", " print(\"scaling series...\")\n", " scaler_air = Scaler(scaler=MaxAbsScaler())\n", - " air_train_scaled: List[TimeSeries] = scaler_air.fit_transform(air_train)\n", - " air_test_scaled: List[TimeSeries] = scaler_air.transform(air_test)\n", + " air_train_scaled: list[TimeSeries] = scaler_air.fit_transform(air_train)\n", + " air_test_scaled: list[TimeSeries] = scaler_air.transform(air_test)\n", "\n", " print(\n", - " \"done. There are {} series, with average training length {}\".format(\n", - " len(air_train_scaled), np.mean([len(s) for s in air_train_scaled])\n", - " )\n", + " f\"done. There are {len(air_train_scaled)} series, with average training length {np.mean([len(s) for s in air_train_scaled])}\" # noqa: E501\n", " )\n", " return air_train_scaled, air_test_scaled\n", "\n", "\n", "def load_m4(\n", " max_number_series: Optional[int] = None,\n", - ") -> Tuple[List[TimeSeries], List[TimeSeries]]:\n", + ") -> tuple[list[TimeSeries], list[TimeSeries]]:\n", " \"\"\"\n", " Due to the size of the dataset, this function takes approximately 10 minutes.\n", "\n", @@ -287,18 +292,16 @@ " m4_train.append(series[:-HORIZON])\n", " m4_test.append(series[-HORIZON:])\n", "\n", - " print(\"\\nThere are {} monthly series in the M3 dataset\".format(len(m4_train)))\n", + " print(f\"\\nThere are {len(m4_train)} monthly series in the M3 dataset\")\n", "\n", " # Scale so that the largest value is 1\n", " print(\"scaling...\")\n", " scaler_m4 = Scaler(scaler=MaxAbsScaler())\n", - " m4_train_scaled: List[TimeSeries] = scaler_m4.fit_transform(m4_train)\n", - " m4_test_scaled: List[TimeSeries] = scaler_m4.transform(m4_test)\n", + " m4_train_scaled: list[TimeSeries] = scaler_m4.fit_transform(m4_train)\n", + " m4_test_scaled: list[TimeSeries] = scaler_m4.transform(m4_test)\n", "\n", " print(\n", - " \"done. There are {} series, with average training length {}\".format(\n", - " len(m4_train_scaled), np.mean([len(s) for s in m4_train_scaled])\n", - " )\n", + " f\"done. There are {len(m4_train_scaled)} series, with average training length {np.mean([len(s) for s in m4_train_scaled])}\" # noqa: E501\n", " )\n", " return m4_train_scaled, m4_test_scaled" ] @@ -319,16 +322,15 @@ "outputs": [], "source": [ "def eval_forecasts(\n", - " pred_series: List[TimeSeries], test_series: List[TimeSeries]\n", - ") -> List[float]:\n", - "\n", + " pred_series: list[TimeSeries], test_series: list[TimeSeries]\n", + ") -> list[float]:\n", " print(\"computing sMAPEs...\")\n", " smapes = smape(test_series, pred_series)\n", " plt.figure()\n", " plt.hist(smapes, bins=50)\n", " plt.ylabel(\"Count\")\n", " plt.xlabel(\"sMAPE\")\n", - " plt.title(\"Median sMAPE: %.3f\" % np.median(smapes))\n", + " plt.title(f\"Median sMAPE: {np.median(smapes):.3f}\")\n", " plt.show()\n", " plt.close()\n", " return smapes" @@ -487,8 +489,8 @@ "outputs": [], "source": [ "def eval_local_model(\n", - " train_series: List[TimeSeries], test_series: List[TimeSeries], model_cls, **kwargs\n", - ") -> Tuple[List[float], float]:\n", + " train_series: list[TimeSeries], test_series: list[TimeSeries], model_cls, **kwargs\n", + ") -> tuple[list[float], float]:\n", " preds = []\n", " start_time = time.time()\n", " for series in tqdm(train_series):\n", @@ -845,7 +847,7 @@ " )\n", " plt.xlabel(\"elapsed time [s]\")\n", " plt.ylabel(\"median sMAPE over all series\")\n", - " plt.legend(bbox_to_anchor=(1.4, 1.0), frameon=True);" + " plt.legend(bbox_to_anchor=(1.4, 1.0), frameon=True)" ] }, { @@ -914,9 +916,8 @@ "outputs": [], "source": [ "def eval_global_model(\n", - " train_series: List[TimeSeries], test_series: List[TimeSeries], model_cls, **kwargs\n", - ") -> Tuple[List[float], float]:\n", - "\n", + " train_series: list[TimeSeries], test_series: list[TimeSeries], model_cls, **kwargs\n", + ") -> tuple[list[float], float]:\n", " start_time = time.time()\n", "\n", " model = model_cls(**kwargs)\n", @@ -1201,7 +1202,7 @@ " },\n", ")\n", "\n", - "nbeats_model_air.fit(air_train, num_loader_workers=4, epochs=NUM_EPOCHS)\n", + "nbeats_model_air.fit(air_train, dataloader_kwargs={\"num_workers\": 4}, epochs=NUM_EPOCHS)\n", "\n", "# get predictions\n", "nb_preds = nbeats_model_air.predict(series=air_train, n=HORIZON)\n", @@ -1433,7 +1434,7 @@ "# Train\n", "nbeats_model_m4.fit(\n", " m4_train,\n", - " num_loader_workers=4,\n", + " dataloader_kwargs={\"num_workers\": 4},\n", " epochs=NUM_EPOCHS,\n", " max_samples_per_ts=MAX_SAMPLES_PER_TS,\n", ")" diff --git a/examples/15-static-covariates.ipynb b/examples/15-static-covariates.ipynb index 29e044d8c7..54be3d205b 100644 --- a/examples/15-static-covariates.ipynb +++ b/examples/15-static-covariates.ipynb @@ -32,14 +32,14 @@ "metadata": {}, "outputs": [], "source": [ + "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "\n", "from darts import TimeSeries\n", "\n", - "import warnings\n", - "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", "\n", @@ -75,7 +75,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -130,7 +130,8 @@ "static_covs_single = pd.DataFrame(data={\"cont\": [0], \"cat\": [\"a\"]})\n", "print(static_covs_single)\n", "\n", - "# multivariate static covariates (multiple components). note that the number of rows matches the number of components of `series`\n", + "# multivariate static covariates (multiple components).\n", + "# note that the number of rows matches the number of components of `series`\n", "static_covs_multi = pd.DataFrame(data={\"cont\": [0, 2, 1], \"cat\": [\"a\", \"c\", \"b\"]})\n", "print(static_covs_multi)" ] @@ -303,7 +304,7 @@ }, { "data": { - "image/png": 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\n", 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Ro8YYY7p162aCg4NNSsq/71mzZk3TrFkz13JiYqIJCAgwCxYsMMYYs2/fPgOY8ePHu7ZJSkoyFStWNBMmTEhXx5gxY0z9+vUz3ZMxxtxwww1m7NixGY69+uqrplWrVm7roqKiDGD27NljjDGmefPmpkGDBuleC5gVK1YYY4z58MMPTc2aNY3T6TQpKSlm7969JiEhwfj7+5tVq1aZkydPGsCsW7cuS7X/V/Pmzc3gwYOvaR/XKq2/iz/PdmL3/oyxf49278+YvN3jkiVLDGAAU7JkSdf/EVmRF/qz1ZGQspl9jpqBlJRkChTwhmz4JTnT7wvs2rWLCxcu0KJFiwzH6tevT2BgoGtdkyZNcDqd7Nmzx/WgtTp16uDl9e+Rl6CgIOrWretaLlCgAMWKFXMl5DS33Xab69/e3t40btyYXbt2Zb74yxg0aBD9+vVj9erVtGzZkkceeYR69eoB8Pvvv7N27VrXkZGLRUREUKNGDQAaNWp02ff4/fffCQ8Pp3DhwgAYY3A4HJw/f56IiAhatWpF9+7duffee7nnnnto2bIlHTt29LjDkyIilxMdHU3//v1dy++8847HPojTViEks6dEnE4nkZGHCA4OdvvPPDf4+/tf8z4KFizotuxwODJc53Re+6mmzOrduzf33nsvK1euZPXq1bzxxhtMnjyZgQMHEhsbS7t27ZgwYUK6110cEC4OXxmJjY2lUaNGLFiwAKfTyaFDh6hQoQJeXl6ULl0agNDQUAYNGsQ333zDkiVLePHFF/n222+59dZbs7dhERELGGN45plnXNfUPfLII3Ts2NHiqq6eLkzNZddddx3+/v5899136cZq167N77//TlxcnGvdxo0b8fLyombNmtf83ps2bXL9Ozk5ma1bt1K7du1r3m+aSpUq8fTTT7N8+XKGDRvGnDlzAGjYsCF//fUXVapUISQkxO3PlYLHxRo2bMg///xDmTJlCAkJcdvfxReTNmjQgNGjR/PTTz9Rt25dFi5cmG09iohYacmSJSxbtgyAUqVKMXPmTI++7k0hJJf5+fkxcuRIRowYwQcffEBERASbNm1i3rx5dOnSBT8/P7p168aOHTtYu3YtAwcO5IknnsiWQ23vvPMOK1asYPfu3fTv35+YmBh69uzpGg8PD2f79u0cPXqUhIQEtm/fzvbt2123y17OkCFDWLVqFfv27WPbtm2sXbvWFXD69+/PqVOnePzxx/n111+JiIhg1apV9OjRg5SUlEzX36VLF0qVKsWDDz7Ihg0biIqKYt26dQwaNIiDBw+yb98+Ro8ezc8//0xkZCSrV6/mn3/+yXTQSus3NjaW48ePs337dnbu3Jnp+kREctLRo0fdTsPMnDmTMmXKWFjRtbPV6RhP8dJLL+Ht7c3LL7/M4cOHKVeuHE8//TQBAQGsWrWKwYMHc9NNNxEQEMAjjzzCW2+9lS3vO378eMaPH8/27dsJCQnh888/p1SpUq7x3r17u92226BBAwD27dtHlSpVLrvvlJQU+vfvz8GDBylSpAitW7dmypQpAJQvX56NGzcycuRIWrVqxYULFwgODqZ169ZZOh0WEBDADz/8wMiRI3n00Uc5e/YsFStWpEWLFhQpUoSEhAR2797N/PnzOXnyJOXKlaN///5uMwleTlq/AFu3bmXhwoUEBwe73QIsImIFYwxPP/00p06l3gL62GOP0aFDB4urunYOY4yxuojclnpNSKQl14Tkhv/2t3//fqpWrcpvv/2WZ2ZCvVb57XNoN3bvD+zfo937g7zV44IFC+jatSsAZcqU4a+//nL7JfJq5IX+7PmVIyIiYhOHDx9m4MCBruVZs2ZdcwDJK64qhMTExDB48GCaNm1K+/bt2bx58yW3/eKLL3j44Ydp1qwZjz76KAcPHrzqYsU6abOdZvRn3LhxVpd3RXXq1Llk/QsWLLC6PBGRDBlj6Nu3LzExMQB07tyZhx9+2OKqss9VXRMyYcIESpYsyZo1a/jll18YPXo0y5cvTzfd9Y8//sjChQuZPHkyVatWdV0vILmrSpUqXOtZt7lz55KQkJDhWIkSWZgoxSJfffUVSUlJGY556v31ImJ/H3zwAV9++SWQ+iyt6dOnW1xR9spyCImPj2fdunV89tln+Pn50bx5c6pXr8769et54IEH3LadM2cOQ4cOdT2LpFKlStlTteQ6T3ks9KUEBwdbXYKISJYcOnSIwYMHu5Znz56dLY/ZyEuyHEIOHDhAQECA22+PISEh7N271227lJQUdu/eTUREBP/3f/+Ht7c37dq1o1evXhne05yYmJjuVlBvb298fHyyWuIVpU3ilZuTeeUmu/cH9u9R/Xk+u/do9/7A2h6NMfTu3ZszZ84A0LVrV+6///5srSUn+8vsha5ZDiEJCQnpJpgKDAx0faDSnDp1ipSUFDZt2sTixYs5d+4cAwYMoFy5crRt2zbdfkNDQ12TW6Xp0KFDjs4EFxUVlWP7zgvs3h/Yv0f15/ns3qPd+wNrely6dCnffPMNkHo3zLBhwzL1RPCrkRP9Va1aNVPbZTmE+Pv7u83oCRAXF0dAQIDbOl9fXwCefPJJChcuTOHChWnfvj0bN27MMIT06NGDLl26uBeXg0dCoqKiqFSpkuW3XeUEu/cH9u9R/Xk+u/do9/7Auh4PHDjgdsH/3LlzXc/iyk554XOY5RBSuXJl4uPjiY6Ods3UFhERkS5YFClShNKlS2d6OlkfH58cCRyX4+XlZdtvHrB/f2D/HtWf57N7j3bvD3K3R2MMffr04ezZs0DqL+jt2rXL0fe08nOY5XcNCAigefPmzJ49m/Pnz7NhwwbCw8Np3rx5um3vv/9+PvjgA+Li4jh27BgrVqygadOm2VK4iIiI3cydO5dvv/0WSL0hILtmzM6rrir6jBo1iuPHj9OiRQumTJnCuHHjKFq0KF9//bXbNRx9+vShVKlS3HffffTo0YPWrVtz3333ZVvxIiIidhEZGcmzzz7rWp47dy7FihWzrqBccFXzhBQvXjzDe5XbtGlDmzZtXMsFCxbkxRdf5MUXX7z6CkVERGzO6XTSs2dPYmNjgdRnebVu3driqnKevU/kiYiIeIDZs2fz/fffA6lzak2ePNniinKHQoiIiIiF9u3bx/Dhw13L8+bNyzeziyuEiIiIWCTtNEza1Bd9+/blnnvusbiq3KMQIiIiYpGZM2eybt06IPXxEm+++aa1BeUyhRARERELhIeHM3LkSNfy+++/T+HChS2sKPcphIiIiOSytNMw8fHxAPTv35+7777b4qpyn0KIiIhILpsxYwYbNmwAUp+zMn78eIsrsoZCiIiISC76+++/GT16tGs5NDSUQoUKWViRdRRCREREcklKSgo9evQgISEBgEGDBmX42JP8QiFEREQkl0ydOpWffvoJgJCQELen5eZHCiEiIiK5YPfu3a7HmDgcDkJDQwkMDLS4KmsphIiIiOSwlJQUunfvzvnz5wEYMmSIniqPQoiIiEiOmzx5Mr/88gsANWrU4LXXXrO4orxBIURERCQH7dy5k5deegkALy8vwsLCCAgIsLiqvEEhREREJIckJyfTrVs3EhMTARg2bBi33XabxVXlHQohIiIiOeTNN99ky5YtANSqVYtXXnnF4oryFoUQERGRHPDnn38yZswY4N/TMH5+fhZXlbcohIiIiGSzpKQkunfvTlJSEgAjRozglltusbiqvEchREREJJuNHz+ebdu2AVCnTh3Gjh1rbUF5lEKIiIhINvr999959dVXAShQoABhYWH4+vpaXFXepBAiIiKSTRITE91Ow4waNYrGjRtbXFXepRAiIiKSTcaNG8f27dsBuOGGG1zzg0jGFEJERESywbZt23j99dcB8Pb21mmYTFAIERERuUZpp2GSk5MBeOGFF2jYsKHFVeV9CiEiIiLX6NVXX+XPP/8EoH79+jz//PMWV+QZFEJERESuwZYtW3jjjTeA1NMw8+fPx8fHx+KqPINCiIiIyFW6cOEC3bp1IyUlBYCXX36Z+vXrW1yV51AIERERuUpjx45l586dADRs2JBRo0ZZXJFnUQgRERG5Cps3b2bixIkAFCxYkLCwMAoWLGhxVZ5FIURERCSLzp8/T7du3XA6nUDqEZEbbrjB4qo8j0KIiIhIFr388svs3r0bgMaNGzNixAiLK/JMCiEiIiJZ8NNPPzFp0iQAfHx8mD9/Pt7e3hZX5ZkUQkRERDIpISGBHj16YIwB4JVXXuH666+3uCrPpRAiIiKSSS+++CJ///03ALfccgvDhg2zuCLPphAiIiKSCT/++CNTpkwBwNfXl7CwMJ2GuUYKISIiIlcQFxfndhrm9ddfp1atWhZX5fkUQkRERK7g+eefJzw8HIDbb7+dIUOGWFuQTSiEiIiIXMb69euZPn06AH5+foSGhlKgQAGLq7IHhRAREZFLiIuLo3fv3q7lN954gxo1alhYkb0ohIiIiFzCxIkT2bt3LwDNmjVj0KBBFldkLwohIiIiGfj+++/58MMPAQgICOD999/Hy0v/bWYnfTRFRET+49y5czz11FOu5fHjxxMSEmJhRfakECIiIvIfI0aMYP/+/QA0b96c/v37W1uQTSmEiIiIXOTbb79l1qxZQOppmLlz5+o0TA7RR1VEROR/zp49S69evVzLo0aNolq1ahZWZG8KISIiIv8zbNgwoqKiAGjRogWdO3e2uCJ706T3IiIiwKpVq5g7dy4AhQoVYs6cORZXZH86EiIiIvne6dOn3U7DTJ48meDgYAsryh8UQkREJN979tlnOXToEAD33HOP2+25knMUQkREJF9buXIloaGhABQpUoR58+bhcDgsrip/UAgREZF8KyYmhj59+riW33rrLSpVqmRhRfmLQoiIiORbQ4YM4fDhwwC0bt2anj17WlxR/nJVd8fExMQwduxYtm7dSpkyZRg1ahQ333xzuu3Gjh3LqlWr8PZOfZty5cqxdOnSa6tYREQkG3z++ed88MEHABQtWpQ5c+boNEwuu6oQMmHCBEqWLMmaNWv45ZdfGD16NMuXL6do0aLptu3Vq5fbY5BFRESsdvLkSfr27etanjZtGhUrVrSwovwpy6dj4uPjWbduHX379sXPz4/mzZtTvXp11q9fnxP1iYiIZLtBgwZx9OhRANq2bcuTTz5pcUX5U5aPhBw4cICAgACCgoJc60JCQti7d2+G2y9atIhFixYRHBxM//79adSoUYbbJSYmkpiY6F6ctzc+Pj5ZLfGKnE6n2992Y/f+wP49qj/PZ/cePbm/FStWsHDhQgCKFSvGrFmzMMZgjHHbzpN7zIyc7C+zz9rJcghJSEggMDDQbV1gYCBnzpxJt22nTp149tln8ff3Z82aNTz77LMsXryYcuXKpds2NDQ03ex0HTp0oGPHjlktMdPSpua1K7v3B/bvUf15Prv36Gn9nTp1yu00zMsvv0xSUhKRkZGXfI2n9ZhVOdFf1apVM7VdlkOIv78/cXFxbuvi4uIICAhIt22tWrVc/27Tpg1fffUVmzZt4uGHH063bY8ePejSpYt7cTl4JCQqKopKlSrZ8smIdu8P7N+j+vN8du/RU/sbNWoUJ0+eBOCBBx5g0KBBl7wY1VN7zKy80F+WQ0jlypWJj48nOjqaMmXKABAREUHbtm2v+FqHw5HucFcaHx+fHAkcl+Pl5WXLL6w0du8P7N+j+vN8du/Rk/r75JNPXHdolihRgtmzZ1OgQIErvs6TerwaVvaX5XcNCAigefPmzJ49m/Pnz7NhwwbCw8Np3rx5um2/++47EhISSE5OZvXq1Wzfvj3DW3lFRERyUnR0NP369XMtv/3225QtW9bCigSucrKyUaNGcfz4cVq0aMGUKVMYN24cRYsW5euvv3a7hmPhwoW0bt2aFi1asGDBAiZNmqRboEREJFcZY3jmmWc4ceIEAO3bt6dTp04WVyVwlfOEFC9enOnTp6db36ZNG9q0aeNanjdv3tVXJiIikg2WLl3KsmXLAChVqhTvvvuuJiXLI+x7kktERPK9o0eP8swzz7iWZ86c6bqeUaynECIiIrZkjKFfv36cOnUKSJ32oUOHDhZXJRdTCBEREVtatGgRn376KQClS5fmnXfesbYgSUchREREbOfIkSMMGDDAtfzuu+9SunRpCyuSjCiEiIiIrRhj6Nu3LzExMQA8/vjjPPLIIxZXJRlRCBEREVv58MMP+eKLLwAICgpixowZFlckl6IQIiIitnHo0CEGDx7sWp49ezYlS5a0sCK5HIUQERGxBWMMffr04fTp0wB07dqVBx980Nqi5LIUQkRExBbCwsL46quvAChXrhzTpk2zuCK5EoUQERHxeFFRUQwZMsS1/N5771GiRAnrCpJMUQgRERGPZozhqaee4uzZswB069aN+++/3+KqJDMUQkRExKPNmzePVatWAVC+fHmmTp1qbUGSaQohIiLisSIjI3n22Wddy3PnzqVYsWLWFSRZohAiIiIeyRhDr169OHfuHAC9evVye5K75H0KISIi4pFmz57Nd999B0ClSpWYPHmyxRVJVimEiIiIx9m3bx/PPfeca3nu3LkULVrUworkaiiEiIiIR3E6nfTq1Yu4uDgA+vTpQ6tWrSyuSq6GQoiIiHiUd999l7Vr1wIQHBzMpEmTLK5IrpZCiIiIeIyIiAhGjBjhWp43bx6FCxe2sCK5FgohIiLiEZxOJz179iQ+Ph6Afv360aJFC4urkmuhECIiIh7h7bff5ocffgCgSpUqTJw40eKK5FophIiISJ73zz//MGrUKNdyaGgohQoVsrAiyQ4KISIikqelpKTQo0cPEhISABg4cCB33nmntUVJtlAIERGRPG3atGls3LgRgOrVq/PGG29YXJFkF4UQERHJs/bs2cMLL7wAgMPhIDQ0lMDAQIurkuyiECIiInlSSkoK3bt35/z58wAMHjyYZs2aWVyVZCeFEBERyZPeeustNm3aBMB1113H66+/bnFFkt0UQkREJM/ZtWsXL730EpB6GiYsLIyAgACLq5LsphAiIiJ5SnJyMt27d+fChQsAPPvss9x+++0WVyU5QSFERETylEmTJrF582YAatasyauvvmpxRZJTFEJERCTP2LFjB2PGjAHAy8uLsLAw/P39La5KcopCiIiI5AlJSUl0796dxMREAIYPH86tt95qcVWSkxRCREQkT5gwYQJbt24F4Prrr2fs2LHWFiQ5TiFEREQs98cff/DKK68AUKBAAcLCwvDz87O4KslpCiEiImKppKQkunXrRlJSEgAjR47kpptusrgqyQ0KISIiYqlx48axfft2AOrWrcvLL79sbUGSaxRCRETEMtu3b+e1114DUk/DzJ8/H19fX4urktyiECIiIpZITEykW7duJCcnA/D888/TsGFDi6uS3KQQIiIilnjttdf4448/AKhXrx4vvviixRVJblMIERGRXLd161bGjRsHgLe3N/Pnz8fHx8fiqiS3KYSIiEiuunDhAt26dSMlJQWAl156iRtvvNHaosQSCiEiIpKr/u///o+//voLgAYNGjB69GiLKxKrKISIiEiu+fXXX5kwYQIABQsWJCwsjIIFC1pclVhFIURERHLF+fPn6datG06nE4AxY8ZQr149i6sSKymEiIhIrhgzZgy7du0CoFGjRowcOdLiisRqCiEiIpLjNm3axKRJkwDw8fFh/vz5eHt7W1yVWE0hREREclRCQgLdu3d3nYb5v//7P+rUqWNxVZIXKISIiEiOeumll9izZw8AN998M88995zFFUleoRAiIiI5ZuPGjbz11lsA+Pr6EhYWptMw4qIQIiIiOSI+Pp7u3btjjAFSp2mvXbu2xVVJXqIQIiIiOeL5558nPDwcgNtuu42hQ4daXJHkNQohIiKS7X744QemT58OgJ+fH6GhoRQoUMDiqiSvuaoQEhMTw+DBg2natCnt27dn8+bNl93+8OHDNGnShFdfffWqihQREc8RFxdHjx49XKdhxo0bR82aNS2uSvKiqwohEyZMoGTJkqxZs4bBgwczevRozpw5c8nt33rrLX0BiojkE6NGjWLv3r0ANG3alEGDBllckeRVWb5EOT4+nnXr1vHZZ5/h5+dH8+bNqV69OuvXr+eBBx5It/3PP/+MMYZbbrmF6OjoS+43MTGRxMRE9+K8vXPk0c5p96qn/W03du8P7N+j+vN8du/xUv2tXbuWt99+GwB/f3/mzZuHw+HwyI9Dfv0cZgcvr8wd48hyCDlw4AABAQEEBQW51oWEhLhS78WSkpKYNm0akyZNYuXKlZfdb2hoKHPmzHFb16FDBzp27JjVEjMtKioqx/adF9i9P7B/j+rP89m9x4v7i4uLo3v37q7l4cOHU7BgQSIjIy2oLPvkp89hdqlatWqmtstyCElISCAwMNBtXWBgYIanYxYsWECTJk2oWLHiFffbo0cPunTp4l5cDh4JiYqKolKlSplOa57E7v2B/XtUf57P7j1m1F///v05ePAgAHfccQcvvfSSR/eeHz+HuS3LIcTf35+4uDi3dXFxcQQEBLiti46O5vPPP+ejjz7K1H59fHxyJHBcjpeXly2/sNLYvT+wf4/qz/PZvce0/tasWcOsWbOA1F9MQ0NDbTMpWX75HFohy18hlStXJj4+nujoaMqUKQNAREQEbdu2ddtu586dHDt2jIcffhhIvZbE6XRy5MgRZs6cmQ2li4hIXnD27Fl69erlWp44cSLVqlWzsCLxFFkOIQEBATRv3pzZs2czfPhwfv31V8LDw2nevLnbdrfffjufffaZa/mjjz7ixIkTemaAiIjNPPfccxw4cACAu+66i6efftriisRTXNXxl1GjRnH8+HFatGjBlClTGDduHEWLFuXrr792XUjq4+NDqVKlXH/8/f3x9fWlWLFi2Vm/iIhYaPXq1a6bCgoVKsT7779v61MXkr2u6oRd8eLFXTPhXaxNmza0adMmw9f07dv3at5KRETyqLNnz/LUU0+5lidNmkSVKlWsK0g8juKqiIhclddff911N0zLli3p06ePxRWJp1EIERGRLPvqq6/4+OOPAShcuLBrUjKRrFAIERGRLImJiXE7xf7WW29RuXJlCysST6UQIiIiWTJ06FAOHz4MQKtWrdxuzxXJCoUQERHJtC+++IL58+cDqadh5syZo9MwctUUQkREJFNOnTrldvHpSy+9lKnHcohcikKIiIhkyuDBgzl69CgA9913H4888ojFFYmnUwgREZEr+vTTT13PAitWrBizZs3SaRi5ZgohIiJyWSdOnHC7G2b69OlUqFDBworELhRCRETksgYOHEh0dDQADzzwAF27drW4IrELhRAREbmkZcuWsXjxYiD1kR06DWMfp8/BTzv9LK3hqp4dIyIi9nf8+HH69evnWn777bcpV66chRVJdjh8wjD1Y8Osz8DpLE3rplCyqDW16EiIiIhkqH///hw/fhyAhx9+mMcff9ziiuRahB809HnTSdXHDG8ugnPxEHfei1mfWVeTjoSIiEg6S5cudT0bpmTJkrz77rs6DeOhfvvbMH6B4ZP14HT+u963IDzS9Bwd7ypsWW0KISIi4ubYsWM888wzruWZM2cSFBRkYUWSVcYY1m+H8QsMqza7jxUOgGcegoGPQGLsKYIrKISIiEgeYIyhX79+nDx5EoBHH32Ujh07WlyVZJbTafh8Y2r4+GWn+1iZ4jCkg4N+D0Kxwg6cTieRsdbUmUYhREREXBYvXsyKFSsAKF26NDNnzrS4IsmMpGTDwm9hwkLDrkj3sarlYPjjDrq3AX/fvHVKTSFEREQAOHLkCP3793ctv/vuu5QuXdrCiuRK4hIM81bCpMWGqGj3sRuqwaguDjreBd7eeSt8pFEIERERjDH07duXmJgYADp16qRnw+Rhp84a3l4O05cZTp5xH2taD0Z3cdDmVvL8xcQKISIiwkcffcQXX3wBQJkyZZgxY4bFFUlGDkYbpiw1zP4C4hLcx9relnrko2m9vB08LqYQIiKSzx0+fJhBgwa5lmfNmkWpUqUsrEj+a88Bw8SFhg9XQ1Lyv+sLFIBOd8OIzg7qVfec8JFGIUREJB8zxtCnTx9Onz4NQJcuXXj44YetLUpctuxOneNj+Q9gzL/r/Xyg533wXCcHVct7XvhIoxAiIpKPzZ8/n5UrVwJQtmxZpk+fbnFFYozh+22pt9mu2eI+VrQQ9H8YBj3iIKiE54aPNAohIiL51MGDBxk8eLBr+b333qNEiRIWVpS/OZ2GTzekho9fd7uPlS0BQzs6ePpBKBLo+eEjjUKIiEg+ZIzhqaee4uzZswA8+eSTtGvXzuKq8qfEJMNHq2HiIsOeA+5j1SvAiMcdPHkv+OWxOT6yg0KIiEg+9P777/PNN98AUL58eaZOnWptQflQbLxhzpcweYnh0HH3sRuvg1GdHTzSPO/O8ZEdFEJERPKZAwcOMHToUNfynDlzKF68uIUV5S8nThveXm6YsRxOnXUfa34jjO7qoNVNeX+Oj+ygECIiko8YY+jVqxfnzp0DoGfPntx3330WV5U/HDhmeGtJ6tGP+PPuYw82hZGdHdxW1/7B42IKISIi+ch7773HmjVrAKhYsSJvvfWWxRXZ3679homLUq/7SE75d32BAtClZeocH3Wq5q/wkUYhREQkn9i/fz/PPfeca3nu3LkULVrUworsbfNOwxsLUu94uZi/L/S+H4Y95iC4bP4MH2kUQkRE8gGn00mvXr2IjU19dvtTTz3Fvffea3FV9mOM4dtfU2+zXfub+1ixQjDwERj4iIPSxfJ3+EijECIikg/MmjWL77//HoDKlSszadIkiyuyl5SU1FlNxy8wbPvbfax8KXi2o4M+D0DhAIWPiymEiIjY3N69exk+fLhred68eRQpUsTCiuzjQqLhg1UwcaEh/JD72HUVU6/3eKIV+PoofGREIURExMacTic9e/YkPj4egKeffpqWLVtaXJXnOxdvmP0ZvLXUcOSk+1jDGqm32T7cDAoUUPi4HIUQEREbe+edd1i/fj0AVapUYeLEiRZX5NmOnzZM+9jwzgo4Hes+dndDGNXFQcvG+WOOj+ygECIiYlPh4eGMHDnStfz+++9TuHBhCyvyXJFHDZMWG+athIQL/653OODhZqlzfNx8vYJHVimEiIjYUEpKCt27dychIQGAAQMGcNddd1lclefZc7AgLy+ARd8ZUi6a48O7ADxxLwzv5KB2FYWPq6UQIiJiQ9OnT2fjxo0AVKtWjfHjx1tckWf5eYdh3Efw5U/l3dYH+EGfdql3u1QKUvi4VgohIiI28/fff/P888+7lkNDQwkMDLSwIs9gjOGbX1Jvs/3hd/exEkVg0CMOBrSHkkUVPrKLQoiIiI2knYY5fz714SSDBw/mjjvusLiqvC052fDJ+tTw8Xu4+1i5EskMf9ybp9o5KKQ5PrKdQoiIiI1MmTKFn3/+GYCQkBDGjRtncUV51/kLhvnfwMRFhr2H3cdqVYbhj0OTGoe4rnowXl4KIDlBIURExCZ27drFiy++CKTeIhoWFkZAQIDFVeU9Z2INsz6DKR8bjp1yH7upVuocHw82BTBERlpRYf6hECIiYgPJycl0796dCxdS7x8dOnQoTZo0sbiqvOXYKcO0T1Ln+Dgb5z52T+PUOT7uavjvHB9Op7GgyvxFIURExAYmT57M5s2bAahRowavvfaaxRXlHfsOG95cbHj/K7iQ+O96hwMeaZ4aPhrV1OkWKyiEiIh4uL/++ouXX34ZAC8vL+bPn4+/v7/FVVnvjwjDhAWGJWtxm+OjoDd0aw3DH3dQo5LCh5UUQkREPFhSUhLdunUjMTH1V/znnnuOW2+91eKqrPXjH4Y3PjJ8tcl9fSF/6PsADO3ooEJphY+8QCFERMSDTZw4ka1btwJQu3Zt/u///s/iiqxhjGHlz6m32W78032sVFEY/KiDZx6GEkUUPvIShRAREQ/1559/ukKHl5cXYWFh+Pn5WVxV7kpONiz5PjV87NjnPlY5CJ7r5KDnfRDor/CRFymEiIh4oLTTMElJSQCMHDmSm2++2eKqck/CBUPoV/DmIsP+o+5j11dJfaDc4y2hoLfCR16mECIi4oHeeOMNfvvtNwDq1KnDmDFjLK4od5w+Z5j5KUz92HD8tPvYrXVgdBcH99+OJhfzEAohIiIeZvv27bz66qsAFChQgPnz5+Pr62txVTnryAnD1I8N734G5+Ldx1rfknqb7R31/53jQzzDVYWQmJgYxo4dy9atWylTpgyjRo3K8DDg7Nmz+fzzz4mNjaVEiRJ0796dBx988JqLFhHJrxITE+nevTvJyckAjB49mkaNGllcVc4JP2h4c5Eh7BtITPp3vZcXdLgz9bRLgxoKHp7qqkLIhAkTKFmyJGvWrOGXX35h9OjRLF++nKJFi7pt16ZNG5588kn8/f2JjIykT58+1KlTh5CQkGwpXkQkv3n99df5/ffUR7zWq1ePl156yeKKcsZvfxsmLDR8vA6czn/X+xSEHm1SLzgNqajw4emyHELi4+NZt24dn332GX5+fjRv3pzq1auzfv16HnjgAbdtK1eu7Pp32iGyQ4cOKYSIiFyFbdu28frrrwPg7e1NWFgYPj4+FleVfYwx/PB76p0u3/ziPlY4APo9CEM6OChXSuHDLrIcQg4cOEBAQABBQUGudSEhIezduzfD7cPCwpg7dy7nz5+ndu3a3HLLLRlul5iY6Jpsx1Wct3eOfIM5/xernRfHaxuxe39g/x7Vn+fL7h4vXLhAt27dSPnf1J/PP/889evXt+xjmJ39OZ3wxU8wcSFs2uk+VqY4DH4Unn4AihUGMLn2TBe7f53mZH9eXl6Z2i7LISQhIYHAwEC3dYGBgZw5cybD7bt37063bt3466+/+PXXX/H2zvgtQ0NDmTNnjtu6Dh060LFjx6yWmGlRUVE5tu+8wO79gf17VH+eL7t6nDx5Mjt27ADg+uuvp3PnzkTmgUe8Xkt/ScnwxaZAZq8swj+H3X/hrFgqmafuO0OHZnH4+RjOnIIzpy6xoxxm96/TnOivatWqmdouyyHE39+fuDj3xw/GxcVd9nHRDoeDunXr8tVXX7FixQo6dOiQbpsePXrQpUsX9+Jy8EhIVFQUlSpVynRa8yR27w/s36P683zZ2eOvv/7KrFmzAChYsCAfffQR1113XXaUedWupb/48/D+VzB5CRw45j5WtyqM6AyP3eWNt3dJoGT2FZ1Fdv86zQv9ZTmEVK5cmfj4eKKjoylTpgwAERERtG3b9oqvTUlJuWTi8vHxyfVzm15eXrb8wkpj9/7A/j2qP893rT2eP3+enj17uk7DvPzyyzRo0CC7yrtmWekv5pzh7eUw/RPDif8cPG9yA4zu6uC+W/PebbZ2/zq1sr8sv2tAQADNmzdn9uzZnD9/ng0bNhAeHk7z5s3TbbtixQrOnTuH0+lky5YtfPPNN9x0003ZUriISH4wduxYdu5MvVCiUaNGjBw50uKKsu7QccNz7zip/Kjh5XnuAaTtbbDhbQc/vuNF29sceS6ASM66qlt0R40axZgxY2jRogVBQUGMGzeOokWL8vXXXxMaGsrSpUsB2LBhA2+//TZJSUmULVuWwYMH06xZs2xtQETErjZt2sSbb74JpB4tDgsLo2DBghZXlXl/RxkmLjR8sCr1+o80Xl7Q6W4Y2cVBveoKHfnZVYWQ4sWLM3369HTr27RpQ5s2bVzLb7311tVXJiKSjyUkJNCjRw/XnQtjx46lbt26FleVOVv3GMYvMCxbD+aiG1l8faDXfTCsk4Nq5RU+RNO2i4jkSS+//DK7d+8G4KabbmL48OEWV3R5xhjWboM3FhjWbHEfKxII/R+GwY86CCqh8CH/UggREcljfvrpJyZPngyAr68vYWFhl5zewGpOJ3z2Y+qRj8273MeCSsCzHR30fQCKFlL4kPTy5le1iEg+FR8fT/fu3TH/O4/xyiuvcP3111tcVXqJSfDxD4G8vxr2RLlPHlatPIx43EG31uDnq/Ahl6YQIiKSh7z44ov8888/ANx6660MGzbM4orcxcYb5n6ZOsfHweOl3Mbqh6Q+zfbR5uDtrfAhV6YQIiKSR2zYsIGpU6cC4OfnR1hYGAUKFLC2qP85ecYwY5lhxnI4ddZ97I76qXN83Htz3pvjQ/I2hRARkTwgLi6OHj16uE7DvP7669SsWdPiqiDqmOGtpYb3vkid6fRiLRvEM6ZXAE3r2XciL8lZCiEiInnA6NGjiYiIAKBJkyYMHjzY0np2RxomLjJ8tNp9jo8CBaBzSxjeCQp5HSc4ONi6IsXjKYSIiFhs3bp1zJgxA0h9PldoaKhlp2E270y90+XTH93n+PDzgd73w3OdHASXdeB0OskDz88TD6cQIiJiodjYWHr27OlafuONN3L94XTGpM7tMX6B4ftt7mPFCsGA9jDwEQdliut6D8leCiEiIhYaOXIk+/btA6BZs2YMHDgw1947JcWwYkNq+Ni6x32sXMn/zfHxIBQOUPiQnKEQIiJike+++46ZM2cCqQ8HDQ0NzZWnmV5INHy4GiYuNPxz0H3suooworODJ1qBr4/Ch+QshRAREQucO3eOXr16uZYnTJhA9erVc/Y94w3vfQ5vLTUcPuE+1rBG6hwf7e+AAgUUPiR3KISIiFhg+PDhRP7vys4777yTZ555Jsfe6/hpw/RPDG8vh9Ox7mN3NUid46NlY83xIblPIUREJJetXr2a2bNnAxAYGMj777+fI6dhIo8aJi9JneE04YL72MPNYGQXB7dcr+Ah1lEIERHJRWfOnKF3796u5UmTJlG1atVsfY+/9hkmLjQsXAPJKf+u9y4AXVulPteldhWFD7GeQoiISC4aNmwYUVFRALRs2ZK+fftm275/3pE6x8fnG93XB/hBn3apd7tUClL4kLxDIUREJJd8/fXXzJs3D4DChQszd+7ca74OwxjDqs2pt9mu3+4+VrwwDHoEBrR3UKqYwofkPQohIiK54PTp0zz11FOu5cmTJ1/TlOcpKYZP1sH4hYbt/7iPVSgNwx5z8NT9UEhzfEgephAiIpILhg4dyqFDhwBo1aqV23UhWXH+gmH+N/DmYkPEIfexmpVhZGcHXe4Bn4IKH5L3KYSIiOSwL7/8krCwMACKFClyVadhzsYZZn0GU5Yajp5yH2tcC0Z3cfBgU83xIZ5FIUREJAfFxMTQp08f1/KUKVOoVKlSpl9/7JRh2ieGmZ/Cmf/M8dGycWr4uKuh5vgQz6QQIiKSg4YMGcKRI0cAaNOmDT169MjU6/YdNkxabHj/Kzif+O96hwPa35E6u2njWgoe4tkUQkREcsi3337LRx99BEDRokWZM2fOFY9Y/BlhmLDQsPh7SLlojo+C3vDkvTD8cQc1Kyt8iD0ohIiI5ICTJ0/ywgsvuJanT59OhQoVLrn9xj8Nb3xkWPmz+/pAf+jbDp59zEGF0gofYi8KISIiOWDQoEGcOJH6lLj777+fJ554It02xhi+2pQ6x8ePf7iPlSwKgx910P9hKFFE4UPsSSFERCSbLV++nMWLFwNQvHhxZs+e7XYaJjnZsHRtavj4c6/7ayuVgec6OejVFgL9FT7E3hRCRESy0fHjx3n66addy9OmTaN8+fIAJFwwhH4Fby4y7D/q/rrawalzfHS+Bwp6K3xI/qAQIiKSjQYMGMDx48eB1EnJOnfuzOlzhnc/g6kfG6Jj3Le/5frU22zbNQEvL4UPyV8UQkREssnSpUtZunQpACVLlmTI8Dd4/j0Hsz43nI1z3/bem1Nvs21+o+b4kPxLIUREJBtER0fTv3//1AW/atR/9GseeqM6iUn/buPlBR3uTD3t0qCGgoeIQoiIyDUyxvDMM89wIqEC1JwGZTry/U4v17hPQejeOnWOj5CKCh8iaRRCRESugTGGsVO+Y9nuntCwtdtYoJ+Tfg958WxHB+VKKXyI/JdCiIjIVXA6DV/+BK+GJbLl77uhxL9jpYvB4EehbcND1Lu+ki44FbkEhRARkSxISjYs/g4mLDT8tQ+goGssoMBxJg4sQ8+24FvQEBnptKxOEU+gECIikgnx5w3zVsKkxYYDx/4zGLeDImdnsXvDK5Qrm3rUw+k0uV+kiIdRCBERuYyYc4Z3lsO0TwwnzriPFYj7hZT9r8OprwhbvoxyZUtZU6SIh1IIERHJwOEThilLDbM+g9gE97H7boWYP0fz84aJAHTu3JmHH37YgipFPJtCiIjIRf6JMkxcZPhgFenm+Oh0N4zo7GD7xg/o/mZqAClbtizTp0+3qFoRz6YQIiICbN1jmLDQ8Mk6MBddzuHrAz3vS32oXLXyDg4dOsTgwYNd47Nnz6ZkyZK5X7CIDSiEiEi+ZYxh3W/wxkeGb7e4jxUJhGcegiEdHASVcLi2f+qppzhzJvXikCeeeIIHHnggl6sWsQ+FEBHJd5xOw2c/wvgFhs273MeCSsDQDg6efhCKFnKf3yM0NJSvv/4agHLlyjFt2rTcKlnElhRCRCTfSEwyLFwDExYYdh9wH6tWPnVa9W6twd83/eRiBw4cYOjQoa7lOXPmULx48ZwuWcTWFEJExPbiEgxzv4TJSwxR0e5j9UNSn2b7aHPw9s54ZlNjDL179+bs2bMAdO/enbZt2+Z02SK2pxAiIrZ18ozh7eUwfZnh1Fn3sWb1YHRXB61vAYfj8tOqz507l2+//RaAChUqMGXKlJwqWSRfUQgREds5GG14a6nhvS8g7j9zfLS7HUZ2cdDkhsw9zyUyMpJnn33WtTx37lyKFSuWjdWK5F8KISJiG7sjU+f4+Gg1JCX/u75AAXi8BYzs7KButcw/TM7pdNKzZ09iY2MB6N27N61bt77Cq0QksxRCRMTj/brLMH6BYcUG9zk+/Hyg9/0w7DEHVcpl/Um2s2fP5vvvvwegUqVKTJ48ObtKFhEUQkTEQxlj+G5r6hwf329zHytWCPo/DIMedVCmeNbDB8C+ffsYPny4a3nevHkUKVLkWkoWkf9QCBERj5KSknrEY/wCw9Y97mPlSsKzHR30eQCKBF5d+IB/T8PExcUB0LdvX+65555rKVtEMqAQIiIe4UJi6rUeExcZ/o5yHwupkPpMlydagV8Gc3xk1cyZM1m3bh0AwcHBvPnmm9e8TxFJTyFERPK0c/GGOV/AW0sNh467jzW4LnWOj0eaQ4EC1x4+AMLDwxk5cqRr+f3336dw4cLZsm8RcacQIiJ50vHThhnLUuf5iDnnPnZXg9Twcc9NV57jIyvSTsPEx8cD8Mwzz3D33Xdn2/5FxN1VhZCYmBjGjh3L1q1bKVOmDKNGjeLmm29Ot92UKVNYt24dMTExlC9fnv79+9OsWbNrLlpE7OvAMcPkxYY5X0LCBfexh5ql3mZ7a53sCx4XmzFjBhs2bACgatWqTJgwIUfeR0RSXVUImTBhAiVLlmTNmjX88ssvjB49muXLl1O0aFG37QICApg+fTqVKlVi27ZtPPfccyxYsIAKFSpkS/EiYh879xsmLEh9tktyyr/rvQtAl3tSr/m4vkrOhA+Av//+m9GjR7uWQ0NDKVSoUI69n4hcRQiJj49n3bp1fPbZZ/j5+dG8eXOqV6/O+vXr0z3Sum/fvq5/N27cmGrVqrF79+4MQ0hiYiKJiYnuxXl74+Pjk9USr8jpdLr9bTd27w/s32N+6m/TXzBhIXy+0X2bAD/o3RaGdoTKQQAGp9Ok21d2SElJoUePHiQkpE6vOnDgQJo1a3ZNH//89Dm0K7v3mJP9eXl5ZWq7LIeQAwcOEBAQQFBQkGtdSEgIe/fuvezrzp49S0REBNWqVctwPDQ0lDlz5rit69ChAx07dsxqiZkWFRV15Y08mN37A/v3aNf+nE748S8/Zr2RyKbdfm5jRQNTeLLlObrdc44ShZ2Y8xAZmbP1zJ07l59++glIvRumb9++RGbTm9r1c5jG7v2B/XvMif6qVq2aqe2yHEISEhIIDAx0WxcYGMiZM2cu+Rqn08n//d//cffdd1+ysB49etClSxf34nLwSEhUVBSVKlXKdFrzJHbvD+zfo136S0yCfw7CrkjYfeB/f0fCnihDwgX3UysVSsGzj0HvtgUoFFAMKJYrNe7evds1E6rD4WD+/PnUrl37mvdrl8/hpdi9P7B/j3mhvyyHEH9/f9cEPmni4uIICAi45GvGjx9PbGwsb7zxxiW38fHxyZHAcTleXl62/MJKY/f+wP49ekp/Z+MMuyNTQ8auSPO/v2HvEUhJyegV/waQGpVSLzbtcg/4+uTcNR8ZSUlJoWfPnly4kHoF7JAhQ2jevHm2voenfA6vlt37A/v3aGV/WQ4hlStXJj4+nujoaMqUKQNAREQEbdu2zXD7adOmsXv3bt59991cDxkikn2MMRw9+e8RjYvDxuETmd9PgQJQvTxUKR1PrwcCeKS5I9vm+MiqyZMn88svvwBQo0YNXnvtNUvqEMmvshxCAgICaN68ObNnz2b48OH8+uuvhIeHZ/jbw9y5c/nxxx+ZM2dOulM4IpI3paQY9h25+BTKv2HjTGzm9+PvC7UqQ+1gqBXsoHZw6r9DKkBBb0Nk5HGCg4Px8rImgOzcuZOXXnoJSP1NMCws7LJHdEUk+13VLbqjRo1izJgxtGjRgqCgIMaNG0fRokX5+uuvCQ0NZenSpQDMmjWLggUL0q5dO9drn3/+edq0aZM91YvIVUu4kDr9edpRjbTTKX8fhAuJV359mpJFcQWM2sEOV/CoHMQlA0ZO3eWSWcnJyXTr1s11R96zzz7LbbfdZmlNIvnRVYWQ4sWLM3369HTr27Rp4xYwtmzZcvWViUi2iDln2LXf/RTK7gOw74j7Y++vpHJQBmGjCpQuZs2RjGvx5ptvun4+1apVi1deecXiikTyJ03bLmIDxqQ+V2VXBheHRsdkfj/eBeC6iu5ho3Zw6sWjhQI8L2xk5M8//2TMmDHAv6dh/P39La5KJH9SCBHxIEnJhohD6S8O3R0JsQmZ308hf6j1n6BROxiqlYeC3vYIGxlJSkqie/fuJCUlATBixAhuueUWi6sSyb8UQkTyoLgE+HOfDz/+DXsOOF1HNcIPQVJy5vcTVOJ/F4ZWdg8bFUpn74PfPMX48ePZtm0bANdffz1jx461tiCRfE4hRMRCx0//e73G7gP/nkI5cAygXKb24XBA1XL/nkKpVfl/YaMKFC+c/4LGpfz++++uaz8KFChAWFgYvr6+Flclkr8phIjkMKfTcOCY+/UaaadTTl56ouF0fH2gxn+u16j1v+s1/H0VNi4nMTGR7t27k5ycehhp1KhR3HTTTRZXJSIKISLZJDHJuKYov/h6jT0H0j+S/nKKFoLalaFiyVhuqlOI66ukHtmoUhbLJvXydOPGjWP79u0A3HDDDa75QUTEWgohIlmU9SnKM1ah9L+TeV18vUZQidS7XSIjTxIcXMiyybzsYtu2bbz++utA6vOodBpGJO9QCBHJQNoU5RnNGno1U5T/N2zUCoYigZcOFyYrE3jIJf33NMzzzz9Pw4YNLa5KRNIohEi+dvEU5f+9ODS7pijP7Yeyyb9effVV/vzzTwDq16/PCy+8YHFFInIxhRDJF6ycolyssWXLFteTu729vZk/f74eoimSxyiEiK3k1BTlaadQPHGK8vzowoULdOvWjZT/XaTz8ssvU79+fYurEpH/UggRj2OM4WA0bNjhx+dbYPdFk3llxxTlNStDoL/ChicbO3YsO3fuBKBhw4aMGjXK4opEJCMKIZJnXXmK8qBM7aeQP64jGflpivL86pdffmHixIkAFCxYkLCwMAoWLGhxVSKSEYUQsVxcwr+Td+2+6C4UTVEuWXX+/Hm6d++O0+kEUo+I3HDDDRZXJSKXohAiuebiKcovnjU0dYryzEmborxWZahQ/Aw31S1KnSoOTVEuQOq1H7t37wagcePGjBgxwuKKRORyFEIkW2U0RXnaxaHZOUW50+kkMvI0wcFFdVeKAPDTTz8xadIkAHx8fJg/fz7e3voRJ5KX5bvv0AsXLrgO1crVy9YpyjN4pLymKJesSEhIoEePHq5J3l555RWuv/56i6sSkSvJdyHkm2++4eGHH6Zs2bJcd911VKtWLd2fMmXK6BqC/8mNKcr1sZZr9eKLL/L3338DcMsttzBs2DCLKxKRzMh3ISQiIgJjDEeOHOHIkSP88MMP6bYJCAjIMJxUq1aNKlWq4O/vb0HlOSe7pyj/78WhV5qiXORa/Pjjj0yZMgUAX19fwsLCdBpGxEPku+/U4sWLc/PNNxMeHs6pU6cy3CY+Pp4dO3awY8eODMfLly9P1apVMwwpZcuWxcvLKydbuGo5MUV52rUamqJcrBAXF+d2Gub111+nVq1aFlclIpmV70JIjx496NatG5GRkRQvXpzIyEj27t3L3r172bdvn9u/ExMzns/78OHDHD58mI0bN6Yb8/Pzu2RAqVq1KoGBgTndIgkXYNeBgvwcDnuinK65NTRFudjN888/T3h4OAC33347Q4YMsbYgEcmSfBdCLlakSBHq16+f4XTOTqeTw4cPu0LJf/8cO5bxfaXnz59n165d7Nq1K8PxoKCgSwaUChUqZOkoSkZTlO+KhP1HwZjymd6PpigXT7R+/XqmT58OpIb/0NBQChQoYHFVIpIV+TqEXI6XlxcVK1akYsWK3HHHHenG4+Li3I6cXPxn3759nD9/PsP9Hjt2jGPHjvHzzz+nG/Px8aFKlSr/CSfVCCxRk/NewURGB7iFDU1RLvlVbGwsPXv2dC2/8cYb1KhRw8KKRORqKIRcpcDAQOrWrUvdunXTjTmdTo4ePZrhaZ69e/dy+PDh9Dt0eJNYoCp/n6zF3/G1YG9tCKgF/rXAu/D/NrryE9jSpiivWCKWxnUKcX0Vh6YoF9sZNWoUe/fuBaBZs2YMGjTI4opE5GoohOQALy8vypcvT/ny5WnatKnbWFyCYfs/F/hx60l+2x3H7gMOok4GEnO+JIYsPN8i8SjE74L43ZCwG68L/1C5ZCzXBQdSzasqxR3Fuc6vIcGFQggqUpWC3sWyt0kRi3z//fe88847APj7+/P+++/n2YvBReTyFEJyyOWnKPcByl1xHw4MxfxPE+g4APG7iDu+lZjDmyB+JySfdtvWCew/Cfv/znhfxYsXv+Rtx5UqVdIDvsQjnDt3zu00zIQJEwgJCbGwIhG5Fgoh1yAnpyivXQWuq+jA37ckUBJoAHTm/PnzREZGZng9SkREBLGxGd9rGxMTw9atW9m6dWu6sQIFClC5cuVLhpTixYtrQjHJE0aMGEFkZCQAzZs3p3///hZXJCLXQiEkEy4kGsIP5Y0pyv38/KhZsyY1a9ZMN2aM4eTJk4SHh7N582bOnTvnFlaioqIynLI+JSWFffv2sW/fPr777rv0dRctesmAUrlyZXx8fDL/QRC5St9++y2zZs0CUq/J0mkYEc+nEHKRs3HGNaeGJ05R7nA4KFWqFCVKlCAoKIjg4GC3H9KJiYkcOHAgwzt6IiIiOHv2bIb7PXPmDL/99hu//fZburG0u4guFVJKlSqloyhyzc6ePUuvXr1cy2+++SbVqlWzsCIRyQ75LoQYYzhyEn7a6cfKbf9O5pUfpij38fEhJCQkw3PoxhhiYmIyvJtn7969REZGkpJBEnM6nRw4cIADBw6wbt26dOOFChW6ZEAJDg7Gz88vJ1oVmxk2bBhRUVEA3H333fTt29fiikQkO+S7EDJyluHNRQBBmdo+v0xR7nA4KFGiBCVKlKBx48bpxpOTk4mKirrk5G2XmgI/NjaWP/74gz/++CPD96xQocIlQ4oeJCgAq1atYu7cuUBqqJ03b55Ow4jYRL4LIdXLO8hovo3/TlGedoRDU5Sn8vb2pmrVqlStWpUWLVqkGz99+vQlJ2/bv38/ycnJ6V5jjOHgwYMcPHjwkg8SvNQU+MHBwTnSp+Qtp0+fdjsNM3nyZKpUqWJdQSKSrfJdCGlwHbS6CSoUP8vNNxShzv8m8yqlKcqvSbFixWjQoAENGjRIN5aSksLBgwcvearn+PHjGe4zPj6ev/76i7/++ivD8TJlynDdddd53IMEJfOeffZZDh06BMA999zDU089ZXFFIpKd8l0Iufl6B1+/aYiMjCE4uIiOcuSCAgUKEBwcTHBwMHfddVe68f/exfPfKfAv9SDB6OhooqOjM/0gwbTlqlWrUqhQoWzvU7LXypUrCQ0NBaBw4cLMnTtXp+dEbCbfhRDJewoXLky9evWoV69eurHLPUjwn3/+4cSJjK8mvtKDBMuUKXPJa1HKly+vB6FZLCYmxu2ox5QpU6hcubKFFYlITlAIkTztUg8SdDqdREZGUqpUqUvedrx3795LPkgw7SjKpk2b0o1l9CDBi4+mFClSJMf6lVRDhgzhyJEjALRu3dptllQRsQ+FEPFogYGB1KlThzp16qQbczqdHDt27JIBJcMHCZI6n8rff//N339nPAd+qVKlMgwn1apVo2LFinh769vqWnz++ed88MEHQOpEeXPmzNFpGBGb0k9LsS0vLy/KlStHuXLlaNKkSbrxhIQE9u/ff8mQEh8fn+F+T5w4wYkTJ9i8eXO6MW9vb4KDgy95qqdYsWLZ3aatnDx50m0OkGnTplGxYkULKxKRnKQQIvmWv78/tWvXpnbt2unGjDEcP378kgHl4MGDGJP+Vu/k5GQiIiKIiIjI8D31IMHLGzRoEEePHgWgbdu2PPnkkxZXJCI5SSFEJAMOh4MyZcpQpkwZbr311nTjFy5cIDIy8pJT4F/rgwSrVq1K8eLFadiwIdWrV6datWqUKFHC1qclVqxYwcKFC4HUW77fe+89W/crIgohIlfF19eXGjVqUKNGjXRjaQ8SvNRRlMw8SDAjRYoUuewU+J78IMETJ07w9NNPu5ZnzJhB+fLlLaxIRHKDQohINkt7kGCpUqW4+eab040nJiZecgr8iIgIzpw5k+F+z549y/bt29m+fXu6MU9/kOCAAQOIjo4G4MEHH6RLly4WVyQiuUEhRCSX+fj4UL16dapXr57heExMDOHh4WzevDndRG7Z9SDBiydyq1KliqUPEvz4449ZsmQJACVKlGDWrFl5OjCJSPZRCBHJY4oXL06jRo0oVaoUwcHBbtPPX+pBgmlB5eTJkxnu83IPEgQu+yDBoKCgHAsF0dHRPPPMM67lt99+m7Jly+bIe4lI3qMQIuJBrvQgwTNnzlz2QYJJSUkZ7vfQoUMcOnSIDRs2pBvz9/e/ZECpUqUKAQEBV9WLMYb+/fu7Zr1t3749nTp1uqp9iYhnUggRsZGiRYty4403cuONN6YbS0lJ4dChQ5e8YPZSDxJMSEi47IMEy5Url+FpnmrVqlGuXLlLPkhw5cqVLF++HEidAO7dd9/VaRiRfEYhRCSfSLsFuHLlytx5553pxi/1IMG0O3YuXLiQ4X6PHDnCkSNHMnyQoK+vb7pgUq1aNUqWLMmYMWNc273zzjuUKVMm23oVEc+gECIiwJUfJHjkyJFLHkVJm2Dsvy5cuMDu3bvZvXv3Jd+3Q4cOdOzYMdv6EBHPoRAiIlfk5eVFhQoVqFChAs2aNUs3HhcXd9kp8C/1IMHSpUvzzjvv5HT5IpJHKYSIyDW73IMEjTEcPXo0w6MnY8aMoXTp0hZULCJ5gUKIiOQoh8OR7kGCTqeTyMhIgoODLa5ORKyU8WXrVxATE8PgwYNp2rQp7du3z/BpogBr1qyhe/fu3H777YwdO/Za6hQRERGbuaoQMmHCBEqWLMmaNWsYPHgwo0ePznCq6aJFi9K1a1ceffTRay5URERE7CXLISQ+Pp5169bRt29f/Pz8aN68OdWrV2f9+vXptr3pppto2bIlJUqUyJZiRURExD6yfE3IgQMHCAgIICgoyLUuJCSEvXv3XlMhiYmJJCYmuhfn7Z0jTwZNe4JpRk8ytQO79wf271H9eT6792j3/sD+PeZkf5eapPC/shxCEhISCAwMdFsXGBh4ySd/ZlZoaChz5sxxW5fT8wdERUXl2L7zArv3B/bvUf15Prv3aPf+wP495kR/VatWzdR2WQ4h/v7+xMXFua2Li4u76udHpOnRo0e6x3fn5JGQqKgoKlWqlOm05kns3h/Yv0f15/ns3qPd+wP795gX+styCKlcuTLx8fFER0e7plmOiIigbdu211SIj49PjgSOy/Hy8rLlF1Yau/cH9u9R/Xk+u/do9/7A/j1a2V+W3zUgIIDmzZsze/Zszp8/z4YNGwgPD6d58+bptk1JSeHChQskJye7/VtERETkqqLPqFGjOH78OC1atGDKlCmMGzeOokWL8vXXX7tdw/HVV1/RpEkTZs2axddff02TJk2YN29ethUvIiIinuuqZkwtXrw406dPT7e+TZs2tGnTxrXcrl072rVrd/XViYiIiG3Z9ySXiIiI5GkKISIiImIJhRARERGxhEKIiIiIWMJhjDFWFyEiIiL5j46EiIiIiCUUQkRERMQSCiEiIiJiCYUQERERsYRCiIiIiFhCIUREREQsoRAiIiIillAIEREREUsohIiIiIglFEJELKLJikUkv1MI8VCxsbFWlyDXyOFwWF2CXKOkpCSrSxDxaLYLIb/99htLly5ly5YtVpeSI7Zt28bjjz/O4sWLuXDhgtXl5Ijff/+dRYsW8dNPP5GcnGx1Odlu27ZtjBs3jmXLlnHw4EGry8kRv//+O9988w179uyxupQcsW3bNrp27cqyZcusLiVH/Pnnn3z55Zf88ccfVpeSY3777TfeeecdVq9eTVxcnNXlZDtP+R60RQgxxuB0OnnnnXcYMmQIERERDB8+nPfff99WP+TXrFnDiy++SJs2bXjsscfw9va2uqRsFRsbywsvvMCzzz7LsWPHeOWVV5g3bx4nTpywurRrZowhJSWF6dOnM3LkSEqUKMHKlSuZM2cOBw4csLq8bGGMITk5mYkTJzJo0CB++ukn+vTpw2effcbp06etLu+apZ0+W758OaNHj6ZVq1bcfffdOJ1OiyvLPufOnWP06NEMHTqUHTt2MHDgQJYvX05CQoLVpWWbtK/R4cOHk5KSwowZMwgLC+PkyZNWl3bNPPF70Bb/izkcDpKTk9mxYwfTp0+nfv36NGvWjG+//ZZFixYxfPhwq0vMFj/99BP9+vWjXbt2AJw+fZpixYpZW1Q2cTqdfPrpp3h5efHFF18QEBBAw4YNWbJkCS1atKBUqVJWl3hNHA4HCQkJ7Nixg0mTJlG/fn1uvfVW3n//fdv8FuZwOIiPjyciIoLQ0FCqVavGl19+yffff09sbCxdunSxusRrknb6bOvWrYwePZo777wTgPPnz+Pn52dhZdkjOTmZ0NBQChQowDfffIO3tze1a9dmxYoVtGrVyuryss3JkycJDw/nvffeo1q1atSrV4+FCxfSqVMnq0u7Zp74PejRR0IuvrAvIiKC8+fPExgYCEDTpk254447iIyM5Pvvv7eqxGtycX9xcXEkJycTEhLCqlWreOyxx3jhhRd499132bt3r4VVZg8vLy9q1KjBgw8+SEBAAMYY7rjjDg4dOsSpU6esLi9bHDhwgOTkZFfouPHGGzl37hwBAQEWV5Z9du3axdmzZylXrhzGGO6//34aNmzIjh072LZtm9XlXbOjR48SHx9P48aN+fLLL+natSsvvfQSixcvJiYmxuryrpoxBm9vbxo0aMCDDz7oOsr64IMPcvz4caKioiyuMPtERERw9uxZvLxS//tr2rQpycnJtjmi5Wnfgx4ZQnbt2sUzzzzDhAkTWLJkCQC1atUiOjqa8PBw13YNGzakdu3abNiwwaMuIMuov8DAQPbu3cumTZvYsGEDQ4cOpVOnThw8eJC5c+dy/vx5i6vOmj179vDBBx+4HSK8+eabady4MZCa6E+dOkWJEiUoX768x/2AyKi/66+/npo1a/L1118zd+5cHn30UU6dOsXUqVOZO3eux13js3PnToYNG8Y777zD2rVrAWjUqBEHDx7kjz/+cB05aN68OQEBAWzdupWUlBQrS86Si/tbt24dAGXLlmX37t0sW7aM9evX069fP5o2bcoPP/zAggULPOrnzJ49e/j000/d1jVr1oybbrrJtbx//35KlixJhQoVPPJurot7TKv/9ttvp0yZMixdupRFixbRsWNHYmNjeeONN1i1apVHXYdmh+9Bjwshe/fu5bnnnqN+/fqEhIQwf/583nnnHQC6dOnCjBkzXNsWL16c6667jvPnz3PmzBmrSs6Sy/XXqVMnZs6cScGCBbn11ltp1qwZnTt3Jjk52WMuIDPGsGjRIgYMGMCMGTPYvn27K2Ck/ZBIW46OjiY2NpZChQq5fmvJ6y7XH8DgwYPp0KEDGzZsoEWLFnz22We0atWK3bt38+2331pYedbs2LGDwYMHExISQkpKClOnTuWjjz7C29ubxx57jPfee8+1baVKlahUqZLrt2lP+M/sv/1NmTKFjz76CEj9OfPuu+9StWpVmjRpwoMPPsijjz7KkSNHOHTokMWVX5nT6WTu3Ln07duX119/nZ07d6a7UyvtP6pDhw7h7e2Nj4+PR93Ndake0wLGmDFjuP3221m2bBn33nsvS5cu5frrr2ft2rVs3brV4uozxy7fg57xk/0iv/32G/Xq1aNv3748+uijjB8/nnXr1rFmzRoefvhhvL29mT17tmv7kJAQNm/e7DHfQBn1t3btWtasWUPLli2pWbOm21GPChUqsHfvXo85pO9wODh79ixjxoyhd+/eLFu2jOPHj7vGLrZlyxbKlSvnuu5l8+bNef7W5Mv1B+Dn54fT6cTPz4+uXbsC0KJFC86dO+dR1xX8/PPP3HnnnfTr149BgwYxfPhw5s2bx86dO7n//vuJi4vj448/dm1/4403snHjRhITEz3iezGj/ubOncvOnTu58847CQoKcvs+rFmzJlu2bKFAgQIWVp05Xl5exMTEMHHiRB555BGmTp16yW1/++03Kleu7Pra3Llzp0ccsbtUj2mnmUqWLElycjJlypShb9++ADz66KNERERQsGBBq8rOErt8D3pMCElLbr6+vkRERLjW16tXz3UR6oULF3jxxRdZsmQJy5cv5/z58+zZs4cGDRrg7+9vVemZcrn+7rjjDr799lsSEhIYMmQImzdvdl2xvmPHDkqUKEHJkiWtKj3T0o4IdOjQgdtuu40+ffpw6tQp1q5d63YYO+2ox/Hjx3nkkUfYtGkTLVu2ZMWKFZbUnVmZ7c/Pz4+dO3e6ruXZsmULMTExlC1b1pK6syLt69Tf35/Dhw+71jdt2pTbb7+dDz/8kPLly9O1a1emTp3KL7/8AkB4eDh33HEHPj4+ltSdWZfrr0mTJnz44YcEBQXx1FNP8cknn7Bp0yYA/v77b66//nqKFCliSd2ZlfY12r17dxo3bszw4cP5559/+Oabb9y2SwtT0dHRtG/fnk2bNnHXXXexfPnyPPVbdEau1GNa/YUKFWLr1q2uu+/++OMP/Pz8KFGihDWFZ5LtvgeNh/nrr79Mv379zNq1a13rjh8/btq3b+9at3DhQjNs2DDz2GOPmZYtW5oNGzZYU+xVyEx/S5YsMaNGjTJdu3Y1rVq1MuvWrbOm2GzwxRdfmK5du5rw8HDXOqfTaRISEkzHjh3NzTffbNq0aWO+/vprC6u8ev/tLyUlxRhjzPjx402fPn1M586dzX333We+++47K8u8LKfTmW7d6tWrzYgRI8z27dtd644dO2aaNGli/vrrL2OMMVOnTjX9+/c3HTp0MPfcc4/5+eefc63mrLja/mbMmGGGDBliOnfubFq1apVnf85k1N/FFixYYB544AGTkJDg9prjx4+btm3bmrvuusu0bt06T38PZrXHtO2fe+45061bN9O1a1fTpk0bt5+7eYmdvwfzXAhJ+yF9qS+qEydOmLfeesu8+uqrJi4uzrV+/PjxZsiQIa59pKSkmD///DPnC86i7Ogv7bW7du3K4WqvzpV6/K8BAwaYKVOmuP0QjI+PNx06dDAffvhhjtR4LbKjv6SkJHPs2DGzcePGHKnxWiUlJZl//vnHbZ3T6XT1fODAAfP888+befPmmfPnz7u2GT16tHnttdeMMcYkJyeb2NhYs3nz5twrPJOyo7+UlBRz/vx5s23bttwrPJMu1d+lltu3b2/effddt/GzZ8+apk2bmtDQ0Byr81pkR4+JiYkmPDzcfP/99zlX6FVKSkoyv/32m0lKSnKts9P3YJo8dTpm+fLlNGnShF9//dXtIqKLlSxZkkaNGnH27FmWLl3qWl++fHkqVqwIpB6u8vLyom7durlWe2ZkV39pF43VqlUrdwrPgsz0mCatj969e7N582b+/vtvZs6cyTfffIO/vz8fffSR67qJvCI7+vv222/x9vamTJky3H777blVeqYtWrSIBx54gPHjx/P888+77gyBf6/bqVSpEjfeeCP//POP2y3wxYsXp3Llyq7lwMBAt7st8oLs6s/pdOLr60uDBg1ytf4ruVx/F7v463f48OF8/PHHnDhxglmzZrF161YKFy7MmjVr6N69e+4Vn0nZ0eP27dspWLAg1atX56677srF6q9s0aJFtG3bltmzZzN27Fi302V2+B68WJ6ZrOzTTz9l2bJlNGzYkDfeeIPly5enmxHUGIPD4eDmm28mPj6eKVOmAFCxYkWWLl3qusAoL14clp395dWZUjPT48XSPk/169fH39+fXr16UbJkSVffee3cZXb1d7kLAa104cIFQkND+eGHH5g0aRIlSpTg008/5bPPPuPGG290XSCc9nXaunVrYmNjCQ0NJTExkVKlSvHjjz8ydOhQIO99H2Z3f3nt+zCz/V0srYdbb72VYsWK0aZNG4oWLcqdd96JMQZfX99c7uLysrPHvBY8ABITE3n33Xf5+eefmTJlCjVr1mTs2LFs2bKFFi1auC6a9dTvwQxZeBTGTUREhFm9erVJTEw0bdq0MR999JExxrgdivqvVatWmfHjx5vHH3/cLFu2LLdKvSp278+Yq+sxLi7OjBo1yjRr1ixPn3M2xv79nTt3zixatMjtNN/mzZvNoEGDzJkzZ9wObaf92+l0mqVLl5pRo0aZhx56KE9/naq/9KcPnU6nOXfunHn66afNHXfcYVatWpWbJWeZ3XtMTEw0u3fvNhcuXDDGGHPkyBHTrl071+mVi3ni12hGLAshCxYsMN999505efKka11ycrIxxphvv/3W3H777a4f7mnn4D2J3fszJvt6XL16dc4WepXs3p8x//Z44sQJY0zqRdAXn3eOiIgwrVq1MmfPnr3sfi4XxKyk/jLX3yeffJLjtV4tu/eY0c8Zp9Npfv31V9O4cWMzatQo8/7775uwsDDX9UdpP4culle/Rq/EYUzu3m+1Z88ehg8fTrly5fDy8iIlJYXOnTu7nsOQkpJCgQIF6NWrF8HBwbz88sskJyfnuUOfl2L3/iD7ejT/O6SY19i9P0jfY3JyMl27dqV58+ZA6vUOXl5erFy5klWrVjF9+nTXOk+g/jLXX17u2e49XunnTEJCAvHx8ZQsWZLExEQWL17M559/zieffGJt4dks1z8zu3btombNmsyePZtp06bRqFEjvvjiC3777Tfg33ughw8fzhdffEF0dDTe3t5ER0cD5LkpZ//L7v1B9vWYV/+Dtnt/kL7Hxo0b8/nnn7N9+3bg37kWDhw4QL169YDU+VvOnTvnNp5Xqb/M9ZcX/3NOY/cer/RzpmDBgq5J1dKe65OSksLff/9tceXZK1c/O8YY9u7dS9myZXE6nfj4+NC2bVsqVKjgSnfe3t4kJSVRq1YtOnXqxODBgxk6dCjPPvus6zfQvMru/YH9e7R7f3D5HtNmWEw7qrN9+3aaNGnC2bNnGT58OOPHj8+zv1mmUX+e3R/Yv8fM/pxJ+9vLy4vIyEiqVKlCtWrVrCw92+XaZynt0HTZsmXZvHmz6wukYsWK3HLLLcTHx/PDDz8AuK4ATkhIIDw8nFKlSrkeMZ1X2b0/sH+Pdu8Pstbj4cOHOXjwIEuXLuWBBx6gUKFC/N///V+e/+Gu/jy3P7B/j5npb/369QAcO3aM48eP8/bbbzN9+nSaNm2Kt7d3np+1Nity7DN1qQ/SY489xrFjx9zue65VqxbFixd3e+Lo+PHj+eWXX1i+fDkvvPBCnpvP3+79gf17tHt/cG09xsTEcPr0aU6ePElYWBhjxozJc9cuqT/P7g/s3+PV9Jf2wNXw8HBef/11/vzzT9577z0eeeQRIG+f6s2y7LzKde/evebHH380xqS/evfiK3cXLVpk7rrrLnP+/HnXFc6DBg0y06dPz3D7vMLu/Rlj/x7t3p8x197jtGnTjDHGREdHmx07duRS1Zmn/lJ5an/G2L/Ha+1v6tSpxpjUW/wPHz6cS1VbI1uOhKSkpDBr1iy6du3KCy+8QExMDAUKFHC7uMvb25v4+HhWr15Nx44dqV69Oq+++irbt28nOTkZp9Ppurgobfu8wu79gf17tHt/kH091q9fH4DSpUtTp04dq9pJR/15dn9g/x6zq78bb7wRgICAAMqVK2dRN7kjW0JIdHQ0J0+e5IUXXqBZs2bMmDEDcD9ktHjxYpo3b86uXbvw8vLi1Vdfxd/fnxkzZtCmTRsKFSqUJ6ewBvv3B/bv0e79gf17VH+e3R/Yv0e795cjrvYQSmxsrOvwUVxcnNm/f79JSEgwv//+u3nggQfcHh4XHR1tZs2a5Xqy38WioqJMVFTU1ZaRY+zenzH279Hu/Rlj/x7Vn2f3Z4z9e7R7fzkty5OVHTp0iLFjx+Ln50eRIkUYMWIERYsWdY0nJiYyc+ZM9uzZw7vvvpvu9Xn93m279wf279Hu/YH9e1R/nt0f2L9Hu/eXW7LUfXx8PGPHjqVWrVoMGzaMEydO8Oabb/Lrr78CqVcB+/j40L59e06dOsUXX3zh9vq0e7fz6gfd7v2B/Xu0e39g/x7Vn2f3B/bv0e795aYsfQSio6Px8vKia9euVKlShQkTJuDv78/q1as5ceKE67xX+fLlefjhh1myZAkAn3/+OREREXn+A273/sD+Pdq9P7B/j+rPs/sD+/do9/5yU5Y/Env27MHf3x+AYsWK0aJFC+Lj41m3bp1rG29vbx577DHi4+O56aabCAsLy3N3ElyK3fsD+/do9/7A/j2qP8/uD+zfo937yy1ZCiFVqlShRo0avPfee651jRs3pnTp0uzfv5/Y2FgAYmNjefzxxzlz5gyvvPIKy5cvJzg4OHsrzwF27w/s36Pd+wP796j+PLs/sH+Pdu8vN2X5SMiTTz7J+vXriYyMBFKTXr169diyZQuFChVybdeyZUu+++472rRpk33V5gK79wf279Hu/YH9e1R/qTy1P7B/j3bvL7dkOYTcdNNNNG7cmNdee821LiQkBD8/P9dUuoUKFaJ3797ZVmRusnt/YP8e7d4f2L9H9efZ/YH9e7R7f7kly7foQupDuzp16kTNmjWpX78+n376KTfddBMjRozIiRpznd37A/v3aPf+wP49qj/PZ/ce7d5fbriqEAKwd+9e/vjjDzZs2ECDBg3o2rVrdtdmKbv3B/bv0e79gf17VH+ez+492r2/nHbVISSN+d9jie3K7v2B/Xu0e39g/x7Vn+eze4927y+nXHMIEREREbkamjFFRERELKEQIiIiIpZQCBERERFLKISIiIiIJRRCRERExBIKISIiImIJhRARERGxhEKIiFy1Pn360LhxY/r06WN1KSLigRRCRCRXbdmyhcaNG9O4cWMOHz5sdTkiYiGFEBEREbGEt9UFiIhnOHv2LOPGjWPDhg0UK1aMHj16pNtmxowZbNiwgejoaBISEihevDi33HILAwcOpFSpUsyePZs5c+a4tn/ggQcAuP/++xk7dixOp5MlS5awYsUKDh48iK+vLzfffDODBg2iQoUKAMTHxzNt2jR+/PFHTp06RWBgIJUrV6Z9+/bcf//9ufPBEJFsoRAiIpny6quvsnbtWgD8/PyYNm1aum1+/vlnoqOjCQoKIiUlhcjISFauXMm+ffv44IMPCAoKomrVquzbtw+AGjVq4OPjQ8WKFQGYOHEin3zyCQDVqlXj5MmTfPfdd2zfvp1FixZRokQJZs2axbJly/Dx8aFatWrExsby119/UblyZYUQEQ+jECIiV3Tw4EFXAOnWrRsDBw5k//79PPbYY27bvfLKK1SrVg0vr9QzvZ9++imvvfYaO3fu5ODBgzz00ENUrFiRp59+GoBJkyZRvnx5AA4dOsSyZcsAGDt2LPfffz/x8fF06NCBY8eOsWTJEvr160dUVBQAvXr1olevXgCcOXOGY8eO5fwHQkSylUKIiFxRRESE69933303AFWqVOG6665j9+7drrE9e/YwduxYIiMjSUhIcNvH8ePHXUc8MrJr1y7SHuo9duxYxo4d6zb+559/AtCsWTM2bNjArFmzWLFiBVWqVKFevXq0b9/+mnoUkdynECIi2WL79u2MHTsWYwxFixalatWqJCQkuE69pKSkZHpfaadpLlauXDkA2rdvT5UqVfjhhx8IDw9n165dbNq0iTVr1rB06dLsa0hEcpxCiIhcUbVq1Vz/XrduHXXq1CEyMpJ//vnHtX7Hjh2uIxlLliyhVKlShIWF8fbbb7vty8/Pz/Xvi4+W1KpVC4fDgTGGdu3a8fjjjwNgjGH79u0UKlTI9T7VqlWjYcOGQOoRkh49erB3715Onz5NsWLFsrd5EckxCiEickWVKlXizjvvZN26dYSGhrJ27VqOHTtGgQIFXEc4QkJCXNs/9thjFC9enJiYmHT7qlixIt7e3iQnJ/PMM89Qrlw5unbtSsuWLXnooYdYsWIFkydPZvHixfj7+3PkyBHi4uIYM2YM1113HUuWLGH16tUEBQVRpEgR1zUiZcqUoWjRornzARGRbKF5QkQkU1566SXuvvtufH19iY2NpW/fvtStW9c1fuuttzJw4EBKly7NhQsXqFKlCqNGjUq3n2LFivHcc88RFBTEqVOn2LFjBydPngRg9OjRPPvss4SEhHD8+HGOHDlC+fLl6dKlC40aNQKgSZMmNGjQgAsXLhAeHo6vry/NmjVj2rRpOByO3PlgiEi2cJi046ciIiIiuUhHQkRERMQSCiEiIiJiCYUQERERsYRCiIiIiFhCIUREREQsoRAiIiIillAIEREREUsohIiIiIglFEJERETEEgohIiIiYgmFEBEREbGEQoiIiIhY4v8Bl8orVtKgNesAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -427,14 +428,13 @@ "source": [ "import numpy as np\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "from pytorch_lightning.callbacks import TQDMProgressBar\n", "\n", "from darts import TimeSeries\n", - "from darts.models import TFTModel\n", - "from darts.utils import timeseries_generation as tg\n", "from darts.dataprocessing.transformers import StaticCovariatesTransformer\n", - "from darts.metrics import rmse" + "from darts.metrics import rmse\n", + "from darts.models import TFTModel\n", + "from darts.utils import timeseries_generation as tg" ] }, { @@ -454,7 +454,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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\n", + "image/png": 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", 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\n", 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", 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\n", 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Nx+PB6/Xi8Xiorq7GbrfXa+xp06bx3//+l8suu4yysjIWLVoUMxPBAw88wJYtWwDo378/f/nLX0hOTuahhx7iqaeewu12c9NNN7F582YyMjJMtTWaMIoU0GK1y5cv5+qrrzbJIsEKfPDBB/zhD38AtHDym2++Sa9evZg7dy4TJkzA4/Hw6KOPMn78eCZOnGiytdFFRaV/uH/1x/MDFiov3990SKesUqWsEq4ZqZCSZP4y5ZYwZMgQPB4PBQUFjB07tsFz+vXrx4YNG/xeq/tzhw4d2L59u99rW7ZsaXS59O7duykqKuKpp54iNzcXgI0bN7b2awSFkPhsXn31VUaPHs2iRYv417/+xejRo3nvvffYvHmzX4Nfd911DB06lFmzZnHHHXfwrW99KyaWJi9YsIC//OUvgBYbfPPNN3Vl+uijjzJq1CgADh06xF133VVPDQutp7Ky/t7v8+bNM8ESwSqcOHGCb3/72/rPTz31lD4PjRkzhkcffRTQvHE333wzBQUFptgZrVRW+j88fPrJIjxud1CunejUyvNbpfBbS+jbty8333wzt956KwsWLODgwYOsX7+eP/zhD/riix//+Me89957PPvss+zdu5eXX36ZZcuW+XlmJk2axMaNG/nPf/7D3r17efjhh+sJFyN5eXk4HA5efPFFDhw4wJIlS/R8GrMIiVC555572Lhxo9+/6dOnM2TIENasWVN7c5uN++67j5UrV/LRRx9xyy23hMIcS3Ho0CHuvPNO/efnn3+egQMH6j/Hx8czd+5cMjMzAa3WzN///vew2xmtNJSsvXjx4iZdoUL04vF4uOWWWzh9+jQAV111FT/72c/8znnooYe4/PLLAU3U3HrrrS1aWik0Td0xWXK2kK1frg7KteNsCl4vnKv/fBIRzJkzh1tvvZX77ruPfv36MXPmTDZs2EBeXh4Ao0eP5qWXXuLZZ59l0KBBvP/++/zsZz8jISFBv8aUKVP47W9/yy9+8QuGDx/OuXPnuPXWWxu9Z4cOHXjttdd4++23GTBgAE899RTPPPNMyL9rUyiqPK43iG95V7du3YKaLHTllVfy0UcfAXDDDTfwxhtvNFiMaPHixcycORMAp9PJ119/rXdOMwhVe4SbvXv30rdv33qvf/DBB1x55ZUtula0tEmwiMT2+Otf/8qPfvQjAHJycti6dSvt27evd97JkycZPHiwvizzH//4B3fffXez14/ENgklDbVHXo8B5B/a5Xfetdd/n5/+8q9BueeRUyp9c+GyC63Z/sHuI3fffTe7d+/2cwpEOtb8zUUpZ86cYfny5QDk5ubyj3/8o9GKiddee62+Iqi6upqlS5eGzc5oxhj66dSpk34s4Z/Y5O2339aPX3/99QZFCkB2djb/+c9/Gvyc0HrcbpXqKm1MJiWn4nBqnoA1nywMmtcq0QkFxdqeQtHIM888w9atW9m3bx8vvvgi//73v/nOd75jtllBRYRKGFm9erWeb3LdddeRnp7e5Pl33HGHfvzJJ5+E1LZYwehmnj59ur4cftGiRbiDFBcXIoPKykrWrVsHaCsQmyvqdsUVV+i1VNauXdtg6XOhZdS4obpKG5Opae0YPlJLoj1TeJIdWz8Lyj0SndpOyhVVzZ8biaxfv54rrriCiy++mJdeeokXXniBu+66y2yzgooIlTBiFBuTJk1q9vzBgwfrK35WrlwpcfEgYPSotG/fnmnTpgFw+vTpqHKVCs2zbt06XWwEMh4VRdHPq6ioYP369SG1LxZwuaG6RhuTCQlJjJs0W39v1fLgeDkjOaE2EN566y0KCgqorKxkx44dfO973zPbpKAjQiWMrFixAtCSiBtbbmYkLi6O8ePHA1BUVMS2bdtCal8sYBQqiYmJXHfddfrP8+fPN8MkwSR84xEIeMmx8Tzj54XWUeMGV7U2Jh3OREaNn47dri2bXb1iQVAezuJsCl4VyiI0oVYQoRI2Tp8+rS8JGzp0aLNhHx/GiVHCP22nrlC5+uqr9WJvCxYEZ2IUIgPjeGqNUJHx2HYqKl14PFrI1ZmQSEpqBkMv1VZYnT51lN07NjT18YCJs8HZsujMUYkFRKiEiZUrV+rHgbiZGzpXJsa2Y8xRSUpKIjU1Va8GfOLECT1nQYhuysrK9NBN//796dy5c0Cf6969u76R2rp16xqsyyMETqlh3XBCgpYvNm5y7ZYWqz8OjpczyQmnzyI1qSIUESphojVuZoALL7xQX4mwatUqPB5P0G2LJep6VAC/vX5k9U9s8Omnn+rJ0y2tNOs7v7q6WoRtGykzxGMcTm08jp5wLbbzVcxXfzw/KOIi0amFfiqr23wpwQREqIQJnzfEbrczevTogD9ns9n01QglJSVs3rw5FObFDA0JlenTp+vlpOfPD87EKFib1oR9GjpfvJxt41x57Xh0nhcq6RlZDBmmtfGJYwfZt2dLm+8T7Qm10Y4IlTBw/Phx9uzZA2g7W7ZkQymQ8E8wMYZ+fEIlMzOTyZMnA5Cfn19vrwwh+jCOo+aWJddFhErwKD5brh/7hArAuMnBXf1jj1NweyShNlIRoRIG2vL0VvczstKgbRg9Kr4aKoDf6h8J/0Q3JSUlbNq0CYCLL76YDh06tOjzXbp00asbf/HFF/oO6ELLKTUoB2dCrVAZM3GWXgwzWOEfmw1KJKE2IhGhEgbaKlT69etHdnY2AGvWrJF9adpAQ6Ef0CoB+3b3lvBPdLN69Wp9dVdrd0L2fc7tdrN27dqg2RZrlJQ2LFTaZXXi4iFaCYf8w19zaP+ONt8r8XxCrRB5iFAJAz6h4nQ6GTlyZIs/byw0VV5ebvqW25FMY0Klffv2egjgwIEDbN26NdymCWGipYUXG0LCsW3H61UpayBHxcf4y4O7+ifRCaUVUFUtDyGRhgiVEHP48GEOHDgAwMiRI/3+OLYECf8Eh7rLk43I6p/YwDd+FEVh3LhxrbqGMa9FxmPrcLmh0jAenQn+43GssUptkIRKZbXkqUQiIlRCTFvDPg19Vp7gWk9jHhWAWbNq4+Lz5s2T8E8UUlRUpHvLhgwZQmZmZquu07FjRy688EIANm3aRElJSdBsjBVcbv/xWNej0qFjFy4cqHmgD+7bzpFDe9p0P4ddS6iVlT+RhwiVEBMMNzNom6bl5eUB2oZo1dVSEKA1NCVUsrOzGTNmDAB79uxh586dYbVNCD2rVq3Sj9syHo2f93q9sk9UK6hxQ1VV/ToqRsZNng2KA5x5zFu6jk+3qSxZq/LBehW3p+UPEgpwrkIeQCINESohRFVV3S2clJTEiBEjWn0tRVF0r0pVVRWff/55UGyMNRpanmxE9v6JblpbeLEhJBzbNmpcUFHZcDItwIcbVObu+gmMqYQRB1m67zv89p/w3Fvw1Osw572W39PpgIKzbTRcCDsiVELI/v37OXr0KACjR4/G4XC06XoS/mk7jS1P9jF7dm1cXPJUog/fuImLiwtoY9CmGD9+vB4qlPHYclyGDQmhtoS+j3++A2fL7Y1+ftEaKK9qmXckKQFKyqDGJV6VSEKESggJVn5KQ9eQibF1+IRKXFycXo3WSNeuXbnssssA2LZtG19//XVY7RNCx6lTp/Rw3rBhw0hNTW3T9dq1a8egQYMA2Lp1K0VFRW22MZaocUNNdcOhn3MVqr6UOCGuAgrnw/G/MDznU0ZcoL1eUQ3vf9GyeyY6JKE2EhGhEkKM7uC2xsMB8vLy6NWrF6BtiGYMYwiB4RMqTa2+kvBPdBKsfDEjvuuoquqX/yI0j8sNLlfDybSHTtSeN/qiGth1A+y/l7Idv+B719a+t2A1eLyBe0ecDoUatyTURhoiVEKEqqr6xJiamsrQoUODcl2fV8XlcvHZZ58F5ZqxhE/cNSVUjOEfESrRQ7A9nHWvI17OllHtUqmuMixPNgiVgydrz7uoTwY9+wwEYNf2L0hWjjKsn/be8UL4vBW14CShNrIQoRIidu/ezalTpwAYO3YsdnvjsdaWIBNj2/B5VBrKT/HRo0cPXVhu2rSJgwcPhsU2IbT4xkt8fHyLNgZtirFjx2Kz2fyuLwRGRRV4jB6VxNoxafSodO/sv/fP6hUL+Mb42vfnt9CR5YyHIllNHlGIUAkRwQ77+JCVBm0jkNAP+Bd/W7BgQUhtEkLP0aNH2bt3LwCXXXZZk0K1JaSnpzNs2DAAduzYoT+cCM1TUdVE6MfgUemRDeMvrw3Hrvl4ASMugNyO2s+b98K+Y4F7SBKdcOYcuN3iVYkURKiECGNYpqW7szZF586d6d+/PwAbN26kpqYmaNeOBQIJ/YBUqY02QjUewf/hYd26dUG9djRTWQOumoZDPz6PSmYqpKcodO85gLzu2ry3bcunnD1zitmGosIt8aokOrVEXEmojRxEqIQI3+qCuLg4Lr744qBee8iQIYC2IZrvKVFoHrfbjdvtBpoO/QD07dtX/719/vnn+jJzITIxFu+75JJLgnpt33isex+hcdxulRoXuGqMdVS0MXm2TKX4/IbU3bNrP+Pb+0dVVdZ8spApIyDlvLb5eCMUnwvMQ5LggOoaSaiNJESohACPx8Pu3bsB6NOnT5vrp9RlwIAB+rFMjIHTVFXahjCu/pHwT2RjHCfG8RMMZDy2nBo3uD3grqnSX/N5VOrmp/gYN7nWy7lq+XwSnQpXn9/j1eWBJQFuYu2rfSMelchBhEoIOHToEFVV2gAM9qRY95oyMQZOS4WKMfwjq38iG984cTgc9OzZM6jX7tu3r55QK+MxMNznhUpNjbGOSgLgv+LH6FHp1XcQOV218gxbv1zF2eLTzBoL55uexZ9CTYB5J/F2KCqVHJVIQYRKCAjl01vda8rEGDjNlc+vy4ABA/R8oDVr1nDy5MlmPiFYEZfLpRfu69evX9BW4PlwOp307t0bgF27duHxeIJ6/WikxnNeqFRrYzLe4dTFntGj0sPgUVEURV/94/V4WLtyMZ3aKYzVVi5TfA4++TKw+yc5oagUPK3YL0gIPyJUQoBRPFxwwQVBv36vXr30qqoiVAKnufL5dVEURQ//qKrKokWLQmWaEEL279+Py+UCQjMeofbhoaqqisOHD4fkHtGEy+dROV+Z1lg+v+6KHyPG1T+rP9bCsddNqH1//ioC2vU80QkVlVBe1eypggUQoRICQu1RiY+Pp2/fvoC2y68vQVRompaGfkBW/0QDoR6Pda8rDw/NU6PpRqrPCxVffoqqqrpHpX06pCQpfp/rN2AYnbK1XeQ3rV/OudJiLuwO/bWX2HsUvtrf/P0THFAlCbURgwiVEOCbqBRFoV+/fiG5h29idLlc7N8fwMgUWhz6ARg0aJC+bcHKlSspLCwMiW1C6BChYj18z1a+yrS+fX6Kz0Hp+WHaPbv+5xRFYez58I/H7eaz1UtRFIXZhgJwgSTV2mwKKuJRiRREqAQZVVXZtWsXAD179gz4D2JLMbqwffcTmqaloR/QJkafV8Xj8bB48eKQ2CaEDuP4CJVQkfHYMmrOp/FU1wn9HGxkxY8Rv/DPci3JffxgSE8+/9rWwJYqKwpUVkuOSiQgQiXI5OfnU15eDoRuUqx7bXmCC4zWhH5ANimMdIw1jfr06ROSe/Tv319f9irjsXmqqlVsikp1lTYmHQnnlyY3suLHyICLL6N9hxwANnz+IeVlpTjsClMv1d53e+D99c3b4LBDSXmrv4IQRkSoBJlwuJnrXlsmxsBorVAZNmwYeXlaEHz58uUUFxcH3TYhNIS6ppGPpKQkunfvDmjjMZCEzlimohpUb7X+sy9H5WAjK36M2Gw2xk6aBYCrpprPP30XgOmjas95Zy14m9lV2REvOSqRggiVIBMuoSK1G1pOa3JUwD/843K5WLp0adBtE0JDqGsaGfFdv6ysTCoZN0NlNXhd9cvnGz0q3RrxqED94m8AXTooDNOqCXC8CDbuadoGhx2qqqHGJaLS6ohQCTLhEipSu6HltCZHxYcUf4tMwjUe615fHh6aproGvB7/8vmqqupCpVMmJCcojXwaLh4ylozMDgCs/2wZlZVaDGeGwauytJmkWkc8VLs00SRYGxEqQcY4QfmKhYUKqd3QMlob+gEYOXIknTtrvugPPviAc+fOBdU2ITSIULEmLg943P47JxeWQPn5lxrLT/ERFxfHmIkzAaiuqmT92mUAjLxIW9YM8NkOOH22cW+JI14r5V8l+7paHhEqQURVVX2C6tatGykpKSG9n0yMLaMtQsVmszF7trYssrq6mnfffTeotgmhQYSKNfG4wWsUKgkJAa34MdJQ8Td7nMJVl2mveb3wXhObWcfZFLxeESqRgAiVIHLixAlKSkqA0E+Kde8hE2PztDZHxYdx9Y8Uf4sMfOPCZrPpRRJDhXGJsozHpnF7wV1TOx4dzsSAVvwYGTx0AqlpmQCsW/MONdVaLtI1o8B2Pmr0zrrmy+SLULE+IlSCSDif3ureQybG5mlLjgrA2LFj6dBBi4svW7ZMX4YuWBOv1xuWmkY+UlNTyc3NBWTlT3O4POB21Y7HhISkRvf4aQx7fDyjJ1wLQGVFGRs+/xCADhkKIy/SziksgXU7Gr+GokBZpfyerI4IlSASbqHSr18/qd3QAtoS+gEtLj5rlrYssqKigvfffz9otgnBJ1w1jYz47nP27FnZxLIJVBVcrtqysE5not+uyXmdArtOQ6t/AGaMrj2nqUq1zngolecNyyNCJYiEejPCuiQlJdGjRw/93vIE1zRtDf2A/+qfBQsWtNkmIXSEezyCeDlbQlVV7XiMdyRy+LxQ6ZwFic7GV/wYGXrp5SSnpAHw2aoluFxaHGdYP8hup52zYTccL2x4bvTVUpG509qIUAkiZk6M5eXl5Ofnh+WekUpbQz8AEydOJDNTi4u/8847VFfL2karEm4PZ937iFBpmpqq2vHosnXSlwkHkp/iw+FwMnLsdADKy0r4cv3HgLaXj18BuM8a+bxdW6JcLXkqlkaESpBQVZUdO7RgaE5ODhkZGWG5r0yMgdPW0A9oO1dfe60WFy8rK2PNmjVBsU0IPiJUrItC7T4/AKXuWnUSSH6KkfGXG8M/tUnu0y4De5x2vOwL8DRQqda3RLlShIqlEaESJE6fPs2ZM2eA8E2Kde8lE2PTBEOogP/qH8lTsS7hrGnkQ1b+BIbd7i9UzlZ11I9b4lEBGD5yCgmJ2o6Ea1cuxu1yAZCZqnDZ+enxbJl/eX4fDjvU1GgVagXrIkIlSJjx9Fb3XjIxNk0wclQALr/8ctLStLj4Rx99RE2NPI5ZjXDXNPKRmZmpFwaU8dg4dhtUG3JUzlS2048DqaFixJmQyGVjrgagtOQMW79cpb83qHfteTsO1v+szaagIkuUrY4IlSBhllAxPinKxNg0Po+K0+nU90lqDU6nk+nTtbj4uXPnWLFiRVDsE4LH8ePHKS0tBcI7Ho33Kyws5PTp02G9d6QQF4e+czLA6TKtnKxNgbyOjX2qcYzhn9Uf167+ubBH7TnbDzT+eREq1kaESpAwS6ikpqbqO/vKyp+m8QmVYNTTkL1/rI1Z47Hu/eThwR+3W5uf7HHG0I9CQanm8cppD05HYCt+jFw6ehoOZwIAa1Ys1Pc+69NVW4IMsL0BjwpAnE1qqVgdESpBwgoTY0lJCSdONBCIFYDa0E8whMrUqVNJTtbi4osXL8btdrf5mkLwsMJ4rGuHAL58Vpsx9JPQA5dHy3ptaX6Kj8SkFC4dNQ2A4jMFbN+iFU+xxyn0057jOHkGCkvqCxJnPJRILRVLI0IlSPgmpI4dO5KVlRXWe8vEGBg+j0prlyYbSUxMZNo0bWIsKipi1apVzXxCCCdWESq+yrhCfXSPStKF+mstzU8xMnbybP141ce1q38uMoR/GspTccRDWSV4G1gVJFgDESpBoKioiFOnTgHhnxTr3lOESuMEM/QDEv6xMmbUNPIhQiUwqs/vzUNSbXu11qMCMHLsNcTHOwAt/OP1egG4qGftOQ3lqTjsUOOSPBUrI0IlCBgnIxEq1kRV1aALlauuugqn0wloVWp9cXHBXIw1jbp06UJ6enpY79++fXt9TygZj41T4/OoJNS6PLq2IpHWR0pqOkMvvQKAwoJj7Nr2BQAXdq89Z/uh+p9zxItQsToiVIKAmW5mkNoNgVBdXa0nGgdLqKSkpDB+/HgATp06xWefNVL+UggrBQUFFBcXA+aMR6gdkydPntR3VBf8qao8n6Niz9RfS09u2zX9Vv+s0LycackK3c7vHbQ3H6pq/EM88fbzRd+kloplEaESBMz2qGRkZOi1G8TV3DDBKJ/fEFOnTtWP582b18SZQrgwezzWve++fftMscHq6B4Ve4b+Wmobh+ao8TOIs9sBbZNC38OJL/zj8cLuI/6f8W3sKh4V6yJCJQiYGQ/3IbUbmiZYVWnrMmnSJOLjtfWPCxYs0OPignlYaTyCCJXG0JNp47VibzYbJDnbds209HYMGTYJgFMnDvP1ri8B//BPQwm1IELFyohQCQK+iTEzM5NOnQLcnzzISJ5K0wSrKm1d0tLSuPzyywE4evQo69evD9q1hdZhdii27n337t1rig1Wx7d7shKvhX5SEmu9G81xrkLl1BmVY6dVDp9UOXBc+7f/mMqYibP081afX/3TXEKtPU67pmBNRKi0kdLSUo4ePQpok1OgAy3YiFBpmlB5VEBW/1gNqwkV8ag0jC/0o57PUUkNcFieLVMpPgfpKZDbSas+e+kAhbEDFbLS4eLhM/XK077wT9cOtfkvOw7VX4rsiNf2AxKsiQiVNmKFeHjde4tQqU+oclQAZsyYQVycVrBq3rx5Uh3YZMysaeQjOztb30FdPCoNo4V+FLBp+2YFkp9SWa2JlKH9YOqlNsYNsjGsv40Leyj0yVXo2gEUZ0cGXjIOgGP5+ziwdxuKoujl9M9VQH6B/3Wd8VBRVVs5V7AWIlTayJ49e/TjcO3Q2hDGWPzu3btNs8OqhNKjkpWVxaRJWlz80KFDbN68OajXFwKnpKREr2lk5nhUFEUfkydOnKC8XEqf1qW6qhLs6aBof4ZSmhEqbo/KsdNwQTfon9ew5zq7nfb62En19/7xC//UyVNxxGsrfyRPxZqIUGkjBw7UBjx79eplmh3t27fXd4g9eLCRbLEYJlQ5Kj6M4R9Z/WMeVhmPAD171v5lPHTokHmGWJTqqgq/pclNhX5UVSX/FHTLhsG9FeLiGhYq7dM1z8zgy2bqr63yCZXutefVzVORom/WRoRKGzGKAuPEFG4URdHvf+TIESk+VodQhn4AZs6sjYtL+Mc8rDIe695fHh788Xg8uN0uf6HSxLA8XgiZaTC8v0KCs/E8wASnQpf2YE/M4aLBowE4fGAnhw/sol+eljQLWp6KkXi7gtsjtVSsigiVNmKcgHr06NHEmaHHd3+Xy8WxY8dMtcVqhDL0A9CpUyfGjh0LaDkJ27dvD/o9hOax4ngE8ajUpbaGSq1QSWlkWJ4pVVEUTaRkpDa/WKFzliY6xk6s3ftn9Yr5OOIV+uZqP+cXaEm5dRGPijURodJGfBOjMfRiFsaJUZ7g/Al16Afguuuu048l/GMOVhUqMh79qa6uX5W2IY9KRZVKSTkM6QNdOgS2orJ9uiZ6ho42bFK4/Hz4x9Al6uapKIqWrCtYDxEqbaC6ulr3XJjtZq5rgzFWL4TeowIwe3btxCjLlM3B2O/NHpMyHhunIY9KQ0LlVLGWPNuvkeTZhkhJUuiUCY6UPPpfOByA/V9v5Vj+/iZ3Uo6Pg9IKBAsiQqUNHD58WM9FMPvpra4N8gTnT6hzVABycnIYNWoUADt27JDVVybg6/eJiYmmFV/00aVLF71qsYR+/GmofH7d0E+NW8WmQPdsBZutZfWpunRQqHHBuMn+q38uNHpU6ibUxkOpLM6yJCJU2oCV3Mx1bRCh4k84PCogxd/MxOv16oKge/fuphVf9BEXF0deXh6gjUdJsK6lOgCPSkkZZKZqoZyW0iFDK8c/fIwhT+Xj+bRLU8hpr/28J18TQz6c8VoybY1Lfk9WQ4RKGzC6c60gVLp3764fi6vZn3DkqIAIFTM5efIkVVVVgDXGI9SOyXPnzlFUVGSuMRaiuqr5HJVzFdpyZLu95YIzLRnaZ0BSRi969xsMwO4dGzh5/LDuVXG5td2UfTjiZYmyVRGh0gastBQSIDk5WXd3i0fFn3CEfgC6devGsGHDANi8ebMIxjBitfEIskS5MWqqNUHZWB2VGreKPa62gFtLURSFvE4KldUwbnKtV2XNigWNJtQ67FL0zaqIUGkDVgv9QK0dJ06c8PvjHOuEK/QD/qt/xKsSPqw8HkGEipEGlycbnh/OnoN2aZCV1vp7tE+HBAdcOtaQp7JiARc3klAbF6fg8YpQsSIhEyrFxcXce++9jBkzhtmzZze6q+wjjzzCyJEjGTt2LGPHjuWGG24IlUlBxzfx2Gw2PRZtNsaJ8fDhwyZaYi3CKVSkSq05WFGoGMOxIlRqqZtMa1O0nBIfZZXQrVPrwj4+MlO1f2kd+tOtp7YX2vYta0m2nyDBoZ2zr4FyU1L0zXqETKg8/fTTZGVlsXz5cu69915++ctfUlJS0uC5d955J2vWrGHNmjW89dZboTIp6Pjc+rm5uXp2v9nIksiGCVeOCkDv3r0ZNGgQAOvXr+fIkSMhvZ+gYaWlyT5kPDZM3ToqKYnoK3tqXFrYp1Mrwz4+bDaFvE5QXgXjDat/1n6ykO6dteMTRVqtFh+K4v+zYA3sobhoRUUFK1euZPHixSQkJDB+/Hh69erFqlWrmDFjRquvW1NTQ02Nv1/ObrfjcDjaanI9vF6v3/91KSkpobi4GNCe3ho7L9x069ZNPz5w4EDQ7GquPayOUagkJCQE5Xs01SazZ89m69atgBb+uffee9t8P6tjdh8xeiy6detmib4aqvEYqfi+v6umjlBJUlHQBEJJmUr7dMhMUfB62yZW2qerJMSrjJ44k/+88hgAqz+eR8+J32f3Ye3ah06qXNhdu3eiQ+VcOYTz12T2uDET37YjzRESoXLkyBGSkpL86hj07t270SeKuXPnMnfuXLp168YPf/hDhg4d2uB5c+bM4ZVXXvF77frrrw9puCg/P7/B131byYNWldYqYRZjoujWrVuDbldj7WF1zp49qx8XFBQ06t1rDQ21yciRI/Xj//3vf8ycOTNo97M6ZvWRvXv3ApCRkUFxcbH+IGEmqqqSnJxMeXk5e/futcw8YTYZjpOAood+slJryMs4CUCe9hJHjwbnXqP6gNo7g+7du3Po0CG++nINk246ynto9fRLzp4hL6PM795m/JoidW5tC4GGaEMiVCorK0lOTvZ7LTk5ucE/DjfddBM///nPSUxMZPny5fz85z/njTfeoHPnzvXOvf3227n55pv9XgulRyU/P5/c3NwGVd+mTZv044svvtjvyclMjKq8sLAwaHY11x5Wx9cuiqLQp0+foNTYaKpNunXrxgUXXMCuXbvYtGkTDoejwT4dTZjZR2pqajhx4gSg7ZpspfHYtWtX9uzZw/Hjx+natStxcXFmm2UaVdUeTp08ypHTDohLA0XrJ06HgyNn86hxqZwugcuHKbRPD04dnK/2e/lqH4yaeAOH5vxR66cHtsJ5obJxfztGDW4HwLkKlWoXTLu06c0Pg0mkz63hICRCJTExkfJy/xJ/5eXlDS4L7d+/v348bdo03nvvPT7//HNmzZpV71yHwxESUdIUNputwc5jfDLq1auXZTpYt27diIuLw+PxcOjQoaDb1Vh7WB1fMm1iYmLQ/1A01ibXXXcdjz32GKqqsnjxYn7wgx8E9b5WxYw+cvToUb2gWs+ePS3VR/Py8tizZw8ul4sTJ05YJvHeDGw27XdUXVVVZ8WPgorCmXMqGSmQldbyarSNkZWm4FFVxk2+jv/N+SMAX3/5fxB/DQAHjmv3BrDbVUoqoNqtkJQY3oKBkTq3hoOQtEpeXh4VFRUUFBTor+3fvz+gBDdFUSKigqMVVxiA5mEyVsMUNHxCJZQ1VOoiq3/Ch1XHI0DXrl31YxmTGjU1lQ3WUCmr1Iq8xcUFTySkJmlVZ3N7XkJ2TncAtm9cSEaK5mU9cAL9b44zHqprtGJzgnUIiVBJSkpi/PjxvPzyy1RVVbFmzRr27dvH+PHj65378ccfU1lZidvt5sMPP2TLli2MGDEiFGYFFStPjD57jAm/sY7RoxIuBg4cSO/evQFYtWoVp0+fDtu9Yw0rj8fc3Fz9WISKRnV1JcT7V6WtqlFxxkPHzOB6MlISISkBqmoUvfibx+0m3aHlxJSWw5lS7VxFUVDQEnoF6xAyP9NDDz3E6dOnmTx5Ms899xxPPvkk6enpLFu2zC/59X//+x9Tp05l8uTJvP766zzzzDN+TyBWxZcYnJCQQHZ2tsnW+CNLIuvjW/UTTqGiKIruVfF6vSxatChs9441rLg02YdRqMh41KiuqqhX7O1sWduLvDWE3a6QlQYVVTBuUq2Xs7qotrbXgRO15yc4tV2bBesQkhwVgMzMTF544YV6r0+bNo1p06bpP7/66quhMiFkqKqqb37Wo0cP0zc/q0vdapiNraKKJcwI/YCWp/L0008D2jLlu+++O6z3jxXEoxJZ1FRX1Qv9lFfCwJ4ELTfFSPt0hb1HVS64+FLad+xCYcExTh94D3rNBODgcRh+Pl0yKQGKz0FltUpimBJqhaaRzJ1WYMXNz4xI2W5/PB6PXn8nnB4VgKFDh+orUD7++GMJxYUIXz9XFMVyyaqSo1KfmupKfWkyaLkhoQj7+EhN0oq5KYrC2EnaQg1P6Rb9faNHJTlB876UliNYBBEqrcBquybXxWiTuJrDWz6/Lsbwj9vtZvHixWG9f6zg6+ddunTB6XQ2c3Z4SUpKomPHjoCMRx/V1f6hH3ucJibaBTns4yM1CRLitX18xk8+vxdXxU7gfELt8dpz7ef3/BGhYh1EqLQCK+7SakR2bPXHTKEC/qt/ZJPC4HPu3DmKiooAa45HqLVLNgvV0DwqtUIlKQHi7aEJ+8D5hNpEzVNy0eDRZGZ1Am8lVO0H4PAp8HhrE2jtcVBUKgm1VkGESiuwcjwcoEOHDnouhggVf6ES7hwVgMsuu4ycnBwAPvzwQ0pLS8NuQzRj9fEI/psTSnXa86t+DEIl0QmOEG6XFhen0D4NKqohLi6OsRNnam+UbQOgxgXHDIvykhK0hFqPR8SKFRCh0gqsPjEqiqLbdejQoZjcQ8KI2R4Vm83G7NnassiamhreeeedsNsQzVh9PILkjdWlbjJtklPzYoSSrHQFl1s7HufbpLB8m/7+wTp5KuWVUk/FKohQaQVWz1GBWldzTU0Nx48fb+bs6CacOyc3xnXXXacfS/gnuFh5abIPyRvzR1uenAGATdG8KfEhFiqp552pXq/KoEvGk5aRBeXb9feNeSoJDi2fpVSEiiUQodIKfE9E7dq1Iz093WRrGkae4GoxO/QDMGbMGD2hctmyZfW2mBBaj3hUIg9jZdqU888O8SEM/YAmVBLPCxB7fDyjx8+AioY9KlL4zVqIUGkhLpeLo+e39bTqpAgyMRoxO/QDWlzct39VZWUly5YtM8WOaESESuRhTKZNSQKPN/QeFT2htlr7efzl10HlfvBobpMDdRzPUvjNOohQaSFHjhzRcz6sOimCTIxGrBD6Adn7J1T4+rfT6bTsDtW5ubn6ZpixPh5VVaWqqraOSmoiqCrE20NbXM1mU+iQrq38AbhkxGSSU1LPL1OG40UqldW1HhRj4TfBXESotJBIiIeDlNE3YgWPCsCECRNo107bTv7dd9+VZapBQFVV/Q9/9+7dLbv7rHGz0FgfjzU1NWBLAUUTbr7ckbgw/OqMCbXx8Q5GjZ+h56moqsLhk7XnSuE362DNUW1hIsHNDOJRMWKFHBWA+Ph4rr32WgDKysr48MMPTbMlWigoKNA9ZlYejyCbhfqorq6ut8+PokBciEM/oIkim6Il1ALaJoWGPBVjhVop/GYdRKi0kEgRKikpKbRv3x4QoWIVjwrI6p9gEynjEeThwUdVVf19flDD41FJTdJqtlRqO2ow/LIrcbj36+/vP+pfysEeB2fOSejHbESotJBICf1ArX3Hjh3TnmJiFKvkqABMnjyZtDStTviSJUv0PYiE1hGJ4xFiO/xTWelf7C01CVDCI1SSEyA5sTZPxZmQyPCB2fr7X33t7+lKSoCTZ6Twm9mIUGkhVt78rC6+JzhVVWO6GqaVPCpOp5MZM2YAWgjg448/NtWeSEc8KpFHXY9KShhzVGw2hQ4ZUGl4brviiiugpgCAIwX+a6Sl8Js1EKHSQnwTjBU3P6uLTIwaVslR8SGrf4KHCJXIo9HQTxhyVADapdUm1AKMGD0NpUpb+VOjplF41qO/J4XfrIEIlRZw7tw5CgsLAetPiiATow8rhX4ApkyZQnJyMgCLFi3C5XKZbFHkIkIl8tCESob+c3ICYQv9gCaMbLbaTQgTE5PpnFGbMbvi09pqtVL4zRqIUGkBVt81uS4SE9ewUujHZ8PVV18NwJkzZ1i1apXJFkUuvn6dkZFBZmZmM2ebS8eOHXWPXiyPx7oeleRETaSETaj4EmoN4Z8hA9rrx59+sc/vfCn8Zj4iVFpAJD29gTzB+bBa6Adk9U8wcLvd5OfnA5ExHmWzUI2qqiqINwiVBG3JcLhCP8mJWpVao1CZNOYi/fjrIzV+vxtf4bcqKfxmGiJUWkCkCZW8vDy9AJYIFQ0reFQApk2bRkJCAgALFizA4/E08wmhLvn5+Xq7RcJ4hFo7a2pqOHHiRDNnRyeaR6Wd/nNSghaKsYW2MK2Ooih0zKhd+QNwQc9kQBMn1XE92bntc/09X+G3EqmnYhoiVFpAJC2FBK3AWG5uLhDbrmar5aiAVudm2rRpgFa0bO3atSZbFHlE2ngECcdC/dCPT6iEK/QDkJmq4DY4tBKdChmJZecNupBVyxfo70nhN/MRodICIs2jArV2FhcXU1JSYrI15mBFjwrI6p+2EsnjEWLXy+mfTKuSEA9xYQz9QG2FWmN9lP7dNQ8nccl8smYjqlr7nhR+MxcRKi0gEjY/q4tMjNYVKtdccw0OhwPQwj+xmrPQWkSoRCZGj0pivAuU8HtUUpM0T44xT6VPnkM/Lipvx9e7Nuk/S+E3cxGhEiDGzc+6detm2c3P6iITY61QiY+Px263m2xNLenp6VqxKbTqwV988YXJFkUWIlQiEz+h4nDj9YZfqCQlaMuUKwxCpVeO4YTUEaxaXuvlTE2Ec+Vwtix8Ngq1RMZfWwtQWFgYMZufGZGJsTZHxUreFB+y+qf1HDp0SD/u1q2beYa0ABmPUFFRG/pJcnp0oRLOZz9FUejUDsoNCbUXG9OcMiax+uMFevjH6VCodmmrf4TwI0IlQI4cOaIfR8qkCP62+pZyxho+j4pVliYbmTFjhu7lmTdvnl9cXGga35js1KmTJUVoQ6Smpur1XmJ1PJZVKaBoCSkpiV68KsTHaeIhnGSlKXi96GOuXZpCD19EP+USjp0oZP/er/Tz4+1w6oyMTzMQoRIgRqFi9T1+jBhtNX6HWMInVKz4x6xdu3ZMmjQJgMOHD/Pll1+abFFk4HK5OH78OBBZ4xFq7T169GhMLks/V1GbNZuSCB4vOOKb+ECIyEjRCr9VGfYFvaTv+QMlDtLHs/rjWi9nSqJW+K3GJWIl3IhQCZBIFSo5OTl6Pk2sChUrh35AVv+0hmPHjulPwpE0HqHWXpfLxalTp0y2Jvycq6zNE0tNAq9XW1UTbtKStX9ltbn2tUIFtPDP8lqhkpqknSt5KuFHhEqARKpQiY+PJydHyxKLRaGiqqqlPSoAM2fO1MWkhH8CI1LHI4iXs6y6dnVNWrINr1cL/YQbm02hc5a/UBnYy1B4LmMShw/u4tABbcPCeLu2maHkqYQfESoBEg0TY0FBgd9S3VjA5XLpy36tmKMC2h4w48aNA2Dfvn1s27bNZIusTzSMR4hNoVLpqt11PiPFruWomBD6gfp5KimJCv18v56kARCf7Rf+SXDAiSJ5kAg3IlQCxDehKIpCly5dTLamZRgnxqNHj5poSfixag2Vusjqn5YhQiVyqXQl6McZqXY8JnlUoJk8FdBX//hISYLTZ6FS9v0JKyJUAsQ3oXTu3Fkv0hUpxPLEaMXy+Q0xa9Ys/VjyVJpHhErkYhQq6WnxqKoWVjGDQPJU9n+9lWNHtB2VUxK0cyX8E15EqARAdXU1J0+eBCJvUoTYnhgjxaOSk5PD6NGjAdi5cye7du0y2SJrI0Ilcqnx1oZg084fhrPYmxGbTSGnTp7KRT20pcgAZGgr8lav0LyccXEKXhWKpZx+WBGhEgDGcEmkTYoQ2xOjUahYNUfFh3H1j4R/msbXj51OJx06dDDZmpbRuXNn4s5vbBNr4xGgxpuiH6ckgRLmfX7qkpWuoKq1eSqOeIWLfHX5ErpBQi9WGVb/JDnheCGS9B5GRKgEQCQ/vUFsC5VICf2ACJVAUVWVw4cPA1rfDnehsLYSFxdH165dgdgbjwAuNVk/Tk0EVPM8KnA+T8Xhv+9P3fDPnp0bOXlc63OpiXDmHJTH1roEUxGhEgAiVCKXSAn9gPZ7Gj58OABbtmxh3759JltkTUpKSigr04pZROJ4hFq7i4qKKC8vN9ma8OJWUvXj1CRAMVeopCZBWkrTeSpQG/5JStBEiuSphA8RKgEQ6UIlIyODlBTN3RrLQsXqoR+Q1T+BEOnjEfztjrVS+l4lXTtQvSSfz6s1U6jYbAqd6+z70y8X3TYyJgKKXvzNZlNQgKJSCf2ECxEqARDpE6OiKLrdR44cianYaiR5VECq1AZCpI9HiF0vp9frRY3ThIpNLcNm0/JDzMxRgfp5KnFxCoN6n38zvgMkX8yOr9ZxuuAYoHlVjheC1xs7c6mZiFAJgGiaGKuqqigsLDTZmvARSTkqAL169WLw4MEAbNy4Uc/FEGqJpvEIsSVUKisrwa5tyminHK9X1ZJpTf5L1FCeypA+hhPSJwKwZoVWUyU1CUrK4FwFQhgQoRIAvokkKSmJdu3amWxN64jpifE8kSBUwD/8s2DBgibOjE1EqEQuFRWVYM8AwK6U4z2fSGu2UGkoT2WoX57KZAC9+FuiEyprJE8lXIhQaQZVVfWJJBJXGPiI1Ykx0nJUQMI/zSFCJXIpLK4CRStS4rBV4vVqe+uYHfrx1VMx5ql07wyZ5/N+lcwJoNj56svVnCk6haIo2Gxw+qyEfsKBCJVmOHPmjB4+iNRJEWJ3Yoy00A9A//79ufDCCwH47LPPOHbsmMkWWQtj/83NzTXRktYTq+PxVFFtbMURV4XHCzabYSNAE2lXZ98fRVH08I9qS4aU4aiqyqefLAIgJRGOF4HHI2Il1IhQaYZoeHqD2J0YIzH0A/5elYULF5poifXw9d8OHTpE1O/USFpaGunpWlJpLI3H08Vu/TjBXoVX1YSK2aEf0PJUkpxN1VPR8lR8mxSmJsK5cjhbFkYjYxQLdA9rI0IlsonE0A/IMuXGcLvduocpkscj1Nqfn5+v7/Ad7RSeNQiVeBdeL8RZIPQDWp5Keop/gqwxT8XR6WoANm/8hJKzRTgdCtUuESrhQIRKM0SLUOnSpYueXxOrQiWSnr4vuugi+vTR/M6rV6+moKDAZIuswfHjx/U/6pE8HqHW/pqampj5/Z4pqRVkiQ6XpTwqNptCTnuoMOSpZGcpdM7Sjt2Jw8CWiNfjYe3KxdpnFP+dl4XQYIHuYW2Mf9S7detmoiVtw+Fw0LlzZyC2hEok5qiAFh/3eVW8Xi+LFi0y1yCLEC3jEWLTy1l0rlaoJDk9WjKtRYQKaHkqKv77+PjCP17skHopAKvPL1OW7JTwYJHuYV2ixaMCtfafPHmS6urqZs6ODiLVowKy+qchonE8QuwIlbPnarNmkw1CxWaRv0S+PJUKw/Q4sFftcVKOFv7Z9PlHlJ07G17jYhiLdA/r4itvrSgKXbp0MdmatmGcGI07QkczkZqjAnDJJZfQvXt3AFasWEFRUZG5BlkAYwE8ESqRR0l5rVBJSQKvCvFxWKbsQ2oSZKRqSbI+jEIlucs1ALjdLtatfifM1sUuIlSawTeBZGdn43Q6TbambcTixBipoR/wD/94PB6WLFliskXmIx6VyKa0ojZrNjURPF6It5toUB0URaFrByg3eFSy2yl0zNCOSzy99Dowqz6WJPdwIUKlCaqrqzlx4gQQ+ZMixObEGMmhH5DwT11EqEQ25yprhUp6ila3xEpCBSArTSHOBm5DfZSLz3tVatxxpHW5AoD1n71PZYWUpg0HIlSa4NSpU/pxpE+KEJsTo1GoJCQkNHGmNRkxYgRdu3YF4KOPPqKkpMRki8zF12+dTicdOnQw2Zq2kZOTg+18ckasjMfy6nj9OC05ThMqFliabKRd2vkaKYZlysbwT9eBdwLgqqlm28b3wmxdbCJCpQmOHz+uH4tQiUx8QiUxMdEycfCWYLPZmD17NgAul4ulS5eabJG5+Pptbm6u/kc+UrHb7XreW6yMx/Jqh3ageklPjddyVOKb/ky4cToUsts1LlRIG6sfbvpM9uIKB5E90kOMCJXIx5ejEolhHx9S/E2jpKSE0tJSIDrGI9R+j9OnT/vlU0Urla7zXk13CYkJiVqOisU8KqDVT3HV1qajWydIT9aO889kkZahefO+2vAelZXR/3szGxEqTRBtQqVdu3YkJ2ujLVaEitGjEqmMGjWKTp06AbBs2TLOnYvNuHg05af4MH4P3wrDaKbK7RMqxTiciagqxNut5+lslwYJTqisrt335+Ke2nvnKhUGjf0+ADXVFaxd/YFZZsYMIlSaINqEiqIo+vc4cuSIX1GjaMUnVCJtabKRuLg4PfxTXV3Ne+/FZlw82oVKtD88qKpKtef8OHQXk5CgHVul2JuR9GTITIHSRpYpZ/W8QT/+aFnsejnDhQW7iHWINqECtd+joqKCM2fOmGxN6IkGjwpI+AdEqEQ6ZZWgcj7O4y7GmZCIYpF9fupisyl06QDlhnL6RqFy1tuP1LRMAFauWBozBTTNQoRKE/iESmJiIllZWSZbExyME6OxeFY04vV6qarSZppIFyrjxo3T++C7774bE/kMdYlGoWLcBiDahUqxMWLpPovDmQiqNT0qAO3TFRTA49U8z727QOL5UlrbD9oYOW4GAOVl5/joo49MsjI2sGgXMR9VVXWhkpeXF5ErRhoilp7gfCIFIl+o2O12Zs2aBWjesA8+iL24eDQKlVgaj/5CpRinMxEU6wqVdmla9dyy888EcXEKF/bQjgtLYOBlt+jnxqqXM1xYtIuYT3Fxsf7UGi2TIsTWxBjJ5fMbItaLvxn7a25uromWBI9YGo8NChWsK1QSnQqdMqHUuEy5Z+2xmj6OhMRUABYvXozL5QqzhbGDRbuI+UTj0xvE1sQYyeXzG2LSpElkZGQAsHRp7MXFff21ffv2USE8AdLT00lLSwOifzwahYriLcEeH4+qWjNHxUfnLIUag/4w5qnsPGxn0Aht75/i4mI++eSTMFsXO4hQaQQRKpFPpJfPr4vD4WDGDC0ufu5cbMXF3W43x44dA6JrPELt98nPz8fr9ZpsTei4sAd0OPdHOPIE8ZWb8HpVLZnWwn+F2qVBggOqarQ8lQu61dZ9+Wo/DB09Wz83Fr2c4cLCXcRcjDUNomli7NKli55vE0tCJVqewGN19c+JEyfweDxAdI1HqP0+1dXVnD592mRrQkffXIXEMy/B4d+R5PkK7/lEWisLlYwUSE+prVLriFfofz7/+Vgh5PabSmKiNrcsWrQIt9vdyJWEtmDhLmIu0epRcTqdZGdnA7ElVKLBowJwxRVXkJKSAsRWXDxaxyPElpezqkobkw5nIl4v2BSw8k4IcXEKXdrXJtQCeuE3gMMFSYydcBWgVRdes2ZNmC2MDSzcRcwlFibGEydORHWeQ7TlqIC2seL06dOB2IqLx8J4hOgXKr4x6XRq5fNtFveogLZMGQW855cpG/NUvj4KV0yL7ST3cGDxLmIextCPb/faaME4Mfri/tFINHpUIDZX/4hQiQ58Y9LhSMCrRoZQaZcGKQlawTrQcm1s56tV7D0K4yZchdOpFVhZsGBBVOcZmYXFu4h5+CaMTp06kZCQYLI1wSVWJsZozFEBmDZtmv59YiUuLkIl8nG5XHqekTMhCa8X4ixamdZIcqJC+4zaPJWURIVe2qbXHC0Aj5LC1KlTATh58iSfffaZOYZGMSJUGsDlcvkVe4s2YmVijMbQD2iia9q0aUDsxMVFqEQ+xvHocCZGjEcFIKe9QlUDy5RVYPNefy9nLCW5h4sI6CLh59ixY/qGfdFSWMpIrEyM0Rr6gdhb/ePrpw6HQ99JOlrIycnBdj6jNFbGo55MGyFCJTNVW5bscmt/F4wJtZv2wPTp04mPjwe08RgLG76GkwjoIuEnmp/eQIRKNHD11VfHVFzc109zc3P1P+rRQnx8PDk5OUDsjEenQahEwq8zIwVSEmvzVIwJtZu+hoyMDC6//HJAy2/csGGDCVZGLyHrIsXFxdx7772MGTOG2bNns379+gbPq6qq4re//S3jxo3j6quv5v333w+VSQFjnCyMm4ZFC7EoVKIpRwUgNTWVKVOmANrqrXXr1plsUegoLS3l7NmzQHQ+OEDt9yooKPDrt9FEPY+KqnkpImEfNUe8QlY6lJ//CpmpCrkdteMdB6GiSvXzcsZKknu4CJlQefrpp8nKymL58uXce++9/PKXv6SkpKTeeS+//DJnz57lvffe46mnnuLpp5/m0KFDoTIrIKJxTxEjWVlZuochmoVKtOao+IiV1T/RWnzRiPF7HT161ERLQodxPPqWJ8fbTTSohXTKVKg25KnMHgfXT4D//Bqc8XDttdcSdz4zeN68eRL+CSIhESoVFRWsXLmSe+65h4SEBMaPH0+vXr1YtWpVvXPfe+897rzzTlJSUrj44osZP3686TvDRnvoR1EU/XsdOXIkagdUNId+IHbi4tE+HiE2vJwN5ahEklBJT9bCVB6PNs5mjlWYeikM7KUQF6eQlZXFxIkTATh48CBbtmwx0droIiTd5MiRIyQlJfklvfXu3ZsDBw74nVdaWkpRURG9e/f2O++rr75q8Lo1NTXU1NT4vWa323E4HEG0Hg4fPqwfd+3aNSrj/7m5uezZs4fy8nIKCwvJyspq8nxfG0RSW5SXl+vHTqcz6Lab3Sbp6elMmjSJDz74gPz8fL744gtGjBhhii0QuvYweli7dOkSUX0w0DYxem4PHjwYUd8xUMrKyvRjpzMBBS+OOIiUr5qWrJKWpFJRDWlJWrgqzqYCCl6v9vOsWbNYvnw5AG+//TaDBg1q9rpmzyNmEmi+WUiESmVlJcnJyX6vJScn1wv9+FyBxnOTk5MbjdHOmTOHV155xe+166+/nhtuuCEYZuv4BJXT6aSystJPuEQL7dq104/Xr1/PgAEDAvqc0Q1vdQ4ePKgf19TUhOz3aGabTJgwQfdAzpkzxxIrYoLdHkahoqpqRI7H5trEWKtp+/btEfkdm2Pnzp36cb9u8VzWW2uTSPqql9RJWczL0P73fYdhw4ahKAqqqvLGG29w1113BZyDE0lza7Do0aNHQOeFRKgkJib6Pc2C9nRbN6HR93N5ebm+f0l5eXmjbvrbb7+dm2++2e+1UHhUli5dysGDB9m7dy95eXlRt8oA8BMmLper2aRhr9dLfn5+RK26KCoqAjTVPnz4cD1MEiys0CZ33HEHv/nNb/B4PCxfvpy//e1vpiUnhqo9jH/E8/LyIirBPdA2GTp0qH5cWloaUd8xUIw5Kp7Ei1izJ5eLe8Kg3pExnwDsPORl0x7onq2NsUMnVYb1V7igm/Zzt27dGDt2LKtXr+bgwYOUlZVx0UUXNXlNK8wjVickQiUvL4+KigoKCgro2FFLjd6/fz9XX32133lpaWlkZWWxb98+Bg8erJ/Xq1evupcEtBoKwRYlDdG3b1969+5N3759sdlsUdl5jBPh0aNHA/6OkdQevlh/Tk6OvpQ3FJjZJh07dmTChAl8/PHHHDhwgG3btuljySyC3R5VVVX6cXJycsT0PyPNtUn37t314/z8/Ij8js1h9Bi079gNt8dGvF3BZrP+qh8fGSkKXlU9v0+RgserhX6M3+G6665j9erVACxcuJCBAwcGdO1ImlvDTUhaJSkpifHjx/Pyyy9TVVXFmjVr2LdvH+PHj6937lVXXcW//vUvysvL2b59O6tWrdKXXQqhI9qT9yorKykoKACiNwHTR7Sv/onmZeY+MjIydK9yNI5H8P9eHTppYzISir0ZyUiBpASoaGIv19mzZ+vH0TgezSBk3eShhx7i9OnTTJ48meeee44nn3yS9PR0li1b5pdTcs8995CWlsbUqVN58MEH+cUvfuH3dCGEhmgXKsYlntEuVGbNmqWHe6JxWWS0LzOH2FiJ55tnEhISSE3PQomAfX7qkpyorf4pa6LUTZcuXRg5ciSg5Rvt2bMnTNZFLyFbHJaZmckLL7xQ7/Vp06bp+5SA1mkff/zxUJkhNIJxR+hoFCqxsKTVR3Z2NmPGjGHNmjXs2bOHnTt3cuGFF5ptVtCI9mXmPvLy8ti5cydVVVUUFhbSoUMHs00KGqqq+oViFUUBNfI8KoqikN1O5URh0+d94xvf0Iswzp8/n1/96ldhsC56ibBuIgSLhIQEfYWICJXIJ5r3/okloeIj2sZkcXGxvsDCt10ASuQJFdCq0qrQpNdLNikMLhHYTYRg4ZsYjx8/jsvlaubsyCLWhEo0x8WNoZ9ozVGB6BYqxu+jCxUiU6hkpECiA6pqGj+ne/fu+kquL7/8sl4NMaFlRGA3EYKFb2JUVZVjx46ZbE1wiTWh0rVrVy677DIAtm3bxtdff22yRcFDPCqRTz2hooKqRl6OCkBqEqQ2k6cC/l7OBQsWhNiq6EaESgwTKxNjLAgViF53s0+oKIoSlvIEZhEr4zEnJwcVtGTaCPwLZLMpdMqE8qqmz4v21XjhJAK7iRAsYmFiTElJISMjw1xjwkS0C5WkpKSI2Gm3tcTCeARNqHi9mkiJRKECkJWu4Gmm4n2fPn30GipffPFFTFaeDRYR2k2EYGAs+hZNE6NxhUFeXl5U/3Ez0qNHDy655BIANm3a5LeFQCTjy1GJ5rAPaMtafX01msYj1BcqHi/YFG2Tv0gkIwWcdvx2U24I48ODhH9aT4R2EyEYROsTXGFhoV7NNFbCPj6iMS7u86hEu1BxOBx07twZiK7xCP7fp3Pnzni9mkiJVI9KWhKkJDUvVKJ5NV44idBuIgSDaBUqsZif4iMa4+KxIlSg1st58uRJqqubKH8aYfjGZKdOnXA6nahqZAsVu13LU2nOVztgwAD69+8PwKeffsrJkydDb1wUEqHdRAgG7du31zd8iyahYtx5NtaESt++fbn44osB+Pzzz/0q9EYqvtBPNC9N9mHsr9HwuwNt5/Ljx48D0DU3FwCvCnERWJnWSPt0BWcA+5z6vCqqqrJw4cIQWxWdiFCJYYxluw8fPhw1Zbtj2aMC0RUX93g81NRoBStiwaMSjV7OY8eO6XNLbq72/TwRHvoBSE/RSuo3RzR6OcNNBHcTIRj4JsaysjJKSkpMtiY4xLpQMcbFI31iNO6cLEIlMjF+j1yDRyXShUpGCiQnNH/eoEGD6NWrFwArV67k9OnTIbYs+ojgbiIEg2ifGGNRqAwYMIB+/foBkR8Xj4Wdk41E+3j0eVR8ybSRuuoHwBGv0D69+fMURdG9Kl6vl8WLF4fYsugjgruJEAyieWJUFIUuXbqYbE34URTFLy6+aNEicw1qA7Gwc7KRaB6PYBAqKsTHEfGlA/p3U8hu1/x5svqnbYhQiXGieWLs3LlzVFcybYpoiYvHSvl8H9E8HsEQ+vFCvN0si4JHx0yFrPTmxdawYcP03+3y5cspLi4OtWlRhQiVGCfaJsaqqipOnToFxGbYx8fgwYPp2bMnoMXFCwub2ZfeosSaUMnMzCQ5ORnwX70WyTQW+okGoRIoxvCP2+1m6dKlJlsUWYhQiXGiTagYl3TGslAxTowejydi4+KxsnOyD+NKvCNHjkTFSjzfvOJ0OunQoQOgbUgYH8FLk1tDtHg5zUCESozTtWtX/TganuBiPZHWSDTExWPNowK1/bayspKioiKTrWkbjW1n4YkxjwrAyJEj9crDH374IaWlpSZbFDmIUIlxEhMT6dixIxAdHhURKrUMHz5czwlYvnw5Z8+eNdegVhDLQgUif0yePXuWsrIywP97xVroB8BmszF79mwAqqureffdd022KHIQoSLoE8jx48dxuZrZvMLiiFCpxRj+cblcERkXF6ES2UKlsfGoAvH2yF7x0xqiwctpBiJUBH0C8Xq9eqnrSEWEij+RHhePtRwViA2hEqdEdrG31jJ27Fg9T+e9996jvLzcZIsigxjsKkJdYmFijFVGjRqlx8U/+OADzp07Z7JFLUM8KtE5Hm22yN7np7XExcUxa9YsQOvb77//vskWRQYiVISonBiTkpJo1y6ASkxRjs1m0yfGSIyLi1CJjvEI/t9LiVGPCkS+l9MMYrSrCEaiZWJsbIVBrBPJcfFYDP106dJF77uRPB6hCY9KXOwKlYkTJ5KZmQnAO++847efldAwMdpVBCPRIlSKior0J/Bu3bqZbI11GDt2LO3btwe0uLjxj7/ViUWPitPpJDs7G4js8QgNV6WF2M1RAYiPj+faa68FtM1gP/zwQ5Mtsj4x2lUEI9EiVCQ/pWHsdrse/qmoqIiouHgsChWo7b8nTpygurraZGtaj29MduzY0e/3p8RojooPo5dzwYIFJloSGYhQEejQoQNOpxMQoRKtRGpcPNaFCsCxY8dMtKT1uFwufRVh3fFoi2GPCsDll19OWloaAEuWLKGmpsZki6xNDHcVwYfNZtPdsiJUopNJkyZFZFw8FnNUIDq8nMePH8fr9QL1x2OcLbaFitPpZPr06QCUlJSwbt06ky2yNjHcVQQjvomktLSUkpISk61pHSJUGic+Pp4ZM2YAcO7cOT766COTLQoM8ahErlBpajzaFG2Jcixj9HIuW7bMREusT4x3FcFHtE+Mgn9cPFLCPyJUonM8xnroB2Dq1Kn6TtkfffQRbrfbZIusS4x3FcGHcZVMpE+MiqLQpUsXk62xHldccQWpqalA5MTFjaEfESqRRZNCJcZDP6D156uuugqA4uJiVq1aZbJF1iXGu4rgI5omxuzsbD05WKjFGBc/e/Ysn3zyickWNU+selSi6cEBGvGoxPCqHx+y+icwRKgIQOQLlerqak6cOAFI2KcpIm31j0+oxMfHY7fHzna77dq105OHI3E8gnhUAuGqq64iISEBgIULF+LxeEy2yJpIVxGAyBcqxiWcIlQaZ+rUqfofwEWLFlk+Lu4TKrHkTQEtfOnrx0eOHEFVVZMtajm+ecTpdOob8fkQoaKRkpLClClTADh16hSfffaZyRZZE+kqAuBfNTIShYok0gZGUlKSHhcvLCxk9erVJlvUNL4clVhamuzD14/Ly8spLi422ZqW4xuTubm52Oos8bHZZNWPj9mzZ+vHkeDlNAPpKgKgPbH6nnpEqEQ3kbT3T6x6VCCyvZwlJSWUlpYCDY/H+DhkL67zTJ8+nfj4eEDLU/HVnhFqEaEi6PgmlGPHjlk+JFAXESqBc9VVV+nJxlafGEWoaESaUGluPMZQulGzpKenM3r0aACOHj3K+vXrTbbIeohQEXR8E4rH49ETUyMFESqBk5qaytSpUwE4efKkZePiqqrqoR8RKtElVOJlxY8f06ZN04+t7uU0AxEqgk40T4yCP5Gw+sflcunenljOUYHoG48iVPy5/PLLiTu/XnvevHkRmTwdSkSoCDrRMDEmJiaSlZVlsjXWxxgXnz9/viXDP7FaQ8VHNIxHaESoSOjHj8zMTCZOnAjAoUOH2Lx5s8kWWQsRKoJOpE6Mqqrq9ubl5UmSXgBkZGRw+eWXA1pcfMOGDSZbVJ9YFypdu3bVjyNpPILkqLQGWf3TOCJUBJ1IFSrFxcWUl5cDEvZpCVZf/ROrOyf7cDqdZGdnA5E1HsHfXmPpAx/xdnmYqMvMmTP1ZdwS/vFHhIqgE6lCRfJTWse1115r6bh4rHtUoLY/Hz9+HJfLZbI1geMbk+3bt29QZMaJTqlHp06dGDt2LAB79+5l+/btJltkHUSoCDodO3bE4XAAcPjwYZOtCRyjrQ09vQkNk5WVpcfFDx48yJYtW8w1qA4iVGqFiqqqHD161GRrAsPtduuVohsbj7LPT8NY3ctpFiJUBB2bzaZvhnbw4EHLPWE3xsGDB/XjHj16mGhJ5GHl1T+xunOyke7du+vHxn5uZfLz8/U9axobj1I+v2FmzZqlH1ttPJqJdBfBj549ewJQVlZGYWGhydYExoEDB/Rjn/1CYMyaNUtPPrZa+MfoUYnFHBXw78/Gfm5lAhmPUj6/Ybp06cKoUaMA2LFjB7t37zbZImsg3UXww/gEFClPcOJRaT3GuPjXX3/Njh07TLaoFgn9RO94FI9K4xi9nBL+0ZDuIvgRyROj0+mkc+fOJlsTeVg1Li5CJbLHIzQhVCRHpVFEqNRHhIrgR6RNjKqq6nZ279693i6tQvNYtX6D5Kig54xBZIxH8LezsdCPeFQap1u3bgwbNgyAzZs3R0zIL5RIdxH8iLSYeEFBgf4HTcI+raNLly6MHDkSgO3bt7Nnzx6TLdKQHBVISEigS5cuQGSMR/C30yi0jIhQaRqrejnNQrqL4EekeVQkPyU4WNHdLKEfDV+/Ligo0AsbWhnfmMzJySEhIcHvPUe8lridkRJ2syIKK6/GMwMRKoIfmZmZpKWlASJUYgkrTowS+tEw9utDhw6ZZ0gAlJeXU1BQADQ9HuOk4luT9O7dm0GDBgGwfv36iCrAGQpEqAh+KIqih38OHz6s10OwKrI0OTh0796doUOHAtaJi0voRyOSwrGB5KcIgWF8eFiwYIGJlpiPCBWhHr4nIbfbbflqmOJRCR5Wi4tL6EcjksKxMh6Dh9XGo5mIUBHqIRNjbGK1PBURKhoyHmOTCy64gAsuuACAtWvXcuLECZMtMg8RKkI9InFizMjIIDMz02RrIps+ffowcOBAAL744gvy8/NNtUdyVDQicTyChH6Cgc+roqoqCxcuNNka8xChItQjUmLibrdbTzKTp7fgYKW4uOSoaOTk5OibhVp5PIK/fTIm244Vk9zNQISKUI9IeYILZPMzoWUY4+JmT4wS+tGIi4uLmM1CffNFfHw8OTk5JlsT+QwcOJDevXsDsGrVKk6fPm2yReYgQkWoR6Ts2Cpu5uAzYMAA+vfvD5gfFxehUotPiJeVlVFUVGSyNQ1Tt0p0nNTJbzOKouheFa/Xy6JFi8w1yCREqAj1SExMJDs7G7C2q1nczKHBKnFxY45K3cJhsYaxf1t1TBYWFlJWVgbIeAwmsvpHhIrQCD4PxcmTJ/2ebK2ErDAIDVZZ/ePrd4mJiShKbBcIM3oMrerllPEYGoYOHaqH/j7++GOKi4tNtij8iFARGiQSqmHKxBgaBg0aRK9evQBYuXKlaXFxo1CJdSIhb0zGY2gwhn/cbjdLliwx2aLwI0JFaJBImxiNeTVC27BKXNwX+hGhEnnjUXLGgkusr/4RoSI0SCQsUfbZ1dDmZ0LbsEJc3OdRieWlyT4iaTyCeFSCzWWXXaavovrwww8pLS012aLwIkJFaBCrP8EFuvmZ0DqGDRtGXl4eoMXFz5w5E3YbJPRTSyRsFiqhn9Bhs9mYPXs2ADU1NbzzzjsmWxReRKgIDWJ1oWLMmxE3c/CpGxdfunRpWO/v9XqpqqoCRKiA9vvwjUmrbhbqmyfS0tJo166dydZEH1bwcpqFCBWhQbp27Yrdbges6WoWN3PoMTMu7hMpIELFh5U3C/V4PBw+fBjQ7Iz1VVqhYMyYMXTs2BGAZcuWUV5ebrJF4UOEitAgVq+GKW7m0DNy5Eg6d+4MhD8uLuXz62PlJcpHjx7F7XYDMh5DRVxcHLNmzQK08bFs2TKTLQofIlSERvFNOKWlpZZbuy8rDEKPmXFxqUpbHyuHY2U8hgcrbXERTkSoCI0SKROjPMGFDrPi4rJzcn1kPArjx4/X83/effddyxbjDDZBFyo7duzgpptuYvTo0Xz3u99tcq+Q6dOnM3r0aMaOHcvYsWN58skng22O0AasvCTSZ49sfhZaxo4dS4cOHYDwxsXFo1KfSBiPIEIllMTHxzNz5kxA2/fpww8/NNegMBFUoVJTU8MvfvELbrrpJlasWMGgQYP47W9/2+Rn/vrXv7JmzRrWrFnDr371q2CaI7QRq1anNW5+1q1bN9n8LISYFReXHJX6WHmzUPGohA+rbHERToIqVDZt2qQrPqfTyZ133smuXbs4duxYMG8jhAmrupqLior0zc8kHh56zFj9Ix6V+hg3C7XSeASpEh1OJk+eTHp6OgBLliyhurraZItCjz2YFztw4AB9+vTRf05ISKBr164cOHCALl26NPiZBx98EFVVGThwIPfdd5++yqAhampqqKmp8XvNbrfjcDiC8wUMeL1ev/9jEd+qH6h17VqhPfbt26cfd+/e3TSbYqWPjB8/nszMTIqLi3n33XcpLy9vUDwEsz18QhS0eSRS2zjYfaRHjx6cPHmSEydONPp7MAPf/JCdnd3k7ytWxkxLaGmbxMfHc8011/D6669TUlLCRx99xFVXXRVKE0OGzRaYrySoQqWyspLk5GS/15KTk/0S44w8/vjj9O/fH5fLxUsvvcR9993H//3f/zVq/Jw5c3jllVf8Xrv++uu54YYbgvMFGiA/Pz9k17Y6qqqSnJxMeXk5e/fuBazRHhs2bNCPMzIy9PoNZmGFNgk1kyZNYv78+ZSVlfH6669zxRVXNHpuMNrjyJEj+nF1dbXpv+O2Eqw+4qujAfDZZ5/Ru3fvoFy3LVRVVXHy5ElA284ikN9VLIyZltKSNhk3bhyvv/46AP/5z3+48MILQ2VWSAk0TNgioXLnnXeydevWBt+74447SE9Pr5dsV15e3miMedCgQQA4nU5+9rOfMWHCBI4ePaqX7q7L7bffzs033+z/BULoUcnPzyc3Nzdg1ReN9OzZk23btnH8+HG8Xi/dunUzvT2MT9uXXHKJn+cnnMRSH/nOd76jx8PXrFnDXXfdVe+cYLZHSkqKfpyTk2Pa77itBLuPXHTRRSxevBjQBJwV2mXXrl36cf/+/Zu0KZbGTKC0pk1uvvlm7rvvPsrKyvj444/JyckhPj4+xJaaR4uEyquvvtrk++vWrfOLYVdVVXH06NGA8ggURUFRlCYLizkcjpCIkqaw2WwxPaB69OjBtm3bqKmp4dSpU/To0cP09jAm9vbq1ct0e2Khj1x55ZWkpaVRWlrK0qVLcbvdjY7FYLSHsTJtUlJSxLdvsPqIcS49fPiwJdrF6EHp2bNnQDbFwphpKS1pk+TkZK6++mrefPNNzpw5w5o1a7j88stDbKF5BLWnDB06lOrqahYvXkxNTQ3/+te/uOCCCxrMTzl58iRfffUVbrebyspK/vznP5OdnU3Xrl2DaZLQRowTo1XctbIUMvw4nU6mT58OQElJCR9//HFI7yfJtA1jxSXKMh7NIZaKvwVVqDgcDv7f//t/zJ07l4kTJ7J582Yee+wx/f0nn3xSr5VSXl7OE088wcSJE5k+fTpHjhzh2WeflaWmFsM48VhFqMjmZ+YQztU/sjy5Yay4Ek+q0prDtGnTdBG/cOFCS25UGSyCmkwLcOGFF/LGG280+J6xTkqvXr148803g317IchYTajI5mfmMXXqVD25etGiRbz00kshi4uLR6VhfJuFut1uSwoV8aiEj+TkZKZNm8aCBQsoKCjg008/Zfz48WabFRIkSCg0idWEimx+Zh6JiYn6MsgzZ86watWqkN1LSug3TFxcnL7Y4MCBA5bYLNQX+rHb7RK6DzOxUvxNhIrQJFYTKuJmNpdwxcUl9NM4vn5vhc1CpUq0uVxzzTV6Uvv8+fOjtj6NCBWhSZKTk/XaDUePHjXZGnEzm81VV11FQkICENq4uIR+GsdKeSrFxcWUlpYCMh7NIC0tjSuvvBKA48eP88UXX5hsUWgQoSI0i28COnXqlN+yUTMQoWIuKSkpTJ06FYCCggLWrl0bkvtI6KdxrCRUZDyaTyys/hGhIjSLbwJSVdX0CqGyFNJ8wjExikelcYz93uwlyjIezWfGjBnY7dq6mPnz51sibynYiFARmsWYC2KlJzjZ/MwcrrnmGn21z4IFC0ISF5cclcax6niUnDFzyMzMZPLkyYBWfG/Tpk0mWxR8RKgIzWJFV3N2drb8ATOJ9PR0PS5+7NixkMTFxaPSOFYcjyAeFTOJ9tU/IlSEZjE+Ke3fv980O8rLyzlx4gQgk6LZhHpi9OWoxMXFRfUeJq2hffv2+l5Ixp3EzcB4fxmT5jFz5ky9/P68efOiLvwjQkVolv79++vHxg3Iws3u3bv14wsuuMA0OwS49tpr9bh4KCZGn0dFvCn1URRFH5MHDx708z6FG998kJWVRfv27U2zI9bp0KGDXuxt3759bNu2zWSLgosIFaFZOnfuTHp6OmCuUNm5c6d+PGDAANPsEKBdu3ZMnDgR0OLiX375ZVCv7/vjK+G9hvH1f6/Xy9dff22KDSUlJRw7dky3R6pEm0s0r/4RoSI0i6Io+sR4+PBhysrKTLFDhIq1COXE6Av9iEelYYz93zguwonxoUXGo/nMmjVLF4vRlqciQkUICGOoxRiCCSciVKxFKOPiEvppGisIFRmP1qJz586MHj0a0H43Znq/g40IFSEgrDQxJicnk5uba4oNQi0dO3Zk3LhxQPDj4hL6aRorjUcQoWIVjF7OaPKqiFARAsLoUTFjYqysrNSLS11wwQX6k7xgLsaJccGCBUG5psvl0jeeFI9Kw3Tv3l3fykCEiuBj9uzZ+nE05anIbC8EhNlPcF9//bVeWEwmReswa9Ys/ThYT3BSQ6V54uLi9JU/e/fupaamJuw2+OaB9PR0OnfuHPb7C/XJzc3l0ksvBWDr1q2mL18PFiJUhIDIzc0lOTkZMEeoyNObNcnJyfGLiwdjYhShEhi+ceDxeNi7d29Y711WVqZvpyErfqxFNBZ/E6EiBISiKPTq1QvQ9vcId+0GESrWxTgxvv/++22+npTPDwwzvZzGhHoZj9bCOB6jJfwjQkUImD59+gDa5oR79uwJ671FqFiXYAsV2Tk5MMwUKjIerUvPnj0ZMmQIABs3bjR9I9lgIEJFCJjevXvrx2ZNjAkJCbIZocXIy8tj+PDhgPZ7aus2CxL6CQwRKkJjRNvqHxEqQsCYJVRqamr0GHz//v2Ji4sL272FwAjmxChCJTB69eql74MkQkUwEm3hHxEqQsCYJVT27t2Lx+MBZI8fq2KcGNu6TFlyVALDbrfTt29fAPbs2aMv6Q4HUtPI2vTr14+LLroIgHXr1ulbHUQqIlSEgOnatasptRvk6c369OrVi8GDBwOwYcOGNsXFJUclcHzjweVyhW1n87o1jWTFjzUxPjwsXLjQREvajggVIWCMtRv27dtHdXV1WO4rQiUyCJZXRUI/gWNGnsqePXv07RJkPFqXaNqkUISK0CJ8oZdw1m4QoRIZGKtitiVPRUI/gWOGUJHxGBlceOGFemhwzZo1nDp1ymSLWo8IFaFFmDkxxsfH67VcBOvRv39/fWJcu3Ytx48fb9V1JPQTOCJUhMZQFEX3qni9XhYtWmSuQW1AhIrQIsK954/b7dZrtvTt21df5SBYkylTpujHrY2LS+gncPr06aOvghOhItQlWqrUilARWkS4n+D279+Py+Wqd2/BmkybNk0/bm1cXIRK4DidTn013u7du/XVcaFEahpFDkOGDKFHjx4ArFixgqKiIpMtah0iVIQWEe7aDfL0Fln069dPr2C8evVqCgoKWnwNyVFpGb5xUVVVxaFDh0J6r5qaGn0/J6lpZH0URdG9Kh6PhyVLlphsUesQoSK0CLvdTr9+/QBtR+NQ127YtWuXfixCxfoYJ8bWxsUlR6VlGMeFcbyEAmNNIxmPkUE0rP4RoSK0mHDWbhCPSuRhXP3TmolRQj8tI5zhWBmPkcfw4cPp2rUrAB999BFnz54116BWIEJFaDFmTIxxcXF6SEGwNpdccomeu9CauLgIlZYhQkVoCpvNpns5XS4X77zzjskWtRwRKkKLCdfE6PF4dFd27969cTqdIbuXEDzaGhc3hn4kR6V5+vXrp1eHFaEiNESk7/0jQkVoMeESKocPH6aqqqrePQXr05ZNCsWj0jISExPp2bMnoI1HX9XYUCA1jSKTUaNGkZ2dDcD777/PuXPnTLaoZYhQEVpMuGo3yNNb5DJixAi6dOkCwIcffkhJSUnAnxWh0nJ846O8vJz8/PyQ3MNY06hfv37Y7faQ3EcIPnFxccyaNQuA6upq3nvvPZMtahkiVIQW43A4wlK7wShUZNfkyKItcXERKi0nHIUYjTWNZDxGHm3xcpqNCBWhVYSjdoN4VCKb1sbFfTkqTqcTm02mqEAIRzhWxmNkM27cOLKysgB49913/XLBrI7MAkKrCOfEqCiKXrtFiBxGjx5Np06dAC0uXlZWFtDnfB4V8aYEjggVoTnsdrse/qmoqOD999832aLAEaEitIpQT4yqqurX7dGjh6z+iECMcfGqqqqA4+IiVFpO//799WMRKkJjROrePyJUhFYRaqGSn59PeXl5vXsJkUVr4uI+l7SI08BJTU0lLy8PCN3KH6lpFPlMmjSJjIwMAJYuXUp1dbW5BgWICBWhVYS6doM8vUUH48eP94uLGxNlG0M8Kq3DN05KSko4ceJEUK/t8XjYvXs3IDWNIhmHw8GMGTMAOHfuHB999JHJFgWGCBWhVRhrN+zatQuv1xvU64tQiQ7sdjszZ84EtKWzH3zwQZPnq6oqQqWVhNLLeejQIalpFCVE4uofESpCqzHWbjhy5EhQry1CJXpoyeofoytaQj8twzhOduzYEdRry3iMHq644gpSUlIAWLx4sb7k3MqIUBFazUUXXaQfb9iwIajXXr9+PaDV4zAmCgqRx+TJk0lPTweaj4vLzsmtJxzjEeDCCy8M6rWF8JKQkMD06dMBKC4u5pNPPjHZouYRoSK0mrFjx+rHwezsp0+fZtu2bQAMGTKE1NTUoF1bCD/GuHhpaSnLly9v9Fwp9tZ6hgwZonuhPvnkk6Am1BrH97hx44J2XcEcIm3vHxEqQqsZM2aMXkZ7xYoVQbvuypUr9eNJkyYF7bqCeQQaFxeh0nocDof+8HD8+HG+/vrroFy3rKyML774AoC+ffvqWyMIkcu0adN0Ubto0SLcbrfJFjWNCBWh1aSmpjJ8+HAA9uzZw/Hjx4NyXePT28SJE4NyTcFcrrzySj0uvmjRokbj4rJzctswjpdgeTnXrl2r/yGT8RgdJCUlMW3aNEDzYK9Zs8Zki5pGhIrQJowTl9ET0hZ8E2xcXBxjxowJyjUFc0lISODqq68GtLh4Y31FPCptIxRCRR4copNIWv0jQkVoE8bQTDDCP8ePH9frNYwYMULyU6II48TYWFxchErbuOSSS0hLSwOCl6diHNcTJkxo8/UEa3D11Vfr9XAWLFgQ9BITwUSEitAmRo0ahcPhAILzBGd80pant+hi2rRpuvhYuHBhg7tui1BpG3a7XU92PX36dJuXKZeUlLBp0yZAW+3j27tJiHxSU1OZMmUKACdOnGDdunUmW9Q4IlSENpGYmMhll10GwIEDBzh8+HCbridu5uglOTm52bi45Ki0nWCGf9asWaM/act4jD4iZfWPCBWhzRjDP22dGH1uZofDwahRo9p0LcF6NLcpmnhU2k4ww7HGz8sKvOhj+vTpxMfHA1r4JxR7RAUDESpCmwnWE9yRI0c4cOAAAJdddpk8UUch11xzjR4qnD9/fr24uAiVtjNw4EDatWsHwKpVq9qUe+Abz4qiMH78+KDYJ1iHzMxMJk+eDGjz78aNG022qGFEqAht5tJLLyUhIQFoWwKfhH2in7S0NK688kpAi4t//vnnfu9L6Kft2Gw2XVQUFxezdevWVl2nqKhI/+ygQYN08SNEF4EkuZuNCBWhzTidTn0ZcX5+Pvv372/VdcTNHBs0NTGKRyU4BCP8s2rVKv2hQ8Zj9HLttdcSFxcHaF5OK4Z/RKgIQaGt4R9VVfXPJSQkcOmllwbNNsFazJgxQ69oXHdiFKESHIIRjhUPZ2zQvn17fdn5/v37W+2BCyUiVISg0NaJ8cCBA+Tn5wMwevRofX2/EH00FRcXoRIcBgwYQMeOHQFYvXp1q0qk+8axzWbz29dLiD6aS3I3GxEqQlAYNmyYXiJ9xYoVLXYfStgntmhsYpQcleCgKIr+8HDu3Dm9FkqgnDp1Sq/BMmzYMH33ayE6mTVrFoqiAPD2229bLvwjQkUICvHx8fpT16lTp/TqsoEibubYYubMmdhs2vQzb948fWIUj0rwaIuXUwovxhbZ2dl6nuGePXvYuXOnyRb5I0JFCBqtnRiN+SnJyckMGzYs6LYJ1qJDhw76ypT9+/fz1VdfASJUgklbhIo8OMQeVt77R4SKEDRau9Jg9+7dnDx5EoBx48bpBYiE6Kah1T/G0I8IlbbRp08funTpAsCnn35KTU1NwJ/1jV+73S4bg8YIs2fP1o+ttkxZhIoQNAYPHkxGRgaguY4DLTQlT2+xiTEu7nuCM3pUJEelbRjzVCoqKli/fn1Anzt27Bh79+4FtBpJycnJIbNRsA5du3bVt0PZtm0bX3/9tckW1SJCRQgacXFx+oZoRUVFbN++PaDPiVCJTTp37szo0aMB2LVrFzt37pTQT5BpTfhHxmPsYtXVPyJUhKDS0vCP1+vVJ8b09HSGDBkSMtsE61E3Lu4TKoqi6KX2hdbTmnCsrMCLXUSoCDFBS5/gtm/fTlFREQDjx4/XKyQKsYExLj5//nw9RyUpKUkPCwmtp3v37nTv3h2AdevWUVVV1exnfOPW6XQycuTIUJonWIwePXowdOhQADZt2sTBgwdNtkhDhIoQVC666CLat28PwLJly5qs36CqKk888YT+s7iZY4/c3Fy9CvHWrVv1iVHCPsHDN66qq6v505/+1OS5//vf/zh06BAAI0eO1PfwEmIHo1dlwYIFJlpSiwgVIajYbDa++93vAuByubjxxhspLS1t8NxXXnmFt956C9DCPjfeeGPY7BSsg3Fi9IV+RKgEj7vvvlv3Tv3ud79jzZo1DZ63d+9e7rnnHv1n47EQOxjHo1VW/4hQEYLOww8/zPDhwwGtRsY999xTr9Lhtm3buPfee/WfX331VTp37hxWOwVrYJwYfYhQCR4jR47kd7/7HaDlhH3rW9/Sw60+qqurufHGGykrKwPg1ltv5aabbgq7rYL59O3bl4svvhiAzz//nKNHj5pskQgVIQQ4HA7efPNNvez2G2+8wauvvqq/X15ezg033KDHy3/4wx82+MdKiA169uxZL4laliYHl9/+9rf6xnNHjx7ltttu83t4eOCBB9i8eTMA/fr1469//asZZgoWwZjkboXwjwgVIST06NGDf/7zn/rPP/7xj/Xlyj/60Y/0EvuDBw/mmWeeMcVGwToYJ0YQj0qwiYuL4/XXX9fzx9555x2ef/55ABYuXMiLL74IaAm0b731lr5vlxCbWC38E3Sh8uSTTzJz5kyGDRvmtytqQxQXF3PvvfcyZswYZs+eHXBBIiEyuO666/j+978PQFVVFTfeeCMvv/wyr732GqCVy3/zzTclYU+o51EToRJ8cnJy+M9//qP//OCDDzJv3jzuuOMO/bXnn3+egQMHmmGeYCEGDBhA//79Aa2qsa9yuFkEXaj07duX3/zmN3rp5qZ4+umnycrKYvny5dx777388pe/pKSkJNgmCSby7LPP6hPfzp07+d73vqe/99JLL9G3b1+zTBMsRL9+/bjooov0n0WohIZp06bxwAMPAFqy+/XXX8/Zs2cB7cFCEmgF0OoY+R4eVFVl4cKFptpjD/YFfS5cu73pS1dUVLBy5UoWL15MQkIC48ePp1evXqxatYoZM2Y0+Jmampp6+1XY7faQFIbylX8PtAx8tNPa9nA4HMydO5cRI0ZQXl6uv37bbbfxrW99K6LbV/qIP21tj9mzZ+vhwcTExKhoVyv2kccee4w1a9bw+eef66/16NGDl19+GVVV6yW+BxMrtofZWLVNZs+erZePmD9/fkhErG8H9eYIulAJlCNHjpCUlESnTp3013r37s2BAwca/cycOXN45ZVX/F67/vrrueGGG0JmZ35+fsiuHYm0pj0SExP5/e9/z/333w9ov+f777+fw4cPB9s8U5A+4k9r22PUqFH6cUpKStT0D7BeH/njH//INddcQ2lpKXa7nWeffZaSkpKwebSt1h5WwGptkpGRQV5eHkeOHGHlypVs3ryZdu3aBfUePXr0COg804RKZWVlvc2ukpOTmxwot99+OzfffLPfa6H0qOTn55Obmxuw6otm2toeP/vZz3A4HHzxxRc88sgj9OzZMwRWhhfpI/60tT26devG3//+d9atW8cjjzxCbm5uCKwML1btI926deO9997jueee49vf/jbTp08Py32t2h5mYuU2ufHGG3nvvfeYPXs2Xbt2pUOHDqbY0SKhcuedd7J169YG37vjjjv4wQ9+EPC1EhMT/UIBoC1bbWpZosPhCPv+HzabzXKdx0za0h4//vGP+fGPfxxki8xH+og/bWmP733ve355TNGCFfvI6NGj9U0hw40V28NsrNgmTzzxBH/84x/NNqNlQsVYC6Ot5OXlUVFRQUFBAR07dgS04mBXX3110O4hCIIgCELriI+PN9sEIASrflwuF9XV1aiqitvt1o/rkpSUxPjx43n55ZepqqpizZo17Nu3j/HjxwfbJEEQBEEQIpSgC5Uf/vCHjB49miNHjvCjH/2I0aNHc+LECQD+9a9/8ZOf/EQ/96GHHuL06dNMnjyZ5557jieffFKvZioIgiAIghD0ZNp//OMfjb5nLCwEkJmZyQsvvBBsEwRBEARBiBKslbkjCIIgCIJgQISKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRVEb2jFQEARBEATBAohHRRAEQRAEyyJCRRAEQRAEyyJCRRAEQRAEyyJCRRAEQRAEyyJCRRAEQRAEyyJCRRAEQRAEyyJCRRAEQRAEyyJCRRAEQRAEyyJCRRAEQRAEyyJCRRAEQRAEyyJCRRAEQRAEy2I32wAz2Lx5M3v37qVnz54MGzbMbHNMZ+vWrezcuZNu3boxYsQI7PaY7BZ+bN26lRMnTtCjRw/69etntjmms23bNg4fPkxeXh4DBw402xxLIH3EH+kj9ZE+EhxixqOiqiper5e//vWv/PSnP2X//v088MAD/Otf/+Lo0aNmm2cKZWVl/PrXv+bnP/85p06d4tFHH+XVV1+lsLDQbNNMQVVV3G43f/zjH/nJT37CZ599xne/+10WL17M2bNnzTbPFM6dO8cvf/lLfvazn7F9+3Z+/OMfs2DBAiorK802zRSkj9RH+og/0keCT8w8OiuKgtvtZvv27bzwwgsMGjSIsWPH8tFHHzF37lweeOABs00MK16vl0WLFmGz2Vi6dClJSUlccsklvPnmm0yePJn27dubbWLYURSFiooK9u/fz5w5c+jZsyfvvPMOK1asoKysjJtvvtlsE8OK2+1mzpw5xMXF8f7772O327ngggtYuHAhV155pdnmmYL0EX+kj9RH+kjwiXqPiqqq+vH+/fupqqoiOTkZgDFjxjBu3DgOHz7MihUrzDLRFGw2G3379uXaa68lKSkJVVUZN24cx44d48yZM2abZxq7du2itLSUzp07o6oq11xzDZdccgnbt2/nyy+/NNu8sKGqKna7nSFDhnDttdfq4cBrr72W06dPk5+fb7KF5iF9REP6SONIHwkuUStUdu3axQ9+8AOefvpp3nzzTQD69+9PQUEB+/bt08+75JJLuOCCC1izZg0ul8ssc0POnj17+M9//uPnehwxYoSeo6MoCmfOnKFdu3bk5OTg9XpNsjR87Ny5k/vuu4+//vWvfPLJJwAMHTqUo0eP8tVXX6EoCgDjx48nKSmJTZs24fF4zDQ5pOzZs4dFixb5vTZ27FiGDx+u/3zo0CGysrLo0qWL30NAtCJ9xB/pI/WRPhJ6olKoHDhwgPvvv59BgwbRu3dv/v3vf/PXv/4VgJtvvpkXX3xRPzczM5M+ffpQVVVFSUmJWSaHDFVVmTt3Lj/60Y948cUX2bJliy5CfJOI7+eCggLKyspISUnBZovKrqGzfft27r33Xnr37o3H4+H555/n//7v/7Db7dx444384x//0M/Nzc0lNzdXf0KMtsnX6/Xyz3/+k3vuuYcnnniCnTt36pOrD9/EeuzYMex2Ow6Ho9450Yb0kVqkjzSM9JHwEJV/jTZv3szAgQO55557uO6663jqqadYuXIly5cvZ9asWdjtdl5++WX9/N69e7N+/fqoHFSKolBaWsrDDz/MXXfdxfz58zl9+rT+npGNGzfSuXNnMjIyAFi/fj1lZWXhNjksrFu3jgkTJvD973+fn/zkJzzwwAO8+uqr7Ny5k2uuuYby8nLefvtt/fzBgwezdu1aampqoq6f2Gw2iouL+eMf/8g3vvENnn/++UbP3bx5M3l5eSQkJADa02R1dXWYLA0v0kdqkT7SMNJHwkNUCRWfQnU6nezfv19/feDAgXribHV1Nb/5zW948803WbBgAVVVVezZs4chQ4aQmJholukhwecpuf766xk5ciTf/e53OXPmDJ988olfmMvnPTl9+jTf+MY3+Pzzz7n88stZuHChKXaHEl8fSUxM5Pjx4/rrY8aMYdSoUfz3v/8lJyeHW265heeff54vvvgCgH379jFu3DgcDocpdocKXx+57bbbGDZsGA888AB79+7l/fff9zsvLi4O0Lxus2fP5vPPP2fixIksWLAg6p4MpY/4I32kPtJHwktUrfrxKdSePXvSvn17Vq5cyYQJEwD41re+xT333MOWLVuYMGECd999N5999hlvvfUWRUVFPPzwwyQlJZloffDxCZB27drpr33zm9/kzTffZPjw4fTq1QvQBl11dTWff/45b7zxBllZWdx///1MnTrVFLuDjaqqet/w/d+pUydSUlLYunUrgwYNAuDee+9l9uzZ7Nu3j2uuuYb9+/fz3//+lz/96U+cPXuWRx99VJ+MIxlje/j6SIcOHfT37777bv7+978zYcIE/alYVVWKior48ssv+fTTT3E6nTz44IPSR6SPxEwf8Xq9eltIHwkzagTi8XhUVVVVr9fb4PuFhYXqs88+qz722GNqeXm5/vpTTz2l/vSnP9Wv4fF41G3btoXe4BDTXHvU5Uc/+pH63HPPqZWVlfprFRUV6vXXX6/+97//DYmN4cblcql79+71e83r9eptdOTIEfVXv/qV+uqrr6pVVVX6Ob/85S/Vxx9/XFVVVXW73WpZWZm6fv368BkeIhprj8Z+nj17tvr3v//d7/3S0lJ1zJgx6pw5c0JmZzhxuVzq5s2bVZfLpb8W632kofYwEot9ZO7cufVej9U+YhYRF/pZsGABo0ePZsOGDXptlLpkZWUxdOhQSktLeeutt/TXc3Jy6Nq1K6Cpf5vNxkUXXRQ220NBIO3hw5fsdtddd7F+/Xq+/vpr/va3v/H++++TmJjI//3f/3HLLbeEy/SQMXfuXGbMmMFTTz3Fr371K1auXKm/53sSys3NZfDgwezdu9dvaXpmZiZ5eXn6z8nJyX4rGiKRptrDiLH/PPDAA7z99tsUFhby0ksvsWnTJlJTU1m+fDm33XZb+IwPEXPnzuXqq6/m5Zdf5pFHHvELY8RqH2msPYzEUh8B+POf/8yf/vQnlixZAqB/91jsI2YSUUJl0aJFzJ8/n0suuYQ//OEPAPXKvavnY4cjRoxg0qRJzJ07l9dee43ly5fz1ltv6WWMo8H1Fkh7GPF950GDBpGYmMidd97JkiVL6NatG0DEx02rq6t56aWXWLp0Kc888wyPP/44eXl5ekVI3+Ti6yNTp06lb9++zJkzh8WLF7N27Vo+/fRTcnNzgcjvI4G2hxFf/7nsssvIyMhg2rRpzJs3j+TkZFRVxel0hvtrBJWamhr+/Oc/s3jxYp577jn+8pe/oCgKGzduxOVyxVwfCbQ9jER7H4HavJzu3bszZMgQnn/+edxuN3a7vd6qyWjvI1YgonJUBg4cSHJyMhMmTODaa6/l9ddf5+abb9Y7ENQq3YSEBKZOnYrNZmPz5s18+OGH3H777VxzzTVmfoWgEkh71KWiooLHHnuMvXv38thjj0VN/BjA5XKRkZHB7373O/r37w9o9Qx27dqFzWbT4+6KoqCqKqmpqdx2222kpKTw+eefs3v3br7zne/oeU2RTqDtYURVVcrLy3nggQcoLCzkiSeeiKoKo4qiMHXqVL7//e/jcDg4efIkW7du5dJLLyU+Pt7vvFjoI4G2h5Fo7yM+bzvAl19+yR133MEbb7zBk08+ye9+9zv9vFjpI1ZAUVXrpmP/73//Izs7m8GDB+sJoR6Ph7i4OJYvX87DDz/MqlWrdJUb7bU/gtUeH330EVdccUU4TQ8ZvjYZNGgQWVlZFBYWkpWVBWgTyYEDB/j+97/PvHnzSE1NbfQ6TYm7SCJY7TF//ny+8Y1vhMvskNLQuFFVlU2bNvH973+fyy+/nL59+2Kz2Rg4cCBDhgzRx5WRaOsjbW2PaO8jAP/85z/Jy8sjOzubu+++mxUrVuieo4a8TdHSR6yGJYXKnj17eOCBB+jcuTM2mw2Px8O3vvUtXaH6Bs2dd95Jt27d+N3vfhfVHSRY7dHY4IpE6raJ2+3mlltuYfz48UBthv67777LBx98wAsvvBDVYjZY7RFNbdTcuKmsrKSiooKsrCxqamp44403WLJkCfPmzTPX8BARrPaIpT7y4IMPctVVVzF+/HgeffRRNm3aRJcuXfj973/vtwpKCC2W7G27du2iX79+vPzyy/z5z39m6NChLF26lM2bNwO1scEHHniApUuXUlBQgN1up6CgACDqyhMHqz2iRaRA/TYZNmwYS5YsYcuWLUBtjPnIkSP6lvM2m41z5875vR8tBKs9ouUPEDQ/buLj48nKytJFvc9z8PXXX5tseWgIVnvEQh/ZuHEjoJW6SE5OZufOnezbt4/CwkJ69epFhw4dmly4IAQXy/U4VVU5cOAA2dnZeL1eHA4HV199NV26dNGVvd1ux+Vy0b9/f2666Sbuvfdefvazn/Hzn/+8QRdlJCPtUZ+m2sRXBdLnTdqyZQujR4+mtLSUBx54gKeeeiqqnghB2qMhAh03vv9tNhuHDx+me/fu9OzZ00zTQ4K0R32aahNfscv9+/fz9NNP89BDDzFp0iS+853v1GsvIfRYanbyhSays7NZv369Pnl27dqVSy+9lIqKClavXg2gJ3pVVlayb98+2rdvr283Hi1Ie9SnJW1y/Phxjh49yltvvcWMGTNISUnh97//fVT9UZb2qE8gbbJq1SoATp06xenTp/nLX/7CCy+8wJgxY7Db7VFVSVXaoz7Ntcm5c+fYuXMnM2fOZMCAAfzjH//gtttu4/bbb+d73/seqqpGXZtYGVNnqMZ+0TfeeCOnTp3yW8vfv39/MjMz/Xb/feqpp/jiiy9YsGABv/71rxvNUo8UpD3q05Y2KS4u5uzZsxQVFfHaa6/x8MMPR/xTkLRHfVrTJr4NSPft28cTTzzBtm3b+Mc//qEnh0ZymFTaoz4tbZOsrCz27dvHqFGj+P3vf092djaqqhIfH893vvMdffWgECaCXECuWQ4cOKB++umnqqpqFfuMGCsizp07V504caJaVVWlVwH8yU9+or7wwgsNnh+pSHvUp61t8uc//1lVVVUtKChQt2/fHiarQ4e0R33a2ibPP/+8qqqqWl5erh4/fjxMVocOaY/6BHNuFcwlbB4Vj8fDSy+9xC233MKvf/1riouLiYuL80tqtNvtVFRU8OGHH3LDDTfQq1cvHnvsMbZs2YLb7cbr9eqJgL7zIxVpj/oEq018e2506NCBCy+80Kyv02akPeoTrDYZPHgwAElJSXTu3Nmkb9N2pD3qE4q5VTCXsAmVgoICioqK+PWvf83YsWN58cUXAX+X4htvvMH48eP1glSPPfYYiYmJvPjii0ybNo2UlBRGjRoVLpNDirRHfaRN/JH2qI+0iT/SHvWRNolCQumuKSsr011p5eXl6qFDh9TKykp169at6owZM/w2BCwoKFBfeukldceOHfWuk5+fr+bn54fS1LAg7VEfaRN/pD3qI23ij7RHfaRNopuQFHw7duwYjzzyCAkJCaSlpfGLX/yC9PR0/f2amhr+9re/sWfPHv7+97/X+3y01XSQ9qiPtIk/0h71kTbxR9qjPtImsUHQfzsVFRU88sgj9O/fn/vuu4/CwkL+3//7f2zYsAHQsq8dDgezZ8/mzJkzLF261O/zvpoO0dJxpD3qI23ij7RHfaRN/JH2qI+0SewQ9N9QQUEBNpuNW265he7du/P000+TmJjIhx9+SGFhoR4nzMnJYdasWbz55psALFmyhP3790ddp5H2qI+0iT/SHvWRNvFH2qM+0iaxQ0h+U3v27CExMRGAjIwMJk+eTEVFBStXrtTPsdvt3HjjjVRUVDB8+HBee+21iF+10hjSHvWRNvFH2qM+0ib+SHvUR9okNgi6UOnevTt9+/blH//4h/7asGHD6NChA4cOHaKsrAyAsrIyvvnNb1JSUsKjjz7KggUL6NatW7DNMR1pj/pIm/gj7VEfaRN/pD3qI20SO4TEo3LrrbeyatUqDh8+DGiKduDAgWzcuJGUlBT9vMsvv5yPP/6YadOmhcIMyyDtUR9pE3+kPeojbeKPtEd9pE1ig5AIleHDhzNs2DAef/xx/bXevXuTkJCgl/NOSUnhrrvuCsXtLYe0R32kTfyR9qiPtIk/0h71kTaJDUKyPBm0zfFuuukm+vXrx6BBg1i0aBHDhw/nF7/4RShuZ3mkPeojbeKPtEd9pE38kfaoj7RJ9BMyoQJw4MABvvrqK9asWcOQIUO45ZZbQnWriEDaoz7SJv5Ie9RH2sQfaY/6SJtENyEVKj7U81tqCxrSHvWRNvFH2qM+0ib+SHvUR9okOgmLUBEEQRAEQWgNUvFGEARBEATLIkJFEARBEATLIkJFEARBEATLIkJFEARBEATLIkJFEARBEATLIkJFEARBEATLIkJFEARBEATLIkJFEISwsnHjRoYNG8awYcM4fvy42eYIgmBxRKgIghAyHnnkEYYNG8Z3v/td/bWUlBQuuugiLrroIhwOh4nWCYIQCdjNNkAQhNiif//+vPbaa2abIQhChCAl9AVBCAnTp0/nxIkT9V5/6aWX+N73vgfAkiVLyMnJ4ZFHHuGdd96hc+fO3HPPPfz973+nrKyMGTNm8MMf/pC//vWvLFmyhJSUFG6//Xauu+46/XqnT5/mb3/7G+vWrePs2bN06tSJ6dOnc9ttt2G3y7OYIEQ6MooFQQgJ/fr1o7KykrNnz5KcnEyPHj0A2L17d6OfKSws5KmnnqJ9+/aUl5czd+5cPv/8cwoKCkhJSeHUqVP88Y9/ZOjQofTo0YOzZ89y2223cerUKf0eBw4c4KWXXuLYsWM8/PDD4fq6giCECMlREQQhJDzzzDOMGTMG0ETLa6+9xmuvvUb//v0b/YzL5eIvf/kLCxYsoFOnTgDk5+czd+5c3n77bZxOJ16vl02bNgHw1ltvcerUKbKysli0aBFz587l6aefBuCdd94hPz8/xN9SEIRQIx4VQRAsQ1paGoMHDwYgOzubU6dO0atXL3JycgDIzMzk5MmTnDlzBoAdO3YAUFRUxBVXXOF3LVVV2b59O7m5ueH7AoIgBB0RKoIgWIbk5GT9OC4urt5riqIAmgip+zlfaMlIQkJCKMwUBCGMiFARBCFk+IRCVVVVSK4/YMAA1q5dS1xcHE8++aTueSkvL+eTTz5h4sSJIbmvIAjhQ4SKIAgho3v37gDs3LmTG2+8kcTERO6+++6gXf+GG25g8eLFFBQU8I1vfIMePXpQXl7OqVOncLvdXHPNNUG7lyAI5iDJtIIghIwZM2YwadIkUlJS2L9/P9u3b8fr9Qbt+pmZmcyZM4fp06eTnp7O/v37qa6uZsiQIfz85z8P2n0EQTAPqaMiCIIgCIJlEY+KIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiWRYSKIAiCIAiW5f8DW3m58jluQYgAAAAASUVORK5CYII=", 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\n", + "image/png": 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", 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\n", 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", 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\n", 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j6tWrADzwwANqTYqWJkyYoNbkLFq0iCNHjmgckRCWt+bMRT45lACAow4e/yGXWicNl9u1XqsZMnWIluEB0PHDcAauzKLHBkMik6soPLg1lqiLKdoGJkQpSBJTRqSmpjJr1izAMFaLcT2Kljw9PdW6HL1ez/vvv69xREJY3p9JZ9Xlxw540ez6KAP7c/bT5p3WNjH7sGtlV3xGefLALwptdxoSmYw8PaM37yLuSuotXi2EbdD+TBJm8dVXX3Hp0iUA7r33XkJDQzWO6IbHH3+cChUqAPDDDz+QkJCgbUBCWJBeUVh71nAuuik6Wn1uaB1N1afya6VFjLp3lJbhmYh4vwsZjmk89p1C44OGRCYlO5e7N8ZwKj1T4+iEuDVJYsqAjIwMPvroI8AwQqfxGC22wMfHRx2rJjc3lw8//FDjiISwnP0pqZzPzAagUZyCc65h/Sdps3n0lUdwcrKdeXedvJzwud8L51x4+kuF6omGxOVMRhYf7o/XODohbk2SmDJg3rx5nD1raL6+6667aNSokcYRFfTUU0+pczd9++23nD59WuOIhLCMdWcvqstNYg13AK3KXElCwAn+97//aRVWkXq+04Nk50u4Z8Ern7jgdv3W6yUnz5GRm6dxdEIUT5IYO5ednc0HH3ygPjaet8iWVKxYkYkTJwKQlZXFjBkzNI5ICMvI70oCCDsASXmJfJ3+Jc8//zwuLtrdkVQUR1dHKjxsmJbEJxWaRqUDcC0nl5Wn5W4lYdskibFzP/zwA0lJSQAMHDiQ5s2baxtQMZ555hnc3d0B+PLLL7lwQS6Qomy5lpPL9kspAFQ5rxBwAWanfYxfgB/jxo3TNrhi9H+jHyedTwLQa6u7uv4XGTtG2DhJYuxYbm4u7733nvrYVlth8lWpUoUJEyYAkJ6ert5NJURZsfl8MjnXu2PC4iBZn0xc7gGmTJmiJvC2yMHRAf+JFQFocBT8kg3dSOvOXuJcRpaWoQlRLEli7NiiRYuIjzcU3/Xs2ZP27dtrHNGtTZkyRW1S//TTT7l8+bLGEQlhPiZdSfsVdubsoFKlSjzyyCMaRlUyQ18ewiGXgzgo0GW74U9DnqLwe+LZW7xSCO1IEmPHvv76a3X5lVde0TCSkqtZsyZjx44F4Nq1a/zyyy8aRySEeShGt1Y75io0Ogw7cqKZPHmyWtRuyxwcHAh4oioAHbffGMn3lwQpwhe2S5IYO3XhwgW2bNkCQL169ejSpYvGEZVcfpcSwNKlSzWMRAjziU9NJyHNMNFj/WPgnJnHntzdjB8/XuPISm74lGFcVpKpfg5qxxu6lPalpLIv5ZrGkQlROEli7NTff/+NXm+4fXPo0KGaTSR3O1q0aEFgYCAAa9euVadKEMKerbupK+lQ7kGatm+q6SSspeXq6kpKdUMXb5ftN64pv5yQAl9hmySJsVNLlixRl4cM0X4eltLQ6XRqzDk5OaxcuVLjiIS4c8b1ME0PwM6cnXZ3bgJU626Yeb79DnDIM3Qr/ZZ4hly9vriXCaEJSWLsUHp6OqtXrwagatWqtGvXTuOISm/o0KHqsnQpCXuXladn8/lkAHyvKASdMtTDGP+e24tOEzoC4JUOdWMNraTnM7P571yylmEJUShJYuzQv//+S0aGoe998ODBODo6ahxR6XXp0kWdT2n58uVkZ2drG5AQd2Dbxcuk5xlaKsL2wxV9Ci51nalXr57GkZVe5caVuepiSF56bXNT1/9yQgp8he2RJMYOGbdc2GNzNYCzszMDBgwA4OrVq2zYsEHjiIS4fSa3VscpxOTsZPDQwRpGdPt0Oh0OjQ31MK33O+OebZj8acXpC1zNztEyNCEKsFgSc/nyZSZNmkSnTp0YPnw4UVFRhW43cuRIOnfurP5r27atOkHg6dOnad26tcnz5b1+Ijc3l7/++gsAT09PevTooXFEt8+4qd24xkcIe5Nf1KvTKzSJK11XUmaWwrOf6qk+DMbNrMy3y+FiinLrF1pQo7sbAuCUByFRpwDIzNOz9OR5LcMSogCLTaf6wQcfUKlSJdasWcP27dt58cUXWbx4Mb6+vibb/frrr+pydnY2ffr0oXv37uo6R0dHNm3aZKkw7c6WLVu4dMlwwezXrx9ubm63eIXt6tOnD66urmRlZbF06VI+/fRTu7rLSgiA0+mZHLiSCkDtBPBM1XO64inatGlzy9fui1e49y2FvdcnjD532YP1e+CxmQoRzRTuitAxrDNU87fueRE6qDaJLxumIWjzXyZxnQzrF504zf9q17BqLEIUxyJJTHp6OuvXr2fp0qW4ubkRERFBaGgoGzZsYPDgoptYN27ciKenJ61atSr1e2ZnZxeoq3BycrLIhGv5tzbrNajWN26xGDRoUKli0DLuwnh6etK9e3dWrlzJqVOniI6OpnXr1oVua2uxl5TEbV1axL32zI1Zq8MOwJG8w3Qb3K3YOBQFPv8TnvsCsgrpocnLg3UxsC5G4YmPIbyxwvAuMKwzhFSzxKcw5VrNlawKmbimuNH1VDArsjO56OJG5MUU4q+mEeJ1YwoF+V2xrvISt4NDyTqKLJLEJCYm4uHhQdWqVdV1derUUYfIL8qKFSvo16+fybfxvLw8+vbti5OTE926dWPixImFtj7Mnz+fuXPnmqwbMWIEI0eOvMNPU7T8iRetRVEU/vjjD8DQQhUWFkZCQkKp92PtuItj3EX4ww8/ULly5WK3t6XYS0Piti5rxv13/I1h+cP2K0Tl7KBXeI8iz82LVx2Y+k0l1u/xUNfVq5nNx49eJDNbx6odHqyK9iDxgjNgSHi27jP8m/I5NAnOok+bdPq3SadWQK7FPpdbSzeUdeCic8FvUwwXexi+YMyNPciEGhULbC+/K9ZV1uOuVatWibbTKYpi9s7XXbt28dprr6m1GwCfffYZV65c4aWXXir0NSkpKfTt25dFixYREhICGFp0EhMTqVu3LufPn+f111+nTp06TJ06tcDrrd0Sk5SURGBgYImzRXOIjY2lRYsWAPTo0YN///23VK/XKu7inD17lpo1a6IoCo0bNyY2NrbQ7Wwx9pKQuK3L2nHn6vU0+GsTKTm5eKQrfPGcwuvZL7Hl7GZcXV0LbL9yO4x7H84bTRn25F3w/gRwcb4Ru07nQOwxWLzR8O/AicLfv3tLeGwoDO4ATmb+Snrq9zPsfWwfAN+7L2XNzGEoQC1Pd6L6hqtfNuV3xbrKS9yatsS4u7uTlpZmsi4tLQ0PD48iXmG4bbhevXpqAgPg4eFBgwYNAKhWrRpPPvkkU6dOLTSJcXFxsUjCUhwHBwer/hIZJ4VDhgy57fe2dtzFqV69Ou3btycyMpL9+/cTHx9PnTp1itzelmIvDYnbuqwV9+7kq6TkGFpDmsRBWu5V6vWpW2DG6swshee/VJjzx411Vfzguxd19GufnwyYxt6iHrSoB28/BAcTFBZvhD82KMQcvrEPQ5cT1KwMjwzW8fAgqFrRPPUzlbv4q8ttr9YlIS+Lw46uHE/LYMfla7Tzr2CyvfyuWJfEfX1/ZtuTkaCgINLT0zl//kYl+7Fjx6hdu3aRr1mxYgX9+/cvdr86nQ4LNBzZDXsepbc4MvCdsFcmUw0cUNiVE8OQYabn5r54hbaPmCYw/dvD3u9uJDC30iBYx0v/07HzGwfiF+n48DEddYzqa09egFe/VQi8W2HMW3q27lXu+FrpFuCKQ03Dn4i6TvVwjtyqPrdIxowRNsIiSYyHhwcRERF89dVXZGZmsmnTJo4ePUpERESh2ycmJnLw4EH69u1rsn7fvn0kJiaiKAoXLlzgs88+s6uJDs0pMTGRmJgYAFq2bElQUJDGEZmPcUImt1oLe3LzVAO79DHqlzFFUfj0D4XWE27cfeTqAp9M0vH3Bzqq+N1ei0mt6jqeu0fHoQU6Vn2kY1AHyC8jzMmFhWug40SFlg8pfPO3Qnrm7SczNXpWB8BJ50Tugkg8HA1/Mv5MOkdmXt5t71cIc7FYW9QLL7zAhQsX6NGjB7NmzWLatGn4+vqycuXKAsW2K1asIDw8XB3BNd/JkyeZOHEinTt35oEHHqBWrVo8/fTTlgrZpi1btkxdLkutMAD169dXuw23bt1q0oInhK1Kzspm1+UrANQ8pVApBTzauePr60tahsLgFxWenK2Qdb1Ur2lt2PG1jifu0pllKAEHBx192upY9r6hdeb5e6GS0QgWu4/Awx8q1Biu8Mynes5cLH0y49+lkrrcMCOUForhw1zNyWXV6YtFvUwIq7HYODF+fn7MmTOnwPp+/frRr18/k3WPPvpoofvo27dvgdaZ8sq4hcIe52O5lSFDhnDw4EH0ej1///0348aN0zokIYq1/lwy+ut5QdgBOJp7hN4jegPw8lyFv2/0vjDpbnj/ER1urpYZ7yWkmo73H9XxxliFX9bBZ38qRB80PJeSCrN+NdTT7JkHFbxLHkPFjjfuQgpzbsbabRuhrWEcr19OnGZoYNWiXiqEVdhfVVA5dPnyZXVY/lq1atG0aVONIzI/qYsR9mbtTfUwO3J2MHjwYPYfV/j0T8N6d1dY8aGOj59ysFgCY8zNVccD/XREfe1A1Fc6Huhr6MICSDwHz31RutYYV38XPBt6AhDqGMru7xdSw91w19Was5e4kClzngltSRJjB1asWEFuruEOiCFDhpTJUW3btm1LQEAAYLhT7ea724SwJYqi8N9ZQ3eKS7ZCvaOQFppKjRo1mDRHIb9c5MX7Sl68a25tGur47iUH4n7QkT823Td/w+ro0iUylTsbupQcdY5Uv1KNcAfDtShPUfg98YxZYxaitCSJsQNlvSsJDLfd5Y/mnJmZWeoxcISwpgNXUjl7vRWi4SHIzk6l5cgWLNkEa3catgkJgCmjNQzyulrVdUx//EYi9fB0hdT0kicyFTvd6FJq5tyMvK3r1ce/nJAkRmhLkhgbl5mZyapVqwCoWLEiHTt21Dgiy5EuJWEvjLuSmh1Q2J27iz4DhvLMpzeSg5lP6HC3QhdSSUwYBF0N42SScBZe/LrkSUyljhXh+scIc2rGhl8W0rKiDwCxKdfUeaOE0IIkMTZu3bp1pKYaLhKDBg3CydzDctqQ7t274+XlBRgG9svvQhPC1qy76dbqxAoJrIhtyInrMxD0aAVDO2sUXCEcHHR8M1XH9XIWPl0Mm/aULJFxruCMT5ghaantFMr5+PN0MRpX9NcEaY0R2pEkxsaVh66kfK6uruqda8nJyWzevFnjiIQoKDUnl8iLhnkDKl9UCDgPFXs35v0FhucdHWH2U+a5jdqcQmvomPbwjZjGvV/yMWQqGXUpNXUKI2vzfzg7GPb1W+JZ8srxIKRCW5LE2DC9Xq+OD+Pm5kavXr00jsjypEtJ2LotFy6Tc/3e6rADcDw3nv1uY8nIMjz/xDBoXMu2Eph8T94F4Y0Ny0dPwevzSp/EhDk3498//6B3NcO0BOcys9l+Nd3ssQpREpLE2LDt27dz7tw5AHr37o2np6fGEVle//791S6zJUuWlOtpJoRtMrm1er/CPvfLrNtrmH3d3xfeGGubCQyAo6OOeS/o1NuuZ/4KUQdufY75tfdD52j4XGFOzdixYwc9vW70KS2/eM0i8QpxK5LE2LDy1JWUr0KFCnTt2hWAEydOFDmrtRBaWXf91mrHPIVGh2Bv8FD1uWkTdKUaTE4LDYJ1vPHgjUknx32gkJVdfCLj7OOETzNDXUywUzAVdBVI2fwffi7OAPyXkkaq1LAJDUgSY8Pyu1McHBwYOHCgxtFYj3QpCVsVfy2d+NQMAOoeAyUrhyjnegC0rAfjip/D1mZMGQ2t6huW9x+Hd3+8dWtMpc5GdTHOYSxfupTBNasAkKVX2HnpqkViFaI4ksTYqIMHD3Lo0CEAOnbsSOXKlTWOyHryx4sBmRBS2JZ1N3Ul7faqTK6D4TI6Z5IOR0fbboXJ5+Rk6FZycjQ8fu8n2H2k+ETGOIkJc2rGf//9RzNPV3Vd1KUUS4QqRLEkibFRxi0Q5aUrKV9gYCCtWrUCYNeuXSQmJmockRAG687dmPQw7ADs9K4GwJhe0LGpfSQw+cJCdbz0P8Nybp7hbqWc3KITGb+2FdA5Gz5jM+dm5Obmcm33DvX56EtXLBqvEIWRJMZGGbdAlLVZq0vC+DNLl5KwBVl5ejadN9xa7XtFIegU7PSqhKc7fPCofSUw+V7+n44mtQzLu47A9J+L3tbJ04kKLQ3TZNdwrEklXSU2LVlMFTdDge+O5KvopRBfWJkkMTbozJkzbNu2DYAmTZoQGhqqcUTWZ9z6JF1KwhZsv5hCWq5hUqSmcZDk4skFF3de/p+OGpXtM4lxcTZ0K13vEePN7xQOnCg6Ebn5VuuVK1bQuoI3AFdzcjl0VeY8E9YlSYwN+uuvv9Tl8taVlK9JkybUrl0bgA0bNnD58mWNIxLl3bqbZq3e6eVPaA2YPELDoMygTUMdU0YZlrNzDN1KeXmFJzImdTHOzUhNTaVC8nl1XdTFFEuGKkQBksTYoPLelQSg0+nUz56Xl8fy5cs1jkiUd2uv31qt0ys0iTN0Jc16QoebjcyPdCfeGKejXqBhefsBmP174dtVaFMBB1fDn40wpzAAzm7ZoD4fJXUxwsokibEx165dY+3atQDUqFFDLXAtj+RWa2ErUrJz2H99osOQJHBJc8CvrRsDO2gcmJm4uxq6lfJnSnh5rsLRkwVbYxzdHKnQxlAXE+BYjSoOVdn8809cr/clWu5QElYmSYyNWbVqFdnZ2YChFcbW5l+xpg4dOuDvbxja/J9//iErK0vjiER5tefyjTFQ6sTDHk8/Pn/Rq0ydnx2b6nhyuGE5Mxve+6mILiXjuhinMM6dOkkwhlqho9fSuZSVbfFYhcgnSYyNKY+j9BbFyclJHeQvLS2NLVu2aByRKK/2XL4xrH6tRIVLQddoEFx2Eph87z6sw9vDsPzbesjIKpjIVOpUSV0Oc24GgGPCMXWd3GotrEmSGBuSk5Oj1n74+voSERGhcUTaM07kVq9erV0golyLOn+jJaZWItw92UfDaCzHy0PHiK6G5WvpsLSQieQrtPLFwf16Xcz1JObUpv/U56W4V1iTJDE2ZMuWLVy5YvgW079/f1xcXG7xirKvV69euLu7A/Dff//JhJBCE9HnDOelS7aC7mIOEcNaahyR5dzf90YL0w+rCp5vDi4OVGznB0Blh8pUd6jOyY1GSYzUxQgrkiTGhqxfv15d7tu3r3aB2BAPDw+6dOkCwPnz5zly5IjGEYny5kp2DheUTACCTkKKf0aZqoW5WecwCA4wLP8TDWcvFUxkKt40XoySkkxFfQ4AMclXydHrrRKrEJLE2JANG27cqihdSTcYHwvjYySENZjWw0C1tmW7hdTBQcf/ehuW9XpYuKbgNqbFvYYuJffTSQBk5unZm3Kt4IuEsABJYmxEZmYmkZGRAISEhBAcHKxxRLaja9eu6rIkMcLadly4UahaK0Gh3d0NNYzGOv7Xx6hL6Z+CLTG+zX1w9DTMHhnmYkhiLkZtVZ+PuijFvcI6JImxEVFRUeotxNIKY6p169Z4eBhumdi4caPUxQirWnsiRV0OTFQI7Vb2pwGpF6ijfWPD8p6jsOeo6Tnn4OxAxQ6Guhg/nR+BDoFc2HajCljqYoS1SBJjI6QrqWjOzs6Eh4cDcOrUKeLj4zWOSJQney+nAIaiXtfUHBzdHLUNyEruN2qN+bGQ1hjTeZSak5d0AlfFUAsjdygJa5EkxkZIElM8qYsRWriSnUOqay5gKOp1Cy0/l8xR3cHZybC8YDXk5pomMqZJTBjo9fhcMsyjdDoji5PpmVaLVZRf5eeMtGHZ2dls3WroT65Zsya1atXSOCLbI0mM0MKuZNPxYRr1qaFhNNZV0UfHoOvTKpxNhjU7TZ/3aeqDk68hywlzboYOHdf23NhIWmOENUgSYwOio6PJyMgADEWsZfn2zdvVpk0bXF1dAdNb0YWwpBWHb8yeHpKoULt7iHbBaOD+Ygp8dY46KoYb6mJ8dD4EOQZxYfuNUbWlLkZYgyQxNkC6km7N1dWVli0NA4wlJiZy4sQJbQMS5cKaY+fU5aAkBe/G3hpGY3392kMlw3yP/LkRrqaZJjIV2/upyw2cGpJ3JE59HC13KAkrkCTGBkgSUzJt27ZVl6VLSVjDSb3hjkHnbIVKuUq5KerN5+Ks454ehuXMbPh9venzFdpUUJcbODVESU/DJ9WQvOxNuUZ6bp51AhXlliQxGsvJyVEnNqxWrRp16tTROCLb1a5dO3VZkhhhaVezc8j1NvwRDjoFNY3+YJcn/+tddJeSbzMfdE6G5xs6NQIgK24vALmKwm6j2b+FsASLJTGXL19m0qRJdOrUieHDhxMVFVXodm+88Qbh4eF07tyZzp07M3LkSJPn//rrL/r3709ERARvvvkmOTk5lgpZEzExMaSlpQGGVhiphylaixYt1LoYSWKEpa04nKwu10qEmp3LT1GvsTYNoX6QYXnDbjhx5kYi4+juiE9TQxdboGMgXjovkqMj1eeluFdYmsWSmA8++IBKlSqxZs0aJk2axIsvvqhObniz8ePHs2nTJjZt2sSvv/6qrj969CgzZ85k+vTpLF++nHPnzvHNN99YKmRNGBepGo9MKwpydXVVW2Pi4+NJSkrSOCJRli2ISlSXayUoVGheNmeuvhWdTmdS4PvTv6bPV2jtqy7Xd2pA7uED6mMp7hWW5mSJnaanp7N+/XqWLl2Km5sbERERhIaGsmHDBgYPHlzi/axatYru3bvTuLFh6Mhx48bxxhtv8NhjjxXYNjs7m+zsbJN1Tk5OFpkJWn99cjO9GSY5M05iOnfubJZ9FsWccVtbfsydO3dm48aNgGFW6/vuu0/LsG7JXo+5xA17UtLAy7AcfFLBs6FnuT0/7+0JL881LP/wj8KL9ynkNxr7tPKB6881dGrIzjM7cMnOItvFlaiLV8jLy7PJFmZbPt7FKS9xOziUrI3FIklMYmIiHh4eVK1aVV1Xp06dIkda/fnnn/n5558JDg5m4sSJtGrVCjB82zYu5qxTpw5nz54lPT1dHYY+3/z585k7d67JuhEjRhTonjKnO20JyM3NZfNmw1Dd/v7+uLm5kZCQYI7QimXPLRgNGjRQl1esWEHnzp01jKbk7PWYl+e4r3kq6ADnHIUaTg6cPHfyzgMrAVs95uENqxIZ58aRk7B03Rla1DF8acypcaOLv6FzI8gA5dghaBhGcnYOmw4fJdjNdifNtNXjfStlPe6SjpdmkSQmIyMDT09Pk3Wenp6FdieNHj2aZ555Bnd3d9asWcMzzzzDokWLqFatWoH9eHkZvhYVlsSMHTuWMWPGmKyzZEtMUlISgYGBJc4WC7Njxw5SU1MBQ1dSSEiImSIsnLni1kJ+7IMHD8bZ2ZmcnBxiYmJsfqJMez3m5T3uY+cz0PkdBQwj9YZ0qGbx3zVbP+YPDYbI63dQr46txtDrdy3l1czjhH8iuRfzaOjSCAccuLIrGo+GYQCccvWkS3B1jaIumq0f76JI3KYsksS4u7urxar50tLSCiQeYPrNul+/fqxYsYJt27YxbNiwAvvJ/4Nf2H5cXFwskrAUx8HB4Y5+GJs2bVKXu3btarVfyDuNW0teXl60bduWLVu2cOTIEc6dO0e1atW0DuuW7PWYl9e4P/nnCLgblmslQoVWvuX+/BzRTeGJjxUysuCXdfDxkzpcXQzdRO5N3bn2XyquelcCHYM4ZVQXE518lTG1a2oV9i3Z6vG+FYn7+v7MticjQUFBpKenc/78eXXdsWPHqF279i1fq9Pp1FmKa9euzdGjR032ERAQUGgSY4+M77CRot6SkykIhKWtPXGj1bhWgoJvc99iti4fvD10DO9iWL58DZbfuAkJ9zA3dbmhU0Nyjx1GJ5NBCiuwSBLj4eFBREQEX331FZmZmWzatImjR48WOpDb2rVrycjIIDc3l3///Zfdu3erdTB9+/Zl3bp1xMXFkZqayrx58xgwYIAlQra6vLw8tUDV39+fRo0aaRyR/TD+PZIpCIQlnHG6cWkMOQXejbw0jMZ2mMxs/e+NW63dm7qryw2dG0F2Fk6nDTVEB6+mcSW7bA2NIWyHxdqiXnjhBS5cuECPHj2YNWsW06ZNw9fXl5UrV5oU2y5cuJC+ffvSo0cPFixYwEcffUTNmoamxzp16jB58mSeeeYZ+vfvT+XKlRk/frylQraq2NhYtUaoS5cuNlm9b6s6dOiAo6Nh5FRpiRHmlpqei76SYdk5R6GBn1e5G6m3KD1aQbXrx2Z5JFxMMSQybg1d0TkbrmHNPZsDmEwGGX1JpiAQlmGRmhgAPz8/5syZU2B9v3796Nevn/r422+/LXY/gwYNYtCgQWaPT2sy1cDt8/Lyok2bNmzbto2DBw9y7tw5kzvhhLgT3/19CIeKmYChqLdSU+lKyufoqOO+3grTf4acXENtzGNDwcHVAZ+m3lyJuYp/TmW8dF5kHT4A/YcBhvFielbz1zZ4USbZX1VQGSFJzJ0xPmb53XJCmMPPOy+oyyGJSD3MTYqa2bpC6wrqcgOnhiaD3slkkMJSJInRgF6vV//w+vn50bRpU40jsj9S3Css5VDWja6jWokKvs3K50i9RWlSW0eLuoblqDg4eH1oK+ORexs6NUS5dAHHqykA7Ey+Qq6dDc4m7IMkMRrYt28fycmGeVm6dOlil7fJaa1jx47qcZPiXmEuOTl5ZFa6cfdjrVM6KeothMk0BKsN/1docyOJaeNnuDkjY/8eAFJz84i7YjrshhDmIH89NSBdSXfOx8eHli1bArB//34uXryocUSiLFiy+ghOVTIAQ1FvwwqeUtRbiHt6wvXaen76F/R6cKvhhmuAYYLW4NwQHHAg99B+9TUyj5KwBEliNCBJjHkYj60jdTHCHBauPomDnyGJCTwFlcKkHqYwVSvq6NPGsJx0HrYfckWn0+HXpgIATrlOBDkGm04GKePFCAuQJMbKFEVR/+D6+vrSrFkzjSOyX1IXI8xt+0XjehjwaSZJTFGMu5QWbzZ0uVW4nsSAoS4m78QxdDmGOZai5DZrYQGSxFhZXFwcFy4Y7n7o1KmTOt6JKL1OnTqp4+tIEiPulF6v57K3n/o4RIp6izW4E/hcn9pu1Q4P0jPBz6guJrxyB8jLI+foQQAS0jI4l5GlRaiiDJMkxsqMi1BlqoE7U6FCBZo3bw4YBg/ML5YW4nas2XQYh2o3Lom1T+vwbuytYUS2zd1Vx93XG0PTMh1YGwM+YT7qoHcNHA3z4uUcMrrVWlpjhJlJEmNlUg9jXvnHUFEUkwk1hSithX8n4Fz5KgBOOQoNfD1xdJVLZHEGd7zRpbQiEhzdHPENM7ReeV7zwlvnTZ5REiPFvcLc5Ay1IkVR1CTG29ubFi1aaByR/TNuzZIuJXEnNsbpTYp6/WWk3lvq0QpcnQ3LK7YZrnHGdTENnBqSe0SKe4XlSBJjRYcPH+bcuXOAYZwTJyeLzfpQbnTu3FnqYsQdUxSFUy5V4XrDQm0p6i0RLw8dEc0NyycvwN541DuUADpX64xy7Sp51yeD3H35Kll5MuidMB9JYqxIupLMr2LFiuqIx7t37yYlJUXbgIRd2rbjELoaFdXHIYkKvs2lqLckBoTfWF4eaTroXTOP5gDkHjaMF5OtV9hz+ao1wxNlnCQxViRFvZaRnxDq9Xo2b96scTTCHv249BiOVa6pj2uf1uHdSIp6S6J/+xvLyyMV3Gu441bNMOhdpSv+1we9k7oYYRmSxFiJcT2Mp6cnrVq10jiiskPqYsSd+m9nthT13qba1SG0Wg4Akfvh0pUbdTG6LB3BjsHkHolTt4+SySCFGclZaiXHjh3j9OnTAHTo0AFnZ2eNIyo7unTpoi5LEiNKS1EUjl0JwMEvHZCi3tvRvbnh2On1sGq76aB3XWt2Q38yASXdMHdS9KUUFEUpbDdClJokMVYi9TCW4+/vT+PGjQGIiYnh2rVrt3iFEDfs3X8EpWYttai3ViL4NpckpjS6NctQl5dvU0yKe9tVbA+Kok5BcC4zm8S0TGuHKMooSWKsRJIYy8o/pnl5eWzZskXjaIQ9+WlJHI5Vb/xRrZWo4CMj9ZZKq7pZ+OaP3rsdPBt54+BiyAqrp9cAMJ1HSepihJlIEmMFiqKoRb3u7u60adNG24DKIOPE0LiAWohb+Xd7Oo6Vb7Te1ZKi3lJzdoJe1y9rl69B1DEHfK4Peud4wREfnY9MBiksQpIYKzhx4gRJSUkAhIeH4+rqqnFEZY9MBiluh6IoHDxdCecqN4p6G0pR720xvkvp762mg951C+pO7tFDKPo8QCaDFOYjZ6oVSFeS5VWtWpUGDQxztezYsYO0tDSNIxL24MiRY2R5Nr9R1Hsa/MMqaBuUnerXDq6PO8nySNNB7yKqR0BGOtkb1zDEXceLTUK1CVKUOZLEWIEkMdaRf2xzc3PZunWrxtEIe/DzslicqjmqRb0hicjM1bepih+0bWhY3ncc0kNuFEfX1huSlvQvZhC0YxN9q1fWIkRRBkkSYwX5SYyrqyvt2rXTOJqyS7qURGmt2HzFZJA7Keq9MwPCb0wI+e9xN9yquwHgetIVh+t/buTcFOYkSYyFJSYmcvz4cQDat2+Pm5ubxhGVXVLcK0pDURT2JfmaFPXKSL13ZsBNo/fmT0Ggz1DoGNQJgG3btpGZKbdYC/OQJMbCpCvJeqpXr07dunUBiIqKIj09XeOIhC07ceIE6Y4t1JF6HXMVGvp6SVHvHWhRD6pVMiyv3QmezSuoz/Wu3RuArKwstm/frkF0oiySs9XCJImxrvxjnJOTw7Zt2zSORtiyxct3gHcNHCoakt0gGan3jul0OnVCyMxsOOpz43g2cmmsLkuXkjAXSWIsbNOmTQA4OzvTvn37W2wt7pRxoph/7IUozF8bknH0T5WiXjMzrotZccEbh+stWz7nbhxbOTeFuUgSY0GXLl3i8OHDALRs2RIPDw+NIyr7OnbsqC5HRkZqGImwdbEnPHC63pUEUtRrLj1bgcv1qeH+jnJQj2l2Ug71qtUHYPv27eTl5WkVoihDJImxIOPujPDwcA0jKT9CQkKoWrUqYDj+er1e44iELbp8+TKXs6qb3JkkRb3m4eWhI6KZYTnxHOTVvdGl1L9efwCuXbvGgQMHCnu5EKUiSYwFGbcESBJjHTqdTj3WV65c4eDBgxpHJGzR9u3bwaOxFPVaiHGX0j7XCupyC++W6rK0lApzkDPWgiSJ0YbxsZYLpSjMmg17wKOS6Ui9UtRrNgM73FhedulGF13AtQB1Wc5NYQ6SxFhIXl4eUVFRANSoUYPAwECNIyo/JIkRt7I+6gKO/tfUK2CtBCnqNafQGjrqBxmW/413w+X6oHf6owquzoa54+TcFOYgSYyF7Nu3j9TUVEBaYaytdevWODk5AXKhFAXp9Xr2Hwcno3qYkEQF3+aSxJhT/sB3ej2kBlcwLGfo6duoLwCHDh0iOTlZo+hEWWGxJOby5ctMmjSJTp06MXz4cLVV4mazZs1iyJAhdOnShdGjR5vcerdjxw7atGlD586d1X+7du2yVMhmJV1J2nF3d6d58+YAHDhwgJSUFE3jEbblwIEDZDrWLjBSr1dDKeo1J+O6mL0uN7rqOlfroi7LWE7iTlksifnggw+oVKkSa9asYdKkSbz44otcuVJw+nUPDw/mzJnD+vXrmTJlCq+++iqnTp1Sn69RowabNm1S/7Vo0cJSIZuVJDHaMj7mMjqoMBYZGWla1Jun0EiKes2uUxh4Xx9VYtmlG0lMHeqqy9JSKu6UkyV2mp6ezvr161m6dClubm5EREQQGhrKhg0bGDx4sMm2jzzyiLrcunVrateuzcGDB6lRo0ap3jM7O5vs7GyTdU5OTri4uNz+BylC/m27xd2+m39yuri40Lx5c5u41bckcduq0sberl07PvnkEwC2bt1Kr169LBZbcez1mJfluLds2QreQ3CouAeAGqehUhMfzT9rWTvmTo7Quw38sQF253mDiwNk6/E87aluExkZqdnnLWvH29aVNm4Hh5J9qbBIEpOYmIiHh4c6XgdAnTp1iI+PL/Z1V69e5dixY9SuXVtdd+7cOXr16oWXlxf9+/dn3LhxODo6Fnjt/PnzmTt3rsm6ESNGMHLkyDv8NEVLSkoqdH1ycjJHjhwBoHHjxpw9e9ZiMdyOouK2ByWNPSgoSF3+77//ePDBBy0UUcnY6zEvi3Gv33oQxzBntR06JAlyA3NISEiwUnTFK0vHvF1dT/7Y4E+ugwPJlT2oeCqV7JM51K1alyPnjrBt2zbi4+MLvaZbS1k63vagpHHXqlWrRNtZJInJyMjA09PTZJ2np2eh3Un59Ho9b775Jt27d1eDDwkJ4eeffyYoKIgTJ07wwgsv4O7uzn333Vfg9WPHjmXMmDEm6yzZEpOUlERgYGCh2eLevXvV5YiICIKDg80ew+24Vdy2rLSxBwUFERAQwNmzZ4mNjdXsM9vrMS+rcScnJ5NwwRMXo3qYkCSFWg/UwjdY28LesnjM7/OGqd8Ylve5VaILhpsdBjQYyMfnZpGWlkZqaiphYWHWDrtMHm9bZqm4LZLEuLu7k5aWZrIuLS2t2GH333//fVJTU3nvvffUdf7+/vj7+wNQu3Ztxo8fzy+//FJoEuPi4mKRhKU4Dg4Ohf4wjGswOnToYHO/aEXFbQ9KE3t4eDh//vknV65c4dChQzRu3PjWL7IQez3mZS3u6Oho8GiMk3FR73kHfJv42MznLEvHvJo/tGmgJ/ogbMr2Jb+kt6XRoHfbt29XC/G1UJaOtz0wd9wWOQJBQUGkp6dz/vx5dd3N3UTGZs+ezcGDB5k5c2axiYi9/MCkqNc2yHgx4mb5Rb2u/oaiXp1eIayKLw7O9nFtsUf5dynFuVdQ11W9KoPeCfOwyJnr4eFBREQEX331FZmZmWzatImjR4+azDCc75tvvmHz5s3MmTOnQBfUjh071HqSxMREvv32W7p06VJgH7YkNzdXvZ28Zs2a1KxZU+OIyi9JYsTNIiMjwbshOn9DS3HAeajeyk/jqMq2/NF7Lzu7kuLjgW8LH2p0raF+YZVzU9wJi339eOGFF7hw4QI9evRg1qxZTJs2DV9fX1auXGlSbPvll19y8uRJBg0apI4Fs3LlSgAOHjzI2LFj6dSpE0888QRdu3YttCvJluzbt0/tSrNEK0zXrl15+umnzb7f22Vr8Rhr1aqVDHonVHl5eWzbvh3HmrVRnBTAUNTr116SGEtqURcCKhqWJ4R2oOXy9jR+syEtWxq6lA4fPsylS5c0jFDYM4vUxAD4+fkxZ86cAuv79etHv3791Mc7duwoch/33XefzSctN7OHrqTs7Gzc3Ny0DsPi3N3dadGiBdHR0cTFxXH58mX8/OQPVnl14MABUrN8cQlQ1HUhSQoVWsucSZbk4KCjf3uFeSsgLVvH+l3Qr73h+pg/2N22bdsYMGCAxpEKeyQdwWZmySTmwQcfZMOGDcyePRudTodOp+PYsWOMHz+eWrVq4e7uTv369Zk9e3aB1w0dOpRp06bRvn17GjZsCBjGT2nevDlubm60bt2aJUuWoNPp2L17t/raffv20a9fP7y8vKhatSr/+9//uHjxYpHxnDhxwqyf+U4Z/wxkdNDyLTIyEjxv1MMANNK54+zjrGFU5YPx6L3LIw1JpPG5uXXrVqvHJMoGSWLMzHiQO3OPLjx79mzCw8N5+OGHOXPmDGfOnFHrbn777TcOHDjAa6+9xksvvcSvv/5q8tq1a9dy6NAhfvjhB5YtW8bVq1cZNGgQTZs2JSYmhrfffpvnn3/e5DUpKSl0796dFi1asGPHDlatWsW5c+fU7sDC4rG1iS6lLkbkMxT1NsKzUoq6rkVQRe0CKkd6tQHn6+3+yyNBURQ5N4VZWKw7yR61bt26xAPT5eXlFRigSa/Xc+bMGfVxaGhoifYVEBBQbLdaPl9fX1xcXPDw8CAg4EZ1/5tvvqku16pVi8jISH799VeT2iNPT0/mzp3LmTNnCA4O5uuvv0an0zF37lzc3Nxo1KgRp06d4uGHH1Zf8+mnn9KiRQumTZumrps3bx6BgYEcPnyYevXqFRqPLZELpchnaImZSm6VdAAqX1QIbi1JjDV4e+iIaK6wZgecOAtxCdAoxPAF7OTJk0RFRZGbm6vWsAlRUvIbY+Ts2bMm8zbdiezsbLPt61Y+++wz5s2bR2JiIhkZGWRnZxcYd6Fp06Ymt68fOnSIsLAwk9qYtm3bmrxmz549/Pfff3h5eRV4z2PHjlGvXj3zfhALCAoKolq1apw5c4bt27cXmnyKsi85OZlDhw7h0L0Bea6GwvuQJKg4SGqkrGVAex1rdhi6kv7eCo1CDF8yfvvtN9LS0ti3b5+m48UI+yRJjJHStCYU9sfw6tWrXLtmGESrYsWKuLu7m/19b7Zo0SKmTJnCjBkzCA8Px9vbm+nTpxeY9PDm29dLIjU1lUGDBvHBBx8UeK5atWq3HbM16XQ6wsPDWbx4MdeuXePAgQM0bdpU67CElRnqoXQ4BwYAxwAITXHErWbZL3C3FQPCYfKnhuXlkQpT79WpSQwYWsokiRGlJUmMkZJ06YCh2yghIYHg4GCTAfi6devG+vXrAcPUA9WrVzd7jC4uLuTl5amPt2zZQocOHXj88cfVdceOHbvlfurXr89PP/1EVlYWrq6uwPXRTI20bNmSP/74g5CQkCKbeW+OxxblJzFguFBKElP+REZGgmswFf3TyLy+LszHG51OV+zrhPnUDdRRt6ZCWiY0rlV4Xcxjjz2mYYTCHklhr5kYD3IXFBRkkQQGDPNJbd++nRMnTnDx4kXq1q3Ljh07+Oeffzh8+DCvvvpqgWSkMPfeey96vZ4JEyYQFxfHP//8w0cffQSgXtgnTpxIcnIy99xzD9HR0Rw7dox//vmHsWPHqonLzfHY4syqUhcjbtyZdGP+ttahlTSMqHzaMEfHyT90fP6MAzqdjhYtWsigd+KOSBJjJnv37iU93VAwaMnxYaZMmYKjoyONGjWicuXK9OnTh+HDhzNq1CjatWvHpUuXTFpliuLj48Nff/3F7t27ad68OS+//DKvvfYagFonU716dbZs2UJeXh69e/emadOmPP3001SoUEFtgbo5nsTERIt99tvVqlUrnJ0Nt9HKhbL8ycvLM3SvejQiM8DQDlMhRaFuu8oaR1b+VPPXmbR+ubq60qpVKwCOHj3KhQsXtApN2CnpTjIT43EOLJnE1KtXr8Af4vnz5zN//nyTdcYTaX733XcABVpJOnTowJ49e9THCxYswNnZmaCgIHVd3bp11a6YksZja9zc3GjRogVRUVEcOnSI5ORkKlaUu1LKi/3795OamopDwzCyPK6P1Htah3fjggXrwvrCw8PVa8i2bdsYNGiQxhEJeyItMWZiDyP13uyHH35g8+bNHD9+nCVLlvD8888zcuTIEhck2xMZ9K78yj83/YJD1HUN8lxxcJLLny2Q7l5xJ+QsNpP8k8/Nzc1uKuzPnj3LfffdR8OGDZk8eTIjRozg66+/1josi5ALZfll+Hk74F/1xp1IzSvLVAO2Qs5NcSekO8kMzp8/T3x8PGCovzAej8WWTZ06lalTp2odhlXIhbL8ioyMBLdaOF0f5A6gTSN/DSMSxmrUqEFgYCBJSUky6J0oNWmJMQN77EoqbwIDA9U7xvIHvRNl36VLlzh8+DB4NuFatWwAPNMUGreponFkwlj+dTM9PZ29e/dqHI2wJ5LEmIEkMbYvf9A7MAzit3//fo0jEtaQX//kUaUV13wNRb3BFxxx8ZVJH22JtJSK2yVJjBlIEmMf5EJZ/uT/nIOCG6vr6uk8tApHFEHOTXG7JIm5Qzk5OergcsHBwXYzHH95JBfK8if/5+xb/cYcSe2CK2gUjShKixYt1JHD5dwUpSFJzB2KjY0lIyMDkFYYW9eyZUsZ9K4cycvLuz6KtiOK0XeLDi2kHsbWuLi4qIPeHTt2jPPnz2sckbAXksTcIelKsh9ubm60bNkSgMOHD3Pp0iWNIxKWtG/fPsMgd+71SK6eC4BrJjSuIwMd2iIZy0ncDkli7pAkMaXz3XffUaFCBc3eXy6U5Uf+uRlSqTuXrt9RXf2yMw4y6aNNku5ecTskiblDxoPcNWvWTONoLEOn07FkyZJSvy4kJISPP/7YZN2oUaMMt7xqpEOHDuqyXCjLtvyfb81ardV1tR08tQpH3IIkMeJ2SBJzB86dO8fx48cBaN26td0Mcqcld3d3qlTRriZBLpTlR/7P1zOwqrqudYhfUZsLjVWvXp3g4GAAoqOjyc3N1TgiYQ8kibkDWnQlde3alaeeeoqpU6dSsWJFAgICeOONN0y2SUxMZMiQIXh5eeHj48PIkSM5d+5ckfvMzs7miSeeoFq1ari5uREcHKxOIBkSEgLAsGHD0Ol06uNjx44xZMgQqlatipeXF23atGHNmjUmcSYkJDB58mR0uhsz1xbWnfTXX3/Rpk0b3Nzc8Pf3Z9iwYcUeg+K2v3z5Mvfffz9+fn54eHjQr18/jhw5AsDVq1epW7culSpVAiAqKoq8vDz+/PNPvL29SU9PL/ZYCPtx8eJF9eeeXePG6K+928pIvbbMeNC72NhYjaMR9kCSmDtgXFNhzXqY77//Hk9PT7Zv386HH37IW2+9xerVqwHDTNVDhgwhOTmZDRs2sHr1auLj4xk1alSR+5szZw7Lli3j119/5dChQyxYsEBNVvJvH58/fz5nzpxRH6emptK/f3/Wrl3Lrl276Nu3L4MGDSIxMRGAxYsXU7NmTd566y3OnDnDmTNnCn3v5cuXM2zYMPr378+uXbtYu3Ytbdu2LTLWW23/4IMPsmPHDpYtW0ZkZCSKotC/f39ycnLw8fFh4MCBeHp6qp9h3759LFiwgKFDh+Lh4VHssRD2I//crOJYg7M1DIPcOeZCk8o+WoYlbkFaSkVpyQQVRlo/rOdscgk2VCAvrwbJl5+Ftk8AMPH7ajz5k/623jegIuyYW/J8MiwsjNdffx2AunXr8umnn7J27Vp69erF2rVr2bt3L8ePHycwMBAwzFbduHFjoqOjC+3KSUxMpG7dunTq1AmdTqc26QJUrlwZgAoVKhAQEKCub9asmUkN0Ntvv82ff/7JsmXLeOKJJ6hYsSKOjo54e3ubvO5m7777LqNHj+bNN9802XdR3nvvvSK3P3LkCMuWLWPLli1q7cuCBQsIDAxkyZIljBgxgjFjxvDXX3+pr123bh3Lly/nzz//vOWxEPYjP4lpUKE3+673JlVOccHZQb632bKbk5iJEydqGI2wB5LEGDmbDKculHRrJ6AyGMZn4kxJkh8zCQsLM3lcrVo1dVyFuLg4AgMD1QQGoFGjRlSoUIG4uLhCk5gHH3yQXr16Ub9+ffr27cvAgQPp3bt3sTGkpqbyxhtvsHz5cs6cOUNubi4ZGRlqS0xJ7d69m4cfftgs28fFxeHk5ES7du3UdZUqVaJ+/frExcUB0L9/f1xcXMjKygLg999/x8fHh549ewK3dyyE7clPYqrWasdeB0NXZg2dt5YhiRJo1qwZbm5uZGZmSkuMKBFJYowElHT4CAUyMzPUcUbcPTyoWPH2x54o8ftelz9gWz6dTodef3utQGAYBO748eOsXLmSNWvWMHLkSHr27Mnvv/9e5GumTJnC6tWr+eijj6hTpw7u7u7cfffdZGdnl+q93d3dLbr9zVxcXBgxYgTz589HURR27drFQw89pM6aezvHQtiW3Nzc64PcgVtITXV945oyPoytc3FxoXXr1mzevJn4+HjOnz+v6Y0AwvZJEmOkpF06er2et96arnZpfDBnDk8++aQlQyuxhg0bkpSURFJSktoac+DAAVJSUmjUqFGRr/Px8WHUqFGMGjWKu+++m759+5KcnEzFihVxdnYuMOvzli1bePDBB9Wi2tTUVE6cOGGyjYuLyy1niw4LC2Pt2rWMHTu2RJ+vuO0bNmxIbm4u27dvV7uTLl26xKFDh0w++/3338/8+fMByMjIYMCAASU+FsL2HT58mLS0NDx1nlypcSPh71angnZBiRILDw9n8+bNgKFLaciQIRpHJGyZdBDfppiYGHXZlga569mzJ02bNmXMmDHExMQQFRXF/fffT0REBK1bty70NTNnzuTnn3/m4MGDHD58mN9++42AgAD1LqKQkBDWrl3L2bNnuXz5MmCoxVm8eDG7d+9mz5493HvvvQVag0JCQti4cSOnTp3i4sWLhb7366+/zs8//8zrr79OXFwce/fu5YMPPijy87366qtFbl+3bl2GDBnCww8/zObNm9mzZw/33XcfNWrUMLkQdunSBS8vL/VxTk5OiY+FsH3552YDpwYkXO9V1eVBj1CvYl4lbIUU94rSkCTmNu3atQswdG/Y0iB3Op2OpUuX4ufnR5cuXejZsye1a9fml19+KfI13t7efPjhh7Ru3Zo2bdpw4sQJVqxYgcP1IsgZM2awevVqAgMDadGiBWD4Y+/n50eHDh0YNGgQffr0UYf0z/fWW29x4sQJQkND1QLhm3Xt2pXffvuNZcuW0bx5c7p37652BdzO9vPnz6dVq1YMHDiQ8PBwFEVhxYoVJl1wOp2O7t27q4+NL5S3OhbC9uWfm/XdWnCqumGd1xVXvFyk4dkeSBIjSkOnKIqidRD25vTp09SoUQOAzp07s3HjRo0jKhm9Xk9CQgLBwcF290fZ3LGfOnWKmjUN9RLdunVj3bp1d7zPwtjrMbfnuGvXrk1CQgJPN/uW718yNMUEX6rMrseaaxvcLdjzMTd33LVq1eLEiRO4u7tz5cqVAnWA5iDH27osFbf9HAEbIvMl2b8aNWqoNUNRUVEyOmgZceHCBRISEnDEEYJvTF1dp4KM1GtP8q+rGRkZMuidKJYkMbdBq0HuhHnl/+zS0tLYt2+fxtEIc8g/N2s7hnIy0FFd3y5Abq+2J9KlJErKYknM5cuXmTRpEp06dWL48OFF1jlkZmby6quv0qVLFwYMGMCqVatMnv/rr7/o378/ERERvPnmmyZFmFqRJKZskAtl2ZN/bjZyasSJG0Ml0beOJDH2RM5NUVIWS2I++OADKlWqxJo1a5g0aRIvvvgiV65cKbDdV199RUpKCitWrOD999/ngw8+UG/VPXr0KDNnzmT69OksX76cc+fO8c0331gq5BLJzs5mx44dgKHftmrVqrd4hbBVcqEse9SRel0bk2goW8PxsiuNakhRrz1p1qyZOiaUnJuiOBY5s9PT01m/fj1Lly7Fzc2NiIgIQkND2bBhA4MHDzbZdsWKFXzwwQd4eXnRtGlTIiIi+Oeff3jkkUdYtWoV3bt3p3HjxgCMGzeON954g8cee6zAe2ZnZxcYaM3JycnsM0vv2rWLzMxMANq1a3dHg8xZW36s9hRzPkvE3qxZM1xdXcnKyiIyMtIix8Vej7k9xm08yF3loObkuBhG6nXP8gUU9HrbvofBHo85WCZuR0dHWrduzaZNmzh+/Dhnzpwx+xdGOd7WVdq4S1r8a5EkJjExEQ8PD5Nfujp16hAfH2+y3dWrV7l06RJ16tQx2S6/kCs+Pt5kcr86depw9uxZ0tPT8fDwMNnX/PnzmTt3rsm6ESNGMHLkSLN9LjAkXfkaNGhAQkKCWfdvDUlJSVqHcNvMHXuTJk3YuXMnR48eJSYmRp3h2tzs9ZjbU9z79+8nPT2dAIcALgbe6D6qqne1q/PUno65MXPH3bBhQzZt2gTAsmXLLDb9hxxv6ypp3LVq1SrRdhZJYjIyMtSZgvN5enoW6E5KT09XnzPeLiMjo9D95A9QVlgSM3bsWMaMGWOyzhItMU2aNKFXr15s27aNvn372tUEgXq9Xh3J155uzQPLxR4REcHOnTsBw23XN491c6fs9ZjbY9z5XzAaOjUmIVCnrm9btRLBwZZJTs3JHo85WC7uPn368PXXXwNw7Ngxs19r5Xhbl6XitkgS4+7uTlpamsm6tLS0AolH/uO0tDQ1QUlLS1P7Qm/eT2pqqsnrjLm4uJg9YSnMiBEjuOuuuzh+/Ljd3aefz8HBwS7jBvPH3qFDB2bOnAnA9u3bLTbEub0ec3uKO78eprFTY3YaFfVGBPvYzWcA+zrmxswdd8eOHdXlbdu2WeyYyPG2LnPHbZEjEBQURHp6ujqzMhgy6dq1a5ts5+PjQ6VKlTh69KjJdqGhoQDUrl27wHMBAQGFJjHWZq+/QMKUFPeWHfk/v4aujdU7k/TXXAkPtfyXG2F+VatWVbsUduzYYRN3pgrbY5G/wh4eHkRERPDVV1+RmZnJpk2bOHr0KBEREQW27d+/P/PmzVPH6tiwYQN9+vQBoG/fvqxbt464uDhSU1OZN29egcn6ypuuXbvy9NNPF7tNSEgIH3/8sVXisWVvvPEGzZs3L3ab6tWrExQUBMigd/bs/PnzHDt2DC+dF25Vgsl0N3Qn6ZK9qSmTINst40Hv9uzZo3E0whZZrCnhhRde4MKFC/To0YNZs2Yxbdo0fH19WblypUmx7SOPPIKPjw99+/bl+eefZ+rUqYSEhACGQt7JkyfzzDPP0L9/fypXrsz48eMtFXKZER0dzYQJE7QOo9ROnDiBTqdj9+7dpX6tTqdjyZIlJuumTJnC2rVrb/na/Atleno6e/fuLfV7C+2pt1Y7NTQZH6ay3hudTlfEq4Stk5ZScSsWGzzBz8+POXPmFFjfr18/+vXrpz52c3PjnXfeKXI/gwYNYtCgQRaJsawqarJFc8rOzrZKDdKd8PLyMpmtuijh4eHqBJmRkZHqJJfCfuT/gWvk1IgTQTeSlnoePlqFJMzg5iTmySef1DAaYYukqMMO5ebm8sQTT+Dr64u/vz+vvvoqxvN43tydpNPp+Oabbxg+fDiNGjWifv36LFu2TH0+Ly+P8ePHU6tWLdzd3alfvz6zZ882ec8HH3yQoUOH8u6771K9enXq16/PW2+9RZMmTQrE17x5c1599dVCY798+TJjxoyhcuXKuLu7U7duXebPnw/cuKWuRYsW6HQ6unbtChhalnr37k2rVq3w8/MjIiKCmJgYk88LMGzYMHQ6nfq4sO6kefPm0bhxY1xdXalWrRpPPPFEsd/2Cts+X2JiIkOGDMHLywsfHx9GjhzJuXPnADh8+DA6nY6DBw+a7G/WrFlqzVdxx0KUzo0kprFJS0zbqjJSrz0LCwuTQe9EsSSJsUPff/89Tk5OREVFMXv2bGbOnHnLkYzffPNNRowYwYoVK+jXrx9jxowhOTkZMNz6VrNmTX777TcOHDjAa6+9xksvvcSvv/5qso+1a9dy6NAhVq9ezd9//824ceOIi4sjOjpa3WbXrl3ExsYyduzYQuN49dVXOXDgACtXriQuLo4vvvgCf39/AHWgsjVr1nDmzBkWL14MwLVr17j//vv59ddf2bp1K3Xr1qV///5cu3YNQH3/+fPnc+bMGZN4jH3xxRdMnDiRCRMmsHfvXpYtW0adOnVo3rw5bm5ugOmFsqjt84/ZkCFDSE5OZsOGDaxevZr4+HhGjRoFQL169WjdujULFy40iWHBggXce++9tzwWouRyc3OJjo7GCSfqOTcgIb+oN92Z8Fqu2gYn7oizszNt2rQBDN3NZ8+e1TgiYWtkLG4jm7tHkn0+65bbKRhaL+IdEzBHb7tLFVc6rSv5HEyBgYHMmjULnU5H/fr12bt3L7NmzeLhhx8u8jUPPvgg99xzDwkJCbz77rt88sknREVF0bdvX5ydnXnzzTfVbWvVqkVkZCS//vqrSf2Sp6cn33zzjUk3Up8+fZg/f756oZk/fz4REREF7kTLl5iYSIsWLWjdujVwoxUFbnSDVapUiYCAAHV99+7dTaZx//rrr6lQoQIbNmxg4MCB6usqVKhg8rqbvfPOOzz77LNMmjRJXZcfd6tWrdiyZQvHjh3j/PnzVKlSpdjt165dy969ezl+/Lg6G/YPP/xA48aNiY6Opk2bNowZM4ZPP/1UreM6fPgwO3fu5KeffrrlsRAlFxsbS3p6OvUd65Pq58I1b8NZmXfBhya1pB7G3oWHh7Nx40bA8CVj2LBhGkckbIm0xBjJPp9F5plb/8s6k0Xu+VyySrBtSf6VJHEy1r59e5NixfDwcI4cOUJeXl6RrwkLC1OXPT098fHxMbkF/rPPPqNVq1ZUrlwZLy8vvv76axITE0320bRp0wJ1MA8//DA///wzmZmZZGdns3DhQsaNG1dkHI899hiLFi2iefPmTJ06la1bt97y8547d44JEybQrVs3/Pz88PHxITU1tUB8xTl//jynT5+mR48ehT5v3KW0bdu2W24fFxdHYGCgmsAANGrUiAoVKhAXFwfA6NGjOXHiBLt27QIMrTAtW7akQYMGwO0dC1FQfutZupLO/m6p6nrnK14E2P4Yd+IWpLhXFEdaYoy4VClZ03N+S4yjo6PZWmIszdnZ2eSxTqdT57BYtGgRU6ZMYcaMGYSHh+Pt7c306dPZvn27yWtuHoUZDIXXrq6u/Pnnn7i4uJCTk8Pdd99dZBz9+vUjISGBFStWsHr1anr06MHEiRP56KOPinzNAw88wKVLl3jttddo06YN7u7uhIeHF5grqzj5/epFuflC2a1btxLvuygBAQF069aNZcuWMXToUBYuXGgy79ftHAtRUP4ftiR9EslDqkC6YW6zmk5yZ1JZIEmMKI4kMUZK2qVj3LWhxYB3NycX27Zto27dujg6Ot7W/rZs2UKHDh14/PHH1XXHjh0r0WudnJx44IEHmD9/Pi4uLowePfqWCUPlypV54IEHeOCBB+jcuTPPPfccH330kdrKc3OL0pYtW/j000/p0qULwcHBnDp1iosXL5ps4+zsXGxLlLe3NyEhIaxdu7bQBOXmC+Wttm/YsCFJSUnqMNoABw4cICUlhUaNGqnb3XvvvTz33HNERkYSHx/P6NGjS3QsRMnl/2Fzc3PjBB6AIYlpVkHuTCoLqlSpQu3atYmPj2fHjh12cWeksB7pTrJDiYmJPPPMMxw6dIiff/6ZTz75xKRuo7Tq1q3Ljh07+Oeffzh8+DCvvvpqkcWxhXnooYdYt24dq1atKrYrCeC1115j6dKlHD16lP379/P333/TsGFDwHCxcnd3Z9WqVZw7d06da6tu3br89NNPHD16lO3btzNmzJgCiVJ+wnH27FkuX75c6Hu/8cYbzJgxgzlz5nDkyBFiYmL45JNPAKhWrZo6N0t0dDS5ubnFbt+zZ0+aNm3KmDFjiImJISoqivvvv5+IiAi1xgVg+PDhpKWlMXHiRLp160b16tVLdCxEyZw/f16dWLZ169YcTjdMU6LPcqJtzeKTaWE/8r9kZGZmyqB3woQkMXbo/vvvJyMjg7Zt2zJx4kQmTZp0R4PbPfLIIwwfPpxRo0bRrl07Ll26ZNIqcyt169alQ4cONGjQgHbt2hW7rYuLCy+++CJhYWF06dIFR0dHFi1aBBhadebMmcNXX31F9erV1XmMvv32W1JSUhg0aBAPPPAATz31FFWqmA7DOmPGDFavXk1gYGCR47w88MADfPzxx3z++ec0btyYgQMHcuTIEfV540HvYmNji91ep9OxdOlS/Pz86NKlCz179qR27drqeDP5vL296dGjB3v27CkwQWlxx0KUjHH3QvPOXbiKob4s74IXTWpLV1JZIV1KokiKKLW8vDwlPj5eycvL0zqUUrFU3Hq9XgkNDVVmzJhh1v0as8Yxnz17toKh5En59NNPzbJP+V2xrOeff179mb31+1LF75d/Fb9f/lXcXzionL+s1zq8UrGXY34za8S9c+dO9ec8evRos+xTjrd1WSpuaYkRd+TChQt8+umnnD17tsixYeyFfNuzP8Y/pwfD2+C0vB1paxrheaYqlStIS0xZERYWpk78K+emMCaFveKOVKlSBX9/f77++mv8/Py0DueONGvWDDc3NzIzM+VCaQdycnLU2q2QkBA8PAO4cFwBvGjSXNPQhJk5OTnRpk0bNmzYQEJCAmfOnKFatWpahyVsgLTEiDuiKAoXLlxQR6G1Zy4uLmpRbnx8vMk4OsL2xMbGkpGRARha0fYfv/FcoxBtYhKWIy2lojCSxAhhRC6U9sP45xMeHk6zOrD8A3hpdDLDIzQMTFiEnJuiMJLECGFELpT24+YkxtdLR9928FC/a3STicjLnPbt26vLcm6KfJLECGFEkhj7kf/zcXd3p1mzZhpHIyytSpUq6gzw+YPeCSFJjBBGAgIC1IkYo6OjycnJ0TYgUaizZ89y/LihCKZ169YFptUQZVP+l4ysrCx2796tbTDCJkgSI8RN8i+UGRkZxMbGahyNKMzNXUmifJCWUnEzSWKEuIlcKG2fJDHlk5yb4maSxAjNfffdd1SoUMFs+1u/fj06nY6UlJTber05L5Tr16+ndu3atx2LKJwkMeVT06ZN8fT0BCSJEQaSxJQzH3/8MS1bttQ6DBOjRo3i8OHDWoehuvvuu9Uai9JcKLt27crTTz9tsq5Dhw5s374dX19fc4ZYrmVnZ7Njxw4AatWqRdWqVTWOSFhL/qB3YJgI9/Tp0xpHJLQmSYzQVE5ODu7u7gUmdNRaYGAgAMePH+fcuXO3vR8XFxcqV66MTidD4JvLnj17yMzMBKQVpjySLiVhTJIYO6PX6/nwww+pU6cOrq6uBAUF8e6776rPP//889SrVw8PDw9q167Nq6++qt5h89133zFnzhz27NmDTqdDp9Px3XffAZCSksJDDz1E5cqV8fHxoXv37gWmvH/nnXeoUqUK3t7ePPTQQ7zwwgs0b97cJLa33nqLmjVr4urqSvPmzVm1apX6/IkTJ9DpdPzyyy9ERETg5ubGggULCu1O+uuvv2jTpg1ubm74+/szfPhw9bkff/yR1q1b4+3tTUBAAPfee2+pRtdVFIU33niDoKAgXF1dqV69Ok899RRgaE1JSEggPj5e3T4yMpJLly5xzz33UKNGDTw8PGjatCk///yzus2DDz7Ihg0bmD17tnpsT5w4UWh30pYtW+jatSseHh74+fnRp08fLl++XGS8xW2flZWlzurt5uZGp06d1KH49Xo9NWvW5IsvvjDZ365du3BwcCAhIaHYY2GrpCupfJMkRhiTJMbOvPjii7z//vu8+uqrHDhwgIULF5o0p3t7e/Pdd99x4MABZs+ezdy5c5k1axZg6LZ56KGHaNy4MWfOnOHMmTOMGjUKgBEjRnD+/HlWrlzJzp07admyJT169CA5ORmABQsW8O677/LBBx+wc+dOgoKCCvxxnD17NjNmzOCjjz4iNjaWPn36MHjwYI4cOWKy3QsvvMCkSZOIi4ujT58+BT7j8uXLGTZsGP3792fXrl2sXbuWtm3bqs/n5OTw9ttvs2fPHpYsWcKJEyd48MEHS3wM//jjD2bNmsVXX33FkSNHWLJkCU2bNgVg8eLF1KxZk3vuuUfdPjIykszMTFq1asXy5cvZt28fEyZM4H//+x9RUVHqZw8PD+fhhx9Wj21+a46x3bt306NHDxo1akRkZCSbN29m0KBB5OXlFRrrrbafOnUqf/zxB99//z0xMTHUqVOHPn36kJycjIODA/fccw8LFy402eeCBQvo2LEjwcHBxR4LWyVJTPkmg94JE2adE9vOdfs3Umm0bEOJ/tVfvLbE297qX7d/I0sU39WrVxVXV1dl7ty5Jf5M06dPV1q1aqUoimEq9Keeekpp1qyZyTabNm1SfHx8lMzMTJP1oaGhyldffaUoiqK0a9dOmThxosnzHTt2NNlX9erVlXfffddkmzZt2iiPP/64oiiKcvz4cQVQPv74Y5Nt5s+fr/j6+qqPw8PDlTFjxphsU9w07tHR0QqgXLt2TVEURfnvv/8UQLl8+XIhR0RRZsyYodSrV0/Jzs4u9Png4GDlrbfeUgAFUDp37lzodgMGDFCeffZZ9XFERIQyadIkk23Wrl2rAMqlS5cURVGUe+65R+nYsWOh+ytMcdunpqYqzs7OyoIFC9R12dnZSvXq1ZUPP/xQURRF2bVrl6LT6ZSEhARFUQzHsUaNGsoXX3yhKErRx6K446214OBgBVDc3d3tKu5bsdfYtYi7Tp06CqC4uroWuG6VlBxv67JU3NISY+RcZjZnMrJK9O98Tl6Jt73Vv3OZJRt5Mi4ujqysLHr06FHkNr/88gsdO3YkICAALy8vXnnlFRITE4vd7549e0hNTaVSpUp4eXmp/44fP86xY8cAOHTokElrCGDy+OrVq5w+fZqOHTuabNOxY0fi4uJM1uVPsliU/NaHouzcuZNBgwYRFBSEt7c3ERGGiXJu9TnzjRgxgoyMDGrXrs3DDz/Mn3/+SW5ursk23t7e1KpVCzAMepeRkcHbb79N06ZNqVixIl5eXvzzzz8lfs+SfrbSbH/s2DFycnJMjrmzszNt27ZVj3nz5s1p2LCh2hqzYcMGzp8/z4gRI4CSHQtbcubMGRISEgBo06aNDHJXThkPerdr1y6NoxFakiTGSFU3F6q5u5boXxVnxxJve6t/Vd1cShSfu7t7sc9HRkYyZswY+vfvz99//82uXbt4+eWXbzk8d2pqKtWqVWP37t0m/w4dOsRzzz1X4uNXUvm3SBaluM+ZlpZGnz598PHxYcGCBURHR/Pnn38ClHgY8sDAQA4dOsTnn3+Ou7s7jz/+OF26dCkwOm/+hTIzM5PnnnuO2bNn8/zzz/Pff/+xe/du+vTpU+qhz2/1M7zT7QszZswYNYlZuHAhffv2pVKlSkDJj4WtkK4kAVIXI25w0joAW7KuV/tbb4ShYDIhIYHg4GAcHKyXB9atWxd3d3fWrl3LQw89VOD5rVu3EhwczMsvv6yuy//Wms/Z2blA/UXLli05e/YsTk5O6pD7N6tfvz7R0dHcf//96rr8AlIAHx8fqlevzpYtW9SWETAUpd7cgnMrYWFhrF27lrFjxxZ47uDBg1y6dIn3339frTnJv922NNzd3Rk0aBCDBg1i4sSJNGjQgL1799KyZUtcXFzIy8sjPDxc/eO/fv16hgwZwn333QcYfgcOHz5Mo0aN1H3mv64kn+3NN98sUZzFbR8aGoqLiwtbtmwhODgYMNQLRUdHm9zqfe+99/LKK6+wc+dOfv/9d7788ssSHYv8RMeWSBIjoGASM3nyZA2jEVqSJMaOuLm58fzzzzN16lRcXFzo2LEjFy5cYP/+/YwfP566deuSmJjIokWLaNOmDcuXL1dbKfLVrFmT48ePs3v3bmrWrIm3tzc9e/YkPDycoUOH8uGHH1KvXj1Onz6tFti2bt2aJ598kocffpjWrVvToUMHfvnlF2JjY6ldu7a67+eee47XX3+d0NBQmjdvzvz589m9ezcLFiwo1ed8/fXX6dGjB6GhoYwePZrc3FyWL1/OqFGjCAoKwsXFhU8++YRHH32Uffv28fbbb5dq/9999x15eXm0a9cODw8PfvrpJ9zd3dVEICQkhI0bN/Loo4+qr8nNzWX16tVs3boVPz8/Zs6cyblz50ySmJCQELZv386JEyfw8vKiYsWKBd77xRdfpGnTpjz++OM8+uijuLi48N9//zFixAj8/f1Lvf1jjz3Gc889R8WKFQkKCuLDDz8kPT2d8ePHm8TVoUMHxo8fT15eHoMHDy7RsUhNTS3VcbUGSWIEQJMmTfD09CQtLU1aYso7s1bYlBNaFlbl5eUp77zzjhIcHKw4OzsrQUFByrRp09Tnn3vuOaVSpUqKl5eXMmrUKGXWrFlq0WxeXp4SFxenDB8+XKlQoYICKPPnz1cUxVA0/OSTTyrVq1dXnJ2dlcDAQGXMmDFKYmKiuu+33npL8ff3V7y8vJRx48YpTz31lNK+fXuT2N544w2lRo0airOzs9KsWTNl5cqV6vP5hb27du0y+Uw3F/YqiqL88ccfSvPmzRUXFxfF399fGTZsmHrMFy5cqISEhCiurq5KeHi4smzZMpP93qqw988//1TatWun+Pj4KJ6enkr79u2VNWvWqM9HRkYqYWFhiqurq1rcW7NmTWXIkCGKl5eXUqVKFeWVV15R7r//fmXIkCHq6w4dOqS0b99ecXd3VwDl+PHjBQp7FUVR1q9fr3To0EFxdXVVKlSooPTp06fIWG+1fUZGhvLkk08q/v7+iqurq9KxY0clKiqqwD4+//xzBVDuv//+Eh0LWywezMrKUn8mtWvXLnQbW4y7pOw1dq3i7tatm3p+JiUllfr1cryty1JxSxJzG+SXyKBnz57KfffdZ5Z93YpWx7xLly7qhfL06dOlfr38rpjP9u3b1Z/FzXev5bPFuEvKXmPXKu6XXnpJ/X349ddfS/16Od7WJXcnCU2lp6czc+ZM9u/fz8GDB3n99ddZs2YNDzzwgNahWZQUENoO6UoSxuTcFCB3J4kS0ul0rFixgi5dutCqVSv++usv/vjjD3r27Kl1aBYlF0rbIUmMMCaD3gmwQGHv/v37efvtt0lKSqJx48a8+eabVKtWrcB2ycnJTJ8+nZiYGLKysmjUqBHPPfecOjbHV199xbx583BxuXH78aZNm8wdrighd3d31qxZo3UYVidJjO3IP/4eHh6EhYVpHI3Qmr+/P/Xq1ePw4cPq3xFXV1etwxJWZtaWmOzsbKZOncro0aNZt24dzZo149VXXy102/T0dJo2bcrChQtZu3Yt7du359lnnzXZZuDAgWzatEn9J4S1ValSRb0Da8eOHaUeF0aYx+nTp9WBBdu0aYOTk9xYKW58ycjOziYmJkbjaIQWzHol2LlzJ87OzgwdOhSA8ePH06NHD06dOkWNGjVMtq1Zsyb33nuv+nj06NF88sknpKSkFJgMsCSys7ML/IFxcnIyackxF71eb/K/vbDXuEHb2Nu3b098fDxZWVnExMSUatwbez3mthb3li1b1OXw8PAi47K1uEvDXmPXMu527drx/fffA4Zxstq1a1fi18rxtq7Sxl3SMdjMmsTEx8dTt25d9bGbmxs1a9YkPj6+QBJzs127dlGxYkWTBGbt2rWsX7+eqlWr8tBDD9G9e/ciXz9//nzmzp1rsm7EiBGMHDny9j5MCSQlJVls35Zkr3GDNrHXq1dPXV6xYoXJhJslZa/H3Fbi/vfff9Xl0NDQAoM43sxW4r4d9hq7FnHnj+0Ehr8XxrPdl5Qcb+sqadz5pSW3YtYkJiMjo8CQ8p6enqSnpxf7upSUFKZNm8aTTz6pruvVqxd33XUXFSpUIDo6mhdeeIEqVarQpEmTQvcxduxYxowZY7LOki0xSUlJBAYGWnXE3jtlr3GDtrEPGDCAN954AzDMIWV84bwVez3mthb3gQMH1OVBgwZRuXLlQreztbhLw15j1zLu/AE7r127RmxsrJybNsxScZcqiRk/fjx79uwp9Llx48bh6+tLWlqayfq0tDQ8PDyK3GdaWhpPPfUUvXv3ZuDAgep645Fgw8PD6dOnDxs2bCgyiXFxcbFIwlIcBwcHu/olymevcYM2sTdv3hwPDw/S09PZtm3bbb2/vR5zW4g7OzubnTt3AlCnTp0StYTZQty3y15j1yJuBwcH2rZty9q1azl16hSnTp1SpyMpzT7keFuPueMuVRLz7bffFvt8ZGQkv//+u/o4MzOTkydPmiQkxjIzM5k8eTINGjRg4sSJxe7bHn9YomxwcnKiTZs2bNiwgcTERE6fPk316tW1Dqvc2LVrF1lZWYDcWi0KCg8PZ+3atYDhb1Bpkxhh38yaGbRq1YqsrCyWLl1KdnY28+bNo2HDhoXWw+Tm5jJ16lT8/f154YUXCjy/YcMGUlNT0ev1REdHs3LlSjp16mTOcIUoMbnVWjsyPowojpyb5ZtZa2JcXFyYPn06b7/9Nh9++CGNGjUymZxv2rRpALz00kvs2bOHrVu34urqajLr8W+//UZAQACrVq3ijTfeIC8vj+rVq/Pyyy/TrFkzc4YrRIndfKG86667NIymfJEkRhRHBr0r38w+2ELjxo1ZtGhRoc+99NJL6nKrVq3YsWNHkft57733zB2aELdNLpTayT/enp6eRdbEifKrYsWK1K9fn0OHDhETE0NmZiZubm5ahyWsRApNhCiBKlWqEBoaChjGQ5JB76zj1KlT6i2Zbdu2lUHuRKHyW+hycnJk0LtyRpIYIUoo/0KZlZXFrl27NI6mfJCuJFESUhdjfampqURFRZGbm6tpHJLECFFCcqG0PkliREnIuWl969ato127dvj5+fHFF19oFockMUKUkPGFcvPmzRpGUn4YH2fjuiQhjDVq1AgfHx/A8DujKIrGEZV9GzZsAAwtMgEBAZrFIUmMECUUFhaGr68vABs3bpQLpYVdu3ZNHeSuUaNG+Pv7axyRsFWOjo507NgRgHPnznH48GGNIyr78pMYgM6dO2sWhyQxQpSQo6OjOlbRhQsXiIuL0ziism3r1q3k5eUBmAzDIERhjH9HjP/ACvO7cuWKWhfYtGlTTb9gSBIjRCl07dpVXZYLpWUZH1/j4y5EYeTctJ4tW7aos1Fr/QVDkhghSsH4hF2/fr12gZQDxse3S5cu2gUi7ELLli3VCYjXr18v3b0WZHxuShIjhB1p0aIF3t7egOHbnlwoLSMtLY3o6GgA6tevr2nhoLAPzs7Oal3M6dOnOXbsmMYRlV3GLV1af8GQJEaIUnBycpICQiuIjIxUx5/Q+puesB9SF2N5xgX3DRs2pEqVKprGI0mMEKUkF0rLMz6uksSIkpJz0/KMC+5toVZNkhghSkkKCC1PkhhxO9q0aYO7uzsg56al2Nq5KUmMEKXUqlUrtYBQ6mLMLyMjg+3btwNQp04datSooXFEwl64uLjQoUMHABITEzlx4oS2AZVBtlTUC5LECFFqzs7O6oXy1KlTUkBoZtu2bVMn2LSFi6SwL3IHoeUYF9zXq1fPJgruJYkR4jZI37vl2FpztbAvcm5aji0W3EsSI8RtkLoYy5EkRtyJtm3b4urqCsi5aW62OAClJDFC3AYpILSMrKwstm3bBkBISAhBQUEaRyTsjZubmzpZ6PHjx0lKStI4orLDFr9gSBIjxG1wcXFRZ7WWAkLziYqKIjMzE7Cdb3rC/khLqfkZF9yHhobaTMG9JDFC3CYpIDQ/W7vzQdgnOTfNz1YL7iWJEeI2SQGh+dlic7WwP+3bt8fFxQWQc9NcbPXclCRGiNvUrl07KSA0o+zsbLZu3QpAYGAgISEh2gYk7Ja7uztt27YF4OjRo5w+fVrjiOyfJDFClDFSQGheO3bsICMjAzBcJHU6ncYRCXsmLaXmk5mZaVJwHxwcrHFEN0gSI8QdkAul+dji7ZvCfklxr/kYF9zbUisMSBIjxB2RAkLzkaJeYU7h4eE4OTkBcm7eKVvtSgJJYoS4I+3bt8fZ2RmQb3t3Iicnhy1btgBQvXp1QkNDNY5I2DtPT0/atGkDwKFDhzh79qzGEdkvW24llSRGiDvg4eFBu3btACkgvBMxMTGkpaUBUg8jzMe41WDjxo0aRmK/bL3gXpIYIe6Q1MXcOVturhb2S87NO2frBfeSxAhxh+RCeedsubla2K+OHTvi6OgIyLl5u2z9C4YkMULcoQ4dOkgB4R3Izc1l06ZNAFStWpV69eppHJEoK7y9vWnVqhUA+/fv58KFCxpHZH9sveBekhgh7pCnpyetW7cGpIDwduzevZtr164BttlcLeyb1MXcvpsL7uvUqaNxRAVJEiOEGRh3gciFsnRsvbla2Dfp7r199lBwL0mMEGYgF8rbJ0mMsKROnTrh4GD4UyfnZunYw7lp9iRm//79jB49mo4dOzJhwgTOnDlT5LaDBg2iY8eOdO7cmc6dOzNt2jT1Ob1ez4wZM+jatSu9e/dmwYIF5g5VCLORAsLbk5eXp9bD+Pv706hRI40jEmWNr68vzZs3B2Dv3r0kJydrG5AdKXdJTHZ2NlOnTmX06NGsW7eOZs2a8eqrrxb7ms8++4xNmzaxadMmXnrpJXX9H3/8wc6dO1m8eDHffPMNP/30E1FRUeYMVwiz8fb2pmXLloAUEJZGbGwsKSkpgO02Vwv7l9/dqyiKdPeW0M0F9/Xr19c4osI5mXNnO3fuxNnZmaFDhwIwfvx4evTowalTp6hRo0ap9rVixQruu+8+KlasSMWKFRk6dCjLly9XZya9WXZ2NtnZ2SbrnJyc1OnYzUmv15v8by/sNW6wj9gjIiKIjo4GDBX9d911l13EXRhrxW1850OXLl3u+P3s9XiD/cZuD3F37tyZmTNnAobfucGDB9tF3IWxVtwxMTFqwX2XLl1QFAVFUW57f6WNO78L8FbMmsTEx8dTt25d9bGbmxs1a9YkPj6+yCTm+eefR1EUwsLCePbZZ6lWrVqh+6pTpw6bN28u8r3nz5/P3LlzTdaNGDGCkSNH3slHKpa9zlpsr3GDbcfeoEEDdXn58uXqHUtg23EXx9Jxr1q1Sl2uU6cOCQkJZtmvvR5vsN/YbTnukJAQdDodiqKwZs0ak98zW467OJaOe+nSpepykyZNrH5u1qpVq0TbmTWJycjIwNPT02Sdp6cn6enphW7/zjvv0KBBA3Jycvjyyy959tln+emnn3BwcCiwr+L2AzB27FjGjBljss6SLTFJSUkEBgaWOFu0BfYaN9hH7MOHD2fChAno9Xp27dpFcHCwXcRdGGvErdfr2bFjBwAVK1akd+/ed/xe9nq8wX5jt5e4w8LC2LNnDwcOHMDX1xcfHx+7iPtm1jresbGx6vKwYcMIDg6+o/1ZKu5SJTHjx49nz549hT43btw4fH191dux8qWlpeHh4VHoa5o1awaAq6srkydPpmvXrpw8eZKgoCDc3d1N9lXcfgBcXFwskrAUx8HBwa5++fPZa9xg27H7+fnRvHlzYmJi2Lt3LykpKVSoUAGw7biLY8m49+/frxZZdu7cWR0w0Bzs9XiD/cZu63FHRESwZ88eFEVh69at9O/fH7D9uItiybjz8vLUng9/f3+aNGlitno1c8ddqj19++237Nixo9B/jz/+OLVr1+bo0aPq9pmZmZw8eZLatWvfct86nU5t7gMK7OvYsWMl2o8QWsqv4JcCwlszroeRqQaEpRn/jsnI2sUzLrjv0qWLTRfcmzWNa9WqFVlZWSxdupTs7GzmzZtHw4YNC62HOXv2LLGxseTm5pKRkcHs2bMJCAigZs2aAPTr148ff/yRy5cvk5SUxJIlSxgwYIA5wxXC7IwvlHKrdfHs4fZNUXZ07txZXZZzs3j2NJeZWWtiXFxcmD59Om+//TYffvghjRo14u2331afzx8H5qWXXiItLY13332X06dP4+rqStOmTZk5c6Y61sbdd99NUlISw4YNw9nZmQceeKDIO5OEsBWdO3dWWxTlQlk045YqX19fwsLCNI5IlHX53SL79u0jJiaGq1evah2SzbKnLxhmTWIAGjduzKJFiwp9zngcmNDQUH755Zci9+Pg4MCzzz7Ls88+a+4QhbAYPz8/tYBw9+7dapOsMBUXF6eOpdO5c2f1y4sQlhQREcG+ffvQ6/Vs2bJFBlcshF6vV79gVKxYkSZNmmgcUfHsr5pJCBtnXBdT3LAA5Zk9fdMTZYdMBnlrNxfc23rRs21HJ4QdkgvlrdlTn7soO+TcvDV7+4IhSYwQZtalSxd1WepiClIURb07xNvbW53XRghLq1KlCg0bNgQgOjq6wJAgwv7uGpQkRggzyy8gBNOhu4XB4cOHOXfuHGCYYdic48MIcSv5rQt5eXnExMRoHI1tsceCe0lihLCA/AulXq9n586dGkdjW+ytuVqULca/c9u3b9cwEttjjwX3ksQIYQFyoSyaJDFCS3JuFs0ez01JYoSwAOO6mKioKA0jsS3G4+d4enrSqlUrjSMS5U21atXUyYVjY2OLnZOvvJEkRggBQNWqVdUCwtjYWFJTUzWOyDYcO3aMU6dOAdCxY0ecnZ01jkiUR/kFqzk5OURGRmobjI0w/oLh7e1NixYtNI6oZCSJEcJCunXrBhgKCFevXq1xNLZhxYoV6nL+8RHC2ox/91auXKlhJLZj165dnD17FjC0JNtLwb0kMUJYyKBBg9TlpUuXahiJ7ViyZIm6bHx8hLCmvn37qkWrS5cuVSceLs/s9dyUJEYIC+nWrRve3t4ALF++nNzcXI0j0lZycrJ6+2adOnVkyHehGT8/P7VLKT4+nn379mkbkA0wTmIGDx6sXSClJEmMEBbi6upK3759AcMf8PI+BcHy5cvJy8sDYMiQIeh0Oo0jEuWZ8R/q8t5SGh8fz969ewFo164d1apV0ziikpMkRggLGjJkiLps/E2nPDL+/EOHDtUsDiHANIkp7+emcRJnb+emJDFCWFD//v3VO3CWLFlSbvveMzIyWLVqFQCVK1cmPDxc44hEeRcUFKSOrL1z506SkpI0jkg79vwFQ5IYISzI19eXdu3aAZCQkEBsbKzGEWlj7dq16ngcgwYNsouRQEXZ17NnT3V52bJlGkainYsXL6pd3fXq1aNBgwYaR1Q6ksQIYWG9evVSl8trs7U9f9MTZZecm/D333+j1+sB+zw3JYkRwsKMv+2VxwLCvLw8/vrrLwA8PDxMjocQWmrQoAG1atUCDLM3p6SkaBuQBuy5HgYkiRHC4qpVq0br1q0Bw4BSCQkJGkdkXdu2beP8+fMA9OnTB3d3d40jEsJAp9OpBb65ubkmgzGWB+np6fzzzz+AYZTx/K5veyJJjBBWYHyXUnlrjZGuJGHLyvMdhKtXryYjIwMw3K3l4GB/KYH9RSyEHSqvSYyiKOofBkdHRwYMGKBtQELcpGPHjlSqVAkwTEGQlZWlcUTWY+9dSSBJjBBW0ahRI0JDQwHDTLHJyckaR2QdcXFxHD16FIDOnTurfyyEsBVOTk4MHDgQgNTUVNatW6dxRNaRm5ur3pHl6elJ9+7dNY7o9kgSI4QV6HQ69ZtOXl4ey5cv1zYgK5GuJGEPjH83y0uX0tatW7l06RIA/fr1w83NTeOIbo8kMUJYSXnsUjL+nMafXwhb0qtXL/WP+LJly9RbjsuysnJuShIjhJV06NABf39/AFatWqUW1JVVp06dIioqCoBmzZoREhKibUBCFMHT05PevXsDcPbsWfX3tqwqS7VqksQIYSWOjo7q7ZxpaWmsXbtW44gsy3gEVOlKErauPHUp7du3j/j4eAC6du2Kn5+fxhHdPklihLCi8tSlVFaaq0X5MHDgQPUWYzk37YckMUJYUa9evfDw8AAMLRV5eXkaR2QZV65cUe/yCAoKonnz5toGJMQtVK5cmY4dOwJw8OBBDh48qHFElmPc0iRJjBCixNzd3enTpw8A58+fZ9u2bRpHZBkrV64kJycHMDTT63Q6jSMS4taMu5TKamtMUlISO3fuBKBly5YEBQVpHNGdkSRGCCsrD11KZam5WpQf5eHcNK5VKwvnpiQxQliZcd/7kiVLUBRF44jMKzs7W52Dxs/Pj86dO2sckRAlExoaSpMmTQDDnF9nz57VOCLzK2tjN0kSI4SVVapUSf3DfuTIkTLX975+/XquXr0KwIABA3B2dtY4IiFKLr91QlEUdfb1siIlJYX169cDEBISQtOmTbUNyAwkiRFCA2X5ds6y9k1PlC9l+dxcsWIFubm5QNmpVTN7ErN//35Gjx5Nx44dmTBhAmfOnCl0u7Nnz9K5c2eTf61bt1bHzvjrr79o166dyfNlsWlPlE9ldeZcvV6v1hK4urqqRcxC2ItWrVpRo0YNANasWcO1a9c0jsh8yuIXDLMmMdnZ2UydOpXRo0ezbt06mjVrxquvvlrotgEBAWzatEn998UXX+Du7k6HDh3UbVq1amWyTUBAgDnDFUIztWrVIiwsDICoqChOnz6tcUTmsXPnTvWz9OzZEy8vL40jEqJ0dDqd+iUjOzubf/75R+OIzCMrK4uVK1cCULFiRfV2cnvnZM6d7dy5E2dnZzXDGz9+PD169ODUqVNqZluU5cuX07VrV9zd3W/rvbOzs8nOzjZZ5+TkhIuLy23trzj582rY2/wa9ho32G/sxcU9ZMgQYmNjAcM3pEcffdSqsRXndo/3n3/+qS4PHjzY6j8ve/09AfuNvSzGPXjwYD7//HPA8Ds9fPhwq8ZWnNs93mvWrCE1NRW4cXOBNX9mpY07/+aHWzFrEhMfH0/dunXVx25ubtSsWZP4+Phik5jc3FxWr17NO++8Y7J+79699OjRg4oVKzJq1CjuvvvuIvcxf/585s6da7JuxIgRjBw58jY/za0lJSVZbN+WZK9xg/3GXljcbdu2VZd/+eUX+vXrZ82QSqS0x/v3338HDN9mmzdvTkJCgiXCuiV7/T0B+429LMUdEhKCt7c3165d4++//+bo0aM2V6Be2uO9YMECdblDhw42f27WqlWrRNuZNYnJyMjA09PTZJ2npyfp6enFvm7Lli04OzubXNRbtmzJL7/8QkBAAAcOHGDKlCn4+fnRo0ePQvcxduxYxowZY7LOki0xSUlJBAYGljhbtAX2GjfYb+zFxR0UFERQUBCJiYlERkbi5+eHj4+PRpGaup3jfeTIEY4cOQJAeHg4rVu3tmSIhbLX3xOw39jLatwDBgxg0aJFXL16lYSEhCL/9ljb7RxvvV7Pf//9BxgaF+69994Cf6stzVK/J6VKYsaPH8+ePXsKfW7cuHH4+vqSlpZmsj4tLU0dZr0oK1asoG/fviYfzLjlpkmTJowePZr//vuvyF8kFxcXiyQsxXFwcLCrkzafvcYN9ht7UXEPGTKETz75hJycHP755x9GjRqlQXRFK83xNr4ddejQoZr+nOz19wTsN/ayFvewYcNYtGgRYBggrlevXtYOrVilOd5RUVHqjTG9e/fG29vbkqEVy9y/J6VKYr799ttin4+MjFSbkwEyMzM5efIktWvXLvI1165dY9OmTfzwww/F7lun05W5QcGEGDp0KJ988glgqIuxtSSmNMrinQ+i/Orbty8uLi5kZ2ezdOlS5syZY7e3JJflc9OsaXOrVq3Iyspi6dKlZGdnM2/ePBo2bFhsPcyaNWsICQmhTp06Juu3bt3K5cuXAcNkXL/88gtdunQxZ7hCaK5z585UqFABMLRI3lycbi/Onz/P1q1bAWjYsKFJbZwQ9sjHx4fu3bsDhjqOXbt2aRzR7csf9sDBwYGBAwdqHI15mTWJcXFxYfr06fz8889069aNXbt28fbbb6vPT5s2jWnTppm8ZsWKFfTv37/AvrZv387IkSPp1KkTL730Evfff7+MOSHKHGdnZ/WicvXqVXU0TXvz119/qS2lZe2bnii/ysLAd8Yzcnfs2JHKlStrHJF5mbWwF6Bx48ZqP+LNXnrppQLrbr6jKN/kyZOZPHmyWWMTwhYNGTKEn376CTBcKHv37q1xRKVnfIEvC5PKCQEwaNAgdeiDJUuW8NZbb2kcUemV9clY7a8KS4gypk+fPri6ugKGAkJ7G28jNTWV1atXA1CtWjXatGmjcURCmEf16tVp164dYBjyIz4+XuOISk+SGCGERXl7e9OzZ08ATp06xc6dOzWOqHT+/fdfsrKyAMNF0h7vUBGiKMZdSsYJgT04e/Ys27ZtAwx3+d5ce1oWyNVGCBtg/A3JeNRbeyBdSaIss+dzc9myZWqtWlk9NyWJEcIGDBo0SG3B+Pbbb285QKStOHv2LL/++itguJujW7duGkckhHk1aNCA+vXrA7Bp0yZ2796tbUAlpNfr1eEboOwW3EsSI4QNCAgIYMSIEYDhduVvvvlG44hKZsaMGWpX0iOPPKLW9ghRVuh0Op544gn18c132Nqqv/76i3379gHQvn17WrVqpXFEliFJjBA2wvjuvQ8//FBNDmzVxYsX+eKLLwDDUObPPPOMxhEJYRnjx4+natWqgGF+sLi4OI0jKp6iKCZzEb7yyit2O1DfrUgSI4SNCAsLU/utT506xffff69xRMWbPXu2Os3Iww8/TEBAgMYRCWEZ7u7uTJkyBTAkCO+9957GERXv33//ZceOHQA0b9680LHYygpJYoSwIS+//LK6/N5775GTk6NhNEVLSUlhzpw5gGHAvueee07jiISwrEcffZSKFSsCsHDhQo4dO6ZxRIVTFMVkkNmy3AoDksQIYVPatGmjjkx94sQJfv75Z40jKtynn37K1atXAXjwwQcJDAzUOCIhLMvLy0sdgDUvL48PPvhA44gKt3HjRrZs2QIYpgAZNmyYxhFZliQxQtiYV155RV2eNm0aeXl5GkZTUGpqKrNmzQLA0dGRF154QeOIhLCOJ554Ah8fHwC+++47kpKSNI6oIONamJdffrnMj9tUtj+dEHaoU6dOREREAHDo0CH++OMPjSMy9eWXX5KcnAzAvffeW+ws9UKUJRUqVODJJ58EICcnh+nTp2sckalt27axZs0aAEJDQxk1apTGEVmeJDFC2CDj1ph33nnHZqYiyMjI4KOPPgIMt56++OKLGkckhHU9/fTTeHh4AIa5/86ePatxRDe8++676vKLL76Ik5PZp0e0OZLECGGDevToYTJny99//61xRAbffvst586dA+Duu++mYcOGGkckhHX5+/vz2GOPAZCZmcnMmTM1jshg165d6nUiMDCQ//3vfxpHZB2SxAhhg3Q6XYHWmPzhw7WSnZ1tUsxofCeVEOXJs88+qw7s+Pnnn3Pp0iWNIzIdhO/555/HxcVFw2isR5IYIWzUgAEDaNasGQDR0dHqTNFa+eGHHzh58iRgmCYhPzYhyptq1arx0EMPAZCWlsbs2bM1jefAgQNq7VxAQADjxo3TNB5rkiRGCBtVWGuMVnJzc00G+JJWGFHeTZ06Va05mTNnDleuXNEslvfee09tqZ0yZQru7u6axWJtksQIYcOGDx+u1p1s2rSJjRs3ahLHokWLiI+PB6BXr15qvY4Q5VVQUBAPPPAAAFeuXOGzzz7TJI5jx46xcOFCACpVqsQjjzyiSRxakSRGCBvm4OBgMqeSFq0xer3e5K4H49YhIcqzF154QR2HZebMmeo0HNb0/vvvq3cvTp48GS8vL6vHoCVJYoSwcaNHj1bHYlm9ejXbt2+36vsvXryYgwcPAtC5c2e6dOli1fcXwlbVqVOHe+65B4BLly7x1VdfWfX9ExMT1TnWfH19TWbbLi8kiRHCxjk5OZmMx2LcKmJphc2GK4S4wbildPr06WRmZlrtvadPn67Or/bkk0/i6+trtfe2FZLECGEH7r//fmrWrAnAX3/9xe7du63yvsuXL2fPnj2AYV6nXr16WeV9hbAXjRo14q677gLg7NmzzJs3zyrve/bsWebOnQuAp6cnkyZNssr72hpJYoSwAy4uLjz//PPqY2u0xiiKYjL2RFmfDVeI22V8t977779Pdna2xd9zxowZZGVlAfDYY4/h7+9v8fe0RZLECGEnxo8fT9WqVQH4448/OHDggEXfb8uWLWr9TVhYGAMHDrTo+wlhr1q0aMGAAQMASEpK4scff7To+yUnJ/Pll18C4OrqyrPPPmvR97NlksQIYSfc3d2ZMmUKYGglMR63xRKMbxktD7PhCnEnjOvF3nvvPXJzcy32XvPnzyc9PR2Ahx9+mICAAIu9l62Tq5IQduTRRx+lUqVKACxcuJBjx45Z5H02bdqktsLUr19f7fMXQhSuffv29OzZEzCM3fLLL79Y5H1SUlLUO5KcnZ2ZOnWqRd7HXkgSI4Qd8fLyYvLkyYBh/Jb333/fIu9jXHPz0ksv4ejoaJH3EaIsMW6Neffddy0y+/xnn31GamoqAA8++CCBgYFmfw97IkmMEHbmiSeeUG+l/P777/nxxx/NNjmkXq/no48+UudpqlWrljoOhhCieF26dKFTp04AxMXFMWXKFLMW+a5atYoZM2YA4OjoyAsvvGC2fdsrSWKEsDO+vr489dRTAOTk5HD//fdz7733kpKSckf7PX36NH369OG5555T1z3//PM4Ozvf0X6FKC90Oh2vvvqq+njWrFmEh4dz6NChO9pvZmYmkyZNol+/fuocTffee686CGZ5JkmMEHbolVdeUedtAcPcRs2aNbvtuZWWLFlC06ZNWbNmjbpuwoQJjB8//o5jFaI86d27N7NmzVKT/5iYGFq2bMncuXNvq8V07969tGnThjlz5qjrIiIi+Pjjj80Vsl2TJEYIO+Ti4sJ3333HokWLqFChAmAYgrxr1668/PLL6iiet5KWlsYjjzzCsGHDSE5OBqB69er8888/JvPCCCFK7umnn2bbtm3Ur18fgPT0dCZMmMDw4cO5ePFiifah1+uZPXs2bdq0Yd++fYDhduo5c+Ywb9489bwv7+QKJYQdGzVqFHv27CEiIgK4MUBdx44dOXLkSLGv3blzJy1btuTrr79W1w0bNozY2Fj1LgshxO1p2bIlMTExPProo+q6JUuWEBYWptacFeXMmTP079+fp59+Wh3QLiwsjJ07dzJx4kQZdNKIJDFC2LmgoCDWrl3Le++9h5OTEwDR0dG0aNGCb7/9tkATdl5eHh988AHt27fn8OHDAHh4ePDNN9/wxx9/qLdwCyHujIeHB1988QVLly5VR9Q9c+YMvXv35tlnn1UTFGPLli0jLCyMf/75R103efJktm/fTuPGja0Wu70wexIzbdo0hg4dSuvWrdmxY0ex216+fJlJkybRqVMnhg8fTlRUlMnz3333HT179qR79+7Mnj3bbHdgCFHW5N+pEBkZSd26dQFDV9FDDz3E3XffzaVLlwDDaKI9e/bkhRdeUAfjat26Nbt27WL8+PHyDU8ICxg8eDCxsbH07t1bXTdz5kzatm3L/v37AUOX02OPPcaQIUPULqeAgAD++ecfZs6ciZubmyax2zqzJzH16tXjlVdeoUaNGrfc9oMPPqBSpUqsWbOGSZMm8eKLL6qV15s3b+a3337ju+++49dff2Xr1q0sXbrU3OEKUabkJyQPP/ywum7x4sWEhYXx3nvvERYWxvr16wHDnRQvvfQSW7dupV69ehpFLET5UK1aNVauXMnHH3+Mi4sLALGxsbRu3Zq33nqLVq1aqVMJAAwZMoS9e/eaJD6iICdz7/Duu+827Nip+F2np6ezfv16li5dipubGxEREYSGhrJhwwYGDx7MihUrGDZsmDpz73333cdff/3F0KFDC91fdnZ2gfvxnZyc1F8Wc8ofwMgSAxlZkr3GDfYbuxZxu7u78+WXX9KnTx8mTJhAcnIyp0+f5qWXXlK3CQwM5Pvvv1draW6OT4639dlr7BJ36Tz55JNERERw3333sX//fjIzM3n99dfV593d3Zk5cyYPP/wwOp2u3J6bJb2pwOxJTEklJibi4eGhTmgHUKdOHeLj4wE4fvw4ffr0MXmuuCHW58+fr05Lnm/EiBGMHDnSzJHfkJSUZLF9W5K9xg32G7sWcbds2ZLly5czZcoUtmzZoq4fOHAgb7/9Nr6+viQkJBS7Dzne1mevsUvcJefr68tvv/3Ghx9+yHfffaeub9KkCbNmzSI0NJTExMRi91HWj3etWrVKtJ1mSUxGRgaenp4m6zw9PdXupPT0dJPnPT09ycjIKHJ/Y8eOZcyYMSbrLNkSk5SURGBgoF3dgmqvcYP9xq513MHBwaxfv54vvviCZcuWqQPj3ar2Reu4b5e9xg32G7vEffu+/fZbRowYwZw5c+jQoQNTp0695d8sW4j7dlgq7lIlMePHj2fPnj2FPjdu3Dgef/zxEu/L3d2dtLQ0k3VpaWl4eHgAhqpu4+fT0tJwd3cvcn8uLi4WSViK4+DgYFe/RPnsNW6w39i1jNvBwYEnn3ySJ5988rZeK8fbuuw1don79vTv35/+/fuX+nVax327zB13qZKYb7/91mxvHBQURHp6OufPn6dKlSqAYebPAQMGAIampKNHj6p99seOHSM0NNRs7y+EEEII+2b2NC4nJ4esrCwURSE3N1ddvpmHhwcRERF89dVXZGZmsmnTJpOkpX///ixevJiTJ09y6dIlFixYcFvZqhBCCCHKJrPXxEycOJGYmBjAMNsuGAbvqV69OvPmzWP37t3qHBAvvPACr7/+Oj169KBq1apMmzZNnZ23U6dO3H333TzwwAPo9XqGDh3KkCFDzB2uEEIIIeyU2ZMY4yHMbzZu3DiTx35+fiaTWt1s7NixjB071myxCSGEEKLssL+qICGEEEIIJIkRQgghhJ2SJEYIIYQQdkmSGCGEEELYJUlihBBCCGGXJIkRQgghhF2SJEYIIYQQdkmSGCGEEELYJUlihBBCCGGXJIkRQgghhF3SKYXNziiEEEIIYeOkJUYIIYQQdkmSGCGEEELYJUlihBBCCGGXJIkRQgghhF2SJEYIIYQQdkmSGCGEEELYJUlihBBCCGGXJIkRQgghhF2SJEYIIYQQdkmSGCGEEELYJUliipCamqp1CEKIMkauK0KYlyQxN4mJieGee+5h0aJFZGVlaR1Oie3Zs4dVq1Zx6NAhrUMpNXuN/ejRo7z55pvs27cPAHuZhmz37t389NNPREZGah1Kqezdu5e///6b2NhYrUMpNbmuWJe9xm2v1xTQ7roiSYyRNWvW8Morr9CvXz9GjRqFk5OT1iEVS1EUcnNz+fDDD3nqqafYunUrEyZMYOnSpaSkpGgdXrHsOfZ8mzZtYt26dWzbto3U1FR0Op3WIRUrOzubt99+m2effZbTp0/z/PPP8+uvv5KZmal1aMW6du0aL774IpMnT2bfvn08+eSTLF68mIyMDK1DKxG5rliHvcZtzN6uKaD9dcW2zyYr27p1K4899hiDBg0CICUlhQoVKmgbVDF0Oh3p6ekcO3aM+fPnU7t2bf7++2/WrVtHamoq/2/v7qOqru8Ajr+BK4LcWcpQwMEQGVLrJEyYpZjaWssCLCRpjVO0M1PXjtY6lOkf4kOddO0IPgQ5PHBO2yDDJ2xn6Vq5qcfCxxJBl3BMfIgrJMqDysP97g+6vyCfyOD3u1/8vP4pfpd7eV/k9+X7e+Q3v/mN1YnXpHO7i8PhICIigpqaGvbu3cvEiROtTrqu6upqTp48SWFhIUOGDOEnP/kJmzZt4vHHH7c67Zra2trIz8/Hy8uL999/H5vNxh133MHGjRt58MEHrc7rFhlXzKFrd2e6jSlg/bgie2K+dunSJdra2oiIiGDr1q2kpqYyf/58cnJyqKqqsjrvmioqKrhw4QJBQUEopUhISOBnP/sZZWVl7N+/3+q869K1vbW1FYCIiAgee+wxAPbs2cOZM2cAcDqdlrVdT3l5OV9++aXxC3TSpEn4+vq67XkaSilsNhsxMTFMmTLF2IMxZcoUzp49S3V1tcWFNybjirl07dZ1TAHrx5VbchJz9OhRNm3a1GWZj48PVVVVfPLJJ+zYsYMXXniBJ554gpMnT5KXl+cWu9wrKirIzMxk7dq17NixA4DRo0dz8uRJDh06ZOx6nDBhAgMGDGDfvn20t7dbmWwoLy/nxRdfZPXq1Xz00UfAN+2fffaZ27Z37t6+fTsA/fr1A6C0tBS73U5CQgK1tbUcOXIEh8PhFgPO1b7fiYmJ2Gw28vPz2bJlC+np6TQ3N7N48WIOHTrkFt1XWzfHjx9PXFyc8fHx48fx9/dn2LBhbnXOgIwr5pIxxXzuOK7cUpMYp9NJXl4eM2bM4NVXX6W8vBzo2GUNkJKSwurVq+nXrx/33HMP48eP58knn6Strc3Skwnb2trIzs7mD3/4A8HBwTgcDnJzc/nggw+w2WykpKSwZs0a4/NDQkIICQkxtlStHujLysqYM2cOERERtLe3k5WVxV//+ldsNhupqalu2/7t7uXLl1NUVGQ8Hh4ejs1mY9SoUQQEBLBy5Uqef/55Tpw4YVkzXPv7DbB8+XKCgoLIzc1l8uTJ5OXlMXDgQN555x1Lu6+2bn77fADXL6BTp05hs9nw9vZ2i3MGZFwxn4wp5nPXceWWmsR4enpy7tw5li1bxtSpU8nKygIwdlMnJCQQERHRZeto2LBhVFVVMWDAACuSgY4tz/Pnz5OTk8Ozzz7LCy+8wAMPPGCcBT5lyhQaGhp49913jedER0eza9cuWlpaLB/od+/ezcSJE5k1axazZ88mIyODtWvXUl5eTkJCAk1NTW7ZfrXut956y/glVVtby4ULF9i2bRv/+te/aG1tZdy4cYSHh1vWfK3uvLw8ysvLCQ0NxcPDgzvuuIMZM2bg7e3Nb3/7W/bs2WPpVuq11s2rOXDgAKGhofj4+AAdW4dWXvEj44r5ZEwxn7uOK7fMJMa1Sys9PZ3Y2FgyMjL4/PPPef/9943HbTYbzz//PKWlpcbVD2VlZQwePBh/f3/L2gcOHEhSUhLh4eE4nU58fHyoq6szBvHQ0FCeeuopsrKy+OSTT4COS/Xuu+8+vL29Let2bfH4+vpy+vRpY3l8fDxjx47l7bffJjg4mLS0NLdqv173uHHjyM/Px+l0EhQUxIIFC/jb3/7GsmXLSElJobKykv/9739u1x0fH09BQQEA3t7elJeXG1f3HD58mLCwMOx2u+nNcON108XLywvoOPkxOTmZjz/+mEmTJrFhwwbLtq5lXDGXjCnmc/dxpU9fnaSUMmbdnp4d87WAgADj8enTp5OTk8PEiRPx8fHB6XQyZswYfv/737Nnzx42btyIw+Fg3rx5BAUFWdINMGTIEIYMGQJ8M2jabDYGDx4MdJyV/8gjj1BVVcXbb7/Nn//8Z+rr61m0aJEx8FvR7vrv0KFDsdvtfPrpp4waNQqAOXPmkJyczLFjx0hISKCystLS9u52z549m+TkZKqqqrj33nsJDw8nPj4em81GSEgIY8aMITIy0m27jxw5wq9+9SvWr1/Pc889h1KKmpoaXnnlFYYOHWpJd3fWTddz6urq2L9/Pzt37qR///68/PLLPPTQQ6Z130y7jCvfj9PpNL7POo0p3e12tzHlZtotHVdUH9Pa2qo+//zzLsucTuc1P05OTlY5OTldHm9vb1dKKVVRUdFLlVfqTndn06ZNU9u3b++yrK2tTTU2NqrS0tJeabyW1tZWdeDAAdXa2mosczqdRv+JEyfUvHnz1Nq1a9WlS5eMz3nllVfUkiVLLGu/2e65c+eqpUuXdnmt6/1b9bTv0/3aa68ppZRqbGxUBw4cUP/85z8t7+7sRuvmhQsXVHx8vMrPz+/V1m/riXarxpUbdXfmLuNKa2urKiwsvGK5DmPKzXRbPaYo9f3arRxX+tSemMLCQmN34pAhQ3jwwQevep29h4cHbW1t2Gw2MjIymD9/PikpKRQXF3Pvvfcas8yoqCi36nb54osvuHz5MhMmTABg3bp1REdHExkZiZ+fX5crOXpbYWEhBQUFhIeH4+/vT3x8vLFl7JrBh4SEEB0dzf79+/nwww+ZPHkyAIMGDSIwMNB4LTPbv0/34MGDjW719datWcfZe6rbz8+P6OhoU5pv1N3Z9dbNuLg4Ro8ezQcffED//v21ardqXOlOt4s7jSvZ2dkUFRUxYMAAkpKSjO+rO48p36fbyjGlJ9vNHlegj5wTc/nyZXJzc9myZQtvvPEGS5YsITQ01LhT49V+GFwn3d1zzz3cfvvtTJ48meLiYmPXtbt2Q8fJXz//+c/ZuXMnjz/+OMXFxaYf621paSE7O5vNmzezfPlyVq1ahYeHB3v37qW1tdVoV18fT33ooYeIjIwkPz+fzZs3s2vXLnbu3ElISAiAabt5e7rbrIGmp7vN0t3uzq61bvr5+aGUMm0C05PtZo4rN9MN7jGuuA5rhYWFERMTQ1ZWlvHL1PWYu40pPd1t9uSlJ9stYdo+n17U0NCgCgsLu+ymLS0tVbNnz1bnz5+/6m45p9OpGhoa1MyZM9V9992ntm7damayUurmupVSavXq1So2NlY9/PDDqri42KzcLlpaWtSRI0fU5cuXlVJKnTlzRiUmJhq7cjtzvQ+n06nWrVun5s6dqx599FG1fv16U5uVkm6zfZduF3dYN5XSt/1mupWyflzpPN7NmzdP7d69W82ZM0ctXLhQKfXN4bjOn+sOP+O6dnfuUUq/dhcPpdzoblHfwd///ncCAwMZNWoU/v7+1NbWGmf6e3h4UFVVxaxZsyguLuYHP/jBNV9n/fr1TJ061azsHukuKiqipaWFp556yrRu+KY9OjraOPlPKcW+ffuYNWsWDzzwAJGRkXh6enL33XcTExNDe3v7FVtErlm+dEv39brNXjd1bu+JbivGlat1A+Tl5REaGkpgYCDTp0/nww8/NPbEXW1PhTv8jOvQDXq3X412k5ijR4+SkZFBUFAQnp6etLW1kZaWZhzHdZ1V/Y9//IOtW7eyYsWKLmdau1xtmbt3u36YzP7h+XZ7e3s7Tz75pHHezsWLF2lubsbf35+WlhaKioooKSmhuLjYtEbp7jvdZq+bOrf3RLcV48qNul9++WUefvhhJkyYwKJFi9i3bx/Dhg1j4cKFXa4EM5uu3bq3X49258RUVFQwcuRI3nrrLbKzs4mNjaWkpISDBw8C3xzfO3HiBHfffTfQcRlkQ0NDl8fNHiR7ots1GzZ79vvt9tGjR7NlyxYOHDgAdNwy29/f3xgEXVt5Vt7bQLr17TZ73dS5vSe6rRhXrtW9d+9eoOPOtX5+fpSXl3Ps2DFqa2sZMWIEAQEBxp2QraBrt+7t16PVJEYpRVVVFYGBgTidTry9vXnkkUcYNmyYcXdG14p48OBBxo0bx4ULF8jIyOD111+3ZAtP5+4btbu25lztNpsNT09PvvjiC8LCwiy9y6R0S3d36dreF7s3btwIQGVlJUuXLmXu3Lncf//9PP3001e8J+m+NdpvRJtJjGuXZ2BgIKWlpcYv9R/96EeMGTOG5uZm/vvf/wJw+vRpTp48ybp160hKSsJut7Nw4ULLJjA6dne3/T//+Q8ANTU1nD17llWrVrFixQrjZk1WHK2Ubunu6+19tbuhoYHy8nIeffRR7rzzTtasWUN6ejrPPPMMM2fORCkl3bdQe3e47STmWt+01NRUampqutySPCoqikGDBlFfXw/AuXPnqK+vp66ujoKCAhYsWGDaTFLXbri59vPnzwMdt/V+9dVXOXToEGvWrDFOajTjckHplu7u0rX9Vun29/fn2LFjjB07loULFxIYGIhSin79+vH000+bdv8UXbt1b78pPXCFU4+pqqpSO3fuVEp13G2xs853nCwsLFSTJk1Sly5dMi77mj17tsrOzlZKKeVwOFRZWZlJ1fp2K/X927OyspRSSjU1NanTp0+bVC3d0t19urbfqt0rVqwwrbUzXbuV0rv9+3KLPTHt7e3k5uaSlpbG/PnzOXfuHF5eXsbJrtBxTK65uZlt27Yxbdo0RowYweLFizl48CBtbW04nU7jjpgBAQH89Kc/lW4T2l13ZxwwYIApfwdGuqW7r7ff6t2uCxvMomu37u09xS0mMQ6Hg7q6OubPn8/48eNZuXIl0HV3Z1FRERMmTKCiogJPT08WL16Mr68vK1euZPLkydjtdsaOHSvdfbxduqW7r7dLt3TfCu09xqpdQI2NjcburKamJnX8+HF18eJF9emnn6qkpCR16NAh43MdDofKzc1Vhw8fvuJ1qqurVXV1tXR3g67t0i3d3aVru3RLd3fp3N4bTL/Z3alTp8jMzMTHx4eBAwfy0ksvcdtttxmPt7S08Oabb3L06FFycnKueL5V93nRtRv0bZdu6e4uXdulW7q7S+f23mTqu2lubiYzM5OoqChefPFFamtr+dOf/sSePXuAjrOqvb29SU5O5quvvmLLli1dnu+6X4rZ/wi6duvcLt3S3dfbpVu6b4X23mbqO3I4HHh6epKWlkZYWBhLly7F19eXbdu2UVtbaxzHCw4O5rHHHuOdd94BoKSkhMrKSsv+AXTt1rlduqW7r7dLt3TfCu29zfR3dvToUXx9fQG4/fbb+cUvfkFzczPbt283Psdms5GamkpzczNxcXEUFBRYfsdAXbtB33bpNpeu3aBvu3SbS9du0Lu9N5k6iQkLCyMyMpI1a9YYy2JjYwkICOD48eM0NjYC0NjYyK9//WvOnz/PokWL2LBhAz/+8Y/NTO1C127Qt126zaVrN+jbLt3m0rUb9G7vdWafSbxjxw6VkJCgjh8/biz797//rVJTU42PGxoa1F/+8hez065L126l9G2XbnPp2q2Uvu3SbS5du5XSu703mX44KS4ujtjYWJYsWWIsi4iIwMfHx7j9vt1u53e/+53Zadelazfo2y7d5tK1G/Rtl25z6doNerf3JtMvsQa4ePEiTzzxBCNHjmTUqFFs2rSJuLg4XnrpJbNTvhNdu0Hfduk2l67doG+7dJtL127Qu723WDKJAaiqquKzzz5jx44dxMTEkJaWZkXGd6ZrN+jbLt3m0rUb9G2XbnPp2g16t/cGyyYxLurrPxOuG127Qd926TaXrt2gb7t0m0vXbtC7vSdZPokRQgghhLgZffcOOEIIIYTo02QSI4QQQggtySRGCCGEEFqSSYwQQgghtCSTGCGEEEJoSSYxQgghhNCSTGKEEEIIoSWZxAgh3MbevXuJjY0lNjaW06dPW50jhHBzMokRQlgiMzOT2NhYnn32WWOZ3W7nrrvu4q677sLb29vCOiGEDmxWBwghhEtUVBQFBQVWZwghNCF/dkAIYbrExETOnDlzxfLc3FxmzpwJQElJCcHBwWRmZvLee+8RFBTEjBkzyMnJobGxkaSkJJ577jlWr15NSUkJdrudZ555hpSUFOP1zp49y5tvvsnu3bupr69n6NChJCYmkp6ejs0m23BC6E7WYiGE6UaOHMnFixepr6/Hz8+P4cOHA3DkyJFrPqe2tpbXX3+dH/7whzQ1NVFYWMjHH3+Mw+HAbrdTU1PDsmXLGD16NMOHD6e+vp709HRqamqMr1FVVUVubi6nTp1iwYIFZr1dIUQvkXNihBCme+ONN4iPjwc6JjQFBQUUFBQQFRV1zee0trayatUqNmzYwNChQwGorq6msLCQd999l/79++N0Otm3bx8A69ato6amBn9/fzZt2kRhYSFLly4F4L333qO6urqX36UQorfJnhghhBYGDhxIdHQ0AIGBgdTU1DBixAiCg4MBGDRoEF9++SVfffUVAIcPHwagrq6OX/7yl11eSylFWVkZISEh5r0BIUSPk0mMEEILfn5+xv97eXldsczDwwPomKB8+3muw1Wd+fj49EamEMJEMokRQljCNYm4dOlSr7z+nXfeya5du/Dy8uK1114z9tg0NTXx0UcfMWnSpF75ukII88gkRghhibCwMADKy8tJTU3F19eX6dOn99jrT5s2jc2bN+NwOJg6dSrDhw+nqamJmpoa2traSEhI6LGvJYSwhpzYK4SwRFJSEvfffz92u53KykrKyspwOp099vqDBg0iPz+fxMREbrvtNiorK7l8+TIxMTH88Y9/7LGvI4SwjtwnRgghhBBakj0xQgghhNCSTGKEEEIIoSWZxAghhBBCSzKJEUIIIYSWZBIjhBBCCC3JJEYIIYQQWpJJjBBCCCG0JJMYIYQQQmhJJjFCCCGE0JJMYoQQQgihJZnECCGEEEJLMokRQgghhJb+Dwh6JfvvWReAAAAAAElFTkSuQmCC", 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\n", + "image/png": 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" ] diff --git a/examples/16-hierarchical-reconciliation.ipynb b/examples/16-hierarchical-reconciliation.ipynb index 2c57bd6f3e..18984f301e 100644 --- a/examples/16-hierarchical-reconciliation.ipynb +++ b/examples/16-hierarchical-reconciliation.ipynb @@ -17,22 +17,42 @@ { "cell_type": "code", "execution_count": 1, - "id": "288c82a5", + "id": "7e499de8-7d98-4188-96b2-166d212d73c3", "metadata": {}, "outputs": [], "source": [ - "%matplotlib inline\n", + "# fix python path if working locally\n", + "from utils import fix_pythonpath_if_working_locally\n", "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from pprint import pprint\n", + "fix_pythonpath_if_working_locally()\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "288c82a5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dennisbader/miniconda3/envs/darts310_test/lib/python3.10/site-packages/statsforecast/utils.py:237: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " \"ds\": pd.date_range(start=\"1949-01-01\", periods=len(AirPassengers), freq=\"M\"),\n" + ] + } + ], + "source": [ "from itertools import product\n", "\n", - "from darts import TimeSeries, concatenate\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from darts import concatenate\n", + "from darts.dataprocessing.transformers import MinTReconciliator\n", "from darts.datasets import AustralianTourismDataset\n", - "from darts.models import LinearRegressionModel, Theta\n", "from darts.metrics import mae\n", - "from darts.dataprocessing.transformers import MinTReconciliator" + "from darts.models import LinearRegressionModel, Theta" ] }, { @@ -47,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "877c48bc-31c6-49a6-805d-5b33a00e2140", "metadata": {}, "outputs": [], @@ -73,20 +93,28 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "12f0066e-7607-4e5f-a3ca-d8a36aec309a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" + "" ] }, - "metadata": { - "needs_background": "light" + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + "" ] }, - "metadata": { - "needs_background": "light" + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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\n", 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" + "" ] }, - "metadata": { - "needs_background": "light" + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] }, + "metadata": {}, "output_type": "display_data" } ], @@ -164,12 +208,8 @@ "city_labels = [\"city\", \"noncity\"]\n", "\n", "tourism_series[\"Total\"].plot(label=\"total\", lw=12, color=\"grey\")\n", - "sum([tourism_series[region] for region in regions]).plot(\n", - " label=\"sum regions\", lw=7, color=\"orange\"\n", - ")\n", - "sum([tourism_series[reason] for reason in reasons]).plot(\n", - " label=\"sum reasons\", lw=3, color=\"blue\"\n", - ")" + "tourism_series[regions].sum(axis=1).plot(label=\"sum regions\", lw=7, color=\"orange\")\n", + "tourism_series[reasons].sum(axis=1).plot(label=\"sum reasons\", lw=3, color=\"blue\")" ] }, { @@ -209,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "c7478d62-efc4-47d5-9936-2272560e7b9d", "metadata": {}, "outputs": [], @@ -226,12 +266,12 @@ "\n", "# Fill in grouping by (region, reason)\n", "for region, reason in product(regions, reasons):\n", - " hierarchy[\"{} - {}\".format(region, reason.lower())] = [reason, region]\n", + " hierarchy[f\"{region} - {reason.lower()}\"] = [reason, region]\n", "\n", "# Fill in grouping by (region, reason, )\n", "for region, reason, city in product(regions, reasons, city_labels):\n", - " hierarchy[\"{} - {} - {}\".format(region, reason.lower(), city)] = [\n", - " \"{} - {}\".format(region, reason.lower())\n", + " hierarchy[f\"{region} - {reason.lower()} - {city}\"] = [\n", + " f\"{region} - {reason.lower()}\"\n", " ]" ] }, @@ -245,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "cff59853-dac0-4cd4-98a1-ffa5ac021201", "metadata": {}, "outputs": [ @@ -277,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "cebbf377-9668-436b-b172-c59f451623f0", "metadata": {}, "outputs": [], @@ -297,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "04f4fb58", "metadata": {}, "outputs": [], @@ -315,19 +355,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "c34e663a", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/julien/unit8/darts/darts/timeseries.py:4079: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", - " if isinstance(time_idx, pd.Int64Index) and not isinstance(\n" - ] - } - ], + "outputs": [], "source": [ "model = LinearRegressionModel(lags=12)\n", "model.fit(train)\n", @@ -344,20 +375,28 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "2116be09", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" + "" ] }, - "metadata": { - "needs_background": "light" + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EhATqgGkBd+/eFb0JxXEcunTpkv81BX9si7Zrg/3794um+xHyOgr+2ABtudWUbmJ+p0+fFl3aXrFiRWYrTer4ZX10Cf4AoLo/hJgYBX9s07Vr15jpegMGDMB3330nOh4eHo7c3FxTTI3ogJXy1bdvX/B80csFFxcX5nkOrUo3P9aqnxYtWhRJ9aLgj23R9npKTk6m1FmiMwr+2ICmTZvC3t5e41hSUhIePHhg5hkRVspXly5dRO+IAWCmfd26dUuvLimk5O7fv4+4uDiNY+p6P2oU/CHEdKKiogyqexYVFYWsrCwTzIjoavLkyaI3ojiOw08//YSGDRuK1hrJycmhtGcLycvLw/79+0XHX0/5UmN1o6XUL/PTVu+nMAr+2BZdsjz27t1rhpmQ0oCCPzbA0dERQUFBouOU+mV+htT7UXN3d0f9+vU1jgmCQOlDZsaq99OkSRO4ubnlf80K/ly6dImW3RJSAqyVI0FBQahbt67GMYVCQc0PLOj06dPYvXu36PjIkSMRFBQEnueLrKR8HaV+WcapU6dEbzo5OTkVSRcqjIo+Ww+ZTMasm6Vv8IcyCqyHQqHQKTBOdX+Irij4YyOaNm0qOkYfsub17Nkz5oWGtuAPQKlf1kTXlC8A8PHxKRIMKiw9PR2xsbFGnBkhZYcgCMyUr6FDhzJXTdL7pmUIgoDvv/9edNzOzg7Tp0/P/7pz586i21LRZ8tgpXyFhYVp7FwKsFf+XL58mVbjmVFERAQyMjI0jpUrV65YrckaNWqgfPnyGrdPS0vD48ePjT5HYpgbN26I/m0LO3fuHJ4/f26GGRFbR8EfG0G51dbjyJEjondFfH19UadOHa37oKLP1oO18uf14A/P88zVP7RqixDDnD9/Hvfu3RMd1xb8obo/lnHgwAFm84OPP/64yGciK/hz7tw5nS5yiPEIgqBXi/fCPD094eXlpXFMLpfTa9KMWClfnTt3hp2dXZHvUccv26HrNZ4gCMznASFqFPyxEawuQ1euXKE7LGZUkpQvNW0rf2jJrXk8ePAAd+/e1Tj2er0fNar7Q4jxsVb9NG/eHL6+vsz3TbrQND+lUslc9ePs7IwffvihyPf8/PxQrVo1jdvL5XKcOnXKqHMkbNevX2d2iOrTpw/z8ZT6ZR30qfejRsEf26BPaQ9K/SK6oOCPjahWrRpq166tcUwul9NFp5loi6zrGvxp0KABHB0dNY49ffoUCQkJBs2P6Ie16qdx48Zwd3cv9n0K/hBiXAqFAhs2bBAdHzZsGADVClixYvr37t1DcnKySeZHNNu8eTMzBXrcuHHFAj0cxzHThSj1y7xYq35atWolGqhTYwV/qOizeaSkpDBXHVPwx7bpE0Sllu9EFxT8sSGsVCEq+mweMTExot3VeJ5Hp06ddNqPnZ0dczUXdT0xD33q/aixgj+RkZFQKBQlnBUhZcupU6fw6NEjjWMcx2HIkCEAVCtJGjRoILofSrs0H7lcjilTpoiOV6hQAV9//bXGsTZt2og+joI/5qWtxbs2rEDe2bNn6ULUDFilCOrWrQsfHx+NYxT8sX7Jycl61ZJMTk7GpUuXTDgjUhpQ8MeGsD5kaXmtebBSvlq2bKlxpYgYqvtjeYYEf2rXro1KlSppHMvKykJ0dLQRZkaMSRAEXLlyhVJkrRSry1eHDh3g6emZ/zUVfbYO//77L2JiYkTHv/nmG9HPQ1bw5/Lly0hJSSnp9IgOHj16xEyXZNX7UWvQoAFcXV01jqWmptLnoRloS/niOE7jGAV/rJ8hN/Yp9YtoQ8EfG8I6YTpz5gzViTEDY9T7UaOOX5b18OFD0Xo/HMehQ4cOomMtWrQQ3S+tPrAusbGxCA4ORpMmTdCkSRP4+fnhwIEDlp4WeUUmk2Hz5s2i40OHDi3yNRV9trycnBxMmzZNdLx69er4/PPPRcdr1qwJX19fjWOCIDCD8sR4du/eLTpWt25dBAUFad2HVCplnstQ6pdpCYLA/DwTS/kCgFq1asHFxUXj2LNnzyiN1gpQ8IeYAgV/bEjDhg3h5OSkcezZs2eiF7LEOGQyGY4dOyY6zvqQ1YR1whQZGYm8vDy99kf0w6r306RJE+YqLqr7YxtevnyJbt26FbmLmZiYiCFDhiApKcmCMyNqhw4dEl3pIZFI8Oabbxb5nraiz3QTxPR+//13PHz4UHR8ypQpou3B1Vgp0pT6ZR7aunyJrRh5HRV9tpzbt2+LliKQSCTM7no8zyMgIEB0nFb/WJ4hrx9q+U600Tv4Ex0djQ8++AChoaHo379/kQ+PVatWoWvXrujcuTOWLFlS5CTsxo0bGDZsGNq1a4fRo0cXye/PycnBlClTEBISgt69e2P//v1Fjrlr1y706tULoaGhmD59OmQymSE/q82zs7Nj3vWkD1nTOn/+PF6+fKlxrFy5csyLEk1q1aqF6tWraxzLzc3F1atX9Z4j0R3r7nJoaCjzsRT8sQ0///yzxvbh6enpWLJkiQVmRF7H6vLVtWtXVKlSpcj3AgICRO9Wp6SkIC4uzqjzI0W9fPkSs2fPFh2vW7cuRo0apXU/rIvSI0eOGDQ3orvMzEwcPnxYdFyXlC81KvpsOayUr1atWmktRUCpX9ZLJpMxV5KXK1dO4/ep5TvRRu/gz9SpU9GuXTscO3YMc+fOxfz585GQkIDw8HBs3rwZq1atwsaNGxEeHp4fGMrLy8OkSZMwdOhQHD16FMHBwZg6dWr+PpcvX44XL15g7969mD17NubMmZPf7Sg2NhaLFi3C/PnzsWfPHiQlJWHlypVG+vFtD9X9sRxWylfHjh1hZ2en1/44jqPULwsypN6PGiv4c+XKlTIboLYm9+7dw8KFC0XH169fT8VILSw7Oxvbtm0THVd3+SpMIpEwX3+U+mVaCxcuxLNnz0THZ8yYAXt7e637Yb3HRkdH08o8Ezt8+DBycnI0jrm5uYmmPWvSqlUr0VVCd+7cofQhEzKkxbtQ6HOPgj/W69q1a8jOztY45lm9Et5+u/jno9revXtNNS1SCugd/Hn8+DF69OgBnufh7++POnXqICEhAXv37sWbb74JT09PVK5cGSNGjMjPO7x06RKcnJzQv39/ODg44KOPPsLNmzfzV//s3bsXo0ePhqurKxo1aoSQkJD8N7T9+/cjLCwMgYGBcHV1xahRo8p0PiOr7g91/DItY9b7UaOiz5aRmJgo2kGBVe9HrUaNGsxVWzdu3CjxHEnJfP3118jNzRUdv3//PgXMLWzv3r3IyMjQOObg4IABAwZoHKO6P5bx7NkzLFiwQHQ8ODhYY8BOk6pVqzI7t1Hql2nt2rVLdKxXr17Mm1lCTjqE3IJV0G5ubsy/Jb3PmkZubq7epQiUz+8i79dg5C6uDyE9iYI/VkzsdTO0AY9bozPxo+95SEWu4g8cOEA3t4goqb4PGDJkCPbu3Yv3338f0dHRePLkCYKDg/H777+jV69e+dv5+flh2bJlAIC4uLgixf2cnJzg6emJuLg4uLi44Pnz50XG/fz88i+e4uLiigQ86tWrh8TEROTk5MDR0bHY/PLy8orVSpFKpTrdibJW6hewUqlkrhSJiopCWloaypcvb66plRkvXrxgBmO6dOli0Bstq3DwuXPnDH7zLvycIcWxTpgaN24MNzc3rb+75s2bixbMPHfuHBo2bFiiOZpbaXrOnDhxAlu2bNG63X///cdcTUnYSvqc+e+//0THevbsiXLlymncN2vlT0neNwnb7NmzRVOfAWDmzJngOI75+y/8nOncuTOioqI0bnfkyBG8/fbbJZsw0UipVDKDP3369NH4N1QmRUJxbDqEe8ch6T4XkhZj8sfatGmDa9euadxfeHi4Tm3jxeZa+P+kwOnTp0W7V7q5uaFZs2ZFfm+CIEC+8xMIqarUWNnFv+DvL/4au3nzpk3+3kvLc0ZT8KeKM7C4lxRSTkCFjFvoWs8e+28Xrw+anJyMCxcuMK8xSIHS8pwBVLW8tNE7+NOmTRv8+OOPWLFiBQDg+++/R8WKFZGVlVWk3aOLi0v+m1J2dnaxHH0XFxdkZ2cjKysLEomkSCCH9Vj1MbKzszUGf/755x/89ddfRb43ePBgDBkyRN8f1eqoi7rVqVMH8fHxxcYFQcDOnTv1Wq5LdHPo0CEoFAqNYx4eHnB0dMxPVdRH1apVwXGcxiKld+7cwZUrV1ChQgW996smVgiwrNuzZ4/oWJMmTXT6W4p1qwFUwQd9C4BbC1t/zigUCnz22Wc6bbtx40aMHz8eUqneH4WkEEOeMy9fvmS+Drt27Sr6OqxRo4bo4y5duoTY2Fi903AJW1JSUv4NPU2aNm2Khg0b6vw5+ODBAwQHB4uOHzx4EPHx8ToXHSa6u3z5Mp4+fapxTCqVIigoSOPfUZryDFXuqlZAZ13ZiJSqPfPH6tevL3q8Y8eOGXR+VJitfy6ZAqtLYuvWrZGYmFjke053t8M94WT+1zk3doPzGg4HBweNq2QfP36Ma9euwc3NzXiTNiNbf86cOnWq2PemdZHCzbHgPfH9dlWw/3Zise0AVWp71apVTTa/0sjWnzOAqu6eNnqd8aalpWHChAmYNm0aQkJCcO/ePXzxxRfw8fGBs7NzkeXbmZmZ+d0enJyckJmZWWRfmZmZcHJygrOzMxQKRZGVPKzHqo8h1vXq/fffx/Dhw4v+kKVg5c+DBw9Qq1Yt8DyPkJAQjcEfQFXnYsSIEeadYBnAKr7cvXt31KlTx+B9BwUF4fr16xrHHj16hMaNG+u9z9efM6SoS5cuiY716dMHtWvX1rqPLl26YPHixRrHbt++rdM+rElpec6sWLFC5+Xqz58/R2xsLLp3727iWZVOJXnOrFmzRjQtz9XVFe+++65oxygvLy9Ur14djx8/LjaWl5eH9PR0NG3aVK/5ELZZs2YxO1DOmzdPp8/Bws+ZN954Ax9//LHGu61JSUlQKBTw8fEpybSJBn/++afoWGhoqGgKl+DlBVl4XSD1HhweX4BX1fLgnFQ3p/r164cJEyZofNz169dRvXp1ODg46D3X0vK5ZAqsFNf+/fsXOQcRslMg21Q0ZdP++XXUreaKgIAAXLlyReN+MjIybHIVs60/Z5KSkooF7xpX5/Bek6I/TxevHHAANPW4jIiIYKbpkgKl4TmjD72CP4mJiXB1dc1v0enr64tmzZohMjISdevWRWxsLNq3bw8AiImJgbe3NwDA29u7SFHH7OxsPHz4EN7e3ihfvjwqVaqE2NjY/LtArz+2cG2OO3fuoGbNmhpX/QCAvb29TQd6WHieB8/zaNeuHVavXq1xm7Nnz5aJJ665sbpidOvWrUS/89atW4sGfy5cuIDevXsbvG/1c4YUSExMxJ07dzSOcRyH0NBQnX5nrLojUVFRyMvLE32fsma2/JxJT0/HlClT9HrMhg0b0LNnT+0bElGGPGc2btwoOta/f/8iK4k1admypWir6osXLzJTw4h+YmJisGrVKtHxbt26Mbt3acLzPCpUqIAWLVqIplQfP34c9erV02u/RDtWyle/fv2Yr2Wlfz8ozi4BBAUQewB8I1XakLe3t2hANjc3F1euXGHWrNTGlj+XTCE5ORmRkZGi4+rarGqyI1OArFeF2qWOgDwHgADEHUFgYKBo8Cc6Ojr/us7W2PJzRtN74oKeUvDqlZD2rkBeBlyEDLTy5BDxsHj459y5c0hJSUHlypVNPd1Sw5afM/rQ6yesXbs2MjMzcfLkSQiCgPj4eFy4cAG+vr7o1asXtmzZgsTERDx79gxr167NP6Fu1qwZsrOzsWvXLuTl5WHlypUIDAyEh4cHAFVxuRUrViAzMxNRUVE4efJkfgHdHj164PDhw4iOjkZGRgb+/vvvMn+izvoAjYiIKBU5i9bk/v37iImJER3v0qVLifZPHb/M68SJE6JjjRo1QsWKFXXaT9WqVeHl5aVxTCaTidY/IKbz008/iaYziNm2bZto1xtiGs+fP2d2qRk6dKjWfVDRZ/OZMmWKaNozAGbrd21YQSMq+mx8d+/eZTYk0FabR+JfMK6ILggicRxHLd/N6PDhwxrLBQCqG/OFUz+U989AEfmP6gv7crDrvzx/THHnABV9tkKvN/AZEsyjrZfqkv2pwg3S7nPzx0a2cte4D2r5TsToFfxxdXXFzz//jD/++AOhoaH49NNPMWTIELRt2xbt27fHoEGD8M4772Dw4MFo164d+vXrB0C1GueXX37B2rVr0alTJ1y9ehUzZszI3+/HH38MV1dX9OjRA99++y2+/fbb/OXDvr6+GDduHMaPH49evXqhWrVq+OCDD4z3G7BBgYGBokWdX7x4gVu3bpl5RqUbq8tX48aNS5xTqy34I/YBTwzDCv5oa/H+OtbqgosXL+q1L1IysbGxoml4LOnp6dQW1cy2bNkCuVyucaxChQo61ctiBX8oaG48kZGRzFVab775Jpo1a2bw/rUFf+jzz7hYq34aNGigtV4EV6sN4FwFAKCMPQBBVtCKmlU8n4I/xqVri3dBIYNs9+f5X0s7TwMfOAhwUF1DKO8eRoC/eL0mCv5YRuFiz852wKyuBYk6sV7vQxIwAOAkAIB+/hLR/ZTl7thEnEEFn8VWnrz//vt4//33NY4FBQVh/fr1GsccHR3x008/iR6zb9++BncKKI0kEglat24t+uZ/5swZBAUFmXlWpZcpWrwXFhgYCFdXV40tj1NTU3Hnzh34+fmV+DhE5fjx46JjhgR/tm7dqnGMgj/mNXHiRMhkMtHxmjVrFsuhV1u3bh0GDRpkqqmR16xbt0507I033tApdZvVxeTWrVtIT0+nzpdGMHnyZNExnucxc+bMEu2/Xbt2sLe311hP6OnTp7hx4wazMDTRj1iqJKB91Q8AcLwEkvq9obi8CpBlQRl3FJL6qtR01sqfM2fOQBAEKuBtBNpWdBSuYac4+z8IT1UrvTiPppC0HAOOl4D37gLlrW1AdgqaVBNf1UfBH/PLzc0tUpfyq3YSeLqpXjf7YhRo8t4YcM6VwNfpAOW946gsfYkG1ThEPSkeKFe3fC8LqUxEd/RssFGs1K/XlwsSwymVSma9H2MEfyQSidbWxcQ4kpKSRFP4OI7Tu1MerfyxDkeOHMGOHTtEx0NDQ5mFD3fv3o309HRTTI28JjExkbn6btiwYTrtx93dXbTDkCAIzKLuRDcnT57E/v37Rcffe+89+Pv7l+gYTk5OzBUjlPplPKmpqTh58qTouHq1vja8f8F2yuiCYFKTJk1E69w9ffoUd+/e1XGmhOXmzZtISkrSOCaVSvNvYilT4yE//urGOsfDrs+v4HjVKhG+XkGAqEbOTdHuiA8ePKDPRjOLjIzMD4Z7uQHj26r+ZjKFgPmXK+YX8uYDBuQ/ZmCQ5r9fcnIyfRaSYij4Y6NYJ0uFlwuSkrl8+TKeP3+ucczBwcFohfBat24tOkbBH+NhXXQ2bNhQ53o/aqx0hxs3biArK0uv/RH9yeVyjBs3TnSc4zgsXrwYffv2hYuLi8ZtcnJymMEjYjybNm0STeWpXr06QkNDdd4X1f0xHUEQ8N1334mO29vb48cffzTKsVh1844cOWKUYxBVCohY7aZq1aoxV9MVxnt3BuxU76WK23sgKFQpnPb29sx9UOqXcbBW/bRp0wbly5eHIAiQ7x0PyFVpeZIWY8DXLDhfkfgWSq2NO8xcXR4dHV3ySROdFb6B/3OYFE52qlU/y84pUD2wff7qucL1t4Y21XxuA1DqFymOgj82qlWrVqLLZ2/fvi0asCD6YaV8dejQAU5OTkY5DhV9Ng9jpnwBQMWKFUVbESuVSly+fFnvfRL9rFixQrRbHgCMGjUKjRs3hrOzMwYMGCC6HSsViRgP6/c8ZMgQSCTi9QteR8Ef09mzZw/zRtLYsWNFC97ri1X35/jx46L1oYh+tKV86Zoawtk5FqwcyXoG4UHBxaq21C9ScrrU+1He2gHlnVcX/a4ekHYuGqjlytcAV03Vwl1IuoTWDbxF90mpX+alfp2E1OEwKEj1efgkQ8DPJxVFbvxz5WuCq6n6DKzrkgXvCpr3RzUNyeso+GOj3NzcmHV9IiIizDib0svU9X7UWMGfK1euIDs7W3Sc6M7YwR+AUr8sKS0tjdnavVy5ckXqybFSig4dOoRnz54ZdX6kqLi4OGZQRpcuX4VR0WfTUCqVzFo/rq6u+P777412vBYtWsDV1VXjWHp6OgXRjSAvL4+5AkDXlC81sa5f1PHLtHJycpgrmLt16wYh9yVk+77K/55dz/ngHN2KbVs49atHfc1pQwAFf8xJEAScPXsWEg6Y36OgLO/UI3Kk5xYv+SEJ6J//7/4Bmm+cnD9/ns5tSBEU/LFhlPplWllZWQgPDxcd16Ujja48PDxQq1YtjWNyuZxOfo3g0aNHzHo/ISEhBu2Xgj+WM2PGDOZJzZQpU4p04wsLCxNN7ZPL5di8ebPR50gKiDV9AIDatWsz0181adSokWhx6MTERNEC34Rtw4YNuHbtmuj4hAkTUKVKFaMdz87Ojvn+S6lfJXfy5EnR2i1OTk7M1DtN+Ho9AF51caqM3pGfysmqR3njxg2kpaXpdRxS1OnTp0VvBlaoUAHNmjWD/NgM4KXqvY/37abq7qWBpFDwp0l58c9RCv6Yz/3795GUlIQPmvFoUE11iX4pSYk1V5Swt7dH06ZNi2zPFwr+DGmkOROBWr6T11Hwx4ZR0WfTOnXqlMYOJABQpUoVNGzY0KjHo7o/pmXsej9qFPyxjNu3b+PXX38VHffx8cEXX3xR5Hv29vZ48803RR9DqV+mxQr+DB06VO9OQA4ODmjcuLHo+IULF/TaHwFkMhlzNV3FihUxYcIEox9XW8t3UjKsFu9hYWFwdnbWa3+cUwXwdVT1uYS0BAhPogAAlSpVYhYBp3PTkmFdxHft2hXc0ygozi1TfUPqCGmvxaLvq5xna8BBtSKoevYtSETefin4Yz5nz55FBUfgx04Fq34m7pNDANC0adNiBdX5Sr7gqqqyQBpXlcFD8wJKqvtDiqDgjw1jrfw5d+4c5cmXECvlq2vXrkZvnUh1f0yLlfKlT5HZ1zVt2pRZf4s6ZZjGV199xXyPW7BgARwcHIp9n5X6dfLkSTx48MAo8yNF3bhxA1FRUaLjunb5eh3V/TGuv//+m9mV6bvvvoObW/EUkpJiBX/Cw8ORm5tr9GOWFYIgMOv96JvypSbW9Yt1bkqpXyXDbPEe1hWyXZ8BghIAIA35DnxF8Vo+nEQK3qcrAEAiS0crL81pQ/Hx8cjMzCzBrImuzpw5gymdpKjkrDqnXHdNgYiHqlV1Yq+rwq/Dvv6ar0v2798PpVJp5NkSW0XBHxtWr149VKpUSeNYVlYWc9k20c5c9X7UKPhjWqao9wMA5cuXZ7acjoyMNHjfRLMDBw5gz549ouNdunQRvaDp0KEDatSoIfrYDRs2lHh+pDjWqh9/f3+DV1JS8Md4srOzMWPGDNHxmjVr4tNPPzXJsRs1aiS6+jI7O5vqGJbA9evXER8fr3GM4zj06dPHoP1K/Asep7hVEPyhos+m8eTJE1y5ckV0vJ/XcwhJqtXGXGV/SNqO17pPvl5B+YKhLTRfTwiCgNu3b+s3WWKQx1HH8FFz1aV5Zp6AKYcLbnCJZXtICrV8HxAo1bjNs2fPaCU6yUfBHxvGcRylfpnIkydPmMEzUwR/mjZtKtrpJj4+Hk+ePDH6McuKR48eMU9eDK33o0apX+Yjk8kwfrz4SS3P81i0aJHoaiyJRIK33npL9PGU+mV8giAwf6/Dhg3TO+VLjRX8uXDhAt3t1MPSpUuRlJQkOj516lSjdbh8Hc/z6NSpk+g4pX4ZjrXqp1WrVqhWrZpB+1V1G1K1dheeXIMy9R4AdvDn3LlzkMlkBh2vrGPdkGzX2BflIhflf23X51dwUs310Aor3PK9a11BdDtK/TK9rMxMvO8VAymv+iz8JVyBxJcF42Irf7jqDcG51wag6hBWwVHjZpT6RfJR8MfGUdFn0zh8+LDomL+/Pzw9PY1+TGdnZzRq1Eh0nFb/GO7kyZOiYw0bNhRdQacrCv6Yz/Lly3Hr1i3R8Y8//hgNGjRg7oOVYhQZGSlaGJwY5tKlS8xUIn27fBVWr1490TSk9PR0umOtoxcvXmDOnDmi476+vnj//fdNOgdW6hcVfTacKVK+1Ap3/VJG7wYA+Pn50ap0E2ClfM3vaQ/kqlLMJY3fAV+ng0775Mp5gKveGABQ1zkd1Vw0b0fBH9O7u38JOtVVBX7upQpYckaRP+bl5SW6YpnjOPCvVv9IOKCnn+ZLewr+EDUK/tg4Cv6YhrlTvtQo9cs0TJXypUbBH/NISUnBjz/+KDru7u7OTFtRa968OXx8fETHafWPcbF+n02bNoWfn5/B++Z5Hi1atBAdp9Qv3cyfPx8pKSmi4zNnzoSdnXg7aGNgdZw6d+4cMjIyTHr80ujRo0fM10BJgz+F640oXtX94TiO6v4YGatjUxdvDo3tXwXXnSpBGjZbr30XTv0K89V8WUjBH9MSZDmoer2ggcW3B+XILYj9MLM8gKIt3wcEaP4bUst3okbBHxvXvHlzZqrQo0ePzDwj2ycIAgV/ShlTFXtWa9y4sWgB8Lt37yI1NbXExyDAtGnTmBeoP/74IypXrqx1PxzHMVf/rFu3Lr91MSkZpVLJrKNkaKHnwljvmxT80e7JkydYtGiR6Hjjxo0xZMgQk8/Dz89P9O62XC5HeHi4yedQ2uzevVt0zNvbG4GBgSXaP1/FH1wlVfBWuH8aQmYyACr6bGxRUVEaU/8dpcD/ehcEZaXdZoNz0f4ZWJikXo/8f3en4I9FKM4ugZugOk88FqfEzuii6cqs1xPwqnObiyp9M8xXAmcNcXpq+U7UKPhj41xcXJitbqnuj/5u3bolWvdAKpUaZaWIGG0XMQqFQnScaPb48WNER0eLjpe03g+geh0GBQWJjtPqn5K7efMmfvvtN9Hx+vXr61WMlhV0uH37NrOwJtFdeHg4EhMTRceNEVSgos8lM3v2bGY3n1mzZhm9u6UmHMdR6peRsVK++vbta3CtrcLyV/8ISihi9gKgos/GJnbR/nV7Cbwrqv6GnFc7SBq/o/e+uZotAUd3AEAXH15jy/e7d+8iJydH730T7YT0RMhP/QIAkCsFTNxfvIuptpU/HM/np2A6SoFuIkE8Sv0iAAV/SgXWmwJ9yOqPteqndevWKFeunMmO7efnJ1q/4uXLl8wgBtHsxIkTomMNGjTQaaWILij1y3QEQcCECROYwc+FCxfqlZYSGBjI7DBFqV/Gwery1b59e3h5eZX4GKy0r6tXr9JFC0NCQgL++OMP0fH27dujZ8+eZpsPK/WLij7rJzMzk1m/sKQpX2pF6/7sAqD6PBR7P3748CHu379vlGOXFZqCP36VOExs/2rlPy+FXZ+lBgXzCrd8r+DEoZVn8X0olUqqhWciskOTAZkq+P7XRSVuJhdddezk5MS8ya/GF0r96k8t3wkDBX9KAdZyQFr5oz9LpXwBqvoVlPplXKzgjzFXcVHwx3T27t2LAwcOiI53797doAtU1uqf9evX00lSCclkMmzatEl0vCSFngvz8PBArVq1ROdAq7jETZs2DXl5eaLjP//8s1FWh+iKtfLn8uXLzLRPUtThw4dFA59ubm7o0EG3osDacDVbAK4eAADl3cMQ8jLh5OSEpk2bij6GbkzqLjs7W2PTiv/1lsL+1TIdSdvx4KsGGHwMSb3u+f/uVo9Sv8xFef8slFGqGyTPswTMPFZ81Q8rkFoYXycUcFDdPO7px8NOw5+RWr4TgII/pQIr+HPx4kXk5uaacTa2LS8vj1kfxtTBH4Dq/hibqYs9q1HwxzTy8vIwYcIE0XGJRIKFCxcadIHKCj48ePCALlBK6OjRo6IFJnmex+DBg412LEr90t/NmzexevVq0fFevXqhffv2ZpyRqquNr6+vxjFBEJjv56QoVspXr169jFbAW5Vy0kf1hTwHyljVDTRW6hfV/dHdqVOnip3Hv92QR2jdV5dw7nUgDfmuRMfgC7V8F0sZouCPcQlKJWT7J+Z/PeOYHKkaYrXa6v2ocVJ78H6qm2Bujhw61tV8TkSpX4SCP6WAl5cXPDw8NI7l5eXh8uXLZp6R7YqIiBCtfeDm5sZMLzAWCv4Yz5MnT5htwY1R70etYcOGkEqlGsfu37+Pp0+fGu1YZcmyZcuYy83Hjh1rcNHSOnXqMNNmKfWrZFi/vy5duqBq1apGOxYVfdbflClTmKvbZs2aZcbZFGCt/qHUL90olUpmsWdjpXypaer6RUWfjeP1Va8VHIE53QrONex6LwFn71yiY3Cu1cB5NAEANPHgUd21+DYU/DEuxZXVEJIuAQBiXzhg5SXN78Xa6v0UJnnV8h0A+gVobgZEwR9CwZ9SQFtbTbp7rTtWJfxOnTqJXtwbE+sOdlRUFLW71QMr5Ss4ONho9X4AwNHRkVlDhlb/6O/Zs2eYPn266HjFihUxbdq0Eh2Dlfq1adMmyOXFl2ET7XJycrBt2zbRcWN0+SqMVv7o58KFC9i6davo+NChQ3WqM2EKVPS55M6fPy96w0EqlaJHjx4axwylSjkpDwBQxuyFoJAxV/5cvXqVzmV09Pp56U9hUlRxUa3quO/ctEjKVknwhfajqeU7BX+MR8hJh/zI1PyvP9+RCYVIg1F9gj+8bxggdQIA9K3Pg9ew+IdavhMK/pQSFPwxDkvW+1GrUqUKfHx8NI4plUpcunTJLPMoDcyV8qVGqV/GNXXqVLx48UJ0fPr06ahYsWKJjjFkyBDRTkbJycl0sWmgffv2IT09XeOYvb09Bg4caNTjNWvWTPTveOfOnVJXK0YQBMhkMmRkZODZs2dITExEXFwcbt68icuXLyMiIgLHjx/HgQMHsHPnTmzcuBFr1qzBihUrsGzZMnz++eei+5ZIJJgxY4YZf5qiOnXqJDoWHR0t2o2TFGClfIWGhsLd3d2ox+Ok9uDVLcNz0qBMOIXq1avD29tb4/ZKpZJWMusgKSkJ169fz/+6TS0OHzRVrehIzxXgOmCp0Y4l8S0I/mhq+X7nzh1mfTCiO/nJn4FMVXD2WaV2OH5PczMLHx8fvVbIcvYuqgAQgGquHFprKN4tCAKzhiIp/Uy/jIGYBSsyfPbsWQiCYNaijbYoNTWVeYHerVs30TFja9WqFe7evatx7Ny5cwgNDTXbXGyZuYo9qzVv3hx//vmnxjEK/ugnKioKy5cvFx0PDAzEmDFjSnycatWqoUuXLqKB33Xr1qF7d+PcWS1LWClfPXv2NPrFp6urKwIDA4tcKBV24cIFq/47XrhwAf/99x/u3LmDnJwc5Obm5v9f7N+CIHKruIQ+/PBD1KtXzyT71kXVqlXRoEEDREVFaRw/duwYhg8fbuZZ2RZW8MfYKV9qEv9+UF7fCEDV9Uvi3Rlt27ZFXFycxu1Pnz7N7O5Git6QlPKqIs9qy29WwRQ/8aLa+uI8WwKOFYCc1PyW74VXo8jlcsTGxhqcZk1UlM/uQBHxKmgnccCW1MYANKez6lrvpzBJQH8oX6Ve9g/gceZB8cDSvn376D20DKOVP6VE06ZNYW9vr3EsKSmJ2mrq4OjRo6L1D+rUqSO6GscUqO5PyT19+pS5TNmY9X7UaOWPcQiCgPHjxzPrkSxatMhoaZisFKStW7ciOzvbKMcpK16+fMmsN2KsLl+vs9XUrzVr1qBNmzZYvHgx9uzZgyNHjiA8PBwXL15EVFQUYmJicP/+fTx58gRpaWnIyckxWeDHwcEBU6dO1b6hiVHql+Hu3r2LGzduiI737dtXdKwkeN9ugER1HqqI3glBEJipX7QqXbvCKV9ftJYguJrqsu1SkhJpPm8a9VgcLwHvq2r57u7IoXWt4jeMKfWr5OQHJgFKGQBA0m48DkSI16XUJ+VLjffrBfCqc6N+/prr/hw4cIC6mZZhFPwpJRwcHJgXnvQhq522lC9zrpxiBX8iIiLMNg9bxlr1ExQUhCpVqhj9mMHBwXBwcNA49ujRIyQmJhr9mKXRzp07mRd4ffr0MepKvIEDB4oGz1++fIm9e/ca7Vhlwc6dO0UDZs7Ozia7+LTFos+pqan44osvoFBoXvZvbp999hlq1qxp6WkwV4QcOXLEZMGv0mDXrl2iYw0aNEDdunVNclzOsTx471dBu/RECEmRzODP2bNnreZ5b42USmX+eamXGzC5o+pCXqEU8PluOcK6GbduE1A09UtT1y8K/pSM4s4BKO+8Krhcvib4tl/h7NmzotsbsvKHc6qgqsEFoE4FDo2qF792oZbvZRsFf0oRbalfhM0a6v2oNW7cmLmS6+HDh2adjy0yd70fALCzs2MWSaUPW+1yc3Px1VdfiY7b2dlhwYIFRj2mu7s7evXqJTpOXb/0s379etGxfv36wcXFxSTH1bbyxxoDBhs3bkRaWpqlpwEAKFeuHL799ltLTwOAamWmWA2n+/fv4969e2aeke2wRMqXGu9fENhVRO9EYGAgypcvr3Hb9PR0CiYwXL16FcnJyQCARb2kcLZTXcQvv6DAjed2Jlm9rK4XAwDd61Hwx5gEeR7khVq724XNRsy9h6Lv/66urggODjboWHxA//x/9w/Q/D5KXb/KLgr+lCJU9NlwcXFxonnpHMcxl6CbgoODA5o0aSI6Tqlf2lki+ANQ6ldJ/e9//xOtdwUAn3/+Ofz8/Ix+XFbq1+7du0WLF5OiUlJSmMUkjd3lq7CgoCA4OTlpHHv69KlVpj//3//9n6WnkO+HH34wagfEknBzc2O+l1Lql2apqak4efKk6Lipgz+S+n0AqIIUyuhdkEgkzBuT1PJdnDrlq78/j15+qlU/j14KmHZUgQ4dOsDZuWTt3TXhXKshq3x9AECj6jw8Xmv5fuuWeIoSYVOc/x3C8zsAAK5WG/DBQ5g35lu2bAmJRHPaljYS/75Qvw77+Wu+1KcVzWUXBX9KEdYH7JUrV5CZmWnG2dgW1qqfZs2aoVKlSmacjQrV/TGcJer9qFHwx3BPnjzBzJkzRccrV66MKVOmmOTYffr0gaurq8ax3NxcbN++3STHLW22bt0KmUymcczd3d2kRZft7OzQtKl4AVRre9+8d+8ewsPDLT0NAMCYMWPw9ddfW3oaRbBSv44e1Vwgtazbt2+faCpV9erVmZ9PxsC5VgNXqzUAQEi+CeXzWOaNSQr+iDt48CBc7YEFPQtq203cL8fLPNM2ILEP6J3/79dbvt++fRtyudxkxy6thIwnkJ+Y9eorDnY9F4LjOOaNeUNSvtS4ch7gPFXXEEFVefhWLJ76deHChfyVZaRsoW5fpYiHhwfq1KmD+Pj4YmMKhQIXL16kLlEirCnlS43q/hiOdeczKChIr9aZ+tIW/KHOe+KmTJmCly9fio7PnDnT6F2i1JydndG/f3+sXbtW4/i6devwzjvvmOTYpQkrRW7QoEGiNbGMpWXLlqIXlOfPn8eQIUNMenx9/PfffwY9jud5ODo6wsHBAQ4ODjr9W2ysQoUKCAsLM2tDA1117twZP//8s8axo0eP0nupBqyUrz59+oim0hmTxL8f5A9UKxqU0Tup6LMBMjMzER4ejp86SVCzvOo5vv+OAltvqor0mjL44xzcD3nnFgIAetTjsfpKQWHg3Nxc3Lt3z6LdAG2R/MiPQK5q9bCk6Xvga6hW9rNW/hhS7LkwSUB/yB+qrhX6B/BYcLpoUFgQBBw8eJC6fpVBFPwpZdq2basx+AOoPmQp+FOcQqFg3kW0xuDPpUuXIJfLjdbtqLRhpXyZ+jUQEBAAZ2dnZGVlFRt79uwZEhISUKdOHZPOwRZduXIFK1asEB1v0KABRo0aZdI5DBs2TDT4c+jQISQnJ5ukUHhp8ejRIxw7dkx03FRdvgqzlY5fgiAwU74+/vhjfPnllxoDNmXlfb9t27awt7dHXl5esbGnT5/ixo0bBtfEKI3y8vKYdTxMnfKlxvv3BQ59B0BV96fVW6PB87zG7kJxcXF4/Pgxqlevbpa52YoTJ04goKIMY1vaAQCyZQLG71WtuKlWrRoaNmxosmNzNVsgXSZFeTs5OnvzkPKAvNCf7ubNmxT80YMyKRKKy/+qvnAoD2nn6QBUKZqsFeqtW7cu0XH5gH75r8P+/sWDPwC1fC+rKO2rlKGiz/q7dOkSUlNTNY45OzuXaOllSXh7e4vWX8jKysL169fNPCPbYal6PwAgkUiYqSeU+lWcIAgYN24csyDv4sWLTX7RGxYWhooVK2ocUygU2Lx5s0mPb+s2bdok+jesWrUqOnXqZPI56BI0twaRkZGIjo4WHf/0008REBCAunXrokaNGqhUqRJcXFzKTOAH0P75S6lfRZ08eVK0NpmTkxMzjc6Y+Eq+4KoEAgCEB+fgImSgUaNGotvT6p/iDh3cj6V9pJDwqlU/s08qEJ+mGgsLCzPpCi6OlyBOqAMAcNPQ8p2KPutOEATI9n0FQPW5KA2dDM5VtfKclYbs7+8vei6iK76iD7hqDQAALTx51CxXfBtq+V42UfCnlNFW9Nkau51YGivlKyQkxORpCmI4jqO6PwZQ3xEWY47Vb1T3Rz9bt27FiRMnRMcHDBhglqLr9vb2ePPNN0XHqesXG6vL15AhQ8wSuKhTpw4zaG4tFy6sVT8NGzZEgwYNzDgb68V63VPwpyhWyldYWJhJCgSL4QPUq4wEKG7vZqZ+Ud2f4srf3YLmNVWXaDefKrHkTMGqDVOmfKm9rFKw6qT7a3V/rOU91BYoozZAeJUCyVXyg6TlJ/ljpqr3U5ikUNevvhoKP1PL97KJgj+lTMOGDUU/4J8/f447d+6YeUbWzxrr/ahR8Ed/rHo/gYGBJq33o0bBH93l5ORg4sSJouP29vaYP3++2ebD6kZ16tQpPHjwwGxzsSXx8fHM1aXmSPkCVEFzVuqXNbxvyuVyZiBxxIgRZpyNdWOtVjl+/LjVrOSyNEEQLNri/XUS/4LjKaN3UdFnPSTevoSxQc/yv/5ijxyyQoszunbtavI5uDbsB+Wrm8XdKPhjECEvE7JDk/O/lvaYB05qn/+1Kev9qPH+BcGffv6aO4dR16+yh4I/pYxUKmWe+FLqV1EZGRnM6Ls1B3+o6LNmrBUkpk75UtOl6DNRWbRokWidMgAYN26cWYvRdujQATVq1BAd37Bhg9nmYktYvxcvLy+jnczqwtrr/hw+fBhPnjzROMZxHDMAWda0aNECLi4uGsdevHiBy5cvm3lG1un69etISEjQOMZxHPr06WPW+XAeTYDyngAA5b1jaN9CPO0rMjIS2dnZ5pqaqLy8PGzatAmTJk3CokWLkJaWZpF5vNzxJdwcValW/15W4PT9gvOFhg0bwsPDw+Rz8GvcDpFJquM2rF40ZejWrVuUKqQDefg84GUiAICv1xOSegWdLhUKBfMc3lgrf7hqweAqeAMAOtThUMmp+DasOmGkdKLgTymkLfWLFDh58qRoW+Lq1atbvJgk6yImOjoaL168MONsbIMliz2r1atXD+XLl9c49uLFC8TGxpplHtbu0aNHmDVrluh4tWrVMHnyZNFxU5BIJHjrrbdExyn1SzPW7+Wtt94yS5chNWsP/rBSvjp16gRPT08zzsa62dnZISQkRHT8yJEjZpyN9WKt+mnVqhWqVatmxtmoAk75q38UeaiRc0P0eS2TySy+IjYzMxNhYWEYMmQI5s2bhwkTJsDb29vsF8aK2EOok6X6XTzLEjD5UNGVbeZI+QKASpUqIfxRQaSgcMv37Oxs0UAjUVGm3oPi9CLVF7wdpD3mFRm/ceMGMjIyND7W3d0d/v7+RpkHx3HgX6V+SXkOveoX/xymlu9lDwV/SiEK/uju4MGDomNdu3a1eBtZ1oeAIAi4cOGCmWdk3ZKTk5mFsM0V/OF5Hs2aNRMdt/SJrrX4/vvvkZmZKTo+a9Ys0SCaKbFWXkRGRiImJsaMs7F+t27dwtWrV0XHzb2ShRX8uX79OvM5Z2oZGRnYtm2b6DilfBXHSv2iuj8q1pTypcb7983/tyJ6p1Wfm44bN65Yynhqair69OmDJUuWmGW1riDLhmzPl/lff39IjuevLYgyV/AHAOL5go5eVPdHP/KD3wOKXACApPXn4Cv5FhlnPd9bt25t1Jslhev+9NNQ90fd8p2UHRT8KYVY7QFv3LhBq0UKseZ6P2pU90d3rHo/AQEBZr37SXV/2C5duoRVq1aJjjdp0gTvvfee2eZTWPPmzZmpZrT6pyhWypefnx8aN25svslAddda7O+nVCoRGRlp1vkUtn37dmRlZWkcc3R0xKBBg8w8I+vHKvocHh6O3NxcM87G+jx69Ii5os1iwZ/a7QHHCgAAZcx+dGgrfi5jybo/p06dwooVKzSOKZVKjBs3Dp988onoKnFjkZ/6BUiNU80pXok1V4qmVjk6OqJ9+/YmnUNh0lot8CxLFfTq7MPDrtAVIwV/xCnuHYfy1qsAv0s1SEO+LbaNOer9qHE1WwKuqlTBrj48XO2Lb0OpXyqCUqF9o1KAgj+lUKVKlVC/fn2NY4IgUMDglcTEROYHmC0Ef6juT1GWbPH+Ogr+iBMEAV9++SVzm8WLF0Mi0Vyg0NQ4jsPbb78tOv7ff/9R3aZXBEFgBn+GDRtmkRWU1lr0mZXy1a9fP7i5uZlxNrahUaNGom2Ps7Ozy/zn4O7du0XHvL29ERgYaMbZFOAkduDr91J9kfcSYX7inVMt1Y02NzcXo0eP1rrd8uXL0bNnT6SmpppkHsrk21CEqxob5CkEfLGneCHzkJAQODlpKNpiIgGBwTgcqwpAlXco2vKdgj+aCQo55Pu+yv9a2nUGOMfiq5fN0elLjeN5SF6twnOUcsUKeAPA/v37oVCUjcCHmiDLhvL+GcjPLIZ88whU3dwZyrNLLD0ts6DgTynFihxT0WeVw4cPi44FBwebpaieLrSt/KGL0ALWUOxZjRX8iYyMLHMftIVt3LiReaf3zTffZNb5MAdWqlJMTAwVmn3lxo0bzDQ4c3X5ep011v15/Pgxc7UppXxpxvM8OnXqJDpe1lO/tKV8WTJ9vXDXrzp5N5jdaC2RTjtnzhxER0frtO2RI0fQunVro89TEATI93wOKFUrixadUSD6WfHzuu7duxf7nikFBgbiQGzB6qPu9QouGSn4o5ni0koIT28AALgazSBpNLLYNsnJyaJ1H7V1qzQUXyj1q7+G1K/nz5+X6puSgiBA+TwWiqv/QbZnHHKXt0Xuz1WQ93dnyA9+C+XNrZBkPoIy0fI1Ac2Bgj+llDXnVlsLW0j5AoAGDRqI3u1JTk5mdkoqS549e4aoqCjRcXPV+1GrW7eu6N3qjIwM3L5926zzsRbZ2dmYNGmS6LiDgwPmzZsnOm4uAQEBaNRIvEMNpX6p7Nq1S3SscePGRitcqS9rDP6sX79etEtOpUqVzH5xZ0tYqV9lOfiTmZnJvJFlqZQvNd6nKyB1BAAId/aidSvx16W5U7+io6Mxe/ZsvR4TExOD1q1bG/U5p7y6Fsp4Vcr6vVQBc09qvjFkzno/gCr4cyhWmd/yvXDdn5s3b9KNx9cIWSmQH5ue/7VdzwXgNNTuYd2Ab9CggUnqHPJ1QvJTMHv48bDXsKi6NKV+CdlpUMQegvz4LOStHYDcXzyR92swZNs+gOLCHxAeRQLKoqvrlFJncBLx1YmlCQV/SilW8CciIsJq2jRmZGRg48aN+PXXX3Hy5EmzzUsQBOYJkzUFf+zs7JjFgymNT4VV78ff398i3U4o9asoQRAwY8YM3L9/X3Sbr776CnXq1DHfpBhYq39YF/JlhVKpZKacWGrVD6CqGSWVSjWOJSQkiLZaNyVWytdbb70Fe3sNxRgIAHbwJyIiQrRzTml3+PBh5OTkaBxzd3c3a40YTTh7F1UACAAyHuPNtnVFtzVn8EcQBIwZMwZ5eXl6PzY1NRXdu3fHn3/+WbI5KJVQPjgH2cGCmjBf7pEhu3jGFzw8PBAUFFSi4+mratWqEJwq4lKiKsgTXI2H56u4REZGBh4+fGjW+Vg7+bEZQHYKAIBvOAx8Lc31V81Z70etcApmeQcOneoWv/y31eCPoJBD+fga5Bf/gmz7R8hd2hi5c6tD9n99IT8+E8o7+4Hs5689igNXJQCSJu9C2ncZpB+fw5Nh5yF941+L/AzmRsGfUiogIEC0dkB6erpVLNmMjY2Fn58f3nrrLXzxxRcIDQ2Fn58flixZgvT0dJMeOyoqSvTk397e3uIpJ6+jos/aWVO9HzUK/hQ4f/482rdvjzlz5ohu4+Hhge+++86Ms2JjBS8ePnxo0SKl1uDMmTN49OiR6Phbb71lxtkU5eTkhIYNG4qOm7tT4q1bt3Dp0iXR8ZEji6cHkAL169dHjRo1NI7J5XKEh4ebeUbWgZXy1bNnT9jZ2ZlxNprxhVK/OtfKFt3OnKvS//nnH2aauDZyuRwff/wxxo0bp1cKt5CXBcXtPZDt/AS5C+oib2UokPUMAHA1ty4O3dW8mqZbt25mT9/jOA6BgYHYXyj1K8yXUr80UT65DsXFV8FAOxfYdf1JdFvW89xUwR8AkPgX6voVYLst34WXj6C4tROyQz8g958w5M6pirw/WkK++3MorqyB8ExDGqdzZfD1ekLa6UfYjdwDh28fw+HTy7DrvxzSZh+Cr9YA4C1TY9ISKPhTSvE8z+z6ZenUr5ycHPTs2bPYhcPdu3cxbtw4eHp64ssvvxTNiy0pVspX27Zt4eLiYpLjGoqKPmtHwR/r9ODBA4wYMQKtWrXS+r7z888/w9XV1Uwz06527drMVZRlPfWLVei5TZs2Fl/BZU1Fn9euXSs65uPjw3yPJ6oLUUr9KkqhUDDTLi2d8qUm8esFcKrLjVrZV0W3i46OxvPnr9+hN76nT59i4sSJouMcx+Gff/5B7dq1te5ryZIl6Nu3L/OGpZDxBPLIf5C37k3k/lITsnVvQBH5D5BZ6AakWy18tU98FZK5U77UAgMDcfBOQfCnBwV/ihEEAfL9EwFB9XuSdpgErnxNjdvKZDLmjQdjF3sujPfpCtipam71rc+Dfy2WaI0t3wVZDpT3z0J+ZgnyNo1AzqJ6yF1QF7INQ6A4PR9CwilA9lr3TN4OXI3mkLQcC7tB/8D+ixtw+PoB7IdvgzT0O0h8uoBzLNuNFSj4U4pZc92fRYsWMQM7L1++xP/+9z/4+fmhb9++OHz4sFHzi22l3o8aK5B3+fJlg5YulybPnz+3qno/aqzgz+XLl03eOtaSMjIyMGXKFPj5+TEvfNVatGhhlasfWKlfmzZtKtV/Qxa5XI7NmzeLjrN+b+ZiLXV/lEol8zUwYsQIixbltRWs4M+RI0fMOBPrcP78edE79VKpFD169DDzjDTjXCqD82oHAODT4tCndT3Rbc3RkGT8+PHMrl2fffYZ3nvvPZw/f16ni/F9+/ahTZs2uHfvHoBXxWWf3oL81DzkruiI3Pl1IN/5CZS3dwPyQiufpE7g6/eBtN/veNJrM05fjRM9RteuXXX/AY0oMDAQl5IEJGeqzr87eRe0fC/LwR9BngdlcjQUt3aoCgbfOw4A4CrUhaSNeCfTa9euITtb8+q3ypUrw9fX1xTTBQBw9s7gfVV15aq4cGjrVfwzZ+/evSY7vq4EpRLyiGXI/bPdq6LMnSA/+A2UNzYDLx4Uf4CbF/igNyHt/gvsPzwOh++S4TA6HHa9FkLScBj4ij70+foazQnxpFSw1o5fSUlJmDVrlk7bCoKA3bt3Y/fu3QgKCsIXX3yBESNGiHaM0EVOTg6zPow1Bn88PT3h4eGhMcUiNzcXV69eRYsWLcw+L0EQ8Pfff2P79u2QSqXo27cv3nvvPfAaityZkrZ6P9WrVzfjbAp4enqiWrVqGlMMc3JycPPmTWZRYVukUCjw77//YvLkyXj8+LHOj1u8eLHZnze6GDx4ML788kuN9X2ePXuGI0eOWM1FljkdO3YMT58+1TjG8zwGDx5s5hkVpy34IwiCWU4KT58+zSzMP3z4cJPPoTRgBX8uX76MlJQU0SL7pREr5Ss0NBTu7u7mm4wWkoD+kCecAgC826YSdkfc0bjd6dOn0adPH5PN48CBA/jvv/9Ex2vWrImfflKl7FStWhVHjhzBRx99xKzXBQC3b93EFwOa4tcve8Hj5WUIqSKBHJdqkNTvBb5+H/B1O4GzV53LHvzrL9F9N2nSBFWrVtXyk5lGYGAgBACHYpV4u5EE5Rw4tPHicDJeKBPBHyE7FcKzGCifRUN4FgPh2W3VfylxgFA83U/abS44O0fR/WlL+TL155EkoB+Ut7YBUHX9Ck8o+jMcOHAACoUCEonlUqAUEf+DvFAdrCLsXMDXbAbOsyX4mi3Be7YAV846OjPbEgr+lGKtWrUCx3EaV8zExMTg2bNnqFy5stnn9e233yIzM1Pvx924cQMff/wxvvvuO3z00Uf49NNPUatWLb33c+bMGdHIe4UKFdC0aVO992lqHMehVatW2L59u8bxc+fOmT34o1Qq8c477xS5o719+3asX78eW7ZsQbly5cw2F1bKl6VW/QAFRZ/37NmjcfzixYulKvhz7NgxTJgwAVeuXNHrcRMnTjTpcueSqFatGrp06SK6WnDdunVlMvjDSnnr1KmTxQKuhfn7+8PV1VVjMeC0tDTExsaiXj3xVQjGwrpwbNWqlVnmUBrUrl0bPj4+uHv3brExQRBw4sQJDBw40AIzswxbSPlS4+v3AfarUq3aVBZfdWPKOmpZWVn45JNPmNssW7asSLclR0dHrF69GgEBAZg8eXKRbcvZq2rg9Pbj0cOPR0WnbOD+Frx+xs1VDQJfvzck9fuAq9FcYwcoVrqNpVK+AFXwBwAOvgr+AECPejxOxivyO37Z+qoKQamE8OJ+keCO8tltCM9iiqbmacH79wfv35e5jSWKPRfG1+sJgbcDp5Shn78EXx8oGvxRt3y3VBqy8vE1yI9Mzf+aqxIAvmYLVbDHsxW4KgHgJBS6KCnru81KjKZ8+fIIDg4WHbdErZiIiAisWbOmRPtISUnB3LlzUbduXQwZMgSnT5/WKyWMlfLVpUsXi0a8Wayt7s+0adM0pjIcOnQIHTt21GvVR0lZY70ftbJQ9ycmJgYDBgxA586d9Qr8VK9eHatWrcIvv/xiuskZASuFadu2baLB5NIqNzcXW7duFR23ZJevwiQSCTMobo7Ur9zcXGzcuFF0fMSIESafQ2nSpUsX0bGylPp19+5d3LhxQ3S8b1/2Rai58RXqgKuuutFRKS8eNUXuDV24cMFkaewzZszIT83SZODAgejfv3+x73Mch++//x5btmxBvWpO+LgFj53D7fBwkj3WDrbD240kqOhUEACRKwXcU3hC0u0X2H9xEw5jL8Guywzwni01Bn7kcjmz+6wlgz81atRA+fLlcfhuQcv3bq/q/qSlpZn1PK+kBFk2lI+vQXF9E+THf0Le5pHI/b0lcmdXQt4Sf8jW9oP8wNdQXFqhqiUjFviROoGr3gh88BBIO06B3Zv/B/tPLsLurfVaA2GslT/muAHGOblDUrcjAMDLnUMTj+LztVTXL0GWDdmW9wCF6vUvaTtBVZR5wJ+QNh8FvnoDCvwYCQV/SjlrqvujVCrxxRdfGG1/CoUCmzZtQvv27dGiRQusWbMGubm5Wh9na/V+1Kyp49fmzZsxc+ZM0fHIyEi0bdsWd+5oXtptTM+fP8e1a9dExy258gco3cGflJQUjB8/HkFBQdixY4fOj3N0dMQPP/yAmJgYvPvuu1Z/53DgwIGibbhfvnxpFXny5rR//368ePFC45idnR3eeOMNM89InKXr/uzduxdpaWkaxyQSiUU7otmislz0OScnB48fP0Z0dDRWrFghul2DBg1Qt654S3VLkRTq+jWsueaCqzk5Obh8+bLRj3316lXMnz9fdLxcuXL49ddfi31fEAQokyIhOzYDvZ/OxbUxSizuZYcwXx72koLPrRc5AjZdV+C9LTLUmpeHwJ/uYsi8Y8iy156udfHiRdH3CGdnZ7Rr1077D2gi6o5fz7OBCw9VwZ+gqjxqvVocZY2pX4IiD/aPL0BxaSVk+79G3v/1Q+7i+sidVRF5f7SEbPNIyI//BOX1TRCeXCtah6kw1+rg64RA0vwjSHvMh92IXXAYdxsO3z+Hw5hzsH9zNaQdJ0MS/Cb4asFaz2OSkpKQkJCgcUwikTDPFY2JDygIcPbX0PXLUsEf+eEpEJJVzyeuWkNIO/9okXmUBRRCK+Xatm2L5cuXaxwzd/Bn9erVzCr3Xbt2hYODg2iKDMulS5fwzjvv4Ouvv8Ynn3yCMWPGoFq1asW2e/78OSIjI0X3Y83Bn+bNm4PneY21R2JjY/H8+XNUqlTJ5PO4du0a3n33Xa3b3bt3D23btsWePXuYF2Alxar3U79+fXh4WDYfmPWBfvXqVeTm5sLBwcGMMyo5mUyG33//HdOnT0dKSopejx0+fDhmz54NLy8vE83O+Nzd3dGrVy/RtMt169ZZVcDD1NavXy861qNHD1SoUMGMs2GzdMcvVspXjx49UKVKFZPPoTTp1KmT6NitW7eQlJQk2hLekgRBQE5ODtLS0rT+9+LFC43f1+XmFmB9KV9qvH8/4LjqptGQJi6Yf0xzAPnMmTNGTTtRKBQYPXo0syX77NmzUbOmqkOTIM+F8t4JKG/vhuL2HuBlosbH3E8TsPu2ArtvKxGeIED22qnZ9u3b0aFDB+zcuZNZooCV8tWxY0eLnx8EBgYiIiICB2KVaFVLFSzoVo/HyktK3Lx5k7kaz9wEhQzyf7qgUtIliP+1C+Ek4Cr6gKtc/9V/fuCr+IOr5AfOyd2oc2OlfDVu3NhsXYYl/n0h2/05OAjo589j2tGivyl1y3dzfjYpYg9BcW6p6gupI+zeWAVOalvnxbaEgj+lHCuH9MKFC5DJZLCzszP5PNLT0/HttyIFvAA4OTnh77//Rq1atRATE4Nff/0Vq1at0lirgeXJkyeYNm0aZs+ejaFDh+LLL78sUsPnyJEjoiliPj4+Vnm3TK1cuXIICgoS7Wp1/vx59OzZ06RzePbsGfr374+srCztG7/avlOnTti4cSN69+5tkjmdOHFCdMzSKV+AKrXJ09MTDx8+LDYmk8kQFRVltjs+JaUuwD5x4kTExMTo9di2bdti4cKFNtvSetiwYaLBn927dyM9Pb1IrYjS6smTJ8xCs9aS8qXGCv6oOyWKreoqqdTUVOzevVt0nFK+9Fe1alU0aNBA9HPw2LFjFiugffjwYezduxeJiYkagzfm6spprcEfrlowOPc6ENLiEVguFe6OQFpO8e1Onz6N8ePHG+24v//+O3OVX6tWrfJrASlubIVs5ydArubAFFejGST1eyOvdlf8MGkeNu0X73gIAFeuXEHLli2xY8cO0fcia633o6au+3PgjhJTX8Veu/sWBH+sieLsEghJl4oPOJTPD/Dw+YGe+uAq1AUnNc37/+ssXe9HjXOtBt6rDYT7ZxBQhYdfJQ4xzwuuiwRBwIEDB8z2+SRkPoNs+0f5X0vDZoOvGmiWY5dVlPZVyvn6+ooWdc7KymKmyxjTrFmzNHY8Uvv222/z74z4+fnh119/xcOHD7Fw4UKDAjJ5eXlYvXo1mjVrhg4dOmDTpk2Qy+U2m/KlZsnUL5lMhsGDBzO71miSlZWF/v374++//zbJvKy12HNhpSH16+rVqwgLC0O/fv30CvzUqVMHGzZsQHh4uM0GfgCgT58+cHV11TiWm5uLbdu2mXlG5pOXl4ft27dj4MCBqFWrlmjw18nJyeouPGvWrCm6+i8vL8+kn4GbN28WveB3dXW1ut+VrbC21C+5XI7Ro0cjLCwMixYtwsaNG3Hw4EGcP38eMTExePr0qdkCP9WrV7famwkcx6lW/wCQQIke9TRfguhbx5Hl4cOH+P7770XHpVIp/vzzT0gkEigfXYFs6/tFAz8SB/D1ekDa51c4TLgLh9GnIQ39Hs51WmL9+g2YOnWq6L7VHj9+jNDQUI0rJl+8eMGs2WhNwZ/LjwQ8ySho+W4vsa60L2VqPOTHVZ2EBY6HJGwO7N7dD4ev7sHh2ydw+OgU7AeugLTD15AE9ANfpb7ZAj+A5ev9FCYJGJD/734WTP0SBAGy3Z8BGaraUbxPGCQt2UXZSclR8KeU4zjO4nV/7ty5g0WLFomOe3l5YeLEicW+7+bmhvHjx+POnTvYvn07c7k3S3h4OIYMGQJvb29mkVJbD/6YuujzhAkTmIEWFoVCgQ8//BA//fST0U7qAFXNGWuu96Nmy8Gfx48fY9SoUWjSpIleBVXLlSuHOXPm4NatWxgyZIjV1/XRxtnZWWMxUDVW9ytbJAgCLly4gM8//xw1atTAwIEDsX37dshkMtHH9O3bVzRAZinqToliTFn3h5Xy9cYbb8DZ2dlkxy7NWMEf1upeU5DJZBgxYgT+YrTqNqe+ffuC11BU2FpIAgoCngMCNa86f/z4sd43mcR8/vnnePnypej4V199hYYNG0LIToNs49uAQpVax/t2g92Q9XCYlAj74dshbf4RuPI1izyW53lMnz4d//33n9bUrJycHAwbNgzTpk0r8vw8duyYaDqap6cn/P39df1RTUYd/BEAHL6rym1ztefQ1ouzmuCPIAiQ7x2fX78ny384JG2+gKRuR3DlPCx+/pGbm4tLlzSsSHrFnCt/AOQHYQFggH/x9wt1y3dTU1z+F8pb21VfOFWC3YA/Lf63Kgus9xOCGA3rTYW1DNFYJkyYwLxgmD9/PvMkWCKRoH///jh69CiuXr2KDz/8EI6OjnrP48GDB6L1SXieZ55QWovWrVuLjp0/f95kJ70rVqzA0qVLmdvokh88ZcoUjB071mgfKidPnhT9mf38/Kym9oMtBn+ys7Mxe/Zs1KtXDytXrtT5ucXzPD7++GPExsbim2++Mei1aq3efvtt0bHDhw8jOTnZjLMxjYcPH2Lu3LkICgpCy5YtsXTpUjx//lynx7K6olmSJYo+JyQkMOuRUcqX4UJDQ0UDHAkJCcyOTsaUl5eHt956Cxs2bDDL8XQxatQoS0+BiavVBnBWrUbv5svDUaT4hDFavm/fvl00VRcA6tati6lTp6pWH+wYDSE1TjXHGs1hN3QTJIEDwDloD2YPGzYMJ06c0Fhn8nXTp0/HsGHD8jtEakv5soYL4Vq1auXXozlwp6CwUXdfHs+ePbOKzz3lrR1Q3nm1WqWcB142Nl5zGWOIjIwUXf3n4eGB2rVrm3U+fIU6EKo2BAA0q1lQwFtN3fLdlJQpdyHf91X+13b9fgNXzrI1OssKCv6UAZZc+bN//35mzYPQ0FC8+eabOu+vYcOGWLFiBR48eIBZs2YZ7eK+ZcuWcHd3N8q+TCkgIED0znpqaqpJumudOXMGY8eOZW7TuXNnxMXF6VT09o8//sAbb7xhlPbY1tzivbBmzZqJjl2/fl3nGkrmIAgC/vvvP9SvXx+TJ0/Wq+5Wt27dcPXqVfzxxx+oWlV7lxNbExYWJlpUXd190BZlZmbi//7v/9CtWzd4eXnh22+/xa1bt/TaR6VKldCjRw8TzbBkLFH0+b///hMdq1GjhsErWYlqVTAroG6O1K+cnBwMGjTIqtI9p0yZYtLmCsbA8RJI6qvq/zlJlejsrfkypKTnpunp6fjss8+Y2/zxxx9wdnaG4uz/oIx+VcfMsQLsh6zVu9hsq1atcP78eTRq1Ejrths2bEBoaCgePXpk9fV+ANUNnYCAAACqlT8KpepGUPdXaXuWXv0j5L6ErFAQQdp9PgR761qBqq3ejyWCfHZBA/L/3ddfUmzclF1MBYUcsq0fALJMAICkyXuQBIivrCbGRcGfMqB58+aQSjXfXklISEBSUpJJjiuTyZhF+3iex5IlSwx606tcuTK+//57xMfHY926dcwVMbqwhZQvQLUKqkWLFqLjxr6QefjwIQYNGsRcuVW3bl1s3LgRrq6u2LBhg9YTLgDYsWMHunbtqnenqNdZe7FntcqVK4vWrlIoFLh69aqZZ6RZZGQk2rVrh+HDh+PBgwc6Py4gIAB79+7F/v37ERwcbMIZWpadnR0zWG1LqV9KpRLHjx/H+++/j+rVq2PkyJE4dOiQwasHf/nlF6td5cUKFERHR4u2rTeUIAhYs2aN6Pjbb78NiaT4yTbRnbbUL1PKyspCv379DOpMaiie51GxYkV4e3ujadOm6Ny5MwYNGoQPPvgAU6ZMwdmzZzFjxgyzzackeP+Ci7y+GlJOgJKv/Pnhhx+QmKi5Sxeg6jrZrVs3KO+fgfxQQU0gu0F/g3M3bBWGl5cXwsPDdarldeHCBTRp0gR3797VOM5xHLp27WrQPExBnfqVmgNcSFR9RgRU4eHlZvngj/zo9PyObLxvd3CF6tlYC1Yw09wpX2p8od+TuVu+K07NgfBQdb3CVfCGtMd8kx2LFEfdvsoAZ2dnNG7cWHQJ39mzZ03Spnjp0qWIjo4WHR89erROd0lY7OzsMHToUAwdOhTnz5/HkiVLsHHjRsjlcr32YyvBH0B1h+nYsWMaxyIiIjBy5EijHCc7OxsDBw5kFup2cXHBjh078ldDSCQS/O9//0PNmjXx3XffMfd/5swZtGvXDvv37zdoyWtKSgozaGIt9X7UmjdvLpqOcPHiRYudAABAfHw8vvnmG2zcuFGvx1WqVAnTp0/H6NGjzdI10BoMGzYMy5cv1zgWHh6O+/fvW3Ub+zt37mD16tVYs2YNEhISSry/WrVqYcGCBRg8eLARZmcabm5u8Pf3F/08unjxolHbFV+5coW5copSvkquS5cumDNnjsaxo0ePQhAEk9xNz8jIQN++ffWufyeRSODu7p7/n5ubW5Gvtf3n6upqFSlAxsB7dwLsXABZJnr78eA5QPlazDkqKgovXrxAuXLl9N7/+fPnmWnqFStWxMKFCyFkPEXeppGAoEpDl7T/GhK/knVMdXV1xdatW/Hdd99h3rx5zG1Z51bNmzcXXWVqCergDwDsv6NEa3XLd19e71WixqRMugzF+d9UX0idIO29GLCy14kgCMyVP+Yu9qzGVQmArJwX7F7eRzsvDpWdgWeFFqFfvHjRJC3flQ/OQX7i51eTkKjauuuQXkmMSCBWT6FQCHFxcYJCoTB4H1988YUAVb22Yv9NmDDBiLNVefLkieDm5iZ6THd3dyE5OdnoxxUEQUhMTBR++OEHoXLlyqLHL/yfq6urkJeXZ5K5mMK2bdtEf5ZmzZoJglDy54xSqRRGjBih9Xe3efNm0X38+++/glQq1bqPGjVqCFevXtV7jtu3bxfdZ7169Qz6uU1p7ty5ovN95513LDav//u//xMcHBx0eq2o/7OzsxO++uorITU11WLzthSFQiHUrFlT9Hfzyy+/WHqKxaSkpAh//PGH0KZNG73+zmL/lStXTvjggw+EY8eOCbGxsSX6bDKXd999V/TnmT17tlGPNX78eNFjBQcHC0ql0qjHsyXGOJ8RBEHIzMwU7O3tRX/P169fN9KMC6SlpQlt27bV+vpwc3MTtm7dKkRFRQkPHjwQXr58Wab/5prkrh8qZP/oIGT/6CC08+I0/h4PHDig9/MlLy9PaNiwIfPvs3LlSkGpkAu5//bMn0PuP2GCUi4z6s/4999/C3Z2dga9x06ePNmocympnTt35s+tiQeX/3vbNFQqdOnSxSJzUirkQs7ytvlzkZ1UffYa6z3GWOLj45nnUtnZ2RabW97B7/N/f+824YvNb82aNUY9njInXchZHFDwNzv2k1H3byhre86YGqV9lRHmLvr8ww8/MJfST58+XbQFfUnVqFEDM2fOxIMHD/D3339rXV309ttv29SqBVbnmqtXrxqlls7ChQuZnWoAYOrUqcwVY++88w52796dXyhQTFJSEjp06CC6mkmMrdT7UbPGos9bt27FiBEjkJubq/NjBg0ahFu3bmH+/Pk2USfL2Hiex1tvvSU6bi2pXzKZDHv27MGQIUPg4eGBMWPGlOi9nud5dO/eHWvXrsXjx4+xcuVKhISEWHVnocLMVfRZLpcznwMjRowoNSs4LMnZ2Zl5XmPs1K/U1FSEhYVprUVTsWJFHD16FAMHDkRwcDA8PT1L1aodYync9aufEVO/Fi1axOwA2rFjR7z//vuQn5gNZdyr2lCu1WH3xmpwEuMmQ7z//vs4cuSIQSt4rKXej1rhlT9XCrV871iXR2z0DYvMSXHxLwhJqg5aXJUASNp8aZF5aMP63G3WrJlF06WLtHzX8Do0duqXfP/XBYXVPVtD0uEbo+6f6MjS0SeinTEikgkJCaKRZ3t7e6NGni9duiRwnOY7OQCEwMBAs660USqVwvHjx4WBAwcKPF80su3v7y8kJiaabS7G4uXlJfr7PX36dImeM/v37y/2e3r9v/79++u87wsXLghVq1bVeqfL3t5eWL9+vc7zbNy4sei+1q5dq/fPbWppaWmi8+U4TkhPTzfrfBISEgR3d3ed70Q2a9ZMOHHihFnnaK0uXLjA/F1FR0dbbG6XL18Wxo8fr9NrTpf/AgMDhblz5woPHz4sdixbulvG+pt5eHgYbWXGgQMHmL/P+/fvG+U4tsqYz5kZM2YwP6OMJTk5WWjSpInW10qVKlWEa9euGe24pZkyK0XInu4iZP/oINz8QvMKrq5du+r1fImLixOcnJyY5xjR0dGC/M5BIftHR9Xqg2lOgjzuuEl/1rt37woBAQE6v+e6uroKubm5Jp2TvuRyueDo6Jg/xz/7S/NXb3SqywkpKSlmnY/yRaKQPbtK/hwU8afyx6ztc4mVeTF+/HiLzk2pUAipP3kI2T86CGmT7YVy9kXnV6lSJUEulxvlWPKb2/P/XtmzKgmK57FG2a8xWNtzxtRs45YdKbFatWqhZs2aGsfy8vIQGRlplOMIgoAvv/ySWTR08eLFZl1pw3EcQkNDsXXrVty9excLFizA6NGjsXTpUpw/f95q2oHrg7X6pyRFn+/cuYOhQ4dCqVSKbhMUFIQ1a9bofMe/efPmOHPmDHx8fJjb5eXlYejQoVi8eLHWfaamptpUvR9AVXfEz89P45ggCLh8+bLZ5iKXyzF8+HCkpaVp3bZGjRr4999/cf78eYSEhJh+cjagWbNm8PX1FR039+qf58+fY+HChWjUqBGaNGmCRYsW4enTpwbvr3Llyvjiiy9w8eJFXL9+HZMmTRL9/LAVDRs2hL29vcaxR48eMYvD6oO1YrJjx46oVauWUY5D2EWfjx8/DoVCUeJjPHnyBJ06ddL6/uzh4YETJ06gQYMGJT5mWcA5VQBfR/V5UrcCh+CqxVdGRURE6Fy/URAEfPLJJ8yVz5MnT4ZfdWfItrwH1bUtIO08HZK6pj1f8Pb2xtmzZ9G9e3edtu/UqZPoe5WlSCQS+Pv75399MLbgHLFHPfPX/ZEd+AbITVfNrcm74Gu3N+vx9cFaLWipej9qHM/DoYFqBb+DlMvv4Kb2/PlzXLhwocTHEdKTINv5Sf7X0p4LwFdkXxMQ06HgTxnBcZxZUr82bNiA8PBw0fH+/ftbtLhynTp1MGHCBCxfvhyffvqpQcUErQEr+BMREWHQPtPT09G/f39mQKBChQrYsWOH3r83Hx8fnDlzhpn6pDZ+/Hh8/fXXzADUqVOnRAOMvr6+Vnuhai2pXz/99BPzdQqo0iqmTZuGmJgYvPPOOzaT3mMOHMdh2LBhouPr1q0zuGuWPm7cuIGPP/4YtWrVwldffcVMd9DGzs4OgwYNwo4dO5CYmIglS5agWbNmpSZdxd7eHk2aNBEdN0bqV2ZmJrZu3So6ToWejatly5aiacUvXrwo8U2tpKQkdOzYEdevX2du5+npiRMnTuS3wya64f0LpX5p6DaUkZGBqKgonfa1bt06HDhwQHQ8ICAAkyZOUBV4zn6uOr5fL0jafSX6GGNyc3PD7t278cUXX2jd1tpSvtQKp34dKdTyvZsvb9aOX4rYQ1De2KT6wqkSpGGzzXZsfWVlZeHKlSui45Zs9KFmHzwo/9/9TZD6JSiVkO0YDWSruvvyAQMgafxOifZpbI8ePcLw4cOtpvOuqdHZfBnCijBry2PXRVZWFr7++mvRcXt7eyxYsKDExyFgtrY3ZOWPUqnEiBEjmHdveJ7Hxo0bta7gEVO1alUcO3YMPXtq76Yxf/58vPPOO8jLy9M4bmv1ftSsIfhz8uRJzJw5k7nNyJEjERMTgx9//FFrzaayihX8iYmJMdlKLqVSiT179qBbt24IDg7Gn3/+WaI6Xy1btsSyZcvw6NEjbNmyBf369bO6u87GYuq6Pzt27EBmZqbGMQcHB5N01SzL7OzsmKsRjx49avC+79+/j5CQEGbHUkB1Q+nkyZOoV6+ewccqqyT1++T/u299zZcjutyYTElJwbhx45jb/Pnnn5CcnAHh4aubY25esBuwApwZb2pIpVIsWbIEv//+OyQSieg2ffr00ThmaYWDm6k5wLmHquCPfxUej24ZdtNRX4IsG/I9BQE0abefwTlbT1e01128eFF09ZqXl5dV3Kjka3dADucMAOhej4fDa0/NkgZ/FOd/h/LuYdUXrh6w67vMqm4qRUZGolWrVjh79iz69++Px48fW3pKJkfBnzJEW/CnpHeq586di4cPH4qOT5gwweDAASmqadOmkEo1FydMSEhgthDV5Mcff8SuXbuY2yxYsABdu3bVa7+vc3V1xY4dO/Dee+9p3Xbt2rXo3bs30tPTi41R8McwKSkpGD58OHNV1ciRI7F69WqrOCmxZgEBAcxi8sZO/crIyMDSpUvh7++PPn364NChQwbvy9PTE9999x1u3bqFc+fOYezYsVbVVthUWCsmjRH8YaV89e3bt0wWSDc1VuqXoUWf7927h9DQUNy9e5e5na+vL06cOIG6desadJyyjnPzBFdD9ZnY2IOHl1vxbXQp+jxp0iQkJyeLjn/00UdoUzEZioj/qb4hsYf9kP/AOVc0aN4lNWbMGOzfv1/j+8Hnn3+OOnXqmH1Ouii88gcADhRK/Sr/zPByA/qQn5wDIfUeAICr3QGSxiPNclxDsYKX1rDqBwA4iRTyOqpz+3IOHDp7Fw0NqFu+G0L59Cbkh77P/9pu4F9WFazbsmUL2rdvn5/2/eDBAwwcOBA5OTkWnpmJWbDeENGRsQpR5ebmMls637t3z+B9x8fHFykG9/p/Hh4eZi9oW9o1bdpU9Pe9bds2nZ8zGzdu1FqA8N133zVqq1qlUilMnjxZp+KHjRs3Fh49epT/2JSUFGZB8QcPHhhtnsb28uVLZjFtU7ZOVyqVwsCBA5m/69q1awtpaWkmm0NpM2fOHNHfpaenp1GKB8bFxQkTJkwQ3NzcdC4Yquk/Z2dnYeTIkcLhw4eNVsDR1ookxsTEMIusluT38vjxY0EikYjuf/v27Ub8SWyXsZ8zkZGRor9zJycnIScnR6/9xcTECJ6enlpfT7baLMLayE7MyS8C+1mr4q8fLy8v5vPlxIkTzL9TtWrVhNS7F4sUB5ad+8PMP6Vm8fHxwujRo4VatWoJgYGBwtKlS4323mwKt27dKnpuVr2g5fue98uZ/PiKp7eE7OmuqmNOdxUUT29p3s6KPpf69esn+txcsmSJpaeXT3ZrV/7f8o9+0mJzNaTlu1KWI+T81jx/v3n7Jppg5oZRKpXCzJkzRf82I0aMMOo1j7WhlT9liL29PXPlQUlSv77++mtmpHTOnDk2W1/HWhmj6PPVq1e1rsJp2bIl/vjjD6Mu0+Q4Dj/99BOWLdO+/PPKlSto06YNYmJiAADh4eHMej+enp5Gm6exubq6MutCXLp0yWTH/vPPP7Ft2zbRcTs7O/zvf/+j16kehg4dKjr28OFDrXWVxAiCgJMnT2LQoEHw9fXFwoUL8eLFC4P21alTJ6xatQqPHz/G6tWr0aVLF9GUg9LO19dXdPVNRkaG1hQflg0bNogWGK5YsaJO6a5Ef40aNUKFChU0jmVnZ+uVBn3r1i2EhIQwVzADQHBwMI4fP26TzSKsDR/QP//ffTXUG7l//z4ePXqk8bG5ubkYPXo0c/+/LpoHpwNj8osD88GDIWnBfoy51K5dG8uXL8f9+/dx48YNfPrpp1b93uzj41OkWcvVxwIevVSdi7Wqnov0FMObDGgjCAJkuz8DlDIAgKTdBPBV/LU8yrIEQbCJlT8AIPHpilxBlU3Quz4PyWun5Xv37tV7n/Kj0yA8UdXs4qoGQdqFXW7AXLKzszFixAhMmTJFdJv/+7//w5w5c8w4K/Oi4E8Zw0r9MrTo8/Hjx7Fp0ybR8VatWlGhSxMoaQpDcnIy+vfvj6ysLNFtPDw8sG3bNjg6Oho0R23Gjh2LLVu2wMHBgbldfHw82rZti4iICGbKlzV2+XqdJVK/bty4obUmwuzZs6lTjZ5q167NfE/VN/UrNzcX//77L5o2bYrQ0FBs27aNmaInpl69evjpp58QHx+Po0eP4t1336WgHlRBZ1PV/WGlfL311lulto6SpfE8j06dOomO65r6de3aNYSGhmqt99CkSRMcO3YM1apV02ueRDOucn1wlVT1ktp5cajkVHwbscLdP//8M27fvi267549e6K/8+mCC9DK9WHX9zerqjdiS+zs7Ip0LBUAHHqV+uViz+Hh2Y0mO7biyhoICaqbKVyFupCGfGuyYxnL3bt3RdOlnJyc0LhxY/NOiIGzc0RaxRYAgMrOHNrVLvoaOXDggF7dExVxx6A4s1j1hcQedoNWgbMzzXWEPh4/foxOnTrhv//+07rt2rVrS236FwV/yhhWpNmQlT9yuRxffvklc5slS5ZQpyATYBV9vnDhAvONWiaTYfDgwUhISBDdxt7eHlu3bjX53c2BAwfi8OHDWuthPH/+HJ07d2ZeUFtzvR81cwd/srOzMXToUOaHWPfu3bUGh4hmrMLPmzZtgkwm07qPx48fY9q0afDy8sJ7773H7A4ihud5vPHGGzh58iRu376NyZMno3bt2nrvp7QzRfDn9u3bzHa4dPPDtLp06SI6pkvR58jISHTq1ElrXYuWLVviyJEjqFy5st5zJJpxHJff9UvCc+ilofCzphWx0dHR+Pnnn0X36+zsjH/Gd4Xy8irVN+ycYTdkHTgHCoKXxOt1fwq3fJfd1n91iC6ErOeQH/wu/2tp7/+Bs9MQJbQyrBvqzZs3L7KKyhpUaf9B/r9f7/qVkpKic8t3ITsVsm2joAoPAtIuM8FXt/yNxcuXL6NFixY6rQbt2bMnTp8+bbIb35ZGV+RlDCv4c/XqVdFOJWJWrFjBbC/87rvvMleoEMPVq1dPNGDy8uVLZrHK8ePH48SJE8z9//HHH8wAkzG1b98ep0+fRq1atZjbZWdniy4BB2x/5Y+uH676mDhxIrNVcdWqVfHvv/9SgNZAgwcPFv3dPX/+HIcPHxZ9bGRkJN59913Url0b06dPx9On+i+bd3Nzw8SJExEXF4fNmzejQ4cOdGebwRRFn9euXSs6VrduXata3l8asYo+R0REMM9rzp07h86dOyMlJYV5jHbt2uHQoUOiKWbEcJJCLd81df16PfijVCrx8ccfi3YDBYClUz+B27mCNBO7Pr+Crxoouj3RzevBnyNxSshftXyvmHbZJMeUH/oeyH4OAOCDBkPiG2aS4xgb64a6NX4mOAX1h0ypOnfo5188/VCXrl+q9LzPgZeqAsp83U6QtP7cuBM1wLZt29C+fXutKb2A6vpo165dcHNzM8PMLIPO9suY6tWri3amUCgUel18pqam4ocffhAdd3V1Zd6ZISXD8zzzLrZYq+m//voLy5YtY+77yy+/xPvvv1+i+ekrMDAQZ86cQXBwsEGP9/Hx0Ro8sgaNGjVidmoztKuCJtu3b8dvv/3G3Gb16tWUwlAC1apVY3bBe32lmkKhwNatWxESEoJmzZph9erVzIsYMfXr18eyZcvw8OFDzJs3j1b56KhFixaiY9euXUN2drZe+xMEgZnyNWLECArGmVj9+vXh4eGhcUwul+PUqVMax8LDwxEWFqa1nlbHjh2xf/9+lC9fvsRzJcVxNVtA6VQFANDVh4fzawsibt68WSSA988//+DkyZOi+2vbvAGGOe0H5KrXsqTZh5A0Gm78iZdBrwd/0nKAcw9UwZ8qfCqUKXFGPZ4yIRyKy/+qvnAoD7sevxh1/6bEWvnDShe3FM6xPJLs6wMAPN04NKtR9HNLl+CP8tp/UN7YrPrCsQLsBvwFzoI3FgVBwOzZszFo0CBmiQsAkEqlWL58ORYuXGjVtbeMgYI/ZZC2lu+6+vHHH/H8+XPR8R9++EH0hIwYB+su9tWrV4t97/Tp0/j000+Z++zcuTPmz59f4rkZwtPTE6dOnTJoBY8tpHwBqlxvVoDLWEWfHzx4gA8++IC5zcSJE9G9e3ejHK8sY6V+bdu2DdnZ2UhLS8OCBQvg4+ODN954Q/SCVJvu3btj7969uHnzJsaOHQtXV1dDp10mVatWTTRQJpfLRYPmYs6cOYN79+6Jjg8fThedpsZxnN6pX8eOHUP37t3x8uVL5r7DwsKwZ88eep2ZEMfzsAtUFX52suPQ1afopUnhG5NPnjzBxIkTRffF8zy2jqoJpMSq9l29MaQ9Fpho5mXP68EfoGjLd2XsQaMdS5DnqVaRvCLtMgNcOdu4pnj58iWioqJEx61x5Q8AuDZ9K//f/QOKvg4vXLjAXJ2sTI2HbM+4/K/t+i4F52a5Biw5OTkYOXIkJk+erHXbihUrYvXq1Rg1apQZZmZ5FPwpg4wR/Llx4wZzRYGPjw/VEDEDVlrW63VDHjx4gDfeeINZg6Ru3brYuHGj6MoUc3B3d8f+/fsxePBgvR5nK8EfwPR1fxQKBUaMGIHU1FTRbZo1a4ZZs2aV+FhEVbdKrGh5RkYGBgwYAE9PT0ycOJFZZ0uMk5MTxowZg5s3b2L//v3o2bMnpemVgDHr/rBW/bRo0QL169fXa3/EMKzUr9eDPwcOHECvXr203gnu3bs3du7cCWdnZ6PMkYgr3PWrn4auX+pz0/HjxyMtLU10P+u+6gi3R8dUXzi4wW7If1ZRaLa0qFevXrFVEQfuFKr7E73HaMdSnF0MIfkWAICr0RyS5h8Zbd+mdv78edFmDT4+PqhataqZZ6Qbj5APoHg17dfr/gCq905NBKUCsm0fAHmqYDrfaAQkQW+YbJ7aPHnyBJ06dWKmZKv5+/vj7NmzZitzYQ3o7LEMYkWcz549K9pGW00QBIwbN45ZUHjhwoVaOziRkmNdxMTExCAjIwOAqlbOwIED8eTJE9HtXVxcsGPHDlSqVMno89SXo6Mj1q9fjy+++ELnx9hCvR81Uwd/Zs+ezVwW7+LignXr1lEHIiNxc3NDr169RMcPHjyodz01QLUSbs6cOXj48CF+//13BAQElGSa5BVjBX/y8vKwYcMG0XEq9Gw+rOBPZGRkfk2f3bt3o1+/flq7uAwcOBBbt24ttQU/rQ1fJxR5nOp33dOPh/S1q5MzZ85g//79zIYPvZtUQ1/Xgtev3YC/wFf0Nsl8yyoHBwf4+voW+d61JwUt34WEkxBkJe+QpEyJg/zEbNUXHA+7Pr+C420nFcfW6v2o8a7VEK+oDgDwq8zDv7JuqV+K8PkQ7qt+Zs69Nux6LjTtRBmuXr2KFi1aICIiQuu23bt3R0RERLHndGlHwZ8yqEGDBnBxcdE4lpKSgpiYGObjd+zYwSxi2q1bN/Tt27dEcyS6qVy5Mnx8fDSOKZVKXLp0CYIg4KOPPtKaTrR69WqravXN8zwWL16MX37RnuPt7e1tE/V+1ExZ9Pn06dOYNm0ac5vffvsN9erVK9FxSFGs1C99tW3bFhs2bEBcXBy++eYbVKxY0Wj7JsYr+rxv3z7R1XUSiQRvvfWWxjFifLVr1xb9LBQEASdOnMDWrVsxcOBArTW23nrrLWzYsIGC42bESe2RU6MDAKCiE4d2XkUvOs+ePYuxY8eKPr6iE7DmDQk4pWpls6TteEgC+oluTwynKfVL3fWLV+RCmWBYSrOaIAiQ7x0PyFVBJEmrT8HXaFKifZqbrdX7KUzh3TP/36+nfmlq+a5MvAT58VfF1TkedoP+AedomfpoO3bsQLt27fDgwQOt23755ZfYvXt3qS7sLIaCP2WQVCplnvyyItY5OTn46quvRMclEgkWL15MBS7NiPW3PHfuHBYsWKB16eOPP/6IQYMGGXtqJcZxHL7++musWbOGmYrGuutrjRo0aCB6YZGUlISkpCSD9puamoq3335bdLkxoKpBMnLkSIP2T8T16dOnRHVBpFIphg8fjvPnz+P06dMYMmSI1bWCLS2aNm0qmjZ39+5dZi27wlgpX926daNC6mbG+hyYOXMmhgwZArlcztzHyJEjsXbtWnrtWUCFVgUr5foHFF3lkZaWJlpbiwOw5xNPOMlUr1vOqy2kXWaYbJ5lnca6P4VSv5R39pdo/8qb26CMfZVeVK4mpJ2mlmh/5qZUKpnBH2te+QMAvr3H5//79RTM11u+C3mZkG19D1Cq3lclHSaB9zJ/cEsQBMydOxcDBw7UuspaKpXijz/+wOLFiy1a4sKSKPhTRmlL/RKzaNEixMWJV/P/7LPPKDXBzFh5qitXrsQ333zDfPyAAQMwdap1f7iOGDFCtOimRCJhFoC0Rvb29mjUqJHouCFFn9UrvO7fvy+6jbe3N3777TcKzpqAk5MTBgwYoPfjKleujB9++AEJCQn4v//7P2Y3KmIcLi4uzKLruqz+SUtLw65du0THKeXL/FhFny9fvsxMVQeAUaNG4Z9//in1nV6sldSvZ36raU0t38VM6eqMxuVedcl0rgL7N9eAk1DwzlQ0BX+OFmr5rryjuS6MLoScdMj2F5zP2fVcAM6hnMH7s4Tbt2+L1qVydXU1uKOtuThX90Nshupcu2kNHl6vLYwpnPolP/gthOd3AKjqMklDtRdXNrbc3Fy89957+Pbbb7WWLalQoQIOHjyIjz/+2Eyzs04U/CmjDCn6nJSUxCwQW7lyZfz4448lnhvRD2vlT2xsLHMVSFBQEFavXm0TxWO7deuGkydPolmzZvnfc3d3x8qVK22yqKqx6/6sWLECW7ZsER2XSqVYt24dtSs2IX1Svxo0aICVK1fi/v37mDlzJmrUqGHCmZHXlbTuz5YtW5Cbm6txzMXFBf3799c4RkynJEX/x44di+XLl1Pgx4I4x/K4z9cFoGo13cRD+02K0DocvmmnPsfhYPfGKnDla5pwlkRT8OdFLhDxquW7kBIL5fO7Bu1bfmw68FK18pn361WkELitYN1Ab9mypU2sNkmp3C7/3/38i74nrl69Gtu3b0fujR1QXPxL9U07Z1W6l5mDrk+fPkXnzp2xevVqrdvWr18f586dQ6dOncwwM+tm/Vd8xCRYq0Vu3rypMWr97bffMpfT/fTTT6hQoYIxpkf00KhRI4NqE1SoUAE7duxAuXK2c1elSZMmuHDhAk6fPo2TJ0/izp07ePfddy09LYMYM/hz8+ZNfPnll8xtZs2axbzgJSUXFhYGb2/xAqMcx6Ffv344evQorl69ig8++ABOTk5mnCFRK2nwh5XyNWjQING6esR0qlWrZtBd9fHjx2Pp0qU2cROktFN498j/t6auX4V5uAL/DXUGD1XQQdppCiQ+4qu/iHHUr19f4+rhIqlfsfqv/lEmRUJx/nfVF1InSHsutMlVyqzSGdZe70fNs8sn+f9+ve5PfHw8Ph4xEMn/Ds3/nrT7L+Arm7eO5LVr19CyZUudulR369YNERERVOvyFfqkK6MqVqwIf39/jWOCIODcuXNFvhcREYE1a9aI7q9x48YYNWqUUedIdOPg4IAmTfQrhieRSLBx40bRApnWjOM4tG3bFh06dEDlypUtPR2DaSv6rG35qlpOTg6GDRuG7Oxs0W26du1qc6lxtsjOzg5//fVXsfTEcuXK4csvv8SdO3ewY8cOdOrUySZPaksTbcEf1uvv/v37OH78uOg41dSyHFbqlybfffcdFixYQK9HK1En7BMoX732+jKCPxIOWDPYHhUdVLVGeJ8wSDp8a5Y5lnVOTk4ab3IciC1c90e/4I+gVEC26zNAUO1D2vEH8BXqlGielmLL9X7U6jbvjrsvVCuU2npxqPravYw/+klR2Vn1Ot0VrUDwiHmYPXs2s+yAMe3atQvt2rVDQkKC1m0/++wz7NmzB+7u7qafmI2g4E8Zpmvql1Kp1Npye8mSJbRc2oJYqV+aLFiwAF27djXRbIguAgMDRVd9JCcn69StAAAmTZqEa9euiY5XqVLFZlL7SoPOnTvjypUrmDFjBj755BOsXLkSDx8+xOLFi20y2FpaBQUFwdnZWePYs2fPEB8fL/pYVrvp6tWr21wB+tJEn9/99OnTMWvWLAr8WJHyHvVwPUX1ugyqysOnoua/zfQukoKOYOVrqlJO6DPObDSlfkU9EZCU/qruT/wJCDLxG1KvU1xYDuFRJACAqxoESRv2NYe1Sk1Nxc2bN0XHWVkX1oTjONyzCwIA8ByH3oVqcH3UnEdPP9X13uMMAWN3yXH7dgwmT56M2rVro3Pnzli1ahVevnxp9HkJgoBffvkF/fv3R0ZGBnNbiUSC33//Hb/++qtNpNqZE71TlmG6Fn1evXo1s/30kCFDEBISYtS5Ef3o84Hy3nvvaQ3mEdOTSqXMFVu6pH7t2rULv/76K3ObVatWwcPDQ+/5EcP5+PhgypQp+O233/DBBx9QnSUrJJVKi9QPe93rq1/VBEFgroJ9++236UaIBYWEhOgU6P75558xdepUCvxYoQSHgtQ9TYWfe/vx+Krdq4s5Xgr7wWvBudjuKmBbpCn4AxRa/SPPgTL+pE77EtITIT9SUC/Urs9Sg2vHrFmzBs2aNUOrVq3QrVs3nVKCjEnscwMA/P39UbFiRTPOpmTq9yno7KxOwfSrxGFOt4JAypgdcjzLKvq4Y8eO4f3330e1atUwYsQIHDx4UGuxfV3k5ubigw8+wDfffKNTYecDBw5gzJgxJT5uaUTBnzKMtfInIiICCoUC6enp+PZb8aW0Tk5OmDdvnimmR/Sg68qfVq1a4ffff6cTXitRkro/iYmJeP/995nbjB8/Hr169TJoboSUdobU/bl69Spu3Lgh+jjq8mVZ7u7uzPdVAFi4cCHzvIZYln3QwPx/v173p4478NeAgotPabc54GvZxmqK0kQs+HPQgNQv2f5JQJ5qlYik6QfgvQxLjfrjjz/wzjvv4MqVK0hOTsaRI0fQoUMHzJs3T+c0+pJiBZtsJeVLzbfDYLzkVK2+OnvzqOwM/DNICmc71fXDH+cVRVL9XpednY21a9eie/fuqFWrFiZNmoSoqCiD5pKcnIyuXbti1apVWrf18/NDRESE3inAZQkFf8owf39/0RzIly9f4saNG5g1axaePHkiuo9vvvkGXl5eJpoh0VXdunW11r/x8PDA1q1b4ejoaKZZEW0MDf4oFAqMHDkSz58/F92mSZMm+Pnnn0s0P0JKM0OCP6xCz4GBgWjcuHFJp0VK6IMPPhAdW7ZsGcaPH2/G2RB9New4EDeeqi4qW9XiUO1VvREHCbB2sB0qOKkuPvmAgZC0+tRS0yzTxII/R+OUkL2KB+hS9Flx5wCUN191KXWuAmnXnwyaT3x8vMbXtVKpxKRJk/D2228zG9YYC6vej60Ue1bjOA7uLVU3M+wlHPa844CmNVRhg+hkJb4/JNd5X48ePcK8efPQsGFDNGnSBIsWLWJeWxZ2/fp1tGzZEuHh4Vq37dq1KyIiIuDn56fz3MoiCv6UYTzPM9OFVq9ejUWLFomO16pVC19//bUppkb0xHEcc/WPvb09tm3bRu2krYy24I/Y3aq5c+fi2LFjoo91cXHB+vXr4eDgUOI5ElJasYI/kZGRkMlkRb6nUCjw33//iT5mxIgRtKrSCnz44Yfo0KFDke85ODhg5cqVGDt2rIVmRXRVp04dnH7qDkBVb6TXq9SvX3pI8y8+uYq+sOu/nF5vFiLWMCa9SMv3u1A+jxXdh5CXBfmegi6ldt3ngHM2LC1q3LhxyMnJER1fv3492rZti3v37hm0f10oFApERESIjtvayh8AkAT0z/93w2qq/8uUHD7crkC27rGfIq5cuYIJEyagZs2a6N27NzZs2CDasGT37t1o06YNswaf2qeffoq9e/dS12kdUPCnjGNFohcsWFDs5Lew+fPnixbMJOY3aNAg0bE///xT76LQxPTq169frDOUWmpqKuLi4op9/+zZs5g6dSpzv7/++ivd+SBEi9q1a6Nq1aoax7Kzs4uldx07dgyPHj0S3d/bb79t1PkRw0ilUhw7dgwrV67Ep59+iq+++gq3b99mrggi1oPjOPj0KKhL2M+fx9AGPEY3f1VLS+oIuyHrwDlSLTVLcXV1Re3atTWOHbhTUN+FlfolP/kzhLR4AABfJxR8Q8PeP/fu3YsdO3Zo3e7atWto3rw5Dh06ZNBxtLlx44ZoEWI3NzcEBASY5LimxHm1A5yLZhU4dZuJnefv45dffkFwcLDII7VTKBTYu3cvhg4diurVq+Ojjz7CqVOnIAgCBEHAggUL0K9fP50KOy9duhRLly6FnZ1htaLKGgr+lHGGLkMMCQnB4MGDjTwbUhIjRozQuJJk8uTJePfddy0wI6INz/PMorOvp36lpaVh2LBhzOJ5Q4cOxXvvvWesKRJSanEcx1z983rxTlbKV0hIiOjFEDE/iUSCDz74AEuXLsX8+fPpb2Njen04GVlS1R38TnV5/NbPPn9M2msx+OoNLDU18opo0ec7her+iKR+KZ/ehOLMq8wCiT2kfX41aBVXTk6OXg1MUlJS0KNHD5PUAWLV+2ndurVNdlzleAkk9fsUfF27PSRtx6NmzZr4+uuvce3aNVy+fBnjx48XvZGii/T0dKxYsQIhISHw8fFBr169MHHiRK1/I3d3d+zbtw+ffkrpn/qwvWciMaqWLVvq/YbE8zyWLFlCy22tjL29PU6ePIkff/wRISEh6N69OzZu3IiffjIsh5qYh651fwRBwJgxY5CQkCC6fZ06dfDHH3/Qa5MQHela9ycrKwtbtmwR3ZYKPRNiPBzHoVzToQAABykHJ6nqIlDS+B1Im75nwZkRNbHgz/WnAl6iHABAee8EhLyi7aAEpRKy3Z8BSlXekKT9RPCVDVupPH/+fNy9e1evx6jrAA0bNsyodYBKU72fwiRtxwFOFcFV8oP9wL/B8QXdLDmOQ+PGjbFw4UIkJiZiz549eOutt0pUcuDevXvYv3+/1u3q1auHiIgIhIWFGXyssoqCP2VcuXLl0KCBfndQPvroIypqaaWcnJwwbdo0HDt2DL///jveeOMNS0+JaKFr8Oeff/7Bhg0bRLeVSCRYt24d3NzcjDo/QkozXYM/O3fuFF1+bm9vjzfffNPocyOkLOP9+xb5mqsaDGmvxZaZDClGLPgDAJFpr2r3KHKLtXxXXFkN4b5qlQxX0QfS9pMMOn58fDxmzZpl0GMBYMOGDWjbtq3G9HpDlKZOX4XxVfzhMCkR9mMjwbmLN/iRSqXo1asX1q9fj8ePH+Ovv/4qVnvNWLp06YKIiAjUr1/fJPsv7Sj4Q/R6U3Jzc8PMmTNNOBtCypYWLVqIjl26dAlKpRLR0dH4/PPPmfuZOXMms4A7IaQ41uvvxo0bePlS1YKYlfLVp08fKjJJiJHxtTsU1BuxL6eq82NPdSatBSv4sye6oF5o4dQvIfMZ5Ie+z/9a2nsJODvDOtCOHz+eWeRZlzQkY9UBSk5ORmys5uLW2hqy2AKO48BJpDpv7+7ujlGjRuHkyZOIi4vDjBkz4Ovra5S5jBkzBvv27UPFioYVBycU/CHQbzni9OnTUaVKFRPOhpCyxdvbG+7u7hrHXr58iWvXrmHo0KHIysrSuA0AdO7cGZMmGXb3jJCyrGLFiqhXr57GMUEQEBkZieTkZOYydEr5IsT4OIkdpG+sQZZ3X0hH7AJfWfPrlFgGq4DxujNJAK8KFijv7M+v3SI/9B2QnQIA4IOHQOLT1aBj79u3D9u3bxcdDwoKwp07dzB8+HCt+0pNTUWPHj3wyy+/GFwHiJXyFRwcjPLly25x8rp162LKlCmIiYnBmTNnMGbMGNFzXhae5/Hrr7/it99+o8LOJUTBH6Jz8CcgIIDapBJiZBzHMVO/hg4diqtXr4qOV6pUCWvWrIFEIhHdhhAiTlvR5w0bNogWWa9QoQJ69eplqqkRUqbxdUPxosMv4D3FX6PEMtzc3FCzZk2NY89e5iKnShMAgJB6D8LzWCjjT0JxZY1qAwc32HX/xaDj5ubmai3yPH36dLi6umLNmjVYuHCh1vMjpVKJb775xuA6QKW13o8xcRyHNm3a4Pfff8fjx4+xefNm9OvXD1Kp9hVFbm5u2LdvHz777DOqaWkEFPwh8Pb21mk1z+LFiynaSogJsII/t2/fZj521apVqFGjhrGnREiZoa3uDyvla8iQISUqbkkIIbaKlfqVICko4qyM3gnZ7oKAjbTrT+DKVTfomPPnzxdNsQKA9957D02bNgWgCjiMHz8eBw8eRKVKlbTue8OGDWjTpo3edYBYwR9brvdjKg4ODnjjjTewY8cOJCUl4ddffxVNwfb19UVERAS6detm5lmWXhT8IeA4Tmtkul+/fvTCI8REWMEfli+++AJ9+vTRviEhRBQr+HPw4MFiLd8Lo5QvQkhZxUr9OpdckOokPz4TwrNoAABXsyUkzT406HgJCQnMIs/u7u74+eefi32/c+fOuHTpEpo0aaL1GFFRUWjevDkOHjyo05xkMlmR5gCvo5U/bFWqVMFnn32G8+fP4+bNm/juu+8QFhaG5s2bY+7cubh06RL8/f0tPc1ShYI/BAD7zcne3h4LFiww42wIKVtYRWfFNGrUCHPnzjXBbAgpWxo3biy6qlVd8FmTOnXq0Ik9IaTMYq38ORWdDJR/lRYmf1WYmZPAru9ScLxhl5/jx49Hdna26PhPP/0kWui5du3aOH36tE4B+9TUVPTs2VOnOkDXrl0TnVPlypWNVui4LAgICMDs2bNx8OBBXLhwAZMmTSrT9ZJMhYI/BADQu3dv0bHx48fTmxchJlSrVi29Cqk7Oztj/fr1cHQ0rEsGIaSAo6MjGjVqpPfjhg8fDt7AixhCCLF1rODPzZu3IKnXo8j3JK0/A1+9oUHH2r9/P7Zt2yY63rhxY4wZM4a5DycnJ6xevRqLFi3SuQ7Q0KFDmXWAtLV4pxo1xNrQWQsBoKqMP2zYsGLfb9asGX744QcLzIiQskNb0efXLVmyhJbBEmJErNQvMbp0kiGEkNKKFfy5desWOJ+wgm+UrwlpxykGHSc3Nxeff/45c5tly5bp1PiC4ziMGzcOhw4dQuXKlbVuv3HjRmYdIKr3Q2wNBX9IvtWrV2P69Olo2LAhatWqhbFjx+LAgQNwdXW19NQIKfV0Df4MHjwYH35oWL48IUQzfYM/zZo1Y9a7IISQ0q5SpUqiaVbZ2dl4YB8Avk4I4FQJdgNXgnMw7HpiwYIFWos865uC26lTJ1y8eLHEdYC0rfwhxNpQ8Ifkk0qlmDp1Kq5evYr79+9j2bJlOlXHJ4SUnC7Bn9q1a+PPP/+kZcSEGJm+wR8q9EwIIVpSv27Hwv69g3CY9BCSuh0N2n9CQgJ++ukn0XE3NzeD6x+WtA5QUlISEhISNG4vkUgMqudIiKlR8IcQQqyAtuCPRCLBf//9B3d3d/NMiJAypH79+ihXrpxO2/I8j6FDh5p4RoQQYv3YdX9uAkCJblhNmDDB4CLPulDXAVq8eLHedYBYKV+NGjWCi4uLwfMixFQo+EMIIVagRo0aqFWrluj4tGnTqLMQISbC87zOd2nDwsJQvXp1E8+IEEKsny7BH0MdOHAAW7duFR1v1KiR1iLPuuA4Dl9++aXedYA2bNggug2drxFrRcEfQgixEqNHj9b4/dDQUHz33Xdmng0hZUurVq102o5SvgghRMVUwR9dizxLpVKDj/E6dR2gpk2bat02KioKmzZtEh2nej/EWlHwhxBCrMS4cePQr1+/It9r164dNm/erFMXC0KI4XSp++Ps7IwBAwaYfjKEEGIDtAV/1PVx9LVw4ULcuXNHdPzdd99Fu3btDNo3S+3atREeHl7iID+t/CHWynjhUkIIISXi6uqKbdu2ITw8HNeuXUNQUBDatWsHe3t7S0+NkFJPl+DPwIEDqQMmIYS8UrVqVVSsWBEpKSnFxjIyMvDw4UNmSrsm9+/fx8yZM0XHS1LkWRfqOkDNmzfHV199BYVCodfjq1evjtq1a5todoSUDK38IYQQK8LzPEJCQvDZZ5+hU6dOFPghxExq1KiBmjVrMrcZOXKkmWZDCCHWj+M4o6d+aSvyPHPmTFSrVk3v/epDXQfo8OHDOtUBKqxt27bUlZVYLQr+EEIIIYSAvfqnWrVq6NKlixlnQwgh1s+YwZ+DBw9iy5YtouMNGzbEJ598otc+S6Jjx464dOmSTnWA1KjeD7FmFPwhhBBCCAG76POwYcOMWlyUEEJKA2MFfyxR5FkXXl5eCA8P13nlJ9X7IdaMgj+EEEIIIVCldTk6Ohb7voODg1FaChNCSGljrODPokWLEBMTIzr+zjvvoH379nrNzVicnJzw77//YsmSJcwGHA4ODnqtEiLE3Cj4QwghhBACVd2fefPmFbmzzPM85s+fj/r161twZoQQYp2M0fFLW5Hn8uXL45dffjFofsbCcRy++OILZh2g0aNHa7yBQIi1oPXLhBBCCCGvfPbZZwgNDcWBAweQlZWFYcOGoV69epaeFiGEWKUaNWqgfPnySE9PLzaWlpaGx48fw8PDg7mPr776CllZWaLj5ijyrCt1HaDhw4cjPDw8//shISGYMmWKBWdGiHYU/CGEEEIIKaRBgwZo0KCBpadBCCFWT93xKyIiQuP4zZs3mcGfQ4cOYfPmzaLjDRs2xNixY0s8T2Py8vLCyZMnER4ejlu3bsHT0xM9evQAz1NSDbFu9AwlhBBCCCGEEGIQQ+v+5OXlaS3yvHTpUqssts9xHDp06IDRo0ejV69eFPghNoGepYQQQgghhBBCDGJo8GfRokW4ffu26PjIkSPRoUOHEs2NEFKAgj+EEEIIIYQQQgxiSPDnwYMHmDFjhujjrKHIMyGlDQV/CCGEEEIIIYQYhBX8uXXrlsbvayvyPGPGDFSvXr3EcyOEFKDgDyGEEEIIIYQQg9SqVQsuLi4ax5KTk5GcnFzke4cPH8amTZtE99egQQN8+umnRp0jIYSCP4QQQgghhBBCDMTzPAICAkTHC6/+ycvLw2effcbc37Jly6yyyDMhto6CP4QQQgghhBBCDKZr3R9tRZ5HjBhBRZ4JMREK/hBCCCGEEEIIMZguwZ+HDx9i5syZotuVL18e8+bNM/rcCCEqFPwhhBBCCCGEEGIwXYI/X331FTIzM0W3mz59OhV5JsSEKPhDCCGEEEIIIcRg2oI/hw8fxsaNG0W3adCggdZaQISQkqHgDyGEEEIIIYQQg9WpUweOjo4axx49eoSPP/6Y+filS5dSkWdCTIyCP4QQQgghhBBCDCaRSODv7y86HhcXJzo2fPhwhISEmGJahJBCKPhDCCGEEEIIIaREWO3exZQrV46KPBNiJhT8IYQQQgghhBBSIqy6P2KmT58ODw8PE8yGEPI6g4I/q1atQu/evRESEoK3334bL1++zP9+165d0blzZyxZsgSCIOQ/5saNGxg2bBjatWuH0aNH49GjR/ljOTk5mDJlCkJCQtC7d2/s37+/yPF27dqFXr16ITQ0FNOnT4dMJjNk2oQQQgghhBBCTEDf4E9wcDAVeSbEjPQO/qxfvx5nzpzBihUrcOLECcyYMQP29vYIDw/H5s2bsWrVKmzcuBHh4eHYuXMnACAvLw+TJk3C0KFDcfToUQQHB2Pq1Kn5+1y+fDlevHiBvXv3Yvbs2ZgzZw4SEhIAALGxsVi0aBHmz5+PPXv2ICkpCStXrjTSj08IIYQQQgghpKT0Df4sW7YMdnZ2JpoNIeR1egV/FAoF/vnnH/zwww/w8PAAx3Hw9fWFg4MD9u7dizfffBOenp6oXLkyRowYgX379gEALl26BCcnJ/Tv3x8ODg746KOPcPPmzfzVP3v37sXo0aPh6uqKRo0aISQkBAcPHgQA7N+/H2FhYQgMDISrqytGjRqVv19CCCGEEEIIIZbn4+OjczDn7bffpiLPhJiZXv30nj59itzcXBw+fBjr16+Hq6sr3n77bbz55pu4d+8eevXqlb+tn58fli1bBkBV3d3X1zd/zMnJCZ6enoiLi4OLiwueP39eZNzPzw83btzIf2ybNm3yx+rVq4fExETk5ORobCeYl5eHvLy8oj+kVAp7e3t9flSrolQqi/yfEG3oOUP0Rc8Zoi96zhB90XOG6IOeL7ZHIpEUuY4TU65cOcydO9fof1t6zhB9labnDM9rX9ejd/AnIyMDDx8+xM6dO5GYmIixY8eiTp06yMrKgqura/62Li4uyMrKAgBkZ2fDxcWlyL5cXFyQnZ2NrKwsSCSSIoEc1mPVx8jOztYY/Pnnn3/w119/Ffne4MGDMWTIEH1+VKv04MEDS0+B2Bh6zhB90XOG6IueM0Rf9Jwh+qDni22pXbu21uDP559/DplMll/mw9joOUP0VRqeM3Xr1tW6jV7BHwcHBwDA6NGj4ejoCB8fH/Tq1QunT5+Gs7MzMjIy8rfNzMyEs7MzANVKn8zMzCL7yszMhJOTE5ydnaFQKIqs5GE9Vn0MJycnjXN8//33MXz48KI/ZClY+fPgwQPUqlVLp4geIfScIfqi5wzRFz1niL7oOUP0Qc8X29S8eXPs3btXdDwoKAhTp041Sa0fes4QfZW154xewZ/atWuLvlDr1q2L2NhYtG/fHgAQExMDb29vAIC3tze2bduWv212djYePnwIb29vlC9fHpUqVUJsbCyCg4M1PjY2Njb/sXfu3EHNmjU1rvoBAHt7e5sO9LDwPF8mnpTEeOg5Q/RFzxmiL3rOEH3Rc4bog54vtiUoKIg5vmzZsvwFBaZCzxmir7LynNHrJ3RyckKXLl2wcuVK5OXlIT4+Hvv27UO7du3Qq1cvbNmyBYmJiXj27BnWrl2Lnj17AgCaNWuG7Oxs7Nq1C3l5eVi5ciUCAwPh4eEBAOjVqxdWrFiBzMxMREVF4eTJkwgLCwMA9OjRA4cPH0Z0dDQyMjLw999/5++XEEIIIYQQQoh16Nixo+iN+GHDhiE0NNTMMyKEqOkd3vrmm2+QlpaGrl274vPPP8eoUaPQvHlztG/fHoMGDcI777yDwYMHo127dujXrx8A1WqcX375BWvXrkWnTp1w9epVzPh/9u48zqb6j+P468yOwdjHrohs2aJmTMZIKUVECsWgSEQJlYpCDKlImyIkkhZ+qYg0ozBKixTKEmMbS1nGMsOY+/39cXIzzdw7c5l93s/Hw8Pcc77n3O+533PPPfdzv9/Pd+xY5z4HDBhAYGAgt9xyC0888QRPPPEENWrUAKBWrVo88sgjPProo7Rv354KFSrQt2/frDl6ERERERERyRLly5dnwIABaZbXrFmTl156KRdqJLmhRo0aTJ06NberIf/h0bAvsLOzv/DCC+mu69OnD3369El3Xf369Vm4cGG66wICAhg/frzL5+zQoQMdOnTwtKoiIiIiIiKSg6ZOnUqVKlVYsGAB8fHx3HzzzUyYMIHg4ODcrlqhY1mW2/W9e/dmzpw5brdfvHgxnTp1ytqKSa7wOPgjIiIiIiIikh4vLy9GjhzJyJEjc7sqhV58fLzz7w8++IDRo0fzxx9/OJe5mkRJCqaCn9VIREREREREJIsUL14cf3//XP9XvHhxt/UMDg52/itZsiSWZaVatmDBAmrWrImfnx916tRh3rx5zm0vpGHp3LkzlmU5H+/cuZM77riDChUqEBgYSPPmzfnqq6+y66WWLKSePyIiIiIiIiKZdO7cOc6dO5fb1bgsixcvZujQoUydOpW2bdvy2Wef0adPH6pUqUJERAQbNmygfPnyzJ49m1tuuQVvb28ATp06Rfv27Rk/fjwBAQHMnTuXDh068Mcff1CtWrVcPipxR8EfERERERERkUJkypQpREZG8tBDDwEwbNgw1q9fz5QpU4iIiKBcuXIABAUFpcrX1KhRIxo1auR8PH78eBYvXsynn37K4MGDc/YgxCMa9iUiIiIiIiJSiGzdupWWLVumWtayZUu2bt3qdrvTp08zcuRI6tWrR1BQEIGBgfz+++/s2bMnO6srWUA9f0REREREREQKmf/OBmaMyXCGsBEjRvDll18yZcoUatWqRZEiRejatWu+HwZXGKjnj4iIiIiIiEghUrduXdasWZNq2bp166hbt67zsa+vLykpKanKfPvtt0RGRtK5c2caNmxIcHAwu3fvzokqy2VSzx8RERERERGRTPLz88vtKgCXV48RI0bQrVs3mjZtyo033sjSpUv55JNPUs3cVaNGDVatWkXLli3x9/enVKlS1KpVi08++YQOHTpgWRbPPPMMDocjKw5HspmCPyIiIiIiIiKZdPLkydyuwmXr1KkT06ZN44UXXmDIkCFcccUVzJ49m9atWzvLvPjiiwwbNoy3336bypUrs3v3bl5++WX69u1LaGgoZcuW5fHHHychISH3DkQyzTLGmNyuhLjncDiIi4ujevXqeHlppJ5kTOeMeErnjHhK54x4SueMeELni3hK54x4qrCdMwX/CEVERERERERECjEFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0REREREREQKoMjISCzLIioqKtXyJUuWYFmW8/GMGTNo1KgRxYoVIygoiCZNmjBp0iQAli9fjmVZHDx4MNU+goODqVq1aqpl+/btw7IsVqxYkU1HJJdKwR8RERERERGRAiogIIBJkyZx7NixdNfPmjWLYcOGMWTIEH755RfWrl3LyJEjOXXqFABhYWH4+PgQExPj3Gbr1q0kJSWRkJDAjh07nMujo6Px9fWlZcuW2XpM4jmf3K6AiIiIiIiISH5RvJ2Dc+dzuxbg5wMnv8y4P0fbtm3ZsWMHEydOZPLkyWnWL126lG7dutGvXz/nsvr16zv/DgwMpHnz5sTExHDPPfcAEBMTQ1hYGMYYYmJiqFWrlnN5ixYtKFas2OUenmQx9fwRERERERERyaRz5+Fcch74l8kAlLe3NxMmTGD69Ons27cvzfrg4GDWr19PXFycy31EREQQHR3tfBwdHU3r1q0JDw9PszwiIiLzL6bkGAV/RERERERERAqwzp0707hxY8aMGZNm3ZgxYwgKCqJGjRrUqVOHyMhIFi1ahMPhcJZp3bo127ZtIz4+HoDVq1cTHh5OeHi4czjY3r172bVrl4I/eZSCPyIiIiIiIiIF3KRJk5g7dy5btmxJtbxixYrExsby66+/MmTIEJKTk+nduze33HKLMwDUsmVL/Pz8iImJYcuWLSQmJtK0aVOaNWtGQkIC27dvJzo6Gn9/f0JDQ3Pj8CQDCv6IiIiIiIiIFHCtWrWiXbt2jBo1Kt31DRo0YNCgQcyfP5+VK1eycuVKVq9eDUDRokVp0aIF0dHRREdHExYWhre3Nz4+PoSGhjqXh4SEEBAQkJOHJZmkhM8iIiIiIiIihUBUVBSNGzemdu3absvVq1cPgNOnTzuXRUREsHDhQo4dO0br1q2dyy8M/YqNjaVPnz7ZUm+5fAr+iIiIiIiIiGSSXx75Fn0p9WjYsCE9e/Zk+vTpzmUDBw6kUqVKtGnThipVqhAfH8/48eMpV64cISEhznIRERGMGzeO+Ph4hg8f7lweHh5OVFQUJ0+eVL6fPCyPnLYiIiIiIiIieV9mplfPy8aNG8eiRYucj9u2bcs777zDG2+8wd9//03ZsmUJCQlh1apVlClTxlkuJCQEf39/AJo1a+Zc3rx5c1JSUihSpAjXXXddzh2IeETBHxEREREREZECaM6cOWmWVa9enaSkJOfjLl260KVLlwz3FRAQkGq7C/z8/FIND5O8KX+HLEVERERERERExC0Ff0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0RERERERERECjAFf0REREREREQkz4iJicGyLI4fP57bVSkwFPwRERERERERKYAiIyOxLIuoqKhUy5csWYJlWamWzZgxg0aNGlGsWDGCgoJo0qQJkyZNAmD58uVYlsXBgwdTbRMcHEzVqlVTLdu3bx+WZbFixYpsOCK5VAr+iIiIiIiIiBRQAQEBTJo0iWPHjrksM2vWLIYNG8aQIUP45ZdfWLt2LSNHjuTUqVMAhIWF4ePjQ0xMjHObrVu3kpSUREJCAjt27HAuj46OxtfXl5YtW2bbMYnnfHK7AiIiIiIiIiL5xZfVvsIkO3K7Gli+XrTb0zbDcm3btmXHjh1MnDiRyZMnp1tm6dKldOvWjX79+jmX1a9f3/l3YGAgzZs3JyYmhnvuuQewh2aFhYVhjCEmJoZatWo5l7do0YJixYpdzuEBsHbtWkaNGsUff/xBo0aNmDlzJg0bNgTg2WefZcmSJWzcuNFZfurUqUydOpXdu3c76zJy5Eg2b96Mr68v9evXZ8GCBVSvXv2y65bfqOePiIiIiIiISCaZZAeOcybX/2U2AOXt7c2ECROYPn06+/btS7dMcHAw69evJy4uzuV+IiIiiI6Odj6Ojo6mdevWhIeHp1keERGRyVfTvREjRjBlyhQ2bNhA+fLl6dixI8nJyZna9vz583Tq1Inw8HA2bdpEbGws/fv3TzPcrbBQ8EdERERERESkAOvcuTONGzdmzJgx6a4fM2YMQUFB1KhRgzp16hAZGcmiRYtwOP4NMLVu3Zpt27YRHx8PwOrVqwkPDyc8PNw5HGzv3r3s2rUry4I/Y8aM4aabbqJhw4bMnTuXQ4cOsXjx4kxtm5CQwIkTJ7j99tupWbMmdevWpXfv3lSrVi1L6pbfKPgjIiIiIiIiUsBNmjSJuXPnsmXLljTrKlasSGxsLL/++itDhgwhOTmZ3r17c8sttzgDQC1btsTPz4+YmBi2bNlCYmIiTZs2pVmzZiQkJLB9+3aio6Px9/cnNDQ03Trs2bOHwMBA578JEya4rXNISIjz79KlS1OnTh22bt2aqeMtXbo0kZGRtGvXjg4dOjBt2jRn4KowUvBHREREREREpIBr1aoV7dq1Y9SoUS7LNGjQgEGDBjF//nxWrlzJypUrWb16NQBFixalRYsWREdHEx0dTVhYGN7e3vj4+BAaGupcHhISQkBAQLr7r1SpEhs3bnT+e/DBBz0+jgvDtry8vDDGpFr33yFhs2fPJjY2ltDQUD744ANq167N+vXrPX7OgkAJn0VEREREREQKgaioKBo3bkzt2rUzLFuvXj0ATp8+7VwWERHBwoULOXbsGK1bt3YuvzD0KzY2lj59+rjcp4+PjzMxdGasX7/eOUzr2LFjbNu2jauvvhqAcuXKcfDgQYwxzoDQxcmfL2jSpAlNmjThySefJCQkhAULFnD99ddnug4FhYI/IiIiIiIiIplk+XrhRd6Y7ctTDRs2pGfPnkyfPj3V8oEDB1KpUiXatGlDlSpViI+PZ/z48ZQrVy7V0KuIiAjGjRtHfHw8w4cPdy4PDw8nKiqKkydPZlm+H4CxY8dSpkwZKlSowFNPPUXZsmXp1KkTYOcgOnLkCJMnT6Zr164sX76cZcuWUaJECQB27drFW2+9RceOHalUqRJ//PEH27Zto1evXllWv/xEwR8RERERERGRTMrM9Op52bhx41i0aFGqZW3btuWdd97hjTfe4O+//6Zs2bKEhISwatUqypQp4ywXEhKCv78/AM2aNXMub968OSkpKRQpUoTrrrsuy+oaFRXF0KFD2b59O40aNeLTTz/Fz88PgLp16/L6668zYcIExo0bR5cuXRg+fDhvvfUWYA9T+/3335k7dy5///03FStWZPDgwQwYMCDL6pefWOa/g+Qkz3E4HMTFxVG9enW8vJSmSTKmc0Y8pXNGPKVzRjylc0Y8ofNFPKVzRjxV2M6Zgn+EIiIiIiIiIiKFmII/IiIiIiIiIiIFmII/IiIiIiIiIiIFmII/IiIiIiIiIiIFmII/IiIiIiIiIiIFmII/IiIiIiIiIiIFmII/IiIiIiIiIiIFmII/IiIiIiIiIiIFmII/IiIiIiIiIiIFmII/IiIiIiIiIpJnxMTEYFkWx48fv6z9WJbFkiVLsqRO+Z2CPyIiIiIiIiIFUGRkJJZlERUVlWr5kiVLsCwr1bIZM2bQqFEjihUrRlBQEE2aNGHSpEkALF++HMuyOHjwYKptgoODqVq1aqpl+/btw7IsVqxYkQ1HJJfKJ7crICIiIiIiIpJfVPvka845HLldDfy8vNhzZ5sMywUEBDBp0iQGDBhAqVKl0i0za9Yshg0bxiuvvEJ4eDhnz55l06ZNbNmyBYCwsDB8fHyIiYnhnnvuAWDr1q0kJSWRmJjIjh07qFWrFgDR0dH4+vrSsmXLLDpSyQrq+SMiIiIiIiKSSeccDs45TB74l7kAVNu2bQkODmbixIkuyyxdupRu3brRr18/atWqRf369enevTvjxo0DIDAwkObNmxMTE+PcJiYmhrCwMMLCwtIsb9GiBcWKFbuk1/diP/74I9deey1FixYlNDSUP/74I9X6N954g5o1a+Ln50edOnWYN2/eZT9nQaXgj4iIiIiIiEgB5e3tzYQJE5g+fTr79u1Lt0xwcDDr168nLi7O5X4iIiKIjo52Po6OjqZ169aEh4enWR4REZEldX/qqad48cUX+eGHH/Dx8aFv377OdYsXL2bo0KE89thj/PbbbwwYMIA+ffqkqov8S8EfERERERERkQKsc+fONG7cmDFjxqS7fsyYMQQFBVGjRg3q1KlDZGQkixYtwnFR76LWrVuzbds24uPjAVi9ejXh4eGEh4c7e/7s3buXXbt2ZVnw5/nnnyc8PJx69erxxBNPsG7dOpKSkgCYMmUKkZGRPPTQQ9SuXZthw4Zx5513MmXKlCx57oJGwR8RERERERGRAm7SpEnMnTvXmcfnYhUrViQ2NpZff/2VIUOGkJycTO/evbnlllucAaCWLVvi5+dHTEwMW7ZsITExkaZNm9KsWTMSEhLYvn070dHR+Pv7Exoamm4d9uzZQ2BgoPPfhAkT3Nb5mmuuSVVHgMOHDwN2zqH/5hVq2bIlW7duzfyLUogo4bOIiIiIiIhIAdeqVSvatWvHqFGjiIyMTLdMgwYNaNCgAYMGDWLNmjXccMMNrF69moiICIoWLUqLFi2Ijo7m6NGjhIWF4e3tDUBoaCjR0dHExsYSEhJCQEBAuvuvVKkSGzdudD4uXbq02zr7+vo6/74wO9nFvZH+O2OZMSbNMrEp+CMiIiIiIiKSSX5eXkDemO3LU1FRUTRu3JjatWtnWLZevXoAnD592rksIiKChQsXcuzYMVq3bu1cfmHoV2xsLH369HG5Tx8fH+esYJerbt26rFmzhl69ejmXrVu3jrp162bJ/gsaBX9EREREREREMikz06vnVQ0bNqRnz55Mnz491fKBAwdSqVIl2rRpQ5UqVYiPj2f8+PGUK1eOkJAQZ7mIiAjGjRtHfHw8w4cPdy4PDw8nKiqKkydPZlm+n4yMGDGCbt260bRpU2688UaWLl3KJ598wldffZUjz5/fKOePiIiIiIiISCExbtw4jDGplrVt25b169dz1113Ubt2bbp06UJAQACrVq2iTJkyznIhISH4+/sD0KxZM+fy5s2bk5KSQpEiRbjuuuty5Dg6derEtGnTeOGFF6hfvz4zZsxg9uzZqXokyb8s899WlzzH4XAQFxdH9erV8bqErn1S+OicEU/pnBFP6ZwRT+mcEU/ofBFP6ZwRTxW2c6bgH6GIiIiIiIiISCGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiI5BkxMTFYlsXx48dzuyoeycv1VvBHREREREREpACKjIzEsiyioqJSLV+yZAmWZaVaNmPGDBo1akSxYsUICgqiSZMmTJo0CYDly5djWRYHDx5MtU1wcDBVq1ZNtWzfvn1YlsWKFSuy4YjyttDQUOLj4ylZsiQAc+bMISgoKHcr9Q+f3K6AiIiIiIiISH5RaUwVzqWcy+1q4Oftx4Hn9mVYLiAggEmTJjFgwABKlSqVbplZs2YxbNgwXnnlFcLDwzl79iybNm1iy5YtAISFheHj40NMTAz33HMPAFu3biUpKYnExER27NhBrVq1AIiOjsbX15eWLVtm0ZHmH35+fgQHB+d2NdKlnj8iIiIiIiIimXQu5Vye+ZcZbdu2JTg4mIkTJ7oss3TpUrp160a/fv2oVasW9evXp3v37owbNw6AwMBAmjdvTkxMjHObmJgYwsLCCAsLS7O8RYsWFCtW7JJe34utXbuWRo0aERAQwHXXXcevv/7qXPfss8/SuHHjVOWnTp1KjRo10q1LUFAQLVu2JC4u7rLqdPbsWUaOHEnVqlXx9/fnqquuYtasWc7nuzDsKyYmhj59+nDixAksy8KyLJ599lnGjh1Lw4YN0+y3WbNmjB49+rLq5o6CPyIiIiIiIiIFlLe3NxMmTGD69Ons25d+T6Hg4GDWr1/vNjASERFBdHS083F0dDStW7cmPDw8zfKIiIgsqfuIESOYMmUKGzZsoHz58nTs2JHk5ORMbXv+/Hk6depEeHg4mzZtIjY2lv79+6cZ7uapXr16sXDhQl555RW2bt3Km2++SWBgYJpyoaGhTJ06lRIlShAfH098fDzDhw+nb9++bNmyhQ0bNjjLbtq0iZ9//pnIyMjLqps7Cv6IiIiIiIiIFGCdO3emcePGjBkzJt31Y8aMISgoiBo1alCnTh0iIyNZtGgRDofDWaZ169Zs27aN+Ph4AFavXk14eDjh4eHOnj979+5l165dWRb8GTNmDDfddBMNGzZk7ty5HDp0iMWLF2dq24SEBE6cOMHtt99OzZo1qVu3Lr1796ZatWqXXJ9t27axaNEi3nnnHTp37syVV17JjTfeyN13352mrJ+fHyVLlsSyLIKDgwkODiYwMJAqVarQrl07Zs+e7Sw7e/ZswsPDufLKKy+5bhlR8EdERERERESkgJs0aRJz58515vG5WMWKFYmNjeXXX39lyJAhJCcn07t3b2655RZnAKhly5b4+fkRExPDli1bSExMpGnTpjRr1oyEhAS2b99OdHQ0/v7+hIaGpluHPXv2EBgY6Pw3YcIEt3UOCQlx/l26dGnq1KnD1q1bM3W8pUuXJjIyknbt2tGhQwemTZvmDFyl59Zbb3XWq379+umW2bhxI97e3oSHh2eqDq488MADvP/++yQlJZGcnMz8+fPp27fvZe0zI0r4LCIiIiIiIlLAtWrVinbt2jFq1CiXw4saNGhAgwYNGDRoEGvWrOGGG25g9erVREREULRoUVq0aEF0dDRHjx4lLCwMb29vwB7iFB0dTWxsLCEhIQQEBKS7/0qVKrFx40bn49KlS3t8HBeGbXl5eWGMSbXuv0PCZs+ezZAhQ1i+fDkffPABTz/9NCtXruT6669Ps9+ZM2eSmJgIgK+vb7rPXaRIEY/rm54OHTrg7+/P4sWL8ff35+zZs3Tp0iVL9u2Kgj8iIiIiIiIimeTn7ZfbVQAurR5RUVE0btyY2rVrZ1i2Xr16AJw+fdq5LCIigoULF3Ls2DFat27tXH5h6FdsbCx9+vRxuU8fHx/nrGCZsX79eucwrWPHjrFt2zauvvpqAMqVK8fBgwcxxjgDQhcHli5o0qQJTZo04cknnyQkJIQFCxakG/ypXLlyhvVp2LAhDoeD1atX07Zt2wzL+/n5kZKSkma5j48PvXv3Zvbs2fj7+3PPPfdQtGjRDPd3ORT8EREREREREcmkzEyvnlc1bNiQnj17Mn369FTLBw4cSKVKlWjTpg1VqlQhPj6e8ePHU65cuVRDryIiIhg3bpwzefEF4eHhREVFcfLkySzL9wMwduxYypQpQ4UKFXjqqacoW7YsnTp1AuwcREeOHGHy5Ml07dqV5cuXs2zZMkqUKAHArl27eOutt+jYsSOVKlXijz/+YNu2bfTq1euS61OjRg169+5N3759eeWVV2jUqBFxcXEcPnyYbt26pVv+1KlTrFq1ikaNGlG0aFFnkOf++++nbt26gD2rWXZTzh8RERERERGRQmLcuHFphku1bduW9evXc9ddd1G7dm26dOlCQEAAq1atokyZMs5yISEh+Pv7A/bU5Bc0b96clJQUihQpwnXXXZdldY2KimLo0KE0a9aM+Ph4Pv30U/z87B5PdevW5fXXX+e1116jUaNGfP/996kCUkWLFuX333+nS5cu1K5dm/79+zN48GAGDBhwWXV644036Nq1Kw899BBXX301DzzwQKreURcLDQ3lwQcf5O6776ZcuXJMnjzZue6qq64iNDSUOnXqZOlr5opl/tvqkuc4HA7i4uKoXr06Xl6K10nGdM6Ip3TOiKd0zoindM6IJ3S+iKd0zoincvucMcZw9dVXM2DAAIYNG5btz6dhXyIiIiIiIiIiOeTw4cPMmzeP/fv3u82RlJUU/BERERERERERySEVKlSgbNmyvPXWW5QqVSpHnlPBHxERERERERGRHJIb2Xc0GFJEREREREREpABT8EdEREREREREpABT8EdEREREREREpABT8EdEREREREREpABT8EdEREREREREpABT8EdEREREREREskSNGjWYOnVqbldD/kPBHxEREREREZECxrIst/8iIyMz3H7JkiU5UlfJfj65XQERERERERERyVrx8fHOvz/44ANGjx7NH3/84VxWpEiR3KiW5BL1/BEREREREREpYIKDg53/SpYsiWVZqZYtWLCAmjVr4ufnR506dZg3b55z2xo1agDQuXNnLMtyPt65cyd33HEHFSpUIDAwkObNm/PVV1/lwtGJpxT8ERERERERESlEFi9ezNChQ3nsscf47bffGDBgAH369CE6OhqADRs2ADB79mzi4+Odj0+dOkX79u356quv+Pnnn2nXrh0dOnRgz549uXYskjka9iUiIiIiIiLiobMzQjGnDuX481qBFfAfsO6y9jFlyhQiIyN56KGHABg2bBjr169nypQpREREUK5cOQCCgoIIDg52bteoUSMaNWrkfDx+/HgWL17Mp59+yuDBgy+rTpK9FPwRERERERER8ZA5dQhO7s/5582CfWzdupX+/funWtayZUumTZvmdrvTp0/z3HPP8dlnn3HgwAHOnz9PYmKiev7kAwr+iIiIiIiIiHjICqyQJYGYS3neLNmPZaV6bIxJs+y/RowYwZdffsmUKVOoVasWRYoUoWvXrpw7dy5L6iTZR8EfEREREREREQ9d7tCr3FS3bl3WrFlDr169nMvWrVtH3bp1nY99fX1JSUlJtd23335LZGQknTt3BuwcQLt3786ROsvlUfBHREREREREpBAZMWIE3bp1o2nTptx4440sXbqUTz75JNXMXTVq1GDVqlW0bNkSf39/SpUqRa1atfjkk0/o0KEDlmXxzDPP4HA4cvFIJLM025eIiIiIiIhIIdKpUyemTZvGCy+8QP369ZkxYwazZ8+mdevWzjIvvvgiK1eupGrVqjRp0gSAl19+mVKlShEaGkqHDh1o164dTZs2zaWjEE9YxpjcGKYoHnA4HMTFxVG9enW8vBSvk4zpnBFP6ZwRT+mcEU/pnBFP6HwRT+mcEU8VtnOm4B+hiIiIiIiIiEghpuCPiIiIiIiIiEgBpuCPiIiIiIiIiEgBpuCPiIiIiIiIiEgBpuCPiIiIiIiIiEgBpuCPiIiIiIiIiEgBpuCPiIiIiIiIiEgBdsnBn02bNtG8eXPmzJnjXDZnzhzatm1LmzZtmDZtGsYY57rNmzfTvXt3WrZsSf/+/YmPj3euS0pK4plnnqFVq1bcdtttLF++PNVzLV26lPbt2xMeHs5zzz1HcnLypVZbRERERERERKRQuaTgj8Ph4KWXXqJevXrOZWvWrOGjjz5izpw5LFq0iDVr1vDpp58CcO7cOUaOHMk999zD119/TYMGDRg9erRz2xkzZnDixAm++OILJkyYQFRUFHFxcQDs2LGDl19+mSlTpvD5559z4MABZs2adTnHLCIiIiIiIiJSaFxS8OeTTz6hQYMGXHHFFc5lX3zxBV27dqVKlSqULVuWe++9l2XLlgHw448/UqRIEe644w78/f154IEH2LJli7P3zxdffEH//v0JDAykUaNGtGrVihUrVgCwfPlybrrpJurVq0dgYCD333+/c78iIiIiIiIiIuKej6cbnDhxgvfff5/Zs2fz0ksvOZfv2rWL9u3bOx/Xrl2b1157DYA///yTWrVqOdcVKVKEKlWq8Oeff1KsWDH+/vvvVOtr167N5s2bnduGhIQ411111VXs37+fpKQkAgIC0tTv3LlznDt3LvVB+vjg5+fn6aHmGQ6HI9X/IhnROSOe0jkjntI5I57SOSOe0PkintI5k74+ffrw7rvvMmHCBB5//HHn8iVLltClSxdSUlIAezTOm2++yY4dO/D19eWKK67g7rvvZuTIkSxfvpzbbruN/fv3Exwc7NxHpUqV8PX1dY7aAdi3bx/Vq1dn2bJl3HzzzTl3oJegIJ0zXl4Z9+vxOPjz2muv0b17d0qUKJFq+ZkzZwgMDHQ+LlasGGfOnAEgMTGRYsWKpSpfrFgxEhMTOXPmDN7e3qkCOe62vfAciYmJ6QZ/Zs+ezdtvv51q2V133UW3bt08PdQ8Z+/evbldBclndM6Ip3TOiKd0zoindM6IJ3S+iKd0zqR2+vRp/P39iYqK4tZbb6VkyZIAHDlyBIC4uDg++OADxo4dy+jRo7nuuus4d+4cv//+Ozt27CAuLo5q1arh4+PDRx99RIcOHQA7PcuZM2cwxrB69Wpq1KgBwOLFi/H19aVq1aqpgkJ5WUE4Zy4eleWKR8Gf33//nc2bN6eKGF5QtGhRTp065Xx8+vRpihYtCtg9fU6fPp2q/OnTpylSpAhFixYlJSUlVU8ed9teeI4iRYqkW8c+ffrQs2fP1AdZAHr+7N27l6pVq2Yqoieic0Y8pXNGPKVzRjylc0Y8ofNFPKVzJn3FihWjbdu27Ny5k/nz5zNp0iQAypUrB0D16tWJjY2lW7dujBw50rndTTfdlGo/zZs3Z/PmzQwePBiwU7fccMMNGGPYvn074eHhAPz222+0aNGCunXr5sThXZbCds54FPz56aef2LNnj3N416lTp/D29mbfvn1cccUV7Nixg7CwMAC2bdvGlVdeCcCVV17J4sWLnftJTExk3759XHnllZQoUYIyZcqwY8cOGjRokO62O3bscG67fft2KleunG6vHwA/P798Hehxx8vLq1CclJJ1dM6Ip3TOiKd0zoindM6IJ3S+iKd0zqRmWRY+Pj5MmDCBHj16MHToUKpUqeJ8jby8vKhYsSKrV69m7969VK9ePd39RERE8NFHHzm3W716NRERETgcDlavXk3//v0BiImJoWfPnvmqDQrLOeNR8OfOO+9MNW7vxRdfpGrVqtx333388ssvTJo0iZtuugl/f3/mz5/v7IHTrFkzEhMTWbp0Ke3atWPWrFnUq1ePihUrAtC+fXtmzpzJ888/z59//sk333zjnEL+lltuYcCAAXTu3JkqVarwzjvvcOutt2bR4YuIiIiIiIh4bn3r1pw7fDjHn9evfHmuj4nxaJvOnTvTuHFjxowZk2b27DFjxnDnnXdSo0YNateuTUhICO3bt6dr167OoEjr1q2ZMGEC8fHxzmDRiBEjcDgcTJs2DbCHT+3atYuIiIgsOU7JWh4FfwICAlL1uPH396do0aIUL16csLAwtm/fTq9evXA4HHTq1ImOHTsCdm+cyZMnM27cOKKioqhXrx5jx4517mfAgAGMHz+eW265hRIlSvDEE084xwzWqlWLRx55hEcffZTTp0/Tpk0b+vbtmwWHLiIiIiIiInJpzh0+zNkDB3K7Gpk2adIk2rRpw2OPPZZqecWKFYmNjeW3335j9erVrFu3jt69ezNz5kyWL1+Ol5cXLVu2xM/Pj5iYGBo1akRiYiJNmzbFGENCQgLbt28nNjYWf39/QkNDc+kIxR3LGGNyuxLinsPhIC4ujurVqxeK7mhy+XTOiKd0zoindM6Ip3TOiCd0voincuOcyQ89fyIjIzl+/DhLliwB4LbbbsPX15fIyEg6d+6Mq3DAmjVruOGGG/j666+dPXluuOEG6tatS6NGjfj888/54osvAGjXrh1dunQhNjaW3bt3Ex0dfdnHmBMK23XG49m+RERERERERAo7T4de5QVRUVE0btyY2rVruy1Xr149gFSTL0VERLBw4UKOHTtG69atncvDw8OJiYkhNjaWPn36ZEu95fIV/PCWiIiIiIiIiNCwYUN69uzJ9OnTncsGDhzIuHHjWLt2LXFxcaxfv55evXpRrlw5QkJCnOUiIiLYvn07y5cvd87uBXbw57PPPmP37t3K95OHKfgjIiIiIiIiUkiMGzcu1XCvtm3bsn79eu666y5q165Nly5dCAgIYNWqVZQpU8ZZLiQkBH9/f8Ce1OmC5s2bk5KSQpEiRbjuuuty7kDEIxr2JSIiIiIiIlIAXZhF+2LVq1cnKSnJ+bhLly506dIlw30FBASk2u4CPz+/VMPDJG9Szx8RERERERERkQJMwR8RERERERERkQJMwR8RERERERERkQJMwR8RERERERERkQJMwR8RERERERERkQJMwR8RERERERERkQJMwR8RERERERERkQJMwR8RERERERERkQJMwR8RERERERERkQJMwR8RERERERERyTNiYmKwLIvjx4/ndlUKDAV/RERERERERAqgyMhILMsiKioq1fIlS5ZgWVaqZTNmzKBRo0YUK1aMoKAgmjRpwqRJkwBYvnw5lmVx8ODBVNsEBwdTtWrVVMv27duHZVmsWLEiG45ILpWCPyIiIiIiIiIFVEBAAJMmTeLYsWMuy8yaNYthw4YxZMgQfvnlF9auXcvIkSM5deoUAGFhYfj4+BATE+PcZuvWrSQlJZGQkMCOHTucy6Ojo/H19aVly5bZdkziOQV/RERERERERAqotm3bEhwczMSJE12WWbp0Kd26daNfv37UqlWL+vXr0717d8aNGwdAYGAgzZs3TxX8iYmJISwsjLCwsDTLW7RoQbFixS677mvXrqVRo0YEBARw3XXX8euvvzrXPfvsszRu3DhV+alTp1KjRo106xIUFETLli2Ji4u77HrlRwr+iIiIiIiIiBRQ3t7eTJgwgenTp7Nv3750ywQHB7N+/Xq3gZGIiAiio6Odj6Ojo2ndujXh4eFplkdERGRJ3UeMGMGUKVPYsGED5cuXp2PHjiQnJ2dq2/Pnz9OpUyfCw8PZtGkTsbGx9O/fP81wt8LCJ7crICIiIiIiIpLfXHvttWly4OSE4OBgfvjhB4+26dy5M40bN2bMmDHMmjUrzfoxY8Zw5513UqNGDWrXrk1ISAjt27ena9eueHnZfUZat27NhAkTiI+Pp2LFiqxevZoRI0bgcDiYNm0aAHv37mXXrl1ZFvwZM2YMN910EwBz586lSpUqLF68mG7dumW4bUJCAidOnOD222+nZs2aANStWzdL6pUfKfgjIiIiIiIi4qGDBw+yf//+3K5Gpk2aNIk2bdrw2GOPpVlXsWJFYmNj+e2331i9ejXr1q2jd+/ezJw5k+XLl+Pl5UXLli3x8/MjJiaGRo0akZiYSNOmTTHGkJCQwPbt24mNjcXf35/Q0NB067Bnzx7q1avnfDxq1ChGjRrlss4hISHOv0uXLk2dOnXYunVrpo63dOnSREZG0q5dO2666Sbatm1Lt27dqFixYqa2L2gU/BERERERERHxUHBwcL563latWtGuXTtGjRpFZGRkumUaNGhAgwYNGDRoEGvWrOGGG25g9erVREREULRoUVq0aEF0dDRHjx4lLCwMb29vAEJDQ4mOjiY2NpaQkBACAgLS3X+lSpXYuHGj83Hp0qU9Po4Lw7a8vLwwxqRa998hYbNnz2bIkCEsX76cDz74gKeffpqVK1dy/fXXe/y8+Z2CPyIiIiIiIiIe8nToVV4QFRVF48aNqV27doZlL/TQOX36tHNZREQECxcu5NixY7Ru3dq5PDw8nJiYGGJjY+nTp4/Lffr4+FCrVq1M13f9+vVUq1YNgGPHjrFt2zauvvpqAMqVK8fBgwcxxjgDQhcHli5o0qQJTZo04cknnyQkJIQFCxYUyuCPEj6LiIiIiIiIFAINGzakZ8+eTJ8+PdXygQMHMm7cONauXUtcXBzr16+nV69elCtXLtXQq4iICLZv387y5csJDw93Lg8PD+ezzz5j9+7dWZbvB2Ds2LGsWrWK3377jcjISMqWLUunTp0AOwfRkSNHmDx5Mjt37uS1115j2bJlzm137drFk08+SWxsLHFxcaxYsYJt27YV2rw/Cv6IiIiIiIiIFBLjxo1LM1yqbdu2rF+/nrvuuovatWvTpUsXAgICWLVqFWXKlHGWCwkJwd/fH4BmzZo5lzdv3pyUlBSKFCnCddddl2V1jYqKYujQoTRr1oz4+Hg+/fRT/Pz8ADt58+uvv85rr71Go0aN+P777xk+fLhz26JFi/L777/TpUsXateuTf/+/Rk8eDADBgzIsvrlJ5b5b6tLnuNwOIiLi6N69erOTOsi7uicEU/pnBFP6ZwRT+mcEU/ofBFP6ZwRTxW2c6bgH6GIiIiIiIiISCGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiISAGm4I+IiIiIiIiI5BkxMTFYlsXx48cvaz+WZbFkyZIsqVN+p+CPiIiIiIiISAEUGRmJZVlERUWlWr5kyRIsy0q1bMaMGTRq1IhixYoRFBREkyZNmDRpEgDLly/HsiwOHjyYapvg4GCqVq2aatm+ffuwLIsVK1ZkwxHJpVLwR0RERERERKSACggIYNKkSRw7dsxlmVmzZjFs2DCGDBnCL7/8wtq1axk5ciSnTp0CICwsDB8fH2JiYpzbbN26laSkJBISEtixY4dzeXR0NL6+vrRs2TLbjkk8p+CPiIiIiIiISAHVtm1bgoODmThxossyS5cupVu3bvTr149atWpRv359unfvzrhx4wAIDAykefPmqYI/MTExhIWFERYWlmZ5ixYtKFas2GXX/ccff+Taa6+laNGihIaG8scff6Ra/8Ybb1CzZk38/PyoU6cO8+bNu+znLKgU/BEREREREREpoLy9vZkwYQLTp09n37596ZYJDg5m/fr1xMXFudxPREQE0dHRzsfR0dG0bt2a8PDwNMsjIiKypO5PPfUUL774Ij/88AM+Pj707dvXuW7x4sUMHTqUxx57jN9++40BAwbQp0+fVHWRf/nkdgVERERERERE8ptrH3Bw8GjOP29wafjhbc/6cXTu3JnGjRszZswYZs2alWb9mDFjuPPOO6lRowa1a9cmJCSE9u3b07VrV7y87Odq3bo1EyZMID4+nooVK7J69WpGjBiBw+Fg2rRpAOzdu5ddu3ZlWfDn+eefJzw8HIAnnniC2267jaSkJAICApgyZQqRkZE89NBDAAwbNoz169czZcqULHv+gkTBHxEREREREREPHTwK+4/kdi0yb9KkSbRp04bHHnsszbqKFSsSGxvLb7/9xurVq1m3bh29e/dm5syZLF++HC8vL1q2bImfnx8xMTE0atSIxMREmjZtijGGhIQEtm/fTmxsLP7+/oSGhqZbhz179lCvXj3n41GjRjFq1CiXdb7mmmtS1RHg8OHDVKtWja1bt9K/f/9U5Vu2bOkMRElqCv6IiIiIiIiIeCi4dP563latWtGuXTtGjRpFZGRkumUaNGhAgwYNGDRoEGvWrOGGG25g9erVREREULRoUVq0aEF0dDRHjx4lLCwMb29vAEJDQ4mOjiY2NpaQkBACAgLS3X+lSpXYuHGj83Hp0u4PxtfX1/n3hdnJHA5HmmUXGGPSLBObgj8iIiIiIiIiHvJ06FVeEBUVRePGjaldu3aGZS/00Dl9+rRzWUREBAsXLuTYsWO0bt3auTw8PJyYmBhiY2Pp06ePy336+PhQq1atSz+Ai9StW5c1a9bQq1cv57J169ZRt27dLNl/QaPgj4iIiIiIiEgh0LBhQ3r27Mn06dNTLR84cCCVKlWiTZs2VKlShfj4eMaPH0+5cuUICQlxlouIiGDcuHHEx8czfPhw5/Lw8HCioqI4efJkjuXbGTFiBN26daNp06bceOONLF26lE8++YSvvvoqR54/v8l/oUoRERERERERuSTjxo3DGJNqWdu2bVm/fj133XUXtWvXpkuXLgQEBLBq1SrKlCnjLBcSEoK/vz8AzZo1cy5v3rw5KSkpFClShOuuuy5HjqNTp05MmzaNF154gfr16zNjxgxmz56dqkeS/Msy/211yXMcDgdxcXFUr17dmWldxB2dM+IpnTPiKZ0z4imdM+IJnS/iKZ0z4qnCds4U/CMUERERERERESnEFPwRERERERERESnAFPzJJzQ4T0REREREREQuhYI/+cCmnXDHmGDWbMrtmsilOJNkWL/ZcPBvRfBEREREREQk5yn4k4edP2+YMM/QYgD8FudP3yg4nagAQn7y+mJDyVsNIQMNlbsY7hrt4Fyy2lBERERERERyjoI/edTvcYaWgwxPvW1IPm8v23kAnpihwEF+MeN/hkEvG86n2I8dDvgoBh56SW0oIiIiIiIiOUfBnzzo1Y8NTfoZvt+azrpP4OsfFTzI63YdMDz2evrtNOtz+GWH2lBERERERERyhoI/edCZs5B0zvX6PlGGhNMKHuRVxhgeeMFwOtF1mefnqf1EREREREQkZyj4kwc9djeE1He9fs8heOw1BQ/yqreXwqof3Zf5KAa27lYbioiIiIiISPZT8CcP8va2mDPKooi/6zIzP4Nl6xU8yGv2HDIMdzHc62LGwIT31H4iIiIiIiL/FRMTg2VZHD9+PLer4pG8XG8Ff/Ko2lUtogZYbsvcP9lw7KQCCHmFMYb+LxhOnslc+QVfwY59aj8REREREckekZGRWJZFVFRUquVLlizBslJ/35wxYwaNGjWiWLFiBAUF0aRJEyZNmgTA8uXLsSyLgwcPptomODiYqlWrplq2b98+LMtixYoV2XBEeVtoaCjx8fGULFkSgDlz5hAUFJS7lfqHgj952OA7oXUT1+sP/AVDpil4kFfMWQZffp/58g4HTFTvHxERERERyUYBAQFMmjSJY8eOuSwza9Yshg0bxpAhQ/jll19Yu3YtI0eO5NSpUwCEhYXh4+NDTEyMc5utW7eSlJREQkICO3bscC6Pjo7G19eXli1bZtsx5VV+fn4EBwenCazlBQr+5GFeXhbvPG4RWMR1mfdWwJJvFUDIbfuPGB591fN2ePdLiDuo9svLUlIMn8ca3l5q2LRTbSUiIiL5izGG978y3DXaQY+xDhauMhije5rCpG3btgQHBzNx4kSXZZYuXUq3bt3o168ftWrVon79+nTv3p1x48YBEBgYSPPmzVMFf2JiYggLCyMsLCzN8hYtWlCsWLHLrvvatWtp1KgRAQEBXHfddfz666/Odc8++yyNGzdOVX7q1KnUqFEj3boEBQXRsmVL4uLiLqtOZ8+eZeTIkVStWhV/f3+uuuoqZs2a5Xy+C8O+YmJi6NOnDydOnMCyLCzL4tlnn2Xs2LE0bNgwzX6bNWvG6NGjL6tu7ij4k8ddUcnihYfclxkwxXDkuC7gucUYw4MvGk6c8nzb8ykwaYHaLq/avtfQvL/h9sftIX2N+hh6Pe/A4VCbiYiISN5njOHhqYYeYw0fxcD7X0H35wyDXta9TGHi7e3NhAkTmD59Ovv27Uu3THBwMOvXr3cbGImIiCA6Otr5ODo6mtatWxMeHp5meURERJbUfcSIEUyZMoUNGzZQvnx5OnbsSHJycqa2PX/+PJ06dSI8PJxNmzYRGxtL//79L7tXTq9evVi4cCGvvPIKW7du5c033yQwMDBNudDQUKZOnUqJEiWIj48nPj6e4cOH07dvX7Zs2cKGDRucZTdt2sTPP/9MZGTkZdXNHZ9s27NkmQduh/dXJPLNr+l3ATp8DB56ybDoOfJk97KCbv5K+Gyd6/UVy0DT2vB5bPrrZ30OT/cyVCqrtstLfo8ztHnEEP936uXzvoQ6VeGpXrlTLxEREZHMev8reG1x2uVvLIF72hhaNdb95+VY0yaWc4fP5vjz+pX3J+zrEI+26dy5M40bN2bMmDHOXioXGzNmDHfeeSc1atSgdu3ahISE0L59e7p27YqXl91npHXr1kyYMIH4+HgqVqzI6tWrGTFiBA6Hg2nTpgGwd+9edu3alWXBnzFjxnDTTTcBMHfuXKpUqcLixYvp1q1bhtsmJCRw4sQJbr/9dmrWrAlA3bp1L6s+27ZtY9GiRaxcuZK2bdsCcOWVV6Zb1s/Pj5IlS2JZFsHBwc7lgYGBtGvXjtmzZ9O8eXMAZs+eTXh4uMt9ZQX1/MkHLAsm9v2bkm56zX0UAx98nWNVkn8c/NtkmHfpzcfcJ+8+lwwvvK9fX/KSzbsMrYemDfxcMOE9Q/xfajMRERHJu44cNwx5xfX9yvRPdC9zuc4dPktSfM7/u9SA06RJk5g7dy5btmxJs65ixYrExsby66+/MmTIEJKTk+nduze33HILDocDgJYtW+Ln50dMTAxbtmwhMTGRpk2b0qxZMxISEti+fTvR0dH4+/sTGhqabh327NlDYGCg89+ECRPc1jkk5N8gV+nSpalTpw5bt27N1PGWLl2ayMhI2rVrR4cOHZg2bRrx8fEuy996663OetWvXz/dMhs3bsTb25vw8PBM1cGVBx54gPfff5+kpCSSk5OZP38+ffv2vax9ZkQ9f/KJiqVTmDoE+rgepsmglw2tG0NwGUXwc4IxhodeMhw76bpMj7bQMcxuj843GBZ/m365GZ/Ck/caypdS2+W2TTsNbR81HDnuusyZJHh2tmHGCLWXiIiI5E1Dphn+PuF6/ZJvIf4vQ0X1Pr9kfuX989XztmrVinbt2jFq1CiXw4saNGhAgwYNGDRoEGvWrOGGG25g9erVREREULRoUVq0aEF0dDRHjx4lLCwMb29vwB7iFB0dTWxsLCEhIQQEBKS7/0qVKrFx40bn49KlS3t8HBdGu3h5eaXJX/XfIWGzZ89myJAhLF++nA8++ICnn36alStXcv3116fZ78yZM0lMTATA19c33ecuUsRNQl4PdOjQAX9/fxYvXoy/vz9nz56lS5cuWbJvVxT8yUfuuxkWfwOfrk1//dEEeOAFw6cTNfwrJyz6GpfBHIDypeCVof+2w9O9LBa7SM6deBZe+sAQ9aDaLTdt3G5oO8z9jdIFMz+HoXcZ6tVQm4mIiEje8ukaw8JV7sucT4F3vtBQ9svh6dCrvCAqKorGjRtTu3btDMvWq1cPgNOnTzuXRUREsHDhQo4dO0br1q2dy8PDw4mJiSE2NpY+ffq43KePjw+1atXKdH3Xr19PtWrVADh27Bjbtm3j6quvBqBcuXIcPHgQY4zz++/FgaULmjRpQpMmTXjyyScJCQlhwYIF6QZ/KleunGF9GjZsiMPhYPXq1c5hX+74+fmRkpKSZrmPjw+9e/dm9uzZ+Pv7c88991C0aNEM93c5NOwrH7EsmDHconQJ12U+Wwdzl+dcnQqrI8cNg6e67yr7+qMWZUr+GxhoWsfiNjefD68thr9PqPttbvnxDzvHT2YCPwAOBzz+ptpLREQKvvPnDT9vMyz51vCXJhnJ846fNAx8KXPt9NZSQ0qK2rQwadiwIT179mT69Omplg8cOJBx48axdu1a4uLiWL9+Pb169aJcuXKphl5FRESwfft2li9fnmroU3h4OJ999hm7d+/Osnw/AGPHjmXVqlX89ttvREZGUrZsWTp16gTYOYiOHDnC5MmT2blzJ6+99hrLli1zbrtr1y6efPJJYmNjiYuLY8WKFWzbtu2y8v7UqFGD3r1707dvX5YsWcKuXbuIiYlh0aJFLsufOnWKVatW8ddff3HmzBnnuvvvv5+vv/6aZcuWZfuQL1DwJ98JLmPxxjD3PQ2GvmLYe0gX8ew0+GXDX26CBHdFQJfWadvp6V6u2+5UIkz7SO2WG77fYrjxUfdD+NLz2TqI+VltJiIiBVf8X4YbHjY0vd/Q+SlD+TsMLy7UZ19eNvJNw4G/Mld2zyFY/n321kfynnHjxqUZLtW2bVvWr1/PXXfdRe3atenSpQsBAQGsWrWKMmXKOMuFhITg728PO2vWrJlzefPmzUlJSaFIkSJcd911WVbXqKgohg4dSrNmzYiPj+fTTz/Fz88PsJM3v/7667z22ms0atSI77//nuHDhzu3LVq0KL///jtdunShdu3a9O/fn8GDBzNgwIDLqtMbb7xB165deeihh7j66qt54IEHUvWOulhoaCgPPvggd999N+XKlWPy5MnOdVdddRWhoaHUqVMnS18zVyzz31aXPMfhcBAXF0f16tWdmdbvHuNgUbTrbW66Fr580dLwr2zwyWpDl2dcv23KloTN71ou8/fcNMzBVz+kv23JQIhbZFEy8PLaLb1zRtIX+5vhlhGGhPSv1xm69mr47k0LL6/8/V4ryOfMuWRDzM9gsGfeKxeUv9sqryjI54xkvQN/GXbuN1jJ+whtWkXnTD5xOtHQYoBhy+6062Y/aRF5a/ZdT3WNuTRf/2j/oOWJ20NhaVT+f40Lwzlz8owhsIhSfGSV3D5njDFcffXVDBgwgGHDhmX78xXMd0Uh8NqjFuVLuV6/8gc7ibBkrb9PZNyNdvojrgM/4L73z4lT8Oonl1w98dCaTYabH8s48FOquOt1P/yO20Cs5B6Hw/D2UkOlOw3thhtuGW6o1d3w/lf6zSO/OZds2LjdsDtebZffnE40DHzRQfW7DK0ehlaPVSZyIiSfV1vmdcbY9zzpBX4AHp2umS/zmtOJhvsne94mn8dC3EG1ZV727S+GkIEOStxiKN/R8PgbDg3Xy+cOHz7MSy+9xP79+93mSMpKCv7kU2WDLN4a7j7iO/x1w58HdFHISkNfMRw+5np9pxvg7jbu9xHe2OKGa1yvf/lDw6kzarfstnqj3ePnVKL7cneEwabZFoFuEvs/OcNw9pzaLC/ZstsQPsTQ/4XUeZwSTkOPsYaFq9Re+cVn6+wAXpN+hivuNoQNcnDwb7VffrBlt91r5M3/2YllAYyxmPel/XkqedvMz2Del67XHz9FhvkPJWc9M8uwy/Us1i4ZAzM/U1vmVVt2G25/wrB+s/34rxMw+X3oE2XSDN2S/KNChQpERUXx1ltvUaqUm14dWUjBn3zsjhsserVzvf50IvSNMjgcuihkhU/XGOavdL2+VHF4Y1jmhto909t1mb9PwJv/u5QaSmat+tFw6wjD6QwCP3e2gkXPWVQpb/F4D9dttvugnbBbcl/SWcMzMx007mtYs8l1ud4TDKs36tqY1/3vW0PHJ1MH8Nb+ChFDFSTP6+avMDTv77rXyBtL4Bu9B/Osn/4wPDwt4/b55Bv4OEbtmBes32yY+qHr9UUD7B+0XJn5mXrk5VWPvJJ+L/V5X8IHX+d8fSRrGGM4cuQIPXr0yLHnVPAnn5s2xKJyOdfrV2+E6R/nWHUKrGMnDQ++6P4DcdoQi+AymRt/2/ZaaOEmyfyUDwyJZ/UBnB1WfG+4/XFD4ln35e6KgIXPWvj52m067G6oVNZ1+fHvGo6dVJvlpq9/NDSMNIx/F5LPuy97Lhk6jTJs2a02y6t+/MPQY5whvR81f99Dpr6YSs5LOmsY8IKDe8cbziS5L9t/inpN5kXHTxruGmM4ey5z5QdP1edfbjt7ztBvUvrXywsm9rcY18/1ferBo/C/NdlQObksa381rHSRKxRg0MtGvWEl0xT8yeeCilvMHOk+4PDEDMMfe3RRuByPTjfE/+16/W0hcO/Nmd+fZVlue/8cOmr/AiNZ64tYQ8dRhqQMbmh7tIUFz1j4+vzbRkUD3N80HTsJE+bpfZYb/jpu6P28gxsfNezYn/ntjp+CW0cYDihnRZ6z95ChwxPugwdzlsF7K9R2ecmOfYaQhwxvLc1c+T/2wMT31IZ5iTGGPlGGPw9kfpuDR2H4a2rH3PT8PNe97ABC6sOgztCwpkXLhq7Lvfk/tWNe8+xs921yNAEGTNHwL8kcBX8KgFuus+jfwfX6pHMQOdEoKdglWrbeMHe56/UlA2HGcM9nVrstBBpf5Xr9pAX6RTQrLV1r6Px0xr9k3tcO3n3KwscnbXv2vgUaXOF621c+Rglpc5AxhrnLDFffZ3jXTV4Kd/YcgvYjDQmn1W55xckzdm4DdwH3Cx580bBtr9ouL/g4xtDsAcPG7Z5tN+E92KoeeHnGy4tgybeeb/fOF/DVD2rH3LBpp2Hie67X+/nCrMctvL3t+5oH73B9v7rqR3RNzUPWbDIuZwi+2Kdr4b0V2V8fyf8U/CkgpgyyqBHsev36zTBlYc7Vp6A4ccrwwAvuPwRfGmRRuZzn0y1alsXT97nebv8R+5dtuXyLvzF0ecZwLtl9uT7tYfYT/94g/Ze3t8Xkga7b7FwyPD1TN005Ydtew42PGCInps4Hcyl+2QFdnzHKdZAHnD9vuHuMYdPOzJU/nQh3P6tAeW46l2x45BUHXUdnPHNiepLP28O/lJ8w96391TDyzUtvh/4vGE4nqh1z0vnzhr5RxplQPT1jIi3q1vj33qVrOJQp6br8W5+qDfOKjHr9XOzhaYb9R9R24p6CPwVE8aIW7zzhPgAx+h3Db3/qouCJ4a8b9h9xvb5dCztgcKk6t4J6NVyvj5qvL6SX66MYQ7cxJsMcMP07wMyRrgM/F9xyHbRp6nr9/JV2okzJHmfPGcbNNVzTxxD9c+a28fJy38sOYOUP8MBkdZvOTcYYhr5iWPadZ9tt3A4j3lC75YY9hwytHjZM++jy9rNmk4Y657Yjxw13P2tIcRNECG9s3/e4sivenm1Kcs5Li+DHP1yvb3wVjOieelmAv0WfW11vM3uZnbtLcte3vxhW/Zj58idO6T5GMqbgTwES0dTi4S6u159Lhl7PK5iQWSs3GLc3o8WLwlsjPB/udTEvL4un3PT+2X0QtzOMiXsfrDLc85z7X8QAHuoEbzxm4eWVcVtalsULbnr/gP1FVB++We/bX+zpvkfPynwi0qa14fsZFt+9aXFjM/dl5y6HMe+o3XLLtA/h9SWXtu30j2HJt2q7nPR5rP1+/G5LxmWLF4Xx91t4ubnrHPmmIV75t3JFSoqh51j3P3ZVKA3vj7aYMdyiWBHX5aZ+CN9tUTvmhG17jdvPLG9vmDUydf7CC/p3cH0fczQBPlqdJVWUy3Ap9yPLvoN3Ps+GykiBoeBPARM1wOKqKq7X/7wdJszLufrkVyfPGO6f7P6i+8JAi2oVLj3wc8HdbXDbZhPmKV/TpXhvhT1TkLtfMQGGdIFXH81c4OeCpnUstwm+v/4Jlq3P9O4kA8dOGh6Y7KDVw4atcZnbplgReHmwHfRpVseete3jcRbX1HS/3bi58PZSvd9y2qdrDMMySBhbxN/9PvpGGfYcUttlt/PnDU/OcHD744ajCRmXb1QLfnzb4qleFkPc/EB14hQMna72yw3j38XtbEJeXrBwjEXFshbVKkBUf9efl8ZAv0mGc8lqy+zkcBjun+R+AosR99j3K+m5qqpF22tdb6vEz7lr9cbM927+r0df1WehuKbgTwFTNMBizpPuf10b/67RsJQMPP6mYc8h1+vbNIX+HbPmuby9LUa56f2zfR8sis6a5yos5i4z9Hre4HC4LzesG0wdcmm9t8bfb+Hv53r9yDcVtLtcxhje/8pw9b3ue+H9V4dQ2PKuxSPdUifuLhlo8cVkiyrl3G8/8CXDF7Fqu5zy4x+G7mPdT1FcqSxsmu0+eHfsJPQYaziv3q3Z5sBfhhsfNUTNz1z5+2+H2Dcsrqpqvw+f6wOVy7oeg/thtJ2cX3LOyg2G5+a4f83H9bNo3cTir1Wr+OHWW+l/4wlCG7guv3kXbhMQy+Wb8Sl8u8n1+tpV7Vw/6TH/3Bw92NH1vc/aX+HXnXov5hZXuX6uSNzMyPiHebz0PFx9aJ48Ywdg1QNd0qPgTwEU2tDisbtdrz+fAr0mKEGmK9E/Gd5Y4np9sSJ2bpjLGe71Xz1vwm3C7ufnKRlmZs36zJ6mNqPPvMd72InSL7Udqwe7/xV78y4l7L4cuw4Ybh1h6DHWcPhY5rapVBY+Hmfxv4mue+VVLmex7AWLkoGu95OSAneNMfzwu95z2S0zU7oXKwKfRVnUqmLxwbMWRQNcl137K4zxIEGmZN6qH+1hXt/8knHZogEwd5TF2yO9KOL/73sxsCiM7eV+GrdBLxtOnlEb5oR9h+1rrLvPy/bXwxM94dCnn7Lx7rs5vn49m3r25O2hZ/Hzdb3d8/MMm3epHbPDnkOGkW7ynFmWPbtXwEXvPWMM8YsW8W3Dhqy99lpO/fEHHcMguLTr55mhxM+5IuZnQ0w6vX4ijn3EtB030/bIfG6MGUrkmddd7uOrH+wAoch/KfhTQI3ta7lNJLx5l2cZ5AuL04kZD/eK6m9xRaWsC/wA+PpYPNHT9T4377q0qVcLmzf/Z7dfRoGfp3vBxAGXH8B78l6LUsVdrx/9jmY+8VTyecOk+Yb6vQ1ffp+5bSwLBnW2e/vcGZ5xuza40mLxeAtfH9dlziTBbY8b/jyg9ssumZnS/cJwkya17Ta9urrFa4+6b9+J72nK6ayUkmIYO8dw07DMBWLrVrfzbPW6Jf12imiUxN1tXG+/9zA8o1kTs13yeTvB819uZkusVgHefcoeFh149dX4lCgBwLG1a0ke9wCj73PdvTb5vN37QD1gs5YxhgenGE4lui4zqDOEXfPv+y9x715+7taN3/r3J2nvXhL//JNN992HlXiK+293vZ93v4RTCsTmuP9+P/My5+l/4Gme3NufAPNvw/fY+Qzhxz9xuZ/hr+seRtJS8KeACvC3mDvKwtvbdZnJ78P6zbooXGzUW4Y/D7he36oRPNQ5e5478lao7GY4yvh31YXTnVc/Ngx8MePX59k+FuPu98qSnlulils809v1fg78BS8vuuynKTS+22K49gHDEzMMiWczt801NWHd6xavPupFycDMt2lEU3uIrDuHj8GtIwx/n9D7Lqtldkr3lwdb3B6aup1634LbnFvGwL3jDYeOqt0u15HjhvYj7aSymfn46XmTHfipf4X799bLgyHITe+7Vz6G75U0OFs9OcOw7jfX63194MPnLMqUtNuyWO3aNPnwQ7yLFQPg8GefccePj3HNla7b6bst8Krr76ZyCd5bgdsZEatVgIn/5GQyDgd73nqL2JAQ/l6ZevaQ09u2sWXIEO6/HZepIk6egYVfZ1XNJTOifzKs3vjv45Ln/yLqzy50/evfXj7FGzVy/v3E/odoeGpduvs6nWjnwtPIAbmYgj8F2LVXW4y61/V6hwN6TzCcSdJFAWDNJsN0NzcpRfztbrSeJAb2hL+fxcjurvf983b4PDZbnjrfm7rI8PC0jM/j8fdbjOmTte33UCe4oqLr9ZMW6EtoRk6cMgx+2UHIwIyDARcU8YdJD1r88LbF9fUvrU173GQx6UH3227bCx2fNCRq2tssY4zhkekZT+n+cBcY0jVt+1iWxevD3E9ucOgo/+T9UrtdqjWb7GFeKzZkXNbfD2YMt5j3tEVg0YzfjxVKw5SH3CcN7j9Fs5Nml8XfGF78wH2ZFwdZtKiXuo1KNmtGo3nzsHzt8V7x8+byaqmJbvNMjnrbsEu9D7LEoaP2tdOdt0fY78FTv//Ohltu4Y+RI0k5dQoAv+Bgrn7xRWcPrkOLF2MtnkH7613vT4mfc44xJlWvn6vObOTV7W1ofNru+n/e8qHq+ClcFxNDpfvuA8A75Rxj9/SkWtLv6e5z9UYFYCU1BX8KuKd7WTS+yvX6bXvhqbd1YT+TZOibQZ6YCQ/YOSey0wMdoHwp1+vV+yetF943PPpqxq/JpAft2Waymr+fxQQ3M5+cSoSxGSTTLKyMMXwcY6jXy/DaYpe5C9No1wI2z7UY2SP9KWw9MaK73UXenXW/Qc+xGr6QVV75CF5b7L7MbSF2rx9Xihe18/+4yzmyYgO88P4lVrIQM8Yw5X1D66Hup/6+oGZliH3don9Hz4bS9r0Nwhu7Xv/LDvWczA479xsiJ7q/lt0VAYPvTH9dmTZtqP/GG87HSbOn8HKVt1zu60wSDJiie5es8PA09zPsRd4KbRsn8+fkyaxv1YoT3/87drpyZCSh69dTtV+/VO237ZlnGFjLdST+xz9gw1a1XU6I/glnTrWbjr7PyztvpULyPgD+9qnArwM/5erB92NZFnVfeokyN90EQLHzJ3h+VzdKJ8enu98nZhi271Ubik3BnwLOz9ce/uUut8W0j+CbjYX7ojB6lmH7PtfrQxvYv0JntyL+FsPvcX3z/N0WO4mb2Ca+5z7p4QUvDrIDBdmlWwQ0v9r1+hlL4Y89hfs99l97DhnueNLQdbThwF+Z26Z8KXh/jJ2wOavyblmWxbQhFneEuS+3+Ft7+lR9gbk8n67JOFjbqJbdzt7e7tu4SW3Lbe8RgKdmGmJ/U5tl1rGT9vtyxBuGlJSMy3cJt6dxv5CTyROWZTFjuPsA3ph3DDv3q/2yStJZw12jDQmnXZe5qkrGk1pU7NqVOlFRzsd1P3+Su62PXZZf+QPMXX5JVZZ/LP7G8KGbmV8rlIaxoT/yXevW7JwwAXPOngO+yJVX0mzpUupNnYpvUBAA5W+7jRqPPAKAOX+eYlP6Ur+U60ivev9kP2MMY2YbvE0yD+1/nBH7BuFn7PHvm4s25/Frvub+J0Kc5b18fblm9mznELAKyft4ftfdFE1JGx1MPAt9ovQDltgU/CkErqlp8ayboS7GQOREU2iTuq3fbHj5Q9frA/zgnScy/iKSVQbeAWVKul4/bm7hbKf/GjvHMOqtjF+LaUMsht2dvW3n5WXxgpsvoSkpdn4FsZPHTl1k9/ZZmv4w9XQ90AF+f8/inhuzdqY9AG9viwWjLa6v777c9I/hpQyGSohrmZ3S/bMoi+KZGDoEdu8Ed4G7lBS45znDsZN6/2Vkw1ZD036Ze1/6eMPUhy0+HGt5lGvrv+pUs3jaTY/MpHPw4IsKumaVoa8Yft7uen0Rf3vGxBLFMm7Tag8+yBXDh9sPjKHf5odoetJ1dOLR6YaDf6sdL8Wxk4aHXnL92gU4TjOr+FP83ulmTm3ZAoDl7U2NRx4hZO1aSt9wQ5ptaj79NKXC7IvnuYPxPHfgfrzM+XT3//4qOK5raLb6+ifY/OMhJv/ZiU5/v+1c/lnpSEZe+Sm9elSkdInU70ufwECaLFpEQLVqANRM+o3Rcb3xcZxLs/+1v8JUN991pPBQ8KeQGNkdWtR1vX5XPIx8s/Bd2JPOGvpMNDhcT1jB2H4WdarlTOAHILCoxaN3uX6+bzcV7p5axhhGz3Iw5p2MX4PXh1np5gzJDuGNLTqEul6/+Fs7h0Zhtv+I4cZH7Z4fp93MVHKxutXh21ct3hrhRani2deWRQMslk50n0cG7NkzPlhVuNvxUngypXuV8plvZ8uyeOcJi6rlXZfZcwj6RSmA4Ioxhtc+MYQNNuw+mHH5quXt9+TQu7ImEPt4D9zOTvrVD3aSW7k8760wvLXUfZnXh1k0rJn5Nq351FNU7t3bfnA+mfH7elH7zE/plj1+ikzl5pO0HnvNcPBo+uuanfya9/a0xG/pG86x08WvuYYWq1Zx1bPP4l2kSLrbefn40PCdd/ALDgagxPZv6XN4YrplE8/CPL0Hs40xhrcmf89r29vQ8LSd3POc5cdLlafySpWXKFLcn0e7pf++9K9QgaYff4xvKTtnRNNTq3l0/yPpjqN/aqbh9zi9Bws7BX8KCR8fe2Ybfz/XZd5YAis3FK6LwnNzDL/vcb2+RV0Y1i3n6nPB4DuhpJuZUMa9W7ja6QJjDE+9bRg3N+Oyb42wGNgp54J2AFEPWm4TX454o/B+Af0i1tC4b+pZLNzx94Nx/Sw2vmOlmrI2O5UNsoeUlQtyX67XBFOoA7CeupQp3T1RuoT1zzAx12UWf2t/xklqCacN9zxrGDzVcC454/Ltr4efZ116kvX0+PlavDXC/f4efdXw13G95y7V5l2GAVPcv35920PkrZ61q2VZXP3ii5S/3Z4v3Cf5NBPjulElKf3uRR/F2MOXJPNWbjDM/iLt8uLnjzF87yAm7upKiQT7RtYrIIBazz5Li1WrKNG4cYb79i9fnmtmz8b65+J596GXuT4h/fF5b/6v8N6/ZLevxs6l14oOlD1v5+w54luR4TU/Y3mZXgAM7UqaXj8XK3bVVTR+/328AgIAuOnYQnofmpCm3Nlz9kQ/55VIv1BT8KcQqVvDYsID7j/Y+0YZTpwqHBeFH343vLDQ9Xo/X5j9ZM4N97pYyUCLIW5yDH31gz1crTA5ecZw33jDxPfcl7Mse5jeAx1yvt3q1bC4/zbX69dvho9X51x98oJzyYbhrzm47XHDXycyt02bprBptsXTvS38fHO2HWtWtvgsyqKIv+sy55LhjlGGLbsL13vwUpw/bwcXLmVKd0+0bGjxXAYz+Q17zbBxu9rsgk07Ddc+YFjkJo/IBV5e9vTRS6P+nfo7K7VsaPHgHa7X/33C7v0gnjt1xtD1Gfe97q6pCa8+emnt6uXjQ4OZMynVsiUAxZOPMnFXF8okH0i3/EMvaRhmZp06Y3jghf+8VsbQ6vhiZm67npuP/ZvRvlTLlly/di1XPPIIXr5uEmn9R6mQEK4aO9b5eOSeBwk+uztNuS27Yc0mT49A3HGcPcuWoUPh5UfwM/ZQrV+LhTCoVjS/F70WgBLFcNnr52JB119Pg7fesm+CgZ6HX+S2v+ekKff9Vpji5ruPFHwK/hQyQ7tC2DWu1+87QqZmTsrvzp6zh3u5S2g5JtKiXo2cDyBcMLSrRWD6vXUBe+avwuLCl5T5K92X8/KCuaMs+rTPvXZ7rq9FMTft9sQMw7nkwtF2fx4w3DA44ymFLyhT0m6/r162qF0199qwRT17Jil3vbiOn4JbRxgO/FU42vJSXJjS/Yv17su5mtLdU0/0hBubuV5/9hzc/WzhzW8HkHjWsHKDHZC9boD7iQ4uqFgGvp5q8cS9Fl5e2fe+nNjfomIZ1+vf/RK++qHwtt2lMMbQf4r7Hs7Fi8JHYy2K+F9623oHBNBowQICGzQA7OSzE//sSvHzx9KUPXgURryudsyMUW8b4i4ailkm+QDPxt3L03v6Ueq8naDZu3hx6k6dSrOlSylWs+YlPU+1hx6i/B129DXQkcDouN74OdKOzVbi56yTFB/PD7ffzv65/3ZlX1KmPyOvXMJx33/HMT9yF5ke8l6hY8dUidgH7x/OdQlfpik3Zrbhtz/VloWVgj+FjLe3PfyraIDrMrO/gM/WFeyLwvPzDL/tcr2+aW17CujcVKakxUOdXK//PBZ++qNgt5Mxhrc+NVw3wLBtr/uyXl7w3tMW97XLvaABQHAZi+F3u16/cz/M+DTn6pNbPow2NOln+H5r5sr3vgV+n2fR65asT+h8KTq0tHg9g1/C9xyC9iMNCacL9vvwUmXFlO6e8Pa2eO9pi/KlXJfZthcGTy087eVwGH78wzBpvqHtow5K3Wa4+TE7IJuUNidoGjc2s4d5hTfO/vdkUHGL6UPdP8+AKYYzSYWn/S7Xm/+D979yX+adJyyuyoJgu2/JkjT9+GOK1KgBQI2zvzN2d3f8HWfSlJ31Oaz6Ue3oztpfDa9+Yv9tGQe3/T2HmX+EEJqwzFmm2I3tCf3uO6pERmK5+7UiA5ZlUX/6dIrWqgVAraRfGbx/ZJpyH62GIxp+edmOxcbyXXg4JzZsAOCsFcDkqq/zeuUoUqx/e22VDMRtDtD0VBswgOoPPwyANw6eiutHnTM/pipzLtke/pWs4V+FkoI/hVDNyhYvDHR/MXlgsuHvEwXzorBxu/uhQ74+9nAvX5/c/wL62D3uh588P69gthHYw7x6jrPzFGT0JcXb284X0r1t7rcZwPB7LCqUdr3+uTkFd3hl4lnDg1McdBvjfjrhC66oCKtetpgzyouyQXmj/S4YcIfFqPvcl/llB3R9RjdR/5WVU7p7IriMxbtPud/f3OUw78uC21674w1vLzXcPcZB+TvsXpNPzDCs+tHu/ZQZlgWjI+HLKRYVSufc+/LOcOjY0vX6Pw9oxsvM+uF3u+edO0O7QtfWWde+/hUq0HTxYvzKlQOg/pnveSquH94mbUKp/i8okOdK0llDv0n2zIiVz+7ghT87MnT/MIo5TgJwzKccBx6aQ8hH8wmoVClLntOnRAkavfsuXkWKAnDLsfnccnReqjLnkmHOsvS2lswwxrB35kx+7NCBc4cPA3DItwrDan7BV6XuSVP+ka52UNxTVz33HGXv6AxAgDnDuF3dqXg29S/eP20jwzQKUjAp+FNIPXiH++7xB48WzFkZks8b+kQZzrsZ7vXUfRbXeDDbRXYqX8qifwfX6z/5hgLZdfOXHYZm95sMf7EEe8rhRc9a3BWRN9oM7Bnb3OUf+fsETFpQ8Npt6267l1ZmezZ1i7B7FbRplnfa7r/G329xXzv3ZVb+YAfMlQzT9lM2TOnuiXYtLB7v4b7MwJcMf+wpGO117KTh4xjDwBcd1Oru4Iq7Df1fsHP5/J3JPFsXK1sSlr9g8VxfrxzPeWdZFq8+4n7I8wsL7aHA4tqxk4a7RrtP4n19fZicwQ+Bl6LoFVfQ5KOP8C5e3H6ek1/yyL5H0sw+9OcBGD1L7Ziece8adsQlc8/hl5mx7QauOb3OuW55qR7M7Lye+8bfkeW9ZAPr1aPetKnOx4P3j6RmYupEPzM+NTgcajdPpSQlsWXQIH4fPhxz/jwAO8u3YvBVX7O9aOM05UsGwiMe9vq5wPLyotFbb+LV2I6kB6X8xYRdd1Hy/F+pyo2bqzx4hZGCP4WUl5c9PW7xoq7LvP8VBW5K46j5sDH9SSgAO+nhk/fmXH0yY0R3Cz83ufsKUu8fYwwz/me47sHM5aIoVRyWRlncGZ73ggf9brOnKXfl5UX29NcFgTGG2V8Yru1v+PXPjMsH+MGM4RYLn7UoGZj32u5ilmUxc6TlNlgOdm+SZ2cXjPa8HHsP2TN7ZfWU7p4ad7/F9fVdrz+dCPc8Z0g6m//a7Ow5Q/RPhqfedtCiv4OyHQxdRxve/J89rPRytGxoB2RvbpF778uqFSwm9Hf9/CkpdrA1JSX/tV1OcDgMvZ837D7oukzpEvDBs9mXUL9Eo0Y0nj8fy9eeYrbdsffpd/C5NOVe/hC+36J2vNjP2wwfz9zI9O1t6XtwHH7mLADxftV5/IpPeK3mq7w6unS25d+q2K0bPp37AeBnzjI6rjeB54871+/cD6t+dLGxpCtx715+uPVWDixY4Fzm6DKYQRU+4oRP2XS3GdbNuqRePxd4+fvTasl8TlW4GoDK5/5MMwzzfIo9/Kuw5KEUm4I/hVi1ClaGuRa6jzU8PNWR74eopKQYXvvEuO0u7u0Ns5/I+dmFMlK5nEU/NzNIffA1BeIX7ITThu7PGR580WRqaML19WHjOxa3XJe32usCHx+LSQ+6rlvSOXimAPzqeWEWtr5R7r/wX1C3Onw/w6J/x7yR2ycz/HwtPh5ncU0GuTTHzoGZn+X/Nr1U2T2luyd8fSzeH20RFOi6zMbtMOKNvN9exhg27TS8uNBw6wgHpW83tHnEMGEebPgdHI6seZ4R3SF6WvYG5TLroU7Qoq7r9d9vhdeX5FRt8pcpC2HpOtfrLQvmP2NRrUL2tnPpVq1oOPNtzD/X+buPvEKXI6+mKuNwQL9J+vJ5QVLCaT66dzRT/2hLraRfAUjBi4/KPsSA2mv4uXhrnuuT/RMitHrjeeJKNQWg4rk4Rux9CMv8e6FR4ufMO/rNN3zXujUJP/8MgFeRIjR4+21G+4zFYfmku01QoD0k83L5BgURLzZJFgAAv+5JREFU8cUijgdUBKDumR95cs8DeJl/hz9s2qmhtIWNgj+FXN/boP31rtcbA69+AnXvM3wYnT+HNazfbGje3zB4qiH5vOtyT/SApnVy/6Y3PY/3sPDxTn+dMTDxvfzXLhf7eZs9zOuDrzNXfkR3+GZ69t+8Xq7bQ6FVI9fr3/3SHuKWX11ot4xmYbugb3vY8JZFwzwyrNITJQMtvphsUaWc+3IPvmj4Ijb/tumlyqkp3T1Ro6LFrMfdP9ern8Dib/Jee+07bPem6znWQXAnQ6M+huGvG5Z/R6aCrJ649mpYPsVi8kCvPJHrDuzk3W+PdP25BzDqLVNgek9mlW82Gka97f41eeo+cuxHkwp33MHVU150Ph4QP5q2x1LPM/3bLpi04L9bFj5Hv/mGlU3DaL1tOt7YgZY/A+rxSK0veavSeJK8itG0NjzmZkKJrOIdEIDjqTmc8LaTF4acXM7dR6Y51/9vLew/oveeO8YY4l5/nZ86dyb5b/sXkSLVq9NixQp+rtLV7WQYw+7Oul7RQTWrUfWtDzjjZf8SEpqwjEEHHk81DHPifNiwVe1ZWCj4U8hZln2DVaq4+3Lxf0O3MYbbRhp2HcgfF4i/jhsemOwgZKDhZzdDvQDqXwHP9M4bN73pqR5s0ctN3pH3VpJv2uVixhjeWGIIeciwIxPDFUqXgM8m5a0vKe5Ylvvk6sbAyHzQ8+C/jDFM/9hw/cDMDc8LLGL/0jzrCS+KFcn77eZK5XIWy16wKOmmN0lKCtw1xvDD7/mvXS+VMeTolO6euDPc/ayJAH2jDHEHc7e9Es8aPl1j97S9+l4HVbvavekWfAWH086WfVnKloS728DMkRa7F1lseMuLdrk4zMuVa2paDE+bA9XpVCIMejl//iiVHQ4dNdzznCHFTU7DNk3hWTf56LJDtX59Kf7QKOfjx/Y+TIuEFanKjJtr2LK7cLZj8vHjbBkyhB87diTgLzsp7znLjzkVRjG41tf8UdQec+zjbc/M5pND9z73dK/Ky7XewoH9fL0PPk/jk6sB+3Nu1uc5Uo18KeXMGX7r359to0Zh/nlDlmnThutiYghs0MDtEPFSxWFIl6ytz/Udr2F7v3c5j93TqMPf79DtyCv/1jcFIifmz2HQ4jkFf4RKZTOeXvWCZd9B/d6GqPfy7uw2Doc900mdew0zP8u4vJeXPdzL3y/v3fxe7Ml7LVzN5JmSAlHz82Z7uJJw2u4p8NBLmRvmFdoANs6yuC0kb7fTf7WoZ3F3G9frV2yAFd/nn7Y7mmC482nDkGnuk4le0LQ2/DTTosdN+avdXGlwpcXi8Ra+6ffWBuyeGbc9bvgzHwZkL8X0j3N2SndPvTjI/ZC946egx9ic/0xzOAzfbDTcP8nu3XPHKHtq5z/2ZO3zBPjBTdfayX1/mmlx6H8WC5/1ot/tFtWD8/b7cnSkRc3KrtcvXQefrM65+uRVKSmGHmPdD7msWAYWjM7a2fUy67rnR3DwhvsB8CaFp+P6UO/0d871yefh/kmFK49T4u7d7JwwgXUtWrD/3Xedy38reh0Dr1rNggrDOe/l51z+RE9oVCvn2q5EMYv6Xdswr8LjgD1t+Kg9D1Am+QAAby81nM+j3wNyU+Lu3Wxo146DH37oXFZj2DCafPghvqVK8cV6e7iuK8O6ZU8uxCETIljUbLrz8f0Hn6PNsX/ruGV34cxbaIzhxA8/cOSFF0j46afcrk6OUPBHAOhxEzzgZlapiyWehSffMjTpZ1izKW9dKH78wxAy0J7p5GhC5rZ5vAc0r5u3b4ABalWx6H6j6/Wzl+WfBMI/bzM0vd+ejSYzHu8BMa9YVM3jw7xcmdDffbBg5Jv546Z33a/2+37Jt5krP6QLrHvd4qpszk+Q0yKaWsx50v0xHT4Gt44w/H0i77fr5fjqpyIMe819mcZX2Xl+cuNLJ0CAv8UHz1oUDXBdZt1vMOadnGmr7XsNo2c5qHmPIXyIYdbnkHA66/ZvWdCsjn3d/Opli2OfW6x4yYsR3e1cS9mVKDY7FPG3ePMx9/V9eJrh+MmC/T7LyLOzDV+7+d7i7W2/ByuUzp22tyyLbh9N4ufKF6afTmTs7u5UT/p37Evs5oyDyPldyunTHFiwgB9uu401jRvz5+TJzim/z3gFMr3SZB6r+Tl7A+qk2q5udXi6V8633YCOFgvKD+f74m0Be9aop+P64OM4x74jZNjbs7D5Ozqa7yIiOPmrna/Ju1gxrpk7l6tGj8by9sYYk3GvnyzI9ZMeP1+LoW/ew5yKTzuXPbZvsLM3F9gzKa7fXDiupae2bmXH+PGsbdKEH26+mRMLFhC/cGHGGxYACv4IYH8wv/mYxbh+7meWutjmXXDDYPtXy6MJuXuxOHbSMOglB837G7fjaP9rUGd7Kuf8YtR9Fq5y5CafhxcW5u2LtjF24u3rB5pMzUpTpiR8Pski6sH8MczLlSsrWQzq7Hr9LzvIdN6c3OBw2L39Wg0x7DmUcflSxWHJ8xbThnrl+R51l6rHTe4TegNs2wsdnzQkFtCu1D9tg6FvlM1wSvelEy0Cs2FKd09cXd3i9Ufd1yFqPqzckD1tdTThnyGuAx3U7mkYNxe3szF5qkaw/QPOoucsDv/P4oe3vYh60Isbm1kE+Ofv92Dba90Pe47/2/5BqrBa/p1h/Lvuy0x4wKJV49w9D/z9vWm94A1+CgwHoETKcSbs6kq5c/+OHR71tmF3fMFqS2MMx9atY/OgQayuU4fNDz3EsbVr/y1gWawvdTsP1F7H0rL3Y6zUX80sC2Y9nju905vVsbi2rheTqr7JQd+qANQ/s4H7D44BlPj5AmMMu6dN46cuXUg+Zo/TLVqzJi2++ooKd9zhLPd5LPzgptfPY3dblCiWfe3c+CqLOiOH8VnpSAB8TTJj4npxReJmwE7A3ntCwb1nSdy9m10vvURsaCixISHsmjKFxN27neuPfPFFoRhGbJnCcJT5nMPhIC4ujurVq+PlatxPFtq21zDwRfe/Iv1X2ZJ21/r72pGjM/gYY3h3uT1jy5Hjmd+uZmV49ZG8O1OUO3eNdvBRTPrrAvxg1wcW5UuZHD1nMuPEKcP9k43Luv9Xy4b2L5V5YeaZrPD3CUPN7oYTp9JfX7U8/DHfokgufVFzdZ05dNSezWvlD5nbT8uG9tCCvJ6MOysYY3h4qsnw1+rGV9lJ27uGk2P5GrLbvsOG6x40HPjLdZliReDb6dk/s1dmGWPo9bzhvRWuy1QoDb+8kzU9JM4lG5ath3e/NHwWS6aGSWZWqeJ2/pabrrVoey3UrJw3XuOMXOr9zF/HDVffZ/j7hOsy375qEXZN/ngdssqeQ3YvWnevS4dQWDIh7/T4evylE1R9oRN1Eu3Zj/b6X8WjNb8gwacMADc3txOQW5aV4/e/WSlx717iFy7kwIIFJO7alWZ90Vq1qNi9O8O3dWPxVtdjGx+5C15+OPeOffYXdv6xq878zMs7b8XP2OP0n682k29K3cnO9y2uqJQ3zi3I+e9MySdOsHXoUA4tWeJcVrZdOxrMmIFvUJBzmTH25DM//pH+fkqXsO/fszP4A5B83hDyQDIdv+pFyMnlABzxrcgjNb/kiF8VAB7tBi8Nzl/vN1fOHj7MocWLOfjxx5z4/vu0Bby8KH3DDfi0bs3VkZH4lyqV85XMYQr+5AO58eFnjD2Dz7BXPQuqRDSBNx6zqFMt+z8INu00DHrZsGZT5rcJ8LN7z4y4h3z7a+gvOwyN+7p+2z52N0weSJ66YfrxD0O3MYY/D2Su/JP3wti+OZfYMKe88L5xm+B5Yn+LJ+7NO8Gfr34w3DvecOhoxttblt1uz/UpeO3mTkqKocszhv+tybhstQowtKtFv9vIljH9OeXkGcMNgw2/7HBdxssL/jch52b2yqyTZ+wZ6twlKr/pWvvL56V8WTbGsGErzFtheH8Vbr+Ue8LPF1o2sHvB3HStnUsrt4bRXY7LuZ+Z96UdvHOlbnX4eVbez9+XVc4lG1o9bPhui+syNYLhp1kWpYrnndfkTJIhpMcRhq5pT9Vz9kXk9yJNGXnlEpK87Wz6c0dZ9Lol/wV/Us6c4fBnn3FgwQKOrl7Nf7tFehcvTvCdd1KpRw9KtmjB3OXQZ6Lrc/qKivDrHCtXJ0o4k2SodKf9w9Vtf89h6P5h9nKvQB6utZJ776/DhP55p21y6pw5uWkTe995h4MffkjK6X/H7V45ciRXPvEE1n+e+9M1dl43Vyb0t3gyh+7/ft1pCO17ignb7uDqRPuX/t3+V/NorWWc9i6JZcHqVyxuaJR3rhueSD5+nMOffcbBjz7i6Dff2F2a/qNkixYEd+lChU6d8C1XLl9dZy6Xgj/5QG5++B1NMDwxw/D20sxv4+drJ6Z7smf2dDdPOG0Y845h+ie4ndXiv24PhWlDLK7MQ79QXKo7nnTw6dr01xUNgF0L4fSJ3L+Q2cO84LHXM5ccuGxJmPd0/uyRlRlJZ+1E5K6GTpUoBjvftygblPPHf/F1xuGweHa2YcJ7ae5d01WhNMx7yuKm5gWz3TJyJslw46OG9ZszV754UXjgdnvmq7yecPdiB/+2fxR4+zOTYVLiaUOsHJ/ZK7N+3mYPPXV3TfI0ELvnkN2j6N0vM35tMuuamnYgqu21FjdcQ76eKe+Cy7mfMcZw82OGr9z0Qhzbz8rTM3dmpUdecTDtI9fr/Xxh7WsW116d916Pr3809BgUx8s7bqXs+XgANgS2YUyNBZz38qNUcdg6z6JcUN7rxfxfxhhObNjAgQULOPTJJ5xP+E/CScuidKtWVOrZk/K334530aIAxP9lqNfLcNxFb2Cwc3bd2Cz322/oNAevfAwYw4h9g7jpmJ0bJc6/NmObf8WOxcXx8839ekL2fmdKOXOGQ4sXs2/2bE78kPpC5F28OA1mzKB8+/ZptjPG/tHB1czDZUravX6K5+Dw6InvGSa/fpiXd9xC5XN2z7SNxcJ46ooPSfbyp2ZluxdsfvncSTlzhiNffsnBjz7ir5UrMefSziQTWK8ewV27EnznnRSpUcO5PL8FmS+Xgj/5QF44Kdf+anhwiuG3tD1XXbqqCrw+zKLttVlz4TDGsHAVPPaa+xkt/qt6MLwyxKJjWP64gGXGhq2GFgNcv3WfvBceuCl3z5njJw39Jhk++SZz5W+4Bt4fY1G5XMFpp/S8t8IeRuXKkC4wbWjOt9mF64xXker0HAdrf83cdjddC+8+ZRFcpmC3W0aOHDeEDjTsyEQuqwu8vaFLK3ucf4t6efP1O3vOsHQdzFlmWP595gLuD3eBV3LhHPbE9I/tGetc8faGb16xCG3oul1OnjF8vBreXW6I/vny6+TjDe2vh24R9lCu3ErOm50u935m535Dg96GJBczRPr5wqbZOdP7OLcc+MsweYFxG/gBeO1Ri4c6593X4YHJDr76cAsv/nkbxVPsLnJfB3VhUtUZGMuLuyJg4Zi81Yv5YkkHDhD/wQccWLCAM9vTfqsvUqMGlXr0oOI991CkWjXncmMMKzbA0zON2/wv/W6DmY/njWPesttQv5d9vfR3nGHajpu5MsnuchZd8k4avzOTbjfmjbpmx3em09u2sW/2bA4sWMD5E6m7c3oHBlKxWzeqP/wwRa+4It3t//etodNTeavX9/nzhpaDDPs2/snUHe0ISrG/WF38Hhx8J0x/JG+0a3ocycn8HR3NwY8+4sgXX5ByKm0ktUj16nbAp0sXAuvVS38/eeB7dk5S8CcfyCsnZfJ5w0sfwHNzDIlnM79dz5vsfECXcyO7dbc9xMuTG2w/XxjZ3Z4ivWhA3r0BulS3DHfwZTrDV8HuWfDNlL1cU69qrpwzP/xuD/PaFZ+58qPuKzzDhRwOw7UPuP4FyMfb/sWzVpWcfS0cDgezFh/m8XfKc+xkxuW9vWFcP4vHe5Bncknktp377dkGPRkqe0HYNfYUrx1b5v5wHmPsLyVzlxsWfEWmzocLbguxh3vl9jFkxBjDnU+7n7muWgV7GFHpEv8eS0qK4asf7YDP4m/x6LPQleZXQ692FnffCOVyoddfTsqK+5lJ8+0eya60agTR0/JOjpussjveMGmB4Z0vMs4f1b0tzH/GytEcjJ46ftLu+VJ6z3qi/rwTf5MEwCdlB/BmxQlgWXw8DppUy/373wtSkpI4smwZB+bP5++vv04znMS7WDEqdOpEpR49CAoNTfX6J5+3f8CcstCwaaf756lYBra8axGUh4brhT/s4Jtf7L8rnd3Ja9vbUMxhfzisaDGJF1YMyMXa/SurvjM5zp3j8Gefse+ddzi2Ju247sD69anSrx8V77oLn+LFXe7HGDsv18Y81Ovngq27DU3uN1xx/Acm77yDAJMIwAflhjCr4rMArHrZok0e6H12gXE4OLZuHQc//pjD//sfyUfT5iXwq1CB4M6dCe7alRLNmmV4Hcwr37NzioI/+UBeOyl3HTAMnmo8muIxKBAmPWhx/+2efVE8dcYwbq7hpUVw3oMhXjc3h+mPWNQuYFNMX2zNJjvvhiuPdD7Oi0ODcvScMcYw/WMY/roh+XzG5csF2cO82rUouO2UnlU/Gto+6rrt7oqARc/lXLudPWcY8YbddplRtbzdS6ulm14RhdXP2+ygwqXO5FSzMjzS1aJP+5wf5hP/l+G9lXYvny27Pd++8VV2gufcntkrs44m2PnT9h52XabzDfDxeIvf/rSHdM1fiUc9T12pUg7uawf33WxRt0b+eL2yQlbczySftwPo7r5Azxxp0e/2gvG6/rHHMPE9+72ZmZ53V1eDDW/lj/fhkm8NnZ8yXJ+wnDG778Mb+wDfCX6GheUfpWIZWDZuDw3rVcu1+19jDAkbN3Jg/nwOfvQR548fT1OmVMuWVOrRg/J33IFPYGCqdSfP2KkTXl5k2Hckc8+55HmLO27IW+33/leGHmP/vW9peeIzxsT1AiDZ8qXy3M9o1PG63Kqe0+VeYxJ372bfu+9yYN48zh1J3WBe/v5U6NyZKv36UfLaazMVXL1wjrsSNcDi8Z6519ZT3rfv/64/sYwxcffhjR3QfLXSJD4t+wDVg+28U7kRnLrAGMPJX34h/sMPObR4MWcPpE0g6lOyJOU7diS4a1dKh4VheXtnev957Xt2dlPwJx/IiyelMXZ396GvuJ/t5b9C6sOM4RYNa7q/iBhj+GQ1PDI98x+WAJXLwdSHLbqE5+ysY7klYqiDGBe9oUoWS2H3Im+CiufMOXP8pD0jxGI3v6RfLLyxPStUpbIFv53S036Eg2XfuV4f+4bF9fWz57U5ftKweTds3gWbd9k9GTL7Zf+OMHjnidS9ISS1E6fsGcCmf2w4mIlk2ekpVRwGdISHu2TveyTprOHTtXYvn+Xfp5sXMVMqlYXv3sx/s/Ot/dUQPsS4/VJ9ZSUynazencAi0LW1HfBp3aRw9pjLqvuZ77fYeZtc3cEGBcLv72XNrG25ZeN2w4T37BkyM3unXjQAvp9hUf+K/HPc3cY4+DAabj46n+H7HnYuf6nyVJaX6cU94SeZ/1zxHL//PXvoEPGLFnFgwQJOb92aZn1A1apU6t6dit27pzvc58Bfhlc+Mrz5KS5n+UzP3W1g4bN5417/YmfPGap0Mfx10ain++PH0O3IdACSSlSk3U/f4le2bC7V0HYp1xiTksJfK1aw9513+Purr9K84YrWqkWVPn2o1KMHvh7MBuVw2L1+XE2OUPafXj+5GahNSbETx6/7DW77ezZD9z8GgAOLcdXnsrbk7fTvADNG5Pw5eXrbNg5+9BEHP/6YMzvTRvu9ihShXPv2BHfpQtkbb8TL3/+Snicvfs/OTgr+5AN5+aRMOG14Zqbh1cWZ/9Lg7Q3DusGYyPQTiW3fa3h4mnE5pCk9Pv/s85ne+ePXrqzy9Y92ollXJvaHJ+7N/nPm+y2Gu5/NXG8Hy4Kn7rPbvzAM83Ll152Gxv2My/dN2DXwzfTL67afcNruwbF5F2zebdi8C37bhUcB2wv8fGHKQxaD7ywcgdWscPacPePTSx8Yfv3z0vbh6wP33GgPCWt8VdblT9uwFeYsN7z/FW4TjmZG7arw0diMg/p51YR5hqfezp5bIS8vaNvMHtbV6YaCkbT5cmTl/YwzCa0L99wI74/JW/dMmbF+s+H5eYbP1nm+7btPWdzXLn+dY4eOGureZzh2Eu46/AoPHHwWgBS8GFd9LutK3sZXL8ONzbKnLR1nz3I+IYHkEyc4n5BA4u7dxC9axN8rV2L+ExX2KlKECh06UKlnT0rdcEOaGZ3Azo8zZaGdAD4zPaAvVqakPdyrfKm82YaPv+Fg8vv/PvYy55n8ZyeuOW2frCVvCKf5kk886nWR1Ty5xiTFx3Ng3jz2v/suSftSTwFp+fhQ/vbbqdKnD6Vatbqk+57F39g9gV2Z9KDFyB6539bb9xoa9bVTevSJH0f3Iy8DcNYKYOSVS9harAVfTrG4ORt66Z8/dYqzhw5x7uBBzh46xNmDBzl78CBHo6M5+Wva5JOWjw9lbryR4K5dKXfrrWl62l2KvPw9Ozso+JMP5IeT8offDQOmGH7alvltqgfDq4/8OxXwmSS7a/Pk9zMez36x1k3sxIb1ClHX+QuMMbR8yBDrYpahckHwRE+LFIcdnEv1v7F/7XYY/v3/ovXpb5O2bPJ5WPZd5m5yygXZeQgK66xQ/9UvysE7X7hev/h5i06Z6Pp96sw/QZ7ddk+eC7163A1n8UStyvDBsxZN66jdLoUxhlU/wosfGJa76e2VkTZN7eTQt1x3ab1GDvxlfyGZs8ywNe7S63FBnarQ8fpjPNO3FMWL5c3PpsxwOAzthrufRcpTDa6AXrdY9GhLgU9i74msvJ85ecZQ7z73vYM/n2TRPiTvv/7GGGJ+hvHvGr7+yfPtvbxg8oMWj92T9481PXOXGSInGjCG/vHP0PWv1wE4Z/nz5BUfc6Z2KJvmpM3faIwh5fRpzp84Yf+7EMT5529Xy5Iv+tuRlJRh/YKuv56KPXoQ3KkTPiVKpFlvjOGbX+CF9w2fx17aa1Ay0B7u1bpJ3m3DnfsNtbqn/tpYOvkgr22PoMx5exrTK4YPp9bTT+dG9YCMrzHG4eDoN9+w7513OPL552kCfAFVqti9fO69F/8KFS6jHoYm/VwPTy0XZPf6ySs/CLzykWHoK/Z7cOTegbQ9vgiAE96leaTWcqwqtfh1TubyUBljOH/8uB3I+Segc+5CYOfix4cOpZukOQ3LolTLlgR37Ur5jh3xK136cg83lfzwPTsrKfiTD+SXkzIlxR7q8PRMw8kzmd/uzlZwZyuLp2d6licjuLSdSLp728LdE2HZekP7kXn/bdy6iR34KazDvNKz/4jhqh6uE6jXrgq/zbXw9fk3QLo1jn968Jh/evRA3CXml8mMHm3hzeG5O967INm8y/DyIsO8FZ4FuS9Wtzo82s3i3puhiL/7dkk6a/jfGruXz4oNlz6s64KSgdD9Roi81eLaOoY9e/L+Z1NmHPzb/uXz8LFL30f5UvYEB73aWTSqVbg/l1zJ6vuZpWsNHZ90/flXrQJsnpt3ewQbY+dPfP5d1z/iZOTW62B0ZPYNE84JxhhuGW5foyzjYPjeQdx0/AMATnsVZ03J26lbJoErSiRQJPkEKSf/Dez898t7VvGvVMk5rKtYrVrplklJsYe6v/C+4fu0I8MypWQgPNjRvqbnh2GK7R5zsGJD6mUNT61j8p93OHM2Nf7gA8q1a5cLtXN9jTl39CgH5s9n/5w5aYcQWRZlb76ZKn37UrZt2yzpufRxjKHraNfXphcGWgzvnnfa2+EwtHnEsHoj+DjOMX733TQ9tRqAeL/qDK35JXd2KMPr/Y46gzjO3jr/6blz7vBhHGcvfzaEEk2bEtylCxU6dyagUqXL3p8r+eV7dlZR8CcfyG8n5f4jdvT449XZs39vb3j4Tni2j0XJwLxz4cwtxhia9zf8+Edu1yR9lgXP9IbRvfP+DEC54em3HTw/z/X6LuF2kGDzbtgVn/ncD5eriL/doy7yVn2JzQ6HjhpeX2J4fTGpcih4olwQPNQJHuqcepiAMYbvtth5fBauuvxhXV5edhL9yFss7giDgH8CTvntsykjK763ewB5IsDPzoPVq53Fzc0p1ENZMyM7zpm7Rjv4KMb1+ke7wUuD89b5mZJi+OQbmPCe61mAMnJnKxh1n0WzAtIjc3e8oUGk4XQieJtknt19L9edXJktz+VTooT9r2RJ5/++Fx4HBRF0/fWUad3aZRDgTJJhzjJ4aZFh5/5Lq0OVcnbA5/7boUSx/NOGroYydT0ynf7xYwA7+e71q1dTpEaNHK5d6muMZVmc+O479r3zDof+9780AQm/8uWp3KsXlXv1oki1allYB3syAVfDvcuXgj8X5p1ePxf8ecBwTR/7PVg0JYEXd95GzSQ7Kp1kFcXXJDkTQl8u7+LF8a9QAf8KFfALDrb//ud/v+Bgil5xRZa2iTsF7V4mIwr+5AP59aT8PNaenj0reyW0bGh/IW1UK29dMHNbRrMJ5JbypezePm2vVXu5knDa7kZ9KdODZ5cGV8AHzxXOoZQ5LfGsYd6X9peIP/Zc2j78/eC+m6H3LRZrNtm9fC51XxerW93u4XPvzaTbYy+/fja58+QMB1HzMy7XqpEd8OnaGv0I4YHsOGfi/zLU7WVcJtX18rKTkV97de63U/J5O8/WxPcMv1/Ce9TLy+559+S9+Supc2Y5h54AAY7TPP/nXTQ8k3pq2fP4cMq7JKe9S3LauwSnvEtiihQnsFxJylQOolK1ElS9siQBpS8K7FwU5PEpXvySe3YcOW547RO7l/ulBu0bXgkje1jc3QZnr978JPm8oUa3dCZ7MYZn4npzQ8JnABRv1IjmX36Jd0BAjtbP4XCwa/NmfNevZ//s2ZzasiVNmdLh4VTp25dy7dvj5eub5XX4KMZwl5teP1MeyrtDNN9YYnjoJbvuZZIP8MqOmymXnPkZD3zLlHEGcvwuDuj8E+i58Ni7WLHsOgSP7dzv4MMVf7Mxrgw7D9hJ8wvyj54K/uQD+fkG+3SiYewcz6dq/69yQTB5oEWvdoVzhpSMZPQrQ26I+GeYV0UN88rQa58YBk/N/Utx2ZIpPNTJmyfutTIcTiRZy+EwLPsOXlxoiHYxg19OCAqE7m3tXj7N67rv9ZWfP5tcST5vuG2kYWU6+X9qVbbz+Nx7E1xRSe+PS5Fd58yM/xkefNH1NbRudRjYyaJWZahZGWoEg59vzrXh2XN2T5Go+Z4Nb7/A1wcib4XHe1jUrFxwz72UFEPYYMP6f4bAeZtkaiRtJdnycwZ8zlpF7C7Fbvj5QtPa9gyzIfUtQupzWTMR7txveOkDw+xluBymnZEbm8GI7nYPwfz+xXLMOw7Gzkm7vGhKAq/vvJFKSfawqsq9e1Nv2rRsqcP5hASS9u8nad++VP8S9+3jxE8/YRITU5X3CQqiUs+eVImMpNhVV2VLncD+LG/Ux/DbrvTX59VePxcYY7j5sX9z4FVP2soTe/pT8vxRjvpW4KhPBY76VuBvnwoc9Q3mqE8FHKUqcP0NFbj9tgq0vtYvz/eAPXzMzq226kc7H+Ou+NTrt823uKpq3j6Gy6HgTz5QEG6wf91pJ4T2dEy7ZcGDd8DzD1iUykSSscIso1kFcoplweje9sxrGuaVOcnnDfV7Gbbvy7hsVvDzhaurQf0roH4Ni/pXQN3qBu/kOK68Iv9eZwqKn7cZXlpkD9m6nKB5Znl5wS0t7F4+HUL/HdaVkYLw2ZSec8n2bEtLvrW/dLeoa0/Pfn39/P+lLbdl1znjcBjChxjWbMpceS8vqF7BDgTVqgy1KlvOv6+sRJrEwpfqdKLhraUwZWE6PSUyoYg/PHA7DL/HomqFwnHubdltJ8q91JxorlQulzoY1LQ2+Pu5f02/32J4YaE9RO9S8qV5e0O3CBh+d8GaMGHfYUP1bunPVlojcQtvxt2E1zk7+FLv1VepfO+9Hu3fkZzM2fj4NIGdpP37Sdq7l6R9+zifkJCpfZVs0YIqffpQoVMnvIsU8agel+LDaEO3Ma7vxV8cZDHs7rx9LsQdNDSM9Cx/6wXlguwhqd0iLMIbkye+B5w8Y+cyuhDsyeiH8jces3jwjtyvd3ZR8CcfKCg32A6HYdbnMPINk6kcFC3qwuvDCs549pzw2KsOXlqUe89/VRX7onljM7WZpz5ZbejyTNZejn197KTRDa6A+ldY1K9hB3xqVkqbm6SgXGcKkv1HDNM/Nsz49PLz9qSnXo1/hnXdxCX10NM5I57KznNm6247aben02unp1LZC0EhqFnZuujvzA3zO3HK8OonMPVDc0nDg4oXhUGd7ZwweXXa7+w0eYHh8Tez9+uJny80ueq/vYPsvHpfrLeTOH/zy6Xtu2gA3H+b3X41KhbM9us0ysH/1qS/ro/fB3T/YSAAXgEBtFixguLXXAPYPUuSjx5NP7Dzz99nDx68rNkJvIKCqHjnnVTt04fiDRte8n485XDYOXM2u+j1U6G03esnq4LL2WnmZ4YHJl/ee7B8KTtvZbcIixuuyblA0NlzdmeDC8Ge73+3ZyrOrK6t4cOxBfeeRsGffKCg3WAfPmZ47DV7yuH0lCoOUQPsJHga4uW577YYYn427DlwglJBJfHxtl9Hby/7105vL/Cy7F+kLv471f8Xl/XKXLlyQXZvkrze3TOvMsZww2DD2l8939bbG2pXudCT559AzxV2MC6zOQUK2nWmIDl1xh5uMPVDw5+ZH3qfrlLF7RncIm+1aFbn8nqy6JwRT2X3OfPsOw6em5Plu02lbEmoVcUOoteqDLWqWPbfVcDCfp++uhiXOYjcKV0Chna1eLgLhbq3szGGF963h8kdO5lzz1upLBT1hx2XmMS5fCkY0sViYCcoXaJgt9/y7wy3jnD9FTK65mMkL54NgH/lyhSrXdsZ3HH8Z0iWJyw/PwIqV7b/Vali/6ta1fnYr2JF9h89miufS4u+Ntz9rOvX5KXBFo92yx/nhTF28GfW51mzvwqloUsr6NbGIqxh1gaCUlIMP2+HVT/aAZ81v1768EyAMiXh8P+sAvsdVMGffKCg3mCv+tEw+OV/kx56eUGfW+3AT9mggvmGyykF9Zwp6LbvNVw/0HDURW9mLy87oHOhB8+FIVu1q15+/gqdM3lfSoo9bfuLHxjW/Zb57by9Uw/rymioQ2bpnBFPZfc5c/ac3fsnKxKe56QKpe2hQQPugOJ5dFr6/7N333GOlPUfwD8zk9422V5vd69yvd9xx9FBmhRFmgoIggIqiKg/xI4gdgQLYgFUFEFBKSL97oDrvey1vW23vSabXuf5/TGTbLK7SbYku8nu9+1rnWRmNjtHJpOZzzzP95kMbo+I197vQGNvMXYcAbbVAO29k71VQ82tkLrm3fiRkXebzXaiyDDr+vg1rG690IfPvXcp7PtGV8ROVVg4EOpEBzzyj6qgAFyCY8dkfS+FQlKrnyONwy8vzgXqn8+ueoqMMbzyIfD7Vxne2pW6bujFuVLrmmvP5XDG4tHf6GdMOsaHw55N+5HykHjvHzksn5s979VoUPiTBabyCbYoShcxvf1SNy8qDpwaU3mfmeoa2hh+/rxU/0erjg155lWk78SS9pnssr1Gqgv04ub4reMXVgO3XMLhUxcCxXmp329onyGjNRH7zJZDDOd9OfU1Y9JhRhHw9Rs43HoZsuqicKIM3l8YYzjVKYVA2w4zbD8C7KtFSrr6jcX6RVIR5yvOmJ4t1X/0LMM3fj/8ZaRGBTT8qhnHP3YR/B1SQiTo9bFhzqDWO+qSknGPDjZZ30v/eJfhhu/Hv6T+5Zc43HNN9u4jVodUB++fG6UBEVIVBJXkAdecA1xzLof1i+J/jlq6pC5c7+6VpmOpoTZSKiXw5wc4XH9+9r5fiVD4kwXoBJuMFu0zZLRon8lOje0Mj/2L4bl3gc6+gTtqn7mEw4q56S1QTPsMGa2J2mf+t53h7sfYmLvvpNuccuD+T3H49EcmdtSxbDOS/cXjY9h7Ath2GNh+RKr1kc4LQ44DrtwAfO16DusXT+/3rrOPoeIT8ets/fJLHL5wkQPelhaoS0uhyMlJe9H8yfheCoWkAslHm4ZfXpIH1P0ju1r9JNJnl4KgFzYyvLNndPV0EikrAD4h1wiaNwPYtG8g7DnRnJq/MRyeBxZV+nDJOjUuWCW1Rpoq79VwKPzJAnSCTUaL9hkyWrTPZDfGpJE5TPqJO2GhfYaM1kTuM4xJF2O1zVINl7o2hpMt0uOmznHVkx2zxTOBB27kcM05mTEKTqYby/7CGENzV2wYtPfE+FsHqVXAzRcBX7mOw7wZ9N6FXf89Ec+/N/yy02YAR/7KTegoiZPxvfTcOwyffDD+5fRjd3O4+xNTc5/psTH850Op3tF7+1IXBKXb/Erg/JXA+Ss5nLWEob9v+pzLKCZ7AwghhBAyPhzHwaSf7K0gJHNwHIcFVdKodvKcyDJ/gKGpQw6FWoGTrSzyuL4dKe8ytvo04Fs3cfjo+unZPWgicRyHGUVSl7rr5G4b3nDroJqBQKi1e2SvZzECd10FfOlqDkW59N4NdseVHJ5/b/jg49gpYPN+4JzlE7tNEykUYnjwmfjBT0kecPvlE7hBEyzfLA3Qc9tHOXTbGP79PvDPTQzv7Z2cgD2eisKBsOe8FUBpVJkRUWTo75vEjZtgowp//H4/HnnkEezYsQMulwvz5s3D17/+dcyePRsA8Mwzz+DZZ5+FKIq48sorcffdd0fS3pqaGjz00EM4deoUFi5ciO9///soKSkBAHi9Xjz88MPYvHkzjEYjvvSlL+Hiiy+O/N1XX30VTzzxBFwuF8477zw88MADUCqVqfpvQAghhBBCpgmVksOcCmBORXjOwIVAKMTQ0h0OhaRgaOAx4PaO/O+cvQz45o0cLliV3i6YJDGNmsP6xcD6xUD4vW7ulEKgbTVS7aC9J2JDv8pi4CvXcrj1UsBARbjjOnsZMG8G4hZZ/93LDOcsn7r//Z5/D5GBa4bzjU9Pne5eyRSYOXzuCuBzV3DoskpB0AsbpYLMEx0E5ZqA81ZIYc/5K+TRGOkYDGCU4U8oFEJZWRmefvpp5Ofn47nnnsN9992Hl19+GR9++CH+9a9/4ZlnnoFGo8Gdd96JqqoqXHnllfD7/fj617+Oz33uc7j44ovx5JNP4jvf+Q7+8Ic/AACefPJJ9Pf34/XXX0ddXR3uuecezJ8/H5WVlTh58iQeffRR/PrXv8aMGTNw33334U9/+hPuuOOOtPwHIYQQQggh05MgcKgsli7+z1sJRAdDjDF09g0EQXVyi6GTrcDJFsDmBBQCcNEaqabPhiV0sZGpKoo4VBRJQ08DUuugfbXAqU5pyPd1CwGFgt6/ZDiOwx1XAPf+evjWLy+9L9UGmoqtpkIhhgf/HL/VT2k+cPtHJ3CDMkihRRq98PNXcujsY3jpfalY9Kb9QDoKzug0wFlLgfNXcDh/JbB0NrWyjGdU4Y9Wq8Vtt90WeX7dddfhscceg81mw+uvv45PfOITKC8vBwB8+tOfxv/+9z9ceeWV2LNnD7RaLa688koAwO23344LLrgA7e3tKCkpweuvv46f//znMBgMWLp0Kc466yy89dZbuP322/HGG2/gwgsvxIIFCwAAt912Gx566CEKfwghhBBCyIThOA7FeUBxHrBhCRAdDAGA082gEKbPcN9TiUbNYd0iYN2iyd6S7HPTxcA3fg94/UOXBYLA068D9386PX+7o1fq0if9SAFee28FVEpAqxahUUkjp4anWlXU46j5A/O4Ea/7zu74LZ4A4Buf4uhYAKAol8OdVwF3XsWho1cKgl7YyPD+gbEHQQoBWLtA7sq1gsPpC6l4/kiNq+bPwYMHkZubC7PZjIaGBlx66aWRZXPnzsVvfvMbAEB9fX2kaxgghUjl5eWor6+HXq9Hb29vzPK5c+eipqYm8rvr1q2LLJszZw5aW1vh9XqhGWY4QL/fD78/9uijUCigUqnG80+dVKLcVk7MpM6TJKPRPkNGi/YZMlq0z5DRmur7jE4+LRVFGkslFab6/jJVmA3AtecCf3lz+OVPvsLw1esZxlNLlzGpTtPeWkTCnn218UZ24+ELAA73mP7S2DcySlk+cOuljI4FgxRagDuulH7ae4GXNgP/3AR8eCh5ELR0ltQa87wVwFlLAIMuvET6xbH+t55Kx5mRFKwec/jjdDrxwx/+EHfddRcAwO12w2AwRJbr9Xq43dKnzuPxQK+PrUSp1+vh8XjgdrshCEJMkJPod8N/w+PxDBv+PP3005HuZGHXXHMNrr322rH+UzNGc3Max7kjUxLtM2S0aJ8ho0X7DBkt2mfIaND+kvmuWqvCX94sGXZZYwfw7H87cfaSkRXMYgxo6VHgcKMKNU0qadqoQq9DSOUmp9XnLu1FZ4dzsjcj4310pfTT0Sfgjd06/HenDvtOqiEyDpWFAaxf4MX6BV6cPt+LPNNAONPbDfSmeFumwnGmuro66TpjCn98Ph/uu+8+bNiwIdKVS6fTwekc2MldLhd0OimS02q1cLlcMa/hcrmg1Wqh0+kQCoViWvIk+t3w39BqtcNu2y233IJPfepTsf/IKdDyp7m5GRUVFdNiCDoyfrTPkNGifYaMFu0zZLRonyGjQftL9pgxA1j2d2D/yeGX/3t7EW4aZtQrUQTq2qJa85yQWvdYHend3nQqLwC+9uk8qFV5k70pWaOyEli7HPju7YDHBwRDgFGnBKAEYEzr355ux5lRhz/BYBAPPPAACgoK8OUvfzkyv7q6GidPnsSGDRsAACdOnMDMmTMBADNnzsS///3vyLoejwctLS2YOXMmTCYT8vLycPLkSSxatGjY3z15cuBIUltbi7KysmFb/QCASqXK6qAnEZ7np8VOSVKH9hkyWrTPkNGifYaMFu0zZDRof8kOd1zJcMfPh+9689o2oLmLg8s7UJ9nz3Gp69bYumdlrgdu5KDVUP2ZsdIP374j7abLcWbU/8KHH34YPp8P3/ve92KGTLv00kvx4osvorW1FT09Pfjb3/6GSy65BACwcuVKeDwevPrqq/D7/fjTn/6EBQsWRIZ6v/TSS/HHP/4RLpcLhw4dwvvvv48LL7wQAHDxxRfjnXfewbFjx+B0OvHUU09FXpcQQgghhBBCyOT65IWAIc6FuygCM69nWHgTw40PMTz6AvD+gakX/FQUArdemnw9QibLqFr+tLe349VXX4Varca5554bmf/4449jw4YNqK2txU033QRRFHHVVVfhiiuuACC1xvnJT36CH/zgB/jRj36EBQsW4MEHH4z8/uc//3k89NBDuPjii2EymXD//fejqqoKADB79mx8+ctfxr333guXy4XzzjsPt956awr+6YQQQgghhBBCxsuo4/DpjzD87uXhl0+BeroJcRzw+69xUKuo1Q/JXBxjYx1kjUwUURTR1NSEysrKadEcjYwf7TNktGifIaNF+wwZLdpnyGjQ/pJ9DpxkWHbrxF1aqlXAkpnAirnAirkcls9hMApNmD2zEv4gB69fqiHj8WH4x/6BxwPL2SjWlQpUz5sBPPBpDuetpOAn20y348y4hnonhBBCCCGEEEKWzuZw+kKG7TWpf22tGlg2Wwp6Vs7jsGIusKAKUCoGAhdRZGhqAnge0Gk46IYvEZsEBThk6qLwhxBCCCGEEELIuN1xBYftNeNr/WPUAcvnDLToWTkPmFcBCAIFM4SMB4U/hBBCCCGEEELG7drzgAf/DNS3jWx9syEc8gy06JldBvA8BT2EpBqFP4QQQgghhBBCxk2r5vC3bwNXfIOh2xa7LD8HWDkvqkXPXKCqBDEjSBNC0ofCnyxBdbkJIYQQQgghme70hRzq/gG8tFkazr2iUAp8ygsp6CFkMlH4kwUanR7cebwN39Tl4KzivMneHEIIIYQQQgiJy6jjcPMlk70VhJBoU388sywmMoYnT5zCWW9vxy6HB1/ecxSuYGiyN4sQQgghhBBCCCFZhMKfDHXS4cJlG3fjG/uPwx0SAQCNLg8ePnRykreMEEIIIYQQQggh2YTCnwz0+9pTOOut7djRYxuy7MnaU9g+zHxCCCGEEEIIIYSQ4VD4k4GCIoNXbu0zGANw964aeKj7FyGEEEIIIYQQQkaAwp8M9Pk5M7A6Lyfu8pMON35cUz+BW0QIIYQQQgghhJBsReFPBhJ4Dr9avRBqPv7b8+sTjdjb1z+BW0UIIYQQQgghhJBsROFPhppr0uP/Fs6Mu1xkwBd31sAXp3sYIYQQQgghhBBCCEDhT0b74rxKLLMY4y4/Znfh50ep+xchhBBCCCGEEELio/Angyl4Hr9evRBKjou7zi+PNuKQ1TGBW0XI9OIIBPFqSyd+ebQBW7r6EBLZZG8SIYQQQgghhIwKhT8ZboHZiK/Mr4q7PMgYvrirBgGRun9lKnsgiO3dVnR4fJO9KWSUmpwefOTdnbh560E8eOgkLt+0B5/dfpC6WxJCCCGEEEKyCoU/WeDLp1VhrlYVd/khmwOPHWucuA0iI8IYw2+ON2HWfzbh0o27seDV93Hz1gPwBEOTvWlkBFzBEK77cB+O210x819p6cJ3DpyYpK0ihBBCCCGEkNGj8CcLKHke36kuhJCg+9dPj9TjSL9zAreKJPP4sUZ8+8AJhNhAN6FXW7rw2e0HwRh1Hcp039h3DCcGBT9hfzjZjK3d1gneIkIIIYQQQggZGwp/ssR8vQb3zKuMuzwgMty9qwZB6v6VETZ39uIHh08Ou+yNth78taF1greIjMZLpzrwbENbwnW+tKsGbmrFRQghhBBCCMkCFP5kkfvmV2OeSR93+d4+O3574tQEbhEZTrPLg89uO4REdYG/tf8EmpyeidsoMmJNTg/u3XM06XoNTg8ejhPwEUIIIYQQQkgmofAni6gFHr9avRB8/N5feORwHWrjdFUh6ecNhXDz1gPo8wcSrucMhvDFXTUQqftXRgmIIm7bfgiOQHBE6//uxCns6LGld6MIIYQQQgghZJwo/Mkyq/JycNfc+N2/fKKIL+2qoeGoJ8nX9x7DfqtjROtu6bbid9RSK6P88HAd9vT1j3h9BuDuXTVUxJsQQgghhBCS0Sj8yULfWDgLswy6uMt39vbjDyebJ3CLCAD8ua4laZ2YwX5w6CSOUaHujLCxo3dMo+bVOtz4cU196jeIEEIIIYQQQlKEwp8spFUI+NXqBUjQ+ws/OFSLBqd7wrZputvT24//23ds1L/nE0XctfMwAlSoe1J1eX24c+fhMf/+r080Yk/vyFsMkYnjCYbw05p6nP7GVix57QN8ZfcR9Cfplkkyi8gYnmtsw5d21eCBfcfps0YIIaPkD4n43oFabHhzG9b+byu+d6AW3hC1WiZkuqHwJ0udXmDB7XMq4i73hETcs+sI1ZSZAN1eP27eegD+MXa122914BdHG1K8VWSkRMZw184adHn9cdeZY9TjawtmJngN4Iu7auALUYiXSXq8flyxaQ8eqanDCbsLLW4vnqlvxUfe3YleX/z3m2QOVzCET7y/F1/YWYO/NbThd7WncPF7u/DXehoxkRBCRsIfEnHdB/vw+PFGHOl3otbhwuPHG/HpLQfo5iMh0wyFP1ns24vnoFKvjbv8w24rnqlrmcAtmn6Coojbth9Em8cXdx2jUoHHViVuqfXzIw3YN4paMyR1fnO8Ce919MZdruZ5/GndYnxtQTVW5Jrirnfc7sJPj1D3r0xR53Dhovd2DlvDqdbhxo1bDlBYl+G8oRA+vWU/NnX2xcwPMYav7DmKD7v64vwmyURdXh929NjQO8KC+oSQ1PjpkXpsHuZ4+V5HL/5QS2UiCJlOKPzJYnqFgMdXL0i4zvcO1qLZRUOKp8uDh07igy5rwnWeXLsIN84sw51zZ8RdJ8gY7txJhYMn2t6+fvzgUOLh2h9cOgeLzEYoeB6Pr14IZYLh9h471oj9ffZUbyYZpZ09Nlz07i40OOMf+7b32PDl3UfAqHVkRgqIIm7ddhCbO4cPeEKM4dZtB9Hq9k7wlpHRCogivnPgBBa++gEu27QHF+1vxD27j8JP4Sshabe7tx+/TFDP8Ec1dehIcAOTEDK1UPiT5c4szMUts8rjLncGQ7iHLnDS4j/Nnfj18aaE69w3vxoXlxYAAL61eDbmmvRx1z1hd+Hhw4mDCJI69kAQt207hGCCz8alpQW4bfZA98oFOYaE3b9CjOGLu2roomYSvdrSias270HfCOr6PN/UTl0uM1BIZPj8jsN4o60n4Xo9vgA+s/UgteDKYJ5gCDduOYBfH29CSD7WMgB/a2yjcxNC0swdDOGunYcjn73hOIMhfPfAiQncKjJWJ+wu3Lz1AGb9ZxM2vLkNT51spmMoGTUKf6aA7y6ZgzKdJu7yTZ19ox6FiiR2rN+JL+2qSbjO+cV5uH/hrMhzjSDgiTULIXDxW448ceIUtlBXhrRjjOGre46iMUGruFKtGr9avRDcoPfrntOqsMRsjPt7R/qdFChMkt+dOIXPbD0I7yjCgIcP1+HfzR1p3CoyGiJjuHv3EfynuXNE6+/p68c39x9P81aRsbD7A7jmg714q334EO/5pnb8tYFqNxGSLg8erMVJR/LBX/55qgNbuxO3YieTq87hwuUbd+PVli5Y/QEc6Xfiq3uP4d49RykAIqNC4c8UYFIq8MtV8xOu860DJ9BGzeNTwu4P4KatB+BK0EWrUq/F79cuhjCoi9Dy3Bx8dUF13N9jAL6wqwYOqomQVs81tuNfp+Jf8PMc8IfTF8OiVg5ZpuR5/HrNQigShHi/ONqAwzZHSraVJCcyhgf2HccD+49jLKdAd+2owa5eW6o3i4wSYwzf2HcczzWO7mbFU3Uto/4dkl49Xj+u3LwHW7ttCdf7v73HcchKx0pCUm1TZy9+f3Lk9Xy+tvcYFX/OUCJjuHvXEXQPM1DFX+pb8XO64UhGgcKfKeL84nx8sqo07nJHIIivUDo8biJj+MKumoR3UjQCjz+vXzJscAAAX5lfjWWW+C1HTrm8+NZ+aoKbLrV2F76+92jCdb62YCbWFVjiLl9kNuLe+fFDvCBj+OLOGjqRmgCeYAi3bDuI39WeGvNr+EQRn/7wAE5RfbRJwxjDg4dO4g+juFiJdt+eozhopXpbmaDF7cVlG3fjwAhCHZ8o4pZtB2CnGx6EpEy/P4Av7kzcOn2wo/1O/HGMx1+SXn+tb8W2Hlvc5T88XId/0A0QMkIU/kwhDy+bi2KNKu7yt9p78M8ErR1Ico8da8R/W7sTrvPoyvlYYok/KpSS5/HbNYug5uN//P7a0Iq32hL/HTJ6vpCI27YfgjtBt6D1BWZ8dX78uj5h982vxoIcQ9zlB20OPJ6gyCIZv16fHx/bvAevtnQlXffswtyErbW6fX5c/8E+2EdQK4ik3s+PNuCxcXxevCERN289CKuP3r/JVOdw4dL3dqHW4Rrx79Q7PbhnF9X/ISRV7t93POEotPH8qKYenVT8OaN0eHz47sHapOvdvesINnfGH7mWkDAKf6aQHJUSv1iVePSv+/cdowP7GL3X0YuHkowMddvsClyXoAVW2Gk5Bnxr8eyE69yz+wj6hmniScbuuwdP4FCC7lgWlRJPDtNdbzgqgcevVyeu4fTTI/U42u8c07aSxBqcblz87i7s7B06lPtgX5pXiRfPXoGfr0zcPfaY3YVbtx9CkFpsTajfHm/CDw/XJVzHoBDw2zULYVAIcddpcnnw+R2HIFKIMCkOWR249L3daBlDF/OXWzqp1UEW2H2M4ZrviPD66DOWqV5p6cTzTe1j+l1HIIjvjSBoIBPnG/uOj6hlZJAx3LT1II5QyQGSBIU/U8zFpQW4ZkZx3OU2fxBf23uM7rCN0imXB7dvP5SwnsiavBw8tHTuiF/zzrkzsL7AHHd5p9ePr+09NvKNJAn9r7ULv69NfHHxq9ULEhZPH2xZrgl3z6uMu9wvMnxpVw2FCSm2u7cfF727E3XOxIUseQ74yfLT8P2lc8FzHG6cWYa751Ul/J33OnrxjX3H6Rg5QZ6pa8G3kow0oxF4PLdhGa6vKsWvVi9MuO47Hb34SU19KjeRjMD2Hhsu37R72JoUI/WtAyewty95mEsmx1s7Gc65h+Ffm4AbH2YQRTpGZppOjw9f2R2/WzsLcfhI3wqYaqvBxOFvXD3f1I5tVPw5I7zR1o2XW2IHPxBdKnh2VSFwKnfI+o5AENd8sA+tVOOVJEDhzxT0yPJ5KFDH7/71WmsX/tMyspFUiFRT5OatB2BN0B2kUKPC0+uXQCWM/CPFcxx+s3pRwjvZ/27uxIvUVW/cWt1efHHXkYTr3D67ApeWFY76tb+2cCbmmvRxl+/ts+O3J8Zej4bEer21C1du2o2eJN17tAKPv6xfitvmVMTM/86S2fhokvf5T3UtSYNCMn7/bGrHfXsS199S8hz+sn4pziiUTnSvrCjClxIErgDwkyP11G12Ar3T3oOrN+9Jend6jlGPVbnxu0QHRIZbth6EjbpeZpyAKGJTfwc88kAX/9oE3PgzP4XkGYQxhnv3HEFfnM+P6FSDe20tnv+7BU1vzoR70zzEe/u+tvcY3bSaZI5AEF8d9P0Y7DTC/vwaeHfMgvOV5fAdLRnye+0en9SFneqokTgo/JmCctUq/HTlaQnX+b+9x9DjpS5FyTDG8NW9xxIWrlRwHJ5etwQl2pG3GAmrNGjx0LLErYW+tvco2j2U4o9VSGS4Y8fhhOHdIrMB3186Z0yvrxEE/Hr1QiTqKfbI4TqcsI+8BgYZ3h9rm3HT1gPwJBnKPV+txMvnrBo2zOM5Dk+sXZSw6DoAfPPAcQoQ0ui1li7ctbMmYWtKgePwp9OX4IKS/Jj53148G2cWxi/IDgCf33EYDUlahpHx+3dzBz61ZX/Sz+QyixGvn7cKT69bDLMi/qlns9uLL+ysoVAhg/hCIm7eegC/sx6G7uJDACe9139/TYnbnqRuzZni741teKOtZ9hlgVYzHC+sgbV54EaV/0gZvLuGH7jiSL8TfzrZkpbtJCPz8OGTMXWb/LWFcLy0Esytjsxzvzcf/rqCIb9b0+/EZ7YeoEFHyLAo/JmirigvwhXl8e9u9/gCuH8fdSlK5pkRDCH84NK5CUeGSubG6jJ8ZNDFTTSbP0jFMMfh50frsSVBE2adwOOPpy+BRojfAiuZVXk5uGtu/NYIPlHEl3bVIETN5MdEZAzfPXACX993DMn+E84y6PDm+WuwKi8n7jp6hYC/bViGUq067joiA27bfgg11H8+5d5p78Fntx9EKMExjQPw2zUL8dFhvscUvPSZTfT+9QeCuGnLAbjllgok9f5c14Lbth1CIMmH8owCC/5zzirkqVUo0Wrw0MxiJKqq9r+2bvzmRFNqN5aMiScYwqe37I+ECsrKXujOHTh3fOrvenz5OeoiNNlOuTz4xr7jQ+YzBngPlMP58nKIbqlHQHFUbyHvzpnwHRnaegQAflhTRzVCJ8me3n78QW59zBjg2VEN15uLgZB0niro5feFcXC9uWjYLmCbOvvw5d00yjMZisKfKeynK+YjVzX8cOMA8FJzJ/7bmnyUnOlqV68N9+8f+mUa7RMzivH5Qd1KRovjOPxy1QJYErxX73T04i/1reP6O9PRtm4rfnIkcf2PHy0/LWG3rZH6xsJZmG3UxV2+q7cfT45jOPLpyhsK4fbth/Cr48kvBtfk5eCN81ej2hD/fQgr0Wrw3Ibl0CfodukMhnD9h/vRQSfAKbOlqw83bT2QNDD4xcr5uKZy+IsSACjQqPDM+qVQJmhyV9PvxFf20MlvOjx+rBH37jmasOUWAFxUko8XzlwOk1IRmbcuR4f75g/f4iDs+wdPYnuCoY1J+rmCIdzw4X682xE7gpB6QTs0awcKtD/2pBEPvDZ8ixOSfiJj+MLOGjgHBd0swMP99gJ4PpgHiNLl3gWrgEPPcPjZXQPHTffG0xBoGr5+zPep+POEC4gi7tl9BAzSe+h6cxG8uwZGoFWd1obfPdqLmy6WZ4g8nK8vQbB96A2v5xrb8GOqgUcGofBnCivQqPDjFfMSrvPVPUdpaNxhdHl9+MzWgwkvUBbkGPDoqgXgEoz2NFLFWjV+lqSr3rcOnEAjdWMYMasvgM/tOJywpcjHK4rwqerko7ONhFYh4PHVCxPe0X748EnUO+g9HCmrL4CrN+/Fv5uT1yi7vLwQ/z57JfIS1DsbbLHFiD+cvjjhe9bq9uJTH+6nFiQpsLu3Hzd8uB/eJF2EHl42FzfPKk/6eqvycvCjZYm/415oaqfuCynEGMMPDtaOaESga2YU4y9nLIV2mID1awuqcVbh0AvOsBBj+Oy2g9Q9fZI4AkFc+/5evN/VN+xyzapGqBbJnyuRxyOPmvDgRqpPOBl+d+LUkNbNoX4NHC+ugv/EQID+f58E3vgph3wzh69cB9x9tbyA8XC+sRjBrqFdof/R1I7tVPx5Qv3meBOO9DshOtVwvLQSgZNF8hIG7fpaXHB9O26dV4Y/fZ3DFWfKJ7hBAc5XlyLYbRjyej85Uo+/NdDNYzKAwp8p7uMVxbikdGh/0LBOrx/fTNK6ZboJiiI+u+0Q2hPc7TcpFfjL+qUJWw2M1scqivHxiqK4y13BEL6wk7oOjQRjDHfvrkk44kGlXotfrJyfkvAu7PR8Mz43Z0bc5Z6QiLt319BQ1CPQ5PTg4vd2YtsI7v7fOXcGnl63ZNiLzGQuLi3AD5KM0rfPasedOw/T+zYOh6wOXPP+3iF3pwd7YNEs3JmgC+Vgn5lVjhuqEge4D+w/jh3UimTcRMbwtb3H8OixxqTr3ja7Ak+sXQQlP/xppsBx+P3pi1CkiR/Wtnt8uIM+dxOu3x/A1e/vTXjs5ThAd9ZxKKvlumgBBb734xw8tJWC1ol0rN+JHxw6GTMv0JQLxwtrEOqRwhyNmuGfD3L40R08BEE63+E4Dr/4IoePnxX+JQWcry1FyD60diUVf5449Q43fnKkXirs/MJqhLrlAvnKIPSXHYBpVTN+uWoBeI6DQsHhhe/yOGuF9N4wvxLOV5YjZNUOed17dx/Fe4Na8JHpi8KfKY7jOPxs5WnIiWpyPdg/mtrxdjsVNg373sHahDViAODJtYswM0EXn7H66Yr5KE5wMrytx4YnaqkWQjJP1bXgv63x92kFx+GPpy+GKUFXu7H61uLZqNIP/fIN29ptw59O0khSiezr68dF7+1EbZJWUhyAHy6bh4eXzQM/jhDvzrkzcEuSliavtnThoUEn2WRkjtuduPr9PehPMvrIPadVJe0ONBjHcfjZitOwxBy/gHeQMdy67SDVrxiHgCji8zsO46m65Bf3982vxo+XJ/9MFmrU+OPpixMWy3+voxePHm0Y7eaSMbL6AvjY5j3Y3dufdF2OB/QfOQyh2AYAYG41vvujHDy0i85RJkJAFHHnzsPwycEMY4BndyWcry4D80nnNuYCH/b8gccnzhn6IRMEDs9+m8MZi6XnzK2G89VlEL2x1ws1/c4Rfe7J+DDG8JU9R2E/lh9T2Jk3emC6ejdU1b24b/5MzIkqU6BWcXj9EQHL50s3VZhHBcfLKyA6YuvhBRnDZ7YewGGqYUhA4c+0UKLV4OEkTeO/vPso7DS8Kl461ZF0WO6vL5iJixK0phoPi1qJx1YvSLjOw4fqcLSfRtiIp8bmwLf2n0i4zrcWz8bKBAWBx0OvEPB4kvfwwUMn0eT0pOXvZ7s327px+cbd6ErS3UMj8Hhm/RLcMTd+S6uR4jgOP1o+D+cUxe+GAgC/PNZIzadHqdHpxsc370VPku7Ft8+uwHcWzx5TSzytQsCf1y+FWRX/Jke7x4fPbj9Io5+MgScYwo1bDuDFU8m79Ty4dA6+OYr38YzCXHxz0eyE6zxSU4cP4nQ/IqnT4/Xjys27sT/B6KaDcUoRhssOgDdLo1mKNj2+91Mjvr+3jmptpdnPjjRERqJlfgGu/y2Gd/tsQO7IbJrdi4N/VGJBVfzPolbN4eUfcphVLh0XRaserv8uBQvGXh7+8HAdurwUnqfT3xva8earObGFnUtsMF6zC0K+C/NMetxzWtWQ39NrObz3MwVmV8kBkFMDx39WRAp8hzmDIVz3wT60JGgRT6YHCn+miRuqSnB+cV7c5e0eH74zzQu7Hel34u5dNQnXubAkH19fODPhOuN1YUkBbp5ZFne5TxRx187DdBEzDFcwhM9uPxS5Ezacc4vy8MV5I+9WMhYbCnPx2QQtSVzBkFTQj06OYzxT14JPbdkPd5KaMLkqJf599kpcXh6/m+RoKXkeT69bgnlJin/fu/soPqQL0RFpdXtx1eY9CbvQAsAnq0rxyPJ54+qCWWnQ4vdrE9dv2tptG1GtGjLA7g/gmg/24q32xAV9eQ54fNUCfHFe1aj/xj2nVeGCBOcnIgNu336IWm6lUafHh8s37cZhW+IbSyVaNb4wqFsmrw3CcMV+cDrp/Ql1mPHwYwZ8e38tfcelyZ7efvxCbhEX6tPB/sJqBOrDIyMyaNfW4bUfCqiwxA/Ew/JyOLz9MwFGkxQeBNvNcL29ANFvnZ2KP6dVk82Pz/9QGFLY2XjVXvC6ADgAv1y1ACph+Mt2s5HDlscUKCmWWteK/To4Xxnaiqvd48N1H+xDP93sn9Yo/JkmOI7Do6sWwJCgJsZf6luxqXN69gm1+wO4ecuBhBedVXotnly7aFzdS0bqwaVzUZmg69ABqwM/O0JN4Qd7YN9xnLC74i4vUKvw2zULJ+Q9/O6SOajQDe0/H/Z+Vx/+TCO4AZBqiTx4sBZf2XM06VDu1QYt3jx/Ndbmm1O+HTkqJf6xYTny1fG7AwYZw01bD+CkI/5+RqSi+R/bvAenXInvMn6sogiPyTUMxuuCknzcv3BWwnWeOHEKL42gBQsJtwTZg63dtoTrKXkOfzp9CT6d4KZFIjzH4Ym1i1CqVcddp8vrx+3bD1HNuzRoc3tx+abdOJ7guxMAynUavHbuKvxg2Vz8cFBrcsHkheHy/YBSuvgMNBTgZ3/Q4Gt7jlHNphRzB0O4a+dhhBiDv64A9n+uhmiTblpw6gAMlx/AfZ9iOLM4cUvWaNWlHN78iQBeKQVAgboieD6cE7POc43tVDstDVq7GVbdGYDrRLhHgVTYWXf+UXCC9Nm5dVZ50nOeQguH7b9SwmyRPoOhHiOcry0FC8Re6h/td+LmrQfhT3KTjUxdFP5MI+U6DR5MUtj0nl1H4ExSl2GqERnDnTtrUJdgJC2twOMvZyyFOQ01YoZjVCrw2zWJR476xdEG7O1L3i9/unjpVAf+mqRLzhNrF6EowQVGKhmUiqRd+L5z4ARaXNO7+5cvJOKOHYfxyxEUkV2Zm4M3zluDWcbErXPGo9KgxV/PWAZ1nEK1AGDzB3H9B/vQ56ORiIZj9QXw8c17cTJJzaaLS/Pxu7WLICQq+jJK9y2oxkUl+QnXuWf3Eeo6m0SL24vLNu6OdCuJRyfweG7DclyZYLCCkchTq/CndUugSBACfthtxY9q6uIuJ6PX7PLgoxt3J/2sVum1+O+5q1BtkGod3jF3hjRgQtQ6igInDJceBHjpotJ3uBy/eUHAPbuPUGiXQg8eqsWJfjc8W2fB9b8lQEBq3SHkOWC8dhcWLfbim4sTh+DDWbeAx0NfdQOc/P4dmAHv/oqYdb5OxZ9TavcxhqW3hdDTKt/slQs7a1acQvhQWKJV49uLE3eNDZtRxGH7rxTQGuQAqMMM5+tLwEKxx9X3u/qo9fk0RuHPNHPzzLKEw6s2u714cJoVNf3F0Qb8ry1xwetHVy3AogQFRdNhXYEFdyUY9SbEGO7cUQMPDUGNJqcH9+45mnCdL82rxHkJuhakwzlFebgpwd1wZzCEe3YfnbZfwP3+AK55fy/+NYKWGJeWFuDlc1aiIEFB9FRZm2/Gr9ckDu7qnR7ctPUA3T0bxB4I4poP9uJIknDl7KJcPLVuSdzRoMaK5zj8bu0iVBvit5x0BUO4eesBqnMXR53DhUvf24XaJK3bcpQKvHj2ypQdV9fmm/HdJXMSrvPzow14J0kXNDIyDU43Ltu4G41JbkDMNurw6rmrUDGoNfJnZpXjN2sWxhTsVlZYoTt/4LvYu302nnqd4c6dhyk0SIH3O/vwu4PtcL66DN69VZH5yjkdMH5iN9RmL55YswgaYWwj0X7jkhyc/rG2yHPPh3Phry2MPD9kc+BpKv6cEs+/y3DmF0X0WqXvwOjCztF+vPy0UQ1OMm8Gj/cfVUCpkbvxNefB9eYiMDE2AHq+qR0/PExh+nRE4c80w3EcHlu1ALo4/UYB4I8nm7E1yWhXU8U77T14JMnB73OzK3BtZckEbVGsby6elbAGSa3DhYcOT6+wbrCAKOK27YfgSNBibUWuKWlR0XR5cMmchN0ZNnb24m8NbXGXT1UtLg8ufm8XPhzBseb22RX48/ql0I1hKPexunpGCe5PUt9ra7cN9+6hu2dh7mAIN3ywD3v77AnXW5tvxrNnLBvzBUoyOSol/rJ+KbQJvudOOtz4wq4a6pIyyCGrA5e+tztpUdBCjQqvnbsq5d0v75o7A5cmGVDhjh2HqWjpONXaXfjoxuTv8zyTHq+cswplcbowX19Vij+eHttiSz2vA9r1A/Vh3O+dhuc+8OPWbYcoLB8Huz+AW19rhOOFNQg2y4ErJ0K74QT0H6kBpxTxtQUzsSzXNK6/88/bC2BYO1BWwPX2QgRazZHnDx+uQ3eSARlIfKLI8L2nRFz/fQavX/rcKEpsMF4rFXaO9tGyQny0vHC4l0lo1Twe//0xN9CNr74Q7vdOw+Cvu58fbcBf6inMm24o/JmGKg1afCfJ3bUv7aqBe4q3KGlyevC5HYeQ6NR/bb45aVe5dNIIAn63dlHCpvBPnDg1rQvQPnK4DnsSdH8zKAT88fTFcQvlpZtJpcQvVyVuRfKtAyfQNo0uZg5a7fjIuzuT1pgApNGDfrR8Xkq7Bo3U1xbMxDUzihOu81xj+4i6rE11vpCIG7ccwLYkNSGWWYx4fsMy6NMc5C00G5N+7v7b2o3H6b2L2N5jw+WbdqM7SXfGCp0G/z13FRamoTUsx3H49ZqFCWve9fkDuG0bjdw2Vsf6nbh80+6khdgX5hjw6jmrUJykq/RVFUX48/olUEUdo9XLT0G9pFl6wng431iM/+z14MatB+ANTe1zy3T5+J86UPfsYogO6bPBaf0wXLUPmmXN4DjpJte986vG/XfK9Vp89zMcVPPlm1IiD9frSxDqk25EUvHnsXN7Ga7/PsP3nxmYp5rfBsNVe8FrY1uiGpUK/Gh54pGaE7lwuQJ//z4DJ0jHSf+xUng+mDskALpvzzG8Ta0ppxUKf6ap22ZXYF2CO3YNTg9+OIVblLiDIdy09QBs/vitRYo0Kjy9bsmkhQZhSy0mfHVBdcJ1vrCzBvZpVqsJADZ19uKxJBdvj66ajyq5TsFkuaAkHzdUlcZdbg8E8ZU9U7P7l90fwJauPjxxogl37jiM9W9sxXnv7EBHkjuHap7HU+uW4IvzqsY1CtR4cByHx1YvSNq64QeHTuI/zZ0Ts1EZKCCK+Oy2g9iYZMCA+TkG/OusFaNqwj4e11SW4HOzKxKu89Dhk9N2oINo77T34OrNe5J+j8w16fG/81ante6WWaXEU+sWx4QJg+3s7ccPplkX9VQ4bHPg8k270ZXk+LvMYsTL56xE/gi72V5SVoi/bVgGjXy+xHGAdsMJKGd1SSsEFHC+tgxvHHPhhg/2wzXFby6mkj/AcPlDLrz7XNnAEOBF/TBdtxPKMhsAQCPweGLNIihS1I32S6dVYdFHm6CYIYUCzKeE89WlEJ3S/vD3xjbspOLPo9LazXDWlxj+uTE8Ry7sfN5AYedo3108G6UJBg0Zies2qPDY/wUATnp938EKeHfGXk+EGMOt2w7igDVxi10ydVD4M03xHIfHVy+IfFEP54kTp7B9Ch7cGWP46p6jOGSLX8hSwXF4ev3SpHe8Jsq986ux3BK/KW+z24tv7T8+gVs0+bq9fty543DCllufrCrF1TMmp8veYA8vm4viBCfSb7X34IWm9gncotTr9fmxsaMXjx1twK3bDmLV6x+i6j+bcPmmPfjm/hN4vqkdx+yupCN6WVRK/PvsFbhqnEVkU0EjCPjr+qWoStASAQDu2nkYe3qnXwH2EGP4wq4jeD1J3bRZBh1eOmsFctXpr9kU7cGlcxOGdyIDbtt2CM3TuPD6v5s78Kkt++FJ0iVnmcWI/567atwXJCOxPDcHDy9LfNf718eb8HprV9q3ZarY32fHFZt2o9eXuNbVqrwc/PvslaP+rJ5fnI8XzlweadXH8YD+whooSqWuvcyjgvOVZdjY5MA17++dljesRqu9h+Gse0S89tbA949qYSuMH98D3jDQcut7S+ZgToISAaOlFnj8eNU8GC4+DKFACgVEhxbO15aB+aX39+t7j1Eh7xHadZRh9ecY9sin6Wq1OKSwc7Q1eTn4zKzylPztL12sxQN3DXy/eXfNhHdf7E0RVzCE6z/YN62/B6cTCn+msVlGPR5YFH9EAAbgyk27ce/uI2hyTp0DwlN1LfhHkovsh5bNxelpGEp6rJQ8j9+uXZgwrHu2oQ1vJrkAmypExnDXzsPoTHD3co5Rhx+vOG0Ctyoxs0qJXyTphvKN/cfRkaQpfiZgjKHd48Wbbd34SU0dPv3hfix57QPMeXkzrn5/L74vt4SpH8Nxo1KvxRvnrcbpBZY0bPnY5GtUeO7MZTApFXHX8YZEfGrL/ml18iQyhocbu/FSklZPFToN/n3OygkbaS+aSuDx1LrFKEoQvPb5A/jM1oPTsjvKn+tacNu2QwgkuYg7o8CC/5yzCnkTGN7dOqscH0sSAH9hZ82UOj9Jl129Nly1eU/C1s4AcHq+GS+etQI5Y2ydt6EwFy+etQJG+VjJKUToLz0IPlcqAC/26+B8bSm2tdvx8c17YKOi63FtPcSw8naGHYfldIAXoTv3KPTnHotpKXJ2YS5uS9LCcSwuKMnHR6tzYfjoAfBG6TMW6jHC+b/FYCEOB20OPEP1YpJ6/l2pxU+73MC0vIgh5xNDCzuHKXkOj65aAD6FLZ4fvs6AWz49cMPbs2UufEdib4x2ev245oN99JmcBij8mebunFOJlbk5cZcHRIY/17di1f+24Is7a1CfZDjQTLejx4Zv7EvcQubayhLcnoYv0vGaZzIkHe7xnt1H0DsNhp/+zfEmvNsRv6uGiufwx9OXpL2uyGhdXFqQsIaMzR/E1/ZmVvcvxhianB682tKJhw+dxLXv78X8V9/Hwlc/wA0f7sePaurxelt3SgqwLreY8Ob5q1N6BzNV5pkMeGb9EggJTsi6vH7c8OH+aXFHmzGGbx04gZd7EjcVL9ao8O+zV6J8AlqLxFOi1eCpJMOI77Pa8X97p1frycePNeLePUcTtp4EgItLpRYdicLPdOA4Dr9ctQCzjfG77fYHgrh120H4qJBwXNu6rbh6c/KWNmcV5uKfUcHNWK3JN+Pls1fCIgdIvCYI4+X7weml74hQZw5cbyzGnh4Hrty0Bz1UPDgGYwxP/IfhnHsGAgPO4IXx6j1QL4wdHMKkVOBXaxamNCiI9vCyedCbgjBcsR+cWgoFgs15cG+Uigc/dOgkvX9xxBZ2luZtWAwsu/UwApb4PQ/uOa0K83MMKd+epz6Xg0uusEWeuzfOh/9kbDHpE3YXbtpygI6nUxyFP9OcwHP41eoFCfvWA1LT/r83tmHNG1vw+e2HcNyeeBjfTFPncOE7B07g+g/2IZjgwnqR2YBfrJw/aTVGkvn8nBnYkKBFRJfXj6/uOZZR4UGq7e1LXuvhwaVzsdiS+mKkqfDI8nkoTNAK4b+tyVtSpIvIGGodLrx4qh3fOXACV23ag1kvb8Ly1z/EzVsPSsMsd/QmrRcxFheV5OOVc1ehUJMZXS2Hc05RHn6apDXZkX4nbtt2cMoPa/zDw3X4/cnEd33z1Eq8dPZKzExw8T5R1hVYkhbv/2tD67QY+aTG5sCXdx/B90ZQtPWaGcX48/ql0E5SkG5UKvD0uiUJW73us9rxnQMnJnCrssf7nX245v29cCapsXNecR6eS2Eh9mW5JrxyzkoUyC3FeKMPxsv3AyopgAo05cO9aR4OWqUaRNnQ4nUieHwMt/6I4a5fMISzOkWpFaZrd0JRNDRo/9HyeWkN1mfotbh3fjUEixuGyw4AgrQf+Y+VwrtzJvoDQXz/EBV/Hmy4ws63XArcdW8ntjjid1WdY9ThK/MT1/gcj9e+YsbKs+UBYhgH11sLEWjKi1nnw24rvkQjYU5pHJvKV4lThCiKaGpqQmVlJfgUFXMb7NGjDaMqnsgBuLKiCF+dX40FaRjxIxUCoojXW7vxTF0LNo9gNKwcpQLvXbgW1ZNcHDiZUy4PNry5LeHJ3JNrFmIV86V1n5lonR4fNnf14ZHDdWhK0LXmktICPHvG0owN8ADg1ZZO3Lz1YNzluSoltl68Lm1BiD0QRKPTjXqnB41ONxqcHpywO3HIaod7Evrw3za7Aj9cNjdlxSrT7dv7T+A3J5oSrvO52RX4UQZ1O0wFxhh29Pbjz3UteD5J11mTUoFXzlmJJQlqlU00xhhu334oYbiq5nm8ft4qLE/QIjYbtbi9eLGpHS+c6sDR/pHdvLltdgV+tHxeSlsVjPV85tn6Vty9+0jCdf60bjE+VpF4dL7p5N2OHty45QC8Se7iX1yaj6fWLYFGSH3Ad8Luwsc274mMLBZoscD5yjJAlN57zZp6aNc0YKZBi/+cs2pIkDER57+ZoqmD4epvD9SFAQD1siZo19eB44d+L3+0rBB/Xr8k7ec63lAIZ7y5DQ1OD/wnC+B6YzGkqwBAd+5RqBe24c3zV2N1njmt2zFSk73PtHYzXPnAwPvIccBP7uBwy8cCWPfmVvQkqLn16jkrcUZhblq3LxAScdpXe1G/Rw59FCEYrtgPZaktZr17T6vCt5OMDD1VTPY+M9Eo/MkCE7FTBkURl23cjV1jKFj60bJCfHVBdcac5J9yefCX+lY829A64hYKHIB/nLkMF5YUpHfjUuSv9a24J8GJsFmpwHMLyrF6zqysPZC5giFs7bZiU2cvNnX2jeiCpUSrxgcfOX3Ci8qOxa3bDiYcIeqK8kI8s37pmF6bMYYeXwD1Tnck3GlwutHgkqbJCn5OhJkGLVblmXHn3BlYmiHHjpEKiQw3bT2A/yWpsfXj5fNw+5wZE7RV6dPk9OD5pjY839SOhhHUV9ErBLx41gqsyaC6aWGuYAgfeXdnwuNJuU6DjReundD6NunQ7w/g5ZZO/LOpA1u6raP63fvmV+OBRbNSfmE51vMZxhi+uKsGzzXGDx0NCgHvXbgWs9M4Elm2eKOtG5/ZegD+JGH+5eWF+MPaxWkd1bTB6cZVm/agWe4a7K8thOvNxZHl4QBhhl6D/5y9MmZ0zulyUfbObqmlSPgUXKUSoTznCFRzhz9HKFCrsOWidSMejW283m7vwXUf7AMAeA9UwPOB3IqSE2G47CBWLffjnfPXQkjSi2AiTOY+s+uoFPyEu+sZtMBz3+Xw0fUcvrCzBs81tsX93Rury/DY6sR1IVPF7gthzt1WdB2VgyZVEMar9kJRGNsd7Rcr56es8HQmmy7HmTAKf7LARO2UfT4/vrb3GP49xi4nF5Xk46sLZmJl3sTfMQ2KIt5u78HTdS14t6M3aQ2Dwe5fOBNfXxi/+HWmYYzhkx/ux5vtPXHXWZ+jw8sXnA4hDXfz0iEkMhyw2bGpow+bOnuxs9eW9MQ1Gs8BL5+d/rsmqdLj9WPdm1sTBjFPr1uCK+MUPA2JDG0e70Cw4/SgweVGo/w8WTP/icJzwByjHkstJiwxG7HUYsRis3HChvtOF2cgiI9u3I2DCUYN5DnguQ3ZEypHsweCeLm5E883tWFrt23Ev6cReDx/5nKcmcGfwzqHC+e9sxOOBDVQzi7Kxb/OXJERFzOj4Q2F8HZ7D/7Z1IG32rtHdQwN+8HSufjCvMo0bN34zmdcwRAufGcHjtldcddZmGPAW+evmbRuapnglZZO3LbtUMIu7gBw9YxiPLFm4YS0uGxxeXDV5j2RQQC8+yvg+TAcIDDoLz0AVXUvSrRq/OfslZG6b1P9oiwQZHj0BeAbv2cI9xSeUSLCfe5uhHLjf7c8e8ZSXFpWGHd5Onz6w/2RUR3dH86Gb798jFCEYPzYHvzykjLcmgH1Midrn3n+XYbPPDJQ36eqGHjlEQ6LZ3F4v7MPV23eE/d3CzUqbL94PcwTeF7U4vBhwRedcDRIpSQ4jR/Gq/dAsAzUduU54G9nLMNFpdl3DjMaU/04MxiFP1lgonfKGpsDPzvSgFdaOkcdogBS3/GvLpg5IaNltbq9eLahFX+pb400Kx6ti0ry8bcNy9JWMC9dOj0+nPHmNvQlqMz/sxXzcOvszG150Oh0Y1NnHzZ29uKDrr6kI5Ek8vUFM3F/gtHrMtFLpzpw2/ZDcZfnq5V44cwV6Pb5UO+IDXeaXJ4xXdilk5LnMN9kwBKLUQp7LEYszDFCN0UvxNrcXlz47s6Exx6DQsAvVs7H2UV5KJigu7RjFRRFbOrsw/NN7fhva1fS7iKDKXkOz56xNCvCrv+1duFTWw4kXCdbmr2LjGFrtxX/bOrAyy2dYy44znPAL1cuwKdnlqV4CweM93zmuN2JC97ZCVeCcHsi76BnmhdPteOOHTUIJTm1v6GqBI+vWjih4Wa7x4uPbd6LE3J4NyRAuGovFMV2FGpUeOnslViQY5iyF2U1DQxPv87w17eArqhGeZecDjg37MVhb/yWep+sKsWv1yycgK2MdcrlwelvbIU3JIIxwPXmIgROSjenOK0fZTfsw77rV05Ya6R4JnqfEUWGB5+Jre+zYQnw0kMcCswcPMEQNry1LWGr2cnqsnqgy4W19/jha5Vu2nN6qbC4YBoYwEMn8Hj13KnXFTqsvYfhla0ML7zjxpeu0eGqM6fOcSYeCn+ywGR9+R3rd+IXRxvwUnMHxnKNeVZhLr66oBpnFFhS2nQ8JDK819mLP9e14I327jFtW9jl5YX41eqFEz6KSar8p7kTt26LXztGyXGYn2NApUGLKr30U2nQoUqvRblOk9am3sOx+gL4oLsPmzqkrlyNKRoWe12+GS+fszJrasaEMcZw89aDeK01fgHATKUVeCw0G7HUbMRiOew5zWSAeoL3qcl2wGrHZe/tgnsEQck8kx5nFFhwRqEFZxRYMqa49RGbA/9oase/mtrRMcZi3jwHPLVuCa4oTzw0dyZ5+NBJ/PxoQ8J1/nrGUlw2wXfYR+qIzYEXmjrwYnMHWsc52p6S5/CH0xen/f1LxfnMv5ra8bkdhxOu89s1C3F9VemYXj9bPdfYJhdqTbzeTTPL8IuV8yflhle314+r39+DwzanFCC8tRCBWumiV2p5sBuCxYNclRIvnr0Ci3MMUyb8sTkY/vEe8PTrDDuPDl3+nc8AmtUN+OnR+rivUaHT4IOL1k3aOetPa+rxSE0dAIAFeThfWYZgm9RyhM9x49avtOAP505urbuJumZqbGf4+zvAs28xHI0qAXjLpcATX+GgVkmfrx8crMWjxxrjvs5HSvLx3IZlk1an8p1GKy65j0ewW6rfyue4Yfz4HvD6gXOBQo0Kb563BpUG7aRsYyoxxnC4HnhlC/DKltjP4o0XAX/5ZnYfZ0aCwp8sMNl3Pk46XHj0aANeaOpIejdpOOvyzfjqgpk4pyh3XAe3To8Pf2tow18aWnDKNb4T3bMLc3HL7HJcXlaY0YWBR+L27Yfw4qmOUf8ezwFlWg2qDTpU6rWoMmgj0yq9FhaVctz/bXwhEbt6bdjUKXXl2m+1jyusG06RRoW3L1g7qUNJj0enx4f1b26DNUELrslmUAhYYjFiidmEpRYjllhMmGPUZV3Yli6vt3bhxi0HRt1Sco5Rjw1yEHRGgQVF2okLg7q8Prx4qgPPN7Yn7Lo2Eiqew+OrF+LaypIUbd3ECIkM136wDxs7e+OuY1Qq8O4FazKmjkyL24uXTnXghaZ2HBlh4eZkziy04OFl87BoAgZvSNX5zFd2H8Ez9a1xl+sEHm9fsDYtQyZnoj/XteAre44mPQbdPrsCj6S4iPdoWX0BXPPBXuzts4OFOClAaJW6ifImD4yf2A1e54dJqcALG5ahwGnL2vBHFBne2ysFPi+9DwzO1lVK4MoNwBc/xsE4w46PvLsr4Xn2K+esxIZJ7FIbXfwZAESvAo4XV0G0SsdHoagfmx7jsaF08lqJpPOaqc/O8M+NwLNvM3w46L5ruLDzfdcjcu5cY3Pg3Ld3xO2CqVcI2HbROpTrJzdUefpwJ257QAfRJr2PfK5TCoA0A61I5xj1eOO81bCos6/Lvj/A8P4B4NUtDK9sARrjXDItngkceJrL+uvCZCj8yQKTHf6ENTrdePRoI55rbEval3w4K3Nz8PWF1bigOH/EHyyRMXzQ1Ydn6lrx39auMf3dsFyVEp+sLsXNM8swK0NO5FPB6gtgw1vbxtztLR6jUiG1FIoKhKqStBpijOGo3SW37OnF1m7riFpEjIWC43BRaT6+v2RuRgwlPR4vNLXjjiR3sidKrkoZ021rqdmEKoM267pFTrRfH2/Edw6Mb8jbOUZdpGXQ+gILSrSpDTS9oRDeaOvB841teKejd0xhfjQFx2F9jhY/XL0YC7KsaHdYn8+Pc9/eESlGO5zZRh2unlGMIo0aJVo1ijRqFGvVKFCrJqTbTLhw87/kws2pOGmbY9Tj2spifGJGyYTezU3V+Yw3FMJF7+7CoQTB5VyTHu+cvwaGLG3ZO1J/rG3G1/cdS7reF+ZW4sGlczLiwsYeCOK6D/ZhR48NzCfA8dJKhHql8FEosMP4sb3gVCHoFQIenVWMjy8+LavCn4Y2hmfeYHjmf8CpYcpoLp8D3HIph09eAOTlSF2Dznl7B2od8etZ3Tl3Bh5eNi+NWz0yb7V14/oP90eeh+waOP61Cswt3bzIn9eH1t/mQqWcnPcr1ddMXh/Da9ukFj6vbweG61V75hLgWzdx+Miagc9WSGS46L2d2Ntnj/vaP1w2D3fMzYzSDD/Y1ozvPZQL0SF9HwhF/TBeuQ+caqCL7fwcA84ssKBUp0GZ/FOu06BYo8q4m4FWB8P/tkute/63A4hXKm7pbODy9cCqme247KwSKBSZ9e9IBwp/skCmhD9hzS4PHjvWiGcbWsdUc2SZxYj7FszEJaUFcS8oe31+/L2hDX+pb0Wd0z3sOiO1Lt+MW2aV46PlhWkZyjQTvNPeg2vlkRgmQrjVULi1UIVOizqnG5s7e9E5xm4jIzHXpMc5Rbk4tygP6wssME6Rk/qRFPBOJZ3Ao8qgw0yDDlUGLarlLoHq/j6snTMra4qEZxLGGO7dcxR/SdAaYbRmG3VYX2DBhgIpDCodQ+s2xhh29vbj+cY2/Lu5E/1jrAcTbanFiOsqS/Gx8kK4O9sz5rtprPb32XHJe7vgE0cXVPMcUKhWoUirRrFGHZkWa+UfjRpFWhUK1KM/MfaFRLzV3j2uws2DFWlU+PiMYlxbWYIlZuOkhACpPJ+pd7hxztvbExa3v2ZGMX63dlFGBB7p8JvjTfj2gRNJ1/vK/Gp8Mw2jt42HMxDEp7ccwPtdfRCdatj/tQrMKR3jFDN6YbjsADiBQc1zeHb9Upyf4UVn3V6GFzcDT73OsGmY07G8HOBTF0ihz7I5se/DA/uO43e1p+K+9lyTHhsvWJsxhcw/9eH+mNEug90GOF5aCQSkc7LzznPhne8asvYYI4oMm/dLgc+/Ng8fHMyvBG68iMMN5wNVJUP/nb+vPYX79x2P+zdW5Jrw5nlrMmZQAcYYPvdWPf70aGkkyFOU98Hw0QPgFIm/G3kOKNaoI4FQmTYcDqlRLs/LV6vSfhOxvo3hlQ+lwOf9g0BomK8GpQI4exlwwdoQzlgVgDE3CJvPj7q2dmjMuXAEQ5iXo8c5RXlp3dbJROFPFsi08Cesze3F48cb8Zf61lEXBgWkUTnuWzATV5QXguc4MMawvceGp+ta8EpL57hOdk1KBa6vKsHNM8unTbPvZM3gs1GhRoWzC3NxdlEezi7KRVmWdu0aiTa3F+vf3DbmYq2DWVRKVBu0qJZba8006qSQx6BDkUY15KQsU48z2SQgirj2g33Y3NmXltefadDijIJcbJBbBiX6PJxyefB8Yzueb2qLjLAzHsUaFa6pLMF1VaVYIB9Tp9I+82x9K+7efSQtr81z0tDMxVGthgZaEQ3Mz1ersKvXhheaOvBKS2qCOoNCwOXlhfjEjBKcVZg76Rcaqd5nXm7uxC0J6t4BwKMr5+PmKTRccVAUcbTfhRdPdeDx441J1//Gwln42sKZ6d+wMfAEQ7h56wGpJWKfHo4XV4L5pG4lqnnt0F1wBBwndS29ekYx5hj1mGPSY7ZRh2q9bsLrFg7GGMP2GuDp/zH8413AMeheJc8DF6+RAp/L1yNSBybaB119uHJT/JGgBI7DW+evzqiCu01OD9a9uTXm3D9wKhfO15YCovSefPf2EL5348R3ERrrMYYxhoN1wN/elmr5tHYPXackD/jkBcCnLuSwbA7ihlstbi/Wv7E1bjAtcBw2Xrh2QrrajkZIZLjixeP43++rI59DZXU39BcfAieMLy5Q8RxKtZqogEgd03qoTKdBjlKRNDAMiCLsgSDsgSBsviB2HGV4b4eAbbuVaGkdfn9TaILImWWFuroHYnk3fIrEZRZumlmGX66auoMGUPiTBTL9BLvT48Ovjzfh6brmMXXxmWfS44ryIrzS0onjCYZwHYmVuTm4ZVY5rqoomrIjDMXjDARx9ft7sau3f7I3Zcy0Ao91BZZI654FOZNz52iyvHiqHbdvH3n3rxKtGtUGLar0ukjQE56OdsjQTD/OZAtXMISv7TmKf54aW4200ag2aCP1gs4ozIVJqcArzZ14vqkdW7rjjxYzUlqBx2VlhbiuqgTnFOYNCQ6m2j7z5d1HUtpya7IoOA7nF+fhmsoSXFxakFHfhenYZ+7fdwy/r22Ou1zN83jr/DVYbMmsC62RYIyh0eXB3r5+7Om1Y5/VjoNWOzwjPNf67uLZuGd+dZq3cnx8IRG3bz+E11q7EGzLgePl5UBI2mc1KxugXTd8AWSB41Cl12KOSYfZRikQmmvUY7ZRjzz1+GsWJtLew/CXN4Fn/sdwbJgGO3MrgFsu4XDjRUBZQfztsPsD2PDWdrQk6Hb6fwtn4v8WZt5Ipj+uqcOPa2LfG9/RYrjfHRiJ7O/f4XDDBRN7DjfaY8ypToa/vy2FPoeHqf9v1AFXnw18+iMczlkGCELif89IWnJ/+bQqfCdDR5J0B0M4+9mj2P3XuZGWXNFBbDrpFYIcEKlhUioiIY9DntoDQbi9QKAlF4GGfAQaCsA8w48ux+e4oazuhrK6B4qSfnD8yM/HrqoowlPrlqTqn5VxKPzJAtlygt3j9eO3J5rwx5PNCZthp5pBIeCayhJ8ZmZ5Vp7cpZIzEMTva5vx39Yu1NqdcKap3k6qcACWWUw4pygX5xTnYXVezpTtmjdSzze24eHDdWhxeyFwHCp0mphQp9owUHsplRd12XKcyRbdXj+2dFuxpasPW7qtODbOYHskFBw3rrpoYesLzLi+shRXVBQlHFVmqu0zvpCIyzbuSlijIZOtycvBNZUluKqiCHnqyR1uOZ507DP+kIhLk7xvFpUSK3NNmG3UY67ccmSOUY/CYVpBTqZOjw/7rHbs6e3HPqsd+/rsYx4M4OFlc3Hn3MoUb2F6BEQRd+44jJeaO+GvK4Drf4shnSEA2rOPQbN4dKGsRaWMvMdzjDq5tZAe1QYtlGPc7/wBhle3SK183tg5tEuJQQtcd57Uymf9ovitQqJ9cWcN/t7YFnf5cosJb5y/eszbnE6eoFT8efCorZ5dVfDukMIqhYLhrZ/xOHfFxH3GRnKMsToY/rVJCnw27x+6XCEAl6yVAp/LzwC06pFvf7JReKsNWnz4kXUZ04VvON1eP9b96Tjq/jk/0pJLObMLQoEDnCoo/4SiHsvP1UFAEFMeEokuFQKN+VLg05wbCYdjMQjF/VBV90BZ3Q3e4h7zdpxXnId/nbViXNucySj8yQLZdoJt9QXwRG0Tfl/bnLIuLMNZYjbiM7PKcfWM4ilT+yVVwvtMTkkpTrl9aHC50eT0oNHliUxb3N60t0wYTqVei3OKpK5cZxVakJuhFymTiTEGT0iEkucm7KQv244z2abb68fWbiu2dlvxYbcVR1M0UlOqVBu0uK6yBNdVlo64APBU3Gda3F6c9/Z29Pgyd/S9aHOMOlxTWYJPzChGlSHzC9+na5855fLgnLe3w+Yf3TmHUamQwgGjHnOMesw2SY9nGnRQp7lLkT0QxAGrHXt7+7HXasfePjtaE7QAGY2frTgNt86uSMlrTZSQyHD37iN4rrEN3oPl8LwfLm7MoJzZDU4bAK8JgNP4wWkC4DQB8NpA5DGnDia92FNwHKoM2kgwNFvuRjbHqIsbmB44yfD06wx/exvoGaZh9drFIj56nh/r1vggKoJwBEJwBoNwBUNwBkLSNBiEMxiCKxiEMxCCQ35+IsFNAY3AY+OFazHPlLmlC95s68YNUcWfAYAxwL3xNPiPlAEAcvTAB7/msHjWxARA8Y4xPj/Df+XCzf/dDgyXqa5fJAU+15wD5JtHv702fwCnv7EVXQlqX7509oqsqCdz0uHCmU/Wo+PV+QAbxbGQF+OHQ6ogkCg8UgWlz7EqCNGqg7+xAIGGfIQ643R5VISgnNELZVUPlFU94HWp+d5elZeDt85fk5LXykQU/mSBbD3B7vcH8PvaZjxR2zTqE7J4tAKPj88oxmdmlmNFrimj7thlkpHsMwFRRKvbi0aXB41OD5pcHjQ43fLUk7LgLkepwJmFuThXbt1TnQUXKNNRth5nslWvTwqDtnRZsaXbippJCINMSgU+VlGE66tKsSYvZ9TH06m6z9TaXfjs9oM4bMusgC6sUKPCxyukws1LLZNTuHms0rnPDHchOlY8B8zQaSP1ZaJbkBSoR99ayBcSUdPvwL4+u9SFq8+OWrsrJSO3ReMA/HLVAtw4syzFrzwxRMbw9b3H8FRdCzxbZ8G7t2rkv8wxcOoAODkQ4jUDwRCvCYDThkOjIHjNwONwd5BcubXQbKMeIY+AA7uNOLYrB9a2oecsvMEL1WntUM1vh5Az/ppqw8mkkaASueGDfUO6ODGRg+u/SxBoygcg1cq5cBWQY5DCoBw9hxwDYA4/j8yXHmvVI2s5NZzoYwzA4YODcuHmTcBwh/R5M4BPX8jhkxcCM0vHdyy9d/cR/DlB1+EbqkrwmzWLxvU3JlK9w42r/9SB/S9XxGltM/E4nQ/K6h6oqruhKLcmLUY9FnNNemy/eH3KXzdTUPiTBbL9BNseCOKpk834zYkm9I7xbuppJj1umVWOaytLkDPKWibTUSr2GZs/gMZIayG3FBLJLYeaE7QaUvIc1uSZI617lltMk15klCSX7ceZbNfn82Nrt03qKtbdhxqbM+UXhoBUJ+P84jxcX1WKi0vzx9XNcqrvM/UON+qdbnR4fOj0+uSpH+3y806vD4EUjMI1EgaFgI+WFeITlSU4q9CSccPqjlS695nvHagdURHk8TApFYO6E0nhULXcWkhkDLUOlxz0SGHPYZsjJSO2JcJzwG/XLMK1lSVp/TvpxhjDtw+cwG+On4Jny2z4DlZEup6kA6eOaj2kCYADpK4lg/+mEIJyZjfU89uhKO8Dl8aP4JmFFvz77JVpHx0pFRqdbqx7Y9uQ0RJZgIfj3ysR6jKN+jUVwkAgFC8gGhIgyfONWob9R9qw+UgpnnsXaO4a+vpFucAN50utfFbMHXvQFG1rtxUf3bg77vI8tRLbL16fsV1yEznZFcD7J3xo6guipT+Ajv4Quuwi+pwMNhfgcnMQ/QKYXwEWmSrAfIqUfHaFPAeUcncuodCR9tpDJVo1ai4/K71/ZBJR+JMFpsoJtisYwtN1zfj18aaETSLD1DyPKyuK8JmZZVibb86qu5uTLd37zOBWQ61uL7SCgMUWA9blW2CgbnhZZ6ocZ6YKqy+AbT1WfNgldRU7ZHOMKwxabDbiusoSfKKyGIUadUq2cbrvMyJj6PMF0On1RQKhcEDU4fGhI/J8bCGRguNwnly4+ZIMK9w8VhPx3XTlpj3Y3mNL+WsnE24t1OsPwJHGLu/DsaiU+PXqBbikrHBC/266MMbwl/pW/OJoA07Z/WAeJUSvEkz+Eb1KMI8q9rlXGVkvXKg2FYRCO1Tz26Ca0wlek/731ahUYMtHTke5fmTdbzPBjw7X4SdHhhbmFt0qOF9fjFCHeeI3ahC9Fvj4WVIrn/NWAApF6q4pfCERZ721DbWDh3uL8uTaRbgmy4PZeEIiQ6fXh1aPF61uH1rdXrS4vdLU4UOzLYguuzg0HIp6jsHPlXKXruoeCKbUdIcFpNaRRqUCpqgfo0KA4PehxJyDHJUS+WoV7pqXHfXSxoLCnyww1U6wPcEQ/lLfisePN6Ld4xuyfLZRh5tnluOGqhKqBzNGU22fIelH+0xms/kD2N5jw4dyAelDNgeS5QlFGhU+MaME11eVYGEahpSlfWZkRMZg9QdiAyGPP/K4w+tDpzzVCQLm5xjw8YpiXFVRhHzN1PoOnIh9ps3txWUbd6PJlZ7uOJmgQK3CilwTlueasCI3BxsKLVNysIRQKIQ9J+vhybGg3unBCYcbJx0unHS4ccrliRuIsxA3EAx5lENDIjk8innuG2hVzmn9UM3tgHp+G4T89Bfrj/abNQtxQ1XphP7N8fIEQ1j/5rZhP3OMAXCr8KeVK1CmMKDfCfS7pB+bA+h3Mel51PzwY5sT6HcyhMSxBTUcz1A5z4l16zy4cB3DHIsaFTotSnXqlNZTfORwHX46TPgVdm5RHv511vJpfRPbHxLR7gkHRAM/LW5vJDRKVtx+uOBmVD8qJQwKYUiLuul2LkPhTxaYqjulNxTCPxrb8VprFzo9PiyxGHF9VSk2FFim9QEyFabqPkPSh/aZ7NIfCYOklkEHbHaITCoSemlpAa6rKsW5Rblp7R5E+0xqMcam/HffRO0znR4fHjvWiLfau9Ho8iQNSjOZQSFgmWUg6FmRa0K5TjPl9xUg8f7iCYbQ4HRHAqFauxQK1TpcYxpxlokcmE9qfcAbvOCEid9prq0swRNrFmble/tGWzc+maDm1opcE57bsBxWfyDy0+eLfuxHnz8Amz+IPr8ffb4AbP4AXEERCPKR9ybcnWjI46h5YIBiRh9UszuHLQLMASjWqlGh06BCr0WFToNynQYVeg3KddLzkbZgP9bvxNlvb4/bulMr8Nhy0bqsKMg/2VzBENrcXrR5vPCLIoyK2PDGoFSkpSvkdDuXofAnC0y3nZKMH+0zZLRon8luzkAQXV4/ynSatI9SFEb7DBmtydhnfCERDU4pFDjpcKPW7kKtHBKkc0TSsVDyHBbmGOSQJwfLc02Ya9RP27p5Y9lfGGPo8PpQa5dCoRNR73uL25uWWmrjNdOgxTWVJfjq/JlZ/V5f/8E+vDWo+HO2MqsUqJCDoHK9HA7ptKjQS9N8tRIMwGUbd2NHgm6m318yB186rWqiNpuMwXQ7l6HCHIQQQkiWM8h3xQghsdQCj9NyDDgtJ3bIbMYYun3+mDDopNyKpGmCWgvNMeoirXmW5+ZgkdkwJbtvTSSO41Ci1aBEq8FZRbkxyzzBEOqdbikQsoffc+n9d42itZBW4GFQKKBXCDAoBegVChgUQsw8Y/ixQiGvE71cqjMSnqeaoMA+3R5ZPg+bO/uGFH/ORjZ/EDa/A4dsjmGXawQe+WoVWtzx69EsMRtxZxaM2EamFzpTJIQQQggh0wrHcSjUqFGoUeOMwtiQwBcSUR9uLSSHBLVySDDWYs4lWjVWyq15VuSasMxiotFLJ5hWIWCh2TikBhpjDO0eH2odLrS6fVDyHAwKOdSJCm6MCgE6hZC1o+2lW7VBh7tPq0pY/2aq8IbEhMEPzwG/XLWA9hWScSj8IYQQQgghRKYWeMzPMWD+MK2Furz+gS5kUbVmmqIKEOcoFTE1epbnmlCi1Uz8P4SMCMdxKNVpUKqj92i8vnxaFZ5vasMpV+pGaMpGd8yZgWW5ox/mnpB0o/CHEEIIIYSQJDiOQ5FWjSKtGhsGtRbyhkJoc/ugEniUatVpKUxKSKbTKgT8cNk8fHrLgcnelElTodPgG4tmT/ZmEDIsCn8IIYQQQggZB40gYKaRRvQh5JLSAlw9oxgvnuoY92vlKBXIVSthUUk/ufLUopYe56qVMMvzc1Uq5CgFtLc0Q5FfhFavD80uaTjxFrcHzS4vmuUhxoNpHO/oZyvnQ6+g2l0kM1H4QwghhBBCCCFk3DiOw69WL0CJVo2/NbTB6g9AJ/CwqFWwqBTIVamkICc61Bkm4DGrFKOumSOKIqw8j0qjDnMGddsMC4kMnV4fmt1yMOTyoNntjTxvdnngHEUB8GjXzCjGhSX5Y/pdQiYChT+EEEIIIYQQQlJCIwh4cOlcPLh0LoKimFGFjwV+oMbT2mGWM8bQHwjKrYbkYMjlRbPbI7ci8qLL6x/yex8pyccvVy1I/z+AkHGg8IcQQgghhBBCSMplUvAzEhzHwaySupMtthiHXccbCkWCIEcgiCq9Lu66hGQSCn8IIYQQQgghhJAR0AgCZhv1mG3UT/amEDIq2RXFEkIIIYQQQgghhJBRofCHEEIIIYQQQgghZAqj8IcQQgghhBBCCCFkCqPwhxBCCCGEEEIIIWQKo/CHEEIIIYQQQgghZAqj8IcQQgghhBBCCCFkCqPwhxBCCCGEEEIIIWQKo/CHEEIIIYQQQgghZAqj8IcQQgghhBBCCCFkCqPwhxBCCCGEEEIIIWQKU0z2BhBCCCGETEchnwjbTiu6N/aif18/RL8ITakGljVm5J5ugXGBEZzATfZmEkIIIWQKoPCHEEIIIWQCMMbgqnWhZ2Mvujf1om9LH0Ku0JD12l/qAAAoDALMq8ywrDXDvNoMyyozFEY6dSOEEELI6NEZBCFkVAL2IBxHHHDUOGCvkaauky4wEdBVaqGfrYd+th6GOdJUP0sHhYEONYSQ6clv9aN3cx+6N/agZ2MvvK3eEf9u0BlCz6Ze9GzqlWbwgGmREZY1FljWSqGQtkybpi0nhBBCyFRCV2QEABB0BdH9bg86XumEdbsVvh4/BJ0AhV4BQS9AoRegMCggGATpuUEBhV6AoFdAYZCn4XXk9QVDeJk0n1dSialswkQGd6Mb9sNSwOOoccJe44DnlCfu79gPOWA/5BgyX1Oijg2F5GBIW64Bx1OXhqmOMQZ/jx+eFi+8LV54Wj3wdfgg6ASYFptgWWOGKk812ZtJSEqIARG23f3o2dgT6c4FlqoXB+wHHbAfdKDpj6cAAJoyjRQErTHDstYC4wIDeAV93xJCCCEkFoU/01jQEUTXW91of6UD3e/2QPSIscv7gwj2B1P293gVByEcJkXCI/mxSQHtDC30M3XQz5JaiyhzlCn72ySx4VrzOI46h+2OMBbedh+87T70ftAXM5/X8NJ7Pri10Gw9lCY6PGWLkCcET+tAsBMJeVo8kfmiT0z4Gvo5eljWmpG7VmrRoJupA8dRMEgyH2MM7gY3ut/rRc/GHvR92IegMzXHzpHwtnrR/lJHpKuYoB/oKmZZQ13FCCGEECKhs4FpJtAfQNcbUuDTs7E36QVZKol+BtEfQMAaGNH6qjwl9LP00M3UDQQEM3XQVVM3orGKac1zxAnHYUfS1jzpJHpFaTuOOIcsUxephg2FdDO0VAB1AjGRwd/tjwQ5UrAjBTyeFi+8rV74e/zj/juuWhdctS60PNsKAFAVqORuLRbkrjXDtMRErQdJxgj0B9D7fh+6N0lduTxNk3MMHU7IFULv5l70bo7qKrbQGGkZZFlrhracuooRQggh0w1dQU8Dfqsfna93o+PVDvRs6gULpKr9eXr5ewPw99pg3WkbskxdrIZ+li7SUkgXnlZpIWiEid/YDBRyhmDdYYPziDMtrXnSzdfph6/Tj74t1pj5vIqDrloH/Rw9TAuNMC02wbTECE2pZtq1FGGMgYUYWFCeRj8ePC/IwMTY5WKIASEGMcgQ8oZgq+mH31sHb6QVjxTuTGRIHObv9qPztS50vtYFAOC1PMwrc2BZY0Hu6VLxW6VpercOZIwh2B+Er8sHX7cfAVtgZN2LRvgxSfZ5YhDhCXrgN/ihzldP6c+fGBTRv7cf3Rt70bOxF7Y9NmDiPxZjIw50yW36UzMASCOKyS2DtBVaKIxSC1ylSZoqTArqOpahRL+IgC0g/fQHAcagyldBXaimG2OEEEIS4hhj2ZEETGOiKKKpqQmVlZXg+ZGdjPm6feh8vQsdr3ai94M+sOA0eZs5QFuugW6mXm4tpIN+phQO6Sq1Wd1yIOQOwW/1I9AXgL9PakHl75OfWwPyVHru7fLB2zzyoqJTgTJXCdNiE3KWyIHQYiP0s/QZ30qIMQZfpx/O407p54QLzhNOKXTxM7CQGDewyZqLz3TgAOMCg1T49nRpWOyp0JqBiQwBWwC+Lj98XT74u/3wdfvg6/IPeezv8UH0Z8axXZGjGAjjq+VgfqYOupk6qHKzr56TGBThOeWRWvds7EHv+30I2lPXDTqawqRA/tl5yD83D7pKHWx7+2HdaYV1py2lXa9HS9AJw4ZC0mOl9Ng4MF9hjF5PCaVJAV4V+507lvOZqYYxhpA7hIAtiKA9gIAtGBXmSIFO0CbP75fnhdfpDwzpoh9N0AtQF0pBkLpQDVWBCuoidcw8dZEKqnw1BHXm//dP1f7CGEN/fz86OjqS/nR3d8NoNKKwsDDyU1RUFPe5xWLJmuA7YA/C2yadG+pmaCHopt7NUjrGZBe/34/Ozk60t7ejo6MjMvV6vTCZTMjJyUk4VSrHfxNwuu0zFP5kgZHulN4OHzr/24mOVzrRu7Vvel8YDoMTOGgrtZGLEoV8Ysqr+dipih9mPidNlcOsr+bBKbkRf/mzEEOgP3mAE/3Ybw1A9NIbOlqCXpBbBxlhWmKCabEJhtMMk3LSyxiDt9UL53EnHMddcB53wnXCBcdx56Re4E0lmlINLKdLrRlyT7fAuMCYEeEfCzH4+/zwdfvh75IDnG4//HKLnZhQp8c/5cJ6pVkRCeQj3XjloEhpntjWW0xk8Pf64evwwdvhk6de6XF7eJ4Xvi5/6oo0D8IJHMwrc5B/Xh7yz8lHznLTsK1smMjgPOGCdYcV1h02WHda4W7InO5lI8Fr+NiQyKiA1+eFRqORzlEYwCAF2YxB+r+ox0yMmiJ2HhiGfcwAQJTnMem/Nydw4BQceIX8WMlLz+X50T+8QlomrccNWo8feB3FwOtySj6yTsgTGghr7AEEo8Mdu/Q4E1pgK82KgZCoKCocGhQYqfJUk3YcTXb+6/V60dnZGRPehC8eB//4fL60bKNCoUBBQcGQgGi40KiwsFDa99Mk5BPhafbAc8oDd5MHniY33Kc88DRJzweXXdCUaaRW9LP00s1SueamdoY2K1r+BfoDcDd6pHIGdXacOtqMU7Wn0NXdBXO+GeaSHJjLcmCeYYF5hhl51bmwzLRM+1bD6cYYg91uHxLoRE/b29vR0d6B3r7ecf0trVY7opAo0TK9Xo/m5mYKf0jmSPTl52n1ovO1TrS/Ko3Sla6TVZKcFA5xw4dDAoeAIyiFOiPtmpGhOIGTulwtMsK4wCBPjeA1PFwn3XDVuuA86YJL/nE3uDOmdQIAcEoOxtMMkdZBpiUmmBYaU1YQlYUY3E1uqQXPcSecctDjrHVlTZe7qUJhEGBebY7UDspZYgIAiD4Rol9EyCeCBcTI84Epk6b+wfPD81ic+bHPg46gFO70+CmMj0OZq4S+Wg6FZunkVkOjL/rPGEPQHpSLy3uHhDuR552+Sbnw1lZqUXBuPvLPzUPembljHtDA1+mDdacUBFl32NB/wD7lwkKSYXhIgVC4JZEcEKkK1FDnq6AqVElT+ScVLawdDgdaW1tx6tQpHDx4EMFgcEjI09HRAZvNNv5/n0zBK5CnyUeOIgce0QOr3wqnf+jopeNlMpniBkRFRUWR50VFRTCbzTE3FlmIwdvhjYQ5kWDnlAeeRje8Hb6UnF9yCg66Kl1MIBSeqosnrosvCzF427zoONyJhgMNaDrWhOb6ZrS2tqK9px3dnm70ij3oFXthYzaII/ii5cBBzamhVWihVemg02ih1+uhNxhgzDHAaDHCmG+E0WyETqeTlo3gx2QyQaeb2gNVBINBdHV1xQQ5bW1taG9uR1tzG9rb2tHR3Ymu3k54/dnV+8BgMODyyy/H3//+98nelLSj8CcLDA5/PM0etL8qtfCx7bKl7O8IegGFFxWg+PIiFJyfD17JI+QOIegMIugMIeQKT0MIuqLmuUIIOeWpS1o/5JTWkZ7Lj50hsBDtbtlEmauEaaERxkVGabrQCMO80bWcEYMiPM3eIaGQ66QLvs7xFwpOCQ7QzdQhJxwILZVaCanz43dXEQMi3PVuOKK6ajmPS0WLJ6NGTqZTFaqgLdNAW66FplwDTbEargY3rNttcB4bWvCbEFWeMhIG6eQab2CAt90bFexILXW8Hb6E3WEmmsIgIO9MqStX/nn50Ffr0vJ3Qp4Q+vf1o2+7LSO6ihGitCjlGkRSGCQFRFJQpMxTwqlyoCfQg053Fzr62tHa2oqWlpaYqd1uT9n2WDQW5CpzYeEsyAmaYRbNsHAWWPhcWHiL9MPlwsAZhly4B5gf/awfVtGGftEGG5OmVnnaz/fDzvXDKlph9VsRYqm9waMUlLBoc2FRmJHDzDD5TMhBDsycBWY+B2beEnmcw5khcOnvxiXohZgwKHo62pacoVAIrXWtqNtbh8ZDTThVewotTS1o62xDR18Hutyd6A31woPsaPEoCAJycnJiWpZE/wyeN9w6RqMxpa1PGGNwu91wOBxwOp3DToebZ7fa0d/dD4fVAbvdjj5HH/pcfWApunutgiryGczl5CmfG/lsqjk13MwNN3PDxVzyY1ec59J6HuYeUQAYz8VLL8aT9/4eqlwVii8vSsm/MxNR+JMFRFFE7ZZaCHuV6Hy1E/37UvelqDAqUHhxAUquKEb+uXkQtOn74mCMQfSJcngkh0Ty1Nfug6veDVe9C646N9z1boTc1EpiooRb8xgXGqRuUnJrnnTf4QnYA8O2FnLVuzOim5umRC13FzNCV6mTWvQcl4IeV52b7rjLeC0PbZkW2nINNOUaaMs10JZpI481pZqEhdj9Vj9su/rRt126eO3f208BGskuHGBekSOFPefmw7wyZ1JqzE2FrmIkO4VYCFbWhx65JUaP2IMesTvyuFfsRa/YgwBGNuJrIjqlDrmqXFj4XJhFM3KC5qggR76I5HORw+VAwU1MEWzGGJzMiX5mg03sh41ZYYuERv2wiVb0Mxv6uX7YRBucodTf9DBxJph5M3I46b+HmTMjhzfDIk/NvAUWzgwNp4EIBgZRmjIGESF53sB8kYXkZ+F54sBjNnSeYBKgKlFBVayEqlgFVZEKyiIlPKJ7oNVOSyvau9vRae9Er693XBfrAMCDh5kzI4/PQx6fjzw+DyYuBwEE4GVe+JgXXnjhZV75uQ++qOde5oUP6ekSOFZGozFpaBQIBEYU5DidTkzkpb6JM0UC1cGBjoXPRS6fi1wuFzou9a2kGGPwwhs/LBKjwyLXkHBpjWoNPqu7HUqLEheePC+l25ZJKPzJYM5aFzpe7UTHKx2wH0pd01OlWYHCSwpRckUx8s7Oy8iif4wx+Dp8cNVJgZC73i2FBPUuuBs9dGE4DjGteRZIU8NcfUaNksZEBk+LVwqCal1w1g4EQ972zPqSng7UReqoYEcLbVnU43INlLnKlH6Jh3wi7AftsG6XLmD7dlgR6Bv/BQMhqaQp06DgPCnsyTsrFypLZha29nX5IkGQdYcN/QftGVFzhmQXD/PgZLBWDnV60BuZSqGOlVnHfSGvhhp5fD7y5Yt4aZofdQFpgZm3QMtlf3H/4VoV2SJTa6SFkRQa9Y/7v202UkMd2Qeiw53w43w+DxYud9wtnkQmwg9/TCDkhRwcDXnuG5gfFSINhAgDAUMwBUFnJtBAAyNvHBLo5PK5MMuBjvT5NEPJZeb34GjoZupwzq4zJ3sz0obCnwzUt8OKw/cdgfNo6u4KqPKUKLqsCMVXFCFvQ25Wj3rFQgyeVi9cdXIoVOeSWw254Wn0UNeyMAEwzNHDuHCgy5ZpYfpb86Rb0BmE66QL9hoH7AcdsB+0w17jmDb1dKTipZAKj8Y8BjghXKh00GMFD45HpFhpZLm8jBc4gAd8Si/yTyuATg51tBVaqEs0kx4QM8bgOumGdYc10jrIXeee1G3KCDygylNF6m+o8pXJi3SO8PA4klODkDeE/pP9CLaEEHRM/a5Ggk5A7oZcFMite/Szs7O+Q7irmHWXDe4mD4L2IIL2IAJ2afSpoD2IoEPq2k0yDCeNDqfMUUJplqaMMamQfJcPAVv6PocNwXp80X7XmH/fwBkiF/L5fD7yuHC4U4B8+WJ+uK5XRAonHMyBfmaLCousUWFRbHCUaS1ZBuPAISfSWicvEvZFwh1Oeqzn9Fm9P/iZf1Ag5Iq0MoluneJiLniUHngUHnh5j7Re0AWHzwGPf3QtNwVOgFbQQQMNtKIWWk4DLaeDltNCw2mg43TQhOdBmobnS+tooZOnWk4LNdQT0p0wk+QsN+GMd9ZN9makDYU/GchV78Lm1R+O+3VUhSoUy4FP7npLVlTuHy8xIA3P6wqHQnIXMledC54Wb/YWWuYAZY4CylwVVLlKaWpRQpmrlKfyfIsSqlwlFGYFOtwdqJpdNS0q17MQg6veDfshuxQGHXKg/6A9a1qLKIwKGObpYZhnkH/0MMw1QGlWDgp3Rj6q3Ghl21CXvi658O2OqVX4lhO4gXoZBSqoC9Ty43CR1aiCq5M4Cg8wsM/MmDEDwb4gXPXy8TYylY7B2RjMckoOmmI1NOVa5K41S125VpsnPQidSCzEpJp/kWAoKI1eZY+dF7QHEXTGhkeRZY5g9n7vpgkncFCaFVCYlTEhTuSxWQlFjjSV5iml73+zEgqjIuFnPuQT4ZdHDvR1+eQfP3yd0mN/tzy/0z/qrvV20Y4bbNcO/feAg4WzyBfxBVGtMvJjWvFouPSNckVieZkXVtEKm2iN7XIm9MOutsMX8oF5GDiRAw8eHCdNefDgwEWmHDjwnDBkLg8BPBdZA/yg/+e42HkqThUT6uTyuRPWHS/bBVkQHiYFQh6VBwGzHwGjH3ByEHp4qIPhcEeaKpHaltjTUf55eVjzz1WTvRlpQ+FPhvrwnK1j6uqlKVGj+PIiFF9RDMsac0YMc5wpQt4Q3I0euOpc8HX4EPINjMrDhozqwxKO4hMz3zcw+s9ILj55NR8JaaTwRhUb4oRDnVwllBY51DErR/VeZtuFfDowJo0SYT/okEMhB/oP2eFtmbwRCJS5ShhPM0A/Vw/j3IGgJxNaY2X7PhNyh2Db1y91Fdtpkwrf2jOjNQqn5KAuCI+Uo4rzWAp0lBYlOD47jtsj2WcYY/B3++VA3g13Q1RX3vpJCIZ4QF2ghqZEDXWxGppiDdQlamiK5eclUjHybHofMhkTmTTog0MKhfz2ADo7O1BcUgKe5wCeA8dhYBrzeLh5kN6X8HJenscNM09+/5jIwIIMLMQgBgYes6D0nS0Gws/Dy8RB60k/orx+5HmIgQXEmPXEAAOv4mKDG7MCiqiQR9ALk368B6RWtL4uH3zd4XDID390YBQ1ZQGpLszTnj/Bwucin89HPpdPF/IZhlNw0FZooJ2hg65KC90MLbSV4akOqryBYCAyGId8o9R1Up7WuSb1PGk0FAYBumqd9G+s0kFXpYN2hga9wV7kKfKkQQFavfC2eeFp9cLb5oO3zZsx5wbTGafkItc3Sot8LSRf/4SveQI2aYTkgFX+iXqezpapJVcXY/nvl6bt9ScbHa0zVPHlRSMOfzTlGpRcIQU+5pU5dMIah6ARYDzNAONphrT9DRZiwwdFAQaFXoAyVwlBlxknflMdx3FSEeIyLYouKYzM9/f5I4FQvxwKuU66Unp3Wl2kHmjJM3egRU+i0cPI+Ag6AXln5CLvjFwA0mfRccw50DJoXz8C/UHwKh68mpOn/MBUOei5ih+67nDrDDdVctJUI0BdoIIiRzFtP/Mcx0nBVqEauadbYpaFu6uEi/2HAyFXvRQSjTYYUuUpBwKd4nDAoxkIdoqloG06tILNFBzPQWlSQGlSAGVSYGhv6oe5MicrQ+apRGFQQGFQQD9Tn3A9xhgCtgB8nX6s7VottR7q8UvTbh/8kcfS80wadW84CpNCPiapIlNVgRrqInk4+1wlQl4R/l4/An0B+Hv98MvTQF8Avh4/An3SvMlo1aguVkNXqYWuUhcV7Gihq9RCU6IZ8Y1CXsFDX62TRiK8IHZZyBOCqyE2EHLLU3/vBLao5gBNqUYKsirlQKtqYDpcvUFRFOFqciK/Mi/uMSZgD8LX7oWnTQqGhgRErd5p0ZU5FTiBiwQ2qqjwJhLshG9qm6N6K1jGH4KLfhGB/oFQyB8dEMmP/YODI2sAgf7krVFVoxy1LttQy58M5ax14f3T43f90lVpIy18cpabUnphYffaYffawXM8eI6HwAtSs0+ehxB5zkPghMjj6Xphk6myvRXHRAu6gnAccaL/gF1qJXTIAedRB0R/4sOjplwDY7ibVrjL1lw9lDmZ+8URCAXQ5exCh70THY52dDg60W5vR5ejC339Vuj0OjAmjeQhMnnsDyZCHPwjDiwXmTjMOkxeL/46BrURlZZKVOdWoSq3CjPzqlGdW40iYxEdU7JAOo8zjDH4Ov1w1w/UdPO2esHxXCTIUZcMtNRRFaqnVZesbEXfTVNf0BWMhEH+Hjkgijz2R4VHPvj7Aim58SLohEiYo5JbVIbDnHDXWXWRFP6mcnCLkCcEf19ACoN6A/D3+SNhUSAqNIo87/UnPa9QmhXQVuqgm6GFrkoL7Qwp2NHO0EFbkXjkzIngt/rlIGigvEJ4OpZRegWdAG2lFvoqHbRV0r9VV62Twq0x/HtTdYyZ7gGRwqiArnogdNOUaaDKU8mtdKQAR2lRQWHMrhvaLMQQsMeGQr4+PzrrOmHkjQjagrCcbkHJFcWTvalpQ+FPBnt/w5aYos+6WTqUXFGM4iuKYFpsHPeHjTGGZlszDrUfwoG2gzjUfggH2w6i2dYy6tfiOA4CJ0TCovBjnh8uLJLX5QfWVwsqFBmLUZZTitKcUpTllKIspzzy3Kg2juvfOt3QCfb4iX4RzhNOqZXQYTsC9iDUBVEteubooTBkTuPJeKFOh70DHY5OdNiled2u7gkd9nMstEotqnKrUJ1bhercalTnVcvPq1FpmQGVglpQpQpjDP6QHy6/Gy6/Ey6fC06/Cy6/E26/G06fCy6/C055mTsQnueEN+CFlmlx1vyzsKpiFWblzaTjzTh4A16ITIROpZvsTUkb+m4i0cSgiEBvYEhLokhI1O2Ds9sFQ5EemiLNQCsdOcxRy11mM+m7OBHGGEKuUFRLIj/81gAEjSAFPJVaKE2Ze/MoEcYYfO2+IYGQq84N0S9CU6oe2nqnUgdVoSql4cFEHmNiAyIffO1eeNt98HbI03Yv/N3+zKx7xkkjVg6EbrqYsEdpmT61g6bb9xKFPxms9icn0f5yJ4ouL4S4MoS5582BIIwt8Q+GgjjRfSIm5DnYfgg2jy21G50mJrUJpTHBUBnKcspQaiqNBEQ5mpxpc6BKZrodyKayqRTqpALP8SjLKY2EQtW5cjCUV43q3CqYtebJ3sQJEQgFYHVb0efuQ5+7D73uXvR7+uXgxiWHOE7psd8NlxzYSEGOC26/Gy6fE06/C0ExNXcvzZocLC9fjpXlK7GyYgVWlq9EsWnq3j0bTiAUgM1jg9VthdVjg03+sXqsA4/dVti8Ntg8/QPPPTZ4g1KdjRyNCeXmcpTnVKDcXCY/LkeFuRzl5nKUmEqgFLLzApG+m8ho0P5CRivT9hkxIMLX6YuEQd72qJAoapqO7pKCXhi2y5y2UgdthZZay8oybZ9JNwp/MpgYFMEr+FHvlE6fEzUdR+SA5yAOth3Ckc4j8AUze+jH8dKr9CjNKUV5TpkUFJmkkCgcGJWaypCrs0ypgIgxBpu3Hx32drTbO9Bub0enoxMdjg5YbTZYzGYoBSUUvAICr4CCF6DgFZEfQX4urSPI6yigEAbWFaLWj5knxL4ex/Fy+CAVhmSDppCWDLuMYWBo6eF/d+B5eB2BV6DAUIBCQwEM6vTVcUonT8CD1v42tPW3oqW/FW39bWjtb0Wr/Hg6hTqpYtFaImHQzNxqVOVWRkKiUlNpxn2xM8bgDrgjIU5fONBxSYFOzLzwj6sPdp99sjd9RMpyyrAiKhBaXrYcJo1psjcrKX/QjzZ7G3pcvTEBjRTaRD2PhDvS1OV3pX3beI5HialkSChUniMHReYKWLTmSfuuY4zB5XfB7rXD4XOgX+5K7vA54PQ54bDaUVEyA3q1HjqlFlqVFjqlDlqlFlqlDjqVFhqFZkp9V5OxmW4XZWT0PAEPWmwtOGVtRrOtGaesp9DS3YKy/DLk6i3I1eUiV5cLi3bgsVlrhkLInNZijDEE+4MxYVB46gs/b5NaxQ2mKVFDW6WL6i43EPKo8lPbqmoihL8/RCZCr9JD4NPfzXG6HWco/MkCiXbKbmd3pDVPeHqy5yRdLMahVWpRaipFaU4JCvQFsOgssGgtsOjM8tQyZKpWqCd8OxljsPvs6LB3oN3egQ5Hh/y4HR0OeZ7c4iN8t3g60yl1KDQWotBQiCJ5WmgolOcVoNBQhEJjIYoMhRPWpSIc7LT2t8gBz0CwI/20oc/dNyHbQiQcx0EtqKESVFApVFAr1FAKSqgVaqgFFZSCCmp5vkpQQ6VQQiWooVaoYn5HJUjPpcdKqBTqIa+lUqghMjEm1LG6+9Dr6h0S9EynzzDHcZhbMBcrypZjZcVKrCxfiUUlCyf8OOvyu9BsbcYpm3TBMPhxu6M9q79H9Sr90HDIHH5cgVJTybD/zQOhAOxeB+w+O+zefji8Dti9dvTL4Y09atrvtUvLB61r9zkgsvHfxdYpdXIwJIdCSi20Kikk0kUFRVqlFnqVfmB+ZB0dNEqNdGElBhAIBREIBWIeB8QAgqEgAmJAeh4KICgGh30cEIMIyr8z3GuFH3McoOSVUAhKKAUllLxCeswroRTCN1uGfyz9nkJeVwmVEO91pN9R8NLNnXBNRj76R+5aL/1wkS74Az8D3fN5jot0zx/6GlxMDUgOXOSCkoM8jbrAHMm8kV6QjueiLPrzG/MYQ+ezQf1ypBta2XXRPFXZPLZhj9Hhx93O7jG9bo7GBEtUKGTRWZCrswwJiiy68GMLTGrTpIYDIZ/Uisjf44dCL0A7QwtBO7k1oML8Qb/03eBzwOG1w+lzyo8dcPiifmKeO2OeO+V50d8fWqUWBrUBepUeRrUBepUBBrUBBpU+ar4RerUeBpUBBnV4vkFeXy+vL/2eWjF0dF0Kf0jGEUURDY0NYEaGwx01kS5bB9sOosPRMdmbN+XplDo5DDIPGw7FC490St2wJw8On0PushMOcaRQp90xEOh02DvgDrgn4V879RlUhkhQVGgoQKGxCIWGAhTJUykoKkChoRBapXbY16Bgh5DUUQkqLC5ZjBXlK7CyYgVWla/E7PzZYz4JY4yhz21Fs+2UfCdYvmCIumigzydQZCxCkaEQ7oAnEuh4Ap7J3iwyTQ0OlCLzGBCeNVyIk47LGJ7joVPqoFNJoaNOpY96rBtYFv14mHl6lQ5aZXgqhZQ6lY5atskYY+hydsUco6XHp+TjdUtGtXIVeAFmrTkqIBoIi0waE4xqAwxqYyRwkJ5L88KP410bTAZf0CeFL34nnOGWmT4nnD7puV2eF/4JhzlOv0MO/gcCHX9oaKukwQRRgCaggSaggTqojjzWBDRQBzTQBAee84yHX/AjoPDDL/jhj54qAggI0c9j12F8/GOCglfAoNJDL78fepUUGvEhDoXmQhg1RhQZi3D/+f+Xyv/UGYXCnwwUEkM43HFYCnnaDkWmrkD6m5OT1FEJqkgYZFQbYfVY0WHvgNPvTP7LJCOY1CYUGApQZCyEXmVAh6Odgh1CJoBJbZK6i1WsjHQbK80pBSDdEOl0dg66YDgVed5ia6HjLCHZhAGagAY6v27IjzaghSAKEEQFBJEHzwT5uQBB5CHIz3lRkB/zMesqIst48KL8POY1BPCMH3jOBDCIEDkGkRMh8iJETgQLP+eGec6L0u/wYtQ6DIwTY34n/JqMF8ELPHhBgKDgwQQgpAgixIcQEkIICiGEhCBCQvTzgXmR53wQIYX8nJeXKQYeh4QQRF4EBmUNAi9AJSihDLdoFVRQ8ipoODWk/2mgZiqooYYKaqiZCiqooBSVUDEVlEwFFVNCyZRQiAooRCWUTAGFqJD/2wtQhATwIem/qd/nQ7/TDrvTDofbAZfbBafbBa/XAwQ5CGzgPQv//sA8YfgfFn8eL/IQeVH678mHEOKkqciJ0n8beZnIiQjyQWldLgSRDyHIS9MQN/D7IheSfycYmS/GvLb0OkEhgIAQjHocQJAPSvPkxyFFCEq1EiqtCiqtGmqtClqdBhqdFlqtBjq9HgadHgaNISpAMsKgGgiT9GoDAkG/FNT4pYDG7pWDGn84wHHEhDaREMfrgMvrhsfrQSgYitn/I58DJv03HJgvQBVUycGMOhLSSIFN/BBncMCjFCemVl2QC0ZCoYAghUVDg6RAJCwKzz+VdwpbZm9BlaUSB79+YEK2dTJQ+JOBHD4Hyr8/Y9KanCt4BSotUtM3URQRYiGIYggiYwixEEJiCCIT5WkIIVEatjl6WSqafBNCUk8lqFBiKkaRsVieFqHEVIJCQyG8/R4UFBZCIXcP4IZ0I+BjugBw4IauN2id6HW5qHUAoMPejvreBjT2NaKhrxENvQ1otDaOuRk3mdpKTCXQKDRo7W8d0V1GQsjEUAaVMYGN3q+HNvq5Txd5rg+HOvJ60mMtBJYZ3VemGhEiguFAgg8iKEgF/hWRkEaessypgUMAv+CPvF9SiBRAUAhFHgPcoMBGCjfD86IDzZhwhz5nce2s2olffOTnWFS8EFvv2TLZm5M29EnPQEa1ETNzZ6Kuty7tf8ugMmBx6WIsKVmMJaWLsaR0CU4rPG3c9RcYY5EQKCSGpAAp/FgUwQaFRXavHW32drTJXWba7G1R3Wla0O/NnGafhGSieKFOsbEYxZF5xXGLnk9Gn+fq3Cqsq1o3ZL7D55ACod4GNPY1oaGvAfW9DWjoa0CzrRkhMTQh2zcdqASV3ERdD71KagKtV+mgl58b5G4K+qj+9XqVDnqVASExiA+OfYiT9jrsa90Hh8+R1m1tt7en9fUnE8dxyNHkgDGRvu9IRspz5uGmbTdBJwc2Or8WOp/0eKLu6KeaiOjWHQOPOcZFLqB5xg88l1tD8MieuiA8eKhCKqhCqsnelHGJfq+iW/MM9yNyYmxrrsEtxqIe8yKfkcFX5D0LTPaWjJ1H6YFX4YVXKf34lF54FT5pqvRGLfMNrCPPEzkRypAKqqD030EVVEEZUsY8j8wbtN6Q+fIyhZj4fQ4I0k0lfZYOIjNSmbe3EwDA0tIlKQ9/io3FWFK6RA56lmBxyWJU51al5UKP4zgInAABwoiHpF1QvCDuMofPgfb+drTa2wYCov42eYQk6bnVY03V5hOSMVSCCsXGIhSbSgaFOkUoNpYkDXWykVFtxOKSxVhcsnjIsmAoiOb+FjTIYVA4JGroa0BDb+O06e5jUBkG6g3ocpGnz5PrDhjlgCYc4oT7tEt93PXKcLAj/YxnyHBRFLHCtAKVlZUAgNqeWuxp2Ys9zXuxp2UPDrUfQiCUxWeuY2BSm2DW5sCis8CsNUd+LNpBzwctz4kqJGr32tHa34pmWwta5J9mWzNa+qXHrf1tCIrBSf6XkumGZzzWNpyestcL8kG4VW64VC64VW54VG64VR64VC75sRsutRtepQcBIYAQJw50tZG75US66QwOAuTuPeHuPIODgXBXH8aNsYU9w5CAKOZHlFvGisMESDHrSFOFKMjdpxRQhOSfqOfKkBJC9HJRAWUo9rk0VQ7zu4ohv8szTm5VEpK6IvED05Dc2iQo/3cLClHz+SCCfPT88PNhpkL4deVuaINCm3D3qkg3K3kbwu/P4KAnUR2XcWMY2vVpUDcy6b/bQDeoSHdCuauaQhSgDCmhCCkj70/4fVOGlFHv1+DHyoH3Sn7/lFHvZezjgXXBYVB3tmGm4c9J5PlA97aQ3F0x9nm8+SL8gj8qoPENCnQGAp7wcr/CP/bPV5pwIhcJ1YYLiRwa6QaWQUXhD5kES0qX4KVD/x7T73Ich1l5s2KCniUli1FoLEzxVk4co9oIY6ERcwvnxl3H7XfHtB5qjbQiCg+h3YYeV88EbvXEyNfno8RUHBUEFKHIUASPw40cixkiExEUgwiKQYTEIIJiSHoeCs8LRZYPeR6S1g9FLY99HpLXib7Ak0YC4QZNAQyaH1vgMd7vxHs9t9+FLmc3upxdsHlsE/mfPGV0Sh3KzGUoM5WizFyGUlMpys3lKDWVoiynFCWm0ikV6qSCQlCgOrcK1blVAM6NWcYYQ6+rF/V9DXKroUYpFOprRGNfI/o9/fCFfBnVcojjOJg1ZuTp8yIjioRHGRluXvhnMkYhTITnecwrnId5hfPwyRU3AJCKSR5ur8Gelj3Y07wHe1r24kT3iUne0pFRCkqU5ZShLKc0UsQ/EtZohoY3Fq0FORpTSoYPNmlMMGlMmF80f9jlITGETkenFA71DwRE4cfNtpZJrUumVqhhVBulf0d4qjFJI7Ko9LD2WyGoBXiCHrj9HngCbngCHrgDHnj8bmka8MAdcGf1iGtTjVs1MAiFyIlwK6XAxqWWghq30g13+LHKDbcc6rjlUMetHnjuUrkQEAJDatFkDQ5gHENI/h+ZPBqFBhWWClSYKzDDXIGynDIo/AJUBjWsHhus7j5Y5ZE1IyNveqxw+aPqqHKI1G4ik0uj0MCoMULgBLj8Ljj9zpR9DzCewcf74FP6Eq5nUOtT8vcyFdX8yVBvH38HVz/ziaTrqRVqLChaIHXZKlmCJaWLsbB4IQxTvMnaWHkD3sioWjaPLfIlYHVb5S+J6OdW2NxW2Lz9k7KtubrcmFCn2FiMYlNxzLxCQyFUiqFNeafbsIW+oA/dzm50OrrQ5exCt7MLnc4udMnPpZ9udDk6J6xLRbJgp8xcDrMmJ2OCnem0z4TEEPwhP3xBH/xBv/zYD3/IJ80LBeAP+uR54fl+BOT1pHX8CV5DWpcxFjNM7EB4kxd5bNbmQOCzsw/+WPaZfm8/9rfuj7QO2tO8F232tjRv6VB6lR4VZvmCQb5wiH5cbCzO6s+By+9Cq601EhCFC2GHw6F4dZNMahOMmtjAxqQxISfqccw8jREmTQ5MahNM8u8lCiZHs88wxuAL+uDyu6MCIjc8fjkoCrgj4VEkMPJHBUnyuuA4KHl5KPV4Q6uPYJj1gd8dGGY9/HhgaHcFGGMIyDdFpGHiox7LQ8cnGjJ+8PDzsUPRR7+u9HohMRjpZi91rx94LEbVYZRqM4oQo5ez6OUspm7jwO/K3fhFEUaHEW6lGz6VDyLEyPsUec/CQ6YPOyrXoHUwdKj14V5LFMWYfWXIaGBx5g2dj6Hzh1mXgcEb8MIb9A6zV5KJZtbkRMKdgWP0jMjjfH1+zHs+0mOMN+CNnO+HgyGrxzbwOM786daadSQEXpC+F9RGaWQzjTHquUG6ga9JsFxjhFEtjZY2uDWyKIrwBD1ywWoXnH4HXD4XHD6nFA75HHD6XXDKzx0+J1xy4WunzyWv44RLLoTt8rvgCyYOfz618pN44hO/Ted/sklF4U+G6nJ0YfYPY1u55GhysFTurrWkVAp65hbMHVezfZJcSAzB5umPCYWGn0aFR/K84Qpfm7XmSC2WeKFOkbFoXHf2p9OF/Gh5A150u6SgqNvZFQmMumLCom50ObriDjGabcHOSNA+Q0YrVftMu70de6O6i+1t2TvukDZXl4sZ5ophLhqkC4fp3qJOFEV0u7ph89igV+mlk2+VMe2ffTrOkNGYzP1FFEUpbAx44PK74JZbpbn9LjmQlB675dDR5XfJ67rlYNIdeRy9visSYLqTb8Q0UGQsirTaGe54bdKYRvV66dxnGGNw+p1RwdDA+b40TPrAiFuDh013+pzykOrOpOHDRFDwCimUCY8mph4YXcwYeS4vV0nLcjQmOawxxYQ3WqU2q75P/UE/XAE3nD4pSLJ77ahvrofOrIc74MYMcwXWV6+f7M1MG+r2laEKjYW4Yfn1qMytxKLiRcgTc3H6wtMhCNl5hzibCbyAPH0u8vS5o/o9URTh8DtgdVth99ph0phQZCyCVqlN05aSkdAoNZGTi2Q8AU+kRZHL70K+Pi8rgx1CMlmJqQSXLbgMly24DIB07KzrrZPrB0ndxQ62HYy0VOE4DsXG4oQtd6j1a2I8z0tdhI1Fk70phGQknucjF8QFKEj564dbNHj8HoTYxHUdY/LIvf5QAIFI69YAAnJrVn8wAH/ILz8PPw5EWrgG5Hnhx+HWsoGQX14n6rXkxzzHo9RUigpLOOSRWu6U55RBo9RM2L99vDiOkwMTI2ZYZoz5dQKhQMxw7JGQyO+IauHijA2UotZz+V1QCcrI/mmICmIGQhz5ucoAkyYqzJGDHLVCPW3PY1UKFVQKFSxaMwDps1ggFkybmxJZEf5YrVZ873vfw+7du1FUVIT7778fa9asmezNSrsnr/0dgIEUe7p+SLMVz/PI0eQgR5Mz2ZtCxkir1GKGZca4vuQJIaPD8zzmFMzBnII5uH75dQCkO3Ut/a3gOA5lptJhu7sSQki24Hk+UnifTC9KQSl3BbdM9qaQaSgrwp8f//jHKCgowLvvvovt27fj/vvvx3/+8x+YTKNrCkgIIYSQ7KNSqDAzr3qyN4MQQgghJGtlfNsmt9uNzZs344477oBGo8E555yDWbNm4f3335/sTSOEEEIIIYQQQgjJeBnf8ufUqVMwGAzIz8+PzJszZw7q6+uHXd/v98Pvjx3BQqFQQKXK3ibioijGTAlJhvYZMlq0z5DRon2GjBbtM2Q0aH8ho0X7DBmtqbTPjKRmUcaHPx6PB3p9bH9YvV4Pp9M57PpPP/00/vCHP8TMu+aaa3DttdembRsnSnNz82RvAskytM+Q0aJ9howW7TNktGifIaNB+wsZLdpnyGhNhX2mujp59/iMD3+0Wi1cLlfMPJfLBa12+BGTbrnlFnzqU5+KmTcVWv40NzejoqJiWlQhJ+NH+wwZLdpnyGjRPkNGi/YZMhq0v5DRon2GjNZ022cyPvyZMWMGnE4nenp6Il2/amtrceWVVw67vkqlyuqgJxGe56fFTklSh/YZMlq0z5DRon2GjBbtM2Q0aH8ho0X7DBmt6bLPZPy/UKfT4ayzzsKTTz4Jr9eLzZs3o66uDmedddZkbxohhBBCCCGEEEJIxsv48AcA7r//fnR2duL888/HY489hkceeYSGeSeEEEIIIYQQQggZgYzv9gUAFosFjz/++GRvBiGEEEIIIYQQQkjWyYqWP4QQQgghhBBCCCFkbCj8IYQQQgghhBBCCJnCKPwhhBBCCCGEEEIImcIo/CGEEEIIIYQQQgiZwij8IYQQQgghhBBCCJnCKPwhhBBCCCGEEEIImcIo/CGEEEIIIYQQQgiZwij8IYQQQgghhBBCCJnCKPwhhBBCCCGEEEIImcIo/CGEEEIIIYQQQgiZwij8IYQQQgghhBBCCJnCKPwhhBBCCCGEEEIImcIo/CGEEEIIIYQQQgiZwij8IYQQQgghhBBCCJnCKPwhhBBCCCGEEEIImcI4xhib7I0ghBBCCCGEEEIIIelBLX8IIYQQQgghhBBCpjAKfwghhBBCCCGEEEKmMAp/CCGEEEIIIYQQQqYwCn8IIYQQQgghhBBCpjAKfwghhBBCCCGEEEKmMAp/CCGEEEIIIYQQQqYwCn8IIYQQQgghhBBCpjAKfwghhBBCCCGEEEKmMAp/CCGEEEIIIYQQQqYwCn8IIYQQQgghhBBCpjAKfzKc1WrFPffcgzPOOAMf//jHsXPnzsneJJLhPve5z2H9+vU488wzceaZZ+Luu++e7E0iGebJJ5/ENddcg9WrV+PNN9+MWfbMM8/gggv+v707C4mq/+M4/lHLHGdaVSzL0FYjL7Iki/aNSLKCFoOihDYzsMWi6Ea7KNog2u1KLworLNSkrqKVEsqLIkMyLbJFy1LKGZds5n/xPA1ajvX8n3rOaXy/bpz5DXPOd+Drx5nvnHOcqenTp+vw4cNyuVwGVQmz8NQvly5dUlxcnDtrJk2apKqqKgMrhVk0Nzdr165dio+P15QpU7R27Vo9ffrU/Tg5g2911DNkDTzZvXu3Zs+erSlTpigxMVG3bt1yP0bOoD2eeqaz5EwXowtAx/bt26eQkBBdvXpVRUVF2rFjh/Ly8tSjRw+jS4OJpaena/bs2UaXAZMKDw9XWlqaMjMz26zfvn1bubm5ys7OVkBAgNavX6+IiAjNnz/foEphBp76RZLGjh2ro0ePGlAVzOzLly/q37+/srKyFBwcrJycHKWlpSk/P5+cQbs66hmJrEH7li1bpm3btsnf318lJSXasGGDCgoK9PDhQ3IG7fLUM1LnyBmO/DExh8OhGzduKDk5WQEBAZo6daoGDx6smzdvGl0agD9YfHy8xo0bJ39//zbrly9f1qJFizRgwAAFBwdr+fLlunLlikFVwiw89QvgicVi0erVqxUaGio/Pz8lJibq9evXqqurI2fQro56BvAkIiLC/bfJx8dHzc3NqqmpIWfgkaee6SwY/pjYixcvZLPZFBwc7F4bOnSoKioqDKwKf4IDBw5o5syZSklJUVlZmdHl4A/x7NkzDRkyxH1/2LBh5A069ODBA82YMUOLFy9Wbm6u0eXApB4+fKg+ffqoV69e5Ax+SuuekcgaeLZ3715NmDBBK1as0Pjx4zVo0CByBh1qr2ekzpEznPZlYg0NDbJarW3WrFar6uvrDaoIf4LU1FQNGjRIvr6+OnfunDZu3Kjc3FwFBgYaXRpMzuFwyGazue9brVY5HA4DK4KZjR49WmfPnlXfvn31+PFjbd26VUFBQZo2bZrRpcFE6uvrtWfPHqWkpEgiZ/Bj3/YMWYOO7NixQ9u2bdP9+/fd14kiZ9CR9nqms+QMR/6YmMVikd1ub7Nmt9tlsVgMqgh/gujoaAUGBiogIEArV66UxWJRSUmJ0WXhDxAYGNhmuGy32xkawqP+/fsrLCxMvr6+io6O1tKlS3Xt2jWjy4KJNDU1KS0tTRMnTnRfa4OcQUfa6xmyBj/i5+enuLg43bt3T3fv3iVn8EPf9kxnyRmGPyY2cOBA1dfXtzkPsayszH1oGvAzfH35NcfPiYyMbPMfeZ48eULe4Kf5+PgYXQJMpKWlRTt37lRISIg2bdrkXidn4ImnnvkWWQNPnE6nXr58Sc7gp33tmW95a87wqdDEAgMDNXnyZJ06dUqNjY26ceOGysvLNXnyZKNLg0l9+vRJRUVFam5u1ufPn3XmzBl9/PhRI0aMMLo0mEhLS4uamprkcrnct51Op+Lj43XhwgW9evVKNTU1OnPmjObMmWN0uTCYp365c+eOamtrJUmlpaU6d+6cJk2aZHC1MIvdu3erqalJGRkZbd5EkzPwxFPPkDVoj8Ph0JUrV+RwONTS0qKrV6+quLhYMTEx5Aza1VHPdJac8XG5XC6ji4BntbW1Sk9PV3FxsUJDQ7V9+3bFxcUZXRZMqra2VqmpqXr+/Lm6du2qYcOGadOmTYqKijK6NJhIRkaGCgsL26xlZmYqNjZWWVlZOn36tJxOpxYsWKDU1FSv/fYDP8dTv9y6dUuXL19WY2OjQkJCtGTJEi1dutSgKmEmb968UUJCgrp169bm6NMjR44oJiaGnMF3OuqZ69evkzX4TkNDgzZv3qzS0lK5XC6Fh4dr1apV7mu0kDP4Vkc9c+jQoU6RMwx/AAAAAAAAvBinfQEAAAAAAHgxhj8AAAAAAABejOEPAAAAAACAF2P4AwAAAAAA4MUY/gAAAAAAAHgxhj8AAAAAAABejOEPAAAAAACAF2P4AwAA8AP3799XbGysYmNj9fr1a6PLAQAA+EcY/gAAALSSkZGh2NhYrV271r1ms9kUHR2t6Oho+fv7G1gdAADAP9fF6AIAAADMLioqStnZ2UaXAQAA8H/xcblcLqOLAAAAMIOEhAS9efPmu/XMzEwlJydLkgoKChQWFqaMjAwVFhaqX79+WrdunU6ePKn6+nrNmzdPGzZs0PHjx1VQUKDu3bsrKSlJixYtcm/v3bt3OnHihO7evau6ujqFhoYqISFBSUlJ6tKF7+YAAMCvxbsLAACAvw0fPlwNDQ2qq6uT1WpVZGSkJKm0tNTjc2pqarR3714FBwfLbrcrJydHRUVFevv2rWw2m6qqqrR//36NGTNGkZGRqqurU1JSkqqrq937qKioUGZmpl69eqX09PT/6uUCAIBOgmv+AAAA/O3gwYOaOHGipL8GQdnZ2crOzlZUVJTH53z+/FnHjh3TxYsXFRoaKkmqrKxUTk6OcnNz1a1bNzmdThUXF0uSzp8/r+rqagUFBSkvL085OTnat2+fJKmwsFCVlZW/+VUCAIDOhiN/AAAA/oUePXpo1KhRkqS+ffuqurpagwcPVlhYmCSpd+/eqqqq0ocPHyRJJSUlkqT3799r1qxZbbblcrn06NEjhYeH/3cvAAAAeD2GPwAAAP+C1Wp13/bz8/tuzcfHR9Jfg53WP1ufVtZaQEDAb6sVAAB0Tgx/AAAAWvk6fGlsbPwt2x85cqTu3LkjPz8/7dmzx32EkN1u17Vr1zRt2rTfsl8AANB5MfwBAABoJSIiQpL0+PFjJSYmymKxaM2aNb9s+0uWLFF+fr7evn2rhQsXKjIyUna7XdXV1WppadHcuXN/2b4AAAAkLvgMAADQxrx58zR9+nTZbDaVl5fr0aNHcjqdv2z7vXv3VlZWlhISEtSzZ0+Vl5erqalJMTEx2rJlyy/bDwAAwFc+rq8nngMAAAAAAMDrcOQPAAAAAACAF2P4AwAAAAAA4MUY/gAAAAAAAHgxhj8AAAAAAABejOEPAAAAAACAF2P4AwAAAAAA4MUY/gAAAAAAAHgxhj8AAAAAAABejOEPAAAAAACAF2P4AwAAAAAA4MUY/gAAAAAAAHgxhj8AAAAAAABe7H8taMug5tokQwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] }, + "metadata": {}, "output_type": "display_data" } ], @@ -378,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "c3b78953", "metadata": {}, "outputs": [ @@ -386,23 +425,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "mean MAE on total: 4141.65\n", - "mean MAE on reasons: 1275.43\n", - "mean MAE on regions: 799.99\n", - "mean MAE on (region, reason): 312.05\n", - "mean MAE on (region, reason, city): 189.69\n" + "mean MAE on total: 4311.00\n", + "mean MAE on reasons: 1299.87\n", + "mean MAE on regions: 815.08\n", + "mean MAE on (region, reason): 315.89\n", + "mean MAE on (region, reason, city): 191.85\n" ] } ], "source": [ "# we pre-generate some of the components' names\n", "regions_reasons_comps = list(\n", - " map(lambda t: \"{} - {}\".format(t[0], t[1].lower()), product(regions, reasons))\n", + " map(lambda t: f\"{t[0]} - {t[1].lower()}\", product(regions, reasons))\n", ")\n", "\n", "regions_reasons_city_comps = list(\n", " map(\n", - " lambda t: \"{} - {} - {}\".format(t[0], t[1].lower(), t[2]),\n", + " lambda t: f\"{t[0]} - {t[1].lower()} - {t[2]}\",\n", " product(regions, reasons, city_labels),\n", " )\n", ")\n", @@ -410,16 +449,7 @@ "\n", "def measure_mae(pred):\n", " def print_mae_on_subset(subset, name):\n", - " print(\n", - " \"mean MAE on {}: {:.2f}\".format(\n", - " name,\n", - " mae(\n", - " [pred[c] for c in subset],\n", - " [val[c] for c in subset],\n", - " inter_reduction=np.mean,\n", - " ),\n", - " )\n", - " )\n", + " print(f\"mean MAE on {name}: {mae(pred[subset], val[subset]):.2f}\")\n", "\n", " print_mae_on_subset([\"Total\"], \"total\")\n", " print_mae_on_subset(reasons, \"reasons\")\n", @@ -443,20 +473,18 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "1d994992", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -465,12 +493,12 @@ " plt.figure(figsize=(10, 5))\n", "\n", " pred_series[\"Total\"].plot(label=\"total\", lw=6, alpha=0.3, color=\"grey\")\n", - " sum([pred_series[r] for r in regions]).plot(label=\"sum of regions\")\n", - " sum([pred_series[r] for r in reasons]).plot(label=\"sum of reasons\")\n", - " sum([pred_series[t] for t in regions_reasons_comps]).plot(\n", + " pred_series[regions].sum(axis=1).plot(label=\"sum of regions\")\n", + " pred_series[reasons].sum(axis=1).plot(label=\"sum of reasons\")\n", + " pred_series[regions_reasons_comps].sum(axis=1).plot(\n", " label=\"sum of (region, reason) series\"\n", " )\n", - " sum([pred_series[t] for t in regions_reasons_city_comps]).plot(\n", + " pred_series[regions_reasons_city_comps].sum(axis=1).plot(\n", " label=\"sum of (region, reason, city) series\"\n", " )\n", "\n", @@ -499,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "5c5f2006", "metadata": {}, "outputs": [], @@ -519,20 +547,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "b2b95875", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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Check mle_retvals.\n", - " warnings.warn(\n", - "/Users/julien/miniconda3/envs/darts/lib/python3.9/site-packages/statsmodels/tsa/holtwinters/model.py:915: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n", - " warnings.warn(\n", - "/Users/julien/miniconda3/envs/darts/lib/python3.9/site-packages/statsmodels/tsa/holtwinters/model.py:915: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n", + "/Users/dennisbader/miniconda3/envs/darts310_test/lib/python3.10/site-packages/statsmodels/tsa/holtwinters/model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.\n", " warnings.warn(\n" ] } @@ -626,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "22e88655-2321-404a-8a76-a24f41c9d8e5", "metadata": {}, "outputs": [ @@ -635,22 +657,20 @@ "output_type": "stream", "text": [ "mean MAE on total: 3294.38\n", - "mean MAE on reasons: 1194.38\n", - "mean MAE on regions: 811.74\n", - "mean MAE on (region, reason): 332.17\n", - "mean MAE on (region, reason, city): 192.29\n" + "mean MAE on reasons: 1204.76\n", + "mean MAE on regions: 819.13\n", + "mean MAE on (region, reason): 329.39\n", + "mean MAE on (region, reason, city): 195.16\n" ] }, { "data": { - "image/png": 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\n", 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", 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\n", + "image/png": 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -702,7 +720,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "616da722-26ee-4630-9728-64d7ab7980e6", "metadata": {}, "outputs": [ @@ -710,23 +728,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "mean MAE on total: 3243.16\n", - "mean MAE on reasons: 1207.85\n", - "mean MAE on regions: 776.80\n", - "mean MAE on (region, reason): 315.56\n", - "mean MAE on (region, reason, city): 198.72\n" + "mean MAE on total: 3349.33\n", + "mean MAE on reasons: 1215.91\n", + "mean MAE on regions: 782.26\n", + "mean MAE on (region, reason): 316.39\n", + "mean MAE on (region, reason, city): 199.92\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -756,7 +772,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/examples/17-hyperparameter-optimization.ipynb b/examples/17-hyperparameter-optimization.ipynb index b6613a7a11..8e1aa3e1bf 100644 --- a/examples/17-hyperparameter-optimization.ipynb +++ b/examples/17-hyperparameter-optimization.ipynb @@ -39,25 +39,25 @@ "source": [ "%matplotlib inline\n", "\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import optuna\n", + "import torch\n", "from optuna.integration import PyTorchLightningPruningCallback\n", "from optuna.visualization import (\n", - " plot_optimization_history,\n", " plot_contour,\n", + " plot_optimization_history,\n", " plot_param_importances,\n", ")\n", - "import torch\n", - "import random\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from tqdm.notebook import tqdm\n", - "from pytorch_lightning.callbacks import Callback, EarlyStopping\n", + "from pytorch_lightning.callbacks import EarlyStopping\n", "from sklearn.preprocessing import MaxAbsScaler\n", + "from tqdm.notebook import tqdm\n", "\n", - "from darts.datasets import ElectricityDataset\n", - "from darts.models import TCNModel, LinearRegressionModel\n", "from darts.dataprocessing.transformers import Scaler\n", + "from darts.datasets import ElectricityDataset\n", "from darts.metrics import smape\n", + "from darts.models import LinearRegressionModel, TCNModel\n", "from darts.utils.likelihood_models import GaussianLikelihood" ] }, @@ -184,7 +184,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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cPxBOHYjy3lapUiWyvAhOnDnyhijXU9mmjeuFCkOcxbEGrDI3QI74yDdxlguDvnzAibPchF7DHdcm1JVFnMUluEzIhzLSxFHeytzG51ftcMQmzkxES7510vR1xrFNQmVugHKZkpKSvAv+Y0Kc4mzx4sWBN2CtyO9NkHbbts4GvZ9z5swJlN6UsrIy/Pe//420HgSp93FYzth84hBncZAP0Rrp+mBLlGXPZ3GWC3XSFCfO8oy4IhFWFrfG8847L6ziWOXvyA1++OEHNG3aFCeeeGLcRckacYmz2bNno3HjxjjyyCOtzwHk3ySQCXSbcPLJJxuljcNy9u233+LVV1+1Tm/D3XffjT333BPPPfdcpPnakgtujRs3bsx6nuy1zZs3L+t5BiHIs5g/f7739wsvvGCUtrKIM7qMP/zwg3XayoYTZ3mGc2sMP9+4G4C483dk8vrrrwMAvvjii5hLkj3iEmcffPABAASONFaR3xv62j777DOjtHGIs1deecU6rS33338/AGDYsGGR5Rmk3tNujXGJsyg8Adg8v/7666znyWIiJILUe7p/+M9//mOUNgxxFiVhiLOddtrJOm1lw4mzPCMfxFk+W84cDiD/1gPYEFdAjbCiLFbk9zbuUPr5RFD3WBPy3a0xCti6G0cgiajcGrds2WKdNow+Jh8sZ3QdNHWrrchtvIqKPwKpYJhWVvrFiMqtMRcsZyZliLsBiDt/Ryb5NqtpQ76Js1wINBAV+RatMc5nkS/1IBcsZ1Hcq8okzoJMNFUWt0a6rpu61VbmNWdF6kOAZDL5AIDDAMwFcEkqldq6/fudALwNoA6AbQD6pFKpxdkpav6zadMmTJ48GUceeSSqVq1qdQ4XEEQMfX1lZWXajV/QF/ejjz7C5s2bjfegCyt/R/g4y5k+6XTa6FxBZpvZfCsqq1evtk5r2/4GmbxbunSpddqg2NaDKVOmYK+99kLDhg1DLhGfXFhzZnqv3njjDdStW9do7S2bh+1YJwiVRZxFSRjibPbs2UZpK3Ibr0J5t5PJZHsAzVKp1BEA5gCgR6DdAfyaSqWOAvASgEuzUciKwtVXX41u3brhhhtusD6Hc2sUQwtIEzEZpAGYN28eTj31VPTq1Qu//fab1TkqcwOUqzhxps+HH35odHxYbmj56oanw1tvvWWdduedd7ZK9+ijj1rnOXr0aOu0QbFpPydPnozDDz8crVu3NkqX726NJvcqlUqhT58+6N69O5YsWWKdZ82aNbXThoXJcwryLIK0ZZXRcvbJJ58Ypa3MYyOdu30YgPHb/x4HoAv1218AyJtXF8Cy8IpW8SDRfILsDJ8P0RrjeqFoQRZVeWlBNmPGDKtzVOYGKFepDOIsLMaOHWt0fFgDU/fe8Nl7772t0r399tshlyQabOrBt99+CwBYv369Ubp8Dwhicq/oyHr//POPdZ61a9fWThsWUVnOgqTN5zVndevW1U4XpK47t0Y5dQGUbP97NYB61G9/AmibTCZnAUgAOCTc4lVMgogkZzkTYyvOgpSXdtGy7bjzseGo6FQGcRbWNZq69lTmDjcKbO9LXGsQg2LTfsfhakdbSuLahNokX7o+BFnDHcd4oLLscxYldH9hYg3NBXGWj+iIs1UoX1MGADsDWEH9dhGAyalU6p5kMnkWgLsA3EInTiaTVwC4AgCuueYadOvWLWiZc4ri4mLtY6tWreoN5ouLi7F161aj9ED5TJ9JmkWLFnl/l5SUGKVds2aN9/eyZcu00y5cuND32fQabVm8eMdyx3/++UfbvaekpMT32eS50Pd35cqVVtdaVlYW2T2KA7oe5ct10jPqtmW2eb+jhHbJMS0nPfmxatUqo/S293blypW+zwsXLrTeVDfXnw2LbbttkpYdCAW5P1G+M6WlpVb9KMEk7dq1a63SAf7nsmbNGqP0K1bsGHaZpFu1apXvs0kfRa97XLx4MXbddVettOz6wyVLlkT+rq1bt047z+XLl/s+29YH07Tr1q2zTkvYsmVLZPeWFllVqlSxqkeA2XWyy1Nsr3XTpk052d43a9ZM+JuOOPsWwAAA/wFwAoAp1G8J7HBlXIZy8eYjlUqNBDBy+8cKJ4NlN5elWrVqnjhr1qwZiouLjdID5ftEmKShX4yHH34YEyZM0E5LuyPUrl1bO1/WD9v0Gm2hxVjDhg2x6667aqVjXVuqVKmiXeZatWp5f9evX9/qWhOJhHG6H3/8EX/99RfOPfdc4/wWLFiAMWPGoG/fvsb7jthAR2gyvc5Zs2bhiy++wBVXXBFJWQlz5szx/ratvzbvd5TQ1oM//vgDxxxzjHZaetZ36dKlRtdZo0YN72+TdLvssovvc6NGjayDOUTxbEpLS/H888/j2GOPxZ577hnoXCZlpdskk7SstSHI/YnynVm5cqVxmgYNGnh/m6StU6eO97dpnrQrWNWqVY3S01YLk3SsS+HOO++snb5evR1OUrvuuqt2v8ha5G37xSDUqVNHO0+2XbGtD6Zp//vf/1qnJRQVFUV2b2m33GrVqmnny/bbJuUN0ibR4tekvLmC0q8llUpNB7A4mUx+A2A/AO8lk8nntv/8OoAeyWRyIoDBAB7JUjkrBGFsABnErfGrr77CH3/8YZW2Irs1BjGd00I0SrfGQw45BH369LFa53bYYYfh6quvxr333muc1oa5c+dapz3ttNNw/fXXY9SoUeEVSAPTdVT5CC2wjj32WOvz/O9//wujOEryLZT+qFGjcOWVV2KvvfaKNN9cvy/Z4Pfffzc63ja8exDXT7pfNHUp7tevn1WeueDWGEd9zAe3xo8++iiWfG2hJ7+DBFwxGR8GeTZBAu/lAlqh9FOp1E3MV1du/341AP0Yq5Wc/fbbDz/++GOgcwQRZ0C5e4LugnHbfcNMIiWGSRzijCaONWd///032rdvb5SGLO6eOnWqdb4m0DOwppDZxZkzZ4ZVHMd22rRpE0odYGePs0UuDPpMmD59unXaPfbYI2NmPdvk65ozoLxf22effbSPt73WsMRZVGvegkRrpK81yIA6islaNk8T8RukHYn7nYmyDWzdurUXAM0kX/b5b9u2Tbv+B7k+Ey+xXKTir3rPIcJokIOKsygERGUSZ/T9jEOcBdlnpVq1atZpTYi7A3PwCWtBuunzta3vQQaa+YZtOHwgnIAgUboQhwHtcqVDHHWHrr9heNHoEFYkwiBb08QhzqKynMXdt0VZj20n69nnb7L9QFjPJh/7CifO8owoxZmznKmJW5wF2dA3qtnbyhD5MB8J8lyCDErCEme5vs9ZXAOCMPLN9XvLYuumGCW5IM5M6gZdRpMBdb5NouRbXc83ckGc5SNu1BQhU6ZMUR+kIKg4szX355s4i2oTaroBsI0GFJfl7Ouvv8att96a9YECXedSqZTVOaJsaNlod6bMnz8f1157LRYsWBBSibJDWKI5qmfDuoRHNeibMWMGrrvuuoxokdnE9trS6TSGDx9ulZZ+jps3b86pQXUqlcL111+fER2PEMQKPGjQIO1j6XvUv39/bNiwQTttWOJs0qRJVnkC0YgzZznLLnQEzrgsZybEJc7yHSfO8gzn1qiXbxyWs2uvvdb6PLYEsZytW7cODzzwAN55550QS5QJfY8OPvjgrOYVBnfffXeg9KeffjqeeOIJXHTRRSGVKDvE5dZoC1tPo+q4O3TogMcffxy33XZbJPkFYdasWaGda/78+aGdKygHH3wwHnvsMaGQCrKH58CBA60mjR599FEMHTpU+3h6kGraR7Zo0cL7+6ijjtJOF8RyRkcxNOln4lgbGteaszig2yHn1lhxceIszzCtrOyL4dwaM4l75ixI/mHMStJ7tWWDMCw0Uc5Q/vnnn4HS//zzzwDMI8hFDftcbCOyRbXmLFvn0SWqqJSA/bUFsaSz2O4hl01EQVKC9jm61nK2rptEPw5iOevdu7fR8YQgQql69ere385yxicOy9lff/0VeZ4sQQOCZCMfljCWnMSJE2d5RlxujSYNrBNnZgTJP47tGUzJtzVnlWUNAlt3ba87LnEW9XMyDTqR70S1LsoE0TMP2ufoPlu27ppYlIKIsziC6NBltL1O3udsEPcEZ5TQHg/OrVEPJ84cWce5NerlG8fsjC1xdyzZbuDzrWE06Txk5Pp1xzHDHSZRW87yIehEkDrHpq3I4oytO7ouvmw6E0tlEHFm+24GsZzReQYZUMfhmlaR3Rrpa3NujWKc5cwRKS4giF6+znLGhxe0JB8sZyNHjjRO89FHH+HEE0/EsmXLjNIFWcdHY3rdX375JU444QRvH7psE5Y4yxe3xlGjRuGMM86wFt9BLGe5KHRURFXmu+66C//617+0jhWVydSFmD2PrTiztSiZ9pHPP/+80fGEsKI1muzPWpncGnNdePK44447tN83mnwSZ1u3bsWcOXO8z06cObJOPoTSZzu+qBowusPLp3CtUVrOnnzyyVDz1yGuhvHUU0/FZ599ZhzgIyzLmWkn2rVrV4wfPx7XXXddKPmrCOJ+RA9K43q+pvX28ssvx/vvv28dAMd0M3W6fOPGjbNOG0U6ADjzzDN9n6OaZLvvvvvw5JNPaq37outo3bp1vb/Xr19vlCfbR9kKb9sQ86bCl47QZ0JYbo0mwYNyISBIVOIsLEz2FQxqORsyZAiefPJJrFu3zjitDWxdj2JsNmbMGN/nfFtaAThxFoggG4Xakg9ujXG5NdCDCRN3E7p89CJoHeIWZ6ad/IoVK6zzsiXuzs/Ucha3W2NUIduDiDP62KBtki49evQQlsEEk/DnNA0aNLBKBwAbN260ThsVe+21l+9zFJYz+hnqDKDo4w877DDvb9N3ja07tpYzk3xzYZ8zE2zfr1xYc2b7XOLCZM/RIOKMHiMF8XgI4kllK85MJgjYPJzlzJF18sFyFtcmlHQjYBJpzPY6gfhfetOOhTcAyvbziVucmbophuXWaFs3wgpxryKI+1EuDDSjti7lg2timC5aUVwvPYmmU3ZRHTVtB22vLSyxkw/vDF3GIJOzcYiffHNrNDlPkP7B9H2jCUuc2cYDMMmzWrVq2sfmKk6c5RmmL1QQoWT7YuSC5SwqcRbGtUVpOeN1WhVdnJlawuJyayREJc6CzHDT9S5oMAZdwpr0yQdxFodbYxzijG6nTcUZfXxQcaabPt/EWVhujSb3tzK5NcYhzoJYzuj3zfSdicNyZuuhwYoz59ZYCQgykA+Djz/+2Cjfjz/+2Pc5ik4/yMxZOp1Gt27dcPbZZ2unIdCNgIkbUZBOPow6YLpegiYMcWYTbMOEsN6T77//3iqd6d5PKsvZBx98gBYtWuCnn36SHheV5WzLli048MADjTdBD8utMaqZ8bgmfQhBrjOqsobpxhaFgKDb6Z133lm5Nk9kzck3cRbVej5SXjI4tfWACSLORBt8T5s2DU2aNMGIESO87z7++GMkEgm0a9dOOz9enlGIsyeffBJ33HGHVVoWk/sblzizJSy3xiDiLG4PJxucODMkSIcQFrobZgLA0KFDfZ9z3XK2fPlyfPHFF1aL9ukON58sZ0EwHUDxZpBEG7uGRVj3yHYjVtN7xNYdtvw9e/bEP//8gz59+kjPYztbZ9qRTJkyBT///DOeeOIJo3RhWc7ywQoQRroo23tb62mYVgBbAdGwYUPtY9mB2mWXXSY9PluWM9s6nC9rzmzEWVhujaJ13A888AAWLVqEAQMGeN+dcsopAIBZs2Zp52davrDSshEP27RpE0kZgliB6PctHyxnts+mVq1avs9OnFUCgiyED4sgFc12BiwqcRaWu5StOIvDchYE0/LG0UiFdY+iCGYDZFpdRQMp1XltO1HT8oY1kM91t8aw1rJE5dYYZNLHdk81Np+mTZtap7UVECaR54K46eez5SzqCQ3SRkTh1qg76bN27Vrtc5piayEMQpMmTazTRuXWGMTjwbbuhynObMvgxFklIBcsZ1HNEsUhzmgXNFN3NPp5OMsZn3wWZ2ENWFWw4kz0nqvuZVT32va+xGU5c2vO1IQ1ERHEHS2KoBlBBpc634uoLOKMlJfUpzjWnImuVXUPohpzhCUAwnQplhFEnIU1ER2X5Uw33yBurrmCE2eG5ILlLI61D1E1drSoMhFYbD624oz32SStLWGFLVYRx8LYsO6RSbhhGtN7xNadqMWZ6f0K677Yzo5HvX5G9DnbBGl3TfcUCssqmOviLIioisNyxlp7bN0ao3pnSJ5B15wF6RNtBbXJ3m65IM6CWAKjcmsM8s7YbgcSVrRG3mddnDirBASxsoSRJxBs1i3XLWdhiTPbgCC8zyZpbbF9phXZcjZ+/Hjf56jEmW76XBFntFujSdCUsCxnpm6V+Wo5C1KPLr30Unz22Wfax8fh+hnWmjMT8smtcfTo0XjooYeM8hHlEbXlLEq3Rt3xiuqc9erVwy+//GKVZzbEpIqffvoJ//73v63SRuXWSB8/efJk7XQbNmzw9S3OcpZ9nDgzJBfcGoM07HGsOTPJ0zYcPpuvyT0Kc0Bji+1AqCJbzu69917f586dO1udx3TT4dNPP933WVSXVA2+rbuhKXQdGD58uHa6sOp9ly5dtNMFId/WnLHcd9992sfGIc7CitaYTctZnOLslltuMcqDhc4jrL0UVZB7FGVAEN16pFO/Ro0apZVnmPU+yLjuqquuskpn69ZoCn2fHn74Ye10f/zxh/A8KtjlKSbLVcISZ1FtTxMmTpwZEodbY1iDEt65dIljn5Mg4iyq8jrLmZqw7tEuu+xiladpPWJFle37ZrsRpun9so1SGtagxLROhSU88smtMUheNWvWDOU8KvJtzVnU4izoxJbtmmjAfnDJBgSxScv+rSIst8YgxGE5A6LxlgjLcmaSlr0uW28o3mcZYYkzt89ZJSDfLWe57tYYZFAdljiLw3LmxFkmbFlNrpU+1tRyplsfVPfS1g0zyIA1DnEWlUiK260xrk2o42rLct1yFuQ8YYkzk3sURJwFfTeDWs7iCAhiQi64NQbBpLy00A5rckNFkHFDLoizqCfywsCJM0PCWnO2YsUK4W+zZ8/2LS6NYs3ZzJkzsWzZMuGxQTrcfBNnovKyz0V2rCkmz5R2iTHdDyboDNKMGTOwfPlyozS692jWrFnSDbltxdmSJUu00wH6bjnZEmemhLG/HxCdOItj0ofGtv2McpuNbFstWLZs2YKff/7Z9x3vPm3YsAEzZ86UXls2LWd0mRYvXuz9HYU4473vJutn6L7D9D0NOhFhI87otjjIM80lcZZOpzFz5kzfmvQwxVkU26fEERCEzTNqy5lp/c0FwR0UJ84MCcutsX79+tzvp0+fjrZt22KvvfYS5hOkovHSzpw5E+3bt8cRRxwhTFeZLGe88k6bNg1t27bN2GgyDnF21llneX9PmjRJe9E0EGwd1I8//ogOHTrguOOOM0qnc48mT56Mdu3aoUOHDsJjbNcRBKlHsnxzxXJG1x3Rhq88gljOgghP2zY0LEvft99+q30sXb58tJzplvmcc87BJ5984vuOtw72qKOOQvv27TFu3DjtMsgIYjn79ddfQzkPoHefgoqzr776yvs7yCSKCUGiNdLlzcYarjAHzLpt9qeffor27dvjqKOOEh4bhVsj7znoPpvK6NYYVJw5y1klINsPnTSI9KwgS9iWs5kzZwIA5syZIzy2souzCRMmAAAWLlyoPFYXeoNYk4AgH330ke/zN998o522Tp062seykJn1GTNmGKXTuUeffvopAOCvv/7yvgvLrbFjx47a6YDwxFlUm1DT5aMHHSbpeJ9l0AI9SHnjWMvaoEEDqzyjCn8ORH+PPvjgg4zveO9bKpUCAIwZM8b3fVFRkWYJ/QSpOzrfi2CvTaccvPfZRJztvvvuwvxlBOljSD5kUs7kXC1atLAqg+4EQTYHzKJzv/feewDKJxpFx0Yhznh55JM4M6n35PmTczjLmR5OnBmSbXHG6wCy7dYoalAqqzjjpdW5R6aEFVrZdq8dU6LaBFqErTgLur7D1q0xqlD6thtCx7XmLKwocFHMhua75SyMgT0Pti1o0qSJVZ65suZMp8y899kk6mJYFmMTyISCjTiL2nobBN16ryOK4rKc6eYblltjEFdKm4iLO+20EwAnznRx4syQME3gPHgvd5gVzUScqdLpHmvbsOeS5Uw3rQn0THyQDsyksQ5S3my66ekck29ujbYEETtBNvi0vb9RW87IfY9anEVpOWMHUXG488jaJLYtCGsiz7ZMcYkz2/ctH8SZbRniGBjnmzjLBcuZyXWy12Uizgim4owuq41bruhc+YITZ4bYNOom6LzcQfZIsbUKxWE5Kykp0U7Hps0Hy1lY4sxEJAQpLz1bHvYATGfSwNZy9vfff2dlckHl3il6LrNmzcrYkNj2XQPsxVmQiaYg5dURZ+l0Gh988AH+/vvvjGNJtLJsDPq2bduGt956y3Mrp/OISpzNmjULs2fP9n0XhziTXS9rOYtKeOSa5czEvSufLWdTpkzRTseW988//9ROq8uXX37pa39122ze9/m85mzhwoUZgdx08w3i1rhlyxbt+0TyIeuh6bWiOulscJazSsgLL7zg+xyF5eyHH37wfdat3DxsG+c4xNn06dO107Fpw7acZVucBRn4ReXWSBPEQqN7TBD3GPZYVgzJMOmsTcP0A0C7du1w4okn+tbXkTWNQDCxkw/iTCdE9+eff46ePXuidevWGfkEnUWV8eSTT6J379445JBDMvKISpy1a9cu4zvduh+V5YxdYxaV5SzX1pw1btxYO898s5zRxy5cuNB4ME6YP3++dp46LFq0CF27dpUGjxJdJ69O54rlzNat8eijj9ZKx+ZBrym0Qdd6Ru4vmVSbN2+eUbpEImHsLRFmOxgXTpwZ8vXXX/s+R2E5+/33332fw/I5luWpk073WNt7ZLLpMJtPPoizsNacReXWaOsqGJY4CxIxzCSIiYkolIkz1XtFW4XoiQhTEZBvljO6UxflyYZ1p48lnXw2OlzSvpMBJZ2H6TsaZvmiWg9omzYqcSYiqDjTSc9rZ5PJpHaetpazMCYAbQK22La/JB0RhKLI1LbXxdvKpbK6NQJm2+nQeXTp0sUqHcF0wqht27YAgHr16hmlC0OcOctZJSDbi9J5LzfrQhJkBtdWeMRhOYvKnzpMcWYrIPLBrTGs/bRs0wWxnAURsLJnKnNrUj0XOh+6fCauUkA8a86CvKd03RGlla29zaY4q1atGjdPINo1Zyy2VotsiTNZPlFZhYKcJyy3RttrzTfLmUk5SDoSGTjs94a39jnfxFkQt0Z6E2pTbCebeGUzrQ9kzZlpOifOHFrYzLiZwHu52VmvIAP5KAZgYYkz03sbljgLsubMNlpePkRrzKY402n8g4gzkw7NxKJkGmxEdN4g4izf3Brpe2YzmZHNNWesOItjzRmPXBdnUVv2guYflltjFO9MvokzchzJM+z3hrf2WbesubLmLEy3RhNsJ5uClJedVItDnDm3xkpAtgOCPPXUUxnfsZazqELpq9KJCGJdzJblrKysDEOHDsX3338vTQfwA65kw3JGp2UX9W7cuBEDBgxAu3btcPHFF0vd50waa5MNq1notY9hirMlS5bgoYceyvg+SKfJHhvEciZ733judwTVexWWOKP3vYtDnJlC1x1eeV944YWMfbToPLO55ozdiFl3pnnTpk0YNGhQoPXAMti8x40bh6effjrjuDAGJTr3d9SoUcJ8ohAeQcXZ22+/LT0f4cMPP8Tzzz8PID8tZ5MmTQKgFmczZszA4MGDfS7HtnWJdWv8/fffsWrVKqNy6yJa9xTEcjZt2jTr8kTh1vjbb78ZlUmUB9nzzTQdwdStUSXOysrKMGTIEG+c4SxnDiPCtJzx1sXw/IfZjYPDdmvUOTaKGbcgebLHs3m+9dZbuP3223HooYdK0wGZAw/A3+jSG1GHZTnr16+f77fhw4djxIgRmDVrFl5++WUMGjRIeB4Ty5lJg8xCTxwEEWfs53PPPZebLkzLmQkmz/TMM88MJZ9GjRp5f5u+3y+99JJV2lxwa2Q3vl+6dCkuvfRSbnS4KMQZu55Fd6b5wQcfxMCBA7H//vtz0waFfTbdu3fH1VdfbR3VUYaO2+iSJUuwaNEibr5B+gpdwppYUA36Tj/9dFx22WVYtGhRYHEW9T0CdjxLMoYQtWUdOnTA3XffjSeeeEKYr+2aMwDo37+/fqE1zw/saEvCdGvs2bOnddmicGscPXq0UZlEeZhM1Ibh1qgSZ6+++iruuOMOdOrUyZfOWc4cWgSxnJFQoqK0S5Ys4aZjfaxNBp69evWS5gmIGxR6MBL2Gi6dtGG6NcoiBLHlo6PoEeh7tG7dOmFaW3H2xx9/+H5jZ8dmzpwpPI/t5tBBCDK4YO/Rt99+y00XpjgLYnWzFXomljM6+E2QjiTIdUYlzmSBZdavX69MZ7PmLIyBsUyc8WazWQt8Np7rihUrlHmY5qsrfmlrSNSWs7Dqru61rlixIrBbY1iWM3rDbxXkXWnfvj0AdYQ9Oux9mOKM52Ggcw9YN2O2HKRtDlOcBSEKt8YghDEZ0rBhw4zvdPJUiTM28J2znDmMCCLObCsMm6fJ7Di71sZWnAW5zlxwa5Q1mjrPhU5P/x1EnMmujy2vrK4EWSBsSxCxzt6jbKznCyLO2HyztdZItOYwSEcSZNCXC+JM5z21EWe291dXnPHKvXHjRu18VIjqvk5bmy1xRovPqKxC5PggEyg8d2JVOTZv3hyqW6MJbLpatWpppyXvF5n8UXk80H2JbftL0vECd5jCOwdvHWi+ibMw3lMbgroR77///t47Y+vWKErHuvMHEWcsTpxVAsJcp2G6wJYQRACYBLuIW5yFaTkzEWcqAUvPoAadXdRF9szjaHjCtJyJnk2Ya86CpI1CnNnOqsvOqXssuf+5Ls6CBAQR3WuTdKYWVJNrMykHTb6LszgsZ/SxunVfJM6i8CixXcO9bds2bNu2DYlEArVr1wagFmeytbmm5aUtZ7ZBLFSWM5E4CxIQJAgV3XJWUFBgHZCJ1AFRujDFmXNrrIRk03ImerGDWM5MhYconzjE2YsvvsgdEP3+++8455xzhGZwwCwghE75wracqRo2E8tZkPVVtoQpzkSE6dYYpLwm7xu9NtAklH62xdm8efPQu3dvb7+3G2+80VvXRaLB/vPPP9y0Dz74IO6++25hPuPHj7cuo43lzGbNGX1tugO3cePGBbKcBYnkqSqbiGyKM/azSJzpMmLECNx2223G6YBg4oxuG3QHfSJxNnHiRO18bUWWbTriwli9enUvjLnKmiuznAVxa1S1haK2XWU5e/PNN6VlkKVVHWuDaJwxcOBAPPjgg1blePTRR3H77beHUr7/+7//s0pHCyWZyCotLcXll1+ON954IyOtStSJ3MCDiDOVIMxlnDgzJEzLmW5FYxuuIJazfBJnQHm0LJZTTz0Vb7/9Nk444QRh2rgsZ7bCQ7WZqey8cYizMNdw6U5KBBFnu+22m3ZatmHXFWebNm3CwIEDjfMB7C07LKK05557Lt566y107twZf/zxBx5++GHvNzJjydvcFQBuvvlmDB482Le+KSwByXbIskmUIGvOLrnkEm7+NGyd6d69eyBx1q5dO+3yiVC5EEVpOfviiy98n+mZbhvL2YABAzB16lTfd8TCoyJsy5mqzFu3buXWTd0Nddl8dfIUHaebjrxbVatW9USOas1ZtsSZCl50VtX5gfJJJvY73mdCtsXZ6tWrM75bu3YtBg0ahJtvvlmap6gc/fv3x9ChQ30BeGxhg4DYiB1Zm/TRRx9h1KhR6NOnT0ZaMgmo2/7SedqKs2xuu5JtnDgzJI41Z0HcrIIIpbDEWZAXgw0xD+wI7sEG+YhKnNF/2w4S2DwOPvhg6fGy85rcXxIJiaZ58+ZaaTt06OD9bVIfWHcFXXHGWh6CCEKTdRqsW47u+8YeZxIQJArLGVA+c84KIhIsQOV6RG/nEFYZ2fNka80Z3VaYuDzRecgmB3jlJgvngxDG4ML0WYnuLxt8RJRHkLpBR7uUEZY4M7HC8t4PE1fVsNwadaFd0XSvM0yXfVro8e4dfX6eqOGVQVSOXBFnvDaC13/YuDXytvgJimkdVFnOeM9RVyixdYTOM6jlzLk1VgLCjAJn69YY9poz0fG5YDnjNUg8P3Q2bRBxZuJyaOt6Z5ouLLdG3rE2fvK2Ip9XBl1xFnVAEFNxpntdbD5s+YJ0JDpp2QAyxOVJdX+DurERbMVZkDVnovxpeNfPW9+iSxgDAtVss06eYVnOZIPDoOLMdBAVteWMPpYmyL6C2XZr5Fk7VGnDsJyx7ylgv97SVpzl45qzOASEaVCPRCJhvZm0Kh1778Jwa3SWs0qEbUPJO1a3wmTbrVHUsVZWcWZyj7IlztjyhuXWyBtM2LjXmjR2tpYz1gUnanFGXIFsxZkKkeUsG26NNOwMJRGhqvLLNqg1IajlzGY2VPbOEnjXH8StMYzBlmpwEaVbo2xrgHwVZ0EtZ2H3xTyCijpanKnuk8xyZmu1EKEjZGzFVFyWsygnUcLAVJyZ1CU2ranlLAxx5ixnFZR3330XBx54oM8lRuXW+Oqrr+Kggw5CSUmJ8vy2jbONW6OscosGLipxlk6ncdppp+HUU0+VDn5E1zlkyBAcddRR0hl5doBeVlaGpUuXZpxry5YteOedd4RloF/8Tp064ZlnnhHmqWrAZddq2zir0n333XfC30wGJkEiD2bLciZC5da4detWHHvssbj33nsz0qrq4JQpU9C+fXv88MMPwrRB3RpF+7cR6DqYbbdGGpE4U6WViTOTsPFhWc5EbdKZZ56Ja665Bj///DM6dOiAr7/+Wpi/6ns6j6VLl+LTTz/lprURZ+l0GmeccQauvfZa4THEcpYLa85YcXb//fdz87Cpv6auquyzGjNmjHYdpO+l7kCTHiDS6LYNjzzyiNGG4du2bUPXrl1x++2348gjj/SVVfceTZo0CUB5vdWNShmG5ey9994D4A/Co3JrFF2TreUsLnGm6tuIazCvHHR/wMO0nB999BHatGkjDVojeqZvvPEGDjroIBQXF/uOo90a2WudM2cOLr74Yu/zHXfc4Su3TJyNGTMGTz75pO87sqRl9erV3no7XgwCHs5yVsHp1asXfv75Z9/u9kcddZTvGPahX3DBBZg2bVpGdDOdRkY3pLjNbJ2sYbcVZ8uWLcOYMWPw0UcfYfHixcJjRY3KHXfcgUmTJvkWA7PHsp1fKpXinouNGCe7tz/88AP69esnzPOQQw7JOL9oQBmV5UxGUMuZTT4mHYWt5ax3796+z+zg8IsvvsBXX32Fe+65R1pWIPN+H3/88Zg5cya6du2akZZcGxkY24qzRo0aSY8ngye2fNkWZ6xbo0oAEGSTKG+//bZuEbO65mzx4sUYPXo0nnrqKZx66qmYMWMGjj76aN+xIvc8leUMAE4++WRh+VhUk2rz58/H+++/jyeeeEJ4jlxya2TvDx0gJGpxxrYpgDhyH0uYbo26be8NN9yQ8Z0sz4kTJ+LLL7/E0KFDvQGyqTjr1auX93eUbo3/+c9/APg3FQ7TrVHnO9Fz4fVh2baq0OcngZh4eQ4ePFia1rScffv2xe+//y6d/BHdpz59+mDatGleNFV6kl8kePr27ev7PGTIEF9amVA67bTTMr4bPnx4xnf0uE2GE2eVBHpGrmbNmr7fRC8MO+tPH1e3bl0A5j7cBBvLmaySihoAlTijX2zZrLqqURFF/TI5lyr0vIlbY40aNTKO0XVBiyJMPEsc4iyIW6PugJyIm9NPPx2AmZujysJN3s+1a9dmpNURATzCskqadiR16tTx/rYZaOiKsyjcGnXSydoyci2A/9nS+Ygi1qnWnMnQsZyx74HOuVXiLErLmYygA1zdd40cx9umgCfYeNiKM9r6Q7bMCNJuy/LkTSAE2YhX13IWRkAQXXSEh65bo644441psj1wp8tG6q3N2M+0nCT6rmiLFEC/zddxa6QDRtHYCiVVZFEZzq2xEsIOwHRdZOiZB7JmylacmXQIOq5AorxU4ow+lu4sTcWZyeyQaCDP7ocS9pozXctZWG6NJjON+ebWaFrvScAKmw2LRZ+rV6+uTBtUnNkGCzDtSGT1UXU8IHdrFIka9hy0KFJhOxjS6XDpwaXIQibafyyIy5OOOGMHGzrn1hXOsnOGJc503zebgZDuIIqUgfcMbVwiTfKlr59M4AVpT2V5yp53Nu8va1WnCSJish0QhEX0XHhjmmwP3Hnvhs3Yz7auya5Px52XPk7m1qjCVJzZ1hkat89ZBUQkTNgXRFTxRQN+mwWVYVjOdN0abS1nmzZtwpYtW1BaWmo845Zr4kzl3mTr1kjfz3x0a7SN1qeyGKiilIrEmQyVW6MoqAxgP9PH3lvdmXw2j2yLM/YYWUAQ3QkY0eL/srIyqVA2mcgh6WRtGX0NtMcDfez69eu559dxazSBTWsTCltlOdPpj0yvQTQpoduGZtOtkdyHsMSZrVsj6W+yZTmTvcc2eUYZEIQHr+7o9HG64kw1EUKIQ5zx3g3dfphuM3THFrL8WcKM1qjaLzdKoUSevxNnFYxZs2b5NlD87LPPvL91rQAigULPPOh2frmw5ox3nXQ5HnjgAVSrVg1FRUUZ5u3JkydLy2gyWBMNAtkNL9ny8tKRY0zFpI1b4z333IMqVarg119/NUqnQxTibPny5d7aB8DOWqcbeILw4IMPAtghztgF/7LOXeXWqCPOTF0iwrKcmXYkQcWZzDpDn5u+/+w5eDPu6XQa++67Lw466CBh/nfeeaeyvGxZfvrpJwDwbaRN4K0XZcv37bffcgf3QcSZjeVMB9UEwfHHH48HHnhAmKfoOxk2lrOg4kz3XZs3bx5uu+02/Pvf/5aWQQb9nEndNxVnpsGCeMjylP1WUlJi3EZEFRBEVMdV70fYbo2jR4/mPhvemIYNRCErjw3s+Gbr1q1o27atMt3jjz/u259T1s9XqVIFf/75J/e3sMWZieB5/fXX8cgjjwCIznK2cOFCnHvuuQDgBRLhBf7KdZw44zB06FDhb7JwwrLvdSq37rmyuebM1q3xjTfe8P7+/vvvfccNGDBAq4w6v4leWJU446Vbt24dNw/ePdpjjz24ZdJ1ayQRBUndsun4RETh1khHwgTsLGdktllXcBNI3TUpu8pyxlpaecfqBGMg60d5edarV0+vsMjswG2xcWuUXSf9nSwgTOPGjTPSbtiwAX/88QemT58uPCe7sbFOW0DKMWXKlIxj5s6dy0170kkn+T6zkfPYcsm+00VlkQ/LrfHWW2/1/g7imkmIQ5yZuBAPGzYMzz33nHEeBPoenXjiidr50u1Uti1nvPM2adLE+1tk/RURVUCQ3377zahcIsg6Y93JBt53q1atyvhOd3sg2+e67777SsuWTqeFbRTLddddxy2TyPX00Ucf5X4fxK2RzbuoqEjo1shrH8477zzv76jEGRscDoBP5OYLTpxxkFUKWaAPGtmaM1NxFqXlzNatUVZe3TLy8rG1LuqIM5HlTNWB2bo10uWweaaickVhOdOdlOBBxJloraXurKqJq6pqICxbW0F3RLx8aQ499FBhHiaWmLDcGnXSmrg1is5N/ibBSEzaFVvBE0Qo0UFTgMzJHCCY5UxnwCj7LMrHNFpjGK6ZuS7OdMogg9zL1q1bG63/psVZti1nvPIkEgmvHpuKh3wJCEImeZo1a8Ytg6gcusfpijPTayVeHjyPjCBeETTkmR9xxBHc323EjO54hdy3oqIi6wiIUUVO5LmPZ9t1NRs4ccZBNpOv2ntJ9L2OOFOdy6ZDCMutMYg4UzUaJh27bG2LaRlE4kw1wLFxa2TLYbPmLE5xZhNpjs3TZCCkU99k9Urlfqyz8F3H5cnUiqojzrLt1iiynKnWnPHeU1mHK7omWRltXb1UsNfGe/5hrzlTTb6JrJI0Qdc98vJVEUW0RtFkSZTirKCgwHrNme7G7TJMxUE6nfbuk2m+smfKW4PHOzbKZ6Pbruici0a1VIMQZrALtg21vY+kTEH6B9E5VdDizHYdF/2Om75vJjhxVoExEWe6rog24oy1GKg6hHQ67dswmz5HNixnopfTtGEzEWdRWM5UjY7pgJxXDtN0vHKapCWEJc5kZfnmm2986w5Zt0ZTyxl9blU6gsqtkY4uSPzS2WN1rBayumsSml3HcrZ48WLuRrs64oyEVeYdI7vOhQsXcvPREWf09dtuw0ATZMaVdSXiiTPVNciwsZzx7olIOKuuff78+SgrK8P8+fOV5SguLsbGjRvx/fffc4PWmFjOiHudqeWMPYbkSe+XaUqY4qysrCyjL+W5NWbDcpZOp/H3339zy60S6yUlJdz1lKTs9PtMEPXzuv0iqXumyPpkmViP03ImulZZedl7umDBAmG+OtvDsPVSRRgWQtJOyMSZSkzREU+jFmfZttZlAyfOOJi4NYoeuo44UzV+v/zyi+84lTi75ppr0KpVK7z00ksZ+WZjzZnORo86yDoE1QCB+JWrxE7QgCC6Llq6ljPVQF7WcbGY3G+TCII0bINH3DhY+vfvjyOPPNK3HyCpR6I1Z0uXLvV9HjNmDFesmHQGJpazJk2a+DpM1q1R15qjI84+//xz7nlU4mzRokVo3LgxWrVqJS0Dr45s2rTJt1Bf161x3rx52HPPPbn56Ez60M9Ad8Y6iOVs1KhRwt/ee+8932fes+F9d/7550vzlJXNRJyRv+m2G9APpd+yZUsceOCBuOuuu6TlmDFjBpo3b44aNWrg0EMP9YLt0CxZsoRbXt771qBBg4xjbcQZeR8XLVok3ZfJ5JwiyL2UibMrr7wy412LynI2ePBgb/NfmgULFkjDmM+fPx9Nmzblrnsi/Pe//8Xq1at939F9gqwd4r23H3zwAVq2bKn9ntDw6oyO14Lud7x7FEScjR49Wnituut2v/nmGxx33HHCfM855xzhb6WlpXjuueesxdmnn37KPacON954IwDgo48+sg6lbxpMxFnONEgmkw8kk8lvksnkK8lksgrzW+9kMjkhmUxOTCaTnbNTzGgJspZF9D09mBFVUPbcH330ke84lVvj008/DcAfxczUciZytTERZ6azFDpWAwL7bEiEomxbzkT3KBcsZ7qzt6WlpYEGEzQikfH4449nfKeynLGMHDnSqL7xYI9lG2zWckJHFLW1nOk805EjR3LPo3L7S6VSAHYMnHXLwEvD3ltRcINvvvlGmC4XLWfDhw8PdC5e/QoS5UvVrvDuzxNPPOE7xmTN2YwZM3yfiRsxXY4PP/xQWAYCmSzREWc8S64OIssZUB5NU8Wxxx4LADjwwAON86atMyJxxgr9dDodWUCQgQMHCtPI3BpJgBxewAn6eLY9EE2c6PSLJDALCQimOx4SYerW2LJlS9/5W7duLS2vbJKoTZs20rTPPPMMAH/wM4JuecmEuwh2EommtLRUGqxOBLk3vL5Ht/7+9ddf3t+mofTp36MIbU+f+6ijjgJQQcVZMplsD6BZKpU6AsAcAGdRvzUFcBqA41Kp1NGpVGpq1koaITK3RtVsKEE0UAvi1qjbIZjOcIvSqkSTjouWDiaWMxZ2ho1sDhqXW6OuuFSlM7Gc6VrDTPYJY7GdxSor27HPlUkofdmsqk6Z2HeEvXZ202T6HmbTcmbr1ihbI6eyWqjqt6hdYfMMQ5yZtD8mvwHqqJ+ishHCHjDYuDWygUpsF9H369fPi1BG52vyHuu0SbzjRN/RyCzZOjRs2BDAjsGXrHyivE3WnKXT6UhD6YuQ1QfZ9iCyidYgljM2T11PItHvpm6NbEj6atWqeRZPU7fGzp07Y5dddgFgv+bM5l3QRVXXVPXfps3jndNWYEVlOaPp2rWrdn65hk5vdhgAEptyHIAu1G8nAtgM4PPtVrX8i1fJwUScBXFr1LWe6FrOZOWI0nKWTbdG9jM74BfNNIdpOTO1lvAwFaGycwcVZzp52zaU9B5nJgNNnckAE7dG9trZwSBtWQvLcsZ7T7MtzlR1l/dZFKglG+IsSCcZZgfLezZhW0Fs3BrZwa6uWyOLzCqkSzbFmcxypgPrKm0CLc5MJi3pMtITN7b10ua5yFzKdMUZW16ROGPhlZe9/0HEKqAXKVfVxskEAH2tvPota89k/U0YAUxUlJaWWvXDoslNck5TbN0a6bRRiSWTfQxzDZ0WsS6ANdv/Xg2A3rynEYBdAXQDMBXANaGWLiaCzC4SRJYRkzVn7PcyP3fRwFs1E7Vo0SJ8/fXX3DKoBow8314gc/8h1f2k9yMxnXFlX3byMv7xxx/KMojEWZBojaoOIYjlTHRvVOJs69at+Oyzz7Bs2TLpcTJ03gl6k2oCCRiwbds27XDO6TQ/mpNJg66ynLHCgzdACWo5M+mo6e95exjJ9h9SiTNVHSWDOhPL2Zw5c3zHqGapyd8q621UlrOwBiomecjamdGjR2PNmjUZA2xby1lRUZH3vvMGrjqYijPZgv/Zs2f7XLrY3+m69vnnnysH+WSfOloclJSUcI+dMmWKb00pz3Kmur/Tp0/3uXIXFhZaR04MIppleers3cj+DajdGkl52f0KeXmaiLOZM2cKy6lrOSO/E/farVu3SgUA3f7wxhKytLbijNcv2lBaWmpVZzZv3ozFixeH1uZF5dZoOyFMpwsjCmxcFKkPwSoAdbb/vTOAFcxvX6VSqXQymfwSwJ1s4mQyeQWAK4DygBXdunULUt5I4PnRkxeMXlgPlIub+vXrc89Bv5S0ACEvxJYtW3zHsIERVq9ejeLiYi8tqXTr16/PeOHJgk2gvIEkv5PGiFTOFStW+NK2bNnS94LS0Zzo79nrAeATdTLS6bS0gbrlllu8zQpZAbFmzRpfWjaa18KFC7Hzzjt7fvSks1i3bp3w/tNpCwsLMzbCLS0tzSgvXa7Fixd7v69du9Z33NKlSzPSDhs2zPt7w4YNKC4uzoiatWnTJl86OtohYcGCBdzNFFetWiW9vw8//DBGjBiB3Xffnfs773pZ2OsEMjud5s2b+z7PmzfPq5fpdNrruOn7x2PTpk2+wRTZLJwtJx2BUPa8gMznwnZK9O9EqJHysu8MW1byG1s3efe1du3avs/k95UrV/q+//bbb731FKQMhGeffRannnqq95nueDZv3pyRJztoZaNTkvZu7dq10neG3IepU3d4r5O8ly1blpEv/XnBggXYtm0bdz0NfRxvgE1+Vw18TAYa7PsmypuHbBKC/p19Z9j6QNeXyy67DM8991zGfmykLi5ZssRokEe3H2+88QZ69erFLZPoesix9G9sHSWQd5UWHcXFxT7BRdzP/v77b1SpUiWjj6Wf7fPPP49GjRqhX79+wuubNWsWAH9fPWTIkIw0c+bM8VybSDnJcy4tLfWem6oNvfnmm32fly9fjqKiIpSWlmL+/PmoXr26MC1LYWEhtm3bhoULFwonOEWQ923hwoUZea5Zs8b7m70Wuq4tWrQIO++8s/eZDsBC3wcy1iHlHThwIM4880zP9Q/wi7Hi4mLfO9S8eXPvnrPt0qZNm3ztFmlbSD0gz4Wtg4C/bSfjkr59+wIoXxtF1p0tXLjQW+ZAzkWzcOFCX/3Zddddvb8XLFiQMelJj/3YMpF7v3Xr1ozfjjnmGJggqodLliyRtnHsmIfm9NNP54r3kpISL6gPDzJeoSF1lu1TVZPEq1ev9tqIBQsWZPSFNGPGjOG+UzVr1lS2g/R7QMYOZWVloYnkMCH7+fHQEWffAhgA4D8ATgBAm0WmALhp+98dAPyPTZxKpUYCICsR80K+8ioNuYnsmoAGDRpwb3C1atV835OoWAUFBd7saEFBge+YunXr+s5Rp04dNGvWzCsPOUdRUVFGnm+++ab3N/07eSHJ/zvvvLMvLfuyN2rUiLsBJHs9wA6/fxWJREJaCYEd95cVurVr1/alZQfA9evXR7NmzVCvXrlBt02bNli0aBGqVKniS0c2t2TL36xZM19nw5aHzoew6667er+zDUi9evUy0tKL8GvVqoVmzZplWEfY8tKdCn0NO++8M5o0aYKSkhL06NEDH3/8MapWrSq9v2PHjgUALzxzQUGBbzBUWFiofD7soBGQNyykvO+++673mdwr+v7xqF69Opo0aeJ9JvcinU6jadOmXgNPd6bs+dh3uGbNmr5j2OdWo0aNjHMQIcy+MzT0vWcX4peWlmakO/HEE/HWW29llJst7/r164X1d8aMGbjqqqu45eE9S3YgzHbG5D2uXr26Ly37ftetWxfNmjXDf//7X+870paR32jowRB510aPHp1RZjodT4CR30nnP2zYMNx666048sgjfWnZdYQyeG2SbhQ0Xl2g31fyOx2xlJfn//7n7y5//PFHn+gGdrT5vPsrg+5Lfv31V1x//fUA4BuUs+VlYd8Ztn8ikHeVbleaNm3KfR677roratWqlTH5xL6PX375Je6//35ufjSk3Sew10IHtSG/EcFQrVo1770jfa0ujRo18sRno0aNuJNmIojYady4MRo1aqSd7pBDDvEmTHjjDvp9ZX+j7xOblu6LSP8E7BjrkPKS7+i0dLtF98MA8N1333kTdmwfxU5E77TTTmjWrJlXf8h5eO0yXQ9Jm/Xrr79635E2ib1OdpuJRo0a+drCG264Af/5z3+839h8aas2+xt5juyYzgZR+l122cU34TFo0CDcfffd3mf62bF89913OPLIIzO+V/XF5P6fddZZePfdd3HjjTfi999/98pDp1W5GNetW9crf+PGjbnjLsK8efPQpUsXPPHEE6hduzauuOIKPPzww+jSpYvRWIWuK0GfS9QofRxSqdR0AIuTyeQ3APYD8F4ymXxu+28zAfyTTCYnArgEwBOi8+QTJuZU3aAY9Nov2zVnNnurZDMgSNhuQLx8dNdtqFzReLMwUa05o+uTrlsjD3IM6SQOPvhgAOr6wLoy8cJn6+ZtAls/dF0aWLdGejG+bnADVSh9Nq0sIIjtmjPed7pu0OwkED3QZTtBmWsl79zsZ/JusM+LHVyzbQkQfkAQEWVlOwLLEAswKx6DBgQJuz0zWXNGYF1Jbd0a6fPQdSmIy77o2em4NRJEba7pmjOC6np4A0badc7WxbCgoMB6PaDNJt8AcOihh0rdGmX3QubWqAoIQtcltn6yz42k79q1q3QwzF478fDRcSlXjUtE/Yyo3SQCs2rVqtZujaJtYlTIQuezsM+cjVSqehd4LuW6W/8QwdOmTZvI3BrJvezevTtOOOEE7XQ0suUAuY7WVGMqlbqJ+epK6rfbQy1RDhBGtEbRgD+MgCAmnYGpOAsjIEgQgoozUcMh66TZY1VrzmQDctX9DSNaI/mfiDSVOwHbodaoUYO7rklGlOKMza+0tBQFBQXeVgDkPDrRGqtUqYKtW7cq88zGmjO67Oy5WVTijH6G7LoklUBUbchtGq2RN1gLS5yp1lVWrVpVOKA2ER5hi7OgAUEIbN9ju4CeFtb033GLM1FwAt2AN6bI2n2TaI0s9Joz0yAYtutg0um09Tq3INEa6WfD1k+2PtH3li27qDxANOJMVCbe2Mz0/upuEyMqqw6i/lQXdkkO75wieEF0TK/VRJwlEglunAbTtWrkHU+nywP3mEaFjRO3CTWHbIgzXkVjjxHtcWEqzujKyTawst3p6bxkf8u+U5VHhWlAENIx8sSZ7rWy98hWwPLKN3Xq1Ax3Ct5xJpYz8j/pEFhx9ueff+Kkk07CCy+8ACBz4KOynG3cuBGPP/64r9ymDTEvjUlAEPYe80SAjuWMvDPfffed73f2HecNUHSsFqbCSHdvQHZQST9D3YX/BHbwSPzwCSILoWgwQ7s1knPT3wHl7sf0fovkOJuB9mOPPeatM6lSpUrgKIRA7oXSJ4Qlzuj6Qq/RMdmrjKzrIoQhzj744AN88sknSsuZ6r0SMWbMGN9nlTiztWIVFBRIhdKvv/6KZ599Ful0OiM4layfUfXtJC3rFqjCJiDITz/95MsTENdPAr3Btwx2eQJZg8b2xTbjDtE7w5swX7FihTdRGSRao8k2Mew5dUUWGxDEVGjwLGe8NaiivAG5OGPrOYvtPmd0u2Ijfm3f8bhx4oyDjpggx4gaUx1xRle01atXZ6zH0B2MyyBpV69eDYC/QaUsT/Z7Gt1Zl/bt22sdx8tHdwaOnXEDgN12202aFyvOZG5stpazww47zPdZNHgxsZyp6sOFF16IsWPH4tJLL8XSpUsz7qFq8fqgQYNw3XXXeW6TQOb6Gdayw0M002ciRIFydx7eO6NjOSNrW77//nvf72xaemF+WPuc0eci6FrOZO5D9LoOkYVeVgYSaZHNiz2OfcYkL7LZPQD89ttvAMqD+tD06NHDW79Bn1tHmLNcf/31XqAHmTiL03LGI0xxZrvvEuBfj8wbCL7//vvcc3z55Ze+z2GIs4svvhg9evRQWmlpZNfO5nPaaaf5xCY9EUUEAT3QDDLoI+0Dz3K2//7746qrrsI777yDffbZx/ebTAB89tlnwjzT6bS35kx2HDmWZo899hD+xpuYogfasv5O5NbIPk82HevOZ2IpUbW5oneG9z5edNFFvnS2kyG2bo2JREJ7razJO8OD3qCbQKKeimDfj8LCQuH9JWNMER988IG194zJ2IFdRhLGZF4cOHHGQcdypvI3Fw3aRQ0PLzofey7dTajpQRXJlwgkXqAJXl46lh3dAcNJJ52kdRwvX1k4b0BsOWORuUewwi6IW6OOuZ53nE7DoSvOaCvRunXruAN9WcP+ww8/AIAXARPIrDdXXnklVITl1njJJZcYizNTVyOZW2OYljNdcSYbWNBrOXQmUdh7wVpOTcWZDqlUyvc56JqzCRMmAAjPrTGKfc5U9VxHnNkOLEQDPt49oqObAeLJG9H18MSZCrbNkrXzps+Fdt+iz0usBGG4NVavXl3LxZC1PgJyDxhZRLl0Oo3jjjsOgHqrlXQ6jZNPPhlAeQCdDh06eL/pWM7oqIsyTxFdt0Y2KiVrZalWrZovLamDPFc8lVujSPzy+lx6AkIlCrNlOdMVZ2ybxb4zqnePjAELCwu9iVfdKKP0s7FdBzt79uxI3BrpOmFrrcsFnDjjoCPOVBtCm4ozWZ6sODMJAEHyJQM63X2GdMSDbmU36fhUg1TR77TfuGleOq4UojJkS5zJOl+2DsosqTy3CVWHIHMFkpVPlcYmIEi9evWks3UiTFx/AXlAkDgsZ7L6IRuc2AhEXTegMNwIbc9BnldQy5lsEBWk8+Zdl43ljL2GIGuiePDuka4I17GcqY4lqMQZjelzEW24zJvIs72/1apV0woIItuPzGbD5gMOOAAAvx9n+yVSPmK5IwNy9n6qNqGW9Xciyxn7PU9ksel44kx1nTxM3Brpvi6MNWc27Zut5cx0zRm5lw8++KCwLqjyDmJdpNdfm4gsU4ElGqs4y1kFwKSTFzWwpm6NvM6JFUqqPAm8xb/E8qESZyQvlUsA7xgWm5fCZJAKZHa4ogZLJi5Za6hqoCWb1VVdq+ie6IgfkeVMVh9oP3r6O1NxpiMCWESzrCYNLEljuubMVJzRM7u2lrMwxZmsXsny5J1fFblSZAEIU5zprjkT/R6WOJN5H2TbrVH1HIB4LGcsuq767PcmLkSsOJMNNIMEv5CJsyCWs6pVq2oFBDEVZ7Lnk06npaKFbRfYPlHkGmYizlSWM9GaM5U4Y8urK85kbo06k010cKUgViHbgCCA3hIBILhbI7mX1atXN7Jg0cfRE6Wm17pt2zZry5nJ2EHULzhxVgHgdRTszK/M35w+jv1cUFDgLZ6nNyLWCYMratRZl4Hp06fj//7v/3DHHXd4aYkrk0qckbKxL8EXX3yRcazu4mXeC0XclFhMxRkJOkDfX11Yt0aZm8qff/7JLUOUbo2kg1O5NdLQM110GUzF2ciRI73zAXbizMZyRu6XKi3ZxJygmsAQWc5mz57tvU86lrOpU6d6x9u6NS5cuBBDhgyRpgvTcsamiUKcXXXVVVi9ejX3HOyG7DyI611Qt0YyGAvbrZEHWz627c2mW6OJ5UwlIkXHsd+LxBkvHdtmsXnqujWqJkTo9+Hyyy/H3LlzQxFnVapU8fapo92/WViXUZIWCCbORo4c6a0Xmj59Oq6++mosX77cO5a1RNHnZtsIEviD/u2xxx7L+A4ArrvuOl9aur6uX78eV199NYDM+jd37lz8/PPP0msj6+jKysqk4uzxxx/3pWMR9RX0WlmSlhZn9MT5VVdd5buf5HcRtm6NgJnljL5eU8sZGXOZiDMCXZeisJyxYwBZukceeQSJRMILYGbrsZNrOHHGgVfpSWei69YoGjQlEgn8888/AID+/ftLy8FuBi0SZ88991xG2ueeew5Dhgzx1g/JGjuaW2+91VdeGbruBbwGlPjOq86p+vzll19iw4YNSsuZLC9WcPOu66677uKWgbXwqDp5spm1jTgjfvokrU4o/UQikbHGSCXOeL/98ssvvrxt6ofuot4NGzYIxZlooPb666/71mvYujV27tzZ+07HcgYAL774IgB7yxkrzHjHySYEVOdXWWxEE01hzjSmUincdddd3HMOHjxYO0/acmaDbIY7bLdG9tnTG9nzfgeQsZlxFJYz9rovueQSblpRGWzEDjuhOHHiROGxsudCB7ogiMTZd999hzPOOCMUcUZvdDtgwADhcawgAPQ9YHjQE2eHHHIIAKBjx454+umnvY3GAb84Y9tQ9lrpdXEkjShIzOeff+77TPe3Q4cO9YKu8Pphdl8umrKyMpx++ukAytticp2sxW3btm3emEaEqK8YOnRoRp7sHoAk7TfffJMhRGWQvdJM65FpQBAydgT0RR2B3FObdVhhuDVedNFF2m6jInHGu7833HADAKBly5bcY5zlrALB68DYhjTImjMevIpDOgCV5UwVMp4ur+qlIA21jWWExSaEqanljJQjTLdGnVkdOm+TtI0aNfKdw6ThYI8VWc5IRwGU3w92AKPqEGzWk/GwtZxt2bIlwxLKs8Ky94zeu83UckagI07pbq1AwlqbWs7Ic2LD0PPS6bo16ogE9hjR4vswLWdA+ew57xxs9EgZvGBHBBO3RlPL2d577w3AbEBE7ieJNMkKL15+rIuT7cBC1A7yvmfPfe211wLYcc2i4wjkXhYVFVlbzgBkRDUkyJ5LgwYNpOdm38kZM2ZwB5om9/fBBx/0iSRVCHGWIJYz2iLFbolBjwPowS3r1mg68SMLGkGXd968ed7fJC86YqsM9v6LnovOBJKua2I6nfa9D2ygLDaSoezZkHtkKljS6bS1WyMbpEvV/pFr7dChg7Vbo0ycsftvslx11VVZsZyxiJaIOHFWAdCxnKncGsMQZ6yVQjSw0KmwOouXAf2GLexzEUzW3tDfBbGcsW6NJutiTNYn0WlVeZqsOVMtrjd1a9RBVzTzyqG6R/Sm0TK3RplLn6nlTOZipiov6ZRMLWekU+Z1amG6NaraC5FVPWxxVlBQwC2faiBPE9aaMx0hTbPzzjsL04kg95OEsFa1bUD2Z3113BpJ26AKh06g3YCDiLN27dpxzy97LolEIkPUifbtYr+jA4KYPNe99trL99nWqmkjznT7NxO3Rvq9590H1vNCx7WO1J3GjRtrlVdU723GAyYCgL7fbPAsdrJK9mxsRD7BNiCIar9SFnrvT1u3RtmaM9n+myRf3U3Ug4gzkdB3bo0VgGy4NdqIM3YgL8ozTHGma2HTyTcbljPR4C5My5nq2nlujTrrk+jfTQUhfQxbB2VujbwOPRuWMx03Md2B0LZt24zdGtnfTC1nvDLx6i/vOZkM+Hl1Ryc6pq7lzLQMQHTijF7kbYvMFS2o5UxWL1UTTbzrIveTDKKiFGdB2lzdNYgE0v7Q4kwFr83iRRnmlY+GF/BIV5zZujWybWeY4kyFrjjj9Ymi/lglztjJI/oYlYXWREzSiJ6Lzj3THYyXlZUZibMw8mQxdWukUW2LxEJbuLOx5kz17hcVFRlZNenz2gQTY8vlLGcVAF4lmz17Nt58803MnDkTgFqcfffddz6/7SCWM9ZSkk1xZhJ1KEy3Rla0EMJya+Rx9NFH45tvvrFya5w8eTKOPfZYzyXL1HJG/g/DcsbWBzaktUqc0XmvXLkSr7/+uvQa6LIQeK5pbdq08X3WrQ/z5s3L2M9Hx62xV69e3t9hWM54HQKv7CaWsylTpmScK4jljB1A6IgzkVvjzJkzfXUpKnGmur80YYkz3vMeNmyYNF/dMhI+/vhjADsGUWy6OMTZ6NGjM74jQRwIor5CdE6ydpr+fdSoUejRowc2b97MTcdzBRTdY5XljG3fZG6NZWVlOOOMM7y0cYqzv/76K+O3qC1n6XQaY8eO9aUTLeGg0wDlLuD0O/Puu+96f5uKsyBujezmxzpeFkC52y5bD2mhTyzCJSUl6Nq1Kz799FNh+W0ssLw8ZbDvgWw/Ql6fEkSc0a7AZF0had8IvOA3NHS+vIjiNKlUimuhVb1rJ510El566SXfd0GsmnHixBkHXoNy/vnn49xzz/U+64TRJZ0AwBdnxxxzTMbvNGFaznQXIZ9yyina5wzTrZENdiFq7ESDTxtxtnXrVhx55JEZViwdgXXEEUfgq6++8tYLBbWcmcwIqdwaWcsKT5yJOgTZAJUuL/vsL7jgAlXxtTuENWvW4Pfff/el0elw6TUCppYzXbfGoJYzet0R+17L0onE2VtvveU7zsatkd7U+rfffuPmyftsisit0cSCMHv27KyJMxL0RpSv6YCGrIdq2LAhNx2vDGHN+qbTaW4Ahq+++kqZVmU5Y9fOkUnIkpISr7w33HADPvnkE7z66qvcsvft29f3edCgQcI2SSXOWLGkspwR/vjjD6v7y7o1kkAWuvz9998AkCEqAHkdvvzyy63EmSwgyIoVK3zpSktLM4K17LLLLr7PJP0jjzzi+562wJFnKSov2RCZPSdB1P/zxBmJ2EzQFQA86PKSfvXmm2/Gl19+Ka1LQayhdEAkGey5a9asyT1u8uTJGc+QTm8iznih9D/44AMAwCeffOIdpwo0BwB169blti1kzTYN3W6YuDWOHTs2IwZDEOEcJ06cceA1kIsWLfJ9NnFHo48rKCjAPffcA2DHhpKi85DKxA7idAQLi67lrHnz5r5z0j7jpvma3CP2WkWWKJGbio1bI/ubrvWL93surTlj0/D2OaPvE53X2rVrpeUXlZfXwNI0atTIyvXDZM0ZjWi/Hfa8hDDcGnUsZ7z91HTWAYnKoAriwSsD+7lmzZrcdVHZsJzxMLGc9ejRI2viTEbPnj2l9VfWfu+2227cY3TOE8Ql57777gMAnHDCCUbpVOLssssuE6Zln4Gua1jv3r2F72rYbo30bzb3l7wrpB9v0aKFdloAOOusswBkRqwE5HU4mUwaWaLoMQf9P31P2DKUlZX5nvu5556bcX/JeekATCwqy1mXLl0AAK1atcooE2Dm1kiX49lnn9XyslCVm06zcuVKZTpiqdq0aZPxu9qzZ0/u96oJRHZSghwveudo9+Mgbo08dNpTem9AOt+NGzdyj7cRZzycW2MFglcB2UbBNNgFPRDjDXB1xJlIYOm8GLrijBUPBQUFQqtbmG6N7OyGybXaWs4INm6NLKa+1CpxppNWFEpfx61R5EKk4zfOK6/ODFwY4kw1G0q+I8fR67lk1xmGW6OO5Yy3vsNUnNH5sFY3HcsUb4DDC3gRlTgzEUq2A2qCrTizCSHN1kEdy1mY4ky3zWcReYXwvD9Y2N8KCgq0yk4P3FhUljNbcUYHBLG5v+S954ksGbp7jvII262RJ87o3+nIvwTWJV9WTpXgFvV/orEDz0uEFimigBU24swkbWFhodcWm9YHEao+Snd9JyEst0Yeuufh9eOi9yAscebcGisQOg2gaWVRVTQTt0advZNYTMUZ/TKKTPZhujWyHYfISmgrzmQvpo1bI4uuWyNr/RI1HGFZznTEmSpfnu83e49sBLgOJmvO6O/ojoggy1fm1qiaRCF1VcdqFYY4o/9mZ091BKIsSlw2xZmozsnWufGODWI5IwNqU8FCvzM6ooqUFTBry8ISZ+l0WiiyVIj6Cp0JMFtxRkeQA/zXayrO6PZQlpYW3DZ1WxSQSXUu2Z6jqjocRJzxrpUtOyvOaAFLCFOcifpNE7dGuhwm4yse9LlIGt20unvJqmDHBgTZBKIOYQQEET1z3fPwrPI64iyIayJr5MgXnDjjoFPhdRv1q666Ch06dPBmU0waj6lTp+LDDz/EE088AYDfaX799dfcTahZdNecpdNpFBcXexv60eKMXZOhazljXwqe65zIcmYqzkxnkwDg9ttvB6AvznibU+pa3V5++WX85z//QdeuXX156rp+DhgwwLt/OtEa6cXeBJk4e+qppzK+C0uc2QyETN0aye+kntCdCV132Ov/9NNPM9yTdC1n7CQKDVtfaRcOmSWiZ8+ewnzp71lxtnz5cqUYGzRoUEZ+OkI0qDhj18cRTIRSaWlpIHG2fPlyAMAzzzzj+161mN1k9pa09eweiGx5o7KcTZw4Ef/73/+006ncGrNhOaPDbNN58crB5mdrOaODy9gM3Ei5RowY4fte1h4DOwbx999/v/Fz1RVnb7/9thd8iFwj+b979+7eml4ytiCUlpb67rdMnMnKolpzppoQ1XVrZC1nQcUZb4JAt26Q5ypy09MlnU7j6aefzrhW4qbMHqsLT5wNGjRIe+wBBHNrBOBtID516lTvO5E4W7hwIQD/M7WZpCJtPm9D+FzGiTMOOg2g7kD+2WefxYwZMzB58mQAZo1HnTp1fIuN6U6eHH/SSScpywroh8hPp9O+KJOFhYWeGGA3ZVRdu0gQvvrqqxnH6ooz2zVnOujeI95AR1ec/fLLL7jooou8z6ZrzuiBAO1WQ6ffd999vb+vvvpqbsdncp/o9LpRtHjYuCawM7Aqt0ZyblIeerE1nZZ3f//55x9u3jxhxNsMWcdyJoskx0KvdRCJMzqYB2HZsmXSMtCceOKJAMzX9KlQ7XlDY7JwXzVQV0EGB+wGvryNwFlk9ZceIJJ6RMoat+UMAG655RbttEHEGduuiNoZer01UF5fbNecsXXNRpyx93fPPfcUpiOMHz+e+71KnNFtx4YNG3y/qdoE3Xb7X//6V0Yaun17/vnnAQCPP/54Rv689p5XxijcGlWWM8D/7tHWULr+mrrd0ZDBvQodTxYdysrKMiKoEkjkV7qP14XnTQLIy0veD5Vbo6hdJi68F154oe/7AQMGKPMfOnSoV4Ygbo0Em4n7OHHijENUbo2iWepdd9014zuAv2mmboUzcWtkLSUkspbpWjcT33rR+jpdyxlvNo8ONUt+P+WUU9C9e3duGWR7IKnQdWtkEYl83t4n7DFktrmszB/+WDTTSf9uIs50LGc6HZIoLanvvAGkqVsjaznbfffdvahWps9VZjkrLCz0orHK3CZ0oi6K3mH6vRG1FeTvrl27olGjRtxyyK6bBIvQEWcmdVu0oTCPKC1nIuh7RLwGaGj3YN5z5j1X1nKm05bpWBBsXLRMBoxB1pyxA3aR5ax+/fq+z6xbI41KkLNhw3UnQGTijATGkiG6D6p2hr5HOpOPNDaTjyQN3e+J7ikrzmiXMkKUbo06lrNsuzWajrFsxg80srI2adIEQHk0VBZVOWlxRr8j2bSckQ3i6Qlp0blZVN5mOuSbIKNx4oyDzgM1DQhCiwdV4yFytSgoKMiY1dRtrE3EGX39vDwJtuKMd3/JudhGO0hAEF6jIxMmtsEC6PKaNh6imUOeOOPNLvL83FUdfq6JM/aZs2UVpdWxnBUWFnLrr8kEDG9gzJtk0bGc0eW3FWf0OWj3TVGbpGNtCluc8fbZEWFiOSsrEwcECUucic4ja/PZ55pOpzMsZ2G5Neo8B9bdy2TAqNrnTHaf2XdYJM5EE01sXrxy0PDaMl3LGS082DLqtOOi+6BqC+l0sskbHkHEGX1/ZcFXdNelZ9OtUddDgxVn9HglqFujKE8RNuJMZ70xD5t6QIszXtRgGSprqajOkDxl5VXlH5blLN9w4oxDmG6NBN6gTtV4rFq1yveZ9q/XcS2gyYY4U127iThjrQ8m2waI3BrpBognMFh01+XxCGo50xFnbLj6RCLBFWeqQXU23Bp1xJlo0kFWl2XRGmVlJfejqKiIW391BvIyyxnvPdYRZ7xnYyrO6HPSIlTUgemIM531dcuXL9d+N2zFmc6x2RBndP0VDZh0LWdz5szx7hOvzSbwrnvJkiW+z0HWRInWW+qmsxFnPMsZDzYMO70OhkVXYBHoOiqrr/S7x9YlEzdXFt3tA9jyrVu3LsMlWTdPnTQ64qysrExbnAWxnNHtFn1Ogm5AEADCNWcrV66UTpzJyk2n4fVtvG0EeOLMxjVc512X1QNRnrSo1hVn7HMwtZzpGBF0xBkpx4YNG3z9IjtGrkg4ccYhKrdGleWMt9M5O0jVbaxNAoLQnd3vv/+O77//HgDQr18/37GqRpz4R+sskBVZznQGNH/88UcoljPRmrOPPvpIWX5by5lI7PA6PjYQCS3OeIEmCNOmTctIx7sHohDAOpYzIsRliK6VfOYJUtIx/vzzzwCARx99lFsuAlnP+PXXXwPwD45NxZnKcvb2228D2LFQW8etkT1GJs7ee++9jHyB8k1RCb179wZQ/oxN1mkQyDuqExBk2LBhOOigg4TnojERZ2RTXl6eLDJxZpInyzXXXOP9bSPO6O9OP/10zwpPi2Yd68zHH3/s+/ztt98CAK688kpfWVTstNNOvvfJxq1RJM5E7Wf9+vW1LWepVMr3mRfYg3D33XcLy5pIJFC3bl3fd3R9v/jii4Vp6WdKl/Haa69FcXGxMB2hXr163t/0OuqDDz5YmZZAnsu6detQu3Zt5dpAG3HGTnDJzsNb78S6oOqIM7LBtiif0aNH+37Phltjv379cOqpp3LPI4JnveW9O+xG7AB/jKUrdFXfsdiKnSpVqiCRSBhbzmzXnJHveW0qWf9rIkaXLFnibYo+YcKEjHdfBu+Z5TJOnHEIM1ojwcZyxsuT7TizbTkDgOnTp3OPVZ2LCAfZPlzsuWzE2ZYtW5QzOzQqt0a2A3jssceU54zCrZElkUhwBSV7LiJU6HS8e8DO2vPOJ6r3ogXMNCq3Rl5dZjcApQdBvHeGCBey/qpVq1bcul+nTh1leVWWM8Kvv/4qLI9KnNFueiz0DLyofSABBRYtWmRlObvgggsA6LuNzpw5U3guGnq9J48GDRponYdFJs46deqkTD9w4EDu93QkWtHz0BVnNLYWTaD8vWWDMAF6fcV5550ndBOUMWLECF8fw3OnFd2fV199NeMdLiwsDORSBgCfffaZME0ikcDw4cN93+kKUbqvo58NG8FQBF2X6AmTkpISrfTAjr7mt99+kx733XffAQjPciYTZ/QEQSKRwJNPPuk7Rset8aeffpIes9deewGAb50sWRvcv39/I7dGUUAQYMdkh6oOdujQAQA/kFEQt0bVeIDdpxKwF2ds8A5ZGXnu9TLoPppMkNJ9qK7l7JBDDvF+IwLdxK0R2NEvPvTQQ8pyA8Arr7yCE044Af/3f/+ndXyu4MSZJWG4NZquI+D5U2d7zZkM9tpHjRrl+yzapFPHrdEkWmM6nVbeDxO3RvYe6WwqGYVbI4ut0BeJMx23ItFAU2dWSpRWZjmTXYvIMkrnUadOHW6AA5MOUGQ5Y9GxnJmsbbGZvOGdUzS4GDx4sPeOBllEz0NlxbKJNgYEd2ts27YtAEgtgEEtZzRFRUVCFy1ZW/ziiy/iyCOPFP6uonr16r73SedZ9u3bF9dff72yXeHdn6KiIpx44olcy5kKEjRA5fXAI5FIoHHjxr72R3cwLXNr1KFx48be3zaiCdghJGXtQI0aNbyJhyjWnLGwW4zoLKlQuTUS17TWrVsDKL//xArSv3//UNwaeWUWcdVVVwHgBxHTFfs24ozXTgZZ7wjI67JOoDFePvT4irSd+++/v3ecrjg777zzvN+IEDYVZzrlpjn//PMxbtw4ZzmrLETh1shCp40qIIgM9lxsYy0KLRt2QBDRmjMeOm6NbAeg09mbinU2nY5bI0tU4kzHrVFncKNacxaGOGPPKVozaVte2QA1bMtZWOJM9M7z2hyTPGXh8lWh9FXrIkTIxJnOPbK1cAPyNl+Ut8ytUdYW26wp4eVti+yd4dVX8p1utEZZnnReKnj5RiXO6PbK9l6Tsga1Luqk0XVrVBHGmjMizohbNTvBGsStkVcu1f0l5cwFcSYrK89F1SS9qTgj0ONN3vpvXXFGWwpFcQVYeOtK0+m01TubTzhxxkFHnJgOxv/880/v3GG6NQZZc8ZrdLZu3ZqxHkCErjibP3++b4G1KOIiYOfWqCPOdCxnIrdGnc6elJfdP0kFuWf0uhtZGWmCiDPefRQJDh23RhO/cVFa3QEOWX+mYzkTiTMdTC1nPPdfleWstLQ0Y00gQfZMFy9eLCyvruVM9VxNxRldh1VtKFtGdr8nEbQ4++abb4xDQpN28O+//8Yff/yhlSc5t43lbOvWrVaWM979mz9/vlcWHUwtZzTknaHrpo44Ywd/iUQiq8KDN1i1cWv86quvtOsggXWnA/S8LGhIWUUu5SzZtpyxe3jKJqFkZdEVZ2St8oIFC3xttqivYINiseKMdWtkyyyCXCfPHdvUrfGvv/7yvqPdpXmYijPZvSe/ZUOc0X00rz+dMWMGNx0brZHuM6pWrYrS0lIvpoEI3mRyWVkZJk6cqCx3PuPEGYcgs9QinnrqKS+dakDdtGlT7jl4kb9MxRn9QhFTPs1zzz2X4Z4ogr12tsEnDc/cuXOx5557Ys2aNQCAm266SXgu8r9JtEYdt0aCzHJGzsEOmnQaZ7IuQrZ4nQfdGdCL0HWEiqgu6cxETZkyJeN7Xr0fOXKklluj6p2hB7ciwaJrOTvwwAMxe/bsQOIsbMvZDz/8kLGhK5sney4AeOGFFzB16lRu/jJxRrtUAXZrm1TPVWedBh0IpnXr1tqDU7aMXbt21cqzrKzMNwFCB/LQgbQrq1atwj777MONusbDVpxt3LjRynLGGxTvt99+WmUlmFpz6AA0JG3nzp0xYcIEAHJxxhMAdJpslJcui63ljLx7v/zyC7p162aUN89yZtr+k7KefvrpxnnqwrtHoj6QTCITeM9Px3JGELk3E3FG1ueNGzeOazlj3yt2M2NAz63RRpyRNCQAhYq1a9d6ZSwrK8Off/6JQw89VJqGt+bMZKKTl052reSZ0a6qJm6NvDHoqlWr0KdPH256dp0qfb1Vq1bFkCFDMGjQIGH+ALBs2bKMicAJEyYYT4TkG06cWWIaEISgs+bssssu46alB2C8gCC8F53AcxN8/vnnvb/32GMP7WsgsIMLtsFgX6hFixYB2NGI8c4VhuXsmWeeAQBhJB9Rx0SCFOhaHsKA3ux04cKF3t/0oJcdiBOCWM5oyPG8RvrVV1/VspyZuJSJBIuJO8oPP/yg7dYYxFLNHisaoH755Zfcc6jEGRuRVXSszG0OKF/0bGo547k5mdyjN954A2PGjPF9R8SOjljv37+/91kkUHnp6IALI0eO1C4vkDm4NQnFbCPOZOl0Fu7TrFu3zsidx9RyRibPAP+7OHbsWN85ZMEI2He4rKwsEsuZjThLJBIYP36895lExpRBBwHhlZcNTqLCJIomsCOQhgkyt8b27dsDAE4++WTpOeh7oyPOyHXttttu3N9J3aet7TpujSzsMgwdyxkbfZKkA/xjFpLmpJNOkpaBQFs/t2zZ4gVxkWHqgqnjVi9LT8QnHflZRwySd4reLJ48wwULFgjTqdwan332WWXec+fOzRjLffrpp8p0+Y4TZxx0OhPbNUaqAfXee+8tnG1SuTXyZpV45eVdn060PRZdt0aC7F4FEWe05SyRSHjhxUUDTdEgoFWrVtw8synOCgoKcPjhhwPg72v1r3/9S1gfwhJnBNHzCWvNmWiTTlPLGSmHruUsyEQKfS76HOw9FN1TlVujbJsJnWdK6uzBBx9sLALYQY1unkB5J9+8eXMvfxYdC9hZZ51lnC6dTguvx8StkWBiZbFZcwb47zN9nEycidbsmYgdmzVcvLR03oCZW2O2y8tzazRpr00FIR0RlPdcTS1bOmWl87GxnMncGolVSBX+v3Pnzp6o0XFrJFYNUbtIzkG/AzZtNjtZQb+nvPzq1auHE088MeN3Uf2ly6WCjq5bWlqqtd+d7tpl+ryAveWMiLOddtrJ89AyEWe8fUNl6Vm3Rp11jywkP9pzQDcuQj7jxBmHOMUZb/EjgRZnPGuDbBG+yIJAsHEp0XVrJOhYOlhxprPxNWs5k12nzK1RFHEx2+KMt5E0QVZekh4IJs5ELiTkNx23Rh1XSpE4Y5+5Dqr6qnKRCdtypivO2PsUVJzp1HuRCKDrlek9Eg3UbWe8dZGJMx1sxRlgbzkD+PdFlkbkBZFtN0FeWrbsJgFBTPrHsCxnutYoWV+ryo+F3BtVIBwWU8tZWGvO2Losant5EzgmljMRvCUEdJsi65NY2PZZZo0S3T+eOCNpdNsIWoxt27bNaDNyGp1J7KDijD6HrThjx2w65WX7F5OxdmXDiTNLTNecEXTEmagBoQdgvJdU5tZI+wrzGhvdF2Du3Lne36aWM1ljFcRy9uOPP+K1114DoOfGJrq/IsGdTXGWSCS44owur2wAQfYBe+yxxzy30TDdGtetW4f//Oc/GWlXrlyJV155RduNDdjxTMePH+9tQEnna+LiIbKcrVy5Elu3bvVEj8hFxmTN2WuvvYZ//vkHwA6XXJ5rLo+XXnrJOzadTntuPOSZ0+8Ti4k4E7UrgJnlTPcekZlx0btkK85MLWd0/jZujWFZznTFGX1cti1nQQKCyKwPvOdG2oEglrMg4szGcmYjzlTrlU03Qtd151Xlb5rm999/B1AeVAfQ37oFkFtQCar1QDzLGXExNnVrJAGiyGeZcGEnGwkkDe+d0Z0MsrGc8YjCckafg+6LAX9wDxLchIwtioqKMsagtuJM977a1PmKQOW8agVxW85k4kzm1iibtUskEt5sFm9AKHsBmjVr5v199tlne3+birMhQ4YI82BfdFFAEN4L/cwzz3jXFIblLCxxpmudUVnOZBDR8OKLL3qbLIbp1vjzzz/jhhtu8JUXKI9MeOGFF3q+66o8O3Xq5N3fCRMmeO5BdDoSVplGttZK9Nvrr7/uK6+tWyNJt2HDBm+tB1kPqrN3H1C+Fo2k+eSTT7zv6TWFNLVr1/b+1hFn9IxwlG6NvHPwyiXisMMOs7ac0etjTcurYzk76qijuPnaujUCfPErG5zILGc2boKmmFrORHlG5dZIIlkCZuLs6KOP1jqWEIY4o4Mx3HLLLaHkL4MndEaOHOmLVmoizmSTeYQjjjhCei6Slt6UmCBqs5ctW5ZxbDqdzgjEpJpYEI0LADvLGVmrTh9XWlpqFLCiSZMmGfnyYMd9tNjSEWd0/SRimHUvJxtyA8CkSZN8ESdN3RrJMeT+kk3HVelogrRjNrEUcgUnzjjYujzpoAoIYurWaCLOCLwIZbIX4OWXX/b+/vHHH72/2ZeLbRTZjmrcuHHCPERujabucypxxisnIWxxpgMtzmgXN13LGc2HH36YkZZHIpHA5ZdfnnF+ncaSvXdEcKjyHDhwoHJN2bHHHiv8/f77788ohyhPeuYviFsjfd/JLOhXX33lfXfXXXcBKN9YU/aM3n77bQD+UPu8e9G+fXvfGi66vLxZ2G3btmm5NWYrIAhgL86GDBlibTmj14zsvffeyjLS6IizESNGcNMGsZyZzhyLxFmYlrPBgwfjxhtv9D4/9NBD3t8yK3YikcCcOXOUeZLyqiD9lqhdlkUyFG0Ho0MikcADDzygdSxBNnEKyL1XCKrgGyz0e2IzoSEaq8ybN8/722QATM4jqocHHnggN3It7xy33XYbt7y8Nnv58uVaZVOJM165eeKMfKey8PACyWzbts2o/aQn7kzEDi8gkixf+vp0I9XSk/mmbo2kfSVpjj32WOHEe/v27X1Bogi24uzII4/EcccdZ5U2F3DiTAPeA84Vt0bdaI2JRAIdO3b05cX+LoLMDLGoLGesOJN1mkHcGmlyya1Rt3EOYjnjoeNi1bZtW++zzkwogb13ZGClSrvTTjtJxRktUnmwM2C0ODvjjDOEG8IGcWtUzVITi3KNGjW0nhN9Pt696Nu3r+8zfU95a9M2bdoUulsj752hreWyc9BpZPd3zz33RM2aNYW/q6DzrFWrVkbeMnTcGmnrJX3ubLk18gI5RREQ5JRTTvFZOOgodrIBbkFBAfbZZx9lnrrlJf2EaBAmqyukzdx9992970za65133ln7WEDdd+h63RxwwAFG+YowabNlk6k6a87YdlR0rf3790edOnWkZSJl4T1bU7dG9rONOOO5NRJUYw464jKdxqTv7tixo2dVMrGc0fdZZDk75phjvL/p6+NtG8CDbot4bo0m5S0oKMC1117rlZdOu88++wjFOovOvb388svzOnCIE2cc2MomW6NlKs4A+UBI13Jms+ZM1uCZuKoQTMWZzMyfbXFGyJeAIDaWM15aHnRwDhobcUaecdA86Q6ZhpyXrVu0WyO9npIto8hSrYNKnNHvk6k4471T7MCBfh484b5p06bQ3Bpl1kXZtdmIM1JOW8sZjakrnO2aMyAcyxnPrZH3XsjcGm3cgXj3pmrVqlzrKZvWZK2RzZoz0oaI1g/KrldkUdZB1Cap0vAwbWNMA4eI0LlWUdnoawnTrVHH0kG3W+w9NXFF54kzldVX13JGUN1jXp2wGTPoXLNsDZdInInea/paZRPnrDgzcWvk5UuLOzptIpHgtnum2w3Q58tnKr04W79+PZo2bYp77rnH+27y5Mm+Y3h74ZAKM3ToUOUO5zT0zM6nn36K5557zvseMF9zphutUTVItRFnrDk/aCj9l19+2bsfpOFgF0zT5n8e2bCcmUbUIgOGFStWKI+lxdkXX3yR8bvNonVToUTOT9avyRBZzmzEWVlZmW/AJ5vxZOsWbTljxRkrOsJwa5T9PmrUKJ97mM75eIMAU3G2ceNGrlvjDTfc4HtvTCxn55xzjtfeBRFn77zzjjCNTJypkA08dGCP561jEeVL0t5222343//+Jy0Xi8xyZiLOysrKfPu86eRJ50VTtWpV3zG8UOs0NmvOdBb9q9waZfeWJ85Ie63K20acqdwadYUzPXFpM8FL0Lm/oklZU3FG6h2556LnIhNnkyZNwuDBg31l4L3TdJ90xhlnYNu2bdx6N3PmTN9nkeWMvOclJSXaa86AcpH15ptvCq+HTkujG+xCNUHGQoJLydykP/roI2Ee9PWtXr3a+5v0LzxXR7otKiwsDCzO6HEWG9yJ9+xs1lkGSZcr5HfpQ6B///4oKSnBvffe6333/vvve3936NABf/75Z0Y6usKrdoGnYRsPNoiDrlsjbxZTFUpf9vLbiDNeHjSiAQbPfW3Lli24+OKLvc9k/xX2nKlUSlkGujPidUima85MF5U+/PDDAICrrrpKeSwtHkhkJCCY5UzHxYo3q96jRw+t8tKQZ6wzOGVd82i/fJXljLfhuY44oz8HdWtk76vpc1ENhmT58Qahmzdv9okzson51KlTcdhhh3nHiWZxyQa0bN49e/YEYC/OaGHYtWvXjDRhWs7IXj06aYHMtoxt23kBCsi5SbnHjRvnu79AMMsZr32VuTWeeeaZ0rx48PJo0KCB7xmI6gNBpz6w6FjOyPWILH2ye7vnnnsCAM4//3zvO1Lf2Sh0LGeddVZo4qxNmza+8ogg95h+vkG2hjCxnNm4NfJ47LHHuOfjnZflqKOOwt133w1AboWk3fXef/99fPbZZ75jjhYEcjnggAO4+ZOgTAC/nRC5NT7//PPcfGjCspyxokcGKS+99xd5HiRyNa8sorEc6V/Ytd0AfJNBvDGoqccNnZ61nPECZdmuOXOWszxn2rRp0t/vvfdersiwffA6PtG2bo2s/zYdASgbbo2q4+j1VDRXXHGF9zsZDLEujyQwgmgdEn1tNGSQL/OPNxVnJLqWapEzgYQp/umnn5THFhQUoFevXsJy2dQznpWFPSfvvDrRpdgysg21bAEuu9EpHdBCZSFkO6yqVasKxZmozCZujePGjRNahQii8rZr1w7/+te/hOUAxG6NvH1/2L/p8tD3T7SORdTZ08KJLtvEiRO985Nzi2DrQzqdxpIlS7zPl1xyCd59913fMeR8QSxnt956K4DyTWXZ38iaBpbffvstw9WalKFly5YAdiywJwNQGvqZLV682PebaIByxx13AJC7IZm6Nc6aNYv7Gw9yPbx7XadOHd/za9eunfe3bbRGFpU4a9u2rTdBaWM5a926NYDy4DykHSWD0Q0bNnjH9e7d25fu5ZdfxoABA4wDmLBlvP766wHsWPPGW38ElEer/fTTTz3PDzpfduKF14+KMBnIB7WcEUgwGBvLmSh/moKCgoy1gLSVBwAefPBBbtr99tuPW4/oSV1dt8bCwkLfhKkIkeVM5z2hjyFtk04IfnJsmzZtvElgHZEkes6kHvEC/ZD3iAgndgxqOikmcmssLCxElSpVcNJJJ/nS2o6LnOUsz1E1JFWqVDEWMzJ44oy28ARxa2TF2ZFHHukrr604U0XZI+iKM5LXww8/7IUoZ2eayOBE1PmIGgR64SnAb7BUrins+UnZyEBAhconn82TWAlFa85MIecRWcJsXCUJooEDKS8dSY89jp0VY6MNmlpSRO+MyHJm4tbYqFEja8vZiSeeKK33gNit0VSc0ZZH2opEY7LmjD2/6DdRurKyMt93VapUybD0hGE5IxYK3jMVBZDYd999M54LeVdIe0PqaN26dTPSywawonedBI0J063RhP333x+A2DVb9Gxt3RrZ56cqb7du3by8RH2xTltYpUoVLwoiaa/J/e3atas3IQiUl//CCy/0BTcgqFzY2ePJJKKqzS8oKED37t29+kBfKzspRlswVUS55oygWlcaVJzxvq9WrRpXyPBQDcplYwJWnJm4jdLYWM5kgcFY6Ht8+OGHAxDXPZ77Oots/zTSFpG2JEy3Rlac0fkExVnO8hzRgJzADpjo78PIE/DPLuqKM3pQS2AHJeyANUzLWRBxxrvWqMSZqdsoXTbdDsxUnKmiNZrOAJHz8PYNI+e0rb9sWXRn1ROJRIarlolbI2/hN31u3ho6URl552PhlUfXcgbw3xmVG1EikRAKMpXljG4bWExm19nzk3KJ4N0jmVCm8wqjDeU9R1lgJJE4Y6+VHTCLAg3Qv/Mg5zN1a5RZzkwg9Uw0WJRZL9hjsuXWyMtT162RhlwrEVgkXWFhoXIigqASZ2xatr9Q9UsE+pmrLGcyTN7tsNwadYSoDqLjeM+H7Ttk90h1/0wsZ7YupzbpTMQZjUlwOlHdl9Vf8pxIPjZujTzLGbvmLMh+Zjyc5SzP4XWWLCbWFx1U4kzWYdImYUBuOcuWOBPt4cETZ6oZeZKmX79+vmNU4kw1SyTrNFeuXMlNW1BQ4B1Pd5hkYKOzhw0AvPrqq/jll1+0G8uwozWS84iuc/PmzVkTZ7IOl7fHlMqtUdRplJWVYfTo0QDKr4fuVNm9i8gmmp07d8bixYuxfv16acAKch08Czd7TTxKS0utxRld3998803pQKhNmzZe8I6CggKsW7cu45gff/zRu08yckWc6VrOyDleeuklrFmzxvebbO0tO+POijNyXp5rkY3ljJ0s6tmzp+eW+PHHHwvPKzqfqeWMvHMi0SF6X9n6u2XLFs/VU9b38d5TXXEWxHIG7LhW1nLGvstBxJnKrVtXnNHPnJ0IMBFKOhYaWb8vKh+blubnn3+Wni+o5UznnEHGX7oBQYJYzmzcGnn7neog8xJi85D1WQD/mbKGABvLGW/NGc+tUVZGU5zlLM/hWUhYwrSc8dwaWXEmapxVa87YdGyHJJttNhFn7733nnSGhT4nL7KYzrUSYUG7btHw9sOgyyCznLGLi+nfCPSiWpnJn8DuVXTcccdpB52Q+Zqb1rN0Ou0NMD7//HPuMZ9//nmg+kvD3uewLWeifMvKyry1TO+//77PDY19X+nO7oYbbsCTTz4pzIdQUFCgXFckKm9JSYlwTRmBJ/R5rnRk03edIC/0ZvEEUYCLW265xfc5SnFGBJFNHeSVaciQIb7fVFuK8MrC1l82kInKcqYSZ+S8kyZNynD9pfdqI5A9x9i1izoDIdqNlBUsLMSFjq177LXS7aHoHvEwEZOigCC64oy1EtITmDquXUBwy5noeq+55hrfZxPL2T333CMsT5A1Z3Ta3XbbTXkeQqdOnaR5z58/3/eZV7/pcumg0/aSDYwnTJhgdC5gx7qqqN0a6bTEpfq3335TpqP3GVSJM533h1wn73rJtbCun+T7sN0aWehgPyY4y1mek4viTBQEQ7XmTDa7RFsmTKM1sgJqzZo12pYzHjrirKysLMNKCOyYFRdFQhSJM7q8osAX9P2irRC0e4wIdmf7pUuX+u7zK6+8IsxTJppVsAujdV2PbOsv+46YuDXKLGemDSl7r3hRnkiQB5qlS5d6lhYZtBVVlKfoWg8//HClOOOtizrmmGMy3gciLE0DFagYOnSo8hhbcaaaqQ3i1siz0NIBSAAzy4PIrXHvvffOODYMt0YAWLBgge94sm6JMGzYMG/CZvjw4dJ83n777Yw86e9YVz+WBg0aYNGiRViwYIHvezYgCP3OkGu6/fbbM84XxHIWdJ1dNtwa//77b1+AGVEfp7KcsRu564qzsWPHZvQtNLZrzurUqeP1q3vttRcaNmyoPA+J5Ne4cWMAO94dNhAR65b3wQcfcM9n6h6rSkuiJKsCW8msQ3TbUVBQEMhyJuKZZ57hfk/GfawHBM8Dhp5MUYkzUVnpMRRJy2snROJMZDmbN2+eV0cIIrdGHXF2+umnS65IjLOc5Tm8wA8spmJGRZjijD6XrjgLY80Z757ouh7YWgnpv2Xp6P95jbrO+gv6/DoCQrUur1GjRsI8eWXVnTlm64qu2LGtv6KOT2cgr3JrlKEKNMC7R6KwvLLF5ATeGi4T8ax6Z3iudwUFBdLJChmm4oy936I1bbxjZfnqWM5MLKQ6ZWIHxiZ1W+TWyMs3iFsj27bQbRr7Djdo0MD7m62rbD48ayt9DSq3RqC8bWLXp8rWSGVrzVnQdXYiyxlrlZeVn+0bWrVqhX333df7LHJrVAXJYJGJMzqPVq1aScsbZM0Z+axrNSMWMHKPyLvDTmSwZRINuk3aLNWkDy3Aee0+ey7Rd2GtORPtywZAGLhJ5NZIAoaJsBVn9PtGrpPXTrBjTfY9Y+9nixYtMiaceG6NuuLMFifO8hwdy5mpmJERtuVMJM7YNTxhi7NsW85415pOp6WL6Ek6+n9eg6Wz/iKoOKtSpYovb9G9oC1nokkAWYfP5pttccbeO3ZwIlu7oHJrlCELCALw7x1PSJqIM1WYbVn95tVP+t6J1qTZirOgHZGtOLNxa7QRUWxaOo8wxJlqkiCoWyMrmOi+hs2THhDy7q/sM4sqIIgIti/hiTNZnSGo6i19PN0+2Lg1itacBQ0IwlpTaHTdGllk0RpNhLCtW2NpaalvDKETzZOtS+TdYcWQbjsZROTzxBlBNH4iyOpTFAFBRKHys7XmTJUO2FFeXp/JWs5YccXLl+3HdEPph4lza8xz6MrB29wzjIaFhifOfvvtN3Tu3Nk7r604YyPWhWU5Yxk2bJhv80NCmOKMHhCw/tBsZ0sTllsjfS06gof9rWrVqli6dKnvekTpVJYzGSILSFTijB2o2bo1ZsNyxusETcSZamBsOpCny8N7PjxxNnPmTADhW85Y1q5dm/EdcXuRWb949U93ICwTbiJk4kwnTxaRWyMPUVv15ZdfCvceI+ej195WrVrVJ5bY8ssGdiLrhwjyzhUXF2fsFSWDrb+6goFdY1xWVoYZM2YIj6efd1hujexamKDijDewZD8T8SBy4ZOdU+bWqKrLtgFB1q9fjxNOOMHLT+URQZdryZIlqF+/vudyy4ozXUFjajmT9Y90+VXiTDapwIqzN998U7uMNLLnQj9v+vkSd8ZHH33UKC+VOCPrltn8eGOcSZMmZaRnx5r0OZYtW2YszkTRJU03hFfhLGd5zhlnnOH9PWnSJN/GlQSeX7/tYCiRSKBNmza+7w499FDf76JOytRyxpqSZeJMNGvx8MMPZ7i8/O9//8P//ve/jGNZP2MdcSYTLSJxRq6Lt+5Mx62R9zzZ8pKNp+lzFBSI95Kijwf0harIrZH+XTZgZTe6psvavXt3YTq6PCbuevfeey/3PLZrzlSWUEKPHj189ZC9V7wNwkUDLZ2om7IAGQRTcUa7/4jE2RNPPOH7jqx3sRFnJs+VXcQP7Lh/vN9Ez9skT5vO8+mnn85Iq1rvI4MMlHTcGkV1VBYYg3e+9u3b+wZvskANLGw9OPzwwzM27KWh67porQsPmVsj+Y2+BrLH2IoVKzLKy24ArVNW2f0Rwa45s3FrpNuMhx56yHdeci4aenJq+fLlWuVkzylza1S9I6r1VcAOi+Rll13G/b2wsNDbb1QGKfNXX33le86sODvvvPN8n//55x/u+bLlHquafJO5NdJjs3r16lkLBl60xvPOOw81a9b03hWW999/HwA/lH7t2rWFeanEGb2GjS4Tz3LGg7Wc0Tz++OPc+8neN947xJZXNaY2taw5y1meU69ePd9nXgXv1atXxneihkU18CssLETDhg1xww03cH9nXUjYc+sGBDG1nIkaoQEDBmg3otWqVfM6NJIn+zudfyKREDYoPMsZOyA899xzM9LpWM5OO+007292wEwGWryZ+YKCAuHeYez37LUHsZyZuCvQZe3bt6/wONUzHTBgQMZ3++yzDzp06MA9D2+wvuuuu/qO47k1ksGFLPw5UL62ZvXq1ejZs6cvP8IRRxyRcQ5RPWcX3PMoKCjgWut0BnmigTx5x0477TShODv88MMxZsyYjN9sxBmbRtbBywY+vAEgGcTwxJnM/YjG1HK2detWHHfccQDCc2tkF7XbWM5k8J5Lu3btjMQZ3Uaxx9asWRPLly/3giHIymwbOVHk1kjXi48++ghApsWgrKyMO+HJg35/6XPH4dZ4wAEHeH20ruVMdN4jjzwy4ztdt0bVAFPkIkd44403vHI1b96c6zJXUFCgNWElqv+sOGMnMEXPj1ybjng3EWemXhg0u+66qxfluLCw0GrCB+Bbzl599VWsWrWKu05Uhej9BuzXnNH1zFacsa6JvHOz+dJujbI0LKoxgqoM+UalF2fsy2ezszuNataGNHCiRasycUYPLE0tZ/RnE3FmAjsAZxtJ0vHRgyhZwy0SWOS8ogEu/b/K5Yl2gaDvPZ2OFjy290kmzmiBo2udIYjWRdGWR1V5ZG5rbF6imWOe5YHt8HmWM1IndAcHsvUuOgE8CgsLfceJGnyRJUp3kCdbp0nXbV6evDLZrDlj2zJZ22YqzkRujaz7kak4kyHaaDyIWyO7qN1UcKvgna+srMxInNHWHFG9Fw2u6PfKZCZZx62R557FE2e60GWlzx22W6PsPpB7TfcLOmvOZPWD12foBgRR1WXVhsVsel7bottPiI5jxzK8NfWyc+u8Vyq3RhNUayXJs2fzNEH0PrJ1Qbetko07bMWZzDWcRraVUEFBZlRjNh8WkVuj6l6I1qSq8slXKr04YyuIap8Tgq3JlFQYUcVhRRUNLc42b96c0SHorjkz3WBVF9ZNkbceZePGjb5Bqugl07Gc6Ygz1cwi/cLTg2Zyf0m5ye/ZEmesyNFdEC8LCCIrK10ekzUCIosguc+i5y9yayTvm+6sGHk+PMGgEyyhrKxMa2aaVzd1gl2Q42TRGkXvuKxNyTVxJsrXZEDDax90B10yy5kJrDiTuTWy75NOXyGabTYRZzpWJJ3BoMlgha3nKssZgb0nYYgzVRtOCMOtkTwX0X2zsZzx7rvMchamONOZ7NOtF7qWMxbR8yPXqZO/Sdugcy4WnrU/SJ6yaI026Igz07D/rFuj6FpllrPCwkLrQCRseVVjanqMYBIIJ1/J79KHAFuxdC1nosXVqgpBfhe5x+lazi688EJ06tQpw3JGfq9Vq1ZGI//FF18AKF+/c+ONNxqVW4QqQiRLgwYNvEAZMrdGnpXQRJyRhemtWrXC3LlzhQ0Pazn79ddfAZTvrXHssccC8AueOnXqcM/DwtYPUUP9559/AthxbX///XdGOlFdAeTiTHdGlN0nSlRenqhIJBI477zzvA2QTSwltFujjuUM2PF82XUNPHh166233sL48eO9zyZhnnXdGmn3YxrVWkvZDKisAxSdb9u2bdoDPdkgRDY45gVmsRVnpaWlXpACFfR1kXaN5w6qgrWymAjuqlWr4rnnnpOen3c+Vpyx52UHHvTgVzQoCdtyRrv7stcuq0dsW2UycOO5Na5atQpff/21VnrdTahl+xzyLPm60RpF7xBvT0OZ5UxkIeahiuzHE2ey9YQyRPVHJc5EkGvTcXtNp9O49dZbtc6rCgjyzTffaJUrnU5rT1yy6KbTnZTUmWj96quvtM5FYN0aRW0vCSjCq4vPP/+8sTgj+7b9+9//9n1P6pfI+4y+V0899ZQyH2c5y3PYxks0G3rzzTeje/fuuP7663HKKadwA2IAwB577CHNj1QY0a7nuuIMKH9p6ArbsmVLTJw4Ee3bt8e4ceOkA0nWh3nVqlXScoswFWfr16/H9OnTvd9FHVqfPn2U4qx169YZ6XgdyPPPP59Rpttuuw3HH388DjvsMO+3vfbay7d4eeLEiQD8gueFF17glheQ72Qv6mTfeust3+dRo0YB8NfL119/XXjeMMQZm5+ovCJxRpePTqey/qncGvfff3+89NJLvu9kIk7Hcgb473lhYSEuvfTSjGNEljNV/a5ZsyYuvvhiroslXX95nTdPnJGgN7IOkKQjwTII7OwtvdbSBNmEFbuOTSTObrvtNt8xAN9yxttwlQedtkmTJgB2tAfVq1fH4MGDtc6j69aYSPAjy/7f//2f9PwiCyr9/NnnwtYNOoCR6FnohEKn6/Mbb7whKXVmeXki/4wzzkCXLl18G2W/9957GWlliKI1EsHy+++/e98dfPDBOOmkk4RBmdg1Z7Rbo+7kD689klnOdKwsvLooE2d0ICdefaQnBLZu3eorE/tcdYJ/yUQ7nb9qfy4Rook0ku/3338vTQ+U399PPvlE+DvdDg0aNEh6LllAEMAvzsJ2ayQ88cQT6NSpEy6//HLpcWRtr0ycNW/eHADQsGHDjGsbMWIEDj74YO+zzK2RrLVjIWNMOvo0YeHChb57RI+1CC1btvR9JutTWUjZrr/+eu7v9D3QEb9OnOU5PMsZPegnFeaBBx7Ap59+ihEjRmDMmDFaGxnzIBVG1NDJ3Bp5M/Kk/K+99hoKCwtx+OGHY/r06Tj44ION3CNsGyGR66QsT501ZzvttJNSnPEW1opEBZvPkCFD8Nlnn/m+E83Y0IJnn332ET4fNpKhqlw82I46kUigXbt2wvvENtq0K4+JONNxHZK5NdLH8CDlp7er2Lp1q9StcebMmbjooot838kGArrijC3vKaecwv2ed37Vc/zhhx9Qs2ZNrjXERpyRSHwqyxmAjO0tWHEmm6WV3SudvOljedc2ZMgQ4TkIJrPUPLdGYkXYf//9ceeddyKdTuPAAw+UnkfXrVE2aaZTTjqiIl0XWrdunfFc2PvQsWNH7zpEbmwq9zZyXmLl4NV5ESK3u+rVq2Py5Mm46aabvN/Y+82KFjIxx4O3poR+Ho888gg++eQTZVtI2hS63VYJCIKt5Yy3XpjA6+tF4uzkk0/2DWZ57c0VV1yBPn36ACivvx07dgRQ3vb07t1bOSlmazkTRQVVWX94lkNgx7XpWI9U4xN68odM1pgQtjhTuTVec801+O6776RBmoBycUWXiUf9+vW9POnrOOaYY3D99dfjrLPO8r6jz6MbEIS8/926deP+Tqc94ogjMn6/5JJLfJ9lSzyA8k232ajF9O+65Ltbo9YCmmQy+QCAwwDMBXBJKpXayvx+K4CzUqlUMvQSZhnemjOdF1I0U6kaEKrWLJlYzgD5vlYm4szWt1pmOVMN1GVujfS5ReKMdy9VLyTPlUqF7B7T6K7xkkEaQt3nwdYH4iJisuYM0BvciSxnos+8QQI9qWHj1mjiQqPzHosicvEsZyILAns+gC+aVeKMnoUnsFYAHqJ1eCbBjWzFGe88QdwadaHTkvxIHWZdlWWwA3lTy5kK3voKti6w8O6DaoNanfe3rGzHhrEmQpM3YaSLSbhsXhsgWjfKQxQQpKCgQLvdMLWc6bg1itbqEOj3VneCk75WWV3SsZwFdWs0jaJHIOXV2XdSNfEW5h5ZtDizxdYdkoXXb7LQdYH3bojuDbvmTAR5J0TnMb1W2Tpv2TGmYqvCW86SyWR7AM1SqdQRAOYAOIv5vTaA/bNTvOzDc2vUeSnZl0W3gqoqjKk4I+6VQcVZGJYzNh/RWhh6IKQT7IK9t7Jr4d0H2fE24kzVYfLQHdCw7rKmopqsYTO1nOmKMzYdKwhE4oxAvzcbN240itYIyC1n7Dup27ny6r4okINq4CQK+EMP3kTijFceVjzwIGVlJ4zY2VvZ/TAVZzKreBTijH4+f/31FzZu3IiFCxcCMBNnpM4Qt26ZOAtiOaPvyfr160MXZzqWb1qcmQx0ZO+4Tp40bL4it0ZRetF3dHqeODN1a7QJCGIrztiolzoTnEHEma3lTHScrTgj5dBJr2ob2L6XvkadfkBkObMlaNRvk/OZiDNR3ZKdn0z4isY3ugF7ePmKvnfiTM+t8TAAZAX9OABdmN+vA/BkmIWKEp5bo87gomHDhr7P9GbWMlQVhl3ET8MTZ2SxZlBxxjOv0zNaohdT5r+eSCS4Lg1BLWcyROJMFYlIRpSWs7Fjx2Lp0qXaHQNxaSCQRb1BxRmvvAUFmSHg2cXVKldWeqPyXr16GUdrlN1jXnAKFYWFhdy6z7Oc7bbbbthll128z7xrJeXjvae0q5Zs/YTMcsYLSEPKwVoHTMRqmJYzXZHF3r9nn31WOx+2HrZu3RrLli0DIA62xGPbtm145ZVXhOclhGk5+/TTT43FGclbFMyA3a+TR9WqVWMXZ7K0bBuwcuVKX3ryXInbH+tmJ7Oc6bYvJG2YAUF491q0CXXYljPeu0BvSiwqH49sWc503qtrr73W95m1tslC1Ou0X/SzI9eZSqW4xx500EHK8/E2oQ6CzPIrEmckQIhInNH7kNKuySxk7b3oOV1xxRWSkmfSokUL7vd0/eK1Z5XNrVGn9HUBkPBGqwF4dy2ZTO4MYP9UKjU1C2WLBFvL2d133+37TCKFqdLSFYZX2Tds2GBkOeOdl/edqqKyaxASiQS+++477zMRgSxVqlTBoEGDcP/993vp6Dz33HPPjDQ6a84AtVsjD5XlLAy3RpXPNA/efRCxYMEC5bWSzb6vueYaYVlMxBlrdRGt3WOvkV1PIZqxJH8PGzbMd7zIrZFda0awcWsU3SMAaN++PY477jhceeWVWiH2e/ToAQDo0qWLkeXsvffeU7rOseUGMiPPDRw4MON4UlZ2s1uTcM6yuqIa3NCdfBDLGR1YwjTtokWLvL9322034XEs27Zt80X+isJyttdeexmLMzJ5IhKet9xyC8477zzhon6gfDNr2Z5FIkz3I6LX8orWIPJgB9yLFy/25U3WVr311ls4++yzM4SqbM0ZO4klgtce2QQEoftTE7dGdl2vau80kTh79tln0adPH5x88snc9KKyyNARZ7LNknXPR3jzzTe9v9evX+/7rVWrVr7PJuKMjGUeeeQRbr6q+i2KFEiPkcKynJFne9JJJwmPUVnORJFWL7zwQu/vX375RVkWW9dRdow3dOhQ7nF0fTjzzDNxySWX+OqAqdgKUxzHgc7dXgWATNfuDGAF9dv1ADJX7lEkk8krAFwBlA+SRIsK44KNUlhSUuJ7sZYtW4bi4uKMdLw9XoqLi5V732zZssU73znnnIPXXnst43d6oEGzePFioVvLypUrM8pJz5ARlx8RdJ4HHnigJzbJORs0aICHH34YN9xwgy9dOp32FnwWFxf77ueKFSu4jQW5vytXrsyYxSMUFxd7aUtKSlCvXj3fuUm5TjrpJHz66ae+PNn7sHbtWqxYUV5tN2zY4PudDqvMe84LFizwnumSJUtQtWpV7ktfXFwsjTRHRzraf//9fY0hm+/ChQu9UPzr1q3L+H3AgAHo3bu39/2CBQu8iE2EdDqN5cuXC8tD7oeoDGvXrs1Is3Xr1oy6ybo00PeAfvYlJSVc16LFixcDKK8TdBlOP/107vNo1KhRxne88pP8i4uLfdGqaE477TTveu666y6cffbZOO644wCUvw+8CKbExePkk0/OuIdAeR0pKCjISLt69WrveYjeYXINxAJE0hUXF3v1VDT5wLv+4uJiX12lQ1bzjueVBeC3dfTzojv/RYsW+cq/fPnyjLxIWvLsCbyJmgsuuMCzbNHn4d17oFy8020d3R7zrpmtvyUlJdy6v3HjRmXYch6kPaLfhU2bNnn1rrS0NKNc5JnTtG/fHpMmTZK+tw888EDGd0C5Nf2zzz7zbZlBthoRccABB2DmzJkAygfGdL68voZmv/32w7333ouBAwdmhK1nnzndvrED2kWLFnnvTKdOnbznWq1aNW9gTZeD9CVbt271tcdsm8+mY/OkzwH469qiRYt8dYbksXHjRu/4XXfdFU8++aQXla6kpCTDHZsWG3Q7vWXLFt/npUuXcgf6RKwvW7bMKw89VunRowd69Ogh7QPoc4nux9q1a73fRJMu9LYxZ5xxhrJtIZD2mX0HSfrDDz9cmJbXX9Hf0e0eHYEZKA8YsmDBAgDlfSngHzfwohLS1K9fH0cddZS3xQNJ16RJE1xyySV44YUXsHz58oz2TPe+0PDGnzVq1PB9R/IpKyvLuNbi4mJfe7Z+/XqrcgD8sQjLokWLUKNGDZ8nzpo1a3zp6H3T6DrFHkeibpLvTN0ni4uL0aBBA6M0UdOsWTPhbzri7FsAAwD8B8AJAKZQv+0JoHMymQSAvZLJ5B2pVOp+OnEqlRoJYOT2j/ZOvFmiVq1avs+77LJLhumXdwN5DVWzZs2Uswu1a9f2zsc7NpFI+GZ+aVq0aCGMmNSgQYOMctIuULJKwP5eVFTEPZ43+8geS5ujGzVqxF3sSxqT+vXrCy0hzZo18zq0+vXro1mzZt6xBQUFXp7sTHLDhg0zyr7zzjt7kR1r1qzp+53uNHnX3KRJE68+NG3aVHgfmzVrJoxKBcAnnthnyJ6zbt26nqtdnTp1uM9V9TyrVKkijAgK+K0dQOaz5bnPVatWLSNfduBAn4d+jxo1asQtM3n/dt55Z9/vvPoM+N0iaXjHptNpNGvWTJimRo0avnT0REHz5s257xqpb3Xr1s24hyRdo0aNMtyea9So4auDsmugXSfJPSfvES/PwsJCrfdVVtfZMqnqF/3e0zPnDRo08A38ybvLKy9bd3jtIR2RlT4Pe38JdNvAlk1UR+i63rx5c+7zqVmzpu+56ELaI9Z6RMpfpUqVjHLttNNO3DYByGzvVM+JTkPqM3uPeNB1pUaNGr66JOoTaUg/wJaXnVxh22MaemBVvXp1ZZ5EPJeWlqJZs2bSNlR0LtLH0O0RXZ+bN2/uqwekjFWrVvXamcLCQl97v9tuu2W4/tH1mv6tVq1avrq92267cdshUoZatWp5742ojVVBj0lkv82bN497DJ22efPm2hYWUvdZy53ONbDHsO0M3few0Rt5599ll12873n7frLp6XENfT76udhcFwuv/UwkEhnfFRUVYdu2bRlipFmzZr7vatWqZVUOQO+9b9y4sW/sBmS+f7SYpKlbt670/LLxFQ/6meYjSjthKpWaDmBxMpn8BsB+AN5LJpPPbf/tglQqdWIqlToRwJ+sMMsHVG6NJi5sQ4cOVbr0qFyntm3bJnVdNHFrtEXkasjLWxatr7CwkFsuMrMehlujaA0ATSqV4rqD0ecWsWTJEu11GjIzOv2bak0MXQdF7oUqTN0addwweKHkWUuxyq2RRRQQxCboCq+8sjRsHiJXJhoyQBEFuxGtOVNF6KOh25A5c+bg22+/9QK96LyDhM2bN2vPNsoCJsis30Dm/jO2bo286xBFcpO5H9Ko3tlt27ZpBRAJuuaMzdN2zZmNuxQpw4QJEwDoRzEVodv+8PIycTWiXWR1+jeZW6MuZGBus+aMiMPNmzdrBw4ix9N50ceL6pzumjMddJ+1TkAQkzKEEXhDdW7APNiZ6hpk4xZyj3Qsljqo9mQjkPrw/vvvC8sEBHP102n/dM4vchFXvaemY1yVF1uuo3W1qVTqplQqdUQqlTovlUptSaVSV3KOybsw+gA/IAhd+UVmUV5Fuf3225W+uyJfc8LWrVuljaWJOLN9EWn3JxpZ+G/e58LCQuk+Q4lEwttkkYeOONMZjI0ZM0Y466cSZ506dcro5Lt0YWPiiPPm/UZvuslbHKsSZ7vvvru0zCQ/uq6ws0424owNJU/KSiPazJy1UBPIwEQ3mprJALlz587SNOw9YAdhPAs2WU+TSCQwd+7cjN9Fa87q1KmjPYiiZ3q///57dOnSxVvPYNKpjRw50vdZNgiSLXIvKytD165dfd/98ccf3t+HHnqo9/esWbOsxRnPZXXffffVSiv6nnccHQBm27ZtPgFI30vaWqSa7BBhI8722GOPjO/CEGf05sUq6Gtl642pOJOFSt9rr72Ev9mKMzIRYiPOSNtMu2XpRmskG5KvWrVKGXGR3kuVFmeFhYW+tKJ2MUxxJtvOhK6LOhNmomN4e4+JBHwYnHjiid7fOuc3sbAUFhZi9uzZ3N/Is/zpp5+0z0fYZ599jNMQyDO47LLLMn4zWYOrk4cOvDZMdS7Ve9q2bVvt/AH+Prj5RH6HMwkBdlBC73N25513okOHDtx0tpYquqHnCT/VIn4TcWY7cyAKV85rPGTiqLCwEA888AAGDhyIdu3acdP+61//EpYjLHEmK69KnP3zzz8Znfybb76JgQMHep2xTt50vrSPPhlA04OULVu2cK/1+++/x/333+9tQErDLhhmB5P0ujz2vEB5vevVqxcA4Oabb+bWwXQ6nVE32DpGXxsAfPjhhxg1apRv4E27BZHOTPd90u0g7r77brz11lvSNDLLWUFBAU499VTh+ROJBH7//feM78k9Z+/T7rvv7ovWKBOjxxxzjPA3k07t448/9n2W1fX77rtP+FtZWRmee+453HLLLdzf6QXyRUVF1uKse/fu3t8PPfQQXnvtNVx44YW4//778cMPP/iOFQlNHXH20UcfeRuzstFx6ePpgEi6lrP77rtPKPYIvAH1t99+C6D82V911VUZaegw8WRd5J133qksD2BnmZCFIjcVZ2RNMhvk55ZbbkG/fv1837300kve36ah/+kZedqCa9JXk7aJdoWmnzv7XtMBQdhgLG+88QZee+01bp9NR3eWibMoLGdkbdcvv/yCvn374oYbbsD48eNx3333ZWwgzKOwsNC7VtG9JusXaeh7J+L5558HUL7m0oTHH3/c+1t2/m+++QbDhw/39Z+8SXMAeOKJJ/Cf//wHVapUEd5rIvIaNGhg/Dx0o9XKLGc0PXv2BOAPFBWVOLvjjjukv/P6P1XZ2HgHNIMGDcqI6GlaZ3KNSi/OTjvtNFx++eXeZ7qxkwmHMMQZG3GI5K+bXlUeUSOjQjSLLgvjzftcUFCA2rVr45577uHOkCYSCVSrVo07GCHpgR2Nq61bowybaI2NGjXCPffck/H8dN0aacjaDLpzEFnODjnkENx+++3cOkCsRAQ26peqrNu2bfPcLUXWzHQ6rdwwlHVlPPXUU3HppZcK8ybiTrfT0O0g7r33Xt8aEB5sXWFngBOJzOiU9O+8d1UkzujBoo7bDDtoZc/PHq+DbJDCWlbp51haWorWrVtnRNok1K1bF+eee66Xh+0GrLSraP/+/dGnTx8UFhbi9ttvzwjqoivOeBx11FFexDB2Moz+m42wqrKcnXvuubjjjjvQqVMn7ztdcda5c2ek02lMmDCBG5qcF5Ht6KOPlpaHENQyYbLPJIFuu8nfbL8ybNiwjGvt2rUrDjjgAADmljOAf59M+gTSHtHeCXRdE+2nxauPvXv35k6kAeX3sHfv3gD8/XRhYSE3rDtLmOKMWDn22WcfvPDCC3jooYfQrVs33HHHHVrtbUFBgfRagfL1SqyXiI7ljFjOZftb8mjRooUntGXt0eGHH46bbrrJd+9EruDXXHMNLrjgAmmZyTYPrAeWDrw1/aZujTSkz1e52OpiIs5oS6lueVXvqawO3HnnnXjssce8z7ptYy5T6cUZkDlYpGe4RYQhznizByprVxTiTOVKIctX5KPOaxRIWtG9zAXLGSBeu8AKFZv6whuwq9waefAGDbR4YOsNT5zRefLy5bk18o4h6AygsyXOaETvjE7dkbnPydYG8cSZ7hYSgNm6T9EzYc9hIproY3UG9/RAy9ZyZlLvg1jOgB33kRVnontZUFCgrHvkunXWFdu+37To0HWztBHLdLmCijPT8tKCx1ac0R4wJn016TPpssr65CCueSQP1nKm87zCFGe2IdIJuvdX1AfJ2kISoIXdh9OkXKauwDrrdEVlpuufqTgTeavoEPQZRp2HjTiTodvu5xNOnMH/IM8++2xvMafsAYchzngVNBcsZ6JNJUXRJUWfZesW6GN1xRm5HjrUbdCXUqcjJKGU2XKy98nGcsYTZ0OHDvVCOgcRZ/SgQiaiAeDBBx/0FrSLxJlOR2G6UThZ36j7HG06CF23RhPhIxJnIsvZ0KFDjQZRcYszuu7opCNlGDduXCBxRr7PtjijB1F0mysT4ypxYSLOSMhrdv8mEaS848ePN96rzEY40PehrKzMePadJ850y0u7u5mmJROLn332GV5//XWjtAAwduxYAP5nKOuTybnJhr8mkDo8fvx477vKJM50hC1pR2fMmGFdrmyIM1GZyb0cPXq0V/900b2PupYoHlFZzmzOFWZQu4qAuxsQV9h8t5yZrjkja+BEi7R1xBldDvpvXhQhU3HG2+CS3XfD9LmYNNzsudn1iLprzmh44mzatGmer71uY8oKxYKCAl9IZtWgdezYsXjnnXekeZJOgefeyh7D/i3Km+xXxOYpWqBt00HwFqPz8uRt6yAbrLOupMCOOsCKsyVLlniTCwUFBUoRe8ghh3C/NxFn7ADCZJBOT+zoDBbJflavvfaatghky03fHxVB3BrZPN59911heuIa1aVLF2XdO+ywwwDoiTOySfH//vc/rfKS/cH+/PNPY0uUjTij3azZ56nzfETijLcVBAttUbG1nPXq1cuLcKpKSwfnIMyfP9/7W7QVByCeyNSBN3g/7LDDvHZbVu58FGeiCUJZ/dTdPFyWn+k4iLfchEUlzoDyKNEEWcAlAu8+BnFrpOsD6Y94fZYuYYoz3j6VYYgzEjTk+OOPD3yuuHHiTILMx1VVkT788EPu9ypxFqbljG1AHn30Uem5J0yYgBdeeAF9+/bl/q4jzuh7pho8kLQq1yMyOOBFQGI3sc6GW6Po3IcddhjGjBmjNQgwcWuUlVdEjx49MvJr2rQpPv/8c8yYMcNoUb9KSE6aNAlXXpkRsNV3jAz6/MRyQL6bOXMmxo8fz41iCeh1EGzggUaNGmH48OHScgDlbqqTJ0/2RdOUPRd6jcXJJ5+MiRMnesfz2g5SV6tXr64UZ2eeeSb3e513kMBazlV1/e+///b+NrWc0RFeVXWAXDt7j7Ihzkw7fDb9rFmz8Pbbb6Nv377K9uziiy8GoCfOTKE3czYVLDZujf379/eivIXl1lhQUIAGDRpgwoQJ+Pnnn5Vpg4gz3vlE5SfBWGjoqLPNmjXD+PHjuUEtZNEmTdlll13Qp08ftG3bFu+//35GEByauMQZWQ9Ik023xiCbCNtazvbbbz/lMSq3RhYS5VdGNq1af/75J955552MdYHb9ygOJQ8TZNE7ZajWHk6cOBFvvPGGNHhIvuDEGcQvhSz4gaoiNWrUCDfddFPG91GKMxbVLFS7du3Qt29f4QuQLXGmaznjDfpsZnVl6WXwzn3KKad4gQPCcmvUScfCho0lZaUX2OueV2U5a9++PZ599lnfRqvsMezfvLLx8tx///3RrVs3Ydl0OgjeLCWJXMXLk6ZLly4+q5XsudCz5u+88w6OOuoo7zPvHSJCVGdxu+jdMbGcse45qrreqlUrbzBkajmjy6BrqYlCnJkOeNjjmzZtil69eqGgQB1Kn9QHlTizsWSxe8mJzs3DNj8ShbOsrExr708aWpyx5T3mmGOEUZDp89u4NeqIMzYkd6NGjbxADgT2WXfr1g3777+/Vn62nHPOOV5ZTz/9dKm1JUxxZgJvMJ9Nt0YAGcGAdLEVZzroWM4IZ5xxhpYFMJtujc2bN8dZZ52VUUdMxG+YdV0UvVuFahuhBg0aoHfv3tpb8+QyTpyBXymqVq0ayK1RtJm0as1ZmG6NvDIFQeflpAdculasIOKM/S5Ky5nq3Dq/hSXO2IEuW1YTy1lBQQH3d/YcvAbQ1K1Rpzw0Oq5cvHOZWJx0jkkk/KHVZZMUBBPLmeqd0DmWtZzpDNLZjXx1kQWQEMFOfkXp1miDqv3jbUAe1joKOu8oAoIAO8pOB7MxTWsTECSIWyOvTdJJKxIOKsIcsJrsoxemOAvaB2bTrRGwv8e2bo06mIgz3XdHd523jVtjGNhuQh1WtEbdYyoKledKJfAqlGp2W1VJSktLuceoLGeqFzlOccaDbaSyYTm77LLLMG3atFAsZ+wC/IoizlSRI03dGnm/s5uT89ZbmLo16pSHxrYBD7szSCTEYfYB/r357bffAOiJMxPLme6906nrpE3SDVRBoPe3u/HGG7XSsOUePXo0AHiBaWSI7p/Nnly6qNqzqMRZFJYzwD85ZivOJk6caCywyHFvvvmmt/dikKh1KrdGIPPZ6golm43JRZiIEHLsqFGjvEkN27puMi4wmRxSpSWW/WyLs7gtZ7ZrcAH9es/2zUCwyUceuRAQJArrcK7gxBnKXQhYaB9/HiqzqchydvLJJ2ufg0cQcUYWrLOcdtppWnmzeyEBmQ0PvRs97Wp33XXXZaTVFWfLly/HQQcdxG0Q2cZLdR/IxsSEIAFBwoDs4xJUnLFlYz+T/dR0zptIJLj3ml6TBPDrr85AkLd/YBDLGXEnJK4jRxxxRMYxYVvOWOsiey946ci6g6pVq2a4PLNuqaL3PEgHSdbisesTeeenN0Tt37+/8txTp061LhfBRESIBi3Lli0LXA4RqoA/5HeeOLvssssC5R3EckYiQ5pCzs+6NeoMGEn9r1OnjnW0RnrfIiLcVdgO+mzFWZD3ccCAAVZ5Av4JjAULFgCwH7iK1vfyCFOczZkzBwBw9dVXS9OJ7jHtnsob22RTnF1//fUAkLEfJS+QlW67phv8hPf+6QYWYiGTHzrQz0HkltyoUSPvbxJo54QTTpCei2DbH7N9Z0XBiTOUr8thByxk0CyioKAA33zzjfB3njg79thjfQPHMMWZTifRtm1bTJkyJeP7t99+WyvvatWqYd68efj444+979iGp3nz5pg1axb++usv1K5d2/v+oYceyjifrjgT5QWINwXVRSdsru25ZSxfvhx//PEHmjZtCiC4OAP8A0C2rGwkQpU409mGgWcd0hnE3Xzzzd57QKLhBRFnzzzzDIDyDuq3337TXhsSRJyRchQXF2P+/PnGLklkE2TC3LlzfZ9N3Bp1ePHFF9G3b19Mnz4d7733nvA40ibRA/oHH3xQWa6ooesWeYdUx7Gcd9551nmy0O8XT5w988wzmDZtGnbeeWejPNnzADvctHSfxeGHHx4oTxvLGQmqUFhYaC3OaHQHYLZud7ZujUEsZ/R7ZXounjXGVpyxE3cydL0SdNMCwE033eQb1Oue/8cff8SUKVMwe/Zs7ngtm26Nd999N6ZNm+abQADK1+2yZdF9d3htg65boy2yiToW+jmQ9ag0//3vf1GrVi3v84wZM/Drr79yhXNYofRnz56dMWlcUciNnjYH6Nixo++zLHwugbYSsWzbti2jsrGBGaIWZwA/RLdJOOAWLVr4rH+8hqJt27bYY489Msp31lln+b6rzOKsXr16vkhfYYgzegaUV1Y6QpKJOONZTAH7NWcFBQVe0A9Tlxxe/Sd1oE6dOsJJlWyJs6ZNm0rbAR4kuAQdwpvdnkCVryn169dHIpFA+/btpe87eab086fzpCdc4oRuC9hgDjRhRoKTnUvkzk3ew6KiInTs2DFQ6HWCydo8wK6fAcRujTrvDclz27Ztxm6YvON421zoYiPOwjy3btqgLpJRuHzJ2l8VontVUFAgDRAjOn+NGjVw2GGHoU2bNtL8smE5KygoQMeOHbllY6/Fds1nFNiscwQyl/00b948Y0uKWrVqCSNfhuXW2KZNG+sJr1zHibPtsJXFNvAAobS0VOkqYeMSYSLOeIOJMH3kATNXJPbYXBBnmzZt0j42Dn9nW59wWURE1XkTiYRPtIoGlLZujXRZTcWZ6rpE2K7VClsk0WltZkB5+Zq4malQBQQJ4x0Ic+YXkL/zsrxMRUsQcUYIY90GeWfCbstZRG6NOtABK2wDgqi+08VGnJkEcQjrOQStG7kuzmTlk93vXFxzJoMtr+2aTxFBAoywmIyX6Otir9HUOunWnKkJb4VfnsN21LNnz1amkVUUnlsjW/nCmEUlRLFDPA+TWaGg4oy3z4wqEIYKE3GWTWSBJ3Sh68C8efOk5zIRZ6ItJWzdGgH/zLqqPDS24sx2hluULshAKgxhZ4ru/SXPJRuuQIQwxBl9DtmzlQ2MwhRndD7ZFmfE5TTbLqa0WyO9958O5Drnzp3ruezqlpcXECZMCxUP9v0wGVAXFhaGYh0xebd5dTGKgWsQt0ZbbM//xx9/AChfThIlbHmDtHfZdmu0FWdsXdVZBiE6l01ZKgPubmyHrSzFxcXKNDK3mCOOOEIZpEFncHDooYcCgOcSOHnyZO5x2W4gRYRhOWM3RiTodFZsZDhyjwcOHKhVJrIWbtiwYQD8AVuiJAy3RroOsGuYAOCRRx4BAAwfPlx6bxOJhLdI+7LLLhPWU973F110EZ544gkAwJNPPinMQyeABg/eO6eTlucWxdvUXPfcOnWzc+fO0rQPP/wwgB11T4eioqKMTbZ1MLWciWabc2XmkqyjOuSQQyKznMlYtGiR93c2xFnv3r0zvtMdzLMBF0wjEZaWluLxxx/XSkMIMviaPn16xne33367Vlpe/ST5Dho0CABw//33K89jsh4xrL43KrdG+vple0ryyIZbIyB/TxcuXKh1/mwicp3kwd4PE+HO7sdJ75353HPP+f6nOfLIIzO+CzppyS5DkVnOTK2DS5cuzfju6KOPVqa77777jPLJZ5w4245NRy0Lt7/rrrsqxZlsk2vCXnvthbVr13pBO0Qz2iazDps3b/a99EEwmcURiTN2fRpB55p69OiBxYsXZ5zznnvuQUlJiTL9WWedhTVr1uCWW24BAFx44YXKNFESZqjbs88+G2vWrMFNN92kFGcHHXQQ1qxZg5EjRwqP470z++23H6655hqsWbNGKiJsxVlRUZFWI85SUFCAjRs3+jZ2DeIOqDOQeuutt7B27VoMHz6cm7Znz56+usfy4osvcvN98cUXldFkWcKynOWKOKtXrx7WrVuHqVOnWlvOTAfVum1dNsTZHnvsgdtuu833nW6b3759e5/YEE3wsbD7TBJ07kMQccZ7Zrr9lSzfu+66C2vWrMGpp54qPccjjzyCvffeWys/IJiouvXWW0M5D6D/bt5+++1Ys2YN1q5d60WQ1SUOt0bV8zKBjZKrQ9++ffHrr79qHx9EuNDBmm666Sbsuuuu3ucrrrgCa9aswRVXXMEtI0sQcTZ9+vSMCRGZ5cx0/MxOCrzzzju+axXRo0ePjP60ouLE2XayYXlSuTXqiLOysjLUqlXLe9HC8G+vWrUqN+SrDUEsZ+R+BF3XQ7/U9D3WCeoC5EaQg7DdGkWQa1WFBSfHivY8A+Ruuap7aivOgPKF4DZpq1ev7qv3tptqAnp1s6CgALVq1cqIJqgbYINn7SssLEQikfClC3PNWb6IM6A8UE1BQUFOuDXSZMutkY2qZ9IX0IvmTS0dNmtmgogz3n3WTavKV6etNw3NHaRPptvQKAOC1K5d2zeu0MU2GmYQggSDYbFpv+rVq2cdOAMwe3/o8vECcYU9VhE9uzp16mTcK5nlzLRdY49X7StMU1EDgLA4cbadsBepA+q9p3TWnLHlCmvxcViDLBOTvWiD2KDirCL4Koft1mibH+830bFB3MKCiDOdTWVF0PdIp/xhhLQPc4PbbEeVUwUEycV3LRcCgtBkS5zZhntnj7Vxa6TRed941xmk7wpLnInIdsAREbIBrylxBQQJsl5YB5OBezYwbSeyHRAkTGQRNGVtV9DgdkECimQ7EFKukHs9bUxk4wVSdaY6jRrbMepY23QIqyEPY82ZCB23RJagIjsbIj0IQUSL7Xl18wwSNIJtnOMQZzodiugYk84ozA1ueWl13hNTy5loi4kwOsYoozXKyms66LLZ+ypXxBmd1lSc8QIxqQhiYQliOeMN5E1FgWnfuGHDBqPjaWQDXhm2gZGCEuT9X7FihVW6MMWZzT0yfWfZ9bokMEk2CTvgVUFBgdRyxqYjG6Hrwo4FTSb5w1wrnMs4cbadE044QboRog06gzIS8AMo313+5Zdf9v3OviCihdH0HlcE2SAorBnwMNaciRg9erRVmWjYxbN33XVX4HPKuOiii6w2fQ3DrZFm8ODBVvnx8hSVYdy4ceYF204Qy5lu+XisXLnS+1un0x09ejR2220338brQLCBVBDLWcOGDTO+oyNl8TYHBfT39CLWfFH0rffffx+77bYbPvnkE9/3H3zwgdb5gfD3/ZF11iNHjkSLFi3w5ptvZvxm2gaeccYZaNu2LVq1apXx28UXX+z9nS1xxkaWta2DQfYb04V1PQ56Pt20V111lVVaetN1NhCCiiARf23FGW9tdK6LM9m6Ldk4Im43NtN39quvvvJ9No1kaAOvzgZZc6YSZ0B5e2jLDz/84Pt8zDHHaKc966yzsN9++2Wswa1ouFD622nTpg0WLVoUagOns8Zs6tSp0nOw5WHXr4jyUhGH5Uzk1hgmbB5XXHEFatasifPPPx8A0KlTJ6P0prz00ksAyjsxW19zne950GW/8847rfLj/SY6NshgKy63xhNOOAGff/45AL1Ot1OnTpg/f37G91G4NfL28FOl7devH1577TV8++23vu95oo4HmaUWDTgPPfRQ7v047bTTUK9ePa3Z8bAHK7I1Kfvvvz93WwnAvP2pWbMmZs2ahTfffBPnnnuu77drrrnG+ztb4oydkY/KrdGGRCKBJk2a+Ky6QdoL3Wd19tln45xzzvF9p5PvySef7AXg4a31yRa24qxOnTrYY4898N///tf7Lo5Q+vfee28o55X1uyaRElVEYTkLEq3Rlho1anj3ULVchEbWt6varvfee8+6ztHnGjFiBOrXr6+dtkaNGkYBWvIVZznLImxja+OSmK0GN441Z6aWMxt4jTwtBFTiKyyXq7Ask7biLMh5o5hZD1OcmSDaLNiUuNac6RBkvQ8ROjbWAN3nErY4i3pNimrdTbbEGetGbFsHoxBnQKZojsJyZps2zD1HTQiy5oy1Tkbhkm+7WXcQ8n3NWdSbYIcBLxBYmO6E9D2K+/nmKk6cZRG2IbPpALK1AD+s82ZzzVlY0A1BVGvKwrJkmtwjk2cRhltjrogzk7R0vkHqXxRrzmwHx0ECiZCOctWqVcb55ps4s33+uSLOctlyBmRORkYREMQ2bS6IM9P7w1r4onCfC/IMZXVf1i/HHa0xHyxnYaPj1hgEJ87UOHGWRWrVquX7bOM7HWQAucsuuwh/Mw0XLCLX3Bp5nQf9HFSdMG+thA2yAQGvDKJ7Qe/hpsJEeMqsuLrijF17YzKzFkScvfPOO9Zp49gw1nbNmW1ZefvFBAmooMuSJUukv9epUwcAjFxYdIhanKmCXWRLnLGD77jWnOm+5+z9zWXLGambUSOrKyrYeh9FVEC2jCZ9zu677+77TNePqMSZDaZ1Iyxxxo4fTQkagdS239KBHptWlgAfpjhxxkA2KaQ3A7Tl9NNPx5lnnolkMomePXuiV69exufgvWBjxoxB9+7duYufae655x6ceOKJ+PDDDzN+u+OOO9C9e3e8//77xmWiMWmcg1g8VAwePBiXXXYZd7H+cccd5/3dtWtX6XlOPvlknHzyyXjmmWd83999991G5ZFd2z777JPxnagBnzt3rnaeJp3zPvvsg759+2LIkCHWQokNOmIy8AwSrZElSDARW0w6KttBqm1neP/991ufKxv7PRImTpyIbt26GQUP0cG2c7cdzOaK5cxkzyMby5loAufYY4/VzldUhlxL27dvXxxxxBF46KGHrPOxgQ4IprMJLw37HFu2bBlKmUzyNKnPX3zxhe8zO8kmQjbJbIpu+//dd98BKF/vdsEFFxjlEVScvfbaa+jRo4dyfJdNCgoKMiaRefeOrG2fOHGi0fmvvfZa7292Q2pHOS4gCMPw4cND24G8qKgI7777bqBz8DqWU045BaeccopSMNSvXx9jx47l/la3bl18+umngcpmSpAB+dNPP41+/foJf5cFwCgsLNQWkUVFRXjuuefQrFkzX+PYvXt37bKq4F23yCUlyD5yqjK88MILAMrvzy233CIsn+g5tWvXTus4HkEsZyxxbIxsIpxs9ypUDXxatmzJDXbBizoblSubjI4dO2L8+PGhn9d24M6+WzfffLNWulwRZybYrDnjWSTfeecda0uG7nPq2LEjfv75Z993Ju94rVq1sG7dOqN899hjD0yaNEk7DxmHHHKI9rF77723rwwmZHOyU0SQpRotWrTA119/jaOOOgqA/9qzQVFRkfV6r06dOlkvgQg6wdWnTx/06dMn0DmCUlBQoOWRMHjwYGVkaB516tTJuW2Lcg1nOctxwgjckA1s8g7TWhI1poNW04YnanFGo+rkddfDxSXO4sCkPrAWmrCEkiwMsu6xLNm0nGULW7chNp3u+5Mrbo0m2Lg18gZnubxujBBkP7iooa2TpgHD4rguNk/TtXp0O0+fK6qBehT9TK60oUEnPN1asHjJjVrkEBLGZsHZwKYxdeKsHN51i2bFg0TD1MV2BpZNZzJYyHfLmUkHnC3LmYk4ywXLWbawnR23TRfEchZkQB3EchaWW2MUEU5573NFFWf0tZq2Y3FcV9AgZyJxVpGoCOKsoKDArQWLmYr5dlQgeGuoCM2bN4+uIAw24ox1Y1A1HvTxQRfH2tCkSRPv7zAHrbzrFu1fZyLOGjdubFUe9tpsozXmm1vjXnvtZXT8/vvv7/1tUh/Yuptty1mQaI1hi7OwAg/JsBVZ7Pui26bx3km6DtIudez9DLKWuUWLFtZpbdwaefUr6IBPh6DiLJlMKs+XTUzdEwmm7qK54M4t6rdEiMSZSVvcsWNHreM6dOiQ8V2zZs2087GFFWem/UxYmD4bmoKCAqutnxzh4cRZSIQ9WzJ16lRcf/31uPHGG4XHXHTRRbjxxhsxYcKEUPPOFvfccw9222037zPdOL/yyisYNmwYbrjhBm9x6ddff+393rJlSwwdOhR77LEHPv7440jKS9ZkAeYR4UwtZ//617/Qv39/TJkyxfe9iTg788wzcfPNNxuv68l3t0bbd48E/9GFDhJjImIOP/xw3+ewLGeie8brVLMdhITHwIED0blzZ+kxn332mW+9ow224uzcc8/VHujR0OKLQN/f1atXe3+rLAuTJ0/WzpdeE2calIMuX5BBV5CIgFEIOwB48803fZ+jtgCYrsF55ZVX8OCDDxoPpul7smDBAqO0ttB5XnnllTj11FON0ovE2fDhw3HttddixowZynO89NJLWnmNHj3atzk8AFx22WV6BQ0A3W737t0b48aNy3qeNBMnTsQNN9ygfa3ffvttxnfVqlXLK8+mikhu2F8rAOl0GhdccAFeeeWVUM536KGH+iI58SgqKsKDDz4YSn5RUKtWLTz22GM444wzAPgb6vPPPz/jeHpmu6ysDLfeeituvfXW7BeUk3+Y/te8Rq969ep45JFHMr43EWeFhYV44IEHjMsTlltjkJD2cYizhg0bGh3foEED72/TDYAfeOABT4REYcW67LLLMGrUKO9z1GvOmjdvjnvuuUd53PHHH4/jjz/eqt4SgrgnTps2zbju8dwLbetvly5dtI+l97Vq27atUT7088/1tSS8e2lyf1lrbdRuZqbRBXl9nw70M43CIgT426QHHngg0H6e9N9169bFY489pkw/aNAgHHDAAVp57bbbbnjiiSfw5JNPAigPpBaFUKfr2zPPPBNqtEkdjjrqKC/oig6dO3dGOp32PY9ccc2szDjLWYi4mQY1tj72cWzkGGRAE9YC5yiu29atMcg+RrlgOTMlSOcV9SbAYW6wbEOUkbiCrMWi0S0zLzBHrrf99LUFeV+ieK5B3RpZoh5oRrVuM+41ZzbXGeeas6jyo+ubW7flsMWJs5BwYUHNMRnQRLHBJku2ZptzTZTaujXmypqzqAZfNgKLd3wUe47FLc5M39cgz9/WcsYSRJzlenCDzZs3h3KeIP1ckLT5JM6iGgvEHa3RJv84xVlUEyhOnDnCILd7lDwj12dPc4HKYjmT7Ytmct2dOnUyytcGlcg65phjAAD77ruvUToZbJCMqMTZnnvuaZ2PKCKfDlFbzmwHPnGJMxreQn4ZBx54oHVeNvACPtB1Q7f8QVzRTO/R77//bp0XTRTi7Mgjj8z4Lp/EWVQD8jjEGf0M47CcmbrzAjv6mij6UgA4+OCDvb/zyT0wavdLh5z8qTk5Duuz6+BjK87isJzRmIYMfumll/D666+jsLAwYyCle93dunXDbbfdZpSvDSpL2fDhw7Hffvt5awVFx5l0trVq1cLo0aO56w9VLFiwwBep1CTfI444Ai+//DLat2+vnYaXj6lYtxFnYVnO3n33Xe10cVrOyMDPdAF9v379tDeQlqErHo455hjccsstWLVqFZ577jkAmetnN23ahKOPPjoj7cKFC3HMMcdgn332waBBg4zL+Mcff+Crr77CxRdfbJRu/fr1xnkB5aJun332sUrLont/Bw8ejNatW2P9+vXec80HyzoAjBw5MrLIwnGIM3q9V1BxZvJMf/vtN0yZMiWjD9Lhl19+wdixY3HppZcap7XhiiuuwBdffIGuXbvmvEWdZs6cOejduzduv/32uIvigBNnoeHcGvXIJ3FGr2UxHRzUrVsXV199daD8+/XrF8nifZXIqlmzJvr165eRLojlDAB69uzp/W3y/jRr1gzvvfcezjzzTKt8L7zwQqPjCXQdNJ0dt3FrDGvN2XHHHaedLqyBbBC3xkaNGhmlrVmzJmrVqsWNopgNEokEhg0bhtLSUk+cse5eV1xxBTdtkyZNMGfOHOu899prL6vQ3Lbr8tjtT4L0c7p1YqeddkK/fv3w1ltved/lg+Xs+OOPx+WXXx5JXkA84qx27dqB8re1nO27774Znhu6tGrVCldddZVVWhuKioowevToyPILi0aNGuGrr77i/rbrrrtGXBpH/sj6PMBZztTkk1sjb21JGNhGQ8wW7IBLt3xBQumzmA7m45gMoetDkA1jo3ZrNClrLrg12hBHfcinWfGw2rIo15zRoiofxFnUdTCO8QbtQZJva84c9jjjQ/S4tyNEnDhTk0+Ws7jFWVz1KYpQ+iymjX/c4swUG8sZe40m7wB9rMkgKK5ojUEnX+JoH+j6nuuDl1wQZ6bQddGJs0ziEDdB19M5cZaf5Hr7VhFxb0dArrzySgCI1J0hn7FtnPfbb79sFEdKq1atQj0fWYPSq1cvreNtFj+Hga04C9LZRj2Yt6FJkybWaW2inNH7WvEg+yP16NEj47fvv//e+9tk0+GSkhLtY2X07ds3lPPoEsbgIa5IhFFA1urwgm2YsP/++2sf26dPH9/nFi1aGOVFi7MglupsizOyHyntph0Fcbs12uDEWX5x7rnnAoi+PXe4NWeBeeyxx9CrVy8cfvjhgdcY5RPr1q3Dgw8+iGuuucYoHd0468zCFRcXY8GCBRlrH6KgWbNmSKVSxpsVi/joo4/w448/KgdI//zzD0pKSrhR4bJBLrg1mlpswtrbyoQGDRpg2rRpqFevnnFaG8vZTjvthJkzZwo3Xb3ttttw2GGHcTerpy0lJsFsNm7cqH2siMaNG2PYsGGBz2NC3OIs7oBFKq6++mq0a9cOhxxyiHHaRo0aYfHixQCANm3aaKe75ZZbcOihh6Jp06bYuHEjGjdubJRvkMF7YWGh90yyvW53/PjxSKVSRhv/hkEc4qZq1aqYNWuWteC19ZxxxMMLL7yASy+9FEcccUTcRal0OHEWkGrVqnkL7itTY1OzZk3cc889xunoe6QzaGzatCmaNm1qnE9YHHTQQaGdq1atWl5YehnNmzf3RSOMGt1BaphujaYDjTjEGQB07NjRKp3tHmn7778/OnTogOnTp2f8VlRUZBTsQ4cw3Br79u1rHN00KHGLo7jzV1FYWIhjjz3WKu0uu+ziiTMTgtbPsNqTbIuz2rVra7XrYROX5SmIR4eznOUX1atXD72Pcejh3o4QqUzizBZTy5kj+7D11omz8AmygXWUQicMcRb3/ktxnCPX3RqDENYm36YEtZwRooh4Gwf5ON5w4szh0MO9HY7YcOIsN9EdaLKDgyBrO0w76mwFa8kWNm6NBJM1Y0GpzOIsCLluOQtCXOIsiPig01ZUcZaP4saJM4dDD/d2hMhFF10EAOjevXvMJcld6MFf1K5PDj6s24JusA1WjAXZCNi0oz7xxBMB2LsZRk0Qy9mAAQMAAJdccol2GrJ5+XXXXWeUF1s2k/2ByLoEsv9clAwdOhQAcP/990eaLxHOu+22W6T5Rsldd90FoHwNWZQEGbwPGTLE+zvKTaijhGxEftJJJ8VbEAPCisDpcFR0KmarFRNdunRBcXGx8SaqlQm6o3SWs9ygVatWWLRokbdgf/PmzVrp6FnQXr16CTfe1cG0o95zzz1RUlKC+vXrW+cZJUEsZ6effjqKi4uNokWee+65OOKII9CsWTOjvGheffXVjIh7MiZMmIBly5YZB36gsbVy3HTTTTj33HMDXa+N9W3FihXYtGmTMrJmPnPppZfihBNOCHRvbQgyeL/22mvRtWvXCi2a83G8Qff/+eiW6XBEhRNnIRNn8Ip8wImz3ITu4HXFGU3Lli0jXXMGIJAIiJogljPArl2xCSpDC5R27doZPdOioqLAzyTIgDxoEB0bcVajRg3UqFEjUL75QBwBioJaVuLaiiRK8m28EdbG4g5HRce9HY5IoRtn59aYm9is5wq6LqWid9RBxVlU0AIlDnewuNY3OXIPZ1mpeDhx5nDo4d4OR6TkyyC1MmNjOXPiTE4Qt8YoqczibNGiRbHl7cikorcJlREnzhwOPdzb4YgUevDnZkZzi759+6J+/fpGC8wHDhyIGjVqeEErbMllwRIG+TIpQYIMAIhsE3QA+OCDD1ClShV88MEHkeXJ8u6778aWtyMT1z9UPJw4czj0cGvOHJFSkUNO5zsvvPACysrKjDrNe+65B3fffXfgjjbKcPFxk8uDzkaNGmHbtm1IJBKRDp5OO+00bNq0yQ3YHB6uLlQ8XEAQh0MPJ84ckRL3fkQOOTYDojAGURV9/WE+1fu4LHtuMO6gcfWh4kG3LU6cORxiXOvniJR8GqQ6oqOiizOHw2GGG7xXPJw4czj00LKcJZPJBwAcBmAugEtSqdTW7d+fAuBOAFsB/JRKpcx2PHVUOtxsqINHZQhH7nA49HFbrVQ8cnm9rcORSyhHyslksj2AZqlU6ggAcwCcRf08A0CXVCp1OICGyWQymZ1iOioKnTp1wkknnYS77ror7qI4coDBgwejW7duOP744+MuiqOSctxxxwEApk2bFnNJHDQdO3bEKaecgttvvz3uojhCYqeddsJFF12EK6+8Mu6iOBw5jY7l7DAA47f/PQ5AXwBvAEAqlZpPHbcFgIv24JBSUFCATz75JO5iOHKEO++8M+4iRIJz581dvvjii7iL4OBQUFCAMWPGxF0MR8i89NJLcRfB4ch5dHzM6gJYs/3v1QDqsQckk8mDATRMpVJu6tHhcDgcDofD4XA4LNCxnK0CUGf73zsDWEH/mEwmmwN4FEBPXuJkMnkFgCsA4JprrkG3bt0si1rx2Lp1K4qLi+MuhoPBPRdHGND1aNmyZd73rm7Fj3vHcxP3XHIX92wcBFcXwqFZs2bC33TE2bcABgD4D4ATAEwhPySTydoA3gRwZSqVWsJLnEqlRgIYuf2j8+2hKC4ulj4cRzy45+IIA7oe1au3w+HA1a34ce94buKeS+7ino2D4OpC9lG6NaZSqekAFieTyW8A7AfgvWQy+dz2n68HsDuAJ5PJ5MRkMnlUtgrqcDgcDofD4XA4HBUZrVD6qVTqJuarK7d/PxjA4LAL5XA4HBUJFxDE4XA4HA6HDm7TKYfD4XA4HA6Hw+HIAZw4czgcjizjLGcOh8PhcDh0cOLM4XA4skzTpk3jLoLD4XA4HI48QGvNmcPhcDjs6dSpE5588km0a9cu7qI4HA6Hw+HIYZw4czgcjgi4+uqr4y6Cw+FwOByOHMe5NTocDofD4XA4HA5HDuDEmcPhcDgcDofD4XDkAE6cORwOh8PhcDgcDkcO4MSZw+FwOBwOh8PhcOQATpw5HA6Hw+FwOBwORw7gxJnD4XA4HA6Hw+Fw5ABOnDkcDofD4XA4HA5HDuDEmcPhcDgcDofD4XDkAE6cORwOh8PhcDgcDkcO4MSZw+FwOBwOh8PhcOQAiXQ6HXcZHA6Hw+FwOBwOh6PS4yxnDofD4XA4HA6Hw5EDOHHmcDgcDofD4XA4HDmAE2cOh8PhcDgcDofDkQM4ceZwOBwOh8PhcDgcOYATZw6Hw+FwOBwOh8ORAzhx5nA4HA6Hw+FwOBw5gBNnEZFMJhNxl8HhcDgqG67tzU3cc3E4HA4+RXEXoCKTTCb3BXAJgMGpVGpN3OVxlJNMJvcBsDeASalUanXc5XHkJ8lkco9UKvXf7X8nUqmU2zQyR0gmk20AXAzgZQDzAGyItUAOAK5PzGVcv+igcf1bvDjLWRZIJpOFyWTybgCvAPjCdUK5QTKZLEomk3cCeBPAyQAei7lIjjwkmUwmksnkHQD+TCaTA7d/7awAOUIymbwIwEsASgFcAKBLrAVyuD4xh3H9ooPG9W+5gRNn2aExgJ0APAWgMJlMnp9MJtvGXCYHsCuAlQCSqVTq/wA0SCaTRwDOxcZhRBUAPwJoD6BrMplsmkqlypLJpGtPc4NqAJ5KpVJ3AHAiIDdwfWLu4vpFB43r33IA59YYEslk8gQA7VOp1PBUKlWcTCa/AdAPwDYAkwA8kEwm70mlUj/FWtBKRjKZPB7ARQAmo3zW9mnsmAX6EkBTAHAme4eMZDJ5IoA+AKYCeCWVSo3f/v1YAPcCuByAq0MxsP3ZnAvgOwAvAlgKYN9kMtkfwFkAWiSTySIAn6VSqbL4Slq5cH1i7uL6RQeN699yD6eEQyCZTJ6C8gp8VDKZPH/7198CuCOVSp2eSqUeAfAFgOO2H+9moyIgmUxeB6A/ytedtATweCqVSlMDtC4onyFyOIQkk8nqKB/IvI5yC8D95B1OpVJDUC4EDkqlUuntIsAREdSzeQPlA8p7AXwK4B0AZwMYivLndgyAZEzFrHS4PjF3cf2ig8b1b7mJE2fhkEJ5J9MfwCnJZHLnVCq1CsCvVKczBeWLbd1sVHR8CaDv9lmg4QC2JJPJWtt9qqsB+BPA3GQyeYtzsXFI2AvAxlQqNQ7AfQDqADiRerfvQnmH1g9Ah3iKWGmhn80gAI0AdEX5LO+3qVTqPQC/AGgIYG5chayEuD4xd3H9ooPG9W85iBNnAaBmF0pSqdR6AH8D+A3lrhtA+QChKJlMXgDgGZR3Ro4sQz2XX1Op1CLyNYDNqVRq3faBQHUAVwD4GsBucAM3BwU9k59KpX4B0CSZTJ6SSqW2AhgN4CxqQFkE4EgA7VD+/juyiMazOQlACYCCZDI5HMAYAMsBrHIWmuzBPBfXJ+YQzLNx/aKDHie5/i0HceLMgGQyeUQymXwymUx2SSaTu2w381Ylv2+v2G8C2C+ZTNbf7ibQCkBHAFemUqkX4yl5xUb2XKhOqQrKZwSRTCbrAmiB8kHblalU6ppUKuVCbVdyksnkodt977G9DpGZZAB4EMD123/7CECjZDJ5zPbfagI4LJVK9XP1KDsYPJsxAFoDaAvgBgCfAPhXKpW6IZVKbXEWmnARPJdC8rvrE+ND9mxcv1j52F4fnk4mk0dut2Snk8lkje0/u/4tx0ik066v0iGZTLYE8AjK1zM0AdAklUpdvf23JgBqpVIp0sjdAuBfAMalUqnLYipypUD3uSSTyatRvialEMAu26NSORwAgGQyeSXKXTreRvmC6O+o35qifJ+s4QB+R3mY9vsBPEzeeUf2sHw2j6RSqT+iL23lQfFcGgOo7frEeNB9Nq5frBwky0PiHw3gPQB1AaRTqdR9239z/VsO4ixn+jQBUJBKpZ7fXqn3TSaTxySTyfYAfsB2X9xkMnkwyn3tn3KdUCQon8v22cLjAfQAsNB1QA4OnwE4HMBEAMlkMlkL8KKafY9y9597Ub531isAFrmOKzJsno0TZtlH9lymwvWJcaJ8Nq5frFR8BuDMVCr1JMrrxGrAi6jq+rccxEVeEZBMJi8F0BPAValU6p9UKvVdMplcmkwmu6ZSqS8APApgAIBzAHRMpVLLtiddCODs7YufHSFj+1ySyeTrACalUqmSuMruyB049Wju9u/rAdgTwFEod4mbBuCgVCq1ZHvSR5PJ5LOpVOr/27v/UD3LOo7j77MfJ89WWtRqBGaJ648ZWPiFAhnYVHIUGCoxEbJRdrSfgjaixLSowITI+mMDbUETjQojBuVcWeQ/rW+RkozShkhCTdOW/bCdWf1x3VtX+0Hr7H7Oc537eb9gnLP7ec7hOny+PPdzPdd9f6/nxzDsiWA2bfo/c3lzlYvnxBGbbzaeF4epqodrMvN3wO6qG+eZlC6dAD/H19AmuXJ2DBFxGnAR5Xrs8yNiuvuUaRdwQURMd/c2PAWcnZlPR9diNDOf9CQ0GvPMZTlAZn7DE5Dg2HVUPfwLypvJMyNiBtifmfsiYnl1A7UnrhExmzbNM5dp8Jw4avPM5hTwvDhER9TDW7v3RfUm0q8Bvt99P+draJucnB0hIqYyc39mbgRmgfXAWZn5AqWz1BJgtnuhOwDsAcjMg+Ma8yQ4iVzmxjVmted4dXTo8cz8B+X+xZdTulbdGBFLMnPOZhKjZTZtOolcDoxlwBPkJLLxDfgA/a966PwdWBURNwEf7H7G19DGODnjcFMJImLpoa5GAN2lAY8A74yIFZn5JOWyuXOAeyntmf86lkFPAHNRH06gji45dE9G503AJZSNWD9TXQ6inplNm8ylXWaj2onWQ7d69iLgfcBm4HngVidlbZrobo1R2ojeStnP4/LMnIuIZfUqWES8CrgZ2EK5R+9RSmeblZm5f+FHPXzmoj7Mo46mgN8Cq4G/dZN+jYDZtMlc2mU2qs2jHpYCeyn3ov0kMx9b+FHrRE30ylmWPRsOAC8BNnXHDkbEmoi4Nsq+LH8AnqDs/fFRSgvag04ARsdc1Id51NH1dC2mfSMzWmbTJnNpl9moNo96uA5YkZnbnJi1b6JWzrol3ZnM/FN30+wccC3wMPARyovZvyiXyH0nM7d39zB9E9iRmVvGM/JhMxf1wTpql9m0yVzaZTaqWQ+TZWImZxFxBWVTxu9l5oeq47dT9oA4FXg9cDew94il4f9aKlZ/zEV9sI7aZTZtMpd2mY1q1sPkmYjLGqO0jV0JXA1MRcTF1cMPUNrN/gV4LzDbLQ0fbkdrYY+GuagP1lG7zKZN5tIus1HNephMg92Euutgs5my8eLDmXlHd3wGuDIi7s/Shn0dZWn4GeBblKYSpG2AR8Jc1AfrqF1m0yZzaZfZqGY9aJCTsygbD98EPEbpVDRLaSUL8EPgAsqnEFuALwPnZeb2MQx1opiL+mAdtcts2mQu7TIb1awHwcDuOYuIS4FXALuAOzJzfXf8TmBPZt4WZQ+IM4DPAruBnZm5p3veknQPkN6Zi/pgHbXLbNpkLu0yG9WsB9UGcc9ZRKyKiB3Au4C1wIXAvojY1D3lFuDyiFiVZcO9U4G3UD6NOFzMFna/zEV9sI7aZTZtMpd2mY1q1oOOZRCTM0r70K2ZuZHS0WYt8G3gDRGxJjOfoHS0eVtELAPOBa7PzPWZ+euxjXr4zEV9sI7aZTZtMpd2mY1q1oOOMpR7zv4I7ATIzKcjYjXwHPAoZe+Ha4CXAQ91nWu2jWugE8Zc1AfrqF1m0yZzaZfZqGY96ChDu+dsCjgNuDszN3THtgIzwDTwfuC5bmlYC8Rc1AfrqF1m0yZzaZfZqGY9qDaUlbPaMuDBiDgXuBj4KvCbzHx2vMOaeOaiPlhH7TKbNplLu8xGNetBwMBWzgAiYgPwXeAHwF2Z+fUxD0mYi/phHbXLbNpkLu0yG9WsBx0yxJWzZ4BPAF9yI76mmIv6YB21y2zaZC7tMhvVrAcBw5yc7c7Mn457EDqKuagP1lG7zKZN5tIus1HNehAwwMsaJUmSJGkxGso+Z5IkSZK0qDk5kyRJkqQGODmTJEmSpAY4OZMkSZKkBgyxW6MkaYJFxA3AF4BNmfm14zxnBbAZePx4z5EkaaG5ciZJmkQrgE8B7xnzOCRJOsxW+pKkRa9bLfs4sA/4GfBuYBPwduBCYAbYC3wyM++NiMeBM6pfcQvwue7fFcBK4H7gA5n51AL9GZKkCefkTJK0qEXEOcAvgUeA2ykrYq+mTM5eCTwLvBi4GjgdWAVcCtwF7AE+DfwKuAy4GdgK/B64AbgvMy9bsD9GkjTRvOdMkrTYnd99/WJm3hkRpwM3AkuBs4GNwHT1/NcCO7vv92XmPQARsa07Nls996IRjVmSpKM4OZMkDcXUEV+XUy5v3AXcBnyYcpnjKcDxLhs5CLwDeKH7v/dmS5IWjJMzSdJi96Pu63URsYRyOWNtJbAGOK869mfgn8BZEXEl8CCwAwjgKsqEbi3wOv6zyiZJ0kj5iaAkaVHLzIeAjwGrKatjP+4emgPuAd5IubTxvupn5ijt9l8KbAfWAZ/vjq0DvgJsqH6XJEkjZ0MQSZIkSWqAK2eSJEmS1AAnZ5IkSZLUACdnkiRJktQAJ2eSJEmS1AAnZ5IkSZLUACdnkiRJktQAJ2eSJEmS1AAnZ5IkSZLUgH8Dos3vmNAOMJsAAAAASUVORK5CYII=", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", 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", 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\n", 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", 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" ] @@ -258,7 +258,7 @@ "source": [ "for i in [10, 50, 100, 150, 250, 350]:\n", " plt.figure(figsize=(15, 5))\n", - " train[i].plot(label=\"{}\".format(i, lw=1))" + " train[i].plot(label=f\"{i}\")" ] }, { @@ -309,7 +309,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", 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", 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\n", + "image/png": 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", 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ntmzZUupmTGo8uk7FqisU7G4v/aBTylg+/2/Pkf93P1z66yQgwIof/E7F2dcoSKWmbn8W5IwBuTln5SKnr6oq/vznP+vvI5FICVsjIMCOr371q9iwYQPOP//8UjelbHPOhiMqntxQ6lYUF7/85S/x1FNP4Y9//OO42xZ7PhbkrPj4wHUqWreVhwBHuS7K7O3INCyRLGFDBAQ44Tv3qvjPa8BTraVuSekgyBkDcj1lyWSyLLxnjz76KO6++279fVdXVwlbI3A4oxz6MwDE4/FSN0FH7iUpl2v0pVvLox3FwpVXXondu3fjnHPOwS233IIPfehDWL58OU444QS92PQNN9yAT3/601i9ejU+/elPo7u7GxdccAFWrVqFVatW4eWXXwZAiktfcsklWLZsGZYvX44HH3wQAPCFL3wBoVAIS5Yswfe+9z393Ndddx0WL16M5cuX45prrsG6devw8MMP49prr8XKlSuxa9euib8gRUS59GmK7oFSt2B0WGO5XKOjPm0gZ+WxPixwGEJV1bLp0xTDU9ivIMgZA3I78sknn4zTTjutRK3JYNOmTVnvDx06VKKWCBzOeOKJJ1BbW4vHH3+81E2B1+stdRN05D6/Wj6i4ju/Ln3M00MvZr8vtwctK+666y5Mnz4dzz33HPbu3Yujjz4ab7/9Nm688UZ85jOf0bfbsmULnn76afz5z3/Gl7/8Zd3r+uCDD+Jzn/scAOB///d/UVVVhU2bNuHtt9/W5+3/+7//Q2trK95++2288MILePvtt9Hb24uHHnoImzdvxttvv43rr78eJ510Es4//3zcfPPNePPNNzF//vySXJNi4Pu/VdH8IRVt3eXTf2JlUE8wN6yx9v0qHnm59NfIeG2SgpwJ2MRZX1Nx0hfLS1SmHMZ9qeAsdQMOZ+QaP5FIBM8//zySyWRJxUEqKyuz3g8NDeHOO+/E7Nmzce6555aoVQKHGz75yU9iYGAA73//+0tu6JczOevoBX7we+B/P1ea9lBUBoBwNPN+IAzc/ncFF54mYfEcieu5Hq/7D9fjUZzbe5ap7V566SXd23Xaaaeht7dXrzN5/vnnw+fzAQCefvrprNDDoaEhhMNhPP300/jLX/6if15TUwMA+Nvf/oZ77rkHqVQKHR0d2LJlCxYvXgyv14vLLrsM5513Hs477zwuv7Vc8b37SAf/8Z9U3P5lvv3GLsrBSMsd9wNh4PxvqlBfLI9rBACyDOxpV/HbJ1R85WMSairKp20C5YtkStVDCNu6gZlNpW0PRbR8AmYmHIKcMSA354wiEomgqqpqgluTQTAYzHq/ceNGXHfddQAm32q6QPFQTuqj1NguB5TRwmIWKv1Au+H9dXepuOcR4Ed/UhF9euoYaYFAQH+tKApeeeUVU+R+z549+MlPfoINGzagpqYGF198MWKxGJxOJ1577TU888wzeOCBB3DHHXfg2WefLeZPKAv0DZe6BRmUBTkrdQNMwO0E3nOVivYeoKNXxT3XTp1xL2AfA+HM672d5UPOymHclwqCnDFgLKITjUZLSs6Syeys4G3btpWoJQKHM6qrq8smX7GcPWflAn/OJXpmI/lfjAecWQ9XsbBmzRr88Y9/xHe+8x08//zzqK+vHxUxAADve9/78POf/xzXXnstAODNN9/EypUrceaZZ+IXv/gFbrvtNgBAf38/hoaGEAgEUFVVhUOHDuHf//43Tj31VITDYUQiEZx77rlYvXo15s2bBwCoqKjA8HAZMRjO6C+jn1YOK+jlOu6NSKaA9h7y+u3JlQYpUEQYx/qeDmDNitK1xYipTM5EzhkDxiJnpVZHjMViWe9FzpmAHZQTISonz1m5Gmnzpme/P9RXmnZMBG644Qa8/vrrWL58Oa677jr87ne/y7vdz372M7S2tmL58uVYvHgx7rrrLgDA9ddfj/7+fixduhQrVqzAc889hxUrVuDoo4/GokWL8MlPfhKrV68GAAwPD+O8887D8uXL8Z73vAc//elPAQCf+MQncPPNN+Poo4+eNIIgxmdaKl3ChuSgHIy0cpXSdzoyr6OG61Thn/i2CByeMJKzchDfoSiHcV8qmPKchUKhHwE4CcBeAJe2trYmDd99AsAVIETvm62treuL0M6yRKnJ2a5du+Dz+TB9erZVFo1Gs953dHTor9PpNBwOBwQExoOxfyuKAlku3VqO05mZqtrb20f1+YlEKbmZqqp4cwewcCYQ8GWHLOU+yMLZ08CkwN69e/XX//znP0d9f8MNN2S9r6+vx1//+tdR2wWDwbyE7re//W3e87722mujPlu9evWkk9KPGNb1ysFb5XQQkphIAqmUCqezdGF6pVyUSabIuD9mIeBwZF+DgA8Y1MLSjPcvWD7rWQJlDiM56x9WAZRunBkFSda+Xdq2lBLjWluhUGgFgJbW1tY1ALYB+Kjhu+kAPgjg9NbW1lOnEjEDxs45yyVHxcDIyAgWLFiAlpaWUd/les6MBs1EtE1gcoAKLAAoefiWsWbVCSecUMKWlHYF/d+vAMd8TsXxV462FKfyKqMAH/RlhnxZhDW2NGRe3/5A6doBlHZR5ku3qjju86ou1mKEcdxHDIRaeM4EzKKcxr1RcfSZ14HWbWUaqlJkmFkKPwnAk9rrJwCsNnx3NoA4gKdCodD9oVAomLvzZEYpPWc9PT1jfkfJGfWQDQwM6N+VOuRSYGzs3bsXJ510Eh599NFSNwWpVAptbW36+/7+/hK2JjuP8sCBAyVsydgr6Ol08R8ij6wj59i8Z/R3sQRQmUrgWwffwuKR0t4vAfP49ysqTvyCgl1tpTdC9htSTEttpAFAtcGiuOaXpb0+pfSc/eoR8v+Oh7I/V1UVcY2cyTKQNoSiukSATFnjh39Qce61CpKpMhj3hsyX/vDY200Ecmv1Pf5KadpRapgJa6wBQOPiBgHUGr5rAlAP4EwAXwBwFYAfGncOhUJXgIQ94qqrrsKZZ57J2OTywVi5XHv27MHmzZtx0kknoaGhIe82PM9tNKIBoLu7GwARdOjt7c367tVXX8Uxxxxj+XzJZHLUeQQy4HF9vvCFL2D9+vX4wAc+gIMHD+qfq6qK//znPzj22GOL1p9yceDAgSxv1fbt20uq3mhcYAAIkS1VewYGAwBGi0+8/k4nNmx345zjYvDYaJqZPhQZqQRA1Ahztx0arsOXOrZi9VAXVg924f1LMnPt7r1tttokUHyc+/VpAIBLb4zhT9/KJAqOxCQ884YH5x4Xy8orKgTWeah1sw9ANQCgb0hBW1tHwe2LjWi0HgDpuEfOSKKtbexFyWIjEqkBMDoP98UNndjb6cTpxxQzDpT0ESWdfU9Ijtk0eFwqXE4V4Whmvf1vzyn4/qet3z/xrJ8YfPMeck///EQvTj8603f2HXJg3yEHTl4+caEQm3ZWASCu1o7uGNra2Bb3WPpQf1gC0Ky/7+4dRltbGawUFQH5It8ozJCzAWQskSoAfTnfPdfa2qqGQqFnAFyfu3Nra+s9AO7R3pZ+iYAj9u3bl/fze++9Fy+99BIWLVqErVu3FuXcxpDK3BtM83MaGhpGkbMLL7zQVmhjW1tbwY401cHj+hjJkPFYv/jFL3DVVVdhzZo1ePHFF/Ptyh07d+7Mei/Lcknvv9vtznovSVLJ2lNRoSLfVHbRD5uwsw34zmeB719mPT/PTB8KBsce92koODIymHe/P70wHdd/dmrG7pc/yD0Nxz1Z9/R9Vyt4qhW46QoJ133K3L1jnYeG45m+HYnLqKufDq+nhHleUqa/RxKuks5BHm/+eOZTvtYIAHjrNxKWzy/WtdLOLWXPwyQ/SIXXI8HrlrLyTMNRGf3x6Vg6z1qbxLN+okDuaSBYh5aWzD2a8V/k8zfulbDyiIkZez3Dmb4djnuZ7z9LH3L2ZT9f+0aCaGkZvRg62WHGglgH4Azt9VkAXjZ89zKAldrrlQB282rY4YCxwhpfeuklAMWVsJeksQctDV2khVWNyM1HEygfGEUvjLjzzjsBAGvXrtUlwYsNY74ZQCTJ33rrrQk5dz4kEtmriPF46dQKjMN+diyMyzu2w5dOYae2UPjIuuKdu8CwRyQG+JT8EntPtU6qdbFJiVx1RFoU9pv3qPj7cxNz/4Yi2eepeb+KoZHS9R1j/kk8OfZ2E4Hxwhq375/4NlABEJ+b/OXiYHfx2yTABuO47+zN3OCjL1Ox8+BEjfvM69ZtwEXfL11idTInrLHU475UGJectba2vgngUCgUWgtgCYAHQ6HQ3dp3bwM4EAqFngdwKYCfF6+p5YdSFnQuRM6oEb1s2bKJao4ABxjJWThMAr8HBwexefNm/fOf/OQnXM95//334+GHHx71+cjIyKjPPv7xj3M9txXk1u4zehknGvqoV1X8ctd6fKhvP04bmJjwr7GG/cCwin2dKjxq5knvNHjX66bewuNhB2Ndqo3bs58tF36P37MmmVLxwz+oeHvX6GOO5ARVxBLAX57hdmrLSBoM11JL+5ejlP5bWoDDrKb8AiCFFnMEygM0jxgAbv1b9pi8/QF+4769R8X//k5F98D44/5PTwOH+kpj3+aSs1KP+1LBlJR+a2tr7nL95w3ffYtriw4jlJKcjYVEIoG33noLDocD5557Lu65557xdxIoCxjJ2be+9S3cdNNNuP76UZHC3NDf34/PfOYzAEb3ZUrO3G637rUqZS2nsiJn2qX6WM9e/bPKdGmlEje+C9TFY3AZ7mNFOol+2QMAGMMpK1BmeG2LisVzgGMvzx6PnjxeEbu461/EG/fNewD1xWzrfSRPYMX2/aWTszYaaqU20sZ72k+EOZB7ig1acM4pK4FX81R16BkocoMEbMEoAvLHp4D7rlOhqsCP/5y9XUM1v3F33jdUvLEDWPcO8O+bxx/36zcDH1rD7fSmkcwZ56Ue96WCKELNgLGk9CcCY9Wc6usjKYG1tbWYNm1a3m2efPLJvJ8LlBZGcvbzn/8cwWAQd9xxR9HOV0i5k5Kzc845R/+slIsR5UTOFAWoT8ZwcVcmL88/Rjghb8hjPKt7h4Bjw9n5pZXpzDX7+3NA72D5LSYJZOP4K1VUnD36PlVz1EHetm/sfkCNtKOPyHy2u4SaIGVFzsYZPpEJiLTObUPvEPlgep0Ebx4C/6kfiDFfjsitIeg5XUXzh0bfq+baUR/Zxhs7yP+XN43+jo57t0E0anc7v3NbgfCcEQhyxoB8xurcuXOz3n/1q18tilGbL6xRVVVcfPHFAIBAIJA35wwAzjrrLFx22WV5Q9cESoexcs6KhUKhsTSsctGiRXqeWzqd1vPfJhKqquKRRx7J+qzUYY3Vqeyna4WBCL25A/jVI8UxivLdsq5+FRd+T8Xxw9kJJu/vO4AF0Uzu4GdvVPH/fiOMtcMRp6zgd6xCoW5UUOKbn5IwRxNM++da4NUtE99vhkbULDn/UhtpuY/xk5Zmv7/kJhUvvlnc62Rsw2tbVPz8QfK6pgLo7Mu/z1W3KhOWsyhgDvkKvA/kkbCfqPVQOu5f/kVmcvjaL1T05AmBLDY25Eg1lHrclwqCnDEgH+n6wQ9+kPX+tttuw2233cb93PkM623btuE///kPACI1biRnM2fOzNr2vvvuw+233869XQL2UYjEf+tbJHq4ubl5zG14ghL3YDCIH//4x/rnX/ziFyfk/EZs37591Ge5nrSJhKoCwXQ2OVzWkB3WeMXNKvZ2TMyD7Z9ryf/5MULEErMqAADv7z+I23e/imPCRH78sfXADb9RS5ZLIGAd3/wU+T9Rd4zmnjTWELU4ihO/MPF95uY/Z58zd0V9oqHkXIIbr5DgyllPO+V/VAwMF+9aGR8R3/515k11EBgcY631Fw/xzVkUYEekgC5bhR+46iPkdW7NLx7INTNUVdXbc/QRwKM/yoz763898f3mkpvKa9yXCoKcMSCfMd3Y2Djqs6uvvhrPPMM3q9p4bvo6Vza/urpaf71hw4ZRx9i/fwLkpQRMo1CJg6qqKgCjVQtZUMhzRslZIEBqaq1enak9P9HhjdSLZ0Spc86op6zfSWKJ/InRZHHux1VE43yvVb5b1jtEGkXDGNWl2R7z83qzi3b35FfbFygRairG/q46SG54nu5lG4U8ZzS8KeAFqisyG5Yiojl3xVxVASWXIU0gjNdgenwE9a4UHHksqCM/VURyZnjdaxjHNRXAwhmZ9x98T9GaIMABhUJg/V7opJ/nuKfI7Z3ROOnbHjfgcEh4j0FHbnjszIcJg/CcCVhGvpyzurq6vNs++OCDXM9tNJBpO/r7swsHOp1O3Hbbbbj11lvR1NSEpUtz4jAEygqUnOXLFaREm6fHyAw5CwZJssvf/vY3/bsLL7yQWxvM4Gtf+5r++j3vIVZHSXPOVBVBjQi1u4lEmjSc/77wTsjPd8v6h1X4lDRcqoqkU8a5dy3E3lUZT7mas1MXW31RAc7IF+JEUUXWRriuHpshZ0Ef+X/bf2c2vuPBiSNGqZSKH/6RvDYaq6U01Ogjd0Z8BHfvXIfOT66DEhvdoIkaX8b8IDmt4I4PhfG596vY/kcJd3xl9E1OJIX3rFxAx/y0POaizw24tf4+EV6j3DFfFZSwfD55/aenUfRQXSP2H8qc68Ql5L8gZwKWketBuO+++3DkkUdixowZo7Z94IEHiiYgko+c3XLLLQCAL3/5y/jKV74CAPjmN7+ZtV85qk1OZiiKgn/84x9obx+dabtlyxY89dRTAIDZs2eP+r4YnrNCoMXTKVGcPn26HlL5wAMPjFoIKBZUVdULb8+aNQt+PyFDpfac0bDGDjd5olUpSTgco7d9+OXRn7Egn13dP5zx5Llr3fAEHPj8vxZBcpKt5zVkzzuHBDmbUAyNqPjbs/m9qI+vVxFLAE4H+ctFlSYEUozwplxE4yr2dhLy1qQJEZyfcZjjv2+fuOfFi4aSijdcIunXppSGGnXaLYoMQAYQPxjDnR/NH0v4zu7iXCvjIzsrR+n6Vuz88Hr8YFkvFs6UMKNRwpLs9Hd0DxSlSQJjYG+HikdeHt0PUikV9z5GPp+dJ0vB780Q74kY91v3kv/TDUTxax/PPGne+5WJG/df/XnmXJ88g7RBkDMBy6Dk5owzzsDw8DAuueQS+P1+7N27F//93/+dtW13dzeuu+467uc2vh4YGAAAXHzxxbj66qtH7VNRkR0/09bWxq09AuPjD3/4Ay644AIsXrx41HdLlizRXx9xxBGjvqfkbCJyrVKpFF599VUAwJo1GS3ddDozSw4OTkxsXG6oLhVNKTU5q9MEQS7/IrGelYEkeu6O4qy+g3CoGTJ01W0qXnqb38Mtn9djIJxRZqxoIk91h0fGcf8IAQDc6eyn275Obs0RMIH/+n8qPn6Diq/8LLsf7OtU8f5vkM8aa5CX3FOVxolYQX/jXSCeAC7ydmL3l95EcjBZMOSymDCG3lbG47js4DbUJWNl4Tm7OpRhRe9fmED0qdGDctnFKlIp/katcX2XkrOttycw9NoA+eyNzIXLrXsmxv3EYu7HVZz/TRXPbczuB794iJSzAIDZTaP3I55i0qeSRehDuWvy67UyqqeszHxmHPcTKUq+x6AMW6vV5hTkTMAyKCmSZVkP/wIAh8Ohr/AbcfPNN3M/NzDac5Yr/kFRWZldiXbt2rVZBrdAcbF2LVFuGI/YnHLKKaM+CwaDkCQJiqJwu2f5whpjsRhmzJiBVCqF6upqnRQC2V67uXPn5s0F443du3dntc3lIuSj1IIg0+MkGL92SWbcrzvxJfxPx1Z8sDc7l/PBF4pLzvqHM+qR7rqMnrbDT6z9XHL2wgSGqQgAj79C/j/4YvbnB7oyrxtrgGXzsr+XpIyBzTXnLM9nm3apWP0l0i/+q3UTDj3WhW3/711UBrK3u/B7E2OpGa9N9R+24pyuA/jffRtLKg6gqqSwu/pcJvIh1hGD15M/TjRXdY5LG/S2ZJQsA+0ZRdbI3kySUGWOCfLCWxAoAaiEPcWTGzLzb2MeQW1jWGNRcs4M0//vn1Bx3d3kg1lNmX6cuyiTWxy7WAgb0u4pOROCIAKWQUlRPiPX7eZYNTQPcsmZqqq44YYbAIyt6Jcr8z80NDTKMyFQPIxVmy4XF110kZ5bReH3+/U+xSu0MV+/ff7553Ho0CEAGFWKwbgAAfBdbBgLRnIWjUbLwnOmqEBLghhBgXl++Of6sr6fHcsmrTyTqnNvWc+AimdeB5qSJHHAN9Orf+fUyJknpwbbrhLVr5nqyF2BNopJNFYDf/qOlEWGfB6DkVbknLP/+dlo4yu8PQw5p7De358DNu8pvqG2u52cQ1JVVLxBmNrs+EjJPWdHRQeA4czNiHWMnTBYzDpRz20khnuFH4htzdQb6PhHJ9Q0uXZzc1KXd7WJRZlSIHfcG8OXm2ok/OMH2WOs2GGNRnL22Rszb4yEbP707H2uvkNFOFL8/mMshl2rtUd4zgQsgxKkUpMzVVXx0EMP6e9zSRjFrFmzRn1GQyEFio+xBDhyc/+8Xi/Wrl2L4447Tv+sqqpK71PF8BrRNhi9YbnkjNY7o/j+979f9Bw4Izm76KKLyoKcIamgIRmFKgH+OX6svCe7CFWPy5v1vpiKV9SobkxQcpYhitRz5lWzrYN89XQEio/ccKK04bZUBoAjZkrofjgzR1T6M0Zab5GjiGmfqEtmrCPZTcyD9x6dve3Sz04AOdPCm04ZzI7FK0aYl1koKjBHW3iRHOQ+xTrG1kQ31mjjBdqHTv8qeTEcAYbeHMra5pXzXsPQpiGcfmz280aM+9Igt8cayVljDfDhkyU8fWvmXlUFAJe2zVjlEbi0K2dCqjGsvU6rH22rfDnPAg7v9hjHTI0gZwJ2YQxrzMVYBaCLAUVRskLl8glKUORK/U+UsIPA2J6zWCz/A76zM2OYzJs3D8PDZOb6yU9+wqU9uQR/27Zt+NjHPqZ/ZizFAAD/8z//M+oY+Uo08AQlZ6eddhpuueWWsiBnnr4oHAAStT7IbhnVx1ThyO9m8gS9OZ4qnkZajiMDz79B/jcmSTyIb9ZocobY6PYIMaCJR+4VN9Y68nnIf7crc4OXz8+oFO7tBDZu53PPcteI7ntMxZta6NUXOjKxeMlBMsYe+eHEK/9Rr9PnmnqyPk+GS2epGXNNq0Mk3LvtL+1QEvlDPfuLQIbyDdvBt7PJWf9rA3jp1PVYJWcz+mKQRYHxkXvPHFmeM/LfSIxOWirpizK/eRyIcS7HQvGZ/8shZzmhjOt+mT3u/8y3GtQodA9kVCxfvUuCR/NvCHImYBmFwhovueQSvO997xv1+ViGuFXkhjVSoxUAFi1aNOZ+L7zwAt773veitpbIcQlyNnEYy3M2Vn0zYx06477/+7//y6U9ueTs97//fdb3Ho8n670kSXjmmWeywma7urpQTHR3dwMArrrqKvj9/rIgZ+4BMoaTdRkiNO9/5qLx7AYAwDR/dtt4rljndqH3rSL/Z0saOcvjOUtH0njg+xLOOZ58nkwVLoIqUBzkhjcZJfR9eQItjj0yE9YIAD//R3GMtMt+lDnuksiA/jo5QDz0AZ+UJakPAENFXNEHMsqCM9LZbud4X2lzTes1z2LVMZlc3H337cdfb5DgcGRINkBKXBSjDUAmF/GOS5OItcXgCDjQdF72wmv6zT5857PAcUfR9nBvjoAJFAprpDlnRmJ0RghZxc33c37E0l75hyezP88lZycsAT59Vub9zNElfLmCjvlFs4DjFpeHQmspIcgZAwqFNfr9fvznP/+BqqpQVRUNDcRw45XjlUvOIhHyELv88ssL5jYtWrQIzz77LM444wwAgpxNJMYiZ/Te5eI73/kOAOCee+4pWpsoFEUZlVOWLzT3tNNOQ0dHh17rLB4vUKTJBoaHh/Huu+/q72n/pJ7ochAEcUQJ+Ur7M4WGJEnC9I+SJI8PHpuG+qKMbX8g97sYwgAU9ME1E6PDGmWvDEiAEldw9uwovrutFRckiUtCGGoTj9wVdGMhWqNRf5JWjvIzZ0lZRpove63ENnKnoYDWZZyKoqt+AhlyBgBf/pgE9UVZr8sU5zz8DnapONRHLpCiqPqCRuIgWXQIa+M+3lu6ca+oQF2S3LS61bX65wf/1IaPnQqknpMReUrGr75OLvCbO/IdhQ+oWMJp08n1Cczz45jfrMR71p6kb5PoSeD7l8n403dJe8SYLw0KhTVSz1mzQcZ+8RxgxLBey1sUZKygifqq7PeSJOH335ax889UOZJvOwBgy14VkRhpEO2flCS6JrDWWzlCkDMGFCJnuaAy9j/72c+4npu+pt4Xn8831i5ZoCFrgpxNHMyQM2Oh8Ouvvx5btmzB5ZdfXpT25PahXDXP+vr6MfelXjXe5Gz+/Pk48sgjsXkz0ffNJWfl4DlzRsnTUvE5sz+v0NqmCQbUGS7nq1uKE5IWjQP+dBLyYAKyR4a32WPYVoJ3Gsl/eyG0Fn3r+nHpu5sRTCeFoVYCFAprnDstc2Mf+5GEPX+VsGi2lOU58/MiZznvqapfjRayJ9WSRZnUUEoXl6DwaOsRPMlZIqli5kdVNH+InGtoRPNSeVJI9iYhuyUcqiALR8kSe86atPDh4JEBnLaJqOoObw5j7cnr9GtFjdzn3kDe2nY8QPuO1ENeeFu8kGQJlYsrsPwX5BkS7yL5wNTYLUaYpcD4yCVDxvHXUE3++zwS2v4hoethCZIkZd2rYuYsGzF9jMd9McY8QFSDl3xGxXGfz0/OhOdMwDYK5ZzlYuHChQD41Ycay3OWT8I/H6ixK8jZxMFMWOM///lP/bXb7cZRRx1VtPbkkrPcflwKckbDGGnZgXIkZ44YOfdY5Kzn2V60XrQRfb/cqSsl7jzI59y5XSgSBxbEyFOtYnFQFyqgqDmhetQxFkcGBDkrAXKNNGNY45UfzLyurpAwRyNrxiHpzjhquYKGyNWmiDHvm+6Fu84FqEC8O1vwh+aBxDnqAOUan7RvLnBmvMERr+Yx7y8dOZPSCglrlABviw/e6Rnhn/DWMIY2k4afGcrsU6zCz3rf6dau0YzMoqynQVP17SE3qUpTAB0MA+l0cciiwNjIHfcxbewcfQQQ9Gfm6+n1EhqqyfsBQ0jsEGdyNpbnzOHIb5/oY57z0Hv4JdKQzXvIe0HOsiHIGQMK5Zzl4qKLLgIAXdSBdzuogS/IWflirH7y/PPPAwBOOOEEzJ8/f8z9qZjLtGnTxtzGCsYi+BSFPHaUnBVLrVFVVSQSCb1QelmRMy2sUfFnk7OKozJB+11PdGPnLbvxg0aSNzicP62QGdE4MCNOEoAqloyuGLzohoVoPKsB0z7SrOeknDzYKchZCWA0ilRVxZ+eJh9c+1+Ax51/bqg1eF9HOPWh3GmIrt5P08pDVMzxQnIR0+CFVWsR64xDVUhbPUWQ+M5d29xJhnwmj3K2DzHtxMmB4qrDFkJFNA4HALnBA4eHNPrYP2WkLCkZCvgkLJ5DPitGbp6qqnpIbKpTu0YzMkTR00xeR9sIcXM4JFRpEevFVP8TyA/juI/EVPztOfL6hkvGthsvOTfz3UR4zs46buzvdM8Z56GXO+5f0aJLBDkjEOSMAVbCGmnI2NDQ0DhbWjs3fU0Na7NhjYKcTTzy9ZN4PI6vfOUrAMZX+HzhhRcAAIFAoOB2ZjFWHwLIYkIhokjz0Xh7zigURcE//vEP/T3Nh6M5ZyUNaxzDc+aqdGLB17IrCS/YsA++dIqbkZar1hiJAVWax8PbNDruzdfiQ+hPx+DoX63AkdcT7/2KkT4R4lQCKAYj7bWtwHoSuYuairGfHy6nhJ9/mXxfLIJPc9lmUZK/KIh4JxnX6Ugazy55Hjt+vAtAxnvH01DL/fX3PEIuVB1dcJzlR1yzENMDpfOcVWkVch3NmWds01mNmHYBEUii5AzIeCN5GdZGQzaRJCITLieQ0OqseQ3kLDCXnHxkx4iuJEnVAMWizMTDOO7v/Gfmda4AhxFL50n4yMnkdbHFdwDg0TyKrBTFqLUIjF4kogIldOzsu2Un/rTteTREpuaKgiBnDLCTc8bLc8YrrFHUOZs45Osn+/bt01/feOONBfeneYI7d+7EHXfcwdyesfIWq6qqxj0+9Zxdc801eO2115jbkq9ttBj2vHnz9GtHPWelFARxxrScM//oOLOF3zoCZ+x4L97zwonwz/fDHUliWaQfw5wKeOYLa6zWyJm7vnBtxcACP5IeJ2pTCQwfLA6pFhgbxhX0dzLl+3DZ+wvv16RpT/zmcWDdJv5haTRE7qRKwtiDRwbR8vHsKrQ7bybkjK6iH3+liqGR4vRpep2Orsh4zuJaWGP8pW4u57SDas116ZievQDqqdMWqgzkjObx8QpJM14jer/8XiB6kHrOslVaaTH6J6Y9hdc/9QbmyGQ7Uets4pE17rUC7kfMyAj/jIUWoiGHy36kZoU58miPYmCMD98kwemU0PN8L7qeHD2+9ILYSeCj38lfNsIOcq0hukj08dMkqKqK3bfsRlU6if/q2DUlS78IcsYAKzlnxfScsYQ1/uMf/5iSHb8UyNdPjHW8Vq5cWXB/qvgJAI899hjXthkJ/g033DCqxlkujDL7F1xwAde2AKR/07ITxuNTcnbjjTfid7/7HX71q19xP/d4cI8QcqZW5k8Ccte6Ubm0EtPOJyvqiyIDRVv9jMaBKk1hbzxyJkkSIjPIItFrjxa5qrHAKBin2d0d5M33LgYaawov7h2/OPP6+TfZ2zGK4GviEnOSpJMGFwWx+IdHYe5Vc/RtPJrQjMfQ5e//D3tbcqEoqp6TM00T3/DP9iHuJ31b2T6Eu++P4+nWiX9m1YxBztwaOUt0ZRY8eHvOjKAhjX5PJnTR15Jd+H7ht46Aq5rMlYf+3YX3HiDh1Wvf4t8egcIw2le0ft+dX5PGzPGiOMNQRHzT7gIbWm0PMnlvPg/wgdUSUiMpvHZBK1r/ayNi7dl1VmRDuMaDLwApToXgc80h2qbZTUDXfzIksTKdxG1/B37yZxXb908dW1WQMwZYyTkrpudMVVWEw2RJzCw5mzVrlv56/fr1XNokUBj5yBmtZTZnzpxx93c4HHjuORKwPjLCbu2PFdZopg8ZyVlPT0+BLe3BuOBgDNWl5x0ZGcHFF1+MK664Aq2trdzPXwjuES3kq7KwfF7FUSSWaEY8wi0kLXeqCUczKnvUSCwExxFkkSi9fahoxU0F8sMY3rSfOIUxu3n8Z8esJgnfv4xsNxJjv2f5vK91yRjSbRFITgmB+QG4Kp1Y9L2FmPZhssAgOclORnLWxylEzkhaFSXjGZK7M4XVD87OiBP97eZunHn1xPfdao3FunLImW8OeT+yO8PEKrXIc16LMsZbpkVXotKVRqIrAckpwZMT0txy4XSc9OQJqFxBxvuCTjJH/+bfYsxPNIzjfh8d903j73f+eyScoYnLjHCuS0kXZPwapzd6zLqfKfw87+XjX8irPAwQwnjwz236543JGK6+Q8W1d6o46tNTp/8KcsYAO2GNvNQajVAUBX19fQCAurq6cbYmWLhwIZYtWwYAePbZZ7m3SWA08vUTq/eN5l4Vg5xZKcdgrIHGq7C6EYqi6Mf1ejOrwvkUJI2hoRMBd5gs8SmVhcmQXzPampNRDHIKJzL2IFVV0TeoYk5MC0dbOH4u4nHvJ/PQ3FgYr2zh0yYBczCSkD7NwKmrzL9tLoLakOQlCmJENA6sGu4BFKDxrAZd7EKSJSy7dQmAjIS9UTGyGEWWFVVbQVdVqJrYhX+OD8EmN+5rOgIAsDBKLt5ER3xQz5kzh5xVLCJz8qHHurD9/3ZgeFtY95zxEuAwPjpo35nj1PLNpnlGqbQCQGB+ACc9eTycVU5UD0TQnIhg677iyfsL5Iexm9Kcv7qq/NvmoqII415VDURIe4Ttu/eA/n1kX+GTHeIkU2DMn06nVSRTpJ+7nCr61mVO0piMQtIu4lQK8hLkjAFWyFltLUkc6Ovr0z1uPM4NEEOWei/MGvkA8MUvfhFAxnsjUFzk6ye5UvHjgYqB8CZniqLo4h5Gr9hYMLMNC8Yii1Sx0oh0euLknNS0CndEyzkbl5wRC21aIoLeQV75OZk+NDQC1MZiCCopuBvdek2zQqhdSdjAvNiw7r0RmBjkM9JqTZKzgHZrwxyMtNxZKJ4EZmpiINXHZluNjqADsltCOpJG11PdWZ4zXuISxpGR1jxnlekkEE3DWeGEq9qFxhpgh5dcrKMiA4CqTlj9J4qaSH5yFlwYRGABmZd3/XQ31q55GdMTZNu+If55eT3a+u5MSVu8mjH2YprslNFwGlnQeh96kEwBnb1cmiRgEnTcp9MqhkbIvawyqelFC8TzGPdGUFl8j5s804beyrjDaB7jWOjiRc4M7IOGNHrdQLIniWRfEs5KJ8JuF1yqqkeHTCUIcsYAKzlnHo8HVVVVSKfTXBQScw3r3l4y41ohZ0KxcWLBk5zRMFYW5HrOqMgGVUQshGKTs7E8Z01No+NBtm7dWhTvXT5E9kYgqyp6nB7I7sLj3lXrghRwwq+kMXKIj4CJsQv1DgF12kPLP9OcSmtwQQAqyGrkwCC/5G6B8ZGPnBVSbDOCkjPe4U0AkEwBMxKEnFGSQSFJEpQEaXjrJzbi+HXb4dQWF3nV8MoNa4wlMsWefbN9kCQJTTUSdvnIxVoQG8a3D7yNVzZPnPcsHVdQFY8jDQmunBBC2S3j5HWrsej7R2o/Amg5QBZLeXkZssa9Rs6mKfnzzXLRcDohZ8uHiI0gREEmFrSH0uteFcjO4yqEYo37pKa86HIA4R0jSEcyC5yxtsIna+OkyWPs00ZyFt6eESbq92vRJ4kMYTxwaGq4zwQ5Y4CVnDMgY1hSFToWCHJ2+IEHOeMZ1mgEKznj4Q3ObU8+z1lzc/OobW+44QZcd911XM8/Foa3kAfHXm9wVMx8LiRJgm82abt0iM/SZ66RVpvUvJ15ZPTzQXbLSFZ64AAw0jb1ViPLBbSUgVlyxjOsMbffJlMkLxIg5D0XdSfX6q8XbdyPC3r3AgAOcjLSssiZFnLVlKAy+ppxVguMOFzocJH3Jw134axrVDz8Ep82jIfoAdKebpcHsmu02SQ5JMz70hws/SlRb6neTeZ1Xl4G4y2jOT/1idE1zvKh7mRiE8zrGwBUVcjpTzBo/7a6IAMYxj1HcqaqQFLjYi4nMPQ26VBBLTw3erDwyfZ0cBIEMUxExnyz8E5tLjoigHg16dtfP7hJ33bWx1R09k5+gibIGQOshDUCmZAs3uRseHgY8XgcPp/PtCAIIOT0Jxq5/SSVSuGBBx4AUB5hjbR2GFVELARjzhkAtLe3M7fHiFdffRX3338/gGzP2dy5c3HNNdeM2v7222/nev6xENlHHhwH3YFR4WH5UDGPPF39/REkknwfKD2DGTEQT6N5T2a6llzPeHuRCmcJFMS+ThWHSKqpec8Zx/Cm3MeVlEyjMRkFHBL8c0c/P1b8YlkWQVs1TLxCxSBn9z4K7O3MrJTTxY0PrAZOXALcMDtT9NmjpPHrxybGSKPk7JDbV3BRpu495Do5t/RBUlVunjMjerQQ6eoorXFW2Gvua/HCVeOCJ5lCbSouyNkEg/bvvz9P/lshZ5lxz1dKn3rOnA5gUAtpbDqX2KfR/VEkh8Yuara7g087jOPoipvJ7/O6gZFd1IvvxzEXkoWF6nRC99gDwNaJTTMvCQQ5Y8AzzzwDwFxYI0DqNQHApk2bxtnSGuzkmwGZulnCczYxyCVnmzdv1l8vXrw4d/O8cLvdcDgcSCaTzLW+WMIajYQJADo6OM3YGh588EH9tdFzJkkSbr75Zj2H0/j5RGDkAFlV7HF5RkkB5wMtCDstEcXmPeznz11Br9FqnHmaxldq1I/RRK6nKsjZhCC3az7ycua1z1Pa8CYAqI9EIYNI1ucL1fVO9+L4h1bh9C2nAgDmxMOQVFUPr2OF0ez86h3kHQ1rpJ6z2koJL/5cwkFPAD1OshBRkU5ixXw+bRgPPdvJoswhl6/goox/nh/eaR6og0nMjoexZS8f6fF8YY2BsBbWOI7nDMiEq7YkIqIA/QSDcoo3dtA0GPP7BryaSivnqTpl8JxRclZzfLX+/dbrt425bwennEVjn/73q+S/LAEjOzVyNj+A93xjFuTZAbhUFUfEMnlxTgefNpQzBDmziR07duB3v/sdAPOG4SmnnAIAeOkl9lgMo2Hd3U2WMPMp2RWCCGucWOT2E+pBPfroozFjxgzTx+DlPcv1nFkhZ7keWl4lIvIhlwgCwEknnZT1nqqhFhtbWolB1O3yjhvWCBhEQZIRvMRhTSbXSLPjOXPOJf3H3VGk4msCWcjtJ4f6ybj7xifNHyPAMbzJ2B5VVdEczaxUF4KnyYNEhRs+Ja2TJx7e4HxpY9RzRscPADidEmQZGHKS+akqlbBk6LLgz3/UPGcuX8FzSpKE2tVk4WiNexBDI8A7PBZlcnJNAcAzoOXkjpNzBmTubUs8IjxnEwzavWmI601XmF9ILH7OmaqHNVatqETFEhLa2PN8NgNzGMgQLyGefGl3w1FgZBc5AV1QoONp2UjGTo0lRu872SDImU3Yke9esGABAD4hYEbDmqoumvXgUVDP2cDAAPecIYHRyL0/lJwtWrTI0nGoJ4lVBIPFc5Yrt8+ruLqZcwHAbbfdllUbzuPxYM2aNfjMZz5TtHYAQFj3nHlNhTV6tVXt2mQc7T18V9C/8nPV4DkzT84CmuR+oFeQs4lArhFCjTQzNc4oqOQ1b6MklSYGOwAEjwiOu320hSyCzIsRC5+HoZaPnDUlNa/QrOyx/8ytEoYc5GJUpxLYcRCY93EFv/hHccMbabHnLvf4izJVmiLq0jSZE9s5lIE0nvPBFwCoKuQeczlnAPFCAET4pRglEATGBjWt6Lhvrh1721z4tGmd97in5KwpEkF6JA3vdC88DR6sfuZEOIMOxNpieigvQIRDKLjV7sszjkaGFET3RQEJeoj1zDPIBfts1049F/XBF1TMvEDBuk2Tty8LcmYTxpCyzs5OU/sUK+eM1k7buHGjpWM4nU5UVFRAVdWiGtcCBEZy9pOf/AS33XYbgPwKhIVAxTio9D0PWCVnE+k5y+dVnD9/Pu677z79fXd3N1566SXcf//9zOGehVAVNZAzE7Y1LQxdmU5yyT/JPWet7jkzH9ZYOYsYc94RIQgyETDes5/8WcU9j5DXjdXmj+HRbm+cg5FmbE8yBdRTImTCAxObTsgZra1XLHJGPcLe6dmLDqceLcGv9d+mZBR/eQbY0wFcdVtxjTS6CNLv9Iy7KFOxmFyjRi2ygcu4zz1HOgnEFVJqoHL8+ZoKvbTEI0KtcYLx7EbgnodVPU+q0Vx6OYBM0fc450eaTs6GSGeoXEb6rOySdU/VcytfRKKX9PtieM7yPT9nKFGoaRW+WT693iJtDwBc0LMXAHD3wyTn9QPfFORMIAfGUMC9e/ea2qdYao0Un/ykhTgZDSK0ceJgDGu89tprdTJtlZzRMD+enjOrgiC55KyY5H7u3Ll5P6+szF8k6sCBA3k/Z4WSUOCLJJAG0Od0QzHxXMgiZ33sbch9nlVTz5mFsMaaGaRN/ugUiA0pAxiNkGvvzHSaudPMH8PL0XNm7EOpNEm2B8x5XxMzaRFzshgzVARy5lHS8CtpyB4ZzorRc9FQFZl7vtSxDQ51YiI+qrRxNuhwjxtK6ZtJ5udKLSeMy7jPGfgNSVrjbHxCDQCBI2jO2YgIa5xgvLED+PxPMp3cbOF5wDDuOayjZS3KaDlnDRo5Cy7MqLQ2nZOpJdr2AMklN+Z48RjzQP6wxi+u0kIa52fsC0+9G4tuWAgg47Gn6JvEPgVBzmzCSGb27DEXVF5dXQ2Xy4Xh4WFdJpwn7rjjDsv7CHI2cRgrN/Hiiy+2dBxenjOeYY3F8pz95je/GfO7XMVICrPj0Srih+KQQFbPFUnGwa7x2Zm7jlzLylSiKMptQVUj1FXj3zOKhjkaYUwkJqxO1FTGWB7WoxeaP0YxV9CpV8jdML73NTmThD7O4+k5y3lPFxzc9e68c2ZsZsa6PbfvYObzePH6Mm3TgNM1rsfc20LmRs9wHLKq6DmGPPG5482LgQCAf44PkIDmRAyDAyKFoVQ49kjA4TAfzqx7zIvkOavTYhQDhpDmlk9M1wWmInvIAHfleM54PDfyjaNzZ2bEQIyY9hGykjUtMcGV50sIQc5swkhmzIZRSZKkCxfwFHMAgFNPPdW0HLsRgpxNHMYiZw0NDZaOUwzP2dNPP42tW7cCsBfWWCzP2cqVK8f8bsmSJTjuuONGfV6svpzoJ+OcChLsMSFQ6fA7IHlkeFUFyeH0+DuMA+OqvaSq8KbJMZ1B8/JVlfVOxCQZblVBdGBsyWQBPsg36k9aak1hlKfnzIg7/gFU09BYE+RMmR5AUpLQnIzCl05xyT/JtfOmOwq356r/rcMjtTMBAB/r2YMGLQ9lsEgplGpaRaXmXRx0uscNLXV4ZHiaPJAUFXXJOBcxh1xv3dG1WtjnNHPkzOF1QG7ywgEVKqeaiwLWccax1rYv1ri/9pdk0NUMkEFTcWSGDMkuGYt/eBSATAkJY1ijqhan3uLZxwOR3dliIBTeaR7ALaM6nYQvPTWeWYKc2YTRAPzsZz9rej+62p9IsI22XHJmpb6ZETQ0TOScFR/5jDGPxwOHw5oubDE8Z5deeqn+2o7nrFi18qgyZT7IsoxHH3101OeRSHFW19Jh8lCIyCTU6twTxjeuJUmCs4HcLx8HK83YhbwKIWaOgANSvhiRAm2KaaGrA11T40FXSuTjYAFzNrUOtzYkkylAMRNPa7I9N/xGRWVa85jXjk/OnG4ZB90ZcQke+Uu55KxR1jxnY4TqTm+QcebtC9Dp8qEulcB/d5BFpUgRygwAQPhQAg4AQw4X0pKM+urx96GhjY3JGBIcvB65Xcg1Qg5qhlBTuLV6aJ5eQc5KhYDPWskXL0fPmXHc72wDZFVF1YBGhhZmP2f9s4k9SclSrnQ9j3Gf+8gKeA1KjfOz7VlJlhCcS/rvtMTU6L+CnNkEJWef+9zncPfdd5ver1jkrJARWwjU0Gdtj8D4yEfOzBChXPDynI0FO+Ssq6urKG0Zr1/nk9kvRsgwACSHCZGJyk784XoJF55mbj/39OwcFF7wK1pIY568nPGQ1BYE4iMixKnYyJejFBgtQFoQkiQVJ8RJVeHXvK+uqvH7kdNBFAsBkvfUxcFJnUvOGlQtB64A8VD9LlwzdxXSAFaGe+FW0ihWCmXPPrIINuhwY+v9EhqqxzewfTPJDW5MRpEowvqHYygT+mkWnpnE4K3iUclcwBasLsrQcOZiSMc3JqNwphV4mj2jRGWCRwQAGQi/O4LkQDIrrBEAl3Gfaw4FvEBYq3EWXDD6ue/Xa4ZOjdBGQc5sgpKzs846Syc4ZlBunjNBziYO+cjZWHlThUDvGc+wRiPMCILkbsND5CYXDodDVzgdC/nGXvE8Z8SIjcgOnBEyH5bm0chZVYTdKDKe0Z+2T85SDjL1x8PsoZYChZGvlwQN5Kz7uR7s/vkepKOF74Wed8Y4Ved6Xx1QEZdlyK7xzQGHDHQ7M+SMRz5V7hFanOOTs0QK6Hd5cMATgANEPbJYnrP+A6Q98YAbi2abG/M0F4yb5yzntNKQ+TxBCk8zmSsDQgioZAhaXJThKgSU04dmxjUitHA0EXL4HYSgAdhw4etwytmjtBjKw7Mqkkh0JSB75by1+yqOIilB5/YfzC/xOslgipyFQqEfhUKhtaFQ6P5QKOQyfH5qKBQ6EAqFng+FQs8Ur5nlB0rOrOZ5FYsM2fWc8SKLAuMjXx06O56zYoQ12mlTVVWV/roYnrPZs2ePSxTztbVY5Cw5RKysqOxEjYWa1zQvJMDhCWt8oPkV6/lmFJScJYTnrOjI6znTbA8lpWDDR1/HthveRce/Cpdk4SUKYuxDAc37OiKbI/hnH08KsANEgr8YnrNpjvGJByU8u70kLH9ebLho5GyoPUPOzEL3nCWKQ84woBFYK56zetKBfDwaJGALlj1nPEto5LzPkLP89Q2X/nQJ3PVuDLw+iFvffAnBdKbf8Bj3uWGNC0AWLwPz/HnD9OdcORvDbhdWjvThpOGMvcEa5l2uGJechUKhFQBaWltb1wDYBuCjOZv8tbW19dTW1tbTi9HAcgXNsbFKzsrNc0bbw7NmlkB+lFtYIys56+zs1Iuxd3R0cFf+MxaZHgv5rmmxyFmkn5ChpNsBt8uC4lYNMXy9Sfb4pmxyxuA505IIhOes+CiUcxY/lJl3Y22Fx3MxxAECaUrOzI35lgYJN36bEI+GZBwdvextyJ02ahXqORs7ImVaHfm/20vVI4cRKdIjLNJJ2pMK2iBnnMIac/tQup8SWPNRO16trIdPLMSWDFbDmYslCAIU9pwBQO0JNVh593IAgNwdw7oPteFrHyff8Rj3uX26OUbzzfK3x1PvxraT5gMAPt+xHTVJMuCjk9R0NeM5OwnAk9rrJwCszvn+As2r9mWuLStz2PWclSs5E56z4oN3WGOpPWderxczZ85EMBjE8PAwd5XEuro6U9s98MADuOWWW3TvcbFyzsJ9mpXls0aGvLX8VqyNXaiSSo6bEHLIRdpJpv5kRHjOio28YY3adB09kCFkiZ7Cc3Ax5PSp5yzsMN+n6+ZTchbD3sLOPlPInYZo/b1CnrMzVwE//oKE8z6V8ZwVy0iLdpP2qNVWyFkmrJGLmEPO+1Sf9Zwzr+Y5CwjPWclgNayxWCU0AGB6Ir8yohH1p9ZhxS+XkTa8OYDZzaQn7u3kL6Xv6yXt8c8b25b90l0tiDYGUJ+K46NaQepiLcqUGmZm5BoAVDR6EECt4btWAEdqr/8VCoVeam1tfd24cygUugLAFQBw1VVX4cwzz2RrcZmgr49UloxGo2hra7O8f1tbm639KHJzfFKplK3jUQO/u7u74P7JZJKpvZMdZq5PPkVMSZIsX9e0lsDf2dnJdE86O/NbVl1dXabyzihmzpyJrVu34tVXX8Xy5ctttycXDofD1O874YQTcMIJJyCRSOCb3/zmuH3ZLnr3kfuX8smWjh+RSA24QCqJ/Qfa4BhjScxUHxr0AyDhpFVamEnCm7D8e5Na2EhvZz/a2sTCTDGhqk3IXQdNxYfQ1jaC3tcyFYoH9g8WvI8OuR6AC/sOdCEg53fHmOlDw8NBACQulxL8sMNlug8ltJyw+mQMu9tVHDzYNm7tr0LoOOQAkMktlQc1aXx1APEC3sRPngykVqSw+RZSFPtgWy/a2vhbaiPtYfgApPyq6WuUBpmj61JxhMNxtLWxVaJW1UYAxNvtQhqp/iQgA92RLkht5i7+CDRjfJw+Ip71vDC6ynxkuBttbeaZ1lBEAtCMaFxBW5uJ2i0FIKEZRppfm6JCN4MFx1l8Dhnvva/0oerjPQDqsG1v4T5tzh7KPMsAINExQM4XiBXcd9nNjdj52T1YFevFrwDs2dd52Ibnt7S0jPmdGQtsAACt+lgFQL8jra2tuqBmKBR6BMAKAFnkrLW19R4A92hvJ0VwaCqVQjgchiRJWLRoUd5corEQDJIwjKqqqoI3Zjy0t7dnvZ82bZqt49XX1wMg6nuF9m9ra2Nq72SHmeuTz8s63nXPh9raWtv7GtHbmz82YdasWZZqMC1YsABbt25FLBbj2keam5stHW/aNPIwlCSJe1+NHoxCefwdcvxaH1pappve1zPPi304gGA6hcbG6fB68l9bM32oskoFnUY/37kdAFA7p8b673V3AwD8rqCl3yJgHQ7HaMNhelMVpjVVYuefMgXT41sTaK6fBocn//Mk6CfHqa5pREuL/T5UVUn6kKSq+O6BtwAAvS6P6T6kNCrYKr2L2lQcybgKb3A66k0oGI6FGDJ9GgDcEUI8Zy6dCXfdOJ6hFmBD5V74h2IIDnnQ0lJvux1jwTlCFkID0yvQ0tJsah91uop3fO/CF03Dm5aZ5yNjH2p2kuvjrnVjxqwZpo8RjoSxG7vhU1Jobp4+ZjFk8aznhdHjfs6shjHHbj7UxcnYSKTY+5AkKwCNYldV1GphgbOWz4KrcmwqoE5XsadpL+KHElimuf4ODRSeL0zZQ9XZ494fl5EA0LSwCc0tTWPul65LY5dzL6ZFRuBNp1BZ3Wzpmh4uMMMq1gE4Q3t9FoCX6RehUKjSsN17AOzk17TyBc03q66utkTMgOIJgoiwxvIHr7BG2udYc7zG2t8KMQNQtHBCWoPPLOgYKEbO2b5f79dfyxbDCF3VJDYlmE4y55/QW1aRyoxXJWZ91VDRtJFTIyLnrNjIN5yCPiCyK4KRHZnKydF9Ufxn+lN4ZvHzGNoyPGofmiPPmtpJ21Nt6EOShYPKLlJkWQZQm4wzhxMaT+1UFEjhFCSHBFeNufDq4WnEC6juHH3NeEAaJNfJO0bdtbz7SBKc2va+Yb7evEaH9ZBGAHD4iQHuURRwSH8VsAGrYY3UvOSRzm2ch3xKGl5VQcIhw1lRWFBKkiTUHKctLG8dAAAuZSty50VFC9X1NBXu1w6vAxVLKiADmF9EIaBSY1xm0dra+iaAQ6FQaC2AJQAeDIVCtLDXhaFQ6LVQKLQOQFtra+uLxWtq+cBuvhlQfnXOeJOzW2+9Fb/73e+4HGuygZcgCD2OorC58nP7UHNzM6644grLx6ECJbxFZSoqLEgigiyWAJmQY64wqEd5TBqNFA4vmWY9qsKs3EZvWWMy80RqOrdwuYF8UDTZ9FSUPRwkFldx9R0KXt40KQIjuCNfffCAD4h3kfFSdUxV1nfxQ3HsunX3qH3o9MFLnKzB0IdOfK81q9EoFc+aD2OchqrSlHi4TBdWT00nz77UwZFxtrQHxzBpU6DZIhnSFnHcEb4qrXodOKvkzEfnoTQXkZLd7Sr++zYFB7vEuDcLq2qNvMc8RY0W0uht8phajK0+rhoAkHhnAAAf9Ujj8HY6gGQ3aZPHxCJI1dFk4XZhdBB9xVmTKTlMJZa0trZem/PR57XPfw3g17wbVe7gQc54izmUg1pjb28vrr76agDAZz/7WebjTTbwImfUc8abnD3wwANYvTpX72d88Kq7lgurnrOmJhIKUQxZ/0Rv5mnknGltrMlamJpLVbgVpKXkzH9UBaqPrba8v+rmR85ufwC49W/ArX9Tob44+cJLWJHP9vF7gLgm0e6f5cPgxsGs7901Lhz8Sxt2/HAnjr53BaqPrdZX0RmHvY76VGa8fuyOuZb29bZ4gdZB1CdjXJXkaqjIjQUVQo9WqiLVXRxlAM8IOW7ldGtkyFnpQhyAK85BpdXwuh7Wa5wBxOMAAG5F4SIw8b6vqdjVBmx8V8XLvxTj3gyskjNe3vJc0HyzqpnmxlnlUrJQGt9JMpl4jHlj0FmlX0V8u/l+XX10FQ789iAWRoe4yPqXI0QRahug5Iyu1FvBZPacCTn+wshHzqwIb1AUK6zRSjF1I8rFc0YLVhejIDaVPP9JyxJU1lq7Z7I7Q85YVxzpLaP5AjWrqgpsXaBNmlpjOsX+1D8gVs4LIp8DyOXMeM48jW4c91Aoq/BqYiCJt7/0DqIHYtj5091Zx2G92nQaop6z2ZfN1A13s/C1ZApRM3vODK9PHCILK4VyYHIRbCHzltTH//mTHErBlVIQl2RUNVi7Rq5q8hvcMb4qrfEee2GNssGDH4+zj9ldmmbDptFOXoEx4LdKzjguyBinodoUDSE098yvWEyexdEdI4Cq8lEgNTSo0Z2CElfgDDrgDIw/9qtWkoXb+bEhLgWxyxGCnNlAOYY1ipyzwxN2cs54hTXmwi45KxfPWX19PSRJQm9vL1IpvkkVVOa83e23VIAaMHjOFHbPGR31tMaZp9q65xUAJK1OWzohiFWxkc9z5nICkX0kR9M73Yv6k+tw6sY1aDiDCFp0PJhRUu1/pR9qWs2EOPHynGnkzEgKzcI7IyOnz7qKbnyUfaKHCKQoCfM/smY2mX/cQ/zJ2d57SB3HIYcLtZXWvENubWy6eXjOjPlCUXthjZIsIaFZ+/Ewv2cHb6/OZIZsMlSXwrigy7OOKF3cMxNCCADuOhecQQfSwykE0ynEEnzb0yxrCw4myWLgiCBUCWhORNHdNTkTKAU5swG7BaiB4pEhu+SMp0CJVSGJqYZ8kxlLWKPwnGXD6XRixowZUFUVW7Zs4dqWRC9ZKhx0uq2TM81z5uSYc+ZjKEANALKm1Kak2I00MewLYyxyFt5GQoSCRxIFX9kpY8E180dtmxxIYeidYX6CINr/Bs1As0POfNPJPvWpODdvsBGzL59lev+Zi0hbvOE40mm+TIEWBt/rrbA87t1abqqXc33D6pS9sEYASGp1POIchYAENysuJF7j3tCHaix6ziRJglcrrD5NiUJVgRRjFzL+ngbFGll0eGQoTX44ABx8i78AWDlAkDMbKEfPWTmENQpyVhj5yNSKFSssH6dYgiB2vHhA+XjOAOCUU04BALz88svjbGkNNOds0GGDnHn55ZzRW+ZXyJPRGbQWakWhe86S/IuJCmQj3/VpqAaGt2vkbFFQ/7ziqMzrmuOrMf2jpDxEz3M93MQB6HHqNM8ZJVpWQEPqKtJJbp4zSVWR0qhj83ljS2nnYt5SDxQAlckEtuziG01Aw5mfqGmxPO49Wlijl7M0YnXaXlgjAKRkMl8U23OWHErlVRwVsA5eHvNscqaRIZPkDMiIALUoZN7g6TGvVzRFVAvtqVpAyOKBdyanXKMgZzbAQs4cDjI58jas6XGtQpCziUPuPfvwhz+Mb3/725aPUyzPWTJpb4W3XDxnADBjBqn7Q8coD6RjaaRH0kjLEqKywzo5c8pQQErIpi2EaxWCP83oOXNqBJ8HOWM+wuRG7vX5+ZclzAkkkehKwBFw6EYPADiDTtSuJs+VZbcuQfP5hKTs/91BrrLagIHg2wiNpTL3Fekkt5yzinQSTqhwVjkt5cDJLhkRrxsOAH0H+EakRDvJnDbg9qDCYnCKK6AVjU6ze6mMfagqRS64x4bnLKXlmiaKXEJj05ffwUtr1qH3pSIo5x6meOIn9mZKXrmmRtTq5Mx8H/JpoczNGjnj6THXyWKj+fZUzyHzpm84BoW3nGUZwN6TfYqDhZwVS2mPGqVWQQUp7BrmAuaRe89uvPFGW+GoxfKczZ1rTbGNopw8Z7TIezgc5taOtGbIxBxOQJIskzMASDtkyGlFy6Wxt5AC8AtrlPSwxsn3UCs3GNes6qqAqy6Q0P4QKQAfXBgYJRl/zO9WItmfRGBeAIEFAcg+GdH9UXjjSQAubrLaeh8KWO+PrirS73h6zmoN8t5WEfF7EIwlMNIWB2CxmFQBxDTPWbrKbTlfiErXuzgkCVKPuy+dwqIoUfYct0B3HqSc5F4nI8XznPU834vOh4ko054796LuPbXcznW44kNrgLOOs0fOeOeaAsCR/jgwYm2s+WYSMtSUJLmyzOPe8LoqqXmDLdQSzIgSxRGJAUF7mT1lC+E5swhVVdHe3g4AqK21PukUQ8zhnHPOsaX6B/DzwgDCczYeeHk7eRF8IxYvXmy7D1HPGW9yZsdzRsN7R0b41TxKRwg5i0rkftXaIGcp7Z6lGVXSMoIgNKzRJjnTPGfgkKMjhn1hGK+PZhvj0KNElXDah5pHbe+ucSMwj/RjySEhuIC8rg+TPs0rvMnL0IeMhdVjrH06h5xZCbWiiFWQfeKd/Lz3qqIi2U2MRrnWeptofUM3h3l6WEurWTFCPFHe6V7451q3RlNazpka45hzlnP7N389k++bDosi90Bm3NsBL485Hfct8RH4u0cge2T4ZptfyKCes4YE/7DGGq2DWyGLNFe2PhlDOMrWlnKEIGcWceWVV+Lxxx8HAMyZM8fy/sXwnDU0NNg+TjEMfYCvks9kQe41KTWhNu5vty1AxnPGO6zRjkBJMchZMkyWraNwwOsGGq07zJHWjCIrKnT5oOec6WGNNp/6siBnEwWjw0XrBojsJ9ZE9arqcfcPHEH6dN0QMWB4GWlU8dNhw3Pm8DmQcspwqSoSw2wGeKY8hDWRAiOSlZqw1SF+c9CuLXEgrWLI4cLMFuumktOnhTUq7ASFes5o+FfD6XWmi3QbkabP+yRHz5nhdXIgiZFdGYGGWMfkzAeyCgeDpU3vMnOuqfZ/XozkAta/tw6uSvMhzdRzVh/Xwho5iVvJqoLm3T0AgLqTzTs8vLooUQwjk7CbCXJmEffcc4/+et68eZb3L4Zh/cMf/tD2cXiSM2ObeJO9yQBe5KwYYY2ybH8qKJbnzI4nlpIznmGNg73EuIrLMuqrrMshAxmjSOVEzljDGiE8ZxOGfJ6z6EFCzuhqdCEENXJWM6R5zjisezkVBS5VheqQ9FIPVpH2kL7HTM60/3reSbN1cpauJvvwLET9j38RstjndGPeNOv762GNKvuz8NSV5P+KWvsEFgAUfZGoOIung28OAQACmrc31hkXC7XI7znrfqYb0QPju3x4F5+n9Q39FrxmQGauqolyCmvUukVNKgFHSoG70W1qPtTbY/CcCXImkAU7YY28PWdr1qzBtGk2nhyc22NsU+5rgfwoJ88ZCzkrp1p5vD1n0YNR7P7+dgBATHbAZiRqxnMW4/OEbfJo5MxmWCNPz5lAYRgNM6cDSPQlkOhKQPbK8JogItTQrR0kfZqHlD4NaYTXYTscXdXEJVKMfZr+nso0WYq3k0ul0LDDXn7krAFkPhtwerBghvVrRD1nbkVhnqtpH1oz037oJwCkZSoEVJzF0/7WAQBAw2l1cAQcSI+kkRqenHWorCCXnPW92o8NF27E2jXrxt2Xd/H5TH1Da+TM0+yB5JIQTCThUdLcysLo7ZlmTTVW95wl4whHJt9zTJAzBth5qPEmZ6x5Xjxz4IzHEJ6z0eCVc1ZunrNihcbaARUE4UXONnzsdYy8PgAASEgyTrFe+QCAIZyIg+dsViwM9zAxHO17zjgvxwqMiaDBBnI6gL71RFCqOlSlC7MU3F/3nJFwMR6eM+p5hQVVxFwoLq0P8fYG2ygPIdURsiL181tC92iF7EdkJ44/yvr+skcjZ6rCPMz0qbqPjZxRz5nK0XNmfKz1v6qJpR1foy88xNr5Fwc/3JBLzrr+TXJOU8MpPcR5LPAWBKGeM5/F+oaSLOn3tDYZZ56H6O71Wr1Fq+1xBp2IuZ3wqApGuiafoJ0gZzbx3HPP2dqPtyAIKznjKQgiwhoLo9xyzvIdk2XfcvCWUs/Zc889x8WTF343Q/KOGenD7f9jb7ylqToiqyEL4Lv739Tf28kXIjuS9kg8whqZjzC5YSRnDhnoe5mIOtSeZC7ywj+PCD9UDUUgqwqXnLNarQgtKqzL6FNQzxkYC5nT38MkUNJADLuB/fyIAPX4RGUH3rPc+v40XNStKOyGLN2/33o9KCMUOlfzzDnT2qYkFQxsIEqS1auq9blp5093cTvX4QpjzlmiN4F99x3Q30f2FC6izFsQpEH3nFmvb0gXBWpTceb26J6zlNYeG/UWR4Jkn38/OvkUQQQ5swiXizzMTjzxRFv7F6tGlV2InLOJA29yVi6es2IokF5xxRW29jPmga5fv55Xc3RUV9gMAdOukcpopakqMC2ZeRDZEQUAAIlaCxzcMCLnrDACBnImScDgGyQvp/Z4c8oyzoAT3hleOBQVzYkoFy/MvBhpgzrXhvSoBoWSswQfQRC/Yj9Ud+5SYjTWJOJIcioPkdRy6Y480mkrz5TmnLnVNLshS18we86KF9bY80IvUsMpBBcFs7wgHQ92lsXCXSlh9Jx1PnZIL88CZPJPxwI3QRA9rNGepwrI9LuaVIKbN1gPa5xuX6W1Z9fkSzoT5MwCUqkUkskkZFnW82ysotzCGkXO2cRBhDWOD5+PWLI//elPbe3f2NiIM844AwCwZ88e5vYYEfbaG/MAoNJcD0bDkduwouF0IudsQhFLANF2LSF/jvmcDyqnPyMRYTbS5JEkrurYBgCQZgdtHydDzti9wQBQKROD1Y43+NyziJFWkU5iXwefPq1ECFmU/DZLnnAOa5RUVfecWakHZUQmrJG/56zvZRLS2HROIwBgyY8X69uEt/NTzz0cYSRnQ28RtURZK7UQPViYWPAsPu9QFdSkE4Bsj+B7Gik54xDWqO0fqtPqG9rwnC0+luyT6Cp9vjtvCHJmAVFNpcbn89kmRVOFnAnP2WiUW1gjb3LGMzSWpV+fcMIJAIDdu3czt8c4Qz76vqNtH4aSM5XDqv4Bd4D5GBJHciY8Z4VhHBbRqKrX4rKiSkhFQabHI8xGWt2L+/XXUhXDgoOWcyZxCmuskGhYo42cM4eEuMsJGcDunXwEKNIjWqkBv715mtY5c3EIawS0nDxFhSPggMOmwib1nKlJjjln2v/+1zK5lABQs6oa0z5C6vj1v9LP7XyHI7LI2TvEa01J7HiKjXrOGQchoAAtwVLlMpXvmgt3PZkvKtNJbt7giphGzmyotNbNIu2RBhKIxCbXQqMgZxYQiZDYYL/ffily3l4PQc4OH5SzlL5dLx7P9hjbxNKvm5uJQdDT08PcHqPMeKLB/rinYY0KIxlSVRW7vcTbMe+/59g/kCalL4mwxqLDOOydkQTUlApXrQsOC2IcdJW7Ks0hnMhwv2S7gjIw5JxxCmv0pKnnzF6bkl6yX187H3EANWLfkwcYcs44ec58jIXngeJ5zpJDKQy0DkJySKg9MROuW3NcNQDgna9tQTo+dW0C+nhV0yqGt5AyL5Sctf25HcmhsRcUdLVGDtyDhg67bI57VzXZL5BOcvOcuaJkvLpqrS8UUU9edTqB3kG29pQbBDmzAKPnzC54K9uVk1qjIGeFkUvO7HqriuE5Y+lH5eY5o+OTR9012ZW5R66A/elSoU9Y1rBGZOomVR9bbf9AQkp/wmAcFu4wMUQ89dYMEXctyXWuTCWZJbVTvowIiMQiCOLSLE5OobruFFth9ZRGzhIDfDxnUpStXIXsJvOFQ1WY71k2OWNQ2KT2B6O3Mxd9L/dBTamoPrYKrqpMn6ox5FUe/ONBrucsdxgf79RzNrJzBOlIGt4ZXlQuzeR77v/NfowFnnXOgrrnzCY504pWB9IpbjlnzrhGGCutt8nTQObR6lSCue5auUGQMwughW15kLNyEwThrdYocs7Gh10CUgwBjnLJOeNBzmhRbLqYwgRDM7w+hulS5uU5y5Azu8WDAUCinrM0+z0TjjPzcCc148iiIeKi5IyD5yztzxjPciW7WqOUZC9C7VQUVETjgAy46+3lU1HSmRrk4zlzDmnlKmrsXSPJIUEB4ACQZgwjVJEpNeBg8ZxRbyfPsEYV6H9tAABQtyZbgdRIQHqe7+V2zsMBLsNtoo+zoXdIvlnVskr452ciMbqe7B7zOLwEQRQV8KftEyEgQ+oqFA5hjTnkzOqcCGRqIlamBTmb0ti4cSMAYMGCBbaPIXLOpi7KTWFzsoY18vScGT1L86fbb5Ou1shmx2rkjLSJrszbAc03mIiwxra/t6NXk4+fijBe4YBWaNmqIZIxQtjDiRRDrond3CXAkHPGqPynqsC0RAQyAP9sn/18Ki03LDXIx3Pm7SYiFtNX2s/xTGuDI82hFpwvze45U/WcM75hjZF9JOUjsDD7WkmyhOMfXgUASPRMMuvZAjSnMIY2kXyzimUVkJ0yztxzGgCg/5UBvPG5t/LaCLwEQdIKEKCKqLbDGg2eMw45Z7Kqwhm3H65L59FAOoXoJCunJ8iZBbS2tgIATj75ZNvHEDlnUxfl5u2crIIgPD1nxp908gqG43BSawQAtxbexMNzVmwp/b51fXjryk149fwNzOc5XGHsQ9Q4cln0WLk0700Fh0R8GBYIHLMY8ihpyC8HctaslYfwz7HfHlpQOxVlXAEBMDSQRnU0hjQkHH+a/UiZlESuUZpD0WeWUgMUes4ZR88ZAET2avdv9uj7R0N4pxo5M45T6lwefDvjOQPIPEDzSTse6sSQ9r0RvIpQp9MZQRCr8w+FS/OcBTmFNfoMZNGOQAltTyCdEp6zqYzOzk4AwMyZM20fY6p4zkRY42jwuiaTWUqfJznj4Tkz1iWb2chwHJkPGXL3RbE4SjKfZbf9ayRxLEI9FmKdcWy85C39PWuNt8MVxmHvt5nz4fBpsuwKu7gEbdALlU1w2DCIdGghcjIHckZzYWj4ph1Ibo2cxdjnoY79ScgAoi4nPD77nqq0zNFzpi3KODh4zljLH+Qiup+Ss9FElir8JXonmfU8DrLIWYo824Y1pcbK5Zlwz5V3L9NfUw+kEbogCGN70gpQnSLuJXe9vXFGxXq8SppLHqU+H9oNs9RIpl9JITrJupcgZxbQ1dUFAGhqarJ9jHITBBGes4nDZPWcFSMHjkdYIw/PWXBhpg5Uo7mawXnBS0q/ef0B/TWT54x6PYroOdvx451Zq+VJTrlAhxuyPGc2cz5oqJ+Lg7gEveeKJMFmDXMAgEq9rxwIPg33NIpJWAW9RmkO5Ky7nRChhNu+lwoA0prnTGHNOVOBIL1GHPIEVY6CIIF0Esn+JBx+B9wNo4VuXDUuQAaSA6miFL8uV+SSs1h7HIneJFzVTngNBaDr1tRh9udmAchf84yb50wB6jRy5plmvaYYkCkP4VbTXARKalJa3T6LAkkUdB4NplOICSn9qYtDhw4BIIVu7aLcBEGEWuPEoRzvGUW5hTWygKfnzDWX5FD8bcZ8eBg8VXSmZRUESRqU9lhyzjABOWfhd7MLzya6J9nSpknQK1yTjOOSrp0AMiu+ZiHrRhE/z5kKCQzDHhIVl2AVuYHBc8ZAzug1UuLsYY29HaQ9aS8rOdM8Z4wy8iqAJi300zfTnmENAGlKzjh6zpoSWrtm5a//KsmSLnkePzTJEoMKwDgqUulMvlnlsspR18mneRwjewp4zlhzTRWgViNn3mn2RHeMHnwegiB6e2zUOAPIMzDllOGAitgw+7gvJwhyZgE8PGflmnPG27AW5Gw0hOfMfJvKRRCErsbFa+3nnQBGQRBGcmZU2mPKOdNC0jisoI91p2IHidHmbiSrovGpSs60W/6BvoxcNs0zMQtKxN1Kmj3nTMn8Y/Gc8SrHoKpAQNG8QtX2yRBd1efhOes/pHl5bRagpkhp8yqzWqOaIUEseXmKVv5A4kBg6RT9/v6DWrvGniN9M7Q84DyeockK4zhNpTNKjZXLK0dtW3EUidDYf98BdPyzE9G2TNQHryLUAFCbpGTIHsGnCyAeNc1FEISSM6vzoRFJzbsd51RCo1wgyJlJJJNJ9PX1QZZl1NbWjr/DGBA5Z1MX5eY5K8ecM4pyEQSh/K7SvmAbAH5hjSmvwbPAMva1h6yDUQZ9LKTCKcTa44AEVK0gxshUEwSgoMPMq2SutXe6RXLmzYQ1MhtpqiGskcUC0PMW2fOplkQGADCGNXr5eYWGu4mhJzOIbwDkGgN8BEHqqWE93b7njJIzcCBnABBMJXF2fxuAwqFpvhlaqPkBDuVNDhPkhjUOva15zgzlBSiqVlTqi21vXPYW1r5nHWId5OHDs85ZBQ2NtVkeQvbIWq1NlUuoLg9ypmi5pomRyeUQEOTMJLq7SR2K+vp6JtnxqULOhOeseCiGlH45hDXyKorN1XMWJ22qDDJW8+IkCJJV0JhFPMFD5jAHhxyQfLeq5/leqGlSlNbXQu7HVPWcUXjUzLWmktRmITtlKJIEB8Cet6PQsEZGzxknxU8lruDIKDFc/fPse4VclJwxhhACQLiXrR4URZrO1RwEQXTvok3DGgDSmiGLGB/P2dJIv6ltg5pnqPvpHubzHo5IpoGhTWN7zty1bhz/zxBqTiSJzamhFA49TiK1eAmCAIbwYYvzD4UkSZnQWEaCbxQCYnmW6eQsLMIapyRovhlLSCPAN4wQEOTscIIIazTfHhZ4PGQVjofnLK6lSFQFC283HnTPGat4gnaNRuZUMUlqQ8sdcKT4hTcZQQvO1p9Wr4sEbPnGVigchQgOF9Bu3ZDMLBZULhm9ej4eMgWEWcmZ1i5GzxkvxU+jOEXdavtRKTQfhocSYapbW9HPI3BhBVStkY8gCDthVGieKgcCK0nA/FhG+n3BV+eNuW3LR6cBALqe6GImqocLjI8zJZZGdH8UklNCYEH+BYia42pw4qPHYdntSwAAXRqR5SUIAlVFkEP4cEpzTiiseZSqQSCJwWNOS3okI4KcTUnQfDMWMRCgOF4PFghyNnEot7BGIyihsQPeZJF1wcHpJA8eHtcnzstz5uAT1ki9FEnGHDiaw+RIKcyEMfd2JQeSOPgXEupUe3wNJMNTpuOhTqZzHY6gV5fmDL1n7Um2xFwyK9YcCgyBcDQHh7BGZm+w1v/iLjYvFQ1rlBnDLAHA00nEbAJHsMUz62qNzJ4zNVPnjIWccQxrlADMiocBACvuWlYwF84/x4/gUUGkwmn0rTPnbZtMqIhpZL/ZA9lZeNA1nF4PAOhd24t0LM1NEMSjKnCpKuKSDIeXoTyEi5PnDBkFUqulRbKOQ73BHBYcygmCnJlEJEJUdCoqrK94GlFugiDFUmsUOWejYbwm/+///T/bxymG5+ymm25ibk+59GnannSa3QChdp7Xy0jOqCAIjyxqgHnmlmQJMVogl0PRXiOGNg1BiSrwtnhRd0otms9v1r8baB3keq7DAaoKSKqKRs1z5p9pj1jrnjNWz4PWhxRIWDKX4Thae5g9Z1r3U1mHmCuz4MCKyiHyvA/MZyiKDX51zhwpYljDzWhYu/kJggCZBYfA3PGvU+P7GgAAXU92cTl3OcP4bF06F7jmTBLSbUaV0DvNi8rlFVCiCvrW93MTBDl5PiFCCVYFUicfDzUJ1WX3nGW8wcJzNiVBDU+W8C/j/uVmyAq1xuKDXp8bb7wR3/3ud20fhzfBP/vsszFjxoyyaQ9rn6Y5oTzIGX0iSkzJOYbir6xNok9o5nEPxGTtOjGSs9yWhHcQr0P9qXWQJAkVi4II/fUYAMC+X+/XQx6nClSViIG4VQWOgAPOCnvGkeLgJHih9aHjl0iQGfq1xNlzprD2aeo54xCq69DmMgdDAWoAUOjzldFj7kkQI1ZiFChROZIzSQIqLBQPbzyLkLPOR7uYSwuUO4zm1KbfyaihYi4m64vVnVwHAOhf36+HHrOaaNRLVdVknwgBBg8+Y94iCWvk6TkT5GxKghp6k5WcibDG4qPcCPVkbQ8lZ1z6NCVnbDZaxnPGGnJFLzEHfZK4Ts7Y2pR7v/pfGwCQkYcGgJrjqvXXW765lel8hxtUFfBpK8SOAHs4EVgVNuntZlxwAK+cM1p3jXUe0tTunBw8Z7I27mUHW5t4ec78CeJ5kSvYDGtFEwKSEnzIWdCC+l9NqBqBIwKItcXQ8WAH8/nLGWrOGlq8MxPWaAY1q6oBAANvDOpTPavnzBPXCD5jH6KeM9bwalVVDfUNOZAzDn26nCDImUnw9pwJQZCph3ILRS23vMViLDgw/0aVHpNTzhnr8yP3qW8TsmwgZ4yJ1MamKEkF3U8RZduGMxv0z12VLlRoEtIyozficIOKjIy+k4GcqdRzxup10L2vbIeRnHwKmVPPGWtYo6yRVy6F1em4d/IhZ6yCIDXDJHxQnsaWa0qV7aRYmjnE2gEVQRqWZqKouuSQMPeLswFAVyKcrMidpmMd1oo/B48kC1sjuyLcPGfeBCHS7OSMjwdfhSGs0UT/GfM4bn4LDuUEQc5MghqeLDL6wOQ1rAGRczYeytHbCZQfWeTRHm65lLRNjHxC5VSwVzesGcmiLAFRSs7C/Ip3Drw+iORACoEFAQQXZIspHPPblQCmXr2zQCSGz3TtAgA4/BzIGauAi8qnD+lS+qx1zvScM0aPuZvsL3OYF+k1YiVnChUEYSRntWGSA+eYyZYDJ7lkhGUnJEVFcjDJdCy/5jULy85MiOs4qDmOSMUPbx0eZ8vDG3qAAyVnnSTf1GzxZ/9sHyCTunBOrT+zdmsvDY21GVZNQcOrmT1VKQU+JQ1VAhxBhges5jGXJlmorCBnJjHZc86E56z4mKxhhOXWHmObmPuhQo/HuqxP/jELgughaWyHkWUgLJPVysQAm5FmvF0DG4ngR+17akZt59ZCn1LD/Mjg4YCPv/wW1gyRUizOAEP4Di+Cr+3OSoZ0KX1unjPG9rgoOePhOeNDztKc8gT9cbKg4aizr6oLED4+6NTGPWPdQVoUe9hh3usRmO+H5JIQ2RtFamTyzgO650x7r4c1mvScyW4Z/lk+QAHqo4SYs3Zrb1zznAUZQ2M5ec7kKLn/aZ+L6Zk/pT1noVDoR6FQaG0oFLo/FAqNurOhUOi6UCjUyr955YPJmnNWLLVGQc7GRrncs3LrQzzJGTdRENom1mUsBy/Dmo/XQwIwrBlpyV5GcmZ4PbyZrIhXLRtdaJWGMyocCuAeTmjpH9Jfs+SccauVp3tf2Q4jOfiEEdIFC9awRgfHsEZZG2cONicDUg4+iqj0N0mMZFGSgEEHqd3G6sFeOUyEfTb7q03vI7tkBDSPevjdEabzHw4YFdZoMucMAALzyXWqHyHkjFlKP0nmeda8RT33lVWBNELJGeMg0/MoJ5fNOe70HAqFVgBoaW1tXQNgG4CP5nxfAWBZcZpXPpjsnjOh1lh8lGsR6nLxvhaDnLGHNZJ/rGqN4GRY01wYZkEQGRjSVrwT/WxGmvF2xbs0I6RldPiO7JYACVAS6pQsRg2whjXyDY1lXpShOWfMUvp8PGdcwxqpIAgjGdIL9sYYvQyg4dXsuaYDTkLO4oyesxkxQq62+ass7VexiORThbeGmc5fzjA+mlVVNQiCmAtrBAD/PBLCSskZa7d2auHHkofNhlWpIAgrOYsSsshKzlRNSl+egp6zkwA8qb1+AsDqnO+/DOAOno0qR/DOOZvsgiAi52w0ytVTxYpyI4vGNrF7zrTjsWpY8M45Yx33koGcMXrOjKDHcueR1pYkSScnCqNC5OGEtOFesYQ16gSftZA53Z3R0AcvQRCtKzCrNWor+lzCGrX/DlZypoWAsXqLJTpcWMmZBHS5iKjIyO4I07HqEiSPqttlnnAAGXI2vG1iyFlyIImOf3aye5wtwCgIMvzOMNKRNBwBB1wWCogHKDnT8g1ZW6+PU9ZQXU71FuWY5jljrLsmaXX/JFYV2zKDGXJWA4DGZQwCqKVfhEKhKgDLWltb1xehbWWFcvWcsULknE0cytHbCZQfWSyrsEa6gs7Lc8atCDX7CvqQtoKe6I4zHYveLqeiILqfqMq56915t3VotajSUyi0MenIPDO80+3nDPHLOSsvtUZw9pw5OIY1skrp07BGVs+ZpIdXs4/7Ax4SLjfCGFbYqBWgtkrOgtRzNgHkTFVVvHHZW3jjsrew++d7in4+/bzaf0kC2v5OygY4LYpe0PBPKgbDalLRPs0aGqtScpZk7NPa/qqLceVTC2uUJ1lYoxnKOgCAJhBUAegzfPcVAD8vtHMoFLoCwBUAcNVVV+HMM8+03MhyQG8via+ORqNoa2uzfZz+/n4AwMjICNNxaHtisRiX9qRSqYLHSSaT457n0KFD+uvOzk6mdh1uMHN9hodJPs7Q0BCXexaJRJiO09PTAwCIx+NMx+nqIrLI4/Wh8UCvDwDmvkMJ3sGDBzEyYt8IUTRyNxwZRltbzPZx4ikSQpSIjn2tzfSheIwQqXiS7Z7193vQqa2g928fYLtvQwEAlbi67R0kh4jnrCfeA0fb6IeuqnG29j3tcCfyE7jJhqQsw6tVH5dPkG1f65QmazgyHEZbW35vp5k+lIiRvhhLsPWhcDSJSgBKkm3c9/cpqAGgQGU6Tt8QuT5SOs0+f2gEr7e/B21t9hdk45odPNI3zNYmOg9Fw0zHiYxUYLeXlLTofrkbBw8eHLUYZqYPRXfEMC0eRVR2oNPtt9SmeC2ZwwbeYZt3xkPyUBI7PrsbyXYyVrb/7w6kF6YRWMGmeGkG0TgATANUFX3biclcd0mdtevkI9epJhwBGoGu7u4xx70ZqFpx9nCEzfaMa3NZbGjs45jpQ7ER8luSCtv8EUmR57JS4NlarmhpaRnzOzPkbB2AqwH8HsBZAF42fLcAwImhUAgAjgiFQt9ubW39P+POra2t9wC4R3t72Ma6VVYSflpRUVHwgo6HhgZS+8fj8TAdp6aGqKH5/X6m4/j9mYmq0HHa2trGPc/Bgwf11w0NDUztOtxg5voEAmQlrLq6muna1NfXAwDcbjfTcWpriRPc5/MxHcfo8WI5zuAgUfqTZZm57zidZGpramrSx5wdyBLp01VVlWhpqR1n67Hh8e0FALgdrjF/m5k+5HGR1V+P18vWh/ao6HCTletUe5rpWJVVKtxKCqcMZRZnZh4xM68HdGdgD5JIoqGqAcGW4KjvJyNSjp0AiCEyZ9VsuKrtJeQ73GQRxO/xoaVlWt5tzPUhsljh9bH1ocpqskjkBNu4r6rUAnMYx33vtAgOAnCohZ9lZiBjOwCgeVoDWvLkT5qF6t4LAPDKbNfaoa2JV1VXMh2nokLBTp+KVMAFtCVRm66Ff3Y2WTHTh7o2k1qGW3zViMsOS21Sm1W8692F5KEUGisamWpcjYXkUArrP/qKTswo4k8nsPDcI7ifLxcjURWAClmWoJJLhdmnzkJNS7XpY6TrFWzDDlTGEpBVFXV1DWhpse/1coKM10rGPuTwEU+eVx7b/jDTh3xuzaPoYbNjArVkXnSrbPNQuWHcJaHW1tY3ARwKhUJrASwB8GAoFLpb++7Tra2tZ7e2tp4NYEcuMZtMKLecs3ITc8g9hghrHI1yC2ss1xy4chIEoSvojMMeKlWSYwwF4RWSJsvAIbcPSYeM2MEYYp1soY2z4tneybHuocPPR73ucIIxXM9ZxZ5zVi618mh4FGuOly4IwtgeJw1rVNmfPbpao4sxP8dFC4ez9XfaHjDOQ7IEKJKE6Eyy2Dy0yV69MZr3mLJxzySHhOARfEIrx8Khxw4h/O4InJVOnL7lVCy9dTE5346JUYg05pzRUG//LGsFxB0eGZ4mN2RVRU0qzi2skTVvkdezjAoJqYztoTlnDtZna5nB1JOitbX12pyPPp9nmxCXFpUpyi3njKJcDP3cYwhyNhqTlQyVG1k0tomXIAhrrofCqxZLbnVTm5AlIC3JaG+uwey2XvS/0o9pH2q2dSwJQH3SXMinQ3uQphlzcA4nuA19kKlvUyOGlyAIKznjXueM6TCQudY5047Jqc5ZOeWcAUC0pQIV23ox9M4wms9rsnwces/SNusx+Gb5MLRpGJEDUVSHqm0doxD6XyVeogXXzIenyYOG95Jok5FdE0TOtP9+JYVkfxKyT4a70XoYt3eaF/FDCdQnY1BhjdzlQuZUjkHR1BpZc870RSZmcqYJAU1BQRABlG+dM1ZMpCBIe3s7nnjiCSST/NThDkeUGxliRTlL6XOrc8bqOdPkfplrseh1ztgOQ6ex/koS0hRtt59PBwANBnK25MdHZX23aZeK9e9oAgtarbN0ZHI9SAuhraqCy3H0Omes41ZX/GQ7DC36zCwIwkmB1KmNMQeHeS1T54zVc6bJjnPynLEa1nTcj0wnfXJo01CBrceGTs7GuGeqquK5jSp2HMh/L3wzCdGIHmCbd8Y69/A2QsIql5Hf6W4kQjyJniS7KJMFNMY1r9lMn63nmruOELrKdJLZcyZx6kOqXueMj9iW6mB7mEmeyek5E+TMJMrNc1aOYY3jSemfccYZOOecc3DHHZO+8kJe8FZXLBfva7l58gCO/ZpyIcYVa5WX54z+HNYVdG33sI8YLfFD9sMaVQB1KbL//GvmYfZls/TvegdVLL9ExUlfVPHmDlWXkk4NpWyf73DDsIdc46prFrEdiJPnTFL5kCHZSaXrGcc9Lyl9Xa2RX1gjc50z7RqprFL6vDxnVKCkhYQ19q3vRypsfSzSOoVjXeknXgVO+4qKoz+nIpmnv/pmkjy+6IGo5XOPh83XbsXAhgEAQFBTPHR4ZDgrnFDTKpKDxV8cpkOsNqnVfZxuL2/RVUPy8YLpJHMRat1zVi5hjbTWJWufpmGNqcm14CfImUnwyjmbKuQs93ipVApbt24FAGzevJn5XIcjyu2elVtYI0/w9pwx1zlzU7lf1npH9J6xNYeuMQ1TcsaQc6aqQCBNDB5XjoT+1n2Z19v3Z4yNRB9bAdzDCbphzVCAGgC/nDNOYY3Qitk6WAuKa31aYfWceTh6zrSL5GTMOUu4SX9XbRAgI2idMx5FqAEgWudHdagKqcEUup7stn4gbRpLS/lNyDd3kv8jUaB7YPT3vhma5+wgX89Z9GAU+39zQH/vmZYpXeGu1+aenokjZ/q8aFMEiO5XmU6C1eGne195kTPGKBD6LGPNNZV92rgXYY1TE7w8Z+UmCMLTC5NLztrb2/G73/0OIyMj+L//y2jF3Hvvvbj99tuxc+dO5nMeTig3MlRufagcwxr13BPGB5rioauNfMgic5y+tvuQT1vBPmh/BVtVAb9Cfpcj6MTG7SoeW6+is1fFDb/J9NHv/UbF5h5ibCT7pk5oMy+vB2j4D3OtPE6hsVqfdjKuWOvFgVlrYmueM6eqMs+NmbBGtjbFvRo5G2Lr77LmXmQ1rOneCoDm80mOac/zvZaPo6RGhzU+8jLxjr+9S8XtD2Su//W/VvHHJ1Uohn6bCWvk6znre7k/673xWeKu10Ibe4u/MKSTM4WQcrtCQK5a0n8qeHjOaB/iFdbI6jnjVBRbFwRhXSQqMzBOPVMH5ZZzRlEuXhggmyyk02mcfvrp2LZtG6655hq9phbFV77yFXzta19DKjV1wpsmKxkqN08ezzbx95zxCbNkDgHTdu8LEiNpZHfEfpNUwJ8m41gOOnHs5aSRlQFgyJB/v30/8PceJy4DkBiYSuRM+88aGstLEIRXaCw1itJ8PGesK+gOp4Q0iKChmlZtG6Gqquq8VWb0nMU91HPGSs60F8w5ZxIAFYoCVB9TBQAYese6YqOq3fO0RvfePaDi/G/m75e/eRz4zeMqXE4JF55GPqNhjbEDUaiqymXOB4DhrZnfsvLXy7O+c9dRz9kEkDPtP50XaTi3VbiraVhjit1zxkkQhBaNllnJECdBECoyxbpIVG4Q5MwkyjXnjBXFUms866yz9Ne5xIyCOeTsMEO5es5YUW6ePIC/54w5FISSM0bPme6F4RTWOOTzQvbISHQlkBxMwlVlPfxGVVUcFyZj/NwbHEBAO3YeYbQhxxT2nDESfL0PcioPwdqHJN1zxvgs04YE64KDQyahkQ5VhZpU7Vs3Suafg5EwJn1aI4aJEIVdgp7pQ3wWZVQVqFhM6gyGt4cJmbVwbHrPFG2XIy8af+7fnok2hKvGBUfAgVQ4jdRgynbYXy7iGvFadvsSTP9wdi1AtxZyPSHkjHrOtMVnp81abo4gmTS8SprZcybxyjlzUs8ZnxB9MAqCyG5tUUZRoSQVyK7JERA4OX7FBGCy5pwZwyzLxdifrCg3z1nu8Vj3L5c+DfCrc0afiKx1zuDlk3PGK1+I7q5AQpW2gt79dP5FlILNUVUc+7Wn9PcRubBF3OsqnhBAuYKb54xbzhkNa2TsQy4ZaUiQVWIU2W4OJ/VIhwNIaTlQStL+NaJhlgokVrsRqiwjIjsABUgN248S0QVKOEnpKyrgqnLB2+KFElMwssea51zVCPlYOWf50FKfeS1JUlFCGxO9ZNHHXTtatp4qH04kOfPTsEabnjPqFfIoaWa1RpqLyew5c1PpetY6Z2R/Zo+5Q0JcC21JRydPaKMgZybBO+esXAxZSZK458EJ5Ee5es4mW3uMbeLmOWM1rF2cyJleQJjtMLqRpgCN72sAQJTbrCLXAxYbJ/7zoJtI94d3Tky9obKAnnPGeBwHL3Km/edg6MfpOGMxingJA0iZMDuVwZunkzNJAuPjHrIMDDgJKWBRROXlfdUXZbTLQ71nw1ushTZm6pyZv2euHH5CQxsj+/mRs6SWT0bFP4zwNGjkbAJzzoIpTRDEZs6ZQys94lHTzGGNvLyvoGVhWKXrdc8ZKzkD4trAUBhVUcsJgpyZBO+cs3I0ZCc61HLWrFnjbzSJUG6es8mcc8ZPrZH8YzXS4HWQ0IukwuRl4FUUWzfSVKD6aCKrPfjmoOXj5K7C9js9Y2xJ0OPyIuXMhFFOBdArxC4IYrhpLNCl9NkOI0lAgnqqGIwiXlL6DhlI0vYkeHjO2AUtZQnocrErE/Kuc0a7UMVRpA7Y8JawpePkhjWaQSJnuBdDsTHerZGzutGeM49W66xnbV/RF5Lp0evj5Lf5WuxJ6VMlQreicBAEoX2I7WFGwxolxnBmiS4ysfZpCUjoi0SCnE05lFtYIwUPQ5a3sT8WPvWpT2W953UNphrKjQyVG1kE+AuCsBajlR0SRmi+FYMYBl39VDkY1gBZQQ8uIqvnEYuhTQCyCrru9gQRzSNv98urM41VJQlD1cR7NrJjanjPeNUVkzh5zqgsO4/Q2AQNJ4oxjLM0nz4tyxkjTWEo+qwapP1Zw5llGejiEMqr9yFOKq2jPGebLXrO9DpnY5uQF743+30iJ6qTd62z4W1hRPdH4ax0wpuHDDWc2QCH34Hw1jBT6RArqNOKUPtm+Wztr3vOOIQ18hIE4eU5k3h5zmSIsMapjMkqCAIUt01XXXUVgsEg1qxZg/vvvx8nnXSS/l0iMXVqHQGTN4yw3NoD8BQE4RROJANhjbgkBxgUSnndM4MTxl3vhuyTkRxIIWmxOLRquLwHPQH99bc/Tf5/81PAlR/M3megmmwXnmLkjLUP0Zsmcco549Gn49RTxbBirRN81twTGUhI7EYalYlXIHEZZx00lHe7Ne9U1nEUqhrLb9wDQOUy6jUfsnQcNY+UvtcNfOnD5PVjP5LwvUuy2xrPedzzzjmjnv+G0+r1XC0jXJVOnYyyqNOagaoCTkVBTTIBySHB01w4omAsOLya50xVwGrx8fK+gpe4VZrPuDfOQ+mI8JxNOfAKayxnLwPPnLO5c+di586d+OlPf4pt27bhkUceAQA888wz2LBhAwBBzuyCtyAIK8rNkwfwFAQh/1gT8SUJCMvsnjOe+UIAMSIkScqEGFk1lHJC7H55tYRdf5Hwg8tl7PiThP93KTFwux+WcM+1pM091cRAGtpkzSA0i32/OYANF76OdLw8xke5hTXSPEpmTx74eM4oOeMR1hjnEN5ExURYi2ID5BLv9JHQQasEyAhaFJs5nNkw7gEguDAAZ4UTsfYY4l3mvUkZ7yJ5v+svEvb8VcKt/y1hx58knHuihMVzyFxACVuu54yGGVIRD1ZE9pK5yz/XP+Y2gflkYWhkV3EXhlSV1CYDAFedC7LNUEJZFwRR2D1nVFSGk5Q+a1ijPo8xhlkaPWci52wKgrfnrJy8DMXwnM2ZMwfz58+Hy+VCS0sLqqqIIpzX68VRRx0FQJAzuyi3PlTOCw68PGesdc5kyeA5Y8m14iUIkhPeFJivre6/azX3JNMHVQBHzADmTScHXzBDgkszBOqrJcxsJNu119pbrTeLzddsQfczPWj/e3tRjm8VmSLUjAfinnPGbuhTAZj0CIM3mJOUvpxlpNmfi9JpfuRMloHdXkLOWDxnujARh/wcIDPuJVmCbzZZmIm1m8/90j1n2tLD3GlAcx0Z7wtmZNo4b7qEKrIWMyrnjBZYTvbxsQNGdhPC5Z87dgghnedGdhXfc1aZ1vLf8ihHmoXDT/qzW2WX0pc5hRFCK6HhSCpMNghVa2RtD/GcUY+5IGdTDuWWc1bu5OxHP/rRmNu53ZpqkiBntlBuZKjcyCLAMayRHo9DIv4gVW1jyHfgpR6ZKwxQuZQYkEObLOaeGLqgBOCkpWNv69bS0doqK/VzKayrrwWQGimPBzWve0aNGInVc8aR4IdluuBgn5yp3BRIJV2gJBWx35409ZyxNYe0SQIGHG6oPgeSAykkbBIRB6d8odxxDwAerf5X3ILEfCasUYbDUXjO9miFvBM5xdNpgWWmSAIDhrTFnsolFWNuE1igec6KrBarqkCV5jmjxa/tgIY1ehR2tUbdc8ZKhpwyorIDkqIiHbY/x/LNOaNhjeURLcEDgpyZRLnlnFGUIzk7+eSTsWrVqjG3czrJAz2VSpVNaN5EYLKSIV618spxwUH3nHEwZA9oOVnD21hW0Pko7Rml9IGMKIjlcB+DxbBkLuD3jt0wt2ajDDrd8M32IR1JI/xu8YwklVXqmRc4ec4kTippvBQ/JQkZkRsO3mBWzxmQEQRJMRhpaZpzxslzBkmC2szmsZF4hTXmeM4AwE0l5rsskDMqpS9JeObWwm2iizKjPGc1pO8k+pLMzzIloWBkVwSSQ0LF4gLkbKI8ZwAqU2PXXDMLKgjiVtnVGnnVOZOMizIs4lZFEQQpjwU5HhDkzCR455yxrujzFATh3Sa/f+yYb3q+qeg9402GeN0vXu0xHrOU7QGKIKXPQSWNhje1P9ABJWHTeOSl/Kf9p5Elfk1NLLrPWs6ZUa1xPLuRGmnxJFC1UgttfGPQ0vmsQE3xmyNZoOecMfYh3sVfmSWsZWDEwe45AydBEABIaOM+yWCkpTnnnAFAWidn9hYjdDEHTmqNaUMXorlf8W4LOWcWyg3QRZncnDOHzwHZJ0NNqkgzerlpeRLZK0N2j22j0Xy0yN5IUb32xrBGF4PnTKaCIIqSdc9sHUsLc+ARGjvMQ3k4zYecZYU1ipyzqQdenrNgUJOuHbYWPpQLnoZsKdokyJl98LpfPMHDm1eO5IxXzlnQB2wM1iHhcSLRnbCvUEYf0IwPtKCWljGsLSD7ZlOjJWrpHhpzzpJ1heWidSMtCVQfWw2AENVioWzIGSfPmadCy6diXB2WOSXiG/MoUwyeM151zgAgqQ1UFs+ZwikHDsiMs1i9Nr5sqgTq8xCjYZ077gHATXO/+s3fQ2NY43iXaSzPGUAUFAEgZVEldlR70ubIqzPghHe6F2pSRe/aPqZzFmyPavCc1dj3nElO4jN1QsVwmG0+0xU/OSzKhDVylrDQZ3KR8ZyxC4Jk6pyVSbQEBwhyZhK8cs6ampoAAF1dXUzH4WnINjY2TnibpiI5o2C9Z8b7VS5kiEceXFmGNWr/HYxkqKmWGDK9VSS0MW4hhCgLnAz9xhryv2uAXHd3nQueJjdSwymM7DRvQBpzzrreN6/gtkZyNuO/pgMS0Pdyv30v4jhQWCXnOYFXzllVLSdypi3Bqxy8MFwUSOl9Yh/2SDrYJbV5qjU2VpP/vTVkQa3v1X5bx9FD0hgXiYzjnkIPL7RCzmhYI6TxyZk27uN5Dk/D9lg9HpQsmvEsTr+gGQDQ/rfiLQwBBkEQBs+ZJEl60efubrZ50kn7kIs1+itDzljGvczLgy8ZwhqFlP7UAy/PWUVFBTweD0ZGRjAyYj/fgqchSwnjoUOHmI5jpU0ejyajO4XIGa9Q1GAwCJ/Ph2g0inDYfv5SuZVjKGvP2ejaypZAjbQ+h/UQouz20Bds1yjgkxDwkdpDQyPkmlevIo20FGqoGWkH3H6ovsIXyWMIb3LXuuGf7YOaVm0VvzYDNVke5EwPSWPs1jX17EqEQCacSOLgOevRCixHLIbDGqFL6XMIa0xq81CSwUhLccw5a6olx9jVXAfJKaFvXT9Sw9a9RBKnsMYmjZwdMjiNXDaEORRd0XJ8Tm1clMkFlYpn9Xjo5MyEoT/9o9MA2CfKptqjApVp9pwzADo56+lle5Y5NBuWlZzJEtDPQdxKJ2eM7XE4DPUWRVjj1APPnDNe3jN6PFZQT8wHPvABvPnmm7aPIzxnhcEzx4tHHyo3T1W5tQcAtzpnTbXkf7ekPdQO2Xyo8ZJlR4YwnnOtioFhVa91Fu+2IAygZAxZq+FNVDmtWMWo1SLmlFiBLubAaFjXNWiCIAnWsEZqFLGHN+3zkns49A5DiDUNa+RAzmJUbIpBRY56ztIcPWftMReqVlQCCtDfOmD5OLrSHuM9o56zTbuBb9xJpNDtkDNjEerxLpNnjJwzAHD42IuYG9tjZowFjiBezNjBWFbOLE8YBUFYcs4AQHKT39TXy9ZWSs5kD6uoXWZRxkr5hVzwWiTKFgQpjzmfBwQ5MwlenjMgQ4a6u7ttH4OnIAhtDwCEQiHbx7FiXLtcWiiFIGe20NxMQjPa2trKoj3lFtbIy3PGq3AnNdIOqGyeBl6eMyBjqK3fDHz7VyrcddqCiRVhAGpYmwhvclFyphlpgSOKK2utlE3OmfafkZzVN5E+LSfSSDL8NpmGETIaRZIEtLn9iDsdiB2M2TfU9LBG9j4dcbGHW9Hi5Wkez3ptjHUPAFVHVwEAhm0QWd37ykhgG6ozr3/8Z+DtXUbPmXmPXkYQZPycM1eBnDM9rJGVnOmG/vjXx+GR4apxQU2rSPTxkfEf1R4VCCjkerqq2MiZQyNTXQxhjaqqwsWJ4EsAujVyFj1on5zJKT6esyxBkDIpn8IDgpyZBK+cMwCoqSEzdn+/fbc6T0OWtgdgM2attMkovz5VwPOezZ07FwCwZ8+esmhPuYU10nIN0aj9cCsgs9ro9LC1yeuR4HUDe52anL5dTwO9vhxm7hqD4vS2/YCnwUa9IwvhTfS20p8QPKLYnrPymFv4CYKQPu1RFBxgCLqQFH65HookY28deX70vWLzeaYZaQpjewAgpsXQxRmM7gw54/Bs1cZY/zAQXGi/vzupmAOjIet2kXBmiu6BTM6ZPUGQ8Tk1/TrfoyGTc8YY1mhSEITC06jNdV32w/IKtkcFHJzUEZ0aOes4xEDONG9wChJ7nTMZaHeTTsRSFoZXWKPTYVCN5VQzrxwgyJlJ8PScVVdXAygfckbbwworbeJVq+twAs97Nm8eEV/4wx/+gBNPPJEpHJUHyi2scdmyZQCA9evXMx2HJlE7Csgzm0V1MCOn37u2z5aRxq2gsdYeinAUcGvFaBMWyBmVQVdMeM7o1EmHvF4QtkjkbN+v9uPNz7+N5FBpH9i6lD7jPaOGrEdN4xt3qTjtywrCEevzp+45Y1RJo/ezvZL06fB2m4aaRs5UDs/WhhnESOttYyBnCY148HjWa2Msi5zZqO1HDX3WkDQA0DSJSFuigKvaumGbWZSRxl2U0cd9vu+8VGWPUUrfQs4ZAHgatNxfu+Hl40BVAQf4RF24/eQa9fcq+Mz/KfjCLdafsVR0KcVjIVYiz7G0Q0J4W9h2jcMMOWNr05xmYCRA7ufQfvuevHKDIGcmwSvnDChvzxkL7JAzUYTaHlpaWgAATz/9NF555RWcd955JW1PuYU10vDcnTt32m+PouoqabKbzyp6l9sH36kkjLjnuR4bjSqO52wkBrjsSGqnLZAz7Xua5qF7znaOFG2Rpv2BDhz8c3tRjm0WvMQcHJqR5lHSeOB54Lk3gF/+0/pxeBlF9H4fChCZeNseUCrAwXh9AGDOEZrnrN++NHtaN2Q5POsNnjOa6xR+N2y5v+ueMw7zkNegT9EzCDgrnJAcEtIjab1e2HjIeGLGD2vMV/iagi44KJwEQcx6hdxN1gtvW2oPMqGozEWWNULuUhXc/x/grn8BwxYXZeh9TcoysyiqJJGSFYPTKgEVGLCRQwkYFok4eIMbFxByFmHIgSs3CHJmEjw9ZzzIGUUxyJldQ8lOWKMgZ/ZAa51R2Mk9K7ewRp4IBIjhzxLWSIUBkpIEB8cQJ+XoOgDA4BtDlo+he854jHsDORuOZHIjLOWeaMPXjCCIlGOkuRvccFY5kRpMIWFBhMQq4h2lfWBnvJ1sx3EGnVAlwK+kdU/Kng4bnjOFn1ojAHQFGT2gGllUGD15AODSlPEcQ/Y9ItTLwIMsUs/Z4AjganDBWan1dwukQE2rcECFAvYQOSAjzAMAHb1kLnFWWfOe0WuUlMcnZ1LOoowRVBCEWUrfQs4ZYCi8XdSwRmuEcSzQotouQ92SPRarAChxjUybqEs3bnu0YdrXUg0A6H9twN5x6CIRh3Gv1JAcOLUnXtTi4hMJQc5Mohg5Z9/+9rexf/9+W8fgaQTnkjO7CoAirNEceBjWlHywoBies4nKWRwPXq+mJhWzb5grWu5JUpLBYdjrZOirDxFPw4gdCXlq4fDwnAUz17mrH3BRA81CGKAug47xc86oMZ/RNJEQLLJiIwDs/vlerD/n1ayC2RMJqtbIaqRJsoSEn5CP6hQx7jtt1NHlVV+IDtNOn5ZHuSWM1IgNmXjN66EykkUAkJt8SAPwDMRs18/jGdbocEiQZWKsX/Yje3lnOhGSZC5zo8tAzvZq5N5q3hnNEUuaMPZzw5mzvvNyEgSxGtZYZHIGZMIaWQm1pJEzp4Gc7bK4Fqsmzd+vcduj7d87vRoA0P/qgK3j6KqxHFIGnH4HOl0+IKUWLUx+oiHImUnw9JwtWLBAf33GGWfYOkYxlP8obrrppqK3aSqHNfJAPnJmtW4ezz7k85EE4RtvvHFCPK/jgZIzFs8ZfaClJBkcHGeo1G5Zu5uQs8he6+TMoRvW7PNQc13mdSwBbDioyZAPJM3fQ605ZiS16dRpHPLFVmyk6H9tAP9ufBLv3rTDHilmQEZhk/1YyYpscrZpt/Vj8PKc+Yh9i7fanVCbyPjfdasNgaIkn6LYAODxy+h2+SCp9sYXkCFDPARBgEx//90TgDST5p2Zz89TDGGWPFpU4c+8vu9x4GCXCjdVbBw0R65pmxIm2pTrMTfCoeeccRIEMXnPqCBIrKN4njNdYZNVeEf3nGXm5PWbLYY16gR//Hl6PPi0sNifbSHqowMbB215qxy6WiMH+8MD7NLyuYfeZijrUUYQ5MwkeOacnXrqqfrrHTt2YMuWLZaPwdOQnTFjRtb722+/HZs2bSpqm0RYIxvykbPnnnuO+bh2Qb2vP/3pT/HYY4/ZOkYxyCKT58yQRM1h2OPC95Lf1ef0ICHJSHQnkByy5mmgXg+Jw2rjvGnZ70/+mgzZI0NJqKZzQKhRpMKEalue8KZiKzbmYudPduO1D2+YkHNR6HXOOBj7iQCxjGpSxKjc1Qb0D1sz1KhaI6unyhgWe5uDCBR1P2OjPEyanyCIzwPs9ZJYwoHXB20dg6o1KjwGPYCLzsy8vmMjadvQJvMGJE/DGsjI+1MsvViFSy+jYS7ckkYVmAmTy801zfpOy6dSTea6jQWrYY0Vi0nn7X91oCjRO8awRmZy5hkd1vhUa6ZYuhkoRcijHHB60O72IT2SxtBb1kP09UUixpwzgORR7vKRhg2+bb0t5QhBzkyCp+esrq4Op59+uv7+pZdesnyMYtSEMuLll18uapumYlhjMXPOAFJE3EpfKpaozLvvvmvrGOXmOcvknPHxnH1oDTmIKkno0KSIra7uOzjJDwPArKbRn0lBLbTRpAKXlSLUelijYcgXQ7HRWeEs+H30wMTmoPHKOQOAaC1xe8yLZTwvte9XLak2Oqghy7hibVT9e6mSiNwMbw1bX0WngiAc8qm8bmCTn8xFdqX9FY5hjQDwq69nftebUiUA4m2w2h4e+UIAsDB7LRaDYcDbTNygsU5zYyM71LLwtgXDGrVFJvob7cJqWGPl0gq4G92ItccQ3s5/YUiFIayR8eFBRWBchoXsN3cA77nKCjkz9CGm1mQvyrwerAcAdDx8yPJxeEnpAzmes02CnE0p0JAxHrk+ALB8+XL99b59+yzvn0wS48ntdo+zpTn88pe/zHp/6JD1wWaFwE7lsMZi5pzdfPPNJWmPkZzRvmkVxfCcHThwAJ2dnbaOYVxtNNYG4oEOLbTRSngTAEia4ev0sk/dLQ3A5R/I/kzxWwtvynjOpHEJbL6wxuCijCeB10KNmmeJfs3Lq9H8wQwb3fLtbVzOZQZUrZE15wwAeufVAgDO6j+oi4IAwOa95o+he1+Zi79mfk/M4YRzmhdqUkVkj7UFEdqnVQ7CAD53plzFsE2jOx6l3mk+YY0+Q43Egx4yb8cOmr9GRiLkZqtnDAD45qcknH5s9mdOWuPQpLS8QnPO5PGjCuivz+s508kZq1qjtZpikiyh4b2EWHQ/Y0M1d7z2FMFz5lazr9GrFgKurJDp8WAkZ69WNAAABmyIgvCahwDNc+YlCx9Dbw/nfQYcbhDkzCQGBgYA8KsJZjRg161bZ3l/6hGgHgJWfOELX8CVV16pv7eavwSIsMbxMBHkjJKSiW5PVVWV/joetxfHT0OHeYjuGMfFWWedZesYsTBpT1qWsuSneWCLvxoA0P2UNcOAGrIeP/vULUkS7rk2+ziK32LNI+0ZmDYhpZ/PSAvM98NV60L8UJybR4s+mGd+ugXBRUHM/twsVCwK4pj7VmLaBSS/du9d+4oqBmCEHtbIQVTm0BEN6HF60JyMYbbBe2Z2BKtpFRJIqiCrtP+oY8/UpOIt1jvTi4Xz8Jx5gAMaAerbbF2yHgCiI2QfJ4fQ4VwMO1yATBRRTcvWGxaJeMxD1RUSnr41+7dJtdRzZpKcGY39cbYt5DmTOJEzvc6ZBYJffypJuu19oZfp3PmgqoDMSRDE6ScTh1uxL5piDNHnSc52agshg5uGLHnMVUWFQ5unHRxyzhQV6Hd50O90IzWcQmSf/YiZcoEgZyZByZnRCGUBrVMFAM8//7zlekw0l8aKMT4e5syZo78uNjkTYY1sGOu++/3+vJ8Xuz1OZyaUrKPDos6vBkrqPB4Pc3uM5Oztt9+2dYyhQS3cysFHJQ0AFmjDfr224tj1ZLdpIw3I1IbhQc4ojjCEOaWo58wkOdM9Z9L4OWf5jDRJklB9DJlTB9+0lyM0CtrlXPzDo3Dyy6ux5EdH6V8t/sEi/XW0bWLCG3kWDp/e7MBWjdj/fPermBEn83TYpC3CW1zCiJEGLX9wm0VyprXJwaHAstcN9DvdGJGdcETslWiIhkl7XBy807lQJQlDDi2/q9ekMmKcGOVJif8iEYVSo6kXmiVncfOemEJ1znh5zuiKj5UFkPpTNHK2rk/PM+SFLM8Zo0eY1oLzqqPJWSJpzn7KGvccydmw0w3XDB+UqGKpuLoxj9LjYZ+JhrRT75xEoY2CnJnE4CAxHHh5zv7nf/4HRxxxhP7+xRdftLQ/b88ZbRMNkwyHrT1gAaHWOB4ooeZxz2pqanDTTTdh9erVuOqqq/TP7RybB/GYPXu2/vquu+7Cb3/7W8vHoOSMx/VxubLjf+wsAgwP8VNGpHj0R+Rat3sC6HJ5kRpKIXrA/CqfrK1OegP82wQASS+5bimLYY12ilBTVC7VwtDe4aOyRT1n+ciQp9GDupNJaKCVYtssoGGNXvY1B1z1EaA2VKm//1TXLgDAkMnURSpZnuCUv+Q0GMO/eJN4zrqetuYNpp4zJydyBknSvWfDW6w/x+IRjZz5+NHXxwxjrE8mYyzRa444xrR8wjTHRSIAePzHmWOlqsicazqs0UKYXKE6ZzSfipvnzMJc7WnyoGJpBZSogo5/2FtQHAuxREatUWYNa9TI2bGzFbz/xOzvhk2OexqGakZdczz4vdlHeCGsEaI3zRMiOg/FZAeXBYcMOSNzo93aa+UEQc5MQFVV7p4zv9+PDRsyqmG9vdZc6zwNfQqfz4c//OEPAERYYzHAO2/xuuuuw0svvYTbbrtN/yyVMq/+F4mQmZ2Hp+rKK6/MEimxU46B9mke7cntgz091vMKdHLGIeyC4shZEtbfSY436LBWWwjICIJ4OJKzhTMl/PcF5HXCbTGsUbtEqjR+aN1YktqVy7RcAU7kjLZprKebWytUnOgrXuFrClVV9dBPn5e9H/k8Er7yq5l6SOLiSD+gqqaNtJQWqhtxOOHhkL9kJB3rKhqRdDsxsGEAYQulEahSHw9yFtW4xQ4f6VP7fm29jijNOXNz9Jyde6KE719GrpXuOesxS874qkdSnHOChJXa+nA8qEnLW/ScmSH5ZgRBVJMeoLFACb7VvM45l88CALT9nS85G4llBEHAGD7s0MIaL1yt4NEfyfjdtzLHMzvu0xEy7qOyAx4OZOiGSzJt2O4mY81K5EN6RCNnkoPLPDSoTTdvBsnCW89z/ENVJxqCnJlALBZDMpmEx+PhSoaqqqpwyy23AAD27LFWH4Z6zniGNQIZ4iDCGvmDkiEroYdm4HA4cN999wEAhofNG7hU9KWpKY9sn0VMmzYNPT09GBoiq2fvvvsuurutyWrz9Jzlwur4AoDwMD+pXyNOWCLhAycZjDQr5ExjNr4A36C0gHbJo9qT0mybqOfMTM7ZWEZaxVIaisLJczZOzSOrBXdZYFxBd3DqR65qF84+dCbcjW7UpRKYHR/RV47HQypMFm8isgNNtexted9xEqJPSXjyFgkxhxPv1JJQsf5XzSslUnLm4hDeVKuFXP21fi7SkND1ZLf5hQYNiSi/vE4jrv8MUB0EwnRRxqQianxEI2ccBFNyQcd9xOcGZCKlb4Yo6WGNsomcs4KeMznreHZhVUqfoulcojLa/0o/1yL1kVgmrJHVc0bDGqm36TNnS7rSrlmPeUyLhEg4HFlhiXbx3YuB2NMSrvlEZiGkv9U8OaOLRDHZycVzdrS2yLDdV4W0JCG8PYzUiLUyNeUGU6M9FAr9KBQKrQ2FQveHQiGX4fMVoVBoXSgUeiEUCj0SCoX4uATKDLy9ZkbMnDkTgHV1xGJ4zoCMRPsTTzyBP//5z5b2FWGNhVEscgYADQ0kh4mSIzPo6uoCADQ2NnJpg8fjQUVFBebOnasf14rHiqfnDCB9mOL4449HZWUlrrnmGtP7jwxr6qMcPWcUc6cBYYfmpeozbzw6KTkL8jXUgloI10ObNNU2k2IZdMyrZgRBDBsYF2UCc/2QHBJi7THm3A/jccciZ+7aiSNnkZixzhm/40qShIbTidrcl9s3YyhszrBMDRODJSo7R9W7sguvR0JIS+XbkyTPI7P5VKRRWlgjB0/VMUdKuPNrEvpdHuz0VUBNqzjzQ4P4yzPmDe+ktqrvDnJQcDFAkqSscW82dDhGyRkHwZRcBLW13VO/KsHdQObdZG/hdqlpFWpahQKTQkCFcs60BQsrebd526QLgli7Ru5aN7zTvVDiCiL7+BWnj0TVjHHN2K0dPq1QdySTc9ZChr7pRZmBXrKv5HdwCY2VJAket4Q1yyVs81cjKUkYfHsIySGTeZQRSs5kLp6z710i4ROnA0nZgf3uAKACp394GO8eOHwX/8ftNqFQaAWAltbW1jUAtgH4qOHrLa2trSe1traeAuB1AB8uTjNLC95KjUZQw9gqOSu25wwAPvnJT1raV4Q1FgbvsEYjKKm24vHk6TkzwigIYkWJlLfn7KyzzsKZZ2YqwA4PD+ueajOIDBAjRfIVrptlB401EoachAhZIQmUnPkr+JIzuoLe57QmDKDnnJlUActnqEkOCZ4m7bwmc17GxDghjQDg0shZwgIptouRWKYpPARBjFj0nYVIBVw4MjqE9EFz456Ss4jsRBMncgYQjxAADGleoeFOCyGjWh6lm4PnDACu/CA5zh4PcRFUdofxX//PQk0ozXD0VvElZwApAK0vypj06MW0eYiGHPNEyqAxQUMbU92F25WOkZ1SsgzYrG+of6fdc2bPmcU6Z0YEF2pCNhzrnY1oip+KLDGTIRrWaCRnlFSPmNQ0Gu4j+7oCfPt0Uy0Qlx046A5AUoGoSZXE9EhmkYhHLm6FX8JPv0SuMy2l4W8fxi1/mcTkDMBJAJ7UXj8BYDX9orW11TiKfQC282ta+YC3GIgR1DDets1a3Z1iec6am5uz3lsJOxRhjYVRTM8ZJen0HGZASRRvckb7JmAtnJC35wywF55LEdeMIjnI3yhqqs0YaQmzyoiKCqc2Xnwcc84AYLq2EkvJmdncE0qGFJiTcx8rtNE7XTtvB5uC4nghjQDgrrFOiu0iEssIgvBOIvA0eRBv1hZ6es3dr8Fuaug7EOAoeEHn/EEnIWf9B82TM4emRuis5DvO9noJY5wTC+vGrBmomhHsq+ZPzppqjGGN5jxn8X6yXdLDfx4yKrUOuDXPWXfhdqWGNMNaU+g1G86cL6xR4uU5S9vznAFA8EiNnFmsOVkIkShVsWUfYzo5i2bImU97REZNTtPDmufMXcG3DzVWk/99LtKgqMn5O6WNsbjMJ+cMABq0tlByNi82jJmN/L3NEwUzd6oGAF0KHwSQFakeCoXOBnATgASAH+buHAqFrgBwBQBcddVVWSvZhwuozL3X60VbWxvXY9PaTr29vXj88cexYsUKU/tRwhgOh7m36Q9/+AM+9alPASC/3e/3I5lMjnuevr4+AMSrN962iQR5eHd1dXFvfylg5vpQojAwMGC7UPNYoLlmQ0NDpq6nqqrYvXs3AFLInOc9qK+v18MZ3377bdPHpp68dDrNrT35PMtmjz3cRe5Xyq1w76OOtAcjMpl+ew4MINDWNm4fStPCr5KEvr4ODA7wa8+q+cBlZ1fi4YcJcYl0REz95v4e0ghFkjAw0I+2tsIrpxKaAUg40NYOoyNArSLGTMeWDkRn2A8v0lfg5bHv85BKxspw+3DR5569B5w6J+s81Alngq9xlAjKCACIdwyhrS0+bh/auY08N2S/XITfPk3Po+w7OGL6+I4oeRZE1DDa2nhJYE/Tydnc+DDqKlNoazOXA6tGyNycls3/BrPwuSrQp437gbYBc2OsbQBOAHE3/3v2xXMlPNtah3fbXOh3OVADINYeK3ie2C5igFNydujQIQTksWtwdfe4ANQjFk+grS1bqGFEi8uLDRc+53jo69Fsj4S5ecuIVCMhm11vdMPbxmdhsLPTgxYAaoF5yCyGImS+GunL/DZJqQbgw8GOPrSZKAnS3RlBEwBPIMW1D6XjADBNX9Tb+UYXUkuS485D/QcGABC1xv6+TrSBVwTVNOz2ZchZT3gQbW38PKK8YSyplQszT4oBAFS7twpAn/HL1tbWJwA8EQqFvg7g88ghaK2trfcAuEd7e1i6Sagsd1NTU8GLaQfG4+3fvx/nnnuuqf0oqZs5cyb3Nl100UX42te+RibdQADTp09HW1vbuOehnsVAIDDuttRorq2t5d7+UmC866Moih6KumDBAt1zyAvU65RMJk1dz0OHDiEajaKmpgaLFy/m2pZHHnkEJ55INH+Hh4dN31/qBebZJ+68806cc8452LVrl/7Z9OnTTXl3HVoNqWCDn3sf/UiVir84CAlJhj1oaWkZtw+FuxMAtiApyZg1k/+Y+canVfzxMWKYKoOKqeukVgH7cRAKJNTW1qClpbDKhCwrQBqY1jwdXkMYW09DHwYxhCpPFdO1TkfS2IQtkBzSmMcJLBjAHuyDFB57G17Y3aMirG4CAEyf1QIXZ++QZ9oQ8FYffFGY6kMbhhNwA/DXuLn/9l99XcVt39OWwS1cW09iCwCgcU4DWlr4XJ9/3ajiv75RgaQkYUF0CDPdadPtcaQOAgBa5tSO25+t4kOnqPjRH4ix7U6ZuwebUmEoANSApwj2B3Ddp1Vc+kMVIxVBAIeQ7lYKnqe/fQDATkQ1u2hacxNaWsaeJ9qHVAAqXK7Rv3fg0CB2Yg+ccLGN+woFB9GOQEXQ8nF8x/txEO1QDhT+3VbgdmqeY4fMfEz/fDJfOaKZY9XVaCVV/LUFrz1FcoSY7c0z+D/Lrvyggr57yKJMus9rah5SPCqAg4jKDsyZ2Yz6aj4eru9fpuLHdxNyNjc2jAFXBVpaqrkce6JhxkJcB+AM7fVZAF6mX4RCIeMywyAAfhmVZYRiCoIAwLe//W0A1vLOaB2yYuQvAZnfSj10ZiDCGseGMQyVNzEDMqGSZsMa29vbAQAzZswYZ0vrOOGEE/Dss88CsNani6HWeMQRR4wKGTbrtVQ0ZTt3Ff9woqqghNnziHET7TcX3jR4iLQ74uQUBzKqTWQlMyHJUOJKVo7DWKB1Uc0UoQYM+Sc5nzu1XAhWha1CNc4oqFrj0NvDiOw3X2PODiJx6KGotKYTTzi0sgCOsLk+He4i27lr+ffpz50n4bMXkGurmLyP6bgCZ1ohxWg5ityc/x4JD/7UjY3BOjgArOgwPw85EqTtlXX8wxrftwqIuLRwZpNhtTQ3TQ0UadxrJkSPh8y7yY7C7aIqk2bDGgsKgniolD5jnbOEffEmPedsxwg3eySqKX6yyugDgLtudI6sn6rrmgxrTGrqiNUN/Pv0nV+T0TSHzEMjXSb7tCH3lWdh9e98VsIxx7rQ5fLCqyqQ2g9fSjLubNja2vomgEOhUGgtgCUAHgyFQndrX5+tKTU+D+B9AO4tVkNLiWIKggCZnB+qnmcGvJX2clFscjbVBEGKKQYCZDyR1Ds3HihZLEb+G5Dpl1b6dDFyzgDA6cw2RM1eI1UzMH1FMGQBwK8dN2Ey96TrANkuXoTcE0Az0iQJw1bqr6mGItQmzjGWoeYIkN9E69/YhU7OCjTGNzsT6nro3+b7px2MRFQ4VWo48l+UcVWReyVHTOYtDpPtpIriGPrVjcT4kyLm+nRKU3cjRhpf8lodBF6pIPPQ9B7zzzFXkvTBqnr+40yWJf2eRcdRRaSgua/eIs1DVZqYyyEHGReJcfIF9Zwzh8mcs0KCIJqUPqtKq07ObNTKc9e54a53Iz2SRsxEiKAZRCN8apwBpH1Adl08mnMWMdlcZ5TcM091ccY9VcCNmqzdl9IWHMIOJ5e6a0YsnJnJO3Mf5FQ7swQwNdpbW1uvzfno89rn/wLwL96NKjcUUxAEsK7YmEwm0dfXB1mWUVdXV5Q2Cc8ZXxRTDMR43MHBQbzxxhs4+uijC25fLCJEQRccSu05o7jvvvtw6aWXAiDkzIwXnBq8gbriPNCCtZq3aMickdbXruXC+IrTHo9bgsetYsjhQl0qjkRfEr4ZhZUUdLVGE5LawNiCIBnPGRs501MXChhFslPGstuXYNOXN2OgdQD4/Gy2cxZAJKrCC03NkoOhlgt3jQspAP3tSQxHTMylmjcYRSJn9dPIcR0xc32aimKEHU7M5mykHb8YmL/cA7QDvqg5o1FRVHg0CcOqInjOgAzJipsUAkppnqpgfXE9Z7td5EVsZwxqWh2zv1JPnu45G+f4hQRBdLELxnFPc03t1qQMLgygryeB8Lsj4855ZhCLUhEg9jHvrHRCcklIj6SRjqXh8Drg1x7b192t4hsXjX8OV4zcM1cRokAAwNdgrTxJvD8TBeLkPMxuukLCN39N7qGzjw/ZLgVEEWoTKHZYo1XPGS3u29DQAIejOA+QiSJnU8VzVmxyRvMiAeCCCy4Yd/tiEiGA5I1JkoT+/n6kUuYMNepd5F0eAgAuueQSzJ5NjHCznjO62hhsLNKKdRO5Z6rJELCBThreVJz2AMRQs+I5U6lao8WwxlxDzRGkRlrxwxoBIDCfGKLRA8V9eEe1+mNKEYgZkDH0g+kUfv3o+NvLI+SeykUiZ3XN5D66kilTC2+UeIzILt0bwAuSJOErnyMH9cfNkbOhERVBhbTJW1ukRRmNZKVNFqFWtBCwiobikrPOlBveFi+UmIqR3WOHg9FyF4OauiNLWCNdlDETQl0IilY4247nDAACNLSRk2JjPGZfPTIXkiTBU6/Vn9SuvXH+7B8ef5y5tVBdT5H6dKCBtE81WecspoVoJjxOLnXXjKivlnD22aRvOgcZS7OUEIKcmUC5ec72798PgL8EuhEirJEvih3WaLzmZuTrKTkrludMlmXLfYhuV6xFEKuhn544eYBQEsUb1Rrpk6LmCEl/hybtXyTDGiCG2pDTAjlL///2zjtOjrr+/8/ZvtdLcrn0kAaEQCgTaSJIVYEvioogggICPxARQRALIogdEEVUpIN0pEvvTcChhFACIT2Xnutl97bM74/PzO5e2XZ7M3dJ3s/HI4/s7c7efm72/Zn5vD7vVngTasi+UPNZgrNUz1m6lH7u48KT1KZEzyqHc86sBsKm15lbrS0gKhIxmtvzL9K8tjircsaG6mo9RDQPHrMwb0jM8hp3e32p8LrhpKJRLRoregsTZy0bE/hNk16PB2/YmY3PqnHK1s3OwgSsZn1ndROcDWts64SqnVU4WPv72atmRtaqe0dLsDBxliusMdNzVkoUje05s8Mki6Vsqto07Vk9TGGNPUPvuzYYZTPU+Lo+VeuIdRll+Zatyf9++14WrHHGhsobLI95gbmvdkivU3mUvrHq+h5oF3G2VTPaPGcvvvgiAHvuuacj4wEJaxxunPacFYtTffIyqa1VXW5bWloKOt4tcVZo0ZSQVTikptGZG8hYqzKdr8AQsOWfqvGMmeSgOKtIe856m/MvaE2rgXBcKyznLFtBEG/58IQ3pZtQ5x5NcHwQzasRXR9NNdV1gogtzoZpkdafZLktzuK0FVAx2muF6vocyj2prVRCC9INr3Nhh/R2en0pD85wUjleibOqWKyge02LVQyjO+DcHBs71kuPx4uWMPOGNCeTJh4rf2/33Zz1nLV1QeVOljhbkEOcrVH3jpYCPWe5who9AQ+aX8NMmCR7SxBnJeScAYQnqvtgZJjEWTRqefKGyWNeMUsp6Lb3rJY5GXN9SR5xZpomoZidP+2MDVVamyD+nsLEmd3bUxvmvms2gQY1nlCXiLOtGqc9Z7W1tXi9XlpbW1MejVzY/any5RWVgoQ1Di+258xJcXbggQcCKtw1H06HNcLoE2f2uS/Ec5ZImJRb4Zi14525gYwd5yWBhi+RTC0uctGzWd3QGqc47Dmzwxqb899obTEV8XiLyjkbUBCkbHhyzgoNa/T4PAQbAmDCp1csLekzc9HTbf2hPmdutXN3TnvOCql5YS+eHBVnVh+vWAEhTnb+UpfHR5UD4qy8SnnyvJgFicV2K3Q4GnJujjXUajT7rDC1PM3em9tNyqzr0PTZTuaaQiwOZbuoa2/r29mNyQ6tawmoe0e+aZ8rrBEyvGcFFpEZjFI9ZyFLnPUMU0GQqO05G6YiN2MOULUF1tynWg5f9O2MNiR55n2iK4HXNIloHkLlzlyHasb5SKDCJwtpKB63ck191c7YdMgKAw1GhrefrJuIOCsAu6GuvdgcbjweT2pBbX9WLtrb1a5WVVVVniOHjoQ1Di+2t8apsEaAe+65ByisVLzTBUFg9ImzYsIaN21KEDSTxDUNf4Uz4mxcnUaX117I5l+Y2EUWyhyoImeTmXNWSKlvO1ckWqA4Sy3U+m2S24uqUktq2zlw+cIaAfzWLvKSK5c65sGPOizOyq38pYpknK7O3H+DmTAJROMkgYBD4iwcTBeK6GrOb9NdVnhTb8CH3wHvosej0WWF6XYWUB2x1fKcxR0qugPQUAstVtPeyPrc4qxtUwIvJhGv15FqnzYpr+X21n3/nXaS8cHnYu9m5VHvsPKcCw5rzPK6r6z0vDN7c8s7VM+ZFeY8XNUaYxFrPEMUi/1pOGws3nIvnYs6iayJsNN2Gud9Q73Wmed2li664x/WsvWZ1FVpdBaRq5y0Nm78DoVZ2gVKQiLOtl6SySTLly8HSBUUcAJ7QWoLr1x0dCjX9pYsziSscfixhZ/tpcvFtug5K0acLftY3dAiAf+wJyzbVFdAt8eu2Jj/JhKwbjQVDhUGsMfUZu/qb8zvxbcXVAV7zrLkn9i5GWasxOuBrfoKqJK20+/Tzde7lzuTe5baQR9C/6VC0LwalBcWRpjyUnl9BIMOjUfTiAXUeNo25F9sd21ytgIpZIjFTfnn2MZV6hivQzl5oIRQsyXOohvyiLMNVvsMv3MbMvaYALqDAQKTAyS6E2x8euBGsWma9Foe/A7rOlFwWGM2z9kwtNFIV2scml0HG4PgUd9HIVEM+Yj1lObJ64/H72HM/sp79vyuL9HTFKHcqpvV1ZP7mmn3petyoGy9TW1lZjh87nlmmiamlZsWrHWqQIn6vWW9hYUzj0ZEnOVh3bp1RKNRxowZQ2VlpWOfY/9uW3jlwj7GyfFIWOPwcu+99wLOes6CwSBer5dYLJbXe+aG58z+WwstwDGaxNmqpfYOunOLospwZn5O7oWJaZqErIpbFWOd9Zyt81u5eTkqttnEbXGmeQvLOcuyULO9AoWExOQiFdZYQK5H3d61VO+ubK23wP48xXL/c6WV+C4Ej5W3kezIPedtT2iHgzvoQEqc9RQQFttjedfMMudsOmIJm64CPGeb1zrb2xCgsixDnK3PbXedG60wy6BzYhH6FgWp/Kz6YfWdTQOOi7fFMeMmvkofMcs9nbdao/X/YDlnkM43LSWkudScM4/PQ2hcEEyIrC3Ne9YTNVmzTo3HN4ybIDPO2Q5QHvDl/1hOeUj97q48w4274DkrDxde5TfZk0SLm8Q0jYpqZ66LZVUqr9NrmnnvraMVEWd5sPO7pk+f7ujn2F6wQjxnboQ12l6P5ubmPEemsYWWLbxysa2FNS5atAhwpky8jaZpBXvP3PCc2eX9ewuolGaa5qgqCLJ2hZWwXOHcoigYgC7rhta1Ifc56uqB8oRd4tu5lXVtpcaaoFUZbGl33l3HTM9ZAdM+60LN9pwlS/ScFRPWCOm+P4V4LoulrdPEb52/QNi5W63Xytsw84izWIY4c2oHHdJent72/IuiSIvdd805MRS1int0FyDO2jdaGyD1zo2nqhxafX1Lo2ej0xpPLOSs56zGEmfNHVB3RA0APSsHbmLZIY2Ben/BTupsvQ1t/DXF9cgajJQ4K8FTZeedlRra+Nzb4HNg3tfsUcPeT6gicGv+vZZyv/qb84mzzLxOpzS+35cO1c3nDY6lGlD7qa5wxoMfDmaKRWc23pxGxFke3BJnthesmLBGJz1ndnl/u6daISQS6mbs8+W/kWxrYY22WDr77LMd/RxbnHV25u7X4obnLBBQC5BCxFlnZyfJZJJwONynZ9twUkxBkI1Ndky8c+JM0zTawmpB0Lo89x22o1tV5HN6TA210OoNEAt4ibfF8xYFycw5K6SZaLaFmscKRzKz5LkUil1Kv9Dmr35L2MRaS+uvNhib28FnqcWqamcWIQB+S6z7OnLPM3tR5LznTBlCIXmUdtEQr0N5naDy2QAiBSz+uy1PXk2jw54zv+05y72QtYsAJRz04AM01Kj/N7ZCYKIyju4VucRZgISdTpln3ucrCBJqtPLvSvBYleo5A1LNp0stCrKpFfzWvPcP86ZMjV5N2Ywyout7qVuyGSgk5ywthpwSZwEfbLJs2q7mmXc8Hj/V5c5cF8uC6cJW+cIsRysizvJgi7PtttvO0c+xvWCFhDW64TkrtvcakGo2XIw421Y8Z3belX1enaKiQm2BjgbPmS3OCilQ4rTXDIoLa7QLAzhZfAOgq1zd0DpX5r6htXenPWe2t8cJGmoATaO1Mu09y0VmtcaCxFmWhZoWsMMahyfnrNA0QZ91LmMFNgQuhpaOtDhzspiDXQa8rDPPjnWm58zBKLlYsPBzapeS9ztUtQ3SXqdoS26xmEya9FohYGMcqtAKUNUnrDH3d2Z7Fs0yZ8Max9Wp/9c3g7fWi7/GR7w9TvPrffOFe628vcCYAHHLMZpv3ucrCBK0xFl07dDLno8mz5ma91Yp/WGe95qmMenYiQBUPKr6mXbluZ1FLXHS7fPhc6ilR8APm+0iN3m+R3sjrNOh3obQ33Mm4myrxPYcjR8/3tHPsYXWu+++m/dYNzxn9fX1eL1eWlpaCvJ8QHHibFsKa4xGo/T09ODz+RzNOYPCi4Js3qx23ZwU+MWENY42cWbH6ZeNcXZR1FOlFgQty/KIsy6T8qQ1v6qcWzjai7QNlpcxX95ZsZ6zrNUah6kgSCqsscD+Qk56zjIXaU7mnJVPVouiqjzxTXbOmQoncmw49ITVpkx8c/55n7SKmAQcqtoG6cqL+b7jjm4IWv2gKhyc91XlpErp56vWaPeDwkHPIkBDjZovG1pNNE1joiUANj3XtyhIpuesYHGWpyCI7TnrWT30ojxJu69YCZ6zVDn9EnudtXSa6U2ZYSoIksmUb08CwLu0HUwzb1hj+wbnQ2P9Xtjkt8RtoZ4zr9+R3oagxGKHVaU1slHCGrdK7IWlk+FfkF443nzzzTmPi8ViRCIRvF6vo/lLHo+HMWPGAIWV94ehec62hbBG22tWW1vrWOU/G9tzls8Du2yZ2nVzMlx3S/acBaLWDrGDIYQA3VZ82fKPc99A2puT+E2TmNeDN1SAChoidnjTKp/tOcst8ntT+QO+ksIateEqCGKFRWoF7hDbpZydyEto6UiHN3mGqd/RYFRNVXZd29OTCjUbjFQJdK+faY2ODYeI5Q1ObMrvCdG6lP2Ea51bOCbDhXnyeuNQnnB+A6QinM7P6WzKfY7sTSKvgzl5oMKZATZYjrLa+TWA6gGYed6im9I5Z4WKs3wFQap2URuEG5/fnLV8fz6SViP5oZbSh4xG1HnERT4yPeZOVGkN1AfUdSuapCbRS0eeFOpmKwrEqQbUAF5vOlQ3X1hoLOOe4ZQ40zQt1Ui+kCqtoxERZ3mwF5ZO5cHYHH300UB+b1hmGX2nF/r2WPLlL9lIWOPgZIozpylEUK9atYoXXngBcDZct5ics7/97W+AO+KskIIgwajzOWcAM3dUvz9fs8xO6wZjV8JzCiuakdW+wsIa7Sa6zb5gcWGN2XLOSvScJewS1gUK2FCjtSArIaQqGy0dEE5aebgOej4qZqsVzuRoFy0d2W/pCxeq67NW6aMs5Ny9o7dSLdKSzfnnvZ2XFq53bp4lywpsNRCHMts77aAY8ng0wmP8xNHQuuIkerIXTklYRV581Q4mCZKe93b+Ut1n61KvNf83HdpYiucs2z5s9W7V+Ov8RFZHWHPP2qLHDmk7KkVUD1cjarUpY3nyHPCcAYSnqi9sXG8PG1tzH/v2O3YLFudsWtM02srsa2nu89e9ySUPvlX1qCdPsa3RioizPLglzuxFsp0LlA0738zJkEYbu4BCIYtZkLDGbNghhrZXy0nGjRsH5M4VfOONN1KPp02b5thYiglrXLFiBQCTJ092bDzFFASxy9Y71STTZu6u6gbi78ktzuwy4E5XbbPur6z0WOJsSfa5H++Mk+hKEPN66PIU5jnLVhxAs5o0D3Xn3Ca1g15gIv5w5ZkMRlcEyhLOL/ZtcTYp2sWGluyiq2W9srFIAdfnUoiWK5tONucJ2etJUJZMkAD22sPBhaNVcTVZgDhL5XU66DkDOOYgraCiIGanFfbpYJ4ppOd9tzUNgmMCbPe9aQC0LUgXKYtZBUr89X6s+l94C8w5y9rnLOhh1o9mAPDe999n5a2rih6/LbxLmWfhYboWdPU4n2saGq9spy7ey4bW3Md6utS5qW10dg3bHrLseV001dJkMFYuTXvOdpnh3Hh6yixx5sDGmxuIOMuDvbB0WpzZYZP5xJkbDahtimlqDBLWmA23BD6kC45s2LAh6zH293TMMccU9F0NlWLCGu3COxdddJFj4ykmrDEcc7ZJpk2gVp2jfJ4zux9U0sFmvaCqXAEs0ayNmWXZy+nbi8rOUBA0rSjPWdZqjaV6ziJqUVRo6KctzlrebC15x7w/bnliArUBukIBQmaSlmXZBYi3R732pQOdtaFYlWVEzdGc1/cVS9V4enx+Dprv3FJEqyisD1yf78vBJtQAQT9stHJ0elZlvx55rLDPoIN91wDKLXGWmb9UPc9q75MhzmzPmc+6bnm9+fuaZsszzWTisRPwW2F3H/5kEfHOwnNATdMcFnEWGBMATRWQSFV9HQKxREZBEIeavQfGqPNfl+ylrRMi0ezjDfYqG9p/P2dtWgt6afEGMGMm8Y3Zv7+2deq1mdv7qatyzoPfXZEWi1siIs7yYC8s7YWmUxQaAra1eM62pbBGt2wICvOc2d+T02KxUM9ZJBJhzZo1eL1eJk2a5Nh4hiTOHF4U2QuSUCSWcyEbsSpumeXOjsfn0wj4odXjx1upKrZF1gx+c+tapq4LLda2ezE5ZwP6nKXEWWnXAztEzBMq7NZWNjVM2Qx1nfvwwo9S+RDDQV9x5lyeIED7GLWR1vFJ9rnmjapzozlsQ56wly6PDy1upnKmBmP5EitU12FvsGYJLa0t93UoM+fMac9ZwA8bLHHWnaNSq6fb+bBPSG/KdGdM9SpLnLW925a6Ntk5Z15LnJWSZ5qJv9rPvs/sBUAykmTD04W38ElGk5gxE09AKykfV/NoqUq4pVRvjcVxtCAIKM8mwAS/+j5yec9CMbsiqsP3Mh+sC6h7bHRV9rnWZTVWd7JdBUCkUs2v2Prhj4pwAxFneXDL6zGaPWcS1lgabnrO6uvrgXQ1xsEo5nsqhUI9Z8uXLwdg6tSpjo6pKHFmnaOQwzlnoTIP3R4vHtMk2ZF9LvS2Od8U26Y8BGgaVZ9ROZKfXrlk0OO6FiuP+oYKdZ0oJazRkwprLM1zlrQ9Z+HCFmmaR2PeNTuDBusf28DLn3stb25SocTiJmG796PDBR16G9R3EF+R/f7hjaq/y+OwOAv4MqoR5ti1brK8fE5vODBG3Vu9rXnCLGPuVEQFCPo11vmtHNhl2e+vPsujXu5gU2yAcqu2WHfGOrZ8ehm+ah/R9b2pqo2250yzroul5Jn2p2xqGTv+ensAll69vOAQ5+HwmtkMR0PsWDxdCMipKq2BBmXTDajvY3Nb9mPDMbsiqsNRIBnirLcpuziLWhtg9ROc3azurbYKE+Vpij1aEXGWB7cW1j6fD4/HQzKZTC2eB8MuzuF0SXZIe84krLE03BRndtERuwjJYLgtzvJ5ztxq9F6MJzhoLaqD1c56PIL+dLPMXF6GhJXwrjlcUhvS+Sf1/08lBKx7cN2gx9mLyo1hdV6LCmvs97xmVTO0+xUNlUSkOM8ZqMp0n/m3jiegEWmK0GK0ljQGm1iC9GLfYXGWnKC+Ay2HGPJHLHHmsA0F/OlqhLmKA6xfaYXIORxC6K8NkEDD1xXLaV+xblURNa5pRdnPUAj6YXlIRb+0L2zPfpwlqMsdbulhe84ywxo1j0bd3up+8vZJC+hY1ElkdQRPyIO3Xr2hlA2ZwZh8/CRCk0K0L2hn/WPZQ/MzcUScleBBzxRnTlVptT1ntUk1h1pz1Gyzo0CcbFcBat4X4jlLWveyxinOjsesChDTNMz2WM6iO6MVEWd5cHNhbXvPci1m3SrtDxLWOFy4lbcIhYkzt2y60LBGu6y/043ei/Gc2eIsVOWwOAukm2UmWrPfQLRuF8WZdWlJzqjEE9CItcZT/cwyiVpVsFoDhS/UsvU8SvUlS5IzmTwfyZ7ics5sxuxfz6RvqpDazo8Lq06bj95YRkEQh78372S1WRdcn922fb3uiDO/D9Zbi7Rc+VStVu5J0MES3wDhsIcWy5MXzbGL3mv1QYv6fY5XQg764ZOwHTbYnnWT0u67FnI4vLp/QRCbnf4wh/KZ5SS6Ery876sA1O9XR9Kv5tdwhTXa+Cp9TDha9ZTt+qSwTeHezXZFy9LtKCXOcmyU5R1PHPxJZ5pQ29g5Z9UxdQ1uydI5JxlPEkqoojsBh73Bfi+sDag1Y+/q7Pd8T7f6viZt5/C8D2npTaItMO9MxFke7IWlG/lC9mfkCm10U5w5WRBEwhqdYTR6zvKFNdpl/+18OacoVJyZpknIKoEecDhXKOCDdq/VtDeHOLNzTzxuhDWmQpw0go3ZyyNHN6rrlL3wLWgX3fp/QM6ZpqXyzpIlFAVJFFmtMZOKHVU11fYFuXsEFkosDhVJd1oyhHZUC/2xq9rpHaRnWyKaJNjTSwLwuhDetN4udrEi+1yLWqFjIQd7nAGEg6rVA+RumWCXY+91uF0FpHPOomE/vZt6iQzS+NhMmITjcZJAyOF8ocEKgoCqYLjrtTv3ea5m9+qCy+hD/j5n/QlPtnLxcgj7THqtJsPBcaWviXx2zlmJnrOw5TH3OrQREhirrrkV0dzizI7G6Pb68DvQc63PmPywxhJnkaWDzzPTNFM5cOOmOCzOgrDZnvcl9q4bCUSc5WEkPGeFiDM3xKIbnjMJaxxeRqM4y+c5c6sPXKHirDdqEjSTJAFfmcO5JwFo91lhjZuz79Z6rFL7HofD46BvcYDQBPVD69sDkxpSnjNf8Z6zwaa9vctcSlEQu1pjoX3OMqn9TA0ALW9mnzvFEItDZcIdcVY9OcgSK0zugx99NOD1nuXdeEzY4A/jdzhkT4U3WfeOHOLM7LC9Qs4v0lr81sZnjrL1boqzoB/QNJobVV/H5tcG2lysXdlOt8dH0KGqfzZhe85HBs7N6l2raTwyvXFWXaQ4K8ZzBhCebHtdC1tQ297QYEPpayI7V9XulzgUMgsB+R26XgctcRaK5BZndmGTLo8fv8NmHfDD0lAleDUin0RS8ymTro4kNbEoSaBqorMOhnAwXXSnZ2VhQn80IeIsD6NNnNmvuSHOxHM2PLhZrbGmpgaA1tbWrLmLbokze87k85yNNnHW06ZWHr0eL5rH+fCmFUHlselemKswgLUT64I4y9xFbzxCLcoW/L+FfcL9TNNMN6D2FuE5y5F/ovmsio0lFAVJWrkF3iEIkMo5FXjLvXQv78m5iC+UWAIq7Op/DrdkqK2EyyfOBWDtg+tY+tdlfV63m4mvCZYV9D2Vgt+bzj3pXp5jY88qE+90PlUfz1mO8CZXxZl1K2iarAo4rbxlYG8v2+vR6fXj9JC8Xo2QNabIIHtpU787BU/QQ9XOlYw5oL44cVZgQRCbUKO1DiqwkIPtwQ82lL7Yt8Oh7X6JQyEWzwhndiiUMFAXAA8EumP4kkk2tw9+cm1PcYsv4Mq8j3q8eHeqhiSse2RgxegNn0TwAi3BEN6gs/IjHMjYJBJxtvUxWnPOxHO25eBmzpnP52PChAmYpsnq1asHPcZtz1m+CqRuibNC7TnaqW7MkXzdVYeBoB8+KKsBoHthjnwhq0eV1+G8AeibfzL+qMbU80uvWZ563Luhl0R3An+Njw5P8ZXbBpv2dihivGvoC6Neq+XAUMSQx+ehRq8BoPmN0r1nvTGoSHnOnP3epoyDlaF0k/tFF39CT1PanlLiLOC8OAv4SVcizOE581mhuhUNDudTZYQ3RXMUKElYnqpY0CXPGbB8khJnLW+0svH5TX2O6bFCHVt9AQLO3zpS874nOnBDqv6zdRy8+PPs8/ReeHye4sIaiygIAul8KjtcMR+9m2J93lcKHusaZHvgh0JvHMerfmpeLeVhbIz1sCJL5xw7rHh9IOzKvAfQDp4IwIobVg5Y323+2Gq/UhF2djAoe14XsDxny0WcbXW4ubDeFsMatyXPmRs2BOmqh3YVxJEaT6HVPltbW4HR4zmLWjvoUTfEWSC9qI4sj2YthuGP2v2XnLehTHEWmhBKCR27jDZAl+URKZtWll6oFbAOydbnDFRVPSgt38P25gUbh7aLXrdXDaCaUpdKPJqkLJnA9GiOV2uc3KAaAl81cU7qued3eYme1T3Eu+K0vK7EZpML4qwspNHiC5AIeIk1x/rYjU0yaRKIqu+5epzznrONlljsGSS3yyZhhVnGXRRnGzOqLvcPbWx7T1VxXBKqdEecWVOmp3fwaAFfuS8VeuxkWGNKnG3uLagZtJ1jGRgG73Q6rHF4PGdO9ssrt/ozTox2sXTN4MfYeXvr/c6LM9t+evcahyfsoX1BOx9f8kmfYzqWqPtGR3WZs4NBzft1fttzVtgadjQh4iwPboakFVMQRMIah4doNOq4985tcTZz5kwAPvjgg0Ffd8tzZostW3xlo61N5TPZIZlOEQ6H0TSNnp6enKGWUaswR68L4izgU2FLzb4AZsSka8ngNxF7Ieu0BwYGFgfY69HPANDxQTqxIdKkXgxPCQ/bLnrAarQbay5sx3wwIuvUuEJWIZNsRHsHn/O256x9Qfby5oViF3Exy52v/uf3aUxrhGeqJ/R5fv0TG/nf199KlSVfHSx3fJFWWwloGm1jrQIrCwcmxHT2wJiYZUMTcn9XpVIRTu+g5/LkJa38nHiZ89dpW2xFe2He31TBjbUPrO1zjB1GvCJU4XhYI2QUAormXxY6Gdbo8Xvw1/nBZFBh3x+7J5m/gNzFbPPexg6HTpYqzlxooVE+Xa3PJvR2s2glJAYRsrbnbF0g5M68B1riPir3VXN/w9P9vMHWeCJ1znvOKsJaKrw6V2Gi0YqIszyMtpyzrc1zNpJhjcuXLycUCnH22Wc7+jlui7N9990XgOeff37Q190WZ7mKk0Davmx7cwqv18ukSZMwTZNVqwbmeNjY5b9bw84uGiG9g76wXJ0ru9lrfwJWGXT/MJSLzkeqIIglzspnluEJeehZFUl5taJ2hbSxgSEt1Aab9SkPXbPqRxXvKr6cte05C+XwnN3znEnoYJO7nx04iort1YKns8Ay3rnwWDlVuFBhE+CzO4Opaaz/xg6p5z788Ue0vNGa+nlxuMrxwgC1VnTlmrGq2MWK61cOOKY7Ag22OJvs7DybMk55DiBPYQCrDUFPjfMLR3veR2PpKqHdy3r4+NeLU8fY+XprAmWOf2eQ4TkrINXLybBGSPfxim4qQJw12328cs+z71+VJHyIydI12dccHttzVkJYY5/+hi54znbwddPWCe8sHnhM9wplQ254zlLirAMmXzYJzavRuaiTT69YkjomsVJdV3vqnfecTRkHm/xBkh6NyNoo8c6ht0cYCUSc5cHNhXVFhbpId3RkL+U8Wj1nt912G48//jjgXFjjO++8w9lnn017e+m72gB///vfAfjrX/9a0PF33HEHf/zjH4v+HLfF2Wc+ozwdH300sGobjD5xFolY3o6Q82IoX8gnQMQSZ81lzi/SfD6NYAD+VzEGgA9/uoiN/QRaT1MPFZFeIpqHYL17pfRtz5nH56FmD7XQ/uiij0nGkvRayfovrwzQaa13S+lzBhCoV9e0d05ewBPjn+apKc/S+XEn971gctktZt6NnEQ0Se/mGJpXS5WaHoxv/FL9nmMvGfj7QhNCeMu99G7qJbpeedUvvjHJw68Uv4nktfr5aC4UcQH4zI5qFfzf7SYz6yczB7y+praSTq/ftUXaa7OmAbD+8Q28ftSbfY7p7khQG+8loWl5vZylMmkstIVCxNGIro9mDVnT1ru5q6/+7+iGqp0rU16fJVcuTfUUtL18G0NhPA4XJoLCPWcbW03OvFLNh1I3ZLIRsCovPv10fqXYW6Dn7K/3q9DKG/6TfSS256y7PcEP/pzk/aXFz/tYzKTM6pPpqOfMEmczUQLsoxUDj7ELYWwMhR333meKM2+Zh0knqNyzT37zKe0fdNDb3IvvbXVv65xY5ehYAKaPh6TmYW21GljrWwMrDo9mRJzlYbSVQXezWmMxnrMTTzwx9biYsEY7pA2UUPv0008xTZPW1lY2btzIihUraG5uZvXq1ey+++5cffXV/PSnPy32TxmUzM/ORmtrKxs2qJCg448/ngsuuIDnnnuOVatWpURFPtwU1JBu5rxs2TKSySRr166lp6eHlpYWNm7cmBJnTtt0OBwmGAwSjUbZuHFj1uPsHDA7J8xJZs2aBcCCBQuyHhNdbXnOyp0fDyhPw1uWOAN499T02CJrIiz7m7rrLqioIziE/l3FUmaV7e6Ophcm006bCsDqO5p4ovFpllylqgE+9H7apovpeRTL2MTsiZqsWm8SGiS87aV9XuWms1Zy0Q0mb32c+3fbFRaD44IFVdmsKh/4nKZp1M6vAWDtC828tAAuvRmO+qnJkia1616otz8lzlzIEwSYbkU0Ll0DM8+bTmiOWpRUfLaeOX/dmZv304HCvqdSsBdp/10bZNyXVUGZ5lda+uQPda6wil2EQukG5A7h9WpsN0ljo11WO0vemWejmvfRWufnfeZCVtM0dr9xXuq1TS9s5pXPv0ZkVYSI5qGt3PlNK8j0nOX+Pn54tcl/rYj5YjZkEom+kTIbW01aO9TPy9aYtHSYbGxV14JNqOvK1TfkF2d2WGOgrrD7a1VZ9r9vc0QN9o13E/zl37Dzd9ScX7OpcJGmRZN4MfGEPI41oQao3FEZ0ZjNHWCaLF2jwjabNqpr1Mcf9hJdEyWqeWhxIQqktlKd1wWfqnM1+4L0BtGKG1fy3NwXAYhpGokxzo9n1mT1/7v+GmD4WqS4hYizPLhZEKQQcTYawxr7V5csRJzZ3sHf/OY3qefOP/98Zs2axXXXXcf+++9PQ0MD06ZNo76+nsmTJ6eO+9///lfw35CLQsTZ1KlTGTduXB9v5kEHHcSUKVMKDod023NWWVnJ2LFjiUajPPHEE0yYMIFTTjmFuro6GhoaUp5Qpz1nkN5MsL15g+Gm52z//fcH4IUXXsh6TMzKc+gJuyOmayuh3RfAs1+N+vzWOGsfXAfA87u9xPJ/KHG2PFjhatW27ow1bOMR49jp8jkDjrWbjgJ4C7ib2Is623sFoJ9qMuXrJqvG1Az6nj06NwOwakPu3x218s0KLQZSPYg4A/DOVeP4yx/aWbs5/fzM40xmHGty+9MF/Xr8VoVNT6X74uxvD8CXE7vz3km7s3/L7hz5RCOdmprvbomzDS3w+M6zU8+/tM+rJKLKZWrv6LslPPafB2ut/JPOTzoHvG4mTHyb1Jhi9e6Js1ZrKPX71TPzPOXVf+uEd2h/T91vNvpDeB1c4GeSqtaYpSCIzfsZXRqKCWsEuPE/6v/emEnD/5nUHm7y6Gsm0481qTtcPTfl6yaPfqiuvbXxXjq6swujZDxJvD0OnsJDCKsrBn/+zmdMzrlWneuOlrRrf8axJhOPNlm8qjCBFrDu906GNAIExwcJjA3gi8QZF4uwdI3JsZeYTPqqyXnXmJz8TXXxWh6qwONz3oZsm37gZXjl/QDBccFUvvKqm1eTtOb+v8bOIBxy3hM8tkZjx6nwSNVEKq+ez/SztnP8M4cTEWc5SCaTKWFSXp7lTj6M2OLszDPP5NBDD+XPf/7zgGNsIWTnpzmJ/Te/9dZbvPrqq31e27x5M1//+td57rnnBojJQhb9S5YsGfDclVdeCcDpp5/Oe++9l/W9hRYoyUc+cWaaZiqEcuLEiQNev+6669A0jYqKCtavz1LLFvfFGaTD984880wA7rzzztRrTU1NgDvizGb58uWDPm+aZspz5oY4mzNHCYxcOWd2QYpkpTvizM4n+Wbrrqnn3jllAU13r+nT82tNoDyVq+Ik9nr5qnuhaWP686d8ZxIE+t4y3itPV9gsJvRqXXP68YfL1f8H31DD/fVTeaNiDP9snM15280HoCGm7GPN4Ol4KSIF5Jtlkk2cNZWp1Vv1pk6OGyT08YTLTLTPJTnk3CTJHFUO/BE1770uec6mjgNNM1m5Ac66yqTDF+Anb6pS7e8vKy5PqKRxpLsvcNH9QSp2UOez69Mulv55KYlIgo4H1DWoo8IdcTZnmsYnYRWam5mDZxNZF0FLmLR4A4SrnS8EVGMJhNZOOPyCJIdfkCQ5f+yA425onJ3qieY09rw/6+qaAXZ9y+MmJ/8uSSJh9ilO0jpQ5w4gM5zud7er35vZNPnICwfOIbsvXX08wrK1A15OEWu1okBq/Dm95Z0ZAs+f5eu96l6TXk1d3yIdA0NfZx+v5v0f78w+503TJBRzvlIjqPNavYsKD5wRaWfJGnjwZfXaP+6M87NVag31YVmN43MeYJcZ6cfXPaYurrV71qSeC08Ns+QH87lvzLRUTz2nmTMNVgUrWDOuNlWJc0tBxFkGK1as4PXXXwfUAvaVV17BNE3KysrwulC5LbOq3dNPP80555zDc889RyKR4Omnn6a1tdVVz9mkSZNSj++44w5M0+TFF1+kvb2d7373u9x3330cdNBBA6rx2flkhbJy5coB4i8X+ZoaF0qmyPvwww9ZuHAhmzZt4tlnn8U0zT5hi7nyALu6ujj22GNTPxuG0SenaSTF2YoVAwPR//MftX3phji78cYbU4+vueYa7rvvvpQYM02Txx9/nGQyic/nc2U848appsqZYtpYpELWkvEkz3/1HRLvWJsN1e7cQd6z9ilakn3tY8GZC/v8vDxU7ornbM609OOHXlFhh8++pRYkPx2/a+q17s9O6LstXiRvf2zywbL0QsfUNG5onM2lU3fjofqpKa9cQ6+ah6s35t65thuuFuo5qyiDl941WbvJ5OOVJu8uVr8/sIPaAt6xuxVfjpzYZwz411PqcTJp8vT/VGiWTcAWZ3kKFQwXwYBGY22SRJZCcx9YHg+ni0v4fRo7ZWxSLz5mburx+sc28O7ZHxB7Uc2/thrnNz0BxtWpsGBQTboT0QRPvZkOq3vjSaUy1gTLqMwR9jZceDNCOR97Xf374j8rKduzjtCsCiofPIAdXtqf/1WOdaVSI8BO26kx9cY1PlimNmbe/tgkmTT5zm9NbnoMXlpAn2vQB8uy/LIsJJLwjJE/RNme+xOj3azO4TG3N9LyldFvzrh9d3TDk2+adEdMXnnPTG1AjamGXo9a5/nN7PP+gr+btHWq93R2mzz5pknM2kRTZfSdzzezqbLE2bc2LGHnpxZRH4sQSCY4tLUpdcwzNROyCtLhZNeMNNfOHo2r/23y3lKYf8/uTDxmAtPv2pOHW2tA01wTZ+OsvcMNre583nDi3tb5FsC0adMAJRamTJmSer6qyvnkRYAJEyYMeO6ggw7isMMO48knn2T+/PmpMbohzqqqqrjzzjs57rjj2LRpE9dddx2nn346xx57LM8++2zquP6es0ISTzN7TU2dOrWocRUr/rJh514B7LTTToCygeXLl/PII4/kDMXrz+LFi9m4cSPJZJL589WOvx1f72ZorI1tJ7lwYzzf+c53OPXUU0kkEpx11lkA/OEPf+D888/njjvu4Fvf+hbgjtcMYOxYtTttf1frmjXmn6a+p08ubqPnhfRKwFdAaebhYKft0oucI+YczCUr3mGPrs0DjlserHRlobbfPI0TDzO59UnY0GJy3CVKpN14ocaishrW+0M0xCJs3KURXhz65+xxam6x1e71E9E8VCTjlCVitHTk/j7sBsOh8YXZkrEI9j/bpDwMXdblqPNJ6KouY0OwnKnRLmZE2vnYahI+GN/+jcmJX9C49Uk46bcmn9kR3rhWXf9sceZzocKmzYT6BGubc6/E3NhF//evNHb4lvp+v/vvSvadtAs/Xf0e7Qs7+pTWbxnnzr21oQYWltWyuaqM+qZunpzwDF/b4fPou/k4ffcuas9/G4CloUrmuKMXB7B8vcZh/j2IBUzMn2ncf5kGmK5syABceLzylm9ogfUtan7G4vDo79P38944JXnvl62FQ87NHx7YFLTEWW93Hy9bf1LFQPKIs670coML/qH+rrnbKY9ywA/RZzXG1sBKa+2SS5wBfP0XJk9dqXHCr00efBl+farGT09QjedTZfRd8JiP//I4lvxpKVOjXUyNdnFk8yoimieV2/vrybuwPFSJtwAPZ6l4vRrX/BC+9yeTNxYFeWORSTgILf8Zw9iDxhI8KEmvta8eDjq/AQLQUKvm0Ppmk3TG85aBeM6AK664gvr6+tTPb77Zt7JUZWWlK+M4//zz+3hgbJ588klA5Vrde++9gHvFJXbccUcANm3alMoPu+uuu/p4rzLF2Y9//OOCRE2+0MQvfelLWSsjLlq0iBdfLGFVaJEpzmzs8Lsjjzwy5WUplClTpvDWW2+lft5+++1ZuHAhnZ3qyuiWHYEKDc2HG54qTdPYeeed+zx3wQUXcOedd/LAAw+knnOjGAioeVNbW0sikeDon0aZeLRJIJnAl0zy7mN9q4D6CkwwL5UHLtNSoWCmpnHZlHk0Wfkx99dP4Z4x0/jzhB2JezyuLdTm76BuZOtblDADuOUJk6jHy/dm7M0LZ+zDqsnpa+btFw3Pje+3p2mceqT1g6almgc3xCJc+7AqIpCNYsMa7TC/zIVbxWEmX/uFmdq5HxPLX5Dg+EuT3Pu8GtebH8H805L0RE18Vs5Z2Rj39kC/MD9/kSI3xNmkfhF6r1YPvJau9YdpmVLj/GBQnjNT0/j3+Omp537Y9AHvvd3LsqvSUQ4fltVQ6XyVbwCe+dPAOdMbA9NaRB79c8uT65L5aJrGAbuqx+8uThftefp/6TnX0tG3mM+b1w7PvL/mhxpTMkxkTaCMJNDY28O3f5XIWoQn3eMs97W6M2OO2+O3c+d6YzD5q0lueQLiVlijL0/Rn6cNuPJuMxVG+LPrTL77+ySdPVDuQgNqm6q5Vex63S59nguZSYJmkleqGnitSp3UbN704WZ6P/9CTxR2PMGks9tMCTPAPc+ZcpazfsuqBQKI54xXX32VH/3oR32e61/owa1FdTgc5uabb+auu+7Ke+y8efPyHjMc2ALl44+zxyFcfPHFAJxwwgn87ne/K+j32oIlG48++iiaphEIBPjBD34AwEMPPcRRRx0FwAEHHMDzzz/PAQccAEB7ezu33nor3/jGN1LekcFIJpNce+21qQV6Ieywww4sWrQo73GRSITDDz889fMnn3zCLrukL5xuirNivZFOMtiN9Zvf/Gafn93ynIFq0v2/VfNoebyFP29azsxIBwvLagguSucgPl/dSEWlO3tXsyZr3HQhHHiO5Wn1ePnB9L2Y0NvNklBln9DBmizJ7MONfVP7x0Pp5+yvscfr441WHy+/pH7+5/ka3zxkeBZpF35LAzTaOpPc8zy0loWY3NtFQyzC8lAlDf9nsuhfsP0U9XmfrjZ55i049Yh0j7Pg+IHirLnd5J8Pw2d3GfDSoGxO5bzkF2d3PNP3Z2MRnPMXk/EdajVSMcY9z9lR+/Tw6ztye6PcEGflYbVjncl142Zz6vpPWFhWw20NM1kaquQrZe7kgUwdp/7uR8xGTuN9APbt2MCcJa2ErcV0TNN4rbKBs93ZJ+KgPTQuORkuvjG3EIgOTxR/Qdjz/vy/p8f0XkZ6+BNvmLxspYN/dJvGDlNLn/cH7g5nfkXjhMNgz9NNPloBe+3mJbImRFlbhHG9PXzz0nL+eT6pkNMX3zVp7YA98vQ4+2i5yb9fpI/wG4zVVjHhmCXO8nnOAM67pu/3dsN/VEh4WdKdgiA2E44ezz1t9Xh/bjAjknYz3jF2eo53OcPMgan5LFsLp/y+77kKO18yoc94rn8UTj3C5DNzthzvWUHWo+v674F9gOXAyYZhxKznjwR+DsSAtwzD+IFD43SMhx56aMBza9as6fOzW2GNUFihj9mzZ6fKpTvNmDFjBn0+Mx/Lrp5o5zkVQj5xZodGnn322Smx3H+R//nPf54NGzZQV1fHGWecwR133MHjjz+eyqmyC7rY/eNisRivvPJKqkhGod6azs5OdtppJz744IPUc0ceeSSPPPJIQe+3cdOOQIUt5srPW7dunSvjOP7443OWrgf3PGcAhxx8CEffegS7rE7ndO3c3Zp6/I/G7XmidiIXuhje1P+m1uP1sSTc115OPbJvroqT2LH6mbz83uCP+++WDpW9d0o/vvsSD3dfAgt/GGbVrTA2lr7e7PAtk97n1OOdvq12ZIN+jelWzpmnPkC01yQYUOcqFjf5830ml95c+Fg2W2XX6zM+96cnwG9uK+z9/3wE/phQc6+q0T1xNq42/6LSDXEGsP+u8OK7pMJGH6yfwkdl1SwOV5G0FsFuLdIqyjT2mWvy0gKN307amZ9Yc782rkLOn69u5PJJysM/WIsFpxhsMdufS092b0E5rnagqH7+nfTjW55IP57WyLBwxD7q76ss0/jwtvTfesOr5ZS1RZgS7eKuZ8tZ32zy1BWQNOGAs9UY3/+ylTJQ56ez26QslC5MFIubnPpHk1cXUjBpz1l6Hn16p8bM4wqr1HjeNSZfdjHnzGbePD8HztgLgC82ryKUTLAi5N5msM2MDHuuroA2a5l3z/N9j3PLc7bPXFWFtDsCby+GzwwsODxqybs1rOv6PGCiYRj7AYuAr2W8vADY1zCMzwINuq7rzgzTOQoJwXPT4zHaKCb0rRhxlivf6eGHHx70+cFy2RoaGvD5fNxxxx0APPbYY6nXPv/5z1NbW8szzzxDe3s7kyZN4sADD0y9npn3lotAIJAKLQWYPHkyt956K9dddx0PPvhgQb8D3LejDz/8kFNOOYXLL7+cu+++mz333LPP67mKnAwn5557Lrfccgsvv/xy1mPcKLhjc8GPLyCa5au4tWEGj9RPIebxulIYwGZSQ/5j6l3U9tPGD3wuW6TP9EGOHQoP/Xbg+baLe9T2Cy8MHGgSODAdKvPuYjNVSn+PC4NMONpkU6vJf99XxxUjzEDluwGcsG86hmtMtcYnt2vc9nP1Lx927kmFi2GNXg+8+BeNk74El5+p9ckXsnG4F22K+y7VuPknGld+T0t98MdlNSlhBu4t0gCuv0CN45XqRn47qa8L9frGdMl/t8IaAfbdOf8xtS7eNooRXKFhyhv6/tGDP982Vm2qbmd5g55/B/wHmgQPSl+I2taqC0BTxE/t4Saf+7567c/3qnlfjDAD5T0FmJyxyTFpLDz/Z40Hf61xwXH5f0d5KufMvXn/+d01/nm+xg++BgdfNIXTbhqZsvGaprHwZo1bzm9mzx2zH+fWvC8LaTz2B41fnaIxfwd3PnO4KCRuZx/AqknFE8C+9guGYaw0DMO+e/UC+bftRhlHHnkkRx55JLvttlvWY4rNPSqV/jlv/Vm7Nkdt2REkM4QvH3buHAwsXrHHHntkfd95552X93dffPHFfOUrX+Gll14iHo9zzTXXMGvWrFQz6WL55S9/ycSJE3nuuefYZZddeOCBB6ipqeG73/0uRx11FHV1dQX9Hrc9ZzNnzuT666/nvPPO45hjjuGZZ55h7733Zs6cOey+++6cccYZrozD6/Vy4oknsvfee3PooYcOesxwVeAshOrqao6/dzeemTuzz/NNgTD3jpmW+nkw75FTaJrGpd/O3dqh3IXeMDYTB3eYD0q+kKFMbvix+hsaaqG+uu9rY2sG/n0hK0Rxeih3eOG19yaIdyaIaR5WdPhobodf3mSyz5mFN4/NJGpVbasNJPnTWRp7zoHvHqFCUL91qPqXj3DS2kGvcDd74HO7atx4oYfzjtU4fG+Nt6/X2Hk6bD8FjtxHnXs3GFOj8e0vahz2GRhbM/gxboXpgvru/vN7jV1mwNIZ4/j27P1YHSjj/vqptPrSLjw35/3URi1v9Uw3xVnmXJ49OftxxXLyl9T/cwfRDD7f4HPpwKPVHz4tmj3K5o4HlOfsz0/6iCfg1YVwyU0m51w9tHm/w0y1JC7zmhx3MHzni6oK6gG7aRy1n8Y5Xy9gU8bFnLNMTj1S46qzPZz7DY0v7Klxy09Vn68ZE+FXp7h375g7XeOg3aP84GvZP9PNeb//rho//7bGHttvOSGNUJg4qwXsTPk2YMBKVNf1+UCDYRhvD+PYXCEYDPLwww/z9ttv88Mf/nDQY2bMmDHo804xf/78VD+mwXDL42Fj53ldf/31fRpA19fX9+kVVow4mz9/PqapOtkvW7asT8hidXV11vddfvnl3HDDDTl/96WXXtrHo/Xggw8WLMy23357TNNMnf/33nuPE044AVCeuAULFgwQj/09cHvttdegv3ukPbAVFRW89tprfPDBB7z11ls0Ng5TXEqBeL1ennzyyaKqYDrFzLkhrnhhOrtcPZcfzNqLP02Yw0+m6X129WcUEHI0nJx0WO5m7+05mrEON15vetG44l6NP56RvrF982B47ir182d3KS7U8uTDNcyXPKx/yMOmRzy8f4t6b7ZeTqFGFV54+KwoX90/+++tsEII272+lGvomgeyH9+fm3+i0fFE+u/412VKnCW6E5xzjMbr//Dk9aT2L4IRssSZt3xkU7t3m63x3s0eFv3Lw8O/8xRUTXc4mdqo0XT/4J+pqqm5x5f21lhwk4cld2ps8oc4fda+3JDhNQOYXIAXezgZrKhGpoB2U5xtny5Szce3e/hiRrDFTT9RFQkBLi1ysX/DhR7MlzwsvEX9/7vT1fuP+Xz290zfV/3h03OIs+q4Pe/TkTi/vKnw62TXUxov/EWNZe+d4LEr1VxN9ia54xcebvpJ3yVyIV7VVLVGF8MaB+PEL2h8eJuHT+/08PNvuy9MvrS3xrODFL0BdzdAtlQKsZ5WwN7yrwaaM1/UdX0ScBXwlcHerOv6acBpAGeddRaHHHLIEIfqPHbJc4ALL7wwVdyiuro61bjXLe6+++5Ur6z6+nra29v58Y9/zMKFC5kwYYKr4/nDH/7Asccey7777ovH4+GJJ54gEokwceJEOjo6eOmllwiFQiV79F577TV6enpobW0d0Dstk0MOOYQzzzyTv/3tbyV93mBMnTqVpqYmbr/9djZv3kxdXV3ecz158mQ++eST1M9XX301l1566YCctPb2dtftaDQyWO+1eDw+IudG2x8i94V5ZlMlVWVJ/m9eDw//V+W/hVhHU5N7wQDxeIyXrtxAb1yjucNDY20CTYP9fqhWi2a8g6YmF2oiW7z2Zw+b2jx443G+8VnYbmwAv9dk9qQ4lWUmj17mZ8b4OE1NQxeNNQH4z2V+JtQnBj3X0Wp1TW55t5Xf/G4ti1fW896ygUrOrpDW7RlafpcnuZm2ligvXekl6Ddp+1htOnW1dOWwy77xnHf/bAP7npNe2dvibEPbejxRd4rLxGKxUXyNGRj/Gou00NRUWHj5cKL2AtV4jtm/m3tfCmOaVoXS9Wuyv9EBxpbBw7/yU1OeZFObh8oykzFVSXY7Q7mxujvcvQ795zIYU+2hqSnJFadpnP4lH8kk6LNjfG5HmD/Djz47RilmdtznYEZDgF1n9mb9PcnyJJpfY0JvN2N7e9gYGJiXPCWqqj43BYYWi9qyeQ0zx5K6lq1rVteReGTw+1Gm3QDsvF2MX5/cxv9dlA41sK9F7bFt935vX4diPT5gYIG2RNRdmx6tTJyYfQe4EHH2GnAucCtwGJDqFqzreiVwF3C6YRiDuiYMw/gn8E/rR/e2fofA97//fRYvXswRRxzBwQcfnBJnhx56aM6T6AQTJ05k7ty5fZ6bPXs2P/rRj7j44otdH095eXnqM/t/9nCNpZjfc8UVV7Bu3Truv//+oj/n1Vdf5d577+Xhhx9G0zQOPvhgTjzxRK644gr+8pe/MHHixKLG8uCDD3LuuefS0NDADjvsgK7r3Hfffey///68/vrrNDY28q1vfasoz+LWTGYDaBufz+e6Tdv8/UcmV95t8rMTvfznv2H4r3p+97mNrnoZmpqa2E8f6M187A8mNz1u8vOTqqiuyO5VHm76fx2TJ+d+fbg+JxNzgsnSMcvp3dTLRG8t9/wqyH5nmWxs7XucnefR5c1+S/voNo0LrzVZ0gRtXXDBcRplIXh1ocnxX6zH49FSY2nuDrGMFfiT/qx2+eCvTW5+wqSyDI7+nMY+uzey5C6Tud82CXhUOWtTg0nTJ7lmR01NTSM2j/IzcDFWV1fLxImFhYUPN789zeTFBSZ/PKuce15ML01G4vwN9pGXn2myeqPJ7nOHKamzYJqYOFF95kRgx1l9X50+bXg+ZUoBYZMbv7iZdQ+v5+bFr3DDuFncnxF2HkgmmNDbRQKNVcHBq7h840D44TEa51xt0tWjnOp/Okvj4VdNpjZqGWsadXy8K84HfATx7HZw2XdNFq1U+a4XnxRgzrQGnrjc5As/MpkwBsZtiEMHNExrYGwx8eFbEfZ1qFczGWzZP2/HRvxZwlkFhZatf0Qmuq7/EdgLWAmcBFxtGMbpuq5fBPw/YLF16MWGYeRqQDWqxVkmPT09TJs2jaqqKhYvXpz/DVs5o/WmHwqFiEbT+SimafK5z30ua/GJ+++/n698ZVAnb0mM1vMzGvnTn/7Eueee2+e522+/fUB5/ZFgwacmu55s8t0j4LoL3G0DKTY0OMbxb7PhiY3M+/vOTDxmAsmkifeA9K2krgreOWMT75/4Dm+V1/OLabv3eb/XC8vu0pg8rvDFQNu7bbx60OtU7VLJZ5/fp+gxx9pjPL3dc/gqvBy64uCi3z9URrMNfeVnyVRfKJuV9xb3vTjF3x80OfNKk3/9XOP4AvIJt2ZGkw31rO7h+XkvpX7+1eR5vF45FjSNq77YwazLX2dFsJwzZw6co9tPURsyxWyMJONJnhj3NHjgSxsPG9KYXz34v7S9084+T+5JjV4zpN+xpWPbULTXZOoxJusz4u2qK6D1MWmxbJHVOAsKijUM4/x+T51uPf8r4FdDH9foJRwOs3jxYjweMaLRzIknnsh1113X57knn3ySNWvW8PHHH/fpO/bRRx+x/fbbuz1EoR9nn302xx9/PF6vl1AoREtLC5MmTRrpYQEwb6bGhoeh1sWEZSE3NXoNG57YSKvRysRjJuDxaIQCJhErCn2P2eCxeop9bm8flx2u8fPr0+Jtw0MadVXFLbi9Vv+tRNfQurfa7xvpfLPRxF0Xa3RFINKr8qjau2Bc3egQQqf/n/J+jpbxCIrwpDB7PjyfN/5P5bpftGoBV4/fkSdqJzJusQrW2u3gShZcrPGja0yeNtT76qvhreuKE2YAmldTy+UkmAlT/Vwk8Xb3qzWOVoIBjcV3qFPa2qnaavjdK8y8RSPWkwO3q+sJxfOXv/yF8ePHs3r1ak455RRACesZM2Ywffp0TjzxRG699VZAhYW6nQwvDMTr9dLQkM7NKS93sbFQAQxWNVAYOWrn1wDQYqSLDy36l8bv7zDxe+H84zTe21E1Fq4ImZzxZfj59en3FyvMIEOc9QwtLyKeEmeyErEJBrQ+hV/c6nFWCB6PlmrALIwu6vetY8p3JrPy5lUAfH/tR5zsbaL8IVWnbtze1Ww3Q+Py78G8k9SmjNdjN0IvDk3T8AQ9JCNJktFk6jpQDLEOW5y5199wNGMXUqpwsUXF1oCIM2GLJhQKcckllwz6mqZp3HjjjbzzzjuMHTtWvKCCsAVSvWsVeKB9YTvxjjjeMi9TGzX+dm568WX3xC7froy6Ko2ld8Gup5ic8/WhfWZKnHXH8xw5OF1LVKGCQL2LjbwEYStl7hVzqN69ioVnfwBA+er21GtjD1YFJ3aZoXHfpXDCr01u/snQN9g8fkucxZJ4KV6cpTxnlbIxIwwdEWfCVo3X6+Xdd98d6WEIgjBEfBU+qnaqpH1hB09NexaA8NQws388k4odK1j8u09Tx25/kSqLvt0EjeZHiyvzn4knqDZyEpGhec42v7gZgLEH1g/p/YIg9GXy8ZPo+KiT5X9fQeVOFfiq/Iz/ciMVs9KRF189QOPL+w193oM19zsgGS1+7id7kyQjSTSvNiSvmyDYiDgTtnrEYyYIWzbTv78d7572XurnnhU9LDhzYZ9jwlPDfXJESlqg+dU1w4wPrYZV9wpVHr5iB0leFIThYvufzaJ8RjkTjm7EXz142GAp8x7AExr6xky8I51vJikUQinIqlUQBEEY1Yw/upGGwwb2y8mkRh++NgOaVebZjJsUUtG4Pz2rlDgrmzKwN5MgCEPDG/Yy9aTJWYXZcH0GqAb0xRKTYiDCMCHiTBAEQRjVaJqGfsfu7HHbbn2er9ihgglfG094Spjtzpg2fJ/n0VJ3RzNRnDgzTZMey3MWFnEmCFsU6XzT4sVZynNWKeJMKA2xIEEQBGGLYNyXGjh0xUH4Kny0f9BBaHyQQJ0zRTc8fg/JaBIzZhZ1p1xz31rinQkCDQHHxiYIgjP4yksQZ5bnzC+eM6FExIIEQRCELQZfhbptVe1U6ejnaD4NosXlnfU09bDg/6lcuIrZo6tFhCAI+UmFNfYMXZxJWKNQKhLWKAiCIAj9sPPOkvHCCwOsf3xj6nHljs6KR0EQhp9hCWusEHEmlIaIM0EQBEHoR7EVG5PxJCtvWpX6eeZ50x0ZlyAIzpHynHUNoSBIawwAf7WIM6E0xIIEQRAEoR92WX4zVpg42/xSM52LOvHX+Djg7c85WlFOEARnSHnOhhDWGFkbASA4PjSsYxK2PcRzJgiCIAj90Px2WGNh4qz5tRYAJp8wSYSZIGyheEsoCNKzWomz8CQRZ0JpiDgTBEEQhH54/Havs3TO2arbm9j8avOAY7tX9rDqVhXSWLt3rTsDFARh2LFDEntbYkW/Ny3OpIWGUBoS1igIgiAI/dC8Vs6ZFdbY+lYrC89+H4AvbT4sdVysPc4r+79GvD2OJ+Shbk8RZ4KwpRIcFwQguiFa9Hsjq63+huI5E0pEPGeCIAiC0I9UWKPVhLrt3fbUa7G29K561+LOVAntPW7bDX+NhDQKwpZKYKzqTdi7obeo9yVjSSLrouCBYGPQiaEJ2xAizgRBEAShH6mwRttzZrSlXuv8uDP1ePWdawBoPHIcYw8c4+IIBUEYboINSlhF1hfnOYusiYAJoXHBVKVXQRgqYkGCIAiC0I90tcYk8c44m17clHqtZ1Uk9XjNfWsBCE+WUCZB2NIpm6byxTo/7iTRnSBmecXz0bW0G4DwVMk3E0pHxJkgCIIg9MPe/U4mTD753adE16fDnHqalDhb+/C6VOPZ6Wdt5/4gBUEYVgK1AcpnlWPGTJ6c/AzPzX2BlbesIt6VW6R1Le4CoGJ2hRvDFLZyRJwJgiAIQj80XzqscfOLmwEYe4gKW7QT/985aUHqeLuQgCAIWzazzp+RepzoSvD+uR/yyW8/zfmejg87AKiYXe7o2IRtAxFngiAIgtAPW5wluhN0LOoED0w6diIAnZ92Y5rp/meVcytHZIyCIAw/449uZMp3JuOrShc0b3m9JevxkbURVt3WBECNXuP08IRtABFngiAIgtAPO6yxe3k3JCE8MUT1blUAdHzUwZp/r00du9cj80dkjIIgDD+apjH3ijkcuuwgDv7k84AKW8zckMlk7cPr1fv8GtXzqlwbp7D1IuJMEARBEPphe866l1mJ/pPDhCeHCdT76d3Qywc/+giA6d+fhr9KyucLwtZIoD6Ar9JHvDNBrHXwxtSbX1Jhzzv9fkc8AVlWC6UjViQIgiAI/bDFWecnKtE/PCmM5tGY9K1JAKlCIBOOmTAyAxQEwRVC463y+msGltePtcfZ9Pxm0KDhsAa3hyZspYg4EwRBEIR+eMNeADa9oHbFa/RqAGb/dCZVVuiSJ+yhckepziYIWzOhCapNRmRtZMBrG5/ZSDKapHavWkLSfFoYJkScCYIgCEI/AnXpUEXNr9HwBbUr7vF5mPe3nRl76Fj2fvQzaJo2UkMUBMEFgpbnLLp2oOfMDmkc94Wxro5J2Lrx5T9EEARBELYt/PWB1OP6z9YRnphuMl25QwXz79x9JIYlCILLhMZn95w1v6aqONbtU+fqmIStG/GcCYIgCEI/Mj1n5TOkd5EgbKukc87S4iwRTfL++R/StaQbb7mXql2knYYwfIg4EwRBEIR+BDI8Z+Uzy0ZwJIIgjCQpz1lGQZB1D65j5Y2rAJjwtfF4fLKcFoYPsSZBEARB6EflnPROeO2etSM4EkEQRpKy7dTmTOenXannVt6qhFnNHtXM/eOcERmXsPUiOWeCIAiC0I+KWeXM/tksImsjVM2VkCVB2FYpn1mGJ+ihZ0UPsfYYS65cSsvrrQBs/4vZaF4pCiQMLyLOBEEQBGEQZp47faSHIAjCCOPxeaieV0XLm61sen4zS69eDoC/zk/dvuJVF4YfCWsUBEEQBEEQhCyMO1y10njn5AWp5+bfubu00hAcQcSZIAiCIAiCIGSh8chxfX6e9ZOZ1Og1IzMYYatHxJkgCIIgCIIgZKFsahmVVu5p+cxyCXkWHEVyzgRBEARBEAQhB7tcPZc1965h0vGT0DwSzig4R0HiTNf13wP7AMuBkw3DiFnPzwbuAXYAxhiG0enQOAVBEARBEARhRKjepYrqXapGehjCNkDesEZd1+cBEw3D2A9YBHwt4+XVwP7A684MTxAEQRAEQRAEYdugkJyzfYCnrMdPAPvaLxiG0W0YRpsTAxMEQRAEQRAEQdiWKESc1QLt1uM2oM654QiCIAiCIAiCIGybFJJz1grYQbbVQHMxH6Dr+mnAaQBnnXUWhxxySDFvF0YJsViMpqamkR7GqEXOj1AqYkNCqYgNCaUiNiSUithQYUycODHra4WIs9eAc4FbgcOAV4v5cMMw/gn80/rRLOa9wuihqakppyFt68j5EUpFbEgoFbEhoVTEhoRSERsqnbxhjYZhvAus13X9ZWAn4N+6rl8LoOt6ra7rzwDzgEd0Xf+ik4MVBEEQBEEQBEHYWimolL5hGOf3e+p06/kW4ODhHpQgCIIgCIIgCMK2RiEFQQRBEARBEARBEASHEXEmCIIgCIIgCIIwChBxJgiCIAiCIAiCMArQTFMKKAqCIAiCIAiCIIw04jkTBEEQBEEQBEEYBYg4EwRBEARBEARBGAWIOBMEQRAEQRAEQRgFiDgTBEEQBEEQBEEYBYg4EwRBEARBEARBGAWIOBMEQRAEQRAEQRgFiDgTBEFwGV3XtZEegyAI2zZyHRJKQdf1ypEew9aKb6QHIIwOdF2fDcwEXjYMo2OkxzPa0HV9hmEYS6zHmmEY0iBQKApd13cETgZ+ZRhG+0iPR9jykOu0UCq6ru8AHAncBTQBci8TisKyoV8D/wFulDXR8COeMwFd108E7gQOAn6r6/rMER7SqEHXdU3X9Z8Bi3Vdv9h6WnYbhYLRdd2r6/ovgNuAZ0SYCUNBrtNCKei67tF1/QLgFmAacD7QOKKDErYodF336br+U+AqoAL4HIAIs+FHxJkA7TFYnAAAC7lJREFUUAWcZRjGecAq4ERd1yeO8JhGC37gf8A84GBd1ycYhpHUdV3mjlAotagb2TWAV9f1b+m6PmeExyRsech1WiiFWuBDYD/DML6H2mQcO7JDErYwpgIrgcMNwzgMKNN1fdrIDmnrRMIat0F0XT8UOBF4DbgRGA/MBv4LPAv8EXgDFfKwzaHr+heAb6LOx22GYTxlPf84cAlwKhIKIuSgnw3dBDwCXAjEgReB3+u6/kvDMN4auVEKoxnLho4DXgduACYC7ch1WigQXdcPA+YZhvEHwzA2A49az88DDgbiuq4/gAqTlXuaMIB+NrQEsNM7pgGLgeQIDm+rRXb/tzF0Xf8B8EPgVmA74DLg78CXdF3/PnA60IISbNtcwrCu6yHg28AdqJCPX9vnwDCM3wA76rq+h2EYpq7rsrkhDKCfDY0HLgXeBX5iGMZRhmFcCTyDCk/b5uaYkJ8MG7oTJcp+AtwLfEGu00Ih6Lp+JGozcX9d179pPafpuu4HdkKtAxYBhwLjRmygwqgliw15AQzDWA7oqHUkEk00vMjJ3PZ4FjjJ8gb9FqgyDGM18HOgGbUYuAiog20ylngW0GMYxhMo4VqFWhDZi5+LUILtTGDXkRmiMMrJtKFfAQ3AvoZhvJdxA3sV5a3eFueYkJ9MG7oEmA6Uoa4/Lch1WsiPgdoA+iFwlK7rVYZhmIZhxAzDuMOyradQoY0bR3KgwqhlMBtKWAIf1AbkkQCGYYgHbRgRcbYNkLmrahjG+4ZhrLNfAiLW84sNw7gdFYf+D1Rs+jZDhndsITBe1/UjDcOIAfcDX8tY/PhQSbBz2cbOkZCbPDb0deswj1XY4e8ogSYIKfLY0EmGYSwxDONfbKPXaSE/GTa01jCMLmAZyk6+Z73usf4/DpXWsALQxPsq2OSzIdKhjD3ABl3Xw+6PcutGM03ZcNsa0XV9L6DG2h2zJ5vH2vXQrLC8w4HtDMP4q67r9ah8hu8Cb27tuTDW+TkRVU54gWEYbbqulxmG0a3r+oHAzwzDsMPOHgWuMAzjeV3XvwwsNwzj3ZEauzA6GIIN/Q7lnT4JuNMwjLdHauzC6GAINvRH4C3geMDY2q/TQn6y2JDfEvX2MdujPK0/QHleG4DTgIcMw1gwAsMWRhFF2tA5QIdhGFFd1+cCbYZhrBqJcW/NiDjbCtF1/XRUSN49qIIWr2e81ghUGoaxWNf17wETUB7UMYZhnDoiA3YZqyT+AcC/URWsTMMwLrNemwB0A38APgZuRvXzuMIwjMUjMV5h9DEEG/oNYCdUC8JQr0OXG4bx6UiMVxh95LGh1L3e+vnHwPeBJw3DOGVkRiyMNoZgQ2cBzxqG8Z0RGfA2goQ1bp08CXwWeAHQdV2vgFSVxv8Cu1pJnYcCRwBrtxVhZvEk8FXDMP6KOkdtkKpK9AYq3PMSIIHqTbVOhJnQj2JtaK0IM6EfQ7kOiTATMsllQ//FyovWdX0+KnfoGhFmQj+KtaG/iTBzHqk2txWg6/opwFeAMwzDWGVV0UHX9TpgJrA/qpP728CehmFssF6/A3jJMIy1IzJwl8g4P//PKn7yZkby6nRU7w5Q4UJ72OcHuErX9X8YhhFxd8TCaENsSCgVsSGhVIq0oT0zbGgNcIxhGK1ujlcYfYgNbRmI52wLR9f1auAQVLPkA3RdD2S8/DZqQk23EjbbDMPYYJVpxjCMu7cBYZZ5fj6v63rA6NtEegrwhPU4Zp0ff0ZCrCyItnHEhoRSERsSSmWINhQAMAyjSRbVgtjQloOIsy0Yq7BHm2EYx6L63hyI8pQBYBhGFHgMqEdV+/q5ruuebeVGn+/8WPQAY3Vd/wXwPes9MSlNLYDYkFA6YkNCqZRgQ71uj1UYnYgNbVmIONvC0HV9qvW/16q4aO+sLgc+QPWiqMh4y27AUcD/gF8ZW3kvikLPj7VbFERVp7wA1VLgD7IYEsSGhFIRGxJKRWxIKBWxoS0Xqda4haDrehmqctdkVN+tmK7rPsMw4hnHjAN+iep/owFLgEag2zCMJvdH7R5DOD9eYCkq9vplSbQXxIaEUhEbEkpFbEgoFbGhLR/xnG0hGIbRDfQClag+SRiGEdd1fZau62foul5vGMZ6YCXwMHAeVgnUrV2YwZDOzzlAmWEYN8mFSACxIaF0xIaEUhEbEkpFbGjLRzxnoxTLxRw2DKPVSsiMAWcA7wFno8SXCVwFPGgYxr+soh/3Ao8ahvGPkRm5O8j5EUpFbEgoFbEhoVTEhoRSERva+hBxNgrRdf04VBPpxw3DOCvj+b+gelJUAbOBO4Gl/VzVfVzXWyNyfoRSERsSSkVsSCgVsSGhVMSGtk4krHGUoasy9+XAqYCm6/oXMl5+HlUevxM4BTjdclWnyudv7RNNzo9QKmJDQqmIDQmlIjYklIrY0NaLNKEeBVgVdS5ANYp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", 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\n", 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", 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\n", 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KGhWGNmIJ+3FLR/b3DSUot8bBB6/dLtJKYwcuDAWP4O6778bUqVNx9tlnD3QoeOaZZ7B8+fKBDqMoKHKmUHI0NTWhu7t7oMNQUFDYRVFMIiu637XvisqZyqwVXEAR/MGHUhL83/72t3j55Zfx6KOP5n1vMpksXSBQ5ExBoQ+am5sxfvx4jB07dqBDUVAggddmrBVoISpnidLmDJ6Bl630vaKce+2891o8uiL4CiauvfZarF27Fscffzx+9atf4Utf+hJmzJiBgw8+2Fpw+uabb8Y3vvENHHroofjGN76B7du347TTTsPs2bMxe/ZsLFiwAICxwPR5552H6dOnY8aMGXjyyScBAJdccgnC4TD2228/3HTTTWnfve+++2LGjBm46qqrsHDhQvzrX//C1VdfjVmzZmHNmjX9v0NcQC1CTQRVkuaM999/HwDQ2dk5wJEYiMViuOaaa3Dqqadi3rx5Ax1OGtQYcoZXkjOFoY+eKMO7y+3xFvcIOVv1GcOvn2D40TkaGkfSXyeUcjb44LW7hRf7Fr0Gr51bpUo5fvGLX2D+/Pl47bXX8OMf/xj7778/nnnmGbz66qv45je/iaVLlwIAli9fjrfeegsVFRU466yzcMUVV+Cwww7DZ599hmOPPRYrVqzAT3/6U9TV1eGjjz4CALS2tgIAfv7zn2PYsGFIpVI46qij8OGHH6KxsRFPP/00Vq5cCU3T0NbWhvr6epx88sk46aSTcPrpp5fmB5cAipwRwSuJNWMM3d3dqK6uHuhQAHgvsf7d736Hu+++G3fffbfnYlNQUCgNCr08z/suw6KV9t/xRPb39ieOuJxhWyuwZjPDy3fS32u8aKWvkBseSTks6Kqs0ZN4bviLWV+bBuC/1vuK2+4JLccW9L633nrLUrs+//nPo6WlBR0dRjPvySefjIqKCgDAK6+8klZ62NHRga6uLrzyyit47LHHrOcbGhoAAE888QQefPBBJJNJbNmyBcuXL8e+++6L8vJyfOtb38JJJ52Ek046qbgf5SGossYhhpNPPhk1NTVYt27dQIcCwHvkbPPmzQMdguexfft2q6RAoS88lhMpEEIkZgCQSDm/r7+xzZgsxqebSrN9L1rpew1qv+SGUl8VikVVVZX1WNd1vPPOO1i6dCmWLl2KpqamrCLDunXrcMcdd+B///sfPvzwQ5x44omIRqMIBAJ47733cPrpp+M///kPjjvuuP76KeRQytkQw3/+8x8AwD//+U9cffXVAxyN9+AVhdPLmDRpEnp6evDGG2/giCOOGOhwFBQGDF5RzjhCJbpjp5k5qMR6UMBrtzLl1uhN5FK4bv87wzX3G0eLvVkarebwww/Ho48+ih/96Ed4/fXXMWLECNTW1vZ53xe+8AXcc889Vt66dOlSzJo1C8cccwzuu+8+3HXXXQCMssaOjg5UVVWhrq4OW7duxfPPP4958+ahq6sLPT09OOGEE3DooYdi9913BwDU1NR4prWmUCjljAheS/q9olh5JY7BAK+MoZ6eHgDwjHrmtTHkkcOkUATcHjPPkbNgabarVI/BB69dh5T6quCEm2++GYsXL8aMGTNw7bXX4s9//rPj++6++25EIhHMmDED++67L373u98BAG644Qa0trZi2rRpmDlzJl577TXMnDkT+++/P/bZZx+cddZZOPTQQwEY3gYnnXQSZsyYgcMOOwx33nknAODMM8/E7bffjv33318ZgigoeBFeIUBO8FpsXiNFXoHHDhMAYFsrg6YBI+s9GJwH4HaveKWskaM/lDN12jtD09S+yQVROVM9Z4MDpbxbrF+/3nr8zDPP9Hn95ptvTvt7xIgRePzxx/Gn5xmSKeCCk4zoqqurHQndn/70J8fvfe+99/o8d+ihhw46K31FzoYovJJYeyUOhcELNYZyQ9cZRp/CS1MUOaOEUs4UvAqfx071NCv9gQtDYZDjvFuN0XPucUAg4LFB3o9QZY0KCgqOUKRocCClZqlLBi9Y6d/yF/s8LCsVOcvy2AvwWjxegdcUfEXwFSixqw8hRc6GKLySWHslDg6vlQ56GV47dl6Bl4eQOmbOcHvMBnoR6i07GH74kH1MlXKmwOG165AqaywcXjl2XolDoS8UOSOCSvoVFBQGAiqxLh0Guqyxszf971L1nKnEWkEWXlZfFQYHxMnFXf1epsjZEIVXZtC9EgeHItGFw2vHTsEZagHh0qFUZY072hj2OFPHbY86H7DeGMNP/sSw5JP050umnGV57AV45YrttVuHx8JR16Ei4LWx5BWkLemxi08SKXJGBK8l/SqxdobXjpMIL8emYMNrR0k809UaVc5we2pd/yDDtlb6nXr3kwxrNwPXPuC87QeeBW76A8PXfpz+esl6zjysvnosHM/Aa7cLlVgryCLtOjRwYXgCipwNUXiFnHkljsEARc6cocZQbng5sR4KEA05qJDvOG3NQgirK8hDAaDGUCFQ+yU3dDWGCoa61TtDjSEbBVWwh8Ph2wDMBbAewPmRSCRhPn8qgO+Zb9sdwK8ikchvShCnQpFQCa0zFAEqHF4ZQ16Jw6tQM9Y0SCadx1l3tJ8DAbDbaA1Oc8fKEESBw2u3MqV6DD54egzt4oMor3IWDodnAmiMRCKHA1gJ4HT+WiQSeToSicyLRCLzAKwB8EyJ4vQ8VNKvoLBrwGvnulpfKD8KOWSxLOYfNZW0sQD54ykPOT//3gr6WABVGjsY4bHLkOo5U5CGImc2CilrnAvgJfPxCwAOzXxDOBweA6AsEolsIIxNQQJeURu8EodC8VDHbnBAKWc0yEbOUqn+jQMAklm+c/Eq4P1PSltm6bXT3mvxeAUe42bpBN8j1yFdZ9jZ4b0B5JVj5zWCr9RXG4WUNTYA2GI+bgcwzOE9XwbwpNOHw+HwRQAuAoDLLrsMxxxzjIswvY/e3l40NTUNdBgWOjo6SONJJBKuttfS0mI99sL+6ezstB57Yf+IiEajnthHHNRjyC28NoY6OqoB1ADwxhjq6NEAjDHi2bwFVeW7+m3NAWw0+Fxktn28tdUHYLT197lf6MafXqrCpq09aGpqJw2nozP3GNrRUgmgzvGzzy9ow6iqHsfX3I6hLc1+AKPMx1tRoQ0AI+2DsQCAjs5ONDV1DXAsgIYxYGZa7YXrUFu7PUa8cB3avF0cQ83wJQeeoX39Fw14/YNyvPLLbdhnggdWlDfHNMA8MYba26sA1ALwxhjqjQN8HzU1bUFN5dC+lzU2NmZ9rRBy1gZ+9IwrwU6H95wO4DynD0cikQcBPGj+OWT3dEVFRc4d3d+oqakhjaepqcnV9oYNs7m8F/ZPbW2t9dgL+0eE18ZQbW2tJ+Lx2hiqq2PglzIvjKGqTjueMWPGorbKY9OhHoDPZyeK2fZxwmfvRwA47fPV+NNLDJ3RSjQ2VpPGU1ebO57qmvRY0j5bV4/GxgbH19yOoW7d/r7Ro0ejsdELY8jYR3W1NWhsdCaq/QpNtw6JF65Dwxq8dR2Ka+IYGoPGMQM/hl7/wBhDry8biaMO9oL/nRGPpmmeGENeu5f1RO14xo4di7rqgR9DA4VCRutCAEebj48FsEB8MRwOj4YqaVQYJPBav5CXocoaBwe86HD1yAsMx1+tmzfbwYHMssapE43/P1zb/7Ekckzyl6InzMtljQrO8NqtTByXXilrVMgNjw2h9BL9Xfw6lJecRSKRpQC2hsPh+QD2A/BkOBx+QHhL1pLGXQleS/q9klh7JQ6F4uGVY+eVOLwKL97QzrmF4YV3gQf+NdCRFI5Mcrb7OMMMZOtOY9FoSuS7W2TrOSsVvEzOvBaPgjO83C+kxtDggJevQ/2Ngqz0I5HI1RlPXSy8dj9pRAoKuygUwVdwAy/f0Lp7BzqCwhGLp/+taRpG1DF09gBtXcCI+v6LJRc5K8Ux9iLBV8gNj90uPKngK+SG18aQOGx29THkhSLcIQGVWDvDK3FweO04ifBybAo2vHaYvOiSxuGVfVWslf78e40PcBv9rn4mmbnIWSmOsUqKBh88cmpZ8PIkkcLggBpDNhQ5U1BQUBjESFtfaODCGPTg5OzI/YHDZhipb3WF8Vynszmia+SbiElkWRC7VFBJ0eCDVyY+OMTrkNfUV4+F47lj5xV4uTS2v6HI2RCFVxQrr8TBodSpwuGVYyfG4ZWYvAQvr3M2mA4XL2ssExaA5soZNTnLh5xljSX4PkXOFGSh1NfBB6+lQ+o6ZEORMyJ4LelXSawzvHacFPJDFxiHF8a110aQuqHlRzFljWVB+zkvkrOSlDWqGetBB6/dytIUfI8NIo/tKs8dO69AEXwbipwplBReSKYVBjeUcpYbaT1nave4Ri5y5qWes1JAmTkMPngtwfeyqYzHwvEMvDyGdvXrkCJnQxReSWK9EgeHUs4Kh1eOnSJnueHlGevBhC0txv9iWWOpes7yYUDdGj1WGqswOKAIfuFQWYgzFDmzociZgoJHoIijM7xGyLx2mNQNLT8KOWRX3mvsvBUb7OesskZi5SzfGMpchPqrn7cfl0JV8/IY8ko8XjvvPRaOp8dQMUgkGeZ9V8eND6tZiv6Gl9XX/oYiZ0MUXklovRLHYIDXyJlXjp1SznJDlTXSIrLSflxTYZyTnT39u2MzCVh9tf04nkHcKKB6zgYfxNuFF66LXlbwi4nntSXAG0uBn/65ZOF4Bt7KOFTPmQhFzojgtcRawRnqOBUOL9zwAe+RM68NoaEyY11KFHPMzjvBfuyVnrOA335cCpt9lRQNbnjhmKlJosLhtXuIV+Blgt/fUORsiMILSSzgnTgUBi8859bosRtr2vpCqhLHEYUcs8ljjf+vO9t+c6ncGvPFk4ucLV5FGwugCP5ghNeO2VBIrHWdIRgY6Cj6D167lykF34YiZ0RQiszggDpOhcMLRAjwnnLmNbAsj70Ar8WTCykzuQwJbo0DZQiS2XMmkrPn3wXue4p2z3ot0VfID68pVYN9DOk6w0EXMxx/TemDV2mIMwb7GKKEImdDFCqJHXxQxNEZipzlhnLaowEnZ37hrujFskYAuOwuhqbtdOeCasQffPDaea8P8jHU3m2o0nwxeqA0JcReQjoZGvjfqsqrbShypqCg4Gl4jZx5jUIPtdnGrh6Gt5exfj/WnBCJRIiTs8WrgDeW0sWUbwxlkjO/v+97trWShAJA2aAPRnjtvPdaPMXCKebeWGm+yyvzsGnqqwcI/mAfQ5RQ5IwIXlM9vJDEAt6Jg8Nrx8nL8Mqx80ocImZ2teDYnZsGOgwAg3/GOhMnXMMw91KGx1+l22Yhp70TOautMv5v6wLmfZfhf4vpYsqFNZvT/57S2PcHbG+j+z6VFA0+eE3t9DLBLySezFJiAOiJ0sfiJXjtvFc9ZzZ2odZHhYFAppmDIkfZofaNM7ymnAHALRuWAAC6Vg9H9ZSqAY3FazdYWcz/0Pj/mfkMZx7Vf+cEJ2eiSjV+ZPp7Nm6j+a5cp/q7yxk+XgfUVQPfORU4dJrmWFZZMnJGt1kSDIUxXQoo1YMWTuSsVMqZV+A1gj/YxxAlFDkbovBKEpuZWA80ARno7x9M8MoY8rJbY7KjBItOFQmv3WCpwEsK+wspR+VMg5gGR+MoOR7+r/F9F38R+PmFRnHLpm19DywpORMfD6ExNJThtfM+zTXWA/EUC6f1A3tKVdZYms0WDa+RIa9NOAwkVFnjEIUXkljAi4m1Vy6LCoXCi8oZhy808OPJazdYKlCSs0KOklNZIwAcHbYf9wc5W7jM+P+0z9lRjx+lYcvT6b+im7DkaqiOoaEMrx0zLxN8t2WN/e3S2t/w2jHzsoLf31DkTKGkEMmZvqtPheSBIo7O8DQ5Cw78JTSt52yQn2KplP1jfv0EsHVn/x3vbOTskes1HDbDeExFznKd6vw7GmrSnx8zPP1DmaYhMvCaCiNCXRadUegx6+hm+GRj6Q/qYF/nzImcHXIJww8for+oemVMe+2899qEw0Bi4DOLIQKvJdZeSWK9llh77Th5GV44XoAHx5CYhfgGfjx5ebax2MOVWUZ08R3994ucrPQBYOwIDUcdYDyOxksfDydd+RbDTaboYvFyUuSVeAb+TE9HoVb6e5zJsPfZDCvWl3ZHei3Rd8J37tTx9Z867ywncgYAt/wF+ONzDJt3lOZ8G0h47bwvJp5laxkeeaH/XX37C4qcKZQUXitrVCgcXjleXiNnvqS3poi9tt6RDDIb8J99i2a7+eZkdJ2BMeN9PgfCXW6Wr8b6oayRJ4mZCl4mSJUz8fHAD2mFIpHrmO1oN/5/66PSxuBlt0aO3z4DPPoy0NrZN0CnnjOO83/BcMFtHv1REvCa028x16Hp5zKcc0v/Oej2NxQ5I4LXFBkvJLGA9xJrhcLhlePltXJYLSFMOHggtKGUWDuVDb68qP/UqmyEqDxk/N8fPWecnOVXzui+c7CXpO2KSDNPKOCYlfq4ek2FKRbZlDOO59+l+y6vpIteO2ZuqkA+2ViSUAYcipwplBQUyhklSfAaiRbh5dgGEl4j+F5TznQdCHdux23rFiGxxcFzfRDBybr6H6/L7+N8pxYvacxGzsqGODkrNilibOiWEw0WFKuYl1oZ0V0k1v0JXRfvI31fz0fOjj2IOCAPwNPkrMB4vvNrhh1tHgieGIqcDVF45cYpm1g3NTVh3Lhx+OUvf0kZlidRLDn74IMP8MMf/hA9PUPbUsrL5IwR9v24BWPAjz9bimk9bei895OBDkcKTuSso1t+u/lOLWuNsyx3RGrlLFc8iRw9Z2nLOJSorDFfop9KMRz8bYYvXc/QG2P4bGuJe5lKuvXBi2J7vEpdgOD18mrxfEk5xJePnFHeerwyD+u1PkG3ZPGHD3kgeGIockYEpXo4Q1Y5u+2229Dc3Iwf/OAHJPEMpeM0a9Ys3HLLLbjllltKsn0vECHAe+RMS9h3ed0j5IxDjxJm7AMAJ/JDZRkf0PWsd3xPljU6xPLxnzU0mgtjl0w5yzOkVzcB760A/rUAmHMxw8QzGFZ9NvDnwa6GYhPZUl86vVway1j6+ZJ0IGKJPOdTf5z7/Y1iSuJfXczw7vL+m4gphixuaSEPZcChyJkEvJY0ivBKPLL7yCu/w8tYuXLlQIdQUrgdQ1u3bsWaNWsQi8Xw3e9+F2+88QZNQB7uOdM8YO0vA1E5u+Irxv89BOSsLJXEkytexc83LHF8vVBy9virwIbm0l6TcpU1Tp2k4ebztLT3UaCYssYNzfbjj9Ya/7+8iC6WTAyd6TRaFKN2AqVXRliWx15BPuUsnsj9eUpy5pU54kLVzp4ow1FXGIp5KeGW4Dsdz8GOwX0nH2AUmzQ+8sgjOPDAA9Hc3Iyuri4kk0n09g7uHpF8kFXOFDlTcEvOxowZgylTpuDWW2/FPffcg3nz5pHEo8W9VdaYdlMd5OSMJ0DHHgScbi7CnGmv7wZTOtoRAMOs7p2Or6d4WWMecgYAD/6boAcuy/OplO0a6fc7v4sTyIFSztZt6ftcMABEYwy/+QfDxhKXOSoY8FpZo7h9r5c1/v4/DOEL9bR1FPNNdgxJ5azASZmYQFwTydKd327LGhU5U0hDsUnjOeecgyVLlmDs2LGoqalBQ0MDKisrS0LQvEJq3CTWr732Gr7xjW+gvb2d/Hd4razRze+75557cP3110ttoxB4ZQzJEvx33yW02UK6W6PXyhq9Rs6KPVxcOasoA6oqjMfdBJfHQB6JM59yNm2y/fjTTfLxZEMhZiAlIWfi4zzHzIks+/3AFfcy/N89DGf/lPacGPgzzJvwWlmj18wlRDCkny8//hOweBXwy79lJ2cTx6T/PeTJWY5jlhL23asOxQdvfciwncCUo5jrkAieIsTiDK9EGGL9sB5lqZHHD8pAOBy+DcBcAOsBnB+JRBLCa2cCuAgG0bsuEom8XYI4PQnZkr2uri4AwJo1azBt2jSyuLwEN4n15z//eQBAeXk5gsFgSeIqFr///e9xyy234NVXX8WkSZPItutmDH33u98l+/7BAK+Vxnqt8T3thuYRcrZbtAs/2bAEHUv2As4bV/DnRHJWWWY8plDO/HmkhXzkbMJoDS/eARx7FSPpb9A0YGpPG0bHewE0Ws/nMgPhGGjlzElhaGkHfves8Xj+h3RxKWSHeJwKUQ52dbdGp/NFfC6z56yuKv1v0rJGuk1JodDSWHE/HXcVQ00lwy8v0fCtE4GFy4B532UYPQxofkbul7kl+AuWAXc9wfD7/zJ8vA64+3saLj9NKpQBR947eTgcngmgMRKJHA5gJYDThdfGATgFwFGRSGTerkTMALoFlkuxjtNgVT3E9/z+979HdzeBVZsAt8rZhRdeiHXr1uG6664jjYeib3GoK2eyhIz8/BKPmVLOHPHdzcsxMhnDHn8rbuVbngCVh/pXOctnpQ8Au5scc+M2+XgA4I51i3B10zJ0rbavcYUsQF0KcpZWkpZnSDt977UPpH/otSUDf14MdYh7uBBy5nQZfXkRw/FX69i8g0D1kJi0evE9hhOu1tHcUppxo8F53IqTIJmTDh3dwDePtf/elZWzzH3X2QNc8iuGK+5heNU817c6V4y7j6eIz/VEDeX+43XG3x+uGfzXn0Lu5HMBvGQ+fgHAocJrxwGIAXg5HA7/JRwOVxPH52lQJa+pFL3DmhcT63wxPf7446irq0t7buNG2hUGiyVnuq5jxYoV1t+xGME0vgCvHCcneCW2YgnslVdeiQkTJhT1maLi0Z0fDxS82HNWpru7pvEEqCxIq5wFUVhZYzYrfQAYYV6adnbIxyMisdPO+nI5NXKUqqxRYwwjEtG8M9aFfO/n/4+hpb3/rh9vLGX45d9Ku/Za5q0jFmdYvWngrpHiTy3kmDiR7i98n+GFd4Grfyv/O2TcGo+7iuH5d4Gb/lCiiUbkJ2drN6d/t86A316p4Y/XGQd+qBuC5Nrz2cbXfU8DP/lTieKRGArttHP6A4JC7uQNAPjtqB3AMOG10QBGADgGwNsALiONzuOgcmtMOvm6DjBeffVVzJ8/X3o7xShnZ555Jjo7O7N+ngLFHrPLLrsM++67r/U3NZEuNh4vjhWOlStX4vHHHyffbrH76Ne//jWampqsv8knP0RVzgvKmfg44A1yFnLJWjk5CQWAynLjcXfUMMqQQTDPuMlX1ggAtVWAz2fMGss2xYvJmSYYfyQHsKzx/5o+xp8/mQ8s3JrzvckCj4VMgpR+zud//7zvMvzgdwz/XuD+O4vF4Zcx7HkWw/wPCh8L8URprheFjIV3PmZp4/bF9+zHax1MXooFy/K4GGxrA7bsYPjDf+n7hvKRsxffM/6/8iuGSv7YTRqqKjR87Sjj+aGpnIkVJtnfR+kMmwtue86G1ab/TaHiDTQK6TlrA8B/eh2AnRmvvRaJRFg4HP4fgBsyPxwOhy+C0ZOGyy67DMccc4xMvJ6CaOTR3d2dlhAWgy1btrj+bDZ0dnbm3eYLL7yA9957DzfccAN8Pjupi8ViOOoo44q0aZPR/Z5IJFzF2NbWZj3evHlz0eYn77//vvWYYh+J8WzcuBH+bPZsAG699Vbcf//9ac91dXU5xuF2/4hKXCFjyGn/9fT0kI8foLAxJGLq1KkADHXy0EMPzfPuwpE5hool7OIi3RT7qafbtq7avq0FrIlmIS63Y2jbtiBMkQnd8d6SjIViIZKzYuLZsbMKQC1i0S5s39aJxhEj0bQjgLcWb8WURvdsxK/b2UVmPIwBz71VAaAeTE+gqWlH1u3o+lgAwNX3duDqM7pcx9Pebje0bG/dge4mg8ls3O4HMAo+LYmmpu2On21rLQMwDF3dUTQ1taa95nYM7dhRhqPbjQw9+exaNJ2VfV/vbK0BkL9IpmnzVpQxd8fMOMWNfd3R0YWmpvRJu5UbA3j4+SpcdUYnqsoZAMO94Z2P2rH/xG4sWR3EfpMSqAiBDmwMeLdQU1MTFq004vvL853YfURnjg8a+M1T1bj9HzV47ufbMGN3+Wy3rd04VwBg85ZtGF6ebZtGnE+9CYz/chLv3LMNiz8J4YyfDrfe8c7HwIIlzZg0OiUxhsphzOUDO3a0oKmpMMl7R7sPxjw/8Mx84Jn5Rla+oakDF50oK4EYv72zswubmnoBjEx7NdrTiaYm4zxu2j4KgB9nHrEVV55qXL+amozrg983BvGEhtVrm1BRBgkY8TCmo6mpMEa8cbsffh9DWRC46ZFa7D8ljm8d15P/gwWgvaMaQA0AYEtzc5rZlYimLQFk7jsnbNrUBE1zfx3autX+nq3btqOpNtf6BmOtR3+7djuOu96Ob/P27NdPL6GxsTHra4WQs4UArgTwCIBjAYhzUwsAXG0+ngVgbeaHI5HIgwAeNP8c+GlmQoj9UBUVFTl3dC7U1dW5/mw2VFdX593mBRdcAAA45ZRTcMIJJ1jPi+rVuHHjoGkampqaXMVYU1NjPR4zZgxGjBhR1Oc7OuwaIop9VFtrT7GMGzcOgUD2U+C+++7r81wgEHCMw+3+iUbtxL6QMdTa2trnuVQqhSeeeAKnn356WjmfLAoZQ07YsWMH6XjOHEPFblssZaWIq7LCVhaGNQxDY2NxYzob3I6h4dt0cJpQWevumFEjpK+yHhcTT0UlA8AwrKEajY21OHBvHU07gObO0fhco/taoBDWZ43nn68zXPOQcWuqKA/middIXn7zVA3u+r+6HO/LjboaOwka0zgaNY3GGO/Rjd9fXuZ8nQGAMVuM9wSC5X3e43YMDVtn35qDFaGc26ioLGxyZOTI0Wh0ecwMpdSIybgOpU+N73mujt4Y0BGtxC0XadZ7//ZaLRYsr8WbHwDnnQD84Vo6JZkJpbHG/tGzxgcAHd0MVeX2kgi3/8N4/++eG4lnb5WPq7bG3kfDho1y3NeGMmIf221tfmihcXjx/b6p2F9fG4X7v+9zPYYaGuzvamgYXvCxP+L7zuNpdXMtGhvri44jHca2Q2XVqKqtQWYKOqyhFo2NddB1hpZO47WZU8cgFEyPfepEHcvWAS3RcZizu0xNohGPz+fLu49jcYY532b4YDUQChqOsUs+AZ5ZUIEfnV9P4jxdI1yHRo0ag8YxztvcGU0fR9kwfOQ4VJS5zxe399jfM2LEyDxjyI79yDmjUB5ilrq5enMACd84TBrrkfpRF8h7hYhEIksBbA2Hw/MB7AfgyXA4/ID52ocANobD4dcBnA/gntKF6j1QGYJQ9zEBxcXT0pJuPyaWzsmW0XltoW7ZeKjLCosdQ/F439qKV155BVdeeSUOP/xw0ti8cLwA+WPmtM9kIFbseaGsURebSTzQAwcUV9aYSDI8/w5Dd69ddsXLjRpMXi7bd5ar5+yx/9n7r76fuqa1pB1PVzfD4/9jaG5htltjf/ecib0euRrvhO/95SUaFj+kYcYezu+TcQc0OAXD7r2daYu+c3BXz9VNQFQYGxuagTc/MB7/8Tn3318MnC5JW3cy1B3PcMI1fV98+2NjTTjp7xUeZzMEcarobusCNjmICm8slYsnza2xiJ+3drPz8xNGycUj4u4n4biAMj/fdnYY+6q+Gn2IGQBrjK/cQBdTPny4BvhgtfE4njCIGQdVpb5bQ5BsaHdfTFBUPJkIBTWseUzDpiftYzfvewN/b5ZBQVb6kUjk6oynLhZeux67KLxMzooBj33z5s2YO3cuzjrrLOu1eDwuZWdPtY+oIOvi9+qrr+Ktt97CYYcdRhJPsfsn11jZsKEf7xwmrrvuOjzzzDNYtGhRyb5DlpwlErlKI1xAvIHQe/kUDTEGVmq/7AJRjCHITX9guPWvwNeOBiablSpBU23gPEF2kdFggWRxZH3u148JAy9HgJPmysUjkrM7/67jjkUMRx0I/Oo7xu8upOfsjaVA45d1vHKnhqmTJC2sxT98ubfF+08qQsABe2uoq3IeczIJJANwdNtmXLF5OXb8fQRw+YGO79MAxPupH0bTgICuI5WhWDhdkp57x/j/JYfL4vY24PLfMDx0DZ3teLbk2en51s70npyyEBCLG72UVPEUchV652OGL3w/+ztD/bCKjtHXpmFbm/H36GHO7+P9r1RjrRDRK1f/azwJ5Cj6KRhuyFnADxyyH3DcHA0/fIjB77fP9W1twJjhjpsgjUd3uM+NG8F3qlkW2+w+Di/AG93jgxRUqpBY2kaFYuLh7/3pT3+KDRs24NZbb7Vek1UdBptytm3bNhx33HF47rns064nnXQSmVEJhXI2kPjFL36BlStX4p///GfJvkN23JDvM+HG4AUyxNLiGcBABPC8ghWQhPzGHDp/f8WYIQbsxIy3wsqeboEc6aI4vPKRs/NPNH5QpVTfCeAXyNn8pUYA/1tc3CLUALB5B3DwJQzL18uNw3TlLPdBs8xTzBhrKp3fJ0OoGQOOaTMklRGrsvcAahoQyzH3ctZPdGkzGY7yVBJPrngVt6xfnPe9mcs/ZF7Dfv8f+XgKIWdOx+DWvzK8/6n997Ca7O8tBmkCfgHbuvmPzCKE5Q69gV0ES2jkAydbnKyObnB+H9UkUTHIZcJBZdAhjspctzI+vg7eD9jwDw3P/VLD9d/QwN70IfmaDyceYry+RrLduVBTGcqqAa9CkTMJDDXlzMlsQjaxHWzK2U033YQXX3wRJ554YtZttLe3o6KiAsuWLZOOp1iSl+94iOYXsijmeInlnvSLPnusrJF5iwylKWceKLMUoeeqzzPRY85NVVX0JSdUSVGudc7EITWiLjcxoYpHE1a8FU9Z7nBYXZH9s5nEraMb2O+bcsc97TJUYFkjJ4nZYpUh1IwBvgLOdU0zVJ9s+PsrwPFXMyQl3TUBYM+eDgTAMKMnve/XKcxO4VaaSrG0kjQquFXOXo6k/z28Nvc23MST79Bt3cnw2vvG4wP2Al77Td/zjmJ9w3zgY2ereUhH5SFnVF0NhWimmYtii3hvhbN6VCwKXchcXH9x3AgN1ZXpv2CK2V62WpacCfH0xpB10inXWJ23v1wMXoEiZxKgUoUGuueMv9cpjl1NOcu08s+GeDyOH/3oRyWPJxP5xgqlS2IxyOzFi0ajeOihh7Bli7xHczH7yOn1UpY1eq/nbODjSUMRlVtjh/Vd58siQ5KJY64yInGPjclS1sTBK/5klTyxrDFh2qtrml2KM3FM9s/mKndyi7QZ6zzKWeYxyuZeJ6ucFZqc8P6zUw4Dnv5539hfjgAL5OfR4C9iJmZHm71HA0cyhC/se16+uVRS7RQeF6Kc7Tne+T3D6/q+1w3S1jnL894n3zBU8gP2Ahb/3oeD93MgZ/QFRX3AlbNtJjnLppzxc84rytkXvs9w6Z205CzX9+VbaoRParV30Sn4x1zJsN83GT5Y3XebuY7D87+0x9LxV9Mp5/0NRc4kQKUKlaKssRjw3+EUh2xiK+6jfCpRLlt7KuRL9MvLywvelkwvHke2MdTa2orzzz8fb7/9dtr785HlpUuXSsfkBplrid1000246KKLMG/ePOltF3OeOe2fkpY1FkAaopuj0JM6NvxxI/633+voXku7QmZ6zxnppvsVVRV2stSnrFHy/iqmfuIYeuoNhmeE5Rz33i33dvxESZoWtzfAFSLG7IVwJ47O/tnEp52oTNFOOBRa1phKMTzyovGYJ2rZEjYpcobClbPTb7TJ7ZcO1/D+wxpW/13D906330ex7lG20linZwvpTfrry3SJbD7lbGQ98I+faI6TFA1EZY1pBD/PT+Mli0fmUDn6o6yRl8RuazUCHtXgPPapznuOQnrOOFnyZcnSH/iXfBzicYrnuKRY6y9mOde5mi9bbuk0blY4tNLnUs7KyzTrmvTCu8A7y+ViGigociYBqr4jpZwZyGVrDwBz5sxBc7Ncl6cYw+OPP4599tkHn3xi15yUlRXeTJIv3kKQjXhcd911+OMf/4i5c9OdB/qzBLaYMZRJzl599VUASNu3FHF4gZyl39Byx9P+YQdenf4Gll7wIT6+ajlizTGs+NGqnJ8pOh698P3T7ygiHE0DUtEUwBh5WWMaOTP3178XMJz2o/QAubrQ29SLHW+0YMu/mvHm3LfQtriNNh7BgdAn7KSf/8X4f2IWS+vm/27Ftq+/jUu3rJQLIANpwyZHWeOTb9iPeQKULXmUuT3qevp+yQZxL+1oN/6ftaeGPRo13PVdH75xrPFcS0efjxYNf5Zza2cH8Mu/MWzZYb8uJo/H7tyEC5pX9VHeeiTnZAspSRMVj5lTNHS/pOHVuzQsuM/ec7zfS7asUTze+Y49tzzPtWbYs2/JL/aeD5yQtJoFM7zEMxPUPWeFXKY50cnV3yq7qDmVckblIOv0a3a0ZY8nG8TSb9K1DvsRipxJwClpXLFiBU499VR8/PHHBW+n2IWZqcFjd1LO+rPnLJ8S9d5771lrs7mFGMMFF1yAVatW4bLLLrOeK4aclUI54/+yOS/2pyGI254zgG7iIjOOfDE5kVf6skY7hqvuzR3P1v8aa6I1/9teGy3WTEuw08qJBnGjdHkigS8+MB83blzal5zJljUKxyyVYEilGE6+Lv3Y3Xgu0DjSSFo/uPQjvPflCN4/7wN0rerGugc2pMcjO7xFcuYwhDKVs+613dj09yasvMkg9ke2N+OJH9Ot4ZOmnOVwa1y10X7ME7Js75YuayykzFs4tXnYsW0xJDuN6xFfgrCl3X0sHP4sZPHRl4Ef/I7hhB/Yr4uJ7ne3rMCpLZ/hlJbP0j4nqwaL10KnZJUxZp03fNxWlGk48gANc6fbR63MvI3JnmPFuDXGTFJRHuo7ekSXxlI77vHx02H2fdaaa8NveqwJ6x7YYO1jq+esH6+vFjkrBy7fvBx/XvUm9ulpS3vPys/6fq4YiMcpl9qbj5xZyhkhwefY0e5Q1pjne8Tf0h+un6WAImcScCIeJ5xwAp555hkce+yxBW9HXMxaBm77u0qpnBVDzgpZoHrr1q1531MsxOS9mLJGauXsqaeegs/nQzAYxAcffOD4/sGgnDHGBoycOY1XcZ+RKEvCT+vqYeiJZt9mxYS+bgntSzuw4/UWh3e7Q5oJiOeUs8LjmdDagbJoAnM6dyBoMhayskZRZUgaLodpr2vAj8+3b4c732pNe71zWWd6PLLDW/hBTgqR2HPWubILb8x+Cx9etgw96+yJvJOn0006FNpzxsu/ADshy6acSZc1FqCcdQhmKokkEN8Zx2v7v4kFRxnl4MNrjd/S0iF/XuTrOVsqOCAmkgAYwx++a18Xw13pg45iraqpPW34fNvmPqThynt17H02Q6u57lSuPkWLnBG6NeY77fnadE4ujSPr7SU1St3jxU1HOswUrLYS6F7Xgw+/swwrrl+JljeNelirrJGInBVU1mh+V2WI4bjWJoxIxnD8zk1p77nintIoZ6kUw5qmvuQ/n3JWirLGHQ4TK/lIsnic+rNPkBKKnEnAKWnkikdTU+G2NaUgZ8Ukx6VUzopJrEeNyr/qZDHKVr54ODThStnfyplTPKlUytFI47777sNJJ50k/Z2lQGZZI2V5naxyRrmouhmE9dAH4ON12d+qx5zPw/dOi6DrE8kVO60vEXvgPEbOCsQR7c2Yt2qt9XfFE8bqq2RlhMIxe/RF1sdV7NO/2deAbS/3XaG369NuJLuSdMqZcMycyuV2Ey6FLW86E/nOZZ34yw006ll6z1n2tIAbJwBCWWOWEGTdGv0FDOU24RSKJ4Hu1d3Qozq61/Sge2231U/VRnCqZStrdEIiBVzR9DFGX/yq9dzM7lZMitqGUxQ9XnesW4TvN30MfUP6D/z1E8Cnm4BHXjCVnxzkjCsLlG6NhZY1lgWBZHcSn96xBrM7t1ufpSq17BNYBji55//XVAI962zG3/KWQc762xCktZPhDLOXcoxm39Om9bQWZUyTD2kl+knDHfHfCxgu/w3DlK8xPP2m8QbLrTHLfDSfqClFWeP2tr7PFfM9VIS6v6HImQTyqUKFLgo8EMqZmEzzhLUUJWHFKGeZCb4TSk3OBtIQJB/E8kuvIfPYUSpnxWwr32QCBTkTh5CPsZyLt/a0Zx/Tbx6ygITEpiXWRez2VDSFpic2I94ysGvnTYx24QebPsKkljbrucCT66AndfIZawC47E4dn/8/e6eNGwHs0WhfAzY85FArpAPt77eTkTPxOGlgGFkPLPythk//pmH5IxrKy+x4/BXOmXXHsg6ccLDxeFiWXpmC4xFIfbYR2d3L8IYg6PNjk00FkHdrLKCsURi6iSSQ7LYHStuSdjLiAQD+Ino7UzEdR7f3nWDbTyhLk71Epp33bc7n8MZtxv+i4pGK6ehc0WltgCfWjMlNqvHdU6an8h45Ts7Ky4DNT27Bp7euxs2fLUWZnoLO6MjQpGgnnlj5Oo7fudHx9Y5u4O1lDK+/D2MH3P0xFp1hr2O36dFNYIzBb85ApIjccPMpZ/PN8+z85k/wo//ZjkVjElFM67ZnSLjTpltkKmf7fdMo977/GeO5B/5lvCFTOevd2Iu1961Hb1Nv2vP9pZyJ4+LGc4FkVxKf/GI1tvyrbx2sUs52QeQjHr/97W8d35uJUpCzfERHTFI5ARvonrNCEudQSK67M18M4vbzuUdSlzVmA4WphhsUc6MWx4mu6wOmnOVb541EOctIrHMlftu35j4P49vkiZEufkURycOa36zDB5d8hEVnLpGOIRu0AsL57Zq3HZ+PbY3b1vXSZY32BjLzosxFlEPDna8x7Us76MhiWlkjMHUicMg0DVPGa5g6KT3CZJfzmO34qJPM2r+Q5Ri+fx9Lcz1MpoCe9T04+JFFCHf2VRv7o+dMxLT1W7DxEbvsq/mZrWRGBQAQEhg1y2NUMX71Nsfnd6dUzsRS3Sydf585kLNVP/kE8w9biK9vW2O9RjGuGQO+vm01nlrxKvzLcpdt816v8hDQu8Eu1Z3W3Qpdt+ORvVxf2PwJqvQkLstioNPRA8y91NiR4+M9iP4nXVKPbY2j97Pefl+EuidmjP/TWvpO8KeNIUKlymm9wAP2Mv7PdGtc9bNPsfLGVYiY9w4y5azIssZRDUY5+uantmD17Wvw/nkfINGRLigocrYLIl/SKBKkXGSJauHgYsoaxSSVJ9YD3XNWiEpXCnLGlbN169bhyiuvtJ7PR3D7Sznbe++9izKYGQiIYyeZTA5Yz9maNWtyvk5iDpJR1pjrhrRsZcaLPsBXbl92ez6TNwNKc2ssYLfvfKcVHcs6rHK59iXG3c9rJZGb/7HZMmGQnbEWSWKmscNDV6cntqmo8wHt+LiTjCyKIfgYQ2UOwV5Ug0S0vtsK1pskiSdt3GTZWKZ1dywOND2xGcM+a8OPP1vaJ7OSKmuEMfFRKGqScZz94TI0/8vuSd76/Db4u437Fwk5E2ZB8pGzkVva0v5eXW7UVx7X2oSzTVJESs4051SOG6GIlaq8bPdrO9ZZr1GQD10Hvrbd2Gblk2tzvjcaB2qTcdT95D2sucuuCz9p58Y05Uz2uIklgE5rB3YI8+KiIpX2no866RehzqOcReNIK4EFgC1Bo395ctQuYaUkQ007+r5eFtTSvocfl9Z3jX3VubwLelwfMOVsmFm23P2pfSCbn93q+N7BBkXOJCAmoCtWrMCUKVMc1auvf/3rOOaYY7JuRylnfWPKhlKUNXKceeaZaX/7snW6m+jPssYjjjhC+ruoIcYuTjAkEglS5ayYMbRixYqcr//4xz/G2rW5E4e8EELQWG7lrG1n+ovVe1alKTO9mwjImfgV+c6x7iTeOfE9vPW5t9H6Tpv1/NKLP8Qr+7yG2Nb+M5wBkHOB0FU//RQT//uJ+T6570lTzoTH912h4fCZGeQsgwyVjTGuOZv/sQXYaoxz+bJGkeCznHbZKUE504Ia9v/jTABA78YoFs2dD40x+RI5sawxyzHZozH97+T7OxHfYd8fqlPp12/ZRL+Y5GS3mPM9tPr81zGnY1u/K2eBWPoX/mu4vYDeWduN64/0MRPGUDLLtnj5YMBvTMq89fm30bPGvlbP7twOv4+GDBXq+MnjOqRjG/wr2tKe37O3A7pOV9YoRnHbxRouPjn99VaB/0zvSSdno08yGj+71/aQr3OWD70xYIZAFrcGy/HoqN0BAJNLpL7yNRbTX08vayxPJrH+oQ3o3Wjnipv/ucVS1Epipd/e977fZQ7hqgpjMq39A3utjM8eSTdN6U+HTUoociYBccCsXr26z6w9TyofffRRvPHGG8iGgSBnooLACZiTpX9/kDNuH18IOZNdqDpXcr9u3bq0v7U801ulMgRxws6dBKuoFolilM6uLmE2j1g5K4ac5VsH7+6778YBBxwgGVC6cpbrBqllGIKMPmE09r5xT+vvVI/8nSNNWcyz2xNtzufY5n9uQWJnAp/92bk3wzXyuWsmgS5f9vLg0W8a/V/ySlX6MeO48It935rsTt9H9QfajR2dX3sLAEE5kTiGGMu53lOyy/iyqbfsg6M+noexJ4+Bv8q4DiZbExgf65ZP0lL5x1Bdlf34oM7tqP5pBBsetsfLqET6/aM/yxr36s3ulX/jxg9IErQAy09grfeaXt6BK/fD7fPm4tW6sWmvh/QUwULm9o/Ss5BFXj4Y8ANrfr0WHUISCwAHd243yhoplLMiydm+vW19nm9IxVETi5FZ14smLj4fUN3XPNfCBJPg7/2jPTHr9zMw/NBhAICOjzrg17iCLxcPAJyxfR0O3J7bdbo3Bowxz6cn9tgL5+91OBbWGutr7B7rsggapXLmZJrDt88VsX3eXovl16aXiO58u7Wkylk8gT593dzAZYw/jjdmv4WdC2wi2/lxZ5piqgxBdkHkS0BTqVRB5EZMbGUgo5zpuu5IEmXLwPKVpLW2tsLn88Hn8xWkaMgqMk6fj8fjWLt2bZ+SyXzKWX/1nHkV4hjq6LBv+gOpnGVOMMybN6/Pe9rb5RY9EkPIpZwxxtDZmv5isCGIcaeNxfAjjBt/NjfHouIRE8U8SWMqS/8SR6KDqG6nQMQTQLme/+4prZwJj32MoaEGeP9hDcFA3ySSK2e7Xz4J9eE67HvrPmmvlxEk1uIg8oOlLZqaCd5zFqwNWKprsNb+wO7RTtIySz3LGOo1RdVQEPjK9nV9Xr9zbvoEkmxZY7Z1xZzw5UB6j1fDQfVpf1OQM9GgRE/k/nHxTuMLAyNCWJmqAtM0PDzanpQZloxJjyFfXEhA484b48pZEDraFve97k3p7YDfr5GQszS3xjwTm7E4w4Fd6X1pyyrrAQATujvJlDPxmPm03GteVZnK75hTxmDcqWNRubvRjLrlqWbUfbCVJJ6J0S6cu201vrPyQ+u5tk6G7W3pYz0aByrNeLoDRtAxnz0xfe+ad0jiYQD27W7F0a2b0eVQxMHJFl83bMTGvqWfHcs6StpzBtilja9EGH76Z4aIyQ+nbduO6GZbxStvLIce0zEuZrM5Vda4C6IQcnbkkUfm3c6GDRtIklm35Cwej2P16tV5t+kG+RLrv/zlL6635wZOMcyfPx977LFHn+UP8hHT/iZnFRXGtN8TTzyR832FuF5mQzYy7bQvspGzgVTOMvs3x40bRxaHEIT1UEP2G9LvngV8ZhK3LViO7qoyjD9rHDRNQ82+RrE8CTkTNpG3dDgPOctH3opGnstHtEdHIONNTw6f2Od9lFb6PjC89hsNs/Z0TiB5j9f4r4/H3BcPRkVj+nR7XTIuT4ZEUxmWex0qTha5WgYA9QL5qNaTpCVy2Qh+j0nOPrg3hakOStWI5ekEqT+Vs7Le9OvT2C+lNxhRkDNxDOUiZ68uZvBHTeWsOmAllk+NmIT1ZdUAgAo9JX3MNGHF32xllpycTW5pRaJVXLHbGIJTop1o7Oq0DTgk9pM4hOJ6nqaqriQakkZwY744Gvvesg/WmX1513/yPsrNWm1K5czvB0I5btnjKsxjVmO8qX5/WzEf9TejXF52kqg6lT5Ok0mGOd9mGHUyQ0e3HWtvjKFKN+Lp8dtBt/qFCeQ8JfWFQNeB29dHcMXmjxF5sa9IwLcfSxhqb02zXVI54ZzxAIDuNT3wm+tSyi5CnZWctRn/H3Mlw40PM1z3oPHG8Tvt69DYL41BzT7G+dUYV+Rsl0Yh1vALFy50fO1zn/sc3n33XQSDQXR2dhZsu19oPMWQs56eHjz11FOO75NJ9IH8iXUhSbyYYJeCnLlFf5Oz2lrDL/uwww7Dpk2b8M9//tOxRE+2FDUztqamJoRCIVxwwQVpr4uETVSjSknO8iFTOSsvL89ZUuwuIPuhL4db46+fYCgzk4x7x07FD+cejlCDcXP1hYxLbypKQc7EKevc70125j6fKeIpBtEOI55uXwB/mDYd5+xzON6osxNrXh5FuQi1BuQsI+RkKFDlzJjKWIrYrZEhx9JiFqHmSSMATL9zP+txZSpJagiSrWSvx5yg5kljJtqXtOOF5LuoMhNQKhWmEATNKf7pd++Hg54O9yFnFKVN4iFKxrMH+IfnGCpMNThYE8CxBxnPf24W0GOqHxWpJEFZo70BPWHHE43Zj+PmJbo6456w++WToY0xJh3G3Pg2yjRjW7Jujdb35iFnXS3mmB5bjgP+NAuTLp6IteXV1uvT1m4GIE/OMpUzJ6UcAC44kYF12wo1YLi28gkRvTpIHk93L8O4LzN8YlYGP/0msGSV0eLRG7OVM5GcXbX7bOtxQzIuX14tHLOGpMNSSpycxRn2iHbCl2Ko2bcac1+ag31v2QfBhiBS3SkEu4zxJV3WmOX5TzYBdz7e99WRW4284+D/HoRZv5+BysmG2nntpg+tyR1FznZB5Esac70+duxYHHTQQZbRw0cffSQdj9ues7a2Njz66KNWXCJkk+x85CwfWaqtrcXZZ59NFg8lOcvXk1YIiomH/3a/34/GxkacdtppuPHGG/u8T4acOSlnjzzyCADg4YcfTnuvSPDb2tqsxwNZ1pipnE2ePBlHHHFEn9il4ksra8x+8a+tMkrgACDm82FHpzFeYnGGyFqTdGQpRyoqHDGxzpOlOyln1VPtpEgnJmf5zpCYSc6iPh9eqRqDHf5yNA+rxdyX5gAAGC+3kk2uMwxBKnKYvqbMJM1fZSdF+90+1Xoc1HXSRah9jBWknIlkMVgfxF7XTQFAo8KIhCyZAH7zD4b1W9LHklXWmKMMNbWqA9eMNBJr2UWodWH0dPcyJHOYcPhNA46xJ4/BiCOGo2x0GQ7+j8GKEppGXtaYi5z1xoAKnSf6fvzpOg13f0/DEz/W0Gv2V1YQlMb6BBcQsefs6z/rGxsnixO+0Yi5rxyMPa/ZAzUN9njiZWCyJi4csRzk7JUIQ9s2Y/+U1dnn2MYy+zpUE41JxwMAPmFX+HzZyxpPO4SBpRh85T5r4gwAjlh4qPFZ08FVPh47oGffSl9g+dxbGQ68kOH2vwMP/xeOyln5bpX4rMxo/qxNxUkNOJxGNCdbsQQwPGEck6o9qlB/YD385X5U7GYQ/OhX3iBR8rLdlr/xM4bv35f+YkDXUb29C5pfQ93MWmiahrr9jQnsIGOYYy7voXrOdkHILKrMSdD06dMB0JMzXdeRSqXwyiuvpJWccYiJ9d///ncsW7YMY8eOxbp168AYw+zZs63tyCAzsV60aBE2bNiAP/3pT7j00kvzbt/n8+EnP/kJ9t577z6/0Q3cfD4cDpNtKxPF7F8+nsReuGHDhvV5HxU549+XbRyLY6i/lLN8SwpwcnbXXXfhyiuvxFVXXQUAGDlyZNZtFo2MErlsN6SaSpGc+dHaaZSxHHsVw7/eM44hBRkqxMyBw4mc3V2zJ4Yd2gAAiHfRGpTkQ6yLkzM/2s2qmoZqoO4Ao6TIl2TQGCMoa7Qf+8FQnkU5Y4xZZY0iGZp4/m6WMUgZIyBnYtKI3OSMHzN/dbpSz5W0Sj0pvYCwSOqXfqLj/+5hOOLy9IkaXtYYjPcdI/x4AUDIzOiklTMhvx92EkPw8wxtnX1/o8YY/PG+pZ/DDmkAAhqCjIERTIKIifXGLdn3dU/MJkOhugBG1Gu4/DQNI+uBXrN+kIScCTVkYs/Zkw6FAuVJW32t378O/nI/Rswbbr0+oUfeYELcI9GUhkSS4bxbdVz/oI41Tfarx1zJrP6ukNA7eeOt9ZayWNdlyLSy1vV9es4cil1qKoF5e5nnfE3GOVZrsDktKj+mgfTJqvc/cd7YD37H0NYFS4E+dI4d0+2XaoiaFTtVqWTJyBCHSM54PAGBUFdMsNcAGZmIkhiC7N3Tjqs3fYSGRG7n4JpUAhoDgsOD8FcY42bUF+z7/G4x44ai3Bp3QRTSc5YNPFmcMsWY/SxFWeNvf/tbHHPMMTj++OP7vNfJGfGLX/yiZVU/a9YsALTkbOPGjTjooIMwadIknHfeebj//vsxf/78nJ/XNA3l5eW47bbbSOJxgxdeeAGpVKrPd1PE4oaciY6Vo0aN6vM+anKWLcZs5Kyrq6tkytlXv/rVtG2vXbsWkyZNspQxXtZ42GGH4Ve/+hXKy42bBy05sx9mkrNUiuHQS3UcfYWO19+37bfLa4xj9uIi4I2lQNzHyxoJyJD4OI8hSNKBfL3eVo3v7zRsmj/+hIKcZfujL7hxgtjs3jjSOO/5enBBptMuQs2QVTnTe3WAAb4KHzR/+uy/r8KIJ0ShVKUpZ8hd1sjJYgY542St0pxhlznlRDHMrHDDRqGFLJE0lJFgAAC3iTdjrp1Rg6o97JW8A+bOkXX+E38OL8/b95ssfcFs2IYy/io/tAyXQK3S2Ef+mHwvpUjwT7o6+4/rjTKrJK1MIB+apiHKyxoJ+gQ1QTmLm6WM7V3Og6BMIGcce99gG5TUEpSiir9nc6uGORcz/Ol54Na/AlO+ZpaYmdcnPmbFeM48xoe5DxuT1WVmZY90GWGGW6OTclZRBiQ7zXhqM8hZtXG8fNEUfASTREFh9mzHztwnLFfObvyOHfSkMcAhB9nnPeWSHk6wyFncjidYZ8ez94/2smOLdZGQxTvXvYd57c24uHmVo5suB+/fC9ba8YSGhTD1FsPAiUINHkgociYBN+SMOwJ+6UtfAmDbsVMk+pmJNe8jc+p7czJ4+MpXvmI95uoMJTlbtWpVn9fzOVXyOKjicUMa/H4/fD5fnzJGL5CzvfbaC9/5zncc3+cGhShnPGZxDIlr5L3zzjsl7TmLRqN48sknsX37dvzgBz/Ahg0brH44rpxVVlamfWbMmPQeFCrlTGPps7vL1gELlwH/W2z8zZWzmnrjmN3+d7Nx2lw0dts2auUs9/jWe4141uwxGluO2A2X7HEIWoLlaIka8XTsTGFrnqQhbzxFfDxqkrOoZo/pUw83zjNfmUmGmE6wzpn92AeG8izkjNvoB6r6TrH7y/12PP2onGXrgQuYhL9S55MoEvFk9MBlgju5VZTZZHHkkSMw9+WDceBf90ftjFrrvRU9cel4WAY549jSAqzPWC3DSvSr+x4zrdLYRyTkTIgo08SGI5ViiJsmNwlNQ6giPcXiZY3lFI6fwnkfj+roiTLseZZzXOXJvsqQv9yPPf5vMgDbqZCq5yya0vD+pxnhphhWmf1V1jHLIENlDcbfZSTqKytIOdtrApDsMFWhjDGk+TSLoJXrSenrUEg4KdraclwoGUOl+WW1o9IZZcgkR1W6vHKmCcY2AYcLt2gIwtcxFJ1iq6dUYcI3DWOQERTKmfB4RCKKsiBQWe783kMmc7KYfswqJxilljW6/ITDQEKRMwnkS/SdEsB3330XixcvxtSpRg8DT7RljTcykUqlclrBZ6or55xzDo466ijrb/5Z2bjEfVTIOmaZ8Ao54zjwwAOtx14gZ5qm4Yc//KHrbWYin3LW0tKCUaNG4Yorrsh6PFeuXOnqWGdD5u+56667cPrpp2PevHl9xidXzjLJ2cSJE3Nus7iA7Ic+pM+mZt4sy8zvqa43xu8bS43n6xqMvz/bRN1zlvu9KdMdMtJeiQta9sZnZhM+V67KdB33P0NHzrQ8m7J7zozvv/RLwP+dYbzm58oZRY9XRilqIIsxgJMzovU5ThZL0XOWxVso2hyzXPaylTVWm0mIjLooKmei2rBsLUMqxfCpua7rxNHCPqr0o/6AOlQ0VmDit3az4imL0RiCsCwdi3ucmf5DeQkhT6JF+EySnbkotBuI1v7+LPeRyi8w7NxmfFevL9CHdB82h071EK9D8SjDZ1vTe5hEcLKTST4CZqLPlT4qt0bd4dhdcz/Dft803lSRylJGaP4disvHk3mIfD6kLVnx9v0aTj4UeOR6zVpCJFjb90S0y4dT0mRIXMj8lbezD4ByPWWMtzIffEE7j2PMJrSVBKYyYt9iyOHmwStno3HnskbAJkfVxGWWGgwyvfyRvmNpn92Aey4wTXfq08krj49iwmEgociZBPIleI899lif56ZPn57msEdJzjIT61yGFbFYej3vTTfdlPY3j4tSOXOzZhonZfy3DDQ5e/HFF8n2TbHxiIYgIjJdI6nJmTg2H330UbS0tOCuu+7C73//+6xx5lsMuhhk/p5///vfAIDly5enxTtnzhx89pmxaDFfdoBD0zT8/Oc/z7rNYiB+J7fSZzpDvDXDPYvZbo21w+xjVh4Cpu1tjOvln6T6rHFTdDxCVpRPOeNli3Et/dLP/w6xFJp2SIVTxOpUQMxUzirr/djwDw33XelDWYgrZ7ZSJV3WKDz25diWU78ZB+9roFDy0gm+UdbY/kEHVt+xBrqZMPVu7MWr+70OANACGnyh9Ot5pdmMPyZmTEiUQjmbfi7DfU8DH601/97d3kcigfWX+XDgX2YBAELRhHQ8DIWPI26+kUlejRhNchaXnywSR0Q2chZPAO3bje/q9fVdqe3gg4xEsjolX9YoZrKJGMtKzAC7DzCTDAWFRB+QJNR51Nc7hRVgnMoaAZt4UJEzkST6tPTk/+D9NDx7qw+Tx2lZyxrFGCnIkGimE8wxk8ZLCP01DvHU0vWciX2LTvGk9Zw5lDUCNjmqTiWllbPMcyIYACaO0TDLrMD9+00abr1Iw8t3aki0ZSGLQjyAUs52Scgm+gCdIpQZT6HkbNy4cVi3bh0mT55ckrjEzzupKfn24b333psWz0AYgogK5PDhw3HdddeRxALIG4IAfccUVVkjP17i9sR477zzTtffUwwK3Ufvvfee9ThTOQOA66+/HlVVhtOVzLFLK5EzHaqWXvghXpnyGlpOeBVj4j2oSCXx8KdvWSVn9cPsY3bIfkCtWeYYZAyL+1b7FoVilLOOVuMN8YwxZCtnKfzj9XQ77qLjKeKj3IDEX+HHbqMzerzKbMIoX9YoEmrnALs+6cKCo9424nEoa+Q9cCE9RbDoc4Zy5tew4PNv45NbV6Pp74bb4Y43d1rvCVT7+1zPKyZWQAtqGJmISi+MrWcoeSKuvI/hjseM52bsoVlulpkElidFIQLzBMYAVqAbrq3COBBqM3ELxYqfGOyzLS03+bDi0W3lrDLDeCY03NhHtak4qfqaiDEsX5/9raGk8z7iiW1FUv6YiYuXZyOvHJwM9onH7B/i5Ex2DImLYfv92bdnkTMnMlRDp3aK6lQgx8Xa6lk0j88BewGaBuw32ViewYpHmpwJypnDfVbsOeNkJ5MMcfW1Wk+QukcCttL52l0a5t+r4aufB679uobxozQk2rnamUEWzfhGhVRZ4y4LCkLVn8pZZ2cnDjzwQNx2221Wj9D++++PSZMm9dlWf5GzTGQSjdNPP500Hq8R6mK2wfdffytnhVrZO62VV11d7fDO4uDGiCVTOeOgPHaAochULtuOLc8YSiHrTOLyzStwcOc2jEkY5xjzaxgukLMvHqoh5edmFymYS/q4RwELCFtvNV3dEoJyNn4k8NZDZkka09HeBTTvdPx4QXBDzlDukFgTljWmK2eC0ig8/vDyZWDmelGOypnYA0fccyYagvSs78Ha+9bjo+8us7/biSwGfKgw+ytGJqJShFHMEzP1nlQKWGUI0pi+O5DqscsaRQQbjCQpGC3NOmcL7nMma7l6zgImYawgIGe+tLJG5x9Xl4zj8s3LAQATJ/kxoj49Zr7OYU0yQaq+JmIM3/5V9gHAyU6musiVM07OpJJrIZ5cxAMA6v25lbxALCVtzc7QVznLdo4kc5U11tKRIZGcFaKccZVq0YMael7SUFmuEStnucnif425KsQS9nnWVznjZY0JyJ5mmec9X5euvkbDYTO0tJw22Z5I+34Oi+BHCcb0AEKRMwlQKCf9Sc6efPJJLFmyBNdee62lnHF3xkyUgpw5lTVmWqOXOqkezOQs8/s5MuMrJTlz2vbs2bOxfPlynHrqqdh///1zxuYGmd+Z7xiGQqGs30ty7IQ7vAaGPR58P+3l2lQcCcHggoX8GFFnn4tfPsIoeQKMPoaXI7Jljc6PHd9r9pxNmWyPoUtP1bDnFN70biRFUv1LBX423hJH4J2txh9l2Xu8yojdGvkvj5y9BO9+aZE1nuI77euTY88ZaVljusoQEGot19y1DitvTJdTs7lwlo0ykv2GZEyujFDYfq6yz0OmCWWNmYl+XTo5ozQEOWAv47vPPd5w83zpV/b5ZPec9U2sgyYZoiBn4tYr9BQOmwGcyLbiyLYt1vPfav4Ee/caS9eMHOcQj6WcJeTVV2EDyTxLBQSzlDXyRLbCvDfLjGs9JSb6uX/cSbOce858IZ+x1hhjKJfs8TKUM2HbPuffF2+Jo/nfxnUod1mjvIlLelmj8z5qjHXjV+sWGe8x4/H5NJSXaWkxVhEoeT5hhzj1nAHAwo9YmpV+pgEHV8yrUklruQ23EHcJQ3qPYCa4chbIIIuBGj/gAwKJFPwUE2kDBEXOJOBl5UzX9bQk/t5778Xrr79u/c3JGbcazwSVIYi4j7hZg4itW7em/T18+PA+7xHjGQhylkmGqPrf3G4jk3RnKmdU5jL5yho5GhoaLIOb5557znEbMshFzp555pk+73cqaeQgGUfCEDqgq8Xx9aR4jAIavnS4Uc74/a8aNfRHHWkkjbXJOD5Y7T4UoLieM77eU02dPaaDfsO5LTQihABjGJmIkrm25cLC499F2XJDotMq+pIhrsTUJuPkZY16Use2F7Zj51utSHYmwVIMPWvtBcwd3RorbAJLmVhrAMqiuckDV6syUTbKmFyrT8blYsowuXHC2/draKjRsipn/mo/fGU+BBK6fJllBjm78Rxj1vyP1/mw4QkNw2vt1yqtnjOHMTTMJB9xAuVMKGvcZ1gCj1zDcOnyD3FV0zKUmyVfE2O2+7BToh9qsMkZpclNa6v9+J3f9VUY+dp0wSxKVTlBWaM4AAPIvaFANEcZoUg+JK9DoqmMlmXSafE330fru23md/f12hfLGmVVGFGVzqYunr/Vtrnk41cEL+OrJFDOtLSyRueNvfAewxtLhbLGzDLCWls564lmfro49FHOcsztJtqdyaKmadZEUVVKXu0cKChyJgGK5Ly/es4uv/xy/PnPf7b+3rHD6PrPppyVwhCE25znwoQJExx75QbSECQTVP1vVNsoKytLO7alNARxilccQ2PGjMF//vMf1NXVASgNOcu3zVzjjIac2ftgWk9bn5er9CQuaP7E+luL65gyXsPC+3244zvG98/Y3yRnBEla2j0+R4bes74HVU3GrH55lX3p545yNfsaJagHdrXI93o4PN8yvwXRLfbdu2eNfZx8DuSsfIwxroYnY6SLUPsYs3pMAGDdbzdgxU3pSpWTcha0XMBoj5mPMVSta8v5/qzkbCRXzuJSSpWeppzZjy8S1hnafZwZS5aeM03TUDbaOGbDEnLHzPgt9n0g6BeURr+GA/bWcPHJwPBEFMftNKwknZSzkJncVhGQMzGxnj0ugeGd9vj91bpFKNNTGBO3JyB5CWNaPMP5eS8/4SCeZHrSiK2mEpizr4ampzR8+jd7/wXizgSWE6FyCuv6ZGE9Z1WpBPSN3cb3O5CzoGDAIesemTYp0+ucqLe+02Y95uRZBKUhiKhKZytrFJ/l4yUtHoG8SpMz4bzPFs9PzdTCLrXMopzphiFIIuk+p+nj1uiwLh1Hst25zFKMsZpiEmSAoMiZBAZbWaOI9evXA+jfskYn5SwTFRUV+Na3vpU1noEwBMkWy0ApZ0745je/iZkzZ0pv0w05y1RfTzzxROzYsQOapiEej6Ojo8N1PEDf3yMueO2EXItwUytnThidiGJsQhjrib7fFWwIAhpQk0rkXTg6bzgFKmevHzgfNTuMpKhMIGe8dGTEPEO1PrxjK4ENejp2vtOKd78UwavT3sDHK5P46aXpbp6ZKgwAlI01xtXwhFzJHtC3rJHf2AFg9e1rsP7+DWnvd7Jl5zPGJLOxLD0pavzd0txvz5LwhEbYCqyccuZsdrHneA23XKThmq8Boxo0fPbIRmz8SxMAZwJrkTPJMksjHCHZN7PQ9g86EG81zu+fXqDhF+sj2N1Uq5yOGSdnlS6cgjMhJkuh3gQWf90uZ54U68JTK15FtW6PK0eCX2+c91WppLxTgahUmYk1VxTHjdBQUwn4mI4rNi2z+m8yCSxPbLnVvhQZSus5yz4Yf7v6bSTWdjvGA2T0eEleh8SxrHUmcPx+Cdy/eiFu0IxyBb6uIQcvO02LxyRnFRRlhMhNhipSSczt3G4/4bAbKa30xTFUnkU5A4Av7diACj0Fza/1Gde8rLDGVNZk1LNshiBOyObWKMZEUfo5UMjx022Ew+HbAMwFsB7A+ZFIJGE+Pw/AXwCsAZCKRCJHZdvGUISXyxrzrXO2c6dRTlRqcibGVIhylq/M0kvKmZfIGUBTiuqm58xpDAUCAcydOxcLFizAa6+9hlNOOcV1TJnf2dbW5npbFMeu2DGkOWTNvoAP/tog0J5AZSwOwLnXsrCA+j7uberFx9esQPv7Hdjv9qkYfcKotI+UC4ksvwEOP2I4gE9RnUpI26BnYufbrdbjjz73Jg5MpifLToYXFeOMa8HIRBTthDdYH2NWv0LW9zgYlPDG8xqdYDZW+PyhHVuzvy8PeN9XhZ6SI0OiIYg5vhsSMez23Gf44i27WcYjy65Ybr0vUNn3mHG1c5ik2qnr6WQo0JNA+9IeLDjqHVTvXYUjFh6GkfUaxglKlZMKU2YqD9WJ7BM2hUJUFIcv34berbknG/Vo3+uw5tes874snkSBKZgjNNY30R9Rb79eFgT279qJo9uNnjhfefqaWQDgq/BBC2gIJnUEdB0pXaJHOK2P0vng+5mOEUm7MSnopFTV2om1bM+ZX7wYdSaAl7Zht1g3dlu2Du0fjsF7py5K+0xomINSVUOnVIljqMLs771tfQRVqSS+u8cc/GTDkrT3J7v6XqeChMqZaCD15VkJvFMG/POnGiaNAaq+YLy2Z287LtxqVIIE6gJ9Jv3FigKNMfREC3NZdUKhPWdNj29Gy/yd5vfnUs4I9tEAIa9yFg6HZwJojEQihwNYCeD0jLc8HolE5u1qxAywk7RRo0bleWd2lGrNrHzKGU9wvaac1dfX46CDDgKQbiahyFl+UIylYqz0ObKNod133x2AHJly+s7W1tYs78wPimOnOXzUaZY8H8omGb1x47q68rwzN8SSNK6cvX/+B9j2wnbEtsaw5JtLoUfTg66s6VvWSDVDbAyh9GuPqA7WJvuqGE7uiFV7GPtnfLyboJwoXRniCztng+5gsCD2MVBa6U+IO09a7Xb+BJSPc56s4uBqUYUut26WeHx4b8lVTctQ/dx6LL3kIwBAqjc9y8mnnEmNIaQfM39XAltfMBSFrlXdjp/JXIwWAMpNJaSKQDkTl2Co3Oocg4hU1HkHBBqIHCT1vsfszK7P8MactxDbFkMoCGudRQB9iBnA+3OIerxSGcQDBhk7or3Z6smrT6aT5JBjTxVNWWOmcsY6EohutmWdBUe+jURbhnLmWNbot+KRrY4V46lLxjE8GcO0njZMjnXh7jXvYt/e9KqQcaeN7RuP4NYoX15txzMmmMDyv/gwSevFzqc249BpxmsjEjaZTuzsuwN8QR/8VX74YKhvMqYgYnoWYrpjzxljDB9c+pH1NzdFEmFfqwlcUQcIhZQ1zgXwkvn4BQCHZrx+Wjgcnh8Oh79HGtkgAE/wMtcIKwZUxhtAacgZpSFIvnI0AJgyZQrOP/98/PGPf8Tq1bZTgpd6zgbaECQbqFUhp7JGp21nUzupVOFi3RpzgbLnrLnG6NHSfRqOWJB5WcyP6hlGDdKEzk73sQDpTQo6sO3l7WiLpJ9ry3+4Mu3vqpq+ZY1BIlcyp7LGbGV5HCGHkrSqvYz9OyHWnUZA3UDsOTtn62q8d1ok5/udVA/bMpqirLHvUw0H16f9XTmxAvs/PAOh4UHs/8eZjpvhxiXSJiXCZ+tSRgI9q9uYmW5f3AYg3c3S+O4c5EyyFFXX08mQvythlTEBQNMTfdefcFI9ykeYazA5TAgUC6dJmUyMPW0Mqvcxxu3Io0c4voeTs8oc5dcFQThmtaaT3tQXV6F7dTdW/GgVWEsUV22yl2MQ+yzT4qnnZWmS61QJ52iNGc+FzZ/gB5s+wqVbVqIhEcMjn8xP+4ij6iGeZ5IEXxxDicUt2PDQZzk/w5VfEbYhSErKKp4xljbhUJtKYK9eu+R/smAmU/PDaThs/lyMOLKvQZo4iZaU6O8C0sd0fIcxHucfvhAfXr4Mv5phKPpjs0weieDHsTqVQLdMWWPaJF4cwQCQiqaw4Y8bLQOQeEv6QQiN7HvMqMbQQKIQTb0BAPeKbQcwTHgtAmBv8/Gz4XD4rUgkslj8cDgcvgjARQBw2WWX4ZhjjpGL2EPYtm0bgL5uftnw7LPPoqmpKe05rgL09PT0ea1YiM6HsVjMcmR0wvbt2633OX1vd7cxM9je3o6mpiYkEglX8YmljJnOjE4YNmwYmpubrXHCv5MbmGSLt1B0uVApMr+v00ymOzs7rdfc7h9+HMrLy62154qNh4MrXc3Nza73kUigo9EompqarN8LOKtg2X47V0p37Nghdcz4WCwG2b6PE7vNmzfnnLzIhYQ5ffrx2OF4pG4SZs7VMAsOro0mUrtVOMYTKzduhmXm/nM7hro6EuDavR5PInLmkj7v2fjnTWl/x1OdAAzTlvb2nWhqikLvNe5ilXoSW5q3o6naXSbS1qWBabCSx3cueBc7n27L+Zko6+7z2xljYH4NlakUYt1RNDXl3kZOCGVWU3udJ4km/KQRG280Yuho6ewTTzxqHC/DEIRJjel4tO+1efzd41D21zI0321cJ3vKelDWGMI+r+4FXUs5fl9HzEjuKvUUNm9pxshat2PIflybTKRNYWtVPjQ1NaFneXrlQ0tvC7qb0s/N3nLjej8sGcfO1g40NblThbc0B9LMEzofWIHOT+x7yQeXfIT47un7sC3ZhkRTOuFJmP2etckENm3a5PqcB+C4TsWUV/bB6qOFiY/dGCZdMQG9q3qROiDpeCxSQdM5Me7uWHHEeu37RU0qgaDQM7T5n1uw+Z9bkJm2On5fjfFfXTKObdtbkBjrLq7OTpscc3L2xZ0bAQBHtW/BOIckf3NzX5IdDcSsbciMoc4eLY0MdT26wfF9428ch9oja8GiOra2bzWyXAEdMeP+V6kn0d2TRFPTdoet5Ecqo1S3PhlLc2bkeK6hEaccHEBnXQc6Nzv3a2vlGvxRhlAiiaamZsf3FIKYoIb3NPdg45qNSJlLZbT88CM8+70OBH6Tvt8cx0aVaUiTSmL9xu2oneRuDO3YEcIE83F9Ko7m9hZE/m8dWv6xE5te3IRJv9oNrc+1pX2meceWPtuJ+owxVJ1KoLXN/RgqNRobG7O+Vgg5awPAjWvrAOzkL0QiEesXh8PhfwOYCSCNnEUikQcBPGj+KS9beAjc9j3b2lyZOPnkk/s8N3r0aABGj06uA1UIxBuPpmk54+IJ76hRoxy/t76+HgBQVVWFxsZGNDU1uYovFLIv2J0FKASzZ892/B5+osvup6qqqqI/k/l9DQ0NAAzLdv6a2/0zbJgx13HYYYdhxIgReOyxxwAA999/Py655JKC4uHgx3v48OGu95FoQ69pGhobG9MmH5wWlc72fbW1xmWjrq5O6phlU3czccEFF+D3v/89vva1r2X9Pr7sQLZxXwiCAePc8YUCeCM4FlMmAeMn+FD570q880Wjh+GdmpGI+vzo9Adx+q2T0NjY196/bVQc7diKkK5LnWNVVTusx9UdhdWUjJ5kz8gOaxiGxkbNKBfRVqCM6WioG47GRnf9JxUdDKthz9jnI2YAMHJcAxob6/s8/2H1J0B7AlXMJzWGfNiU9z2NBzaifWYnOj7owOQvTcLoxvRy9URNEivwCar1JBjTMGbMOPj97pL9ULDvRMyEPSZgwk0T8NzdLwIAxk0fh2GNDTm3Uz6xAuvwGcr1JEaOHIOgvtnVfqqssEuF/WCoS9nEPNWaQtcfe7Dm12vT4913Qh83udA+ZdiIJgxLxtBeUYPGxrqiYwGA7T0MOyCsgflJ38R+3fnpKsi4vcahujH9+s4Yw2LfKlToKdSXj0b1iBz2b3ngY0YS/MCYvXFWqBkHfXssJs7cDWdOrMTPzF6h0fuNxripY4Gp2bezcdg2RNGFshSTuy4G7d9fm4rj/h2Lc7zbgNP3NY/dhp6lPahLxVHfMAbBYMxVXNUVNlGv1FOoSKUrdU6TIk7fE50Qw3bsQE0qgVC5+zHU3sXgQ1/yk4ndDt8N9TOyf0fF5FaswwbDgIO5zz+SSQYfa7P+Pqxjm+P7dgTL0ThuFBobs19bVtZ9ilg0hlAihXHjxrmedAgFbWIT35TARwctT3s9k5jte8s+jr9/48gmRD+NoUpPoLpmJIJBd9ehYXX2mAkyhtEVNWj5h6ngv9yBll+0YtPf0kmf0/f0NkaxHTtQpSdRVe1+DA0kCpF8FgI42nx8LIAF/IVwOCysNoLDAEiu2DO4wEujNE3De++9h1tvvbXobZTKSl/X9ZwnbEuLMdNfagMO8fPchCQXpkyZUtJ4vNpzFggE8Le//c16/vjjjx+QuJzKGsVeQaeFxPu7rDEbPv/5z6OpqQl/+ctfsr6HsqzRZybmptEZ6va3bwBLq4bh9vHT8bux+yA01nmSJFBhxBKU3D9pvR6dhZGzcqHXg5d9aJqGmNmA5tSIXnA8Lk6x8lpnIqiZZXuBmNySDFoBMVVNrsScZ2bjkBfmYNRxI/u8HqjxQ/NrqNCNxU0TMiGZO+nfwybgpfpx6Ll1jvXSQU+Fsc+P90LDnPq8m+GlhRWSZY0sYwiemzGjn0nMAOceL/5cdSqBhKTzX7b11jiiTekE16l/SdM09ASN57d9RlNGuCVUgY8vOwiTL54In0/DzoA9eVQ7vTbLh23wYxaSrY0VLmG7R7swdnv+tgEnhMzlGOqScbker4zS4//b/LHj+x4abYztva5zvtfz8tSaVAJRiUPGWHrfIgBU7FaB43d8AbUz7eNU0Zinr1MoI5Qqa0T6mB6VsMfvlqB9j2gJlCGURzYR+86k0pACy8XfrxqGl674HCZdPDFnPNWppFxZY0aZZuiW99P+FolZ+bhyHPL8Qc7xCP3BUtfpAURechaJRJYC2BoOh+cD2A/Ak+Fw+AHz5a+Ew+H3wuHwQgBNkUjkzdKF6j3wRNbn82H27Nm49tpri95GKd0ac60HxcsNuXKTiVKQs0LK9kodj1fJmaYZi6wuX74cH3/8cZoZSrFxUbs1imWFTqWy+dbKkx3bhR6zQCCAcePG5dx3lFb6XBTmyx35hbW6evz23TWb4xR/f1CyKD7fwtNOKBPsh8VdkTA7sHlpi6t4WPp6PtnwxMn2jTUrOTPdCLUeWXLWN6DyjKQsNCqEYG0ADbPrs661GKizk6K4REjcwbPLH8BvGveDtpdN7Ed8bjh2v2xyQbPhtlujZJKWsX++0Na33CwTmoNqKNqgyyRFxhpV+d9XNaUKBz0dxv5/mOnYcwYA0TIjUduxSa7vjCfWeobZTXOoAj0+P9AQskxscoEvGyFLzpzGdCZWVtThqsmzEQ8FMPOB6Y7v4epnXVLSPCHjOpRNGfr38Ak45Lk5mHLVHo6vc4Jfm0ogJkHOdGavTffsMKNYbvKlE6FpGoYJ/Z0hB0OJtHiIes50vS9ZBIDz9zwMd4yfZv3dVFaV00IeSHdspFo4nCNzSYqmUAVumHQgAg7r9lnxCJMyUlb6mfGsdJ5waDi4Hp977zA0HORcWWD3nMmNoYFEQT6ukUjk6oynLjaf/z2A31MHNVjAE7xCe86cUEpylmu9Jw5eopcJKkOQYsjQpZdemjUhGaqGICLBB4CpU416mC1b+tZR50Op3BrFvkGn5RBKTc7E3/PNb34T9957L37729/2mQzhJYu5QLJenvnZijIAPUCbQzl7QuvrhpiJAE/SZMeRi59SVWU3hYn3Q52f9wn3+4cVGFT7WGH2usr5vA/UBBAHAFlylvF3/YF1mPvSwdATOjb+tQllo0IFkaFgXQCJnQnjpp8orNzWEeYhT5mRZRsj+eAvN9VXxozj6LKlqlCCXx+u62M2IyIozOjLkDPRae+BMXvj4uZVmPnb6Yhti2HlzfYC73vfuCdGHNHXNEFEoioIdACtTXLkjJMhPWOcxHx+fGvPw/DYT30FjSG+tlcox+RpQXA4ZrMemoGlF35o/b0tWI4VlfW496vz8MrpzoOME2pZl9Zsy2SlAIjfnNJy50vcMbE6lcAGIuXsiZGT8b0/7oaJhxhlr7udtxs++/MmTLli97zHTFyEWibRN8a0AT3og8/sh9waqkC3MJm3qqI2LzkLZDha5lqsOSfM461Pa4BvmVHafOhrhyDeksDbx70LANhuqnrlOTistRC1rHLmMP5CI0KYdse+WHLuUuu58N8PSJsM7RMPNyjRk9gySMmZWoRaAqLq4RalJGdOJWiZ6E/lLB/mzZuX9bX+WoR6wYIFOV+njAXITvDdEH5KRQ+wx6RIyJyWQ6irc67nLsUYGj16NGpqahyJWCFqI6VyVllunPetXcDfX2H4xs90bBpnnE9Lq+zzKis5oyprNJO0pJCZjz87d71/lSAaiQkZTzx1GXLG0pMxAJhwznhUZqgKwaCG1yfthjdqR6Oq0Zno8DWPqJUzblHvC/ow8bwJGHPi6IK2ExQWXJWy1WacwprkzOVyV1rAGEMBpsspZ+YY+qzM7tlaUdH3vD7oyTDGnT4W0++e1uc1IF05i0uMIV1IrF+qH4d93zkS474yFpO/Mwl1s2xSzxfhzgVWbRyzjmZZcmZuD31LdzsCIbDKwjLkIHFZY3TqMEAz1ILRJ47G5yKHW29pCRrn1fb27DmKWBor5dbocD+8Z+xUnDH182nPHbxf7s3wJRrKpd0R7bkKHRrKd6+y1N7qPatw7Kajscf3d8+7Hb+pJFXqScTiNGN668l74t2aEfjNOGMytssfxJWTD8IlexwCXfPlnWOhWqjbIvgjyrH3jXtiyjV7oGr3KtQJZZ+tAeMcKw9lj8paV0yXU854mWWH3z6XJl86EWO+ONpyggWAYG3uc00krzKlsQMJRc4kkKl6ZIKbauRCqXrOkskkPv00fzNsNnJGtf5a5udrampcbae/yhqnT3cu/ShFLOI28pGzGTNmFBwXZVljPB7Hpk22mYKTcpZtnJdCORMNSzJRjHJG0XNWbpKzFRuAs37C8NeXgEvq98fp+xyJjoDgXJYl5CBXzrIs2FpwOObH15fbZi0rvrhv2nsCNQEc+Oj+1t8iORN3he7n5Mx9TLrDjH7jGeMQfvQABM11px4bMRlBP/D47nvjlxNmoKrc+cYfMtcY0hys7YsB3/ojo/bAh5UNmPrTvXO+Pxv4LHq5LkvOzP/MwPwu78S+oLEBP1fOXIKrHq2BkEXQfje27z4KVAcw64EZmJCF/PvL/WABzVDyYjJjSEisNQ3lw4JG6bdPw/DD7XtWWQHkTDNn2OMSpbpAduXMirnA/c/JUJksOTPjSU6oxrzFh2PuK4fAX+ZD1WT7gjNljnEsL/ty9sTaL5AzqUTfTKzj46rQ+LVx6PYFsKR6OGI+v5Xg18+ux4L78ihVAjmT6jmDTYZ0TYMv42t5K0E++AI++Ct98AHwx1OO17eC4hHU4GRVED/ZbX+81DDeen1VZR0+M6/h9XnSJFGhliLU5m/R/Br2+N7u2OsHRh+gL2RfkDaa14OKHIUCfmFJD5l1znSz5ywFDT+fMAOhI0djt/N2A+C8nlk2UI2hgYT75ekV8ipnr7zyCsLhcM5tlEo527lzZ0EGHPnKGqnI2R133IHm5mZcdtllOP7447FixYqittNf5Kzf1BcThZKzbMdJBHVZYyqVwjnnnJM2jriRTCGxlYKcXXrppVnf13/kzPiv0uzhFktddM2H3oxMe0QWo6hApfE+6Rl0M57twXI8uNtUtLAgmn8K/Fd4S+XulRj1hZF4cvQkfFhWjzcFjxLxlGBmBiNV1shLZQAsOW5fnD65Gw1z6qH5NByx7EgMOzKOHl8AZwHoNW/k2W78QbNsL+WwKHQx4In1hw0j8IU79kDFBHfVDrxfqIzpUrP6fKfrkmWNmknOAoyR9JwxaLh60mwsu7UHC2fWw7/qILxzwnvFbaoyAK0jAXQnAbirt9KFRFaHlrZ/AsKseSHKmWYuvpyUIIuAkOhneb3Q/R+spiFnvG8RPg2VE9NngPb58V5oWdCKK347Dqe2adhzvMMGTNhr5UmuK2Z+lvk0zLx3Og74IGUR2Zt3m4Wvbl+HHz68L3yZLCkDYqIvq5zxMZSCBonuEwRqAkj1xE1FOIhyFxXNonIGh5zx1bs0zNrTuJ9UlOUhsOY5UCm5cLg1hhz6R2fcOw3b3tyJpz8yTECG5fC6EdXX7qjMvcM8XpqGhbWjMfyWMQjWGrHtec0ULP/hSsy4x1m1F8Gv0xV6yrrHDDYociaBfMpZQQ3dJSJnhaK/yhoPOuggHH64UW7x1ltvWcsQFIr+ImeBQAATJ07Ehg2GhezNN9/c5z2lWIQ6c6y4KXOkdmuMx+OWtT8HX29ORH8pZ6+88gpGjRqV9X39XdZYkUXtyURtltUbApXG5beMpaRKZPkNjUHDx0GbCaaumA7/rz9C+bhyzLp/OnQG/GHEntA0o3/goKnAeyuAL8y2t8V7zmTKGvmC0Qwa1k9vxL7X2mO3Owr0mCUrL7wH7DSX8anvu0IDACBo7mMmEQ9gl6T97y4NYw+SKEM3VZgyPSVlCMIzfP6r6opf4QOAMasPAH6mS7o12klsVyCIiZ8zJ1zmNEALasXt//IA0JEAJNROwzzBjE1LN9VJtNqzIU6LGGfCZ5KzVExyDPHYshSdFUpsLIKv60ilmOvlGKxSXQeys/tlk7H7ZZMBAHvlUWF42Z50WaOeHo+oMK6uqMPPd5uFnzXmv4+JiT6VW6Ouaa7VacBQjGNb45YpiBtyxgSDEpZxzC4/DTjygMLHQYBKOWO2cpaJ8V9rxNivjEP8SOM92SYZAXFMp+TKGpPp6rTYSzf6hFEYfUL2+78IPuFQppSzXRP5DEEKWVOLqnwQKJycPfTQQ7jwwgtx7rnnZiWQVIYgTvto2LBhqK2tRUeH8wKLTii1Ichuu+2G22+/HaFQCG+88Qb+9re/4bTTTsOee+7Z572UPWfZCH7m38UQD6qyRr7Iugi+aLaIfMoZFcHPtw/6TTkzkxC/H/j7TRp++TeGb52oobIcOP8XfcdEtnPMb/achXSdxCVNz/ia1+vH4Fct46y/O3uM91WWGzEtuM8wMxlRb3+QJw1y5AxWPJm/q1toWeTErK4aKMvSzxAss8kiY8x1f69mJkVanln7fPCZx6xMT5H0nPEkZFr+1hdHkCln5meDQWDxQ+n7qFhirAUI1FdmH7NM5ax6H4PJB+oKS198ZZyc0ShnLFtZY4Gb52TRb6qvlS5VU4vZS45psQRM6jqUyk4WiwFP9Mv1FKIShFokQ7pkWLycuSrl3k4/zcVWch9xB0lpt0bzeDuRMwDmxIERdLYJNCC9NLaFwK2R/6R8xihZ4+FjiClytksiX1nj3nvn72ugIkFAYWRh/vz5OOywwzB16lRMm5ZdHqZWzjL3UXV1NTo6OqBpWkFxl9oQ5JRTTsFXvvIVAMDEiRNx3XXX5Y2lP8saC1HOqMsaneDkIllqx0+nfeQUZ3+XNWo+DWceZfzjOO4gYNyXCxujPGkMyqoellFB+jl25xPAx+t1zJul4ZqzbGLE+80CAQ0j6jO2ZZEziTEk/JieGPDCuww1lcCh0zVHJ68fn5c9UeG9DwGmIxrP3feQC1w587lNhE3YyplkWaOldhrIV8aUDZwIBcCQLHDNIifw82nSOA0H7J0lUasqbOfxMkJdohQ1pTOrIZ4hPUlr/Mo4gBlLDhQCPoZkyVlmKWomCl39RCTUsYQxWeIKllLl8vMmeBlhhZ5CUqqskV8Yjf8iD2m46x8MG5qB+R9m/1wmfCEfENTgTzCkojr62gsVGA8TTVwkyxoFR0u3jo1iqW4wYzLqoi8Wd/5ba69RKWcFkMVsFSCAWBqbknJr5ASfT1oF3brYCkper0SZ5UBCGYJIIF9ZIwB86UtfyrmNUpY1nn322Wmrp4fDYRx22GEAgEMPPTSry54YF5VSlbmPnn32Wey///54883ClsYrdVljIT1d1LGI28hHxo444ggAyFkOSl3WWChqa52L0anLGvMR1P42BHFyhB47QktL0m6/JPtNT7NK0hiJ4xbfxJx97RKUF98DrnuQ4eyfMqzaaDxXlSMZtMhZUiLRt3rONPzzdeD4qxkO+w7Dc28zxxv3eSdk35ZPSGRlymV4UuRzWULGYfecySlnWhZCXfR2NA0pAodNnhTlcjnnJgR5YwoSqK9myWgKALR05cwX9GHC18ejYoLz4u6Z8JuJsAxZBMQSufTnb7lIw5H7AyccXOB2uMMmdDn1VS88sc4FWzmTdf4z/zfPsQP31vCXG3wYU1wHAwDAZ06C6L0SpbFi36Im33MGmI6NUsqZEc/Rs4G504B7vqeh60UN03Yv7hiKE3sy5EyzDEHyv3fy2OyviQ6bMuXe4r0DcK+caX4NKDNMXFK9NO7V/Q1FziTgpAoV20tVKnJ2yCGH4JFHHsGLL75oPXf00UcXvC1q5SwzsQ6Hw1iyZIlFFvsrnkzycfnll+PSSy/FeeedV/A2BoKc7bvvvlixYgXWrl2bNy6qskaOp59+Gn/961+L3lYpydk3vvGNrN+XCyQEln80y/30G18w/j/nOOCqr+VQhQKC0x5FI755Haqr6qswPfY/YN53zXLMHFd95pfvOWMZqhDHJXcya8FujuF1QG2WNc4AW4XxM12KnHEylGeJpbyg6zlLnyGWgU5QiiqaOWRi9++ZvUvX9y3xdgI/ZozA8ZNBw31XaHlNJHLBz0tjJWzQgfSeswMFdfG6r2t49Tc+hIKFxaiF0pUz1zB3r+yY5sqibKLPMnrOpGIy+3Hl+hZt9VW6rLHatmZ3S2B13SaLFZU+LPitD5edpqGqovjARPVVhlBrXG3PMWnV/IyGtY9pOa/TfoHgS42hjJ4zt+QMsAk+kyD4AwlV1igBJ1Xo2WefTSMc+XokSmGlP2XKFCxYsACapmG//fZDLBbD008/jRNPPLHgbZWanBULShMOETfccENOk4lSx1KoIQgA7LPPPjm3VaqyxlNOOSVrf+BJJ51U0njEz4v7ZPTo0Xj22WdxyimnWM/1l3KWr3/pgas0HHsQcMTMPNvhN1joJMoZP3JV5UCX0Nvl96f3frU6LJrNwcybNJNQzviu5TfYmkrDCOSzrcCTb6Rv97iDcm+LK2dBxqTIEFXPGe8TLNd1qQVp+cHSAfz4fLmYrLXpZJQhrgY73K/2vmFP7PbN8aiYWJhS5Qtq0CFJzvj48wGXnip5zEJ8woFGOfv5RRpOKWxO0Xk7gmIuM4YsQxBJNTgt0adQYTLOsX0nFr8tThhlxjSzbNkBSCpnXKkKMOaafDDYYyhbj1fB8Qjl3qUyBOEYPSx/rAHBHVFmDOmCMREgR878VX7obQkp9XUgoZQzCVAQj1IoZ5nrd4RCIXz1q19FdXWOjs4MeI2clarnrFhiRhmLuA3KRagplbNvf/vb0DQNdXV1uPXWWwEARx55JN5//32sW7cOTz31VEnjAbKPoVAo3Ua7/wxB+LacXw4FjT60cSNy39TEskYK5YyXglRXptvjt/1Xw6t32bFck0PNs8saJZKijN6nOfsCu5slMfdmDJd8xERUzqTKd/h5RtVzxuSUMx7P8Ydo+NE5cjGl+HkmU9bID7fDmNZ8GionVRZsxqJZZIhAySNQFgNl3PFTdjkG4/9jDy5sfays27HIEE3fokwsgJjoy6kw2SoKfnC2McY//GPhcfpMddFHOEkkMy9j9XZKXIcM5Yxv0H0sgFDuDTlCTVUaS1VmmdlzFpJRzvhabZLn/UBBKWcSyGcIUggoyRkVERK30V/9QoXGU2or/f6MRdwGJTmjVM7uuece6/EPfvADhMNhhMPhghZYL3XPWWYZYyHkjEL1tHaR7A1NKGuUUs6sNaoMVJWnlxRWV2o48gCg+yXg9feB4+bk2JS1CDVBUgT7Bru6Kf09558A/OwCDWPzEFifUAImRc749mR7zsSyRoLEemSDXKIP0JQ1WuYSBGSIuxHKJEXWcgwU5Kycl1nKXft5SZq06iEsHE6xkLlsPJp1HZJMrLOsmVVRpuEn3youRqs0lmCSiF+HpJQzgcC6Vs6EnjPpYxakUc4stTNAR/ClyhqtY2ZARjnjk0Q+4mqr/oJSziTgpHrsscceRW2jFFb6FOSslCVp2ZDLoGRXJ2fZTDdEUI4lwCjRFQmPpmk4+uijCyJmYjz9Rc76q+eMqx6yvR6akKTJuDUiQ2XIZvhRWa7hhEPy9O/wskYS1cP4PxgArjoz/T2//0F+YgbY6qJsEuIjPmZBWdWDb4+gP4eTM5mFulmWxNoNuAGHTGmspb7Kh2MrZzL7hzFbfZVMZHliLT2GrGMmFU6GckZA8Cl6zszzzCfjQGqtt2hAZp0zn6B2SpEzMoJPYyZF1YtLRRZZRi+uVM+ZGZMmY0E6gFDKmQSclLMxY8bgo48+shZ3LrTnjFI5k52JBejLGnPF9MQTT2DhwoU45phjSh6PV8lZ5v4R//7Wt76Fz3/+8wXHRVHWOGHCBJx88smutwOUnuBn/l1MWaPUOLByEJoZ6wAYkjKJbIZyVl1Ya5Dztvj+IUmK7Bvsj8/XMHca8IfnGE77XOFqkVi+Q6KcEc0QUzlsUkyP8mOWkFlkOY/JTTGgKCfSUwbnoFDOghVmSiyjwoiJtaxiLkzKyJCzbD1eRW9HuA6lJM57UnLGx5DESZbKMJeQKmsM2gTWdVkjoXLmEyaJKNROqnikS2P5MbPUTvdxWaWxUjOfAwdFziSQTanKtX5YJkrRc0ZZ1tgfytkZZ5yBM844I+d2Sr0IdTEohSFIrv1z5513FpTMUpY1UhB8L5Y1ku4jyRlrTdOQggY/GEl/Dr+h1VVr6OuVWCAC8v05qQyDkmDAUO1OPQI49YjixpVPmJFNEPR4UZWAyZbviGvlyYKXosZ65Qk+RTxcOYNMv1CSkZGzUIUPMcl4xAWE5ceQneiT9JzJxqNpYD4Nms6Qkln0OUffYrGgUM74cgwkZY2ieZOUcmZA+pgR9QlqVORMNCiROM94z1mKsLzaJzWLNnBQZY0SKCSx/slPfoJgMIjbbrvN8XWv95yVap0zt/FQGoKMGTNmQGMRt5Fr/xS67yjdGinV11KRMxnljMIQhEJl4LO6SRmbb06GzHjqq9MNQYqCj6AkLXOtGgkSK5Z+ui8nslcTk5mJBcRyIh1JGSt9ojJLwCZncZlFlgkTa64yyPR62D1n8vEEy+XLm8Q1s6St6/mYll3njJ+iBPuIEUzKUCl5gO2wKcM8uOMnT/RlbmmaUEZIopxRjSEit0bZXlzNrwE+4/KRkrp3pCtnMuCEUYbgDySUciaBQkr29ttvP0Sj0awJdn+UyLnBrmAI8tJLLw1oLOI2nPbPddddh+7u7oJdNinLGgejclbIGKOx0ufbkt9HKZ+GYMpw2iuAWzqDpd/Qci0ynXdT5qy+zCLUfMZa7DlzC4o1mChL0jSqMku+RhVBjxdvpolH5WesKQxBuHIm1S9E6NYYMteR0iQSfcbs855OZSAqayQYQ8b6hrrc4vP8eFP2nEmUpOkZhiBDqefM7luUvQ7RGIIAAAI+IK5L9poa/1Os/2iXNQ5O5UyRMwkUqgrlet2rZY0DYQiSC9Rllo899himT58+oLGI23DaP7fccku/x1UKctZfPWf9VfpJVU4E2LO6qQQD3PaKsbT/UBaUUM74byLsgZNy3BIcLWWSIt74LqsMiWtUkfR6EKoeUuSMiLwCQjmRzHWI0BCkzFybziejnOm26iF70ERbdrm18gjJmRmT1Fp5JkiUszK7JI0x5up+xMmZ5gda/yu3kDlJz5kwhugcP92XWQLCJBHBMdMCGlgccr2dGeucycCqchikPWeqrFECu4KV/lAlZ1Lr1PTDItRu4LWyxv5WzvqNnBGWpPEZQimnvQzb8bJQrnfnAXdrlCknMg83I1hIlKLnrCTKGZNcOByEM9bmMUtEZcaQ+YCiX4gn1lKqh/E/I0gaQ6Zbo4wlKgO96iFvCGL+T7CPuKwko3qAVDmzyZDbSzVX8OHTUF9DZXghoZzBHkOyrqg+ArIIiL24UuEY4MsfyPRPW+ucyYdj9y0OTuVMkTMJUCSyvE8mKdXAYMDLbo2y5IzaEEQmnv7uOSsUu3pZY+ai1E6gsdI3/ycpazSPmUzPmflR/otG1EkExMmZ1OKv6S5pFD1nsi5p1pEi6/UgKmskmbEmKGtkhIk1VztlCL6ePuEggzKrrFFmTNP3C8maOVBOElEYAWlE1v5A+iSI6/M+STeGKMgQY4Cf6N4hLulB4RrLx6RcTOZAlFFfM+4dMuDqq3+QljUqciYBnuAVsr5SNnBylkqlyMwuhrJyJruPKAhsf5U1FgvKskavkEWgsLLGb33rWznXycv8DIVy5iNIQnSxrFEynoY6Ddd/Azhwb/eb0gjIGbOUM3n4CBrx0xZ/lbYdl++Bs4ICTWLN1TcKK32KMkurJE3mOsTHH2FZoyajnFH2nAlqMAk5I+xbpFjInLI0VobAZq6ZJQPRmMhtPLpOp76KS3qQrP9IMIb4PgLBMiwkZY2D3BBEkTMJUCTWmqZZBC2RkFvVtBSJPlViLavEeMk9kpNxSrXTK32CXosnV0zipMiNN95Y0LYolTMKlYFiAWGeFE2dqOHnF/oMa2yX9yNuCCJzg9UzFn+NS5wmvKlb1hDEOlKy16GQqJzJq50UY4gTxmRM4lpNuAg1hZkDpSFIWTmBuQQzjrmxIbl4RDWYpF+Igpzx8loJ5Yx0TIcolTPpcNIItVRZI1EZITXBl3VrFGOS6TmjVM58whiiqHLqbyhyJgFOXGQT2WAwCICOnFGWpMkSEGorfS8oeVTHiyoeDgpCXQpy1h/KWXl5YRaFtMoZBTmTd0e01hciSEIolDO+a3nPmYxduCYYcLjuOQOgEdmgi2tUUfR6EJxmFhlKyihn/KMUSZGZ6PskEiI9o49SBiI5c5uk6TqdcmaZ3Mgu+kypnAXoes4olTMp63rLNZZOFZI2BCHrW5SPB6Al+JZyRuD4SWGl7xdcUQchN1PkTAYUZY0AXbJPWZLGk91oNCq1Ha/1nFEQWN7bREnOvNInWAqC3x89Z2VlZQVti0Y5o2uitg1BCO4ehEkRI3Dc4r9NxvSAzMKaqKxRXKPKK4tQ86RIaq08wjFtWenLKGeE5hIB3nsCCVMZCIm1rFujplmKuUwZob2umFQ4BnjPGQFZpOg58xH0VFESfBLljLS82rwugiEpM4Y8p5yZ/1EQ6hCRujhAUORMAlTEg1o5oyBnFRWGr7dXyBn1ItQy8XByFo/L+CDTxcNB6dY4GJQzkZz1q3Km823RlTVS9HqIk41utxYIyZdZcqc9vgUpRzqeFEmsKyaqHvIlafK9MEZQdGSIJ0VJmUWoCdc548sNyPR4WeorhRosLMcQdXnJ1nVxUkY+KL5wuJR1PT9khMqZVFkjYc9ZmnW9S0JNaggSEsgQiTGRPMFPWSXxBASfgpyF5I13IChnVW6XluHxWGvB6a4dPwcSipxJwKvkjEL14OSst7eXJCbZfcQT8UQiIUXQKPYRP14U5IySUFP0LnrNoCRXTOIxLMSpUfwMiXJGYaVP0nMGMx4KlUGeLLIMpz2ZtZzE9XwoLKyllbOAfDyAOIboygjlTGWM/yiSNL9gnuA6nCQdWRRLY92SM8aMMkQAJFmTzg04JErAKK9DXH2VsUGnLJFLW8dL0hCEhuDTGBP5icoaAb5wuNy9g5d7+whWPOYGHFLKmUnORg7XsO5xmv7gAGMyq2gMGBQ5k4DXyBml6uFFckbRB+c15YySDFHENZh6zsRxUCjZplBgLSt9gjWq+DpOMkmabYPe96liEaQgZ0k++2lAzhCEz35K9JwxwGeZFbiPBSDs9SAkQ9Y4lBlDhCVy1gw6gYLPKOIRlDO3EwU6oz1m/LynsK6nXI5BKrG2FumWDkdQPWR6zvgYoiOLZGWNFCXxBMqZj1I5C3LlTOK8N+MZPRwYWS9b+skJtdxC3QMFRc4k4DVyVoqyRq+QM8DuK4rFYgMaTyl6zobS/uEoNTlzs/9JDUFIyhrpLKwpVIaQ2Z+TIjAE4U3dUmWNBEqV4dZI1XNGY2FtjyGpcADYJWAydZYWmafoNRX2kdtJEGtRbEozB+iuxyLlQuYALFdMXaYkjdQQxNyGREkaZYmcRqCc2aWxtGWWbm9nujiGvKKclaDnTJMyBDF+Cwmhtpx+lXK2y4HarVFWifFyWSNFTJTkw2tljV7bP4OBnFVVVRW9LdpFqF1vwgLF7KdtYS0fT7Bcvrwps6zxoi+6H9saweKvus7sXSN5mlmN+NJljeYDEtXDPGYyJXKEPXA88TTcCN1tgzn0UbqOR1DO3B6zVEoYQwTnGV+yQiewrqchQzyxllc7SZZjCMmf94x0EWoq5cx4THHM9ACdqYyPoAqEHzMZxdwqjSVdK29wGoIUVGkaDodvAzAXwHoA50cikUTG69cCOD0SiYTJI/QwdgW3RllyRhkTBfnwWlkj5f7xWlkjxfHKFdOkSZNw9913Y8KECQVvy2tW+jyRSRKscybe0P7zCw1fuYnhz9cXF2OozNCYdIkkjZcTVVYAq/+uYY9G9/tJ7BuQSYoAo8xSer1FgqRRDIpiIXO7nIiirJGOLPolZqwtpz2SnjyCfiFLDaaZSOOqh4wrqkY4hihKY2lt2eUTa9utUTocwfmPoYfCSp9gXDN+L5O4d3CySHEv85sl8T6pZWHoznvRrXEwGoLkJWfhcHgmgMZIJHJ4OBz+IYDTAfxdeL0GwPTShehdDOWyxlAoBE3TkEwmpXq8vJbse9VKfyjtH47+WI7h8ssvL2pbFH2LlFb6nJwlJJQzp5K0Iw/QsO1fxR/HULmGGIjWOdPkiBlgJ9Yki9ESyDB2iRyTKv201Ve6fiGpNaoolTzBEMS1mQNlWSOBcsbHEIXFNyC4NZKonTRkiAFmaazL7VH2LRL0djJKK32K8mqIPWcE5IwrZwSlsSSGIFx9lZGp+AQToXvkUDYEmQvgJfPxCwAOzXj9ewDupQxqsMCr5IwisdY0jcROvxTkQ0YZolCqvOrW6BVlkcNryzEANKWWlIk1t7BORCl6ztKfdnMdKCsnSPQJ16jShBtswmVM3NqfZAZd06xZXTl3RPpEVqp2h9L5z2+TIbcz1qUoawyAIe7ymFkqjHw4Bvhi7wTlzDQlYPLqK6kDqbWEhnszB1JDEMGYKOlyH4nKGQX54OqrjHJG2bfI1zf0pyQWfdbp7h2ilf5gNAQphC83ANhiPm4HMIy/EA6H6wBMj0QiPwuHnSsaw+HwRQAuAoDLLrsMxxxzjFTAXkJbWxsAoKurC01NTa63wxPizZs3S21nx44dAAzSILMdjrKyMvT09GDt2rWora11tU2eBG/ZssVK1t2CJ5ufffYZqqurXW2Dl2m2tLS43kec/Ij7OZFIuNpee3s7AKCzs1P6mHV1dVnbdLutrVu3AjCUJdl4qM4PPoaam5ulxxA/djLHn2ecbe2taGqS7Mk0PQ3bdnYikQi6iikRNyZ1ovGo9DHriXUDAFgi5XpbO1vjqAKgMyYdD782+sHQ1t6BpqbuorexY7vxvw6N5LoIPwAd6OnqQVNTp6tN8Mb39s52NDXJTV5EE70oB6DHEkgkAu6u06alZm+0V3oftXd0ADAS642btqCmsvhMrbPDiCdJMIYAoxzRB6B583Y01RWvmm/ZbJIpjWYMJc0Znu6OLjQ1dbnbiDnr0NHVjqYmudLxaCqOIIBUNI5Ewu9uDJl2qj0x+THU1tUGwJiU2dy8HaOqip+4bmuNogpACvJjKN5iTMT6mY7Wtk5Xx2zrtqClnG1p3izt9psyx1BHW6fr6xDvD2vraEVTk9xkczRpXMf8TEc0prva57FeM7dKyuewbV1GbhVgDE2bm5GKeq+2sbGxMetrhZCzNgC15uM6ADuF1/4PwD25PhyJRB4E8KD55yAUF7ODE4T6+vqcO7m/ttPQ0AAAqKyslNoOR2VlJVpbW1FfXw+/3+9qmzy5mjBhgqXsuAXFfuKq16hRo1xvgys5iUQC48aNg2besN1sj5tayB57ABg7diwAQ2Fyu63hw4cDMEoSKcYQYOwnmW3xMTR+/PiCF5vOhrq6OgDGWHIbkw8bAQAjRg5DY2ODVDyBMmNCpSxYiWAw98U6G4JBI1GoqKyQPmYtY7djE9ZB093FAgC11TuRAqBJjEMRS/3L4Usx1JRXo7GxvujP+7QkmrAMTHP/m9LiCawAEimU+cvQ2Fib/wMO8GvGGBo2rEF6DFXXRZEE4Nf8CAaDrn6j32+Mocpq+XtHcFQI6/EZfIxhzJixqK8pPgmtqmgx4gq4u+9kYrFvOXy6jvq64WhsLL6Gq7cngZ0wyhop4vGHjONfGapAY+OwPO/Osg1sAgA0DKtHY+NwqXiW17QjASAgMYY09ikAoLZWfgz5RvvxGTYhwHQMHz4SjS7Ko2uqtxuknOA6FAvGsAKfIMAYqmtq0NhYV/Q2NrbpVvLcuFujdIWTPYYqXV9DfOYYGjF6uPQYaq1vx060IsAY/AF3Y6gsYEx2lhHkH/7RAWzARgSYjtGjx6BxFE1Jcn+hkCKGhQCONh8fC2CB8NoUADeEw+EXAOxp9qTtMhjKZY0ATRnhUCzb8/l8JL1LVPFweM2tsT96zooFX6hb6rgRGoLw2dOkTHkTYa9HyGzq1mSK9C0zB/l4AHnzBKv3hKJGDrBKUaVc0kpQ1ijjtFcaG3SZnjO68ibAdkVNxlxa+3OjApJoYI0hGUMQa01sSht0idJY3oMZDNFdF0l6zgj7KANwby6hm7ccBiJTGe7SmnBfs8cNQfyE65wFZQw4+H2H1PFzcLo15r01RCKRpQC2hsPh+QD2A/BkOBx+wHztG5FI5LhIJHIcgE8jkcjPSxqtx0Btpe8lt0bAewYTXoqHyrHRa+TVy+SMYgyRGIKALrHmSYiUlT7lOmcVPEmTNwShiAewzRPgul/I3A4RN9MILKytxJrSwlrGrZHSoESw0nfL8bk7ItViP5ycJVySMyuxJhrTVs+ZRwxBeGLtkxhDqRQdOaNIrCnJmW1MJNFHmaI1lbEmiaSMieit9ANMdz+Jwfct4YRDUOKYDSQK0vcjkcjVGU9d7PCeXcpGH/CelT5lYg1415p9oA1BAGPf9Pb2euqYee14ieSMMeaaXJVCOaMwBCFRzswbiGGl7/L3ETrt8cRKRjmjdEkDYKsMLrM0XSdOinhiLbFGFa15AoHaSankCe6Irtc5S9FNOAB2gu52wV76Mc0nHCTGkLWgsXw4PLGWkRg4gQ0E6RJrOeXM9JOhMEgjIB469RiyTGVkrPRpHT8BY1KG6S63R2kmJaxNNySVM4XsoC5r9NIi1IC8EsMEyx6vKWcU5Ew2FjEer+0fKpUqGAxC13UppYpSEaYoa6RcX8hHoZwRJtZlFeYMusRUI6XTHiCvnHFbdqqyRp6ESKkefBcRjCG/5bRHu1aeW1Csc2a7tkmHY2yOlzW6PM9SlupBEw8Ilj+wFhD2iHLGFRzz1igFMbF2XUZIqZwJpbq6y33ECNddA0AzhrjhEolyJpQ1Sp73lBMOQ9lKXyELqBL9ESNGALCd8tyCuqxRVonh8WiaRpLsUxAiKvLBjTMWLVoktR2v9ZxRjyHZxcypCT4NOTP/J7T7TUnMoPNSEIp4eM+Zj7G0fV9UOJwMEfULWeTMbc+ZTp0U8USWYCFzgn3kJyhrLMW6azRW+lTKGVeoPULwCRZ99lkEn7LnTIKclaCsUYbg2+qrdDjQfMYSGj5xu0XHY/xPV9bIFXyZMcTLGuXD0QIEZIjUSl+e4A8kFDmTABU522OPPQAAq1ev9kQ8HLLJvtfiAejIxwknnAAAWLJkidR2hnLPGSDfd0YdD+Ui1CR1+uYNhGLNLIokxF9m7B+ZBXuzrbvmGn65GWJqQxCehMhMx9pkSD4eP0EpqjWGCPso/ZAwBClRz5lbckZekiZZqmt8mE7B52NIRjGnJGd8DMmYS1CXojLJPsHeKG08XMGXKY3lBJ+658ztMSM1JhKUM1XWuIuBmpytW7eOJB6vuDV6kZxR7aP6+noA3jIE8VrPGeA9ckbSc2b+T5IUmeRMlyhr5Ik+SZ2+UJIWczmMqBNZZqkMbnvOzO0QG4JoMuVEoCf4Mok1pfOfPYYkEmvCGXRxO27Lh6nVVxrljNAQRFJ9TaWYpeDTlMjJqzCMXME395HL69CajXRlqABs5cxj16EAc78INeV5b/ctKuVslwOVW2NtrbFWTnd38QusiihVWaNSzkoTC6CUs/6Oh7LnjKIEzCJnEjfYUpg5BBhD3OUu4qoHVVLElTO3iaNObqXPlSoZQxDzfwrljKBfSCNUO3nySWKlT6x6uC0f5mYXZCVpXPWQWf7A/J8isebKmd/lmI7GhRI5CrLoRYLPlTOX59nqz8x7GUEJIWAr+DLGRKTHTFTOXM5iaKRW+kJZo+o527VA5dZIneh7xRCkVOTMC8oQtZW+1wxBFDnLDkpDEH9Q3g7Z6jmjcCXjjltMd62ckRuCSCpnjNxK39hHUmSoFD1nUmSRbgbdWudMykqfNrG23BrdmspYyhltz5mU+ir0dMtCtucsGgd8lnskXUlaUEaFsZz/pMMxtsfLmV2Ooc4uunMMEMoaJa5DnJz5CRw2NQLljNKYyLbS1127xg4kFDmTgFcTfa8YgniNLFLGREWoKdVOyrJGqmNWUVEBwL0hiCd7zqxtESTWZbypW6bugi4JEUtTYi5XibD6hcgso7nK4DKxZiVKrKV6zuhcyQJWv5A8wacpjbXNHNwmRUlz7FElsrwkzS0545Mn1ARfJrG2CbV8PLLKWW/MTvRJrkM8HgnVwyLUxMqZ20kQPqapJhw4GZJRX32EE40iGXJ7zDTCdc405da468Jr5Mxri1B7LR7KmChUPKB0ZY2unfZ2EeVMbp0z+qSIRDmjmG3kNzTo7ssa+W8hdkd0Tc4spz0aaJbKIF/WSFFOxJUzKXJmXRelw7EXoZZIihImiZIsSrHByxrdGoIQL0Jt9Zx5ZQyVyY2hWMJOJkl64CgWfebXL7LeVzkDjkScTlkEBOWMgpx5xBDEVs6kwxHKGgfnItSKnEmAKnH0alkjlXLmlR44MSbZfUS9zhnFPvL5fBb5cLs4dqms9L1Gzkis9AlusnzBVhk7ZMqeM03oOXNd1siIZ6wtlUFuEWrqxJqirJFkDPHlDwjcIynXzJLpOeOJLJVyxsmZ7jKxJldfOcEnMAShIfhypjLJlGBQQqG+8sQa8soZ+SSRy/M+maDbP4ConLmMJ8msUlR/QP7moYlrwblVzihNbgRDEOXWuItBKWe5sSsYgnhJOQO8d8yGIjnjNzSKpChQxm3i5Z32SByuhNlP18oZFyWJ1/Nxm8jq1D1nFMqZ+T9FOZFV1shkxhBhv5Bgpe92xjphXlb9ROYJlnLmkbJGTjrl1FfzOkTQL8TVV7/LrDqRpO05o1DOOIliREoVkyyvtsgZUTxWn6DLe0csAdq18oR7h+uesxS9A6kyBNkFQeXW6PVE3yvKmRcNQZTamRteI2eU65xRlFzxNYHcOoCJ8ZAoZ2aiF2QM0ZiceQLdjLWccmb1nhAvICynVBE6flKUNep0vSe2+up+xjqZ5DP6tIm17toQxPyfTDmTU2EA0RBEPp6gpb7KK2cka+URJNbUjp/2JJHLskaLnNGEw/eRW7JolKKaMZFY6dsmLm7dGkHo1qj5Nega4If79Q0HEoqcSWCoJ/qyJXJeU4XEmKgMQZTamRucnHnFEISm58z8nyCxDpTLlaYAoDVz0DSkzHMj7pKcoUQW1tI9Zx40BCEh+GXczEGmNNb4j2SdMz9X8iSUMzORDVA52wV4WaOsckasesiQM74tCiv9MgrlzIyLsDQ2yJi1FEaxsIRksnXF5ExcUmYaRaecyamv0bhRgkgVk+3WKLEINWE8AJDy8UmZwVfXqMiZBKit9L2W6PPfpbs807xIzqj2kRd7zgDvkjOvKGckPWfWwp3y8QTL5Ju6Kc0cAEA3SZXbhbF14sVfZcsIyRcQDnIrffmyRookhDt++iXKGinVV048/HDfc5YsWVmjV8aQfFkjpdNeQHIMJVP2dZEk0fdp1ppybs2SbOVMOhwDkkt6UJc1+iUXDo8Jyx/QlsQz9+ZLhBONAKBzchZX5GyXgleVM6pElm9HlpwpK/388Xil9LNUVvpDiZzxOn0KK/1guVw/FWAreVQ3NOnZRkacFPnl9lGpyhoplDOKYR2UVD0AWEkRiXJmLSDsXjnjZY0Bgn4qIxi5MkJrIXMPKWe0hiAEyhnhumsAoFsmLpLl1cTGRG6NgHi/I8XxAgSC75IsiguHU5Ra+iiUM52WwKb4+oZuq0AGEIqcSaAUhiBuLdDFeKgujrLkzGslewC9w6aXFqEG5In+UHdrpFnnjO4GEqqQN5cAYe8JYCtnKZezjZYhCFVSxC2j3V6HrH4hmnB4EkLhjkji1hiyyZBrUC5C7TPOEB+AlGsnOeN/qrJGa6081+qr+T/VvTVEYQhibotgHwWEdcXcIJEylFKALrHmqofrNSDJyZmkcpak7TmTVc6MhcNhxkRw3ovKmVu3Rk7OiM573S850TiAUORMAlSJo2iBLpM0UifWVMqZVxbFBlRZY3/HI7ufvNxzRtEvFCon6D3hHyWbsealIN4wBNGk3RpLo3q4XbAXsAk+zULmZjwSBhwaYYkcIBB8l6oH/5w/SBMPk+1bJF67z0dgKkPqGlsul1gbhiDmH0RjiMkqZylqciZ3rU6ZqR3V8hDWJJHLk95wayR0RxSs690aglCWVwO2cqbKGncxUCaOlGt4DVVy5sWyRreKEIfX1MVSKVVeGUOkVvoEN9lQpdwNFhDGEHFi7ZUZa81KiryhnFnlRBTrihH0VFm24xLW9ZSGIAAsUxm3M9ZeK2ukXueM9+dI9S1aaidBPEGb4LsZ1omkUFFA1i9kbsftdYicnMmVEVKXNfrKKJQzQnJmLfpMsAg1lfoqOdE4kFDkTAJUVvoArU28V8oavZboA3RkaPz48dA0DevXr/cUoZZVhoY6wadZhJquX4grZxQlctRN1CnXypn5gNrC2rVrG1c9aJUzmcTaR5jIkiz+qtONaQBgfAy57aXU6ZRFY0OSPWfm5YLaSt8zC4eHBBt0F8M6bRFq4sTa7RqQXO2kWudMdpKITzjQKWf83uGy5yzK4AOgazTXIVs5k78OUZfGKuVsFwOVWyNgK2duLceB0pU1ei3RlylJoyKwVVVV2HvvvZFMJvHMM89IxzNUyZDX4qHpOTNAkRSVVZiJtcSEA3nPmV9OOaN2SZNWzrh7JE04pOuc0awrJr8mFGX/EiDv+MkJPlV/jmWD7pIsJrihAJXqEZCflKEsa/RZJi7ulTOrf4lokohZ1yGX+yhFe8wgaeLCyzOpyxr9bssae4zP6UT3VnuNTPeLUFvkjEgxlzWVGUgociYBysRx/PjxAID169d7Ih5xO15JrGXjoY7ppJNOAgC88MIL0vEMVbXTa/FQqK+UZY1lFXaS5vqGRmguAYizjW5VD/N/sqRIjgwx6p4z7mwnYcBBSfB9onKmu90ebckV39due85s8kqkeljKmUuVwUxkqYwKbFMZb1jpi2YOrskZofMfYC8c7rq82lTcWIAozbUUfLfxmGpwiGYMyS4+H+s1PqcTkWnxuuj20sidMPlyJbKw1FelnO1aoEwc9913XwDARx99JB0PVaJP1S/klXgAWnXxgAMOAAD09PR4Ih5xO145Zl4jZ7LqK2PManwnWfy13BjTAYnZRhA3UVsz1m7LiagNQXjy4DIJ0YnXqLLNHAj6hUgXf3Xfc6ZZVvrS4QAQ+hbdGt1YyhltWaPbeHgiS5U0+iQTa8aYlbz5CFQGnzCGmAuCn0zR9i8BYlmjyzHEyxqJyJkmYaWfTDLresENfGTBe7zcXoe6ukzy6ieKJ80QxOU2eP5BdMxsUxlFznYpUCaOBx98MADg8ccfd70Nryb6XkmsxZgoyIfsGl5iPF45ZkN9DMlPONA2vqetDSOrnFH1nFG5NVIZgvBkz+2ya/xyQWyDLlXWyP8nGNZ8//jBkHRJPqiVKns5BtmeM5JwpPsWo73mdYio3EpWOeMkMwXARzCuKQxBgmYtKhWBZZLl1ZblPfG6Ym7U13jSIL6AXdIqC9s1tvgDxhjDjQ+YY5pIydNCvKzRvYLP+3jpyhrNe5kqa9y1QJk4HnfccQCA1atXeyIecTteSfS9ppxxcibTJ+i1Y6biyY2Ubs9YUyhDPJEJMubaBt1WzohK0ni/kEvlzFI9qMiQmaC7dUdk5MoZT4rkS9L8FOuKaRpS4Gqny20QO35KjyFicwl7nTN3YyhuKmdc8ZKF7Fp5vA8rqflITFzsfiF3ZY3JlF3mS7WPmKxyxo+1B5SzeMJeQ04jIkMyytnGbUDcnHAoryDaP35j2tIPBt3lMfNZPWeqrFGRMwlQujXW19cDALq6ulxvQ5Wk9W9MsgssU8cjbsdrx2yoxMPXqtFBc56RKmfEibXbRN8iQ0R3F1s5kzOXoF5AmKIkjeqYWdb1Li+NnJz5iWasuVuj2xlr6jJL2Z4znshS9QvZTntuzSWMeFKaRiJQ8146P5grh81E0riGAXSqh+Wy6FI545b3VG6NvPfVTc9ZPGkQX8A+9rKwerxcjKFY3FbyyiqJlDNNQ4qf927JGbFyJluiP5BQ5EwClIljdXU1AIOcMZfNJ0O9JI3SEISyrJFCOaMm1F5z2PTaGHK7f5JmssmIGqrSmqjdmjkQuzXybI+5ZYvEZZbWItRu49FLdMwk9w8A+KnUTnMzyYS7zweI+2Fs5cwtOaMtAeNljW4JfjzKyRmxcub2Xi8oZxTnvaZpSGo8kXVBzlJi2R51Yu0R5cxaQsMdGfITk1f7OlT8vTWl22SaiiwC9qLPbh02+WQFVUyW+qrWOdu1QG2lHwwGkUwmXa915rVE1mv9QoD3yhoVoe7feGTHUMK8yKfI1jviZY2667JGrnpQ9XjxRN9tYk1upS+pnHH1RqeyQbcSa5fl3uZ+TUIjI9QpTXLGmitnRMqQZaXvMknjiSyZUmUtIOySnJlW+gHqeCTHdFLTyNam49e0VNKdIUiAuqzR/GFuTVysxaKJyKI9SSTZc0ZEPLjK7WaSKKUbi9YDdGQRgKWcuSXUliEIlXIWkHT8HEAociYB6sRRVM9k4lFljfljothHXixrpHLYHKrkTDaeZIyWnPESOZmFO22liiQkaWc7lMgQxHXPmXljprKMlkmKALskLUlUkgbIE2r+W/xUM9ZWaazkDDpRos/VE7djKGGWNQaonPaklTM+hnxUcyDQeWmsC5WhFGWNlpGHpFsjGTnjypmLeOIJUakiuneUeU85k56U4T1nVG6NlpI3+MhZoJA3hcPh2wDMBbAewPmRSCRhPj8TwP0AEgA6AJwZiUS6SxOq90BNhqqrq9Ha2oquri4MHz686M97zYDDa4k14D3lzGv7aFch+O7JmdnrQbU2jGg/7HJY+4jLibic45qccbJIZcDByZnbRNZMpqgWW7X6hTxSkgYIibUL1QMQlKoy2pI092TRJENUTnL83HA5prs6jc9V19AqZ27HkNVzBjrljI8hN2vTpRmCELs1ui0p4L1hVIm+FjQML9wYcMQStnJGZXbBVWU3YyiVoo8HEJQzl2WEvOfMR3UdCsiVWQ4k8h4Vk4A1RiKRwwGsBHC68PLySCQyNxKJfA7AYgCnliZMbyKRMAr8g8EgyfaolDOvJLJeXOdMGYLkxq5SZum658xSzugcrgDjQuxWEOb24BrRejWyiTX14q+yPWe8GZxssVVLOXM5pkUzB+LE2jUZIjcEcd+IzxizZtCplDNbfXV3zDq7jHhqamhL0txa6XN1i5Lgy5TGJpJMsNKnVc7cr3NGXdYo59YYJFbOeP9jQFY5I5oAAezScbfKmY9aOeP3xCFqCDIXwEvm4xcAHMpf4AqaiQoAq+hC8z68Ss68onqUKtH3Slmjlw1BvEaG3JrceK3sM2GSM53MfQOWDbru0h2RkwSypEh2AWH+Oar1hWTXOeM9Z1RjyExm/G7HtJlYJzQfWVmjpXq4TGR5gkdVtgeJskbGgABoS640iXXOGGPoNhfsra3zhnLGSS9laawMwU8kDadHgN7MQbas0V9GY/kpszZdPGlfL6iVMzeTRGKPIBURAmzlzM0xYzoT1FfVc1ZIWWMDgC3m43YAw8QXw+HwcQBuBRAH8AvS6DwOanJWVlYGAIjFYq4+v6uoHl4pa6RQztQxG1zxpLjqQZURAdA1wM/clRMBIF8bxiZnLj9PrJzRlTXSlqK6Vc54Yp3S6AxBmOW05+7zPCmiKiOUcWs0ZvW5cka0gyTcGlMpAOa5WVFJlVjbLq1uUBJDEOuYuTUE4ZNERAHJ9r4maUtjYf4uNwQ/reeMbJ0z92OoZMoZv7+6KGvkYzqhaSijMreyFjIffGWNhZCzNgC15uM6ADvFFyORyAsAXgiHw9cAuBgZBC0cDl8E4CIAuOyyy3DMMcdIhuwdcMVk586daGpqkt5eMmncWZubm11tr729HQDQ2dlJEk9bWxsAoLu7G4lEouhtbt26FYDxuyji2b59OwAgHo+73h5Pyjdv3oxQKCQVDydWjDHXMfX09AAAWlpaSPYRJ4o7duxwtT1+zLu6ukji4WPS7faox9C2bdsAGBMgbra3ZbNxjqY0jSQewJyxZgzxmLvfqJlKcltnG5qaOqTjSZrb6+7qdhVPoteYXIom3e3jTHT2dMEPgCVTrrbX0dqBMgAJppPE09phtFX7GHO1vdgmY/8kNR+2bNmMAMHEPl+FIR51N4Z4grdt5za0M/mAkqaK0tnWiaam4tyHY0Ii29bZBjTJJ1Y90R5UAdDjxe+fmLCAcGcvzb11R6u5Rqrubgx1bjLuG0nNh82bN0vHA9gmR4ne4u9lrW011jpeW7ZtJllGI26WEkS7e1ztIz1h7ONoohtNTfJWCF293SgDABf3oqbmMssdsbOX5t66badtJrVp06aiqm+at4asCZAY0XUaADivT8aKv1anelLmNnzYuXNH0dcNJ8TN2apol7sxVGo0NjZmfa0QcrYQwJUAHgFwLIAF/IVwOFwWiUS4zNMOGGNXRCQSeRDAg+afg4++5gBPzsePH59zJxeKyspKAMCwYcNcbY+X2bn9fCZGjBgBwLb5L3ab69atA2AoTBTxiCWAbrfHj9mECRMQCBTkh5MTPp8Puq7D7/e7iolfUBsbG0n2ES+Nra+vd7U92c9nghvbuB0Dsp/PBFelfT6fq+3t/KQT3TBsniniAQCmrQAAaAi42qafrQYADB81DI2NVdLxBMpaAAAVoQpX8YT8rQCA8ip3n8/EmoYt6ALgd3neV5cnkQCgubiGOSEwZic2Yh18jLnaXmdHJ1biUyQ1DeMbx8FPUP6p+9YCADS4uw4F2HIAwISJYxGska8E8YWMSZXK8io0No4u6rM9UQY/Mz4/fPRwjCny806oqTeKfwJa8edtT5QhwFYCAOqG01wXU90xtGCF6zHUtKoFwFowv/t7YSaYxsdQ8dehijIjCWY+DeMnjCeJJ1RuTOyVB8vcXRd14/c0jKhFY2ONdDz1w7eiF0AQuZNqJ9SuZQiw7eZ2aMaQVsbQjGUIgGHcqHFF9WcO28oQYMY5UVlbSTaGENgEAPDrxZ9n8dY4lmEFUtAwYewINDbKXxeDFcZkdbnf3RgaSOQ9mpFIZCmAreFweD6A/QA8GQ6HHzBfPi4cDr8RDodfB/AFAA+XKlAvgq9HJqvAcPB+GLc9VdRllkOtJK2UMQ21YzbUe+Bk3RqpSuQAodfDZfmOn7pfiJeCuDTgsNcXIur18JiVPu8Xki1JozQEscoaXVyGGGN2v1CI5phBwhBEL4HNN+8587lw/ku3HSc2lXE5hnq6zaoNIhMgQG6NqpRZxsaoHGMBwUrfrVujEVOonGgMhSQMQdLWOSMaQxqshcP1eHH7KK10mNJK3+/+vGdCL26IJh2yy5kHoSFIQdJBJBK5OuOpi83nnwXwLHVQgwXUibXXyJnXrPRl9w9gK2eUDpLJZNIzx2yokSGvxZOyzCXokhBmkTN3n+c9Z8FyWkMQ94345r6lMgThZg6SPWdUiaxfco0qPW4c6ITmI7sOWWPIjQ16klklaVSJo4zTnpg4UpncyDh+plL2gr3UCwi7HUPRbtpzDBAmiVyMId6LC0KyCMnlD/gkUbCCmOC7GENiqS5VT57PZ5QAgulFn2cpHfTumgB0PzfgcNFzJvTillGRM76vByE5IzyTdj0MdXI21FQY0THQK/b+XjtmfOzx3+WVeKjHkKyVPpXzH2CbELq1QbfIGbXTnttVsbm1P9EsuqwhiGW0QhUPT9JcVunzZvkU2fLBwuKvbshHwo6HolcIEBrxXYxpnZVOOXNj5pDSRac9ovuGJDnr7eVKFf0Cwm5MZfQY7TkGCJMpbisKODkjMnGxDDjcuDUmQO5EKKWcpYCQORvoLydSyyFMWrohZ1HjN8R9hMqZ7ELmAwhFziTgtcTaa/F4dVFsqkSfIibq0lhZ8lGqeGTHNHXpsKxbo044Y20727n7vM9MZKmVM9cuaeY+8hORRZ+lerj7vB6jVRnERajdLBGRKAHBZ9yMMJH7fU7gEw5JwuuilHKWolfOfCaJcaWcCSoD1bprfsmyxmhPCVQPGYdNTg4IyaK1hIZrcmbcA8uqia5DlpW+uzEU4mOI6Lro95vKGey1E93E4ydSFgFBOXMzhjg50/xKOYMiZ1LwmnLGE2uvkDOv9XdRk0WKmLxGqKnJ0FCbcLB6KyiVM8myxoBO3FthWVi73ECKeIZYopwIsJUhqvV8uJLnZ0Z/VLGI99Ivx8DHo5sxxBPrVAnImZvEWmeiykA0poOc4LvoOUsBZcQqg9W3CHcEP9bD0rZDAbs0tvjPWoSOcNIKltrpon+JMQRTvKyR5pjxckQ3fYu6Tq9UGcqZO+v6ZAoI8fyMUjmTUcyjxv6Jaz46csav+YNwnTNFzlyCMeY5cua1xJovDeCVEjkvKmde6xOkVs74vvZKPLJkWi9lz5mrBXvthTvplDPzf8myRiqDEp9kz5k1q0yVyPp5WaMONyFZyhlhfw5XzlzNWFsGJYTpgPnb3IzpdEMQ2tJY16oHT2SJVAa/X0MS7hXqWC/xmmKwlVw3YwimOq0RKYsA7N5XNwTfjCehaSivICpFlVj0OU05I5pE8/mM3wcUv8iyEQ8ni4TXIRlTmVKUNQbdE/yBhiJnLsGTO5/P5xnDC6+pDKUqSXPdL2SSRQoL/cyYvELOZI+Z19RXr004cJWBlYCcuXLaS9r9QiEyMwd+g3V5Q0vyskYiMwdzbsc9OSPugbPcGt3x10S0BI6fvCTNRSLLyxoplTyZxDql2wYcVOTDVs7cxVPG6FUPrlS6SWTjPfRkSKasETHz4lVGp8LIGIJwchbX/AgR3e55SaubPkFdIPhUZMgYQ7yssUhDkBRQVgrljJcPu5mUMY9ZjLCsUeOOwarnbNcBdVINeJecDZUyy1KQM1XWWNp4qMeQfM8Zd/6jdEkzt+3mhha3Z4jJZhslDUH4jTlAlDjKKmecZJIl+pZyxlyVNXLljNIGHRbBL35cpsykKEmqnFFZ6dP2nMkqZ2SJtU8gZy7MExKcnFGZAEHOEARx+nh4GbIrE5deW4UhS/SDcsoZJ/hUZMhwa3Rvpc/7KEmVM7/7MZ3qNV1sKZUzfu9QPWe7DnYFcua1Ejmv9S8BdH2CXtlHXovHa2SR1/ZTJtaWcpYsPhG1lDPNhyDRBKhsIz7MEpJAiKicSMIGHbDXzyGzZefkjDFXyhlfK4+U4MuYOcS81XOWbqVPq5y5ImcpuwTMR9S/5NMAXaKsMWX252iEShWTUF8R58SDnuC7Us7M/RPTfCijuXXAb7k1ujSVKYFyltDclRGWgiwCwn3RBRlK9pg9Zz4f1RKZliGIGyVvoKHImUtQz+gD3iMfXkusvUZexZjcNHUzxqzfQqXmeU2p8mo8sj1npShrdJMU8XiSmoYgkSBsCSguJxv5TDe1W6PrdcXMRMFHZAiiWYYgLpWzKP0YknHYTJgqQ4J0jSr3Nug6ExJZcsdPl2WNJVTOXPXlmaWxICxrZBI26L4EV87oEn2rDNnNwuFW/xJ9WaMb5UzXgTKr54xOOUtJKGfUajAg9NFKlOom/X4yXwBN9ZzteqAmHoD3yIdXE2uvkFdA7pjxeAKBgGfWgvOaUuU1K32deIFlQG7GmjeCpwjJmVWS5nbxV+6SRtRz5vPLkTNLOSNS8jRhnTM3ESXN8p0UldQJcTkGmXIiwn4hibLGVEqY1Scy4LBt0N2aORCXpEn2nOklMODQZZbQiNOaXQCSC4f32s5/5UQpms8yBHFbGktrwJHu1lgkOUvRG5QA4vqGxX821m1eFymXY1BW+rsevJboA94rSStlPK7WFyrBMZNRYkoZjxePmUw8XiH4qVKWNUrYoCc1H51yxomnyzJC6kWx7Z4zlxuI05oVyCpnyS4jniQlOZPoOUuWQDmTKY3VmTirT2td74bgpylnVGTRZyfWzI1VvGnAoVGWpPFrowuy6EuY5JXSEMQvYS5hKmcxnx9VFTThcOXdlXLGSu3WWHxZo0UWiUp1AZucuTlmvI+yFOTM57ZEfwChyJlLeJGceU05o1Y9NE2zFCavkDOZskZVGpsfXiOLzOxl0EnLiYz/3ShnCaFfiGyJCMmeM74OEJW1fzAk13Om8USWiCymGYK46TnrMqaVk2V0xkQyZY2lVM7cjKFkXEcADDoIF6HmJWluSuQElYGKLGpCz1mxqgdgkzNS1YOPIRfrnPGyRsoFja1+ITfHrNfuOasqpwmHu8+6WTi8NGNIc70IdTJll1nSGoK4V6oS3aaLLVnDmaC+qrLGXQeKnOWH18iH145ZKUpjhxoZ8poDKW80Jy1Js3rOik9E4112bwUVpJUza/FXmttLqFzOrdEquaJajFZSOUvx8p0QfVmjq/WFenmvB+WMtYQhiKXk0fWe+Mrcqx7JJCNXPTRNs1SPVJELCAOw1hWjNHNISSwgzJUzKuIB2JMpPheJfrTNuG9E/QEy5z97CQ1WtJNtKsUspYqSUPPSWG7qU/DndKCcK2dVdJNEjKtebnrO/r+98w6zqyr3/2efOr1m0ibJpFdIgOzQQVRAlHYp/i4gwsWLoigXVBAUK4gFG7aL4kVUBBQEFZWaAEIglA0kJCGE9DpJZibTZ07fvz/W3udM+ilrz5yQ9/M8PEzOnJm8Wfs9e6/veluv/oNPN+1XxNkhhO5NLBSvOCuWNMtCbSq2VvqS1jh09ti2nV8TF3dGlc6NdSHzjpwHmtaUtAJb6QedB2GoXM8ahUOFiTMj3UlOkzgbsEnLZ4mSTleylAed9vLr/OdFWmP+DUHcmjyd9rjRgUA+URi3i5zfl/5s6CDuPjsiedwbnUiJzjTCpLP/yKchiN+dbehB5CwfcdbX7vhQSKPA92UEda5zxexECj+qrs+ncXB40vWhPGrOSlJqP+TXdJ8GsN0mLvk8y7rVNbM1ZhT4pFvjoUc0GgUgHA5r+53FKs6KJa0RCmvo4GXkrFjskblr+6fQ1Fi73z3dK44W1m7kLO73InKW+8/atk0godYoWK4pcubcYvNpgw5gxPVGGQamNeajF1O9alOU1JnW6Hd9KPcfTZ9Y60wnKqSVfr9+n3bTGgPJ3OuVE93qekW1ddxxfq9zb0xEcr9o7oGDTjHkDhAmxygMZCJnAY32FBI56+906jp1tWpktzrBHNMI6VM+pPNABjI+lGv0NZmCUudmEdAoztwar3wOGuNOLS46m8q4jYAkcnbo4IU4K7YUsGKbcwbFm9ZYLPYUa6SqGFNj8xKwTq2HrTH1ggIagsScU32tKWnuszoPMWTHbXxAAoOQpodsuEIZFLRTpPKwyeduZHU99H2Z/+U1OLzPPSHWL/DzObF2C/FTGk/0KaQNer9+n/aHfCQwMMi9eYLbwCWmWZy5kTO3IUsuGDH9kaqkP0/hAYQS7kZf3xq5KWn+RO43xkinfjGkuiO6kbPc1site03pPJCh+NIaS8rcA4fcfzbhHBJRqtGHpCHIoYcrzooprbHYOtsV28a62GrOIhF1Byst1dROiuIT1MUWOSvUJrchCF7UC+XxsXeLqBMaowwUkJLmDseN+Xza5gv5y9QvCqeSxPNo0ZyuhynTV4ifdJs55CGG3OirrbE+Jx31yCMlLe6mWWqso8zMqMpDvPbr92mfoXwSck8jTDoNXOIaI4sAcecUJJ+0RvfAIaDJpyHjQ3YeNXBBN1pepc8et37NyEMsRrscny7xKHKW6xq5GReaI2epfCNnCTstznRGzsqcbIlEHj6UdKKLhsbukW6tqZHn/mMoEXGWJ+4mtpjSGvv6+gAoLy/XYk8xbqyLNVKVzzVzr5dOcVZs16wYBX4hPuQ2BNHVXALAdrMI8+nW6Nac6Ww/7PzT8pov1JdpYa0r0OBGB8KpJNF86mE0pzUCpNwT6zzssfsdhVmmb+OY2Vjn0fDCEUM6o8GFNJVxBX5Co0+rtuPOvSia273aTUONaUyRg0xNXT6RM7/zb/BX6LMp3cI8j4112Dk1CVXqs6e8yq0Xyn19Yl36N/oGKiMABsy7zPZn3S6/Gu9BAAmf21QmN3vsSFJF/oO+zGdVA2UVbiQvj0MZ51nm03jgkJ5JKZGzQ4diqzmzbZuenh6geMRZsTUEKbZW+q44Kysr02ZPoddMd0S4GMVZQTa5XdJ0Fr4XEDmLpVPSNJ42FhL1iGRaWOsSZ76gj4Rh4AciPTkW4tt2Oi1KZz2Mm06UT+TMPUXXOaMqff3zEWdOZEhnmqVRQDMHN5KkNXLmUwOJITPAOWt7nLTGuMbPGGSiMPlEzgIxdc2CGsVZJvqauz1h5zMW0hg5K692fCgPe9z6JZ3iLOAfOPQ5t8+9L+p8xjRG8iATOcvVnsxIGL0+XV6Rnz0AqX79kTx/yE1rzG827lAi4ixPii2tMRKJYNs24XBYWzfCYtxYF1vkrJBr1t/fDxRX5Ex3NE+XWCwp0TSshsIEvltzpjOdqKCGIL0e1MA5D+xCNtYxn58SfbfG9KiASHeOUY9ICsNWw1qDOuf5FCDODFeceZCSZuSTTuQ0vEhq3DimmznksbFO9eiPVBWS1phy0q3imiNnbk1drvVCAEEnVTdYqfE+lBb4eUTOkk7krErfGlU4kbO8ZtM5PmRojE6HggPTGvOLnOk8AAHV/RFyr4Fzo/e6a+DKK/PrZglARL848wVUCrpBfk1KhhIRZ3lSbGmNuqNmUNyt9PPZ7BdbK/1ijJzpFozFJhahMIFvOA81n8aHfkGRs7Q403hCXOJu9HM3yG07HjV8hPWdgRBzrlk0R3EWd+Yd9fiD2mrgYEAhfj4P/Kj+jWy6mUcekTNXDKU0FuITzl/gZ9II9UbO3BqvXCNnrj06O/9B/q30bdsm4KYRaoxUFRI5K3EiZ2GNPl1ZXYA4c+6LgQp96xMODmgIkuPn3uc2ktKc1pjM14c8GOcBUOkI6rzEmfNsDWiMBgf8EHXXqD+/fexQIeIsT4qtW2Nvby/gjTgrlhS5gTYVW1pjPmvkCqFiEWfxeJxkMkkgECiapjLFtkb+PuVDgRqNysPttJfHsyPhpDWiMXJW4p7G57PRd9MaNUfO3Jq6aG+O4szp2tbjC1Kq71adfnL29eaeZulu1Eo1bqzdznb51JzZjviwNR44uK3r3WYsueC2rtfaBt0YkNaYY+t6u9dty+7Vxjo3e5J9SXw2RAwfYY3R4Hybyti2nZ6ZFa7Wt0ZVtep3BfJ41qdT5DRu9MOhAeIs18iZcyCD5rRGt24xlWs02IOmRJARZ/l0jXU7WoY1RoOryqHfybpw05MPFkSc5UmxpTW64qyiokKbPW6EyRU1uaK7QQkUb1pjPvYUW0OQYkyzLDabAo4481fr86FCGoK4G1lbY9Sj1C3Ez2NjHXfsifj8hHWKs4IjZwGt4sxNRe3szrFLWl8Sw9lYl2uaAweZNNtkHi2sXXGms0FJoCz/tMa2beoal9To7bTnpjXmGjmznbTGhOYUsPSMqjxr4Pp9Aa3RYNsd2JvjNUuloDTpplnquy9WVavPWNC2sXNtLNPn1uR5U3OW6zXzKnIWdfZobt1o1vR7IxarapxoZ65plradTv2cPFmfTdXlEPE5a5Tjwd5QI+IsT4qtIYgXaY2VlZW7/O5c8SJtzxVW7vrnQrHVnBVbWqOXYjHfYlwvxVmu1ywVTxGMJUliENa4cUynNebT2a5T+XSqTJ9PlzkRnXw21r3NKpW5KxRKD/vWQcJpYx7L8fQz3p5Ja9QZyXOHPnfnKs56Mxvrcn1llITK8ztBB9IbWTTOOyqtzL8N+s7t6prVjfQmcpZrepObAqY7rTGRZ9TD3Yj3+/1U6Lst5h05iyegNOWmpGlsCFJqpKMe69bmKD4i+huUGIZByvncR3O8Zn43RVxjgxKAqHNfTOYoPFyf1m1P9TBnJmUimdNMylTMxm/bxA2DU0x9sqSmYkDkrDdHHxpiRJzlSbHVnHkROXPFWVdXV16bay9SLd1/Xz6CsdiGPhdbVKjY7IHiqjlLp8j5A4RD+oRHITVnKSdShcb0HbdLmj+PyFnfNnVo0q3xvggQd1LK3GuQ9c85kbNuv960Rrf9dFdXrgONM5FFnRvrsFNEb+fRXMJwxJlPYxt0N/qaT1pjzLnGtcP1Rs76/E4mSFduPuRGzpKa63PcaHCuYjExIHJWqe9cL925z8hx1EAsZlNip0gBfo3NHAzDSLeen/98btfMF3HSLKs0pp+TSUWN9uQoztw11Rw5izmRs1yFh+GRPeFqZU9pKklXb/Y/5844i/j8lGk8tKqpUL8TJHJ2yFBsaY2rVq0CYOTIkdrsCYfDhMNhEolEemByLngRGSokmldMG33IiNdiiApBcYqzYqo58yoKYxdQc2b3KJsMjeLMjZz582jm0L9NHVr1lugVZxFnwWNtuaVYxzs9Smt0Otv1dOWYkpaOnPm1Rs5KKvIfIOxzm9x44UN5NHOwHQFbUa83ctbjUxv1RI4C300B09nNEgZEPbpzsycdOdMs8JNO2qYvx7lrUUfsRn1+rdFyAF+Fumbt23L73LtiqKzGGzEUyzG92u/WnOlsJAXE8oycuZFFnTPFIFPjV5JK0t6d/c+59kcMv9ZGUtUV6hBj4N9xsCDiLE+KLa1x0aJFAJx44ona7AGoqqoCChNDXkTOurtz+OQ7eBFd1CGox44dq82e91rkrJjSGgdGYXQ+QPxO2/Fofx5F1K4401jrUenUDQTz8OloqxJnkVK9J9apKiXOuh3xly3xDjfaqTmt0blm3e05Rj3cTYjPT7nGjXWZm0aY6zDapI0/miQFBDSmNVa40dc8BL47aqBqWHFEznBSwHRHziLOIMBE3uJMb+QsUeKKs9zsiXY6Pq1xLp2L4fhkZ0v2NiUjSfzJFAkMSiv1bnFjzqFMNEeB72Yh6Jy7NtAed9xDtriRRZ2jBgD8jtgrTSXYtjP755kbDY74Alrv0xWlGb+M5vq5H2JEnOWJu7EeNWqUtt9ZSNSjtbUVgHHjxmmzB/REqoolclZs4wYWL14MwJFHHqndnmKrOSsmcZZv3aIbOev2632AuEXrkRzrlyCTkhbQmL5T6Zw2B/MZV+E8ABOaowzhBkecNecqzgZEzjRes5CTvrViVY4Cv9utFwpojXqU1qn19ufY+c8VBv2+ABUaG5S4M6ryEfhulKFmhL6NbEWp8gGARGduURijV71fd+TMFWfJHJs5uOKy3683Bcz99/lyrKeKOT4U1TiixiXopNpab2a/RtEd6h7RHghRWqI3kufWHUa7coycOTVnPp3jKoB4IL+okN+jyJkv6CPh9+EHtmzJ3o9ijk9H/H4CAX3XzDCMdLpuz04RZ4cEL7/8MgAnnHCCtt9ZSIqcKzx0RoWgsMiZG6nSKc50RM6KoXukbdts3LgRgClTpmizpxAx1NXVBRTPOAbbtj0RjG7qb3Nzc04/NzCtUWcnQrd1fSzHtuwAqW79rf3deqFQKsW7G3NsVuBs1FKaN7IVI9WCR3fkK6iD6My0rBum1mjtutw2ReudxgbxoJ950/XZUzVCXf9AfyKn+mB3o9/rDzBvhj57Kpw26KFUKid7bNsmGFM21Y3UeOBQBn1OelM8R3Hm26lS+mNVGpUQA+qFcoyc9TrR2ngwoDWN0A75SGLgi6dyGmrsRs7cNE2dlNSqNepvT7BmS3Z+FN2u7hE7g2G90XIG1L7mGIUJeNQQJB05y9GHQr1qjfx1mhcISDprdOW3srepz8lwiHoQfe2sKmNlaVX6MORgIStxZprm903TfME0zXtN0wwOeP1s0zRfMU1zoWmaP/XOzOLCtm127NgBQFNTk7bf62703WHJueCKJzeypIv6+nog943swI11sUTOvExrzFV8dHZ2Eo1Gqaio0CqG3BrIfLpZLlu2DIBp06Zps6cQcdbe3g4o//FrvGmPGTMGgM2bN+f0c7EOdSLb7Q8ypkGbOenOdvEcxVkqlqIklSKJwWEz9K2PP+QnHvTjx2bhotzuRW4UQGdrf4CaCWpjbLTkVvsaGzCEWmfkzJ1RFu3JTXy0bXdS9ur8lJfq21jPdR7LpfEEa7dm/3OJLrU+fb4AsydpM4fKcoOI2x0xh1P9LS1QmlQ+VFanz4eCAYNYiVqj/tbsfToVT+HripEEAsP0bmSTpW4b9NwEfl+7Nw1KSsMGfe4eJIfNvtvAxYvI2Ygx6ndWJBN86gdZijOnKVF7IKw1/Rwy4xTiOUY7Q/3q2eHTmH4OKt07BSTbY6RySCEu6VP2+Ov01gYDGANSG7Pt2Ni33alV1nnq6fDq4RP4wsRjSB2vrx/DYHBAcWaa5hyg0bKsk4B3gAsHfHsJcIJlWScCw03TNL0xs7iIRCLEYjHC4bDWE31XxOTTfMONJOmOnB1zzDEAvPrqqzn9XGdnJ6lUipKSkvS8NB24/z5XSOSCF2mN+dacbdu2DdDbwAVg2LBhALS0tOT8s0uXLgXgiCOO0GaPW5Pppifmgpv2efjhh2uzB/IXZzs2OCfuFUEmjtZnT5lTn5MeKJ0l0Z3qgdbjD3DOiXrTd2I1Sgx1b8ztXpTyYGYWwIgZ6t5Y1pGbH23bMCBypjMVtSwzIDeXrmTvrlLrE6rSuz7ldQGShkFpKsnOnTmkEznitdcfoFbjo6MkBL1+tRHtbc0+UvXLv9qUpZwBwhq7RwJEq9S9qHdz9j7dv7Efw4aOQCgzYFcTQaezXbIrt0ieG2XQfQBSVwW9bnSxI3ubYk40u0d3mAooGa3uQ8PiEV5/N7uf6XizE4BtwVLtkTM3KpRrnWCpI4bQHKkqK/fRGQiBDbGW7FO+Sx2xGNR84ABQ36h+Z3Uizvad2f1MvyPOejwQZ9XOfa0jv4lQQ0Y2d5vjgaecr58A0nl8lmVttCzL9dIYkF9hyUGGe6JfW1ur9fe6oqGQeirdkbNZs2YBsHVrDsexwEsvvQTAUUcdpdWek046CYB//OMfOf/szp3qTqFTwOYbGdq+fTugX5yNGDECIB3ZzQVX0I0erU95uJ8R9zOTC8uXLwdg9uzZ2uwBmDhxIgCrV6/O6ee6NqpNSLBB7wyvihq3pXZuPtTerG69fYEgfr9ecZasU5ui6JbcxFDc6WJWXqf3VL/p8BKSGFT1RXJqPe5uHNsDIa21DH4nPSmcStHWld3PvL7SZpGlbC+t1rs+hmEQccIEnduz31i3b1Sbor5wUHutR8yJMmxYk709f3nWpizpzszSLM5q1UFqLj695SGVMbK8rJaqcr2fMX+t2oim2mM5RV/73KY4mtvE11UZtAeUgHWjT9mQFmc626E6lI5V96Hh8QidPZBI7H+dbNtm26Pq4PPlqgbtkTM3XTuRQ7dGO2lTEompUQO1esVHZRnsdK5ZJMtrZts2lb3qgCLYoF8MlY5S9tQmomzYnt3PRJw6wT4PBH6Ns93rPMjEWTZ3v1rAzWnrBOp2f4NpmvOA4ZZlvbGX730K+BTA5z73OU477bT8rS0SVq5cCahN/pYtW7T9Xjdi1tbWlvPvdeuF3P/rwk2x7OrqyskmtyZv2rRpWtdo9uzZhEIhmpubeffdd7OOgnV0dDB//nxARXF02gS5r89VV10FQHV1tVZbXJHY3Nyc8+91BWMikdBmk23bhMNhIpEIq1evzinSvGLFCkAJPJ1r1NCgchIXL16c0++NLmjGB0RrA1rtsX0q6m1HUjn93rVvq/tFJOTX7s8JJ5rXt6mLLVuyOyW2bRvD6fR2/JE9bNnSps0eOwWbS8poivSy8OH1TH7/get/bNumpFttWM79SFzrGvUn1QY/lEry9qoWwvaBBcg9/6ikPKneN6oxqv2aRUoClEdibF6xgy1Zprk+8mAXhwOd5WH9PlTqh15Y9mYb9dMPXCPc1uVj66Y6AtgYpT6aW3JLpT8Q0cpakoDdEmXT+k34ggc+m974500APF43hktiHWzZ0qfNHiNcQ48vQEU0wcYVGwlUZydGuzd1UwLEKn1ar5mRKGVHsIQZ/Z1sXdZMf1N2IrZrgzp46wrpvS8C9JaosPTolLLlJw+0c/rcKMOq936QFd0co29dPz2hIMvLaunq3JH1/Ssb+h392be1J+t/a7wljs+GDn+QWHc7W7bkXnKwL/zUsDMQZhLdbF2+ld7hB1Yg0Y1RKiIxOv1BkuEetmzp0GYPQKLCqRlNRHnutQ7G1hz4M9O5XtnQG9bvQyFfNVDGuk3tbMnxsNFrGhsb9/m9bO4GHUCV83U1sEug0jTNMcAdwHl7+2HLsu4C7nL+mHvFexGybt06QG3y9re4uTJ+/HhADUvO5ffatp2up5o6darW+pwJEyYAKjKXi01uzdOUKVO0rhHApEmTWLFiBX/5y1/4yle+ktXPvPnmm+mv582bp82mSZNUoUZra2vWv7OlpSUt8MeMGaN1fdy0xtbWVoYNG5bTqAc3+jp9+nStNtXV1dHc3ExpaWlOv7ezU6WnzJw5U6s9J598MtVGNSNWj2Tk8JH4gwf+vLQ804rPaVNeObGKxkZ9Eer4BB9vsxkjZjN69Oiso3IbI0pMx0pD2j9jFY29QAvvLt//A2Qg8Y44fns5EcPHUXPqaWzUG2lIjW+Dd3pZ/UqI9116YJtW/3gtvpRNjy/AycfV0NioL9Oha1gPbewkZKfY1t2Q1b91a3uKaXElqI88eRiNjRpzY4FoTTN09BHqCtPYOOKA7+/ottm8rJ3Dga7Kcu0+FKrZBq3Q11mWlT3be2zq40rElY0u0W7PhHEpdgbCNCSi1FNPWeP+a6Ft2+atZhW9X1FazdjRAa0+NGJYitZgmIpoglq7jqos7yn+TtVIqnRUBY2Ne5yV583EcTZLgyrjoqQ3+3v18nWbSQC9NRU0NurrXg3Qe0Qva1nPCOdz86Xf1PDkGzD/J3sX1m3r1fa0pbyclGEwtnG41vvQmFmd8G+w21JZr8/2JWpNN4fLmdWg9744vD5FmxM5q0hUZGXTxvnqwGFpeS0Xjta7PgC9E/vZSbsSZ8uqufHymgM+05avUXvqjoYqrT4NMGlsijENMKy+Vvvv9pJs0hpfAk51vv4Q8KL7DdM0K4E/AVdZlpV7HtVBipseV1en90K76Xa5pjW+88472LZNeXm5VmEGKrIDuXdHdKMwbpqdTtwmJW5NUja44uyss85KC04duKMUckkjXLNmTfrrfBp37I9wOMysWbNIJpNYlpXTz3rl1+7va2vLLZKyaZN6iOjepI0cOZJ7p93PZ4zP0vlGdpHmrX/JnOKXT9Q4XAgoc07MA8kkvTkc7HVvUBuWfs1d5ABKRzh1A8k4qzZld6a24mvqwKErEGK43oxvAOomqX/nzvXZ1Qy9/SP1wN8WKmWE5meyv1Q9OktSSRa8nt36bG21mdXnNLlp0utDAAknFTW2Jbv1eXdplNPaVbr6MvSmwwOEa1VOWdvmA0cV27tt/vi0zdiYOmQsGaXfpy88xaAlqH5vfxZ1Z/GOOHbcJhoMEPf5qdJXqgxAdTm0OfZEtmZ3zZJ9SXxbVSQiPFrvGo0dTnp9OtdndyPqXddHYlkHEcPH+lH1Wu0BKGlU9lRHovhsdTi24HX2mQaabgYSUoJFd5bc1Dkq88PXnn3dYuu/1XNvcXkdfs390StKoT2o/pHZpjVueLYDgKVltYQ0p30ChEeotW9IRXnyVbj/6f2/P9YWI7G+l6jho7Whav9vzoPvfMrHpod9XP5hvSLUaw7oKpZlLQa2m6b5AjALeNg0zV87374OmAD8wjTN50zTfJ9XhnpFMpnkrrvuyqmhww9+8ANAb20O5C/OZs6cCWS6EeokH3GWSqV45ZVXABg+fLh2m77+9a8DuW32169fD8DZZ5+t1RZXnLliNBsG1jrlUmuQLW5fHrdmKxueeOKJdF1YTU2NVntcH3jsscey/plLL72UF154AYDJkydrtQdgce14AFb+tTWr90ea1cP4q01HMaJe7xPW3eiXppI056Bf21apDVSyQePALIeps9UDvzYR5Q9P2nz51ym2te3bV+2Uzeb7VTrKplA5IzwQZyMmqYd+39YDb0LspJ0etPr1pqOYoPdAn2CdW/QeY/227H6mZvVO6hIxjBIfFVM07/QB2/GD1PbsNo6b/radADZ9Pj83f0f/Basco65XTxZC6GO32PzkQfjENjU/tESz8ACYOAp2hNQa9W86sPiIOXUwbqOLKs16uqbSSEc9shVnr/9xB/5IgndLqihv1LtGR0zJCL6WVdnZ0zxf3T9fqWwAzQ1KAPwlfsIjQvixqY9nPveb93EW6t6n3Tos3TVnjRMCRA0foXgy66YgbQvVoefiinrt4qyyjLQPRbdld822vK18f0u4HC+6y5c44uz40erzc93PbTp79v3saH+tA4CVpdWESmS6l0tWK2FZ1g2WZZ1kWdbHLMuKWZZ1lfP6rZZlNVqWdYrz37+9NVc/n/nMZ7jqqqu44YYbsnp/R0cHCxcuBIoncuYl+cw5u/POO3nnnXcA/QIWMpGzXMSZ25lPdxRmxgw1HGjx4sVZNwUZGGW7+eabtdoDmdTGjo6OrN7f39/PWWedBSghpDv66gri119/Peufue+++9Jf6x6sbhgGkSlqM7r+5ewOHfpa1Ol/lz+oPQoTcjpmVSXjvLXmAG926OlO0vWc2hj5xuiPwlQ5G7/hsQjf/gN87z4YdZ7Nd+7d+0O2d02mruA3o6ZqjzIANM10UnR3Rmnvtkkm9/3Aj7bH8AE9vgCdgRBNmgP4JU7Re30iyoZt8NybNm+t2bc9fRGb0ZvU/WrkhaMJapxL51I2VgmPRHN2UY/uLWrztG52I2efrH9TNHa2sieyoZ/+6L7XpqvX5vFXIJxKMiyhNpiNF+l/bowZDjvSkbMDr1Gz01iiOak+n/XVeu2ZNBpac4yc/eUnyocWVo+gvkZvJMAwDEZNdxoBZWnPcwuUYNoSLseDfiAAlI5T97cXvhbhg3PVa6+u2PN9ka0R1v9GpXxuCamf0R05Gz3MoC3oCOrmA6+Rbdv0bVD3xvXhCnyaP2Y1FcaA6Gt2kbNwj3pfWyBM0oMWfmEn62IEUWY0QWsnvLhUrcXeDqPdZ8e6kgpGHjxZh55zyMvU885TpXIDN4P7wx0cDLm3Tz8QAwcsZ7vRH+jst99+u1Z7QIkzn89Hd3d31i3+P/e5z6W/1t0GHTLirLU1u6hHf38/Tz2lGo66bdR1MWXKFJqamti5cydvvLFHP5y94kYhv/a1r2kdQO2Sa4fElStXpn35t7/9rXZ7XB/IVky78/FAfT59up9owGHHqAdaPIvTxjU/XUv/CnXNuvxBhtfotSXUkOlu9eKy7CKpz/xiB2NjvWwNlbKpSePQNYfqOVUQNJgc6aIykWnRfPNvbKKxPW3sdwa/riitpmp6hdZuli71zqyzuliUujNtjviEvdfubXbSZtWD6gCkKxDk/JPR3s2y1IkwNCQiNLfB+6+1Oelz+752v3scxsSUX4/8oP70L4DGWU4kJYtZcB3dNtYr6prVTtUv7gEaD1e/t76/nzX7qfF3I4+jY30EbZvm8nKGvU//Go2ohbawMyJiw/7XqG3hTlZ9T52UPF+tOuo21Oi1Z0YTtKYjZ9ltrKucvOf14Qrq9GeiMmq6EtT2juye9ZHtmW6oHmQ1AlA2XtlU1tHP+45Qn+OHn9/zs7bytlVENkcom1TGghoVKtfdmX1kXSZStT2L6GKiK0GqP0U04KffH9AeOZvcCNvdA4csosG2bePvyAzpHu/B6K/wSEfgb49yhprExP/+zeYD19n43mfzydtTdPdlrp8rcluDJVrnhx7sHPLi7LTTTsMwDFpaWg44/DmVSu1SL/SFL3xBqy3hcJimpibi8Xi62+H+aG1t3WUm1fXXX6/VHlBzvGbMmIFt21nXeLni4JJLLiEY1H9C7IqzlpaWrETsww8/nP5aZ70ZqNPGD33oQ3v8PfvDFWe6xx64uOufbeTMFXEnn3xyelSBTnIV025L/9raWh588EHt9gDUT1QPkNLu/W+KItuirLxlVfrPXYGQ9hP0UF0QfAaVyQRPvnhgf27vtvnnI2qj/1LlcBoa9N/GA5UBqqZX4AOOrt91E7J07Z7v37gmE1l8+kfe5PaXObV+kyLdBFNJlq2Di75l75FuueXBrWz62tuAmm/252/qt8dNuxtlZ/ynqxdaO/Yu0J55w6baEbnh4d6EGCYfqTax5d0H3qT9bSHUJNQ1Kx+pv301QFmTsmdkvH+/M4bWO+Wc7vpUNnqzPn6/QWCUsmnn6v2v0fbHlbj/R91Y/lU3FoB6zeUw08ZBW0j5UV+WdYI1zhq1B0LU6S/P4YKzg/T7/ISiCb77qxhvvrv/w6LWdZmBz2P1VzAAUDZefe771vVz2Ycg4IcH5sNdj+5q286X1HPs3Ytm0xZT+Xq6I2fVFZk6weefObCgjjSr93Q7YcWQ5jTCqWNhR1D5dN+m/gOWSbQ+00ogmqTdH+Ibnw1SVqL/3hgeEYKASgs+/Qj1PPvXInjO6cn2f/+EK7+fsTPanInkjWk4uOrCvOSQF2eBQIDhw4dj2/YB64Y++MEPcv755wNw5ZVXpuuNdGEYBh/+8IcBshJnd9xxB2+99RagOj16cVoNcPTRRwPw2muvHfC9tm2nxYcXURhQ8+DGjRtHLBbLqo7JrV268sor02maOrnkkksA+POf/5zV+920WK/EmVszlk3krK+vj4suugjQP7fPJdc0VFecNTU1aR1gvotN48LEDB/lkRg9q/ddq9n+amYNdwZCRH1+JmnOuDJ8BiFn3kzX5ugBH7Cnft4m1ak2aZ2BEF++1JvPvSsi7r8mzvI/ZP6OPz61q329a3rZ/oXFAFSPCjJqmDf2hOpC2M7sqzvXLMJn2zz8bzj/q7vas/XxzAD25lCZ1vldaVuGh8AH5ZEYgQEHRG+v3/O90ZjN0xZUJ9U1C9V7I4bGTAuRxKA2HqOva/9ZHU+/mGCm05zEqPVGDJWOKSVlqAHCHe37PnRY54izcw9T6zN5pjfrA1AzxUm1XLf/9t4dr6tOsS9XZo7ydUdfy0oMwk567I4sa7xqHXHW4ZE4mzrOR1uJEh//d3cf53zZJhbf+/1o8b3bObpbHbgFGku54WKPDmWcyFnf+j6aRhp85Fj1+j2PZ+yKtcfo39iPr9THZ/6amWMa0Juhj2EYjJmp1qdnw4EPQXreUacSm1NOmrjmR+ykRogEAvT4A6T6U8Ta9t18x7ZtVt6m6t0fGdbElLHeXC9fwEdohDqUP3FEhEtO3fM9f38R+qM2dtJm2yvqs9YSLOG4wzwx6aDkkBdnkBkE7HaH2xt9fX0899xz6T+PHTvWE1sOO0x559tvv33A97qtxoGcWqbnyrx58wAlzrZv377PaNVrr73GCSecQCKRoKKiwjObDMPg05/+NJCdINqwYQOQSWHVjRu9dEXF/njkkUfSzVK8jpxlI85uu+22dA2c1+KspaUlq1Rgt7OmF81kXIbVGTznpCu9+P5FxDv3/lDrWqK6OS6qbODG8SZv3m1QEtb/UKuaoTYU47q79htl6I/avPEuVDlRjwvODnly+gmOAAGiO6LMHG/wyLfV37Nmt3n0y27IFIDUVHl78jn5SnXfHRXr50cnqUL7196BVCqzUdv2qrpmfxg+ibtGTvPEDl/Aly58f+V7MUarMk8W7aUHz6srINaVYHTMmY02zIMWaUAg5KfduedueHvfm/14wmbqfW9Rk4zTGwpy9mUe7PIBX8hHf0UJPqBzHx02+6M21/1cXbuJfvWekuHeibNhU0uJGwa+1gix9the32PbNj0r1YdwfYn6XOquM3UJjFCb9lQWqahty3qoTaphxp2BUHq4rm5STg3syHg/m1vg6b00/bVtm83fUHuUZ6tH8thDlVSWeSTOJqjIWfeKHmzb5ndfUX/Py8uh6owUv/q7Tc+76oAtML6C1IBDai8OrBtnZVfbmexLsvLWdwF4tVLdIHSLs9KwwbgRA2op95Pa2L2sm64lXXSHQ/yzbiwjPUpDBQiOVj7UvznCfV/3EV1gEF1gEH/GYFQ9RGPQ3g1b/9qM3dxPc7CUEz9azTEzJXLmIuIMmDtXVZnuLy1t7dpdc3l0NylwmTVrFpBdm/iBNrlixQtccXbvvfcycuTIdLfE3Tn66KNZtGgR4N1G38VNJdxfnVcsFuOiiy7iySefBDIiQTeVlZX4fD56enr2mxq7dOlSLrjggvSf3RpD3bjz8pYtW3bAKMwDDzyQ/lp3l0aXsrIyJk2aRDwe54knntjve//xj3/wqU99Csj4nRcMq4b5NSoEluxL8vbN7+z1fTudVvsLakazNVzuWcFy9VEqV3Jyfzd1Z9rc++Ter9trK1TjhFM7VbihfIR3G1k3cua2aHY3g90Dgg6peGqX6GJNo3f2AEz72lTGf6YJgJO3baG2EhJJVXQOkIql8LX2kwKeHjeex+/y7tDKTW0c74tw+6fVpmLR8j2vm/VWkv9do+6Lht8gWO2NOAPoqVD/3rWL971Je3NBL/O6Won5fJz2j6Mor/EmOg0QqVMb2YVP9e31XrTYyRhujPYy7DFVMlAyRn/3UZcRDX5Wlygx+vQHXqWlbc+Dxuj2GImuBN3+AB1+5c/v3ufNpvGrVwfp8/kJxZPEdu5dLAK8/U6cF/9DqaTXKofxnc/4maR5PpXL7OPU7/3YbCUYz7rRpr17t2j56l58nTHa/SE6rjqMijLvtpIVk8vBUFGoFV9bSW2lwclz1Pe6++CG/7XpeVeJ6fjITCei397kzfqMnOH4547Ifp+vWx9ppm99P+vDFTxRq2rdKz0o7xxek0lt3J84a3O6IloldcR8fk866rqERgd3sScUNAgFDQIBI51q2h+Fdb9cD8CDDeOZNE5zmPMgR8QZmYjK/gSRO3jaxavI2bx58wgEArz55psHbF/vRtduvfVWPvvZz3piD8Ds2bN3+fNtt912wBlaXV3ZzY/KF7eRxpo1a/YZyXvxxRd3iax5Jc58Pl86Cra/Oq/dheTAxhc6mT59OvX19WzdujXdpXJv7NixYxe/Li31ZlNkGAann346sOchx+5cfPHF6a/POOMMT+wBKCuBDfU16T9veWBruqvWQHYuVn68ukRd31pvgp2UO6fDw+PqYXbZbXvvRriuGa5uzkSqasd7Jz4qpqqNTuebSvlUOO7RM+D537euj1R/ihTwSH1TWjh5Se28GgC2/X07s8PqxNwdQdC7tg+frTYrz/zSz3GHeXcSWzlLOUP7ax3MVrPo95rWuOH5jvQQ3Sk3TcbweWeTuyl66MH+XaKJA1m5QF3P5kkN1Jg1ntkCmTEP65f088en9vx+S4f6/0Ut6yBhU3tsDWM/rrdp00BG1cOvRqloqrGxl29/cc86WHejvzlUDk7kparcm2s2daxBs9NZ0O1a19tvs3H7rtfupmvaCbRH2RIqY8UFh/GlS7zzobKxavc8JpZJ9/7qb3a1Z/kCtTdZWVbNKUd6u40M1YcYf5W6r6z/9QbinXEe/a7BX24x8PvV/ahlqbK1rVrdsy45Fa74iDdrdPhxyqeru/t5cem+39f+SgcAT9Y2EvUp4eFFJO+6jxpsCqt/99aHm/cpGN1r5h5OeBUNBgi5kbONe0aE3a6efW1xut7qJm4YPFM9Wjo17oaIM2Dq1KkAzJ8/n1hs76dXA+vRZs6c6dmpfnl5OVOmTCGVSqVnc+2N3t5eNmzYQCAQ4MYbb/Sk8YZLKBTi8ssvp6QkM1fliiuu2OU9u98QvGihP5DKykpGjBhBNBrdQzi77C6G3BbzXuBGnfYnznYfVO1GSXVjGAZNTephtm3bvocwbd2ayU9ramrSPgNuINk2KRkYTTzmmGM8s8cwDCaMNrh24tHp15476gU6F2dShbvf6cHoihMzfLQESwgFIRzy5oFfOk498IfHMw+zhXt58LesjfCBDhU16zxjPCf+h0dqEag/QT0tW59ppXNxZ/rUd6A4275MbYrerKjn2aOmcvhR3olFlxFnDqf2OOVPH1++nEAqlRZnbYtUFG9VaRWT9U7N2IPaY2oA6FzSxdSx4POplM/du1l2vK3WqOys0Uz+wkRPbZo5V/nn2LUt+5xLt8Oxp3KyN10aB1I7Rf0do2L9XHabzcqNu9r02Mvqz2ZAHYLM+PZ0glXeRfIaG2B1aTWP1Kv7486XO5h7ZYov/CLFrMtS/PShTIqcu+H1klH1mbbvix9Rabqzr7Bp+qjN+ubMWlVuUvelVyobmDPH2+h0qdNOP/D0Fg7vVTYt2C1BZb2l1sgeW84lp3lqDgAzb5uusgtS0PlmF9UVBhecYjB7glqj9hXKnjtfV2vpRT2eS7nT6GZYPMLfnt13mn7XUuXTq0qVMY/d7s2zY+40eKpmNAnDYPs/d9D+csde39f5jlqjjSXlnDRbpUR6hXtI5KYHD8SNnPU467O2pJKEz+fJ+JWDGRFnZNLAYN8t9d16ohtuuIHly5d70ljCZcQINZRnfw1K3nrrLWzbZtasWZ4KM5fbbruNvr4+ysrUzW/ZsmV873vfS39/YP3bGWecwR133OG5TSeeeCIAf/3rX/f6/d3FmTtQ2wtcf9hfnZd7Pa+77joWLly4R0RSJw0NqpB9f3VwrnA79dRTWb9+Pccdd5xn9rjidaCf7I3+frXzf+211zz368mNaqP252Hj068t/UKm1vPls18FIGSnuON/fKy417uHWelYtSGaWxPhBid4+MQre26u/35Xh5rfNXMYF983zZMxAxmbShl1wUhSMZtXzrcoTamU3YFpjVsXqwd+/7Ay1vzJR9CD5hu74wv4mPPLw/CFDEa1dvLd9RYty3qxbZv7f6o+f5uH11Be6q0tpc5csWhzlHDIYEwDpFKwacAZTFunTekOtUZjTI+KhAZQMU59Zo7rbuHWK/Z+ih7dqD5jY+Z4L86OOtnt2Kic5neP22xttXn1bZvDL0/x60chmEpSsbMPfFA53ds1Ov4wuOxD0D9Vifuzdm5i07J+fvVAglGLt/K1n8XSQ3E3hJUtV/+Hd/YEAwb9s1RGR/f/raZndS9rnTOzF1SvLxIJm6ao2uRuqqjk4r00WNBJ+bxyRp6t9iAf71AHnys3wlOvZnypb7WyZ9Sccs8ake1O3fHqmr1zy7sk+5Ms+dxSbn7y30zs76Z/qXqurHeu2SlHeGeTv8RPqiasBriv2nszKTtl0+N8b2O4nO4nDD58rDc2jaqHreFyHmtQpTbbH9v7vrFku7LnSzdW8PwvvN36VxytUlF3PNmyy4EnZMRZp3NItC6sDhjP8m77cVAi4gwVGTr33HOBTCe93XHbgLubXi9xxdnukZaBuMLjqKOO8tweF8Mw6Ozs5MorrwTYRZy5tk6YMIHHH388ncbmJZdddhmgBPPRRx+9hxhzhx5PmzaNa6+91tOHiBsZ2l+7+KVLVShkzpw5nHDCCZ7ZApko4f7EWXOzisDo7jq6N1xhvL/IWSQSoauri0AgkK4D9ZKPvl/5w58bMtGMriVdrPvVetoW7iS+UzXdiBsG/3ESTBztnf+UjC4BH0S2RTjSOQ1etVtG6rqtNg1O2mNwvPcba4CZ35lBoMJPojNB/5PKX9zIWSRq89B9atOdGj24x55lTWXM/l81P29mfyf1X3qRld9dzahtSpzNOrPGcxvcQdRuTZ476HrDgL3R75+AsVG1CanyWHgAVH+wig4n/3Ty4k28vnLX7/dFbEp3qms27RjvfajCSdc9vkFFhL93HzSeb3PMp22WOQkPY6O9+Gyb8onl+Eu9rTsJBQ1+f7OPPz46nJr3D6MymeB3qxbyl3ee5QtblvPFjctoflLdw98ZMYz1Dxr84vPeio9rfjmapWU1BFI2b9+dGQjnZr9290NjVF2ze35Z7ul9CNRzftYPZgAwO9bJ1eeo+9HfF6r/L19n07NK2TNu7uB97iddO4HSsSV0LeniyTHz2fLAVkojca7buhx/d5wdwRK2hsqwfqOial4SOFI97+c9sQx7L+nDka0RUpEU7f4Q02cGqfCoWQqoOraaClVLBiqSvzux9hil/TEiho+xM0r2+L5uQqNCjP34GOyknR4M7uKKs5ZlzoFDuJzOxw1PuuoezIg4c7jpppsA1f5992HL8XicBQsWAIMjztwudR/72MfSnf12xxUeRx55pOf2DCQQCHDXXXdRVlZGZ2dnerN9//33A94Mnd4XZ511FmeddRagIi0nn3wyO3eqNIx169axcuVKysvLWbJkieeRPLfj55Yte5+2un79ep566ikMw0iPJvAS10/3JxZXrlQ7t4GRY6/IJnL24osvAsr/B+M09uJTDe6+0WDUaD+/GTE1/fqKm1fyyrmZsRE3jTcZN8JbW3xBH8HhQUjBhIC6/zz8b3hgfmbQ8sKl0OCkPc6Y6/0DFiA8LMTM76mN2tqbV/D35fO5cO277FjQwjOv28zq6wAgNEhicSCjzxvF5qsz97+1P1rLiHiEPn+AG2/2LkruEna6NUa3RbBTNk3OQNeBA5effNVmnBP1qJjmvTgL1Abovf04UsDkSBcnXpngyu+n0hG0t9erFEOAumneb6zLJym/8K3p4vJRO/f6ntOHq/WpnOn9+rgYPoPZ35mGEdz1PjOvpxW647QFQnzw7DKaRhqe34smN/lZd4xKs9zxyp73x65um0ZngHnDrMFZo3BDmNJxpST7kpyaUs+Qux9JMvXCOP/7SIpG58Dhg+cMnjgL1YWYfMPkPV6fFFG1VIvL6zjzeIO507x/doy6YSq9vgAj2nuYf8+efu1GzbaEy/jyx7wX08cfBqud9Mn21zrSjTh6VvaQ6E3w7N+cOspwOZPGDI4IGv8pFclrW7jr+rg1Z81O1sWEY8s9q+k8mBFx5jBQ5HzjG99g06ZN6Qfavffey5IlSwBVb+Y1bjt9UClnuzfX6Orq4i9/+QvAoEQYdscwjHS3ytraWl544QV+9atfAd4Mwt4XPp+PRx99NB1B6+3tTXeLdFMdzzzzTE/HDLi40Sd3ptruPP744wBccMEFg+JDjY2q4Oaxxx7jG9/4xh7NZXp6evj+978PDE701Y2c7S/t89RTVb6OV8129sYnzjRY96CPPy8Zzx+OPWKP76eAC66sGhSx6Obpx257i4awSiG85BabPz2jvr+uWbW3Bqie6F1Hu90ZfcEoqo9UD/4ANhe0bsD6f2/Q9+cNjI310uUPMvOMmkGzZyB1H2zgnJkf3OW11VXVGJpnUu2NQHmA8IgQqZhN3/o+jp6h/s4/PmWnnx0dzXHqEjGMEh+lYwZHUH/yP4P0NVYStG2u37yMu/8FzzpJBW+/1EtVMk4sFEiPSvCSUF0onY72X2tWsPVhNXzZ5cuXwv/MdWrgBlGcAVRMreC4x45hyk2TGH3hrtkDy8pqueLMwdseuZ8ve3kHw5wDmD5nxnH72n5CdorOcNjTerzdGX+VulClP1/KvO4W7nn3BW5auIiF97VTYqeI1ZZQ6tHMvn0x5pLRHHnPHOY9eBRz7931YPrZ6lFMG6RHx+TZJSyoUT7z/IMde3z/7YXKp1sryjjnRO/t+fS5Bp2BEM9Wj8SO2Sz74tu8+73VPH/8i7z0oVd4+nfqubumpJIRdYMjhCqmVuAv9xPZHCG6IzOw242cNfarNZp+wuB+7g8WRJw5hMNhJkyYAMDtt9/OuHHj0htqtyvi3LlzByXq8YlPfIIbbrgBUJvov/3tb7t0JHzjjTfo7u5mwoQJHHvssZ7bszcGjhI4+eST2b59O6FQKF0HNlgYhsHvf//7dGe/s846i3g8zrPPPguQTlf1Glec/eEPf+DHP/7xHt9fvVoNfzRNc1DscWevPfPMM9xyyy3cddddPProo+n0U3eWGKjr5zVudG7BggWYpsm1117Lxo2ZdIeBnStvueUWz+3ZnVDQ4Gd/HM7F0963y+uLy+v4+n8PTovfUqdFc89bXTwydU369Z8/rDb7O9ptJkScKMMgRGFcfCEfR949h4bTdm2oU/ZHNcNnaXkt/+/Dg7dpHMhZx0F1tY8EmQ1H5NTBE/dVc9Shw7/nLeTDZTupLFO1Qg89C3c9mpmXVTKp3NMujbtzxI9VtPP47h3c/85zRE5/ir+OfYZF31WfuZ7ZDYNWKzTnVyqbom9dHxVtfSz/vcGq+w3s531851M+upapg6PKmd41t9kXNUdVM+WGyRzx69nsmJTp5nv3yKmDEoFxmT2vlBeqRuBPpPifrW8zo6+Dqh+9SfPft7HpVeVDbV70Yd8P4z/ZROm4UlJ9Sb65cTHVyTgj4/1c06z2Q/bswW+vZxgGo84ZScMHGxjxkeGM/h/VJvU7Y2bzVkUdJ84enGtWXmpwzn+pz/6YVduxkzaJngRbHtxKvDPO60+oA/WxcysGpQ737BMMzj4e7ndS9FsWtLL6B+oZ0rOih+PeWQ/A4ed7ONxsNwy/kT6YWX/XRmzbZtkNb3P8opU0xPqpTcbo9QX4j/MH59DqYEPE2QDefffdXf7szmRyU9Wuu+66QbEjEAhw++2385Of/ASAyy+/HL/fz7XXXquGPzrt0Y855hhPGwLsj1/84hd7vNbY2DhoD/zdGdhl84477khHsKZN82YI7e6cfvrpaaH8xS9+kU9/+tO8/vrrrFixglQqlR6E7XZR9Jp58+btMuT6+uuv59xzz+Xzn/88kKk3O++886ir8/4hO3Xq1HTq5+uvv87PfvYzmpqa0g143M/Y+PHjB6VecW8MrzV44EdhHj5NRRL/UTeW6E1HEAoOjk9XnZK5XpFntvF7Z9jqqyvA9z6b+x+MMjwewQ75KJs0uBu1sqYy5v1pLvNrRhHf7TN+1Cnl+AZReAykJGzQ+g8Dfnk8P2ycxc/ffyzfuNO74eW7U39S5rPTed9GPuvMuf/Pb9pc9UObo3tUStiwed6nWQ5k6qm1TPmKSgGrTqrayXBfnLN3blKvedxCfyCljaWMOl999rc+3IzfbzA2FKPlmVYiWyPsfHEnht+gdhBt2hvH3Hck/zXlRM6f8QHGzhzcDeOZx8H9o5XQmNvTxg/XvUbl0hbe/MQSXvmDqht+K1QzqDYZfoOpN03eY5foDlOvOHbwNvr74vCvTuLMWafxYvUIykrg1ME5+wTg6E8MpzUQZmxnN29euYTnjnqeJZ9Zygsff4vKtSpS9eFPeDvvdSDnv89ga7icl6v3vP+VOvNXT7lscAX1lOuVT6/5yVqenfM8G3+7ielvbuST29Reu3tiDcNrRYbsDVmVAQQCAe6+++70n3/+859z5JFH8tBDDwGZVLHB4uKLL95l4/yzn/2MCy+8kI9//ONDYs9ApkyZwubNm5kzZ076tTFjvJtPcyBuv/329Ndf+tKX0rVNg2XT8OHDeemll5g+fToAv/71rzFNk5kzZ/LDH/6QVavUtNXBEmfV1dU888wzfOUrX9nl9fvvv59rr72WW2+9FfB+5IGLYRg88MAD3Hjjjbu8fumll3L11Venx1kMpU8DfOQ4g7v/1MBH2j7EnatmcuvV3ndCdak8poJ5D6k05diOGMM+9hT/2bKWgBM1N52Nvv/IOnyBobl1p649jP+c/v7Mn4GTPuV9He7+MAyDcy6q4O8vN/LIfYOTguoy5j8zn5+2l3Zy5ekpKvxJrty2kml9HRzWqzZpI8/yuGhxL0z+wkRG/Npkc2hPIX/Uhwc3StX03yrTYs2P17LwlJdYMOM5Xvvo6zxnvoCdsKk9piZdwzdUzJjiZ+XTZdxydYD7vza4hw01lQbjjiznn7V7Pq+O36QOrsqPHryNvkvjf47mtDUf4IztpzHr9hnp1yM+P0d9zLvRNNniH5C+PKoeKj1svLE79SMDPDJWiY9tj24n1qYOQSIvttIY66fP52fiyR729N+Niz6g/n9b4+FcM/EYPn7MqfxueKZGb0H1KMZMHdzPWI1ZQ/URag0iWzK9HE7oVhk89ScNvcAvVoYmF6WIueKKKxg/fjyXXnopzc3N6cHUZWVlg9rsAlTXxmXLlrFy5Uqef/55brvtNh555JH09ydO9HZmzoFobGzkt7/9bbruzctW7Adi7ty5tLW1cfbZZ/PSSy+lXx+MBi4uhmHwpz/9ieuvv5758+enX3cFSVlZ2S5i1mtM08Q0TVpbW7nrrrvSr//sZz9Lfz0YnRpdTjnlFE455RT6+vr4+c9/nn79zjvvTH99/PHHD5o9xUjDB4Yx+YZJ6ZSUy3as4bIda1hU2UC3XwnF0SfVDJl937vK4OQ5AUaE38eUqjgVk8vxhYrjjG8wN2YuofoQH95xOs/Ne4H+Df2sNBfgjr0/ry2Ttltz5OBGzkDdj+ZeWE/g8OMJvriN9TcsU7YcW8PoE2oG1Za6Y2sZec4Itj26na6lmfrXVFQdPFTNHrxN7P6orTT40iVD83d/7yqDE9+awf3DJzG5v4tbNmZSz5PAZ78y+OIMIFil7jtjLxtD55IudqyJMv3W6dQ3Dm692YGI7n1ErWcYhkHv0SNZu3UjTdEe2oIlu8ypfKu8jgsH8RCtJGxw5Vk2//dPH2tLq6AH/lrfxKZwOQnD4PWKYfzIw9lm+2L6t6bx5ieXUDm9gr51ffRvyqzR6DOGXuAXK8a+pol7xKD+ZYXw8ssv8/GPf5wNGzYwZcoUfvrTn6YbFgwV//rXvzjvvPOIx+OUlZXR3Nzs6by1gWzZsmWvUY3e3l7Gjh1LIpFg27Zt6TloQ0lzczPXXHMNEyZM4Ac/+MGg/J27r8/3v//9dAdQl8svv5zf/e53g2LPQPr6+rjnnntYtGgR9913H+Xl5fT29lJfX88zzzzj6by1fdHb28uWLVu477770jVmV199NT/+8Y8HpYFLMeL6UDKa4snRT+/zfUfeM4dR54wcRMuEA9H24k5eOee1vX6vcmYFJ73g7egMl33dp23bZssDWzECBqMvHDWo9W8uXcu7eeuaZXTtpdX3sf86mrpjh0Z8FBO9/TY+H5SdZnNq+xY+v1XVd/1i1HQeeX0cJYOwud6XDxUrxslK4I8dDhv/MrgHRd/8bYpb77EpTSXo9Qf5+sY3OaZbZTg8dOkJ3PPTwW12Yds2/3gRXntHbbW3t8Nzb8Lp8+An1xiDUv8Ge/qQbdsYhkGyL8nzH1hE/6petnz2SK781uDVvhYp+/zHizg7yNiwYQMtLS2D1ljCZX837E2bNhEOh9MjAA5Fdl+fWCzGzTffzLZt2/j73//OyJEjmT9//i6NVARFe3s7ra2tTJkyZahNGVIG+lC8K8HbN66g5blWYjt2bL2WegAADsNJREFUPRI++aUTBqUtu5AbW//azI4nWtj26DZSscyjbvo3pzLxmgmDYsPBsrFO9iV5cqzKLhBhtieu4ChLxvHZcNicIIvuHBzhcbD4kMufF9h84vs2f7vN4LR5g7vRj8VtXlwK53zZpqcfDGy+fE6cj54VZEqTj/LSQ1N4HGw+NISIOBMKQz5s+2d/6xONRgkEAvj9g9P1Tzg42ZcPdbzewUunq3mHNXOrOf6poenQKmRP86PbePOKJdQeXcPRf5uHPywb692JNEcwgj7Cw4orPa4Y+OvzNnf+zeYrHzd4ZyN89BSorx6aqMfBQDJp71J/NthsbbV5eTmcdzKHeiQIODh9aIgQcSYUhnzY9o+sj1Ao+/OhnYvaaX22lfGfaSJUK5tZYe/IfUgoFPEhoVDEh7Jmn+JMGoIIgiAUOXXH1VJ3nKR+CYIgCMJ7neJosyUIgiAIgiAIgnCII+JMEARBEARBEAShCBBxJgiCIAiCIAiCUASIOBMEQRAEQRAEQSgCRJwJgiAIgiAIgiAUASLOBEEQBEEQBEEQigARZ4IgCIIgCIIgCEWAiDNBEARBEARBEIQiQMSZIAiCIAiCIAhCESDiTBAEQRAEQRAEoQgwbNseahsEQRAEQRAEQRAOeSRyJgiCIAiCIAiCUASIOBMEQRAEQRAEQSgCRJwJgiAIgiAIgiAUASLOBEEQBEEQBEEQigARZ4IgCIIgCIIgCEWAiDNBEARBEARBEIQiQMSZIAjCIGOapjHUNgiCcGgj9yGhEEzTrBxqG96rBIbaAKE4ME1zKjAZeMGyrO6htqfYME1zkmVZa5yvDcuyZECgkBOmac4APgHcallW11DbIxx8yH1aKBTTNKcDZwN/ArYA8iwTcsLxoduAfwG/lT2RfiRyJmCa5mXAA8AHge+apjl5iE0qGkzTNEzTvBlYZZrmN5yX5bRRyBrTNP2maX4duBeYL8JMyAe5TwuFYJqmzzTNLwG/B8YDNwAjh9Qo4aDCNM2AaZpfAe4AKoCTAUSY6UfEmQBQBXzOsqwvApuAy0zTbBxim4qFIPAaMAc41TTN0ZZlpUzTlM+OkC21qAfZLwG/aZqXmqY5c4htEg4+5D4tFEIt8DZwkmVZn0UdMjYMrUnCQUYTsBE407KsDwFlpmmOH1qT3ptIWuMhiGmapwOXAS8BvwVGAVOBRcAC4AfAK6iUh0MO0zTPAC5Brce9lmU95bz+OPAt4JNIKoiwH3bzoXuAfwA3AQng38D3TdP8pmVZrw+dlUIx4/jQxcDLwN1AI9CF3KeFLDFN80PAHMuybrcsqw34p/P6HOBUIGGa5l9RabLyTBP2YDcfWgO45R3jgVVAagjNe88ip/+HGKZpXgt8HvgDMAH4NnAn8BHTNK8BrgLaUYLtkCsYNk2zBLgcuB+V8nGbuwaWZX0HmGGa5lzLsmzTNOVwQ9iD3XxoFHALsBj4smVZ51qW9WNgPio97ZD7jAkHZoAPPYASZV8GHgLOkPu0kA2maZ6NOkx8n2malzivGaZpBoFZqH3AO8DpwIghM1QoWvbhQ34Ay7LWAyZqH4lkE+lFFvPQYwFwhRMN+i5QZVnWZuCrwE7UZuBrQB0ckrnEU4B+y7KeQAnXKtSGyN38fA0l2K4GjhgaE4UiZ6AP3QoMB06wLOutAQ+wF1HR6kPxMyYcmIE+9C1gIlCGuv+0I/dp4cBYqAOgzwPnmqZZZVmWbVlW3LKs+x3fegqV2tgylIYKRcvefCjpCHxQB5BnA1iWJRE0jYg4OwQYeKpqWdYyy7K2ud8CIs7rqyzLug+Vh/4rVG76IcOA6NhSYJRpmmdblhUHHgEuHLD5CaCKYA/jEFsjYf8cwIc+6rzN5zR2uBMl0AQhzQF86ArLstZYlvVHDtH7tHBgBvhQs2VZvcA6lJ981vm+z/n/xaiyhg2AIdFXweVAPkQmlbEf2GGaZungW/nexrBtOXB7L2Ka5rFAjXM65n7YfM6ph+Gk5Z0JTLAs6xemadaj6hmuBF59r9fCOOtzGaqd8BLLsjpN0yyzLKvPNM0PADdbluWmnf0T+JFlWc+apvkfwHrLshYPle1CcZCHD30PFZ2+AnjAsqw3hsp2oTjIw4d+ALwOfAyw3uv3aeHA7MOHgo6od98zDRVpvRYVeR0OfAr4u2VZS4bAbKGIyNGHrgO6LcuKmqZ5GNBpWdamobD7vYyIs/cgpmlehUrJexDV0OLlAd8bCVRalrXKNM3PAqNREdRhlmV9ckgMHmSclvinAA+jOljZlmV92/neaKAPuB1YCfwONc/jR5ZlrRoKe4XiIw8f+g7gFlQLQr73oR9alrV6KOwVio8D+FD6We/8+UbgGuBJy7L+e2gsFoqNPHzoc8ACy7L+a0gMPkSQtMb3Jk8CJwLPAaZpmhWQ7tK4CDjCKeo8HTgLaD5UhJnDk8AFlmX9ArVGnZDuSvQKKt3zW0ASNZtqmwgzYTdy9aFmEWbCbuRzHxJhJgxkfz60CKcu2jTNeajaoV+KMBN2I1cf+l8RZt4j3ebeA5im+d/AecBnLMva5HTRwTTNOmAy8D7UJPc3gGMsy9rhfP9+4HnLspqHxPBBYsD6fNppfvLqgOLViajZHaDShea66wPcYZrmryzLigyuxUKxIT4kFIr4kFAoOfrQMQN8aCvw/yzL6hhMe4XiQ3zo4EAiZwc5pmlWA6ehhiWfYppmaMC330B9oCY6BZudlmXtcNo0Y1nWnw8BYTZwfd5vmmbI2nWI9DjgCefruLM+wQEFsbIhOsQRHxIKRXxIKJQ8fSgEYFnWFtlUC+JDBw8izg5inMYenZZlXYSae/MBVKQMAMuyosBjQD2q29dXTdP0HSoP+gOtj0M/0GCa5teBzzo/E5fW1AKIDwmFIz4kFEoBPhQbbFuF4kR86OBCxNlBhmmaTc7//U7HRfdkdT2wHDWLomLAjxwJnAu8BtxqvcdnUWS7Ps5pURjVnfJLqJECt8tmSBAfEgpFfEgoFPEhoVDEhw5epFvjQYJpmmWozl1jUXO34qZpBizLSgx4zwjgm6j5NwawBhgJ9FmWtWXwrR488lgfP7AWlXv9ghTaC+JDQqGIDwmFIj4kFIr40MGPRM4OEizL6gNiQCVqThKWZSVM05ximuZnTNOstyxrO7AReBT4Ik4L1Pe6MIO81uc6oMyyrHvkRiSA+JBQOOJDQqGIDwmFIj508CORsyLFCTGXWpbV4RRkxoHPAG8B/4MSXzZwB/A3y7L+6DT9eAj4p2VZvxoaywcHWR+hUMSHhEIRHxIKRXxIKBTxofceIs6KENM0L0YNkX7csqzPDXj9Z6iZFFXAVOABYO1uoepdQtfvRWR9hEIRHxIKRXxIKBTxIaFQxIfem0haY5Fhqjb35cAnAcM0zTMGfPtZVHv8HuC/gaucUHW6ff57/YMm6yMUiviQUCjiQ0KhiA8JhSI+9N5FhlAXAU5HnS+hBkW/ZVnW/zmvlwIfM03zacuyksBJqFD1TuAvQB/Ae73VqayPUCjiQ0KhiA8JhSI+JBSK+NChgYizIcY0zSDwdWA1qrPiVajW9wDPAB9EnYr8Cvg5cIJlWX8cAlOHBFkfoVDEh4RCER8SCkV8SCgU8aFDB6k5GyJM0zwfGAbMB/7PsqwPOK/fDaywLOuHzkyKJuA24FXgKcuyVjjv81nv4Zllsj5CoYgPCYUiPiQUiviQUCjiQ4ceUnM2yJim2WCa5j+B/wfMBE4FdpimeYXzlm8BF5qm2WCpAYBVwLGo05H0h+u9+kGT9REKRXxIKBTxIaFQxIeEQhEfOnQRcTb42MCvLcu6CNVhZybwMHCYaZpTLMvaiOqw8yHTNAPAXOCLlmV9wLKslUNm9eAh6yMUiviQUCjiQ0KhiA8JhSI+dIgiNWeDTxvwFIBlWa2maY4EuoFVqFkUnwZqgSVOJ517hsrQIULWRygU8SGhUMSHhEIRHxIKRXzoEEVqzoYIJz+4GnjAsqwPO6/9GigFQsCngG4nVH3IIesjFIr4kFAo4kNCoYgPCYUiPnToIZGzoSUALDRNcy5wBvBb4F3LstqH1qyiQdZHKBTxIaFQxIeEQhEfEgpFfOgQQiJnQ4hpmh8GHgUWAPdZlnXvEJtUVMj6CIUiPiQUiviQUCjiQ0KhiA8dWkjkbGjZCXwF+KkMBtwrsj5CoYgPCYUiPiQUiviQUCjiQ4cQIs6Gllcty3plqI0oYmR9hEIRHxIKRXxIKBTxIaFQxIcOISStURAEQRAEQRAEoQiQOWeCIAiCIAiCIAhFgIgzQRAEQRAEQRCEIkDEmSAIgiAIgiAIQhEg4kwQBEEQBEEQBKEIkG6NgiAIwnsK0zSvB34AXGFZ1u/28Z4y4EvA+n29RxAEQRAGG4mcCYIgCIciZcA3gP8aYjsEQRAEIY200hcEQRAOepxo2U3ADuA14DLgCuBM4FSgFFgL3GxZ1l9N01wPNA34Fd8CvuP8dzFQDjwNXG1ZVssg/TMEQRCEQxwRZ4IgCMJBjWmac4DFwHLgZ6iI2GiUOBsOtAMVwCeBsUADcD5wH7ACuAVYBlwAfBP4NbANuB540rKsCwbtHyMIgiAc0kjNmSAIgnCwc4rz/59YlnW3aZpjga8CfmAWcBEQGvD+8cBTztc7LMv6E4Bpmvc4r1014L2neWSzIAiCIOyBiDNBEAThvYKx2/+DqPTG+cAPgWtQaY4lwL7SRhLAWUDS+bPUZguCIAiDhogzQRAE4WDnOef/15mm6UOlMw6kHJgCnDDgtS4gBUw2TfNjwELgn4AJXI4SdDOBCWSibIIgCILgKXIiKAiCIBzUWJa1BLgBGImKjv3b+VYc+BNwBCq18ckBPxNHtduvAf4InAR813ntJOAXwIcH/C5BEARB8BxpCCIIgiAIgiAIglAESORMEARBEARBEAShCBBxJgiCIAiCIAiCUASIOBMEQRAEQRAEQSgCRJwJgiAIgiAIgiAUASLOBEEQBEEQBEEQigARZ4IgCIIgCIIgCEWAiDNBEARBEARBEIQiQMSZIAiCIAiCIAhCEfD/AZcL15dCeQaNAAAAAElFTkSuQmCC", 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" ] @@ -383,7 +383,7 @@ "source": [ "def eval_model(preds, name, train_set=train, val_set=val):\n", " smapes = smape(preds, val_set)\n", - " print(\"{} sMAPE: {:.2f} +- {:.2f}\".format(name, np.mean(smapes), np.std(smapes)))\n", + " print(f\"{name} sMAPE: {np.mean(smapes):.2f} +- {np.std(smapes):.2f}\")\n", "\n", " for i in [10, 50, 100, 150, 250, 350]:\n", " plt.figure(figsize=(15, 5))\n", @@ -431,7 +431,6 @@ " likelihood=None,\n", " callbacks=None,\n", "):\n", - "\n", " # reproducibility\n", " torch.manual_seed(42)\n", "\n", @@ -487,16 +486,16 @@ "\n", " # when validating during training, we can use a slightly longer validation\n", " # set which also contains the first input_chunk_length time steps\n", - " model_val_set = scaler.transform(\n", - " [s[-((2 * val_len) + in_len) : -val_len] for s in all_series_fp32]\n", - " )\n", + " model_val_set = scaler.transform([\n", + " s[-((2 * val_len) + in_len) : -val_len] for s in all_series_fp32\n", + " ])\n", "\n", " # train the model\n", " model.fit(\n", " series=train,\n", " val_series=model_val_set,\n", " max_samples_per_ts=MAX_SAMPLES_PER_TS,\n", - " num_loader_workers=num_workers,\n", + " dataloader_kwargs={\"num_workers\": num_workers},\n", " )\n", "\n", " # reload best model over course of training\n", @@ -564,7 +563,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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\n", 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", 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\n", 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", 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" ] @@ -612,7 +611,7 @@ }, { "data": { - "image/png": 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9OOqoo3DSSSfh888/x88//4z77rsPH3/8MYYMGdKZzUMFKnLG4B32BOWMJGTpSs4qKys72wQGhqTAyJkz7AGPXgYGBoaU48EHH8ScOXPw008/4e6778b48ePx6aef4scff8Q555yDpUuXAgA2bVgJjJoN+LJw9dVn45prrsGBBx6ILVu24Oijj8aqVatw7733oqCgAL//Lvmh19bWApDyNnTr1g3RaBSHH344li1bhtLSUkyfPh2rV68Gx3Goq6tDYWEh/vznP2PatGk45ZRTzExOOzBy1snY05SzaDTaiZYoWL9+vWqgdhY5e/rpp/Huu+/i+++/R25ubqfYwNA1wLiZMzByxsDgPtJtkWhPH+dfF3/nyXn/VH001XG//PILPv74YwDAYYcdhurqajQ0NAAAphxyPNatyAIAzJw5U+V62NDQgKamJsycORPvvfdefHtRUREA4IMPPsCLL76ISCSCiooKrFy5EiNGjEBmZiYuvPBCTJs2DdOmTXPlt3YGuj4zSHPsCcpZOro1XnHFFfHVGwBoaWnpFDv+8Y9/YP78+XjppZc65foMXQdpMrR2G3T9Jy8DQ+qRduSssw1gMEVWVk78b0EQMH/+fCxduhRLly5FeXm56YL1xo0b8eijj+KHH37AsmXLcNxxx6GtrQ1+vx8LFy7EKaecgi+//BLHHLP7Rl0x5ayTsSeQs3R0a2xtbVV97ux6c2S5AQaGRJBuk6J0xx7w6GVgSDnYYyi9QKtweYWDDjoI77zzDm6//XbMmjUL3bt3R36+vuTLUUcdhaeffho33CCluFi6dCnGjRuHI488Es888wyefPJJAJJbY0NDA3JyclBQUICqqip88803OOSQQ9DU1ISWlhb86U9/wpQpUzB48GAAQF5eHhobd68aoEw5SzF27dqFl19+Of55TyBn6aicadHZ5IyBIVmwSZEz7AGPXgYGBgLpVspnT8Bdd92FxYsXY8yYMbj55pvxxhtvxPeRz+CnnnoKZWVlGDNmDEaMGIHnn38egFR2qra2FqNGjcLYsWPx008/YezYsRg/fjyGDRuGM888E1OmTAEANDY2Ytq0aRgzZgwOPPBAPP64lED+9NNPxyOPPILx48dj/fr1qfvxSYApZynGcccdh4ULF8Y/72kxZ+lCzrSkuLPJGXtpMCSLNBlauw0YOWNgcB/p9iojzRFFNu5ThU2bNsX//vTTT3X777rrLvyyTMSzi6Q71L17d7z//vu643Jzc1WETsbrr79ueF1yfi1jypQpu10q/a7PDNII3377ra7j7AnKmZFb40svvYSLLrqIkRIGBpfAhpJzvPqViAseFCAI+sa76TkBD77NGpWBwQnS7TlE2kP+XbZaxEm3CthUkWYG70FIVRHq3RFMOUshjj32WN22PVU5u+SSSwAAZ599Ng455JCU26QlxYIgQBCEPeJ+MHRNsCmGM3AALnxIarXTDgOO3lfZV10v4uH/AoCIm89mMwgGBlqkMzkTRED2kZl0ibRjV72I2f9hY7wzsAdoEwmDzUQ7GXuCcmbl1thZWRK1qKysRG5uLq688srONoWBISGk26QoHWGm1DdpHkOR9Kj4wcDAkCTIKYeR6/f26tTZwqDGHjD9TRiMnLkEQRAwe/ZsNDc3O/renqDUWGVr7Cy3Ri0pfvvtt9Ha2opnnnmmU+xJB/dOURQxd+7ceJFHht0LadCF0h5kmUVVLErKLWFg6JpIt7FE2mPgvcxS7XciGDkzR9dnBinCc889h6lTpzouerenK2edhT2h3Z3iu+++w5QpUzBmzJjONoUhATByZg9SEVMRNdZ2DAyuIN3GklnMmQw2FUgPpMMCdTqBkTOX8MEHHwAAZs2a5eh7XV0527JlC2688cb4ZxrlrL29Hbfeeisee+wx3H333a4RutraWtx0001Ys2aN5XGbN2925Xq7G2bOnAkA2LZtWydbwkCLrVXK+En3V9u3C0Q89I5oOOaXrhNxy4sCWtu9/RVR0sXJZtKWTvholohnPklzIxn2OMz5TcRdr6oT6pCv63SYcKtizgymEl18CpbWsCPOezJYQhCXkGgq9q6u4EybNg2///57/DMN0XriiSfwr3/9K/553LhxOOGEE5K25ZprrsEbb7yBZ555BpMnTzY97phjjsGqVauSvp4TpMNLrKv3xa6I424iyFnndyFLHHuDZOAh4zlMHqHeN/5CaZ+PF3HvRd71w6hJ/Em6t92pd0gG/uVgoE93Nk4Z0gMHXyX1y6H9gTOOkLaRYywaBfydPMvUJgTRgo2mzoOWODOirIA1hUtIVAHr6soZScwAOuVs3bp1qs8VFRWu2mIXF7h69WpXruclHnvsMRx00EFobW117Zy7Iznbtm0bQqGQYX2UPQG/b1D+ThOPYUOIggg+Ntbrm8yPW7HRWztMlTOL76TDwomMxvTIn8TAoMLWHcrfZmOss2CnkO+Gr70uAzvi7ATTZ4uYeFHXKY3QtZlBCsGUMzp0ZswZ2da7e7tff/31+OWXX/Duu++6ds7dcaHg5ptvxuLFi3H66ad3timWCEdE7Kzz9qWRzq+kOQfNxbN/zAVEEcGA+XEdEW/tIOPMrJSzdHW3SSdbGBhkkGPJTJ3uLNgqZ7v3VKDLINm+ctJtIpasBf75dNd4SO5+s7E0RaLkbHecECeDaFSdo9poVdqrlep0JmSJ/uaOjg7XbEjn9jGDm8qhl5h0iYiefxaxYbt3L450nbgLYQFNq5vQr6MFflFEhgU5C3tNzkwmkdq2EzTuNumCdL3HDHs2yG6pWgBJg/5qF3O2G772ugzsMmkmgvawO+fpbOxZzMADiKKIt956C5s2bUro+15OiNvb2/Hqq69i+/btnl3DKWiUs1SQs0jEehaYLlkl7eCmnbsjOdtd8Nsf0v/fL/LuGunaZSONyljjYU3OPFfOKGPOVPu8NckR0mGyy8BgBbKPRtPgmaQqmWEwfnj22us0eOGh0FVuJyNnSeK9997DOeeck3ACCS+VswceeAAXXngh9t9/f8+u4RSCIKjIlxER0xIOL0jDzz//bLn/7bffdv2aXsBNIsvImffwsonTdd4eaVAYFwfroO8Oj1c9Iyar+tq2Y8oZAwM9yH5p5jrcWRBsYuDYa6/z4GbMmYyucj8ZOUsSixcvTur7Xk6I5bT+W7Zs8ewaTiEIgop8ad0cjeBGGy1btgyLFtHLFr/++mvS10wF3CRne5qLrdvoCIs4+14B7/9gfk88JWdpOnEPE+SMF0XLCZvnbo0JxJylQq0SRRFXPiHg5NsENLeaX5ApZwzpCNFELUuH/mrn1sjQefDi3jByxgDAenJM43Lm5YQ4EZc3r935BEFQuRQauRd64dY4depUR8enmqgk+puZcmaNVLqnvj0DeOd74PS7GTkjQSpnPETLCVsq3RotY85SnNSgqgZ4ZjrwyWzgf7+bH5eu95gh9RDSgfnEYObKmA5kyNatkc2COw1exJx1FTdV1i2ThNnk74UXXkAwGMT8+fMtv+/lhNjpxP2RRx5BdnY2VqxY4ZFFUnuRalmqyFldXZ2j4xNN8JJqMHJmjsbGRvTo0QN/+9vfUnK9OosU8TK8bOF0nbirYs5slDOv3Rppi1ALKVbOSHdLq4D2dJjsMnQ+ylaLCB4u4j8fp8eg322UM1bnLK3AlDNzMHKWJMzc8i677DJEo1FcddVVlt9PJ3J24403or29HXfeeadHFiWmnHUGaSCVsx07dqCsrCzlNtAg1QlBWltbMXv2bCp31M7GjBkzUFNTk7L4Qe1wK1utT5+/p8ecHVm3HbWXzEdHjXGWUS+UMzEqYsMzm9Dwe4N5QhDNd1JdoJq8RsRiaKUrAWdILa75j4hoFLjq3+nXIchXQzq8Jli2xvSFFwlBuooS2kV+RufBbnJs5x6Xbm6NgLdFVxNRzjqbnO21116YNGkSli5d6tn10sGtkaYvnn322Zg6dSoeffRR167rFTpTCfztDxGTLhFR8pcUkrP0m6cBAMINihR0YdU6RFc1YMNTxtWmvYg5K/9gO1bfsQa/HDLPNM239lGpcrdJgVpF2mLVBumgRDB0PtKNT6SzcmangjNylh5wLSGIO6fpdDByliSMCNDcuXPjf9tNeL2cQCZKzryM0+msmDOnIN0aGxsbAQA//fRTZ5ljCq/dGh9//HF8+eWX8c+ffPIJAOCll15y7brJwGr8pD5uUPl7wUrpf+3KcTqRs5oGETc/L2DdVu/G29fzRMz6n36MCx3Gzxgv3Bqb17fE/6aNh7HL8OY2REpyJh/38Lsivl3Q+c9JBgZAeQ+t3izipueVfkkzlZi9VMTdr4mexdC99Z1y3s2V+v2MnHUeyOfeTc+LaGhOvg90FeXM39kG7O4wIjJnnXVW/G878uXlBDId1BgtOivmzCmM7ovTuLVUwEu3xuXLl+O6664DoL8nzc3Nrl03GVj1lVQrZ1YxTDK8tMhpV7jySRH/nQm89KWI6i/dtUwUREQaIzjuJh/OrwzjFO0BmnvDiwKOr9mK9f5iAPmu2kKCdBm0WuEXRCk+7sjacrRsKEa3cTme2QRYuzWSfVwQgfkrlAmwOJvNLPdEpBuhkHvo+AtFtBEeyzR8a+o/pIOGD+Dw18NctksUsW6b8vnSR0WselvdeGnWlHsUyO7x+jdATqaI/1yT3B1Jt7GRKLoIx+w8GMXekAWpUz1BXLduHSZOnIivvvpK9VJ/+eWXsd9++6G2ttb2HF6So+effx6TJk2Kf96dyBnZdv/73/8wYcIE12LR0oFIa39zTU2N6bHpQs7M8N577+HEE0/sbDN08PJx4LTga9OCGpyxYz1q690fb2VnLMH3g39ESXsLsgX9GOc0w+uo2u24pHItHvptnuu2kCDbKGIRGyMIwBF12/GPilVYdvgvntoEaNwadeRMbdcO+0f4HolddSImXyrg9W86//2xp0FeGGrThJLK42rGQhETLhSwcpP5vSnf5b5d2nG9frv+mK6itOyO0E5fVmxK/pyMnDEASD7mzG0XwosuughLlizBtGnTVOe++OKLsWDBAjz++OO25/CSHL311lvYtk1ZytodYs5kkMrZ1KlT8euvv2LatGkptEqPVCcEkdHURJGaMAUws/mMM85IsSV0boVedmWnXkGXzV+Ms3duwOTGna7bsnOmNNPar3EHcqIGfnqahijt8JDsE5ciJ2uk+6CW2IoiMLit0TubNLBSzsgh3taRvrGFnY0H3haxcBVw/gPuNJAoiqisTs/G3l0moPK4Ovp6Eb+uAy58KLXtqV3oOPMI/TG7S1t6hfYOETUNndPPrUqYJIqucjsZOUsSdpNjuwmv20SInDQbnbujwzhLmpc2WSFdyZlRKv36+vr437Ji2tDQkDKbjJDqVPoZGRmuX7erQJV5qhOun2hmtKJIu7uGEGjh/YbKmfYN2s55V7qC7NamypnmMZ7qRAZWCUFIUw77p9gpfUsURUTb0zuPf6v9q80RzrlfSujDYvvsYfY6OE9DlA0zkYoiMgRv0jpqY1gLDLyTu8pkPlHsc5aI4mn6zMKpgLbfJDqt+ORn5YtdhWwzcpYkjMhZdnZ2/G9ZgXn//fepv58MyEmz0QSaJsYtlRN+GnKWjD2rVq0ybXsrGJGzOXPm4Ouvv07YFi+QanKWm5vr2vW6MsjbQrpZpYNbYzQq4g3CJtHD6Umrz48sg4mXth06UlRXkCQ+5ETRSK1KJRWhVc46C2Vn/orv+nyPjlqJAW2tEvH6NyIikfQhLm6vF709Q/r/2enp8xtlpMMEdNEq8hliDG1B9ZxM/TG3bv0Nn6z6Ef6aVveMi+Hjn9WfjchhOrRlZ2JLlfT/krXq7e//IFq6oboBXQmTBC/32PvKF7uKmypLCJIkjGLOsrOz0dIiZQjjOA6zZs3C6aefbvh9txUIVfC4wVu9M8iZ1floyFkyBHbEiBEJfc8s5uy4445DVVVVfJtbql46xpwZ/bbs7GxUV1e7ds2uBPJOkLeFdLPyNCEIZVd49WvgkkdEfBX7PKa5Bh3VvREsDrpihxhVDPnH9pWG5EyLiD815Iyso6Zya9QSItFb0qoF+YjTKWcurS4ng50zJNfXXT9Vo89JJRh1noiGZqChmcM/dNleOgdMzE8t9r2UXAim+052hn7bATG36rwlVQAGuWCZhPXlos6NkpEzc5DN8ONiEaff7X3SIbeebRkB5e+ucjupOGYoFHooFArNCYVCb4VCoQCx/S+hUGhW7N+WUCh0tXempieMiMOuXUpkK8/zWL58uaPvJ2MLWYvLaOKuVYTq6+tx6qmn4ttvv/XEJjeUMy9T+5tBJiqPPPKIbl8qXRn/+OMPnHTSSfjtt98M9992222WiTucgIZoBgIBw+3RaBQXXnghXn/9dVds2Z3w0hciLn1EoFI4PFXOKD2D5q9Qj6+pDVX4Zepck6OdI1yv+BKZEjNe3RDRgPIqct1llrgU6eZkpqJJNgCiR/dq+/QKLLlgKaJtykXJX6wlZ1rSnQoS0hEWcfa9Aj74UcTClXp/3YZYiODVT4lYX961WVE6/rpku6YoirjkEQEvf+nOrxMEoKrG/lw5WerPy9Z717rbDRKMGHkX8F1lNu8i5q3Qb7v5eQEPvi3iwgcFnHmPgI5w8vdOF3Pm8JQ/LhZx6h0CmtuUbe/O7BphF7bkLBQKjQVQWlZWdhCA1YCSFbmsrGx6WVnZIWVlZYcAWA/gU4/sTFvQxJxZHeNmJ5o1a5btubXqyJ133omPPvoIxx57rCc2uUHOjNRJryG304033qjb5/crgnMybWXnggoAF154IaZPn4799tvP9Ng77rgjYRtI0JAzI3dPAPj666/x6quv4vzzz3fFFlp0ZqFpGZc8IuLFL4B5K+z7QjokBDE6rq3CvbizSIN9JWltO/A+ZYPQ5t1iTIeKkCkNoYs5E7yblC+9aBkqP6vC1reUxEjWqfStP3uB178B3vkeOO0uEZMvs35OnXF3ekyEusB8jBpGzxEhLKD6lxqq2MCflwIvfQFc/LB7jXb1UxTkTOPWOPUq726az2B2y5Qzc5DtsEuTwbe6XsRD7wL/96KIV78G/jtT7wbpBpyuwx9+jYiPZgELV6m3L13nmkmdBhrl7AAAMe9rfAtgivaAUCjUG0BGWVnZZhdt2y1Ak63RilysWrUK7733niu2yK6UVrZpydmaNWt0x3z33Xeu2APQk7NIJII33ngDW7du1U0AFixYgK+++sro654hFQWn7VxQAcRdCNva2uL9SHvsG2+84bptRvZs2LAB69YZP/XcUu+sIIoi3nnnHZUN6bRCNnOx8rehWaKY0pizjuoO/HrRb9jy+lbVdq+F6MbVFJk8NQ1RFFWyOYTr7cldolhAqEBkJjf/jhaV3alwawzXKTKe2q1RsnHechEzFop65cwjeyp2iXjtaxHtHSKqzZwDDC6+vYt7ObvxiJk+W8Rvf3j7rPrlurVYcMIirLplte2xdS4n2xVFYHOV/XHZGnLmth0kjCI4DBOSMABQP5J31qn3GbWbG23p1cJTh3evkJSBhpwVAZAf1fUAuhkccxKAj90yaneCnarD87wlgfv8889xxhlnYObMmUnboiVCNMqZ2aR6x44dSdtDA5mcPffcczjvvPMwatQond1vvvkmpk2bpkrB7zW++OIL01pebil5ZL8w6yPdu3eP//3ZZ58ZXr+pqQnl5eVJ20O2ezQa1fWnIUOGmH6XJgtosvjss89w9tlnY5999vH8WomgnWgC7dA7v3IdXln3C7gWTfowF0F2IVEUsevnalRMr8Ty61aqVtO9prOLz/rV/iCia5V/tB0nb/oj/jlc63JfIvrxo8Q6GCnaj37gF8yZ8j9EW+QFEHU7rfw/zdKsCxCJYUxeS570HHCFiKOvF1GXooz+B1wh4oIHRTzwtqhzrbRCME0i173q18lOGJdvEHHSbSLGXeCmR4p+W9M70tq4djHGCG6TFBF6d1wjGMWceQUj5czQrbGLJJBIFmSfqtU8c4zGgNO6mkZwKyGIFkb3fncDzWO1DkB+7O8CAEaz+VMAGPozhUKhSwBcAgBXXnkljjzySOdWpjHsivG2t7dTqQpz587F8OHDk7JFm6jBaMKsncjv3Glc42jDhg0Ih5WJZDgcTogA2ClndXV1eOKJJ3DttdcCkOK5ZBKixfLly1PqxrZ+/XrD7dp2KC8vT6h9yPtTX19v+P3MTGWp8eSTT8bTTz+NY445RnccGWuYKMgi21u2bFER9K1b9S98s340bdo0PPjggygqKkraJhKzZ8/WXbutrU23zQhukFcjCAIAsTd4iBCIysp19XWQHpcSTqneBACo/n4DyifkGZ4rsTFWEv+rta0D5eXViNRGsPZ0dd8t37gNvjzJJbWpqRCAJvgD3rWRERqbGuPXW/0vtX/M9rUVyM13L66zsdH4XLX1TQDU2Ue3rt6KQK8AduwMgGSQm17cgoIr8+EmGuoa4m1QWekH0EOyq64J5eWNgNgb4DisXr8jvg8Adu2qgbRmqr9niT6nAWBTpdSXZixsx77DOgDo+2ltTS1QLoLsdxwiKC93v1aeUzQ1FQCQMiW705el39jW1oby8lqbY82xeEUG5DVtt8ZYe3s3ABmG5xRyfLbX2bEzE8n3IaUPNDQ2oaU1A4A+Jlk6l3Rsc3Osbxuco62t1dVn0K5dypiSUVvfhlNuFXDYuHbIv7+jvR3l5d57fmixocKH21/Px+Hj23HBMS2mxz30fh5yMgRceaJXtSCle1C9axe2bevAnW/m4+v5Ss2B8vJy7KjjAfRSfauqahfKy40X0mj70K5dytgAgI6OMModVSMvMdxaU70D5eXpL5+Vlpaa7qMhZ3MBXAvgTQBHA/gfuTMUCvWChUtjWVnZiwBejH1MHx8klyDXfTJDVlYW8vKMJ2MkCgsLLW8UDUiVBTCOD9Jex0zx6NOnj+q48vLyhOyzI1OZmZlxYmaHXr16Jd1GTtCjRw/D7cXFxarPpaWlCbUPSSyys7NV349Go/D5fBg2bJjKzfSqq65CY6N+KT0/Pz/ptiH7ae/evVWErHfv3rrjyeuR5SO++uor9O/fH88++2xS9miRn69MjuVrk+TV6vd71W+aW0XcvvVXjG6uxd+GHox2XhpzBQWFMHrcZeflmtqS2BiTli/P2rEeJSJQWro3/vjveoQr1Apd7x5KNsbMLAGjmvWTEbfa6DeYJ0CSkV+g9Nf1GRvRDuU5tP6CjZj0wQT0ONx4/DlFc34rqqAnD5lZ+rIQvXr0QlZpFjbsEiFw6gm52+2Tm630hR3NIuT+kpGVi/ZPa/D22tm4ZtC+6FbcE2Rfys3vFv+stSnR53Q0Slw/IwPZJhJHUWERSkv7gCw0kJ3pT+lz2QzZ2YpN7tgjnS8zMzOp83XforStW+2Uman/rXK/4vIzbK+Tl29uE30fUmzIyc41ndxJ55KOzcvLRWkpucihnKP/V1tRePdA5AzKhhuoalJ+o4zvyqT3xcdzlGtkZqrbSxRFCALg83m7EPzkZwJ+Xgb8vCwTt19ovJDZ3Cri6U+l3/DA3ws9skS6Bz16dIcQAF79Vt1mpaWl8GXq27JbcXeUlhq3EW0f6rZJfV6/P+BwjBjLdyW9e5ratrvAVvwrKytbCqAqFArNATASwMehUOgF4pA91qURoIs5o8k26EXsDI1bo1lqfbdc9xJJCJIuaG83TpLgls1mbo3ffPMNcnNz8cEHHyAY1Kc3N7o3brgVkjZEo1HVdex+s/b627dvT9oeN+FVxs+WdmC/xp3IESIY3lIX3246nD144ftEAWfu3IBDV27Aj2N/RvUveuIlEJm1/C1hPLRpse6YlIJsBoN0aYv+usSb68ZuTM+OVqBRIrAccbNW3y2peFLMmRodNS67WxI+PKQ7T96mWqy+Yw2KIh04ZdcmlbtYn/Zm+D7dhICLRXs//EmE/1D7lOOA8TslG1HULqxNq/hPN5Hsz0p5NsA844y6JJy4rdKitLoOZ+xYD17TYLOXKp+1TcFpjl19uz4GPlHQut1ppyiXnL0Lb/WfgyqD56ibqKcQwsjfEE5BTUGz16Rnbo1JZms0Q1dI8kLlmVlWVnZDWVnZQWVlZWeVlZV1lJWVXUrse66srOxx70xMbySbrVGGGy82LRGiSQiyO5GzVL/8W1uNi2J6EXO2efNm/PqrFKtz7rnnoq2tDaeddpqhS6zR9c2IZKL2RCIRbNq0yfKaALBw4UJUVlaqXGC15/IStG6uXmX8bHZYN1X04K0RJNq6bVsbqufo+4wYEdDaLuKnJSKO/WCh6zY4BdkMnNclzoisjJOadiEv0oHX1v2CY56YhSn1VejbrsySKqZXAgAqqwGf5nGz8BR3CS1ZD05+tA1tqcOUN8sU0zlONZF+6Y+5KHh/LY6tdc/966936p+rpjFJBo/gU39YjHnHLkT5e+4syDS2iJj1qwjBrZlakkjWCi9imrQ1qdo7iL6U48fy61ei8it9ho71/96AXw6Zi2iTu+xMBHDtwkU4e+cGHFZXAQDwCwL2am3Ap7PN3wV+Ub1PCLv33rAiD93DbZhaVwEulqRpzm8i6hqlNjz0+9/Rs60Vi0/1dgGrnWKthxyHX8+DK+nrzcBxQLtJSLRRW7rxStWRM5duf5o8OpJCFwib61wkm63RTSSSEMSMnLmlDrlJzlKtsmmzX7ptB9l3PvjgA0yYMAHbt29XxWq98soruu+lgpxFo1FcdNFFltdctmwZJk+ejJKSEp1y1hm16azgpXImgxxtZusIggfL6EHR/rcJYRFn3yvisH+KKKg3j29IGVTkzNtlToFYcb59y1KUdCiM+pZty/D8+nmq46OtUZx+twifxmWm4Td36xvK5Kx5fTMaX/4DWdEIRhDqKwBEON5Q5Ti8bjtK2r27jzTkjBcF3LH5V4xorQcAVH5Bka6PAsdcL+LQq6XyFE6QrsKdF8oZ+Vo9/BoR/3xK6avc7zXY8tpWLDlnqe57a+5Zh4bfG5H5s7vxpWTbT2iSYoauqliJf29YgMhXSiIv7aRZ++zaOdNJvJE1rJKevLDuf7ixfDkOq6/AzDLg4KuUkhFRuXE7vH2HGREh7ZyN/A0n3irixuc6h5wZkR03CJD2FG6N4TSbfiQERs4cQBRF3HvvvZgxY0Z8WzopZzTn/O6773D//ffH95nVrUpH5SwVGQFJHHXUUYbbb7nlFlfOb9QvNm3apIsdpPmeGZFM1B7t/TfqDwsXKgqMm+Rs6dKluOmmm1TJdh566CF88YXD2RoBNxdIfl8v4sbnBDQ0iyrljCb1ushzCNeFXV0hDlK4uLU0C/hktvUxKVWmiecC57Hfl0iQMxEcBJtn0nd9Z2JwayP8HreHbNf/jpiP1tc24IKqtbprRjgunhaaJyaye7U14uU/VOHfroLG7W1iUzUmNymTadGl5eq5sZDFj392dr50zdbomYuVKOJPNVsxoK0Rb3+i3DCOwv1NbHV3wZhso6kNVYAo4oiYgjZx1RacsWM9LqtYrZs0BwwWlprWuZP4wko5y4xd99ryFXF39LWxEOtavxJvueCkMrRVtGm/7gq0RKhlSyt+Gv0zNr28Jb5NSzCf/dQTUxSbTKZYRq9PLzQHmkdIW7uI/3tBwKJV5ge74XLZ2UiTJLi7B3744Yd4wV95ImNHGDpTOTOaIM+YMQMzZszAIYccgilTpnhOzgoLCy0zWqYzOTPLsvnDDz+4cn6j++Pz+dC/f3/MnTvX9HtG98Yua6hTe7Rkz67ttS6gyUz0x48fD0AaOw888ACWLFmCm2++OeHzAe6SszHnS78tHBHxl4NICUj5U/XzyQ+NYXw/5EfkDsvFwf/TlYxMCDTK2UvT7X+/GBbBBZObSdLedy6FypkYIdx1OQ400/gTajajg/N27VJOpS8X7R7S1ogavzoRR4Tj0RGbxOVEU+M5wMEq5kz526fzSXLXDqdDdk9TzqY2VOLvFVJNs0v3OsCZTXXuFZ0HrBWxYDiCs3duAAD81DwEcpZJAAgYvAM7drYDe+fotjsFbf95dOMiHDdSySLOE8+H6p+rseq2NRj/ytik7dGiQ0PO1j30B9oq2rHyplUYeFF/APpFEi/rtHFwppx5EnNGcc4nPwQefAd48B3zAc+Usz0MVVV6t426ujrL70yfPp1qMp8q5UyGbLdXbo0rVqzAyJEjbdOpdjY5O/74410/Jy2MyBnHcaqshEZIBTnT3remJutqodoMkm64EX7xxRcYOXIkJk6cmPS5vFggWbtV/dIifzE58nzEp53z6wAATTSFmimRQdHW67fYP1+ibcnfMzKOygrvvdOCh2IvVzNy5tYzkVTOIhxP1V6cCO+VM82MhxdFBDTXjHIc2mKPvSwDhdQrtdN8EqhcL6p1o3c50IP2dG3tIva/XMDr37h6+ThEETj9LgFn35vY+PCqjtY+LYqbbV7Uun5iZbWI0ecq9nebbV8LLRlkEH01h4hHPvThWaj4vDL+2WhhSXAhriraGkX7/ctwQIMzV1tBEHWLDm3b3VPO5vwmYtjZAmYvFXVEyMiDID4ORRFXla/E0dXu1nolnx/XPqO3SYYREXPFrVFzDhHA2fcKOPUOAUdcI4A7WMBJtwoqOzdUULzLGDnbs9HR0YEVK1bYHjd//vwUWONsghwIBLBx40bPEoKcf/75WLlype1xnU3ORowY4fo5aWGWsMXudxrdG7fdGrV1zYzS95NoaFDH47hBzlasWEHVh0iIooiNGzfqtnsRcxbwA1Fi4m+2QE4m7FjvQSmxoGg/VjvaJDtPi61gG0FoT57AipQZxSZs3o63H6+G0CGYvoV2fLMT1b/UoGZebVI2kTFnYZ43dKXSgofoPTmLiti4Xd1/tAkSeFGZMGUL+mdltNmbpXSamDMBWnLmrg20E6xnpgPz7V/DCaM9DLz/I/DODBGr7lyDbe86G8ReuDVyHJArKDNpO9fmu18Xse88dT1BbaZEJ9AuCmhPRZIu7Tj69fzfsP1jyeWxe1hPfCIuJCvZ9m45xJ8rcevWZSgKSyphVjQCn00nXbdNrwhH293r2AdfJWLNFuDKJ8X4ogsgkUKjxEjyOJzYVI1j6spxVcUq12wB1Ori4jVSIiQjGL0+vUoI8s73wEezgB9i+VimzwFqiOkFzXBiytkejkmTJumy1CUKN1ZATz31VNVnqwnpbbfdhsGDB5uSy2TJGa2S46T9vCBnfn/nefaakTO75B5G98Yss2Si9miVMy35AtR9tr6+XrXPrXHhFDfffDMGDx6s2+6Fcub3AVEiSxr5UieHM0kGDo3FYbgJGrfGcGyCcc4O48LqACC4MAmhJWcA8NCmxVh+w0pT5WzrO9uw4IRFmD9tYVLPR9KmPzLzVav6VrCbyCWL9d/XYvDp1uQsQJAzI+UsXO/NOKMiZ1rWQama0oJmgvXTEhHXP+stiZZJ4l5tDdj4n01YdpV9HT8Z4fowWq8vw9R6ady7mYGSdHPta5McpqYBOLlaXYqWdhwYQad4aD7bnXvpJcsAQJWcR0akPnlyRsb0/nP7CmQIUXy0+ie8sM48XAAAhp2tX5QR2tx/d3TLB1oIXhoVYFhmJRwBzq9ch3u2/Oq6DfHrEjj7Pn3/FATRO+WM8pzk84hmsYMpZ3sYtDFdy5Ytc+3cyZKzyspK3bZdu8wzHy1atMjyfMm6NdIqFU5IxZ5AzjiOS0g5e/zxx7F9+3a8//77qkQdidrz/vvvq/YZkTOSgH3//feqfamOD5Tx8MMPG253m5zxooDNmyJ4/UvlvHnRMJ77Y65OnSLJmd+DtAU0CUEi7d65NYpREVvf2obmDc3Ubo0ytr1djqho8rYlThVtSmIiSZAzvyjQKWeiiAyD497/LoqFK925hxk7WlSzWk7UTwwDogCsqsNlFavRLaxftAnXeROHRhPbom0dt10saSZ/b83wPtBMfnTkJhDzt/7JjRB+rcGN2yRC51bMEAc1ObuicrXl8U0Gr1kjsk8L7atLe+szKd7/Dc0CBrbr3bvDDckvOATylVpvo5prURojryVh8/lGj45WZEYjOk8EN9y9tejdTZ3pt7YRWLHZ2K3xlOpNrl9fBs0YiwomypkHMWdmr2mn5KwrpNJnCUG6CH7//XfdtmTSqyc7md1dyJlZQpRUwKyNEiFnra2tKC0tjX9OZKJE2rN4sbrGi5Fbo5U62lnKmRncJmfP/TEPfVe24Py9D8TZsW1H15ajf3szztmxHqvEIfFjzciAEBbAB5JfH6NSztrs+0Oiytm298rx+z8lBf6ItYfq9rdxPBp9AfSIGD+P6ucaJ94hu3C4IQx/XmKvK4FIiR0UBUPSpQUPIMvAjfCieyJo8gUgznbHV420hYe+rwREAX0eXIg+ACY26Rfb5GQiboOmCLVOOXN5QkQzZFNR4Dkcs0OXACUGMSpi1+xqdJtcBF+28j4RRREbnlK7WEeiQNC+RrQtOA7IoHBnlmFEzq4vX45bB0xI6Po6xSOi7rc0tr1w+nocX6OPfXNjwcGXo9yHDt4XSwQkwcids0dHK15f9wtq/UGVGzrgzBuA2j5erZxd/piI7r8BJ2iO86JYOAmaMRaNpq7Omdlzh0yeQqWcpSYHn6dgylmaINlVRzcSQpDYU8hZuilnP/74o23KeK+yf1rds7POOku37cYbbzQ9vqysDKeccgra2rxJQyyDtgi12zFnfTukldiRRF0qcmJNDmczF59oqzs2aScThteiSN2fKDlrXKmsfhtNZObn98TarALH5w3XKOM9GRIiECvfGULUMEOcFgc1VGFssz7WLRlXMCOoEzmIenJG2NrHwAXMDVdU7RDiOItJoVXMmctujTQr82bDfw1FAhxayBNGv2p8i4i2C4g0RrDk/KVYdMpivP53tdtgxy79+8rNbHu8gzlDc4v+2HHNNRjeWm9wtD10Q0jzfKFR80fONY5/jbjgqkv2xfxoGDnEQst15Xq31NfX/QIAKIp0qI4FAC+Stvp9QDPxavx6vj7BDmDcX0RRxAUPCvj3hy4kTqEYYxETcuZFQhAze8hEJTQLMsytkSFt4EYRYhLMrVGPkpISV69t1MbXX3+97fe8IjxuE5iPP/4Yb7zxhqvntENmZqbhdq8I7fXEi95sZf3Z9cYJgQSXag1lUkyEyNg4MyQ60eeDymtEMCBnAjiYeS5aoXWb0s/DScShRImYEUk5S7zd79+0JKlECoA6O+V5VevifxspZ3aqqOBCoVztnLDvjlrsO2+tccwdmUpfo58ILpOzjrB60TJi0LfMyNlfbnWRnMW6Hnkvok1RzJowGz+MnIWqr3YAABq+VzIDhuvC+GHYLNV5ztqx3jVyFgxHsHebdZImEmETt+bS9pbEvCy0X9E8O5JxmXRDOdOOi0c3KmEch9brQ0As4ZI8S7az36dWziJR/WKHvF2LX9eKeO1r4J9PJ9/HaQhWJGrs1miW2dEJtJenIWd7ilsjI2dpgmSVM7cn7F1FOdtrr70s9ztxazS6fjL37c9//nNC35s0aVLC17SCFxkN3cgi6QS5ubmG290iZ/NXmN9vbTIHO0RdCjQvjNiPC6E+jFu3/OaJPTxRG23Dv/Wr4QIHRKlybKnRXqksOCWT+IKMGQkKUSql0Qz9OpoxpE0ff0kLURRVq/qTG3fG/+ZFURfXNJxQZo3gpnLWs6MVJ+7ajNO+K0NoxWYcU6PPSkg+7rTKjcuiIpZvBE6+TbrGNU8LCBwmYsN29TXN5s3rt7tnhzxBzibuTf1vDWivbFdly2zhlYW+nT/qXVDP3LkBDSvpCZUVJpf94ej4DM64n/QItyZUH85OOetm4sJshpd77Y22ntkAgPIPtqPiM4cESgMvXBGTBUm0ogJU2RojUQM3YRgr2OSiSLLzRiq3RsGYNF3+mIhr/+PunMFs8YIsjs3cGhl2K7itnKUjOfvqq69sU7prYffwcqKcGZEzWrc6I9CUYUglvCBnqYzpE0XRNAmOW+TsH/8270/kS5Nm5e6P9e7YVEQxETq0rgIHNO6wPKatJpzQy55Uzja/oo8hkZSz5Fafk3JrJBTKDFGgai8rnFNlnvHS3hj1x2yC0XAQkR9VP2OsEhgAQNSF8gfynfn3hgW4uEpJt94jElvwI/sE0bG1dzTqsnIGSGm0AanwLAA896mGnJnMYDrCwOrN7tgjTxj/QaQxX3CCcUKt3/6IXdPk0q2bks+qCwDFtc7qJAZNKoQXRToScgHTPd861P3wsoo1js63PjMfW05Rytr8eoH1QpIZNm4XsXiN6EqtNBmCSwlBSDLWaLBmSauc8cQNM4oldILl+qozhjaYTQ2e+ED6f0uViEWrnLd5Im6NVKn004+bOwYjZ2mCZFdA0s2tkXYy7ISc/fjjjzjppJMSNckQyZIzL2FWg84reEHOUhnT99///td0n1u/LRKVgseNQCoJNJc79cbk3XeCAaAbhXI2psW+VtjvlyzD5pe3OLaBJGdGEDjOcOLhBNGWxEiIKIqoX6ooXUFBwD6tiStfADCx2aQYEI09FgSmtKMVxQYZGa3gRiY5jgP2a9iBfE0hYy5mKk+u1BP2e62cGUHL8Q2VM1HE4NYGjD7LHYPClKfJEKIYd4GIphbR9H3ebhCHlgiiDt8NQRO2WBxpT6gmlPY7nEY507q82qGd5xHOSj5TyuDTRYQuFtFQ7967zC0Ph5YmAX/fvhITG3eh3iBFAG3MWZiIH6xOLGQwjkOvtr9PZjFnJAacKmLfS0VsrnR232kTgjh1a2TKGUPawG1ytmTJElx33XWGKdRp4IVyBgAzZ850dLwd6Q0E6F8IRhkI3UgfPXDgQMPtRvW6vIQbBObkk09WffZSOfviiy/w4Ycfxj9/+eWXpse6GXP25IYFhtvJNOjyy8wqPskojbRTTNyHTjmjxcqbrVNyG8GOnEXBmazb0yNRErL9A3VduRwhknASBDdglzSjIOrMfdONArkcB9y+1UipkGzlCZM//VlAayx+iddMwN1OCEIDI45ybO02PL1hAW4o/x1hF9zbaOPERrTW4+KKNdLE2+SyRklCEoFgUhvQDEETt8bicFtCKoP2O1py5hRtvA8dGep3RTIp9Wtrkx8Xc/J7AQCiLsUGV31aiT/VluOeLb9SkzMjt8a6FxSX1uok1ploa+5FBXolarXDtT3t6zGuLmp2sJgzhk5Duiln9957Lx5//HHcfvvtCX3fCxUmEbhJzox+UzL3LScnBwDwpz/9yXC/GWmjQSLt78Y9e/XVV1WfvSRn2pg9K/vdImc+H1BoMoEmyRnX1AGIommSEADYu7U+6aK0ItwlZ4kIXByFckbr1rgxIxfVp+6j256ocrb17W0Jfc8OP46ahcbVzsm127EwbpCzAW3Gv4OL/6/YPPtXES98KuKBjWX4v63qOp8pUc5sPvOigKNrpVi5Axt2oNkFL8JoVFpkoWnpE2u2QIwI+iJwMYTdImeUytmKm1ZhyflLTbOMdvdIOXOKKMejQ1NjYN0DzuLqVPYkuVDQyvvwYN/REACIHaIrCw8dRHxiR10YvEWMshh7L0SiQFjz7Gz7RHEdr0tifY/WnTUSpVeinL5mdQlBosCRteV4f/UsDCE8HMiYM5atkUEHMr7ITMEZMmSI4XY7/N///R+eeeaZhL4LuE/OZKxdu9b+IAOkCzkjceihh2LOnDmqbcFgMKlzOq3n1dbWhn333Rd33HFH3HXUjCB27949ZXYB7twzbXum0q3RioC5Rc6sVtFJJWHc7bNwecVqyyQhOdFI0i+Rqcv+QO+wi8mARGD7xxX2xxGwU844n+lcVYdlOUXYeehA3XYhQdeiVhcWBy7cewraNPm02yrasfLmVSbfMIfowpJuvS+AJTndAABrb1+DTQm4opJ4cvU8031BIYrTdyqBKTyAxqoOjGmp1dWLC69zJ9mFFYzS/svo3dGC91fPUmUxbHZhaISjUqZG2snStjtX4bcr9HVHASDS6E7hKoEyg+Dml7eg8vMqFC6tMtxfFOlAJAGCr11zyqlNLvFTM+9HOKgeq80bkjhn7MGa6FNfBACOi6tZ9cuSc4UGgDCRsvaBH2bh/dWzcPl26RkiJQNS3tlytslIVJ1oRndOh91p1SYRw84W8MnPIjWR+nIucPg1+ufW+KZqHFOjXvxy+j4zWrv85/aVyBUiuLxC8eKwU878goChLXU4tmYr/IKQ0IJDuoGRswRx5JFHGm4fNGhQwue88sorE/6uV+Qs0Ql7upAzUtkqLi7Gvvvuq9rvRDkzgiAIjuLzvvzySyxatAj33ntv/HtmBKa4uDhhuzqLnGnb0yvlzIhsWd2HZGMoZbRbLHxrU49Pq90Gv0XsRZYQTcg3XhRF/LFNims5eDVFRLdDLL1kGZrW0ddN5DOsJ4o9u3GIUBQLWpTbHe/1GKwqOCoj0ZpwZVuUsaUlWACwJSPH9hyVwWz8EnNxIpGIai4rZ4mUFpDx3x6DsS4rP/555U3OSSIgqYq1C81jETkAJ1Zvwem7lD62b+NOhC3KMrRXufse4kR9hkYAqGsUsbNOVLk1HtCwQ5VgBXCHnEWidOUqZNR8rM9yKcOt8hmCz9nU7bQK45piANC63fk9q9J0m6mzVuqO4cd3sz3Pw31H4d5+Y1EbyEBUUA8Kzs8l7JkiK2fNPvv3u1aZIhGIXX/uEfPRvCG5WrIdmnIg2UIU02olcnPz1mU4qVpZZIk0RdG+qwP8V1sso/eckrMrnhCxZgtw8u0itbvu1U8ZW3Df5iW4qmIVmtYp8p3TUhFWt5fcJZMzURDh79D/6Ec3LsTjGxfhyorVOG3XRqacMehhVmfJazByZgztw12bZMOJcmamZDlpe5JUyH+bEZjOImcvv/yy7bF9+/Y13K79LV6Rs4svvli37dNPPzU9/oUXXnDlula1XYoNEnP0MkkeAgBZQiShukc3Py9i7zNFPP6+8+/SonUL/ao177d+jeTncuiwccP6pHgA7howHg3+oGEChkTjPpo5pf81ERO1GwaG8Pch++HBvqMtv/9VkdTPjdJccwn4gMruUWJO4otCEY5DTjS5xYa6X+vx+9UrMO/YhRZHiejXrp6Qjmqpw/MfmT/bwy4pQwAwpLUBn6z6Ef84Uq8MFh0nouefRdXktMGnf5Y3uVDJIxKVxqobcCt+yUg560iwWnJ7pTMG2xEWMepce9LE97Ne+KgMZOHnghLMz+8JQK+67Ph2JxaevNiRbXFEpJM1+uw9N6r9GbptRgmMyMRCiSBskXF2iiaTbl1ZHZb9/XfkvLHa1I0ecE7OyGkZDYHJikYQsFmYeOU9xT43lDMZ5DNXXhRdeHIZDr7vJxRpEieRavnEpl1MOWPQo6uRs0RXrmpr7bPDJQonNtmRMyfK2Y8//oiJEyfqtifT9hzHmWZlzM/PN9xOg2TIGU2bFBYWUp1z4cKFllkUE8Vrr73m+jlp4LTw5lMmyUMASTlLhJw9HGvOe97wLuo50uzOJBIADg1x2Hug9auG/CXrtoloDPVW7U90UhsmFge6E7F5bbwPmzLzUB1Qntff9B+o+u6lex2A50uGAjAmZ4kgrpzZEFpAinsxQoTjkZskWWjbbj8h5wC0GTybtIWyVXAxKchFlWsRFAVcUam4N8m3oVdHK87asR4ffqEMyOKI/jc1bk1eOnOqnFnBrbTsokFCkH/1G5PQudocKme7aPPpZFsTI06jCT31sf6Y6p/pM6OS7/rKHdLfC/N62H6PfAbIkBdyrh6seNrwGclNlyP19GO2eV0zds40LgtDwik5yyBe7XZEKiBE8dHqn/DOmtmq7b07WvAwUdT7za+VE7lVZB1QvxMiUaCtsh3Vs2sAABOblH7h1zAxN8IF0gGMnLmMZGOYEkU6KWfLli2zPygJJEI8ZGjrkjmJiRo9ejSmT5+u2+6k7Y2ub1YrLTs7m/q8WiRDzmhS+Ofl5VGd89///jfOPPNMbNhg7laTCiTTZ0hYuTU6RaLKmYw2E1se6jsai3ITV10BqIrr2sFusSQ7m8cJB+v71N+H7KccQ5CNt74Dzm8arjo2kUmtKIjwxd7Sz/UeqtrXHiM+Tb4ArhiyH87b+0D8kVcQ3/96z72wLSMHQkyNMCyinQBfk2POaNwad/mNF/oiHKeKT/ES7QYE0W9xv+VYGa8gPyof3FSGM3duwDkbJeJ2YH0Vztmhrz/X/FxiMdMkIlHg3KrEk1OQEF1Kyy4adL5ElbP1Vy9zlNxG+9wxzUib4yzeOBwBqg7q7+g7JEgX8ZkLpH64I5Cl7Oc4vNtDnwH51xy9+6WsuP2RVYD1gyWX5mQLvkfq6cdspDGCQJHxIilJzA08/CxB5lyxe/cUxjxBcjQLQRdVrsXIlrr45wCRzjXZhCDDifMWRsKY1LgTEEVEIiJ+HDlL+V6sCSY07cJnq35QnSMnGmHZGhn02B3JmVm2QMA5Ofv73/+Oa6+9NmFbaOBkoq2dOHIcpyJDLS3O/F6M7m8y5MzK7S8rK8t0HwCMHTsW77zzjuE+r8mZU1Wvrq7OsT1u4s0338T555+ffFZUF+fE2UI0qRW+DpPYn6pAVtJKT0etgx+qMeOror4qd0GOl/5psSlTIfh9Ne5zrRp3JKfK2YZnN+G7fjNRWiu5ImlJRhvxeXNmHnYGs9AiKtumFw+I/50ZBHxBl5Qz2a2R5/Bed+v45J0BvbsVICln24L2sXLJghOBNk7/fMoQze/FH5vcWzona/MVhdtx9+YlKFonqQk9Y0lw9omVRTi/ap3hOaIOJsRm6Nbain2b7FUMGoguZNcEAN7gvRxOkJwBwJwp/8PWt+gym7ZqXndmmSCR49clAlqRXYj7+41Bgy+AR0tH6b7yyVB9plZakGRDzpJLpqff2rMITQZujh/00I/DRsIFOhpTube+sQ0rb1mV8DtErDB2cTcit5HGCBpNPFii3ZRFm6SUM5uhqnqHEDZqVeQgSc4cdO9/PiXgtpfVv/30ncoibr+OZty1ZSleW/cL/JuMkw3du/lX3bZcIczqnO1poMn41lnkLJkCyc8++6zpPicPourqajz77LP44Ycf7A9OAk5+q5H9JPkYOnSobr8Rxo8fDyB5cqaF3+/HmWeeabjPzkW2ra3N9LuJ9Ae5f5PtY0YQb775ZkfnTkYFdAuvv/46fvvNqJ4TPdwkZ1lRSTlra3f2ss+IdUGy0KuWjHEUp3yw72i8130Qdv5lbzzRZ6RqX6QucXL2XVEp5hQobolCRDTNf/xKr70BAB8aEBVS7XKi5AHA6tvXQGgT0K9eImcdPI/1BBnMyteTjhZB6fdkex47GcalABJRzuIJQTjsNHCnItFokswgynF4p6daAdj2bjnaKhNfJDLDGUQyEBnZFvFut/zHnVmRNr3/eVXrEGqqxshX1ZMxuRB2wIQwuuHedEll8uqbDLeUM95AGghTLKo9XTIcpw47BOftfSAe0sRb/v7PFVTXbtF4imqzdsaR40cHsQgyP68HHi0dhbn5vXDG0KlYmVOk+wrPcxh2j/qdTJvhlCRnsutthBikPt44liyqIbVRcHgt9lwCFHJW/UsNNr2wBfVLnNdJbPqjGb41dYb7sg1clMONEWxsNAkvaFd+qFNyFjRwa5zYuAsn7dqkO5YsMk+q5c0agntiufKMoPUEaWwR8e+PgPKd6u1tBkp9z3AbBj00X7VtPOHWqEVAFJlb456Gv/3tb7bHJJv9L1GYKVwPP/yw5fc2bNiAAQMGmO53opzRKC5uwE1yVlhYaBsft27dOixcKAXOG7lBPvbYY9T2aOHz+TBq1Cgcc8wxun1Gv3O//QhXMAvCk4xyRqp5ZosNhxxyiOl53n9fn6kiVX3DDm1ticegiKKIYys3J/z9tZmS2lgfm3QHRAEzy4CsI0Xc6yB+LDN2S1TuZRz5p3l9tR8KSvB0yXCcMXQq5hT0xlu99kLhuYMws6hP3C7AWdFn7RjTuleJEdFQOQOAT7oPxCnDDsXiPH2ynS+L++PgBQcCAJrWNiWlenZwPtwyQIkXjRrEfDVFCHJGbM/NBlqC7jzXSeXMTt3UqocywhyPFl8AMwtL4tuWXbUcC/5sldzDOfp2GGeny7UgZ00NyfsTDSkFCjTJdQqjymeyH/QOt8EnCrosjfFjXZik5QjurciI7e6QM5/Be5nGrfHbbn3R4gtgZzALfxCLFU6gzYBpGo+XE8AbA4cBAF7svQ/u7T8OO4KxxT6i7x+3v/IVngcCBep+T+vSfPMLSr+Qn38R4r2jLZpOoiH27FueXYi/jDgMG4hsqFG/mjAkUvOsdr75HONPNXrFct7CiM6dMA7CdTgZ5SwSleK17tnyKy6sWqeqKwaoF/+CxOKHNrX/sHrlt9GSIrNpZbuBUm+EQ+srrQ+o9SbMJ5VIjxnTbgi5gLAWBQUFhttpsX693m+eBmYkyk4ZsiJmQPLFsZOBUfINIPn4IZIo+P1+2+QW3bt3j5Myo1irl156KWFb5PP26KEPXDZyuSwqKsJPP/2ESZMm4c033zQ9byJtZKScJaIEZ2To3bHcqjOWLNatM3Z/okFHOLlV9GsG74t7+o3F34dIsxEfgJufl8btHa/Qj7OsODkzHvMcAL1DkYR2nse33fqiwS+dxO8DArF3Lbmq3NHk4H5pTNeu4AsdgmXlUDMSAgA5Q7IRKPSjvaoD7Q6UIS06OB5NPj+qApnYHsxC1CAVeQehnJGTxwcv5fBj3/5YlJt43UEZLS3Sfamq5xCxIWdNfADfFfbRbZfLErzTQ11Ts3k9vYt2+S77/maW+OOWbeYxxRkJV5YirmswPyNbav3Tm1T77ty8FFlm5Cxpa8zdBctL7VPF69Au4JdlItaXJ26ZKIgY8odUt6w6qDxrWw3qYT3XeyiuHzQJjT4/HtG4EVYE9Yt7NO97kpwdXbMNEwiXz/snKAk0xCwffunVB6cPnYrPis3nGaMI0fyreUBU86yIUhLaZ4hwcL+BcmY13G4aGMIPBSV4tHQUohyvUpiEgPr+8xnOMxBHYoSqLLcYVRrF/Lwd+njG/ut3oH+78eKIvy2CQ+u2A4BhZlsraJWzG8qVenx5mjhWcvGvOJZMKSBEUWSQlVgGrXJmJoa2u7WI2+xe1tjOAiNnCcJs8turl74ejtn2119/Xbdt2LBhCdljNvm1i1uyUzSSyYyYDA455BCUlZUZ7nNTOTNSwrRp2rVxYc899xz19bUwizkzij3r00c/MYtGozjkkEOwcOFCjBql99mXkQw5o1HOnCJdSiucc845CX/XyqXRMGGEFhyHBfk9URvIiNfWSaQArKFyRl5GNFfOtmsmZD6f8sImywz9b3ES5EyrnIVFcJoJ10cWkzUSHMchs1SazHTsStx1u4PnAY7DRXtPwWV7HYCJQ/X3y4gs9esJ9OnOIRL0464B49WpuROI67vn1VgsDDhVn/msWz/dsY0+Pz7uPlC3XY6jiSYRV3jF4/bPahrXWC3O27QajauSK0Ztt/q+9m71AsnEZnMXJzdeSWSNPrJW3pLJQ4wOt4TYGsVBfxew1xnODWtY3oAtr2/F5leU0gJFA5WJvlF2zzDHY1V2IU4feghmEUorILnW/qdEPd+IUizKyOUJhrQ24B8Vq3BlrFjw2sx8lBcRi9Oi1P6Nfut3yKASpR+HI8D7P6n3CwnUOJRVH9Jl0WqmsyUzF4/3HYWdMWUvl5g2aRdynChndYvr8Ps1K/Dzz9LLozKQZbsoQ4Pry1egb3tz0tkaD2xQUvhrf5UczwkAT2yQVPkX/5iLSUbxl7JSSfnaMFurNUp0Y4YTqs29WNxSqDsTjJwlCDOC0LNnT8PtRpNcI79/p8Vy165di4EDB+KLL74w3J+sm+XcuXOpj02GnD399NO47777UFlZidtvv92QuMpIlpy1tiqBuUakaNq0aTj00ENNj7noootw//33q2L1ElWGZHJIEsbrrrsOCxYswPHHH4+HHnpIdQ9or+OWW6NWBbv77ruxcqW+4CgJo35NY7coirjppptoTE0K//jHPzBz5kzH37MiZy0O67nJE76oRUFfM8jkjFQ2yOx/HKAiZ03EivoX3dTZ0Hy8opyRk9AtWxMnZ1r3KiEsqNwaTxx+GF7rTR/4HyiUnmFhJ3FwGsg2CRyPKMfjzvPtydkRIWD209I22bOJioRb4Lt5SqICcuJolpTAiIDJ5DcZcqaNoYuC003uE6ntVdzRjp8PnofaxsTfBYKoT7OeqATmBjkjFxtIdSocdJaNEAC4iIAPVs/Spf+mwS9T52H5dSux4alNysagYluHATmL9xGTvqJV26xqccmQlbPiiFrJbud5ZASAt3sMRlluMTCMzotogLpqBn77XW1DNIEFrHjMGfG7OU5UKTPbgtn4d58Rht/PJKZrgkbKFSP09sw9agG2vrkNPb+U1LEoxzkiIFYoDrc5jzkjbreWSJHPhIFtjbiuXIlBzBKi8AtCPBGP7ryx9qadBpktwDgpWWHlxeJWbGdngpEzl2HmImdEktyIwzn00EOxebP5CoKV8jFokHW2MBlr19K5cpmRM5rfGQqFcOutt6JXr1645557LN0tk8nWqIWRcsZxHG655Zb4Zy058/v9uOWWW3D55ZfH25fWJhrl7I477sC+++4LjuNw4403Yv/9Fad8IwXKSJX1yq3xjjvuwPDh6jTnWowdO9b03FaYMWOGbYykG3j66adx5JFHOv6eVRp9I5ciK8iT/ETImd8HXFi5Fm+snUNsJSYhmpiztbH4ifJglioGA5DImfzCfqWXQphMM7AZQDvGjJQz0q0xbFK/ywwyOXOUQVKDMOdTuU/lZgEnT1UfU69Z4b/7fA4DY6v68vCMqiZ7zu2Qg+wFcKpzRQxexauzjSe3MtE0+k6iiHKcLimMWRyXHXhBxM3PJ0HOBH2uFSt1zAq+mrakPTrI/kwmLCDv32IHpStyhAiGt9YlbA9Zo44j3LeMFg60yS600E6GIw32Y0xOpa9t1Tbeh6Af+G/PIbhzwASMHMLbjpHiAqC7ppv3zFDbJCQw0Y7HnBEG8Bwwq6AES3O64dmSYbh07ymYUVRq+H2SxAQ0voNCAs9sGTTkjFwksfIw4AGEI4klkwL0RIo80+hmfYxcd4M6gjJkt2LamDMzha2kw4Wq8QDgUlbUzgQjZ0mgW7duWLRoEd5+++34NnKyT9b7olXOnGL79u2W+63I2ffff5/09UmYvQSNYpC0cOI+l0xmSi3M6pyR9lilu5dJt1PFU3t9K7WKhBE5W7NmDU4//XTVtkSUPLfcGgcMGKCLdaSxp7PT7dvBUjlzSM7kCd9/V83CuSZpwM3Q0RDGSVqXDg74ols//JGZh1XZhapg7sdLR+HTbv1xx4AJAICnrlZnMJPdGv9X0AtPlUjEO+gkk4JWOeN53HiG8lmICOhxqDR5rQxYu1lrMfQsAWJu8spZmOfiCiEg/e53buew6EUOH98rtUezL4DrBk3CFXJMIDHsZeVMFduUwOPbz5PKmXIC8u+5eT1xw8AQNmfmqSZyn3frh10H9cPGWCKHpJQzzWfB4HylSUyUNlYk/FVEBfN4SqfIqGh2XGRZizDRLuTEmVzoMKqVpUUL70NrvvRsH9riPOOfIYYqzMYowczmDOuSC7/k90JL79z4ZxrlzDSZA+9DMADs/JzDqrc49O1p3z8nDQPyNSbWDlUTXSfJiWT44+RMuUccJHXx1oET8ZWBG7GMj+9VPysyNYXdhHDifVMAj6hNs8wnCmdXBc2flxlC1HmdM+J3aYmUTxRR0t4CiKLh879Xh3EpAEBR2WnJmdFxWdEIRrS6My6aaiMYepaAB97efQueMXKWBHJychAKhXDKKadg2LBhuPjii1WT/dGjlVS1XpEzO1hNrmmTl9C6RjolZ8XFykPY7BrPPPOMrqZWsm6NJOT7RSbXOOKII1T20JCzRJOUyOemTcJhRM4KCgp0apUTslhTU4OtW7c6ytZoBy05o4k5s2rnzoQoilixUcQOi6SeRvEeViAnwn81SGNshX9WrNJtWz+uH54vGYarh+yHKMerlLPaQAZeKhmKyli82WRC+PQRCUEAYGtsMudEOdNC4Hicd6zy+4QOAfmj83HwvCmqwtM0WLsVWL5TMjBcl3iQdwfnAyka+nggI8ghNIxDiAi7WZ1diM2ZufFjyOMBtZokOly1BoBMX0w549QxZ2R/2BHIjKcZJ4nBCyXDUPnX4fFJeDLkTAuBs09Q4gTJDGVBkNJhu4VkSD2gVs7IenlksgmzsgcPE+nqOzgey0ZLSkj3SPLZ5N7pMRjcKCUdPfmEXZDbHXf2H6fKOmiEJn8Apxbvj57HSISgY4f9u1XU/C+jjfMhIwB0L+QwbIDUNtPvs+5TB4zikK/JS8IPysOhvx2MgnGS7YkUoPfJbnYa5YwGE4eqn4lrJ6pdwcWwt8pZjV+ZL+2wKLeRGw0j4rBt/EQBa616dfX2lXj5j/9hfHMNggbP/zu2LDU9r7xoRetmaaScad1kk8HPC6JYuxW45UVGzvZIyMk2MjIysHLlSrz44oumbo125IycTCfjhkGqeIA1saIlXbTJHJySs7vvvjv+txkJuOKKK1BXVwdBEHDQQQcBcNetUSZFf/vb3yAIAgRBQFZWloooWJFo2e5klTNSWbK6ntm90LqOOrGnuLgY/fv3jytXtETRKtmMtt1plDMzFfOrr76y/a6XeP4zYNS5Io66zrgvdXC8YX0WAJiT3wtbhvfGz6dOUG1PZnLWf12Vbtuyg/ZSfTZLCAJI6aplV6Jh/ZXsj4ASt5LhJCZGc6kbzwCGD1T6sExicvfJRZtFZkYzRGPLvdEkMnCFOU41OSPJg3ZyGD+GGFJ+g9ubyAp6gJfdGqFTzuR03r8T9Z9qApn4pHhAvB4c+RvcJFMCOJXKkCwCEBJ+jwmieabIRBBJshA12S4kUYsSz8kWTb/+b/dB+LGgBOEDeuHnfMntfFZBCX7eJA22/u3JlYYAgEV53cH5yWBT5e+KYDbK8vQZgM2Q2UciAaTLpB20zxhZOSNx8DgOx0w2P8fF04A8zfjLzway+mbBnx8b9wm4NZopZ3bo1Q3o3wtYuUnZ1lyUgyP+UGLQk1HOohynig82AuleTdZCXJFdiN+zC+Of/7ZjPQ65+wfUWKTpt7RF8zPkeLL7Ni8xTOOfaTEmc2OZHtspiavRdODyWGKZZo0XygriN9PCiFzubmDkLAmQdabkCfXYsWNx44034rXXXlMda0fOfD5ffHKaTKp4LeGymlzTqiK0LnJOyRnZfla2cBwHjuPiv81Nt0aj6wD0Ko5T5UxLvJzec7N7obU3EbIoxy7SKmcLFizAOeecY3hMIuTMrM29UNScxHs+M136LS0txv07zPG6wpwyGnwBLD1xNNb3sIlJcTBRiwTV7bE6q0AXW8KbpNIHYrEXT3E452jg3Ts47NUXcfVIDpjPMCnqawSt6VqOncxKMwAIMUYiJHEerXJGkhzt5FAG2e0MMu87+l1CRMCun6uRj5j7D8eryRk4XDFkf9zZf5zKrQkAXum9Dz6JZW0kHx/agrqcL3GypnWzTBbnv/0zyk5fQn38uq0iHv2viLZ2EYKgTPaSgdyDly5N/Fy8KMbrY33YfaDKdfDLRUqn0E4o3+61Fx7rOwr/vYvHv0tH4s7+4/Bar71RF5t4j22uxarb1yRsl3xNFTlLAnFyVmFPzuTxriXQ7bGYMy0yLNaAu+UDOVkcSokuL48qPpbC3olCPa6pGkfUbldS6auUM/vz9CzUv6N5HggWBdH7BIlkJ6WcgTMshE2CVGFJd/kbB03C2izF20le4FtzT2LlXf56h/k7Yr+Gnab7jDCkVcrQWt9kc2AMRsrZuOYaANARQ5r6fVpkubi401mg+tWhUOihUCg0JxQKvRUKhQKafaeHQqEfQ6HQrFAotL/ZOboijJQDjuPw0EMP4bzzzlNtN1KptA8BmcS0tye+qq5VH6xqeLmtnJkdd+KJJxpuJ9uPxhaZBCTq1khmYLRDqsiZfB1aMkWrnLkVc2amZgGS2+4bb7xhmCBES86ScWv0gpxpXWWtIE/kzVbzA6KgWqF9ukRpjzDHI+AHam2yi2c4eJm05agXOyIch/1Gaki/jXI2chCHN27l0b+XtCDxdCwOTS4CGnSUTU59Ld3UI8klQJmcJVL8VUaY59XKGemyaEJq3FTONv5nExaeVIZ/rv5N+i40rcZxqA1kSGqHBUlS7dIcF+yeeGZegeMcTYJWE5NEI2R0RLBzpkHKbROMPFfEDc+JePi/0or+32Or6MlAXrC4//nEyNnx1Vvw4aofsVesOO+OQGa88PfC3O6qiX+zz49nNGnpzzgC6N+LQzvvQ1leD0R4HnWEy9qm5zYnpZ7tDGSCNyFnVgWXjZDZW7KrrcJ+7iGbrI1LajNQzgDgqEkWnicBad9VJxELFbFXFxfb54QM3b95Ca7ZviIeLxmFM+XMaJzLt1kmi8kqZ3bkrIX34dx9DsKZQ6fG61HK2GXg5sgH6Mct2ZJGHhgySsLm8WVGGNQmveCqG2wOjIE2Ng2IlUFxiDyLWmy7C2x/dSgUGgugtKys7CAAqwGcQuzrA+AEAIeXlZUdUlZWNs8zS9MQpPJjB5qYM7fJ2WuvvaaK69KCdtKbrFvjHXfcgQ8++ABz5szBU089Fd9Oq5xpj0nErfHZZ5/FpZdeSv09p+QsWbdG2u+bka5E3Ro/++wz3bm15zrzzDMtz2G0AOCmW6Pf70+6JIQWTorFy80RIMbBS4cqxVaDoqBy8ZlRpNSnC/MSOauxIWdO0pZXDlCP6QjH4/gDgCtPUrbZuTVqIROR9rhbY+Kp9OXzj39tLHKH5WLE/YnVbpQRJ2dJTIrCHKeOOaMY3j6b450oeds/UWfIiHIcrjvNuephNZcPFtPHh2pPEwWHMGffKDcPnIhVWQV40iQFeaKQY1UWrxFNC9TSwFeqLPjJp8mJJvZsvqxyDTJFIZ6kIMJx+KmgBFcO2Q/39xurUqtbeL/OtdlIMarVTLYjFAk4jFBRnI8w79MpZ1uDUsxomUHBdKs+H+hGn3Qnrpxp5gXtsYUoLS79M/DRPdZ9valVuemyqiL/NsFB6noZcmwoSaCjufbvELmN/vcMmf029n+MLCadrdGkKXbeMglv9RiCufk9sSuQiXp/EO28D3f1H4dbY8mcvi7qi7kaZZ3PcEDOCNMPra90bL8ZCmJKNy05o62HVh7MtiWzRpiwa4f9QWkOmrt6AIAZsb+/BTCF2HcMgHYA38dUtVztl7syaLIQnn322QCA+++/X7fPa3J2zDHH6PaTSUpoE5Ik69aYnZ2NU089FQceeCCOOuqo+HZSObNSaGTIE3Qn7SPbdMoppzhSYLxSzrRtJF9n27ZtVN83I8qJujWSqqaRcgZIZQ6sYER0tHYm69boVjFsGYkoZ7KrXzg/A1sKC1QFaX3EKjL5MomAQ8AH3Huh9VhzUt9FO7Nu53nwPHDiQYSbtMXKuVFgvExEZPUk0WyN24NZ8fOX/Lk3Dv7fFOQMsc4YZ3v6JN0ao+AgcOq03jTJAeyUs2TcmwRwOH6Ku+SMz0xcYRY4jmqF+vecbrh+8L4oz/bmVR8VYLqsbpfpc59b9sKRSw6Mf14fy2rphoskEFPiOA4bM/MQ4XmVi2Mb70OVxj4jctakSRzSvpN+hT+rv3J+2QVVq5xdM3hfXDNoXyzO05Mzvw/Yf6RuMwBntQTlLmiknBk+W3wcTj5Ev2MikTPq7KMIEhU7bdytMYlxFuE44F8hdD+sGOWn2C8oyON8MnGozp4EyGL8XCbKWbcpRZh6ZiHe6zkYgkbBXpTXA0tjpRoiPI9/9RsL8m0hk0YaCMTKR6Ixq/4pPeIJZGTkR6V+XE2ZbJFWOXumZFhCiyulLc04oMFcGdwdQEPOigDIfLgeAJkztheA7gCOBDAPwJWuWpfmoCFnb775Jurr6zFp0iQ0NzerYtEyM9UStRvkjFQYZMJDFsZ+7rnnHJ8zWeWMtIlUZcy2m0E+/pFHHqGyhwRJRN955x3b42nuLWlTom6J8j0iC2M7+b6MZBKCaL9DkiRRFG3vf2mpvlaM226NbpMzJ0qc3HVkt0YxwKOtQ525TaVUEX1N4Dj4/cC0A6xfhMfWlFPbo52hN/MB+H1qtx3Hylnsp8RjzoQotcvVJz9L7bK+Xw9cPuQA8LRp0SghxGxK1K1RJh1mCUHMYJRKn4STSVpji7pNEo3vslKVnLSPNmOcAPPYjsqJfXTbmr6js9+p214kCoypNXaHbLRIJlM+pgR7XTcEvJ/H9WMOwMOlozAvX3rvnb1zA3bNTqxOmso2ixX8Rl8AK3KK8ESfEbhqsJQBw4icadPdd+yiJ2e+bOK5HDsP51ffs1afH2tN6uP5eGDGY8a/IVAgkzP694ZRzJlVt9buI9Wpof05PHmV9FmnnHUkToYiHI/AhG7Y98MQuJ7mmQ9lyOPc5+Pw0o1qe/hA8vZE9eXVkTs0B/t9vm/cxdMOIsepSL4j5Yz4u7TdeamMIfcPx1GfT0B2f/VCRH4kOeXsomnGx3FQLwKcNPwww+NejiVMInHrVqmUVbKJdzoLNKmz6gDIy8wFAGo0+34qKysTQ6HQDwBu0345FApdAuASALjyyisTKv6aDjC6wR0dHSgvp5tUNTbGAibrlaWF0aNH49BDD8WUKVNQXl4en5xu2bKFmhxoUVurZO7ZuXMn2tvb8cYbb+C2227DnXfeqaolRdoeCARM1Z/KykoUFBTY/taKCuPiNpWVlXFytGOHIjfX1NTgoosuQkNDA5qbm9Hc3Gx5/qIiKYuZ3++nbndZsamoqIiT3pYW5aFkdp6MjAyccMIJ6Nu3L9W1WltbqY6bO3euzr7y8nLccccduOOOO3DPPfdYnqetrc1wf0OD+qm4a9cu6jaSIROoXbuUyVF7e7tpn5Fx+eWXY/HixTj33HPj+9va1IHlVVVVtvaQfZdEIBAwVFaHDBmC7t27Y8GCBbp9+++/P+bNM/eydjJ2o5FiAMH4ZCTKi2huiUjkLLYq/2GPQZjctAsLhvVVfbc40o6W5kaUlzcBKDG9xjG12xAOD6GyKaLJV9zC+7BjRwV2VQcASCusVuRs544qlAfUb8ddu/wAekDgeIQ5DgFRxLZN28AH7V/8384H9gawrjGISD6PpsaG2O81gr4NHr2kDvNWBbGzzofZv+ufey2xcdtU3+S4TwNKhr1IuB2AdP6qqgp0NJNtpLdr546quFoaCXcDkIEn+ozAqbs2oW9HC8KtYWp7yneJIJcw8nNF1TijRV19PZTXsRodbR0Ih2ltUjOHKMfpiocDwIzCPij5S2+Ef6/DLwVKsfuaXdb1NWXQ9iG5/Vta2nHz2t8Mj2g2SVcPAKvG9Ij/7sqsXlhVmIOjapV2WPiXMvScNQYl3ZJRPfS/46K9psAvCvHC6jOJosYd7U0oL2/EZ3cHcMKdeiULACrWVKC1H90kuaFRKTsejbH0hrYG0C5bBXwC6murcNrUAuRkiXj1W0XRrm6T+mJ7TZttH6qrywGQr4tLbeN9CLa1ory8zvB7HHqrFgV2VG1XLxRFswAUorGpBeXl9WiLSAuWNTtrwFF06d7deuq2RTkOdXW7UF7egZambEhTWHNEwu0oL5emuO0tmQCK0NQk/abmGJmpq65DebnzrLOyPSRBH/XLcHAZHMrLy1HTwAHobfi9nEwBzW1KYzX6AnFXwvao8ZzACPUNuQDywIki+pjUMWzjfaaeHJEM6RkcmOoHXgQ2ZOZicFsT8mO21DVGUV5eYduHKioDkHSd2Hk7lLlfrT+IoljMGAd15sV2jetwrS+IF0uGoiy3GBfF6oV+0a0fjq/ZiqZYMpX1m7arMhKnE4wWtmXQ9LC5AK4F8CaAowH8j9j3PwA3xP4eB2CD9stlZWUvAngx9nH3pLAwdsvKysqybFwjdOumCI8DBw7Ejz/+GP+ckyM9LAsLCx2fV0afPsoqZ//+/ZGTk4PS0tK4i6OckQ9QdwwrclZcXIxAIGBrk5la07evMmElr1FSUoKXXnrJ8pwkzjrrLDz33HPgeZ66fWRS2KdPn3j8Xa9eyiTD6jyffvqp7fnJuDkam5544gnd90tLS1FaWopp00yWjwj4fD7D62hjC/Py8hz3Ifn+lZQoE9WMjAyVC6DROUtLS3UESatMFRUV2drTu7f+xeT3+zFp0iSdygwAc+bMQUlJCX744QccccQRqn3vv/8++vfvr/uODLN2NEJmpjQJ4WOEhw/40RH1o52YrK3KLsSpww7Bwfv7gYXAmqx8DG1twO/ZRTi8KB+lpQWARQbFpbndMIVijAGA36cmsc0+P/r3LQGfAciP2CW5xTiooQpLDYrjlpT0QmmpepW2ISzGv9vO+RAQI+hd1Dvu7mSNLdJ/sVMWFsq/1wj6Nrj8lCJcl8lh5SYRI8/RvyKC2dIqbXYwm/qe/Ybl8b/lWmGZmQrx69unBIV5ZBvo7Sop6YXSXtIxOdnS/plFpVic2x1vr50NTqR/DoHfrPo4ci8funfvjvXYKO3mzYv7ksjLK4DZa9TP+ame0xLUMRkCjN0aOQD7jOyOk/aeotpeWlqKquN3ovKLKmT1y8RN3DD8uXoLJjTXqI7rXdwbgXyaPhSrsRgwX5RsslDOggW5KC2V3BgDAcHw+MlX9UJ0Fr3KQPYhwNgNrCLDPO68e7dclJbmo7QUOOZrAd/GHpE/5/fC1JjLVR5H95xetUlE+c6NiJdOjt2rnlMGQDysFs//bu9mmpkh9df37pU+v/qt0uH6DeuHlViDaKMAv99vaVN+vvSs0CpnbbwPJdlZKC01dmPmeQHknL9fP/U1ehRL5w1mZKO0NBc1BXWoQR0Kcguo2qggRz9Hi3A8+vTujtJSDt2LlWecGbIyM+LX6tkj9jszpN/UWNSEXahGXhb9u1Xbh6Q6Zwr6D1feUXmF5vbdf7EP//1BxIKV0mcyo2NOYS61Pbk50j3rFmlHhiig1hfE90V9VLU2rVzsS4b2RrfSbkApsOaVUlzzaBAfrv4JOUIEmdEIWjukvlNeXm5p0/qd6t+al6OMozv7j8dTG6TBwouiabKs5dmFuGlgSJJkRREr8rthUH8er0b3xnE1W5EpROETBRR164PiAne9OVIB2ydVWVnZUgBVoVBoDoCRAD4OhUIvxPYtA7A1FArNAnABgKe9M7VzYaScJVJE2kpidTvmzEhtGDBgAF555RV8/fXXqu1Wbl7JuDVOnz5d9Zl0v3Oahc9p8gzSJvJe0cS30SKRDJIknLaBl26NRkWora5phUQSghhB/h1Gbo2ynYcddhgee+wxS6VMCye/ieMAThRxRF1MGfZxaG3Xr+K1+AJxl76bBoZwR//xmF3QyzBIXmePk6BnzTATOA5+HzBiIIcXrpfO81Sf4Xi2ZBgeJIrgyjCOC1H+lifp0Va6eyZPN2SznD4WZXsyTVY346n0E4z1kBNdOE0IQnZhMv5MdklMJhaG93EIFFmTltAw4LN/KY3522ucLubs6sGT8VpPqcZdIkWxZZhla/SLAjgOmP20/qaOeWYUQu9NwMELDsJ+5/XAnQMm4OG+o9TndVAk97iarZi6aA3+yJYWg/wl6gWZJt68vaKE36n8p/b4BB5j6ms47NgZhJsaed8e7jsaPxRIC2C07bNmK0AOfPl54fdzGP7KeLzdcy/jL6rsUX/+4G7pHP17Ab5MHzgfBzEs2vYj+bdoM8K2c9ZujXaRC/J9i8d4+eljzr6aJ2L9Zv1xUU6K+QWABgqBkoyFksd8NAo89ZGIReuSH/cCeNMi1Nr3xGFEaUxt25ELBU66pQgAohhXPVt8PkPF3AxF+xfF//b1zkKE5+Oxlr3DrWhtB6IU7tWkW+PRNdvQfaO0qBPmOKwnCqdXBzJMa5a1cz7lx3Mc/jVsAqqvGY8O3oedgUz4IaKkQ7JpdwTVLLWsrOwGzaZLiX23uGpRmsKIeCTiyzp5suSPnpurX+lym5yZEa4LLrhAt80qpidRcjZmzBhdGn2SRDglSU6Tb5AgydmYMWMcf9/OpkSzNdLGPp111ll45513cMkllxjud6POmQwt0ZNVqX79+hkdbohEYs6sxpMVOeM4Dtdeey31uWjtkcFzwCH1FTipOqZ++Di0dQA1/gwA6jSM8gs9zPviQflG8UpaWLkh6qA5tijcEb/GJX/mcOmjIlp8AXzVzfh+WWVrBJR0+vTkLGaWfH6H5EzuumauJ3KAfKKTInnyYZZK3wxkFyHvoTwxSib2hPNzyB+Rh2F374OcITngHtYfc+vfOIwarHweM4TD57+o2+CPrHw0836cv+MPiA7SHGojXwQYuzXK/fKgsRw++xdwwi1iPKmEP8ePnkdKiQH+tJ+Ipz8GtgXVqknUATm7omI1UCEllQGAAbcPw/orlsb3k7UEBQCbMnIxuF1ynw0bkDOz2oO0iIJTJdZxWqQ7g+jPqiHLcfF6Z5EW+jFGXl1uVZ+P7vkC6I+bElu36Yi9TrkgB7FVhGjzehUN/gKkxSqrkiF2JEIek/KrK54dkWLRYdpNIjIMVKcIuDjpqapR9k8cCiw2KDNHeozL7RWOAlc/JeKMHRzOhgup9E0aQlsj7sSDOPy4JLa4rDlWpfm304/7rG2NeHvNEsyIud8K4NHK03Wg/hf0U9fmjd2vimAW+nU048/VW/BU6Ui0UExfZRI8qK0R/6hYBcTSAMhj7NpB+6JPRzMOmJaHz9/rjwur1uHLInXIQLvmRSZyHPx+DoCIbRk56BVuQ9/2ZjS17p55ClkRakokoh4YYejQoVi1ahW2bt2q2yeTlURVBkA9sXZSaJckCWvWrMHSpUsxcOBAR/Zo28goyUUyylki5Mxokl5aWoq1a9eiujr5IPFkyZkRSTfCa6+9hiVLluDqq6823O+GciZDe18mTpyI5cuXY8WKFdTnSEQ5M7pX8piwIme053KynwTPA9eXK7+d83GICsCzJcOwKqsAd/UfpzpWC+2K6CXH64+xyq6og+ZR9EdWHhXZkGGVrRFQXnq0E2uFnMUyyDl8q8SVMxOPtngq/QSVoXhCEMIuMwJJ1mkiuQ7ZPvIEIhmlSs6yN/jKQeh1bE9T3VS73Yh/yZO9iAOyqD1vlOMMZ89+UdF0j58iqXc/PKk/Tm6fBk1cmNDm/F2WHcvOlpXN4Z0eCjslyWO9P4j5+UqMkZFyVuNXd6jciLNFPe2k1blypvytfdzIfbK9mXKMcYCRcubj9QsNLd9zWPKy3lbtFEa2r7IG+NdbYlypAmW/1g7zNp7HdoswSrtFG/m+xRNw+GWliq6N/AbubxGOjy/+kMrZ7Kc5/Paa3qAw0V3l77W0KecCEs8aC8jZGo2hrbdIfuI4dfvNy1P6vpNFor2mr0RRtAOn7doYt6feb74wv/B4xfPCn6MeD3L7rMouBAAcXbcdReF2NFPkNpPvcVFEzeSisV+9JrsAPxX2QcAPTC8egKsG74cXSoaqjv2DUNjiNsZs2hGrB1ccaadOUpJuYOSMEm5mfBk2bJhhbahE3Pa0yM7OxhFHHEEVv0SCJGf77LMPxo4dG48VS1Q50yaFAOgLHBtBPj4RcqZ1Qd17771V8X+JIhk1D1DiDGmuM378eFPCrf19yRB8I9IzcuRI5OXlUZ/DLXIm22KkMBr1n9NOOw0HHHCArcrnZLElt13jsurj8NVDHPjeWbh+8L5YRNSdMZq/acmZIAKjnhiBYI8g5u4rZZnKy3CgesTa6fWee+Gx0pGYUVSqIh5v3WY9C7JVzmKTUoFyVV87OXSqnMn2mClnokO3Rm0/MlTOTHj90H5S8eD9RgKDiBwhZFeLK2dJrKBz2omYQZsZvXKMtsmkoanJiXKmhplbbYC4IMdxGDOEQ1aGAYmTJ8CaiZ7Q7ryNZHe5jEwO9X8aFN/+a67yvM6NhlXLGRHi5sq21AYyVFncXls3B2EHdcW0dcucuH8BGnKm2Ser0x3NlMoZB/DESeRW9RsoZ1kZHMbvY0DONEaQ9t36kgg+KPdrOrdGrfrazvvwxFXmg9/WrTE2xqKC5Bq3o9HZODPyPohwHOTH981ncejTHXj+Og7ZmVJf1qKDeI3L7SorQYo7c3LZGt/tOQQA8FnfQZbHku0lisDz13PIj00XPivuj5/zpdh5R2NMO0/QZH4c/i91Tcptw5U48My+6gyN8nP/C8JDoyjSYVvTE1CUM+1zRxvXGQxIitiGrLy4B8VlQ/bH6z33wsfFAwEAXz/MoXc34PN/KSqp/JuuqFiN6tXOs1KmAxg5o4RbypkV5IkoLTn77rvvDM8xY8YMfPHFF46uPWKEVNiDJAsyEUi0ztmQIUN0x7ihnCUbc+YmklXOaMmZHbT3yE23xkTglluj3EeGDh1quo/Ee++9h19++cX0N6xatYraHhk52nhCH4cjJ3GomK7vUz0L9d/XTp4EAeh/Tj8cvuoQHPNXifBm8A4Wf2KHVgcy8GNhH3B+XtW/zz6Kw+KXLCZJdjFnnDPlLDtDHXNG033OO1b5W7Y9wybmjNat8T+fqD/Lv4emzpkoAu/ewWPus5yqJAB5D+MTCoE+fb32KG19KjNbsjRqomAwRuTVZs7JHE07Pk2ej75YzJkdtEXM47Y5cGuUISsgfJDDl48rrDjM8VgXq122PZgdzxAHqEsDkPdqdoEyucwWomiv0C8YmkGrZjdaZIs0gqlbIxTlLNxC1z48rybUG2KLuz6e3q1RW1tKO944SuUsTs608Y9n+XFEyNlzh0TcrTEK3PWaiOe/cjbudQlKYnXp5An7Pv04bPuYw6UnmBtCxkLFYxdjc/uwS8rZquxCnDT8MEwfYB0nqB13Y4Zw2Pl5rE04Dj8WxuIWHShn2gLYEY5TJbYadOkAjH12NAonFeKItYdCFIE3ew7BjMI+6H+u2q1Qvl+tPj+Wx9SzpzfMx7+usi8ALbez9rmjdR3WunoCwNbMXHzYYxAisTF07H4ctk/ncPA4jog3JZ4bb/xha086gpEzSqSiVoJTt8YPP/xQt43n+YSIyCuvvIILLrhAlVRBnvwmopxdcMEFeOONNwztk9FZMWduIlnljNat0Q7a67vp1pgIEilCbUXOnnzySVx44YU499xzdfu00N7r4uJiXHrppZg5c2a8/zkiZwbKmfY6vbsBl50gxXxpYaScyd+Xa+doJ8uWEGUyFEsKYNAMVkPLaCgYue3RrlhnxyZ5cu0lq0nYj09yuPTPwDV/1R/EcZxhUpCoQzfCFz5V2y1PqkQReOJKDo/9ndO5EBnZQiI3S7UTkONhaFfRNefTkjOzx1PvYg73XqgkejHqJvIEh3OS5EZ7DpPjtEkfzGBGEBJxa5Qn2bymhpdfFHF/v7GY37sED/QbgzUxt6YNGepnKGlLMmQxoGnPeqfkzMKtUVbOwpTqNE+4Nb7ffSBmDhgIQFpUoV1L03YP3T2T3QgpyRmvIa9clvV7w+4VTCYEue9NejfCjth+rXImq7hjCQ5kNw8gY87yYwkEd9TF7HJBMS/Il87RzvsM75s8ziVb9fvJd0mcLDpQzkTNSQVweP4BtSJWelofHPDtZASLpfZ7v8dg/Lt0ZLwItwzyndFCkKEz5y61tUOeDmgt1ypnNMm0AOW+yn1IpXpvZ8pZl0YqlDM33BoTnViXlJTglVdewejRio+x04msPLnee++98corrximMk+GnCXj1ugV5Fgor90a7aDtM271oUTbzy23Rrm/dO/eHS+//DIOOOAAQzuNcNxxxwEAzjjjDDz//PM4/PDD4+dz8rty2tTkTOuSBgAHjgGeu45H/166XRgzWP2ZHE7yJJ138nyJmU4mBdAd4vC2kS/a+EufckU2W6PuWM1/Dp3A4fnredPMjFqlCHCerdGvmTSGiWfOP//K4drTzA00a7YizRoK5yCTnNF5eRtyCCj38LZzuTjpN+ominLmII5SY5E88byr/zi8QrgC+kUBe6sXzA1B9sFrBu0b/1tMwLtaPhWnIbA+UcTOYBY+HD8KWzNyMSe/N+7sPw7XDt5X1d9PPEj5u43TkjN6g7QxTLKr1GN/p1voy7RQzuQ+GaFUzjhOmax9W9QXHbFPPp5+4VFTHlHvCs8769Paq3KZ1s9jJ8oZQLgR2oz7xtjcW3u/GnwBPHI552hhtoNoo+IC6f8dtTG7kkxMBAAnTFWeRUZWkYt7RvvJ3+L0OQ3olbNBfTnsd3QuRj02ApM+mKA/nvK8TpPvyPdYS6i1tQRJ5eyO8+zPK5M5clHJV02vlqcTGDmjRCqVM9qJtdFDxw2XNO25nLo1Wj0MyQl1KhOCeKWcyfW3Ek2l7xY507aJW0llEkUibo1G0N43MjGInZ3//e9/8dFHH+Hhh5VUeE4XHMIREVvWafqbwcRa5m89CjnMeorDfRdxmP88h+8e5TBhqGa1kmgamZw5mVjLJxA1q4WqQxw2NzkU45OQDkp3oth3426NSQw1K3JGOynSvvBJ5cwOZseoa6IpxCHR9P58QHM+yjYzMi/KJdCHNJDVt0V5PfBJ94Hx7aMmZmD4QHvjyD64NrsA2/tJ8WFOMkhqIRPYgZf0R3BoHlbGXKfkPhLheZTl9UCYVzsg3vo3DiMGKsfMIlwbo5RKFaCOtwOU8dbNuAa4DlbHya5kEdqkO8QtEKG4KMqE5pRD7M9hl0Vvww5K5Uy2STvOBOexriS0qfRplbOGWP1i7biv9wfxz1Otr7nhPQ7fPkoQHpKcae6fW1laZdi1h90zIa7kOUhMpEvjH1tk6n9eP/Q4vIfBN6zOpYBUzupy9DVJtZDvsVaZN4o5k7H/SPrnEJm8h0tC6exMMHJGiXSLOYtGo6isrDQ9h5v2OFXOrIiQG26NiahCXpMzo+QnNOiqbo1uKWdakIlB7O5pXl4eTj75ZGRlKW4b8ndo+/RX84AsQdOWBuyD9HycOo7DredwmDyCw1H7GgTmk7V0YpN03sHijxzrQSYF0CI55cyZ+44gk8XYZxpub3bnDN0a4WwSok1UYFS/yykKtcPU74wwauHTujUaHGN0ZqNuG4/bcKKcaQ7VKkw37jUJvY/vhUOeG051Pm1SGNkm2pg8ow4rT2RHPDAc477ePx5jYkTgya8HAxym36+06CN9R2NBrlTWoq2RcqExKsZjzpp4P+YRSX8OHU91CtXkXvvrOnjn5SrkZ4TIcTpyNtEgAYgWLTavKCXRDW1CEDXabBZzaFPpV9dL/9PWE2yNPXu1ZLrBF4ilVjfHoD4cjt6Xw5OxRCZPEglN8nNMFq2SydJKLOzZZdilJWfUYwx65cxooVF1vMWpyWcRWcC+PsO8kLwMWTnTxglqM6IeNUn5nBEA8sxrvkvni00pZxb2ibs9+8KJL1R3Jhg5o0S6xZztv//++PLLL3XbvVDOvCJniRahTie3Rpmc0dSmM7LFK+Wsq5KzZAuIO3VrbO8AMjQvEK27FaCJSbKBSjmTY86cKAwUMWe0vvoyjGLOKiopF6RE+b8Y0UziEWREzgSfPCmiTJ6gdWuUJ1UU3zVVznTkzFlcnha0CUFotjl1a5T6vvrY2uws3HaO8nllZiEmvD4OmX3sV8EBfR+UbaKdOBp1GXKcFRGJYo36iLZd9unHofFbLq4oyWSovYlywSF2X8Mch78NPRj39xsLAJg8Aigp1h8/qASomM5hzTuKzSpypk0I4jDpjipzH5SYHflRbTdppUFEvgsJxpy12TiP0KbSX7VZtodukUi2x6eJYCLVHDtcfarUX049lIz54lTuzG4oZyDi8uzIl9ZdXAvB4RgD9DFnduSswGJ6Qj5ulmcXKdegIK9myhmZEbV5hrrOY0aAXn1t9flx7aB9EQUHXhCTihPsLDByRomffvrJ82s4cWtctGiR4fZ0cGu0siHVCUFS5daYKDmzKv7tBG6SMy/cGt0iZ8kSx0QSgmQKats5gsnMeIzDYROAJ/9B378MY84cJQSJ/WdxyVGDgbOOpD+lmpxJJ168gnJRRnNYMm6NQYOcC0s20Llbxa+v+RyOE3L779KSM45SORMEER/9JKBfXYPaRsqEILrzGVwurpzRZo4U9W20KyMTmUFOdYwTdGgeyRW1ziaOfYqMlDNyIY/DDWcAl59IvxCSm83FV+hlN8L2Jsp3Wey+RjgeHbwvPqnNypCUOS2CASl5C9lP5JglwCDmzGEyB45TFGEjt8YLjgP+tB/w6s2KbR/f62wgJqOcvdZzL7TavALtXivaR3uEMgZOtkc70deWQrBDbra+vciMlk6Vs3YDJVEoUhhXXxMvwuev43D8AcCph1qfX4nJS2IB2oac3X4uhyNDwCf36Y8jm3t+fk/cG1vA8GvTghpAHpfaOMEo0auyM9XjqT2sf7do+4xKjeQ4pWZnKyNnXRK1tbU4+eSTPb+O01T6VudwA07dGuXj9sSYMxpyZtSOySpBMtxWzgoKpJlFKBRK6BxupdLXItn2curWyHFAhpacERPrIydx+OFJHv172fcveXIydi/CtUV2a3SgnHEa5ayqxshuDm/fzmNAb/2+bAMxxMh9J5OnJGfypJGTr23/nQITb14jFfD3Tc7cGnlNju+wg7FvdgVSuQFAKGfWNr07E7jjhjrddp9mgn/4ROn/A0Yp2/r1hA6G2Rpj/3Mi3RgSRSArqn4+VAgZ2KtU+XyYPjeAJXQp/3V/WCM/y4CcaSaOD1/O49lreeqacIASQyRnbWyjJWcxUhk1dcBVY6o0L1UlMCBJnNa+SNxlj1I545TJmgi9W2NWBoevHuZx/p+Ua540lYM4m8c5R0ufxyt5XgwRT2NO6z4c+1HP9R6Kj3oMslXO7FpSO/bjypmNYh5XzjQT/az85OdC/zhZsTruXk2pnN33puYdCACFwfjz9/TDjVvk0hM4fP4gb7gIoDpfAm6N2tT1RsmtSBQXcJjxOI+/HGztng8AWzIkmc1H4eGgKGfqY7Wp9Ml5W36OnuBrVXSzrKiJZI3tbLgzM+ziqKkxmP14ADeyNe5Obo1OJ9qkS5ogCI5+a7qSM7fItLbPJFuE+tdff8X06dNx2WWXJXSOdHdrTEY5s1ttNMOatzl8MRe44kTCnlh64oSUM4pDf/kPh36nKEe+fxeHojy9/QE/h0/uA066TYyv6mdS1l6T36/xmDOK5ulZxOG/dwLdC9Tbjb5KG3siw+eBJ7POrdEnr1pb96N5y0VkR/XPc61y9sYtHF79CjjnGGDDdmD5RmA/gwB4w27LcQhzHAKiSLWKLorAPypWqbaFOR6nHiq5lG2ooM9IKENKGqJcOz5xpHW1NJhgGrkPA8bk3+wy8gq9HBPTXE+50BhrR20MjBbPXsuhpQ245M/S58I8Du/dqSfzWvPkrHROkjmQJ4krDxSvj6f/yWH0YKm4uhVolRh5r9wyMvFNWjnT7I9SZkeU92qVs/P/kvy7lSyHEHaYrfHr+cD+xOd6fxC8n8f854AfFgN//0tiNs1+msPvG4D7H5TuQIQycZMh7ALfLKAdc3L7+IWorfIuxMmZ+sAmg6yP856Tfu/EoZxu4S1D42mhvWwHzwNR+tjOdAJTzihgNmk8++yzXb2O0zpnRnBTOfMiW2MyMWccxzlWz9Ip5sxL5ez4449XfU6W4A8aNAjXXnstsrMTC2a49NJLVZ/TjZw56RdackYTL2SEvfpyuOavHDII9zF/AglB5DefWeFgEn17cnj6aum4K08C/nqY+Xfk1VF5VT+DsqqxYrqzmLPTD+d0RWuNWkFe/aRNg66rv6Sz0xzUMWcBOY219Ul5Xh0sH/96gbofd8vncP0ZHHoWcdhvJIeLphnfJ1MS4mDiaHRElJOKbt91AY83b+XRo9B5Hx9GVE5xGg9jdJzZOCO3Th0n/X+aSb+WlbOOWB9qrqd8l9mQMzkT4wkHAtedziGPcIk77XB9IiBT5YySnImi4tYokAlBKF6h+TlS3yrtYX5POY6+D2ljzuSj7ZSz6yxKWAD6VP+KmyWtcqa2u7hX8u9W0q0x2TpnzbwfPA+MHsLhn39VF7l3goPGcrhomvL8r6NccAAM3hkJLjQCwKhB6s8ySQ8KAuxycMhTIW1CkOye+jCP/UZyuPh443fLzWdZ298eT7zD3Bq7JMxW2U844QRXr5NuypkX2RrJfYnYmig5SwflzKrIcrLYf//9sXbtWvznP/8BkHjdNbdsOv/887Fy5UpcffXVANLHrTEh5UxbrCmJF5rOngTcGnVL1zb4+0nAijc5VSYyK8iTtABllZtElDPTcxlcsjX2go000D0XteRsQSzTHs0isZkSoSVnq8vp3NI4TiEGJDJ62mc0M4JZ20YcqItGbaxNYZ0IyMe5U5crI5Ji5nJFmjrjMSkJxzGTjY+V77mcgKOVMiGIGGM/Zm6NG9/nsPF9Dn2607Wbts3jpI8yyY0gEosMIBKCuPS69/tIwmh9bPy3yAp+7Ht2oUbX/BU4aIz5fm2qf6cxVVoXOX+2u8oZbWp/M4Q5PuFno/aKAT/RN5PgHXZujVboXcxh0wccLomtC8vKWVAUYLcWK7/utPesTSuFaUC234o3OVx/unq/WeKdCFPOuiaMJnJuJXIgkW4xZ14oZwBw8skn4/jjj1elRafF7kzOvFTOAKn4d16e5E/T2X2I4zgMHz48Pk7SRTlzHHMGERObqlXbElXOjJBIQhA51sMuTXT8eI7DiIEcfJQv4vhEnWKFuLZRxI4a9Qp6MutDRs3Qxkt52IQ2wTb+pCMsYvn62N8cjwv3noLfc7phcB/rpC2f3s9hYG/g7duMj8nMUG8Pi3RxcDxnrL4EeyX2/rjxTA579wWeuprDC9cr53WknBmSs+SnAuTPdJxJziKVvtV1ggEO+/Qzv6//jt1z+R60tToji9p7J3/Kz+EwsMRBLKNWOZOnXg6SuKjImSbmLFn4eHryEU/8Fft88Z85DO4DPHy5dXtwHIeh/c33Txqm/izb02GjeijZGtV2+1wnZ87iBLXDPsJxrt0vjuPifdNPXSraQDlL8l02oLeStENOvhEUBESi1uc1S+KS024tv5LmjxioLzCuHWdyUpjWBkbOuiSMJnKJFh22wp6gnAHARx99hM8//zwhwpRoG6VDnTOvyRmgLBok0z+9SCrjFjkrLjbIY+0ATt0aue0t+m0ukrNE3Bplt3sD93xXoEz07cf9gFPJsqax3+KycjZ0ABdPix21Sehw6aNinLzW+YOoDEpuuevf4y0n8SccxGHjBzzGU9SLAujbSOpu+h+VmaBy1qsbh7Xv8rjqZA4X/EnZ7iQexjPlTEXOtH/YIMmYMzPIBbTdImeJQnvVfn1k5YzOHkEk6pwZJARJFqRyRmvTmMHScQeO5WzHlwyrx1wwoO7Tsj3hNjo3y8G9vCZnzpQzbdcJc3xSC1daXP1XWTmjl860liejnMmQf5PA8YiCgw8iwjbu3mbK2ZZeRdbXsjFX6+EgPxcZOeuiSCYGzAnciDlL94QgySJdY84SJWduEiFAIWc0Sp4Z3Lx/Tgg+zb0aPXo07rnnHrz33nsJ2eO0T3ONEsklR6QbLzQZctY+bbyEJWLH2mXzSgTv38U5mug3EtzVSbZGMxhdMegnXBubrBdlXv9Geal5GWVAm3ac54wTlAQLkmfW6gybDtwaDbZFXVDODN0aU5QQxA5KGnTKd1mMoAguTZG0w9sXizvlKNKOA5r5N6d8duv1EfA7UM5kM8yqUVvArjsEDchQh025AbOYM1fImSqVfnIxZ5Ek3BqNEMyU1Vf67+jqnLmw0EiO+2jsB0Zsph9KzJl0z7ZPHYDQfydg1aAS6msZYfw+HO46n/QocLYok05g5IwCycTvOIEbylk6uDW6SRC1kIs2V1dX2xyptskrwpiRIa2A0yhVu4NyxvO8J+SMpk/TEunbb78dp512WkL2OCZnLZLdGzOV9GtuKmd80LlyJsML5eyvh3EYPkSO9aB1/ZTghlujEQJ+oCX2YyON9v2IVBi8QjzbHoVyxhnQoUCOG26Eibk1CgZEyEm5AXN7iGvE06BT9muDV4zZIsgxsWQbe/elty0en0PpRii3kWvKmeay/gxnbo1kzFm8dAKX/HstFHMlPOlgJT5HpCRDsoLPOWAcR0yUjjUqEwFIY12GfM+iNiqMvNenYX7BYudhE1qQypnTrLFa9O1odvXZmJEZI/gO4pW1tTFdUc6IU8htFGmnUztl5SySE0TPo3rY9qUzYxlHj9vf/Jg7CXImP6fbd0NyxlLpUyAZsuQEbsScdaZbI02ds2QxYcIErF+/HgsXLsTo0aNtj/eanDlpo1QoZ07IohG8UvJo7PFa5QScx5yJzdJYbPIpb2lv3BrpV2Pll7HfzwEerBuJcvFf2tgKTdY2t90agwGglacnZ2RWO69Aq1TxnLGwEHBhVT8RewBjwvTg312IMyWvIYct0qb5NhiPZBFqEucdC/TpzulilKwgTxo5hwlKdDFnCXYpHTkLOrNHEMhxFlPbXXjVf/8Yh7nLgf69gCdfib3LKGO8ZDghZ2ccIdXOmriP8X6yThxtRksl5kxtd2aJQUFHhzBKpU9b50yLCMcj6CY5k+NgHbk1apWz5A3yGSjmUZvnkNatUYy9U+3G190XcDhwjFJX0A5yH2pvY9kauySMlDM5K56bcEM5c5OEyESP1kUuFW6NQ4cOBQBs3brV0fe8Jmc06uLuoJzJyqRbcEIWU0HOnMacic3SfSXrr7iZEMTnT8CtMYYE8ulQQfTRx5ypESOaLicECfqVYHOalMipcGukzSTHccaqKG1yFlo4cUU1mjj1osw6aAXyvsvuuhFalcHg8Wk2znheys5YXEBvc5xk0ZKzsHW2Rqd48FLlPBlBYMIIqbFo3RrJhCAyaGqc2aEwj8Of9ueQGVSSJwgtlIsySk59anAch6P35dDdpFQDOVSilHGdZm6Nfhdch9VujbJrLF0fytDUBhDgrldBZpasnNF/x4uYs/OO5WL/A9HYD4zYvO61dc7kd87dF0jnuvM84+8FAxyO259Dbra13fdfHCOJsVHTsRsqZ4ycUYAkZz169EBbWxv+/ve/u36dRGPOrrrqKtdtAYC6ujoAoP6tqSBnMhminlx7POF34ia3O8Sc9evXz01z0k45cxxHGXNrbOJJ5cxFdTronJzJk6J8g2LSbkCUX9iUkxDZvckN5aynQTx4wK+s+NLEMMXdGj18DtGuopu5Nbo87B0lKzBSzuJudkmAvO9yDSdacja0sdbghEmbFEfEqXJm4taoLS5Ni8MmcmibySE6i0PDNxzych26WYrKuJfVCTf7UMBPkDNa5Ux2a3RxnLUSr4m4qy3lc0j7DHXDLiO3Rlrl7Ijl61SffaLoasyZQs6cuDVqlGAXFhoHlkh9+7X/4xXljNIVNV7nLPZOnTJaOtddFyQ3+G/5m3Se7sUxgcEmqUw6gpEzCpDkTBCEuBrgNhJVzhJJSU+DBQsWODo+FeTMsfKRIrdGGkL9ww8/6Lalm3LWt6+DQA4KpBs5c+zW2CiNfZVy5mIiDl+AQxQceNAHmstk6IwjORw7WUri4Spi5Iw2DbrOrTGJt8qL13M4MgQcEVK2BcmaPhQmxd0aPY05o08IYtQcbj+PnLg17vhou26bL5i8PeRPklfkI5Rut5dtWWVwPvfaKO7iSqtUGbg1FuUpqfkTQUZQKjwcDHDwxfzbeEG0fe5V7BLx9gx9BKVbmRqBGDnjZHLmXUIQO5CFrEftTZfEJS7gxUhK3ohcHLzgQFfsMXJrpI3FLa1tUH32QXRXOctU4pVp+tC734s6D0jOhUUZQOrbACDIizJOlTNiwTPDhWdR/Dx+2c2SuTV2SZBkyUnxWqdINObMq0ktTQZCIzu8JGdOJ9fa77kN2piz+vp6nHXWWabfdwvJxpz16dPHTXMcKXnpppw1r29G7hcbASgJKQCAdzFwwOfj0Bg7d7SeUjGPtVNuNoevH+Hx18Pc7duivLxLW6NKBqf6LyEM6sNhxuM8jiWKCgcDSiA7jXImZ0f0NFsj6CZqPE9MYr20x4Fb44Y7Vuu2+TOS70MHxkKA+/YA5Lrb1G6NHkO+X7Qqg2BAzmY+zqG0hztjze/n4gsOdm5yoUtEfPyzPiGIm+TM7wPanSpnMTiJObMDSc5Ke9MpZ/GEIDHDehzeHbl7ueOen0m4Ncr3S6CMo9TGvPpEMeF7tlepgW0ZnOINbPOwm3SpiLPuFbGtSqMuFrhbr1d2a7RTzsxiztyG/C4TaWNf0wgsIQgFSOXMywlkom6NXtRcA5y7xqWSnKViIk8DJ+TMCOmmnBUUFLhpzm4dc7b68Y3xv8NEqnFfpnuzIp4DGn0BFEbD1ORMVs48E4Z8zsiZbIboQsyZjNws5W+VWyOFTXElz8PnEK1yJtnjHX55hsOmCmDtJXTuRGZww63x3os49O8F/OVg4KnjHMaceQzHCUFkt0bi7rm5jibXFfOJokSoLZxftu+S/vciIYiMRNwaE4k5s0MrMeWIxxzaqJ1KzFksIZmL8ZykGyuZoEQURct5jiiKqG/jQAYJ+ETnytmvr3BYuAo4ZrJ+X2YQaIz1ISEiWsaxlu+U/ufhLTmTlTO7hCDabI2udmbyOrHzUmeNTSMwckYBrVujV6B1a9TasCeRMyfKBzkB98om2nIDZvvTLebM7YQgybo1/t///Z+r9jjpP8vXi+gR+5skZ7xLriCA9E6SM0FGHCpnnlWscOzWKMENt0YZOUSitaDfWVFjxa3RO4QpkxVEo4kle6HFlNEcpowGbpP7dcLkLPnnY3Ymh6tPlf7mHMScCQ5iZhJF1AW3RjcScJDninIcIAJCRIAP9ifnNM3kasyZT6klaEfOtPa4qZypyFkg9iChzNYox5pyAfcejAVkUWOOQ5jjEIgRas7C/W5mmd6tmofzmLNxe3MYt7fxvswgUAcOAYiSgm/zXuJEEQc27FDblO8uBRDj5Mz6OHnMBzy4Z0b2OE9u1flgbo0USLVyZldXTTvR96oOW6LkzMs6Z06Us1QoMbTKmRk5cVs5c+rWOHDgQNXn3Nxc4wMTRLLk7IYbbnDVHiduse3EsCJdVNx0a+R5oEEmZ3XOlDM3V4hVcJoQxMVU+jLyspW/gwFnRY3jbo1poJxFosYJQbyyJ1HlLODiggOgqB5RCg99D9c744grZ7RFseUi1B6RM58Pjutmxd0aY3+47dboPCGI+zFn5JCNx/baLBJpszW6WepEu6iruDZat1F1gz6ZTDJujUbIDBLPRYpXx6gWfdIdLtPdxWFZObN7LmrdGrNzvXlWiw4XGtMJjJxRIFUxZ3JiDzvlTLvfK+XMKVJR58xptj0gNdkj7ewxi9/rzLpiS5cuRVVVlWpbuqXSd/vekeezI+/kZIx0b+JdClgGpKx27UFp3Dfvou3TsUmIm6m/SMgzCNoxpmlGN9ZmjpkM7DscuP50tVujM+UsNiH3oJmUBBzWBknkzPi7biKapHIWyHTXptg839a9CaAWs5JCPObMaZ0zYorkNhmSbaJxjQVIku+NWyNteQh9EWr37HjiSg579wXeu5OLL4JxdglBYv/LypmbpU60oE2nH47oF4e+6tbX3YQgGcQ9oxhEAaKW5rLsIvxYUALfPvnuGQRAjMec2RynI2ceuzWmiXu1EzByRoFUKWe0E2utcpYu5CzdYs5SGcNk59ZoRs46K+Zs1apVGD9+PFpbW1XbvXJrTJeEIBzHUfcho+KaAOFu4xLklMi1DXTjRp4Uee3WmHDMmQvDPxjgsOAFHo9cwUtujZQJQXKiYRxWVxGzJ2afS4+jYf2Vv2lT10ei+jpnTX73s+vGV6wTLJHpczEDKUBma0wPcuZYOTNIpe+JWyPoJtaAvn5fTaN79vh8XDxG006FEbV/uLhINGwAh7Xv8jjtcC6+CMZF7bMRAoBPXhz2lJzRKWcdYfWC3geHT8DrvfZ2N5V+UFmAoqm9FiGm+x91H4jH+o6K19l0C4KPLuZMUc6kP7wjZ7IXCHNr7JJIVcwZ7UR2TyZnThI6pLLuWropZ3Z96NdffzXcnk4xZzNnznTVFhm0ro0qckYmBnBROQOArFhBzfpGWnIW69ceuTU6zdYYJ2exP9websEAMQmxmVzfsnUZjqvdBkAh1G7Z880jHK46WVrVjyahnDUE3A3CB1xQzlyepMl9U6DoQ1pu8kt+T+z/rUEGhCTgJCHIp3NEzJgvqL4HeKCcOXFrJJ+Nse81t5ocmyBo75k2IYhX71cpo2XsmhY2eRlzBgA//ZvD9afL90xelLEhZxrlbHPPbohyvLvKWZBe7QSkVP4yZBXN7VsnK2e2bo2C2o4cj2p2xuOnd8OEIIycUYB0I/RydZ/WBSxV5OyEE05wdHy6pdJPJTlLJ+XM5/MhHA5b9guzWn3pFHN2wAEHuGqLDFrXWHK+GiVjIVwsQg0AWbHkF00t6aGccQ6VM61fo9velgEfcQWbYT+uuSb+d9yt0SU7BpZweOpqHgN6E0WoaZQzTftsyUmwkrEFaGM9jHD9oEmuqkIAMdGnmBSRj87yYBYe6DcWRZMKXbVHUc7s3xt/uVXEW1/HyBnRe1yNOeOBqIPC4d69wRTI7oA08UsD2xqRUdUsffDIOEldtC8RoYs5c3nR6pDxHB65gkckSk+otcpZTZP0f5aLJXIzg8o1aGJxM4So7m+3n9ViXDmzOU5zz3LzvFXOaOM60wmMnFEg1cpZupCzZ555BgCQnZ1tc6SEdFLOqqqqEAqFUmZPuihnHMehpKQEALB9u77YrAwzcpZOMWdeZ9i0d2tU9pPKmejyy9/np3uhyVCUM1fNIAxyRs7kF6ybqfRJOE0IIsMrJY/niBV0G/emSFQh0/W+AH7O74UPhuzjrkFQ2scpOasIZGFVdqH75CzWB0SK12WEaMOrB+/nriExRB3GnMl9WuXW6OI6mko5o3C5kq1wVmTHGfjY77NLnsB1RPHM+vkI1EveGW7GnJEg28iqX2uVM95lF10StOM+HFGePwCwbAMHvw8YPdg9W1QJQSgWQTII1p0lkzO3712CCUHy8j3qRDG5m9Z1OJ3AyBkFSDKUipizdHFrlEmZbJcd0kk5u+OOO7Bs2TLP7JCRbsoZAPTrJ1VX2bp1q+kxZuQsP9/dAOFkUvt7Tc7s+hB5edJFxe2EDlw8sx1F3EBTBIGo/GL1asmajpwJgoh/lq/AKdWbAXgSgiKZ4+Mc1TmTIXqUEITnyUkjjVujZPOynG54uN8YtGZ459YoOkzcK9vmdpkhJ8pZpEVqwxbeh1af35MELk5jzuSJPjnu3XZrdFYrT50MBJCSeLgJXlYZbOax/hZNJ/PoOaSKy7MgsHLL+AT3szWS4MhFGZt+3RHRp9Lv30sqN+EWMgJEH7IhiwCQQbzv5H7dWcqZ4tYYiznLYW6NWlAN71Ao9BCAAwBsAnBBWVlZOLb9EABvAVgPIFpWVna4N2Z2LshJXDoqZ4nWtLKD08yI6aScNTQ0xP/ek2LOAKBv374AgG3btpkeY0bOiouLXbUlmYQgXpVkoHaNJcwhX7RuJwSRJ312teej7QJmDPgB3WKfvYo5o3VrjArAkXV6ddbtRUofryQEcVK8LOpighKtPfGsbTYxXpJbowTZdC/qrSoJQZxNQmRTPHNrpCDT4Wap47fzXknBCpmmJWdyLAxZ39DthCByggaaGkyy+kpa7/ZrjaOcyGqzJ3qVNVYV42UxznTKmctu5zJmPMZh5QmxNqJRzjTbMl1ek/H5uLjbZ3ubCLuAhCDh1jiroDcA95Uz0W/vhgroszUGs72SX+WEILsfObNtkVAoNBZAaVlZ2UEAVgM4RXPI+2VlZYd0VWIGqCeNXpIzecI8b948y8lsqlLpO4nvAlJb58zOJtIVNR3ImTYrovb7bkKOGzO7JmCuhnbr1s1we6KQlUE7ZRFIP7dGcjfp3rTPOBcDB6DEethl2msrV99Pr9yJ4m6bNhNZs1u6o9Zde3ieSAjiwHPBM7dGHghTrliT2RrlbHhePB6FBFNGyy6ybpMzmRHTKJ3hlhg588hPd9ZTnGPlTCFnKVDOKCaO8VqCHgafye6AdsoZr1WxPHNr5BCJZyO0UM7i8UveZmvsXkCfpbUjIuoWGzLcT9KKSKxTtjVRKGex9vm4eAA6Yra5zqvlRRmHbo0ZWd7cMzmV/u6onNEMqwMAzIj9/S2AKZr9J4dCoTmhUOhqVy1LI6QixTegnjA/9thjpsdpJ7pTp04FAIwYMcJVe5xkRgRSU+eMNg26V4W5taBNpW9Gtr1oKxpXS7Pr0sYX0kImZ3a1+4DUkzM7Qk2aI3Acrh68L74+ZhyyS7PctYeSnNVWqQ/wyq2R88nxOfbEg4TcXD2L3LWH55Rzp4VbI0eftY10a5QJpifKmYPYExJy07i9RsQ7cmuUOlJbbFy6fb+mjuPw4BXSuXnKhUavlTOfw2yNcpOIHqYG8fnpCDWn6fNeve7JhCA0iw5KtkZvDOqWT1/fMBxR7tm/+wwHIMXOug0hNmbaWuz7taycdRCrQ67fu7j6an2YPAzlVPrBLI+8ZHbjVPo0bo1FACpif9cDIJfWywAMjf39WSgU+qWsrGwx+eVQKHQJgEsA4Morr8SRRx6ZnMWdgLq6OtXn8vJyT65TW6ssOf/4448499xzDY/TXv/KK69ESUkJjj76aFdtk5WXaDSKcDhse+6dO3cCkIiIV20kuys2NjZaXqOxUSkCw3GcZ/bIfSMajVpeg7y3JLywS3ahrK6uNj2/tvi0DKskIolgx44dAEDVf6qrq3W2eKnCbtu2DQUFBab729s74q4iAoA/sgowpFeb6/csEqvYGYkIludet1KtnFXX7kJ5eZ2rtgBAS7ukDIo29jRosktefFwzpkyow949W+FmEzU25sRVp7qaOvjK6fpE3BVSFFBeXmF5rBPs3OmPk7OWhhbLNmpuKdK5NQpCBOXlO12zBwCiMb+3aIf1c0gLmTi6Pe7DsT7d1mr/LqjaKi1cxZUGUXR9jNU3+9ADAKLWfVpCiSE5q6ra7hqxrqsNxonHzsqdOlVca4+hV6/L7SRAyiIqRq3P216v/rxz1y60lLe4ZoeM5uacOBmqLK9EZtDYY2HnziCA4vhqUW19LYRy91On7Krl4+N+R8VOtFrcs/r6POTFiEe8DwntKC+vMf1OIojGhkzFtmpkWt6DEmTGGBGpUFft2InyPPcWsiOxcROxeQ41NuUDyIkrZ9W1VajvcP9d3x6RnkMdFM+hzkBpaanpPhpyVgdAzhJQACDeu8rKyprkv0Oh0BcAxgJQkbOysrIXAbwY+7j7aYsA8vLUqY+tGjQZkO6JhYWFptchiQcA7L333rj55ptdt0cmZ6IoIhAI2P5uOV4pKyvLszYqKpKW5bOzsy2vwatWhzjP7JH7hiiKltfQ9iEZXtglJ/XIzc01Pf+WLVt02wYNGuS6PbIabNc+gHJvZfTt29dTZbF3796WbpyBgEIWDxzLoWwDcPVfs1Ba6q66mJUdS0kdte6nm1EFcjrQs2d3lJa6m10TAHLypavwonX/zGoQsRG/xz/vNSAPR53prlssAHQrElEH6cVakF9gadNvWB7/W4hnj+Rd7dcNYRERTlp0yPBlWJ47M1NAROPWmBH0uz/u/Zuk/236EACsHbMVrcukGbb8lHTbnsws6fkS9Adtz12TVY1GKJNGjnf/eV1ULBFAn0DzWwUEYn5XEYKc9evbx7XnUe9KEes5KZFOt4Ju6Fnaw/TY0sIIDl+zQbddhLvtlJEZm9zbtFGOb4fqc89ePVBYWuiaHTKKi8Q4OevRrQfyS43focWVIgARgdh4796ru2V7Jgp/logIJz2H7O5ZZpYQd7OUVe28XOtnRSIQfdKiSm5WIUpLu1scKSAo6mM7e/bogdJS996xgcw66Q+b51B2ttQ2ckKQfoNLPYkVDOZIi3IB3oNnrsegaY25AI6I/X00gP/JO0KhEJna7UAAf7hnWvqgM9waMzMzTY+jid9xA07dGlOZEMTOJY0kuqmwx+6eaNuwuroa9fX1JkcnB5o4L609a9euxcqVKz2zJR3dGm1jzoj4lP87l8eWDzkcu5/7NsmxHnbDurVWfYBXgfi0CUG0bo1eeVzxPOHW6MA7RSZnOe56ocZizuhSau+9qhzXbJfGVTyrnAdisEjpThSJiPiVeEv3LATqv3H/xjlJCCK7NbbLbo2uW6O4DtPGnPkNlDM3n0eqTIQ2LntndmzDabs2Scd6+S6TY7Ucx5x5ma2Rrs5ZcbgN/SulxTSvYs6k2nRyQhD7Z6NMPOQyDkH3EzMjGnuYRNtpsjXKsZ1Kn3Z7aiu/yyI2aRAEUXJD9UGEAA+TW1FmHk5H2L4mysrKlgKoCoVCcwCMBPBxKBR6Ibb7r6FQaGEoFJoLoLysrGy2d6Z2HjqDnGVlmc8oUkXOEk0IsifFnNEmBNHa261bN9fT1mttckLO+vTpY7kg4KUtZjZ5hURiznwBHv16efTSl725bF4gbfXqmbfXLzS7iazulnpFpjliUurgJTtiMIeRg4AvHnDXLp8qlb61PYf8rCx4CB7FwAFKtka7rGTrtytJQADp73wP0ljHM/9R3K9oi35F33XE+rTjmDOP3Kp9vJKtUbCJhymuVTxlvMzWyFPGnPnCmkUir2LO/MQ4s1gEEUXgzbVz4Pc4lb5qUcbmnomtURzQKCmMMqHzIuZMLqFBk0o/GOvTHR6OM18wttBoQ15FUUngEuF47+aMPrrnYjqCisuXlZXdoNl0aWz7ywBedtuodEOqJo1kenOmnCVnU6qzNdrdEy+zfGqRCDnzqo3SkZzRLjqQ5nhFhAAlEF+waaL2FgHkU8GrcDwxntvfZnVYOyHwUDmLJpCtsWc3YPkb7jcSr0qlTz+u5Ux7Kza6bhJEeZJmMwnZvktJyw44UyKdgHOQwjrSqo6F8eJR5EQ58wsCRrVIMcJhj1Ki+h0kBPER4zAVCUHsxj3foXlQpSCVvlWiG+0jgfeKnHHKc8iODBVtVmLL4uTMA+VMkOuKOVHOiBeH229cXzCm5NkpZ4KSDMTtuqEkONlVkhWh7ppIN+UsVRP9dKxzlm6p9OlVmNSt3CRCzrxKvJGMW6NXSCSVvkdZvgEAvgAlOdO88DpbOQu3piZrm49XiI0TMuFV+zjJ1kgWMdYWpXUT8iTNbqK/fRfAE1OyzBJ3y0LI4GKTNFDU8JKVsw4Pk/9wATlbo/0z5oqKVRjQLsWBeknOhLj6at1GPmJiSVrv9uPSL2c5tHNr1JA3r0p6+H0EGbJoI6FR7SXjmVujT+kPdm6NbT6Ficm/wYPa89TKWcCvpNInE4K43odkckaRSt8o6Y7r6MpujQypI0MkObOqf5VqhcGpcuZlhr10Vc4EQbC0aU8lZ+monCXi1uhVfBdAr5yFNS88D0rkAaAnZ5G21ClnccWAMmYI8O6eqeqc2UxCIn7vVqlVoKznU92g3KbqzExMeH2cN/ZkxH43hbIYqYxll/V7QxQBKbW/AOm3izZ96GiisLq3yhndPSNdMb2sc+YLUI4x7X5PlTM5ltLCphp1mRrOoyLUPEfWprOJeScTksVGvjfKWexd1m59zwpyjJUzt+HLiCl5FEWo/XG3Ri+Vs9i7bDd0a2TkjAKpnDReeOGFAKxjplJFFklSY9cGF110EU488UTd97yyyUlCEC/BcRyVTZ3h1milVnWGW6O9UpVebo2qSYinbo3S/3axHlrVyKthJr+7OUG0vCd6cuadUiVfyUmdM69CK0jlzM6tMUpk/xA8fC4KlLEVoqjEnL00bhxyhrif7RNQlCqE7RdlIrGU5JVByVvEi2biyIm1g0LdYY/umSohiM09Uytnij1um+ajTAiiXWXwahEk6CfrnFnEnGnIopcJQeKKuY1yRi60ZcY+eBJz5rNvH0Dqb8HY+67DQ+UskEE3xgSRdGv0UDGPK2fMrbFLIpWqx+jRowFYk7NU2kOrMrzyyivxv/ekmDOALinInqqc0ZJXI5u8ArVyRvztZcyZ7E4k2s1jNZMQPujRCrGPU2K8LMhQWEPOvGohnieKLDt4x/JeuTWSkzQ75YwYV166NYIyAYcoKvfJgQjpGFxGLH6Mxq2xUiJnVQGX02oSIOOFRAcFab1NCELp1kgqZ55YE7uOTB5sO0aKYvADdHF5upgzj4pQkwlB7PoQGfuZJZOzTow5iwpAhkEqfbfhD8p92vo4KeZMSQjiFWQVlWNujV0TqZxYBwLSE9JK+UmlPU4zNpLf8QLpFnMG0KXTT+U9SySVvpeuqDT2GNnkFeTfapfRkyQCXq3GAgo5o0k7ToIPeqVUcfECzlaEMdqmXUL3xBwp5iwBt0avyJkqW6ONchYhlDMvXdLEuFuj9XGCqLhZeUkWnZAzIUby23gPE4LwRFp2By5O6eDWaBZz5rpNsguYaPMs1grmHr06ggHK2E6tPR6O+7j6apeNkLinsotjhgfKmezWaKvgC0BGbM70+7teKmd0fVoUlVIDXqnTAFjMWVdHKl3S5LizdHBrBJxnbATSI5U+uT9VypkV+dhTszXS2mNkk1cYMmQIAGD58uWWx6mUMw9jzuTJDWfTRbSqkS/gzeObo1QZdMTEKzdL0q3RQRfxKokLzyvExi5+KUoEBnqZaU+kTKUvispL38ucv3xsksbRZLOM9TGZPHnRShxHGb+kgZfkjNbN0syt0XWbAly8T1gqsDqpyiO3RmrlTPMu8+i5qFbM6ZWzX/J7AfDGrTEec0ZDzmIrbYFcDyS8GIKZTtwaU6mcMbfGLok9WTlzmrERSI/siF4TMhJdwa3RS9DEwAGpsykUCgEAli1bZnkcOfH20q0xnvrZ5udriYB3yhnhRmgxg9cRE8+KhiNe58xJzJnPw2yN8fgxu3IDZMyZJ9bEQBlzJhAxZ4KHUh4nkzOKmDOZLHmZGIC8Z85izlKRSt/OrVGx10v1NeCjG/f6mDNv7MkI0BFY7aKVV6n0OY4jlDMbl/hYn/41p1t80SHogbulECMfdllao1El5syXpdyw3sXu2qMoZ9bHpcqtkd+N3Rq9o9BdCKmcyMrKmRU5S6UK4zRjI/kdL5Bu9gDp59aYbuTMqVtj//798c4773hmT7du3QAAzc3N1geqsjV6Zo4ymXAYBMR7mI1QoHAj1GaX9CxBCaGcOXNr9MScWPtIsCOLZMxZRoaHypk8SUsb5UxqfJ4mviuunHn53lBivBzFnHE8XrmJw/i93bVHUs7oatOZxZy57pLml8e9aL3ooL2wR/ct6FcKdVvdM12iJA9d0OUEHFGb7IiyizrZp3PMS9cmDDnmjEo5iz2wfVk+LHqRw7adwOA+7raVHO9nV+xdRGrqnEEmxLshOWPKGQU6g5zRJgS54QZtfXB3kW5ujemm5AFdw63RSzh1azzmmGNw4IEHemaPXOC9ra3N+sAUPdDl2CjO7p5oV4g9JENRCqVKZ65XMWfkir6DYeTL9Iad0SqLgDpb42mHSd85drL7NsXdGm36rCAQMWceyjCyWyNPEXMm9zF5Bd27mLPE3BpPngqM38ddo3wOilCTjIx0a3zwUndt8quUMytypv7oZcwZ1T3TujV6SM7isaY2/Vq2NwpvyZlIGXOGqAA/pJUZLsAhNIzDiQe5304KObN/Dsl1zvLzvVTOYu/WFM6/3AJTziiQyom1E7fGIUOG4KGHHvLUnnQjQ4koZ14jXcmQk1T6XsKpW6PXZJqWnKWqieIKj20Ka7VBAY+e3qqYKotJmpAqt0ZOmZRak0X1PtJ9x1V7eCImz2YSQu7tVShi+yccehZ5YJQ8IbVTzgDwsUO8zNboy4yRMyrlTD+RdRscyLTs9H2og+c96dZkgWU7JU80+Puqk4FrT3OZMNLGUurImTf3LYOSnOncGj2KOQMAUfaSsUkIAgPlLNeDZKSKW6ONa2xEdmn0eTs/iz2HeJuXpyACwdiNmzjaw5izGFlkbo1dFOmmnMlEqW/fvp5PZBPJ1pgORajJdkmVWyNtzNkbb7zhqT3pRhadujWmCzlLFTujVc60kxC/h3W84tkarSZFqXJr5In5oMWkUdt8/kzv0qALtCoMqXp0CCjp7k0jySvoNMqZbJSXZEgmxr6oAFEULce0GBVj5MlLj4vE1OAw5x05oy3HQEKOvRxc4oHqQasu6opQu24KALVyZpnJVquceRgfHKV0I5T7GFnbMMcDchZ3Z7axxx+RXRq9dZaTibGdciaKSv03f453NES2h/NyJcojMLdGCqRrQpBUJL1IN7dGWrKYSnLmxK3x/vvvxznnnOOpPYmk0vcSTt0aU0HOpmUcjzFLx6J5vUXcWYoEc562+Ktmydor5Uwq2Bsb91ZkSKeceWOPys3Sas6oscef7ZFyxtEpi1pEmj2M8qJVzoiYM0+VMx+HDpl8aIuVaxGREwPEFim8cGskY86sChobkDMvoCpCTeH6KUM2z+fBwoyPNpZSt8u7mDNFXaQn1JxHdc4AQIiphHbKmayGDh/ssVuj3759BEGMp9H3ZXlX4wxw5tYokzPeQ8IoE3WWrbGLQp40ZmRkYMmSJZ5ey4nq4aVCJSPd3BrTjSwCzu5ZKgh1uiln6ejWOCV4IIZtHYHWcnP1TFWOwUNXmURiziLgvFPOKCdpuvgvr2LOKJUz7SPK71XMGamc2ZEz4p6G62wqsyYDOfbETjkTlX7mpXLm90EhZzYFchUXMO9izjgiTtAqAYehcua+OepaeXaEmmgQ+ci1W91/ftP2a+1zKiUxZ1aEOoUJQQR5PmRHqGPtJ2cLBLxxa6SJOYsKQFAmQh49E2VQkzMROLq2HADgz/bOJvm9ze+Gbo2MnFGAVD3Gjx/v6bWcqDCpmOinW3bERNwsvYaTVPqpINTpSs7SSTkTYvTDcjIbu53RHlnI7u/BmzUGXlbAbGPOlD+jHOddtkaObpKmVc68um88UYTaCVkMeBVzxhG3SrQeSxyxK1zvJTmji60QRYVDe6qc8Urx3aitciYZElfOPLCHrFFlqcJoTeM472LOZHscuDVmxMpnHDrefaOoY8404D1yH5ZizuwXHXTPIQ/dGuPZEW3umezyHSQytHrh1oiAfcxZNKrUOPN5SIQAwq3RJnMTH45iWGu95zb5CLKYTnkKaMDIGQU6Q/VIl4k+jVKVyoLG6aic0aTSTyWhdkLOBg8ejE2bNnlqTzrGnEXl6bVl4Lu0r/mC4Z7aQx1zRuwXPB1jxCTN0l1Gs8HDmLP4CrrFO18bl+KZWyMPgOMUlytLQkQqZzbFf5KASOnWKIgieDnmzMNsjT6eUM7arJ9DcjyI9wlBnLvIwSNyxvMOMv8Rf5d0B355hsNfDvbAJtWijMWBmmdmoMCD6sqIKWcUzyHtopaX74+4cmYbcybtLy5SbMnwsAh1tF3AuAsE3P+mvp2kNPoxt0aPiLQMWuXM36Y8C53EXDq2x09ZWD0NwcgZBTojxitdJvo0bo3afemgnKVbzFm6ujUOGzYMAwYMSIk96eTWKMRmHzTFVnkPV2IBIt2v3YIDqZx5OJFVTdIshlkqY85o3Bq1tnoVXyF3T5qkKeQ989KtkZPdp2iUs9ghntY544EwF3sOWShnqpTjXicEgT0ZStXiOsdx8fIHtvFLxN+8j8OU0Zwnz0ifj1yUsRr46o9eZWsM+omEIJSp9AddOdATW2TIZIjWrTEji8OphwJ79wUG9vbAoBgZ2rxNwG9/ALe9rG+nCKmceR5zFksEZEPOfB3K0yfa6t2TyEcq5nYZNtMMLFsjBdJVOUulWyNtJkLyO14gHZUzmnsm70ulWyNNKv10IYuptInarTG2i/P2fQZfnJzZHEi88DzPbJeAG6FX7MzHK3E3lmRRY6pca8ttcBwHnhcRBYcAROrkCSPuG+aJPQDiCUHs3BoFAXHlzNM6ZxzQLi/sWcScyS59EdVimlf2xJ5DLVaLVvptng012ol+KmyBZE6YpvZaiua4fj+nJOCgINQVfYrwp7uHemqTbI9AmUrf5+fwwd08BEH0xA1dztZoVRRbijmLzT88Vs58MW+FgE0CjkCHMjexGo/JQlok4pEhChA6BM/dOt0EU84o0BluhOmWEMSKDKW7cuY1nNyzdCFDqbSH1q1RRtqQM0EeZ56aA19sicwu3S85BAUvXcBoY860K+geujXGE5RYJQTR2OrL8O5FTOsCJhd8zn90Anof38sze+SYM1vljPjbSy8fnldI4JbXt5oeJ8QUGi8XGwCpbzb6JL+ycK25gmnUJF5ZJvpolTPCAq8qz0O6Zx28vdqZMnkRiKuLTpK4eIl4Ag67+n1yQhDZzc+r+xYjZ36LVSvJrTE1MWeyMpdh8673h4n9KYp9Xfibl74C7oORMwqkW7xQZyQEcaKceUka0y1BCZB+GTbTjZw5dWv0GpmZmRBjbwTLVPGxXb4UuTXavqSI9vFUOSMSXlgWodbu89CtMU5GHdQ583KVmIzLs45bjB2f47E7kaycCXYLaYRy5rFr7OC2JgDA1je3mR6nKGfKvTrxIA/s4YGm2CqIVWKWVCpnMjmzy9ZIIlDgnbMTGScYtYgTVH3H434dV6qslLzY+BNT8C6L3zM75Uxe2PMwcySAeEKQgBU5iwIZohxz5u398udK/TNoR84I5Wzvm/fyzB6eU/r0qnW7Fzljbo0U6IyYs3Rxa0w35Swdi1CnG6F2UucsXRYcUmlTcXExRowaAayFZYZEOQbMc7fG2As/J2ITk0QqZ14nBKEqRqv57KFyRuPWqCWLXk5kaTNaxl1jPVQ8AGllPsxxCIgixLAILmh8PVXMmcfKGQ3kSXeU43DyVOCEAzn8xQNyxoFQziwSs6TUrdFP59ZImhT8//bOPEyOstr/n+ru2ddksk8CCYQ1rFKAgKAim3KBiwKCIAhewCWCS4CLiIDbVeQqivtPgYuICAIqooAgu2wFhshqIASSyb7PvnTX749aunqp6go99VZN5nyeh2d6uirUmaq3qt7znnO+p6M2ImPsiawz/+j3t8kb4Y8qddjBDOGcuc8EBckyjiBIpd50mirnzP7/e52z4qbvBZGziJtQO5G5SpGzGrvmTDt0Kk1zGqOzJw0D9gu8QUuOwncYJHIWgqRNZJOW1qiy5uydSOmrUmtMikOdtMhZ0tQaW1pa2G2epcAYamId8VvfeYHP7dnCSHdAdNEbOYtaECSEpHZx0+eorltBc9yt6HPWtFNTJPZAUV1eiMhH1HdZWMGLnKkuchYGJz1sBI2JrfDxozWaG0ffLityVjmt0TumP7Pju0fdjgJCyrJ7qZkQjTIiOI3D7XfHYMCz2mNuOmrnLExvOueDAufMbfpc4ZqlTDXOmSNQkvG8Gzb1FO6TzUGt7bylIhYEydiR1NpsNnDOmBm23nNaxPbUpPMLDvUVe9UkC3HOQhBHSloYcYmkpDUmMXKmkqQ51ElzzpKm1gj5XjhhZNCjjpyZffnz0vd2v/+Ontss6siZmzaZgCbU3shZkMSg91reM2FmQQPYUbdJ86g1hmjYm4o6+hq2j5eZv0xJiJy5ao2a5uhjRIKmedIaA1QznTGdA96qb3H/bRQ4KXI9z2wMlGYviJxNjM45KxBxCVlzFrXARL7pc8BgdQaygvdG2CbUjpR8lA2xATYOWQ+W+lz+wTjxWJNLfpq3L5vNN6GOWko/ndEY1FKkCI6+ppxnVG209jQ15NMa6yVytu0Rh3R9UibWYzVyNp7TGpPmnNXUWBOKRDlnqcrOmfMsj7KpKUD91Lr8LyEXHaKsOStoRrsVzV+jrDkbcaJCASpgzsS6N5XhJzOi7U1XIDseouYs8rRGzSM7HjCRzeXyDuNIlGqNKehOV04rzac1pmisq7BzNfZo0Gd3ex/pCUhrdOqXPIM5srPk8UaX39Llb5Pnc017hJGzFPnIWZC8ucegVMSTaycEG6Y3nYrlWjdyViFavvemdUD0bVi22JVJjdmRgnfH1b+FtZtM/vS4yYPPQ73T5yxqKf0UDNgrUSOBKoz2uz7i52JTvaXWCPno4VhBnLMQJFVKX2Vao0jp+5O09gdJk9J3nLOhoaHA/VTahP2OCp5Y2/dZxOa079/mfg5asfbaGmVaYyadj8wFphMpjJyF6VVjFv2MkrA1Z5o7CYnWnnQ6L0cfVA9jkn/pR3me0im4art9AWjcwb+mxPSoNbY2RessOpPGbE/AIpq9KecxJbqas/z/eHBd8LPRIUoBjnTa2zg83EQ2FbHARC6MaIozF1CR1uioNVaInM3t3QLA0KqBSO0ZyKUY0lJkMF3RD4cPf8XkhC+bnHu1Sa0tKRt1WmNK89xnvQGLsYoWPpvq82mNdZF2dhx9xDkLwXhuQh1GHVGk9JPV/iBpkbPaWquIPUnOWbi0RpuIvbNMWuPFxnaAwPSmgibUEZ6jTNrj/AU5rwprzkbcqFBAenXWUW2LxIwCUiGjiw5RD2krclY5rTGXyzuMI7kInSENttg1XkGTZmc+mUWjNboSQTSg34mcBUwanWtpKpjpm57nSibA6fLaEqXanlcQJBvQm07zpjVGHDnLS+lXjpirSGt06gQr1Zw5dBw1JUprGB6xMgXAjp55eHxR/rMqQZCCRZCgyJmT9hnxJWtuzC84SORsGySpE+vxKAiS5MhZUhzqpDpnw8PBaoTxOGcB+6iqF/KsWAdNilRJ6VuRM/uQld+veaJMa3RW9INqc9x6IY1MxNfMm0YYmOLkLOpH3Y7B00A48ByZebXGKKcq3rrFYMXPvEPdGp1oG6kU9DnpVkGRszIpclGrNUKFiJjn+FGqI3rTGnNBaY2egRO5IEhq65yhqHGaPlOhz5lT39iut0dqz3AW+uxjNeb8Fx1qFUnpF6Q1BtxnqtK9C9Iat0JELgmIcxaCOCbWSREECZPWWLxNRZ+zrVFrXLbMvwnqaJC0tMakSeknMa0xTM2Z+wKJWu1Tg+FQhfj5j1EKgmTS4aJCxQuRYVOhthbL8aic1uiV0l/9x+idITetMUw7hoiHdDqVj5wF1pyZ+chZlNGhAsXPEGMoF3XkTIN+exKbDVVzVvhvo7LJIXDSrKjGK5XKC4IENqH2GBS1IEioJtTONVMxmw0ZOXMubSriZlVDw/lFh6as/7h2pPajjnQWpDWGiZxFfM2stEbLnow4Z9seSUtrVDqJfQdNqJMQOZO0xuSMoUSmNTqXISBtz4kwRK3W6HU+Apu/esZ8lDLombQn6rEVgiADqwYjsScVMq3RLT3RYGJr9A51GOfDnaQp0E1wRFPMoBQw0y23jDxy5i4gBI2hbN4ZaovQOfNOGoNW9E235swjCBLR86jOcwWCBSby27Sa6MZ12vMcygU8hzSPqS27t0RmD5AXBAlaRHNPo4JU1IyTOlxBrdF930dr0/AI9Nrpw0GRM/eaRf0cSuX7igU6Z87DOuLz0+xRa8wEiEklEWlCHQIRBBl7NWcqkbTGYBKd1hhCECTq1AtN0xh2Jo4B8sMFkbMIbfIKgmxN5GzmqTMisccrEx8mrdFUcY+lwknpK4u+etIIc0HRRe94j3IRTfP0XQucWFvbcppGS4RpjZrmSWvsHSlp1OuaU0atMSrWtDXnjxsw2fc6Q6maqCNn9rsjhJS+2ZRhp4t2jMweyPfxChM5UyEI4oq4VFBrdKLTIQRLq2JoJJ/OXBMQwtcUqSNqeBZBAmo73YXPiKewdbXwh47teKh9Ok8cF+EDJgLEOQtB0iJnSUtrLHbcHOcgCsIIlHj3U0HSnCFRawxBmJoz+6eCNRA3rTHQOVPU5yx8WmPhtvoZ9ZHYU1BzFpSy54nCRM1WC4JErdZY0OcsaAypqd0JHTlz69806mujtifFiKaRyZnkBnNlUwldBz86U/I2pTXumTCTYzcuD0yT8wpwRB05GwoROXNOzuDpO5NujDatwAwhpe+i4r2Ryo9pM2f6OjuO8xG1lP7wSD6dOR0wJ0orcs7CCoI4746oz4+maVy8oJn+QZiwnbo54WgQyjnTdf07wMHAUuAcwzCGi7b/N3CSYRj6qFuYAJLWhDqOtMatiZxF6ZwlsQn11jiwktboT+LUGt3IWeTmMOI4Z0GCIJ4pY5TRobCRs0jz4jykUjDsTNIqiF2AmshZeEEQ576P2J6C1E9/ezpfWBGtIY49nshZGAffhEhFXNw5dSpFJpu1nKEyawlmkeJnlEOpINoZMIa8JkQZOUunPGqNgRF8NXWUllFOjVeISJ4Cc1JpjWFNo8Y0yQ2bpOvKn4QUapyPoeH8fZ8JmBO50VcFQkmOKmq2N8g5s/eP+PwAXHjy2HLKHCre6bqu7w10GoZxKPAqcFLR9hZgz2jMSwZJbUKdlLTGOCJnktZYnT3jPq0xTBNqZ/Uz6kZnwEg6RM2Zd8hH+FILGznLqYrCaJ5amKCaM5VRD2/kLKhu0fmppAl15dTPOc8vj9QO1x5v2meQ85rNpzVG6Zw5j5RKtYtegRLvv4sCb4uIIOejQLo+wshZyqvWGCil7/yDyExxyWYqCyW52XwK7CmIUAfcZynn3aEgrdFZBEknIK2xIDU2wDlzBUEkd8+XMMP5YOB++/O9wCFF2y8EfjSaRiWNOFLSkjLRfydqjY5aYJT2JCly5r1mvb29ZW1L2hga72mNrrR5oHPmvGEjN8eNnAWvWHs+R+ycOXUMAysDRD4U3YJWg2X7OTQYIgqjOHIWPIbsnwrSGodDRPIGGiPMHfRQIJgSNKQ9DrWqyBn41+Xlyqg1RmZTSIVNb82ZFnHkLK/WWDmtUUXobDgTQtrfPT8q5kMeB98nfdg0TdcZSmWivfGHR/LPxkzAqHWsiPo5VCC8EyJyllYQORurhLlUE4At9ufNwERng67rbcCehmE8GYFtiWE8C4K8E7XGJETO4qg5W7JkCc3NzXzkIx8p2UfSGpPmnNnHDBH9UZF6kbUjZyMhCvEh2r5ZmTS80GQ95tc9vM53P1XBa6vmLDjiAZALmL+NNuFrzuz7PuIh5J3oB9UvKXPOvH3OQqQ1KoucOYt7fhNrewyZCiJnBamfQYIgXun6yCNntrOYkLTGbI01KMwAZ9F9hquKnBHc1iObNfOmRHyO2pry91lQ5CzlLjRGHzlzo8GBjcPVlQyMVcKEODYBrfbnNmCDZ9vngeuC/rGu6+cB5wHMnz+fI488cquNjJu+vj4ANm3aRFdXV6THGhy0Vqqz2azvsdavXw9Af39/5PY4ohLDw8O+x1q5cmXB7729vZHZtW6dNVkcGhoKPEaxGEaU58m5ZjfeeCMAd911V8nxenp6ANi4cWPk1yzMOdqwwbqNVYyhgYEB95hBx9q8eTMA3d3dkdvU3Wtdjy2btvgfy36BbNy8ga6uzZHa49RUbVnv/7cPD+fH9FAmFdk5Wr8+w6raBgAGNg34Hqe3uxev/lVU9qxZl2JEs5zF7MCI73HWrLHOjxmhLQ5mbpJbl7d29ToGugZ8drR+rN+wnq6uLeX3GQV6e1totu1Zt3odI13lU4g3NRS+8qN7TmfIaR2AFWHwO073Wus+NNFYt26V2yx3tFmzJg1McSeOK5etpE6rK9lv1SrLCXBrzjAjO0fZkYmuPZs3+D+HvGmNG7Ib6OnqicSezZvq3Z5Q/Vv83wvZEesc9fRH9553GKDNOmaf//yjp6ePCcBIwJxptBgeyl+zFctXUDtSutgxbC+O5IAVK6Kt8fz5hRmevMCETRVqzuyf6zesZ6grmpYnACPZfAr6lo3+7zLTlrXvUzCGkkxnZ6fvtjDO2T+ALwI3AUcDT3i2zQUO0nUdYCdd1y8zDOOb3n9sGMYvgF/YvyYnF20rqKuzHuIdHR2BJ3M0cJyKXC7ne6z29nYAmpqaIrenvt6qmk6lUr7HKq4lmjhxYmR2rV27FrCiQ0HHKI7eRXmempstSWRvxKf4eA0N1mRXxRgKssNB5Rjq6LAmaXV1dYHHammxeua0tbVFblN/+wCrWUNzY4vvsTTeAGBSRwednRE2YQJymaUANGQafO2pSa93Pz+2785c0dkQiS3dIyZZzXJGM6mMrz2NjYUv1ciuWa3JsGY7plnN9zh9K3rZiJXWGPX4qa3NuZOQCS0TmNo5pex+zhiaPHUSnZ3NZfcZDdrbcgzY9rS3tNPZWb6tQareciLvb7e2R/ac7jPJOUWSWZgxY0bZiPjaietYwlJywKzOaUzriGZlf0gzAdONLk6eMIWWMtdjeGM/63jFU3MW3Viqr8+59jTXN/s/h8zXAZh6wVzm7DcnElsAJk8yGXLue9P/vs+krMW/lhZ/m0eLVJN93w+YvsdqarAcoHTG3+bRorExf82mTJxKU2epPPtAX5aXeQmT6J9DnZ3QecQG3vplsFqjEzmbPGUSHZ0dkdmTy5lktbcBaKxrDHi3vgZAW1v0Y2isUjGoaBjGQmC1ruuPAfOAO3Rd/7m97eOGYRxjGMYxwOJix2xbIWlS+nGkNSZFECRszZnKmrQw0vVOGqbKtMakSOknMa3RbW6agCbUkE9rzAapNdrn57ude5BrL131Hy0y6XDKf2FSQkeDgrTGEH3OVJDSPClgIcQTIk9r1DxNqAOumRsNzkQ3fsBK/zI1La9h43OK8uqI0aY1Oo/drHPf+6TH5ty0RgtlgiCBaY0WE46fHp0x2GmNYWpfbaIWlwBLSj+LBlnTP6XZ7T4fuTm2OFGwcqzTCzqnwB7It1fobA9Ia0TNg0jTPIIpge8O2xypOfMllHKDYRgXFX11fpl9tkkZfYjHOQNrQl9uMh+HIEhSpPTD1pypVHPcGod6PAqCJFKt0RmigQ6GswiiQq3RrjUNqjmzMYHGCOfWmbSnXihwoh+dDV5SKW9tjn9/obwgiBqbnN50Qc6ZQ/QqaVpelj1Idtyt8YrUHNcZMjUNTBMza5avk/SoI6oQBKnUzNzrLALURqgmVyClH6LPWZR1puD0OXOaUPu/OzSFC5+ptMZAKk1TboRsX5ZUW5nFTYUCJV61Rr/2B46oTE6Ftwik7MbYx+wHP1tYfh9VqrGa5nkOBSykaW6fs0jNGdNIOV4IVEaqvMfxczDi6HP2pz/9yXefYsdNhVrjSy+95KY4hrEpShxnKMj5GM+CII5ao1OblwSb1my2jvHi6/Er7QHkMk7krLL8sKlpNETsnIWTrlcUOUsBmka/ExHuLh8Rdh6XpgrVNs1zjoImIc5PlU2oQzjUUU8cnTlgpX55riAIUBOxIwSVI8LOM8jZOnNydDYViEsEOWfOTwU9qsJEg92To0iAw1WQ9FH/M1VGzrwOtV/kLKtukQjyCp41AatlKdfBj96eXMo5P5WzrSRy5o84ZyFQGamCypNr1c4i5MUayhFH5Azgl7/8ZWibomRr0hrHY+TMqVtMknN2x2PWz/ufDpoUqYucOWmN/d05lq+pkLILXP3paNUasyFS5FStf9inhu6M5eQPbyy/COLao2iSFm4iq0bZrlDiu3IUJofG9+ZHq/wHHufMzybHoY44rdEZQ5UcarPIwZ9ZvpRwVPC2Y/CTZYf8NUtFPIjS6bwjFEa6XkVao9XU2F783OLzflWoZGC1rHAUP/3UGq2fKhaJIK/gGSSl71qi4N3qtKvIBkbwrR9O1E8oRZyzECithaGynL7Kif4FF1wABE+s45DSh3y6XBibosRxkpNS45U058wRQ+nv7w/cT6VNfcO2/HDQmz2GyNmz/8ox6ySTbLlIgx1l+K/jNHaYEa1zlk/dCVfjtc8v94rMHmcO2Ju2++Vt8HHOilLSosQ7SQusObN/Rr1C7G1CHdQc1xlDJ39A4wunROicFUfOfKKszjXLEW2fM+eVNFJhYu02obbt75wUnU0FsuOJiZw5aY0hpP0VLYJ01VpiTN0vd5fdJ9+EWkWZR+XImTO2VDyHALRMiD5nzrtVxbssXTmt0Vm0inCqOOYR5ywEqp2zSjVMSYt6xNGEGvK2hbEpSrbGGVIR7XTOf1Kcs8ZGS9HKaUmRBJsGRqxjpAKceHfFWoUgiP2CrbFnGsPl/HxnThTx6fEKgoQRl1iyVyczToxOrCBVHDnbVN45U3jL2xPr8IIgatIaK9cvqYp6lETOfNIac7akdk7TiPLR6EbOCJmSZu/X0Vp2t1GzqVINHHhqziK+Zmn7vjexHAzfhRk3rVHNIsjrDdZF6H6pvHOmWhCk0jVzXruqHkdO5CwV8ABUJQgCnsbqIyYbtph88yaTrrVF979EzioizlkIkhY5UznRd6IeSYycBTlnSas5G89pjWEjZw4qbOq3L1UqRCqIivSdnD17rLXHSdlAg6LC94LIWZA6oqJJmvO/73YjZ+VVP916IUWTNEfZLnCFGDUr1gXOYkA6kVOIryIKA/kJaqUUME3TIr3vtz6t0aKtOdrUz960tZA24peyR36SFvUiUdqu7Ry2T5afOJGmMAqTSsGmjC0otcnnHClclEl7F678BEEURvAhr9aoBTR7z0dfVbzL8gsg519j8pVfmhz1pSLbJHJWEXHOQhCXIIjf5FrlRP+dRM6idM68OP3nyhFH5GzLFv8ms3GlNfo5qUl0zlQ61P2eyNmSFeWPqzJy5qQ1Ok14s+WGr6JJUYFaY0D9kpuqFnVUyD7/vangiaypsNYj5UlrDE4Bs/ePul7ImyIXIq0xcoc6ZORsZETRGHIEQSrUUuYFQaz9WkvbWI2qTc6Cg180GNSpNbqKlukK49qN4KupOXPu++EtlWpN1dgTWvEzcmssnLRGrexLw95HZVqjffNv3Gjy+4et715eWryTCIJUQpyzEIxnQZAwzplKtcbe3l73czabpaenp2zEKo6asyBU96ar1HIgiWmNzhhTEzmznTNg/vf9nDP7p4rImaO4ZXsY5W59VUXdhX3OwtScqZk0DtrhnlV/XF3eHpWCIB5lu6CJtZtNFHWkKhVOPdKZFEWe1mj/710n36fmLOucOgViFwDDVJpYWz+dmrPWCHvPpzzO2VCQc+b8VLQIko+c+WReKJzop8NEFxWmNRb2pguOBucUzRedtMaWuoCaM4VpjY5a47+XVk6vTktaoy/inIUgrpqzJAiCOM7ZwMCA7z4q0xq9ohsbNmygpaWFXXbZpaJNUVLu7/3+979f8HtSHfwkRc5+8IMfAMFjbbRYcFo+cra5N3hfFQFz04mc5SpHziJPb0pban45gJx/1EPVpMg5/wP2bHD94xvo+XdPqTk5dSvW3knasv9b7ut8aKrSGj3OYmAkzzEz6iiMEzmjwkRWUeTMmZM6DYT90xqLImcROmfpFPSkgxVIwRPBV+RQOzf05oXlM0G0kg/RkUp5Imebg9UaVaQRFqQP+zahjidylsqZbLm39BzstWP+9lKy0Gjf/BnTb/5qerJSxDnzQ5yzEMRVc5aEiXUY50xlWuN+++3nfl64cCEAb775pq9Nhx12GD/+8Y8jswfK/71f/OIXC34fz2PIiZytXbuWm2++me5un8Jum1deeSVym9rsZqZpTF/nS1UhPoBpS9XVBKY1OoZFa4umaWQyIZoa21+rcDwABjyFUv1dpc8jpelNKZgynLch6yM9riytMe2pgQsh7R/1gkNJ5Mw3yqBmRT/tOvjBzd6La86iTGtMadBjR4WGNw3H7uA7aplt/VYGw/NnLSy/o8L7zIqcWQ7siG9ao9rIWYd93790Ufn3lDuGFEfOzBGTpjJl+Hd+Q6O5Tr1aY8ZngTyb8zge4oH4IqcmBKprzpIkCJK0tMZUKuXK+4eRrr/rrrv4zGc+E5k9AC+//HLFfeIaQ0lwzpzIWU9PDx//+Mc59dRTA/cPai4+WtQ2WH93TS7nOy90J9Yqas5qi2rOyl02hbUedbWVmxqrmoQ4t8ygZ2ZRrv7GrTlTlNa4tiY/E/J1iBSpNVpNsdPBtoDymrPhCqIpqtMaB7Gfiz7OtBs5s81pa47WpqyWIjuhDnLQ+0b5tO+Umxob7TkK2wRctSBIxbRGxVL6e/ZtsuzpLm+PakGQVL0dMe/PlkRX95gDO3Zq+WbqCs7R+t7gyFk26xlDis7RWGTcO2c33HADhx9+ODfddJPvPqpT0kQQJBinv1lS1BEfeOCBivuoHkNOfZef4xhH5MzhL3/5i689oMg5a8xL1/udApWRM0etMThypiYFDKA2421qXKFuMWJ7rBrKfA8m8HHOlNaZwr0TOt3fs/0+ynZO1ENBzVmYpthuHWXEtR6uQ22vbGT7yr/LVKU1OmNoIJWfyJbD7XNmL83M6IjOJjfVcsc2ADY/v7nsfs4Yinpdr7Ym3H4qa3ELBUGS0YT66pl7BO6Td85UWASZFtt5LeMs3ve/TghbnUPtvDckclYd4/7ULF26lIceeoglS5b47qM6Jc2JPPlFhpLW50xlzRnknbOhofKS2l6bVESqLr300or7qB5DDieccELZ71XaU1NTU3FMeB3tqCOdALUN+dW9SpEzFeKjWsaq8aoxTVKmWdY5Uzkpqq2BESpFztQpcKRShW0Pnj7+2RJJfZXpRCkNhlNpzGlWVDg36JPW6PgeCtL2nLTGVXevZvlvu8rvmFMzSXPSCN06OB/n1ZXSV9Qzy3UWfewprlucFqVzZp+j7ATrHTu0sfz7zBUEiTpyZj/nemoqeGmK0wgHUmnMFGR7s2UXilSmNaY0eLY535m8+9XS2tecPW1ToRoLUNNSPrJ44UkwY5IjyGN9p+I+czIu0n6Rs5x30UoiZ36Me+csTBRG9cS6xn44+tkUR1rjwMCA78q0yibUUPn8eG1Scc2+9KUvVdxHdVqjw6pVq8p+r3JMa5pGa2twN1evo33++edHbRJ1TflIVeWas8jNIZ3RXLW9GjNX1jlz5yAKHkO1GSrXnDmPAxXpTVppT7q3r19WaI5PzU4k9jh/c22wCIcqpT0rrTF/kEXzXywrPa4qGuwqbKaC0whVRc7AWmRxFD8rRc6ciXVNhBFGx4HN1to29VZw8CNeJHLSGn+8114AtMxrZnDtYEnEKj+m1SzKmJqGZl+XNX8tk1WhsgYuDf2p/PzmsUOeKNknZz+HVKk1ZlrLR8680zUzp84Zcp5DtT7O2UjWEuICNWNorCLOWcKiMF6b/JwPlY5HJpOhpqaGXC7nGz2LK3L22GOP+e4TR3QxCNVpjZVQveAQ1jmbMGGCmpoqN63R5N/LYOW6ABliBS+0tEcKvcbMsb5MhpOmcIW4rsarShZcc6ZKGCBVvDhUdFiVkTNnYm3a4YY3vu+XeWG/OxQoWg4XeYD9y8qIODkr6IrUGsM7Z2qinYMV0xrzKWkXnxa9PQC52uDUz7wgiJqasy0pa/Gz+9VeHtz1YR7a65HCHdUFzEsWzp7/xMLSndx3mQJ7tMrPFzdypjitcbjIORvy/urW5UVvj5OG2pwdKfQQbazImTp7xirj/tQ4UZgg50z1xLqSw6i6Z5YzsfZrshxXzVmQ6p/qc1SJuNIaAe69996S71Tb09bWFrjdcfydaxs1TlpjjZlj2RqY8eHSl4jSyJknLa0ml+OA8/2dRVVpjd2OktwGn2ejyklRqvRllWkujNA78wAlgiBFkbOVd65i0z9LPWpXVEZhWqND/1ulrSuUR860YMcjF1NaY843rdH6WV+n8Z1PR3vjO69Jxzlbct3SUntM0yODHqk5rnPWZ9qG2XVKI90jhQuwCqMe6TB/s7soE6kpQLi6P7eRecyRs17P2ozTDkXF+34klWJAS5HGpCFXet9ns/ksCImc+TPunbMkpzX6OWeqncVKzplKtUbInx8vxQ5iEiJVX/va19i82ZqwxZXWCPn+YV7ijpytXbuWq666iuXLlwP5sa7KOUvXBStKgfr0naEK6SAqaz1qa2Bjpg6AwdU+i0QqVdI0eKZlUsF3xWmMStMa7T/ZrMnfzyPl+jA5l0yhIIjDwOoymQ7Ofa/AHvAKcPjUn4woXABJV47kFas1RokzhkZs58zMmvyl4z56XvPUMdnjJ0f0Dr7jnPWapYMj25M/X0rVGsP8yYql/QFWHTc3f/ii+Y/qPmfpxrRVs9yfY5NHVKbHI/7pvu8V1Xg5/fuas/l59YtLbGc/m0/VVVSWNyYR52wr0hpVR84q1ZypjnokLXLmpVjZMk5nyOGKK65w69HidBZff/31ku/ids7OPPNMrrzySo499lggf//V1dUpsSdVW6iOWA7nBaJCEGTl+rwUeq1ZfuLovO2jnqSBVXO2MWPdZ4NrfMSAVBbip2BZXTP1c/LKn6985bUCUZCcwjRL57HiXR0vp4CYUtiEujit0SwjX68pqj0pjpy9tOBl1v59Xcl+7sK6qsiZI1DiE8mLIzU2V1P4gHn0Pfk6Jm9T7KhNcszoK+OcDZVpkq0qYg5w66Q57nclizKKBUEA1hyxg3sPFffwc8e0onerpmlMPGQiAP848im3D5s3cqYyrRE8zlkuv2C15yes81QgCCKRM1/GvXNWHKVavnw5Bx10EH/4wx/cfeJyzpKQ1ghbHzmLwzkrVrZMQuQM4LnnngPUj6HLL7+87PemafLRj36USy65RKk9xc7Z3//+dwAWLVoEqE9rTNmRs7aRYaYMWelfxeNY5QtkeASG7ePU5HLMHuimf1lhWprKFevaGthQYznK6x7dUHYfVwFMkWobgFZb+Mcv/rZn4UGhhLU7JAbyk3y/aAxAWkFao1eoAMr3FlOl+Om8mhqy+efysyc/V7Kf0siZrfwHsOjFEf7xL7Pkno9DVKbkkLnSzzktet/DjZyNlF6MJde96X5u6bMXaxQ6sL+eOpcee3wPrS+aFykWBAFbDt7uL1bc0NxULAgCMPGgdvfzdoO9gE9aoyJnyGmu7o2cOXil9MU582fcO2fFUaovfOELPPXUU5x44onuPs5EX5Uz5DiMK1euLKuQON7TGidNmlTy3Zo1a8hms6xbtw7TNBMROfMeX/UY2nXXXUtsAFiyZAm33XabG2lUNYamTZtW8HtxVFh1WmPKTkdryo1ww+LHOWpjF31rC1/6zllT0YTaNPN9vDpGBvnxG0/x0D6PFu1UZFiE1NXAM81W59JVPs6ZSvlIZwiPFK3gexv35p9C6uzBI+1dLhqjTK0xBRtrCqPOfT3lnDN7khZ1nzP7f78xHXw/ZxVOGtMp2GLbk+4Z5stnrOCBnR9i03Ob3H3iiJzRX5oOu+ru1fR3DRQopUYeObNf20PZFA+1FT6v375+GYsufJFXrniN9h570UhFWqPnGOvt8f3mj5cW7uREgxUuyuTM/AJf8SKIEzlTJQgCUNOaL/Xot19YBc6Z23w+eltOeb83rbFwbG/sNunt94g7jXsPxJ9xf2qKo1QbNpROROKKnH384x9n/vz5sdvjOGdO/VQxqtMaDzzwwJLvZs+eTSaTYfLkyfzXf/1XYiJnjmMUV90iFF6P4gijKnsuvvjigt+LHXrlaY11hY++C1e8zLMnPOv+7rVPRRphzszXDHUO9pXdx416KLhmtRlYXmenEG4c4u97PsKWF4sWZxSmyjiXoO39hQsztZPyk39TYeTM7eN14FT3u3JS6K4zFLVao/3/Xzmpxf1uuIy8v6aoz5kzqf79pNmB+zlZTypqYVIp2Jyxnott2SEuXv4iwxuGWXThS3l7VEZf7XPUN6OlZNvzn1jIQ3s9wvNnLrTsUpHW6FlTvWbmnhy7+xEMTMqnES+/uYs3f7TU/V3FNfMewamBXXLdUlbcsTJ/vyusX3JFXHL51PjiNhrOGFKV1giFiy1Oqn5bk9coez8F1+zWKzXfyNnEY032OtuUtMYQjHvnrLhnVrlUwricM4Cf/OQn3H333WXtURWFmTJlClC5Z5bD7NmzI7Vn2rRpTJw40Xf79ddfnxjnzHGM4mrHAIXnoNiRVnV+pk6dGrhdeVpjbel16Pt3Lxv+YS/O5PI/VFyybBZ67RdaS5lUEAu1aY2DnpDhwIoBHn/vk4XWuJE8dZGqGZfswtwFO7jfj/TkFxuGnl0PQP1wGWGOiOzp++BsJh1udSoe6Sute3XVGkPJzr1znEnjkOc4/3w5x6buolRd5zkU8STNeaz0pArFmwbXDrLmb2vd52Euq25Mp1PQl8owrGmFKnKlQoTKGpkD9G7f7rvPuoetMZ3VonfONE0jky74gpdTzf77Z03fbaPFbtvn/+gNmfzC3cLzFrHw01ZKfF5cQsU1s46RzZkMUj5ypnKRyGHGR6a7n6/7jMlR+8MvL84b4PY5U9TX1Lnvy6U1gietUUFWylhl3DtnxZGzJDhnxWqExx9/PM8+m1/VV+14zJw5E8BV1iumeMLf0dERuU1XXXVVqP2SltYYxxjynoO4nLNK10F1WqNWW/7vfuo46z5z5ZAVrFiDFTnbZAtwTBzJC3AUSljbP1VEzmogW2bG3LO412OPunQit+asMcPOl+7EQX89AIA19651RUEGb1kKwPRN/i02Rgs3vUnTaNndinwUpzXmvP18FEXO/rHLLPe7J1/I8dEriybQilJjNU0rK6Tz4K4PY5z6PCvvshb6soql9NE0NhelWva82sOgrWxpKnA4XHuc+qUs1HaUKhB7WVHbqETYrqaoIqF3IOCoG32EgkaRI/fPf3ZaezisvGMVq/+6hqbFlgOrJHJm3zcr1sFbG+13+2CxUrT1U5WUPkBNew1TjrbS0HedbnLf/6aYO7PUOVMnCOLpdVYGt2enRM58EedsK5yzOKIeDgcccAB9fX0F9qiaWM+aZb3wly1bVna7dwLpdSKjJKj1gRdV5+jPf/5z2e/T6TQvvvgiTz/9tFJ7vM7Ziy++yEEHHcTee+/Ngw8+WLCfysjik08+6btNeVpjpsK97CnEV/H+yObyztmk4XyxQEEDaFfMIXp76nzmit7URtVS+pCf+GQ8NRYP7PQQL1/6SuQ2FNjjiDnkLClrKHXOTDMvKhP1xNGZ6P97UoerbJcxTXb9w0sYpz+ff2coipwBtNtBlznX7FGybd1D1oTajZwpWEF3ztHamvqSbc9+1BYrUan4aR/i8l9ZdThBLK1vVrIIUuycBbmq2vTGgK2jQ63HnmdaJpdsf+6Mf9KyeKNtUOTmuPd917q8OmrPaz08d8Y/2bzQKvtwpfQVZ+341cBZX1o/1AmChIycxZzZlGTGvXPmTGJ7eqzeIuWcs7iaUBfz/e9/H4gvrXHt2rVltzvn54gjjkDXdSU2hXXOVDFv3ryy32uaxgc+8AH3d9WiMg5PPfUUixYt4sILLyyxTxXvfve72W233cpuUx05A5h68oyy35umWaDapiRyloONacsxnepxzkY258e5UrVGH02fhZf9m2W/Xs4L8/+FlnWei9Hb49w29iGpaS00cOkv3o7eCK89Wt6e2nbrXnv9u2/QdfsK1ty/ls0vbGHhJxdSo0g0xbFnYCjvfNSYOY7etII1965lcJUV5XDrFhU4ZxPtUqqh7VtLtjkTSEeGXEVdp3OIrtpSp2LLv6xoa76BcOTmFDRY7qsQhFqfqVNyn23prbwPwJK6ZjK7lF7X0abW8xpb2NzBfe3ln9mAUvXIoeG8c/bCZ//F6r+u4YkjnwLU9srz4jhn2YEsvW/2FTSlNhW10HBwnLNWH+fMXbQa9x6IP9HK6o0BnMngs88+y89+9rNEpDUW9+xycGq+VDuLDQ0NQL4uqJg4lBHjTlcsxk+hMpVKsWbNGvf3uFJj/VC9clVOfRTU15wBNM9pYHWZ7587/Z/s+6u9AasQX8Viozetccpwfhn9wd0e5phVR1rqkm4QJnqDilfQXVYP8K/PWwIK7Y5YgMIog+MzZ1rjfXV5I2f1M/ORmBc+9a+y+6sS4Fj0Zi1TnGbmnhRmxxlyBEFURM462mDxcvjGrRpnF21znDKn9EtF5GzInqsur2squz03nMOvxWAUWA6pPbeosG9PukalvkRFnmydwj4K7CleJHqofTpHb1pRfmeF6pFDI3nnLNfvKVCG/BiKKXK26LMvAlDfWc/hi95bYJsqZ8gRk5oz0M1RG5ezOV3L061T3O2qWnqMZZI1w40B72TwkksuKWjYa5om//jHP1wJeVUT2SeeeKLs9060SLUz5KSaDQ4O0tfXx+OPP15QuxSH+MYnP/lJZccKg6ZpfPSjH+XEE0/k6quvdr9/5JFHSvZTQdh2Bklwzp5//nm6uroAtc5Z2meCuua+tfkaFNS8Y7PZvHNWU3SOnH5nKlcbw+hX1PRaC1lKxBw8KmkA6aZ4K8ndBsImNMxqqPwPIh5DXg2UYXvANuXyq9YjPXbrDIXR1w47sPLQv0oPtvIPqxjePJyX0lfgLPbba4tPl0mPA3jxSy8z8oYVQeutj/451FnaEcaXnnQmlhQwv7TGv06YqeS5WFd0GZyITFw4vsTQMAz6zL9UKn56SRcpEA905TMwVPc5e7OuhUEtxayhPi5c8QpfXfYCaTM/Z0wpFLcaq4z7U+OdDBb38brttts45JBDeO211wB1zlBvb/ncAsc5U+0MeZ2zk08+mUMPPZSf/vSn7nbVkUWACRMmKDtWWG699VbuvPNOFixY4LuPX+QoLpKQ873ffvu550xVzRkQOGF+eL/HAKg3c0rUGnMmbMyUnxC+/N+vWh8c30zBRDZMN4yM058phshZ3OPWWwPXOLty7U3U9hY4Z/aAbfQU47spTqY6Z8hxzoZ8ZmBv37jMjTKoSGscsJNiVpRJawRY/psu+v+ftTi7sTn6eqoDd89/7ksFL6b1xuyUALTubV3QvtoaNtbUKREoKY6c9QSdJ1WiMliRsxGfca2yV56X4vYwQEHPV0BJXR7ASCpVsgjy69ceJePMXZ0vJXLmy7h3zoJql4ol7FVNCLq7y6uNXX/99Zx22mlcc801Su1xJsyvvfYaf/nLXwD43e9+525X3WA56WiaximnnFJ2m1/d3mgT1glUPcmtNEZURs7CvhhUnKGcRxCkmLUPrgOgfsB6VmmN0UeNtkr5XaGUftdauPTnOR56Pt5FjpQnclZc/1aWiE/RMZ7Wj86kcc++Tfnvuke442GTfjtjNuOjVjqaOMGnYZ9J7NoH12EO2hE9BYFQxzkbCfGe2tQcIhpaJdt7+jx/a9ZevFFf2u/MoTejxjn747f8x8WOF8xhwgHt3Py+/ZTYAoU1ZwAbaupY7uNc1/aUlqSMNm5a4zCsrikdI2bWJJdfQYrcHi+putKbqPuVnoKURpXv+9W1heenLTvMNDtlP6Uwgj9WGfenxhG7KCaVSpWs4qsa2Oecc47vtltvvdX9vHp1uYqZ0adcNKOxMf+AjCNylnTOOOOMst/PmTNHyfGLJfP9UH3NKqVbqnTOOk8JKC73oKTPWc5aPR/weVstuvBFWnoHGNY0tGnRTxydfkd3dWxXcd+eeVuRn/UOcfy/079u8u3fwOGfN7nfRxzgXzuEu66jYY9zm+11XakiYQER32Y1Gc2dyI6Uuac3rRnmpK+a7qSotkZd3eKwzw204YmNtDxgCbmoiJwNbYWGVF9xPl0EeP2/NxpauWDHd/vuu7a2VGEyCo5/T+F18EZ/pv/nNA7664GsarOcSBWvjuLIWVZLcf7cg3m+qbTPaU139NL+zqLVwBDcMmUHFjUWZvD0L+uPpc8ZQKq+9D574fxFeTEQxVGq/lSps9hkR/NTxOPAjiXGvXM2Y8YMV+bcy1577RWbc/btb3871H5OnU7UhHXOJHKWZ926dQW/f+hDH+Lxxx/3VSscbZIaOavknKlMa2zorOdPE2cF7nNnx3bK+pyhaayqLe94Lb/ZutdX1TSiVWoDMAo4k5B7J8wM3G8EjaEp0aeAOfasXJ//7gedpQqp13TO4/6Ddy/5frTxqkdeeX2O9909nTcntvvur6b5q/WzXLpV33prUpS2J0UqImeOc5YN4ZkOtylMZw7BkJ9c6SgSNCYGM2nm/WhPXrrsPXxxzgF0ZaK/x8qx0HaCGmZZzuGDz5k8/bK1TcVzMZMpcxBNK5vmWdutLnK2ZIWVanrZ7HcVbO99o5ec3VJjJKO2LjZTpg63++Uelv/WeneojlINlHHODttsidq12vWwobIOxikym8bqIVZcw9Tb28uSJUsKvlMtwFEJrwpglJSzp6kpr3gVhyBI0jnuuOMKfu/s7OSQQw5Rdvx3vetdlXdC/TWrpCKpNK0RaMyVb5LpsClTp0at0Y7AFKeCFNOdziiVrvdLSXPoT6eV1MCFffSuqWlQskLsjZxddSO80QXdPg17w8Wwq8dZjyl3zfqXW+IAdbY8Yl1L9BNH17/RNJb6KCQ6dM9pj9yerWFQgXNWDifasLBhAhv16Vz8hwZea2yLxRaAB9pn8PVZe3Pw36yo3hFfUNtixI9yE3+VkTOHXNG91vdWPxvfsFL3+hSIyniZ8O7ydfgvfsHyplO1aqf75SJn/7nhbRqzw0yx28V4lW6FQkI9gXRd/w5wMLAUOMcwjGH7+72BnwLDwBbgVMMwQnbKSBbFK/qLFy9m8eLFBd8lzfnwRq+iRNIat55JkyYxNDTkOhuqz01DQwNLly5l9uzZgfslLXKm2jnrSQU7i0NaSo1aoz2D35wO/vv7FTlnzqJvuRS5AntSap3FYl7dZxa7Llzm/r4lU8N0FT2q7PMz4FmsX1+TjOjPqjK1MP2LrT6e9Y5z1hz9RM3bjuFLcw5g9mAP//vms2X3zSmOnF3duQfHbOxir76NZbfH5Zx9ac4BHL/+bW6esiPHJqCVp6lpPNU6hbrJpeMlzre9ViYzZMURO0R+3EqLRC9d9AqOu6FC8dNL2z6t7PatXXnly6+W3d60c7NSe/p9xFtuf/VhAGo7asg0SeTMj4pPaNsB6zQM41DgVeAkz+aXDcM42DCM9wLPASdGY2YyCCtProof/vCHSo5TLtrxm9/8hg0bNgDQb1eZO/3QBIuwvcaiIsz1SJpzplStEfjd5Dn8btJs3+1DWkpp5KwnHXx++lNpJZOitP1HV4qc9aXSSs6PX5bu4g/uwr437O3+3lXbqOT8uA2N1+UN+78pc8vuayqexm4s4yQOPLqWL7/9AhNH7H6CCiJnNZ6UtIF0hvUZ/3u7WAY8ah5pn86lc3Tf7SrSGgHu/IbG9I7872/VN3Nd5+5srKlj1QYlJoTixSUm1/w2OUrDT3l6Zi2ta+KLcw5gwwGdkR+3nFBSucUQgP4Gtc6ZpmnMOX973+0tu6t1zgbLRM681M+QqFkQYZ6IBwP325/vBdzcLCeCZtMAvDZ6piUP1av6QdTU1CgTlyg3ge/t7XUVCR3pf2+qoxA/YRYTkuacqb7Hjji8lpum7uS7fSiVVldzhpWWF0SfokjV3rafUTGtUZE9Iz7NgZsbNdpsiW/aasgpinQ6K+jLPeKra2sb+Op2+5bsm/btFhUdX5yzf8l3h3SvcV/4Da0qnLPC39fWNnD91J2oO6y0z1hNudqiUWZye+l3X5+1N7/vmF3yfU5RvdCJh2msuCvF0QeUbntbjd5XKI77b5OLflo4juNMlHmydQp/e+8e3PjBA/ns3IN5rbFNzX1f5hifmXsQKz+4Y8n3ayf6q29GSeOO5TOqVDtDDdngkoHajuTMp5NImOWhCcBK+/NmoEAmR9f1Y4D/AYaAEiULXdfPA84DmD9/PkceeWQ19kZGGHW79evXK4sO3XvvvRxzzDG+27PZrDJBED8efPBBjjnmGGbNskQVTNOM3SYvF110kTJ7hoeHA4/V29ur/Nz4tWQo3kelXdlsfpY9e/Zsli5dWrC9r69PqT3fPEtjcLCNBX37c8raNzmgp1DIZUhLsXLlishf/LncNEDj/gmdvKtnfYkdDv2pNOvXr6OrK9rid30OnHxYG3c9HPxCb8kOs7pnE11dfZHa0z8wmXKvq/6+HjZmutnlD3N5ckUT/BgGBwfo6iqfrjZa9PY0Ay2s2zgI5CNCfT6RTyVj2rTGEMBrje2cufOhXP3ms0wbHijYLQes27yKnv5oB3VfbxPQWvDdHZNmc/YBi9EeXVu07xa6unoiteeOr6Z535cmk/PI6D3VOoWnWqdw0vqlAOSm1PI7s5OhoUG6utSFrrIjE4DCe+1zPyh0htQ9F6eXfLN0VelemzauoasreAIelT0A/5wxkS19+WvZvSX6MbR5cwPQXvDdYCrN6ok1JVa+1dYQy3xo+5/MZOOfNrHqx4WaBP0Zle/W6SxuaA3cY6RxJFHzxTjo7PSP9oZxzjaRf8K2AQVPLMMw7gXu1XX9YuB8ihw0wzB+AfzC/jU5cfEiwoh9bLfddkyfXv5BMdpMnly6uugll8sFXlhV3Hfffe7nadOmKbVJ07RAVcIFCxb4tkoYbbq6ugL/9qamJuXXy0k3dTjwwANLlEnb2tqU2nXRRRfxyCOPcOaZZ7LPPvvwxS9+sWD75MmTlZ+nS880ec/TDVy1/b7c89LfCrYNpVLMnBn9Pf+t80wu/qnJYCrNVdvvy2Vvv8DB3aWCP83ZESZPnkRnZ/TLxBd+1OT3jwQvWj3QPoP3d7TT2RltU3jTR1ajubmZ6dNbMKfBS09aezY01Ec+htraLHuGc4Wpen4Nl5WMaa3wHK2vqefcnQ7h7pcfLPh+SEsxZ/vOyOXrJ000KffKb65vobgwfeKEVjo7oxW+6OyE+R/O8cM7Srd9bocDeebaHC/UT+DmL5i8r17RNbNpaaq8OKzOnnASNrM6pyh5DvnZk87UFcjVt7ZGP4bmzCo/ps0ZpdL+DQ118czROmGODn/58X0FX0+ZO4XpndN8/tHo8t69+nlkURNtvz6EmhteZd3f15fs0z5L7dxjrBEmrfEfwBH256OBJ5wNuq5730ybgWiXT2NGZcpV3PVK7wTVaY1BDvU999yjzDELQ1hp+9GkOIWwXEqh6rTGY489lmXLlnHDDTfQ0dFRsj1MtG+0Carb9ptsjzYLToVnfrSa3CMa/306/GraTqyqKY1atWaHldR4gRWDMcuMjwVz9ufcuQezYM7+3D5pdkn6WhT4pTWOZOG9F5hk3m9ywpftlh4K05t6Ctc/lI2XsOS0VIlqWr2ZU9JXrLiBsEN2pPRZqEp13O9eX9LQSschE91pt+qMPRX30Gjjd31Hm0d+WP5qjGRhwCPQqOJVtv+u5b/vbSnNqFLcVqwiKtMI/+/iDXTdqXHIh5rZ9cpdytszKRkCSkml4pvEMIyFwGpd1x8D5gF36Lr+c3vzMbquP6Lr+sPAUcCvojI0Cah0mDRN46yzzlJ2vEoceOCBFfdR7ZyVcyxOP/10TjjhhMCU0PGC1xm78sorE+GcAcycOZNUKsXcuaUCCqoaq3vxTtj6iiayqs6OpmnM6MihaRq7bKexqraRT+58KC82trv7rKqp5zdTdlBe6/Hl7d/F3+flaypeaWxnRV0TrzS2g6ZRo2BiPeyTPfWXp+DxRYXfqTg/TQ3WQdZuKvx+qEIRfBz0+qimRY2fw/HcltKJrCrnrCFgPjgyYvK3Zx3lYTX2OChuiRVIU324yJkqQctGn8zqR18oTLdUcc2mdWh0lAnO3fpoir9OKIwCJU28unUvdTVwmTTMmGSdgNZ5Lbz98w+wrLawFq7j0NJoo5An1O1lGMZFRV+db3//R+CPo21UHAwNVa7hUC1WcOONNzJ//nz237+0uFs1l112Gccff3zgPs3NatWADjzwQJ54wg3kous6N998s1IbwrLTTv6iE1HhdbwuueQSHn/88cB9VLPvvqXiCdOmqUm78OJ1zj4992Dm9W7k4q4XAcjFcH6aPXPXazr34Kw1r3P7pNm8VW+9XFWZ5BznheYOJk2o5XDeKLufiomlX+TslbdKv1NxfjrsRP8NWwq/T1rkDOw6uJHoe0AV4+ecXfxIO3/8/B5ccVctJ69byl8ndHKaool+Q52GX3XF926D79xifVZ92ycpcnbCwQPc8vfKbXpURc4mBpQueVtZqLpmt1yucfSCwjH05kr40Yzd+f2k2Xx65avcOnkHZsfsnO13y768ff0y2t7VxsSDJlDTGl82Vqo2zbWd87jyrX/yROsU6vefyIf2b4/NnrFAgh4J8bJ58+aK+8SRapiU3mFhJM5VR85uv/12fvCDH/Cd73wHiCd1sBKGYXDXXXdxwQUXxHL8X//61wwNDVFfX5+YyJlDOXGdT33qU8rt8Dpn62rqeaR9Oj3pGub1beJfjdHWUpWjybNSvLa2gWtm7lmwXV00L/+5Z8B/PxUTy61xAOsUPKY7fCaM3ekaelMZmio0N1dJd7rwhPxy6k58SMFxgyKq9zdPZ2EzLGy2Ups/noDI2SU/i6/BcpKcs6vO3Mzm/kbueTJ4PxX3GcAOMzR+cAFc+MPg97uqS3aEDld8wmo+X8yq2kau2P5dAOyhpg2tL1OPnsLUo5NR2qFpVsbFR3d9H2gaJ5fPdBQ8JG+ZLyY+9rGPAXDFFVf47pNOq889SIrDESZqOGGC2ons9OnT+fa3SwRCE8V+++3HN77xDeX9uxzOOOMMzjnnHKB8zVmYRYko+dCH8tPEXXbZJZZeecUTtivP1niuZRI3TZ1btuYqair1LlVl0mxPEPOI/2yiN5Xh1YbSnB4VE8u6rUhaUNH7tVxqE8BIKsV5Ox1M93cO4th5R3LjlLncuadPocooc75PYkNdrjDs+LInVTZKgsZFcdQl7pqzYlTf9UlKa2yog59+sfIZUBU5A7jgpMr2qHouplIaV55TeercGrNzliTc+jv7IiUj5JBsxDmz+fnPf87f/vY3LrnkkrhNSSRhnLN3vetdCizxJymObFIpF/ldt668ZLsqbrvtNvdzXE3eiydszTH3Uq/k7KiahEzr0Hj+lxpv3a5xxblphv7vfSwo0z9LxcRyaxwuJc5ZQKrVpkwd2e2tFNTbJ8/hmdmzojcIuOYz5QdGsTPmJ/c/2gSN0+/fVvh7EmrOvIznyBlAa4gkGFU1Z2FJSJKRS5hzOF6IIa4x5hHnzKa5uZkjjjgicSqJSUlrLKesV0wl+f+oEecsmHLOz4YN6nr5lMObCrtmTal0vAqK03OaxDlz2Xdnje2matRkNI46OFU2kqgkcrYVj+WwE/BqmFkhW8hbI6fqsVRbo3HYnqW1ZTdO3Ykbp+TFdyo1Fh8tBraiFZ8q5ySpzlnSlP3CLFCl08kyOp2w2WxbU7LOT5yc8J7436tjjYQN5/iJI3UxiPp6tV3d/agk9nHttdeqMUR4x5RrPRC3cwaw2267AerTYh2KJxkqIi9BtFRIh4nrle8X3Uiac6bi+rU0Bl+FAucsYlu8TJtYqpwymEpz++Q5/K19BosaJ7C6Rs0sqX8rNEiSltaomlzC1hW9z8QT3hOjIVuBqhq4sFR6jo8nJrRobLpHnNWtQZyzIryRqptuuilGSyx233135s+fz/e+9z1OP/10fvKTn/DhD3+Y+++/X7ktf/7znzn99NM58cQTOfvss/n617/ubmtslCdR0lm/vrQRZBKcs7vvvpvjjz+eW265JTYbvnVe/r5XIQ0fxG7bw/wP+2+PK5juN4FWMbFOWs0ZwIxJ/ttu+Gt+tq0yoH/Zx7r5yHvLb7u2cx6XztG54ctqXvtJdM7CRs62Juo3GuTCqdfHgp9Sqmqe/lnwg29rnhEqWLspYR53zGQy+euXkISwRJOwrOFkcfLJJ7NkyRKuvPLK2GzQNI3rrruu4LtPf/rTsdhy7LHHcuyxxxZ8d/nllwOw1157xWESAPPmzeOll17i8MMPj82GscBbb5Xqjh922GExWFLIjjvuyB//GG9HjkvP0PjyL6yXadzBc03TuO7zGs+8kuOZV8ptV28TBETOFJyvo/bXeHxRuMlOfa2aE3TecRpX3lDeJm/vNZUT747WHL//egrtsPIH7WiDsz6o5vzssl34fVU5Z5Pbw+03OBypGSX0Bqihxs12U+O2wOKA3TUWnGpyza3ltyctcrbLduKBFNPWDJt74OA95NxUQpyzMjz66KMMDg5SX18fm8reWGHRokW88soroZpUR8WDDz7IPffcw2mnnRabDWOBxYsXu5+/+93v0tbW5qqUCuU577j4jn33tzWmnlA6+Y+rPiXOtMZLPgY/uQtWhQj0lsnejYTLz4K5nRp/f97k+r/475ek9fOswijI0QfA50+Ga2+vvK+qmrM9d4CbLtM485vBV0V1/dLC19UeLwyLbtB4bRls6oGkjOKgKHSSnLMPHginHxm3FcnjXzdoPPg8nCHnpiKS1liGQw89lCOOOAII199rPLPnnntyyimnxGrD1KlTOeecc2KRYR9LXH311YDV3HzBggWce+65ynvTjQW8vk+cK3xTJmj8v4tLjx9X5MxvwqpiYl1bo3FuSEe5b0DNRDKV0jj9KI1ZFcRBkqRTpLK2SdM0rjon3GBVFTnTNI2PH60xtzN4P9XqiZeeXv48bT+t7NdK2HNHjZPep3GkHp8NW0OS0hrPO94SURIKmTVV4xMf1ApSHIXyiHNWgdbWAM1kQRhDLFiwgGXLlnHmmWfGbUqi8To/cc+r/+s/NFbcqfHn78Sfr5/yCdmpmlinQ4YM+xSniFVS1E1SPVFWsS2tTRqr/lD5uqnu81XJ+VJdd3rK4RrLfl9GCTUB+mTbT9NY8yeNgQc0fhKi/1mUBC0uqI6cFQt+/Oby/LlJUt86YWwizlkFPvaxj/Ge97yH7373u3GbIghVNFU+GAAAE6xJREFUoWkaM2fOTEx7BiEc0ydpTGzJ/560y6cqyhA21Ux1/c4kn4bUcfL1T1qDZK8d4QuexIapMQiihqnzUj2ZrXS8OPqOzZxSemPHXf/qMLldo65W45PHwnv3KRRQUsni5f7bVCtx/vDCwnPgjcYmrW+dMPaQIVSBhoYGHnvssbjNEARBEdMm5j8nxQ/yNjRNmhy4qhq4HSukojnMma72qh2yZ/D2OKKvXzlL4ytn5c/Dr+7JsaUXTvJRcowSK+IafBZUT2YrRs5imhlN74CVHlHd/XaG194ufCbFSW2NxsM/jO+pONNupTplApx6OPzwjvw21dfsEx/USGlw1ressV3vqYCRyJlQLeKcCYIgAA98X+P5f8Ohe+e/izut0WH32fDNczX3c1zcdpXGoy+Y/OhO9cf+6OHwepfGNbeabOnNf7/vTrDzLDhonsb6LSYXnKTWrn120vjKmSbf8Om8koS0xod/oHHHIyaXn5WU5YZCEpfWGNPM6PEfa3zqGpO/Gdbv131eY850k3OOTeZ1U83XPqkxocXkvOM0rr298Okch0PkTbP0RqXFOROqRZwzQRAE4AP7aXxgP+tzbY3J0DDsv2u8NjlomsaXPx63FXDy+zXmzYEf3ZmflahKs0ylNC4/C556yeQvT+W/v/rTGkfojhHxTGIvOEnjGzeVd+VV13mVY9+dNfbdOb4J/sRW2LDFf3vS0hpPfl8852qHGRq3fw3aP2SNpYmtGt84Vxwzh8ntmptSOTRSeL+pVtiEwoUXb/ru8IhyU4RtDHHOBEEQiui6Q2P5Wpg3RyZGxdQWvTVU18AVS+Unob4jaGKouqFxEnn+lxqzT/GPQyfJOfvaJzVOfr86W4ppa9Z45dfQVB+fDWOBNRsLf487cuZN3127Sb0twraFCIIIgiAUMaldY5+dxDErx5zpVr8ogHlzYLZiue/vfEorUEqLY8W8GHHOgtl+mhbYzDhJaY0HzauswBk1u26vMWuqPH+CKHbO4hBPOfl9MHUizP+w9fsFH4HOyVafM0GohgSsOQqCIAhjhXRa44Ub8r+rnsjuPltj818h9V5rlToJzo84Z5UZ8TTAPmxvePSF/O+qG6sHSdSrbsUgvDOKVUDjiDS2NWusvCv/DPzBhSmuvcCM3bkXxj7inAmCIAhbRdyTD+v4lnPWPxirKUDwqv3QsDo7koy39i5uhzUocqa6FYPwzrj2cxp1tSYtDTChBXbsjOeZVPwsjPvZKGwbiHMmCIIgjFm2V5xWWY4kpFYmnRMOgV/cDUfqsGpD4TbVqqiHv0vjT0+UP+qOMxQbI7wjZk3VuOWr4ggJ2ybinAmCIAhjjpdv0ni9C/baMf4Jmtc5+5/zNC79RVKaMCSH739O49C94T8Ogv3Pi/f8fPZE+MPj8PA/rd9/e4XGPnPh1bfhgN3jH0+CIIxvZL1PEARBGHPsNlvjuEOSMZH2Kkh+4oPhG2aPJxrrNc44SqO9RXN79jnMmqLWlkxG4/++bNnwnr3g1A9o7Lq9xn8emozxJAjC+EYiZ4IgCIJQBd4aOE2DF2/U2PMTJq93xWtXUjnlcKunYEujVZPXUKfeKdpuqsbGeyhQ/hQEQUgCEjkTBEEQhFEilYL6Oo27v63x7nnw92slGlOOjjaN2hqN5sb4zk97i0Y6LddHEIRkIZEzQRAEQRgl6mutn7tur/HkT2XiLwiCIGwd4pwJgiAIQpXc9U2Nnn5oiTESJAiCIIx9xDkTBEEQhCoRMQlBEARhNJCaM0EQBEEQBEEQhAQgzpkgCIIgCIIgCEICEOdMEARBEARBEAQhAYhzJgiCIAiCIAiCkADEORMEQRAEQRAEQUgAodQadV3/DnAwsBQ4xzCMYfv744CvAMPAc4ZhXBiRnYIgCIIgCIIgCNs0FSNnuq7vDXQahnEo8CpwkmfzC8AhhmG8B5ii67oejZmCIAiCIAiCIAjbNmEiZwcD99uf7wXOBn4LYBjG2579hoDcqFonCIIgCIIgCIIwTgjjnE0AVtqfNwMTi3fQdX1/YIphGM+X2XYecB7A/PnzOfLII9+5tUJsDA8P09XVFbcZiUXOj1AtMoaEapExJFSLjCGhWmQMhaOzs9N3WxjnbBPQan9uAzZ4N+q6PhO4Fjix3D82DOMXwC/sX80QxxMSSFdXV+BAGu/I+RGqRcaQUC0yhoRqkTEkVIuMoeoJ45z9A/gicBNwNPCEs0HX9RbgVuB8wzDWhPh/ae/ESCF+5EYLRs6PUC0yhoRqkTEkVIuMIaFaZAxVT0VBEMMwFgKrdV1/DJgH3KHr+s/tzZ8H5gA/0nX9YV3X3xuVoYIgCIIgCIIgCNsymmlKpqEgCIIgCIIgCELcSBNqQRAEQRAEQRCEBCDOmSAIgiAIgiAIQgIQ50wQBEEQBEEQBCEBiHMmCIIgCIIgCIKQAMQ5EwRBUIyu69JWRBCEWJHnkFANdjstIQLC9DkTxgG6ru8MzAUeMwyjO257koau6zsahvGG/VkzDENkToWtQtf13YBzgK8bhrElbnuEsYc8p4Vq0XV9V+A4rB61XYC8y4Stwh5D3wTuAa6XOdHoI5EzAV3XzwR+C3wA+B9d1+fGbFJi0HVd03X9MmCxrutX2F/LaqMQGl3X07qufxX4NfCAOGbCO0Ge00I16Lqe0nX9YuD/gNnARcC0WI0SxhS6rmd0Xf8ycC3QDBwGII7Z6CPOmQDQCsw3DONLwDLgTF3XpcW7RQ3wLLA3cISu6zMMw8jpui73jhCWCVgvsh8DaV3Xz9B1ffeYbRLGHvKcFqphAvAycKhhGJ/FWmScHK9Jwhhje+Bt4FjDMI4GGnVdnx2vSdsmktY4DtF1/SjgTOAfwPXAdGBn4EngQeC7wNNYKQ/jDl3XjwE+hnU+fm0Yxv32938FrgLORVJBhACKxtANwN3AfwMjwCPAd3Rdv9IwjOfis1JIMvYYOg14CvgV0AlsQZ7TQkh0XT8a2NswjKsNw1gP/Nn+fm/gCGBE1/W7sNJk5Z0mlFA0ht4AnPKO2cBiIBejedsssvo/ztB1/ULgC8BNwBzgG8BPgQ/puv454HxgI5bDNu4KhnVdrwfOAm7BSvn4pnMODMP4FrCbruv7GYZh6rouixtCCUVjaDrwNWAhcKlhGCcYhvE94AGs9LRxd48JlfGMod9iOWWXArcDx8hzWgiDruvHYS0mvlfX9Y/Z32m6rtcA87DmAa8CRwFTYzNUSCw+YygNYBjGUkDHmkci2USji5zM8ceDwNl2NOh/gFbDMJYDXwE2YE0GLgcmwrjMJd4J6DcM414sx7UVa0LkTH4ux3LYPgPsE4+JQsLxjqGvA1OAQwzDWOR5gT2BFa0ej/eYUBnvGLoK2AFoxHr+bESe00JlDKwFoC8AJ+i63moYhmkYxrBhGLfYY+t+rNTGtXEaKiSWcmMoazv4YC1AHgdgGIZE0EYRcc7GAd5VVcMwXjQMY5WzCRiwv19sGMZvsPLQf4aVmz5u8ETH/gVM13X9OMMwhoE7gZM8k58MVhHsHoyzcyQEU2EMnWzvlrKFHX6K5aAJgkuFMXS2YRhvGIZxM+P0OS1UxjOGVhqG0Qu8iTVOPmtvT9k/T8Mqa3gL0CT6KjhUGkPkUxn7gTW6rjeot3LbRjNNWXDbFtF1/d1Au7065txsKXvVQ7PT8o4F5hiG8SNd1zuw6hn+C3hmW6+Fsc/PmVhywi8YhrFZ1/VGwzD6dF0/HLjMMAwn7ezPwP8ahvGQruv/CSw1DGNhXLYLyeAdjKFvY0WnzwZ+axjG83HZLiSDdzCGvgs8B5wOGNv6c1qojM8YqrGdemefXbAirRdiRV6nAOcBfzQM44UYzBYSxFaOoc8D3YZhDOq6vgew2TCMZXHYvS0jztk2iK7r52Ol5N2GJWjxlGfbNKDFMIzFuq5/FpiBFUGdZBjGubEYrBhbEv99wB1YClamYRjfsLfNAPqAq4HXgBux+nn8r2EYi+OwV0ge72AMfQtwCqoF4Z0+h64xDOP1OOwVkkeFMeS+6+3fLwE+B9xnGMYn47FYSBrvYAzNBx40DOMTsRg8TpC0xm2T+4D3AA8Duq7rzeCqND4J7GMXdR4F/Aewcrw4Zjb3AR8xDONHWOdoM7iqRE9jpXteBWSxelOtEsdMKGJrx9BKccyEIt7Jc0gcM8FL0Bh6ErsuWtf1/bFqh34sjplQxNaOoZ+IYxY9oja3DaDr+ieBE4FPG4axzFbRQdf1icBc4L1YndyfBw40DGONvf0W4FHDMFbGYrgiPOfnU7b4yTOe4tUdsHp3gJUutJ9zfoBrdV3/mWEYA2otFpKGjCGhWmQMCdWylWPoQM8YWgGcYhjGJpX2CslDxtDYQCJnYxxd19uAI7GaJb9P1/Vaz+bnsW6oHeyCzc2GYayxZZoxDON348Ax856f9+u6XmsUNpHeDrjX/jxsn58aT0GsTIjGOTKGhGqRMSRUyzscQ7UAhmF0yaRakDE0dhDnbAxjC3tsNgzjVKy+N4djRcoAMAxjEPgL0IGl9vUVXddT4+VFX+n82PQDk3Vd/yrwWfvfDIs0tQAyhoTqkTEkVEsVY2hIta1CMpExNLYQ52yMoev69vbPtK246KysLgVewupF0ez5J/sCJwDPAl83tvFeFGHPj71aVIelTnkxVkuBq2UyJMgYEqpFxpBQLTKGhGqRMTR2EbXGMYKu641Yyl2zsPpuDeu6njEMY8Szz1TgSqz+NxrwBjAN6DMMo0u91ep4B+cnDSzByr1+TArtBRlDQrXIGBKqRcaQUC0yhsY+EjkbIxiG0QcMAS1YfZIwDGNE1/WddF3/tK7rHYZhrAbeBv4EfAlbAnVbd8zgHZ2fzwONhmHcIA8iAWQMCdUjY0ioFhlDQrXIGBr7SOQsodgh5gbDMDbZBZnDwKeBRcAFWM6XCVwL/MEwjJtt0Y/bgT8bhvGzeCxXg5wfoVpkDAnVImNIqBYZQ0K1yBja9hDnLIHoun4aVhPpvxqGMd/z/Q+xelK0AjsDvwWWFIWqC0LX2yJyfoRqkTEkVIuMIaFaZAwJ1SJjaNtE0hoThm7J3DcB5wKaruvHeDY/hCWP3wN8EjjfDlW78vnb+o0m50eoFhlDQrXIGBKqRcaQUC0yhrZdpAl1ArAVdS7GahS9yDCMX9rfNwCn67r+N8MwssChWKHqDcDvgT6AbV3qVM6PUC0yhoRqkTEkVIuMIaFaZAyND8Q5ixld12uArwKvYykrno8lfQ/wd+ADWKsiPwOuAw4xDOPmGEyNBTk/QrXIGBKqRcaQUC0yhoRqkTE0fpCas5jQdf3DwCTgAeCXhmEcbn//K+AVwzCusXtSbA98E3gGuN8wjFfs/VLGNtyzTM6PUC0yhoRqkTEkVIuMIaFaZAyNP6TmTDG6rk/Wdf3PwCnA7sARwBpd18+2d7kKOEnX9cmG1QCwFXg31uqIe3NtqzeanB+hWmQMCdUiY0ioFhlDQrXIGBq/iHOmHhP4uWEYp2Ip7OwO3AHsoev6ToZhvI2lsHO0rusZYD/gS4ZhHG4YxmuxWa0OOT9CtcgYEqpFxpBQLTKGhGqRMTROkZoz9awH7gcwDGOdruvTgG5gMVYvik8BE4AXbCWdG+IyNCbk/AjVImNIqBYZQ0K1yBgSqkXG0DhFas5iws4PbgN+axjGB+3vfg40ALXAeUC3Haoed8j5EapFxpBQLTKGhGqRMSRUi4yh8YdEzuIlAzyu6/p+wDHA9cC/DcPYGK9ZiUHOj1AtMoaEapExJFSLjCGhWmQMjSMkchYjuq5/EPgT8CDwG8Mwfh2zSYlCzo9QLTKGhGqRMSRUi4whoVpkDI0vJHIWLxuALwM/kMaAZZHzI1SLjCGhWmQMCdUiY0ioFhlD4whxzuLlGcMwno7biAQj50eoFhlDQrXIGBKqRcaQUC0yhsYRktYoCIIgCIIgCIKQAKTPmSAIgiAIgiAIQgIQ50wQBEEQBEEQBCEBiHMmCIIgCIIgCIKQAMQ5EwRBEARBEARBSACi1igIgiBsU+i6vgD4LnC2YRg3+uzTCFwMLPXbRxAEQRBUI5EzQRAEYTzSCFwBfCJmOwRBEATBRaT0BUEQhDGPHS37b2AN8CxwJnA2cCxwBNAALAEuMwzjLl3XlwLbe/4XVwHfsv87DWgC/gZ8xjCMtYr+DEEQBGGcI86ZIAiCMKbRdX1vYCHwEvBDrIjYDCznbAqwEWgGzgVmAZOBDwO/AV4Bvga8CHwEuBL4ObAKWADcZxjGR5T9MYIgCMK4RmrOBEEQhLHO++yf3zcM41e6rs8CvgKkgXnAqUCtZ//ZwP325zWGYdwKoOv6DfZ353v2PTIimwVBEAShBHHOBEEQhG0FrehnDVZ64wPANcDnsNIc6wG/tJER4D+ArP271GYLgiAIyhDnTBAEQRjrPGz//Lyu6ymsdEYvTcBOwCGe77YAOWCuruunA48DfwZ04Cwsh253YA75KJsgCIIgRIqsCAqCIAhjGsMwXgAuAqZhRccesTcNA7cC+2ClNt7n+TfDWHL77cDNwKHA/9jfHQr8CPig5/8lCIIgCJEjgiCCIAiCIAiCIAgJQCJngiAIgiAIgiAICUCcM0EQBEEQBEEQhAQgzpkgCIIgCIIgCEICEOdMEARBEARBEAQhAYhzJgiCIAiCIAiCkADEORMEQRAEQRAEQUgA4pwJgiAIgiAIgiAkAHHOBEEQBEEQBEEQEsD/B/apQpYmrcdyAAAAAElFTkSuQmCC\n", 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", 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\n", + "image/png": 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", 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0.00018522348742177705, 0.00023520299083359408, 0.0002406744276076448, 0.00024170249960374756, @@ -1989,7 +1988,7 @@ null, null, null, - 15.455841833876597, + 15.455841833876596, null, null, null, @@ -2000,12 +1999,12 @@ 17.052607997446447, null, null, - 15.233435808001337, - 15.704231508378241, + 15.233435808001335, + 15.70423150837824, null, 16.300355711498774, null, - 15.526767256795555, + 15.526767256795557, null, null, 15.323101489024388, @@ -2029,7 +2028,7 @@ null, null, null, - 15.468282486135895, + 15.468282486135896, null, 15.18098728429224, 15.46624621024003, @@ -2065,7 +2064,7 @@ null, null, null, - 18.480700748796398, + 18.4807007487964, null, null, null, @@ -2143,8 +2142,8 @@ "x": [ 0.0005556390413520811, 0.0001641683748936943, - 0.00017765085917960965, - 0.00018522348742177702, + 0.00017765085917960963, + 0.00018522348742177705, 0.0009221396184837396, 0.00024170249960374756, 0.00030356543042572154, @@ -4074,7 +4073,7 @@ }, { "data": { - "image/png": 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\n", 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", 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X4xe/+AWuueYa3HzzzTjppJNw4okndvSduVwu8CWEEHBdqaD1rW99CzMzM7jhhhtw0003YevWrahWq9EPqzWHBOAte1i8cbEnvzMJZJGzDBm6gEnOshQ+iYycdYb6tMYMUeQSjrYcFCBItUYuUiFh7QtTSh+hSEDCKbskAMqWogyPIAhf1nl1WldFlx+YGAyvqpsXg6w5q4qAHK2srGB8fByDg4O48847ce2116JareLyyy/HAw88EElrHBkZwfJy2KvxcY97HG644Qb84R/+IX784x8HvtbS0hK2bNkCx3Fw2WWXYdeuXY2/Veh/Ce6sC2/54L34s8hZhgxdwCQfGRGRyMhZZ1idM6OuCRqSUji5rK9ZWwiSd29OSIPihWcqkKqaszTUfaRNnGTuqrn2b+wT5q6cB6+l4CRlWBPIUz0Ok57X+vBMPuarHMKXSonPec5z4Ps+nvzkJ+Nd73oXnv70p2Pz5s248MIL8Qd/8Ac44YQT8OpXvxoA8JKXvAQ//OEPA0GQN73pTfj1r3+NE044Addccw2GhoYAAK997WsxNTWF4447Dt/4xjfwpCc9qdEmg5wJV/53sCKLnGXI0AWytMZGZDVnnWH+jlUAUqAgi5xFccNdhC//+OC9ofYLQujIGdLAzSLz+JK783jhYyqpIEWC0pPWKGoCvCwgfAErl/y+OAmCqAnYheRtydAZhCtw9QM57N7l4A+fmLCkrUHOSBB4hcNmNiCAQqGASy65JPZjL3rRiyJ/H3XUUbjlllsiz1177bXB449//OMAgE2bNuGaa66J/c5SqaRMUunUAhAuT8213w2yqzJDhi6QkbNGZJGzzlBdDZ1W7xEg+dtLTL6JsCc9wYX0gpPsc8YpFWmNrkHO3nzJOIRIRy0cQJLApgCSnHFQSnbzJXlOhy0ZOkN1TxWv+N44/uI7A7hrv5WoUjQRAQxgTG4SkSfrYBNVr6ZwXvOKOKind0bOMmToAhk5a0Qmpd8exAlVwznLpk6GrkDKEeKq/iThKJWp1gjobMvkPaM0NaEWVQFe4RBeKlirdFzTMTQZOgQzIq5zqyzZ82ccm4hkSiMSlrEnqMVHpX+mYA3qFhk5y5ChC2TkrBFZ5Kw9iFNEECTjsBm6AQVpjTKFx2z+mgRcHiVnPqzECSOAoFFu0r0oSRBWd5YlOXNTQs7ScH4yrA3GObNJJHuNae7DAAhg8DEDYAlvFBFRkOotvIM7ctZRzdnk5OTHATwTwE4Ab5yamvLU86MA/gPACICpqamp8w6QnRkypAr15My2M4m5rOasPerJmZcNU4YuQJzAckylDhJ4gqpkRBQRBAEAVyAV6YTadwwfJAO/5MNf8mV0wUuHx0gCB7Xz+miEMBpjsoRFd1RWY9BXLDeSA6/wxIWASFAQMT+IA2ftI2eTk5MnANg+NTV1GoA7AbzSePnNAH40NTX1XABDk5OTv3NgzMyQIV0wyUeWwieRRc7agzhFmlD7Cdd0a3zjUsK1tx/Ed7I+Y/7q+USPT5zAbCmlL2oi2fouaoyceZQCNTmotEaWcPoXpAPrlXypOu5yeMvJX/i0Bjn2RytW7iqhuqfa/o19gtkWgiHpdOYwL3b0uFH5FEtBzRkn+a9/cEfOOklrfCaAn6vHlwI41Xjt8QBuUo9vBHB6zyzLkCHFyNIaG2GSs1qtlqAl6QVxiognpCFydtv9hNf/E+EZb03nnSzplLR6EKdAMjpJG5jNwv5CSYJkpMyERywVgiBydz8NaY0AL3M4E3m4sx4Wb1xK1B4AgACWd6ygNpOt1c1QfbiClR0rSZsRgCKRM0q+vqsuHE2E5G3SjagP8j3zTtIaxwHsUY+XAEwYr+0A8DwANwB4PoD76j88OTn5ZsgIG84991yceeaZ67E3Q0LwPA/T09NJm5EaLC4uBo+np6cxMDDwqB+flZXwJrZv375H/XjEgTjBPaQQ/L13/wympyVDS+oau+v+PICNAJCCc3ZIwzMPPbwbdpqqownwt/io7U5uR907yoNnuxATAlbeQo1qmJ6eTmwOuc5E5O/y43zMVmfAppPKJZTziIYI7tEu9uzdk2haI3GCOMmHYBzLtgsaBGo7q2BOckZ5j/HBLKBSKsNywwssu9eH4IfLGsHagzW5GZK0PbYAIPt+0ZEe9sztAVtIxi7iBP9oH14u3KimcYG9C3vhCx8f/OAH8c1vfhPHHnssPv/5z/fFJu8ITxE0wLfl4+npaVx66aU48sgjcdRRR/XFjk6xffv2pq91Qs4WAaiYJcYAmPkcXwVwweTk5C8g69H21n94amrqQgAXqj/TtQWaoWNMT0+3nEiPNgwODgaPN27cCMdxHvXjUywWg8fLy8uP+vGIgzvvgh4sBX/bewrY/vytAJK7xo5cDdNTtm49FLlEm0A3brsecsihqWpMzSsc83fNY9NzN8l6iz5D+AJ7frMPhY15rNxdwuATh+Av+zjkpdsSmUPCF/BWo2l6dG8eG48ewcD2gb7aYlgl/1lhcO4sYNuLtybaz8tb9DAzNYvccA6CC/iLPoqHFLDpOZsSsYcEYd9v98Eq2hh+4hAGt4f3syTv9X7Jx+Jvl7DptI2JHL8eC1OLKN29iuLhRQwdOYSB7cX2HzqAmDcirvY9OWw9dSOc0WTaFes5XdgSbjbW9tew+YxN2F/aj29/+9u47LLLcNhhh7X9Lt/3kcut/3fs/e1ecJcgqgK5IRtEhENffgiuuOIKjI2NHVQ+SSer1dWQUTEAOAvAVfqFqampytTU1Bunpqb06z/psX0ZMqQSZipjltYoYaY1zs1lzapiQYiIJ/i+dPaThJnxtVBq/r6kIFKQHgdI9a/gvyRPmRZyYAA4SYKYcJ2HK6Ik1eUJS2obYIGsXHIgIW2wChZERc4hZ4OTsE0A+ZSK9FMNb8mHX0rP/ZQ4gbiQ45SC+rya0YaFKzGgpNA0VZiAd73rXbj//vvxohe9CP/yL/+Cl7/85Tj++OPx9Kc/PWg4/f73vx9/8id/glNPPRV/8id/gpmZGbziFa/AySefjJNPPhlXXSWpRqlUwjnnnIPjjjsOxx9/PH7wgx8AAN761rdicnISxxxzDP7xH/8xOPY/fePDeN55z8UZf/U8fPDfPoCrr74aP/7xj3HeeefhxBNPxH33NST4pRJtqerU1NRNk5OT+yYnJ68A8CCAT05OTn55amrqzycnJ08E8BnIZfibU1NTDxxQazNkSAkyZcJGmOSsVCphZmYGmzdvTtCi9OGaO4H/2RVGE3yPsHTTMiaeMZ6YTa5xw9/9gIvNJxVavLv/4ClwHv2Sj8Ubl/Cj2xw8dlzguHFKzC8iQWBMikwMP2lYFuEnLQjiN5KzNEjpS6nv5AVBoIRJfrs7hwfuB154mJ+4DHrFBS66JY8/2ER4whHJmWJCuBy8IkBEYCwF0XKSdV3kJz2BJC65PhwT7olklT+bcTMifOxjH8MVV1yByy67DB/4wAdw0kkn4eKLL8Yvf/lLvO51r8NNN90EANixYweuvPJKDAwM4Oyzz8bf/M3f4FnPehYefPBBnHXWWbjjjjvwoQ99CGNjY7j11lsBAAsLCwCAj3zkI5iYmADnHGeccQZuueUW2MsOLv3Npbjsn38F8giruTKe8swn4aUvfSle/OIX45WvfGW80SlER3HEGIn8P1fP3wTgOb01KUOG9CMjZ40QdSGOr371q3j3u9+dkDXpxHPenYO57Pq+TAtLEgu3rEB2QwHmF9PhhJhIQ+Ss/EAZdz4E/Nl3hgEAe/5uRjqQSRQyGeNhqaa0iQbOCKjvq+yJdAi5aBOER7CTyrCEItREeMEX5XX261dUcVyi54zwqauHcMHUEC64QWDXD5OzxQRxAL6MVCVZjxeAFLdPQdTMK3H86fdHwieKuY7EgP5n488OiD3Pv/u5jfyMGjetrrzyyiDa9bznPQ9zc3NYXl4GALz0pS/FwIC8MH/xi19gx44dweeWl5dRKpXwi1/8At/5zneC58fH5Ubm9773PVx44YXwfR979uzBjh07cNroacjn8njHV96BM054Ps469cxUrEPdIJlk1QwZDnKY5Oxgvfh7jfqWAjMzMwlZkk6ImJ1y30/+xu+lTD2yHmmInDHHwvT+8Dwlec5IUIO4RaKKhETw69MaRTrSGlnBAhGwdPMSNj5rIrlojI7gKcyW7cRV7X4zLdMqH5xLj9oOcQHiKlKVbNantEcAxJQse8LwvGizPrIYeDm5/GoS1Lg1tcYo9dDQUPBYCIFrr702UrveDA888AA++clP4vrrr8f4+Dje8IY3oFqtwh7J4acf/x9cfsPluOSGS/CNy/4dV77qis4NShEycpYhQxcwiUh9xOjRCj0OZ555Jv73f/83qzurw9Jq43Oel1x6nIZvEI00EKF6pCA7DlaegWrGNe8nm9bYeOxkU/fquaqbEhlrZjGQyyGqItFG1PVppwxIPK0xPZRMonTPKmr7PQhOiUc6NWQKMYPwAb6a7M5VfTtVsqyO6pV/d+6sA2JPbX+tIWQfN6NPO+00fOtb38I//MM/4Fe/+hU2bdqE0dHRhve94AUvwPnnn4/zzpOJejfddBNOPPFEnHnmmbjgggvwmc98BoBMa1xeXsbQ0BDGxsawb98+XHLJJXjOc56D0uoq3IEanvuU5+J3jj0Fp/7lMwECRkZGImrSBwPSdn1myHBQwExlzMiZhB6HLVu2AMhEQeoxu9j4nOcjcfZh3vRTsEHcgDT0eLccCzDrO5IcqJjeqhSTTtQ3UCOp90RK+pwBEBwQNZ4sGRKhIioAlf6VpJgDwFgKdj0MVPdWQR4HlAhHKiAILMcgahzVvTWI+vzdPoLXzV9h9PRKBAQ0xM5ipvX73/9+3HDDDTj++OPxrne9C//+7/8e+3Wf+9znMDU1heOPPx5PecpT8KUvfQkA8N73vhcLCws49thjccIJJ+Cyyy7DCSecgJNOOglPetKTcPbZZ+PUU2X75VJ5BX/8ntfirH88C3/wj7+Pf3zD+0CC8JrXvAb//M//jJNOOumRIwiSoT+44YYbsGfPHrz4xS9O2pQMHSCLnDVCj4MWAbntttvw9a9/HWeffTby+XySpgEA7r77bkxNTeGP/uiPEklvmltufO7+OQuUcLfMCDnjKajzqIN0ShK2iwG2ZXgdOStZQZC65xgSTK8mNKY1poBQA8AND+Vw214bJxRkulxSHg/VNexlSZJpQCpHJnj4OBCXY0KcUiPAQRz4+a4itjkeTiz44Ksc1oZkRo7XBe4E4lPl+wWiSKYuAPW3Wod27twZPH/xxRc3fP79739/5O9Nmzbhu9/9bsP7hoeHYwnd17/+9Ybn9vx4L37+pf9FeVcFhW0F+Cs+QMCpp54aqWc7GJC26/NRi8nJSbzkJS/Jmj8eJMhqzhqhx2TTJtm7Z9euXTjnnHPw2c9+NkmzAhx99NF47Wtfi5/+9KeJHH8hJqviv+8ZSDzC4PP4x2lBorL1GoRoT7Pk1dkjICRX40VEDWmNNW6lZl38vR/LPoLJRhnqJgwlr7CZQIu+1uBS1p9ZSFaF0MB9swx/+tNx/N7FW+CX/I4EOA4U/DrCKgAgybWR6qLB8skkLDEOL+txh44cRG7QliVw6ZhKa0ZGzlIA8ya2tLTU4p0Z0oIsctYIPQ71Bb1TU1NJmNMUus9KvxFHfBiQ+N3DjJalUXg0DWmNRIBlnKdEVb6bBF0Sm0bUmOVZS8E5M8HshKX9CRAUTpqB7cXE7TGncBqINAX9+1jiIkkaDy+GLrKoikQJfv06KCj5Od04bZKtfSWBxiSLFMztbpCRsxSgVAo7v9q2naAlGTpFRs4aocchl4vmDmnp27QgKUckroxioZZ8P6iIWmNKdqxNpKH8hISAMxrKx8lN42TGKm7DOslGy0JEiQcAVH2WeC2lCZcjUXVEwUVkOChpJ5YokqabhjRCIgKpkB6vpeCiRzS66WxwEiWxvI6wyh7UyaY11jOh2GBan0BEKtXSULQkpEI1thtk5CwFmJ2dDR7XarUELcnQKTJy1gg9Jo4T1UBOGzlLCnHTZL5iJX7zEMYN3nfTN5fTEDmDktTW4Ak6ISCKidwl5+zHnZ+Kl4LGzwYWqyzZ6JAAPONPnrSYAwDLyGtMRRqhkP9ZOQaRQnIGhkTX6oaasyTXIABA4zqUdN9wVjcgzEpYFXUdyMhZCmCq2rmum6AlGdrhJz/5Ce64446IWmMaUkLSAE1S68U/0kbOkjpfcfeIKrdQrlKiaTxmWuPCCvAfPyeslNMzp9NQc0Z1GU0Jirbh5geAb95SxKp5q2BIzFGLS9et+ukiZ/PVZDdBSESvM56CmjNmeH9JqhBqEMl1kNnpSWs0BTcYkxHQpNBQc0YJK6IS4HLCRTc5mCnVRasSsqchp5Eo8ZrubpGpNaYACwsLweMscpZe7NixAy95yUsAAC960YuC57PImYQeh/rI2fDwcBLmNEViaY1NSMbcDMfhpeSKvcw+Z+/4bhEA4fUvBL7+nnQoBqQirZEIwrjxewmSj+f/Qw4rlTHUChX8v9PU/YIlN6/9GEe64iefrmtiuZKsPUQUIfQ+JbypR4BtXN4iDRFzHU20kIqmz0Bj5IwSrMmt36QSSL5X3henhvGJa4bwxM0c1/zNCpJUSiJBMUtyutKr14IscpYCmIQsi5ylF7t37w4eZ2mNjWhGzjJINLtHuCI5WXagUQYdAH50ZQKGNEF9rUUiEBQ5f0mW6KxU5PnavVR3+04qcmY4rE/ZJtfFip8OYq/hJR5lIPhkiEtQcuqayhxYxoRJQ80ZSBIQZrPUaDiYt3ZmsUT7rzXUnAnW2P29jyACrnxIZsncM2NoJSQYOWP1TbEpS2vMsA54XpiNnpGz9MJMz8uaUDdCE9Z6QZBsfCSaDYNIeBc9bpN6LEXBTi/BqGIAEfWDOCVcw4SYJsIJpzVODAq87VlVAEDVS1e6NxcJ76BT3fwRSWt8U0RKPxUOLJFs+myxdJBF1M3hhCNnDWqNSDY1loRA3q4jQ0n2o6TG4zOWbMnAepCRsxTAdPSztMb0wiQd8/PzweM0OSFJolnNGU+FokOINKk1AoBPydbnxNm1IQXkzFE3/qV7yglbomvOjLRG1TA3SUSK75PLJgrIWc4CimqJrPjpci18kXCNl4hugnCkoObMJGdpWKIJQc1ZWkJnZj0lJSzx78X0OUta5MapFxdPcB2KP26yEer1IF0r6KMMKysr+Nd//VfccccdwXNJR85uu+02XH755YnakFaYEaA777wz9vl+Y2VlBd///vdRqVQSs0GjWVpj2iJnaRIEAQCetxMmZ427nWNDCRhSB+3oczd5R00Iwv/cUwj+9in5tL3GJsLJjNNVt8t/LQsYyEsbKj5S5RT5hEQd/l/ebuGuuXBz7+Lb8pgrtfjAAcbtu4DfTIebaA/uSf4aI5K1byzHIFyCt+i1/9ABtYcibUY4JVcLVyoTLromyoQST40VgGNF540k/Mmxs3q1RgIO2shZJgiSID760Y/iox/9aOS5pMnZcccdBwDYt28ftmzZkqgtaYPp1Fer1eBxkuTjjW98Iy666CKce+65OP/88xOzA8jIWTs0CyAKkexGcVzkbPOGvpvRgGKOsFJjECm4uf7PzTb+45aB4G9PJBPtNOduGriZ6xH+6BPSacxZwIDyKKopqznzeXI1Z3fuIrz8cwMAwvnz/ZsLuGXaxp2vTMamk86NOvrHv6uA6s8FLCfB/XpFzmAx+Ks+lm5ZxqbTNyZnj1DXuYJHLLEI459/kvDtX9TdVxPecCAiOHaKrnNCg5Y/w8Gb2ZRFzhLEXXfd1fBcWtIa9+3bl7QJqUMzkpHkxX/RRRcBAL75zW8mZoNGsz5nGTmTaBY58wUlWg8Ttxm8NYHuB/XnpaimkZ+CkrNr7o06szwhQm2mWTVMmUTIWfj4iSsLyC3KCL7L06WSJvl9Mvbc/VD883fNpmdv3OPJ9xaTkTPVO4sASljeX/gUEUsSSE4Q5LuXNT7HkXA6qqCGmjNV+JUM4o5LKUnZ7QIZOUsQxWKx4bmkI2caLOlugilEM5KRNvKRFJpFztJWc5YUmk0Tjyet1hjzXAKnrH58io4cFOETeDnhOVTHxDyezProKaKaFxxOKbqRl4Sog75NbHYreMctN8L+0E0A0tH+wARXDY6TwEFzK028hpIw9ITBoGdf0v0NyReRtEY/QUd/PKYGOHElwriaMyDBrEZqTCdg6WnLsFZk5CxBZOTs4EJGzlrjYElrTAr1DqvedeQJFlED8Y50IuSsbgw0OXNXOap7qzGf6B/qz48vc4r6boc+L+956Bac8Z/Xo7xLiqVQMuYE52yjHxLFIvfhiZQoACpIdc1kjp22rKpmrSmSPl9EgOVY0vcgStypJl7fmy65XnlxAk2CklZrDEWbgueSrMWNOzUsYeGddSAjZwliYGCg4bm0pDVm5KwRzdLh0kA+lpaWkjYhGIe0S+knltbYEBmS/yYtVhDX5+yn1/bfjvrxKahdWe4S7GLcFm3/4NWlVs6Vk7HH84GNXhUnl2ZhC8LDl8wGryUxhfQ5KxhhjsPdVfgiPb2qAOC7O4rYPZfMOpSmcQCAShMXI2nhBBKQkQ/lUJOXsD0+RSLkPMFU3Q0jjc8JSlbVUtachX//z46caqGRlEHyH58DP7szh8UKA1jyLU+6RUbOEoRtN97gkyRnphN9sE7oA4k01pyZ2LFjR6LH1+mL9fM6I2cSOkK1zS3j4w9cj5PKcwAUOUpwCnkxUbJ988DPruuvUfV+j63vToO5xK+x+dUogf3ejoFEzpnnA0M8ZIq/vln+y1gy4Vd9zoZEaNOY7yLhcqEGXPVQAX98fr79Gw8Aai1EB5OY1+UUkjNSSnuMMUnOOCVOahsjZ8mlNcZGzoBkmb8ATP2Y1/3HMN77q1FQQvd7nT1w4TUFvPYbw/j9rw2BMaA6XYW3nILC5TUiI2cJIm5hTrI+x2yGndUJNSKNaY1mY+wHHnggMTuAcBzqyVna5lLSgiDn7r4Dx5YX8c47fgtA9WBK8B67VI2/DVx5S3+NMqfJ116xEqQ1Vv3ke9WU67LNC7lkCt89H7CNA5dW9MAkQ/D10mcSxiHhw+fJnzMQwTIurN/cm0y0sxkZAqTaZb/RPHLWXzsiEEZKHJNRq6QFZfxlPxo5Q3L3jnyMdgyn5BRIAQCCGpac7+xozAbrH6Q1/3unHKxbd6tBEwR3Lh3lQmtBRs4SRJxTn6QjazbDTptDnQakkZyZhHpubi4xO4BwHCzLin3+0Q49DCM8upUu+4wl54gsVOJTmPst6hBEYfKEFx/jBn3OapxBJKwwUXGjY5SU3+hxwDY8sqKpmpBgzVmEnHE/VmSm33jfQzfhG3dfDidhZYlmZAgAvAR8xnKT8s1EI2eCZPQXsqRCuAJElGjEvLK7Cp4L72W+SG7DIRezr6AFQZIaIxIUf50nde2rYYiMFZObDgdjlU5GzhJEmiNnfhr0q1OGNNacZeRs7Ug6rbH+PuGLBOWHAcxX4m8D/V6K9DSxmVTdGlCRs4rPQAkvRxUveta4SKaWwfMB2ziuSc6SECvQ52xQNJKzpAUmTlmZxbjv4gnVleC5RNIIW2jZuAnM62ZkUSTJqAkghJEzURXSyU/SJI8ifc4kOUtmTtsxSzQnVVOVEKkmUjbUP58wOYu0XjuIa846arQxOTn5cQDPBLATwBunpqY89fwAgO8BGAXgAzh7amoqa5DVIeImTZKkKCNnrZHGmjPznH3nO9/BX/3VXzWQo35BbyzUHz8NUVjzHKVFEETD4yolLaHdvYUmaY1JRc4sBkAAQ5acN1WeXH8hjUpdhCOpIIPPgZxJzohjtsQwTMkQ/Li0xmHuR5zapJEzvMVSBRgZ7O/xW0XO3ARKzJulWdb21DD02D4PjsLiCuGKB/M4foDh3hkbxwsCs1RELaFGx7LmLDz27fttHD2WTHpcLsZT14KxxNGhJ99b7Jph+PoNxcb7VpJS+gBM94Op+sWk7q3rQVsvbnJy8gQA26empk4DcCcAs6f9iwDcNjU19WwAXwfwpwfCyEcq0pbWmJGz1khbWqMQInLs6667Dl/96lcTsUXbA6RTEMS8rpK6xpptuiYp8w0Ai9WUpDWq41kMuON9d+FV378GjuCJR85W7i41ONgJKek31JwdWS3hJR/LgbFk5lCcIMiQ8GS6U0p2rE0yO7/c/+NX3ObjkKaaM2+hhXLJAcaZ5wF/9F/jOOajY3jZV0dwc0HWUieaaumJiJLt234w3HSD7UAjLq1RkErXS2iMfufDg7jw3qvwN9O3RZ5vrETrE2IjZ5DjcxDmNXayxf5MAD9Xjy8FcKrx2r0AhtTjcQCzyNAxsrTGgwtpI2fm+dK4+OKL+2+IQprTGs3rKqm53Yzs+AKJRoaqvrxxvW6yig89fSF4vt+9zrSjzxiwevcqHI9ju1tGjSeXugMAtb21xrTGJMlZ3X3j9x7aqR4ll9Y4ZNRR5oVIrEk3ECr/aZiRs1ZRrAMFvfRs8Gs4urwUIa0xS/gBR7PrWvjJXWM33hudL7/Zacv0Rje5dVFwoP7wSfR/BFQ2AYBjVhfwngdvxga/Jq89lhyBPaa8iEPdCp6/uAd//+BNGPfUxZVUOrNKHrAt4/gsYaGbdaCTYOg4gD3q8RKACeO1ewA8ZXJy8nZIjvo7vTXvkY2MnB1cSFvNWdrOUZrJmTlWSY2bHgZW50QnrbrlqiXnA79XxbVXG4qtCUXOHGO+uMxCxQcoKa8IACygUudEi4SarUpyFj0xecEReCZ9NisucpYnoSJn/bXFRMHwbMzIWRI1XoIARoSv3HMVBgXHux/7NNwyLN2oJCJncdd13k6+6XMjGPgqhzPqJHJ08kVDD0g/ISKkr7NP7JwCAFT32hD0eJXWmJBNRjTqmSszKNkOPrv9mATTGuV1Fq3PYyASB2PgrCNytghZUwYAYwDmjddeD+DKqamp909OTr4SwD8A+Dvzw5OTk28G8GYAOPfcc3HmmWeu1+ZHDEqlUsNzS0tLmJ6eTsAaRI67d+/eyN+e5yVmV1qwf//+2OcXFhYSGZ+4xtPVajWx8+S6Mh+/fpySnNMaKyuhKEBS9iwsDQNo7CZa3eRjga2AezwRuzyxDQDgbi0hd5ID/K98fnllFdPT/csD2ztvAdiKISus68gRoTQssDy8jNXp1b7ZYsLfzFGlsehzgwL7S3NgTVJCDxR27803RM58ZsE9ysWsNwvO+juH9szYALZEas4cRc5WJkqoTFf6ZosJi22J2KPx8PQMNhb7G65aXh6BQwMYVOIth7pl3KL2uHfPzWJ0ur8bD/tnCojuscvovXtUDdPT0wnd6w+J/OUOuXCPrmGmOgNrOpkaau8oD+VrfQCF4LnyY2uJrNGl0gYAoUz9Rr+G+QEX7lE17FvaB7baf/bBUYz8rdeAOZoHPOr7OJFP8I/2Qdf4AGRPw9rjKiBBmPVnE5tHrbB9+/amr3VCzq4G8LcAvgHgLABXGa8xhKmMs5DkLYKpqakLAVyo/kxHEnoKsLy8jBtuuKHh+WKx2PKEHUjMz4e8e3x8PGLH9PR0YnalBRMTE7HPj46OwnGcvo9PPt/YVDXJ+cPU9lT98QcHBxOdOysrK5FrrVAoJGLP8FD8zvSNNw3ir84oYHl4ue92CUHw1M7rxMIwRpbD4oZicQjbtzeSyQMFcmSu4HAtdFZzJODNORieH8bEUfHX34HGL7+7hFK9aMqKhc2Dm1HYXIj/0AHC+F6Cjb2R5zb4LvJ3FTD2tFEsOot9nUM1Rjhr/qGIIqIjBAQxDO4fxuaj+3/OiBMsgywWDEXLsfHN2L69v47s4JCAbRRNmgqbowObsH17f9UcNtzbmJMriOGG/xvG7791Anv270lgfaxbG5cLmPq/YTzvBQOYOGIo/iMHEF5N4Ec/mMetN1h4bLWEXUXZBZrdVcD2p23ruz35QuO9I1fKI393AaPHjWLoMYPB/bdfcGgm8veqLecxvymHwjOo73Ootq+G2al5FNzwerrhilGcOFbG1mdNYGB7kj3Y1o62VHJqauomAPsmJyevAHAMgB9MTk5+Wb38bQAvnpyc/BWADwH41AGy8xGH008/HXfffXfD81laY3pRn563ZcuW2Of7hbiasySR1rTGs846Cy972cuCvxNLayTguYt7cGQ1GjH/4Z1FXHVnQrvDaigcm8BYWNsA9L++QqdbmX3gHCJZ75XQtt49DxHOuKCRoCZVAmfWnO1z5M71CPfkNmkCl5kQwF/uuSPyXAFy4rgJ1TARUVj/gigZqrUQ5zhQECJa92ba4yeQ1tisJOiVF03g5nvSsX/+L5cV8aqLJvDObyUgQwjggh8Cr7poAp/+7ZX4wn3XBPNJJNR7LS4VVcrYE6p7q/CX+n9P+6OZ+yN/v2BxN14+uwur5b6bAkClNbJo24FXfGsM//CrhpjRQYGOZv7U1NR5dU/9uXp+CcALe23UowE333xz7PMZOUsvzEX5L/7iL1CtVvGVr3wlI2cKaSVn11xzTeTvxARBOPCOOmUrfUe58m4bx03236aqclbzKmBmuh1J1ZwNiXBe50jAE8kVdd98X/zzPKGaqkotrKFasvPY6lXleLFk+gsJAu4ujuKo6jJy4w78BQ95ZYiX0Dkjn/C5u8Jr3iRDlWWOfrd3FdTY/kAjkZqzFufl4X2ELSnyZb/2awdJ6A//5y+jBZyHumUsOAVwYSVT2xlzbdd8AEhGLImIcGx5seH5N+27G+XKsUgkx4FkRDgiCALg2zsG8VWWgBLQOpG+JMxHOTJyll5okvGyl70Mn/vc5zA6Kksxk+qblTZyluY+ZybSJKWvnTY/oSiDKhNE3pbHNwlZvzm1Hh/HuJ5yJIvyk2porKdKTggcWQlT95ISBClXw8jZUk6mNW93K5j91Vwi65AQQNWSzP7Q35fpXkeVl/EHszvhJ6TYyFd5QBABYNiIxJaX+s9ghYi2PzDJouslUCvUYghWkykRTB3qz4oWcfIEJbIW8ZjWFBVXNVgW1HdP3mvhHpZrySQ6yPPCkFBbvJ4jI2cpQ5KObBoU7dKM+siQ/jdptcahof7n5MchzX3OTCQWOYshYHm1i+4nkG4FADXlt+ZVDoXpdyQVOTNTwN689y6p/JcQOdOpne96+Bacf/+1eO+DNwFQGYRJRM5c4G923w4AKNlh4svD/zGdSK6lIAREyB4Kr/s/3XcPkhL/8yvRe+hGP9w1r9USIGcUndOFCDnruzktr+skWg2kERYDDq+F6ed6Q0RQMinWXADHlcM2J5u8Kiq+rPMmFUHrJ1qRMz6URyKDJCR/zYFS02NxPcjIWcqQlOM4Pz8fEU3IyFkjNMnQhbf636TTGsfGwjyUfhcFm0hrWmM9kprbDzzQuPFSUGPj1RIiZ0HkTP5rcqB+8yF9vJxxY39cbRUe722rAd8nXHMbwesgWsmFlK5/xoosfn/Gygy2uWVwkYyE9W33hHNo3HcjryWR+ilESDZyw9EqiSTqqQCAl6MDYY5TLyJVu/YS7t/d+W8TItqb7gWLu/HRB6YwwP1EIuatluOVuey+D8japQvuDVNjB9Qc90RC5IwDkythG+HD3DL8Epe94BLokdmKnF0z7STSrFt4AsITeP53r8PfPXwrHMFDknYQcrWMnKUMSUXOHve4x+Ed73hH8HdGzhqh04bqI2dJpzXq9ErzuSSQkbPmqNYIv742hpwpj5onFDkL0hpz8vgjhvhgv0+b9jGcOlboU2+vsXdfSHjm2wh/c35n5KyeBI1wD5zQ9/Qm1yN8/zth3plVNyaJpDVSGP3NDUcj5p5IZqNI1JGzjV41eNwLQZDH/SHh8a8h8A7JeX3NGQAcX17AixYeTl3kbGl/ulLlk4IFwJzNOhWVi2SCMoIDC7moMqyzosKcfqP65oFGK7Gof/z1KP79F/3P5hGewEM3lzFWqeH05X24+I5f4q9375AvZuQsw3qRFDkz+0AlaUeakba0Rk3ERkZG8KpXvQoAUC4nJJWEg6fmLAlytloNiZgJHTnr1NHrNaou8NTSLD547VVYvn0Fxx3KccwWOT793pCtKl+jYEUP7AvWUyXCz/1A/nvBD9u/lwtgoxfN9RrzXQjBIPqct1fzgK0G0cjXhxMTisLoOWwPRSNn1RU/keiiX0fORo2as16SoVbRAxNCNDYOB4C8EKit9n9t1Nf1YzZwvO1Z1chr5dJB6MUeAFh1+woDwlgTE0ix9jlF+vUBACrSJuFT38mHa2xyXDe8qeH1/7pqsJ/mAADIB/bPRgfizMXd8rWDcFpn5CxlSIsjm0XOGpFWcpbL5fDOd74TAFCpJFfRndWcNQdj0VoTDR11SKikCq5LePvDt2FDrYY73nMnAOC80yTB7/dpq6gAVZHVkTOOxO6unAPjfpSc/d78wyi4LkSf0/bqJdnzguMrW48KX09Cll2EJLE+cuavClR3V+M+dkDhl6LX2Qj3gihjzY37ROcwo5OdtprwqwLb3cZNM2JAeSE5cvbsx3v44O/WkbNq/x39VIJFB0GnNXJKJp2Z8+i1DwBQtZWUwKaMa6Thf+/4pzS8bifALMgTYEvxFzilzAfpBBk5SxnSQorSYkeakLaaM32OHMfB4KDcqUoycnawpDUmsQHi86hKm0bBSJdJAtUaYdV2Is/p09dvm8rKTyzWhck80VuZ+LWUZXIhI2Umfqc0izfefAuoz6movK52qUAC/73xcPmHLdN6+g1BkiQCjZEzb8Xvu+Q40Bg5swBsVhHH9UbOzKWs01YBQgDvevjWxufBIhGIfkH/hjgHusIzlxBonLaHKnLNKRnlWCHQEDmzpJa+JGd9NsmrSltqloWKXdeRiwiMJTBGHoFVmvit6XJBOkJ2JaYMWeQsikqlgptvvjmxui4Taa05M8lZkpGzZmmNSZLXG2+8Mfb5foM3IWcTyvFPSgXsvmlgdz6agqIDn/0+bZqcFer61Mi0xt4Nzlr4Ao9xigDg8FIJv72/v+moQgCWMUnygoPrX8MBUev/vePOXYSiVmss1qUzl/namHATzC8T7nmo83H2So3j8K/3XAkQwV3npW9Ow04jZ82mrmCs49TIXkJvugyvVnHHP9yJv3047L1Y9fvPpoP7KhGevbgHT6gs992GelgMME/v8xb3YNyrwRfJRKo4b0xjzlVV1oUvuvJBSmXC7Q9091uWd8rFmlsWBAHf3Pz44DUL4QZfP0FcwKrGX1ApcB/XjIycpQxpIWdp6aH1u7/7uzjxxBNx8cUXJ21KatMaHcfBwMAAgHRGzpKa0+94xzvwtKc9reH5JMiZH3NzBYC/npay6EmkNfo+4U8/bWHWCQvNiVNikTOd1lio2+b0fUrs5irTieIP/px/HsLffal/hnEBmLw1TwJgDL4iaLza30HatZdwzofkufIZA7MYHvPGw4PX/SoH64GHsfHFhKNeS3h4f2e/77c74tcbG4TqOtMaI5GzTmvOmix/ORLwEkhF9dWGwgnXP4DlW1ZwxtIeWGptqvj93yTSJPcVszvxzunb8I6YKGO/YQG4Y3BD8HeBBB5XK8mei0mQM0FwdOaO2gTJuT5yozmVa7n27zzpTwnHvl4q164V1Tnpe3DGcNgGwne2HAlPbcRYRA01e/2A8Ais2QZVRs4ydIMNGzbgU5/6FICMnNXjV7/6FQDgv/7rv5I1BOkjZzpKNjAwkHhaIxFFIouXXHIJtm/fDiC58fnsZz8b+3wikTPRqK4HyJs+kEiLqsBRFUYsyZ1zA3LWb8KoI2f5upoz4pRInQcgHUcdrao6uYbX/+W7/bOF10XOvrJN1pv5Or26WUrPAcLN94Z1MFx5Y9teuhUPjwzL56rU03TU2x7o7H1L8/EHzQvRcbSrGcxronNBkPi5WxAcbgKRKu1iDJTDWkod1fcFQAkp/+m6vMNj6vP6DYZwU2ZRpX2fvrQXvi8SipyxIIJvj0l78q4Pu2DDHnW6Ih/3Tst/L/nN2j+sNxWExfDB363gpce6ILUO2UiInLkiqMOLwPBNDiZk5CwF+OpXv4qTTjoJQHrImeuuc4uxxzDl4pNC2mrOTHKmI2eVSiWRhUgfkzEGxhhe+MIX4vOf/zyA5ManWCzGPp9U5CxusRWQSm7UfzXkwCky1RHdWTf5tMa6yBn5kpwJtzcGrbXmTDtptXwjOetnW0HOQ4I/euIoLttwKADAV+GpfrdjKOSlIwYAQtnAGANXxUy82ttmsJ1+FTXZPXd6EKnqKnLWZNoWBU8mrVEd0zJI40BAzvofOdMuj9/kYupF+4O1grFw42FV1VS9YHE32I1ziWwUCQIcdQHkFDkrqvsYw/ous24yJHxVc8aZhRO3c/zr2WVYOUXOSCRSc0Y1AYohZw6J5BS31oGMnKUAjDHkcnIBSEutV61Wa/+mPiIN5CxtNWc6SjY4OIhcLgfHcSCESIRYx9WbJR1ZHB8fj30+qcgZq/N6/IkiLABPLi/KQvM+e0X6pmxq7PklHux6JpXW6NSTM5W2s3jTUn8NgiSwWgbdjSFn/VQlExRGzkxlRK6c2mak5EAhnwsFSoSxVc71+li0eho563iZVeNw94mHw3ps2G/JIQGXr49NmzZ0TKyatFyQ5CyBKIyyxzLWZR05S8KH1esMNyL4ZpbB7FIC5AwUzO1VKxRMsnaVwHu0SbQWCC5wVEWuf86YXIeKwQRc3yZIN/EA3WCezBCZpclZMpEzv8aBlcaMr4IQmSBIhu7AGAvkx7PIWTzGxsaSNiG1aY06pTFJUZA4GX09PknN6Q0bNsQ+n1jkrO4G6j5JksdjVxcTSWsMyZkhD17yYds6Itxfe4K0xjqSSr6QkbNa/68z1w+dNC+GnOXshqcOGMyaM2Z4PzriQLy/jbELjkHOjKiHjpztWWI9i3YCnQd0SO3qk2OB2aFdDol1R6oiaY0dLGvCE7Dd+Dfme2BPN9DLH4uNnPXfHm1GzjjDZtuRucU+G4RoWmPZUCPkGwcgqv0fpOffdT8OU+me+QlJFgeC+9j6op1dRc5qOp3ZoBBWmNa43k0rIaRYSbOU4HoQJ+y534NViiFnxHu6SdQvZOQsBbAsKyNnbWzQaXtJop6cJZ3WqCNnemw0OVtdXe27LXFiIHpOJzU+GzdujH0+iWuMiygJAgAaUekpxOUNMqF0onpyZikG0H8pfYJFhE176iJkquasVymEa/ke1wudRrcQEznrJzkz6t9M0qHTGoXf3x1i2zbTGhsjZ/9+YxHX3dO743UaHNCiAJS3UThqJHjeET0gZ8b4ttvj8ZY8zF+70FRBziHRsRx/L8H9UB1Ro6iaLCdBzvQ6ZPbxGhThmM0u9NsigBHBVheTOU4ixxJRRX3uzl3B48K4ImfcD6+JfpMzNyZyZvcucvbhbwDHvp7wri939sN8l/C2/4gvYygIntWcZegOaYycpSGt0SRnaUj3rK85S1vkTEeKFhb6fzeLI2dJj89TntLYHBNIKHLmN/Q1hVWQ41MQvK+1S4FNmpwZNy5e4rCtZCJnrg/87vxDGJqvEwTwpbBEr26va60502mN3AiTLSuRgH6mNUZEZSyG//rTEgCjVoejrwSf83DumClpQjlpDglcclPv2Ota0xrhWBh/xaHB0/kekKG1RM6EJ9Nxc3X6/R877DhpnhDJCILE1JxtzcsneRI1ZzqCb5zgcaO34FwiaY1h5MwkjeT3rvZ1LVh1wtTK/Ab5+OjKMlZ2SR9gXTVn3aQ1quihmc7M1KVu0fo30j707/IH/fN/dvb+WpUwrCb2LYPjuHMgzLTKI0trzNAlzJqztJCzNETOzLFIg3pkWmvOdORMR4rm5ub6bksaa86aITG1xjqvhzkhOUuy1sOqT2vUao19Pm2eDzxvaU/jC1yAuEiinzGE0fh5ceMQhk/fBMBovNxvcmZEzk55rHKoVeSM+hzqjBBXZpIzaY8jqKdFTGslZ+RYKAzagaPmkFh/n7M1CoKQoEha43se+9RAYMIhIdURezRGlYcqcOfb37e1sIxtsMtzT5H3kgRK4IIxNfsJnrMvDLnOLvbfKM+nYCPEbKVBPiWS1lgyyNng48Isons/fDfWXXO2nsiZEcHX6fA2KMi+6BZ647BTkud5hGEufcQHC0N4+5G/g9XNst7UEkhM7Xc9yMhZAqgnYGbkLA0RIiAjZ3FIW82ZKQgCJEvO0lhz1uy8JHGNlWe8hpozyityRgJCJLCDrnesjZCeO+cGN8R+pzX6HChbjamD5KPrXj7rhanWCNvCljcdAQCy8TKtv7ZiLRBG5IxZQRZR0OesWT+tA2YPhWmNkciZuu4d4n09Z8QJwhdgOrLhWCjkKOi/lKcEpPQ5wVZk8aahCdw8vBEe0+Mj4AnWM3K2urOMykPVtu/zOfCxnVNwjHRLy1MkO8F1yCRBJ67OB4/n+q8DBM8PI2Y5I+zSS9XYtcCsexs+ajh47M24oERqzhrTGkO1RoLdo2lUcNq/B5DnS5OzkspqoKAGTsBPYtdhncjIWQKoJxpprDlLQ1qj6Vw//PDDCVoicd999wFIT81ZfVqjJmezs7N9tyWNNWdpImcru8qNi61dFznrwf1DeALLty539F4e7FiHB65OV4Mba6fF2L2C54ey1SYmahUpdtGj2NlavsWMnMFiyOVYQERsUN8EQYgIdz0U3rCZzYK6Dl1zBr+/ep+mtL9vdJvmOU0+ets8vN13rdxZwvy1C2AqUsXyFhwbcFW+lSPWl9a4ay+hYtwW25EzxqTNjjpo2ZJ2hOSMpD09GiPiFEzucrV50+79S8Ax5cXIc7YKKfIerUMAcP9u6sgpDslZuF7fZjSAToKc+ZwFZPFrW48Knl+tACIBaf+HhyQhcx4/BGYzlPQ6OZGH6689cFYqG6nsXdyeeUDOjPu9Qc56leZQyHf2PtdDkNaox0anXOaIUK1m5CxDB6gnZ2msOUtb5OzCCy/E0lICq7TC5Zdfjs985jMA0lNzVp/WuGmTTLlKMnKWprTGZtdSWtQaa0vSjoLgPVNr5GWO2lxn1y7nwKtmHsDx8yGZr+6pBZL//V6KPB460gBQ2FYAAJw+v1cRxf7fYM3oEMsx2BYFKXy5PkbO3nEB4VXvIyNyxoJm4doe0UvPugOYUUXf8MbIDiNDvTSn3VeJCgdxgqXTCPMW8jbgW2ENXLeCIDfcRXjcHxKe/pbQik6IHomw5qyiosJm5MwXrHdKciKs9Tn6tYTDX0nYtTc6ar5P+MavGncULBUN6pUgyI+uIDz+NYTXfKD9BPB5Y/rgmB/6SLML/b/uXT+87u8aGMMXtx0NALj5QQv7O9v76im0subwaZsBAP921DEAgJtqw/i970xgrRfaxpcY5KyLdV4oRieMEFlORblkE+renLNih+TM8wlDQpMzaYiO5OVI4JxvDPbEnn4iI2cJoFVaY1rIWRoiZ/Vj8cADDyRkCfDd7343eJyWmjMt/KGFQNJac5a2tMZE1Bp5Y81ZJK2xl05shykcPgfesP/ehs/aKver32mNtYoIVOOe8M7HY/zpGwBIx7/Uw+VoLcXqQhg7+pb8rBbgsPpIzj71PW2CTrEMX9P2+F5vyVA7cNFGEESIvjahJgIgAKYYBnMs2Bbw+G3y9fXUnP3oSnnwvWG2HarLHXyZIOQUi6s0RM4EPIGe1eWZJO/hGfnvVbdG37NSQexA6mgjF/XdGLvDV38qv+UHv27/XsGB41fnI6mMozzcYKpUEtiUMa77//2LEqqKWOeEwI59dt/v+frc6qoBLy8fFATHHbPOmq9714gPdDP9dOTMzF9khlpjr9bFTsgZCUJtVWBARDdBtqk2pzki/OKuDvMjU4SMnCWA+p17y7JS14Q6bZEzAMEYJYG4WqqkI0OahGlSltaas6TTGr/5zW9Gnu/3NUZEDWqN9rANGIIgnHqUB0KSYHXiPDQjX6ySTENat0IYVIVTuSEbI0+SMuh5EijlC8gN9VG3XkGQkdZoM9gMRuRM9LXmDAhtMfucaUEQr89LtjDaQ/jMjJyF49NLtCVnWoBEO7LqdnH0dvlBKV3PunKs46TBl+5v30+SiOCocF21LnKWFypy1qOwOYnGdLL6a7xak9dTPVgkcrZ+e5w1XKqcA6+Y3Rl5bpiHNbrcS4Kchf0Njz6EcOpRcl1ySAoT9V1gQi3GORUN0gMciKj0ueZMqA0QMhZAZjShXm9tp0YnNWeVh6tYuq8c9OqrWDY2DAjknAOzDvULGTlLAAdD5CwN5KzeqWdJ6I0rxJGOpGvOdG1ZPTnLas6a2wQksAFC0cjZ+DPGcfQ/PBFMRc6Kgsu0vR7c74kTyBMdOQ/NyZmqP+l3WqOPYPfTGrAjrQaWbadnQZi1S+lrhUQZifHVbVOmNfbXSQvUGg22oAUv+q2ZZEr7R9Ia1fVmIyRKvUAn5IwoTAGzFEm0lJMmyRC6ssmKYWftUiSJAHCCoxzZIHJm6fkja+B6JQgia86idtZf48vzPvIx63EQOevRdF7LPqrPY2rgAEz4MlyeRO814iSvcqbqO50wNZYx2fC9r1BzJBBtVPYU1Lnst5R+QJjNyJm6zVog+Lw3vlonkTNRE/A8ChqXVy0bHmdgQVrjwVdvBmTkLBEcDOQsjWmNSRJGk5zV15wlldaoI2S61iwNkbM01ZylhZyRIPiGk1/YlIeVt0BFOaeGuA9BDL2Qc5CKddSR89B0qamqyFkflyLhCdRWRbATbDksIGd5EphZtRLpVTPxwDxOXdkPQDppZuTMRn/VGgGzz1n4nI6c+T5SkdZopjf1cm1s903T86p+S7c8yWlyVqeO2AUDiTvPbevXSG64WIohuWodalBr7JWSHAX/C1B/je+/pYRCzOKgG3f3qs9Zp0I50zME1wOqRq2pJrFbXBmZ7FUUZi1wdP6rWqOtfLgWWQx9TSsgoobImW7DklfnktZxj11L5IyIcPsDBFe5h8yKu+7Xr4qq0VHNmSB4HoLIWdWywQUCcqbbfcwk0JJhPehof2NycvLjAJ4JYCeAN05NTXnq+d8H8FfqbUcC+JepqanPHgA7H1GoJx2ZWmM80kTOzJTK+rTGJM5ZrVbD6uoqcrkcRkZk+leSgiBprjk70OSMBEVuUo1vkIX4QTUHk/8rjso5Ncrd3kbOfNHRFnj9DZQ5DOQRoCJn/ZQfJi6V6/QuJ8tZsNSNuSg4/vnKQfzuKeUW33Bg8PSLbw4ea4VEv4dpjUQEb8FDfqKzyvdArdEK0/OCmrM+y0VzbqQ1gkF4AuWd5SAH0O7xplWrr/vXnxL+9ONjeNPvVPCcusgZM6IeHu8uJc2KOc9uu0glyVohHcnTnfp0pDPoc9arcFUMYai/xstVBBGGCFTkrFdRqk7I2XU7CKe8hbB1HPiEEfHbkx/AkdUStnpV7EAy5GykKv0fNiTXaDNyZln9TWv0PYIO0Os94pCcqcjZOsbIrRJ4TcAutF/MLvgv4C8+S3jhfB5PBEC5+E2ZXrVkyHeQ1kgkr8WiQc4OGRPBJrpWI/7+ZcDbfr8nZvUFbc/G5OTkCQC2T01NnQbgTgCv1K9NTU39cGpq6jlTU1PPAXAfgIsPkJ2PKMRFztLWhDoNtW9pImdxaY2aFC0v91++SY9FsVgMFqE0RM7SWHNmWRY++9nP4q1vfWvwfC9tmrtyPsjBjwMJ1aNK/c0sBljA5GE+VnIObACFmtcbciYIwuvMefDr3uMosshK8trva82ZkJEIvctp5aKRs4EceiYusSZBEHNnOCeddB2p6kXhu7/iY/n2lY7fb6o1VqdlTysdtfL6XJsjKKzn4MyCcIV0EtWY2OjNhkMn+Oh/yAN95boBMDVGeikyI2fd1njF7b0sV9tMJFIbN+pCOnwj4Z1nVPCqST+wxxOdC/i0BQH1RWdxNWeFuBqcsk5r7F/k7KJfywPtW4g2oJ7LSZXWLV5ykbO/uPFGAADNS5Kmo7COEDIg2sfFsVozhYDU+c2HqbrA+uxZnPGxenepo/f+7PqwfhMICRmASBrhelpWmOhofSUC5+GmwzOPFvjWn6yC2XqdlrYOFHpjU7/QyU9/JoCfq8eXAji1/g2Tk5PbABSmpqZ29dC2RywOhibUaWr6rJFkNC8uIpS2Gq+JiQkAwPz8fN9Jfhprzsxo3l/+5V/iC1/4Qs8j1CQIfsmH30q5TRVImylpjElHf2iTJESDvSJnHCBfdHSzrh+C4vaifH6fdIr6edp05EzvcjKbRWrOaj3sB7WWPd3yUHhH19HRDaoHbC+k9BlbG1kInDQLECr91EtDWiNjYIzBKjAjcib61ufMJCFMi2vm6iJnQpGhrmrOGp9bKDPUZptvGGon3lLX4lnH+HjnGTV88pWSVDtE8Pwe1pzFiHnUk7Oyy2IjZ2JJ/o5+Rs7GhtSVSIQhHq6fdw+MAQC2uNWe2tQpiCisy9N6G7mQ4HebGtstXC+6KQM0pjViHQR/udL5ihhoI1EdWYRJzkRfm5kLn+D6YVrj+WfXcNQWEUQ7dTbG4COQnI0D0KGBJQATMe/5AwA/6JVRj3TUE7A01pylgZylKXJmpjXqSFXa+orlcjmMjY2BiLC4uJi4PWmJnJnRvF5HqEkQRE1A1Fr8RhEVBJGRMyYZ2kZJiLYurfTEkRWuCHbsW4GXeYO63+BjZS8Yf490ivpKzoRUtNSRGJZjQZ1HQXCpspfANFoZC/vjaIeEDPKx7poz1rm6JmA4aYwFgjK6Bo73eV/PnNMcTBKRvBWEJm2jVqYX6Jic1UfOjHohX7CubIqLnM2XGVbuaBH1JCmIwkQ0ysAYA5QjSz5BeL2Z2HHjYy5zJAhlF7GCIHzRkxGIHp2uTtQax4bkvzki5NQ8+osjT8HdA6MAzMhZn4XAYk4Ht8LG4T6Pf8+BQs01VVrlcyxnQSh7LCLV47A7LFfR8Y7Vqrw1RNZpDZYLhZL6Ge0kXzY712mNVlEJttWpxg4W+2dTL9BJzdkigFH1eAzAfMx7XgngnLgPT05OvhnAmwHg3HPPxZlnnrl2Kx9h2L17d+Tvubk57N27F4B0Gqenp/tqT5xjUKvVcP/996NarWJsbAye5/Xdrj179kT+3r17d99t0FhdXQ0el0olTE9PBwR2//79fR+f+fnwMjSPu2HDBiwtLWHHjh048sgj+2aPntNCiMCemRnZbKdWqyVy3nST7oWFheD4mqg9+OCDGBoaWv9BCPCe6GGmNgNrOt5TJ0GobioEjrU77IIfK+eOf+IA8rcvYPvyErg1tu5xEkyAn+Rj//J+sHLzO65f8jGzr4hx80k1XfxyFbAAH4Tp6WkQAXPLFjaNHTiPZGaeYW91U+CElLeVg4hHngSqFqG0aQXV6fby5e1AtBV6X7LdeJcN52P5CAelzSWQqoPIEUGQh+npvWu3QUu+WwDfzjG9a1pGVO1m5+wQAOFuqjvqgh+vagOVx1Yb9uAzv2/X2uzcQCRyVn1MBUQEPqg2RUDwRta+Ls4uWdg4Koz0U/nb5+bnMa1SOevheVugm7/p2k5/Qw2lzQLeBnmtOSTgMsK+pf1gq2tz+FdWhhC6QcpORljdUkKtiU3kEfwn+2Cqh4Y34qG0WaaPkcPAfALZArO1OSw8uNDi3HcG74keFgseVqZXEIzZwhKmp8N71/JoPohOa1gjFsSKwCj34DMbPtY/h6rVUQByfW32XdwdALAhSJErWzbuHxhFrrwEABhRyq2e3WefiOR8tolg//E2lDaX4I5IV9khgeqhPvaX9oO1S2vtEaZnrGATpDpeRWlzCWwoD5dZKJIUUVoqLKI03Xl6tJ4fALBQY1gsLql50xp7ZzcBcIJolChSMKf5kGqDomop13fOpH3Sd4ijHCH4EMfqRA6DIAgGlA+R931/xFf2SFtXV2YxPZ28CrmJ7du3N32tE3J2NYC/BfANAGcBuMp8cXJycitapDROTU1dCOBC9efBJZdygGA61gCwZcsWPOYxjwEgyVmrE3YgEBfZ4JzjBS94Ae677z4sLCxgdXW173ZpwqoxMjLSdxs0dMogIAnQ9u3bMT4uXduFhQU4jtNX2xylqZvL5SLH3bhxI3bt2oVvfOMb+MpXvtI3e3SkLp/PB/aUSnLRtiwrkfOWz0uRhc2bNwfH1+O2ZcuWoHn3esBrAnuu2oOJZ0xgcPtA7Hv8FR9sz0rgWBeqBTh3SGn4Qm4QBCDvCtjcXvc4Ld22jMWpRWx+8WYUtjTP45j51SwKpejrg5BRIrYCYAwgzrB9+3b87ecFPv094PsfZHjlc3rvkPzHzwl/8uFoLcPw4jDsog0wYFBwFJd9DO0ZwqbnbFr38SwWbuu2G+8cyaj4l7YdjecMDGF4xgHTNWcgFOquv06xfOsy3AUPYyeOYe62Odh5C/mNeYydONbkEyoyrRU/KwU4d8nzpyNnbMaGLdY/hzrF2AaCTbsDGwo7ixAuATVJoG0iuAtrWxd/8CvCK99H+ItXAJ/7K33FyN8+Pj6B7dvj5x+zwnuYrvEarBYxPGNhtSadtTwJ+C7D5oHW10YcxjcQ6t2XpZkchvYPY9PRG2M/U5muYO7WeViqFrDoFjA8I3NiZT2MACsTBvcMYfCxgxh6zGDs9zRD+cEKajM1jD9tA7xlH3t/vQ9jJ4xi9Akj0GM2PDyG7ds3AJCKqP70MnIUJZPFLUWUV8o4rLaK3f4YctTdnDYxNBSejy1bD4WTazxvExNyTE2CDwAVtYE2qMgZVfs3pwG5ps8V7sOWagUjT96C4ZkCbDdcn/yHHGwubkBh6/ry5HhNoHRPCaNPGWkpKLVS4bBIEp2B0iCGZ4ZR9HLwLAtFLpAXHCPLoxh/4oY1HD08P4urFkYrY9jwhNFgo75Z26LlivxcTn3eFrlgTue58klA8H22znMmv79QKLT9nvlrF2A/rNq/WFZoj7cg7VH3lMO2bcT27QePQH1bS6empm4CsG9ycvIKAMcA+MHk5OSXjbdkKY1rRNoEQeLImed5uO+++wAAU1NT/TYJQKNdaRMEGRiQzrjrun2X049L2QOAQw45JLCpn9DzNq2CIBq9bva+et+qavzcQhCEAI+H6VZgABgDsxDUVeW46E3NmStkqly7IRcE7kbfpJs8k5bSV/Z8+nvy3098uzsDiQjuQvP5+IF/C79X73JaOQvMZig8WUYrjpqf611a41p+hko5W7VzeM1T5W8w0xq7hs3UnCHAl+dCdCDoEabGyuzBDzxnJYic8T43xuWGuiYHkyl8FiCMtMa1pjd9TM2x82M8is5rzuQbc7rPmUprzOmmz12ctrj0VbetlL60Oag1NZZqnQ4mfOpaEMRb9OCv+CjvLGPl9hXwCm+4D0XGnwDPZ4HCJgA86YNHY+hISQofX12R6pE9mEZmVUQzVUttm96Q0f0Dy6pZt64h6nufM0Gw9b1D1XYJ26g5W0dvOl7hUtEUsma0fF8ZvNL6InHdUBU16CXGgJpqP5AnsS5RGfOaWLh+Eav3xaviEhHmVIGTJtS6rgswas5E72rOOhFvIk7wFXkWLMYeZWuxA+XHNKEjKf2pqanz6p76c+O1L/bUokcB4qT0zZ5ZQogG+e8DiTjn2XRe+12/pFE/TmkRBNG7SrpWkHOeCgEOAHj1q1+Nn/70p32359FKzmp7q1KNsWXdCEHU1ZwxBhDCuqGcED0h+MQhyV8b54E4GsiAPSzHRlR0z6PoZzqRNY7D6r1lVHZXsPnZ7aNe9bUM+UOLqO1YhuM1Op7dolO1RuGJwDP8s2e6KOSkE6vJWU6phHVlg8UglICH4JJQ8FZ1iwqhqIy04c3PqOILPxPAnHT010PwKw9VUN0vIzGdgAvAUjvcgjHZALpOzbKXtSdyfOKLmczzoGXHrRzgLftgyssJ1BG7ILFxc6ZtfyhV+6k3ZSxTPMGRqwHzqXtBEALACdXdVQifpI11Npk2kiD4PIxUTZw6jrETRlG6S2Y4jPuurMnrwS6RVA6Vv7fmAUMxSQXatqAdg46cqT5nA6qIslfkTLgCC9cvYOOp8ZHOABSSD1tH/Ay1xm4JPgC48x5W7l7F4OMGZdN03v6arXnUcN0TQiGgguDwFj3wKpfZBl3AcwnLO1bAyxy+E39vLFdl/RtgrNMRQZDe15x1suSTAHy1ASCMXZSg5kxdFIO5gytx7+CJ8T2CECcIAiAxUZBmkTONtJCzJCNn5hiZdvXa2V+rPfXkLCl79PHi+sGlqc9Zr1VReU1uNbeMegi5SxyR0mcArFD0wuG8N5EzX0R6YDV9nyBwN/oeHTkTFS5/E0U90o4agsagvKsMUesgKkQkx4jJG2v5wQocVdzt+LxnRfgd7+lSGIUxVcmEFZKPtTRwjdhgAcSlqiYJkq0YOvBEIqIyAOy8HTAHsc4p7c578Bc7F4KqV2uEIMBmQT8vG72T1AaA0gPN+9zFCoLkGCoPVQJHOk8CHu9SECTGUxLUei4RyTdptUbTkQ0iDnx9UQ/BAWKKcLLG38bNv0kSnUBcQkcWi6GTz1Uwd70w91HbRc7MdgwAUDUiZ2wdGyD18JZ9mXbbBiTIiODL57TTr+up1hNe9Jc98JpsOyGv/9bvr7nGda/OmSCgZoXRPPIEavu637yurnD5+RYbF7NL4eNcTOQsaDdAAlzEaxkcEJARObNMe0KyCADFjJxlaIe4tEYgXeTMfG5paanh9X6gfhz27duXiB1A1BaTuCZ9zurJWa/tEUJ0RM7jImdpktLX6GX6sHClSiMRWkoZExF8EZXSB5PRM0v1qyGX1u1cAyp6YrG2RIZEeEPTsPIWWF5+Nk+iwYctdBE5E56Av+yDas3HW0cltJOmd6n9VR92Qd3we5Vvhaj/2syBWF4l+F7Yo8rKyd9CgozImeg+cmYzkEuo7KqAPNFR03AgjAoxS9pOggJfej2KbYBSHl0DUeBG6wOPWUEzdp3RZJPAXHl9LoZ5fkQLUmWSM32d5WzpMGqHVsug16fzdoJazL5gO2JOQqYqW00iZwDAOHV87hu/nwBBMlDuEfbUcvDrlD/NCIaMnBmN3uvSPgvEexalMnvu1QxytlohzCyqSJla77Q9OnImGAuiZ0UR2lRzCeVq93P8wetXOkodJgFYai2yFfkw0xp9wVDeWemKfPAKlwSRy3MnI2fNv2d5lVCpUYNaI8iMnAkIlyLKiWvFcgVBn8Jmkdw5ww0M0hojao3huuhx1rv+fW1AXMBT9zKyGu3R6edFJyNnGdogbeTsIx/5SMvXFxYW+mRJFPVO/Qc/+EG8613vSsQW85yYxbJpi5z1eg694hWvwPj4OO6+++6W70tzWmOclH4vzpcmZx1FzkQ06kEAwBhsndbIBd79rfGuZLWJQoeMPOUgt/McOeHvv1fHtizAHgiL8YWI3qi7SWvkFQG/wjtyisxUGeIkRTIGlQw69zv6jk5gTse4y2TPLGHsRYTnvD2MnDGLoXT3Kmr7amHNGdaRvmMxeMue7IXlk0xH7eDnhX3OZF+xwSMGAbVD3EmKVCvo+qd2NTDB+yk8Zz6zZKSRscBhs4nw03uK+MnV3Rtl+q2tlhHzNTNyxhwWOLR5ZavXQfpo1AbC33y+8TdQa78aENE+Z5ZRRKIjDswXXZPqsAUDwxeuLOAZ396Kv/jPYmQsIlErAnyfBamoATkrhE4+gfWkhYZJZrUNvk846rWELS8l3HZ/eO3Y5hxSCFIbha9SLYFDfp8w9AIpm75WXPgjgaM+Oo4vXpVvT6qIkBNR8jFUlHWVNiTxFJ7oKpLPV31Ab6oItLzu55bkOvSC99gNEXMCUFNj5JCAcEXQdLkbHPfxDZhbJBCoaWRZ15vpYwJ1Gw5GjRfn6Bs5gwC+eZPUyY8jZ3oDKW9n5CxDG8TVnAHJOfr/9E//1PL1pHqexRGMj3/84wlYEj0nZ599dvA4KSGXuKgQ0HtydvHFFwMAvve973VkTxrJ2YGqOROeCPuKtREEERxg+t5gSQeSsdBJyxPHd68axtLNa49SL920jJXbpQwy+UKSmzY/j4wddA1msYCcDXAOQcDqvaEMdzeRM+ICuQFZVdLOKdL22I4F4Qswx0KuIO0pcAGvi4hHHExCFUeufn69/PeaHSwkZzaDlZekUZg1Z92axGR/PMtR3ymaO0UmzGa0BGDwMQOBE7Luxri+AHHCwvWLWLhhEV6rxuqQkSPtpL38JEk0mQ08/4nSM9e1RB/5Ro/IWYslzRw6yyRnOSsYn6esLsIiwsqD1TVFPZql5bU7XToQHJwzm4GXOYQnYAeRM9H1eSOSERjucnz3Vumc/uf1+ebzmwCfU0PkzFbkbIjJ872yhqbEzVBbDQ+shVOWy8DuWfl4x05TECQaOQOAimKygzrVEsCCUnpfKK3dnr+9QB7jA5eNtCdVxqaDFgT5k5NdeLqfoCvX/W4jZ0RqbggK0prj8Jsd4eP6mjNBgMvCiKdwBdg6vfnfPmzLsWnys6oG4W4pCKKii93Ma+ELrNy5thNMgjCWUzL+pj12GDl75ZMrGHkENqHO0GOkLXKWVqRpHLQt73//+/HkJz85eD5tkbMDRRZHRkZavq6Pl/aaM/N8dV2Ir0AegVyVntJSD0SmNQaKW4wFao3aSdONYXll7d6+v+LDXZAepPBkegu1YQ2yrqLuPRSNnHERtac7ciYdbKa+vxVMMRDyCFaeBfUwReJw3c6bNbeCOTRx5MxU4YuQs4ItbTPUGrsmZypLk4TaYfapo6zNQGlPTiHZC87uDTkjLtOseJnDX/GxeH3rjAmvwuGoeXv45rC/2OMmFDnqwbkyL9FWy0hwHnTdIiRRtJwwhTAHwuv234vVOX9NUQ+vydLeTpGOiAARJWerD6yiOl0NI2e8e7VGcBWxlzsfhl2IfUxqMyFIkctFI2fDqsXEwkp3biEJQm1G1j3VKkZao9toS7lmCIIENWfheJaNyFn9NdbNXp8p6NLuOhGegGx0ACgzMJgP0z95TWdMrN0OUZX9+0SNy82PFtHuidHwcXDvUPYIAjx1Xxu1VKrkevdmXCVI12Q+mudBr9W5SM1ZeJ3xLoV3eFmgui++b2BTiFAd1BSe0X0yX//UGj571nLX6cNJISNnCSAjZ50hTeMQFxkCkoucNZPS77XghUY7cqaPl6aas3bkbO6qeVT3dl9ELTwhU8E6kFYW9TVnAMAAS+3mFUjNr8Fu1LYouBF2EjnTtUr1zWhZjgXHHxS+dIyN9xS6EQQJbojtVelyptNIOlql2lYIHzXe3W6sCRIUqauJJWdGpIQFkSrAKjAwi4HbIaHulpyR3qEW0qnibmfOXjCHlGgKYyyMnK0zjUiSRYJfls6et+zLeslS/GQqPVwN05scpe2vhG6A3pCzaFpjc3Kul1+d/sUB2BaTojvF8Pp/xexOcLS/Xk00EzWR10eLDwqKSOmzHAtSz2xVa2pxWecDoGUUJfbrOSRJqIM5JyNLL0lC2SAIosjZIBQ5W+1S8W/Bw8K1C+AVHhkzvdlh2lKumoIg0h4vktaoREG4rDkr7w7X6W5Sic02Yu3GWNckcsbkHNKfU+PFPQoyJtYKXpXrc/mBMqp7qy3PuSnAFNaaqmudWCClnxMC+Qln3SW5vCYgKs1re81xD7MczDpKI3LW5VrNbETqt0VNYO6q+ZafkWqN0TRU+V1aKEkutuvdjO03MnLWI+iGu52g3nHWzmOvyVm5XO7Jd/W7hxcgx2h2drbvx22GfpAzIsLq6mr7N6I/NWdm64JisdjyvQdLWmOEvBLAy92TWOEKQJCsGWl1iZDsJaMjC0HDUaPmrKDHqJuhIgbXJazscUGcYOVYyzRLCPmfGTl7/NuPRH5jHvaAJkMcnBhWjB3wfEeNV+pM4wTGCETNRUrqBUFYTsuyh+SsKDg8bgWObLeQ5Cy8gcc53mbkzCTUVs4CEeA7elefd11zRkJqGpKQann5iQ5qYWDcsC3DuXbCyNm6lmqVWsnLHLzMQb6Av8Kx+Nv4VFvhU0DOWI4Fl4D2sTUJWM/lH6kPbJFyFYhq6mMyJh1yxmAXwjXJZRZcYa3JsW6W1sgJsIdaEBkBAGbNGQsiHzpdzhIErq7V5duWsXTTGtKaicIITpMIY4OUvkCDDLomZwPq+YVSd26hV5IRfFETkWtIC4KYtlRqcWqNRlqj0YjaFwylmTCnrtn5aIVIjLPNfPTLqjaRWZH+dloUxC5Yqt5v7XaIGpckPWeBr3K5njWxx/x+s9YUiKY1Oly1GVlvJkiOBaIgcTDnlZand2LTGmU9YVebRYRI5K5a4iivtF5kSVAgLmPHqEf2rfatx8jIWQ/w5S9/GSMjI/j617/e0fubRc566egvLy9jaGgIz3jGM9b9XUlEsLZu3YrXve51fT9uM8Sl7Zl/9yJS9ZrXvAbDw8O499572763H+Rsfj7csWrXxuBgIWf1aY3rcfaFK2AVLNVfqMUNgFRPqGD3Uz1vAZZtqUJzgq0Ku9cKAnDUxyfw2Ddqzw8tVfdIkCx6V07RTUMT2HS67P0TRM5Uj6HD/y7MrVlLWmN1TxXVvbVAsICx9jvW0VoYCqJVgCRnP7mn0Jp0doB6Rcy4yzYSOdOONZO7uowBVjFMuep6aqt+dMQJ9oANZ0OuI8IQRKpyVkBqa6qYIj9fxXpym2QEWKZe+Yuu/NtvHiHwfWZIalty84GFAhxhs+N12GQ8Fn7zOaTPg760OBhAUslQE3xAiijwDiLdJpqlNcK2Wtb5kApCmGqNQSph3lCQVL/LnfPgl9e2IOlWHhFy1iStMZDSr5NltxR5HVK5YXNLrCthIlOB0Oehk6yvp0jkrCbr3wBDrdFwR8NG1D58jkj/qlo35GwNaY1n/r08ls+sSMRNqE0ZmXJtrZkMEcmxYWpjRUekm5G8OAVSPd8IwEpOLsgjvgem+gyuByxvy/PeLDpt2BP0gTPuCVEp/S6zHAhSvVbh+occHPWJCfAW30WcwNX9zo5Rj5y/auGgi5oBGTnrCd7ylrcAAP78z/+8zTsl2qU19sLRn5qaAgBcf/316/6uftdTAVFikAbEpe0BvSXUWnTjW9/6Vtv39oOcVath7vd6yFlS6altpfQ5dZ0mt3TrMqq7a8hvLmDwiMGWNWekImeR3U8mHWwbFPSryYvuyJnLgbLHsLDKIIkQi9zgGg2SNmmn6OhDwvcGgiAxuv65NWQ7le5dReneUlSNrM1PC4vw1Q3WYoHjOCA49q7a646ceXWKj+1qzjShBpO73QTgpMdTYFP3kTPI7XyltMnA1kTO5O67HKfSNplyPLi/tC4nRNdI2cM2iocNwMpbMhrX5Ds5NyJnjpp7FjMK8ZVD2bVFUT+xkx5ceg6FkbMwdRiQ0RlXNI/ixsFtcvsT7QLmijTpmirLZkHU3DJqTTU5E54Inu8IRlqsaYk5J82ll0S0N11wnlS0fGS1ikNrq6h5rLtNoqCOkmB+XJMpcxqVq4Zao9GE+qXHuvjP15cwMqZSLQUHJ4AbLKnZ+WiFSFpjGxZzz0PyX84YLBa+VxTCPpDULqU1DgKQ2wWSuPgrrSNeIoYMMZtB1AT8qsBsTmazTLj6Pr3+yBm5La73SM2ZfI+Tt0BEWL5tOah9PWZ1AR5nXamQEjWqxVZ9hqVS8+8SPgXHiqRZ5sJrafGGtQttJY2MnCWAftScmXLv60XStV9DQ0OJHh9ontZ4oGq82qEfTajNiJeZ4hiHuCbUaak5ayalT2JtO+gaRITqnirIl7ugcpO+9fdwMqT0dVZjTkbMdO1AgXhrUtUEZjCJCICFNjVnAAjY4Mg5vXEUqM26cOfcgJydfsgai7Lrj6GcRuEK1det/RiFNWcq7cwKiVpRcFRcoLZ/fXZ5db3d4tIaPSPqGNScMZlmyQAMjIQEttsmy0QyyiT9dqafbPs5LcDBcqEQCAYdeIzB4gTqoNl3U+i0SE6wi7aK7KGp8yj7nEUJNbPDtN311pwt374M10hp4gJtG/Y66hoTUJshDGBGeIup6FEvIme8nYMuABAZ6pFqDuVYkEqYJyHFDASBXBFpVN0WguCMOSAelTSP1JyZ9pFM6a0XBMlvyqOwRRY4HVEtwePoahOEhAjS9EyCqCNnphtRqYXXntmE+l/PLuPMo3285GnyuQHugxODa8zruJ5z7bCWyJm2x2NW5HMwyBnrYD2rB6medFA1wrkRG/lxp+n3RBRIjZR4b9EDdwVmHEnONnk1pfa6JnMa7WMMwhdNr7FYQRClYMtyFoqHSnsOd8vICQG/G3VdihfFaqbUSkRwfQLTkfOYmjMAqD68vvtGEsjIWQ/R6cXaTEq/l+Ssl3ViSUTOTAwPDyd6fCB9giD9kNI359AjMq1RSVGvFe6MC3fGlREPm8moR6ufSCTJmTpUbdaFqApZg8IIrmX0GeoiP94zHBdBSma9Vfqfds7U3dZ2GGr7aqjurcKZUKkyq403szXxWJLHIU+ENXYdRs6YrQRBrNCJLQqOGix4S+tbi+qXsnaCIJZJznIyVU5HGgYFh8e73AQT8juJS/U2GUVr/7EgUmWxIMri5BAQfHK7X/f1ZkXgnBFa9k4TwrDHseS8slkoCKJPeJcmLd28gupsuCkkVDpuK+RUpIPryFndwFog2SB3LZGzJml0RGg9p4WOnMnjW7YSTGGIzGuXm5spnQ8WCQAWk+uQERpqVnOm06uDTRD1GcYYxp46BgCY8GsyJbEb/4HLg5AguGZaoxYEMSNnNYA3aUINhOnVw8KHLxAhZ2uNnPEKj0TO2kdf1RyCnEPeoic3mYohOev0ejURzjnZoNnKqbRYAirTFbhz0XusedtsFJNimHNkSHjCr4Fo/WmNxBgGDh9sOqcjNWc6cuYA4DJNd/iJQ4F9DglUF9fug5Ag2EONxc1eM/dDABXfitQra7AmRO1gQUbOeohO+4HVk536yFm5XO6tYetEr4jH/Px8R6Sx/j0HkpyZqXut0I6cdUtgiaghKkVEcF23Jak5kGmNtZqsFVpL5EzP/ThyVqlUOhY66SXa9jmj1rVZcajuqaK6uwpeUf2J9F2/1bxWTpHe/bSLltzRzzFglcNVjnVe8O6KzJ1wzDU566RhcyBUoBr1Mpth6AkySj0+u9J4nLVwbOW4Ck+OUdVrL4MfUWuEXBe1AtiA8LFYs0Dr7HXm1Z3vOHJmRkoCKX3lUBNjsIJecH73Tai5GTkjFVls/zFNhsgXQbTK4iKQ1RbV7jy0mqsktNVmRU2PgarLjDt3XEQjecRk2p43Lz2pTV4NA7x7Mk2eADecMiHQnuArciaYFOCREY7QMXuoMCQjzUSoPFTB3FVzbe1oFjkT1DoCp6PHQc2ZFgRh4aZDgaSyoU6BXIvDT4LAbPWv4XtGas7q+pxxGE2oDefVGZebMuN+TUXOusgo0Fy8ThE1Vko/otYoXzCbUDsbpD1jvisjZ8Y8WEvkzFvyMH/dQjQC1uanBc3KLQvkyrYSpXtKgBaYqGp5zc7tAGCwU5IKgpacmySA1fvLKO+qxL8dUp4eUFkFTM7vkir4GuK+UiBd5yaoY8EesJpuEEbVGuWxnIIVRM5IEHJDYWPs6mIX1z7FbwI2azxOnFDlrKGOEqiLouUycvaox65du9q+p50gyIknnohzzjmn98Z1iV5Ezj796U9j48aNePvb3972vfXjc6DI2Yc+9CEMDAzg8ssvb/veuLQ98+9uydBrX/taFItFPPTQQ8FzlUqlrZhLOyn9bu1ZXl5GsVjE8573vAg5axc5e+1rX9twXJMUbdu2DdPT013Z1C06aUK91tqKlbtK8FZUjzSBMK2xXc0ZsTBFTu2gs7wFvuwGNWcF6q4hrW9U9RAgb/ptas5AAAvy9C0ZAbQYitvkbmyx0ni+1yQbr2TERU3gHT8fweM/vw33Phz/1kCtEVrsIkyRgyCQxeAQ4dIdDnbuXYMNMfDr9s/a1ZxpMqfFHBjMujyObvVJhHKsZf2fjix2kNaoHcdlHtRUOBZQU45tN7VCK2XC4AsIr/rOGMgnTO1zsP19G/DJa4YgPAFCfPTMbEItBUEAMCA34YBtLSJPAkdXlrre0SehnFh9vCYk0UQQOQMzgwx4wtuPBACsWjl4goGEbBXQyeZMs0gNp9Z1gkGwQz+wmaxdNFpEFITApbsGZZ870Z4UEREqDysnXqiIGScww5DPXhQ+jvY5A0TOamhCDQD5CZnWOO674F3KoBMpMswRiZzV4gRBqmEUOy5ypsnZhC83BauGauxaImdLtyxDVESk7rFdSmte5XS6zALzCbmxHJhtBYSaVTl03e5aQJxUxE3OPzAtlCRi55E5XnqTQ0fth/KEVSWaMsR9mUG7TjeNEyTx66DPmW1EzkgQLIeBeFjn5ZBAdWltPoi74Eql2JglzG2Srk0+oeqx2Dnd7PHBgoyc9Rj/8R//0fY97dIaAXSs/NjJMdbiqJ9yyinYuHFj0+/qFp/73OcASJLWDvURyImJiXUfPw7ve9/7AAD/+I//2Pa9Byqt8T//8z8BAN/5zneC52699Vb4vo/rrruu6ecOVOTsyiuvBAD86le/WlNao8aDDz7YYAsgW03cddddXdnULdpK6aMNiYkBuUpERGjlLZWq1EGfM9vY/dQy8ZbNULPCyFk3nqy5qxhEzlo5nSTt0VEhW9UvMZsFxCPnNs6ftZAzmfJF4FWOb98saxH+7X9b3yDNyBkRkBtzZOqeTgEjju/d0EUnbAP1O7BxUZHYQnyLSccoktbooxtRO3kQqMhZGPVYS+TMLlpB5MzJETyd1hjT96odrr5N/uarHsxDcMJnb5ACI5/+zbDcRY8R4iBB0RQ5vTPNpAKpvU2e84Eu57Q8CMCNNE3eQeQsqDlTjq/2yu1huU47JOBq+XIRRgpboVXkrDU7k//oBvNOUQmmgMLImeBYzBdCwtDGHPIJqw/IzBqd1ghCpKjqM9+vt1F/mGIFQQBEWmj4HKjt66L/o6qfJF/E9hI0bXH98Fo840h5rOc+KVxznHF5vjaq0OmcEchfi5S+t+CBV3lUVbNd5Eydr5plw4IUbrLyYbST6bVxjfPanffk9WQzwKdgflITFVL9lEUCRRKgIB2W8IHnreCJj5GvDwtP9rJeZ14jqXtHsxQJvf6PFUQQOcsXGMoPVgL1Sr3p4JCAu8YU66VbllHeWYGIOUFNU4s5oeKHojvMENRhTkbOMhjoRIijnSBIL2CmobVLSTNx7bXX4rDDDos814vI2VrIQj0527Jly7qPv14cqLRGjbUKuKRNEETDvEHU29bv2rNWkTN9PtdSewIAggslBKJS0rS0ccv7kKw50/L09qAFqN1zx2ZBv5o8Ne8x0wquH84dQdKmVjvfpGrgtCKjXbRg2VJyXAi5u5vzRbjjr797TZEzObZrieRE6gZIiq0QY0Eh/gD3MWh3P4cWb16CW5f2Fxc5My/FsF4IsAdysAp24AAMCA4i1lLmuRkCUqZTY1tc/uY1pdMIrQErsCNnIahbFF0IgphLsz1gR1KAgjq0urlAQs6hBkEQyNS9sKbKB3WQYtsM5iYDF+39Yb0BoiNnpvgOYPRgUpsrnVxvrdQa2/U3ZDZDQakZFIvyPBOLCoJwFYXpxLeORNeEukZE8+lTr9YYJwgCyCi+tIfD8xlqs12obqi5LHyK1GJqp960xfXCa29rUT443LjN68jZOJf3nX2L4WudSumTkDWvCIQ49AutP1cwImcWSAkTWcipjSu7xusFMjvC6gOr4BUuFRd9Ck9ak3mkx21AzR8q2IFk/mPGCT958ypcZsEhkum/HRYFNyNxmpyJZmnM6nxtHBTBpkw+z2AP2bAGZFqjJkSOEGtfF31S5RSNs7k+HT2wWRCqZkuPLHKWYT3oBzkzIx2dRj2aod/qkfXE4kCrNXay43SgBUF6Rc7WGzkzx2ItaY1xn6//TWkQTaknr2tVaySPwt1PQfI3tisOJ5kCNaTJ0FBO1XgBlm0KgvBASljvjncCUx6eSErpoy4lrN4eYTOMcunl5EZsMMeGM+GAkWyKDEjH2sSaImdQaY3V8EPtLrOIWqP6l4HARqWjttGvYcgWXe0Q+ys+qg9X4dUpgbWrGdPOvmUz6aQVLNi6aa8an6Z9sJqAiMBXfSnmwGVqLGPNRSpMUqzrYXIDNqCWIscJG9KSK9Y8p83zOnTEYKQvGKnG1A0RK+Xoa8dR934DpCMU9oLjzedhGzCbwTd230kASzcvtfyM3lAwpfSBkDw6JOApKX3RYdPuljVnJAUjZq9orF0jQShsKyJHBA4ZZYBaLsLm81w2RVfSj23tMaJ92pludb6j16yKnAXKf+Er+pznhYDHu0uPlam6DP5KNKKsI2SmmR431BrVRagJKxCSsxHPAyPC/kVDYKRDciZcEbDzqJR+888QURA5cy2ZqssYwPIMORVdtFxfdcFY472jRgFhD4SSGAORCCeTab8aQ72ph6KteqUB1oANXhNYVfczb9XvOK2xmdlcp1cTYiPUei5tGhDBHMoXALuoVCd53XXWTcmZT6CYrQavLDB35Xzs+yseQ1E0ziHznry+mGIyyMhZj1HvLNfD9/22giDdwvxeM9KxXnJmfm+3EZm1kI/6yFkvSWu3SJOUvu/7fSdnnUbOWkXH+kXOXNcN/gOaS+kDWPOqTQIgX95YpSAI2jYAFaoGQ99kc8NyB5RZDHbeMqT0BQQxuPMuyrs6I2e+T5GboFCGkADmr1mAtxzjyRDALQtjvhwff1UWvWtHT6c2DtbpF6+p5owAEEWdPJK9nJo5ftHImXQeCQzWhKyD2+xVkc+1r8uJg/AEeK0x1SY2rdF4i1nLwJh0PrQjq8dnrXL67oyL2n459iQMpb0mk8gkkEFa45AdiKM4thE5c2nNc1pET1GUsCpRC6JoSqjvA56QqZ2A3GjwlcQ4s5kUvQGU09T52h95pxXdPOG+rGFshUDMgVlgukcVi9bC+KSjKp0NVDMyoCNn/qofzElv2Q+JmprvgEyRK+RCtmimNQYtAtpF4oBATZOIgrRGz6em2ZlVF2EEgwCfEKtsF5AzEvA5CzZV1kSsBUBcwJvzJAFW0NyiPnKm/7bUMUzH2nIs2MM2bCKMcA/TC401bO1AHqlliEVq8lpFmDgPI2c1ZstLUs3p/KCaQ/qCX+t15smNE5YL086Z2tiLu/S1mcE6XLBBPsEZyWHwiEGIGkdF1Z3xWnvBJY1mb9OnWtbBNb5JrwsbB0WwDuXzcl10NjiScJrXWQc1ohG7IFud1GKiZLUaQcTcgIgTSi4L7mW5sRyEL2RLAON7uu1nmiQyctZjtCIh7373u+E4Dm6//fbI83E1Z2vFzp07MTQ0FAhunH322cFra0lrBIANGzZE/tZO9V/+5V/CcRzs3LlzzfalmZx1Ylu7JtT9Imd///d/D8dxcOuttwI4sOTMfNxNWmM9+kHOFhYWcNhhh6FQKAQiK3E1Z5/61KcAQDUD9eNJTByEVLVjKnUwmDstfvdLP+7gJ7sGw8jZcC5QbXPyLHCs84LDVd/Zyc71h/+d4DyPcP29ZlpjaA8JwtJNyw2fI5KEQkfO7KFc4FSTCGWs65X21hQ5E6qZqTkuBCzfvIyFqcXIe7X1QRNq7TTa0qmicSlWsMWrSke2mx5MXKY4+Z2QM+N3RmyylE15aV9RcDCiNSs2esuyx15uyA4Ifsv3GzZqp8gZc5AbkdGFnI1A8bNVE9lmMN/+2m8O4Yr7w7o+4pI4XHqdnGtf+wmBc8Lj/5jw7dsGMazmkJQG10IuITkza84q0xW48603Ck3LmRWNnKFgwRlrlNk2oaOZFcuGKPlBTZa3qJw3IpmmSFDEqfVYeUse5m5tVC4FAK6ERSAQED1R5aEjSIBbDcmZVo+MSOkTV0EzRYLbnDutnEkeAULgDT8YwePO34Yd++PH5WfXAbnnkkwLIxntHFeOrD0YfsYyInmcGIQnUJupYf6ahZb2RGwTBHswB3fZi2xY6Mh+JHLmh86+rclZPnohBKmNvovPXjEQPN+pIAivSaENxjqPnNVWeCRyBpJKhJbNUBhSxMPtLq1ReEKKSNlqvuifq6Zh/dfpdUjPaWvAhvBIkta8TCP09aaMF0ZU26EpOatLga2HXv8niiJIjc3lZQ2cMybPVVyEulNUOcP2j2zEcRdubXjN86D6MEaN/+j3GM7573GMqdpEZ9RB+YEyyveVkd+UN4zPyNmjHq0c/Y997GMAgC984QuxnykUCl0f90tf+hJc1w2cThPtImenn346AODZz342AOCLX/xi5HVNPM4//3wAwFe+8pU127cecuY4TuR39bKHW6fQ5KRYLEaePxBpja1+3z/90z8BAD7xiU8AOLB918woWKfks5Xt/ag527FjB2ZmZiLPmeRs61a58A8ODgKQN6Hl25exeGPrdCn5XgodIzMNTea4NP3cL29TkRYdORuwgwbCdt7C4RPqRkcETp2np/3D1+Trn7giTPvVJpESTxBunAIkwefRtEYwBEXdTSNna5lSKgpQn6IidFqogUCtMZC3k72JZNoVw7ZtSqGMe1IFrpvG4VwSCK8uWhL3VSYJDWqqcgCzpAgHY8xI/eRrT2sUAvagLesghCEq0y5yRgRHvad4WBFDR8g5PDAQ1i1SjWIdq1Ywf+/P74wKrug5/+efln//2ScI+xeAh2cZLCIMCumo+is+LFUbyHIMOSM1Vv+sOLnwlmCI9OvjttXWIS6qSVq2cvDnXJmOSgS/LJ/PkQBXPaGEct5bwZ11W/c5U/sPOsLEbBY4giSAWiUkZ+EHAUepIz6mtgquo2HqtZYgWSs3/5sFkAB+dk+jz/D0oxu/5KH90k7OCU+syA2boScMQtQE/JIfqYHzSabb8YpYU+sK4gRnLAdel5oWpDUaX+X6wIrqruLEpaQhKqdvolMpfW/RC9R06yxt+pmV+8uhIAizJblzGGAzDA6HkTOG9kQ6ckQV6RQ+wXIs8DKXm0ysedQ0jJyp6PSAjJbnhuwgY4Mr1sl96rgtTLN3BQFBQiyZ0evExkGBIotGX/V6qOtgHVL1Y65AdU9n7Ypua7LBAACuR0GDcxMf+J78THAvG1XfYcn5M3biKIDmCpRpRkbOeoy11g6Zn3nTm97U9XHHx8ebvtYu6uE4chF873vfCwB48pOfHHm93tGvl5PvBOslZ3/zN38T2NlpP7leolKRTsXAwEDk+aRqzjThaBY567UgSC/IXj8iZ7Ozsw3PmWP08pe/vM4WSbREB8pSum+Rv6Lq1fTdSt9gW8A2FbcGrSCVzbIZnvUEaYvcbZTH4S51XPdh7lIH/j1JZ0l4ojEVjGRKWiAIMiDJGXOiaY0D9TVnHZ4+rWYpC/GjkTPizUmnJmeiIuAteYAlxSVyQQqYGp9uBDh05Kxu6YjbLwieM8iQTmtktlWX+umvnZz5ISE1s/6azSFNzoIeZ0pZU2Pj0YNGWqNY0y7xyp0l+NXm84y43O0vGEu+Xv71/KCCLaMLhTByljNqzvQUkJHmjk0Dsxi4cb5k82jZn6xZup22qWrZsGwAllJKLYTpVp6QNT/est8QrWn4/QINhF4jiHgJkvmCgBLj0Z8l1JQAjWdZcGfdoM6rsC0Pf8jBuO+isOLKcdbf1coeLiMkJFrVpzW+4PlyU8DyBYaED3Is5CfyqExXUXm4AktFg/NCpjUydaxOndravpq8ZgHUC71ynzB39Twqc+HJ9PxQgbGoU/TryJmp/Gei08iZt+zJ7yCAmT+jxU8ixwoFQSxL1vWpNhpjY4rAcj+IRHYKed4Ehp4wCGYx5EZzyA3bquaMYol5WHOm6zplTzG5CSJX1iByxtGx8nCzPVJfpaIyFh991+mxNgMKmpxZanzUemRGznxiqO2vYeXuUkd2FfLNFwevRoGITxxG/ZCcWXk7mDvDT5JtmLK0xgxdQTuP64mc1acimmgXOYsTTjBR7+h3k2a4HkEQfbx8Xu42rreGrhvoxuA64qJxIMhZJ2Ol39OvmrNOvy/ptMa5ucbC/Li0xsAWUrVNMdLx9ZA7nQwDjx2Qu5dqwW9Wc+YteZi5XNqjC5bJsWBZLDh/LM+CdDCHhOzl5BPgd97zzDfqOwJFbpKOJcWRPOknBDvEtropakfGlIo3wTt01Kp7q+BVoaIBZj5R65QinUIIS5IgxhhgWUY9DEetiex0WwiC8AG/biziUjX112uy6DEGW/pD0vkQ0Zohd41qhCQoUGgkM3LWBJqc6d1hGshF1ojhISNyViUs3LDYsS21mRpqy83nvpb5zhsBNZ0iNqQiwaJow8rJ+kntzOaKRupn8GUdmwVAKceZ0RYVYVh9oAyvSYNbfZ1VbBu2JfPZGELBkoKQTZ+FRyBXtG9OK6htzZkQBGGsBUJvQJAUMgCAmm3DnXVVQQ9h9Z4yeFHeOyyPB1EB0eaaJ05BVJw1GdByNUZQwZfrl+5tCK2uacnr3nIMtUa9NKoNjU7Aa0Kl6jH4PHp8tyTrTL1yOM9cD5hVGddB5CxvgYiCzS9mrIuR7+s0A70iJGmwEPSYlD+s+Wc81xAEYTZAJHscWqEgyCD3sVC110bOfAKzLdgqulzYXJC9ARlUNKgxZa8+rVGnoTJbkSIQuLruOaeONhiBFmmNQSZIvMiMXistZpAdBsCyAnIWRM6EWv/VPagTtLq9uK5WV41/ky4ZyA3ZsIoWcsM5CFcEdjVrrJ1mZOSsx4hzrNulc+nPaPKhQdR5kadJzuqP1ypyJoRoKnah0e/IWb299eR1rTV07dDJGK+uyhyMZuSs31L6/SBn5uNW39eM0NUjKXJmjq1JpplMl5cpb52YpuTP9Q2WOGTTzDKXtVF184iv8kCAQt/w4ViQjWiB/LgDZluA4YR4JIvFhY+W+frCuHma9x2tuCVVvYRy5urs4gRPsEA8gRWkk6BJYhAVqjtfHUfOBFCd92QdROTQFAhMoO5poE58Qy1FzAp3Y6WSXHcNcoUnx6I+ytUqcqbJos8s6egzADkWFPUDUmp/zYH8oMcRA7gUl9EEn5c5/NWokdrmjZ5c97whJ0LmBguAq9LmRDUmUtoK1LqWUNcNOsaSr6dTJHLmMLC8JZ3GHEPOkK4Pvl813u0YliFmASmqI9NTmxs8oCZpxcrBsiXBg82QG8opm7mUeY+bhzEgaqPWKJQ9Qr9fRbZU9Fg3z/UsSwmTQErp51lw3ZOvnU5qX6OjHF6I5umr5WrjD6t5kvjVdKuFQBFV9VvUaY0qcgYAUA5/J/dHxmTqGEMjearOefK7jOc8DsyryFlOTRC7YIGXOMoPyo1Qfd3n6o5f63AzhFe5nIvDuQg5a7W541cpaEJds1QarYqcWUYEf7HWWgSqAaLFxpSei3Wv6+tGb5LlBpVAiSKLAAsjZ74IxGfaoZkZmpQLV2Dp5sZaZW2Precpk/dWy4mPnAmw1qrBdah4zdcGtyKvK2/Bi/2dWlnYKqoU/QFLkjN9jWWRswz1Tvb73vc+DAwM4IEHHmj6Ge3Q1kfOnvWsZ+GpT31qR4ujSayWlqL1M83IzC9/+Uvk83lcccUVDd9hOv29IGedgnOOpz71qZHnko6cXXXVVbjlllsANE9r7KdaI3DgyJmJTmvOOiVx/ag5iyNn5vyPpn2qlJIYAhOHwIFS/xeCMPVHN2Lqj24M6k8AwJ13UZt14S37gXMW7P7mLEk8LAZng4P8RieMnAmlcOXrtKV4m1bKhC0vM8iZETkLPqKFCijqxHFOOO7/2fiD74+HPbPyMr1G3sgoFATpUkr/Tz5t4zk/PhTP++YEfvfbRrq1jpw1/C5CTgicPXMfAOkwMpuFKTNarIAEaspBXyuEJ8AswFtD5MwJyBkLokWWwyBq4Y5sDoS1LkfCEAERbkj0QDLqVV//qCNnE74mZ/kIxxkomFL6BFHpfIBIEFZbNByWxAHIm+RMjZkm93BkdLOwuQCrIHfRc4FjLXDTPgflqghqbDoFs6Pn2uXS3laS4XrOli076HnELMAeknGmAgn4XCq/UYxD3PD7W0bOGIQgkKevMzI2e+R156uaM9+2gygDhEpNUwQJahMFsddGoz1Qkbp/u2kg9j337mt06/7044SzP5/HfTOalOkaIQZmKJAWSDahBoOMfreIVkRgQdansUb1Up+YbFtgTNpyFVguM1iMYHthWiORjDBJG9cZOavK69TKW5G0xla/x60KFILImSRnll6PDEXL+craal9J9aRrAEMwbxrsV8+FtcpKoES3bwGB68gQR8fqo82m2Md+IeeTvv/UQ69DRbUbZQ9IQSMrbxnkLBwjQeo66DByVmmxjtYqAoIDi79dim2Qrlt62EVLRZSZzIo5iMnZgfOyH6WoJ2cf+tCHAACf/vSnm35GO631kbOrr74aAFCtVhtIQbPvAIBSKZrj26xG6/Wvf33Ema7vB6VJUC/SGtu1GNCYnp5ueK6enPU6ctYOZi1gfeQsKcLYjJytlyx2k9bY6fv6ETmrn/sA8JjHPCZ4HCGvkouAlMQxEbWMYEofIXT4zGJ5bogLLN++opoMM1TULrauY0DOUvJhOg0q6oT4YErVq/ku+qW/AeYM/93cmAzuqerGWN8/Z3oWuHc3A2AHNll5lfrjWBA1Coq7G9IaO7zB/deUvq1E1wl3yYuth2IATl3eF9Z3OZbMnVHkLLjhC46SWJtTpCFc6URU62rTW0fOVNSDhTd8f0VFSnMh+Vi8qwQ8cbRjW8gPFTm1AykPHO+oaadoXJGzQw+xItuqg0WDnPH2qXH1aCWwoElDIY6cCWPDIS8dx+Gjh+HOe2Dq/Tr6uGsPMIG115xFei2qiFecpLaGdmQrVk6KuDAl4OLY8B0bjschagLkyZS1tjVeHkXIxp89vYYf3+Zgf0mNN6k1U4TkTHApekNE8FQ9n2dLZQotnW5Gzpgv0weJOki/0hzOFfiHy8dav9fAzfcCNyOHw0leALpZcLDe5QBiUsiBe/I4/oofNupu6ymGjZWrfvQk+65MeeRGCumSEgMZyBH4clhTpdUMAcBSBDJXR846kdInIoiaQG5UpgBH9FhabDL5tVAm3rVsgEklwvymQiBskSeOJc/qOKuptq9F3RUL51D914VpjUrMZkgJ7liAbuJHishWa+i4RrmZ2VtGzF08+abZK+YwdsIYnNFckDkxtCzHoXBIAcRJ9slUmQXWYCjexKmo6hZFtGVIDGavmENpqblf6VZkJogQ8f0gdTqzPWDDL8kIriAK0xofqeRscnLy4wCeCWAngDdOTU15xmuvAfBmyNvFu6empq45AHYeNGjm3HWiYldPzjQqlUpbcmY6vq7rolAoBCSmGTmrt8kkXebjfqY1xkVX6tMae02E2tlmKjTWnwdN1rRgSC9sMMegGWFIU81ZmshZ/dw477zzmtacaUdJuJIMEafWNSiCAr85vykfqNMBAC/5WL2/jKEnDAakTQiCp8xxgrRGKTPOVeoZs8OaM50CpslZs2he/SVi1nUF50+nXNUpXNnGdNFOCHOkQInlsMCxBEKnIPiN6z19SlW9fjoTyYiGRm7IDlL9YLPAkSyQbJDbzY22NuMiN5xDeU/0ed9Tu6wGwpqzMK3RUsRi+Khh1PbVAkcjR4Tqwho3QoR0Yu0BO9iNBwAtp15PYDU5O7wgJ1Nh2I5ogw8WVNoc1O75mtKt2qRlKqexYNScaW6UN+aPlWMobJF1NLlhO4h+jDmKnHgIIrktzfEEAuZpMQijfskTau5zauphHlKVKXF78gOwLVnTKZ19Bq7IGdUE3EVXkpw2/iz5QpJCAP/vaSV84KU+PvbSCra+dwxcqJq4INVQ8b2qAK/KVEdfXed+TqpzCl+o2kUrTC30BYRvKWLX2h5plEp77gKOmtx6w4NI1S8Rk+mpVQ676ssasUVX/q5OJdpVBKxaN5+4AHjZBxn7Fzr6ZTG5dgJAbjgn7alLHa4nZx1FzpTJQW2vHbNGxsB3zT5nSurRApyxXPAdDhEqLjqO4PurftPoGMDC9bm+5kxHztTJLgxJ8goLQRTOVmvjajncEGjnzzT7+aZGFvlSUIU4QdQkO9fX/cCy9HWKhxRBPsFW9ZzMZnBUe48x7kEIGX3Vab6tyBlxahk5c/X3WGhIa2ZKNRZQtcAlyCWEwlT9g5GctQ1nTE5OngBg+9TU1GkA7gTwSuO1QwG8DMAZU1NTz3m0EzOguaPfKqVLLxbNBEG0GEUrRHYYXTfiCDeLorQiZyYB66cgSLV+axvJR87MaFl95EyTtTi714J6cq3RLhWwWVPsXhChbtIVW0XskiBn9Rse0ciZ3LaUDTzbp8u5825QuyCbSIev8RJHdU8V1ekq/BKH8JSMsEprzBtpjbmxHDY+a0IZxALG5JCQ4gIuBYIecWiVXhjUnGkHts6P1eTMUkqEBFUnwBhYzoJdDOtPBuv6nK03KzVI+6p73hdSXU9j4pRxJYHNYFmIRM5qnK2NfKjj+ose7JFcgwPglhrna8Cjg4bGDEwRKmc0h8HHDRry/wJui1qJOAifAIvBHrQDOXxpJ4K0OBM6clMMhBNYZD0dKCBoZC5qrVT8YkBtIhEkx8/JGZs26mFeMwm14ZDfKK81U71NO9aup9eT1mMlDOVIZrMIL3C5VFmMk9TWOLQiQzIPFYZh53RUWBI07qha0RqXhMPYbGlqj0+yRg2yn5yG9jN5EJ3W81s6s/6qL9cWFTnnthVEhGCpXnk67dIPJeupTe5wIN/fYX1RPRwzWg6EtY8AMCwda6ciyYS3xGEX48UhGiBIiqwIoFIXOeM+5JoYY7LNAH+VB3+QJ8BsCys7VgxyFj1+R5EzrdaiEOEFLRx1jxuCIJatvkHNIcbgqwW0VkPnNV6KuMch0vC5SeRMr8POkLSHqfpFAMip81hWe8OdpDZ6K/E3umBY1MbgwnWLIC8kVXpq6ibczmhOpjUqoRQrxwIp+zHfhSAWCkO1iwhzapg3JmqrQq2PjTVsBbUOCccQJlEbrzp995EqCPJMAD9Xjy8FcKrx2gsB1AD87+Tk5DcnJyeHe2xf6lHvjDZzQNcbOWsH00Gu1WoROzqVnj+QkTMTRNTUiY/7rQc6ctYO/YicNSNnzeZTv2vOehE560fNWT1xr9/wiKR96lIfT8j6lBY3beEJVKarkboJYdwIvZJ0aEr3rcJb8iBcAV7lwS5v3oic6Rs9gEjKjUMCnCzwFV/eEJs4Ra38t9Wf7MZNf34LvBUvdBiN79HmB/VUloyaSdU2BnvADgRBij2OnBFXxKHOfs5DJ37TczbCGlDCEgwqchbWMXi8ffQl7riAdB7KdXsoXDQ6BGHNmXzgMQtU5sHg5SecsE8dEby1plk2kZQPhB7qfp9eKsO6CjsqCFI0ImeuAKPmUdeGY1Kj9Hn96+AUn9aoI2c6VVfDZnAXlLS1HkO3w4ie8R4t2KPhcgbyZb1VU9U25cgu5PKwHJnOqEVBuBOOkVCNqNsyWQoFQfIGOdOBGK7r4LQDrua3qMpB1RFynrNlhJop9cicFdZ9qdRBoQljW5sIostuMvq6t53wvhEQBKUG6FR8DD5mEIOPHQAQn0ZWD6GEbeyi1RA5831J3OKatduMwBU5q+2roravBisHeb7UGHYlpW+koEcftY54CZ9CKX2VzsyMSJVQ5Mx1qaUwTQSEYFFpGAOGMGJeT87U3/q6LwzZYEyuHZowOap9RaWiNgFatMXQ8GI2pICwdpnUfcMvcbiqjhAwBEG0MqkjxZm0EipzLNgj8vGYryLTNa3a2HpOE28k9RGbjTpOqptfenxEcIGSJPoURkwPRnLWiZc9DkAngywBmDBe2wpgE4AzAbwVwLkAPmZ+eHJy8s2QaY8499xzceaZZ67T5PTg4x//OL70pS9FnnvHO96B17zmNQ3vXVlZafo9e/bswdDQEBYXF2Nf37lzZ0PEph5mf6cHH3wwssDv27cvtpar3pGenZ0N3jc8PBzYU6lUIgRvZWUl9vtawXSaP/nJT+Lv//7v8ba3vQ3nnXde5H27du1q+GypVIocb3p6Gtu2bVvT8VuhWq22/D0mkdyzJ5oXpcelXC6veUxMzM/PB4/NuqmHHnqoofE1EBKdWq0WOa5+3vf9ruwx59H+/fuDx5dddhl+9rOf4dhjj234jGkvETU97tzc3LrGqBPUi+FUKpXIMXWDatd1UT5kVZEXAAzYM7cHbKHJDYIAf7MPGif4jurnYzTN8ra6KG9blalJ4wLC4iACynvkbrRWAMMIsGgvYHla2imKAv5hameUBGqPcVHe7oG2ATPlGbDpRnte95FDYk0c92qo/Uqes8pABbmjbJAgzFRnYE3LtKn9SxaArUHUw89Z8I/xAAbMVGbAH88h7lY1DnVOUcV3MT29L358Ioi3T4z7qB4uswDMc+L6m8MUwhFf2gNgls+CthK8Fbl2FARHdVhg1luQv6dTEOAd7cGzXaxsGIq8dM5nbVx/9yLe87rV4LnS6jiAYiStESdxzNbk+SCfgFG5vjokUN4k1jSv/W1qPaljaGJMoJyTTZvN79u91wGwCQWmnJDHCCyweSxNL8rv4zoFS6bouk9ysXvP7s5s2e6jPNzcDahsL0NsFqBbawDkxtTuPfsAbAnImRglVA4P1z8xIMBOlONjKyb+zL9iuOajLg4drKE03fxeKJyNwePqYyuoOSEjqtoc1cdVAAJm3Jm6OSDnnKXue4Ix8KNdLA15oMdJ55erehiR91A5zANtE6g4rdft1RGO86cOk9+91UNps5wnzBoFwLC6vQo2VIU4TmB69zQsx4KY5FiwF0GHC7hWDQMAxBCDOMFHNcdlVMp2QWP6Hu3DO1bmRAqLYff07qYBRuEJ8CfzNdcVaugNBwwLLA8vgW2QtUKuzSA2MFgPAg/dD8yOlVDMAzQusG9xL9hKm4inEMAzBFZzq1h9bLSRuTdBwCkCi/cvA9gYeW2YyWvdGrHAngmACJ4ja1Pp1vAaM7G0XMG0mvtNoa/5nFqjnUHoGtjFwgJWphvVCAFgdUu4kVazbLhHVzFbmwXbz+Ad7cm6ORcoD3lYsMtYbvI9JoQlIA4V+O79g3jv+WN464tW8LcvU5/bSKjZDGKUULOrqE6Hm7xzcwMANgRpjd52F+7RFmb5LNgSg3+0D3tIjtEK9+AeXcPe+b2wllnLAPViFYhbo30ilDaXIEY5qjmOqXvyeP1ntuA5v6zhwr+dwdLyGIBBsAFpjz/mwz2qhnmaw+L0AvzDfYh5+doo91A9zMPqVhdiI2Hv/F6wxRbk6zAfK0MtmlBvF/CP88AYw2LePH+HBJuIosBQ2lwCjQt4OQ+0meBOy3sHL3J5HmPup0li+/btTV/rhJwtAtDZwmMA5uteu2xqaoomJyf/D8B76z88NTV1IYAL1Z8HH31tgfPPPz/2+UMOOaQhohFHrs4880zUajWcdtppsCyraYRreHi45UkEgLGxseDxyMhI5LWRkZHYz9enxB1yyCHB+z7ykY/g9a9/PQAZnTHfu3Hjxrb2tMIXvvAFeJ6Hz372s/jMZz4TeW3Hjh0N75+YmMD27dsxPCwDs6Ojo+s6fj0KhULL7zNfq3+fJom1Wm3NNpmRpGbk+5BDDol9TTfkHhoaajiubdvgnGPLli3B+zrFxMRE7GMAeP/7349rrmnMXDYJ0Q9+8IOIPeeeey4+//nPA2g+D3uJ+utuy5YtkWOa19jQviF4S75MEWIMm5+/Cc5o/HjxKsf8XfPwFjwUtkmyXN0fhmHEzcDAkYPSOZ5zYeUt2QC6JKPh2pG1fRsTmMDQdkkSVu4pYWVWOnwOCbD7HeSrFsgjbDx1AsVDG4l5sxyZTX5oj7PXgXNnHnyVY+DIQeQGbQhO2HbkBgAU2MMtC84dDmAxbH3hFpTmVlHaLwnUWCF6HBv5Ds9fvH1sv43irgEwG9hy5pbg+W3jPIiw5P088nfnAcYwfvIGuMsexN5FALLmTCzY2GhtwuD21jW4JvxVH/uvm4Ez7sDb7eDwagmzThEVW97+vnDJKC5494bg/QX1uzU5KxaArbty2HzUJhS2FFCbdbGz9pAcEyKw2QFs3z6GTrH/5/tl0+a6Bsir96+isK0Icjm2vTjcfNo1L7fViyqdKL+Qx4QVziEA8C1FjCpA/s48tr10W5AO2gr7bt8PMdd8jSg+OIDyrgo2DIRZHZs3bwVAAcHP8RwGdw9h8/M2AZBzunKvdDIdY8f86z8Yx8ffwDH2hOZjZbnhJt7A7kE49xtR+UoOhXuL8Fc5Rp48jNFjRrB63yq8lXDzzFbziINh4KEitp+xCUu3LMMv+QDJe5i1wFC4Jw9vwUXx0CK2vCCci/X4wS9C53uwnMfwjIqYKmKduyuP4riF2kwNA4cPYORJI9g7tQ9jvzOG6kMVsEWVbiosOHc6yI05oJqAM56H5cqNMLsG2DdK5bvcBgfbfm9r0DS7HpWHK5i7fR7Vpe7C2PqcFZADrrNgD9lwRnOwB3OYGMtjGcAI9zB39yiO3ipQ21/D5udtgrOh+Ryp7a+htlDD6n1l5DfmUdkRjczQnA3nzjyG8qMNnx1RqQXOoAPrBhvCFchvK8Jf8WDPyrGrT2u0cgPYvn2o4btM8ArHvuv2o7BFZk7YPPRfxsobMPaERlsA4N6fzwZro8ss5O8qYOOzJpDfnMee3+wJ8sLF3hzGVgZbzmWNlTtLKO8q498vGYXHGT73k1G875lq42fVh5WzwCs+nA0ONh4dktfRMXnda3I27A4hd1cO46eMwxnNYd/UDPJaeadkI393Ac6EA9uxMPHMiXozAlir8SUYnDMMzwyjdHcJzoSDKy8fRLlm4X+ul+NdLEqbHUXUC14B+bvzmHjGBIqHFDF31Ty8PeFGo73LQXHARm3GxdaXboMzGk83iAj7frsPlb3N5xjttGAN2LAKNoaPHsLIocOq1i28lzHbxvDMMKr7aygeUoC73w0jiQvApsImFA+Ju5+mE51sP14N4Pnq8VkArjJeuwrAierxiQDu75VhBzPiUrviUhW+9rWv4de//nXbJtSdpMyZx6x/fzeCIK973etw5513ApBRGPM71ppWpL9Dw4wS1SPut2q7kkpr1PVkF110UcNrmjh1U3NmjokZWTQfrzWt0bSpk1rFejTrc2Yesx6aZG7YsAGnnHJK5LXzzz8f5557LoC01pwhKEhoWTSsxDXMITFrDoQrpEKYp2tHSCm9ybq2QE0qH1XaYyzs6eUIAZ9kZIZZbM3X2Zgf/naWs4K0RuIEb8WHqPIGmXhhy/oFrbaV35gPUkGee6SH3zksvO57UXMWV3ckhCFOYjMlYamc4CE7SCnKCw5OrG1dTsNx1bRjFoO9p4wv3XcNPnf/tU3fX19z9qStQtZ2MP09MGqqqCFVshlW71/F/HULMvIWdymp9Kf6NDJdc1Ywit5Z3XUvVIocd4VMve106og2aY2eVJM0eWRJLdGmIIhZf2nZDEyJ5djGpLHQeO4bDxg+ZBaD0cJaSumrVMTKQxVU98j6Tm60DgjIGWMojDmwB23kRnIyBUuLA3hCpkZS87pOjaITGlQohLboGia35Mu+ckLW+5BOWVXfL9Tgipyt0hgZrKINeyBMa7T8sJ0HA9qrkRIgimuv+wbCyJmVt2Qqt5JlJxCKw2E6c1nXUXaQIrtyVwm1GTc4tW6dZrw1aMPKWbHrx6CugStaQZ2pvL6sdQmC1NtsGVr6rdIRfU7BdVbT1xhT9z7GQOqceS7J2tFOoMbQjvO2GQOvqfnbpOZsc041oR5QNWcMQesC0lPSF1KpcNFvK37R7HQGw6Lbr9S9L2hCrVuwOPIeo9dCXhXwl8NUVK7uP3Ipb26TbtexUImuDU/czPGnT5e+kK/qTImksunijUtYfUD6N7kgvVqVCgDq/olAcKnTNgNpQltyNjU1dROAfZOTk1cAOAbADyYnJ7+sXrsFwEOTk5O/AvBGAPGhpEcZOiVn9ZGrZjVnaxUEqX9/NzVnQLRhbyd1UK1gEhHd0DkOcb9VE5CkBEG0TXGKmesRBGlGzpoJgrRSsoyzqZs6uFYiIO3IWbN2CXpe9aPmbG3kTD1Jdf/GICQWxk3eWPAtG5GeQMRlnUWNA/+06wa8ffp2AEDOYRHHWjbt1ZLRUsaafAJ10pC2DqPcIFKuTJOCKsYml8AMB0nn6fOcViRjgciJJovkU6SQ3l+n4hVzrMhvEq7A7BVzOGRuCW/eezeAUD6bWdJxtotW8Fxe3/DXuPyY4gnDKh3mULf5tRGqNaoHhmKkNA5BD60cCVQ7XI74qpThF35UuUy4AqV7VmXvLd3vyoCuUylwg5zVXYpaqEBUKShj6QREBLdFrYfwCZZjwTMI0P5F+W8+cNJYRHGB5Sy5CYFo1EOpyXcMZtX1OfNllb+Vt1CbccErSnHVmFOWQc5UYBTDRw9hYPuAbAAPAJ6QcyKmzqceZn83nWHJqxx/f/cNeOH8wyDVPJxUs13yBWAhqIsj3RBciRUwJomIs8EJGvZaQgSbKGBtNomUzbUu97n0hoNsHKzrOuV1qaN1BRJB3VgnRF+37NBzWozL6IQmRJwkAeQxmyoFdfKsgqWEW6I9DoHupPR1qnoA43Gr9YNzGJEzW6lZKqffZoHCpreGmjOhFFSHnMaBZFD1UNR4zQYbaaro0R60ZUsPTRYJAUFkXMAeslHdXwsEnZqh2aYfkVQflXVdItK4GzDIGdebMpKwBiIcNgJxkLwQIJJ13MxqPYf0fXO+XEfqWVjbGXSEUfWLZKgKh+TMuK/m5KaovsY6FW9JEzpSdpiamjqv7qk/N157T08tegQgaXJW75R3qtbYTFyiPnLWDTkzP9Pq863IWVJS+no849IL1yMIYo5DJ5Ez8xzox72OnLVS+eyWnOnn+xE5W4sgiBJrDNBqF13K0lPTyJk1YIeF3yTfO3B4Ef5dDCesLgTvcwZZxLFmuXCH+ITVebi1wyE4IedYLfs5xcGMnIma6ptECJqK5nI5VPbVAOSx2ZObCZWhAmBBFuEzqJutcvQ5RXac18utuQC02hZxqWZJrsArbr4reI+8qWoxCAKzbalsB6AgdGPTtRmyvGMF/rKH/KY8Vu32tzz9O011RCsXRvOY4TXkiFDukJwxxwqiK6azeN+n78f81Qs49A8PwbbfLTQKgqjLJq+uH7vQeB3qfkdCqYNCEHiFB+IuTUGtI2eiJsByLOjXBQAP3VEFUAicWMuxIr+HMQQpm5HIGWskng3mmK9bADeiE65yHJkNFA8pwF+V5CwgM0TQ+noCDJZyqq2cFYlUwaPAIW7HYo1uGQE5m796AceuyP/s8acCwpebIC4pkSDplPplLo0GJDnLSZKy6bSNqO6tBTLflroeBo8YAi95LSNV+pp2eet7VjM4ZpQhZ6mNBgCEwKkvCI6Kp6MQHYjLCLUuKpM0sRspEJaqTF73DPBWG78nvMZUBN+WJEg3opc2Rz/XUeSsjnibo+XOu5i9Yg4bT52IbJIQEbgI03VdywqiVIC67hXB912Cv+x3Jl2vmEXRUDx1fUX89fy1G78jiOCr0Lm74EJUSa7RjlJG1K1fVO/EgUOLrdvBoPWU94W8J4m6lhxybORj24ycAQY5Y8F6E0bOIH9bqymkBEMWqlG7bQuwLIPgQzag5yUOK8+C36E3gMwNNKaOaT2SI2cZ1g7thF5xxRXBc3FRg3rVw/VI6ZuO74GInHWSatcKnTZFbpXWqMnrBRdcsObjrwedRM66IWf//d//HTy+9NJLg8fmWNercGrcc889AOKbLveKMPaKnPVSQbId1pzWaIbNWt21lOrhHTM5PPFDo/ijfx+KKkBx+R69Kys4wRl14FJ0zEafNBxNa7QBW+025kDIXTYtZbWttUeIDjGiQZ+4NI8dM/L6JV8AgvDj6ywcfa4cj63qvZXhorzZa+eoLnJmnvI4tbV2GOA+zljcjUHuwddRL06oPFwJoovmqDNH3chtqRInnSMGYoANRWzWSBJFRSqGMYuhzMI1zmrCxrUvOqIikfagrRwM9QIze9MJVNzOHGWWU32uiDBXtrDpPRuw6T0bMH+1JO+LNyzFym5rtcCcMozlomQIQJBuFSiIEjB/3QK85Tbrrog2Wa6Ht+jDHrThVcKztGe3qn0zImeRHkYWgkbmtuHYW9qwDrH578fxgu9tDv52fQZe4bAKUoGRPCHbwnH9/cqRg4z4WIbDyxiTSqkAoMgQtbnk5efCx0EgdSC8gDknvP/Xwzjj4q1YrRJEVRLGn10rcOv/u+3/s/fecZYc5bnwU9XdJ82ZtLM5aHe1yhHBkBEWCIwQhgsYyzYZB5LJ6WIM12AbYxu4gIEL12BfLLDBJoNAmCAQEhKgQQjlVdiVdnc2TZ45c1J3V31/VOjq6uo+Z2Z3Qfqs9/cDzZ7Tp7u6urrqfep53+fFwJ0ihF8AjoTxFOqo8r2PY9S2i7xQgPQMS+vGwJO/kJ8nV2RlAwzpZ+aJEGoXOINZhyvH9JglwFyT4B1XiPWnLt2ZL95Uwuv/awjdxawfkmLOiGLN5H8DBc7SDWj3szdrbzybz5FxxM0YC7+yxDyYUCwsK0EQooo+q02ZBABcdYeHrixI37MpkmEzAfVsU4DWp/3zMN5w5aDztWAc8BmDx0Qx5bgZyzIaAhgNnzcET75ne48QnP2+Idx61E+pCDvbU/B1zIGBk2tAxFKqwD+9Dfj898XfVKnfBkQCatk/JHk3As6EeKkK0S/ccBAv4mwr7T/cftjToaBMBYLEYtOJx0BX3mdghTUCIrKBG+CsV588EO0hcHYCTDm1l156qf6sH+YsT7zBVqBz2WrCGotyzsx/R1GUctRXE57WLzgzFStf8IIXYNeuXXjRi14EQOQ0Afm0/Gqt1/kUKHIxm8fC5r3gBS/Qf9911136b1PZ0+xr13P86le/mvlMAcYHCnP26wRnNnNmb4Bkwhp19xY7IVwCrxsOljDXovje7gDddjJuuJTD5iqcRi5gNqAZ2FCC6VkTSlLOXvCLabCu2B3tVw5d2SntxNnotjj+1zVDAERuBGfAy/5TCAWd15jFKw8LtqozVBKLvVrYDOZM5M8kbe0HnDGrza89eDveNHkbXnVoNxgT4IRJ1oKFHKzDEJn94ckFn0owpCLm1C5txFasVMciplnOllGTbDhyz4vqlVPgzK/7eicWkCGXcs7wOEer2197qC8YFQ6CT12f3YijZSrGEezwYshrGU6I/SrqfCrRZ3EnBmsxkIIVXl2jKKxx6OxBlNeWUs9+Se4B+GaIXOo6SfkDz3CsBQm7OsYHABodsWvu1TzJOAiHUEWXKYaFESJYOrNNNGkTiZjcSc/m92XMYI7VdUyhlWghwqd+OYB7FwJcuydA+3AHIMAnP50Oc6e+ZIOVo+8TDYaCiMGXipkc6DEPMUwuUSx2V+e61Y0NB8WWq9whaoQ1toxXo6eyPwM4xFzxmZ8na6SZr/el26uinIJlqvh8wpzJHFxCtLN95lqxDp08KlULezjanSMddI6m1+PU0sWB7kwoRGJS9yHKNNRlnYLnPVIWjybJMxsaEn9XWIwO6xGCqkzmb5mRdY0uwe6jFDcd9PHFO2u6Xamfmbm4AYFX8kQ/yUfvDXh6TLOQ48gSxZu+PdizTeaY37EmPalHTDwDHqfn+z/5h+ScOozQyjkDAF8qLoqanTK83twDdTZItKnpqBepQDWTUSm6qLSxqaTnIVXTjAkmkhCuc84ejGGND4GzE2DKCTWZi37AWZ7zOzMz0/c17esC/TNntnNtMmf9hiXmWb/gTN3r+9//fnzuc5/DPffcg6GhIQAJmOn3fo6Xqft1gWf1Wb/314/lsZT99vuxMGf9FpR2/eaBmHNWBM7sdIoiJ0SFNXaNW1g0wnRUQr8IxSMYPHtQXid9UuJbYY0egVc12ljxUD91QIsKZNrhaOSGbgsDcYg1YTJuSjxGJ5L5MFaIyvvu/0VyvpInd6mTemfpnLPkh2Efw8F+xL+1KKT3L1w8jIiJsDseMsGGdQVoig3PnvpEJr2TBOgC4MqxjjkQC+GVfjdpaMXDwMlC3U0VBQeAKnPfkMKXLzpdsNJe3dc5HqIRyY5swJnTqXCZcnoAoDK9jJccuRtVo9C3V6JiDBnsIGci1ApIxC6EM5S+pmLOuHSA4yYTQhV9OEVFYY3KYgN0qzpTyinyMmCR6/wlmmLOeG+xiwJbaFNUT6uDyrEqcgkJGOM4szmPr9xxlWgrCCiBDmsEoNkGAIDcIFBFbYuMGcy3YhLMXfi4kXRenUcIF2S4uXUe6skCuSpEzieaXfQNL5igt8PfddTmA4CA9X6QF4yKOcIb8ACDyYubsW6PyZxx3sczU3W6jGFwamsBz9y3J8VOx05wZuZRylBGmXOmxtDD1nYx/bfz+MTT58V5etzm0l0NdI6kN+lS2CzmiJajjAIlZyJcMeAcIaX483NmRVivepgeweCw+EeNRWCsN8spTiy6KDSYs1aXpNk8np3bw3acvPO+iG4QIFZtpEFvymgQR1BY0yvuMDT3i42DtdUYF59m1emVY4uxdF06atBoClBD5pAqEF0/ta5zzgLOEIXQeaGFr5nYJ8ktUg4AdLSE2s4B1E+rQwmsqLVVi9wEBN3ZLuIOk5t7ROecPRjDGo+tmvBD5jTlRJvOdD/gLM/6AWdFOWerDWs0mbMTAc5cYZzqXsfGxjLfKSD06wZnqu2u4tsnApyZ1m+BZ9N+3Tln6jcPxJyzvFBdfW9cMBmCRSuYwJXqobHAzv10LvnaCJUiSPrK9v+piFzSRjwCYjBnvOzpXWRXWKPdhaNhB/9y97VoUg9tmtxrmTFd8JlHHNyRjA5A51KtvVC+bzTZseaMw3yknQKGRVmeaMgy9QXmaMUAPCn4IXJ/ImNcEV8WfpV5MFq5UTrWNGTgMTD70zkM7Kihuq0PSX2esCjm8LDDpZTpxPeWfO/rsr2aOSPa0ffA0Q6Fk0YceSMpI2IdIAAe94UbAQBrDUDtD/kpQRniEcxNzKN5oAygkgAdLw3wxc1IcBZJT5BxKWJT0B7pFPWTv2SOu2THWjpFRj6eOq9y9KmZc8axYjbYtvkOxbpAPE8REssRhxxvlII7ABATKl4x83lQkTso2iQBB+3lNaYdRnXfphBEywBnVcLAu4JBpxYqpoFogykuoULSSqmXOr/AtmiQm8F+7vR9+OMjd+NtO8Zx28Bo7s8rcu30qjI0lBCwNkNnuqtFXAQ4E8cTUqwayw01R+JRDTg+vOfnAIC9G0v4+th2AEDUyZ6nojb2Skn+khIE8WVZk3BeAl75817LCI95Ju/KvHIcK2be+iHjotg8gHbgy7C9BHESSgRrC1HsPGZeX8WNufw/U8SlHaWLmjMHCG7sa8OXtdlEWGx6c49QovMotdALLWaJ4uVIb7QQACU/fU09tmKuFWABoIIYahINwBBBkMqVTRX97MobyiivK8n2cBBfAUXe8z3jTOWVpU2FNXJKZNgv9FrNLEEQGgilYyrHD2ccrCW+e4g5e8gAuJ3QfnLO8mylzJntlPcrCFKUc2ae4yFwdnza5CoubdtqQPGx5MEVXS8PfKmxnbfZ8JvMOcvbcNBtYYmEdT/MmQJnlHNE1ycFuxFDq5aZDmiGOfNoypElHoFnLIJ6fSLuXVlbC2NLVyif1liyywqIHWkivW9WkKdFfQID06VU0pr3NrFxIQmx7fax/5D3iJueD+Z7oFUPvCvj0bjoszgFzogAy5QIBTjlQylwFsfgEQdrM0TLfY6nmINID+uyuxLxEVNowJwLmQ3O7JwzSrSj73OGdiTyQVi3DwfAeqRPXkiK2rMuE+yB8ezjZoxQ3qdSIqSO14yYYY0gBjDr4Vhz3h84M25NCTL4clBxauWccZmbQ4RDrdgTSnqAxRyjnOF9eyfwp4d2Y64pwQ0lOuw27EgHULWVSFbCnK4IgSefGYkYKpsrKK91C3CZZt53pFgFwyFfnE8OqGypCCeQcf2slHmS7VvzaAGciE/hSbWRh01PIW4r5FfMnHGWtMO0Pz4icpBfcvSewvspSYRphjWCAF4pCbOs8BhtzQbzoiEk5hWpMCo2pdJfn2OIIUUOB1mLyshcKuITUYuKSsELJODMU+qPvV57xUKZ77RxD2EowuMyaot68whol3wRTUBsQC3BGYsQgzg30PLMZs7My3fC7PrD4vQGCPUdqrHSJVEAxafcGXGRuk3VGSQteAMkobv10+qAURS6apCMWoHVEG3RTfIoYtVfZR+DZw6iZ8oAF//n0nlKwhrN68j30A5rVDlwUj02XAjRmRb+wIOROXsInJ0Ai+M4lTsFHBtzNj093fOYEyGln8ecrTQ87bbbbnP+xs7hWl5exre+9S0AwNq1azPHK3B0PMDZSvLWThRzpsI1i+xYmLOikgV5Zl7jpS99aeq731TO2Q9+8APttBbVyANWFtYIQAtU9FQHYBxX31fC+28Q4YrrwzTwnW9wPP6z6/Clu2opZ8ZORCY260EhVN2kTTUo/vBfB/Q1bbMXsNAQuKgbIXIlxnDTdBlkbQV+lSbj3U6Ut2TQzQRvAHjR1Tfqv/sBZ3mvQQyCz99Uxh5UpXMrBRlijtAIayQ+EaBSMVVy41rlVpS6Mf7Pf3l47MeGcfhof/OQCo+0nV6TOTPZCO27SLBFK14qb4JQ6FyGgDM0QoILX83wsnf0lm10hS8pu/4Ogp8166Iml1YgFKFWG7otDLbE+VVenmmLkWhPU77yPOLaaXbZK97P8FuvE45uShCEp4HFfbMU5/7dEL67P1Gq1QIlSrae0pQnwbnI/1DsmSrbQAvKQ3zmSo5df8Bw/0J2TTy1tYjzmnN49uw+vOErNTzpY3W0Y6LfLVNJEhBjjYAjnE3WCUIFQAIAGnGUxkoorS/1zDkzv9ePxABnP7/L2Fgoe1oZ1QZnfpVi8Iy6wRClVTe/8//m1VmEaIyzLRzhXFgo4NKkxRu+ZYms9YYDUePZMwRBklBdzkkPFVsZHsrEO2svEaOGguzf/Hwk8/vAqN0nwAZBMOSDUKKLFodzCpyJ38S92Ff9ApPMRwDwVz+q4+lfW49mK30ezjjQFIO7XQokM4UkT9Aj8GoyrDGOwGL0pxwrlxYTmza7HK3rprGtI0KnH/ZP63DrZHr9ZDG3ZOIJqEeNeSzLnHm0t/iF3mNCljnTUQMlmgJE1+xJ0JkXqxyvpEanMkIJIqpY/OSGi90trsj+jKmwevs7zgC13KlNNk7FBgj1qQ79Vvnc6r18MNlD4OwEWBzH+MIXvpD6zLUg5zmztvXjZJtOvH18v4Igthrh8WLOrrvuOufntuN8221JaMo555yTOf54Mme96oeZVgTOjgUwXnLJJT2PKQozBIB///d/z3ymGLnV1IMr6ovfFDj77d/+bf33pz/96cJj7edgbzioNirWgDPpYMAR5mLZC7+dsLllK7/jl/dR7Fv08eYfp0OKYivsxXasCSUoDftoB2Ic3Vkdxvd2B2jHcApf2L5Aydi69YwbUIv6YVrR4Y1A4ijr31tiDsSjaRbEsH7AmRnGadbJUWd827frwrllAGciFDRVtJYDHAYTo/5zUGw4PeXOe/HO79WxZ87HR7/bZ1Q+E9v6sbVzb4KzjoHp9SvQNfJhqLFLTAB/QIyrahzjxkMBfnq/j8t/7lbbNe8NMqzR+XWX4Xf/pS76QDkSXDA1/3L3tfo4m30FgP3z8h2TeT1CCjsfnP3TN4FrbwXunPbRMZ7ZWyZvxRfvuArv3HcTzl6ew/V7fRxaTL/X3YigxGKc1loAAKwf5BmnnPhEM0MJOMsPa3zZ+zj2HATm29k5pGQ8p5/v83HLQR9X3hGARwzhXBdRN92nMSGgVIiZJA1Kwhofvu+gXvt67dFpgRvO8Wx6FN2ZbgqcmcwZ44ljbIc1Bn4SugeIeaBaS1r9lZvkWC5wrsO5ENFyVCjM05S06qYhhno5fZ5nT9+P9UeFaBApe7rgOykJhkiFEW7uNrXIjShVkL8miFxbeU+EZMa2yYovhdlnW7ak9DUbQwloTZQfYG2GuBMnapw5OXe6TVzN6Ykx4zefuWUAt88E+K/b0vMHZ0BbArY48KQ6o6fnR+IloeYDLELMSX+FqDkAxtExmLPK5+9BfPm9+OQ91wMAGl2KV35VjNfG7obMNTVk4pUiorFxQyh0TpUCKB4tZl6B5B4IAf7o0V1sGDQ2qYx+yivjpsa/UtZMGQWYEpTS823xxmfcFGyzut72UdHAN13U1mGN5hBUZRJiK6xxqA4p/EPglSloicCv+boUS9xvpMUDxB4CZ6s0F9jasWMHAOFE20DHdnoppT3rYyjrx6k1jzHV/oD+gYNdxytPrXGlTnaew2+fR4XhPeEJT3CySsczv2sl96OOdTGdqk2rAR55pRNM68Wc/eEf/mHmM8VIrkZBsug+jrXO2WoFQczf9Xpn7Gu4RHcopdjlnYKZn84iXAwBJsPYi5wQ63Wv2NcxnU7zT3vxlknUuj0+gT8S4Npzd6WPI+5QENsxs0GiMq1cx6BBAZAt6FqrIpWbQ32Sqblz77uEE95fzlnyd8Vom/qbcaC8viz6mgFg6V3RzpEOCAFGHj6MgV215HmvEWN628Jicq0+h5Ni4GIr58XsCxN4ZpizMsHAzhpK60UbCCXwZMhPjUVo9CmlL04ORG13w02HXjtgHsk6446cM8U+KmeOR6J8QM8IAc4RMoJ6HOJNB27FkxYOo8IZHrs0hX+4bwIzzey9dSPgr++/URfy9gKSei8rm8sY2FFLmDOD0V1NXKPn+M1cm8rCz0DUTn/PZFijWYyXUAJvS7K+8TBRIi3qIzUWfq9yFIfefxd+9apbsHRHsr76BrLlHBjYVUNte03nR+l7CNL9SAjBYD35bNELdDt5jvonl05+xMTGxxnNefH+G+1XdfxKHsfe/7WAqffOY6gkxtufHkkUgWkAXXR+9BEjICWK2o4qwoEA68M2vBm5sUcAXrTcStaMM4i8Pis+vFskF4qksLpXETlwSjiFUIASgmBU5Z1FevOpp2qsnPOI8T4xDjxzZh8es3g0aVumnzmWpcimrwqElxIAYtY3rMcRIqC/UGYixlgUiTac3FpE9eZsNJRiK9tHO2BdBsZ4WqEVIpdOg3wCHRJvMme9wJlaugiAtXWOW9++iJ1StdGcU13L4f/8rWXNiJGAJnXW1K0S6LBGtcnQa+OzcbdgD1XO2Y9fv4Sp987jHb9tgDPj9yqfTKUMjAai7b5ipX0Kr+bBq1B4VQ/+oHgnujPHtz7uibaHwNkqzeXEKqc4jmMMDw+nvuuV37XSa9lmOqV27at+mTPbTJW9YxEEyTve/lyFY9brdefxx5M5Ww04O945Z/2AldWENSrQtxrmbDUg80QzZ+a7klduQpl9Ddd75vs+Xl57Be557x60D7SEL0FRGBdvWjWOMBxb4ZPGj83XKsN++Wlmyqt4QoxDFzWWeTxw71jbXVjOGUPqPLHaRZaHedYqOVhLO9YkyOYRqNCXonAq3T4DjCrZbiBRZfMkOOURNHjwjXsYe+IaEI/AH/BTxY39NwkmPaRUM3L9+vmiXhpBZDlSgXFds7BtljnzQCue7idCiV7wBTjrbxlVOWBT/zXl/N7jiTqmJkQpEFso1BXWGEknOGAMqkwBV8C8yJi4zWfP3I+Ljfw3ZXMOcNYJgXOa87ntoT5FMBpowYuaBOZR3Ed7HOYCZ/MtIkBByDJ5nUqt0RbeKW0oa2ZJMR6kR4PUWDhpUWwKsA7DzNVJaDUx2FjGxftMyzTDnPlB9pnVNiSbc4HBkPAC8QLOxHv41PlJfHDvDXjbgVt0QXkgAemKXCUkEVUwzSxZQUpSdpwSxMOyTcvihSCUFIopqAgEdbuU8BQjzXtsplXVxueACLMkMg+OyOenFBXDuVBHX/eKJOQqT8649prGMl55eDfetf9X+rPMHi8DmgqcmWUG1Gk8EeUAAMNRF5zkA+mMMY7zZqfwysO78dE9P3MeonC+KFJPwBhJmDNfdMjgGXWUxuQmESE650zNZR4FoJ5JjmmmEwk7atYTU+bKbfQY16CLeNnNUkIJmPIDFIhD8VwtVI6TjRCPJI8uJaVvXINHLGHOYPQR46JshVQgpgY4U/lnDxZ7CJyt0lzhU6YTarMitiPerxiIOt9Kjlktc2abYhmANAuzUgakX+asqNgzcHzB2Upy6NSxxzvnbKXPtV9w80Bjzo4VnJmArBc468Wcqc+WuQj9jZuxdiyK62fJcCXO8KU7f4i/3HdTuo2GQ+IPJte0mTNbrTH5kQJnkvHK2W20HZNyTka6ak/MiN5JBrKObr2aVjYjjjwCpSompPmLnRHzEW808vKU80ClQ8hjIYXP4+S7re88A9Wt1XTInvzTW1NCl1AEjCWMV7+OvmyzrRbnG/fSNpws7Qh0pMNTphmmauBkwcLU4gitPhhF3RQGtA60nd95nCNQfW/knMFqN/GyN64UL32I3ArWZSmnuaBF6MYEcU6w5cxy9nMbpBOfpPMWAeHYVlRYY6RvqWddMYelVDXlM5tvEbBQglCPmuXIRFgj0vMVLVFUakSDlwQAFe/qq0uzvBQEY85QfxGPpOq7AYAfyGuZ9zXs47pBUUxaM+A9coaUIMhT5w4CAB6zNIXzDdENFQJqaAzBJzxTcF0VxCY+SSsX1mS9NZl71QssioMFSCNUgOIBI7Y56LG2KnDm171Ubqny0IORRBQkCWssbo5SqDU3wYba2Y1Ke8nmjKMpDwtKkr0rG5tpHAgkgz8adcEIQftQG62D7vc5de4YGM7ZLFWbU52IJKU0uAprlADHkyVG7GLvgWLMk+fOewhwMAX4jFPVeAzKWYo5czGUHrgW1yA+FWDaNEoQS6SXGsdF64Zxv0B6M8EFyImfvPtAtgi1PxTAHxR5i7RMUFIAf+bXKyR3rPYQOFul2QDB933tCF544YV43etel/q+H6cxz1bKnP3sZ+mdmWMJA1SAJK/2Vp7deuutWLt2LQgh+NjHPuY8Ji+s0Q6vtNvyQGTOpqamcM011/R9/c997nP4zGc+0/O41dQdWy1zNjU1hbe+9a2533//+993Ar4TDc7Mfjf/Zozh4osvTr1r9jVcz8zzPDS52Ai49zDBk760HtcdKLkzki1bF1r1c+SCcGjWCOvamKhwZnISbGU7dZzsO+XUfez6qjOfIZNz1gdzJnZS5eVtcFZL7+gTQrRTrcxcLL/j3vQV12rHCNtJ/z9tblL/XRE0mVATU6FSUtJcOSd+WTgnaTVCGSKzFGoAkieBn2dfu6OER314CJd+fCD1uXmeQuasQjMrpVKSq/VRW0oZl6yWCd6BxI/yIMAZhzFuOMAtNEQIyQJ8QhCqMD0ArBMLZskxpO0C12EsQgFd9rmJbOi1Xe9OFFe2m5MoWqqcsw/cOLziAuJAOnRXAeqFltg95yHLAMuYEM286PYEBJUK0WNIbZpwXgyG1JQQ++65zQwhM6ePEkmf0w+yvyc+xaGS2IgscYa17xjBNfeX0J3ton3I4fBL1YQwTj/+Vx6+M7muepeMIebRLMOuivlqcCYl84kM1731HuCM9w7hkstHsLxc8My48T/JeNSMMFY7jNq2hDnzwTnH8LlD2rEmFJolWr53GXrfohdWVPL+cp59+RdqmJvLAgWbceUMaMoQ5VIg6KTh84z0Cg6U1or1fjTqIAZB1IhSYa45LQJnwDx1bywOySiMbkySHD4JeE3mjADpB0+Ifo4ndxoYirqgkugrEr/QIdPy33Erxvt+fDX+5a5r0bl1IVGKdfQzjVkS1ujalKEAk5EX7SbDkz9Wx0d+lhbJSrVF5n1/c09VhzWagRsJoyc+vPmghyd8ahRX3lPCU/+viE4zwRnngC/ZTeLJKAcJzh5izv6bWBE4m5ubwz33pCVt+wm3AoCPfOQjmc9WCs5ss8McXfba177W+blq50rB2Z/8yZ9oWfzdu3c7j/lNMmfHG5wBwNOf/vS+r//nf/7nfR1XxJy98pWvdP5mtczZT37yk57H/PCHP8x81m+ds9XmnJmqnmZ/33zzzbjqqqvw0Y9+VH/WL3PWlMzZv99ew555H5d9caQ4t0IuLpu7aSXUjhTyyHNCMswZJc5Z9/4ldR5x/Eeucy9o9lCt5IADraTHkMo9ssMax+o8Axb9WrqBh684gt+dvg8AcOnb8hf9uRvmsXhnIkZku/sBZyLcihLE7RjtQ2JOUeBMSZ2bC75ysqPprmY9tDpXn37+q781gr0zHkIrNykFzoxXJZY5PariOC1l84P9unheA/HKN75sWWdVn87jHCWPi3AjdXME4B1rbOWItmjVSw4tuuLqJHMMRYyg0aW5uYsus6X3hQKp1R4vCf00i30vtvpnGZVVUuBM9EUzJGBdUROMW1hVhDXyzKZDtU51H6lQPUK4ALI5pi7tCq0UFzPAmfGYytZ8UK0gw74Sn+DhO8QFSvJCl31+GDxk+t2wjXNRzsMMmzRFfpLw4eR7n/DsPCFFN3Q+lS/YFiqFbupxiOllil8dDnDHZMEzMzZ/OOcgjKeYM7/gJa3HIbbPzIvmDIiw4WAkgJL3ByEYGR8BACzevJRI6ffDnMWiVmLMgK/cXEop2eq50X51OUco300vEGF/ahMGAGo7qqhsrCAGQZ1FiCMO3mVCoKfIuFCYZI46XgAwLMO/O7Go06XAZcyIHncKhKXHtDFnArh4/qCeOoswsSlmOXXVNCb+4EYAwLqog85H78TBL4vwZldOL41ZOqzRmocIScIaf3K3h5sP+viH6wfz52ougORrfpSIbSk3on2wjbM/8XP89tykfs1e/Z813Dvr4xXfH8PRhlTw5ck8Dc6NMS3m7bHHj2LHq0/C2idlyzM9kO0hcLZKc4GzolDFXipyyl73utfhSU96UuqzfhiTIoCRVyfN3EF997vf7TxG3dNKwxr7KYBs35f6TR5z9kAEZ+ZnKwFDCwsLfR1XlHP2iU98wvkbBWZWypyttJ6esl9nzpnZ3/b5dP5Dzm/Nz5Ylc+ZJx4yjd8geAKyzJPS7pTSosi0jCELc4aEqPMQEea4CpxlBkJ5hjSJpPZZy/baTWSplwxi9gfQ4v/9T+/BHR+5GpQcQ4RFPFRK1AWtZgjMQCJlzueKqvvOkg+gq5hz4puhF/2GN6pmOL03huTP3pb7LFQRhcsHnEpjRLFPlD6VD9voyuSNu12czwZkIa7Tk1K2wMgI4V+4EnIlwUZXLkTnOuHw7AhY6BNWcceQ75vpM7qHKFTI/KlPNejxje/LOxKuoN1ThJjhLGMXyOiGFz6yHowRBbDRUXxvo3DxuMJMsR6AFSBzZUt7cZTAUJllRtuLKRgd4ZgzRgOAJp4jzmmCOhdljtcVcbh64v1bMmc0+2PME8SSoVgyaJ+Y/tekwaOSLziwi17jcR2CdGL96xS3Y/uU7UhsWLpb7qTsEK/g4Q5zDr3p6HiJE5FYBQGlMrPmsw5Kwxl7LCOc6rFEV0z5/OckTVOMpiqz1giWsEpX1Fk3wUV5XRm17FZH8LI6FOqo9f7osjnlKddS0IaPcAI9kEXLJRGpWSKk1mmOaJnXOAKBLPRGZAORHgRCkarPt+cjezCGT/yFCZl1FoSnjemNDiLdYpzfUGktmHnaOz8jj/KLrez66F6WlLl5/8HZ9O659lIohKgOOZBzJsV3bUcPIBcOobu5dV/aBZP0nPj1kKStizvo5vuhYO6/mWJmzvDpp5sSUB4hWy5z1YysNa/xNqDUKlaXeao3238fLjkUQZKXMWT/19Fx2ootQm8/K/NsGU653IE8QRDFn1RSN0CN8B9kwwlbgo478UDsXwHI5Xqo2jAnyXGAxk3OWy5wl4Ky2vabPZYc1QoY0pX474H6OJc5QlF3BI64X0TKLM4C1xGJ4VKiycW44QkpJMiAimd8EHirnzDdELzQ46wedif+8x8oRFOdJfh9aYY2KaaBVKk5iPbNAyo4PrAScQTQ5blrzngRnFFzk99GkhhfnPOORcLgBvg7Zi2VYl5FraJoZljjT8sBBMEjc48gDh32HkbUJQ0haGVFZaZ0AZ+VWMg91VxDWuLmzjCUvSLE+6tkzDgRrAoSNKJNes73TcA1r1DeVDeYsoQ+KBC9Ufk45Z+4ixgtpCvrb88GIA5wJ2W/pyBr3yEPmLmfBBSMSMqCUszPhyjnzCM/ME9QXUuMsVP8WGyN+zUOMNBs30ztqD92pLrpTXQxOdfFezOmvXBEFVSkwZN6BP+Kn5l81nlROFQsZPAWKeoU1ymgBUIKWVEA8zwBnZRaj4QVod4lQ7ZTsk8j3Esdk6lFKIwHVIcAslsxZgegJ6zLEyzHiKD8EfdgAwjwW7+38LxcQRSUd6UCczBmBN5RElQzEETqSdSwKa1TNyKmYotcsF3M2sKmUMP8+AbE3ZGlSPqFGjHcjZ+kXNfLc37X2J6uNzkdztLki52BapeAcuk2qz+JmrJVjH0z24GvxA8RsgNALnLmOzzP7PCsVjrAtjxExf5Mn6+5izo4XOGOMpRzQXmGNJyrnrAjYqu9McRTTTEC2EpGXfu3XKQjSD3PmshPNnJnPynz2qwVnpiBIjakCp7xn8U4gy5C1pHR1bh6U5Ulwlg0FAYA2UzlnHMNRVyTw91HnrJ7DZingEXMCWqZiVxHZsEbiIyUIAojFWYWkmdYr9I3HHFHM8dtzk/jKHVfhMUtpVcISZ/BUyBIXieXljWXN5nk+AY8Th0w0ECJPy2DOtMhAP35+wTGm42gCFsYTRsyreMgkMAHw6qIwdZXFWcCb1xTVbAsMdDRzxhB4XEhFqzpTgBYmUdaTOZMMgGI1MscZ93pECn4Mkrxx5GDOrB114ovCr7Yp5ixoJu9sv2T+um4Ln7rnOnzi3uutnLMEnBFCgJgjignaxtwTcO56ZCgHBoCNejuOAMBlrks55z2jOcyZvSkzVs0+CBoQXafKzAkT81AOE8+AKCap+mGmqb5KgzNH+DMlCNYEGD5/WBw/HCBuM5SqMufKOH52qSgUVbA8eQA3cAzAqieOVQzbht9Zr9X1lIk5kuti77zLEEgc0hPfcwBMjIF2CIBzDJnKsfLeWrHIGWvuayW3Ih9iqq6hYbRERdF1ACwS910UcbF0RwNxyMAY8GojN9A0kzljHVH7MV6ORf6UJXaRzjkDfI/jnzecCkAoSOrXIM+lMeaEXgHGLhAcqL0qj4B6gF0pgdAEvFLjBAt5eYvMzZx1Z7qIlowNdHmIK4LUZM6E6qj4vLqlitLakqiHOP/gEgMBHgJneN/73oe1a9fiAx/4wIp+t1LmzAZnRcfaTn6RU/uGN7wBp5xyCj70oQ/lHjM5OYnzzjsP1157bepzxVQB7p1Ys51vf/vb+2rPSs10qHuFNXqeiEvnnOMRj3gE5ufnV33dfpmzopBGIN1v/datW4mtRhBEgbNPfOITK2IZ+wFnL37xi3HZZZelFqQTXefMfNfMv+1QXNdzdD232dlZtLjYlVM7xCUv7bDZpm7XdlZVXSE/1R/G72xlO5sZktaRH27tLuPfd1+N9953ozNvIAvO3IuOWtQX28CW/zWMte8YAZANa3TtEHMGnP7uU4W8tWF5+W36d7Fgzl5/8Hbn92XGcOUdAbqcgMUcL/7iEN519ZAGjNQXznaqPhURLJvnE4QS1KpnULQ7rNuUh1CQfpZhyPGrezjOfxnDHfcbzJnccbX7iPoUkO00QxtZrzZxnhF6acnYJI9z7Jnx8I3dFQ3geIxMWGNWGUBYQ9bKipZjubvtvncTnB1dljlGSD/b5SARPrBBeWhJbBPP7ciW1olzBMsGc9bndHSBZDpGo26KxUmYM4IP/6iM531zLdodDlvA8nDDy7KdQQJgVbFuEOTWqmIR0+UXSjnFtdJMd/K5DdiHyjyzPpAS1U63GXYogF3OWsJ4ZjiYpt7veSO3TzBn1o84B6EUgcypKq0rgXVilOR4H4m7+MQ91+EPj96LmUZxzhnnAjy5zMWcVeTDqsn3xh/wgdiqSyd3cRR4ZSHHgAxL6y0IIucGCrRCggqLUyGFqkZlOwTahzto3t+Uv+MaBVDfQb1CgBEFjBkBqttquWjxzvs5Hv++Kr67O0DYyZ8XTOasM9cVobodUSLCLkJtjgtCCXyPYNET6/1QHEopfXfUhbjJZI4qclce878H8cO7s5FAKsyZBASZZE/ZPCUIUjLee5lamG2OUebFtF/+0a/03zGSNdWly6Nr5VVlzLxsU/3UAZTXldx024PA/tuDszAMMTMz03cOkDIX2CoCXDaLUXTsSpizj3zkI7j33nud3z3hCU/Qzuktt9yCiy++OPX9+vVCxnfDhg19txtYvZPtMvPeFFjMY86AhKm68cYb+1I77Oe6Rf1bJKP/67C8dhYJipgsqK3cWWR2CQaXzczM4Itf/CKmphJWpBc4W20OnLJHPvKR+m8TnH37299OHdcvc9bpdBDLYC0dUudxMFt4wWG2s9GUDnEqf8kYTnatMk7cao1tyUYo9b/zmnNOoQJ7qNphdUqxTzmxn72hnCoenQVnWXGJ4YcNw6/78GoWOOMxnnx+vpOhwFmelaQD+tN9AQ4tUHxvTxn/cmNVK1RSX6iVeWaIHBGOX3nQywiC9Muc1XNC9gKLOfv9d3PcLKdSBQhUra6MWuOgD68q+sdUpytyHDljQj/BAmc650ze0Cu/OSQk4lUOpe34cp7Zsf6bPyGYDoTzGs6HmjnrFdZ4tClrkVngTBWS/eQ91+Nzu69OfWfvYRCfZNhXAKhsEu2pLSbhSZ0+yfxNhvBOxaHWyDjwN9+t4oYjZXznQA3UQRXY45p6iVhBq5UwJHnvfePOhhYEscHZ4pBYo8z6hubYX19LHx84SmgEw752uks2c+ZKKpOgI2L5AiVqHrrzqFEb0pFzBqRZD69EES1FIPLhPmHxKE7qLOOFU3uwXDRtcwCcZwq862s7PO+yzItSzJk34AmVVqNQtxpPquAy6zJdGLqnIIgsmkWICGscsjawVF90YwLeZXqTp7m3pR+iixUSDQOYUWSZlkiuAukrP8hx20EPL/nKSKbGIiCwDZBmKeOlWLNJ3S7SRagdeLFc93RYdJnFKMuQ0TzmjPOEwQ8K9PbvmXb7p7okSkAENssgvER995Jb79afLjVyaq+x3ptsMaFaTIXa1d2RvJu0qoNAky8Jegu2PEDtwdnq42gKCJgsUj/mkuwucuDb7XS2xkrA2WpzrD784Q+ngE5emNvPf/7z3HO42KnjyZyZ5+rFnAHHL8freDFnpvUjKNHLiurjqXZedNFF+Nu//dvcc6w21DIvXNQFuszNjF7gTD3Plb5jysw+KXoX+gVnABBJaUbl7Pk96gsx6RHYYToutcbUWmM54nnMWZtl28lClgkX6hXWqBx9dV/TVp2qbFgj0TXWlAVDwmm084jKLEa1VDDGeY7jJE+jHNDAR0ptzjN2h7nFnCnlNhOcmfl0vYwzoJ4jwxkYDyqMgJbhhJY1cyaTzB3FVpWyXc0AyFHOGAoXI7QPdqCVFA1rk0QQRLc7Ypi5dhasFTuKkWcB/lv+AJjxZa7pbFewwDmkoYkzppoe1nVbOGlqXn+27PvaARX3l54fO5ZaI/Wpkzmrn1oHKFBfbOn+7PQZXXTZGclc8dvzB/XfZlijvp/AcwvyOHb1ldO/KEVFi+p4sS7XLIMtCBKXHM/MaIItpe9qTzAYJDlnEjAQwhM5dcu4DNeLCM3c756yENAok+wPA8KzYI4jPQ9RIG4x8K5jQ4ghV7RBnSoP4LqeS0UCiLoMKfclOFMhjIBkijhP5ZwlBYl7iDcpAEKAVphI1StTY5FxOecrPNONU8yZa0wDRIc1xqFkQ3Pmoabh8sWOYtWxnw01Z51YsEmxqLmm1hsdNmw1aeTcOkKa5OLqfaui5yXH9O/s2+v8vsi702AxoGIty3QRwUAz62e2Z0K0HfXgTLZSfoDWgbSfEBGShDXaEQycYWdDKNbQCkU4G6bLZNKs4NWDxf7bgzPlOPajLmjaSnPObHB2vHPOXFYqlQqBjrqHIpDjFCU4juDM7MdegiBAut+GhoaOy3WLmMCVgLPjYf2As7z8QGUmQ7USljMPnLnOYYZA9gJnq90AcbWrKN/QNS7z3slYLkFq59v38sObAOGg+IylEssBoFNKgyHAinLJCII4ZMcBdBzKWKzDMk6PHV1lhzUqRz8Ja7QcaaOdWy7bDL/mudUjPZJlzlhcyHxwnlO4VJ5HhRYRmmZv/EJwJuodlQf9jCBIN+pjQ4RzDGUkLdR1Dbazw1KLeoY5c/hpvmTOTHW6nChTXXdMKCm6mTPz2bCQg7WYcDLtPnVEvQU+MBOIeSGcDQEQyew5whqN8x1tUbxl8tbU90ulEnhe0WXr9wDgDVAnc+ZVKEprSyAA1oVi/bNrpOVZXh6fLqNgfBZbTNKsX4LTDHC21JDHG+IrLstjzpjjvTcBI+kj5BY02QBRYYcekZsjLrUGFQJW8zNFrtWzdwmFVKokA85M4QTZYoACQTX73CNGnDUXxXkES5Wfc+YIa5TAtRYnjAdYOteUUMnGyilI5GByUSIBPRQbJTiLmhHCq49gayft2+nQWBDEzUg/uLiV5PlSjziZM0Khc6riiEv1wxwW05g+Ywd4jfwkz1gf12ZawbAdEYzJ98aZcwagXPeNiAKGmIlcvVw2iifAf+eiO1osclKGwkqLYgEQz8oRBcLlBqRlYchF/9pmMWd/dugO3Pxn6fkoJsRZoBoA/mAqAZheicKr0nRfE+SA7Ae+PQTOVrmr72LOisCZHdJ1vHLOiqxUKqWYM/uaytFdKQN1opmzorBG05EsAnG97EQwZ8cj58wGXq6wxl5tMcMTV7LpsNr8tF51zla7AaKsX3DmApF5fRVJh10tjAEtzjmLQlHs9cxWekHrBllBkJQfxGynyM2cNXh2PggX4gxgNEkUwjnWROl5RTn6qj12XSl1v/eNjWDrC7bIHWtH4rvvAmesZ86Qay9AiZEoMNSOCUKDfVEsmmIxaDm5LiEiNyeokkzIZjcmvUMbOTCQsxdsPrN2g6XUy14n8+ai5Vh6stnf+y7mzLFDrtqhi8v2CGsExDFxKxbH284fQUZqjVKCRV+WGlmIUNlcgT9Ae4Y1TjU97Gyn62Bet21rYZ5Gts6Zu+4aMcCHCt1q95uXn+NcauaMmYcS/fnHN52B15/8aHF9F9spndxGU31WsCnDDeYsF5y5GXMTnEUQeZN2TBqhSf0qFWrnUxEy68pjUiFpESMZRmpJ5h25QtXKFZJhzAGeZhiIaItfyk5OMYezPfI0YozmMmcsQ99WfJnHx5MNEB6nwxoBgPhp1T0ecZ1z1J7Pn4g4hMM/9Z1plL6yF2+1Nh/UHMiJqCumcrTiDtPAgnpwvwNGHS8mwVneWDXdrdKG7IZq5DvYV1mfkDOgRBheOLVHHFN1M/gijzKZF8VjIvkiN0Z0Q5CzcVvkxYx88hYA8p1xzYucixqR0tQmi2AZHc1hPNXWS+cmM8cEjOn33X4kFy2ImmzEFwI33oCnQ2HVzbg2jh4M9t8enCkgsFLHcaXgbO/eNIV8vHLOiqxcLqcATF645EpZoX7YmH4Zm5WGNZoFtY8l9+1EgLPjYWbBZcAtCFI0doB0H73kJS/BeeedlymK7rKVqGD+zu/8Dt72trel2thPWOP+/fvx6Ec/Gl/96ldzzz0zM4PHPe5xuPzyyzPteve7341LL70088wYYytjzpQzZOScaQlyh0UMeLpj4fjVbDl1HgDpYqMG4CtvLIsdYccqtZQT1mhvPJu3uLHbQo3FWPSSzZWuBmeCpZlaTj8T5aRx1QbGURrKbs4Qn8BfIXMGcFxxW5a1UKIaCgy1QoLQACgmc0YCIpK4dUPELrZfpqkdYkDoZBTlK/CYI1qKMmGNqktTRahbaedB5aks37ss+srxzIK6ZM4McBbmgTNIcMY4YotlMOuc6ZCrLgPrSiU4A5GX1oq+cW1uLyuV1gXRHs7d4DUFzloeusZ7+5adj8QN2zaDW16Q6XCFDOjIBmx9wRbhNLqYHkrgSUdJ1fGK4j7Dv3OYmAqLUY2jtDKin4CPa4Y2YDbIqWdEAE8CgI/9sISf3SN2/l3Mx137OS7+3wO46j7xfgc2OCs7HGvzUqaKIxGiO5ki1JRoR3I06uIPj94rmHmaJywj5qdPTVQzYGvJIUykrOSl27nmcaNiDjLbI2X9/Ur2OcYFzJlqhh22F0PkVFFkQ6mrMm9IhTnTEhWbREF6UNMKFfXKlChIl2nnfGZi3t0cJQDEOJbuajiPUX3BAFkPEFAReioalfru3GBi5Jx973Yfz/j0EKZy1CxNlsfFqHc9xxhiHLtnPDzuc+tw8JfLybnqqnhX+hy+VWIkiiU4LQDTatpzibUAqsZj8TsaLoQS0WeZM99Y89QcG4VwavcXrbnKKpzh67cEePon6ymhmzKLsaXbQkgIHvmfjwCR8011i/n+u5/jg8H+24Oz1e7qrxSc2VbEDp2osMbjBc76kWivVPor+LdSQZA8WfWV2krBWT/P9oILLlh1e5TZ4MzVzl5tecELXqD/npycxC233ILvfe97Pa+d15/Pfe5znZ+///3vB5CAs7x2mRsgb3zjG/Hzn/8895wA8N73vhfXX389XvKSlzjbdeWVV2aUR+M47jvn7Atf+IIOa1ThQRUfbpZCWl44VsMhCJJit+TCs3T2WpzzwbNyQyw++IJuds+b8cyurHnuTaGYs/ZUBpPrcY4uUXksjv5QzINasGiWIQPErjWtpj8v8biYOePA//uho3SBPI/aqW3HJAVidM6ZT8C5letBAPgEfolkwFk3zs/3AIDlPU0s3LaIt99xY+rzjpWXBwBhyJw7u6e8ZRcI3IVmvZqYN81aeVGelB6HCEvjyDi6TanWSAF85Y6r8LLDdwlgFguRGq+azM9nf+As4YA62qrAWbgo3hcCt4+lxvL5jRm85MjdmPaTXf07aiPCmbEcGtOBDGOiVUrXPWUtdOMtIxQ6j0iBTtczc4E1ktOPf7Xvl/jSnT9EuW3IjyMJ21OO6kU7XCoWYhwBwB8fuQvdt0ygNdlyMh+v/TDHjfs9fO62AQBZcMbLxcwZNeaiEhdCMJl3n0IDkvVhGy+c2oO/vOdGoVDqAkNyDAUezzjWS3IeqktW6q1PTtIoSh7X7/3cGWtxylt3ye5I2qOZM0c9qIjBkfdoGOPglnhRRKhRwy392zIV/1ZsoQBnSDMeAILhAMt7m/pzHnItFR+2ejB5MUdp2B0RpELZVY12re6IZL71vBzGhQJcvhs/utPDDft9fOynbn8lBc4c7GxXHpACrzHHm384ggMNP5WX6w/IOcBq0vpRYESkGyLgTJSYQHbdUJbca7bAfJtQRPICzhxOwxTgsTeJOAc8Y6yo9z7quudXMPRRG0Fs7tywz8ddU4bQjRzTHeLJHMXsMyMPMWcPXjteYY291BptGxsby/3ueDJnRWGNqwVn/fRVr93RdevWAVg5c2basYAz87rHmnN2xRVXAOidC2aayZ7s2LFD/22fw7zHfsHZtm3bcNlll+WeJ8/yjtm2bRve+9735v5uJczZ7Oys8xjT5ubmerbLzuFkjPUNzn7/938f373quwCA01uL+Pg914kda+LeRQfcuVQA0NQ71m4nTeWcxWsroBW3cAIAPO+Rsd5JVcajrDCA6SMpsNMx+r3EmVbvcknf65Ae5Xxzd0w+CYiUJk7M57ynoMOZzfnsNeV5HrtN/LgVkgTkcZ44J1QIIpirEiEE1KMIyjTZIZYdHPVgzqJGhHg52wcdC+QBQBgSjUd0Pg8Fhh82JMRRHBrOfj0b1phXbUA5RazLYQuOTZUq2ikCgOfN3K8dTB5xHWp14KyNKI0GIDmKn82SlNJflO0hAHe8E2os/+39N+Ky6ftwaluEQX9pbLu4fwJwK7kjxTLGlogL3MwZ8RJm6LULd+Flh++CiIayNhxcfeYQpjBty0wSXhyzxJmMCcG5myL82/MWs+2hQCDB2WjURYkzHPi3SWcobmozhnOUrJBv5hQEMcBOJoqQZ7wtQokzpDgvVI4zMSZils0hU+CMMoYD75nH/3xKMj8GBnPGy7IIPCwmjwLEgwavpsUc+UWEJVEVW3l71DdCWq1JbHijLE6u1o0yFU60NVUPnl5HeV3JKkQtrpMrTS8xFgrIH82ccZLcABPPb730QTwvh3EhSY05NeZUoevMdVLMmbjXBk38iA7NjiEWA42uOJ+56QMKZ2hsuUzwL/9jHoCYF8X6QLSAlcvU0LLB2a/qazSLrt738W3u3bgEnGXv/Y7Hn5y0T06IYQTnhpLKr1OmQzTXpIG1q8amWnN17ULOHfT0gzfnrC+vfHx8/O8BPA7AfQD+aGJiIpSfXwTgswDuBRBPTExcnHeOB6odT0GQlYCclYCz1ao19mLOVptz1k9f9Qo5VAyReW/95JyZ9utkzoqereteepnpoAwMDGTOVdTOfjYBRkdHU//up6/y2k8pLRRf6RecNZvNvlhXG3i52mWfJ47jFYU1VmoygZ4z7Ogs42U33AS8/LxcJmY5J0SmRT3EEGFVlHMwI3kZgJamhk9zgRAgmKrQ93RBTUAmnFtOmgkSlXMQkjQ4a1MPw3GIKothu6gKCDG9oMGZW0EDmglr9DlDt2gYcXfRWSpzzk45OAUMnoRWF/BPqqTaoxdYWzCFAMQHggrVIFQv+KyYOYuWIlnvK21d6gFxaEnpJ7vyQ5EsTD4YACzJmbNNMY6mIEiUG/4lnuXR/zqa+WrOLyEm6Twizjm8qofy+jJ4LDfDqOojOB2dVlnM43EjAWeu/sljge+ojYj7osiENQacQW3JmeCAeCRXgRQUoNLZ3zjfwPPQwL7GmkybbIER8WHx5p4JhBhPAHVECIbKDNT1yAgQlNP31T7Ydu7cDyVTsiw0Lpld+XyVUxpwIR0eUZoCedRa/6jvCGemgJcDzlxgiDMmcoZ4ltlYkvmGPOaoWMt52edo6w0Q6VTbSnuEAB5FaSTrC7A4myeZNErQT3adMyZDNhnSUvEAUB9OK1QKEMczjr4/6KM0VtLMB+skYY1xDvLisn4E5zyTS9gmFBXOEiBvsEicccz9bBbnzIlcauq5Vf4ISTa2dBkFLq5rP19z6WEyUmDZ81GXmzltXd8waSePmJ4TR418Yh5zUWDZahMhBJWqIQjCSbHIDee5zNnasCPXElEXrgVZAsJhQ+fLaA37veccMyeP4eg1FawP2xqYxyFHc18LlS2V1EaXWedsXbeVrB/Wa+ECZzZbnrvR+CAFZz2Zs/Hx8fMBbJmYmLgQwJ0Anmcd8h8TExMXPRiBGXB8pfRXwpytXbs29zsbCDBWXIU+z2xBEPO8JtOQ51TnWT/grBfbp/rKFdb462DO+gVn/YhwKHC72vYUSd+b51wJ02n34bEwZ5TSXFawn3FkvmP91DqzwZmrXS5wthJBEDu/QXzoDmvknOP+V9/kPA/lyW6f2skzX1ViFjVlBQsFJeha7EzUzcbjm8Av2TlMfhcSonOYzl+exSZLqUwlaHelc+ta8AGRpB+sSW8UBD3AGYe7npJSPNy0bxZDURftiOhUPF23CiIsxeVUE58iKBEdQnpaewGXzB5A3CtfgbtV5DqWJD8AhGHiDyjpbTrog0U8E96pzK+LsWUyZ7lTiWQfZq7OMsf7SwN6N14bM3KYVDM9InJziFt8KCqJ9rBmrNcLl5PvBEMwdvIJMmGNtuqerihECfLyOkzmTFncjDOMhnNM9WDOzHMwpkX9wEBAwZ1gghCCwA7bI8RJsQwb4OxDe0SpGfOcvuzEbd1lfP2OH+CCxkwqs4pa49JVa4lQAprj/brC4JRQhKhz5g5rdN23yZylihmbYY1U/M+vevjxULruKY9YflijvJytKsso1c++bLVVvftmzhnglq6nJaoZONZmWhAkjnLmUcmc8TjbpoSpsuZpybRNfT8RufJ89zMzwxo9E0g4usf8uQJL84aSqJqH0lL6DJ4Eo2Yu68ApYkA6+6icrD8RE8+R9xAmGoq6GLTW1XocJiHxEgzZAlTKTn7tTjmE0u3hcg5vyIgSHdYYCQBvPxPFBgPAC6fuTT63xrE9hgAjn02tf8wdwvj/W3AGwZh9V/79HQCPt77/3fHx8WvGx8dff1xb9msy5cTedNNNOPfcc/HP//zPhcd//OMfxyWXXJIBKCsFZythzoDViV8UMWcm6FipymA/QLZXe1Vb/vIv/1J/9usMazyezNlKwNk73/lOEEJSiormufPYTbOd/Ywzuw/zGKuvfvWruOiii3DkyJFCcGYzespuvvlmPOMZz9DHFbVlcXERExMTuW2+9tprsWHDBnzta19Lfe5ql100fqXMma0MBgCqyKtt3Rl3vxwNKthfrmUk3lOnkO8BCajYXc1ZKKgHMMsZCENkwhq/+uPk5IHeOUzOGRKqwdnrD96OT9/zEzysYTgd8jdmrSrngl+jmdASn/PcnLPrb+V4zn+OOkMpzQXz87uvxg2fn8E3bpVhWGoXmwpwllW0IxjYWYMXJODsKfOH8NpDd+CM/Uec9aCUxYzgdV8fyHzetRQtAeA93yzrS6u6Xl7VA494IqdvmUrSN4tQ54W/Atlwvj869Ql4085HYqpUFeDUPpYDn7quhKvvkmNYEAxaxc42WqJoEwow4cgSSpxhjXnMmXIWCUGGTc2AMyus0cXkEUJStasAwerFTatwuqM9NhNTZMqRiyTg4B3mrm1EgWrV6mePOMfQcD35e1t3OfN9XEuvB689eDve+90KOAf+7Is1DTg7hOJtO8bdeS8EgGMeIgFBNNtF3LRESORGw3C7g4rV6GUqdRqZAMDRctKpZd8AEl7CmKdeNSKBNgHuLxs3DwARyxcEAUSqlrUJUm11NWtqM2ctGban5gpaou6cPIi5Q71/cSvWAkc/ncyJ9pFRivs/ewDzN8ynvrLDCFV/iVxQrtVXAZlz5mTOiJ6n0wq92f4xpfQVizcbGDU7l8Qz2tVawusmb8No2MHfX1/XoE6FrZcuGEX91AHBUrvKnpSS+Ywx8RzzlIclgY83Tt6W+W4gjlI104A0qWy+84HK57OmIhpQeB5BR3aeAlVxxHWZAGV/+g8MAy8s4zU/XgMAGInkpliVZiIeXOtKEjmShDW6pP1FGx98AK2fOLxRAIfk3wsA1hjfTQA4Xf799fHx8WsnJiZ+Yf54fHz85QBeDgCvec1r8NSnPvXYWnyczcxvufXWW/GqV70Kl1xySe7xr3nNawBka2zt2rUr5XD3MsYYJiez6m+AG/zs27cv10F2WblcxsGDB1MOKyFEX1Ndw/f93HYAQgziK1/5SuqzxcXFwt8A2dIBtq1fvx579+7Ft771LUxOToJzrvtvYWEh9/cf+tCH8MY3vhGAUPXr1Y48O3LkiP676Fns379f/513jBpDzWazZ3tcuVuuWmbKjh49qs85PT0NQPRtr+vY55mdnXX+RglzvPGNb9Rj4nGPexyuu+46fUyj0cDyctZBAYDnPOc5+u8wDHPbRSnNAHb72AsvvDDzu8nJSef7cP/996f+feDAASwuZvNMDh8+7ASN7Zns+Oqe3sGRhSMgjfREHh7NgrO/2P5w3DIwiphQDY58zrGh28JCrYHhdWpxkbt7gxFaW5oABSYns9dmZYaxSgQzDrGztovp7hS8A55eW+47OApAhAReNr1HnDvFnFG0aHpaf+/9N+I5Zz4ZXerpJPMOCBrrlsBHOQ4ePAjbWMBAHpVeIH3O0Oi634PHvXoTgBKe61hEo0ra+37toTvwh7+8WJ8TEGGWyxsa4Osc56cAX+KojxEgeW2xobGEI/OHQXLU0n445+Haeyv4E+vzrmbOkvtbahOwOAQQaLDFhzjaO1roBm00J7PRAt014jlWjXueac1icjLrGLGQIz413Q9HSlUcKQlW2WbOwtOFo/LnH9mA5ykAW+ZY3tAA1rufweH5jVj2fFSiLhari/A3e4j8EIuT6ffiyNEy0su4sP9xYRu33wi85rlz6OxOf2eH0SlHbXljA9gI5xgCA7DRYk3KIQ5PHYbXTDzXw7MUQJqtYT1qNsQ06fPQF++neg+9UY7OLsccyYHS1nR7pkKK9kmtzLFRdxCABVIA7Pj4dixdt4RbN41g40+StWEoDnHPtId7gyb+45cjeJTsn3fseASe+XygvT17DQCIzgxBaxSsmbSrMdIA3UBwaPpQCrDEXgz+yBhvec/NmfMQcMSEgHKOX7zoRnhDHk77+qni94PJex9XYjTWNcBHGKbaUyCT0rGNOaJTIoBBi70oY+UYs/Ei5ifnUp/HTVEwmY0zxN927ErI7rNZj4c/agH4woAOeWtuaYJ4wNHloyBWXcZ4KBYeKIDlUrIGvfLbI/idFznWGg6EZ3TRuCMbhq7yqdRY7lZjtHeKtWXywCSwgQC3y9NsirA4tIjlyfS6x2MO6ol2P21uEtcNbQAbiXHo8KGM/9/pJHN1pyrmitj3cCSoYEPYxt3VYTx8eRabwhY2zbcwHIf465sfhvO2i2MVQBq+uIzl9Q3wte73Pjq1q4/v+jE6u9rokDYak9k+YCEDO4PiguWZzHc+Z2jQAEALQ3GIgwBCyvDIUzq44Z5ywvD5EGNolOHI7BFg3jhJDTjlXB9HJBDWDNyZBO0dLRyZOwKyRNBoEXz6io0AgG/vFxtoat496QNbsXjVEma/nIy3v7tvAt8f2YxPbjoj1V5ArH+NdQ1gTGx+ticTf4EzjnhjDD7CMN2Z1uP9gWJbtmzJ/a4fcDYPQCGRYQA6LmNiYkI//fHx8W8COB9ACpxNTEz8E4B/kv9ceWzeCTbbYQzDsLDDlA0OipjbpzzlKfjUpz6F7du347WvfW3f1127dm3udYaHhzOfbdy4se9cLHWOLVu2YM2aZBEOgkBfUzmy5mcu+9KXvpRxbrvdbs8+Ur/54z/+Yycb+Y1vfAMbNogFecuWLVhcXEQYhqjVati1a1fued/whjfgrrvuwic+8QnUarW+npXLzD6O4zj3PHv27NHH5x1jAr3VtMdkuexnPDg4qM+pNgTMz/Ls5JNPTv27XC4X/oZzrnf/PvvZzyIIApx00kn6uhs3bnT+7ujRJIemqI9+9KMf4YlPfGLqs376asuWLU6gbu9UbtiwwRl6uW3bNud5m2ETu3F36rPS7hLGLhxDeV36PM24hduR9lZDQhFLJ39E6iS/+Og9uGRuEs3XVFD/p3MBAF5XLDDlsITKvir8uo+x07OO8fKeZVQCDpPfJAd8VPfXUFpTwvD54tkrx+APju7Blm5Lt0VZl3oafJhWZjG61NNOdcwpaocGEC/H2PiMDZnj2wfbmDmaXsADztBoU6xbvxmlzI4/09exzW+nd7k7hKIrd8/Vgh8TgtqhAbBWjA2XZtsTLoZ40+PvwYHbk8+8Dsf6wfUojbo3rfzphWQHGEnom9pBt+v8lKSghgJbZb+E8j1l1E6uYfjUbM5lSIQzYe7qDnmj2LIlq1Tbmmxh9vY5+IMeoiVx/OUvXMaLPyccEz8gqWLT/q2BdsxVH3nMQ3V/DTQgWHtmNiz+nc/ronlHgDF0Ud5XgT/ribbvSrd9ZI+M/bLsVY/keMUz51EvB/imVXdP9WPJE+ypatPAVB3RUoRNz8zOD3E7xuGldI4dOQyMxqMY3JIojEaeaM/DGjP4s0N34CObzwLmisEZiZIxroCeeh+9BkHtyADWWX3EQobB9gJMmB1zD+W9lcw7MFAV92uHD27Yuh4bLluP669Jv2NqzAzMyucpf/fJP2zizHPLoEcGnM/s0C8OIxj00Wkmb37tUA2sw7DuyWsRGDlgczfOY/6GBZzWTMD20PlDOHxHC3srgwgJRcBj8AiIZmNU91fhD/gY6ARYUCVDYh/1qTraRzpYd9EYSmOJwufMbbOIl2M0PAuILwJDzWEMn5weR9M/nkHncBvhYgwul8AW9ZLNinlxnrccuAV/fFqy8XZSu4b7/tccbvt90UeDC4PoTnWx7ilrEVhlPRZuWYTPBDcQHE6/55s3bs6wbXE7xqFrD2f6GQA2ryPAgWTs0mUflXsrIrSOcwSdxB0e6FYw3B7B8Cnpew4XQ+w4IFincRmRQOc8bBzbqFVplZVKydihByliAGHJw+t2PQabO00MWvr6m7piZAbMKIcCYDCsonZwAKzLsOGS9Zn7OvSLw/J4BnQ8DEzX0ZnqYtOzNmSEjFqTLfBb5xCDIJD9cMZfn46J9+7F3204B8+Y3Q+0Rf4ZAPCQ4pt/0sBss41KFOKOlwLUo6hP1dE52sGGZ2yAV05f4znb5/DT00LglwkwJ/t8eAiw9qIxVDZXMLeUnYPWyhy7YX8Y635/HcZOX8Duzx8B3buEKovxzNn9OeCMoC7noWBNgDWnpfPtG/csY+YXM1j79LWobOpPRfyBYP2ENV4H4Cny76cB+In6Ynx83By5TwDQu5jSA8xWIuJhOoQKnA0MDGDHjh0ghKyoKHLRdV3fuUQRivLQRkZGAKQFJswQr37zl1w0+krCGvMApQoFVO1QRY2LcvGUKbGL46XWWBTW2E+opX0vKzXzudhAeLVhjXbY7EoEQYIgSIGaOI5zc87McMkiZvdYCoa7chzn5+dT/87LOcszt7yuu3gna2c/bHhB5rNLZB20+EiSM6eEAWhAZMJyXnso6JAtBCTDYYymKkLhRWZ8PoDBc8R8dPXQRjyqMZ05v9ql1uAMIgfOGd4J0T/EAnl1CQxnswSlNlf4iR0quuz5aEfpvA1GZP/kyh4TlAfT/eMxVigIQr1kl7xjJLOp8L3T2ukbUVNdReZ6+DVP5MHl9JEnw6Aq3JxLchojQ2YrW8V8+KWxHVhTM5w3a1ywLtN9oUI/IWty2aGCykp+kutxy2tvxcEvH3L2T16aHg856vI1Z5bTqxyhdXWuWRhGIPMEc8aQRzJiKrwVZ8QKVAjge++/EZu7Lbzn/l/23MYlRtiWKleh2EePAuW12bmIUIL6QLqtjLrDGpcPtVGNI3xgzw3u61c8Z2lz1RXqmdWrcNbw0ufxCMLF9LoRSyVC+73hXYbYClc7492n4dOXPAYd6mXYVxVCVvITKX0FZrIS40TkWFGix5D+Js6KawDiveCxuI5iqmaNnKr2pJgHN4btzG+rSPLNlI/hDGukSVijPQ87/Z+c0HQAOt/QN0L2VEmvzpGunpvn/BKGzqw7x7UzrJDmhMMbjzWSa8L8QBUNL8BdteFsnqm+L/FfLZgSyFzTnHlI5eQFnOOM/Ud1OKxrLevOhGCM6I0MABg+bwifuvRx+GV9DDOyrMafH7gZz5rZB8YIfA9YP8h1fTrzObnqLRJKUJWs6WMWj2J9t4U4Blgn1v1k5plSzjASdbSwkl/3QcsUax49ClK3/FPjmSdh/VR+xZ3rhz/g4UEY1dgbnE1MTNwE4Mj4+Pg1AM4G8OXx8fH/K7++bHx8/Ofj4+PXAZicmJj48Ylr6okx29EtUi40w6bUS2r+fiXMVhEocjnfLgBR5IwqcGY61eY1j6W48koEQfIcdnVddZwK2SvKxVN2rAIcQBZI5fVlPyIl/bYnDwSaz7so52wlz8wGuf2oJOapd7ZardznaPZjUSmBlSqCmm1y9Zsr52wlAjeuvB2mAvIti9vZsXHUKHT7s8H8DQUiM6q9QNTycSaZQyxy616aHvtxJOpcmYuhS8qeco7T/uJU3PcHZ+PKNVvx/eFNmWMUo2WqI3LGgZz8JVUka80TEpZvQNYnml5w/wRwJ27byd3LNBkLCTgTOXmuQqWAFJOz1CO9mBUKgnjEAGcGKjaB2qOWpvTfsczTUTv/flUUx6U5jrUSCqn2KQiicsEA4CdD6zFYTtrOrftm3eS5m+UPOOMZkQ1lgZ/UTAOAI1ccdeae5Gk7VLcb65ctCKLyX3yu+5SrPME8cEYJqFWOgYUs0yZboMTOp3IZNXJX1Pk0OPOA+unZkERQwDmNO5z8KAR+d/q+DIBX5lGCZccGjTqTZjt7bMrwmGPbi7bqQu0A0GozADwjThR3GO79+3tTnxFKEMrxHFmessrbKXmG6IR6rhlwJuYgGtDsxhPjzhxAFom6fYu3JH005+evAQD0mFdgT4lZcMD5nhE/KWIft6w2uIYJd4s6AUAgx2KSc0bEo2ciUS2SoPjf1p0MjxAgL0/QviTPAUJyri6zGGxKsEKLg8k7xnLAmXofFFtN5NqROw8FVKurPuem23HPB/aIZjrmxnA+BDm0nBIbMc3MiXvF4d0wX1WtVOonz8xdfB7wZW7n0+YP4v/dfS1iJsYLkyc017H33fcL/NvuH+s2mQwkKdu1NpMGKZAdGoIgNKf8wQplFR4Q1pdM38TExFsnJiYunJiYeMHExER3YmLiFfLzT09MTDxqYmLicRMTE289sU09MWY7w4oRc5lid4DE2TV/f7yYs+MBzlTYXh5ztloZfUCAs17qkapteedX928zZ/2AM/XbG2+8ERdffDF+8Ytf9PhFYldeeSUe/vCH44UvfGHq87xzvPWtYlgXAe9+wVkeQDJ34+wQvl8Xc/aFL3xBh2faY7PdbvdVw62IOVstOMvbCLj88stT/zYFQfrZJHGxISRn19XFnLWM3eXrBrOhJs19Lez95H0YbInnSX0A3K2MKC5OUH/kAF566hPwXyObxXUZB+vEaeYsTKtmAQAFh1/z0D1lBIwQ/Oe6nZnT28xZBMEY0BymilACTghOefPJOOmlWwEAVcmczayUObOcpdAY7zrnTDr6zsUeAEiyQ6yMMl7InPmUI5BeU9cA7GbY5wWGWIq6cs1gzoqcoqCerScXFSTiH/3eFJp7xXhuUQ+DRoRNZOmf+3Uf/qCsoaeUET3BVNj9oNvjkxTwVNe1zQUgw2fvSN0ns0B7EtZogEVC3En4ymgWUMchcpmzlRg1+vlHu+VaIp+gFJDMGCFEl3XQn6k6V5aFETAWZcOplXmUp4CwMrUkKwDrScGRvE0Zf9DH0HlDGP/8wzFZUfUgubgBayiFc+66far1kXXTSsSj5CdMnt7o4fYGlcjX8Wo0VYsLEH3NHOp/PBSh8HEradNNA9mQbdMGKxIYSdU+xQKTHNaDeIlao3kdwD22Oc/WhlRWriW5wQDwzdtK+O49ATjjCEYCLMvAZ+ZREHA3I0yA7114NoD0POJqTCcEtnaW8ZU7rgLZLXa0VLkLQEYvOKwjowp8uRbRgACM50c5BAQwBGoWblyAC9wDQNyKEPz7vZnP1SBa8NJruLmRo86n2decEhrUYDuVee0IiLl+9813/hyjNmaTemkG1dqIMudaHQViIi8n+4rcTb8HsvUFzv7/bLajW+SENhpJgqVydk8EODv7bPHymwyAC5wVAaR6XewcmvezmrBGAFqAw7ReTIxqr+mUKzGYpzzlKbngrJ+wRnXOK664AldddVUml6nI3vKWt+CXv/xl5vM8oZp77hGRuocPu+PYgQSU9BJB6Qec2WqGq2XOduzYkco37Ic5U6b69+KLhWjDk5/85L7EaE4Ec9ZviQtT0n/rVgEmVKFzl7lCRJij6DOQZc4a56fP23bov9/2tttx9MopbJkXIje0RAuV9ohQk8ZUqarVBGNOEC5Y4CxCJldBLVIlLxvCp0wzZ6mwxvwFH0SEUXVnughkTleViH6YL9A92iDz4EYu24rSWICT37Az4ySYO8apumusIKzRAc68mOXukgMivO1v7r8RADAWJu9m0wDWZo0yNZ0q5oxWRXHcvFWS1rzU8QAQuzekEc51cfjrSW5qm3qoG8zZ1MnpzZTq1qoGS1ppT6qw0Rz1yCDg2bHoGs8u59UCLVE1/b6WOMNzzutK5kwCao8Wh8YSklLAAyB2zq0NkDz1yPKG/PmEGjeR5C2KfvmjhzVzFYjtvCDK3O98FKefKwCc9XdJvgslyIT/AYkKqnpmQYkUbsrUTx2AVxalCJhc79stLhThjbEdNSIsHnHP4a+5UArT2EqUUkGxGnDdHlkOTTBV9rtGxYaELQhC4qwEOiAZS8bhDSTHXz+0Hu/fcg4ab30YRh+b5P5sl4XO/+rpYn5QYM8rUxGq6+eVYwB8ef5wPpn3LtjYdQKihZsXkzp/lvnVtFojALzsG6Pi+gHF0UVx/Ra83LInhBIsrxN+ngnOnGGNIXDJ3IHUZ5Eh4ZgX1qg+TjFnBeHM1Ccgg+lnxjlBOOcoQeP4DABeLceQPabf8dQkJFWDM5/ogs/O98wjmhHVH3Eu8r3khkGe3k/Neufsuc4EZ7aUvhpHGXvw4TIAD4GzDDgrcmJNpsrFnB2vsMbf//3fx759+3D06FEtmrFS5kw5yqZTbTrPK3H0P/jBD2Y+68XEqLaZ13/hC1+I/fv348orr9T9FkUROOcrYs5sR38lBcSdqmLIhsnZZtfeMk2B8l5AIg+8mROc3a9m2KAac/2wWAMDA9i7dy8+9KEPOc9bZKp/v/Od72D//v14/vOf3xc4+3UyZ7aZzFmlUsHy8nJKadM2O1EagAjfcTAfkbHY375hHWafe2rqexcYYlYIjufLvIGCEDA1DhSzFIVAMOyn1pZO1wHOkOyQA0Db4QmqcEO1oHUhQvbywSIB6zJ0proayJakp9/KmSIp5zitJd6j6/A3cQAAw+lJREFUNRevwwX/8jCse9LazC6q6cakQvaA3IWUEAJqhbh4jCF2sJr6N0gWe9+46qxfBr9AzDNmeI/y96uxGV6TzwwpKf3RqIsP7Pk5zmzO5xah7s6nvZEu9VALkmM7fvrezI23FOvBuHYybQt86ELdypijXhjjyDi2dqhkt5Z+X//xmQ3838uaKHnJGOIeyS1irsy3ncaIZ5mzHEetNJY/Z3gmOJP3Uq0A975rAY/dGuaOo6ol1kK4CM2zNzrDOMk9BICbLtiBwTOTqBqfZlUNgSRcS+V4VcvoyQirxsaSXWu3uYgqNhz+uBVj+bB7/fitUyLc+64FbD45Pf82dgulwTU1rueIWlmynSQNPmiJgnoUXs3LhDXm5ZyxkMtw3aSdR4MKXvWGQVz8hAC73pgw+P/n3p/izOY8nn2e6CAF9mhJhg6X3WOaeBQlmT949DtT+OI2Icw0Unaz5vFyDJYT6qyEKy45zepHmdarwhovOTcCL3jvR0bEecyajt3pEN3Z9MQYRlnRobgPcNboiM9PWyP6igai+Llfy0H4lMAbTD+zeDlCY4+lNMk5ImseOuVtQnztibsifOWPGpln/6xzk7VGhzV6ciMtDz3QLKhioVj71LvfkV1lC+5kTmVtGpkbJhkp/ZgjGHL4s4Q8KAHaQ+BsleDsWJmzXqFp27Ztw9jYmLNYs7Ii5kw5w73AWT9Os7PgaQ/xCxc48zwPW7duhe/7oDRJBGaMHRM4W4n1Ko6dZ0Xgpl9wlje2ip6jeV0F7votqTA0NIRNmzYVXttlqn9939csVD+AsOiY1eQ2AisDZ2rMeZ6HWq1W2B4Xc8YZwBxMTHfK6Dua9UVd4Mw2kTzPQfLK83Do2VjtBIZdDlqmqTZ1o3xZc1nuJuOgA4mksWI9uqSY9VCLGQ2I3o1U6l7LrZycDs5Q4QwdQlFbkzzvbS/emjrObL9WU/SoYPIKmDNbFcxnDFEj/32OuftcjBAE544ASOeLqRplygEQYhbu4rgA4FeTezyztYC/33sD4pxpIlpIfxESArP+MBlIDwyzzk8qX4iQ3DDLUkAyzJkrDC6O04Vvgez7EFrgbIRG4GGMS3ffjbNlGBJXzyxvDCELzliULRDdDXPmv4JQJJM504XMKcFwlaNITSQYCUAMpofIfCOTlQGAMCYpR5BbY8Cjjk0QzhPmDAlzxjnc+UuiBfovJcLSaXOoYsHJlyIvM8+GqxwjF6SVBff8414AwGgtYc5qFUiG2mKhKx7GnrAGtEQ1c6+MRoI5s+uu8ZiLDS05P929ZhRNL8C6umLF0uc5b3k2uZ1uEtZYxAoRCpTXJmOx9p19WBu2ETL3uslasbMQN5Aw7/XrD6U2JxjjAEkKf48MEodgSnKO0ZFEgINyjigGlu9dRuPuNBjqhtn6gLHx0tthjepfC7KcQImrsEYKFnHN1GfaFBD4NihhPFuImgu1Sft+lI0NMKfQlf65wZyJH+cweR4BKVksbiQQcOdIB3Ez1hsyw1GxX2Jv7JkbJr7FnAHZMGpAMpEPwqSz//bgzHYai5xYczJQx5m/P15hja7jXGCoiDlTDrzpyJt/K6d/tU5zv+IX5jVtoQbz3lYT1rgaW4miovm8i+63UhG7se12u/CZ5DFn/YKzlTBnylT/r4Q5c42J3xRz1m9Y48oFQbKTdTeCDrswLTKcEtcU72KqbPNKwpGtrM+R8mVcLyBqsYm6cvfWaFOnC62Wp0wxK4GndhJdUvqKOZNhJYo5y3H0QQBQUURYASblaLRySGS1CxoTooEiAJTXlVHblcyN5o5ziRngTF03pz3EWvQJT7OapoWLIeKckEcOoCRDwMwwGc2c6cT0/OK4AHSRXWUekpyjTHsWrMLLhIIbrBaxlDpNdknvLstDbMdHme9lx+LCVPa9j1kW4NvMWWSBs3AhwqEvH8Zj9h7A2w/cIi9YnE8FAKW11lwVccTLEWZ/Oqc/yosGL8AieMaB+7CtI9IMVP8oURUuU7ac5/QIBt5xNv5h6zniAyaA0MyPZ1NhhII5M8CIdY8ehS7Yq8ws0q7AEPUF25nHwkiRUtFueY1uW+RyLd+TpFHwmKOznB5cO1+9PfVvmsOorqkx3Z4BqR6Zu5/kyiFiHOFciOX70xtlShBE2WxJRusYUvwHS0k0UcMQAlLgzCvLTaLcTRmCklXaZHu7gW5MMhicM464y1LiJWb+nDnnj8SGnxdzHcYNAPU6AUCccyOhBOs2+1rxNeAM3VCEXNobEWHE9Xyr79snIMSRK2WYYs6UmBQJxG+8PADr0wxjxBkyhcE545nNGrPfAw9YKgJnBnMmck3dxwm1xfS9sVC8A6wjCqQr5swOHfYfNpr6tx2GnGbOEnDGmQgFtp+BaDAeYs4ejGYzWJ1OJ9dZNh3vL33pS5nfH6+wRlf7VhrW6HKUr7nmGnzgAx/A85//fPzFX/zFitphW56zv7CwgGc961mYmhIqaKZTbve1uvZznvMcfPzjHwdwYpmzz372s4XOvplTCKTvseh3hBAN0IqOWwl7ZbZhenoaz3zmM/H1r38dQP/MGZD0Vb/X9n3fyZQ+0ARBbGOM6XekH3D28vdn3/F2RFIs1fwSx7PeznDTHcl7RglgR4q5cs5s8wPx47ycKs6SxS7SYY3CaVBt2nOQ48BUVhBE7dKX1avseH5KljkwmDPO8h19QkQOCKVE77KrxXBZDvEo4viDdzN86ptMfp84HXa0pLmbPRBHeudaATXmy/bkOLG0TEH8tDQ74VyrH9o2/4sFdHO+YyA6RKjqBGcq58wT63quVDzFrY85OfVZlMMC2cxZRCia9yQ77fFA+t3pGjk+Zs4ZSE5ILoAgyLK4C/MOdoFnw4lsNi602rP/8gNoHbDmNl9sOBTtTQQjfoqV4xFDtByn5OO7DrEJAFj/1CS3s7Yzu7b+3V6Rn5tSjwTyc2EgniUbLOHuimCZCAPAOMK5LkLjGUUWgLXVNCnhKQVQQIzljrwtnS8k3/d8ISAgnO0iasZahKXbYiitKaE7H2lfhDOOyBLEqG5L90meiudwxWTOSGFupwLah4Pk3CRiCMZKKfDKGccnJ2p4yXfX4jO3iI2XULqTJcOl+NNTHq//fvXhO7Van1ZrLAmV1vxwb7EJ4htlNDZ2WwjjbAkE1mHgYTps9kNbztZ/m4Iia4wc1C/cKe5V5egNynILeYzw2FDynpVZjJAThAthJu/MyZz5no68yFNrVKbKRdBA5SvnzI0BQWXYAc6M92qpyfGsdwBv+sZA+hrGe+97ooxAnjFTEIQ7lxnRnjIFscAZ5G+j5RjEI3oTg1prWfm30uJafq0o50yekxCwkCEY9N1RBQ/OqMaHwJnLkctjV1ygzQQcSoSjH+sXFBU5/UWMi3KUbVD31re+FZ///OfxjW98AwAwOzub+a3Ltm8Xu3SKHcwDZ+973/vwzW9+M9MOIJ85u/LKK/VnrgLctq0EBJv24he/uPD7T3/606l/m/f47ne/u682FYGzPObs1FNPdX4OiLH4nve8B1dccQXuuOMOACsDZytlzvLOvW7dup7P5lgFQVyhvktLIpG81/tihzX2sj2O1MN2BymW6h8+z/HN64B/+UFyPkoSNS39u37AmRQEyVslaED1dyZzBkI0sHnVB+VuvLVlvHFA3nfBbP6IxgxeffAODMi4u4j0DiOkgVjVlFOgnNVWW/z3W9cD/3EV8PL3i58k6lk0tXA372+CGY7RcBxiU7eFEos1cyYcU54vy04IgpEglctAOdcOlWk8FuxMnmR8TAjKctGvsBhlX7ZbdqsOa6x6mrFztokC7aH0mO823WGWdjgRJyQVthdbao3NVgIWFfjW7ch5ZIGfHYvLjr2NOM4yZ3ZYYzSQfV87R9LzlxAGQG5IGiDGkFlImUSCrfXrSTu7new6tua5m7HmwjU450Nn4eH/+jCc8e7TM8eo4u+qfzhN8qnyjPgE24fjpM4T46JWF7MEOOJ0SpMNznwHc1ZmsWB0kPQv9SVDnfdySiAZLYSIJN0ctpgUEEpCBlnIEFl5rPYzy1Px3DCY5Jx5vqoHVSBMBOAtOx+pP/NiBq9CU0IbPOb4m2sHsXsvw8b7hQ/RVeDMLGJNCL625iT9T8XcpNQac8Q31O8BgmCNkZ7BY3RiwKbOWJeJUg0S+E0ND+DeSpInaLJGJ3WSjZH/ee0agBBUJLKujYrnkBc+PFxPxEDesf9XAAGqW6qZqIswToOzDqGgXhJVkKfWqE0xVYFQzs3NW/QJysNWzirj4DHTPuL//g/gWz8FYG0emYAvoDwT0poC5GbOGS+IKCjTbM6ZDGtkrViwwHI6zESBWH0e5AiCvOLx7bSUPkfh2vFQWOOD0Fw7bHlOtIupMh3BflgfZf2CM3VOU8a/qD3KlDPcK8eqH5YBAH74wx9idnYWGzduBJDv7CvGTFk/4My0fhgaU4XweJrdV+Y9Pv/5zy/8rQLRRUyPi72am5vD0NCQ4+ikDWZ9PWBlYY0rZc7yQnOr1Sruu+8+fOc738n97bEyZ67xoMb9+vVZuXrTTEGQvsIaCfBnux6D74xsQUNetx3xFHO2JB+luYCUPJ6py7RYEAqijAbSkc1ZJMobynrnWzFncSTZNLnAzktil1q+7DNe1XveedLCYTxj7gCeNi9QqUii5rksDKEy14okO8g+SzNnTWua9I2wRtOiRoTO0fT4e970ffjqHVfhuTP3A5DgjBWvoeX1JQycbIxPDsQO1iVux4hbMVhOzhkBUKqL72pxpBnHmInPzLDGIicEhCCuWGUnpruZcCIgKxDz/v/RTDlbdk7TfCOpd6RAb29wlpXSd7GHIqzRYs4sgBWXs+9iFpxJgF8gCEJ8ms4bicQOt8mkuqYmvy4Y/IGTB0SeWAEATIU1FmyAAKIPawHH/7pUxOYSxsGkYqNZqypkEkBL446wxq4V1/WM2QOCOeNcl64gvnJkc9pDku+UWARXwMXYmGEh0zmRymxHNo85K/nA886Vz06JOeQxZ3I+mAvKuHnXFnHeWCiEtg92kgLHcvj8890/wXlNEaLale9bybpXM79R348SBClL5ixP8ZMCAE+NoYAzhIxkBEFYh8GveaieJOaImJCUMqt5z2+dvBXPmDEFoziGpN/njZTEMMrpo0o5yes9tzmP8+48IMIiezBnHerBo8BAKdnEKjSDOSNAwcYeQdliziq7BgBjw2FhWYLzDBhKTqq0Sj6y+Uz9manSmco548jNCSWUZMAZj8Q77dU88Iijo9Q67U0iK5LDVntV4OzPn9LWAlWRjA3Ofe3pgxKbPQTOXJbnyPYCZ/3kSyk7HuBsNczZattRKpUwOjpamAMHZEFbP2GNpvUDPFbSzysxmxlS99gPGOyHOXONq5GRkVyHHRD9affJiWTOiljJkZERzaC67EQwZysBZythzgDgvsogPrrlLLQ0gCUpIQYVnmODswxz5lBts80PSOECCyTATTFnsQprlA7NUIll2gMAI+fLEK0CIQTbIqXdn7cCSMZIhBWJg5RCnmLO7GGbSJpbXxCCtU9OA8hL5icBIBGXkE5hLhCCALCVTUnOHuFudU0ei5AekzlbMAA0AUd1QIoDsAinS4VJO6zRq8g7yg1rJIDlIDMQp7KduQMdQ8ibm76ZHeK00OBQGE+DM1ULLme+KFlFqAEgdLBSMQMuWkiXBrEdfdcufbSUXktIQIrzFiGcP1NxjcYcoOl8GFdYo52zksvwwgxrpIXhVoAMo4s5hqVoBeUciEWOl6k6GDHrbbLGAOUcXQtx/cH0XnQiouvkxUpMhqOAOUvOHct+5FKBlIUM3WmxZrBuNp/RBg8u5kyNu1StvCLVWONjJVDixckmTtyJwSKGxu5sPY2OI6wRSIfQqncjk3NW8I5xpFnmEmPoxkC0nPZB7PeOEZJ6H7Y+f0vq+1cfvlP/HTVjBIyhST14VS+3hhcAVMoUM34yD1XaXRHhYLlaYZxWa2xTD5RAl9DoOVtHSc4ZUBDy7dFU2CcAUZaBJm1SP7XDCE0GVf353dGtesORRQ5w5hHwgv4BBbwhG5ypzQrBJC/PRPLQHsyZlXP2e9N7UWIxfAqMlNRmoFw7cqehB2dc40PgzGErUdUzAcbo6Gjf1+jXgVwtc9YvOOuXOVPWq+Cy/XkRc+bqg36Ax0oYypWYDc5WUqi7H+asHyl921zg7DfBnPVz7aJnZ6pz5plrPExPTwPoDc7MOmcrHdNMrlztbtqJLsnHTozXvuRnc85ctvPPdqT+7ZcEU1W4SMjvlDQwC0XdFsW61GXX2wvsakwlUec+E0rEjj4h2unz5NZ9s6UP0fa4xSP457t/AiALzggV4OycD50F/xHujQ4e0J6y7KXRErwBI6wRHCxOPzNxMhEOFkVJDsW7tj88aQ8HSoYj+7g5UX/MFgShVXFMfqFuwE6ui1ka4CtjBlPECIFH086EzQwtNBImr1/mzA+AmcAKs1yOU7lUgHDy//jI3anPvMyry/HFtTvcF1K/CSTwKNibID5FYOzqk5gJJ9NUIO3yrLS/BTSKQLsux6D7p2AMrQkEwyKfKWWiVp5dgD6M086zzWyyRpgqbK6s/qW78cU7fyTOodpB8sUTQBKnOwrSzBnrMDR2S7o8ZJkw3X6Ys1idS4WkKYCfB3aNU6gNE6r8DM7BQ464ydC6P7sJ6QxrRDqEVjExqZwzxjPvkdkegjQwKUkRjsad6RzxaDkCPKrng4hQcEIQ/cEu7Hz1dpTXlXHGXyfhsaExTuanRXvm/DIoEfRrHhgqlTgOlJN1Umk5cesB2czZghekwJlr/KRvSG4gqOecN6wpQTCc9lE0plJhsTIk1WaqbEEQ3XY5YM28NcV6CpGbfL+FEAJvxJoUzPkvZFoEyBoqGdETO1xzY9jGk+cPwaNJzlmoVHXyAxwelPYQOHPYasMa+wVcwMqZM+WkmvamN70p93f9hjWu1FYKzk4Ec+YCZ0VAtV+zJ5uVgDPFOK00rNF1XbsNH/vYx1KfrUYQ5HgwZ72u3evZ9erHY2HOvvvd764orDFlchH++X4fcSNJwi/L5pq7e4GXzTkDgNtqI/pvdvIgqLVYeiW5A9zHSqGYMxZy0BJFuBSBRQxVWRern7Umr1CxspBQIR9etKB5Ysdag7Mwnzn7i/0367/tcB1CAFryMHDyQC7Q4b7IsekF4M16SJRz7FvyMuBMSXzHRt7QvJXoXgoIrh4SNSRrUjpstpMuUks8yXbmkR4eAbeciR/fX3IW7F0w/MgYUjDFcP5IheK1Jz9a//svvlHW4hI6BFH1XUFY47RvgbNGVgLdlYtHAxFetnirCKGOF2N8ZsOpeOFpT3RfDMpp5KAFyY6EAlv+YHPym1jmnhgA9vPf5Zkutp20QnCmCpkzoHF3o/AdC0YD8V4p5UuZcwaaCExwzhFGEO+rNDvnzCPZsEYAqN+UhPVriW9esHNPklINTHnHIqFKhOZKpzbuxBnmLLDqwLnAWShrb3Ezf4nnFOtFei1S4KzT4lhqi7aK2mZcy86b1oE7rDFwMWc650w41jRnaVB9Y5KUAWc41PSxbIvfzMR45beGcOWt4vP5tpy3HrsO658m1g+zzp2pHtng8l4phYqOzXtm5YDgUMkEZ/L5WkDDDn8/UqrCoxwD8hWd98s4tCsnCojzJOfML97YIx6BX0/7UlEsXgPNfMkxZTNVZjipb8TLK8VfM6eXSUaXVlS4t7s9oIA/kv7y27f4uOouH6AErMPQbrrbY2cI2OGaAFBjETwC+JIWjIg8S1EtwQchQHsInDlsJcxZESB71rOeBUCwKqeddlrqu37BmQrhs5mzhYUFXH755ZnjH/1oscA/+9nPTrUhz971rnf11Q5lvZx9O9xxpTln/QCPwcHBzGcrkYo322OCEbvtqwFnqxEE+b3f+z0AwBOf+ET86Z/+KQChYAkAN998c+b41Ujp/zqYs127dhX+tlc/HkvO2bve9a4VhzUqi+TMvX+eIlqOEMvaWWUZ/24yVQHleMZZ2bH2ju2PSP37hoPpew10aEpxW154bjMBZxETzhLjWLxlCRuG3XkDys7aKO5/rMaw6407UdtZxZonuJmqSCba50tuiVAsgmSXXamHLTTkDmrOTzMqZAZLlOcU+oSLBb9H/5iMCgXww72llAobIMUdOBAzrvPzTDbvYWs7oB7wrTXbAABj1jurdvoJEeqa+ewi8OTT0mPh3jkvFQqk7Ipbk7HNSBacwaPYUx3C7qoIUaUx8MO7xW+0c5vHLkgLArHzb1ocsQwD4FKUpEqYQRZLf/Jmsck0F5TxD1vOcV5PTBWk2IvwCYLREk77mzPEdSTNYIajTi2RTEiuXdMOAL6zeTvuKw9kPlfglQMyrzO/OaU1JXhlAt9L3m0eC8VJ1hUCCo3dy4IBNc/jqHPWi/nwZWgmJ0UsQ+Lk2uCstqMGzjimr5lBdzZEZDToYKmayRdt3p/dGLz5z25F466GDiOlQXEYIQjwuNNFPt7DdojflDjD1ffIMLeOEHRw5XqGnIISDrOe+tYRhukgAUR2WKNizvLCPgkhGcdbhQpec096vfj49wN8c3cFn/6JeAdU7q7JCAVrjHnZ6IKlpmQvQUEgmM7c8OESwYKx2cNk/pXJjncXIkRx+vdHgwo8ArzmQtG/f3huC5PnbXJeQ89BvmTwChVIs0Ap6nJwxrF0u9gVUt1nM2em+q3ZT0rsRjO3AGKZv6rDvQtyzjyL8Qo4w2WfqYP6BPFyjKV5I6zYbI/FBpcHs2u5zzkoTepuRr0Wjf72RB9w9hA4c9hqmTPTtm7diq9+9auYnp7GzMwMbr75ZvziF7/Q3x9rzlkeGLnmmmtw8OBBnHXWWQCAs88+G1dddVXu+V/0ohf11Q5lxzOscbXgjBCCd7zjHanPViNT73keZmZmcMEFFwA4NnC2WkEQALjgggswOTmJH/zgB/jkJz+Jffv24SUveQkA4MiRI5njTyRz1guc5V37yiuvxCMe8Qjnd3Zb8swF/FzgLG+RWi1ztk5GsxLGwUF0KJDKnTCv5lPgnA0Rbn7zXOockXFNzoC2tWAEFSrq2/QIa/y7Jy3iopPFOCEqJKXDEC6EGpQ957Q0mOAxR/tQGyNVhptedgQ3vmEOAycP4NwPn4Phh7nFZpTjkrdoEUIEu0CNnAfp4M0IciWvBqkOE03OBb2Q56nEDc8ty7BG9zmVmU67xxnCGOhMpd8r1haUWcxIkgdndPwzdrUAQvCl14p+HIvS871ZV6yIsSEewanr085FxIh2YkxbbifnYSDwaFpVUDEzasfa50wr/2lxCSkqk/fQgoCkxiEgWDKbWYwcgiVeSRZJlzlSpwwl88Vijrx2tSrbUzCoCSEA5wjkRofPGUIOEY7KhQPJYp51Gh3g7IpTduEfN5+VbbshpS+U2XKbI9rkU6hlm3KRF+cP+ELdLuRo7muKfjNOFNvMGXXXEzRtSIrOCPa1YFdffscU5aQKNFMRIhsvx0Ii3gi7ZI6bNB1t0/Z/dlKH2tISKQwjJJTgX197FLe8fQE75dj2OcNMUzB8rMPAmbtweERoijXjMcf1r5nHW9+XRLqoMLlMzlmeIIhkr82u3lYX63TTqrc4NSc3ryxhIvNWzbVjnwH0F5tc/4Zw5IY0AkClkhaBYmpaN57P1M/nM79b9EqgFLjkzAi/etsCPnBJA93RBLiaPapKn2hmixczZwBJbVxFXS6KPc90wWOe5JzZOV6m0IrRT0rN8t4P7U3u02TOCmr3gWY34XxjDotbDNP6WVntsQRBAsdmnjqXEqiK5ByTX0uw95zwQLSHwJnDVisIYtrAwAAopRgbG0OtVkO5XMbWrVv198cKzvLCFYMgwKZN6d2YzZs3O4/txUa4TLX7NxnWCAhpd9PyAHWREUJQrVZx7rnnAvjNMWeAeEa+74NSim3btmmQNDc3lzl2NYIg/YLX1YY1nn56Vuratl796Dq3C5wpxVDbVsucaQdceGQ65MYV1ugRoDXZRv1wNiE+aQjP5BA1714CRwELo4wSjA3KXVPlVHOAdzlC6RDVYIWptYQ6IY841tYZqmWiU3jy5LU9JTme1xwKwCO63hmoAK8eZ5hdKr6HjCAIJRpP5IU1RtVALrDFS1JK+plzdDnNiIIs722Cc46YJU6/yeZRIu5neJ14wGvCjq4dRzhP8GGPHVetaGlYKxLOh23mGIoJQTTdSZ08lqyIdig5Q7nVxWsP3o5zpGgKkeUW8tqkciTfbMigsyirIhc7pm4llEECiqXbl8BCjgFf3MehUtU5Tqgsf1DcRxCMqPT8AsbQjkRYFJgIt4sZ8DZV2FqaC5yVPTcg0jlNUsCm1ytGPAJP5ZxxjvppddAKBZd9FS1EairQFluukkeBzd3iGozmWM1tE0nmn1giG9JRKg6if6KmeL/NJd8czzzmaN7XRGltgOr2KoYfMZy6xOLNi1i6TcxXJBD1BPPCCEEEmNk0lMjt+5xhToGzkAMxR9ThmXqLIaEp9qU12Ua4t4EN6ylGHz0i2q3z6eT7VqIAyRcoARVAzpTBH/TE3+aGB485IjmufeudD6zEJu+yHQCAWYNlXpLK+hERLG7RpkylStEwwBnnXDwr4/l05P2ZVw4J1QTslhEBmLyA4q+3nZ9qN5DkUyV5hfk5cKqtF/zz+fqzqMtQ2VJBuBAhbsV6/rGZKjNvsdeeptp0UoAuX4FUtGf7n5ykPzNLS4QLIaYlc1Zn6ckoI0zkuGUFztTcrqX0C96xByN19hA4c9jxCGucn5/PfGaCkX4dyLywxpXkkuXdz2oKA6vf5Kk12p+b93y8BEFcx62GOVPPU7XR7lN1L/0A6WPJOXOZAmeu36xGEOREM2f9PLde/ei6L5cgSB44W3XOmQZnwplUOTpqeJqCIJRCFt7NvwZnHNiY7kff763WKL42ahBJcMZjBhYx7XwQC4zwmMMf8kV4n0dhSgfnyWsH0qHIzzkTzhgtJY40AKwL25hdliyPewoQSmHq75AJZkA2KG+HfM8Tdorr9opQMe5HSGqnxTYAgHfF7n7MiHZKTOaMUOHQUI/AH/Thg2M4Fu9ZUquNgCC/aLg4T7YQbjtCqqabspQoAiEgHZZWxpNfR5o549jwb7fjkrnJ5Ho9JLXVdH5nbQT/NSI25OKIZ8IsI0dOnO8L5oz6ROQZegmgPlyqYezCbHgsCXozVcSnMqdIOsqcoSVTHVmXCXDGCR67lC7BYuecAUDZ5xlwRngCwLlqSwHrAchnTxJnlctddx4KcRAW8kxeXmjdpEeBa4bEPHTt0HpcP5jeLARMR5Pn5y0a9atU/iJRzBkhgs3rMLB2OufMZM7idoxoOYJX8XDmX52O0991KobOy4b+6zYxnlvDSxU6BxLWJuAcs02xocFkH3XDhNFVFhGiawYCQqFPAQc1D9k5Z2LzKH8MKcVYEyypsMZmk+twzcVbFjV4Ve98JDvdxll0UKxD5jvZkMt2TKiYFgvAWXWApvINfVnDy2So1XJrlmMICU0BIC7rTN4jQ5nLqQLLaaXGQsEL+cz8QR8LgViHoy6HV/ZAy1SAM/lbm6nqx6JGhOb9TRz4nJiLvIon25MPqMGBjc/cgB/tFABNM2e+ULOda4nfmrnKgGDwbTsUpDeM1XNTfdQrCsQc0w8mewicOWwlYY15DqcLnJlgaKXM2XXXXYerr75af54HjlyWBwpWwzatJKyRUloIzuz6XUD/wMM+rte93Hrrrbnf5ZUHWE1Y49vf/vbcY1YCzooYrN8kc5YHfPp5bsfCnJlMaS/mbLWCIDwWTovKYbIXewDwKBfOaw7IAICFJoFfoniTwWAEPtCXWiNJdvWJvB8lDa+Ys1IlfRIe8SRniApnT7U4D5zpxbLAkR17/BoQDykA8vrJ2zHfJohjlg/OjFWysbsh8mpkM1zMWZN66AxWhHPT49GZOQkBYwg5SSmBxTHDG79ew4u/vxY3HvJTYEubzN/gjCMYFWNyOJLgTIlLyILGReqRxM8u+mGXI25nwVmKOQMReTnGb7kGZ3LXudNA9fAyTBOOWv4YCowlJdR5ixzcek4uMQdKhLNIfAHK7V3xwbOyzr7eQS8YQ1pIzQBnH/1xBSDCQedxFggBgGdJ6cedGL5DJnWARUYYKkmNtdw2eSLnT+Waqly7aDkGjwGvQsF8mnpmkVUzj3Ngd20Yf3TqE/APW891hluKIt0iVDq3j4z2csWcqfskoj4U6zIMnFK3mDPZL5IxByEgvpjDCCEp4RzTaEm0KQ+cETnEgORd9TnDbJPiJ/tKePm/VjC/LMCZCSYAAWxM5kzNR+K6FjhTYY16fsphheTGzvqnJfO/cspbnSSnavm+Fj53W022N/3OL4fpc6v7MiX+Ve1GwZwVg7OgRnHACIkMoliH7yrr2gqyEHlc5mk5E5t9bfmyVUxwptYyU6mxiDlT84dc+/RGHgE6010dhWGHNfZj3eku9n78Pv1vryqUdXuFEXLOscRV3izXbeUxx1zbPf5c68NbTn4kvjW6Fd+UOcI6rJFbOWe5G43q/x5c9hA4c9ixMGd/9Vd/BcAttLEacDYyMqL/vuiii/TfK2HO8oQaXAqQvWwl4MzzvFT/2A78/v37YduJYs7MvrPteIAzdY6jR4/mAueVgOEiBmvbtm19n+d4M2d51s9z6wX8XCF/3W4XpVIJ9Xpdf2aHtCpT70Q/rPSr/oe41nPObIPUxPGlbgTiEcRNWadIrt12WCPxsozKqWsifHKjCO38h6HT4Xkcu6vD+K+Rzfi3dScjkGqNPcMakSxQakElPgXvxogkCPGtRZFFog4Rj0Uie217Ta9TGZl5aQ873ysOa4RcfGWhYWXnNefAOMHcXFaNTLeHCuCjnX1KtGPhArUhoTh9LER3Luwd1mjsrPrg6EZIFQ6/fS/wbzdVce3hKv7ttgFnWKMWKGGAJ599TdU2U+IShBTKRQMCcNjNjbs8pUSobMfcfHIMIfB9pMEZJRjf1MU5y+I4W+oekKGHBWNo3WjydyIqk1ZcA6BzmlLnJkK1kPhU9E1A8bqHi1C41z6xDb+efaeCIVVrwtkc/R1BAgYCzvB/rxPrwOKtS4iWIkQsewI7rJG1GcoBz4jN/MedP8LrDt2hbqIvJ4z4BD5JnHcecXg1D52jHd1XMU+HgHUtcQfliB8pVRETihbN9g/xCJZuXSpU/DTrnDELnBFKwOKEHYqMTYiqXEqX9yyjdaAt3jGP6hBWL0etVYSXklxHH1TcmigMnTyzhRbB8/59GF+6qYS/+7KHKMqCs6bnWSGESQkBkgPOiCpCnct6iP6pnTyAM94jRNV8OTE32wK0A0DL6Bt7Q2bHmvR4V2IoJnPWbieOfq+wRkJFsff3bRXpEKU4lkDaYM6kom8mrNFQRATn8DxR/wwAyjzLnKn5soiogpdsxqncyEgJrpQpWvvbuhzCapizcDFK3Rste2KQ9EIPDHjkzjh1P6KRHIudnHB7R7/P+2X8n81nYr8ExOq5nblWrNOP2RUXd9CDkzh7CJy5bCXMme24v/Od78Rdd92VEaywj+0XnOUxEisBZyMjI3jzm9/c9/FFtlLmzHSU+6lP1i84s/ulFzhz1YlTTs7xAGfmmMl7NqsJa7StXC6vKFfweKs15lk/zFmvYt55/TYwMJAaRwMDya7lhz/8YQBCpVQ9g37a8rsXEfzyPcv4x6cvAoOij6rtLohP0J2LwFmyo2+GNQ4/eZ1Qx7LA2Vcvm8U3x07Cs866GLcOrAFjwlH8xy1n49/X74IX0N65MBA7oCqdQYGz2vYqQIjeDbVKWUnBDcF8eFUPpdFAr0b1UwcyeWf1l+7EWy9DX+EeVKr3Kbt9VDzDqeli5mx5TxPN+5oyLyvZifYcO6Mjg8DTt7UQDPo9+8je8Y+6POU4LJlkE+cJE2aGNRIiHJqYa3A2IBOxdL5Kr3pQ8jxezcP5nzw3uWSHARYYWrqzgR1zC/rfDASBn/YlvLqHLz5vDgMsPyKiXCkOja3XKM7eJt5zDc7iLFiMHWGNHoEId5PPygsIXvXIFn7+5kX8r6e14dWz65Xou15sMAGL0yFygHD+WcQQLkaIHP6iDS5YyFEOgMlSDd8b2Yz2cCXzG+4RzT4XmQprVGqLLGQ6RFWxnoylnaOljni/Okc74n2wwx4ddB2hSIRwctlFsXlBSAJgqNr1oAJYezXR9+1uco5d6xImAvKW1YYDkJ9rSgN5rSLwAQGG1dgPOEPbGJZHZzjo2lImrHHJC1LiGyLMVG7KSCaUGTXcALHhULRJRAiBXxcbSd6AeFeVEESzS/S73+wkJzDB2RUvX8KampVnJe/9UY1prOsKykxt8ESEgPZ475UpZdRSFAsgHXG9gR85lrKQ0LTSLhNgJCIEMcS7oTaHkrBGyU4XvGfUT5gzVcZEqWkST4TrEojNhufN3N/zvmyLlqLUePKklD4tCh8mgqF91K4kBHFNTT77CGiFaeZSn7vglKouneqbHcNiUD7/UWGxSmuPTcgHqj0Ezhy2EkEQ23EnhODUU0917m6aDmY/O+hAPlhZaf2y7du3r+j4PFuJlL7NnPUDzvoFrXa/rCZEU5lq47GAs3Y7kY46HsxZHst00kknOT/Ps+Nd56zXdYpM5U/mWd6YrtVqqXFksmhDQyJePwxD/d72Gxq7a6NYDJhceGqtLkhAEM6F6M6GCFWdGbnyfWt0K0YfMQJCScb5qfjKIRCf26xSbk2Y1EFiQ1KF7tUbLflbASbUzrm9gKmFSYcFGt8HIwEe8dkL8K/rT9Gfnf70EeEz0t5MnghDAba8YAsAoF0V7930LHM6IIAANixkAgQopzMWzrgrnKpUoUDM4FU9uxZxxgZ21VKOE4t4itlbNvR41JVipHM/QIDy2pIAsxnmTIIHGdZY5MQCgDfgwx/0EWwUY46FLAOGFn65kPp3TAg8HylvgnGSqQ9lW7VK0AsMbV4j5p5QhepGPFNqgDnUGgmRuZIeEcxiiYL6BCePidw4f8DBDFEAPVRTCAGixUgzJdrp9ET4cF5Yox2Oy2Mu3jFC8OEtZ2PP87JhhFAMbc+wRlEyriNfSiV0QAikmAtHKYywq52I/ix1COJmDNZl4hnb4WGEZHJjIDdN9MldbVFgkhCheALAS+WcAeUN4p1rG26JrtOWyPCJC6pcuhzmjAaCGsubj6hPxRjgZlgjR8sAhtFiBAwGGRZm0QBnLGSGjKEjrFEp/5VUzln+GBp5+Igg++Tc4ckB0+6KccE5R1sub/U4xDNmRUROBJpizViHIe7EqdC535u+T/wRJ7mphBQoERqm2NJSHGuxlO5MiPahNrqOWpgRIWCLYRKBRaSKIiF63RiR4dWnttIpH0WhsSovjzOO2FNhjWpnUc4BEcf5y7Op32WEm3IsWkyDs2BM+l49AD54woCXWYx2qOYkhnZEMswrAJ0L6myHkY8rziPXQ1lvMf8de3BSZw+BM4etJKxxNaIaK7HjwZwVnWellscyKbOZM7PPzBDNY7WVMmdFdjwEQUyVxry+OR7M2UrNvLd+CnWv9rr95HnZ4Nx+n/LGdLVaTZ3fZM5Url8Yhhr89p2TJ0N4mJz0h5ttUJ/Cq1DwiKN5SJxPRepMBRVRCJpnZavtu7f9X1d+kssI4cJxB1DtRli+V1BBBMlubKaQKBF5cKp+EdFVVOXxZYo1hly85F90InlhezwAjGvnfICIRkzP8lzmjFMK6hFQX+zS04DCH/ERN2NQx6tEA+FUEw/ohc68qofz/+k8xFU5rkOeEgRRBbKBJCzNDoXzKh78oSAd1hiLmzFzzvqqu1YS6neK6SHdOAN+omULHBFZZ8s4Nyu+bQBAuVoc1ghA15jSRWQZR7QYYfqaGYSL4h6VUp5pnvJv5Pixcz+8qiOscbRUHG4FCKe6lNyryqXhAFhLCF3EPHsCE5xxEWen1VMBAAOOgSSZs94bDoLB6EjnWhcN5xydwx2wELhsz12p3yx2xHMmPpHsVPYabz75UXjJaRfqf3enQ+0YFo0jJTbHJTijxs4Oj5l+XztmbTHNyCnWTLxrXPZlXq6pCgvO23TwB32xIWOGNTKGprG355UIGGim3uKSF+jxxxlPFUZW7eEhR9SI0Ngt5rVgyNf3kWfBkA8SUH0OTzFnoXTQGdCMxHdv238LTmsLYNOhFLVS0sbmfU009zZTodV6boiVuIQSBMlvjzIdjqjWLQK0J9tY2t3QG3tmaGxMZH1DNdxYArIVC/nqQ3cCAF4rQ3Wbe4RSCSmQ0gfE8w/nQh3WyKSrIQC+mKttMBQViFqljluKUuOpsrEspuminxOI9cgAZ81QqAizLkMrIqkcO/2zgntU4OzJC4cQNeNUYXWgoD1E/9+Dyh4CZw47lrDGfs0F9FyWBwxWIggCrExEosjU/X796193fm+Ds4WFhdS/j5f1w5zdeuutePGLX+zMbTMtD3B+//vfB7By5uxrX/ua85jjwZyt1ExHxVXQ+kRd12U2ODPB2NLSEu666y77JwCyzJkLnN1yyy145StfCaD/jQjiCXTGNgtAetqRaa1sx2OGblPuYCNx8su+cKTseix2mptdgFTsevZCQvJchvPwhX9ewrduC8C6TOfmODgM4VAxnoQqWtPLlaNbEYHg9rNlOQ8idlt7NknmsqnFuUpEnzz3HwL88m73HMYo0cISIELxsbyhLEM2sxckShHSy6ofZkyCPh6okDSOaClhZpoGOHPVOBMXlM4g4fAlOKtzFdYod/aVx9xjB50GRIBX6YTQiOPgQjoUtHMk/d7HIPBpmpVTS8vOP9uRe61qpVi2HgB8muTOAFJivCFyRlg7xoHDDJdfm11TqGRtdXkAFSKozjuY/c3I+HBPv4dQAhrQJIQcQoI9ZELMIlqMnMyZyRIs3d5Ad6aLlMZFzYXy+6xz5gEe4ejI9ejD3w3AmGAnwoUQPGTYuZQwF9N+Gbd6Q4Jx8oWD6Rr5C34J00EF35VKmRufuT4pJdBDNIUQDloiYAC8iOlc0dpJNdCyEGC4ZypZP/X5dChncq4io75Ac4VhjdKxVm04rzkHNJO18Qu/quCxHx5yMGcleJL5WL57Gd6Al1Vr7DC0J8VaWVpXQnVbFT1DY6noH+WEUzlgvnRvHcsdjpf8LcdfXS0Eax6xnKQvdKmHqllzukRByzR172puUKI5MZEKr30wZ22DOQMk89qOwbocHekGDRpqPAwCnPGIY/meZZHraLlEj7FUS5VxUiAqAzGmO1MdrZT75i9X8D+/UQWjcg7o8kwXF4GzheckOgXhXJiuh7YmKK5zBgjGjiXrRkUi0td+uQZS89GOs+DsP9fuKOz2yBjc0z+c1ptyKqyzKKzxQagH8hA4c9lKmLN+w/Bs6xXipaxXwV3TXvziF+ee53g7+3v27OnZLs/zdP0r5USbZgs79BvqCWTvx/XMHvvYx+Kzn/0sXvrSlxaeKw+c3XbbbQCK5fGVXXbZZfrvvOvZbVSFpl1GKXWCjDe96U0925JnT3nKU3oe0w9zNjo6CgCpun39mPqdMhPIf+hDHypsUx44c/VRvxsRynHy1lWw4AUIGEPckDknHZbE7eu8JTXJZxdvGzBlmDOj1ldhmzySWrCvn67iJf82AFqi6Mr6WfaONSBzw1QRYQcjtvmMCn7vzCdh58u2aVlmHZZWZFyACwVGzTyTz1zp/okGZz4V1ykR4RQaIZupe1YslUd7UkhUKZPJ+CkWMcRthuZ94h1dbond6noU5jJnSUFXopmzIelEqd8MVNFXWCN8IcKg1PHKLMZ37q1ox7ZztIPmvnTtQ0YIPA/wB3ycNiregac/WrRr9DEj+fc+03tzRyk2KkeGRwJJsFDc86v/LtIA1DSPinFOqGRjLMctGAlwyltOxsmv34ltL9qKR/zbBYlz1oM5E2xNctBl03vBqr6oXxVyZ85Z2oTnZcq0P+EUx667kX9VZMQX7IgKa/z2rzzcPSWEUOJ2jOq2aorx+PMdj8DLn9BNQBbj2D6cH7nyqY2n4QsXXYC1T16X3HaRpyU3bkjM0ZTUcrggNwskKL5nmsLUktBCG+o5qXEq2z107pDzuRDFLvZyrDlPiQk98f7sBqcNziJKcccRT7eHeMn7pZz0cCHUYbaVTconKPaeCSHgnOhz+Aaa//YtAT77PeCq+7PrQMdSj6S+OIfnYs5YknMmypkUD6KRCkNLPisFMmhAES1F4N0YYQS88tCd2NZOkmBjIlRa41aMuBNj8Iw6SsPpjd/bq8M6Dw4Ahi8YEsJKvUB3SQBhJp+rzxn++adlfO+ukmDOuhwE2edlGmccwyXRt2f+jzGc/pdCgGXh5mSjYv0l67TSbR47C0CGeTLNnI14Yn79wo0l3EXrkjlLnuOS5+NfN5zqBGeP2SF+Gxn0ajgXJsyZBmfuTtIbNv3xIQ8YewicOexEMmfT09M4cODAMYet2eDstttuw7/8y7/kHt9Pvlc/9prXvAZAPnNn9hGlFBs2bMDevXtx+PDhzLE/+clPUv9eCbNm348LnDUaQmb3vvvuKzxXHjhT9/KGN7yhZ3ue8Yxn9DzGbuOnP/3pwuPtMfK0pz0Nr3jFK3peJ89coii9rumy+++/H3v27MGOHTtWdH373GZ/zM7O2odrq1arPZkz0/oGZ6oGEwUaMpYwWpb5Ax2mc7yUU/SWJyQhhqbj89LfduxKytfz9PUxbnnVVH85Z7JNZny/CmEsry8DMpTPBmfhQghSEvkpxAMsVWcAwJf+qIGr37yMi06NNDgDJT3D9lgnFqqEciHeMtCbseeSLaE+4Nc81HcNaGbPxZzxWAAH6vWMagSoLLDtq/AdjrgR6byhdgf4+7034D92/wibZJFgZj8dybCYDGgZaXnmWgVSCrxHcwIZniR3lissRisi2rGNliKEM+n3ngEIKFA/bQBX/O40bnhHA485S7KMefWnANRqvVkhX8bgqrBGEjPB9EhGeHKGOBXbKBGS70o90CE+iLELx7DuyWux+XmbdMiaCGssaBSHrq2l7MVH70VEPQTDPnjMEDvUGtMmxqkJzk5d51iLKU+JcOSezSPwjbDGMovRjQUzEbdEbS5TrODzL13G63+rIzC9ZHdHygyveFTLef6mF+Do2LC+FkdvMAQAfDHUNa8ad6XLKLRCkg5nNsIagcQBVbmVIw8fxiM+d0H+NfsISTM3JsqdbM6yS5ZdsfvUS4ffqvlj5sezOPQ14Qt4NSqFYXqAD82cSXbaAGfTi3k/Ah53OhMiLlMdhPMh4nYswiMNRUmddyXfj5jQfjRlMPGGeXz3NeLiZRZrsMK6TJQ9iTieOZsGtIwIxlzkBhPAJzrM++ObzgAAHC1V8Zm7r9W/2fGK7TrcuMholYrxK8F6SfouS6FQ/AwjDjudSzFncTtG1IyxdPsSfnLZIVzze4ewbZRj8Kw6aJmifaCNmavF+jx0rhifnOWHzgJivLMu12vZ+etDrVR5eJEgZgQ1JGuJArqu1+TzL2lg19o4xZxFS5Ge03Rd0KJXzCOZepgPdHsInDnsWARBetnY2Bi2bNmyqnaZZoOzs846q1BC/HiBs8FBET6QxyaZ7KJqz44dOzA8PJw51lbvWwlzZt9PUchg3jNSbc0TBFHjQN1zkfXTdrONY2NjPVlXG8ycffbZK+qj1Vg/DOvg4CB27ty54nPbQMp8z0yRD9uKmDNXe/sOayQAB4dPORpSIjFuSDn9VqzWa+2EDGjMlw4x2Thm70nqtR4XnhxhTYVlwsTyjAYEXsXDL+pifA/E6bAY0Z7Etr/iJKE+JxkcootQp1tU9hOHVn/TT3FOInPHylnHKM8i35PlBig4IfCHfFGDibnDGnko5RU80ps5q1KRh6OZM464zdA51AbnHMttjrNaCwCARzTEZoSd+F7ZJOpsmQW/FSOogK8uZ+AXL5FKYMFMfDfBGTHq5imLCYUfAKWxEqol4JT1LFMPzLZvrtmG+kCagXJZIMFmpMGZZA+7DGDAQJnDdzjVvBsjnAslYCdaFKKX2RsVmfNymZdnlXToRvKHMQfvNaYkw2uX7try+5tT/65PL6dC/HLb7KfDGissRszFDrwuQG+Mw1M2SPDAJJPKODgh2DaS326t6k9QKDuuAR8X7OX9Ui7cDoUN43T+EiEijI0EigmT4ZHGb3xLYfOMvxJRLGb9MbcRXSBZX9+RF+hS21PzjghrTj43w8AXfilAjVfz0J3qiqLtRY41kWVI5O3wkGPnGvGcFnPqZQHAYF2Ape5MKEKzy57IhTWupcIa1XsSEgLCe7/3QxXglDGOjsxRizuCJRLgjGkpfdNiiE0PVYuOUKKl4+d9sbiULB/TG/DkvF48qIfPHYI36KPtpWumBRRAzOENlzJdHMvxv7ynida+FkAJBmsEJ41Kv6jiYfjhab9N5ev1UvyEByBm+rl7EcOzzxUA/+CCuO4wTeZFIse2PSw7Ux1gzxIesS1Ogcuj/zWl8xYVO1u0KeNVKeLllaUC/aatL3A2Pj7+9+Pj49eMj49/dnx8POPpjo+Pv318fHzi+DfvN2N5jv5vQhAkz1YqCNJvGGUvU4DBFMAwzWbOisx2rFfCnNkhckViG72eUR5zthJp9n7MbGM/IMvun37zFI/FjpcQicuKatMVAeB+BEGKrpNrVDwHSgmWqcGc+QSdqS6MPG/xX7kYCcYlaY/nJaGPykKZc6aYDOL1GdboU/jguKEu3ldTWt0Gi+suXouRR4yI2lTSaRRS2T08VLUQWrvbzvYQATBUsVjSx+5jNxCqi7TiCRlnxQhxjoGTs+OLx2JxJn0AgtJoCYiT/BMec9S2V0UB4YijZewZKVCm/nvaX5yC9Zeux/rfXqdzAFX4kgIsZkFjncNXYDQQQhE68Z3HaIWJA8pClpL6V+2hlIAEVDCd6hrKUXdc58tj21Gt9mY6E0GQxOkkFIibIo9psMSdTjVfihAM+7qIuQIMPOZY2t0ovmhRF+nnaoGzWMQZcQYMWXP35euTfJelO5aEI0vSzBkAbH1+epOzc9qIzN/qFQNG4IHr4r9lHoMxwB/yMbBLjM8Uu2iMASKZWwJkatylbhtJDl9RzhktJXlyPuVoy3mIWeUOulG6m0fGh8EjjmAwSEBgwW0PnDqA4fOHdNuKxrVQ7kQqtC9y4FAXA6vwmA6v5uo+s53lVT2wkPcllqRKPCj1wVogTrzQLbgPBQyqYnPAq1AQn4AZQFNv3EgwrpiznoqfYg8MZfku3f2+ewQ70xZqnl2HODKTgiCdqa587wG1P9slapMo7dd5NU+yz706SHShyoOrynWDeqK/IpZlOiMJ5KgE+FQVcTcuZau0EuM5FgNGAk6SiIv2oQ7GZEmDSQnO1nFTpErer9XvcYsBhIDxdB04Pa4qFNUtlZ4CJYNnDaGy5cTl058I6+kNj4+Pnw9gy8TExIUA7gTwPOv7QQDnun77YLUTyZwdL1spODtezJly3vOYM7OPXKGMptmO9UrAmc0Svu9978s9Ns9Zt+ucvf/978eVVyaJNGocHA8xlYMHD+Jzn/uc/nc/QOtEAqU8W0lu4krBog1yX/KSl2gRkKLr9iMIUnSdfJM71oRjWTFnyxFIIHbQVbiV2rH2ah6a+1roznZTzhalSEu1A/jnn4oxE6hSUH3K+ZKAwCdch1nWZf0txoDv7RZt1IusPCX1kYhp+NJrKEI5XC7EfThFnIjFW4Eh4vLSLIt8T4Q0Dno6ZJH4FGBAdVsV7znpYfjbrefp4/0hH5wQDJ09iNrO4jHvlQUbp8FZyOFL5T4ecywcTbwiVchZ7Y7XTx3Auiev1U4ekDigSuJdhTUqoOgK70uZZCwUOKswhlaHgMlCwtwQd1DGAARggpnwqGYwFbBgjkT9Rb8EPygWcgDcYY0gBKwlQq8GStzpVBMiygooWe7h84XYB+syHT7ktF5giAPEy74f3VgoC8Yxx8ZueqPvP9adrP/WTilBYamBab8MfsFa0IqHqFG8Q0496STrsEaGiIn78JScvQFgFSAWDAa0I+8VBD6ILuNycOSLOXgVT4Mm0aYk1My0y28oa1Z3+OHD2HDpBhCfCCaZG+fPeRTmM1S5hbkm2b5Umx3TiesUKnSNWGqkLhPAQxaq75koSECQjP9hKeDz6V8mawG1Nh1IWQKbklDfpRKcmQXFFThT81oEAsq5roWW3/j0Jsrir8RmTNyMMXEwwFuvHsn8ZLJUg+8ReHVPi+6ouo9duVEw3kinHlBfKl73ao988C05KKuSOfNlIfOYkXQhaCRhjbREZZ6pOpWxtlklY0yQXVgDUv5PHR83Y2xpidIUH/2xWLPXRYmImlrT7GFA5PrDuXhPbatsqRibP/ntqWwsa2XQB4v14w0/DsB35d/fAfB46/vXA/jY8WzUr9t27RI7dc985jMBAMvLy87jjqcgyLHaStUaa7UaNmzYkPrM/nc/ppzofsIae9nxVG+89dZbc9vUC0CbC8Oll16q/16xNHuBvexlL1vxb06kcmKeucBOnl188cUAgIsuuqiv4+1+/MEPfoAnPelJAIrHs12E2gyBPKacMxn+51FgWeWcNWJQnyJqRIilE6aWYUIJokakQzq2D4o2X/Joccwzzko2dfbNifYutYl2UPsxGhD4nGFeFjhVEvg/vCeZZ9SpoqVIOHE+lc4EBw0UECSF7yIpUR0SVWS1k6opGes+1BsQBr7IOQsU4hB93ZnugHOOnw+uw0+Gk7mnvEGE3AQjQWEeA2CAJpl/Ehvy4qzLsDSbHUeMEPzOGR3AJ2Bdli4OLJ2eQDNniXPJGU/tFDvbQ2W/W5LRrCtyS3gELHTSnRwTKtQ9qXT8tUCJvBeH0zM2DOntFDZHCyCYYY3BsBDfYBHDQMA1ADXNI4LJUwqVIhxJAutejmERNpOOpXlIl1DBBBEgjIDtbTczx7oMtOppVdGLdor3a/Nw4qTVzhGM+8c2n4lqWQD9npsgFCkp/QqLM4qRZh+ZzKZmVJEtqP7iRyZMQCzKpelwwyIjvugf6iXMR3s5/Yx+cAPDJXOTAIA1jx3VuW/ifckPm1Rm1t4rqpklGgQRwmncn6s4uouBDSjEHOkBwVCy7sYthwiNDEfrZ5NIyfsrFvopv0wr+xLO8U93X5c+f0moXKpnRksUXs3Hmmq6LwDgMXfeD0CqiQJ9MGcKEKStvLGC3/3Wehxppif8v9j+cLHB4smyJDLvdfOYOAcvYsZi9ASL6h1JmDMpUkI5EAvRncACN98//1TxW6WsqwR1jHu36+WZJTUK1zTZXHM+37ovDTw3mswZB0arUik3ZIiaqm6MmJeedU6ImwbSaTCirUm/PBgVGYusH2QxCuCQ/HsBgO6h8fHxYQDnTkxM/M34+Ljzx+Pj4y8H8HJAiEk89alPPaYGnwj79re/jf3792NiYgLf/OY3sX//fkxOTmaOm56ezny2sLDgPPZEmbrWkSNHnJ8X2RVXXIEPfehD+Pd//3cAwGc/+9m+2x6GISYnJ4WCGSHodrvYt29fhsGyGb2V9E0cxys6/pZbbsHLX/5yXH/99QCEUIUqStyvTU5O4uDBg5nPgASAzs/P92yXXeR5//79KfBpy9hzznue0+7bpaWlVY21n/3sZ3j0ox+NWq3W8/f93Kuyl73sZTj77LNx7rnn9vWbpaWlzGcHDx7E5OQkpqaEhPBjHvMY/PSnP00dUyqVUuNdCb0AwNzcXOac/b6TPOSITo9AhjxdULTlt7C8cRlsOEb3LrFYKEGQzlAH1UeXwQEsDizgir+cw+FWGdvXMHRPD/HB047gW6/elm4LjdA9vYPQF2pvRe0KwxDxWAx6NsVUIADoulDsLs5UOgDEZ1G1iwAAX88RnROC+BHmShH4oxjmg3ksTi4iPC1E6HXdK9aYDNfjHIenD/d0jMLTQkQS9MR5xc0M69QI2ie30aEdxAMMR2aPiBIFj+VYLjXwF79H8N4vjuCjm87EG7x7MfbGNeiu6eDgoYM9zw0OhKeHoKyLGMAf3fQrzManIDjdw5G5I+iWs8xbrcbxwdccRScAsJVjvjQPskwQnR6he1A4CL4UBFHgjFUZuqd1MOfNYmFyPr85kRhD0c9Ev5RZjPlBjta2Jg4eOoi4FKNbA8ztghgE/lkdHDx4EOGpIaJSiMbhBsLTQ4R+hFIViK3I+q+98zBao6znGKKeuP8keT5Gc3MTZD0w3Z5GVBnJMGc31Mdw+ukdtEtyvIyJcRGeHiL0QvB1DI2KG0DxUYbDc4dBFtyDiMcc8UYGvpZh45vW4/D/Por7ywPwhlrobumg3SV4/OLRzO8a6xqCcdyQqAae5jfwvV0dbFzD0JAO9sb/vRnPeOUo9lQH4Z9yGK0tomxAUR8xzsDOiLUyYi2O0Bhuo7Eu6XQTnC2vX4Y/7IOPMIRBF3ytAK1zk2UAor+veOcRnLE1xOU3CAXbjhejvbOtCzEXzkdyTHf2BzrUssU7aKxL+vyPjiTiEp1h8R0fZYhLIfgjGUK/K0JZOUfoO2Lq6rJPQwaMAgcP579rMWLEp4s8sMHfqmPp6gbWhu3McU4G1geam5ZBthEsDzUQBiHCIAQ/LwvOog0RorNDgAJHFo6ANPInomhnlAJMj54/ChhRrQNxhE1hmoGNxkK0tzXBmQifZdVY1N5qJs/ZBpjnLM8hOqWC+aCLxcl8tZF4XQy+Jn3/jXUNYJ37+MMlsdFKNsUIT++CRRxzdBannUzwtf9JMTrXROP12d811jXEGKJAZzI/r54zjui0CJEEU8+f2oPPr9uJaLQNei5BZ3+UUtr9x01normmgsa6KfARnuBMyZg2AuHHRSPJfD/2gjGw8RgNT4y9o0tHQZruZxaxCDg9grm4xOsJYOh+XbhxAeE+8TcFx5XvOYzGqMjZQ8xBqx64zOt8oj+Hf9sVgE3sAHn/fanrqHchqoRYKnhmD0Qr0p/oB5zNA1Ae7zBS3Ys3APho0Y8nJib+CcA/yX+e+KSZVdppp52G+fl5AKJmlavTXEWUN23adFwEPvo1dS0756qfNmzZsgWXXnqpBmc7d+7su+2Tk5P62Gq1imaziTVr1hQKOfTbLmVxHK/o+C1btuCss87S4Gzt2rUZeX4gHQZnGuccW7ZsyYSrqjaoz7dt29azXfbiOzY2VhiWSCnteU77GQ8MDKxqrCmGtN1uY/PmzYUhJFu2bFnRNVai2Lhp06bcayqW8Pzzz8+As61bt6Zk+08+OQl7cgmTeJ7X1z10jnYwc+MsSpNMO0X+jI/6VB2dIx1wWVdIhVxUliugvxChMiOPHgG/ZR7bnziIwS11HPrFYdSHAxDCdSFYAGjOl1C6q4T6GYOobq2kdpNtm5ycRH1pEP5tDUwFghHc3G1hV2sRI80kVLPclIVbpwjohIfyxjJGHz2KqZ9NYezxa1DdUsXhm47AG/BAfYrG3Q0M7BrQu4zRUoRgTQDWYRjdMZIpqG3boYnDqEimLZqNUV8XouEb92E5aYwHqM9U4Q94WLqjgY3P3ojW/hbm71hAZWMFp0om+ztrtuIzf10XLMbuLrY81z0+UueOGA797DDi3YkjduQvj2Dn63Zi7HFrwI9kox6qJYrB+8qobKlg+e5lDF8whPKaMqZ/MYOqrN+knHEF0nzmo7S7jLHHrxE12nIsXAgx9YtpVCPxvP5wei8+dHgz/FsDbPidDVg60kA4czAFzhgBhu4vYcv4Jhy+6QgqG8sYPnkYB392GJUNZVAQmFtcrVKArVMB/LYPMI51Z+Z4gADKwYJol9z+3rp/HqXd26XIB8DmfPhIO3l/u+18/MndUyivL8OreAjnQqy9aAyHfnYEwbCP1v4WBk4ZyMwbLGLoTofYeOn6JCnfYd25LqZ/OIN6MAjgKCoshjddgzcdg3UJtnWzz6w+VUdnqgNvwEfnoBArKK0JcF7Vg9fwAIlbagzYUxXs2eBkgOFtdXSnO4V91DrQwuzdc1j2xLMeYBHKs1XUp8S45DytiFqfq8Pv+ugcbaOyuYr2wTY4BzqLSWTDY2plYDYZJ7zto7KngrgVw6t72Hh2fpQK5xyHfn4Yjft8zeZh3kN9KllbR6Nkc6qyXBH9c7SN2s4BzP9iAaW1JVEnrc2Esqu0M/76dOy//ABO/rOdqB6tYOnOBsprS9j8nPx3bXJyEt5tPliHYdNFm7B09d2Z0FPAXdIDMeDd5MMf9LHh0vU4/POjqGwoo76mjvDFEfZffkAfWotr8G8L4Nc9jD1xbWHY2dHbjhaKdFR4Ns2jGlVRuV/Mc+FihOELhsA6DGQ6Ab0+51qMAgAm6mvx7HtKWPPIIQxscfsNADD70zkhoGOY+bxsU6HVlXkf1QM1dGe6GH30CLyyh/HOHLhfxc2O39Wn6ogaEYIRH2tOzzJH+vytGEduOIoKS9q0s93AwFIF9WkfZDlIhTUSAH7ooXZwANFihGgpBPEpWCTEjVSZg7mFef2bUy47WXv/nakO1j9lnbP+IQAc2HcAdLeH8voy1j99HY5eOYUtX74f607dhikJVHFPOufslKgGTIli4VErRv3MOjpH2uCEoLyuhEdVCBobqrjNuA5pEdSn6ujOdFE/fQCDu3qLtz1YrJ+4susAqAJJTwNg6p+fAuCd4+Pj3wFw6vj4+F8c5/b9Wk3lZeVJjj+YBUGUmWGYqw3JLBIFORbRCldOXy8zw+HycgV7PSOzULZpxxLWaPeN/bxWIwiyWvN9H0EQgDFWKJyijj1RVpQLVtTXhJDcnDPX8SazVmgSrHiUaKdIJeITn+hQJ/WkVA0o0CSnghjhcZxz2EroSx0RfR8M+4XATDepJM7TMZKdzl2eSyXkK5KWUNmGgMCrUng1T4dcEY8AMbB877LIGzLU57hMhRk6ezCTU+A0GfdfPUmMx1Pa6d1J20nrqnArFa5ERMiiVxHt6xqvgq9y8vqMcHYJqyzd0RBpMhEDbzvCp8pCtMOryKx5muS4KIfvYdNT+PP9v0qFNQLIVU9URktC6CQ2rjsQhqLPYw4eMsSW0t2CX0I5MMaNZFfUfZlT6Jt2Pgqfu/RROn2pl0CJ8l/NmkCHvnYYoATxcowwToc3NaiPLvVACEH9lAGU1pZEuBKR4bxSpASOqbmxWzIx/YSkMa4B3LZuE902Bw85OvMRRqOcOUnl/ClVUSVpaZgZGe8TBn/Q15Lr+Q0i6M6GWgSoFkcw0wLtEMckrFEol3Znu+ARw2In/zr6HB7pKeZAiFA2XOxSHZYWWTlnZsHe5NGSRFVUhrXZNnzeEM75wFmonVQVohilPuIsAZTGAsQdhtKaEmJCsDbqoGQVDbZzmAA5twQi1FLUdkw6ds1j05uNXk3W/fB6C93AKw7TrsZZRp+WaRJSzkQeGaUEwXCA6nYxl/mcpxilL6/dLoZaD7VGJZjTr6l6aj5N1hGVh0xQXDOMM2TqDmYbBIBzDLUThvPU1qIWv40YST0/NWezkOs6hHpuNS7Fujk+mQ7FzjfVPebGzbNmBVU27EUIZxMgaSqRmnmsovg29PxDLclWJhfGHilnD0rruSROTEzcBODI+Pj4NQDOBvDl8fHx/yu/e9HExMQlExMTlwC4e2Ji4r0ntLUn2JSi4TXXXON0Yh/MgiDKTOe7SHq/yGxRkPe///1a7GI1AOtYzOyHK664Qv9t5p/1EgRRjKltagysRq3RvD5jTIftrcSOpyCI/cwuv/xyfPCDH8wcdyLHcxHILRJfKQJnrrxFV/ikywgRdbw8yrVTtNxIxq9Wa1QLByXSsU9Agoq7V7lQgfVKLenNwf5WDhpQvcv6+XWCFayxCLcfFieuRyHK37gvaY8U9wAXiyCTIEEXAa3QlGqaMBHWWBor9bVJoPJmSmvE2Bg2nGnCOT645+ep4yOfioKvKqeNEMnKCNW0ph2dw1GcA2O2hWRFMZZGa/jkL6pgIRey3JbRMhVhMqYDJE9h5tU8YfGoDt9SgKKo7pg6N4eQo1Z28N4uaFUwD602sGcqfY7/t+HU5LqqbhsBXIElu2vDiEq+dHr7EASRioYmOAOXmw0dhk5IdD6KbbpQuhJMUWk11O0YE4/q44pMKRaWRpO1J75jAfFwCR+6Pp+dED+W/1UAjQHhYpjabFBGKVDdVsHY4/IZBkCMZUIJmjLPdIBFqc0PS79Fj01OIHI1qSh3Md8qCMP7/9p773BJjuru/1tV3ZNv3pyzVqtVbmUJCQkUkYVksDFJBJPBBAthY+MXDLZ5wQEHXsB+MQ5gePEPm2SCLCFhDBjRRkIYJKGVUNbmdMPcCd31+6OqOsz0zPTM3L1z9+75PA9o70xPd3V1dXedOud8jx8ugrDGh0ISnGOyFjHOfnw4JiSTj+Z8RfNsLP0cMqIlbQ4h6+oeSKUam+HKoLEYKjl13w954WT6+fsfw61P/k/zMaDaIqEWjqIVIBsFJITOJ4wK9LTCiHqs/OUVAIC9djzXuJAwpkU2FNOQOjTQ5FUtvULN9baXD2OFrod4RNiocaFqqnXqI95suESvF2+MJjBlUJgW9whqc6kbqG2urZQdc5YZV+qIP1u/IvhsdXUGdR/YP8Xwz/dmYkaoaZ+s+RB5ASn185HpGpAmvLvWaj7X+ZqZXO3oufna5BhjtYZto3+EZRhYw7eiYTExMx6ZL6R8hxwvpFqvdF33na7rXuK67ktc1626rttUCdd13eSks+OItWvDfJHvfOc7Td8nGR7zIQiSJHrQrSCIITrJ7dU4i4qC7NmzB7feeite9rKXAejec/b85z+/pzYYosbZ61//+uDfd9xxR/DvTqGXN954Y+Lnc+U5u+2229ps2Zr169f39LskGr2dN998M2655Zam7Y6lcXbSSSe1/M70dVJY6mWXXRYzwqL3XFJ7r7rqqnQN4mpVMjtiB56qHz5kXpphIemCnhh5s14gEc0sBpYR4HaY1C59VTMtyq+cUesqlpvbHCuH1XPG5MTk/To+eLu65y6cjObnRAQkJJBfl4c9ocaqX/Ux84sZPeFueGl1u8qoFedE0ShIhs+eZbVZbJ2NG8MVJgCLgQkemziCM0w/PIPtntp+NB95nnaTzc0YVt4UTkJ+WB3C+78zjH0HfaCSEN6kizebiakRTAHQZOxMaAEWe8RCZtxuGbYTNIUziCzHyJlhruvO6UN45KiFyr4qPvrtTMzD8GerduCwlQ2OywWCVeHmUuYKoVfFrZLd0YhtlNIHENSckhUflYoMxnMcGXiEmTBFulmo2pcwiHkm4tFqQyDikrfwi2XKcPIOVvBfB/I477sPtv0tz4pgJd2UGSg/UU5UZFxWlInGe/NOVX9YOj9n+8zhmLesVms42fCRoJRLuTIsrjlVteG89c1tqdTDvuNtVO0MjANXbKoEHhYAeOIzYQhg1KCOdnegimjGM9Q7OMl4lXUJluWpvNTcCOJwoK69FdH7/gbtAWk+Eb2IAG3sR76yRuL3UiAIwjqrNYqcgPSA1S9Qte3G/WosHDGfMKYzeRYs/ATeJ46gthwA7Jw5jP/zsAqjN4ax0BEJ7agdqqGyJ56HN7UrDM+1GryKRhXS0oZoEHGhT7tdWLAqa9DZc8akxKU7JT64Romnr6zOwPcZvvQT9Y4crteim+PG02qqHMOwDSY4eI6DZ/W56+Znl7RemO4oKmO8X433E+I1ztTm8W3Ce1gZymZeGY30KG4rYvNbN+rN5DGvATvfzJ1c3iJgZGQE27ZtA5C8+p64ejgPA+Kee+4BEM/ZWQies3K5HGtHtVqNGbB/8AedHamf/exne2qDoZWRevRoGHrVyYB+yUtegr/+a5UWaZQ7gbnznKX15DTy7ne/O+YN7CdktJPKpuFYGmfLli3Dz372s8TvTF8PDw/joYceCj7fuXMnzjrrLDDG8Mgjj+CRRx4BYwyPPvoodu3aFbu2a9aswd133x1T3GwH00aWlQlfzOVpNX7tERvIC4zWK7hwUnk9pa/VqhhgFS3YIxZEQT1C7TEb/qyP6Dt0/ZiH115Y0S+OlJ3EGcZKDN9/52REsCC8x9YtDceAN+0BUhWbFSWBoW0lZJfohQSzSsxNWE/DuXfz2OJqgm/pCcTvXjyJj166H3/17IP4yAXNHuFZJpQRZCHmWTGSx9tGa7jzzZO4+zfVfWEmgWmxhixMXDaBf9bGvjF+Dh0FVh840rR9Vl+jqCocYypPo3H1e3lVTbgyE5nOEyKNKFoYOXUYy69XeUVnTB9EhXHUj9bw8z08Fk5k9hhMPswk2ExkpGwyhBiDus4jAhMXt/cKzWhlyFqkQ40am1/14ecsFBJCwKRUBmtuVU5Ju5uuMiGnSQvogoXetjaYkF/GgQPjaqHMP1zDZIVhUwulRoOZjDFuPMSmIG940J+86wi+//ajGMqkez6qscnw3mer8be0XgH2hhPt+nTDxNF4zqSeJFscPM/xa8+z8OUXHsDnX9l8DkdmjUHKYY+lW8C97qQaHs2GC4nPfCEsRdPWc2Ym/NrjUdlbxeTPEuYvOrQvXUkPXTicMXi5eFkPQC3KJCHBkBm1I/d9eE1EVuDMT50enkaGhyGNnexp7XUXBQF7zIbl+XjWRNjvL97Z/F7L5nVYI2OqNqLQ/w7GUBwjClU7WO3YICXxHn8+PPCecKGhMeTTeM4srjxUQdQ6B5AQ1lhlHCf/4Xb9Y9lWth4IPWfXrC2jsES9w5fXyvB8YO+U9lbVw5CFN1wwi187q6r6tCSCUGZu8ZjnbPn1y7DihuU45UMnxw/YIayRQeU/1me8WFkIU6tslMWfQaNrwzlWNHw7WLDSw0iYumsM2PnhHcitijguFpdtRsZZI2effTaA5ElskudsPoyz8XH1Qo4aIoPMOYtO9KNtOnjwYMyAWLFiRdNvG8nlci0FO9LQqh+i16+Tl5ExFki6B+583w8UGHsxWKLHb1dguR25XA7XXXdd8Hc/xlmn+nSGY+0JPvnkkxM/jxb83rJlS/D5BRdcEPx748aNgQDI+vXrY4Y0oPr5nHPOSX9P6hVM2w7DicxEmgkGKTjOjtSd4RZTK5xc1dmyhi2IguqvzIRKPLci6x3nb/AgTIpHyiZxi4FlGLavBcoiXlAUALYMhxMkWZfwaz5yy7JN58wYU7XMdI2l2NjpIoxQtUm9sM3qbqZax7XrZ3HtmmmMfmFX0/YVpopic4s35Hex4O9TV3kYL4Rt6qY9hQ0FcM5Q15NGU/9mzzMeLnu0eUXfyI2bemgmb5DneFMNLxPiZI/bnT0wmtGzR8EEUDpZPceGvRrq4PCrEjOzjRM1ndNmcoUiBZqDS9jouNGbsBSlRya1elo0rNFci+Gdw6h6ySFgxsPJBFO5kWZirXN2ZILrjDGV69gpL88YEIwx1ApadONoDeXZDs8zabxzegKvPXCmKLVh5YjE1qV+xwljAFdelI2nhzm99v+E9/nME7NN2wNmQq48YcVNRWTHMzh7TR0Fq/k8TMhjYX0ewztSKAhLCWGrenYHrEzDVxL5yHsuuLQMYQF6/YxhjEFW/eS6WPpadszJg1pYkR4AweDr+2xr+Wj7H6nTUN5m05aGCxINQxN5XfA5RfH56L2aW60m5M8eDo2zMd48pgsFFtSmU//VXkOJRGPHFAC37PCebAlDU5mNaOHwJuPMrMUIYPSs0fD6JLwcdts53LjjCgyfouYNacKrTe4a48DJa9SxR+pVeBI4pJ8Jo14Yjr5xkwjuIasg9D3KwwUsYwxlBda/ah1KJ8Wjj2Qk+qBVe3hewC97Qag9oNRsAWCI6TpswxaWXDaBk9+9OfbbwLMY/J+C2xxn/f0ZOPszZzYfkoyzxU07D0M/k+N+MBPmuTbO5iLnLNqm/fv3x/oo7f57bQfQuh+iYYVpQkBNG8z+ooZZLwZ49PhJeVHz7YI347pcLrcdx4PKoWyVc9ZN3bVuMSFQVkYZFACQiYwnT4YvE7W9DmvSidxLLpkIYuC5LSAbcs5MQWCgw4ssgigIWEULPMsDz9kZ06FAblQYwK/L1t4dbjxF8TCV8Pv0449lVN0rUVTHqk95yK3MIrcsC3+6+f6bgdB5VLqeUsRzllj3ySTtp0TkVHiS0EaBmQjtP9AilyqjluZFySwPa8MiJ5oMix1l5XmzSlZqb57IK2+KVQw9DB4YvLKH2RqL5XoYfQRj+I2dPYrRM0f1hwAkMHyampT9PKcm9crAT+ddnCxrT1NktvvUZ58O7vlqnbUIa0RsEmQm1ibnLMlzZsL7Oj3LjIeacaA6rFbIM09PoTrV+h3mzSgpd+OpBlOeW7/qB6v8ie1PM6wZA7cAlhW44wxV60k8E4l0cA/FNpeexOzTymDjWrU0CEvN8KYi4wAwVYn0XRoYCyb7xkgwx/ZmfFiR6yl9JYQgayonLBCv4XqOwpBcF0t7/tKEWXJb5ZwxBsi8as9r9vwcy6vlJmGQhkNAREKBGUNTiOVJ79mKTW/dCHvEBrN58+JRAlZOwNxGtg6PLEbC9HKTjYmsQL6ojb4glBChoZ1onOmwxpwI6qm1pEO4amNNMU/fvBbXeVMR26zxSJOi4R3so6NwE+OhISxKqn9G6jV4nsTBGVVYe5mOCvjPiRWYuCj0wHNbL6YJgNkqVzgpLDZ2PKDj88gYfStuCBfpzft0GOoZVNxSxOa3b0JuRfw9H903Y4g9f+xRO3jWBkgsOmtmkZ1O/xjD45ZbbsGdd94ZfP4Xf/EXeNvb3jaQNkWNs5mZGdx66624++67O/yq/b6AuQlrjNb3uu2222LexbRFpvvx1qTxnDXWIGvXBmPIRT05vfCCF7wAH//4xwG0VpGcT8w1+973vtfWWB2UcWbKOzT297E0zkyOjciwYEKUrUeMHz9unCGSJ9DkqdICIXYk58wsdhphgDSIkpLGFxHjbMSrYVVlGmdN7Uc+cu1kzQe3kDg5ZGAqZEivQkYnP7LLF9noGSMorC/AGlJjo3qgCnvERmYikzgZLmtLy+QvhcaZ7reGY8tuOgh6si9MoeTQ2/kf9yc/z3hGGwcsbIuUEiLHW3rHRJannlgzrryUIs/hM4aC7+HGT5Zwz6Mcdz6WRSbyTDR7LG5U96MoiDDfRKq+XXnTCjx6/jp8YJ0KAeNm+ThFc6a0xyaq9gkA9aNq3FS9ZGU7IGFMMwR5U0lzZ8aBoZPTRQUYI31y5RDKXCC/Zxpfv6P1c7myrwKe5UH+EjiQW5Nr7RWCCoFKK3BjQjInS2rRih9UE9cfPyXww/vj2089MAWv6sOvS/A8h8iGoho8y2MT2ZFcZBbZTR4MCw32aP0wv+ajejR+vWafqWD64RmAycBQDLzT2lOeNK6l9pinUWhlljI6GWdAIXw/by0fjQmDNB0DxusR9QzHtxl1RrH08iVBqCvjrKNnSN2/akemELJZqOLSR/HhQ02/yWci49YsEumxnGS8GuOMZ3msaHcSpS3N0T759Xnc+qVQBTKKGRWBDoh5tkRu65U3rYAH4GMrm6NL0ixemVu+mGOY5hYsSLz/izaePsJxzuQ+jHlV7LZz+Nj6HbHxwWwW9AcXWpykja6bGu8dvNQMyC7NABIorMtjyzuVZ8xcs5I2zkQu4brzBvVOluy5jzcKqSILjicW19nMAcbDcOTIEVx++eXBv9/61rcmSnSvWrXqmLcpajh88IMfxIc//GH8+Z//eV/7AuZGEKSdcZZ2/0bI4+Uvf3nXbbnpppsSP+8mrBFo9pwZz1evxsHhw4fxhje8AeVyOTD0+qUfz+2sltj91re+1dZYnQ/j7MILL4z9ffBg6BlqvJ/S9P8LX/hCAMCrXvWqrtrBtDDAyJYiDtjKKByJXKuKx4KQufBHkf9FP9arqFOV8AvjResmrJExhtEzRsAsjiey4QTg9x6/F+9/7B6s+UFYjNavScDmyaucOkeHCTQLOnSpO2yP2GAWQ3aF6qPykxGvcMLL9ez1HvJrcshM2BjaUQonqNowawphNKvbadH7sPSEznimvnBP8tjlGR43EjlXE/9MawNMCSekbJPO62OcYUY/X4e8Gq75jFqdbgxx2jxWhz2W0FYGVA+oJP1nLliHA1qRTnA1NUkz0b/iLDV+D9g5HI6swAclImbqeM6RZ1qeR6w5QgspcAYkjTHB29aAi+3aVl7cbJ7jv0uqZM0pTzcXnw52rY1WxpWrlTEdJssSxjO6fDYyFarLBTAzpNpvaePsio8OYd9e1Vf3D49i7ctXA5zBHrLgV5QgkMiLQMKbZzmiJbZec6FaiHvhGeq/qXMp9XldsK4W1FUElLBPpaGelj9bV96UjADPChWmGIn/8mY8JRLUiK+MucL6zirALFLCgI2E46jCOU4ZSs43i52LMV6tNl4YvSYzcfF4e7VCaA8lGOozXmCcjQv1Xv+TR36I7BPNOXb5jAwWF4zoSJBzlmAMBsZZhgXeuVZkl2eb2jzFLPztD9R4aqy7FpXSBxB2UOQyrX3ZGrxo+2V4sDASPxhLeGYmwTmkBM5bXsERXYdy676DuPtxCxfrQu//Nr4WLz23QSlRh5vDUv9jrd4pmsmfTUJ6KZ5Hke6xSqpvz5vaj7xXx/PuUSsg5lrG2sMR9E/Qis622aKDjLMGkuTLGwUdtm/fjgceeAD33XdfUBvtWGIMh3q9jgceeGBO9tX4726IhjVGJ/qTk5M9hTW+973vxZ133olPfOITXbfl5ptvTvy8MazRPEieeCKc3Ca11Rhnptadyffrlenp6UTPWS9hjf0YZ29605sAxHPpkpgP9dHPf/7zsb+feSacLDYabo2FuJP4u7/7O9x55514+9vf3l1DGADBYGcYDlrqpTpcreLhP/8FABWrv6Q+G9ueWTwxgd2sbL72otC4E1xqgYeUuTDR/XGVf/LTwigAVRuqEVlTCf4J9VfD3AFtEDWmPXU9/BiQXWKD2Qy1AzXUZzxIX8byCQy/dfUs7GEb3OYobQlzFYzsd1NfSKCrRU+jHqk9AFtmJzFWq6DYIlwvZpwFk0aO4qYC6pPJv1Ges7TNYXpcMEzrxY2RSH5HNIzwLReX8eVfPZjo2TB5fdxisf4IHH4pJmg3XVTGt/+C4ZO/dBj/MRKGE3m6xMAVDzyc+Dv1feOgVkaZydOJYsLnUveRNiByQuIhHa4ZzWE6ImwsvWIJ/F/ZiLt/7RnwDNfGGQKBAGYz+D4CgZGmY6Qd077Kh4FgqOYz8ACIcj2QDTfX6xtrNmL8gnHl2cmLIF+yuLkY5E5xm8e092959iy++OtT+NMbZ7oKtTIh1n910xT+clXoOfErPnb/S9yYVsqDAMswiALX9ftYkEvqVXzwFmMltyqL/NrO9TNNexgD2FhogO8cq+IPRx5t+TvjnQuGEmeoHW4TuZLWO20zMClRfrwchCIv+++n8bmbp7Ctoe6iIW8Bpk5fULtPXw+RTzDO9EPcKlmxQt6J7eEqcPiPV58SfFabUeNnU3kSv7E77n41giCmXFm4XhUGIDPO8J13lfFXL5jGz377SOTXnQVBwoYBp0zUsLegFvZOnz4IJiVOn1bzmZuvF/idK+PGNc/wINQ7M5bp6DkLQuU7NSlSCy76vLt57y4IbfwlqVQyxoLFOmY8yimmPpRztshJKvzbmH922WWX4aSTTsKpp546L22Kes56ldBv3Ffjv7shmr8Unejv37+/J8+Zbdu47LLLevJStQqdjF6zarUavMxXr16duH0r48zUvuuVmZmZOfOc9YM570aDupG0oaj9MDo6Gvt71y4lKnH++ec3Ga1pjLNCoYDLLrus68UGE/5j2SwmYb3/W/shdaz+NYeeCj6vHaqFUvpNYY0c9RkP62qhnLItgPITs6gdrXf95jArpYdF6zIOft1XE5WEVc7AOONmQhLfpht1RPUDdY7GU7L3G3uVYZbw0iy1CJviuu8avWSyS8+ZUu4D7Ez4m+cdfCJRhRBAqGgHxMQkpKfyF5KoHqiCdWFRG+Nvv164WlNR48DyfdgRQ2LDuMRITiauhDOhQzBtHlP9DARB0i2e41lnMIzmZUx+/Se/8VPUyx6etffp2PZPZ3R4pa45FduXFlKAxZpDZ331fdpFJq6Ns7wt8Yw+5pZICYYa49j0GxtxxmVFLEENIs9hlazAoDYKi9CGSdO4k+nHNM9xiIISPxACOKrvsemHZ3DW1P4gx7Oi80iZiOSaciVIY1b8mVB9Y6T9LQFcvKmOvI1QKTBVo1T7h0sMPyotwX7tyferEn5D/pNxfIsMVx7AnFb/M33EoZRSG7tIi2KkweSHggHy1NHg83NxGEe+1azQ+qHVO4O2GXEZc8zKvtZFxlNfM0sJ7NgjIriXq/uquGxVshfviLD1Aoeu7abDPaEXGkRjzhJCz1mqZxEDGCTuHF2F12xRC4q5vdNgUuLPHvkBtk0fiW0u9TgwlzIm6hLZbv24jxedVcOyoYj3tOKnEidiTCqjnAG7TlERKCurM7jm0JOY0LUpT90om2pxcptrj6IqWs4z7XPO1LOOdfTmGS8lgJiHfVVkobGxvAKgQ2OZfvaYvM02xpk346kFkkVmzSyy0+mfJM9Z1AsDzM8ENul4Usq+jbPoBLbX82glCHLgwIGecs6OBdFrZowjznnsZRn9dyvjrF/PaLlcXhA5Z60M6kHQmFf24INKgjipr/v1XLbF5DskrEpWD9VQnmxwSZmXe0IdHBPWNp6NCIjM1uGVvdBj1AXmZVxuY3DKqq+UFBPaP37eWLjqmCTo0OWtyThDfaqu5PsBPPH3TwbemOZtW+wkotYYQxe77aYt4AyZyDASkBitJ99n9am6EpJg8ZlQfnWuObFcw3Nh6FqqNulze0Yrz26oqBD4RvENKWWTAll4Eizw8kXr5am5UHeTD86BHxfjCxuPfTZumO1aOo4/Wnua2j7Pm9okPYnK7gq4aBa9kLKNGE0CZjznLeDJbPM71oTyGdGG3Ko8SluLAExorvZWeDJ5stZFUIFVtDB65oiqUceBMe3l/Nm77sf7H7sn2K5iWyoEzqhNJoXkCqB2pIaZJ+JzhKBJKW98BpUXacb0tA5J9at+033GJFQ4ow6rG9o+FITYMTPRTrhmQHpjiFksEBTKZTn+ZWIdAGD5rv1N2+7KDeHbo6rMTzCnj+TAtby3JVL3D89zZCYyEHkBL+Ktrx5Kfpe9ctsl+hBMya9rQ5NxDkjAKrZ+rqarSxcKcJQjAi7O1P6YeEsjRyv6AgQ5Z+2P5ZW9VEW6zT6NV/Ggft+vrJbxvINhtJDIhectPRXeGdTKM/dghkO2ePQFeYIpmhP1uGeXZTF23igAYG0lXMBMDGtkOs9VDyZmNVZBizPz+AxqR+upFx6OFxbX2cwBjcbZww8/3JTLMt9GB2Ms8HJ95Stf6XtfSf/uBtNH73rXu3DRRRcFnx86dCgm0NGPCmM/7N+/H5/61KeCv7/73e8CaO8pbBQEmSvj7L3vfe+CyDlrZVAPgsZx8a53vQtAcl+n8Zz1ivGcmYmjWf0FgN1f3YM3P3hffPtAmr55kmYmbzF5eF3fhVuthSdaoref4a3H7NCOEhgHChsTckgY1OomjJR++JWqK9atdab+N35ReD2e+PRTydu2WFHlVnJdrFRFVmM7Uv3t58O+eeH+R3HJ0T3Jm+dE+KaLHJsJBnukhYhIjqUOuQIQGOAHcmpStKSm7vlG46w+5YUhgY37EEqsQRlnkbYwNcnsxsIXArhrZGXss3274s+hfzzjVOzNaEEMize/D5ie7CeJzvjobkxrQZacJfF4ttQkF/94wQiLqH63hoT2eAAmjFX1j8o982t+cyHqbr3TgrUNpy1nbL2dzlHkzeesPBUMIscTns9d1Ddk6pmS0V5nox77P2/7KcoPNOS6SwluMwxtUyHDuRVZlfvJAPO4SFRA7OI+E3mhQv84kBcyeA5lp5sXQKIeWqnPJbjftMHgzXrNBj7SLzgwxlDcUoAoCHiR6149kLwgYwRxGJQhZkItjTpiUjhdUDg65X1vHG1HImN5U8QbbPj28PLg35MmJzlqm7U5nPQkrGFblSfoQCAEw4BD2SzqYFhSr8QEiaI5wn7dD+p0MjBV6xA6P7RdnmCakEYAEGpxwYS1Ln3OUgANNfL0YfyaHxmv6l1aP1IPa/O1mfvwLA9EnxYTZJw10Bj29p73vAc/+tGPYp8NohL5XBmEc9H2Vt4M3/d7Cmvsl0svvTT2t8mvaqRdexo9Z4cOKfWnboyDZz3rWU2ffe5zn8Mdd9zR9Hk34idmTF5xxRWpf9NIK4XNQdBqDCb19Um60PCxaQhixtm3R1ficV2Tbve/7sbp04caNpf6Bd+8kskzagI3mg9fIhlbKSqaUKOumsaBZ6+vJBpnS549gQ2vX49Vv7pKb9vcn0HehzEkfYmjPz2K6sGqqqmUpJLVvkVggmP49LBm0/5vNa+i600TEQUL3FIThGhNIFUUtru2gDOsGAM+smpH8OkFOun9O2Px+opKXY+Fv43AMy2MM8G7ShMsbiqAZwWO6FIQ47UKhutVfHTX9+Mb6oLTideMhZ4zwcJxJDjAZHeFugUPQ6kMjzwcN2auPVk9By5YMQtmJ0zmmVbE5AyV/dVYqJOUsmN9syjcgproWxKSsSC00XDo+bq2ob6/4iIyLDDIRYbDKglUdlcw81iDt6qbd5vORRIM+MelmxM32eNngrZzmyvDpvEa6JhTkeOQ1YbQ4eD/OmMMv4y+3Wfb3BChAmHk9zoXVnlC4l6L4HdI30WqeHYG3GLICz/w5Bk2vWVD8O/oAsQrr/BjIW+Mq76b3jWNyt6GRcpuwj6hFqHy6wsobgrHTi1inOXXJqVFSOVlNGGNTD3HRYLnTDTUIOyIbrvPGD66UhWMXlGNj8mhnUP4kPZOA8BpK1VfRdUa2yF9icxERonhdIKHba8zgT0Z1R/La2Gbop4qWZOwjJHK9fsN6nrVjtRjwk/hj6DGfCpVVI7qgSoq+yqQvsTo2SPwGn5nwhqnfj6F2uF4ofXZ3RU12DvknDHBIfKiq+iL4wEyzhq48sorY94pkw8TZRDhel/84hfn/Zit+PVf//VU282XcfalL30p9ve9996buF03xpnJWSuVSi1/08hXv/pVXHvttU2fP/bYYwCAq666Cvfeey+++c1v4gMf+EDq/f74xz/GHXfcgRtvvDH1bxpppbC5kIj29a5du+C6LpYtW3bMjscYU8nxguET1x4GAOwutr/eahKV4DnjDDzHkY+E2tmmgK7VOT6/6TiC4RPXHcHpm5vfSsXNBSy9YokqgN1qt3pF1rRX+ioczJtRwg9pCtFG4RYDt+NhMS3b3koBUSjBi/yqHLyIcdat54wxtWr9K6fM4iVvCJXNctJHhXE899nxiSTPhsZxk3Mowfuz8c0bIqE+6chMZMCYxGGdKzRer+C1ux9ELhIfNHHpOEbPH0UrGWom9OSJxxfvmZTwa8l5aq0wQQL3RxbSVkZyPTa9bSNee94sPvvLh/B/n3tQ5S41KubpiTXTaoWxPBQPybW0WqBCjhjG86o/oosOlevW4x036H5iCJUCocevFisAB1iWB16E6LWT3RqvBYHc8iwEk/jucPMz5l8m1iEIAtFlEniuWcGTaUGeRIW7hEWc1g3SHrgMh64w13LT3PJMk6XFhBKlKW0pBt79xlBmFvVodYAxhmXPWYrMRAZZLnGkwTjjeYFlVytPyL+NrQEA3LS9jD97jQx+D2iPslDn1xQq10XOmdmnyHCMnBPe87/4mHq35tfnMbQjLOtwzzuPqOvBVN2uQMBCW8xJ0v2mhEHacR0dfyZ3c1V1BlORsT1yZrwA+QUbQuOM53QNvzaGh/SQqvQBoBc19Hl5PrBbtyn6ax41zuoyyL1jPGwGtzm8mbrKlW5sjy+DEi0d26NTAKyiqU/IkB2PjyNTb03lvUUbikDhtZOxrOot9hCdssAh46wBzjme97znoahzB5KKUQ/Cc9aoYjdITN90Yr6Ms5GRkeCaeJ6XKOrSqT2tjLNW+0piaGgIN9xwQ9PnRsb+vPPOw+mnn44rr7yyqdhyOyYmJnD55Zf3Ne4WkucsylVXXRX8O9rXmzdvxtlnn33Mj1/cWABjwM5l6kU02SaM0IQJBmpVDYgsRyTSDpbNgiKx3b44GGcoZiXOPbd5nGQmMh0VFwPvg1G+8sNJh4R+8XXTHi2xzLPtf7f1tza3mXBJZXxYDWEqvuy6PeAMtgCed1odmYgi5IywMDLcsGmGh+IeHYwzDwxLn7NETUC6aA7Tq8mHjeesXsGWcjzEae3L1sDKqlli4r3MGYpbiuA2j3nOvMm6EoXo4m1taSPic9tPxlNDqn8K0Zp9PmBB4tINNZRyEsOnDTVNWBljqq5XQUAUBbwZH9KT8Cu+9px1l3PGODCmjbNpEd4o2zeEHiPjdQkcZ8IIOeiwRpuDF9SkNnardhnxzThDfn0eggFlEb/n7yuM4VPLt8ZU9OwRWxcmb1yUAaTnJ8r7A+kNamVcqXO0RXOdLAB4z6nnYtNbN6C4vaT3HX5X2JBHcXNRha3pUNBGY1EGMYfpYRmOHPdjoXuAWkRY/5p12PiHJ+P/Ld0EAPj1M2eQzcSNwMK6PMCZCv+UCe3pctFKlQgJT9wUixa5eO7t2jEJ6clAdTUMxQvN3rU3r4ntutKNIAjiz46ntCG0ujITeBK33LIJKyMFmFcUvaD7xy8YQ25FVqk1tjXOZMdnbtge9YzXIqvYbTfPXUTE0PNrfhjeGbkO1pAFkTDWVYPSz38l9HNRv38AoB7xdG591+ZAeZVlosaVLn9gs8CT3ioHDlDvMpEXZJydKEQ9DY0MwnM2PDzceaMUzKdhOZ/9ZOpz1Wq1vowz31cTDyMokiQQ046kY+/dq8Ktei1oPRe0Kn8waKIhst329ZzBgbytnv5HeLs6bzLYPunFlV2egyXiWzObBYViu8HIB+dPihf5HbtgDGPnjal8DWN8tdwJYEI3lVKbXs1GdyFpgDLOuMUCdbhWBAp7CWSXZlHaXtITNTU58Ct+oAjYFTrUDwDs4YgXhnFkGzxAPBPmnDV5zhpOpa7FU4xUdjftkVJi0srAA8NIQ6Hebe/ZGjSgVcQaE9qYZ4jlnAlI/Xn6PjL2xqSVwVd2bm/6XnoSM4/OBPmTiRMxptQsR84YBhMM5SdmUNlbwdSuKcw8OgNvJn3uqgnbG8upXp2OWFYmxCwQS4l4LQMhDj1JYxaDyIrE0KquRXe4yjkrNxTs3mfn4JuBwXT/ZDgmLkkI57e05yxR3j+9x3zMGcXQySUwC7C4xJRoXiR6uljC+IXjOp8s7pWzh23kV+diqoTNUardeRcB5bXJCYlDTcaZUooc2lYKlG6H/Fr4zNFtK27SnrykMd9LiRGm7svGsESeE00lAmRdgueE9rqGRr83pYSaVt20Euf+q4Ovja/Bg/lhfFYbmamf1ZHN9ts5VBnHmFcFB+BlBSYumYgteEgg9AibYzAV+u7NJosrwZPpPWem3h1j8HzgmUzzXCR2bn4kvD3ysTVsQeQ4ktYpAw91Gs8Z1AJKNP+xuDl8x1u6fp6sq+db7UgNR//nKKTUdQ21uA0TytqceTShnIz25OXXFVL30/HC4jqbOSTqaWhkEMbZXBlV82mczacgiFFEfM1rXtPy+rQTBGGMBb/zPC8wyrs1GJK2N0WWu/GWzTXZrFqlq1QqgbG4EIjmmQ3KOGOMwUTrHUF7zxmAlupZxS3F2Od16PpXIiFXpRMcavJSsPDSbWEu4+a3bQwMt07J5MFECQzedD1iALF0OQzRXXEGllEei0aFLVOcOjxmcqN4hiO/Oh+KnTwyg6mHplSds25C5DJcKQhCJZxbxfBcqlwgM9QwccuymHR1DMGw9hXhCrqZaKZVJAvapFUoPbBAknttNVQlG9pe0tY6WvbP6FkjsEdVwe+orRqobnbRIDO39yRweKKIpxomaiOnD8OE3bU2zkzdNe19EFzX2OJ6Bb6LBulJ1kjOeM7CRZCg4K/un1h79Mq7uX5LLpkAs/V4aTx8t14YBlhMNhlnMxHDKAyrTH53MqYUJJNrMaX3doq8UJNRwTFT47hf1zc0fHF8HYTxzmkvVKtrFvRfk6hDh8WcBLjNkE/wnHH9wIw2Ybwgwz6K3s8MyXlDMnmRq32DlEd727u3xD4WOY5lVy7Fmpeuxs4/U3mo3qyvcprMYlrkeWnyqRhn+Niq7XjHpvOCc+xUENsQfe5JxpCJuHfqxeZFPj/pfLny+rSqt6g8xOnaoxRI1W/qPpryOk/6X9ua9h0s2EXaxbgO+2zlOTM5jZ2QAHT9R3Ptt/52eN1MeLJZOPSrEtzm+n5CUH/Nr/koP1lGfTo5zJJbHKWtRfKcnSiY0L2pqamm7wYR1tiOFStWdN5Is3Llys4bzRGDUGv89Kc/je9973uJ35n2vPjFLwYAvOxlL0v8PmqcdRPWCLQ3MHqp4zZXqPAJ9YT8zGc+E/tuy5YtST+ZF6Jqn9329ZwR8ZwdRjvPmanjk+zFYAKxFeI6VJ4WF12sxpom2RySMYwXJA7ZWbx660X4wJnnhjlfPmKTjSQC4QmOILnfqAWyTPfPMG6zIP/GsPKXV2Dljer5E4ShdDpXrl6qZtVTSiTmgLRCZDnGLxgDYwzTj8wg+pipMA4r4knLrckhvyYf5rQ1els4U4aT2bcWxmBW0mS7NcbT5kskFsMOVvpbGUJAKOvPGJYUwolexUPXeYuWvgaeDwzlgQfyo8F3X77wVGSXZ3VhXu2lSiyKHZm8CaZXwbVARq65Llo78mtzyC7LIqu7IeoZssf0xD8wzhDxnCljMKayyXmzJ6ZDmG8SxnPmN5zIjDbWXnRWNTBgW82UVD2xZCNMyh5yTXW3fGFiQ+zzv1l5EgSX8bCyJNvM9B1LHr9de84sjqyQOGxlcThiUJtnQNRuGMojXHxg0X0k53j10h6z/fDOeCQR12Ftq1+4CsVNau7mV30U1hcQejDVf3mmfX5SUoHqJPIrWr/PvWLzQqxSXG0+H6skIOsStaPNES2yCxUXURDBtr5keCIbpp8csDIYPWuk4RdhPpfIiZhRyjItFoN8hJW0OyD1opvKeVafZZeGBm1mTI8niSD0lOcF/LIHoUMrAQa/7CmvWCtjcZEJgRjIOGuBCbdKMs4GVb/rkUcewSc/+cnYZ1/72tdw3333pd7H0qVL8f3vfx8PPPBAX21xXbfjNoOS0je8+tWvjv1t2vM3f/M3+PKXv4y//Mu/TPze87yewxqjBth5550X++6Y1uxKwdVXXw0g9OQZzjjjDNx11134xS9+MW9teeSRR3DXXXfFjLNBes4srhTyDrYp+hxs32qi1hBq5UmmXmRdhqQBZjLMMJzx8Y3XT+KNz2f41C3h97JNO6LtNEWXldywUXLr3nMGaAOBMdjjYR/xjFqx3vjG9dhyyyYALEW7wolj0C+9eBYhASkxfGo4USsWeUx2+rS/3AlmMQydrA2wxsmREUvRFHIseOF3W7mCCzUhaUyPyG8vwRR57ejthLr2F6wPJ2ozdR4Y2WkprVPPIa0DE/MGHR0rKAM08Jyh2dhBOH4A/V9LT5BsroVL0o9pe9hGYb1afPnNZ8/GVvXtMRtSSq3mFy9VwQRTkuWxlX3ErQIg9Ep2A0+Opi2VGP7xZVP4nSu15Ldoff8qQ1HlojbmxXQjwBE0SU9865zj/6yMh6Ma5xML/ptgUEfC95oESnoIIwTT+WIAfnuDE7aloMaT4MCXr9mNrz1vt5rc6+dN1Cg1+UBJjsWuLerIfje/fWN4jIRQawbAKojQ+6m9SsxuU3sN6T1V7YyCeiHBcwY0GUjc5sitykHWJeoJAhxdhaJGxltjWON9RTX38Ks+ph4M57TGaB45YzjWtlaesyCsMc29L/ViQ4MU/mkf3YlTPnRy+JyOLDjYwwIQqkyEPSSC4WGVRDAOY4eIhOsvNsg4a8GSJUtafjcoz9nGjRvx0pe+NPj7Oc95Dq655hosXbq0q/2cf/75fUuUb9y4seM2gyxCDajzjGKMr0KhgOuvv77JU5PkOevWYIiGTr7uda+LfddvzbR+OeeccwAAR44ciX2eyWRw6aWXYsOGDfPWlo0bN+LSSy+N9cnAcs6gJtZ5SwaqW60wqodJNIaH1c1KbY/FMUVBFUJ21nl448UVLClFpjeys6uAmRh8riTRuc2UB61b8Q3NmDMKkeUYPjXMgxNZZTgsu2oZssuyQMPELLFd2hCICj/Yw+09lk37YCxYiQ5WYAHkiyxWw8iE6NijepumlWsWK15qJm2MM5ROSid8FP6Yw5cMv7M+LmSz6dfXAojUgOoAa4iCrdSBJIXQdpTWKOOs7jOUaywWuucV7SA8SU3oWuRERkIHlcdMG/pC5VGmqb0UQ7f/jDV17MpFxlBeF0yWui6YCBc5mGA6rDHqOmN6Rb7xYna5AMLCOmev2Hpx8LmV47jm5DqyltpnO8OYcZXzqOpFNVvzXU8cI8f5mQ5tNNlIjCE0sPwWhpbuv0QpfdaDJy/igHs8V8LhGzZj7c1rYnmepy6t4eTxeiBGkV2ZxXBEOZFleKLCn5Td9090H7nV4UJoYx6sqSUYFFc3MvqWCiNEG0996nzchl3885INwb/9hNBICSQWvWeWUvpNzpliqcd1ECnBlHHmM45/WLYZ9+dH8M9L1HzNm/Xh16PKqGrf3OYx76YSUUpARo7TCSnBBG8K+c2vyaN0UkQVWXvOpC9DIRJmxodqJ8+KIK83hk+esxOOdhPpQRodRvgCGKzARJoQtEF7zroVUYkWou41rDF6zo2/HbRxZoyfRuPM5OsNgrTKn/NBTkjstZtDVczDv7ilGLxIWhF9wcqcqusVeG26RBQEZK2F+8aEgrR5cQdhKlyJYjCbQxQ4sm3CcdrBBINkquhtcIxs5B43Ten04uZaSU57kRhLrjvUsT1MHTSaA8d5mGge0sZjIOJiBTzHAalW4rs1GLlQl+XRXPx6W0VL5VHoiUXHCamIe+2qHouJZKTB2JueBMq1uPKkyPIgd8lMtJLDdEMDKBQbUJNXe8zG6NmNYVLtMR4AKRn2ZfL48Oqd2Pa7W9WxPQShp2pC19CGSPO4pZXcIuOsm4LGUSyhOnpfxMtQzke858zcYy2umVT3gCiIZpcpa39/JhHd/he5Idy6wcGrtl0CQF9TafqxxQTZeM4SpP0Z0H0fMSDq85o6ezlW3RRPjTDeeWap68QtrhRlNUPbS2CcoT5Vb2hTD9cssr1ViojKNJb40JP2aGg3Y2rcDp863PYeTJtz1riPf1gW1surDzXPzXzZ6pisRc6i/rabPtIiUabe9/9bugm3bDoXj0WfSYyhsq8CtDGOh08ZCvIKo6icU6T3nGmvc5MXN76Zzt2EzruMLNwZpVYjqtVwj8lFHNbY5dLXicNCNc6iD+92AhfHmjT5U4P2nDUaZ9EC2UkYw+rLX/4yvvOd7wDo3psTNc4af9vOGzsfGGPxJz/5SezzycnJpM3nheh4TlJGnc925CwJnwkcXVrC8D4V+lF70Wacd80QJn8+jdLWoioi3ObFFDMyhu2uJ9VRuM3bSgirhrf+auT0Ycw+XUH1YBXDpw3j8A8PAYJhzBntqT3MYmCQsEZDo4VHc9dM0n2asEazvZShXHq37dHheNEJhl3zUNldwbpXrkFG5zcY9S+gebJsPDDrXr0We766F1t+c1Nv4VZQ/eN7SmnPR9gNoiAw/fA0/JpEdnm2o/eMATGBgIqvvHtdqTXqYbh7SmD3FHBK5NkneKhyFtR/SmDs7NFg4jNyxjAqu2fVfNriLQUyOp2YBDCjizXfNboSY+ccBqDUI82xOI8YapyhsL6A7LKIwaQl9aUXUbgzQhldwiPn8KHVO3Hm9EE8vHYpgLJpctvxbNrdsk5f18ZH/Bx+WhwL/h0Mc8bAWPKCg/GQi4JAdV+l6fuu77MGw9RLeh6Z/ECbJbZJRQ/ohasG26yXPEFD1HPb6HWKhbsxxAy07JJM23zSqOe9bVsaDBufcdwxshKXHXkGh89YnmofqrFKPbflQlwX18wYconXKbJNZV8VmSWZloZNo+hTtK1qASBle0ydu3Yh4trLKX1VNsCUlDCe3sy4jdmn/eR+8OWiKz5tIM9ZC9rlByXloQ2CQQqTNB47SWhk0MIpjcZZs9RxHGNYveIVrwg+69Zztnbt2pa/XSies0Z27Ngxzy1JZuvWrQM9/mRNPQ6/Vw89AiOXLoU9aqO4Ma+EQ9bmUdrc2ts3ft4YRvTXF+7sXkI/hsmrSsIof7XZfWYsA3vUhsgJZcTYHPlVvYvSqBV5ERPciE6KghX0TuGW+mVtclO6EQOJ7cfiTYZdYboCWfcxfOpwEH7Jop6zxkNxNbEeOWMYZ/z1aShuKqrnRC9N4gzXbJyFzzhmcqExIYpChxCZuW77/qmXPVT3hxPrqmfyrtI3ZagA2CIcO1kZGjLlGovkebTOpxKFUCRAFETg0WMZ1pMhZBQEV002vz+lryaojEHlaEbmhoV1+ZhnhFusWSClh4k+AAznwz769uhKfGT1Kfivx+N5p+0mooFRyZul63tRI2x3LOM5g8kRTDKEGAPPchV2mHTorj158b+3LWue9Ztz5K28PzpHSRRE/B3ci1pjpEHRhTC/2uhSQRC+HXiGI0qS0fN67knxXK+0bUoKUfzz1Tvwq9ufjW0nJ6s1JmIEkRIO2+06kbGlrz25oZTHUi+yjS7M3e45x1sc13jDUjyLAq8Wb+85M95gJiW4LQJ1Y6bbN3LaSOBNa6rd56P1wshxDnnOWtDOM7RQjLMkmf/55Ec/+hEOHTqEgwcP4sorr8Sdd96Je+65B+973/vmvS0f+tCHcOutt8Y+W748vnqV1nMWpVvP2apVq3DnnXdi6dKlOHz4cOy7Uqm38La5IulcPvrRjwbqlYNi165deOihh3D66acPrA3Rl81MJD9nohiOmcLafFMtnUZEXuD+f5T4wf3ADRcD++5oDsVI3SaG1rZZVLWtDfnVOeR1bsbIacNh7lWPiAKH7UXUEFeGz0ljmHUKUWScxwQcuilmHNuP0CEvAPhEBv6BKjIZdQ18LzxAPNcr3mferAd7zIJfjlykHnJhDO+5fAoXrK9h9Q+GcOTbBwDofI4MDyaQreqcRc4slgdX9VlbUYokhgoMX/99D8/5HXWtfp4fwTWHnsIMF5iqsCCxP+0ugwmulOCC9+jpVNfrzE0+PnfzFDYvCftcekpG2/e8ME+lFTpsjwkv9nur1H1o7K+cOovfui1eS3Cm1nDsNucqPV2DLkG6Xnb4bRLMYvjuK/bj3/cU8d6vx581uvvVPTZqt6zrZMR/ZGMYHesyRA4IFojcW47i4f0cZ6xOqMfFocJSW3j6mc5p5BZvfp71kQMXvR9qB+PGiJQydDVGF0Zg7r+wIX964wxu/nQR7hO9T4fXj3n4/WtnsWzIx5EywznrmvuppW0mldcx6ftOntsm9PPwlnOOYgefxCUXZHDnLhsXbjQGqDJ4WIa3N44Zg1/1MfngFIYi+WHSl+AsWSykqSkZFoYlt/WcqXYbY45bCARkQmXSFgahlCmep8cnZJy1oF1NqkEbRYZBhoEBwJlnnhn7+4YbbsANN9wQGGfz6Tk75ZRTmj4bhHEGAJdddhkA4J577ol9PmhPYuOCw7ve9S688Y1vHFBrQjZv3ozNmzd33vBYEnEGzEaqb44V9FulCzW4lUsYnn+J3q3oXvUvaBJvVoALUWp73ey7X8MMUOpnsiax4Q3rIfICxS1F1A7XIEq6BlpBhHWrWqAmWDKYaPVTPFRkVK7Czt/fhl987DGs/tVVysszoyZHUgunBGGNDb8f2l5CdX8VR+49Gn7Yh3FWyDI8b2sF+x7P4kjkc55hkH57T6eBMcQKwFb93trzrNPDft21eTn+lDHcVxjDtgpUTo7xvqTZNQOMe4plevcIm3yoK7bWYhND6UmwAgOqKnev3bPSyPl7kW1kPX2x3ijFfPvzkGjf99JHk6cv1tYuJ46iaGH9iIdXrK80GWc1T7eIASLb2lifuHgcfsVvNrx79FR5ZR/r13jYMJ78MGKcQTIV7p0oQ8+YFiVqFO1AbzlwkVNY8uwJ7L/zAJZe0ZAy4IUh12ZRIAi/bOiD5UMSN59b7cs44xy47pRmKfworZ7VxU0qbHf/XQeaf4Mu5w0cAFN1O6/bOIuhURsvdeI55YypkiHtynoE0axJ3mCBVKGEhQ0FzDxeDpRqWyHNu5Ux7RUPFVADg8x4ixsdpOQ5O/FoJ7YxOzs7jy1pzUIxElsxnzlnSderUWyiU1hjUg5fP7W3Bla3qwW1WvzlMegwy4VKVNkub+yZLoyzKKxBRrgrOMPsU2XYw1bzBFG3Z77NfZZRynrLr14WfFY9VAWf5MELtKMUtREy0ZL6vapHSl/CGrFR2VNBdnkWJ3/gJEw9OK2ksnM6ziwQUDAnEN+HPWzHvWZIr6rY3CCo1fqajAkiqFV8Hqzed5wgM8Sk4qs+6ykcNWYYMIY7RlcBAFZV63pibGTPU+xLq2MySBTW5ZFb3duzjVuAX2fqmq3IBhPPoJCz+V+7tljKwItO+KQnU+cKxRvU4VjQnqoEqXYAyK/JwR6xUGsQuzAetW5LVogsB7cZcrz5mVH1EMS5tTMYWbvvu3wlM6bOpbKnglzCGFTCPmomX9iYTwxRVgqf+nLFcs5k9wuWDZtv+o2NWPeKtU0LT1JKMFuEv2GhkWNKnshI/rBI6O+5plVkH8+o2oxJ85NuPWfmfGpHai2eM/q5m6CeGW+UXgRpqIdpclVZCgXimHoxa+86CwRAtKdN1Y0MvZ9cG9jNao2y5/fHQieVceY4zv8GcCGARwG8ynXdmv78dAAfA1ADcBTAi1zXnT42TZ1f2nnOKpXmRNtBMGjPWSfm01OUdL0aj5825yxKP/Lu7cbQIGg05sk4ixDJ35rmyY9F1sNiQ35tHnaTemDaNkHl+XgJtVxkPMRnvuANk+LgMzPJZ2ksxnADxnoPazQTLm5z1A7XwbMc2aUZJQNtMciqMc4ix0s6VJOHob3oSyukFjcBAyaeNY69/75PKRr6eqJjhkEnwRSm83c0FY/BHu9+DMWUQ73wok1VWChs08V5cqEMK1GwlOJjDzCbg/k+apN1ZCYyQTF0M1E2IXDtEDmB0TNHsPe2fcFn0pNNcuqp2tPx/CWGTxtqqeBnlSxYJQveI/Fpj1/3W4sqdEDkeKDsGaVaZ2GuZgejzxRFN5SfLCNNmYvmHalj+fV2ua9ob2jpSTd8NAuCdG0sxh96jLPkiAAZWZxgaFL8ZFwVsC9tKer9dteOpna1y6nStNvEnJc36zUrT3axUsSYKptSPeK1WKyQgces3Vgw+ahN416qCIzipnTzotyqHOqHa+3PwSTWaYMwEAayOKQZd6a9DX0ofbloPWcdz0obYKtd170EwAMAXhD5+meu617ouu6lAP4bwI3Hppnzz0IOa7zkEhUzde211w60HQuJNIZQp7DGaJmCdp+lZWQkFJZYs2ZNz/uZKxpr261bt25ALVmAMOCGHWrR5a7RldiVG8Knl24Kvu42MdtQ3FhILc3c1CSm6jm1W1SY70hZbrPYRB9ARJSjfahMsD0Lww1NKEsvqIk8VJidCcFhwMgZIzGZ7cZOSu7ORqGC7tuTGbPVuUhlQOz88A6sedFqZVwbQ4gBHSdbWkXx7GVqPD57baUnY5FbHJdsV+GdV64p46KNynP+nJNqcUGZtPtmDKj7PfVN2CYVqiSKInHCzxjDkks6LxqZCZz0JI7+5KhyWmZ6KMdgMSwpxt8Lz9ocjTAw8V0dMGIduk3TD033lAMHKKNaJvRNVacNKZXSDm3iiEngezOeLh7cZWOY9p60sCyUKqI5ZpvJvg4rlJE29RrWKH2JqYfa5/2rdmnFT8aQX52L3UM8y+HXwut+UoLQSRou3Kn+++wVyXNCb8bD1RtVpNWN57exzrRq6vTDysiffHAKlb0VeLPd3W+jZ42E75sWYyQwzNoNIYZQaTGClEB2IpO6zEhpS1EV2W63kYw8ohkL7u3h04Yxds5o2B6eEMbvA6z/aP0FSRrP2YUAbtP//gaAVwL4LAAYD5omD+DBOW3dAGkMk/vXf/1X3Hijsj0HHdb4xS9+EV//+tdx0003DbQdnRh0WGMjnYyzsbGxuWoOAOWZ+uY3v4nbb78db3vb2+Z0371w4YUX4jOf+Qzuvfde7Ny5E5dffvmgm7RgYILjvVfN4LGDAnf+IoO3bj4fH/uVaaigALSUrj62jVIvSJmQgx/E6c8zjfWTTOI9g/F+pGgXZ0HugAll6aktTIXniAyHV/YBX4mBZJdkMLvbQvVALRC+CH+UtCM0rej30rcjp4+gPlnHvm/Hc0ekL5VAgzZqO022jBra3z53P769r4ir15UB1lsY4adfX8GXvlHDtZvL8Io1fOvRLK7bUYOcjfRLynOVvo4U7VHgBtBe0qoPq2RB1n0AAn7ND8Iru83Rkp4ef1qOvPsGAbe9+gh+uCcDBuDBvQKvv6gS+z7NAghjDNWDVdijNnyd/9ZrLiXPcnjlOm5/0ySOzjLc9EklyFDxWFB7vlM/GXVC45EUeZFq4aRpP5wF9csS0eGx7RY0RF4gM5FBZU8lrrYnZarwuKZD1mVrT57Bj4dLFzfFUxx4nscWhU5b5eHvf/kotm1iAEaQlq98kOGfPnoYV66chvTyTUbzzONl/Mmza3jeyXm86MUFtJpyM50Xa34vPank7sft7p5FDLoGYAsvqRk/HZ7TTOcIeo0LcQw9GdR+2UuOAAmOp72gIxbsUSXQE/XOMyPfnyCJ2m3o8PEC6xTq5TjOu6E8ZF90HGcLgN93XffFke+vBvBHAKoArnZd91DD718L4LUA8OY3v/ns5z73uXN8CseGO+64AzfffHPw95NPPhl4P0477TR87WtfG1TTBkKtVkvtRTL9dPvtt2P79u3HslkB999/P6JjK5vN4uGHH455rEZGRvDTn/605T5e8YpX4Pbbb4999uSTT6Y6fjf9Qyw8TF2p+x+3cM0HVFmI2967G9tWq89l3Ycoip5l39PQOIa8WR/ejKdC3BpearLuBy+6aL2fY41f9VE/WtdqeQyQMvSkaaPGHmk/oZB1idqRmqpVVZfgeZEsJNCB+tG6mij4kZwlpvrDK/vwK15QuNTSIiX1yXqgZhdtT32qHoTHmHyhXgpjA0DtcC3cV81X55jTxhlTq9Exz14jUu0jyImRgDVqpZpYN44hf9YPVCJlROgkZihKxMojtD8vNdHvVRBE1iW8GU95QLQMvV9WY5lnleGfajzLsD1+xQezGKyhhNzMDtQn6/BnffAWIYiy7sMa7tz35l6FJ5USnq+ueS+hjd6MB78WTmQ3vEa9w3IZH/f/+ZPK25xhHfddO1zT9wSDrPkAR6oQ6+gYkp5EfbIO6SMhxE2FDUtfPQPsEbttQeD6ZD005qCfqZFyDamQQO2QWnTh2YTzN23y2ve/X/XhTXuxkGqlGMq6zl2sHdaeaK0eG2tO1Q+cr1bJats/tcM1/awQ8GfVihyzmPJSpe0iqcd0Vd1TjSHj0pPKC8oBgLUVb6ofrQfXllkcjKv71ypZHRdCYmOopp73kMoojr4cpCchqz54TqnZ2qN24j1cn/Yg61Ibh5Hf1/xU7VmorF69umXD07zVDwMwBaNGAByMfum67jcAfMNxnFsBvA7ABxu+/2sAf63/nO8UiZ5ZtWpV7O/Vq1cH//Y8L/b3icBTTz3V9TmvWLFi3vrp4MHYsEShUEg8drv2dLt9lF76h1g4HP3pJLyZOpbtDYMBJqYKKO1TE9vKngrGzh9FvkchhDQ0jqHJB6Zw5KdHYA1ZTZOq6oFqYHAs3T5/xc3LT5Sx190H35MY2lHSk5w6/JoKWOIWw/Krl7WdcFX2VrD7W3swcfE4jvzkCIZPH8bQ6qGW27di34P7IHICXtnD1MMzyK/LQxQElmyfwOT9k5jaNQ1r2IJf8QMBk3137UdxUxGFyHWsHqhi338fQG6Z8r7P/GIahU1FTGwb77pNUko8/V/PgNscmfEMJu+fBGMcQzuHUNlbAbMYMmM2xk9qvW/pSzxz9zOoz/hKHdPzsfya5e0NOk3jGPKrPg589yDAldx4Vhfmrh6sgmc4REHAm/Wx/MqlHfe9+8d7MLxjCIV1vefhAsDkg5PwZn1M75pBZsxG+ckyREGgtKWI6uFaqvHsV308c/ce2CMWyo+XYQ3ZWH7N0sTaU+3Yc9telB8vo7Q9ucxJZV8Fy67svF+v4mPvN/eieqAKe8QCsziKW4sY3tL9uJ68fxJTD03HRGUAoFpjyO7KKe/wiixGtgy32INiz4/3gOcFmMWUF2bEwtIdna9zdAzVjtZx4L79qE16KDSUEZl8YBJMcGTGLNSP1rH8huVtjb/9/3EA/qwfGD+zeyoYO2c0di92wqv4eOY/n4Ff81FYX4gZzX7Nx9TPpwMDbeyyMQy16P/KvgoO3HMA2WWhyEntSA25lVmMbh1N3R4A2H3PbjCLw5v1VYHrCLPPlMEzqs7k+IXjyIy3Tr/Y/aPdqOytonRSCdO7puB7QH5tDks2TyTkoSXjVXwceOAAaodqAANyK3LB+XplH9aQBb/iBQssUWGnRvY/fADVg1VU9laVYIkP+LMelly7FLkV7SOVomOosqeCPd/eC6/io7ClGHs3VA9U4ZU9ZJdnUX56FsuvX54YMnnwB4eU8epB1fDTVPZWMHHROLLLO0dOHW+ksce/B+A5+t9XAfiu+cJxnGiPHAGwsBUquuB4UGskQho9wElCHp28xCSQceIyfMoQMkszGMuEMYRZK5p5Hvzf/BGENTaPWzOUe8lF6ge/LrUHMfTA8Iyqb8SAVHktzGawikK9hFk65a/k/XC1Mmwx2MOWyqsxK9Nmlx3UGpM+5gULpZN6q0loQpMqe5V8NdOqaCLPI7LQHXLyuFZGNPLfPYSjGXiGY+jkIWV4ai+nX/djQimpa51ZvO3Kf1qGTlLtYUzCr/gQOQ5RFN15vkyknSkz4Ps9ebUZWuc8ehUPYbHu9oisMnSZYPDr0AW1e+sra9jWdfr0vrWSoC9NLah0OZFM5z/CV2Iuxc3Fjr9p2ocOO046nppky2DMp1EhNe9gKSX8steDIIjaT5IwkV/xlQfe5FO1GavmHoshe3yecl3UuSFtIlBptXXkQ6dd61zT4N8ywVuZAia0SErkeNWDNXgzdR1KqtrS6V4bPXtE1Wg06ol1pYjadc6pvhZJ95n0JKyigBF3anXPlLYW1e+Tch979OIvdDp2s+u69wLY4zjOdwCcAuALjuN8Qn99teM433Yc5y4AVwL45LFq6HyTJDBxwQUXAACuvPLK+W7OccmSJfO3or90aXxFMCkfj4wzoh1c8JhBVsxEcquQfhI7d6jCvFHjrD7j6RARmVqrYE5bVIuHIklfhSVCytCQ6NBRVsmCPZ5RoXQ95BkZxs4Zxcjpw+CCwx63UT0UkY/mKt+m8cIlto0hlsvAWO/y/oDyHgYS1DpHgxdEMGlMl8Nk/sdCifkeya3IIqcLkVf2VFB+rBwKObB0E31A9Umv4YxN+8qo8CaVn6XGU35tHkMnp/Q0MUDFr+lJpsV7zjlLUgutz3iY+UV3a825lTk17mrKUOz1eWGVREzM47ItKrT6wo16cm1xWGkEGbSYkMp5FChs6MHjyRBIz0epHqgGsudmm06Ge1Rtr360Dm/W6/6hau4Li8Xz16BCS4NFEBZXPG2CNz86TXmJbmG8+f6sz3jwqz64Lr7dSR3R7CcslK1KgeRW5FJ7zQAoFVbBMLRjCIwxzO5R+ZPmWTL79GxqISCraOki0oARhrFKXYahAsG1YBaDN+OhPlWPfCmDvmuXB5cZzyjlx8m6WjSJ7v5ELkLtuu47Gz56nf78SwC+NNeNWggkec6+8pWv4Etf+hJ+9Vd/dQAtOn64++67sW/fvqYi0MeSlStX4t/+7d8AALt378aLXvSipm2Ghtq/+OfTmCQWIJzB94D/78o98FYVMBQp68Mw/4IgDOrF6Ot32ezuWVT3VQGLIbskA8YYrB7lunslvyaP6UfL4BVPTfw8PdHWOQzGGGkHtznGzhlVoTUpjLlWmEnL0MklTD08jdrBWjBBD+pnGaPRkHQohuZJYh+WOMsIiLyahDCbA56EyAqtNiZTTQDD1W0AXrLnohtEQUCCQXq+zu1COKFM7TnrPh+n5b50+QVZV/kmRoCgMSys5e913TVTvoCL3saRqeUkG6XgpVTiHjU/df/k1+Zx9CdHAZh7orcxpIyc8Ld/duMMvnifjeftrAM1IL8uh8K6zqGAXDAdbiyDkgVdt0Xfn7Gu8SRmd1eQWZqBN10PhFw6G2eRP2So3NldgwAj79+UJOP52tBm4TatdpM0VlLem03oQ9anPPBMDfaojZlHZyCyHJklqlyErHbcS6y0BdPrXd3mE/MsR3ZZFvnVORxiQO1gFbnlWW2dSVjDtq63yNCiYky8TVZoePIMR2FDIbl0QdudaM+qxVF+qqyMx+1qLhZECJjr2qb/eZajdrQOr+yhtC2MbJirBaOFBhWhbkGS0uDExARe9apXDaA1xxfnnHPOQI7bqbTA8HD7GP1Gz1lS3TNi8cIEQ/1IHedtFsgsqwKIVfEdUKMY/Nk6pJSo6jwCYasV6PzaHIobuw9V6gdREOBCT659AL5Uqlp6AsdSTpJNbspcvFgzExmMjdooP14OVsuZUKvzlb1VDJ0U9hFjzfLQTe3twpuUBM8w5FbmMftMuErNLTXxYn7KczZt4qrwc7/hq9zisEcszD45C5HnqB6oIrPEVkZOyn1PXDQ+Z2G0gYEI5SXoZRwY5xkT6F0QQHvO/NmGumS+Ci+sT9XTG6/GmAbDyGnDED1K6Tf28aoRiZeO7Ue2llX2Q9owYKFq/TEPPdWAU43R5xT5eWVPBfaIBZFh8MralZWiPh0YMPPoTBheafMeF0EYuFDCKXEvjprhG9GItvcwV94tMVkPDSCpwj+7bg1jgSKmV/Zgj9rgtvIKi4JAZiyDyr5Kx3FknktSe7/S1Pxr2gdnGNL5k7Hfa2PVHhJBiGWafStvOUf1YA3ZJXZPuV1G4ZFnGESWQ8rwuAwIoi2CMO5Wbclw8EzD+6WDQXc8szg1KAkigWjdsTTfl0q95Z0QxydMqNwgIyLRvMEAXgK6JlT9SB1ch8+wjFAKdT3mavWLyjtQYUXSV+p0QThOl0YsEwm1a3qA6bAZGfzN1eTN5HtoRs8aUeFn7eg190STGcuoekNZHoRIMuMlSLlbbkIOTUjjHFzq4oaCUqDkTIV/hRctFXM5CeJBbToZyGh3hf6t8gDyZOW+VA1RKpHTDSGM0pNqEYIj/X3PEeQYWUNW7xLfDQWkzWeVPZUwnDkFTDDUj2rVz35yBfXkWXoS1QNV+DUf1ogFnhVash+pcs6MAePXZLBg0VMRagAQDOWnykGtMr+mPLA8kJFH2+vGdH3FeIgderrPmNDnVhJBP7OM8pjzLMfI6cPpztOoPUoEz4t+Fomi3kzGAZFhgKVzfPWiRCd4TigPGwe8So/jSBtQ3OYqqiAb34d51kEri7Y+H6bu8+g4S7vYdRxCxlkLeg21IRYunTxn+Xw8VKRTGCSxuGCcwR6zIXIc0gOO/vRo7OU9348EqY2E/Jo8yk8pRTtuMWTGbdQm6wOxFQFg7NwxiKJQMsieDpnSk7Ru3yhGjn8u4BYLivcyBjURseOTRmNYxhuRtLPe2zFy+jBEQUDkwkmQEohA6hVrs/JvVpTn4n2UW5NXohsWD9oGDGblmTEVImnksXsJb5OACpPirOeCz/mVOVglC9xmyruhUYsOoimkr22TmLpm3OovN48JoNE8Y1zJonvl9Hla0lPeduOJ6YmIRPzkA5OY3VMJ80714kPa3FflSeLB/c7thMSvTuhnDOMql8vUS5R1JSxhBHg6GjZcGVDRfpF+b2GNzFJGocgL+LO+NuzVFQxEalKGMjOmx54uxtyXUS1CsSUTVqhCgNUiWpp9i4LytAlbhUT3NI4YAKYWQZjOS/arPqYfUQW3g1DODteM2VyFQDcEtJBxdoIRzT8iD8ri4Iorrmj7/bp162J//9Iv/dKxbA6xwJB1FaKXW5MLwvXq05Hk40G8A5iaeIq8AMsqdUKe5eBdeGHmvElCFyid9lA9UA0nRO0XPlvua64KrAztGEJ+jfaKMUBkdLhlp3l7Q5sl5sZg4TmlbMZttWqsinWnm+yrCQcL8kPmAqMoyC1AlCyUthQxtKOEUg8qfnOBPWopVcIeBE9MkeXMuK2ECnqsSVc6qaRCEPMC9SNhGQ3pSfBsKIaQCuMB7HOyqLzSwMxjoTePCRWaKGsytSHLmAoj8yt+zwI3pgA1FzqsTOcgjZw6jJHThgOvSJoxKms+RE4Z5NIUie723mf62nOA2cqb51d81I7WQ1VJE2HQ5h5mXBkc4Eypl5rPe7jXRk4fxtApQyhsyKN2tI6ZX8wow8+oOMI8T9rvu7C+AJ4VmHpwKghD7M/IDw0ZpsVbgpqZvINgiobbXO0nI1KJmiS2Qy9wqBBJZYB5Mx48HZ1iQuG5zdvu3yqIwACP7X8OFGQXImSctWDlypX4/Oc/j5tvvhk/+clPBt0cokfuu+8+vP/978fHP/5xvOMd72i77dq1a2N/f+hDHzqWTSMWGKIoVLhOhusipgIIXtzzLwgCqBeZVbSCiZEJBZI+BmacAQhCggrrC2r12sw9up1kizlznKG4qRjU9TFlCFQeULrXnFEBY5Bz8mbk2qPIMkJ5zzrkVMR/jKA/51KNbMklE2AZjtKWErLLsoEK2iDILssqIQ/BYPUgNMIAiLyAyHJkV/R+DtzmKk8oqornQ3kXjVhBmvZw4znrb/CYSbBX9lGfrgeiJyKrjLbUE2QmIXJqEaWdOEY7eIZj4uJxQIeUMR0WJ3JCeVW0VyRVH/kAy/KgEDJLkMPvRGCYMeUJmn5kGlO7plE/WosZxh3vNaY8Z950HVMPTKnPdP5it9gjtlI21MXBpS+VqqGlwxt1e3i2/TXILs0o41Ibmf2qo3IRepWCEMdoLluKccRtVahaedrT3wsxtCfTGstow1m/Xy0GQCK/No/siqzqnzb753ldrkIbY1KH+C7WnDMSBGnDC1/4QrzwhS8cdDOIPjj11FNx6qmnpt7++uuvx1e+8hUAQLE4mBVlYjBYJQtjzhjKT5ZhjLEwd7kHt9AcwBggSgL2uK1CCC0WqCMO8qXELA6/JjG0vYTc6hymHpjs7eV9zEJSdA6ahxQGkcp7m941o5Ppe68rFkXkRJAIz20WTFLS9JFVEKgdrqmcvL5bEocLlrgCPd9YJQsiLzDmjPZUzwlMGWdgSK3ymATPqjpr3kzoJZe+hMhb3YWUGmO6jzIMwa4spaZX3V+FKFpqvxYH4176e4wxsAxH/VAKqcC2u1H3kshxeLORib4OQ0sblqrETBg8qUO2rd7yTU17uK0MaCnVWDLhgKYuYCfPWRAGbbaT6RdykpA+wLMq3FvklfFqwhonLh7vvAPtMeU694pn+qwraPJwvbDeWm55FtUDtfBZ1IHCJlV+YXKqDr/S4zuHQeV2WpHQzbqujymBwoY84CMUUGq1G8ZUoXodfqzeh4vXv7R4z4wgeoByDQkjUhEVKpjrCXJqdEHRkVOHdYiJDv9gc2NA9Aq3GCBlPIerh1XM7HgGmfEupZlTtk8JFnQO32FMCQqIrK4LBcyJV5LpsEp7xAbjDBMXjmtPTOedi5KlQ1q7KMzcRbvmwoDoF54T4Hmh80i6b48JFxV9lpNggmH0zBHE7nKuPBlde85Ef96OYF+2yskzuZ3B/a4numkxNfc61fjs2B7t+Qkm2BYLQkvTt0mFsEoGJQRj8jC7bov2itsComQF45lBG44MHcNReYYjsywbE/9R5RS6b0+AL8FzKoy0tLUEa8SGX00Qlmp1XgKA9APDl2f7M85MGKNf9dX1EwzFrUVMXDyOkdOHUdzaOV3HCL7A6n7sRfaihK1yIrhf/aoKtc2M2kEtx/Hzx1LsisGblahP15V3u8cSEccDZJwRRISkEgrEiQXjDPWjdfUimFF5VZADcZzB5FjkVuZ0SJESBojWnxkIRoHQTPL1xLGjEmIDw6cOIzPeu9ejFaIklGx0mlBLBsBTAhBTD0ypcKA5uNgmAV5N/BHULkuDVVKG5dCOIVWnaA4Zc0aR6cPTNFeIHI+rRnbJxCUTGDtvFGPOaN9tYZxB1iTKT80q5T+bK89FN68DvTgxdnb/7eEWhxiyVBiz9nIwHaaWOudMi4iInOh7dSm41y2ulQRZEHobiIJ03In2aEF5mbITGWSW9jAO9TNR5LkScxFMl6pAuPiRIj+KW+EinPQkvKl6zHvaLVIqY8GE3k1cOK68QinhFgfPiDA/rM+wxiWXTECULEw/Mq0UESMLB/awrUqgpEF7FE2+WLeYAtg8q+53nheQNR8QDIWNYWF0nkI9knHAL3uYeXQG9ak6RJd14I4naCZKEBHOPfdcAM3KjcSJgzVqh6vmvgwUygaTcxYeNL8qFyp49VLAdQ4x9W+idXQAoLRlYYQCW0ULwztDwYKOMKY8OFavtZea4SYBP3qYlJ4YkRVKAS7LYwVX5wKRFwsiQiDIZ+oRe8QOvMl9o73l9ck6ph6aVp7LQJI93S5MztlczKqYVv8LSgQwFUrcSegijgxUMZHegZPI6JkjEDkRhAMCOrxQj6N0CqSqj/yKD0jAHrV78/5zhsySTBg6mOV6AcYomyJdThUHACWwUp/2kF2V7yv/MrskA54RgTiNNWTBKnZnPASKk5zpEiX9jW2/5sMaslXOoNXb/qSp/yZ6UNcEYBQ2s8uyKG4uQhQF/Lqq3dh1WKJZCOAMsupj5NT2CtzHM4vX7CSIHnjHO96BUqmE6667btBNIQaEyHJkl2VRfnoWLCPApV5Nnef5bHFzITZZKG0rYfrRGRUlMkfhU72ilMTC1U6m6/MsJOwJW4dAddhQL7YLm8HLzF2/MqGM6CjDO4bUZLkDPNtH7a7jiIVgJAImhBmwhpSaoF3U4brdljHoUKspLfaQBZ7lqB6qof5UDUK3h3clKqPEZERR9FQ8ONaeERtWUaA+WY/fH8Z7luLhmF2ahTftwZv1gFmvo0BGSxhgj1gobi5A1iX23bU/rLem28Mt1lmhkjNltNpqES67JBMWpO4Be9QGE9rr3WvZPe114xmOJZdM9NwWw/DOIRz58VFwu4+FEIlQSKQXtUZ9D/EsR2F9QV1/pDTok/YlGJgPSM7A84vXv0TGGUFEyGazeMtb3jLoZhADxhq2wZ6p6Bd8mPcxn3CbN4V6cBFZGR7gxJbr0MogJyJtaNM8YnJiWIdQZcYYmFYKYxZXSphzQYJyX2YiXRiXMs4W78RjoWEMMVt7zIZOGUIvNZQC71mfjOrQyNrhGiq7K7otJqeqizYx5aHMr54DRU6mF2Mi5xfcY6LzyszwjiFU9lcx+eCUFqXpbfppDARuc8BWi2l+3YRXanXTFDXCGFNiO4Gi4VyIS3B13MxYb2HDIi+C8PW5IL8qj8n/mepNcMeg1UK78SLHiBjNgIpqCEL0u92fCVOvSS0UusBeOnMIPf0JgiAa4Bn9khcmLGiBuIW0UcYGHNbIRLygM0udeDKPmFX9FHXOuKX6lWcYGJuba61yfnrrE6tkYfTskTlpB5ECna+UGbcxfMqwEmKxGHLLuvM4TVw4nip3Ji1SSuXNMZNSkd5zZpQLxy8Ym5O28JwWb4kaPZEws1Rt4upeY7YKI+6FQIhEU9hQUAqACMMZudVZTINxABLgnPVXqDu6zx7LQhh4xuQ6zt2zlFnouc4dAOTXF5BdkgnqkfXWiNCQ4jaHMOOoy90Z7x3TIamL2YIhzxlBEEQDzNKFN+seRJ6jfhQLwvZgWr2xV4W7uSK3MofMknDiyjj6Vs2ba0xOTJp+MhO5uZwU2SM27OHelSh79SwQ3WMK5drjmcDLxBhD6aTu8v3m+p60R21Ywxa8GS8Qikh7DKskACb7rrtmGDqphOqyGip7K8FnwQJR2tNmCARFer7XGk6nuKmoCj9zE9rItBJkipwzqSf4MkX4cwrGzh3t6xlilURouMwRudU5JQrTIyLLkRnPqJD6Xon0Cde5gt0IJAVEvHASfRiLxwH09CcIgmiACeVF8WfVC7OyFwvCOFtyyYSqwZQTA20P46oormHs3LGeV8KPKanEQBDUA0pbnDUNoiAwehZ5v44XRNFacHmTjDHYwxaq+6oY2l7Cof86lNo4K22fWyEZJhiySzLxmnIRgyjVPjjr27PY4DgL24HQsyJS5GsyzoOi3tKXHcOf09CPEQQA+XV5lJ+ahSjO3ULX0ElDc7KffmrARceHVVIlEPQ3Xe4oXJwYZOTIfEDGGUEQRAOZCRvW0xa8aU8JMyygsD3GGYZ3DC2onKR+EumPJSLHO+dbMF0PyAcg5EK5zMQ8s+SS8YEX5k5CSoBlWFA+I7UhNA9eBSP2UIxIorf/AetZLCMg6fy1yKopf5AmlFN1j6695qcIf54HGGMYP29sQT6DeK/90/DqZFyd4yH3cKD8mXpXpsbdYFOu54WF+UYlCIIYICInMHRyCZX9FV2vag4mFXNIv+prJwo8xzurI0o1QUNddq/ORywaFlpYboAnIbSs+lwVuZ4zOIMoCuTXpCs9w3R+Wl/nkLBOVlxfALMZvCmvSSG17X6g8pdkTc6NIMgcsKCub5QeIwpErjmCQOQFxs4Z7XqBkWf1YhtnwCKvSUvGGUEQRALKIOOqADSjHKDjkdGzRmEPt79uTNcA8qt+b3kQBHEMya/NI7MkAynlnOZEzgXM5G2lhZv7rffzSLpHCxuU527qoen0YZOMaY8bg5T+og+T64selEujJL07ewkBNWIiwOIPa1zkp0cQBNEjOvk4M26rgsbEcUcnwwxAUAOI6QTzNDWbCGK+yExklGdKYk6FIuYCZZylbxNjWgykj7wzxljLiT3PsNSy8cwoCDJtXy6srl1QWEMWrNLCWJxkGV32ZJFfr4XR2wRBEAsNlrxKSyxStDE+Z3XOCGIukQsw5E2w7j1nHBja0btIRbtiysbLmApdZoMxoD7pUThzG0R+4YgbjZw+gtmnZhfevTDHkHFGEATRCj4/ifXEAkDXkmILTbKPIABYw9bcFJOeQ3LLc93X0NKqj8cCxrsobs2UB4/ZXCnNLrCQUSIZZtYDFvn1IuOMIAgiAdaFMhpx/MO0p1QulILjBBGB2xzFzcVBNyPGUA9y/QvF48GYKs7McxzcZos+h2nRYIbPIr9ei/z0CIIgeoPbHBMXtg6hIRYZRqmRDHKCOCaInMDIaQskf5cp9b/CurxSDaT7/riAcaZCfBd5RAt5zgiCIFqwUFZ5iXlAL1WSt5Qgjh32iD3oJgAA/JqEyAkUNhYAwWCP0nT4uICZKIdBN+TYkmo0Oo7zvwFcCOBRAK9yXbemP78ewO8CqAH4b9d133qM2kkQBEEQxwxjlJW2dR+qRRDE8YcoCFVEe33KItrE4DF17hb5IlrHsEbHcU4HsNp13UsAPADgBZGvfwzgItd1LwawzHEc59g0kyAIgiCOHWNnj2L0nFHkVlCBb4JY7ORWZTFxEYWtH3fo8geLPcIhjefsQgC36X9/A8ArAXwWAFzXfTyyXRUAiRATBEEQxx2isMjjZAiCCGCMAXTLH3cwru2zRa6Ykeb0xgAc1f8+AqBpqcFxnHMALHNd90dz2DaCIAiCIAiCIAjlMWOLvwp1Gs/ZYQBGXmcEwMHol47jrAHwEQA3Jv3YcZzXAngtALz5zW/Gc5/73B6bSgySWq2Gp556atDNWLBQ/xD9QmOI6BcaQ0S/0Bgi+uVYj6HaKXXURQ3lp2aO2THmg9WrV7f8Lo1x9j0A7wDwDwCuAvBd84XjOEMAPgfgda7r7k36seu6fw3gr/WfVEDmOOWpp55qO5BOdKh/iH6hMUT0C40hol9oDBH9cqzH0GymApEVsIcXr8Jmx7BG13XvBbDHcZzvADgFwBccx/mE/vptADYC+CvHce5yHOfSY9VQgiAIgiAIgiBOXHJLs4vaMANSSum7rvvOho9epz9/P4D3z3WjCIIgCIIgCIIgTjQWud4JQRAEQRAEQRDE8QEZZwRBEARBEARBEAsAMs4IgiAIgiAIgiAWAGScEQRBEARBEARBLADIOCMIgiAIgiAIglgAkHFGEARBEARBEASxACDjjCAIgiAIgiAIYgFAxhlBEARBEARBEMQCgIwzgiAIgiAIgiCIBQCTUg66DQRBEARBEARBECc85DkjCIIgCIIgCIJYAJBxRhAEQRAEQRAEsQAg44wgCIIgCIIgCGIBQMYZQRAEQRAEQRDEAoCMM4IgCIIgCIIgiAUAGWcEQRAEQRAEQRALADLOCIIg5hnHcdig20AQxIkNPYeIfnAcZ2jQbVisWINuALEwcBxnG4AtAL7juu7koNuz0HAcZ7Prug/rfzPXdalAINEVjuOcDOBVAN7vuu7RQbeHOP6g5zTRL47jbAdwPYDPAXgKAL3LiK7QY+gPAPwbgL+lOdHcQ54zAo7jvBzAZwFcAeCPHMfZMuAmLRgcx2GO4/wOgIccx/lf+mNabSRS4ziOcBzn9wD8I4DbyTAjeoGe00Q/OI7DHce5FcDfA9gA4J0AVgy0UcRxheM4luM47wbwEQAlAM8CADLM5h4yzggAGAbwZtd1fxPAEwBe7jjO6gG3aaFgA/ghgNMBPMdxnFWu6/qO49C9Q6RlDOpF9lEAwnGclzqOs2PAbSKOP+g5TfTDGICfAbjEdd03QS0yLh1sk4jjjPUAHgdwneu6VwEoOI6zYbBNWpxQWOMJiOM4VwJ4OYDvAfhbACsBbAPwfQB3APgwgB9AhTyccDiOczWAF0P1xz+6rnub/vzrAN4H4DWgUBCiDQ1j6FMAvgLgtwDUAXwbwP92HOe9ruv+9+BaSSxk9Bj6NQD/BeCTAFYDOAp6ThMpcRznKgCnu677Idd1DwD4qv78dADPAVB3HOdfocJk6Z1GNNEwhh4GYNI7NgB4CIA/wOYtWmj1/wTDcZy3Ang7gH8AsBHABwB8DMC1juO8BcDrAByCMthOuIRhx3FyAG4G8E9QIR9/YPrAdd0/BHCy4zhnu64rHcehxQ2iiYYxtBLA7wO4F8Bvu657g+u6fwrgdqjwtBPuHiM6ExlDn4Uyyn4bwD8DuJqe00QaHMe5Hmox8VLHcV6sP2OO49gAToGaBzwA4EoAywfWUGLB0mIMCQBwXfdRAA7UPBIUTTS3UGeeeNwB4JXaG/RHAIZd130SwO8COAg1GXgPgHHghIwl3gqg7LruN6AM12GoCZGZ/LwHymB7I4AzBtNEYoETHUPvB7AMwEWu694XeYF9F8pbfSLeY0RnomPofQA2AShAPX8OgZ7TRGdcqAWgtwO4wXGcYdd1peu6Ndd1/0mPrdugQhv3DbKhxIIlaQx52sAH1ALk9QDgui550OYQMs5OAKKrqq7r/o/rurvNVwBm9ecPua77Gag49I9DxaafMES8Yz8BsNJxnOtd160B+BcAL4hMfiyoJNidOMH6iGhPhzH0Qr0Z18IOH4My0AgioMMYeqXrug+7rvtpnKDPaaIzkTH0jOu60wB+ATVO3qS/5/q/vwaV1vAYAEbeV8LQaQwhDGUsA9jrOE5+/lu5uGFS0oLbYsRxnPMBjOrVMXOzcb3qwXRY3nUANrqu+1eO40xA5TP8OoC7F3sujO6fl0PJCf/Ydd0jjuMUXNedcRzncgC/47quCTv7KoA/cV33Tsdxng/gUdd17x1U24mFQQ9j6INQ3ulXAvis67o/GlTbiYVBD2PowwD+G8BLALiL/TlNdKbFGLK1UW+2OQnK0/pWKM/rMgCvBfAl13V/PIBmEwuILsfQ2wBMuq5bcRxnJ4Ajrus+MYh2L2bIOFuEOI7zOqiQvM9DCVr8V+S7FQCGXNd9yHGcNwFYBeVBXeK67msG0uB5RkviXwbgC1AKVtJ13Q/o71YBmAHwIQAPAvg7qHoef+K67kODaC+x8OhhDP0hAJNQTRC9Pof+2HXdXYNoL7Hw6DCGgne9/vtdAN4C4Juu6756MC0mFho9jKE3A7jDdd1XDKTBJwgU1rg4+SaAiwHcBcBxHKcEBCqN3wdwhk7qvBLA8wA8c6IYZppvAvhl13X/CqqPjgCBKtEPoMI93wfAg6pNtZsMM6KBbsfQM2SYEQ308hwiw4yI0m4MfR86L9pxnHOgcoc+SoYZ0UC3Y+j/kGF27CG1uUWA4zivBnAjgDe4rvuEVtGB4zjjALYAuBSqkvuPAJznuu5e/f0/AfgP13WfGUjD54lI/7xei5/cHUle3QRVuwNQ4UJnm/4B8BHHcT7uuu7s/LaYWGjQGCL6hcYQ0S9djqHzImPoaQC/4rru4flsL7HwoDF0fECes+Mcx3FGADwXqljyZY7jZCJf/wjqhtqkEzaPuK67V8s0w3Xd/3cCGGbR/nm24zgZN15Eeh2Ab+h/13T/2JGEWJoQneDQGCL6hcYQ0S89jqEMALiu+xRNqgkaQ8cPZJwdx2hhjyOu674Iqu7N5VCeMgCA67oVAF8DMAGl9vW7juPwE+VF36l/NGUASx3H+T0Ab9K/qZE0NQHQGCL6h8YQ0S99jKHqfLeVWJjQGDq+IOPsOMNxnPX6v0IrLpqV1UcB/BSqFkUp8pMzAdwA4IcA3u8u8loUaftHrxZlodQpb4UqKfAhmgwRNIaIfqExRPQLjSGiX2gMHb+QWuNxguM4BSjlrrVQdbdqjuNYruvWI9ssB/BeqPo3DMDDAFYAmHFd96n5b/X80UP/CACPQMVef4cS7QkaQ0S/0Bgi+oXGENEvNIaOf8hzdpzguu4MgCqAIag6SXBdt+44zlbHcd7gOM6E67p7ADwO4MsAfhNaAnWxG2ZAT/3zNgAF13U/RQ8iAqAxRPQPjSGiX2gMEf1CY+j4hzxnCxTtYs67rntYJ2TWALwBwH0AfgPK+JIAPgLgi67rflqLfvwzgK+6rvvxwbR8fqD+IfqFxhDRLzSGiH6hMUT0C42hxQcZZwsQx3F+DaqI9Ndd131z5PO/gKpJMQxgG4DPAnikwVUdc10vRqh/iH6hMUT0C40hol9oDBH9QmNocUJhjQsMR8ncFwG8BgBzHOfqyNd3QsnjTwF4NYDXaVd1IJ+/2G806h+iX2gMEf1CY4joFxpDRL/QGFq8UBHqBYBW1LkVqlD0fa7r/l/9eR7ASxzH+XfXdT0Al0C5qg8C+P8AzADAYpc6pf4h+oXGENEvNIaIfqExRPQLjaETAzLOBozjODaA3wOwC0pZ8XVQ0vcA8C0AV0CtinwcwF8CuMh13U8PoKkDgfqH6BcaQ0S/0Bgi+oXGENEvNIZOHCjnbEA4jnMTgCUAbgfwf13XvVx//kkA97uu+8e6JsV6AH8A4G4At7mue7/ejruLuGYZ9Q/RLzSGiH6hMUT0C40hol9oDJ14UM7ZPOM4zlLHcb4K4FcA7ADwHAB7Hcd5pd7kfQBe4DjOUlcVABwGcD7U6khwcy3WG436h+gXGkNEv9AYIvqFxhDRLzSGTlzIOJt/JIBPuK77IiiFnR0AvgBgp+M4W13XfRxKYecqx3EsAGcD+E3XdS93XffBgbV6/qD+IfqFxhDRLzSGiH6hMUT0C42hExTKOZt/DgC4DQBc193vOM4KAJMAHoKqRfF6AGMAfqyVdD41qIYOCOofol9oDBH9QmOI6BcaQ0S/0Bg6QaGcswGh44NHAHzWdd1r9GefAJAHkAHwWgCT2lV9wkH9Q/QLjSGiX2gMEf1CY4joFxpDJx7kORssFoD/dBznbABXA/hbAD93XffQYJu1YKD+IfqFxhDRLzSGiH6hMUT0C42hEwjynA0Qx3GuAfBlAHcA+Izruv844CYtKKh/iH6hMUT0C40hol9oDBH9QmPoxII8Z4PlIIB3A/hzKgyYCPUP0S80hoh+oTFE9AuNIaJfaAydQJBxNljudl33B4NuxAKG+ofoFxpDRL/QGCL6hcYQ0S80hk4gKKyRIAiCIAiCIAhiAUB1zgiCIAiCIAiCIBYAZJwRBEEQBEEQBEEsAMg4IwiCIAiCIAiCWACQcUYQBEEQBEEQBLEAILVGgiAIYlHhOM4tAD4M4JWu6/5di20KAG4F8GirbQiCIAhiviHPGUEQBHEiUgDwvwC8YsDtIAiCIIgAktInCIIgjnu0t+y3AOwF8EMALwfwSgDXAXgOgDyARwD8juu6/+o4zqMA1kd28T4Af6j/92sAigD+HcAbXdfdN0+nQRAEQZzgkHFGEARBHNc4jnM6gHsB/BTAX0B5xFZBGWfLABwCUALwGgBrASwFcBOAzwC4H8DvA/gfAL8M4L0APgFgN4BbAHzTdd1fnreTIQiCIE5oKOeMIAiCON65TP/3z1zX/aTjOGsB/C4AAeAUAC8CkIlsvwHAbfrfe13X/RwAOI7zKf3Z6yLbPvcYtZkgCIIgmiDjjCAIglgssIb/2lDhjbcD+GMAb4EKc8wBaBU2UgfwPACe/ptyswmCIIh5g4wzgiAI4njnLv3ftzmOw6HCGaMUAWwFcFHks6MAfABbHMd5CYD/BPBVAA6Am6EMuh0ANiL0shEEQRDEMYVWBAmCIIjjGtd1fwzgnQBWQHnHvq2/qgH4HIAzoEIbvxn5TQ1Kbn8UwKcBXALgj/RnlwD4KwDXRPZFEARBEMccEgQhCIIgCIIgCIJYAJDnjCAIgiAIgiAIYgFAxhlBEARBEARBEMQCgIwzgiAIgiAIgiCIBQAZZwRBEARBEARBEAsAMs4IgiAIgiAIgiAWAGScEQRBEARBEARBLADIOCMIgiAIgiAIglgAkHFGEARBEARBEASxAPj/AXsPRmsPvu5xAAAAAElFTkSuQmCC\n", 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", 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\n", 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", 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\n", + "image/png": 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", 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\n", 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", 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" ] @@ -4134,7 +4133,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", 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", 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\n", 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", 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\n", 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CgUAgEOw31mw58NSPCGvcvyQSCfs1pRTPPPMMotFo3c9t27YN11xzDZ5//nm0tbXh/PPPRz6fh6IoeO655/Dvf/8bf/rTn/CTn/wE//nPf8byJ4wZQpwJBAKBQCAQCN7QvB7DGhv1cI0VJ510Eu688058/etfx2OPPYaJEyeiubm5Yru3v/3tuOmmm3D55ZcDAF566SWsWLECp59+Om6++WZcf/31AHhY48jICBKJBFpaWrBv3z7885//xKmnnop0Oo1sNot3v/vdOPHEEzFv3jwAXASmUqn99puDQBQEEQgEAoFAIBDsNw5E7SNK6QfPN7/5TbzwwgtYtmwZrrjiCvz617923e7GG29EZ2cnli1bhiVLluCWW24BAFx11VUYHBzE0qVLsXz5cjz66KNYvnw5jjzySCxatAgf/ehHceKJJwIAUqkU3vOe92DZsmV485vfjGuvvRYAcNZZZ+FHP/oRjjzyyIOmIAhh+3cGiul+kNLV1YUZM2aM9zAEBzFiDgn8IuaQwC9iDh0YXHItxc/u46/ZEweGn6Crl2HmB7iZ+uA1BO94k3sunJhDBxcH8Pmqmmx5YFwRAoFAIBAIBALBOFGSc0arbycQjDVCnAkEAoFAIBAI9gt7+5jtNTuQEE2oBQcKQpwJBAKBQCAQCPYLn/nBgal8SnLOxm8YAoEQZwJBORdccAHe//73Yz/nYwoEAoFA8Lpn697xHoE7IqxRcKAgSukLBGXccccdAICBgQG0t7eP82gEAoFAIHj9cGC0nK5EhDUKDhSE50wgEAgEAoFA8IbGKchSmzLjNxCBJ6j2+nF3CnEmEAgEAoFAUAdGGfJ78+M9jIMWxhhWrWVI58Z7JO44Qxn1vDF+AxGMCqpSZLdnMfDM4HgPJTCEOBMIqkDIgRp8IRAIBIL9jZ7Skd4sPCpeufcJ4IRLGHb3lv49d4AIXmckIxXa7KAhvyeP1IY0mMHAjNdHPKoQZwKBQCAQCAT1INx7diBhZL2riN4htl8LXz2wyv27hp4bgp7Wkdmyf4UvYwznfoPishuo+e/ie9TYv8dmPFEH1YP6t+pZHTRPwTQGRhn0tD7eQ/KNEGcCgQPnDepgvlkJBAKBYAw4wNJaBl8Ygjaijfpz9z3JMPlMhstu3H/PuWrBKFSlyO8tILtr/8Y77u4B/vQYcOOfzXE4zi3TGZj+xrABRtamoA2Nfg4dKDCdC2lmMGiDGoZeHEa+uzDew/KFEGcCgQPquDtTUUtXIBAIBDaktKTfAQAzvImIH9zFP3PTn4Mdz/BLw8jtdhdZUhWLk2oMRs4A0xhyu3Loe7Ifua6xF2pG2SPeeRQNA6+bELm6HIQ/U8/o6HuyH0bWAC1QMAowndqiOrUhNd5D9IUQZwKBA6cgE54zgUAgEDhhB9qaHWWejOuxCv3SRnSoA+5eGLmKxcl0CqZRUI2i0KvyEMet2f0e5uhcj6UeRe9YQfWxm3gHo61j5LgQG3hmAGq/BhgMVOe/hekMOIDOnReEOBMIHAjPmUAQHAfjQ/9AQ8/o4jg2AKMM/U8NjP33sNHlIlGdjun5Y4a3nlx0jIxXVkMsStVqbFEGPcs9Z3pah5E1wFS63ytjUoenzChQqH3qfv3+cgo9BXvuDKwaRGZbdmxEmsepoA1pSG0cHw8VIdzjSjUGPaUDjIEZpufMYGM2v/cXQpwJBA6cD1EhzgQC72S2HVyljfWUjoGVY2/cj5ah1cPjbiQeDDCDgRpjK4QAxnPORvEVA6sGkd2WHbsRMTbqUEuqU7CxqkZYIxSwWlgjo+ChaYzByBswchRGntZQc2OD4XAmUoMhvSnjq+CKX1Lr0/b3M41i+OVhaENj4PEc5fwxsga0IQ25PXmk149f9VKmURDCx8MowDSG7M4cqErBKEOu68CoAuoFIc4EAgcirFEgCAYjo4MWDp4FDj2lV6y2jrXXoxEYZXYRCkYZBp8fGtfxHLAw8FCmsZxyjJ+DRis2MoOBFii0wbErtsCM0YU1pjal8Zs70tibGhvzr1bYZ9WwRgZogyoYBYwsBc1TGFkDZD+LsxLPmQ7oGQPZncHmvjHGoA40ttjCdH4cGOOeID1jwMiOgThjo2sdlN6YxtALQzx8cJxuj8xgoBoFLVAYeS7OqM5zF6lGAYMhvTE9PoMLACHOBAIHIqxRIAgGIpOaq+i1GA9BxFhlAYCBlYMYfHawYWNqTCirIGfkxm4lX0/poOrBed/j5w8wxrB5MBfKjRmk6qCKwWcHQfMGtJQ+dufNaEwsUo1i4LlB/OGvGj51VxLdGdl+L8hKfYxVbzdQ1f5nDFQDaM4A0wwwjUJP7f9wXqc4IxKBPqJBCvNBq4PB3AP0lIGRV+uHAvLcKWDktRRgeoWgUWgjwc+j0RxmqlPoaQN6xoDaW/AUUtvQmOrMaWYwLuLzBpoWNYEZlBeVUSmYysAMHFSLg+UIcSYQOBDiTCAICELqNnLNdeUr8oTy3QX0PzUQ+Ip1XVhpA9NcVw5G3oA2YiAzjo2HnWNizCyeMEYW0dBLwwfUanO+u4DUugZzWigPaxxaPTx2Ao1xz1AjRUFGXhnhoixPYaR15Hbn+Yo+gIFnBwMTRMxAQ2KxsK8AdV8Bz26sNPv0TIDHq8bcrOYIYwaf13rGANOByNQIpIgEOSK7f2AMMAoUIxuK1zkDYGQMu7fdyJpUYD3uGtoPBahBQQsM/U8PgGpccMDguXiB9ttr0NRRB1X0PzWA3O4cCj0F6OliwuPQi8OBCdhCbwH9T9cOMWeUQRvRoQ0bgASE28KIHxLjokzj98iDWZwpjWzU0dHxAwAnANgO4NOdnZ2a+fcYgD8CaAagA/hoZ2fnvrEZqkAw9oiwRsHBijVfRxOeMpYQCWDltarLMNI6aNk2+a4caI6vnO9Xylb805syvEDBiIZwW0OPyrEZlvPwGNxAM9IGiEIgx4I1XkcbIjfWqL0FqIMamhY3sDEDPz4qrfgNRtaAHA/gWJnirBF3gdKkILN1BFJEBlUNZLZlEGoLITIxDCNrQE/rCLWG/A3HLE7SyLNKCktQ+1VIsYTbnnyNo3xX1YRDtZwzgHuFkwsSyHXlIQEItYXG/Bns3L2eKfUaU4MhOj3K85d0BqZRMIMFE2rZgGZgBuMVCDXDLBFPQSlfQEptSEOOywi1+Js/9nc1uJ2eMmCkdTAKxGbEoI1oICEJQy8OI7czBzkmI9wW9j0eI2PUrZTJDCDcHoaSlEEIQag1BGaYYY0qg5ExIEWlg9aOq+s56+joWA5gRmdn50kA1gM4x/H2uwCs7ezsPAXArwB8ZiwGKRDsL4TnTHCwMrBqENnt/rxNVKPBhfBJDYY1lvcaogy0YOz3flKMFj1nmW0ZUJX/2yiYxQnGC8oAAqQ2ppHZnuV5Zy8MIdVAaNSoMdiYhSl5wS3UtOq2lJfSNsxiAMV98OMVhJHGmPl/DUwHIhGEWkKgBQO0YEAf0aENaRh4hntBAjHyzePTSHEPRgGlOeTqvqJ5anv1fA+JVRf41XLOiEJANQYpJAFmNUJCyJiXsnfeYhgh0NXiH5SWEIhMQAuMCzedBdJGgZDG5vTAs4OgOqCldOT3FLgXyDzXTGXIbssGV6ykwWtDChFowzoiE8OQYhIXs5Qh35ULtEJibne+7rmnKgWRACVRXDgjMjGvB4rwhDAPBz1IqzY2EtZ4AoCHzNcPAjjR8d5mANYyTBuAvuCGJhDsf4Q4EzTK8MsjgRk0QUC10XubrJXifDcv2ZzvymN4zUgg4yESQBsYDn+YMvQ92c//rfPyyNRg0EY0DL4wHMh4qEYxUuu3mZ4XAMhuzYIVDDCDIT47BnVAgzakgarBGbGNYgmN/J481D6VG0EqA+TgPaT1wlDHg4YbAVvFOsx8E2s+gZr7COK0MZ7f1YjQoxoDCUlFkZ8zoA9pvMCDRnlOpu/xgP++Rp5VlHHN5OJZT2/JIr0huHDWavli1TxnsZlRJOdzU9IWmgRjvkBT0teMouTaluIKIBEUugsY6hwGM4tMBAGj9SNzaIHy0vB5A0qTAiNHEZ0W5UUwVANqv4p8t/9qhGwUcxoMMHIGpIgESZFACwxgDLm9BTCD8kW1AKAardsywMgbIErlhKJ5isikCJQmhYu117E4awNgPdGGAUxwvLcJwJKOjo5XAVwE4K5ghycQ7F+EOBM0ijaijWlxhlEzyhLf6qCKgWcHYWQNDL8ybMfnMy2gh1kDxhUzw8So7vBSZQ3+oNcY1EEN2R3ZQPIrjKwBtUaeD/ec8ddUM/su6eZqvsHsfKyBVYPIdxd8j6cRSsqkG2Zolc7AVAOSMgbhq6zxSoRqv4qh1UPBj6FkPI3ldwH8WFlV/uxQNNMbar0OYjygjQk9plNIIYLk/ASU5pBZKt7qzUQD8VByg5oXjqi7LWUgcL9F6CkN2ogezDEyvRX6SOWgqjkLpYgEKcLNUWqF1kpkzHtVOSOqDYNBcyxuUQbIMQm53Tnk9+bNnK8gvK+NFZWhBQqmcW9dqFmBFCKQIhL3cqr8Xjlm7RCqwBhDeELYXliQQoSPQecLRiyAYkJWO4V6QtjIGK4LHEQmCLWZ4Z4NeikPRBoJpB8CzykDgBYAziy9TwJ4qrOz85sdHR3nAPg6gK84P9zR0XEhgAsB4NJLL8Xpp5/ud8yCcUDTNHR1dY33MMacnp4e+3V3dzcSCbf4fIEXXm9zSJ+kIz+UA0kdGDle+jQdOTmLdFcD4W4UMAoG2CSG7J4M6HSKvb0qfzBOp4GcJ8oojIVGzX3REAWdRdHdo0KfoiO3Kws6lQKTAV0ZRlojoAsp9uzZAxB/c4gZDMYkA4UqvW8oKIz5Bp56oRuf/mUrTluaw5fPGoIe0cHaKFSiIitngElALpeF1LV/6mlpczX0ZnthzDBKDA01oiLVyLkezXfN0lEI5ZHpqu9FoXkKGqPIdI1dsRQjboDNYg2dc2YwGMu5cd2b7oEx1cCePVxE6zN07N2n+p9DOoO+WEf3YDfISO3rXp+gA63gimQmAJ0hFRoBTVCAAn35XpAun/cOBmiLNQxKAxjuGqq5KdX49ai/Uima1IUqNFlDdmcWUsjfmLS5GgACdbgAki7dVzqTBNBU8ZnstGIfOHI8g6ao0MDPV7XrNQj2disAJgEAuod7YCwsXtP5ZB7Z6VlgGoNRMEAlwz7vfu9D+iGGfU+rhjZHs6/3fCQPzAPylIets0kMkACVFOqe90bQZuvoSfWAZGufe6ryOaSFzEWuSYCmaoDBoBMNqUgKuS7/hZy0QzSAoOYx1lt1oBkolCv+SUAWfD6xNorugW7oVD8gbY8ZM2ZUfa8RcbYSwP8A+A2AdwB42vEeQTGUsQ9cvJXQ2dl5G4DbzH8enBJWgK6urpoT6fWCs5jCpEmT3hC/eX/xeptDvf/pRevRrTWTsvufGkDLimZIUQmEkGBCmarQ83AvQhNCaFvQWnfbzJYMsrtzMHIG5LCE/D4VU94zBdqAiuFNI5h21lTf40lvzmB4/TCmv39a1fya1Po0MlszmHhKO/pfHUB0RhSDzw7ylfSwhMikCAo9BUw9eyrkqOxrDmkjOoY2D2LSWye5vj+yPoX0xjR+8vJkbNoLbNobxpXzMojPjvOqYEMaolPD0EZ0tBzRjMSMsV+4oTpF9/P7MOHYNqR3ZZDZmIHSqgCEILkwiZb5lYauH7pf3ofI5EhDcyi1PoX0pgwmntxuhxAFzeDqIRT2FTD1XVPqbqsOqujr7AdjQFtHC0a2jGDSWyeBMYa+V/ox6dSJkOP+5lC+u4C+lX2Y8u7JdYsx9P6nD4wyyFFeiKSwr4DotAhye/OgeYr2U9sRmxHzNA4LqlLseXovWpY3o2le7bmQ2ZrB8KYRKJnKccsvSJBiMiae3I7otKivMXW/2A2qM0x660SEmku/q6XJ3bOS7E1W/M0oGDDSBtrfPAHhdv9FJtwYLHDPIwBE9sSh7Cy6osKpKJK9XKxpwxrUPhWT3zsZ4Qlhf/ehIQ39r/Rj8tsnQwq7L/AwxrDn2W6o/SqaFibta8vq4aX2qZCTCpJz42iZX2F2jwpmMOx7cR8mHF//OKc3ZzCyaQSRSRH7b5mtGbNsvYHwjAimvnuKr3sBowx7Vu1FqFnB5CWTq26z7589kGNy1WMIAIWeAia9dSJ6Mj0Hne1Rd+mvs7PzJQD7Ojo6ngRwOIA/d3R03Gq+fReA93R0dDwG4NsArh2jcQoE+wVn3LUIaxTUpJEQfcpzTYY6hxvqbeN3PI3E1zPKkN9bMMNQ+PiYzhO7GeO5XsEUTzBDd6rsyijwvjR2Ho/OG4oqTSEwnRdMMHK8uWhQoSm1QuSYzseqOxwL8dlxAICSlKGPaDwkLU9h7K+8M6vgg/l1VOfhejDo2OTjUNZwcQAjy8cy9NIwCj1jFObZYJl4ADycjgGE8BArqpoFCswwu2DCGlnDTaiZTksrp0qAofHS6FQNJiTNjGqsu6+RtSM8H5UQV2+NoTLoKSOQ5XNGrXypyp2RUZwDIhFoI9qYFqhxDkft05ArFA+O83tDLSEoTUpgYY3MqF1O37rGIxPDpUJH4oOmqhm+G1SYZYOXPdNpxUIbMxiUJhnNS5tBVea/yq41p2vcYrVBDXpKB6nj5WV4fYc1orOz8/KyP33W/PswgHcGPSiBYLwQpfQFjdJYhTQGEAIjb4z5Q4IBDRnsvNgGBVMNGHnDzqXghSYoYFAU9hYQne5vBZ3nDHChR1wswtSaEWhmWWZQXjyEqRRSRIKeAiARs3R1Y8e6EaxEfNd2AwYFAxCSGCwLVjcAReaGYnxOHEyl3LDeT9Ubh18Z4caYWSRFSSi8qp1CuKAeAxo91uqACmoJ7DFq31Cr8l/lttYwCAr7CqAqhTakFSu4BVIQpCiI6kE1BtnhOCKKBKYy0Dw3qgM5f+Yzqt6zqtCvArR4fMqJzYzByOnB3KMYqopPworXVj2kkMS9jmNYFMS5/vrZe5vwn53Fe57b1wZyH6L157VVst/poQJ4ZA9VGULNIaiDWkBzCHXbQ+gZHUMvDvPWDy7izHk/9TuH7DzKGvthBoMUJnXbxhAWTJ7geCCaUAvGlV/+8pe46aabxnsYNqIgiKBRGhLvBuMGkVUevgy+MhzUw2MUpYx1Bm2ECzM5IiHUpCC/Nw89bYAZwPDakZKeP55GY3k93IwcxqDnzKRvyzukc+EjKYT3RzOTuYlCAMoCKBtdOxGf6tzyzjpSXAZzxYc/MQsUUJXCCKC56c59DJfdQLFzXw2jKKVjx4CMOReFcPOjIUSnR+1S3GNRDKDRHl5Uo9CGNRCFwMgY0Ic13/PF/YsaGw/Aj5Xl+VP7uXBMb0ybYVcsECPfKubQiNCjamlFxlCzgkKvapYfRyBeD/v6qnfd63zc1cLNQs0K91QH1LSXae7HWxrlvY7IJBhRXQXnI94pzAD3aVevb2MjWEWQanrODOZePYXweaU0KYjPiQVzD2jAa8YrjPKCH6RMNfCxWsMLyEMNfoz0lI6cS84hnxMNiHwytvNnLBHiTDCufPrTn8Z///d/I50OroyvH4Q4EzQCazC8iVEAhBv2TK98IA+tHubhRIEMqsFVS8pDF42cWdI7JAESkN9bQKGnwEvyD+u+xRBvjgtXMaT2miXhDdi9rJjOYJgGbWJuAoSBl68mBNqwhsEXhnyNxy61XuWcMZ0Lt0ymeN0PZBwGgMzDepjGAhEi7/saw41/Bs78ao1zRhm+9WQT+jISvvV4E2B6Ea1ePoHDGiunrw1oIOC9oNQBDbndOYy8moKe1kF17rEKZDij8Hhlt2ehDunQUgbUARVyUrab2QZZrbERoUd1HmPpDAEjMoEck5CYl0B0aqS+oGpkOJSZxn6d+5AB6Gbj8mpbEpl7+H1DUDUEcDRhjUDx3jBWjGo4DMG00WCML0rVCrGu1gye8DEQhfBnSgDj4cKsjoea8D50hR61QuAnDk3YuWoMxH/LCgbwlTmG7K4cMltcCg5ZbSHq7WqM589YIsSZYNzQHckdhnFglCQXYY2ChqCwvT61YGYDYRBAT+vQUzpS69Om0clM4zEgI5uhsQcj4+GDSkLmvZjMUCc9wxvlMtXMifFrzJql/V1XoCnsBqZgDKkNae5JoAAkQI7LIGGJG5QwyzT7NWYZapZBZ6bnLD1S/J5UocxzpgHR6dFAxNnqjfy/L2+uPeYdKdn+p6RIvM2AGTYW1Cq1/XXO0v01oDoXzUpcRnRKBEaBQu1VMfTiMAp7Cxh6OZjedI2GNTJm5bowRKdHzUIbZs6iWY48qLDGRlpWMJWBscqV/cikCOS4zMvEB9GywtxFPQ8KNXj4qZKsnslCFAlGJqBEOMn0fGTK8o9G+ZMJCcbjWY1a668VXysTu92IL6xFojphe24QQsBUM5pACsr7yhqc0waMXGX5ejkq24sQhASwCGJ93MqFc/mNVluIeoz1/BlLhDgTjBu5XLHkqlOojSfCcyZoBDuJutbqp6NnlByVeWNWypDblYOe0Xm+TtaAkQsolIg15k1h1GxiDCA2PcrzCAhgZHSEWkIgCjHFmc8BWQ9FF3VGCMCsogEMMHI8j8uZDxZqVviKrESgp3Xf4oyfj+qCxhpL1lHbIqeVijNmUJAQAQso/KseclLBvoxc+reoBKUpxCtrvhSMCLKpE24FAL/+J8P5NypQzfNBZJ4HwxtAm73y1IAMogZCrgDwvk85A1JIghKXuQAC9xZRg9oFb3wPh5Ve11XHo9OaaXiSQoLpkdjgeJ7doWBNDxdm1Y6nFCKgAYzJvm9QhqHVwyXNhEfrOQMI6Fh4iE3UGmZH+XGSFMKbLvuEmQVqhl50v3ZTG9NIvZp2P6eSObcUnm8VVE9KO8rBheyOLPRhDUaOgubrNU/3H0bIGHi+sRnSXC7OqEaR2ZZtMNqZBOPtHAcaKggiEIwF2Wyxt4mmBRMG4xchzgQNYQmzWk8Ih3eNMe5Bo3kKPWOgsJcrAKNAQdXgvMaNhHDYXiuACzMAoeYQ9GENSlKBHJOh9hZ8G7PMyu+qEp5jqBRyWAJjDNqAyjfVGGD1D5UJwu1h6GmdVwP0a4dY56zKZW01Bs6qxb/lHcYbkQmvIikT0CDCv+rAQ+MAtcwDE5sVh5EzkN+bh9Jau5z7aGGoP4fO/x4DIONN8RjOn86PB8sZYGECqgPZHbngDCKGhtQZLVBIIamkiE1kYpg36rabUAcwHEob8sIxrbqxC5iCNgghZF1fVa5VPaMjxyScfVcbAKDvqKHqYY0K9577Gw8zC7Nwo9hqmm5ZmqNeYCFAbnceiTnBt60YGGE46dIa3qvyP0gBXfeMLyRUay+S3ZoFowzakI5oWUcTYrbQIISASQEVu7C9we77ymzPQpIluzF0bXHW4GpKHQgpzu1ykWrk+KJmQ54zhUDPGkCk/rYHGsJzJhg3nJ4zIc4EQTE8PIwNGzaM6XfYHpgazyE7X4aZYUeEcE9Z1kCuO4/cnjxgFsEIZlANhm4xBqrRkhAnKSIhMZ/3GiIyKa5c+hkOdRiPLu/RAs99o3kKLaVDScj8oVvmcpBCkmnkUX8CzTRiq4YrW54ztfj9ea10LFTnnr3AcphqMLBqENqwBoNWmiFE4XOJFigy2zJIb/XfCJp7g1nN1fhCX1G5bh1W8NRWBT98Kg5d5eFDTKOgOuWetCDCwkljtp6Ve+OcO0QmUFoUUJ2HfwVmyDYgGIuVI90hIQIjkIqf5jmrsqvB54fQv7H4nK0pGE1vnl+PHgEDJILszhyvwOoQZEaVuVVtXMwwQ7/H4Fr7+8ra75ePiciwIw58wbhH3K1AFAAYeYpCdwGhNnffiRTiZju/DwUTZgkw5HZXafZtnj8jR2GodcQZC6AAh3ngCYqtMJzQvAE9YzR0fwksVHccEOJMMG4c6J4zkXN2cLJ8+XIsWrQIGzduHLsvYVYifo1NzKpudsI+5QnOTKcwUjxMjzfvDC4ErCFvFwOUhGJ7zartyrfnzDSGXa8js3ol03muUGJOHNFpUehpo+KpRBReJdFvZbLieXB/n2oMOgVGHHlmubLbEgFAwoRXAQugoANg5kW4jdcUgG5OKCkkoWlxEwo9KtKbsshty1ZuNFpM4VErF+qv9xcN/b1pCWffkcS1T8bx0OYwn8sa753HNCOY42MJ6nphhFXGTGReNIGMon9b3fHUK54A1L12iGzmw/kdk+mlqnZ8ysVWVnM/DP0ZgoJOQAj8FXNhAEDM484rsDpFsVFFIFc7XFJIssPBvZLdkUX/qoGKfUTqOJ1pmceaSCSQhTTGGKQQX1wpP/9WiGp0WhTRKXVamUgwCyr5vE+b4r4iP9DE6nsZnhBCYm687ph8C0b+qMTH7m3D1+4J2b/POvZWj7dGFuqs43wwIsSZYNw4EHPORBPqg58dO3YAAFaurLM06gcG09NSJ6zRNAqZzr1Vhb0FXuGu3yyhLyOYJPNRwBowVIm1na8vMr/H1XPG+3YxnSFxaAJyTAaRCWIzovbKsD0Wy9BroABL3fHU8JwxnaJzbxgZrfj9l9yTQPdI0UhrWtwESeHvB1UFrJqHhWn89xouhSUAM+xzYhjaoBpMCXTz59Qyrj746yb79XCheJx6Mzw8leap2SgbweTDWNdPvWqEGnU9r8SsMscCyIUBrMWY+nlQ9YQpkXh7iECK3BDw0E2Xuc00hoKjGmym4F6t8bCrW9Dx42az9LiPMZmXKJHMXmcaKxHO1QpYVPvKcHuYXyA+zl12exZan4rM1iz0dNHOiIRrf658SDysOZjrjIALqwpPnLl7pal+xhEhvHuk7+vMCh/UGAr7KpvJW4tERJEgx+TKz5cOKpAiJa/1KnhsRwQ/fSoKMCDfXcDAs4MAzPDzQmM50UQhoPupJ2XQCHEmGDcOdM+ZEGcHH06RryhjmFLLip6xqpuYK/40z3MvwPjqZKQ9DCXJS01LcjCrscXvbGwbVqd5J2NAdnuu5jb1v6joaCiH6hRyTOZ9zBxhMtWMEt4om6KxAso1xlMl58wq678vU/lIvPqhaGVVN+Ihf6bGuNygRmkRC1mq3FCOSTDSRiDhVozxcLRGxBAA5PXiedsxLMMwWwwwzQxXDaRUfPVz5kTtVytEvb0PnfdmCsKwZsxsil5jLZGqFOktGdRdSQigoAMz4ycZBQaeHURmc7bkPaYzqOmiOEurpOq6TPeItejgbzwEzPYyUYM1FNZYa7oR4nOhiBAU+lToIzoGO4fsP9fznFUcJ6m2V7lhGMDAvZTlOWyDq4dKyuPXM0EYAljcsz3mFCPrUtBGSm0xpvFQZylUP8uLEPhvEVG+BkcZUq+O2MWGaIEvAJE6OpEPKIBFxnFCiDPBuHGgizMR1njwsW/fPvv10NDQmH1PMTyu+hxh5sp4ZlsWtEARmxVDfDYPC1FaFf4Qk6Vgm/c2MmVNA6omlJkFMrxfA3a+nZvnTOchJ84GpjX3ZXpj/OacsSq5R7TAxXOBVg7m96sjmPL1FvSlHcYJG3vPGRhAHSLATXsQQhCZEuENvH2Kj2weeL5L4c6qBgyaHoeQvW1dMw6/ZTJyebMHXEB92JhZ5tvtWGtDmlk+n4euhVrcrW1LuPo9PuaAeD5mjeuCmkUwGin27btwCjPLlxsMtEBLPENWLzzNUQ3W6QV2hcDf/cgMa4TErymmlo6pWg+9utPNr71vmCGejhA3uc59p3xMwYU1mpEJDCVeHWYwGGnDXojYsE/CET9oxmV/jtXcn1+vuRXgQFVejXFg5aDjPXNuabTq4kcJEvG9KMMFfinasA7DbO9CCxSRyRFEJtev8kEIEX3OBILRIgqCCIJm27Zt9uve3t6x+yLLgK3lOTND8ayy9FKEh4U8u0PGDU/FoKsMUpjYK4J+0HWGLQNybaPR/J7U+jTP7aqBNW5fjio7fLJyJ0yjxeqHVaqWlW7PfIfL2M2u3cSZxkufG0l3A58xgr+8UvpeEJ4hANVNeIaSVfRqxmSoNcSr//k00j72HeB997Tjt6/EG/KedKVKl65TmoT1fQquXRnHS/3hgMIaUXX1e+jlYQy/PIL01gyogaqFCpjlpQ3iEcN4yF4t7wAz+HytkkpYgm9PDIMZ9scq+i7yyqISdMe8+NhvkrV7DfttbGxe8oQU23Gofap9/owqx40yLrarGdK+vB+UIdTMC3DQfHHBSavjba4Ia5RI8b7vA2ZQgJinzXE8Rl5NlfSXvPGJKPalJNz5QnURwsMafVfgAGO88ApVaWkupLkwQgsMpBHPmRTAeKxrzPon5T1CmWo9TxmkiNzQc8NWwQchQpwJxo1Gcs6y2SwyGf+VyBpFiLODmzVr1tivnV60oHn6VeDeTbGauSdWThVVeYl262Fyxq1N+O6/47h3bRgZosAIICTtw/+P4c23T8D969wf5EbOKMbs52jdMDhmVlH0u2JNCHHdB6Pgq6xGg+LMMj79YDZ+dRVnKq/cktOqj0Upe1oGFtZYDcZQSBXvi+UeNkqBn68KY123ZOeM+OGvZormb9fEXI91z2Dp39yO1R962nDtC804675JARUEYbj2uSTefSXB8I58aSVBg4EaFOnXUjV9VE2HNyHUGgpmBZ2awqNmA2HTi1nXKGS+DVnGABKSoGeNyh5+5q41R2uIVIHU9FIx4q85tvMnU5UrNXVAxeAzgzwnscrPzW7NYvUnX8KW67ZWvkngK+eMGszs3Whe+xTIdeUw8MpI7Q9WOQx+5hGlDB/4cRg/XJXgx4oyGHkuhmiBFkPgUXq9pypTwfhYGHznUn7nLoJPPDABapZ7fGmh+HxgZl4103hvtboEVCjJ+U2MMhh5CqrxysZqv9pYSKO5o0Caz48DQpwJxg3DKD5o3TxnmqZh8eLFOO6440q2HUtEWOPBjbOE/q5du8bse079soTL/tOGTV3VH1hMp1ygaQxMNUBkgpe7ik+V//53Gw67YSJGMv7n2Z8f5//9/dqY67ylGn/oqv0qr5JYZ/VXCkm+wxrtYgVu4szKNWvQ0EnMT0CKSv48ecz0arj8dqYzgBDkXbwrbVoBh2cGoRnlnxnbpz5RJGiJothOFwi29BUf2f94LYSv/C2Ok25s5uMJ6H41kJNcDdC/PFZ//527ijmDQeWcXdfZhIdfJHjgX2ppwQKGEkOyGoQQM0cwgJA0c1GhpudMZ3X7nJkj8y8YGePVPpllqPMeWsxaiACgl4mtOzure2IK1GeoJWMgBJBiEg9FZvx4aCM68nsKVUXt83f1AQD6nxxw2adP54clzgrcK5XZkkFmaxZqvvZOXYfK/C3KdK4H/vWKjBueTYJIPAd5YOUABlYN2iGEVl5eylHZfueAu6lOAugr9q27JDy+O4qn94TN8GgGta9g931klD8/ylucuI5HCiDcu+zjTOVtX6jKoI/oUAc0SOHGpEutSqYHOkKcCcacVatW2RX0nNQTZ6eddhp27tyJtWvX4qmnnhrTMVoIz9nBjTOU0W3OBU3PQO0wQqsoiNXT6LSbmyq229CnBBYXL1VZZWYGX3UcXjMCagDxObVLIkemRmA3J/WK+VB0y9NgOoWkEDQvbW5oV1JIaiznoSasaiiq9QC3ilz8z6l5/PTcDO76RBo3bH0GP9zeid/do2I4ZxooxH+uh0VVk4cyaLHSAiln3Jq0X29zGGy0gaIZjTKQk1wNmlS6/hzd3FdcfGAB3D+d10X3UNl74Dk7zGiggElAK/qMmf2uqvXrMhgKPardQLwevvPgGMBj5GC36Bh8fgj5PXleYQ+V4qwWKpUCCJPjRrGcUHjuqs6gDmjIbstWeM7eNtiFmzevRPMrPbV36DP3VYpI3OtKeZN0fUiDVue4jORdrkziLxTV+UkiEeS7CyjsU3kvLsbPYaSdl5Ecdnz/jkF3V1FQVUgBQNV4E3kCIL05g+yOnFkttXJho9rpIJI/z6u1b6cONDQg3BaCHOX7lqOjexY0IioPRIQ4e53BGENnZycKhSp+8P3M1q1bccIJJ2DOnDkV79USZ7/73e/w5JNP2v9+9dVXx2yMToQ4OzDZsGED+vr66m7X399vv3711VexadOmsRxW1b49QLHIBNMZjFz1uWQl9AeBVCU/h6lmeKXKiwTIsdq3fiKZjaj9eqo0itSGtOt75U+fbf0Svvq3GPaaRQsoBVbvll29Wd4GxL/YzTPADP5+1vyulhjD+w7NYsYvXkG7zuPCjk734bed3HAicnAlmqvZDtQAckbpQepzFOFwfq4nK/lOrVBkMxeHco+OOqii78ni9dTdU/p7CRgOm2zgnk+lccXbKit7+u1LBwAZR0je3mFSomQl2ewVVSf3EwhoRR8AwD2+6oCK3sf7So4PABR6VeS68qCFBjxnkv88Qcval8K8sJCe5nlVmW1Ze1FEbq9TM95BnhJ/5638N1PGi4KkdBh5o0ScHZ4ZxBf3vIY5hQxC9Zp6+/B+MINBCku2R9HIGtDTBvQ695XedJXqnz7Ea0neqGQ2klcplGa+CEN1INTGc1ud4uwTv0vgjFuT+P4jUXzid3Fcck8cuwZ5xcegPPjy5Cjfl2RWEbXbM5SGTH/7X1Es+0EzBrIuNy4pOI+5haFSM5/UvF7K20VQhlxXvnqLlCrtXA50hDjziWEY+2WFvlFuueUWHHPMMfjQhz403kMBAGzZsqXqe05xpus6du/ejauvvhrXXnstzjvvvJJtd+/ePWZjdFIuzi644AK8613vEkJtHOnq6sKiRYswadKkutta4iwc5gbJs88+O6Zjq7VKSDWGuzfHcepvJ2JkShJ6FaOHILj8JZm4l/e3KnFRg5c6byhExW/IjBVqpVKMvJZCamNRpDG9cgzn/jKB21dF8OX7uVfvrtVhvP2nTfjcPXUan45iPCDEVZwZBQM5Cuwc4CvUsRDD3r90I72hmO8aMwx07pRN7wmBkR/jUGtKkVUrz5OqA6/skbHD4Tnry5AGSt7Vxnk6GAWMtGELGj2jY8+e0uO254p+PP2FFN6yQMfkpsrv1gOY0wO54m/M5kvfk6ISz7MyGhBeJBixCAq7+IqR0qGndVsEMcaQ3piG2luoEBM877T0b0QOxssAmIU8DAYtrUPP6mDm9Q4AJB6q+FCzrsKNguG/z5kFMY851Xmoo57WS24npwx3N75fP5GWlB8fEuIC1mpirNYRZ/0jDF39DGpZOryfc+YUZ4QARka387yMjA5mUDsHdyRXvCAVSnHGE6+g6w9d+MdrYfzxxTCO/FEL7t8YDaSvGADoklWgCXZIo72gZV7LqQJww+NR7B2R8OeXKosnEYkAvsUiK7mVqSoPS2UMyHfnKx5JXX/cg1cuWYOuu/Zg6MXhivkrPGdvUD72sY9hzpw5uO+++8Z7KACAX/3qVwCA+++/f3wHYhIKVW8mUu45u/zyy3HVVVfhf//3fyu2Hcv8ISflOWd33HEHHnzwwZoiUzC2vPbaaw1vOzDAcxZOOeUUAMVy+sPDw2OSQ1hLVFGN4qvPtmPzkIJvPZJAv9tKI4ACDa7cLw9rdDGU0zoP/9JG03/KvZhHo1jH2yhQFPbmkdvp8K5QVuIF6U0TbDeF0T/XhfC1B6K45SmeG3P/Wmvl398KqFWUxc24YirDRQ+04p/r+P0qmSsguy1bsk3S0PD3V8O49xVeHTGIKptA9YbfjAJZlzpJVz0Qw1t/0oRfPlvMHXp4e9T36nDJ5WGtmptzZWj1MPbuK/29oeZiyOWkZOWxCCKXcsDhKdQ0QB9xlooHwu2hhjwZgeWeUGZ7vKgOGGndLs/ONB6+bOdSmgd02y078Py5L2D9NzeWjimIBvSWBS2ZuW4FCrVPg6FSZLZnsXKLjH+8VvoMvnjvevx+w+P4zvYXMFXN4v192xExa9zn9QCueVL6b2Ya+vqQVtLnTKryRc5y9yU/0yuUV0dMzImDqrwpNtVozcUDiTGc+2AnHr1kAz70q4RzJP48Z47oRKoz7unUGfSMDqPAC3JAAvYME+wa4nN/dj6N9/XvwNHpfny8t7RgyiX/aPHfV8xEZ8QunsJUxnMGKffKJubyBbIP/LwYVr1ym0tPSp9hnwAAVprvp2vcW00IX+ijhdL9d/1+D//vH/dgwzc3oveR0gibg7V0gBBnPrn77rsBALfeeus4j+TAZDTi7OGHH67Y5vzzzwew/zxnTgPeOb7h4eH98v0Cf1ies0MPPRQAF2fr1q1Da2trhTc2EGoYfM6H+KY+Cct/4J5flVWDE2ey5G7oayM6GGXQU3pV4yKrAp+6M46/reXXLLP/zyNmnzMjT6GlDLMwgMYNNrNEusXVD0VLPnrr01Gs7ynNs2ikb1Qt/rMG2JJSXI2ZFzZQPLKVi502rYBZN72A4ZdKq7lN1bi4/Pa/YoH087FwE2fMbASd1yof0b94trKgwzWrkv5LfDs+zvO4+H8zWzJgKkWvS4Nui6RL5Nxwxv+Kddbh4FE1oDCg2nlazODeV6rRGol7JkE1DWc8RJKqFMzgYYSWmDByBmiez3Pnuej5J8+nGimbT77L1oMLeAZies74gpDSrICpDHpax7l/m4gfP2peW4zhzP6deM8gf5YemRnAzzc9jc/s24Rz+7YBAAoG/Hlgyz7KzGbkclwG1RgkR5P5aqds4/c2V/7Rx5ioUfyyxKFxMI0idkjMNazxvYeruPujw/jdhscxo5DFgnwKqzbLeHqreS+SJOi5Gh3I6+C8xgyz3UBsVgyggJEx7Dn94m4FlBGcMqeAn25ZhfN7XI6J/fuCuQ8ZlIAQwotB6dzDqKd0EJlAjsso6MDq3cXz97dXwxXCh5gLF/luf2k1ziLI8UVNfMGDMVPA1r5m+p8uLSpzkDrOhDgLiv1VTbAeB5oLdzTirPwY7t27F1/5ylcAjE9YozNvz5nLFDTPPfccLrjgAtvrc7CzY8cOvPzyy/v9e3Vdx/DwMCRJsnMch4eH8etf/xoAcOeddwbyPdRhKNTKW3CuIK7Zo0Cn7tdmXg/QcyahJARIHVT5KnHGMJvUGtjcL2PnoAQ9o6Prnj0o9PB5ftvKCP72ahifuouvFBMCf95GxnuTsQJFYV8BzGAYWj0MfUS3jRAA+N/7YvhdjQpyFrzwo7fxbN3D8N4fRPC2e6fa58UyoKlKceZPi6vjcwspSC7nY1aBhzjuGpKwL9V4pcl6MACggDqgFo834/fyjDm/Tluo4ZqzsiCM4e2DuzG94NJexP+CdfE1ZTxfSGfI7sqBagy92ermwtJplc+/IMSZ4bjWVLPiqCWyLC+DklQQaq3+nAHAPUsBeM4Y46v4Vmgc0xj0DP/tQ6uHuYFNmR1C2fXHPRWftwkk1NLspyYBzKBIHJrgRThUWpHnujA3gs92b3Ddy/xcCoB5L/KjFx2R0JEpEUSnxxBuCSE2K4qmxU2AoxcdcRyLK+Ycbb8eeWUEv/9THpmtxTnudUy8oXHxXiNFeBVaokjQysT6tEIWF//lCSS/9hxajOKNPUF1fP8R3ghaUgho1vsBcpo4rCmE5MIkpAj35lO16Km2ci1nhyvDT2XHwZiaNALznGkUaFqUBAlxz5k2YiCzJWM3Ebc8eW8b3IPDM4P46q6X8dwHOkvnuAToKd1uDu8JBlBWnCfWc5MZ3Kvq7GfoFlpuLXbtvb8bmS0Ze58HG0KcBcSBkpN0oIkz2eHHLz9GTjFWKBTsEDSLSZMmYebMmQC4ONsfpe2dY3T2VxvLnlnHHnss7rjjDluIHiykUiksXLgQl19+ecnf58yZgxUrVgTWBLrROW2J27a2NkyYMAEA95zF4wHlLJlojueOWquMd4PGe14PqliBmXPmuE5G1qSQ25UDyxtIzE1AnpvA2/46DSd+P44XPvoidv+uC+u+wQ22nYPljwP/idSSzD0b0WkRXhjFNGSdYY2/fi6CD/VuxRkDxdDlpZkBnDTcjWNSvYgZVq6K98Fs6Sq+ZgaFUaDoe6IfjDHksxRptfjbf3baoOs+WgwNX9rN++jdsDJetWfTaCEEKOwrYHjNCLQBbhTe9CeGv28KI5djaNJVJCMM719WwBnpLly2Zx1u37wSSaN0ZcDv/bGkR5VOubA2+/QZKsVAvrq50BZnWHflMJ66rOgdGgqgPaXhWNBQDQKa4zmTRs6AkeWl5WIzY1CSCvbe140XPvEir1RYdj0Rq1GzT6x8Q6oxyHEFkakR5Lpy6Huyn1cEzBtc1FKGQm8Bu+/sKvk8dQqmIEItzeuCWPlCMrFLn5eHBzYZ1VeSms0isimV+JpHzqhGOSYj3BZCdHoUktkk0PlzZcf1nJZLxfW8367B2i++Bm1EQ4niGy201ONOCIGkSJBjEqQJpQtCn+3e4Opd/e2GJzD8agrPbJdBFH+5pk6PkKpzLxUB4SGEOkVsJheB6QIf8wS90gP1/MUDuO59POR6ejMNLufM/Fk89JtCG1B52K150nYPSpiXG8EX97yKH27vxJtHegCDYfedXXYPTSt8WI56lxaMleacWeOywqxjs2L2e11370EFBOh/oh87f7ELa//nNTM6/+BTZ0KcBcSBIs4k6cA6pdU8UeXvOb1GTU1NOOeccyDLMpLJJFpaWpDP58fUe+U2pttvv91+3UilQL/s3LlzzL8jSB5//HFs2rQJ11xzjev7+8vbaWHNj/b2drS2tgIABgcHkUgUvSJBCPwScVanIEhYYlAoxTsHdmN+zj00NqdLgZVDLi+lb4WAGAUKEiIYKPDFkuNSxdLVhb38ukwVSkUw85tzRoH43DgvQKLw3ll6SucevIxhP33atAI+0bMFl+xdD4kxTCtk8YPtL+CK3WvwzZ0v4XN71+HxzQrAvI/HaRQxg+cu6MM69BEdNzvSc9++SAPbXL057VvMIgZ7RqTAPGcAL13NzCa5O7oZLvsJ8NkH2tB+2xr8YcPjeP8jq7H9K6/i4p5i3tLn9qyzX4ck/0LayQPPERz/fxG82iPjp09G8PwOGQYjaIlQfGHZMO49cjt239WF1766Dtkd3FCclGRYNIXinYu5CBgOQJw5H6u6zlfKmcEw2DkEPW3YCzep9Wns/OUu6MM6Xr54DV74xIvQhhxiJKiwRgqAEMRnxRCdEuFVEvMUupl7xouTAKAMm75bGYqmOZuKA/7nkHnarbBGq5gEMygK6VIR0R6rfpNRIvxi7EtLPlsy1P49zCG2mx1hBwYIMm+bVbG9NqSDMe/ePN5OoHRMifkJHg4rl9pKTdRdvMpg+ObOl/DCLgVEIdB9eM6cRaFU67WVL2gw8wYOZMxCQIfurGwxMFnRceRMPo9yWjAtIoBiHQ8i8Ybd6oDG57S5+3SBYLKWd/2sc66HWkL+WkSU5ZzZ926GYn9Mk/TGypuMkadIbwrg5jPOHFiW/EGMCGt0x3lc8vl81fcs8XPIIYdgeHgY99xzj/2eVaVvf4szZ2+1bDbrtnmgHGxNr5PJYnLwWHoWndQ6Rk5xNmXKFADAn//855Jz5/SGesXZjLjg/qwCwHPOpiQpPtGzGZ/fuw7fTbm3gwjScyaVhSIy3cxLYfyhO2hWv4vS0vvV+m9uwHv/+QLuWv8Y3te3HYAZHuIr58wMGzR7msFc1U9vzPDQIhBQCkzRioVCLpq4D3dsfrpkN28Z7saWrXr9vKIaOI0iplE71DO7K4dNm4sG8/VnZzDwDF8Fjs+NoXlZM5Z8fxEO+8YCAECfwlfbDRbcOSPg3imq855dXTuKRmJ0L5+vE/tSyO3IAY5V++OyffjWu3MghEGjBLqPSCIAYI5Qog/9JIpXu2Sc/tt2fOvxJpx9TzsAYHITw+Wn5RH+3SZ03b0HqdfSWPPfpfO6JcqPi19xxlipF1iXuRBSB1ReJbFQzDXr+kOph8pIGxh4ZhDpTWl+noiZe+QTy9OlmLlTROF5Y/qwbnqFGZRmBcnDkq7NsV++8BXku82bBvEermePh5k7Km8VYDCED02WbHtoonoeUNj0dPRkJH8XfZ2POj0ik0xDvyscw85IAgs+Pg3XzDy8dPu8YeYMeRwTq56rapQd+4Jc1kssWcyvSlAdc1duhxSSYKS9X2jO77SfI6SYm2cde8tzNu/l4ryWzPYna77wKtRrX8O0QhZZLRiPMFAMHyQKATMolKTMF9HMMWc1oLVKlc8SAsjHLSkIUrLYWLpfKVR5btPr0tj3d4eoZRBhjW9kDhTPmRdxNpZjd+67ljizQuBaWloqfoPlBbnzzjsDKcxxyy234L//+79dDf1qxyIIo/71hvN8rlnDw73GQmA650mtRRCnOFu4cKH9929961v26yBEZHlYo9tvZgavCBaLAIcUeAn5RG8G1703jURZeFFGlQJ7wMqm56zQxx+i1OA5OlZ+x5BZMfLSvetLPjf84gimDKXRYmj4r33O3nD+QpwAoGlRE+S4jEh7GHJUAmMMRpYCEpBWgalqUZyd8cQa1309fF+WP7C9es4c04YaDPqIDpozUNhbQH8vv+anNFE0Z/JgKoPSouCI65di8bcPQ9PiJiQXckN3ol7Akel+pArB5QlarRSYykPR+jZU9gxzIxYBLnlzAVHThswF1HetFpNcSuYDwPBLxfuyJc6Gcj4XCstyT2iEh5Vld+RACwyGWhRnbgUItv9sB1790jrsvquLl7sP4t7ESgsMEIVAzxjQhnVQjed8RadFoQ1WDyFc9zUz78tvTicARikIzKbPMcm26KgO6GXmXayGeo9L/ALZlw7Oi++G89da4ux/574JIASz2hg+94HSMfNqlt69wlYjbjfKW5uoZeKs9bBEyb/bV+7mJeY15tm76BRnBbPhPSHmvURnkEyRnFF5+XyL2RceguYlPPaUFii0jSncsnklspr/dgwW1vGQwhKaFjUh1Boq8ZzlMxSf37uu6uetuUwkgNUK969H2XVvOF6XizPf1U4PYIQ4C4iDVZz94Ac/QHt7OzZvrl4NyA/leWXV3rM8Zy0tLRX7sMTZt7/9bXzjG9/wPaaLL74YN910E1555ZWK96qdx/3hOTvYcB6T008/HU899VRJM/HyxuJeaXSfVmjshAkTbM8ZwAuFWPT0VIaJjHo8DhtHo8DIK6mKbfqfHgBVGTQDoI5r8ohfrcYfNz6G695T/MzOEdm195YXJMIw8loKI2tHeGiJzksiWxbKYI7g+JH6x8BZmMIXBCVhKGAAzVMw1QAIMJiVSsSZk9ZjWtF79DT+Wi/gX1ujrts1Qt6x4KsVGFIb0qA6L+qwfZAbZXd8OINNP+AtM8oT2uVE0XD7zo7VCPdmAxNnADc6qM7DcdKpxuaC1ccqYRrWuRpe3KDY3CcV8wYdrP+/jdjw7Y3oeaQXTaY4G/F7y2SlaxYaJYhOj/Imz3kD0ckRO5epljdjz5/2Ys3nX/UXamVCVaNEUBGZQE8biE6N8PBdMywtt5ufjKalTRX7UPtUrPnCWugZ3WcIIfjnTSsuMS9hP/+ZwVAoOyTzu6tHnrS/1ov/6t6AnrRPT0yDnrM2rYAk1ZGTZKTkEE6Zz+/rJ51Ymnv2+DriK6yxFlpZgaZo2ZdEplQWKdIGNV+i2rlIZHvOzLDG5MIk5Ai/z2RUgvcO8DSH6Iwopp4xBaTMS6SAIa8yX88O5+9wRoQQmUBJKig2OwMiG9xzcS2sfEq/VUgZKxWx1mXLWGmDa3VQQ+o1vvDpvD9X7M/zSMYXIc4C4mANa7ziiiswNDSEH/3oR2MynkbDGi2vhyXEnDibfN94442Bjc1NcI2nOGvkhv/SSy/h//7v/yqKp4wHuVypUX3SSSchlSqKjvLz7RVVVV1fl+P0nBFC8P73v79imxNOOMF3/mBpzhmBOqiWJPYzyvsdUY1BNQhkx3ktdBcACnxgZhr3/xc/Vuv7lQDFGRcWRs4AU3m1RB5exe8LG3YyXLWrspLmQ63TS3/jkPkjfeacVYwvIkEb5v2OCCEY6tNxXq97D0GiEGhNvEZ7i6Hi92u99/LKOtaFchkGbVhHfHYcesbANlOczW2nMHL8nhSfHSv5vGV0W0zrGQ7GEwMgJJvhcoyXYx8Z5vsNlcXhhdpDmH3BIfa/mcHw2lfX4derH8VxIz3I5fx4ORv7bGu0stCExVDnMLbdtB0hme9L8x1mWVkYQApJkCMSaIEiNMFs+WDwflG1UPtUfk34OGd6Wsdz7yvtV0YIQWxmFFJUKinpn9vF742xWe4LCtltOaReTfueQ27XWLg9hHB7qMTYBoDp/dy72bKi2Q7LdPK+/p3466sRDOe9ezzrHV/rfB6R5Ya+dGgTvvy2PG77EH++yhEZM780H7p5IH/+eJhXjfW6+F3D2+4UAIQxTC3wMSgtCuZ/+VCE2yv7Q2R6/S04lhQEMYqeM2qwEms8XSB4fz+3e2adx4uiuS2KxDIF1/DZRqEu43FiNTcHAD1X+xp7eYs5Dgm+wxqd171lJkamhBGdXryedtxRzNEPT3Tp5WFyYCX6NI4QZwFxsHrOLMLh6pPbD42KM2dYYznOBtSJRCKw0LmDMazxyiuvxLe+9S184hOfKPn7tm3bcOONNwYiiLLZLG644Ya6BUrKxRkAvO9977NfByXOGvWcOcUZAHzoQx9y3e63v/2tv/E4PWcGX/Xsf3rANlr7nx7geU15A5oBxFySXYY6hzB7Ap9r+zJSYKEpMjND9vI8r4pR2IIDAJ5Z7/6530yej7RUNNgyg/xH+i2lX34/kuMy1AENoeYQIAH5Z6qv5sdmRCE1cQO8RdewZp/i2ZbNOKZqXmUgEq8mNzhoYDAvIRZimNLEEDYN/tmfOaRiH4f+7zzIplE7OZWG4VN8WCSiPDyHGbwowLBZOTJBS79A69cw9T1TMPkdk+y/WSvHV+56BTkfrYUafXzd9uEM9CrizCJsxkeV5/SMGlaqXSzjMdQW4gVuzLllZA2A8dXz9pMnVN2dkaa+FhtG1vDFlOyW0oU6JanYRRRsz5ktzmKY9v6prvvL78kHkHNWFmcJQEkoiE6NlhjbMqMImSdkwVfnY8oZk6vu82fPxqq+V48HniM44/cTsH3A3bS0zuc0lR/D6Jw4vnxaAe2J4omZ/uY2PNbCj1mUGnarCc9UMYmcjtQZahaT8jkoTQqO/PlytJ84AXK80hsz2K356v9YUhDEeXnrpYUuugYJWsz8rtZjuE008a0TK/a3NDsEteB9UrvmwDmIz4sjMZ+HdzLzut+9YjoKXziiYtvP/zyE1btks3Kon4IgzDXnTI7w6p8W+T1F+2Le5+e4eqkB+Dpf44kQZwHRiOfskUcewQsvvDCm4xiNOHOGe5WXG//CF76AK6+80vd4Gs05qxXWeO6559qvM5kMBgdru9cbZTTibHh4GJqmBRaq55V//vOfAIBVq1aV/P1Nb3oTLrvsskA8oFdddRW+8IUv4G1ve1vN7dy8iU8++aT9OpVKlcwxrziPeaOeMwB2Of1y1q5d6288ZQ9YpjNesc18eNE8N7SVlhBUA0i4WPG7fr0byl5+/HIaAXyIs5J5zJjdPNTIUbvnkT32nPv8TskhfHHem+x/Z0es+sWeh+WKFJbQvKQJsRlREEKgdxVVU9PhTZhyxmQsvW4Jpp45BdM/MA3HH8HvZ62GirQmeR5QxnHrSffrCLWFeEjaNJ5L1h6nIAT2sZLClY/GiSe3Y+5nuWhL6lYzbW/jcVb5tCstMmAwA/zmFW4cT6xSGW32hZXCEWDI+lgLaeRnEMKwZAqFYXqpkouTOOKGw0GU0mfOnKe2I2roFTk9o4YB1GFZW/uTozISc4vPK6sPk5JUMOei2bbwsAooWBhpzVfp+vLfWfKezIsoWFhhjbGZMcw6byaOuGkpFn3rMCz+7iLMvWQ2AMuL7vMCo6xqk12n8W8Z+qFWBXJURmSqe19Bwhj2pb2bhe+/WsZL+0L48v3uAs/6uRM0vpJAWisXhQkBChIXRhFqIKMR7+XiWXXnpFM/WHnAkakRSCH++5OLkxWfSe3TfHliSj1nxdfNRzQXK20yYNteQAZAopIdujvx1PaK/R2V7ofuI+qiRJy5RTqEJDvUkmTNCZVQcPJborj00ONKtr1223M456YILwjiZ7GRlXqEq/UHdRYDic+JY8nVi9Bx91FYccey0g0PEMfJaBHiLCDqec4GBgZw+umno6OjY0zHMRpxtnXrVvt1Op22Xw8MDOCGG27A9773vZrGcCM0mnNm5Qu5hTXedNNN+NWvfoVZs3ip3T17XHpbeMDtnH31q1913fYf//gHwuEw5s+fP275Z9u3b7dfOyslAkVxWy7avPCvf/0LALBp06aa27l5zpycc845OPLIIyvO+2jx6jlrbm4uef+MM84A4D/vrMRzpvPqf0bOgJHWke8ugBZ4I9rIxDA0gyBO3QVq+t+8OElG9VfdyprGEmO8MqPGIIUlpDemYeQpjLxhiw3JkUDd/vaiB0aXJNx9uYFXW7igHRmkvlYcDYPh0a0hpOqceiPDj82Dxy7Gku8uwpwLZyMxL4HZnzkEclxGrJkbBnFDR07zXpEw61hdlmfEbYMjx0wvlWmrWiFCUsT90SjH+Odi1IDuo7T/iMMRr1PCqwlqFJ/8+wS81sdXh48bKe0T2LSEX/OWseYkLYdQ8LOCbgBgrKJ3mpPmCIMkwfYQxw+JIT4njiN/vrxkuykv78VXd7/i33OG0v66TmPWOg8AMLiXj3mnHoaSUDDnwtl40186cNQvVpTsS0sZvjwwtcSZVUTBwlrVj82MgkgE8UNiaFnejObDm+wQLN503Pt4ACCzOYPhV9xbPzg9Z5/s4TnlVrhybLp7uGWUGpjd4n9BrVoxGOv3Wv275Bb35uEdh5p5TszAYMFfwaRqJpFuECR1DR/r2YLZZlN3Z3+uxNw4Dr9mCZTJRSGb7dfhZ8WqNOfMfWA9aWILoZCjYiSRCJZetwRT3j0Z8y6bCwCIUx2UwvOig3M8epXx2N+f42OS4nxMP/8SsPqCY0q2OXm4m3u9fObjGg4JXO0+Qhz3wRGd3w/kqIzIpAjmXDzbfo/xIqoHHUKcBUQ9cebMc/nxj388ZuNoVJwxxvDBD37Q/vdvf/tbfPGLX8TGjRtLwgj37t3razxOAVbuRXHzNrp5zlpaWvDJT37SrsB39913+xqThZvnbMOGDTU/s3PnTixfvrzmNkGOx8m9995rv96+fTv++Mc/4rzzzsPKlSvtvwfRSsEp1GtRT5wB3Ev1wAMP+BqPl5wzADjyyCNxxhln4Pjjj8cjjzxie4L9NscuyTnTeXw9zRvIbM9i8NlBGAUeTggJ0PTqBm//w734+q6XAJ2i4KPqlEGB9/bvxL2v/RstfWlQnZfzVvtVMJVC7VXthGnJdPspU6M49KLZOPKXy7Hg9hXY++0hLJ5KEUry7e57TvJVSv+GPwEf+0sbPnN35eozAGjDGtZcthaRNfycsVhlDgxQFEkx8HFnCt7mt/NWs2NEwZV/i2HnoISs2U8oHuY/tJbnDAAkS5wZOijzbhQ5i2Vohlm+WmVYva/oSWgva0C78OvFCqTO0EaAexiyjRV5dIUy4Pyezbh7/WM4Ku2ek9kSM3NPTMPRmlOh1koDuyPd79tzxsq8HtVW0Ne9xq+vdXqxuh6RSUVYmpExfHnOJIc4c7tXW2FpzKwGCrgfm1AbP8faoO47rHH1J17G1uu3lYR4WTg9Z28bKn2OR6uIszjVoen+nyHVotpsz5npyYu2u4uzo0xxFqMGb/8xBs6Pgg5cuncdPtq7FZfteQ0A7GqJFskFCRz2pXkYSnJPoNavwk8WU2m1RvdtuoYk+5mhJEvncGJeAnM+O9sW+DHDgE69V46tF9boJJTh50w2BeOy6QbOPpnglXibvc2C/Ah6spK/xthVCoJUbGZeg4+2TOV9MB1Meedk+/oPKmVgfyPEWUDUC2t0loD/0pe+NGbjaNQ4f/jhh/Hyy8XCAKlUCtdffz0OO+wwrFixwv57V1eXy6cbx3lcyr0ebsfMzXNm0dbGbwKPPPKIrzFZeM2n2bx5c2ChlaOhPO/tQx/6EH73u9/h7W9/u/23IMSZs6hHLSwP4he/+EWcc845Vbfr7u72NR6vnrNQKIS///3vWLlyJU477TRMnMhj9oMUZwWDgBZ48Q8jYyDfXYCRNsBUCoAgquqIUwNSTMLh1yzG0b8/Cod8pths9biRXrxjsAspHymNlAIXdW+AAoaj1+9EYm6cJ/wTgsShCcQOidmhOor5BA5Pi4DIBOEJYUyYHELItAHa2kxP0ppedKeJZ3V2+9/MB+emSuOLMeDf39mF7PaimiDxauKMDyzK+LjTWW+PLMpg/5Zzf5nEbasi+PRdcfRl+PXSLOvYfN1WHmqGWp6zYp843fBeRc7pOSvoPE+ovJpgucdVcYiNuZfMwdxLZmPe5+eAEl5pLj+gljZeHgUGBc41+9t9pGer6zZWiXyrMIGzQtrh1yzGYV9fULK9M5zWE4yVTL+1e2Ucd20T1u8rPTeRYX7O9oWiuHt16XyziikAgJ7R7QqXfqnV+FfPmDlwSbm0UqmJldeoDqiBFZUp9FS6qC1PY8SR87rgikMBoKQgyMzzZtivY9RALgBx5iyF7sQqbG95zqqJM6WFj69FV7FtWPZs7KsaQ1eVME3VIDgqXZrzylwM+eRhSbx8Aj9uxrB5fXmNKKDA8SM9WJAbdvWcXfaXGN7+syYkzVB4Oel+X7TuQzGq++q5WC+s0YIZDFO7h/g/ZhcXQdrjDP+YULzGDs2l0J2SSl3eHigpBFRlXKrpBb5n4lxXYWktxNCcAXYQus6EOAuIep6z8up6V111FW677TZs2eJeqcwrjRrnjYZ2+RVnzuPi1XNmcfnllwPAmOacTZs2raHPTp8+HbfeemtD3qOgqBYe6BRt+1OcWd87d+5c3HPPPSX9xJz4rSzZaM6ZVcDEEmflWM3MgxBnH+nZgq/tfAmqIvNiDjoDLVDEZkTBGIPSHIJOHYnv06JILkhCicuYduZUxOcUczLelOoLLF9IogxyXAYhBJGJYRCZlITBhSzPWawy2R0ADsnwc39sqg8vdoc9GyG9NdoRPrxBQW57aWgwSdT2nFlNszMeK8nRAsXNW1bh8+YKOQCs2Svjo7/hnr3FW7rR/1jRUKvmObPC6eLU4Mfdq+fMIc5Ug0BpUtB8eGkYbqzOgt/kd0zGpLdNQkHhx04f0e1CFKPF+fgKVVGczWXiTHEI6uSCJFo7WrH02iX23yL1YlrrYGQNJJ/vxmRHq4XNfTK+8JfS/GijzxJnMTy4rtTYn37ONMz4CK9EaqR0aCPeQ/acocduRryFPmJ6PVwqIgKA0qwAhDfK9lU4wYHbKbv5Se4dO7Of3xf7lQgmHF+Zh9u0uAmJ+fyYxqiOgu6//1q1UDRrkaTNFGfxie7izCr8MEEv4JndYc+es/d+g+BNd0zCi7sr73d5rbLojlGlImG8yfTC5P3FyNG9WVy162Vcv/U5V8/ZnZ08hHK6+dywhHw5zvBqg9ZeLKhFiYeqRlijNqIjrBkYlkNodVQgDSvAOz/ajI8cdgoAfr5GCv7C9Bkr63NWxWNutTsZVkKut2FrIab3P30irPGNTD1xVi4orr76anz2s5/FggULcPfdd+PjH/84/va3v/kehyQ1dkqd4YpLlvAHqlu43gc/+EGsW1e98WA9annO3I6ZZUC7MW/ePAD+Ggk7Hzpu39+ouMnn87joootw3nnneR5LrbFV+06geBz++te/VuQwBiHOGi3iYc1pq/DGV77yFSxatKhiO7/izCnIqnnOHnroIYyM8NyLauLMEv6pVMqX8aEZwMd7t+KEVC+aetK8dL5KQVUKOSkjOT+JULMC1QBmmbkM0WmlCfgLv7YAU8/kvdhmqRlfnjPnA1aqcx+SzVizUNz9PjH13cUqbjuGZc8PtYEhhmWZAcRciqFs6JFRIKXGktRcZYXYFGcR83el8t4eWU07hzCnkME7B4uLTXGH3RPbVxrKW0+c8RVr7z2GnCFnOY3gvN8msHe49NqtlqtYTiFsirOUbnYhHz1GQ+LM3NbMOXOrZpc4NIH8VL6yruT85S5tvWkb2h/fheu2Plvy97QjtFVP6Yis5ost+8IxTEhUTthQMz/ResanGHJ4J2qdd8toDFWZ00Qi9qKDEVAT3VzZbTFVAP61nv/u8818s/Iw2RW3L8PCK+ejaUnSPpcJQ0de955LaVErrLHZ0BBiDGlJQSLhPl9DE3jY3gS9gK4R7zln/36R7//u1ZWFR1QXHVbN25s0x8k05svZyXqKq3Dl3h6nWDssx1e3kgvdw8KlqBlRQA0YzEdYo1vfNRcKppdqRA5jSlPpMfrsiSoe/CIXkwlDh069e/LscdXwnDGDoftv3aDmdZaS3cWZFaIahE00HghxFhD1whqreXsYY/jwhz+MO++8E2eeeabvcTQ6EXfv3g0A+P73v49XXnkFhUIBL730ErLZLBhjuOqqq+xtf/WrX3kez2hzzubOnVt1XxMmTIAsyxgaGvJcqKSWJw+o7zX63ve+V/LvP//5z57G4QVLnF122WVIpVJ473vfi0ik1OgPoppko20VnE2frc85C5JYon9/eM6cXrtYzL1SmCRJ9uKFn76ETv1D0jqPaafcO0NkAsaAp7fK6EtLODLDj1F8Xulqf2RyBDM+yFf044aOtB/PmXM8NR6KlALNmlm1rUrTzkmnF8s1d/V7r0b4jsEufG/7C/iaS0+1//dgDHmp+P1Xz1qGcJXxWA9Yq+dXzmvOmUuYVVQp/jZS9jNJuEqFMDucyIBBJc89higFDssO483DPOT3n+tCuOofpfPWrQWDG2qIiwCa1iv6sY1mPBbVxFkx56wyrLFkX6bqVXKar0WQ4Zf4YktrWc6mUxcNPFN8rnaHY65GmmKKJD1t+MrxcobW1QrZtPLNqnnOAAQuzh56rfS7Ug14mCOTI2g7tg2EEDs3rlVXAxFnbp4zRhmOe3YjLt3DF3v7QxFEqhwiy2PUrhXQ6zeHCUB/tvJ4qC7hm7TK+WgxNRL3UBHPh8eZ/1TeV8y56HBKC18sqtYnzwprjFN/Ysh5nqp5O//4YggfuInbGcNKCJObKr9r4TReVEoBg64yf+KMsZq5cH2P9mHHHbwuQkpWYBDJNYx25kd5uK7VQ/JgQ4izgKjnObNyzs477zxceeWVVasCWnkzXmlUnFlehsmTJ0OWZdsgtwxbRVEqtvWC87g0knNmVWR0Q5Ik36FpTkFWLs4YYxXFMBYtWoT169fju9/9Lnbu3IkrrrgC3/72tz19t18scRaLxexqjd/5zndKtrFEtx+8ijOA5wxSSrFz5058/etfB7B/xNns2bPt769FKBSq2OdoKWlemtGQODSO+CExu/fSA6+GcNYdTXjvjVGcNMy9vM1HNFfsx2noZ/M+QmVKwhqr34cKBjAvzxcf4nPirtsQQqCbjZ/zKerZmD1phP9uS5zaYzX3Rx33qZVNkxFzj97hFfIkQKEMEqOei0xQlyT+3kzx8WfltNnfW+U+KoUlUABhRqGZoaxeMCgvPf3V3Wsww/Su3r+m9LqLNeg508yEQSNrVK1M18h4LEJVBJWVc2aFTipVxRl/dig5zfYieaGa97Lk+nP83p5QzLVqtiWSjLTuvZkxULKcXyusUTN/s1LFcwYUxRkrUM8Cdjhd/C3DZdGsOW10EyFkVk1s0wvI68R3WKObSB5Zm8LizXtxYoqnVGyNNtm5rhXjabPGo6I/QzyHNTbrKr60ew2iuyttmLzL1Kx2v2tuMo+nTs0553FRxjGH1LLv70vz77ho33pEdvDxVmuuLDs9ZwYLJKyxmjP4knsSmJPm48koClpj7t+lhvmYWI6a+aLBPNPKRXSuq7iSOSLz4+MmLK3qqswQ4uwNTb2VeCs/Z968ebj66qvx3e9+F0uXLq3Ybn+JM0uElJdktzjllFPs185Kk6NlNAVBWlpaSkShG5YQ8Grw1xqP5TW0kCQJ69atw2GHHYavfvWrtnAMov+bG42GNUajxdW0U089FYwx2zO7ZcsW3w9WS8DUozys0YIQglmzZtkFXPyKM6eIriaqrFDXP/zhDzX3Zc0vP/3XSsII8zrkiAw5KSM2ky9sPGZWjvr8hlcgg0GTJDQdVnmdSYoEXSKQwaBXyXVoaDyOj0o1PF2qTjCzwMNP4rOrN5plZv6XlPNeNlqT3R8tZsEvDCoOo4MQNEXcv4cQYhvpEUrh1eFZ72NvmtJYfhQhBJrpfaUqBa1X4qwKztM02a2fGWOu/fHcMMycQpqnqFKHof54nOKsioBJRhhyu3JQe8xKe1Uq/jFTnIULumfPIlC9KIuzauM6s7DwA20zQQlx9ZBKpheU6qz+RKiBM4+m3HM2lCN2aGFDnrOwNYe8G47rtjjEYtlUyZtjOXxS8X6Z+NjsqvtyiqG8v2rxANwra6Y3li58vphsr7qYIIUkyE0Kv38O6549Z+fv24S3DHfj40++WPGem+ds3n+7R+4k4nxb2aDgjbg8DaemOPvIb3g48Hv7itWyq4kzIhMUJAkSAC1PPRffqVfaf+8I/9tn9vG2OoYsV++rZ3rwjazOC794FNTakIb4pkHIplIuOMbIKENhX/FeXSDmdeRyPpzVU/3aROOBEGcBUc9zZomzRKJY6ea+++7Dpz/96ZKiII0WY/BLPXF26qmn4hvf+AaA4MRZvbDGl156qe7+LM9IEOJM13VQSm3RU+41mzJlius+JEnCL3/5SwDc2N9fF76bOLNobW3FxIkTkcvlfLc/aNRzZs0LS4SVY4Vc+u2V57y23PbV1dWF5557DgAwZ86cmvsKwnNWEkZoPjkIIXaOybRmvsGyLBev1YxdAMViDhnvK/r1PGcb9kk4+44Ent4m281Wa63qE6c48/qAVdyXxEfyBAlDw5tH+Or51bOWAQAmuuQKWVi9h6LUqJrLUg/nMSIu12ub1LhYt8SZrnuvSOg00tzCCA/Np5B0eM6mfWBq1X0xuWjoe/WcOY9PtbDGJknHK5fyBu5Nhze5lokHAJjiLKTqYD4a5FbznDn18OOv8m1yZpisq5FmilemM19hjaxKzllGBeZ/uwWHf4/ntOoNeM6sXEqqGp4N/cxIcQzl4ZF5U3i0EHMOScDSD05GNZzirGB4D2uUGMXMQgaGi5hSe0vv3f9pqV18K2JWcmwqqCVN20eDVbK/nH+8puC5naXn55g/H43mw5tct1cipjijFIwRUJV6CvlmjsUKvew37RyUcXSq1NayPGRuFGT+nj6kI73ZW9KyQbmA/eG25yuqxf7xxRCO+H4LJjkK8jw7ufp9SDNzX2mWdynzGtr48ufWYvY/NuEcs3qsU0Tv+0cPBp4uhjJb9yo3z5nk9JyNQSuGsUaIs4CoJ84sw98pzg499FD8/Oc/x7x582xPlZ8QQqBxz5klAquJMwD4wAc+AGD/ec6amtxvjE6sog5exVm5F+aUU05BLBbDyMhIhTC+4YYbqu7n/PPPRzgchq7rvsVHo1jirDzPzGL+/PkAeKl/PzTiOevp6cHQ0BCampqqFuCw9uPHSwXUnkMAcP311yOVSuHEE0+0e+FVI4gxOQ0PQ2O44A9xXPTHOLLmNKgWquOGFZJGC4Znke+89cguD8Qv3hvHU1tD+MYdDJOs5q9VqiMCgJTkxyiU00E9dhKmDs+Z83eN5Ikd6gkAOYmPoz1R/XussZ45sBNGnUapVdGri6EwNZDeUDRuZnxoesXH05vSdiEMzRICOvPsGXJWhYu7eMjOMivs3TfhEBz5i+UlJeHLscQZM6hnh0dJqKDL+ycfquHEh1+1/11TlJpiSPIRbgUUPV5AqaB2jjWZ4oajlcPoaqSFTCNN955DCZSJM8d53zHAf++Imedl9zhrrn4fDSKsUXe0BTDKzocV1tgCPpZqHhgLS2gXwxo9DQmf3rcJt25eiTf1VrZPyTvE2d8mzAKrY6sojjw41eNaml7lOz7xu6TtmQGAtmNbXZu7W8jmQoFszR+Px4c5rnvV5d7xrZ2VHr5qFGRTDJHinBstVguNw7NDmNldWhfhh//mi8DvMIsovZSYgA0TJ1bsw0I3n2Usb4Y1eq1k+zK3gY83F/CchVu67y+dV1YIttt8tZtUE1498mDznglxFhCNhjVWE0OWMPHrOWu0WqMlFmsJIqsvlB9xNpqcs0bEWdCes6eeegoA8Nxzz9nHZNmyZRgYGMC5555bc1/WuQzK2+klrNFJUOKsEc/Z2rV8BX3p0qVVFwSCCCEE6nvO9uzZAwD4zGc+U3dxwhqTL8+Zw+AcSTHc+0oYf3opjB8/ys9LRiWu3hk3LA8Tf6B5G09JmKXL96bMRPPvbn/B/pscrWGImGWjlYIO5jEMLO4oZpHvymPt5a9h4NlBjBR4GKeFZp6v9hqeM6uH0wf7tnvOOXOKhHCZOHtgfrHx/IIr57uKM2YARt4UZ6YYMjRm9rMbPTRT/CHNLk3KF+WGAACPtE1HuD1cc15T0wghivf+QiXeYMf5OXa2jkc+l8JfPpOBuq64cEjz1U+EJYZgUF8FL5wCyJl/15eRsGuQYO1f+3FCN48SqCXO7NwTnZWWgRslrKSUvuO5VrZgoA7U907bBUFU76v6TkFW3mjXCmtsZuZYXCprOnF6znjknrfj9D5zUeFDOzZVvNe1nZ/DV+Jt+MWUBRXvl+MsF+/1EcKqFMiZpObsBRASk7HwytrjCZnroQqlRfesh0PEHNeNUSbOpFG6dVXz2UEUGV5d5iW5poXSazoeBsAYPtK3zRwfQ0Sp/qMNx0IjiPcKkhYEfOFs+h9eQ89DZo2Bsmq0iuU5cxNn9n2IcbHoczz7GyHOAsJLWKMTS5jsD8+Zqqp2A+panjPLI9LX1+d51WE0YY2NiIIgxVm5gW6JrKampqqhek6sc1YeDjlW1BNnVk6c3950zvNQ7bxbgqhWGGFQ4qye58yaC7XaMFgE4jlzPFQjjgfqDY9Hcdy1Tfj96rDdlwsA1hw7r+q+7GIOPsSZ89bjFtZoJXC3OERArap+llEZKWieVz8Tju/aePUmZDZmsOm7m3HGrU1o0h15MKbXqJY4C08ozkevnjPiCNlxhple974smjPFHIamxcmSxsHaiI6RtSOQopL9d93KOdOZ97DGTHH+NZeFXkWogRlqDhoh6E+6F25xwqwVYp2iwQKPFVRzkD7w2TRWzKj06raf5O4tB4piSGLMFrSexpStLmAvvzKNzM+32v+2PLButyurATvTvYWiWVTznDmPHWMM2a38WV8rr9MSZ9RHaXbD4TkrXyR4bJOMSWoOx27l+Uu1POVAsa9Ym16AzryHNVqLLS26hn3/7MHe+4qeDslsFv79WcugSnLVPFMLS1DGqV6zQXItZEfeojUXv3e7hl9tesrOo2IN5PuGQgQUgGx5hLxOo0KpOPvls2HcvyYExoAWVjrHa+UsAkVxZuQMuFbCaQCnCSZrOlIFoNvMM4uHmN0sHAC2xJqqVtcEivchay7WKprTCBJjeMdgF5q3DGLbzdsBVD63HmvhYZZu1Rqt+7W1mOrHiz8e1D77gobxK86am3k1tyA9Z4ZhQJYrV8xuvvlm+3UtcRaJRJBMJpFOpz2Pq9ywHh4eRjweRygU8lTOPMiwxnIDvV4eXjnWdvtLnFlNqKuJMyvc0W85fachpqqqaxildfxridggvFRAfc+ZNZZ6lRqDGpMz3yRSZg1v7uPX24cGttt/Gzl+OoACqE4rQmes8D+m8/Am4hpUVmc8jmeO4mJ8TkqO7sEdblagAYiommdPjDN0ML+ntNjGJEcBjLUJPn8mxKt/z4IrDsXaL/Lm0R7rb5QY1k7P2VlLC9h6W3FBTEmWPhL1EQ1SWEKoWbH3oTs8Z+Uei4bH4xRnDuFx/fuzUHsKwDpAT4Rx72eLzbqt7ydlq8e2ODOY535Q9Wy7wVWlIU/T3j8Vhd4CiCLZhr2FletBDO+eRaA09PPts3L4zb6iUP2io5k4AJy4iOLBnior6ErRSPPVo6pKzpn1cpKaw9DqEWhDOpQmGZGp7uHngCOsUaOevVSGxmA93Z2evKev3Iy52xiuogbmm9VZpSqtISyUJt4Yu8XQYBh8XsvVtWVVUnLIzvPafssOAEDrCW0ohBUkdB0qkTAsh/DDM7M4e1nte7DVe21GIQtdd7eb6mF7cQG8dvNOsH15vHXD6J/XikygEQkRRm1h7ul+7RBn3YPAj1fzOX1Im4GY474YnR7Fgq/Or7krS5zRvAFGGyviVY5zYUHRDJzziyRe2KVgdpsBRQa+sfMl+/07Jx2Kw+Tqc5WaiyAwveVGnsLbqDjzCmm8dagsf95xuK+dcTieaJ5S8TssJKfHHP57r+1vhDgLiEbDGsfac+YcR6FQQDxeufLq7EVliZ1qTJw4Eel0umqfttGMZ2BgAK2trTjssMOwfv16T+LMEiaWUPEznlqes0bY32GNlgis1sdrLMII8/l8TXFWSxCNxXisc/a1r30NK1euxC9/+UusXLmy7lgsgvCcOVfNy8WZxcd6ikV+2uIMekpHdmcWzYeXltS3xZnmfTW2XlhjuMG7vNqvQk/riLQoyACIqt6rpLmGdZoP1umq2bD00oXQn1Rw+cn5qlE5VKeIz42DmR8vT6JvGIfn7BcfTCE6Q0drjCHUn0ehRwUIcPSdR5aszGZ35cBUChKWoCRlpDdlEGoLFT1nGvUuzly8Qr/5eAbvXqIhsy2HtQAmtEtYNqO4XWZLBkQmSBxa+gxhVlUyjXlusuyWc/Z/7ywWAshsyZZsL4UkXviCoUKcEdNIk6h38Qrw6pMWM8O183rnzJKAHsBwqRJohTcxnzlwpZ4zxz1SIwhRA7/a9BQ2mu0Wm5c114xisb15qo9wZsfxIaY4692Sh/LqIMrrQDu3dYPIBAhLQIFC0imMrFG1iXY1GGMVzeUB4Jd3q1i1QcelAPpCEYAQnL1Mq7kgAxRbjZzTvx2aWj3XqRaG4xxkn3BPz4jMq++dDskoijPN+zlzugD3DXKv+aV71uEf6ZnImt7f+JwYjrihspJ3Oar5fKUF6nnRQXcWJtIMtKzpweeyQ/hLYQ7AKBaY4n5NvBU5WUGuxuoYtUrpq3w8RsG719xiYb7UHnYWCfp3azH83L0QULk48z2c/YoQZwHRaEGQal4Zv6LDwik+8vm8qzizqiJed911VQ19i4kTJ2L79u2eS/w7j4v1vRs2bKh4r1H8VttzHp9stmhwaJo2as9ZUNUIG8UKJZw2zb3K1VgU4Mjlcq4Cfn+KM+d4VFWFruv47ne/C6C0afn+8pwZDoMzUiVP4IXkRLwp3Ycnm6dgmswNecsgc2LlC1HduzijlIcTVetPlXWUr2/TVfzrrUfgWJfttGEdVDUQbTWNBE3znKNT3tQZAMCA6YUMZqh8oWr+sih2nTZcM10iszmD6NQodEVGSDfACh7H4/gdSyfqSMwwwAyG7seHAADNRzRBKQ/9ogxG1kC4PQRIvKQ/0xl0uVgQxHPOmUMkNBv8BL17CZ+TVjifXJYnxChc+3Q5wxq9GiCGw7pJhBh2/78hRB2aSxtyuV5kd7+B5a2QKA1MDM3Z2I0HLo5BHdDQ/K/tqGg+sLAZeMHdSLO81XwBJKicMwY9rYPIBLkhhqPTpeK1vI+gUTBK2kJYgpEa3nNhnD323rJtBzY/EUH/jze6b9vAPCUhCaxAQTQKI88AVPf8uWEYgOqS877uZRWX9vIQVEuA1BNmAEBzjogJj9e9XidzZ6QlhlOuqp//pki8JQoo91J6bXXmPNfnrH4VX8xxm+PEVA++PvtI/l0tjfmbrHxlmjNAwAteNFoQzsJwHuNhHf87yIv+vHuwNDXi24esAFDMX3bFer6pBogEX/mdbnT9aY+9YHP4NUsg/YrZ4Yy1xZkZZumxuNV4IcRZQPj1nAVR4rt8HG5Cb3BwEJs2bUIsFsPnPve5uvuzhEoul6uzZf3xOG8cXV1duP/+++1/X3fddQ3tz6/B7xyP0+OVz+dH7TkL6pw1QiaTwfDwMCKRSNXqiGPlOXPDEme1PK9j5Tm78847XbdrJE8wCAHLaoQ1Wn97U5qv0j7UNh1XtfKVVuJSGpw5whq9Qhlf1Q1VscyzKoFCKVp0FQYAdU4LgAJG1o4guShpG69E4qEgktmdNa5png3HagVRbt/MvZyIK4hMDINYuiKtQ47JJSF7VKcItYSQ25WDoUgI6UaJN2VUuISkbbl+K/qfGAAARCaXGqJUo1CaFBg5CqJIIBKBFJHBdAbDUbre6/Fxnm9nDh4AGBl3cUYUAiIR0AKFFJGg9qs89C9UDGv0Oh5nShczWIkwA9zFmVQeXkkZ9BHdDieSKPNVSt8pYGds7QMu70MYqBBmv12xFO81j0H5z2cGK3rOfCyAWPuy0IY0vHzxGoABbSkdXy/bNjypNH86szkDIhEkFyb5+Ssp7+9RnJUd22rCDEBDzdJJWAKD2cuLjqLkrIluAGGXxQOr8T3Ac7Z+/8nGwgrVgeKip+5RnBl1xMqJ35qHcHv9XHdF4vdYAFD7NESYt4A95y16Vq70OLRr3F5Tko0de7vSb54CVl+xUZ42Z8XPFsN9kZlNiyMj89+briHOmC3OTDEUsBba/dsuOx+atYVL8sxqtdCgB6nnTBQECQi/OWdjIc4sw3rlypVYtGgRHnvsMdv7csghhzRUMt0ysL2EIJZ/7sEHH7RfX3TRRfbrv//97/jCF77Q0P78HienUe4UZ+l0Gr/73e8AAFOnVu/lEeRYyqkV1mgV+Zg+fXrd6ohB5njVE2f723P24osv4vzzz6/YZtasWVWvLSeB9Dlzes5McXbnJ4oP2veb/VkA4EPHGzjpUB2MspL8BwvmWNX3XBjAAKjDh/GxX8fxo39HMJgl6B4G3vXsa7h/3b8hAegNRTGhmRuackwG0xiMAvcigfDV80iM76tJ10A9nrp61SonnzKhRIjl9+aR3pguMSKpyiAnZSgtIbvRMgoeSyI7xBAtUPQ81GsLMwCIzijN42QaN+qVhCkYCa9wmdmaLQlr9CqqnZq+2dBKimcYWbNPVpk4k8ME4UlhaCkdtEBR2FfgXjaH58zrJHKGr7oJBrsCYZOChV/jngYik2KRlLQOdUBDvjtf4jmjAXnOavGFUwp2ETeeB1g8J6l1KbsdBDMYCgOqd0Ht+NzwyyPQR3S7p1k55eeOyBJAeOhwZlu2RDB695w1/rnoNPc8ZSeWV08yqCfD2qBA1MUCXpYppkSEGMWUpsbGPen0YoEnzaPI12vkhC2/5YgKD6cbhd4CjCEVOyJmGsM6027w4jmrcb/4gplHWZ73Wg3dqvSb4wV7vNwXndVUrXDzciRHS4hUrcAuM4/SCrH1dHzq/AarZcAPV5U+6xvKOXs9FgTp6Oj4AYATAGwH8OnOzk7N8d6HAVwILvS+2tnZucp1J69z/IqzoAxZ5+ctw/p973sfenp6cPrpp+Ohhx4CUL3Bcjl+jdlqx8Uqxe78jkYI0nPmzO/7/Oc/j+HhYQDAJz7xiVGNZX94znp6eM+PWudtLMSQmzhLpVL2eKp58YIcj3MOWQK6nM9//vMN7SuIc+a8yceZgeNm63j7YToe//wITrmpGYeoxZ5ZHzmRghC+iiiFJTDKSvKarHwhX4Y1AyTHk3DNKxpeWCvjB/9uwYLcMK7v7rHf6wnFcEgbBdUp5DgP08vuzEIKS6AqQ6hFQdRc9W8yNM9GUT1xdsj5pX275LiMcHsY2oiGyCTuxcrvyiG5OAmmMrtMM/Na/c9hAA8+M4h9D/SUvF2Rx0UZlKQCYpaEJoTwf8vErkhHVe/VEVlJWKOGb+9YjdWfHELbm1pt0VjhOYvKUJIhpHalUNiXhxQ2K0haol/3nuNVXtbbyBq2kdj/ZD+yW7OABCy/9QgoCYXPY5nYcza7MwcYDHJChqwwUPjvc9aoaJl7XBN2mVFYapYiv7eA+CE8XJ+3F+BeR6Yz0DwNRJxlNtZu+tu8vJhbqqd100PM+4dJIVKSD+N1Rb+RfFApJqH9hAmY+bEZdbclpnEt6956r+mGeySBs5n6QzNm48wp7j9Yz+iQwpId/t16ZDEqQ/fYT9DtnjrzozMw5d2T61ZDtCj0qGAasCbRhmPSfXwByWsIegPnTE405v6y+4qZIbNexqRli/f3eXl3j2bzJBnRFDP73/F5a+QNSBGpdKHYHA9RzVL6XgbU4OPmZ6tKRXXNsEbrmL/e+px1dHQsBzCjs7PzJADrAZzjeG86gLMAnNbZ2XnqG1WYAbU9S4wx1ybUTsbKc6brui06dF3Hvn28AezkyZMb2l+QnjMnTqP/xBNPbHh/Qeac9fb22q+tYwQAM2dWb/ga5FjKqRUvbhVkaaQ6YpBiqDycddeuXWhubsaTTz4JoHr+GzA2c7oa9QrblI/JX0EQR6GCmIa/fzYNQoA5+TS2f6oLZy8v/l7F8aAlEoGeLv1e5ghv8pNzJjsePLdvXolfb3wS7xrYheu3Pley7WvxVpwwVwdTueeMqhRElkDzFHJMAlEIlBYFFLxXWd5rjpfLx/SYAhKWcOQvlkOOlhogUkhC02HJEo8NCZkeq4Rs5+ZB9XacnKX0y4UZACQXloszLqYnntpue4dImCB2SAyFsLmYlDFAAE9haeXVI1dkBkDztMSbV248EtOwt7x5ljizjBCi04pQt0YxykTUi596GS984kWMrBnB5mt4vlC4PWzn5TGDQYpIYISg0KdCNqsBylHJ9grJzHvBFOs76rHs5qWQ4zKs9Q5WZtGYKU72MTIaKJtedTyOY1Qth6vlM/Pwpvs6IEdl3PNSCBf+IY7hHTk+h2QCI62DOASIn95r5efMdTzLmzHvv+c2FLpnec5kj97gauLMgk6M4spvNFk2PACzCJGZY5nfzb3nqfVpex4PWRWIPaZ1yy7XJmOsYWEGAEQGQgqxwxrL712jwW2xYtnNS+3iJwAqc1+roDmaPjPAmzjL1T/XsYmhirYHmS0ZaANlz/VwmefMw33Rq6fdtRCQJc58lvQfLxoJazwBwEPm6wcBOC3pdwIoAHi4o6Pjtx0dHY1VUngdUstzVigUQClFOByu6iUaC0N21apVCIVCJblnH/nIRwDsP89ZNcO6u5v3P/nLX/7SUDiahV8B4vzcf/7zn4r3f/rTnza8r6AKcFjUWq3s7OwEUFucjUVBkMsuuwwTJ07E7t27AQC/+c1vSratFQI6FmLRyd/+9jf7daM5kUF7zvSUDsYY1EENa7/wKl790mtQ9xWvN2sVlBD+sLDKymvDPJ+LlRQr8DYegwKKy9y5dO/6kn+TeU34wOWTML2Fh1JJUZn/17QF5CgXZ3JIsnM1dI9GkVvVyBPuWoFj7j6qwlBk3PUHpVnhOV1ZA9qIDqU5hPisGPfqWeLMYz84UsMAnvGR6XbD2+KgeBiqFOECKD4nhuSCBBLz4lCjpkDJ6vDa3LQRIyQ0oexZQRjCbSEoSYULM4WHFdrVEQ3qufWBUWbAUJVCH9ax7qpig+4JJxbvPYwySFEJSkKGNqgCigQSkkDCEmRTzHLPmffEE+s6G5arR1ZIpqFsiTMKAqZRpDeleYGEkMSFtikYjSyFh24VfDwNnGcyv8leZLv4jwn85ZUwHt6bACTg9jUJfOE/rRjRJUd5f++hn414YZqXNdfdxsLqCaZQw5NhrTlK+1vcPPdw+/WSy2Zj9oTycFkV+T383k1C3DudmBezcxypVPRSe4G4ibMaxnqhT0V2Z67kXBNCIMvFHm5WCLgXR0z5OYvPiyM2M4ajfrGi+H112h5YlHjO4G08aqb+YkV4QhhvWcif4YtNryeRpYpFKcvzKmnUa0/shu4Xa+KtlZ+r5TnzcQ8aTxqR6G0ArGYDwwAmON6bAmAigNMBXAzgUgDfd364o6PjQvCwR1x66aU4/fTTfQ75wMFpNBqGgd27d7t6PyyvRywWq9og2PKsDQ0N+Woi7KxAWKvgx6JFixr6HsuILRQKnsZVrwS/ruuj2q91nEZGRjyNZ+/eYt+M8v5k//Vf/4Uzzzyz4f1ax2bfvn2+Gz8D1Y/xqlWr8K1v8RrNhmFU/S4rTNPrsbFwiqnnn38eAPDd734XX/va17Bp06aSbbPZbFVhZOX0aZqGrq4u+7+jpfw8HXvssfjTn/4EQgh++MMf4q677sJpp53W0L4t4dnd3e35GA0PUtj+Qgp0b9kHvbco9tIbeMjT9K9OQ24WPzaslfLQqlkUIxgGmgAQQI+a+TARHXv794IMjv6ptrdbhlxHsWyaNxFn/24SgBTSAFgbBZF5yB6jAAwGLaSZhisBJdz6GEym0dXlnndYC7ewxsyUKqFglIFNZOhJ98BYriNT4KXtpbCEATIAY6oBGrE2ZdjTvWfU49EL1VVm4sNxpCeVzjE2gUENF5DZk4E+S4fKCvzengYKSWvRoQB1kYq9+/aO2uDPpLKo1zLdmGkUx8UY2ARgODkMbYEOUAYKPpf1x/l/GQxkZ+U8zeve7trGgJSU0HZZK9IRczymoAYAOo0ChM9jVSpA3cWvBYlRqAsK6Nrd5UkQ6Rq/D31p7jH41AkjOO9tGWj7NCSOSWDdqVw05qZnobWoUAfDAJLQYwzGch2QCVJIgbQBOtVg1W/Q5qsAC3k6RkP9Q3W3SU9WkZ5kzbVWAMA3nmvBd19sxp5BfoRPPDmPt7TybYwmHX1qL6Su0af/p4YLqIh/iUmAwxuS+HgcaaWxAhzU7IcoGRRDyhBGukbX1mdvj7mgA4JPL3wzCkRGWgnhhr8WYAwakBdISKN0LLSZz910JA3aQkEkIB9lMHIGNFkFNYV+tln1dM4kl4U9shgV13vJeBiQgmoLf9psQGaARvj5K0gqWIShu3f0172eL10UbL+grWIseSXvOj5mMPBOBfxLtbgl8FWohxXQ3bN31FUk+vfpqLdMr03ScNXpvZg9J4mzj80i3W6ANRtQZQpNKf4eo8UUelRHYX4euqKNeg4ZKXexOOu7MzC0OodNbW249jH+9P3fs4aRKRDc8mAzcjG14phpMKvfUgoWYegr9IF0eVSNY8SMGdXDjRsRZ0MArOWXFgADZe892tnZyTo6Ov4N4KryD3d2dt4G4Dbznwenf7EK5avvU6ZMcfWMWQZhU1NT1ZMxaRJ/VEcikZonrB6NlFJ917vehYsvvti1QXU5VnNsxpincdUrS79gwYJR7dc6TqFQyNN4du3aVfW9N73pTaPapxVKV+u8joaenh7Xgh+/+tWv7NdDQ0NVv8sKVfV6bGrxs5/9DJ/73Odwzz33lPy9VgiotVBgGAZmzJiBrq4uT+Mq77M2Y8YM+3svv/xyXH755Q3vy6rE2dLS4vkYJSIDJf/e+b+VcypxaByzjpsJmJGz+Z4Cmpc0Yfi1YS6KdAZtRAdy5mrjCDC1bWrDyeBO9gwY1tdUJX/sDCR7ucdKHdSg9hXQfEQzXyVWeV+jyLQIJFkCY8z2nIX3xDDjzY2FQDuR2JaSf+ckGcle93tBekMasVkxTFkyGf1PDyCzJYPwhBCiM2NoXdSCodVDINRcVCkwTJsyraKZdz3CZHPV95oLzZB7S++Far+K5GEJNM1oQt8T/Wg9qsU+NzTOjUR5iCG8IYIp75xckR9Wj1i4vqGZMBL2MdPTOpQmBe1LJqB3Yx+0Yd32xoZU7o2VC0BkUxjTzqoealyNLRvzqGbCR6ZFsOR7ixEeKT7btBEd4fYQmg9vQt9jfdDTPAdFikpQdhnQwUPKwhvCaD+pvaIaZiNsYPycGUSC2tKGiS0xboEMA5PePgn6sIaWQgtIL0Fi2PRQ5yXgWYLk/ASMAoWRp1D3FSARCQYMsOcBZZ7s6dovJFXsQXfNbZRcE5K9DKojWGAgXTo3UtvDiGi8QAfpk9BmTEByxuibLMci+0r+/Z/Wafjf66fgxfNfAgBEp0fQNFhZeZhqPO+uPDwvQkLIAwjpFK1qK5oObaxqsYVm6OgBQAlBf6hYgKSNtXGd6nKTymzjCzah1jBowYDSrKBpURMGnhvgfQTNUEKp29szTWKlvc1mX3gIphw5GaTX3U7KbM7wsMekgsjkCIhMkNnKx2h58aSUDFIgmDpz6qhDHGWp9Nkxfcl0+7jM/9952PevXsw6eSaU3tLnADMYsrtzUOKynZPLiHndZ4DQ+gimvmv096F4qHaLpNCEEKYvmwYlo+ArxzCAxoBeILMjCyUqIzyRP1OITBCmfCFSKTBENkeRXJgY9RxSB1SsxToAwMuJNiw3i8lMnj0Z0w9XsATAMSepCMkqJiUZbnycf7+SCVc8X6yWJDRFQQoEExMTGyqMc6DQiCWwEsD/APgNgHcAeNrx3tMALMtoBYCtQQ7uQKc8XOvxxx9HIpHA8ccfD4CHWkWj0br5ZkBwYY31Qsguuugi/OxnP2t4f0EW4CiHEIL58+ePan9+Q/dqfa5W/pQbgYTIOTwM27Ztw65du3DIIYfYXllJkuziG0BtsTsWBUGcLF1a2hjz9ttvr7mfsQprrFZBshECGVMDYRL57tKyVgRAZHKYh32FCYycASlcDEmDj5wzo4GQn/e8OwLrC5hGEWoLQY5KMLIG5KhklrGXeEiNCtMoMuC1hV+55+z2JUfg1CrbKi0K4ofwBG9eNAVILmqyizpIURlwhhR5weVzcy+dA1qgkCOVBg2jzC7FHD8kVmL06DHzsZk1eHiTj5yzXXMm4h0XT8Le+7oxuMqMMpCASW+diBZHUQkjayB5GH9+NC9twuCzfNvIlDCMPF8EIQY3ur30O6qWG3bkr1ZUNJnmH2CQIxKUpILotCgyWzIItYYACZBDxBZnIN7PmRXWaBACqSyJcd7n5pT82wqjNRiBpEhILkqi0KMityvHi5REZWjQPPenAtx/x7zPz8HWm7bb/zbAr5vedPXj35OTbU8eKKCPeHt+lI/n9DNipefKZQ4YBQPZrVkwBjQvKTWc5agZ1uixWqNVtMP50SNn1rnPSgSEEBgZHVKYQI7JkGMSqMEgJxVbEOkecyklx7U540PTMfWMOn4iCZAUcww6BaGEjxEANVtolIcAj4oa10L7ye1oP9m9wFZqQ5oLIWr211NIsYdbgfcV81LERXPJKV5wxaFIzE8gPKEovMqRZALGGLLbsyAKQXx2HJJVUEaz7oujHo59zeciIVw5pwP3ZJ/DJFkruf9Ob3GGnPL/FkZ05LvziE4tii8pJgGS2aT7IMw7q7v82NnZ+RKAfR0dHU8COBzAnzs6Om4133sFwK6Ojo7HAHwawE1jN9QDj3ID7/TTT8cJJ5yAfD6P3bt3o62tDRdddJGdr1OraEFQ4qze548++uhR7W8sxNDAwABSqRQKhULDDZ/LxxN0DtzZZ5+Nt7zlLft1LECl8Ojp6UE2m8W8efNw1llnYfv27XZo4YIFC/DDH/6w7njGKsernPe+97013x8rsTiaHMVygi6lX422Y1or/kYUAjlMuNiQJUgR2dGjinovpe9SyeyBtpnYGUlAn5HAku8twtTm4jaMMbunmBQiUJoUSFEJkgxEp0SgNMk8rBGA5rFKGjEtYPlNE/GTM9+MKy+t4TkhBGEzv0oK83DKcFuoWKAgLsGqlU41eMs5cxFQk0+fhKnvqTTWGGPcE2R+f3xOvKTCph7h85rkeQiUJyPENNJoSEbToiTmf3EeIlP4MVp201LM+/zcCqPIMpbCE8I8v0uREG6PFHM9dN6OwYsYMqoYv6Fm9/VbqyAIYOZVSgQTjmtD86KmYnEQSsHgrYocUBS9FMWcsmoQ3dqWQIrJkGMypCjPFwy1hCCZwoMokvfCO+Z3tB3biklvm4jozCjaTyoa013hOKzDuLmvugfjZ8/FcetzfOGBUT7XvFBeEGRmnN9nrd8am1npJchsyfJzZ+bg5fcVkN7MF4+LOWcUDT4CSrDa9TGJ4NPHFnDsbB13nle7qiUhBJLCK5MSWcLEk9qhJBQQiUAOETA758zbSbMKgky7bD5mfrSBipUyMYvaSDDSBtJmfzpGYIdYUs3Me/UwJGdlzgVXHNrw5+SEzPsKEoL8njyy23OwDgmxFuc8jEd3LOwlFydxxE1LMeH4CYhMipS0yijHyueyrjGgKO4lq+mzhweaJc6s50/vJ5bg8B8tKbn/OpGtx2eBQRsutTMIIXZxFaOB3LoDjYZiaDo7O8vjhj7reO/KQEd0EFHN6Fy/fj3uv/9+FAoF3HbbbXZPqLe//e1V9zXWnrPjjjsOiUQCH/zgB0e1P78Gf3kj7LVr1zbULLgaY+HJ++IXv4hrr7121PsK4pyVj6e3txe/+MUvsGPHDuzYsQMPP/yw/d6qVav2S+n68jEpioKpU6di9+7d+NKXvoSrr74auVyuboVESeKldhljDQs+N5yfnTVrFn7wgx943leQnrOhSUm09nLDpv3kCQi3h7H3Xh72NPu/DrE3ZwbvYUUIXxmGzJsJy7FiZTvJx8qeW0GBvy45DD8dkrD2imE0NZe/bxa6UCTIURlyXObNghUCpVkB1VhRnHms1mh5MpZeOAO/nVSrOY65+mk+fElIQnRa1D4uACCHZft9r2XHSVkjnJajq89dPcVDCKslklMrlKlg5Vh495xZ1QWliITltx5hVs10MexZUQwRmUBSeFn26LSI3ZhVMigvYW2gwSe7czyVfzvipqVVjTOmM7vCHBeKfG4zgyGclJEFEDKo50IF1ncAPKxRkmqfeGsxgIIbiXKUG7MkJIGEADlmFd7xXgbdOmex2THM+thM20P5zDkr8NQqHY+2TsO9Or8frN5dO7zs5b4w3gUu7K3wK6/jsbAqUR7+g8XofqAHsxzl8zNbMojOjHID31HhU09pAOWftcRZhBqgXkrpq0XD+odnNVagiUgAFMmeO4DZbF3mTbFtz5nHoimSuXJi9byq/wF+b5QUglyvCiWhQFIAqhM7xJKqzHNRGashV/85C3Ds8a0NfYRRczEtTMDyzG5Gz8JmKK95H/Lk7czz47p3ZhvO/n5jUUx61uDfz8yKuuapSSRNkaYZrl7bRrAXrczPK+azshp2ISCJuPYRlRMy9JQOI3XwFQUZfYKDwKaaUf7rX/+6JPfsvvvuAwBceOGFVfcVlGHtNqazzjoL995776hDXYIYlyXOTj31VFx88cU4/PDD63yiNkE2obawim14HYufc1b+Ox5++GFcd9119r+d4rZWw2dgbJpQW2M6+uijMTg4iEMO4aIjHK5fmtkak6ZpgQjYe++9F2effbbn/QDF/DU/oZGW0ViIF48BLVDM+PB06BkDrUc2l5RqLuwr2GXPk4uaoA1poCrlIXymF0bSDHi1Gp2NRAEg+ZZJePqSEYzkSInHzIIQIDYzhvicGDLbspCjMowcRWJ2HOH2MPQR3TaKVA+njVIGyfzaRo0i0+5B0+IkkgsTJTllJORYwfUY/imZD31jUSsWvKMN7SdPqLotVRmSC+KIz3ZvUGudM6KaRpEX9WEZog7DwxbvJWMxqwuaRqONQkAYL+sdjTubUKNm6FQ1LIHfn4hh8clNiM2I2GGlrhBAsmqi06KHjcgE02YoGAKQ1FWoOvOszixDzQCp6zmzCuIwWUJ4UhhSTDLbDZgLIWZYFFU9NjFHscqf3VrBfJ4OTWrCo63cS2Xp+RfriDOrLDvTvfddK1+UsTyv8TnxirBPI09hpA1eUEKRQCSCkVdT3GuVUJDvytme4jClthdsNOjm8WEN2hm5rjyIzEPklKRi98UiChePkkJs69vw+PiwPGdSA5YuLVAYGQORiWHISQWhVgVyRAKJSLxsvOU5s+5BXjxn5vyQqyx6uJFal0J0WpSPhRAujohDnKmGeY15EdR8QESuG0Rnk92eRXRKBDRvQDYr/lKdoskSZ9ZF4EEP0TLPWahOCp1MSueckTVKQiCVpIwCAFql0MiBjBBnPqhmlF9//fUl/1ZVFW1tbZg1a1bVfY1VWOOuXbsa7ttVa1xeBYhqJq189KMfHbXXzg2/YtHt+I42tLJ8LH7OWflnncKsnHoFXIL2nF1yySU455xzcOqppwIoFtMYDaFQCJqm+esr5si/84tV4MaqJOkFS5wxxwPWyFPIUbnCKAL4ymeoNQxIQGxGFNGpEUQmhxGeGAb5Dc8XkjXDe7iVaRSlQiFMvWgeFp+UhBQGEuFqO+TNpiVFsnubSWGC+BxTjCjEfjh6yfXQjWJT7EbXgyyRQkhxVd9CChXDGmEwT7a+7Tlb3IpJb51Ye2NKEWoOVfUaUbOUvlTw/sCntues9gFKb0qDSATR6dGSxbVQc8jumTdnMkMBPPeQEF6eXa4oal4bK6yREoK5F81u6DOW8E7MiyM+22r6TJCYEEJekhGlBnb3AFN8eqooqT+PrPnGFB4aB5hixVyICLfxhRQ/DYSZeYzK56fTKWvqdbzcVTStZk4CcnmG/pQjNNbKodSZJ48HUDw+qYkJLD27He1vrr7gQKzpIBPIEQJGATkpAwZviUBVavdeizADXlK8bHHW4PZGRkdkUhggQPKwBM9ZBL8XKE0hQCKg5j3fa7sBy4PfyCKRUTB4+4yohLaOVqgDKsJtISQXJtH7714754xq3tsxWAsncoOLVkbesNtmxOfEkevKQxokfHHEXMiRVMOrNiuGxI/idkEkwr14aQY5KUMb1JDbnkWMmgVlDMNHKf1yz1ntH2V7zswm3LmuHBLzeNqDnik+U4208Jy9oRiNwVmvpHzQYY3PPPMMQqGQL2EG+BcgluenvOKeV/wep6AaRgcxFqAoXutxxx13NDyeoHLOrr/++qp9+RolCMFojaeR6qL1sMSZ1XbAC3a4m0TQ+qZWDD03hIk1PDEkJPE8BmvFXSZ2xS3JrAAoq37EGR+PFpKx9G2N9DUqeqImntQOo0ChDxfnsBQitufM8FAQxKBFY7muy8P+0trvWZ41rw17rZyzcsO6ytbFQi0uxFqLK9aMejT2Lc9ZHSuGyARMY6VeMwCtR7bYHqB504F14Dlned1b01V7waFRq4oxO/TUmZtCZG7cZiMhRHMGuroZjvIpzgwi1c85M7/DGWGqJBW7wqbtOSt4N9KsRZDySqHO71R1PtDhPP/vbefl8KGPxvGmCxj6HetBOnGIDo+ePEvgZyYmMPW9tQtd2GFoMg8hpBqDHOVGNhjPxVOH+MXOPWcevDBaqWFdD6IQkLBkfn9p+Fr78W3I7srZYb9em5nL1rOjgeueEN7YPbkgyaMc5icQnR618+GsnpTWWPx4zKUGH2WZLRm+UKQQJOYleGGbAQ20YCAeBfJEQpRRHmrpI+esEc8Zowyp11KQIhLksAQak6EkFOgjOmKHxNCymyIPPn+y27OuOY91v8O+5k3PWZ1hWeu1TCKQ4xKYAWS2ZUFkAiUh2/m4fq778cL/UvQbmAPN0Hd+fsGCBTjqqKMCG1etqou1CFqcBeU5s8bzP//zP57HEsQ5a+SzCxYswGc+85m62wVdHTEIMRTEmKy5F6TnbHh42PtOTDHEFAnzvzQPS76/CBNreGMIAZoPb7JXpp1EZ3GPg6IZnsOtqDo6owgoDaeTI1JJqXNJlmwjXfNgFBkG7LBGUueUMbOiXy2RQggB7Iai8GTMWmGNjaygMwbX/AWLuTMICkQCYQDzeOnbYqiueCW8gly48kBax6x5FjeCQoyiKyV7athrmJdn/fHYX25Xsyz5s0TQsqIFepzfG3/+dIgLWA/YOWcNhDXSFP8BRpWvKoY1+vB22oZs6WAoLf67YB5HzfyaM5ZqaE4QRKPFba6+gDjCGv2HfTZixVnXoRSV0HZMG0LNCpSkDDksmSHXsMvCR5gBL7drY5QCXwpLkELcU15+/StNprA27/nlxU8aJWzOPSXayEHijZStBZzkgiQvTkIcYZYoLoZ5wlTyjYY1Elmyc+AAINQe5jmmMkEywpA3VR7Ne3t+WM8O0oCbhlHG8xVlLqrbT25H8xFN/N8hCfGRYqpA99qsx4IpowtrdHrO7GPluFdar1ne4wrROCLEmQ8sg3PhwoX44x//iGeeeQYf/vCHXbe97LLLau4rKMPa+rxfj4eFX8+Z5RlqNEepHkF5zt73vvehu7sb11xzzbiNxfnZWmX8GxUlQcwhxoo5GV5yFMdiTAee56zoFZIjMpoWN9VMWgZBhefDQm7hc0jRfXjOrFCQ0YjXWuOVilXSvJSNdnrOah4XAKl1aRg5WtdzJslWQRBvhhGh7iFprtsS1BzP3KnENorUrMfzZnny6kxpq1iKFK4+7pYj+JwOUwPr++Ti/BwFlje4Yc8ZqgtdJSEjH+HzOjNoINNYbYhKrNQVUnu6MsbssNVqa4hygh9oI+/NwwAUj1FFWKNjfwWde6gsDWh1abjsHP6Z898FzJhY9JwxjXoucsNc8harIhNbBIbaQ7xku+m5kiK8OqFV5TFCaSPdQiqwQuQaFfhEkexiMm5hgkSCHc5seBkQeHETAFBijXmG5BBxLfBDFIKY6QiyvXhewqstcdZgzJqk8MWA5EKeeiFHJLQe1QIiETTFgZyZTEfz3jz4hp1H2cCzg5nXvEzQsrwZkYlhXjDFzIWLRoA/t/OQ6Ff3SL6qNRrmhFCk2vuwhs3MIjdSiCA6NWwvxlj3TeE5e4NhGdaKouDcc8/Fsccei9///ve45ZZb8MADD+B73/seAOADH/gAvv/979fcV9CeM8so9svr1XMWCoUwZcoUXwIkyJyzeLy0+MAJJ5xgv250jEGMx5nfdaCIs7HwnPkRZ5blwhpY/VQHNdACrZq/FG03m2j6yjkr5gs1Si1RQGRiG+lewpsM6uhzVueUyTGJN1OuYdARidjiDDq8FQSxCgPUOWeFPtWuRlaNuTOA/hC/n/31r94KyzhDY2tCiuFW1bCM6hBjuGttHNSDgDVGGZIGsJKKmqUDAiZP5mNq0VV09Y/+PsIYK66ig9iJ/67bGgwhsyhKtcKZiinOmAevogUtKwhi/71EnPH5zxjvzdaymBvV578LePU3BLdfTpCMAZoz54x5K1JCzVtqPe8034aYoW/MbKHB86rajm7FhOPbIMnFYjRhZtiev9Ew2oIgRALaOlqrl2wnxb97KVACcKEJAEqVxbESKPecueUAEkVC1BJnOe9RDhiFOGOUASFe0Tc2s1icJ9QSApEImpMEaXNHusdFIuvZ0UjBFFAuUpWEXBLaaxW7ksMSlszk+8tmvOUGW/dFo0HPmfWcYbKZOx2RS0LSpYjlWRSeszcU1bxUn/3sZ/Hud78bX/7yl/Hkk0/i97//PaLR2vG3QeecHSies7HKOQtCnI33WMrHc/nlxY4V55xzjv16tOLsQBFCQFF0Wo3YvRCk58wq/+8rrNFaKm9EnPUWIEXlqh6bKTP4bwrpxv9n783jLEnO6tATEbnctfaq3rfZd41mSstII2TtgAAtYGQEQmCMBGJYZBDwBNi8Z/MwGAsBAiTxDLKMjRCWMEggJAzal5FKs2j2mZ6tu6v32u+emRHvj4jIzFtVt+pm3MjuOz31/X7zq567ZEVlRkZ+J875zmdsOa6TRt7njrUANpVY6iAs2f02kjfxxD1vu1196qrG19sxjyp5MGGFgETWuFUt2er9q+ic74A6W9s3X7aH4OuVaQDAvQ+bJSGaFdqW9SDyetBNGmXroA6Vzc0hcNfTFE0DvJhFIicT043GLfGQCcGhffJAY2EHJxcNdtBTZiAgW8saRSjgl5X8rZesUTNnA5i4xG6NWxiCtEMSm2m4DLFrKyEE1x0mcByCUiFxa+ShMG6MnVyzPthgRhAsd6RET5stlB2wkky0qU/jdgM+j4zAkHZUFFmwOCWYuG180/YRhCabSCabRJyLmDnbtD3FuhCKGXJHNyIV6gBszMOi44HWQ7Qe3bo9SO9BZZA1CrmZtL7GEZDnpVIEakzmII+fNFPHRj3m9KbDEQLUpRIcpj5f2FOANyn7Uk6Pyde+dZz2vBe3/B0ph1YAcLaTNeoWGlwC6Oq1FZQOlWJDEt0eQrR2mLNnVegkuBdLRSnF7bff3hcQsMF6CCHi5NpGIgsMzpzZljUOep7yAGc2mDPXdfHbv/3bOHv2LD72sY/hjjvuiD/TL1CyARZtOiMCiHvabWeIc6HGdOiQlF3Mzc2ZX7cwQ1LkJo6Im8X+3apxJ8wle3r3s285EbZ+GBNG4kJ8E7fGdM3Zdk8Y4sgm2Fvt/hNK4mRGRGayNKrn0BZ/N9XXipEtxz1aBVYc1RCaR0ZZUb9giFAJFHvJYnV4k/Ler7Q7OGHAVPEM9UK6AfVWm0aOct4bjQKcNGHO1ONGj2erYYlQwJ/Uz6ke41HNaAczBOlH1pjUm/Xa9S8X1skaldNc1sgia5RAh/acb96EGwMYn3MEPPs1i6L+5xAgbxtCNhqsJGNO6hpN3Bo5l/VzQG9ZOQAEywHaZ9sAF9KyfjOg6FCAEjxekI7FD9xnds0SWWMf9xlXPc02kTRTjyHiBGsKnJ06y83QWdifogAAwCVL5k95XUx+9ZoKmDLfKJcTgP/4mezP66S3oWLOtjMEUedTOASUKWfWCRf64ujrvsOcPcvCpoTQJgvjOI4VSZo+VvrYWePZwJzZAmcAMD09jTe+8Y1d47uQzJlNlgoAJiaki+Hi4qLxMWyyeddddx2uuuoqnD9/HnfeeafZQbQkrQ+dPnXIlonsvulkl9DULlrv6HNB0FnY2l5RcCEbvG4lI2QEIi7Ezz6efpkzwQWoRzD23NGtd25pAqpEZOaSph/inTOb00rBagCiHDXXO8etD0IJbjss79vqNs2RewbvE+BToho8bwPOJiRYHA87Rr3p+jcoAURno3vk+nBUzcdI2EGjbQDO1tXAbcecedrwo8fU0MwZb/OBGer1Erz08VohiR0bvR5pQbmYWOlL5sxM1ih03WI/yyKT9Ti91qGxW8fgqjlU5iGaBs3no6yyRiL6qDXVx85+34eRQEEzZ1vM1/aZNtrnOvI69liHRm8awf3nXHQUlXfeYMMBSMBZPylja74F6lI4lY3PYm/KxfgoYlnjyXMwBItaot9HTZ6Qjed1y4PuNwHmEVw2I4/nC25krKufgSG0rHHrg+hWCXBpfO3ks4QgakQpcLbDnD2rwqaEUDNL/Vqr5z0eHYOCobzAWbo5c5awCc50/dLy8rLxMfoZT7/gTM+hQRos25Y12gBnNgEjIQSvetWrAACf+9znzA4S9mfmAADEoyju693Md6RMoDepzyyYZY1pWWOwsvV9KiKxpaQRUIBKfcTEECRMP5W3SqojAeJQ+NP+tm6N8U6zqaxRG4Js8reHtRDN4y0wTxoTyL9/a5nly6+Q960XDWilv8WONe/I3XDq0Z7sgg5vSq4fE2EbbROWIQNzVn+yvu3c93fJtajEw+750GfEskZ1HbZajnjA4Wwja9TMWdTiMC3u7N3nLPn/Too585zNf0+5AAQ05dYIYtSwN5MhCJGOer2AnGzvIa/ZWNg2AtSh7pnV96Nj+00QvSljYggStjgoZH3fVvcZIP/+cDXoySC5Yy5+6iUtdNR1W10xI6qyMGdhUzYNH71pdMN7ozeP4o3fW8LuKXmc42fMFAUk3H4dikMA3riL4oFNnmdCsnm+qv0s8AihiTHROiv97WSNcQsNSlE+JEsoqEsBIRA2IjBVjyt2DEGeXWGTOSsW5YRvNk2treybgaSPNWgTalvgTNcwmZ4nm+BsZmYGAHD27FnjY/Qznn6Bkm6mPSz1XQBQLsuGkD/xEz9hfAzbgPF5z3seAODee+81HJBa6LdJmAEp29FJT6/QCfFjJ8x2YzXLIBPZrR+Igou490vPoEnCZ1R7oq39sXFjIVgN0DypNg/41pb16fHEBfTGskYlc9lkaWwea4J6UkJFqGbOeh+LUAJHWaM73JA566NeqHGsCVakYJXeNYs63HG5foxEQczcZImEOdvmc1yC++0AQflyed+XoxBRZCJr7AaLvX4dDzlEkDBnvd0aB+93pM/RemnserfGTrQNc5aWNYayZtHIPEH9rdsBZcFlXRt1sWWdrAb4Y2GAhglztq6B8Jaf1aYaW32UJiCGR8h83wc1eYJaWzQV4x0uXRqLVLrGbvE7fugFAQ6ojil1Qz+pmDnrI/2gDlFOrZu3rGAewcuuVcxg27BmWVPNfYAzHoie970344N4BOGqzBN9HvW8F7cKETNnSh65DXMGzfwBKB2WuSFhRDZbLyYOpDuyxmdZbFdzliU0OGs0GgOPxyZzpkGVKaOnGS5bNWcanJmeJ5vgbNcu2fjzzJkzxsewCc6qVamHHwSc2QZCe/fuBQDU63XzPl6WAeONN94IALjvvvvMDpChEF9mRdvv2gLAt44a7uinLay3OYQIRNzPaMvxaJc0g91P/VBOJ2k84IgaEaImh1OkiNoyOdusV9aG8RACRzc8NmxCzfjmiTUg/17qswSYsa139AHAU3UpjilzFkvSNv89QghVq0hlr6Vt2E5tPFGOQnQ62QfE+5jTPORYe2hNgvttkjk9nhIP41qkLCHWJfppS+2oHSFqqsT7RAtRK4Kr5Us9flVcc2ZoOQ4kfc6a893KhG5DkLgNIrweS3q5mBiCiICDwBCd6Tm0HRgSkBsubu/aVwBgBQct1wGDQHMp+2ZsLMvuA5y1Tre6zEk2C0KJtLbXx854isKGvBDBFuCs9lhdqgk8iuI+f8vLwByCXePyA0FTwGQiaXfBvppibyOvBgVcJXksBoHRHNItKPphzlonWwhWN58XzJfNzbUE0xeRkaxRrJM19trgiH+vGvZ6xpwyAlZgiVvjDnP27AqbYGhQRgjIhznTLpOmUjnbhiBpEGuS7OfBnD3yyCM4ffq00TH0eDY7P+9+97sBAP/+3//7vo7l+z5c10UQBMayT9uGIL/wC78Q//vcuXNGx7ANGK+77jocOnQIV111Vfz3ZgpdtNyMELV6bw8KLnfFt6sX0pjzH+/OPhRAJs2AYs62A2ehiG3Fe0Xa2trIJW0TW/bO+Q6aJ5ogkA2BeZNDcPS2Y18Xji6Kz8MQhMp+OETVtm1nCAIAnrqmHucw6rGsMpewEW7qICgCAVZkIIxg7JZRuCNbr+kJOAtg0md5PRjq9RnqUTCPbNsXKQ0WTWzZ14/HJyI25Gg82UT9SbU5Rwn8GR+e+n093RorOkkbwK0xBh/dr6+30g9U/roVcxakas5gaAiSxTUWkAzMVsYPhCHuT9deyX6e4kbR22xsCC4kWN5uOSckXhu5QaYfqfkSbjGnqUuU5JOgem0V5ctLPT9LHBJvkhCDDRAgqZHqp60YKNlSWUAYiV1KS2FoJmvMMId025PeB0sY/GoYDMScBcry09viURU1o3jYG9ZgVaera85Ec4c5e1aFTTDkeR4IIQiCYCjMLnRo0GgKzmy7R1JKBwKMNs/R5ORk/O8PfehDRsfYajy/8Ru/gYWFBbzuda/r+3iaPVtbWzMaj22Wqlqt4rbbbgMAfPWrXx2KMRUKBTz11FP427/9WyPAR1KyRr5F3yTe4vAn3G1rvFwFPL7xGMFq3ZxlEH38LTzgoH2BM/lvE+aMawetVBZLHAJ3zEVnKQAtMkStCM1jjcS1cJuImTNuZl0fJ0Ue2bCpQ5iSDpGEXdiOjdCA2xMcHZPlOm5CTWJDl3TIejwFFPvIvR0F3io8RLuTXUbIU4m16EE/iVACRuoxaebQx3hKPIKJ8lOPITbLOddC/WgdgGotoOYD9SnKV5ThVVTNWU9ZowJvA7k1qmuWSpg7Cx20U2xCKyVrdHuwI54LgBJZZsbV32qSOwrNvm7zMS6kK6K7NeNJHBr3bmwbyMASQ5DN31+9fzWWoRJGtnUIJBQxcwYDIyBtZiR63EA84CCuXOsoIyjsKWx531OWMHkkNGNgNXO2lWus7ntHCOlpUAJIxrSg77MwNAJDpI/aVx2sQLcE3qM3jKCwV+Zmu4KWGWOuN61i5qz3MepPNOLdmPWbMsyX65SuOdthzp5lYZM5I4QMXHdmU2apQ4/JFJzZTqyBwaSNNsEZpRTve9/7AACf/exnjY6x3Xi0oUa/oa+9aT2VbZYKQAwuTQ04bLN5g0b8QHM3L+RvnWqh8bQEHrSP/jpxkhIBpw18U3RyL2gfibxInPR6Rbrpc2RQc8Y3S9K43FV1RxncqoOoxaXrV58PcNdXybihIUgsa/TohmsWW9WTRNa47fHUOfR4NDA4W5/ktU610DnfiZmzfgwfdG+mchQajSeu8eLSJS5+nQu0z0gWXoQCrMSkbfY2W/+uZs54iMCAfY2ZM/X/PpWgnAdc9nVzKdYeqSFaC+BNuHDVI6/X367lVoPIGkXcsDf526NmBJ6a6AtrwFOLCri7m/8iQggKPknqziLRExBvGZsYgvRsaE0A4hF4mznt6Y8wEoOzVjN7Msu3YM6EMrbhLY7aYzUpr9xG2kdoImcWBmCIR73BYmcpQO3RmpQMUzWW7e57h4Co+i9i0GIESNW+bnH71B9voHWiBeoA1aurWw+pKud1JQoQGIChZKNxG7YzEiAe3bQHnA5WYigeLKLDKMo8RLiWHS1qFUigHmRbtHcE9VLM6rrLUdxXACvTmMnbqTl7loVtMGTL7GIYwZnNxHpYwBkAfOd3ficAczBkezzanOSVr3yl0ffzANMHDx4EYO6wmQdgHCRinb5DNmV+okYkE65IbGnhHB9PJQUUAm2D0s44saboAmerD6x2MXs8kDbi2yXWQOJqGG3BDPaKdM1ZfH4IUDpUBPUYaIEByjWy383w0i5Z++qHIUyya6bntbOROQNRTAyVgLt0qLe7Zny8gq6t4OgYgI/ErTFhBHjAsXr/KqJGhKAWghWU8UYfRJg7ovqcRYGRlX4MkgmJ5YMAUH+ijrAeSgv6SMh+WGW2uZ12KohDEChNEm1kH5BQQ4hljY4AoUDt0TqidiRbVBQo4Mq6vIIHuA7QCYDWJmYWTiVVc2YYetMhLTUjjHQl/1940sOP/YU0Q/G2SHh9N2WnHxgyZ5vULTaPNdF8el3+ICTrMXbzKEZvHu15OMKStSEwMARZ35tOviYQ1kKAA8SnksFQsmm63TpEACduoWHA5PVgzsJGBOpJpswdd5VSgG5rrEK9xD2SBWYgX5dObrUBJEKBqBECDlU9u3qHU1Eywigwenb0Ys54wOV1U9E62QIrMIzdMrbl8ahDsVaQazVfM7jvdckAKCgRPd0ao3YE6lF46l5sr/tV5avKmHjhRLwps+PW+CwL22BoUOZMJ7E74Kx32AZDhw4dQqVSwZkzZ3D+/PmLPp50nDx5MvN38gBCg7aJyAMwDhRRkqTFlt+phJYWpZyiebK1bT8oIJElUSE2PGT6iXjXfZ0kh7Bu2WXt0TrC1WBb5z8AINo0xACc8RTrsfZwIq9lBQbiqZ5duv9bn9OsckjKZXwT7Q5SskZnk/oeqmzGGUXpYAmlQ73rTnToGiafR2ZgKE7SKJrH5HofNTlYQdZJsAIFK7Fte9Lp8FJujW0D5ixmGVgyNu3y51QdRK0IIhQoHSlh8rYJVK4sb3k8Qgk6RTkmWjNI0vR9pRLrgiMZJ+oSsJIDwiQLxApU1gsSgjFpVouV+sbj0YJswCwiEYOszGPSVvosAfiEEogepja9DEEAoOAlpiCIzKS62vkvncVFTa7aBaQHDoAQuKPbPGNYwmZ1jMBZisFXESwHaBxvSkdEj6B1qgVKlenONmkKcZPaT2og/Yzl1etun6geonJVGd6kJxtOq35q2zHm1GexrNGUwSe6OfK63xW1oqTJOYWqQxTbZujajdAT3OjZoWWW64Fp+0wbzRMt6arJBcJ61NdaTRgBV7lDaAKI4k0ZwO8xP3jIUT9aB/UIioqdbq7b9yWqfYJT3WHOnpVh2x1xUMfGPIDQMIKzQc6TbTBEKcXu3bsBAEtLSxd9PB/5yEfif3/605/O/P08rpcGZ6bNuoeNOevasVYPt9pj9XinkToErOrAKTPQbcxAgKRvEsXGHcB+Is1OaUlRc74J6kvWZfWBVbTPtcF8tWPdBzijSrZHDQwUEjkRkeyYqhMjHgXzJNNBVVPcvhwvAZTG5JO6aFr4HtecAeCyBiFqR1h7aE0m/D4FZUD5yPbADEhkciVuJiOMAT4jsWOlCDhYmUnTjSKT0sm+zB4AT7VrmAjasSFFltCsRxrg8zYHKzugPpXNzQm2dfqMgwKBAmdO3UQb211zVnBlospKTLlYEjgjDliJxRsgowovLm9iVhtLVwEIA8kekGw6EDcB+ALo2cC3X3Amk3IDMKT+jDR4p6qGSjP3gGJm+1g6aUrWuFUtbc/xpNehVDBfSompx2S7CtWqYjsHUurQWHlADWpNtSR7Q+8+IeBUHEy+eALly0uSLeyjzpQ6FI6SV7OQGxmSxevQutuo/kQDzeNNWWumFBCEbL8xw5TM0hXcbJNokzkkXyDwplw0TzSllFg5x24bFIhoyuwmY+iNRg4Cr5eNPpfydOoxlFSHpkYPUY42UtqpOXuWhW3mbFBZYx4MwzCCM32e6vVNtki3iTyYqkEMSmxLY9/0pjfh7W9/OwAzGWEec0iDM1Ojm6FjznQO4pA4mXXKDLzD0T7XhhBAaX9Rmidsk4AAiAEKFWayxlhvT0icF/EWBysxNI414Y64CGshiE6OMtRU0U6UOQmJGxpD1mg0n24iaoSSBXIV06Hs+ov7Cn0d01PgrBSZ9c/RzBllFEII1J9ooH60AafioLC7AGfE7RsIAZCfhzS8MDFPQGrHmroE4VqoLL0l2CjsLcCf8ftyjgQAd9IHJ8B41DEaT5xYpxqQ80Da+es5HLV4vFO/XRBCEBbkOWImzJneQVf/XyjIXXmnzOINBlZgsi5P3YNjqjxnZTNwRkic6EeG4EzE4IwmawDZJPlXUdlCHZuWNQpDB1LCkzmkg3oU1GfoLHSw9nANYS1E46lmX/e8NARJ2Lyskd6USQf1aWxwQz1537uTHkaf01tiqcNRNbvEAAglxkQyolaE2iM1CJB4TsuaTmwvsYTadFProm+yAwLETZPTzJmIJKsISFdbuFTKhsn29aZUgUVXcLRMHCRTDLCOYDkAYbLxNisxRPVISay3PxwhCXMqBlCBcJCYOdPP1fRniCdlqOUROaj1zJkOR9XImW7IXMzYAWcDhG0nQluyxkudORuEickTnJlcN9tzCEjOjwk4G0ZZ47AyZ5QmyRUry4RIBBwi4CgeKGLkxiq8qe2br6drzkwesElinbxGPQqn6sIbc0GLVJlLyId5P7JGpmp0WCcyKMRX4yIyoREQcEdcsCLD2K2j8CY8EJeiuK+AypWVvo7pqYdskRu6kqmfjgtZ/8JkDYlTZRh/wZj8TAZwxjyKlrpn2waF70glacSlaM63ZNLhELACxeSLJuCOuph6yWSXAUXP8bgEHWWDLpaz32eJNBYx2BEhl/3fGODv8kF9ImVg/QQFeEGzr9kTWT2eNpd/e1GzvkUmHfZ8WY8z/ryx+DtbMWcAYuaMm4KzVH1OsmEh0Ovq/7u39D5WwU/1OgsNDUFSpjLt8x2sPVID1b2mCMBKFFGLy/M0O7bt4QhFskExgIlLeh0SXPZVrD/VkPJBX7LmhJFt20MAiSsq5RxZqTO+jg2uH60rF8TEcZMoiWX1+u3XIeIQOGpzomBEl8s1HkgMQTSIJqqxO29LabM37cVmFluOydXMmdi01nLbEN3jAYDW6TbC1VDKuwXQOtWWDF0/AJ8S2dIFSSuDTMNJPTtcBgQr0rglXElyPcGl6y4Ywe7ZEQBAo0d6GteatnsY5QxxDEm288wM2zv6GjCYyr+GkTnLI7HWf19kkKXlcY4GYc7yBK8mYChP5mzQeT084Ez+kLmVAA+5lApyyajwUACUoHJlJZshiBAwuc1ieRORMkYhBGiBwilJiRwhSkbECNyq2xc407I9ZtCkKkr1OSNMMhbOqANWYHEzYFagmWp/EqYqRBBmf+ivZ86k85gL4klmqHxZCaPPGen7eMRNwFlnzSBRi2WNAHUl8zp26xiIS2Mb+kxBgEBtNfN69mumkyJCECNZEcp5RByK8pESnIrTV/2bHlBci2XSjVbvoKtfVygocObL2kDNfOj5BGDLmjMA0ogGA4AzvSnT5bBJ4rq4itt93Osv632uCh663BpNgqTAWVQLIbiAO+bEPeZYiaF9piUZx0ofc4okjoXC4B6LAWbaPVJJ4jxlvMEKqo6yz2mkmTOTmjPdd03XnBFHyaxDkSga1Lq4bSNv9X3NcBWDweTV+lHWPtsGqzDVQ00qMViJYfyWMUy9ZHKLI6nhuzRmYDsNA5ll/OxIXnPUeNwRB4TJtZr4/SkuQAl4atMhc6RljY5AsBTIez7d8IxLhQGhBCMT8vVeskaqx80Bbtib7mKFPeeIZ2HYThq1tM0EdOQxHmA4mTMNHEwaCA8bu5gHGPJ9ydYMG3M2TJsOA0XaXIIQ1B+ro3S4BFpkMpml/dV16dCyJAphJpFLsR4AgejImgrtPgguIEBBqMDY80b7klp6yqLZCQyYs7gQX/Y2C9dCTL64uyUELdIMib7cQeeQO8ShwUNWJ0WOD0AoiVLFga9qtWQNQ/9znhUp2q4DdDpm4EwkiTVxKIhL4E26GKuOxsl1liCUIFIJDG+YM1VIuUMKAKUDRfi7/bhPXf/jSUmlTGpPtOOnBj5TDkg9QvXaKla/tSKd2qa8ru9sx5zFPY9MJU4ph830PaEdJa+aCHHXmWRMW7n/dckaTd0a1/U5o4ygsNsH9SiWzrbBCkxuQPS5bBIKQAFqYpJYx/0WU3WLoUyQ3TE1jgKV8sc+731WVBtXBsyiTg/0N4liqeN1Ub/Wp3QYhMCvMtShmj4bhJY1EqbccyE3ZiCAYEmaNTHfjR0ttz0eBSJK4USRUfuDmI3kqjaRy00MwpS0nRKwEgV1+lNcgKbqFk3YV20ERAh8pjxRfAZCEffIE5Fs6UEcgqISpvSSNRIiN3SiRoSoYd6A/mLEDjgbIGwDD518mtbm5CGR04CRc540RswQeSTW+nxfCszZsMkIh43JA4ZP1pjesSZUNjB2R125c11x0PE6IF4GcKYSFSZgJGvk6XohCLTPtVE6UoLgcoeYBxwUkmHzxr2tDhWHU2IQANxQ1pyRfvzc9XhSO9bumAvejjYAwskXTWQCZ4QScEJAhUBo4kqmfjKPonmiCXfEAStSjD13+7qXzYJ6FB21NgarA8oaFSNEHQp3xHBdogBX4Ey0DJizdN0iTdAZqzhJ24CZ7SW6OkgqSYMJC6PaVei7YebmKmp3BnHdYvXaygb3wZg56yVrLGrwasicxbWmVNYGMZn86Rqr2y5bD862Zs5iQ5DQtOYs/hfCtRDOqBu3VIhaHO6IA6fiJevDdsdLXTOyvqtvH5GeQ4BMtJ0Kw/itYwhXQ7ROtRCshqAt3rdmK2bORPZzlKxDCRCThik0zmNYkfXVixKQQKio7s9SGBhJUbVbI6MEwVIgXVm1yQ4BwuVA1p32u7nHiDTgiICOETiTP8LVAI2nmojqIYr7CyAFN2adpJSY9mUGRFlyviOTTZkEK8Y2+tQlEFxIh0afgpUcFPcXULm6AqJut2ZbbgrS9c8UIh0to0aEqG6WV1+sGI5s5xkaeTFngxon2E5ibYChYWHO8hjPsMkaNXN2qckah4c5kz+oi1iOwkoUE7eNw5vx5QO/H2crFYP2OYtdE9SDKFgN42ahxFW21W6fO58qYjBlUoif6ndE3c3rlKjbp0xGB0nkY6HBjmxspe8SuBMeQAgmbhvPfJx4OA5FqEwEQpMd2ZSZA9FtBTIA+g3jSTFnJg6bcT0Vlf/p2pwsbGL3gJDMNwPZXl2V73JCMFHk8IsEzqgDd8TF6I0jm9bjjFbk71uubf77dPN1Xh+85iyshxAdARBA45jv/c7ujY+tNjIlc5aqOTNhzjQ4EEIa7fjSgId4UkY4+txReJNeX+08ALl2xM2IDRJrnqp9bZ5oImpzWftacVDYq5JpV61FfW72uCU5dmbEnKUVBfKepR4FTVEShMoaz36AH6EEfokiBIErhGQ8M0a8DilHTafqgKq2GdSTPyvXVPpeG2WNlzxHJuCsSxpbDyVzpxvNKwUIKzMUdhdQva6P+mAK8AGkse2W3pQhWGxoV0+5YeRUHHhTHqK1UJ6rAgOlBAV127U2eXbG1xdAZCD3vpixA84GCNs7+oPKGvNiGGzUeNkc0yBgMY9zNIghSJ5g6FKRNeYxpj/+3wL/9n0cR0+Yywgpk6wZYbI3lTfuSTlIH/b56UjXnBk1odYPWCKZIb0bW9xXjJ3tqNOfTEYHUw88Agy0Y13YX0T1+v5ruXoFYYl8zASc6R1rx1eufX3Khnoez0kYE6PlWteeOLq9Ac0E6DcEBbir6gQHMOAABZxRF+2zHYiIS3c9g5B26RqcZU/SVla10x7B5962DFZgmPq2SVk3OeZuCnw0c9ZT1lhWz7F6dgdSAMkmiJJ4EZdA8MTuvzTt4td/VP77u67f+ka+52jCnNVbwmgTJG1MJBskS/mZN+li93ftQmF3ASM3jWDiBf1tQsi+ekrWaHDNYkBHZf/HYLHTBe6dioOp2yelCUe/xJD6PhPZrev5uibUhCKuoUzHxIvGt+8BBwAUcF0gSjcPzxj6N1MlpWRFBn/GR/XqCqhL4U54fRml6CAp6/rIpPIkluhDsuZMgjNd78YqDipXVFC+vNTf+pQCiyY1ZzVVL8oJwW99T0MCRKbuN4eAebI1i5tSgGhp46amIOqaA0C4A86ePTFsssa8GAb992VlqtKLaVY55FZxKTFneQCPYWXOhqkJ9Uc/K/C7HwWOnTH4cvxAo0qOljAM/oyP6nXZwMigzFnco0rVCzkVByBA6WAR3qQnk8mMTBXTVtMGcqK4vxLkeSns7l8O1/ugKUtsE7dG9WXmUNkni/ZnAtAraKpBLjdZrtXSRRV4Lu7vr6VAryCMxLJG1jGRNWqAT1A+UkLUjODN+MbMGXEScwliwJxpAO4ygd1joruGrUfomrNehiAanPE6N5IRihQYSkxJEtaLUeDdbwE+/TsEH3nP1nP+2BkgVH/PH32tv95664Okas4086rNLfS5ksxBf+smdSngmgNqfX4EB1jZAQ/EpqxdFjmzdkc0MgSJrf0VE6wY6vUbDv1uihBK4LkkAWcG87rbEESOp7Dbl6oHX6ovMt1zlCDSTZ8N7vukBk7NF7cbvE6+eALFfYW+rxkhJK4nNAFna4r1nqpwfPvVAXjI5fNLlRDAkUqMNIDdpfYenjq96YBAfbUp8wyrOdsBZwOE7aRxWGWNpszZMMssh6XmLM8ar0uFOctFGquN5Aw2iHWtB3WA8edJhz0tA6SuARjpYs4MdPpp+Q4l0h1tUp7z0qEiRq6pxjuPfQ/J0+As83C6+h3Z2pNxKk7MnAUG50hbWPsTsoaKDlptnao1GihJYwmIGWw4A8oaeZLoe5Me/GkvZjlMxxMn5gbnRy/tgsrz0884tmPOXGW0YlxzpmWNPpEOmxUpX06XfLoOwaufT1Aubj3ef/pdEjNnD59lg/U5I9I0gbo0NrowDZ2YUyMTl2RThpUYEIlNgUa/BiWA2gRBwnxnG4/+hQRRLZJrICMY66OtwOaDARwnkVcHBo2N9dlglIBAthlwqg68aQ9jt4x2uY/2E2nrej6AlX68Qeh0m1n11aezazyI6xaNmDPlOFlwgbAWori3IGsEHQn6CN0o0X/etfLnV+7ffDxaybIja3wWxbC5NeZhCAKYM2d5g8VhYc6Gza1x2AxBNJM3TLLGQcBZ/EBTu3hO1UHUMm9yqRNPCoFWYJBcpRJrEHRZZztlB/4uH+UjJZQvK/d9SKaamxKRvR4mXXOWwUdk24hlhAb9c6j6G4qTPgr7ChukTVlDnmuVFBnUVsTMmUoY++4f1iso4nohYjKeFPvqjrvS1n9A0KhdBIlBvVBautqvm97oNoYgTIGzqGbGnOlr5o56KB2S7qyszGIgmWUJf/mtJK45c4XZeOKNEwJZb5axrnSziBPzAfqugRL4U54EM9VNTkoW5kyDMwFz5gyyATVAMPHC8dg0JWvonmimhhfSWCmpNRUgqFxZlqCa9NnuYP2YGBCphxnvmNecUarbrSDunWYShJGkCbUBOGupyhBPcDSfbqJ4sAjqqLpcpq6B060CecUt8t9//cWNv4/QhDkLn2GGIDtujQPEsMoah4U5y6sGbthqzjT4GKQJdR7jMWHOhg0s5jUmDc4M8H38QGMqkRl//njfRfebHk+7UgkgNNmx3sxpb10U9mSTzbEUm5cVnYl0fyGL4Ez3vIoy1noIIcB081cXAAZPYmUSosZlcM2ISObQ5O0TA8u+CUsBYRPTthQLQ3RyPWBoOZ0JWIzZSEJQmPH7Oj9ZmLOsDqRAco78aRejN44iXAshhAD/7/J9lnEJePG1AvgaUGHRYIYgnMtegiVnsLpFpExcTEiYFIVYPFhE80QThb3Fjb+D9b9p46h1lRqcoHT/OHfEQVjb6BqbKQhkbaf63yhjmiZEsklUe2hNrskDPvbThiAm4Cw2t3Ig/z5GQcywazyehDkb4L7Xz0SfSSMXwRGpOlbqdtcLf/eL5c+vPgAEoYCbXtuJ6tPmEbO+axcxdpizAWLY3BrzNgQxZc5sM3k2mLM82g0Mi2HKIGBoGGWNeYxJH8qMOVPHUA8Bd8Tpq9l0r0jXnJmAxUSSRiRAs6AlZOkd64yR7nNmM5Id62zfSwPeuLZiQHAGda6BhCnMFDH7aqcel9C0Bf5grIet8JT1vglzpq+xIIA70V+2uG3NmXJrFJ3s9UtA6j7TDbCr0j0y1MxZxiVg74z6R2RmCBKzHi4FMZFTbxJ6TTOxiRepxNopMTgjzqbMmTflbehR1yv0OkQNGPxkDilp7ICPfEJkrSE3ZM44R7xJFNdjDippTskaTcBQLK925E4aZQPuqClpPQAjObOeQ5RItpwQoHxZCVCmSdqZNB0TIwRXHQDaHeD+J9aPh2DfD+zFtf/nKuz7/r0Gf9DFix1wNkAMm6wxb0OQYak5Gzb3yGEDi8NmCJJmhE1c0vJkzoxqzlJ2yDZCAwUmhJnzX8ppj6jd3UEjrvUwMQRJMWc2jYBM5UQaPEWQ43HH3L77vfUKLSFNHz/T93WONshOfjpS4zFKitR9MGgCmw5XJeZGhiD6GpMEBG8XY1X5sxdzpl3bTJo+6wa9wMaEOlAgwM2oQ3J0XWdktikTr0MUGLt5FIV9g5nKAKoGEjCTNcbtGKTj3+SLN2eER66rwpvos99ieh3KGF21uARWajtpyjU2q1tjlDqnmj20wZjH4MzAPVLfB9JIBrKdh7lCv+s8mzBV8X1A1UWjQHF/MW4HAyTPpnRcc1D+fPLUuuEQGNfNXuzYAWcDhO0d/WGXNQ5Lzdmw9V0zrckD8mWqhsUQhBAyEIDNteZsADA0iDY/HWnmzGQ8cWJNJGiw8ShinnkhftoQJI+as6xW+vrzXDmJuSMORp8zmL0/oUntiTAAH7EN+oAGDvF40q0BBqgXsuqqW0g1EM4YMQDvH5thRJkertZTPa7S41F1fUZ9xdL1XZbAmb7HXMHRMWmsrlkPl8KpOlaSUOp2HztTaMZc19BaWB/1OZJyQLNNGQE5rweVfMqBJIYgWTeJNJMXIVXbN6is0Usx+AZpY1peDQown0IY1PSmjggRP1wNnh1hUpMH5WgJAFMvmZTtaqa8TVUPB3fJnxvcl6Wh6jMydsDZADFsbo15G4IMG3M2LIn+sIHFYTMESR9vWM6RvkXMmDN1jEGlcfp4qfquyEROlJakWarz0owONSjEF2k5kVVwJn+GGRPZuO8a7LlHgiJ+ekYmk8j2HCIpcGYk2dPHsTIcACmAb3B+uMEcYoygqgDa33114/u6LtRkRz8G4Jsw06bgjKjxOEKgaVCOq11jBzW36TqmPpbJlNbMmcU5pNlFmu6l0WfEm0RqXRxYygxVa6pVexnTtCi9DjHJUA0KqKmbqn01uM+62zEwsIoDf8ZcHptWbhjdZ6lNItkSInlv8rYJVK6qYPL2iQ3fOzAjz+Oj6/qWPlNZM2AHnA0UwyprHBYwNMyJfh6yvWGRNdqQfeZVJ5h1TEKIWAo5LG6NyY61ZXAGIIoMjpmuOdP/DRhsADkR75IT2Xs4JrUe2b6nr7EArD3x0r2khJGs0e4cAhJGx6TGK06KLEi/4vFogG9Sc5YGQxmG5Khl6yv3b/yd2tZdyhozmspEqaRx3ToUqOufFZzp8XgiQsuksbpmPQwbhW8WTP8Ng7g1WpxD2hDEbB3S/5KN522AM3fEMa85SxslMSKn4KDgjNF4jRUD9VsE3DEHozeNwN81QO0iSa0hJvmQrjlTzpGbPT82Y/dfdIP8+dHPbhzPMzV2wNkA8Wxxa7yUmLNng6xx2MYzyJj05/VOmq0YxK0xbkI9gKtVOtLMWThIvZBOZK0wZwk4y1onKHLoc6aPBwBRxsL3qJOSNdqsgRvAMrq7EN9OxDLbgWzQrQ0nacdgMJ5YorWFA+lm8TvvkJ+dP7/ZeFLMWdYhxY6oG6dQzJxl3M8qKgfVQ626UfP52DXWIjijA/Q3RMph01Y4qjSNCpG40vYZPAXwC3sKGH/e2MDjqV5fTfobZpU1qvSJg4BSIs0tBr3faMq63uA+05JjVqQYfc5I3w3LewWhyXM66/UCko0uaetP+j4/L75RqmEWVtDVK5Q4xKjOfRhiB5wNEMPq1jgs7ojDBhbT37mUZY2XEnOWn8mN/DmYrDGHmjMTsBizHuh27RsgYgtrk+FEqR1imzuX1KzmLN3vaOBkKBVkgKRIgwPHYmJN9LGM6oXUMSzKgHSiz0w2icIUWMyQ7O+blj/nz218j6h+R0aGIFFqPJZkjaO3jAIArmitom3Q3zAG+Js0ejYNvSljAvATUxmLm2gpWWPWaaQTfaGc/wYFHoC8PyLDfovpWlxtvDPo/ZZuS8BNal/VHHIciuK+jW0Psgb1aXJ/DOL4qRUgfZ4eQkhcc7rWSI3HkT3knmk2+sAOOBsoni2yRlPwMWzW/unvDItbYx6AehBwNmzXLLfxWJE12hmLltvQAd0aCaNwiix2pRskdM2ZkVtjqqGxzRCG8h0tg+QZWZhtQxNVAxTi2zBNiIczSL1QqvbEVmjgaSRrTBuCZBjTQWVPf+/jQLPd/XtZQc2fwIAN1ueHbNxxMHZrVG6WHo/QMmHOLDP4AMAUgDUC+Cm3RluRrI3IrCpIy6utqS4oUsxZtq/q/owCUNb+ZHBDkJT0z8SYSJ8VG5JPAHDH3KTnp5G5VUrWmHGjsaKwZW1du9ksffWGKXbA2QAxbG6Nw8qcDYtBSV5juhSZs2EB+LkxeRbAmWPLrVG7mxkyZ2kb9JGbRzD23NGBx+SosoNBmr9aNwTR18y41sPueLR5glFSFLuk2RsPK+pKfPPEer0T4SChJZtG9UJa3pQRUF9zSNpqL6wAX39o3XgGkDWmmbM0WBRCxH3OnIxLVNwvTQgzcBY7flrs2emZX7Ok5szacOJ7zGRt5IZ1i1uOhyT+tVlljTp90ptErMKsWOlrWaNZSw+9DtlnO03khCJdc0aR6bpVN2HOAHQ5Wj6TYgecDRDD5tY4rIn1sLAweY1pWMczLOA1fbyhYc60W+MAPaps1Qvp/i2uEJtagG8bcf0Sjf8bNOIkDTBPZG3uWAPxAzbMmBSFKVmjzYd0bDBhcM1szyEAcMuqr9gAMktbO+hAqkeVUc1ZWt7U//cIIbjpcvnvE2e736OxrBHGNWdkXd2iXj8ohawjyhBa/scg0OpkP0c0vu8zf7X3MbU0lmdPrnMxlRmgB2TM4FtmzOM+ZxnRYuIaKzfk+u31tlWkWaGBNokssq+xcsPESj8lr9Yy/X6jFzizIWe9GLEDzgaIYZU1Dlu90LCAxfR3hs2A41KXNQ4dwLfAnNnabXTK8r73eTSQW6PNRD+u9RjArTEv5izrDjFX1vvcskGJBjImO9bIYcc6NgQxKa9IO35aCjqAqUzarTHrmOK6s3WmIMySlX56Dpk6NQKJpNUR3MwQRNct2gTUsSGIQaKfg6wRaVVB1vs+7Rpr8fERGxNlbOmhGX9OCIhH4JQHf+Y7I07C4A+ySWTVYTORxmZmz+I5hEw1Z0AKnK2TNQ7kPnkRYwecDRDD5tY4rE57w8ic7cgatx7PsAH8vGSNJm6NtiVpTD2kizwyNATRB7IzHgBw1E4qFdkb9oqU055Vrb+phTVPdqytPvEGcEdMWA/7LMMgbo1Wk7QYnBkMJ20IkvGa7ZtSjo3nun8xKZg3oRZdSWPyuqlTI5CAVybM5Mxxjyqbbo3pFhqZ2cUcZI3qWNTgHMVugZZdWrm+zTKmaemm2KzAktqsAYIQErcuGIQ5o8zewkhS7Kup6oJq1izDdauqmrP1zFlxXyHbIIYkdsDZADFsbo15g6GhYT2GrKbqUpI1DhvAz2886vgDuDXaMnPQO6gFHg7UhJpZfMCylIOk6XgEtStrjGsrslpYpxoaW2WGnEGSIvnTJutBBgBDOpGlFhNr7fxHDRL9uObMwH10YkT+XN0gb9KGMgJZB9Qr0Tc1AwFkPZWATMLCAWSNtlxjgXQLDYMv58C+pqWfYVbmzNBUZruImbOM932UqsX1pzwUDw7ujggk59us36L8aVMaq1UX4Aa1nXodogTUzaZ0KKvT2Wite+OZ6QeyA84GiWGTNeZlCDJszNkgMsJh7XM2LLLGYQPUw2gIEidFlobEYnBmJmvUD1jCTDKqHmPqStIyJrI59DsCEEucssrS8jIEiXesB6gTJDbrhbQBxyDjsdgU2xmAhRlE1lhSKqb1SRobwEo/AR7dif5A4IwAEdUSuQHqhXz7cmYYMWdqXBbZ16QHZPa1OgHUsCuv1jVnWa309SYRiHQ1tAWq9QaPkVtjDgy+rl8zWYbSzJnHMl03fQ9uBPHPRGgG9LWkzM7O/haAFwF4CsC/npubC9a9/8sAvm9ubm7W+giHOIbNrXFYE+thGU/6O8+GPmfDIvsEho850+BM9szKWMgfF1HbrTmTskZzporYZM60hfUAzJn1Z2K8Y53ta1FehiC6tMJguU4Afg6yRpN6oZg5s5jop5gzIQRIhgkhUsxZ1igpFVOj3f26NiowcmuMm3R3A/xBwBmINpcQiDrZnx00B9ZjILdGLf3MAZwxiMxMVbIO2WXMhSGg7ur/aPNxpvH0ADVnNmtftaKE8Oz3fbodA/VJpr9Ju6WuB2ebdL94RsS2U2R2dvY5APbNzc29BMDDAL5v3ftVADfmM7zhjmF1a7zUnfaGzUp/R9aY35jyZs5ayxmrupGs87YcrrqYswGaUFuVpqhjUWR/6GuwImwaA+jBwMRKX/20bQgSy4kGKcS3Nx4N9IyYsxyc9py0rDHrcCJzgB+Ds3XMWexsFwE8zHaj9XIgjQ1BTK4jAbh+lhnIGuNNohyaUBOR3a2R5DCH0qAqa/P5uMbLgH3dKpJ+i2YMvlyHbAJYCy09bNYtxv0WzTdBKAVGbxyBU+3/oabBWbA+fX6G6gP7GfaLAHxG/fsfALx43fs/C+B9Ngf1TIlhqzkbNjA0bGAx/Z1hYc7ykDUOG3hNj2lYAP5gVvp2az0GNgTReaPFBIQxEvcQzfrQz4050zvWWZvRDpDobxkDyBq1nIhZbEJNBugrhhwMSrqksZlrzszrl2JZ4zrmjBASA4esiXXcS3Cdtf9gzBlJSeQMas7iOWRT1ji4IYhVt0YAEcyMgGJFgWWmKu63mBUsqjkkgHgtsxEkZs6yf5fmIGuMgd4A0ljKZEPrLCC2N3P2DKTN0N+UHQewqv69AmBCvzE7OzsK4Ma5ubmv5jC2oQ/bif7k5CQA4MyZM0MxHh2DNqEeFrCY/s6w1JztMGdbR25zSBtKDVRzZmfR98YlBXeoXcvsAAYgF6c9SknSzyejzXdaTmQzzA1BtKzRbs0ZGcglTf602YQ6ZiyMzBzkD6tujd4AhiDqPjAxcujFnAEwBmfoAviWDEEIwPWGwwCGII5N5mwQgB8zZ9aGIw+rS6oMmTOkQLmNEGpSGq9DhFg1KIHeJBzImMjecHTNGTF4tibMWXYpqtsDnD0TJY1AfzVnywCU/xFGASym3vs5AH+w1ZdnZ2ffBuBtAHDHHXfgVa96VeZBDmvUajUAwPLyMubn5wc+XqVSAQA89thjRsdbXJSXptlsWhmPjiCQ0q/Tp09nOu7p06cByITc5nj0eV9ZWcl8XP23nDlzxho7tLy8DACo1+uZx1Ov1+Nj2DpHeh4EQZD5mHnNIQ2yTp48idHR0b6/d/LkyfjfNsdTr1cAVFHjTcyvb4i0Tei1fqmzADK/PPBYxC4BXmGYqbVQatUyj4erp1HbbVk9RwKyHubU8hn4vP97pVGTNnm8IOzOIfW0b4bZ/s7z5+Q9Lxhw6swpaw/rdiCpGU6zr286+V1pL2F+fnWbT/cXa7U2ygAIyX7eudo06Xj25tDCotrogcCps9nOe6MumxWJYva/ZXWZAZjBaj3E/Py57jcdAG3g7PJZOBncWJqnFNIrAWdWToOsyT9m/qQDYBoQQeb7VkQCXIGhBhqYn29s843u0HNolSxjfr6e6bu9YnlVnneK7PcKV4qfqBLaXYcIAYTA+foi5ueX+v5evVbHGABRBE6fP23tvueq2E9es/7/znNn5fkRFDjXPAcyb2dAkavOu2u+DtX8NczPt7f5dH/RaMp1CCz7fa+fZWIq+9/SbFYBVLCwtNJ9PwhAFATOt89bO+e2Yt++fT3f62d1+gqAfwvgwwBeA+DLqfeuAHDb7OwsAFw5Ozv7K3Nzc7+R/vLc3NwHAXxQ/a/Jnt7QRqEgt+impqa2PMn9hgZnx44dw969ezPTsdVqFQAwMjJiZTw6SiXZ3W9ycjLTcTUD6Pu+1fGMj4/H48p6XH1O9+/fjz179lgZz/S07HrqeV7m8XieFx/D1jnS10sIkfmYIyMj8U+b18z3pdYo69/ZaMiExXVdu3NoTGqtvKiIffvGMn2XiIcAALtGpzCzr2RlPPeUnwBqTbjNrRfszYIReZ+VSNHqOboTDwIAJgpTGMvwdxa9pwEANKRWx8NctZ7wbOvJiZFVLAAQgmDP7j3WXNJKJUkpkhbJvg7hEQDA9Mgk9u0rWxnP+OQCOgBIlH0OUZwFABRRsHbNfNHBPB4CFQK7p/fETaD7+q77JACABQZzyJP3did0Nnz3fiLv3Ul/CpUM5311eQ2P4ihIm2D3xO7YxOevvyZ/V7mYfX3qLHUg8LgcctPHvn1Tmb5PhZxDU4VJ7NtXzfTdXtHetYRFPA7CBfbM7AHNwMoxyPvTC7I/B7eKO9U1G6Hjme6VYkGuQ6RDsHfPXnvsGVXrUDvbvXJ2bA2nINehmeoMvAnPynBcKjerqdE69CgAYJyOYd++cSvjGR2XmxSkI7B7ajdYof+NPQK5oe+ubbx3t4vxMbl5VyqNdj3TeYfj2GPHMFWeQmHPM6fn2bZ33tzc3D0AzszOzn4RwPUAPjY7O/sB9d5b5ubmvn1ubu7bATy2Hphd6mFbAjY6Ogrf99FsNtFsNrf/wrrIu15oWCRpw1ZzNqzjuZRkjUNlpa9rPSxKQXRDY5bRqABAIieyWDcAIJY1RhmllrHMz7ohiGEhfqq/kNVC/NgdMft3kx5V1oaTWOmbuDXmUHOmpVJS1mhaJziAW+Mmskb995kaghCazCEhBH73r+TrP/gqs/OmZY3CRNao1yE7Ob48pnZpNWg+T3g+930sr85c+6r+YdDIfMswlFen3Rqt9lvUj0aD+ul4HbJZt5hehzLPIfnTpP9jImtc90uHiyzrO/p6NMzNzb1r3Utv3+QzzyobfSCfxHp8fBynT5/G0tJSzID0G3kn1js1Z1uP51IAZ88aQxANzgZo3GnzgUa8AcCZzoksu1LF4CxjrYfIKUmLa84yImptyy4sP6R1bY2ZhbV9METp4PVCNu8z1zd3a9T3pYmbZVmBs7UmEIQCbvocm9acpfqcgQLtjsDP/YHAEyeBkTJwxxuzj5OkDUGyjgfJebXWLwtpgG/wZZ7DphVSTZ+zrkObAGqb48kKFtM1cDbBIlEbe4MYE8W9ySwE1c9FE18ibj4eh0kZ/qVSc/YMNZkcjsgjkdWSvaWl/rXV68czLGBo2MBi+jvD4o44bE2oh5U5y82tcQDmzLGYFEGBMzoAc2YzSQN0zVl2d0SRlzuifuZn7XOWlyFI7CpjwnrIcG0CfAuJtdV2DOpvY8i+g65t0x2DZdFzCa7YJwHe/U90vxcbghgm+rLPGcH7/wZ4/9/Il647bGgORADBNDud/b6PDUFssh4sdWxTK33La7U2BDFlqqyabwDmDH5uVvrm931sCOJZzIc0+2pgpa8/b+KE3NOt0baC4wLFDjgbIPJizoDBwFle1vXDk1gPzgw9G2SNwzKe9PFM2VfrTN5Abo3yp60+Z0Cy+8kCAypPYyHrciL5M8raCi4nWaOpOyJPW/tbHFLMehnMoZg5s9n0eQBZo5aAeRbndNLnLLvNtwZnpo6oz71K/nzgqe7X9TUznUP6Hvv8Pcn3f/CVhteQpHtmZf96HvJqzXpQA0Ct74MMPit9RcJUZfye/nxeDH5Wt0bN4AN21yHX3DU2nkM2+y3Gcm+RuVdezL4ajKdnn7NnJjbrT9a4E5tHnuBMOwBmibz7nA1NYj1kMsJhG88wyhpNx5S7rDHjJRNCSCYAqYeQhSC+fEwyAyqPxHIiu35LppKrvGSN8Y51RqZK27ILy9b+iXX9AEmRTTmRumXNwJn8jmuxGa3u4cYgEIYcDvpfU3SdoynwmFD+GKvrTAz1Ncs8p4ME4BMCPCi9JnD3fyW4+UrTc0aSRN+kz1kO8mrKzAF+HlJdwFzWGANq232uBqw5A8luE79VENdsHeJcpKSxNu97ghCGzJl+lhm0h9DtLHr2OXuG2RHuMGcDRB6J9djYGIAd5iyP8aS/82xoQi0Mdq6ePY3M5c8oY6Ifph7INnvnUF/XnJkwZ8L6eICk5izIXHMmf1qXk+jjZTxFXYX4FhO1uB7KiDmTP20C/EEMQTTb6Vk0lyCUxKcmK/uqpaimss+qKtdeW+dOr6WfIqshSJiSyBGgE3T/HpMgJGFhso4HQLJJlEOfMypgYOZgnw0GzA1BkJO8Or5mhnJvQWA186a6kX3GKdQJklND8qhbNPmy3iQyqjmTPzd9hBJkZ/EucuyAswEij0RW2+nr/lcXezzp4w2bIciw1JwNs4xweMCQ2TnK3xAk2yOko1zV5KhyMAQxKYLTu585uaRFpg17rdd6yB/ZC/GTHWubEYPhAQrxrYKzAWpPYlmjReYMACIN8NsZN4m0rNGQOauW5O9da3T/3gScZWXOdJduohK95H+NgwJCb6RlHE/QkitQhHwAPjWgGahau2iGlgn9hDGDn7drbNZNIjWFBCFW16LYgCPjOtQJk+tsYrzTK5z0eLICfHXNnIK9mjNAbspFTYNn60WMHXA2QOSRWJfLso+HbrScJTod2XfHs7n9iUuLOctT1jgsTB4wfDLCoQOLsawx29Oj1UiKum2GTmhcA+ZMJ+M2zRyA5Lk6fElRRrZTszaWh2Mqa5TSWBlW3RrjGq/hkDUCialMkNEqnqvPO4aJvma0aus60miGILOZQ9BdcxYrdwdYJgkhiXtkxk2ZZk1+vkOZ1XntKMaCGKgumJYRWmTyANm0GTDYlNEM/pAwZ+l11OaY4pYeGZ9l7SCRxtpcq1lqHcq6FDF10dyiuaxx07JtAvCWgSrlIsYOOBsg8kgcB2HOWi3Z2EU3x7YVw2qlPyxM1bAxeenjDRugHhrmTMsaM67XrXqSFNl8wOpmr47BHNI71rb7nAlT5kxLtCy7R8LQHTFUib6wDBZj+VZWOVEnnaTlUy+UNbGm6pz6lsGZ3sQIs8oIVYal7fizRqUof26QNcY1ZxnXIZXYEUc67fEUkWYcJAEeyHiPtRQ4C2w/Wx1zgE/1s8NiDRwAcGW3mLndQNQNqK2FnkOZ+65pWSOxawiidxqzMmdBwuBblegrkCTl1RnXIXVO/WL28WzFnLESQ/ny/huYD0PsgLMBIk/mzASc6cbVxWLR2ngAc9YjCOSW9bAAj/R3LnV3xKFr+jxkYNHUBV0nRZHtB6xKaIiBRE73RnMLdq+ZqZyIKAmYsAwWieGOddBS42H5JLJZa7zixJoQuwA/ToqQWU6kd6z9kt1zpGWNYUZZozbgcAzBYlxzto45o4ayRq7mEHXkfW+DOQNJDsAz1uQlc4haXYfYAG6NepPIZg0ckPQnNG0+b99KX40n4zrUacjzw5nlZ0fqvs+yKdOsRaAAIhC7rrEDMPga4DsGzNmWskZGwIp2n495xw44GyCGDZwNG3OmxzMsYDFtkLEja7yw4xk2x0+dp4cZLazbee1Yq6JuamCHrMFZoWL3HCVNqDN+MciJOdNNn7P2OYvBmW3mTI3HEJzZlqRp23mSMasWQiTgzGDHestj65qzTsaNKzWHTGWW2xuCZKyBayt22lXgzApzRuI5mVXW2KrLmyCk1DL7qtYhkb39ga6X9UqW51DcfN7smjHLbLBpn7OgJq8ZZ5avmWPGnLXW5PkJqV2Ar2vOTDaJtHLEM5Azb2kI8gyMHXA2QAwbOBs25mzYwKJOoojlJpCDMGfDVifYbrcBAL7vD8V48pY1Zr1kbbX7GTG7DzS9o88M5hBT56hctV1UJX9kta4XKhFnhpK0nkHNkhCdpFlnzjQYynjJmipJsw3wu3asM5yiTgC4uqGxQSH+VhED/E7GL6oNB9Oas4JaTjvrNha07XhmQxAF8AlTskYrNWcwZmFaawqcWV6HtGSPQmReG2NprGX2NZFXZ/ueZjttM3kwBIvBmvwDIsfysyPdQiPDkJorcjyB5TmkwXDWdQhI6hb9QvYBxTVnBj0DhzF2wNkAkUfiOIghyLCBobzGYwoWh40VAvIHQ1nHpAF+qTSAR/QmMeg1s82clf/no/iPT30TIwvZ7jMtTYks71gPIgVx1Y51aSSfxDo0dLaznRRpeVJWsBiDM8syS5pqtpolOmn2NQeAn1Xe1OoArqJJiGebfZU/w4yGINq1zSRJAxJGaz0hpVkGHmRbF+OaM1ev9WqcA9acaQqfZLzH2mvJOmRzDhF1+ZkQmXtA6o2lgmWAr08yz9rfsG0ukdsyYrYz29cCxXYKy+BMs8GEi0xrY3tVohjJnFl8lqXqFjObyghz9nWHOduJOHaYs61j2MBiXon+ILLGYWPOGg2pA7I9hwYFi9bn0BMreG59EX4zm2av08wnKYqd9gwAvqu+U6nadkkzS0K0JM02CwND63ota4RtWWMKDGWJlkqKAsosy5vMdqzbQQLObLOdmvUIM/bKI1oiZziemGRddzuZyhp5J1VzhlTN2aDgLGbOst337XqKwbfZ0iPFnGUHZ/KkFHKSNYqMgBptZSpjCPB7BjGrfQ0bKXBmMeJ6MZFtrW6tqHYMlFqtfY1ljQZ1i46aQ55lK/1nYuyAswEiD3CmGQudJGeJ3BJZw8Ras0J5jeeZDhaB4ZMR6nk3LMyZ3qTQmxa2QjMEJKNrW0c9YCPbib7akWdZd4c5h8vlmKpj+RiCRFkT61jWmI+cKHN/IbWDDtd2UqR/Qbbz01xWO9bWZZbqZ9YauE4C8KlltjORNWa7zzRzZlq/1MvATssaecY5ra39Y1mkNSt93dMj23e7wZm90KY7VIjMTJVmPfyybYAvf2Z+vKo551lnztRPQ3AG13LNWdpMKsOY2mv6WWb3/GhwxoTIXLfo6DlkUPu6I2vciTh0Ym2T9dBJumZUssSwGXDkLWs0ZWHyOj874Gz78WQ9R3mBM6gklGTcHg6aqs9ZTjp9ljGxbjZkz6wIBI51lzRd62Fmpe9Z3rEmhhabkdpBt21QYurWqGWNtusWHcdsx7rVSZIi2+DMFOBr579CTrJGkXFThqs5pDdRbDNnWWmqoJE2lxhgDOuHFDNn2W4zIURct1goWd4kMjTgIB15jmyDs7hxeNaNtGZem0SJvDqLs24QA3y77pEsbQiS4RyFIYej5pCJ83Cv9hnP1HjWg7O//uu/xo/+6I/ib//2bzN/N49kXyfpOmk3Gc+zhakalvEMo6zRdEzDVnOWG3PmmtV66KRIMLs6fW0ZntUQpLaknf/sJmlAysI647TWbKRtWaOuh8la46WZM9t94ExljZ1aasc6h5ozKrKdorWGSGrOLLOdXCXWWWrOwlDEDLLphkMvwx9TQ5Bo3RyK3RoHtNKP+0tlNZeo6XXIbmIdgzORzRBEn88QBCXbMkJqyOAH+bSHMK195c2EObMZXZtEGcaka+BsbzQ6KUOQLNLPTkuAQYDDrFfexIj8ubia+atDGc96cHb33XfjQx/6EO66667M3x02cJY3czZshiDDwpxdirLGvM7R0IAz9QDJzpyletVYDGpoCFJbSTn/Wc6J4qQoY+JINXNmecc6SWSzfU+DM9vNcVmq6XOWCNKOnxbD7XJJ639Ma6scDJJss9mMFkjVnGUAQ901cGbnqCdzppmvrLLG2ErfHnPWLWs0k8hx2+YSabfGDPeZZqcDSuHb3WdM+i1mNJXRLUZ8yzVwMGw+r8EZscxO09R4soChoJWPtb+TepZlkTXqFiMhoUZ91yaq8ufiWuavDmU868HZIDLCPJJ9zaAMMp5LnakatvEMI3M2bLJGU0CdHzhT/XwyyptC3TPLsctUaTkZ49kcruqr6oFmOdEHBmhCHZoXdW8ZWt6U1dpf15xZlzXqX5Axsa6nE+sckqKMVfj1FcV6UAo6kE5vY4iY9ej/O8122j3SkDnrZQiinfYymkusB/hWmLPUeEhW5ixlLmEzsdYZIUW2TRltlBQSAst+W3EvuKwOm7rFSKFseUDxOpTta3odsg7O4ibU2ZgqLWu0zb52rUMZ1moNziJiNp6xivy5tJbd2XMYYwecWZARDgtzNmw1VTvM2dbBOUcQyKzlUgdnejxhxq7PeYEzalhzFmppim0piHqgOYJnqheqryaOW7aZM9Pmr3EzWsvGAMTQrVEbA1DLciJmKGsM1ByKLINFT5HvWd0aa2oOcWo3SQPSjcz7H1CzJeLaE2ooRdWgaf3tHcsSTd0aNTizUXMGJOYSGed02Ey1h7DJnBESE0JZTFyaqzqxtuseCSSbRFndETVzVqzksw4JzrNZxasaOGq5KXbapTWLXDdoJhuNVmvOFFikAKKg//yj3Ugaq5uMx3EIRspyr2wlu9n50MUOOBsyGaGN8VzqTNWwjceUOdOsmeu6dnc/YX6O8qo5cxzHaDy5M2cZH/gd1V8Ilnc/dbNdljGxbiznk+gDSJpQZwZncky+9ZqzpPA9S4iOlhPlVXOWsX4pVbdo1cJayxqBbHNoVUt1c5hDBvVCzXZiUGKdOdNW+oayRr2pY4050/etIXNGLCfWQAKotXFNP9GsJcyZ9dDXMuM10w6kpUo+DD44skkbc+r/mN4kylTj1cinFtd1CCJkl6IuPySf9REhIIY31ohKW1Z3wNkzP0xljVEUIQgCEEJycWvMCs7CMEQYhqCUwnVda+MBzJkqzcI8W5gzU3Bmu95skDHlVXM2tOAs4xzqqB5VtJBPETXLuBvbWE3qBmw7gpi6Neqm2AXLfdd0IX7WWg+opM56zZkhOIulsa7dOeQ6CeuRRfrZyMlcAkjJGjPIhxvNBJyZJrJxn7P1Vvqa9cjKnLW72Vd93EFvOT2nSUbmTNct2mZhgAScBav9a1FbKQbfujGRdkfMCGBd9awpWwZnaUOQLGu1NiihvuU+q26yDmnjmn4iBmeeXbaTMSVNBMBb/T/vF07JZ+sgc0j3Osvao28Y41kPzjSwygqG0om+TdbDtOYszQoNCwuztiYrM6vV6lCMJy/mTAOPrJK9vMxAAHOpZV6yRtNzlLesMStzFtTk+J2C3VoPbXzgZCyibq8m9Uv2kyKdyGb7nq+6gPojzjafzBaxlX5GGaG21La9Y23q1hg1U2DIYjgM4HrHOgMYasVziNkH+AbMWaslUFRuFNSQfe1tpZ/9/ABAqAAsK+l1tfv3mEZicpNVXq3Aq2t/U0bPofZK/zd+UxkTdRjLwZhIjSvDHApCAS/uu5bPfZ+VOdPgzDXo4bVVpDeJeAZwpk1lWMHus4PR5LRkYc7qMj1D4DnGc+hSakT9rAdnpkxVXixMejxZdmXyGg9gnuhrcDYyMpLLeEwle8PGnNmuNzMdE+c8dynqsIEzljEpipSZg1u0bIPu6ZozgSzTSBdRizxkjVpxlYFl4AGHKzgiEJRH8nJJM+t3xIqWd6xTSVGWtTrUrIdveQ5RYlTj1V5SO9YGvYW2i4Q56/87zSZHRTmIuGNmAL+nlb6WNWa10lebMqwiD2yjCTWQMgTJasve0swiya2Fhjau6SdqaXm17QHp2tcM16zeFPDVxWeW+64lNWeib4W14CKWNZYsu0fSdPuDdv/XTDP4zPI65LBUrWkjwxxalPdY6JlfL33fG3izDV3sgDNDWWOeiT5jDEKITIlsXk6NgHlN1eqqbDhhmzkbVNaYF3OW9fxcCOYsy5jSc5oOmnWsi2GTNVJfM2cZTVwa+TQ2TZgzjiz4tV1LNVi2nqRlL8TX4LVJGcZyKsTPCs6oSliKlg1KWMolLcsOetRKJHK2VQ5cJ9YZ9hqDZQmEhM+s59VxYp3Baa9xLgQD0HKYcVPsXsyZqVtjsKbAWZl1AfFBr18sScvcWF2xMH5+Ji5aOtlP1FVinQeDH7sjZliHamuyZ1YEYt0IqGsd2gadRa0I4VoIEYm4/2PV8qZVuukzz2DiItQ65OSwSaRlje1z/efVjSXF4BfM2dcdWeMlFMPGnJmOKa8eZ8Clw5zldY4GZYXyvGYm4My2pBEYQlmjNuDIOKdJU46/ULKbFFHF6jiCZ7IB1o1EieV6Kjmo7G6N9QWZ6LcYQ9GyIQhlSRKS6XtBPpbaXYX4Ga6ZljU6edQLqUkZZGDOIi1fGyAp6hWahcnC5NVVQtf2XOPx9DYEURt7GViYqBXFmw5O2bFmBgKYM2dCJeGulx84Cxth34ywNiay3XcNSMmrM8yhmmKDO5ZddYE0c4ZtN2VqD9ewfPcKRCRilUbVcg1cmsHXjGo/oeeQVyT2N4nUSW+tRojaHEtzS9t+p6WksaTAYHrR9H2/I2u8BMK05mxlZQWAfeCRHlMWNm+HOds+ho05O3v2LABgZmbG6ngAM3CWlxlIejzDAs40U5VV1ggFzoolyzVnSlJGITLt+sW725bNJeRgssuJls8ntSfE8tOFGNqOsyAfS+10rUeWOkEesx75sZ2ddgYp6poE1NSy7BNALEXlGcbTOi/H0yk4xhK57az0s8gaRShiQM0qzJ6NPsyZM838ub79xDrpb9g/W9VYluuibZMbAAlzFvK+NkFEJLB2XknkqH12OnY3jATC2tbPM8HltRKhiK39x0atDidZhyAQrAQ4/8UFtM/2zmdbp1pYe7QWW/tb70eJxBAkDAWiWohwJdz22rU0O10yZ/BZj/v+mRjPenBmypxpcDY6avlOMxzTsDFnQoihNQQZFubszJkzAIBdu3ZZHQ9gBqjzMgMBhpA504YgGVkYph5oJcuJvk7SWMaaM13UnYdETmj2IcN4Vs6qHWvHPgtjKmt01TZq0bJ7pJYTUZGt2aresXbsq5kT1iOLbK+RJEV5JdZhFhe5JQnOQt+8xUjMnK13azQAZzzg4MqAwykyq8xZzHpkmT8iqV/y/RzYVw3Oov77ZrU165GDQQlS61A/hhfL96xg4d4aACBk1PomEY1begC1x+pYvnul52cFlxs3PBKxtf/omOV1yEvWIRFJZn4reWPzRBPNY83YoKRQsM++xgCfy7qz7cYEAJ01ZSQ1ALO4YwhyCYVpzZlmhYYFnA0bc9ZutxGGITzPs15TNWzMmSlYHFbmLE9wNjQ1Z7pxZ5YNBy7gqN3Psm2beDUehozgTLu25SCRQ7yD3n/iuPiE3ACJHGZ/x9rQrdFTJ7QyYru/kPyZVdaom2J7ltsxAImMMOjjcSYTRwHWkGDIKdu/ZtqRMsrg2tZZlOOJiuaubTFztu5e0gA/Sw2ctpRnRQriUqvMGVEoMoupDG9zIEyBs5zmEO+zb5YQIq59JW4ONWfxpgxHuLr95l64FqKhPhfl4B4ZbxJBMmetU62enxWhvK6NhoCndrlKlt0j46bPQs7rqBalxpgEDzmClQDBSgjejMBiuXd+vfKiSCCsR4iavCc46yx0UHukFhvQlEfMr9mlZAhi1+v4GRjDyJyZJLIXQpKWBQxp8JqH7HNYmTOuelT1m+CcPHkSQD7M2bDVnJmwi0KI/GSNXiJJ63s8kYCvzufIqO3eOQlzFmashwHU35ObnChDz6yVEARA6DM72Wt6OIZ9xXRSVKnmU3NGkc0QRO9Y+5bbMQAp5qyPa7ZyzyqIS1BsSCTnjtntjwkgBvhRBjAULcvxiJJjnOjTFMDoet2AOQtU/RJVzKJ+DNq4dFQDfK6az/dxzKgZYbQhn2V+MYeaMyQ1VVuBMx5wRPUI7XOdmPVgOYDFZB0haBxvwt+1zWavAJrLHAVo90jLw0nVmkZNDt6MEDWiTV0hRSjXhvOLPLb2Z5ZdUbvk1R2OoBFtKrNuPt1E43gTrVMtUIfGioJSmdhvx6DNpEBQf6KOqM17sp4r96wg6nBASYerI+bj2WHOLqEYZnDWbyIrhMBrX/taAMDExIT18ZgwZ3lJGtPjGRbmjBBidI4efPBBAMDVV19tdTyAGTjTc3pYZI21Wg1CCBSLxfj7tiKWpGVgPDotAV95gttmYUjKDjnTnkOqV4310DboGRzAmspxK/LME+teYVJzFrZDuEKAA6hYXopYqvlrv8yZEAJuW84h37KlNpDIicI+mKrOQgft022Um/LZV5gwZ6p6hmaqMswhvqhov5JjnKHYtNIPlWGKU5LMYsycWbjlWKrmrN8xNZ5qYKbZRI06KB0p5SBnVvd9uPWYWqfaWL57BVEzQqhr8rz8as6A/mSN4AJtpSjgjNrfJErJGsNaiLAeIVjZvGG3iOTacOLra6CQRknUsnlTWl5dOFAEK7FNQTUPZO0kKzBEjQgjSi1WmbDv0qrXIZQcOYe2kMhGzQjhSginI++z6uhOzRmwA84GNgQZBnC2sLAQ/zsPMGSS6D+bmDPAjO184IEHAAA33nij9fGYsJ333nsvAODaa6+1Ph6T8zM/Pw8A2Lt3r/XxUANwtrIqUODatc1yzyy1m5pV1ugq1oNV3fxqPTLUC7VUvZAoMOtPl9jZLoMErHZOW/s78CwnRU4qKeobnAUC1aZch8b22e9vqJOioB8ZoRCIGhGqLfnsK02YG3D0jBjgZ2A71waXWfZjpb+dtJEHHOFaIltj65gzK4Ygmn0VSYuF7aKjDFNOe0VUq/kB/O2YM+pRRM0IIuIQepOoaF8aG59oIRAsdVB7rLblxwUHwhVdt2h/PDS1SSRaEQjbvEG2ttAHF1h6Wt5jdd+zDhbTzBl1KKhH0Trd2rBGRo0IvM1BPYp2M8J4IMdUnTZ3Re0VsawxFPAmPLCKs+k5EkIg6giEtQiFQOa7g/TH3LHSv4RCg7OsRgXDBM40EAKy1871EyZM1bOJOQOyy/aEELGs8dChQ7mNJwsY+ta3vgUAuOWWW6yPx4Q50+Bs37591sfDXG0I0n/SuLTMURAcHLL+xGYU9kkGnwmRSZLhN2US4o3ad0eM3RozJNaBqvUgBWY90U8MQdC3jHDlpDw/TcdJatYshWZniB5TH1E71UE5DNEmFFdcmUPTZ50UbZPoB8sBohZHe6GNMfXMGJtx7M8hzZz1wQoFywEW71yCUA54hRHzObSdIQgPt2fz6o83sHjnEupPyJIBVpT3mK0G1ECaOePgbWmc0Avorz64hvrjdXQU8GhQB2X7+D4xBOECIhCI2rwnSIuasp4IqpegU8zRrZFLkL/2YG3LzRkhBLjq3deu+LnJGiEEKtdU4Y57iDZp/rx01zKC1QAiAlZOqPYQBdf6OuSkzKR0BMsBWie7CQcecIgOB3EIWo6DguBoUCavmeXQ/Ra1mRRhBFFr47OftzkgALKngKJSpRQHqIHbsdK/hMLURW6YwNny8nL8b80O2QyTRD+vHmem4wHy7U2XdUwrKyvodDqoVqtDMR4gYWB3796d23iGBZzRtMNVn6zH0hll8c1Y3C/J2ngYBYdckMOw/02HQks+9P0R++AjsdLvfzyhMgZgJftgkWZkzprzLSwfk+thy2GJBbalYAbM2RP3ykR/yS+gMpIHOJM/gy2s66NWhOW7V8ADAVL14AmOOmUYqdD85lBHgHd6W6ELIbDwpQU051sgdXmf7Zommxob9PVrexmCaFljJJ3ttgriEDSPNxEsJq0GCCNWDUG8qnzWUyHA2xyLX1lEa37zZ3hrvoXG0w3UH5N1uA3moGx/nzFhzkBQe6KBhc8v4Ow/ntsA0EQkwNsC4WoYN3r3yjluygjJiEXNaNN+Xp2FDha+tAgRCdBVuS4GFc8+GazXEV17yEjs5pkO3uIIlwPZiFptTgQlN5FnW4q4flr9vwgFwpUAtYfXuj8oZL9BwgiadTmexUJRbjbkJGvU9x91CFrHW13yT97hCJZDEAI0qIuKAmdOeQBDkB1Z46UTGggFweaa4V4xrOAsD0maCVOVV48zwLwptgaueTBnWa9Znk6NgBk4W1qSjSLzqFscPuYslVj34Ui28KUFLB1TD3yHJXUHFiPesd6i2aoQAq2TLUTNCItfXUS5I9et4ph9GSFSO+j9xPkvLMQ9syQ4y0lOJNAXU1V7eA2Lj0ow1HEc64Da6bLSl8mqNmjpFacflONpFdy4nYPN0PVCnbaI3RjXB29zRPUQCDmWF+V4VzwflIjcpKicC7TPtHH+Cwubfo63OcJahMa5DjxVe7JvF0kS4YzRkzlTb2i52VYhhIAz4qJ9TjIQTlkCfJtW+qXDcmOOCCBYCdBZDnpuhvC2NJ5Ye0puMrYdFsu4bIZuoUEYQbgaoLPQQWcx2Di3uQBvRWifbsFRJ8UvkxwYfDUuLuBNeqAuiR090xGsBNJ8ohaB1uT7vOzan9MxOJPzhzACvsl9HzU5iM8Q1SJ06qoGruQYbzj0CpqqVwbk2husRPFGWRzKMASUoHFKzulz5RJAcuiVl6pbBCTr3DrdRutksvHQeLqJlXtWAEKw2iYoc93Sw1xeHRuCZONahjJ2wNklwJzpsQDAu971LuvjGaTmLE9Z4zOZOdM9zoYRnI2Pj1sfzyA1Z3mAM0ftNjLeW7KTjrAWYflp+WAJXJbYO1sM3oeZA29zrD1aQ9SI0D7bRkVtKhVG7YMhqAediLAtUyWEQLgWAnW5Znk52DN31Zz1cc2iDkdzWc63wGXWk6JYkgYp26s/2cDi15Z6fn71wTUsPCzXoKDg5GLiEtecNSPUHqlh6RvLGz4TNbmsPwkFVlVNXq3gySTN+hxKmCqd5If1jc+11qk2CCMIDo6gGukNB8f4PuvVhJqmmDMttQxWg431Oc1IMkIuiV34WMUB8exa6Ttxv0WBzmIA3hZxn7f1wQOB9rkOArW2yw2H/NYhQaQ8NlgLITrRhtpTrur2aMmBqwB1oZQf+0q4rF8iDt3UgCNcCcGKDGEtjBs+0xIDZbZVDsk6BEiwFm1WlysEivsL8Hf54EqqS8uu9WeHo0xWNThzR12UDhfBO7wL6PNQgHcECAGaSvK4XC3KZTqvmjO1TlNf9r+jKQklK1I0jjXhTbhYqQsUeQQOgPjm7RjYTs3ZpROuK2f2pcCcvfWtb83Fac+EOdP1VHv27LE+HlPmTJ+nPKSWWa9ZniwVYAbOFhcXAeQDzkxkjXoO5cKcpZKi7RJ9oYwTmk9J1qNWLVpP9IEUONvCgEMEUv60+sAamifbYEKgRh2MlmF/NadJIiu2YPMAKVsLayGYciIsVOw3o0037G2fa+P8Fxe2lhOGAk3d0NhzQCw3jnFiMwehiu0jRLWN81sDk7UH1tBRckNeypc5C1ocYSNCZ3FjDbJQvYdACNaUTXzgu9LJ3fa8Ttmgt893EK2FskYpPR4hsPbgGsLVEOfWEIMzVmbGzFlspd/DrVG7xwkusPyNZbmxkIrFO5fQPtWS7NFaIreirl3mLG4gzKUUjTKgfbK9ASzygANCoHS4BEWWI/Tst6sAUrJGIUFqcW8BzojTBc6iNkewEsLfVUBxbwGe6plVrtLc2FdtD09cgqix8bkW1EI4o640vVBFR04pv00ZjdKpRxAsdQN8IQSg2uq4Y24MzljFvupCA3yWagDJCvLvFumNvkigsNuXAFati52qD1n0ZXVI8TrEU2k1K9INqhB/ygVhBKdPy9fbrgM6wJzeMQS5hOJSYM50fVelUrE+FsAs0T9x4gQAYP/+/dbHY8qcnT59GkC+gLHfMdVq0nEqD2YRyH6OOOcxeB0bG7M+HpP77NSpUwDycWtMyxq3a9jLW5JdE6ck69GYKNqX7iABZ//5owTN+SbqT9U3fibk4C2O+uN11M7I3c8Vx8OYj9x2rEXY23pcCIFgNQRvc/BQwFG1J8VqvjVn9ccbEB2O+mP1Td3bAnxI+gAAZjlJREFUOgsdRIFAa0E1NPZZLGmzFbGsEQLhmmQ9os7GeriFryxi+e4VsCKL+0GRsgPkwHroxLp5pgPe5OCb1Z4JAepRFPb6qC0puVXRAUEeskY9LnlNeEdskIDxpkz0C/sKWFgQcIVAmzEwjxon1tsZgggusPbQmqwpWws3yGSjtRAdNXd042NWoiDMLnPWtQ6tRuCRBD7rwUfU5BCCgLoUSwtaQpgDS4U0OFO1VLrkK7WJ1XyqgdbpFgiVbP5YW6oKSuP51b7GMkJHukSuj2hNMp0jN4zA6Wj3SApi2aWV+goMaVbIodL9M73pIIAuxKMVBRX78nOmmLPN+j92MWeRAKuoPEUxj1HFvkEJkKo5S92AhHaDRcHlHAOAux9TLxYduSuwYwiyA85MwZlOZPMAZ1kT/Twt4tPjycJUHT9+HEA+4MwELHY6HSwsLIAxhqmpKetjyirby9PNEsh+jlZXVyGEQLVatd5TDDCTNeoNkFxklrqImgtM/EuCv/rs5uCj8bR0bCOMwF2WYKgzWbKe6ANApB5of/d1gtOPtNB4srnhM6Ije9VQl6JRlLWTDV9ZIduWEcYuaRxRK4prb9IRrkVY+toSzv3zeSAQcb1QuUpylDUql7g2R/NEE80TGw0U6k830TnXiZOBsGifOWPKKY8Kgc5SiHA1gOjwDWYFIhKI6hFYmcVJkVM1Z4W2DDUt2wsBwkYEEUgZbPeAAFqgoA7FqupL51VZLrUnicOmgDvuobDH32B4ISIBVqRwKg6Wzsn50y64ckPftiGIZmFCgfbZNsJ6FG++dI1JyPYWrMRi5oyVHFCPWm1Cne6VFzVCRI0QBALRunqh5W8uI1yVc2d5WQ5gfJzmImuMWY9IwJ/24FQcQJCuRJ+VGNqnpRS19lgNPud4vFDFyHgOzNm6Gi/qEITLAZbmEgmx4LLeU39WM/jlEQrqWu5J6ctnK03TM5Sg/mSjazxIMVmOctUtjuS3ScSwUX7eNa8jEd/fYjVp9O6MuLnVnPFUWk0Y6RqP4Dy+h06c7l6HTJ8dO8zZJRTpJLbf3jlRFMXOdnkm+v0CxjyNLgAzpkozZwcOHMhtPFnAYrrGS19zm5FVtqeZs2FhO/OsNwPMNkFy7ZWna84g0OoQvOnXBfa8nmPu4e41oHWyhUeOAcstwF9QD99dBeuOWwAQqF1EJgROLBOIYKPkkodSQgdKsLIgr620Z0YO/YXkD8GB2tE6lu9a2fARQoDWuTacMkPxQBG+ur7VEfu7+lSBKwLpEMdbHM2TrU3lgdFagPKREqDsm3nRtV974mg5EdA830FnKQB1yIbaHBEI8FBaWEPJm/wRJ/6+1VBzgBSZrBMKBBpPNdZJrgCisp/GipxDpRH78i+gW5LmlBicqoPmqXZ3kpbKY9fUnA6LSmZpKmtMzb303x4zZwIIlkPF5vGNzLAQKOzxQV2ayBqrTPa0sllzlrJBLx0qoXiwBFAi2bz058oM7rjcDQjq8hxVRnOwrUfi+CkiacBBGAGoZNDjzwjAGXHhjroIVuV4zroFVDxh/b7nvnK0VGwYcQh4IBAsJwOS91yyueCqz06MEvsurQXNnCX3uTfhoX40ZfG/jjlz1TpUGrOvKGCMJMRv19LT3fhZhAkjpQ1TSNmFO2Z/M1YzZzz9/KLo6i0oQsTjaZ2RYLE4Icdi+izbYc4uoSCEGDV95pxjcnIyV5ZhWMBZVuZMCDF0zJmup5qcnLQ+HpMxXShw1u81yxucmdSc5QvO5NLnqIepEMDpReDtv9OdpD1+nuHlfzqOF39oGkSxHuVp17rzHwBESADjk+cZzqwKnPniYvz+Sk3gv/wtxceeKuN7/24Kf/919b1yPtKUxMJagqGoHqFxvNH1GSEEnCIDK0q5TlHN/+qI/cRRgxnKZdPSsCHd69bXna3ev4pwNZRJmUpyRcWxv6OfSiDqNS5rqSjZILkSQtbsEULAGmoO5eGuieSatSJZlxM1IoRrIZpPJyys4IlUTTu6lUbNJYRbRsqAIx4f5wi7avPke2fWCD76RfX5itP195jEZuyZTtSpQ+CNuwgWA0RKkpsOIXN9AIms0anIhsZW+5ylal9ZkcEpMbACQ/tMawOgZkpOBzW/CmWaE/uqEuvUo4NQAtGVWPMYZDSUVDdwGCi1byrDi8qQRYMzSqTZRapPnYgS4MEDHm8STY5fGHAmWSHEtbli3WO30Jb3fXWUWn92MJqoLtavhWlwxsMEOBN17vwSAfXt7zRydd9GqWu0YQ4JARCCWhvYf15u/E3dqPIhU1mjZs52wNmlEcNmg551PO22lBsNC3O2urqKWq2GUqmUS/3SINb+eST6QPZrNmzMmQaveRmUZD0/URTlWkvZZYOeisWknzuOnxF45R9UEHCC5RaFrzpqjlZJLoX4+gHLhMCP/o8Sbvr9Kfzmp/34/f/3vwv82l84+PnPjeMbx12cV1IQp2rfnhlAV/NX3ubSqvpoNzhLKXdQe1TWyB3zyxjxhXU5kTsi55AXRtJtrxWhdKgEvg4MtU62ZQ8vRkDreofYsc8sAgjVMU/VGUQkwdnaI7VuO3SRNDz2lLypMk5zGY9ORFsdIKxHKO4voHM+QHqaiwjJDrra0fer+TBndJPiLyFIt/RT/fOn/qoUu4/SqiOHOMAU2qzuLGHyBFjFQetMW5qDdDF5QrKL2vlSAXyqbNBjQxAbskZv4zrEygytU+1uaWOqDocqyZ5fyYc54/oPSwNWSrrMHHgkYpa2WZMnJHTt9xIEAFGURVUanAFS1tzFCnHEa9HyN1dAATzlVzAzIuxb1xfks9VZp52LmhEWvqo209Y9V0pqXo9PEvt91yhBoJBy2Oped7rmdQrAUmXgUioR63JvAGgUJMvL0wwwJeikjVNC6Rz5+HmGmY7cPCofUqU5g1rp74CzSyOyOjZqidyuXbtyGc8znTlLu+zlkYAMYu2fFzjLOqZhqzm7ULJGE8MUamOLev14VFLkrNvibKeWgF/+gMBqK0mePMHBAYyXRS426Ak44+BKW/T7X07cVx870f350VBKQQrj+YCzdL1QWAvBSg54wNE+k6o9S+Ugq49L4PZgeQylonkD4V4xNiPnkBtGGLm+iuo1VbAiQ7TO9IK3I0QtDsIIHMVUuVUnF0AdqmxggXooHS7BqTC0T7W7DR0E0Gxy/Moni/BacjwTU/mMR/eC6wgKp+zAqTjwZ7x1tR7KMbLN4TflHPJKLJc57aq6vPR9RSC65U0qWfvcURcjyqnRG5E28YM8Pzaz009b6bsjDqrXVlDYU+hm8kRirhC1I4iOAHEImE9A3FQTaguna7NNIkLl5k83M4Q4sdYgpVShudS+tn2ZD4nUOSF0XTN6lVgDQEsxZ5Gfj3skVCsDmgJn1KXdku/U+aspidxDlTGUXfsOpI6yg3f4etOWKLnveTKksM1R5BFCEIyO2K/JA4A6k2ujNhwCAJBkDgkhnUkJlXVfjIv4WZbHNWsUFThLtTygLkGwGCBYVj3o1Jy+b55iVN337qgDEBhLP3eaUF9i8Uxnzoat5kwbOeTBmqXHk4U5yxsMmYKPYWHOhkXW+MlPfhJvfvOb8ZnPfAZAjkxnIZE1vu66BGykc8GnzyT/1qxZizKM+RxOKQcpCDZaIgNAqHaIrz+SvPYDt7QxFskkpDLJrDuSAUBUlHPaCTlogcEddxGuhF3NaNPSq4bqKdb23VzOz+ReecxCeg4RKbFKB48EuHKNdBVT5Y6wXOoEuZIEnT0vmRZWYCA+7W49QIA/eXQMH/yyhxF1zaamcmjWC4Bp5iwCCnvl82C9nAhcYOXeVcy9+S4cWpRrdaFKwar2t9BHR+QfWU97gDCKsNkNXgHgMGtiPJT3YnHMGViyt5mdvpaUCfXrCSFgFYbWiZT5jkhqZmJJ44gDwqQkLW/mDFAANlW7KHjCVGnTnUoln0S/VZJsfbiS2NjKObSOOVMnoK0bLHv5mNyUxlXNWTuZM6XDJWm0o+ZRWs63qgxTRFHWd9mWWTKfIgIBFd2AtXiwFNc6fuSzAu+bK0EIYPmkPI9rjgt3wA2HXrGmLBs7KylA7RCEjUgCNI54/uiebC3KMFES1q39AaBZlHNIrKbBGQUrMER1DWAFlr+5git/4yu4pinXIWfEheDm12zHEOQSC51Y7zBnm4cpKzQsLBVw4Zizfq+ZlhHmBWCHDZz1O6ff/e534y/+4i/w/d///QDyMdwBUvVLEJgsRPjD75bzY62RPOTVBjLedFMLv/daOadDh+HwGAfNkTm7bXcbv3zbKkY8+YT5+gNyTI56iP7YzQ3cdiSKmbPiqAOWQ91AVJEnwGmH8Cc9UEYQrITdO9Gph2BTGQOEvhM37rUZU4o5K/IoBqyEEoCL7loLLnexO0sB/E6ICEBhJB92USiJ09pqqrYC0kgmDpfi/XeV8R+evkv39UYlB2YRQCwl7aR+PWHdJiVhI8LxD5/okoUViwRO1f41m9ir7vvUjj51SFdfMcGBxvEm/vBbX8IbFo4BAA7spSADymI3lTVq5iz1t1OXyr5v8XiEbCsAoHNO3mPuuHSPpJ5l5kwbE20wIyOIUkyRdNqTmyGljpbG2u+ZBQBCG48spyVp6JZZhtLF5cRH5hHdeV6+5ucjja1OyTk0sVhbx5bJ9gwAJFMF+f/1j0uJASvmMx5GgY7eIE5ZwzOfxsDnh36L4j99uYqPf4PiyV9/GADw+OiYnFc5kIsNT14zbfADANSjaM23sPDVRVlTKYDmfAtPvPcJAECbMkwU7Ms+AaChAD7WunNq4shnCCA31Z7846e63ndHHYkhB2TOdmSNl0hoWeMOc7Z5ZGWqNBDKu4fXMDFnWcBQu93Gpz71KQD5AfxhA2e6zYOeG5vFwsIC7rvvvq7Xbr311lzGQ10taxRYbhC87vImfCaw1iCoN+UDV0ux3nxzG995hdzRn54gYAy5NBDW4OzG8TZ+8vo1fOchKRO88z45zzsqobxsZRkzj5zFhAJnpTGWD1gsywe+o+qSqE/BSgxRqq6h3QF+6H+P4Rf/poiOYhlEgeVyflyfoql0e0vL3cyUTtq0hfXqvWu4+0fuAQCsMg9lL4cGywCIYhdrKXAW1kK0Tids7J/d6eN1p57Ec+uJuQsdIAHZMpSRRlTvrvVI1wud+thphCvdz5ZKlcIp2WfOdl8hkzS30YllXqzM0JpPNVoWAit3r3R9rzjK4nvUNDYzBGFlJdFPgVVCpclHzIIIybY9+f6n8OD/JRNrf8aX9vquXebM6cWcOQRRPc2cASCy35nHOVqEwq+3gBzmtDOpmbMU6+FQtM+1U5I0gbWHa5j/i5MgimHzSzSX+35snxf/e/W+5PlBHBI3NBdcgDdC3P2v743fd0v5MHmMAh1Fe4u04QWT95lei0bDDvb/xp3g6px97vrLAdg3TAGAltpJrC8l9zX1KbjqmSdCjrOnQnzrHfdh6c5l+R3CMFGM8hmPenZgtbuJKHGIdEgNuFwP1qUn+v4ctL/hs8YQZHZ29rdmZ2e/ODs7+99nZ2fd1OvfPTs7e+fs7OyXZmdnfy+/YeYbWcFQ2pY9z/Fk7XM2DMyZECJuM5AXS0UIgad2irQZynYxLIYgn/rUp7qu08UGZ1/+8pdx1113xUxeXuDs6quvBgA89NBDPcf0+te/fsNrN998cy7jifvhCIFzNdlEdKYkx3VKTt8YnHkOYht56lOZpOWQhFy2X47puw/UQADsLcq5dPKc2sUPACo4bvjHh1H9y8ewryPBW7lM8gFnmjlrJUmaU3ViWaMQAre9i+GzT/n42y8JcL2DXhg8se4VbaVbOX8+PYeS/jn1hsDHHyrg5P86Fb+77HgoufYtvgGAKobwa48mx3aqDoKVIE72f+3TJbxeMUI6eJvns6s/Iq8ZTzFBhKKrfmnhK3Ij5ksjyfOrOkJymdOTR2SiP9LuYEXXb7qqaW8rAUPre/o5I04uzJkTg7PuNYiIxHZcCKD2wBrOfupccqwSA3VUAp5Dzdl65kwCj2SMtUdruPtH7sEDv/AgAGDJ8TFSJLmAD39aJdYp1oP6Enh1lmSyHSwFePoD3XPaHXdBPfvjmdzr4pwj51H7XEpqmW5GzYFH/7/5ru955cHZ183CYZJ1AoCHfu0RPPDLDyVupFSy9hAC33/uya7vlXZ5QA6N3gGgWZHXrJYCr9SlCFTLEREJ3POe7us1GbYxVcpnPMVdcjzhSoDaYzU0lWyYMIJwNUD98QYWntjYn5IQyVkPKmt8VjBns7OzzwGwb25u7iUAHgbwfam37wXw4rm5udsBzMzOzs7mM8x8I6usUTNnwyBrPHr0KL72ta8BuPjM2V//9V+DUoq3v/3tAPJjqYAEZG3FxKTj/HmZOOZdU7UVGOKc481vfnPXa3kB/H7Gs7y8jNtvvx233nor/uRP/gRAfudnfHwcu3fvRqvVwpNPPrnpZ770pS9teO2yyy7LZTwxOIPAv7lqGeFaiFFXzu9l1bO33VHGAH/6CJ7+E/lgixrS8D6PGq+xEXnMkb0FFA+VcOCIXAdOK7DYCYDLW2tw1iWWJcoh2v2zyP0GV+BM9+gBVHKqdquPngAeOEbw6qV5/LdHvxh/hpYcUD8PWghoqh3ZU09078hqeeV3/TLwHz7hd7113vWxfyTKBQzpZL++yvH0ovyb/Wm/q0bnJ/15lFPdWD9x8Ai8KQ9ODjVezqgyc2h0155oQB3WQ6zdK9fMP911Fb5ZnsTnRnejVCC5XLPiHvlMmgjbOLbUffzYrIAD59clat6MD2d0sPOzmSEIK6uNz/X3CyWJ1FGI2HkUkGB76l9MynYRQC5NqBnEun5stAucPfEHTyFq8riB94rroegjl2tW2i3vMVbrzoeoT9E510HUjLBy9wqCpe73SzP5tBiZGAE+O7YHAHDyr5JNF8JIV81Z+75u9tUr59OkmzGgocBZ81gTtYdq6Cyq9UhIcPayldN4/WI3GJqqCIicmLMHr5Iti8Ivn8OTf/RUbLjjVBhEwBE1I4yvdjvteoKj4gnkobN87vPlGuwvtvDALzyE+3/hQYQN2SQ8XIvQPtfGN/5yYcP3NIgzljU+y2rOXgTgM+rf/wDgxfqNubm5Y3Nzc/op0MG6FnjPlMgqa7xQzNl241ldXcWVV14Zsx4Xmzl74xvf2PX/wwTOdN+1gwcP5jIePYe2YvKOHj2K5eXlrte03M929GPisl5CCORnpQ8Ahw8fBgA88cQTm76v59m/+3f/Ln5t9+7duYyFEAKqXLe+/UUOypeXMVKVD6lVlZe1A+C6xjL4A8lDnzgErJCvDTqorEfaPSb/97QkOhBEwL52Y8P3pmYc+Lv8Da8PHFWZpHWDsySpXm0A5SjAz558sPt7lcFZj16xslca6Hz0r1PgLCVr/Py3gHeceqjrO/vQxuGxKJdEzVN1WgUe4YmFdeAj4PiDjwmIx5M1qrm/ih96l0z085hD1V1qo3EtjG2rqUsR1UIIIXDyYzK5XWMOzrgF/L9XPBdn3nAVKIH11geAlAMCwFTYxsI/nOl6T0sLz3/2PHzlqnnaLaDy4klQRlAYcE7HzFkqK4mZs1Z3qiJEqg5N1SwCwJ437satf/5clC8vgWpwppkzG7JGh8T9Dbvq4Fh3u4H2ue7nSuhI6XAeYKiqZIRes3sDhBYo2mfbWLxzqUu2q2N0l5sLWDwwAxRVatM+08bSN5bleBwSm0u0Wutr9qQDaS7gjAJ3Vbr7pXYWNVAV4C2OX5i/P37v4eIo3rPvety8Lxq4PUSvCPeU8UhR5kRnP30O5/5Rbkb7Uz6IQ9A62YInOGrUwW/uvxEAcH9pDJSIXIyJLr+qe2OFNzlW71mRzFk9QtTkcB6Rz9V/HNuLE2MjuOrXrkS4GoK55qYplxJz1s/W1DgAvV2xAmBD9jY7O/s8ADNzc3N3bfLe2wC8DQDuuOMOvOpVrzIfbc4xPz+/JaD48Ic/jA9+8IN46qmnAEhZz/z8fM/Pm0azKXcPFhYWeh7/1KlTeNvb3rbhe3mMp9GQCeHi4mLP4//TP/3Tpq/nMR4gATVHjx7dFJSGYYiPfOQj+OAHP4hf+qVfwuOPPw5AAtg8xqQZp29961u47rrrNv3M5z//eQDAC1/4QvzKr/wKDh8+nNv5qdclwlheXu75Oz760Y9ueI1SmtuYDhw4gK997Wt4zWteAwD4+Mc/juc///nx+47jIIoi/PAP/zAeeeQRPPbYY5icnMxtPGyEgjc5VrxVeHtclMaKwNMunji+gKt3t1FvTuP5zQSY+Uc8zPz4FDpXd3IZU8BlMtSsNkCnCSoNF0AFp1YizM+fxdLKKCbCjUmRc1Mbi2QRsDwkt1BAh1B4EcdqdVVKJydlQXtrvon5Uy6mg40J9NTNBEtsESvzy3YHBKBwmQc8APzo2aN47K8msecdMxDjHKeXToOsEgB7cEAB2Bp1UOEhnjoygeDqNk6dO7X1wQ1ifCbAAoCZoIUnIh/Pm5a/W4xxnFk6g5/5vb34VVUbGPzMIdz6piIIr6HuELTmm1sc2SzcShMBgJuPn8LdP3ICe35xNyZePw4xwXHi6XksnJAbeV+vTOOtL6/j/37zMgCgE8hziCXrQ4qj9DdPovZLcq0W4xxn62dB5ym+dUeSyD79azfiFa+soRO2ca55DmR+gORazABgODF/GqGuTRJSxiVCgXPnz0FEQOmGIsQ4x6nzp8HqFI1Hmlj4vDxPzixDbbqGJZ/A8YD2fAtnzjgAphFFAebnz5uPD0AnBJqUocJDrJZW4YyplEzI2qX5eTl3ogNFsKeS+cKYAL85xEJ0HkvzdrNrUZIbjcVWgLNnzqF4bUGxzrKZOiiJny/pGL+mjdVyHbX5NavjAYDClT6gWokc/S9HcfXfXwU6Ldnp9rEWHvqJpzd8J7zeRWNXHfPzG+Vzg8TaioMnCt3lEaudVZBpOa/PNM+hQRlKPMK3SuN402d246bTFJfvXkSHc5xdOwvSsAsaXWcU59wCrm7KjaB6VEdtWkpAxBjH0mPympxzC/jS6G68y/Fx0i/h+68+g/Od84PdZ5uNBwy/duAm/Mrxb8WvnfzsaXivcyEmOO473YJzSs6h+24/iB975yrcouxd2fbaxs/XZqMCoIrFpVXMz9fi14MgyC2PGCT27dvX871+wNkyAD0TRwEspt+cnZ3dD+C9AN6w2Zfn5uY+COCD6n83bm8MQejkfmJioufJmpubw7vf/e6u12666aZcrNC1g1+5XN50PKdOncLznve8Da+/9KUvjRkcm6EZvMXFxZ7n561vfeuG11784hdvOfkGiclJuXPl+/6mv+OOO+7AH/7hHwJALLMEgOc///m5OABef/31+PjHP47l5eWef7OWrVx33XX4ru/6LutjSIeW3DqOs+l4zp49i/e9730AgOnpaZw7dw6EELziFa/IZUcfAK644oqu/3/HO96BU6dkwiyEQKcjE5HDhw/jIx/5SC5jSMcTk08hOFODf8xD2SljQklVGJvAvn0UR5YW8G/OPCbH9PZD2PWdM2ifbWFyZBL+tH2man7kFOpowDvjo3Kugr0tmXSt1uQ1dD2ON6g6hso1FdQeruHsi/bj1hMl7LrBPov/nGsETjMXU2Eb/hM+/GkfPODgzQi7vnMXnlwQmA7Obvjeq8c5prxpFPfZZ/Jvf8kKTnxC/nvxzxew9xUHgHNt+Ed8TL98GgDHmAKwb7vyxbixvohDN47CfWQVe9+w2/rcJq+mWPirRfzI2aP4xG8dwOdOT+O7/gVD60wbU7dPwOURbluTtUsTxTKq58sIlgMU9voYu2rM6lgAgL24gLvecwIlxZif+u3TOPjiA6g/Xkf15hHcc7YNBmDB9bGHMVTOVSAigWAlwJ7vyYelvhcJ+OKfExi5vorOYgely8oo7S4B6v0nD8/gZ54D4FwF7XNtzLx6Gk7ZXNroKpnyzMxu7NuVXPf7iw+B1zme+LGnAACzf3kLBAdYkWLqJZO45z8kCeVYeQydY2W89LermKgCT3yM4UwNAAR8zx34+RZFAp9xnkSlE6L5v1poPNHA4Z88BFZgaJ1poXykhNKREthT93d9TwgC//ECRp87itI+u+qLq9YEvlao4LJWDU++7SmwMsOe1+3GvjftxdrDNVCfYPFTMtlvEYqC6hW5f4lhgoygvK9sdTwA0L66gQ88BLz99CPgTYG1P6lh35v2on2ujdGbRxDMPRB/9m8mDuBpv4LXdjyMR0VU99lV8DhFgVV2rus18hhB5cYKwrUQbUHAFRv6yHdfg5GFEM91ASwA7bNtTL9yGu6IXUnzgZkI36hM4fZVuR6zM/LeBoC1h2v4P38Z4UoA834J/+X1Dfz8/x7HDTMByJ0U0981HTPctqI6xvGVEeBf3fgyfPbgAzjzd2dR/3odhaeKiBoRPvO/Cf6F4DjtFvD8yx3sqZWBGlA/WkPxUAnT15rlaJPj0hCqUKxi377R+PX5+fncctG8op8tl68AeKX692sAfFm/MTs7WwXwEQBvn5ub2/iUfoZEPzLCb3zjGxtey6tH1XbjWS9Hm5qawr/6V/8qF2AGJPLNs2fPduniN4uXvvSlePnLX44jR47gZS97WS7jAbaXNWpglo4DBw7kZs1+5IhsQqVZ1fXBOY9BogaWeYYG+Lrn3Po4dizRw99zzz344he/iMcffzw3YAYgtsfXcfr0afzX//pfAUj5pRACjLFY3ph3OGNKzqxsvqu+nNvLql/XDxx9JP6sP6Mdw/IxTgAAf7d8QOpajrGiHM9SXV6T0ukaKqp2add3TOPm/+8mfMdPTedW33VwF7Cq+ufc+28fkiYbFIiUrDEIgVtqsm4geP4MPjJ1BP/xwHNw+Xg+9V0AUNjTvcb93WcC+HsKscbM5xEKgqNNKPxRhi+N7sZVuwWYn48UtXJt8gz47sXj6Pzhw3HtGQ8FfoIej9+f3iPXdcEFiJfPHJ++bWPN6OLXluBNekDE8c0H5LULCcV0RTWjDnjskpZHhG5y7Ife/TBap1qgHkW4GuDuLyZsUOuNSSM/wQeXWXoq/w3WSZx4vVvS2DzeAnUIuGogvHKPfKbs/b49KB0u4VMPulhtUzx1nmJxFVYNQSgFlhy5thz70+M4/7kFnPr4aQCS3essBPjmfz2z4XuLkBLCPJbryRHgK9VksyeqRzjxP+cRrAYoHylh8StLQC3Eg8VR/PTlL8TDxVH8+sGbMVOKQHNo6QEA0yPA304mJQkn/qdkQViR4aG7EzXBH119Iz645xp8emI/SoznUtc5PQa0/O7jHv9vJ7A0twxaoGieD+AowPpbb1yndCB2ahXXx4uuA/55bC/mLpe1Z6c/cQahcmwt7i/g7GNyHF+rTuOtz+/gr3+shj/9trPy2ZFDDdxImaLscqxxB/TyBBx3FgJ4Ux5GlqXC4LhfwdUzyf1Ii2yg+95Tj4f19/wzMbY9C3Nzc/cAODM7O/tFANcD+Njs7OwH1Ns/B+AIgPfNzs5+bnZ29qV5DTTP2K7m7Etf+hLe8Y53XLDxbAfO0qDQcRycPHkSf/7nf57beIrFIiqVCoIg6Jnsa5nhJz/5SfzjP/4jHn74YZTL9nfQdIyOyl2RXuPZTOqoa57yCM1UnTt3btP3P/GJT8T/zrMWT4c+P+tr3HRo85sbbrgBe/fuxe233x4DzLxienoaH//4x+N6OAB473vfCwAxa6ZdOC9EeBqc1eR9psHZ0qKyrk8tj96UGldOTo1AYp7QPt+Ox0OJQK1N0Ak4po4looWx2bGYvcsLnF11QDZOBWQz0eP//QTO/p8F8BZHZ6mDTj3CK5dPAgAOvWYS/33XFVi5ahIuQy4NnwHA2+d2PbUO/cUD4O0IvMkhhMC3L0ntU2HcwRd+tob3vKGBH3xOK641sh3r66L2dRr4m/vUOYsELl9djt+b3K9e5wD18wGvzKc4eribRX3sN49KI5cOx4FxOcdDQnDDHmWmEIrczg8AHL2j2yeseaIFVmRon23H997jhSpecVMCFgkZ3HTHVflzZ53PV/H6bqbpgXc9GDfsFUKgfUbef7tftxvHlwh++mOl+LPfehxWrfQJIWis24ya/8uTEMo9MmpGePT/LAMAnvAruPtV1+CxQhVX/9BuUJfAnbC/ITs5CpzyShteD5ZDUJ9ieUHOm0dKo7j1Fhc/f9nzce7QBEZ82acqj5iuyrnxuSvlM6p8pcwtWJHFrn+rzMU3xpO5P+ZHuazVlBKUJjced/4vT4K6FO16BFdtYqeBhohUY/EcluvnXA5wQvAPo3vj1x7/L7K226k4mHblTXD95XLSvuTyELunCFiJ5VJzBgC7KvJGaVyXbEYHix3pyKicQKMxD99xXXej6srV5nljr3v+mRh9XZa5ubl3zc3NvWRubu4H5+bmOnNzc29Xr/+Hubm5fXNzc/9C/ff5fIebT2zn1viXf/mX8b9f/epXAwB+9md/NrfxaGCha73WRxq0hWEI13VzZxs0ezY/P4/Pf/7z2LNnD/7+7/8+fl+fO9/3QSnNPcnes0e6N6UZoHS86EUvAtANhPJy1wSSZskanD388MNxiwNA1sbp0M6ReYZmzrYDZ3k1we4Vb3jDG3DixIn4/tFujBqc5cX+bhaesoxun5JJ4hSRY/jHe+SyWKfJPeXvUUk4EbkUmQMJc7b01WXc87ZvYfXeFYwW5EP+zBMdQDlwNW7bDUf1s+KdfHaHAZmEXHV993389B89BeISBKshwuMNlHiExXIRB2YrePD/WsGnf1LKnfI6R86Ygyt+/jLULk8YovrjdUTNCEEoYsv6qVtHMFUR+OHndVCmPJem2ADANmncfPZYR1pqtyMQZVQS/djVSaIWidxaDQDAkZeNbXiNBxy8I8DUjv5VuzluPZCAM1bI7/kxdsDD50eStXdFGQOAAx3VS4s6JAGOqyHKV5YHZjpj5mzdHueh/7S/+wUBLH9jWfY664jY6Y66BD/8592J4re/S1hlzgDgaHVsw2ud8x3wNgfvcDTOy3NUfcM+/JufrOIVf3ItXj1L4O3yB5J99opqCbhndBKPF6qY+d69sdmQ7nF2rK1s0gnFH39/A1995yr+6e0rYD7NjYH9tqvkRfwzIq9dRxmkEEYQKqXBsuPhx17Qxud/ehX/+XUNvHB/mIurLgBMXc5wwiuhedkodn/3LjUm+fwgYx4YhHTHS82R5nwLhJrbxG8V+6blz3vCZON++Zsr8eaHr7z6XnJ1iqXyGViZQeRk47erLA/8i58sY+0GmR89/niER89SdNqqxONgYtyjY5Dz4/a455+JsdOEGonM7L777sMP/dAP4dZbb+1KoDUQAID3v//9uO+++/A7v/M7uY1nelreab1YmDSIvOOOO3IbRzoOHDgAAHjyySfxHd/xHTh9+jT+5b/8lwBkvZAGjBro5h1XXXUVAODRRx/d9H09nk984hP4wAc+gMOHD+O3fuu3chuPvmZ33XUXCCG49tpr8Uu/9Evx++lr9vKXvzy3cejYTtaox3MhwZCOPXv24Pu+T3bk0HP8YjBn4y8YAwCs3LuCe3/iPtzyp3NgguOupyiCUICpLOyKX7wcTMt1RD7NjAGgeo18sDaPNdE+08Yjv/5oLG2cv78BKGv2dG8jHgh4Oeye69hd2fjkDrmUgGmThUBZZM1UBYpqKGT9E9diVK6qoPBjl2PekwxI8+kWIIB2TaAayXl96McSCZQIOFglLwCb/J1LalPNe2hRMlVtDqp6nbE0UyYEWCG/dfL5317GP9x4JX5n3/Xxa52FAOFaCK4s9cdTinweiti5NI94+fUR/vP+G/G/J+Q1OfPJs6g9Jov126pxeXpOi0jAqQye5GuJU2ddoubt8zDxbd2+ZktfXwYBwFsRREf1rHII7jvVfZ06QeIEZyvH/sLlhza81ppvSav4FgdRMmK/QOEwYM+IALgAzWkdIoRgZpriZy5/IVZecRDlKySLFipwphPrGw4KFFzgymmOMji8KS83WfyhKeCKiRCLjo8QBMFyiNYpufnZqckLElCKn/62Nq7fw/GjL+hI9jWndei6yyK8/YoX4Z9feSMO/qjMjYKVAIILQDWmj0i3lFqEQso+cwBnlRJB1eNoRgzBTyaGZM3jUjbsKCbPSaksCJHMYx698gCg7Mnf+ZUnHXzqtFxw/vzvOV703hE8tSTvb7+48XcP8nztJWV+JsYOOANid713vvOd+B//43/grrvuwhe+8IX4fZ3I/uqv/iqOHDmCG264IVcQohke3U9tfWjg4fs+fvu3fzu3caTj0CH5APnQhz4Uu0nqhScNzPKsWUrHtddeC0A6aL70pS/Fe9/73i4ApP/tOA7e9ra34cknn8ytZxaQgLN0/P7v//6G8bzoRS/C93zP9+Q2Dh0anN155504ceLEhvf1eC4kGEpHuo7xYo1n8iVyU2b1W2sI10KIZoTL0EAnIjiziLhuwB1LaoVA8+lTAwCjt4xuYJx+/v5vggiBpVUJMgCAOSS2IScEubIeR9680STi8bsaCBsRQjUGvi4BEgK5PfAB6Sr1nCsIPr5bSpwWjzYBCLRrEVylOUvvmPNQJI11bQcBLn/nERz81wdw5I1yTr/mgaM4uULQPtkGVQ13WEpeJQiBO54foKaU4ofvGMVnx/ZiTll+dxY66Cx0EJyXmyBpuZeIBJxSfs+zyVGCn3xeo6tmaO2BmmpqLLMoTrvBqw1b/xicrRfEEGD/m/biOR+8CVf/utzkk9bwQq4DUQLOGN1YY31MlYDZYs7GRwg+NNNtlrT2UA3ly8twSgxEZZpptlUIADneY9fMyN/5rZMMrpJ/dxRDRfQpWQeo82KndfiOlO45ylfu/nfJFh5MKQfcAo2TcyDfdej6wwFAJHj/1CMuSJHJFgyNCIEC9+G65wRhUoKeh4yQEGCyKNea139hH75WlfmIZjuZWofWNwkfub4aX1/b8fLLklYMDxdlmcW3rZwGFTweT6mcnAweclknN8D5edbJGi/10Il+OrlP225q8HGhWAaduOp+autDj/MVr3hFbn2y1seVV14JAPjYxz4Wv6ZB5MVgYWZnZ2P55xe+8AW8853vxAc+8IH4/QvN5JXL5ZjNS4dmrvQ5es1rXnNBAOzevYn2/MCBAxsA2sWQEaZj/Ry/GMyZP+WhdFl3bcXVVEqJ73tCNoYFUnUDArn0FdLBCgylI93jObK0gqmghcUVxLJG6hE0nm4gWA1lgXlOPcUAoHpdFTe893rc/MGb4tfmHhBon2yhqWQ8PLXTGTUjQOQn/QQACKDiA3SfPFeP3NMCCEH9fCdO3NK7r4Qg18Sxem0Fe163GzNHkvqzh+7rIKiFoLoZrJsej9iQJFkNAhwc5fjD76vHtUML31xB6XApNiPgAUfrVEs2Vc9RqgvITbwfeU4DZ7wiPjp1GIDc0adFirZKHrsAPrHDTmsfkvXMGSDBaWGXj4KSErdPtyEEQe0xae9NHNlraUzJil+wr4NDM6omVTnFe5YeLRMV4K+mj6DzSzfjyB2HAQCLX5U9DVjFAVXMGXPXAdicmDMAuP2wvC5/94CLgpJ06wbYiMFrOrHOl30FJWCkGyhHyshJ1wBG684HIfnJq288Is/PF59w8cN/XsGpUEk910KEbV3X2X0+iENA3Zw29wjw9luT9gbaZCZYUnnQJptEQH4qEAD40Vtb+I3vkGO6pzKJGnWwJ2jimsYKPHXRdqe80TrnOgP3f+wlZX4mxg44AzbtS5UGZxcafOjGu8eOHcPf//3f42d+5me6GvdeaLAIYFPnRZ1gXwxwVigU8Ja3vKXrta985Svxvy/0OSKExM6DQOImOTc3B+DCg6GRkRG86U1viv//wIEDXcD6YsoaAWlY4nkearUaGo3GRQFnIMCe1+/qKqq/JpBObT/zewKuYs70A15wkSsjBADTL5sEcQhmvmMa1eukFGRfp4GlmgBUk1rGCJwxV0rUxMbdUKtBCZyqbHK98lJZ77FyNkRnIZDgEIBQyQYPOBpPS3CbW7Kfcju7N5LnZ3fQBC0xrKrEuqPkRJ3FDsK6bL6cl2kKiPoPwMhzkvrWU6cFwqUAVCUhrpI1ikiAOPk4R8ZDki2p8KZbAjy0V67Ry/eugVACwpMEN1gJUX+6IRmGnExuAIBQYN8Ixyd+fA33l2StYONMB9SjCNbUHFLzJVgNEDW4lfkTO7dtkqjps+BNqCR2OQBhQOuUqmVSv7+l9mv/2+uWMVqRr62qUnDf0lI1oabNykgJE8pts32mLd1rfRrPIZaewzzfjaLvvj6AQwX+6VEX/+WBMQCyZgpAvEkEhyCshfIeCzmcHBl8UOBXX1rHCw6FeN+ea+RrClhwBV6jDXX3+W067B7neOWRxIlRz6fFry4hVMxZlAJhQgjwjpAbaXkwZ5TgDde0MVqQ52LJUYD6pLxmmqlyUvJqgXzq3+IxMYIff2Ebj//aCrhL8U3F4u8OmvCFBNbuunXZGXGtMGc74OwSiWuuuaardgDotkS/0InsoUOHMDU1hdOnT+O1r30t/uAP/gA//uM/vmE8F4oVAqTT4dVXXw0gqcFbWpK7exeLhXnPe96Dl740MQhNX8OLcY5uv/12fPKTn8Tv//7v4wd+4AcAAA888EDXeC7kOfqzP/uzrv/XdV4XazzpIIR0SRsvFjirXlvBLX92M678ZSkrunlFWsMfnQeY6N4hFlzkVmCuY/Klk7j1fz4XR37iMAp7JTO8p9PA/BKNkxDHBSgjUoLFRb6JNYjSUAG79st7ia514E17CDU4YxRCCNSO1uFUHFCX5vrQ1/Hzrw6w5HhwhcDyskBDuciFlCBYDhCsBGidbIO6FDSnRJYQAqHQGfMZFm+Sc7q9HMIdd+OkiLnA6kNrCGthrtcLQFdNS7RHMmfRokz2NcCnHgX1CbwJF7zF8x0TBSCA245EGFetEGrnAhBCYpZBMIqoEYF3uLzPLOzoe1tInPTRqU9BCxQilA2WI2U/rpP6dih/Fnz5HwCs1OSYfUtL57gCZwsNAlZmYCUG3uII12RLijixdruTfZYjUzVVFfjua+Wa/A/n5EZj56wCI2oOCS7QONZE83hTgtucN65uP9jB3729htVbdiMCASJp3hIpMMT1Oi3k69Rnua5Dv3L7GvaOymuzJ5ClHsf/2wkECrxGlEr591oI3uJgBaLY63yYs6on8OWfW8P7vq+Op3y5cbXwJdVMnSfsq1AbNAT5MmdE3fejRYH9oxynVY3wNU4DByuq1tSjCGtSSkyocrEdoBtyLGvcAWeXRpTL5Q3GGl/4whfinl4XOpGllOJ1r3td12v//M//HAPGi8GcAcAf//Ef4+d+7ufwT//0TwBkU2rg4iX6lUoFn/vc5+LxpAH1hZY16njta1+Ln/7pn47ZT214cTHOUbFYxIc//OGu1zQDe7HBGZDIYr/0pS9dNICvE2td9D6VckjVNWeEETSeaoA3ee7MGaEKECHZ1Z8IO/hP3xjF6ppcj6pFyZYV9xeUFXK+EjkdU3vltSnWOwAlCJW5hGAErZNtuFUH7pibm532+vihWzs448oH/rljHYRB0sOrs9iBN+aCeCT3WhiCpME8qvKa3XjnE/Bn/BjgUy5QmPERNaL8WLzUgHR+MzZOUacOSCDrqbR7JHUJnLIDd9QFdUmuYyKUxgO69kr5e4JF5dJYlNeGU4JgNUDpYEmCewv3Wb+JmqMaAmsWCJBJY8SBUBAQCJQmXRTUvtGKUo/ZAmf7Vbnyr3yyiN/7fAHRhJYRyoSfxcwZQeN4E6sPrIJQInvX5RWM4EdulazLeVeOp7MQyHq8SDNnFMwjUorG8uv/CKAL0ExXBU57cuOqNd+KN604o6g9WsPawzVE9Sg3F1sdV01G+NYvreIPv6+OD89cHr8eqlIrTgnap1ponW5DhALuuIfivo0tfqwEAQCB3SMCRyY5vjwyg4BSBEvSCMhVTel5I0TjyQZ4wOUtmSM4A0kWopmqiMHZ9+xew0sO6tpXguaJFtYerQGEoHJFOXYiNomt2PJnWuyAMxXvfe978Z73vAc/9VM/hXK5jFOnTsXM0MVIZH/jN35jw2tf//rXL9p4AClt/N3f/d3YubFer1/U8ejQNYP333//RQPU62O94+bFGs9b3vIWCCHwgz/4gwCAv/iLv+gaz8UyBAESk5lf/dVfRbvdvuDjIamHhzfhgTgE0WqIv33rMgBg1NXgTEpngtUw1/ouAAAlEGpQ2jRiLJTnRsssyz7Ayg7GXzCOiReMb34cS5EumxjdLccz2mmjERLwlkrSGAUhAt6EB1ZmsdFK3kEpEI7JxHFlvh27tkWUghUo2IgD6pBc3SwByb4IxSaQ/RLkUy77rukda8oFvEkXvBmBFfKdQ4Sm2M6qwDmdXJ8P4sSauhSjzx1FYZcP4tKcE+sELL7kRiAgBF4nRNSMwNV54xwQgYA35Uk2K0dDkEiga9PBVUl81OKyZhLSSKK+ooy3HGD0xmoMxmJwZmmpukyZQbdCgv/4mSI+XRsDgLj+TbvGOh6VzqNFlvumDKEEz9kTYO8oR0AZlhwPIhKSIQsSuTcrM3jTPqjPQHOc15qFAeScfrIg6cb6E42YOROMgDgExT0+guXAiuNnP7F7ROCvpg4j0gZp9dQ6VJTtBXgo4O/yc2Oq0gz+ZFkAhOBMUa5FrdOteB1yCxRO1ZG2/shbEo/4WbarynHaleNxltsxoKYehTvC4ntwUECt60x3wNklFIQQvPOd78T73ve+2ExBmxVcjMR6165dWFhYwIc//OG4J9R61uNCs0I6SiV5kzUaDXDOLzoQ2rNnD3bt2oWVlRU8+eSTAC4ec6ZDgzPdkuFig6HXv/71ACRLlR7PxWTOfvM3fxMA8PTTT8f32oWWNZKUgYTue3ZzsYnH//U8xl35kG2dakupUTPKtT8VoBNr+W/tEvnCCbWDrlkYTybTTsXJd/cciCVpgDRQAYDJsI1aSBEpt0ZBgeKBEkZuqGLy9olcpTLroz0igUfzTACukGRECYhLMXbzKJwSy6VRbzr8PT4ilZCxW6RFO+MCvMnjpMjxCPxpH0IArJrzPadqzgBgpsJx3pW79Z3znS7mrHSwCEHya2IeB03qBCeqiCVXtUdqEKo9BBwCWqBwKjLJtwEWNzMH+LY7OL7jV6a7XtPMGW9xlI/IvmbEoaidUb07HQHq0Jg5W7XMnF2+r/v/n1DAo3lcMlfpOcTKDM6Ikz8bTIECA77x86uYLHGcVXOofa4TG4JQV7JmrMhAffnvCxG7qhzHPXmd2mfaEJo5cyhokcGb8hE2IrgTOa6NqSVud5WDE4o1V/6+aEkZJVEK4jHpSnq+k/81I5LBny7L63PKkUxV+0wnsdL3CNxRB6VDRdBC3rWvKeasInBGMWf8XKurtpN4FMSTD5pBNxx6tc94JsYOONsktCTt9OnTAC5eIjsxMYG3vOUtsVOiBmcXS9aog1Iau0Q2m82hSPSf+9znAgDuvvtuABf/HOmm1OvdCC/WeHS9oAavw3DNrrrqqpg90wzjhQZnaXm7brbaPtsGi0S8qw8hZIJPVcFynkNKgSFtcXzQaeMXXt5KZJYEcCcu4KaDzp9HHESEYCQKsNaUBe4AIChFcX8Bhb2F/J1ICSAiHjcKbo/IpJGfb4Gq5CeiVPYSKzGMv2AcpQOlnoezEd6kFwPVsg+cVolssBLEgNp1CfxdPrwJF17Oss/0Jdg1ImJwduLJILb2pz4FYdKRMPekMeXEOFnieECZgtQeqYOrPnDCoVIyTKTxhZWas3WJWhAKfPFbwEPHXHzpeLLOFJTUrPFkI2FAXYJAbcMXPIAWaVJzZhmcveBagmsmE3rvrEpiO6rtQVwvRABX1XTSvIGQ2iTyHeDQBMd5RwH8c+24d5+sW2SgLkCdnOtMU5nqTLV7w0G3GBEOBaVAYY8Pt+rAy8kiHuiWWe4akXNmhcnfFy3L6xZRAm/cQfmKkmw1kDdjrhq7jxQEXCawSFQLBNWwu0UoXEZQuaYysHywr/GknmUTZY5zro8IBMFigNYJufFAHAJWYKAuhQgHr4HbMQS5xEODs4vJnKXj8OHDAKR7Y3o8F4sVAmSdHgDUarWLfn6ABJxpd8SLfY6uu+46EELw9a9/HSsrKxf9HB05cgSABGedTueig0Uduh+b7nd2oWWN1JEFyQDgzyhwdqYNVmFxouaMuHBKDkoHi7knsuhizuS1CVZC/MLLWvi2Q0nfGG/0wpwnkjLZIZRgTWm56otRYkjA8q1Z6h6PLKoPV6ULo5Y1koU2AnV6IqqTRgp/Jj8pkQ6nyGIGtuQBy8opLVgO4r5rjitB0ORLJ+Hv9nsey0pQAoXjUfEEzqvxnDoWxG6NvpLMVq4sY/x5Y7kOhzDEMsvxksDjRSVJe7KRMGeMAI5iYiwljet7Hq0l5aR4Yim5jytXymdZ/YlGDPoJI2gp/4tiSRrKxLJG2T/bGjhzHODj37+Ee35xBY/96grOKeCxcteKTOq1V3wQoXR5WVqy5ylHg2TFdL+3ibKIx9Q+1wHRNWeRiFnOvI2SQImeQpgqi1QdXAc8Zl8pBCGx7DNvAKtLKEYLAgVHYJnKCSGOSfQuKAGrOijuLcAps9xMiXQwZWxDiJQ21hRYPPYh2UbnrFeEwwTcEReFPQVM3j6x1eEGj9SzbLIkwAmN29PoiFqRNAOBAMjgRkBbtc94psUOONsktFHBxWbOdOzfLy2sda+qi80KAQk4q9frF/38ANIpEQA++9nPArj4ssbdu3fj5ptvRqfTwUMPPXTRz1GlUsH111+PTqeDj33sYxd9PDo0ODt58iQAxL3rLlSULishaihwtksCj/bZNor7ivGDhZUoxm4dxeRLJ1E8kPP4Umyeo8HZUgCHAeO+SvQrTizBzDsIBcJWFCetdaXtapwNoNyQZbJ4gcAZAHgzPoKljjQDmJbXw11sItKW2pTAHb1w85oWGITaSS97Asuqx1BnMQBTKKkwQlV9hWulnmqr8Hf5ss0CgKtmojix7pzvwG+rZ4eSw1KPwinnvIPuEEQtgc5iB2UPcb3QyYeaMTtNXArCKFiBWauj9NYZgqTBWS2gcXKtGfPO+U68ISMigbAoD6BryzYYgti6BQkw6nHsHxMYKQic9ZI1ZnluGa6WpLlyA4l6NNceZwBAfRa7+k2lwFnnfAdMMWesxDB286gcT961uEAM8CVYlOziyt2rQEu3Y6CgTM5pVmS59xIkhEAICYZ2j3BMBBLNO9+QKpCIEFA1p2mR5Q5gaZGBR0mNV5N2g9OQELBU77fcHXVJAmC/9+YAM1Xe9bY75qByRUXOnwKDM+oODM52DEEu8Vgva7zYYGg9OLvYrBAg64QA4F3vetdQJPovfOELAQAPPvgggIsPqIHE8OL48eNDMZ43v/nNAKSxzMWugdMxOjoKQJq5AMlcv1BR2FOAEPKB4E9r5kxSMDpRG71pRD7sc+5PBcgHZtSIELUiAALEI+BtaVSgx+OOORfEqh6Qkq/q1eW4pqpZVudoMYj7HV1ocOaUHbjj0qAABQcBIfBrHUSKAeWUxmYqF2Y8LHZsrHgCp5Qsbfkby2AAIgBjl+UrrUxH2gDlmokQV1wpnxPTD5/DaK0JDoDmzd6lgqhWC+2zHRACHPfKCAhBcbkFojZG4FDryfT6RE3XigFALZASMCDV62wxYWGoT8H2yOtYWAfOkpozS+MliME9o4BTYnigNAYAqD8uEWVACDxf1uRVr62idCjf+UR9ChEJVcPEY8BYP1qH31ROe5OerFtyaO73f3rdHS9wnPCTv3/qTrmxF1KC4oGSNJQpXoC12iEQoUD98TomWYCxqNP1PvdVbZdPwUo01750AMAKDNGaVBS84GCEAu8GQ58cPwCX5ttwPh3p+umxosB9v7SK/W9JCiypT+GMORi7ZRSjzxmJXT8HiR1Z4yUew1JzpmNiYgKFQgErKytYW1u76GAxHR//+MeHItEfHx8HYwxra2vodDoXnTkDukH1xZ5DAHDTTTcBAO67776hGA+QMGdf/OIXAQAHDx68oL+fFaTTYLgWxgxa+6zsCaVlPd7MhZvXgguIToT6Ew20T7cTaeNyELMMbo61FOuDEAJnxAEPBVqnWmiqjDc63YzPDy5AcpYOVqBwRh24Ey6KNHEB46dkHUPEyAWz8wekBI6VGdqn2mDzNcx7cjznPyt75gWEopw345oKp+IARFrDt8+2ccMV3QnPkuPBLV/AOcSk+yH1Zf+5339TMzYFGX9UGiYRh6B6dcXq79XM2c//oQD5No6bfjSRVNUC2R4jakRw1FzpLAWxuQR1Cbhqi7AenGnmzLN0CgklaX8JjBUF/nlUWjie+Qcp925RhvIuD4QSFHb7udvEU49CcKB5rInpsBXXnNUeqaOswJkz5YO6VEpjL4BrbPtMG8FKgOJiAxHZuN4ERRflK0pgRYapC+AYSz2KcDUEqzq4ejzEf953Y9f7nfECnBFHrg8Fln8bFk+aNEWNCLMHQ0SpX/ef992AT0/sh+PgwrCc6Hb6FVyg/tAa9r5hD674xcvBKgz737IfpQNFeJMe/BnfyjUrq2V2/jxQawzQMG0IYgecbRLDVnNGCInt64cl0U/HMIyHEIKJCamhXlxcHAoAu9k1u5gAVtcunjp1aiiuGSBNQdKhx3ihgnpSpx+sBHHT5+axJoIlVajCsKFBfZ5R2FeAN+3DG5PSRV/JF5/+0+Mxc3YhgRAAOCUH4ALBSojVaZlAj332OMaOSvDh5uz6tWE8FQcTLxiHU3FQFBFO+pLhIHfLRJ/TCwsWAQmYo4BjdNrBQrXc9d6a4+YuQ0sHoQSEEbQXOijs9uGNu7FZAQA4QsB1LlziQl0qrd+ZbKT+6sNtfH5UPmP9mkz0w7ECCnvsAtjJ0d7nfK1NIbhA83gT9Scb0hmVA42npDMq9Rk6XDHq6tTpn7r2yVbNme5RpWOsJGJmKFR2/kuOj5HDRUu/sI8hqfkatTn2FsMuqaWO8v5i/Fl2Ae436lI0T7RQCkI4ROCnL3tB1/th2b1gjpGANI3hHQ6n4uA7rmjjm9Up/MQVt8Xviwk/dnWceOF47vXKlStKKB8uQgQC1+2O8DeTh/BwcRR/tPsafG5Mgv1C+cKdH91DI2xEqD1agzviQIQCky+ewK1//lxUr6pYRyD7poHnXgnUm8Dn77V77AsdO+Bsk+jFnA0LCzMMrNBb3/pWALL2bFgSfe2QeP78+aG7ZsNgwKElhMNgUKLjF3/xF/G//tf/wtvf/nb87M/+LF772tde0N9PXALqM/gzHrwpD9UbqhCBwOJXZY9DwsgFXSUrl5Ux9W2TcMZcOGUH/m6ZFC1/fRmNp6TEKc9+QpsF9SmiRgR3lGHq6iRBnFYUwtgWSXBewYoM1asrGB0hOKZYGHZCjqflu6D+BUxCAIzdMiqd9DyK1ky35GzBLVzwJy1xCAq7fBQPF+EFId525Yvx6B65Pv7N5EE4FxAsUo9ifHYMTplJQE0jHCt0s2TNPeUe3zaPH9tiKVkLKLwJD7QoeywVD8h5ff4LcsPBm/JQ68iLVlK45CXP6T5ntsCZ7lEV1qQkbawoMO91n48mZfAKF/CaOQSEyPVv95jAsuPjn5RMX4d7IR+tVK57rCidPKeKPK5d1MHL+ddzdg3Jo+CBdGHcOynB9XGvjE9MHMDXK1OIrh6NN63y7COoQz4vfPAOxxVTHBMzDD9/2fPxd5MH4s941Qu3LrpjDqJ2hGg1gDvuynY0Sm0h5zxAmd3zQgjBbdfLf//4bwvce/SZy57tgLNNYtgMQYAk0T927NhQjEc3ya7X63ED4Yud6E9OSlpcO/8RQi4o67E+NHP20Y9+FI2GTKwv5jnSEsLl5eW4gfiFNuBYH67r4nu/93vx/ve/H+9973vjFg0XKgghcEYdEJfCKbHYuW3xKxKcyYf9hQUfhBGUDhXhjrmYelniqKWbPrs5WyCvD3fclc1mZ3y87NaN52Jq78XZABEcuPUK4BuVqa7X2xXvgjNn1JWOddSlODQl8Ad7rsXXqtN4pDiCTx08fEGZRUCCM6fqgPkMVU86t/3lDdfho7fegL+aOhxL/i5U+Ht8OBUGVmEIFzuol7pr3sJp+/f9xAiB+AIF/zwB/zzBHW9M3ptflc2BW0yCtMJ+xZo/LZkzb9rDqUX52b1KbfWa5xN870uTY8xYVPIRArROtRDVI4y6HEtOt8Li6uYqLuijgxEILuuTpifl3P3o/itw+O2HcHTPJH7oqm+7oOCMgMTgzKkyTBR5XKenI5y6sM8O6hKIkMMdcTAzo9YbQvD+Pdfg/z70XOzbc2E3iADAHXUhQg5RD/GZd9Twuhu76+Cq+y/c896b8hEsSTk+YVLN0HgyKfwkROTyaL36oDzoqQXg5n8t8P986JkJ0HbA2SYxMzMDQCb5URQNBRi67LLLAABHjx4dCuZsfDx5Mr3uda8DAPj+hSsy3yx08/CjR48CuPhgMW1uoS3+L+aYKpUKKKWo1+t49NFHAVz4Gq9hjIkXjqN8WQnOmIvR546AMIK1+9cAAO6og03KG3KP6tVVUI+isKuA6/7TNckbFCgeusBJiEMxcds4KpeXsXscqFzXzXrsvezizGlvysXV17p4oDyOJ/1kTNWx/Os7NovC3gL8XT5+8UU1/MPEfvyHgzfj3172AnhXj1zwJy11JFB0R11M7JeJ/nLo4IHpaXBC4eRtfb4uCCGoXFMFKzIU9xbQrCZJ4h/vvhpejswCIbKf2x/8HMWJjxFUixwPnXVw+x+P4Zo/msEHjo5i+pXTcdN3VmIYub6CeUmiYd90cqzf+xmCl98C/OTrgdfdbm+M1Kfwpz1EzQiVKAAIwUemjsTv/+muK+FewDlNGBA1IzCXYHxanpfVFrDrO2fwsefdiCXXh3MBsQfvcDCPglWkGdBDC3JMv7PvepytlvHD/397dx5vZ1Xfe/zzDHs88zkJmQgJmRgSTCQ/pQgUCLMIKFovVKpioKjIlV7UG0FBb1GrOGstFMFaULFqrcpVRHBC6lWWVLxaqdTZKjcRAhnIGfe+f6znnLNPJkg22evZJ9/36wXJefY+ycp5fvt51u9Za/3WsuNIWzyjICr4qbHFmSXmNswoeMbcUW588RZOXDyym+/eN9KeFKKI0U0j9BTGOGfF1DaU+1rXZ0w7E4oDRaoHV4lTiIsRyZTKsNHUTRmfJi86ARbNnfz6mpvrvPGmboaG2ytJU3K2E8VikYGBAWq1Gvfee28ukrPxTYTvv//+XLRnZyMc4yNFoSxf7sezxzeiDpm8gq/WeNhhh005FjKBjaJoYmrj3XffDUzuf7Y/S8oJlXkVBo7tpzKvTNeKyY5+oa/Q6oGzCVHqi3E0ls2PSzFJi6fsAZRnl0m7C0RpzKLLFtKzumfitc45YWI6TmPKc0u88TmbuXLh6onjHcu79vneZjvTu6qHykFlFvXVeO2xk3Xbl88cbfnIWdfh3XQsrpJUEw46sReAB9cnPLjex06h9SHkP1uHddJ3VB89nXDuYWt4wWFruH3gIGb11J78D3gazJsZsXqZH014aENCrR5x9W0pX/p9J4dcs4xFrz2YI29ZxS/jKv9yT/Y9M6Ip33/3+2M+8j9iik9jgls5sEyhr8Do5lGK2f6BX+ub7GF+fuCglo6cRXE0sVZw7ko/m2DTYMxVt1fYMuT/3a0cfR0brBGXEpJyQlKJueZ4//DsG71zufCg5/BIodzaaZYwsXFynEZUOia70gf11Thn+XDLH4BAthl4GpF2+H06eyqTCUkS1Vv60CopJ1QWVOizXqJiTJTF03h5/Tr7JDdj7oyIn98W8/rzJ4/dclcH625QcjYtzJnjF1BefvnlE1PSQhZzWL3adz7uvPNObrrpJmByDVEIURTxxS9+ccqxkO0BMDMAbr31ViD8yFkcxzzwwNRVqYceeugu3t0atYbyul1dXSxevDhga/IlivyTvQNOm3xUnnYXWt6xHtdxcNVPt2rot0ZR5Df3DCCKfcIYJTGHXr2M/mP76Tykg3Kg5Ax8B+kvl29m1WI4c/kpnHX4yRy9OtxDmSiJGRus8ZoFj0wcO6R/pOV32vLs0sReb33dk/Ey3rE+oC9MRyUp+3VnV764xlCcMJztxTR3Ruti+uDZYzsc+9D3O9g0o5OZa2bw3m9VOOmWGfz4l/61NUfu+zb1PKOHzkP8Gqr+bA3nw8UqD1ywivMPOd4nSy383EdxRFSISDpTOuaUqBZ8vNzwryXu/53/fLXy2WcU+TVn1QUVZhw3wNrVg7z9ORunvKfv6V+2uFtxKSJOs61EGgqRrN8cQ40gMy4gWy9YShjdPEpXafLm4Wfot/be0buyhyiJ/CyQ2WXiNOKJX/j+dDTxv33jXa+Kue/vI/7iNFi1eJg3XhDoKeteUnK2Cx//+McBPwrz4IMPAq2vJNdo2bJlrFu3DoAtW7YAMG/evN19yz531lln8b73vW/i6+OPP3437973TjvtNJYvXz7x88nDqFBjglgul4NP/bzggguI45i1a9dy3333TWwmLl7aldCzsodZzzuAtCuld3VPsKtklEQUugrUgbkv8g+L5rxwdqvvrw0N8gVUomxz0aWvX8yyK5e2vPjGlCYVIkrdKZ96/kbeedYTfPDcrU/rWqA9lVT9fl2lrpTPvHwzbzx5G6ctGW7ZvnQ709cVccrioSnHFswK21E5+2g4c+ngxNfz+luXLJ66evLvPT0r+PezR1OO+JseZr2ph7+5e3JWyBlHwdL5rflZJaWY4kCRKy6efAh831gPm9IiaUxrr0OxH4XpXu4TxqEd89mndeTwyXQd3kX/c/roWOTvV4VqwjEHTV1PNaOrNaOv4+o1KPQVSXtS0q6UUlYBdUZnzY8OBRi9B4hKCXEhYuTxEQaSyQ2/FvWPBksYO5f4h3hpdzqxuTns+42w7dCIf7wq5kt//QgH9Ck5mxaOPPJILr744inHuru7A7XGe8UrXjHl6/E1ViFddtll3HLLLXz5y19mzZo1QduSJAnXXnvtxNcrVqwI2JpJN954I3Ec84UvfCF0U/jwhz/M4OAgH/3oRyemysqkpDOlNlxj4cULWH3rM+lZ2d3yp42N4mJE7Ykx5p43hxXvPZyBY/uCXbWTSkLHAr/J63hZ/3qtHmQK4bi44Pc8I4a1Rw/zZ0cM7fPNXnfbnjSm89BO0o6E4xeOcMWaIeKoHvxOe9N5WyZ+f/S84WCdtHFRHHHNn25i2cwxDuweY8Hs1sXQcUcMM/x1XyzkPZdO/XvHapNfrz4ErrmwhdPAqgkdS6rMXVDgA2f78/X5H/lEbVbHWEtH8KMk8vvTZQ9eGn8u42a1cPQ1SiLihs91oTth/nZTYWd0tfg6VPdrvOI0JinFfOvaUc5eto1rzxyEOi3dPqNRUo4pzSpRObDC7OpkVr2odyzYtbpyYIW0M6X3yN7JfTrr+6YgyM4EvIXvNSVnu3HDDTdw+umnA/CSl7wkcGtgyZIlU74OvcYLfEJ0wQUXcMYZZ4RuCuCLk5x00kkUi0XWrl0bujkAXHTRRQwODnLqqaeGbgoQfrpnnqWdyUTiMS5kRzZKfZWy+nCdjsUdEMXBRmGiOKK60G/yOr4Bdb0GUcClnXEx9k/Tx9cxjNWJAqz1aNSzoptCb9oQR1HQkTOAjp6EO17+GJ962RY+86KNwdsTpRHzOmvc89rN3PPSP1JqcdHY8eIai+bAgTN3fP3vzt2MuzHmqMNbmxB1ZVMbVy6Ymnicsmiopb01P60xJsoGxdcsGd7hPfNaOBV1e33P6qNYjjnt0MmCF/1drZ2q27msg4FjJqvprloW8ZHnbuKgvpq/HAVKhPqe3UvXoZ2U55UZ+t02ytmI3hmLh4JNiR+XlP3m5kA2V7UNs6YWUXK2G1EUcfPNN3Pddddx/fXXh24OURRNjJ6tW7cu+BqvPIqiiK985Sts2LCBE044IXRzJighag9pRzqxyazXuqd7O1PoKVDsL0x09KOI4FftpDI5ckbdl0kOKS5ETJykGi3d62hX4nJDAgtBYwh8YZsj+oc55ZBRH0OB2xNlyVESQzGtTxkVaaVyKeLBWyN+8YY/8ukzNwBw7MIRzjmi9ZX2Gj1jIfzg1Y+yat4oxaTOBUdsa+njfz9yFk2MtHz2bQnXHLuJ6188WQo9ZL0tP5IGH/tvWVXduM78gdYmZ3EaT7nWpJ0J1P2UbwLOKEjKycTDl9LsEp895WE++IKtnLNsW/gR80JEFMMTv93mB86Um+1S2HJ2bWDOnDm87nWvC92MCR/60IdYt24dS5cuDd2U3CoUCkqGZK8UegtEsb/BRlkvNlRBEPBPsMvzK2z+yZYpx0JqnNZI4JEz8KNn4wl1faxO0uI9znYm7Up5/IePU+gtEhfDxhBA2llgbOsoQ/U6o9vG8pGcNfxMQk6N7ahEdPelHLN0iA3XbmRs0JdtDykuxfQ9toXPnFdnU1Rg9uhYazvWE8V//Hnpm1PgkqO2kfYUeWxbxJLiNojCPhzuWNLBlp9t5aE3Pc7WR0aoVsPu2ZmUE9KOhJGNI37fw8DX6c4lHdSGahyxZTPPrGzx1RFDj5hHEXEhYnRzjagUB3/QmGf60bSZarWqxExkH4kLMcUZRcaeGJ+rH369UFpNoGERdejHjUlHwtCGIYbWD0FUJw49cpbtb1Sv1/20xhwkZ4XulKSSMLZ1NGjiMS4u+k2FR7eOUewvkJTDFXEBP+pQr8HQ/xuiXvfV3EIaOKafQm+B2nANaq0tOb4zcSkiKcdUhkdY0F9r+ehrFPk1Xo2xm5T9deiio4c5ev5o8I5+caDE6KZROkeGOaBaC94eAGIYe2KM0S2jJB2BP2PFmOrCCsXelOFHhkk70uD3MvBrK5Psmh36oVWe5eBUiYjkR6GvQG247jv7gUfOwHeK6pPLlybWgYSSdhagBsOPjlCvh183kFQSkmLkO/q1+sSNP3Sbin1+9L7YH24LlnFxISbtTElKEZU55eDJUFyKoV5neOMwRARvD0ChN2Xwd4Ns++224DEdpTFJRzox/TOi9aMeA8f2T/k743I8dT1u4H51Uk1Iu1MGHx7yU+QCXxcBkpLfeHnk8RFKA+E/98W+ImlPgSiJSKpJ8HsZQN+z+4iKfgPv0DGUZ+GviCIiOZJ2JtQGx7KKW6Fbk3WKRmqMPDbin+oH7simHb5TFGdl9ePABTiiOKKyoMrYljHqo+F/PgDFgSJdy7tIqgnleWGnW4GfopZUEuJSMtHhD9qebO+j8SmyUQ7OWaGnwNhQbeJnFVJaTfzm83VfETUP6xbjUjylDHro9hS6U3pW9VDsTXOx9hWg76heynNLlA4o+fOXA/WxOmlPSs+qsNXGx6WdKYXuArWhWvBZIHmmNWciIg2iOGZscIzRTaO5uHkk1YTeZ/ey6YFNEEfBC16k3SmlWSUG/+D3iwpdHRH8pstbOhNGHsvHNEKA6oIqcSkJukn3uKgQE1diSrOKpF35uO1HRT91rzS7lI91gpWEpBL7z1glbHvKc8rUa7D+zvUMPzpMsb8YfNQjLvpEuj5Wp14nWBGXRsWBArWROqNbR4OvfQU/y6FjUQeV+ZXg1+lx9VqdtLtAoTs/6/DTrpRoQ+RH0GWn9JMREWlQ7C9QmV+hNlLLRUc/iiI6F3VQ6EmDj1KBH6nqO8rv9Dw2WMtFJ40Iin0F0o58jAyBHx2qzCvnYi1MUorpf1Yfvc/spbqgGro5gO+gxaWEQk8+Oo1xOZv6WYxIA4+cAcRZQY5Cf5FCb/jMIy4njG4Z5YnfbqM8u0RSzcPPyK9dTCoJ1MN/zsC3JU+JUIRfc5onSacfxc/DQ5m8Cv+JFxHJkbQzZeYJM9j0402Mbh178m9okc5DOqmNtLZc9K4kpZi0OyVK4lyMnBX7i3Qu62RseJNf+C47yENnulHfkb08+v2NuZiGCn7UozizyOiW0YltEIK2p5pQml0miqAz2/8spCiG2ljdF3QIXOxiXFSIiAv+818frj35N+yP4vyNUFUPrIBO127pLiYishNdh3dlm1TlQ2VeJXQTphg4ZoCRjcO5uPFHSUT5wDK10XrukhDZuaSa0Htkr59KmANJNWHGcQNs/fUTFHNQzCHtSpl16kyGNgxR6As/ElMfqVOZXSLtLVDsDd8eyArddPuZDh2LO0I3J5d6VnbnYgZIo7QrpXt5+AcOeabkTERkJ/IwHS3PCt0phe783EKiKKLj4HxM2ZOnJk/xM64jJ9M+x5Vmhl+zCNCxrJPqomruRqZ7V/eSdia5S0DyotgX/kGD7Ll8fcpEREREJFeSUgw5GCXfXmmGkg+ZfvL3SRMREREREdkPKTkTERERERHJASVnIiIiIiIiOaDkTEREREREJAeUnImIiIiIiOSAkjMREREREZEcUHImIiIiIiKSA0rOREREREREckDJmYiIiIiISA4oORMREREREcmBqF6vh26DiIiIiIjIfk8jZyIiIiIiIjmg5ExERERERCQHlJyJiIiIiIjkgJIzERERERGRHFByJiIiIiIikgNKzkRERERERHJAyZnswMyi0G2Q9mVmXaHbICKie5mItCMlZwKAmR1qZq83s/mAbmiyx7IY+hzwZ9nXiiPZI2a2uOH3ih/ZY2Z2mJldZ2bdzjlt5Cp7zMyWmdlz9aCxPUzH+4aSs/2cmcVm9gbg48BC4PXA7KCNkrZiZqmZXQm8H+gE/hRAHSN5qswsMrOrgIfM7Jrs8LS4yUprmFliZlcDtwB3Oec2hW6TtB8zeynwKeAk4B1mtiRwk2QXpvN9Q8mZ9AH/DhznnLsUH9gzwzZJ2swC4DfAmc6504CqmS0M2yRpMylwH7ASONnM5jrnamame5Q8VX34h0N/CyRmdoGZHR64TdJ+uoHXOOeuAH4LvNTM5gVuk+xcgWl630hDN0Baz8xOA1Y6597lnHsEuD07vhI4GRg1s88D92j0Q3Zmuxj6OfDz7PhC4CGgFrB50gbM7FTgZcC9wC3OuTuz418B3gpcDOj6I7vUEEPfwc/++CJwFTAEfBt4p5m9xTn3g3CtlDzLYuilwL8CNwNzgGXAd4G7geuA7wH/FaqNMsnMTgf+HH9+pu19o+2zS9kzZnYWPoCPN7M/z45FZlYAlgN/BTwInArMCtZQya1dxFAC4Jz7FWDAwdlxXWNkB2b2Wvy15uPAQcAHxl9zzr0dONTMVjvn6mamh4iyg+1iaCHwHufcd4ArnXPPd869F7gLPz1t2qxFkadPQwz9I/6edS3wd8Bzzewy4BJgIz5hUwwFZmZl/MOYT+KX37xt/Jxk943Dpst9Qx2n/Y/D36z+CjhnfNG0c27EOfdJ59wdwJ34qY0bQjZUcmtnMTSWJfjgL5xnATjnNIImO3M3cGH21PNdwLCZdY4n+cDV+Bvvq4FVgdoo+dYYQ+8EIjOrOuf+raETfS9+FERrYGVnGmPoHUC3c+53wJuAR/Frz94M9INiKAeWAtuyfuq1+Cmopzd83t/MNLlvKDnbTzQ8XfiDc24r8Ev8WrNLs9fj7Nfz8UP7v8bf7PSkSIAnjyEmpzJuA9abWaX1rZQ8a4ihHzvnHh4/DAw557Y0vDXFF5ZZgY8xEeBJY+iJ7PXEzP4CPwpyb6CmSk7tJoYGs+MPOec+gV+Dfz26BgXT2Ad1zv1fYI6ZneWcGwH+GXhRQ9I8be4bbT3sJ7tmZn+Cn0d9G/CAc+5xMytkAY1zbsTMbgPebGYDwEYzm41/MnG5c+6BYI2XXNjDGJoBbAbG8BfF7zrntoVqu+TD7mLIzKLsplrAr1ME/4R6A9AFPMc598MQ7Zb82IsY6gEGgGcClzjn7g/ScMmNPYihn2XvHwA24UddL9eaxdbKzlevc+6ObIpiBBSdc0P4NYBXAV9yzn3JzC4xsxOdc98AOpgm942oXtco7XSTlRQ9AfgcvoJV3Tl3bfbabKDLOfdQ9vX/BC4DvuqcWxumxZI3exFDrwHuds69PEiDJXeeagyZ2aXAXPxMjpnOuYsCNVlyZi9iKAH6nHOXBGqy5MxeXodmOOcuDtTk/dJ4kmxmlwBvAz6NL/jxfxreMxd4Aj8V/j+Af8je+57x/sh0oWmN09NXgRc65z4MfBN4HCYq7H2XbC6umT0Lv3bob5WYyXb2NIY+osRMtvOkMZStMTsVeB7wByVmsp09jaHfKzGT7ezNdUiJWeuNb+H0ZeA4/LkyM+uEiaqa38NPP30rfpbOLcDD0y0xA01rnBbMbC3wAuCV2WLW7zcUYliE34cK4AfAUc659dnXvwde7Jx7rJXtlfxRDEmz9jaGzOyTwLedc39odZslXxRD0izFUHsxsxPwhTx+bWafA77pnPttNrV0CXA88L+B+4HVDX2P95vZ9c65wRDt3tc0ctbmzKwHOAU/X/pEMytutwnfQcAd2e9HnHPrzawI4Jz7L3WqRTEkzdrLGCoBOOc+rQ6RKIakWYqhtnQu8O7sv2fi9ykDnzz/HliUFRd7PDtfhYaCLtMyMQMlZ20tm6P7uHPuPPx+HGvwTxoabQNmmtnVwKXZ9wy3uq2ST4ohaVYTMTTU6rZKPimGpFmKofbSUIXxN0CHc+7f8QVbVpvZ8qyg2FfxxX3+GXiTmcXOb/s07YtlKDlrM2a2IPs1aahiM77570/w+051Zk+LSsBFwBvwJWLftT8EteyeYkiapRiSZimGpFmKofZiZouzX8eLf8T46rw9ZjbgnPtP4B7g2OxbngGcDdwH/LXbj/ZNVbXGNmFmVXyFmvn4fR1GzCx1zo02vGcW8Bb8vhwJ8Av83Ot7sqCX/ZhiSJqlGJJmKYakWYqh9mJmZ+NHM7/tnHtndix1zo2a2XLg+cC/Oee+bGbnAYlz7hNm9gzgj8653wdrfCBKztqImb0XXyXvNufc32fHlgInA//knHvEzN4IvBL4FrBufwxq2TXFkDRLMSTNUgxJsxRD7cHM1uD3JrvCOfdNM6tkUxYxs/GCH4P49WY/w1fN/Lpz7iOh2pwHSs5yKhuCrzjnHsuKL4wArwJ+BPx34AqgDrwf+Bfn3K3ZosnPALc7564P03LJC8WQNEsxJM1SDEmzFEPtJTtfVefcRjNbAZwBHAX04rcy+AB+v7IrgM875/7JzBYBFwK/cM59LEzL80PJWQ6Z2fnAtcBXnHOvaTj+QfwCyW78zvWfwgdy41D+lKF92T8phqRZiiFplmJImqUYai8N5+sO59yl2bGzgRXOubeb2QuBlfhNwX/aWFxsfC1aiHbnjQqC5IyZlYEOfDnRyMxOb3j5G/i9HrYAa4FLsjm7xfE36EIkiiFplmJImqUYkmYphtrLducLM3tu9tLdzrm3AzjnPke235xzbtjMJvZbVmI2SZtQ50BWcegN+I32fuSc+2h2vAK8xMy+5pwbw++a/irgUeCz+GFhnMqa7/cUQ9IsxZA0SzEkzVIMtZcnOV/nm9kdzrmtDe/vxg8MjZ8vJdA7oeQsMDMrAFcD/wnMxle0OSd7+evASfinENcDHwKOcc7dGqCpklOKIWmWYkiapRiSZimG2stTPF9rgRuz954P/CXwBefcXa1vcfvQmrNAzOxcYAZwF/BR59ya7PhN+Hm47za/Z8cC4G3A94E7nXM/zd4X7097PsiOFEPSLMWQNEsxJM1SDLWXvThf3wO+gB8Q+qNz7vEwLW8fWnPWYmY208xuB14MHI4v+7rezC7M3vJW4EVmNjObf9sN/An+acTExUcXov2XYkiapRiSZimGpFmKofbSxPl6AVB0zv1cidlTo+Ss9erADc658/AVbQ7HV61ZYWZLnXO/wVcgOi1bKLkavz/EGufcfwRrteSJYkiapRiSZimGpFmKofayt+frROfcQ8Fa3Ya05qz1HgHuBHDO/dHMZgObgYfwez68EugDHsgWSu73+z3IDhRD0izFkDRLMSTNUgy1F52vFtGas0Cy+bg9wKecc2dkx24AKkARv2hys0qLyq4ohqRZiiFplmJImqUYai86X/ueRs7CSoHvmNlq4HTgZuBnzrmNYZslbUQxJM1SDEmzFEPSLMVQe9H52oc0chaQmZ0BfBG4G/iEc+6WwE2SNqMYkmYphqRZiiFplmKoveh87VsaOQvrUeBK4APaOFH2kmJImqUYkmYphqRZiqH2ovO1Dyk5C+v7zrnvhW6EtDXFkDRLMSTNUgxJsxRD7UXnax/StEYREREREZEc0D5nIiIiIiIiOaDkTEREREREJAeUnImIiIiIiOSAkjMREREREZEcULVGERGZVszsdcB1wIXOuX/YxXuqwBuAX+3qPSIiIq2mkTMREdkfVYFrgJcHboeIiMgEldIXEZG2l42WrQPWA/cBLwUuBM4ETgYqwC+Aq5xznzezXwELGv6ItwJvz/47H+gAvga82jm3oUX/DBER2c8pORMRkbZmZiuBHwI/AT6IHxGbi0/ODgA2Ap3AxcB8YCZwLvAJ4KfA/wJ+DLwQeAtwA/Aw8Drgq865F7bsHyMiIvs1rTkTEZF2d0L26/ucczeZ2XzgTUACLAfOA4oN718I3Jn9fr1z7jYAM/tYduyShveeso/aLCIisgMlZyIiMl1E2/1awE9vvAt4N3AZfppjGdjVtJFR4HnAWPa11maLiEjLKDkTEZF2983s18vNLMZPZ2zUASwFjmk4tgmoAUvM7CXAd4DbAQNehk/oDgcOZnKUTUREZJ/SE0EREWlrzrkHgNcDs/GjY9/KXhoBbgNW4ac2frXhe0bw5fZ7gVuB44B3ZMeOAz4MnNHwZ4mIiOxzKggiIiIiIiKSAxo5ExERERERyQElZyIiIiIiIjmg5ExERERERCQHlJyJiIiIiIjkgJIzERERERGRHFByJiIiIiIikgNKzkRERERERHJAyZmIiIiIiEgO/H8ZeUx8jSZ78AAAAABJRU5ErkJggg==", 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IAYgZmi/cAmiMgue4Vz/Jbe6ojx4bc7iu2HZvb++NJU+913l+DMBlUQ9KoVAoFA0iBHgF21CU3Mwmtk/AGrOw4LwF0zQwhaJ5hC1ml0/B5T/3vHKdx8zzWdhpBnAxbWIhggmYQyZa1rXAnrDBLUep0eZe5Mx9H5gjpz+b9qVi9jJTvjwhEBaH1hlH7mAONEmR25+DsIQTVTs2JnA0SdwKhUKhmDGEEMjuz3nNZotf9B/mD+dhjdoQDArFnGDWNU725OkFBAOEKaSDZgnY4zYKQybMIRNjT4xPvq2QcJsj1h0DjVGAEgibQzCATdiOce0oOLrRMyGVHBWKSRHwnPvphFACbgnoLRraTmoFz3HYaeZEzMisiqJPJco5UygUijmOYALcSWUSlgA3A3fVgHE7sSst368sNMVcgc8eB+3o5iHYGealCXrpVrafKixsGc0yR80pHw/PMxBNagoQAvC8k9ZY4ABxfDMBrwZNLtTMjn2p8LEnbAw/5Etocptj6L7hGRzRzM4SYXOAAkQjsrE6FxAmB6HyNW7yWXNNmCqUc6ZQKBRzHGEL8IK8iaWfTWP4Yf9GX+SHMSeyNr/va4p5hBBysWF82zhYnoFbM1cvKWwBnmO+6IbNISwBc9iEMIX/HltUrP+MGl4QnnMGAtg5x2gVAkSnMg1MBJpQq/N+VmJnGFghWBc8GxbQZmi+CIHWk1o9dWFuC3neM0f0hgHDD48guy83A4ObPpRzplAoFE1gjVuz4AYq8UQAuLzR84I0YIe3jPjpTYAvDDA7hq2YhRRFXWcDHGBZhnx/QSo37kjP2FCE5dRxcYDbQL6vAG5zZPflZATNcdjMIXNaUoeFzX0xbkLAc8yph4OX0uhFzZjwn1PMKghFmTM/45EhgVApsF/+8pdxyimn4K1vfWvdn8nuz8m6yYDAfHxxzMsKIQQwR0yZKVKo/zp111134Zlnnmlk+DOOcs4UCoWiCcaeGIc9Mc3qbFUQDFKpjctVRleswBq1io0x7gosKAtNUZnhh0dmzby2Ri2ZlssFWIbJqNQMOo+ccdgTtnTCuICdtaXDZnFP6pvb8hybauO6MFiQCzJu/0IuZINst76My+cgnBRMhpkTeVDUhqDcmZ+B42SNWmCOsq8bgW2Wr33ta/j973+PH/zgB5O+17bl9Sa7NwvBRXFPTkKc+es4bbzxBaTf/va3yjlTKBSKY4FZpSLHBWhSg5bUANvtcSTA0kzKITvjFBAqcqaoiWAVRGVmgPEdExh7yk1ldBwPNvVOTy2EJWCNWI76IQdsAW5xcCYdInvMkqqp1tSPc2J7GvaELaMukMY0cyNnrhCIIwbi1u3NhMCDojLWmOXVlRFKiueLKzozzefh6NYxTOwMRKab/PrrrrsOzz33HC6//HJ8/vOfx2tf+1ps2rQJ5557Lp588kkAwMc+9jG8/e1vx3nnnYe3v/3t2PGrnbjmw+/AK2+8HC9/7yV4+MmHAQDZfAb/8OW/x6UfeQUu++il+NWf/xfC5PiHj/09enp6sGHDBnz0ox/1vvtDH/oQTj31VGzatAkf+MAH8MADD+B3v/sdbrzxRpxxxhnYs2dP0/tnOlFt4hUKhaIJZpNqlBsp4xaHnWEgFBjtHQUvcNA49W/ybu2JTmpsTXEsI+f1zM8Pe8yGnbbBchwsV3AiaLIGZaYQTEDvNMDzDIBUlRPuPy5gZxioQSFM5ke0pmosloyUE016Z0angfyRAggBiOPEEvjBD2FzkJg2KxxvBWCnbXDmeMvEXxQhhPhKidN8Krp1k/KP5rdz66234q677sKf/vQnfPzjH8eZZ56JX/ziF7j77rvxl3/5l9i6dSsA4JlnnsF9992HRDyBq15+Fd77F+/FWSvPwiCO4g03Xo0Hv/8QvvCTL6At0YY7P/Jb6C0axu1xcBv48N98GGtevAYcHC972cvw5JNPYsWKFfj5z3+OHTt2gBCC0dFRdHZ24hWveAXe8IY34Kqrrgq3g6YR5ZwpFApFg8hGtAJjT45j4QUz3y9MMGmIcYvDsgTi3YbT94gXRfgElwpzIjajw1XMUqQ8/OzIfaM6hTVig5synSnWqcvI70w6ZwJILIkj81zGqy/jbv0LF+AWQHQBZgLaFJ9j3DmvXR+QUCIXXwA/nRG+sIQ1xhBbSGfDoVW4uHVmbm0gEyDuwpmzkEZoOO/s/xb8NtwYq/DKoUvret99992Hn/3sZwCAl770pRgaGsL4uGwz8ZrXvAbJZBLc5rj3sXuxc+9OuQ8MionMBNLZNDY/sRm3XPcV6G06Yt0G2gbbILjAT3/xU3zv2u9BGAJ9fX145plncOqppyKRSOCv/uqvcMUVV+CKK66Ykt8+HSjnTKFQKBqFy5VoYc58hAEIRM5sAIKD2wIC3K99cQ0yldqkqIErDz/TBrwQAnaWwZ6wAA60n9aOwkDer5mcoTERFCsyuk2fmSNhD1sAcSqFOgwt0u8f3zYOalC0ntQqx+CqVgaKU1JrksgdyPnNp516PS2pgRhEnfuzDLfOTAiAM+dvHY5jPfPn4VTT0tIiHwiAc45ff/430LI6UquTgXcJwObec9n9ORwa34ev/viruPs7d2PtJWtxzTXXIJ/PQ9d1bNmyBX/84x/x05/+FF/5yldw9913T/8PiwDlnCkUCkWDuA1oWUEaZZ6cdQnjz0ygZV0KWiJaQ618QM4quSXV27jJQWzi1b6I4PuUpLaiCsIW3j9uctnceAYwB0zYYyZSa1ugp9xzh3gR6xmByzEAgN5hwBwoOBEzgBeYk3LJnZozDiGiPefNEQtUl8dDMOkcEkqKIit6qw69VZepzZo8nq0ntgIEKPQXvIiaYnbg1ZnxgKImnGinm9YYkskiXNn9OcQWGBh5aATWBEN8UQydPZ0Y/P0ASIyi+4VdMDqNpr//ggsuwA9+8AN85CMfwT333IOFCxeivb296D2CAxedeRG+9cvb8e5LrgUAPLXrKZx2wmm48PSL8J0/fBefPf+zAIBcKoeJQxNIxVNoS7bhyJEjuPPOO3HxxRcjnU4jm83ila98Jc477zysW7cOgHQCJyYmmv4NM4ESBFEoFIoGERxeOlOtvkuFwQKs0alXvpOr+oCW1EDjmqfQllyVLDLIBIcjqT3lQ1LMQQQT4Exg/JmJoqa4045GYI0zT+wCgJyzXKqSsgZktKNCCF+gIbEkjtTxLZ4Dy00OIaQjJIRsnkujrusMtMTgtqxzq1ST5IqApI5LofXEVtAYBTWcHcmFEmqdRYiitEZe9Lecb1P7/awg+wfK+k4GYXOnIfawHIPJQ9d4fuxjH8Ojjz6KTZs24UMf+hC+853vlL9JCHzyPZ/E1l1P4NJ/fjle/LYX4du//DYA4O+v/nuMZUZx/l+eh4uuuRAP7XwQp6w6FRvXbcQ5bzoXb3nLW3DeeecBACYmJnDFFVdg06ZNOP/883HzzTcDAK688kp89rOfxZlnnqkEQRQKhWLewmWqBecANwW0RJX32TKtaMoR8j/JNTL1I3+4ANmIlviF5XAV24TyzRQVEc68ZhkGasxMyi7LMkBIeXoaD3hnGpFRPSYwsmVk2ms9zUGzSOJbT2nIWwJ6mw5ucumoGdSTq+dT0OeMu+nLBQZmchCdFMuOe28UvkPmQlyRCXX2zxbcFF0hZEqjW/PpKmxO9bHieQZr1IY1bsPOyPOOx2T2hRAAt4S3QJLdlwUIEF+SgBafPK6zd+9e7/EvfvGLstc/9rGPBQYCdLV049YP3Ap7zEbL8S3eS53LOvHVj3wdRrt0V+wsQ2GggC/+7ZdgdMWw8ILuou1u2bKl7LvOPvvsOSelr5wzhUKhaBDB3X5GHGNPjKFlXQrJFcmy9/FpkiWXRpdvqBEKsByXi+qOc8ZNRxxE1Z0oquD2wxI2h9CnOBW3CiOPjsLoMKC3akWOB6FOxKjA/XqraUJwgYmd6bKUSm5zEI0gGaiR8eq6OIc1ZsHoaD4lrHwgzqZzHCLPIRK0LP8psSwOvqDyd6qas9lDob8gnTEhex64vbxGHhmB5x1N8a2D5zl4jqEwWICwZURaS2pgjEl5f1MqzLACx0jvKGKdBnL781hwfvfkG693DCbH6ONjTh+zkpYCkFFo1zEDHAEcV7hoHi80qLRGhUKhaBQO0BiB1qKDFzjsicrL5NPlDAnGi9KbCCUy9YvCMwB4gcvoA58danwKn9zBHMwRc6aHIdMGeaBh8QzgKoqWStETXRqL3Km3ms5+Z4IJcLO83q1lfQtii2JFtV9+9EP2jYpsDIG0yvxAQfZXK4iyyBk1KPSWyuvuYhoMfsXkWOM2CkMmBBOwRi0vIiyYkHXMtijKeJgquC18tVEAbSe2ws7YXpNnbnIvUs0mbHneRewQsTwDLzCZputee2pBXeEiPnP1p9OAipwpFApFgwQljnmewTxqgtvcK9j33mdPj4CBkG2XPGILY6BJGXlwjViWZyBCKEGQWUjmuSyMTgOxrpnrcSC4oz7oGImV0nHtCRs0ScvmeaR46VzF30/j1OkvJmthBAsvM14vggmIAis7b7R4eXRRb5XPCQ7wQnQnGiF+5Cu3LwctpXn7o27UuT8r4DkGe9wGhMD4UxMySuVErcEBaPJYT3nWBRcgRDqLcGolwZ0USwFvQcJO22AF4fTWi/rcJwFhrcl/MwFxovvzOxKsImcKhULRIK7D4650shyDOWCWreYLLrw6kSkdD+NFkQYvFYTCS2u0J2yAEiUIMgsRTFTsWZw7mENhoDAtY8jsySK9K+0YiZWFAIYfHkGhb4rH47V7KIkIxag04rhsGeHW5ow/NT6144Fznpu8riyq+MI44gvjMlWtEGHhmducWEglTaPDAGlUTXMeG7NzCcHdSKxMIQakQ2SPWY5oi5OyN8XHi1syLZeNW94iYsvxLdCSFBACRqeO7N4cWNpGanXSadUS8SCcrA5XAXnSGm0SuK9yjqH7hyIe0OxAOWcKhULRIN7KoiXACwIsy2CNWxj4/SAKRwPpaY663JTDy7LAJMRNawTMYQs0Tuf1auNcRdZbANaYJeXYHdK7M0jvykzLGHiBOREpgBWkAzCxIy0XHoZN2eB8zCpTB4waL62pxBMihKB9Q7uMKFhypZ0XOMwRa2oH5IxJmI1ZpbwgohcF8VoJCMQWxNByXKqxjyu1xlmBcNsvCKc3JeQ1ID9Q8ILGrn82lXCLw+iOyfPd8r/MTbEUJgfL2gCl0Fo056YX7aDGnhyXNaRCplmWCdmU4tScwZYLSLzAi66Z8wXlnCkUCkWjOOkg4AI8zyCYQOGIicJAAfn+Auy07dWIsMwUyLaVDscx7kshhDjGrkxJIZRMT7qMoiEElzVWI1tGkX0uG3gBYLmpnz+AjLZyW4DGKeILYrDGbGT2ZJB5PisNKCbl2wuDBRzdPHWr1UK4iqJVvEAhDcn0zjTyh/MQNsf4U+NTmz7s9i5rwDB1VSejghBIo92uoJ9f7waUWuPsQDiRM+FkPTjPuQIcwuaOEzTFao2W7NGZXJlAcoUvOSyYX2dpZ5gcizP/oh6SsDi4JbcrLC778tXAbcTObQBu5kpWOWcKhUJxzOOuXrsy2oIJsBwDIQSFvhxGtozAGrakcZ2Z+j5n4Khcf0MAOB2qpSxyuRqWYuZxI2cyAuvPF6KTmn30Ih0DkcaR3kKht+lIrUnKGhQmZNSIyxX/wqA1pY6Q2+6hYiQYQMs6KbNtjVrIHc5DMNmg2RUxmJIxOTV4jahExrqNaP0gQqQxb1VeiJkMqhO1KDPDuLVVgjt1wQkKOCIbRCNe6iy33dAZMPzgSNE1IUqEyUEoYHQYRY2mBZMRdC2lgWUYuMnlQh+i9e09oR3LTWusb+PUoF4jdl4QGH1sNLpBzRKUc6ZQKBQNIkU1CGILYogviTtRBQ6iyTRGc8jC2NYxQDgrj1M9HptXjpxR4invcYsDGpQowGyEO3UUNi8+Nm7T8GmAgDg1JW47BgKWZygMmeBMGk9agsAeNRtO8WsI5tTaVHFAaJxCS1CnXkc0ZNQ1i2tQN/I98cVxX4AnsoE4Y2hik7HFMejtujr3Z5CRLSNI78pA2NIpIoA/ryiRjhrjMr2Yy/nNTAaWnhrnzF2wK39BOmiJ5QmwfEAIx+2/FtX3u2mJTkplIyn3yZUJmXaZscHzKnKmUCgUihIbWnCA5TmILo1GO22DxDW52j0N+fCihjHrrvgLU96Ik6uT0BIz08NKUQUuo1PCEn6aE9wUv6n/esEE8v15CMtfSCCaVJCzx2w53510OsFkf6+pisJIo7RaEaUDJTJibQlfEXUqsxodNVRRQSSl5ueA6JwzJ60s35dvKnxBCAHRqMpqnEGEcy3mzG8VIYRUSCXUuY4z4QlvjG0bB+zmIqX1wE1e2QsgvtJoYlkCCTflUQAiwhNNWNzLPBEMaD+1re7PGh0GmCXPfS0x/1wZJaWvUCgUDSC4QPq5TJHtKBgHswEtqckUFZ0AjoKicBpRl/YjinRMrPr22za2oTBggmgEWkoDjVG1eD7LkAqEjhKhs4pcOFKQ9WbTcLDstOxtxAoCWizwhUzAyljQ2w2Mb5vwnA3hGEVEj3ZOu86oELV9My1BpfpoyldtnNJ0XSFgdOiIL05M/t4iSGSRBkesEYWjJkST1xLipjkrZgZ3LtjCKwEkHOAMvpy+k7oqOMAmbNDY1C2k8Ryr2BYjtjAGlpYLNXpr0E2INnIm0xq5I3TTxOdNLvfjPOx3Nv/cTYVCoZhCuCXVGYtSnJgAy3Hp+FjSUeK2Xzcz5b3OePXVVUKIJ14CCmcFfv7dzOYiuUN5ZPdnpXBMlsmomTNXrHFbSkxPwbFiWYbcoZz/hAB4joPniyX03RV8nmdgeZle5aZfNhpFqoXgAkP3DUvDj4tJFU6JIXsj2RN2oOnzVDpnqC5QUgOCCMdFiK/WqE7fuYmjlsmdNEbiLCrwPJORamfhg1tyQc8ctcHZJFHkELAcq7jAorfoiC+Jl39ARCsm5Tacjy9NoGVdY8qj3ni4U4c5z1DOWUje97734UMf+tBMD0OhUEwTE2Mcb/1hG/7nGf/mJRjQelILjG4DrMClkEOeg+U5hCBNrQrWC7c4CoMmiFHjBi5ckQUCEuFqviIcuYM5ZHZlZMTMlI6ZGzkjFOB5PiVpjfm+PDIBVcixp8bBcgzC4jA6/JVyQp3mzwUOc8iUToFjDEVpEAmnt5MbAbPHrSKBglKoIRtSc9NxElm0K/pl42sys0xEeK4J9z+8eY11IQSyz+dgjU99+wGFj98X03nCWbgTjsKGNW6DJjXpvNkCwubgOe4J8kwF3JGiJ1r9M7tCb/hwCNm0Pb4wBi3ZeIRQOM4ZV86ZIkihUMBXvvIV3HTTTbDtaVBkUygUM8637hK4e18Cf/vHLu85zgSoTmXELM9BDCoNR9O5G09h5IzlpIGqxavf3IRwpL2DTyhmFHPIlAX/BSkA4DpirvKn29wcU52yB1lLxfMMyTUpGB2+U5RcmURyZRLM5GBpJlf0ncgWi7B/HyFO1MCRehcMNY1GQgniSxJevZlgEdZ2VaS5bRMS3bGTxrzwBH6a2wYBZ3xa2nsofIbuH5aLH86Bk5EzeI2XeYFDS1K/ntMRutESFMxs3hmvhZvh0diHEHmfszBRwfjiuFxM4vNvtVE5ZyHggQlx9OjRGRyJQqGYLkjJfYAatOiGpaU06K2aNLzzHIQIsMLUGUPC4pPeu+OL4kitcdJG3H41ihll/JkJWeflSFXzApOOjyUw+vgY8ofzMpo2FWqEJXOAOzUuRnvlMvTksoTjZAinSbUcb1QIRzYcrH7nI9Zl+I4Zm2LhFI76BlUGiS790+1zFqY7MYGM2lRS6FNMGW76HrhsNG+NWHJBwuTgFkfbSa1emxPXMSMakc8VpiZ6Lmqkwtf+YJSDCOd3xroMJJYlvLru+YRyzkIQdM4GBwdncCQKhWK66E4V3wSMTgNtG3yVqdTqpKw9swVSa1KwMwxjT45P2XhkD5raNyaiET8SQVTgbDYgjTUBO2ODEIAVnD50Foc1YsFO24h1x0B0isKRAoa3jET7/YFJICZpsGx0GqAJzREtkOmXdjbiBQdnX3BbgNe5Oq8lNQibe33IosYatzGxIy3Tp0RzDk2kKYSOlH7Tvhkl4AzK8ptu3EiZUzPILamSyEzht6VwnDPuRINbT2yRejKOYEbkNOHwiakYSgTrBAJk6uu6pxl1ioYg6JwNDAzM4EgUCsV00Rb3bwIFJ5u5ND1EGkEC0Igs8I4wBawU2bi0wTucakQ947jRnvjiOPQOA8LiaDm+Bfa4JaOuWe4V65tHTVijEabOu9J/7ljsydXSUmuSaDm+BS3rWxBfEo9+TgvAHLLAJuy604CTa5Lyo3xqImfmYEG2GOC8KRuSaAT2RDROLKGQljFH83VspI42BYpIKW2H4dZrEkoQ6zagOzWeUrQJzvMxWR9MpfDNlETO3Gh1Yx+KNHIWaQ3bPMtsVFL6IVCRM4Xi2INb/nk/nCFY1lHh7kIAOI1GtdQUSiHbHJk9GWjxxtbZVORs5nEjVYQSr5aIaETaP0yAFxhozDHQsgwi6tRYZw4IIdUXk6uSNd/upsJpmgYe59E6Z25hv/Mb610FJ4Q47SoQ+YKD4AK5w3kvHa2ZpWyiRdfnkOgU3BagGtB0DRyVaZZELctPH06DeVdMx60pI5QgviigiEjlvG87qdV7Sm/XQQfp1PTKbCZyNgX3jSjWCWQz7/l1U1OnaAiCzlk+n5/BkSgUiumCBZSh8nblOwuhBNx2VkEXxCr2kmmWwpEChh8YBgBYozYK/Sa0lgYcQIJpaWysqI45Yjr1UsI5Hn5dmdEVkzVolpDNwilgjlmevHYUkGA7BS6dnGr1ZhU/r8n0y0gRTgoX0JC6KSGAMBmsdLQKhJ5Evx0i8k1R1JogDDLlVYCzEEYygTfnFNODd547yoxw1BgrWd+l12VCCYxOfUr6eDU1L3nzKbUViTJ0Ns/uaco5C0HQOTNNcwZHolAopgs7YKhZNYxIwThA/OaikX1/2vZv1kJAixMpSlInhJB5qW41m+EWBw+sfucP5mWtCROOAWYgvijmv98WaD+1DUanAb1FhzlgQrBoe4sFI2cNG0iUgJsRR6rgO0GNnC/EoGAFgdzBQqTjcQVKeIEjf6TQ1BK/W0MY2ZAcsYgwhqiwuRIEmkYEl+e5m0bIbeEt3JW9t8K8J5REnkKc7y9g4pmJxj84W+cNibb/2mxAOWchCDpnlqX6higUxwJ2wECu5Zy1rGuRThPFlEQZACfC0KDRSDTpLPKpSJVRVGS0dwzjTwWMIY1IdUZHnIEaFPHFgb55pn9stKSG+JI4iEajVf5z4Y2LXRCtOL03NG7Kl8lhdBmIdVfvcVZKYmkcqeOS4Llo0z4Fl5LnLMdkGlpTRWeAiLBUkNscYOH2e5TjUdQBh6dAKoQjvlMhckY0UjE1V2/Xkd2TLXu+GcafmUD62bRsKt+EoI+r2OpG/kMjgCjCuMK5fswnlHMWAhU5UyjmP1K4Qd6IhBCwAz2CTFb9xuI21fQU0qIiuErIRVOLmYIB1pBaUJouuF1co0UokUptrFzWnOqkzPHRW3XH0I9uddhNoRKi8aCQK/EdJULInm+GI49d91g0Ai2lgRd4pKvno72jnphPs/td1nhFtJ+8BsUhN+Om2CmmBVfm3Ry2ACEVGrlZ3mOM6hTtp7WXfZ7oJLJ6KnPIhDlkyUWHLGs8RZEDEMD4tgnk+yOOVIdAZmnPr0mtnLMQKOdMoZj/jD425qWAsCyDla0vrdGDIvqCbi9yJkCauCmxjI3xbVMn768ooTRzkEL2L7J5mXOmd+hoXd+KMkRj6X41cdQahZApV40a69K55Mg8l4lkON7Kd5M9uIjs0Byx8yolz5lznJpa4Keyh1xUcFuEqj8iGgHsyfsiKqKDm7LVQ+5ATkbOClymvDdAZMdLAIJzsLTt1LU2MQ4hz41IBD8jrV+LcFuzAOWchYCxwAq6cs4UinkJyzMvWibs4khVrciZCyFy5TPfF5FokGNYc5sj83wWzViNWpsBqEa004aU0w5EzuBE02z4/efc1wgBraK+GZW4BCBtmczuLEYeHW14Crmpsdl9ucjGAyFgjdtNN0gWkIIZkQ2HC5mOzGTvqaZrzqKsE7RFqBRpvVUHTelKrnWaEFxg/JkJJ+opw07c4kgdl6p7G6URtlDjcVRhC/0F6TSajTpnzv1PYNaJyii1RoWHqjlTKOY/ngwy5CpoUEvDrjNd0R61MfrYWISDEuA5DnvCbuqmZLTrqtXRdBPsK+b0q2o0ohqVoS+PvZDiMgWOppadmfDUFaNAcMBOM6+3W6MQEnEdJQe4JRtwN1tzJlVbozlmggvQhBZaiIWqBkrTRr4v76h+AtwGIIC2U9ugtzR2EAiiU2oVQqBw1AQ3mzh/OQCby9q4CG4g0So/RritWYA6TUOg0hoVimMDr+bMEgj6Y2adzllyTTKyPkxSBt2J6E00V4BCKIk03UoxCaJ4ZZdbAI3ThtMUI02PdcYkmowKcRvRqcg5KYlakpZFEuvfRLSRM3BZa5pYmUChv9BcpIAgUmXU+OIYrBELRmf9ginlkHlnyM5WMs9lpVKjsxAjmo04uWnRIf0ht4MGNShojDa+PSGVJgWbfX3M51t7GBU5C4FyzhSKYwABz7FiFocQBETIWi+rjrRGwE0Di2g8srwG3OSw06y5m9JUKEgqaiI4wFxFQaeGqaGoCiWwo1IkdBx82ceLN7WELexopf25xUHj5f36hBB47ivPo++X/TU/T0i09V2CAxDCS+EEZCrq0OYhWKN1ZspMQU/B1HGpUM5ZlK2lFLUR3HHMuJDOfoE3l6YYlSfkpMQTHUgsSyCxtH7hHQBIrU05vymiXnk1JqPgAsMPDMMaq+NcEyLyJvQzjXLOQhB0zr7whS/M4EgUCsVU4kY9eJaBZiz8944/4b39O/E/T9ZpJJGIc+KFjFrQGEViabz4pToM7SjTrRT1M9I7CmvMQmHQhJbSG7JvqEHAmoyUVkK4DXEZ0Iyl5QodRJlKmFgWL3suty+Hwd8fxf5vHaj9YSEijSy6diMhxGsiPPj7o9j9uefwzD/vaGw7UYwnms3MuojHvCbomHHAzjR3/kbqUHMBNNg6w0Vv0WUk0I7KOat+fvT/+gh23bQHOz+5a/LtkPkXDVbOWQh4SbrCzp07Z2gkCoViKnFXv1mOIXlwAinO8OrhA1j5292OKEdtPAMvCpwbkTliw+jUZXqKQ3Z/Do+9YysG/jBYexvUETdRwgDTglPiBcEExp4ch51h0Nt0tJ3SVv82DFrUxiH0gISUVbcm7KY8CKPDCN0Q2UNIKfFSMRCWZRh9tM5aTUrA8lGmffq1pm47jfSzaQBA/nB94j5ulCSS8yzKtR112k8PXLbLcJ375Mpkc9uJqMkyIfB6KzaLsJ3fE2U0rwRr1MKBbx8EAGR21aMIK5QgiMKn1DmbTamNtm3jVa96FT760Y/O9FAUijmNZ1gLATvNwANKeq8cOYRtf/80WGESo5lEKIMOuRqb3Zctq8/Zeet+2GM2nr9lb+3hECJtPZXZOD0EpN6FyWGPWQ3PB6IBPErngwvY4zbii2KIBRpgZ/ZkcPhnfZOOL7EsAWrQ6IyiCrbeni8+hwPfPej9XWtMNE5hjUQnzOWlb7nfK4D4knjR6y7DD4147TZKIVF6VVXs4d/v1HHJV1ux+2g9Jl2NcIUiUoSAbFXhZA7TRHMmd1RzSMDpmRbCseLOeRGFb1YtIvjU321r6Poof1f48cwmlHMWglLnTNPK8+Vnis2bN+P//u//8IlPfEKtjivmPNu2bcOKFSvw3e9+d/q/nBCAC5gDJswhs+JN4NCPD9feBJU3xehWPwWs4XJDtP9AY2kzUTqMihq4NV5cyrITjcDoaFCxjUYYfYWsgRS2ADVoUR3Mtvc/gwPfPYjBPx6tb1MRRc5KGX18DCMPjxY9x/LVF0G0pIbCQISNcQO/SzipXId+5J/nLCvHYg6b2PXvu2ukOkaUclVlG0IAb/5OK7Ye0vHBX9YZmZmi097tDaeQCC4jVcLxQpqWxY8qrdFJrydhLH/nXIjKrKy0S6xR/z6mt01uVzvx6WgGNEtQzlkISp2zwcFBjI1FKJfdJPv378dLX/pS7++hoaEZHI1iriKEwOHDh2eFc//BD34Qhw8fxjve8Y7p/3Ln5sEtjsKgCVrBQB66f2TSegJCSHSRKi5gtOvQ230D/w//eRTd45OnWHrjQcR1cIqaCCGdIZ7n0Fr1xvt5RS54IQ3pauqI6WlOJwpeZgpHTez82LNl72G56icQ0Ql4gUd3vRLCE/FpOb4F2YPFPd3scXm+TyYOIrz/hB1P5ac/eqcv6nBgdHKTTkxhg6rCgInBPyl7w0MIr7cYOJqv04qopopAAKxyo/d0ARivI1s3sSwR4SKRmPR3JVZMvuDgNbGfRyjnLASlztnFF1+Mzs7OGTdmv//97xf9vXv3bjzzzDM4fLj26r5CEeQHP/gBVqxYgVtvvXWmh4JYLDaj3y+ErDNJLIlXVIUyjxSw9T1PTraVaK4NjgIcTWpFK7Ftv36+6G2cA737NWRqZFuryNk0IgBhyd5CzcjFE0oik2Unzgq6sERVK2B4qJ76tujSiYIr6NZw5UnLa6hVTkWqrut4agmtLFJtOeIsk8n3R9mjqpJ1/7X7fOcsbxHkLeCR/VrVqeLKqU8JQoDn2IzbQLMFr1UFZPPm5husRwQh4Axl00gI4CVfacNpn+mAOUnyhac8HFnozH/ICxy5kkWQeiKxUzqnZwjlnIWg1DlzyWbrX72eChYuXFj097Zt27BhwwasXr16hkakmItcd911AIDrr79+hkcCtLXVL5wQOQQAB7J7s1LCuooiHMuwmrVnAhFFzpzi8MkyZL69JYbLbm3DtT9uqbqd+bbaOGtxBThMKT9PmsiAd9MaIzN8mRM5qzKR9h+u83siGE/pb3JTBkupldbobSvKBYfAQkxphMyNnAVVTysemyiV9iYhrgv87c9SuPzWNnx7S60FranKa3T2wTwzlJvGVTbkCCf1HmWZYIWas4kC8PyQhoxJ8MyRSS5OBBCMRxcNDvywnZ/ehSf/ZlvxW+rJFqCItgfkLKAu56ynp+emnp6ezT09Pd/r6ekxSl57U09Pz909PT339PT0vGhqhjk7qeaczXRqY+kN4u677wYAMBZVoyXFscDy5ctnegge7e3tM/r9AlItj2ikqnMGAL1veAzj28YrvhZlGqFUy/L/tisYs9/+PcGmzDB+u71KbZOoYkwqpgTOOLjlOERNr6BHlxoreG3DR5h1ftEUBIXsdBXnLFt7TATROWeEEqTWpgLfXTwmtwF8MJpXse9bRClp9Vw7bE7wP09Kp+yOx6c/20AI4fSHk4+HHx455q8xrvMepicgiSo05NQrl9ac9Y35Tzx5uI6VoygXQALn/fgT5fdOXud1KLI+orOESZ2znp6e0wGs6O3tvQDADgBXBV5bDuBKAC/r7e29uLe398EpG+kspJpzNjo6Oi3f/9WvfhU//vGPy56fmChWjXr2WT93XzloinqJx31lsmpzfbpoafGjP7/73e+m9bvdG6N7byST3GQzeypHzqPIiy+YAv/6Ix2PH9a91U/BBR5/19ay936m9z78+95HcUK+srMIqLTG6YIQ6Qh5qYQhyn6iOmaCw+tRxrIMB390CKP7AkUndS4kTEXdol2ln9ukDWlJOCO4aFM6ATV8E6lUKXPvbfuQ3Z/D7puf856r9N1RjGZvn8C//iqBwUz5xGmJ+d8wnvdf705V++boIlssx4qjmQJSPl7I2kp73D62o/PCX+SYDX0lCWTdauniUN+4P88PTVK3SAzqpDVGMKAaEUHmXCTrSmukBGKGbZSoqSdy9mIArjV0F4DzAq9dBqAA4PdOVK014vHNamYycjY4OIgbbrgBb37zm8teGx8vNsYee+yxqq8pZg8DAwN485vfjPvvv3+mhwIhBA4dOuT9PTg4Sd+sKcayfKPs0ksvneZvlyvfxL2LTHKTtdO27GWVLjYwCQ1vyH75Z8Dn/lfDq3+y0MtMYXkOXkEowV3/7LYqK9hFXZ+jqIXT9NtVawykFfXu1/DuH6VwZGJyjy3a6CtH0im2P/jDQzj048PY+bdPea+zOo3JSGrOStKbXHGdkVhxU+r84XztSIyI0Fks2UxpDzWe53jqfdvAAr3nxh4vv/dHEfS49AMCX703jut+WZzePb49jfMHDuEfDm5DjLO6nLMoGxqP9I5i/EnfphBu+p4A7AwDy7FjXnSI20I6NCVOxo8fM3DjL5P1lZFGdMxIjMIet6CliqNjzw35rkD/eO3rkJ7SoHcaEdZ4Vf6+cU0m6eVzdV6HmuvvPWupR8u3C0Cf83gMQHfgtSUAFgJ4OYC/BnADgM8EP9zT03MtgGsB4IYbbsDLX/7ykEOePfT391d8fvPmzfj7v/97/PVf//WU/d6g4Rx8DMAT/li4cCGOHj1adDO77bbb8Pa3v73h77Msq+x7FNHyj//4j7jjjjvw4x//GAcP+r19Dh8+jPe///14xzvegcsvv3xaxvL8889jZGTE+/vxxx/HaaedFmqbYebQ0aPFst67d+9GMtlkQ88GsRcxiG65xGfpNhiVjuKPFq3Fi8YHcVwhXfT+w//dh8P/3YfkqQms++Za73nRxdE/0g8y1nzYZNuudgAyipg/LgdQArO/djTBEBzPajks7y6OmosujoH0AEg+RBhnmpmr1yF7EYPokEqCVKfIaVm4Rsll/28lAMCM2fjadcPeZx7cGcfNv2jHTe8YwbqljvhEF0ffYF/TaZEu3BJgp9soGBwFWsD4wfIeXdTimFiYrlnbKDo5BjMDIIVw4xFcwD7JhqXL35kekefU3cetwuuf3e2979CPDiOTzmDFP1dOuRadHEfGjoCkq4+n3jlkr7blIXJ2gInJZfp33bQHJ92lQW/3jV/RxdE/2BcqWvrsgWUAgKcGdKQXyX0zfs84Dvy/Q/hb5z39rS34Uad/vbnj8Rg+/d4B6CVZaqKT46h9FPRQeMkBe7ENQgiyh2S2AGcc7FSGw/2HIZgAO56h70i43z4bqXcOWess4DgAmowApRP+veKGn8rz/mUvHsf5p/pz6/Y/tOLBHXF85dohJJzMVNHF0X+0P7RKBOtgIBcSpLXie9YjQ11YlxvHq4cPYOfxq7w5Vg3RxXE0PwhyKOR1yOZgJzHYRvk9bFw30MVMcFtg9w/3wFhqYMEbuytsBRDdAlbMxMShysGH2XrfWLFiRdXX6nHORgG4BR8dAIZLXvtTb2+v6Onp+SOAD5d+uLe39xsAvuH8Oa+WUPbt21fx+X/6p38CAGzZsmXK8q2D2y09wG6UYc2aNWVG7T//8z/jQx/6UMPfd+jQoZoTSRGe4WH/1Aru63e961247777cN999+EjH/kIPvGJT0T2nYwxEEJAafFV/6mnnir6+/LLL8fjjz+OM844o+nvCjOHKvUQnK75OHT/MKwRE9wUiC+OQ8/J1fEs1fH+dS/E/zvwBDZlRhArCSHknsmjddBPJigMFLDokkUw2hvrbxWkrc3/jsSBFPSUhvHttaPhMcFx0/cX4vY3F6dbFgYK6H5xNxJL41U+OfuYq9ehofuGkNmbA2yOlvWVE0z2HYoXzZc3f64TAPDSjyzF996exuWn2CgMFrD4kkXQ25qfQwAwsT+P4UcHkFiRlCvhrHx7CdvGZ6/O4q+uIFjzqkUVtxPVHLLTNgZ6BxFfJLdjbZf3sP2JNmxPduCUnB+RGv3VGE5694mVx3MkjwUXLvC2U4l659CRpwZA49RLbaRj9VnGfR/qR1dPB5a+eqkzpgKWvHIxtGSYPqjyvBcc3hzZ9539Re9426HdOHfoCJ5o6ca3lsr988SWTly0vjikUDhSQOfZnUjVIVE+GUfvHQI1KFLtKaR3pZFckcDItlGkVifBmUDhSAFLr1gCLTF7esBGQaNzSHCBzK4MWjeWn/u5vhRaF8ko0UiW4JN3dAAATv6blXj0A+NY081RGChgyWWLyyJejXL0kVHYAwUYHUXSEThwKI5bnvuT/ONe4EMdJ+ArV1UXtisM5NH9om4kliWqvqcesvtzGN05WtTc3WVvvBVrChkYJsPQHdI2WnXhSlC9/Dy0Ri0kVybQsb6j4vfMxftGPVebBwBc4jy+FEAw5+p+AGc4j88A8ByOIWayDqeawpYQwqvJOf/886dzSIqQBOXi77nnHgwNDeEPf/hDUY3VJz/5yci+jzGGVatW4eyzzy57rVJvvL/4i7+I7LsbJZ8vbsBSKETYbHYSiEbATQGiy3POrTmzCEWBavjomrPwhpMvxuFYubFTXB8UPjel0mm//V92eo+3tXaVvR7jHKO58g/KtMZ5tV42axEMEBavmQr0dL+Go2mCo2mCT/+E4uyJQS8f7ro7nJrLCFTbdh0QaH9bDJ96uMMLaFRqSrvILuBVh/eh/xt7a2xNRDOHSn5Xvl+e33tjrfi3VZuwO1GnWish0QkDlPwuVpD3+0qGZJDxrePY980DXkNs4f0niiHJIzb66CjSO8v70K3PT+D1Q/6i8USVqHhU9TnC6ePFcgyCCXDneseZ0/+OH+PKjW4rhriG9o2+qFVwrr/nxy3ImsBEHjjhU8XOxfd7Y4HPhNuR3/+dwKJ/bMedu8qFYkaz/jzptE3cuQV47I6jVdVRo0qNFZxXjaruTJU7Wo9c9SjGnqywGEmmpvZ1JpnUOevt7d0K4EhPT89mABsA/Kynp+c257UnARzo6em5B8C7ANwydUOdfdTjnO3YsWNKvrs00uFy3333ob+/Hx0dHXjDG95Q8T133nkntm7dOiXjUjRPUIDjJS95CRYuXDilacD9/f3o6+srqkl0caN4L3zhC73n9u7dO2O1Z88880zR39PpnNEYgZ2x/VQyJs97K2DRWlTDdxavL/tsenfQgAqv2hZ0zird0/546ekYPr449cMQHH/ebWDHEYq9w/6YZS3M/LqhzVYEE7KB9CS7++R/68DJ/9aBxf/zLD62fyu+sVuuhaaMUie/eb74E/n5bz7T5hUmTtZ3LT1RzeMh0bU7ck+vHAPLMBCDYHc2jmEjgc+t3Fj3dqZK5IY7ztm6v1uLzrMrr9AH2fOl5wE4YjAR7SR3Kzs/savm+9z62Gt+2IKdR2jx4gxBdA6TIwAC4QjduM6Y80+w+Wc0R0Fp78nVH+vEaTeVz6lFrY6NGUFa6Ns/JY/D3/66fKGjEBDgGdVj+Ni+x2H98Hnsu/1A5Y1N1selSYLXoWeTHciXrhoJKcRTaTzHnFojAPT29t7Y29t7QW9v71t7e3vN3t7e9wZe+3+OUuOlvb29R2ttZ75RyTnbtGlT0d+nnHIKvva1r0VuBFWKnA0MDODCCy8EIIU/Vq5cWfGzr3zlK3HmmWfinnvuiXRMinAYhjH5myKkWvQV8J2zV7ziFV6aLiBTZaeb4eHhsjTL6XTOAKlm50bO4uPyu82SBZIH2xeXfe6ZD26fXGGuAYpKjSocvqVtHIWTi50zN93y/C+1o+dz7QHD4Bhf1Z5G9A5d9jyqsp63ITOCy4b9OtNNWVnvucLMoYVZOOe46Krdg6e9Fne8s4BRtDNZ3rbiC7/UsO/2/ej7ZUmdtYgglIfiTZhDcoKy9pg32CG9OFp14HsHK/YUFK6zEAGlP4s7UQS9VcNJHz4RnS/srL0BdxxR2rF1/rQW7s+X877Ujpd/NZBOF6Vz5iJk/ZCwnSnhRM3k/yP+rjmEEKh4/McrRDTTFeo2izL4pkbnBnaG4Su993p/j+kxnOgo/A7+brDi/Suyps8cRfvHvccCwHOJNgwa5dko+YN5b6HE+9yxGDlTVKeSc/b5z3++7Lm/+Zu/wS9+8YspH8fOnX56kxACy5Yt8/7+yU9+Uva5H/3oR1M2JkXj1Gpe/h//8R8AgO7uygWxzVCPc9bd3Y3PfOYzeNWrXgUAyOVy+N///d/IxlAPd9xxR9lz0+2cJVcmobfqYHmGzgOj4ACeSskUwjedZeLaFxXACMV/LimvhSmS1o8wclZ60799yQkYyxOkLihuQh/jxUbsXkeZS4ho+i8pJsc9bpUMiMW6hf/Y24v39W3Heqe2Kk39GrC1+QkwHjjYUc4h97mAJbA/3gr9facUvd7/52H0/+8R7P/WgeKFxqijMADMIWkM7rF9wyxLi2viDv+0D30/LxfkIhRg9fZnqwdnXw38dgC5AzK12q2finXXXkxjQQXVyNIa63vfW04pTnl8flgrMqYjUdh0t+X658EG6W7ULBrffd5RyTlzWdzKcd15cq7lLKddSoQKm0L43z3w2wHs/treotffeWau6O8DD1VII4wyVze4Wadk4KOrz0CBauirUCoAAM999fniJwjmXYq+cs5CUOqcXXnllTjllFMqvvd1r3tdpBL7wRukO46gwt8ll1wCwzDwi1/8Aj//+c9x1VVX4fWvf33VbSimnkKhgI9+9KN4/PHHy177zW9+g1/96lcAgFNPPbXs9aVLZXF5Lpcre20q2LZtGwBg8WIZDfrOd77jvXbllVfWdCSjRAiB66+/3vv7ZS97GQDANM1qH5mCMfiPx7aOgwigL5bEoHPjeOkJFs5cJVeqt7T5jpEbgcgdzge2Fe6cqyTSZ8ak4fr7zuV40XE2Xn4yw853neG9bpRYYs8PBZTk1CVgytl/ROCTv4ljhOvQ4sW33B98cwL/9cQ93t+vO7oP//58L1oDkY+b9j4KLT19ixECwJKe4tSnvxzY4z0ONmMOGnpR4UbODkKKDZyxwkbCAN5y0kVF78vsLr8GEUrqb57dAM9/zU+lii2UNTsr37ICXed24uSPVxYnYZlAtHOKoh4upLXYeX3TM9vL3jMU6JEW2b3fTV/kQqaVMeF5ZH7N2bF5kbnvSYEv3p8qO0fSBeADv5TNzU9aXB79bUsIJB2/P+8EraYii5AVGJ7/2j6M3Tdc9Hz690eK/r794Qo1lhGNJ3hrKgwUvJTk3taFeP3pJo5Ucc6G/lw8ZtlcO5oxzRaUcxYC1yk6++yz8Y1vfAPf/e53sWLFCtx555146UtfWvb+q6++OrLvDl5c3cbSrlToggUL8MMf/hCANKRf+9rXApCqf0EeeeSRyMajmJxbbrkFn/jEJ3DWWWeVvXbFFVd4j1/84heXvR50zqK6sVaqW3SdobvvvhuapuGVr3wlADmnOjs7vff9xV/8xbQ0NN+7d6/3ePny5UgkpME23ZEz92a069+lrPcKM4c3nGnia1dn8BebLLxuk4VbXp/F4XgL/nnNC/C+defgvvYlAIAnn3TusCJ8GmFRSpqzgq/Z8jhcvIHjXeeYIAT4yysNrH7XKgDAOSuK01Lue073ftJ8a9w5G7n0HwVu/n0c/7hlIVqO95upP/OUifW/Kq5Jvmj8iJfSGKRtJLroayUHP5/250GqjWJlZ/Uvye4tXiCK5nLkhwZc52xIl+f6G880YWgy3SoIy1W4/lAia/uiGJGzS0pr2Ny6GKPDwIn/fAI6zqhcf2a7TmyE/km14EAsVXwtz+1M46VLi6Nn7nnvFMFFMyBnUMKWUTM7w7ygirffjk3fDBfcIHDTfS341TPFEdYv3JPAQ3vlsdi4rHwOdyQEErrcaXnbDblHd8jczRT66ruHPn3EwL17StVco0+J3/Uf/gIQCMErT7UwopeLl1SCEKLSGhU+rnPW3d2N97znPWhvlyvll112Gc4999yy9//+97+P/LsB6Zyl02nceOONAIB/+Zd/waJF5dLHxx13XNHfW7duLZPaV0wdu3bVLuJ2qdTqoLu721NzjMoxqZTW+NBDD+HrX/86AClV39Xlq/8Fnbnf/e53uOSSS8o+HzVbtmzxHo+NjXmiKdPqnFXwXx5v6cb6hQxvONMCIYBGgTe/QBqVT7Z247lkO/picnU0fdAZK4lWEAQAnjpIoHEBBuB9LzUR9LddCfAOrdgA+LN7o52K2hNFGTscxfOtfcUGzsTR+uvI9JzvYIddnCmdQxPbJ5DbJlOXDsRasO+c1c4bK39++//bgdyhvP+WyAx9p5+Y45wdNeS5njYJ4nr5d0xsm8DDVz6Cx//qCRQG5TlGnH5SUUEIKYoUbviPypkxWmu5xDnLMudYRZeTVm1X0wpS9S/vLxZzcM97EmkqqpC/zuIQloCdZk4kDTKK5tadHcMcHC02s7cf8Y/VugUcb+spvpctoiYSjj+XK1pXi3YO5et0znTB8brbW7HziP87IlNrFMK7IGV2FS8mLO/gsCvJyDo8ecNTyO51Fq2InxI5X1DOWQhcB6lSBCKVSk3pd5emNQbrgHp6eip+plK63GxszDdfqaaw6falczn++ONx4MABLFzop8itWrXKS+X7n//5n8jHJoTAwMBAUdSutC9I6ffec889dTuczRJ0zm677bbIHdT6kTeQ2GL5/bcvPbFiBKIl5p+Xhx3nrGVMRhqiuHWUfuenfuXsD6phw7KSIumYfPOatmInoG/cmYfKOZtWSg1ry6x/548OMBwajSaXqNQ52/Yz2TbjiZYuXLf+RViwSBqPnS+oHBECgP5fO6lPESoRurg1Z0OOc/YXmyzEarR1M4+a2PruJyG4kCvoURlpzu+y0/L8iS+No/Wkyj3qTrt5A9a97zgsfOkCKf4CABzgOUfxYIrTGq2RctGGTU8f8GoYgcB574wtygHxAgc3OQTj0mETjpy+AIQlInWY5xqlxyym+c+cQDO46aXj+NyVWbTbJv5tby/+9s77sWS3VEW+9f6EKw4cOX8oF2muSMKpWb7hZ0GbNqq8xsozuiPBsbhVIE+r93XLHchj3385CxB0/pXpKOcsBLWcs2AkLUhUtTqlaY227RtglfpWudx5551obfVvMMo5mz6qOWfj4+UFtytXrvQckUQi4dV+AcBb3/rWSMYTjL5yzvHNb36z6PXS+XvRRRd5UTUXtzYtKjjnRbWZrsjNN7/5Tbz1rW/1ImfTWXNWNL683GfDeryic/bdt/mrf24xc3smD8FEJApXpYb1AkOe93mqIV5iwLqRs/E/H8UnThtEZ1KOPZ0HJhzfNkphAEVtSnd1bqL2zj9ynC/+c13/Tnx1cyKS1NhSDm2TiwffWbweIATL2n3J+BVvXo4zvnl62WcyRwPOQARzKJsDCjbQ/5sjGN0yCgA4qiew5R/HcVw3R9w1aGtYLNaYcw+M2EizHZlxvaW6oRhfEseiSxZh3d+uxZm3n47EcpmS6fZriwpepcZv6WuXVNw3Fy307Y3+wKJMpOe9kD3guMkB25mfQnhpjePbJzD+1ESEXzi3KA0cGhqgc46P7nscK770GLa+YytecoKNzz2/BadnZErzknv8Gsd9I85xi3bNAb97oj7z/31923HRaB8Gxv25F5Va46hzuxx5ZLTo+ac+NI64LnB3xzIcSFVeEAH8FGMCIiO18wjlnIWglnO2ePFijIyMgDEGzrmXUnjzzTdH8t2lzplr0F5//fVeXU4lLrvsMoyPj+Oaa64BoJyz6aSaczY6Ouo9Xr58uff461//OjZs2ICHH354SsZTGn0N9lkDUFRj5nLddddBCIG3vOUtAIBMprwRahhe8pKXoLOzE/v3y1wwd366LSpmIq0xmJbj1rnkqVaxSPui9TYGPjWKI58aRe8/pzGox6EJgW1PW5FIl5VOoYRTb9beXj63tKRvTF746C7s+vA4XqIP4/vP3ov9d4/K3jDzbLVxNlNqpOWr9A7b94KV+NTlF+Gif1+Hzjet8p4njEeiDFC6iXZLnksDjmz1ugXyvma0G1j5phWIL/LrPn66RvbyK7hORwTlS7Yt0Hm1ho23LcK+b+z3nh9PxrG22xmLM5VjFy6puh3BRLSy9e740vI4aW01wncOhBBQgyK1Vu7L7POOcxThaVZ6zq6/8Xgse+3SIhlyl9dt2YbH3nwYANA3RtxBRnvecwGe5+CmAHeilrzAASZAKAHLsCnrPTcXKHWoDQ24eKwfL0z7JSWrOjlWmH4tJwucpF6Puoijr0lef1r1Bw9tw0v6DxY9F7Ze+ck9Aqv/rgXX/m8bnv2Un4Ez2JZCKgbEdCCn6fjQqedU3YZ3baLzb6FROWchqOWcuc9TSkEI8XqOBQUOovhuQDpnroFfyaAuJTge5ZxNH9XmyebNm73Hv/71r73Hr3nNa7Bt27ay3nlRUeqclfZZO+ec6hfFlhYpbBC1c3bvvbLfym9+8xvYtu01S3dTLGek5gzSoOW2rKsQBCgQWjFyBkgHSqPAsnaBw3GZCnLkf/rAbR55zRmfkBEMUsFw1AIiAYTKz7772afRZZtI37pLbusYrweZTkqNtKefL9/3PT86C1d/ZCl+dV0WHSmCJa/126G05/ORKFiXTlu3D57lXJ/OWlXuNJ7yqZOw+p2rsG2ZjOCz8UDkLOR4xh3/JWsVXx+T3bo3392aM/LaNVj/weMrbke4qXMR1lMBflpjrchZKfGlToTfqZ+L0hkqTdts39QGqlO0byzP1AGAvg8/jQQYhrIUBdu5hkRcn2NnGGILDJlZIKRzZmcYaJw6giHzzHJugKBfmreA/348hlZWnIY61jta9HdyRPY3BICj6alp+JzgjQl6vfn5Z/0/IhjSd++SO+YPzxaLfjzwkg0A/PRP067+ZW4mi/xjft3LlHMWgsmcsyDvf//7AQCDg4ORfHepYe1Gzjo6qtcJBHGNXeWcTR+V5kkul8M73/lOALL9wZlnnln18x//+Me9x/v27av6vnopdfBLWz3ccMMNVT/rOmdf+tKXpkRWn3OOn//8597fS5bIFfMZcc6cc829Edi6BhBS1TlzIQToXCXH2/b4AI78Ovy5X/qdS/pGAQBaR3nPJS3lO2zESXFM2iW1KVNgMwkhZIqTooigff7oAQ0H+uQTh05fhrYNbTjuujXQUlqRUE9nUuDZNnlN/8MDHBmTRJ4a67ZaMAnFK0810Z0q/4L209qx7LVLIVrknLInbO8eFEV7iMuGD+Ilo33F3xkocXFrziyNYsF53Vj2uqVl2+GWiD5ljwAs4zafnjxy5qKlpCPHcm6zr3DDCN46CvnijVGnU/Hxf7e26uf/7YAsMOofp84+ik5cQgjpwOrtBnhB/l5W4EiuTIIYcr42EKSZdwRPj28+JO8HsZJJ+uyny2u3v7LnIQDAbQ/Ene1EKwiSrOGcLXzpAhz33jVFzw0YCTy8z1mgIAh979CcOR0vGUd8ufy9bpp+hV7zHp5iKwEa9DVnPco5C0EjzplbMzQwMBDJd5emNTYSOQN85+yb3/xmxWbaiuipNE+CPc8+9alP1fz83/3d33mPKyk6Nkqpg+/OoVgshoGBgbJIWhDXOdu+fTv+4R/+IfRYSuGce6mNa9asgabJm4LrnP3Lv/wLbr31Vtxyyy2Rf3dFiH8jsJ0cK0rquFku8lOMx54cjzxy9tI9ewEAeq7c+glGzmhMPjZKqsujMmQFF2B5J+XzcAHDD49Es+F5RNAevv85HV22jKpsOFnHqf92MpZcvrji59aeIb2UC8f68dPt1VPW64VAYFN6GG22ib6f90F3BvayUxm+89baCy1GQkOeUGhMYPPTJBJZdpGz8b6+7fjAoeL61YuO9+d0zLEJP/XbBL52Xxw7zlmLM765CQsu8uvyhC2V3yJzPJxzgzvzmibrN5fclGKWY5GrtI5PFG+MGI60f6eBrnM6K37+pPFRAMBvd+jROrCOap+WoCAEYAVHDCTPPYEWCEDY88xyboDg0brfaWew3pi8X+liKw8IgT/vNvDMoB5ZRJgLwLTLI2ep41NYcPECnPiRE3D8363Dklcuxmm3bMSpN0mFUoNzvOq2Nu90DzuHXHMo2Idza0s3Xn+GXEDUKUCIAOPVV0HdezKJuj3ELEA5ZyGYSeesNOoxNCQVt4LS57U46aSTvMdu82PF1FJpnjz55JMAgGuuuaZmGiEgo6JuBDbYcLxZqkVfb7nlloqtGIK4zhkA/Nd//VfosZTCOfeEUtz6SMBXQbVtG3/913+Nv/3bv52ymrxS3BV0y1nGnyxyBgDGcr+Jpt6mRxr1sAMS34ll5Y1CiyJnTuE0DRiuA1ka2f0sdzCP0V45f4TFj+kak2oEfQbtj4fw6mGpNNbZXfv+sfFSGTlbVcigb4KGNkIW7RjEv+97FP+5+37s/7a8jjAQfOSy/KQlbS0xgYRjTA3ecTASWfZqfckuXu87ZylHBfX+5w386/8l8ZbvtiK+KI717z8eLSfIa1HkaY2AXJRxIuZaBbn6avjOGY9kOMHDcvTJdPFrFWrNqvH1+xIASLTnp5DNv7WkhtYTnWPBuBM9lP3PxDEcOQumMz/TL+fFSxbVl23iRrf609GZ6gLAeJ6UOWen3bwB6/9hHbp6Or3nUquTSK1JOmORB7F/vP75VgvXHIqVBAdOWCT/JgRIVVkfTq2TdgDLOZ+NSqFkFqGcsxA04py5TYQPHz4cSaSqNHLmprmtWrWq2keKOPHEE/Ga17wGAPDb3/429HgUk1Npnhw4IA200h501XBTIIeHh0OPp9Q5m5iQilqVVEZLCTpnpa0AooAx5jlnbW1t3vNr15an7kSR4lkTZzeZw/J35pMyR74e56x1gz92o0MPLwgSLBTfl/cer3/XyrL3aoGV/tFHRnH03iHv7zyhuP9ADJH1zrF4cdqNcs7KCDpnPU887z2erJYp6bRvWGjlMVoIf8vuek5eO9qYbzFrEGiLT37MUoFWER3PO9HRkLczXsUjbA2MZ3VX+Zd4PaCceXfgB4ci7rsmcSPCWqKJyFmWRWI3Bm8dI4H6pDO/dXrFfpWVODE7hgOjFH3p8A6+h5exKf9LDQpwGamX0QwZOYuqMfhcxN3VjAP9EwRLzSzMrfVlFrzhBFnTPZKnkS46pE3fOVv40oU4/dbTqr6XxuXkSwgOKgSeG6KRiMq498+YYIHnRJHqsKscG2TBhd04/h/WAVBpjYoqNOKctbW1YdGiRcjn8+jr65v0/ZNRalgHU8Dq5S//8i8BqLqz6aLSPHEjYKU9xarR3S3TeKJwzkql9PN5aezXUvt0Ke3jF7XqXzByFnQW169fX/beN77xjfjJT34S6fdXwhyWaWjZlDSW67GJjltOsKVV9qujsfA32OB3HvytdLae7lqAeFf5EqMrpe+y5/PPeY/TmoEjaRpdzVmwjoUAYp7dKKOgmgy61lK7lsnolse22zYxlg2fIldlGIhXz2L2SBrAZ1ZKQ25Ud2thQo6nShpi0Flcu6B8op7ybx3YNUiR2S2jEONPjEfau084whaDv5eqepUaPVfDrTnjbs1ZyFRL77wXAuRpadjvWr8UsQWx6h8q4QvPy56R/RM0svPT+1WBScXddEbAvy7MM7GGRnAzyQfSBIwTXD2+v/YHAiyCvOeMFqITBRECKFh+JGzxyxcisaz6PZ9QgpzTbyzOmRf9C3tM3ZqzYP2dVvIz3QWaw05bmptWnoYPdpwG5qTpuw3iCSEgTpR2vqCcsxC4vZbqcc4A37DcsWNH6O8OTsLx8XEMDQ0hFot5wgn1oERBppfSeZLJZPDd734XQP3OmZu2Ojw8HPpCVOrgN+KcBSNnQPRz6Oc//zm+9a1vASiOnG3YsAGrV68ue7+70DAVuPcOy4mcpRPSKK0ncra6i+OBDpnSbBXC3zhcmyfGGQp/ko2ASY1amOVXLav4vC64TJGL0mgSst8REJ3gwHxk68Hi4+Ua8tWgBoVoM6BBwBqPIEpdZVXBVUSsRSomsCspF0sSmUIkzlC1Fe9g5OyCdeV5cekCwX8+EC+yYrjNIxUEOfj9Q146s9ZQzVnAeCTh0wjdI3buxCD0rNwX9nEVMhzquCb1ZbRoz08uir+XCS/aAiL/PpavB+5Pv+1+J/W8tY5VEIcFXNqYY/noUtAFCPK2HzmrZ9HBbQad4AyPH9LrWpicDOrcQINpjVrJKfb2s+Xv/4d15+BDx70A97Yvwb3PxfDMiFyU8CJnAGT4LPy4ZgvKOWuSsbExL8WsXufsxS9+MQBEssofjHo89dRTAIDVq1fXPRZAOWfTTemxCYqBnHvuuXVtI5lMIplMwjTN0DL2pamxjThnpamPUbWIcHnggQcqflcqlcLu3buxbt26ovczxtDf34+RkakRorDTNg7+QJ4n9w5Lx7SeUy2uA8sWypvQ4ZEI0pucm2IqIH+WjFe/UybXJCs+n+IM/WkandFECMAFRraMoDBgettlOYbs3ujVPOcSpfPkvieL93k9Eu20y4mQjFvhI0NV5m28jsBQW1xgyImYdZkF2Cx81LzSx7e0LkRrwn/htOUMez86Wva+zmSxEqI5aEayei4cGcKj9/i9qGiy/siZ7qinmiPSmY5KPOGknK+o21WhVjGxtPa1+7q+HeifiDJFzolWBC5BrSe3IrHCH4fgmHf1QI3g/vIdR+T8WRhzIlaXL8Lyq5aB6AQnfMhvD7HybStgOJkQHULOn5FctIIXBduvZ6tn0cGNnCW5jf1uU+ywc9qZM0FBkN8vK154fccLTfzmvRNIawaeaun2FpYKmsw24Dnu3WuEUJEzBVAkQmDb9VW7ujVebu+mMAQnoRs1cGuG6mXp0qUghODIkSNTUjekKCbonG3evBl33XUXAOAtb3lL3UIugO+sNHq8SylNa3Tl6etxzkpbNkQldFOJUkfQMAx85StfKXrOsiwsW7as7ghkvVhjFo7+echLbQKAPifFojQFoxqb1sj9LH3paNKbksxfMUxq1bdJqoT3YoJjcDy8LHsRQjYCFrbwas7MYQuZPdH2wptrBA/Bowc0bHm8OFRk1JGaZnTL9yQyhfDHrMqyt16H75E0BCyqYUwzoENg6IgdPnJW0nNre7IDn1m1CS0lAYbWOPCRS4tV7m6+J1H0/daEHdnqOcsx2IFG4aX9xWoRXxgDMQisYQt2hkWW1sgCXlB3d/lxXPHG5VjyqsqqnwDw6uED6JugDf2WSeEomlNUp0XXHcHnXz1QIxwapdjeT/GHZ+WE7tku0xqTK5JY9faV6PnxWeg8uxPxxTG0n9GOFVcvR9e50h7o3ivLF9JWhCnoQNORsxZuy75rESh+uuaQK6W/O9GGP8bKhcjOXsVw5Uaz6LnXfbsNeSI34La4IRGoos4mlHPWJK7sOOBHribjhBNOAADs3r079PdXWiEIKjDWg2EYWLp0KYQQkdTBKWoTdM4uvPBCfPrTnwZQWeSiFq2trQCAdDo9yTtrUy2t0ZWrr0Vpy4ao+vdV4vTTTy977vLLL8eDDz5Y9nwul4s0Evz4Xz2BPZ9/Dod/6p8fRwzpnNVrby1ZIA0VsxBdE+pg5CyhV79L1mr+OjIafjzBcQnhypn7xhiNEbA8P6bTmoKRs0u/3obxw76hsebdq6FPktYIAPGF0rBLZc1J3lnPgJrPSXL7jXU4DXL7/3Nv6DnESlL+NncsQYFqFSPTf3dRAVdsqL4PRJ5HE2EQQObZ4kWF9tPaqry5HKIRJJbLRS5zsABuhbNk3SPGAk7Q0oXlO0hLaTju2jWILazu8PdN0MgiDG4j+1ppboKLeVcP1Aj/82QMF3xZLjCuLPhzyk1npgYF1SlOv3UTTv7Yic5r8ti2PTOENttExiLgETbyNm0gIeoXuhmJybm80CpgKENAKAndWNy9DJ2ZkQ7oUSOBy04uDxJQCtz+lvLsi6wTPXNTGwUi7nE4wyjnrEmCBuC2bdtqvNNn6dKlSCaTGBoaijTq4XL77bc3vB2V2jh9VFPVctNj62UqnLNHH30UTzzxBIDZFTn7yEc+UlRzFmT58uUVn48iMu1y9E9DZc8dikkxlG3bbfDC5HeDJc4KN4+g5sy9oSUDztmazupjCKqkla6opyd4WdQiFNyvNRPcMca4rEOrZz/NV0pP+3V5ed4ufd1SLH11fTXCiS5piCRMK7zzEaJeJFbiR/K9mdBRoVKDaliP48F/GK/6/rNXVw/DuLL3URCMLp3zy7NhtNdfKwQAMUfIxZpgoQ1r11ENGmzLu5rb7/0TEUZhhHP8as0pJiDE/KoHapa1hq+wa08UZ1wRjXg2ghbIMW5nFrIWAcuGdPADx+jr98W8tEavPrAGWafn6YcPPIGRHIXFyyPejUIpQIXwWoqcOzGIL19VPQX+jBXF+ytHi50z4nZEnyco56xJgs7Ma1/72ro+QwjxjNooDWtARhIqKdlNxrJlUjCgv78/1HgUk1OpHjAWi+H444+v8O7qROWcBR38K6+80ntcj3NWGjk7fPhwqLFU421ve1vV11avXo0vfvGLZc+7Pf+mgj8ef5x3lzup2wa3OKxRy1ONModN2Onim0hrm3PDZSx0lMGdQqlAnhCtsdGggbn6nauw8eZTvTq0uMUxnI1IBczJKBG28NXZHGdNmPyYltIuDVSd4NQNtZ3UWvc2kp2uc2YjdCeWENX8MSeF9l/WnOU9x1m4AZWmvPFWw+t1VIn3vKiAc9ZULiVgORbZ6jk35YYWXrygqc+7dWcsY0OErBrwak0D7Q/09hoqnzWM1IE0jbTPmRCiavo04ETOyLEbOQvyV7av1FitYTjgzz1A1oZlbAqWja5Z3MPPyhuJVZKCWo2xRCCbRggM5rTQqbGxkRx+vOMe7++n1y1DW42kne+9vTiSnXVSLSeecewg4rZt4KEj1bMB5Zw1iSuBfsUVVzQUsXIlyLPZcEXypRe6RmqWgriGftjxKCZH08rTl0ol6ethKiJnQepxztwxuOzZsyfUWKrhNm+vxnve856y59xm2lOBlpZW1scvz+F1J8hzJt9fQO6grIXJ9xWQ7ysUfSaRkjc/nfHIgh5BI62Wsb3gwm7El8Sx/KploAZFy/EtngBFktvIRRDN88YgZL2ZYNI5E84/bvHQKTBzmVIFsoW2U9u5dPL0YRfDMcTbmIVsYZI3T0bJeGwQfO74TXV91E1r3Nq6AGln5doeD2c0lqa80s7aNXgxHXj3i/yd8OOFflo4y4ev75KDAphzbtQTWaiEe8xYmvkNspvEPcVbnIi5fnon4ourz5/gdWbj508tei1nAeAC2b3Z0E6aEJg0IiYYVOTMYdEuWbvc8YKOmvL1wUyDFLORMUlR/WMzeNFXIfCKUbmYatdTaArgT4H+uXHBsXdUCz13uh8+7M1nANj14trlHcvaBTYs9feBGzl7/qt7vecEFxh9bAxjW6tH3ucKyjlrEjdy9v73v9/rPVUPU+WclUYypns8ismpFDmrxxEqZTY4Z4SQImdz165docZSieOPP37SeZ1MlqsRTqVzdm9CFiy/+9w8jDgFyzFQXRZV2xM2iEbA86xo36ac+gGDc4iQYQ8tb+Nzz23BjYf8VOpVby9vQO2it+g4/bbTit7jqs61cBtZM8L+Oa5DxgQEB8whSxpmpkB2X27yDcxTSk/7DlvWTLmRlXrQ26Qh0s4sTITdlSXO/F+deD7uTdVeBHG5eL0fAvJrPqKNnK1aN/l+yVv+b/je4uNxd4fMADEHzMgym9zV92rOmZ1lNaNBRoeb1miHjhx7zpmzKNN54cLaHwh8Xcv64rYnZkGAMyB7ICfFSkIihKhpSQqnJk0FznxWXF25xYlLUGW3hdvI2ATmcDglUveMef3RvXhv/04AgGHVd/xvuNR/3zVHduH5kfCRM97hL8LkCcWG4yb/TC4QgQ6qPLIc89p6cJODFea+Ao1yzprEdc4aVYeLyhkqrTlrNnLmjieXO3aNp+lirjhn9QiCAMDIyAgOHz4MwzCwb9++yJ2ijRs3TvoeQgjOOuusoueCYj1R83DLQixq4YhRgGoE9pgF6BSJZXFk92dBNACUgKX9m0OqVR73GA+f1rjg7r04JSCnnX/bCUiuqD2HSmsd3YjN2vwEchE5Z8S5MQpbSptzW2BixwS4ycGZgHk0AiGLOQolQNIxqqkQaHPENPS2BqTZW/3IWSY/yZsno+SQ54kGxuubB6u6BJ7+5zH8z7vSKDhqaaGds0Ckq0AoTlo++UnyipMtvy8bIZhwHMUjvxmIRHxGCJmOCwC0imBC9rlM0Xleipt2yDI2RMjUz9IWGm2dtU23+BKnF2MFmXRiysbYUaQ2EqePWbWiM0Jliw0IHNONqAF5vXVpPbF2SvOil/rOdwuTi2jCCle7694GTsmNes/lWuu717/pLN8res3wAWw/GoFz1u47Z+N6DKcunfy33fQa306NB1Z1rFFnfCLwb46jnLMmEEJ4NTaNOmfuSn9YZ0hFzuYelQRBwjhnUyEqA9TvnLW1tWHZsmU444wzIITAo48+Gmo8pdTbUH3Lli0wTRNf+tKXAExd5Iyc0A4QgqUdUn2Q6ARamw4tSZFYlgA15OWUGsRL5wOAWNJvthm2Y4VWUndQuioOSDn7Wjn3bafI+XNibhzZqNIaAU9KH1x4hoSwOcAE7Ix9zNacvLJ/H36640947dF9uGzkICgAO6GD6vXffuOLpCGzxMxhPBu2cLH4OuTKZNfLkjaBF6y2YTqfy2XCOmf+43HNwKLWybe3oEVgz0fGMPjpUbxmo4ndSb/dhjkUvi0MtznMIbmgUDWtkZKac1pz0odZloPlOIYeHG56PH7kTP62ZHvtY7b+/euw4IJubPjMKWWv0bwtxRwicJaITsHM6mqNRrchr1ERNCufy1Ah8JU9D3l/k0n6sBCNeAJOSW4jXSBOL0l5jbXGmp/jHbb/2bvPKZ8f9bD1kAFh85Im0I0RrHX7+YI1dZ33LznBxsGPj2LgU6P48wo/G+SJ656CsPi86nWmnLMmGBoaQqFQQEdHB1payo2jWsy2mrOonbNdu3bhwIEDkWxrvlEpclYpLW8yXOcpbG+6ahexSrVxtXAbQh85ciTUeEqZrN7MRdM0ry0EgClrRJ139suydrkSTAwCLaFBi1PQGAGJyeJqYhAUBgpIPysjm1pMHve44MjkQtZ4lBjWHUvLU8DyfXmkd1aPqroNTluZhZwVPnImhMCjzxOk85C1OiYHNwV4XqpBJlYmpcMWoQjBXOKt+54FALznyLP4m74dAADeUkPMoQJ6h468rqGV27BGIlKXgOybZTchENISA7izGDE6Es45C0a6TEqxqLW+eZIwpL26uFXg3val3vO5A+GzQB65+jEM3yevI1oV54zQ2n2VdM85s8HyLHTUo8sq4AQn+qJPMn/iS+JY/4HjkTquvKY5yRjyVnjZ8eywjYd3E5jZ6qqvhMhrJCCO6XYaS8zG56Qrtd/CbFiOIqZgArlDeYw92XhNlXuadzo1r+9efx6OLqi/PUSQ0XEOluc4eu9Q0+IblDi9MAnF/y5YjQUt9Z/3lALPrl2Ko7q/kJw/YnqZG/Mhh1Y5Z03QbEojMPvSGl3nIArnLJvN4sQTT8Tq1asnf/MxSCWnp5nImevkMRYur7rUOTv//PPxH//xHw1vJ6o0y1Lqdc5cVq6UK2n79u2LdBwubo3NsnYZOaM6gdaigcY02avGkM05qUEhGEA0inxfHoQSmE4KWCZkUTcvWXHtais3rIUtavay0lLyd6SYHUla450PARd8OIbLvtcFwaVARMvalIzg5TnkTyfRNr6d45D2yRtPF72fEIw412r7SEhFkIAzpkHg6jMt/OAvGz93mRP5y4WsWwpoAsAitG4jzWV5B4dNKQ4eL+tBWSa8qt3IA/4CD40XX7e5xWFnGQgFcvurG92a40C5Sq7CbH7+X3DkEL7/7L3e367jVy9rr1/jPW7hNvKMOM5Z82P6my9wvPr7Xbh5a7vXl6sq86xBcKOsMjOTv6kEzznjNhiH07bAaVXSxEKX75zJiPCLTgM+eElzOdIX798vr/Vpu0hZshGoY39saVuIlphAqrFLIpZ2Ak+0+HoPhALCclNomxrSrEI5Z03gOmeuMdgI81kQZKoiFvOZZpwz18mrlpZYL6Wf/+IXv4gbb7yx4e1ElWZZSqPOmdtKYteuXVOS2jBBpLG1vJ1DcIDGKLp6OkETFMSgIDoB0SmIEymjMQLTiXKYjmRfZjxklCFgWKepjo5khd9JUFMe2U23auU2cmGV/wD85kE5hj3DOqhBYHQY0FIahBAwh0wQjcDOsUgiGvMF2oAYiAtzFmVE2LYEJXf9r78hi0tPLnZo7Cyb1ADkupvWGFbxz/+8RSgWtZSfIyxffTxrF8j3jzHnutiksViN0rTG/KE8snuzctGhxnmmtzqRswwDOEL1OnvXrmeK/tbqaFweZPGli9HZI9v4yEUZ+LVgTfLtP8vr4feebS3qy1UJO21j/Jlo7w9ziWYiZ64D/rqhfdLZEALmsOU1/m4UAkDnHAnBYYPgtncUsH5h/XPylE+d5D0+cWxE1i2aArzQXHojcc57DoIFFc75yVi7gBU1ZRe2kOeYEPNiHUA5Z03gyujPZORsNqY1VkrbU/hUchhmU+Ss0RRdF7dJdNSRs0WLFjX8/s7OToyNjeHpp5+OZAxE9y/+g7o8VkvbZR2V3qZDb9XR9cJOEE3WCVAd0AzifNaJpgGwHYc6PxHSOQtEzm5ZfiqMSjYRIVKYpAq6Y9ilmI1sBDodwdOeaMQTrwAHeJ5LgZQsQ3avcs5caGfjzplw9jMLmx5aRxpj/lAOhYHanrtw0hrzYWvOApcxk2oVI2f5IwXkj1Re5V+/UG6gP+s4Z1HWUQJoO7VcvIEaBKCkZu2QuwhiZx0BDiu6ehgaa/xe6zp0KW4jZxFPXTUsop6u5gJg6ej6dM01Ftj+ubT+g/X1NQ064C+cGISVYcg+JyNwzaSkEgLEhNOPk9KKNfC1aD+tHR1nytrOfbFWmAXZT2xiexojvaMNj8cV0xEAulONz8P1C3mRc8ZdwRQlCHLsEiat0TXG8/lwklulF/n29vYq76xNlM5Zoyf7sUalG/Ob3vSmhrfjRs6ids5isQbzChxmS1ojIcRrCP/b3/42kjEEja+DVJ67C+0CsvtzMLqlga0lNFlrRgmgyygaAFDdd+5sJ3KWC5nWGIycVasVIqR2wXnQSMuGVf5DDVtfCLC8TP9KrUrKHjSPT12bg7lEd4VaQRc7bVeUgnaPPQubHhq46/cZfs2rnbZRGCxACOE42LW/x3XOCtnoas4sQr1eaoBM37KzDFqMVL2/nLyEY203wxiTHwwToapEfFGJQBIl8hzXai+CyForZ4HCGVcUdZffXry+qc955z1jMnLGwkXOXOrxN7kpqtalHQsssKRztu59x2HBefW1XtICdYW64LBtwBq3YWeaa7ROILCyIO08izRn+rsN2Vu4jYmsn0bYTB8/20k/5oSgs1IGyCRcfooFhmDkDDBHnAWAeTDVlHPWBGGcs6gM69KUtGXLavfNqEZUziKgnLPJKD1mt956K97+9rc3vB03chZ1WmO96oilzBbnDJC90QAp2hMFQSfnOS4XMhbHbBidOmILAkYbdSJnGpFqjYwDOnXqzwQsN9oZtj4nkEZV9QZLUDPdimgEtk5BAVgR9DkKflPQ4ee2TDEjmnRcWY4h3x+BNzgP6F5WXdAhuy+H3P7y/SScHR1WyCHoTX9vsb+KXxgooDBoQlgCWoIClNbsF+Sm75ohRW6KImclc9ocNmENmdIhqjbdCXDcAu591lVtmyoIBaguncVaiyDua7L3H5wG7c19Z9AIXffi5jIcXGn/BXYBeVvWnIWeS6jPOdOSFIILsAJH7uCxFUFfZObw0rE+AIDR3dwCaIFqsC0OlpUNzZtpzfCGvufwhee3AIC3gNgobr1yC7ORnvDbYIhmgqLOvGFozjlb2i7wp8W+DS64TKMXKnJ2bPKv//qvuP322wEAa9fW7mheiamIepx77rleBGymxgMUO2fzRc40Skr3ySWXXNKUQzsVc2jx4sVNpzVOlXO2YMGChj/jNoQfHm5etjpI0CDcOREDIQKrujiMrphXUyLfJw01YhDQhAYaUG5keQbuOGd2CFEAABCaP6CPXlHF0SGkZs0ZANhxeZMVufCpRkVTOGAzCMYhTOHtG57n86JQu1Eq/WSjq9hI45YfValq8Ds7OnRkKLD5N59jF71AdYLCoAm9wwDL2MjsqZ5R4Tpndgg5baA4cnZfh79AxLIMhYEC9FYNRCNFUeNSOpMCprtoZUYTEQKA1pPKr4lyPstrA9FqyOm7p6pbb8bRtHx9MEr+1y9pbn+nVsso6ZpCGkwQOd8iuE/Xs4X44jjABMYeG0N6T+PiGHOZj+9/3HvciJBLrNuPrse5TA93FXCbuY5e3fec97iruUQrL1W3hVkwEzq0GJXy9c3Ma9exIwRdTaQ1AsBYdyu2pTrldpgAz8v6TkHmvv2pnLMG+eQnP+k9Pvvssxv+/FQY1t/73vea3k6UzlnRqnnIqM58pHSfNJtGGFXkLHi8mokCu0yVIEijkv5A9M4ZAoZynmpYkBJoSxHoLZqXtgTAc4iITkE0QEtqoBpgtBmwx2xPAp+HdM54wEg7ZUU1+WpUjTK42E7uGA1pWLvfVwmvQJsC0ByhhmNw0YZX2EFuFMMlsyfjRRSqpcq5zgkPLQjijyeZKh4b0QlY1pbGNCXQEhTj28ZhDpcXJxpOyq4ZUtk/eBm7b4Evic9tAaJRUDdtuIYb0JUUgchZeOcstU4udq69/riy14hOoKV0zzmrZigTQjwLS1jCiaA1NzAR6IlnVGgsXQ/JNfI3rSpkZEYj40jvDO8o1XNKE0rACnIBIrSgzRzhFSMH8fF9j2FNwd/HVXvmVSB1XMprgB4TDPE1LdA7dKl6G7JWkDYZOXOdyxZuw+xIQG/T5TFtYjxu1JYDdfU4q0RXSuCoIbO/hC2dMwGoyNmxjmsINkLUztkFF1zgqdQ1Q5TOWdBZiGJ7842oarymwsFvxhFyiarvWhS45+TmzZsj2V4wApXTdJy5kgFEoGVdeaSaaIDsy0vkTZUSkBgB0f1V/7BiDsG0RiNWLcIyeZNT5qirkXz45tBB3yO4KcEEYl0xL/2Lm1Llstm+OHOVUucstjCG5IpiISCa0DyDgji1TIIVC0h4Dn7IS2twNK54jfxeABrxosKEErnIYFCvbqpozM4lI+x4XIfluXgrVncH56IAjRFHDVU24a02V7tSwmuKzaNIa3SMzUrCG7zAZU2eE0Gr9V3ueehGRpttJxFsoeEa7I0Sc/obdtomuCAQdjTnYl2/iPoOaiTHZw7wd4e3oyddnF7fqJDLggtl9kjMiZwRSmTtIm88Oyl4pEm1e8ck6G1yUandtmAzyEbsTTqLIhA5W9PV3DzsDC7K2AIs7/YcaGpzswrlnDXJXXfd1dTnoq45C6uQGFUUpnQbyjkrJyrnbCpqzsI4Z1GpR0aBq/DY39+P3bt3h95e0MnJEw1ffF227HkPjXi5/FpC82qtAAHunKZhox7BJtRV6wYoJr2ye5GzAgudahjMoAwWhgtbeCIEhBAYnQYggJEto+G+cI4RVNi0KMXpt51WZKRZYxb0Fk068VzIHUqAiR0TmHh6oizawsOKSgQMKS0YwKOyjspo06C3G6C6bH5NkxrMIRN22oY1bhW9H2iu51IQdwVdEIJzj/PTLK1hCzRGQTXpJIJUl8lf0OLXnIWOLCKwyFBymrMCk69pbnojlbZgFUPZc86c5rj5weZ6V/BAKLxZ50xr1cAIQQu3pbIdE6EaY7vUs7elUyH8yNmxtT7j0UjkDJDtWAAgLhiYkItc9gST14kGp7kdmENas5GzDsc5YyaYLWT7umZrKZ1zhoHghMXNTYgFKY6CsygjbFnTqNQaj0HcyAClFC9/+cub2kbUMuhhRTimKq1xNhjqs42o0hqnInKm69UFCqZrPEEeeuihpj53+umne4/vvffeGu+sj6DhefY6jiVtAgApktgH4Enpt53UKoVB4tRXcASJLCUtGIWp5CCyAoNwRDhqGc1u5Ewr2KHltIOXoGCEpe2UNumQOcQXxaVBmD+2rg3BWqkC1UD14tuusAUSy+KILY7Lpt1Onzrq9M5zoy1+5Cyk5RH4ONV90Qo3HTaxPAEtSUHjGvSUDi0lFUizz2eRO+jXObq2Xtj5414WBYBPXO6LRbA8B41TJFYm0fWCThBCkH2+cg3cladZXs3ZRCaClXP3N5XUbgpbwOjSnciZcw4KYOKZCa/ZdBBPFMTkoAkN9nhzNZ7B874ZGX1A2grZuDwfedoGbCk9HjaKVdfhJ3LhhuWZjJxFIOE/F2n02Lnvj3MOLgCj04A9bjWVSmgXzaHm7EaqU2QNHRqkFD7RCITd3BwSgfP+zBXN3RNevdEKLMpw6EnN2S9zf34p56wBxsfHAUjZ+mYjVlE1EHZPhrCRs6lKa1Q1Z+VEndYYZc1ZmMhZVOMBfPXQTZs2NfV5SiluuukmAMCTTz4Zejws4Ei0J/z9VeoY6S06ul7YhfiSOGicQktQkKC/G1W90CQ3weyeLOwMA53EOeOOIIhWmLzZcCPEl/gKlmXOI4U0CI8xSe2gc5an5eeZ4ALxxXFQgyB/OA9qEPACB4lTUDeaBnhRnGYV//wvDEbO5Eazz2XAskwuMhhULjAkNaTWJuV8TmkAlWqkHq7jEvK0d39fwgDaAtmeWsoR1iFOOhUB9Fbdu27ZE7bnEC1pEzh1pXx+IoPQdU2u4Vi69imYPFZGm+6k68rrKDVoxXntpkULi4MapKIDVw80IKsYZkE2F3MWS7KWp6Ya5flfDTdyZo3YMtJyjJoHDUfOnPefkRkC4+5igJNG2OA+ZDR85AwAsnFpt4hxU6arMjRXd+a8/9TlvJ7WixX5i02Wl5qdzjqCJU5mY/5IHqOPj2F8+9xsft78cvkxyOjoKACgo6Oj6W1EndY4myJnKq2xNqXOS7PRqqiir7MxrTGKee2mNrrna6jx5Px95DtnomLUSnNupO0b2iCEwPCDI9KgJn7Ug4VV2gt8vOIucupgoEtjiFbx/7kzVmqFV1AMBheMGs2VCSHgdnM9ceYywahHvoJSi+CyQJ/GKFiOIbbQgGAA0QEO6hnPfuQsbF8x/7EXOWOQdV2aXFSQ9WYUeosOalDoLZp0LILDdyNnYeso3chgyfPUkGqnLoRKoZ38oTyIRmAOW6A6AWcCiSVxtDjiJswUMEcsJFPNX9O83xSY3EIIsBz3hYAIAELkeOK04vnoirtwS4AYFDzHvP5Oekv9138aUaTJ1p0UMCtQ88MQyhKsO2hiC7AskyqEx0DNWSWC87ke3NYrp2dGvAiluzjj1gvWG40LRs7GQ/SbLBiu0i8DIRqYxSAsmSKrJRs459zrUAgTlhDAXePOF4R8ggsIQTD2xLi/mNSkOuVMoiJnDfDggw8CAFauXNn0NqJOSZtNzplKa6xNaeSs2WM3WyNnUc6hMBFhtyG7G+kOQ2yx790s75D7WwhSs08M0QioTtF1difaN7YBwk9Fa6ofTJDAMau0Ckt0mXJJNVpbfMBd0RfNK8h539nANBaMh4/8zDGCkTO3CWwZlEhjSyfQUhqMLgN6R0z2zPMiZ74jFW5AgQhwp5zfxJBKo1qMAM72F5zfDRqn8l9S81J3vc96NWchr0PeBoufd/sGen8bcix2msEas2RKlfM5lmNIxF3RnfAOo2sJB+e2NWzBGjGhpagU3XFqO7WUBi1WRawkUHNGDdnrb2zreMN1l5G5Mm70lQLgwkmbDVm7LOq7ALSd2obYQud6emytz3g0es8P1gR66b9Of7rMrgyGHx6pe1vNNp4uxVto5ACoo0BM0Xj9YpXU4UbRHYe3YMrzVXCZRsxzHKwg/81FlHPWAH/6058AAFdffXXT25jPzpmKnNUmKoWqqOsWgWgiZ1GKyoSZ121tbQCikfbvPFNGyX+8cC1es1HWnBJIQ2sytIQGGqcydUiLJnIWjHrEF8fLXqearAsgOmAO5GFPVPYGPSObN9mjJritRpwzWxxzi+Z8EuODEFeh0YlUxTTQBEVqdRLEoLDHbFijlmdYh0+Nlf/bnuyA1iEjnUSnoLpUjXS3T4gU4ug+twuJpXE5Z4LOkvs4pDPkOptBJ5ZbXA5TI958oToBjREkVsQhhNNnjMBz4BzRWHAmwPIMLESbCG+OFkXOgNjCuKw3I3JfEEpgdBjQklpFp9mrObM4iE7ALQFeYA2nN+5fIlVoxdJkU7/H+w3O76ExDW0b5MLRdKQ1AnJfxBfGZCbBMVpz1igr3rTce8zcSCeXKq52hjWk/mlHZO67vrhgAoQQCJOBGhT5I1X6blbbTgSRMwAwnGQNy5RiSoILuYAzYsn72xxt3aCcswZw1d82bNjQ9Daids5UzdncIap9ElXkLKq0xvkcOXMjXjtSHThxsazPIgadtMmzC9GlOIhr5PGwkTPnBn1ow9LKr+tSdlxr0aG1aLDHK7c38OxgHt44ayxyNj+KtRuhUp+zUohOQADorRoSKxLeogCNU1ijFnJ9eT/aGbJmzzWMn2rpgtvTnOqyqbTRZZSlplKDQm/XobXqxYsmxHfwq6ko1jWeCvMv/Wxa7o8WHYajEAedIL4ojvjCuKz30okU5XBSMxMB5yzfl8dI72jzY6oQOQMXiC2Q+4cQ6bxSg8jWCCuTFZUx3OtEanVKpkMSKWHf6DF05xC/pPl+lIDvAHPHsJZ/hNpkE4MAxraOFzVeV1Qm1h0DgxsRdhdDBODUeDUyj4KHef0/rmt6TME5JLgAMwX0Vh2FvgaVSCOLnMmLmGk56pFc9teML43P6SwN5Zw1gOuchekrFnW90GyKnKm0xtrM5shZFGqN8zFylnMWA7tSHElDGlVaA1LWVKdY9NKFgOH2YgmpjuZ+tMr+kfLeUulPS2owx+yKDYSLImchjf1GI2fHmG8Gw6rukctCeqdHXpw6/cUAw4loUUM625SSQN1iNKIyAoBOneuALsVHOk5vR2JpeUQ2tSaJhed3SylvJ+rjNcsWIlQ0z6ulCfp9GgUo0HV2p7cvWte1oOX4FqTWJKVzpFOvhx4AJBO+8iREyOhMhZozQKB1fYvfwJfK9Obk8gS0Nr3i97lj8/4PWTPY8DWgkrPYDAHDGpBTYSYcJG5zjG+bwPAWmZZ3LDhpS161uKnPcfdS7R4z5s/vRuZRMrAy6PZPawbhpTNLAY7kygSIQcByjd3/g33OgtgZG4XBArjJ60qVdGvOLBPOCSbvacRJCZ+rmRrKOauTTCaDw4cPwzAMrFq1quntqLTGY5fZFjmLOq0xSmcxzLyOMnKWzckxSQl9qW6mtzfmyBodBogjny5YuMiHZwCWXLmFkKuYhDoOmk5kpIOSyjc49wbLw0diGjlSrSe1hvquuQivEQXOPp8FKzAQjaL9tDZ0nNEhozAOfuQVfr2QLRv58mZTZF1nCEROA+7MmZhWtXk5IQSxBTEQCuQP5SCE8KPHHOCFECmEFYw04jRzD5JclZSqkY7IjluPRzQCa9RCMhYQuGhCza54TM44ig5doNbUqTlbeMECxJfEocVoUcoxKzCYQ6af1ugY1kQn4IUmxuaWHYa02DxRGc/Zaz6tmZDmPieEnMMsw8ALHNa4jaEHhpva1lzhqZNW4rhr1zT1WTdqyiw3rdE5Z1hj82h/PKJrL/HnNCEERrsBohGwPGts0aHKgkNhwERhoIDs/iwyVVpnBIk5JQa2JZ1WluNSgIe49bpz0ztTzlmd7NmzBwCwbt26WZECNtvTGpVzVs58j5zNFucsyshZ1mm7tKRDjo3lGYzu6oqEVXGbUPOQjV9LZNVdcvtyyPflpXOmAYklcaRWJUGrHFbPCI9AEKSRSxA16JxdyWyWwws6vMefXbGx6DVucWgxIuupDIpYd8w7NtRwnOwEdcQvfHGJ4YdGkN6ebm5AnjPkRM6YkLWKBql93lEAGkGsO4b84bxvPYiwaY3lzxGjsvoh4KQKEkfERCOgCQ1Ep0i6AT8nslBfA65qY6p8nrnqliCk2FGixUamedRCvr/gRReDDhDLMnneNTA+4i7GhkwB8xU2vS037cQ2OxTBAtccJyV2OmvQ3F5r04kZ4n7mOWfByBl3FvoauJiO6TLEdMuyU5oeCxB08P3nCCWNZ2FUSWskRD5HY5pcjJkEwznvbcd5ZTkO3V3EaaJZ92xBOWd1sm/fPgDA2rVrQ20n6nqh2RQ5U2mNtYnKOZuKmrNzzz13xscT1YKD2z/ObRofhnxejmmxK8UrAKOtcedMOMU9ggOjj48V9U9rbEPO/0tvaLpjZFHiRen0Vl1a3xUIpjWGNVQavQQRNJaOM/eRv/XTqzZh4OTi1CaiSSOkksR26rgUOk7vgJbUZCqtY8zwPIOdtpvuF+c3fyVY2MIhbOGkVNZeoCFECmDoHTogAOrOQQFPHr658biOUCBy5raEqIQTHZYCIRQ0TkDjBLrjOBEhwFllp6/+QfmZKdaYJR0qx2j0hho4BwkhJYtLjsiLlwLmv2Zn7IZFMVznrFqYemL7BKw6GlwHU9IAOTPdsY1tHYM1Vv81s2nTgwkIi8uWEO4umEZfaWTLKNI7mlzYqJPS69virvDOmXu+x7oMKZTDGnM84lTu5KwWroOWm37MSxf1CGlskaaGIAghUvyHxquooAbQAtFpo9NA28mtSB2XAqFEzvM5eqtRzlmdmKas20ilUqG2M1vTGqOsF4pqe/MNd59s3LgRjz32WNPbmYrI2fve974ZH09UCw5uFDAK58xN/4l5/pioazWvDHfF2lldbFZBqpJQgeACNOZEpIg0XAUX3uNKuM8TAbB8SOesmU8cQ5cH4vzWc46z8d/XFBuFRCcgMeKJWhS9phEY7Tq0Nh1ai+5HX52eQtVSECfFmXpL2jiWdcgIjt6uo+vszkk/SjUguSIJvU33jDTCBXg+RM2Z6yw628vsyTgy9dVTLEGkgImW1KDFZY84qglvPOAilE3mOXYaYKdt5AcKYDnuR85Qcm7RkqgjcVIztWJnyOg20Hp8CwDSkPNIuLtwVXmfCI761ClLas4I/PuAOWZJVdB6x1T3O4uRgg0AL/j1QNNZc8ZNDjuEkmc9lPoTL1xX//eJEgVdzzlzNpFYnoDRrktHqIGD0O6k/V59ZnkNcil2lqEwUFngw3PwKywuNHIcvXTmCnPaTc+nxiQtYeDXvrrnU/C6KHh0i+LTjXLO6sS25apUmJTG4Odnm3Om0hqnHveYve1tb8OZZ57Z9HainkOXX345DKOJVL0pGk/YyJnrnNm2Hf7C7ExpSv3xNeWcuZEzJsUTmh6Xt9ron/fc4tBSFEan7ggkONERSqqmNdKAmIPb6LRZGr0GCczdG2ZTOL/1kpNsLG0vX23WYrTmPuzq6ZSNZl1BELdQvlmD1hnP+kXyuAtbQEvVt5ru1lzFFhi+XSgQLvpaEjnjFi/rqVYE9aNSbae2Of3PNG8FnXqpuiHSGj2vQabishyD3qJ5TrTWqhU5Z0ST6YquQcst4bRHKKk5I8RfSGnk+HkR88ovV40yllIayRPSqM0dzgG2aCgcFsb0ECaDMJm8nglRVK831YiwqeX1fEfpoW3AGUw/m0buoC9L76U1Bp0UJx20mrNeCTf6unbh5L/dPGqiMFjFiasQDQac63oji46sug3rpgzrKc2LxhVFn9M2cgdlzUEwC6R4nPDEgeYidVlBPT09N/X09Gzu6en5Xk9PT5kV19PT86Genp7e6Ic3e3CdszC1OUD0UY/ZVHOm0hprE1VkKKq+YvN5PO42QqdaeivWAM9z6J0xX62tAbyfxIRUbAyruBe4KfMCR6w7Br1N91fruZQVRlUD1xdzYCFS0oAm0hrF3L1hNgOpYVhTg4DEas8nGqOgCVp8zLLMU01sFE/swt2cxetecHD7+xkLYv5CgRAQIWrOvMiZ+x06AbTq7Src9EqiE6TWJNGyrgU0RqDpJc5ZmFPfa/grnSwtQaEFnLNYVwwLzu/2x+Q0ES8MmuAWly0sKjhn/vsbS2v0xROqnWykriCKCETwAUgD1uYYf3ICrMFj2OxVmlAClpfNgd34ZthG5oD8TUP3TS4sIphckArTB28ySg9ttX6TlSkWcfKk6wO7iCY0cFOA26L+39FI3aIz7ysPrzga7G3eFhh/pr4676K6w0qXP0oAIUATsn8gtzgmtvvbtkYtLwWXuqn7pecYIfO75qynp+d0ACt6e3svALADwFUlr7cBOG1qhjd7cJ2NsM5ZVM7QbKw5U5Gz2sy2aOdsG487f8IuOADwIoHuokrTOHdZTZPpMEZnk+e/a6Rxubo4vq1JsRLXSPNS3DgK/QXoXQYSyxIgMeqv2FNISfIKuJEz4jYzDRHJalQYQBAyZ2+YzVE5JY3lGEAJ2k5pq/lpalDZ98z5OE1I6UZzyERhsMHeQoC/rO+e9wLQkvU5Z27kzGjRvTlIhIwUZQ9JUZqGhxMQ3xBcSNVINy2w4iDgR9YIkFyRgNFugDn7gjppYUIImMNmU3Pbc164FB7RUhr0Fr26YeuqpGqQh1uT7Q98RcuSMTQtCOI/xwu+JD/RUJ+3VFJzBg5k92bltkzR0EJLs5fp+NI4hCWkamWENWel0vKCico1dEzAHLbCL5DVGkvJphupxaJ68dx3m9gHa0yNTgPJFQlwS9TVz09wAep+vMYxHt827i9IVrnVlTn4DmzcqjsLY/SxUd+ZqjQeKgWQiA7/XCk999zUfHdfVU2znJsOWj2n14sB/M55fBeA80pe/zsAX4lyULMRldZY/5ii2t58IyrnI6pI1WyrW4wqGgwUpzaGImDH5g7kpApUMwRW0LnNYaebHFeJWqM1ZoMmNMQ6DK+/Wfc5XYgvk416aZXh+mqNgJ1hMIear89rXBCkQtrPfMaTZS/eUZnnMuBZu679p7fqATEKguSqBAoDZnOr/16IypmTQvZYq4eO09uROi4p1SOdVEYiZJpYZke6aHW77uF4RiOR4iSGfFwt6kuofC21JuVFsYkOaEYgcuYYskP3DTeXtutm/QkhFTPjFJ0v6Kj6dk9BksqFB6pJR82NtJXX4hCY1dLGSociRGBRxt8n6V1pmEOW00KjzpOwREofFDBHZfp3o4qbzd41pGKrkKmNXCC9MyMjKRFcFIIOWu5gDmNP+u1UuMlxdPOQ50RzS/6d2ZOpuj1zpL5jVErprXDFGxtoHq4XR41LBUGKEMI7D2sh0/tqR84EFyA6lcfCGUfFdh2lDr5DcnWq7vplO8P8tMbAeLy+j0T+owb178GUoDBY8OoiXbvFjZhXTBPmmJOOGVCfc9YFwJ3hYwC8WH5PT08HgNN6e3sfnIKxzSpmW+RsNjpnShCkNrPtmEXtLM6WaDAQnXPmrSISSMW8ZvWjA0XUvCDAMnZzdTolhjU1COILYzA6DRidOrSUBurWMFEAFYQmAGk8AlJogOgkVFpaU4frGPLOCHxD2CW9Y8JptEzqrvymcXneCy6gJTTEFhh1KfSVUSIqQwigt9S36GB0GNJg0oivMOkYiCzPmxKXcR0X4UbONCmjr1VLH3aiVFrSf51oFJqTHkq5kzbMATvdePqaCKbdCpk2RRPyvKoKIVIURJPqckQjjpHt/saSnxCjdae6CVv4qbHBc40QgAlk92alI9tE5Cy+KA7i1Hw17JyFuExrSQ3cllkE3OYy1TasySAC/+AY+oFrLMsxZ3FMHp/c/hzstC0dhUqb4zLDoanIa+DxyR8/EckVibo/SwPpsEAgrbHU+aDyMsrrmUYB7ZBgVE6Ks3Dvsdc70HGO0jsrOK6lDr77dII2dF336gwD8yizJyPPV0IACBCDIncgB3PQBNGAwlHTi867v4NWqzmDPJfn6q2mHk9jFIArJN0BIJjU+/cAbqn14Z6enmsBXAsAN9xwA17+8pc3PMjZwNGjRwEA+Xwehw4dano7o6OjAGRT6zDbccdTKBRCbWdkZASANGJrbceyrEm/p7+/33vc19cXalzzEbfv1sTERCTHLJvNzoo5NDg4CEAqmoaZQ+7+IYSEnjuuw7h//350d3dP8u7quA4j67DAe4BRbQTjh8Ya3o5NpAHAUxz2Rtmc9nD/4Yb7FpkFuZLLEwzpRWmIbgEaI+gb7JNvWAkcOiQbdwomwI6TBoltFN/BrRa5+igIh3ViAYOFQdBDzTnpE+MtcG8R6UWTS1SLLo7+wf6m5KjquQ7NNtx6Gqu9gPQix7Bpd+YVgIGJAZDs5POAx+UcsnXbO/a2bmFs9yi0hFZ3KMNVHmYJhvTCCYhuoH+4f5JPlf4mAbLUdao4cmty3muNHp/xsRzaAQgNyK3IQiwTAAF4B6u8LQFYJ1oYzA6CHJI/mnUyaJsEGGTkzDrVhK1bEJbAQHoA1PQn22RzKFgLkz8uD6pLh6vmZ7gAO12eYxmRBpYQcMrAUvKY5VqzMBb55pboFjD1AtKH6pB0F/AKF60Ofw7hXA5TK0jnTSMwqQ1Lrx0B5zE572zD8s5V0ekb50fto/VfB8gSuJOunvO+iEWAyDEU9DxMjUAsEjjcd7j5cBwgswCW2jjc54iygIMv495xE7YAW8IgFknneyxhg63kMFMm0ocqRHwFYC+yUTiULxvXZHMoVwCyVEOKM+BFQLq1/v3jHo+04dwznMORTeaRXlR8fEUXh0Vr33fd3+I2DTfbTP/YW04rjQQFz3NgAUFOz4Iv9kLHSMeLx84N+ZqtW2XHXXTzus5/e5kNJJy5mOTedngHB7gAoxYYBezEGPBiAUuY8n7hrJvY1IJwxma3OcJG4OXjaeWwNQuwG78uTQcrVlSPqNbjnD0A4P0AvgvgUgD3B15bD+BFPT09AHBCT0/Pv/T29n46+OHe3t5vAPiG8+cc9WGB1lbZXb2jo6PmDp2MxYtln5tYLBZqO11dXQCktH+Y7bi/i3NeczuHDh2a9HsOHDjgPV6wYEGocc1H3DYMnZ2dkcwhwzBCbcd1WpLJZKjtuJEuQkioOeQuXFBKQ88dt9fZokWLsHTp0qa3Q8U+AEB8wkBin44F53fD6Ghc2dIw5E2DjFPQRzTonQYWXbwAsQWxxrajyZVMzdLROtgKc8hEywktaF9fXrdkT9gY3jECe8JCfEnxym3clIYktYHY7gTaTmlB63GtTUUtOzr85eqWIy0Y/ONRtJ3aVnW1uDBQwJLLFjelelnPdWi2QYk0CuKZBFoH5fU2uz8nRSGYwJJXL4XRPvmt2NCkAanlNbQOtsLOMmhJCjtto+sFnYgvjk+yBUlMl4kwmqUj/lwSsW4D3ad2NfSbrDEL2vhB+ftsAuPpGEBk1GvZXyxtaB61tB4BABBBkDiQAs8zEAosvaLyeSuEQB55xBfHvWjW2JPjiD03gRykc0Z7NcQXxZAfNNF+Vjs6zvBTEiebQ9zieBJPAwRIPJdAcnUSHZvaq74fkAIthzdLB1dLSUNXS2rQTXlc48P+sQek2pzRqaP7xMkXjuyMDcrlvk5k4t52JnakYXQZsEYsqdSqU8QX1r6eUD4qx5jVvO3kD+cghLxedF3cjdYVLZOOSQgBGgh1BX9bvdhpG9aIBb1DB8swLLtyae3o5CRwk2No5xAWXLQAVKdIP5tGem8GSy9fAkBed0aeHgXLcRACtJ7UiuGHR9B1YSfaVpRfP7nJcfSZIZA4QWJxvKg2dLI5tO+PQ54/1zrcCi1X37VOCCFVEgWQWCLPZ+GEX2NjcbQOFl9Tcwdz0JIall1Z+x7HsgyEOdehrD+Hcgdy4CZHYlkC2f05GB069FbdTx3UCeJL4kWLiFTIxUktp5Ud9/xAAcuvXDppm4+xJ8ZA8nKRWXfuZe7vYVkmM0AMgvZN7RjqHQY3ObQ2XaYuEsjImhBoWdeChCkXhqhdPg8zz2WhxQnw4tqO0Gxk0jOht7d3K4AjPT09mwFsAPCznp6e25zX3t7b23tZb2/vZQB2lTpm84mo0hqjVmucLSlywOSCIHfccQfe8Y53YO/evaG/ay4StcLmbKnxmm0KpECEgiDOcpJGZYPLZhwzuQG3vkem1YCgOTln77z3/yQ1LklSKKD8GuGlNQoBqhPkDuQjacw6tHkYz39lL568/ilvfP/xxzg+9L9JmO6hIOXF5PMZV8whGCQlVBo+UhK+zu14ojLO9jQCe8wGS9uwsw3M84BaI88zJJbV59QVjYUS6IY/h7jNwZ1F/YaFFtx+R47BRShq5swRQpBcmSw25CmBHnNqUJxzTAhAi9GGGisDfrqV1y+wnug2dY6pBmgJTQrzUL/thjlcUrtEAHPEqq9xNIOfLlbUqNv/P6nSbL4U114Inn+CAXCEiupVTczsytRMYZvIA//w8yRuu7+6s0hjsqaJmxyCO+mZIRACRemRnAnAEQUZenAY49snICzhpRnLeSpk+BoAy/v3r6Obh8CdRtmF/kLD6bosz/3G4Q3czgQTMq2R+GqGfs1Z+fs5E+BscnEZwYV/HQq2gNCJIzAE2fQ5JmvO4NTlaQlafp/y5lD59xDnu0YfHS3an5V+Jwl+yH3oXBdB5PWAGE7at5PKDJ0guTIBlrW9e6orXEIq7IOWdSlw5jR+n2PU5Wn09vbeWPLUeyu8pyeSEc1SZqsgyGyS0q/lnHHO8aY3vQkAsHDhQnz+858P/X1zjail62dLjddsUyAFohQEcQ3ryo2C68U1rAkliC+WBkt6dwb5w3m0n9HuywFPhlfI73tnNT9LScV0BU+t0ak5YxnWtEhJcPvp54oNrB1HKP7jj0kAwEtPtPCKk225S48d3yxQ0O4/FRSLqDeVyzPAXedBJ7DGHTGHBhx9z8535pDR1Vj0FpDz2VVuBJcOiTlsQkvEZK+jBtYw8oMFtACOZYfq7R9qQDUgZhBwSFsu1m3IVC6DNi4I4vVfgiPuMflHCJVONjEoqEHk4gfxVTDzh/Jl7+cFjty+LIzTakflEFDaK3LkCTxnluqyvm5SKtTnuIZ9wlH/qwdr3AJB9Tqqnzyq484HBL5npPCmsyx0JCtsl0gHiec5IIDc4TxaT2w8Aufh1Bd5LeosuV2W4zAHTHCTORLtRAZebO60QJAfGH5wBO2ntkFwATtty3lsC1hjNrSWxuwsxgANtRfQhRCY2J5G6/EtyA8UoCXkQkJ8YQyFQRvWsIXWk9u85uyVGjwLkwMxIp2dGosIgvuLREXXGyr7LHKTy1rlGJGqnVTWe0EnyOzNou3EVn9xyD0fqvTpE1w2sWZZJtOtK70nuOBQtGoFWc9KnTJOjTqqrM5zIIgviiO9J+vtV7cxPKnRN9CV7p8sojebUE2o6ySqPmezzZCdKrVGzjl+/vOf4/zzz8ef//xndHT4aSU333wzCCH46le/Gvo75xKzVR1xtvQ5m5VqjW4TaiK8Hk/NELwpxBfFoSU1FI6asNI2WLaB/VZaRM0nWTWnlW1/b/VUyEJua8xqusI/uIAevD3+agvBBV/2Dc+3fLcV6z/Zji2HjDlbpN0MrphD0e4l0oAnOqm77tBT/nMFPTQCvVUDzzEpS14vQSNNoLl5rfkNzokQiC+OO4a1qKzwVgN7ghWNh1DS8DWJ6BS6DjDnc0anIaMQOmlYeMeLQhAnclbHogzRiNwniYBYCiWILZKOb2GgJHJGncbddRiLMspQIerhGsuQjrI1ZlU04Iu/tzzqQTTpKBKdgOfq21fCFkXXFYsB1/wghc/+MY6b/pDAwG3P4zvPbsZJ2VEc/8kOvOSWVhxNl/xWIo10XuByzjTbVN0bFGRz5gKXUS9LCp2YgwVnvxC/6TWc/eoeBwD2uI2JnWlMPDMhWwswIR035kfX3MWQSSGkZn9DAOA5DqNDB8sxsIyNQn+heHHCPb6uAEeF/cMtAcEqO27F+8Y/XsF7ESHy+s9yDCQmRXUEFwChoE5kP9ahFy3ckRqRMyC4v2sMh/siN8WZA8SZ405GAYXjrBEv0wDO9cH9nKvWWCly5v522Wx9bt10lHNWJ0qtcXKCxvkll1yC173udbj//vtx8cUXI50uT5m64YYbQn/nXGK+phHOtgUHwD9PLat5iXgAgcgZJm0WXAtSsmKttWgwB025OtvIcAKGo/NM1bS4WoY/CdzQqC5rZOwxq8FmqZKgT37bA/5qeuIzjwMAXjZ6GJ/e+yhSzMJojuL9v2vHsRQ683tU+amtboqO0aZ7aUWTbifQ/sAl1h1DYnmysRTZYBNqIpqKCBMCTx2xSOZdNJ7W6Bu7RKbpukqjDUANAsMAmGOCypVyx+GwBcaeGp9kC4HxuLvSGQOtZ7WdyuOjJTUZVXTOPc05tqVKiDJyVp8EvmCiYvTVd2bhGNJ0csfBU4/03xdfHIfRZYDqtO6G9MIuXmxY9pFO/PrpGG76YxKfvTuBl4zJ+rvXH90HAHiqT8cdj5dHaAVznCjHkQqFkHNp7IkxjD8xDlHgEEL2cZNS/fDTwt12C4TAHLLAHAfRztieqqNgwktndB2j0cdH/cWEGpA49RzqavczVuCgcQ35vrxsZUEJaFxz5hC8yLTwnKHyY5tanZS+R13OkHu/L36NxihYjsNo1WF0Gk6uPJzUXAKtTQcPKp7S6uMBkRkhcHzhqgSbQwf3j5Ma7CpREiKdNZqgchHSUbIMKk56UvpV5r7gyjmb18zWtMaooh5ScjTc5FXy+bWJOtoZdn/PtvHMysiZm3khnF5TTVLqnBFCEOs2pNHWwHnnrQ5SWcdijlSPeGlJDa3rWwAI5A7miiII7i52b9iJlQmwPK9/ZThA8B4dtEHbmYXXHd2L9x96GmdkhnGxY7BpEMjsrt5baN5RalhzSENMo+h8QWfd51/pHPKfb0wG3TWqpNHXXLoujVEkl0lHnJTMl0adMzfyKwikEUibuCZRAiNOwZydLFPTmDRwmUDhSAPNuoPO9GSRafc3EOmMxbpj0BKabDdAAwa2Ve6cCZPVFawWTJQ5+KWPoTsG6yTToNIcojEqJfVjpKqsfCnc4mW2tyY4Flh5vKdvp/fcKtM/z0tTG6lOocU1x0HzG2qLBht0F+EY4XbWBjO53LYbWXJ6aAkuIECkw0UJWJZhZMuIjLrluBctE7ZcuEisTAK209i8UH0xLAizODxLscL7hRDIH86BunWSLbp0sKnT3kAjXrqeGzljFc4rvVWHFqdlrRrKvo9VieALqdQobI7EygQSy53FNQIvckY0Cm4J5A7lpVBIjabPEM61aLIauEBaY/GCg4xAG+2GMwi/9o3q8mVC/TRiwK85o1W+U9h+pHQuES4MdAwxW9MawxqycmWCgnMOznko5zOKJpLzmdkaOZtvNXBAlIIgzjHTBGLdjdfmuLg9oWiJkcYKvCFj1tcFILCGTdCEVjOPXhqHBNaYCb1VB+10ir9LUkEIJbAnbFjDJjLPZbDwggV1j8ntd3N8bhxvPLq36LW/OrLLe1xwinc2dFswh0NGNOcQpYX4smkwQHRAb6v/flIqCBJ8vrHImfN5IetKmj3fYh06OAAS/GoCjG+fQEueI7U62dgGCUHhaAFGp1571b3SRzUgniReWiME0HqCrF8yR0xoDaQ2emmZFDKyWHdNIPH6DU5sT0OMCa+5d1kDYQJHtGTyc18Ee1SVGrKQoiNubc5kTo23eyr1FtYprIIphRyIbLxdCZZjMEcsb0wxziAA3LS3FyfliiOUC6182eeDpNalICwBlmPQU/JcyOzJIt+XR8txKSRX1T+HvGbdXDphzG10TJzGxlJPHt7/nBokO2PDaNNkiqXFQWNEpgpy4Tgo8lixDJN1a3Vcrl1HiqPK/YxLp9htoq4lKVhaOkN6m+5FiIBA5KxajRcAa9isqfha6M/7kbxgDzXI48wtIWvOdCfFNa7JawMlAGeILYgh81wG1mggrbHaeAocIl7bjhTByFnwuggAmuxhyLJMLjjoFFpMpu4Sxp3URgAEyOxOQ0vKOZLMmxUbsruOa+jI7DSjImd14hqeYSNnUdfnRGHIRu0wVuOf/umfiv5evnx5qO+ba8zWSNVsSY2djZEz1/DUCIpSKRqlsErKMC/YP1L0PHdSaOomEDmjcQ1avLbanyOAV3RDzu7L+jfqgHHIcgx2ljWsIsmdw/6ugCNWC1aHuth8wjOsPcNYGu1Epw3dgb0IV6mdT0ljDYSdz9tpJmWmm8Rz8EscDDZhw56o3/n2U3Wlg+CKAjQCIQRGjMB0TobgHBZ5DjSyAGIGroui/hRLosmTzWg30H1OF4gGT1GydAGGUAJhCeT7C5OnEotqkTN/W+58EkzAHDIrbUVSo2GvszWMPTaG0d7RqpvgljSsCeS4vvXsffjF9rvLHDMAss+XQ77ClHDHTt1aJ0jZd2FxpJ9rMLou4ETGpEFup5lTjyWjcgJEvibkohU3BfQWDXpSk1E2DvACkxE0LrxjRgiBsDmye7Nym3VMJVdkRJSsMgguHXLBhC/AYQtoSRltdVP2gqm9buSsWnSMFzgyezI1r6mFIwV/IS4wJELkfQQEvnPGAaNVx4LzuuX81QmSq5PO9YrUnEPCFjBHLQgIsDSrWn8qbOFFurw5LSDvazrxmqpTQ9a+0YTmRRZBnXR9Kp/XumI4qseRtGzkDpYvBgjmHPQ5ds9RzlmdzLbI2Wx3zv4/e98dJ9lRXnuq6obO0xN3ZnPe1SqtpFWWkE0wyGAyiEcwD2ww9gODAWGMZFugB7YBAybJJj2CiQYhBIhoSSYJSbOSVtJqtTnv7M5Ons431Pvjq3s7T/fMdI9mV/f8fivN9HTfrr59Q536zndOR0cH3v72t2Pnzp249dZb8d3vfhf//M//jHvuuQdveMMbAFCI8tMJi61Stdis/Re1W2PJSuZckN/QCQAIpfLF44AzuDl3VuTMVT0QDKryktBnzgtTrlfePs0ez1G+0GmawHk3bK5T34G0Zuf8BwBO3sELR49ga3psxudd0EXvWTB1iLCgSdgirbZLVyI/MsMkdxaotNSWDlU7omsiszrW68kawWcp2SmRE80nV6o0jsHftAPYaQfOLI6h4vxdGWmw2Z9rTCdZY0bQee9kitdGiearVECJRFRxs9nITstW5xn8nrNKWSMAhJaG4BbcxgYqsprgA4qclXwkrtP1ITeDhNOfCM/wlo4185ikQzlhEdvCS0YPo9OZ+Ty5YAmxsow1w37k5dVF15aALWe0Y68eGIoETVnou45UUQFFa3qoxRE369B3DMDNS9/1VLogWaPXp8bouHYKbtMydO/7lhUfeXrXNPKnyDCDmxxccOidepGUKfMLkvBR+HkjciZtCWvantFt18k6JdXX8kFxndE/jUGEBIykjuiGKBhnSJwbR/KiJMxeA1xT1f9SeXYFzH4T3BCQjkT6YAbjv5+oSRqllOAqtkGWyKqZ4NTrBs/9tEjePcJW3EecSBskjpuUIWuNV68ARFZGaDHkDJM1BuSsSSxWQ5BWVBlaPSYAuOmmm/CJT3wCW7Zswc0334yXvexlAIA/+IM/wG233Qbg6UfOWiVFXWwGHIuNLAKtIWeu7fqTHyGaa96vB6Fz5BkHQ3FV3+g1AM4wvWcW+WIlVQamcyTOS8zYC+d/t2qXWlOW6mdQD3u8weTQkzqcrKMmKM3fyPoeGsJfnNzd8HnXraRJY9ai1eLxwYmaN9PFgMLpAqZ3TbdmY5WyRptWzWcj2QIApqRCldJYxmllv2mUOH76dvhzgFB2+aUuadKWsKedpl3/ABRd+pgkmRcD4rO0VDd6DJhRgbQ6sEt7p+KbYmTv3USmGFCsejDOaELbrJumqpz54Mw3EappUa8WZxpBSpRUzkrfED5rYwxgGkdhrACu16+kspn6hejd6PuY4fSXDo3pzUefLJMt18Mtv/8two6NbGEG+TVjgDqGpSJSriUxsX2y4fb9ccmitNHNu9Q/pUgWEbciQRcm9+WbzJWQtgtZcJE4L0G9aGoM1ONEE3ufYDcja1REtLJy5vWHSYeqZSJaNJBBCUGDku5N75z2ZY1une9MOi6cjFt2LI38erSMYHOdV0ljnYxDpE1XhEgjQ5vua7oQWU3XJhER0BMaXSe8PsoZDEEYJ6JHPXuUE1dTtu+UkrOi74FnbiMdSe6nJkVTMK9a5pmBcFIScINBSImckszXIvOedDwgZ2cpFpshSCurDO2onL3rXe+q+zzDMMA5R6FQmH9l4wzCYrWuXyymMovNrdEPKQXA5+AgVwrBgay6gfjVL84Q6jdhT8ziHPBDfABhCoSX1c8aojdR/xPkhMc1siHmJQHCHrjBUBgp0Gr+LGRy5mhxkYWHOC7/waXQOqoJo27R9SVn0751825TE52nAs04oDWLqn4hV/r5V7Mak6qQaoXy48WfPDZ77pWc9/PJ7vMyjPSS65A5YCLUb86uGuwUJWDeBNFcMrtgbMYYkpd0+JWzQoWjHuNA/nRzlVD/2Pdlg02OoaJypic0v6eo5gRVkYiGZg62W2I7Tt8XhVd7JzfgGTjABZjBkdqbqvkdFCtn9WWNZNYww4DUds+dmrlS7sHMWHjWxAlkZ7oU8+I+kjYRRDc/u+sQIClM25Gw007xvFBkC25x7F6fpnQkGYbYsli5UwHm/v5TY/PknFOPN160cWz6Ut2K04uHhOppo8pZdF1ExVLwok28cvpkjMiJ33NWT5prS7g5xzd8kpKIZKkCwrUleIU0Nn0gTQt1BoPQmd8XrXfoVfdgT2rJShQkda3rAbg2/fOMVCoh7SI5g0Yuo6knU0SGdQ4R4mCC037ROTq2JpA4P+6TNMZpvIwzaEwiqxZl6i4KsYCcnbVolawxGo0CACYnm18RqoVWyhpbNSZvcn399dfPWP1gjCGsmjiz2ey83vNMQqsqQ60+hlphKsP81b25E8ZWVRaB1lTOyCVNbY/PL8AyGgZy6gbilNgSkyRxFjcNtXtd20V4VROVF2/I3g1VrdKGVdWj9MZp9pqw0w5JgWZh8W/pxWuiZ5rCS8wEvNBt/Pw4NmYmkbWZTwCnd82iariQkDRBbIXs0juG3JyDzNEsVTfm0OtleA3/daReTU8+/Akq5pXdJ5J0EEUKxVk3SY347ExuKqrBcwXjrEjOKlwHRVigcLo5x0YnQ9cM5jdsNvf+0XURhJcWF0s6LuqAFqHPY41byA1V98M4hcaGQLUMQbJHM+TYOmHBURNSZnCIiKCJdh1bfV1933ah/ntWTqZT+9KKDKq/O7RPJkINFoZKsCqfQnYGWSPjrChvdaXKP5tlPy6KlTO4EiIilKxRSRsZ4BaK13En70Ja5OgoXcDspuuUR8K892aMwUnbJMN2JVyn+h7nWm6ZlNf7LKWVM+nKkqxCkgDqCd0nS4wXyQ/XVc+VKQDl6JjPkNz0yP87iqnHi/19IqbBLbhwcqR4SD2ZgmtLTDxUnB9QVp63r9UPighS75sAEzPM2Tx3RAZfWzuTwYZ0vcqZrPkduiU9Z9ApysWT5/dc2w2jx/DD33uu7YYW06gC5hFErZgTKVBc+Cy9t57pCMhZk2iVrHH16tVgjOHw4cMoFObe09BKcrZ27VoAwJNPPjmv7cxmsh+JkEb46SRtbFVlaOXKleCc4+jRo8jnZ2ER3abxAK2pvrbymG6FW2OphbUQ8xvXuqVAxr+BFO9qTDRf9SjNqmGMQe/QG76GMXWT5zTRZBrJV/riNAbHBlIlh1Co3wRjswvutfViFUjvojGV9jKFV0f8n999/DFkLUa5QgWX3M/aBCfjwMk5mNg+0fRrpp9MIXs8R4vwDmb12nrwvjNrtECTQUuCh2ZfOVu6Uq0O1yBnUrKGFRgP3oo6YwAz5q4EEQkNFmMwHaeswsHY7Po7vEmeBJsXWQSAnFcxT1VUzmbhaOk7iXJAsuZDwsPLwmX9n4yxsgy7HW95rOz50pWQOSIHM0G6blXVA6AFMWFwknkxNcnWiOByDTV7grpitJ1z9p6AXfN4oUpH6aUuezyL/HDxIuGFYk+Hyqubq/9yVd3PEHadmStnjCqE6YMZFaws4RRqT+xLkT+VL8r3JACXCG94RZiuQYoceNVZT74NAJHlYRg9BlV3HBdmn+l/Pul6CgdJEQMpp9jHWNYbR2Yj00+kMP7AOHIncorA0XPKKmeyvHLp9WxWW8RTthjjpEQIxeiYmhh3cfoXIxi64yR23VSUkYeXhxFaFoaTcfyeQ1lB+mvFMXBB0kC9Q4feqZUtqNX6fjwzjsamMgAcus5JW5ZdlzKHMph4eBLSciG8414nZ0YtphUXBkrfxxuCRxD9yhn1ognmlqhSAnL2tEOrZI2maWLlypVwXReHDh2a83ZaWWVYv349AOC5z30uvvCFLyzImJ6O5KxV5KP0GDp48OBTPh6gNaYgi7Fy5kHMY0UfANYvA7JqVf/I/ztS9T7NBJtKu6QXhLPmLL4ZaPVfmQcwThNVQ00io66N1e9P4vEh+nx6UocI8dlVzrQScqbkjKU23NF1RXK2rJBFrgA4KRvWRDH8tdVwbRfj2ydgpxxYNdzwcidzSB9MV03+cqdy5HwmqZfDTs3/Zu9LRxmR1siacFmFpVmsWkP7lufrjKkJQuRLvAByO5uHW6MmGCYFVRusyZKZd4lErRl4kzfqm5rftSirVfecAaoy0+Qx7U3Ciz1ncx8Pq7AUL12EESEB13IbL4Q48N1VOS8eS94E1ug2VIgxAEE9Op5UrBJL1hUJ1ZUfidYmTLYsN7KoOK7ofGX+vgbIEn/J8/rqfgRNSmRm6jlTAeaZwxlYUxZVgaxys6TCWAGpiv7c6T0pFEbUh1B9ZfaU7VfHYptifsaZFtcQXRPxe3S1OMlOPVmj/1lWh6EnBKyUTQYVjPaxPU3J26XGJWP3jSN3PAe3QOOd3pNSFTY1pJKLtJSSvjOplMXeoaEk514/lRbX0PMMijLhgiGWoAMwNeEgX6f6y3Wq7pGkUcItkAwzdyKH1IG0sqn3yJn3IjIBYRpD73U9daMT/O9HSS29c5TXu24zQNokV5Z2sX/PyTjIDedhjRcgGSuRNSqZZ7j4/kwZf1Rul+5fdJ0I9ZvQ4xo0lKhSmujhPFMQkLMm0SpZIwCsWkUrTEePHp3zNlo5sfbGAwBvetOb5ryd2VRiDEPd1OfRE3SmoZXk47zzzgMAPPjgg3jggQfmVLFq5THUClOQVhqCeJWz+Ug/pV2UNRrzmMQCwLLeYs7X1KPTZRIn6QATD080NR4/0Jg1KbdigHRppdGz1Gc6BWBLAN1WDkK6+Ie7SiSSUs6qctZodTtxbrzs9ytODcHJqcpZzkX+VGtcEcsHVSQisoaEa+LBCUw8NEkh3qVQjey+MYJD+6JyUjgXMFXd6Lmux+9Fmg0GVOVsWS6Dx38xiZ1DJZMZXrtBvxJlsh82v0oVZ8CUpirUkyUEeJb9HeVW+s1VhOshryS22amK6xBvPqjbX5igefS8ro+V+9cpIY1aXENoWRjZw1m4BRfWhIXCWPW5IN2iNNYzlfEnqpwhfk7cd7BjnIEbwu8/q8SaqxJ+3EBqxMaDRyrmM5Kq2qXVQlmxtuFkKWvKKd0vDSr/QrpovAaj+lBtCTtlk8NmCRHKDxeQrZCGktmH6lWTKs+sZFhc54isjiC+Je5XxqrftrxCp0U1UjVnnGI+ZYiTeQYrvw67eZITQkoiJHlPXuh9otJ9pM5TRSKZus95RjJM8KL8nCkypDP0dNNj0xMOpuqsZTOdAsS9rDbXciFtFxMPTSB7JAuUSPT96A0JUlI0UxlmarGCAXac5m6hVH3VjutKwHZV5UwiezSLsd+PwUlTBZIBfuXMkzIzwX3zHbPfrDIF8veJkmOGV4YBnUEw6VfO7IyD03ePYGpni8ycnkIE5KxJtCrnDACWL18OADh+/Pict9HKibU3nvliNpPrVjn8nUlo5Xd26aWXAgD+9E//FJdffjk++MEPznobrSSLrZA1tlJm6e2fn/zkJ3PehnSKVY/55EEBNKkqDeYsXeHzbKMbj6c4EVH++A1fQ3lKri+fZKofRYQE0hETAkCvlcNEtrgtpvGyHpOGKKnkdF6WBADENtONVYsLhJaX98b92cFdiK2PQpgCTtZFam8KI78ebZz3NAv4dti29CtzXsbQyK9HIUFOeYUKu3xpq8moykpyHXL5y56YOUy3HtyCW+a0x1SW0Vygx4ukJf3pPXjpxwxsP6qOKdkc+fCd60Cr8lybh1ujACa8ylnJ8UKyxtm7NUoAxhIT8c2zc2osRXcvfZ6jQ3N3tPTOTcbmXznz+pg8FEYLKIwWfJLMdYbCuI3U7hQmHpnE5GPVWWGejJA+B9TYilUELSoQXR9B57YkSeRMmrzW4kuMASci1LPcZRcwkqquTjgZp6xaVkn6nZRNkrKSxxtVLEzXhVPpjlH1OUlK6FoS8U0xaFGNwp8zZHbBGKqvk6WLAJLIkd5Vvs+1qDbjOWd0G9AT5QsCTspB7kQeWpTIK9nKJ1QvFb1n7kSOFrHUEKQr4eZcvzcNqJA1esTRdpE/mQdTvNhbOOMC4KV9X4yuxUt6aCPPPHEM//lA+WfzwDUOJ+1Q/5wlVSVPUjyKMjNh/vwMSO1Nw7OlbwbMs9BngN1JJDc6NcM10ZOGFpTpiithTdrUu5d1IWIauPfdCXJo5QL+giPXeU1TIMbpvucRNK4xrIg7YEo+fOrOUzjwbwex633za9FZDAjIWZNoZeVs2bJlAIBbbrllzjKwVlYZvPF4mOsEezaT/VY5RJ5JaOV3VlrtBIB//Md/nPN4FoussZX757LLLgMADA0NzX08JRNMfSY9fpPQSwieXSIDo6yzJsbjlqx+NsfN1A0YgCJpXNnoMw4UOujm11/IYiRd/Hw8xGcOsq0cl6pMTS9PoFvJcZa/djn6/2QJzvnQOdCT5dfMo6EoWcmvDvuujU7WqZKizQuS+j5cm2Q+uaEcRn87Rvvaon88JJA7noWdLhIL11H2zw6ROum4SqbTfCWobBgl1Vch4De5zwVaXMAumbx9dt99+M59xdX37PHGBLK094TsqOdBzjgwqdWWNbpNfpVO1vENThxd+H04c8XSpbSDux4+hXRJiPGsejs9EucZIDTrCFID5pJy+Wr2cBYPv3EHHn7jDnoLg5NDqiVhjVm1r8VuedWDqr1MyeBo0sw1DhEW6LqyC8IQlK9Wp5Lq9Yp12nnsHyn//kVEUJxGKTmrILVOxqFcrBJHiNJcuVqIuHYzqls4WYfcKQVDZCUt6oz9fowqIZyVjQsoP86mn0iB68w3JWoWRrdBtvslMPtNhAbMavIi4ZtcjD84AVlQ1wkJ2NM23IKD1O4UbNVXXGYIogLrnIIkcitKK2cAOEP3NV3+85ngRNhKdv/yfO1gbiZIbunmJdyCQ6TMJVImXSJr3jft2d17jodNganFCg7YcTp+wul83fNJ2oqc2RLZEzlkD2bgZF0Uxiwai2AQ3mKsGoOIag3nIqxEiknGIBwGl8hu7MK4NrvvfbEjIGdNolWGIEBxYn3w4EE873nPm5MjWCurDCtWrCj7/W/+5m/aPqZWBRefSWjld9bd3V312GzNQdphwHHzzTfP+Ttt5f5JJBIAgKmp6pXo5gdUrJzFVs2+R6gSWsmN8Ml/3OP/LCKiTG9fD6XW/uCsSVkjZS0xplzINGqkhmBAV5GcnZjkuHuP6mkyeJn8quG4PIv8JVFfIqNFBFb9+UpEVobBGMOF/3E+Op9LPSmjBu1LrpGBgZN24KRm78w2I5SltmfHbadowjL+4IRvOABHwpp2yvv9HKpiOgWXfvbcMS13btdEu1j14KqJfa5gjKEQKU5AYq6N+H0naNsGh5NuXHksq5xp87PS1zX4PWcPPVFuCMLQnMzSTjsQORp3QdfmRRYBwCxZCNjzf6szuJo5xrxjn+tKbjafnrOK13o27B6Z4QaH0aXDTtsojFk1Kxll/UKMTA/8ybVgKL0QGN0GEufFyeilzkfNm7SP4o6Ff/nvMA6MFgepxTTENsRIzmu7sCYtUhCUDMtOO+SSV/L9lslaAUQ3RJG4MOH/fk52EsJqfHw6WafMLMfJOHDSDlW4ayySlC6gVTomzgd6h15TXuvlnbm2VDJCidzRHOxJCoG2pxxkDmcgVdNeWe+eC1okcyW4wYpRDYIRAa/47r1KktlfPOd7rRkWYCSQ2puCk3EpT00ZnUi1SFWsnCnXSG0WYe9epdbgcKYsWIyBS4nBWvGWjD6jV0nMHcsifTALqUijm3fp+FHfXX7cAiRVgGc0JQGIvCqXRup/o8/NkgYejPWU744WOO0+lQjIWZNolSEIALz4xS/2f/7FL36BX//617PeRisn1ps3by77/VOf+hR+9atfzXlMgayxNlpZGerq6qp6bM2aNRgdHX1KxtPf3w8A+MxnPoMf/OAHc9pGK8fjkbPp6blrz13L9ac9raicrRsov1l4kn2jS29K9186sW6254xxkq8xAciCq9zdaGKXXEo3/Tef3A3DdfDKL8cwnQfAUNb03vA9nBLCWAeh/hCS19KCQtQuVllCS0Nwsg6cnNtSG2TpKpKVtv2sIydlozBWICcz5ajm5pxiuCxUPo8iddIF9ay51IeU3p9BYXx2/XFlGVVewOw8YJnli4PhkQzedUcYTQcal0h1PVnQXLGks9hz9ouHGAplE+Pa+UZV47EleF6RM1PzpV5zRbi7OKEujFb2M7NyU5068A1kGIOTm1/4fOU5UUsuzA0ON++CK1vzStB3poakDCXgTU49B7sSaHENRrfu738n7yC9v1hx0aLKDMihsVz2r4my1zPB4OTJkn30N2Oql6u8ksZEOTmr+kw6o+pdCa7Yeaju8733dQuyrPKlRQXyIwW4OQe5oVx1BbvkmjibPtm5QkpVFSoo+aIjkR8pUI9c3kV4ZRixDTEYKj6krHImQQTTUmHr6m9mn4nouiiMCgks06kHS4vreDTSCQBYXVI5+8b28ue7eRe5Ezk4ORqH9Aw5HAk9rvmOn1wt7HGNIbm1o6nP7fV7Gd0G+mISutrWoX/YVfVco0snJYgyArGnHcQ2RcFNAaNLJ/k1L/acuS7J9cMrwoitjzYcizA5Os6nY5aryIh4CH4vpYfZmFotRgTkrEl4roKhWWR71MOyZcvw7Gc/2//97rvvnvU2PCONVlTydF3Hxo0byx6bC2EMZI0zo5WVoVrkbGhoaFa9Z60cT6k0dvfuWstpCzueeJxMKPbu3YudO3fOaRt2zoGpjs9QeP5jWtVb/vt2tbDvhaU2Ak0sS2+wzTVyQ8k/QitCiKyO+CGnq7eG4QIwpYurpoYBAI8eFzQBbLJnSKrGbwBV1seV0ONqUljioMk1riY8RXImXeoLmw+kS5KnwmgB0gXyw3nYGQf5U3nqj1GeH67lwi6xX5YuySAdtSpe2ruWPZxB5uDschllWeUMxVn2HGFVuP+FXAdfecDE7jGtKacy6RQtrFlIzEioG6EjBl9K9IqRQ3j4hu0Yf3CC3gfNWftLR/qVM8vQZsxaagahniI50yt7VtTE2FaEveZ4XAlXHYciwovZTnNEJXEav2+8+jkag5t1wEPcJxiZI1mMD05AShWorI4hTaMxegsuTKAmeeSeVA7UP1VKXM5fS8+PuUXyWmqrT7EDDjKHMrCnbVqckERCMkczcB0lLytRSESUI2t0Pf0/eWmScqlKkEzNfO4YPUbV5NzsNRHqVws4Werlyo8UkFM9oNJRRiCSCFO7Q+3NbgN2yqHFK0nGJdJyYadsRFZHIMKC9p+Xc1b61UjpxyswU9nnAohvjsFcYlYZJyXOjcPsJWnlrkg1ifrr70XKfpcu9efFNkbBde6TM9cGQgOhYhyD8KSNfHYMgNN2lvUWd/LKfLpqEYZxRj3aKmvOTtuUbbsijNBACInzEsgPF8AAWExl8oFBRATCK5rI7gT8OARwmi/EDIlCxbyzkdR2sSMgZ03C613xKgTzxZYtW/yf3//+92NiYmJWr/cqAl6FYL548MEHcf311/u/j42NzXobgaxxZrSy2tnRUXvFazY9Vq0cT09PUVIwVxdSb8HBk0jOB6XnxRVXXDGnbZy8bwqmdHE4FEXnkvmPqfsZ5YR614f20w9NWo9LV8JbywjpTXIzZX3MNIaeZ3QjsiZClTODIb5ER/IFAwCAZQVafJrIknuXazfXn+NaEsw7hRtM9HXVXB9xK6oHKjTWq/x4la75yVKkP1lxsw7stA035yC8IkwukWq1VloSruoPGd8+QeOwJOxpy5fmSOXY6BRcWFMW7DQZODQ1ipKeM24KuDOE/zYDyyif8J6fHofpOkg5NKFuOB4H0DwyHdKgxeeuBGGMYVQrWayUwMg9I/Q3VPcq1R6PBFfk2A1p1B85D8Q3RrEjSlUG61S+LJ8LkoKH4cLPo6qEm3NhZ2jcIiyoH2YeBHamqpv3/kxnsDMOuMlROF3A+PYJ5IfzyBzJIns0B2vC8klH1FTHlDcuwWrP4jTub186sqxCunYF/exVzgBgMlc+TmlLZE/kSf7mSkiLbO6ndxUrN17lTLukC5tu2gAA2HzLJmx473r0v3AJtI7yY5U3uNczzmrK2rS4Bnva8StV6X0pTO9J+edn+kCGFlQKjaui84VHorjGENsUQ3RdFJG1EQq0LiX8viFIyX6VoIqRySF01nCsZFSigRsMv4/Xjiko26UOSXA9J1g/ry3rgAmGkLp58DAtyoiomNW933NrXBItH/gDLxmschl1HVogi6wOI7KqnEQCwMQDEwCAnZFO6EKSEmQObITOLwnLBayKlZAz3bExIGdNwpv0DgwMtGR7b3/728t+//GPfzyr13vkzKsQzBeJRAKf+cxn/N/nQs4CWePMyGZp5bAV1dclS5bgoosuAgDEYkV3s9mQ9VaSs9K+xc9+9rO48cYbZ72NVIomvKWfZ67wcvS87c7lOBs5RjeckWhkXn0nHjqvSmL4Nef4v1+UHsPQqGw6tNe1XMQztGIcCjfZc6bstpnGIEJUFeMhDmFwaDEN3WuourBMyWXGPddGxmpacVeCZHuKVDWonHk9d0JWbNiloFIn5yB7LIv8qXxZv5fnsjgruABsF44lYfQYcLIutLgOLU422UzSZ3QtkjY6GYdMUCSdF07W9fvW0gcyvqOmPWXDnrSRPpxprn+pRBobjvF5n2vrV5UfiKZ0ccvhhzGaoWy6sfvHZ5R3uQUXwnMe7mzcgN8IqWh5dWp4r6pooDxsvR6cvAOWL6lUzXM80TBwW39Rpv/oXz9e/KNUMlYJTDw0iczBDCYfnSx7vZ22/coZDwnqi5zPkDiZS9SC9z1xjUJ4uSlgp2xkD2dhjRfgZul8SI1ZMFUpxjRVz5egqgcTtSvoWkz47p3k0lo8bkSESNMlPUXiOp4t34abd6EnNDqPXIn8yRzgSDiZYtXRO4/NPxrwJXlaXEPXlZ3gGkdiS/ncRMzGwbMCRrdBocaWJKt6u2ThJO8iezxHlbWFUrJ5lu4q+ys8EPLz0wAgZ5VcRwHkT+fVAhwRUGbwpogk0+j5f/daViXbA8pJNS1qoex3xtUxbbkwpAsHDMzkECaH2Ws07PEqG4uSNvZEq7/Hh//iMRz6/OHidVpCxRowiHD1ios1QQuxj0Y7EdUBoPmw94pBAWA4nWJV5Gzfh/fPfnuLCAE5awKWZeH06dNgjKGvr37Q4mywdu1apNPFVahHHnlkVq/3jA5aVTkDqGfp9ttvBwD8z//8z6wzyAJZ48wYHydJS2dn57y3xTnH4OAgXNctM73wCGAzOHXqFIDa5iKzxXvf+15cffXV/u8f/ehHcf/9989qG60kZ5WTvK9//es4cuTIrHLPxkfV8WyKJq0RZwYXAn/0hzoir1/jP7bz7imqnDUx0c+PFLB8OoU844htijbXc8aUq1UJcWKgmyzTGKLrSEZ0deY0oo6F3x9SEwzZ3JikI8G8CkAjcmYos5CKGZQnm3JzLlK7U8gczqqVV3re2H3jyBycXVi9dGklXRZcaHENTtqG2WPQcaFW3ENLTYSWhkjuOJKHPWWTbNSVSg5ZrJrJvAvXkbCmbVhTdtMGJvmU40tR4x2zlBHVwNqX0v2n45Ji5fyCzDhG0pzGN25RVlS9/WK5YKqilUjO//b/lVvLqyP26TxOTAIMRXngTJAFCaZIigbMS0IIAGAMGVEck5t1sWeYIW/T35wsZVJZ0zZyQ3kUTpev+NsZx6/gihCnytF8DEEYw9p3rEH/C5fgvI9vKfubmy+eN9KR4CaNT0oiYNwk0jY5KRFxHbigap4Icz/jrFbPGQCE+kx/sitdMoDwZMNC9ZytPj2Oq3vovHrsREVYtgptdjJkfZ49lkN+tABr3PLt673KWb2+xY5LOrDub9bCffU6AMCqsUkcvO3QnIx/jG5DWcKTUY+bc0hi7BI5m3p0Ck4LozhmCz2pl13/ptVt2PturAkL2WNZMEZuuFxnTfVkMk6Las/bmEfPBdX3xdIoBNeSZT2L0lH9bGuiKKSVXFYIdKp1y8T5iVn1wDJ1G6x5OhRcnPrRMHLHlGFJRXZcJTxyNikMRA2XeOocuZmUQEgDCjWscM9kT5CAnDUBbxLb19fXkh4vD5FIBN/97ncBzL5Px5uQt6py5sGTpx06dAjvec97ZvXaQNY4MzzpajKZbMn2OKeVZsYYfvSjHwEATp8+3fTr9+3bBwDYsGHDvMfS0dGB3/zmN5BS+mTviiuuwK5d1Q3D9dBKcgaQXNjD61//eqxatQovfelLm3799ISazIRac5n0JFvnv7QHOy8jgmbceVhZtjcx0T9FK92n9RBis6jCMI0heVGFDFZ9pOiaCOLnxaHZLi6bPo1vPWTgh4/rdNNrxtDBaV7W6AXyatItu2mS8QY5tXlua9IhQw4n78JOO0WThiYgpcTkY1OQlgtHOcs5GbdoG61MPrjGoSc0FMYsTO2chltQ5h+29OMNvIqeq0wAuMbg5qiHJ3+6sTvq+FhR1qipfpT5IHF+Aud+5Bxs/Lv15e8z7sLsNZFXPXb14DpFMrRy6fyP663naui7vthMGZIuPv6+MVg2YKeacOfLOz5ZDCV4U31zM+HKLcA168vf92/fn8Un7g2BCYbM4QwdayqLyrXK+xvtSZucOlGUNc7PEAQwewys+rOViK4t76cqzaWLb46D65z6tFTfo9eLOj1O16G0Ml8RISJnoYGQ3z9aCTrWFflTBC6tFjjCy0i5YY1bePNDD4NJiTd9K4oj48XjIbYxBr1Dh7k0DKPXRGggBGukAG5w//gq9jDV3j+MMfT8QTfY8qKKYfinpzFVI8utGbgOBcq7DmVmZY9k6BqRc+AWHOqzauKatRBI+eRMXfNiAlznRYOmZl1bOZE5SNRkGiPp4r6PbYqW9WxJh0yjuMlx/JRyRtQ0hPXysTUNhobX+Lxa7DCXmDD76lvbW8rdc0IzEDWowjenqjknc6ybn5uDWaNA/Ztfzm5RbzEhIGdNoNWSxlKsX0832R/+8IezqlS1uufMw5IlS/yfP/GJT8xKThTIGmdGKytnlfC26b1HM3jySQpq9I7BVqHUMfKee+5p+nWtJmf/8A//gMsvv7zssdmY76Sm6Hg2QqwVhTO62nqua1f2khnHVB6Z/c1J5AqKnI1rBuJGc26NADWcl0puPBtuTzaYOJ8WeFYoaePH76W7XHOVMxTzlBoQD8+2XZcSdmmOkpTkNmhJuAXK5IEjYY0WMHbfGDXcT1hNX4uctANrlPp0QivCYJwhvjlWrCQ48Cc6TDBYYxZZdavH3IILo8dQgbMuVTiconW2NWXDtSUFuTbA6IRbNAQRDHrXPHsXOWD0mn5fif+Zh7LgBocWFnXzrQBgcsqFpuRly/rn7zwMBix9aT8u/spWHDNoEv7yY/ux+8fjTQWZWxM2hCJn4Ri5uc0HmmD4zItTft8ZALx05DA+cncIelJH5lAWUkroHTo5cjrFXsepx6eQPZ4lYwkAPMzJWGNehiD1e4tkRWg44wzhZSE6Dyyqdrk5F06UJrlZgxwYeUgAglEluE7PGdOYb2nuhSJ6iyPhFWEkVeW1ZzqDW448DAC4fUf1vtciAnpCg4gIRNZEfdkvAD+nijfIypqNdG4mkIU9fWdO1kH2eI56qvISrgVoCQ2iRe81X6TS3opMUd6oxel7g2DouDCB8PLG5hdGt0HnukRNg52JElkj18oD7vWk7puyHDqhrm2GIPMUYPazf168D0aVAYy2vrw4kB8mcmZ0GTB7a8t5gWIu4qQiZ3M+yZSscWWnixufW+2ka3x6bmZgiwGL40he5GgnOVu3bp3/8x133NH069ohawSoivLhD3/Y/302MrC5VM6eLuRMStlWcuZV45r9vrLZLB5//HFwznH++ee3dCyvetWr/J+3b9/e9OtaTc4A4HnPe15TzyuMF5DaVz7Zzqr8q1CEz19uBYBx7i9+rlsl8PNOcrgc2plpyhAkq25845qJiN48Oau8ScbPjaPjwg7fSczopEnZORGSpFiOmpA2U8RwJLhHBhqsqjLGYKtBOyWf10jqMHsNMKZMPHIOXBvIHqf8INeSyB7LIXuiccgyQKTSyVHlTYvQdaZ00uLaLkRJllJ0TYT6cDiDdIHYphj1CblEFqXtwlxC1QPGmS/tasb6f3xCwlClhviWGJIX1jbyaRbM0/EA6P6DohxZDBdXiGeqHjxyWPj9S3qkBbd/NTnSkzqGQsUKycSedMNQcelK2BOW3wMXSfCa2VKzHw7Dty+/AO9YS0H0/VYWa7vJFIErIwYvbJlkcQ5c20XuZB5uXvrVW6qczbMPboZzwi3UPsHcLJnYcIPDTjvIqipD3tSQ3pv2ozCgyFyt8THGKOvKosUOxinv0MPGmzcgcQHNHbalRsGli2MTTRwPnPmkUlPHdaNsOlFZJZrj/qTFIgY365L7oMHhKgdHOPSYJ9N+qpH2yJnB/cUnZghEVobBdQY9oTdVRWecwehRtvwV5zWXEtO5+tsILwv5izj7jirzlqjw+wlne1x7CwFSSmx+/yZsvHkDtvzfjUjz4sLf8LHmCgxeLt6EMBANMcp9mwNKP4I+TyfcxYaAnDUBj5wtXbq05duOxWK49NJLAQBPPPFE06/zqhOtksh5YIzhxhtv9KVuJ0+ebPq1c+k5e7rIGrPZLCzLQigUaokhSCU898ZmXT/37NkDx3GwceNGRKOtvaH9x3/8B17xilcAKEonm0E7yNlNN92ET33qU2WPFQrVK2z5UwVkj5b36+WVTj8Snd/quQe6kdAN5Jo1Nk6oSsPICZt6rhq4200O0Y0vE9LVnG9ug+I6h4hwwKGsHs+W+IIjJxFzLAxP0wS20eQaKO85a1Q5AwBH3U2dkgya0ECIekocAI4LN+sqy+w8nKyD/Ok8tLhGpKgJUA6Rg3oli/jmOELLiucgNzlCS4iMQUplAsEQXR+FFhOUxRUS0GIaQstCcPMOTXrzjScDExPUL+QwBhFvgSS+qFbD2reuRuZ8kqGHRujYlbZb16VMSoknjgCmuuaKFpAzVlIYuubi4vamuA6ngazRtcjoxXOPjMZacZLRmP7zDVlsvjgEF0DCsdBhqIWW5TRh5RojJ0+HyNjY78ZJrqqIEUAELr55fteiyn6w0uOuXm+g3q3DXGJCRAWkK1F4dAIAkIqHi+6MjKoYlVLJUvCQQGHCgt6pETEt7Tvl5T1PMcfGcKoZsqDC7FGUNTbKymMV2W31SGkjSJt6quysQ5LLPtpHcJrL1FtIZNNeVZEDrurVMhgiqyPovnqWPd6ciFnfs8tDlv947Cgmm1TtnVKyxlBcwMm6MPvqV7XqDsOT3YJyMDsvTSIc5gh99BLc3rMKADA50pz83KucTWgGYiFZM9+vuUEVfzzTc80qEZCzJtDOyhkAvOUtbwHQ/ETWcRwcPHgQAJl4tANeZMBsyFkga6wPr2rWajLtwdvuyZMnm+pf9CqvrTADqUQikfCrr081OdN1HW9961vLtllqoGJP25h+MoX86XzVpMFWk7RYjLXE0ZImjrQdzoFzVN5QepRyYKSyWXcyDqaeqO7LGFXkDAnDn4TOeSicgekC2aNZaIkiaXjR2BGMZjgKBYnJR6dgN+j1kq4shho3Qc5sdd7bNW6k1O+l9rvtojBcgJN2YHQZ4AZH9mi26WBjKRmMntoTEL/yUAGqkhW3L0ICoYEQXFv6/YLMc3i0XNUXNzOpnh6h7yxn6pRNN0+Ufudc52BbKZ4hfyyLdIE+g5t3q8KypSORPZpD4VgWAhIuZ+ANKh5Nj0n9f/V1Sf+xwjT16VlT9Qm1tEiixkB5R9EWZAl6MsKBhMSHXpTzHe56R4iwClOo3D8yyHAyrp99lh8uqDwtr3LGoXfOs5LHWdkSwaa/L/b3pg/UnlmbPSYZTDCG6OoICidJzpza2A3OvfBpNFyb8WzwwwMhMnPQWZk0uFS2TIsyjY8Hs99EZCXJ8TSP5DeQNVZKDedKzuLnxGH2Gn5+FTc4wsvDyrxk/lEnrUQuq+ZCXuWMMdVzNvtj3Dume57Vg/M+tsWftf/lyd342neqFxprwZwk1YHWawAumc/MFl1XdEJP6OA6R/5UUcVw1ToX/VvomEiPN9FnmiPTnQLjyHKBiDl36StjxfNrIULIFxIBOWsC7SZnXs9PsxPZo0ePolAoYGBgoOVVDw9zIWeBrLE+2ilpBMqt41/84hc3fH47iFApVqxYASEETpw4gXy+sXECUNxH9TLc5oP9+4u2up70UzoS49snkDuVU9WW4sWdpHF02U/EW9Fwhio21dFL50BeSTzsDAWt2ikb2SPlOUz54TymT9LzQl5fzjzJmedGF15Z7H149fABaK6L05NA/mSuapJfCddywbzqdzPkrEblrHJ7ToZkjW7ehTkQgtlj+BlQzUgtXVtCS2gwe+o3pNcEQ/U+5crhsOS7cwskc3RyLk7fO4Kx343XJY1pZeZQMFtkJMXLfQEiK6gSs7yQxlcfMKkSlHYwMTiJ1O4UJh+dQmGsgNHfjsHJOIjspXOsENZbc/dn8AeUvLgDePVaAIBIFQBXYuKhybqTJs8GHQDyTCBRHYc0t/EoDCQkQkp691c7Hil/mk4kW9rKhXPcBtO4Mo8pWunPu2LOgVJ6FhoIYdWbVgIApp9oLoepoMbT3Vd0j2Ql/av14LlAenloXONlhCxSct5/+OAgRqYaL3xwjfv9q7p3v29Q9aiUrM3kJjoTmGDQOjRE15YfKLGNsTlVgtqJrCJnwiRixdg8CEjJwlB0XbTsGthj5zE02fggDU/TPVjrMWlBrYkF9HrgBq/6LJ3qXmY30Wdql5iBxEwJzWBV4ePND6Z4ipZWzh5+bjGyZi7uoIsBATlrAouNnH3/+98HAFxyySVtGQ9QJGdzCTWeTeXs6SJr9OSG7SJnpZNHz+hjJrSbnAkh/PPlxIkTTb3GWwhoVdB7Kfr6+nDhhRcCKJIzJ+uQ/bJNfRluycXdtVyEckRMOrta1HMmWNnEuncJTXLktA172sbE9gmcvncE+VN55E/ny2SWbsHF+DDd1PqXq4n+PMfETQ4IBi2qYevnLvAff9XIAUxEwuAGR/5UoSoLyh+TTflC3PtMTckaVcW8TkHFLUjApvBoocwIqFeI+xLDmXq9XMuFNdbcanIlomsiiKwun/j5GXQlH81V1TU9oVPuWdqGky46TZYiP0HfmRPRW3IMgbMydhZSBLTTLuDwGAcsF9aEBTfnIHsyh9xQjiqgaZtyoNI0nrFNPfOaoPlQPV4eei+iPqZotgC34MKasOu6NnoGMACQ5xxLEi24F5SQxVLoFRaWXg5XdG0UsuDCzTuIrAkjujZatJwPzy6kt+ZwVE9eacXK7KXvrJmeRccFrCl63rrlRLK8KIyG761RD6WXh8Y0VlYZXvG65f7PnU4Bz9x3aFbW45pakBANilZahezxwCcPYvete+YUMk+9dC0wsmkzCqr6qpskaxRhjo4L5+gPwFnd7yXi2Hj4eOOFn1iaKl0eiZ1P2DvXWZUhUbJb9fZmmiBn6nqQEhr6om7d8PHmUDy2kpfSom50XQTrn5VESvXCeYstZxoCctYEvMllu8jZwMAAIpEIRkZGmuoZeuyxxwAAL3jBC9oyHmB+lbMg56wa7ZY1AsCLXvQiAM0RwHaTMwBYtowML44fP97U871jrV3nmWee45Gz3IkcrGkHdspRzm3kBuZaLjIpiZ483dC6lrZmYq3FRFnxbNkyOge0rAWz30TmUBbZI1nkxwpwcy7SBzJ+HgwYYKSIdFx6rrcaO79BcZ37xhjmEhMr30BB4henRnE6L2B0GcifziN/qlDmvJfam0JuKAdrzEJmf8Z3bWMNek+A2j1nHkSEemxim2Mw+4yy/hwAkJLBtVyM3V/uSOparj/Ryx7LITuUa9gHUwv15I7SlmWfzejUoSd0GJ06heJmXYw/NIHxinEBKucMAFo0oaz8zjetJElgxHUwNuGChwVcWyJ3io4he8qGkyGpXuZIBiznZV21ppJXOZ6lK+hzdhQKsC0Ja7yA6Z0pTO+ehjVhldnWS1siryaxBSbQ3YpUGMYgS8a07j1kuHXYjMKqc6uRtiTpKmOwUzY5dwLQoq1alEFZtYOrUN5mQrqPbZ/GsiwZFZ2/RsmRRfVCT833VbJGkkEyMJ0hvS/tVzK1uIbOy5P+8182fAjTzYkcIB1J8lg07jkTHDhqlFRGJDAxOOlXUM5GWPmi06+UEtDmbnZTagJUiTW5aRyfaHyQdmToXhYZMKkHbh6SZmbwql7KbrXQaGYa9wV7ixIZrshZnTiIpsbCaHvSkYhtiOHCfz8fW/75HMRDEmmVd9hMpMdiREDOGsCyLOzcSXacrciDqgXGGJYvp1WsZipVw8PDANo3iQUCWWOr4eV9tatyBgDf+ta3AADpdLrhquRCkrPFUDkDinJJr+esMFoAYxSI6RZcSJts2Ed/N4ZHnrDRY+XgAgh1G2jFLE1ERNlNdkDlTEUKFrSohsS5cURWReBkyKnNyTjIHsti/IFxZA5n0JvLwgHQv0GRlnkOKbImUnYH6Lqajs1eK4fhaU49KhZZyE8MFolH5nCWnO0KLrS4QFhVGN1YYxmhR87sGn0nRqeOxBbKezK6jKrVWSaoEuAZTTg5B6m9aYz+dgyjvxnD2O/GkD2WRf5EjvZ1i5A4L1E2llB/yF/pdS2q8tmTtm/JLl2J7LEsckM5HB6mzyvMVjl+lv9u6gxagiZ9hUkbkVURxDZGEV0bgTVuwck5cFI2VYeyLljeM7vgrYmHqNhGKMwxpekQkJgSOkREg2u5yB3PEUlMO2TlLSWsiQJOPkDn4lTIqKqwzGk4Fbu541xifJ12AdP52tuPrI0guooqppM7piBtSdl/Ma1F1c7yL81zEG1mRX/4q0fLXsc4AEHBxCLcQE4omJKwlSw8iPLqWWXe1qkm+s6AYn9PgXHwBgcSY8C7116K+5eUX9fPth4hD1ICJ0boZyNElbMa+chNgwmUSViXPL/P//kF48cQ/9WxBuOR6MqRCiM2oANMzuvc7766q6q/uH+lhjzjiBUKOHH7zHNYJ6MCsbmG3oirYh/mOBgVxp4bIvLpuXgmTIlRjaqE3mLLmYaAnDXA448/jlwuh/Xr17fFPMFDby8FeXrEayZ4QcPea9oBb4Lc7MQamF3l7Okma/zEJz4BoPXRB6UIhUKIRCIoFApIp2fOYPLIWatDzEvhkSEvk28mSCl9claatdeO8ZTGDeidOlxb0uTakbBTNIF8+DdZcACZmFkML54nGFfSIiUH6umhcyDsOkgdo5sLNzjsSZusvtM2rEkL6QMZjD6egoDEKSOMzoQazzyHxSr6YYwuA5IBSbuAU+NULbLTDtwc7RMvdNnNuwCnFUkmOKIZelwmDaT3pWecdHqGIG4T0QGV4AZVAlxLIrUvjfxwAdmjGSIflgvXpioWDwmI8MJIn+w0vbc1YcHNU89g/mQeU0+kMLxjGifH6UsyIi3qW6zRa8QVOXOUAxpjjI6jlA0n68KeptBs13KhKxt9I9q+W3/KIJKeGnOgxciy3rWJkNnTFkbuGcH4AxNI70sj/Suaxf563ep5Sa18MKB0B2kJDVkhkHAsjB+sHcUgTOGT+Zw6D2MbopBojREQF+U26GIW5MwOFSuc3jEtIgKdlyarFi+qoDFyQPXIGQN4hUQuvLS8Ou0dr43g9ffYrDHJFxxICR0jZvl7zbX3bLFj8KjwZbThCFXO5lWpErzsnF/95lVY9qqic/ia+w7P+Hon7SDiOMhwgUS3AMDmNfOvvI8BQEcYGNaph/HoV44hP1y/BOtVzrJcoC9Cssa5x5xRRbhCtYxEiO6VAJA/1WQ5eJEhIGcN8MADDwAALrvssra+T18frYZ4xGsmeATOe0074LlAHjhwoOnXeCHahtF4Bf3pVjmzbVrtv+GGG9r6Pl1d5N42NjY24/MWonIWDtPFMZvNNngmVbNyuRxisVjbxlSLnAFAfFMMkTURiDBH/mQOTsrGwR00SWPdISizrZaAady/qWklq49P3kx9gkxnsCZtaHENTs6Fm3NhTds4fZBW/1KmURzLfPtheLkDBhMMdtwABzB21CKZ17QNt+AidyqP8QcmYE1RfxwDYE3aAGeIZAtwAYikAafgwk7Xl5E4nkvrHBzbRFiQiUTBxeSjU1SJybkkS7UpH83JOGXuk+1GbH2UpI0Zh8jikSwmH5+CPVnA4SGJkEqOXd4tWzLRZ5xXkTMvuFmbLl8hjq6JIrYuCnPAhLTIiVJTlbNQrAVmF0DNbaRNuv7nxi1wnZPsqODCmrJRGLOQP11AYTiPwpgFTFtwAUwt72jJScYYg5QlxzRneKKXLMgnH6l2QK1E9jhdq0LLQi0851nZd1aUNTa+9+WM4rHMDMoqS25tzjCJK1kjOENoiQmtQwfTGPInc37Vqv8l/ei4qLhgePRUc+el93qLcxgNTjfvMleoKB+5Z5n1uYfdwwKGWnSORpUEfT6VM1bt/dL/gvIFzJmUMtkhIien9DCiBi3HzbuXsuQ+BgBhXSJXsig/9Vj9BVlvUSIjNPRGHSUZnys7U661FS9PhCROKrLoBWOfaQjIWQN4ksaLLrqore/jEa0vfelLDZ+7EORs7dq1YIzh0KFDNXOhasFz5ZsNOXs6VM7y+TxOnz4NIQSuueaatr5Xs+Ts1KlTANrjjOjBI2eZTOMwFk/O2y5JI1AtaywFNzgYgNzJPKwpB6mjagV9qeHl7LYEXGdAjTmZM16seiTOjYMJ5pOi0BITY8oMxFauf3SDnedgKqoMACBWKWJ8iG6uiXPjcPIu9KQON+8iezQLJ+cgf6qA3FAObt4BBzCpGdBN5ts+VyI/nIebd7E0Q4sCw7c8MeteAK5zX+5pT1oojBRgT9mwpm24WZLtOWl7Hs3lswfXOWKbYohviUPv1JA/XUDueA7WpIMTIwxhlz6jEW6RrLHG7o0uJflOdKp8EYSb5KqmJ8iZUdrSr3JGOlpVWaz+UFlTySwn6Ltw0kSe03vTiG2MQTqAnXJ8Z7dpoaM35jbVs9jUcKQsk8yNdpI6IH+88SJRQUUfmL1GlYR07mMqNwTx8uWaIWdpvdinxBijvd3kbvIm0FwwxDbFqGKnIgS8fa9FBDbfsgm5OB1DJ042R5g8YlVgHLqY+TVchQOPa+WOioUZqitnMqb2pfG60+QOzA0+/4s1K1c4ANQvmH3hav/3mSJPRo/R/G00FILwLkPzNZOquI+F9HLy7aksaqFYOVM9Z4Kcg+c2EKqcVX6eqAEMG1SpzQWVs7MTnjysnUYOQFGi6JHBmcaTyWQQCoXaWvUwTRPLli2D67pNSxs9EtcMOXs65ZyVEg+PlLYL3nE0U6/g+Pg4vvjFLwIAtm7d2raxzKZy9uUvfxlAe8lZpSFIJURUQ2G0gFC/ifi0WkHvVxOKVq2i6zRhcpQxQ1ojspXXy48LbnLoSR2RlREYXQZsVY2QIY36SBjmf/Xm1awzsZ56b/TR4ncWXRWB2WvCyTrIHs+CCY7c6bzfawUAI5oJQ1T3R3jIny6gMF6AXjJJffxdTzQM3q415vDyMBhnVKFjZAlOVTQHturXW0h4/TzSBazxAskqQxxDEwymkhG2zGGOAYyV7+DYMjpGe7OUdVYLkdUR7MhF0JvLIs841m9qjclNrYWLXJiOVWvCBhNEBty8CzfrqH5CHdxg0LvoeVNCR290fn0wxQFRpSpzsLgglOqk65Az1AQ5U06feqfesnI5E6zcEMTgFDBdkEjtSc34WktZsu959nr1iJwFOYPv1kgPkKyxltMjV1LLzgebu9dLr3LGOIwGh7ZQ738gWi7p3/OhfXNybFzs2PrtHf7PifPjStY492NJ2hKF8WqjjfhzlvhGK4UZqkO/+B19V05nCPlTVLGeqwGHB26UV850AdzVXXT/LIzWNwax0p4hiEBv2EV4eWjO12ymKmeV5yrnwFRULQ6fDCpnZyVyOVpB9yaa7cKb3vSmsverh9J+s5YE484ArwrTjIMkUCRnptk4c+TpJGscGaG+inZWOj2sXUs5Q6W5XpW45557/J8vvvjito1lNuTs3nvvBdC+fjOgvqzRgxbTEF0bJRezrGow7jcb5gnNBlznsKdspPfT5HHkf20GULyReGCcIdRf7NFIT9J5wiICTsaBucScd4hwrcpArIvOSz1XXIkVEUETTMaQH8pDhDlCvSYiKyMojNI5P6KHYAiahNSacHmvL0X+ZB57/7n+cVoPIiygdxmwxi1Il8Ki7bRNuWg9Rtuvi/VgdBvIDxcgLRehgRCmIPDS0SMAVGxBy2SE5RuKrqFjZ312CodGax8TXOPYeTdVQ3fFkgiZLSoH19hEPlzeA5c4NwE368ATSWgxDeaSEDIqsHZKM3Ben91UiHnD4TAGrpX3VblJdT+amHmSJqWENaak+d1G68hZhayRMYa+59Ii2tAPTs34WltVGSIx+l5n0wfHBYXieZEJ1CukJG0Vm/DI2Xm7jzcVEu1FIDRDzrzhnjSr51Anfzjz5z8TIUpIS/ycuJ9zNlfYGQciUq0dXd0tcULt01xJFdLJO5h6fMrP90oPeRlnBvIjBXAN85/5C1aV7fhg9xJ8u4faYWrFmXhKiRPH6Jie0gxs6rLndxlSizGVlUUAyMTp/pkfyp2RiwABOWsAjyyFQqEGz5wfvMpcIyOHhZA0evCcBT0b+EYIZI214R1DpUHR7YKXmbd37966z/HI0nOe85y2jqlZcmZZFh555BEAwL//+7+3bTyNyJkHJhi6lI1+eImpes5aNFHT6abGlclIV78KdLVmlvhlpzwLdMr6mrMMpGwwdMMslaBEO+m8fM7ocWQqVj/DK0Iwl5gwe03f1MAjZ6OaCUOrbl7330pN4KwKRjjx4ATyIwV/O83C6NThOpKswgVDZGUETt5tbJLQRnCdI7wijPDyMFU+S3rArMnGFtNzRXRdFC4D1uamcfBw/cWu6Gm6t/Rc0jGrPKtmkT2WRfZ4DnaEyJk7VfzM5kCoynziwGHl2qbr+JNN+aZy8poB08qtvoWq0OljuRmltE7WhZt3SQ4aqrYLn/N4RPWCRd8fETmbemxqxomjZ5oRjyuC5f+nufdlWkk/DqPzkwlUsQXtBcWqh1WjSlOJfK7Yc9bQEET93aox3TzyxaMzSuDOdEhHEjmbx0JaeGkIPMSrjpOuiMSEmpeefozO7eyxLAZf+RB23bQbJ39ExDehbPSvvkQQkdH5vCtntV4fNoD/6aDF1fSBTNl4h35wEttf8zDGfj8O/nuawy5fp2NZwp1f3qIiZ9a0U2UwY3eYSHMN7qSF8Tsm5v4eTxECctYA3sSy3eQsGqXydCaTmfFi7ZGzdjo1evAIY7PkbDaVs6eTrHGhCD4ArF69GgBw5MiRus/xvqelS5fWfU4r4BG/RuTsscceQz6fx4YNG9DT09O28czUc1aJsDJwiXSKlvacCZNDOhLM4MieyEFXJM0ozEzOLJWXZcYFkcUWSPeYkjiVSlD0eHGFduh/ys97xhi0mFZW4fBeS5Wz2tU4QE0UAXx026X4Wu+6sr898mc78PAbdyB3cmbVQBUc6a8Oi4hAZEV4Qc1AakFEhC/RMU4VpXXxzTG0qnTm3R6klJh6bAoiLDAykAQHIO447O+TSvACHUN6XCh5ZEuG45NxO2VTKHeUyBAbLU66tahWVRk7cpTG09kr/DyuVoBp5dK9SLxY2jn+7fqyPUf17WgJbd7VjrLx1JD6mv0mtLgGe9Ke2epbGbjEPYfWWcgatbhGiyjq+Yyxull+nRcksDtMssNaErpKFFSOl9NMpql6iltnWvPInz/akiwqKSUmHp4sZkM+RUipnkv72n5M76J8v/nMtPWkTlb8FfuPMWCil/opJ+86icyhDE58r9jOMPSDk5BSIpGj89DsM8E1RguD8zy2WQ3X2JAOHDZj4Ekd1piF8d+P+wZOR75EkRB7/2mf//ytF+rzbsfjGvcXCyujGeJhYFinOdfJjw/XvS4uVgTkrAEWStYohIBpmpBSzjiZ9WzJ290DBxQrZ83KGudSOXs6kLOFIvhAc8HP3vfUDImeD5qtnD344IMA2u+I2qjnrBQhhyYLYW9i16KJmtZBLoxcZ3AzNgzVP2TazoyLMjJL49FjGqCa/OcNxiAMVl5lKLGgzzWyYQNKZI0myRrrSZzUeE/HovhW39qaTxm/r7lFIA9uQZb18mgxrakV4cyRLJ78wB6kDzY2qilFfjiPzKHmXyMm6TybWN2J2OZYSyb7jBd7zmRBkl26K3H6GgoQ79p1Grtv3VN7PIqc8RCvG2o76/FoHFJSViBVagBXBVyHjkwhe7T+uT98isaT7BVqWy0ZEpjGynKl4qHiZ80eqT8ez1RBi6mBzLO6UBxQtdU3Ywy6ctk8+tX6OVWa6smMJahyMitZY5gqgKXXLiYAKFv9UiRC0jfssMYbV7FzqsfJaWIfeU9xZhDJ5I7PcmGmBiYenMTuW/bg0bc+Pu9tzQeWughqz+wHGIObd+e/8FDLCQjAyY3FRfrH3r4TI3ePFMcxamH/xw4gqly0zQ5B330LKme17ochjVY0WIyO673/vB87/uoxPPS/H6m5ifPP19CKngEtTlEYsiKeJW5KfHLpFv/3A588OO/3WkgE5KwBFrLqUVo9qwfvbwshkZutrHE2hiBPR1ljuwk+cGaSs0OHDgEANm3a1NbxNCtrtB0goowczGhrDVy0qAY4UpkCaIhGgWmhQUjpO6jVglAGIlpUkNa/FYG9DOAmSV08SYjeXTx3sw3mS07eweivyBV0VAtB19RGawzNI5PeKrp2aXVmpJ0qX6hx8g5O/vgU9n5kf23Zo5Rzcmbc88G9mNw+id3vr01iKlEYLWD3B/bgkTc9isfevhPTu6abWukXaZoUpde0zhG1tH8ptS9F/VWuRGxLMa9w8uGpmnEGHjkTkdZVg0meK1EYyRM5EwxusnivHP5F/WgYS5nJdPYqkxvRmukI1ziRRrVSnjAlPrjiAgAzV5y971SLq8pZq2SNWkl5sQRexWz0V2NwbRc73/ME9n6EejCtCQt22kY0T88Jd+rIHs3N6isrVslKXsWpJ68SybDEmEbnfmGsgcT6aBbDXzoEADgSa5zbKbiqskmGTbdsrPkcx5NJTlgYH5xAam8Ke/55H45/t/mc1emdtHA9k3PhXJE7kcOxbxyHk2+8mGyohT0zKsglVbZg4aEOuVuSnPllo78aQ9Sm61AoqUGYnO5B80UNshjW1aJRyfbzJ/Nw0tX77Ju9a7CuT7bkOiQd1K6chSQOhIrXxSV/3P5WoFYiIGcNsJBVD4+czdR35pGzhZjoB7LG1mAhCf7SpUvBGMPQ0FBdc5nFRs48IukRy3ahWXKWybowpAsbDNxUMrIWaZz0Th16pw4eIse2iAnY6jJ8+Iv1pagiTzd8ERWAhN+zNh8wg9NYdI7s0QzcAhlqDCfUIlFq5oWTzP7iItL+cBymUJKrGvuKqexToao+2hvWo/u6rrLnlBKekV+NYvCVD+Hw545g7DdjePiNO3yHSw+xTTGEls/+nPICUpvprQGAsd+NY2J78Zh54r1PYsdfPdawyVzLqniEuLJDbwkZ4nByLnJDOcr30RjgAEs7JEZKrMonHy6X7kpHYsUpuo5rYY5W6RoZZ2CCQyR06m8SDKxD96WrY78ZqysnMqfpe+joEaovZ97DAQB0X9OF0LKQb9kdL6kKpXancOKjJ5HeX32P9StncdGirAoCr3Br9FAq88wdyyG1O42x34whvT+Nh17/CB7+sx2IW3RPjXRrdH7MYsbGmFcVV/ufwQ8QrkRIB6YM2keZGfo/T98zgkff+jikIlMPN9H77hVppASSF3Vg/buqK+d2yoZ0JR7+sx3Yc+te7Hz3LozfN45jX6u/yFj9Rs0/tRmkD6QxMTiBw188gp3v3YXj3z6BwVc+hMwM1dfMoQyiqn84HONUMZNy3lVYxlFlwAEAyzoaL26brguLMYSiHBAMsc2xecu/vY9VipC6zLkvX9Pw9fmYCTIonr/EElL1YNcIorY5x7EXbED/3/Qhtql97ubtQEDOGmAhqx5eNawZcraQlbNA1jg/LCTBNwwDW7Zsgeu6vslGJWZDoucDb7HBk+LWw0KTs8qeM7fglk0gc9N0lc8K4Tubtar/hOscPEyrl1pMg1mw0emoFfT/GcPRrx3Do299DI+/+wmk9xWvA7oiZ0ZMQEpJTmzzhDA5khfRPjGWmL5L29AAnffGnYdmfL1Hcu7v6kNa6NCEVFlMsszxTUpJJbOSPh5XsqoepOmd09j9wb3IDeWw/18PVL1fpfSJ8foSLzttY3xwooxASanGNctifa0eFnvShj09sxRVV+RMxLUWygjp89opG8zg4CrL6tx+B7esush/3tH/PFZWPRv99aj/sxZubTWYa4AwGLgGMJ3DyFn4du8aZDpCKIxaeOQvHq0yfbAzDs4bIwlW/+YwVRfmYwxQAREVPvlOhCQmBN2T7Ekb47eP4/F3PoH7X/Qghr4/hF1//yQOfPIg7GlVnY5pkJCtmx1VuEf6KHnwsbcXI3Qef+cTAAA360KTEimuIRrlStI6uwsRExUVDs7qmlPklZHL6e8N1T2uD3yiXBo2Fms8L6qUNXY/oxuXfueSsufs+/B+5E7kqqRpwMwBy6Vohdundx9wbReP/80T2H3rXpy885QfGQIAx79VThhd28XJH5/CE3+3C/tL9o8ZYopUYd4y9HoyxPMGmps/6VKSYRMALSZaIGusrgZHDPo9lYzg4q9sLfvbhveuR/+fkFmIDYbpvtYRpfDyMMw+s2o8CSVnPrZxCbpe0tmy91soBOSsAZ4KWeNiIWftrJwtBlljLpfD6173Otxxxx1tfx9gYQg+AFx11VUAgB/84Ac1/z4bEj0fDAwMACjmvNWDF4jdzowzAIjH49B1HVNTU5iamsLI/4zikTc9igdfsb2sTyeXpmMyp2lUrXGbb8JvBsIggpa8uAPxTo67Owb8v5347hCyR3NI703j1E+GMfb7cRz92jHfMMSMazRBa6GTnIgICIMjc5iuLV6rGXMkckP1tY151XdyUqfj2lQFGTvnIrU37a/0WipXR6Ioa5QSiKwqv4Zlj+Yw8cAEdrzlsZrvVxi1MHXPFHInGvenPPaOndhz696yitfBzxzCgzdsb/jaStQzGHjodQ+XNbhXwsjR63hcryv3nC2YCuLmpgDXVY9X3kVnREJbEcGfbbgaAJAfymP7qx/G4GsewmPveBzDPy/2omjKabNlhhcarciLCElke3UbkjEcWEn9MIXTBey8cVfZa07vy0GXEodDMXRvioBBti70GTSZtVI2pJToiUoMGyFYNcjfkS8fw9Sj0zj93yM49B+HAQBGjwFZkNBaJGmmylk1wXBrEJFaGNFNJRlj9Xs664CJ8n43xolM1+I7bqQYeD1+3ziOfOUo9v3rfkhHVlWtAeCXHQNgZuMKjHfOlxZQucmROL8oOeMGR2p37XnP1I7GBk5AY3L2syc1/MW3I3WzAA9+9hAGX/MQrEkL1kR9aSSrcIQd/tlpHP7cEUw/kSrL1wvpShrrzp/o18uPvGSFg08tPafh6w+YMXhRi/MmZgB9norxLInTA6emObQODeGVYYADm9+/EV1XdmLVn6/ExI0X4c0broa9jMgZHYfzJK6CQYtrkBXb6YrQeE5Mnpk058wc9QJiIcmZt7I/ExlarD1nN954I370ox8BaF8I9U9/+lM861nPajoUuxE+9alP4T//8z/xkpe8pKnn33TTTbjhhhtmnZmxkJUzAHjNa14DoDE5a3flrLT/baZ95lWy2m1yI4TwQ7fv//4D2PeRA3AydPxNPjyFfR8/gMff/QRO3Uor13lNg5tzYXTq8woRrURkbQThpWHKVUto+PyyTfj3/up+u8mHJ7H3n/bhxHeHkPRy1zrortiSGywACAYe4tC7DRidOuyUjVimSH6mHi+velqTFvKqV2bqSQrQ3a/RjVaTLsAYuGDgBsP0Lvp7frgAEaHmC++G40hgyfP7sPy1y7D8tfUrptGNUYxupP60fR/bj6M3HccT73tyxiwm13L9UNbMgeJk6fQvRsqqZo2WhU79ZBj3v+hBnP6lyil8XrVD7vj9E5COxPHvnMDIvfQ8J0e2zj0TNNnUk1rLsvK4zhBaFgLXaELLNI7CpAXXdvHyCws4qYcx1l1clXZSDjIHs34/DgAYEd5SK31hcpI2xjRoEYGBLtr4YRWQC5RLSKWUmHiCjo2TMXqObGV5GlRhEiGO6V0pbOqxIDnDvpL+k5kQ6jfh5F0Kom4F5um/8mCiF7rnhDrLCgzj5cSQcUWmawxovLe4f/b+y34M3X4So78awwMvHcTgDQ9hfHCi7PkfX34edNH4g3mXqkp+avYX7z9uwa1r2PDkPzbuDXXyTtkcf+y+cTz2zp0Y+sFJn1j+xZdM/PBhgW8M1p6fDP/sNNysi7HfjuPwofrkbPqJaRx+1xFfOVB6bnn44pINJPPjtCgzb6VDHUOQ3phE/rIl+Js1RTOtWrLRf1p5ATSP2LfGS6oKAwmlvJgkRcO5HzkHF33xQnRsLfbcTkbDOGWE0eGZ9LTotGc1TG4uWErf+8PHWqsUWCgE5KwBFnJi7Vmb7927F5Zl1ZzQeuNZSHJ28GDti6ZX9XJdFx/96Ef9x5shZ95n+9u//Vv/sTvuuAOrVq3CAw88gP/1v/4XIpEIli5dikQiAcYYrr/+etx9991461vfOufPVIqdO3c2fM6b3/xmXH/99bAsCx/60Ifwne98B5yTlvxf/uVfmpJ8LnTl7PLLL4eu63jyySexd+9ebN26Fe9///vxwhe+EM961rP88bSbnMXjcTDGkM1m8drXvrbu8zxyFo83N3maD6677joAwORN1TfU0XtHkd6bhqsqJePhEKQjYfaHWhpsHF0TRXgZXU+EwRCPM/yweyViN56DJS/uR88zKU6gUJEzNqKZCHcISFnbDnsuYIJWsTsuSECLaSiMWYiU2Pof/PQhHP/2CV+WtvM9u/DIm3Zgetc0Jh+lffhQlMhT4UAKXBBpIMMTAXvaBuPkAmlN2Dg6RuP+ygMmuM6x7BVL8T2rdvD4j/pX4Q/0q/CjCboOuVllGjBuYftrH67rBDj886IJxdAPTtZ8DkA3P89oJH86j+zxHLLHczj0zRN45cc1HPr3w2XPv2HPOjweSVZt54GXDuLY149j/8cP4ti3jmPwhofw4CuLFTqzg6oLrTiGGGfourIT3BTQu3Rwg8Po0uEWJK5cYwOM4SMXbYP7uvU1X28xBiOptzQegocFuAC6LuuEiGpYmqTHfzhZLiV65M2P4oFXDGL7ax/G1Depv3IqSucBY62tnIHRMWd06ghLFxctdXBH1yr/z6Gl9e/n4VVhwJWtMU4AwARHfigH13bhZB1kj9Fxu/G91d9R77Oqo0R2d3T639dsTVOYqOBhXs9ZjYk+6zLx4eXn1d3WnluL2Znf7V4NAA0DqIEin3Rc5k+UpQR+sad58lvab5U5ki2TyVqTFgZf+RCOf7O4aLv3n/chsz+DI186isOfOwIn5+Bre36Fz+y/D5bL4BZcHPvm8Zruq/v/6xS+9NP6YykMF5C6L41H3vQo9n54H9L7q7eR5UK5F6L4bz7gIKltDVy52saeSAd+8/wLsPMt23D9g6tx6jXFatqk0DFWOl9s0XWocjgeOfunX4ax+xSHCAkYXeVzwVSe3jtm0ovZLKIhZh5P9WMXL7cR0iQeOqbhyOkzj6AF5KwE2WzW74/JZrMYGxvzf4/F2t9M6AVLv/Wtb4VhGHjZy17mS76Ghobguu5TYgjy5JNP4gtf+AIAyllzHAeDg4NIJpP413/9Vz97zUMzk/5SYpTP5zE5OYmXvOQlOHLkCC6//HJ861vfQjabxdDQUFXPUjOkqhmcPFmcuKVSKUxNTcFxHP9x13Xx+c9/Hj/96U9rEs73vve96OzsBGMMv/rVr/zHx8bGykwwFrL66r3P1q1bIaXExo0bsWPHDtxyyy344Q9/iLvvvht33nkngPaTM6BI1L/xjW/g4MGD2L17d3k45dCQb9CxEOTsFa94BQDgsHu4wTOBsY4oucgZLdQ0ViB+ThxZmy7D1921HNv2no8fXLAZWnf1xOXejgGEvbG0KLCXaxyJ8xJgnMHsD8HJ2Bi9tFxeeuwbx/HImx/Foc8dRv5kHnDJFINZLvaF4hjX6TjSDbJp1jt1kkuGObJHs9ASNEnmIY7hDN0kv/WQgVQeyBSA2x4qThzev3Irfhvvw086l+G2bnJ2+2HXiqpxu3kXj771cdz/4gdx9GtkRW6nbJy4fQiHP1c0VnFSDnIncpjeVbvv8fGbdsN2KGvp0b96DE/83S6c+tZxvOve+6qeeyAUwz+sunjGCkzpBNGDGVa32VbJCBmDFhfggkOEOIykDmm5uHCZA84kHh3S8aLtq3z3vVL8vyUbYGoSs8nLagQtoYEZHCIioEU41i2lSdpRM4b/LpHs5k/lIQsSTokrZz7qXYNat+AAAHqHBhEli+3MoSyeu2Qav0v0Ihs2oC/VccGnzyPZVQmYwbDi9csRWRWheWfLctcAaAxuQcKasmEpV9aOrR24+NuXYLq3WGHsvH4JLvrepYj/QbFKeypOcw9yX5zlm1dIKinuoPbnSoYlDpmN5zk2Y/hWL5k+PHGy8aS3VE36jtsjGM8wHBnnOD5V+7X/uuzcqscefuMOWFMW7JSNx972OB7580f9v5VKl2vh9H+P4MkPH4AhXSwtZJGAhT3fOonj3zqBx96+E5URk2Ish5c8WD3HyNfY+WO/HadrYgWyXIOpFQ1z5gvGapvKACRtBIBPD/XhPb/uxIFRgTc+shzvW3UxJoWO7/as9qXqZAoz7+HUNDjxKlUA8OG7Q9h1kmOqRIFesIFDY/TmcdM7Jlu1QlS9nXgIeOZGWuB85FD75zqtRkDOQO5tP//5z3H55Zdj6dKlmJycxPOe9zx0d3fDdV10dXVB11skcZgBlYTr+9//Pvr7+3Hrrbdi6dKluPrqq/Hoo3RRWojK2caNG/0J/L333ouf//znGBgYwPvf/3684AUvwPT0NN797ndX2bZ7/WQzwZPWAUB3d/esJG179uxpSa9aKpXyf+7v70dHRweuvvpqDAwM4POf/zx27NjR9Lauu+46/P73v8fU1BS6u7tx4YUXYseOHWW5dQtFzoCZM8MOHyZishDk7Oc//7n/87XXXovNmzfj//yf/4N8Po/f/va3frU4HA4vyDm2cSNN+D8z+cmGz831RACXesTaBXOJgahRvgT5oV+EcVPPhRgZ6MBX+9ahEDXwi+RSfK1vHSI62Q/zFo7J7KFJfOL8OIwuA9suM33rcR8ucOrHw1WvHYwVV/q9yWPnpUllq86hd2oQYUFGKBUOk6vfn8TKW5JIi2KFYnusGx9aeSE+XZJPkxUaftS5vPbgJfXpPXjDdmx/zcM4+pXqzKg9H9qLI//vaM2XW0M5/MXNxRlEaeN/KXLkxoA8F7h15VacMMLYFW7OIt/U0TJZo4eOrR3k3Kkx6D0G3IKLqAGcs0SpGRjH6zZdh08OnIN9oTgOmjEMxrrx087l0KULJ9u6Xt/4phi6rqAqWXhlGMl+A685jxYRP7b8PNzRtbLua12/OtVCAw5QP6MwOEUtSIn1nQ5cxvG5P7wM6766FkwwnPPBzf7zN968AZf91zYsfSmRyVb2wHHBIXSyt2MoN4d4911R/F3ifP/31/yyF6/8chRferxI2Oxo0e1ztqYXTBR7HcNLQ4iujSK0LORX0/LDed+C/EXnF3CkCXJ2tKsDWXXOZqzZjWfnSYEN/7cDl3w0gVG99v3n7uRSfHpgc9lj1riF0f8ZQ7bEFEg6ElJKnPrxqYbvm9o+4f/8i3ss3PXzoirh6n+L4/hE/c/xw64VeP65z8H7Vl9S9zluxfeiC88mnmTjtYxOZoN6PWcAsHUZkaLpfPkYdsS68epN1+H2ntXIeH121Lo4f9Rwa7x0ZZGc/eAxA9d+MoFNH+zAk6foRHr1V6P47G9oDlQkZ2hRJQ81pbrre+jYPnS6RVawC4imRrxt27Z/AXAVgEMA3jg4OGipx/8EwM0ALADbBwcH396mcbYNJ06cqHKJe/WrX11WCelrwi62Ffjrv/5r3HnnnXj88fIQxX/4h38ACrhtpwAATFFJREFUAPz+97/3H1uIKoOu69i+fTvOO+883Hvvvbj33nsBALfeemtZJelTn/qU//M111yDbdu2Ndz22NiY/3M9A5TnPe95+OlPa+sLVqxYgbvuugsXXnghAMC2bfzkJz/Bs5/97BmrilJK3HnnnUgkEr6BSekY7r//fgAkZ5wtrrzySjz/+c8HAF9O+KY3vcnv/fJkoguBN77xjfjMZz4z43PabQgCAM94xjOwefNmPPnkk/5jt912G4aHh8uMWBbieAaoGtzT04OD2SRefM4z8TfHd2JUD+HJcAf41k4M78zgYwcpFNvtjwDMamm/WSUYY/i36yfx0m+X28oPap14fRcR7G/3FnsIIpqETLtkHdxicJ1D79Kxxsnjpa9N4FufX4NXjcwc3DkYJ3L2zstTtJ84o14onYKAJRPgpqAqRh2XwIzQ8e/9m5ASOrYsdQG4eGyo/Nb09b51eNbkEMKug8iVXcjcN1b2dzdXn2xkj85sIPLmJ2obkJTih91FgjGih/CmDdcAAH688xczvu7BWDf+OCwBBy01lTG6DDCNw805NLlW85Lnbraws6Sa8bOu5fhZVzmxlSN5sE7WMoLv5ZsBgNlrQotreP91p/D1x2kB8fP9G/HNvrW4ZvIU3jZUbgwiQsoYqtDCPkqF8Mow8sMFMI1jdZJI988PhzHhaOgAoCc0rPubNZjamULy4kqi3dpKHjOEPzEuvZ58fdAEQiZuWbkVh80Yho8SEdvduRx/ODGE73evgu+5wasdThsheVEHBVGDsqDCEYHUvrQ/kc2PFgDOYPYYuP4cGy+7yMLz8Wz836MP46Lp0ZrbPG4V7xv/+bpUzec0gzu6V2FpIYN7OgYwcEEEF927B/cmqWr/k64V+F1iCaKOhQ8cfhgDVhaHv3AEnVcW76E7nrChfWUv0vtmFyY/dsqh/liFo6cZ/uQDArfVeO6DsR58pY/kp1lef7o8zE18dtk5+MCRhwEAcY22zzj1iTZr/lIXnNWVNXaEJVZ2OjgyXuP6qoiP7RaPm5Yc1wxVZIgx4HtvTOFlXyoSfMthuObfEvjCq9K4d19x8TVWQs5a0nPGazuirumm7+FzP0/gtdc5uHig+jmLFQ2vztu2bbsQwLLBwcFrATwJ4OUlf94B4OrBwcFrAPRta2ZWvsjw3ve+t+qxu+66q+z33t7qRvB2YMmSJX5lrBGe+cxntnk0hLVrq5tLAZQRm6985SsAgPe85z349a9/3VTlbHS09oXfg5QSP/nJT3D//ffj+uuvx+te9zq/4gMQqd66dStuueUW3H777bjwwgvxwhe+EO9617v859x999348Ic/7Pc0ff/738eLXvQivPjFL8Yzn/lMPPzwww3HCZCxRbME/cc//nHZ75///Od92edCkXwAuPjiixs+x5PMthseYS3F9773vTIzmESicZhp68bzJ8DFg7C4wIdXXIAv9m/EbzuW4NcHDewOd2BXuAPHjQiMgRBaar5RB1esdHDfX88szQEAjUuEM3loSaOllbNS9FzbDQiONT0uvrZkPX6ZHIADhs8MbMYxo7pavzucwNf/NIV3XZUGExxaVIAJMgWBqqRxU0neZnC/+2H3Shxa14d73pbCPW9L4bOvSOMZ6yy87Rk53P7GFKY0A3++4Wq8ZtMz8IdT9VewW4XfJOhc/VlyKW5ZuRX/qXK7KvH5JRvhAPiX5efj4WgXPrDiQv9vo5qBD6y8CL1Rt2VW+qXQExrMPhPcLO7Xv/nDxk6WZojB7DWgxdqzmsxNjrDBsKlHVSEZQ0ro+GnXcnylbz0+vnQLPrT8AvyoczkmVyVhp2wyeGnxaRbbEEN4RQjcYFiTdNARoonax+8sXmt6/qAHa//P6hqkp3WyT6lyCbNHsrTNGgTrwXgvho3iouJpI4zXb3oG7uhZ5dulMz57wqjFNPAK63xW4rQnDF42OV7TRaY+tyzfiiG9fJHzrs5lsMHwDXUu7Pv7STzvnLkHPue5wMeXnYdn/UkUX/vzPN7x1VW45iUJPGOdhX/+kwyesw04YUZxU0nFavy+ojlZ/uZHkN5b39m6Hlbk07g8VXQv/Yejj+C2/dUyZgD48PLzcP4ab7z1r7kSDNvjRRXBUal6KTnATTH/yhmvUaoqwYdeMHOWaBlacD9jdQxKLl1Z+3j4829Fy36Pl4qIWnGecZK6e5JhD8/cYIEziXSe47dPtvc+3mo0c3W+CoCnTfopgDcA+CYADA4OlqamFjDrBJmnHm95y1vwta99bcbntNviuxSMMTz72c/GL3/5y7rP6erqWhBZIzC73rbLL7+86ef+0R/9Eb70pS/V/Nt3vvMd/+fLLrusiiyX4v3vf3/Z77fddhtWrFiBo0eP4rbbaC1sz5492LVrF373u9+VPde2m7uxXHrppfjgBz+Ic88lLfwb3/hGfOQjH8Ftt92Gnp4evOUtb2lqOwtF8j3s2LED7373u3H++edjw4YN+M53voN77rnH//vKlfXlRq3EBz7wASSTSWzZsgUve9nLFuQ9Z8I//dMHsesd/40Hhp5V/UfG8J412yDB8KEOuuG1Ij9nJnABrO1q7Fr69uvygATim9vX/8oEA9cYzltC4/m3pefiP/o3ISN03KV6v66bGMKLxo7gy30b4DKOi5c7QI6B6yrTiwPQmL+a2XlZkprDe0xEdBcZq/Yk50dvLq7Cv/IiC6+8iKRHx5TkaKIkZPl9qy7GBw4/jBE9hMejSTx7Ygi5rjA+ve0SvPvnv6m5/d/Fe3HV9Omaf6vEbQObseJ5PbjqvBg++W2qqnzr9SksiUvcd0ggazHc+rMw7uhZhZ92LkNOaPhVR/l94teJfriMIWYCBdZaN8JSlBL1sA58/U9T+NzvTJzb72Bjn4t33F5+rzDyFiKr4207rhmjY+hz1w7jrqlOrOpy8Vf/RZOz76heJQD4bccS/EUkh8KYBRHmLSdnAGB0GwgtDaEwZuGGiyx87j4T05nGCxtSspZMYtXG/GpyYdQqW6TY1Odg9/DMi5mv20YLocLkSF7SnJR2RihyJqUkW/iSz+kRQZtzvHnD1fi3A7/H2lwKd3Uuw2cHzsEX+jchz2m8yXBrFhxufCYtKHAOvOdZeQDU8qBx4LuPGDilt7Yd4PXD5fEXl6TqLxRnuIY73zSJX+7RcDrFsePOVZg+lMU1U+USb68w9e41l+KczAQeDHcDmCSnRpPD7JufSqWUUNfCczfb+MfnZXH/YQ3rexxs6HXx9tur54hStqpSVfvxiAF8/89S2DfCsXNI4MsP1JauJrzKWavyHxlTyozy7S1LSvzHDRnce9zFOcsWrq2kFWiGnHUC8IKKJgF0VT5h27ZtlwLoGxwcfKiFY1sQXHXVVbjrrrug6zpe8pKXlPUhefDstxcKd95554zkq1QSuBB4+ctfju9+97u44YYbcOWVV+Id73iH/7d3vvOd+NjHPgYAuP7665ve5ic+8QksX74cK1aswJIlS3D8+HH85V/+JQDgj//4j+u+bseOHXjmM585Y+Xtfe97X9nvX/ziF5seFwC87W1vwx133IGjR4/iFa94BT760Y9i5cqVGB0dxfe//33ccMMNiMViuOmmmwAAb3/728t66OphocnZBRdcUNbz9Za3vAU/+tGPEAqFcOLECbz0pS9dkHFEIhHcfPPNOH78OO6880688IUvrHrOQoaRDwwM4Hff6Mfn/j2Fbm7jhm+WT3hcdec5t4/c79pVYfDANAYG4OEbJ3HRR+pPvlKqWN12smhwdJouYqZEKs/w18+1MZJy8YXf0412sK8fzsU9ePSAjq3LbLJzzkqIsKCAU8bABQcT5EXgSdc6Lkzg3j87jU/fbeBF1zAMTTE8dFTDl+43YWrSz8mpxPKkxHf+dwqv/HKRlO6IdeNF5z7b//3jy5TL3HHg3d5zop34r541+L+H6bb0+0SfT872h+JYl6sfjv6O59l4/jVRRA2J+5ZO4dETAs/aaIMx4HzV+H7rz2jhKifKj48bV2/DM6ZO4atLSA7lLTK3iZtBhDmYxuBaLrjO8dzNNp67ubjwdMNFBdy+Q0dfXCI3YSNuSurFaic0hrX9DO+6MAtXcExmM/i7H1Xf01YkJexpG6EBc97VhZrDiGrourwTw788jUtW2sB9Zq3IsSqwVhknAFSEM+g6Yk/bYJoiRoz5WUwzoT9RsubdiqoH55AApC3BzXLDkEtWFI+bj78si//asQ3b9wBprmHbSgePDQnABt5+HREqr1+N6zPvrLv+Yhq37zDwgvMsHBjl+OT/mDg0JvCi8wpFi/cK/OmlBeRt4KYfL8xCdCXcFVHc+eYUNAG/Qpi+sA9X/p2Oa6aGkeYavrRkA942tAufHqAe2V2RJHaVObqSFLXStXDWYJiRnDEGvO0ZebwNxXnIMzdauO+gBo0D63tV9ZW1qCI8w9d97Tob164DRlIMQ1McP3uyupd8ZZdbHHiLZJYiImruo5dcYOHZ102hv2vhiiytAGuU2bRt27a/ApAaHBz86rZt2y4B8IbBwcG3lvx9OYBvA3jJ4OBgVcf4tm3b3gzgzQDw1re+9ZLnPOc5rRx/S/HNb34TN954I1avXo1Xv/rV+NCHPgQA+K//+i9ceeWVCzqWsbExHD58GIwxJBIJnD59Gk8++SRuuukm3HTTTT6RWQik02ls374d1157LRhjGBwcRCaTwZo1a7BixQrs2rULkUgEq1ataryxGXDgwAFks1m/QlUPUkp85Stfwc033zyn91m5ciWOHDlS82+f+MQn8PKXvxxDQ0MYHh72e9pmwmc/+1n/WOnv78ddd92Fr3/96/jXf/3Xsuft27dvQU1BFhssy4Ku61i3bl0VmV25cmVVZbPdcNIOpCPxri934Xv3RfHsC7OQEvjvR2nSvfMTRxHr4G3p7yqFPV2Ufx0aFrBshuFJgSVJB1ICH/l+B37+SBg/eN8pXLAiDy2mtbUPzp62yWhjQsN4iuPclRYcF/jVzhAMTeK8lQV0RCXu221iy4oCOiIS0nZJvqj6yvzPJMnNz9/2FEnYeKg4I9u+z8Cybhv9nTMLLzJ5hr/9WhI/vD864/PWZafwl0NP4j/71mFXJInbd90NAPj3Z1+Mt/ySiNr/3nANeuw8Pqr6Cytx7u8aB7u+5mM9+O0uOp+ff0kG73zxFG75ZhK/fqJ4jm9aZuFnt5yCtF3oHXpbqkMA4GQcOGkHvE5fnwepJtNaTAPT23gMTdlkf86KfVKr31Rt6vJPrxvDDZdNU/RCTLRHQuwChQkLP3o4gr/+Qg+ef2kan3nzzNmd0nahxbWWLIS4BRdu3oV0ycSCcc+og+EV/9KLB/eZuPmVE/iD83KYynAILjHQ6eAPb+5HpsDwxKdOIGzSOaYn52+a5FounLRDAdUqGZqVSB93HdMRC7lY0eNgIs3w2GEiFldtzsOygQf3mbh6cx6cA26W8sVKz+dmYDvA7540sW19ARFz5jno0JjAp//Wxqt3Pjnj8ypx2IxiVb4oe/xZcimeO9E4I/Wc/96E3N4ctD4dRn/1/l79puVYl53CaT2Ed74mhXP6C3jLF3sxnirugz979jT+/oZJSMsF0ygkeT5w0g5cW877eGzZMZSnWAimzXxvPHBSwzP/vpoUPfbJ44iHZUvPs5n2kSsc6Jre1mveXLBs2bK6A2qGnG0F8M7BwcE/3bZt2/sAHBwcHPym+lscwE8AvGVwcPDxGTbjofVLYy2E67p48MEHsX79ekQiEWzYsAHLly/H7373Oz80+amElBJ79+7FunXrmurraiWOHz9eZZzyVOOJJ56oInL33nsvXvva1+LYsWrXNoBy1W699VacOnUKmUwGnHMIIbBixQrs3r0bW7ZsmXUekeu6eOKJJ9DT0wPOud9btnfvXhw7dgwDAwOIxWJYvryO49zTBN4x9MlPfhJvf3u5d9CPf/zjGSum7cDYfWOwJm3YpoYdJwS2LnOwf4TjDz8dx5uvyuOmSybRdVkS4RXtja0YuXcEbkHSyl8N5Czg1DTHqi4XueE8eq/rhtHdPjOX8e0TyJ/KQ080fxPPncqj89IkIsqefOTXo2CMwc076H1WsWJ8+t4RZI/nEF01t9Xwqe4UntjegRd8rtxA5quvTeFP/7O23HNpPo2PvDiDZ13Csf1/ETlLfnob+ro5xr9/HKe/U+42Gz8vji0f3FxrU2XIWsCxCQ5TA7oiLmImqXR2nBDIFICuiMSypIu4CeRP5dD/wv6GFYa5YurxKYw/OAGz1wQYiAjWQG4oB6YzdF3RhVB/+9xaT987AmlJOBnHP1bf+I0I7ny8eNyamsTgOyaQmMggsiqCnuu62zIWJ+/i1F2ncMcjOv7PPV14/iUZfOVlhRlfkx/Oo/dZPXX342yQPphB5nAGkBQnIMIC3OTQYhqe/x8x3H9Yw4/ePI0rVperB8YyDLYD9MUlpCNhTVoYeOH8V/+zx7MYv38CWkyDa7twsy7MJXM7FqZUAHPi3PYaOmUKwI/ucbDqsw9hdzgBi3Gcl5mY8TX/1bMKrxgp9qkbl3Wj8MDMve5ah4ZLvnrRjM85nWKY6kzDPR7Dmm4XmgDyNoUd6wII6xIbe+nx3KkczD6TennngdJrsnQl3Lxb12BpJuSH8xh4cf+8F0FS+9KYemwKRrcx47byNnD5xxI4NlG87m3pd/Crv6bjJjecR98ze1pCGGe6b013UuUsNLDoFsfr7ryGdH5wcPCRbdu2ndq2bduvARwB8NFt27b9x+Dg4F8AeAeANQA+rbxA/nFwcPB/WjPmhQfnvKxvav/+/f7jiwGMMd8KPACwZcsWbN++HZdcQg3DN954I6677jrs378fExMTmJqawoYNG/znj4yMoLubLpK1iFKjil09cM5x3nnV4Z0bNmwoe/8AhLe97W14wxveAMYYTNNEKpVaUCfLSoR04PJVNDHa0u9i399PIqoDhZH2SwgBkjXaE3ZdchbSgVVKBsKAtlbNAECLCeSOzW4drTJEOLm1A9aEVRYWCwDRNREUTs88MZ4JnANXrHbwby/N+D0VX351Gn+8xcaRWyYwmWXYPSzw8v9XJGq//ycLEYNu2L3P7oF0JdatIJ2QXBuG14W25Z82I7ox2vTiTFgHNvSWV/sYK1pbe5CunDFfqhVgGofeoaMwkod0VPZYjc/BOFm5tzTwuQaMHgPWuAV7qmhZ/oVXZZC1M7AchrgpkS4AMSaRS/G2hvowDvAQp7wxoClZI72wNd+XCJNJTnxTDKMTFnhYIHcsi9jGmD+WWvPbUsmjtOWcJuO1QW8mXc/saB5r5gtUiIgYwCufK/C+oUvxlSfjSMDC984/jOnvlCtg/qN/E/7i5G4AwFuvK6D/Oedjzz/tw7IbliJ7OIvjD8z8Ps0sEPTGJMJJFzGreO6bGqrINQCAMXRe1pp7m7ToeyqMFJAfziNx3lxMtFoka1TbmH5iGtENUQiz9rFpasAD75yCK4GczaALidJiGwNadu5zwc5A14v6aKrWOjg4eGPFQ3+hHr8VwK2tHtRiwULkQAWYHy6++GI/88ybjBiGgb6+PvT19aFQKOBVr3oV+vr6fGIW4KkFY6zMOv+pJGa1EFen/UIQIQCIbY4jd++I34cyI1zZtuqLBy2izcH6mZXd9LW4Bi2uVVUdwyvDGB+cmPcYX7OtgFdfQiTP22URA4gYEgMdNh7720n8729E8dptBURKioxr37ambDvRdcUKXnRDtMrZrhWQNlVFZ1uRnw2i6yJIH0xDFwacnANp1QlP5wxYgGO647wErAkLw784DSfjwLVdcIMjLBiiKpQ7EQJSu9N1iWSrwDiDMDg0xYBcOfN7SYcmsK0aUqjfhLnEoF5MncLZjW7KpvPIWaP3Sh9MV4VmzxW+0Z4EINj85ExswfgZAOCDrwc+xKaVscUSPHz3SRRGios9f3ZVAbidftYEQ2gghAs+SQunziUkY+++tgtaQsPDb9hRtf3lr22dOoiu52TkMl9wnaEwYcHJuXR8zmHRUEoJCdaSc40pTs80RqRxhqmyF4Ad0mscaaxF1v5qW9mhHERUtP0euRA485LZAgSowEwXG13X8b3vfW8BRxPgbMJCkDOzx4AIC+SO56AndWgxDdljWUBKhFcUyYOdtqF16nUrbK1CaXBtsyDDi8YvYsxzcWxMRF3bpefXmYjM9PKBDomf/WXjDCazz8S6d62FntDadkN3LRdatL23Wq5zMMEg4mQO4uRcv7+CMQYnQz1GkBJc8AVpMNCTOpKXdOD03aPgJoObd8E0jtj6Ys8g0zi4Ido7w+cAM5qvnE3vTpEJUAsPB+9YZwZHqD8ErjNYE7ZvVtdofqrFtLL9Nh/wEIe0JKQpwQ02r13vTdIXCt457/3/3A+fg6md08ifzGPst2PY8opODN6ushkrLpMiJLDidTO3FtSrAM0JrQp8BhDbGEN6XwbWhEV9i3MhWC1yagTgO37OW1kiW0jOQPEidsqB0RmQswABAgQ4Y5EfzkO6dFEHaHXRmrBhdJIMbiHIGUATJndCInssB71Dg5N1/ElPYbQALa7BSdvovLz9VUYmZj9hI3lKc69iHCQ/aTAPyhykPh29ywAYYLapz67nGe2tqEtLQkTaP1ngBgdXZCd9II3CCEnqQgMhZI5kwU2OxJYYrEmrHbFrNWH2mdCiAjzMYUvbP6Zdy/VtxkW4veSMMYbua7rAf08mIJWf3bVdpJ5M+TIxEebgWmsnjR64zmlybdD/m62ckR17a5Q8RpcBbjJIW0JP6rAm5p5VRqWP6oPJyTkQIQE7ZbfV7dboNvzzd9krl5aPrM35lA3RQjLkuauKiICTdua2cCBb50DKOIetFnykLZtTfdRDqy6NjIGr80pKCbgL05bQLpz59DJAgAAB5oHCSAHpfWmk9qTgZBxYUxacvLIeXqCLOwNDqM+EntQgXQktJvwKmTVeQGo/uY7N1/WrqbHMkZA2fW9u0MPr5El+JB348jLGADs1n0nkwsG1XApVVpCOhJghgLtV6LgwgeTFHdA7NWgJHcmLO8A47Te9Q8DNOeAhQQ5rC8TOmM7BTQ6uMd/yHyBDgezRHGW0cbRdG8cYg6ZOHe+jS0fCyTtwC+XRAlyj0PR2jCk0EILRrfuTyJl6zsrgypYuFAmDI3cyD66x4mLQHFF63ruWCyfjIL0/DTttI3M44ztCLjQia2c2HVrzV/Nzl24KLWJnniSW6czvYZ0TWuWf4ErlRMmRP51HfqhxlFAp6PouSZ7aMhItfQfS/Mk80vtnH1C+mBCQswABAjwtIV3QpJnRjcLoNpA9mkWoz4Q9RURAzNIieq7ourITnZclocU0iLCA0WWA6RyZwxloSR16XAPzJo1tBtMYpO1Sj4IjkTmSrXpOfqQA1y7pvpay6bsJ42RGAMAnwaXIHMxi+olpiIiACAvEz48jeUkH7LQNqImeNwkEUD6OBYR0JDKHMjSGvEvyqpECMocySB8oTgxc24UIt59UG50G9bYJDm4wRNdGySFwOA8RIrLPDIbEufGWVWEagesMPETHrdFl+ARDhAWcjA3puCpTrP2LIF74s0eIcidySO/LQNouREiU2MoziKjWlmC6+OYYzF4T3ORkra8eL30nKdXKf+ljjLVUdisloHdokA5g9of8c2m2YAz+frJTNlK7U0gfTBN5cNG6IO9Z4MLbzse6d65tHNjdxj7HtrwHg1+pmst+lVK2rHImHQmtQwPXSHLr5hyk1bWwGaT3p5E9mqUDv5XyYU5xAV5VuJEb/WJGQM4CBAjwtIS0JfS4rmRnAiIqoHdoiG2IwpqwqB9jgSpnIiKgd+gwenTwEEfn5Z0wewzwEFlu60kd0XWRthhWVML7zPaUjendKbh5B4Vxi8gRyB7azTmwJmy4+WKYaLP7ynueW3CRO5YrbkNBRIhIaDEBLaFBmPS7tCVc28X0kymkD2SQPZaFa7lI7SYitNA3YifrwMlRhlVqfxpGpwZ7ktwJuUY9VpnDGTjTdtvdEUshXQkR4uBhDh7iCC8LQcQ18BBVsIxuY+EqwpxBmByxTXEkzkuAmxzWhAUR4mCCEwkSbEHmydHlZKPtGYK4tgTX6XviYY70vjTslA1uMHRsTbTEyKEeuMEBR0KqsXBG31tuKIf03jRyx3L+c8lVsbVVfCYY9KRO/ZAxPvfqFoOf35Y5nAHTqPeRCYbM4Qy46i9dSISWhtBzXXdDmV27M69aSYYAAIKRI6FGfbv1qp35U/mqv1kTFnJD+ZadZ9JRxlQahwhzuMpEZ6bv2sk7fmg5M7lqyZt/dpsPFWjtpB0ajyefP0MRkLMAAQI8LdF5aRLJSzqgdxnQ4pQ9xE0BXZluhJa3N9+sFqLroohtjEKLaei6uhOxdVHEN8cQWRtBeNnCjIcbHHqnAdciOZ4WEyiM5JEbytNEVuOIbY6hMJJH6kAa2WMU3t0okNQD40DueA754Tz0pA4n51T9nYc4eEgo10cBEaHKExMMoSUG9A4NzORwcy5EiMO1iLTNtQIwJ0jqT0ofTEOPazC6Db+Hiukc6YMZgDGY/aEFkTV6iKwKI3FuAowxdF7Rha6ru9B1ZRe4xtvqilgPWlKH0WPAXGJC79SRP0VB6txkENE2BU/XgFd5ko5EYbQASAmmM+RHCmSoYjBkDmcRXR+F0dm+HEGAyDtYiTmJS1JYa9ICJIWK2+pYdnJuy81qqDooEN8UgzCFX8me/YYAJqgvlmucpHecyBmZ1GDRTpC7rqro3231YdhKAw4oUxlBzqOMAZnD1YoGAMiPFuj4BvwFNWvCony0Wbvw1oZ0VY+roCozY4DRpVM/nEKloiFzMIPMIRqzMLyDvzXukT4Yme5wjbY75+N6ESAwBAkQIMDTEl6FxuwjW2uz16RmfY2BmwKRVQtPzoxOw58Yco2jY2sDaU4bwA2O8KoQRn81DrPXADNof9gp6seLro8isiqMqcenwTijCgRzacLZDBhJT+xpF3pShz1tl4X9MuH1KjEkL+7wnQ7jm2OY0CaoCmRK8KxDkr0whzVmwew2kD6UQfycWNtIiDWhcrs4Q/ZoBkaPQT2CSQ3xcxMo/HYUZo+J/KkcRFSHFtEQXR9p+2S/FFpU8/eZ2VN8X2Y8NZOVzkuSxZ8vTiJ3LAdziQl7ykJ8Qwzc5AtivCNK3BpzJ3IQMSKGekID0xn1c0obWnz+gbiNwASHNW7BUdlV9rgFdBWli67lInMwjcS5CWQOZhBe1loZanR9BEbSIOmXI6kqnXfLeu+aBTc4Lc7oTPUzOvR/LgEpkdqbQmRNFNoCSLJnA2EKXP6DS5E/lcfkI5NIXDCX3LAGaOXCA6cKs/cdafHa+5MLBifrwk7ZyBzJIrElDh4WMJLz6y0shXSp6iwdyu/LDeVhdBtI703DzbvQYhpS+1KIbYr5ag+m8yJZ5Yw+Rwv3D1OyT65Rbx47wytnATkLECDA0xqd25KQkvJoYhuiZOAQ4mdFVspcoYU1khZ2aGR970o4aQfWhAU9QZN/ERGUBadCjZudYLsFCaPPRPZwlvJ7xqhaQLJS3Xfd6jg/UWZB33FhB1LHU0icGyc54540ucLFNdhTNswlJtyCC1mQYGZrJ/tOxiEZo8rs0qKa38fFdQ4tqiHUb0KYHCJKvXIioqHnD7ufetc4Ba5Tn9NTCRERVEXrNZA7LsBDHEbXwhBXoXk5ZwBUBlZ+uACjk+TDkIAsuAsi+eQGg4gJkl+BfBqkK8ENJRGzGVWd1HObrUo3i+jqoi2/3qkjNBCCPW2jMFZAaCA0q21xnfn5i8xgcNTYyZyBgRvS7xVdjDCXmOh7bl/rN9xCd0TA25bnSOgCYLCnbdgpG2af6R+3TGNE7g9lIMJUFWUMc8pGqwuVtcZ0RvcBTotCTtaFnc7DsCjXMLU7hcS5CWSPZX3i5IWfa506ZKGFSgepolpURQ+CwZqwYC45M/OKn76zjwABAgQArfx6/SVM3XAWajV/sYJpDHpCoOuKTnRf24Xuq7pg9BoIDYQoNFgwkqYZjBrDQ6L5/cWVEYu6gXKd+ijyp/JIH0j7fUpaovbaodlrIrw0jK4rOkmCmdSx5PolCC8NQe/U4WSdlleICqMFWNM2jB4DIiTgZBw/k06La37EATdpohLbEkd0U3TREDNA9Tk9xWCCoecPehBaFoJoo8V6LQj1+aVkECGO0NIQGAfMpSE6lkIczBDNV4DnAa5zMiFRvzPlXsdMmlhyAT8PkBu8rccR4wzhlSEUxgpw8u6szh2mZGSFsQJN1NU+JvUBJ4mywZE7mWuwpbMQLeajfmXI5PSLYMgez8KatGGXyAmhqeqR+g6cnOrz4qyF3v5KuioY5QiqcXGdruf2pE0kSR0PJIenMUmHjmk9IVpKGKUk1YVXOQMjieeZiqf+ah0gQIAAiwiMMURWhuck8TlbYPQa6LmuByIkyMbZ4DC6DWgdmi/RC68IgRkciXPiNLFtNueMMXCh+lMErXRqypBFWiSXYSqzayaIkCBi50joCQ3RDVGEV4SRPZ7zXRRbBW5wGEkNRg+ZabgFF1pSQ3JrB5IXdfjkPnFuHJE1EcTWRRF5CnoWZ0LH1o6nRKpbCWFS71vnJUnone2XEPrvqwwgJKhXMLw8DCZAVXKDo3NbEtxkCzIrYgYHDwu4qtHJSdvU42moipky1UjvTfsW6u2EntABZSQzq4UNRkTA7DXBNAam87KJuzdBb1Wv05kE2eqmM3VMMGUIwhggIhp4mCN7uHi944IBOvVdcYMjcygDV6lBWheKHSXJtGAAaFwixP1jQIsLCIN6uFN7U8pFlirA1rhFx7RGpkAtgwQde962uVr4O0MRyBoDBAgQoALRddHGTzqLwTUOHi+/cSbOj5etBifOiSN/Mg8toaHn2uaDnL0bpjfJ8FZbyT1OgpkC3Vd3NbUtZhSleowxiAhXvRCtXrYGtA4dkVVhZI9koSV1JDbHq3LnQktnJwlbSOh1KpFPFRYis68UXsSTK6nCKSIcEMWquRbVqPKzADyCG5T75r2VHuEkqYzoQJ5Mbpy8hLRIPhZb397rkYiKkgy8WbyQAeAMegf1pDJBFQvGyTmPa6Dq+NNRhSBb7LDJy/ctAHCTgXEOmXfhWi7saRtgaiEirIOHVfyKI30nzVaA6xwhZVDFOPweMm5ySEtCS+qQBReQDpwMVD4bwDUge6IAPUmqAt7CS4AnpRUGLRC4Ft1jKg2nzhQ8fZeGAwQIECBA0yA3tvJbBtPmsKqvXsPVZEGEBHhY+AYtYhZVOOqjkiW/F103G8ELIW4W8Y0xGF3Un2R06gta9Qkwf3iHlISaSGocWlSUVciZQfED7QZjDF1XdPpujXpUg1twVXwHp4qIYEqmxRFe0d6KJ9ep6sG1YgZh9mgW6f0p3/687mfhDHqXQcRBJ5c8mrCr33XVJ3UGZ07NBtIlExTpyJaqL2IborRvhXK01eD3u4qIQG4oD2vSItJjCvQ8o5uOcSUf9pw0WwU9oSF+ThxgABe04MENImjxjTHwkAA0Mnbi3kJcSFB1WGcw+4yW9oPFNsbQeUmSjEc0kgZzjSO9v7UqioVCQM4CBAgQIMCc0HNt96yz1zrOTyC6JkLETvUqJLd2oPOyTiS3JpC8uHmHSrPXKKvAcIM1PbnOHs8hvS8943OsKRupfWkqJghvtZomy4uhhytA8/AqZx45A4Duq7vKArl7ru0mid9CjEcnl0MA0MIc0napiqypRQ9VCWBG+6tOnvU9OIM1aiF3Mgc77UBL6HCy5eQstTddrEyrSo4W08qz2ESRCDBNyctcsldvRPbOdHgGHJmDmZZWYf1+X416+TxDDh7mtKigk2QYnIx3zB4DnZcmocWE74DL2mCYyThT37eKQAlz6F06uMGhxQSNSfVRirAAM6g/ObycIj9aBW5QlTBxfhzd13SRxJbRPeFMRHB3CRAgQIAACwaqPgm1yq6kKBG6iUfXRv2V3mYQ3xJH8qIimdMSOoyk3tQkRDqNA1DdrAMtwsmNTkmzomujCC9fvPLFALXhfdMSVAUFMKtjrR2QalRmnwFmCIiwBuYZgHiGOa3sy5kBTFVWCqMFylYzGURYwK2oLru2W0LOVI8co8l/x4UJn6QxVvLPEJBSInMwi/Se1IJ8nnbDyw4rhXQkrHELIqq1xe03vDxEfaOc+UqGzss6FZGnUGjGGWIbSAarJ3WIsACEkjS2w4lUEXSmpI0iLCBCAp2XJtF1WSf0ToOOZ42k6xRA3z7CZHQaYIyC5D1DmoWQKrcaATkLECBAgAALCi0qqAdKNbjPFd6kwIMwOZKXJMF1DjvjzLxKL9FQ5iMhoXXogJTgYWJ8kZVhiNDiymwK0BilPWeLpTfQOzqNmIAWFRBhMtBgyiyH84WpnAEqpJsTyRIh6sXjOgNK5IjSoUUKa9IigqZ6zjxC5smJKYgaVLkwaXuQlG8oZymtc/OLs9KWPZ6FnbLLHrOnbeRPk3MlM1tfqTK6DERWRWj/KsMPrjEweD1+5MgYWR0pvkh9P3pCa4vrp38N5kDyog50nJ+gKlZYQE/q4GFlKqVxyqBUiol2Q4tqfizLmYjF1SEcIECAAAHOejDB0H1NN7LHqic484WIkEV55mAaWlxHZGW9fh3ZkBgyxmD2mQivCPvGEQHOTHjz0qcgh7suvKEYCQ1aRkNkbRShgRAmH58CFwxygSayAFXOYJE0zZfOcVaWjScdCWZw5EcKVB2DkjIyz0UQ/j8qi4MqK0IFoDOAgSIC6gXFu5ZL21J/T+1LIbIysuAGMo1Q83vhgDCIJAmjhdb1Ve9D+9voIvdYJihrjukczCkns4zRvjaXmO3J/OLKjIOzmpJgpv7uSV2pX24BjmnV98hna3KzSBDcbQIECBAgwIKDGxyRVRF0bku2dsPenLCBHIz5/5kZ0XVRJLbEWzGyAE8h/J6zRVSI8WWNnTp6r+uBMCm3j+sc0BhFWszCCXU+iG+MUbSASbbo3KQeHr1D9xdQUnvTRDxMXiRlnOSPnlMjmHIT5PDJG9MYrHELAEmZp3dOw7VrfxGp3SlYYxYAUM6bxpE5lgVAYfBuYXF8gZVVKNd24WQcMIPyu5ghYHS3J2CdMSC+KYb4lph6QJmE6NXSRb+K2SYwlblWbxHBc5nUYqI4+AXIfyxKatkZaUYTkLMAAQIECPCUgAma2LV2owAgaTV3hjmA5DNPEpy8Q+GpZ3BWToAivGNBNsPIFwrKTIdXzMSiqyMQYYHwsoXLpQstpWBwJjhElKRoTGMILQ350kIibeTKB0a/Jy/uQKjfRPfVXUW7dxVS75EGpjEUxi0iCaoiBwnkhnJVlXOmMUgvF82lip5XtU4fyrQ8w3CuYBXfmTVuwZ62VX4jB9fg93614709Z0wA/j7lNfIhXZuqnW2DZwRT5y0885LObZ30+wxEruVD02tX884EBOQsQIAAAQKcNWCMQUrmu3XVfR5nM07Ts0dz5HQWkLOzAr6scXEUXgAUe84q1wjMJSa6ruhEdF2k6jXtRPKiDjDBEN8c8yf/IsqLZEkwaBEVP+ANXo3dI2bgDJEVYTLq8SSNtqRoAC+kWn3gwriFgqqSefAm7q7lIrUvTYRDkYtacjgpZetzDWvATtnl/W+CIT+Uh5N1yCTFltA7yKWQqd69tkGwMlkoUxXMWtEmIsRh9rZBzljy3qGBUF2ZamxjrMyVkcxi2jac8rEZHJHVYTCNL9rexXpYXCLeAAECBAgQYD5gAANNBkvhFlyk9qYQ2xhDbigHaVGPRvpgBtE1xUmwm6eeF7gSIq7NOiogwOJEqSHIYoHrB6hX/+2pMJ3xJHBeOLyICAhDQIIIkBYlW3ZucmSP5qB3aGULIIwx/7xjXLmxqt4zERFgHHCyElAfjQsGJ+vAyTlgjCE/kvfJmZNxSAEXEpC2i6nHp/wqu3Qk9aYxBmvKQn4431Jb9lrIHM5ChMlRlirzAq50kB/OU/WPMYSXh4r9em2s0FZKKplgkBJInBcvEmmFjgs72jkUAJgxJF1PlleumNbGXrwKeMSeIlbOLBOngJwFCBAgQICzBwyQjIEJWSZhK4wWICIC1oQFvVNH4XQBjAP2dLlVeGpfCiJE/SIdF7Z3whdg4bAoDUHUWCpljU8ZlAQNHOi8ohOMM1gTFpy0g+mRFIxuHeBKZlhnfs1NDtcqVtrAgI4LEjg9NgqhM/qbA3JL1RjcgovskSz0Th1aRMCyJfKn8zD7QxRaHOKK0FFlSFq0yMJU06iIirYuoLh5VcHTyd0kezQLdKserzCHPW1DT+okgdYo50u6skr22FIwlJkZ6V067AmrtiHHAkkImwXTWEvDuWeCCHNocQ2iIBDqb1/1sB0IyFmAAAECBDhrQI3g1FPGwGCnbDg5F4UJC2aPASdlI7wyDmfahpTVkxeucbgFF+YSA0Znexr6Ayw8/MX6RUTOPKK4AP4ITYGxYj+QR3j0Th16hw4wy5cpQitWyCqrIFxnkMrsg6SORct3EdNgT9uAYEjtSVFl2mVUJXQlmKmBCQciIuCkbbJh56qSpzHwsIAjAT3KYKeconW/QVU3s6f1E3An60CYjPoDJWCnbUhbQhMMQmNwMgx6lwFrtABmEnGNrIqgMFZo+Vg8dF3eWWYRH98cg5NxZnjF4kHXFZ0LRhi7ruhakPdpBwJyFiBAgAABzi6oYFQpJfKn8nAtSblNJoc1AegxjSaAGQdMAKk9KRjdhp8x5abdMzYfJ0BtLGZZ46KpnAFVcmDqZ4LvPNh5aRK5oRwyBzN0nlXMs7Wk7tve6wkNelL3e6FCAyHkTuT84Hly06N8NTAGLSpgTzFoIW9qytQCijIPCnHIggtuCDDNJYMRSfmGTqY9PUWu5UJEKcjZSdngGoMjXQoI11QPnkHVoPiGGPQuHSIk2mNbr1B5bdKiGuV6nQFoRzj32YhgLwUIECBAgLMOTDBIS8LJudBiZGLADQ5Ici8TYbJzZIImiNa0jezxLESIg+kcUi6SckaAlqAoa1w836vHExfPiFBlxe49JkwKphZhAREix0amcsxK0XlJEuHl5DLZsbUDkdURkrJpHEaPAQkiW0JVxchAgwGQ0OLCt+Zf8rw+1bMG6uPSKMyaaSqAWVevU+HXrZYRSimRG8oBLsANCgh3C9Sn6gXYM41cKT05o96pBwH1AVqCgJwFCBAgQICzDJRv4+ZdiAhXroscZq9BmTtRgfCKsN9jwzX6xzg5yukdYsEcxQIsDPzK2SIybVuMlbNakjPGyVBB76LqmYgKqo7VIGf1tsl0dY4xBgheZsdPhJDkjVxXZiQh4edzkUEJVMWMKm1aQqPtSpI1tpqcuXmXJJiQlPlmcIgw910puaHIWYeG8IqQX5kPEKAVCI6kAAECBAhwVoEx6pmREtQQHtXAGP3srXjHNsbIXU797pUxuM6gdRiLS/8WYN5YhC1ni67nDKD+Jb2jwmFPuTcmL+oA4J1TAhQq3XjwjDGYPYZv88448wkVNwW4xiAdIkHM5OBe9qHqWWMawHSB0IAJrpGlfs+13ZTtpSp6rV5NyQ3locUEpGRExPxqO30eYXJwQfskvCKM8LJwlYtigABzRUDOAgQIECDA2QVGJIsx6sfwJFBc9bd4kygmaOKnJXRaideochZeHkJk7cJmTAVoL7zqlFxElTPPrXExVWnNXrOGVXu5fTvjDKEBc1Z6zMR5CTrfOMCFV00jOSDll0kiajpH1xWd/vtISVJCbqpzViMipsU1CnreFKWKVQtNJtyCSyTQFPRzSMDsNcHD3A/SZgZXVXYdjDPENsVa9v4BAgTkLECAAAECnFVgDOBRAQiaRIaWmv4Ekwnu3/m4SaG4oX4T4WUhiBBNFOPnxGB0BU6NZxMWo5X+YpQ11oIXHl3+YMX/m9qQerJGTqoiItBzbTekKwHBoSeVgQjziBjJHc0eA0KZYJSSRBHTYHQa6L66G4yDttMCpPamoQITYXTr6LwsiVC/6fepgjEkzo0jeUlHS94vQIBKnBn2LgECBAgQIECzEAxaiORSeocO1y7mJHEd/uSPmxwsx5RTI0PuVB5M501JtQKcWViUbo2LUNZYC53bktWVKRUuPRtyxnhJFY4Vg7aZwWF2kNGIJ50EyDTEyTiInxNHbjhPzy3x2+i6soskjQAturTou2XCC+MG9A6dDD+SHJHVYRTGLDAOMgIJHF0DtAnBkRUgQIAAAc4qeEGnXLnKATR5o0lXsXKWOC9O1TRO0sbImggS58efwpEHaBc8vr2YXDi9ytliXwsQEeGTIA9MVZBms5DhSRm9EOWea7sBANG1ESQvrq5CeY6OABBaEoLRa/ivB1A+JlcidzIPNz9/3SoTRflkKSkVhoAwWFUIdIAArUZQOQsQIECAAGcVuFBGAWFyUGNZp9jnIooTSrPXJHc61eAf3xj0jZytWIyyRm8oi71yVhNs9hEAnk1+pZFIbH3t806LCdhpImDxzfQcpjNIt4ajpADsaQvWlIXYuui8qlpMVdnByyt1zODgpoAW0wLzjwBtRUDOAgQIECDA2QUvCynEfYc4CAbw4qTLQy3r8ABnHxajIciZ0nNWE7OUNHoQYQE35zal24qujyKyptyYx+w2YGed6uEYHACDFuGQcn4MnGl03aAe1eKH1CICPHQmflkBzjQER1mAAAECBDirEF4ahtFtoOuyTmhRjeRRKs+MqTwzD4yzRS8rCzB/LMrK2SJ0a5wV5jDuris7FbFrwoKfU9h0KWIbY0heWC2B9EKxeYgD8yTg1BunHF5LDUgiAt3XdM1v4wECNIGgchYgQIAAAc4qxDZEy343l5gweg24lqSV8LLKGeY0yQxwZqHYc/bUjsNDaXXnTDSgoTHPftzc6zlr8Uc2uw1kDmTABJu3ayMTANdQND0p/dsZ+F0FOPMQVM4CBAgQIMBZD5IooapyBjGH5pkAZxwWm1vjGS1pBAAp50yw5sjrZoQ5ECLSxxngAvnhPOyUPbeNcTIuYRojY5AAARYYQeUsQIAAAQI8LeAZgpRODP3m/wBnNYqyxsXxXZ8pNvozYj5jb/Hn9ogi0yi4On+6ABEh847ZQLoSjDMkt3aA6dWyygABFgIBOQsQIECAAE8LMMbQfU1XmTSpc1tSOcgFOJvhG4IsksrZGd9vhnmM3atwtRJKKskEg/Tky3Vgp23ABbR4jSmwJEOQmn8LEGCBECwJBAgQIECApw0qe0YCYvb0gPetLxZydsbLGoG5V79q9HLNG8pkhHHqOWMCdUOpM4ezyBzJ1vybdCV4UEkP8BTjTL4sBAgQIECAAAECNMSi6zk7G2SNcx18GwxBGCuGQ0tH+pU5J+fAmrBqjqEWpCsDmXOApxwBOQsQIECAAAECnNXwe84WSc7Z2SBrnHvlrKWjKNsmEwz5k3lA5RnmhwvID+fLnsoFqy97dAGun8lfSoCzAQE5CxAgQIAAAQKc1ShWzhbHxPtsqJzN1Vae8XaUzkAui8rwh2sMjDFwnUGLlUuXfav8Ekw9NgXpSNgpe9FIXwM8fRF0PAYIECBAgAABzmostpyzp3PPWWRVBNxo9QenMHmuM4AzcIPDcRwwwQGupI5etayWPb7GkD2aBTiD3rFIDpIAT1ucyZeFAAECBAgQIECAhuCLjZydFbLGuQ0+sjKMUL/Z8qEwVTnjglHlTH3pjDM4eRdTj035v5e6RUopwQUROD2poevq7paOLUCA2SIgZwECBAgQIECAsxqLzRBEng2yxqd6AKVgADgoPFpn1HMGUFi2YHBzDgXOg4icm3WQG8rRc1wyEuEmR/zcOIQZTI0DPLUIjsAAAQIECBAgwFmNxWYIclbIGhfT2FXljOsMTONggoNxkL2+xpA7nitWyxj9K4xZkK6ElJJew4HwQOip/BQBAgBosuds27Zt/wLgKgCHALxxcHDQUo8LAJ8HsAHA9sHBwXe0Z5gBAgQIECBAgABzg99z9tQOw8fZYQjyVI+gCMbInZEJBq4BXMDfuVpEgOmMss8ARdroudO7phHbEKO/sRlcHAMEWEA0XPfYtm3bhQCWDQ4OXgv8//buPEqysrzj+PdWdfdMz8oIExGCgIoLENDw5GhUIiIIuCtGQQ3KQR0VTDQIMYIoCeJxjRo1YNwRQRMkKiogKLJI0McF44KixCUDOLLIJkvPdOWP9/ZMMTDMcp2uW93fzzlzuvve23NeuL+pW0+9G1cAz+07/TTg6vrc/Ij4y83TTEmSpE2zZkGQil4LJp7N6qX0N4e62OqMdahGOmVYY1XRA7rzR+iMdsoeaL3e6v/pVRc6Y52656yC+1piX5pGG9Jz9ljg3Pr7s4FDgdP6zn2p79zjgEv/mA2UJElqoqoqqqpHrwe/uhY6ncEWaCtuLF+HueesVY2v6v3LRis64x063Q70enRGKrrzu1SjU5MOKT1sU0vvj3To3dWjM9JpRdEuwYYVZ0uAa+rvbwLut9a5m9dxTpIkqRU6HVi1CnZ8fnvehA/1nLM2qUrP18iCERb/2SLmbjOXq8+8hs6cLp2xDt15HSbvmOSWK25hbOmcei5ah+7cDqvunCz7nq1qUbGpWW1DirPfA4vq7xcDN2zgOQAi4uXAywGOOOII9t13301sqgZpYmKC5cuXD7oZGmJmSE2ZITXxoict4qs5p1VjCZ//hD+wfPmtg27GRptkkskHTLJ8+Z2DbkrRg5U7rmTFzSvKhJ1rYOIRE6ysJpio7mLVw1bRW9WDyR4T3TvLvmdVxaouZbXGejjjhry++Do0XNp6v7bddtt1ntuQ4uybwN8DnwT2Ay5Z69w+wIX1uY+t/cuZ+SHgQ/WP7fm4Shtl+fLl9xkkaX3MkJoyQ2ri48e2MUOL6z/D5barbuP2FXew1cPasSfY5MQk111xPVvuueXqpfCv/cFv6YxULN1nKdd943ombphg1R2TdOd1WXnLBN3xLiMLRpi4cYJ5O4wzb/t5zN1m/as1ti9Dui/DeL/W26Gemd8HfhsRFwG7AGdExMn16bOAB9bn7shM55tJkiTNYKNLRhlduEELfk+PqQVBRtb0ina6nTKvrKo3pe5WdOZUa+ab1UvvT67s0Z3b3aDCTJoOG/QvKzOPWuvQsvr4SuAlf+Q2SZIkqaXGlowxtmRs0M1Yrar3M+tfbbEaYfWiJd3xLtXYSrojXZgsQxqrbkU12qE3Mbl6g2qpDZyKKkmSpKFVdSsW7brw7sdGOqs3nl602yI6ox2687r1htXQGe9Sdaoy/8ziTC3Soj5pSZIkaeOt3ZO3eLdFq7+f2uesM9JhcqRD1VlFd7wUaz3qXjapJYyjJEmSZpTRLUbv9nPVreiOd5hc2YFbKxY+dAG9HlTfrai6DiRTe1icSZIkaUbb8vH3gx7cdf1dXH/RDWUzasoQx6o76NZJa1icSZIkaUarqgoqGFk4UlZcqMrKjaU4s+dM7WFxJkmSpNmhquj0L7vfraiszdQixlGSJEmzQlVBZ06Xzni3DG3sdspBqSUsziRJkjQ7VNAZ79Cd0yn7nVmbqWUsziRJkjQrdEY7ZXEQygqOVbdavVm11AYWZ5IkSZo1qqmusk5doPluWC1iHCVJkjTrVJ2KasSeM7WLxZkkSZJmnWq0Yos/X8zYktH1XyxNE5fSlyRJ0qxTVRXjfzo+6GZId2PPmSRJkiS1gMWZJEmSJLWAxZkkSZIktYDFmSRJkiS1gMWZJEmSJLWAxZkkSZIktYDFmSRJkiS1gMWZJEmSJLWAxZkkSZIktYDFmSRJkiS1QNXr9QbdBkmSJEma9ew5kyRJkqQWsDiTJEmSpBawOJMkSZKkFrA4kyRJkqQWsDiTJEmSpBawOJMkSZKkFrA40z1ERDXoNmh4RcTCQbdBknyWSRpGFmcCICIeHhFHRcR2gA80bbQ6Q2cAf13/bI60USLiwX3fmx9ttIh4RES8IyIWZaYbuWqjRcRDI+IpftA4HGbic8PibJaLiE5EHA18AtgBOArYeqCN0lCJiJGIeAPwHmAB8FcAvjHShoqIKiKOAa6MiDfVh2fEQ1bTIyK6EXEccApwXmbePOg2afhExCHAacCTgLdGxEMG3CStw0x+blicaQnwY2DPzDycEuylg22Shsz2wK+Bp2bmfsC8iNhhsE3SkBkBvg3sDuwTEdtk5mRE+IzShlpC+XDoA0A3Il4UETsPuE0aPouAIzLzSOA3wCERse2A26R7N8oMfW6MDLoBmn4RsR+we2a+PTOvB86qj+8O7AOsjIgzgYvs/dC9WStDvwB+UR/fAbgSmBxg8zQEIuLJwIuBS4BTMvPc+vhXgOOBlwG+/mid+jJ0MWX0xxeAY4A7gQuBt0XEmzPzO4NrpdqsztAhwDeBjwIPAB4KXAqcD7wDuAxYPqg2ao2I2B94AeX+zNjnxtBXl9o4EfF0SoCfEBEvqI9VETEK7AK8FrgCeDJw/4E1VK21jgx1ATLzl0AAO9bHfY3RPUTE31Feaz4BPBB479S5zDwReHhE7JGZvYjwQ0Tdw1oZ2gF4V2ZeDLwhM5+Vme8GzqMMT5sxc1H0x9OXoU9SnlknAP8GPCUiXg0sA26kFGxmaMAiYi7lw5hPU6bfvGXqntTPjUfMlOeGb5xmn6Q8rF4LPHNq0nRmTmTmpzPzbOBcytDG3w2yoWqte8vQqrrAh/LC+XSAzLQHTffmfODQ+lPPtwN3RcSCqSIfOI7y4H0V8MgBtVHt1p+htwFVRMzLzO/1vYm+hNIL4hxY3Zv+DL0VWJSZ/wccC9xAmXv2RuB+YIZaYCfg9vp96gmUIaj79/17fyMz5LlhcTZL9H26cE1m3gb8L2Wu2eH1+U799WBK1/6vKA87PykSsP4MsWYo4+3AiogYn/5Wqs36MvTDzLx26jBwZ2be2nfpCGVhmV0pGZOA9WboD/X5bkT8DaUX5JIBNVUtdR8ZuqM+fmVmnkqZg38SvgYNTP970Mz8H+ABEfH0zJwAPgc8t69onjHPjaHu9tO6RcRjKOOoTwcuz8ybImK0DjSZORERpwNvjIgtgRsjYmvKJxOvyczLB9Z4tcJGZmgr4BZgFeVF8dLMvH1QbVc73FeGIqKqH6qjlHmKUD6h/h2wEHhsZn5/EO1We2xChhYDWwKPApZl5ncH0nC1xkZk6Gf19VsCN1N6XV/jnMXpVd+vLTLz7HqIYgWMZeadlDmAxwBfzMwvRsSyiHhiZn4dmM8MeW5UvZ69tDNNvaToXsAZlBWsepl5Qn1ua2BhZl5Z//wPwKuBczLzsMG0WG2zCRk6Ajg/M18ykAardTY0QxFxOLANZSTH0sx86YCarJbZhAx1gSWZuWxATVbLbOLr0FaZ+bIBNXlWmiqSI2IZ8BbgM5QFP/6775ptgD9QhsL/FPh4fe27pt6PzBQOa5yZzgEOzMz3AxcAN8HqFfYupR6LGxF/QZk79AELM61lYzP0QQszrWW9GarnmD0ZeBpwjYWZ1rKxGbrawkxr2ZTXIQuz6Te1hdOXgT0p9yoiYgGsXlXzMsrw0+Mpo3ROAa6daYUZOKxxRoiIw4BnA6+oJ7N+q28hhgdR9qEC+A7w6MxcUf98NfC8zPz9dLZX7WOG1NSmZigiPg1cmJnXTHeb1S5mSE2ZoeESEXtRFvL4VUScAVyQmb+ph5Y+BHgC8CXgu8Aefe893hMRJ2XmHYNo9+Zmz9mQi4jFwL6U8dJPjIixtTbheyBwdv39RGauiIgxgMxc7ptqmSE1tYkZmgOQmZ/xDZHMkJoyQ0PpOcA76z+PouxTBqV4vhp4UL242E31/RrtW9BlRhZmYHE21Ooxujdl5kGU/Tj2pnzS0O92YGlEHAccXv/OXdPdVrWTGVJTDTJ053S3Ve1khtSUGRoufasw/hqYn5k/pizYskdE7FIvKHYOZXGfzwHHRkQny7ZPM36xDIuzIRMR29dfu32r2Ext/vsjyr5TC+pPi+YALwWOpiwR+/bZEGrdNzOkpsyQmjJDasoMDZeIeHD9dWrxjw5ldd7FEbFlZv4cuAh4fP0ruwHPAL4N/HPOon1TXa1xSETEPMoKNdtR9nWYiIiRzFzZd839gTdT9uXoAldRxl5fVIdes5gZUlNmSE2ZITVlhoZLRDyD0pt5YWa+rT42kpkrI2IX4FnA9zLzyxFxENDNzFMjYjfgusy8emCNHxCLsyESEe+mrJJ3emZ+qD62E7AP8NnMvD4i/hF4BfAN4PWzMdRaNzOkpsyQmjJDasoMDYeI2JuyN9mRmXlBRIzXQxaJiKkFP+6gzDf7GWXVzK9l5gcH1eY2sDhrqboLfjwzf18vvjABvBL4AfC3wJFAD3gP8F+Z+al60uR/AGdl5kmDabnawgypKTOkpsyQmjJDw6W+X/My88aI2BU4AHg0sAVlK4P3UvYrOxI4MzM/GxEPAg4FrsrMjw2m5e1hcdZCEXEwcALwlcw8ou/4+ygTJBdRdq4/jRLk/q78u3Xta3YyQ2rKDKkpM6SmzNBw6btfZ2fm4fWxZwC7ZuaJEXEgsDtlU/Cf9C8uNjUXbRDtbhsXBGmZiJgLzKcsJ1pFxP59p79O2evhVuAwYFk9Znds6gJfiGSG1JQZUlNmSE2ZoeGy1v0iIp5Snzo/M08EyMwzqPeby8y7ImL1fssWZmu4CXUL1CsOHU3ZaO8Hmfnh+vg48MKI+GpmrqLsmv5K4AbgPyndwqTLms96ZkhNmSE1ZYbUlBkaLuu5XwdHxNmZeVvf9YsoHUNT98sC+l5YnA1YRIwCxwE/B7amrGjzzPr014AnUT6FOAn4V+BxmfmpATRVLWWG1JQZUlNmSE2ZoeGygffrMODf62sPBl4OfD4zz5v+Fg8P55wNSEQ8B9gKOA/4cGbuXR//CGUc7juj7NmxPfAW4FvAuZn5k/q6zmza80H3ZIbUlBlSU2ZITZmh4bIJ9+sy4POUDqHrMvOmwbR8eDjnbJpFxNKIOAt4HrAzZdnXFRFxaH3J8cBzI2JpPf52EfAYyqcRq198fCGavcyQmjJDasoMqSkzNFwa3K9nA2OZ+QsLsw1jcTb9esDJmXkQZUWbnSmr1uwaETtl5q8pKxDtV0+U3IOyP8TemfnTgbVabWKG1JQZUlNmSE2ZoeGyqffriZl55cBaPYScczb9rgfOBcjM6yJia+AW4ErKng+vAJYAl9cTJWf9fg+6BzOkpsyQmjJDasoMDRfv1zRxztmA1ONxFwOnZeYB9bGTgXFgjDJp8haXFtW6mCE1ZYbUlBlSU2ZouHi/Nj97zgZrBLg4IvYA9gc+CvwsM28cbLM0RMyQmjJDasoMqSkzNFy8X5uRPWcDFBEHAF8AzgdOzcxTBtwkDRkzpKbMkJoyQ2rKDA0X79fmZc/ZYN0AvAF4rxsnahOZITVlhtSUGVJTZmi4eL82I4uzwfpWZl426EZoqJkhNWWG1JQZUlNmaLh4vzYjhzVKkiRJUgu4z5kkSZIktYDFmSRJkiS1gMWZJEmSJLWAxZkkSZIktYCrNUqSZpSIeB3wDuDQzPz4Oq6ZBxwN/HJd10iSNN3sOZMkzUbzgDcBLxlwOyRJWs2l9CVJQ6/uLXs9sAL4NnAIcCjwVGAfYBy4CjgmM8+MiF8C2/f9FccDJ9Z/DgbmA18FXpWZv5um/wxJ0ixncSZJGmoRsTvwfeBHwPsoPWLbUIqzPwFuBBYALwO2A5YCzwFOBX4C/BPwQ+BA4M3AycC1wOuAczLzwGn7j5EkzWrOOZMkDbu96q//kpkfiYjtgGOBLrALcBAw1nf9DsC59fcrMvN0gIj4WH1sWd+1+26mNkuSdA8WZ5KkmaJa6+soZXjjecA7gVdThjnOBdY1bGQl8DRgVf2zc7MlSdPG4kySNOwuqL++JiI6lOGM/eYDOwGP6zt2MzAJPCQiXghcDJwFBPBiSkG3M7Aja3rZJEnarPxEUJI01DLzcuAoYGtK79g36lMTwOnAIylDG8/p+50JynL7WwCfAvYE3lof2xN4P3BA398lSdJm54IgkiRJktQC9pxJkiRJUgtYnEmSJElSC1icSZIkSVILWJxJkiRJUgtYnEmSJElSC1icSZIkSVILWJxJkiRJUgtYnEmSJElSC/w/l2sFKB+9+lUAAAAASUVORK5CYII=\n", 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", 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\n", 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", 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7xACE1Kjg8OlPfxpbt27FGWecgR//+Mc488wzsWzZMhxzzDF2w+lrr70W559/Po4//nicf/756O7uxllnnYVjTjgGp3/hNDzz3DMAeIPpiy++GEuXLsWyZctwzz33AAA+85nPoL29HYsXL8a3vvUt+7u/9rWv4bDDDsOyZcvwla98Bc899xwefvhhXHXVVVixYgW2bAmjkk4xqmLO2tvbbwBwHIDtAC7p6OjQzPcTAP4EoAmADuBjHR0dXaNjqoCAgEA4EMZV6zA6RaHyQRjCVzMUovEJXaCI8NkU1vaGoUOYBilMtgAhuymaCNimm2++GQ8++CCeeOIJfPvb38bhhx+O++67D48//jguuOACrF69GgCwfv16PPPMM0gkEvjYxz6GL37xizj2iGPx6t2rcf5nP44Nmzbgu9/9Lpqbm7F27VoAQH9/PwDguuuuQ2trKwzDwCmnnII1a9Zg1qxZuPfee7Fx40YQQjAwMICWlhaceuqp+MhHPoKzzz470N85mqgYnLW3ty8HMKujo+OE9vb2bwA4G8AfzI/PAPB6R0fH19vb288H8AkA3x81awUEBATCghA9Y23CI4wP/jBBDI+AD4RuwSFsCOP4hM0eYExt+ufkh6rc8rWajvve3tOq2u6ZZ56x2a53vetd6O3txdAQF0L64Ac/iEQiAQB49NFHsX79ejDKYCQNDGWHkEwm8eijj+KPf/yjfbxJkyYBAP70pz/h1ltvha7r2Lt3L9avX4/DDjsM8Xgcn/jEJ/D+978f73//+2v6TWFCNWmNxwF42Hz9IIDjHZ9tBlBvvp4EoCc40wQEBARCjNA99MOXkhYmhjFsLAyAcJ0vQLRjmEAI0aXlRhgDtDBBjI2N+vp6+zWlFC+88AJefv5lPPqLR7HjjR1oaGjw3G/btm340Y9+hMceewxr1qzB+973PmSzWSiKgpdeeglnn302/vGPf+D0008fq58SOKpJa5wEYK/5ehBAq+OzNwEc1t7evg78XnFU4c7t7e2XAbgMAK644gq85z3v8WWwwPhA0zR0dnaOtxkCExhvpTlEdQpjvhGe38MAfbaOfT37QlNJrB+gI6dnIXUGZ5CfOaRP0wEAuc5sYPb4AaUUdAZFZ2duvE0BwJUs9QU69g3sAxkKh+uvzdWhURX9nf3BHdPvHKIIxXU/PNwAoBFAOOwBwO9DB+jY162GJnrMzdKRo9nwzKFZ/D40Vuds+ZolZT+nKoWRMRBpjtR03Er2G4aBvXv34vDDD8evfvUrXHnllXjuuefQ3NyM4eFhDA0NwTDyz9B3vOMd+N73vodPf+rT0BfoeOyZx7Bk6RIcc8wxuOGGG/Dtb38bADAwMIA9e/YgGo0imUxix44deOCBB7B8+XK88cYbyGQyWL58OebPn4/jjjsOnZ2dSCQS2LlzZ3iuExOzZs0q+Vk1wdkAeE0ZADQD6HN8diGAZzo6Oq5tb28/G8A3AXzVuXNHR8etAG41/xRrBhMUnZ2dZSeSgEAlvJXmUGZXBoNbBzH9/dPH2xQAgJE10PN6L6acOBlynTze5gAAutZ1o3FRA+pmJQI7pp851P1kD0CAtkVTArPHD9Lb00h1ptB2Stt4mwIA0JM6ul/pwZSTpyDSFA4h532vdaFpcSPqZtUFdkw/c6jn6V7QDMXUU8f/nDU15WnOsNxXqUrRvb4XU05shRwf//vQF2+kuPEvDOt/msPCI8Ixh7of6waA0Fz3mV0ZDGwexIwPBvssk2UZM2bMwI9+9CNccsklOOOMM1BXV4ff//73mDVrFpqamtDQ0GCP4+23347PfvazOOOMM5AbyuGd73knTjv9NNxwww347Gc/i9NOOw2yLONb3/oWPvzhD+Ooo47CKaecgtmzZ+OEE07ApEmT0NjYiI9//OPIZrNgjOFnP/sZZs2ahQ996EO4+uqrceedd+Ivf/kLDjzwwEB/62igmjvwcwC+BOC3AE4D8KzjM4J8KmMPePAmICAg8NZHyJaawpe2FzZ7EDqTQnXKwmSLhTDaJFASfD6H56T97M8AQPC/Tyi4/ojxtiaPUF33BKNyyrZv326/vu+++4o+v/baa11/T5kyBXfffTdfJHq8B20n80W0hoYG/OY3vyna///+7/88v/ell14qem/lypUTrs9ZxXyTjo6O1QC62tvbnwawGMA97e3tt5gf/x7A+9vb258E8F0APxklOwUEBAQEKiBUAVqITAktQjlGITIqTPMZCF1ZZygh6hbLImxTOnQQ8wdAlVL6HR0dVxW89Snz/UEAE7fiTkBAQKBGMMbwp+ckHJKTEI6kRuQfaOKhNrEQsvMlHMdqEI5BcortMMZAwqC+IyZQdRDDVBEjnUo9T/di0sqWUKTV+kFISscFBATCgrvuugtnnnkmMpnMeJsSStzzb+CimyI49vZw1C7ZCNsDP0T2DAwz/O7lGIayIXBgHQjREIUuJS2UCNH0ccViYTptYpGoPEI4NmGKqWu1ZfC1IeS68qJKTKUYWjMEfVgP2LKxhQjOBAQEXPj4xz+Ov/3tb7j99tvH25RQ4uVNIXqSmXi4A/j3NiVUD/4wpViefx3Dl/5Wjyv+Wl954zFFeMYIQPgc6zDZAoRvfEKIMF33FkJnUtjsCZlBtZwvdUCF2qcCAHLdORgqhZ4xoPZro2Td2CAckkwCAgKhw/Dw8HibEEqEIXvICcNgeO83CIBJ0C6k421OKPGP5/i/D2+qTS561BEunyh0YCIYqg5hGSPTjjAGaKFBiIaGMYbbH5OwWJMxs8K2qa0p6MMGmpc3Ib09DXVAQ8uK0dAArDFHX+cNNRljGHp9GExlQHTiN9sTzNlbDKtXr8ZNN90kbo4CAm8TGI54LFSXfZhsCStCNUZMMEOVwIQiSEWI8SmLMF1if/038Pk7Ijjlt5VT9LUBHdogZ6Oy+3PQTLZqVFDDAFGD/9v7TB+YzqAldVBj4l+ngjl7i+Hwww8HAMyYMQMf/vCHx9kagYkMEeBPDDhPU6hOWZhsMRGq8UH4rrFwWYPwGWSu0IcBrpIzxkDCUBA3wYWJGGUYfGUQLe0t420KHnqJYV8vcOEZo3deN+yoYWMCMIOfWCNt2OeYqhRUpVAaAgwnaphDzFydZAYDYwDNUDB9gk5ABwRz9hbFm2++Od4mCAi8JRG2tEZ3cDbxH0oC4wjGQjOHfnkvwxm/m4TB9Hhb4kA4hgZA+O5DNmqcP1Sn9r/jPfeoxpkXxhjS29Pof3lgFL6lusjj9K8wXPQDhs6usUlVrzT2hACgDEPrh0BVCgbAyBro7xhA/6qBAA2pcXuD37OYxlMZpRgB0xgyu7PB2TQOEMGZDzDGcPbZZ+OCCy4Yb1OKMN43OQtPPvkkDj30ULzwwgvjbUpoceedd+IPf/jDeJshMAKE4TqjzuAsTCVnIRib0CNMQxQmWwB89qcMr3VFcOujE1sSe0wQknNn3w9rsKfv+X4YaQPdj/city9XeYdRBNMpqMrADAa1T4M+oMHIGJwpCgCUMry+T3aloldC5/ODGN4wDD2lgxkMwxuTSG9P2yyWH9QU4BMCagC5vTkwg7du6HuxH3pSB9WCnYC1HI1RILef28Qot4tRBpozQvF8HilEcOYDqVQK99xzD+68887xNqUIYZmUJ598MjZu3Ij3vve9421KKKGqKi644AJ87GMfG29TihCWOSRQHtRZczZ+ZgiMBGE7YWGzB4Cqh4wiCuEYhcqmGm1hOkP/qn7uYNPR+SHVHpXpDEw1wAyAqgaoAfQ914e+5/sCseMbtzG8+/ZJ+M5j5VVjnc9ehTHkulUkNyTR80wvcvtzSO/MBCIVX8sjnoEzU4ZKATMwZCpnrJhBkdqaAtUCWB1kqK22k3E7qEoBBhCFgGkURpYCYVqsrBEiOPOBMDuvYbMtlxvfFbGwQtcndi+OtyNcrmIILrPwMmfjbUC4wSwnJCyY4PVCY4K30NiMio9giuRVe2jGGJjOoKcMMJVCG9bR83Rv8HZVCaYzzgIZDDRHAcqgDupQ+1Tu/PvEf5sJMre8mCi7XdahtWFojDN6OoM+qINRzhAFfv4qHM5mpLI8CKIa5amoOgN0huTmNAZeHRwTW1ybUgaqU9CcAVAGohAYGoORFcyZQAgxkSelQDgg5tDEAA2pWmOYbAktQjdGoTMoXBYRVG2QNjS6gYYzJa2Wa83IGNCGdPQ+0wcjE0y6ngu1nDBq1pxRBqVBgZHkQRpjLJC0vVpBNQamUTCD2emN0BmITGBk/Y9VtWmEGcdatqYxMN0MHHMGZ60CUiOs+RCUC4AwygU41G4VjDJowwaYavDALQiw6vwPxrgICFMZjBwfEyITMIvdC9XNozaI4ExgTEBCW708vhAB0ARHCE6f0wRjlNKCRoQQmRJGPLORoCcVsvuiYM7KggFVR0I0Z4xamh4wMkGQ7N4shjckMbh6kAcgAdcK5VHdcZkj0CARHgAZOQqao+h9rg96cmwzS5hBeYBGGahGYaQNM32QBFPjVeV2ruBMzTNlVOPS8dRA8NdpxeNxlpMZpt/CwFMadQZ9WM+/79eMWo5BAZjnimZ50EgUic9rWQpXJkmNEMHZWxTC6Z8YcJ4ncc4mBsK2zhBW5kygNB7tYDj9BzEcf8fkqvehKg2mpqMErFXosCGMNlUNh0Pf+1yf3ScqcFQ5SMObkjDSBpjGmQWtT8XgawGlogF2WmPVm1MGqjMwCkgRCUba4PM8R+30vbEEMzhDpfZpYBqDkdKRmG2mII6hKa7gTOfjxIMhCmaY/wUwNrU9yswAlTHOoGkMJMLfq59fx0VCAhujKoN7M4dWG9DAdM54Ko0yEgck+HN6At88RHDmA2F2rMNmj4A3xBya2AjDGLlqzsbfnFCDhaEXFIB/r+YnaihX/SO474V+DK9PjpZJNsI2h9gEpvKYIxOOZg3oyVFIIwSqDhyomTJIDW6bnjKQ3p4J+D5WfToZM0xBCQZIcYn3qDLZGNBgM24YY8juLS+vTlXu4Kd3pEFVCiNHISX4NRoECzOStEZdZQDlKZbWuMBgyO7xX8df01knXM3SYvGYakCKSjwoU4jdZywQo6pl8CkAWULjokbEZ8TBDAoiEUSalPyxJihEcOYDzhsapeHiT8PgNApUhnPehO2chc2esCDUzFmIbkMTffpkOjNIbkmNtxk2aJbaDVdHDSE8Z2GaR6SGwAOAi92gOhu9e0cVNqV3ZsByPPhgBgU1KMC4mEJQ9V2sxrRYZsBmY6SoBKoaYDqDNsRl44P0UI2kgeTm8tcz0yiomWpppA00LW4yA0TmXgUbIao9/+lC5sxk9BgzA1oDUPvU0gcYASo+7wmBNqRDikgAZTxwjZnBmWyyakGVnDEgt1+tGEwzykBMu6W4BD1pgEjOYsxg7BkPiODMBwTrUT1EzZk3nMFZ2AJ8gSoQgsssvGmNoTKmZiQ3JpHZlRlvM2xQnQLSKN5HazxdwxuGofYG6yB6ImTTqNa0PT3F66aYSmvNIysL56Gqed6nt6e5DDrNsy9qv5m+lzYCEbzgxqD6c8bMtEYGEImA6QA1GNI703yhKeBzX5gKmNqWdgUAVGMgEu/n5R4PMqbzMJ3Jf5lhim/YjJWZ7mlkRremsRgMsWlxPhQUoDkGKcKDMxDTxqDyGhmg9qgVg2lQ2BGvUq8gPjPuOshYp8UGCRGc+UCYHeuwBWcC3ghzgB8mDA0NjbcJoYXz+TPaxEpNmODT2cgYkGO8AbKe1Mf9Qc80Nvq0LUPV0Ue2K4dc9+gHZ9X7+Qy5rlFu2UJqcNIZAIOh59+9vI5KZYEy266pUIVNzFRFpBqz2Rde2wUMvjaEgVeCqD2rLQmV6cysf+MDw6iplphjo8MSFxwyszuDbKczOKOQzD5ZdfOdvchMaXafDGO1l2/fm/lFIV3Pp1vCCtQMBiNLR1HQxQOGyfyawTeRTWVEwwysg2LOzJ9kBaLlkHwz5WpxoNQr+Q9ruVZDCBGc+UCYHeuw2SPgjTCnNYYFN910E5qbm3H77bePtykAaneKRhvOaROqKVSDox829D7XB6oyEB6bob9jANm9wTn+I4mxGGW2wISR4ap2QeLq30j4/vON1e9gjGKa3gjAdIah14dGVTSlFnC5ccBI6uh7sT/vYAPQh/VgA8mqgjO4AjJmMNAM7wVFMzSQtD3+RdX34DKyBqjGZevNXXkGoWoAem0ppOVNMg9U8BsLnX+mUZCIBKpSSHJ+cjMQpLankdqa9mVHtZdL/1DeLo3ycwZLjMPgKZY0S3lgGxRKjLXV1sCau8xgoAZDw8ENJmNG7fcDX8CqcDh1QOVpqCX2naCPHwAiOPMFUXMm4BdhDs7CYs8VV1wBAPjsZz87zpaEE25BkHCcs4kOfUjnbIdZ/0KzFEZKh5E10Pdi/5jawhhDZlcGjDIMpRjSWc7GZLb7cxQLv+PH90r41eqGqtkdqo/NXKvWHmbwZsajLb9e9SVmNsc1VAam8lqm7G7OiKS2pjG8KThxl2psshT/eJ8qi33hfbOMrBFMHFRDSqM+rCO9IwMpQvJBBuXsoqEyGCoN3LlmhZmb5tdqQxqG1g/xtEbChVJIJB9KEfCaT+LTY66aOXO02KAUMMyAGsysP6O8xor5vAarGd/s7ix6n+vj2xLTFrOfmVwnIz6DpxIyA4EwZ3bdYpm5RHXKr/NKAfwEfh6K4MwHhGNdPUTNmTdEauzERhiGKKw1Z35NGX4jiaENw4HYUiuMtAEwBn1Ix+DqQdAc5Q71huToNO4tA6YzDG9KQtOAud9oQtsHGfS0EWj92UjmkF/HMGgwg3FWSB1tu6o7Ple042l6VGOAzpDrVrkAh/lfcCaVtsmueTNrlajK0wiZwRv38nEzQIIqiGN8btAKvy/5ZgragAY5LtuBPmdnGIykbp7H4GqYuOp6AbtjvjaSXLWSZg0ozREoCdklLMEYZ6qk+Ni4zL2p/PdojCDWFrXttsaVoJj584UShzKypmw/5V8qRwikGLePSATR1ijf3aCB+QyMmb+txO/L7Mxg4JVBPm/KjUG4blE1QQRnPiDSGgX8IsxzSMAbYVtmcPkaVfp7uR7VToujenAPVQvW8fwcV+1Rkdk59oIcnGFgiE6O2c4sMxjU/Tlog9rYByUMeGqDhN+t5f2W0lmYDn9wzr0rNbZapkpnYE5hNMZZxvGCrWhnMFcdSpDjRAiqZwcY/27LHqbzf2mW15/5dRyrTa8eeGWQX+smW1Y3p46nODJzHqm8uXFw9wCG5KZU5Ro2wlM+lSYlP+cYAInwfmdBBq8AT7ekxePOwPh3ZimMDIWckBCbFnNvRBGIPdWuUfc6mDPdIHyMrLRCjf8GhuqvVV8w5zyjDIQQJObVITE3UbSZ0hiBHAsgpHD5RCU2oSYDrNGyc19Pje1CWpAQwZkPiLRGAb8IM/sqUAVCcM5qldKnOkX/C33QTCnmvuf77V5Hvc/0BWeYz7EhhKcSjTUYNUUNZJi1HQaYQSHXK1zUYYyDs4FhhnP+PAnffLrZfo/qPFUuKLASr8vuw+ASSNCHdQy8PBCYTbWCGZQvNFCG3uf7kN2bhT6so++F4NJQa+FyGOWONDMYD9LMQH9oLa+L83vrIAWBcUk7zBoli9Hitpl1SzpDtDXCa638mWN+GUyWilVmmClXiVQaFDQd1mjaBYACTUuauLhDYDVnpm3UzTblUwVNIRLK3FLs1nZmP7ZAA+oy6HMFZ4yzmpTxejhz7pAAiqq8dk++mSpe0KCMpxESngXllQkVmxYN9nFY4mIbeHkANGOAahRUR0n2UB/SkApRK5RaIYIzHwgz6xE2ewS8EebgLGz2hAVSVsd3tr+Cdw7srfphHfgqsPPYTtajmtoTlTtFViDHNMoLzs3V4UBTZXz8bIsFGfN5SAGAcAUyCuhJA0bGdK4zBk9JCyDwtFDpWE+vKX7PUBmy+3PIdGYD6cXmNKFkgb0Dg6sH85Ls1jF0no5GAwwaC20ru53JnKW2pWGkDWhDGndkA7anGsGL5OYkUltSZtoVX2SItEQQbYshvSMDfThghrGcOVZgRh3n1owzJJlAaY5wUY4Aep3tHwD2p6SKUaw2pEPt1WBkKYhTeIMWpB0GeOkPrh2yx8J1fAbOKuqmmqaXV8ysWj1/NpSKzfSk7pLuH3a09zIYASQ+vxOz4lAaFDOFkATWVwzIX2fZvVnoDgacEP7dtEKwTUBswaJAjGHFTa1zXTlowzr0YZ72Wi41mJnnbKJCBGc+EOZ6IYGJgTCzr2FDWILF6c/twpGpXlzV+XrVzkP3471Id45Oip5z2lQTV1kiBVbASB2r6UwPiBmqhWLwQKYzg2xXzqzNGdvzzijjq9JmPx8enHHHxMgYtsM9VhhKFf9+HYCRMpDbn0Nyw7Dv4N85h4wqMoHUAQ2MMmQ7s/n02ByFngqwX5aFaq97qzlvtwqmMeT2qaa4RMCKdlXM7fS2DIysAblORqQlAqpSxGfEoZh/Z/ZkfYtLFNnl9TbjY8IsR9cOzjhz1rCwAUqdDKbTQOrCD7hAxhG/ngZapl4IAJhOoSV1JOa40+MIgUslMTBY7BgtSOEF/9uwlDQN73Gw742j9IweeHUQw+vzAjFp1cGc6da9iP9LZAKmMxDiXx3R65RbfcvyG/H5oycrXNdSQGmWVvxsnRwHht9IAqZaJDPMZ1Wp72TBpjSPNURw5gOCOaseQhDEG2FmzgS8IeVqdz6NlI7UG0kMdAwEbo/L2ahi5ZJaIgWmE800KzhDcMGZZU/Vyn8UPU/3AuDy2kNrh7n6lyzZ9o0ZKMAs5kzjjYMbD20EkQmMlGGnqgWGSo5+tvg9gxLoSb6CrKcM3yIYLjKhCoePmemdaq+KzPY0D/izBhfkGCehED2tc8VGM4BWe1UMbxjmtgZ8viqNETMlzyNNCohMXP2olAaFMyCNkeBMKmUPNW2hACEMjWb6IAhxnafEnDpIMX/PaOfzyzBKX/vZfTkMrRuGMaQVpRDWH1yPugPrPI/pyzaYKaaOdhT2J4xnD1i930odIIhFmVKMeeE1k3a0D9Qp958YZYBEzN5iDAzEdzDkHN70Vs7AFwdn/D6oJ6toeh1YO4Z8KqqzZQgzzKblBk+z1JMGXwjwOgRlY//sCBAiOPMBEZxNTHR0dGBwMIiGm/4R5uAsbPaEJsCvISXN2oZRBiNlQBsFme9am1BbjkhmV9aW1LbqUphOg3Vkq4SR5kIJjDFkdmag9mmIT4/xurMxXv3khe+89sTIUe5gSwTRKVFb4MF36idjeOfAXszIVZbDT3kQrkZUhpGh0Ic021nxaY6NasgBqnNBEpozkNyagtar2X27gp4/1d6GMjszkBMyWNZMZTTrnnhtSoDpVvbSfrnteCoYJILo1CjqZhcKKJDqf1gJuO6GZZkznoZJzWbBABCbFoPSmG/YS8y0OT8omkMU6Hm6t+ha0Qa4qI7cqEBpUFyfSYoESZHyBwxqKtnjAPQ76yJNPQmqMrMezXv3SGvEZmz8wPkI0/q0vHmO9G09pSOdy2+4LyVzT91gIACkKJfQ51sEd61ldvNVIEv4w2kz1fmzgShlQgb/U5qD5f/VkzoGXnX4aobVtJxBrpMh18klU0WliDQ2gimjBBGc+UCY0xrD5liHBY899hhWrlyJZcuWjbcpAMId4IcNoRmfWptQm44KV5jKO/Z9L/QhvcN/ryq3DHoVwaLBoNQrUHtUHhCZdR5Da4d4gXUAzvUTrzB89+lG6FXGogOvDPDUQcp759CcYdeiBFE3IDGGBZlhSBXGh+rUdAbM784a9ngQiXAGTZF8j1Hr2v24qvN13L752crMmUevYsoAOS5DHdDNdL4Ag7MqgmGmcZn4xJw6ZDuzoCpFbr8KuU4eN+aM5iiURpkHYyoFg6n6V6E2pSYw2CIS5Y3h54QQHnA4AyHArOMJkswr9fMo+PywnXkOOSEjPj2ef0MivhccXOnVlIHB3bxYG9TsYI1qVaRREhJY7MGzArhTb9UgDqwe5PdLZgq2MGZnExQi0hRBfGY8OGYIPAgyctQcE9i/tb9jwMWc/Wh1i8mcASA8UGxY1MDnj2/mLP979JTB1Wg97iVMo0jMTkCpk0sey7YxCJhpuFRlnDrMvw2q84UGuU5B4oA4GhY2eB6ibn4dCAmJzzACiODMB8LsWIfNnrDg4YcfBgDs3LlznC3hEAH+xIY2qFXcxpZApgBTDSTfSIEZDNqAjsweD8+7RtQsCEL56qsUlWDkTGEQnaeJcMU7/+f9lC8Bt7xaj98/V90jxkjqdj0Xo9wZApB/QPvERV1v4satL+CC/ZvLbpfrUt3pXrMTLlltIhMQGb6dovq91TUg3tvD8M3f8jE8/dD8XFM1gCg8+JDrFd/BGWXAR/dvxbndW3mNSwUwBijNCqSohNjUGAZfH+KKfzKBkTECCfA/2LsTN25+HnKmcoSv9qowMhSRSREkDkiYTAhPa5Jikm9WyAVW+TqzlSzLTX+/SnuO13oJsQZmMnhGhpb9PkLgW8zBubdBTTbIoW6qJw2eNq0xsByt2Kcv0KcPNdkfU6ES4LWJ1hdZkuxymeDDOo4fuNIaTcVVppsTyrqR68xVc2ZvbwZwhBBIisQV7qtJlagSjDLe/sBgPEijDGq/iszuLKjKXMItJRGUD8P4/5juFoyBOa+MtAEpxlPPS9lFJMLP+QT1Y0Rw5gNhFnMI24QMTUpayCDSGic2UlvLM1+2+hiBuULLkNmdgTak8bqvABoa1yKlr/apXELfvPMbaQMweIqakeOphZndwQmX7B2o7rqXIqbCm9WjyrSPSEB2X9Y3e3ZW7w4AwId7tpfdLrUlBeqoKVPqFW6bE6xyzVEtKHedffK/858dO0/HzGY+Dgblym0NB9ebamp+WQ+G87u34IL9W6pizghhiLXxoDXaGoXWr/PaEAakd2YqXhfV4FP7NmFBLokDVndW3Lb32T7OVEkEUlzi6bnmtcfHJ5jnc74fV6XaG5jMWYn5H8Dj0HndJ3eUuGbNlGq1VwUr96VSdSqd1dpDDfA6LcORJk35/Y+qfBGmkrNv3jKDAeNBoZUCrA1o/N5nBjxUZaibW4fEAcX9u5wG+U5rdLwefiNppgczl0olo0Bac4+N6iUZH0Aqqmt+Gmbzb4Nfw2q3iqHXh82m6cwVTFPqEYeRys+famCfc2thzhnQggexWp8KKVpF+EKCVbQcS4jgzAeEYy3gF2FmXwW84XygVXLSe5/tg9bHC9+ZuUKrJw3owzqoxmtirFqdkaIW5mzo9WGoA9weEpWQ2pzmCncDui3mkNmVDW4uVnkYqjNANlc6DWrXxsgJGcMbk1D7KjOU1aCST8zVISuzh6Odukcpwz+eY3hmbf69uiiDIpkpYgZfMeaBYwBpjQ4nz6iiiJ7XUznsVSnkBK+N0Yf0QFtHkGoESswGz0QikOMympY0OaT+AxYGYCg7r60a05Ky7M7j+DGjIBVVG9TQ8wwX1dGTOvpXDZgtMxjS29NQGsqkpAUgg+6ufeV9uZjKz8vAywOmQAOFnubtKCqqVbIqKMpqwfJMFc1SpDan7MbgRsbgPbwqkGZE8u/oO2MhfVCH1q+BqYad+g4ABmXI6u471YfuaMBglrPlzoP5XSRy3ecZ4+fGTEXNdGbNJuU8gLTOF6XACb9oxAduK0gnDDJV17zGmEHdAajJBCdmJ6pb8LcY7AkIEZz5gGDOJh7CxuCFOcAX8AYr+YfHtrr5wAM4K6SbqY0bkrw2RqNIbvbXp8o5bYwKDyKmU+5FSQRKgwxtQOPpcSk9X39m9T0LAKWs2b6X4aPXUqzZYq4Wm8XuVKXIdmbtKEppUKAkKnhNAYLpvJ6qUm1JoMFZwaEYZfi/fwEf+BrDkGNq1EcZrHp84qphIv6ZM8f+lfqCMVMUw3kvbTioHrG2GKSIxB3OUXCItAENqW3FjBwz+44VsquMEb7qz4I7X5YkfTmnuPeZPp7CXEKWHUAgLAMDMF1NI0oN6Bqz0wYBLnqhp7map5WmptQrpQ8WgAy6qx0D48Ep1SmMtMEVRQ3OnKndKqiGymlyBIHlNlKzETjVGYwstRk0qlE71dKr+XQRApzX2rCpNqibKbjm+GXU4m1f3KHgJbnVxRYRAiBgZV1qtxRg0IY1UJ336HMumA1kCTbtl/HijsI6Sh6dBeLHmMeJtkZBc4Z9bVuLHnKVzwRCyl+rYYYIznwgzKxH2OwR8EaYa84EqkClYEjLF+Pb6SsGePNVidiqcn5AKexV5kppLrxRL08XlBSJsw3gYgpUpXlnyOftY1YuhXcN7CkZ5FxwHcPdjwPHfoZ/Ts20Ga2fqw+6HaUAV9ArgBpcgbAsM4LRXY3teaIHj7xQfC9IRGAHZ4g7HKMAVoepY97olVgms9WAF6xGy0YquCIvuznuvpy3gI7JOhTWJkoRAiPLVRSCPV+s/PwwzGtRZ6PKnMV2DOKON5/Fz7e+aKaUMvt+RHOO1zqqCyr8CoI4mTxTSt9qO2EFQtZGTK9ccxZI7qcJq08ZIQQ0x3vxWX2yeHPpKmqqCErKtlcLVxZh2mw9oVGz/xo/tmomCTRFKS5Ymc+qKKpDCyDN0rk3NWsEeVq3de74cyHSHLEDw7RH8OgwyX9AbQb2YIAUk6AnDXQ/0WMbXEuwFYRoynihzFKKQCWI4EzAL8LMnIXNnrDAFTZUuPFTgz98AdiKcZQAxODBiJGhvuMOgzJ8f8fLMEBA6eKy21o9jywvweq/xAzKazICWmW8dfNzAICNby4GcEDR57v283/TWZhNnwEQvoJMMxQk4kgdRXCqbRVXI3VebB6ZzOupbnwqhs4BCd9/fwaStXPQqTKuzCIGbViHRCkK86zqogyK+ZYra1CCq4/WiExwnHdaxvmytn16RwSTsjL2JwkWTKZYNC1vkKSQwJhX/oX8HyLBs58bozwYKhRziE2NIdIcgZ7SzSAtIFRQEWQas4U4SsYXhPi+vza+zh3WObkUqM6Q2pYGNbh8Pc0aIHGZO7gRgtis+vIHCyAlzUutkZp9E5nB7FYZkMwmytVQA0FdZgYXsIlPi2No3TAPiqyaRDPoqcicBVC/5AzOoi0Rs9UDQCKcvTMyBvQc/9EyYfje+zL47Sp+L8oWZHYTiQTLnJlBqpHWwWjEFnShKoMUzRuedMj8U4r8fREAC0hh0xY/kQioSqHnzFTpCs3NC0GkievHiODMB8LMeoRtQoYtnTAsCHOAL1AZFVXbtLw6GNMYV/oDAJ2CMQKW899XjGoMy1P9/HU1QgWOfjVG2rDTh5jZNJcb68skG3X7vVM2Y1GHSVYqjUSQ3Z8DzRmQZUcPJowucZbZlUFsRowziWYj2uikCPYMEnz7QS4QcPYKFe1zOBtEJHdjVL8YWjeMlsOb+R8m2+DltyYijpoz6uhDJBHfghcux7qEIAjVKPpe6Mc/hybhwntbXZ/1fH8gb49MSkqS+wFv5lxsG6MAZAl1M2NF28t1MqhKofWrACoEKNWAesvgWxLk0dao7USWW+EPgmGgThZGzaefMcrHSYpKYMxSGK38/A2yobFh8LxNpuUFL/Q0NReGTKa8mpKhoNIaNWozq9HJEWj9GpfVZ5xxrSZQDEJ4xwkpKkFP6bzOVSPI7M5gcI2MTB9XJ1UUgroocPk7svjlM/GiOrQgRFycR6QaQ3RSxE5BZQbjDCNlAMtvuXsgP1g6BZy6HAQ8HZH4YT2Z+T/GAIm3LTEyvE57RAtjE9StEmmNPhBmxzps9gh4I8zMmUAJuJ5o5XPsmaU+aK0iG0CkSbHTkKhGfWfvuArxKyjtWWmV1ipx3fwEErMTiE6JID49hoTZLDeouVjqKLFI/nX/SwOm0h6X/DZyXmzi6F0bya0p6IO6nfpksYdr9uSZGGd9BZEDYIacwgDDeal4q8G15PF766L5tEZXLEaCZc6MEqvxTGN4Yx/BhTdUOJgMGLngz1ep4Ay0rA4hlCYFmd0Z33PayBgYeHnQvp6dGN6YxODaIXND07GslCrnm6V2MhiMp+PqDLDYKotpqOZIATRec92HdAam84UXtUfl98G0bqZeUkgKyTebHgNYYkcAEJ0c5fV5himvr9HqgpwAVomcs4HIBEaSj4mVOkhzFMRszC2bPboS5r2yiDkjxM7KGCmcvyYxK47olCisHn1U4+IpzhN79ysRfPQ3eSGQ4mELiDkzmWciw+4XmNqSzjPSNR3Mvz3jARGc+YAQBHn74L777sM73/lOdHV1BXpcwb5OQDilfa00wQJYhfCMmewUCLpbGvD7nlagJQYiEURbo7xRZjWF6GVAHW2gWKWWUFYDWNNplGMylDoZsSkxRCdH878vqFPPwJtbF4g1RB3B2b5+hiseasbXHm/CzU8qGEwDkRbFfZAAp+Lga0PudgFmbZCVHqeYzlHOsVKdcTpGJNgaJivd1TCV0sAAeDBP9RGGiBkvao6Pg0hvcjo8pdoWMMYwkKw8V4nMncbAAnzzOIxacujUZaPdqqKUPZZSqs9ztu4/N2DzD7eg8497uNCPI6i2lCEZ44swVAOaljSVDkACSCRx/hpD5wyQxfyCId8SotrzEETtq22PuShFAaoaXKY9Y9VWAQ0LG6pg82qwvdKRGOx+hbxth7kIo5uiINW06ghAxKUwgcjIUMh1CnQzg4EZzE5Zls2pE4/wMfBizoLs3xdpjkCKSKbgDV+MMbI0f08C8IdXoq59Cgl7Zv/PP5jBQBR+r41OiSGzM2NOiRq+ILgpNOYQaY0+IFiPiYeRpld+6EMfAgBcffXVuOOOOwKzR7CvExyGdxpHemsamT1ZgDJkMgzn/7UFT+3kzkFaI7jynebuWcM36+GsE6ukSmcVerskmUcTlEHt16CndESj+Qe7kzlb+mNnilwC6/dH8MvF+cInxoKrOQMAdUAFCOyeRtRgSL2ZQvOKZp4eN4Ofp5zD99YMRxohQQCsRx5M59da34v9aDy0kTcJ97j2ElFmO2y6M6ctgJozp8pnKSl9ZlSXRsXvsaYAQ8T/PGMal/WmptqfPqhjaN0wppw4mW9Aq5gehHBH1ofHs/v3vN9a/4sDyOzMQBvSbRuYwfIBms64PHpZewJII3S8pjoFMwiYBtAYtZX31B61umsnAHucl4SeZTBAQaISZ8INM3DNGpAbqjsJpihoIIi0RKDU5c9JbFoMzGCQ4zLSO9LVBTkBSNd7BWd18+sQmxrjwT5l9jha9aUxc7iyBb3PSMCLRDaoKQRiMEgR4hqbWMGpM6h7JS+IFHRmrrVYCpFWkAYwU+Cj1rTGienHiODMB4RjXT3eKjVnvb29gR5PBPgTG8zRn8YJfYj3DQNjeHWXZAdmAPDCDgUAV+EihNjMyYhtcDygK7GvjLmZs9Ib+jIpfxjdu6bOGZwVYtW+KIB8cBZ4zZnO02T6Xx5A3ZwEmMagZ4y8MAm4ItmWnnxao5OpCkK1zQlehG9AG9RAcwYYY57BWV0UiMj8feeKdRA1Z04HzBngGzmK/pf60XRYIyABWtVBoOk4ljnPVYMyDK4ZhJyQwXTez4tm3fdNUmHCEvM4vuDwQ6lq1VNxhpwr/gGDa4a4g12JwUaw93vOmBHoSR1yXDZT5RjUQQ1SXRVuXsCCIHrOQMPSen4fzOajZ6UpgsikKicFIaPmV0db8wtFDQc3VJVDFogSoQNKs4KG+nruG8mwz4G18GIzZ4rFnBUcQCpdH+oHUly2e8IBsPv28dfubT1jwyBOGoGpKmzeRyQAjL+um1tX9WGCZPLGGiI48wGR1vjWx86dO/HrX//a/ltVK0iZ1QgRnFWP0IyPc6HBI23CyFGo/ZqZ2kUQLbg3uPrYBLFi7diflenVTFWevlMoOV6EAFNBrH5ChQ/IcsFZc6xgQAjAAry/WrLx+qCGbCex+xw5g8h33tiIrb354EwtCM4QYDoR03lfOSNlwMhQkBI1Z1wQhL8uChYDrDnTsxQ9T/fytNs5CTCDYfC1QTQta7Zlvqs7qC+T8odhgNqrIdZGeKpVygBVzd5HZtPr8lVnsFPGgoKe5ox3cnMK2d1Z3pIiQqD2qmAG0HBwZXVEv6COg+iamYpmmKmNDGAqn9dKo4ysBuwbljCv1fs6shZPGWMjXkgtbELNG6RzdkiKEEAC4tPiVR9vrJZzpVhxZJZ8M4nhdUlM/49p+fEIgDF3Di0hxM0sm0yYdYasaz1fc1bAnEn+F/ZAGc7t3obX6vPZC3VzE8juyYLqDNGWCNS+0j6P55pQUM8OMyizaqStBSilSuYVsBb2QuI31AgRnPmAYM4mHmp98JxxxhlYv369/Xculyuzde0QAf7EBvOS9jUYtAEVJCJBkoENXW6Jb1ftQAD1XbSKlDSA91YzVFYFyxJgPpHpMBYFZ1HvzQGgOVocnAXaq4YyMPAaHbVf48p2usQZI/PUOAMzANBdaY3+05ucYAag9mlcMjqlgyiSJ3MWV+BIa8y/TyTiuzmukwmkplJbrjtnM4s0x+uGtEqBvRMB3j6YRrmKHONN3Q2VYeDlAehJA1JMAqkom1pePbFme0whCX1Agzasg6kUSrMCmuV98iox0wRBzCFHOplkHo+CTw6dmrVvFEQh+P6/4vjlM3Hc9tEUPrTMO8KuxD5WtMaxe3xuHayLluYMEEkeoYLf+DyD1n1lAwAgNi2K1mPNwCWImrNyn0n8XmmYNJ6V1mjXnBWetgAWZVpf3YcL9m8BsAXAStMOAkaBSIOM+Iw45DrZbvpc+G2esaHvU5afx3bqJuGLaiMK2CeoGyMEQXxAiDm89eEMzADBnI0nwpgay0UkPN4jnJH53doEvvx4i+vzTMEKqO9CfAeLYpRw0vWkDpozoNTJaFzUWOGIwdZ4MYNhaP0wtAHuXWQ6M1zKugQalRJy6UHZY9cGURgpHfowl7MeWjtc9D0xamDlcDd0p2BAwHmW1GBcoIRyFojIBJKH404IEDGl9J3BIiTY7RpGbINLVIbl2UTKBTiMLFfR1KpI1+PGBns/YwZgJHVbVhsGQ65b5X2i1Gp6BQZQn+MkzM36NyNt8DTGLFdKpDmjeqVKn+PDHPfDxIJ621NmBv9PaVJ4fSkh+OUznLG68q/lUsL8XfeUAqcM7MG7+/fYPy0yKcLZc4rafy8rvreONtR+DdpQ/t6U2+943hMEXnPm/pBfe7rplltqjTE7rdGDOfM5p2P9mZKfMVM+39mAuvAUFrm9AYyR9T3WPUlpkPliB629D6dIa3ybQjBnbz+8nYIzYY83XFmNBqCndFcDXEYZiERgZCm++WRT0f5FaY0BMmesYCW15+leNC5qwND6YcTaeN1bNT2PAoXBnX1tUEOkhfcYYnrpvEY5WmBfAA17KRwrkWb7A2YAmtk7h5oNYC2J/Bm5NGarKZw0uA/vHNyHjatmAmfPso/nWyXN+RMNU7JaIfjEH+sxmKrHkhb3feaUg7nDaKc1OmPFIOoWnc8yU0rb+hcGr1+iGWNc0ho5+0phZAkizREeOFKuikjA624qCuGYxwkKzFQjNNIGoq1RJPs0GGbKcOKAKlL3AmDMnbtThzNLKU9tJBot+opMmfPn15GlDPhS5zoAgK4fBYAHEARm7alUIxcQoGpsNYehGsWrF612m+Dq4UUCTa8uhMUSWbbKhWmNHjVn/hccvJ8FzKAgcvH5Kvw2g7lPUhAiLkOvDyOzK4PEHC7YZNeYMdTcOiDweuUxhAjOfECkpFWPMLIeI8FopjWG7ZwJeMPlFFGGodeH0XZyXvDDemAy1fAsUUg7mDOeIufTHkertMKHNVMpjAwFzTGXiELlg/qzKX8cxtNRNIb0Tv7AtVfSSyHuTikMQh3RuTejpmw9Y0jMjCO9PQ2mU2SHdDy+JYLljRL+Z8vziDuMXLC5C4AZnEnBpjVSjfdhgkxw/wY+j+Yl3MHZJ47l9526KP/etOpOjfXrpDnPBzX4vLGOS1XeM0vPGtBLMGeUAkW+d2CONUN8ZgJyncQFJjRTYMJUSqW5ytcQV38LLhpiOgPNUdC4BEUmiE+PQe3l50wumL+lDApC1c5pmv37TMEbprMidc1yXTv8NhB2svZOhoMoEmdha/Q2GQvIZ6hyoPWkx4qLVHCdBSyl7/6QnzMDJQRBPNQawZivOsGSoADxOF+Fbm7hmlAQwdDLH3sVALDkZ4e53lcaFeR6yi+O60kdRsqw2ybwlbeJ6VeJtEYfEKzH2w+jyZyFLcAPG8IS4DsvLV1lMAp6UjGDO0tyQvZ8GOuFPoDf9CanlL5ZT6anuBdtqBTagAaW4yIKVR8zqPsHM5kYzVRtpGZD2DKH9/wsyEuDAUbKADNFHBhlMHIMf3o5gosfmYJ3/U+DKzAD3NL2Qai2OacF0ymoamBYzz+OSYHCntXfrMH0OZLONaIgHH2nWiNlMDRmp8dRlTNTNEO9a0zgUXvCgl2xlmMS5JjMWVSV5cUvdIBmjPJOr2VSgHOIajzVk2m80XS0NYr4rASvAarWq/K74OD40YzxJsLxmXEwg6Hx0EbUza1DvUPZjjCGz+9eh/2Pdpc4os+0Rld6dd62+gV1XPiiwoRglGHLz7Zi3z/yvUT9zqHz9m/BHzb9G7FU5ec29egtSAqCM7/nrEnL4aP7t2KS5rHIK/FrzTDPq8WSW+JJFuvJGPCDR+K457UID2BHwW2w6rwKoVH3m8XlywFQZyasVicWopOjaDykoWg75rjXvHLBaqy+bA20Qc38LNgU/bGECM58IMysRyV7DCNAubG3MKSC5eANGzbgiiuugF5qCblGiAC/eoTFHqcVRtYwm3Xy62lo3TAyOzMgAOKzEsVsAjye7wHWnFGDpwAOvDLIm/XmGNTuHK8ZytLyS+cum3yZ5IZB7dQzRlnF4Mzp6BsUgaxYO8FtMJURCbGDtX0KdwYGMsUnTTEcTZUDVv6zFAgHMw71vYKJYzlq9SZzlswFy766BUE4c2alNVKV9zwysgZY3Jv+8M42CjAnzRLMI7ynFxf44GlORtaoQp49gGjRcenogzyV2TkPlDoZiZnxqnsI+g6oC44VaYnwdgPmuZRikivdekWqD6f078W2G7eXPp4Pm1gp5kwmnG32OHb3o93Y/ftOMMbQ+ac96HmiFztu28n3C0Cl9WPdW9FkaJizbk/FbY1MsU/kYsgDWAS5bP1anN+9BVfveq3oM0IIv9YkN3OWMJmznE7w0ydjaPtGC378RByfursehDBfz8VSe5a6n6gOt2dudhhDN25CZpezbs1fMOROr668fc/TfXjuwx249/wNSHVm7TmY2Z3l1gRwzsYLIjjzgYma1rh+/XrE43F8//vfH0OLwoFC9mXz5s04//zz8cYbb3huH4vFit676aabcMMNN2DVqlW+7QlzcDbRQCnFP/7xD3R3l1oZDgr5OWQYPEWu74V+5PbnkOvKQhvWYD2hvNw0gxWsxvqEy7HOGXYjXJqlYJoBQ+UOrJGprCIXNHi6FU8ZslQbmUGL2UMHVLPw/Rv/SGDRdU3oTksBXBsOlkHntUGGmeYZnxlHfHrMXiUvpfzX9Y/9/IUE32mNrgDfZGGScj7ASKnu8xSRKIycgYaYGZypBC/tkHH5n+vQlyEelfm1wZn+Ri22zOCMJ1XN1CCDQS+RauYSKLEQWGos8p6KxOX0+Wo5M1s15OW1LQGT4kMEu4Jet6AOdXMSRav7kZZIdQx/AJehK73a+qNMHVKcll+Q9ZuS5pyChV9FAE9pv603bkfn3Xvw0pkd6PxDPoCiGg1EjdBGFYcx0sXjs+PWnTYLE8QiyMED/QCAwzKDnp9TjYEStyBIPAIolOLKl1ZBu+NNF4vfl5H8zesS85AzZ8UfagZw8sBefH3Xa/jvbR3QXxvAmitedwe2fuxx9sqrYgFsy4+2QKEMs4aTeP3ytR5bBEzhjyFEcOYDE5U5+853vgNd1/GNb3xjzOwJS0paIT74wQ/id7/7Hd773vd6fu4VnAHANddcg6OOOgoDAwO+vj/Mc2ii4c4778QHPvABHHnkkaP6Pc4ye0Z5rxojqXP1uBw1RTn4fPciqlx+fQCskFNpz8hZTrXJnmkAUxmMDIXalQOpohzG7wq6E8TMcuGBosXGoKwke878Pbc8F0N/RsJ9G+OBpu5ogzr0pI76g3jKV6Q5gujkqP0MryvRQXjH7eaKPvynEjkv9cT8ejQe1oi0o79AuqC+RH65Bx0feQUzdvViZi4F6Y9bcOGNCv70ahQ//Xc8kLpFCzRncAEQlUFPciVLrgwHlNLdGB1J7fxxnM8PauSDRW67uVremcWaz67Fa59aW9ScN2hhACkiVbXQwYVnir84kFpTh2dNzaBZjsuoX+DdY62qr/MxSM42HkXqoQRFjbnLsc9qj8rnXFDBWRUwUt7B6ysXrIZuBW4BKmwWgkQIEjPjdqqgIvP5k71vF77U+TrmpIZx0lAXlqT77X029yuBZhVYkOtkRJqKWfKcTvCVztfxjqH9aHCc0O237rBf+8kqoI48yQprCWVh130HvCgzlhCCID4Q5nqhco5+Yare2xkbN24EAGzZssXz81LBmYXe3l60tLSM+Psn6hwaD1QK8B999FEAwK5du0anSNoD/Hv4imeuOweaMc+hlQHnFZwVKu35TW9yMWfUbERLQVPUbHAM1M/ndR/VOJTBOrKc5ZDjEnf2h3hPqEJJ9qkNFPuT/L6kGjy9p8HQkJQjaIyyQIOz+oPqYSQNXsPkgBUU1hulU5YZY4HUnjjHl8RlEAmutMZUQUkKvZPfn+b+dSN+QmQ0GjoShoHrZy/DQEbiQa+POe90qIhM7HmT68rx4npCQA1akjnTPByp4O4fjvoXxngOpbnqUT+vjrNlKsUax8p5tjOLunmFsvH+7CGEgNVwDGYwvP6ldUjvyODgrx7E+7FJQPOK5vxv8QFP5gywe1IVbe+sUTMVZcsetEa4GPxCoQg530TYglUX6wW1R0VidqKiCmeQ2N9T+iaz7+/7MOsjM0eVhSGEINISgb6P/y1LfByG/r4HJzm2+8H2l7GmbhK+Me8IbO2XfdrkfT3Xz/duuVCXznq+n9lppTb66ydYWPs6UlCTyZvIao3CS/eBicp6hJXFGgsU/vZK561ScJbJlO4TUg1EWmP1qGV8zjzzzFG0I/+aUgCM9zQzMgb0jMFVEc0gyDM4K1pU9lc34Ko5M3tUQed1TMzg6XtyQoakSNVd+84Ka59ggC3zTlWKgdVDMHIUNOJ2IK10vTY1g7NXrcWqc17G3RufRJ2hoSFGA1VHlBQJkZbiGiXNTM1rMErrjQ/16jBYsDVn1k/rTeXPjcWcTW+ieM8heXsIZWg0g8dG0862hnzt3IhtcPilkSkxNC1pQrQ1Cm2Yp8ISCYBeJq1xlJkzEC6IwChnYBKzE4jPivO6qoRsp55ZKK4fYmO+gq4NaEhvzwAMePP6zdj07Tew8VtvBHafLxWclULUcaMwSim3+plDzl55BQbFZ8RRf5Cb0dOHSgdnuR6VC2TUKJ1eElX8rh/8vXTdYucf9gRe+1oK1rUkEyC9Ne25zbJ0PxanB7C5TxmVtMZS+MT61z3fl2yFUuKrzYgzvbpYbKR6GBnHzhPUrxLBmQ+EOTgTzFkwqBScpdPeN89qMVHnkIW+vr4xsKQ6OAOPv//976P2Pc5hic/nDgfTGbReDUzlPasshqpiWiMAvwJXtKCI2sgaoGawyAye6uhE17/2Y/WlryG3n9Mz+x/uxs7f7spvQPzZ4wQBAVUZYlNjoAZDbl+WS5AXDExjnH/hJ7vewGHdPfb7c3MpMBYAu1jFNpaYZUMZ5uxT31Txkd80BCqDbs2H7qQjODNrzq44IYc/XJgCKez9BmBXrN4+lt9UVGewaTmgSqNipzQSiYDqzHYcFcn9ZaoGPLZewoZ9ZrNav/Y45zSA1/YomP/tZtz0cj2YTvHqvgie78zfmwtl0LVB9zkcD9W29HbvZwPN0ECW9F3BWRlH1vqauDNLI2Mg+UYSmd2ZfABE/D2DnMyZUcB4EZkUMdX6cOnrLN1lpTWOTTbJ3iGCVr1YQbFuQZ5Bqq7ZeXlUszulllojQ6pEcAYAEUbRlZICXbiqhAXJIc/3nSI4/tIamefrWrH1xm1chMq+OU48CC/dByZqSposV1F4EjAmKlsXiZRXAXsrM2eV7LnhhhswefJk/PGPfxwji8pjzOaY43vkBL+FUp2ibl4dqM5ANWpvQjyeDEXPHJ+sB1xqjQxqr2ampXFVu8IUle0370Buv4rOP/IC/G03bcfee/Yhu9dKWQlwHAnADMpV40z1Pz2pF6XB1ZvlVq0FEtMxaiBHMSpy0U9tUfCh2+uxo89Kp+TvN9DSzNkX96zHEc++6b+vmON1PjjLP46t4Ey21Ng8fv/hM7idwzkCwnymEzkXms3j8PRGxr0EiUDPUmzq5ZUQk2Udy1J9OGlgL76yey32Xr8RuW++inf/rN7+gX5uZ64eXgz4+gN10CnB9/9djw8+MQun/aoRZ97egPfe3IBXdslFjv6bP9iM7bfusM/TeKQ3bfrOm57vp7ZxhzvYmrPS21kB9YpUr/3e7j90Yt1VG7Dms69j7ZXr8vd6XwF+/jWlle8h+/bmd1jVMMX12d+fMEARnCCI133Yic4BCVMc9545F8/G0p8vxuL/PhTxWbypeHZPzneWQzls65Xwfy9GkTOHRZFQNjiTGePn3c85q2VbgyErlUiZVfkk4/cqPwG+9+tS0Fq8F8+ZzjCwagCA/4W98YIIznxgorIeEzVQGm2cfvrpuP32213vVepr5pc5C1uAX8s8/trXvgYAuPLKK0fJGn8Iqt1BIdyONeEBiOlExNpiXN3ORDXMmd87h3OFUY5ISG9Pg8cXBA0LG0o2xWWU18nZx3EybAHdziSF22Adk0gE9Qvqi1JWEhHuzWckdxl0nBpQjWCbPlv48B0NeHprBD98nDtf+bTG4nmzuyHfX+fUgT3+WQ8P5qzHkdZotWWSJbOO0KP2ZpLM7RzKEl5PFBRTZZ0biafJWs1uv/3vBvxlDXeGrnnzFfxg+8v4z87XcfLgPiibhzBZV7Ek1Y/OAcKdYV8sjNM4IOs4Ja/vzc/nl3Yq+PETcWy/ZQcK0fXAfnQ9aCpsBiDLXsuahZ4sfe/ZcPVGaEOlFwCqhXN0y60VDJi1jCcP7rPf634kz05nd2dBMyYr5IfBr4L1YAz42t8TOOkXjfjxA/xaf7x5Bq6de7hru0O7e7BzUBozQZD+DMEUnS9OpT55KGacOR118+ogRSRE2/jKkdanwrdUfJnPTv6fRnzlb3W4cxX/PkniNZ+lwIOzsWOEe7bnSip+Ws8OQoinWmq1cArJVMOcqRm+zd5IwvNzIhU81yYQRHDmAxNVSl+kNXrjoYcewqWXXup6rzA4+8IXvuD6+62W1jgSe7q6urBp0yaXhP2rr76Km266aUx/U+GiQ39/Pxhj+P3vf48HHnggsO/xcqyth0psSpSngkUl0yb+ucyovWNhmp7vmjPnrSdKoA/roDkDsanRsgIgRCJ447rN9t/7uvIP3iC1HKSIZL8GACkq2YGQhWM278AfNz2JSYbbGZmbSyKn+29WU6JUCgAQlfmxrR4+Z/dsK9pmXWOr628/SmJAMTMEAIPZ4rRGWSqd/lW/pgeTtBzuXRPFI1ujvobI+XusuUhIPq1MTsh4vT+vJrkg5Z3eNF3NYEOXGTwF5MSSuIzGMtnldVGG7G5voYLkGynzIBgV9rUUcvtKO9UAkOtSA02RM8owVev3Vc6U0QY13+yi4UqN9T7QK7tl3P5CDOv2yTDMeT0kF2enNBo6Nu2R7HpVv6j0szIbhrA81Q8AUONue5R6Pn560ghUybYQVu/CZ7by71fKXPsAoJjM2Vg8Y7Ma8O0fcF+no2Gy/T5r4rbaqfNm+vNI4WbO8nN6Z79UNDeHs7DvT6qHT8sob7GR3unPRxsvVOWlt7e339De3v50e3v7ne3t7ZGCzz7a3t7+eHt7+5Pt7e3Hjo6Z4UTYHGsnyjWZrpTW2NXVheHh4aBNqhn79u1DMpkcVxsKgzOLLbLwVktrrGXBwRnkL1q0CFOnTsUvfvELAMARRxyBK664Avfff//oGFoFpk6dioMOOgjnnXcePvKRjwS3gOJ6gDBEWiKITck7ro2HNUIxm79KBGjUVfx201P4Yuc6AMCp/Z3Yevc+9Py710y78rka61xhNKOQ2JQooq3REnuYkID0tvyD62e3qdjUZQVSozAXGbMP6zRZYhTHrd2GRkPHgqz7er9g/xbEdwwG7hA5f96z2xSc+ssG7Bviv32Wyq9pZ6rVY/GpSDbnV2fTueCav1oy6M6ANas7gzN+L6+bV7w6/Ls3nsK53Vtx8d8n+UoncjrTrhRHg58zKSqhX68s7nz5vo2486f96ByW/bEwBSSu5aOfvzKHn3wojfcelr8vW2IyABCd4p7zvU/22izVWIg5WNA9ema5QJnvRZlq0hr3DxNc9sc6V28sL2gDVm9GH461I44odavtHMg/Myxhm2EzOFtTN8m17dad3ozxiFDhMNN+vc5+rRak7imNfN7rSZ2fMz8sfg1ZSzJhZYOzw5O9/tMaq7CHMeDhtRLO7+aKsX1KfqVEU/hYZXdnkclQEAlgZdqkVIKTLbOea//3YhRH/LAJ330o7tp2f1JCzMyl1Uh+Xk05mQePNEdBFAL2VmXO2tvblwOY1dHRcQKAjQDOdnw2E8B/ADilo6PjnR0dHc+PmqUhRNgcayfKBWfl0hr37duH6dOn4+STTx4Ns6rGrl27MGPGDJx22mmBHreWlE7GWFGQ2tbWhgULFth/B5nWGIY5VEtw1tzcXPTeF77wBZx33nn235s2bQrOuBFg69atAPh56unpqbB1dXDKaVPKHdfY1PwDyylRTcDT4FoMDe8e3ItP792IL+xZj54/7MKWn2xF14uD/tOJCpzp+Iw4IpMqBGYoVks7r3srHn5a92dMGfDfyY/tTGucrpZf4DjgmR2BO9aq4/a4pUfGK7sVvLBDQcxBId07eY79ul+O4rKDjkPadNySw/CZRph/bfl6Xo25ZZJfPVcaFcw+/4CibS7Yb7YB8WOPR80ZwGs35JiEHz4Ww5vd1dUqHze0H/e/GQ+s5gwsL9V//koVF6xUceVJeWZqR58EtY47+AecN6voWN2P9gCEjGlaY6meWc7PGfPXL6+atMYL76pHb1pyzWsvcAEVv4qflVPSnNfd8UNdAIBhhZ+7H8xehtunHWx/Pu8P6zxbNIw2crLbLbYanNvnbBTSGpMFRGu9oeGUR9aUTes8faDTd3BWDa75Zxxf/32ekzEIgWYuzD5J8xkFd13VyUVc/KQ1uuoW+b8/fZIHZb94yh2c9QwBEcZgmDZZkOLcNiNrmCI3IzZnXFENc3YcgIfN1w8CON7x2ekAcgAeMVm1hsKd38oodGQHBgZw+OGH4+c///k4WsVRLjgrldaoqipuu+02AMDLL78caM1OrXVuDz74IADgueeeAwC88cYb6O/vD8yeavDUU08hlUq5FBtlWcamTZtw+eWXAwiWOQtDamwtbHCp/m6///3v7ddjmUJbaY51dnYG80U1FC0TCZiu5gP4D/Ttcn3+h0etVMeRm8OqSCeyP3d8Uf+LA0WfP/ikji39PuWZS32388HrOP7sXKrsfon+jKs5aRDIat5zxZKnH5QjeK1hMu6YdjCemTETe6J1GMxKSJs1calhw58AhzM4M3+alzCdLDEk3+BsYmxaDDPPnoFF3z2kaLuEoUP1UZ9TijlrWtIEKSbhhsfyrN2t51Y4X1Q3nUYfK+gFaZ8Wq2hlyB4x28Avz+F2PLUlAt2Uho9PL85/3PWb3bxeaIwcfW1Aw5vX83ThSUe3oG5eApFJEbvpOcCZNd8sjANGiWBo1U4+Xz/Um6/Jq1/WVLSdpdwaVI+qUrGg5bdHqYEDzPvih0/g7w0pUTwwba697VQti519wTw/yj0ZCp9zWeJehJDttEbd3D4Qk1wYyrot/HDPDszsGqi4n8HIqC/q3vJsHM16nqmOUorPzT8af2ibj99OPch+f+kus6bRV3q1O8B/cIOCzkHvOdA7yP9VJdlVqyzFTJGuHA2k2ft4oZom1JMA7DVfDwJwJt9PAzAFwHsAfAbAFQCud+7c3t5+GYDLAOCKK67Ae97zHp8mhwfOGpv9+/fj+uuvx+rVq3HllVfi7LPPLrMnRyaTwVlnnYUTTjgBX//61wO1bXh4uKQz6gwoCCH4zW9+g1NOOQXnnHMOnn8+T35+7nOfwzXXXAMA0DTNl3NLKa1p/3vvvdd+ffrpp+Ohhx7CnDlz7GBtpHAyYaXssd5/4YUXAPCeWR//+MeRSCTsz6xAYNeuXb7Gpbc3r6DV1dVV1bF0Xce6deuwbNkyPPDAA3j++edx5ZVXoq2tbcR2WMjl8kt45eYQACxcuBDbthXX5zgxMDBgH8PvHALKB1jOef39738fV199tevzNWvWYOrUqb6+H3CPUaoliWS5YZcaMUkvLSqzdSeFtiiHfd17RyySODjAYJmQrc8h2Vb6aVRpRX+SruLCf03Dv0/phtTpXziIKgaSbTy4YA0GmAQkY0noJL+ON0stzz7LOsVwwxDUztyI5xBz9AdItiXROyABKGZ+m3QenFmpO3+dMg8XnzIMPMbHIiUrmKLnMNSoYu++kZ+znJoXhBienEK8iSJHihu/as1Z9G8aAADE3hlDsi2JbEvxgtAULYs3dicxJTkyz6jXIQyTrc8i2VZ4nBb71anv7se6/yo+xhfnH4WfbnsJk3QVm+spenI9ICOcQ2mHs0plAzlCAcjQ21JItnEnWW9JAKgHGLOZodxRQNMpTUi+kARN5a+Dvu4+kAjBUOfQyO9DjiGx5rQX9t3dZb8mBxDM/+k8AIC6W8WbH+EsZ5qkETskgr1dI59D2Wz+nKUbM0i2eYmMtADgrLhtex0p+sqh/UNoWFiH/UP7QdIjM6inh8HiV/gcKq67G07UA6i3F0EA4F1XgHuPAI48KAesyW+fOSAXyKKa4bgPOfHshhhe26y4mjw/3iXjxp/H8NNP9GH5fA3GTD63MpkMtEP4vVrT/T/LnPbs1xUAzZjTpmNnt4ImI//MkJtlGIPchqSkoMGRP8riOvYP9Y74nGVz+e8ZnpIskXXZghaHPRlJxq54A34XPwiSI/IZlCPILshCjeSQ7BxZOUrvnvxvSzal8fGfTnN97hyzPbkoJgPIEQk3zViE27RXMOezU5DZwOtPs0oWqWlJsCkM0AJcnA0Qs2YVM/0WqgnOBgBYSy3NAPoKPnuio6ODtbe3PwbgmsKdOzo6bgVwq/nnBCUYvdHamo9TJ0+ejEQiv7roNehDQ0PYsGEDjjrqKBBCcNddd2HNmjVYs2YN/ud//idQ26LRaMkT39TkXjl7/PHH8ZGPfMQVmAHAzTffjOuuuw6tra3o7OwsO5EqQZKkqvd/9tln8a9//cv++6GHHgIA7Ny505cNgPu379mzx3Mb6ztSKb4ye8ghh+D973+/a5ujjjoKN910EzZv3uzLJif71NbWVvFYd9xxBz75yU8CAD7zmc/gV7/6FQBg8eLFuOqqq0Zsh4VsNl9Yn0gkytpTqc0AwINY6xh+5xBQ/mZWV5d3bmfPno2vfOUr+NGPfmS/953vfAcXXnihr+8HgFgkH0zc91ALprZKOH+ldwDGdIKDM97iCQDQZGiIbGzA9PdNLamqWAmNjYP26+hgFA3d9SW3Te8sduyfb2zDzlgDzu3Zhs/t3YCPNE/DlMhkJGZ5K2BVg9fAm5VKqoSGbh6IaUMapAhvGsz0/GroAWWYs/2ROKZqWUQ3xzDriFkjnkOrsN5+3dDdgJ4SK/KWUzSk5Of254+g6OzU8PDGiM2cqW9ImH7B9BGfs4hjDiW669GQY6C54mPVJ+NQN3ObJre1It4d57kqBWg2VMhkKmbNquaRXoxdzf32gz0yGLPPWSHuvigJ+mT+Mf5y/WQcaUq0/+1bFFsv4u0QhjsVTI5MQWJW3PM4lTCcZngTa/kfugwjx89X02AdGiLcIWxI8nN06sAeyOCE9o//MBU/+Hwj8Hkg+WYK677Cz3s8k0BzthnNBzWNeA6tIfm6pFLjAwCR4byLFCfx/LYxYPp/JLHvb12Qu2RE34hh2hlTISdGNodikTwbFu2No6G7mDVMRBgyBSzxP95owgcw6HpP26Qj+kYUk1ZOGvF1v6U5Bct1jgzE0NBdnFotDfD36s16s666Ohzd32h/LmvusZC2RjHrlJEvqNn3IU0uOmeMAef9pAUJQ3cFZ/e/Wg8Qggt/2obN3xwCWoE92IvcehXRTTFMe+9U7OvbF8h9yAI1RVsmRYFbPj2MR76b36d+Th2G1vJF5WE54grOYv0UbfVTXTXPtSAa257/nv0NKJXo0uJYYPxjW76sgxIJ97XOwZl9O0FBEN8eR6wthkkHt4zIHjmdwW5sBADEe4vnoXPMpv6ZLwzLYNgTq0fjVcsxYzrFnrWcS1L6FdTvb4DWr4K2U9++x1ijGs74OQDvNl+fBuBZx2fPAlhhvl4BYCveRihMAauUVvWe97wHxxxzjJ2yt3//fvuzQgl3vyiXklhoZzKZLMmAPPXUU4HaVQl79+7FV7/61TH5rqOOOqrs59ZKi9dFfeSRRwIA1q1bV/RZLai15swKzADYgRngv/bNy4ZK9mhaZTnosWxS7ZzXiUQC3/ve9/DII4/gG9/4BgBef7Z58+ZSu1cNZ+rPT55I4Iv31iFbYijmDQ9iikdzUwuNhobtgwGKJ1RI4XBKM2+LNeDnHz4J35uzwqWY9s7Bfaji1NaMSFPEdkSdWVjlmLOkbBbjDwWXYg2g5Plakuap00Ny3tk5oIXh9xek0PP9AcSauT3pQeqvXogVv/ZKa1SyGrR+DVIsX9dYf1A9Zp9/AA788gLEzDS+Zl3DwAhZs0J7Cueic36dfJCODVdvtP/Wz12AtvdMwdIbl6BtEoFRp0ABgzpkFB9ohPYwyqCZym2WsiYAKObrL+zhDq8E4Lbn8wGKM42Q6cy/WmOV5ISVVgXkU+IsJA7gDmdqsznnA6rLK1XF4FX+8+cp89AViWNfJIFdCb6Qk9mZAQgp2wKgElyKnyXGWjXTU+vNACMXcS8mEAAH/eeB9t+GSkctbe+Zrfy7o4XGms+RgQw/j3Xz6wCJi14wg/mjGErMoZSp1FgfBVbOMfD/jszfoBJz80FKSnaPVzSn+8qzLHPZu5Awz9ffWmejP+JeBPjNNJ7ayM+pv7YnTlGZuzpKL/4yg2HaZl5DbgnLWF8rxfhY7v3rPrOFxojNGVdUDM46OjpWA+hqb29/GsBiAPe0t7ffYn62BsCu9vb2JwFcAuDG0TM1fCisOasUnL300ksAYMt6O1PaLr300kBrvGoRBOnu7i4KzhrMvj5DQ6VX/WtBNTVnPT09WLZsGZ599tmS24xFXZZ1XssFZxZLU02AUs13AdX9tlLjWKkf22jYY/325cuXY/r06Xj22Wdx6aWX4t3vfre9zfbt2wOxqxo4xyYejyMWi+Hd7363S520q6vLa9fa4Hj2WCpo+4bdt1KDAt/8Zxx1Q94S33Mv42IT87JJ/H1TLDC1xkrPaau25NnGqfj8gcfg4U08CHm2Kb86fUAuhT2jXN7pnFptmnuM5HoZk09sRf/HF9lKbv/3byVQJ62QTQB43db/6+b3QWew6rzkaD13jjKDemD2GKZX4SUIEu3mTGdidtwWmiGEYObZMzDlxMloXs4zAZoNFcPlS8HKwl236P7M6rlWH2VFK+uf+wDDgivmo24OdyCJwm2c/EIncj5ujc75YbC8kITiiHUiFQgnQgimvIsrtzGN+lKzrAXO4KxQPbJpKWeJhtcPg/mtOXMKcJS4VXsFZ72ROC5ZeAI+sfAd+PSC49AvR0GzFJ17qK+mz656oRK/yxL4qDMdarXgJBICTD6+FcNRPm560hhxUF3p+nxwA7/Go2XEUjoHiM32A4CR81drWgpJ8xqzlUcd3xGfmWefnaqEABDV9MBEZcoNV9ScYJpZj3fJ0flFPtW0KcooUltS/oJFx+/+yyul2cDMnuLnKjNVb51zmOlsVGoExwJVVVt2dHRc1dHRcUJHR8d5HR0dakdHx6ccn11tKjWe1tHREYwc2gRBLcyZc9vGRn6DLnRcd+1yiwX4sadc0FAYuD311FN43/ve53pv2jSe6ztajXy9cNddd9mKevX19UWphEA+1XA0YQU65YIzRTFX9H2OT63M2UknneT5fjabDSRwHUlw9vOf/xx79+7Fcccdh1tvvRWPPPII1q/nq9nWv0GAMVZ20cGJeDz/QJs9e7b9OpC8c2dwZv6xd9B97T+yScFfHmf4z06eVrNq5gzM+MhM+/MpJ04GjcuYn0ti9XrDn6S2c0gqOA6WxPeeWB2o437VHU3gO7OXAwBm5dKump/RgK3vwVhRTV6sLYqDvnwg3vWhRrAEv84eWyNhY3Gf4arhUraj3szZJAfDmZMknHSQhg8tK1j0aOIOw759PpkzZ/BhskKaR6+qaDdnWBJzvFPNlCY+Ps26iqT3OkDN9hQ6n1b/pfpY8dwiBV3WJVO2/n39u7G9a+RzyPlNlBFo5m026gzOJLhqXgBgfqv7/iCZ/QapGpz4RiVY3wnwuexEbHoMcr0MfVCHPqD7WtUnTlEZD8l5g3KHVXJM1FfqeSnG5Lr8e/uj/F55x2OSrwDWLaXvfRwryG6gfJ5o0QLmzJwyqrmgRtWR3xtZhcgjbd4DipgzB9abPfvsXmdpw2dxjvc1kWfOzIUaB4M5+fh8+Ux3xJ0mHNH0wFRRS50zAFCYu5/YhUc5skEcz5Edt+4ILAukVHCyo0/CL64tzsixzJ9ycr4FipGhIBO0mkp0I/aBWhxrpxDF9ddfb9ecOXHWWWcFZk+5oKEatscSThjL4OxnP/sZAOCaa67Bli1bcPTRRxdtMxb916y6qzAGZ6UYsp/85CdYsmSJ7zRCpz2V2DhrHnnVnh14IE9N2bHDh0ddAMMw0NbWVjLttTCt0YKzzuzcc8/FqlWr/BnieIhZDtI5v27Azv787XQoS3DlnnxgevpyA61HtQAAGhY1QGlUED+Qs9NKby4wCetKAcPgEN82R4pv/btiPMVptppCJhgituTvshbZG6iOKKOQEhIO/f4i1C+sx4IreU1DVAFaJ3E749TwZJZGggXfacaLO4prs6z0GICvFN9zSQq3fdSdcqmZku279zLofnowOa51q/m1F8sRMT+MNHun+ETMNMtmQ0PGV3DGil5bLEfSbIjdYDqOllT17AuLZf1bzDkOAMM+hGwNFwuTD1wjjrRGWQJijvvV480zsK1Pxpv783PbDs406nsFvVrFYWdvLmeLDesYVqCd3ZvzxzI40xo9HGtrPl1o9qcCgH+esBRXnJDFhUflL/AehTv8B27r8qlA6phDJa5VS3XTYs70wuDM/Ncwg35dG3m7gUpDq5q9BD+1r7jdy9xJ/Af87wsxTLm6Bfs0fv3RtOEvGHJMIedxkgXBGVX5j55/xTxEWiJY+vPF+E37cuyLuhdpFJ35k48sUEcsBSuAtZi7Uqx1tC3qa8HByeB79ebrHCA48kdNOLmnWC/AmrrRSRFEJvHz1ftUL0asuDPOEMGZD9SS1lhNTdCrr77qSy6+WubMCiiuueaaknVX4xGc7d69GwDwjW98A9OmTXMJrlgIKs2yHLLZLDKZDPr7+xGJRDB58uSibazgrFompxRqbWReLmDasGED7rvvvsDs8ROcRaNREEJAKfU9Rk709/fjv//7vytu52TOIpEIvvnNb9p/H3vssf6M8GDOsjrBB2/LFysTAszN5ZWl5ISMhoPrsfTGJVh07UJul5muMjXjjw1mrlqP0nOIahTJv/OHWs7s17WwzYAi8X26ogkYhGCqlkVycHSve4stslIao5OiaFrciCU/PAz18/O1QmZ2EyRQZEqX7tWElErwvYeLmSinQlqpO3l9E39kJgwd+32kfjodGE0rndYYMVOunGyME0oTv/aadRWpgJgzUODFHTIOua4ZX78/YTuODTHOPlGzqeuM/5hedByrD1tnNIEhH2WwTnsoZXagGHGlNTLbaUxJCn48azEA4PV9+Y1sWW01gJqzKkHN4sH4zDiik4tTs6yA2kjrgaU1ejnEVnB21BBXlZ7yrim4/zNpXHtGFo3x/L4Wi7V4Z9eot/SwzuNcUwRIrXePj+VCUZOhsZqgj8ieCvvV9aXw+c51ODLZ63p/WiPFWcv5mDyyyVyMyfF/jQwNTEvf2fMtpbrZaWo2crbSYuvm1eGLn4pj7cIDYDTmx0ymPpk8pw/rcf+x1j4iFnNmBmcFreBw83Te3kNpjPirOXPY4MV4nXZzYxFbbsG5tdbPz9+O23Zyn2YCkmciOPOBco71dddd53IIq60J8sMMOW0oF1RZnx144IF46qmn8Oyzz+K73/0uvvzlL9vbWCqCQTrW5aBpGnRdhyRJdl8xJ/th4ZZbbvH1PdWsfmazWTu9sq2tzbNX12gwZ9WkJVaaR36ZKuccckrGe6FccAbwAM253WjDabszOAPg6lXne047vsc5m3YP5OfJYIa4Vv4Uk22om5Ow6xeUVj4+Taoa2AO23Kpl79N5VlUlEt51sIaHPjNs+3gGkZCKc5sy+4M6Z94/zPrOs3q2AwDqDy6WkQeARTP5D5IZQzqg4KwUmvX8b/7rlLme27zjYG6PAoYXNvj4Mud1Zq7gW6yCE4qZ/+msY3Iiz5ypvoJXF3PGgK/dn8BQluC252OO4Mxc0Wc8WCSyh71m+lecGhhKBVO/xGjeqY84hiEiw5bQT8kK6s1L3OkbWkGt1quOWVqjxXpMOaV4UQ/INzXWUyOvpwLgurQMDxZXN+dTwhyjWefOsD9rdKSoPtqST7f2Y49LmKjELVY1gCZdtfs9ppvd92lrRlGTOaM6G3GqpTursXiufuDhV3HaQAEDIxM8+blhzGl1D0TKrEHVUn7TGvNwBmc5040ws7h5jSQAKZK3++CpFA9/NYdj71wGdgxvnhLxm07g+C1e14d1GeZrzvj1pBTcjnrMdEua9Tc+ziwQyeM4+4YkNBjePlepmHli8mYiOPOFQsfa6fhfc801+N73vmczZtU6qH4cx3LMmaZpuPPOO7Fnzx77M0VREIvFcNxxx+Gaa67BpEmT7O0tyfmgmLNKQZHVoyqRSNjbOh1sK1gcC+Ysk8lgYGAAAFxj4oQlMhGWtEYL3/nOd/zVLwXEnDnfH43gzOs6cb5nBc8WrEAxCDgDoMLUC6uWaSDj7iVkBWdOyKaTVqdrvhZjXY5smQNZq4kAcPAs4M7zU2iMFzgxplOUzQbjgZASh6EMOH6wCycP8saljYc2em4Xi3J7FMaQ9sMMVfGEnq7xe/VfJs/F4ctLLTjAtIfi3BtGJoHODcq/1MqkNcopfs5KMmfNFnOm+Roft+Ins1O+gLxYQX2UgZrNnqWEtz1WymOCGr6YM+cKOqW87owQ5lqxVyQgZkYBOSLZSo7OS8Cqiet5shfMb01ulV4eM5lQKeI9RnKDKS6R8iku4crcKTbOmk9WAOts++BkzsihLQCATMwn6+EIEEu5MZpBXBkFucbCtE9zf/O8GfrIWY9KNWcxj8Bm0bUL0dbAcObS/LNvUoIiZbXQSPurD3bCeY1ZCzRxYmD7rTswvJ6PEfGYQ4QQkBg/l7LuM13Xldbo8XEBc6ZJVnDm/tK0mYlhpA1/dYsOG6QSJ77B8PYnSo0Dm6DhmQjOfKCalDSrfslydBctWoS//vWvJY8ZVHBWGDTcfvvtuOCCC9De3m5/VujAejEPY5XWaAVnzl5VTubsM5/hXSrHgoUZHBy0gzNnHzIngmLOaklrXLt2LTZu3Fh2G8Cf6IUzWLSYs76+PhiGgSuuuAI33pgXZHUG+V6wgrOglCSdsJhNJ5xzw6nQCLiDMy8mtCY4zlPhA+SF7XwsBgqYM69eRhFTzKFe0+FnudE1bco8GI10/t4ytRWIWau0jsjFDs7SweSAlbLGoMA5Pdvsvy1hi0JYzvVYMGdLUzxP8bLzo/j1x7xTTS01Qq96iFrgdIKtftRawa1/kpZD9lk+z0sxZ0odn1d1VPeV1ogCpspwnP7eFP/uhhiDkTUd/RL2WAFAjBoY9iHt71xw0B2smXONLyIzu+YsJ8mwypecl0DWoepGA1pwqASLOSsZUJuKn3o6OOaMeTBn1nyKmz6F5FgganIEZ011/DVhzKfSXv61UaJ+STWAr+x+3f67b0az53Z2WqM+ciGXwp/CGLPPjRcikyJoXsYXpRtiwIpZ/Nn+iWNVwLzuDQ2BqSOqDtfBYs5mbdqPrgfyLZZKBfjEnFsRg8LPs8O5697f7ERqm3tFhTJgbnYYp5oMo82cFTzOMmbwqmdpYDVn5/Rsx6/feBrTC1qtNBYwZw+2zLJtfStBBGc+UI1aY2FwFo1GXQFIIUaLOXv11VcB8D5iVkBRyHg4A42gmKFq4WTOLDiZs/r6+kDsqSatsb+/H3fccQeA0szZeAiCXHrppfbrwuDDCT8BbGFa48MPP4zJkyejvb0dN910Ez7/+c8Xfc94pDXu27ev6D3nuSg8b860xlLntFo42aBCJ/2e1yL47J/rcPOz7nQd6tHEKtLIz2GDrvl7vlbRXwgAjEx+w0i0xHVgBWc+xBycKHW1Gcz9kFUaSwRnVjAEf8yZE1Mc0v0Ht+XHZLrKf/ScwxJFzodtj0ndKIyh/cBgvDRV446kXsB+HD3cbb8u5eg7g0U/wWuhIIiTxbvyr/x5FVcYr7kBIJVonExkAkMmkAEMD418fLwc/UIRAkXKy6CrREJMKWbOpr033yJCGxqb9GrqkZLmhDXX/YpLOHf2EgQxKJ8XUUYB4p5DzrTGFrNnPaH+anOcAXVJ5kyH3fdxQ6IZEY/UWCC/SER99BUr7N2367e7seqcl9HzVK/n9od9f5Hr79+cl8IP/yONr5yctdMsjQCl2bM6XzxgLM9UN3e5M4NKzSFiLo4ohr80Qud13/94D16/cp3LBzAYcOOWF+y/1RJpjWnZqqP0xyw60xqPGe7GVC2LL3W6e8m+c3Cv6++bZvLzVio4E2qNb0OUS2u0UJjWGIlEPGupLPhx9ssFZ3Pnzi36rJDx8EoLC0twZgW0Y2HP3Xffjd/85jcARp85qyU4e/HFF+3X5dL0gppDqqradYirV68u2nY80xrLBWcXXnhhkZiMc7wK69FqBXMxZ26s75Jx96v8uxSHh1l/UH3RcSKmk1ava/5U0jyU9rygDzvUCD18a4kwu44omwsmNdZ6yQwG6vD4DUqQlfJGlAzO5HxaYyog5uM3bzyN4we7cEbfLnxI2Y8P9O7ExfvetJ1GS+nL0x4lz+R51YhVDSdzZjD0rx3CaX27XZvIDqfCaqxaZI+ct+cXD4w8zdIV1DOgP138fYNZYs+hUucLAKgZRWV8BGfUI90qJhku9leRgRjLM2dW8EYdTHD9QfVQzEUQfWD0nx2pLSn0PM6df1IioM6LlPgTl3DJoHsEQzolOG7IZGGYu+1BgyM4azPJK4mxwFiPUsGZlaYL8NRXq5H4J47h195n38H/pXK+Z9WIpfQdr2du7+FNiQFs+fFWTwZNaXbP6VktDBcfrfKFGks90qc6InP4iMP/2ofXPrMWu3/fiazVKiLtflaWmkNWWmNED04QxMIrF65G/6oBAMDQq4Nw3lXyaY3ufax7uZH12WLEY94sTg/gXSZzNzOXwgf78i2nrpu9DItmFC/KHPRlrvjLFyTIhBQEKX2HFaiIalLSrKDDyZyVC85GK63R6q0GwE7ZKwzOnPsEHZxVYqysILZUcGa9H6Sj39jY6CnA4lQ8LBWcSZIEQghPlaB0xKlytQqCWIjFYvbcKkSQzNnOnTtLblttcDYaaY1ewZllz5lnnln0mTM4K2Vv1XCcJqXAm8mYqlsxaiBufvaV+Svx9CHFh5HNup0IDa5nVjnnSnMoMMZI8YaEADCdItVHIORKaTLn09ov8FSmpTcuAWMEs3IpzHOqWdaVYGLstEbqkzlz33+u3r2Gv9gLnFSwZSmWitvD/5XBoJeRnq4Ix67DaeDN/96Ez5tWHje8Hz+feRimqfnr26v2BHAEZ+YBDYNBLsFGlDXHYY/xSi+y+uyilYdkjqDrn9zZLxecsagEZIFs0kdw5mLOgMlaFjduegEvX6Dj0OsWofGQBkQkZtefZCXZTtMtXJ9QGhXowwb0lE91xCqGdfOPttqvS6akmQE+88EK8QM4XmoU2X0aIi0RO7VUo8DXrXlegLmtFJPrKA5qo1g5z0x7pP56wbkUNg3mmU106Mb8AsTeaMIOqK//QAZff08WLQnT0XaoNQbBnNWl3LSy2l/8jHTW5BUdy7TH0BGY6mfm73ws9vxpLxYcWYe4MQfRlPtZKSnek06KSTAAKD7rKD0eA9AHdXT9cz8mrWzBwGPdrs80W63RfVJ04pzTwTBnTny5cx0eb5mJqxwpsd+eswIvNbZhucyfa85dGxdzf1dukCdiXAZAMGe+UCil7xXIjFdwVuigOx3kwcFBAOXTGseLOStVczYazNn8+fPx5ptvFr1vBa+Ad48zC0GMUa1S+hZGizkr7HNWToAlbGmNlq2WmI2XLUAAwZlThbHgnA2azZubzcbKPUoMmO8tdmFlpsrw6xQVB0Ne0Ify8yLiIeggkXwwpPphzpzBIuO1NZldWWR2ZUGzFAYDbt38nGufaGuJc+IYo3RAqZZ+YDnWCqOBMWc3/in/+nN7N+DIZC/O6tmOs3rzyquWyEQR5Lw9QF5cpFY4WY/I2j7c8uazWJHsdc2n4RRD//P9fHsv9RITxFxWz6SCYc4YZThuaD+adQ1MY0huSsJIG9DWDmBZiiuQbok32oIghZeSZLIMhspAS43jCFBYw7Tzt7tcNW5yfenUT8Csp/JjjuOHKvvTeO1Ta7HuP/MSouVcicYYsOZrQ7jvk0kkzIxvyafkuPOczXxxF169+DWove5g4/ANedbjVzMW2QwMIbADM8CR1hiUIEgBtL7iBUMv9VEbpp3Uh7R/ERzHWfnyFlzc9SaUZEFwVop9NdPSI4bPvmslnju2MEvaexIVphjrcLQ+8KP4WcH9nemoPxuU+TM96sGY2wsgOgMhAJuAIZoIznyg0LH2ckILg7NKaY2jxZx5BWdjyZxVCjyqrTkL0tGXZRlTpkwpu41XjzPn/kBwwVC5MSr8zFlDVYiggsVSUvrWNmFhztavXw9VVe0egV41Zc7x8h+c5V/KBU+iYTM4m6rx+dw0VcETV3i3x5DNBwhhzCdz5nRkS29n1dy82DAFZHHxGBHAdvapjwbLTuaMMC5jboHmKHqSbifocwuOrsgyyD7VGqsV7Ioe11b+MI40wpEGQgBcc2iZKUTixH84UncAlBQyIFbwal6T6kiDs4LDt+k5XLfjFTyw/lEcY6bGffvALvtztaf0fdhi+bJpHwG+4zF42sAevGMo/91G2sDmn2xF94/fwHv7ufjRKw2TSzJnVhohy9HAVAP2PdCFbf+zHR0fewWpLSns+ete7L3HvWCkNFQIzijgSwjI8Vui2/nCVGZHxn4/t7G8snFM4U52NEpAwS+RckF3ZXvyr5v2DkPr17DvH12ubfrr+DP9xhmHojcSL+qXZYG6AtgRpjWW2W33H4qbGJc9lmwJgvhjhlzHLDjM+/t3Q04WpDWWqDmTzKhWpsyXCmkpNV3re42U2x+1lD8L0xoNM5rL5Ziva4xVyEaIOW4MViqlFSg693QugExQsUYRnPlBYUqal1NspetdffXV9nbjIQjidJBLpTWOZs1ZJQn8sao5c44RIaRskAMAZ511VsnPghgjr+DspZdewuLFi/Hvf//b/qzwfJYLMIIKzrZt21b2+JbYTanFhrGoOfvb3/6GxYsXIxaL4bXXXgPgHZyNFnMWKXjCpjWCD/dsxxc71wMA6NzGkuISssPRD4o5Ix4Pt+SbKay+9DVovfw83DB7mb1a7oREYD8RdDUY5gyUuVbPjRzFqb9yM4nDsvt8MMYwvNGUkh5FtcZVDcULM5P+Y4bHllxMZWjdkCs48+HHuubQIZnB8tsSoPnwYjYYcIyP6ZqMmDkr43B+c9dreC/ZD/nWvFLs1NNKB7HWqn424yet0W3PkvSA/Tq1JY2BVfm/+5QoNiWaESnFnJkqhTRHfTFDTh9vx6070f1oD5jG8PqX1mPXb3YXbS/Xlxe5YYY/e5z7UkeU0/8CD/az/+IBSH88hiU/OazkYWIys51rP8wireBYM4NBNmVAOxr5tRfxamYFZ1rjiM0pO7RDr9XYkse0RzfKL4DVYlM1WdGlFq2sa0zyKeJSKtC0mHqtM5+ukCUSOsx7plQQ8FjpjoT6YxbLxZknDu5zPW+t4MyS9Xe10Ig4grMJChGc+UAtzNnLL78MAHjyySddzmxzc7Nr+6Ac68LjvP56Ple3VFrjF7/4RSQSCVxzzTWBBWfWcTKZTNljWdLoTsfaGThZYzaWwdn27duLhCWcCDqtkVKKe+65B0cffTTWr1+Pd77znVizZg0GBwftIN9peykEVXNW7vi6rsMwDMiyXDGtcTSYs3/961/YsmWLZ32Z1zlzBmd+e545Vxsn61lM0nK4oGszJmtZtKkZfKLrTUw3mTMs8E5pBADFwQpVSucoB5ezkHMsGKkUzGB484bNyO3n50CTJOQkGYlI8XkmJL/i6Ic5c/0WBqi9+fm4+b83I9GVdG1/+/kF+YqU20J16gqGfPU583jv9ukLi96beUCxQ60ndaS2pnk7BPO6U0D9MWeOczZVK/3DolOjWHn3kYg0eV9jTsEUMGbL8teKSivWh2bzAWT9gXWYempxcJbamuIOuOlQan7qFsuM7cBLA66/t8UaAUIcrSHc28sOAY6gelRVA6VSWqOBwIKzSS/kmaA3b9iC3X/sBNvKGftbjzkS9QcWCxJZiEUAAw51xJHCw7He+9d9GEwzaEM6Xrl4NZpy/D40aC7IFKbHWbCYKuojgK3lVC/6jkdRsBMWO2Mw+Dtpjud2FQtypVItrdRhhfkM8EvYMPDyIIbWDoE5GPur5x0J3QxSC90PK7iPMLfwU60odx/66u61rr8zZnBm2eI83xazSHVzfCZgjCYEQXygMDgrVXPmZEcSiQRaWlrw3e9+F5MnT8Z3v/tdO1gCRoc5279/P+65556i7QuZs/nz52N4eBiyLOOmm24CEKx0fW9vL6ZNm1a0zd69e/GJT3wCADBnzhz7fadcfFAsTGFw5hTymDp1Kvbvz/cYmT17dtljWePn55wVMmdnn3226/Ply5dj7ty5OPHEE13vlwvOggoWS0FVVfs3l1M+HE3mbHBwEAcddJDnZ07xGwvOueS3z5mTqfrGrjVYXd+KFak+HJbuxz1T5rm2JXNKO0WWTLIUZM2Z2RSK5iheuWg14gfEXc2nU1F+ThIe8ankEATxF5w5l/TdzFlqcxo34kXX9scc7P4uRhmkuITkhqTty8hgGAq4T9XuWD3m/HAp2mYqGHxtCEaOIuqRisYMBjlGICdk6Gaaj8yYqxdYzXBcZ1NKBGfTz5yG6e+d5tnjLLUlhcTcBHdCJACU/1PYK61qcyr8lnoHhVG3oM7lNGpDOpR6GVRloDkKOUqgA9jbx9XtlBKiBuVQShjAC7tjPKvCTm8qkdbI1RFrNmXE8OptCDiDM3/GlLtXd5ppe0NyBNlEDEDpe3BMZqDEVLQbBeZs4//rcP2dkhRoFutRMjjL9zkLMjiLTIq47ocWmpd7M9M2nPdFhhGnyjlNUkprv1f8zdazQ6b++oqVS6ff/NOtrr9VqcTJAsAIgQEzhvUxr2vJ0MyZ9lgsnms4rVsmLV1XF3YI5swHnI51b28vfvrTnxZtk0ql0Nub76th3VCvueYafOYznyliboJy9FVVtb9rw4YNntt7NQ+2nNjRkIrv7u7G9ddfD0IIzj33XHtcLLl2wB0QTZkyBccddxze//73j4o9hQGOs75s165dFZ34oNMaS9V47dixA3feeWfJY1x99dVYuHAhjjzyyEDtKQVVVSumNAKjKwhSDl6Bq/N3+bnGgOI8/RWmKMHS9EDR6l50amlm1lL+kxiDn2ntfDiTjIFP3JXAF37Fa3NSb6RcReVDZhH1pIT3A4s4aytGCOcUiqg6dt9Vvil6YfDBKIMUk3hqimmGX7XGrOy9Dlk/Jw6lQcHk41sx9V35NEdGGbQhDcxgMDIGpLgMOSHZjp0SYM1ZA+UHMqISmlc04eCvHYTlNy/F3IvnIDbNe/4wg4Fm+UBbqY2SH+asggNz9JZ8DdyMD7lTPzO7MlD7VEhxCYwx1NXlU4oee2Vk9tAaxrY7wheIYqXSGiMOR38MUZL1sN73Ky5RhSObleSSdV0WFCnPfDAfKw7VOsHDjmuxVFqjrd/uy9F37yslJCz+4aFoe3f5OnNve/Jqjb6CoSoQaYlg3qfm4sAvLShtTtQKzkZekwegKIJtWZnP5NJ63TeTHCk9kS4+OgedWIsgAS00VoBlj1dwRghxq6JOQIjgzAecF8Xll1/uuU0ymcSePfmUg0L58yCDs0ImL5VK4bXXXnM1L3aiXO1NEKwQ4HaKly5diq9//esAgD/96U/41re+BcAdPDrZEEIInnnmGfz9738PLDgrZM6ccKr8TZ8+veKxgk5r7Ows78RaKKwFu+6667Bp0yY7uBxt5kzTNHseV8OcBZ3W+L73va/kZ1OnTvV83zmP/c+h0p8lCvITIyUKugG3DLruqxA/b1D9U51Y/q/16NiRX+V0NsC2GLGZzfn3DpnKbV4207AdfT/MmdOe6bv7Km5f5MQa3KEmMnGnNfqoOcsUBGc3zeCNS0ulVenDOjKdWWQ6s1D7NMh1MkhUsht58z5nI7enMIKQEhKOvfsILPr2IWg9dhLiM0pfV8xgkOtkO9ggDsXGEQeMVU6/BVfOR2KW2zZJ4UG0HCVgRt5xjDCKLdXd0orNqcFJ64vwZ2jMfJwV7hqYdH2FdjBOHHb9oqL3hjcloSf1vD0+xRyqiexUItm1eKWgyI60Rh+LMtUGLXWOe2TptMZ8QD3S2KNwaBd8bj5ibbGiNhCzPjqz4rGsuIT6PGesiimk9WuY9t6pmHJSaTGyvCCIT5Gbgn3nfWpuyU3fv9R75efeTyRxzanZfN2in6yLWvY1v88a0sI9RXD2NkY1juyXv/xlPPHEEyX3KQzqggrOAGB4eBhf/OIXPeXiAW/mzEIQSoReNjnx7LPP4v7773c1OPZK3yOEjFpaoxPOlMpyY2MhaLXGK6+8suL2bW1tmDdvnic7FHSwWIoVq5Y5G620xhtuuMHz/TPPPBMdHR2enznH2bfITYWHYbcSwzNNU/GNuUeUdD4AN+Oh+xmiAl/h6OEevHMgrxznlGHfF01gagO163MA4A8XpnDJomHc/OG84MUL22Tcdv/IHmrOeiF5BI4DYwwkQngDViutMcCas5ws45+tnKEvUW8PEAI5JoFmDchxCUq9DCIRm4WUGbUySP0bBGDyCZNdTYI9d2EM6Z0ZGBkDckK2FRxtRUswH2qN1Z2niEd/MxIhJtspA5TZYxSlFNv2jmwOlWLOZn10JtrePQUzz8mzd30KD84sNpgy3n9yaC0XfXDKagddcnbAecWtVpqWNqLx0OLUaiLx+kXJWXPmKzW28ia5MqloFiKygznTfAQeJZzgNXVugaakrODLJ2exsM3AWSu8F+6YnA9gg7LHqj1UmtxzeNr7ikstCuGcQ77gsft9M+fjutnLajqMZC5EyD7bHxTuu1eKQzree4GTlbhZRmQGSWI2c8b0kdd2Fu7WfP5cLPp2vjZ4QI7gt1MPxI0zDrXfs1yhsosyExAiOPOBaifgF7/4xZKfXXHFFXjppZdwyimnAAiW9RgeHsbmzZtLbl8uAAna0ffC6tWr8cEPftD++5ZbbinJ5o1VWuOmTZuwd+/eqo4VdFpjNaiG7QxCEGTu3Lk455xzPLdxBmflmLPREgRZtGgR7rrrLvvvd7/73Tj11FNxzz33lKwTXLJkif3ad3BW4bIfVKL4wezlWN0wuXTaDmDffWUAmupjUcbDgTmzr7h5+KqGKbhx5qGYM4nPuaF1w6A5itnNBq59ZxJTFcPFVF32wxE61lU4VE80T8fTTdMw48xihtrIUBBFgqQQV9NnP8wZcZy0mGMBrGTKF2NQGhTEpkYhxyVIMRmRlgikmCUI4pM5K7g3zr24fH0rwFUKjbQBtU/jAZH5vlPRcuRqje6/H5ntvYJOCvouMYOBKBKoyiAlZFCd2WIFEUaxt9frKJVBSzw7Dvh/s7Dgc/Mx++MHYMq7JqMrEseWeCOWz9IhkXxaIzN4gM8YK6jx8uGoecyV1uPcgcfkE1pxyLXFQjMAr0FjBrPrl/z2hKomsDNA0DVc3s2TJQbDSknzMacLH2VfnXckzlp0clGA+KNZS/H192Tx3BeH0Vgi69teqNBZbcoeTnsKfos1d+MO5ndTQ3PJlgcuyI7xCTCtMSPJuG3SQdgbLa3g7YQ2qCHXk4NiBkoKDVYQpP3HzThjYHnRZg/Nmwe13vtkTa5jkEm+ETX8NA4vCKSUuISmZU14uWEyhmUFVxx4LO5uW4AHWw+wt/GsOUO+gbcIzt6GqNWxBrjAgxOSJGHlypU2AxEkczYwMIBdu3aV2Hr0g7Nax6ecAEcQgQdQnjlTFAULFy6sKqXRaVOQ56wSrODsC1/4AgDgkksuKfosiHNGCMFhh3nLL9ea1hh0bzpZlvHRj34U3/3ud/HMM8/gkUcewUMPPVS2RnDWrFl44IEHAAQRnJU/Z85ZFfG4xJjBkN2XBSHETifS/fTwqvIyu3bu4RhQYmifw39/pEmBNqyDaQyRRhlGjkIy7ZV9PPG9HM5om1uB5Nbph+D62cswpyAo0ZM6cl05EMmsRQtISr9kP58SZBWjgNKoQGmOgEQkNBxSD6VBtuuXJMZAGakp/c79Be4/5brKDiLTKeSEBH1I42lN5jGcvc6Cqjl7Ye4BaL5uOa6ee4Tr/UgB68B0BilCQFUKpV4CM6gtVhDxwS56MWfTz3QzHAs+Px9XLj0eaTmCUw/Reb9AAIbOwAzGnTMa3Aq6F2sYaXYvlskNip1yVgiiEFeqLvNZL+S1b45IuPzAY+y/p+jZitoVEQmgCIAZKphDr9e3Iisrrqn+vsXvQdsS70CEqhSq1RzaGkM/jn7B+FjMWeOhDQCAtCTjR8vayzLWmd0ZaIOafY35nUO04IbzRoKXUmRJFQEigFxXDpq5OANYbVj8GOT9e25buNh+/e+maXj0wAV2+ruFP1yYxPUfSOPgqRSyhDxz5idTt+B+EYlJIBLBf809Ah9ddDL6I8UBopdaI1DQ62wCQgRnPuB1c/zIRz5ScvupU6fi/vvv9/zMSpEL0tH/3e9+V3b7aliYsQzOCgNXJ4IIPIDywZlT0a8ajMcY/dd//RcAng67bt063HrrrYHaY40PIcRVg+fEgw8+WFVao1VPaW0bBCw2TpIkXHPNNTj++OOr3nfRIl4H4reO0stZmPa+fCqIk6WJekwpPaXDSOpgjIFaC8QlmgxXZU6NAcK7DubfTWISYFBQjYJEJJeUvuzDaTQ8HoZzL5ntErdISyUWhhhXIiOEB2fUbA2gMIaMr+Cstt/DDIb4rBhaj5nE7TEda0u8RDE9kBGnNo5ofAmkuAS5QUHdvITtlDhrF0ea1lgY4MsRgoMPi2Ly4U3YdRCf2/UH1aFuntuxphrlaYyMIdIcgdav2/eQKPURnBU4wXMuOgBzL57jeo8Qggc+lcTX3p3BF9+ZtZ2ZbHcOTGUgUc5UuZgzP8RZvPhilgvl8ktci5YdRCL2ij4M5qteyGvB4S9T5mFHPJ9SGacG/uecdPGGDkTkPOsRVBNq1/sFf//irIzndrn9OWhWcCaXoENqQCFzZl27kaYImq9bjssOOh5xpfzx9aThagXil+1MFfhcP53JgyDd4YsUsrFOkJgEKSZBMYMzhfpsD+HY1Sn4cV/EXYcnE4aLj1ZxzakZPPk5ni78nkN0fPJY1fzcUbfoQ2Gz8HQrcX7Mf39uCCce6L3yZDHmrKCgj5gLaVTzmfo5ThBS+j7gdVF89atfxZ/+9CfP7W+++eaS7NBoBGePPPJI2e3DxJwdccQRmDmzdGHuWKQ11iqxPpZjtGzZMvz5z3/GwQcfDMCb2Qo6OKuv95aBv+qqq+ygsFxA29DAVyiTyWTJbWqFnx5lgTVW93AYGg9rRNcD+4ve90prZDqD0hwBVakpYc1g+JCJL/ds/uvkOTh9aA8ebeQ1Oh9couKkg3TOKEgAMwhojiLaFoWRNmzH0U9w5uW8KI0KFl59ENZ+YR0A2P1yikDM9COZ13xZYy3DX41XzcrXpugGkQkPXC3H2nTQFXN8NB2IjqCnORmBg0dkLgRAGiQe6L4+jExn1l469pPWSA3mWqmVohJkCfjTxSkYuQOQ3DQJTYuL66ioZilrSpDrZCgNsn1fjTCK3EjtKRgfKeZ9nzl0OsWh03nUbgfgigQjq0KK8IWLwARBPK6JQtal8TDvvoaMMhCJFAeLAbEet0w/BDki4YkWt5JmHQysmFX+wpEIAhFzKMUqsYLnbKlUbyI7gl1njVdAKXJOVVitNYH+SAwzIxXGJkpgpHWeXulxzFrhZM5unb4Q3VG+uNkdiYOunIK2eoYDPz+/5P6EEIAAkpQXAQpqTn9j3pHe3wnegzsiA1e+071Clt6ZQWJWHLJE8mqNPsaocKExYooLLZ5Bcc8lKdzxQhT1UaA7RRCVgcMP0PGrZ2KFPwVAvt2Anzk9nhDBmQ8UOtY7d+7E7NmzMXnyZJd8voVywcdoBGcbN24EkJdav+OOO/D000/bn493zZkTM2bMKPv5WKQ11sqcBS0IUum7Fi70rmWwEGTNGSHEDq68cNlllwGAS4m0EFa/sbdccOYxrRNzEpjyrsnoebwX97fmF2C8BEGYTiHXK2Aas50i3U8hfonV5Q8cdgookXDFj6Zh/boYNh0xiMn1Zl2OxswHPQMzKGJTY8jty9liBX6CM69Uv4aFDbbSYfnfAsgJCZEGBbopYQ/wNEI/0vWW4x5pi0I/ZwHw70o7mIGQREwmzxyXeJ6lAjBym0YwvkQiIBECJSFDaeTpYvpw/lqXGR05c1a4Yu24zOSYjOZl3iw6T2dUICm8D5zSpNjBUIRRpEbad63AoZLilRfOrHNMYjJy+3OITYuBUXdfMV9phAWX6NxPupm8A86bhckntnrvazAQ2UzbCqjmzPopt087GH+f7LaFwVyQKHF8PalDaeD3Q0LyQYPmq4WG976FJjh7m+lJHVJMMtVZeRoz1ak9RiRAQRBnS5GseZ3EI8XHpzoFzfL+YUSRAJlCUqzgrPZSBCecDH7cSe0RgthFB+GgA8pfMHYNrlVz5lMQxFok+unMw7ChrsVzmzcSTfDK/GQGA9MoqEp5D0hitWHhbB4ZQTO4wnMWcQTUhMBm6py4+Rn+b1HNmXm+mY+slPGESGv0gcKL1GLF2tra7PdeffVV+3W5AGQ0g6GFCxfiwgsvxFNPPYWLLrrIfj9MaY2VgtKxSGs8/PDDazrWWAaw1YxlkOdMluWSzJkTlpCNF8LGnAWlQOrlWMdnxDD/8nlYd8EK7FiYr1l0BmfagIZcdw4AZ2HS29Ng8O8UOR28fZF8mik1n+TTm4HPnpCzAzO+U/5Bz0DMehqWD8781Jw5LuWuxnq0/+kISDEJkZYI2OcPw6cPOrZoHz1tILM7A1AGpVFBYnYCDQc32E65wlggzNm8axfh0JP4osGMptLXFGPMZkVIROIBB4GbOfNj08iyGiEpElqPngQicWZRSsh5h82PIEjRinV1rgEBg5yQQRSuZCnXyXY6ka+as4JTE2muYh3Z+g2KBCnKHf70lpR9HoNkzhZ+82BM/wCvgVtx+zIcdv0izPrITM/6pezeLPQh3RaksIUKaDCOfmEdEwAYJereAD4O6R1p5LpzdoN4ai8SBXPdF1jq+st5T0zvyCC7J5/2LinmebKUenz0giuV1ggA963hz5HCtEZmMCQ3JpHemUFuX5bXvsoEkYh/Vghwj0Sv4q6fqpRiCXB2kcj5xRMlICn9QnYTAH4x41A80Twd/2idjcJuJwC4amyDbAdUNvsaYBNqJVY5wCspCGKebz8KpOMJEZz5QKkbqzM9bsWKFZg7dy4aGhrKBmejwZxZmDYtX0jtFHAIU1pjpe8ZzbTGTZs24fbbb8dHP/rRmo41lmNUzUM8SEEQSZJw9NFHA+Aplaqq4qSTTira/rzzzit5rLAFZ6PFnC2/eSmkCHcGLzkrgmeuTKKtnmJ6E0V9NL9xpjPLnSHGV9GliJR/oPkhhM258UjLTGyLF7OdnuwdZZDisj2vpBhP3bMFQXws6VsP534lihtXHgnZkZKmzmvGrlixjcawDhIh0Id1xKZEEZsWgxQhkOOWdD2Dj3IYe4wkAjTGgS3fHETHV4ZKbk4IsZ/6rcdMgpyQAULs/muAz0bUI/E4Zc6cWeIhShNnrOCsORvpPCoY22ocRWYwMBDIDZwti0yKID4tZst8R3z0XSt08JSmyrmjlpPGCE+DJBEJTq/Sb0qadUnM+8xcTGpvsd+PtcU8pfMt6Ekdao8K2Qzsg0prtKYQ9WAohlv5wpriEdQyylMrc90qsl08TY3ZzNnIA8ZSDH7hu7IjrZHIBEaWYnjjsPkhQWZHBsQ0mwTYFNtiUl7aIeOW53hgRAvqlHgaLFeKletkSHG+8BA1GTbqu06Q79s5fwoeL0hBjVeTHi0RQJbslD2FMV/tBlBmDj3UegB+dMBSaJL3Uh2jDHJUygdnpt9r+KjxKsd2loItCFL4ftRqij0xgzOR1ugDpRzrwvS4jRs3wjCMskxVkMFZIpGw1fQURcGRR+ZziZ1Nr8MUnFX63aOZ1rhw4cKKKYNesMYylxu5UkG1YzRWzJl1HiRJQktLC4aGhhCPxxGJRPDYY48VzZm6utISwFZa4/Dw8IjtKUQQwZl/QRD3Y6CwYTAhwOqvDkEz8ik8etpApEmBkTXACO/hRaLEdop01c/qp2kWgK3xRhw73I1UKcEN6ydQQI7zVCIjpQMST0uTJAIKnzVn5q4GCNK6+15YSn6egYHIEhgxbCl2kLyKYdRnXzHrEW8xg82JSr+P2YyU5OjvQxTCz1uGIcoMaEZtqdD2cRxfH5teQk+8cB9C7JRBAGha3Ihct+oScQmKOYtW4RkMb0oiNiXK6/EIZ4Nj02M2c+ZLEKSQyWupbJB1lijhQb0kc5YqX3MGX8yZNUYtRzbXtJ+ckKGpGuSEVCztH4Bj7XWEtacuwpnbt2Dm2R4LwtRRK2exDubfXmI+tdoDcCVE++0Cv9/ZLkuKEF7rai7CEJnwAMhizigb2UIG3BkFO9ta0NIv48GNUXz3oXx2wexJBc9VyuvMQDhDFZ0cgZHSEY36Z4WA/HW/9ei5oGvdgUeiwoIIo8xmXYnEb/symL+aKnP+VfIuPBfGKA+A0lvTIAuIvdBo6LWnM1ooXBMkJRtROrYpxZxZwZlgzt5+KOUwFwpLxOPxiiliQQZnkybl1X6OOeYYV5plWIOzSt8zFmmNtcJiIf2oEY5GWqOfANaZ1gjwAMsae1mW8a1vfcu1fbngbDSYM+f8rRVBMWdOx3reZ+Z6bhNTgAaHqTRHEZsW5XVDEq/jkaKSnU5EfamkWQ9YgnumzMOvpx6ELyw4uvxOlEugR5pkSHGJM3kJGdatSwYbkdAFkG9CzYAiQYjSAhGctVMaZPv3EIVArufnLMoMf2mN5nWmdldeSNGHdWiDuu1EW5ATXCDEbrLMKLIjbeHHrHQi4JBvHlx20+y+LNLb05DjEiatbLHfJzKBZAqVAHwVfeRNqN1/x+TK9yWlXrbpKksJnMiS7UBGGMVIL7VCKf1IDcxZbEacO9iyqbJppTXq1F8TanOMKjULt2CkDQytG+IKmwk+VlJEsu/5vOYsgJQ0D9ZDb4nj4KsORP189/3ZyBlIbk6a6qP51GY7rVGHb9ZjOBbFZQflVXS3FzDlLiZfAl8YMhdhiEQgRwoULUec1mgy+HIUfzhmOY7/ebMrMFs+S8c1p7qVIxljPLCPcJGbSStbQGQC67HDdPhiO+3a10jxOYtVmOIWqwdJQm5LytH02U9AXTqt0QmD5j+nGlf4tYNFmcBIGXaAr/thzkayKFMhOGN+Fj7HESI484Fqg7NqEERw5kxJs5zYY49113c42btydo61IEi1wZmqqr7y9MupNdYKS0beT3BWbQBrMaHlEGRAXWpuXHvttXjXu95l/z3WwVko0hrNp0Dq1DmYdvrUChtzMM1AdHIMYAx1c+t4fU5ctp0iXyvWFnNGgJwk4y9t87E3Vr6pqSWU0HhYk52qJ0WIvcqtMIalC0ZojqOOIVuwiqqWWFUlBIDM1RAtJy/SFEHL4ZyliPhgYVy2DVZeuFD7NZ6CVnAJxKfHMPkdrfngjFL0Do7QEPN06x+aj8QBpdtRAIA+qENP8x/vDBiJZDrYpiMrIbg0y1gFn4iZapY2QWvaJTmYPT81Z05zpp7e5qoXKgX7di4TkKiptKlI9nnkNWd+ajvNfat8bDDKIEUkRJojvM8fY4hPj9lpVjwt1D8KmSkAUEooItIc5UIgVr81CqR3pEGdKWkjtcMcn21TJrn6Ud0zZR5+17YAlx/IfRFX43dCEGlSTHbabDcQlW21RsmPIIjjvtixqzjyufb0LGY2s6J95ITM04cTvK8hUQjiVu2TjzRLID91vLTH6jzESSxQlULr0yBFCCQZAM3L7/tJ27MEQSoyZ47rOLU5hdSWFJjG+D1b5uynEYRao+N7bp5+SFWLMqX6nFn3aUMEZ28/BBmcBS2D/vOf/xyXXXYZvvnNb3p+TyUEGSxWg0rfI0mS7ZgHxVQFxZxVEziVQrVjlEqlKm4TdM1ZKTjrFidiWmNgTbETtaS0EbupcmSSgoZD6iHFSD4482ESs1fQa9mJS4wr9ZzB4yvpElrM06kwisQISUoruGIAsob7GhvM8r+nN1G8bNZ8UZVCikmQo7yHj+xwxCMtfE5HGTWbPo/MJovtlKo4ZbweT/JkSAjJ9zqLMorugZHZY3kSpIQT7f7S4kAMgC3zn68/oT6aULv/jlZKsTKbPJMIb3dATI+bKARyHb/Ogqg529dQj5kfLq/ka8Hqd0QZbIVNKZ6viQlKEKRa5gzgjn5ibgJyXAYzeEuJTKf5/KL+6uDICK57qjJEmhVIkhmcSQAIgSHla85Gzpzxf5viwKzm/IRSJRl/mHogdnjUwxICRFojvKehDkDi505yNKEeaTztzChIa+5z1hhjOH6+x+SkvP8jkSWbKScyQazOnN869aWwKTkY80KUqjnThjSkd6R5M2wzWJQVQLOCIT81VWVEZZwonKaMAbmenHkPItDTRj6tUfNxzswdn2maivsLFEhLQSoVnJkBtRAEeRui2pqzahBkWiMhBJ/61Kdwyy232A6yhdZWb6nfQowFC+NENd9jMVV+giFncOareSPGNq2xGvZprM5ZtcFZ2JgzWZahKAoYY/4CNCvtrsq7pzbEv0uK8rQmOSpDjstcvdDxQBsprCnkld5Uch+zXoBETKl4mQAyQDJ87vhKSXPUwA1kJUy5ugVf+zu/dvvS3MaPrFAxt5VvSDWKSHMEkSlRNBxU7xJXkB2BEDDyhWsJ1Z8zu1FwCYdFNoPyKB15cGYFi1U5+mb9i9wYcS8omY6jVfiuMFaypq8SCtOJ4pWYM50BpkIjo/mgl0QIFDMVyQ/bac+hGhbQ7NoTsx2D0iBDjvKUKwBl1ASrg+2UV2sS44IcSp2C5mVNaDykgcvGK9yhBQCa9ZHOXOa6LxS6sPcxKBdLUayAn6cU6nLe0R+xIIgluiPDXnipCMKFZKSoBBgMDQvq+QKEOZ98MWfm+fY6wg8+kEbhI47meKpetCUCSQEaD+HPLyITJBJm+q7vmrMRBNQ5irr5dXwhxuwBqUjEDs58ScWb4zulvrxFzvuulU4tKXwhjSh8gchO0fdxnVn7egmUlIIdnJl/q70qX2Cz0ho1n73gxgkiOPOBUjexMARnpVCpn5iFIB39yZMn47jjjqtq23IIIjhzfo9fYYgg7fnxj39cdrt0Ol3xWGMVnDlTY8vVgIUtOAPywWQ141kKNgtTxfOD5igyu7K8n48pAmI50yQiBVOIb60Q18IEUy6bzwUuJC7ogLxtEUpHHAhZbIDTnttfiOGJNxX0pkwFRIczwAzu1Lcsb0Z8RtyVumcJBUTNp/ZInH3ec4eDVLFQRGQACikZyFkKklFmBMCcVROc8bqpSIP7uUIkk6mK5ccoKLXG+lj5+Uh1zpwlZsb5/LP6UhGC+vm8vpqnNY7Q0beuhxqmtDVtKANaj2lFdEoUUkzOr6D7Zc7stMbqjGKUIdISQWxaFNHJUURaIlzVNSblU66yPp5B5hya3Vh8vy8Z0xAzMJQlU5adQIqRvNJeAMJEIO5eZuUgxyQ0L23ibDQBolNjIEpeEZVQipGeNIt99QrwvToNJN9MgmY4i08UKS9SEiFI1JlplsbIg1dujGlbTfdqID49zheNohK/b8v5tEbDT1qjaU+l+mJXcGY9M8xFPSnC/7bYV6rRkacPj4ANJrAYc/792f050CzNC4KItMa3H8JWc1ZNcFauEbYTQaZZSpKEZ555Bvfff3/RNueffz6mTJmC2267reLxgmbO/NYeBcGcWXPIb4olEKwgSLk5XG1qaNjSGoFggrO8Y13Npsxu0Gv1XpJM5S+lPi9R7EtRirn+qbw5ZcjuyZqS0RImH2+y6RLytjGGkd6KSq1+nvPrBjz+BvcCJtc5rKXMlcrohFTAnOlG7dcJpfkHeDWXmbUqXCgIYn8ez9uUyo7swZ9nzqrYViauHmvO97lzbfUVC06tsbWu/O/K7M6AyBIaDmlAZFLETj8FgMgkS8TFR1pjGbELLzDG8sIA5qUUnxU3HUlz3HykyAF55qxaxhwMUBoU1z2SMUCOy3nHMefHseY/5n1zi+9lXr9TT+mINCu8abicF0yRoxKobNWcjZxlsFM0a3mWEXChlChfDCFWWqMVnAUgCGLdhz64JK/e49VeBDKBNqCBaszsa2gGZLKEiNVvcaR51SYsBp8VuN4rZpW+UJjB7SGEj43FmFuCIFrO/8JeBXFfO/ChKgXTGOQor+ckETP7QiZ23aKfIbL2rSV4dQqCWPL+ILBbV9BcAMXK4wARnPlAmNMaS+Goo47CypUrcf7555c9VtAsDCHEs97tYx/7GPbv34/29vaKxws6OPNbexSkIEipYOi//uu/MG3aNPzjH/+oeKyxqjmrdtzCyJxZ58xfcMb/qYb1YNSqwzGDs6hk11PEpsYczJkPc6jbCbEwtZHijxcWjz1v8kryvYQsxT2SL6KOMP/MmZdj/UY3v8+1NfCDD28YhtqrlpRMdgYeZIS9zihzKGxW88yXiN10uqxNPthFZ9+1SrDFUgoashKJYPLxrY4xosiNND3Wsdvv2hZUDM4iTQqIGcjXza1Dy4pmx2cR254RC4JY+5EqfXPqUB60YwQzbc8cN6ozX4Ig9r41CIIUBfiMQU7k6wT9pDWiTCzkRVjqQzpik6NoOLieM/gysVUJ86yHn/Tq2tK9LRDJlEwn/JpjOrXvIX7SGu1FInN8nD0nvYIzSSaQEjIaFta7azsjBHLcXLQyaCBqjQ7xQ9x/6TDu/YT3M1JP6tCHedYFJLMfpcQDNN18Rus+gjPLHqXCjajBZNJz3SpvOB/jYkCSqaxJZEdaow+1Rs+JWwGW5VRnYDozU60Z5Hp+kvX0xKw5E33OfCDMgiClEIlE8NJLL42JPYWOfqmgtVrWKOi0xqCYsyCCRa8589JLL2HlypW49tprqxqjoPuclcLAwEBVx7JYqlQqVZM4TCEkSbL3DwdzZtlVxbbmqqeccNR3mU6tFJPsAMZfrxrTLHOKzG6h+Pp7MjhnhVZyEdsSTHCB8FV0wJ9jba1YWySXRJirBuagKQZOPJDPUQbAyBglWSpCeCoPVSkPGEfInEn2SatiBwKXBHshrBXZGDNGvkpcQ4DPnV3iqVhIJO4oAaYAh8+asz9NmYc/TD0QH60rzXZb9RytR0/y/NxK+/RXc8a5hRKlU8U2MWazLYXnxGIc/aY12sxZ1WmNAClIF5OiptCEuRgRRHDmFeEXxjRUp1D7VJBIIyItES5QojGQGL++DJs58+NYm//WwHowgLPCMR58EIkAEgEzGUVCfTBnBexrvePR4aVmSWTO3kkxq2UG3y/SrEBKcDP8MmfWyBw9zwBeAKY1Uhw7v/RFwnRLFdVkFqNcVIqQfF8x3ZcgCP9HrtA6o9FKc2aM174leABkNXqXnGmNPuryrHNm1FI/ba7YZfaryO7L8cwGCjs4MzITkzkTwZkPBBmcBdHQuJrgrFoEGZxZ9ng51rXkb4eNORvttMaVK1eW/MwLQZ6zcuxvf39/VceSZRl1dXVIp9NVqU2WO45ll58+Z0BQNWc1pDVSHpxFp8b4qqeDOZOi0v9n77vjJLmqq897r6o6Tp7dnc1Ju9Iqh5EECkSTJRAYEwwYg4UAIYIxySZjmw8bZEAEk22CP0AgPoMAkYMEQkIDynG1Oe/kmc5V9d73x6tXVd3T3dNd9XrVK839/aSZnemuuV3xnnfOPRdCY89Zb0Lgh387i8dtXWRbQj7ka6VshASSPSMGc8b9RnyPWbGAXOi2dvVlBX/IMWUEJEGbOo7RBAGvRB9qzEVQFNFFwBB3OHiZo++MXp9FXJhPfOaM+IsyLbyYEVBSPQy7Kp9kAKjj9pypY9aMOfMLxkb5hAB+VLDIXQ9Ht/osE4FFe/iR0nNiFtO3zcifxzRz8K+XlmWNInAd9CIxkoA96/iFvxtDctXsHFpAOAmAGgFjB0gwwlIMIAHrzuM47dXsn8E0x1Sh+c4iQni9VMTvrWQJCqYWsGKAITUrT20h3EdZy5wJIWSfKZM9p8MXD/m/6zuzD6m98yhAyr1FDDt95dZ46moXv33j3MIh2DUhuLzWpGGTdLRV8k8la3Q0SGOZUX2d1S6oKeZMcCl9Z0km7wOesyVJCl8FIhnqiAlFMAJSOJtYFE7eQXooBeEIOYcRAC+4x6MfyJKsMU40AmeLDZyuFzqd/7oNnCmweuGFF+IJT3gC3va2t0XaXrf1nOlk8moB/Yknntj2to5Vz1mr4AzQ03cWlsN2B3OmiqIWZI2u8O3OqUExMNrv/44YJHigaXBrHB2p4Nw1i5/T+YfzYCZZYAktBPxhsJKliphPjSFI2hL4n7+Rsp3XXlDGBd5KsQKuRtaANdC4Iz0Y+uxGAkOcB0XIYguyxT1FyXQmGy9OhIdQx7X2b6nnjHj9HQ2ZvAAsRu45U3Ir7989yfrno+ACvMz9v1kvwjLLyLLGNo0BhCN8x8gwMEmsSMDolfcPYWsaQt0Oc1YDzgiREmefOdNQWFNG0J+q3s4CcMalPE+BXeFKyWX/2dKER/WcxboP1Rim/OT1OVx6SuMp7UIICA+QERL0mZn9pm9MRHl0sBietwgEAAOoYwgi5H6EGjEQCkIJEj0UFeWOGGMfqeveYMApKzl6k81fLxwpzxMCss/LpL7LpmKq3Dg9Z97OtUM6y19dNY8PX1Jd0wT7Ts7uy2xOg3nGNpR5KhDVcxZnzpkT3IcSi4zzUBF2wFTtA4IDLCOve8mcHX/wbAmcxYhGrM8111yDM844A9/5znda3tajEZzVSvYMw8Bvf/tbfPSjH420vW6TNepkOyml+PSnP43R0VGMjY3hz3/+c9vbOlZujW9/+9sBAP/wD/+w6PbUeV2pNH5ILxZhFq8rwJkXrRTW3OZgpnRCBAIbdkBep9wr0pw48ibe+nXvFl0YvYYsDuusrqv8TCFiMGfVhXXKEHjGSQ4mPjyDf72k6JMhhT1F39QitbbxIGbfQVKIaIYgIeZsMSZGuMLv72iYjxqQG2MftePWSJicj9UQnIUAdWzmzNs/dQ0TAJSPllHYUwisEevl6+0fJoSWnrNWIr8z7xe+tYBZAW1ZVMfvqWpV7UFI/XsE8WRgQDxwFvQ+Efz8yhze+dTguch59Y4TXEjQHPjvywHZvSaoQcCNsO14VOqsmjnbNMTx5ZcW8Bcn2lX9Xv7LHRGMP7CFf+/Jbs3CSAXgLGrwUKEPVPecLZjjJ+R1poBibVhpwwdncRw2lby6lYU9/z0JCpakss/LMwTR13Mmv1ZC58vpq9wF4PUV51ZQmbbhFlx5P8oY0rHRkrn0nNQTLDS67SmiwqEANScE/akWrzMEzxvmyYbBA+bMLS71nD3mIlzoX3TRRf73GzZswB133NHWtnRK5KIYktTGsSr0H0lZY1xwpgw4dDFVr3vd6/CGN7whdj6dPmZXXnklnva0p+GEE05YdHthNi9swd9OdBs4I95lHx5orNwWa6VehBDAaPwg5glvdU+HMUAdvLXg79nCc40kflGvwuwz/J8ZcWzQvY+ieiIanUq8zGH2Mn/mU6OgfrEfTUZY1XPW5E8pNoFajZ0agRD4gGhsWb5IBOMYmm/ALbpw5h0MPn4Q1vL6574RAmdx3RrV7rUaPEKEIzyXzyb7xyvSKKK7R3J/WG8L7LQnH2R1mDMAMNIhVkigZcC3MCn5pbCrUDWLr25OXIBX+ALmDIBvPw7E6znz5dUM2DjE8fanlnHzLgM37TRx2enVi2FCyF4q/zIwCRLLE34+wjvfhQZJWhjcUAp865V57JqkOPfqXmweDoCNPWMjuUrWPZI59z6PQfzzi3IOEZGerpVXh3vOahW5QshB6o0WQAwTsKnsZYoz9DlgO1t9h0D2hIwcw5CUwIMa8l6vlAluDKdflU+ZN74obvuHOWwc4sg/XPHnrBFGMHzxEOw5G4QRmH2GL9EXOsbCgGCgBXAmXOE/X4TXl8uSFG7R9Rn8pZ6zx2CoQvY1r3kNPvOZz8Talk5wFqXnrTZUQdypQn/t2rXYt29fSy6NKhRTFQcM6ew560RfXrfk0+wcIoRg69atLW1PB2AMg7OoAE+FDoCviggaOmb5nQWAi4VFmwAIow0LQu5RFG6cFfTwivUiix3C5rK/gxKkVldratIb0hg4tx9APLdGtWKtiqKGi8QEvp1/swgzMbbT/nUS3iVqxXru7jlkt2arTDbyD+elaYtVf/W8Xj5RpZ9hSVqzsOccsAwDTdKGOelgzhSgVsfMMgTm7plDz8k9VQWrlFShqc2kArZUIJK7ZjiflsKT7DU0BPEYGeHwWODMB7AtFMPlo2U5C85a+MeIQfxFHC2FfuhPfOdVeRyZJ1jdX3Mf4AI0EQy/HnzcQLCQxAiEGWLOIkaznryNQxx3v2u2updRAKk18h6UXJlEcqX8njDisyGUi8juiL5rrLd/wszZgjlsAlVjKWrDtEjAnMVhqryvrEXmTIjAmGjowkGvP4+BUD2z6Yh3zOwmioSNQ16NYhDQFPVHewDSmbX/7L4qiX4cgC9UnyAh+IenLF4LCy58IM9JYLhjzzqBu20M9vWRjCVZY4xQhey6detiF43dBs5UoR/H2r8Z8Ni+fTvGx8exfPnytnPSAT7ibgfQw5w1c2tsN44VOIuSU1wgrCJuXjr2UdAvFDqvuVjwPHIKrnQia7JKyjXIm6rmLy3yHCodLXu21fV/X2V2EbfnzPt3I/yheicWY84U+8AQTUbIRdA0HlRHBJXpCkpHAkkyYfLh3sjWX4UCk0xEZ878v7nI6Sy48E0bGuYTMgSJasDhD39VRRfxLKlr+2uI52TZhDlTB5wijqyxun+p6nc1CxBKisq8peaFzJlnDBCjV0huwPvaihGQI+S4gToLD9QIzvk4A3LDzJkKg2EhMIO8RxhZE+kNUjnAkiwY9MyCBYdYhbXaPw0WEVb2CiRq6IB6CzOEEVjewHUaVWIJLJi3WGUIUsetESRw+KsNZhDYqucs4oqDECKQNbbAnPmLAOo4qZEnVC5GOBrAmTrWtcxZ3UNIiZQz0monWyNjgBhET8+Zd7yHMhzPP72FmoEHC1yCBAOxqRX0VIvjs+VsCZzFCZ2F7GOh5ywciUQCw8PDkXLSxZzFAZ7AsZMRthrHyhAkSk66AHXcc1vHPMFgRlWYkiFVfQRCCBR2F+SDvAk7wi01KDM+c9bKvrH65VDcRq9Vq+lmjH4hXmMIonaLPWtXS0xodWHYKFQRaUTMKTyEWoEcQgkq4xW4eQeVGXm9EIuBpVgwlLtRPmYga4zr1qjAmRAChT0FvyDjDsfcvXPgBVeagTQ5tr47Io/BnKl+LQSfjSbZQpaISCa4GeOnwAITAkIQuBGKtaDQr/556XAJ8/fOy4UPyHOqfLQsC7IGzBlJqaI6hhNh6I2tXGfUJDAyrL78M2Q6Ecf5z7/9tMLCCAEjU1+2p5wAZT7xJWntVJWkHrPICBLLpAaRCRF5zAgPSeSARZgzLo11wi6N4TDMYOiziAiGRGiRqJV+5dxDebjz9oLXqvl0XJ3vOmSNLTyelRS+nuSbEBLIGmOMrFDnX7pFU2bBBVgoH+JJ9qkVaPxrXYmPl1gCZzFCpyStW5mzbgEeunMC9MkadYAhHeeQDrDYypyzKDnp2EdA/LzU++PMXfP7hUIPBcpQdTdVNsPwBuE2smWHYs7izKppcUVfuiN6TdyLMWdxhlBzlVZ1z1nxYAmlQyGmikoXwqELmoMh1Z/DhIDTpDeiUXAR2jXeN4RJkGUNmrBnbLmqbUqJ4GImHWFZY1y3RnU+unkXLGPAzcvrzy24MPtNWMOWXJVuUmAowwsrTs9ZjYkLEd4IgwXMGQE10ZRdVMUb87YWCVD7krTQgocrwCvyHFbyu/LhMpycC2qQum6NQLDgIBwRuX8JVWCxhXOQEBi9ps9qVuVjhD9TtHSAQJLGWrklCtRl8QB1HXqFNReRzRzUOdqKyY2KRvfF3k0Z2MpZMio4U/JqL51sqOBfYKUP0RTkMkbghIcsR8kntEik9hEvc8zdM1f39Up+vgAMUcm8cmUIEqOMUfeh562Xo26ueHxjczPCCDKb0rJHsc45IkwdPWfqj7Uo++TCXwwSxJPqE3k+Bwsgxyc4W+o5ixE6JWndBs4olau1QojIhaxucKaTqYq7HV35PFZkjTr2EdAdzFndOWeEVP1b2J47GgEGzu1vWIQIb4CwiDVINMScNXsOeQUaoY3BmWKFaAxWqLYoYkTAnrWla2WoKJJudmRRUOkX+xHt/d2QzJIQgvzugu8SafSacHIuSgdKMLKsoaypYT4x3RqpN/xVOAJmj+yVyD+ch9FrILEiIVfIbd60B0sBakOI6G6NNQYcQggwi8KelzO5hC2kxTkg0UAzN0tlCCICcNbudMJ6zBmvSAMZ4cj9wStcgmxCQJMUpmedvaDnzAqKtKir6IFNPBbFZup+ld2SqQtUaIr6fYJxClm15WbmNSqcvNvwmBEWAFvEGdTdgO2sF6VDJRBW7V4bDsbkHC9TuJGZIV6zSBRmzmpljW7ehZFtXA6bJnxZI7c5aARew3FF8C5vH7llt+6zoXykDJZkkv2reeYlllkQNvcHh8da2PPO1VOGbOx65YwPYHvrjtKQM+msIavuqBESGvYemaFWizIt7l7hBswZJ7JnUM6BC541xys4W2LOYoTOQlYZFXQLOAPiF9Y6WSEd+YRzAoD169dryadbZITHagh1O6EbUHcDOFPFi6q7BBeyyKKy8AekLI0mKUDlzKyGq8keOEOsB6z8QkhzPxDBvVlQTVb/VZ5xgEetRI5SoHSoDGLVSGIoATEWP6ZhGWEkK/3aQ83lsTKyBgYvGABNUvSe3oveM/oWZfGAgIlhMaz0a+eucZtL4woh4JY5hCOQ2ZiWJgBMDuZtuK0QoI4+V8z7qn7gAjAInJyD8pEySofLEiRCeIYNTT6bEeQDIBKbF/ScBT/jNpd24hZF+VAJue05yeAZBKnVKaRXysqyFn8RQoMer4juiH5fZwv3n+K+knRfbXDNE0J8IBBL1ugzVYu/ljscZl998CF7CD12MY7reIvMGS9zuHkHNNH4vmgYBK4ajF2JdlLXssF0PDCBMkKFu1Nw5f2pyX6UskYP5EcFi34PXHAeCVuA1Lm27XnPCIiRBXkZWQPZrVnfYTOq7BMI34cIeiTpBF7mePYJJbz4rAo+96J8+NUAJeg9paf+goAGaWztrLzFori/BHjHw2f9KfEdJQEAfGGf6vEQS8xZjOi2nrNOgDPbtmODs24Bi+GcTNPE//7v/8bKR6dkT2efYLeARd05AV0CzpQ0xbv5VyYrssChBNzrqeJlDpZmcAvN/45abYTNpZ1zlM/XqpyIw8+z0Z/RMqPKny8U9JzRFIWRkcyQU3BhpJlkzhZxagTCTFU0a3Z/3hGRn5ulKLgtGRVqULC0gcSQhcSK1vidsJV+VDdCtfup0qRxaSBROiSLisp0xf/cjfpgVIQNSsqaes4El6vkzCJgKQpqynO6MmEjtTbZlIEKuzUC0WSNVeYSXnJuwQUbkn2B9rQth7sbkgU2e43AUrs2NS4H57q2C7ckgL7280EbzJlbdGH2GVUscW0osBinkA0Y/OYJCS5gZhhSa+rPEgxb+wuuwxCk+evcoguWNQI3vTphhJiqyLJGxZx5N7tE6BlkMqCwtwhecuXgYoM0XdhiRtBzFpXJc2vui4Anf69jiEQtApZicnRFA2bUHxwey1TG+xo6V/M78hBC4DN/Vav9FE0BbNU4hogn0WKmMrVhZFiQEwm+EurdhygkGj4O3fSXmLMYobOQVTbxOgYsdwsY0inZ05EPEOyja6+9Flu2bOmafLqNOetWKWrc0CNrVNsKyS1N6RBFTCJXY2dtWfAsxgqpVdOoVT4QMGc0+IdTcJHbnvNfwh0umQaDNi0wA9YDMXrOFBiS/2ZEDgdNrU6CV1wUdkl2UYKjxR/CYaaqmeVzo1CnjwDk8WBUFiMK9KVoS3JGFWGwGJc5o9S7Tyo2RcgeimbjF5rlY0ddRa9xaxRcLoQQiwFMyvDcoiub7Q3qW143zSdGz1mVjBCAk3PkIgiTdt4AQA0ABoU5aElDHrVQXrMLkquSARiKWFiH8wlf0vVmKFEmz9lmckMlZWu2HxeLuvLqUL5CCHCbozJRgTlgNVz4IZQEpgoaDEEWY/J4hUtX1CbPGMMI5iSKiNb1wUgPGanQ2gslgDNvQ3BIua7ZXBJumAgMQSL2nLnesRahwyAgFhhG8QoHL0vlBUuzhgxswJzFNwQJC2WEQP3nFiFN+/L83s4451CbskaaoMgulzVG0fHe5A0SD7tKxuqDe4RiCZzFCJ2FbLc5/wH6ZI3dkg+g1/Ci26z0u/kc6hbmTI8hSE0RQghogsDsNwFGYM/aSG/MwByyFl0ApGE5UdTnRzgfbxtu3qmSuwjXMwNZjDlj8SV7vpmDV1kTCAhXwBw0QVMMhAHzD+aklObU3kW3pwCjEdHwQrm2+XPXPMmLenAPXzzUtN+kNsIGJbENQfzj4PV0MWUg03zWWtW2fCYvzhBq+ZX7UjIOlmFgSdnDwVLMl2CRBGlagFXNRYvssBnIm+xpG4U9BW94OvUH4cKgIAToO60XyZUJnzmrPSbWkOX3NrmlqBakXjpUrmzYszYqUxXkdy0cZk8sBmLVMXIIhWKNdLg11vs7uYdyKO0vIfdgDrzC0T/anC70x1nwOI6WamONP7dwBcrj3jDjZjJCFmKqIhbWwSIRwT+eMwMr5AyZMoVntgGASmDRjA02LQQGJVHBWc19EfCeZ2QhODP7TfSe0oPe03rrzsoDAuYsaj4AAoOSUA6EYsHCEC9zCIc3Z85CxjvR2df6ssZ695vKtFwAXbZK1j3zFQmwCQmYwMAUJFo+j2QsyRpjhE5Jmo4V/W4DQ93MwujoqdKZT7eMP+jmYwZ0h6wxKKwp5u6dQ3IkCWJSpNelYE/b4LZA5oQ03CJvKm0CACNkVhA5/H4YVD0Uq1gpLkFFvQdvOAIbdA4hCDgXVSMCWgn1IFRyIlHyjBsMCmZRiBL32KLW+mXCMsIoQ6i5OvVI8N9i9vRN81FMXgzTFBXUk90IF6ApBmvIklLYRVapq/JRbGdE2SeAgDnz/lk+UkZmcxpOzvFX8bkjwCyC5PKEPy+rYTAAruw7i9RzFpLICUdIZjrBYA2aqEwBzJJDp92i6ztsqtEW9WrsYI5XROaspkewMlnxHDbrGCNQuejSFJz5LEOkdLxUvIW9UAr2jO0Zn0hWj3gz1ZQBSaNgIXAWtbL2e+CanLZybh/1nVobhcECMBTV8EL1ePVaAn99ag7UYPjNi46g7AIJI4GKYleIp3hoAs6qwWLEfHx5dfAzAXlKKRmx+iHLGDD7TFiDjXemL2uMZaUvv1aNxiALe0pzD+fBUouw+crBNY4KxB/HEPwhe85GcW8RvaGFPCEEKhNlJJZZ6MvIn+XBYA4web54plfEIEAZ8RjhRyiWmLMY0QmnvThFo845Z+GcorIenTIE6ZaeqqV8jk1O3ebWGPScyeZstygdt6whSzYiE2lvTg05ELNpPuGiKCZzFn6g8TKvKg4FlwWuEB5j1MisIDTwGYho5uDPF5KxdUD2B/kDp71+AOGKxfvkUN0HV4kCzmrmHRHiAdWI6zPUz4fHYM4CttOesZFcmZRsmWcB7QPpVrZVtX+i5SN8WaMH9CxZ0LM0AzG9c0YIUIuBJllDlz0/p5BjY5yeMyUBo4aUn0rzlh7QpGRgM1syPkBqxJzJ93uFflSWoaafysgaAJU51YYa09Ds3PYNODT0nIUXT0qHSiiPl4OBxSwAIM0isUxq/ogGSZq6t5QOleoylSzDPOlu400xBrgxe85ESKorhLxONvW7OHmFV5dQyHsRlTPOmtncMIpQz1lUmaWXV5g5U/8LbVK4Aolhc1EXTqHOaQ2GIJSGn7FYAMLkLLxFzmn1LIvDnLnBMfN/ZosFQF44AuaAieyJPejPyp/lDVMOxFagO5Tv8ejYuATOYoTOQlYVjd3EesQtZLu556xbZITdZqWve86ZbofNuHlpZc6oXOF08q6UnqiHrJAyMGIQf4W8YT5KshJjUKbaPYoMFq6APe/4DzRe5h5zBhB/Cmr9bdFQoQ8gUrGvHoQGA644M493jc7KOVTef4QR0IR0Jqw3hLY2wuAjClis6vVQzJlJmw4HbzWf6D1n8itlBNwRSIwkZFFhSNAoZV8tMmehHq/ossZqQM1ScnFh+OIhb16fJ421mszsq5MTRcSeM7dmodGUwAzCW/hIUtAkQ2ZDOgCC6lKqc0wU2xl5gDAP8iFE/jFqUjlzLXTtzt0zBzAKa9Bquj2qxRDE21Z4CC8jvnRYDgxvXlCrMFS6MeTVweKw/Lc968CeqVmUE7L/tO78rnA+DL47ohuTOVOmMgo8qOtXgjO5UEUshtSG+oYpgLL2jydrdNRIjyqgLHV4whUBMOFC3h8XCaEAixt9Nl1wDoV+6AHWqkVR6p1nTUC+P/swxjmtrt3ac7ZW2ilsAavfRHZLBn0eOJvJhd+gVnWC1x9vsQTOYkQnWI9ulDVGzanbZJa6c+o2pqobe87i5lT70NmwYUOsfLSAM19O5LmcCflgYkkG5vWesTSD2W/C7G2uHFfgLM7wV7VyaniOY8opkppy/+V25OGWOYjJwLxZUA0LIxr6EhEMKalWNiHwvovm0ZeUrJ0vJ2QELE1BTLbo6jAQSMAMEVHWWNNzBkJADbRkRlIvSMgdMSqmDno9AHAB5g0rljbQ0vq9VZKYamDOVEEuQLCxz6lixqzlCSRHEhACsAYsJFct7moZZs7sKOBM5aNqLEbALApz0PSHurNUNYPnM2f1ZI0xB9LWOhFSBtAkk4V16A+qOYJ9pzfvpfRlljEKWVoDhtR2jQzz2A+PPWiBIa6WNUYMbx85844c6u6d0/acE0gThdxvRq8Bs9dsnA+N79YYZs4kBgqkbk7O8Y0jlMlLz9Zsw20RQuB457RTjggW7WrpsPxeXue57TkU90kzOLUIsmiw0P6Jeh+qcfwUQvg9yfP35YJFEirNN2gdZ0kV/qKNw6Nb1zv1pbFh5syesVE6XJajRwCfOZsNgTN/ZIy6jyz1nD224tHOnIXBkGU1Xwk81vlEjU6AoW6z0u+WnrxwTnGlsQDwz//8z3jpS18aKx/1ueIZgsiv1OvfogmCzMY0WJph6AnDfqFtZA30n9PfdFuGz5xFTscfzGwyAXABXuEw0kw+mLiU7PCSC2vIkn0MQ1bD3htCSFW/UJShxjwkkatMVwKramXl7zGKhLVmeqHAGQVHlJFHVXPOiCerNCj6R/vb3xhChiAQkYZiAwtZD1V8EEP2UrUyYsDfVhicRbXS94qwU1fY+PgLxsHSll/Y9J3aC3vGlo32ZmvHzO9dhECU21Ew5yxgO5Jrkn5BTy0qi8WQrLApcxaSNUa609Za6VM5+JpQoHSwhNRaybrQBKlmIRoE0eBs50vSQosMhBEYaQPOnOM1NLV2LjGvsCZxrPR9eTUkQ5aQqKd0sAjhAr2n9MjXUKD/7L7m+dCQW2NE5kyEmDMh5DkpPJBW3F/yehU94N7CcVAySzemtb8IXT/qO2pSuEVv4DZHS+y0Ys7igDM1i1BJY505B8Sk4GUXLEUlWPPmm9Ek9Qfe192WepbFYs68Z0ctc2bK3jpqUpSOlOXwaS+XHq/9dT7szaN6CdXi35Jb42MrHu3MWVzA2I1DqHXK9nTk022yxm5jzsJg8T3veU9s0Kg+VzzmTG1LrjiyBPVXHs1eo6UHqwpDvdaN/oBV7oimQcBtjuKBoiw8vL4umqBwixyJlQmkN6Rg9hpNZVdx54r5D2ciiwzCqCxGufAMCjzXvWRr+0k527GozFm4j8HLKbM5DZaMdi6FwVBc5kxa6QeAjzIipYNtpOazVIjGUgHwFwdGejjSVBqAmP0Bs6EGFbfMNsbuOQutoKv+kRDIoAmKzMa073bqvxb1yR+1f6OaJwRDqAEQwOgxfCdLgAAcmL9vXi4ktDIeQgNzFiwSVTN3NEWDXjManK/NwgdnIjqD7y8wUa+f1KQSxyaZX+grBkv91yikrNGTZUeVNYZPBC78nilC4S0YScBGzdYkxFw9OyJKYxWoE7V/ypOfggD5h3IQaJHVV6/RImtUqgI5O8zIMJh9JvwBzlTOXWvecyZvWsTl0funa8YxuEUXwpG9rvkdBZWiPI7eAoepRoX6YBw+slGLRHyp5+yxFZ1wa+ymwrrbZI3d6tbYLbLGbpNZ6shJd9+inp6zYLWRpRhYj7Go8UejUEUjEdHBmfoohiFgzzqwliV8wwv5YKOSTesxWmQ94snkwivEhABmD0P2hIxnBOCBWYugd1tPS9urljW2n48vkfNa/o00Q2p14/6SxYKErPR1zDmDQLAiTSABB2s+FLdqWzoMQdTf8s6P7JZsFTiTkia0zOgFPWcRZY0hGaEvYwyt2g+eP4Dk6mTVe5oZgsSVNQbMmbevk1JWCSqNUwSX1xlNUvSesvh5rWMItWI9wsCCmoA1YEow7QHJVsCZoaH3VZ1ElBAIW3hsuZTssiT1GZ5WZJYGC7BeZFljDRiSgFWyloQCMILet1bk1W7MuWLqPi1A4Mx7FypRvW8SkAlKQCBaG+3hm8rEHwtDmX+TlM6enhxWuiJWQA2C7AmZpttSi2hxTGX8uX/eOZ3fWUB5vAwj1B6gzGSId3syvV/5zwbiLViprwBE9JLoEYuW7rSjo6P/Njo6etPo6OjXR0dHFwiFR0dH3zU6OjqmP73ujm5za+w2GeGSIUjr+egA+I/GnrNuM7kBqgtrwoDhi4aQGG5f9gsATDWnxzEE8R6Gltf/Zg2aclWREuR35X3nKtpiYa1WUSMbTISZKkqQXJtCclUSZp8pDSayDEbGgLW8tX3mW9dHdWusdQCLeampQs5AdLdGdSQolYW+X4z5piCtDegGqsFZXCv9WibPz9ekft9gSzkpcBaTORPEYzpStIqRptbCUQiWKtLq/D2/xyuiMUCtlT61KHpP7gE1qByGzQGakMxCet0iYwaAYL5hHCv9ugOESXCsIAv/xcxJgIA5ozFkjSTUn8QdDnhmNwrICi7kplsxKGGB/C8qOOMhJg8UIPDMdpJyCL0aUtxzUha9py0OqH3mTMNQ7MKe6vl4hFE5H88boWH0LA7OfEOQOD1nvvOwt685/FEHoMQ3iGEpBmuRZ5xaPCExVCBh4x1ASiXVfENzwIMenhujuoZqwZnZa6LvjD65OOEPV4858+QRiEWf1qOjo2cAWD02NnYxgAcAvLDm9z0ATutMet0d3dZz1m1gqNtYGN05dWs+3QTOdPWcdRU4875SKlp+kDbMxwzAWWQ1kc+cATRF0XdaL4YuGoQ1YIIlmdfMTZo2c4cjPDcrkltj2JadEqTXV7NUwxcPYeDc/ghDlqPKGsMbI/6g7aihQAKNwZz5pAclMHpC1bXHFA2cN1DFXDWL8PgDxyXRJE6h+ULKCr42zB4DVqs5xXVrrJERmgMWzMHmf9vyfl2v786fKxZ1BlMoH0IAGASJFQkpzSUEue05aXLT4qmlxxDE++rt6/LhEkSFS5ZTsUSsNSbPSAT3oegjPeQXQiUIJoxCuAJGnwlzwPKYs9bGZ1QxZ5FNXAIwpHpNwSSLByaPFfHu39bA4gBW9UFFlcip93HPhMRPzpC5yMHz3uJaKyNG1DUaAwyR0H1IJicZYLWgJ1zhs8OL5aTui8Tl0QeZhwA+IBfmEiMJZE7I+LJlQgGwwAl5AXMG2V4QZo0frVb6FwD4mff9TwBcWPP7NwP4tM6kjpfQWTiqbQghIpsV6O7x0tVz1i1gUXdOj0Yr/W47Zt0IzlQQHde9WuGOyJy5rvALa8NztGNZw1+BVYNDWYLWLbjrJxWPOQtbWJOQtERFK4VHVToxmSF/xVrdF2MeNi1W+iG3xrDjYGZTGr2n9lRJ+BbdVkiGCkSdTQc/H8JoXZZ1+CnDSK5KLvh5s5yiDsYWIRkhoQSptalFmV/FnNVbUAiGUEcs9MMGJYT4gGhgtB+gAMt6fZytLjgoWWMMl9agb1HJ7YSUDpvetU6Jb8CxWPjgLGpVDQTaWCr3s6Hmz5kENEElSyjQEoA1WOCuGnWOlw+ilMRNSQiJAq5tzjpUstmIj45A1hjUaALSsVdJd0mS+uYyi0bIuj7qYaMh9rUyVZED5xMKLFIU9xVRPlKCNWgu2kut7lk0DhDiwTkEQPbAZQ0YWRYAWnX9qT7vRow5Cc85i57SIxWtLPkOADjkfT8LYFD9YnR0tA/AaWNjY/8yOjpa982jo6NXALgCAK666io87WlPi5VwN0UuJ707Z2ZmcODAgdjbY4zBdV3s3bvXL/zbifHxcQBApVLRko8qjA8fPoyRkZG2t3nkyBEAkjXRkc/s7CwAYH5+PvL2SqUSAGBiYiJ2TqrAj/P5ikVpnzs5ORk7n3w+DyDe8Z+amgIAFAoFLcdM7e/JyclI+2l+fh6AfJjpyGdubs7/GnV7qoBxN5eRHxYoHShGzidXcJEAQIjA4fFDbUvuiuXgLc5wBfbJFRyekLdrd8AFz3LYxAEgcHT+KEhh8T8gvP4DJgQOHBzHSLY91jM3n0MvAGEBzukOjsweAclFXzDKFXNePhylimj7uI2PB7JG9zQb88k5FA7kI+cz73k2G0KgVKrgwIHJtrehVqydzWXklgkUa8+hNj5iebIMIBgcvnvvIWSS7RVIjiOPMe914Z5m4/DUYWC6rU1UBYdnvASBI0cnceBAua33FwvyviFSHO5ZDsYLR0EOND+HZqYNAMuQL9g4cGCi6ncVUgEA5I08qN3+vaR8yMs/BbhnOZixZjB3QN5LnA0OsAEosDz4cGvn51xZvpenXRw8eLCtXFT496ETbOSW5SD6ORzqYNKZgLtROv8RRlrafqFkYMjb5tHcUZBS+9ereh6KZQKVE8twkjbcXo5cUl4vPCPhVtkgC8/3BfkQfx5YqbcY6V6dm8sjCUCYAM7jKCQKEK6AY9ngWe4D6Va3rWTRdtKO9Cw7esTbP5RAnM+RS85DDAg4RgVihWQUhSswbUxj5sDMottzvDu/MHikZwcQvIVvc1EaKQGuwFzWhbuZg7AKxAAHKDCfnUd+kXtmCVLOSznHeP4oSLn9hCoVeZ26livP6QGOUm8RR2ZKsLfasFkFok8ABJi0J0AOEJQqALAStl197dkbHSDrXSPc1VI/6I7Vq1c3/F0r4GwGgBra0QdgKvS7twD4VLM3j42NfQHAF7x/Hn/cYpNIJuUq4vDwcNOd3GoocDYyMuJvu50YGBgAAKTTaS35pNNpf7umaba9zUxGNpD29/dryWdkZAQAIuWiQjEnIyMjsXNSK55CCKxatSoSY6lA+PLly2Pno4CQ67qRt9XTIyUwvb29Wo5Zf38/ACCbzUY6bjMzMwAkc6Yjn6GhIQBAKpWKvD2KhwAAyb0JZNNZDG4diJ7PMgk+qSMwMjzStoPg1JwAERIcWNMJJHYksfK58jqZumUK9pwD4Qr0nJRFatXi7AMAPGhshwMHVAj0DyzD6tXtndeZpEddOATmvSZGnrMilvSzMFDEEYxL63re/nmwt7+IaciVeOMeEz2n9aJvS2tmJPVicsUUdmI3KASYYUU6jygeBACkDqUwuLoP2dWNZywtFkVexAPY7jNny5avxEBPe8fMIEfl1zyD+YCJlZetXHSAerPYbu5ABTaoEOjrH2r7HEom9gAAaIWC3WVi+XOWLyo9myoJAAICC+8zh5JHMId5JOeTke5DuWIeD2A7SIWA3WFg4IJBZFfL59vRe8dBUwyJYRPF/SUsP3nZotsjfRR7sA9klmDVylVts8kAQMV2AIC110Q6lUZl1obVZ6Dv7D7M7phF+WgF1pCF5acsns+KEVkUUy6wLLM8Ug8tExIQs2kGdoeB4ScOYXJsCj0ny2utuLcIUILEsImBRe6Z5YoAx2H5+SYSka6xlLUfAEBcAnIbQ/qENNyCi+RIEqWDRRCLARBYcfLylrZH2E75dd6IdA6NP5zDQXjM2W0M6c1p8KILljVQmarA7DNRmapg+dOWNZ0BpyKR8BaFygQjQyNVDHyrQcT9AABrh4Fk0URlooKBiwaQ31uA2W9i9o5ZsB4DgxcOILO6uSFIX68DFwATwJA1hNTK9k2XTOItqswSpPamUB6vYOVlIyAmwaFbD6MyaYNaDGa/gaELBpFYnpDqEQjYLqk6JofvOgJa8djbCtFSPxzLaOXuezOAv/C+fwaA34d+dwKA94yOjv4EwJbR0dF3a86vq6NTVvHd4o4YV9ZYKMimVwXy4ka3zfEK2wFHlabolDV2m5slEHyuqFLd7nZrRGyJnN9zJp8vbUexHEohpLEHpEEB9Wz+MxszLRuChHu8ovWchfLRsI/UNUYEYg2hBgAjyxYdDL5oPmo2Geo7Ay4WcnaQjOyGFDIbmxc9reZjeF06UWad+TJCSqGGF+vIKao0NphRhbrS2Hrh95zV+Xs0PBMqStTIrUj4FPJ6OqWZS2ubC3phEMPMwfvzlCD3cF72mZnUtxlnKdbyuArLK+yla2zUhiEvLyo/n9lvelb1Xv8S925yLZxbYVlj1BlVYTmkOoeIQWENmp4tfJsSa++lUfuXuC9rlDJL4QqQBPX78FiK+SMQWgn/fh6jT9BXD3qDp2mC+mZS1JL3AmYS0Bb6dE0Gn+1ExMeruu4JAfIP56Xs1JOgQkjzJGIQGFlDukoCYEzmLoQn8w9FIGeOls8jGYvu8bGxsTsAHBkdHb0JwCkArhsdHf2897tXjI2NPXNsbOyZALaPjY39a0ez7bLoVOHYLf05ccFiN4MzXfsoLvjoRN8i57wr8glvp9sWHKLmEwbhdJFZPS3lY8Xr9ShVQv1LFFVW572n9sDsba25PByx+4V8d0SvYI27eBWa41WsRDEECfLpPaMPyZFErHRUz0xUQxDXDXo9zH4rPhAKGbgA0XrOfMMLCmkKEoHJqcrJP4eiGoJU93i1Itnye87qgNPYPWc1BiXhflM1OBxAS8ADCBmUxHBpVcdbjqcgICZB/9l9MPtM9J3RC5pi6GlxXIWlZgnGMgRR9yHvnub1vRGTghjePhStteUxRnwZYdShzyLca0oBOaYCSK1LoffU3mAAdYuhXhu55yyUDyFyfpuRZnIwNgV6Ts76DpKthKHINRH9PKpaaCRef6Dp9eRZ1B890Mr1ZxoAV1LLmCMr/PsQI37vGKHEc/6k6D+nr2rcQD1TEDXTDohhBPQIRkvrPGNjY2+v+dFr67ymftPZozi6DQx1m5mDbnDWjbPgKKVwXbcrTFwIITAMA47jwHEcWFb70pROgaFuAYvxwRn8+UJqVS9WPmr4MI82uLNih56bpHp4MTWl/Xgrc47CUTXnLA4LQyD3T1zmzPtMVAC5YoTrxEtHECCxzGprSHj9hAKwGKUmst1Qb7sGgjrMdAL1maNFo5o4ix+hOWdRwJnjBEUjoa3dH5syZ8qAIypzJkJgMVT0AYDZb8LqNz2mqLWdF3bak0xqBFmj95UxgFgM1AxGMpi9pjd+oLXtWhZQgTwvIxf6ocI6MNxQDpaSIYJoA/h7x9yJOPQ5MHFRmyOgaUMW+VlvllcbJ7vKO+r4DJ85IwAoQelgCam1KfCK7edCGGn5fmmETWUij/TwzKQMSBBkUn/uGzXhA7ZWnk2G4TFnIr7DppqzpmbTyWQJiCWfabUGH6Yhn4W2C/gNQSR4lj1aDUGWokF0m4yw26z0OwXOohb64fd2C3PWiWPWTeCsm5hFHfm4PDBziDsvCwiGv9KINWPFCVY/5WDV6qQII+2Ds7B1fRTWI8RUyeIsJgsTYs7yEYwKAjkRtBwz3+ZZBA5s7YQE1NUsQ6x8Qu6RAFCuRNgID/LRklNM9tX1FgUo8/Z3K8xZEyt9NfcrqvNflVS3hsnrP7PP/7718QchhiGqrFE57XlgsXbUwNBFgy2Dj4RFMAsCBhEDwMovioUBAViaglcEaJJAcKA8Xl4wPLzh5pQZYURLVP+YeZK97IkZf1aWkg+mWswFCLliRgUeYcdPLmANWkguT4CXXbgl4oOJVgG+oRaZOBD1JPKt9D0gpCSooIrdlYCtFbClkzmj3nWmhkkDCvRLuaXZXw1dTG+Rq/peQ3zVBY5DK/0lcBYjHu3MWbf1nMUtrMPv7ZZiXzlQplLtN8/WC9M0USqVukYa223gLC5zVq4Eq41SyhSvkDVCM7Oi9C1W7GChlSbZggG4mc1ppNa0Zy7kz82KypyFihBqtiZJaxpKJgWBfKn984CHiyINQUKsUBSSoeKEdomG07rWSj/ObDrKoAfAhvZRlHwcNwAeYUvsZqEKtE5Y6fvglbTO5DULXwYZmcgT/vFWPV59p/VWvaadHC0D4ESaOUSWWopqdEYIgTVoyRlZBHDmHRh9JtxSa88Clb9jR9vXvIrBl4tUqk+LJRkGzhtoa2QF9arl2HPOANk/lWZIrEigPF4GtVx/YaRlWaNiRYUIhsK1GeFFIilDlUoLBYIIA6hBWgJblkHAVUpRmSr1PjU4nIVksh5wZcmFoz6MOuBMMbhAdCnqIxlL4CxGdBsY6jawqGzidYOzOGYO6r3dAD5KpRLuv/9+UEqxbds2Lfl021wxXeBM9+y+yH2UYUdwg0Rvnlf5+IYg0bZjO8EDliUpMhurrzVq0rZd95TUjkUdQu33VsA3KIgTKh8igFwU5sw79SRzpuE88sFiDOaspj8nTigwTXUxZ63Ow2uWky9FFZHyUcwZU7LYmMyZ3+OlgYVRxX6sUGAxohGQUyWNJVVfo4RlAi4ITIjo5gkhQxAlZx4Y7QcAFA8UwW0Os8cAWu3/UcwZFxC8teHVVelUzaZb+AxpB5gBwf6NO+fMl0V7C3PpjWkklic85gwtL9iY4Z6zCM8PwQWUqpoyCfAJkXPFBs7phz1jezPzWus5s8wQcxYT4FPVD8hCzJmaCVrnPK/bc0ZC99fjkDnTU4E9RqNb3Rp15/NoZM66wY1wz549cF0XGzdu7BrTlG4DZ93m1lgshxrxNYCzQNYYTd5UcQJJpI5CHwhYBgqBYnvjqWT4LAORPS+xW7wCmWU+Qs+ZduYsZAgSiTmriGCXaLgN0RpDkHIEthOhc6gVZ7bFImDOouWjzBOYctRrxa2xyRDquD1n4T5KoqEvTzFncgh1++8PM/i+7DPGYbNUvxAAHkXLDASGIAyoNQIihIBZFMQirTNP3vvdiI6WYcdPhORxUUMds7hDsYUnuFDgzMgYEpwBbfXBBbLGaMwZtz2mnVAwJllFad5CJNvJpNFNZnMG6fWL1ycJKwTO4g579655Etof1IBvyFMb9cAZQoOqhQfwj6dYAmcx4tHu1thtssZHW8+ZYhbVPDgdocCZbUep0PQzi3HBULfJGsNDnwmlkRuxVVSBswgR7l/SIUcDqmWN3rzltsIvQijkAz9uJauYASEiMWcVxdwQPfgssNKP5tZYDjtsamHygi9EiGjgTAFqJkBaNJFoFuGes1IE5kwZgjBPVtWaw5/XzsPrWGr7zFlcWSPxJVdxwu8DjZhOqVLNvkor9OjHLVxYu+VoNzXivY3SOsfLk8kxi7U+89DbBnejSb6D8RDecYsrRfXSdp14YEh4TF6tWUs7/WYAYIZkjVEeH7wkn4EOkXY0hALDFw8FMkKDeLb1rKW8LCska4zojihsdR9aKPGUKgzUdURV4CxsPkQoCQyXeDxn1EcilsBZjOi2wrFbZY1RBmrXi0dbz1m5LGkJXfsHiD/rrJv2Tyfy0cKc+T1niG477YV6wLKIzJntBvnEllp5EZ5RNVeIUBSFVqyHLhqMn48y4ABQLNOquWWtRKUSAq86dpHv1hjNua1cDhnAxLTRl+kEYIEgIjhTJmkWhdnXmqlF05xC51DZbv8ccr3bF6UesGrh8ieE+NLGWhMSVQjzCLkA9QxB4kp1Q6xHhMq6VAncOaHYxRhhGfCt61XR3naEpbo16RAKkARF7+k96D+7r86b64R3DrmcRAJDwTEj0HDIArYzoozQqah5DN72alxjqUUxdGHr90tDAXwORKHw7bw8zjah0uyk9pgpM6kWd1wqGQb4UbWfSv3l/Re6Pw5fPCSZxTo3cd8QJPxnQ7LGOMY7j1QsgbMY0W3W9d2WT8Vbso7iGlgvdPScdRP4KJVKAIBEIt7cpXB06zHrFnAWN5/anrOow8dVsBA4i2oIUtXroSHCrMdcPsIGXM1g0duO4X3Qdg9duaKRpUIILEacc1apCJ85o5oBNRXRes6UDTpNMvSf1WLx3Gx7NDiHShGksUrWaPi9MK3tp0bSRhKSEUYKEQL4NP61plb0o/aclW34TnuEtD8uozbC/UJuMdo+ImFZY00BbWQNWAMmWJK1fh0qmSWPph6vstLXsDCjxpSoeW3thl0K5NVq4HNttAOyTfWIFtGs9CsF+SbXy6cWhCl5I23x3EolSSCNjTj+QD07qOcYWcvYsUx95rWerHHw/IFgH0c8Zo9kLBmCxIhuW9XvNpmlktaZZvyVWEAPc9ZNhiCdYM7igjPFdupyj3y0GYIUQ6wHNRe6RrUbpkHAIVfJIj1gQ26NOlgYICgcKQTmCu2/X0lahG7gocBZmw9ZW4GVFo0lFo2QtX+UGV5Vx0wXoKYEAsJjqiJswDfa01PBhI9ZpJ4zb78yQ7nItfa+RqYgJMwyRAh/CLUnH4zd3xk2Koiwy0vhnjMWyDajRlXPWTHi4icPFhxqb9cszbDsycNtbc532uMiEjPkv4eQlscxNAtmqGdZRAl6mcOClweRM7vihFJdEIFI6FWBM4dQ1MFmMLIGlj15uOW5kIkQwOflePcRf1ZezXWmDGZqox44k26TalHm+JM1LoGzGNFtMsJHO3P2aOs56yRzFrXnrFvBWbcsgMieM3mTz2zKILky3rEzGCDk6NdI/TCVkFujJvwamzkjyjJa0zELeqqiMWdK1qhp91SxVFFmeJUrwQfQZuISOmZRwJDwWQ89xyxuPqpv0TCA7KaMBuZMT88ZCECs+I6WvtwqokSuWA6ZypCAgY8ayq0RiNFzpnaRsVAiF2l7/tBnEs0QJCRFrTUoiRKq5ywOcybBGfGOWbxrzQg5/UZa2Ct6zJm6B9VZ3GsVmAFe36LqOYsqa/R7X6WucfCCgapfNzqGdQ1BENyHELFt4JGMJVljjOjUqn63yRqjFrKdYs50yBq7wa1xiTlbPLrSrdH7niZI2zb1tWGw4IEWqW/ACYoi3TJCAmA2AjgTtmbmLNTjBURhzmqMAeLmE+rvijIHTsmbXKKniAVQ3XMWRdboM2eaEgrNXotiCKIc8QyLILW29XtRQ+ZMraDHHEJNqHTXMzLx1rXVOSQiOu2VC8E5JF1R4zL4AXPmRuw58+/VjGiRWIeZsyishwLiRBm4xMyJ+bJGARGh0re9+W7CM/6IM/oAAKxEAM6iIA87xJyBoG4vVzsRZs7ciLJGqhZPlNNiiympOWe1MnN/H0d0/HwkYwmcxQjdheOjjTlT4Kxb+pfC7320MmdxDUF0g7NuM7mJPeesFNzhdbAejMWbDVNxQvIm3YYgQkRihlTfgNAms/TyicqceYW6vp6zACxGmQOnGvEdTSxVVU4imnW96jmL27sU5CO/RrXSV7OwjDYZqobMWRgMRQiujAooQf85Gnry1LXBozFnJa+wVoAqrkSOEN/zFU5E5kwNEE6Ywnd8jRXeOe1GZKqqxh9ocARRTJeIWOi7pQB46ABnRmjOWRSAb5cVc0b9Xso4kbSC81FUoj1fqbrODGXi0to+Uo++2mdDuNf0eJM1LoGzGNFt1vXdxuQpWWM39Zx1EzhbYs4Wj246XgAwNevlBWjREUrmzHuARJE12sHiItFDBlf1C0XaTR5zxnWxMLWyxojMWaMZOZHziSjZq+S8FWumYcyAF+FjFoXNM7zmOZLUL2uMYggCZaXfLjhrxJzR6NcYAHCvkA3PXYoVoflLkZizopIOS3aBahgcLrzPZZei7SPi3SyshIaePIRkjW7UIctqQ/BY83j5qHORRzRxUW6NgtHACTFGWFWyxgjMWY2sMS7bWdVzFpE58xeJzPbAtCoPFhg0haz0l5izx1B0Cpx1SyHbbbLGpZ6zxWOp56x5xGXO9h9W+iY9RbXsOZMhIqRkOwiGYutqOlOGIBH78AO3Rs2FfkTmTDc4C2Sfou5MrUXzKSrmjGmTNarCikUEjEytWMdkYPx8YhqCUI85MxMRmbPav8nigTOhziFd/VR+L0w0Nq9cCoEz1Hf+azd8WWNk1kPmlEgSEA1uBlWGIHHcGimRjoNxwZk/my5aj5dvpe8Bs7jMmenLGqONiKh49yFXuaXEfH4kQsxZlHyEEKCKYDBoW+mEJ1OEg9Cg1zSmsfIxjyVwFiM6Veh3mwSsWwxBdPScPdrdGmP3VHUpOOsWt8YDh5U0RQ8+M0KyxihIqEq+pYk5o96TLjJzptwadckaQzJCIAJzpmSNGp0Rw/m0K21UskZXh/RLRWgwdpS5YoZ3PZCUJnBmhJizCD1nqkhjbfZ0+sxZI2OAiOBM9WFJo4JIm6jOJyyzjOLWWJD5CEJAExQsFf/iV5e6a0djqpQkLZkkWmSNgSFIRDmqGyzKEJPGPm6GpdhORHJH9Nkkb8By3B5h5dZIhfD7fNsJpwbgx32eJczQQmOEfMABFpJXt3O/pj6Qr/m5WiSIeMweyVgCZzFCgRZd5hLdxpzFzacbrfQf7cxZ3H3UreCsW5izQ0cCeZOOIi0sa+RO+/tI2rKHHK50hHc7I0JEY84cvT1nYRkhEIE58wp1osGxrSofb7+3KyN0lZyIxS8YVYQBY5TZa4Z37tGEHgPnIJ9ofYtMuTVG7TlbIGuUX6PWZ9wOX/f6JHuRmbOQrJGlGMyB+M9YJWvkbjTnWMO7MNP9BpIjGp5pPoBFNEmaG8gIqRFfammoSyPiOeSUQ+CMoeX5YY3C8nex8GW37eUTyBp1qAoSpmdyhOB6aSeEEEHPmdne89WfTLFA1hiSDx9f2GwJnMWJ+fl5AEBPT4+W7XWbs11cpqrbBhqH39tNbo06wVm3MWfdOrsvMjg7GjiSacmHhlpOIjw8Ko4AUxWndnfEaFIQop6QumzZYzJnjpKk6WbOvJ3TLnPmeqwHNz05keacotweTe+Y0aSe+2JgKhNNGquYMzMZseesEXMW0RRAhFgPLRGz50zZoAtG0XNiFmZvfHDmyxo5ieRqyRQ4GzSQ3ZqNnU94H0WREapFIhjeTDFNskYRcTYd9+zliUHkeRTzXGLhaywCW+6WVG9wyL0nZj7C28l2BLYcAqBqVp5B23qesUbMmf+LaGzwIxlL4CxGdAqcdZussVuYs7iFNdCdzJlOWWOcfIQQmJ2VjhfpdPoRzyf8vm44XuWKwPS0B+419Z5QGlqxjvCAncvDB2dx5+aoiFtYq6JIlyFImIUBojBnoZ4zHfmwarDYLnPGvcKaG5p0qAjMYKJIUQUXPuuho3dJ5hPPVEYV+kxTz5lvCBJxXY9XwoYg0bZRlY869BGZM9tjzgSNX+SrEH5LVfvMGecChvc5UhldF5p3zIT6X5uhlAgGxcBof2zW3DeniVjkCztQOBBGYxs4hVnlKOMPbI/J40oSreHEVueQG0EFAhFc920zZ416ztRsOheRFkEeyVgCZzFibm4OANDb26tle90ma4wLFpes9JtHJ5mzKPl87GMfw8TEBFasWIGVK1dqyafbwFkcgH9wIgBC2izHCfGfGTzCZTaTAwwR2A9rySlmYU38WTVa0vGfUmrft1vLVmzFvurNJ6qs0SlJWoe32fTeLMLuiO3uH+6toJcIBdXm+InI+QBB74mhu+csKjjzZFqUQaupTFRziXIpZC6h6RzyF4mc9o1TyjZgeh/E0MW+htmQmMyZjp48Xy0houEztxLcpymLL0OnJDQ4vNT+DlLvEWqemAaQ77OvtmibqRJh5sykAevVQjSy0keIMT/OiLMlcBY1OOfI5XIAgGxWA4WPRx9ztmSl3zw6yZxFOYduu+02AMCWLVu6Yv+E39cN4KxQDgEhXZbaCIoiJ4L98PQ8YCrppzbmTH6NKpELej30SuTU3m6bOSvrlaKq46621q6sUShJmgaTAj9C0s92949TkB+gQpk+ABsD4HMeSHWNNpkzs5FbY9iAI0LwDrk1RjWX8O3udY2rQHAfcnn7w7qL5QCcEUuv9BMCyG3P++xly+EEzn9a0qmSxkYwBFEMvkE9UB0TnFGAe5twZu222TMFFjmlIDR+DxwQOociMFXC4f51TxkBaYPFb2SlT2NeZ49kLIGziJHP5yGEQCaT6TpDEF1FY7cxZzqs9LvRrbFbmDO1b97ylrdoy+fR5NbouIChmDNNhiAAfJ1+u5bsgGTOGJQkTZchSKiwjtRzprc/x7eJjzjnzCnqlezVGpS0LWv0ek9Em6xQs4jDnKkeuDKh2sBZVQ9cu8fLDRZB2l1waDiEOraVftBHqdNUBtHqfDgh5kxX8LAhSJvgrFASPjjTJa8OA2rucDi59lZBtN+HQsxZJGt/7xyihh7zJtmv7PV4TdsoT7TX6OWDM0ZBLKZlcU+BM8HbXwhRfZ02IWAGwNqYudio58zHY2603s5HMpbAWcTQ3W8GdC/L0G3MWVxzCUA/gH00MGe63UfD2+oWk5s44Mx2QuBMk4QQCElBIrjaSVmjZqmlslYW7bMwAEA0W+mrFXQSsedM9XjpYxar82mbOVNyIkOnW6P8GoWpUu6RZcq0ETHhvry2mTw3YGHaHa7ccAh12LUtQijmjGqz0lf9VCKSs51igwnVkw8QdmuMwJzlhXQKBZH9Qhoi2EdonzVDcB/SdV9Ukl8iIjr/OYHZRc+J8dVWkjmTn83sN2G3C86UeyQloCYB1bBYpHrOOG+/b1HN17OJdNds537dqOfMGpLEgODHHXG2BM6ixszMDAB9/WZA98ka4+QjhPCL/W6RNYb3Tzewi93GnKnjZRh67LSB7jun44OzQNaoryiSX90IDmmzuVAha+nq9YjHnEE7cxawMED7zJnw5D6GLrOLEHgF2mfOoFzbTH33oXjMmbzuy5SB6jJxiZFPeBGkbXDWiDlThb6LaE57vpU+9PacccCZa39VRtmgQ+MiEWIU1sW8zMfRML/Lj5BbIy/y9o+bmpmliaEO95xFCgUWTcBaFl9NFGbOBNo3lFL2+9wbim30xn/uVwH8dk1lvHwcQmGYFD3bWgewjXrOfGDOIzYKPoKxBM4ixt69ewEAa9eu1bbNbmMZ4oAhJWk0DKMrWKrw+3Ttn7g5dRtzpt7TCXDWLccsTj6OG0jrOsGcRZpz5gRGGTpWPoGg54xF7Dkjfs+ZXlmjb6Xfbi9DWYEzXUWj+hKNOYMqrHXJv4BYPWduQb6hQmgHmLP2z6GwrLHdwloxZwtmq1VJ5Nov0vyhurp6TUOFvh0DnOlcJPLvQ4K0zeYVc/L1rsaFz7CM0C26bbN5vqxRl6JAbUdEHTGiJPGanmUk6DkjkD1b7YRbDs7pvtN7tZimiNDg8Mp4BbntuZbfq9hphxCYGQbWhrGMEvvU9pxV9XYeZ7EEziLGrl27AAAbN27Uts1uK2RjsQyabfTj5gN0Rrb3aOo567b9E35fV5zTtcyZpqrIl8VHOK2r+nN0yYk09ZwJXXPOQvkAEez9PUmUldLN5Hmbb5c58+Q7OmYv+TmFmKp2h1C7Ra/njDIkNN2u/T7BCPlUXWftgrNGVvqq0HcFeATbcdfvF9K7ACKEgDMfAZyFZZa6IlRYt7tQpMAZ1yiz9AeHc4CX3LaZGLVIpMPoAqiVNbZ/Y1QyS2roAfiMBcwZSHTmTHQC4LsCxf0lFPYUW39vSNbIaHufxWfOaq30Y7q0PpKxBM4ixv79+wEA69at07bNbpOAxSmsdQ+gjpsP0BmmKg4YUuCsW5izTsoaHxXgrMYQRL+FdfsP/HB/DtHcUxXVrdEfQq2LXayREbabE/Ee+lZKr6wRABABfJCyAtMdAGcRALVvCEIpEppu13GZMzOirNFcxBCEO9H6hYTv1tj6exxH4Gs/EZicXfgHSciJsF3GAwDcCPksFtw3cxAQbS44lAth5kxPPmHTDLfM2wZnVDFV2hQF3n0oYqHPHO+Zo+k+TUktOGuTOVN9fJRC341IfuHc66cstv6cDcsa233c+z1nDd0ajy9JIwBovLQfW6EK61QqpW2b3ebWGCefTjBncQv9ToKhOLLGbmHOOiFrjHtOq5y6x61Rb5M5ELIfjmAI4jiB7biuFeIqiVyEZxpVRYKpd95RVOaMePlYumSNMhmAR5MRElsxZ/ryiTP+wGfOCEPC0FPExO85i2biokj/Bb0nNJRXBO1meAh1q7HqBQLjM8ALngBc9y/V7wuDM25zCC6qQf8i4TN5Gkd6+NJPtN8vVMoLmJDOf9qW/EMtXonliUCG12oo5kyTooD591dPGSBEW/veKssbPNUgHwSUIYj3Dx4MuW41fJMVjeyr79bICNy864PqVq4bBc5sQn0GvNVQIo0FC2U+Y97e9rohlpiziNGN5gmFQgGAPsAYJx/dNvrhfLqJOdMha+w25qwTssao57RiYHXto7iyRhZizvS7pEXoheHSJQ1Eo1tjyI0wSm8FVU9IXSvWtYYgbVxqjiNgevmYaX3ntW8KgvaZM+oxedSi0EUzBPm0D14D5owhqX0cQzy3xnbZYIVvFhyTsO4pwkntg7MWZY033iGBGQB878bg50II3LtLoFASIWc70nY/lasGq3eisObts3mlvDyHOCMgmlQOgSRNgJrUP09bDZ850yZrDDFnon0wZKpFGU3gLK4hiGKDYehXgRBGYU/bEE7rbqTq+DqEIN1m2djQEOQ4Zs6WwFnEUMVdN/Xn6Lb3j8N66LbRD+cTtdDvNnDWbcxZN8oadR+zWCY34RV9TcNogcCtMcoQajh6CxAgxFRFYD2EK0BdAQ5AaGfy2mfObBdIcO9erWvOmUxGfokga1TMIk3ol4CxSFb6wZyzdlesF8uHCtG2C2lYPkzbZBeDFfTqv+kXaRFnVAXgrLV8fvD74I8M9QU//7f/AU59pUDm6QKOdwO59t5E++DMs0FnWu9DQc9Zu/lUCqp/Sb/JjRByv9tzdtU4nEXfriTx2npxvW+EAAjgzDuYuWO25fdbtmbmjARSVEIA0eaNKGxyo+0cogFYdIuyT7BVEMtLAXOWarMkamSl7x+zpZ6zx050opCNKwHTDc66jTlTEgIhRFs3aRWdAEOPJuasG90adYMzbUOoddnaIdxz1ubDVYhglo9Gg5Jwoe+2uRrremYLFUK1rer7krQIPWdOCJxpG9KN8LiB9o1cqLeCbmh0a4xnpS93aJkypHT1nIXHMbTJCFeZ3LQJ8Jm3H2qPSdj5Tw01bicUK9FqPgcmgu8nZ4FdBwX+eJ/AP34h2Bdq+PzdR832bcc9sMg0SmN9WaNo30q/4s3KE1QfC6MKbsElOCsfKiO/s9D6+7leF1slayQewK9MVFCZqLRciyQqesEZY8E5JKkzASfntJyPD840zlv02WBHILs1C5ZiLRveuOXo4KzREOq4w+cfyVgCZxGjk8xZVGZIgbNsNv6AQ6D7mDNCSBVAaze6reesk+zio4050wWodQ2h1mapjQCcOaU2gVAYLGqUpoTdCNst9P2+gQ4YA/jMWRunku0AVsT+paY5+SYl7TNnhgcMWELfinUcAw7VtF/Raggiv1IhAtOCFqNii8AQpN2es0Yr6H6PlzQEKexq3UUOgO/wSFtkXw9NVv9700sEzn9ddVLqMOXzAvldrYMOABAeODM1LjgIou7VQGXGxtSt0y2/V4EzaGTywlJUalJUJiUYavntut0ajeCaBwEq0zbcfOsW/wlHP3PmkgB8cAeYvnUabm7xZ5tbcH1Gi2jtWwwx1PI7zG/PoTK1+HGzZ+SCvkNIZOZsgZV+aJ7g8RZL4CxiLDFnzaMTzFncnLpN1thtAL8TPWdxz+luYs6qZI2apDJASNbYZsN7VW+OTkvtUKHf7hqI6xVpZcLadtxqGCGWCmgPMFYzZ/rO6/Css3YBrHJt6wQ4IxEAtVMIjpk2K/04bo3lYN5RuzOh/J6zBcyZ/Cq4J71tU+Kq+mFYi32LCpytWbbwd6+/DPjm+wksj/Wa5+33UynmzLB0StK8r4oZOlpueRHULuhnYUgoH8IIMpsyLY9BqExXkC2UYRMC9OupQdRCAXM5QAicOQdu0Q2MNZoEtzksl8MFAUvqch6G/HyQ57Sbd1Aeb43Jc0tuMBS7o/3TBG7BBa8IX1VR931coHRYLp47hCJlRbTSX+o5W4qlnrPm0QlWKG5OjwVw1m1ujY82WWOKe6ufCZ1SEE+KVWrTCpl3xqAkzswsXg5m1Zia+5dIFEMQF0goQK2pKAKqZY12m9JPw1FDsTvQAwcB3uap7SjmjFHouvSDc6h9sGh71wGPIB1uOIw2xMJEYZnbBWfKDOSjVxL0Z4GnngPM3kAwewPBZ99K8ZKnEp+JmS1S2HN2W26EvgOpVlljyBDEFrDnnJZYGACwFeuvU14dZjsBEJP4Etxm4RZd7P/mQVAADyd72xpm3Cx8cMY5CAVKh0sQNm+pp8rxBo0XGIOhi8kjgOshWOEIJFcmPTOXxfMRrgA0G6YAoZ6z0D5xZh3kH85j+raZhu/jFQ57Wi7o24Qi3SYj3KjnTB2zds1buiGWwFnE6Ea3xlxOTmPvJuZMNzjTYcDRLeCs286hbssH0C9FjcucJX0WRiM48x5odgTmzIrYm9Ms/B6vKOYSJdW/RJHQVegTlY+M6MxZh2SNbRq5GB5yMBL65ER+n2CUoc8e8HA1LhKp/cMg2u7JU/Jel7Rvy76YpbbwnPbyuwttDX9WjHAr4EwIgXlPpfiCJwCTPyT4xccpejMEvZngeKuCcrYoQUf+oVxLuTjzjt8vZCX1gSH4LQNyIDAv85b3keOxIlqNicLHzNu2W3IXZYbckuub3OSYCV2ntTIUolwAhCC9Pg2WMVpyIywflc+xPDW1OddTKtllQAIyI2uAJUlL/VXCBUgYnGnKyfXcTNXMTmpR2HPOon2Mwha+271LSdtTWBpd9+qeL6KYbT3CsQTOIkY3SsAUOMtkMo94Pp2WNcYx4OgWQ5BuY187aaXfLcxZLLdGF0h1wvlPLeq3yZxVMXlJps+WPcR6tCtrVP1LNqHaJHK+rNFLpnCw1PJbbQdIcGWeoJ+pIgCcdi21PebMSuqUgEWXEdp5j6ky9Fn7I8S+TuUJym0UR6p/yWW0bfDa0BjA74WR/UKiwluSo6lohzkrV7yFExOwTALagAFUCyoHZih2HBSoTNtw8ouDodyRCgzvA5oaz+mw0x61GIweo6kULRxqUYaYGntN1UdTzBkhgBAtMUM8ZExkaHqcKedQJoScS8cIgNbymfb69/LM0MZOMyolgEAAhgCC4t7iojJZ4QoQNyxr1HPQXO8CVNeW2W8iMWxJ18Ym+4nbHGVvUUYwEhz7FqORrFGpJZbA2WMoOikB6xaWIU4+nZI1LvWcNY84zFA3yxp1AWoaaoRqNyfHDTFnGsGQbwhSjADOXJWPzqIoYD2iGoKUKYOlrX9JflWGIHa+9XNbyho7x5wxIVCaslt+n+ACliqsdbpHhnvO2nQmU8yZMDpn4vKGj7cBzsKyxjbzaWgMUGOp3e5MKMXEsBbMHHKe10h2kXGjKlcKgZ3jFPaMDXtmcXBWKAeMOdHZ+6qYKkfAGrJAExROi9eaAmc6R4yoOYkkVNTzikB+x+LmKSqfCtUHzhgjqCgZYej8aYWpsmflcc1TAwbVAxQoCTFnKgcB2PMOiostYHEBsyhz4mlD23XvejtbhNhEalHAbcycCS7gFFx46/lyHEPE6772eaUWUfkSOHvsRDezDLqd7bqJOYuTU7Eon5qPZnDWbTLLbjMECefULoC1nYA5IxpnVPk9Z2XeluWv4wJpjznT1WQuNxawHpzLvOzZ1gCI37+kkTkLJITy36KNHR+WNbJUJ5gzgcps6/I4xY76M8W0OdupL9HnnHFNluNAtVsjAHz5R62/t1L0egsj2LI3kjeFBxoDcmW/HVZYHbdWmLNWwRnCgJoS2HMOnPzi11mhDJg8AGe67kOOKqxDM92Kewqw55ym0j1e4XCqwJmehLjXK6aGtgNy4aF0eBHgIaoZfF2j1yiV2wNCTAwhKB4oLSq1tD15aJ4Z2vJhDHAQ9Jyp4GW+6Cw/bnMkinIBXWRNbfchrmSN4fOFoqoXzi3zKoOOyd9PIXf/PGzuJRGhf7oRY+7LGlschN1NsQTOIkYnWIY4hawQQrtsr5uZsyj7SMk+dY0aiJtPtzFn3bzg0AlwZpfaa8KXPWfePuqAmQPnwmeeWgmXB7JGlmQds2XPP5RrediqktPYlCLZAec/IHDzayWq+vJ0MmeqsAZQyS/eB6NCycTKlMFk0N5zRoWA67Y3B1K5NRJTo8zSP2btRzAzS1/PGamx+OY2b7l5kVe4LCwZWhqKrfrNetLNXxeWopK0AV5yYU8tDvRLNglcWg1957RrVkvSWJqheKiM2TtnMXVLY1v9/I68b3jBNDJ5PCVrKxYCZ3AXv0cKVwTMmcZ5i4yG3BEVc8aBynjZly02zMnLuUAN6DpkVVb6Cpwx4i+2NAs7Z8O0XdiEQKT0PTtUz1mYWSSUQPCAOZu+dRpzd8/7v+clDrfAUfjuPgByGgPaNANqxJjTpFpwWGLOHjPRyUI2jgGHYRjacupG5izOPtLtZhnOp919FH59WGr3SOUDPDZkjUBwXo//fhyzLYIOAHBcEfScaewX8hvxOdrqg5HMWSBr1BVVM6qElAi1+nBTRVqZ6JM1KlDFvHOoPGu3zORVbNERK30FqiQYas0hDQAcz/2uTCgsjc7+4UK/MuugMtm61NKX7Gl0/gsblLQbPjiLsoLu7dMFt5uQWyMgi7VWWWp7Xu5LlmQtWfu3ypyFATVJG0gsT8Bp4by2XRGa3afTzMHbeZ7jKjUokiNJ6UjY5L4kSHDfohqZPCQXgjPREjgLmM4KpWBMT2HOQsyZksmxNEN5vIJKE2mzPWP7YNGmVJ9BSajnTDjqfKBw8y54A1egynQF9pyD8hGZ7wyzYGgcw6KYs6rzhRJ5DilJcUUqMdyi6x3P6nEEZ0webfvvNuw5O44NQfRVYY+x6DbmrJNmF48W5qybwFknWLPw9rpF1hi3j7JQkMvQ6fQiy9ARcnKKLmzaehFrO0C/X+jrtx/mrhxsavQYLc0tkz1nyhBEv7mEbHeXwKMVRzIAcPOBrFE3OFMmCHahdfmnU+Sg8GZmaXSSC2SEclyQsAXQwuct7M4DkEWjaegrGMJMlWu35+Siil2dDqRK/pds19cfQG5PEcugwFmbK+jeyxv1nIWZs1YXQkoH5AIRS7W2fxQ4W4w5U9uiADgIzH4TOXmeuAAAdupJREFU9nTzYb1CCBSOVAJwplGKqmSt4UKWmgS80pyJJQCg5q6Z+sCiz5zZIXBmNzeWcEsuSgdLPkNdIUyvrJGGmCEGGD0GUiwFOycdCWuZcLfMMXP7rG9Qoltm6dQwZ9QgcIvcZ+pqY+ZPszD7DTiz8jwrUwaT6mPweZ2eM8Lksw1cYPy3Ex5rDSk/dQTcMq8yntk72N++EZBalKntOUsu9Zw95qLbJGCdZBi6iTmLk1M3gbNOAKFwPt0ma4wKzvJ5WczqlKKqzydoazNqVFTPOdMnBfHdGrlAfkcehd2LN7wH+YSMCnRJ5DwQY3jMGXfkfpq4aXJRa21lIFCh+nrOfHDmVdxOkbf8sFW9HhVdrgAqJ88lzxAcHEB+Z74lAMs9SaZNKCydPV6GYqo4XFdg/oFcy4NXVeFoJNp3R2wURo+8r6nrpZ0oT8v3CBrdrbFRz5nwe84EctvzLck/Z8ZmAAA0xYLZW01in7fwP9zX/HU0fMyEJ/9ymxtMVI5WUDhUCXrONC44uMq/PARaaYJ6A4sFCnsW3peKB0pwCxxG2XuepTXeF9PyHDJC4CyzNQPhCjjzDnLbF44eKB0qw561q5gzI8K8vHoRZs7CzBBLM0AAZW+IcjjKR8pw8w5c7/UOad8mvlEQstCtkaYozF6jobSRF13QBPONZxxCYRChXdaIGlkjdziEw+HOO4ArfNZM2By8LPz7BQD87uSNbf9dqvq2a+55xzNztgTOIka3uTXqdmqMm88Sc9Y8upE568Q5rfa12vfthgJnusZDAKF9RFuzQVZRqYQNQZi2QjawsCawZ2xUJpuvnqsIG4Lo7KdSrEeCuxKclSRTJSoc3Gkib3IFykfkfahCGJKa1mV8cCYkI+QUOdxya/ckJSOsGEZLhXWrUbWPOFDYU0T56OLHTZ1vDqGwTH0Fg9pHFufgriweWz63vVV2QyMbbGTlPSRdB5wJ0ZyJNWbkQiM3aduW2v4Q6prTw2eivZ8Ll6N8tOIzvc1CFbIsyVrK54/3y/1+ztbm+5N6zo8JwaEuKyHCtujVIYRAebKC4oyDrOtJLTWCIQXORAgMUVPK5Nyii9KhavAhXIHc9hzsmQosbzyEmdW3SBSWNSoQTQ0KMHmfnLtnfkHPMKGSPVMA1yYUTKN1vV3HrREA3LyD+fsWPuNyD+XgzLtV1z3T5NZICIGrFiOcYNyAOWjCybuSkaoBadxzTVSLbA4hMJg+cCYU++qEmTMCXpZya+HKmYFuwQNnrgBLUiSGE4FbaNps30p/MUOQJXD22Ilum3PWbTO8OjWE2jdzsFuXo6lQAKG3t1dbPt0GzrptCPXAwAAAYHp6OlJOnTBxCcAZb0veVM67vkxL75wz78lIZM9WqyYlbog5o0mmb0SVkqQJF1wQVCYqEC4HtxfKdsJhz9j+Q79CKXSR5oQQn6kyBYc5aCH/UB7F/cVF3xuAM82LIKlAtuc60iq7FVCtQKUEZx0Ai4KDc2kVvxg4Kx8ty+LNy8lK6JOkBcxZcM3bXj9icU+xqcFEdq/sA51a0x/dta3moxu98jnkFl0QgyBzQgYsIc0ThDe3ql64RRf2tOo5ayxr5Fxgak5u44/3y5+dt22RXJMKULt+UUmolJlWphaeS/aUjfKRMmi/iT5X/t7I6DuvVWFNaoCzk3fhlhbeJydvngKvcLh5F0lbHltTYz7UoigTKntfQzMgCeRx4SW+0IWPELjzjs8oap1z1gScsRSDPessZD25XIhQr3cIAdPY4+V6+bhVUlQJqKf+MIXp22Zq8pHsq7ovSpkl0bfQ6DNnoeNFCZx5B8nlCXCbw61IGWNhdwGzd83J2XVC+EOxTQttI5OaltLg516fcTu93N0SS+AsYnQrc9YJWWMcgxLdskYFrObm5tp+r3rPEnO2MDjnweqkRoMSwzAwMDAAznmkY9ZR5oy7cHIOKhOtMVW2jY4Yggi1HUZgDlqwp22UxxdKZGojLLPUaaWvgIcy0nDyLrjtudYt0uvl5DxwpnMINQDimVVYggMJisKeIvI78ou+r+TtR9tojfVoNZQtf0JwkLQBlqQoH138mKkZbS4hsj9HVz41hT4v84YMjIq5++cxecsUiLfKndB4ThtZeQ6pnkgAyD0gF8ekwUz9+5MQAkRJwHqs9ucdNeg5M3oYiEHkfikLMFW0lTlyD+Qw+fspTP1xIWB05pygn6eBIchHvynAniQwdInAnx4UuHunLOTP3to81+A643BFUF0W93hFa01wWzIeriDo9Zgzo8fQVli7yqGm5thkt2Z8Fib3cB72nPzbvMil017RRdJb2LMy+kaMUArMGrJ+UMBeRfFgCbzCUR6vVLH5wuGw5xzffKJThiC1TExypVQs1TLCridjVj+3CdXm1gh4swBRzbZKpoqjeKAEJxcARsHlIoQ9U/EZNZtQmJr2DxAYgqBmP/ScnIU1ZMmewTKH2WvAyblevyvxrzGbEKQiPDcaWukrkyOntXl03RRL4CxiPBZ6zuLk0ylZY39/PwBgdrZ1lz0VnZA1KnDeLpPXbcxZJxYbVAwPDwMApqam2n5vJ8CZf14LDkIp3Epr+8ouOLAEhyAAMaFxRpX3gOWANWTCybmY9vpcmkVh3PaHUDONdsi1Zg5swJS9AYuAM+EI30q/QhkSGt3/WEi2JxhBZmO6pbEDRQ/A2abGXhgALBOSNVICa5kl50E1kX0CQGnOA2eU+nOudISyjE4IDk48RqiBYxvgFWq2gDNtgwivKEoAunaSYs7SIebMH/wshN+DsyAvR/jIipkEpM2FooayRkJgDnjs2awnTTbk4OfKlA1R4bBnFt7DK3PBuA2apMEway8mZwXe8Z/BNXH+6wQcF9i0Esimm+9LH5wJNwCThKB0qFy10u8WXQk4bA4n58ItuDCFQJkxrYYgQjVD1RTW1KBIrUzCmXdQ2F1AZVw+27kr5WluiSOllDJZjfchCswyeczCLpYsw+DmHHBbYP7eOeQeCHrPigfLMPtMn0KpEKaNqaIEKKoF6/k6zwxS7doqhICwXVCT+vcqh+gDiwDgqGdZzX3HLboQtkBlvOIz+tN/nAG4gFvkSI4kvHz09cABgKsccUvV+0ctIHBbqlUE5D2IF12ABsyWTSiShmj7um84fJ4Q38m4lRED3RRL4CxidKtbYzcM6wU6x5z19cku65mZmbbf2wlwpva32v+tRrcxZ+FRDLpjaGgIQDRw1lFZo8tBDAI339q+Eh7rISwGrZW+6jlz5cPEGrbg5j1NvmjslDb7UD7oOdPJeoRkjQBgDicACjjzDuYfzKHSwFGuMhWsyFYIRVLfOlGo74xDCAJiErnyuohchXu27I6hsRcGNbJGLmVBBGLRHqaKN1PMpQQaCerqHjjIlfNmskblwKmYvDJhSFtCG7uowGuKO75Foj1dQelwSbIIdUB++WgZU7dM+7I6ZpH2e84aFGkAYHngzJlRjqsUhd0F2HM2uAv/mguHM+v4/TMsSav6FvcfFRi+tKb3yDv8W9YsnitV7CsPwJk5YAaSPQ9gzN0zj5nbZ+DkHPCSCzfvyUN12aF64Xg9XrSwsE/Q7DfBbYHKRCXob3WFfw0mvQ9uteho2UpUMWehQe8sxbz+JdnHpHoC3TJH+VAJ5oDp3xcqVMr2dARjwLghn/f2kTq9lDzIM7cjj9LBEiCA5Kqkf79wQbS5NQL1mTNAHq/E8gSMHoa5++aR351H+WgZgnvMtXeeOhplnwBQSsubPpmvv1gtbIHE8gQICVhOlmS+7NMmFKkIJUgjK30g6H9dzMyq22IJnEWMbnO267aes8cKc6bAmWIuW41uY846cf6oiMqcCSE6wpylUnIAUaFUADWlHn6uTjP3gvCAB0/IQl+b/TCrdgAz0lKC5+RdTP1hGvmHq13SKpMVjP92AuUC74ysMVPNnHEBpNelYfRIBq0yUf/Bm99Z8GVfZUqRTmlkhrwVWcvrqSKUQDhS1tQs/BVrk2l1tqs1TZFBFmXzyl6vB6dEK3MWyBo5YMpt53cUGjKd3Jaz63hBDcWmGEhwfYW1QUGTFBTBeWTPuZi6ZVpas7tYAPJ5mcMtclAvZwnOork11i3S+j3mbC5gm8vjFbg5B0KxQIXq+6ZbdH1nUJpg3oRcGTfeGbzu1c+u/ltb17aQa1JdZ9w/h6gp3RF5Oejx8t0S9xU99lq+1laUh6ZjVu6V9342X/+aSq1KVoEkEAIjy2ANWr57pJHQd1+UzJkEZ2HmjFACM2sgszkNa8D0+/N4yQVhBCzJfJbWJnrnih215LPDPrzwHsgsgtk/z0C40nU3tz0PXhHSjMbbJ1LWqLHnTNVoNelYgxbMXgMAQflIGYUdBfCyC8GlTFUtODiEamXOyhkLHADN23XvPdzhEtxT6u8bo8eokn2uyLptr6M1stIHpKQZCGZwHi+xBM4iRidnQnWLrHGJOVs8ug2cRWXOFJjWfbyA6ODstttug+u66Ovr0wryBwcHAQAz8zMgBkFlquLPMqoXEzdNSmvyvPcEtPTeNstJ+dlEMXh4CEEwd9ccnJzj93j4v3MEnBkHjiOCIdQa3SMV8Bi2y4Bnp0+8gcBuWcq/nHz1g04Ibzisl4JNGLL6SHy/d8ASQSELRus/jb3Ibc+Be/uUGxRUIxgywgYcoRTyuwoNjUEqUxVUPJZWUNLSLLtWg/myRhcCBMmVCeR3FmS/YB2XTeFK5kwxnWXKMJjieh0tffbM+xuzNoQtUB4vgzt8gSkIYQS86IL4skbaNqBuJG8CghV07rGXhBGkVqf8Ieu8xH1wxm2OyT9MwZ6yfcDNktXW/p5BIS65AHjZ04KfGwx47ysXzzswcXERPjwKmM3ePofx305Id7uSXBRJb8rI4YOQfZTQp5BDJWPBJgSs6NRdZCCMILHM8s8lXuFIrkpKhsZr8jI03hspadxzZg1ZYEkGs1cyesIVKB4oQe0QtdCl2xBkwpD1lT2+EJyZA5bsC8w50tgp5/ijRQIwRDT3nMmNTXz3ACZunFyYs0VhzzjSHdGReVCTYspb27ZJ++x0syAmxSyzpFS6jkw4uzULs9cE9fo/TW/BJOxmecGa1nrAw+H3mtYpVdV1X3sOdXssgbOI0Yni+tEywwvoHHOm3P8eeOCBtt7nOA6KxSIopVoHGkcFZ90256yTzJmSNU5PN3Zoqxe33XYbAODSSy/tSD5Ts1OgCQZe4r718Nz98wsKWV7iqEyUQRR4SuhzRgSAiicnQkhOxAuu/5CvlacJV/Z2OXaHDEHSwT1tNDfhgw/pJCeL68KeaqdEJcH0i31GkdQ5qNtbhV1XzlXl08zZMr+r6M8V40xzj1eVrFH9kMCedVDYW99Fcvb2WVQ8mSWn7dvEN89HSeQkWGQJBpqk4BWOyZunMH3rTJUjYWXSlqyQl0+ZSHAGjeDMyFTb6Vsn9oJXOOxpB+684x8bFYLL84c6ioVpH5w1kzUqZ0P1mQF5rvOykOwUAQr7irBnbcw/kEP+4bycmRXuOQvdrhU4W9YPDIQeu9//MMFQ3+J5KwfSS6b2weXB63lFzn9yCw4qkzYqExVwm8PJO7Ko9fp5XMOTEOrq8TIIpjzwUc8tUiZNYE/bstAVwgfzATjTmA8Les7s2cY93YRIoJjfUYDZL8FcIGtk2kZWsJDMUklja0NwgdxDebg5B27RRXqdZNoq3rlODKLN2h8A8qH6asfVOxf83uw3kT0xA8G9Hi+PBc4XAzB06kp9vViMAvOGJx+uI/FWPZLEILDnbL8fTDGdFUrlfahdI6AGLq0AwJZkjY+t6CRzFoWpUk54Om3iu3EI9TOf+UwAwA9+8IO23hfuXdLFMABLzFkrEZU5KxZlkbt8+XKt+SjmbHpuWsoH5x3wsovKRAXz987DzQXXn3AFeFnKjIiarWVRrayH7TFn4V6PxMoESgdLXmP3QtbDLbko5l2kXWWlr7HXIzQm4ITivGoZko3tJRflI+UFwGLurnkIW8D2WAluMTCNboRK2vnSo7tC4EyuvpYOVV97Qgg4eQei4oJ7+1QYVCsrpPqXhuyS3z5FTeJJ5KorhMnfT6F4oAh7zoGtwBkjdZ3/oobqabFESGYpZMO9W3Th5F1/Bh0AlA6WwMsCjne8ypShPyG0LjpU9Z0BKFY8t8SiC2JSCIdj4sZJv6dSjbVgbkgi1+Z11gycqUWHMDgDPBOeeRcsY6Cwt4iJ304i90AOqdUpHzAC3nVBFzJnBgP6Qy2x4e+bxcztkr5YV85XteBlt2QkS+31njk5aQaSWpUEtULmEprHQ1gGMK8MOHL1axDCCEoHSpi9cxbqhiO4gOkdQ9OAvvtQiDlzZpsX1s68A1HhPuBVIKRCqGd0Ez/C4MxtAM7cvIvC7jy4LYGQOudUeZDSqCYAgOkWPAbkcHPp1Kju27YHFnszwPIeffQrY3KfA2gq8aZJCmpRObcO8J9xNqGI0tLdVM7cuwTOHlPRbcyZ6sFSsj8dEQcsdmrO2fnnnw8AOHjwYFt5dQK8At0HzrqROVPgbHJyoeyiWShwpnrEdIXP5M1KJi+zOQPCKHLbc1V9J8IVyO/Mwy1KZs3HGgmmtdC3lXdwCJyxJENydbKq9wTw+vB2F6RVdMoEg5DmEqY+cEYIwYpLJCAWxPdz8K3IzV4TzpyD/M48psemwW2O/K68bIhX+85iIBrB2YrnyHxS3AnAGSMoHy5h5vZZ5PcEfXn2lI3psRk5l00NNmWkivWIG31nyfvsScVZHwiwNJMr/DXLt27BRflIRR5LBTbYQue/OBE2KPH3DyEo7JHuepWJStWQXHvGlo6NlcDAZSDlaq0IFFOlpLclB0iuSSG5Kon0+rSU585LyZVbdJHfXpAFbRxwpnpP6oEzxZwVqn+ZWJZAekMKyZGEHLhckEODqUWRPSELtxgwZ2FpbBU4CzFnfS2Csw2vWed/z0PojDAC7sD/u+m1KXAbPssgQn2U8g16rrNMEsh54MzNNyhkuZD29SGAG/R3ERgGha4bEWONZY21Yc/YVQpP1cNUIRRpTaoCSoE5b/+Ud5ZRrjOCRTgCzpwDs8+sum+XPTCUThGt7PRMCO01ulYIJRBcgKUYslvlyemtxYIaRNv5A8iPVqZqmHkTcGZQpFYHz/Vwj2AigptlM+bMyDKABtfT8RJL4CxidBtz1glwFgcsdoqJsSwLy5cvh+u6OHLkSMvv64TrH9B94KwbmbO1a2V3/IEDB9p6X6fAWZg5U8ErridF4Zh/KAcn76B8tIyZP836Dw7mHTOS0CtJq3jgjBarCxAjYyA5Ipvw1YPemXVgT9uwBkwIrxzxjQE0OkhmNknpb9qVg6gBCc7cMofRY6Cwv4TCniKcWccHtLwcyOQQQZLWLE54+2YAACckKMIogVvhsGcczP5p1pejCscbWstF4Apo6mWqek6R95FVlQKEt3+oSWFkjQXMGS+7sOdtEJMG86xY+2YXzcIaltdtv1PxwbTRa6AyaQNCzp+rzNgoHiohvzMPZ95BYsjy5ZlFypA0oFVVoMBQ2pt1VrQJjDTzbfZ5RQ5bzj+cR2F3AaWD8no3vHuXZbUPzhrNOQMC5qwe68GSDMQb0eDk3KoCWjFnrIYx9+Yuw2BAX8ivqKdF1fzK54/439cWsukNKX9mILGk+Y06X4TH4HPNzFk2DeQ8zV09SRrguQAuS6B0sOgzEm6osKZUX61PCTBuyudrYXeh4aBwQKB0pFz1d31rdqqZOWPB8/HIDxfWHzRBwTw5b9hBUYGzrN5HGY6mMyh4YMgaarwQLlxRhVxsxWoZRCsKCM+Ca2fws2LZbEIkc9bms6zZdb/pqg046edbsOYlq9ra5iMdS+AsYnSiuI7T4/VYYc4AYPXq1QCAf/u3f2v5PZ0q9LsNnEU9ZgqcdYI527hxIwDg5ptvxsTERMvv6zRzNjUXyCwJI6hMSdajMl5G7v6c7I+ZcyC4gDVowfCKf5LQ6/xXzsp9TuvYD5v9JiCA8ri0IS+Nl1E+WvHkVqEVdAK9Q5Y9nX6YqWJJhsymtGSIpmzJepS5NCzhAsSQTnOAt480Sj9T3pDXIbvsgx/CADfnwpl34Hj9U2roqlvkECIoeqlBtTJnZr8JTgnS3EVyOugxswYtKbFyg5lv3AOLLEHhqMV2Rqqc/+JGcpU8h4acss8asaR0IzT7TWS3ZgAOTN8i3T9pgiKxIuHLiorMQMrkepmz0DkESHAWDpaSJjb5XUWUDpZALIbMCWkw7wOYmmWNysRl4huTdc0KVGQ2pZFcGdwHuZI1JqsZ8zBzRkM/H27xEUwIQdlbmLGmqvsUmecIa/YakvmwxQJw5poUDaZsRIpMkqBA5TEb/2X9+zRhBGa/icwJWd+Exg7N8AKg7RxiFNidyKKYMGFP2th5za66rzOyJor7SlW9skrWSEx9C2mUBHPFgPpMVXpDGqk1KVjLLP+eBQCOB2DTSWidwiJMildvuUj+jUKTZz6HP5gbABy1fwx97pqAZLDK3g6f/uNMy++b3ymVD0cSqUjEYsbb1bk67b7UpFo/47GKlk7b0dHRfxsdHb1pdHT066Ojo2bo55eOjo7eOjo6+rvR0dFPdi7N7gtl8a3TXEKxFu3OzAI6A85Uod4u8AA6ZwgCAI973OMAANdccw3Gx8dbes9jBZypz6c+b6uhzrlOMGfr16/3v//Wt77V8vs6zZzNzM34P0utTiGxPIHMlgwqkzZKh0ooHSoDrsfCAGCV+r0ncSOwsC7XnWkmAOQemMfM7bMo7ikiOZJAYlnCL/4d05Av0vj8UYXsc6b3wwkNFFUFmdlnwJ62IRyBuTvnkFyZQmZjGsJ7rZGiIBptyWiCopCQMk7iWX2zNAO1KHq2ZWEkKexZG9N/nIZT9GZBiUDeREwCqpE5I4SAeqj1wp/cFfzcIrDnHEzcNImpP0xJ6/MKBy/JmXpOJXAK1Ooe2WMACYoUd2E5wfHKbsnAHDRBDWkO4sw7KB0p+aMJVEFXoAxJprdQM/vkvb/PkUCoVIOHkiuTSG9Igxdd2LMOjKwEa4a3XxMRzCUaDaEG5Bw1Fc0KR2pV9yeqRRCWolUVUxicAcD+6wi2/1+CdLL1pBNFuVNOu+6eBb/LnpCRizNEAnwfDHjnEFeLMpoOWTYFbCxJ6evsn2Zbfp/tFfoOpYDQx75SCghCsGeVXEyb+HV9WTxLMyRXJPzFACBgbZip0drfO87fXL8FAHDkhqMNX0tN6jsRAvDneOl0swTkYyjvsZ1uwW3ILrpen6cKBc6ooVcFwijQ68pz+siPGu+f2sg9LOvpHdn+SOf0cL/8OjFT55fHKQW1aNqjo6NnAFg9NjZ2MYAHALww9Os7AVw4NjZ2EYDlo6Ojo51Js7vCtm3kcjlQSrW6Iyonwig28coJT80B0xGKYZiYmGg4CLdRdMoQBAA+8YlP+N9/73vfa+k9nSr0owLYToEz1VPX7hy4TjJniUQCp59+OgDgoYceavl9nQRnQ2QYG/ZurHqYGVlD2jP3myhPVGDPObCGLSSWyXPYcALzDZ2SNJ40kKcGqCOqzEhUEAhppT1lw8k5MHoM2ZeiXNukDkSvDXoyOC/nb13osplcmQQYgVt0UT5aAUsz2XDuraKnMkRrTxUIUPAGnNIZea4SQmANWfJzE4Lcw3nYMw5K+0r+foJXFFFLX09ebWRyQdFPCIE9Y8OeslGZtnH0l+PgHLBzDohJfAkYMfQyZ4QQ0AF5nq7y+msBCQJVcSpsDkKA5EjSn/2j5q7ZhgGqef58YkQeryfNHgIAlOz6n5dXuO/Gp8YQTBkWLCPGnLM6j6vU2uA+oiSDrYQbYs7CJ5ECZ6a331YvIzhhTRv5hl5qlhvnQw1alTvx+sHshN4DlkkCD6baX9yteCykkrPpus68yx03bwkW92pHeKgImxgBwaIMS+jr8bK83T1LVV+ei4nftthH7S3ssYReOTOlACcUJGsAHLCn6zPC6Q1pJJYHtZjwxrOIJNO60JhOynEnKmqdjxtF+ai87sfTKZAI8yEUWz1Rr+zR+PmOZbSCKS8A8DPv+58AuFD9YmxsbO/Y2Ji6WioAjq+Ou4gRZqko1QfLFThr19UOgM8g6XS2y2QySKfTKJfLfs9Wq9FJ5syyLLzqVa8CABw+fLil9zxWmDPFnLYLzjrJnAHABz/4QQDAgw8+2PJ7OgnOPtL773ja5DOQe3DheZ1YnoDZZ8LNO2ApBmvIQu7BHJ6+Q8pqaFKvrDGTBCZMz8K67owsacRBCIGTd/0VdPOwlIJUMpZ07dD4DAo/VEsP17/2MxvScAsukqvlNaAc3nLUwLZlrlZZIwiQz8h9ZBwpLPy1QVA5WoY9a6MyYyOzMSNX0pWs0dTYDOPFvuds8b+f+kNwz85uzcKek7OiyofLSK1OAq4ANSgcr/eEMiKb8TWGWhm/8o476v7eHLCQWJn05YQAUPbmrlVMFnTVawrVA7OlNI+V5QKKjTwmPNmn2W/6YwgOWWmYtH2ZZbPek9V/vSroO2vQU1Uv/J6zZPVMqFrmrO0gwOGty/x/Nu6pgjdQWIKO1D4JvisZS17ymk6jTAr47xXBOa3cJJtF6XAJu951HwDAUTtfUz7KZOUQSSK5St5jKnVMOGpDcOEvyhimvjleyoUz7wQb3PEfC+3r64VRkGCIZPUCassrr+hKTzGzr75ihlrV0j7roLynF5ZntD43+rMAC4GzOy6/C5M3LQ5g7XFZf8wqg5N2mTMPnI3PtPe+bo5WzpQBAIe872cBDNa+YHR09FwAy8fGxv5c53dXALgCAK666io87WlPi55tl8SuXbJI6+3tbdvkoFkotml8fLzt7R48eBCAdHPTmdPg4CAKhQKOHDnSFkuowNzc3JzWfFSoPqYPfOADuPnmm3HNNdc0NfsI56AzHwWydu3a1dZ2Dx2SlxTnXGs+CiROTk52RT4qVJ/g7373O+zYscMHtc1CscGFQkH7dXZT5Ua8OPUSTDwwCfPZdRYQVgkpRWEEZVbG3o/uC363hSC/Io9yk8HV7YRFspg0ElhfzmPWngNfVlNZDskhq4QScJsjl8xBOAIr/yhzOrKtD5WTKjg8cVifjfVFRA5MFQK5WyYw/77BuoCU93KUrCJACErz8gE7bVjYeEoJBw/OL3h91BBcwB5gwH5g5Mc7cOQSB5kzQ5LyITkEm5gEFVGCbRIABEIN6V7OMWlPgBzQV4nMPT4L/Eh+v/0jO3DKzaGFjRWAU5bSynwyDywH8sjDrch9ZA9QTGEKMwdmtOXjzgWF6zSmYS6rOa+XYUEUXHkOOwmK8uaS3utsXbCKf0phBtPJBHLLFhaPZFiAgCBP8jh8k1xs22+lcd5W23+utRqTkwaAZSiVbBw4UN03JVyB4cuHcOSaoyihhNyyxRccBQ9kzc7pDibKwTk0PZMF0INCfh4HDrS3eKnioReuQ99HppDiLqb5DKwVTUwdhMDet+1D6oiUgBUHCSonlnHoyCEt130pn0CRBeXdgx94CNt+feICViocE78ICm9LcFROLGu7D9lFeSxnALDVDDgIHH1wHMOjQ03fp2z389RAIiswXtR33SetFTjKqtUls+lZ3/ymURiefNVdRZAbzqF0oL22g0aRMocAWCivMWFsB2ZmZsGWNc9l/pYcMoc9cLY5gfxyfc8yyjNVzJk9bePhj+2EOBNIbqqvyqkcqMCdtmETgnyPicqJlarrrJWolAiAEYzPCOzff6DaHIZxIIO2t3ksQtVF9aIVcDYDQPmP9wGoonVGR0fXAPgEgOfXe/PY2NgXAHzB+6fG9tVHLtQDY2hoqOnObTfUysbc3Fzb21Vs22mnneYzcDpiZGQE+/fvx2233YYnPvGJLb9PfZaVK1dq3UcqTjzxRP/7n/3sZ7j99tvxkpe8pOHrFfsyODioNZ/h4WEkEgls374d6XS65X2/Y8cOPy+d+SgX0UKh0NZ2f/jDHwKQTGAnjtfq1atx+umn46677sLHP/5xfPrTn170PUpKu2bNGq059fX1Yf0z1gE3ApV7bWTH64P6/I48rGUWjKyB+d8FxVfKzKJnKovBExesU0WK9asF9nquZOQhApY2JNtSJ4Qre08KuwtI5G1MGAnMbRyB9cAcRp474g/5jBslVsY7zxzE1bf/HiLHcfCth3Die7c2fY+7Sz6UZwwLK2ZMrF6tj8V3Cy4YCeSVu6/cg9Frz5bGCQ1CCIGBccmymQUDy1LLkFihT7abmJvBb3tX4Ilz0rUt95UcRi4NHPjKE2VQI+g9ccsusg9KwCqIhWFruMpOOm7c27/TL0z57wWyFzZ3phWugH2TzMc1DKT2prDimfqOWYmW8MPUJE4qzmJtOQdM9SE73rxwLN8nweuv+lfibw5wrH5Se/mMFwQAAcrMBfcMZ97B9Lxkg+gkbXjdq8g9mMOO/whMKBLbkxi6aBCJ5fIcSqXl+T440IvVq6P1eltTs9ie7MXphWnQuyiyRuOcZsZmkPtD3v+3MNKwHqhou+43Tsl9F47y/6tg2VOHG75nYmcAzjKODfPBBFZessKfNxYnykTmMz/H0Lu2B/nb8jjymaNII43lT1+40iC4wPivJvzaY8awkHKB5dnlsIb0KEKGelzcUx6AdckgKj+UNZdxr4nMxsbeA27ZhWFz2IQgWTTQb/ejb7WekT7LBzmwA3ATGRiYwuTXprD+Sesavj63PY+9b5OLegXKkEYCPdNU67OM1TG02/umfTj7v86s+57DP5f3zz/0LIfFGKwHExi8YBDJkfbu1akER7FMMDC0CplUAMJyO/IYzx/F8PJhKcc/TqKVK+hmAH/hff8MAL9XvxgdHe0B8C0Arx0bG2u9++84j070dwHVssZ2HBu/853vYG5uDoZhaM9JDX3+xS9+0fJ7/vjHP+I3v/kNgM7J5MImEwDw5z8vIG2ropM9ZyeddBKAAHC1Et3Wc/b973+/6msn4v3vfz9M08RnPvMZfPe731309Z06ZtlsFu/6+DsBAOXDjc133DIHBDD+q2AFfo6ZsFaltOrYh3qBCQ+c7fnCXtx15d048uP6t1MlFZy9U0qbHkj3wbIIBNHby0AIMGcG1+7M2Cymbm4ut566Wd4XZ4wEMvpbF72enyAWMy04dN1hpIqSTaIZpr0x3KDAR9ec5v+7sLd69TkxnKgyBdj3tf2w5uT5xtOGdgexgTcFkrSjPznadDbU7B2zePCfHwKfkSvWR/p79MpQAYAQ/HRAAqSz8lML3BprQ7gC5YkKOIAHUv1IRFDE+/OOGjw+1Ty4iV9PLhh5UBv3vuP+hfeH0EeILWuEzHd/Qhb2Oz+5s2lvd353IOe9MzOA8kBK5qPpsK2rg4N3XrPLv9fUhhCiylgl7bpaZZZKRjhbIug5OVDt7PrM7rqvn/r9FHZ9arfv6rjcLmIkq9eBdKAHACGwX7EG/edKQF4+3Jh1OvSDwxh7kaxN8tRAgsn7gq5QM/XyXoOePWlj52d2N5TIztUcy+E01yr3Huip7jlTYU8t7IUTrsDe/96HPV/YCwDYmeyBaqOMklKjvrPj0KgRQAun7djY2B0AjoyOjt4E4BQA142Ojn7e+/VbAGwE8OnR0dHfjI6Otk6tdEn85je/wdve9jbceOONLb1eCOEzNMuW1dGJxIhUKoWRkRFUKhVfOrlY3HXXXXjRi14EQPab6X7g/9Vf/RWA9owc1KBoAC3J16LEeeedhy9+8Yv4l3/5FwBY9PipQr8T+axbJ1eq9u7d2/J7OgnOKKWYm5uL5LLZyTj//PPx7ne/GwDwqU99CoCUUyq2rzYUQ62zj1JFck0SxCCwp2245fr9J+l1KRg9BsZ/HoCz151wAUyj8cDPKDHUB9yfrl553/35PQ1fXzpcwt6vyNXPnckeSNM2vT1noNIefJYFFfL2f9uBSoOG8/zDeRz13Mv2JTJIa57nAyLnXoWjdLC5q+2+r+8P3r4ipRW8AkBPUrrJ/ei8kwEAzmxje3YAVecRyRjaG9WTW3vw7vVnAwDm7ppvaD0uhMAD738Is7fLQu26oQ2Y6s82la9FCULgnz+bS/Mwdsq/VzpSrjsDqbivKO3+zQQcSpGMAM6aWemDACw0kPjwD1rrV1YhgIZW+lGDUYEDlhySZs84OHjdoYavVX1y+85fi3/aMApmEK1W+quXAVadAcDb/+3huq8v7JADzlU82NcPaLwP9WUAQgTmKhTu+uqWionfLOxjyj2Yr/q3KQSesqGstSYa8NKYyVEkR7x+8wP170Nu0cXeLwdy+H7XRpIJrUZJCsDOhYa5jf9sHEd/Vt/FunQoqAkOWmkMpYTW+2J/Fvj2sGw5Wf6M6vr4Ty//s3RBPlKGW3Rx55V349D/C67BacOCxUTkc7ph39mj1a0RAMbGxt4+NjZ28djY2MvGxsYqY2Njr/V+/s9jY2Orx8bGnuT999vOpqs/fvazn+Hqq6/GT3/605Zev2vXLp85U5bcOuOss84CANx+++0tvX7PnqCI0zkQW8XWrVthWRZ27dqFe++9d9HX1zI2YfmhziCE4PLLL8db3vIWJJNJ3Hrrrbj++usbvn7nTtm4q5uFAYIhy+973/tafk8nh1Bv3SrlZ/fff39L75mfD3qDwk6YnYg3vOENACSYXrVqFVatWgXTNPHRj360atXYtm3s3bsXhJAFLKmOKFQIKv3y4VrcU1//b2TljCGzP7iu5pmJoRTXaj+8fgVwV3oQOzOtSV2O3hA8ePdbGflAg17mDISAEoG/33Q+si9Y4//40P8ewszts1WsgxCiCgj9rH8VMvovMyRZdcW97+v7/blqtVHcHxzTd204BwnNw1YBoDch85mg3qr1bGOmSgjhD1oFAAwktJ5DAMAoqXLbm7ltpu7rah3d9iUySDIBamleYibArBGwr1u/dTcOXHsQd15xF3Z/sXohSwiBu98sny/zHqCLYhyb9P5coV69TKodG/f+9/6Gro21ixDbPnySxBw6DUEgx93NGwEK3f/1AyjsLdZl0BSDVUzLD8koAKLPKp5SgvVDHP++5tSqn7t5Fw9fvWPBbLiCx+SxVSl8bflmfOXU0+V9SJt1PcETtsn98IOdKaz560CmuuPjO3HgOwex50t7/XtRLVv0iVUnoy+hlznb6KmWdxwxfDfSfV/fj7l75jB542SVoVPtdXZ3uh8W0+uqq4afHx7uq+oJnvztJO55232YvauaKSuGesv+fc1pGM64WvfP6mHgf4fW4b1nPQ4bXrcep3xsm/87Z97Fna+7G3e+9i489K/bF7DSexNZWIbHdGlkzo7XOE4xpb5oFwyFX9cJZuLss+XKZzOgEY6jRwP5UzvMTauRTCbx8pe/HABw6qmn4pxzzmnaNP7hD3/Y/37ZsmVaRw3Ui0wmgze+8Y0AgOc+97m+sUU4brnlFnzmM58B0Blwdu655wKQ+7/VkQMKVOuck6fitNOk1Oruu+9u6fXqeA4PD+PNb36z9nzCMTw8jGc84xkAUHWs3vGOd1RJZ//rv/4LnHOsWrVKu72/4wic8xqB20vyyXbv2++vKubDUT5SxvQtM/6/r7s8jwSD1hlep26Sc1reuOF8pF++wf95I5ermT/JfIpJE3/sGYZFBahejA9CgYwlcMRKwfmLNchuk0u0h//3CB78wEOYvCmQOOYeyGH2jjmwDMM/nn8Rxq0UUrpljQSw6MJra+9/7VvwMyEE7npDMDfq7swgLCa0y/Z6UzKfw0IWy/md+bpSOXvGrsrnsm1PwQnL9efDTDlM+qubg4KonntboWYxYleyB70WrxqfoCMII7jmudVGGfv/R95rxkMr+0II3PMP9/n/7vEmddcypa3EkLe+MVVHiUcIgZE1cNIvggXD8N8Nx5EfH/G/NwdMZLdkJHNG9DJnBgN+37Mc8z2BouPuN96DXZ/eXfW68ngZhR0SDJU9vadR53qIGxuHBX7btxLTbzkTI5eu8H8+eeMUdnyi2plQDao2zh/Gt5dtgmMZWmWNAPDcc+VOvvMAw+oXr8JpnzzF/93+bxzA4euPYOoWuVheDrF41zzrQvx8YDVSpr65awBw+ib59f59JnpOCvoD73/3g3j46p24/32BG/F0zQiSj645DSlTaK26lw/Iz7Z90sDmt27yfz5/Xw757Xk8/O+S9RRCwJ6zkffmif39eU/AgUQGwymuVfa3fkRKde+ws7A5QXZLnR5KAczdHSwIJ1YmMPc3J+GhdB96kwv7HlsNf9ZZraxRswvtsYrjM2uNceaZZwJovZD9x3/8R/973bJGIACLX/va13yGrll0wlmvNlQxDcjeLmWLXi++/e1v+9/fdNNNHc1LxXvf+17/+xNPPBFf/OIXq3r23vWud/nfd8Ls4pWvfCUAyUCFwXKjKJfLeO1rXwsAWLVqlfZ81EyxVgH+vn2ywD311FMXeaWe+MpXvoIXvehFOO+886p+/pOf/MT/XrGQimnTGYZB8OKnAL/rC4qPXf+5B7N3zOLQ9w9XAexd/7nb//5f156OtQNcrtBq7e8iOHu9LEIe3LIKmc0SsD/8sZ0oHaleXSwfKaO4rwSaovju8x8HmzKYDPqF9QRY0yuvoX3TFL2nVi+y5HcFEiL1oB1+4hCOEInKMprXHAgjSA8uVAYc/ck49n41AGilwyX8+RULF9oSptAu+e7z1nn2Qn4jKgI7P71QSvjw1TtQ8las7+vph00ZTltua3/6przhxz/tD+4pd111zwLAWMuo7UlmsTLr6pc1GgQnn9xYzTF3j2cJf7TiAw9ASnV7EjzSkO7ejCwO5wuA7dQv8lg6+Jzlw2XsrNPDNP0H+ezNnpjB6Z86VVqRA9qZM0oJSszA9c8/DyOXBfej8V9UO02WQoxH0bNyzyag3WJtg+cU+wDJYv3l1cYSs7fPyfujK1DcX8T8fTnQFAW7UNZBcj/olVef5eGN2/bK8yi9YeGNpezdIxUTc+p/nIwZIhdMUobefE7fLDd2/34TmRMyWPa0arOU0n55nLjNsfe/pZqApiiuvuxJmDSTSDC9MsJLL5Bff/KAiYHHDyBzQvX+ceblSXrw2kP48yvugLAFkquT2FuW+2c4xbXmY5kEa/oFuCDYO9Pa/WTbP5+I8Q3SgXMgxWHPOPGYs5n239uN8ZgHZ+vXrwchBAcPHsTRo0d9+Vu9+PnPf47t27f7/37nO9+pPZ9nPetZ/vfNZGlCCNx11124555gRfarX/2q9nwA4JJLLsFzn/tcXHihHHH3xS9+Eb/73e8WvG5sbMxnhG655ZaOSRpro6enBx/72McASIB0xRVXVA2nDvc0dWKUAyHEZ88WA/lCCJ/pAwJ5o85Q4Oy73/3uojPz7r77bjz96U8HIF0Rj0WsWrUK3/72t3HrrbdCCIHPfe5zAILxCxMTEzhy5AgMw6gC1jrjKWcT/LJ/Fa5+4uNBDIL5e+bxwPsfwt6v7MP8vcGq3vz9wcp/mTCM9HBwW8DI6r11nrlWFkW37TWw8aoN/s/vvOIuHL5eruILIfyelL4z+zBdkVVh2uTaWRhCCdb2ynPz1w8bWPmClVj7yjVYf4Us2PIP5SGE8B3SAKBnW9Y3fchm9Oez7cm9uH7FOr+vSsWh78mCcf6BHO587d1+QQIA+5OSHV3do1e+AwTM2YRtIL1RArSJX00uGEw7d1dwPr1nzdkYSnOszLrae+CGegBGBGZLtEridPff34udn96FIzccRXF/EUd+FCwg3XfRZgDASMYFTWoGZ4yAMIJ73nUh3rzpPEwsr5bt7vjELggufHkcAJCT+vCJ1adgXX+0c5pSgkFvHWEBe0ZQF8yM/2wc+78ZLHLyMvflXyd+4EQ5zNx7v3bmzGO/XEEWOApyhyP3UA6CCxT3B+Bs+7Bsp+hPicgSsEZx7iZ5H7r2dpnLyR85qer3e7+yD+O/nMD8A/K+2H92H5yUJ7MkQqusEQDO2STQY3E8NM5wYKb+dstHyxBC+OAsMZKAN1YMKUMvGDrjBPn1wQMmOAdW/9XCxdXCnkJVL9XqF63CfFnmkDSE1oW0Ez38PFOk+OivUtjwug0LXnPkhqM48J1gJEVJUJQcgpQpkDKhvfd1w5A8h/ZMyfvJCe/Y3PC1a162GollCUwVZA79ptcyEGEfDffJ90zMVl/kj1pDkEd7WJaF4eFhcM6xYsUKnHLKKQ3NOMKmE1/72tc60nOWTqd9Ew4FFIUQuP32231TCwC49tprccYZZ+A73/kOAOC2227D3/zN32jPR+X02c9+tgqQXXzxxQuAxUc+8hH/+1pWpNNx5ZVX4o1vfKM/hPnWW28FIOd2/f730mD0m9/8pm/eoTv+4i+koennP//5pq+744478MUvftH/dyfMLhTYAhYf+nzppZf63+scqN5OKGlnoVCAEAJbtkjXOcdxtLMdKtZ6u/0PMxkYp/VX/U7JvtyiC6cS3Oh3pHp8kwIjq3e4+pNOktfSbx42kNmUQWp9IL/d86W9KO4vYuI3kzj6UykH6zunD2N7ZVV40jJX/wOIAKcukxXOV/+YwIdvzGDVC1b6Up75+3I4eN0hlMcrKB8qo5ww8KTfrsdcSSaSyWrWWVIgkSDY/fS1uCM7hN1/fUrVr/d9bT/ue2f1Yhbf3It3rB/F6ascrOvRu0IMAL0pgBKBXJnghH8JpIQ7/mMn7v77e5HfmcfRnwfyvatXn4IyZTh9tQsQ4g9E1hWUAUMpWRit/I8zYPRJUFHcU8T4zyew+3N7quSVp/z7NtyyVioJVma5FvvzcBCDAAJIWcDDqT5MDFRLnCrjFRz50RG/0H/wlNV4NjsPM0YC6/qiDzEf9DDgZD1w5qGz4adUz8o68K2DuPV5t2Hv1/ZhemwG4EB6YwpG+BjVDHrX5dYISAOT2jEPe/9rH+59+/3408tuxx6vR2/k0hUYr8jj2p/m0OoIAuB5Z7lgRGD7BEXJBnq2LWxLmLplGrs+tRsAkN6Y9s1XDP1z3mGZwOkr5H3oviNyR6+/fG3Va+bunseuT+8Gr3AQg8DIGChUZCJpzTLC4X5gRcZFoUyxe5oisSIh+6pCn/vuN93ry3fXvnINep89gjsPMBAicNIyR2uvKSEEZ4zI/fP5mxP+NR+O3Z/bI2d2ejFxVJ64CUMAXICamsHZsDwhdk/KDzp04SC2fqj+Qr3Zb2KuBHzgBvm8G0hxIOJYiIaGIEvg7PiNcMFeKpWwadMmnH322b4Zwf79+1GpVPClL33Jf12rvUVRYtMmyeV/6EMfQrlcxnXXXYezzz4bf/mXf+m/prboVn1GxzLUbCxA7g8FXn/wgx90rKhuFKlUCtdccw2+8AU5Um/79u14+OGHqww3nv/853fs71955ZUghOC6665DoVBo+Lows5ZMJqtksroimUziBS94AYDmfYgTExNVhjKvetWrtOfSSoTB2bXXXouZmZmO/801niK56BC8c2pj1e/2fGEvHr56Bw7/YQaUCzyc7MGbH/9E3POvQaO+oRl8nL5ebnf3FIXjYkHhftcb7sHOT8hFo+n+DM745WZMFigG0xybBnndAdFxghCC52wNJJWfuimB6QKpkhXt//oB3HnFXQCAOZfhUDEoDJIZ3c5/BJQBjz9R5nSzOVS1qn/ofxc6712aOA+zhoWz1rgQBNpXiKlBsKbPk34WTAxeFCzWFXYWcM/f3+f3Dj2Q6sOvPLnhEzdUYPYY2mbSqSCESGtsAJNGEmf852nY8q4T6r/WIPjYzmFcd5cEBCt7XFCNfZSAcjQV6PcA4157YSPini/tw6HvyWP34+l+/+enrnAig7PlA/LroZqWTWpROY5BCGy6amNVT5WKQ9cdxsP/LkeiLH967cJZtemO7Qky4oAzZR1ediQLFY4jP5QMp1sIFkGtZRZmix7LkBRarfQBIJkG1va6EILg3sPyg535hdOriv7wCIvslgwcD5wxCu0mNwCwZVDu6Ac8cDZy6QjO//65PiNT2l/yZaDCk7IqBj+pmTkjlODkYZnPvYdkPtktWZz3vdG6r1/x7OXYMWmg4hKcuIxjMKk3HwD47xdKZn6uRLC9FFxjRm99SfFtPVKKOVOkEK7QzpifOCJPiG+MWSh5DOaP7WHc1LvwehOuwH/fGuTcnxKgjEQC+cv65dcvXA/8vxuP/5HKS+AM0sTiqU99apWkUBl/7N27F7/+9a/xkY98BIcPy4fImjVrqhgH3aGs8bdv345vfOMbvlzxhhtu8F8zNxcsC37wgx/UbprQKL7xjW/437/jHe/wv//tb3+L8fFxDAwM4JJLLjkmudSLbdvkCvaf/vQnXH311f7Pn/rUp3Z0H61ZswYbNmyAEAL79+9v+Lpf//rXAIB/+qd/QrFY7IghCBAsOITBV22o8QjnnHMOOOd40pOe1JFcFotacKbit7/tnPlrwiJ485OkVOj27BB+9/RTsf6KdT4omrxxCvs/KZnr23qGccObikiasq/I7GHazRPSWYKRjAuXExyco8iemGn42qv7ToTjLbn/x2VFUCKk7ZvOIEDW5DjyLzMAACEI7j7IQBjBSR9cOIz6vnR/1b8NzQ98ACCMYstK+bR/8AhDz7YenPAPm+q+9q0bz/OX8c9fawMu12phLRMCtg7Lwvk/fpME39bf8KWfXSmBZE9C4AWnlLUXRCqfkawsjL57hwmWNjD4+AGc8bnTkD2pmrUa+ew5+PRNgQnFykwHAL63vSdsttGX5LjNbm4OpcZJ9CQEXnRSEWYdFqCVONEjVu7fLb8+sEfg7Z/lKFYAa9gCuASO6161Fpv/fmPD7Qw/dWjhDzUzZ8MZWUSO5yioSXHSPzdvBejZlsWMB856maubOAM1KU5dLsHH126TcsXEigROvfrkBf1VANBzag8cLvMxqN5eXAAAAU4ckvk8dLT6mhm6cBDr/rZGiu+9xBtviLSp160RBDjF2z93HggOfCPAxZIMB2ZlAmsHeFWOumJlL8crR+Wz7Mmf6/d/Xm94+DeHN+KLI/Ic+8vTy6hM2toXiV52XgXLMhx3HzLwxusks3rLboZPrDoZ3xzeiL/dchGObJALWT9ly/ChnwYqkfPW2CAGaTinrVlsCZ0KL3iPwONexzE1p3nEzDGMJXAGKQP7xS9+gR//+Mf45S9/CaBa4nXzzTfj/e9/PwBpeLF3715/YHQn4uyzz/aNEK6//vq6LJ1iFz73uc+1ZeEeN172spf58sqHHnoIr3/963Hw4EFfUvnUpz71mLNm4TjllFOwYsUK7N+/H9/61rcAAFdddRV+/vOfd/xvK0DUaMDyvffei69+9aswDKNjElQVp5wiZV/vfOc7MTY2Vvc1CvRv2bLlET1mykGzUCj4kuIvfelLeMITntDRv/veZ5bwvqdIY4v/KSzH7ZtWw9y4EBRtvqgHA2l5DXKbBz0oGoMlGNb1yWrvxf+dgfXctRi5bAUGL1x4n3nAs0v//uXzuORUG3Chn4WhckmeUeC1F0i26gVfyeLbfzbRd2YfzvzC6VWv/8pINWDTPtAYstg/abUNQgTuOsgwXSAYesIQzv/+uQtWiR8MzY17av88qMW0r1hTg+AZW+S++e4dFl5813oMPXUYQ09YKHffncziL0608cur5rE8xbUfL0Aes80DsnD83O+TeO4Xs8hXgOTKJE75t21IrZPX2bK/GMafjlT3N63qcUE0y5sIISAmQ8YA3nBx2QfwFUJx15vPX/D6A4kM+pIcN715DqsybsOV/8Xi5A3yczywV16z214h8LFvAf/8VSELP5UfIxh+0jDO/vpZyGypvu4HLxwAS9RBXZrnnC3r8cYx5OV2+07vxbZ/qQ/Qhp40hMwJGYznvL7OfAlMs4kLNShefYZ3TxxL4PCc/FuJZQlseE31SJP1r1kHTigu+5IE/ilTbz+Viq0ec/bNPyfw6ZuqF1iHnlQNQLZ58jnFnKUMvdb1hBCc5S0Q/Xl/9YE/73ujVY6JAPCZmxL4m2/Ic2tNP18wK09LThR480UlrPDOpb/ZejG+fcl5WPbi1Ri5bAXMwUCC/+1lG1H2rH0/eUkO1CLa79X9WYI3PU6qh/7fXRZWvKcfDxxlKDEDyeetxbiVwhe3nY5T/vssvOf3/QCAp2yxceBDM9g27Eon5AZD5JvFmVsIvvD24LPceh/wzV9I5W8HhW4diyVwVhNPecpTIISA67q+UYH6CgAvfOELj0khq8DZ7bffXuU8qELNE+skSGwUyWQSF198MQC5bzZu3Og7/n384x8/5vmEg1KKk0+WQ2EVgH3Xu951TI7Zxo1yFfbd7353XRfNT33qUxBC4PLLL++4Wcqzn/1s//sPfehDC36/a9cuXwIaHhr+SIRizm688UafsX7pS1/a8b9LCPB3o0VsGnKxd5rhFV/P4h3zC5uXn/MsWRC4BRe8zMFSuikYWSg+cb1c7t0+zvDq7/Rg/avWYf3l62Atk4W0NWTi4GVbUWQGLjmlggs3yepQdKBvIGye8PqLSrKRHcBV16Vx90Hm5wQAb9z0OKzeYOC0VYHxDtHcvwTIfTTYw/HkExxUXIL3/CiF270CKbs1KK7LK4Pvv3/5PNIJKfHRXRSBAC8/s4yTlsvjsD9n4HGHz8LYk0+qYqp+depmuITiFaMVbBriUkqke6aYl89JQ8Ex+MNuA0/5dI9fRJ768ZNx7nfOwaY3bsSX/xAUuZedVkHK6gygZkkK4Qg8c5uNGSOBK064AK/ZciG+vz2NZU8P3I6/O7QeGUvgz2+fx5p+ea5F7YFb5dXrR2eq2w/2HKm/iGH2Gjj1Yyc33aY9Y0M41fMNFTgzY6zVKBmqAlwA0HtaL9Ib5T1xzcsDd+HNb96ImRLFdJEiYwks7wNYxtD7bKMEo6sc2asF4NSP9GHO8yKhCVrFVLk9Jkbe2+//e0W6E72vBCcNB7LOD9yQwpH54I9YAyb6zu4DTVGc/JGTMLW6Hy/4cgYlh4AQgYRmcAYAZ3n3uT/uMRAuzQgjGH7iEE7+t20gFsHQc1bg/TcErNDGAVc2F3ag6l7Tx3HPu+bw9JNsTJpJfG1XH9b+8yD+cMbmqmHQNmV4z9OLuP3tszApwDJMu+MnALzk1BJOHgmO2+375UVywUa57w7OU/zucBIFm+CUERfXviovJb5CgBoLZ9a1Gq+5lOCXHw+O91WfENh6lYXLPjZSfzB9F8cSOGsStaYWH/rQh46ZacLWrVvR29uLvXv31h3+rNwIlQHGsY5///d/92eYVSqyqNyyZcsxc/xrFmG7/M2bN3fErr5ehGWeta6NDzzwAL70pS+BUoqrrrqq47msWrUKn/rUpwBIeWAt+3rffcF8n1e84hUdz6dZ1Eo7n/Oc53RM7lkbCQO4/ooczlgtHxp/6hnGlZsfhxdsewreuvFcXLn58VjpESGFvUWUDpZAM/rBGTUILj+rgMdvkHncttfApg/14S2/7Mf8287C6PdGceaXz8T/M1cCAJ68xZFuiUJAcL1z12RCxJeDrOkX+PyL5UqoEARP/nQPpooU2/71RHx+22nYmerBX59dweWPk/eBF24rdoRd5DYHr3C89cmSuf/27Rae9tke7Jig2PTGjRh64iDMl27E3/RJ59T3PaOICze5oBZFcmWy2aajBZGGF9f9XQ5vuDhw03vvj1JY//YTsO7Va3HWt87Gfxpy0ea0VR6YdgVoPVYmblCCS7eWccXjyzhnrTyPdkwwPOfzWWwfp6AGBbUoDs4S3LLHgMUE7n7nLL700oL0utB9DgGgSQruCpw8wvGq88s4kkpjwkzilj0GDj1jE5a9aDW+O7QeX19+Al53YRl9qeA+FRWchd0a50Ptv4x6ALTBMnrYhGfkhavwtM9mMfxP/XjlN9LYvpeDWAwgAOcC+aLAjXcG240agz1SDjhTpMiFpmac+L4t2PKuzVj1wpVY/eJVWPfqtSCUYKdnsrBpSBqm6D6vCZNs+edeHIzKuPHhgHkZee4Ihp8yhOwpPfirW6uf9X+3bqbunL+40Z8U+MCzAjO0uw5WXzsnvX8rRv/v2ejZ1oMrr03jxh0yXyEImP7bEFb2CawccFByCJ7+n9kFp1PPSVmc/dWzcPipgWQ2ZQo8cyQHYupn8MEIhJDOnVddXD1796rvZvDn1SvBzxjEe9efBQD4ixNtrB2QTrssw7TLmUEJsgmBX181jzX91YhI3ZcOzlG84TvyGf+CM4L5dPDuQyIGkHrKOQQ//4/gMx2YIrh7bwK3PXx86RuXwFmTOPPMM6tmfIXZiE4HYwwveclLAFSbOggh8NBDD/ls2iMFzh73uMdhbm7O71sCgBUrFjZ8PhIRBmcvfvGLj5lkb9u2bXjNa14DAFUjF2zbxrZt2+C6Ll7xilf4ksNOx5VXXonh4WHMzc35s+B+8IMf4Ec/+hF++tOfAgAuv/xyDA3V6a04hlELxL7yla8c07+/okfghtfm8KPXzuOqi0t4x8spPvfyEh5M94OuTiFlyuuOpiiISWFo7jcDpGQvYwlcf0UOzz5ZPqzmSgTX3m7hBV/rxYr3DuA130rjZw/IwuMvttooHy5j/v4civsaG9BEzocCwuWoTMpcnnOKjV+8IbCE/8frU5hc2Y8f0BEAwKZhjr8+p4JfvH4OH33GvHa5FSAlV4QRnLvKwbqBYFX21j0GzH4TJ7x1M551z2bkmIltK1y88QletSsEEsutBluNHrKoEVie4fjAM4v48Wvn0Z/imC5SfGt7BiufN4J//nUGBZtg/YCLtV6hIrjQPlMMkFgxYwl8+NIifvK6HP7lOUUkDQHbJfjxffK8OThLcOa/SzvDJ57gYGVfCAzpLtIge26UScNHn1fEwQ/NSvkbgOd9pRffW7kR/zWyFQ6luHCjU/XeqEXjkPdInJwDjoQmiew7CpAmjOUpH9mGM/7zNJx77Tm4qdTnr/b/6D4L334wI1lAAOe9ViD7DIH5gmTNTm3ctrZoGAbBxn55Lm/4YD/uOyzPC2vQwuDjB0EIwZq/Xo2VzxvBtbebeMZ/SuS5dbkcHqx7EYR4s8qefbKDizdJ+d7f/t8M/ryPeb8n2PzmTbj9BafjoRl5TV240cbEh2eweVg/c6a2d9XFZbz5iRJ4vPSrWXzh5urrmVCCN38vhT/uDfbHVReVAjtMzTm9569mAAB3HDDwvhsWAmQjzXDD/TLHvzyjghvfNI/VfbJfWXcPFDWILwO8YKOL37xxDuevD66la3f34FLnHPw5KynlE5er+xBg9ltILNfci0/l+gejwJ/fNod3P10C62dus7G6X6A3Ke9JsyV5bJ50QpCr4KLpAkqr8eSzgDf9JXBSyJz7p7cfX3Dn+Mr2GAchBNdffz2uueYafPKTn8TZZ5+9+Js0hir0w7F79+4qJu2MM844liktiC1btvguiC972cse0VxUhN03L7vssmP6t5Uhyde//nW8//3vx7Of/Wz83d/9nf/7Cy644JjlQinFP/zDPwAA/vVf/xXJZBLPe97zcMkll/isWjcwnWFwtnnz5o6MF1gsLAM4f72LDzyrhBefbePSU23c/a5Z/PT10ua7dLAMZkn7c+0rjQCIKXX2ggt89hkzuGhDZcFr/vduCw4nuGCjg9X9AsLhSK1JgmUM/e6xRBbW5YkK5u6V5kNblgWA6KFx5ruVAcDoOgeEAKevcJDWPANOhbXCAgiBKHO845Sg6v7KLbIImi8DrmdO8LHLCiAEsGdtuOXOgCEQORy4dKCEyngF56138aFnywLyph2ySFQF/kWbnaBwFaIj4DX8NCcEeN2FZXzo2bIw2jkhf3ndnRa4kIlsGPSKNCGk6V8HZI00RSGcYBmcUuDlo8G5/ZFfBGzVxZtlkebkHE/6GY85m5gB/uuG4LrYdxRNB1uzNENyVRI0QfGRX1QX3P95RwYkQXHHDuBPIaPkd70MWLsi+n5zyxxPWxmwVC//egb/dH0Kb7ou5csmAWnwcuV3Arnu6R4Lq32+IQvsH9/7jICFednXMtgfmjM2Xwq+V4sg1KIgHTiv1a3ttReUkU3If/zTD9NVDNpknuB/xgKQ8aPXzuOdTyx0xD2SGATPGS3iVefLz/2fv0vi879fuPhz00557b/6cWVsHJJyxtTalPbFYmLQqvv/qSs5rr6sAELkz35yf8B8fvmleZhqtwkBlujQwrWXD6XA3z+pjN+9eQ5feok8z1f2VtNiJ60ITnS3oKf3lTGCT76Z4v5vUIx91MZXXn8E73i+/pmynYwlcLZImKaJN77xjXjTm950zE0TRkdH8da3vrXqZ5s2bcILX/hCANLoQpkpPJJx7bXXYufOnXjd6173SKcCIABIQNAHdqzi8Y9/PAA5d+5DH/oQbrjhBnz961/3f3+s2cUrrrjC/75cLi/4/bEGr/VicHDQlwuHmeqOxyKX88pegUHPCEQ4HEafBZaU7JnuMHsNmIMmeIWDT5TxjSeP4453zOJNTyjBZNXA62/OLYOXOdwSh9lrwhoygYga/UZBKAFJSNtxlmIoj5eRKDs4d50soO85xPC3/1cWi6+9sISeBFCZriC3PaffOdKL/jP6QA0CJ+/g0q1l3P9PMwDk6vVzv5jFxg/2AwCG0hznr3fh5BwUD5Zg9rKO9AmKMgcIQXpTGqpx4+w1cv/86D4L7/pBCrfslgXa3z8pfO0R7TPFgKC3RrjCL9Y2ezOHfv6QiYkcwcd+FYCOLWZRytA4OnJOA/K8VsyZig89u4hr/rKa7f3ci/I+eC2Pl8FSNDZztn8c+D+BuTB2HgQ+9mNz0R4b2wV2eGD26stknlwQXPOHNP54f5DTp95M8MFXxzzXCfCWs+bwkUvl39k7zfCFPyTwf/+UwB0hN8AbQgX2ictdPGdbBfasrd2BNAz2zl7rYu8HZrB1mYvxPMX/3h0AkO/dJfNZN+DiqVvlOU8tisQyvSxM+BxY3iPwtZcHQPZXDwUsmZJ7AsCvr5rHOUNl2Lvz2u+LAABGwCsc7zo3cMx+94/S+Phvgs8uRDCEeesyD4xwdIYxZ1hgoHHSCo4DH5wFo9Wf/zkn20GOvEO9wZ7cuzYfNSM0DM4uPaXij5MAACfnyXVX6ZPrnrIeeNIpJaT0iyc6GkvgrMvj6quvhhACn/zkJ/2fKUnj1q0LLa0fiTAM45iDoGYRBmfHWrI3OjracJTAxRdfjGc+85nHNJ/BwUFwzjE2NoabbroJf/7zn/HlL38ZH/7whzE/P/+IM6+AHAR/6NAh3HHHHfjEJz5xzP4uSxvgdmsPb5pkMNIMNEH1m2+ofBLe3BmDAERgTb/A+55Zwt3vnMOXXxoUJY/f4CD3cA7WkIWebVmwFNPvRkUgnbwIgZE1YE/bKB4o4vpXzeGsNdXys3PXyhVJJ+civSHdkb4TPyhQmbTB0gxDCY4nniCLjZt3BU/4M1a74A5HcX8RlBEYfab+Pg8AIIDRw2D2mVBIf8syjo1Dcn986ZagWFvdV109NZPXRc9HbnP+wRwqkzbcootz1zlYnuU4Ok9x0of7kPeG856/3sbz1+XAyxzzD+Y6wjAAgJFdKLszGfDX51Tw2b/K451PLeJnr5/HX54hj6MQAkaaYdlTlkVeDO1p0q76gWsN3L67eZX29dsslB2CDYMuXnJ6wB5d/csEtu+X5/a/XE5w1V+S2Au2hBJYBsHlj69gx3tnq3p0lAX7PYcovu8Bo++8Koffv2Uea5K29lmLKp/wolXaAv7qLMl0TnmOkjfuMHxG+K1PLgeMMCPoO6NXaz7UolX5PGGzg8/+lbwX3h5yS/yfMbl/LjutgtNWuSgdKIGl6KILcFFCgY9kycbrLwqdH79K+vfh3z5sIF8h6EkI9Ks+SqJ/xhkgF1bqKScsAzhpeXA+/e155Wpn0U4YSUH2mTYz9AiDs789v1ohYmQZEssSnblfH2exBM6Ok7j00kuRTMrVhJUrV+L5z38+XvnKVz7CWXVnjIyM4Ic//CFuuummY852GoaB66+/HvPz83jWs56F//N//g/uvPNOOI6DG2+88ZjNowsHIQTnnHMOLrroIpx11ll49atfjX/8x39ENptd/M3HKJYvX44zzjgDpmku/mJNYQ1JpqqVIETOGKIJ2hH5FyALkcqUDcKq7Y2HswLPPdXG1ZcVcP1r5rEy5YJlDbA0g/n/27vzOEur+s7jn/Nsd6+1u+lutm5oEBpQkIOAihghqGjcdTDJYIhxMG6JY3Qct6hxi1lGk5hoJuu4JhPjJDEqRhNFjTEe1ygoW0D2ptda7/o888d56nb1CvStu1T39/168aquW7e7DvX86t7nd5bfbypm/FFjjG09fA+ph8sYQ1gMKRyXENUiTGQori+wcMs8H796vluh8JoLGzxja5OZH85ijN8adritYz2PKzCE5YCw7M8yvefpi5y+dt/tKu94yjxzN84RVf24x89Z2RvGJRmGaCwiWVaqOgzgj56/76rQyx5fJw79ilZrpkXaTPuzchbiE8ZqRGNbg4U7Fign8OKLDlwxf/eT50nKAZ16x/f+6pOoGh1y4uAF57V47WUNHn1iB8iYvWGW2RvnCCvRAU3YHw5jDJ/8DcMlj4SfvwKuuAD++wv2fv3tfz3JzpmMxdbB//7r/t5ndxdt6tC6Y4FbXn4/APWW4au+5zqbNxzx8PYda2C6q0PjpYwvvXK2O+Fwe77y8v4v711FOCnvlZW2MpKpZMVXYkxoDijGsLR7YMeCH+e37tx7bS47vUWWZqRtfwZuxdtVFIIDVmGWtuPeM+P/32/bEfCRfEvjVY9ukqUZ0XjkX6v70bIi8gmsCeE3rqzzwDt3A1BvG2by7Z7P+3P/3trqLO8uYFa+Dxz5Vt1DvI39wfP2vha9+on7FgvJ0qwvRYDCYnjYgh7nneBfrzdPd7o7MZaYJKB62qF7fB6JIXYJ6kkfatlIPyyVqx8bGyNJVtn67BA87WlPG+r3r1arfOYznxnqGOTwokr0kF+4M/wMZb/e8MHfiBgDpuDLjzd3NmnvaREUQ4obirzoMX6WsfFAi2QiJiyFGGNIJvvzejB10SSdxQ7NnU1au5uEpZCwGDBdSvnyq2bZPm84rpbRaaQk0zHF44tUNpVpz7Uf/B8/UsaXZ48nY2Z+OMeWR1T511fPsmvBMFbMCANf9ryZJ66tHa2+nBH0Q8kwYUBhXQETGhbvXqR0fAl7Uoe/+6VZ9tQNV271P4sszZi/dZ6gGBKW+7PN0oSGrJX6BLlgSBv+rva/Pa7BZ26MuWNnwJnHpTz75Dk2Lc4RTkTU721QOaXctz5AQRIQVULSZsrcTXNUtlQO2sA9a/seZPFEzNg5vU80POdSw3Mu3fe6v/UXM06/KuW7txc4/T0Fjh9P+djVc5y1Ye+d5PyyifxrL6oTRRHpjib2uAbu/gLf/JH/2vJCA70wIftsA54oZVx5Zosv3xLz8W8lXH1Bk0993/9+P2Jdh1OmUxrbm2StlPIja8RjKzuZtZR4LDedJ2c75/3r3gOz/uPbr1xkw1hG/d4GrT3t/hTdiY0vrd7JuhNWSysvP7g3pNne25z68ae0uPwRbToL+e9AFKx843n875kxe5NqY3z1zNt2hNw/ayjGe3+ZXnRhg4XbF4jGYyDrzxm4+NArVeds7PCB581zypqU4/MWFd1VtjQj7MMk0YNNGFxzYZMnbGlz0kRKsn8GkmVaMsopOVtF1qw5sOO7iByZsOy3A3YaHVq7WhTX752h9qWJDWkrpXF/AxNCWApYc0n/tslmaUZUDck6fmytPS3Sdka6f7KT+bH0awVvubAUEk/ERGMxRIawFjF38xyVUyscV/Pvolkro7CuwNRFk/1fqTb+ZmTsnDHa8/5cWTwed5uEQ/5zHIsISyGdYqcvq1Tg7yOWbraiiYjmLU3ac22iatTtQbektadNcWORrJ1SPqXSn+1Noekmr2E5pvFAg+bOJtXxmM9etZPWriblzRUW71oknir4LavjEeXNJdoz/Uuoo1rkW1AUAuZvXaB8cumA7Y5Zx1+zZDImnujP6nmtbPj9l6Y8/z3+jv3uPQFP++Mazzy7ycWb22wcz7juR35cm6c6nLW+TXtPQFgKOGVNivMLaBw3BeeetkKDMoagWwrdx8QLHt3kDf9Y4pbtIT/9h3t3N1z3y7OkTb/KmDZTClN9SIZCv2Wvs9jpTiBM5r3YPntjzLfvDNmW92RbW82TWgPxZNSXflnGGJLpQvf3Cnx1XYBWx7DxLRPd525Zk9Keb7PwnwtMP2GaZDJm7pb5g/2zPekstGHSEEQ+KTKBYV0t47Yd8Nj3jbFlWV+2N11Rp3N76ifZSn0oow+YiMP+7P/Lo/ddIm7c3/Bbw4uB73O2wvx2+L2fL/2Mup93UjaV2kTRfq8B+fNW/D1kla6cKUcVkWNSkPiZ1ea2BmHBb/FaMnvDLJ2FDs0HmsQTMWE1OmgD25VkAvz3KQaUTih2tzwtL3GepRkYKKwvUDhuMFtkw3JIPBlTPrFEMhFRXF9g8a46jR1NsiyjPdMmHlvhZriHEUSGeCymenqFTj2lfm+dtJH6yoyLHbJ2RjwWEZVD4rGob8mZWZacTZw7jkkM9bv3bh1K2ymNbQ2yjr+ZTqYTkrWFFd+C2h1P5LdNJWsS1jxhmrAQ+DNlP5qjvq1BYWOJ9nybsBRQWFMgKAVElYjqlioTj57oy5gAokpIa0+boBASJAdumwO/chZVI8bOGSOq9G/O+JmPh9c/exfnTDc5sdZmrmH46LcKvOJvKjznT6t86Gt+gmbjeOoLpQQ+/l/3pGWVCy+HYIVusv0W5n2LuNQKe89x3r7T3zz/yqV1qgXoLKTEtchv/ezHmbPQ31gv3LFIY1uD1kyLU6b3XrAr/qjWLQyyrprRnm2DMT1tQ30whfUJ7Zl29+eTRBywHQ7g9HV+ki0oBMTjEcUNRaYfO7Xi45k4f8K3VTAwe+Mcje3NfSrZ3rLd/yxOnEgpxX6bX9pM6cy1+3LHbUK/0yJtPbQt+lk7I6z63RjR2Mr/ri1/n2zNtJi9cXafrzfua7B4596+dXM3zfmxZ/2pGOsnrVZfhqaVMxE5ZgWhIdlQJFlXYP7meVq7WqStlLAYsnjnImmaMX7eWF70ob9qW2tkHdj59Z2UTiwxe8MsQbT3UPvMjbMEoaF6RpWxrf05R3UwxhimHjMJ+FLQczfNsXjvLn+gPDS0ZtqEByn80Lfx5MlWWAjJWikmNjS2N0ibqb/xKIYkawtUH1Ele4g3LEcig+4ZEl8oJoBlYVK/p0Ha6NB4oEn5pBLj5431dbXTGOPP5OWz4WEtIqz6Vbuo4rc6thYyyKC8qeTbQgzg4H1YjfJEMORQ63NZJ/MTDn08/wa+V961T57jZcfVMYWAyz62hpseODCxeOzmDnM3zeWtKkJOKmf823tbuHsSrl7Rmk5+a+ziHYt0FjtUTqsQFkJedWmdH/xVhbmG4fWXL/JrT/LnBn28R8Q1+jcZEhji8dBvT5431Jp1nrCpxPX7FVI5dU2HhbsWCUshyWREp92fUuXlk8os3LZAZ76Tn2HM+IeXzLFr0fCv/xnxlVsjzt7Q4QXnNcnu95MBJu8M3o/ft2QqwcwYDBBWQtLFDu99xiIf/ua+k2XPOqfpd13EBhbwK8J9uGZBEtDY1qCxo/mQJn5MbIhLIaWTS/79ZYWZ2K8Ep42Uxv3NAybHsnZGkG9t7tQ7RLWI+VsXqJxa7k9yFgercvFMyZmIHLsiP+ubTCXsnt1DEPob3KC8tL0nJSxHK9+o8yCCKIAIqlsqhBW/nbC5uwUZpK2UwtqEsbNqAxnLIccYB36bY76a1tzeIpnynw9sDPlKYpAEvpFqJWLywgl2XL+TLIN0seN/hoUA+tFPbGkccdBdOQsKQXcb2OJdi0S1iLAcEETQCVPKW8p9uRE6YEyFoLvytObx08zd6iccTOhXG1s7Wn4rXRz0dYVqubAUEESGoBQQtFOyVkqn3umePcs6Gc0dTcqbDlNmcYWY2K8MJWsSsmaHv3z6Tt7zzXEurs5xZyPh4z8qM1XJ+MWLGpi7Agj968PUBZOsKQVcuMI31+WTy/53aG1C84Em7Zk24dqQK85o8+3XzvDFmyKecbYvutFZ7NDY3mT83P5OFkXVkLQO0XhMa3eLLAj56LN20RkvcP9swGUfqHH56S02VjosFgKS6Zh4KqGzsPjg//gRCJKAypYKu7+zh7SZUr+3QW1rlbVVuLyyh6c/tUBYyH/3AggK/elFuQ/jz7ZGY347ZxzC9a+a4S++UaDRgV9/Sp2xTov6fS2SqcT3N4voy2RREBl/HvohVIDtNDqA387cj3Ovfjz+933hJ75VR1gO99kma0LTTZY68x3fCiY0zN88T3HDyr+3BfGB5yhXAyVnInLMCuKAIPHnSgAqp/kzOUsJQJbykN70VlLpBN+7sHZmjYU7F1n8yQJzt8xTOqFI9bThV9g0oU9EamdU2XH9DuKpYl/65Rz0exvTLcgSjUcka/xNajKZEJR8Zbelao79Nn7uWPesh285ENJppGTzvpHyusvXsv0rO4nigLg2mCqkUdWf24L8JijzhW8yfL+6tO3LZ/elKfchBFFeSCcwhIWAxv1NzG7f6y1tZyRTMWkzI6z0f0x+6yesu2wNMz+YZeOuPbz/gm1+AqQxx69s3cXEmTWCAObzVhLTF0/1bWts6YQSu7+9p3v+Z3kbiqlyxvPP9eeFZn44RxAZokroV4an+7fCOPmYCXa73US1mPrddconlfzqb5By6hq47c17aNxXZ+6Wlt/OHPrKrv2oRLgkLIY0d/qJqngsYvGORcqbyqTNDp25Tjc5C0KDSUzf2p0sZ0JDPB7Tnm3TXuhwalDn7RctEo9HhMWQ1m5/jjIoBKy7Yi0zN8wS1Vb+ljssh/lZ5Yz52+Ypbijuk3gt3rVIcaPfJj9/2wJRKaT2iEkK6/s3yRcWQ8haBCV/dGDhjkVqZ+TvXaHBkJGlGa3dLSYePc7sj+f8ts9+tD5YpcmZzpyJyDGrsL5AsjbxVeWqEZXNZX8WLQoobCgSFIOB3sguV9xYZPIxEwRJSHFdgcqpK1ti+EiZ0PeyKW4ssu6KdUxdONn37Wjd7x0Zph/vz5EEkU+sl0r3h8WQIDJMXTQ5kLEsJfZLameNEcQBhbUJUd5UPCgYKqdUKKwdzM9nzROm97kBjMZj4il/7i4ejwhifyatX7PmB2Niv1IXxL6gTNr2Wxjb8x2ydsriXXW/7TLu/5iMMb5Ka2h8j8BCQDyZdFdaCqUAOintuba/tkF/GobvOyb894kDOnNt6vc3mP3R7D4V+MKST2SjatTXJAggHouZuniK6mkVTOInYjrzHRbu8CtjQQAmy6icXCYa8+0qTEBfV6tMaAgCQ1gMKByXdJPYIDLU729Qv79Bp9EhSzO/vXhAhYmifAtxe7ZNlkKYGBrbfMnP1p4W8Xjc3ao3trV20L5/vQorIYW1CSY0JGsTGvfVyToZjQf8edfOQofOYsev6Fci0lbqt/r18WcU1iL/e5/vRAlLAa383KAJDSYKqN9dJ56IKWwoEFX9Sl4/KiEvHQ3I2n3sv9kHWjkTkWPW+Nn+7FaWZsS1MO8dlpC1UmpnVGlsa/T95uxwTH7Y3iRmxfu/HLHUN8wOiyHhxsHd5C9ZflMRJEG3V11hbUJ7tr3i5cUfquL6Ao0HCoSFgNIJJX/tkoDCumQglTUPNabi+gL1e+t+EqI4mCqfy5nQJziTF01Rv6fOwi0LfkvsjhZkGVHVr1wP7Mx+/n2CJPA3kcbQWQgg6BAUAuZumfcrQknQt+bc+4/H5Cs+8WRC/d46heMKdOop5AmaiQJKJyX+RvMwDX5XSlgK/Sp0bHz7h6pP0JaqSpo4oLa1ytyP5/z5rsD0t79hZDCFgLAYUN5cobWnzcwPZ4mqIWEMnfk27ZkWY48cozPfGch1C2JD8fgi87cv0p5rUzyuQDydkLbyvoLG94aM+lR9dO84AqYvnebeT91HYU2BrJHSWfCFkZq7WoTlkIU7/G6QZDqh1Un3KTLVD/FEjAn9qpmJDIEJaNxfxxhfcCeqRjQbTcJq3vIg9q8R/VrxNIGhPd+fM5H9ouRMRI55JjCMnztOWAqZumCCNJ9lC4vhUJMzgOLxBSqbKwNvqH4oUS0i7uO2qoejenq1OyNaPaPal3LeD8f+Da9NEhAUh79BpbjB3xSFSdD/8zj7MYFPPMJCQHF9gerWKoW1Bdq726SNjj/XVQoHshV1f9OPm2bm+zO09rT8Wc4M4mpEPBn7ao2DSGTzvllBFGBKhnCP/1ks3DaPifzqTBDB9MVT7PmPmcFtIY4MJgkJigHJZEJa6VC/p9FdJQ/LoR97ZPzPqc8rZ2HBF7tJpmMqWyr+/JbxW88JfY+88XPGfJGJPhcoWloNCgq+96MxvuhNXIto3NcgyzLCYkDtrNpAXrd9Tzdf2deEhoWfLFA6vkhrpuP7DLYzyLK8CBB9f02KxyOCQt4PLl89i8ZjFm5fIFmT+DOWu/YWCzGh8Qlan1b0w0pIeV3/z7SuJCVnIiLQPcdhQkMYGrI08ysgA6hodziDrMz4UITlkIlzx4c9DIB9tlMaM3pnC6YfNzX0+Flu7JFjfW8Jsb+oFnXjJSyFTF88RZb6s2YmMOz8xi6iWti3/maHE49FRFVftdIXLkiJp2KfPM62B7KleSkBNLGhtLFIe0+boBh0i34U1iW0ZtpE4xFTF08OpLDM0niCxFBYV6B6eoXZ/5hl93f20MCfww3igNLxRYI4IFmb9HdFL/Dn2sKyP3NXmE5ori3QmW+TNjOCQkCyxm9PH8hkmoFoLCYs+kIcJvTjivJrVr+77lepBzihZkJDZsAUAqJaTFgKWbyzTrImJuqEdOop0UREMlk6aCP4lbQ02ZI1s25j86Dpt4KGlZCxs2vM3zrfXSkz+bbe6YtXvvXB0r8fDul4wpFSciYichAmGKGthLIqjVJiBlBYO5xKn/tXFzSB6T5WPqVM1OebxcOKfFXGoOD7RZVOLlF7RBVSBnIqPwgN8VRMVI1IFztEYxGl9UXIoL2nTW1rza9OGTPQVU+TV/U0Jj/fWfE31iYyeeGdcJ8CRdGp/budDCJDUAiZeswkQRJQ2FAgSzNmbpgjLkH1zGrfE479TV8y5VdXkxBDRm1rjWQ6Zs93DZ3dnYGvBC8lQeVNZbLmnD8vWA39NawZ0maT8XPGBvJzCoshUSWi3Wn5bY2hodPxP5OwlI8pMN1V4GQq3ueMpSg5ExERkSGpnjLcCRAT+WbTk3aCXd/aTWEq8Sseg7q3Dv2sfvmkEp1GSuG4AvFETPnUMg/883aisWhgbQ/2Z5ZVqg0LIYU1CYt3LlI8oTTYs4v5VrxuOXbji0oEiS8ANOjEbGkMhH4V0QT5+Spjuo3LB759OP9+tdOrlDYW/RnBZkqQGKLJmNZse2BjCsshkxdMsP3LO/IKmgE0U6JK2C1s5Ruw++SsdEKpW6VYPCVnIiIicmxK/cpMVI2YfuzUwLd9mnBva4OwEHS3X3VbEAypmAz4M5RL201LJ5UwkaE122HSTgx0HGExZOqx+1ZhTaZiyieXh5KYLVfdUiGZ3lsBtXJahYXbFwd+3YLIt84AX3AjqkZMFCb8dt2FDvVtzYGOKUjy2M3PVIZJQOHkAuWTfBJmQrotbORASs5ERETkmJQ20u6N9aATM4DJx0wespfi5PkTQ00+kql9z3SGxdBXJRzC+cD9z9oFSUBl8/CLPOy/4lM9vUp7pkOnPtjqgGEtItyvEMrSdTKxYfycwRQn6crbKwRRQHlzmbTe2acdTFgKh55YjzIlZyIiInJMqmwukx5fHNr3P1yhgn5XHXy4kumEqYtHo1LrqDLGMHnBxMDPUE1eMHHIrxljuhVbB2WpUuPkhRMHnfTwZ/QUS4cyWr/5IiIiIgMyjFUgOfoNuhjQqLRaWa6yuXzIc26DThZXGyVnIiIiIiKyYkonqsjHkdJpPBERERERkRGg5ExERERERGQEKDkTEREREREZAUrORERERERERoCSMxERERERkRGg5ExERERERGQEKDkTEREREREZAUrORERERERERoCSMxERERERkRGg5ExERERERGQEmCzLhj0GERERERGRY55WzkREREREREaAkjMREREREZERoORMRERERERkBCg5ExERERERGQFKzkREREREREaAkjMREREREZERoORMDmCtNcMeg6xe1trasMcgIqL3MhFZjZScCQDW2jOsta+11p4I6A1NHrY8hj4JPD//XHEkD4u19tRlf1b8yMNmrT3TWvtb1tox55waucrDZq093Vp7pSYaV4ej8X1DydkxzlobWGtfB/wlsAl4LbB+qIOSVcVaG1lr3wC8D6gCTwDQjZE8VNZaY619I3CztfbX84ePijdZGQxrbWitfQvwYeALzrmZYY9JVh9r7dXAx4HLgHdba7cMeUhyCEfz+4aSM5kEbgAucc69HB/Ya4c7JFllTgZ+AjzNOfdkoGyt3TTcIckqEwHfBB4FXG6t3eicS621eo+Sh2oSPzn0ASC01v68tXbrkMckq88Y8Arn3GuAO4GrrbXHD3lMcnAxR+n7RjTsAcjgWWufDDzKOfde59wO4NP5448CLgfa1tpPAV/R6occzH4xdCtwa/74JuBmIB3i8GQVsNZeAbwI+BrwYefc5/PHPwu8DXgJoNcfOaRlMfRV/O6PvwfeCDSA64HftNa+1Tn3reGNUkZZHkNXA/8K/BmwATgd+DrwReC3gG8Adw9rjLKXtfYpwM/ir89R+76x6rNLeXistT+DD+BLrbU/mz9mrLUxcBbwauBHwBXAcUMbqIysQ8RQCOCcux2wwOb8cb3GyAGstb+Cf635S+Ak4P1LX3POvQs4w1p7vnMus9ZqElEOsF8MbQJ+xzn3VeANzrlnOed+F/gCfnvaUXMWRVbOshj6P/j3rHcAfwRcaa19JXAtsAufsCmGhsxaW8RPxnwMf/zmnUvXJH/fOPNoed/QjdOxx+HfrF4NPHPp0LRzruWc+5hz7nPA5/FbGx8Y5kBlZB0shjp5gg/+hfNnAJxzWkGTg/kicE0+6/leoGmtrS4l+cBb8G+8LwPOHdIYZbQtj6HfBIy1tuyc+86ym+iv4VdBdAZWDmZ5DL0bGHPO3QW8CdiJP3v2ZmAKFEMj4DRgMb9PfQd+C+pTlv2+v5mj5H1DydkxYtnswr3OuXngP/FnzV6efz3IP74Qv7R/B/7NTjNFAjx4DLF3K+MisM1aWxr8KGWULYuhHzjn7lt6GGg45+aWPTXCF5Y5Gx9jIsCDxtBC/vXQWvtf8asgXxvSUGVEHSaG6vnjNzvnPoo/g/9B9Bo0NMvvQZ1z/wFssNb+jHOuBfwt8LxlSfNR876xqpf95NCstRfh91F/Aviec26PtTbOAxrnXMta+wngzdbaaWCXtXY9fmbiV51z3xva4GUkPMwYWgPMAh38i+LXnXOLwxq7jIbDxZC11uRvqjH+nCL4GeoHgBrwWOfcd4cxbhkdRxBD48A0cB5wrXPu20MZuIyMhxFDN+XPnwZm8Kuuv6ozi4OVX68J59zn8i2KBkiccw38GcA3Av/gnPsHa+211tqfcs79C1DhKHnfMFmmVdqjTV5S9InAJ/EVrDLn3Dvyr60Has65m/PP/wfwSuA659yLhzNiGTVHEEOvAL7onPuFoQxYRs5DjSFr7cuBjfidHGudc780pCHLiDmCGAqBSefctUMasoyYI3wdWuOce8mQhnxMWkqSrbXXAu8E/gpf8OPflj1nI7CA3wr/Y+Av8uf+ztL9yNFC2xqPTtcBz3XO/QHwJWAPdCvsfZ18L6619gL82aEPKDGT/TzcGPpDJWaynweNofyM2RXA04F7lZjJfh5uDN2jxEz2cySvQ0rMBm+phdNngEvw18paa6vQrar5Dfz207fhd+l8GLjvaEvMQNsajwrW2hcDzwZemh9m/fdlhRhOwfehAvgWcKFzblv++T3AC5xzuwc5Xhk9iiHp1ZHGkLX2Y8D1zrl7Bz1mGS2KIemVYmh1sdY+EV/I4w5r7SeBLznn7sy3lm4BLgX+Efg2cP6ye4/3WWs/6JyrD2Pc/aaVs1XOWjsO/DR+v/RPWWuT/ZrwnQR8Lv9zyzm3zVqbADjn7tZNtSiGpFdHGEMFAOfcX+mGSBRD0ivF0Kr0HOC38//Ow/cpA5883wOckhcX25Nfr3hZQZejMjEDJWerWr5Hd49z7ip8P44n4WcallsE1lpr3wK8PP87zUGPVUaTYkh61UMMNQY9VhlNiiHplWJodVlWhfEnQMU5dwO+YMv51tqz8oJi1+GL+/wt8CZrbeB826ejvliGkrNVxlp7cv4xXFbFZqn57w/xfaeq+WxRAfgl4HX4ErHvPRaCWg5PMSS9UgxJrxRD0ivF0OpirT01/7hU/CPAV+cdt9ZOO+duAb4CPD7/K48EngF8E/gNdwz1TVW1xlXCWlvGV6g5Ed/XoWWtjZxz7WXPOQ54K74vRwjcht97/ZU86OUYphiSXimGpFeKIemVYmh1sdY+A7+aeb1z7jfzxyLnXNtaexbwLOA7zrnPWGuvAkLn3EettY8Etjvn7hna4IdEydkqYq39XXyVvE845/44f+w04HLgr51zO6y1/xN4KfBl4PXHYlDLoSmGpFeKIemVYkh6pRhaHay1T8L3JnuNc+5L1tpSvmURa+1SwY86/rzZTfiqmf/snPvDYY15FCg5G1H5EnzJObc7L77QAn4Z+D7wKuA1QAa8D/h/zrmP5Icm/y/waefcB4czchkViiHplWJIeqUYkl4phlaX/HqVnXO7rLVnA08FLgQm8K0M3o/vV/Ya4FPOub+21p4CXAPc5pz78+GMfHQoORtB1toXAu8APuuce8Wyx38Pf0ByDN+5/uP4QF6+lL/P0r4cmxRD0ivFkPRKMSS9UgytLsuu1+eccy/PH3sGcLZz7l3W2ucCj8I3Bb9xeXGxpbNowxj3qFFBkBFjrS0CFXw5UWOtfcqyL/8LvtfDHPBi4Np8z26y9AS9EIliSHqlGJJeKYakV4qh1WW/64W19sr8S190zr0LwDn3SfJ+c865prW2229ZidleakI9AvKKQ6/DN9r7vnPuT/LHS8DPWWv/yTnXwXdN/2VgJ/A3+GVhnMqaH/MUQ9IrxZD0SjEkvVIMrS4Pcr1eaK39nHNuftnzx/ALQ0vXSwn0QSg5GzJrbQy8BbgFWI+vaPPM/Mv/DFyGn4X4IPD7wOOccx8ZwlBlRCmGpFeKIemVYkh6pRhaXR7i9Xox8L/z574Q+G/A3znnvjD4Ea8eOnM2JNba5wBrgC8Af+Kce1L++J/i9+H+tvU9O04G3gn8O/B559yN+fOCY6nngxxIMSS9UgxJrxRD0ivF0OpyBNfrG8Df4ReEtjvn9gxn5KuHzpwNmLV2rbX208ALgK34sq/brLXX5E95G/A8a+3afP/tGHARfjai++KjF6Jjl2JIeqUYkl4phqRXiqHVpYfr9Wwgcc7dqsTsoVFyNngZ8CHn3FX4ijZb8VVrzrbWnuac+wm+AtGT84OS5+P7QzzJOffjoY1aRoliSHqlGJJeKYakV4qh1eVIr9dPOeduHtqoVyGdORu8HcDnAZxz262164FZ4GZ8z4eXApPA9/KDksd8vwc5gGJIeqUYkl4phqRXiqHVRddrQHTmbEjy/bjjwMedc0/NH/sQUAIS/KHJWZUWlUNRDEmvFEPSK8WQ9EoxtLroevWfVs6GKwK+aq09H3gK8GfATc65XcMdlqwiiiHplWJIeqUYkl4phlYXXa8+0srZEFlrnwr8PfBF4KPOuQ8PeUiyyiiGpFeKIemVYkh6pRhaXXS9+ksrZ8O1E3gD8H41TpQjpBiSXimGpFeKIemVYmh10fXqIyVnw/XvzrlvDHsQsqophqRXiiHplWJIeqUYWl10vfpI2xpFRERERERGgPqciYiIiIiIjAAlZyIiIiIiIiNAyZmIiIiIiMgIUHImIiIiIiIyAlStUUREjirW2l8Dfgu4xjn3F4d4Thl4HXD7oZ4jIiIyaFo5ExGRY1EZ+HXgF4Y8DhERkS6V0hcRkVUvXy17PbAN+CZwNXAN8DTgcqAE3Aa80Tn3KWvt7cDJy/6JtwHvyv97IVAB/gl4mXPugQH9b4iIyDFOyZmIiKxq1tpHAd8Ffgj8Hn5FbCM+OVsH7AKqwEuAE4G1wHOAjwI3Am8HfgA8F3gr8CHgPuDXgOucc88d2P+MiIgc03TmTEREVrsn5h//l3PuT621JwJvAkLgLOAqIFn2/E3A5/M/b3POfQLAWvvn+WPXLnvuT/dpzCIiIgdQciYiIkcLs9/HGL+98QvAbwOvxG9zLAKH2jbSBp4OdPLPdTZbREQGRsmZiIisdl/KP/6qtTbAb2dcrgKcBjxu2WMzQApssdb+HPBV4NOABV6ET+i2ApvZu8omIiLSV5oRFBGRVc059z3gtcB6/OrYl/MvtYBPAOfitzZet+zvtPDl9ieAjwCXAO/OH7sE+APgqcv+LRERkb5TQRAREREREZERoJUzERERERGREaDkTEREREREZAQoORMRERERERkBSs5ERERERERGgJIzERERERGREaDkTEREREREZAQoORMRERERERkBSs5ERERERERGwP8HREonOVslCgcAAAAASUVORK5CYII=\n", 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", 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\n", + "image/png": 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", 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\n", 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", 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\n", 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", 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\n", 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", 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\n", + "image/png": 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", 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\n", 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", 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" ] diff --git a/examples/18-TiDE-examples.ipynb b/examples/18-TiDE-examples.ipynb index ed021e0308..4506473c93 100644 --- a/examples/18-TiDE-examples.ipynb +++ b/examples/18-TiDE-examples.ipynb @@ -42,20 +42,17 @@ "metadata": {}, "outputs": [], "source": [ - "import torch\n", - "import numpy as np\n", + "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import shutil\n", + "import torch\n", + "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n", "\n", - "from darts.models import NHiTSModel, TiDEModel\n", - "from darts.datasets import AusBeerDataset\n", "from darts.dataprocessing.transformers.scaler import Scaler\n", - "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n", + "from darts.datasets import AusBeerDataset\n", "from darts.metrics import mae, mse\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import warnings\n", + "from darts.models import NHiTSModel, TiDEModel\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "import logging\n", @@ -228,7 +225,6 @@ "source": [ "# train the models and load the model from its best state/checkpoint\n", "for name, model in models.items():\n", - "\n", " # early stopping needs to get reset for each model\n", " pl_trainer_kwargs[\"callbacks\"] = [\n", " EarlyStopping(\n", diff --git a/examples/19-EnsembleModel-examples.ipynb b/examples/19-EnsembleModel-examples.ipynb index b28bf23dc6..0b7fd4ba87 100644 --- a/examples/19-EnsembleModel-examples.ipynb +++ b/examples/19-EnsembleModel-examples.ipynb @@ -42,8 +42,13 @@ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", + "import warnings\n", + "\n", "import matplotlib.pyplot as plt\n", "\n", + "from darts.dataprocessing.transformers import Scaler\n", + "from darts.datasets import AirPassengersDataset\n", + "from darts.metrics import mape\n", "from darts.models import (\n", " ExponentialSmoothing,\n", " KalmanForecaster,\n", @@ -55,14 +60,9 @@ " RegressionEnsembleModel,\n", " TCNModel,\n", ")\n", - "from darts.metrics import mape\n", - "from darts.datasets import AirPassengersDataset\n", "from darts.utils.timeseries_generation import (\n", " datetime_attribute_timeseries as dt_attr,\n", ")\n", - "from darts.dataprocessing.transformers import Scaler\n", - "\n", - "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", @@ -323,9 +323,10 @@ } ], "source": [ - "ensemble = NaiveEnsembleModel(\n", - " [LinearRegressionModel(lags=12, lags_future_covariates=[0]), ExponentialSmoothing()]\n", - ")\n", + "ensemble = NaiveEnsembleModel([\n", + " LinearRegressionModel(lags=12, lags_future_covariates=[0]),\n", + " ExponentialSmoothing(),\n", + "])\n", "\n", "# encoding the months as integer, normalised\n", "future_cov = dt_attr(ts_air.time_index, \"month\", add_length=12) / 12\n", @@ -816,7 +817,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have a good idea of the individual performance of each of these models, we can ensemble them. We must make sure to set `retrain_forecasting_models=False` or the ensemble will need to be fitted before being able to call `predict()`.\n", + "Now that we have a good idea of the individual performance of each of these models, we can ensemble them. We must make sure to set `train_forecasting_models=False` or the ensemble will need to be fitted before being able to call `predict()`.\n", "\n", "**Advice** : Use the `save()` method to export your model and keep a copy of your weights." ] diff --git a/examples/20-RegressionModel-examples.ipynb b/examples/20-RegressionModel-examples.ipynb index 30a84de818..371e15407f 100644 --- a/examples/20-RegressionModel-examples.ipynb +++ b/examples/20-RegressionModel-examples.ipynb @@ -99,22 +99,21 @@ "%load_ext autoreload\n", "%autoreload 2\n", "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", "from sklearn.linear_model import BayesianRidge\n", "\n", + "from darts.datasets import ElectricityConsumptionZurichDataset\n", + "from darts.explainability import ShapExplainer\n", + "from darts.metrics import mape\n", "from darts.models import (\n", + " CatBoostModel,\n", + " LightGBMModel,\n", " LinearRegressionModel,\n", " RegressionModel,\n", - " LightGBMModel,\n", " XGBModel,\n", - " CatBoostModel,\n", - ")\n", - "from darts.metrics import mape\n", - "from darts.datasets import ElectricityConsumptionZurichDataset\n", - "from darts.explainability import ShapExplainer" + ")" ] }, { @@ -976,7 +975,7 @@ " self.weights = weights\n", " self.norm_coef = sum(weights)\n", "\n", - " def fit(self, X: np.ndarray, y: np.ndarray):\n", + " def fit(self, X: np.ndarray, y: np.ndarray, *args, **kwargs):\n", " return self\n", "\n", " def predict(self, X: np.ndarray):\n", diff --git a/examples/21-TSMixer-examples.ipynb b/examples/21-TSMixer-examples.ipynb new file mode 100644 index 0000000000..4da314d98e --- /dev/null +++ b/examples/21-TSMixer-examples.ipynb @@ -0,0 +1,1022 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Time Series Mixer (TSMixer)\n", + "This notebook walks through how to use Darts' `TSMixerModel` and benchmarks it against `TiDEModel`.\n", + "\n", + "TSMixer (Time-series Mixer) is an all-MLP architecture for time series forecasting. \n", + "\n", + "It does so by integrating historical time series data, future known inputs, and static contextual information. The architecture uses a combination of conditional feature mixing and mixer layers to process and combine these different types of data for effective forecasting.\n", + "\n", + "Translated to Darts, this model supports all types of covariates (past, future, and/or static).\n", + "\n", + "See the original paper and model description [here](https://arxiv.org/abs/2303.06053).\n", + "\n", + "According to the authors, the model outperforms several state-of-the-art models on multivariate forecasting tasks.\n", + "\n", + "Let's see how it performs against `TideModel` on the ETTh1 and ETTh2 datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# fix python path if working locally\n", + "from utils import fix_pythonpath_if_working_locally\n", + "\n", + "fix_pythonpath_if_working_locally()\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "import logging\n", + "\n", + "logging.disable(logging.CRITICAL)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import torch\n", + "from pytorch_lightning.callbacks.early_stopping import EarlyStopping\n", + "\n", + "from darts import concatenate\n", + "from darts.dataprocessing.transformers.scaler import Scaler\n", + "from darts.datasets import ETTh1Dataset, ETTh2Dataset\n", + "from darts.metrics import mql\n", + "from darts.models import TiDEModel, TSMixerModel\n", + "from darts.utils.callbacks import TFMProgressBar\n", + "from darts.utils.likelihood_models import QuantileRegression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Loading and preparation\n", + "We consider the ETTh1 and ETTh2 datasets which contain hourly multivariate data of an electricity transformer (load, oil temperature, ...).\n", + "You can find more information [here](https://unit8co.github.io/darts/generated_api/darts.datasets.html#darts.datasets.ETTh1Dataset).\n", + "\n", + "We will add static information to each transformer time series, that identifies whether it is the `ETTh1` or `ETTh2` transformer.\n", + "Both TSMixer and TiDE can levarage this information." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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componentHUFLHULLMUFLMULLLUFLLULLOT
date
2016-07-01 00:00:005.8272.0091.5990.4624.2031.34030.531000
2016-07-01 01:00:005.6932.0761.4920.4264.1421.37127.787001
2016-07-01 02:00:005.1571.7411.2790.3553.7771.21827.787001
2016-07-01 03:00:005.0901.9421.2790.3913.8071.27925.044001
2016-07-01 04:00:005.3581.9421.4920.4623.8681.27921.948000
........................
2018-06-26 15:00:00-1.6743.550-5.6152.1323.4721.52310.904000
2018-06-26 16:00:00-5.4924.287-9.1322.2743.5331.67511.044000
2018-06-26 17:00:002.8133.818-0.8172.0973.7161.52310.271000
2018-06-26 18:00:009.2433.8185.4722.0973.6551.4329.778000
2018-06-26 19:00:0010.1143.5506.1831.5643.7161.4629.567000
\n", + "

17420 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + "component HUFL HULL MUFL MULL LUFL LULL OT\n", + "date \n", + "2016-07-01 00:00:00 5.827 2.009 1.599 0.462 4.203 1.340 30.531000\n", + "2016-07-01 01:00:00 5.693 2.076 1.492 0.426 4.142 1.371 27.787001\n", + "2016-07-01 02:00:00 5.157 1.741 1.279 0.355 3.777 1.218 27.787001\n", + "2016-07-01 03:00:00 5.090 1.942 1.279 0.391 3.807 1.279 25.044001\n", + "2016-07-01 04:00:00 5.358 1.942 1.492 0.462 3.868 1.279 21.948000\n", + "... ... ... ... ... ... ... ...\n", + "2018-06-26 15:00:00 -1.674 3.550 -5.615 2.132 3.472 1.523 10.904000\n", + "2018-06-26 16:00:00 -5.492 4.287 -9.132 2.274 3.533 1.675 11.044000\n", + "2018-06-26 17:00:00 2.813 3.818 -0.817 2.097 3.716 1.523 10.271000\n", + "2018-06-26 18:00:00 9.243 3.818 5.472 2.097 3.655 1.432 9.778000\n", + "2018-06-26 19:00:00 10.114 3.550 6.183 1.564 3.716 1.462 9.567000\n", + "\n", + "[17420 rows x 7 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series = []\n", + "for idx, ds in enumerate([ETTh1Dataset, ETTh2Dataset]):\n", + " trafo = ds().load().astype(np.float32)\n", + " trafo = trafo.with_static_covariates(pd.DataFrame({\"transformer_id\": [idx]}))\n", + " series.append(trafo)\n", + "series[0].pd_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before training, we split the data into train, validation, and test sets. The model will learn from the train set, use the validation set to determine when to stop training, and finally be evaluated on the test set." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "train, val, test = [], [], []\n", + "for trafo in series:\n", + " train_, temp = trafo.split_after(0.6)\n", + " val_, test_ = temp.split_after(0.5)\n", + " train.append(train_)\n", + " val.append(val_)\n", + " test.append(test_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets look at the splits for the first column \"HUFL\" for each transformer" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_col = \"HUFL\"\n", + "for idx, (train_, val_, test_) in enumerate(zip(train, val, test)):\n", + " train_[show_col].plot(label=f\"train_trafo_{idx}\")\n", + " val_[show_col].plot(label=f\"val_trafo_{idx}\")\n", + " test_[show_col].plot(label=f\"test_trafo_{idx}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's scale the data. To avoid leaking information from the validation and test sets, we scale the data based on the properties of the train set." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = Scaler() # default uses sklearn's MinMaxScaler\n", + "train = scaler.fit_transform(train)\n", + "val = scaler.transform(val)\n", + "test = scaler.transform(test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Parameter Setup\n", + "Boilerplate code is no fun, especially in the context of training multiple models to compare performance. To avoid this, we use a common configuration that can be used with any Darts `TorchForecastingModel`.\n", + "\n", + "A few interesting things about these parameters:\n", + "\n", + "- **Gradient clipping:** Mitigates exploding gradients during backpropagation by setting an upper limit on the gradient for a batch.\n", + "- **Learning rate:** The majority of the learning done by a model is in the earlier epochs. As training goes on it is often helpful to reduce the learning rate to fine-tune the model. That being said, it can also lead to significant overfitting.\n", + "- **Early stopping:** To avoid overfitting, we can use early stopping. It monitors a metric on the validation set and stops training once the metric is not improving anymore based on a custom condition.\n", + "- **Likelihood and Loss Functions:** You can either make the model probabilistic with a `likelihood`, or deterministic with a `loss_fn`. In this notebook we train probabilistic models using QuantileRegression.\n", + "- **Reversible Instance Normalization:** Use [Reversible Instance Normalization](https://openreview.net/forum?id=cGDAkQo1C0p) which in most of the cases improves model performance.\n", + "- **Encoders:** We can encode time axis/calendar information and use them as past or future covariates using `add_encoders`. Here, we'll add cyclic encodings of the hour, day of the week, and month as future covariates" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def create_params(\n", + " input_chunk_length: int,\n", + " output_chunk_length: int,\n", + " full_training=True,\n", + "):\n", + " # early stopping: this setting stops training once the the validation\n", + " # loss has not decreased by more than 1e-5 for 10 epochs\n", + " early_stopper = EarlyStopping(\n", + " monitor=\"val_loss\",\n", + " patience=10,\n", + " min_delta=1e-5,\n", + " mode=\"min\",\n", + " )\n", + "\n", + " # PyTorch Lightning Trainer arguments (you can add any custom callback)\n", + " if full_training:\n", + " limit_train_batches = None\n", + " limit_val_batches = None\n", + " max_epochs = 200\n", + " batch_size = 256\n", + " else:\n", + " limit_train_batches = 20\n", + " limit_val_batches = 10\n", + " max_epochs = 40\n", + " batch_size = 64\n", + "\n", + " # only show the training and prediction progress bars\n", + " progress_bar = TFMProgressBar(\n", + " enable_sanity_check_bar=False, enable_validation_bar=False\n", + " )\n", + " pl_trainer_kwargs = {\n", + " \"gradient_clip_val\": 1,\n", + " \"max_epochs\": max_epochs,\n", + " \"limit_train_batches\": limit_train_batches,\n", + " \"limit_val_batches\": limit_val_batches,\n", + " \"accelerator\": \"auto\",\n", + " \"callbacks\": [early_stopper, progress_bar],\n", + " }\n", + "\n", + " # optimizer setup, uses Adam by default\n", + " # optimizer_cls = torch.optim.Adam\n", + " optimizer_kwargs = {\n", + " \"lr\": 1e-4,\n", + " }\n", + "\n", + " # learning rate scheduler\n", + " lr_scheduler_cls = torch.optim.lr_scheduler.ExponentialLR\n", + " lr_scheduler_kwargs = {\"gamma\": 0.999}\n", + "\n", + " # for probabilistic models, we use quantile regression, and set `loss_fn` to `None`\n", + " likelihood = QuantileRegression()\n", + " loss_fn = None\n", + "\n", + " return {\n", + " \"input_chunk_length\": input_chunk_length, # lookback window\n", + " \"output_chunk_length\": output_chunk_length, # forecast/lookahead window\n", + " \"use_reversible_instance_norm\": True,\n", + " \"optimizer_kwargs\": optimizer_kwargs,\n", + " \"pl_trainer_kwargs\": pl_trainer_kwargs,\n", + " \"lr_scheduler_cls\": lr_scheduler_cls,\n", + " \"lr_scheduler_kwargs\": lr_scheduler_kwargs,\n", + " \"likelihood\": likelihood, # use a `likelihood` for probabilistic forecasts\n", + " \"loss_fn\": loss_fn, # use a `loss_fn` for determinsitic model\n", + " \"save_checkpoints\": True, # checkpoint to retrieve the best performing model state,\n", + " \"force_reset\": True,\n", + " \"batch_size\": batch_size,\n", + " \"random_state\": 42,\n", + " \"add_encoders\": {\n", + " \"cyclic\": {\n", + " \"future\": [\"hour\", \"dayofweek\", \"month\"]\n", + " } # add cyclic time axis encodings as future covariates\n", + " },\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model configuration\n", + "Let's use the last week of hourly data as lookback window (`input_chunk_length`) and train a probabilistic model to predict the next 24 hours directly (`output_chunk_length`). Additionally, we tell the model to use the static information. To keep the notebook simple, we'll set `full_training=False`. To get even better performance, set `full_training=True`.\n", + "\n", + "Apart from that, we use our helper function to set up all the common model arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "input_chunk_length = 7 * 24\n", + "output_chunk_length = 24\n", + "use_static_covariates = True\n", + "full_training = False" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# create the models\n", + "model_tsm = TSMixerModel(\n", + " **create_params(\n", + " input_chunk_length,\n", + " output_chunk_length,\n", + " full_training=full_training,\n", + " ),\n", + " use_static_covariates=use_static_covariates,\n", + " model_name=\"tsm\",\n", + ")\n", + "model_tide = TiDEModel(\n", + " **create_params(\n", + " input_chunk_length,\n", + " output_chunk_length,\n", + " full_training=full_training,\n", + " ),\n", + " use_static_covariates=use_static_covariates,\n", + " model_name=\"tide\",\n", + ")\n", + "models = {\n", + " \"TSM\": model_tsm,\n", + " \"TiDE\": model_tide,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model Training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's train all of the models. When using early stopping it is important to save checkpoints. This allows us to continue past the best model configuration and then restore the optimal weights once training has been completed." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1ab2f4e3c6a14b4687d70b402b9920ac", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c8efee5bcaef467499408860f691509d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# train the models and load the model from its best state/checkpoint\n", + "for model_name, model in models.items():\n", + " model.fit(\n", + " series=train,\n", + " val_series=val,\n", + " )\n", + " # load from checkpoint returns a new model object, we store it in the models dict\n", + " models[model_name] = model.load_from_checkpoint(\n", + " model_name=model.model_name, best=True\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Backtest the probabilistic models\n", + "\n", + "Let's configure the prediction. For this example, we will:\n", + "\n", + "- generate **historical forecasts** on the test set using the **pre-trained models**. Each forecast covers a 24 hour horizon, and the time between two consecutive forecasts is also 24 hours. This will give us **276 multivariate forecasts per transformer** to evaluate the model!\n", + "- generate **500 stochastic samples** for each prediction point (since we have trained probabilistic models)\n", + "- evaluate/**backtest** the probabilistic historical forecasts for some quantiles **using the Mean Quantile Loss** (`mql()`).\n", + "\n", + "And we'll create some helper functions to generate the forecasts, compute the backtest, and to visualize the predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# configure the probabilistic prediction\n", + "num_samples = 500\n", + "forecast_horizon = output_chunk_length\n", + "\n", + "# compute the Mean Quantile Loss over these quantiles\n", + "evaluate_quantiles = [0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95]\n", + "\n", + "\n", + "def historical_forecasts(model):\n", + " \"\"\"Generates probabilistic historical forecasts for each transformer\n", + " and returns the inverse transformed results.\n", + "\n", + " Each forecast covers 24h (forecast_horizon). The time between two forecasts\n", + " (stride) is also 24 hours.\n", + " \"\"\"\n", + " hfc = model.historical_forecasts(\n", + " series=test,\n", + " forecast_horizon=forecast_horizon,\n", + " stride=forecast_horizon,\n", + " last_points_only=False,\n", + " retrain=False,\n", + " num_samples=num_samples,\n", + " verbose=True,\n", + " )\n", + " return scaler.inverse_transform(hfc)\n", + "\n", + "\n", + "def backtest(model, hfc, name):\n", + " \"\"\"Evaluates probabilistic historical forecasts using the Mean Quantile\n", + " Loss (MQL) over a set of quantiles.\"\"\"\n", + " # add metric specific kwargs\n", + " metric_kwargs = [{\"q\": q} for q in evaluate_quantiles]\n", + " metrics = [mql for _ in range(len(evaluate_quantiles))]\n", + " bt = model.backtest(\n", + " series=series,\n", + " historical_forecasts=hfc,\n", + " last_points_only=False,\n", + " metric=metrics,\n", + " metric_kwargs=metric_kwargs,\n", + " verbose=True,\n", + " )\n", + " bt = pd.DataFrame(\n", + " bt,\n", + " columns=[f\"q_{q}\" for q in evaluate_quantiles],\n", + " index=[f\"{trafo}_{name}\" for trafo in [\"ETTh1\", \"ETTh2\"]],\n", + " )\n", + " return bt\n", + "\n", + "\n", + "def generate_plots(n_days, hfcs):\n", + " \"\"\"Plot the probabilistic forecasts for each model, transformer and transformer\n", + " feature against the ground truth.\"\"\"\n", + " # concatenate historical forecasts into contiguous time series\n", + " # (works because forecast_horizon=stride)\n", + " hfcs_plot = {}\n", + " for model_name, hfc_model in hfcs.items():\n", + " hfcs_plot[model_name] = [\n", + " concatenate(hfc_series[-n_days:], axis=0) for hfc_series in hfc_model\n", + " ]\n", + "\n", + " # remember start and end points for plotting the target series\n", + " hfc_ = hfcs_plot[model_name][0]\n", + " start, end = hfc_.start_time(), hfc_.end_time()\n", + "\n", + " # for each target column...\n", + " for col in series[0].columns:\n", + " fig, axes = plt.subplots(ncols=2, figsize=(12, 6))\n", + " # ... and for each transformer...\n", + " for trafo_idx, trafo in enumerate(series):\n", + " trafo[col][start:end].plot(label=\"ground truth\", ax=axes[trafo_idx])\n", + " # ... plot the historical forecasts for each model\n", + " for model_name, hfc in hfcs_plot.items():\n", + " hfc[trafo_idx][col].plot(\n", + " label=model_name + \"_q0.05-q0.95\", ax=axes[trafo_idx]\n", + " )\n", + " axes[trafo_idx].set_title(f\"ETTh{trafo_idx + 1}: {col}\")\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, now we're ready to evaluate the models" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: TSM\n", + "Generating historical forecasts..\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "809ff39dfd7b4192b102d9151b2c1417", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating historical forecasts..\n", + "Model: TiDE\n", + "Generating historical forecasts..\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ca2e5b2a7d634d7ea619998ce8a11dd7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Predicting: 0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating historical forecasts..\n" + ] + } + ], + "source": [ + "bts = {}\n", + "hfcs = {}\n", + "for model_name, model in models.items():\n", + " print(f\"Model: {model_name}\")\n", + " print(\"Generating historical forecasts..\")\n", + " hfcs[model_name] = historical_forecasts(models[model_name])\n", + "\n", + " print(\"Evaluating historical forecasts..\")\n", + " bts[model_name] = backtest(models[model_name], hfcs[model_name], model_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see how they performed.\n", + "\n", + "> **Note:** These results are likely to improve/change when setting `full_training=True`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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q_0.05q_0.1q_0.2q_0.5q_0.8q_0.9q_0.95
ETTh1_TSM0.5017720.7695451.1361411.5684391.0988470.7218350.442062
ETTh1_TiDE0.5737160.8854521.2986721.6718701.1515010.7275150.446724
ETTh2_TSM0.6591871.0306551.5086281.9329231.3179600.8571470.524620
ETTh2_TiDE0.6272510.9821141.4508931.8971171.3236610.8622390.528638
\n", + "
" + ], + "text/plain": [ + " q_0.05 q_0.1 q_0.2 q_0.5 q_0.8 q_0.9 \\\n", + "ETTh1_TSM 0.501772 0.769545 1.136141 1.568439 1.098847 0.721835 \n", + "ETTh1_TiDE 0.573716 0.885452 1.298672 1.671870 1.151501 0.727515 \n", + "ETTh2_TSM 0.659187 1.030655 1.508628 1.932923 1.317960 0.857147 \n", + "ETTh2_TiDE 0.627251 0.982114 1.450893 1.897117 1.323661 0.862239 \n", + "\n", + " q_0.95 \n", + "ETTh1_TSM 0.442062 \n", + "ETTh1_TiDE 0.446724 \n", + "ETTh2_TSM 0.524620 \n", + "ETTh2_TiDE 0.528638 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bt_df = pd.concat(bts.values(), axis=0).sort_index()\n", + "bt_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The backtest gives us the Mean Quantile Loss for the selected quantiles over all transformer features per transformer and model. The lower the value, the better. The `q_0.5` is identical to the Mean Absolute Error (MAE) between the median prediction and the ground truth.\n", + "\n", + "Both models seem to have performed comparably well. And how does it look on average over all quantiles?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ETTh1_TSM 0.891234\n", + "ETTh1_TiDE 0.965064\n", + "ETTh2_TSM 1.118732\n", + "ETTh2_TiDE 1.095988\n", + "dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bt_df.mean(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the results are also very similar. It seems that TSMixer performed better for ETTh1, and TiDEModel for ETTh2.\n", + "\n", + "And last but not least, let's have look at the predictions for the last `n_days=3` days in the test set.\n", + "\n", + "> Note: The prediction intervals are expected to get narrower when `full_training=True`" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "generate_plots(n_days=3, hfcs=hfcs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Results\n", + "In this case, `TSMixer` and `TiDEModel` both perform similarly well. Keep in mind that we performed only partial training on the data, and that we used the default model parameters without any hyperparameter tuning. \n", + "\n", + "Here are some ways to further improve the performance:\n", + "\n", + "- set `full_training=True`\n", + "- perform hyperparameter tuning\n", + "- add more covariates (we have only added cyclic encodings of calendar information)\n", + "- ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/22-anomaly-detection-examples.ipynb b/examples/22-anomaly-detection-examples.ipynb new file mode 100644 index 0000000000..9e35ef1bfd --- /dev/null +++ b/examples/22-anomaly-detection-examples.ipynb @@ -0,0 +1,1790 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Anomaly Detection Darts Module" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook showcases some of the functionalities of Darts' Anomaly Detection Module. We'll look at Anomaly Scorers, Detectors, Aggregators and Anomaly Models.\n", + "\n", + "- `Scorers`: compute anomaly scores time series, either only on the target series or between the target series and a forecasted/predicted series. They are the core of the anomaly detection module.\n", + " \n", + "- `Detectors`: transform time series (such as anomaly scores) into binary anomaly time series. The presence of an anomaly is flagged with `1`, and `0` otherwise.\n", + " \n", + "- `Aggregators`: reduce a multivariate binary time series (e.g., where each component represents the anomaly score of a different series component/model) into a univariate binary time series. \n", + "\n", + "- `Anomaly Models`: offer a convenient way to produce anomaly scores from any of Darts' global forecasting models or filtering models by comparing the models’ predictions with actual observations. Each Anomaly Model takes one forecasting/filtering model and one or multiple scorers. The model produces some predictions, which are fed together with the actual series to the scorer(s). It will return anomaly scores for each scorer. \n", + "\n", + "The figure below illustrates the different input/output for each tool:\n", + "\n", + " \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The notebook is devided into two sections:\n", + "\n", + "- How to use `ForecastingAnomalyModel` to find anomalies in the number of taxi passengers in New York. \n", + "- How to use an `AnomalyScorer` and the importance of its windowing capabilities on two toy datasets. \n", + "\n", + "First, some necessary imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# fix python path if working locally\n", + "from utils import fix_pythonpath_if_working_locally\n", + "\n", + "fix_pythonpath_if_working_locally()\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dennisbader/miniconda3/envs/darts310_test/lib/python3.10/site-packages/statsforecast/utils.py:237: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", + " \"ds\": pd.date_range(start=\"1949-01-01\", periods=len(AirPassengers), freq=\"M\"),\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from darts import TimeSeries\n", + "from darts.ad import (\n", + " ForecastingAnomalyModel,\n", + " KMeansScorer,\n", + " NormScorer,\n", + " WassersteinScorer,\n", + ")\n", + "from darts.ad.utils import (\n", + " eval_metric_from_scores,\n", + " show_anomalies_from_scores,\n", + ")\n", + "from darts.dataprocessing.transformers import Scaler\n", + "from darts.datasets import TaxiNewYorkDataset\n", + "from darts.metrics import mae, rmse\n", + "from darts.models import RegressionModel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Anomaly Model: Taxi passengers in NY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load and visualize the data \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Information on the data:\n", + "- Univariate Time Series (represents the number of taxi passengers in New York)\n", + "- During a period of 8 months (2014-07 to 2015-01)\n", + "- Frequency of 30 minutes " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, anomalies are subjective. It can be defined as periods where the demand for taxis is abnormal (different than what should be expected). Based on this definition, the following five dates can be considered anomalies:\n", + "\n", + "- NYC Marathon - 2014-11-02\n", + "- Thanksgiving - 2014-11-27\n", + "- Christmas - 2014-12-24/25\n", + "- New Years - 2015-01-01\n", + "- Snow Blizzard - 2015-01-26/27" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# load the data\n", + "series_taxi = TaxiNewYorkDataset().load()\n", + "\n", + "# define start and end dates for some known anomalies\n", + "anomalies_day = {\n", + " \"NYC Marathon\": (\"2014-11-02 00:00\", \"2014-11-02 23:30\"),\n", + " \"Thanksgiving \": (\"2014-11-27 00:00\", \"2014-11-27 23:30\"),\n", + " \"Christmas\": (\"2014-12-24 00:00\", \"2014-12-25 23:30\"),\n", + " \"New Years\": (\"2014-12-31 00:00\", \"2015-01-01 23:30\"),\n", + " \"Snow Blizzard\": (\"2015-01-26 00:00\", \"2015-01-27 23:30\"),\n", + "}\n", + "anomalies_day = {\n", + " k: (pd.Timestamp(v[0]), pd.Timestamp(v[1])) for k, v in anomalies_day.items()\n", + "}\n", + "\n", + "# create a series with the binary anomaly flags\n", + "anomalies = pd.Series([0] * len(series_taxi), index=series_taxi.time_index)\n", + "for start, end in anomalies_day.values():\n", + " anomalies.loc[(start <= anomalies.index) & (anomalies.index <= end)] = 1.0\n", + "\n", + "series_taxi_anomalies = TimeSeries.from_series(anomalies)\n", + "\n", + "# plot the data and the anomalies\n", + "fig, ax = plt.subplots(figsize=(15, 5))\n", + "series_taxi.plot(label=\"Number of taxi passengers\", linewidth=1, color=\"#6464ff\")\n", + "(series_taxi_anomalies * 10000).plot(label=\"5 known anomalies\", color=\"r\", linewidth=1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_anom(selected_anomaly, delta_plotted_days):\n", + " one_day = series_taxi.freq * 24 * 2\n", + " anomaly_date = anomalies_day[selected_anomaly][0]\n", + " start_timestamp = anomaly_date - delta_plotted_days * one_day\n", + " end_timestamp = anomaly_date + (delta_plotted_days + 1) * one_day\n", + "\n", + " series_taxi[start_timestamp:end_timestamp].plot(\n", + " label=\"Number of taxi passengers\", color=\"#6464ff\", linewidth=0.8\n", + " )\n", + "\n", + " (series_taxi_anomalies[start_timestamp:end_timestamp] * 10000).plot(\n", + " label=\"Known anomaly\", color=\"r\", linewidth=0.8\n", + " )\n", + " plt.title(selected_anomaly)\n", + " plt.show()\n", + "\n", + "\n", + "for anom_name in anomalies_day:\n", + " plot_anom(anom_name, 3)\n", + " break # remove this to see all anomalies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The goal would be to detect these five irregular periods and identify other possible abnormal days. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train a Darts forecasting model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use a `RegressionModel` to predict the number of taxi passengers. The first 4500 timestamps will be used to train the model. The training set is considered to be anomaly-free, the five considered anomalies are located after the 4500th timestamps. The number of lags is set to 1 week, assuming the demand follows a periodicity of 1 week. To help the model, additional information on the targeted series is passed as covariates (the hour and the day of the week).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RegressionModel(lags=336, lags_past_covariates=None, lags_future_covariates=[0], output_chunk_length=1, output_chunk_shift=0, add_encoders={'cyclic': {'future': ['hour', 'dayofweek']}}, model=None, multi_models=True, use_static_covariates=True)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# split the data in a training and testing set\n", + "s_taxi_train = series_taxi[:4500]\n", + "s_taxi_test = series_taxi[4500:]\n", + "\n", + "# Add covariates (hour and day of the week)\n", + "add_encoders = {\n", + " \"cyclic\": {\"future\": [\"hour\", \"dayofweek\"]},\n", + "}\n", + "\n", + "# one week corresponds to (7 days * 24 hours * 2) of 30 minutes\n", + "one_week = 7 * 24 * 2\n", + "\n", + "forecasting_model = RegressionModel(\n", + " lags=one_week,\n", + " lags_future_covariates=[0],\n", + " output_chunk_length=1,\n", + " add_encoders=add_encoders,\n", + ")\n", + "forecasting_model.fit(s_taxi_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use a Forecasting Anomaly Model " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The anomaly model consists of two inputs:\n", + "- a fitted `GlobalForecastingModel` (you can find a list [here](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms). If the model hasn't been fitted, set parameter `allow_model_training` to `True` when calling `fit()`)\n", + "- a single or list of `AnomalyScorer` (trainable or not)\n", + "\n", + "For this example, three scorers will be used:\n", + "- `NormScorer` (window is by default set to 1)\n", + "- `WassersteinScorer` with a half-day window (24 timestamps) and no window aggregation\n", + "- `WassersteinScorer` with a full-day window (48 timestamps) including window aggregation\n", + "\n", + "The `window` parameter is an integer value indicating the window size used by the scorer to transform the series into an anomaly score. A scorer will slice the given series into subsequences of size W and returns a value indicating how anomalous these subset of W values are.\n", + "\n", + "The `window_agg` can be used to transform the window-wise scores into point-wise scores by aggregating all anomaly scores from each window that the point was included in.\n", + "\n", + "The following figure illustrates the mechanism of a Forecasting Anomaly model:\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using the main functions: fit(), score(), eval_metric() and show_anomalies()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# with timestamps of 30 minutes\n", + "half_a_day = 2 * 12\n", + "full_day = 2 * 24\n", + "\n", + "# instantiate the anomaly model with: one fitted model, and 3 scorers\n", + "anomaly_model = ForecastingAnomalyModel(\n", + " model=forecasting_model,\n", + " scorer=[\n", + " NormScorer(ord=1),\n", + " WassersteinScorer(window=half_a_day, window_agg=False),\n", + " WassersteinScorer(window=full_day, window_agg=True),\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's train the anomaly model with `fit()`. In sequence it will:\n", + "\n", + "- fit the forecasting model on the given series if it has not been fitted yet and `allow_model_training=True`.\n", + "- generate historical forecasts for the given series.\n", + "- feed the historical forecasts to each fittable/trainable scorer:\n", + " - compute the differences between the forecasts and the given series (controled by the scorers `diff_fn`, see Darts \"per time step\" metrics [here](https://unit8co.github.io/darts/generated_api/darts.metrics.html))\n", + " - train the scorer on these differences\n", + "\n", + "You can control how the historical forecasts are generated when calling `fit()` (the supported parameters are [here](https://unit8co.github.io/darts/generated_api/darts.ad.anomaly_model.forecasting_am.html#darts.ad.anomaly_model.forecasting_am.ForecastingAnomalyModel.fit))." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "961e3b451e6c49e6b04c4c1cd0f3aa8a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "START = 0.1\n", + "anomaly_model.fit(s_taxi_train, start=START, allow_model_training=False, verbose=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We call the function `score()` to compute the anomaly scores of a new series `s_taxi_test`. It returns the scores from each scorer in the anomaly model. We will use the results in the next section. With `return_model_prediction=True`, we can additionally get the historical forecasts generated by the forecasting model." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "50a7f565ce2a4cf6b49a69fb5cf209a0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00 MAE: 595.190366262045, RMSE: 896.6287614972252\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# compute the MAE and RMSE on the test set\n", + "print(\n", + " f\"On testing set -> MAE: {mae(model_forecasting, s_taxi_test)}, RMSE: {rmse(model_forecasting, s_taxi_test)}\"\n", + ")\n", + "\n", + "# plot the data and the anomalies\n", + "fig, ax = plt.subplots(figsize=(15, 5))\n", + "s_taxi_test.plot(label=\"Number of taxi passengers\")\n", + "model_forecasting.plot(label=\"Prediction of the model\", linewidth=0.9)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To evaluate the anomaly model, we call the function `eval_metric()`. It outputs the score of an agnostic threshold metric (\"AUC-ROC\" or \"AUC-PR\"), between the predicted anomaly score time series and some known binary ground-truth time series indicating the presence of actual anomalies. \n", + "\n", + "It will return a dictionary containing the name of the scorer and its score." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AUC_ROCAUC_PR
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WassersteinScorer_w=240.8849150.609469
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" + ], + "text/plain": [ + " AUC_ROC AUC_PR\n", + "Norm (ord=1)_w=1 0.658074 0.215601\n", + "WassersteinScorer_w=24 0.884915 0.609469\n", + "WassersteinScorer_w=48 0.950035 0.687788" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metric_names = [\"AUC_ROC\", \"AUC_PR\"]\n", + "metric_data = []\n", + "for metric_name in metric_names:\n", + " metric_data.append(\n", + " anomaly_model.eval_metric(\n", + " anomalies=series_taxi_anomalies,\n", + " series=s_taxi_test,\n", + " start=START,\n", + " metric=metric_name,\n", + " )\n", + " )\n", + "pd.DataFrame(data=metric_data, index=metric_names).T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the anomaly model, using the `WassersteinScorer`, can separate the abnormal days from the normal ones. The AUC ROC is above 0.9. Additionally, a window of size 48 timestamps (24 hours) is a better option than a window of size 24 timestamps (12 hours). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We call the function `show_anomalies()` to visualize the results. It plots the forecasts, predicted scores, the input series, and the actual anomalies (if provided). The scorers with different windows will be separated. It is possible to compute a metric that will be shown next to the scorer’s name. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anomaly_model.show_anomalies(\n", + " series=s_taxi_test,\n", + " anomalies=series_taxi_anomalies[pred_start:],\n", + " start=START,\n", + " metric=\"AUC_ROC\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convert an anomaly score to a binary prediction with a `Detector`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Darts' Anomaly Detectors convert anomaly scores into binary anomlies/predictions. In this example, we'll use the `QuantileDetector`.\n", + "\n", + "It detects anomalies based on the quantile values (`high_quantile` and/or `low_quantile`) of historical data. It flags times as anomalous when the values exceed these quantile thresholds. In this example, the anomaly scores were computed for the absolute residuals of the model. It is lower-bound by 0. We set `low_quantile=None` (default), as we only want to flag values above `high_quantile`. \n", + "\n", + "We set `high_quantile` to `0.95`. This value must be chosen carefully, as it will convert the `(1- high_quantile) * 100` % biggest anomaly scores into a prediction of anomalies. In our case, we want to see the 5% most anomalous timestamps. \n", + "\n", + "> Note: You can also use `ThresholdDetector` to define some fixed value thresholds for anomaly detection" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from darts.ad.detectors import QuantileDetector\n", + "\n", + "contamination = 0.95\n", + "detector = QuantileDetector(high_quantile=contamination)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# use the anomaly score that gave the best AUC ROC score: Wasserstein anomaly score with a window of 'full_day'\n", + "best_anomaly_score = anomaly_scores[-1]\n", + "\n", + "# fit and detect on the anomaly scores, it will return a binary prediction\n", + "anomaly_pred = detector.fit_detect(series=best_anomaly_score)\n", + "\n", + "# plot the binary prediction\n", + "fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)\n", + "anomaly_pred.plot(label=\"Prediction\", ax=ax1)\n", + "series_taxi_anomalies[anomaly_pred.start_time() :].plot(\n", + " label=\"Known anomalies\", ax=ax2, color=\"red\"\n", + ")\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.91/1\n", + "precision: 0.76/1\n", + "recall: 0.32/1\n", + "f1: 0.45/1\n" + ] + } + ], + "source": [ + "for metric_name in [\"accuracy\", \"precision\", \"recall\", \"f1\"]:\n", + " metric_val = detector.eval_metric(\n", + " pred_scores=best_anomaly_score,\n", + " anomalies=series_taxi_anomalies,\n", + " window=full_day,\n", + " metric=metric_name,\n", + " )\n", + " print(metric_name + f\": {metric_val:.2f}/1\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using the functions show_anomalies_from_scores(), eval_metric_from_scores()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Internally, methods `eval_metric()` and `show_anomalies()` call `eval_metric_from_scores()` and `show_anomalies_from_scores()`, respectively. We can also call them directly with pre-computed anomaly scores to avoid having to re-generate the scores each time.\n", + "\n", + "Let's reproduce the results from above. Both functions require the window sizes used to compute each of the anomaly scores. In our case, the window sizes were `1, 24 (half_a_day), 48 (full_day)`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " AUC_ROC AUC_PR\n", + "Norm (ord=1)_1 0.658074 0.215601\n", + "WassersteinScorer_24 0.884915 0.609469\n", + "WassersteinScorer_48 0.950035 0.687788" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "windows = [1, half_a_day, full_day]\n", + "scorer_names = [f\"{scorer}_{w}\" for scorer, w in zip(anomaly_model.scorers, windows)]\n", + "\n", + "metric_data = {\"AUC_ROC\": [], \"AUC_PR\": []}\n", + "for metric_name in metric_data:\n", + " metric_data[metric_name] = eval_metric_from_scores(\n", + " anomalies=series_taxi_anomalies,\n", + " pred_scores=anomaly_scores,\n", + " window=windows,\n", + " metric=metric_name,\n", + " )\n", + "\n", + "pd.DataFrame(index=scorer_names, data=metric_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the AUC ROC and AUC PR values are identical to before." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For visualizing the anomalies:\n", + "\n", + "- if we want to compute a metric, we need to specify the window sizes as well\n", + "- optionally, we can indicate the scorers’ names that generated the scores" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_anomalies_from_scores(\n", + " series=s_taxi_test,\n", + " anomalies=series_taxi_anomalies[pred_start:],\n", + " pred_scores=anomaly_scores,\n", + " pred_series=model_forecasting,\n", + " window=windows,\n", + " title=\"Anomaly results using a forecasting method\",\n", + " names_of_scorers=scorer_names,\n", + " metric=\"AUC_ROC\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A zoom on each anomalies: visualize the results" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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MmTKFBx54gPHjx/PZZ59x4sQJ8vPzWbFiBVdeeSUA/fr1w2azsXLlShwOB0uWLCE5OZno6GgAxo8fzzvvvIPFYmHPnj1s2rSJMWPGADBu3DjWrVvHwYMHMZvNLF261Nuu5PwyatQoIiIiWLRo0VnLPPnkk1WWfl599VXatm3r/XvatGlMnjyZhQsXEhUVRXBwMAsWLMDpdDJnzhxCQ0OJi4tj6dKlVdo/ePAggwYNQqfT0a1bN3744Qef/P379zN+/HhMJhNRUVHcdttt5Ofne/NHjBjBfffdx8MPP0x4eLj3e3cmbrebp556iri4OPz9/endu7dPmABFUdixYwdPPfUUiqLw5JNPVmlj2rRpbNy4kddeew1FUVAUhWPHjuFyubjjjjto164der2ezp0789prr3nr2e12unXrxqxZs7xpR48eJSgoyOsPtnz5coKDg6vtO3iW4hRF4aOPPjrrfJ2rHwA//PADl156KUajkdDQUG644QbvDsNfd/xK+77tCQgIIDAwkH79+rF9+3Zv3S1btjBs2DD0ej3x8fHcf//9PtbWtm3bsnDhQmbMmEFAQAAJCQm89dZbPtffsmULvXv3RqfT0b9/f7788ksURWHnzp3eMvX9zJ988kkSEhLw9/cnJiaG+++//6zzKfHwwgsvMH/+fN5//30fEVN5T7/44otER0cTFhbGvffe6xM6o6ioiKlTpxISEoLBYODKK68kJSUFACEEERERfPbZZ97yvXv3JjIy0vv31q1b8fPz81rpFUXhnXfe4ZprrsFgMNCxY0fvztmz8f7779O/f38CAgJo06YNf/rTn8jNzfXm//DDDyiKwvr16+nfvz8Gg4FBgwZx6NChKu107NgRrVZL586dee+993zyFUXhzTff5Oqrr8ZgMNC1a1e2bt1KamoqI0aMwGg0MnDgQI4cOeKtc+TIESZNmkRUVBQmk4lLLrmEdevWnXUsM2bM4Oqrr/ZJczqdtGnTptpn50WFaADz588Xy5Yt8/69dOlScfnll4sRI0aIV199Vbjdbm/e3r17xU033SQGDRokZs6cKbKysrx5NptNPP7442LIkCFi/Pjx4ttvv/W5ztdffy3GjRsnhg4dKubPny/Ky8sb0u1GweVyibS0NOFyuc53V84Lt99+u5g4caJ4/fXXhU6nExkZGUIIIb744gtx+tdq/vz5olevXj51X3nlFZGYmOjTVkBAgLj33nvFwYMHxZIlSwQgxo4dK5599llx+PBh8fTTTws/Pz9x/PhxIYQQR48eFYCIi4sTn376qdi/f7+YOXOmCAgIEPn5+UIIIbKyskR4eLh47LHHxIEDB8Rvv/0mxowZI0aOHOm99vDhw4XJZBJz5swRBw8eFAcOHKh2vC+//LIIDAwUH374oTh48KCYO3eu8PPzE+vXrxcul0tkZ2eLbt26idmzZ4vs7GxRVlZWpY3i4mIxcOBAceedd4rs7GyRnZ0tnE6ncDgc4oknnhC//vqrSEtLE++//74wGAzi448/9tb9/fffhVarFV988YVwOp1i8ODBYtKkSd78ZcuWiaCgoLN+XrWZr3P1o6KiQgQFBYm//vWvIjU1Vezdu1e88MIL4ujRo0IIITp26SjoiThw4IA4fPiw+OSTT8TOnTuFEELs3r1bmEwm8corr4jDhw+Ln376SfTp00dMmzbN28fExEQRGhoqFi9eLFJSUsQ//vEPoVKpvJ9JaWmpCA0NFbfeeqvYt2+fWL16tejUqZMAxO+//96gz/y///2vCAwMFKtXrxbp6enil19+EW+99dZZ57OS1vgcuP3228WkSZPEI488Ikwmk/jPf/5TZfy33367CAwMFHfffbc4cOCAWLlypTAYDD5zOnHiRNG1a1exadMmsXPnTjF27FjRoUMH4XA4hBBCXHvtteK+++4TQghRWFgo/Pz8RHBwsNi3b58QQoiFCxeKAQMGeNur/H5/8MEHIiUlRdx///3CZDKJgoKCs45lyZIlYvXq1eLIkSNi69at4rLLLhNXXnmlN3/Dhg0CEAMGDBA//PCD2Ldvnxg6dKgYNGiQt8ynn34q/Pz8xL/+9S9x6NAh8dJLLwm1Wi2+//57n77FxsaKjz/+WBw6dEhMnjxZtG3bVlx++eVizZo1Yv/+/eKyyy4T48aN89bZuXOneOONN8Tu3bvF4cOHxeOPPy50Op1IT0/3lklMTBSvvPKKEEKIn376SajVap/f1q+++koYjcZqn0eNxYVwDzRIyLRmmvLDc7uFsFqb/99puvOcVAqZtLQ0cdlll4kZM2YIIeovZBITE33msnPnzmLo0KHev51OpzAajeLDDz8UQvzxw7xo0SJvmYqKChEXFyeee+45IYQQ8+bNE1dccYXPtTMyMgQgDh06JITw/Kj17t37nOONiYkRzz77rE/aJZdcIm699VZvv3v16iXmz59fYzvDhw8XDzzwwDmvd88994jrrrvOJ+35558X4eHh4i9/+Yto06aNyMvL8+bVVsjUNF/n6kdBQYEAxA8//CCE8L0HcnKEMBiNgsmICldFlXZuu+02MWvWLJ+0H3/8UahUKmGz2YQQnofyrbfe6s13u90iMjJSvP7660IIIV5//XURFhbmLS+EEG+//baPkKnvZ/7SSy+JTp06eX9Ea0ujPwfcbiFKSpr/Xx1u/ttvv11otVoBiP/973/Vjr/ynnY6nd60G264Qdx0001CCCEOHz4sAPHTTz958/Pz84VerxeffPKJEEKI//u//xPdu3cXQgjx5Zdfiv79+4trr71WLF68WAghxBVXXCEeeeQRb31A/P3vf/f+bTabhaIoVV6Ma+LXX38VgPeHv1LIrFu3zltm1apVAvB+DwcNGiSmTJniMwc33HCDGD9+/Fn7tnXrVgGIJUuWeNM+/PBDodPpauxfcnKy+Oc//+n9+3QhU5l/+v08efJkn5eFpuBCEDL1iiMjaVrsdjgfVu3/+z+oj5vFP/7xD0aPHs3s2bPrfe1u3br5OIpFRUV5faYA1Go1YWFhPmZfwGdrvkajoX///hw4cACAHTt2sGHDBp/ddJUcOXKETp06AdC/f/8a+1ZaWkpWVhaDBw/2SR80aBC//vprLUdYM2+88QbvvPMO6enp2Gw2HA5HlSW52bNn89VXX/HPf/6Tb7/9lvDw8Dpfp6b5Olc/QkNDmTZtGmPHjmXMmDGMGjWKgQMHkpiYyLZtcMnwqWz8+nXGjB7D2CvGcsMNN9C+fXvA81mkpqayYsUK77WEELjdbo4ePUrXrl0B6NmzpzdfURTatGnj/cwPHTpEz549ffzjLr30Up/x1fczv+GGG3j11VdJSkpi3LhxjB8/ngkTJqDRNPMjsqwMgoKa95oAJSVwxk7UmujZsyf5+fk8+eSTvP7669WW6datG2q12vt3dHQ0e/bsAeDAgQNoNBoGDBjgzQ8LC6Nz587e7+OIESN44IEHyM/PZ+PGjYwYMYKEhAQ2btzIrFmz2LJlCw8++GCVflViNBoJCAio8sw4nd9//50nn3ySnTt3UlhY6I2Fcvz4cZ+Ar6e3W+kSkZubS0JCAgcOHOCaa67xaXfw4MFVlmVPbyMqKgqAHj16+KTZ7XZKS0sJDAzEYrGwYMECvvnmG7KysnA6ndhsNo4fP37W8cycOZO33nqLuXPnkpuby6pVq1i/fv1Zy18sSCFzAaLTeUTF+bhufRg2bBhjx47lb3/7G9OmTfPJU6lUVXYwVXfEhJ+fn8/fiqJUm1aboEuKogAev5YJEybw3HPPVSlT+TACqoQGOFe7lZw5rvryySef8NBDD/HSSy8xcOBAAgICeOGFF/jll198yuXm5nLo0CHUajUpKSmMGzeuUa5fOa7a9GPZsmXcf//9rFmzhk8++YS///3vrF27Frd7EIOu+DMbk15ndPxovl/3PfPnz+ejjz7immuuwe12c9ddd1Xrd5KQkOD9/5o+cyHEOT+D+n7m8fHxHDp0iP/973+sW7eOe+65hxdeeIGNGzdW6VOTEhDgERXNTUBAnYrHxsby2WefMXLkSKZPn8769esJOkOAneuzrI7TP+Pu3bsTFhbGxo0b2bhxI0899RTx8fE8++yzbNu2DZvN5t1cUptrnonFYuGKK67giiuu4P333yciIoLjx48zduzYKkFVT2/39OfLmWnVjaOmNmpqd86cOXz33Xe8+OKLdOjQAb1ez/XXX1+lb6czdepUHn30UbZu3crWrVtp27YtQ4cOPWv5iwUpZC5AFKV+lpHzyaJFi+jdu7f3jbeSiIgIcnJyfG7s0x0zG8rPP//MsGHDAI9j244dO7jvvvsA6Nu3L5999hlt27Zt0Jt1YGAgMTExbN682Xst8DgbdunSpU5tabVaXC6XT9qPP/7IoEGDuOeee7xppzv9VTJjxgy6d+/OnXfeyR133MGoUaN83hprQ03zVdt+9OnThz59+vDII4/Qv39/PvzwQ0aPHoSjwgXhMOPPM3h87uPcfPPNLFu2jGuuuYa+ffuyb98+n8CXdaVLly6sWLGC8vJy/P39AXyciaFhn7ler2fixIlMnDiRe++9ly5durBnzx769u1b7z7XGUWpk2XkfJKQkMCGDRsYNmwY48aN47vvvqsSX+xsJCcn43Q6+eWXXxg0aBAABQUFHD582GudUxSFYcOG8dVXX7F3716GDh1KQEAAFRUVvPHGG/Tt25eAOgqw0zl48CD5+fksWrSI+Ph4oOr3qTZ07dq1Sr0tW7Z4x1FffvzxR6ZNm+a19pjNZo4dO1ZjnbCwMCZPnsyyZcvYunUr06dPb1AfWgrnf/O55KKgR48e3HLLLfzzn//0SR8xYgR5eXk8//zzHDlyhMWLF/Ptt9822nUXL17MF198wcGDB7n33nspKipixowZANx7770UFhZy88038+uvv5KWlsbatWuZMWNGFTFxLubMmcNzzz3Hxx9/zKFDh3j00UfZuXNnFQvUuWjbti2//PILx44dIz8/H7fbTYcOHdi+fTvfffcdhw8fZt68eWzbtq3KOLdu3cp//vMf/vSnP3H99ddzyy231Ph2Vh01zde5+nH06FEee+wxtm7dSnp6OmvXruXo0aN06dIFm83G2k//AUfhyNEj/PTTT2zbts37MH/kkUfYunUr9957Lzt37iQlJYWvv/6av/zlL7Xu+5/+9CfcbjezZs3iwIED3rdV+ONttr6f+fLly1myZAl79+4lLS2N9957D71ef1620rck4uLi+PDDDykoKOCKK66gpJbWpI4dOzJp0iTuvPNONm/ezK5du7j11luJjY31HmUDnufHBx98QM+ePQkMDPSKmxUrVjBixIgG9T0hIQGtVss///lP0tLS+Prrr3n66afr3M7s2bP57LPPeOONN0hJSeHll1/m888/569//WuD+tehQwc+//xzdu7cya5du7zf/3Mxc+ZM3n33XQ4cOMDtt9/eoD60FKSQkTQaTz/9dBWTcdeuXfn3v//N4sWL6dWrF7/++muDb/DTWbRoEc899xy9evXixx9/5KuvvvL6jsTExPDTTz/hcrkYO3Ys3bt354EHHiAoKKjOgZvuv/9+Zs+ezezZs+nRowdr1qzhyy+/rFX47NP561//ilqtJjk52WvKvvvuu7n22mu56aabGDBgAAUFBT5WkYMHDzJnzhz+/e9/e98cFy9eTHFxMfPmzavT9Wuar3P1w2AwcPDgQa677jo6derE3XffzW233cZdd92FoqixWorhC7j8ksu58cYbufLKK1mwYAHg8Q/YuHEjKSkpDB06lD59+jBv3jyf5Z5zERgYyMqVK9m5cye9e/fm8ccf9x4+W+k3U9/PPDg4mLfffpvBgwfTs2dP1q9fz8qVKy+a+EhNSZs2bdiwYQPFxcWMGTOG4uLiWtVbtmwZ/fr14+qrr2bgwIEIIVi9erXPcsvIkSNxuVw+omX48OG4XC6GDx/eoH5HRESwfPly/vvf/5KcnMyiRYu8wrguTJ48mXnz5vHSSy/RrVs33nzzTZYtW9ZgofXKK68QEhLCoEGDmDBhAmPHjq2VdXD06NFER0czduxYYmJiGtSHloIiGmuhv5XhdrtJT08nMTHxgoiqeD5o7XPQksZ/7Ngx2rVrx++//95oRzqcPv7PP1fxzc5feNfvMnbetZNebXo1yjXOxYoVK5g+fTolJSXnJSBgS/oONAWtffxw4c2B1WolJiaGpUuXcu211zb59S6E8UsfGYlE0mCEgIoKN/hBuau8ya7zn//8h6SkJGJjY9m1axePPPIIN954o4xqLGn1uN1ucnJyeOmllwgKCmLixInnu0vNhhQyEomkwbjd4HB6fFDKnU0nZHJycnjiiSfIyckhOjqaG264gWeffbbJrieRtBSOHz9Ou3btiIuLY/ny5c0fOuA80npGKpG0Ytq2bdukB3m63eCo8DgiNqVFZu7cua32UEKJpCaa+h6/kDn/C3oSiaTFIwQ43U1vkZFIJJIzkUJGIpE0GLcbBE1vkZFIJJIzkUJGIpE0GCFOEzLSIiORSJoRKWQkEkmD8VhkTi0tSYuMRCJpRqSQkUgkDUYIQJEWGYlE0vxIISORSBqM2w1uaZGRSCTnASlkJBcsP/zwA4qi1DrkOXi2IL766qtN1qe6MmLECB588EHv343RvwttjOARMkgfGUkL4UK8hyT1RwoZSb2YNm0aarWaxx9/vErePffcg6IodT5QsTWwbds2Zs2aVauyy5cvJzg4uEFtNBdCSItMa2PLli34+fnJ+1xy3pFCRlJv4uPj+eabb7DZbN40u93Ohx9+SEJCwnnsWeNS1xOmayIiIgKDwXDe22hs3G6kj0wrY+nSpdx3331s376d48ePn+/uXNRUVFSc7y5c0EghI6k3ffr0ISYmhs8//9yb9vnnnxMfH0+fPn18ypaXl3P//fcTGRmJTqdjyJAhbNu2zafM6tWr6dSpE3q9npEjR3Ls2LEq19yyZQvDhg1Dr9cTHx/P/fffj8ViqXWfp02bxuTJk1mwYAGRkZEEBgZy1113+YiVESNGcN999/Hwww8THh7OmDFjANi/fz/jx4/HZDIRFRXF1KlTKSws9NazWCxMnToVk8lEdHQ0L730UpXrn2nSLi4uZtasWURFRaHT6ejevTvffPMNP/zwg/cwREVRUBSFJ598sto2jh8/zqRJkzCZTAQGBnLjjTdy8uRJb/6TTz5J7969ee+992jbti1BQUFMmTKFsrKyWs/bufDZfi0tMhc9FouFTz75hLvvvpvLL7+cd9991ye/cll4/fr19O/fH4PBwKBBgzh06JBPuddff5327duj1Wrp3Lkz7733nk++oii8+eabXH311RgMBrp27crWrVtJTU1lxIgRGI1GBg4cyJEjR7x1jhw5wqRJk4iKisJkMnHJJZewbt26s45lxowZXH311T5pTqeTNm3asHTp0mrrpKenM2HCBEJCQggICGDs2LGsXr3am79v3z6uuuoqAgMDCQgIYOjQod4+ut1unnrqKeLi4vD396d3796sWbPGW/fYsWMoisInn3zCiBEj0Ol0vP/++4DnxPCuXbui0+no0qUL//73v886rtaEFDKSBnH99df7PMSWLl3KjBkzqpSbO3cun332Ge+++y6//fYbHTp0YOzYsV4hkJGRwbXXXsv48ePZuXMnM2fO5NFHH/VpY8+ePYwdO5Zrr72W3bt38/HHH7N582buu+++OvV5/fr1HDhwgA0bNvDhhx/yxRdfsGDBAp8y7777LhqNhp9++ok333yT7Oxshg8fTu/evdm+fTtr1qzh5MmT/OUvf/HWmTNnDhs2bOCLL75g7dq1/PDDD+zYseOs/XC73Vx55ZVs2bKF999/n/3797No0SLUajWDBg3i1VdfJTAwkOzsbLKzs/nrX/9apQ0hBJMnT6awsJCNGzfyv//9jyNHjnDTTTf5lDty5Ahffvkl33zzDd988w0bN25k0aJFdZq3mvDZfi0tMhc9H3/8MZ07d6Zz585MmjSJ5cuXVxse//HHH+ell15i+/btaDQan2fDF198wQMPPMDs2bPZu3cvd911F9OnT2fDhg0+bTz99NNMnTqVnTt30qVLF/70pz9x11138dhjj7F9+3YAn2eA2Wxm/PjxrFu3jt9//52xY8cyYcKEs1qNZs6cyZo1a8jOzvamrV69GrPZzI033lhtnXvvvZfy8nI2bdrErl27mDt3LiaTCYATJ04wbNgwdDod33//PTt27GDGjBk4nU4AXnvtNV566SVefPFFdu/ezdixY5k4cSIpKSk+13jkkUe4//77OXDgAGPHjuXtt9/m8ccf59lnn+XAgQMsXLiQefPmVRGRrRIhqRcul0ukpaUJl8vV6G273W5RYi9p9n9ut7vWfbz99tvFxIkTxfbt24W/v784evSoOHbsmNDpdCIvL09MmjRJ3H777UIIIcxms/Dz8xMrVqzw1nc4HCImJkY8//zzQgghHnvsMdG1a1efPjzyyCMCEEVFRUIIIW677TYxa9Ysn378+OOPQqVSCZvNJoQQIjExUbzyyis19js0NFRYLBZv2uuvvy5MJpP3sxw+fLjo3bu3T7158+aJK664wictPT1dAOLAgQOirKxMaLVa8dFHH3nzCwoKhF6vFw888IA37fT+fffdd0KlUolDhw5V29dly5aJoKCgKumnt7F27VqhVqvF8ePHvfn79u0TgPj111+FEELMnz9fGAwGUVpa6i0zZ84cMWDAgLPMUu04/R745z+FGPGX9wVPIu5aeVeD2m1JNPZzwO12i5KSkmb/V5d7XwghBg0aJF599VXhcrnE4cOHRXh4uPjf//7nzd+wYYMAxLp167xpq1atEoD3Xh00aJC48847fdq94YYbxPjx471/A+Lvf/+79++tW7cKQCxZssSb9uGHHwqdTldjf5OTk8U///lP799nPieSk5PFc8895/178uTJYtq0aWdtr0ePHuLJJ58UQlT9Djz22GOiXbt2wuFwVFs3JiZGPPvssz5pl1xyibjnnnuEEEIcPXpUAOLVV1/1KRMfHy8++OADn7Snn35aDBw48Kz9bA6a8rewtshDIy9AyhxlBC0KavbrljxaQqB/YJ3qhIaGMn78eN59912EEFx11VWEh4f7lDly5AgVFRUMHjzYm+bn58ell17KgQMHADhw4ACXXXYZiqJ4ywwcONCnnR07dpCamsqKFSu8aUII3G43R48epWvXrrXqc69evXx8TAYOHIjZbCYjI4PExEQA+vfvX+XaGzZs8L51nTm+8vJyHA6HT59DQ0Pp3LnzWfuxc+dO4uLi6NSpU636XR0HDhwgPj6e+Ph4b1pycjLBwcEcOHCASy65BPAsRwUEBHjLREdHk5ubW+/rnokQIBTp7NtQysrKCAo6D/d+SQmBgbW79w8dOsSvv/7qXVLWaDTceOONLF26lNGjR/uU7dmzp/f/o6OjAcjNzSUhIYEDBw5UcVofPHgwr7322lnbiIqKAqBHjx4+aXa7ndLSUgIDA7FYLCxYsIBvvvmGrKwsnE4nNputRj+emTNn8tZbbzF37lxyc3NZtWoV69evP2v5+++/nz//+c+sXbuWUaNGcdlll3mfHTt37mTo0KH4+flVqVdaWkpWVpbPs7By3Lt27fJJO/0ZlJeXR0ZGBnfccQd33nmnN93pdJ6X78uFhhQyFyAB2gBKHi05L9etD9OnT+f+++8HYPHixVXyxSmT8+kipTK9Mk3U4tRWt9vNXXfd5b3W6TSGc/Hp/TMajVWuPWHCBJ577jmftBMnTtC/f3+fNfraotfr69/ZU5w+hzWln/lQVRQFt2fPdKPgdoOils6+DSUgIICSkvNw7wfU/t5fsmQJTqeT2NhYb5oQAj8/P4qKiggJCfGmn/69q/w+nv69q+mZUFMbNbU7Z84cvvvuO1588UU6dOiAXq/n+uuvr9Fpf+rUqTz66KNs3bqVrVu30rZtW4YOHXrW8jNnzmTs2LGsWrWK7777jkWLFvHiiy9y//331+q+rs24T38GVY7t7bffZsCAAT7l1Gr1Oa93sSOFzAWIoih1toycT8aNG+d9SIwdO7ZKfocOHdBqtWzevJk//elPgMcLf/v27d4YK8nJyXz55Zc+9X7++Wefv/v27cu+ffvo0KFDg/q7a9cubDab94Hz888/YzKZiIuLO2udvn378tlnn9G2bVs0Gs9t43a78fPzw2g00qFDB/z8/Pj555+9oqqoqIjDhw8zfPjwatvs2bMnmZmZHD58uFqrjFarxeVy1TiW5ORkjh8/TkZGhtcqs3//fkpKSmptoWoMhAA/P+ns21AURam1ZeR84HQ6+c9//sNLL73EFVdcgdvtJisri5iYGG644QZWrFhRa5+1rl27snnzZqZOnepN27JlS4O/tz/++CPTpk3jmmuuATw+M9VtHDidsLAwJk+ezLJly9i6dSvTp08/53Xi4+O5++67mTVrFvfeey/vvPMO999/Pz179uTdd9+loqKiygtEYGAgMTExbN68mWHDhnnTt2zZwqWXXnrWa0VFRREbG0taWhq33HLLOfvW2pDOvpIGo1arOXDgAAcOHKj27cBoNPLnP/+ZOXPmsGbNGvbv38+dd96J1WrljjvuAODuu+/myJEjPPzwwxw6dIgPPviA5cuX+7TzyCOPsHXrVu6991527txJSkoKX3/9tY/DbW1wOBzccccd7N+/n2+//Zb58+dz3333oVKd/Xa49957KSws5Oabb+bXX38lLS2NtWvXMnfuXFwuFyaTiTvuuIM5c+awfv169u7dy7Rp02psc/jw4QwbNozrrruO//3vfxw9epRvv/3Wu4Ohbdu2mM1m1q9fT35+PlartUobo0ePpmfPntxyyy389ttv/Prrr0ydOpXhw4dXWR5rStxuUGmks+/FzjfffENRURF33HEH3bt3p3v37nTu3Jnu3btz/fXXs2TJklq3NWfOHJYvX84bb7xBSkoKL7/8Mp9//nm1Tu11oUOHDnz++efs3LmTXbt28ac//alW1seZM2fy7rvvcuDAAW6//fYayz744IN89913HD16lN9++42tW7fSpUsXwON4XFpaypQpU9i+fTspKSm899573h1bc+bM4bnnnuPjjz/m0KFDPProo+zcuZMHHnigxms++eST/OMf/+C1117j8OHD7Nmzh2XLlvHyyy/XcmYuXqSQkTQKgYGBNb5JLlq0iOuuu47bbruNvn37kpqaynfffec1QyckJPDZZ5+xcuVKevXqxRtvvMHChQt92ujZsycbN24kJSWFoUOH0qdPH+bNm+dde68to0aNomPHjgwbNowbb7yRCRMmeLc2n42YmBh++uknXC4XY8eOpXv37jz00EMEBAR4xcoLL7zAsGHDmDhxIqNHj2bIkCH069evxnY/++wzLrnkEm6++WaSk5O9wghg0KBB3H333dx0001ERETw/PPPV6mvKApffvklISEhDBs2jNGjR5OUlMTHH39cpzlpKEKA5pRFxu60N+u1Jc3HkiVLGD16dLV+Gddddx07d+7kt99+q1VbkydP5rXXXuOFF16gW7duvPnmmyxbtowRI0Y0qI+vvPIKISEhDBo0iAkTJjB27Fj69u17znqjR48mOjqasWPHEhMTU2NZl8vFvffeS9euXRk/fjxJSUneZfWwsDC+//57zGYzw4cPp1+/frz99tte68z999/P7NmzmT17Nj169GDNmjV8/fXXdOzYscZrzpw5k3feeYfly5fTo0cPhg8fzvLly2nXrl0tZ+biRRG1cU6QVMHtdpOenk5iYmKNb90XMy1xDqZNm0ZxcXGVZaz60BLH35icPv6XXlKx2fFvvnbey5CEIfw4/cfz3b1mQX4HLp7xW61WYmJiWLp0Kddee22t611Mc1AfLoTxSx8ZiUTSYNxu0Gjc4JRLS5KWhdvtJicnh5deeomgoCAmTpx4vrskqSNSyEgkkgYjBKj8XKjQSGdfSYvi+PHjtGvXjri4OJYvX+515pe0HOQnJmlVnOlALGkcKi0yfsIgLTKSFkXbtm1rFf5BcuHS+hb0JBJJoyMEqDVuNOilRUYikTQrdRYyzz77LGPHjmX48OHcdNNN/Pijx6lv5cqVDBgwgKFDh3r/5eTkeOvt27ePm2++mcGDBzNr1iyfcy3sdjvz5s1j2LBhXHXVVT4HaFW2PX78eIYPH86CBQvkSaASyQVG5fZrtdBLi4xEImlW6ixkbrnlFlauXMnGjRt54oknmDdvHqWlpQBceuml/Pjjj95/bdq0ATxxO+bOncuUKVP4/vvv6d69O0888YS3zTfffJOSkhJWr17NwoULWbRoEenp6QCkpqbyyiuv8OKLL7Jq1SqysrLqFKtAIpE0PZUWGbXbIC0yEomkWamzkGnbti1arRbwxLBwOBzk5+fXWGfHjh3o9XomTZqEv78/d955J/v37/daZVavXs2sWbMwmUz06tWLYcOGsXbtWgDWrFnDmDFjSE5OxmQyMXPmTL799tu6dlsikTQhbjegcqF2S4uMRCJpXurl7Lto0SJWrlxJeXk5w4cPJykpiX379rFr1y5GjRpFaGgoN910E9dffz0AaWlpPmHl9Xo9cXFxpKWlYTQaKSgo8Mnv1KkT+/bt89Y9/SC+jh07cuLECex2OzqdrkrfHA5HlTM1NBqNV3w1FpWRIhvzvJqWRmufAzn+P8bvdiuguNAIj0WmtcyJ/A607vGDnIOmHn9tYtPUS8g8+uijzJkzh+3bt5Oamgp4zqL56KOPaNOmDfv37+evf/0rYWFhjBw5EpvNVuUQPqPRiM1mw2q1olarfUSJ0Wj0hmM/s27l6cM2m61aIbNs2TLefvttn7QbbriBG2+8sT5DPScZGRlN0m5LorXPgRx/Bg5HLDabBY0w4HQ7OXrsKCql9ewlkN+B1j1+kHPQVOOvTeTiem+/VqvVDBgwgA8//JCkpCQfq0n37t2ZMmUKGzZsYOTIkej1eiwWi099i8WCXq/HYDDgcrl8LCwWiwWDwQBQpa7ZbPamV8f06dOrHKrVVBaZyoP6WmM0R5BzIMf/x/jVajU6gxa18NyXbWLboPdr+OneFzryO9C6xw9yDi6E8Tc4jozb7SYzM7NK+ulHkiclJfHFF194/7bZbGRmZpKUlERgYCBhYWGkpqbSvXt3AA4fPkxSUpK3bqXVByAlJYXY2NhqrTHgOTG4sUVLTahUqlb55T2d1j4HcvwqhFBAEWiE5wWkQlRgVBnPUfPiQX4HWvf4Qc7B+Rx/na5qtVr59ttvsVqtOJ1O1q9fz44dO+jTpw9btmyhqKgIgIMHD/Lxxx8zdOhQAPr164fNZmPlypU4HA6WLFlCcnKy97C/8ePH884772CxWNizZw+bNm1izJgxAIwbN45169Zx8OBBzGYzS5cu5corr2zMOZBIJA3E7QahuLwWGenwK5FImos6WWQUReGrr77iueeeQwhBfHw8zzzzDB06dGDlypXMnz8fu91OREQEU6dO9YoRrVbL888/z9NPP82iRYtITk7mqaee8rZ711138cwzzzBu3DgCAwN59NFHadu2LeA5kv3BBx/koYcewmKxcPnllzNjxozGmwGJRNJghAAUNxrhsZTKLdgSiaS5qJOQ0ev1vPHGG9XmPfTQQzz00ENnrdutWzc++uijavN0Oh3PPPPMWetOmDCBCRMm1KWrEomkGXG7AcWNItT4q/2lRUYikTQbrXdBTyKRNBpCgFu4UFDhr/GXFhmJRNJsSCEjkUgajNcig7TISCSS5kUKGYlE0mCEADcuQIVWLS0yEomk+ZBCRiKRNJg/fGRU0iIjkUiaFSlkJBJJgxECBC7v0pLdaT/fXZJIJK0EKWQkEkmDcbtB4EY5tbTkcDnOXUkikUgagQZH9pVIJBKPRcbj7KsoatyidR6gJ5FImh8pZCQSSYPxWGRcKEKFoqikkJFIJM2GXFqSSCQNRggQimdpSUGRQkYikTQbUshIJJIG43ZXBsRToygqBOJ8d0kikbQSpJCRSCQNQojTfWRUqJBLSxKJpPmQQkYikTQIccr4Ik6dtSSXliQSSXMihYxEImkQ7lOaxS1cKIrH2VcIubQkkUiaBylkJBJJg/BaZIQbFdIiI5FImhe5/VoikTQIr0UGFypUIH1kJBJJMyKFjEQiaRCVFhm3cP+xtCR3LUkkkmZCChmJRNIgTveRUaEGubQkkUiaEekjI5FIGoTXIoMblSIj+0okkuZFWmQkEkmDqLTICHHqrCXkriWJRNJ8SIuMRCJpEJVCxiVcqBQVcmlJIpE0J9IiI5FIGoR3+zVu1KhALi1JJJJmRFpkJBJJg/BaZNwuVMqppSW5a0kikTQTUshIJJIGIQQoisfZV0EuLUkkkuZFChmJRNIg3G5QqTxxZFSKWh4aKZFImhUpZCQSSYOotMh4lpbkWUsSiaR5kUJGIpE0CB+LjAyIJ5FImhkpZCQSSYNwu09ZZE5tv5ZLSxKJpDmRQkYikTQIIf6wyCiKCpU8a0kikTQjUshIJJIGUWmRcQs3atQg5NKSRCJpPqSQkUgkDaLSIuNyu7ynX0shI5FImgspZCQSSYM43SKjkmctSSSSZqbOQubZZ59l7NixDB8+nJtuuokff/zRm7d8+XJGjx7N5ZdfzmuvvebzMNu3bx8333wzgwcPZtasWWRnZ3vz7HY78+bNY9iwYVx11VWsWbPG55orV65k/PjxDB8+nAULFlBRUVGfsUokkibAa5E55eyryF1LEomkGamzkLnllltYuXIlGzdu5IknnmDevHmUlpayefNmPv30U5YvX84nn3zC5s2b+frrrwFwOBzMnTuXKVOm8P3339O9e3eeeOIJb5tvvvkmJSUlrF69moULF7Jo0SLS09MBSE1N5ZVXXuHFF19k1apVZGVlsWTJkkYavkQiaSg+FhmVCkXuWpJIJM1InYVM27Zt0Wq1ACiKgsPhID8/n9WrV3P99dcTFxdHeHg4t956K99++y0AO3bsQK/XM2nSJPz9/bnzzjvZv3+/1yqzevVqZs2ahclkolevXgwbNoy1a9cCsGbNGsaMGUNycjImk4mZM2d625VIJOef031k1MizliQSSfNSr9OvFy1axMqVKykvL2f48OEkJSVx9OhRxo8f7y3TqVMnFi9eDEBaWhodOnTw5un1euLi4khLS8NoNFJQUOCT36lTJ/bt2+etO3DgQG9ex44dOXHiBHa7HZ1OV6VvDocDh8PhO0iNxiu+Ggv3qZPyKv/bGmntcyDH7xm3y+VGpVJObb9WPGluV6uYF/kdaN3jBzkHTT1+lerc9pZ6CZlHH32UOXPmsH37dlJTUwGwWq2YTCZvGaPRiNVqBcBms2E0Gn3aMBqN2Gw2rFYrarXaR5TUVLfyGjabrVohs2zZMt5++22ftBtuuIEbb7yxPkM9JxkZGU3Sbkuitc9Bax9/Tk4eLlc4jgoHbqeg3FZOUXGRd3m4NdDavwOtffwg56Cpxt+uXbtzlqmXkAFQq9UMGDCADz/8kKSkJAwGA2az2ZtvsVgwGAyAxwJjsVh86lssFvR6PQaDAZfL5WNhqalu5TX0en21/Zo+fTq33HKL7yCbyCKTkZFBfHx8rRTjxUhrnwM5fs/4IyIi0WpVKGoFrVaLQW8kMDCQxMTE893FJkd+B1r3+EHOwYUw/noLmUrcbjeZmZm0a9eO1NRUhgwZAsDhw4dJSkoCICkpiS+++MJbx2azkZmZSVJSEoGBgYSFhZGamkr37t2rrVtp9QFISUkhNja2WmsMgFarbXTRUhMqlapVfnlPp7XPQWsfPygoinIqIJ4GTkX2bU1z0tq/A619/CDn4HyOv05XtVqtfPvtt1itVpxOJ+vXr2fHjh306dOH8ePH89lnn3HixAny8/NZsWIFV155JQD9+vXDZrOxcuVKHA4HS5YsITk5mejoaADGjx/PO++8g8ViYc+ePWzatIkxY8YAMG7cONatW8fBgwcxm80sXbrU265EIjn/VB4aWXn6tUo6+0okkmakThYZRVH46quveO655xBCEB8fzzPPPEOHDh3o0KEDKSkpTJ06FbfbzeTJk5k4cSLgsZI8//zzPP300yxatIjk5GSeeuopb7t33XUXzzzzDOPGjSMwMJBHH32Utm3bAtChQwcefPBBHnroISwWC5dffjkzZsxovBmQSCQN4syzluT2a4lE0pzUScjo9XreeOONs+ZPnz6d6dOnV5vXrVs3Pvroo2rzdDodzzzzzFnbnTBhAhMmTKhLVyUSSTNxehwZjUoNMiCeRCJpRlrvgp5EImkUTo/sq6CSRxRIJJJmRQoZiUTSIHxOv1bUcmlJIpE0K1LISCSSBnF6ZF+VogIhl5YkEknzIYWMRCJpED4WGZUaRZG7liQSSfMhhYxEImkQ3u3Xp3xkVHJpSSKRNCNSyEgkkgZx+vZrlaJC7lqSSCTNiRQyEomkQVRZWpK7liQSSTPS4CMKJBJJy+fTT8GiziAz7D88PuzxOtUVAlA8FhiVokIRcmlJIpE0H9IiI5FIOHoUlh5+nue3PF/num43XiGjVmRAPIlE0rxIISOR1JLnNj/Hf3b953x3o0k4ml3EVttSSstLKbGX1KmuEKCoXMApi4w8a0kikTQjUshIJLXkq0NfsTVj6/nuRqNjNsMOsZQwRz9MWhMZpRl1qu92A6rTlpbkriWJRNKMSCEjkdQCt3Cz++RussxZ57srjU52NpQYt5Hkuoo2+ngySuohZE4tLcmzliQSSXMjhYxEUgvSitKwVFjILss+311pdHJyoFyXSUJQPKGa+DpbZDzOvnJpSSKRnB+kkJFIasHOnJ1oVBqyyi5Oi4xZyaRdWBwB7oZZZFQqedaSRCJpXqSQkUhq4HDBYeb+by67cnYxJGEIOeYcXG7X+e5Wo3Iiy02xK4suMbH42xPqZ5E55eyrlmctSSSSZkYKGYmkBpb+vpQXtrzA69tfZ1z7cbiEizxr3vnuVqOSlpOHU1TQo20slMZzvOR4neq73QCVzr4yIJ5EImlepJCRSGrg8/1fMyL8JgpsBVwaeymh+tCLanlJCMgyZxKmC6d9og5RHM/x4vrvWpIWGYlE0txIISORnIXD+amkFR0hYddbvDLoIwYnDCYmIOaicvi126FMySQ2MI7AQGijjyezLLNai0p2WTY3fXpTlTwhQJzh7CuFjEQiaS6kkJFIquGXX+Dv760k0T2SyVcGYv7lJjSKlpiAmIvKImM2g1WTSXxQHACdo+Mpd9nJt+ZXKZtSmMIn+z5hb+5en3TPWUu+cWTkriWJRNJcSCEjkZxBRQW88w4cc27lpktHctVVUFAAR45w0QkZiwUcukziAmMBaJ9gIEAVwZGiI1XKVkb8XXl4pU+6EAooLhQU1GoFIZeWJBJJMyKFjERyBsXFoFKBLjyH5PhYtFro0gUOHYIY08UlZMxmsGkziQv0WGTi4iCBIaxPW1+lbLG9GKgqZCq3X6tVahQFubQkkUiaFSlkJJIzKC6GoCA4aTlJlDEKgM6d4eBBiA6IJtt8YfnIHD1a/7qepaUTPkImouRKvk39tkrZkvISLo29lG0ntnHSfNKbXukjo1JUHiGjyF1LEomk+ZBCRiI5g+JiCA6GHHMObUxtAI9F5sgRiNTHcKLsBD//7FmCOt+YzbBwIeTVc0e4xQJl/GGRadMGYq1X8nPmzxTZinzKpmWVQH4nukZ05ZcTv3jTKy0ylUJG7lqSSCTNiRQyEskZFBWBMdhGaXkpUSaPRSYqCkwm0Fs7s+/kQd5e4uRIVTeSZqegwPPfnJz61S8rE5SIP4SMRgM9EuNIMHRlXdo6b7n8fNj0SwnmgiBiDG19Ys14jihwo1ZOLS0JubQkkUiaDylkJJIzKCoCVcBJ1IqaMH0YAIoC3brByqVdEU4NqjZ7OXnyHA01Aw0VMidLi3AIG7EBsd60Hj0gyT2O74585037/nswhJQQGRhMgCuR9OJ0b57bfcbSkty1JJFImhEpZCSSMyguBpf+JBHGCNQqtTf91lvh2WdUDG57Ke6Yn8nNVc5fJ0+Rf2qXdH2FTGZpJgZ1IAH+Ad60nj1BlzOSjcc2etMKC0HRFxMXHoTGkkB68XHsdk+eEJ7t15XOvvL0a4lE0pxIISORnEFxMVRo//CPqUSjgYgIGJx4GZn8fMFYZEym+guZHGsmkbo4n7TYWEhSDyGtKI3M0kzAMycOVQmJbYKoyE/k18PpfPKJR8i53eDGY5FRqQC5a0kikTQjUshIqkUIwWf7P6PTPzvxn13/Od/daVaKisCm/mPH0plcFncZKbafyc1t5o5VQ0EBJCd7TrCuDydtJ4g2+AoZRYF+3QNJ0vfzWmWKisAuSugQH0RFfgJ5juNkejSOxyKj8vWRkbuWJBJJcyGFjMSHQ4fgnY+z6LLocqb99x7cuV1446cV57tbzYbb7bE+WJSqFplKLou7jGNlh8koyMd1ng/CLijw+O6UlXl2INWVfEcmsQFxVdLbtIEkZSQ/HPsBtxtKSsDqLiE6OJgBnROxqrM5kVPu2XotQCB3LUkkkvODFDISHz78EFYdX4FGW8GXo1K4PepVfs3b4I3qerFjNoPLBSWus1tkwgxhjE4aw+bgeyguVldbprkoKID4eAgMrN/yUpErk/jgqkImNBTalA9nY/pG75yUVRQTpAvisb+0wU/lR5Erk7Iy9ant1384+8qlJYlE0pxIISPx4nB4liiC2qUysddQRg0JZGj3JCJUnasNkHYxUVwMNpvnv0Yj5NtOerdeV8cH162g0H8H7x34qNn6eCZWq6fPYWEeC0pWHQMOl5d7DoxsGxpbJS80FHQlvUgtTCUnvxyj0RMQL8g/CJWiIj4oHlVoOvn52lO7lnwj+8pdSxKJpLmQQkYCeHxi1u7eicEAmdYjdAjtAEBMDLS1T+LrQ1+f5x42HU4nPPYYzHown2efsxMS4hsMrzrCDeFcapjC3oK9Zy3T1BQUgF4PBgP8HriA2zYNYN7382pd32IBizqTduFVLTJhYeAqjkHvp2fviSMEBFdgrbASpAsCICEogdLArbx7+G2fyL4qFXJpSSKRNCt1EjIOh4MFCxYwfvx4hg8fzqxZs0hNTQVg5cqVDBgwgKFDh3r/5Zxm6963bx8333wzgwcPZtasWWSf5p1ot9uZN28ew4YN46qrrmLNmjU+1125cqX3mgsWLKDiQgipepGx4dgGJqzqQ1BcFqmFqT5CJrD0Mnbm7D7PPWw6SkvB7rawuesAbMNmc9ddvscTnI2E0ChyzIXN1MuqFBR4BEdZeRlf5j9PaPFoFm9bXGsRYTaDVZ1JYjVLS4GBoFErtAvsyP6TKeiDSgEI1gUDkBiUyGr7E7yfO58KlxPwOPuCPGtJIpE0L3USMi6Xi9jYWJYtW8b333/PsGHDmD17tjf/0ksv5ccff/T+a9PG80brcDiYO3cuU6ZM4fvvv6d79+488cQT3npvvvkmJSUlrF69moULF7Jo0SLS0z0Bt1JTU3nllVd48cUXWbVqFVlZWSxZsqQxxi45jYU/LgSgLGQzx0uOe4WMyQRR/u04VnT0ot2JUlICO8MeQ6vR8PmRd3EZTpBVllWjRQagT6dICmwF3ngqzYndaScn10VYGHy6/1M6h3diYsCTWB12DuUfqlUbJ4vKcKhKvVF9T0elgpAQiDd0IqXwMNrAEjQqDXqNHvA4PA+PuBaAEleOT0A85K4liUTSjNRJyOj1embOnElUVBRqtZqbbrqJrKwsiouLa6y3Y8cO9Ho9kyZNwt/fnzvvvJP9+/d7rTKrV69m1qxZmEwmevXqxbBhw1i7di0Aa9asYcyYMSQnJ2MymZg5cybffntx+2s0N7+e+JVfTvxCFyays+ITdBod0QHR3vwuUW2xuax8t/n879JpCvIKy9nlv5jPbvyMnlE96f92fy6Lu4wu4V1qrNclLgq7Joc9e5qpo6cx5dMpfH7kXaKiYPmu5UzrNY3LLvUjTnUJWzK21KqN1NwT+KEnRBdSbX5oKERqOnKs7DBqYzFB/kEoHm9e7u5/N29f8Ql6dySFFVnes5Y8cWTk0pJEImk+NA2pvHv3bkJDQwkODgZg165djBo1itDQUG666Sauv/56ANLS0ujQoYO3nl6vJy4ujrS0NIxGIwUFBT75nTp1Yt++fd66AwcO9OZ17NiREydOYLfb0el0VfrkcDhwOBy+g9Ro0Gq1DRlqFdxut89/WzIbjm5gVOJYCrZfzk/qhz3WGIH3xygxxoD+cASvf3gUI2EMHuypd7HMQVreCQA6h3Zm7qC5PL3paT657hMUlBrHFmGIwKbOZcMGBavVzaBB4OfXPH0+XnKcvKJtjG8/gR/3/8hH135E8Qk34ZsvY0vGFqb3nn7ONtLyMwhWxyCEqNaCEhqqEOLuwGbbZhRdEcG6YJ/5CAx0o3fGUFiRSZAwnVpaEl4fmZb+vagNF8s9UF9a+/hBzkFTj1+lOre9pd5Cxmw2s3DhQu655x4A+vbty0cffUSbNm3Yv38/f/3rXwkLC2PkyJHYbDaMRqNPfaPRiM1mw2q1olarfUSJ0WjEarUCVKlrMpm86dUJmWXLlvH222/7pN1www3ceOON9R1qjWRkZDRJu83JnqOpZByMYXhMdzZbyonRxXiX9gAMBhOBrrbE9djDl1/2pU2bDDSnfXNa+hzsP55CoBJFZkYmvXS9+PSKTynNLaWU0hrrOcudOIQVuzuPTz8NR4iTtGvXPOtMJ8tOorLv5XDZeuJMcdgL7DgcmQSVDmJj2l99Pr+zcezkCUyqkLOWValCUBfHk+tModh+HL2i9ynrdCoYXLGcKMsgzByDy+mitLQYu60cu9teqz5cLLT0e6ChtPbxg5yDphp/u3btzlmmXkKmvLyc2bNnM2TIECZNmgRAbOwfWzi7d+/OlClT2LBhAyNHjkSv12M5I1qXxWJBr9djMBhwuVw+FhaLxYLBYACoUtdsNnvTq2P69OnccsstvoNsIotMRkYG8fHxtVKMFzLHvzYTG5TEs/dfyuLntfSI7UFiYqI3Py4O3rO1JS4uD3uOmtzcRAYOvHjmoNi1gzD/GJ8x14Y4Vxx+Kj9uvrOI1e9HoFJFUccm6k2JowSnsp8y0zEuibuExMRE3G6Idur4X+kRgqKCvI65Z6PMaSPcGH7WcbdrBz9/1QtzcDamGDMR5RE+Zd1uNwFEU+zMxRTQAZ1DR3BwMDqLAT+tX53nsyVysdwD9aW1jx9axxwU2gp5+7e3eWTwI1XyLoTx11nIOJ1O/va3vxEREcGDDz541nKVa+kASUlJfPHFF96/bTYbmZmZJCUlERgYSFhYGKmpqXTv3h2Aw4cPk5SU5K1buTMKICUlhdjY2GqtMQBarbbRRUtNqFSqFv/lzbWepF9sNHqtjr7RfekS3sVnTCoVdIpMIr30GJcmKhQVKZw+5JY+BzmWHCL10fUaQ5gujDxrHpGRncnP952XpsLutGOpsIDKwqasbxnbYeypzwASwiMJ9Y/kcOFhLou7rMZ2CqzFhEWHnnXc4eHgMocTGBHCxhNrCdIFVSkbpGpDofM4qMQpHxkFRVHjxt2ivxN1paXfAw2ltY8fLu45+OXEL8z/YT5zB8/1OUj3dM7n+Ot81WeffZby8nKefPJJH7GyZcsWioqKADh48CAff/wxQ4cOBaBfv37YbDZWrlyJw+FgyZIlJCcnEx3tcSgdP34877zzDhaLhT179rBp0ybGjBkDwLhx41i3bh0HDx7EbDazdOlSrrzyygYPXPIHhY4c2kd5duh8cdMXTO01tUqZdsHtOFp8FIPBE4jtYiLXlk20KaZedcN14Zy0nCQ8/I+TqJuaAmsBAEEikU3HN9GnTR9vXkQExPh34nDB4XO2U1xeRLipekdfgE6d4M6ZCk+PepJvDn9TrYUnzC+aMjzOvmqVGpVKnrUkkbQ0dubspPO/OpNVVn1UzfSSdCrcFd5DZC806mSRyc7OZuXKlfj7+zNy5Ehv+v/93//xyy+/MH/+fOx2OxEREUydOtUrRrRaLc8//zxPP/00ixYtIjk5maeeespb/6677uKZZ55h3LhxBAYG8uijj9K2bVsAOnTowIMPPshDDz2ExWLh8ssvZ8aMGY0wdAl4zhYqdZ+kc4xHyJxty3Hb4LYcKz6GIQEKz1/olCahwJFFj6D6LYOE6cM4aTlJuwj49ddG7thZyLfmY9IEk6D0Zk9FOn2j+3rzIiIgPOfcQsZz5EAhUUFnF3BaLQwYAAO4H2uFlWhTdJUyYdooLK4TvmctyV1LLZL04nQijZHo/apftpdcvBwtOsrhgsNM/HAim2dsRqfxXfFILz4VDqUwlcTgC2/JuE5CJjo6mu3bt1eb16dPHx566KGz1u3WrRsffVR9OHedTsczzzxz1roTJkxgwoQJdemqpJZk55VTriqka3zNMVPahbTjWPExdJ3dWK0Xj/lUCChxZRMfUvMyzNkI14WTa84logPk5Z06CVo5d72GUGArwKSE0z6gOwUV23yOUoiIgIDjnThcUP19WklJCZSrimgT1K1W13x0yKPVpkfqo7A6shB44sgAIGRAvJbI+A/GM3fQXG7vffv57oqkmTE7zFwWdxknSk+w8dhGxnYY65N/vPQ44BEyo5JGnY8u1sjF84skqRcH0nNBKLQJDK+xXOIpi0W+OHRRLS1ZrWBRskmKqN/SUpguzLu0ZLM1z7JbvjUff3cYI+LGMaO3r3UyMhL8zR1JKUypsY3iYnD5FRJhDG1QX6KNbXCoirG7zagV9akjCuRZSy2NE6Un2J+3/4JdOpA0LWaHmTB9GIPiB7E9y/cl6PBh2Lo/HQOhpBYeOU89rBkpZFo5h07kEKCKQKOq2Tjnr/FnXIdx/FT02UUlZEpLwabJJjG06rJJbQjXe3xk9HpPFOS8vEbuYDUUWAtQO8IYlzyEpy9/2icvIgKUIs/SUk1+KkVFUKEpIkR/dh+Z2hBhCkIl/MhzZJ4W2VcuLbU01h9dD0C2OfscJSUXI2aHGZPWxCUxl7Ata5tP3tq1UKocJ7p8JLszU8/SwvlFCplWzpGTOYT617ysVMmNyTeyLvuTi0rI5BU6sKnyiAmop5DRhZNryQU8IqI5hExWcT5qezjx8dX0JxwCKtpjq7Cd1XEPPBaZclURofqGWWRMRoHBFU2ePcPn9GspZFoW64+uJ8g/SAqZVkqZowyT1kT/mP4+Fhkh4PARB8WuLC6LupyDudIiI7kAySg6SZShdkJmYueJpJsPc6L8QBP3qvk4lncSBcXHz6QuROgjvA//5hIy6XkFBPmHcSrUkg8aDXRuryNKl1Cjw29REdhE4VmPJ6gter0bgyuWk+UZ8qylFooQgnVp67gh+Qayy6SQaY2YHWYCtAG01fUlqyyLl97KZvFiz/Ms35GJWqXmmn5DyLKn4nJdePe2FDKtnHx7zjkPR6wkwD+AEfFXkKKs4mKJxp1Zko1JiTzn0trZaB/UnmPFx7BWWImPh9RmsLyeKMonugafpuRkCHbVvHOpsKQCuzA3eGnJYHBhcMWQa8+QZy21ULLN2WSVZXFt12ulRaaVYnaYMfqZeO35AMLpSr52G4cPw3ffgSH6OHGBcVzRryNOxcq2Aznnu7tVkEKmlVNUkUN0YO2EDMAlsf0o0Ow+Lyc+NwUnyrII1tRvWQkgSh9FsC6Yfbn76NsXDhxoeoff3LIC4sLCzpqfnAx+ZR1ILUw7a5lCazFAgy0yfwiZTNSK2muRkUKm5VBiL8HgZ6BdSDuyy7KlNe0M7ll1D09tfKrGpdqWjtlhRnGasNng6l4DWWn9G+5uH7BpE2gj00kMSiRArydIieWX5nhbqyNSyLRiPDFkcogLqr2Q6R3TgyLtHs44caLFklF2lCj/tvWurygKvaJ6sevkLiIjIToadu1qvP5VR6E9n6Q2Z7fIJCZCkDuJnelplJefpQ1bIVqVrsExQwwGN0Z3LGUVxd6lJUXuWmpRlDnKCNAGEG2KptxVTkl5yfnu0nkhx5zDd6nf+aSV2Et4ffvrfLDnA97c/uZ56lnTY3aYcZhNxMTAK+NeYmbfmbx1cjoVigWXKd0bOybB0JWdJ/af595WRQqZVozNBhZVNvGhtRcyPaN6UKzZT5nF2YQ9az4yrSnEGzo2qI0ekT3YfXI3AP36wY4djdGz6qmogDJXAR1jz26RUamgT7skdh5L4+WXqy9TUl5EkLZhjr4A/v5uTMJj0ap09pVLSy2LsvIyAvwDCPQPRK/Rt1o/mdlrZ3Pft/f5pKWXpBPoH8jEzhMpsBWcp541PWWOMuylJuLiIEgXxAMDHiAuKI7OY3/gUPlGb/Tw5PBuHCrcd557WxUpZFoxZjOYNUfpFHHu00UrSQpJQoWGfTnnDoHfEjhRfph2gZ0a1EbPqJ7sOrmLrLIswttmN6mfTGoqlKsLarTIAMyYnESFKY2sLM/OgzPxCJmGLSuBJ/hfmNYTg6fSIiPccmmpJVHmKEOnBPDNNwqRhuhW6SezM2cnn+7/lCOFR7A4/jA3pxd7llXC9GEU2i6ykOanYXaYsRQGeHdCKorClR2u5FfnO/yUuYmbut0EQL/47hy377vgfCSlkGnF5BVbsaqzaR+aVOs6apWaSKUb+/L2NGHPmo9cZwrtQxpmkekV1YtdObsYvnw4bx96BosFnE1ksNq9z4FDKSXccHaLDHjOxipxFFNSXkRZWdX8kopCQnQNt8gA9GkfC+D1kVGQu5ZaEpl5ZRRkB7BlC/g7olulRealrS9xd7+7CdWHsi/vD4tDWmE6lqxENM7Qi9oiY3aYKc03+YR0GNdhHF8e/JLL211O9KnwFAM7dKNQvY/c3PPU0bMghUwr5nDuMfwwEGGIqFO9WL8eHChs+ULGVmGjWBync3jDhExyRDJmh5mMkgzyyk8AVCseGoPfDnjeCsPOIWSCdEGE6cMQIWnVPnTMziJCDQ23yADcc5vnIee1yMiAeC2Kg2llhBgDmDoVRFk0J0pbn5BJLUxlYPxAekT25J2v97Bjh8eHcE/GMXT2RKz5F7dFpqzc7F1aqmRk25Fo1Vpu7XGrN61HVDI29Un2HLmwRJ0UMq2YlII0wlRJPqeY14YE/x4cLmn5QuZI0RH8hLHeUX0r0Wl0/Gv8v3hu9HPkWLIxmTwRgxsbiwWOZOcToA1Aq9aes3xSSBIEVxUyFRVgE0WENZKQCdAGYPQz+jj7tjQhU1AATz1V/TLcxU5qRhnRIQF07gwmEc3+DI+Qmf7VdNanrT/PvWsessqyiAmIIaSiB7+d2M1HH8Hq1XDoZDomZyIlOaHeU+cvRsrsZsICTBiNf6QZtUbWT13PTd1v8qYF6YIIUcfxS9qF5ScjhUwr5ljxUaL8ar+sVEm0IYF8e8vfiphSkEKQsyMmU8NPeby7/930je5Ldlk2AQFNI2QOHQJjRME5rTGVJIUkUW5M4+RJ33S7HcpVhUSYGmdpSVEUYgNjUatOnbVEy9u1lJHh+ddUlrQLleJiyC0uIy4yAJUKOsVEsysjle9Sv2P5zuXsyG5Cz/ULBCEE2WXZhGiiKUvtgSp6D1OmwNatkFGaTteYRPIzLl6LjBACc0UZsRGmKnlDEoZUeWlqZ+zGwQvM4VcKmVZMRlka0fraO/pWEqg3YnW1/P3XB/IOE1DRsdoIufUhOiCaHHMOAQGiSYRMURFog/MJN9Ts6FtJ+5D2mDVpVaIN22zgUBcRog9utL7FBMSc5uzb8paWKuco58KL9dWk7N0L+uAywkwBAPyp30QOW3/m+v9eT7ghnBzzxT8hBbYCKtwVHNkVTaegnqRZdtOjh6CsDHLL0xk3MBGXOZSS8hKc7otjt+bplLvKceMiPjKgVuU7BHbjuPXC2oIthUwr5oQtjThj3S0ygToj5a6Wf+DSwdwUgpydGk/ImKKpcFegDihoEiHjsaQUEKavvUWmQBypsrRks4FLU0JoA6P6nk5MQIyPs29LFTJnWq8udgoLQaXzbL8GGNunG9fm/sadPR7k/kvvbxVCJrssm0D/QH750cSUy7tRWl7K/21/ia69y7CpcxncLZHO8Z57rshWdJ572/iYHWYAEqOrWmSqIym4AzmOCysonhQyrZiTjjTaBdddyAQbDdjdLd8ik158nGAlEU39TieoglFrJEAbgEufTUkTxBRzOMCm1N4i0za4LbmOdHJzfX0/bDao0BQTrAtutL7FBsT+cdZSC9y1lJcHWm3rs8hYrVCh8gTEA/D3hx5tY7km6GkSghJahZDJKssi1C8apxMG9jey9ra1LPl9CW9YR+Gn6IgLjuLKMTo0wsD+Yxefn4zZYUYRKuKjdbUq3zG8PfnuC+vwSClkWilCCPJdaXXael1JsMGIQ7R8IZNnzSNEW7cdW+ciOiAah39Ok1hkysvBrtTeIpMQlEC2JQOrze3j+2G3g0PVuELm9l63c2ffO1v00lLnzq3PImO1QoXyh0UGPPNw8CC0MbVpNULG3xHDkCGeQ1dHtB3BlhlbEGo77UITUBSFfv0gSBvKR19dfH4yRRYzGmEiOrp2voJd27SnRDmGy+1q4p7VHilkWin51nwqsNIxom2d64YGGKnA1uJ+rM6kwJZHiH8jCxlTNDZ1dpMJGYuovUUmLjCOclc5GPJ8+mO1QrnSuEKmW2Q3hiYORaVqebuW3G7Iz4cePVqnRaacPywyAF26eBzLo4ytQ8hkm7PROqJJTPwjLUQfwrqp61g6cak3LdIUxsnSi88ik5FbhlaYCA6uXfnOUQkI3BzJz2zSftUFKWRaKfnWfPyEkYjgujuIRAR59uhZK1qun4wQgqLyPML1jW+RsShNKWRqv2tJ76cn0hiJ03Qcs/mPdLsd7DSukPGhhZ21VFTkWXpLTvYImqYKZnghYrGA3V2GSfuHf0RSkifqt5+jDUX2IsqdZzmw6yIhqywLjS2GM89hjTRGMjhhsPfvUH0oxY6LzyKTmWtGpw44tePw3AQH+mFyJbAv+8JZXpJCppWSZylE6wrBVDv/Lh/CTwkZc3nLXV4qc5RRIRxEGhvfIlPibjohU+bKr/XSEniWlxy64z6HfFqsbuyihCBdUKP30fMwbFlLS7m5EBoKERGeIxfy8893j5oPqxVsbt+lJT8/iIuDspPhqBQVJy0X93pbRnE22vLoKkLmTCKMYVhFAQ5H8/SrucjON2PU1P6HQKWCENGegyelkJGcZ3KKi9C66ydkggP8UISaInPLtcjkWfJQoyHcFNyo7UaboilyZjfJMQXl5VDmLKj10hJ4hIxV62uRKbSUIRBNYpFpiWct5eV5RIxKBVFtBFktP0RSrbFawebyXVoCaNcO0o+qiTRGXvTLSxnFWYRoYs65ezHCFEq5qtDnpeBiILfYTIB/3X4IwtXtSS2QQkZynskuLsKghOLnV/e6er2CRhjJL225d3SeNY8AdThGY8OD4Z1OdEA0+XZPZNTGDq5WXg4lzvxaLy0BJAQmYFafKWSKUaHyWU5oLBQFEEqL2rWUn+8RMi63i3f8evLMz4+2qP43BKsVLBW+FhnwCJljx1qHw2+OOZuYgJhzlgs3huHUFlx0Qqa03IzRr27Pgjb+7TlaLIWM5DxzsqQQo7p+cURUKtBipNDccu/oPEseRiWi0WLIVBJtiibbnIXR2PjRfW3lTsoqiutskSlVfJeWiuzFGNRBqJTGv/09QqZlWWQKCiAsDFYeXkm5UsgP+R/x3E/Pne9uNTkVFWBzOHC4HdVaZI4fhyjDxS1khBDk27NJCDn3MSWh+lCcfoWsT/v+ohK6Zoe5yud/LmL1SWRY0pqoR3VHCplWSm5ZEQGa+gdE0ypGilqykLHmoRcRBNTt/j0nXSO6crT4KLqgskaPJVNS7gnGVVcfmSK3r0Wm2FZCgF9w43buFJVxZFqSkCks9PjIvPrzq9zR/X66mx9g24lt57tbTU5lDBmgikUmKsrzwhKouriFjNlhpkI4SIg498tBmD6MNPUqpm0Yxd7cvc3Qu+bBUlGGqY5LS/GBbTlpT2+iHtUdKWRaKQXWIoL963/Wjk7VwoWMJQ9tRQQhjRfcFvBEuI0PjKfE9EvjC5mKfPQaA3o/fa3rJAQlkF/ha5EpKS8mQBvcuJ07hUqFZ2mpBe1aKiiAE2xjW9Y2Hh5xJ2pnCCdLL74IrmditQLaMhQUjH5GnzyVCjp0gPLCi1/IAMRVc87QmUSZonDjxF+lx1LRcp99Z2J11t1HJswUgM1lPnfBZkIKmVZKoa2QEF39f8X9VQZKrE3j7Jtdls11n1zXJG1XkmvJQ22POOdOhfowKH4Q2ZotFBc3brulzgJCdXXrcEJQAsUVuRSbbX+04ygmUNv4O5ag5S0tOZ2egxP/tf9x/nLpX4gMCCU2NIS8sotfyFgsoDZ4tl4rSlVfsSlToCijDQcys89D75qHMkcZauFPVMS5nQXHdRjHgoiDhGvjWnToiTOxusoI0tVRyAQYcVJ+wQTFk0KmlVJSXkSosf5CRq8xUmJrmreSvbl7+fzA5016rkl2aR5+TWCRAY+QSXc3rpBxuTzB8ML0tfePAYgwRqBV+ZNtPe5NK6touhgyLU3IFBfDcd03/J77K48MfgSAdtEhFF6EZ+qcidUKan1VR99KoqLguoH9+DVnEw7XRbbn+BRl5Wb8RADhtbitVIqKxKC2+GHA4riILDKuEkKNwXWqExbkcS68UASdFDKtlNKKIiJM9f8VN/oZKbU1zZc4q8yz//VA/oEmaR8guySPQE0EutodL1InBsUP4rBtK0XFjfe2Ul7uOTAyvA47lsDz8I0PaEeW/Q/HPLOzuFFPvj4dRQHRgnYtzdvwOOtDb+TVca8ScuoQzXZRIZidrUPIKP41O3qO7zEIjTuQb1O+bcaeNR9FFjMat4mgWhoojUbQCMMF8wPeUNxusFFU5zAUF1pQVClkWilmZxFRgfX3kTH4GSlrooB4J8pOAHAgr+mETK4lj4hGDoZXSc+onghcpBQ3Xv8dDrCqc2gTEFXnuknB7clzpuF2ex5cFncxoYbgRuvb6SgK4PZE9r3QxYzFYeHtA89zj+4npvWe5k1PiAzBTukFYzZvKiwWQHt2iwxAmygVSeZbeHfn+83XsWYkv7QMP2FCX0u3M6MRNG7jBfMD3lAqz12LDKjbS21IoBaV0FwwvkJSyLRSrKKQNkH1t8gE+BsxN5F5dW+6xyKzv55CpqICfvnF98TnMymw5dHG1DRCRqPSeKwgluPnLlxL7HYo98shOqBNnet2CE+iVJ2GzXbqwaUUN3ogwEoql5aAC97hd/PxzYT7xdEzoo9PelKM574otBafh141H1YruLVVg+GdjskE3dy3sDp15UXz4306hRYz/tQ+PL/RCCq34YL5AW8oNhs4VEVEBATXqZ5O57FMXShLbFLItEKEENgoIia0/kLG5N90X+J9x7MIdLZnb07dhYzbDe+8A/94d1uNJxkXO/KIDWkaIQMQbgijwJaPu5FcRRwOKNfkEG06d7yLM+kU3h6z3xEsFs9buEPVtEJGCI/j6IVukfn+6Pd01l5O6BmGyfgoAyrhR3ruxb28ZLWCW1Nao0VGUaBTSBcQCovfy2DXrmbsYDNQZClDp6q9o6vRCCrXxbO0VClkQvV1+y3wCBkjpfYLYx7qJGQcDgcLFixg/PjxDB8+nFmzZpGamurNX758OaNHj+byyy/ntdde83mQ7du3j5tvvpnBgwcza9YssrP/8IS32+3MmzePYcOGcdVVV7FmzRqf665cudJ7zQULFlBRUVHf8UqAIrMNt+IgPrz+S0uBuqYxr+bmQo4liwTn6HpZZLZuhf0ZJ/gq6lI276k+zoHdaadcWIkPq5vjbF2ICgjHrspvtOi+djvY1Dm0MdXdItM+JAmzXxpms+fHy6kpJrgJzlkCX4vMhe7wu+HYBuJdI6vsXPPzU9ARwrGTF7eQsVjArGSdUxxHRSmE+sWyZW/mRXd8Q7HVjF5d+2BSRiMoFcYLxhLRUGw2QblSd+d/f3/PElux9cKYhzoJGZfLRWxsLMuWLeP7779n2LBhzJ49G4DNmzfz6aefsnz5cj755BM2b97M119/DXgE0Ny5c5kyZQrff/893bt354knnvC2++abb1JSUsLq1atZuHAhixYtIj3d8yOUmprKK6+8wosvvsiqVavIyspiyZIljTX+C461R9Y2+Q6BjHzPCa7RtT23vRoCDUZszsYXMj//DA7dCfoEj+aE5VidxVJODkS298S9+O7QxmrLVMaOiAlv5Gh4pxFhDMOtK2i0nUsOB1hU2fUSMkkhSZSo0jCbBRYLVKibbteSJ47MhS9kiu3F7MjeQVjZyCoWGQCjKoSMvItbyFitUOhKp21w2xrLRUaCtjyOIteJiy48f4ndjEFdN4sMFc1jkcnPh6ee8iyVN9k1ysoQitvr6F5b/Pw8S0slLVHI6PV6Zs6cSVRUFGq1mptuuomsrCyKi4tZvXo1119/PXFxcYSHh3Prrbfy7bceT/cdO3ag1+uZNGkS/v7+3Hnnnezfv99rlVm9ejWzZs3CZDLRq1cvhg0bxtq1awFYs2YNY8aMITk5GZPJxMyZM73tXmzYnXbGrxjP0t+XNul1ThQUoRWB+GnU9W4j2GDE5rbU6IdSH7Jz3JS6s+kd2Re9KpBD+YfqVL+4GFw6z5rStryzCxlFqGgT1gRblk4RbgjH5Z/faEKmvBwsSv0sMu1C2lGhWMgozD0VzbWkibdfn1pauoB9ZHbl7CI2IJbyvFiiqvGfDvQLIavw4hcyeRXHaiVkNNZYLOpMmih01Hmj1FaG0a9uFhmV04i5vOknYv16OJbh4HjjudpVIa+sGAWFQP/AOtVTqTzR3Ztq52pd0TSk8u7duwkNDSU4OJijR48yfvx4b16nTp1YvHgxAGlpaXTo0MGbp9friYuLIy0tDaPRSEFBgU9+p06d2Ldvn7fuwIEDvXkdO3bkxIkT2O12dNXsnXU4HDjOOGddo9Gg1WobMtQquE85P7gbywkC2HdyHy7hYtHmRUzvNR0/dT1OdKwFWUUFGJTQBvU92KCnAgsVFUqjzkF6fi5uXHRs04a22QNZn7aeXlG9al2/sFChPDEbrVrLUedGiovdBJ5xj5bYytAIIyEhAre7/j+2NX0HwvRhOP0OUVzsbhQ/mUKzGQcWooxRdZ5vnVpHoCqalIJUjERgV4oJ1AY2+HM76/hPCRmny4lbdWFaZdKK0ogzJqEo4tT3wDc/WBdMdnHhOeeoKZ4DzYXVqpDjPkZCYEKN/Q8PB6MrDpcxE4vF955pyeMHKCv3CJna9l+v91giiq1ZVcbemHNgs8GSXz9iU+yfufpgOu3a1U1o1JbcsgJ0ShCIultQtYqREmtZk38HVLXwxK63kDGbzSxcuJB77rkHAKvVisn0h4nOaDRiPSXfbTYbRqNvCGyj0YjNZsNqtaJWq31ESU11K69hs9mqFTLLli3j7bff9km74YYbuPHGG+s71BrJyMhotLZ+OPIDfSP6UmgvZPHGxVzT/ppGa/t0UjKPoyfIu3xXH5w2K07Fgs2matQ5SC/MJzAkGH91KYnWSXyw8y2ui6l9lN/8/DgKI48wqM0gNmZu4ptNuxncw9cf5EDmEfyEiaKi41itDbcaVDd+YRVYOUl6egkJCcUNvsbBzDzU+FF6spQype6ONxGqtuzP2keYvSM2UYSt0EZ6eeOclXL6+HNytDidnpeGY+nHqoS+bwr27jViMLhJSrKdu/ApdqXvwuSMIiSkgoyME1XyTRoD2QX5tb5HGvMeaC6KyiLIV3LQmDU1jtPhUGNwxWIzraGw0E56etUjC1ri+AEKLYVEqkPr9Cz0V+nJKy6sUqex5qC4WMM73x7lh8A78VP5sXrPBvok926Uts/k+MlM9ATX67dAi47M3BzvuJvqO9CuXbtzlqmXkCkvL2f27NkMGTKESZMmAWAwGDCfdjKdxWLBcOpoYb1ej+WMxVWLxYJer8dgMOByuXwsLDXVrbyG/iwb/6dPn84tt9ziO8gmsshkZGQQHx9fK8VYGzIOZKMpuITBCYGk2FNITExslHbPxKH6nkBtWIPaT7Qn4lJbKC9XER8f0yhzUFEB+eX7iA2Ko0ePSCK/vI7/+f8FVbCK+KD4c9YXAsxmBcVko6ehJ0dyykixH+RPib4i9vfiVDTCSIcOCbXedlkdNX0HOjs649hejKIEk5jYcMda+7bjBKra0LZt23rVjw9oT57zJHaVE4Ggb6e++Gv8G9Sn6sYvBKgUzyFT8fHxNe6IaSw+/VQhOBhGjhTM2zCPcR3Gsfvkbn449gMfX/9xtXWKdxUTqe1CfLxftfdBfHgbDmSXnvMeaYrnQHNR6ErBX62jX+d+1R5RcDo3j4/hjX25uB06nzlpyeMHcOAgIii8Ts9Ck7+RcsXtrdPYc/DJJwoHNC9wS+dbKbVUsPvgzyQkTOIcH1G9KFftJMAvpF6/BTpNACqthvj4+PP+HaizkHE6nfztb38jIiKCBx980Jverl07UlNTGTJkCACHDx8mKSkJgKSkJL744gtvWZvNRmZmJklJSQQGBhIWFkZqairdu3evtu7pO6NSUlKIjY2t1hoDoNVqG1201IRKpWq0D2/TwX20cU4gcy+UJ6xqsi9FiaOUAE1wg9oP8A/ApbLicKgabQ6Ki8Hul0V8cCwRESr8XREMihnOF4e+4MHLHjxnfYvF4xRb5sqlnak7EX7tyDKfqNK3EpsNLSY0msaZ3+rGH2GMwOzOx2JRUKka/gTKs+US4hdd73mODYzjaFY2OZaTmNQh6LW1P3jyXJw+frUaEKd8r5TamYUbgtPlIitLRUkJZJZl8NxPz/Hy1lfQaXSUOUpxuB3oNFWfFekl6XQoH0GbmOo/n+iQULZV5OB0qqjN46QxnwPNgdsNBa504gISUKvP7Ss3oGs8T/+WidVa/Xw1xfgf/u5hIo2RPDrk0UZt93SszjKC9AF16rvJ30ihw1qlTmPNgdkMJVG/M7z9HbicKv526HWKilS1OkahrpSUlxCgCalXv/VqI+bT5uF83gN1vuqzzz5LeXk5Tz75pI+KHz9+PJ999hknTpwgPz+fFStWcOWVVwLQr18/bDYbK1euxOFwsGTJEpKTk4mOjvbWfeedd7BYLOzZs4dNmzYxZswYAMaNG8e6des4ePAgZrOZpUuXetu9mCgshMPFe5g2vgfXjU4kJS+d8vKmuZbZUYZB07A3ZaOfEadiweFovC9uQQEIUxYxAdFoNJ61+Z6BI9iRvaNW9YuLPfEN8u0niTRGEqqNIt9eNZhMkcWMv1K3Q9LqSrghnFJnAWVljePwmmfLIcSv7o6+lcQHx5DvyCLXcpJQbf3bORfNuf0635pP1ItRZNmOcPIkfHvoe9rrLmVy5kGejdmPwc9wVmfxY8XHUEra0uYsUxEVGEKFpojCwiYcwHmkvBzK1Od29K0kLjCOAnsuZdbmOXPp3Z3v8srPr/DriV+b9Do2l5kgfd2ehSZ/A5Ym3LVkd7g4WPI7/aL7MbrDcPK129m6o2lOmi4uLyJAG1yvujq1ocmCotaVOv0KZWdns3LlSn7//XdGjhzJ0KFDGTp0KL///jtDhgzh2muvZerUqdxwww0MHjyYiRMnAh4ryfPPP8+KFSsYOXIku3bt4qmnnvK2e9ddd2EymRg3bhyPPvoojz76qNeE3qFDBx588EEeeughxo8fT1RUFDNmzGi8GbhA2JtahEV9gjE9uzOidyJl6nQ2barfj2B6Ouzff/Z8S4UFo1/DfsiNWiMVioXy8sazd+bnQ7kug7iAOABiY0FYQym2F9eqflERBAfDSctJokxRhPlHUVCNkCm1W9CpmtZ3I9wQjlNUUGhtnEAyBeU5DRIg7cJiKHZlkWfPIVzXdELGs/26eXYtvfrzqxTaC3CE7yAsDD7dvh5T3uVMHpmAs7gN3SK7sS9vX5V6TreTjJIMnAVnFzKh+hBc2otXyNhsHiGTFNq2VuWjTFEoikKBI7vRgjzWxPwf5nNd1+vINjftyds2t5kQQ92ehYF6I7YmFDK5zsO4hZuuEV1JDE4k2hDHm/9b32gxqU6npLyIYP/6BUY1aIxYHS1w11J0dDTbt28/a/706dOZPn16tXndunXjo48+qjZPp9PxzDPPnLXdCRMmMGHChLp0tcVxoHAfgcQSog9BrVLjwMxXa4sYOTIUTR0XANetg7Q0eOYZql1XtVaYCa/jzXsmBj8DFXh8ZBqLggKwqDOIDxoAQHw8/H40hCJT7bbBFhVBSAjkWnKJMkYRbohkb+lPVcqV2sx1iuZZHwL9A9EoGvLN+UDDdxwUOrJJCD63n9DZSIqMwUwWuZYcIkLrfl5TbWkui0yxvZh//vpPEvTJ2PV7STDdwL9OrueeditIToZ9+6Bb227sy60qZE6Uepx7ldK4swqZEH0IzovYImO1gs3/GO2Ce9SqvEaloY0xGovqBDZbIsYmfA9wup1klmby1MineGLDE+eu0ADKRRkhxroKGQNWc9NZIk6IHXQL64VG5Xnwzx32EE+uvZ9Pvh7OHbcEN+q1zM5i4nX1FDJ+RoorMhu1P/Wl5SzqXuTkWk4SpIoBPD+CwbpgbNrj/LSt7ibFlBRPhNyUlOrzrS4LAdoGWmT8jLiowFreeAfr5edDKZnEB3p+sOPjwVoYTJG9eiFz7Jgn4m0lxcUQGOTyLDmYoog0RFHizK1Sr9RuQa9uWouMoiiE6MIoshc0SqydooocIvT1t6R0iIzBps4h15ZVr1g0taW5hMy6tHUkBiVymf8Mivz2oGlzCLtSxO2jBhIW5hHFyRHVW2SOFR+jjSGOoADNWQ8LDNGFUK4UUVDQZEM4r9hsYNYcrfXSEkBcYCxWTdPHkskx5+AWbvpF9yPbnN2kR12UCzOhprotLQUZDJS7m2YS3G44qdpB76h+3rS/XPoXuoR0Z8mxxxr9emXOIkLqGVPK6GfAeoGcOSWFzAVCaXkpBvUfb+6JQYnEdE9l/Nq27Dm5p9btFBZ6LBODB8OPP1ZfxuY0Y/Jv+NISQKnNfo6StSc/X1BQkeHdoRQXB9aCEIptxVXK5ubC88/Dl1/+kVZUBOqAfNzCTaQxkjamKErdVZeWzA4zhgYurdWGMEMYZpGPoxHcCsyuIkL19T9SIjqgDUJxUajZU68TtGtLc521lFWWRVJIEvrSHmQ595Lu/w0d/YaT3ElHWJjH6budqRt7c/dWqXus+BhtdG2pKbB1iD4Eqyi8iC0ygnzVPrpFdqt1naTQJGz6lCYXMpmlmbQxtSFYlYDD5Tjri0xDEQIcShlhAXV7FoQYjU0mZBwOKPDbSb+Yvt40RVG4LmkmR50/N/r1LK5iwoz1s8gYtUZsrgtjaUkKmQuEUvsZQiY4kZ3uFVgpYFtaag01fUlJ8VgyRoyA33+vvozdbSHAv2EWicr4IGZH7WN3nIuTJSVYXWavRSYsDEzqEAptvg8yIeD996FzZ9i0Ce+PTXExCEMugf6B6DQ62gREYRa5VSwDZQ4zBk3TxzeJNIZTrirA3EA/vfJyj/hqF1N/B21/jT9GwinQ/k5ccMu3yGSXZROkisad051M6xFWZ67goXHXoiiec2ACAqCN0p20orQq4eSPFR8jXJNIUA274pNCkigXFtLyW2Z8lHORVpiOU7HRJbxLrev0j+5Pvv+2Jj+mILM0k7jAONauDMBfMZJdVnc/mftW38c3h7+psUyZ1YFbcRARWLf7KsRkoFxYmkSoOxxgV+dXuUc7h3ekQKTU65pHjnBWy6LVXUSYMbgePQWT1ojNJS0yktMoqyjFqPG1yKw+4jmr6kBm7dchU1OhY0ePCCgvp9qdT3a3mUBdwywSapUavSqAYntJg9o5nUJnBia/AIJOHWioKNA2Ohi7y0a584+B5OR4bs4774TevWHNGo+4OX4clICTRBk9FoeYoEjcOCk6QwhZHRZMDVxaqw3hxnDcuvwGP/h37waXuozE6Ib1OVgTg0NVQkJo0zr7CnfTC5nMkmzS90Uz6fJognXB7MrZxaTOk7z54eGgtkYTpAviYP5Bn7o55hxMxFSJ+Hw6Jq2JrsF92FO6uamGUGvcws2x4mON2ub+gt1EqbqgVdc+VMUlsZeQrdrWLBaZuMA49u0Dozu6Xg6//0v7H/d/ez9259ktxvklnhszPLBu91VogAGBu0nOxCsvB6diweRv8EnvHNmeCsXCSUtVC3NNmM3wz396zrCrDhvFhJvqaZHxN1AuLTKS0ylzlGLy8xUybuEmWBXD0YLaC5mUFOjQASwijyz/DdX+iJYLC0H6hlskAjVhFJcXN7idSgrdGcSYfB1aO8QGA/jsXMrL8/xQfXXkQ7peeoLt2+HECc+6vyrQs2MJICzAhEYYyLX4+slYKszNImTC9GG4/Btukdm+HdwaM4ENDC4X7u/xwYoLbrqlJThtaakJdy2lnszGKKKZPFmhe2R3hiQM8X7u4Pl+FBQodIuo6vBbYCvAzxlWo0UGYFDcEFIcPzbLLp2aWJe2jiveu6JR2zxUvJt4/551qtM3ui9mckgvqhoJuTHJKMkgSImjrAy0jmgyS+ouZLLKsjA7zPzr13+dtUxeaRkIpYpoOBdhgZ5np6UJ/EMcDnCqLJi0vs/nsEA9Rmcch/LP4vh4Fr74whNfqzrx6XRCuVJEVGBwvfoaqDNid0uLjOQ0LBW+QiYhKAGNSsOoiNvILK0qZMrK4JszLKcWCxw4mcI/j82i3T8TWBN+BWXmqk9hhzAT1MBdSwDB/uGUVDTO+rUQUOLOIC7AV8h07eSPH3qfdfK8PLCFbOeWz29hydG/IwSsXAmdOkFq0UESgzxRKvV60LuiqrzF2FwNX1qrDeGGcCr88hpkkXE4YM8ecNBw8RVl8AiZ6ICmXlryCJmmtMictGQTHxKNWg239LiFBwY84JMfHu5xHu8e2b2Kn0yBrQB1eViNFhmAUR2Hkq3Z3GAh2lBSClIotDWus06qeTft9HUTMiatiWhNMjvztjVqX84ksywTlTmeHj3AJKJJyc5m6e9Lq7yQnA2zw4zZYebJEU/y7s732LqVah3uC81m/DCiUur2MxgW6PEQb4oTsCstMkbtmUf6QKCzI/tzai9kjh71WGIc3d9he+H6Kvk2m0fIRAbVzyIToDNSLoWM5HQszlICtH88WUe2G8m/rvwXPdp046StqpD5+Wf46it8nBFTUgTfRV1BOWWsn7oet+Iku6jq0o8Dc6NYZIK1oZQ4G+cB63SCWZ1BXGCcT3qXLuDnCuFEQbE3LTfPzefl93BLz1v4aN+HxCSn89tv0LUrrE5dzbgO4wCPkNG5IskqPVPImAlo4NJabYg0RmJX5zXoh9BshvKKCspd5Q0WMrGBMSAUIowRDWqnJlSqymMKVE0qZPLLs4gN9ATUvKv/XVyX7HseV6WQ6VbNzqV8az4qe/g5LTLD2w2myG8vaVnn5xRs16kNgUeLj1LmqF8QkbSjLtIznFXSj1l30z6gbkIGoIP+EvYXN52QcbvhcE4mxelx9OgBkYZo9mQd5u5v7uan41VDKVRHdpnn0NgBgdexN3cP/15WWO09WFBWVq/AmAEmFWqho6QJ1tjsdkEF1ipnlGm1EOzqyIHcqkJm24lt5Jh9z79yu2HFChg7FjY5XuOrgoVV6hWby3GpbETWc2kpSG/AgVxakpyGxeUrZMIN4dzV/y46R8dR6Mqs8kaxfTu41Vb2nvayufVAOmYlkyUTlzAwbiBqtJwoyvOp53RChWKpc+yE6gjVh2FuJCHjcHhiyJwZKyUwEIzqYPakFrFmjUe47Tq5i+yKQ7x19VtM6jKJLZqnPf1JzGJXzi6vkNHpQO+OIqvY903O7rIQqGsOZ99IbEpugy0yaD1P4YaeW9SjbQwh/uHe+BRNgWfXEigoTeIMmV6cjsPloMxVQHxw9FnLhYd7fKmq24JdYC3AbTn30lKUKYowVQf+d6h2P6CNhRAeJ/YHHvAEtkwrSsPhctTZJ+OLL+Dafz3BLR/+2SfdVmEjpyKFzrWMIXM6nQP6k2KuXaTt+nD8OKTmZpAcF8cll0BccDTrsj+hwl1BvjW/Vm1klWURbYpmzadRxPp3pihgMyXVuPIVWczolLrfU0YjaNxGCsoa3xpRZreDIjD4+S53KQqEqzqQUvCHkKmogNdfh3u+uZ8H1zzoU37HDs9L0MARJaRb95FSsYn0Yt+DIXNLiwEI0QfXq6+BBiMVQlpkJKdhdZUSpKtq6+6REIdZlUlp6R8/Cseyy1hWfBtLo43c8eNwb4CvDWk/0jmwHwY/A4qiYFIiyC7xvfntdoFTMRPSCBGtwvXhlLkb5221vNxjkUkMqRr0LdQQwoatRSxeuZlNP5s5WLSbLsG90Pvp+ceof7Dh5KeED/8vv5tXMyBuAOEGz6EkWi0Y3FFVLDJ2YSa4EZbWzkWkMRIzuQ2yyFRUgNB63sbPfLjVlR4xHekUkdSgNs5FZQDGprDICCHo9K9OrD2yFgUVCWGRZy3bsaMnxpDR0o1jxccwO/74EApsBbjKzr20BNA3eCTrjnzfGN2vFW43fPwxfP019O8PH30EaUVHASgrr71VRghYv8FJeshSUuxbffKOlxxHg5a4wNg69y/KGEVpRdMF1zFbXFhUWcy4Pg6DAdqFR2Nze8ZdWyGTbc4mQImmuBjGJQ8j37ixeiFjrV9gTI0G/DBQVNb41ohSm0cYnLm0BNBG25GjJX/sYC0shB2/Cfbm7OeTfZ/4hOnYsQMGDoSdeb8SY2hLR67k3V3v+rSXW1qMRujrfXhskN6AU7HjcjdeLLH6IoXMBYLNXb2QaRcei1txcCD9j5v4rysX4AhIZe01uyi36Hl5y6seX4rSHxnRbqi3XKAmnJNlvhaZUms5QnE3ikUm3BiGxd04DzWHA6yaEyQExVXJaxMUQr65mJ+ibuHj/R+QbttDj0jP22RSSBLLJy/n5WO38si6uVzd8WpvPUWBAFUk2WW+QqZcmAkyNI9FptR1skFCxuEA4efxj6nrWv6ZjGo3io3TNjaojXPRlELG5rThcDn4aO9HGEUk4aFntyz5+cGll8LBHZGEG8LZn+c5s8NaYcXutKNxnNsiA3BFp1H8XlLVv6CpWLMGdu2C+x620G74T7iFIDU/DcBHjJ2LoiI4ItbhVFnJcx/EVvFHmIRCWyF6wjAY6n68SLAhAJu76ZyGskpPIhQXMQEef67OMR6r24DYAbUWMoezs7HnxXDttXB50nBO+G2qVsgUW8swaOr3HNQqBorMjS9kSqwWFBT0mqqRGmP8O5Ju/mMLdlERaIKzsYsyroq5g2d/fBYAi93Bnr2CPn1gS8YW+kQMpKPjRlalrPJpL6+sCL0SXO++hpg8z1Cbs/FCcNQXKWQuEOyilGB9VSFj8DNgIJT//u84L73l8d4/ULCXq+OmMrpHTwaLR1ny+1IOptrI9f+RK7r8IWSC/CLItfje/IUWz0OooduvASJN4VgpbJTItQ4HOFTF1QZ9iwsPpufgExSJ4/xW8CN5qt30T/hjfX9yl8nsv2c/r1/1Onf1v8unbpC6qrOvAwuhjSDkzkWkMRKLq4QSc/1P/3Q4PBaZxthlpShKvd++akvl4beKojT6rqXKH/KvDn2F3hV9TiEyeDBs364Qo+nGj4c8y0v51nwUFEyaEHRVD8WuwpQBI8lX9rI/vXaOpg0lIwMSL91Dl6VhjPzPUFxdPsbiKsVP5VdrP5kiWxGvbn6DfaELub37LLQiiF05f6xBF9oK0YnQs0Y1rolggwm7uwkO/TlFVlkWRsK939OhXbsSXT6MEbHjybedW8hUVMDqTVkkRURz2WUwNGEoJ9y/cbKwqugotBUS6Fe/IJP+KiNFTRBQp9RuwQ+Dz4HMlcQbO+BwlZNR6oltVFwMrtD9xBjaEXHsHr5N/Ran28n4d69lXcj1RMc62Zq5lUujB6K2R1JaXurTXoGlGINSP/8YwGvVL7Of/+UlKWQuEMpFKcGG6m3dEbo4vih7hL9n9sZqFWSVp9ArvhOKAqPaDydAieLB9bMoUh9iSMJgb71Q/wgKbL4WmWKzBYSCTlOLp/g5iAoMxa4UUFHR4KZwOKBCqf4HO1QfQqrdcwputt+PFGn30DfWd32/fWh7bup+UxUhFOAXQslpW7fdbkEFZkJMzRBH5tQS10lz3jlKnp1Ki0yAtmH+Mc1FU1pkLKdO2jU7zOgqzi1kEhM95nX/0mS+23EA8PjHBGpDCA5UV3sO2ZnEhUTQRtWDT7Y1z/JSeTkcK/+NS2Mv5d5L7uWbwpfQu8OJNEbWamlpa8ZWui7uyn9TlhOpj2P2kPsJc/Rh69E/omMW2YvQukPqKWQCKBdNZ5EptpWhV/3xwXaIjOP+wI24SiNrZZHJz4dCZzbD+sSgKJ5Tu7WKniOFaVXKFtoLCPYPq1c/dSoDxU3g7Ftmt+Kvqn4JOdDoT5yuM7tP7gY8Fpky3QF6x3YlwtULxe3HurR1/JSzlhL9bgYuuYxN6ZsYlDAQd7mhyi6rAmsRBnVwvftq0vuhCDXFVilkJHjW/sspJaQaiwxA9/h4jirrsatz2bTnKCUcY0CHjgB07qwwSjxHcZmTmW2fI8zwx40Zpg+nwO77I1pkNaPFVK3irytRgWHYVfk+5x3VF1u5E6diq/YHO1gXzC+Zv5AckYxFnYlVlUP3yO61ajdAG0Sp4w+7srXcgVBchJqafmlJo9IQrA0j31b/t/mKCk8wvOaIe9MYNKmQqbCg1+jRqrQY3NHn9HFRFLjlFhjSK46M4hMI4fGPCdTUblmpkkvCR/H90Q0N63wtKS+HElcWcYFxTO4ymb2F2zE526FXm2plkXl317tM7DyRR8K28njnD2gXGk+Mqg/bM/8QMoW2QrSuUAz1cLkKNphwUNZkx0+U2qv6rXTuDJa88LMKmdPj/JSWQrlfFjEBniUpRVGI0bUnrfhIlXrF5QWE6uopZDQGSptAyJgdFvxV1T+bjEaI1/b0ETJF6gN0i0xm7BUqElyXM/d/cwlzd+XjK7Ywq98s3prwFpcl9kHtNnpfBCoptBVhUtffIqPTKWiEsUl2b9UVKWQuAOxOO27FSZip+idz2+C2jO84nihVV5Zt/QIVWrrFe3xJOnUC/fEJDM7+kL+PnONTL9IYQUmF781fYrOgVRrnR7xNQDjl6oJGETIlp5zcqtuZE6ILocxRRv+Y/rQ39iFC045A/9qdKB3kH4S54g+Tan6Z522yrtE860u4IZICe/2FjMMBLo25wTuWmotKIdMUu5YsDgvBumB6hl1KiCbau4x1LrrFx1BKNjk5HouMSV07R99KhnX5//beOzyO8t7fvmf7bNOqd9uSZWOwAWODMbhRbIoxmGYgQDiBBAhphPAmIZyTAAmQQOipHJrzy/GBQ2jBxDRDwBBaqDYYY8tVtvpq+872ef9Ya23ZKltmJa0093XpkrRT9nlmZ2c+863T2eZqzntFW0gGKLvibVRbq1k4cSEOk4NKQyN62XaQReZAVwEkU7WPqTmGtjaBmmSYCZNMM1nftU/IOIM96KLZuZZKLDZkIZG3uAhfuH8h49rTv5DZtQtuvBF+9OKP+az9MzwekLRtqRgbgHrbZFr8BwsZb8xJmTk7IWPWWfDmwaXiDwcwDSBkzGao1u4TMm43dMS/5NCyQ5k1Cxw9i9nQuYG6wFkcO6Ocq2ZfxaVHXIpZ1GDgYIuMW3Jj0zuyHqtOB3rZgke1yKjAvgtSsaX/m9WvT/41/3f+/zHVfiRvdj9FmaYJ7d6reEVFUqkLQrLJ4v5U2Mrwxg4I9pX8WdVO6I9SsZSw4CIQzD1q3R1MXqQPrJ8ASYsMwLTSaSw7fCHHNaZf/6LIZMcf3WeR6fElXWs2UxZX8SyoMFfgiXVmXR02ElEtMr0EoslCYddOu5fjTFekvV1dUTUxYxubNydjZEQ5M4vMERPrCRlb+PDDLAadIZEI9ERaqbHVoNfqOXfauRxSPB1N1HaQReaoB49i3c51fV7b5trGJEcDbW2khMw0x0y2eNenhGWXvwdjIkshs7dTdCaBx5ngC/kPCsCdPBnivjK6AgcLmTVroNmzkXs/uIvXtr+G1wt+oY1q277U/EbHZNpCBwsZX8xJuTU7ISPqzfjD+bHIiNqBhUx5oq9FpiW0kUPLD8XhgNnFiwGYW3Jmn/gvQUgWr5NiUp/vpCfiosiYvUUGktlbqmtJBQC35EUrG7Gb+w/EtBltWA1WZlYfSYf+PerNU1PLBCFplZk8mYOeUKvt5fgTB1pk/BgVssiUmktBkOnw5p6C7Q35MWBGq9EetKxYTH7ZDik7hP9a+J/84YyBy44fvG0Rwbg3dRHvCeyt5qnJ3bWWDlW2CiRNZ789r9IhGoWYRo2RgaRFxqK3UCsczSRHQ9rbVduqCWiSQsYpOTHEyijN4P5VZ0+WQHj77fy1XOglFAJnZN+N+MEzH+QHs24kEeprkUnICXZ5dvHmjn1ZaPFEnB3uHTjkRkIhqNpbwHlK6WTCCSlVNK0rkBQy6QQ7H4hNNCDIOjxSfgJ+A9GDhYzRCA2VZfgi3j61dNrakhleHZMeQIuOTd2b6HIHkWQP1dZ9QmZq2WS64gc33g0knFTYshMyVr0Vf5ZFCgcjGA1i0vbv87NYoDR6BF85vyIUC9HudtET6Uw1/lx4eCNndL7B4kOPOWhbm8mc2n8v3og7ZyFjFGy4gyNc+hpVyIwKnAEv+oQdwxD92+Y3HQlAU8mUPq+fdhosW3bw+jWOMgJ09bEG+MIBRK0yT/dmvRmdLNLhyz0F2xMauMpmyiJTNo0SseSg6r+DUWwuIkE89QV2BwIYyH98TC9VtgpC2s6s3RKRCMSE4ekNpQR9XEt5yFqyGCy43eBwpL9dtbWaQNzDl80SzqATTaiU8gyKG9cX1RNOSHy5s4enPn8uq27M6RIOQ3eoLXUj1ml0TKzXkpD6WmTcITexRDIrpZfd3t3Issxnb9Uzc2YyBR2gstREkaaa7e5kPZpufw82XUnarrn9MZkEDLItlf2oNP6IH7P+4HN9UmVScDiD+641//43TDnCxUfRvzIz8n2+7PqSLT2bETW2PrGCh1VPxq3ZetDDRBAn1UXZCRmHqRhvxJ3VtoMRiAQw92OVhr2F+IJ12I12NrRvZHewmTKxPHV9POIIqI4s4tBDD35I663kvr+Q8UVdFO/dNluMghVPSBUyKoDT78UgDy1k5u0VMkfW9RUyEyYkLTIHUl9STljTjbSfO9sX8ismZADMQgmdCggZb8iHSdO/1aFELEEraJlc3M8kh6DXXecJJ91L7oAfg0KutXSotFYQ0Xf2+QwyIZmWXniuJSFfriV9Ushk4hoqNZei0+jY42mj3ddNIpiZkLEarDhMDgLaFn70yg9Zu21txmNPB1mGUFimI9jaJ8ajpASEqA1XcJ+Q6e079O7ud1PHeZtrG9WWCXzyoZ4LL9y33+JiKJIb2L63sF6P1IPDkGXasRF0CSs9/jxZZGJ+rP0ImbpqPaJQ1CdOprMTvjL+lZnVM2kIXMTGzk184XmfQ+1z+tRcml49Gb92Jz3ufa0aYjEICU6qHdkJmRKxBJ9CVc33R4oNLGTMZpAkgUniEby6fj0eXTNNJU2p5Q0NsHRp//cCm9mAVtD2CfgNxN2UmHOzyIgaG948WecyQRUyo4CeQFLI6IaoHF9jq6bOVsdJ09MrLV5bXE5U46fbvS8adzAfbDZYhFK60ixUNRjesB9xACEzo2IGr//H61nVQLGKegyY8YT2ChnJj2kYhUyFJSlkcrHIRCkc11LvU37eXEsGC9u2JVOr0x6ToKHKWoVgb2W3M9kwMhMhA1Bvrydo2UiLb2dKFCtNLAYh2U04Hu4T42G1gkmw4fTue/LtDHRSY6shHAvzwbaveOGFZKCvLdbAwoX0cZ2VlIA10piyyLhCPRSbshMyWi0YZBuuPFlkpFj/1sfKShDlvgG/HR0yr7ke4qrZV3LUhEPoCXfzWfAFZpbN7bPtpOIJAHzZuiv1mtsXIarxU1ucnZApNRfjjysvZIJDCBmXCyIth/P02+sJW5qZUrpPyGg0cM45+yxx+2OxCBg1fQN+AwkXpWZHTuM1aax4wqpFRgVwB70YGTqNQhAENn53I3Nq56S131Jz8mLV4tz35ff344POBaumBGcgd4uMP+wb0FKkETQsnLgwq/2azWAUilIB1V5p4KyAfFBpqUTSZm+RiUYhTAFaZPKRtRQNoJMtdHYm48IyocZWg6G0jTaPE4ehLONA1zp7Ha3ml4CkWycfhEIQ1LZh0Vv6CFdBgCKTDae/r0Wm1lbL0TVH8+A/3uXvf4eNbdswBRs55JC++y0rA6PUQHN3Ush4o65+C0+mi0Gw5i1GJhjzYzUefK5XVYE+VtbnoWmD6wM6wrtYcdgKptQXUaytYbP8IsfW9hUyOo0OhzCJLzv2Bfzu7klesyrt2R2HMmsJAYXas+xPKB7EMkArEoslmWp+8uFH4NStJ2hqTttKbbGAQbAQiO6zyEgJN6WW3CwyZp0Nn+paUgFwSV5MQnr5oJmk4eo0OkS5hD3ufZlLwUgAcz99PLLFpiumJ5T7k0lSYClvdbDZwJCwp56ivaGBBVM+qLBUEBQ6cnMtUXjp1/myyET9VhoayLgGSrW1GsHWRrfUQbW9LOP3rrfXs02TFDK91j2lCYdB0rVSbas+qM6Tw2JNZfZBUsgkfBUUS8fw4Z6PKS2FT3ZsQ+drpPGAdloOB1ToG/iqczsJOYEv2kNZlh2PIWkd2t/NpSRSwo+9HyFTUQGGaBm7e5JCJhiETfLfOWvK2VgMFmproUSehizEmTfp2IO31zX2KYrX5nZikG0YtEP48wegwlqCJOfBtRQP9NtnCZKf4zXXwNdOPAK/eQMR69Y+rqXBEEUOSsGWcFFuc+Q0XovOlpeg50xRhcwowBNKX8hkikVTRqtnPyEzgA86W6w6e7/1LDLFH8m+78lg2O2gixelbj6esA+LLj/Huj/KzGUE5W4CgeysE5EIhOXCs8jkK/064LZw2GGZb1ttrWYnbxEQOjiifFbG29cX1RMg+T3Kl2spHIaIsa1Pxk0vpVYbntC+G0aHv5NAZwWx7glYqnczaxZs6thGraWx3xo5U8ob2O7ehi/sI0GCcmv2FhmTxoY3T0/hoYS/3/YpJlOyd9z2jm4+/hi6usBt+pS5E44GoLYWTP5p2GOTaaw82G9YbpxAq78l9X+bpxsz2bmVACrtxUiCMu1Z9iecCGAz9i9kBAFmzYLDK2fgDHeyXfo4bSFjsYCefUXx4okEYcFNZVGuFhkr/qgqZFQAb8iLqM3PzbVIV06nb585NhgPYFXQImPWWRQ5kYMxHxa98lYHqxV0sSLcoaTY8oW9WPXDK2SihHAHsguSiUaTF/dCiZHZZ0hQPmspEAngc2YpZGzV/Kv7OarDJ1BfmfmxrLcnu7LXGQ/Nq2spbOhbzK2XMnvf9Os9nk4M0QouXV5LwtKaLBon7+Cw6kn97vvwukY6wy10BDrQoKXcnv35JGqseDPoxJ0JYbl/IQPJSuXvfNbBn/4EGzeCy7CeI6uSCRDV1VAjLWFq6NJ+08orTRNoC+6LkenwOrFoshcyVY4SIoKHUFjZzs/hRACbafDrs9VgZXLxZKSYlJFFRiebU64lp88HgkxVjkLGarD1cVeNFKqQGQV4wl4s+RIy+jI6A/ssMtIAPuhsseqtBKK5W2SSlqL8uJb0CTtd3uRTtC/ixTqMosBhciAg0OHLLiA6EgEpoVpkIBkQnghbqKzMfNtqazUxOcZE6ayMA30haZEBmKyfnzeLTCQCIV1rvxaZSoeNQMzH7t3J+imt7k7sugomltTQ6mulviGEpO1gdlP/UdBHNdUioOWz9s8wCyVYrdnXURK1Nnx5CvAMy34c5v7P9WPLl7BesxJ//bOsfs2Jlz0cUZksjmkwwBz72ZysvbnfHlq1lgl0hPYJma6AE5sueyFTXVSyt4aWsudCWA5gMw7tNz2i8ggcJkfasU4WC+gSlpRrqd3jQpA1OVc4txmT5+VIowqZUYA/4sOszc/NNdk4ct9NVEr4BzRdZoPNYFXkRA7GfVgUdHn1YjCQTNv0JcVWIOpLu72BEmg1WqzaErqD2QVER6MQihdOjAwkxYyAoLyQkQLoEpasKtL2ZgEdZVl2UAXsdJjkmIRW0FGbOD5vFplwGFzar5hccnAAZ1WxDSnu47HH4OWXod3fSbm5ghpbDe3+djojO9AJBhbOruh33w0TtVhjE/nXrncxySXk0jPVrLOm1cAyU2Q5GQ82kJC5Zsmp3Dl3Fc8IX2NT5FXK9BNSNVQg6V4aqPVEra0eZ3Sfa8kZTPbcypYisxmNrKfNpVycTCIBEYJDWmQgKWSaSprS7plnNoMmvs+11Ol1Y5AdGPS5SQCbwYoUH3khM0TCr8pwEIgGqBwg5S5XSsVyWl37LDLhRGDAC0U22AwWgoncLTJS3I/NmHmdmHSw6otwBpJPToGYF7vpkCG2UJYi/cAN74YiGQBaOAXxIJkGKggaxbOW/OFk6YChyhT0x+zq2fxo7o+4c/FEtAcXjx6SxuJGnj7hK55b15y3YN+glGAP7zG37lcHLaspsxHGxzsdr1Kn1dKt62SurYJqazVxOc6HrR8yqXgCZaX935hKS2FK9Fz+++PfU5I4EksOlxuL3kYgmn3/sIGIRiEqDCxkGhrguoblPN8+nw8jP+OIoiP7LK+rY8BWIJOKJ+BKtJCQE2gEDc6Qk2Jj5kHfvQiCgEkuUaSqeS/J4pcBisxDfziXHH4JM6tmpr3vpJDZF+zb6XNhwpHlSPdhN9mQ4mrWkgogxYKIuixa0aZBmaWsT+PIUMJHkajcTbHIZFFEkUsJX7/ZCkpgM9hxBZM3n2DcR5FpeK0bDmMprnB2FplIVCYYK5wYGUhaZDQo71ryRQJZW+2qbdXcferdWYmYXg6taiQecOTNtdTs+ooYYY6sPPKgZTWlNiKCjy1Vt7MmeDM9kU7qHBUYdUbKzGW82/IuE4omDLhvQYCzSm9AiwF9LDeLjFVvy0uAZzicrGJdMsTgvnfsNfh0Oziuse9xOvlkuOii/reZVFJHnGiqkGAuna97EYUSuvzKWWRSQkYcWshMKZ3C2dPOTnvfZjMI0X3p106/BxMZVJUcgCKTjVBi5C0yqpAZBYRiQcyG/AiZKls5nv0aR4bx5Jxytz9FJishOXeLTDjhx54ngVFkKsKzN9hXSngpNg+fawmgxFSGO5ydRSYYkUiQKCiLTL5cS4FIAKuCbtFMsdkgFijKm2vps553mag/Gr324IpmRaIVWYixU36bNt2/CMRdTChNupFqbDW8u/tdJhYNXiVwSl0xF5b8htLQ3JwsMlaDlUAehExQihPTBIe0GJ91yFnU2euY1zC7z+tmMwP20Cq2mTDLFezyJONkPFEnJVl2vu7FIhQrKmSSQi6Q6oukJGYzaBLmVMduV9CLWaOAkBGthGRVyKgA0iBFkHKlyl6GP5EUMtEohAW3wkLGTJjchUxI9lEk5kfIOMR96deS7MWRp/cZiDKxDHc0OyETiCWPbaHFyGgEjeJZS8Ho0Bkd+cRmAyFcRCgW6tO8UCk2uN9linhc/++99/Ovs9dRk0iuM7E86RqpsdWwvmP9oBYZSMaQTOi6mqO8/5WTRcZmyI87oTezzz7Eua7X6vns259x1iFnpb1viwUssQm0eJJxMr6Yk/JchYy2hO6AwhYZTVDRrNJeRDEZ7OuVksfYFfRgViDBxGG2EZZV15IKEI4HByyClCt1xeUESd5EJUkmovFQkUmjmiEoNluICL4BfdPpEsFH0QBpl7lSYrbjiyaFTET2UWIZXotMmaUUXyw715I31o1Vn33hrpFAEPLTaykY82MfQSFjNpOKK8hHnMwm/7tMs/YvZESdiEbQsLhhMceYV2BMFFNZljwnam21xOX4kBaZ2lrYvRt0Oobs6zYYNpOVYB4CPHv8yRtiOtfCErGkTz+lobBYwBKdwHZX0iLjjzupsOcmZOy6ElyScjEyvRaZfNwLdDowaS14paRFxh3yYtHlfh8osdiI4Fc8Hi5T1GDfUUAoEcCaRspdNtQWlxESnIQjCZy+ILIQp8ziUGz/DrOZqOBFkmQsluxTOpPZCvmxOpRaiwi0epFlCAteymzDK2QqrGX4Ex9nvJ0sgy/RTamYfVDiSKDR5Me1JMUD2NOIH8gXGk3SRWHQGHCH3JRbssjjHgBZlmkNf0WTtf8+aoIgYDPYWDJ5CV7fYrr+baR4bwmQ3rozQ1lkamqS55TVSr8pyumSjIvIg0VG8qOTTeg0yt+WzGawxiewzZkUMkGcVOUoZGz6YlwKVDXvJRROEBMkLHlK/DDrzPjDSYuMUvW0ii1WEGSkWJalyxVCtciMAsKJIHYxP0JmYlk5spBgd7eLTq8bgCKTcjfyYrMFWUjQ48+yKyJ70y4Fn6LZVPtTYS8iGPcQCslEBC+ltuF101TZS5EEJ9FoZtvFYhDWdFNmVu6GORwkY2SUzVpKyAnCiSCONDI68onNBla98gG/4XiYODEc4sDfzUfOeoQzp55JU20JhwW/fZCQmegY3CIjiskGkrnEx0D+Ajw9wfx1ptfpoEioZ6drFwk5QYgeqotyEzIOYwnusHJCxrO3s6w5T2EGFoMFfzhpkfFGvNgMCgkZIBAb2aJ4GQmZBx98kBUrVnDMMcfw8ssvp15fvXo1xx57LAsWLEj9tLe3p5Z/8cUXfO1rX2PevHlcddVVtLW1pZaFQiF+/vOfs3DhQs444wxeeumlPu+5evVqli5dyqJFi7jllluIZno3yBO7PLvY4NygyL4icjAvAV4AdtGCThZp6emiy+fGINvQanJI3TiAXn9utzf7C1skIhMV/BRb8iMwyu12JNmDJxBGFmKUD7NFprqojLCmO+N+S5EISJouyi2FZZEB5QviSdHkwXPkehfOEYsFLFrlA379kaSFYzCr5HmHnYeoF6mrSwqS3jT0XiFTZx+6QE5tLTnFxyTHaCVCfiwyxjx2pi/R19Lqb8Xp9yALCWpLchMyxaYSvFHlXEu98Sv5CjOwGMwEIsn38CskZOwWAxrZkLeWFemSkZCpr6/n+uuvZ/r06QctmzNnDm+99Vbqp6qqCoBIJMJPfvITLrroIl5//XVmzJjBL37xi9R2Dz74IB6PhzVr1nD77bfzm9/8hp07dwLQ3NzMvffey1133cU//vEPWltbeeSRR3KZr2KsaV7DvZ/em/N+ZFkmSjCtlLtsMVPGHlc33X4PJsGh6L71Gj06WcTpzz7g1x0MgiBTkichU11cREyQaHMnn56GO2up0lZGSNtNMEOjVSSStMgUmpDJh2upN2209wlwpDCbQdQUKR4j4wv70KDFajIOuW5TE+x3CeWw8sOYUzsHk66f2vwHoIyQsREjTDSu7EOlN+THlEchU2qspiPQxh6XE42sp6Iot/cqFovxRpWzyHhDAQQ0GLVDnwPZYDPuS7/2xzyKWOaNRtAnbHiy7YqrEBkJmaVLlzJ37lwMGUSKffTRR4iiyPLlyzEajVx55ZVs3LgxZZVZs2YNV111FVarlSOPPJKFCxfyyiuvAPDSSy+xZMkSDjvsMKxWK9/61rd48cUXMxly3jDpTITj4Zz3E4qFkJEpyrSdbwZYhXLaPF30BNyYFRYyAEbsOQmZHn/SmpOvm1RdWbKM9+aubWhkPUZdfi4UA1FqLiWscWYnZHTdlJsLS8j0BvsqmbUUiATQoMVhHdmgZ7MZREF515Iv4sOADVFMv1JrL00lTbz/rffT2m7JEjjnnGxGuI9eF7BP4a7H3pAfUx4701eI1TjD7bS6nJgSpRiNOQQKAaXmEvxx5YSMW/JhEmxpV+vNFJvJTHCvCygQ8w7qxkwXvR70sg1PKPvQAiVQLKrqs88+4+STT6akpIQLL7yQ888/H4Bt27bR1LSvsZUoitTV1bFt2zYsFgtOp7PP8qlTp/LFF1+ktj3uuH1R/FOmTGHPnj2EQiFM/XUGI2kBikT6pkbqdLqMxFc6mLRJIZPIMV3Hv7dniV005byvgbDpymj3dWLUmDFrihR7n979mAQbTr876/32+L1oZAN6jS4vx6C0SI8Yr+KDHZ9hxK74/IfaX4mphJgg4fT6mZRIX7CGwxDVdVNmnpG3cyMXBpq/IAgIaIjFY4qN2xf2oceCKMokEiOXIWE2CxidRbgkF4lEIu1zYCi8IS8G2YrBkMg5A3AwrNbkTy7vUWROXnv9YT/2vQGjSnzO3pAXUWPN27leaa4kIoX5qmsLIqU5v0+puYhgwq3YOeAOujEJyl2fDsRmMhPyBEkkEkiJZBkKJd7LgBVPKJi3cWs0Q9tbFBEys2bN4oknnqCqqoqNGzfy//1//x+lpaWceOKJSJKE5QC/tsViQZIkgsEgWq22jyixWCwE9z66Hritda9NVJKkAYXMY489xkMPPdTntRUrVnDBBRcoMdUUAXeAUCxES0vL0CsPQmugNbk/j4udO7uGWDs7rEIxu7pbsOqKMGFNue6UwoiVls7WrPe7Zdd2DLLy49ofB/V82PIphjzMf6hzIJ6Igyzw1a7NlGXQbXbPHiOSphNBEvJ6bHLlwPknEvXEojE6OjvYKSoz7uau5mQdDG8HO3eGFNlnNkQiRWgiFnZ27OzzmeR6Hdi6Zys62YrLNbLzS4cepw6tbGLLzi3E7cnuz7nOH6Db60SPKW/nuoEyjIKFD3Z+gJmSnN9HCMeRZFdq7rkeg/aedoyCJW/z1yYSSDE/O3fuJBj3IETiiryXESu+UFCRc6A/GhoahlxHESFTW1ub+nvGjBlcdNFF/POf/+TEE09EFEUCgb4RzYFAAFEUMZvNxOPxPhaWQCCAea/d9MBt/XvrDIiDdI27/PLLueSSS/q8lg+LTF20jnAiTH19fVqKcSBC3SF0skhjQ21WXX3TodxSSQA/eo1IkamUiRMHz25Il0QiQUtLCxa9g7hOznq/hj0tGLAqNq7+qDRNYpe0GbOmSPH5p3MOiEIxCTGzYxQKQVjrZGrd1Lwem2wZaP46nYDRYKSsvEyxcTcnmtHJFhoaKpkweJZxXtm2DUy7S8EUY+LEiRmdA4NhDprRJ2xMnFjJKPyo+1BUBDpZpKi0hPqqekXmDxAVohSJjryd65WVAg5vNbvD27Hrcz83p/mMxD6RqKiuoLOtM+djENe/j1Wfv/nXVQaJtEpMnDiRiOBjcu1ERd7LpLHjDQcUOQeyJS91ZPb38TU2NvLss8+m/pckid27d9PY2Ijdbqe0tJTm5mZmzJgBwObNm2lsbExt29zcnNp2y5Yt1NbWDmiNATAYDIqLlv4wG8yEYiE0Gk1OH54/EkKXsGA2a8jXOVBmrmSL+ws0sgG7waH4yWbW2vCG/Vnv1xuSMAiWvH4J6mwT+bJ7LbWG6Yq/TzrngEUopUdyZfTe0ShIQjcV1ooRu0Ckw4HzTwb7alLLlCAYldAlLFit+fuepIPVCrq4A2942wFzzvU6EEAXtyGKIzu/dBBF0CbM+KRQas65zh+Sx8BqsebtXLdawSZUsdX3BZN0J+f8PlWOZOydb2+7hlyPgS/iw6Kz523+xRYbUYIIgkAYL+VFytwLTBor/khAkXMgWzJ611gsRjgcRpbl1N+JRIJ33nkHlyuZhrZp0yb+7//+jwULFgAwe/ZsJEli9erVRCIRHnnkEQ477DCqq6uBZADxww8/TCAQYMOGDaxbt44lS5YAcNppp7F27Vo2bdqE3+/n0Ucf5fTTT1dy/lkj6kRC8dxNwO5AAJ1sxpjH+NPJxQ20h7bji3qwGxyK79+qt+MLZx/455UCGDX5TattLJlIWOPCrB2ZUv8mjTVVwyFdIhGZoNBFeUHWkVE2a8np96BPFJHHmPi0MJtBGylSPNjXFfShS9gY5hJHWWE0gg4Rd1DZTBV/1Dtke4JcsFjAkqimLbSdIkNuqdcADqsJrWzCGVQmBdsf9WHR5W/+JTYzMjLOoIuEEFWsMKioseGPFlDW0q233sq8efP45JNPuOmmm5g3bx4ff/wx77//PhdccAELFizgxhtv5LLLLkuJEYPBwJ133smqVas48cQT+eyzz/jlL3+Z2ufVV1+N1WrltNNO44YbbuCGG25g0qRJADQ1NfHDH/6Q6667jqVLl1JZWckVV1yh3OxzQKmsJa8URCubcyoZPhSHVU+mK96ML+qmyKRce4JerAYbvkj2WUseKYBRk9871LTqpAlViWqW2WDSWPBHMhMyHilIjBBlatYS7d5kpskgxthhwWwGIlYCGX6WQ+H0+jFgHXGhlg4aDRgEMz1eZTNVfLEeSnPsfzQY5eWgDyUfoItz7HwNyXPBkHDQ4XHnvC9QrrbLQJRYkw+LO3uSGcMVdmXey6y1ZnxtU5qMXEs333wzN99880GvH3300Vx33XUDbjd9+nSeeOKJfpeZTCZuvfXWAbc988wzOfPMMzMZ5rAg6kRFhIxHCmLAnFdz8qxJTQSFLrqjuyg2LVZ8/zaDjW5P9kLGHw4iavNrkTm8foSFjDZzIdMd7EZAQ7GYfoDwaEAQQIOyBfHavd1YhLIRd7uYzZAI76vHoRROf/JpPE+Zt4pj0phx+ZV9CvfFnZRZShTd5/5UVYEQqIIiKBVzFzJGIxgTxXT53DSUVOW8v2DcR2ker08ldhPIAjucrWhlI0VWZdwAZr2VQKxDkX1li9prKUvkqIloIko8Ec/JL+gNBjEI+X0Ma6wuwZgoplX+jGKzQ/H92412duZwIvvDAURdfoXM9LpkhKjVMDK2e1FrIZjhza/D141VU5pRc7zRQPJmLCjaoqAr4MSmzd/TerpYLEDUgi+sbCVTd9CP1TCyxf4ywagTcefQlqQ/Aokeyq35EzIOB9iEpEWmzJL7uSQIySaiXT4XKDDsYMzLJGPugmggLBYBS7yO9e2fY5Dtij0UHGM9m7h2ZIVMYV0hRxFfbkhmToViucXJ+ELBvMeHaDRQQhMxQaLUorxryWGyE4jlYJGJBDDnWcgUiw6M2LEbR8gio8v8KX53TxdF+sJyK8Fei4zCLQq6g93YR8GxMJtBL1syjncaCrfkw24qgACZvZh1ZtwBZYWMRE/OjRwHQxCgzpEUMuVWZd7HrCnGGXArsq9gwktRHq9PZjM4YtP4YPcHGFHufQ61H8sU3SLF9pcNqpDJEvveFPBcu376QvmPDwGoNEwGoFTBzte9FIk2pET2QiYQCSDmqVHa/kywT6SpfmRuFmZd5haZNk83JaaRv3lnSm/TSEWDfYNORdwBuaLTgahVPkbGG/JTJBaORcasF/EqWJY+FoOQ0EOlPX8WGYCGsqSQUUowWTQOeoLKBH5LcR92BRv6HojBACWJaXza/T4mQbn3MRohEhlZKaEKmSyxmZP+xVwtMv5wEJN2GG7i1mT15HKbQ/F9O8y5CZlgNIg1T63r9+ecGaczt+HwvL9Pf5h1FqR4Zje/Tp9TERP4cNObtaRksK8r5KTCNjqOhc1kIRBV1rXkC/vy1jQ1H1iM5lSTQyUIBONENG6qHfkVMlP3ZsvWFCnzgGDROehRKGspJCer7eYLQYAG2zTaw9sRNcoJmUmTZKqqco8XzQVVyGSJaNKglY2prrzZ4o8Mj5BpKt4rZOzKu5ZKLXbCcvbp18FYIG8dX/fnjiV3sGTykry/T3+Y9WakDFrdyzL0SG4qbIUV6At768go7FpyR7upcYwO65TdZCEYCygaAxSI+vLWNDUfWI0igbByFpnWveU7qoryK2SmTSjnrM73qCtRpqSBXV+sWCf0MN68N7Q9vHoaAGatcu8zaxYceWQBdb9W2YfRmKxumatrKRAJ5j3QFeDQyqSQqciDkCmx2AnjJdvreigewGbK/zEYSSyGzCwyXi+EZDeVRY78DSpPKO1akmUZf9xJXcnosMgUiVZk5JytsfsTjPsptReOa8kumvFHlLPItHt60MpGzPqBq7YrQVUVVESOVSzN3W5w4Am7FdlXGJ8ijRwH49jJSSFj0Y1MrGC+UIVMlphMoJVNOV/MApEAZl3+LTJzG49gmv9qHFblC3GU2mxEBC+xWHbbS/EANmMBFNDIAYveQjiRvpDp7gbZ5KZYVF545puUa0khi0UgGiBGmAllo8Mi49jb/80fUe4pNJTwUW4vHIuMTRQJRpSzyHR4exApzVvn514qKmDhQlCohEpSyETcOe8nkYAI3ry7F+ccWo0+YcOqL7zrymCoQiZLjEbQyiLBHF1LUiyI2ZD/m/ghk2w8cMqf0WqV33eZzU5ME8AfiGe1fTgRHPMWGavRQihDIYPRg8PkyNuY8oXSFhln0Ikga5hQ4VBkf7liF00ICIrVkkkkICz7qXAUjkXGYTYjxYKKderu9PVgFvLrVoJksPbXv578rQTFYjH+mDvn/YTDMlGNj1Jrfi0lFRUCJYlpeS28NxKoQiZLkkLGRCCUm0VGigWxDEPGjl4PS/IUHlJqSX4pnL7snlDDiQBF4tgWMjZjZhYZpxNiOndBChmbDeIx5YTMblc3xkQJpSWj43JltQoYNRbFMpeCQYhqfFQVF45FxmEViSERVMi71B3owarJv5BRmmLRoYiQ8UlhEkI0dS3NF4IAh5kXMcEyJa/vM9yoBfGyJOlayj0FUYoFsQyDRSaf9Abqdvu8NJG5yTJCIGWuH6vYjBYicmZCJqLJT0uJfFNdDeEW5bKWdnU7Mcmlo6Z8v9kMRsGqmEXG6YoRF0KU2grHImMXzSS0QbzZJyv2oTvoxKYrPCFTZnEQSOSetdTtSx7I4chc+9/LfzvirT6UZnQ84hQgOl0y2NeXo5AJxYNYC9ytohE0GGQbTn/mmUvxOEQJUDRa7lJ5wmayECEz11KYwnQtVVdDJKygRcbpxKopHTXl+1NF8RSKkWnvSe7HNkJVp7NB1InIOglf9smKfeiRerDrC0/IlFqKkWR3zvFgrqAPjazHpMtj9+C9VFUlqxyPJVQhkwN6wYgvR9dSKDE2Al2Ngp2eQOaPZ+EwRIWxb5EpEjMTMn4/BOKF6VqqqoJwSDkh0+oaHVV9exFF0KOca6l7rxoYjhIESmHWm5F1yllk3KEeHMbRkZWWCeU2BwliOWev9gS8GLDlPdh5rKIKmRwwCCL+cG5CJpIIYjMVvpARsxQykgRxIYjDXDgX8WywiRaiGQgZKSTjj3koMhamaykaFYhElHEtdficFI+im5wogi6hnGvJF/JjwFJQPbVEvUhcE1TMIuOJ9OAwFp5FptyW/H56IrlV93UHfYq2DRhvFM43ZxRi0Jjwh3JT4hF5jAgZjQ23lI2QkYkKAWzGsS1kHKKFmBAkHk/v5u6VAsTleEFaZGw20Gs1uL3KWGS6At2UmUeXRUabUM615An7MAqF41aCpEUmJkj4/cpYELzRHkrNhSdkrBYtBtmeu5CRvIq2DRhvqEImBwwaI8FIjhYZxoY1wqy14wll/njmCYRAkAvKrJ4NRWYLCHLaweHevbUpRqrJZa6Ioga3Rxkh0xNyKtbkTwnMZtDGlHMteUM+TELhBPpCUshECeJXqJSOL+4sSCEjiqBP2PHneC4khUxhidnRhCpkcsCgMeVcpjtKEPsYCHS16Ox4QplbZNzB5AXAPAwp6CNJr1jtne9gxGLJ+BibwYZWk4fCP8OAaBLwepVxLfliPZSNopucKIIQt+R88+olEPFj0hSWkBF1IlFZIqBQ78xAoocy6+j5jNMlKWRseHPshu4N+xTtfzTeUIVMDhi1RqRo9hYZWZbHTMaOxWDDG85CyASCCAiIuvyWJh9pbKIJZAFPGkImHIaIxkNRAbqVejGbNPj8ylhkAnEXZaOo55Qogi5uxRtSxhwRjIQwaArr/DfrzURk5SwyYdmf9xoq+cBkAr1sw51jA01v2IuoVS0y2aIKmRwwak05NY2UomEQZOxjIEbGprfjj2TuWnIHA+gxj/lofa1WQC9b0hIyoRBENW6KC1jI6PUawhFlhExQdlFhHWVCRrbgkZQxR4RiYQyawirsIepFIokwXoXEaowQVjH/qcdKo9OBQbbhybEyYDASRNSObfd6PlGFTA6YtKacWhT0nvxjwSJjN9rxRzO3yHiCAYzC+PgC60nv5idJIBsLsxheL3qdQFiBrCVZhhAuKotGj5DR6cCkseBVSMhIsRAGbWHdxHtdwZ6AMv2WYoSwGgtLzPVi0uTuWgpGJUzawrLKjSZUIZMDJp2BUCyc9fbeveZIm1j4J7DdZCMQy1zIeEMBjJrCF3LpoMeCN5SeRUYu0D5LvRj0GiIKWGTCYZmw4Ka6ePQIGQBRp5xrKRQNYdQW1k281xXsCUhZd73vRZZl4kIYq1hYx6AXUQEhE4pLmMa4ez2fqEImB0SdiVA8+ycSdzCANiFiNBT+x+Aw2QnGM3ct+cNBTOPEpGrAgi9NIVOofZZ6Meg1hCIxVq3/35z20+H2IQtxakaZkLHolAv2DcfCmApMyJh0yfGG4hLRaG5uYSmafBi0FKhFRtRacz4XQjGp4MTsaKLw76AjiKg3Es5ByPhCQfSY0YyBT6HYbEdKZG6R8YUCmDTjQ8gYNRZ84aF96ZK0V8gYHfkfVJ7Q6wXcmq1c+uwlxBPZdUUHaHO5QBYoNo+uQFCLQbn061C88FxLgpAM0I9rgkhSbhcw/97q6NYCbQBk1trw51gcMRwPjfmEh3wyBm6hI0dSyGSfteQLBdHJY8OtUmy2E8pCyARjAUTdOBEyggV/GiboUAiiWk9Bx8gY9BoiGjdATuXbO7wuTDhGXdVbi0G5gnjheChl4SgkzHozOjGIJOVWIqBXyNgL1LVk0dtyrvIcikuIelXIZMvoujoUGBaDkXAid4vMWKDYYiNEFkImGsCkHRvHYCiMmgyEjKawXUtaQUNcl6x2mktmX4fXhSiMLrcSgM1oJRhTxiITiYcLUsiIehGDWSIYzF3ICLIGk0Gn0MiGF6veRiCWm6iNJCTVIpMDqpDJAdFgICpnb5Hxh4IYxkjGTqnVTgQfiQzjO6VYEPM4sciYNOm5IyQJwkJhB/sKgkBUu1fI5GCR6fK5sGhGn5CxmyxICgmZpEWmsFxLsL9FJnfXklY2odMVZgkGq8FGMJ6ra0lC1BeemB0tqEImByx6I5FcLDLhAAZhbFgjyu12ohov4QyTuKR4ALN+fAgZUWtJywQdCkFA7qZELLxKp71ohP1cSzlYZJwBF1bd6BMyRaKFUEIhi0wihGgovJuYqBPRmoK5W2TCIbQU3vx7sRttSPEcLTJyCLNBtchkiypkcsBiNBIl+4t0IBIcM0Km1GojLoTx+CMZbSfFAohjvD1BL6IufSHTE2+h3l4/DKPKDxpBQ1jI3SLjDPZg149CIWO2EkooEyMTTYQL8mncrDejMUo5W2QCoRC6QhcyOZ4LkYSkCpkcUIVMDliMBmJk71oKRIJjpoaKQ0xmlXS6M0vBDiWCWMeLRUZnIZiGkPFJITyxTuqLClfICAiE5GTMVC4WGVfIRZFx9AkZh9lCVA7llJHVSyQRQtQXpmtJMOQe7BuMhNEXspAx5S5qo7KEuQDF7GhBFTI5YDUZiCFlHBfSSyAaHDOpxyadCY2so82VWTv7cDyI2TA2xNxQmHVWArGhhV57cDd6wUCFpWIYRpUfNIIGmeQXIxeLjCfiotg0+oRMiSXZF0eJzKWIHCpIi4yoF0EfIBjM7TYSjBS2RcZhthGSc7TIIGExqhaZbFGFTA7YRSNxQUKSsittGYwEx0zGjiAIWIQydvd0Z7RdJCFhGScmVaveRjAdISPtolKsH3Upx5mwf++sUCx7q6U34hqVsUKl9mS3al8W/cUOJCaHMRdgjMyxtceyzvW/uQuZcAh9gfWa2h+HaCOCHzmHEscxOaQKmRwo3CvlKMBqMiALCTy+WFbbB6IBjGNEyADYdeXs8XRmtE1YDmIZJxYZu9GWVvXjruguqsyF61YC+oiwXFxL/piLUsvos8hYzVr0sjmrju8HEpVDmA2F51q6bu51dEZ28XH4mZz2E4yG0AuFK2SKLTZkEgSj2TeOjCJhVYVM1mQkZB588EFWrFjBMcccw8svv9xn2cqVK1m8eDEnnXQS999/fx91+sUXX/C1r32NefPmcdVVV9HW1pZaFgqF+PnPf87ChQs544wzeOmll/rsd/Xq1SxdupRFixZxyy23EI1Gs5lnXjDvLWDU7c3OrCjFgohjSMg49BV0eLsy2iYiB7Eax84xGAyHaE/LItMTbaHOOmEYRpQ/+giZHFxLgYSLctvoEzKiCHrZnrNFRpaTDRMLsTy/xWDhmuk/433+lNN+gpEQhgIWMr1uxlzOhRhSQZ4Do4WMhEx9fT3XX38906dP7/P622+/zVNPPcXKlSt58sknefvtt3n++ecBiEQi/OQnP+Giiy7i9ddfZ8aMGfziF79Ibfvggw/i8XhYs2YNt99+O7/5zW/YuXMnAM3Nzdx7773cdddd/OMf/6C1tZVHHnkk1zkrhllnBlmg05vdCSzFxo5rCaDUVEFHIDOLTFQeP75hh9mGlBj6XHEldlFfVNhCRmCfaykXi0xQdlExWoVMwoY3lJuQicUgTrhgb2Izqg7FT3tO+5Cihe1aspkNaGUDvnB250I8ESchRMfNdTAfZCRkli5dyty5czEYDH1eX7NmDeeffz51dXWUlZVx6aWX8uKLLwLw0UcfIYoiy5cvx2g0cuWVV7Jx48aUVWbNmjVcddVVWK1WjjzySBYuXMgrr7wCwEsvvcSSJUs47LDDsFqtfOtb30rtdzSgETQYBSvdWQqZUCyIZQxl7JSby3GG0hcysgxRgthMY0fMDUax2UZYHvpccbOLCY4x5FrK0iKTSEAIF1VFo0/I2GygS9jo9uXmWopGIS6EMBsLz7UEUF9SjqTpIpJZ1YU+SJEQhgIWMiZTbta53hgym0kVMtmiSE3o7du3s3Tp0tT/U6dO5Q9/+AMA27Zto6mpKbVMFEXq6urYtm0bFosFp9PZZ/nUqVP54osvUtsed9xxqWVTpkxhz549hEIhTAM0GItEIkQO+FbpdLqDxFeuJPamKpkEO90+d+r/TAjGA4g6MattRwO94+79XWEtp3nP5rTnE41CTAhiNZgK8hgcOP+hcJgthGTvoOtHo+DT7KKhuG7UH5OE10vpz38Oej2yIBCIRvnH7t180NXFRFOAhxOglQ3M/OCvyJWfZrz/cBj+uLGHWVt/jWy0KT+BHDACv/yyi/YTPFCc/jlwIOFwUshYjIZR/3n3R21xKVGNjy6XRG1ldjdiKSph0BTmNQBAr0+gT9hwS4N/twfCF0qWZCjUcyDT62CmaNLoqqyIkAkGg1it1tT/FouFYDAZ+CRJEhZLX6uDxWJBkiSCwSBarbaPKBls2973kCRpQCHz2GOP8dBDD/V5bcWKFVxwwQU5zHBgTIKVXR1tKXdYJgQjPuQoWW07mmhpaQHAihF3tCPt+UiShpgQJOj1FvQx6J3/UMQliagQYPuO7QNmJPn9AgFtC+aoftQfE/2XX1Lzt7/hueYaHl2/nlv+9S9qrFZmV1Xx4PoWJhTBeU0WysxaJtoz717d2iOxqwgiZVW4hdxqlSiNIEl8570WfrhrDzSlfw4ciNutIy6EcTtd7BRG9+fdH7FEMtHh400biYXKstqHO+BGm9CO+vN9IEIhDfqEjS27tjNZ25Dx9jtdSe+Eu7uLHOKFR5xsvwND0dAw9DFVRMiYzWb8/n0Br4FAALM56S4QRZFAoG8RsEAggCiKmM1m4vF4HwvLYNv2vocoDqz8L7/8ci655JI+r+XLItPS0oJVX0RMIzNx4sSM9xETwpQXlWa17Wig9xjU19ej0Wg4rGMygc8fZ8KEiQhptE1xuyEuSEyZ1MDE2sI7BgfOfyii2iJYByVVpRSZ+r+xf7Kph5gmwHGHzcFuGl1WiANJdHUhW62snDiRX61cyVOrV3PyyScjCALXPXMdD/z8AW7/PMAZp09k6X/dk/H+//riWn734Wv84uf352H0OeJywX//N9F4siBeuufAgRgMSYtMQ90kJlYW5nfAJBcT1gnZX8e0YDZZCvY6GI0m0Ms2tCZjVnNwayJoZSNNkyei8G1qWMj0OpgPFBEyDQ0NNDc3M3/+fAA2b95MY2MjAI2NjTz77LOpdSVJYvfu3TQ2NmK32yktLaW5uZkZM2b0u21zc3Nq2y1btlBbWzugNQbAYDAoLloGw6q34w75svoAw4kgFqN1xD58pdBoNGg0GiaWVSEJXYRCGixphP7EYknXkl0s7GPQO/+hKC9Kihe3FKDY7Oh3nfc378AilOIwFyk5xPyQSHB/OMwtt9zCK6+8wpw5c1KLbCU2uBDK/304L97+LDcGb+SXv/xlRt/NTV2bqTVOG53nxt55ePfGyKR7DhxIPL43RsZgHp3zTAOLUEabpzvr8YfiYUxasWDnr9eDAStuyZ/VHAKRMFpZxGDQUKCHAMj+O6DIe2eyciwWIxwOI8ty6u9EIsHSpUt5+umn2bNnD93d3axatYrTTz8dgNmzZyNJEqtXryYSifDII49w2GGHUV1dDSQDiB9++GECgQAbNmxg3bp1LFmyBIDTTjuNtWvXsmnTJvx+P48++mhqv6MFm9GGN5RdwF9EDmIzjp1g37riCiRtJ15veoWhIhGZmCaIeZz0WrJZdGgT4qDB4Z+1bKVGnDyMo8qe3z3xBLcEg7z00kt9RAzszVoSYOKcuSz6xVm8/PLLHH/88WzevDnt/Te7NzHJOk3pYSuDLvkM6A/kFuwbDsvEhTAmXeEGu9o0ZXT4MiuEuT+ReKig5w9gxIZbyq4Mhy8koZNNBS1iRpqMDt2tt97KvHnz+OSTT7jpppuYN28eH3/8MfPnz+fcc8/lsssuY8WKFcybN4+zzjoLSFpI7rzzTlatWsWJJ57IZ599xi9/+cvUPq+++mqsViunnXYaN9xwAzfccAOTJk0CoKmpiR/+8Idcd911LF26lMrKSq644grlZq8ANoMt62j1cCIwporBVVjKSQgRWp3pXdz9oWSrbFE3PqL1dTowyDacvoHPl686m5lS2jTg8tHCAw88wE0PP8ya8vKDRAzsy1oqNlSgKTXx3nvvsWjRImbPns3f/va3tN5jZ2ATU0pGt5AJSPs+y2wKogXDycQEo64ws5YA7NpSOgPZC5nQGBAyJo0Vj5TdfcAfktAxPq6B+SIj19LNN9/MzTff3O+yyy+/nMsvv7zfZdOnT+eJJ57od5nJZOLWW28d8D3PPPNMzjzzzEyGOaw4RDs7IllaZBhbxeDsRjtaDOxydjGboV0jXil54R8vFhkAI3ac/v4veIEAtIW3cnbN6LbI3H///dx000289OtfM/vuu/tdRyNoEHUiNqOV9kgIo9HI3XffzaJFi/j617/Oxx9/zK233opWO3AQb1tsEzMqR6mQ2Tvu0F5rrDvkZtL9k+j8cWdGN2V/OJl6W8g3coehlO5gZoUw92csWGQsmmK6g9mJuWSvKVXI5IJqzMoRh2gnEMtcyMiyTHSMCRlBELAK5ex2pVdLplfIiPrx8yU2CjZcgf6FzI4dIInNTK8evRaZXhHzyiuvMKepCVnX/7OQIAjYjXasJpFgZF8dmbPOOov333+f5557jmXLluH19v/d8YV9+NjDUfWjVMhoNCQ0ApGwD1mGPd49+CK+jK0ywXDSKlnIN/JiQwk9oeyFTDhRmE0z96dEW0dbILusHX9YKugWDaMBVcjkSLElvUaABxKJR5BJYBfHTowMJNsU7HGnJ2T84SA6jAXdHDFTRI0NV7D/m/fu3eDVbWVy8ei0yPQRMXPmJKO1BxAyGkGDzWjDZhIPKog3bdo03n//fQDmzZvXb9rt+tbNGOOlTKnNLqV3OJC1WmTZTyikoTOYPOfDsXBG+wjstcjoNXrFxzdclJiKcUdziJFJhDAVuJAp1dXSJmUnZAIRCb1qkcmJ8XMHyROlFhthvBlXtux9crOLY8ciA1BirKDdl97TmT8koWdszX8oRK0N9wC+dJc/iCfRSlPJ6LPIHCRiAGKxAS0yGkGD3WjHLoqE+qnsa7fbWb16NQsXLmTOnDkpYdPLpy2bKU5MxTyKTw9ZrwN8eL06Ove25ojEM7sQBMIh9Jj6dAsvNMrMJXhiObiWEiHMBS5kyvS1dIayEzLBsIRBowqZXFCFTI6U2uxENT6CGcb5BaLJ+jhjrSx1ubmCzkBHWuv6I0EMwii+U+UBs86GZ4D+PDu92zBqLFRYKoZ5VINz3333cfPNN/Pqq6/2DeyNxVKxIgcikHQt2c0ioXj/LQp0Oh2///3vufHGGzn55JP7BAHvdHbg0FWnVY9oxNDpQAjg82npCiRv5OF4ZhaZYCSMrsDdChWWYvyJ7C0yUbnwXUsVxjq8MSeBSGDolQ8gGA1hEMbWfWC4UYVMjhSZbMS0XgIZnr/BaBCtbMJoGFsfQW1RNV1S29ArAoFwcNx9gS0624DN5Xb5m6kxNo2qp/P77rsvVSfmmGOO6btwCIuMzWDDYRGJJAbutSQIAtdeey2PP/443/zmN/n1r3+NLMu0eTop1o8uQXcQOh0kAvj92qwtMsFICL1QuBlLAJW2YoJkb5GJymHMhsIWMiUmB3rBRIs3c6tMb4sGlexRpCDeeMZutBPTZCdkdLIZfeG6xvtlQnENb8beSGvdQETCoBlfFhmrYWAh0yptpdbcOMwjGphBRQwMapE5supITDoTDkQi8tBNI88880zWrVvHsmXL2Lx5M52zoNgwujuACzodiYSbUEigU5ddjIwUDRV8oGd1kYOQ0MO9//odixrnMat6Vkbbx+RQwQsZiyVBiXYCLZ4WppVlFqAejKqupVwZW+aAEcBuTLqWMhUy3lAQXcJSkCWpB6OxvAYfrYTTuJ4HIkGM40zI2Aw2/NH+hUxXZBe11tFRpn1IEQMQjQ5okTmt6TSunXstxTaRqCwhp1EjcebMmXzwwQesX7+ed+9fjSUxus8NQadHlwBvSEpZZDJ1LUnRMHpNYVtkaoqKQZD50dof8OKWFzPePiqHsBgLW8iIYgK7XM8uz66Mt5WiEkatKmRyQRUyOWIzJIN9MxUyPikwJi0yk8trCWr34HINvW4gGsQ4zp5E7EYbgQGEjDPWQp2tfphHdDBpiRgY1CLTS4lNJCZIhELpvXdNTQ3r1q0jYUzw6r33s2XLlgxGPrwIesNeIRPM2rUkRUMF71YQ9UbO8D3N8savZVUUMEbhCxmzOY4tkZ2QCcVCGAv8HBhpVCGTI3ajnRhhejyZXcC8obHpWqq11xDUtOPsiQ+5rhSRMGlH91O30hQNUnfIndhNfdHICpl7772XW265hVdffXVwEQODxsj04rCaiAsSPl96bSsALBYL4oUODj96IXPnzmXt2rVpbzucCDodoqzHEw7ss8hk6lqKjI2b2AzdORTpKg5KtU+HGIXvWhLFBGJ0Aru8WVhkYhIm1SKTE6qQyRG7MdkIcLD+Of3hkfzoZetAZTgKlmprNbIQZ0fX0MF/UiyISTe+hIxDtCEl+j9XvEILDSUjJ2TuvfdefvnLX/Lqq69y9NFHD71BPD6kkDHrRWQhgccfy2gsPrmLy6/5OXfddRfLly/nzjvvRE7HPzWc6PVYBAveUFLI6DX6jC0y4VgYg7awXUsAZjNoZXNWFpm4EMI6SCPgQkAU4xilelo8mQf7hmISpnHSpiVfqEImR0w6Exq0dPkyq+7rDfkxYB3d6aVZYNQZsQilbOtqHXLdYCyIOM6eRIrN/QuZUDRCUOigsXRkhEzGIgbSci31Vm12etJ/Ug9Gg4TxU2Ov4PLLL2fdunX84Q9/YMWKFfgG6VM17Oh0WDHhjnbji/ios9dlHiMTC2HUFvZNHPYKmfjBxQ+HIh6XiVH4QsZsTmAITWBnNq6luDRu+s3lC1XI5IggCFh0dpz+zIWMUbDmaVQjS6mhhl2uoYVMKBZEHGcWmWKLjbB88M14W9ceBLRMLK0a9jFlJWIgLddS7wW6x5v+Da4r0IUga6hylAAwe/ZsPvroIzweD3PmzGHTpk3pjzGf6HRYNCKd0R1oBA3VtuqsXEuF3J6gF4sFhLiIFM1MyATDURDkghcyohjHmCjBI3ky3jaSGBvnwEiiChkFsOrtA/bPGQhfeOwKmXJTDa2+NIRMXBpXDSMBSq02whx8rmztbsESr8FiHtzCoTRZixhIyyJj0BoQEOhyZyBkgl2Ichk2y759l5WV8dJLL3HOOecwZ84cnn766czGmg90Oop1drbLb1FmLkPUiVkE+4YxFXDn615EEYR45q4ln5SMAi90IaPVgtVoxh/1Z7xtOK66lnJFFTIKYDMM3D9nIPxjWMjU2GpoC+wZcr1QPDiuGkYC1JWWEhZcBKS+MSM7elqwyfVohvEbec8992QvYmDQ9OteBEHAIIh0udITMu+2vEuHvwNjvPyg9gRarZbbb7+dv/zlL1xxxRX85Cc/IRbLLPZGUXQ6Tq05nc26Z6kwV2DUGTN2LYXihV/VFpKuJSKZu5b8oV4hU/hiziFakWJBEnIio+3CcQnzOLsOKo0qZBTAbrLjDXvTqpXRiy/ix6Qdm0Km3lFDd2hoi0w4ERx3FpnGiioQZLZ39m2sudPdQpEwfPEx99xzD7/61a+yFzGAMEjTyP0xakWcabiWOgOdHP/o8azZ/BKmeMWAfZbOOeccPvjgA/7xj39wyimn0HnAsRw2dDpmlk2nLnQq5ZYKDFpDxhaZUKzwM3YAzGYZouaMXUu9QsakL/yCWkXmZAPgTI9BWJYQx8A5MJKoQkYBKqyl+OTOtIrA9eKP+DGPUSHTWF6LO95KNDr4euGEhMUwvoSMSW9ATJSztbOv0GvxtlCiHR4ho4SIAZIxMkO4lgBMOjGtGJnNzs0A/HXD/0NMDCxkAA455BDef/99KisrmTlzJm+99Vbaw1YMnQ6jNsYC18P8fN4tGLXGjGNkwrEwoqHwrREWCyQiYsaupUA4hFYu7KaZvZRYk9dzfyQz91IkERx310GlUYWMAkwta8KnayaThApfyI/NOFaFTA1B3R56egZfL5IIYjaMP5OqXVPNDmdfIdPqb6HMkH8hc88993Drrbeydu3a3EQMJGNk0rDIiHoTLv/QQmaLcwsGrQFfxIuFiiF3bbVa+d///V/+8z//k9NOO234U7T1evSaGJZ4HTNLj8/KtRSOjxWLTFLIZONa0lH48wdw2Axo0aUaAqdLSPalynioZIcqZBRgaukUAsZm/BkIcX/Ej900NoVMQ/FE/LodOJ2DrxeWg1iN4+9JpFhbQ4s72VhTlmVuXXcr/3auZZLpyLy+b6+IefXVV5k9e3buO0zTImMxiHiDEokhQge29GxhxWErELVm7NrytIYgCALf/e53eeONN/jjH//I2WefjSudstJKoNOhicfQ6RJIEhg0mbmWYrFkn6FCr2oLyWDfRChz11IgPHaEjNUKJo01Y4tMSPZSpAqZnFCFjAI0lTTh1W3JyCITiPopEsemkJnkmERY8LCj3T3oehE5iN00/oRMqbGGVm/SIvOV8ytuXXcrt059jcPtC/P2nnfffbeyIgbStshYjWYiBPEOEQ+/pWcLR1YeyYqGa5hoyGyMxxxzDB9//DHxeJxZs2bx4YcfZrR9Vuh0EI9jNCaFjFGXmWspHIa4EMZiHBuupVgoc9eSOxDAIIyNa4DFIqPHQiCSvkVGlmXC+CgSbXkc2dhHFTIKMKV0Cm5hOy7PEEEh+xGM+XGYx6aQcZgcmDUOvurYPuA6iQREZYkiy/hzLVWI1bQHk0Kmw99Bta2aSdrjBo0JyYW7776b2267TVkRA8mspTQsMg6xCI3oGbL/1hbnFqaUTuGayXdxpHlpxsMpKSnh+eef59vf/jaLFi3ivvvuy6+rSaeDaBSTaa9FRmvIyLUUCkFI202p1ZG/MQ4TZjPEpMxdS90BF2ahOE+jGl6sVjCQmUUm6YaSKbWqFplcUIWMAtTb69GgZatzZ9rbSAk/xZaxKWQAqowNbHEOLGRCIYgJQYqtY+NpLBOqrTV0h5Kupe5gN+XmciQpaZ5XmryJGEjbIlMilqCxuAYVMrIs09zTzJSSKQSDZC3qNBoNP/3pT3nllVe455578utq0ukgFttnkdEaM3IthULg1n/BjIrp+RnfMGI2g7w3aykT8egMuLBqS/I4suHDagVdwpJRjIwvnDTjl9lVi0wuqEJGAbQaLRX6Rpp70u/UG0r4KbOPXSFTb2ugxbtjwOWBAEQFP8VWy/ANapRQV1RDTzRpkekKdlFmLsuLkOkVMWvXrlVexEBalX0Bik3FJIw9uN0Dr9PmbyMYDTK5ZHJOQqaXefPm8emnn5JIJDj66KP57LPPctthfxwoZDJ0LTl9fnzaHUwvHxtCRieLyMgZWaV6JBc2/dixyGjimVlk3JIXrWyiyDrGugcPM6qQUYg68xR2+NIXMhHZT4lt7AqZxuIGWqWBLTKBgExY00O5pXQYRzU6qC+uxh3fK2QCXZRbkhYZJV1L+4uYWbNmKbfj/UlTyJSIJcT1rkGFzBbnFuqL6jHpTIoIGUi6mv7+97/zjW98g3nz5rFq1arcd7o/+wmZYDDpWook0rfIbOzeiFkuo8JSoey4RgCtllQGYiYBv65QD/YxImSKi0GIWvCF07fIdPu96BP2vLmVxwuqkFGIibYp7A42p7VuPBEnJoQoH8MWmakVk+iObSce7395p8eLLMQpEceGWTkTGstrCNBJLBGjO9hNmVhOT49yFpm77ror/yIG0mpRAEmLTETbM6hr6dP2Tzm07FAAxYQMJF1NP//5z/nb3/7G97//fa699lqiQxU4She9HmKxvTEyQsZ1ZDZ1f0GFMH1M1FABsO89gTOJk/GEXRQZx4aQcThAl7Di9KVvken2+TDINgq8Q8OIowoZhZha1kRrKD0h45GSir3CMXaFzGHVDXi12wd8Cm/zONGgpchYNKzjGg00lier+7Z5O+gMdLHzyzISCTj00Nz3fdddd3H77bfnX8RARhaZkODCM0g/vSc3PsnyQ5YD0NOTzIJRktNPP50PP/yQN998k5NOOom2trbcd6rTIRzoWsrArfKV63NqdDNyH8cowWrWoRP0GWUueaMuSkxj42FGrwdRZ6HHl75FxunzYsQ+rK1JxiLq4VOIQ6rqcMV3D1krA6DLk1TsFY6xGx/SVNZAQLeD7u7+A/86vD2YhZIx8zSaCaXFekzxCpo7W9nZ3YW3rZzrr0/62HNhWEUMpG+REYsJJlwDlifY6d7Jv/f8m/MPO5/PP4cdOyAfw29sbOSdd95h0qRJzJ49m3feeSe3HaZcS3IqaymTYN+tvi+oN44dIWOxgEmbWS0ZX6yHYnFsWGQAbIbMLDI9AR8mQc1YyhVVyCjEITW1BDStQ6aYAnS6/ehkEZNxeDsdDyeTHJOICkGa27r6Xd7ld2LRjI0nsUzR68Em17GlYxd7XN3MmFyGw5HbPoddxEDa6dclYgm+WM+AdWSe+PwJTm48mRJTOX/9K5x3XjLeIB+YzWb+3//7f9x4440sWbKEP/zhD9mnaKfSr+OprKVMXEvb/V8wwVT4gb69iCIYhMxSsP1xF6WWMSRkTBbcwfQtMq6AF1GrZizliipkFGJCcQ1hrZPdbUNfyLq9fgyMXbcSgFlvplQ7kU9bN/S7vDvoxK4ff4G+vZQLh/BJy1f0hLqYMz29KrYDMSIiBpIWGf3Q2RbFpmK80aRFpj+L5SvbXuHsQ86mvR38fliwIA9j3Q9BEPje977Hyy+/zK233so3vvENJCmz+idAyiJjNifwejOzyLT52uiJtjLZdkTm7ztKsVpBL2RWFC+YcFFuHTsPNA6zFU8ws6wls1a1yOSKKmQUosJSgQYtm/YM3fXZ6fNjFMa2kAE4zHY8H3X1b753BnsoGsdC5tjJ03jtsy+RhC6Ompq9kPnTn/40MiIG0m5RUCKW4It4icsxAv08rO5072RK6RRaWqCujmGLF5g/fz4ff/wxW7du5fjjj2f79oGz7Pplr5Cx2WJ4PJnFyLy5800mGGdSYh47MWIWC+jJzLUk4aLcOnYsMg5LZllLnpAPi161yOSKKmQUQiNocOiq2dIxtJDp8fsxaca+kJlVcTwbff/qd1lPyInDOHaexDLl9KMPJVz2b+JChAprWVb7ePzxx/nJT37CP/7xj+EXMQDxeFoF8VIxECb3Qe6lhJxgt3c39fZ6WlqgfngagKeorq7m9ddfZ/78+Rx99NG8/PLL6W+8V8hYrXE8HjBo0nctvbHjDaYaTmAMdCdIYbWCVk7fIpOQE4RwUWkfO0Km1JZZHRlv2ItNr1pkckUVMgpSYaple/eeIddzBfyI2rEvZOZPOJ4dsXdJyAf7EzwRJyXi+LXITCubxq7gV2gFLQ6TI+PtX3zxRb71rW/x1FNPcdxxxyk/wHRI0yIj6kSMWiM6+8FxMl2BLsLxMHX2OnbvTlpkhhuDwcDvfvc77rvvPs477zxuu+02EulE7e9Nv7Za48TjAvFo+q6lN3a8QaPmhDGVdmu1gjaRfoyMW/KCIFPlGDsPNGV2C8FYBpV9Iz5sasPInFGFjILU2GrY4x3aIuMO+rHox76QOa7xCOJynA3tXxy0zBvrocw8foXMlNIpaAQNZeayjDO3/vWvf3HBBRfw6KOPcuqpp+ZphGmQZosCQRAoFovRWg7OXNrl2UW5uRyTThwRi8z+fP3rX+ett97i4Ycf5txzz8UzWL44pCwyer2MKMpEpfRcS22+Nrb0bKE+sWDMCRlNPH3XUrvbhSBrqSgaO9fCCocVKe4n3fhxf8RLkVF1LeWKokLmqquu4vjjj2fBggUsWLCAH/zgB6llK1euZPHixZx00kncf//9fTIFvvjiC772ta8xb948rrrqqj41HkKhED//+c9ZuHAhZ5xxBi+99JKSQ1aUiSW1dAT3DHkSe0PjQ8hUlOmoiBzLa1sOdi/5407KLGPnSSxTTDoTDY4Gyi2ZxcesX7+eZcuWcdddd3HhhRfmaXRpkqZFBpJxMoj7LDKynNRBLd4WSrQTeO+9ZKDvSFhk9ueoo47io48+IhQKMWfOHDZu3DjwynuFDEBREYSD6Vlk3t/zPtPLp6OJOMackBFi6buW2jw9GBPFmExjpwRDucNClAD+NL1LgZiPIpNqkckVxS0yN910E2+99RZvvfUWDzzwAABvv/02Tz31FCtXruTJJ5/k7bff5vnnnwcgEonwk5/8hIsuuojXX3+dGTNm8Itf/CK1vwcffBCPx8OaNWu4/fbb+c1vfsPOnek3ZxxOGstr8AmtA6aZ9uIL+7Eaxr6Q0euhRjObj/asP2hZIOGkyj5+LTIAh5YfSrk5fSGzdetWTjnlFH784x9z9dVX53FkaRKNIqeRtQTJzCXZlLTIOJ1w553wxz8mLTKyu55HH4WKCkZFzEhJSQn/+Mc/uOCCC5g7dy5/+9vf+l9xb/o1JIVMJJhejExXoIsaWw2hEGNOyBAzp+1a6vS6MFHMWColVWKxEtf66elJb/1g3IvDrFpkcmVou7ACrFmzhvPPP5+6vY9bl156KS+++CLLly/no48+QhRFli9PVvW88sorWbx4MW1tbVRXV7NmzRruvvturFYrRx55JAsXLuSVV17hyiuv7Pe9IpEIkUjfpyKdTofBYFB0Tr0+9P196XX2akL6l+npSWAb5Nz0R3w4Sizp+eFHMf0dgwOpE5vY4nyyzzqJBEj0UGkvLuhjkM78B+OQ0kPY5dmV1vatra0sWbKEiy++mJ/+9Kej4rgJewvipTOWYlMx8YQTj0fmz39OtiDYvBkSE3eil+r5zncShMP9p2ePBIIgcMsttzBr1iy+8Y1v8P7773P77bej29+VptXuZ5GRkfx6wvHwkMfDJbkoMhYRCskYDPKomXM27P8dMJtBiIv4pGBa50Sn14kolIyKczkX9j8Gok4kpgmwZ08iLTeplPBSbLYW9DHI9To4FJo00hgVFzK//e1v+e1vf8vUqVO57rrrmDJlCtu3b2fp0qWpdaZOncof/vAHALZt20ZTU1NqmSiK1NXVsW3bNiwWC06ns8/yqVOn8sUXB8dc9PLYY4/x0EMP9XltxYoVXHDBBUpNsQ8tLS2pv7VBLZJuD5s3dwEDm1e9IS9lCc2otSxlyv7H4EBqDBNY5/6qz1wlSUNI40QIRcbEMRhs/oOxonYF/kr/kMfA7XZz0UUXMWvWLL73ve+xa9eurN5PaWolCVmrTWv+hrgBV3gXL+z+H0paL+X7329h27ZaPty8jXLDcTgcyWMw2k6HmTNn8swzz3DNNdfwzjvv8MADD1BamrQk2rxexL1BP4LgpbsjSiQ29Dm9s3Mn2piWYDCBy9XOzp3pVwMerbS0tJBIgE42sbuzI63v9fb2XYgUjYlrACSPgcfvIYKfjRs9VFe7h9wmlPBBODYmjkG218GhaGhoGHIdRYXMD37wAxobG9FoNPzf//0f1157LU899RTBYBDrfvXXLRYLwWDyRi9JEpYDGqtYLBYkSSIYDKLVajHtZ3/df9v+uPzyy7nkkkv6vJYvi0xLSwv19fUpxRgwB/CvbUWvL2PixIHtpWE5SFXpJCZOnKjomIab/o7BgXztVAN/fG43n35RwfKlyaZybR0xIho3Rx0ynQmOEYzuzJF05j8YExn68w8Gg1x88cUceuih/O///m9fi8AIIwgC6HRpzb/+q3r+3+6/4BK6+FntWUyfXseUKQJPdbWzvKphVH8XJk6cyIcffsg3v/lNzj33XP72t79xzDHHQHl5qiBgfb2Njs2lRBIRJkyYMGgAt7xRpt5WjzeqYdKkaiorh2smynPgd0DUmUEXSOvzDL8bw6YrHdWffTrsfwzMkhmZBD1eU3rHQPAyZcLEgj4GuV4HlUDRq+KMGfv6hvzHf/wHzz//PF988QVmsxn/ftFPgUAA8972tqIoEjigSlYgEEAURcxmM/F4nFAolBIz+2/bHwaDQXHRMhgajSb14U1wTCCKxHZnOydpagfcJpjwUmKxj9iHrjT7H4MDmXdEDeYXrDz7xnbOWZY8Pzr3BhFV2MrHxDEYbP65IMsy3/rWt9BqtTzxxBPDel6ng7w32Ded+ZeIJbgiyXYV9Yf0oNEUMWUKeLp3cfiEiaP+PLDb7Tz55JPcc889qYSFbxmNyHtdSw6HQNCfDPCJE8egGfizcofc1Fgm0BMTsNuFMdEwsPccEPVm/CFnWp+nW3Jj15eM+s8+XTQaDfa9gbs72wJoNIO3s48nEkQFPxVFjjFxDPJ1HUzrvfO6872TamhooLl5X2fozZs309jYCCQbue2/TJIkdu/eTWNjI3a7ndLS0gG3HW3YjXYOt57Eq21PDLhOLAZBuqkuyq0sfaEgCAJTSqbQGt5MeG8cZIvTiVY2IuoG/6KPd2677Tbee+89nnnmmT5WyVFDmunXALOrZ3P59O8hxispn9ANwISGCJKmg9lNhWGVEwSB66+/ntWrV/Ozn/2Mq/76V8L7BfsGPEkhM1TArzvkxkQxWm2yP9FYwmoQ8YfTy1pyhropMoytgH+TzoSAgNMXGLBJai+9zSXLi9Rg31xRTMj4fD7ee+89IpEI0WiUVatW4fV6OfTQQ1m6dClPP/00e/bsobu7m1WrVnH66acDMHv2bCRJYvXq1UQiER555BEOO+wwqqurAVi6dCkPP/wwgUCADRs2sG7dOpYsWaLUsBXnzPpv8KZn5YCN6CQJQppuah3ZVXMtRKaVTyVg2kx38v7Fri4nVk3puOx8nS7PPPMMd9xxB88//zwVFRUjPZz+ySD9+oypZ/Dns35HsbEMU0nyRIjYNmPUiMyaWp3PUSrOSSedxMcff8ynLS0s/OgjWltb+wqZIWrJuENuDHFHsu5K4T+I98FqNBOIpJe15Ax1UGoqYL9aPwiCgNVgxVocYM8QtVE7PHst06qQyRnFvkaxWIw//OEPnHzyyZx66qm89dZb3H///VitVubPn8+5557LZZddxooVK5g3bx5nnXUWkHQF3XnnnaxatYoTTzyRzz77jF/+8pep/V599dVYrVZOO+00brjhBm644QYmTZqk1LAV58wp59AT38lHbR/1uzwQgLCmmyr7+BEyU0qmELJsSQmZ7c7dlBoGdr2Ndz799FP+4z/+g7/+9a8cccQobioYjabVNLIXgwGaaktxhZMnwqaeDRxRPR2DvvDu5vX19ay7+WaO3JtxuWHDGyRiSXfSULVk3CE3urhj0MzGQsVqEtMWMq5oBxXmsSVkAKwGK0UV3qGFjNuDXrag16X3MKAyMIrFyBQXF/PXv/51wOWXX345l19+eb/Lpk+fzhNP9O+OMZlM3HrrrYqMcTioKbNQH13CP7f/k6Nrjj5ouT+QIKRxUjqOyvNPLZ2KT/8GXckQCXa4d1JtLtzgtnzS0dHB8uXL+dnPfsbZZ5890sMZnAwsMr2UmctwSk4APu/8nMMrDs/HyIYFk9nMf0+cSOM553DOOWeycOEf0dZr03ItaSMO7GOwDppdNCNJ6bmW3NEOauxjT8hUWavQGzuGFDI7utuwUljWyNFK4T0KjXIcDjCHJ9Ps7L+TbofHgyzEKTOPH4vM1NKpdMv7XEutgV1MKJowsoMahYTDYc4991zmzZvHz372s5EeztBkECPTS5lYRncweSJs6NzA4ZWFK2R6K/tedNFFPPfcc7z55nXIT8hs3zF4F21XyAXhsSpkRKTY0EJGlmW8iQ5qi8aekKmx1RA17aGzc/D1drlacWhrhmdQYxxVyCiM1QqORANbuvu/mLW6u9FiGBeVfXuZUjoFT7yTls5k75rOyE4ml6oWmf2RZZmrr76aaDTKI488UhjxQ1laZPoImQK2yOzfouDkk0/m9ts3oRF1LJu/jB/96Ec8/fTT7Ny5s0+8XCgWIhQLIUtj07VUai0iGB+itDngCXuIE2FiadUwjGp4qbHVIOlaae+Q+ar7qwHX2+1pVV3sCqEKGYURBKgyNbDD3b+Q6fR1YxUybxRYyJSIJTgMpWzp2UI0Cu7ELqZVqUJmf+6++27Wrl3Lc889h1goqSxZWGRKzaV0B7vxhX3scO8obIvM3u7XvTQ1VWA+u5jf/e/v8Hg8/OpXv2Ly5MlUVFRw2mmn8Z//+Z/85X/+Au0QcpvGpJCpcjgIxt0A3PmvO3l8w+P9rtfub0cni1QWj70HulpbLV728FXwbY575LgBEz/aAq1UmFSLjBKMnupaY4gJtgb+HthBQk6gEfpqxQ5/Nzbd+HEr9TLZMZXtWzbT03M0ft1OplWrrqVeXnjhBW6++WbefPNNamoK5MKWSCAkElnHyHze+TkVlgoqLKM0Iysd9rPIQLI+npAwMO2oaXxz+TeBZDmJ9evX8+GHH/LRRx/xwksvwOfwo2gJ9fVTOOGEY5k3bx4LFixg2rRpBf+AM3WCAwk3oZDMOy3vUGmp5GuHf+2g9dp8HZjildhshT3f/qix1fBOyztEzZtwhVz0SD2Umg+OieySWplZevwIjHDsoQqZPDCjfiKRrWHa/e3U2PremLqD3RSNQyEzrWIKX27ZwhfNXiIaNw0lqkUG4PPPP+fiiy/m0UcfZfbs2SM9nPSJxwGQM42R2etaWt+xvrDdSnCQkKmoACFu7JN+LYoixx57LMceeywA7+9+n7OfOJuL9nxAU9N62tre5fHHH+faa6+lvLycU089lVNPPZXFixfjcDiGe0Y5M7HSgSzE2LwjSI/UM2ADyZ3dHYjxyjFplaq117LHt4di2xaQobmnuV8h44y0UldUIA8uoxzVtZQHjp9jwpKoSQX8vtvyLrs8yf44Tqkbh3H8CZlDy6cSsW3m2dd3YcRKsal4pIc04nR3d3PWWWdx3XXX5a0XWN7YWwguY9eSmHQtfdL+CUdVHZWHgQ0j+3W/hl6LjBG/NHD6tTvkplgsRhDqOeOMM7j11lv55z//SU9PDw8//DB2u52bb76ZsrIy5s2bx+2338769esHdE+MNor2VrbduNWNK+Riu6t/F3uLqwMLlZlk7xcMNbYaWn2t+I1bANjq2trveq54KxOKVSGjBKqQyQNNTVCUaODdL5Nf4m8+/00e+fgRAFzhbkpM40/ITC2dilC+mebunZTqB+9FMx6IRCKcd955zJ49m5tuummkh5M5ey0R2Qb7ftT2EbOqZ+VjZMPHARYZiwV0goFO58Dp166QC4fJgd9PH2uEKIqccsop3H333Xz++efs2LGDK664gn//+98cf/zxTJo0ie9+97u8/PLLRCKjt8mkTqND1NjY3OKmR+php2cnCfngrsitng6KtGMvYwmSMTKukIv2xHqsmlKae5qJJWJE4/tEryzL+ORWGspUIaMEqpDJAxoNNJY08O6m7Wx2bubL7i/Z0pNU5+5IN2Xi+BMyU0qnsMu/hcPnqxlLsizz3e9+F5/Px8qVKwuzz0rvDTwL11IsEeOTtk/GnJARBDDqjHS7BxYy7pAbm74YWWbQ9Ou6ujq++c1v8uyzz9Ld3c2DDz4IwLe+9S3Ky8u56KKLePzxx/F6h84QGm6KjA627XHjklxE4hFafa0HrdPu66BYP/YyliAZ0K7X6GmPbGMyp7DVtZUb1t7Aj1/9cWqdroCThBBlcqVaR0YJCvAKWhgc09TAlq7t3LPm74g6MSVkfLFuyq3jT8g0lTThCXt4x/e/TK8d30LmgQceYPXq1fz9738/qPN7wZClRcZutCef2vUiU0qn5GNkw8cBQgbAYjKwu21w15JZ40AU0y+KbDKZOO200/jDH/7Arl27eP3115k6dSq333475eXlLFu2jEcffRSn05nLbBSj1OJgt6cdKSYh6sR+3UudwQ7Kxlh7gl40goZqWzUaQUOpewlbupt55stn+Mq5LxV7S8ceDIkiqksL9Ps/ylCFTJ44ZtKhdBW9yBObH2JG7Ao2dW1OmhPjTirGoZCxGqxcfPjFTCmdwrVzrx3p4YwYL7zwAjfeeCPPPfcc9fWF0SyxX7K0yAiCQKlYysyqmQdl9BUcB6RfAxTbjGzbFWKgkBZ3yI1I9jVkBEFg9uzZ/PKXv2TDhg18/vnnLFiwgD//+c9UVVWxePFi/vSnP9He3p7dGyhAidmBXJQUL0dWHcn2fkpRdEsdlI/B9gS91NpqmVg0kQnmQ/l367/Z7t7OTvfO1PJtna1YEjVjMkZoJCjwK8no5YLpF3DLSb+gplLPBbXX44966Qp2EaCb6qLxJ2QAVp27iseWP8a0smkjPZQR4f333+eiiy5i5cqVzJ07d6SHkxu9N/As3GJl5jJmVRW4Wwn6tciUO8x4JYmOjv43cYfc6GIOlEpImjJlCj/96U/54IMP2Lp1K8uWLePxxx+nrq6OhQsX8rvf/Y49Q9XKVxiHyYFQsg2TYGVq6VR2uHcctE5PpJ0q69gVMjW2GppKmjjl6CZicpTG4kZ2eXalgra3O1txaNRieEqhCpk8odVoueaYa9j4vS+44JQGLPFa3vlqMxLdTCgbn0JmPLN582aWLVvG7bffzooVK0Z6OLkTiyHr9cnAkAxpKmliwcQFeRjUMNOPkLEazRRXBFm/vv9NnJKTkNtBU5Pyw5kwYQI//OEPWbduHbt37+aiiy7i2WefZdKkScyfP5/7779/WESNw+RAMm3HkCihwdGQssjc+a87+cunfyGWiNETa6XeUZf3sYwUjcWNzKiYwSnzSzEk7PzH9G8TiAbokXoA2O1upVivxscohSpkhoH6eigTpnLPs/8kqnUzpVKNVB9PtLe3c9ppp3HFFVfwgx/8YKSHowzRaMZupV6eu+g5zjv0PIUHNALodAixGPv7kcx6MyUVEp9/fvDqspwsEqfpnMXUqfkdWlVVFd/5znd4/fXX2bNnD5deeinPP/88kyZNYsGCBTzwwAO0th4chKsEDpODrtg29LFiSrUNqRiZlZ+uZO32tbR4WgBoKClg1+oQ/PLEX3LbSbdRWiqwQnyQI2NXUWwqZqcn6V7a7WuhwjR25z/cqEJmGBCEZNbOO5pfs7TxbCrHsElVpS8+n48zzjiD448/nl//+tcjPRzlyKI9wf6MifT73vnvLQ4ISSFjLgqybVuflwHY2LURl+TC4pzH5MnDN8yKigq+/e1v89prr7F7924uueQSnnvuOSZOnMjChQv5/e9/T1tbm2Lv5zA52OnZgcNYguiZyYetH7LFuSWZvencwjbXNoqYiMM+duuxmnQmRH2y1cg3j72Irz4rYqJjYqqe2PaeFiaXqUJGKVQhM0wsnD6VuEbil4v/c6SHojJMhMNhzjnnHEpKSnj00UcLM816IHIUMmOCvfMXDhAyGmMQjQZ27+67+stbX+bIohOYPNGEyTScA91HZWUl3/72t3n99ddpaWnhoosu4qmnnmLChAksWrSIRx99lEAgkNN7FBmLiMQj1JaU0LXhCCY5JvGDl36AzWBjS88WNrZtwxptHJNVfftj1izYuhVKdRP5x9s76eqCDqmFBUeoQkYpxtCVdXSz7LCT+P+O+/84qrrAq5mqpEU8Hueyyy7D4/HwzDPPYDAYRnpIyqIKmX3z3y9Oxqw3I8WCTJ4Mzc19V3956yvUSKfk3a2ULr3upzfeeINdu3Zx/vnnc99991FbW8v3v/99Pu/PP5YGDpMDgMbqYlpaBJZPuoyXml/iksMvoUfq4dGXP2SSfTKTJik3l9FMUVGySGr7pgl8vHUXd90Fkr6FQ2tVIaMUqpAZJo6qPorfnvLbkR6GyjAgyzLXXnstn376KWvWrME2Fh89VSGTKgRzoEUmGE0Kma17K9O/ueNN6u+tZ+3WtZS5T+Okk0ZisINTXV3N97//fT777DNefPFFfD4fxxxzDAsWLOB//ud/CIVCae+rV8hU2EqYPRtqey5GQOCcQ8/Bri1np+5llh3fSIYliAqaM8+EE4+aSM30nUgJLyG81NtVIaMUqpBRUVGYX/3qVzz77LO8/PLLlJeXj/Rw8kMsln5Ft7HKABaZXiHTa5H5vPNzqk0NXOD6hN/+dBolJSMw1jQRBIHjjjuOlStXsmfPHs4//3xuu+02amtruf7669m8efOQ++gVMsViMSecABvfq+PpM99gbsXJmENTcMk7mVzSmN+JjDKmTYOFR06gQ9rFlde3YNFbUsdJJXdUIaOiohBOp5MrrriC++67j5dffplJY9l2rlpkBoyRCUaDNDZCKASffQY9Ug9iqJFTjjx80LYEo42SkhKuvfZaNm7cyDPPPENrayuHH344p5xyCs8++yyxA1LPe+m9QZeIJUyeDAsXwvrVC1n5mJZaMVnNubF4fAkZgEmOSWx1bWW3bxf1RfVjI+B9lKAKGRWVHPH7/axcuZJDDz2Uzs5OPv30U2bMmDHSw8ovOaRfjxl6fSP7dcDuFTJGI1x8MaxcCbt7evB3lXDssSMzzFwRBIFFixbx+OOPs2vXLk444QR++MMfMnHiRH7xi18cVJsmZZHZ2+H+nHNg8mSoroYls8avkDmy6kjCsTAvNr+oupUURhUyKipZ0N7ezkMPPcQ3v/lNKioq+M1vfsPvfvc7Vq9ezYQJE0Z6ePlHtciAICBrtf1aZADmzoU5c+Cf7/UgUsK0MVDQurKykhtvvJFt27bx0EMPsWHDBk466SROP/10nnrqKcLhcB+LDCQ9kJdeCitWwFETplAqllJkKhrBWYwMBq2BExtO5K/r/6oKGYVRhYyKShrIssxnn33GrbfeyrHHHktdXR1/+ctfmDNnDp988gmbNm3iwgsvHD/mYlXIJDmguu/+Qgbga1+DSdN6OGVBSTbdHEYtWq2WpUuX8uyzz7Ju3ToWLVrET3/6U8rKyrj6sqvhM9BIB094SeMS7jvtvuEf8Cjh1Mmn4g65qS9ShYySjKGvloqKsgQCAV544QW+853vMHHiRObNm8cnn3zCt7/9bfbs2cPbb7/N1VdfzSGHHDLSQx1+VCGTRKcb0CLTS0DuYWr9KI7wzZHKykpuuOEGmpubeeuttzj88MMp31DOkiOXMG/ePH7xi1+wbt06IpEIxWIxlx5x6UgPecQ4dfKpAKpFRmHUK5GKyl5kWeaLL77glVde4cUXX2TdunXU1dVx+umn89BDD3HCCSdgNBpT6ycSiREc7QijCpkkB3TA7k/I9Eg9KTfLWEYQBGbOnMnMmTO56aab2L17N6+88gqvvfYaDz74IH6/n4ULF3LyySdzwgknMHPmTHTj7BxqKmlievl0Di0/dKSHMqYYX2eRisoBtLS08Nprr7F27VrWrl2Lz+dj0aJFnHXWWfzxj39kypQpIz3E0Ymafp0kDYvMeBEyB1JXV8cVV1zBFVdcgSzLfP7557z22mu89tpr3HrrrSQSCebMmcNRRx3FUUcdxcyZM5k6deqYFjeCILDhmg3jxwU9TIzdM0ZFpR/cbjdvvPFGSrg0NzczZ84cFi9ezJNPPsncuXPHXhXefKBaZJL0EyMTjoeJJ+JoNVpkWR63QmZ/BEHg8MMP5/DDD+eHP/wh8Xic9evX88EHH/Dpp5/ywAMPsH79emKxGA0NDTQ1NTFlypQ+PxMmTEA7BqroqSJGedQrkcqYJhwO8+6776aEy4cffsjUqVNZvHgxd955J4sWLaKoaPxlUOSMmn6dpLcD9l7MejMAUkzCarDij/iJJWLjXsgciFarTVlieonH4+zcuZMtW7akftasWcOWLVvYvn07giDQ2NjYR9w0NTXR1NREXV0detVCOG5Rr0QqY4pEIsH69etTwmXdunUUFRWxePFirrnmGhYvXkxtbe1ID7PwicUYVzXmB6If1xJAMBrEarDSI/WgETTYjQVUCW+E0Gq1NDY20tjYyKmnntpnWTQaZefOnTQ3N6dEzvPPP09zczPbt28nkUhQU1PDhAkTqK+vZ8KECamf3v9LS0tVa8gYRRUyKgWNLMts3bqVN954I+V/lySJE044gdNPP527776bww47TL2AKY3qWkpygGtJr9GjFbSpOJkeqYdiUzEaQU0QzQW9Xp+yvpx22ml9lsViMdra2mhpaWHXrl2pn7Vr16b+7unpQavVUlJSQllZGaWlpX1+el8rKipCp9Oh1WrR6XQD/m2323E4HKn1VUYW9RNQKSgkSWLDhg188MEHvPXWW7z11ls4nc5UnMv3vvc95syZo5qZ840qZJIcYJERBAFRLxKIBHj+q+cx682qWynP6HQ66uvrqa+v5/jjj+93nUAgQGdnJ06nE6fTSXd3d+pvp9PJJ598Qnd3N16vl1gsRjweJxaLHfR3LBYjGo3i9XqJ7q3oLIoiVquV4uJi7HY7RUVFqd/FxcU4HA6Ki4sP+rv3f1EUh/NwjUnUK5HKqEOWZVwuF62trTQ3N7Np0ya+/PJLPvnkEzZu3IjD4WD27NksWLCA7373u8yZMweTyTTSwx5fqFlLSQ5Iv4ake2mnZyfLn1jOfy34L1XIjAIsFgsNDQ00NDQosj9ZlgmFQng8Hnp6eti8eTNmsxmfz4fX68Xj8eDxeHC73Wzbtg2Xy4XL5cLtdqf+DgQCABiNxgFFTu/fdrsdm82W+n3g3/uXhRiPFISQcblc3HzzzXz44Yep4ktz5swZ6WHlTDweJxAIEAwGU78P/Lv3/3g8jkaj6fOj1WrT+t9gMGAymTCZTBiNxtTfJpMJq9WK1WpFMwxlR/cXKG1tbf3+7v07HA5jsVhoamrikEMOYdq0aZx77rnMmjWLuro61VU00qgWmSQHWGQgKWS29mwF4O9f/Z06e91IjEwljwiCgCiKiKJIRUUFoigyceLEjK6j0Wi0j7Dp7+/29na+/PJLvF4vPp8vJZR6/47vPfcMBkNK1AwmeOx2O+Xl5VRUVFBZWUlFRQXl5eUFL4QK4kp0xx13UF5ezmuvvcZ7773HDTfcwHPPPYd9BFvJfvrppzz99NPYbLY+psdoNJoSH36/n0AgcNDfvf+Hw+HU/vR6PWazOfVjsVj6/K/T6UgkEn1+4vH4Qa8d+Ho8HicSiRAOhwmFQqmfcDicMo1C8oll/y9Cfz8WiwVBEJBlObV/t9uNzWYDSL0uyzKRSKSP6barq4v29nbC4TA2m43q6mqqq6upqamhurqauXPn9vm/pqYmtV+VUYgqZJIcECMDe4WMKylkNnRu4IjKI0ZiZCqjHL1eT3l5OeXl5VltL8sykiT1K3AO/N/r9dLa2sqXX35Jd3c3nZ2ddHR04HQ6AXA4HFRUVPQROL2/e8VObzxRSUnJqCtRMeqvRMFgkDfffJPVq1djMpk44YQTWLVqFevWrWPZsmUjNq6Ojg4+++wz7HY7er2+TyCYxWKhpKQEq9WKxWJJ/fT3f69oGYmYjlgsht/v73PyD/bjcrmA5NNI74/f7yeRSKDRaBAEIfXbYDAwY8aMPsF0vSLFarUO+1xVFEZNv05yQPo1JIXMNte2VHE81bWkkg8EQUg96FZWVma1j1gsRldXF52dnSlx0/v3jh07+OCDD+jo6EjFFPn9fiApwnrvd1qtlnPOOYdHHnlEyellxKi/Eu3atQur1UpZWVnqtSlTprBt27Z+149EIkQikT6v6XQ6xRXkKX4/J7a3Y3S5QAE3h6zAmDJFCxTt/ckKWSYcDifNkhkcg5GYaz4QZJnqcBjBaEQeb66uXbuQ96bIjudWDYJej+PeexGeeCJ1DjzSsZlY4gt+oxdxSRJ1Tz2HfNvHIzzS/DCuvwN7KeRjoAWq9v4MiMkEdXVQV0c4kaAnFiMQjxOXZeJAPJFAMJnydh1Ix1036oWMJElYLJY+r1kslpQyPJDHHnuMhx56qM9rK1as4IILLlB0XLrSUoyXXaboPlVUConwzJlAss3DeEX/059iaG7u89pzG3rY4tnC/Jpj2endyayKWYg1C0dohCoqyqLj4Iff6IQJebsOpBOgPeqFjCiKqejuXgKBwIApa5dffjmXXHJJn9fyYZFJ1NfT0tBAfX39sATKjkYSiQQtLS3j9hio8x/f84e914FDD+1zDD792xs8u+lLjli8lKsOW0GxqRibcWzGe6nngHoMRsP8R72QmTBhAn6/n+7u7pR7acuWLSxfvrzf9Q0Gw7AGIvVmCI1nxvsxUOc/vucPfY+BxZC0INfYaphUPGkERzV8qOeAegxGcv6j/qibzWYWLlzIgw8+SCgU4s0332Tr1q0sXKiaalVUVEYfZl2yTUG1rXqER6KiMj4Y9UIG4IYbbqCjo4OTTz6Z+++/n1//+tcjmnqtoqKiMhC9/ZaqraqQUVEZDka9awmguLiYBx54YKSHoaKiojIkKSGjWmRUVIaFgrDIqKioqBQKZr0Zk85EkTHrwgYqKioZoAoZFRUVFQUx681UWavUNhoqKsOEKmRUVFRUFMSsN6vxMSoqw0hBxMioqKioFArLpy1nesX0kR6Gisq4QRUyKioqKgpSZa2iyjpo0XcVFRUFUV1LKioqKioqKgWLKmRUVFRUVFRUChZVyKioqKioqKgULKqQUVFRUVFRUSlYVCGjoqKioqKiUrCoQkZFRUVFRUWlYFGFjIqKioqKikrBogoZFRUVFRUVlYJFFTIqKioqKioqBYsqZFRUVFRUVFQKFlXIqKioqKioqBQsqpBRUVFRUVFRKVhUIaOioqKioqJSsKhCRkVFRUVFRaVgUYWMioqKioqKSsEiyLIsj/QgVFRUVFRUVFSyQbXIqKioqKioqBQsqpBRUVFRUVFRKVhUIaOioqKioqJSsKhCRkVFRUVFRaVgUYWMioqKioqKSsGiChkVFRUVFRWVgkUVMioqKioqKioFiypkVFRUVFRUVAoWVcioqKioqKioFCyqkFFRUVFRUVEpWFQhkya33XYbp556KosWLeLCCy/krbfeSi1buXIlixcv5qSTTuL+++9nLHZ9GGj+H3/8MVdeeSXz58/n+9///giPUjnOPPNMli1bRjQaTb12++238+CDD47gqEYWl8vFtddey7x58zj33HP54IMPAFi9ejUXX3wxCxcuZPny5Tz11FMjPNL8MND833jjDc477zwWLVrEqaeeyj333EM8Hh/h0eaHgY5BL7FYjAsvvJDzzjtvhEaYXwb7Dhx77LEsWLAg9dPe3j7Co1WewT7/DRs28I1vfIMFCxawdOlSXn311eEbmKySFtu3b5fD4bAsy7L8+eefy4sWLZI9Ho/81ltvyWeccYbc0tIid3V1yeeff7783HPPjfBolWeg+X/xxRfyiy++KD/00EPy9773vREepXIsW7ZMPvHEE+Wnn3469dptt90m//nPfx7BUY0sP/3pT+Vf/epXsiRJ8j//+U/5xBNPlD0ej/zUU0/J69evl6PRqNzc3CwvWbJE/uijj0Z6uIoz0Pw7Ojrknp4eWZZl2ePxyNdcc438t7/9bYRHmx8GOga9rFq1Sr7iiivkc889dwRHmT8Gmv/zzz8/pq5/AzHQ/Lu6uuTTTjtNfuutt+RoNCq7XC65paVl2MalWmTSZNKkSRgMBgAEQSASidDd3c2aNWs4//zzKiZfCAAADdVJREFUqauro6ysjEsvvZQXX3xxhEerPAPN/7DDDuO0006jsrJyhEeoPBdffDGPPfYYsVjsoGVPPPEEy5cvZ/HixfziF7/A7/cDcM011/DCCy+k1gsGgyxcuBCn0zls484HwWCQN998k29/+9uYTCZOOOEEJk+ezLp16zjvvPM4/PDD0el0TJ48mTlz5rBx48aRHrKiDDb/iooKiouL+6y/Z8+eERpp/hjsGAA4nU6effZZLr/88hEeaX4Yav5jncHmv2rVKpYtW8b8+fPR6XQ4HA7q6uqGbWyqkMmA3/zmN8ybN4/LLruM4447jsbGRrZv305TU1NqnalTp7Jt27YRHGX+6G/+Y5ljjz2W8vJyVq9e3ef1d999l7/85S/cd999rF69GkmSuPfeewFYsmQJa9euTa27bt06pk+fTmlp6bCOXWl27dqF1WqlrKws9dqUKVMOOtfj8ThffPHFmDs3hpr/p59+yqJFizjppJNobm5m+fLlIzXUvDHUMfjd737H5ZdfjslkGqkh5pWh5v/ZZ59x8skns2LFijHpXh1s/hs3bkQQBC644AJOPfVUfv7zn+P1eodtbKqQyYAbbriBdevW8Yc//IFZs2YBSZVqtVpT61gsFoLB4EgNMa/0N/+xzlVXXXWQVeaVV17hvPPOo6GhAVEU+e53v8srr7wCwEknncSHH36Iz+cD4NVXX2XJkiUjMnYlkSQJi8XS5zWLxYIkSX1e+9Of/kR5eTnHHXfccA4v7ww1/5kzZ/Lmm2/y97//nfPOOw+bzTYSw8wrgx2D9evXs2vXLk4//fQRGl3+GWz+s2bN4oknnuDVV1/lpptu4uGHH+af//znCI00Pww2/66uLl566SV++9vf8txzzxGPx7n77ruHbWyqkMkQrVbLsccey7///W/effddzGZzyq0AEAgEMJvNIzjC/HLg/Mc6c+fOpaysrI+7qLu7m6qqqtT/1dXVSJKE3+/H4XBw1FFH8cYbb+D3+/n3v//NSSedNBJDVxRRFAkEAn1eCwQCiKKY+v+pp57i9ddf584770QQhOEeYl5JZ/4AtbW1TJ48eVgv4sPFQMfAZDJx1113cf3114+5z31/BjsHamtrqampQaPRMGPGDC666KIxJ2QGm7/RaOTMM89k4sSJiKLIt771Lf71r38N29hUIZMliUSC3bt309DQQHNzc+r1zZs3jzmzen/0zn88cOWVV/axypSVlfXJSGhvb8dkMqUsc73upTfffJMjjzwSh8MxEsNWlAkTJuD3++nu7k69tmXLltS5/sorr/DYY4/x+9//fkzM90CGmv/+yLI8Jr8bAx2DyZMns2nTJn70ox9x6qmn8pOf/ITdu3dz6qmnEgqFRnDEypLJOTAWBd1g8588eXKfdeVhztxVhUwaBINBXnzxRYLBILFYjNdee42PPvqIo446iqVLl/L000+zZ88euru7WbVq1Zgzrw42/0QiQTgcJhaL9fl7LHHcccdRUlLCm2++CcDixYt55pln2LFjB5Ik8cc//pFTTjkltf6JJ57IJ598wrPPPjsm3EoAZrOZhQsX8uCDDxIKhXjzzTfZunUrCxcu5L333uO3v/0t9913HzU1NSM91Lww2PzXrl2bErYtLS2sXLmSo48+eoRHrDyDHYM1a9awatUqVq1axX/9139RU1PDqlWrMBqNIz1sxRhs/u+88w4ulwuATZs28X//938sWLBghEesLIPNf9myZaxevZrdu3cTCoVYuXIl8+fPH7ax6YbtnQoYQRD4+9//zh133IEsy9TX13PrrbfS1NREU1MTW7Zs4bLLLiORSHD22Wdz1llnjfSQFWWw+X/44Yd8+9vfTq07b948li1bxs033zxyA84DV155JT/4wQ+A5By//vWv84Mf/IBAIMDxxx/Pddddl1rXZrMxe/Zs3n33Xe65556RGrLi3HDDDdx0002cfPLJVFZW8utf/xq73c5jjz2G1+vliiuuSK17+umnc+ONN47gaJVnoPnv2rWLe+65B6/XS1FREYsXL+bqq68e6eHmhYGOwf7Y7XY0Gk2foNCxwkDzf//997npppsIhUKUl5dz2WWXjZmHmP0ZaP5z587l4osv5pvf/CaxWIy5c+fy4x//eNjGJcjDbQNSUVFRUVFRUVEI1bWkoqKioqKiUrCoQkZFRUVFRUWlYFGFjIqKioqKikrBogoZFRUVFRUVlYJFFTIqKioqKioqBYsqZFRUVFRUVFQKFlXIqKioqKioqBQsqpBRUVEZVXz44YccffTRHH300bS2to70cFRURjWRSIRbbrmFpUuXsmjRIq666qo+bXNWrlzJ4sWLOemkk7j//vtT7QNisRg//vGPOf300zn66KP7tB7Yn9bWVubNm8ftt98+4BhaW1s5+uijDyqCed555/Hhhx8qMMvBUYWMiorKiHH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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_anom_eval(selected_anomaly, delta_plotted_days):\n", + " one_day = series_taxi.freq * 24 * 2\n", + " anomaly_date = anomalies_day[selected_anomaly][0]\n", + " start = anomaly_date - one_day * delta_plotted_days\n", + " end = anomaly_date + one_day * (delta_plotted_days + 1)\n", + "\n", + " # input series and forecasts\n", + " series_taxi[start:end].plot(\n", + " label=\"Number of taxi passengers\", color=\"#6464ff\", linewidth=0.8\n", + " )\n", + " model_forecasting[start:end].plot(\n", + " label=\"Model prediction\", color=\"green\", linewidth=0.8\n", + " )\n", + "\n", + " # actual anomalies and predicted scores\n", + " (series_taxi_anomalies[start:end] * 10000).plot(\n", + " label=\"Known anomaly\", color=\"r\", linewidth=0.8\n", + " )\n", + " # Scaler transforms scores into a value range between (0, 1)\n", + " (Scaler().fit_transform(best_anomaly_score)[start:end] * 10000).plot(\n", + " label=\"Anomaly score\", color=\"black\", linewidth=0.8\n", + " )\n", + " plt.legend(loc=\"upper center\", ncols=2)\n", + " plt.title(selected_anomaly)\n", + " fig.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "for anom_name in anomalies_day:\n", + " plot_anom_eval(anom_name, 3)\n", + " break # remove this to see all anomalies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple case: `KMeansScorer`" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's have a closer look at the scorer's `window` parameter on the example of the `KmeansScorer`. We'll use two toy datasets to demonstrate how the scorers perform with different window sizes. In first example we set `window=1` on a multivariate time series, and in the second we set `window=2` on a univariate time series. \n", + "\n", + "The figure below illustrates the Scorer's windowing process:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multivariate case with window=1 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Synthetic data creation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data is a multivariate series (2 components/columns). Each step has either value of `0` or `1`, and the two components always have opposite values:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " comp1 comp2\n", + "State 1 0 1\n", + "State 2 1 0" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(index=[\"State 1\", \"State 2\"], data={\"comp1\": [0, 1], \"comp2\": [1, 0]})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At each timestamp, it has a 50% chance to switch state and a 50% chance to keep the same state. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Train set" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_data_ex1(random_state: int):\n", + " np.random.seed(random_state)\n", + "\n", + " # create the train set\n", + " comp1 = np.expand_dims(np.random.choice(a=[0, 1], size=100, p=[0.5, 0.5]), axis=1)\n", + " comp2 = (comp1 == 0).astype(float)\n", + " vals = np.concatenate([comp1, comp2], axis=1)\n", + " return vals" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = generate_data_ex1(random_state=40)\n", + "series_train = TimeSeries.from_values(data, columns=[\"comp1\", \"comp2\"])\n", + "\n", + "# visualize the train set\n", + "series_train[:20].plot()\n", + "plt.title(\"Training set\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Test set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create the test set using the same rules as the train set but we'll inject six anomalies of three different types. The anomalies can be longer than one timestamp. The types are:\n", + "\n", + "- 1st type: replacing the value of one component (0 or 1) with 2\n", + "- 2nd type: adding +1 or -1 to both components\n", + "- 3rd type: both components have the same value" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# inject anomalies in the test timeseries\n", + "data = generate_data_ex1(random_state=3)\n", + "\n", + "# 2 anomalies per type\n", + "# type 1: random values for only one component\n", + "data[20:21, 0] = 2\n", + "data[30:32, 1] = 2\n", + "\n", + "# type 2: shift both components values (+/- 1 for both components)\n", + "data[45:47, :] += 1\n", + "data[60:64, :] -= 1\n", + "\n", + "# type 3: switch one value to the another\n", + "data[75:82, 0] = data[75:82, 1]\n", + "data[90:96, 1] = data[90:96, 0]\n", + "\n", + "series_test = TimeSeries.from_values(data, columns=[\"component 1\", \"component 2\"])\n", + "\n", + "# create the binary anomalies ground truth series\n", + "anomalies = ~((data == [0, 1]).all(axis=1) | (data == [1, 0]).all(axis=1))\n", + "anomalies = TimeSeries.from_times_and_values(\n", + " times=series_test.time_index, values=anomalies, columns=[\"is_anomaly\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the anomalies. From left to right, the first two anomalies correspond to the first type, the third and the fourth correspond to the second type, and the last two to the third type.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_anomalies_from_scores(\n", + " series=series_test, anomalies=anomalies, title=\"Testing set multivariate\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use the scorer `KMeansScorer()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use the `KMeansScorer` to locate the anomalies with the following parameters:\n", + "\n", + "- `k`=2: The number of clusters/centroids generated by the KMeans model. We choose two since we know that there are only two valid states.\n", + "- `window`=1 (default): Each timestamp is considered independently by the KMeans model. It indicates the size of the window used to create the subsequences of the series (`window` is identical to a positive target `lag` for our [regression models](https://unit8co.github.io/darts/examples/20-RegressionModel-examples.html#Darts-Regression-Models)). In this example we know that each anomaly can be detected by only looking one step.\n", + "- `component_wise`=False (default): All components are used together as features with a single KMeans model. If `True`, we would fit a dedicated model per component. For this example we need information about both components to find all anomalies.\n", + "\n", + "We'll fit `KMeansScorer` on the anomaly-free training series, compute the anomaly scores on the test series, and finally evaluate the scores." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Kmeans_scorer = KMeansScorer(k=2, window=1, component_wise=False)\n", + "\n", + "# fit the KmeansScorer on the train timeseries 'series_train'\n", + "Kmeans_scorer.fit(series_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "anomaly_score = Kmeans_scorer.score(series_test)\n", + "\n", + "# visualize the anomaly score compared to the true anomalies\n", + "anomaly_score.plot(label=\"Anomaly score by KMeans\")\n", + "(anomalies - 2).plot()\n", + "plt.title(\"Anomaly score from KMeans Scorer vs true anomalies\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice! We can see that the anomaly scores accurately indicate the position of the 6 anomalies.\n", + "\n", + "To evaluate the scores, we can call `eval_metric()`. It expects the true anomalies, the series, and the name of the agnostic threshold metric (AUC-ROC or AUC-PR)." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AUC_ROC: 1.0\n", + "AUC_PR: 1.0\n" + ] + } + ], + "source": [ + "for metric_name in [\"AUC_ROC\", \"AUC_PR\"]:\n", + " metric_val = Kmeans_scorer.eval_metric(anomalies, series_test, metric=metric_name)\n", + " print(metric_name + f\": {metric_val}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And again, let's visualize the results with `show_anomalies()`:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Kmeans_scorer.show_anomalies(series=series_test, anomalies=anomalies, metric=\"AUC_ROC\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Univariate case with window>1 " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the previous example, we used `window=1` which was sufficient to identify all anomalies. In the next example, we'll see that sometimes higher values are required to capture the true anomalies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Synthetic data creation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Train set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this toy example, we generate a univariate (one component) series that can take 4 possible values.\n", + "\n", + "- possible values at each step `(0, 1, 2, 3)`\n", + "- every next step either adds `diff=+1` or subtracts `diff=-1` (50% chance)\n", + "- all steps are upper- and lower-bounded\n", + " - `0` and `diff=-1` remains at `0`\n", + " - `3` and `diff=+1` remains at `3`" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_data_ex2(start_val: int, random_state: int):\n", + " np.random.seed(random_state)\n", + " # create the test set\n", + " vals = np.zeros(100)\n", + "\n", + " vals[0] = start_val\n", + "\n", + " diffs = np.random.choice(a=[-1, 1], p=[0.5, 0.5], size=len(vals) - 1)\n", + " for i in range(1, len(vals)):\n", + " vals[i] = vals[i - 1] + diffs[i - 1]\n", + " if vals[i] > 3:\n", + " vals[i] = 3\n", + " elif vals[i] < 0:\n", + " vals[i] = 0\n", + " return vals" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Training set')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = generate_data_ex2(start_val=2, random_state=1)\n", + "series_train = TimeSeries.from_values(data, columns=[\"series\"])\n", + "\n", + "# visualize the train set\n", + "series_train[:40].plot()\n", + "plt.title(\"Training set\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Test set" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create the test set using the same rules as the train set but we'll inject six anomalies of two different types. The anomalies can be longer than one timestamp:\n", + "\n", + "- Type 1: steps with `abs(diff) > 1` (jumps larger than one)\n", + "- Type 2: steps with `diff = 0` at values `(1, 2)` (value remains constant)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "data = generate_data_ex2(start_val=1, random_state=3)\n", + "\n", + "# 3 anomalies per type\n", + "# type 1: sudden shift between state 0 to state 2 without passing by value 1\n", + "data[23] = 3\n", + "data[44] = 3\n", + "data[91] = 0\n", + "\n", + "# type 2: having consecutive timestamps at value 1 or 2\n", + "data[3:5] = 2\n", + "data[17:19] = 1\n", + "data[62:65] = 2\n", + "\n", + "series_test = TimeSeries.from_values(data, columns=[\"series\"])\n", + "\n", + "# identify the anomalies\n", + "diffs = np.abs(data[1:] - data[:-1])\n", + "anomalies = ~((diffs == 1) | ((diffs == 0) & np.isin(data[1:], [0, 3])))\n", + "# the first step is not an anomaly\n", + "anomalies = np.concatenate([[False], anomalies]).astype(int)\n", + "\n", + "anomalies = TimeSeries.from_times_and_values(\n", + " series_test.time_index, anomalies, columns=[\"is_anomaly\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_anomalies_from_scores(\n", + " series=series_test, anomalies=anomalies, title=\"Testing set univariate\"\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From left to right, anomalies at positions 3, 4, and 6 are of type 1, and anomalies at positions 1, 2, and 5 are of type 2. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use the scorer `KMeansScorer()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We fit two `KMeansScorer` with different values for the `window` parameter (1 and 2)." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "windows = [1, 2]\n", + "Kmeans_scorer_w1 = KMeansScorer(k=4, window=windows[0])\n", + "Kmeans_scorer_w2 = KMeansScorer(k=8, window=windows[1], window_agg=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AUC_ROCAUC_PR
w_10.4602120.123077
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" + ], + "text/plain": [ + " AUC_ROC AUC_PR\n", + "w_1 0.460212 0.123077\n", + "w_2 1.000000 1.000000" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scores_all = []\n", + "metric_data = {\"AUC_ROC\": [], \"AUC_PR\": []}\n", + "for model, window in zip([Kmeans_scorer_w1, Kmeans_scorer_w2], windows):\n", + " model.fit(series_train)\n", + " scores = model.score(series_test)\n", + " scores_all.append(scores)\n", + "\n", + " for metric_name in metric_data:\n", + " metric_data[metric_name].append(\n", + " eval_metric_from_scores(\n", + " anomalies=anomalies,\n", + " pred_scores=scores,\n", + " metric=metric_name,\n", + " )\n", + " )\n", + "pd.DataFrame(data=metric_data, index=[\"w_1\", \"w_2\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The metric indicates that the scorer with the parameter window set to 1 cannot locate the anomalies. On the other hand, the scorer with the parameter set to 2 perfectly identified the anomalies." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Prediction (SCP) due to its simplicity and efficiency.\n", + "- You can apply conformal prediction to any pre-trained global forecasting model.\n", + "- To improve your experience, our conformal models automatically extract the relevant calibration data from your input series required to generate the interval.\n", + "- We offer useful features to configure the extraction and make your conformal models more adaptive and efficient (`cal_length`, `cal_stride`).\n", + "- Conformal prediction supports all use cases (uni- and multivariate, single and multiple series, and single and multi-horizon forecasts, providing direct quantile value predictions or sampled predictions).\n", + "- We'll demonstrate how to use and evaluate conformal prediction on four examples using real-world data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3ef9bc25-7b86-4de5-80e9-6eff27025b44", + "metadata": {}, + "outputs": [], + "source": [ + "# fix python path if working locally\n", + "from utils import fix_pythonpath_if_working_locally\n", + "\n", + "fix_pythonpath_if_working_locally()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9d9d76e9-5753-4762-a1cb-c8c61d0313d2", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from darts import concatenate, metrics\n", + "from darts.datasets import ElectricityConsumptionZurichDataset\n", + "from darts.models import ConformalNaiveModel, ConformalQRModel, LinearRegressionModel" + ] + }, + { + "cell_type": "markdown", + "id": "6ec264e9-af99-4d88-9fcc-1e71db03b294", + "metadata": {}, + "source": [ + "## Conformal Prediction for Time Series Forecasting\n", + "\n", + "*Conformal prediction is a technique for constructing prediction intervals that try to achieve valid coverage in finite samples, without making distributional assumptions.* [(source)](https://arxiv.org/pdf/1905.03222)\n", + "\n", + "In other words: If we want a prediction interval that includes 80% of all actual values over some period of time, then a conformal model attempts to generate such intervals that actually have 80% of points inside.\n", + "\n", + "There are different techniques to perform conformal prediction. In Darts, we currently use **Split Conformal Prediction [(SCP, Lei\n", + "et al., 2018)](https://www.stat.cmu.edu/~ryantibs/papers/conformal.pdf)** (with some nice adaptions) due to its simplicity and efficiency. \n", + "\n", + "### Split Conformal Prediction\n", + "SCP adds calibrated prediction intervals with a specified confidence level to a base model's forecasts. It involves splitting the data into a training (+ optional validation) set and a calibration (+ test) set. The model is trained on the training set, and the calibration set is used to compute the prediction intervals to ensure they contain the true values with the desired probability.\n", + "\n", + "#### Advantages\n", + "\n", + "- **Valid Coverage**: Provides valid prediction intervals that are guaranteed to contain the true value with a specified confidence level on finite samples.\n", + "- **Model-agnostic**: Can be applied to any predictive model:\n", + " - Either adds calibrated prediction intervals to point forecasting models\n", + " - Or calibrates the predicted intervals in case of probabilistic forecasting models\n", + "- **Distribution-free**: No distributional assumptions about the data except that the errors on the calibration set are exchangeable (e.g. we don't need to assume that our data is normally distributed and then fit a model with a `GaussianLikelihood`).\n", + "- **Efficient**: Split Conformal Prediction is efficient since it does not require model re-training.\n", + "- **Interpretable**: The method is interpretable due to its simplicity.\n", + "- **Useful Applications**: It's used to provide more reliable and informative predictions to help decision-making in several industries. See this [article on conformal prediction](https://medium.com/@data-overload/conformal-prediction-a-critic-to-predictive-models-27501dcc76d4)\n", + "\n", + "#### Disadvantages\n", + "\n", + "- **Requires a Calibration Set**: Conformal prediction requires another data / hold-out set that is used solely to compute the calibrated prediction intervals. This can be inefficient for small datasets.\n", + "- **Exchangeability of Calibration Data** (a): The accuracy of the prediction intervals depends on the representativeness of the calibration data (or rather the forecast errors produced on the calibration set). The coverage is not guaranteed anymore if there is a **distribution shift** in forecast errors (e.g. series with a trend but forecasting model is not able to predict the trend).\n", + "- **Conservativeness** (a): May produce wider intervals than necessary, leading to conservative predictions.\n", + "\n", + "(a) Darts conformal models have some parameters to control the extraction of the calibration set for more adaptiveness (see more infos [here](#Darts-features-to-make-your-Conformal-Models-more-adaptive))." + ] + }, + { + "cell_type": "markdown", + "id": "d5dc6eb5-2eeb-4495-9074-1a44ac9154ab", + "metadata": {}, + "source": [ + "## Darts Conformal Models\n", + "\n", + "Darts' conformal models add calibrated prediction intervals to the forecasts of any **pre-trained global forecasting model**. \n", + "There is no need to train the conformal models themselves (e.g. no `fit()` required) and you can directly call `predict()` or `historical_forecasts()`. Behind the hood, Darts will automatically extract the calibration set from the past of your input series and use it to generate the calibrated prediction intervals (see [here](#Workflow-behind-the-hood) for more detail).\n", + "\n", + "> **Important**: The `series` passed to the forecast methods **should not have any overlap** with the series used to **train** the forecasting model, since this will lead to overly optimistic prediction intervals.\n", + "\n", + "### Model support\n", + "\n", + "All conformal models in Darts support:\n", + "\n", + "- any **pre-trained global forecasting model** as the base forecaster (you can find a list [here](https://unit8co.github.io/darts/#forecasting-models))\n", + "- **uni-** and **multivariate** forecasts (single / multi-columns)\n", + "- **single** and **multiple series** forecasts\n", + "- **single** and **multi-horizon** forecasts\n", + "- generate a **single** or **multiple calibrated prediction intervals**\n", + "- **direct quantile value** predictions (interval bounds) or **sampled predictions** from these quantile values\n", + "- **any covariates** based on the underlying forecasting model\n", + "\n", + "### Direct Interval Predictions or Sampled Predictions\n", + "Conformal models are probabilistic, so you can forecast in two ways (when calling `predict()`, `historical_forecasts()`, ...):\n", + "\n", + "- Forecast the calibrated quantile interval bounds directly (example [here](https://unit8co.github.io/darts/quickstart/00-quickstart.html#Direct-Parameter-Predicitons)).\n", + " - `predict(..., predict_likelihood_parameters=True)`\n", + "- Generate stochastic forecasts by sampling from these calibrated quantile intervals (examples [here](https://unit8co.github.io/darts/quickstart/00-quickstart.html#Probabilistic-Sample-Predictions)):\n", + " - `predict(..., num_samples=1000)`\n", + "\n", + "### Workflow behind the hood\n", + "\n", + "> Note: `cal_length` and `cal_stride` will be further explained [below](#Darts-features-to-make-your-Conformal-Models-more-adaptive).\n", + "\n", + "In general, the workflow of the models to produce one calibrated forecast/prediction is as follows (using `predict()`):\n", + "\n", + "- **Extract a calibration set**: The calibration set for each conformal forecast is automatically extracted from\n", + " the most recent past of your input series relative to the forecast start point. The number of calibration examples\n", + " (forecast errors / non-conformity scores) to consider can be defined at model creation\n", + " with parameter `cal_length`. Note that when using `cal_stride>1`, a longer history is required since\n", + " the calibration examples are generated with stridden historical forecasts.\n", + "- Generate **historical forecasts** on the calibration set (using the forecasting model) with a stride `cal_stride`.\n", + "- Compute the **errors/non-conformity scores** (specific to each conformal model) on these historical forecasts\n", + "- Compute the **quantile values** from the errors / non-conformity scores (using our desired quantiles set at model\n", + " creation with parameter `quantiles`).\n", + "- Compute the conformal prediction: Using these quantile values, add **calibrated intervals** to (or adjust the\n", + " existing intervals of) the forecasting model's predictions.\n", + "\n", + "For **multi-horizon forecasts**, the above is applied for each step in the horizon separately.\n", + "\n", + "When computing `historical_forecasts()`, `backtest()`, `residuals()`, ... the above is applied for each forecast (the forecasting model's historical forecasts are only generated once for efficiency).\n", + "\n", + "### Available Conformal Models\n", + "\n", + "At the time of writing (Darts version 0.32.0), we have two conformal models:\n", + "\n", + "#### `ConformalNaiveModel`\n", + "\n", + "Adds calibrated intervals around the median forecast of **any pre-trained global forecasting model**. It supports two symmetry modes:\n", + "\n", + "- `symmetric=True`:\n", + " - The lower and upper interval bounds are calibrated with the same magnitude.\n", + " - Non-conformity scores: uses the [absolute error](https://unit8co.github.io/darts/generated_api/darts.metrics.metrics.html#darts.metrics.metrics.ae) `ae()` to compute the non-conformity scores on the calibration set.\n", + "- `symmetric=False`\n", + " - The lower and upper interval bounds are calibrated separately.\n", + " - Non-conformity scores: uses the [error](https://unit8co.github.io/darts/generated_api/darts.metrics.metrics.html#darts.metrics.metrics.err) `err()` to compute the\n", + " non-conformity scores on the calibration set for the upper bounds, and `-err()` for the lower bounds.\n", + "\n", + "#### `ConformalQRModel` (Conformalized Quantile Regression Model)\n", + "\n", + "Calibrates the quantile predictions of a **pre-trained probabilistic global forecasting model**. It supports two symmetry modes:\n", + "\n", + "- `symmetric=True`:\n", + " - The lower and upper quantile predictions are calibrated with the same magnitude.\n", + " - Non-conformity scores: uses the [Non-Conformity Score for Quantile Regression](https://unit8co.github.io/darts/generated_api/darts.metrics.metrics.html#darts.metrics.metrics.incs_qr) `incs_qr(symmetric=True)` on the calibration set.\n", + "- `symmetric=False`\n", + " - The lower and upper quantile predictions are calibrated separately.\n", + " - Non-conformity scores: uses the [Asymmetric Non-Conformity Score for Quantile Regression](https://unit8co.github.io/darts/generated_api/darts.metrics.metrics.html#darts.metrics.metrics.incs_qr) `incs_qr(symmetric=False)` for the upper and lower bound on the calibration set.\n", + "\n", + "### Darts features to make your Conformal Models more adaptive\n", + "\n", + "As mentioned in [Split Conformal Prediction - Disadvantages](#Disadvantages), the calibration set has a large impact on the effectiveness of our conformal prediction technique.\n", + "\n", + "We implemented some cool features to make our automatic extraction of the calibration set even more powerful for you.\n", + "\n", + "All our conformal models have the following two parameters at model creation:\n", + "\n", + "- `cal_length`: The number of non-conformity scores (NCS) in the most recent past to use as calibration for each conformal forecast (and each step in the horizon).\n", + " - If `None` acts as an expanding window mode\n", + " - If `>=1` uses a moving fixed-length window mode\n", + " - Benefits:\n", + " - Using `cal_length` makes your model react more quickly to distribution shifts in NCS.\n", + " - Using `cal_length` reduces the computational cost to perform the calibration.\n", + " - Caution: Use large enough values to have enough example for calibration.\n", + "- `cal_stride`: (default=1) The stride (number of time steps between two consecutive forecasts) to apply when computing the historical forecasts and non-conformity scores on the calibration set.\n", + " - This is useful if we want to run our models on a scheduled basis (e.g. once every 24 hours) and are only interested in the NCS that were produced on this schedule.\n", + " - Caution: `cal_stride>1` requires a longer `series` history (roughly `cal_length * stride` points)." + ] + }, + { + "cell_type": "markdown", + "id": "eacf6328-6b51-43e9-8b44-214f5df15684", + "metadata": {}, + "source": [ + "## Examples\n", + "\n", + "We will show four examples:\n", + "\n", + "1) How to perform conformal prediction and compare different models based on the quantified uncertainty. For simplicity, we will use a single step horizon `n=1`.\n", + "2) How to perform multistep horizon conformal forecasts\n", + "3) How to perform multistep horizon conformal forecasts on a scheduled basis\n", + "4) An example of conformalized quantile regression.\n", + "\n", + "### Input Dataset\n", + "For both examples, we use the Electricity Consumption Dataset from households in Zurich, Switzerland.\n", + "\n", + "The dataset has a quarter-hourly frequency (15 Min time intervals), but we resample it to hourly frequency to keep things simple.\n", + "\n", + "To keep it simple, we will not use any covariates and only concentrate on the electricity consumption as the target we want to predict. The conformal model's covariate support and API is identical to the base-forecaster.\n", + "\n", + "**Target series** (the series we want to forecast):\n", + "- **Value_NE5**: Electricity consumption by households on grid level 5 (in kWh)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "90b31843-8f60-4dd8-b6e4-87206d67e585", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "series = ElectricityConsumptionZurichDataset().load().astype(np.float32)\n", + "\n", + "# extract target and resample to hourly frequency\n", + "series = series[\"Value_NE5\"].resample(freq=\"h\")\n", + "\n", + "# plot 2 weeks of hourly consumption\n", + "ax = series[: 2 * 7 * 24].plot()\n", + "ax.set_ylabel(\"El. Consuption [kWh]\")\n", + "ax.set_title(\"Target series (Electricity Consumption) extract\");" + ] + }, + { + "cell_type": "markdown", + "id": "ab445a33-9a50-4695-8de4-09bcc007f787", + "metadata": {}, + "source": [ + "Extract a train, calibration and test set. Note that `cal` does not overlap with the training set `train`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "29a5b91e-543f-46e0-8dbd-12da2f09522f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_start = pd.Timestamp(\"2015-01-01\")\n", + "cal_start = pd.Timestamp(\"2016-01-01\")\n", + "test_start = pd.Timestamp(\"2017-01-01\")\n", + "test_end = pd.Timestamp(\"2018-01-01\")\n", + "\n", + "train = series[train_start : cal_start - series.freq]\n", + "cal = series[cal_start : test_start - series.freq]\n", + "test = series[test_start:test_end]\n", + "\n", + "ax = train.plot(label=\"train\")\n", + "cal.plot(label=\"val\")\n", + "test.plot(label=\"test\")\n", + "\n", + "ax.set_ylabel(\"El. Consuption [kWh]\")\n", + "ax.set_title(\"Dataset splits\");" + ] + }, + { + "cell_type": "markdown", + "id": "cd792a32-744a-4815-86d9-d3d7b3677859", + "metadata": {}, + "source": [ + "### Example 1: Compare different models on single step horizon forecasts\n", + "\n", + "Let's see how we can use conformal prediction in Darts. We'll show how to:\n", + "\n", + "- use conformal prediction (predict and historical forecasts)\n", + "- evaluate the prediction intervals (simple prediction and backtest).\n", + "- compare two different base forecasting models using conformal prediction.\n", + "\n", + "To demonstrate the process, we focus first only on one base forecasting model.\n", + "\n", + "#### Train the base forecaster\n", + "\n", + "Let's use a `LinearRegressionModel` as our base forecasting model. We configure it to use the last two hours as lookback to forecast the next hour (single step horizon; multi horizon will be covered in Example 2).\n", + "\n", + "- train it on the `train` set\n", + "- forecast the next hour after the end of the `cal` set" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8a9952be-a6c4-4da1-aabe-70c8f019b222", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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W0k8//SQ6rqbv+ebNm9yIESM4f39/TqPR1DtbLDIykhs8eDC3evVqrkOHDpy7uzsXFRXFffbZZ6LjaisDz/bt27nBgwdzgYGBnJubGxceHs4NHjy42vG///4716lTJ/adzps3j/XNutqA48wzjV599VWuZcuWnJubG9esWTNu8ODB3OnTp9kxW7Zs4bp06cJ5eHiI+lrVWUs8S5YsYeVp0qQJN2zYMC4jI0N0DN9HqlJTuQnCUjQc9/+nOBAEQVjBuHHjsHr1ahQXF1t1ft++fZGfn48TJ07YuGTSEBUVhdjYWKxdu1buohCEU0IxMgRBEARBqBYSMgRBEARBqBZyLREEQRAEoVrIIkMQBEEQhGohIUMQBEEQhGohIUMQBEEQhGohIUMQBEEQhGohIWMlJpMJFy5ckHSVY6Xh7G1A9Xfu+gPUBs5ef4DaQAn1JyFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRdGDduHB555BG73oOEDEEQBEE4MTNmzMB9990ndzGshoQMQRCKJC8vD8uXL8e1a9fkLgpBEAqGhAxBEIpk3LhxeOaZZzB06FC5i0IQimfjxo3o3bs3/P39ERQUhCFDhuD8+fNs/9WrVzFq1CgEBgbC29sbDzzwAPbt24cVK1Zg5syZOHr0KDQaDTQaDVasWIGLFy9Co9HgyJEj7BqFhYXQaDTYtm0bAMBoNGLChAmIj4+Ht7c32rZti4ULF0pcc8BV8jsSBEHUg8FgwJ9//gkAOHToEK5evYqIiAiZS0UQyqWkpASvvfYaOnbsiJKSErz33nt49NFHceTIEZSWliIhIQHh4eH4/fff0bx5cxw6dAgmkwkjR47EiRMnsHHjRmzZsgUA0KRJE+Tm5tZ7T5PJhIiICHz55Zfo2LEj9u7di9TUVISGhuKJJ56wd5UZJGQIglAcGRkZKC8vZ3//9ddfGDlypIwlIpyVBx54ANevX6/zGKPRCK1Wa9P7Nm/eHAcOHGjw8Y899pjo76VLl6JZs2Y4efIkdu/ejby8POzfvx+BgYEAgDZt2rBjfXx84OrqiubNm1tURjc3N8yYMQOXLl1CZGQkWrdujd27d+PHH38kIUMQhHNz8OBB0d+7du0iIUPIwvXr11URp3X+/HlMnz4de/fuRX5+PkwmEwDg8uXLOHLkCLp06cJEjC359ttv8c033+D69evQ6/UwGAySBw6TkCEIQnHUJGQIQg4aYqWwl0XGElJSUtCiRQssXrwYYWFhMJlMiI2NhcFggE6ns/j+Li7mEFqO49hnFRUVomN+/PFHTJkyBW+//TYGDx6MJk2a4OOPP8a+ffssvl9jICFDEITiqCpkjh07hqKiIjRp0kSmEhHOSn3uHZPJxFwr/OAvNQUFBTh16hQWLVqEPn36ABCL/06dOmHJkiW4efNmjVYZd3d3GI1G0WfBwcEAgJycHHTp0gUARIG/ALBz50706tULTz31FKu/MMBYKmjWEkEQisJgMODYsWOiz0wmE/bu3StTiQhC2QQEBCAoKAj//ve/kZmZifT0dLz22mts/+jRo9G8eXM88sgj+Ouvv5CVlYWff/4Ze/bsAQBERUXhwoULOHLkCPLz81FeXg6dTocePXpg3rx5OHnyJHbs2IF3331XdN82bdrgwIED2LFjB86ePYvp06dj//79ktYdICFDEITCEAb6+vn5sc/JvUQQNePi4oJVq1bh4MGDiI2NxeTJk/Hxxx+z/e7u7ti0aROaNWuG5ORkdOzYEfPmzWPusMceewxJSUlITExEcHAwvv/+ewDAsmXLUFFRgQceeACvvvoqZs+eLbrvCy+8gEcffRQvv/wyevbsiYKCAkycOFG6iv9/NJzQAUY0GCWYE+XG2duA6m+f+i9ZsgTPPfccAGDy5MlYsGABAKBv377YunWrze5jC6gPOHf9AWoDJdTf+VqdIAhFI4yPGTZsGCIjIwEA+/btqxZs6Azk5+fjP//5D/75z39i7dq1cheHIBQHCRmCIBSFUMh06dIFvXv3BgDo9XocPnxYrmJJzq1btzBo0CCEhITg6aefxrfffouUlBT8+9//lrtoBKEoSMgQBKEYKioqWKBvTEwM/Pz8EBcXx/YrzbVkT5YtW4aNGzeyfCA8zz//PL744guZSkUQyoOmXxMEoRiEgb5du3YFACQkJLD9c+fOxSOPPIK2bdvKUj4pEYq2V199FZWVlfjqq6/Y31lZWZgzZw68vb3lKqLd2bZtG77//nv4+vqiWbNmcHd3R2FhIYqLizFw4EAMGDBA7iISCoCEDEEQikGYs+OBBx4AALRv3x4jR47EDz/8gNu3b+ORRx7Bvn37RDOaHA2j0YidO3cCAJo1a8YCnoOCgjBr1iwAwMKFC/H777/j3//+N/r37y9bWe1FQUEBhgwZgpKSkhr3f/755zh8+DA6duwocckIpUGuJYIgFIMw4db999/PtpcsWYIOHToAAE6fPo1x48bBkSdcHjlyBLdv3wYAxMfHs1WJZ86ciS+++AKenp4AgAsXLiApKQmHDh2Ss7h2YdGiRbWKGMAs9iZPnuzQ/YBoGCRkCIJQDCdOnGDbnTp1Yts+Pj749ddf4e/vDwBYs2aNQ+eV2b59O9sWutYA4OWXX8axY8fY50ajEf/73/8kLZ+9KS8vx5dffgnAnCNlzZo1+PHHH7Fy5UqsW7cOrVq1AgD8+eefNJOLICFDEEqiqKgIW7ZsccppxhzH4fjx4wCAsLCwaqnU27RpI0rI5YhWCJ66hAwA3HPPPfjll1+g0WgAOF4Q9KpVq9iK048++igeeeQRPP7443jyySeRnJyM+fPns2OnTJkCg8EgV1EJBUBChiAUxIgRIzBgwAA8/PDDqKysBADk5ubilVdeweLFi2UunX3JycnBzZs3AQCxsbE1HiN0N506dUqSckmNyWRi8TGBgYHMpVaVwMBAtsrw0aNHWdupHY7j8Nlnn7G/p0yZUu2Yxx57jK0pdO7cORYETTgnJGQIQiHo9Xps2bIFgHm2xkcffcRmZ3z55ZdITU3FyZMnZS6l/RC6lWoL4GzXrh3bPn36tN3LJAfHjx/HrVu3AJjjY+rKlpqYmAjAPPgLrThqJj09nU3B79GjB3r27FntGI1GgwULFjCL1IcffojS0lJJy+msREVF4fPPP5e7GCJIyBCEQrhw4YLo7xkzZmDgwIE4evQo+4x/U3dEeLcSULtFJiAgACEhIQAc1yJTn1tJCC9kAMdxLy1ZsoRtCxc+rErXrl0xcuRIAEBeXh6WL19u97I1FKORw7bDHL7fYv6/0UgByfaEhAxBKITz58+L/q6srMTu3btFn8mxsqxUNMQiAwD33nsvAODGjRsO404RYomQ6dOnD7PYOIqQ4Vc59/b2xqOPPlrnsVOnTmXbH3/8sSJiy37ZziHqCQ6Jr3J4cpb5/1FPcPhlu3LEjKPFFJGQIQiFIBQy/PRaAHB1dWWD1d9//y15uaSCt8hoNBq0b9++1uMc2b3EcRx27NgBAGjSpIlo5lZNNGnShCUOPHHiBG7cuGH3MtqTgoICXLx4EYB5eQpX17pTnXXu3BmDBg0CAFy6dAmrVq2ydxHr5JftHEZM53A1T/z5tTxgxHT7iZm+ffvipZdewksvvQR/f38EBQXh3XffZVPTo6KiMHv2bIwbNw5NmjRhi7Lu3r0b8fHx0Ol0aNGiBV555RXRlPcbN24gJSUFOp0OrVq1wsqVK+1S/sZisZCZM2cOBg4ciISEBIwcOVJk6l6xYgX69++Phx56CAsXLhTN78/IyMDo0aMRFxeH1NRU5OTksH1lZWWYPn064uPjMXjwYGzcuFF0z7S0NCQnJyMhIQEzZ85UhOomCFsjFDJff/01mjZtCq1Wi3//+9/MQpGRkVFnbg21YjQakZGRAcA8O0mn09V6LG+RARzPvXT8+HHk5+cDMFtbtFptvec89NBDbHvbtm32KpokCNfZ4hMi1sfbb7/Ntj/66KNqSzpIhdEIvPoFh5qkCv/ZpC/t52b6v//7P7i6umLfvn344osvsGDBApGb7uOPP0ZsbCwOHjyI6dOn4/jx4xg4cCCGDx+OY8eO4YcffsCuXbvw0ksvsXPGjRuHixcvIj09HatXr8bXX3+tTLHMWciFCxe48vJyjuM47sSJE1xCQgJXVFTE7dy5kxs8eDB35coVLi8vjxsxYgT366+/chzHceXl5VxycjL366+/cmVlZdzChQu5CRMmsGt+/vnn3Msvv8zduXOHO3LkCJeQkMBdvHiR4ziOO3fuHJeYmMhlZGRwd+7c4VJTU7lvvvnG0mLbHKPRyGVlZXFGo1HuosiGs7eBres/aNAgDuZnHpednc3dvHmTu3r1KsdxHDdhwgS2b8eOHTa5X2OxZf3Pnj3L6jd8+PA6j/3jjz/Ysa+//nqj790YbN0HPvroI1a3L774okHnbNy4kZ3zwgsv2KQcDcXW9Z8zZw6ry3//+98GnWMymbhevXqx89LS0mxSlobCt8GfB4wc+tT/b+shk83LkJCQwN17772cyXT32m+99RZ37733chzHcZGRkdwjjzwiOuepp57iUlNTRZ/t3LmTc3Fx4fR6PXfmzBkOALd37162/9SpUxwAbsGCBdXqL+c4YLFFJioqCu7u7gDMJmCDwYD8/HysX78eI0aMQEREBJo2bYp//OMf2LBhAwCzytbpdBg2bBg8PDzw3HPP4eTJk8wqs379eqSmpsLHxwedO3dGfHw8Nm3aBADYuHEjBgwYgPbt28PHxwcTJkxg1yUIR4K3yHh5eaF58+YICAhAeHg4AKB79+7sOEeMk2lIoC+PI1tkhNbopKSkBp0TFxfHXDC1xclwHIfDhw+jsLCw0WW0J9ZYZDQaDd544w3297p162xeroaQ08BwrZwC+9y/R48ebBYXAPTs2RPnzp2D0WgEUL09Dx48iBUrVsDHx4f9GzhwIEwmEy5cuIBTp07B1dVVdF67du1YUkolYdVaS/PmzUNaWhrKy8uRkJCA6OhoXLhwAcnJyeyYmJgYNrc/KysLbdq0Yft0Oh0iIiKQlZUFb29vFBQUiPbHxMQwM3NWVpZo+t0999yDa9euoaysTBRHwGMwGKoFMrm6ujLxZSt486VcZkwl4OxtYMv6G41GNmspOjoaHMeJXLN8HARgjpNRQpvbsv5CIdOhQ4c6rxkWFgZvb2+UlJTg9OnTsraFLdvgzp07LFtxdHQ0oqOjG3RdLy8vdO/eHbt378aZM2eQnZ2N5s2bi4758ssvMWnSJLRo0QJnzpyBh4dHo8sL2P4ZwK+15ePjgzZt2jT4uomJidBoNOA4Dvv27ZO0T/D3ah5gQkOiNUICOJhMtncvcRwnqnfV78bLy6va/tTUVLz88svVrtWyZUv2klD1ulU/s/c4UFf6AR6rhMzUqVPxxhtv4MCBA8jMzAQAlJaWwsfHhx3j7e3N5vXr9fpqK7R6e3tDr9ejtLQUWq1WJErqOpe/h16vr1HILF++vFrisMcffxxPPPGENVWtlytXrtjlumrC2dvAFvW/du0ai/0KDQ3FpUuXRPt9fHzg4eGB8vJy7Nmzp9p+ObFF/fft28e2AwMD661fq1atcOLECVy4cAFnz5612cBsLbZoA2FG5169euHy5csNPrdjx45shtvvv/+OgQMHsn2VlZWYN28eK+fWrVtFVi1bYIv637x5k9W5ffv2Fl8zJiYGZ86cwbFjx3D69Ok646zsQWTAFTQPCEfuLS04aKrt14BD80AjWvpfg61/vmVlZdi1a5fod7Np0yZERUXh6tWrqKysxM2bN0X7Y2JicPDgQbi5uVW7Xk5ODpo0aYLKykqsXbsWnTt3BmA2LBQWFla7FmC/cYBfjqIurF79WqvV4sEHH8T333+P6OhoeHl5obi4mO0vKSmBl5cXALMFpmqAYklJCXQ6Hby8vGA0GkUWlrrO5e9RWycdP348xowZI66knSwyV65cQYsWLRqkGB0RZ28DW9ZfGOgbGxuLyMjIasd07doVu3fvxuXLl+Hj44OgoKBG3bOx2LL+vDXKw8MD8fHx9c5W6dSpE06cOAGTyYTy8nKEhobi2rVrohlNBoMB69evR/fu3REWFtao8tWGLdtAuOTCiBEjauwDtZGUlIRFixYBMPcl4bnr1q1j6f4B89u0JdeuC1vWXzgDrVevXhaXsXfv3jhz5gyMRiPy8vJY5l97w7dBVGQLfDnZBU+8B2gAUdCv5v//94tJrohuZZu2F+Lp6Ynr16/jiy++QGpqKg4dOoT//ve/+PjjjxEZGQlXV1cEBgaK2nTmzJno1asXPv74Y0yYMAHe3t44deoUtmzZgi+++AKRkZEYOHAg3n//fXz77bdwdXXFjBkzoNPpRNdSwjhgtZDhMZlMuHr1Klq1aoXMzEz07t0bAHD27FlER0cDMJtJ16xZw87R6/W4evUqoqOj4efnh6CgIGRmZjLfeNVzeasPYE5HHR4eXqM1BgDc3d1tLlrqwsXFxSkHcSHO3ga2qL8wGV6bNm1qvF63bt3YW/fBgwcbHENhbxpb/7KyMpw7dw6A+U28Ib9f4fTsXbt2Yfjw4cjKysIrr7yCzz//HBUVFRg8eDDS09PRtGlT7Ny5UyRybE1j24DjOBYf4+bmhn79+ll0vV69erHtPXv2iM5dunSp6Njr16/b/Pdqi9+AUMh169bN4uv16NGD1XX//v315uCxNS4uLhjR1wWrP+Dw6hfiKdgRzYDPX9ZgeEJ1S42tGDt2LMrKytCjRw9otVq8/PLLeOGFF1jcjEajEbXpfffdh+3bt+Odd95BQkICOI5D69atMXLkSHbcihUrMGHCBCQmJiIkJASzZ8/G9OnTq12Lr78qhExpaSm2b9+OhIQEuLu7Y/v27Th48CBeeeUVhIaG4qOPPsKAAQPg4eGBlStXMstI165dodfrkZaWhoEDB2Lp0qVo3749QkNDAQDJyclYsmQJ5syZg6ysLOzYsQMrVqwAYH7TeP755/Hoo48iIiICy5YtY3kDCMJREFpkhPFiQqoG/CpFyDSWs2fPsoDE+gJ9eYSukSlTpqC8vBwA8MUXX8DPzw8XLlxAeno6ACA/Px8DBgzA7t270aJFCxuX3jacO3eO5U/p06ePyE3fEJo1a4bWrVvj/PnzOHDgAAwGA9zd3ZGTk1Ntdehr167Zqtg2RRjoK4wJaygPPvgg2xa6KqVmeIIGw3oDO4+ZA3tDg4A+nQCt1n4iBjAL4M8//xzffPNNtX1836pKt27d2MSammjevHm1/vPUU081qpz2wCIho9Fo8Ntvv+Gjjz4Cx3Fo0aIFZs+ejTZt2qBNmzY4d+4cxo4dC5PJhEceeQRDhw4FYLaSzJ8/Hx988AHmzZuH9u3bY9asWey6zz//PGbPno2kpCT4+flh6tSpiIqKAmB+qE+aNAmTJ09GSUkJHnroITzzzDO2awGCUABCIdO6desaj+nWrRvb3rRpE15//XXJ4wDsQV7e3VfXiIiIBp0jtK7wIoZHuEI2z9WrV/Hwww9j586daNq0qZUltR/WzFaqSq9evXD+/HmUl5fjyJEj6N69O/7v//6PiUSe7OzsRpXVXvCBvr6+vrjnnnssPp+f2VpcXMyyA8uFVqtB3y6yFsG5kGnat+pRwtx5uXH2NrBl/bt06cIB4LRaLWcwGGo8xmQycQEBASxfRosWLbiVK1c2+t7WYqv6r169mtVp3rx5DTqnvLyc02q17DyNRsM9++yz7G8AnIuLC7dkyRKuTZs2dsuzYqs2GDx4MCvjsWPHrLrG119/za6xYMECzmg0cq1btxa1CQAuJSWlUWUVYqv65+bmsvIlJCRYfZ3ExER2nWvXrjWqTA1FCc/BhIQE7tVXX5Xl3kqov/MGNhCEQuA4jllkIiMja5xFAJgtojNmzGDZXq9cuYIxY8awIE+1UlRUxLYbmqPC3d1dZLmaPn06lixZgvfff5999q9//QvPPvssNm/ezNp0z549tim0jeFjpHQ6XYPda1WpGiezYcMG1q8SExNZv1Gia8ma/DE1oRT3ktRs27ZNcStSSwkJGYKQmYKCAty+fRtA7W4lnldeeQXHjh0T5WyaNWtWNfeKmhAmaWvSpEmDz3vllVeg1WoxcuRIvPfeewCA999/H5s3b8aOHTvwz3/+E4A5iSc/hTMzM1OUn0cp8N+/v7+/KKmZJcTGxrLYml27dmHKlCls34svvshyyyjRtSSM4ahrna36EAqZvXv3IjMzE4sWLWLLPhCOCQkZgpCZhsTHCGnfvj3WrVuHYcOGATAPTN99953dymdvrLHIAObBubi4GKtWrWLWBo1Gg/79+1ebessHUJeUlIimIisFvg0sEXJV0Wq1LCA8OzsbZ86cAWDO/Dt8+HCWJTo3NxeVlZWNLLFtuXXrFttuTFoBoZD5v//7P8TGxuKFF15A9+7dFZ/VmLAeEjIEITOWChmet956i23Pnz9fEdl+rUEoZCwdyGtLw1AV4UwwYToHJWA0GnHnzh0AgJ+fX6OuJXQvAWZht3DhQmg0GpZLh+M45ObmNuo+tkYoZAICAqy+TmhoKJuZlpubyyyVFy5cwDPPPKNIaxzReEjIEITMWCtkevbsySwPp0+fRlpams3LJgXWupYsQclChhcxQOPrL1zOBTAnCOWnMguTAiotTkbYBxq7lk+PHj3YtouLC8sMv2bNGixcuLBR1yaUCQkZgpAZYXwAnwiyoQitMnxaBLVhrWvJEpQsZPj4GKDxQka4cKCvry/mzJnD9gmFjNLiZGxlkQGAl19+Gf7+/ujSpQv++usvrFq1iu174403cPjw4UZdn1AeJGQIQmaEb6OW5jhJTk5ms1z27NmDMWPGoKSkBBzH4dixY9ixY4fixU1jXEsNRclCxpb1DwwMxIwZMxAVFYUVK1aIFo/kY2QA5QkZW1pk+vTpg/z8fBw6dAg9evTAkCFD8OabbwIwrztVNdMxoX5IyBCEzAjfyH19fS06V6PRiBLAff/997j//vsRExODzp07IyEhAdOmTbNZWe0BP4hptVq2xpqtiYyMZAHBjixkAOC9997DhQsXMHz4cNHnarDIaLVai7Ma1wT/XfMILZcnTpxo9PUJZUFChiBkRhgjYc1DfNiwYfj555+ZCDp79qxosP7oo4/w119/Nb6gdoIfyBsz9bg+3N3d2SJ3SpuCLRQyjQ32rQslx8jwQiYgIMAufSAwMJBZpzIyMmx+fUJeSMgQhMzwQsbHx8fqRdeGDx+O/fv3o0OHDgDMQY58Pg6O4zBu3DiUlpbapsA2hrfI2MutxMOnvb99+7ai8opI4VoDlG2R4fuAvWKkALDfRn5+Pm7cuGG3+xDSQ0KGIGSGdy1Z6laqStu2bXHgwAH8+eefyM7OxrFjx9gMjszMTEW6mDiOE1lk7IlS42RsGexbFwEBAfDw8ACgLCFjMpmYkGlsoG9d8EIGIKuMo0FChiBkxlY5RABzXpWHHnoIISEh0Gq1WLFiBcu1snDhQsXFB5SWlrJFDe1tkREKmXPnztn1XpYglUVGo9GwgF8lCZk7d+4wV58UFhkAOHnypN3uQ0gPCRmCkBGO45iQaaxFpibatm2L6dOns7/Xr19v83s0BilyyPAo1SIjlZAB7rqXbt68Cb1eb9d7NRRbTr2uC+HSB2SRcSxIyBCEjJSWlrKMvPYQMgDw2GOPse3t27fb5R7WIkUOGR41CBl7BvsC4jiZnJwcu96rodhy6nVdkGvJcSEhQxAyIpyxZK9BLCYmBiEhIQDMiwnyrhwlIKVFplWrVmxGjFKFjFQWGUA57iWpLDIBAQEIDQ0FYBYySpq5RjQOEjIEISNCIWMvi4xGo0F8fDwAc2Dp0aNH7XIfa5DSIuPh4YGWLVsCUJaQkSrYFxAnxVPKFGyphAxw1ypTUFBAM5ccCBIyBCEjjUmGZwm8kAGAHTt22O0+liKlNQK46166desWbt68aff7NQRnt8hI5VoCyL3kqJCQIQgZkcK1BAAJCQlsW0lxMlK6lgBlxsnwQsbNzY1Nj7YXShQyclhkAJq55EiQkCEIGZHCtQSYH+CBgYEAzBYZPsBYbqR0LQHKnILNt0GTJk3sltmYR4lCRkqLDM1cckxIyBCEjEjlWnJxcUGfPn0AmKfeKuVtVGrXUlRUFNu+evWq3e/XEIRCxt4ocZkCuSwyJGQcBxIyBCEjUrmWAHGcjFLcS1K7lpRmkeA4jolZKerv4+PD+pkS6g9IK2T8/f1ZH6CZS44DCRmCkBGpXEuAOE5GKQG/UruW+Om3gDLyqEiZ2ZiHH8izs7MVMZBL6VoC7lplbt68idzcXLvfj7A/JGQIQkakci0BQOfOndk9lCJkpLbICIWMEiwSUibD4+GFTElJiUhIy4XQIiOFkBHGyZw5c8bu9yPsDwkZgpARKV1Lrq6u6NKlCwDg+vXrKC4utuv9GoLUMTKenp7MfaEEi4zU9QeUl0uGF7M+Pj5wdXW1+/34XEKAMupPNB4SMgQhI1K6lgCxRUIJZnV+INfpdHB3d5fknkpyrUiZDI9HaXFCvEXG3vExPEoTckTjISFDEDIipWsJAJo3b862r1+/bvf71Qf/Ni7VIA7cFXNlZWUii4gcyGGRUZqQ4fsACRnCWkjIEISMSOlaApQnZPiBXIrYCB4lDeRyCxm5B/KysjKUlZUBkK4PCIWM3N8/YRtIyBCEjAiFjI+Pj93vp6RZO0ajkdVfSouMklaAliPYV0kDuZRTr3mEvwG5hRxhG0jIEISM8K4lnU4nSaCjkiwycsSHAMqauSS3RUbu+ks99RowB3wHBQUBICHjKJCQIQgZ4S0SUr2NK0nISJ1DhkdJA7kcYk7YB+SuvxwWGeCuVSo7O1sxy3UQ1kNChiBkhBcyUgT6AmJrhNxCRuocMjxKcq/JYZHx8PBA06ZNAchvkZDDIgPcFTIVFRUoKCiQ7L6EfSAhQxAyIUxPL5WQCQoKglarBaCsQdxZLTJyxMgAdwfynJwcWS0ScllklBTwTDQeEjIEIRPl5eWorKwEIN0gptVq0axZMwBkkQGUJebkCHiW2yIht2sJICHjCJCQIQiZkDoZHg8fI5Gbmyvr27hcg7gwu6+SLDJyzdySsw3kdi0BJGQcARIyBCETUifD4+EtEkajUda3cblcS8DdNsjJyZE1uy/fB1xcXCSZfs+jFNeKEiwycotZovGQkCEImZA6GR6PcNaKnK4VuVxLwN2BXK/Xy5rdl7+3n58fNBqNZPdVykAul0VGKUKOsA0kZAhCJuR2LQHyxsnIaZFRSlI8oZCREqW4lpRgkSEho35IyBCETMjlWlKikJHaIqOUpHh8G8hlkQLkHciFFhkphUzTpk3h5uYGgISMI0BChiBkQi7XklJyySjBtQTIZ5EpLy9HeXk5AHnrrwSLjJubG3Q6nWT3dXFxYb8DipFRPxYJGYPBgJkzZyI5ORkJCQlITU1FZmYm2zdr1iz0798f/fr1w/Tp06HX69m5GRkZGD16NOLi4pCamip6eJSVlWH69OmIj4/H4MGDsXHjRtF909LS2D1nzpyJioqKxtSZIBSBElxLSnCrAPIF+wLyDWRyLdEAAM2aNWP5hJQgZAICAiSNEQLuupfy8/OZoCTUiUVCxmg0Ijw8HMuXL0d6ejri4+MxZcoUAMAPP/yAzMxM/Pzzz/j9999x8+ZNrFixAoBZ5Lz55psYNWoU0tPTERsbi/fee49dd9GiRSgqKsL69esxd+5czJs3D5cuXQIAZGZmYsGCBfjkk0+wbt06ZGdnY+nSpTaqPkHIh7O7lniLjEajkbT+gDIsEnK61rRaLesHSgj2lVrIAsoJeCYaj0VCRqfTYcKECQgJCYFWq8XIkSORnZ2NwsJC5OTkIC4uDk2aNIG3tzf69u2LrKwsAMDBgweh0+kwbNgweHh44LnnnsPJkyfZ2+D69euRmpoKHx8fdO7cGfHx8di0aRMAYOPGjRgwYADat28PHx8fTJgwARs2bLBxMxCE9Chh1pISYmR8fX3h4iKtl1sJSfHkyurLw4u53NxcWazcRqORtYGU8TE8FPDrODRqud1jx44hMDAQ/v7+GDJkCBYuXIhbt27B1dUV6enp6NevHwAgKysLbdq0YefpdDpEREQgKysL3t7eKCgoEO2PiYlBRkYGO7dnz55s3z333INr166hrKwMnp6e1cpkMBhgMBjElXR1hbu7e2OqWg0+kZgzLzjm7G3Q2PoLLTLe3t6StaOXlxd8fHxQXFyM69evW31fW9Xfz89P8j4UEhLCthuzcGBj2kA4Y0eONuDFHMdxyMnJQUREhMXXaEz9q1qk5Ko/AFy9elW234HasXf9G/KSY7WQKS4uxty5czFx4kQAQEREBHx9ffHwww9Do9GgW7dueOSRRwCYczV4e3uLzvf29oZer0dpaSm0Wq1IlHh7e6O0tLTGc/mkUXq9vkYhs3z5cixevFj02eOPP44nnnjC2qrWyZUrV+xyXTXh7G1gbf2F5uw7d+4wd6oUBAUFobi4GNeuXWv0fa2tP/8bd3Nzk7TuPH5+frh9+zauXLkiSxucP3+ebZtMJsnbQGgFOnToEIxGo9XXsqb+N27cEP0tdf2FL7cnTpzAgw8+2Kjr0XPQPvVv1apVvcdYJWTKy8sxZcoU9O7dG8OGDQMAzJs3DzqdDtu2bQPHcfjwww/x2Wef4c0334ROp0NJSYnoGiUlJdDpdPDy8oLRaBRZWEpKSuDl5QUA1c4tLi5mn9fE+PHjMWbMGHEl7WSRuXLlClq0aCG5WVwpOHsbNLb+woyy7dq1Q2RkpC2LVyctWrTApUuXcOfOHTRr1syqGSONrT9vOfX19ZW07jwRERE4efIkbty4gZYtW1oVbNqYNhA+k6KioiRvg7Zt27Jtk8lk1f0bU39h/w8ICJC8/p07d2bber3e6vvTc1D++lssZCorKzFt2jQEBwdj0qRJ7PPMzEy8/vrrzHoydOhQfPrppwCA6OhorFmzhh2r1+tx9epVREdHw8/PD0FBQcjMzERsbCwA4OzZs4iOjmbn8jOjAODcuXMIDw+v0RoDmB8OthYtdeHi4uKUnVeIs7eBtfXnRTlgNq1L2YbCOJm8vDxERUVZfS1r6s9xHMrKygCY1z6So/+EhYXh5MmT0Ov1KC4ublTArTVtIIyR8vf3l7wNhDEiOTk5jbq/NfUXhgDodDrJ69+iRQu2nZ2d3ej703NQvvpbfNc5c+agvLwcM2bMEL3B3HvvvVi3bh3Kysqg1+uxdu1atG7dGgDQtWtX6PV6pKWlwWAwYOnSpWjfvj3zUSYnJ2PJkiUoKSnB8ePHsWPHDgwYMAAAkJSUhC1btuD06dMoLi7GsmXLMGjQIFvUnSBkRa5ZS4D8uWSEg1htLyX2Ru4p2HJNv+eROykeL2QBwMPDQ/L7K2HmGmEbLBIyOTk5SEtLw+HDh5GYmIg+ffqgT58+OHz4MF599VXo9XoMHjwYQ4YMQUlJCV577TUAZivJ/PnzsXLlSiQmJuLo0aOYNWsWu+7zzz8PHx8fJCUlYerUqZg6dSp7Q2zTpg0mTZqEyZMnIzk5GSEhIXjmmWds1wIEIRP8QObh4SGpFRGQf+aScBCTS8jInRRP7jaoapGRGmHuFjnq7+3tzaxwNGtJ3VjkWgoNDcWBAwdq3T9//vxa93Xo0AGrVq2qcZ+npydmz55d67kpKSlISUlpeEEJQgXwQkaOt3G5k+LJPYgD8ltkhFYpZ7RIyG2RAcxtUFRUJGtiSKLxOK9DjyBkhnctyS1kyCIjz0AutEjIMZAHBASw+8phkZDbIgPcnYZfWlpabUIKoR5IyBCETPAWGTmSockdI6MEISN3UjyhRUZq1yJgzqjMizm5LTJy9YFmzZqx7dzcXFnKQDQeEjIEIQMVFRXsjZQsMmSRkcu1wsfJ3Lp1S7Q2nhQowbUkTIxYNa8NoR5IyBCEDMg9Y6Vp06ZsW443UeGgSRYZeSwygLwBz0pwLZFFxjEgIUMQMiCcei2Ha8nNzQ1BQUEA5HmAC9/GrUnGZwt0Oh1brNBZLTJyTsFWglWOLDKOAQkZgpABuS0ywN2HeG5urijLqhQoYRAD7g7kOTk5kreBUMgowSIjtZhTgmuJLDKOAQkZgpABJQkZPrOtlChFyPDupdLSUpGVTArknn4NyCtklOBaIouMY0BChiBkQG7XEiB+iEv9NqoUIaOUGBG5LDLCpHhyWmSUECNDQka9kJAhCBkQ5qyoujK8VMj5NqqEQQyQNyme0iwycsbIKGHWErmW1AsJGYKQASUEesoZH6AUIaME14pGo4FWq5X03jxyCjkluJa8vb3h5eUFgCwyaoaEDEHIgBKEDLmW5J2CzVtkPDw8RAvwSomvry+L0XJG1xJwV9CTRUa9kJAhCBkgIaOMQUwJFhm5vn8ePk4mOztb0plbSnAtAXd/BwUFBaisrJStHIT1kJAhCBlQQnwECRl5g335PiBXoC8P3wYlJSWSztxSgmsJELtY8/LyZCsHYT0kZAhCBsgiowwho4QYEbktMnJZpZTSB2gKtvohIUMQMqAEIUPBvuLsvs5ukQGkFTJK+A0AlBTPESAhQxAyoISHuKenJ8th46zTr4G7VhmpY0SUYpGRK5eMUvoAWWTUDwkZgpABJSRDA8TLFEiJUgYx4K5ForS0VJRx2d7wfUBJFhkpc8kopQ+QRUb9kJAhCBlQgkUGuCtkbt++LRpY7I1SBjFAnjgZjuNE06/lRAmuJTc3N8nuWxWyyKgfEjIEIQNKEzKAtG+jer2ebcu1+jWPHDOXKioq2LaSLDJyuJY8PT1ly6MDkEXGESAhQxAy4OxCxtktMkqYfs8j18wtvg/IXX+yyKgfEjIEIQMkZJQjZOSwSCjl++fv37RpUwDyuJbk/v4DAwPZEhFkkVEnJGQIQgaUMpDJZVYXChkluVakci0JLTJy1x+42wbZ2dkwmUyS3FPoWpITFxcXBAcHAyCLjFohIUMQMqAUISOXWV0p8RGAPK4VpXz/PLyQqaioQEFBgST3VIprCbgr6G/cuCHpFHzCNpCQIQgZUMpAJrdrSe63cUCehSOVapEBpGsDpbiWgLu/A4PBgKKiIplLQ1gKCRmCkAESMsoRMl5eXmz6r1RrDSnl++fhsxsDkCSXDsdxiuoDNHNJ3ZCQIQgZUGIODWcVMgDg7e0NwLxwohQoJSEiD19/ACguLrb7/SoqKpgLRwlCjmYuqRsSMgQhA8JkaHLGiPj4+MDLywsACRlAOiGjpOnXgLkf8EghZJSy8jUPWWTUDQkZgpABpayzA9x9iJOQcV6LjNRCRknT7wGyyKgdEjIEIQNKEjL8Q/zmzZuijLP2wmQyMYuEEgYx4O5AThYZadpAKGSUUH+yyKgbEjIEIQNKWTAQEL+N5uXl2f1+SnMrAHctMpWVlSKRYS+UZpGROkZGaX2ALDLqhoQMQciAEi0ygDRvo0pzKwDigVwKi4SSLTLO6Fry8/Nj21KugE7YBhIyBCEDJGTMyL1gJI/UQkZp06/lDPZVQv2l/v4J20JChiBkQElCRhgfIIVrSWlv44C8FhkluJbkjJFRQh8gIaNuSMgQhMRwHCeafi03TZo0YdtSJITT6/VsWwmDGEAWGWd3LZGQUTckZAhCYpQWHyGMD5BCyChtEAPIIiNnsK8SfgOurq6sHCRk1AcJGYKQGKU9xH19fdm2FIGOJGSU1wec3SID3O0DUtSfsC0kZAhCYpQ2iJFFRl4howSLDJ/dGSAhQxYZ9UFChiAkhoSMcgcxwDmnX7u4uEg6kCvtNwCQkFEzJGQIQmKU9hCXOocGCRnlWWQAaV0rSu4DJSUlbEFLQh1YJGQMBgNmzpyJ5ORkJCQkIDU1FZmZmWz/8ePHMW7cOPTp0wfJycnYvHkz25eRkYHRo0cjLi4OqampyMnJYfvKysowffp0xMfHY/Dgwdi4caPovmlpaeyeM2fOlCSNOkHYC6UJGWGMDFlknNMiA9yNk3FWIcPX32QyiX6jhPKxSMgYjUaEh4dj+fLlSE9PR3x8PKZMmQIAyM/Px5tvvokJEyZg69at+O6773DvvfcCMP9o33zzTYwaNQrp6emIjY3Fe++9x667aNEiFBUVYf369Zg7dy7mzZuHS5cuAQAyMzOxYMECfPLJJ1i3bh2ys7OxdOlSW9WfICRHaULG29ubrcBNQsZ5XStSChkl1l/qmVuE7bBIyOh0OkyYMAEhISHQarUYOXIksrOzUVhYiJUrV2LIkCHo3bs3XF1d4e/vj4iICADAwYMHodPpMGzYMHh4eOC5557DyZMnmVVm/fr1SE1NhY+PDzp37oz4+Hhs2rQJALBx40YMGDAA7du3h4+PDyZMmIANGzbYuBkIQjqU9jau0WiYe4mEjHNOvwbuCpny8nJUVlba9V7UBwhb4tqYk48dO4bAwED4+/vj5MmT6Ny5M5544gkUFRWhe/fueOONN+Dn54esrCy0adOGnafT6RAREYGsrCx4e3ujoKBAtD8mJgYZGRkAgKysLPTs2ZPtu+eee3Dt2jWUlZXV+AMwGAzVFn1zdXW1+cPCZDKJ/u+MOHsbWFt/YUI4Nzc3RbSfn58fioqKcOfOnQaXxxb1d3d3V0T9hUslFBcX270NhAO5UvqAcCC/c+eOKFFibTjSb0A4c0uK34GjYO/6u7jUb2+xWsgUFxdj7ty5mDhxIgBzavONGzfiyy+/RLNmzfDBBx/g008/xcyZM6HX60U/EsD8o9Hr9SgtLYVWqxWJEm9vb5SWlgJAtXP5twa9Xl+jkFm+fDkWL14s+uzxxx/HE088YW1V6+TKlSt2ua6acPY2sLT+ly9fZttlZWXMjSon/G+pqKjI4vJYWn9hfNydO3cUUf+ioiK2fePGDbu3QWFhIdvOy8tThFWGdy8CwOnTp9G8efMGn2tp/YVLYRQWFiqiDwgH4szMzGpjVn3Qc9A+9W/VqlW9x1glZMrLyzFlyhT07t0bw4YNA2A2kQ8aNAiRkZEAgAkTJiA1NRWA+W2nqqmupKQEOp0OXl5eMBqNIgtLSUkJU8dVz+V9l7UtNjd+/HiMGTNGXEk7WWSuXLmCFi1aNEgxOiLO3gbW1v/48eNsu1mzZuw3IydBQUE4d+4cSktLERERAa1WW+851tZf+Ntt0aKFIupftfwNLZO1bSBs3+joaItEg70Qrrnl7+/foDawtv7C53FUVJQi+oDwO/Dz87N7H3AUlFB/i4VMZWUlpk2bhuDgYEyaNIl93rp1a9Fxwulr0dHRWLNmDftbr9fj6tWriI6Ohp+fH4KCgpCZmYnY2FgAwNmzZxEdHc3OFc6MOnfuHMLDw2v1q7q7u0v6duPi4uKUnVeIs7eBpfUXzrrz9PRURNsJp2CXlJTA39+/wedaWn9hoKe3t7ci6i+cuVVaWmpxmRyhDzSmDRrTB3Q6neLqr9fr7d4HHA0562/xXefMmYPy8nLMmDFDZIocMmQI0tLScPXqVZSVlWHFihXo3bs3AKBr167Q6/VIS0uDwWDA0qVL0b59e4SGhgIAkpOTsWTJEpSUlOD48ePYsWMHBgwYAABISkrCli1bcPr0aRQXF2PZsmUYNGiQLepOELKgxBkbUuaSoUBPZfYBKVfAFtZfiX2AZi2pC4ssMjk5OUhLS4OHhwcSExPZ51988QV69OiBJ598Es8++ywqKyvRo0cPvPHGGwDMVpL58+fjgw8+wLx589C+fXvMmjWLnf/8889j9uzZSEpKgp+fH6ZOnYqoqCgAQJs2bTBp0iRMnjwZJSUleOihh/DMM8/YoOoEIQ9KHMSkzCWjRCHj7u4OrVYLo9HotAnxpFxvSYl9gGYtqReLhExoaCgOHDhQ6/5Ro0Zh1KhRNe7r0KEDVq1aVeM+T09PzJ49u9brpqSkICUlxZKiEoRiUaKQkXKZAuGMFaUMYhqNBt7e3rh9+7Ykb+P8zEqNRgNX10ZNHrUZUloklPgbICGjXpzXoUcQMqHEh7izu5YAadfa4fuAu7u7yEUvJ2SRISGjVkjIEITEKF3IOKNrCZBWyPAWGaV8/4C0MTLCPqCUNpCy/oRtISFDEBKjRCHj7DEywN2BTEqLjFK+f0Baiwxff1dX1wZN9ZcCNVpkOI7DunXrcPToUbmLIiskZAhCYpQoZMgic3cgMxgMdk/Rz1tklBLoC0gbI8P3ASV+/4B6Zi1NmjQJQ4YMQY8ePXDmzBm5iyMbJGQIQmKULmSkipFxcXFRTKArIO0bubNbZJQuZJRokeE4DgaDgeVoW758Ob744gsA5vZcuHChnMWTFRIyBCExShcyUllkPD09FRPoCkg7kCnRIiOHa0kp/R9QvpCZMmUKPDw80LFjR7z77rt44YUXRPv/85//iJa+cCZIyBCExChx5WM5YmSU9DYOkEVGjmBfJfUBJQuZc+fOYcGCBQCAjIwMzJkzhz1HmjZtCsBc5uXLl8tWRjkhIUMQEkMWGeUNYoB0AxnvIgCUI2QBci0pWch89dVXNX4eHx+P9PR09ve//vUvGI1GqYqlGEjIEITEKF3ISBUjo6RBDJBuIKusrGRxDkr5/gF5EuIpqf5arVa0cLFSKC4uZpYWT09P7NmzB9OnT8fkyZOxZs0adOzYkS3pk5WVhQ0bNshZXFlQTqQdQTgJShQy5FqSTsgocXkC4O7ilSaTya5Cxmg0skUzldgHysrKFDVr6T//+Q/7TY4ZMwY9evRAjx49RMe88sor2Lx5MwDzkkFDhgxh+3gLjVKmudsDssgQhMQoUch4eHiwQVUqIaPT6ex6H0uRSsgIY6SU8v0D5uUSpMilo8QFI3mkTIrYEDiOw5dffsn+fvnll2s8Ljk5GdHR0QCAzZs34+LFiwCAQ4cOISgoCO3atcOpU6fsXl65ICFDEBKjRCED3HUv2dO1VFlZyXK0KHUQA6QbyJX0/QN342TsaZFQcv2VJmS2bNmC06dPAzDHw3Tu3LnG41xcXDBhwgT294oVKwAAb7/9NoqKipCZmYm+ffvixIkTdi+zHJCQIQiJUeqDnBcy9rTIqOFtHJDOIqMk1xJwtw3sKWSUmhAREGd35uOY5OSXX35h27VZY3jGjh0LFxfzkL58+XIcOXIEmzZtYvtv3LiBxMREHDt2zD6FlRESMgQhMcLB3M3NTcaSiJFCyCh5ECOLjNgiY6+BXInrLPHwfYDjOFE55eLChQtsu2/fvnUeGx4ejqSkJADA5cuX8eSTT7J9AQEBAID8/Hw89thjMJlMti+sjJCQIQiJEc7YUFJCOD7g12AwiAZbW6LX69m2swoZJVtkeCFTWVkpKqctIatcw7l06RIAczxZUFBQvcc/88wzbJuPiQkICMCJEydw3333AQAyMzOxZ88e2xdWRkjIEITEKHHqKSDNFGyyyKjDIgPYrw3U0gfknrnEcRwuX74MAIiMjGzQS09KSgpLkMfzz3/+E2FhYZg8eTL77Mcff7RtYWWGhAxBSIwahIy93EtqGcSc3SID2G8gV7KQU5JF5ubNmygtLQUAtGzZskHnuLu74x//+Ifo75deegkAMHToUNbffvrpJ4dyL5GQIQiJISFjxlmFjFoGcnsJGeoDDYN3KwENFzIA8Oyzz7Kg37FjxyI0NBQA4O/vj4EDBwIAcnJy8Ndff9mwtPJCQoYgJEapQkaYFI9cS86XEA+QxiKj5D4g5XpT9cG7lQCza6mhxMbG4tdff8UHH3zAVsfmeeKJJ9i2I7mXSMgQhMTwA5nSBjGyyDh3QjxAmoFcLRYpuYWMtRYZwBwr8+6771ZLOjl06FDW5j///LPDrMtEQoYgJEapFhkSMmSRcXaLjJKCfa21yNSFn58fm6LtSO4lEjIEISEcx7F1ZkjIKGsQ8/T0ZDNDnNUiQzEyyrHICIWMpRaZuhC6l1avXm2z68oJCRmCkBAlD2LOHiOj0WgkyWyrZNcKzVpSjpDhXUsajQbh4eE2u+6QIUPYApJbt2612XXlhIQMQUiIkh/izm6RAaRZa8fZp18ruQ8oMdg3LCzMpv3Ez88PXbp0AQCcOHECBQUFNru2XJCQIQgJISFzdxBT2urXgDRCRsl9gBLiKcMiU1ZWhtzcXAC2dSvxxMfHs+1du3bZ/PpSQ0KGICREyYMYWWTEiwbaC2e3yCj5N6AUIXPlyhW2bW8hs2PHDptfX2pIyBCEhCj5Ie7sMTLA3YGsrKzMblNTldwHKNhXGbOW7DFjSUjv3r3Z9s6dO21+fakhIUMQEqLkQYwsMuKBjE8Pb2uc3SKj5D6gFItMY3LINISgoCB06NABAHDo0CG7vbhIBQkZgpAQJQsZ4SBGQsY5E8JRQjxlCBl7Tb0WwruXjEaj6lfDJiFDEBKi5Ie4VqtlA5m93tD0ej3bVlr9AemFDFlklCVmlTJryd6uJcCx4mRIyBCEhChZyAB342TsZZFRch4dQBoho+Q2cPYYGS8vL7btyK4lAOjTpw/bJiFDEESDUbqQ4eNkSMg4p2vF3d0dbm5uAJxz1pKLiwtLC6AEi4yvry+aNGlil3uEh4ejdevWAIC///5bJDDVBgkZgpAQJbsVgLtC5s6dO+A4zubXV3r9pbbIKLEN7O1eVLJFBoAk2Z3rwmQyMSETGRnJls2wB7xVpry8HPv377fbfewNCRmCkBAlv40Cd4WMyWSyy4Nc6YO4s1tkACAwMBAA7JbxVVh/JQsZuSwyN27cYL8Te7mVeB588EG2ferUKbvey56QkCEICVH6INa0aVO2nZ+fb/Prk2tJ+WKuefPmAIDCwkK7uBuE11RiH5AiKWJdSBHoyxMVFVXjfdUGCRmCkBClD+RCIZOXl2fz65NrSflilhcyAFiafFsirD8fj6Mk+D5QWlpqF/dqfZw8eZJtt2rVyq73Elp8hAHGaoOEDEFIiNIHseDgYLZtDyGjdGsEWWSA0NBQtp2Tk2Pz6/O/AXd3d7vGf1gL3wc4jhOlC5AKYaxK165d7XovoZAhiwxBEA2ChIyyB3E+PgSwj2sNUH4fEFpkrl+/bvPr831AiXUH5E+Kd+DAAbZtbyHj4+PD+jwJGYIgGoTSBzF7Cxm+/m5ubop8GxdaI7Kzs+1yD6GYc3V1tcs9GoO9hQzfB5TY/wF511syGAw4evQoACAmJsZuU6+F8FaZq1ev2m19MXtjkZAxGAyYOXMmkpOTkZCQgNTUVGRmZoqOqaysxMiRI/HYY4+JPs/IyMDo0aMRFxeH1NRUkcmyrKwM06dPR3x8PAYPHoyNGzeKzk1LS2P3nDlzJioqKiytJ0EoAjUJGXsG+yrRGgPY360CiAdypYs5e7qWlNj/AXktMidOnGDt88ADD0hyT17IVFZW2q3P2xuLhIzRaER4eDiWL1+O9PR0xMfHY8qUKaJjfvzxR1GaZ8D88HrzzTcxatQopKenIzY2Fu+99x7bv2jRIhQVFWH9+vWYO3cu5s2bxwKPMjMzsWDBAnzyySdYt24dsrOzsXTpUmvrSxCyonQhY+9gX6ULmaCgIBaAam8ho9Q2kMoio9T6y7lMgdCt1K1bN0nuKZwZpVb3kkVCRqfTYcKECQgJCYFWq8XIkSORnZ2NwsJCAOa8A2vWrMH48eNF5x08eBA6nQ7Dhg2Dh4cHnnvuOZw8eZI9KNavX4/U1FT4+Pigc+fOiI+Px6ZNmwAAGzduxIABA9C+fXv4+PhgwoQJ2LBhgw2qThDSo3QhI5VrSYl1BwCNRsMsEvZ2LSm1DShGRj6LjDDQV2qLDKDemUuNctAeO3YMgYGB8Pf3BwB8+eWXGD9+fLUkR1lZWWjTpg37W6fTISIiAllZWfD29kZBQYFof0xMDDIyMti5PXv2ZPvuueceXLt2DWVlZTUmUzIYDCIfNGD2Q9ta/ZtMJtH/nRFnbwNr6i/MoeHm5qa4tgsICGDbeXl5dZbPmvoLLTJKqztPaGgoLl++jLy8PJSXl9c5RdiaNhCKOSW2QdOmTaHRaMBxHK5fv27zPqD0+gvjUur7DQC2fQ7yFhkXFxd07txZkvZp0aIF27506ZLF97T3OODiUr+9xWohU1xcjLlz52LixIkAzKLm8uXLeP/993Hw4EHRsXq9XqRyAbPq1ev1KC0thVarFYkSb29vlJaW1ngub/bT6/U1Cpnly5dj8eLFos8ef/xxPPHEE9ZWtU6uXLlil+uqCWdvA0vqL8yWevPmTUW+Afn7+6OwsBA5OTkNKp8l9eeFnEajUWTdAfFAdvDgQVHMSG1Y0wYuLi6KbYPAwEAUFBTgypUrNu0DHMeJrJJKrL9wXDl69Ci6d+/eoPMa+xwsKyvDiRMnAABt2rRBfn6+3WbOCRG+5GdkZFj9ndhrHGhILh2rhEx5eTmmTJmC3r17Y9iwYTCZTPjkk0/w1ltv1Ri8ptPpqpnoSkpKoNPp4OXlBaPRKLKwlJSUsFVIq57LR5HzC3tVZfz48RgzZoy4knayyFy5cgUtWrRokGJ0RJy9Daypv/DtPioqyu6ZO60hJCQEhYWFKCwsrLN81tSfD9T38fFRZN0BsIX0ALPYsFcbeHt7K7YNwsPDUVBQgPz8fLRs2bLWoGRL619RUcGSzPn6+iqy/sIpz3fu3Km3jLZ6Du7duxeVlZUAgJ49e0rWNsKx8ebNmxbfVwnjgMVCprKyEtOmTUNwcDAmTZoEwCw8Tp8+jddeew2AubOWlJRg4MCB+O233xAdHY01a9awa+j1ely9ehXR0dHw8/NDUFAQMjMzERsbCwA4e/YsoqOjAQDR0dGimVHnzp1DeHh4rWt0uLu7SxpE5uLi4pSDuBBnbwNL6i90e3p6eiqy3YKDg3HmzBncvn0bFRUV9cYyWFN/d3d3RdYdAMLCwth2bm5ug8rpaG0QGhqKY8eOoaKiAkVFRaL8OjXR0PrzAzVgdi0psf5V0/Y3tIyNfQ4eOnSIbXfr1k2ytgkNDYW7uzsMBgOuXLli9X3lHAcsvuucOXNQXl6OGTNmMJXu4+OD9evXY+XKlVi5ciXeffddhIWFYeXKlfDw8EDXrl2h1+uRlpYGg8GApUuXon379sxkm5ycjCVLlqCkpATHjx/Hjh07MGDAAABAUlIStmzZgtOnT6O4uBjLli3DoEGDbNgEBCEdSg/2Bey33hLHccwaodQZK4B9px8LXStK/f4BccCvLdtADf0/NDSU5feR0vUlx4wlwCxA+DgZJbr6GoJFQiYnJwdpaWk4fPgwEhMT0adPH/Tp0wdHjhxB06ZN2T8/Pz+4uLiwoDF3d3fMnz8fK1euRGJiIo4ePYpZs2ax6z7//PPw8fFBUlISpk6diqlTpzJV3KZNG0yaNAmTJ09GcnIyQkJC8Mwzz9i0EQhCKtTwILfXzCWlrzPFI7TI2HrmktFoZK4VJYs5e81cUkP/12q1bGCXcjoyP2PJ1dUVnTp1kuy+wN0p2Ldv30ZRUZGk97YFFrmWQkNDRaqxNh544AH8/PPPos86dOiAVatW1Xi8p6cnZs+eXev1UlJSkJKSYklRCUKRqOFBLoWQUfIgbk+LjBq+f0DcBvYSMkruAy1btsSFCxdw69Yt3LlzB76+vna9H8dxOHv2LADg3nvvrTV0wl5UXXOpY8eOkt6/sSjPQUkQDowaBjISMtIIGSW3gb1cS2qxygkDXqVwt9y5c4fFD4WEhNj9flVRey4ZEjIEISHCB3ld+UnkxF7LFKhBxAHm+mu1WgC2dy2pZSB3ZtcSIH2225s3b7Lt+gKr7YHas/uSkCEICVH6OjuA/ZYpUItFxsXFhQ3k5FpyTteS1BYZuYVMVdeS2iAhQxASooYZK87uWgLuDuQ3btyw6YrAamkDZ561BEjvalGSkCHXEkEQdeLMQkYtgxhwV8iYTCbcuHHDZtdVi0XC19eXJR21pUVGLa41Z3MtCZcpIIsMQRB14sxCRi3WCMB+U7CFa23Vlp1cCQgXz3TGGJmq6w/ZG+HSJXIIGZ1Oh2bNmgEgIUMQRD2oQch4enqyNc1sGeyrJiFjr5lLQiEj9RRbS+HdSzdv3hQJkMagFouUcGB3BtcScNcKlZ2dzRJXqgUSMgQhIWoQMsDdgF9nTIgH2E/I6PV6tq1kiwwgjpPJzc21yTXVYpEBxAO7sO/aAyUIGd4KZTKZbD5bz96QkCEICVGLkOHdSwUFBTYLdlXL2zggjWtJ6RYZe8xcUpOY5YUMx3G4du2aXe8lFDJBQUF2vVdtCN1p9lrJ2l6QkCEIiTCZTKpYawi4K2Q4jhM9ZBsDuZbUJWTsMXNJTRYZKWfyKMEiExERwbavXr0qSxmshYQMQUhE1ZWvlYw9An7V9DYuhZBRk2vJVhYZNVnlpMwlIxQyAQEBdr1XbZBFhiCIelHT26g9hIyaBrFmzZqxhIW2dC0JY2SULmbt4VpS029AyinYvJDx9vaWrV1IyBAEUS9qeogLs/vaauaSmlxLrq6ubM0bZ3UtCdf8ceYYGUA6i4xcbiWAhAxBEA1AOIgp/SHu7K4lAKI8KiaTySbXVKuQsVVSQDWJealiZIRxaHIKmdDQULi4mCUBCRmCIGpE+BBX+iDm7K4l4K6QMRqNNmsDNU2/5vOoAPaZfq30PhAQEMDyKdnTtVRSUsJEvpxCxtXVlfV5CvYlCJkwmUxITU3FgAEDbL7Yny1Q09uovS0ySh/EALFr4dy5cza5pposMh4eHmjSpAkA58wjo9FoWB+4dOmSzaxyVVHCjCUe3r2Um5trsySIUkBChnAYNmzYgMWLF2PLli2YP3++3MWphpoe4uRaAjp16sS2jx49apNrqknIAHfdS7ZyLamtD7Rq1QqA+bdry6UahCghhwyPME7G3rlzbAkJGULxHD9+HPPnzxetR1ITmzdvZtt//PFHtf0mkwkTJ05Enz59kJmZafNy1oeaYmSEbgVbmZnV5FYAxELm2LFjNrmmmqZfA3f7we3bt0VltxY1iXkAiI6OZtvnz5+3yz2UaJEB1BUnQ0KGUDQVFRUYNGgQ3nrrLQwbNgwcx9V67J9//sm2T506Ve2HmJaWhm+++Qa7du3CvHnz7Fbm2lDTQ9zX15clyDpx4kSd7d5Q1OZaio2NZdu2EjJqmn4N2D7gV21itnXr1mw7KyvLLvdQkpBRa1I8EjKEovnrr7+YifOvv/7C9u3bazwuNzcXJ06cEH0mtNAAwLfffsu2t27dauOS1o+agn2BuxaJwsJCmzzU1OZW8PPzY66F48eP2yRGQq2uJcA2cTJq6wNCi4wzCBmyyBCEHVi7dq3o79osKenp6dU+27RpE9u+cOGCyN2UlZUl+Q9VTRYZAOjcuTPbtkWMiNrexoG7bVBSUmKTgUytriXANkJGbb8BEjLqgIQMoWjWrVsn+vuPP/7A4cOHqx0ndCvxbN68mS14uHjx4mrukdqsO/ZCbQ9xW8eIqM21BNi+Dci1pK7fAG+RAyhGRsmQkCEUS1ZWFk6fPg1APPBVnZHEcRy2bNkCwPxwHDRoEADzA+LQoUMwGAxYunRptetv27bNTiWvGTUF+wL2FTJqqD9g+zZQm2vJnhYZNYhZnU7HVkK3l0VGOIlBbiETEhICV1dXACRkCMImCK0xU6dOZVOCf/zxR9HbUVZWFsu8GRcXh2HDhrF9mzZtwm+//cbeJocMGcIeoHJaZNQwiMXExDDBYYtBXG2DGGA/IaPRaFTRBs4eIwPcdS/l5uaipKTE5tdXkkVGq9Uy4UbBvgRhA4TxMSNGjMCkSZMAmKdRL1q0iO0TupX69euHhx9+mP29aNEiTJw4kf09adIkPPjggwCAzMxMSX+sajOru7q6okOHDgCAM2fOiNwi1qBG11Lr1q3h5eUFwDZxQryQ8fT0ZItSKhl7upbc3NwafT0psHecjJKEDHDXvZSfn9/o37xUkJAhFElxcTFz/bRo0QKxsbFITU1lZs/vv/+ezSKpKmRatWqFNm3aADCbR/lFD2NjY5GYmIi+ffuy46W0yqhNyAB3LRImkwknT55s1LXU+Dbu4uKCjh07AjAPYnfu3GnU9fiBQQ0WOcB+riUPDw9VCDlAOiGj0+kUEQAujJNRi1WGhAyhSP7880828A0ePBgajQZNmzZFUlISAPMPbMeOHbh9+zabneTn54euXbsCAJKTk9m1XFxc8Pjjj2P9+vVwcXFBQkIC20dCpm5s6VpRo2sJELdB1Sn+liK0yKgBX19fVlZbWmTU9P3bO5eMEhaMFKLGgF9XuQtAEDUhdCsNHjyYbY8ZM4btW7lyJXbt2oXCwkIAwPDhw5nF5p133kFJSQmCg4PxwgsviNbN6dmzJ9zc3FBRUSFpwK/agn0B2woZNbqWgOpt0LNnT6uvxfcBJbx5NwSNRoNmzZrh8uXLNo2RUUv/B6SzyChFyAiT4pGQIQgrqaiowC+//AIA8PLywkMPPcT2DR06FD4+PiguLsZPP/3Elp3XarV455132HHNmjXDkiVLary+l5cXunfvjr/++gvnzp3DtWvXEB4ebscamVFbsC9g2/WG1OhaAsT5dBor5tRmkQHMcTKXL19Gfn4+Kisr2cuCNQhdS2rBnssU6PV61ieUImTItUQQNmDLli3sLWXo0KEs2BIwi5BHH30UAFBUVIRbt24BAJ566ikWF9MQhHEyf/31lw1KXT9qdC0FBwcjNDQUgHkQb8xSBXz9XVxcoNVqbVI+KeBjZABg7969jbqW2mJkgLsBvxzH1bveWX2oUciEhISwZ5CtLTJKC/QF1OlaIiFDKI4ffviBbY8cObLa/jFjxoj+1mq1ePfddy26R69evdj27t27LSyhdahRyAB3rTIFBQXIycmx+jq8RUZNbiUA8Pf3Z21w6NAh7Nixw6rrVFZWorKyEoC6hIwtA37VGCOj0WiYVebChQs2WaqCR4lCpmXLlmz71KlTMpak4ZCQIRRFWVkZ1qxZA8AcvMsH9wrp16+f6OE6duxYUUBeQ+jRowfb3rNnj5WltQy1Cxmgce4lNcZH8Lzxxhtse86cOVZdQ/j9qyVGBrDtFGy19gFeyBgMBrb2my0QWriCgoJsdt3GEBwczJ6nu3fvRlFRkcwlqh8SMoTs5OXl4Y8//kBZWRk2btyI27dvAwAeffTRGt9cXV1dkZqaCsDsarLUGgOY337atWsHwPyWLUW+BDUG+wK2ixFR49s4z6hRo9hgtmnTJuzfv9/ia6hteQIeW1lkOI5TpWsJsF/ArxItMhqNhk2wqKysFK1Zp1RIyBCyYjQaERcXh6SkJHTu3BmffPIJ21eTW4lnxowZ+O6777Bnzx7RQ8YSePdSZWUlDh48aNU1LEGNwb6A7WYuqdW1BJjF81tvvcX+tsYqo7blCXhsld23srKSxViprQ/Yawq2EoUMYM6AzlN1vTslQkKGkJVTp07h3LlzAICzZ8+ywNvAwED079+/1vO0Wi1Gjx4tGmQtRTiNVoo4GbW6ltq2bcuysNpCyKip7kKefvppNrvtt99+w/Hjxy06X20rX/MILTKNcS2ptf8D9pu5pFQhEx8fD29vbwDAhg0bbBoXZA9IyBCy8vfff9f4+YgRI+yewlwY8CtFnIxaH+Tu7u649957AQCnT58W1cMS1OxaAszf2euvv87+/vXXXy0639ktMmqdfg+ILTJff/21Va7FmlCqkPHw8MCAAQMAmMXrgQMHZC5R3ZCQIWRFKGTGjh0LT09PeHp64sUXX7T7vdu1awd/f38AZotMY6YWNwS1ChngbpxMZWWl1TMZ1Oxa4unevTvb5qf+NxS1xsjYKthXzf0/JiYG/fr1A2D+3vv162f17DUhShUygDgRqTBBqRIhIUPICi9kXFxc8NVXX+HGjRvIzs5ulMuoobi4uLDZSzdu3MCFCxfsej/hyseNSSomB7aIk1G7awkAfHx82HZxcbFF56rVIhMYGMgSTzbGIqPWJSoA8292zZo1bHmTO3fuYNCgQY2excWvAwcoT8gIl3lRepwMCRlCNvR6PYszaN++PXx8fODr64uAgADJyiBlnAz/IFfLysdCGitkKisrmZ9dbYOYEFsJGTXFyGi1WgQHBwOwnZBRo5j19fXF+vXrWexeaWkpNm/e3KhrCoWQMBZJCYSFhaFLly4AzDM7a8ohZTRy2HYY+H2PF7YdNv8tBxYJGYPBgJkzZyI5ORkJCQlITU1FZmYmACAtLQ1PPvkk4uPjMWzYMKxevVp0bkZGBkaPHo24uDikpqaKGqWsrAzTp09HfHw8Bg8ejI0bN4rOTUtLY/ecOXMmKioqrK0voSCOHDnCEoQJTfZSImWcjFqnngKNzyWj1nWWqtIYIaNW1xJwd5C9ceOG1S5YNcfI8Hh5eYkScvKpIqyFFzJNmjRRZJsIZy9VnYb9y3YOUU9w6DcZmPRtMPpNBqKe4PDLdunFjEVCxmg0Ijw8HMuXL0d6ejri4+MxZcoUAOZO+vbbbyM9PR2fffYZ/v3vf+PQoUNs35tvvolRo0YhPT0dsbGxeO+999h1Fy1ahKKiIqxfvx5z587FvHnzcOnSJQBAZmYmFixYgE8++QTr1q1DdnY2li5daqv6EzIijI/p1q2bLGXo3r07s45IZZFR4gOrPpo3b84GM2ssMo4wiAHO6VoC7sbJGAwGqxOkqd0iw+Pr68u279y506hr8UJGadYYnsTERLYtfF7/sp3DiOkcruaJj7+WB4yYLr2YsUjI6HQ6TJgwASEhIdBqtRg5ciSys7NRWFiIxx57DB07doSrqytat26N7t274+TJkwCAgwcPQqfTYdiwYfDw8MBzzz2HkydPMqvM+vXrkZqaCh8fH3Tu3Bnx8fFM/W3cuBEDBgxgrocJEyZgw4YNNm4GQg6Ekf9yWWT8/PzQoUMHAMCJEyesnpHTENQsZIC7VpkbN25Y7GJQc3yEEJ1Ox4Svs7iWANsE/DpKH7CVkCkvL2eiUKlCpmvXrqy/889ro5HDq19wqEmq8J9N+pKT1M3UqIjDY8eOITAwkM384DEajcjIyGDBQllZWaIF/XQ6HSIiIpCVlQVvb28UFBSI9sfExCAjI4OdK4xjuOeee3Dt2jWUlZXV+FZjMBhEb3+AOZmVrX84vL9f6fPr7Ulj24BX+B4eHujQoYNsbdmxY0ecOHEClZWVOH36tGiRwLqwtP7ClY/V2G86duyILVu2AAAOHz7MYgUaUhfhIO7m5qbK+vN4e3ujuLgYJSUlFvWB0tJStu3u7q6qNhAOtJcvX2bPa0vqL3Stqa3+Qvj8KoDZtWTtc1D4MhAcHKzI9vDx8UHbtm1x+vRpHD16FGVlZdid4V7NEiOEA3DlBrD9CIe+XRovZvhA87qwWsgUFxdj7ty5mDhxYrV933zzDYKDg5kA0ev1oi8fMHcGvV6P0tJSaLVakSjx9vZmP/qq5/KmXb1eX6OQWb58ORYvXiz67PHHH8cTTzxhZU3rRi2rg9oTa9qgsLCQJcLr0KEDsrOzbV2sBhMWFsa2t2/fDj8/P4vOb2j9hbOWeNepmhC2044dO9C2bVsADau/sL6VlZWqrD+PTqdDcXExCgsLWd0b0gbCPl5cXKyqNhAKma1bt1Zb26wh9b969Srb1uv1qqq/EKEVJicnx6I+IOTEiRNs28vLS7Htce+99+L06dMwGAz4448/cLGkB4Dges87fiYPrQJL6z2uPlq1alXvMVYJmfLyckyZMgW9e/fGsGHDRPtWr16N9PR0LFu2jJmkdDodSkpKRMeVlJRAp9PBy8sLRqNRZGEpKSlhy6ZXPZc359Zmmh0/fny11ZHtZZG5cuUKWrRo0SDF6Ig0pg3OnDnDtnv37o3IyEhbF6/B9O7dG59++ikA81tSQ8tiaf15S6Gvr6+s9bWWhx56iG3z9W5o/YW/4YCAAFXWn6dJkybIy8tDWVmZRW3AP9MAoEWLFqpqg4EDB+Ltt98GYM5sy5fdkt9AkyZN2HZISIiq6l8bHMdZ1AeE8KEXgDnhnlLbo2/fvmwh36tXr6Jjj/pFDAB0bBsMqapksZCprKzEtGnTEBwcjEmTJon2bdq0iVlEhO6m6Oho1hCAWY1fvXoV0dHR8PPzQ1BQEDIzMxEbGwvAnKqeTwkdHR3NZkYBwLlz5xAeHl5rsJy7u7uk/lcXFxenFTI81rSBcG2j7t27y9qGQlfSyZMnLS5LQ+ovnH7s4eGhyj7ToUMHaLVaGI1GHD9+nNWhofXnUWv9eXircHFxsUVtIIwR8fLyUlUbxMbGwsPDA+Xl5Th06FC1sjek/sLZpmruA8Kx7c6dOxb1ASHCHDIhISGKbQ9h/OKBAwfwwgsaRARzuJaHGuNkNAAimgEJ92ng4iJNmgmLW27OnDkoLy/HjBkzRLkw9u7di48//hiff/65yAQNmAOG9Ho90tLSYDAYsHTpUrRv3x6hoaEAzIl3lixZgpKSEhw/fhw7duxg6ZGTkpKwZcsWnD59GsXFxVi2bBkGDRrUmDoTCkCY8lquQF+eVq1aMQuf0NxrS9S68rUQDw8PtmL4yZMnq8Wi1YWjzFoC7gqZmuLx6kLNs5bc3NxYdudz585ZNXOJZi2JUXIOGSH33XcfS+C5f/9+aLUaLHzFPPZXlSn835+/rIFWK12uLIuETE5ODtLS0nD48GEkJiaiT58+6NOnDw4fPozly5fj9u3beOaZZ9jnc+fOBWC2ksyfPx8rV65EYmIijh49ilmzZrHrPv/88/Dx8UFSUhKmTp2KqVOnIioqCgDQpk0bTJo0CZMnT0ZycjJCQkLwzDPP2K4FCFng/cH8LDc5cXFxQfv27QGYzebCoERbodaVr6vCx8VUVFTUmCCrNhxlxgognoJd1WVeF2rOIwMADzzwANvmU2tYgqOIWVdXV/b9OYOQ8fT0ZFbrU6dOobi4GMMTNFj9gQbhVbxMEc2A1R9oMDxB2oSfFrmWQkNDa108atGiRXWe26FDB6xatarGfZ6enpg9e3at56akpCAlJaXhBSUUDx/4GBoaqgiTamxsLA4ePAiO43Dq1Cncf//9Nr2+o7yNCrMuFxUVieIe6sJREuIB1ueSUfP0a8BsWec5ePCgKMdIQ3CU3wBgtsqUlZU5hZABzHm+Dh8+DJPJhEOHDiE+Ph7DEzQY1ts8O+n4mTx0bBuMhPuktcTwyD+CEE5HRUUF+xFXdUPKBZ9LBgCb+m9LHOUhLhQulrgXHOVtHLCNkFGjRaaqkLEUR7LK8e4lZxIyPML8X1qtBn27AEN7lqJvF8giYgASMoQM5ObmsjTnShQy9oiTISFDFhm1u5bat2/Pyl2bZb4uHOU3ANhWyGi1WknXl7OGqkKmtLQUeXl1JJORGBIyhOQI82koRcjwM+YA+1hkHCHYF7BeyDjS27izupaEAb+ZmZkWB/w6klWOFzKWBnwLES5PoAT3el106NCB9dmffvoJ3t7eaNasGWbOnClzycwou/UIh0SJQqZFixbs4WRvi4wa38Z5hFNPLVkwz5EGMWGCTmdyLQFi95KlAb+OaJEBrLPKcByn+HWWhLi6urK4QWEG4hkzZrBs33JCQoaQHCUKGY1Gw9xLly5davRicFVxlIc4uZacN0YGEM9cstS95EhWOWH2b2ueFUVFRew3oQYhAwBvv/02goKCEBQUJHLFjxs3Drdu3ZKxZCRkCBlQopABxHEywqybtsDZhYwjDWKNnX6t0Wjg5uZm83JJQWMCfh3lNwCILTKWWCZ51BToyzN48GDk5eUhPz8fx44dY7necnJy8Pbbb7O4RzkgIUNIjlKFjD3jZBzlIS4UMoWFhQ0+z5FcS9YKGd4iI1xBW20IA34tFTKO1Aca61pSo5ABwPqti4sLVqxYgaCgIADmrP5V1ziUEhIyhOQoVcgILTLHjh2z6bUdwa0AkGsJaLxrSc3fv6urK+655x4A5lWwLXkLdxQxDzivkBESFhaGZcuWsb//+usv2cpCQoaQnGvXrgEwP8yUNO2wS5cu7I1j/fr1NjWVOspDnFxLjZ9+rWYhAwDBweZ0rgaDwaL6O1IfICFjZujQoXjttdfw4YcfYsWKFbKVg4QMITm8RSYsLExRJvamTZsiPj4egHk9mePHj9vs2o4oZJx11lJjLTJqnHothBcyACzKJeIovwGAhIyQjz/+GCNHjpT1WU5ChpCUsrIy3Lx5E4Cy3Eo8I0aMYNurV6+22XUd5SHu5uYGLy8vAORaApzPtQSYBT+PcAXn+nAkMSsUMpb0AR6hkAkJCbFJmZwZEjKEpAgXGlSikBk+fDh7s/jpp59s5l5yFCED3LXKkGvJOYWM0CJjiZBxpN8AWWSUBQkZB0ev1yM5ORndunVDVlaW3MVRbKAvT1hYGOLi4gAAp0+fttk0bEcJ9gXuChmatdRwIVNZWYnKykoA6v/+hRYZa11LahezjRUyubm5bFsoDAnrICHj4KxcuRIbNmzAgQMH8OKLL8pdHMULGQB4/PHH2fZPP/1kk2s60tsoL2SKi4thNBobdI4juZaEmX0bOv1a7csTCBEOvAUFBQ0+T/gbUGseHZ7GJsTjLTI+Pj7MVUtYDwkZB+f3339n2xs3bpQ9nbQahMzw4cPZtq3iZBxRyAANt0g40tu4m5sbq0ND6+9IFjlrLTK8mPXw8FBUkL812CohHrmVbAMJGQemtLS0mnB54403RGtlSI0ahExERAR69eoFwJwYzxb5ERxVyDT0bdSRXEvAXfdSQ4WM2le+FtLYGBm1C1mgca6liooKNuGBhIxtICHjwPz555+iBygAHDlyBCtXrpSpROoQMgAwatQotv3YY4/h0qVLjbqeIwkZ4cKR1ggZRxjILBUyjuRaamyMjNr7P9C4WUtC8UdCxjaQkFEZeXl5DbaoCN1KkyZNYtvvvPOO1UvPNxa1CJnU1FT06dMHgDkwb/DgwRbN0qmKI7kWrLHIOJJrCbgrZKyJkVH79y8UMtbEyDiCkPHy8oKLi3n4tNQiQzOWbA8JGZVQVFSEf/zjH2jWrBn69u1bo5gxmUzYt28fbt68CZPJhLVr1wIwvwHOnTsXSUlJAIArV67g77//lrT8PLyQ8fb2Fr3VKA0PDw+sWbOGpWPPyMjA+PHjrb6eI1lkyLUkFjINebFwJCHj5ubG+oC1MTJqR6PRsD5AQkZ+SMiogN27d+O+++5jLqGdO3diw4YN1Y6bP38+evTogejoaMybNw/Xr18HAAwYMAA6nU4UxLpv3z5pCl8FpWb1rYmgoCCsX78egYGBAIA1a9ZYvVy9owqZhgY6OqprieM4kUipDaGLV+2uJeCuVcZZY2SAu+4lS4WMUPzR1GvbQEJG4Rw9ehR9+/bFxYsXRZ9/+eWXor85jsOSJUsAmK0377zzDtuXkpICAOjRowf7bO/evXYqce0UFxezgS88PFzy+1tDmzZtMHr0aPb3kSNHrLqOowoZa1xLap96C1i+ArYjWWSAuwPwrVu3UFFR0aBzHMm1BFgvZPhAXwDsJYloHCRkFM7ixYvZg6J3796IjIwEAPzxxx84c+YMO+7s2bM4f/58jdcYMmQIAKB9+/bsASyHkFF6Vt/auP/++9n2oUOHrLqGIw1kjXEtubu7K94S1xCEQqa0tLTe4x3p+wfEcTINSYzIcZzDCpni4mKLMoALrbokZGwDCRkFw3Ec0tLSAJjfYtetW4eXX36Z7f/Xv/7Ftvl4GADo2rUr246Li0Pz5s0BAFqtFt26dQMAXL16la1CLRVqCfStilDIHDx40KprOLtFRihkHAFnFzJCl4jQwlAbfFZjQP39n4dPisdxXIP6AI+wvQICAmxeLmeEhIyCOXbsGC5fvgwASExMhJ+fH5555hmWCXLFihXMVbNu3Tp23sqVK7Fu3TpMnDgRS5cuFV1T6F6SOk5GrUKmQ4cObAC21iLjSLN2rJl+7WjxEZa6lhw1RgZAg+LGHKn/81g7BZssMraHhIyC4a0xADB06FAAZgX/1FNPATD/eJYtW4aioiLs3LkTANC6dWvExMQgOTkZX331Fdq2bSu6ppxxMmqN1ndzc0OnTp0AmF141qQk5x/kbm5ubNqmWmmMRcZR3sbJImOZRcaRLJI8QiHT0Gn4AFlk7IG6n6gOjjAPDB/nAgAvvfQS254xYwYWLVrETLdDhgypMwbhwQcfZNtSW2SEM1yEb/VqgHcvcRyHo0ePWny+I8UHNGbWkqO8jTt7sK/QIkNCxnqLDAkZ20BCRqHk5ORg//79AIDOnTuzIF8AiI2NxZgxYwCYZyi99dZbbN/gwYPrvG5ISAiioqIAAPv37xf5ru2NcNATLrqmBoRxR9bEyfADmSMMYtYsmOdoriXhwpENscg4mmtJaJFpiGvJ0fIIAdZbZPj28vLycpi2kBsSMgpFGLzLT58W8vXXXyM6Olr0mY+PD+Lj4+u9Nm+V0ev1+PvvvzFjxgw899xzjcpc2xCE11ebkGnszCVHssi4urqygZxcS2SRsdQi4yhitrGuJYqPsR2ucheAqBmhW4mPjxHi5+eHVatWoVevXsyqMmDAgAYNFD169MAPP/wAABg4cCAzi/r5+eHTTz8FYM4SnJubi+bNm9tsuqzQIiN0T6iB2NhYuLq6orKy0umFDGD+/kpKSmjWEihGhlxL1rmWyK1kO8gio0CEq1Y3b95c5NYQ0q1bN8ybN4/9PXLkyAZdXxjwK/wBrly5komiiRMnIiwsDGPGjLEoR0JdqNm15OnpidjYWADAyZMnLZpuCTiekOFjnBoiZEwmE8uFRELGMYQMxchYZ5HR6/WsL5BFxnaQkFEg27dvZ5198ODBdc5yee211/Ddd99hxYoVeOKJJxp0/S5duogGFN7ikpubi82bN+PkyZNYtGgRAOD777/HggULrK2KCKFrScnrLNUG714ymUw4duyYRec6mpDhLWqlpaX1xlkJM786Sv0tFTKOFiPj5+fHMjRTjEzDhQwF+toHEjIKRLiOUnJycp3HajQajB49Gk8//XSDXUAeHh5466234O3tjRdeeIGt4QQA//3vf/HJJ5+Ijp86dSoLPG4MvEXGy8tLlWnqrY2TMZlM7EHuCG/jgGUzlxxtnSWALDIajYZZZZw1RkZoVW6okKGp1/aBYmQUyMaNGwGYgyr79etnl3vMmjULM2bMgIuLCwwGAwIDA3Hz5k2sWbMGRqNRdGxFRQVGjRqFQ4cONSq2hR/w1OZW4hG6+CwRMo74NirsB4WFhSJXQ1UccRBz9mBfwBwnk5OTg5s3b9brfnZ011JDY8UoGZ59IIuMwjh//jzOnTsHAOjVq5ddg2J5l5W7uzuLrykrK2OugNdff53NcMrKyqq2UKWl8K4ltQqZdu3ase1Lly41+DxHfIgL+2V9s90cUcg5u2sJuBsnYzAY6g12dcQ+YI1riSwy9oGEjMIQupUGDRok2X35bME87u7umDJlChYvXsw+O3z4sNXX5ziOWWTUNmOJp0mTJsyikJub2+DzSMiQa8kRLTJCK1x+fn6dxzrib6CxMTJkkbEdJGQUBu9WAoCkpCTJ7tujRw+0adOG/f3000+jefPmuPfee6HVagEAFy5csPr6JSUlzPysVouMRqNBSEgIAPFyC/UhfIg7yiAmzMxcn5BxRNeSTqdjMWnO7FriycvLq/NYR+wDZJFRDiRkFERZWRnS09MBmKddd+7cWbJ7azQatvSBl5cXXn/9dQDmOB0+q/D58+etnoqt5mR4Qnghk5eXVy2WqDaEg5ijvI06u0VGo9Ewq4wlFhkXFxe4ujpGaCJZZCzPI0MWGftAQkZB7Ny5k/nSk5KSbJaIrqG88sorWLt2Lfbu3YuYmBj2OZ9B+Pbt2w2aalkTak6GJ4QXMiaTCQUFBQ06xxEf4tbOWnKU+gN3lymwZPVroSVH7VhikXHEPuDm5sbqQhYZeXGMVwMHQa74GB6NRlPjWk3CpRCysrKsepNQczI8IbyQAcxxMg2pi6MLmcLCwjqPdUS3AgCLLDJ8/xeu0aR2hBaZ+kS90GrnSG3g6+uL8vJyipGRGYssMgaDATNnzkRycjISEhKQmpqKzMxMtn/FihXo378/HnroISxcuFDkhsjIyMDo0aMRFxeH1NRU5OTksH1lZWWYPn064uPjMXjwYFGcCACkpaWxe86cOVOUYMtRyM7OxtKlSwEAWq0WAwYMkLlEd2nVqhXbzsrKsuoajuZaAhoe8OvoQsYZXUtAw4UMx3HseRcaGmr3ckmFJRYZ4Sy/Fi1a2K1MUsM/y8giIy8WCRmj0Yjw8HAsX74c6enpiI+Px5QpUwAAu3btwurVq7FixQr8+OOP2LVrF1svyGAw4M0338SoUaOQnp6O2NhYvPfee+y6ixYtQlFREdavX4+5c+di3rx5rONnZmZiwYIF+OSTT7Bu3TrRgO9ITJo0ib21Pf3004rq5FUtMtbgKK6lZs2asW1rhIyjBHpaImSEOTYc6W2cFzIVFRUisVaVgoIC9vIVFhYmSdmkQChk6gt+FwoZPubOEeDjZCyNkdFoNKp+DioNi4SMTqfDhAkTEBISAq1Wi5EjRyI7OxuFhYVYv349RowYgYiICDRt2hT/+Mc/mKvk4MGD0Ol0GDZsGDw8PPDcc8/h5MmT7C1l/fr1SE1NhY+PDzp37oz4+Hhs2rQJgHkWz4ABA9C+fXv4+PhgwoQJIheMI7Bu3Tr89NNPAMzm2vnz58tcIjG2FjKOYpFp6MwlZw/2FVpfmzdvbrcySY1wCnZdA1l2djbbdiQhI6zLtWvX6jz28uXLAMxCXiiA1A7/LDMYDA1yMfIWmSZNmrDZoETjaVSMzLFjxxAYGAh/f39cuHBBlE4/JiYGX331FQDz4Cec2qvT6RAREYGsrCx4e3ujoKBAtD8mJgYZGRns3J49e7J999xzD65du4aysrIa324NBkO1tyNXV1ebm7RNJpPo/9Zy69YtTJw4kf396aefIiAgoNHXtSVRUVFsOysrq1rdG1JWYRyFj4+PoupnCcKHMG+Rqa8uwmRo7u7uqq27EKEYLSoqqrNOQiETEhLiEPUHxNalO3fu1BrzcPXqVbYdGhrqMPX39/eHTqeDXq/HtWvXaq0Xx3HMItOyZUtwHGezhWjlJioqCjt37gQAnD59WrSMSU3wFpnAwECH6Qe2Ggtro661BnmsFjLFxcWYO3cuG4RLS0tFbyje3t5Moer1+momZW9vb+j1epSWlkKr1YpESV3n8vfQ6/U1Cpnly5eLkrgBwOOPP97gBRUt5cqVK1af++eff+Kdd95hb/ZxcXHo3bu3RVljpYDjOPj6+uLOnTs4d+5ctfI1pA34NzLA7GpRWh0binDKNW+dqq/+wrfVkpIS1dZdiNFohEajYfEfddWJz1QNiAc1R0IYK1iV48ePs20PDw+Hqn9ISAguXryIy5cv11qvW7dusRiSZs2aOVT9hTFPu3btQlBQUK3HmkwmJmS8vLwcqh2Axo2FdSGM0awNq4RMeXk5pkyZgt69e2PYsGEAzF+M0LxaUlICLy8vAGYLTNVgqJKSEuh0Onh5ecFoNIosLHWdy9+jtjTf48ePx5gxY8SVtJNF5sqVK2jRokWDFGNVXn/9ddGq0oGBgVi+fLnI+qEk2rRpg8OHD+PatWsIDw+Hq6urRW0g3N+mTRvV+smFoprvl/XVX5hvIjQ0VLV1r0pYWBiuXbuGvLy8Ousk/P126dIFERERUhTP7oSHh7Ntd3f3WttAaCGOjY11mO8fMFskLl68iOLiYgQEBNToNhYGuMbExDhU/Xv16sW2b9y4UWfdhJbL5s2bO0w7NHYstAUWC5nKykpMmzYNwcHBmDRpEvu8VatWyMzMRO/evQEAZ8+eZbEV0dHRWLNmDTtWr9fj6tWriI6Ohp+fH4KCgpCZmYnY2NgazxW+7Zw7dw7h4eG1Bk26u7tLOjPCxcXF4i/v+PHjIhEzaNAg/Pvf/1b0A75Vq1Y4fPgwjEYjrl27JlLJDWkDYYyMv7+/bB2+sTRt2hRarRZGo5FZ0uqrv3CWnU6nU23dq9KyZUtcu3YNubm5MBgMtf4mhUHRzZs3d5j6C9/Gc3Nza62X0LUWERHhMPUHIHpmZWdnizI+8wjf1KOiohyq/vyYBQCnTp2qs27CWLLAwECHagfAurHQZve29IQ5c+agvLwcM2bMECV2Sk5Oxs8//4xr164hPz8fK1euZLlQunbtCr1ej7S0NBgMBixduhTt27dnD4Lk5GQsWbIEJSUlOH78OHbs2MGmHyclJWHLli04ffo0iouLsWzZMllyrNiSzZs3s+2pU6di3bp1ihYxQOMDfh1l1pKLiwubudTQWUuOGOwLiKfRCuNAqsIP5EFBQQ41/VoY7CoUK1Vx1GBfoGF9wFFnLAFmYcYL+FOnTtV5LE29th8WCZmcnBykpaXh8OHDSExMRJ8+fdCnTx8cPnwYvXv3xvDhwzF27Fg8/vjjiIuLw9ChQwGYrSTz58/HypUrkZiYiKNHj2LWrFnsus8//zx8fHyQlJSEqVOnYurUqczF0qZNG0yaNAmTJ09GcnIyQkJC8Mwzz9iuBWRgy5YtbPupp55SRaZPWwoZNc9aAu5Owb5x40aDghYdMY8MYLbI8AhjoIRwHIfr168DcKwcKoC4Pg0RMkIR7CgI3Wu1xUgI+4awzzgCWq0W7dq1A2COk6prGj4lw7MfFrmWQkNDceDAgVr3jx8/HuPHj69xX4cOHbBq1aoa93l6emL27Nm1XjclJQUpKSmWFFWxGAwG7NixA4C5Pe+9916ZS9QwGitkhGZVYcyIGuGnYFdUVNSbnh9wbiFz+/ZtZpFypKnXgOVCJiQkxGHWWeIRWpKd0SIDAO3atcORI0dgNBpx7tw5dOjQocbjyCJjPxzLSacC9u3bx4If+/XrpwprDCAWMtasgs0P+D4+PqrPnyDMJVPfYnmAYybEA8RuhdqEDG+NARxPyAjdREL3kRCj0cjawNHcSoBlQkaj0SjehW4N7du3Z9snT56s9TiyyNgPEjIS8+eff7Ltfv36yVgSy4iMjGSiqzGuJbW7lQDLhYyjxsjUZJHJy8vD66+/zhI8OmoyPMD8Vs1/n0LBJkS4SrojCpmGxMjwfSMsLAxubm6SlEtKhFb1uoQMWWTsBwkZiRHGx6hJyLi7u7O3qca4lpxRyDiTa+mDDz7Ap59+iieffBI5OTkObZHRaDTMvVSba0loqXG0GCFAHMBdU4yMXq9ns/sc0a0EkEVGCZCQkZA7d+5g3759AMz5FNS2eBrvXiooKKg3Lb0Qk8nE1ttR84wlHhIyZgICAli+J17I/PXXXwDMaRoOHz4sEjKOOJDzdSooKBB9zzyOPGMJEIu5miwyQnHjaIG+PK1bt2aWprpmLpFFxn6QkJGQHTt2oLKyEoC6rDE81gb8ChMlOoJFRjjzxJmFjEajYYPz5cuXUVFRwZYWAcwp2x3ZIgOI61STe0loqXFEIQPcbYOioiLRAqGA4wf6AoCbmxubZXvmzBn2jK8KWWTsBwkZCRHGx/Tv31/GkliH8EFUV96QqgitN44gZCjY9y784KzX67Fnzx5RXU+dOuXwQqa+XDKObpEBxJa2qotHOoOQAcDWCjQYDLW+5JFFxn6QkJEQfnExjUaDvn37ylsYKxAORA1NBgc4TjI8HqGQKSgoqPd44aq4jmSRAcSDc1pammjfqVOnHDrYFxAP4jXNXHIGISP8XqvGyThyDhkhwkWPa4uT4S0ybm5u1dYeJBoHCRkJ4YPemjVrpkrTYn1m9NpwpGR4gHkFbH4GV0MsMvxgptFoRKtnOwINFTLu7u4O+RYq/E04q0VG2AZVLbXOYpG555572HZtQoa3yAQEBKgm7YZacKzsTAqHV+Q1rUeiBqy1yDiaa8nV1RVBQUHIz89vkJDhH+aOOP1UODifOXNGtO/mzZssZ1Lz5s0d8uFdXy4Z/jOtVutwIpZHaJWqS8g4i0VGGCcmhH/+q/ElVumQRUYiKisrWSCcWt9MhS4Vay0yjuBaAu62RX5+fp3LFDj69NP6rAx8zIwjupWA+rP78kImNDTU4RYJ5KlLyPCuJX9/f4d4iamNVq1aQafTAQC2b99e7ZlQWlqq+ue/knHMX5YCqbr6sxoRChlrY2Qc5WHGt0VZWZloVlZVhA92R3wjbai7xBmFTGVlJfudOKpbCag9RsZoNLK/HVHEC/Hw8EB8fDwAc8BzVauMcGkfYd4ZwjaQkJGIwsJCtq1WIaPT6ZgQscQi42iuJUA8BbsuUefowY4hISHVXEadOnWqdpyjCpmgoCDmLqzqWsrNzWVv5o4sZAIDA1lSPKFwF05FdsS+X5WkpCS2vXHjRtG+vXv3su2ePXtKViZngYSMRDiCkAHuDkjOPGsJaLh1ytGFjIeHRzWRMmrUqGrHOWIyPMC8onXTpk0BVLfIOEOgL2BuA34VbKGQWbFiBdtOTEyUuliSM3DgQLb9xx9/iPYJhUyPHj0kK5OzQEJGIhxFyPAD+O3bt6HX6xt0jiO7lgDnFjJA9Xo9/PDD1fq4o1pkgLvWuby8PFRUVLDPnUXIAHfXXLp16xZKSkpgMBiYkHFzc8NTTz0lY+mkISYmhrnQduzYwQLdOY7Dnj17AJiff8K1mQjbQEJGIhxFyFgzc8kRXUv1BXnyOMOsDeFSG1qtFh06dKj2sHZkIVNbELwzCRneIgOYrTJpaWnIy8sDADz66KPMauXIaDQa5l4yGAzYtm0bAPPLDN8vHnzwQYcN+pYTalGJcBQhY83MJUd0LQkH75oWy+MRWmQcNeBRKNDatm0LT09PpxIywmnVQlF7/vx5tu3oQoZfUBYAlixZgsWLF7O/J0yYIEeRZEEYJ8O7l8itZH9IyEiEowgZa5LiOaJrSfjgboiQ8fX1dRgRVxWhkOEDfZ1JyAgDv3khw3Ecfv/9dwDmGJIuXbrIUjapGDJkCNv+5JNP2CAeGRmpynXlrOWhhx6Cq6s5PRsf8EuBvvaHhIxEOKKQsdS1pNFo4OPjY5dySY3QIlN1fRkejuOYkGnZsqVDJoQDzLEBPN26dQMAtGvXTnSMswgZ3p104sQJnDt3DgCQkJAgOsYR6d27N/71r39V+/zZZ591KleKn58fevXqBQA4d+4czp8/z+JjAKB79+5yFc2hcZ4eJjPClU/VnBDJmlwyvEXG19fXYR5qXl5eLENnbRaZvLw8lhDOUeNjAGDAgAF49dVX8dRTTyE1NRWA2CLj7+/vcItlCqnJIrN69Wr22WOPPSZ5meTgxRdfFIkZFxcXjB8/XsYSyYPQvfTyyy/j8OHDAMyCPygoSK5iOTSOMaqoAGe2yPBCxlHcSjy8VebatWswmUzV9jtDoC9gHrA+//xz/Oc//2EWt6ioKLYwntB65YjUJWQ0Gg0effRRWcolBy+++CKWLl2Kdu3aYd68eSIXrLMwduxY9rK6YcMGGAwGAORWsickZCTCUYSMNcG+vGvJ0YQM/5CuqKioUdQ5w9Tr2tBqtfjoo4/Qrl07vPfee3IXx65UdS2dPHmSLRwYFxfn8IG+VXnmmWdw6tQpvPHGG3IXRRbCw8Px448/QqvVij6nQF/7QUJGIoRCRs1Bn8KHNr+GUF0YjUaWT0HN9a6J+mYuOcOMpbp48cUXcerUKYwYMULuotiVoKAgNmgdP34cX3zxBdvn6HUnaqZ///6ifgCQRcaekJCRCF7I6HQ6eHh4yFuYRuDh4cHMpg2xyPALpQGOZ5GxRMg4m0XGmXBxcUGrVq0AmPvBokWL2L7hw4fLVSxCZiZOnIjXXnsNANC1a1fExsbKXCLHhYSMRPBCRs1uJR5Llingk2IB6g5yron6pmCTkHEevv7662rLMPTo0cPh44OIuvn0009x/vx5/PXXX9VcTYTtICEjEY4oZEpKSpjbqDaEU5OF2T8dgYYKGRcXF6eLk3A2+vXrh9OnT2Py5MlswOJncBHOTXR0tKqt8GqAhIwEVFZWori4GIBjCBlhwG9+fn6dxzqykBG+bQsXy+PhZy2FhYWxFZIJx8XPzw+fffYZMjMzsWfPHowbN07uIhGEU+AqdwGcAeFaQ44gZIRTsIWuo5pwZCFTl0VGr9eztiG3knMRFRWFqKgouYtBEE4DWWQkQJgMzxGEDFlkzHh4eLAEV1WFjPBvZ5yxRBAEIRUkZCRAOPXaEQJeySJzFz7AMzs7G5WVlexzCvQlCIKQBhIyEuAoyfB4rLXIOGLAK18nk8kkWvn46NGjbJvcDARBEPaDhEwj4Tiu3mMcTcgILTINFTLBwcFwd3e3a7nkQDjlVuhO2rBhA9vu27evlEUiCIJwKkjIWMHWrVvRt29f9OzZE0uWLKn3eEcTMkKLTF2uJaGVwhHdSkDNQqa4uBg7duwAYLbGtG3bVpayEQRBOAM0a8kKKioqsHPnTgDA+fPn6z3e0YRMcHAwNBoNOI6r0yJz48YNFjfiqEJGaJ3ihcyff/6JiooKAEBycjI0Go0sZSMIgnAGyCJjBa1bt2bbzihk3Nzc0LRpUwB1W2QcPdAXEFtk+FwyQrdScnKy5GUiCIJwJkjIWEHLli1Z9s6srKx6j3c0IQPcHcBv3LgBo9FY4zHOIGSEAcxXrlwBx3FYv349APP07MTERLmKRhAE4RSQkLECNzc3lhskKyur3oBfRxQyvFWqoqKixvT8gHMImWbNmjHX0ZUrV5CRkcHao2/fvvDy8pKzeARBEA4PCRkr4Ve7vX37NgoKCuo81hGFzD333MO2z507V+Mx2dnZbNtRhYybmxuzTl28eBGrV69m+8itRBAEYX9IyFiJJXEyjpbZFwDatGnDtjMzM2s8xhksMsDdNZfy8vIwc+ZM9jkJGYIgCPtDQsZKLBEyvEXG29vbYRYPbIhFxtGT4fEMHTq02mdt2rQRiT2CIAjCPlgkZBYtWoTHH38c3bp1wx9//ME+NxgMmDVrFvr3749+/fph+vTp0Ov1bH9GRgZGjx6NuLg4pKamijKglpWVYfr06YiPj8fgwYOxceNG0T3T0tKQnJyMhIQEzJw5k01rlZvo6Gi23VAh4yjWGMAyIePh4YHAwEBJyiUHb731FrZu3YoXXngBwcHBAIB3331X5lIRBEE4BxYJmRYtWmDKlCno0KGD6PMffvgBmZmZ+Pnnn/H777/j5s2bWLFiBQCzyHnzzTcxatQopKenIzY2Fu+99x47d9GiRSgqKsL69esxd+5czJs3D5cuXQJgdlksWLAAn3zyCdatW4fs7GwsXbq0kVW2DUIhU9/MJUcUMmFhYSyQtT7XUnh4uEPnUtFoNOjbty+++eYb5OTkoKKiAk8//bTcxSIIgnAKLEqIx/v8ly1bJvo8JycHcXFxaNKkCQDzbI2///4bAHDw4EHodDoMGzYMAPDcc8+hf//+yMnJQWhoKNavX49PP/0UPj4+6Ny5M+Lj47Fp0yY899xz2LhxIwYMGID27dsDACZMmIDZs2fjhRdeqLWMBoMBBoNBXElXV5unx+eDfQGzRcZkMtVantLSUgBmIVPbcWqkdevWOH78OLKysmAwGODqerc7lZaWMgEXHh7uUPXm4eskrJtGo4FGo3HI+lalpvo7G87eBs5ef4DawN71d3Gp395ik8y+Q4YMwcKFC3Hr1i24uroiPT0d/fr1A2C2VghjBXQ6HSIiIpCVlQVvb28UFBSI9sfExCAjI4Od27NnT7bvnnvuwbVr11BWVgZPT88ay7J8+XIsXrxY9Nnjjz+OJ554whZVFREUFISCggKcPXuWWZGqIpzR5O7uXutxaiQ8PBzHjx9HZWUldu/ezaakA8CFCxfYdpMmTRyq3lWpbfq5s+Ds9QeoDZy9/gC1gb3qLzQa1IZNhExERAR8fX3x8MMPQ6PRoFu3bnjkkUcAAHq9Ht7e3qLjvb29odfrUVpaCq1WKxIl3t7ezIJR9VwfHx/2eW1CZvz48RgzZozoM3tYZEwmE1q2bImCggLk5uaiWbNm0Ol01Y4TWodCQ0NFg73a6dSpE4tp0uv1tQqZmJgYh6o3j8lkwpUrV9CiRYsGvTU4Gs5ef4DawNnrD1AbKKH+NhEy8+bNg06nw7Zt28BxHD788EN89tlnePPNN6HT6VBSUiI6vqSkBDqdDl5eXjAajSILS0lJCYu9qHpucXEx+7w23N3dJVtluUWLFjh8+DAA4NKlS8wFJuT27dtsOyAgwKE6utCSdv78eVHdhAHdERERDlXvqri4uDh0/erD2esPUBs4e/0BagM562+Tu2ZmZiIlJQXe3t7w8fHB0KFDceDAAQDmoFhhMKher8fVq1cRHR0NPz8/BAUFifafPXuWBdJWPffcuXMIDw+v1RojNUIrQ20zlxwxGR5PXTOXnCWHDEEQBCEvFgmZyspKlJeXg+M4tm0ymXDvvfdi3bp1KCsrg16vx9q1a1mela5du0Kv1yMtLQ0GgwFLly5F+/btWTbU5ORkLFmyBCUlJTh+/Dh27NiBAQMGAACSkpKwZcsWnD59GsXFxVi2bBkGDRpk4yawnpYtW7Lt2oSMMBleQECA3cskJSRkCIIgCLmxSMjMnj0bcXFxOHz4MN5//33ExcXh0KFDePXVV6HX6zF48GAMGTIEJSUleO211wCYXT3z58/HypUrkZiYiKNHj2LWrFnsms8//zx8fHyQlJSEqVOnYurUqYiKigJgdl1MmjQJkydPRnJyMkJCQvDMM8/YrvaNpKqQMRgM2Lp1K3OBAY5tkWnevDmLYSIhQxAEQciBRTEyM2bMwIwZM2rcN3/+/FrP69ChA1atWlXjPk9PT8yePbvWc1NSUpCSkmJJMSVD6Fo6fvw44uLicODAAaSkpOD3338H4NhCRqPRIDIyEidPnsSFCxdQUVEBNzc3cByH48ePs2N46xtBEARB2BrnjUyyAcHBwSzwePv27SwuaO3atcylJJy907RpU+kLaWd465nRaMTFixcBAHv37sXZs2cBAL1794aHh4dMpSMIgiAcHRIyjUCj0Ygy/PJwHIddu3YBALZu3QrAPAW8a9eukpZPCnghA9x1Ly1ZsoR99uyzz0pdJIIgCMKJICHTSGoSMoDZQpOdnY0zZ84AALp168by4DgSQvdaZmYm7ty5gx9++AEA4OvrixEjRshVNIIgCMIJICHTSO677z62zQc4A2Yhs23bNvb3Qw89JGGppENokVm9ejX+7//+j+X+GT16dLVkiARBEARhS2ySEM+ZefXVV3Hr1i3ExMTgpZdewqZNm3DixAkcOnQIv/32GzsuMTFRxlLajw4dOqBp06bIz8/Hzp07mUsNILcSQRAEYX/IItNIAgIC8OWXX+Lll1+GRqNBQkICAHPa5p9++gmAeQp6r1695Cym3fDy8sIvv/wCX19fAOb4IACIjY1Ft27d5CwaQRAE4QSQkLExffv2Zdv8oN6jR486l1VQO3Fxcfjzzz9FCf+effZZaDQaGUtFEARBOAPkWrIx8fHx1T5z1PgYId26dcO2bdvw3HPPISgoCM8995zcRSIIgiCcABIyNqZZs2a49957cerUKfaZo8bHVKVTp07Yt2+f3MUgCIIgnAhyLdkBPk4GMGcufvDBB2UsDUEQBEE4LiRk7IBQyMTFxVFmW4IgCIKwEyRk7MDAgQPZcgRPPfWUzKUhCIIgCMeFYmTsQEBAAE6cOIFr166hS5cucheHIAiCIBwWEjJ2IiQkBCEhIXIXgyAIgiAcGnItEQRBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWkjIEARBEAShWjQcx3FyF4IgCIIgCMIayCJDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqISFDEARBEIRqcZW7AGrAYDDgww8/xL59+1BSUoK2bdvizTffRJs2bZCWlobZs2fD3d2dHf/TTz+hefPmMpbYttRVfwA4fvw4Pv30U5w/fx6+vr6YPHkyBgwYIHOpbUdd9Z87dy42bNggOrZXr15YsGCBjCW2PXW1gcFgwLx587Bjxw5wHIdevXph2rRp0Ol0chfbZtRV/7KyMnz66afYtm0bNBoNnnrqKTz11FNyF9kuzJkzBzt27EBZWRmaN2+Ol156CX369AEArFixAv/73/9gMpkwbNgwvPLKK9BoNDKX2LbUVv9Dhw7hm2++walTp9ClSxd8+eWXchfVpqSkpGDu3Lno2LGj3EWpGY6ol9LSUm7x4sXc9evXucrKSu6///0vN3ToUI7jOO7333/nXnrpJZlLaF/qqn9eXh6XlJTE7dy5k6uoqOBu3brFXblyReYS25a66l+VJ598kluzZo20BZSAutrgP//5D/fUU09xhYWFXHFxMTdx4kTu66+/lrnEtqWu+v/rX//iJk6cyN25c4fLycnhHnnkEW737t0yl9g+XLhwgSsvL+c4juNOnDjBJSQkcEVFRdzOnTu5wYMHc1euXOHy8vK4ESNGcL/++qvMpbU9tdU/IyOD27BhA7d48WKHHA+GDBnCHTt2TO5i1Aq5lhqATqfDhAkTEBISAq1Wi5EjRyI7OxuFhYVyF00S6qr/ypUrMWTIEPTu3Ruurq7w9/dHRESE3EW2KQ39/i9cuIALFy6gf//+8hTUjtTVBjk5OYiLi0OTJk3g7e2Nvn37IisrS+4i25S66r9nzx48+eST8PHxQfPmzTF06FCsW7dO7iLbhaioKGZ91mg0MBgMyM/Px/r16zFixAhERESgadOm+Mc//iGyVDoKtdW/ffv2SEpKQkhIiMwltC/Hjh3D2LFjkZCQgCFDhmDVqlVs36JFi/Dee+/hrbfeQnx8PMaNG4ecnBxJykVCxgqOHTuGwMBA+Pv7AwCOHj2Kfv364fHHH8fq1avlLZwECOt/8uRJaDQaPPHEExg4cCCmT5+O27dvy11Eu1L1++fZsGEDevfuDR8fH3kKJiHCNhgyZAgOHTqEW7du4c6dO0hPT8eDDz4odxHtStU+wHEc28dxnMMJOSHz5s1DXFwcxo4di549eyI6OhoXLlxgrmYAiImJcdg2qKn+zoKrqyumTZuGrVu3Yv78+fjmm29w+vRptn/r1q0YNWoU0tPT0bJlSyxevFiScpGQsZDi4mLMnTsXEydOBADcf//9WLVqFTZv3oz3338fS5YswdatW2Uupf2oWv+8vDxs3LgRH3/8MX799VcYjUZ8+umnMpfSflStv5A//vgDgwYNkqFU0lK1DSIiIuDr64uHH34Y/fr1g4uLCx555BF5C2lHqta/R48e+P7773Hnzh1kZ2dj7dq1KCsrk7mU9mPq1KnYsWMHvvrqK9x///0AgNLSUpGA9/b2RmlpqVxFtCs11d9ZaN++Pdq1awcXFxe0b98ecXFxOHr0KNvfo0cPdOnSBa6urnj44Ydx7tw5ScpFQsYCysvLMWXKFPTu3RvDhg0DAISHhyMsLAwuLi6IjY3FqFGjHFbI1FR/Dw8PpKSkIDIykpnf//rrL5lLah9qqj/P0aNHcfv2bcTFxclUOmmoqQ3mzZsHnU6Hbdu2IT09Hf7+/vjss89kLql9qKn+zz77LMLCwjBixAi88sor6NevH4KDg2UuqX3RarV48MEHsX//fuzZswdeXl4oLi5m+0tKSuDl5SVjCe1L1fo7C+fPn8fEiRPRv39/JCQkYOvWrSgqKmL7AwIC2Lanp6dkYpaETAOprKzEtGnTEBwcjEmTJtV6nKNF6fPUVv/WrVuLjhOa2B2J+r7/jRs3ol+/fqLZa45GbW2QmZmJlJQUeHt7w8fHB0OHDsWBAwfkK6idqK3+Op0O77zzDv744w+sXr0aGo0G7du3l6+gEmIymXD16lW0atUKmZmZ7POzZ886hcuFr7+zMH/+fNx3331Yu3Yttm/fjsTEREU880nINJA5c+agvLwcM2bMEImV3bt349atWwCA06dP44cffmDTER2J2uo/ZMgQpKWl4erVqygrK8OKFSvQu3dvGUtqH2qrP2Ae4DZv3oykpCSZSicNtbXBvffei3Xr1qGsrAx6vR5r166tJnAdgdrqn5ubi/z8fBiNRuzduxdpaWl48sknZSypfSgtLcWGDRtQWlqKyspK/Pnnnzh48CC6dOmC5ORk/Pzzz7h27Rry8/OxcuVKh3Oz1lV/k8mE8vJyVFZWirYdDd6F6OHhgcOHDyvG+k55ZBpATk4O0tLS4OHhgcTERPb5F198gX379uH9999HWVkZgoODMXbsWIfKoQLUXf8ePXrgySefxLPPPovKykr06NEDb7zxhoyltT111b9Lly7Yu3cvPDw8HNpfXlcbvPrqq5g3bx4GDx4MAOjcuTPefvttuYpqF+qqv9FoxPvvv4/CwkJERUVh7ty5Dula0mg0+O233/DRRx+B4zi0aNECs2fPRps2bdCmTRucO3cOY8eOhclkwiOPPIKhQ4fKXWSbUlf9Dxw4gBdeeIEdGxcXhyFDhmDGjBnyFdjGaDQavPzyy5gzZw6+/fZbPPjgg4iPj5e7WAAADacEuxBBEARBEIqkX79+WL58OVq2bCl3UWqEXEsEQRAEQdQIH+8WGhoqc0lqh1xLBEEQBEFUY86cOdi7dy/eeecduLm5yV2cWiHXEkEQBEEQqoVcSwRBEARBqBYSMgRBEARBqBYSMgRBEARBqBYSMgRBEARBqBYSMgRBWMyBAwfwwAMP4IEHHkB2drbcxSEI4v9jMBgwc+ZMJCcnIyEhAampqaLlI1asWIH+/fvjoYcewsKFC9kSA5WVlXjjjTcwaNAgPPDAA8jPzxdd94knnkCfPn3Yv27duuF///tfreVYtGgR5s6da59KVoGmXxMEISIlJQU5OTl1HtOnTx/ExsYCgGLWlxJmV/39998RFhYmc4kIQnqMRiPCw8OxfPlyNG3aFN9//z2mTJmC3377Dbt27cLq1auxYsUKeHp64p///CeioqLYAqj3338/xo4di/Hjx1e77o8//si2CwsLMWjQICQkJEhWr7ogIUMQhIi2bdsiKCgIAHDjxg3cuHEDABATE8NES0JCAh555BG5ikgQRC3odDpMmDCB/T1y5EgsXLgQhYWFWL9+PUaMGIGIiAgAwD/+8Q9s2LABw4YNg6urK0aPHt2ge2zZsgXt2rVDixYtGnS8yWTCW2+9hSNHjsBoNKJbt26YNm0amjRpguzsbIwYMQJvvPEGvv32WwDAK6+8wpY8aQgkZAiCEPHJJ5+w7UWLFmHx4sXsc97KwbuWgLvWjxkzZmDt2rUIDQ3F888/j2+++QbFxcUYOnQoXnzxRXz11Vf4/fff4evri3HjxmHEiBHsPnl5efj666+xZ88eFBYWIiQkBCkpKRg3bhxcXc2PqePHj+Prr7/G2bNnUVpaioCAALRt2xZTpkzBunXrWDkBsHV++PVu/vvf/2LDhg24fv06SkpK4Ofnh/vuuw8vvfQSIiMjAQBpaWmYOXMmAGDevHlYtmwZLl26hK5du2LmzJnYtm0blixZgrKyMgwYMACvv/46KxvfFpMmTcLJkyexc+dOeHp64rHHHsPzzz9fbaFRgpCKY8eOITAwEP7+/rhw4QKSk5PZvpiYGHz11VcWX3PDhg0WL5KbmJiIWbNmwWg04u2338aSJUswZcoUAEBFRQUuXbqEtWvX4uDBg3jrrbfQr18/eHp6NujaFCNDEIRNyc/Px7x58+Dm5oaSkhJ8//33eOqpp/D777/Dx8cH169fx/z583HhwgUAZjP1uHHjkJaWBr1ej1atWuH69ev49ttvMWfOHADmN7pJkyZh//79cHV1RatWrVBRUYGdO3fi+vXrCAkJQatWrVgZYmJiEBsby948Dx48iCtXriAoKAhRUVG4ffs2tm7diokTJ6K8vLx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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "horizon = 1\n", + "\n", + "# train the model\n", + "model = LinearRegressionModel(lags=2, output_chunk_length=horizon)\n", + "model.fit(train)\n", + "\n", + "# forecast\n", + "pred = model.predict(n=horizon, series=cal)\n", + "\n", + "# plot\n", + "ax = series[pred.start_time() - 7 * 24 * series.freq : pred.end_time()].plot(\n", + " label=\"actual\"\n", + ")\n", + "pred.plot(label=\"pred\")\n", + "ax.set_title(\"First 1-step point prediction\");" + ] + }, + { + "cell_type": "markdown", + "id": "f8a80d6b-2818-4079-b39a-1848a2f049c1", + "metadata": {}, + "source": [ + "Great, we have our single step forecast. But without knowing the actual target value at that time, we wouldn't have any estimate of the uncertainty." + ] + }, + { + "cell_type": "markdown", + "id": "8e5bbfe1-2e10-4675-844d-d965c0371ca3", + "metadata": {}, + "source": [ + "#### Apply Conformal Prediction\n", + "\n", + "Now let's apply conformal prediction to quantify the uncertainty. We use the symmetric (default) naive model, including the quantile levels we want to forecast. Also:\n", + "\n", + "- we don't need to train / fit the conformal model\n", + "- we should supply a `series` to `predict()` that does not have an overlap with the series used to train the model. In our case `cal` has no overlap with `train`.\n", + "- the API is identical to Darts' forecasting models.\n", + "\n", + "Let's configure the conformal model:\n", + "- add a 90% quantile interval (quantiles 0.05 - 0.95) (`quantiles`).\n", + "- consider only the last 4 weeks of non-conformity scores to calibrate the prediction intervals (`cal_length`).\n", + "\n", + "> Note: you can add any number of intervals, e.g. `[0.10, 0.20, 0.50, 0.80, 0.90]` would add the 80% (0.10 - 0.90) and 60% (0.20 - 0.80) intervals" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "358f91ad-770d-4389-bf95-53004d8ec93f", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d89437eb2ec14fa997bdc230faa8e1e5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "quantiles = [0.05, 0.50, 0.95]\n", + "four_weeks = 4 * 7 * 24\n", + "pred_kwargs = {\"predict_likelihood_parameters\": True, \"verbose\": True}\n", + "\n", + "# create conformal model\n", + "cp_model = ConformalNaiveModel(model=model, quantiles=quantiles, cal_length=four_weeks)\n", + "\n", + "# conformal forecast\n", + "pred = cp_model.predict(n=horizon, series=cal, **pred_kwargs)\n", + "\n", + "# plot\n", + "ax = series[pred.start_time() - 7 * 24 * series.freq : pred.end_time()].plot(\n", + " label=\"actual\"\n", + ")\n", + "pred.plot(label=\"cp\")\n", + "ax.set_title(\"First 1-step conformal prediction\");" + ] + }, + { + "cell_type": "markdown", + "id": "3897a238-4543-4542-895f-e2e62dda32bc", + "metadata": {}, + "source": [ + "Great, we can see the added prediction interval (turquoise, dark blue) around the base model's forecast (purple).\n", + "It's clear that the predicted interval contains the actual value. Let's look at how to evaluate this forecast." + ] + }, + { + "cell_type": "markdown", + "id": "80001270-a5af-4514-83ac-5c392b10bf37", + "metadata": {}, + "source": [ + "#### Evaluate Conformal Prediction\n", + "\n", + "Darts has dedicated metrics for prediction intervals. You can find them on [our metrics page](https://unit8co.github.io/darts/generated_api/darts.metrics.html) under the *Quantile interval metrics*. You can use them as standalone metrics or for backtesting.\n", + "\n", + "- `(m)ic`: (Mean) Interval Coverage\n", + "- `(m)iw`: (Mean) Interval Width\n", + "- `(m)iws`: (Mean) Interval Winkler Score\n", + "- `(m)incs_qr`: (Mean) Interval Non-Conformity Score for Quantile Regression\n", + "\n", + "> Note: for `backtest()` use the (m)ean metrics such as `mic()`, and for `residuals()` the per-time step metrics such as `ic()`.\n", + "\n", + "Let's check the interval coverage (the ratio of actual values being within each interval) and the interval width:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9470a0bc-0ac9-407b-9749-0d6ce19e4d7d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IntervalCoverageWidth
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" + ], + "text/plain": [ + " Interval Coverage Width\n", + "0 0.9 1.0 3321.12" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "q_interval = cp_model.q_interval # [(0.05, 0.95)]\n", + "q_range = cp_model.interval_range # [0.9]\n", + "\n", + "\n", + "def compute_metrics(pred_):\n", + " mic = metrics.mic(series, pred_, q_interval=q_interval)\n", + " miw = metrics.miw(series, pred_, q_interval=q_interval)\n", + " return pd.DataFrame({\"Interval\": q_range, \"Coverage\": mic, \"Width\": miw}).round(2)\n", + "\n", + "\n", + "compute_metrics(pred)" + ] + }, + { + "cell_type": "markdown", + "id": "bb765655-53f4-41a2-83cd-96c87c88fc26", + "metadata": {}, + "source": [ + "Okay, we see an interval width of 3.3 MWh, and a coverage of 100%. We would expect a coverage of 90% (on finite samples). But so far we've only looked at 1 example. How does it perform on the entire test set?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "23567754-d132-47d8-aa1c-33a048ff0d28", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0643a2e4c65b46c4967e73a5286e76cf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_historical_forecasts(hfcs_):\n", + " fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 4.3))\n", + " test[: 2 * 7 * 24].plot(ax=ax1)\n", + " hfcs_[: 2 * 7 * 24].plot(ax=ax1)\n", + " ax1.set_title(\"Predictions on the first two weeks\")\n", + " ax1.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.25), ncol=4, fontsize=9)\n", + "\n", + " test.plot(ax=ax2)\n", + " hfcs_.plot(ax=ax2, lw=0.2)\n", + " ax2.set_title(\"Predictions on the entire test set\")\n", + " ax2.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.25), ncol=4, fontsize=9)\n", + "\n", + "\n", + "plot_historical_forecasts(hfcs)" + ] + }, + { + "cell_type": "markdown", + "id": "10b8f9f4-a1f8-42c5-96dd-294440290fca", + "metadata": {}, + "source": [ + "Nice, we just performed a one-year simulation of applying conformal prediction in under 1 second! The intervals also seem to be well calibrated.\n", + "Let's find out by computing the metrics on all historical forecasts (backtest)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "73bf5226-e09b-447d-991d-f6efd71cbb7d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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00.90.9016092908.944092
\n", + "
" + ], + "text/plain": [ + " Interval Coverage Width\n", + "0 0.9 0.901609 2908.944092" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bt = cp_model.backtest(\n", + " cal_test,\n", + " historical_forecasts=hfcs,\n", + " last_points_only=True,\n", + " metric=[metrics.mic, metrics.miw],\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + ")\n", + "bt = pd.DataFrame({\"Interval\": q_range, \"Coverage\": bt[0], \"Width\": bt[1]})\n", + "bt" + ] + }, + { + "cell_type": "markdown", + "id": "36eb467d-adbd-4538-9b11-a2bd4927bd9b", + "metadata": {}, + "source": [ + "Great! Our interval indeed covers 90% of all actual values. The mean width / uncertainty range is just under 3MWh.\n", + "\n", + "It would also be interesting to see how the coverage and widths behaved over time.\n", + "\n", + "The coverage metric `ic()` gives a binary value for each time step (whether the interval contains the actual). To get the coverage ratios over some period of time, we compute the moving average with a window of 4 weeks." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fc72247b-8e34-4a43-b82d-f9f096c9bd37", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_moving_average_metrics(hfcs_, metric=metrics.ic):\n", + " \"\"\"Computes the moving 4-week average of a specific time-dependent metric.\"\"\"\n", + " # compute metric on each time step\n", + " residuals = cp_model.residuals(\n", + " cal_test,\n", + " historical_forecasts=hfcs_,\n", + " last_points_only=True,\n", + " metric=metric,\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + " )\n", + "\n", + " # let's apply a moving average to the residuals with a winodow of 4 weeks\n", + " windowed_residuals = residuals.window_transform(\n", + " transforms={\"function\": \"mean\", \"mode\": \"rolling\", \"window\": four_weeks}\n", + " )\n", + " return windowed_residuals" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "da696430-0bea-4adf-8bb4-5315e4a18ca1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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BpUuX0KNHDxgbG/O29fDwQEpKCm7fvs2L8/z582jZsiVcXV1x8eJFlC5dOt/rAAD//fcfXFxcYGpqCl1dXejp6WHnzp286lyyvqenTp1CyZIl0bVrV17M9erVg5WVlUSV3zp16qBKlSoSMT18+BDdunWDhYUFdHR0oKenh0GDBiEzMxOvX78GkF2NKTU1VeIz0LRpU4mqSqdOnUKtWrVQr149XlwdOnTItWdkkVu3biE5OVmi+pqdnR3c3d2lVlUsDIFAgK5du/KW1alTh/vs56Ugn5tevXpJ7MfLywsGBga83lH379+P1NRUeHt7c8syMjKwZMkS1KhRA/r6+tDV1YW+vj5CQ0N5nx9lePXqFT59+oSBAwdCKPzfz4WpqSl69eqF27dvIykpCQAwdOhQJCcn4+DBg9x2vr6+MDAwwIABAwBkV997+fIlvLy8uHMTv3ZRUVES1dukXTtZr0lQUBBq1aqFGjVq8F4vqoosIut3BgA0btwYfn5+WLRoEW7fvi1RrY+QgqJ7BbpXoHsFulfISZ33Cq1bt8br16/x6dMnxMTE4NmzZ1xTBzc3Nzx8+BA/fvzA+/fvER4ezqsaL82VK1fQpk0blCtXjlumo6ODfv36yR1bft9LlSpVQqlSpTBjxgxs2bIFz58/l/sYmoaSdhUQCATw9vbGnj17sGXLFlSpUkVqeyQAiImJgZWVFQQCAW952bJloauri5iYGG7Z0KFD8fHjR+4HSfRHLGu7HQsLC968gYEBgP91PiI6lvgfFwDo6upKvFaa2bNnQ09PD2PHjsX379/x/ft3JCQkAMjubfP79+9gjCEiIgJ6enq8f6KbgObNm8PY2BgBAQEICwtDREQE90N8584dJCQkICAgAE5OTqhQoQKA7OFigOwbhpz7XbZsGRhjiI2NRUxMDDIyMrB+/XqJ7Tw8PABkt4sTd/z4cSQnJ2P06NHc9crP0aNH0bdvX9ja2mLPnj24desWdzOWkpLC21aW9/Tz58/4/v079PX1JeKOjo6WiNna2loipvfv36Nly5b4+PEj1q1bh2vXriE4OJhrZ5XfZ0Dass+fP+PJkycSMZUoUQKMMYm4xImOIy1WGxsb3udeEYyNjSV6JzYwMJB4P6QpyOdG2nmVLl0a3bp1w+7du5GZmQkguw1p48aNUbNmTW67yZMnY+7cuejevTv8/f1x584dBAcHo27dugXqKMje3h5fv36Vqe1gfu9LVlYW18t8zZo10ahRI/j6+gLIbl+5Z88eeHp6cjesor/NqVOnSly73377DYBs107WaxITEyPzZxfI/zsDAA4ePIjBgwdjx44daNasGUqXLo1BgwbxOushpCDoXoHuFehege4VclLXvQLAb9ceGBgIHR0duLi4AABatGgBILtdu7T27NKIvrdykrYsP/l9L5mbmyMoKAj16tXD77//jpo1a8LGxgbz588vsg/bacg3FRkyZAjmzZuHLVu2YPHixbluZ2FhgTt37oAxxvsx/vLlCzIyMmBpackt69ChA2xsbODr64sOHTrA19cXTZo0kShVKijRH8Tnz59ha2vLLc/IyJDpi/HZs2eIiIiQ+sc4ePBgANlPh21sbBAcHMxbX7VqVQCAvr4+WrRogYCAAJQvXx5WVlaoXbs2nJycAGR/kVy6dAldunThXiu6RuvXr0fTpk2lxlauXDlkZGRAR0cHAwcOxJgxY6RuJ/pxF1mzZg0OHjyITp064dixY2jfvn2+12HPnj2oUKECDh48yHtPxTsLEpHlPRV1BnTu3Dmpx8s5pEnOmzog+4YiMTERR48ehYODA7f80aNHvO3EPwM5RUdH856gW1pawsjICLt27ZIal/hnNyfRcaKioiTWffr0Kc/XqlqpUqXk/txIew8AwNvbG//99x8uXrwIe3t7BAcHY/Pmzbxt9uzZg0GDBmHJkiW85d++fUPJkiXljr9Dhw64cOEC/P398csvv+S5bX7vi1Ao5IZ2EZ3Pb7/9hhcvXuDt27eIiorilQSI3sdZs2bxOksSJ/rbF5F27WS9JhYWFrl+dsXJ+p0h2nbt2rVYu3Yt3r9/j5MnT2LmzJn48uVLrn+ThMiK7hX46F6B7hWkHYfuFZR/rwAArq6u0NHRQWBgIAwMDFC/fn2YmpoCAMzMzFCvXj1cuXIFsbGx0NXV5RL63FhYWEh9wK2sh961a9fGgQMHwBjDkydP4Ofnhz/++ANGRkaYOXOmUo6pTJS0q4itrS2mTZuGly9fcj9C0rRp0waHDh3C8ePH0aNHD2757t27ufUioi+DtWvX4tq1a7h37x62bt2qsJhdXV0BZJcs1a9fn1t++PBhmYahWLt2Lb5//85b9ujRI0yaNAkLFiyAm5sbVwWsYcOGue6nbdu2mDVrFkqUKMFVazMxMUHTpk2xfv16fPr0iVsOAC4uLihZsiSeP3+OsWPH5rpffX19tG7dGg8fPkSdOnWgr6+f7zkZGhri6NGj+PXXX9GtWzccPHgQnp6eeb5GIBBAX1+f92UcHR0t0SMsINt72qVLFxw4cACZmZlo0qRJvjHnFhMAXgkAY0xiqK8mTZrAwMAABw8e5CVZt2/fxrt373g/xF26dMGSJUtgYWEh8UOUn2bNmsHIyAh79uxBnz59uOUfPnzA5cuXJaqAqkLOp7YixsbGcn9uctO+fXvY2trC19cX9vb2MDQ0lKi6LRAIJEpqTp8+jY8fP6JSpUpyH3PYsGFYsWIFpk+fjpYtW/JuskWOHj2Knj17omrVqrC1tcW+ffswdepU7nOTmJiII0eOcD3Ki/Tv3x+TJ0+Gn58f3r59C1tbW97NatWqVVG5cmU8fvxY4sZCHrJeEzc3N6xcuRLPnz/n3cweOHCA91pZvzNysre3x9ixY3Hp0iXcuHGjgGdDyP/QvUI2ulfIRvcKfHSvoLp7BSC7tNrZ2ZlL2kU1BETc3Nxw5coVxMXFoXHjxlxCn5vWrVvj5MmT+Pz5M/cgPDMzk9esTsTAwEBhww4KBALUrVsXa9asgZ+fHx48eKCQ/aoaJe0qtHTp0ny3GTRoEDZu3IjBgwcjIiICtWvXxvXr17FkyRJ4eHjwfnCA7CpSy5Ytw4ABA2BkZFSgdiG5qVmzJvr3749Vq1ZBR0cH7u7uCAkJwapVq2Bubs5r4ypNvXr18tx3ziFActOmTRtkZmbi0qVL+Oeff7jlbdu2xfz58yEQCODu7s4tNzU1xfr16zF48GDExsaid+/eKFu2LL5+/YrHjx/j69ev3BPKdevWoUWLFmjZsiVGjx4NR0dH/Pz5E2FhYfD398fly5cl4tHT08P+/fsxfPhw9O7dG7t375b48hQnGkblt99+Q+/evREZGYk///wT1tbWCA0Nldg+v/f0l19+wd69e+Hh4YEJEyagcePG0NPTw4cPH3DlyhV4enrybuKkadeuHfT19dG/f39Mnz4dKSkp2Lx5M1fVWaR06dKYPHky/vrrL5QqVQo9evTAhw8fsHDhQlhbW/M+AxMnTsSRI0fg6uqKSZMmoU6dOsjKysL79+9x4cIFTJkyJdcbh5IlS2Lu3Ln4/fffMWjQIPTv3x8xMTFYuHAhDA0NMX/+/DzPRxlKlCgBBwcHnDhxAm3atEHp0qVhaWkJR0fHAn1upNHR0cGgQYOwevVqmJmZoWfPnjA3N+dt06VLF/j5+aFatWqoU6cO7t+/jxUrVhR47FNzc3OcOHECXbp0gbOzM8aOHYtmzZpxbd/27NmDx48fo2fPnhAKhVi+fDm8vLzQpUsXjBw5EqmpqVixYgW+f/8u8Z1WsmRJ9OjRA35+fvj+/TumTp0q8T2xdetWdOrUCR06dMCQIUNga2uL2NhYvHjxAg8ePMB///2X7znIek0mTpyIXbt2oVOnTvjjjz9Qrlw57Nu3Dy9fvgQALjZZvzN+/PiB1q1bY8CAAahWrRpKlCiB4OBgnDt3LteaA4TIi+4V+PumewW6VxChewXV3SuItG7dGitWrIBAIMCyZct469zc3LBmzRowxri+avIyZ84cnDx5Eu7u7pg3bx6MjY2xceNGqc31RKXkBw8ehJOTEwwNDVG7dm2Z4z516hQ2bdqE7t27w8nJCYwxHD16FN+/f0e7du1k3o9GUX3fd9pB1l4rpfWOGBMTw0aNGsWsra2Zrq4uc3BwYLNmzeJ6Rc2pefPmDIDU4ZYYy71H2JyxiXpkFB8GKSUlhU2ePJmVLVuWGRoasqZNm7Jbt24xc3NzXm+UspK3R1jGGMvKymKWlpYMAPv48SO3/MaNGwwAbzgpcUFBQaxz586sdOnSTE9Pj9na2rLOnTtLHDs8PJwNHTqU2draMj09PVamTBnWvHlztmjRojzjzsrKYuPHj2dCoZBt3749z3NYunQpc3R0ZAYGBqx69eps+/btbP78+Sy3P8H83tP09HS2cuVKVrduXWZoaMhMTU1ZtWrV2MiRI1loaCi3nYODA+vcubPUffj7+3Ovt7W1ZdOmTWNnz56V+AxkZWWxRYsWsfLlyzN9fX1Wp04ddurUKVa3bl3Wo0cP3j4TEhLYnDlzWNWqVZm+vj431MmkSZN4vYXmZseOHaxOnTrcaz09PSV6/lZEj7AmJiYS20p7PwICApizszMzMDCQ6AW5oJ+bnF6/fs0AMADs4sWLEuvj4uLYsGHDWNmyZZmxsTFr0aIFu3btGnNzc+N9d8jTIyxjjEVHR7MZM2awmjVrMmNjY2ZgYMAqVarERo4cyfXgLHL8+HHWpEkTZmhoyExMTFibNm14wyiJu3DhAnc+OYckEnn8+DE3XI2enh6zsrJi7u7ubMuWLdw2eb3Psl4Txhh79uwZa9u2LTM0NGSlS5dmw4YNY//88w8DwB4/fszbNr/vjJSUFDZq1ChWp04dZmZmxoyMjFjVqlXZ/PnzWWJiYr7XnJCc6F5BOrpXoHuFvNC9guruFURDCeYczo2x7F7shUJhrjHl/E5hLPvvsWnTpszAwIBZWVmxadOmsW3btkn0Hh8REcHat2/PSpQowQAwBwcHxlju1yrneb18+ZL179+fVaxYkRkZGTFzc3PWuHFj5ufnJ9N5ayIBY//f9SYhMrp58yZcXFywd+9erldool3Cw8NRrVo1zJ8/H7///ru6wyFELj4+Pti/fz9iYmIKVWWREJI7ulcgdK9AiOJQ0k7ydPHiRdy6dQsNGjSAkZERHj9+jKVLl8Lc3BxPnjyR6FWTFD+PHz/G/v370bx5c5iZmeHVq1dYvnw54uPj8ezZM6m9xRKiKf744w/Y2NjAyckJCQkJOHXqFHbs2IE5c+bgjz/+UHd4hBQLdK9A6F6BEOWiNu0kT2ZmZrhw4QLWrl2Lnz9/wtLSEp06dcJff/1FP8JawsTEBPfu3cPOnTvx/ft3mJubo1WrVli8eDH9CBONp6enhxUrVuDDhw/IyMhA5cqVsXr1akyYMEHdoRFSbNC9AqF7BUKUi0raCSGEEEIIIYQQDZV3l56EEEIIIYQQQghRG0raCSGEEEIIIYQQDUVJOyGEEEIIIYQQoqEoaSeEEEIIIYQQQjQUJe0KlJWVhfDwcGRlZak7FJXS1vMWp+3XQNvPH6BroO3nD9A10Cba+l5r63mL0Plr9/kDdA0AugbqOn9K2gkhhBBCCCGEEA1FSTshhBBCCCGEEKKhKGknhBBCCCGEEEI0FCXthBBCCCGEEEKIhqKknRBCCCGEEEII0VCUtBNCCCGEEEIIIRqKknZCCCGEEEIIIURDUdJOCCGEEEIIIYRoKEraCSGEEEIIIYQQDUVJOyGEEEIIIYQQoqHUmrRv3boVffr0QaNGjXD+/Plct0tJScHcuXPh6uqKzp0749y5c7z1/v7+8PDwgJubGxYuXIj09HRlh04IIYQQQgghhCidWpN2Ozs7TJkyBTVr1sxzu61bt+LHjx84c+YMlixZgqVLl+Ldu3cAgLCwMKxZswYrV67E6dOn8enTJ+zcuVMV4RNCCCGEEEIIIUql1qTdw8MDTZs2hb6+fp7bnTlzBj4+PjA1NUXdunXh6uqKCxcuAADOnTuHdu3aoUaNGjA1NcXw4cNx9uxZVYTPk5GRAQ8PD4wePRpXr15V+fEJIYQQQrSRv78/vLy8sHPnTsTHxyM+Ph7fv3/Hy5cvER8fr+7wCCEq4Ofnh5IlS+a5zYIFC1CvXr08t4mIiIBAIMCjR48UFpsi6Ko7gPzEx8cjJiYGlSpV4pZVqVIFISEhAIC3b9+iWbNm3LrKlSvj48ePSElJgaGhocT+0tLSkJaWxlumq6ub74OD/GRmZnJV/N+9e8fFpw2ysrJ4/2sjbb8G2n7+AF0DbT9/QPHXQCikbmcIyc+DBw/QvXt3ZGVlYd++fRg+fDhvfalSpXDx4kU0aNBATRESQlShX79+8PDwkOs1Q4YMwffv33H8+HHlBKVAGp+0JyUlQUdHh5eAm5iYICkpCQCQnJwMExMTbp2pqSm3XFrS7uvri+3bt/OW9enTB3379i1UnOLt6D99+sRV39cmkZGR6g5B7bT9Gmj7+QN0DTTt/NPS0gr9UFZeiroGFSpUUMh+CCmuGGOYNWtWng/K4uLi0KlTJ4SEhKBMmTIqjI4QokpGRkYwMjJSdxhKo/FJu7GxMTIzM3kl54mJiTA2NgaQ/QYlJiZy2yckJHDLpfH29oaXlxdvmSJK2hljKFu2LL58+QKhUAgHB4dC7a8oycrKQmRkJOzs7LS2ZEjbr4G2nz9A10B0/ra2tli9ejV27NiByMhIlCtXDj4+Pvj999/x9OlTTJo0Cbdu3YKxsTF69uyJVatWwdTUFOfPn0ePHj3w6dMnXvW2CRMm4MmTJ7hy5QoA4ObNm/j9998RHBwMS0tLdO/eHUuWLOEe3jo5OWHYsGEICwvD8ePH4enpCT8/P8ycORPHjx/Hhw8fYGVlhQEDBmDu3LnQ09PjjrV48WKsX78eycnJ6Nu3LywtLXH+/Hk8ePCA28bX1xcrV65EeHg4HB0dMW7cOIwePRpAdqepo0aNwsWLFxEXFwcrKyv4+Phg5syZKngHCNEu9+/fx5QpUxAUFMQt69KlC1JSUiAUCiEQCLgakF+/fsXGjRuxYMECNUVLCCkIf39/DBw4ELGxsRAKhXj06BGcnZ0xdepUrFixAgAwcuRIxMfHo0OHDpg4cSK+f//OvX7p0qVYs2YNkpKS0LdvX96DuwULFuCff/4BAAgEAgDAlStX4OjoCCC7NvekSZNw584dVK5cGVu2bOFqd3/8+BHjxo3DjRs3kJaWBkdHR6xYsULukn55aHzSbmZmBgsLC4SFhaFWrVoAgNevX8PJyQlA9g1aWFgYt31oaChsbW2llrIDgL6+vtJKXUqWLIkvX74A0M5qjUKhUCvPW5y2XwNtP39AudegYcOGiI6OVsq+82JlZYV79+7JtO2cOXOwY8cOrFmzBi1atEBUVBRevnyJlJQUrh+T4OBgfPnyBcOHD8f48ePh5+eH9u3bo2TJkjh27BiGDRsGILvZ0X///Yc//vgDQqEQT58+RadOnfDnn39i586d+Pr1K8aOHYvx48fD19eXi2HlypWYO3cu5s6dCyD7PTEzM4Ofnx9sbGzw9OlTjBgxAmZmZpg+fToAYO/evViyZAk2bdoEFxcXHDhwAKtWrUKFChW493P79u2YP38+NmzYAGdnZzx8+BAjRoyAqakpBg8ejI0bNyIgIAAHDhyAo6MjIiMjERkZqfV/E4QoWkJCAtq3b4/Y2Fhu2aFDh9CnTx/edu/evUPFihWRmZmJjRs3YsaMGcW6JI4QeRTmniIzMxM6OjoFeq089xSurq74+fMnHj58iAYNGiAoKAiWlpa8h3WBgYGYNGmSxGsPHTqE+fPnY+PGjWjZsiX+/fdf/P3331wOOXXqVLx48QLx8fHcPUTp0qXx6dMnAMDs2bOxcuVKVK5cGbNnz0b//v0RFhYGoVCIefPmQUdHB1evXoWJiQmeP3/O1fZWGqZG6enpLCUlhQ0fPpydOnWKpaSksMzMTInt1q5dyyZMmMASEhLYkydPmJubGwsPD2eMMRYaGsrc3d3Zixcv2M+fP9moUaPY5s2bVXwm2apUqcIAsJIlS6rl+OqSmZnJ3r59K/W90xbafg20/fwZU801sLW1ZQBU/s/W1lam83/y5AkzMDBg27dvl1i/bds2VqpUKZaQkMAtO336NBMKhSw6Opoxxtj48eOZu7s7t/78+fNMX1+fxcbGMsYYGzhwIPPx8eHt99q1a0woFLLk5GTGGGMODg6se/fu+ca7fPly1qBBA26+SZMmbMyYMbxtXFxcWN26dbl5Ozs7tm/fPt42f/75J2vWrBljjLGxY8eyZs2asYyMjHyPT4o2bf3O05Tz3rx5M+87auzYsblu279/f267LVu2FOq4mnL+6qLt589Y8boGmnxPIa5+/fps5cqVjDHGunfvzhYvXsz09fVZfHw8i4qKYgDYixcvmK+vLzM3N+de16xZMzZq1Cjevpo0acL7XR88eDDz9PTkbRMeHs4AsB07dnDLQkJCuONkZmayqlWrsvnz58t1HoWl1pL2RYsW4dSpUwCAhw8fYv78+diyZQu+fv0KX19fHDp0CEB2tYdFixahY8eOMDMzw8yZM7mqC5UqVcLEiRMxadIkJCYmwt3dHUOHDlXXKRFCiNJYWVlp9HHfvHmD1NRUtGnTRmLdixcvULduXV4fJC4uLsjKysKrV69Qrlw5eHl5oVmzZvj06RNsbGywd+9eeHh4oFSpUgCyq8OGhYVh79693D4YY8jKykJ4eDiqV68OILv0IKfDhw9j7dq1CAsLQ0JCAjIyMmBmZsatf/XqFX777Tfeaxo3bozLly8DyK5eGxkZiWHDhmHEiBHcNhkZGTA3NwcADB48GHv27EH16tXRsWNHdOnSBe3bt5fp2hFCZMMYw8aNG7l5UTOY3EyZMgX79+8HAKxevRojRoyg2i+EoHD3FIUtaZdHq1atEBgYiMmTJ+PatWtYtGgRjhw5guvXr+P79+8oV64cqlWrhtu3b/Ne9+LFC4waNYq3rFmzZlxzu/zUqVOHm7a2tgYAfPnyBVWqVMGQIUMwd+5cXLx4EW3btkWvXr142yuDWpP2BQsW5Nq+qFOnTty0oaEhFi1alOt+unbtiq5duyo6PEII0SiyVidTFwMDg1zXMca4NmM5iZY3btwYFStWxIEDBzB69GgcO3aMV+09KysLI0eOxPjx4yX2YW9vz02LPxgAgNu3b+OXX37BwoUL0aFDB5ibm3PV36XFIR6z+LGB7CryTZo04W0nunGpX78+goKCEBISgsuXL6Nv375o27YtDh8+LP2iEELkduPGDTx79gwA0Lx58zwTdgBo0KABd9P/+vVrnDp1Ct26dVNFqIRotILeU2RlZeHdu3dwcHBQyQOwVq1aYefOnXj8+DGEQiFq1KgBNzc3BAUFIS4uDm5ubko5rnifN6L7A9G9QL9+/dC/f3+cPXsWFy5cwF9//YVVq1Zh3LhxSokFUPM47YQQQoqPChUqwMjICJcuXZJYV6NGDTx69IjXceiNGzcgFApRpUoVbtmAAQOwd+9e+Pv7QygUonPnzty6+vXrIyQkBJUqVZL4l1dfJTdu3ICDgwNmz56Nhg0bonLlyhIjfFStWhV3797lLRO/oSlXrhxsbW3x9u1biWOL9/JeokQJ9OvXD9u3b8fBgwdx5MgRXrtbQkjhiDqOAsB1ApmfKVOmcNOrV69WeEyEEOURtWtfu3Yt3NzcIBAI4ObmhsDAQAQGBuaatFevXl2i9D3nvL6+PjIzMwsUl52dHUaNGoWjR49iypQpEqOTKZrGd0RHCCGkaDAwMMD06dMxffp06Ovrw8XFBV+/fkVISAi8vLwwf/58DB48GAsWLMDXr18xbtw4DBw4EOXKleP24eXlhYULF2Lx4sXo3bs3r1PRGTNmoGnTphgzZgxGjBgBExMTvHjxAhcvXsT69etzjatSpUp4//49Dhw4gEaNGuH06dM4duwYb5tx48ZhxIgRaNiwIZo3b46DBw/iyZMnXIc1QHbtsPHjx8PMzAydOnVCamoq7t27h7i4OEyePBlr166Frq4u2rZtC11dXfz333+wsrLi9YZPCCm4lJQU/PfffwCya9T06NFDptd5eHigSpUqeP36NYKCgriRPgghms/c3Bz16tXDnj17sG7dOgDZiXyfPn2Qnp6OVq1aSX3dhAkTMHjwYDRs2BAtWrTA3r17ERISwvtdd3R0xPnz5/Hq1StYWFhwzd3y88cff6Bv376oVq0a4uLicPnyZa6JnrJQSTshhBCFmTNnDqZMmYJ58+ahevXq6NevH758+QJjY2OcP38esbGxaNSoEXr37o02bdpgw4YNvNdXrlwZjRo1wpMnTySG56xTpw6CgoIQGhqKli1bwtnZGXPnzuXamuXG09MTkyZNwtixY1GvXj3cvHmT61lexMvLC7NmzcLUqVNRv359hIeHY8iQIbyHBsOHD8eOHTvg5+eH2rVrw83NDX5+flxJu4mJCbZu3YrGjRujUaNGiIiIwJkzZ6j9LCEK4u/vjx8/fgAAevXqJdEUJjdCoZD3fSJK/AkhRUPr1q2RmZnJJeilSpVCjRo1UKZMmVyT5X79+mHevHmYMWMGGjRogHfv3knUzhkxYgSqVq2Khg0bokyZMrhx44ZM8WRlZWHcuHFcHzZVq1bFpk2bCnWO+REw8UZ7pFCqVq2K169fo2TJkoiLi1N3OCqj6rYtmkjbr4G2nz9A16A4nn+7du1gZWWFf//9V6bti+M1INJp63ut7vPu1q0b/P39AYDrAEpWr169QrVq1QAAjRo1kmgOIwt1n7+6afv5A3QNALoG6jp/7bvShBBCSA5JSUlYvXo1QkJC8PLlS8yfPx8BAQEYPHiwukMrkhYvXowOHTrAzc0N/fr1w7Vr13jrMzIy0K9fP/Tq1Yu3PCQkBP3794eLiwt8fHwQFRXFrUtJScHcuXPh6uqKzp0749y5cyo5F6IZfv78yb3nNjY2aN26tVyvr1q1Kte7s6hZCyGEFBWUtBNCCNF6AoEAZ86cQcuWLdGgQQP4+/vjyJEjcpXkkf/x8vKCv78/goKCMG/ePMydOxfx8fHc+kOHDsHU1JT3mrS0NEyfPh2//PILLl++jFq1amHevHnc+q1bt+LHjx84c+YMlixZgqVLl0p0KEiKrytXriA9PR0A0L179wINNyWqWssYk7kaLCGEaAJK2gkhhGg9IyMjBAQEIDY2FomJiXjw4AF69uyp7rCKLEdHR65Hf4FAgLS0NHz79g0AEBMTg2PHjsHb25v3mvv378PIyAienp4wMDDAiBEj8Pz5c660/cyZM/Dx8YGpqSnq1q0LV1dXXLhwQbUnRtRGvGZFx44dC7QPV1dXbjpn7Q9CCNFk1Hu8AonG8KNuAgghhGi7pUuXwt/fH6mpqXBzc+N67F2/fj28vb15nfwB4IbTEzEyMkL58uXx9u1bmJiYICYmhre+SpUqCAkJkXrstLQ0pKWl8Zbp6urmOTSgPERj9Yr+1xbqPG/RUE1CoRBubm4FiqF58+bc9LVr1+Teh7a+7yLafv4AXQOAroGiz1/WdvGUtBNCCCFE4WbOnIlp06bh3r17CAsLAwA8efIE79+/x/z583H//n3e9snJyRK9gZuYmCA5ORlJSUnQ0dHhJfomJiZISkqSemxfX1+JMXP79OmDvn37KuLUOJGRkQrdX1Gh6vPOyMjgHtBUqFABMTExiImJKdC+KlSogPDwcNy7dw8vX76EkZGR3PvQ1vddRNvPH6BrANA1UNT5i0agyQ8l7YQQQghRCh0dHTRp0gT79++Hk5MTNm/ejBkzZnA108QZGRkhMTGRtywxMRFGRkYwNjZGZmYmUlJSuMQ9MTERxsbGUo/r7e0tMWSgokvaRWN9a1Pvyeo67xcvXnA1J5ydneHg4FDgfbVu3Rrh4eFIT09HVFSUXB3aaev7LqLt5w/QNQDoGqjr/ClpJ4QQQohSZWVl4cWLF3j58iUmT54MAEhPT0diYiI6dOiAEydOwMnJCceOHeNek5ycjA8fPsDJyQlmZmawsLBAWFgYatWqBQB4/fo1V+U+J319fYUl6HkRCoVaedOq6vN+9uwZN123bt1CHdvV1RW7du0CANy4cQNt2rSRex/a+r6LaPv5A3QNALoGqj5/7b3ShBBCCFG4pKQknD17FklJScjIyMClS5dw//59uLq64syZM9i7dy/27t2LOXPmwMbGBnv37oWBgQEaNGiA5ORk+Pv7Iy0tDTt37kSNGjVgbW0NAPDw8MCOHTuQmJiIp0+f4urVq2jXrp2az5aowtOnT7np2rVrF2pf1BkdIaQoopJ2QgghhCiMQCDAiRMnsGzZMjDGYGdnh0WLFvE6kQMAMzMzCIVCWFpaAsguHV++fDn+/PNPLF26FDVq1MAff/zBbT9y5EgsWrQIHTt2hJmZGWbOnAlHR0dVnhpRE0Um7Y6OjrC1tcXHjx9x+/ZtZGVlaXVpISGkaKCknRBCCCEKY2RkhC1btuS7XcOGDXHkyBHespo1a+LAgQNStzc0NMSiRYsUEiMpWkSd0JmYmBT6QY1AIECDBg3w8eNHJCQkICIiItdmFoQQoino0SIhhBBCiByysrJw/PhxnD17Vt2hFHuJiYl4+/YtgOyHOoooFa9Tpw43/eTJk0LvjxBClI2SdkIIIYQQOezbtw+TJ09Gly5deJ2kEcV78eIFGGMAspN2RRCvYi9e9Z4QQjQVJe2EEEIIIXIYPHgwN7179241RlL8iT8UEY0cUFhU0k4IKWooaSeEEEIIKSAdHR11h1CsKSNpr1SpEgwMDABQSTshpGigpJ0QQgghpIDS0tLUHUKxJl4SrqikXVdXFzVq1AAAhIaGIiUlRSH7JYQQZaGknRBCCCGkgKKjo9UdQrHFGMP9+/cBAGXLloW1tbXC9i1qH5+VlYVXr14pbL+EEKIMlLQrkEAgUHcIhBBCCFGinKWylLQrz7t37xAbGwsAaNCggULvs8Q7tRMNKUcIIZqKknYlEPVySgghhJDi5cePH7z5qKgoNUVS/IlK2YHspF2RxJP258+fK3TfhBCiaJS0E0IIIYTIKC4ujjf/5s0bpKenS2wXFRVFpfCFdO/ePW5a0Um7qE07QCXthBDNR0k7IYQQQoiMcpa0p6Wl4fXr17xlgYGBqFChAhwdHfHgwQNVhles3Llzh5tu2LChQvddoUIFGBkZAaCknRCi+ShpJ4QQQgiRkbSexq9evcpNZ2RkYPjw4UhNTUVqairWrl2rwuiKj4yMDNy9excAYGdnh/Llyyt0/0KhENWrVweQXVuCepAnhGgyStoJIYQQQmQkrSr8sWPHAGT3RL5x40a8efOGW7d37158+PBBZfEVF0+fPkViYiIAoHnz5ko5hqiKPPUgTwjRdJS0E0IIIYTISNq47FeuXEFkZCTs7OwwceJE3rqsrCycPHlSRdEVH7du3eKmmzVrppRjUA/yhJCigpJ2QgghhBAZSUvaMzIy0LRpU3z69IlbVrZsWW760aNHqgitWLl58yY3raySdupBnhBSVFDSTgghhBAiI/Gk3cXFhZsWT9gNDAwQGBjIjStOSbv8RCXthoaGqFu3rlKOQT3IE0KKCkraCSGEEEJkJN6mvXXr1hLrx4wZg9jYWFSvXh2VKlUCkN0+OyMjQ2UxFnWfP3/G27dvAWT3Gq+vr6+U41AP8oSQooKSdkIIIYQQGYmXtNvY2MDW1pab19HRwaxZs2BsbAwAqFevHoDsHudDQ0NVGmdRJt6eXVlV4wHqQZ4QUnRQ0k4IIYQQIiPxpF1fXx8zZszg5nv27MlL4uvUqcNNU0mu7MTbsyurEzoR6kGeEFIUUNJOCCGEECKjUaNGcdN6enoYN24cAgMDsWrVKmzZsoW3LbWZLpjbt29z002bNlXqsagHeUJIUaCr7gCKE1GHM4QQQggp/kTt1N3c3ODm5iaxXjxpp97JZZORkYF79+4BABwdHWFlZaXU41EP8oSQooBK2pWAMabuEAghhBCiZAkJCXmur1ixIvT09ABQQiirp0+fIjk5GQDQpEkTpR+PakMQQooCStoJIYQQQmRUoUIFbjq/UmA9PT1UrVoVAPDq1SvqQV4GqqwaD1AP8oSQooGSdkIIIYQQGe3YsQO6urqwtrZGhw4d8t1eVJKbnp6ON2/eKDu8Iu/OnTvctCqSdupBnhBSFFDSrgQ/f/5E06ZNceXKFXWHQgghhBAFcnd3x+fPnxEQEIASJUrku32VKlW46bCwMGWGVixcvXoVQHbP/KIh85SNepAnhGg6StqV5M6dO3B3d1d3GIQQQghRsJIlS3JVqvNTsWJFbppK2vMWERGB8PBwANlDvRkaGqrkuNSDPCFE01HSTgghhBCiJJS0y068hqIqCz6oB3lCiKajpJ0QQgghREkoaZedeNLeunVrlR2XepAnhGg6StoJIYQQQpTE2tqaq0pPSXvuGGNc0m5kZITGjRur7NjUgzwhRNNR0k4IIYQQoiQCgQBOTk4AgPDwcGRlZak5Is0UFhaGDx8+AABatGgBAwMDlR2bepAnhGg6tSbtcXFxmDBhAlxcXNCzZ0/cvXtX6nafPn3C2LFj4ebmhp49e/LG8Lx37x4aNWqEli1bcv8ePnyoqlMghBBCCMmTqIp8amoqPn78qOZoNJO6qsaLiJL2rKws6uWfEKJx1Jq0L1u2DGXKlMGlS5cwfvx4zJw5E/Hx8RLbzZkzBzVq1MClS5cwZ84czJ49G9+/f+fW29vb49q1a9w/Z2dnFZ4FIYQQQkjuRCXtAFWRz83Zs2e5aXWMvlOpUiVu+u3btyo/PiGE5EVXXQdOSkpCUFAQ/P39YWhoiFatWmHv3r24evUqunTpwm2XmJiIp0+fYtOmTdDV1UX9+vVRvXp1XLlyBT169JD7uGlpaUhLS+Mt09XVhb6+fqHPSRptqAYnOkdtONfcaPs10PbzB+gaaPv5A4q/BkIhtWArLsQTwlevXqFVq1bqC0YDvXv3Dv7+/gAAKysrNGjQQOUxUIeBhBBNprak/f379zA1NYWlpSW3rHLlyrk+3WSM8abFt4uKikK7du1gamoKDw8PDB06FDo6OlL34+vri+3bt/OW9enTB3379i3M6QAA0tPTJZa9e/eu0PstKiIjI9Udgtpp+zXQ9vMH6Bpo+/kDirsGFSpUUMh+iPrVqVOHm3706JH6AtFQ69atQ2ZmJgBg1KhR0NVV/e0pJe2EEE2mtqQ9OTkZJiYmvGUmJiZISEiQWFarVi3s2rULPj4+ePz4MR48eAAbGxsAgKOjI/bt2wd7e3tERERg5syZMDY2hpeXl9Tjent7S6xTVEm7tH2sXr0a69atK/S+NVlWVhYiIyNhZ2entSVD2n4NtP38AboG2n7+AF0Dkru6dety05S08zHGcPToUQDZ91FjxoxRSxyUtBNCNJnaknYjIyMkJibyliUmJnJDboj7888/sXTpUnTs2BFVq1ZFmzZtUKZMGQCApaUlV1rv5OSEYcOG4fDhw7km7fr6+kqrCi/Nhg0bMHPmTNja2qrsmOoiFAq1/kZV26+Btp8/QNdA288foGtAJJmZmaFixYp48+YNnjx5gszMzFxrBGqbsLAwrlZiy5YteTUwVals2bIwMTFBYmIiJe2EEI2jtrsKe3t7JCQk4Nu3b9yy0NBQXmctIuXLl8eGDRtw6dIlbNq0CVFRUahRo4bU/WrijZK0avOEEEII0R716tUDkN2nT2hoqHqD0SABAQHcdLt27dQWh0Ag4ErbIyIiuOr6hBCiCdSW4RobG8PV1RVbt25FSkoKgoKC8ObNG7i6ukpsGx4ejuTkZKSkpGD//v1ITk6Gi4sLgOwh36KjowFkt5PfuXMnWrRoodJzyY8qxxolhBBCiOapXbs2N/38+XM1RqJZLl++zE23adNGjZH8r5f/9PR06p+DEKJR1FY9HgBmzpyJ+fPno02bNihXrhz++usvmJmZ4ezZs/D19cWhQ4cAANevX4efnx/S09PRoEEDrF69GgKBAADw8uVLzJ07Fz9//kTp0qXh4eGRa9V4dRHvRI8QQggh2kc0DjiQfe9CsvuBCAwMBACYm5urfcjenO3aHR0d1RcMIYSIUWvSXqpUKfz9998Syzt16oROnTpx8wMHDsTAgQOl7uPXX3/Fr7/+qrQYCSGEEEIKq1q1atz0ixcv1BiJ5ggJCeGaSbq5uam9nX/OpF3dJf+EECKieQ3Ai6Fbt26pOwRCCCGEqFGVKlW4WoKUtGcTrxrfunVrNUaSjXqQJ4RoKkraVaBPnz7qDoGoATWLIIQQImJoaIgKFSoAyK4eT78RwJUrV7hpStoJISR3lLSrAP0wa58zZ86gbNmyaN26NdLS0tQdDiGEEA0gateemJiIDx8+qDka9crMzERQUBAAwMLCgtdRn7rY29tzVfQpaSeEaBJK2glRgqlTp+Lbt28IDAyEv7+/usMhhBCiAagzuv95/Pgxvn//DiC7PbsmDNmrp6cHBwcHANlJOxW6EEI0hfq/IQkphsTbK/r5+akvEEIIIRqDOqP7H/H27O7u7mqMhE9URf7nz59cJ3mEEKJulLSrgKmpqbpDIEpw48YNNGrUCDVq1MgzMT916hTCwsJUFxghhBCNJF7Sru1Ju6a1ZxcRb9f+9u1bNUZCCCH/Q0m7CpQoUULdIRAFe/78OUaPHo179+7hxYsXGDZsGBhjeP/+PbZv3y6xfYsWLahtOyGEaDnxknZtrh6fkZGBa9euAQDKlSvHe5ihbtQZHSFEE6l1nPbiRjSUS05U0l68+Pv7o1u3bhLLnz17Bk9PT6mv+fz5M+rVq4fnz58rOzxCCCEaqnTp0ihbtiy+fPmCp0+fgjGW671DcXb//n38/PkTANCqVSuNugaUtBNCNBGVtKtA48aN1R0CKaR79+7BysoKAoFAasIOAH379s1zH9peFZIQQgjQpEkTAEBMTAxCQkLUHI16nDlzhpvWpKrxACXthBDNREm7Chw4cEDdIZBCYIyhUaNG+Pz5c57bpaam5rsvqiJPCCHarVWrVtz0pUuX1BeImmRlZWH37t0AAKFQiC5duqg5Ir4KFSpw05S0E0I0BSXtKpCZmanuEEgh9O7du8CvFY1BK5KYmFjYcAghhBRh4kn7gwcP1BeImly7dg0REREAgHbt2sHW1la9AeVQokQJlC1bFgAl7YQQzUFJOyG5iIiIgEAgwNGjRwv0eisrK7Ro0QK9evXiliUkJCgqPEIIIUWQeGd0r1+/VmMk8nnw4AG2bdtWqB7VMzMz8ddff3HzgwcPVkRoCieqIh8VFYWkpCQ1R0MIIZS0EyJVVlYWr4pcTkKhEA0bNsTFixelrh8/fjxOnDgBoVDI64jw06dPCo+VEEJI0WFsbAw7OzsAmp20M8bw5s0bfPv2DZMmTULDhg0xevRotG/fHhMnTpSpSVhOkydPxvnz5wEAlpaW6N69u4KjVgwa9o0QomkoaVeRjx8/qjsEIqN58+ZJHabv6NGjYIyBMYbMzEwEBwejbdu22LNnD2+7I0eOYN26dVwHhB8+fODWUfV4QgghVapUAQDExsYiJiZGzdFIevHiBapVq4ZKlSqhTJkyWLt2LRhjALIfaq9fvx7Vq1fHokWLZOqr5ePHj5gyZQr+/vtvAICuri72798PIyMjpZ5HQVFndIQQTUNJu4qEhYWpOwQig7dv3+LPP/+UWh2uR48eUl/j5eWFqKgohISEIDo6Gj179uStb9u2LTcdHR2t2IAJIYQUOaKkHdC80vaUlBR07NhRIi59fX24u7vD0NAQABAeHo65c+fC3d0dnz9/BmMMKSkp3PaMMZw5cwZdunRB+fLlsXr1am7dtm3beL+NmoaSdkKIpqGkXUXMzMy46WvXruHKlSvcU2uiGRhjvB9qce/fv8/ztWXLloWRkRHKlCkjsU78vd+4cWPhgiSEEFLkaXLSfvfuXd5vXv369dG7d288fvwYFy9exOHDh1G1alVu/Y0bN2BlZQWhUAgjIyO0aNEC586dQ5s2bdC5c2ecPn2at/8FCxbA29tbZedTEJS0E0I0ja66A9AW4eHhOHjwICpWrAgfHx8AwPXr1+Hi4qLmyIjIuHHjcl0nan9YEI0aNeKmb968iWXLlmHGjBkF3h8hhJCiTZOT9tu3b3PT27Ztw4gRI7j5rKws1KhRA8+ePcOJEycwatQofPv2jff6GzduoFOnTrxlpUuXhqenJ4YMGQJXV1flnoACUNJOCNE0VNKuIr169cKyZcu4hB0AFi5cqMaIiLhnz54prRS8QYMGvPmZM2dSL/KEkGJt8eLF6NChA9zc3NCvXz9cu3YNAODv748BAwbA1dUVnp6eOHz4MO91ISEh6N+/P1xcXODj44OoqChuXUpKCubOnQtXV1d07twZ586dU+k5KVJRSdqbN28udRuhUIhevXrhyZMnuW4DAI6Ojvjvv/8QHR2NXbt2FYmEHciuPWdiYgKAknZCiGagpF2NSpcure4QCIDk5GTUrl1b6joDAwPuZrOghELJP7MfP34Uap+EEKLJvLy84O/vj6CgIMybNw9z585FfHw80tLSMGvWLFy+fBmrV6/Gtm3buLHK09LSMH36dPzyyy+4fPkyatWqhXnz5nH73Lp1K378+IEzZ85gyZIlWLp0Kd69e6euUywUR0dH6OpmV3bUpKSdMYZbt24ByG7aVb169Ty3t7a2xpUrV+Dn54dTp07h8+fP6NOnD2rWrIk///wTISEh6N27N/T09FQRvsIIBAKutD0iIgKZmZlqjogQou2oerwa2draqjsErZecnCy1lGD9+vUYO3aswo7Ttm1bBAQEcPNU0l4wmZmZXE2F1atX83oevnLlCjZs2AAfHx906NBBjVESQhwdHblpgUCAtLQ0fPv2Db169eKWV6xYEY0bN8bz589Rv3593L9/H0ZGRvD09AQAjBgxAm3btkVUVBSsra1x5swZrFq1Cqampqhbty5cXV1x4cIFXvVtkbS0NIlezXV1daGvr6+Q88vKyuL9Ly+hUIiKFSvi1atXCA0NRUZGhtQHvKr2/v17rsNU0Qgo4uco7bx1dXUxcOBAbv7AgQO8fRb0Gqmbk5MTnjx5goyMDLx79w6Ojo6Fft+LOm0/f4CuAUDXQNHnL+t3PyXtCiQQCLhpY2NjqT2QiytXrpyyQyL5+Oeff/Do0SPesiNHjqBbt24KPc7+/fthbW2NjIwMAJS0F9SePXuwcuVKANklPPPmzUN0dDTMzc0xZMgQvH//HoGBgRo5hBIh2mbp0qXw9/dHamoq3Nzc4OTkxFufmZmJkJAQeHh4AMgevaNSpUrceiMjI5QvXx5v376FiYkJYmJieOurVKmCkJAQqcf29fXF9u3becv69OmDvn37Kur0AACRkZEFfm358uXx6tUrJCcn486dO7CxsVFgZAVz8OBBbrp69eq51mQozHkXFeK1IYODg3n3eNpw/nnR9vMH6BoAdA0Udf4VKlSQaTtK2pWkdOnS+SbtmvBUXZvFx8dj9OjRvGUTJkyQGLJNESwtLTF58mQsX74cQHbnPlu3blX4cYq7IUOGcNPz589HnTp10KtXL5QqVYpL1GNjY5GUlARjY2M1RUkIAbL775g2bRru3bsnddjTzZs3o0yZMmjWrBmA7JpPonbEIiYmJkhOTkZSUhJ0dHS44cZE63L7nfX29oaXlxdvmaJL2iMjI2FnZ1fg3/K6devi0qVLAICkpCQ4ODgoJLbCuH79Ojc9YMAAiZgUcd5FRY0aNbjp9PR0ODg4aNX5S6Pt5w/QNQDoGqjr/ClpVxIdHZ18t9HWaiWaIDQ0lNcRkMioUaOUdkzxUpRPnz4p7TjapEePHgAgUbL+9OlTNGnSRB0hEULE6OjooEmTJti/fz+cnJy4BP3w4cO4fPkydu3axZVgGhkZITExkff6xMREGBkZwdjYGJmZmUhJSeES98TExFwfzunr6yssQc+LUCgs8E2b+LBpYWFhaNeunaLCKpCEhASuGZeNjQ0aN26c67kV5ryLCvFRYz59+sQ7X204/7xo+/kDdA0AugaqPn/tvdJKJkvSPmPGDPTu3ZurMk1UR1rCfvToUVSrVk1pxxw8eDA3HRcXp7TjFCepqalgjAGAXB1OxcfHKyskQkgBZGVl4cOHDwCACxcuwNfXFxs2bEDJkiW5bZycnHgl8snJyfjw4QOcnJxgZmYGCwsL3vrXr19LVLkvSjStB/nz588jNTUVAODp6anVN+NAdvMFEdFnlxBC1EW7v5GVSFdXV6axuI8cOSIx5A1RvU+fPnGltspibm7O9RZ848YNPHz4UKnHK+r++ecflCpVCjVq1MCPHz+kVq/NTWhoqBIjI4TkJSkpCWfPnkVSUhIyMjJw6dIl3L9/H87Ozrh9+zZWrFiBtWvXSrThbtCgAZKTk+Hv74+0tDTs3LkTNWrUgLW1NQDAw8MDO3bsQGJiIp4+fYqrV6+qvXS6MDQtaT9+/Dg33b17d7XFoSnES9opaSeEqBsl7Uqio6Mjc9W8KVOmKDkakpc9e/ZwN4XKJBAIeO01V69erfRjFmVDhgxBcnIyXr58ibFjx+Lx48fcOmm9RYsbM2YMVq1aJdF7NCnekpKSsHPnTly5ckXdoWg1gUCAEydOwMPDA23atIGvry8WLVqESpUqwdfXF/Hx8Rg6dChatmyJli1bYsmSJQCyq7QvX74ce/fuRevWrfH48WP88ccf3H5HjhwJU1NTdOzYETNnzsTMmTN5vdQXNdbW1txvwqtXr9QaS3p6Ok6dOgUge6i3Vq1aqTUeTWBlZcXVNqCknRCibtSmXUl0dHRkrqL76dMn/PjxA+bm5kqOioiYmppyPbgPGDBAZcdds2YNhg4dCoBfqkH4RFU0Rfbs2YM9e/Zw887Ozjh9+jSOHj2KKVOmIDo6Gl26dOF1SjV16lQYGxtLdDZIiq9ly5bhjz/+gEAgwPPnz5Xa3IXkzsjICFu2bJG6Lr8OOGvWrCkxZJiIoaEhFi1aVOj4NIVAIECVKlXw8OFDhIeHIy0tTSXt8KW5du0avn//DiC7RoO64tAkenp6sLKywqdPn7S+l2xCiPpRSbuS6Orq4ufPnzJvL8+2pPBE7aRr1arFG8ZF2QYNGsRN29raquy4RU1+pU4ODg5cVdnq1aujdevWUjv3++2335QVItFAolJZxhiqV6+OqKgoNUdESN5EndFlZWXh7du3aouDqsZLJ2rX/vnzZ6q5RQhRK5lK2uXtUVsgEGDz5s0FCqi4EPWYu2vXLpm2p87oVIcxxpXIqnpYMB0dHZQqVQpxcXESvSSrAmMMBw8exMePH+Hm5oYGDRqo9KGFrDZu3Jjn+ubNm0ssMzc3566tuJ8/f6JEiRIKjY8oTmxsLG7evAljY2O4ubnJ1ImnrLZs2YKFCxcqbH+EKFrOdu3qqB3CGMOJEycAZJcud+rUSeUxaKry5cvj7t27YIzh06dPsLe3V3dIhBAtJVPSfv/+fQgEAq50Mj+amASogvj10dXVxdChQzFy5EiZXktPcFVHvEdydYzlXbZsWcTFxXHV81Vp+fLlmDlzJjd/+vRpeHh4qDyOvJw/fx7btm3LdX2vXr14PU6L279/Pzp27Mhb1qlTJ97Yw0RzZGZmolGjRlwJ48yZM/HXX38VaF/BwcESy/744w9K2olG04TO6B4/foz3798DAFq3bg0zMzO1xKGJxPtMCA8Pp6SdEKI2MrdpL1OmDDw9PfPd7sSJE/j69WuhgiqqxMdd19HR4XoKlwUl7cqxadMmjBkzBuXLl0d4eDh0dXURFBTErVdH0m5qagoA+P79O+Lj41V6gySesAPZJdqalrTnTLrFXb9+HS4uLrmu79ChAxhjmDx5MtasWQMgu6f+lStXYurUqQqPlRSOl5cXr0rw0qVL4e7uLleP4JmZmZg6dSrWrl0rdX3jxo3x77//8sbEJkRTaELSfvr0aW5alvs8bVKhQgVuOjw8HG5ubmqMhhCizWTOKsuVKwcfH598t7t586bWJu2ZmZncdF5VPO/du4cZM2bg0qVL3LLk5GTs2LEDhw8fxvnz59GvXz/s27dP68dJLawxY8YAyO759dixY+jTpw8vKXz06JHKYzIwMOCmfX19MWHCBJXHIBIdHY2kpCQ8efIEtWvX5vVurw7SSkvnzJkDKysr1K1bN8+EXdzixYu5pB0Apk2bhj59+sDBwUFhsZLcZWVl4caNG2jRooVEzavExERERkbi8+fPOHjwoMRr27dvj8jISN4YyXk5fvx4rgk7kP2Z8vb2xs2bN+U6B0JUoXLlyty0upJ28XuRvB6aaqOcSTshhKiLTBnh/PnzuR6v8zN8+HDMmzevUEEVVeJJu6iUvW7dutyyyZMnIyQkBA0aNEBAQAAmTpzIrduwYQNGjBiB8+fPAwAOHjwIHR0dGm+6EHL23v/gwQOJbUqVKqWqcDgtW7bkpqUlLcpUq1Yt3vyDBw/g6OiIZs2awdHRET9+/FBpPDlJ63F6wYIFGDNmDFq0aCHzfoyMjCSG/apUqZLMTXxI4ejo6MDV1VXioWNcXBxMTU1RvXr1PIeUsrOzk7k/kN69e0ssW7ZsGW/+1q1banlAR0h+SpYsibJlywJQT9KelJSEGzduAMhOUJ2cnFQegyYTvx7q7CiQEEJkStq7dOki8w1zixYt0KVLl0IFVVRJK2nfs2cP7O3t4e7ujmXLlqFGjRrcNunp6dz07t27pe6zSpUqEAgEWlt7oTDWr1/Pm1+6dCmcnZ15y/bt26fKkAAAw4YN46Zv3bqFlJQUlRx3/fr1ePbsmcRy0Wfr27dvarkeIg8ePJBI1H7+/FngjslatWoFPz8/bj4jIwPe3t6YPn16rn9vpPBya+rDGOOSE1kMGzYMHTp0wNWrV6Wuj4mJwYwZM6SumzBhAsqUKcNb5urqKvOxCVElURX5qKgolY8kc/36de5vtm3btio9dlGQs007IYSoS6HqXk+bNo3aP4mRlrTXqlULERERuHTpkkQbd/GEIj8bNmxQSIzKlJ6erjFt83Pr6C1naVudOnVUFNH/5OzIRnz8cWUJCQnB+PHj891OncnskCFDJJaJ2v8X1IABA3jz//zzD1asWIHBgwdLfYBBCi81NZU3//z5cwDA3r175R4l48KFC3Bzc+P6C0lPT+dqS6xYsQLLly+XeI2zszMMDAxw9uxZ3vLk5GS5jk2Iqoi3a1d17bqAgABuuk2bNio9dlFgZGQEKysrAJS0E0LUq1BJ+7dv32gcXDHSqscDufemL8+QXy9evCh4YCoQGhoKQ0NDGBgYYPbs2WqLgzEGgUCA0qVLY+nSpWqLIy8GBga8doyqSJRlvRG8ffu2RLMCVUhISMDTp095y3r06FHo/erp6aFixYpS19WuXRtv3rwp9DEI37FjxyTmFy5ciIEDB0rd3szMDLGxsbyaRznp6OggODgYVlZWaN68OdLT0yWqwAOAoaEhFi1aBABo0KAB72GnLH2yEKIO6uyMTrw9u7u7u0qPXVSIqshHR0erZahWQggBCpm0Ez7xUiRZqvTK0wGZOsZulYd4ieaSJUvUFoc8Nzzq7HTN39+fm1Z2dcgnT55IJMB5tc2TdZhCRfj58ycWLFggdRz1vIZ9k8fixYtzXdekSRMqgVUwb29v3vycOXOwYMECqds+ePAAMTExKFWqFHR1dXmdNObUuHFjxMbG4vbt22jdurXE+ilTpuDLly+80RCaNGnCTYv/zRGiScST9pcvX6rsuAkJCXj48CGA7FpnOZuUkGziD36pXTshRF0KlbRTp058svYeLzJq1CiZ963p1zomJkbdIQDIrhYvq99++02JkeRNvBdzZXaQlZ6ezusMEQC6du3K6xE3pwMHDqhsXPPFixdLHUd76tSpsLS0VMgxevXqhUaNGkldFxMTg40bNyrkOES+G9q9e/fC2dmZVyvp7t27GDVqFBo2bJjna0UdZ4lMmTIF8+bNk3j4I968IjIyUuroBISom3gHoY8fP1bZcR8/fszdW4g/4CJ8lSpV4qapdhYhRF0KlbRrc0/x0uRWPT43eZWeN2vWjDf/5cuXggemZNKSLnU9ZPj06VOu67Zv386NR1uuXDmpcauKoaEhN62sseKXL18OfX19ieVz587N97Vz5sxRRkgSclZxNjQ0xLt377BixQqFHUNXVxd3797F7du3pQ6hOG3aNBw6dEhhx9NmsnZk1bZtW4n+BoDs0r7NmzcjODhY5vdk8eLFWLlyJczMzCTWid9sA8DRo0dl2ichqlSxYkXugZO0UU6URfxY9evXV9lxixrxknZK2gkh6lKopF2be4qXRt7q8dJMmDABL168wM2bN7nh3wB+VeG4uDj4+fkhMjKy4MEWwufPn7F7927s2rUL27Ztwx9//CGxjap6RM/p8+fPUpeHhoZi+PDh8PDwQEREBMLCwmBkZKTi6PgaN24MIHvIHWVU0c6tZ21RqY74iBC3b9/GyZMnufmgoCClt92T9mDnw4cPEh31KUqTJk3w7t07qU0o1FnroiAYY7h79y6ePHmi7lA49+/fl3nbnJ3ESdOnTx94eXnlu52NjU2u63R1dfHPP/9w87dv35YtQEJUSCgUcjWi3r9/r7Kaa6Kq8QAl7XmhknZCiCaQKWl3d3fHuHHjZNrhmDFjtLYHUnmrxwNA3759efNr167lSuCtra1565ycnDBnzhx07doV3t7eSktu8pKRkYHmzZtj8ODBGDZsWK7tn3MOt6Yqol6mcxL/0XVwcCh0r+SKULp0aW565syZKjlm7969uYcVfn5+6NKlCxYvXowmTZpIPICT1pu7Il2+fJk3f/78eVhYWCj1mOXLl0flypUlhpaLiYkpUm3br169iiZNmqBu3boSHfipS69evWTa7tatWzLVRAKQb40LV1dX9OnTJ89txJuHBAYGUptUopHEk2bxZFqZRCXtOjo6qF27tkqOWRSJl7SHhYWpMRJCiDaTKWn/+fOnzKVuSUlJKh9nVFOIJ+3SquFKI97WNmfnSqIeS0XCw8OxePFiXntOVbZ/A7JLrGW56ZXWs7MqJCUlSSwbM2aMGiLJn/h7//fffyt03zmH3RIRL3WsWLEi/P398fvvvwPIHuVAvGTz8OHDSmvmcOTIEV5V6nr16qF9+/ZKOZY0Q4YMQb169XjLbG1tIRAIIBAIMHToULi7u6N27drcMlVWW83L+/fv0apVK25+5cqV6gsG2Q9bOnXqJLXmz/z583nzffr0QdOmTWXed84Hl+IqVqyIy5cv59uhpPhIDaIYCNE0zs7O3LQ8tVYKKj09nRuOsWrVqmqveabJLCwsYG5uDoA6oiOEqI/M1eOfPXuGxo0b5/svJCREmfFqNPEER9aS9pEjR6Ju3bqoWLEi/v33X946WX5E7927J1+QhTR69GiZtsurF2hlEn+41LRpU+zcuVPtSU1ucrYtT0hIyLfq3adPn/Djxw8A2ef66tUrqdvl7AOha9euiIiIyLf9/NatW3nzJ06cyHP7gkhLS0Pv3r15y7Zs2aLw4+RFIBAgICAAdnZ23DLxTgx9fX1x5coV3ljuDRo0UHuHi6GhobxODIHsoTfVhTGGjh074ty5c7zlzs7OuHLlCubPn4979+6hXr16+PPPPyW+42Qxa9Ys3vzq1avh6+uL69evy/Q9m/Mz/+DBgzyHlyNEHUTNpQDg5s2bSj/eq1evuL+DOnXqKP14RZlAIOBK29+9e4e0tDQ1R0QI0UYyJ+2MMZn/ySouLg4TJkyAi4sLevbsibt370rd7tOnTxg7dizc3NzQs2dPiXaJ/v7+8PDwgJubGxYuXKi2GzLxqtm5jc2eU4kSJfDw4UOEhobC1taWt06W0vqSJUvKFWNhxMTEICgoKNf14lXQ5SlNUyTxpH358uUYOnQor9M3TaKnp8ebt7e3R6VKlXil4eJu3rwJOzs72NvbIzIyElWrVkW1atWwZs0aiW1Fib3IiRMnJJI9aUxMTHhj9fbo0UOhVTUZYxK92QP8G1ZVsbCwkLsjTUtLS7x7905JEeVt165dvKGhRM6cOaOGaLLlNmyljY0NWrVqBYFAgAYNGuDhw4eYM2dOgR7m5XxQ2LFjRwwZMgRWVlYy72Pt2rW8+aVLl8odByHKVK1aNZQqVQpA9ne9sjtzFe8Pg5L2/Inub7KysvDx40c1R0MI0UYyNSxUVinYsmXLUKZMGVy6dAm3b9/GzJkzcfz4cYlegOfMmYOGDRti7dq1ePLkCaZNm4YjR46gZMmSCAsLw5o1a7BhwwbY29tjypQp2Llzp1zDqSmK+I+srNXjAdkTfGlOnjwpc1vSwrp27Rpv3traGlFRUdz877//jqFDhwIAjh07hp49e6JcuXIYOXIk9uzZA3t7e4wfP15p8R05coRXqq6sXtkVqXPnzlyP9qKS3mnTpmHw4MES27q4uAAA4uPjef0ZTJ48GYmJifj999+5z11CQgK3fsKECXJ9xubOnctrb+7n58erulkY27dvlxiHeOjQoYX6GyiMzp07y/2aFStWYMOGDUqIJncHDx7EsGHDcl3/6NEjier+yvbhw4dc+66Ijo5W2HHs7OzQunVrXLlyBebm5rzaEbKaMGECIiMjsWrVKgDAvHnz0LFjx1yHAswpMDAQ27ZtQ9WqVTF48GAsX74cMTExGDJkCDp16iR3PJqkIO+VPA9MiGyEQiGaN2+O06dP49u3bwgNDZX6kE5RxPvCoKQ9f+Lt2tX14JYQTSN6iGVtbS1zXzWk4GS6wg0aNFD4gZOSkhAUFAR/f38YGhqiVatW2Lt3L65evcrrECsxMRFPnz7Fpk2boKuri/r166N69eq4cuUKevTogXPnzqFdu3aoUaMGgOxh6BYtWpRr0p6WliZRtUlXV1fq0FjyyvlkPLdO0eRx48YNLlmTZvfu3fD19S30cfLy5csXpKamIj4+nrf8+PHjvLFdc3ZAeOzYMQD8hz716tXj9VquKLGxsejXrx9vmampqULeA1mIjiPv8UTt5MR9/fpVYj/5tXGcO3cuqlSpgl69ekEgEPDeKxMTE7niatmyJcaOHcslpqGhofm+XpbzT0lJkei4cP78+RgzZozK3qecypUrh65du8Lf3z/XbSpXrozQ0FBu/vHjx1LjLehnID9fvnzBL7/8wltWqlQpXnX+MWPGSDxUUzZpo0aIVKpUSaHXwdfXF/v374e7uzuMjY0LtO85c+ZwSTsAdO/eXaYROBhj6NGjB75//w4gu/+J2NhYAMChQ4fw+fNnWFpaAlD8Z0Ceh78F1bVrV7kemgkEAty5c0eJEWkvUdIOZP/2KzNpp5J2+YjXJHz//r0aIyFE/Rhj2LdvH7Zt24b379/D3d0d58+fR2BgIDZu3AgDAwP88ssv8PT0VFuhTHGktsci79+/h6mpKXezA2TfHOfWyYd4QswY47Z7+/Ytb0zzypUr4+PHj0hJSZFaLdrX1xfbt2/nLevTp49EL+4FIR5jUlKSQp7GWltb46+//uK16zQ2NuZ1uKbMp75z5szBvn37pK6zsLDA1KlTsWbNGnTr1k2mZgkHDhwoUElZfu7evcvrCBDIrn6u6ifi8g7D16FDB5w4cYJXrd/W1lYiblk6z+rXrx9q1KiBY8eO8cajTktLk/s69OnTh1eaLOvr8zr/69ev8+bNzc0xePBgJCQk8GoGqNrSpUtzTdpnz54NFxcXeHh4cMvKlSuX5/VQ5FCMjDFeCQ8ADBgwAIsWLcKGDRuwevVqANk9Gqvys56VlSXxPSpu8uTJCo9H9FCuMPsdPHgw1/zk06dPGDZsWL5NJF6/fs0l7AC4hF2kXLly2Lx5Mzp06MAtU9RnoEKFCgrZT36UXRWbyEb8Af2NGzfg7e2ttGOJknZzc3OUL19eaccpLsS/hyMiItQXCCEaYNGiRViwYAE3f/nyZRgYGPAeWB88eBC///47Fi9erIYIiye1Je3JyckSvf6amJhI3LybmJigVq1a2LVrF3x8fPD48WM8ePCAG5s3535EQ3klJydLTdq9vb0lxv5VVEm7uBIlSsjUhlgW06dPR9OmTTFs2DBUqFABe/bs4XpVbtu2rcKOk1NwcHCuCfs///yDChUqYNmyZZg7dy5iYmJkSsbj4+MVHi9jTGK4mvLly8PR0VGhx8lLVlYWIiMjYWdnJ1fpmIODA3r27IkfP35wN04JCQncNbp06RJ27twpc6Ly/PlzPHnyhLd9+fLl5b7m4n0lPHv2LN/Xy3L+OZvZLFiwQGmfXXmlp6cjISEBKSkpKFu2LNf7voGBgUTNHKFQKDXugn4G8vLixQve/ODBg7nh6lasWIF9+/YhOjoaKSkpKr2Wol6npWnfvj0aNmyosljksWvXLnz48AGXLl0CkN30Y8eOHVJLAhhj2L9/PwYOHJjvfkePHg0zMzPY2dnB3d0dCxYsUGl/I4VVtWrVfIfXA4CpU6fyap0QxWrUqBF0dXWRkZGBq1evKu04cXFx+PDhA4DsUnYqCcsflbQTku3p06dYtGiRxHJpNcyWLFmC2rVrS9QWJAWjtqTdyMhIYhi5xMREqT2m//nnn1i6dCk6duyIqlWrok2bNihTpozU/YiS/tx6XtfX11d4gi6Njo6OQqs2tmrViutZXHw4r9TUVKVUoUxJSUHLli1zXV+iRAnuuKampoiJiZEpjr1792LPnj0KixMAtm3bxvsM6Ojo4NKlSyqpWpqTUCiU+7impqYwNTXlqjz/+PEDQqEQHz58gIeHBzIyMuTa37lz53g3fF26dJE7JvF+JSIjI2V+fV7nL17Fv27duhgxYoRa3iNphEIhL8kS//4wNDTEgQMHuB+dxMTEPOMuyGcgNzmbpHh5efH2bWNjg+joaCQlJan0WkobWlGkVKlSGvO+SrN9+3becJrh4eG8G3KRgIAAmRJ2kfj4eISEhCAkJARz587V6GuQk76+fp7D64no6elRqbwSGRsbo2nTprh+/TpCQ0MRERGhlIfP1J5dftbW1jA0NERKSgq1aSdabfHixdx96cyZM2FpaYm5c+ciOTkZpUqVwrx58/DmzRuutub8+fPRp08fmUfVIrlT212Fvb09EhISeMMVhYaGSoxNDmSXFG7YsAGXLl3Cpk2bEBUVxbVhd3JyQlhYGG8ftra2au8xXJlPrvX19bn9JycnY8CAAdxY0mZmZnmWgsnq9evXeVZ3l2U4OlFtCHHlypUrVFzS5GwnHRERodS2gMoi3ka5bNmysLOzk0jYZWnGsXPnTl5SJe1vKj85v1y/fv0q9z5yKl26NDd96NChfMfX1iTdu3fnpvPqrZ0xhl9++QWVKlXiejhfsmRJgY/7+PFj3nzOB2mi74GMjAwcPHiwwMeRl/jna+rUqbx1Of8eNU2FChV4HSvmHI1E5O+//y7wMXJ2pqrJgoODudob+fHz80NwcLCSI9Ju4s0szp8/r5RjiLdnz1lLjUgnFAq5KvKRkZESzfEI0QZxcXFc88vSpUtj7ty5mDJlChITE/Hjxw98/foVEydOxN9//w1XV1cA2fnEyZMn1Rl2saG2pN3Y2Biurq7YunUrUlJSEBQUhDdv3nBvsrjw8HAkJycjJSUF+/fvR3JyMtf2q2PHjggICMDLly+RkJCAXbt2aURvvsosZREIBFzSfO/ePezfv59b9/PnT0yZMqXQx8hv/HdZemYXrxEg8vnzZ246Pj4ee/fuLfRDBvGbDmNj4yLbPk88kZCWJOvr6+PAgQO8HrtzG3JLpGLFigV+gCSeIIo6RyoM8THoy5YtW+j9qVLO2jnSShsfP34MLy8v/Pfff3jz5g3++ecffP78GbNnz861Y7KUlBQcPHgQkydPRq9evRAeHs5bL97Hx4wZMyQeRorXXlBlspycnMxNm5qaIi0tDadOncLNmzfh5uamsjgKqlu3btz0iRMncOvWLYltxK/9smXLUL9+fan7qlKlCq/tsZGRkcRwjoTIStVJO5W0y06UtKelpdGwb0QrHTt2jCvQ8/T05O5JRIWGogIfgUDA64srt5FmiHxkqh6fV4dDOY0YMULmbWfOnIn58+ejTZs2KFeuHP766y+YmZnh7Nmz8PX1xaFDhwBkd2Dl5+eH9PR0NGjQAKtXr+YSkUqVKmHixImYNGkSEhMT4e7uzg07pk7KrhppaGiYaxXVc+fOFXr/0m5ixeVW0n7x4kWsW7cOY8aMQY8ePaRu8+3bN1haWmLIkCE4duwY9PT0EBERIbVkXhbdu3fnqvspuyd9ZbKxsZEYE71+/fp48OABWrRogRUrVkAgEMDHxwdPnjxBWloaFi9eDGtra8ycOVPqPvPq4Ts/rVq14nokz1lNuyAePHjATZcoUaLQ+1OlnA8+UlNTeQn0mzdvck3qACAoKAitW7eWWD5r1izeGOJHjx7l9cchXtPC3d1d4vWmpqZck6AfP37IdjIKIP7dI0pSO3XqVGSqjYo/6Dt8+DAOHz6Mmzdvcp2aMsYQEhLCbTN16lS0bNkSc+fORWZmJubOnYtbt27h/v37WLVqFcqVKwfGGEJDQzFgwACVn4+iJCcnc6XpMTExEutPnDihhqi0S4MGDWBhYYGYmBhcuHABiYmJCq+VJJ6016pVS6H7Ls7Em9GEhYWptN8cQjTBgQMHuGnxh9/SdOjQgRt9JzAwEFFRUTI1wyJ5YDJo2LAha9SokUz/tBkA7t+ECRNUdixp/xS5/zNnzkjs/9mzZ9y2mZmZ7O3btywzM5O3j27duuUaX/Xq1Xnzp0+fLnCs06ZN4/Zz9erVAu+nMHK7BvK4ePEi75r8+uuvcr0+MTGRlSlThnu9vb09y8jIKHA8/v7+3L4GDhyY57b5nf/169cV+vlUh65du0r9vKampjIdHR2Z/iajoqJYt27dmI+PD8vMzMx12379+rGUlBQ2YsQIbtmTJ08kYlq9ejW3XkdHR2XX4t9//+WOu379esaYYv4GVCXn51H0Ly4ujjHG2NevXwv0eS1K10CaOXPmcL/lDRs25P3T9t/3nJT5Xov/3e/du1eh+87MzGQmJiYMAHNycirQ64vyZ7wwNm3axL0vW7ZsUXc4aqHN77+Itl6DDx8+MKFQyACwChUqsDdv3uR7DebMmSNxr1AcqOszIHNHdEyGzmeoB9L/UXcnRO/fv4e9vX2BXpuz2lfNmjUlxrIuVapUvvsxMDDIdV3OXrFzdkooD/Eq97JU29dUbdq0QXBwMJ4/fw5zc3NeNUlZGBsb4/Hjxzh58iQYY+jdu3ehOv4QL90Rb28vr/j4eF7PofKel6awsLDgpjt37oy9e/eiXLlyCA4Olql9Y2JiItasWcO17cqrtsHBgwfRp08f/Pz5k1smrbRtwoQJmDx5MgBwnXOqQs6S9qImt+YZAwcOxIYNG3Dx4kVuWdu2bVUVltrduHEDAFCtWjU4ODhAV1dtfdVqNS8vL66G4969exVaeyM8PJz7vaX27PIRH/ZNvLkXIdpg5cqVXFM/UV9a+enXrx/X0/zBgwcxduxYpcZY3Mn0iyze8cyjR4+46ujt2rUDkN3L7ooVK7Bq1SrlRFkEqfsBhoODAxISEgpUre7YsWO8eXt7e7Ro0YKXtMtSlb1atWoyHzOv3qjzkpKSgt27d3PzRTlpFwgEaNiwYaGGy7K2tlZY22bxzvwKevOekJCASpUq8droi4/tWZSMGzcOfn5+3HzOoSPzY2pqyruO+X1fRkVF8aqiiYazFCcUClG1alW8evWK185c2cSPVRT/5ipXrix1+alTp3Dq1CneMvG+Joo7fX192NjY8L5Tieq1bNkSdnZ2iIyMxPnz5/H161eFPZSj9uwFJ149npJ2ok2+fv2KrVu3Ash+UD927FiZ7jlq1aqFGjVq4Pnz57h+/To+fvwIW1tbZYdbbMldHLx8+XKULVsWnp6eMDY2hrGxMbp16wZra2usXr1aGTEWScouaQ8MDOTN169fH6NHj+YtMzU1LdAPy927d7lpUVvpX3/9letcSdZhkEaMGMElKSNHjpToOf7XX3/lpnPrpIIxhuXLl6Nz5874448/JDr0Onv2LG++KCYQmkp8CLSCtGX9999/UaJECYlO9Zo2bVrY0NSifv36aNy4cb7blS9fHunp6WCM8TooA5Dr8H0XLlzA4MGDectyjtMs3vu+OFFJt+jB18WLFyEQCNCnT598Yy2ogIAAieMXNR07dpRpu+HDhys5Es3Rt29ffP/+nTeqC1E9oVDIla5nZmbyHt4VFg33VnD29vbcPQ0l7USbrFmzhkvSfXx85OpMWHzUo//++0/hsWkTuYvP3r17B8YYbt++zd1837lzBx8+fFB76bImUfa1yNlDc9OmTaVWWZ8+fTqOHDki177Fq8eLOpOzsbFBYGAg7t+/L3MJo52dHa5cuYInT57Ay8sL48aN43qerFu3LrKysrgx2+/fv48PHz5I9Px+8+ZNzJgxA0D2UFuNGzfm3WznrP4trTSSFIyxsTEEAgEYY9y/3D7XSUlJiI+P52p3PHz4EIMGDZLYrlGjRsoOW6n+++8//Pnnn9ixY4fU9ePHj8fIkSO5G7vRo0fL1Dli27Zt4erqiqtXr3I9yOf8ccuttoPoQVV6ejoyMjLQvn17ANkdrL1+/Vrhwx+mpqbySqOLWqeCIr6+vtizZw8MDAxQtWpVbN26lRvKRsTBwaFIDh8pj4ULF/LmMzIy0Lt3bzRq1Ij3fSoQCDBv3jxVh6e1Bg4ciGXLlgHIHmpv3LhxCtkvDfdWcLq6unB0dERYWBjevHmT528iIcVFYmIiNm/eDADQ09OTGOY1P3379uVqWB4+fBgTJ05UcITaQ+6kvXLlyggJCcH48eNhaGgIgUDAPX0RjZ1OVNOm3crKCtHR0QCy20NXrVoVixcv5m1z9OhRREREyNXLqXhptfjrmjdvjubNm8sVY4sWLdCiRQsAgLm5OWrWrMmty1lKLi1pX7FiBW++U6dOyMrK4n4ocw6BJd7umBSOKGEXef36NapWrSqx3ePHj1GvXj2Z9lmY3uw1gb29PbZt24YvX75IHXd04sSJcHBw4OZlaeogEAggEAhgYGCAR48ewdzcXGKbMWPG5Pp68b/XnH1DXL9+XaFJZ2RkpETpXM6x44sKKysr3s1Hu3bt0KFDB1579qLa/4I8Tp06xUs8GGNITU1FUFAQbxkl7apVs2ZNNGjQAPfv38eDBw/w8OFDhTTVEI1QYmRkxKvuTWTj5OSEsLAwJCQk4MuXLxI1CAkpbnbv3o3v378DyG4WWL58+VyHsZWmevXqXDO+27dv48ePH1Lvc0j+5M4sZ8+ejTJlyoAxhuTkZCQlJYExBktLS8yePVsZMRZJqnj6umPHDhgaGqJly5bo1q0batasifT0dIkO4E6cOIGUlBSsX79epvG2RUNIAcotRRPv1AWQ3hmdtLHeZ8yYwSU64jfVOR9YkMIT74Rr165dUrcRlezm58GDBzJXSdZkAoEAJ06ckDoklrRtRaVluREfotHMzEzqNkuXLs319eLV0//880/eOllilMeyZcu4H28g+yFGzjHsiyqBQIALFy6ga9eu3DJptUWKG2dnZ96/+vXro379+hLLtKltv6YQb5oxfPhwtGrVCvXr10dkZGSB9hcXF4e3b98CAOrVq1eojkq1FbVrJ9pG1JYdyO78tiBE9+qZmZm4cuWKQuLSRgUqaT927BjOnj2L8PBwMMZQsWJFdOzYMc/ewrWNKkraO3fujNjYWK7GA5BdfevKlSu8EvG4uDisWbMGv//+OwDg5cuXUktMRaUpoh6rhUKhREm2IlWpUgXVq1fnepKXlrRLGx88Z+m7SFFtW6vJ+vXrx7Vf3rhxI5YuXSrxQOrLly/57sfPz6/Y3fSLt/kHwHXMmdPo0aMRExOD5cuXo02bNhg6dCgmTJiAb9++Yf78+RKvMzMzk/jc59XsQ7ykPWfndtOnT8e0adNkOR0A/FFCpD14/Pfff3nzsrTxL2o2bdqEypUro3LlynLXLCqKtm3bpu4QSC4GDBiAKVOmICkpCQ8ePOCWL168GFu2bJF7f6JSdiC7jw4iP/HChtDQUK34jiDa6/v373j8+DGA7Ae8staqzKl9+/b4+++/AWT34dO9e3cFRahdCpRZGhgYoHv37pg0aRLGjx8PT09PSthzUNWQb0ZGRhI3182aNcO1a9e4+YULF3IJO5DdpiSne/fuwcHBAQKBAPfv3weQnSgou8aAj48PNy2tB/mbN2/KvC9K2hVPvDOzxMREmJmZoXfv3lxyJxoiKi+fP3+W6GStOBAKhejVqxeA7M/ehg0bpG5XokQJLFu2DIwxBAQEYMCAAfj69SsYY1iwYIHE31i3bt14823atMkzDllGcshPZmYmGjZsCKFQCKFQiBIlSnBt2ERmzZol8TBBnhEiiory5ctj1apVGDVqlNa0V92yZQvu3LlT4FE8iHKYmZlJHe7twIEDSE9Pl3t/4ol/gwYNChWbthIfeSI0NFSNkRCifOKjhxXmAZWbmxvXmfWFCxcKHZe2KlBmef/+ffj4+KB58+bw8fHB3bt3sXDhQu5pDFH/kG8524aLu3btGq9E7erVq2jUqJFElTtVDMsgPiTdyZMnsW7dOuzevRs/f/7kVT3T19fnerLPjbW1tdLi1Fbm5ua8z0FCQgKOHDmCXr16oWfPnrmWLovMnz9frl5Gi5r//vsPT548wadPnxTWPjRnu//cRlYQmTRpUp7rxdueZWVl4cKFC3j06BFvm9u3b3MP64DsBzS//fYbAgICwBjD8+fPpVbRp9EaioedO3di3LhxcHd3x6BBg7B69WoEBgbymkIQ9fjrr78watQo3nftjx8/5HqgLSL+GippLxjxPkJev36txkgIUb47d+5w002aNCnwfkxNTeHi4gIgu1kJNS0pGLmT9nv37mHMmDF4+PAh96TXysoKp06dwvHjxxUdX5GlqpL23Dg6OmL16tXcH4m48+fPc+1ob926JdETvUh+yYIiiCft+/fvx8SJEzF48GBMmjQJ9+7d49ZVrVqV60U+Nx4eHkqLU5tJS7qPHTuGY8eO8cbpXL16NXbs2IFmzZoByB7mb9asWSqLUx0EAgFq164tUVW+MCpUqIDVq1ejRYsWWLp0KapXr57n9nZ2dnmuF3+Pjh8/jg4dOqB+/fp4//49tzy3vi7atWuH48eP8zqQFCf+90uKLiMjIzDGkJmZiRcvXuDAgQOYPn062rdvjz59+mDx4sU4c+aMusPUSpaWlti8eTMuXLjAjbYCgDeCgyzi4+O5v/MyZcrk+jdN8ubo6MiN5EFJOynuxIeALkzSDvA7dqXS9oKRO7PcunUrsrKy0KpVK26Zvb09SpcuTSXtYtRd0g5kl8Bdv35dYkx3IDvBdXJyyrW6S69evfKtlqsIzZs3l9oZzs6dO/HLL79w861atULJkiURHR3NVYPft28fl9BMnTqVq3pDFEvWapSenp7w9vbGzZs3wRjDli1bqNlMAU2aNAnXrl3L90GViKjaq0AgwKtXr3gPsExNTbF69WoA4KrzM8awZs0abpvr16/nuu+ePXvmuo6S9uIhMDAQ//zzDyZOnAg3NzeYmZlxwzxGRETg+PHj3JA9RH06duzIFQjI0qmsuICAAKSlpQHIbvaU2xCSJG96enrcg9LQ0FC5etEmpKh5+vQpgOxmfuJNQwpCvNNiStoLRu6k/fnz57CxsZHoDMzS0hJfv35VWGBFnbpL2sW5ubnh8ePHEm1lReNBS5NbZ2+K5ujoiNDQUOzfvx/79+/PdbsuXboAAMqVK4dXr17h7t27+OWXX3Dr1i0EBgbir7/+Ukm82mjgwIH5buPq6qoRD6q0lbOzM168eIHQ0FBUqVJFYjjFKVOm8ErWgf/1R5CcnMzrA0OeMVRpiMXiQSgUokaNGvDy8sLKlSsREBCAPXv2oHPnztTDuAaxsLDgajK9ePGC10Y9P+JJvuj3lBRMhQoVAGT3w/Pp0yc1R0OIciQmJiIiIgJA9hCUhb3Hq1evHiwtLQEAly9fLlC/HNpO7sxSV1eX1x4ayG4n+fXrV41KVNVN0xKYOnXqYM6cOTJtO2fOHO5HSRUqVKiAX375Bb/88kuuJbPu7u7ctJ2dHRo1agSBQABzc3O4ublRqYEStWjRAr/++isqVaqE8ePHo1atWhLbFPXx14uDatWqcT0b5/yOFq0XFxwcjFWrVvFqTQHZw7r16NGDmxcf9tHa2horVqxAlSpV4OnpiU6dOinwDIg6paSkIDg4GNu2bcNvv/2G4cOH48yZM8jMzAQAGo9aQ/z666/ctKg35vwwxrgHeUZGRhJ/80Q+Tk5O3PSrV6/UGAkhyiP+2c6vmZ4shEIh1zdHfHw8r+o9kY3cmU7VqlXx6NEjLFq0CED2cGKzZ89GXFwc9UYqRhMfYOTVadT48eOxbt06FUYj3ezZszFv3jzespMnT1JSrkZCoVBiqC8A6Nu3L/777z/Ur18fLVu2xLt379QQHZHmxIkT8PT05C0Tb9suMnXqVN68u7s79PX1cfTo0Tz3n/N1pGgbMmQIXr16hczMTO6Bj729PW+sdkWMUkAKb+DAgZg1axa+f/+O/fv3Y9myZfk+UHn69CmioqIAAK1bt6aRVgpJvFDj9evXKmlKSIiqiSft0oaJLoj27dtztWrPnz8vtd8tkju5M6HBgwfj0aNHOHnyJAQCAT5+/IiPHz9CIBDIVI1WW2haSTsgvf3piRMn8OXLF/Tr108NEUmaOnUq7O3tkZCQACMjI1hYWKBz587qDotIsXnzZnTq1CnfHuSJ6nXp0gW7d+/GoEGD5Hrdvn37lBQR0WQhISEQCAQoVaoUBgwYgG7duqFUqVLqDotIYWJighEjRmDFihVIS0vD1q1bJR5053T+/HluumPHjsoOsdjLmbQTUhwpK2kXuXDhAtXSlJPcSbuLiwsWLVqEDRs2cE9ura2t8dtvv9ETEzGaWNKeM2n//fffJdq5q5uRkVGxHNO7OLKwsIC3tzcAUGc8GkYoFGLgwIF4+PAhr8O5vFy9epWqQGspIyMjJCcnIzY2Fhs3bsShQ4fg7OyMevXqwdnZmWt2QTTDmDFjsGrVKmRlZWHdunUYMWJEnkOeikaLAfg9OJOCoaSdaANlJO02NjaoVasWnj17huDgYMTGxqJ06dIK2bc2kCtpz8rKwpcvX1CnTh2cOHECP378AGOMnshLoYlJe8mSJWFhYYGYmBh06dIFixcvVndIhBAlklYNtl69eti5cyevOdOPHz9gZmamytCIBgkMDMSrV6/w8OFDPHz4EI8fP8b58+e5Hn5LlCgBZ2dnrFy5Us2REgBwcHCAl5cX/v33X8TGxmLu3LnYsWOH1G0TEhK40SEqVKhQ6B6gSfYwqKampkhISKCknRRboqRdR0dHoQ9u27dvj2fPniErKwuXL19G7969Fbbv4k7uzNLT0xPDhw+HQCBAyZIlKWHPhSZWj9fT08OJEycwe/ZsbNy4Ud3hEEKUTFo/FjVr1kT9+vXx559/AgD8/f0pYddyQqEQ1atXx4ABA7BixQpcuHAB+/btg4eHB4RCIeLj43H16lV1h0nErF69Gubm5gCA3bt3IzIyUup2Bw4c4IZ669ixo0bemxQ1AoEAVapUAZA9Co/o+hJSXDDGuAdSFSpUgL6+vsL2LV5FPiAgQGH71QZyJe1CoRBWVlYwNDRUVjzFhiaWtAP/a95gb2+v7lAIIUomLWn/7bffAGSPEsEYo+GfCIDs3uPv3r2LrVu3YuTIkfD29saZM2eo6YuGsrS0xNixYwEA6enpuXYku3nzZm566NChKolNG4hqLGRmZuLt27dqjoYQxfr48SMSExMBKK5qvIiLiwuXI1EP8vKRO7P08fFBZGQkDh8+rIx4ig16mk0IUbfy5cvz5ps0aYLmzZurKRqiqYYMGYLWrVtj7Nix2LlzJx48eIDU1FQwxiAUClGzZk14eXmpO0ySw4QJE7hhUn19fSVGiHj37h03lnv9+vXRsGFDlcdYXIlK2gFq106KH/H27DmHiy0sU1NT1KxZEwDw5MkTJCUlKXT/xZncHdFt3boVOjo6WL58OdatW4dSpUrxEtQTJ04oNMCiSlNL2gkh2sPT0xOTJk3CgwcPULp06Xx7mSbaKSQkhJs2MDBArVq1uKHe6tSpQ7XrNFSZMmXQp08f7NmzB7GxsTh06BCvI1d/f39uumfPnuoIsdiqVKkSNx0WFqbGSAhRvJcvX3LTii5pB4DGjRvj6dOnyMzMxMOHD6kjcxnJnbSLeowHsqvTic9T6fL/0LUghKibvr4+Vq9ere4wiIZr3rw5nJ2dUb9+fdSoUQO6unLfGhA1GTVqFPbs2QMAmDZtGurUqQNnZ2cAwPHjx7ntNG2kmKJOPGkPDQ1VYySEKJ4yeo4X16RJE+zcuRMAcOfOHUraZST3L/OIESOUEUexQyXthBBCigJRe+jg4OBcE/ZVq1ZhypQpqgyLyKB58+Zwd3fH5cuX8fXrV3Tq1AnPnj1DYmIiLl++DABwcnJCrVq11Bxp8SLeCz8l7aS4UXbS3rhxY26a2rXLTu6k3cfHRxlxFDtU0k4IIaQomTx5MtauXcsbDjArKwsLFy7E2bNnKWnXQAKBAIcPH0bnzp1x69YtfP78GT4+Pqhbty4YYwAAb29vuidRMAsLC5QqVQpxcXGUtJNiR5S0m5ubo2zZsgrff82aNWFsbIykpCTcuXNH4fsvrgpUHJyWloaTJ09i8eLF2Lp1K6Kjo/HgwQP8+PFD0fEVWVTSTgghpChJSUnB5MmTuc7L0tLSMHXqVJw5c4aSPg1WqlQpHD16FBYWFgCAY8eOYcGCBQCyk3rxdu5EcUSl7ZGRkUhJSVFzNIQoRnJyMt6/fw8gu5RdGd/9urq63MPhiIgIfPnyReHHKI7kziy/f/+OgQMHYtGiRThx4gTu3LmD8PBwjBo1CgcOHFBGjEUSJe2EEEKKkv79+yMpKQmTJk3CtWvXMHbsWFy7dg26urpcEkg0k5WVFddGVNzQoUNhZ2enhoiKP1G7dsYYDftGio3Q0FCulo4yqsaLUBV5+cmdWf799994+/Yt9PX1uTe1cePGMDQ0xM2bNxUeYFFFpRKEEEKKksmTJ2PYsGFISkrClClT8PDhQxgbG2PdunXo1KmTusMj+fD09MTcuXO5+YoVK1JHlEpE7dpJcaTs9uwi4kl7cHCw0o5TnMidtF+/fh2mpqa8Xkl1dHRgZWWFjx8/KjK2Io1K2gkhhGi66Oho3r/u3bujT58+YIzB2NgYCxcuhL29PaKjo9UdKpHBH3/8gbCwMBw+fBh37tyBmZmZukMqtihpJ8WRqpL2Ro0acdP37t1T2nGKE7k7oktISICjoyMsLS15y7OyspCUlKSwwIo6KmknhBCi6XIbCkwgECA5ORnTp0/n5qnDoKKhYsWKqFixorrDKPYoaSfFkaqSdkdHR1hYWCAmJgb37t0DY4xyp3zIXRxsZWWFt2/f4tGjR9yyq1ev4t27d7C2tlZkbEUalbQTQgjRdIwxmf/JY/HixejQoQPc3NzQr18/XLt2jVvn5+eHtm3bwt3dHevWrePtOyQkBP3794eLiwt8fHwQFRXFrUtJScHcuXPh6uqKzp0749y5c4W/AIQUEI3VToojUdIuEAh4D6YUTSAQoGHDhgCAL1++IDIyUmnHKi7kLmnv0KEDduzYAR8fHwgEAjx79gxTp06FQCBAhw4dlBFjkURPiwghhGi6LVu2KGW/Xl5emDZtGvT19RESEoIxY8bg5MmTePLkCQ4fPgw/Pz8YGhpi9OjRcHR0hKenJ9LS0jB9+nT4+PigY8eO2Lp1K+bNm4ft27cDALZu3YofP37gzJkzePPmDSZMmIDq1avDwcFBKedASF5Kly6N0qVLIzY2Fm/evFF3OIQUGmMML1++BJBdEm5oaKjU4zVs2BDnz58HkF1F3t7eXqnHK+rkTtqHDh2K58+fS3Q616xZM3h7eysssKKOStoJIYRoOvEx2RXJ0dGRmxYIBEhLS8O3b99w5swZ9O7dG+XLlwcA/Prrrzh79iw8PT1x//59GBkZwdPTEwAwYsQItG3bFlFRUbC2tsaZM2ewatUqmJqaom7dunB1dcWFCxcwYsQIieOnpaUhLS2Nt0xXVxf6+voKOb+srCze/9pCW89bJOf5V6pUCXfv3kVkZCSSkpKUnuSom7a//0DxvgZRUVH4+fMnAKBKlSq5nqOirkH9+vW56eDgYHTv3r1Q+1MVRX8GZM0Z5U7a9fT0sG7dOjx48AAhISFgjKFmzZpK++EvqqiknRBCiKabNm0aHB0dMWbMmHy3Xb9+Pd6/f48VK1bItO+lS5fC398fqampcHNzg5OTE8LDw+Hh4cFtU6VKFWzcuBEA8PbtW16VYyMjI5QvXx5v376FiYkJYmJieOurVKmCkJAQqcf29fXlSuhF+vTpg759+8oUu6y0tUqntp63iOj8raysAGSXUN64cYP3+SzOtP39B4rnNbh9+zY3bW1tjXfv3uW5fWGvgXiz6uvXr+d7PE2jqM9AhQoVZNpO7qR948aN6Nq1K+rXr897QkL4qKSdEEKIpgsMDETt2rVl2lb0sF5WM2fOxLRp03Dv3j2EhYUBAJKSkmBqasptY2JiwnVim5ycDBMTE94+TExMkJycjKSkJOjo6PBKMsVfm5O3tze8vLx4yxRd0h4ZGQk7Ozut+r3X1vMWyXn+devWxcmTJwFkf7aLe1MNbX//geJ9DcT7CWnYsGGun2dFXQN7e3tYWVkhOjoaISEhsLe3LxKFnur6DMidtPv5+eGff/5BrVq10KVLF7Rv3573A0yyFbc/ZEIIIcXT27dvMWrUKJm2k5eOjg6aNGmC/fv3w8nJCcbGxkhISODWJyYmwtjYGEB2yXpiYiLv9YmJiTAyMoKxsTEyMzORkpLCJe7ir81JX19fYQl6XoRCoVb+3mvreYuIzl+8ZP3t27dac020/f0Hiuc1EO9QsXr16vmenyKuQcOGDXHq1CnExcUhIiKiSI18oerPgNxJu7W1NaKiovD06VM8e/YMq1atgpubG7p06YJmzZoViSckqkDXgRBCSFGQmJiI+/fvy7RtQX/bsrKy8OHDB1SoUAFhYWFo0aIFAOD169dwcnICADg5OeHYsWPca5KTk/Hhwwc4OTnBzMwMFhYWCAsLQ61atSReS4g6iCft1BkdKepUNdybuEaNGuHUqVMAsjujK0pJu6rJnbSfPHkSz549w/nz53Hp0iV8/foVAQEBCAgIgKWlJc6cOaOMOIuc4vb0jRBCSPEjrRO3wkpKSkJQUBDc3Nygr6+PoKAg3L9/H+PHj4e1tTWWLVuGdu3awcDAAHv37uWqsTdo0ADJycnw9/dHhw4dsHPnTtSoUYNr9+jh4YEdO3Zg8eLFePv2La5evQo/Pz+Fx0+IrMTbokZERKgvEEIUQJS0m5qawsbGRiXHFA37BmR3RtevXz+VHLcokjtpB4BatWqhVq1amDJlCgIDA7F06VLExMTg27dvio6vyKKSdkIIIZrOx8dH4fsUCAQ4ceIEli1bBsYY7OzssGjRIlSqVAmVKlVCaGgoBg0ahKysLHTv3h3dunUDkF2lffny5fjzzz+xdOlS1KhRA3/88Qe335EjR2LRokXo2LEjzMzMMHPmTF4v9YSoWrly5WBgYIDU1FRK2kmRlpqaivDwcADZnXyqKo8RT9rv3bunkmMWVQVK2pOSkhAYGIgLFy7g7t27yMjIAECJqji6FoQQQrSRkZFRnuO/e3t75zpEbM2aNXHgwAGp6wwNDbFo0SKFxEiIIgiFQjg4OOD169eIiIgAY4zu/0iR9ObNG24IM1VVjQeAsmXLwt7eHu/fv0dwcDDS09Ohp6ensuMXJXIn7dOmTcPNmzeRnp4OxhgAwNbWFp07d0bnzp0VHmBRRV/ahBBCCCHFm6OjI16/fo3ExETExMTA0tJS3SERIreXL19y06pM2gHAxcUF79+/R1JSEh49eoRGjRqp9PhFhdxJe2BgIIDsoVbatm2LLl26oF69egoOq+ijpJ0QQgghpHgTb6IRERFBSTspksQ7oatWrZpKj92yZUvs378fQPZ47ZS0Syd30t60aVN07twZrVu3hoGBgTJiIoQQQgghROPlTNrF2+gSUlSoo+d4EdFoIgBw7do1TJo0SaXHLyrk7uJ8/fr16NixIwDgxYsXePHiBVJTUwt08Li4OEyYMAEuLi7o2bMn7t69K3W7jx8/YsyYMWjVqhU6deoEX19fbt29e/fQqFEjtGzZkvv38OHDAsWjSFTSTgghhBBSvOVM2gkpisST9sqVK6v02DVr1kTJkiUBZJe0i5pfE74CdUTn6+uLXbt2ccm6gYEBhg0bhiFDhsi1n2XLlqFMmTK4dOkSbt++jZkzZ+L48eMwMzPjbbdixQrY2tpi3bp1+Pz5M4YNG4aaNWuicePGAAB7e3scOXKkIKdCCCGEkP+XlZWFyMhIxMbGStw41a9fX01REaK5aNg3UtQxxrik3c7ODiYmJio9vlAohIuLC06fPo2vX7/i9evXKi/tLwoKNE77pk2beMtSUlKwadMmWFpaokuXLjLtRzSOq7+/PwwNDdGqVSvs3bsXV69eldhHVFQUfv31V+jq6sLW1hb16tXD27dvuaRdE1FJOyGEkKLk2bNnmD17NqKioiTWCQQC3LlzRw1REaLZxEvaRUNmEVKUfPv2DXFxcQBUXzVepGXLljh9+jSA7CrylLRLkjtpP3ToEACgVatW6NChAwDg/PnzCAwMxIEDB2RO2t+/fw9TU1Nehx2VK1fG27dvJbbt06cPzp8/jzp16iA6OhpPnz7F8OHDufVRUVFo164dTE1N4eHhgaFDh0JHR0fqcdPS0pCWlsZbpqurC319fZnilhVjjBs6obgTnae2nK802n4NtP38AboG2n7+gOKvgVAodwu2Qlm6dCk+ffqk0mMSUtSVK1cOhoaGSElJoZJ2UiSpsz27iHi79uvXr/PyPJJN7qQ9PDwcNjY2WLFiBbesbdu26Natm1xPGJOTkyWqX5iYmCAhIUFi27p16+Lw4cNo2bIlMjMz4ePjg0qVKgHIfsK5b98+2NvbIyIiAjNnzoSxsTG8vLykHtfX1xfbt2/nLevTpw/69u0rc+yy+Pr1K969e6fQfWq6yMhIdYegdtp+DbT9/AG6Btp+/oDiroF4tVtViIiIgK6uLiZMmAAnJ6dcH34TQv5HIBDAwcEBr169orHaSZGkCUl7w4YNYWBggNTUVFy7dk0tMWg6uZN2HR0dpKamIiMjA7q62S/PyMhAamqqXD/wRkZGSExM5C1LTEyEkZERb1lmZiYmTJiAQYMGoXfv3vjy5QsmTpwIJycntG3bFpaWllxpvZOTE4YNG4bDhw/nmrR7e3tLrFNGSXvZsmXh4OCg0H1qKlEbSDs7O5WXDGkKbb8G2n7+AF0DbT9/oOhfAycnJyQnJ+OXX35RdyiEFCmOjo549eoVkpKS8O3bN5QpU0bdIREiM01I2g0MDNC4cWNcu3YNb9++xcePH2Fra6uWWDSV3El7lSpV8OTJE/j4+KB169YQCAS4fPky4uLiUKdOHZn3Y29vj4SEBHz79o1LukNDQ+Hp6cnbLj4+Hl+/fkXv3r2hq6sLGxsbtGrVCvfv30fbtm0l9pvfjZK+vr7CE3RphEJhkbxpKwxtPOectP0aaPv5A3QNtP38gaJ7DaZMmYKxY8fiv//+Q6dOnWBqaqrukAgpEnL2IE9JOylKNCFpBwBXV1eulP3q1avo37+/2mLRRHLfVQwcOBCMMTx79gzr16/H33//jadPnwIABg0aJPN+jI2N4erqiq1btyIlJQVBQUF48+YNXF1deduVKlUK5cqVw/Hjx5GVlYXPnz8jKCgIFStWBJA95Ft0dDSA7HbyO3fu5LWLUBeqGkUIIUTTNW7cmPs3YsQIpKamYsWKFXB3d+eta9KkibpDJURj0bBvpCh7+fIlgOxa0HZ2dmqLo1WrVtx0YGCg2uLQVHIn7W5ubli4cCHKlSsHxhgYY7CyssLChQslEu78zJw5E58/f0abNm2wbt06/PXXXzAzM8PZs2d5bcyXLVuGM2fOoHXr1hg0aBAaN26MHj16AMj+oHl7e6NFixYYO3YsWrVqlWvVeFWipJ0QQoimE/2Oy/KPECIdJe2kqEpPT+c6Aa9cubJaa4k1a9aMy58ePXqktjg0VYHGaffw8ICHhwc3PECpUqUKdPBSpUrh77//lljeqVMndOrUiZuvWbMmdu3aJXUfv/76K3799dcCHZ8QQgjRZvPnz1d3CIQUeZS0k6LqzZs3yMjIAKDeqvFAdofkjo6OCA8Px4sXL6hTxxzkTtpDQ0Px6dMnVK9eHWXLlgUAfPnyBS9evICNjQ0qV66s8CCLIvqQEUII0XTiw7RGR0dDT08PFhYWaoyIkKJHfKQHStpJURISEsJN16xZU42RZKtWrRrCw8Px8+dPfPr0iTqjEyN3HYhFixbh999/53XmZmBggN9//x1LlixRaHCEEEIIUY2uXbti2rRpEsvHjx+P9u3bqyEiQoqGsmXLwtDQEADkGv6YEHXTtKS9evXq3PSLFy/UGInmkTtpj4iIgJ2dHUqWLMktMzc3h52dHdcmglBJOyGEkOIhNjYW379/V3cYhGgsgUDAVZEXjdVOSFFASXvRIXf1+IyMDMTExEiM0x4TE4PMzEyFB1hUUdJOCCGkKFi4cCE3/eHDB958SkoKQkNDYWRkpI7QCCkyHB0d8fLlSyQnJ+Pr169cE1JCNJkoadfT00OlSpXUHA3/wcHDhw/VGInmkTtpd3R0RGhoKObMmYMBAwYAAPbv34/v37+rvQMDQgghhMjn1KlT3IPm79+/4/Tp09w6UYlh7dq11RIbIUVFzs7oKGknmi49PR2vX78GAFSpUgV6enpqjghwdnaGvr4+0tLScOPGDXWHo1HkTtq7d++O5cuX4/Lly7h8+TK3XCAQoHv37oqMrUijknZCCCFFgbOzMwQCAR48eABjY2PeA3hDQ0M4Ojpi4MCBaoyQEM2XM2lv3Lix+oIhRAahoaFIT08HoBlV44Hs35wGDRrg1q1beP36Nb5+/YoyZcqoOyyNIHfS3qdPH4SHh+Pw4cPcE3iBQIC+ffuid+/eCg+QEEIIIcqzbds2AECjRo3g5OSErVu3qjkiQooeGvaNFDWa1p5dxMXFBbdu3QIA3Lx5E56enmqOSDMUaJz26dOnY+DAgdybXbNmTVhbWys0sKKOStoJIYQUJcHBweoOgZAii5J2UtRoatLeokULrFy5EgAQEBBASfv/K1DSDgDW1taUqOeBknZCCCGa7v/au+/oKMq2j+PfFCCNSOhIj/TQRUBKEqQGQZQmIvKg0kQFFESOhaICImJ9LBE0qKAGUYGgiUgL+CDV0HtHktCLaaTt+0dO5s1SNIHdzG729zknh9nZmdnrHrK599q7jRw5Ml/Hubm58cknn9g5GhHnpbXaxdls377d2HakeUs6dOhgjGtfsmQJH3zwgfIqbiNpFxEREee2detW3NzcrJaouvbDkcVi0QcmkX9Rrlw5vL29SU1NVdIuTuHPP/8EwM/PzyFmjs/l7+9Px44diY6O5uTJk/z555/cfffdZodlugKv0y75ow84IiLi6Jo1a0azZs1o3rw5zZs3x9vbG3d3d+rWrUudOnVwd3enePHiNG/e3OxQRRya1moXZ3Lu3DlOnDgB5NQD7u6OlRLmndx88eLFpsXhSBzrf0hEREQKzWeffUZ4eDjh4eF06dIFNzc3IiMj+frrr5k/fz6RkZF4eHgQHBxsdqgiDi83aU9NTeXMmTPmBiPyD3Jb2QGHbMV+4IEHjAZQJe05lLTbiVraRUTEmURERFC+fHmqV69u7KtevToVKlRgwYIFJkYm4hw0GZ04i7xJuyP2pKpYsSKtW7cGYNeuXRw6dMjkiMx3y0n71q1bmT9/PvPnz2fr1q22jKlIUNIuIiLO5NKlSxw/fpyPPvqIvXv3sm/fPj7++GOOHTvG5cuXzQ5PxOEpaRdn4ehJO6iL/LUKPBHd1atXGT9+PBs3brTa36pVK2bPnk3x4sVtFpyIiIgUjnbt2rFy5Uq+/PJLvvzyy+ueE5F/pqRdnEVu0u7t7U29evVMjubGHnzwQV588UUAlixZwvjx402OyFwFbmmfO3cuGzZswGKxWP1s3LiRzz//3B4xOiW1tIuIiDN5+eWXCQ0Nva5+Dw0N5eWXXzY7PBGHp6RdnMGlS5c4fPgwAE2bNsXDw8PkiG6sTp06xqz2GzduJDU11eSIzFXglvbffvsNd3d3xo4dS7du3QCIjo7mvffe49dff+Wpp56yeZAiIiJiXyVLlmTWrFn89ddfHDlyBIvFwl133UWVKlXMDk3EKShpF2cQFxdnbDtq1/hcwcHBHDp0iIyMDDZt2kRISIjZIZmmwC3tp0+fpnr16jzyyCMEBAQQEBDAwIEDqVGjBqdPn7ZHjE5JLe0iIuKMqlSpQnBwMCEhIUrYRQqgXLly+Pj4AEraxXE5w3j2XO3btze2161bZ2Ik5itwS7uPjw+nT5/m7NmzlCtXDoAzZ85w+vRpfH19bR6gs1LSLiIijq5Xr17UrVuXt956i169ev3jsUuWLCmkqEScU+5a7Xv27DHWatfnQXE0ztTSrqT9/xU4aW/evDlr1qyhb9++NGvWDDc3N/78809SU1Np2bKlPWIUERERO4iPj6dMmTLG9s0o8RDJn9ykPS0tjdOnT1OxYkWzQxKxsm3bNgCKFStGgwYNzA3mXwQGBlKpUiUSEhJYv349mZmZeHoWOH0tEgpc6pEjR7Jp0yZSUlJYv349ABaLBR8fH41nz0MfcERExNENGzaM8uXLAzB06FDVXSK36dpx7UraxZGkpqayb98+AIKCghx+1S83Nzfat2/PwoULSUpKYtu2bbRo0cLssExR4KT9rrvuYt68ecybN4+9e/cC0KBBA4YMGWL1h0pEREQc2/Dhw43tESNGmBiJSNFwbdLeunVr84IRucbOnTvJysoCcmaOdwbBwcEsXLgQyOkir6S9AGrWrMnUqVNtHUuRotYKERFxBsOGDaNp06Y0a9aMJk2aaH4akdugGeTFkeV2jQdo1qyZeYEUQHBwsLEdGxvLc889Z2I05slX0r5s2TICAgJo27Yty5Yt+8dje/ToYZPAnJ2SdhERcQbbtm1j+/btfPnll7i5uVGrVi2aN29O06ZNadq0qTHmXUT+nZJ2cWR5J6FzlqQ9KCiI0qVLc+HCBdatW0d2djbu7gVeAM3p5Stpnzp1Ko0aNaJt27ZMnTr1pgmpm5ubknYREREncu+997Jjxw6Sk5OxWCwcOHCAgwcPEhkZCeQsAde8eXNeeeUVkyMVcXxK2sWR5W1pb9KkiXmBFIC7uzvt27dnyZIlXLhwgT179tCwYUOzwyp0t/Q1hcViueFPdna2reNzWmppFxERZ/DBBx+watUqvv76a55//nlCQ0MpVaqUUbefPHmSpUuXmh2miFMoW7asMcRESfv1MjMz+fPPP9m6dSuXLl0yOxyXkpWVxY4dO4CcOcr8/f1Njij/8naRX7t2rYmRmCdfLe2bN2++4baIiIg4P3d3d+rVq0e9evXo168fu3btYvHixcTExBiTFonIv8tdq3337t0cO3aMrKwsPDw8zA7LdOvWreP1119n3bp1pKWlAVCiRAlmzJjhsmOUC9uBAwdISUkBnGcSulzXjmsfNWqUidGYo8AT0c2ZM4cKFSrwwAMPWO3fsWMHV65coV27djYLzpmppV1ERJxBamoq27dvZ9u2bcTFxbF7927S09OxWCwAVKxY0WnGPoo4gtq1a7N7926uXr3K8ePHCQwMNDsk01gsFsaMGcOHH3543XNXr17l+eefp3Xr1tx7770mROdanHESulxNmzalZMmS/P3336xduxaLxeJyuVaBk/bPPvuMRo0aXZe0v/vuu+zevZtNmzbZLDhn5mq/SCIi4pw6dOhgDG+zWCzUqFGDZs2aGT9aZ1qkYOrXr8/ixYsB2Lt3r8sm7VlZWTz33HNWCXvVqlVp3bo1iYmJrFu3DoBp06b960TXcvuccRK6XJ6enrRt25aYmBgSExM5dOgQtWvXNjusQnVLS75dKy0tjXPnztniUiIiIlKIsrKycHNzIyAggIEDB3LfffdRtWpVs8MScVr169c3tvfu3cv9999vYjTmOHPmDAMHDmTlypVATmPWBx98wMiRI/H09CQ9PZ3atWtz4sQJfv75Z/bu3Wt138T28ra0O1v3eMjpIh8TEwPAmjVrlLTfTMuWLYGcN92uXbuMx3mVLl3adpE5ObW0i4iIM8idPf7ChQt89NFHfPTRR5QuXdqqtd3VPhyJ3I5rk3ZXc/DgQTp06MCpU6cA8PDwYM6cOTz++OPGMcWLF2fs2LE8//zzAHz11VfMmDHDlHhdgcViMVray5cvT6VKlUyOqODuu+8+Y3v58uUMGzbMxGgKX76T9tyxbW5ubsb2tR566CHbRFUEKGkXERFn8MEHHxhLvcXFxREXF8f27dtZsWKF0UpWsmRJY1tE/lm9evWM7T179pgYSeHLysqiX79+RsJeqVIlIiMjad++/XXHPvroo7zwwgtkZWXx3XffMX36dH1+tpNTp04ZvaKbNm3qlPe5RYsWxnrtv/32G5mZmXh62qTTuFPId0knT54M5KzZXqVKFZ588knjOS8vL2rUqEGtWrVsH6GIiIjYlZubG3Xr1qVu3br06dOHXbt2sWTJEmP2+L///tvsEEWchp+fH9WqVePEiRPs3bvXpSbNWrx4Mdu3bwdyehysWrXqpvNilC9fntDQUFauXMmxY8c4cOAAdevWLcxwXYYzT0KXy8PDg86dOxMZGcnly5fZuHEjbdu2NTusQpPvpL1Hjx4AbNmyhSpVqhiP5cZc5Y+ziIg4t9zZ43Nb2Xfv3k1GRobZYYk4tfr163PixAkuX75MYmKiU3ZHvhVff/21sf3OO+/860SW3bp1M3rxxMTEKGm3E2cfz56rW7duREZGAjm/L0ra/8GUKVMAyMjI4OLFi8aMs7k0y6yIiIjzuHb2+Fyenp7Ur1/fGNcuIvlXv359fv31VyCni7wrJO3nz5/nl19+AXK6xXfu3Plfz+nWrRsvvPACAL/++itjxoyxa4yuqqgk7V27djW2Fy9ezOuvv25iNIWrwEl7SkoKr7/+OmvWrCErK8vqOTc3NzZu3Giz4JyZWtpFRMQZ5NblXl5eNGrUyEjSGzVqRIkSJUyOTsQ5NWjQwNjetWsXHTt2NDGawvH9998bvXQGDhyIh4fHv54TFBRE5cqVOXXqFGvWrCEtLQ0vLy97h+pycocseHt7O/XEopUqVeLee+/ljz/+YNeuXezcuZNGjRqZHVahKHDS/vHHH7NixQp7xFKkKGkXERFn8Oyzz9K8eXPq1avnUpP6iNhT3t4pW7duNTGSwpGdnc2nn35qPB40aFC+znNzc6Nr16588cUXpKamsm7duny10Ev+JSUlcfjwYQAaNmyYry9THNkjjzzCH3/8AcAvv/ziMkm7e0FPiI2Nxc3NjSeeeAKAKlWq0KdPH/z9/ZkwYYLNAxQRERH7GTx4MA0bNlTCLmJDjRo1olixYkDOfFBF3U8//WS05rZo0YImTZrk+9y8XZ5z1+EW29m5c6cx9Kkg/y+OqlOnTsb2//73PxMjKVwFTtrPnTtH5cqVeeqppwAoVaoUEydOxM/Pj3379tk8QGellnYRERER11SiRAmjBXDfvn0kJSWZHJH9ZGVlGatMQc5KUwX5HNypUyfc3XNSktx5AMR2cr9MAWjcuLGJkdhG3bp1CQgIAGD9+vU3XYq8qClw0l68eHF8fHyM7TNnzpCZmUlGRoa6zYuIiIiIAHfffTeQM8Fj3onA7O3cuXNs3bqVxMTEQnm9r7/+mt27dwPQunVrwsLCCnR+6dKladWqFQC7d+/mxIkTNo/RleVN2otCS7u7uztt2rQBciY/PHDggMkRFY4CJ+1lypTh7NmzQE7X+LNnz9KpUyfOnj1L8eLFbR6gs1JLu4iIiIjryk3aoXC6yGdmZvKf//yHcuXK0aJFC2rXrm2VsNnDmTNnGDdunPH4jTfeuKXPwN27dze2Fy1aZJPYJEdRa2kHrJZ6c5Uu8gVO2oOCgkhLS+PgwYP07NkTi8VCcnIyYP2Gc3VK2kVERERcV4sWLYztwpiMbuLEiXz11VfG46SkJIYOHXrdak+2YrFYeOaZZ7hw4QIAAwYMuOVZ8vv3729sf/fddzaJT3ImCNyxYwcA1atXp1SpUuYGZCN5k/b169ebGEnhKfCsM3nXw6tduzZly5Zl586d1K5dm169etk0OBEREXEu6enpzJgxg40bN5KcnEzdunWZMGECtWrVIj09nTfffJO1a9disVho06YNL730Et7e3kBO19g33niDEydOEBQUxNSpU431rdPS0pg2bRqxsbGULFmSZ599lm7duplZVJF/1LBhQ4oVK0ZGRobdk/a4uDjeeeed6/Zv2bKFhQsX8sgjj9j8Nb/++mu+//57AAICAnjvvfdu+Vp16tShWbNmxMXFsXnzZg4fPsxdd91lo0hd15EjR4zG1aLQNT5XixYt8PT0JDMzUy3t+dWtWzdeeOEFHnzwQY4ePVqgcy9evMiYMWNo27YtvXv3ZtOmTTc87tSpUzz99NOEhoYSFhZGRESE1fNRUVF0796dkJAQpk6daqwRaSa1tIuIiCvKysqicuXKREREsGrVKoKDg43us5GRkRw6dIgffviBpUuXcuHCBebNmwfkJPsTJkxgwIABrFq1ioYNGzJp0iTjuuHh4Vy+fJlffvmF6dOn8+abb3L8+HEziiiSL9dORvf333/b7bVmzpxpTMj11ltv8dtvvxnPTZ48mczMTJu+3tGjR3nmmWeMx+Hh4VSoUOG2rjlgwABjOzIy8rauJTnyzqVQlJJ2Hx8fmjdvDuS8t86fP29yRPZX4KT977//vq6bza5duxg/fnyBv8WbOXMm5cqVY+XKlYwePZqJEydy5cqV646bNWsWlStXZsWKFcydO5fIyEgjwT906BDvvvsub7/9Nj///DPx8fF8/vnnBS2WiIiI2IC3tzdDhw6lQoUKeHh48PDDDxMfH8+lS5dISEigbdu23HHHHfj6+hIaGsqRI0eAnO7D3t7e9OrVixIlSjBs2DD27NlDQkICkLMe7/Dhw/Hz86NJkyYEBwezfPnyG8aQnp5OUlKS1U9aWhrZ2dk2+wFsej1n+XHVct9q+Vu2bAnkdCVft26dXWI6fPiw0eJdvnx5nn76aTp06EBoaCgABw8eJCIiwmblT09PZ9CgQcaXEIMHD6ZPnz63fe2+ffsa7+ElS5aY/n9tq98BM382b95s3NNmzZoVqXuQOxkdwO+//+60vwP5le/u8fHx8Tz//PMcOXIEPz8/Xn31VZo1a8brr7/OunXr8v2CuVJSUoiNjSUqKgovLy9CQ0NZsGABa9eupUePHlbHJiQkMGjQIDw9PalcuTJNmzblyJEjtGzZkpiYGDp37kyDBg0AGDp0KG+88QYjR4684eump6eTnp5utc/T09Pmk+hZLJYC/Uc4s7y/vK7K1e+Bq5cfdA9cvfxg+3uQuwSSs9uxYwelS5emVKlS9OjRg/fff5+LFy/i6enJqlWrjDGwR44coVatWsZ53t7eVKlShSNHjuDr68v58+etnq9Tp44xY/W1IiIimDNnjtW+fv36WY2btYWTJ0/a9HrOwlXLnasg5Q8KCjK2ly5dSv369W0ez9SpU42/O4MGDeL06dMAjBo1ijVr1gA5re0tWrS47THNV65cYfjw4cY44qpVqzJu3Dib9Hpxc3Ojbt267N+/n82bN7N161bKli1729e1B2d5D/z+++/GdsWKFW3aO8nse1C7dm1jOyYmptAn2bNV+WvWrJmv4/KdtH/wwQccPnwYyGltf+ONN6hVqxZ//vknAMWKFeP+++/Pd4AnTpzAz8/P6s1Yu3Zt4xv3vPr168evv/5K48aNSUxMZOfOnQwdOhTIqeTvvfdeq2ucOnWKtLQ0vLy8rrtWYVXkiYmJLtdtz+w3ryNw9Xvg6uUH3QNXLz8UfkXuyJKSkpg+fTqjRo0CcladKVmyJF26dMHNzY177rmHBx98EIDU1FR8fX2tzvf19SU1NZWUlBQ8PDys6nVfX19SUlJu+LqPP/44jz76qNU+W35Bn52dzcmTJ6latWqR+XIlP1y13Llupfx9+/bl2WefBXLGnVevXt2mMa1fv5758+cD4OXlxYsvvmh8tq5evTphYWFER0eTkJDA6NGj+eWXX657n+XX5s2b6devn/E3zsPDgwULFtCwYUPbFAbo1asXb731FhaLhd27dzN48GCbXdsWnOk9YLFY2LNnD5CTsLds2dImw3cd5R706tWLp59+GsiZD8XW762bMav8+U7a4+LicHNzIywsDIvFQnR0NHFxcRQvXpx+/foxaNCgAn0bdrPKOSkp6bpjmzRpwqJFi2jfvj1ZWVkMHz7c+Lb92uv4+fkZ+2+UtNu7Is9VqVKlQvvlMZujvHnN5Or3wNXLD7oHrl5+0D241tWrVxk3bhzt2rUzJqp988038fb2Zs2aNVgsFmbMmME777zDhAkT8Pb2NiZMypWcnIy3tzc+Pj5kZWVZfSGfnJyMj4/PDV+7ePHihbIMrbu7u0v+X7tquXMVpPwVK1akQYMG7Nmzh61bt3L58mUCAgJsEofFYuH55583xrJPnjyZ8uXLWx3zySef0KpVK06fPs3vv//OlClTmD17doFfKzw8nNGjRxu9VQMCApg/fz7t27e//YLk0aNHD9566y0AfvvtN4YMGWLT69uKM7wHDh8+zKVLl4Ccids8PDxsen2z70HlypWpWbMmR48eZfPmzWRmZhbq8uOFXf58J+2XLl2iatWqTJ06Fcj5RuPkyZPMnj2b1q1bF/iF/6lyzisrK4sxY8YwePBg+vbty5kzZxg7diyBgYF06tTpuuvkJv3XXidXYVXkHh4eDv9mtjWz37yOwNXvgauXH3QPXL38oHsAOetFv/TSS5QrV46xY8ca+w8dOsT48eONL9sfeOABI4EIDAzkp59+Mo5NTU3lr7/+IjAwEH9/f8qUKcOhQ4eMVr0DBw4QGBhYeIUSuUVdu3Zlz549ZGVlER0dzcCBA21y3fXr1xtjlhs3bswLL7xw3THVq1dn2bJltG7dmqysLMLDw3nllVfy/cWBxWLhjTfesJoUslWrVkRGRtqlcap169b4+PiQkpJi1bVbCm7Lli3Gdt7lB4uStm3bcvToUdLS0vjzzz9vKSd1Fvn+VJGdnc0dd9xhPPb39we45ZtTrVo1kpKSOHfunLHv4MGD11XAV65c4ezZs/Tt2xdPT0/uvPNOQkNDjaUzAgMDOXTokNU1KleufMNWdhEREbG/adOmcfXqVaZMmWLVHbN+/fr8/PPPpKWlkZqayrJly4xlne6++25SU1OJiooiPT2dzz//nAYNGhhLvnXv3p25c+eSnJzMzp07Wbt2LZ07dzalfCIF8cADDxjbixcvttl1v/nmG2P7+eefv2lLaosWLRg2bBiQ00D2ySef5Ov6FouFCRMmWCXsQ4YMYc2aNXbrTVqsWDFj8r4TJ07w119/2eV1XIErJO3t2rUztm9ljjVnUqCmgP3799OrVy969erFgQMHAIzHuT/55ePjQ3BwMOHh4aSlpREbG8vhw4cJDg62Oi4gIIAKFSqwePFisrOzOX36NLGxsUYl361bN1asWMG+fftISkriiy++ICwsrCDFsgst+SYiIq4oISGBqKgo4uLi6NChA+3bt6d9+/bExcUxZswYUlNTuf/+++nRowfJyck8//zzQE5PuLfeeosFCxbQoUMHtm/fzmuvvWZcd8SIEfj5+dGtWzcmTpzIxIkTqVGjhkmlFMm/du3aUbp0aQBjqcPblZGRYcwY7+XlRe/evf/x+HHjxhk9gN577z1SU1P/9TVmzJjB22+/bTyeNWsWkyZNsnuP1bZt2xrbrrIGtz3kTdrvvvtuEyOxn7x5Y2xsrImR2F++u8dDzh+I+Ph4q315Hxc0UZ04cSKTJ0+mY8eOVKhQgRkzZuDv7090dDQREREsXLgQyFkabvbs2Xz44Yd4eXnRpUsXHnroIQBq1arF2LFjee6550hOTua+++7jiSeeKFAc9qCkXUREXFGlSpWsPixeK3e86o0EBQXx3Xff3fA5Ly8v3njjjduOT6SweXp68thjj/H+++9z9epV5s+fz+jRo2/rmitXruTs2bMA9OzZk5IlS/7j8bVq1aJfv35ERkZy9uxZ5s6da0yQdyMxMTG88sorQM5n2k8//ZShQ4cWyiTLeZfyWr9+PQ8//LDdX7Ooyc7ONnolV65cmYoVK5ockX3Uq1eP8uXLc+bMGdatW0dWVpbNx+47inwn7c2aNbN5IhoQEMAHH3xw3f6wsDCr1vKgoCC++OKLm16nZ8+e9OzZ06axiYiIiIjYwrBhw3j//feBnJbuESNGUKJEiVu+Xm7DFsAjjzySr3NefPFFIiMjAZgyZQqPPvqo0QMgr8OHDzNw4EBjgrvXXnuN4cOHF9qSnnlXhVJL+605ePAgf//9N1B0u8ZDzhdKwcHBLFq0iCtXrrB9+3aaN29udlh2ke+k/bPPPrNnHEWOWtpFREREBHIaoDp16sSKFSs4evQo7733Hi+++OItXSsjI8MYG587ZCQ/mjVrxqOPPsqCBQu4cOECkyZN4r///a/VMZcvX6Znz55cvHgRyBkG+9JLL91SnLcqICCAoKAgdu/ezbZt20hJSbnpShFyYxs2bDC2i3LSDhASEsKiRYuAnB4oRTVpd+3pbUVERERECsHs2bONceVvvPEGiYmJt3Sd1atXG0l1jx49brpi0o3MnDnTWL3hk08+YeXKlcZz6enpPPTQQ+zduxfImTjyq6++MmU1jNzW9qysrH8cbiM3tn79emM773CDoqhLly7GdkxMjImR2JeSdjtRS7uIiIiI5GrcuDHDhw8HcpYovtUW7LzzPvTr169A51auXJlXX30VyBn33K9fPw4ePEh2djZPPPEEq1evBqBs2bJERUUZq0UVtryrU+VtNZb8yR1W4OHhQatWrUyOxr5q165NzZo1gZwZ5HOHBRQ1StrtREm7iIiIiOT12muvGUsoz5s3j6VLlxbo/IsXLxpJu7+/f767xuc1fvx47r//fuN6PXr0YODAgSxYsAAAb29vq+UYzZB3XPsff/xhWhzO6NKlS+zevRuApk2bGj0riio3NzdjLrSMjAx+++03kyOyDyXtIiIiIiKFoFy5ckyePBnIWQe9V69ejBo1iuTk5H8912KxMGrUKGO5tiFDhtzSWG8PDw+++eYbGjRoAMCBAweMCerc3d357rvvTG+drVevnvHlxoYNG4xJ8eTf5f2So6h3jc+Vd0LyJUuWmBiJ/ShptxO1tIuIiIjItZ599ln69OljPP7kk09o2rQp+/fv/8fzpk2bZrSy+/r6MmbMmFuOwd/fn6ioKCpVqmS1/+OPP+aBBx645evairu7u/HFQWJiYqEsNVdU5J1x31WS9g4dOhjLHi5btoy0tDSTI7I9Je0iIiIiIoXE09OT77//no8++siYRO7QoUO0bdvWamK4XJcuXeLDDz80xqK7ubnxzTffEBgYeFtxBAYGsnHjRkaPHs2DDz7IypUrGTFixG1d05Y0rv3WrFu3zthu166diZEUnhIlStCjRw8ALly4wLx588wNyA6UtNuJWtpFRERE5Ebc3NwYNWoU27dvp0mTJgCcP3+eTp068dxzz3Hq1CmWL19OkyZNCAgIYPTo0ca506dPt1lreNWqVXn//ff56aefuO+++2xyTVvRuPaCu3r1Khs3bgSgRo0aVKlSxeSICs+4ceOM7TfffJOMjAwTo7E9Je12oqRdRERERP5J7dq1iY2NpWvXrsa+9957jypVqtC1a1d27NhhdfyTTz55y+u7O5u84+rV0p4/W7Zs4erVqwC0b9/e5GgK1913321MSHf8+HFjYsWiQkm7iIiIiIhJ7rjjDn755Rfeeecdihcvft3zdevW5YknnmDNmjXMmTPHZRqGAgICqFevHgBxcXFFcpyyreXtGu9qSTvAK6+8YmxPnz6drKysmx576dIlZs+ezYQJE9i0aVNhhHdblLTbiav8QRURERGR2+Pu7s5zzz3Hnj17ePXVVwkKCqJFixZ89dVX7Nmzh88//5yQkBCX+3yZO649IyODP//80+RoHJ+rJ+1t2rShQ4cOABw8eJDZs2ff8LgjR47QsmVLxo8fz6xZs2jdujUTJkxg165dhRlugShpFxERERFxAHfddRevvfYau3btYvPmzTz22GO4u7vux3WNa8+/rKwsY+b4cuXKUbduXZMjMkfukooAEydO5Pvvv7d6/scff6RFixYcPHjQ2GexWJg1axaNGjVi6NCh/9irw2KxcOLECdsH/i9c96+AnbnaN6EiIiIiIrakGeTzb/fu3Vy+fBnImTXeVXORkJAQpk6dCuQk2I888ghPPfUUn3/+OU888QR9+vTh4sWLQM6cEo8++qjV+Z9//jn33nuv1ZdEFouFQ4cOERERQcuWLenZsydXrlwpvEIBnoX6ai7EVd8oIiIiIiK2EBQUhJ+fH0lJSWpp/xeu3jU+r1dffZVjx44RERFBVlYWn3766XXH9OvXjzlz5nDHHXcwYcIEoqKimDZtGqmpqWzbto02bdpQv359KleuTFxcHOfPn7c6/7PPPmPChAmFVSS1tIuIiIiIiOPx8PCgZcuWAJw6dYq//vrL5Igcl5L2/+fm5sbcuXMZO3bsdc95e3szZ84cIiMjueOOOwBo3LgxL7/8Mhs3biQoKMg4du/evaxYseK6hL1hw4bUr1/frmW4llra7UQt7SIiIiIit+fee+9l1apVQE4X+b59+5ockeOxWCxG0u7n50fTpk3NDcgBuLu78+677zJq1Ch27NjB+vXrKV68OI8//jh16tS54TmNGjVi27ZtzJ07l7lz57Jjxw4yMjIoXbo0rVu35t577yU4OJgqVapQo0aNQi2PknYREREREXFIece1//HHH0rab+Do0aPEx8cDOV9yeHoqxctVu3ZtateuTZ8+ffJ1vKenJyNHjmTkyJGkp6dz/vx5KlasaDTIZmdnc/z4cXuGfOO4Cv0VXYRa2kVEREREbo8mo/t3a9euNbZdvWu8LRUvXpxKlSqZHQagMe12o6RdREREROT2lC1bllq1agGwdetW0tPTTY7I8axZs8bYDgkJMS8QsRsl7SIiIiIi4rBy12u/evUq27ZtMzcYB5SbtHt5edGqVStzgxG7UNJuJ2ppFxERERG5fdeOa5f/d+zYMWOMdZs2bShRooTJEYk9KGkXERERERGHldvSDhrXfq28XeNDQ0NNi0PsS0m7nailXURERETk9jVq1Ahvb29ALe3XUtLuGpS024mSdhERERGR2+fp6ck999wDwPHjx0lISDA5IsdgsVhYvXo1kDOevWXLliZHJPaipF1ERERERByaushf79ixY5w4cQLQePaiTkm7nailXURERETENrRe+/Xydo3v0KGDeYGI3SlpFxERERERh6YZ5K+n8eyuQ0m7nailXURERETENipWrEiNGjUA2LJlCxkZGeYGZDKLxWIk7d7e3saYfymalLSLiIiIiIjDyx3Xnpqays6dO02OxlwHDx7UeHYXoqRdREREREQcXqtWrYztjRs3mhiJ+aKjo43trl27mhiJFAYl7Xai7vEiIiIiIraTN2l39cno8ibtYWFhJkYihUFJu4iIiIiIOLymTZtSrFgxwLVb2s+dO8eqVasAqFq1KkFBQSZHJPampN1O1NIuIiIiImI7Xl5eNGvWDID9+/dz8eJFkyMyR2RkpDER34ABA5R3uAAl7XaiN4+IiIiIiG3l7SK/adMmEyMxz9dff21sP/bYYyZGIoVFSbuIiIiIiDiFvOu1r1+/3sRIzHHs2DFjaECTJk1o1KiRyRFJYVDSbidqaRcRERERsa22bdsa2//73/9MjMQcixYtMrYffvhhEyORwqSkXUREREREnEK1atWoUqUKkDODfGZmpskRFa7vv//e2O7bt6+JkUhhUtJuJ2ppFxERERGxLTc3N6O1PTk5me3bt5scUeE5fvy4MY6/SZMm1K5d2+SIpLAoabcTJe0iIiIiIrbnql3k83aN79evn4mRSGFT0i4iIiIiIk5DSbuSdlejpN1O1NIuIiIiImJ7jRs3xs/PD4Dff/8di8VickT2d/LkSTZs2ADklL9OnTomRySFSUm7iIiIiIg4DU9PT2Ppt/j4eI4fP25yRPb33XffGdtqZXc9StrtRC3tIiIiIiL24Wpd5BcsWGBsDxgwwMRIxAymJu0XL15kzJgxtG3blt69exuzIV6rf//+tG/f3vi55557mD9/PgBbtmzhnnvusXo+Li6uMIshIiIiIiKFyJWS9h07dhiz5Ldq1YpatWqZHJEUNk8zX3zmzJmUK1eOlStXsmHDBiZOnMjixYvx9/e3Om7hwoXG9qVLlwgLCyMkJMTYV61aNX744YdCi1tERERERMzTunVr3N3dyc7OLtJJu8ViYcyYMcbjQYMGmRiNmMW0pD0lJYXY2FiioqLw8vIiNDSUBQsWsHbtWnr06HHT81asWEG9evWoWrXqLb1ueno66enpVvs8PT0pXrz4LV3vZiwWC9nZ2Ta9pqPKLaerlPdGXP0euHr5QffA1csPtr8H7u4awSYicjMlS5akSZMmxMXFsXPnTi5dukSpUqXMDssmkpOTiY2NJT4+nmXLlrFmzRoAatSoweOPP25ucGIK05L2EydO4OfnR9myZY19tWvX5siRI/94XnR0NN26dbPal5CQQOfOnfHz86N79+488cQTeHh43PD8iIgI5syZY7WvX79+9O/f/xZLcmMnT57k77//tuk1Hd3JkyfNDsF0rn4PXL38oHvg6uUH292DmjVr2uQ6IiJFVdu2bYmLi8NisbBhw4brcgRnc/LkSSZMmMCPP/54XSOjm5sb4eHh+Pr6mhSdmMm0pD01NfW6XzpfX1+SkpJuek58fDy7d+9m1qxZxr4aNWrwzTffUK1aNY4dO8bEiRPx8fHh0UcfveE1Hn/88eues0dLe7Vq1QgICLDpNR1VdnY2J0+epGrVqi7bMuTq98DVyw+6B65eftA9EBEpbG3btuW///0vkDOu3ZmT9pUrV9K/f38uXLhw3XO+vr4sWLCALl26mBCZOALTknZvb2+Sk5Ot9iUnJ+Pt7X3Tc2JiYmjZsiWlS5c29pUtW9ZorQ8MDOTJJ59k0aJFN03aixcvbvME/Ubc3d1d7kObK5b5Wq5+D1y9/KB74OrlB90DEZHCUlQmo5s3bx5PPvmkMbyqbNmy9O7dmwYNGuDr60v37t258847TY5SzGRa0l6tWjWSkpI4d+6ckXQfPHiQXr163fScmJiYfx3HoQ9KIiIiIiJFX9WqValatSonT55k48aNZGRkUKxYMbPDKpBDhw4xbNgwI2Hv3r078+fPd5keu5I/pmW4Pj4+BAcHEx4eTlpaGrGxsRw+fJjg4OAbHr9//34SEhIIDQ212r9lyxYSExOBnHHyn3/+Oe3atbN3+P9K67SLiIiIiNhX7uf+lJQUtm3bZm4wt+C1114jMzMTgKFDh7J06VIl7HIdU5ulJ06cyOnTp+nYsSPvv/8+M2bMwN/fn+jo6OsmhouJiSEkJOS67vP79u3j8ccfp127djzzzDOEhobetGu8iIiIiIgUHc7cRT4+Pp5vv/0WgNKlSzN79uybTqYtrs3UddoDAgL44IMPrtsfFhZGWFiY1b686xPmNWjQIK1XKCIiIiLigvL2sF2+fDljx441L5gCCg8PN1rZR44cib+/v8kRiaPSAHA7Ufd4ERERERH7aty4MVWqVAFyZmC/cuWKyRHlz9WrVwkPDwfAw8ODp556yuSIxJEpaRcRERGbSU9PZ+rUqXTv3p2QkBCGDx/OoUOHjOd37tzJkCFDaN++Pd27d+e3334zntu9ezePPPIIbdu2Zfjw4SQkJBjPpaWl8eqrrxIcHMz9999PTExMoZZLRByTm5sbDz74IJDz92fZsmXmBpRP33//PadPnwagd+/exhcPIjeipF1ERERsJisri8qVKxMREcGqVasIDg5m3LhxAJw7d44JEyYwdOhQVq9ezTfffEP9+vWBnA/bEyZMYMCAAaxatYqGDRsyadIk47rh4eFcvnyZX375henTp/Pmm29y/PhxU8ooIo6lb9++xvYXX3xhYiT5Y7FYrIYIP/vssyZGI87A1DHtIiIiUrR4e3szdOhQ4/HDDz/M+++/z6VLl1iwYAE9evQwxqCWKlWKUqVKAbB161a8vb2NpV+HDRtGp06dSEhIoFKlSvzyyy/Mnj0bPz8/mjRpQnBwMMuXL2fYsGHXxZCenk56errVPk9PT4oXL26TMuYuzZT7r6tw1XLnUvkdt/zt2rWjVq1aHDp0iJUrV3Lw4EHuuusum7+Ore7Bhg0b2Lx5MwBNmjShTZs2Dnlfb8SRfw8Kg63Ln9/lypW0i4iIiN3s2LGD0qVLU6pUKfbs2UOTJk3o378/ly9fpmXLlrzwwgv4+/tz5MgRatWqZZzn7e1NlSpVOHLkCL6+vpw/f97q+Tp16rB79+4bvmZERARz5syx2tevX7/rVqa5XSdPnrTp9ZyFq5Y7l8rvmOV/6KGHmDVrFgCffPKJXVuvb+ceWCwWXn31VePxwIEDOXHihC3CKlSO+ntQWGxV/po1a+brOCXtIiIiYhdJSUlMnz6dUaNGAXD27FliYmL48MMPKV++PK+//jqzZ89m6tSppKam4uvra3W+r68vqamppKSk4OHhgZeXl9VzKSkpN3zdxx9//LrlX23d0n7y5EmqVq2a71aSosBVy51L5Xfs8o8aNcpI2n/++WdmzZpl84mhbXEPZs+ezapVqwCoWLEizzzzjNXfNkfn6L8H9mZW+ZW0i4iIiM1dvXqVcePG0a5dO6PLe4kSJQgLC6N69eoADB06lOHDhwM5LevJyclW10hOTsbb2xsfHx+ysrJIS0szPtwmJyfj4+Nzw9cuXry4zRL0f+Lu7u6SH1pdtdy5VH7HLH+NGjUIDQ1lzZo1HDhwgP/9738EBwfb5bVu5R6kp6czduxYPvnkE2PfRx99dNO/Y47OUX8PCkthl99177Sdack3ERFxVZmZmbz00kuUK1fOas3ka8eYWiwWYzswMNBqlvnU1FT++usvAgMD8ff3p0yZMlbPHzhwgMDAQPsVQkScTt45Lq4dImOmixcv0rt3b6uEfdKkSfTu3dvEqMSZKGkXERERm5o2bRpXr15lypQpVl9i9+jRg6ioKP766y/S0tKYN2+eMSnd3XffTWpqKlFRUaSnp/P555/ToEEDKlWqBED37t2ZO3cuycnJ7Ny5k7Vr19K5c2dTyicijql3796ULl0ayFlS7cKFCyZHBMuXLycoKIiff/4ZyOlxFBERwdSpU02OTJyJknYRERGxmYSEBKKiooiLi6NDhw60b9+e9u3bExcXR+vWrRk4cCBPPvkk999/P9nZ2Tz//PNATpf2t956iwULFtChQwe2b9/Oa6+9Zlx3xIgR+Pn50a1bNyZOnMjEiROpUaOGSaUUEUfk5eXF4MGDgZwhOl9//bVpsVgsFt544w26du1KQkICAP7+/kRHRzNkyBDT4hLnpDHtIiIiYjOVKlViy5YtN31+wIABDBgw4IbPBQUF8d13393wOS8vL9544w2bxCgiRdewYcN47733APjss88YPXq0KcNWZ8yYYTVLfNeuXZk7dy5VqlQp9FjE+aml3U40pl1EREREpHA1aNDAGHazZ88e/ve//xV6DOvXr2fSpEnG4xkzZvDLL78oYZdbpqRdRERERESKjBEjRhjbn332WaG+dkpKCo899hhZWVlAzoRzEydOdOmZ1uX26bdHRERERESKjD59+hAQEADAwoULC3VCukmTJnHkyBEA2rRpY9VFXuRWKWm3E3WPFxEREREpfN7e3qZMSLdhwwbeffddIGeW+C+++AJPT00hJrdPSbuIiIiIiBQpw4cPN7bnzp2LxWKx6+udO3eOhx9+mOzsbACmTJlC3bp17fqa4jqUtIuIiIiISJHSoEED2rRpA8CuXbvYtGmT3V7rypUr9OjRgxMnTgA53eLHjx9vt9cT16OkXUREREREipyhQ4ca23PmzLH59bOysoiOjqZ169Zs3LgRgAoVKvD999+rW7zYlJJ2EREREREpcvr374+/vz8A3333HZcvX7bZtX/99Vfq1q1L9+7d2bt3LwClS5fmt99+484777TZ64iAknYRERERESmCfH19efTRRwFITk5m9uzZt31Ni8XC22+/TVhYGIcPHzb2N2/enN9//51GjRrd9muIXEtJu4iIiIiIFEnjx483uqq/8847xMfH3/K1rl69yoQJE3jxxReNie1CQkL44Ycf2LhxI/Xr17dJzCLXUtJuJ1ryTURERETEXIGBgcZM8snJyTz33HO3dJ1Lly4RFhbGDz/8YOybMmUKq1evpnfv3hrDLnalpF1ERERERIqs1157jbJlywKwcOFCYmJiCnT+lStXCAkJITY2FgAvLy8iIyOZPHmyGuqkUChpFxERERGRIqtMmTK8/fbbxuOnnnqqQJPSvfDCC+zYsQPImWxu9erV9O/f3+ZxityMknY70bduIiIiIiKOYfDgwYSGhgJw7NgxmjZtyuuvv86BAwf+8bzo6Gg+++wzIGdiu4ULF9KyZUt7hytiRUm7iIiIiIgUaW5ubkRERBAQEADkJO6TJk2iadOmbN269Ybn7N69mwEDBhiPZ86cSWBgYKHEK5KXknYRERERESnyatSowTfffIOPj4+xLzU1leHDh5OZmWl17KlTp7j//vu5cuUKAL169WLEiBGFGq9ILiXtIiIiIiLiErp168bhw4eJjY2lYsWKAPz555989NFHxjGnT5+mY8eOHD9+HMhZg33BggW4uyt1EnPoN89ONKZdRERERMTxVKxYkeDgYKvl21555RVOnjzJsWPHuO+++9i/fz+Qs2RcVFQUvr6+ZoUroqRdRERERERcT5s2bYwu70lJSdSvX5+goCD27NkDQNWqVVm5ciV33nmnmWGKKGm3F3WfERERERFxbG+++abRTT45OZmUlBQAateuzapVq6hRo4aJ0YnkUGZpQ/Pnz8fPz49nnnmGEiVKmB2OiIiIiIj8g1KlSvHzzz9zzz33UKxYMapVq8Z//vMfNmzYQK1atcwOTwQAT7MDKEoeeeQRWrVqpaUgREREREScRPPmzdm0aZPZYYjclFrabczDw8PsEERERERERKSIUNIuIiIiIiIi4qCUtIuIiIiIiIg4KCXtIiIiIiIiIg5KSbuIiIiIiIiIg1LSLiIiIiIiIuKglLSLiIiIiIiIOCgl7SIiIiIiIiIOSkm7iIiIiIiIiIMyNWm/ePEiY8aMoW3btvTu3ZtNmzbd8Lj+/fvTvn174+eee+5h/vz5xvNRUVF0796dkJAQpk6dSkZGRmEVQURERERERMRuTE3aZ86cSbly5Vi5ciWjR49m4sSJXLly5brjFi5cyLp161i3bh1RUVF4enoSEhICwKFDh3j33Xd5++23+fnnn4mPj+fzzz8v7KKIiIiIiIiI2JxpSXtKSgqxsbGMHDkSLy8vQkNDueuuu1i7du0/nrdixQrq1atH1apVAYiJiaFz5840aNAAPz8/hg4dSnR0dGEUQURERERERMSuPM164RMnTuDn50fZsmWNfbVr1+bIkSP/eF50dDTdunUzHh85coR7773X6hqnTp0iLS0NLy+v685PT08nPT3dap+npyfFixe/1aIYsrOzrf51Fa5a7rxc/R64evlB98DVyw+2vwfu7pp2RkRERExM2lNTU/H19bXa5+vrS1JS0k3PiY+PZ/fu3cyaNeum1/Hz8zP23yhpj4iIYM6cOVb7hg0bxogRI26pHHm5u7tTs2bN276Os3HVcufl6vfA1csPugeuXn7QPXAlrvp/7arlzqXyu3b5QfcAdA/MKr9pSbu3tzfJyclW+5KTk/H29r7pOTExMbRs2ZLSpUvf9Dq5Sf/NrvP444/z6KOPWu2zRSu7iIiIiIiIiK2Z1veuWrVqJCUlce7cOWPfwYMHCQwMvOk5MTExhIWFWe0LDAzk0KFDVteoXLnyDVvZISdB9/Pzs/pR0i4iIiIiIiKOyLSk3cfHh+DgYMLDw0lLSyM2NpbDhw8THBx8w+P3799PQkICoaGhVvu7devGihUr2LdvH0lJSXzxxRfXJfYiIiIiIiIizsjUWW4mTpzI6dOn6dixI++//z4zZszA39+f6Oho+vfvb3VsTEwMISEh13V7r1WrFmPHjuW5556je/fuVKhQgSeeeKIwiyEiIiIiIiJiF24Wi8VidhAiIiIiIiIicj2tJyMiIiIiIiLioJS0i4iIiIiIiDgoJe0iIiIiIiIiDkpJu4iIiIiIiIiDUtIuIjYXHx9PmzZtzA5DRERE7Ej1vUjhUNJ+E+np6UydOpXu3bsTEhLC8OHDOXTokPH8vHnz6NSpE/fddx/vv/8+uZPwZ2Zm8sILLxAWFkaLFi04d+6c1XX79+9P+/btjZ977rmH+fPnF2rZCqpnz56EhISQlpZm7EtKSqJt27b06dPHxMjsz5XLfjM9e/Zk586dZodR6P7880+GDBlCSEgIHTt2ZMSIEZw6dcrssApFz5496dGjBxkZGca+6dOnEx4ebmJU9mWvOuDUqVM8/fTThIaGEhYWRkRERKGWS66n+j6HK9d3rlz2f+KK9b0r1/XgevW9M9X1StpvIisri8qVKxMREcGqVasIDg5m3LhxAPz+++8sWrSIefPmsXDhQn7//XeWLl1qnNu8eXPeeuutG1534cKFrFu3jnXr1hEVFYWnpychISGFUqbbUaZMGdauXWs8Xr16NRUqVCjwdTIzM20ZVqGwVdnFeSUlJTF+/HiGDBnC6tWriYqKYsCAAXh4eJgdWqFJSUkhKirK7DAKjb3qgFmzZlG5cmVWrFjB3LlziYyMZNOmTYVSJrkx1ff/T3W96npXpro+hyvV985U1ytpvwlvb2+GDh1KhQoV8PDw4OGHHyY+Pp5Lly7xyy+/0LdvX6pUqULZsmUZNGgQ0dHRAHh6evLII4/QqFGjf32NFStWUK9ePapWrWrv4ty2rl27GmUEiI6OpmvXrsbjuXPn0qNHD0JCQnj88cc5ePCg8VzPnj358ssv6d27N/369SvUuG3hVsseHR3NiBEjrK71yiuvOHRLS0FMmTKFefPmGY+joqJ49tlnzQvIjo4fP46XlxehoaG4u7vj4+NDhw4dqFixIllZWYSHh9OjRw+6du3Ku+++a3xgDQ8P55VXXmHs2LGEhIQwatQozp8/b3Jpbs3AgQOJiIi44Yfx7777jl69etGpUycmTZpEUlISAE899RTLli0zjktJSSE4ONgp7oG96oCEhAS6dOmCp6cnlStXpmnTphw5cqQwiybXUH3//1TXq66/EVep71XX53Cl+t6Z6nol7fm0Y8cOSpcuTalSpTh69Ci1atUynqtTp84t/UdER0fTrVs3W4ZpN61atWL//v1cvnyZc+fOcfLkSZo3b248X7NmTb7++mtWrlxJq1atmDx5stX5sbGxzJ07l++++66wQ79tt1r2Dh06sG/fPs6ePQtAWloa69ato0uXLqaUQ25d9erVSUtLY9q0aaxfv96opAAWLFjA9u3bmT9/PosWLWLfvn0sWrTIeH7lypUMGDCA5cuXU6FCBWbOnGlGEW5bq1atKFeu3HXfvv/xxx98+eWXvPfee0RFRZGamsq7774LQOfOnVmxYoVx7Nq1awkKCqJMmTKFGrst2KoO6NevH7/++ivp6emcOHGCnTt30qJFC3uFLbfAlet71fWq612Z6vocrlzfO3Jdr6Q9H5KSkpg+fTqjRo0Ccr498vPzM5739fUlJSWlQNeMj49n9+7ddO7c2aax2ouHhwchISGsWLGC5cuX06lTJ9zc3IznO3bsSEBAAJ6ensY30HnvycCBAyldujQlSpQwI/zbcqtl9/LyIjg4mOXLlwM5f8Dq1atH+fLlzSqK3CI/Pz8+++wz0tLSmDp1Kp07d+bVV18lOTmZJUuWMGrUKEqVKkXJkiUZNGgQq1atMs5t3rw5rVu3pkSJEowcOZLY2Fin7DoKMHz48Ou+fV++fDl9+vShZs2aeHt78/TTTxu/8/fddx9btmzh77//BuC3335zmr95edmyDmjSpAk7d+6kffv29O7dm169ell9KBBzuXp9r7pedb0rU13//1yxvnf0ut7zts52AVevXmXcuHG0a9eOXr16AeDj42P17VtycjI+Pj4Fum5MTAwtW7akdOnSNo3XnsLCwvjvf/9LWloaL7/8svHGBPjpp5/49ttvOX36NG5ublgsFi5fvmzcF2evvG617N27d+fTTz/l0UcfJSYmxilaWuTGatWqxeuvvw7A3r17mThxIl988QWJiYk8/fTTxoc7i8Vi9ft+7bbFYuHSpUuULVu2cAtgA61bt6Zs2bJWXeDOnTvH3XffbTyuVKkSqampJCUlUapUKZo1a8aaNWvo0KEDmzdv5tVXXzUj9FtmyzogKyuLMWPGMHjwYPr27cuZM2cYO3YsgYGBdOrUyW5lkPxRfZ9Ddb3qelemuj6Hq9X3zlDXq6X9H2RmZvLSSy9Rrlw5xo4da+yvWbOm1cyCBw4cIDAwsEDXjomJISwszFahForGjRtz5swZUlNTqVu3rrE/Pj6ed999l9dee401a9YQExODu7u7McMiYPVttTO61bK3bNmSxMRE9u7dy5YtW+jYsaNZRbA5b29vq5l2HX3cki3Vr1+fDh06cPjwYcqXL8/cuXNZs2YNa9asITY2lu+//9449syZM1bbbm5ulCpVyoSobWPYsGFW376XLVuWxMRE4/nExES8vLyMb6dzu8zFxsbSpEkTpyq7reuAK1eucPbsWfr27Yunpyd33nknoaGhbN261R7hSwGovv9/qutV11/LVet7V67rwXXqe2ep65W0/4Np06Zx9epVpkyZYlURde/enR9++IFTp05x7tw5FixYYFUhp6enc/XqVQAyMjKM7Vz79+8nISGB0NDQQimHLc2aNYsZM2ZY7UtJScHNzY077riDzMxMwsPDrSrxouJWyu7h4UGXLl2YNGkSLVq0wN/fv7DDtps6deqwdu1akpKS+Ouvv6xm1Cxqjh07xoIFC4wxi8ePHzfGa/Xq1YuPP/6Yc+fOYbFYiI+Pt/rDHBcXx8aNG0lPT+ezzz4jODgYT0/n7eR07733Urp0aWJjYwHo1KkTP/74I8eOHSM1NZWPP/7Yaixnhw4diIuL46effnK6rnK2rgMCAgKoUKECixcvJjs7m9OnTxMbG8tdd91VuAWT66i+t6a6XnV9Xq5S36uut+Yq9b2z1PXO/dtkRwkJCURFRVGiRAk6dOhg7P/ggw9o164dBw8eZPDgwWRnZ/Pggw/ywAMPGMf06dOHhIQEIGc2VYAtW7YYz8fExBASEoK3t3chlcZ2ateufd2+WrVq8dBDDzFgwABjFsZixYqZEJ193WrZw8LC+Pbbbxk2bFhhhWp3bm5udO/enQ0bNnD//fdTo0YNunbtyq5du8wOzS58fHzYsWMHX331FcnJydxxxx107NiRIUOG4ObmRmZmJk8++SSXLl2iYsWK/Oc//zHOve+++/j222954YUXCAoKMrrdObNhw4YxevRoANq2bctjjz3G6NGjSU5Opk2bNjz33HPGsSVLluTuu+/mjz/+4J133jEr5AKzVx0wc+ZMZs+ezYcffoiXlxddunThoYceKsSSybVU319Pdb01V63rwbXqe9X11yvq9b0z1fVulqL4NamIAzl37hx9+vTh119/xcvLy+xwblvHjh2JiIigWrVqZofi8MLDwzl//jwvvfSS2aGIiIgdFbW6HlTf55fqeikM6h4vYkfZ2dksWLCAzp07F4lKPPcbxEqVKpkciYiIiGMoanU9qL4XcTTqHi9iR126dMHf35+PP/7Y7FBu27Rp09iwYQMvv/xykewSKSIiciuKUl0Pqu9FHJG6x4uIiIiIiIg4KHWPFxEREREREXFQStpFREREREREHJSSdhEREREREREHpaRdRERERERExEEpaRcRERERERFxUEraRYqALVu20KJFC1q0aEF8fLzZ4YiIiIgdqL4XcU1ap13EwfXs2ZOEhIR/PKZ9+/Y0bNgQgOLFixdGWP9qy5YtjBw5EoClS5dy5513mhyRiIiI41J9LyI3o6RdxMHVrVuXMmXKAHDmzBnOnDkDQJ06dYwKOyQkhAcffNCsEEVEROQ2qb4XkZtxs1gsFrODEJH8CQ8PZ86cOYD1t9k3+pZ7ypQpLFu2jEqVKjFixAg++eQTkpKSeOCBB3j66af56KOPWLp0KSVLlmTIkCH07dvXeJ2zZ8/y8ccf88cff3Dp0iUqVKhAz549GTJkCJ6eOd/17dy5k48//pgDBw6QkpJCQEAAdevWZdy4cfz8889GnHn16NGDKVOm8PXXXxMdHU1iYiLJycn4+/vTtGlTnnnmGapXrw5AVFQUU6dOBeDNN9/kiy++4Pjx49x9991MnTqVNWvWMHfuXNLS0ujcuTPjx483YmvRogUAY8eOZc+ePaxbtw4vLy/69OnDiBEjcHNzs8d/j4iIiE2ovld9L5KXxrSLFHHnzp3jzTffpFixYiQnJ/Ptt9/y2GOPsXTpUvz8/EhMTOStt97i6NGjAFy6dIkhQ4YQFRVFamoqNWvWJDExkU8//ZRp06YBkJ2dzdixY9m8eTOenp7UrFmTjIwM1q1bR2JiIhUqVKBmzZpGDHXq1KFhw4ZUqVIFgK1bt3Ly5EnKlClDjRo1uHLlCqtXr2bUqFFcvXr1ujJMnjyZ9PR00tPTWb9+PcOHD2fmzJmUKFGCy5cvs2jRIpYsWXLdeR9//DFxcXGULFmSixcvMnfuXCIjI+1xm0VEREyl+l71vRRdStpFiriMjAz++9//8uOPP1KhQgUATp48ybfffsuiRYsoUaIE2dnZbN26FYCFCxdy+vRpypQpw+LFi/n222+ZOXMmAMuWLePkyZNcuXKFy5cvAxAREcE333zDb7/9RmRkJIGBgTz44IO8+OKLRgxvv/028+bNY+jQoQA8++yzrF69mu+//57IyEg++OADAE6fPs327duvK8MTTzzBokWL6NatGwBHjx5l8uTJ/PjjjzRt2hTIaX24VlBQEFFRUSxdupRmzZoZ8YqIiBQ1qu9V30vRpTHtIkVcblc0gIoVK3L69Gnuuusuo6tdQEAAiYmJXLhwAYDdu3cDcP78eTp37mx1LYvFwq5duwgLC6Nx48bs2LGDvn37UrVqVe666y7atWtnVLT/JDExkenTp3Po0CFSUlLIO0rn7Nmz1x0fHBwMQKVKlYx97du3B6By5cps27bNiD+vjh07Gl3oOnbsSFxcHOfPn+fixYsEBAT8a5wiIiLOQvW96nspupS0ixRxvr6+xraHh8d1+3LHe+VWpLn/+vr6WnV5y+Xl5QXkdEWLiYlh+/btHD16lJUrV7J8+XLOnTvH4MGDbxrPX3/9xfjx48nIyMDX15f69euTmZnJgQMHgJyueDcrQ278AH5+fjeMX0RExBWpvhcpupS0i4iVoKAg1q9fj4eHB9OnTze+oU9OTmb16tV06NABi8XCjh076NmzpzGL7WuvvcbSpUuJi4tj8ODBRmUPkJqaamzv37+fjIwMAD788EMaN27Mr7/+yssvv2zzsqxcudKYcGfVqlUAlClTRt+6i4iIy1N9L+I8lLSLiJX+/fuzZMkSzpw5Q58+fahZsybJycmcPn2azMxMevToQVZWFqNGjcLX15cKFSrg5uZmTGxTq1YtAKpUqYKnpyeZmZmMGjWKSpUqMWjQIGrVqoWHhwdZWVk8++yzVKxYkfPnz9ulLPv27aNnz564ubkZS+f85z//sctriYiIOBPV9yLOQxPRiYiVgIAAIiIi6NmzJ3fccQeHDx/m6tWrNGvWjOeffx7I6bbWp08f7rzzTs6cOcNff/1FpUqVeOyxxxg2bBgApUqVYvz48VSoUIELFy6wa9cuzp8/T40aNXj11VepXLkymZmZlCpVypil1tZGjRpFixYtSEpK4o477uCJJ55gwIABdnktERERZ6L6XsR5aJ12ESlyctdtnTx5Mj179jQ5GhEREbEH1ffiKtTSLiIiIiIiIuKglLSLiIiIiIiIOCh1jxcRERERERFxUGppFxEREREREXFQStpFREREREREHJSSdhEREREREREHpaRdRERERERExEEpaRcRERERERFxUEraRURERERERByUknYRERERERERB6WkXURERERERMRB/R81FQ9YM5Xf0AAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "covs = compute_moving_average_metrics(hfcs, metrics.ic)\n", + "widths = compute_moving_average_metrics(hfcs, metrics.iw)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 4.3))\n", + "covs.plot(ax=ax1, label=\"coverages\")\n", + "ax1.set_ylabel(\"Ratio covered [-]\")\n", + "ax1.set_title(\"Moving 4-week average of Interval Coverages\")\n", + "\n", + "widths.plot(ax=ax2, label=\"widths\")\n", + "ax2.set_ylabel(\"Width [kWh]\")\n", + "ax2.set_title(\"Moving 4-week average of Interval Widths\");" + ] + }, + { + "cell_type": "markdown", + "id": "62f26595-5286-4c6b-9191-cf6535971e47", + "metadata": {}, + "source": [ + "Also here, the coverage looks stable around 90% over the entire year -> the conformal model is valid.\n", + "\n", + "The interval widths range from 2.5 - 3.5 MWh. The adaptivity/responsiveness of the widths to changes in model performance is mainly controlled by the value of `cal_length`." + ] + }, + { + "cell_type": "markdown", + "id": "c4888c37-8cde-4c70-a807-f0f74e3536e3", + "metadata": {}, + "source": [ + "#### Comparison with another model\n", + "\n", + "Okay now let's compare the uncertainty of our first model with a more powerful regression model.\n", + "\n", + "- Use the last week (7*24) of consumption as lookback window\n", + "- Also use a cyclic encoding of the hour of the day and day of week as a future covariate\n", + "\n", + "The process is exactly the same as for the first model, so we won't go into any detail." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6ca89f61-3da1-4e89-86a0-edee7474ee3f", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6f1d228446304cadacfc27e9ca1be4ef", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IntervalCoverageWidth
00.90.8984131662.243896
\n", + "" + ], + "text/plain": [ + " Interval Coverage Width\n", + "0 0.9 0.898413 1662.243896" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "add_encoders = {\"cyclic\": {\"future\": [\"hour\", \"dayofweek\"]}}\n", + "input_length = 7 * 24\n", + "model2 = LinearRegressionModel(\n", + " lags=input_length,\n", + " lags_future_covariates=(input_length, 1),\n", + " output_chunk_length=1,\n", + " add_encoders=add_encoders,\n", + ")\n", + "model2.fit(train)\n", + "\n", + "cp_model2 = ConformalNaiveModel(\n", + " model=model2, quantiles=quantiles, cal_length=four_weeks\n", + ")\n", + "hfcs2 = cp_model2.historical_forecasts(\n", + " series=cal_test,\n", + " forecast_horizon=horizon,\n", + " start=test.start_time(),\n", + " last_points_only=True,\n", + " stride=horizon,\n", + " **pred_kwargs,\n", + ")\n", + "plot_historical_forecasts(hfcs2)\n", + "\n", + "bt2 = cp_model.backtest(\n", + " cal_test,\n", + " historical_forecasts=hfcs2,\n", + " last_points_only=True,\n", + " metric=[metrics.mic, metrics.miw],\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + ")\n", + "bt2 = pd.DataFrame({\"Interval\": q_range, \"Coverage\": bt2[0], \"Width\": bt2[1]})\n", + "bt2" + ] + }, + { + "cell_type": "markdown", + "id": "027d41cc-7f43-414e-bc7e-9658fadc5851", + "metadata": {}, + "source": [ + "Nice! We achieve again 90% coverage, but our average **interval width decreased from 2.9 MWh to 1.7 MWh!**\n", + "Finally, let's also look at the metrics over time and compare our two models." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "aa8a446d-5d58-4b5a-a7fb-2d2c33069909", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IntervalCoverageWidth
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Model 20.90.8981662.244
\n", + "
" + ], + "text/plain": [ + " Interval Coverage Width\n", + "Model 1 0.9 0.902 2908.944\n", + "Model 2 0.9 0.898 1662.244" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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FEydO8PbtWwICAtizZw916tTBwcHBaF8PDw8CAgI4duyYUZw7duygVKlSlC5dml27dpEkSZLP5gPAX3/9RYkSJXBycsLKygpra2sWLFhg1NzK1Nd08+bNJEqUiBo1ahjFnD9/flKmTBmuSW7evHnJmjVruJjOnDlDzZo1cXFxwdLSEmtrazw9PQkJCeHq1auAoZlRYGBguGugWLFi4ZoSbd68mdy5c5M/f36juCpXrhzpyMKhjh49ir+/f7jmZenSpaNcuXIRNiX8Gjqdjho1ahity5s3r3btf8qXXDc//fRTuOM0bdoUW1tbo9FFV6xYQWBgIC1bttTWBQcHM2bMGHLmzImNjQ1WVlbY2Njw77//Gl0/MeHKlSs8ePCAn3/+GQuLD18HTk5O/PTTTxw7dox3794B0KpVK/z9/Vm1apW2n5eXF7a2tjRp0gQwNK+7fPkyTZs21dIWNu8ePnwYrvlZRHlnap54e3uTO3ducubMafT80KbCoUz9zAAoUqQIixYtYtSoURw7dixcszshvpTcK8i9gtwryL3Cx8x5r1C2bFmuXr3KgwcP8PX15Z9//tG6Iri7u3PmzBlevXrFnTt3uHnzplHT9Yjs27eP8uXLkyJFCm2dpaUlDRs2jHJsn/tcypw5M4kTJ6Zfv37Mnj2bixcvRvkccY0UyqOBTqejZcuWLF26lNmzZ5M1a9YI+wMB+Pr6kjJlSnQ6ndH65MmTY2Vlha+vr7auVatW3L9/X/vCCX2TmtpvxsXFxWjZ1tYW+DC4R+i5wr55AKysrMI9NyKDBg3C2tqaLl268PLlS16+fImfnx9gGK3y5cuXKKW4desW1tbWRn+hX/I//vgjDg4O7N69m2vXrnHr1i3ti/b48eP4+fmxe/du3NzcyJgxI2CYTgUMNwQfH3f8+PEopXj+/Dm+vr4EBwfz22+/hdvPw8MDMPRLC2v9+vX4+/vTsWNHLb8+Z926dTRo0IA0adKwdOlSjh49qt1sBQQEGO1rymv6+PFjXr58iY2NTbi4Hz16FC7mVKlShYvpzp07lCpVivv37zN9+nQOHjzIiRMntH5On7sGIlr3+PFjzp8/Hy6mBAkSoJQKF1dYoeeJKNbUqVMbXffRwcHBIdzovra2tuFej4h8yXUTUbqSJElCzZo1+fPPPwkJCQEMfTiLFClCrly5tP169erFkCFDqF27Nps2beL48eOcOHGCfPnyfdFAPOnTp+fp06cm9d373Oui1+u1Udpz5crFDz/8gJeXF2Do37h06VJq1aql3ZCGvjf79OkTLu86deoEmJZ3puaJr6+vydcufP4zA2DVqlU0b96c+fPnU7x4cZIkSYKnp6fRYDhCfAm5V5B7BblXkHuFj5nrXgGM+5Xv378fS0tLSpQoAUDJkiUBQ7/yiPqTRyT0c+tjEa37nM99LiVMmBBvb2/y58/PwIEDyZUrF6lTp2bYsGHf7I/pMiVaNGnRogVDhw5l9uzZjB49OtL9XFxcOH78OEopoy/bJ0+eEBwcTNKkSbV1lStXJnXq1Hh5eVG5cmW8vLwoWrRouFqhLxV6wT9+/Jg0adJo64ODg0364Pvnn3+4detWhG+25s2bA4Zfd1OnTs2JEyeMtmfLlg0AGxsbSpYsye7du0mbNi0pU6YkT548uLm5AYYPij179lC9enXtuaF59Ntvv1GsWLEIY0uRIgXBwcFYWlry888/07lz5wj3C/3yDjV16lRWrVpF1apV+fvvv6lUqdJn82Hp0qVkzJiRVatWGb2mYQfjCWXKaxo62M727dsjPN/HU358fNMGhhuGt2/fsm7dOlxdXbX1Z8+eNdov7DXwsUePHhn9Ap40aVLs7e1ZuHBhhHGFvXY/Fnqehw8fhtv24MGDTz43tiVOnDjK101ErwFAy5Yt+euvv9i1axfp06fnxIkT/PHHH0b7LF26FE9PT8aMGWO0/tmzZyRKlCjK8VeuXJmdO3eyadMmGjVq9Ml9P/e6WFhYaFOfhKanU6dOXLp0iRs3bvDw4UOjX/JDX8cBAwYYDUYUVuh7P1REeWdqnri4uER67YZl6mdG6L7Tpk1j2rRp3Llzh40bN9K/f3+ePHkS6XtSCFPJvYIxuVeQe4WIziP3CjF/rwBQunRpLC0t2b9/P7a2thQsWBAnJycAnJ2dyZ8/P/v27eP58+dYWVlpBfbIuLi4RPgDdkz9qJ0nTx5WrlyJUorz58+zaNEiRo4cib29Pf3794+Rc8YkKZRHkzRp0tC3b18uX76sfclEpHz58qxevZr169dTp04dbf2ff/6pbQ8V+mafNm0aBw8e5OTJk8yZMyfaYi5dujRgqBkqWLCgtn7NmjUmTdMwbdo0Xr58abTu7Nmz9OzZk+HDh+Pu7q410SpcuHCkx6lQoQIDBgwgQYIEWrMzR0dHihUrxm+//caDBw+09QAlSpQgUaJEXLx4kS5dukR6XBsbG8qWLcuZM2fImzcvNjY2n02TnZ0d69ato1mzZtSsWZNVq1ZRq1atTz5Hp9NhY2Nj9GH76NGjcCOqgmmvafXq1Vm5ciUhISEULVr0szFHFhNg9Au+UircVFhFixbF1taWVatWGRWijh07xu3bt42+aKtXr86YMWNwcXEJ90XzOcWLF8fe3p6lS5dSv359bf29e/fYu3dvuCaaseHjX11DOTg4RPm6iUylSpVIkyYNXl5epE+fHjs7u3BNq3U6Xbiali1btnD//n0yZ84c5XO2bt2aiRMn8ssvv1CqVCmjm+hQ69ato27dumTLlo00adKwfPly+vTpo103b9++Ze3atdqI7KEaN25Mr169WLRoETdu3CBNmjRGN6PZsmUjS5YsnDt3LtyNQ1SYmifu7u5MmjSJixcvGt2srly50ui5pn5mfCx9+vR06dKFPXv2cPjw4S9MjRAfyL2CgdwrGMi9gjG5V4i9ewUw1DYXKFBAK5SH1vCHcnd3Z9++fbx48YIiRYpoBfbIlC1blo0bN/L48WPth+6QkBCjbm+hbG1to21aPp1OR758+Zg6dSqLFi3i9OnT0XLc2CaF8mg0bty4z+7j6enJzJkzad68Obdu3SJPnjwcOnSIMWPG4OHhYfSFAoYmTOPHj6dJkybY29t/Ub+MyOTKlYvGjRszefJkLC0tKVeuHD4+PkyePJmECRMa9TGNSP78+T957I+nyIhM+fLlCQkJYc+ePSxevFhbX6FCBYYNG4ZOp6NcuXLaeicnJ3777TeaN2/O8+fPqVevHsmTJ+fp06ecO3eOp0+far8wTp8+nZIlS1KqVCk6duxIhgwZePPmDdeuXWPTpk3s3bs3XDzW1tasWLGCNm3aUK9ePf78889wH45hhU4z0qlTJ+rVq8fdu3f59ddfSZUqFf/++2+4/T/3mjZq1Ihly5bh4eFB9+7dKVKkCNbW1ty7d499+/ZRq1Yto5u0iFSsWBEbGxsaN27ML7/8QkBAAH/88YfWFDlUkiRJ6NWrF2PHjiVx4sTUqVOHe/fuMWLECFKlSmV0DfTo0YO1a9dSunRpevbsSd68edHr9dy5c4edO3fSu3fvSG8MEiVKxJAhQxg4cCCenp40btwYX19fRowYgZ2dHcOGDftkemJCggQJcHV1ZcOGDZQvX54kSZKQNGlSMmTI8EXXTUQsLS3x9PRkypQpODs7U7duXRImTGi0T/Xq1Vm0aBHZs2cnb968nDp1iokTJ37x3J8JEyZkw4YNVK9enQIFCtClSxeKFy+u9T1bunQp586do27dulhYWDBhwgSaNm1K9erVad++PYGBgUycOJGXL1+G+0xLlCgRderUYdGiRbx8+ZI+ffqE+5yYM2cOVatWpXLlyrRo0YI0adLw/PlzLl26xOnTp/nrr78+mwZT86RHjx4sXLiQqlWrMnLkSFKkSMHy5cu5fPkygBabqZ8Zr169omzZsjRp0oTs2bOTIEECTpw4wfbt2yOt+RciquRewfjYcq8g9wqh5F4h9u4VQpUtW5aJEyei0+kYP3680TZ3d3emTp2KUkobK+ZTBg8ezMaNGylXrhxDhw7FwcGBmTNnRtidLrSWe9WqVbi5uWFnZ0eePHlMjnvz5s3MmjWL2rVr4+bmhlKKdevW8fLlSypWrGjyceKU2B9b7vtg6qiPEY0u6Ovrqzp06KBSpUqlrKyslKurqxowYIA2qujHfvzxRwVEOB2RUpGPqPpxbKEjGoadJiggIED16tVLJU+eXNnZ2alixYqpo0ePqoQJExqN5miqqI6oqpRSer1eJU2aVAHq/v372vrDhw8rwGi6pbC8vb1VtWrVVJIkSZS1tbVKkyaNqlatWrhz37x5U7Vq1UqlSZNGWVtbq2TJkqkff/xRjRo16pNx6/V61a1bN2VhYaHmzZv3yTSMGzdOZciQQdna2qocOXKoefPmqWHDhqnI3mKfe03fv3+vJk2apPLly6fs7OyUk5OTyp49u2rfvr36999/tf1cXV1VtWrVIjzGpk2btOenSZNG9e3bV23bti3cNaDX69WoUaNU2rRplY2NjcqbN6/avHmzypcvn6pTp47RMf38/NTgwYNVtmzZlI2NjTYVSM+ePY1G24zM/PnzVd68ebXn1qpVK9zI2dExoqqjo2O4fSN6PXbv3q0KFCigbG1tw40i/KXXzceuXr2qAAWoXbt2hdv+4sUL1bp1a5U8eXLl4OCgSpYsqQ4ePKjc3d2NPjuiMqKqUko9evRI9evXT+XKlUs5ODgoW1tblTlzZtW+fXttBORQ69evV0WLFlV2dnbK0dFRlS9f3miaobB27typpefjKXtCnTt3TpvOxdraWqVMmVKVK1dOzZ49W9vnU6+zqXmilFL//POPqlChgrKzs1NJkiRRrVu3VosXL1aAOnfunNG+n/vMCAgIUB06dFB58+ZVzs7Oyt7eXmXLlk0NGzZMvX379rN5LsTH5F4hYnKvIPcKnyL3CrF3rxA61d7H050pZRgF3sLCItKYPv5MUcrwfixWrJiytbVVKVOmVH379lVz584NN/r6rVu3VKVKlVSCBAkUoFxdXZVSkefVx+m6fPmyaty4scqUKZOyt7dXCRMmVEWKFFGLFi0yKd1xkU6p/4a2FOI/R44coUSJEixbtkwbVVnELzdv3iR79uwMGzaMgQMHmjscIaKkXbt2rFixAl9f369qUiiEiJzcKwi5VxAi+kihPJ7btWsXR48epVChQtjb23Pu3DnGjRtHwoQJOX/+fLhRKcX359y5c6xYsYIff/wRZ2dnrly5woQJE3j9+jX//PNPhKOtChFXjBw5ktSpU+Pm5oafnx+bN29m/vz5DB48mJEjR5o7PCG+C3KvIOReQYiYJX3K4zlnZ2d27tzJtGnTePPmDUmTJqVq1aqMHTtWvmTjCUdHR06ePMmCBQt4+fIlCRMmpEyZMowePVq+ZEWcZ21tzcSJE7l37x7BwcFkyZKFKVOm0L17d3OHJsR3Q+4VhNwrCBGzpKZcCCGEEEIIIYQwk08PmSmEEEIIIYQQQogYI4VyIYQQQgghhBDCTKRQLoQQQgghhBBCmIkUyoUQQgghhBBCCDORQnkU6fV6bt68iV6vN3cosSq+pjtUfE8/SB6A5IGkP36nPz6Jr691fE13WPE9D+J7+kHyIL6nH8yTB1IoF0IIIYQQQgghzEQK5UIIIYQQQgghhJlIoVwIIYQQUTZ69GgqV66Mu7s7DRs25ODBgwBs2rSJokWLUqpUKe3v0aNH2vN8fHxo3LgxJUqUoF27djx8+FDbFhAQwJAhQyhdujTVqlVj+/btsZ4uIYQQIrZZmTsAIYQQQnx7mjZtSt++fbGxscHHx4fOnTuzceNGAIoUKcJvv/0W7jlBQUH88ssvtGvXjipVqjBnzhyGDh3KvHnzAJgzZw6vXr1i69atXL9+ne7du5MjRw5cXV1jNW1CCCFEbJJCuRBCCCGiLEOGDNpjnU5HUFAQz549++RzTp06hb29PbVq1QKgbdu2VKhQgYcPH5IqVSq2bt3K5MmTcXJyIl++fJQuXZqdO3fStm3bcMcKCgoiKCjIaJ2VlRU2NjZfnbbQwX3i20BH8TXdYcX3PIjv6QfJg/iefoj+PLCw+HzjdCmUCyGEEOKLjBs3jk2bNhEYGIi7uztubm74+Phw7tw5ypcvT5IkSWjYsCH16tUD4MaNG2TOnFl7vr29PWnTpuXGjRs4Ojri6+trtD1r1qz4+PhEeG4vLy+thj1U/fr1adCgQbSl7+7du9F2rG9JfE13WPE9D+J7+kHyIL6nH6IvDzJmzPjZfaRQLoQQQogv0r9/f/r27cvJkye5du0aAAULFmTlypWkTJmSixcv0qdPH1xcXChbtiz+/v44OjoaHcPR0RF/f3/evXuHpaUldnZ2RtvevXsX4blbtmxJ06ZNjdZFZ0353bt3SZcunUk1HN+L+JrusOJ7HsT39IPkQXxPP5gnD6RQLoQQQogvZmlpSdGiRVmxYgVubm4UL15c25Y7d24aNWrEvn37KFu2LPb29rx9+9bo+W/fvsXe3h4HBwdCQkIICAjQCuZv377FwcEhwvPa2NhESwH8UywsLOLlTWl8TXdY8T0P4nv6QfIgvqcfYjcP4ndOCyGEECJa6PV67t27F269TqfTHru5uWk16gD+/v7cu3cPNzc3nJ2dcXFxMdp+9epV3NzcYjZwIYQQwsykUC6EEEKIKHn37h3btm3j3bt3BAcHs2fPHk6dOkWBAgU4cuQIL168AODy5cusWrWKUqVKAVCoUCH8/f3ZtGkTQUFBLFiwgJw5c5IqVSoAPDw8mD9/Pm/fvuXChQscOHCAihUrmi2dQgghRGwwa6F8zpw51K9fnx9++IEdO3ZEup/MWyqEEELEHTqdjg0bNuDh4UH58uXx8vJi1KhRZM6cmePHj9OgQQNKlSrFwIED8fT01ArWNjY2TJgwgWXLllG2bFnOnTvHyJEjteO2b98eJycnqlSpQv/+/enfv7/RKO9CCCHE98isfcrTpUtH7969mT179if3k3lLhRBCiLjD3t4+0u/unj170rNnz0ifmytXLlauXBnhNjs7O0aNGhUtMQohhBDfCrPWlHt4eFCsWLHPDtSydetW2rVrF27e0ti2bds2PDw8GD58OL6+vrF+fiGEEEKI+Mbf358RI0bQsmVLpk6dyvTp05k0aRLjxo1j0qRJXL161dwhChHrFi1aRKJEiT65z/Dhw8mfP/8n97l16xY6nY6zZ89GW2yxKUOGDEybNs3k/U3JE3OI86Ovv379OkrzlgIEBQURFBRktC46pkm5ePGi1sw+e/bs9OvX76uO9y3R6/VG/+Ob+J5+kDwAyQNJf/SnP76PbCvEp9y6dYudO3cyZcoUrly5Eul+/fv3Z8CAAYwcOdJoYEEhvmcNGzbEw8MjSs9p0aIFL1++ZP369TET1HeiR48e7Nu3j6tXr5IjR45Y+cEizhfKozpvKYCXlxfz5s0zWle/fn0aNGjwVbEcO3ZMe/zvv/9y+/btrzret+ju3bvmDsGs4nv6QfIAJA8k/dGX/owZM0bbsYT4nqxdu5bGjRvz/v37z+4bEhLCqFGjCA4OZsyYMVIwF/GCvb099vb25g7ju6SUol69ely7do0LFy7EyjnjfKE8qvOWArRs2ZKmTZsarYuOmvKOHTuyZs0aABIkSBCv+rTr9Xru3r1LunTp4mXNTnxPP0gegOSBpD9+p1+I2HLgwAGaNGliVCAvXLgwnTt3xtLSEisrK6ytrbGysuLo0aNMmDABgHHjxmFvb8/QoUPNFboQX2zTpk38/PPPPH/+HAsLC86ePUuBAgXo06cPEydOBAyDYb5+/ZoVK1awaNEievTowcuXL7VjjBs3jqlTp/Lu3TsaNGhAsmTJtG3Dhw9n8eLFwIepKvft26cNpnnjxg169uzJ8ePHcXV1Zf78+ZQoUQKA27dv06VLFw4dOkRQUBAZMmRg4sSJkdbUZ8iQgTZt2nD16lXWrVuHi4sLM2bM4Mcff6RNmzbs2bOHjBkz4uXlReHChbXnrV27lqFDh3Lt2jVSpUpF165d6d27t7b9yZMntG7dmt27d5MyZcoIxyB59eoVffv2Zf369QQEBFC4cGGmTp1Kvnz5TH4tpk+fzu3bt1m0aJEUykOFnbc0d+7cwOfnLbWxsfnqAnhEwv4apdPp4uVNmYWFRbxMd6j4nn6QPADJA0l//E6/EDHp7t271KtXT+uG2LBhQ7p27Urx4sUjfN/Vrl2bDBky0KlTJwCGDRuGi4sLnTt3jtW4RdxXuHBhHj16ZNK+ISEhWFpaRst5U6ZMycmTJz+7X+nSpXnz5g1nzpyhUKFCeHt7kzRpUry9vbV99u/fH+lAmqtXr2bYsGHMnDmTUqVKsWTJEmbMmKGVmfr06cOlS5d4/fo1Xl5eACRJkoQHDx4AMGjQICZNmkSmTJno1asXTZs25dq1a1hZWdG5c2eCgoI4cOAAjo6OXLx4EScnp0+mZ+rUqYwZM4YhQ4YwdepUfv75Z0qUKEGrVq2YOHEi/fr1w9PTEx8fH3Q6HadOnaJBgwYMHz6chg0bcuTIETp16oSLiwstWrQADM3v7969y969e7GxsaFbt248efJEO6dSimrVqpEkSRK2bt1KwoQJmTNnDuXLl+fq1askSZLks6+DuZi1UB4cHExISAhKKYKDgwkMDMTa2jrch27ovKWjR4/mxo0bHDhwgEWLFpkn6P8opcx6fiGEEEKI70lgYCB169bl6dOnAFSqVImlS5diZfXp29WOHTsSEBBAr169AOjVqxclSpSIk4M5CfN59OgR9+/fN3cYkUqYMCH58+dn//79FCpUSCuAjxgxgjdv3vD27VuuXr1KmTJlInz+tGnTaNWqFW3atAFg1KhR7N69m4CAAACcnJywt7cnMDCQlClThnt+nz59qFatGnq9nh49elC5cmWuXbtG9uzZuXPnDj/99BN58uQB+GTlaCgPDw/at28PwNChQ/njjz/44YcfqF+/PgD9+vWjePHiPH78mJQpUzJlyhTKly/PkCFDAMMYYhcvXmTixIm0aNGCq1evsm3bNo4dO0bRokUBWLBgATly5NDOuW/fPi5cuMCTJ0+wtbUFYNKkSaxfv541a9bQrl27z8ZtLmYtlI8aNYrNmzcDcObMGYYNG8bs2bN5+vQpXl5erF69GjA01Rg1ahRVqlTB2dnZbPOWSh8lIYQQQoiYMX78eK1GMWPGjKxYseKzBfJQPXv25O7du0ydOpWgoCB69erF3r17YzJc8Y2JqCAameiuKTdVmTJl2L9/P7169eLgwYOMGjWKtWvXcujQIV6+fEmKFCnInj17hM+9dOkSHTp0MFpXvHhx9u3bZ9K58+bNqz1Onjw5YGgunj17drp160bHjh3ZuXMnFSpU4KeffjLa/3PHS5EiBYBWqA+77smTJ6RMmZJLly5Rq1Yto2OUKFGCadOmERISwqVLl7CysjJq7p49e3ajEehPnTqFn58fLi4uRsfx9/fn+vXrpmSD2Zi1UD58+HCGDx8e4baqVatqj2XeUiGEEEKI79fTp08ZO3YsAJaWlqxduzbKTU3Hjh3Lpk2buHbtGvv27WPPnj2UL18+JsIV3yBTmpCDYfyQ27dv4+rqGutdlcqUKcOCBQs4d+4cFhYW5MyZE3d3d7y9vXnx4gXu7u4xdm5ra2vtcWhFZOhsI23atKFy5cps2bKFnTt3MnbsWCZPnkzXrl2jdLxPnUMpFa4CNGzL5NDHn6ok1ev1pEqViv3794fb9rnp48xNOsUJIYQQQgizWrJkidbMtlOnThQoUCDKx7C1tWXEiBHa8qBBg6S7ofimhPYrnzZtGu7u7uh0Otzd3dm/fz/79+//ZKE8R44cRjNFAeGWbWxsCAkJ+aLY0qVLR4cOHVi3bh29e/cON9PV18qZMyeHDh0yWnfkyBGyZs2KpaUlOXLkIDg42OjHlStXrhgNdFewYEEePXqElZUVmTNnNvpLmjRptMYb3aRQ/oXkQ14IIYQQ4usppVi4cKG2HDpo25do2LAhuXLlAuD48eNaN0khvgWh/cqXLl2q9R0vXbo0p0+f/mR/coDu3buzcOFCFi5cyNWrVxk2bBg+Pj5G+2TIkIHz589z5coVnj17ZtKUg2CYt3vHjh3cvHmT06dPs3fvXqO+3NGhd+/e7Nmzh19//ZWrV6+yePFifv/9d/r06QNAtmzZqFKlCm3btuX48eOcOnWKNm3aGA3EXaFCBYoXL07t2rXZsWMHt27d4siRIwwePNjklhIA165d4+LFizx69Ah/f3/Onj3L2bNntQEoY4IUyqNA+pQLIYQQ8cPDhw+ZNm0aN27cMHco371Tp05phYcSJUpE2mfWFJaWlvz666/a8vTp0786PiFiU9myZQkJCdEK4IkTJyZnzpwkS5bskwXhhg0bMnToUPr160ehQoW4ffs2HTt2NNqnbdu2ZMuWjcKFC5MsWTIOHz5sUkwhISF07tyZHDlyUKVKFbJly8asWbO+OI0RKViwIKtXr2blypXkzp2boUOHMnLkSG3kdQAvLy/SpUuHu7s7devWpV27dlr/dzCU1bZu3Urp0qVp1aoVWbNmpVGjRty6dUvrw26Kdu3aUb16debOncvVq1cpUKAABQoU0Eaqjwk6JVW+Jjt+/DjFihUDoGvXrsyYMcPMEcUec/aviQvie/pB8gAkDyT98Tv98Yler6dQoUKcPXuWvHnzcu7cOXOHFCvMdY136dKFmTNnAjBv3jxt9OgvpZQiS5Ys2sBON2/eNHmA4Pj+Po/v6QfJg/iefjBPHsTPnBZCCCGEiMTNmzc5e/YsAOfPnzeaB1dEr4CAAFasWAGAvb29Nl3S19DpdLRq1UpbDp2TWQgh4ioplEdB2Obrv/32G1WqVOH169dmjEgIIYQQ0e348eNGy6dOnTJTJN+/JUuW8Pz5cwDq1q1LwoQJo+W4zZs312q4Fi1apI3wLIQQcZEUyr/Cjh074lUTdiGEECI++LgfeUTz/B44cICWLVuydu3a2Arru6PX65k8ebK23KNHj2g7dpo0aahSpQoAd+7ckTnLhRBxmhTKv9L//vc/c4cghBBCiGh0584do+W5c+dy//59AHx8fOjVqxflypVj0aJF1KtXj5UrV5ojzG/e5s2buXLlCmCYn7lw4cLRevywTdgXLFgQrccWQojoJIXyr7Rp0yb++usvc4chhBBCiGjycde0V69e0aFDB5o0aULu3LmZOnWq0Vy/rVq14sWLF7Ed5jdNKcXo0aO15dBpj6JTjRo1tLmJ//77b3mNhBBxlhTKoyCyKdEaNGgQy5EIIYQQIqb4+flpj0P7JW/evFkbkOxj/v7+LFmyJFZi+17s2LFDa22YL18+PDw8ov0cNjY2NG3aFIDAwEBWr14d7ecQQojoIIXyaPLu3TtzhyBi2cyZM/nhhx9kXAEhhPiO+Pr6smXLFm150qRJ4fYpX74869ev559//tHWzZ07F5ll1jRKKUaOHKktDxkyJNKKj6/VvHlz7fGiRYti5BxCCPG1pFAeTS5dumTuEEQ0UEqh1+t5+PAhCxcu5IcffiB16tQkT56cEydO8O7dOzp27Ejjxo3p0qULJ0+epHv37uh0OiZOnMj79+/NnYRvnlJK+9PrDf8fP1cs3mb4L4QQMenq1atGy+3bt+eHH34AwNbWli1btrB7925q1apFrly5+PHHHwFDX/OwhXQRuUOHDnH06FEAcuXKRZ06dWLsXPnz5ydPnjwAHDt2TOvDLoQQcYmVuQP4XsTUL7zmopTiwTNInAAc7L6vtIWllOLKlSusW7eOQYMGfXLfhg0bfnL7L7/8gq2tLd26dYvOEL85z14qbKzB2dG06yYwSGFtBZduQ6txiv995vctdeD7vR6FEOaXK1cuo2UHBweOHz/OpUuXcHFxIUWKFEbbmzRpwpEjRwBYuXKlVgAUkRs3bpz2uF+/floXgZig0+lo3ry51md91apVDB06NMbOJ4QQX0JqyqPgUwXvrl27xmIkMevlG4WFuyLtTwrHSgrHSnp2/A/cmrviUBHuPfm2ayt9fX0pWbIkRYsWxcLCghw5cny2QG6q7t27R8txvjWr9ih0pfXoSutJVlORsKpi9J+fv07GLlXYVVBYllHkbv75AjnAuKXf9vUnhIjbnJ2dtdrv0Cm1dDodOXPmDFcgB6hXr55WqFy5cqU0Yf+MgwcPsnXrVgDSpUtHo0aNYvycYcf+kSnsxPdi0aJFJEqU6JP7DB8+nPz5839yn1u3bqHT6Th79my0xRabMmTIwLRp00ze35Q8MQcplEeTI0eOMGvWLHr37s1ff/1Fzpw5+fXXX80d1hcp0dn4huJdAHj8Yngc+B7S1VP4B35bNx1KKfbt24etrS1Jkybl8OHD0TKdXZYsWcJ9wRctWjRc88fv2cZDikYjwl8Pg+crqv2iZ9cJxcFzisAgwz7vAhQbDylS1NIzcG7Ur6NNR76ta+9LBb1XHL7w7b3XhPgebN68mT/++INly5Z9dt8UKVJQtmxZwDC/+fnz52M6vG+WXq+nd+/e2vKwYcOwtraO8fOmS5eOIkWKAHD+/HmuXbsW4+cUIqY1bNgwyvebLVq0oHbt2jET0Hfi3LlzNGnShBIlSuDo6EiOHDmYPn16jJ9Xmq9Ho86dOxstDx06VGvS/K1466+4eOvz+y3bBW2qx3g4Xy0oKIgOHTrg5eX12X1//vln6tevT6pUqfDz88PZ2RlHR0eyZs3K4sWLmTVrFi9evKB+/fp07dqVwMBAMmTIABhG59Xr9YBh7noPD4948aV/+5Gi1sDIC41bj8HWYx+2T+kCvX7/dCHTyiIE/fsXlEyzld/GNqfPb/68eP6Ek7fSA3DkH2j162Om9EhOogTfb1N22/If8qnSD4olg3UkTxz303vviWLNfnj2SpEiiY4Xb2DYQkNa+jaGrOl01CoJyRLF/bSI+CthwoRUrlz5s7VQoWrXrs2ePXsA2LJlC/ny5YvB6L5dq1ev5sSJEwDkzp2bFi1axNq5f/rpJ+3H+LVr19KvX79YO7cQMcHe3h57e3tzh/HdOXXqFMmSJWPKlCkULlyYY8eO0a5dOywtLenSpUuMnVdqymNY3rx5Wbx4sbnDMFmXaabVyu0+Gbdr75RS2NnZYWtr+9kCeYoUKXjx4gV//vknNWrUoHDhwpQpU4aCBQuSLVs2dDodnp6erFq1iitXrjBmzBhSpUqlFcgB9u7da3TM69evM2vWLKN1b94pxi9TbD8et/PucwIC0ZqqZ2gQtbREViBvUe4+j9br2DF4N8HeNuiPpODAXy3Jl9mCXdMdObkko9H+XruSkbia0n4I+V4opVi4xdAVIKydJ+CP9eaJyVRKKZLV0JOunqLn74rRS6DbdKUVyAEmroC2ExTJayreBXz5+8Df3z86QhYi2lSrVk17HHbkdvHBw4cPjQrCEydOxNLSMtbO/9NPP2mPpQm7iIs2bdpEokSJtHubs2fPotPp6Nu3r7ZP+/btady4MRBx8/Vx48aRIkUKEiRIQOvWrQkICNC2DR8+nMWLF7NhwwZ0Oh06nY79+/dr22/cuEHZsmVxcnLCw8NDG4wR4Pbt29SoUYPEiRPj6OhIrly5tG4oEcmQIQOjRo3C09MTJycnXF1d2bBhA0+fPqVWrVo4OTmRJ08eTp48afS8tWvXkitXLmxtbcmQIQOTJ0822v7kyRNq1KiBvb09GTNmjLA106tXr2jXrh3JkyfH2dmZcuXKce7cuUhj/VirVq2YPn06RYsWxc3NjWbNmtGyZUvWrVtn8jG+hBTKo+DjPuWFChX67HOuXr1KixYtmDdvXkyF9UWUUvx7N3zT2CcvPjyuVRJ2TNKROY1h+bdOT7Vtq/bC++C4W7g8ffo0gYGBEW4bMmQIZ86cQa/Xo9frefTokcm1IZFxd3fXBvoBwNaVzpMDqd77Gct2KvadVjhXUfSfo6jaV1Gsg56D5xS+rxT9Z+uxKquneEc9e099fZ4qpThz5gwXLlwgODj4q4/3sXT1I17fqTaoAxb479IxsaOO9jUhj5sJBzxflkUj0pPSxYLKlStHvt/dieFWWbqN+S5G0vUPVJy5qrCvqGg9PuJrYLjX1xVkY9L7YMM4FM9emf4cx0qKPM31PH8dtTRZWVnhkDA9Op0FS5Ys4d+7isYj9CzapqQvrzCbjBkzkjNnTgCOHj3KzZs3zRxR3OLv70/FihW5c+cOABUrVvz0530MyJQpk9aC4cSJE1osIv4o3FZP2p8+/5e+HvzYIw3p62HS/p/7K9zWtAqE0qVL8+bNG86cOQOAt7c3SZMmxdvbW9tn//79uLu7R/j81atXM2zYMEaPHs3JkydJlSqVUQVRnz59aNCgAVWqVOHhw4c8fPhQGz8DYNCgQfTp04fTp0+TMWNGmjZtqt1Hdu7cmcDAQA4cOMCFCxcYP348Tk5On0zP1KlTKVGiBGfOnKFatWr8/PPPeHp60qxZM06fPk3mzJnx9PTUvrtPnTpFgwYNaNSoERcuXGD48OEMGTLEaCrDFi1acOvWLfbu3cuaNWuYNWsWT5480bYrpahWrRqPHj1i69atnDp1ioIFC1K+fHmeP39u0usQkVevXpEkSZIvfr5JlDDZyZMnFWD4S9ZUteiz6cPyZ/50Op169eqVmjRpkho3bpxq0aKF+ueff5Sfn1+sxT9vk16V7Raiev4Woij14a9U5xB18rJePX6uN1qv1+u154aEhKjr128Ybd9zUv+Js8UOvV6vLC0tFaAWLFigrbewsFCk6aHIvUORfqiqWtVDrVu3Tr158+aLzhMSEqJu3LihQkJCtHVv3urVxkN6tXa/Xj15YciLa9euGV7z3NuM8ioqfz+PCoksDJPY29sbXXtv375Vp06dUvv27VOBgYFffNyQkBDlc+lmhDF3n258vYT1/r1e7TiuV4PnhaiMDcI8L91Ahc7K5PcQ6BRF7hifu/gLbfvcuXONXp+YENF18LUePtObfG0s3WHe91zY9I+df1vla/ZMbTmiV7UHftm1Hvr3OUePHlWlS5c2vNY5/vrw3NRdwx2r6cgQdedRzORTTLz+Im76ktd6zJgx2ufRgAEDYjC6mBNT1/iQIUO0vHF1dVV37tyJ1uObauTIkVocU6dOjXCf+P4+/57Tn6bu131Xfelfmrqm52XBggXVpEmTlFJK1a5dW40ePVrZ2Nio169fq4cPHypAXbp0SSmllJeXl0qYMKH23OLFi6sOHToYHa9o0aIqX7582nLz5s1VrVq1jPa5efOmAtT8+fOVUoZrYMeOHUbnypMnjxo+fLjJ6XB1dVXNmjXTlkNjHzJkiLbu6NGjClAPHz5USinVpEkTVbFiRaPj9O3bV+XMmVMppdSVK1cUoI4dO6Ztv3TpktH7ec+ePcrZ2VkFBAQYHSdTpkxqzpw5Simlhg0bZpQnEQn7Pjhy5IiytrZWO3fuNDn9X0Jqyr9E0gaQ/U8WHfegUr0RJj1FKUXChAnp06cP/fv3Z9GiReTOnRsnJyd0Oh2tW7eO9jAv31bYlNNrTY3bTlDsOwNTVxvvd/A8FG6rSFHLuJbp45YBb968Jn/qC9py+Z6GZrbHL8Zu7ZRSivnz59O7d2+Kl29LSI6tkKY3rVu3RpfgB3T5vNEXvglukyFxBXAdRkiOTdSpU+ezv+qZ6pGvIkEVRc0Bip+GGJrjeo7W4+bmxu7deyBxpS8+9pIdhhHwo+ry5cvodLpwTXsdHR0pVKgQZcuWxdbWlocPH35xbLceGw9DUa4gTOuqY1o3i0hnJ7Cy0lGpiI5BzYJIcr0IHLQ0/N0dAyrimvxy5cqhlOLJkyc8efIEpRRPnz5hw6AzcLpgmIM7Q6kQsM2g9ffR6XSkTp2aynV6sHzbkzhfe1p/WMTxLRui4+oy4zwdNF/x9wHF+oOKrUeV2VqrLFi2lwGL03LudmKq9VOsP/jRDufLfnidT+aCQ7Yflh/8Ee54Kcv/j/fv3/Py5Us2bdqEn58fer0eT09PrK2tKV6uJQd8kkKCopC07ocnZpoW7ljLdkH6+orbj0zPm1d+H2YPSFJNz5ELcfuaiStGjx5N5cqVcXd3p2HDhhw8aLgQNm3aRJMmTShdujS1atVizZo1Rs8rXLgwJUuWpFSpUpQqVYqFCxdq2wICAhgyZAilS5emWrVqbN++PVbT9DVatWqFlZXhM9LLy4uQkBAzRxQ3PHjwgEmTJgFgbW3N1q1bSZcunVliqVv3w+fHpk2bzBKDMJ+USSBNMhP+kkLKxMGkSWri/p/5SxmFCtYyZcqwf/9+lFIcPHiQWrVqkTt3bg4dOsS+fftIkSIF2bNnj/C5ly5donjx4kbrPl7+lLx582qPkydPDqDVQnfr1o1Ro0ZRokQJhg0bZtKAlmGPFzpzRdgpI0PXhZ7j0qVLlChRwugYJUqU4N9//yUkJIRLly5hZWVF4cKFte3Zs2c3au166tQp/Pz8cHFxwcnJSfu7efMm169fNykfwvLx8aFWrVoMHTqUihUrRvn5USEDvUWBVuhwm6Ctc8w2GBgGGAaGyZMnD40bNyZdunTUrFnT5GMvXLiQadOmkSBBAsBQ8AwJCdG+4KPq/lNFjp+//MbS0gLevHkDgK2tLcuXL6dly5aQvDlkW2i0b7EOin8WQ66MsTNw04QJE+g/YDCkag+Z5kJiDIXvMK/Lx3aeiNo57j5WrDtgeBz4HuqWBhXm/ipVnfB5u2QHLNmhgDJRO1kErt2HwhF/5hpZvXo1/fv3j1JTydSpU1OgQAFOnDgR5f58J65+GLRwZGsdQ5p//jWvWrXqJ2+sGzVqRM6cObl//z5FixalcuXKpE6dGoBkyZJp+yVNmpSaNWsS/KoaNfv5s/V/YQY3KXIdLlSBPIbzPAQe+sLOsdB0+D0WddqHp+fPn5zW0BxuPVQc+uh7LaET3F6tI6GTjpCQEDaNs6BGf8P1dvsR1B384dozvAaxF69er8ctc24o8Sbyna62g1cHPiz7Xzbefr0L3OgFxX3B0gGAx+8LY1MeCLECy2rQrAy8/q+kn7AM5N0T5VgzNFDsnwHu+T//mudv9SFPX7wxzECxZyqUKxS3rpe4pmnTpvTt2xcbGxt8fHzo3LkzGzduJCgoiAEDBpAjRw5u375Nx44dcXNzo2DBDz+orV+/nqRJk4Y75pw5c3j16hVbt27l+vXrdO/enRw5cuDq6hqbSfsiKVKkoHr16qxfv55Hjx7h7e1NuXLlzB2W2Q0dOlT7sbhTp05aM39zyJkzJxkzZuTmzZscOHCA169f4+zsbLZ4ROw6Oc+0uki9Xs/t2/dxdXXVpjuMLWXKlGHBggWcO3cOCwsLcubMibu7O97e3rx48SLSpuvRIexMCKH3S6H929u0aUPlypXZsmULO3fuZOzYsUyePPmTU0JHdLxPnUMpFe4+LWzFSujjT93L6fV6UqVKZdRXPlRUu6r++++//Pzzz7Rt25bBgwdH6blfQmrKv4TFh9rWBPaGX/b379/Pw4cPOXjwIJ06daJGjRpRrqELHe1bp7PEIm1XrFPUpXfvPlE6xoNnimcvDXOMf0629BDZZ03IYRecnZ1xdnbG1tbWUCAH8I14kINJK2OvZmnMmDFQ5C5kmhGl53n01ZOilp4tRz8d6/PXinI9FD1+M/z1m63I0gSytnJl0TbCDcL1SYH34cVuADwrvGHTaD/6NdHTpjr0bGAoVM37RUfQXh1j2334kPmhnSJlbT1TV7zF3z8gwkOfPXuWhg0bflHfxTNnzmBlZcWECRNMvk793sGGIx+u/RSJP7Gvnx9DhgxBp9N9skB+8eJFVqxYwZAhQ5g9ezYtW7bUCuSRsbS0ZEZPh/Ab8kRyHts0tJhdGQsLC0qUKIFOpyNHjhwMHjyYli1bUqdOHXQ6HU2aNOHRo0fRWrOu1yvuPg7f19nvnaLtBD0ZGxqvD9oDp3+/xbbNq9DpdFhZWTHz96mRHn/ogph93+n1itF/BpOlzjWs3N9jna4jFLkV+RPenIDHCyLcNHPmzA8LKgiOJAy/03+FdPLtB7uMgKVpBXLfLXDZE4KNO7WX6WZoURCZ248UTpX13HoUftui7VJb/jkZMmTAxsYGMNwkBQUF8ezZM3766Sfy5MmDlZUVmTJlokiRIly8eNGkY27dupV27drh5OREvnz5KF26NDt37ozJZESr0AGYAFasWGHGSOKGf/75RxtsNWHChAwZMsSs8eh0Om1QvuDgYHbt2mXWeIT4WGi/8mnTpuHu7o5Op8Pd3Z39+/d/sj85QI4cOTh27JjRuo+XbWxsvrgVT7p06ejQoQPr1q2jd+/e0T5eVs6cOTl06JDRuiNHjpA1a1YsLS3JkSMHwcHBRoPDXblyhZcvX2rLBQsW5NGjR1hZWZE5c2ajv4h+CI6Mj48PTZo0wdPTk9GjR3912kwhNeVfwvpDacTe1lCTHNmb5O7du+GaaRUsWBCdTsepU6eMd9ZZ8875Zyj0YVCGKSdgol5v0i91Pw3Wa7W7H7OwgGI5Yc9UHXa2Hwp/D58pWo4J4Oqd93Stfg8XizM0b94s8pOEvIHjruCQE/Js01Yv2gYhlzxp06YNpUuX/mysX+Lt27e0b9+e14EJwSZ5lJ+/7bjhf/V+iqI5Fbum6EjgYPxr2y9/6Jn4ifuo1uPDr9s+SUeVPpHcwN+fAvenAfDnQfhzGKRPn54LFy6E+3XehkfAh3Q9fg69/rCn1yw9HE3I32sWkyFDBvLkyYOlpSXDhg2LNE5/f3/s7OywsbHh/fv3AHTs2JE//jBuOtyvXz+GDBmCv7//J6+xxiP0rNwD8KGmvHQEM/4EBARQsGBBLl26FOmxihYtSunSpRk5ciR2dnaR7vcpmdLoeL8XrMuZWHCyTgZpenDkyDTA0NT/4w/ZFStWsGLFClKmTPlVTfxDhYQofuyk+N9/WfFra6hcxFCDH9E0cs7Bh7CxCf85sn1lfyjZI9Lz6ErrCd6nw9Iyemt1Hz9XpKytMPx2+9+IfVnmfPpJZz8MGDNs2DAGDBhgNCVkp06duHnzJnfu3MHHx4d+kzrilzZ8c3YAcv4NgXcj3DS7t44KheHAOUiT+AWVf/yvVdLTZZB5NqRqq+1brZ/i3lpIk+xD/vx7V5EoAZ+cPWDJDri/vwaezerh4eFBsmTJvrvR/qPDuHHj2LRpE4GBgbi7u+PmZjy6Y0hICD4+Pnh4eBitb9asGTqdjqJFi9KjRw8SJUrE69ev8fX1JXPmzNp+WbNmxcfHJ8JzBwUFERQUZLTOyspK+6Hga4S+1lF9zT08PHBycsLPz481a9YwY8aMb2pa1C9Nd2T69u2rHWvAgAEkTpzY7O8jDw8Pfv/9d8DQhL1OnTpG26M7D7418T39YN48SJAgAfnz52fp0qVMmzYNvV5PyZIlOX36NO/fv6d06dLh4gv937VrV1q2bEnBggUpWbIky5cvx8fHBzc3N20fV1dXduzYwaVLl3BxcSFhwoRGxwn9CxW63LNnT6pUqULWrFl58eIFe/fuJXv27J/MI6XCz5IT9vgfn7dnz54ULVqUkSNH0qBBA44ePcrvv//O77//jl6vJ0uWLFSuXJm2bdsye/ZsrKys6NWrF/b29tq5ypUrR/HixalduzZjx44lW7ZsPHjwgG3btlGrVi0KFy6sVZREFruPjw/ly5enZMmSdO/enQcPHgCGSqGwLTijwpRynBTKo8ra+FcW+89816ZNm5bdu3dToUIFevbsyZQpU4y2L137P35u3Q+CX0DB0xEeY+aSs+TN8IYbN24QEhJChQoVePnelQKtTSuQqAMRXwi+vr7kypqFFy8MQ673MnWGkKB7hr+Dlob+vP9ZsgOWLHHn9evXWjP86LJs2TKaNfvvx4IMY8Jtt7FWBL3X0bAcDPpZR96WhrzpUR+m/RX+eMcvgnMVxfZJULlIaPMZ9ckCeUSuLNORJW3k24e1S8GIj8rOd+7coW/fvpQqVYpbt27h5eXFjRs3wDo5FIugMKizgB9fUKdRRgg0jBY7YsQINm7cGG7XJUuW0LRpU61pz5s3b7h48SJ58+bF0tKSmTNn0qZNG6M+nEFBQcyePZtOnTpFmIbu00ML5MayRtAlMGHChOFukkNduHCB3LlzR7jtS1hZ6ejTSDFppfH6qkWhV0MdgW8fUn1Iyg8b3CZDyDt4NPeTx3306BEXL16MtImlXq9n3759pE+fnuDgYB4+fIi7u3u4rgAz/0YrkAMMWaAYEnElMig9r09H0g5dvYfDztRt0p21S0bz7KUiWU3j936htoqzC6O3UN5h1DUg0yf3yZniJl3L76Zjnz/A/yoODnZcuHAhXMEsrIwZM5IxY0bc3d3p1AnuPg4iff0Ivooc8xj+/uNgB3P76CiRBzKkMqQ1UxqAJFy/fp1Mmf6L9VoHo0I5QNqfFB7FFPN/MXw2RDpK/PlykPfD9IZ7L2Zg73/zKLu4uODr6wsYChoTJkTeXSY+6d+/P3379uXkyZNcu3Yt3PY//viDZMmSGfVrnDdvHnny5OHNmzeMHz+ekSNHMmXKFN69e4elpaXRj3WOjo68e/cuwnN7eXmFq6mpX78+DRo0iKbUGX5Yj6ry5cuzYcMGXr58ydKlS6lQoUK0xRNbviTdHzt8+LDWUip16tTUqlWL27dvf/Vxv1bGjBmxt7fH39+fzZs3c/PmzQhvmKMjD75l8T39YL48KFiwIKdPnyZLlizaeyZz5sw8efIEBwcHbZ2vr+9/Te0Ny8WKFaNLly7069ePwMBAqlSpQuPGjTl48KC2T+XKldm+fTs//PADb9++Zfny5aRNa7iRffjwIYkTGzeDfPz4Mbdv3+bly5d07NiRhw8fkiBBAkqXLk3//v0jfU8HBwfz/PnzcNufPn2qrbt//77ReV1cXPjtt9+YNm0ao0aNIlmyZPTo0YOyZctqzxk5ciT9+/enTJkyJE2alN69e3Pz5k2jc82aNYvJkyfTsmVLnj9/TtKkSSlSpAgeHh5aWoKCgiKNff78+Tx9+pQNGzawYcMGbX2aNGm0sVOiKmPGjJ/dR6fi+ihIcciZM2coWH4A5P4wL9+AZjCm3Zf1Aug/W8/45SbseKUlPPkTLBw+3ZfzIwmd4NlGHVZW4W/W9Xr9Z/sTe3h4MHHiRHLlygUYpkPIlCkTvXr1+rBTsUeGWshQfufgTEEWLlzIgwcPGDlyJGnSpGHkyJEfCtVfwKj/SJgfAno1gEmddZH2L/nnhiJPi09f4vkyG272i3YIv9+zTTpcEuroOFnP7A3G2wJ267C1MZz30i1FTk9F4ezg1V9HQkdIl8KwrXnz5vz555+mJBMSV4Wk9SBli4i3H4z4NQsICIhSbcz169eNaqPAcE2EzceAQEXlPooDEUztOKuXjo61P+yrlOLQoUMRtpIYNGgQv/76a4z1507soeeln+FxlrRw8c8P17zvK0XSGh+9rm9OGZpP3xkNL7Yxfvx4o7lzwfDBe+vWLcDw66afnx/du3c3mpbjA8O53r71w8HBgfOXn5CvnelNpHh3EU7l+fx+wPHjxylSpAg+NxW5mxuna0Z3HV1/ir48dvU4yx2/vJFun9tXR9sa0XO+oPeKPkMX8Pb1E1Ze/YV3geE/Uz/XGiAgIIAxY8bw66+/glNBKBCFgSQe/AHXuwIKsi2D5I3+O+hNuDUYnv4FfPjcqVix4jfVpDq29OjRg4YNG2oF8DVr1rB8+XIWLlwYaV++Z8+eUa1aNQ4dOoS/vz/lypXj0KFDWsF86dKl+Pj4MHbs2HDPjema8tCWblHtU7plyxZtTJnixYtz8ODBODeeRWS+Jt1hvXjxgqJFi2oDKy1evPir7gGiW506dbQfto8ePUqRIkW0bdGVB9+q+J5+kDyI7+mH6M8Dk44Ro2O7f2dOnz6tSFLTaJqDkYu+bOqdf258ehqkZgMvG6/78bXJUy94jgpRGw99Oq5cuXJ9cvqpDBkyaPvq9Xrl7++vbty4oe7cuaNsbGyMpoYLF4NDngiPGdkUKL6+vqpfv36qW7duav/+/RHne+hxnEsYnSsg8PP5v/24Xg2cG6LGLNGrDpNMn8Lild+HYz9/FaIqdHujKBWiqv0SooKDo/a6v379+sN0aab+fZRWQ97mCLdfo0aNohRLKF9f3w/HsUygDh8+om27di/i63Puqkvq1Zv36vXr1+rp06dq4MCBkcYfdsqKmPTyjV7NWKNXp69E/Jos2/np91rz0SHq4bMQ1apVqyi8PpaKYk+Nj5VxkkqWwlVR8r1p11heb7Vsg4/RcZs0aWI0jcebN2+Mtv/6669KKaWGDx+uHBJlMjpejmbRN32Nr6+vouBZ7dhz5sxXFLmtLe8/Exxt5/rYhoPhX685G0x/v4WEhKht27YZ8izfEdNei7CvrVXiiPdJ0VLbZ/r06TGW/m9Z165d1erVq5VSSu3YsUN5eHio+/fvf/I5vr6+qkiRItp0jZUqVVIXLlzQtg8ZMkTNnTs35oKOxNdMCxUUFKSyZcumXS+HDx+OgQij3+PHj9WqVavUsGHD1OnTpyOd5vJzLly4oIoUKaKlv0iRInFueq25c+dq8Q0dOtRo2/c8JZgp4nv6lZI8iO/pV8o8eSA15VFw5swZClYaAzlWaeuGNIcRrSKvqT1+UeF9FlpXA5eEOu4/VTQYpjjyT8TnqFoUlg7RcezcM6oNcjEtsCstGDpkEE8e/ovfnb/o1asHBQoUCLdbcHAwly9fJmnSpKRKlcpo27lz58iX70Mn4Zo1axo12QhtHuPq6srFixe5f/8+FStWxNLSCkpFMK3VsVTw/onRqlKlSnHggHGnd6WU6b9AZZwAaXtri+mSw501Ufv1SinF3tNw4Tr0/D3yS//pRh1JE314TcOm/2t+MZsxYwbTpk1Dp9Px/PlzevXqRbJkyQgODiZ37tyUKVMGMPSf37p1K0WKFCFLy7S8D/4Qi9WV2gQ/MUzlMm7cuHC1vFFRtmxZ9l/KAFnmgs6Seyufcf7qCzyGZgm/8+Vm8NS09v27d++mfPnyXxxXdPuxo56jEXdL1ei9daa/tmFrUz/n9VG4PRzsM4NDLnhzEl5shfdPjXYrXLgwJ06Er91t27Yt8+fP15ZDm10CkLiyUcudMe10XLun6N1IR84M4T+Tbt68ye3bt7GxscHV1ZU0adIYbff19cXNzY3XAU5Q1NBsTxd4k5BjbgSHwOYjikQ293EvkiZGfz1XSrHrBDx5CTVLgLNj1GsZx44dy8CBA8GlLuSMoA9LqOPp2bR2NtWrV+fy5cvs27efPuta8i7QOtyuExtsIJGdL56entFSG/ste/fuHd7e3ri7u2NjY4O3tzdDhw5l8eLFPHv2jCFDhjBr1iyyZDH+LLl+/TohISFkypSJt2/fMn78eN68ecOMGYaBO6dPn87NmzcZPXo0N27coGvXrixatIgMGTLEavq+9jN/8eLFtPiv60OvXr2YPHlyNEf4dfR6PRMmTMDLy4sXL15gZ2cXrqlu5syZyZUrF2XKlKFZs2aRDpL08OFDjh07xuXLl9mxYwfe3t7atqRJk3Ly5Mk4N3r+/fv3tSa7BQsWNBrjJ7q+779V8T39IHkQ39MP5skDKZRHwZkzZyhYeRJkX2K0vlRe8P4tfMF81R5FoxGG7O1QC35y11GxV+TZfWSWjuK5P/RvtixjwktzJDGEvA63evbs2WTPnp0nT56wdetW/ve//0U6+u379++xsrLi6tWrtGzZkoQJE7Ju3Tqjfn2RXZxXr16lVatWpMnZhNWXOxgdN4/zXzy+6c2Tix8GcipWrBgAr169on///jRvHkk/2o+l7QsZxxmtur5Sh1vqL28SGFET4GSJwOdPHckSGR/XnB9Qxy8qin3UtP7X1jpyu0HeTJicB3ceK+oMUoToYf4vOpzs4eatu3j0egWOuT795MtN4enKT+/zn61bt1K1alWT9o0tdx8r0tf/9Pupxo+wbNBb6tSpg7e3N8HBEfzYZJ2C1Lnb8MBppGknvt4NHhhGHW/evDmLFy+OdNdVq1ZF2Bf21atXn5zGw6rYDYKtjW947fU3WD/0BpUqVdLWeXp6smTJf59dOitI6A5WSciUvz6F8qRh9aHkYBe+L7jj+//hd9Twvv2WvqiVUrRt25arV69y8OBBMmfNw7UHduB3EkMFmcHUqVPp0aOH0XMj6q4S1vUV4JYmbqc/pvn7+9OzZ08uX76MUop06dLRunVrypYtS/v27Tl79qzRDxdVq1Zl4MCBnDhxgrFjx/LkyRMcHR0pUqQIPXv2JEkSw0S+AQEBjBo1Cm9vb5ydnenatStVqlSJ9fR97bX+4sULkidPTnBwMK6urty8eTNONWEfPXp0lKb4sba2xt3dncePH/P8+XPc3d2xtrbm8OHDEY4lAIbR+VeuXEnRokWjK+xoVbBgQc6cOQMYCumhM398S59zMSG+px8kD+J7+kEK5XHe2bNnKVBlOmQNP1rTkOYwsrXhRdPrFdfvQ9amn8/aOX10tKsZ8Rf1pBWKvn8YH8PaClb2u8G4KYs5sXsS6COeLstUM2fOjHSAr7A+d3GGhCisyn4ivZcaw7PVXx5okbtg+2GqrC514bceX/8mCQhU/HMTlDIMGpXEOeLXwtwfUJ8af+DSEh3ZXT99s/f8tcKl+he81f3OwJnCn93N3t6eyZMn8/PPP+Pk5PTZ/c0hJERx+ioUyAJvA+DuEyIcb6BLXUP/7CdPnnDt2jVKlixp2GCZkCSVfXn+xsQb62Op4f1j1qxZw08//aStjmg8h4/3+VjGjBm1Pu7hZJkPKVuGX//fWBTZs2fn8uXLYJ8V8h8Fq0Smxf+f6Z3f0q1hAi32b/2L+uHDh8yePZurV6/Stm3bCOeRVkpx+AL8vk5x/QGc/Giq9YJZ4dT8bzP9wjTRca1XrlxZG3vg5MmTFCpUKDpD/GJ+fn64urry/PlzAFKmTElwcDCZM2emdOnSWFtbc+jQIQ4fPhzxj5OfkTVrVjp27Ei7du1wcIhg+so4YujQoYYxKDAMPtimTRvg+/ic+xrxPf0geRDf0w9SKI/zzp49S4Gqf0CWiKfwUQcsWLBZ0WaCaVlavhDsmhJ503elFBbuhmPZ2oDfduNB2wICApg3bx63bt1i6dKlPHnyJMLjRGbMmDH07t3bpGaYplyctx+pSKcY0oW8Qh1J8slzBAQE8M8//9ClSxdtXsUECRLw5s0bo8HdFg3Q4VmFWK11iAsfUJHNj54yCdz+S8fNh7ByD3gUgx9yfMibCAc7M9VBa8Bw3pQpU/LXX3/h7e3NrVu3ePXqFe3atfsmRxYOFRCosK8YPm+OzdZRNKfusy1WZvbU0amOjqt3FXM2GApw+TJDy6o6bYTwiAQGBjJx4kRev35N+/btP4wcHomgoCCKFi3K2bNnAViwYAGtW7f+sEPmWZCqfcRPDnkLlo6fPP6nvN/74XMnLrwPYlvYz2Gj9ZHMaiG+D9Fxrc+dO5f27Q3vywEDBjBmTPiZQ8xh+vTpWuuQJk2asGzZMm1b2HQHBQVx7do1Fi9ezOLFi3n69ClWVlYopbR5jm1sbChSpAglS5YkX758ZMiQgaJFi8apVgGROX78uNZ6r3bt2vz9999A/PycCyu+px8kD+J7+kEK5XHe2bNnKVBtIWSaFuF2dcAi0oLTxy4v1ZEtffR/ac2fP5+2bY2nA0qWLBl+fn4MGDCApEmT4uzsTO3atXF0NP1G3dSL0++dYtE26Do9/GXV9SeY0d2Ctm3bsm3bNu7ff4Rtwsy4plB4e3uTMqVh+qo37xSjFite+BmaaS/dCX1mGY6XPgXc/iv2PyDiwgeUf6AikYci6P3n9101XEfD4Qq31JDDFbYcNd6eysUwX/anNM7QhRVL/sDR0ZHp06fTsmVLs+dBTIioG0PBrIa5sCObUzxU0F4d1hHMbhAbZsyYQffu3T+scC4FyZuGmw7sc6wtAnC2CyCbyznOPcrH2/eJAHCy9Wfwz0H08/wwPUpceB+Yw7KdihZjFcH//Ta4ZDA0qxR/0h8fRce1/uTJE1KlSoVerydr1qxcvnzZ7IXVJ0+ekDt3bp4+NYxp8fFUlZGlOzg4mDt37pA6dWr8/Pw4ePAgadOmJU+ePEZd3b4ler2elClT8vTpUxIkSICvry/W1tbx9nMuVHxPP0gexPf0gxTK47yzZ89SoNZacB0R4fYnG3Ukr/n57IzJGhZ/f386d+6Ml5cXW7ZswcPDI1qO+6UXZ6Veenad/LA87xcda70V24+H2ecHePQczl+P+Bi1S8H6/6YFbFcD5vSNn4Xyj5n6A9DH9N6G1hnHfBTFO364Xvs2hvKFdFhZGsZJsLGOO/3qY9rAuXrGLjV9/18awy9NDNPlmZuHhwfbtm37sCLfQXD+8ZPPaVoRlgyOvJVOZL7na8AU8T398Ul0vdZlypTRBj77uAAc2/R6PdWqVdPmDq9Xrx5//fVXuH3i0zXetGlTli839A07cOAApUqVind58LH4nn6QPIjv6Qfz5IFVrJzlO6HT6UBF3r9q8LzwBfKkCeHZqw/L99bG7E28vb09CxcuZOHChTF6HlNN66Yjl+eHfGkbQdP+nZ+ZTji0QA6G+bHFlwvZ/6EgViyXDr03TF0Nd58oBjTTkThB/MzfMe0sGNPOtB865nR/Qps6ybGwiBt5NXXqVFKmTMnz58+xsrJizKT81BgEV40HUuaclyHePG6x2/VDiPisbt26WqF87dq1Zi2UT5o0SSuQp0iRgpkzZ5otlriiUqVKWqF8586dlCpVyswRCSHiq/j588fXCH4V6aa5m8Kvm95Nx4stOqZ303F6vo40yeLXzXDODDo614meY9nagKVl/Mq/T7n9V9Tyom5pwhUkdTodvRrqmNrVIt4WyMOa2DHyPAjZr+PVVqhY0D8WI/q8bNmysXDhQtavX8+aNWvImsGJK8sseLNdx63VOl5t06EOWJA3k468maJeOy6E+HJ169bVHq9bt85scfj4+DBo0CDA8Lm/dOlSkidPbrZ44oqwM1SEDsonhBDmIIXyqLKwNXnXbj9B4wqQKIGObvV0FMgaP2+Gx7XXMbadIe1JnCPfr0Qe2DlZR4tIZtMqF37q9XgtfQpDYUsdsCB4n+Gx/y4dXgN0LOyvI3CPjkUDdCRNaOhDvqBf/Lz+oqJPYx3B+wx5Vyjbh/W7puiwsNDhFHcHEg7HyUGHa0rdF83xLYSIHmnTptWmBDt//nyk04fFJKUUXbt21UZS79ev3zc9QGd0SpUqFXny5AHgxIkT+Pp+ZrAVIYSIIVIojwKdTgc60wrlq4brmN7dQmqlMBQO+jczFBp9N1toBUl1wIKXW3X8NVLH7b90HJppQcUfdHgNsCBkv45mH37AJokz/NpG8jIyoS0I7Gx1tKiqo6WHDhtrHc2r6ni6yYIHf1uQSGrCTWJpaci7k/MsODxTx5kFOioUlrwTQnyZsNMdrl27NtbPv2bNGvbt2wcYplccNmxYrMcQl4XWliul2LNnj5mjEULEV1IojyoTasod7aFBObmJN0VCJx31yuhIn8I4vywsdCwZ/KHw7rvZgkLZJE9F7Poxj478WeS6E0J8OXMWyt+9e0fv3r215WnTpn2zo6XHlMqVK2uPpQm7EMJcpFAeVRaf/zI7Pltu4oUQQggBbm5u5M+fHzA0kY7NJuwLFy7k7l3DqI9VqlShRo0asXbub0XJkiW1Hyp27tyJTEokhDAHKZRH1WdqypMnhlwZpVAuhBBCCIPGjRtrj//4449YOader+e3337TlsePHy9d6iJgb29P6dKlAbh79y6XL182c0RCiPhICuVRYOhT/ulZ5I7+IV94QgghhPigVatW2NoaftRfuHAhfn5+MX7Offv2cfXqVQDKli1L3rx5Y/yc36qwTdh37dplxkiEEPGVFMqjzFJ7VKUoeA0wLoSnlxlGhBBCCBFG0qRJtdryly9f8vvvv8f4ORcsWKA97tixY4yf71smU6MJEZ5Sihs3bvDgwQOjdQEBAWaM6vslhfKoClNTPr6DYaRrj2KG5dL5wMpKasqFEEIIYaxfv35YWBhuuyZOnMjr169j7FzPnz/X5kV3cXGhZs2aMXau70GuXLlInTo1AN7e3gQGBpo5IiHMJzg4mEmTJpE2bVoyZcpEmjRpqFSpEs2bN8fNzQ17e3uSJElCixYtuHLlirnD/W5IoTyqdB9qyi3/y70/B+lYPFDHquFSIBdCCCFEeNmzZ6dJkyaAodA8adKkGDvXsmXLtIKlp6en1nReREyn02m15e/evePUqVNmjkgI81BK0a1bN2bNmsWjR4+09bt27eLPP//k1q1bALx48YLFixdTsGBBduzYYaZovy9SKI8CQ5/y8IVyl4Q6PKvoSOkihXIhhBBCRGzo0KFYWRla3I0fPz5GapmUUsyfP19bbt26dbSf43sUtgn7gQMHzBiJEOYzefJk5syZoy0XKlQIGxsbbdna2pr8+fPj5OQEGH7Eql27tvyQFQ2kUB5l4QvlQgghhBCfkyVLFm3e8KCgIBo1asS7d++i9RynTp3i/PnzABQtWpRcuXJF6/G/VxUqVNBGp9+7d6+ZoxEi9nl5edG3b19tefHixZw8eRJfX1/Onj3LmTNnePbsGWfOnOHevXvUqVMHgICAABo1asSbN2/MFfp3QYqVURWmT7mV5Sf2E0IIIYT4yJAhQ8iSJQsAZ8+epUOHDtE6N3bYAd7atGkTbcf93iVLlowSJUoAcO3aNekrK+KV2bNn06pVK225Z8+eNGvWDAAnJyfy5ctH/vz5cXZ2BiBhwoSsXLmSIkWKAIb3TOfOnWM/8O+IFMqjKmzzdSmUCyGEECIKHB0dWbduHY6OjgAsWbJEm088MDDwqwrob9++Zfny5dp5GjZs+PUBxyO1a9fWHm/cuNF8gQgRi+bPn280Q0O3bt3o0qXLZ59nY2PDihUrSJAgAWD4LFu9enWMxfm9k0J5FETWp1wIIYQQwlS5c+dm4cKF2nL37t1JkiQJdnZ2FCpUiOfPn3/RcZcuXaqN6t6wYUPtZlmYplatWtrjDRs2mDESIWLHuXPnaN++vbbcr18/pkyZonXl+Bw3NzejPui9evXCz88v2uOMD6RYGWUfCuXSfF0IIYQQX6JBgwYMHDhQW37x4gUAZ86coWvXrlE+XlBQENOmTdOWTanpEsYyZ85M7ty5ATh27BgPHz40c0RCxKwxY8ag1+sBw4+DY8eONblAHqpx48ZUq1YNgPv37zN69OhojzM+kEJ5VIXpUy415UIIIYT4Ur/++isdOnTA8qP+cMuXL49y8+mRI0dy+fJlAEqUKEGBAgWiLc74JLS2XCnFpk2bzByNEDHn/v37rFmzBoDkyZN/UYE81LRp07RR2idPnszVq1ejLc74QoqVURCu+brUlAshhBDiC1lYWPDHH3/w/v17lFIsWrRI29akSROOHj1q0nH++ecfxo0bB4CVlRXTp0+PiXDjhbBN2NevX2++QISIYcuXL9dqyTt06IC9vf0XHytz5szayO3v37+nW7du0TqAZXxg1kL5ixcv6N69OyVKlKBu3br873//i3C/+/fv07lzZ8qUKUPVqlXx8vKK5UjDkD7lQgghhIhGobVTnp6e2jRDb9++pWrVqnh7e3/yuUopOnfuTEhICACDBg2iUKFCMRvwd6xgwYKkSpUKgD179sg0T+K79ddff2mPQ0da/xoDBgwgXbp0AOzYsUMGS4wisxYrx48fT7JkydizZw/dunWjf//+2gAlYU2cOJE0adKwe/du5s+fz6pVqyItwMc43Ycskz7lQgghhIguOp2O5cuXU758eQBevXpFuXLlWLlyZaTPmTFjBgcOHAAgU6ZM9O/fP1Zi/V7pdDoqVqwIGPrpb9++3cwRCRH9bt26xYkTJwDInz+/Nk3j13B0dGTKlCnaco8ePfD39//q48YXVp/fJWa8e/cOb29vNm3ahJ2dHWXKlGHZsmUcOHCA6tWrG+378OFDmjVrhpWVFWnSpCF//vzcuHFDmxvvY0FBQQQFBRmts7Ky0vo6fCm9Xm/Up1yHQq+PH00zQpu3hP6Pb+J7+kHyACQPJP3Rn34LC2lyJYzZ2dmxYcMGateuze7du9Hr9TRr1gwHBwdq1qxptO+RI0fo1auXtvzbb79hZ2cX2yF/dypWrMiff/4JGJqw169f38wRCRG9QvuSA9F6ff/000+UL1+ePXv2cOvWLRYsWCCDTprIbIXyO3fu4OTkRNKkSbV1WbJk4caNG+H2rV+/Pjt27CBv3rw8evSICxcu0KZNm0iP7eXlxbx588Ido0GDBl8Vs2EUTltt+d692/Gutvzu3bvmDsGs4nv6QfIAJA8k/dGX/owZM0bbscT3w9HRke3bt9OpUyfmzp1LSEgItWrVok2bNowYMYLUqVPz6tUrPD09tR+J+vfvT9WqVc0c+fehSJEiJEqUiJcvX7JlyxaCgoK+umJHiLgkbNP1evXqRdtxdTodkyZN0gaanDJlCh06dMDKymxFzm+G2XLI398fR0dHo3WOjo4Rzm2XL18+1qxZQ6lSpQgJCaFdu3Zkzpw50mO3bNmSpk2bGq2Ljpryd+/ege6ltuyW0ZUvHKTwm6PX67l79y7p0qWLlzU78T39IHkAkgeS/vidfhG7LC0t+eOPP/Dz82P58uUAzJ8/n9WrV/PLL7+wZ88erl+/DkDx4sUZNWqUOcP9rlhbW+Ph4cHy5ct59eoV3t7eWpN2Ib51t2/f1roB582bl6xZs0br8fPnz0/lypXZsWMHN2/eZO3atTRs2DBaz/E9Mluh3N7enrdv3xqte/v2bbiR/0JCQujevTuenp7Uq1ePJ0+e0KNHD9zc3KhQoUKEx7axsYmRXzQtLCw+DPSmQrC0tI72c8R1FhYW8fpmNL6nHyQPQPJA0h+/0y9ij4WFBV5eXri6ujJr1ixevXrF69evGTx4sLZPkiRJWLp0abhp1cTXqVWrlvZjyPr166VQLr4ba9eu1R7HVNeMvn37smPHDgAmTJhAgwYNvni6tfjCbHcV6dOnx8/Pj2fPnmnr/v33X9zc3Iz2e/36NU+fPqVevXpYWVmROnVqypQpw6lTp2I7ZIPQPuUqxDznF0IIIeKA0aNHU7lyZdzd3WnYsCEHDx7Uti1atIgKFSpQrlw5pk+fbjQ1jo+PD40bN6ZEiRK0a9fuv65hBgEBAQwZMoTSpUtTrVo1GWQLQ0XDmDFjuH79Oi1btjTaljhxYv7+++9w907i61WuXBlbW0OXxQ0bNsTbsTTE9yds0/WYKpSXK1eOggULAnD69OnPziIhzFgod3BwoHTp0syZM4eAgAC8vb25fv06pUuXNtovceLEpEiRgvXr16PX63n8+DHe3t5kypQp1mM23FR8qCkXQggh4qumTZuyadMmvL29GTp0KEOGDOH169ccOnSINWvWsGjRIlavXs2hQ4e0qXGCgoL45ZdfaNSoEXv37iV37twMHTpUO+acOXN49eoVW7duZcyYMYwbN47bt2+bK4lxiouLCwsXLuTgwYP07duXoUOHcvHixXD3TSJ6JEiQQGuRef/+ffNVBgkRje7evcuxY8cAyJMnD9myZYuR8+h0Ovr06aMthx2VXUTMrL3u+/fvz7BhwyhfvjwpUqRg7NixODs7s23bNry8vFi9ejVgmDpt8uTJ2qiilSpV0ubxjE2G0ddDm4dJoVwIIUT8lSFDBu2xTqcjKCiIZ8+esXXrVurVq0fatGkBw/y327Zto1atWpw6dQp7e3tq1aoFQNu2balQoQIPHz4kVapUbN26lcmTJ+Pk5ES+fPkoXbo0O3fupG3btuHOH1MzrUDcnmngxx9/5Mcff9SWozPGuJzu2BI2D2rWrMmWLVsA+Pvvv+PF/O9yDXzfefDxAG8RpTG60l+3bl3Spk3LvXv32LRpE5cvX472/usxJbqvAVO6vJm1UJ44cWJmzJgRbn3VqlWNRhDNlSsXCxcujM3QImRUKFfB5g1GCCGEMLNx48axadMmAgMDcXd3x83NjZs3b+Lh4aHtkzVrVmbOnAnAjRs3jAZqtbe3J23atNy4cQNHR0d8fX2NtmfNmhUfH58Izx1TM62EFV9nGoiv6Q7r7t275M+fH51Oh1KKNWvWRPjj0PdKroHvMw+WLVumPS5evPgnWyJFR/qbNWvGuHHjAPj111+/uQEpo+saMGWmFRmfPgqUUtKnXAghhPhP//796du3LydPnuTatWuAYaYSJycnbR9HR0fD7CVEPvOKv78/7969w9LS0mie7bDP/VhMzbQC8Xek/fia7rDC5oGrqys//vgjhw8f5t9//yUwMPCbqen7UnINfL95cO/ePU6fPg0YKjzLly8f4X7Rmf6+ffvy+++/4+fnx99//83UqVONpsOOq8xxDUihPAoMTRikT7kQQggRytLSkqJFi7JixQrc3NxwcHAwmt707du3ODg4AJ+eecXBwYGQkBACAgK0gnnY534spmZaCSu+jrQfX9MdVmge1K5dm8OHDwOwceNGfvnlFzNHFjvkGvj+8uDvv//WHtevX/+zaYuO9CdJkoTWrVszffp0/P39mTt3rtHsEXFdbF4D38+VFgsMzddDs+z762cihBBCfCm9Xs+9e/fImDGjVmsOcPXqVW10cDc3N6Nt/v7+3Lt3Dzc3N5ydnXFxcYn0uUKYQ+j4BwDbtm0zYyRCfJ3YGHU9It26ddMKttOnTw/3w6wwkEJ5FBhqyv+bY09JoVwIIUT89O7dO7Zt28a7d+8IDg5mz549nDp1igIFCuDh4cHatWu5f/8+z549Y9myZdo4MYUKFcLf359NmzYRFBTEggULyJkzJ6lSpQLAw8OD+fPn8/btWy5cuMCBAwdkfmhhVlmyZNF+GDpy5IgUKMQ36f79+1qLj5w5c5IzZ85YO7ebm5s21sezZ8+YNWtWrJ37WyKF8igw9CkPzTL1yX2FEEKI75VOp2PDhg14eHhQvnx5vLy8GDVqFJkzZ6ZkyZLUrVsXT09P6tevT4kSJahZsyZgaHI+YcIEli1bRtmyZTl37hwjR47Ujtu+fXucnJyoUqUK/fv3p3///kajvAthDqFTowUFBXHw4EEzRyNE1K1du1Z7HJu15KGGDBmCTmeo2Jw4caJRFydhIH3Ko8CoplwK5UIIIeIpe3t7Zs+eHen2li1b0rJlywi35cqVi5UrV0a4zc7O7psbnVd8/ypWrMjcuXMB2L17N1WqVDFzREJEzfLly7XH5iiU58yZk4YNG7Jy5UqePn3K77//Tv/+/WM9jrhMasqjICQkBGzT/rckWSeEEEII8b0rW7asVsu3e/duM0cjRNRcu3aN48ePA5AvXz5y5cplljiGDRum9S2fOHEir1+/NksccZWULKPg3jPrDwu2qc0XiBBCCCGEiBUuLi4ULFgQgHPnzvH48WMzRySE6VasWKE9btKkidniyJ49O82aNQPg+fPnzJkzx2yxxEVSKI+CDf9Lae4QhBBCCCFELAs74ODevXvNGIkQplNKsWzZMm25cePGZowGBg0apD1evHixYbwuAUihPGrkwhFCCCGEiHdCB3sD2LVrlxkjEcJ0Z86c4cqVKwCULl2adOnSmTWerFmz8uOPPwLg4+MjAyeGIYXyKJFCuRBCCCFEfFOiRAns7OwAQ79yqeET34KwteRNmzY1YyQftG/fXns8bNgweS/9RwrlQgghhBBCfIKdnR2lSpUC4O7du1y9etXMEQnxaUFBQdqo69bW1tSrV8/MERk0adKEzJkzA7B//36jPu/xmRTKhRBCCCGE+Iyw/cplFHYR161du5ZHjx4BUKNGDZIkSWLmiAysrKwYP368ttylSxcePHhgxojiBimUR4k0rxBCCCGEiI+kX7n4lvz222/a465du5oxkvDq1q1Lo0aNAHjx4gVt27aN983YpVAuhBBCCCHEZ+TLl4+kSZMCsG/fPt6/f2/miISI2KlTpzh69CgAuXPnxt3d3cwRhff777+TMqVhZqutW7ca9X+Pj6RQLoQQQgghxGdYWFhoTdhfv37NkSNHzByREBH7/ffftcddu3ZFp9OZMZqIubi4MHfuXG156NChBAUFmTEi85JCeZTE72YVQgghhBDxmYeHh/Z469atZoxEiIj5+vpqg6clSpQozoy6HpEaNWpQqVIlAG7evImXl5eZIzIfKZRHRTzv6yCEEEIIEZ9VqVJFq3XcsmWLmaMRIjwvLy8CAwMBaNmyJY6OjmaO6NNGjRqlPf7111/x9/c3YzTmI4XyKLC3tzN3CEIIIYQQwkySJk1K0aJFAfDx8eH27dtmjkiID/R6PbNnz9aWO3ToYMZoTPPDDz9Qq1YtAO7fv8/MmTPNHJF5SKE8ClKnTmXuEIQQQgghhBlVq1ZNe7xt2zYzRhJ3PH78mDVr1tCrVy9q1qxJyZIlqVKlCkOHDuX169fmDi/e2LVrF9evXwcMU/hlzZrVzBGZZtSoUVoLlDFjxvDy5UvzBmQGVqbsFNVfWXQ6HX/88ccXBSSEEEIIIURc5eHhwZAhQwDYsGHDN1EbGROUUhw+fJj58+ezZMkS9Hp9uH127NjBkiVL2L17N5kyZTJDlPHLrFmztMedOnUyYyRRkzt3bjw9PVm8eDEvXrxg4sSJjB492txhxSqTCuWnTp1Cp9OZPH9cXBzhTwghhBBCiK9VoEAB0qdPz507d9i9ezfPnj3TpkqLD5RSrFixgmHDhnHt2rXP7n/r1i2qV6/O0aNHSZQoUcwHGE/dvn2bzZs3A5A2bVqqV69u5oiiZvjw4axYsYKgoCCmTZtGhw4dSJcunbnDijUmFcoBkiVLprX3/5QNGzbw9OnTrwpKCCGEEEKIuEin09GoUSMmTJhAcHAwa9asiVe15UOGDAlXi+ns7Ezbtm0pW7Ys+fLlI3ny5Pz777/Ur1+fS5cucfnyZfr06cP8+fPNFPX3b+7cuVprhfbt22NlZXIxL07IkCEDHTt2ZPr06bx7947u3buzbt06c4cVa0x+tVKkSEG7du0+u9+RI0e+20K5NAAQQgghhBCNGzdmwoQJACxdujTeFMr//vtvowJ5mTJlaN26NXXq1Ak3yneuXLnYsmUL+fPn5/Xr1yxYsIBWrVrx448/xnbY372goCDtBw8rKyvatGlj5oi+TGht+ZMnT/j7779ZtWoVDRs2NHdYscKkgd6GDRtGq1atTDpgmzZtGDp06FcFFVdJoVwIIYQQQuTLl4+cOXMCcPjwYc6ePWvegGLBwYMH8fT01JbHjx/Pvn37aNasWaTTbmXMmNFoyquOHTsSHBwc47HGN5s2beLJkycA1K5dm5QpU5o5oi+TKFEipk2bpi23b9+eFy9emC+gWGRSobx69eqULFnSpAOWLFnym+vDIIQQQgghhKl0Oh2dO3fWlmfMmBFr53748CG7d+9m7969vHv3LsbPFxwczMiRIylbtix+fn4ANGrUiL59+5r0/I4dO1KgQAEAzp8/z/Tp02Ms1vhq7ty52mNTWjbHZY0aNaJRo0YAvHr1yqiQ/j37qinR+vbta1I/8++FVJQLIYQQQggAT09PbeCyZcuWaTWVMcXPz48uXbqQPn16KlasSPny5cmUKRPe3t4xds5///2XkiVLMmzYMEJCQgCoVKkSCxcuNHlgZysrK2bPnq3tP2jQIM6fPx9jMcc3N2/eZOfOnYChZUL58uXNHNHX0el0jB07VusTP23atHhRW/5VhfJnz57x8OHD6IpFCCGEEEKIb4KTk5PWdzcoKIjZs2fH2LnevHlDlSpVmDlzplHz70ePHlGpUiXWrl0bredTSjFnzhzy58/P8ePHAbCwsGDIkCFs3rwZe3v7KB2vSJEidO3aFYDAwEAaNmyo1bqLrxO2lrxt27ZYWHxV8S5OyJAhAy1btgTg9evX8aJ1xbf/qgkhhBBCCGEGXbp00QpBU6dO5fnz59F+jkuXLlGsWDEOHz4MgKOjI506dSJ//vyA4QeB+vXr07lzZ+7fv/9V5/r333+ZOnUqWbJkoUOHDlrz+MyZM3P48GFGjhyJtbX1Fx17/PjxWsyXL1/+bsegik3v3r3TCuXW1tZaQfZ7MHDgQKPa8pcvX5o3oBj2VYVyU+ct/17I/OtCCCGEECKUq6urNvjZy5cvo72gee7cOYoWLcrFixcBSJIkCQcPHmTmzJmcOHGC5s2bA4Z78lmzZpErVy5OnDgRpXO8f/+e1atXU6pUKbJnz85vv/3GzZs3te3t27fnzJkzFCtW7KvSYmdnx6pVq7CzswNg+vTp/PPPP191zPhuyZIl2g9BjRo1+mYHeItIhgwZtOv71atX331t+VcVyr/nkdaFEEIIIYT4nF9//VVrzj1z5ky2bdsWLcd9/vw5derU4c2bNwDkzp2bI0eOaIOmWVlZ4eXlxfjx43FwcAAMhZdKlSpx7Nixzx4/JCSExYsXkyVLFho2bMihQ4e0bRYWFlSqVInt27cze/ZsnJycoiVNWbNmZfDgwQDo9XpGjBgRLceNj/R6vdEgaD169DBbLDElbG15TLVEiSu+qlAe30Zal4pyIYQQQggRVtq0aRkzZoy23KBBA/bu3ftVx1RK0apVK63GunDhwhw7doxs2bIZ7afT6fjll1+4ffs27u7ugKHGvkKFCixfvjzSY2/atIn8+fPTokULbt++rW3LkSMHAwYM4Pbt2+zYsYPKlSt/VToi0rNnT1KkSAHAmjVruHDhQrSfIz7YsWMHly9fBqB06dIULFjQzBFFPzc3N6Pa8rFjx5o5ophjUqG8XLly2uAMn9O5c+dvftS/yMSz1vpCCCGEEMIE3bp1o27duoBhlPSKFSsyefLkL+7qOW/ePDZs2ACAi4sLa9eujXQucICkSZOyZcsW7R787du3NG3alObNmxsNynzo0CFKlSpFzZo1jZqOV6lShT179nDhwgXatm1L6tSpvyhuUzg4ONCvXz9teeTIkTF2ru9Z2ALq91hLHmrYsGFal4cZM2ZoP0R8b0wqlL9584a3b9+adMB3795pzWyEEEII8f0JCgpixIgReHh44O7uTrt27bh27RoAY8aMoVSpUtpf0aJF6dmzp/bcwoULU7JkSW37woULtW0BAQEMGTKE0qVLU61aNbZv3x7raRPiS1hYWLB06VI8PDwAQ9PiPn36ULVqVVauXMmrV69MPtaaNWvo0qWLtrxw4ULSp0//2ec5OjqyefNmrY87wJ9//knmzJnp3LkzlStXplSpUtqAcQDFihVj//79bNu2jXLlysXa+Ent27c3qi0P7TMvTOPt7c3BgwcByJ49OzVr1jRzRDEnXbp02ndIUFAQ7du3R6/Xmzmq6Gdl6o7//PMPRYoUiclY4rz4NrCdEEIIEZGQkBDSpEmDl5cXSZMmZcWKFfTu3ZsNGzYwcOBABg4cqO3btGlTrVltqPXr15M0adJwx50zZw6vXr1i69atXL9+ne7du5MjRw5cXV1jPE1CfC17e3s2btzIiBEj+PXXXwFDE+MdO3ZgbW1NmTJlqFWrFjVr1iRdunTa80JCQnj27BkHDhxg/vz52pzTYGiBGpUCl52dHYsXL9Zaub5584Z3794xa9Yso/1y5MjBmDFjqFWrllkGMnZwcKBPnz707dsXgHHjxvHnn3/GehzfIqUUw4cP15YHDRqEpaWl+QKKBYMHD2bVqlXcuHGDAwcOMGLECIYPH/5dDcJtcqE8KgXS7ymDwpIiuRBCCGEofITOzwzQsGFDpk+fzsuXL0mUKJG2/ubNm9y8eZMKFSqYdNytW7cyefJknJycyJcvH6VLl2bnzp20bds23L5BQUEEBQUZrbOyssLGxubLEhVGaC3M91gb8ynxNd1hfW0e6HQ6hg8fTsGCBenYsSOPHj0CDCOc79q1i127dtGlSxcKFChAkSJFuHbtGkePHtWmHgurTp06TJky5Yti+fnnn6lcuTJjxoxh9uzZvH//HjCMaD1o0CA8PT2xsrJCKWV0jx+b10C7du0YO3Ysz58/Z/ny5QwbNoyMGTPG+Hk/J66/D3bv3s3+/fsByJIlCw0aNIjWWONi+u3s7Jg9ezaVKlUCDF0e/P39GTt2bIyUO6M7D0yZO96kQvns2bO/OpjvgVSUCyGE+JaEFgii4kum1Dl//jxJkiQxKpADbNu2jZIlS4YbublZs2bodDqKFi1Kjx49SJQoEa9fv8bX15fMmTNr+2XNmhUfH58Iz+nl5cW8efOM1tWvX58GDRpEOf7I3L17N9qO9S2Jr+kO62vzIF++fBw4cIATJ06wc+dOdu3axYMHD7TtZ86c4cyZMxE+N1WqVPTv35/q1at/9bzjPXv25Oeff+b8+fMkSJCAggULYmlp+dnjxtY14OnpybRp0wgJCWHo0KGMGjUqVs5rirj4PlBK8csvv2jL0TE3fWTiWvozZ87MwIEDtUEVJ06cyNWrV+nfv7/WFSJUcHAwx44dY9u2bezfvx8bGxvKlSuHu7s7OXLkIFmyZCadM7rywJQfm3RK2mSbbOgCPb8u/rCsDnzV4PXfFL1ez+3bt3F1dTXp157vTXxPP0gegOSBpP/bS/8PP/wQpVoEnU7H8ePHo3QOPz8/mjdvjqenJ7Vq1TLaVqtWLXr06EHZsmW1dWfOnCFPnjy8efOG8ePHExQUxJQpU3j06BG1atUyOv/ff//N/v37I5yfNqZryu/evUu6dOm+mdc6OsTXdIcVU3mglOL8+fNs3LiRjRs3cvr0aW1b2rRpyZs3L25ubtSoUQN3d3esra2j7dxREdvXwPPnz8mYMSN+fn7Y2Nhw/fr1GB1kzhRx+X2wceNG6tSpA0CePHk4ffp0tMcYl9MPhm5OnTt31lp42NnZUbduXTJmzEjatGk5efIk69evx9fXN9Jj5M2blzp16lC0aFHc3Nzw9/fn7t273L17l3v37nHhwgUsLS1Zs2ZNtORBtNWUCwP5+UIIIcS3JiZ/ew8MDKR3796ULFkyXIH83LlzvH79mhIlShitD51jOXHixPTp04dq1arx/v17HBwcCAkJISAgQBtp9+3bt9r8yx+zsbGJlgL4p1hYWMTJm9KYFl/THVZM5EGBAgUoUKAAw4YN4969e9y+fZu0adOSPn36ONf1M7augaRJk9KpUycmTJhAUFAQ06ZNY9KkSTF+XlPEtfeBXq9n6NCh2vKvv/6qzeEdE+Ja+kN17NiRJEmS0Lp1a96+fUtAQECk0/8B2NraEhISQnBwsLbu/PnznD9//pPncXZ2RqfTxVoeSKFcCCGE+I5ly5aNiRMnfna/Pn368O+//5p83ODgYAYOHEiyZMkinI5n+/btlC9f/pMF59CbHaUUzs7OuLi4cO3aNXLnzg3A1atXcXNzMzkmIb4VadOmJW3atOYOI07o1asXM2bMICAggNmzZzNgwABcXFzMHVacs2rVKm1O9yJFinzXI65/TsOGDSlbtizjxo1j3rx5+Pn5GW13cHCgWrVq1K9fHw8PD96/f8/WrVs5deoUBw8e5MSJEyad58GDB0aDMsYkKZRHgdSUCyGE+NbY2NiQKlWqz+5nbW0dpVr10aNHExgYyPjx48PV8gUHB7Nr1y7GjRtntP769euEhISQKVMm3r59y+TJkylatKhWcPfw8GD+/PmMHj1aG2V30aJFJsckhPj2pEiRgtatWzNz5kzevn3LjBkzGDFihLnDilOCgoIYPHiwtjxq1Kg417oitiVPnpwpU6YwevRozp07x7Nnz7h//z6pU6emXLlyODo6Gu3fpEkTmjRpAsDt27c5ePAgZ8+e5eHDh9jY2JAuXTrtL2PGjFhbW5MmTZpYS48UyqNAyuRCCCG+JabWBgBRKvw+fPiQTZs2YWtra9RffMaMGRQoUIBjx45ha2tLwYIFjZ73/Plzxo4dy5MnT3B0dKRIkSJGU/u0b9+eUaNGUaVKFZydnenfvz8ZMmQwOS4hxLfpl19+Yc6cOQQHBzNjxgx69+6Ns7OzucOKM2bPns2NGzcAKFeunMkzWsQH9vb2FCtWLErPcXV1xdXVlWbNmkW4PXQMmdgkhXIhhBBCREmqVKk4efJkpNtLlizJli1bwq3/4YcfWLduXaTPs7Ozi1OjLwshYkf69On5+eef8fLy4uXLl8yePdtolPH47PXr19q89wATJkyI97Xk3yOTCuUfTznyKRHNJfq9kObrQgghvlX+/v4sWrSIEydORDgq7YYNG8wQlRBCGPTr149FixahlGLKlCl07doVe3t7c4dldhMmTODZs2cANG7cmEKFCpk5IhETTCqUz5071+RfZKRQLoQQQsQ9Y8aMYceOHUD4Edml1kUIYW7ZsmWjfv36rF69msePH7NgwQK6dOli7rDM6v79+0yZMgUwjPsxevRoM0ckYorJzddNGfxFvtSFEEKIuOnw4cMAZM+eHVdX1xidSkcIIb7EwIEDWb16NQATJ06kffv2ZpuzPS7o06cP/v7+AHTq1ImMGTOaOSIRU0z6Rg47UMzZs2fp0aMHPXv2pGLFigDs3r2biRMnMnny5JiJMo6QmnIhhBDfKhsbG1KnTs2ff/5p7lCEECJC+fLlo1q1amzZsoU7d+6wfPlymjdvbu6wzGLp0qWsXLkSABcXF4YMGWLmiERMivJs6BMmTCB58uTUqlULBwcHHBwcqFmzJqlSpdKaVwghhBAibmnQoAEvX77U+iYKIURcNHDgQO3x2LFjCQkJMWM05vHPP//Qrl07bXnKlCkyd/t3Lspt127fvo1SimPHjmnDzx8/fpx79+59983XpaZcCCHEt+TjuX6Dg4OpV68eP/zwA05OTtp6nU7H0KFDYzs8IYQI58cff8Td3R1vb2+uXLnC+vXr+emnn8wdVqzx9/enUaNGWrP1Nm3a4OnpaeaoREyLcqE8S5Ys+Pj40K1bN+zs7NDpdNpFkzNnzmgPMC6RMrkQQohvyebNm41+MFdKERgYiLe3t9E6KZQLIeKSAQMGaJ9To0ePpm7dut995V+oPn364OPjA0CePHn47bffzByRiA1RLpQPGjSI7t278/TpU60wDpAsWTIGDRoUpWO9ePGC4cOHc/LkSVKkSEH//v0pUqRIhPtu3LgRLy8vnj59SsqUKZk8eTKurq5RDV8IIYSINwoUKBBvbmSFEN+PSpUqUahQIU6dOsWZM2fYuHEjtWrVMndYMW79+vXMmjULAHt7e1auXImdnZ2ZoxKx4Ytqyv/++2+2bdvGzZs3UUqRKVMmqlSpgq2tbZSONX78eJIlS8aePXs4duwY/fv3Z/369Tg7Oxvtd+DAAZYuXcqkSZNwc3Pj/v37JEiQIKqhfzVpvi6EEOJbMnfuXHOHIIQQUabT6Rg2bBg1a9YEYNiwYdSoUQMLiygPh/XNuHbtGi1atNCWp06d+t23QhYffNF8KLa2ttSuXRsw9E/7kmlV3r17h7e3N5s2bcLOzo4yZcqwbNkyDhw4QPXq1Y32nT9/Pr169SJTpkwApE2b9pPHDgoKIigoyGidlZUVNjY2UY4zLL3+42V9xDt+h0LTGp/SHFZ8Tz9IHoDkgaQ/+tMfGzeYs2fPpkCBAuTJkwcHB4cYP58QQkSH6tWrU7hwYU6ePMm5c+dYv349devWNXdY0SIgIID79+9z7949/Pz8OH/+PJMmTeLVq1cA1K9f32igN/H9+6JC+alTp5gzZw7//PMP2bNnp0OHDmzbto3atWuTL18+k45x584dnJycSJo0qbYuS5Ys3Lhxw2i/kJAQrly5wrVr1xg5ciRWVlbUqFGDNm3aRNokz8vLi3nz5hmtq1+/Pg0aNIhiSo29fp0ISKgt3759+6uO9y26e/euuUMwq/iefpA8AMkDSX/0pT825pxdsGABOp0OCwsLsmbNSv78+SlYsCD58+cnUaJEMX5+IYT4EjqdjhEjRlCtWjUAhg8fTu3atb/Z2vJ3796xbt06li1bxq5duyIdVT5HjhzMnz9fuh7FM1EulJ88eZIuXboYXUgpU6Zk8+bNACYXyv39/XF0dDRa5+joiJ+fn9G658+fExISwokTJ1i1ahVv376lW7dupEiRQmvS8rGWLVvStGlTo3XRUVOewLhVfbzq067X67l79y7p0qX7Zj8Mv0Z8Tz9IHoDkgaT/20y/vb09/v7+hISEcOnSJS5fvqzNfevq6kr+/PkpUKAAHh4eZo5UCCGMVa1alaJFi3L8+HEuXLjA2rVrqV+/vrnDirK9e/fSpEkTHj9+HOk+Op2ORo0aMXPmzHBdecX3L8qF8jlz5qDX6ylTpgz79+8HIH369CRJkoRz586ZfBx7e3vevn1rtO7t27fY29sbrQvtp968eXMSJEhAggQJqF+/PocPH460UG5jY/PVBfCIGTdZ/JZuyqKLhYVFvEx3qPiefpA8AMkDSf+3lf79+/dz+fJlzpw5w5kzZzh79qzWRPLWrVvcunWLDRs2SKFcCBHn6HQ6hg8fTtWqVQFD3/LatWtjbW1t5shMd/DgQapXr240QHb69OkpVqwYadOmxdnZmSRJkuDh4aF11RXxT5QL5RcvXiR16tRMnDiRH374QVufNGlS7ty5Y/Jx0qdPj5+fH8+ePdOasP/777/hRlZ0dnYmWbJkRuuUjLgmhBBCmMTCwoKc/2/vvqOjqNo4jn8nCekEQjG0UEKTokgRVCB06YLSVERAmiIgTYj00IuIoKAIGHgVKYIoUZp0FUGqNOkIUXonjZDsvH+sLKyAJJBkk+zvc04OM3dmZ597s+TuM3PnTsmSlCxZ0jaK7ODBg8ybN48VK1bcdwiliEhaULduXZ599ll+/fVX/vjjDyZMmMCAAQMcHVai/PbbbzRs2NCWkNesWZNhw4ZRuXLldHVyV1JekpNyNze3u5Jii8XC+fPnk/Th8vb2Jjg4mOnTp9OnTx+2bNnC0aNHCQ4OvmvfRo0a8b///Y/ixYsTHR3N4sWLee2115Ia+iPTuQAREUmPYmNj2bNnj+1K+Z49e7hx44atPw8ICHBwhCIi92YYBlOmTKFSpUpYLBaGDBlCpUqVqFWrlqND+0/79u2jfv36XL9+HYB69erx7bffJvlpVeIckpyUFy9enF27djFy5EjA+qzxgQMHcvnyZcqXL5+kY4WEhDB06FBq1apFQEAAY8aMwc/Pj+XLlxMWFsbChQsB6Ny5M+PGjaNBgwZ4e3vTtGnTu2ZoFxERkbu1a9eOgwcPkpCQYEvC8+fPT9myZW0/efLkcXCUIiL3V6FCBd577z1GjRpFQkICLVu25LfffkuTw72vXr1KWFgYQ4cO5dq1awBUq1aNb775Rgm53FeSk/K2bduya9culi5dimEY/P333/z9998YhkGbNm2SdCx/f3+mTJlyV3n9+vVt944AZMqUiUGDBjFo0KCkhpusdKVcRETSm3379mEYBv7+/rz66qu88MIL+Pv7OzosEZEkCQ0NZdeuXfzwww9cunSJJk2a8Msvv5AlS5YHvziFJSQksHHjRr744gsWLFhAdHS0bVv58uUJDw+/a94skTsl+WaGypUrM3LkSHLlyoVpmpimSa5cuRg+fDiVK1dOiRjTDCXlIiKS3nh5eWGaJpcuXWLq1Km89tprDBo0iEWLFnH06FFHhycikiiurq589dVXlChRArCecGzevDlxcXEOi+no0aP06NGD3LlzU7NmTcLCwuwS8rZt27JmzRoyZ87ssBglfUjSlXKLxcK5c+d48skn+e6777h69SqmaTrNGfes+v8kIiLpzPr16zl48KBt9vXff/+dlStXsmrVKgAyZ85M2bJlef/99x0cqYjIf/Pz82Pp0qU888wzXLx4kdWrV9O5c2fCwsJS9bne8fHxjBo1ihEjRtw1Waafnx9t2rShS5cuPPHEE6kWk6RvSR6+3qRJE3LmzMn3339P1qxZUyCktKtCcQOwXi6vVc6xsYiIiCSGi4sLJUqUoESJErz66quA9Wknc+fOZcWKFVy7do2NGzc6OEoRkcQpUqQIS5cupVatWsTGxjJnzhwKFSrE0KFDU+X9jxw5wuuvv86vv/5qK/Py8qJ+/fo0b96cJk2a4O3tnSqxSMaRpKTcxcWFXLlypatnA6aUGkrKRUQknYiNjWX37t3s3LmTHTt2sG/fPocO+RQReRTPPfccX3zxBS1btsQ0TYYNG0bBggVp27ZtiryfaZocP36czz//nAkTJtj+frq6utK/f3/69u3rNCOHJWUk+Up5586dGT58OIsWLaJ58+YpEZOIiIgkkztnXwfsHmvq6urK448/TtmyZR0VnojIQ2nevDkTJkygb9++AHTs2JHHHnvMbrLoRxEZGcmyZctYtmwZ69evJyIiwm57UFAQc+fO5ZlnnkmW9xPnluSkfPr06bi6ujJ+/HgmT56Mv7+/3T0c3333XbIGKCIiIg9v3759tmUPDw9Kly5texTak08+iaenpwOjExF5eL179+b48eNMnTqV+Ph4mjZtykcffUSnTp0e6h5z0zT59ddfGT9+PD/++CMxMTF37ZMpUyZ69+7NoEGD8PX1TY5qiCQ9KT99+rRtOTY21m49NSdYEBERkQd77rnnKFu2LOXKlaNkyZK4uSW56xcRSZMMw2Dy5MmcP3+ehQsXEhcXR5cuXVi9ejWfffZZkua/+vHHHxkwYADbtm27a5uXlxeVK1emevXqtGrViiJFiiRjLUQeIinv1KlTSsQhIiIiKWDy5MkAbN269b4J+cSJE+nTp09qhiUikixcXV2ZO3cuOXPmZOrUqQB8/fXX/PzzzzRu3JjAwEBefPFFSpUqdc/XX7hwgZCQEGbNmmVXni1bNlq2bEnLli2pXLky7u7uKV4XcV4PdU+5s9JzykVEJL3q3bs3H374IeXLl7eVWSwWQkNDWb58uZJyEUm33Nzc+Pjjj6lVqxZvvPEGV65c4fTp03z22WcAhIaGMmrUKPr27YuLiwsA586d46OPPuLDDz8kMjLSdqyyZcvyyiuv8Pbbb2sWdUk1DzWGLS4ujhUrVrBnzx5y5MhBkyZNOHXqFIULFyZLlizJHWOapJH6IiKSnsTGxtK7d28mTZpEuXLliIuLIyQkhJ9++sn2JVVEJD178cUXqVChAm+//Tbh4eG28vj4ePr37893331H5cqVOXr0KOHh4dy8edO2T+bMmRk7dixdunTh5MmTmm9DUlWSk/IrV67QpUsXjh8/DkDp0qV58skneeedd+jQoQNdunRJ9iBFRETk0bzyyivMmzePXr16MXLkSL744gt27tyJm5sbQ4YMcXR4IiLJIjAwkKVLl3L+/HlOnjzJokWLGDduHKZpsmnTJjZt2mS3f6ZMmejQoQODBw8mT548WCwWB0UuzizJp8anTJnCsWPHcHd3tz1WpWLFinh6et71IRcREZG0oXfv3nTo0IHo6Gj69OnDzp078fb2ZvLkyUl+hFBcXByhoaE0aNCAatWq0blzZ44cOQJAeHg4lSpVomrVqrafM2fO2F67b98+XnnlFSpXrkznzp3vmkB28ODBBAcH07BhQ1asWJE8lRcRp5MzZ07Kly/PmDFjWLt2Lfny5bPbnj17dvr168fhw4f55JNPyJMnj4MiFXmIK+U///wzvr6+fP3117ZO3NXVlVy5cvH3338ne4AiIiLycO5MhgGaNm3KtWvX+Prrr/H29iY0NJT8+fNz5swZcuXKlejjJiQkkDdvXsLCwsiRIwfz5s2jT58+tseiVqxYkY8++uiu18XFxdGvXz86d+5MvXr1mD59OkOGDGHGjBmA9bGrV69eZdmyZRw9epR33nmHEiVKUKBAgUdoBRFxdtWrV+fw4cNs3bqVmzdv8thjj1GsWDFN3iZpRpKT8sjISAoWLEiOHDnsyi0WC9HR0ckWmIiIiDyaF1544Z7lhmEQExNDv379bOtbtmxJ9HG9vLzo2LGjbb1Vq1ZMnjyZK1eu/Ofrtm/fjpeXF02aNAGsT3SpXbs2p0+fJnfu3CxbtoyJEyfi6+tLmTJlCA4OZtWqVfd88ktcXBxxcXF2ZW5ubsnyJfvW8FVnG8bqrPW+k7O3QUauv7u7O5UrV7Yru1c9M3IbJIaz1x+Svw0SM29LkpPyXLlycezYMXbt2mUr27hxIydOnCB//vxJPVy6otnXRUQkPTFTqePavXs32bJlsz0T+Pfff6dWrVpky5aNVq1a0bx5cwCOHTtm93xfLy8v8uXLx7Fjx/Dx8eHixYt224sVK8a+ffvu+Z5hYWG2K+y3tGjRgpYtWyZbvSIiIpLtWOmJs9b7Ts7eBs5ef1AbOHv9IfnaoFChQg/cJ8lJed26dZk5cyadO3fGMAz27t1L3759MQyDunXrPlSg6ZEmXxcRkbTu008/TfH3iIyMZPTo0XTt2hWAcuXKMX/+fHLlysX+/fvp27cv2bNnp0aNGsTExODj42P3eh8fH2JiYoiOjsbV1dVuxmMfH5/7jsJr3749rVu3titLzivlERERBAYGOtXM9M5a7zs5exs4e/1BbeDs9QfHtEGSk/I33niD/fv33zWp27PPPkv79u2TLTARERF5NHc+kzwl3Lhxgz59+lClShXbkPS8efPatpcuXZqXX36ZdevWUaNGDby8vIiKirI7RlRUFF5eXnh7e5OQkEBsbKwtMY+Kirrvc4Ld3d1T/H5QFxcXp/xS6qz1vpOzt4Gz1x/UBs5ef0jdNkhyUp4pUyYmT57Mjh072LdvH6ZpUqpUqRTv+EVERCRp3n33XQoWLMjbb7/9wH0/+ugjTp48yYQJExJ17Pj4eAYMGEDOnDnp2bPnffczjNtjy4KCgliyZIltPSYmhr/++ougoCD8/PzInj07R44coXTp0gAcOnSIoKCgRMUjIiKSXiU59Z86dSonT56kXLlytGnThtdff10JuYiISBq0fv16tm/fnqh9d+zYwYYNGxJ97FGjRnHjxg2GDRtml3hv2rSJy5cvA3DgwAEWLFhA1apVAeuV+5iYGMLDw4mLi2PWrFmULFmS3LlzA9CgQQNmzpxJVFQUe/bsYePGjdSpUyfRMYmIiKRHSb5SPnv2bObMmUPp0qVp1KgRzz//PL6+vikRm4iIiDyiY8eO8eabbyZqv8Q6ffo04eHheHh4UKNGDVv5lClT2LJlC0OHDiU2NpacOXPy+uuv2xJrd3d3xo8fz4gRIxg7diwlS5Zk+PDhttd36dKFkSNHUq9ePfz8/AgJCaFgwYKJr6yIiEg6lOSkPHfu3Jw+fZo9e/awd+9eJk6cSLVq1WjUqBHPPvus3dlyERERcayoqKhEXy1PbB+eO3dutm3bds9tZcuWpVevXvd9balSpZg/f/49t3l6ejJy5MhExSAiIpJRJDkpX7p0KXv37mXlypWsWbOG8+fPs3r1alavXk2OHDlYtmxZSsSZJuiJaCIikp7c6/neIiIikrYkOSkH62yqpUuXpk+fPqxfv56xY8dy8eJFLly4kNzxpVkaECAiImld586dHR2CiIiIPMBDJeXR0dGsX7+eVatW8dtvvxEfHw8kftibiIiIiIiIiDxEUv7uu++yadMmbt68iWlaB3TnzZuXhg0b0rBhw2QPUERERERERCSjSnJSvn79egB8fHyoXbs2jRo14qmnnkrmsEREREREREQyviQn5c888wwNGzakRo0aeHh4pERMIiIiIiIiIk4hyUn5Rx99BMCNGzf4448/AAgKClKCLiIiIiIiIpJEDzXRW1hYGJ9//jk3btwAwMPDgw4dOtCuXbvkjC3NMfVMNBERSccsFgsRERFcunTJNi/MLeXKlXNQVCIiIs7toZ5TPm3aNLuy2NhYpk2bRo4cOWjUqFGyBZeWaaJ5ERFJT/bu3cvAgQM5ffr0XdsMw2DLli0OiEpERESSnJQvXLgQgOrVq1O3bl0AVq5cyfr165k/f77TJOUiIiLpydixYzl16pSjwxAREZF/SXJSfvz4cfLkycOECRNsZbVr1+aFF17g+PHjyRqciIiIJI8///wTNzc33nnnHYKCgnB1dXV0SCIiIsJDJOWurq7cuHGD+Ph43NysL4+Pj+fGjRvq4EVERNKooKAgYmJiePnllx0dioiIiNzBJakvKFasGJcuXaJz58588cUXfPnll3Tu3JnLly9TrFixlIhRREREHlGfPn04c+YMX3/9NZGRkY4OR0RERP6R5Cvlbdq0oW/fvuzdu5e9e/cCYJomhmHw+uuvJ3uAaYlmXxcRkfSkYsWKd5VNmDDB7hY00ERvIiIijpTkK+XVqlUjNDSUgIAATNPENE1y5cpFaGgowcHBKRFjmqTZ10VEJK271U8n5kdEREQc46GeU96gQQMaNGjA5cuXAfD390/WoEREROTRDR061NEhiIiIyAMkOSk/fPgwp06dokSJEjz22GMAnDt3jj/++IM8efJQtGjRZA9SREREku7Ox5SeOXOGTJkykT17dgdGJCIiIv+W5OHrI0eOZMCAAbi7u9vKPDw8GDBgAKNHj07W4ERERCR5NG7cmHffffeu8h49evD88887ICIRERGBh0jK//zzTwIDA8maNautLEuWLAQGBnLs2LHkjE1ERERS2KVLl7hy5YqjwxAREXFaSR6+Hh8fz8WLF+96TvnFixdJSEhI9gBFRETk4YWGhtqW//rrL7v12NhYDh8+jJeXlyNCExERER4iKS9YsCCHDx9m0KBBvPrqqwDMmzePK1euULx48WQPMC3R5LQiIpLefP/99xj/PDLkypUr/PDDD7Ztt2Zdf+KJJxwSm4iIiDxEUt60aVPGjx/P2rVrWbt2ra3cMAyaNm2anLGlaXoimoiIpAdly5bFMAx27NiBt7e33Ql0T09PChYsSJs2bRwYoYiIiHNLclLeokULjh8/zqJFi2xn2A3DoGXLljRv3jzZAxQREZGH99lnnwHw9NNPExQUxPTp0x0ckYiIiNzpoZ5T3q9fP9q0acO+ffsAKFWqFLlz507WwERERCT5bN261dEhiIiIyD08VFIOkDt3biXiIiIiadibb76ZqP0Mw+CTTz5J4WhERETkXh46KRcREZG0bfv27RiGYbvdDLBN+naLaZp3lYmIiEjqUVIuIiKSQd2a5O2WP/74g7i4OIoWLYppmhw5cgRXV1fNvi4iIuJASsqTQE9EExGR9OTWJG8Aixcv5sCBAyxYsIACBQoAcOLECdq0aUNwcHCSjhsXF8eYMWPYsmULUVFRFC9enH79+lGkSBHCw8OZN28ef/31F/7+/rRp08ZuItgKFSrg6elpO1nQvn173njjDcD63PRRo0axYcMGMmfOTPfu3alXr96jNoOIiEiapqT8IWmkn4iIpCdhYWE89thjtoQcoECBAgQEBDB37lxeffXVRB8rISGBvHnzEhYWRo4cOZg3bx59+vThu+++Iy4ujvfee48SJUpw4sQJ3nrrLYKCgihXrpzt9d9++y05cuS467jTp0/n6tWrLFu2jKNHj/LOO+9QokQJu5hFREQymodOyrdv384ff/wBQIkSJShfvnySj3H58mWGDRvGtm3bCAgIICQkhIoVK953/1OnTtGiRQsaNmzIgAEDHjZ0ERERp3PlyhXOnTvH1KlTqVmzJoZhsHbtWv788088PDySdCwvLy86duxoW2/VqhWTJ0/mypUrNGvWzFZeuHBhKlasyP79++2S8vtZtmwZEydOxNfXlzJlyhAcHMyqVavo1KnTXfvGxcURFxdnV+bm5oa7u3uS6nIvFovF7l9n4az1vpOzt4Gz1x/UBs5ef0j+NnBxcXngPklOym/cuEHfvn3ZsmWLXXmlSpWYOHFikjrDcePGkTNnTtasWcPmzZsJCQnh22+/xc/P7577f/DBBxQvXjypIYuIiDi9KlWqsGbNGubMmcOcOXPu2vYodu/eTbZs2ciaNatdeUJCAvv27aNBgwZ25a+99hqGYVCpUiV69uxJ1qxZuXbtGhcvXqRIkSK2/YoVK2Z7/Oq/hYWFMWPGDLuyFi1a0LJly0eqy50iIiKS7VjpibPW+07O3gbOXn9QGzh7/SH52qBQoUIP3CfJSfnMmTPZvHnzXeVbtmxh1qxZvPXWW4k6TnR0NBs2bCA8PBxPT0+qV6/O3Llz2bhxI40aNbpr/19//RXTNKlUqRIXL178z2On1NnzO0+WmKYFZzqB5OxnzZy9/qA2ALWB6p/89U/M2fPkMnDgQBISEli/fr1defXq1Rk4cOBDHzcyMpLRo0fTtWvXu7Z98skn5MyZk2effdZWNmPGDJ544gmuX7/OuHHjGD58OB988AHR0dG4urri6elp29fHx4fo6Oh7vm/79u1p3bq1XVlyXimPiIggMDAwVX9Hjuas9b6Ts7eBs9cf1AbOXn9wTBskOSn/8ccfcXFxoWfPnrbJV5YvX86HH37IypUrE52Unzx5El9fX7t7yooWLcqxY8fu2vfmzZtMnjyZCRMmsGzZsgceO6XOnp+/4A3kBKzDAE+cuP5Ix0uPnP2smbPXH9QGoDZQ/ZOv/ok5e55cMmfOzIQJE/jrr784duwYpmlSuHBh8uXL99DHvHHjBn369KFKlSo0adLEbtuiRYtYu3Ytn3/+ud0M8GXLlgXA39+fvn370rBhQ27evIm3tzcJCQnExsbaEvOoqCi8vb3v+d7u7u7JkoD/FxcXF6f8Uuqs9b6Ts7eBs9cf1AbOXn9I3TZIclJ+9uxZChQowCuvvGIre/XVV/n222+T9EUlJiYGHx8fuzIfHx8iIyPv2nfu3LlUrlyZwMDARB07pc6e5zh+ezlr1qwUKJDtkY6Xnjj7WTNnrz+oDUBtoPpnjPrny5fvkRLxW+Lj4xkwYAA5c+akZ8+edttWrVplO0H+7yHtd7rVjqZp4ufnR/bs2Tly5AilS5cG4NChQwQFBT1yrCIiImlZkpNyb29vzp49y/nz58mZ03rV+Ny5c5w9e/auJPu/eHl5ERUVZVcWFRWFl5eXXdm5c+dYunQpX3zxRaKPnVJnz10Mk1sPRnPWs0fOWu9bnL3+oDYAtYHqn37q36RJE4oXL8748ePvupL9b999912Sjj1q1Chu3LjBuHHj7K6Eb968mQkTJjBt2jTy5Mlj95qjR4+SkJBA4cKFiYqKYuLEiVSqVMnWZzdo0ICZM2cyatQojh07xsaNG5k9e3aS4hIREUlvkpyUlytXjvXr19O8eXPKli2LYRjs2LGDmJiY/5w5/d/y589PZGQkFy5csA1hP3z48F1fGvbv38/Zs2d56aWXAOu96BaLhdOnT/PRRx8lNXwRERGncerUKbJnz25bvh8jic/5PH36NOHh4Xh4eFCjRg1b+ZQpUwgLC+PatWu2Z48D1K9fnwEDBnDp0iXGjBnDuXPn8PHxoWLFigwbNsy2X5cuXRg5ciT16tXDz8+PkJAQChYsmKTYRERE0pskJ+Vvvvkmv/32G9HR0WzatAmwDjvz9vZO9P3kYL3iHhwczPTp0+nTpw9btmzh6NGjBAcH2+333HPP2Z29//LLL7l8+TK9evVKaugiIiJOpVOnTjz22GMAdOzYMcnJ9/3kzp2bbdu23XPb9OnT7/u6p59+mm+++ea+2z09PRk5cuQjxyciIpKeJDkpL1y4MLNnz2b27Nm255SXLFmSdu3aJflsdkhICEOHDqVWrVoEBAQwZswY/Pz8WL58OWFhYSxcuBB3d3e7yeC8vLyIjo7+z3vUREREBDp37mxb7tKliwMjERERkftJclIO1tliQ0NDH/nN/f39mTJlyl3l9evXp379+vd8jb5UiIiIJF6nTp146qmnKFu2LGXKlEnS/C8iIiKS8hKVlH///ff4+/tTuXJlvv/++//c917PGBcRERHH2LVrF7///jtz5szBMAyKFClCuXLleOqpp3jqqads95yLiIiIYyQqKQ8NDeWJJ56gcuXKhIaG3veeNMMwMnRSbpqOjkBERCRpnn32WXbv3k1UVBSmaXLo0CEOHz7MggULAOsj0sqVK8egQYMcHKmIiIhzeqjh6+Z9stP7lWdEyTRXjoiISIqaMmUKFouFQ4cOsXPnTnbs2MHvv//O5cuXAYiIiOCvv/5SUi4iIuIgiUrKt27des9lERERSftcXFx4/PHHefzxx2nRogV79+7l22+/ZcWKFSQkJDg6PBEREaeW5CvlM2bMICAggBdeeMGufPfu3Vy7do0qVaokW3AiIiLyaGJiYvj999/ZtWsXO3fuZN++fcTFxdlGt+XKlYuyZcs6OEoRERHnleSk/LPPPuOJJ564KymfNGkS+/bt47fffku24EREROTR1KhRA4vFAlhvMytYsCBly5a1/eTKlcvBEYqIiDi3h7qn/N9iY2O5cOFCchxKREREklFCQgKGYeDv78+rr75KzZo1CQwMdHRYIiIi8o9EJ+UVK1YErDOs792717Z+p2zZsiVfZCIiIvLIbs2+funSJaZOncrUqVPJli2b3dXyokWLOjpMERERp5XopPzWvWeGYdx3lvUXX3wxeaJKo5xocnkREckgpkyZYnsU2s6dO9m5cye///47q1evZs2aNQBkzpzZtiwiIiKpK9FJ+dChQwHrM8vz5ctHhw4dbNs8PT0pWLAgRYoUSf4I0yg9EU1ERNILwzAoXrw4xYsXp1mzZuzdu5fvvvvONvv69evXHR2iiIiI00p0Ut6oUSMAtm3bRr58+WzrIiIiknbdmn391lXyffv2cfPmTUeHJSIiIv9I8kRvw4YNA+DmzZtcvnzZNqPrLZrFVUREJO349+zrt7i5uVGiRAnbfeUiIiLiGElOyqOjoxkxYgTr168nISHBbpthGGzZsiXZghMREZFHc6uv9vT05IknnrAl4U888QQeHh4Ojk5ERESSnJRPmzaN1atXp0QsIiIiksy6d+9OuXLlePzxx3FzS5YnoYqIiEgycknqCzZs2IBhGLzxxhsA5MuXj2bNmuHn50e/fv2SPcC0RJOvi4hIevP6669TunRpJeQiIiJpVJKT8gsXLpA3b17eeustALJmzUpISAi+vr4cOHAg2QNMqwxNvy4iIiIiIiKPKMlJubu7O97e3rblc+fOER8fz82bNzWsXURERERERCQJkpyUZ8+enfPnzwPWoevnz5+ndu3anD9/Hnd392QPUERERERERCSjSnJSXqpUKWJjYzl8+DCNGzfGNE2ioqIAaNCgQbIHKCIiIiIiIpJRJXnWlxEjRtiWixYtSo4cOdizZw9FixalSZMmyRqciIiIiIiISEb2yFOx1qtXj3r16gFw7NgxgoKCHjkoEREREREREWeQ5OHr169fJyEhwa5s79699O3bl1deeSXZAkuLTD0TTURERERERJJRoq+Unzp1it69e3Ps2DF8fX0ZPHgwZcuWZcSIEfz0008pGWOapEeiiYiIiIiIyKNKdFI+ZcoUjh49Clivlo8cOZIiRYqwY8cOADJlykTDhg1TJkoRERERERGRDCjRSfnOnTsxDIP69etjmibLly9n586duLu706JFC1577TVy5MiRkrGKiIiIiIiIZCiJTsqvXLlCYGAgoaGhAOzbt4+IiAgmTpzIM888k2IBioiISNoSFxfHmDFj2LJlC1FRURQvXpx+/fpRpEgRAGbPns2XX36JxWKhSZMm9OjRA+Of+7727dvHyJEjOXnyJKVKlSI0NJTcuXMDEBsby6hRo9iwYQOZM2eme/futslkRUREMqpET/RmsVjIkiWLbd3Pzw9ACbmIiIiTSUhIIG/evISFhbF27VqCg4Pp06cPAD///DOLFi1i9uzZLFy4kJ9//pmlS5cC1mS+X79+vPzyy6xdu5bSpUszZMgQ23GnT5/O1atXWbZsGaNHj2bs2LGcOHHCIXUUERFJLUmaff3gwYM0adKEJk2acOjQIQDb+q0fERERydi8vLzo2LEjAQEBuLq60qpVK06dOsWVK1dYtmwZzZs3J1++fOTIkYPXXnuN5cuXA7B9+3a8vLxo0qQJHh4edOrUif3793P69GkAli1bRufOnfH19aVMmTIEBwezatUqR1ZVREQkxSXpOeU3b97k1KlTdmV3rhsZfEpyPRJNRETkbrt37yZbtmxkzZqV48eP06BBA9u2YsWKMXXqVACOHTtmG+IO1uQ+X758HDt2DB8fHy5evGi3vVixYuzbt++e7xkXF0dcXJxdmZubG+7u7o9cH4vFYvevs3DWet/J2dvA2esPagNnrz8kfxu4uDz4Oniik/KyZctm+KQ7KdQUIiIiEBkZyejRo+natSsA0dHR+Pr62rb7+PgQHR0NQExMDD4+Pnav9/HxISYmhujoaFxdXfH09Lzna/8tLCyMGTNm2JW1aNGCli1bJku9ACIiIpLtWOmJs9b7Ts7eBs5ef1AbOHv9IfnaoFChQg/cJ9FJ+WefffZIwYiIiEjGcuPGDfr06UOVKlVst7B5e3sTGRlp2ycqKgpvb2/AemU8KirK7hhRUVF4eXnh7e1NQkICsbGxtsT8ztf+W/v27WndurVdWXJeKY+IiCAwMDBRVzgyCmet952cvQ2cvf6gNnD2+oNj2iBJw9dFREREAOLj4xkwYAA5c+akZ8+etvJChQpx5MgRqlSpAsChQ4cICgoCICgoiCVLltj2jYmJ4a+//iIoKAg/Pz+yZ8/OkSNHKF269F2v/Td3d/dkScD/i4uLi1N+KXXWet/J2dvA2esPagNnrz+kbhs4d0uLiIjIQxk1ahQ3btxg2LBhdre3NWjQgMWLF/P3339z4cIF5s6dS/369QEoX748MTExhIeHExcXx6xZsyhZsqTtkWgNGjRg5syZREVFsWfPHjZu3EidOnUcUj8REZHUoivlIiIikiSnT58mPDwcDw8PatSoYSufMmUKVapU4fDhw7z++utYLBaaNm3KCy+8AFivbo8fP54RI0YwduxYSpYsyfDhw22v79KlCyNHjqRevXr4+fkREhJCwYIFU7t6IiIiqUpJeRJo9nURERHInTs327Ztu+/29u3b0759+3tuK1WqFPPnz7/nNk9PT0aOHJksMYqIiKQXGr7+kDT5uoiIiIiIiDwqJeUiIiIiIiIiDqKkXERERERERMRBlJSLiIiIiIiIOIiSchEREREREREHUVIuIiIiIiIi4iBKypNAT0QTERERR7hy3eRwhElktL6NiIhkNHpO+UMy9Ew0ERERSSEWi8mXq+DLVSb7/oRTF6zlhgHlipm0ed7gudKQJwc85g+Z3PTFREQkvVJSLiIiIpKG7Dho8tYHJr/9cfc204TtB2H7wdtXzF1c4KVgk0ndDPI9puRcRCS90fB1ERERkTTiy1Umz3a1T8iz+UHVJ+HlWlCmyN2vsVhg0Xp4qoPJpj0a3i4ikt7oSrmIiIhIMkpIMDl6Ci5dg/gEyOprHWbu7QEe7mDc5x64jxebdJ98O6kuXQg+7GFQs5z9a/YeM1n5Gxz+y+T0Rdj4O1yJhItXoWYvky8GQosaumIuIpJeKCkXEREReUhXrpt8vR6O/GVy6iIcPAl7j0PMjXvv7+ICft4mBXPDMyXh8fwGri7ww2aTFVtu79flBfiop3HPe8VLBxmUDgKwbrt83aTFEJM12+FGHLQcavLhBXinhRJzEZH0QEm5iIiIyEO4cMWkYheT46cT/xqLxXpVe9dh68+9nu0yoA2M7Gjc94r6v/lnNlg2Ht6caBK2zFrW8yOTyBgY+LoScxGRtE5JeRKYuk1LREREANM06fL+vRPyInmt937nzQFurnDhKpy9DLFxEBULl6/DsVN3f68okAuGv2Hwer2kJ9LumQxm9Yf8j5mEzraWDZppEhljMrpz4hP8jOzv8yYL18GeYyZRMVCumMHrdSF3DrWNiDiWkvKHpL5NRETEec1ZAd9stC5n84PP+xsUzw/5coKv94O/JFy5bn3U2eG/rIPQC+aG50o/2qPNDMNg2BsGvl4m735izfjHzoXIGJMp79z/Xvb0KjrW5MetcO6K9b59b084fgoizplkcgNPdwNvT4iOhTU7TDbssj8RsnCdydAwGNcFejTPeO0jIumHQ5Pyy5cvM2zYMLZt20ZAQAAhISFUrFjxrv0++OADNmzYwOXLlylQoAC9evWiXLlyDohYREREnF1UjEnI9NvZ3az+Bk2qJi2hy5rZoPITUPmJ5I4O+r5i4OMFXT+wxvjxN5BgMfm4J7i4pO/E02IxWbUVZi83Cd9kTbjv78FDHG/EWYf67z0O03qDq55LJCIO4NCkfNy4ceTMmZM1a9awefNmQkJC+Pbbb/Hz87Pbz9fXl48//pi8efOydu1a+vbtS3h4OD4+Pg6KXERERJzV5EVw9pJ1uVk1aJrEhDw1vNXUwMcT2o81sVjgk2+tQ+6n9kqfiblpmizeAMPCTPYdf7hjFMkLretA48oGnu7w6XcmH39j3Tbze9h6wCSkNZTLb032vT3NdNlWIpL+OCwpj46OZsOGDYSHh+Pp6Un16tWZO3cuGzdupFGjRnb7du7c2bZcu3ZtJk6cyMmTJylRosQ9jx0XF0dcXJxdmZubG+7u7o8Us8W8c9mCxfJIh0tXLP9U1uJMlb6Ds9cf1AagNlD9k7/+Li66LJfeXLpmMn6e9QuBi4t1Qra06vV61pndXx9tTcw//Q5uxpt82gfcHmGYfGoyTesQ9QEzTLYftN+WzQ9eCobyxQ2uR8P1aJP8AQZBua3XyGPjIDIa4uKtM90XyWc/RP2jngbPlTZpN8Yk7ib8fgReCQU31/zEJ0AmN5MCASbVy0Kb5w2qltEQdxFJGQ5Lyk+ePImvry85cuSwlRUtWpRjx4795+tOnTrFtWvXCAwMvO8+YWFhzJgxw66sRYsWtGzZ8pFivnjBB7DGe/nyZU6ciHyk46VHERERjg7BoZy9/qA2ALWB6p989S9UqFCyHUtSx6CZJlf/6f7b1YPHC6TtJK318waGAW1GWRPzWT9YTyx8NQQ8PdJ27Ef/tk6mt2a7ffmzpaDfqwYNn/33PfhJr88rtQ0K57HOXL/zsLUsPsF6nJvxcORv68/M702K5oOOjaB+JShVKH2OOBCRtMlhSXlMTMxdw899fHyIjLx/ohsfH8+wYcNo06YNvr6+992vffv2tG7d2q4sOa6UZztwe9nf358CBbI/0vHSE4vFQkREBIGBgU55ZcfZ6w9qA1AbqP7OXX+Bn343+eRb67K3Jwxtnz6SslfrGLi5wmsjTW7Gw5KfoH4/k+9Gg59P2qzDhSsmwd1NTl24XVamCIzuZFD/meS9Yl2xpMH2mbB2h3VI+65DceTI6k70DetEfLeeOX/4L+j/qUn/T8HHC4JymwTlga5NDZ6vmDbb8VGYpqmRASKpxGFJuZeXF1FRUXZlUVFReHl53XN/0zQZNmwY/v7+dsPZ78Xd3f2RE/B7cTFMbk0a4uLi4pRfypy13rc4e/1BbQBqA9XfuevvrG7EmXSecPs+ttGdDPIHpJ+EpWVNg6y+8NJg6+PA1u+E6j1M1n5onXQuLTFNkw7jbifk+QNg3JsGLWuk3NVpwzCoVR5qlDU5ceI0BQoUwMXFhagYk29/glk/mKzbeXv/qBjYc8z68/2vJus+hKpl0lY7JoVpmvy8G5ZvgfXbA9h/Em4mmLwUbDKyo0GBXOm3biLpgcO+VeTPn5/IyEguXLh9CvTw4cMEBQXdc//x48dz/vx5RowYoS9DIiIikqpm/QAHTlqXK5WEbi85Np6H8XxFg7WTDLJnsa7vPAxDP3/wDOWp7bOlsPQX63LOrLD5E4OXaxkOGS7u42XQ+nmDtZNdODTX4P2uBi9WtU4ad0tCArQcavL3+bTXlg9isZjM+t7kiXbWkQljvoRf//DkapR1srsvV0HptiY//Jr+6iaSnjgsu/X29iY4OJjp06cTGxvLhg0bOHr0KMHBwXftO336dH7//XcmTpyYIlfARURERO4n7qbJ2Lm3k5KPexq4uqbPK4cVSxr89JH1+d0A076FQxFpJ+GKOHv7GesAn4cY5M6RNtq6aKBBn5cNvhnlwuF5LsStNahe1rrtzCV4caBJVEzaacsHuXzdpGZPk47j757RPm8O8PvnLtPIGGgywGT+mvRTN5H0xqGXnENCQjh79iy1atVi8uTJjBkzBj8/P5YvX243KduMGTP4888/qV+/PlWrVqVq1aosX77cgZGLiIiIs5i3GiLOWZcbPQcVHk8bSeLDKlHQ4N2XrcvxCTDqf2kj2TJNk66TTK5HW9c7NIRGz6Xdts7kZrAw1KBALuv61gPQbLBJ3M200Z7/JTLapEE/kw27bpc9VxrC3oNfJv3FyUVw8muDFjWs2xIS4NXh1qvqIpL8HPqccn9/f6ZMmXJXef369alfv75tfdu2bakZloiIiAhgTRQ/WHg7EXmvddpNEpOi78sGUxabXL4OX62GER1N8uV0bEzfb7L+AOTKBhO6pv22zpnVIHwMVO1unZV/5W/QcbzJnAFp9/FpN+JMXhpksnmfdf0xf5g3xKBmeQOLxcKJEwkAZPE1mD8U/H1NPgsH07TWLcECnV9Im3UTSa90c7aIiIjIfaz8DXYftS5XKgnPPZExkhFfb8N2X3x8Akxa6NgroKZpMvKOK/ZT3jHwT2MT0N3PE4UNwscYeP5zh+UXK2HhWsfGdD/x8SavDjf58Z/rXVl9YdVEa0J+Ly4uBp/2Neh1x1OF355k8vNuXTEXSU5KypPA1N8fERERp2GaMHjW7c6/78vpI0lMrO7NDLw8rMszvodL1xwXy9od8Nsf1uUyRaB5dcfF8jCqljGY/d7tz0f3ySYXrqStL44Wi0nn902+2Whd9/aEH8YZlCny359rwzCY+LZB738S8/gEaDHE5NSFtFU/kfRMSflDyljdsoiIiPzb/j9h2wHrcpki8NLdc9GmazmzGrzRwLocFWOd9M1RRn9hf4tAWh36/V9a1TJoVs26fP6K9ZnmaYXFYvL2JJOwZdb1TG7wzUgj0SM/DMNg3JvWx8aBdWK7FkPSx/3zIumBknIRERFJkunTp9OiRQuefvppVq5caSsfPXq0bULWqlWrUqlSJXr16mXbXqFCBapUqWLb/vnnn9u2xcbGMnjwYIKDg2nYsCErVqxI1Trdy+INt5ffaOCYR3KltD6tDFxdrcsfLYaYG6lfx837TNbusC4XyZv+rpLf6eOeBll8rcthy2HbgbSRtE5cAJ9+Z112cYGvhhjUrZi037Wbm8G8oQaBj1nXN+2FPlPTRv1E0jsl5SIiIpIkgYGB9OnTh1KlStmVDxgwgJ9++sn2U6RIEapVq2a3z7fffmvb/sYbb9jKp0+fztWrV1m2bBmjR49m7NixnDhxIlXq82+mCVOX+hE6+3ZZRrtKfkuhPAYt/5lh+8JV+Ponn1SPYdQdV8lDWqffx80B5MpuMLSdNX7ThB6TTUwH3/+4Zb/JgM+sMRgGfDnIoHn1h2vjnFkNFo8wcM9kXf/4G/hylRJzkUfl0NnXRUREJP1p0MA65vnOK93/dvz4cY4fP07t2rUTdcxly5YxceJEfH19KVOmDMHBwaxatYpOnTrdc/+4uDji4uLsytzc3HB3d09kLe7v0+8sTFzsb1t//mnIk8PEYsmYyUefVtbHvgHMXJ6F/q9bbElXStt+8PaM6/lywqu1HdvOFovF7t+H0bUpfLYUDpyEX/fBFytNXnveMXW6GgmvhFrvAwcIaQ2tat6/jRNT//LF4aN3oMv71vXOE0xKFjB5qmiyhu4wyfEZSM+cvf6Q/G3g4vLg6+BKykVERCTZLV++nCpVquDr62tX/tprr2EYBpUqVaJnz55kzZqVa9eucfHiRYoUKWLbr1ixYuzbt+++xw8LC2PGjBl2ZS1atKBly5b3eUXifbEiAPAEoEaZaAa2usyJE/GPfNy0Kps7VC39GD/t9eKvC25MX3yeF56NTpX3HvBpTsAbgE71L3LmdGSqvO+DREREPNLrQ1p60u79AAD6TYunXIFT+HimbmJumtDz0xwcP20d/VCuSCztapwlMQNQHlT/Ok9Aq2rZWLAhMzE3oOmAm3w37AxZfTNOIveon4H0ztnrD8nXBoUKFXrgPkrKRUREJNmtXLmSnj172pXNmDGDJ554guvXrzNu3DiGDx/OBx98QHR0NK6urnh6etr29fHxITr6/olh+/btad26tV1ZclwpN004+Jc1ecqbw2T1ZG9uJY0Z2ZA3oE5v6/LnP+agWyuDlJ5rbedhWL3Tupw3B7z7WnY83LOn7Js+gMViISIigsDAwERd3bqfNgVg0T/PXT97xY25G/Mz6t6DPlLMnBUQvtm67OcDX4/wpGDuAv/5mqTU//MBcOwsbD0AEeczMeB/gYSPsd6znp4l12cgvXL2+oNj2kBJeRJkzEFrIiIiyev333/n2rVrVK5c2a68bNmyAPj7+9O3b18aNmzIzZs38fb2JiEhgdjYWFtiHhUVhbf3/ZNhd3f3ZBmq/m+Xrplc+edi7eP5Daf5UlqrvEmF4ibbDsLuowartyd9IrCkGjHn9lXVkNYGXp5p515yFxeXR/7dT+pmsmqrSdxN+GAhdGxkUDhv6tRx33GT7pNvf3P9rK9BUBLeOzH19/aCxSNMynU0uXAVVmyB9z6DCV0zxv+Z5PgMpGfOXn9I3TZw7pZ+BOnwSR0iIiKpYsWKFdSqVes/k+ZbX3RM08TPz4/s2bNz5MgR2/ZDhw4RFBSU4rH+2/HTt5cL5Un1t3cYwzB495Xb62PnpuyliB0HTb772bqcJwd0bJSib+cQRfIZ9GphXY67Cf0+SZ3LOyfPmtTtaxIVY13v0ND6uLaUEBhgsGCYYbs6/v58GJfCnx1JeRYLhG+C8V+ZTFlk8vsRx09YmNEpKRcREZEkiY+P58aNG5imaVu+NSFOfHw8P/74I/Xq1bN7zdGjRzl06BAJCQlcu3aNiRMnUqlSJVvi3qBBA2bOnElUVBR79uxh48aN1KlTJ9XrVig3LBgG/Vte5sWqqf72DvViVSgYcBOA9Tuts3anBNM06fmR/Yzrnh4Z82rHwNcNcmWzLn+zEdbvTNnE5uJVk7p9TP4+b12v8DhM7pGybVuzvMG0XrffI2S6yYxwJXDp1eZ98NLwXDQdAP0/NXlnislTb5gENjd5830LP/2u321KUFIuIiIiSTJy5EgqV67Mzp07GTp0KJUrV2bHDuuDpjdv3oyHhwflypWze82lS5cICQmhWrVqtGjRAhcXF4YNG2bb3qVLF3x9falXrx4hISGEhIRQsGDBVKyVVTY/g+bVoUvDa9SrlOpv71CurtClwTXbekpdLf96Hfy027pcNB90eSFF3iZNyOxtMKrT7YS198cmCQkp065RMSYN+5scOGldL5oPlo038PFK+RMeXZoYjO58+326vG/y9Tolb+lJxFmTNiMtVH4bdh/3uGv73+dh+lII7m7y9geWFPscOyvdUy4iIiJJMmzYMLuE+k5VqlThhx9+uKv86aef5ptvvrnvMT09PRk5cmRyhSgPqWnlSKYszc7pi/DtT7D1D5OnSyRfUhcda/LuHcO4P+hm4J4pY14lv6VtPfjoG9h12Dq53ZwV8EbD5H2PhASTVsNMtuy3rufKBivfN8iZNfXaNqQ1XLwKExdYJ0xsPcIkfwBUKpmxf7/p3Y04k/HzYMyXJjE3bpeXLgTdmxnExsGKLSZrd8KNf55COe1biI0zmdnfeuuLPDpdKRcRERERADwywXt3TGrf/9PkvZf0/flw8qx1uW5FaPhssh06zXJ1NZjU7XbiMnCGyfXo5L3KOHyOyQ+/Wpf9fGDF+waF8qRusmQYBhO6GrzRwLp+Mx5aDTO5fF1XVNOqE2esE/UNmXU7Ic/mB6GvX2T7DOj8gkGP5gbLJrhwMdxgai8DN1frfp8vg48WOy72jEZJeRJofgMRERHJ6Do1hsJ5rcvrdsKqrclz3KN/m7Yh8W6uMKmb4TRX2aqXNWxzFJy5BKP+l3xfKn/ebTJijnXZxQW+GWlQpohj2tUwDD7ta/Bcaev6iTPQbZK+QKdFFovJq8NN9v9pXXd1hZ4t4NBcaFMrErd/jaf28TLo+qLBl4PuuB1jqsna7fr9Jgcl5Q/JSfoQERERcTLumWBUx9tfdPp/amKxPNoX7+vRJk0G3L4a1+0lKFHQub5MjX/LINM/ic74efDNhkdPZvb/adJ8iGm7cDSig0Gt8o5t10xuBvOHGvhntq5/tRpW/qbELa2ZvhQ27bUu5w+AnTMNJnV3sf3e7qdVLYOQf0bTJCTAy6Emf5/X7/dRKSkXERERETstakD54tbl34/A7OUPfyyLxaTNSJN9x63rj+eH0DecKyEH6yPShraz1ts0odlgk14fWbgR93AJzR9/mtTsaXL2knU9uAz0fzW5on00gQEG73e9/Tt+a6JJdKwSt7Ti7/MmIdNv/z5mv2fwROHE/58c2dGwTYR5/or1NoWb8fr9Pgol5SIiIiJix8XFPqkKmW5y5SHvDR4y6/YzybP4wndjDPx8nC8pB3jvNesJj1s+/Bqeecvk4Mmkte3eYybV37mdkJcrBt+ONnB1TTvt2r4BVHvKunz8NExe5NBw5B+mafL2JJNrUdb1NxpAjXJJ+9y4ulqHsecPsK7/sgfem66k/FEoKRcRERGRu1Qva9gSyPNXYFhY0r50m6bJiDkmo76wrru4wIJhBsUC007imNpcXAwWDDOY3MPAPZO1bNdhKNfR5PMfEjep3q97TWq8Y3LusnW9XDFYPcnAP3PaalfDMPikt4HLP9nGuK9MLl1T4uZo32zAdpIsIBtM6Ppwn5vsWQwWht6+JWPiAvjhV/1+H5aSchERERG5p/e7Gnj988jij5fArsOJ+9J9I86kwzjrrM63TOpmULdi2kocHcEwrDNa/zbd4PH81rLoWOgwzuTlYSab9pgc/dtk2wGTFVtM5q02GT7bpNkgC4VaWniuq8mFq9bXPf142kzIbylR0KBdPevy1UiYtFBJmyNduW7S7cPbv4MpPQyy+T38Z6dSSfsRNe1Gm5y+oN/xw1BSLiIiIiL3lD/A4L3XrF+6ExKgYX+T46f++0v3mYvWK7lhy26XTXjLmojKbWWKGGybYdCp8e2yheug8tsmRV4xebqzSf13rTNkD/3c5JuN8OeZ2/s+Uwp+/CDtJuS3DGl3+2rq5EXoarkD9Z5qcuafWx4aPWd/K8XD6t4MGj9nXb5wFV4f/egTQzojJeVJoEeiiYiIiLN592Wo8Lh1+dQFqNPn/lfDth+0JpO/7rOue3nA/KEGfV9J24mjo/h4GXz2rgsLQw0yez94f29PqPqkdaKtHycaZPFN++1aINftZ5dfj4YPFugLtSMs2Xj7RFlmb5jWK3keSWgYBp+HGOTObl1fvQ3en//Ih3U6bg/eRe5Fj0QTEUm72rVrx5UrV/j2228Ttf/69eupUaMGly9fJmvWrCkam0h64+lhsHw8VO1mcuAkHP0bqnY3+d8A69VaFxeDUxdMRv7P5LNw6xV1gHw54bvRBuWK60vTg7SoYVD5Cev9vn+cMLl4DbL5QY4s4OdtUCg3PFEYCuchTU3mllgD2hh8vszkZjxMWQy9Wppkz5L+6pFenblo0nnCHcPW3zEIDEi+9s+R1eDLQVC7t/XxfANnmNQqD+X1fz/RlJSLiIgkwmeffcbs2bPZv38/169fVwIvTiVHVoMfP4Aq3UxOnLEm5pXftn7JNwzzrtGEz5WGb0YaBGTTl/LEypPDoFszgIzXZvkDDDo0NPn0u3+uli80GdUp49UzLbJYTNqNuT0PwYtVoW295H+fmuUN+r9qMnYuxCdA6xEmO2aCt6d+z4mh4esiIhlIXFyco0PIsGJiYggODua9995zdCgiDpHvMYMNUwzb88tvuTMh9/WC4R0M1n6ohFzsvffa7XvLpyyCi1c1jD01jPoCVv5mXQ7IBtPfTZ5h6/cS+oZBuWLW5YMnoc9U/Y4TS0m5iMhDslgsjBs3jiJFiuDh4UH+/PkZNWqUbfuePXuoWbMmXl5eZM+enc6dOxMZGQnAypUr8fT05MqVK3bH7NGjB9WqVbOtb9q0ieDgYLy8vAgMDKRHjx5ERUXZthcsWJCRI0fSrl07smTJQqdOnQDo378/xYoVw9vbm6CgIAYPHszNmzft3mvkyJE89thjZM6cmY4dOxISEsJTTz1lt09YWBglSpTA09OTkiVL8sUXX9i2xcXF0a1bN3Lnzo2npycFCxZkzJgx922vdu3a0bRpU0aPHk1AQABZs2YlNDSU+Ph43n33XbJly0a+fPn4/PPP7V73X+0IkJCQQO/evcmaNSvZs2enX79+dz1WyDRNxo8fT1BQEF5eXpQpU4ZFi5L20Nx33nmHt956i0qVKiXpdSIZSYFcBpumGUzva/BCZahYwjoDeOUnYNDrcHS+weC2Bh7uSsjFXv4Ag46NrMuRMTBR95anuMXrrZMEgvWRhHMHG+TMmnL/N90zGXw15PYTGz79DsJ/0e85MTR8XUTSpAoVKnDmzJkH75jMcuXKxbZt2xK173vvvceMGTOYNGkSVapU4fTp0xw4cACA6Oho6tWrxzPPPMPWrVs5d+4cHTt2pFu3bsyePZvatWuTNWtWFi9eTIcOHQBrcrlw4UKGDx8OWJPRunXrMmLECGbNmsX58+fp1q0b3bp1IywszBbHhAkTGDx4MIMGDbKVZc6cmdmzZ5MnTx727NlDp06dyJw5M/369QNg7ty5jBo1imnTplG5cmXmz5/PxIkTKVSokO0YM2bMYOjQoXz88ceULVuW7du306lTJwIDA2nfvj1Tpkxh6dKlLFy4kPz58xMREUFERMR/ttnatWvJly8fGzdu5JdffqFDhw78+uuvBAcHs2XLFhYsWMCbb75JnTp1CAwMfGA7AkycOJHPP/+cWbNmUbJkSSZOnMiSJUuoWbOm7X0HDRrEN998wyeffELRokXZuHEjr732Gjlz5rQ7CSIiD+aeyaDzC9D5BSXekjTvtTaY9YNJ3E34aDH0bmmSIwWTRGe2drvJqyNu31oS2t6gVvmUb+vi+Q0mdYM3J1rf+I2xJntmQ67s+j3/J1MSbdb3FpOqCSZVE8xPv0twdDipKiEhwTx27JiZkOBc9b7F2etvmqnfBnnz5jWBVP/Jmzdvotrg2rVrpoeHhzljxox77vvZZ5+Z/v7+ZmRkpK3shx9+MF1cXMwzZ86YpmmaPXr0MGvWrGnbvnLlStPd3d28dOmSaZqm2aZNG7Nz5852x/3pp59MFxcXMyYmxjRN0yxQoIDZtGnTB7bn+PHjzfLly9vWK1WqZL799tt2+1SuXNksU6aMbT0wMND86quv7Orfu3dv89lnnzVN0zS7d+9u1qxZ07RYLA98f9M0zbZt25oFChSw+wwVL17crFq1qm09Pj7e9PHxMefNm2eaZuLaMXfu3ObYsWNt22/evGnmy5fPbNKkiWmaphkZGWl6enqamzZtsounQ4cO5iuvvGKapmmuW7fOBMzLly/fN/5bv/81a9Y8cF9J35z1b76z1vtOzt4GqVH/rhMTbN+nQz5Ne+2cET4DW/ZZTN/nb7dzu9EJie6rk6P+FovFfCHk9vvX65tgJiQk7v3TAkd8BnSlPAn0SDSR1JMrV640/b5//PEHN27coFatWvfdXqZMGXx8fGxllStXxmKxcPDgQQICAmjdujXPPvssp06dIk+ePMydO5cGDRrg7+8PwPbt2zly5Ahz5861HcM0TSwWC8ePH6dEiRKAdVTBvy1atIgPP/yQI0eOEBkZSXx8PH5+frbtBw8epGvXrnavqVixImvXrgXg/PnzRERE0KFDB9uQeICbN2/aJjdr164dderUoXjx4tSrV49GjRrx/PPP/2e7lSpVCheX23dOBQQEULp0adu6q6sr2bNn59y5c4lqR09PT06fPs2zzz5r2+7m5kaFChVsQ9j3799PbGwsderUsYslLi6OsmXL/me8IiKSvN57zWDmravl30CfVrpanpxW/mbSbLBJVIx1vfFzMCMF7yO/F8MwmNkfnmhncvYSrNgC7003GfeWfs/3o6T8IekjJZKyEjuE3FG8vLz+c7tpmvftAG+VV6xYkcKFCzN//nzeeustlixZYjcs3WKx0KVLF3r06HHXMfLnz29bvjNhBdi8eTMvv/wyoaGh1K1blyxZstiGp98rjjtjvvO9wTqE/dY91BaLhb///tv23uXKleP48eMsX76c1atX07JlS2rXrv2f92pnypTprhjuVXbr/RPTjg9y61g//PADefPmtdvm4eGRqGOIiEjyyPeYQadGJlOXQFQMvD/fZOyb+madHL5YafLGWJP4fx5LWL0sLAg1cHNL/fbN+c9j0uq9a5KQAOPnwWP+Jn1e1u/6XjTRm4jIQyhatCheXl6sWbPmnttLlizJrl277CZl++WXX3BxcaFYsWK2sldffZW5c+cSHh6Oi4sLDRs2tG0rV64c+/bto0iRInf9uLu73ze2X375hQIFCjBw4EAqVKhA0aJFOXHihN0+xYsX57fffrMru/NESEBAAHnz5uXYsWN271uwYEG7+879/Pxo1aoVM2bMYMGCBSxevJhLly49oPUS70HtmCVLFnLnzs3mzZtt2+Pj49m+fbvdMTw8PDh58uRd7RgYGJhssYqISOKEtDZw/+d87MdL4PwVDUd9FKZpMmGeyeujbifkLwXD8vEGXh6OS4JrVzD46J3b7993msmkhfpd34uSchGRh+Dp6Un//v3p168f//vf/zh69CibN29m1qxZALRu3RpPT0/atm3L3r17WbduHd27d6dNmzYEBATYjtO6dWt27NjBqFGjaN68OZ6enrZt/fv359dff+Xtt99m165dHD58mKVLl9K9e/f/jK1IkSKcPHmS+fPnc/ToUaZMmcKSJUvs9unevTuzZs1izpw5HD58mJEjR7J79267q8/Dhg1jzJgxTJ48mUOHDrFnzx6+/vprJk2aBMCkSZOYP38+Bw4c4NChQ3z99dfkypUrWZ/dnZh2fOeddxg7dixLlizhwIEDdO3a1W5W+8yZM9O3b1969erFnDlzOHr0KDt37mTq1KnMmTMn0bGcOXOG/fv3c+TIEcA6Ed+uXbuS9SSEiIgzyPeYQefG1uWoGHh/nhK1h2WxmPT+2KTfJ7fbsGtTWBhq4OnAhPyWt5oajOhwO47eH5t8qMT8Lhq+LiLykAYPHoybmxtDhgzh1KlT5M6dmzfffBMAb29vVq5cyTvvvMPTTz+Nt7c3zZo144MPPrA7RtGiRXn66afZunUrH374od22J598kg0bNjBw4ECqVq2KaZoULlyYVq1a/WdcTZo0oVevXnTr1o0bN27QsGFDBg8ezLBhw2z7tG7dmmPHjtG3b19iY2Np2bIl7dq1s7t63rFjR7y9vZkwYQL9+vXDx8eHokWL2mZw9/X1Zdy4cRw+fBhXV1eefvppli1bZnfP+KNKTDv26dOH06dP065dO1xcXHjjjTd48cUXuXr1qm2fESNG8NhjjzFmzBiOHTtG1qxZKVeuHAMGDEh0LNOnT7fNjA8QHBwMWB8b165du0evrIiIEwlpbTDje5Mbcdar5X1fMVP0cV2OsO+4yYotcPKsyZVIyJUN6j9jEFwGXFweva4xN0zajDRZvOF22ciOBgPaJP4Wr9QwqK2BCQyZZU3Ge31s4uUBXZqknRgdzTBNTV+WWLO+N+k43tpc0/tC5xecZ6CBxWLhxIkTFChQIFm/cKcXzl5/UBtAxm+DOnXqkCtXLrtnkd8po9f/QZy9/s7EWX/XzlrvOzl7G6R2/XtMtvDRYuvyu6/A+Lcc3+aP2gYJCSZLf4EPvzbZ+Pu99ymc13pSom09yPSQ93tfuW7ywnsmP+22rru6wmd9Dd5o+GiJbkp+BkLDTIaFWXMpw4AuL0Dt8gaVSlpHT6QVjvg74PhPvoiIpLro6Gg++OAD9u3bx4EDBxg6dCirV6+mbdu2jg5NREScREhrA49/pkiZugTOXU6/1wovX7feL13kFZOXBt0/IQc4+jd0Gm/y1Bsm63cmvc5nL5nU6Hk7Iff1gvAxj56Qp7Sh7Q36vmxdNk349DtoPsQksLnJCyEWjv6dfn//j0pJeRI478dERDIawzBYtmwZVatWpXz58oSHh7N48WJq167t6NBERMRJ5Mlh0OWfe8ujY2FCOry3fN0Ok7ajLOR9yXpv959nbm8rUQAmdTPYNM1g3/8MvhpiUKv87e37/4Qa75i0Hm7h9IUH1900TeavMSnd1mTXYWtZjiywfopB/WfSdkJ+y/i3DIZ3MHBztS8P3wSV3zbZfTT9fQaSg+4pf0hp6DYNEZEk8/LyYvXq1Y4OQ0REnFz/1gafhZvExlmvlr/7islj/mn/i/a+4yZ9ppqs/O3ubfUqQc8WBs8/bX9vd8mC8Eptg1/3mrwzxWTrAWv5V6th+RaT4W9Aw2ehQK677zmPOGvSfbLJdz/fLsuXE378wODxAmm/vW4xDIPBbeGtJrB+F+w6bPL5Mjh9Ec5eglo9TX6eCsXzp586JQcl5SIiIiIi4hB5chh0ecFk8iKIuQHjvzJ5/+20m5DFx5uMmQvDZ99+/BhAFl94vS681cSgRMH/jv/Z0gabP4VZP0DIdJNL1+Dydeg+2aT7ZOtw9NJBJoVyQ3Y/uHQNvtkIsXG3j9G8Onzc0yAgW9ptq/+SI6tB8+rQvLpBn5dN6r9rsmU/XLgKz/cx+WVq2rrPPKVp+LqIiIiIiDhM/1cNPP+5t3zat9Z7ptOiQxEmVbqZDJl1OyHPHwD/G2hwZonBlHdcHpiQ3+LiYtCpscHBLw1e/dedY5ExsHkfzFsNH39jvZJ+KyF/zB8WDTf4erhLuk3I/80/s8HK9w3KFLGunzwLdfuaXLyaNj8HKUFJuYiIiIiIOEzuHAZvNrEu37panpbE3TSZMM86MduW/dYyV1cY2AYOfGnQpu7DPxM8R1aDuUNc2D7DYEg7aPwcFMx1936ZvaF3S+v7NaueMZLxO2XxNVgxwaBwXuv6/j+hUYhJVEza+iykFA1fFxERkSSZPn06q1ev5s8//2TkyJHUrVsXgPDwcEaOHIm7u7tt36+//ppcuazfMPft28fIkSM5efIkpUqVIjQ0lNy5cwMQGxvLqFGj2LBhA5kzZ6Z79+7Uq1cv9SsnIg7R7xWDT7+z3lv+8RJ4tY5J+eKOTz5/3Gq9l/vgydtlRfPBF4MMKpVMvvjKFTcod0d9I6NNzlyCi9cg7iZUeBy8HjLxTy9yZTdYNREqd7XWffM+aDbYZOkYcM+UseuuK+VJoCe6i4iIQGBgIH369KFUqVJ3batYsSI//fST7edWQh4XF0e/fv14+eWXWbt2LaVLl2bIkCG2102fPp2rV6+ybNkyRo8ezdixYzlx4kSq1UlEHCt3DoOeLazLcTehxRCTK9cd9+X7xBloNsjC831uJ+QuLtC9GeyclbwJ+b34ehsUyWd9n6pljAyfkN8SlMdgxfsGWXyt6yt/g3ZjTCyWjJ2IKSl/SJp9XUREnFWDBg145pln7K6IP8j27dvx8vKiSZMmeHh40KlTJ/bv38/p06cBWLZsGZ07d8bX15cyZcoQHBzMqlWrUqoKIpIGhb5hUKmkdfn4aWg9wiQ+PnWTsYQEmLrUj1JtrZOr3fJcadj2mfW+cR8vJQIpqUwRg/Axt+cZmLcaWg0ziY7NuIm5hq+LiEiG065dO65cucK3336bqP3Xr19PjRo1uHz5MlmzZk3R2DK633//nVq1apEtWzZatWpF8+bNATh27BhFihSx7efl5UW+fPk4duwYPj4+XLx40W57sWLF2Ldv333fJy4ujri4OLsyNze3JJ0ouB+LxWL3r7Nw1nrfydnbwNH1d3OF+UOhfCfrjOPLNkOX900+6W3ilgpZy+Xr8EqoyY/b/G1lAf4w7i14rQ4YRsa/Yuvoz8AtlZ+A+cOg2SBIsMCi9fDzbpNOjU06NYK8OVPuvZO7DVxcHnwdXEm5iIjIA1y6dIkhQ4awbNkyzpw5Q44cOWjatCkjRowgS5Ysjg4vzShXrhzz588nV65c7N+/n759+5I9e3Zq1KhBTEwMPj4+dvv7+PgQExNDdHQ0rq6ueHp62m2Ljo6+73uFhYUxY8YMu7IWLVrQsmXLZKtPREREsh0rPXHWet/J2dvA0fX/qKsHbScEEJ9g8Pky2H0kllHtLlE0780Ue88jp9zo/OFj/Hk2EwCuLiZt61ynR9Mr+HmbnDz5gANkMI7+DAA8mRc+6+lJj2k5iYp14cwlGDEHRv3P5JkSsVQtHUuJ/HH4ellwc4X4BLgZb3AzwSA+HgL8E3g88OZDj3BOrjYoVKjQA/dRUi4ikoHExcUly5VCsXfq1ClOnTrFgAEDqFatGhEREbz55pucOnWKRYsWOTq8NCNv3ry25dKlS/Pyyy+zbt06atSogZeXF1FRUXb7R0VF4eXlhbe3NwkJCcTGxtoS86ioKLy9ve/7Xu3bt6d169Z2Zcl5pTwiIoLAwMBEXeHIKJy13ndy9jZIK/UvUADIBG1HWxOtbYc8aTg4Dz1bQEhr8M+cvO+3aiu0HAHX/zkPmC1zAl8PN6he1g/wS943S+PSymfglnYFoOKTMPRz+O5n61Vzi2mwab8Xm/Z7PfD15YrBgDbQpLJ1ToDEcEQbOL6lRUTSKYvFwrhx4yhSpAgeHh7kz5+fUaNG2bbv2bOHmjVr4uXlRfbs2encuTORkZEArFy5Ek9PT65cuWJ3zB49elCtWjXb+qZNmwgODsbLy4vAwEB69Ohhl9gULFiQkSNH0q5dO7JkyUKnTp0A6N+/P8WKFcPb25ugoCAGDx7MzZv2VxhGjhzJY489RubMmenYsSMhISE89dRTdvuEhYVRokQJPD09KVmyJF988YVtW1xcHN26dSN37tx4enpSsGBBxowZc9/2ateuHU2bNmX06NEEBASQNWtWQkNDiY+P59133yVbtmzky5ePzz//3O51/9WOAAkJCfTu3ZusWbOSPXt2+vXrh/mvmTlN02T8+PEEBQXh5eVFmTJlkpRMly5dmkWLFlGrVi0KFy5MzZo1GTVqFOHh4cTHxyf6OM7GuOPyRFBQEEeOHLGtx8TE8NdffxEUFISfnx/Zs2e3237o0CGCgoLue2x3d3d8fX3tfjw9PXFxcUmWHyDZjpWefpy13mqDtFf/V+u4sPZDgyL/nOuLT4D350PRV2HiAoP4BCNZ3ufn3QYvDrydkD9ZGL4deprqZR3fBs7+Gbj1UzrIhcUjXfhzofWxcUF5Et8P7TgEzQfDs11hw67Ef2aSsw0SQ1fKRSRNqtDJwplLqf++ubLBthmJ+wP63nvvMWPGDCZNmkSVKlU4ffo0Bw4cACA6Opp69erxzDPPsHXrVs6dO0fHjh3p1q0bs2fPpnbt2mTNmpXFixfToUMHwJpcLly4kOHDhwPWZLRu3bqMGDGCWbNmcf78ebp160a3bt0ICwuzxTFhwgQGDx7MoEGDbGWZM2dm9uzZ5MmThz179tCpUycyZ85Mv379AJg7dy6jRo1i2rRpVK5cmfnz5zNx4kS7IVYzZsxg6NChfPzxx5QtW5bt27fTqVMnAgMDad++PVOmTGHp0qUsXLiQ/PnzExER8cChXmvXriVfvnxs3LiRX375hQ4dOvDrr78SHBzMli1bWLBgAW+++SZ16tQhMDDwge0IMHHiRD7//HNmzZpFyZIlmThxIkuWLKFmzZq29x00aBDffPMNn3zyCUWLFmXjxo289tpr5MyZ0+4kSFJcvXoVPz8/3FLjRsc0Jj4+noSEBEzTJD4+nhs3bpApUyY2b95MiRIl8Pf358CBAyxYsIBevXoBUL58eWJiYggPD6du3bq239etR6I1aNCAmTNnMmrUKI4dO8bGjRttv2MRcU5Vyxjsng1jvjQZ95V1VvbL16HfJyYL18G8IVAk38NPurbjoEnj96yPYQNoUgX+NwAunk9IngpIssr3mEHoGwbD2pv8cQJ+2QMR50yuR8PNeHDPBJlcIZMbmMCKLdakHGDbAajZ0+SFyiafhxhkz3Lvz018vMnoL+HFiqk8mZ8piTb9O4tJ1QSTqgnmjPAER4eTqhISEsxjx46ZCQnOVe9bnL3+ppn6bZD3pQTb/7fU/Mn70v3rd2cbXLt2zfTw8DBnzJhxz30/++wz09/f34yMjLSV/fDDD6aLi4t55swZ0zRNs0ePHmbNmjVt21euXGm6u7ubly5dMk3TNNu0aWN27tzZ7rg//fST6eLiYsbExJimaZoFChQwmzZt+sD2HD9+vFm+fHnbeqVKlcy3337bbp/KlSubZcqUsa0HBgaaX331lV39e/fubT777LOmaZpm9+7dzZo1a5oWi+WB72+aptm2bVuzQIECdp+h4sWLm1WrVrWtx8fHmz4+Pua8efNM00xcO+bOndscO3asbfvNmzfNfPnymU2aNDFN0zQjIyNNT09Pc9OmTXbxdOjQwXzllVdM0zTNdevWmYB5+fLl+8Z/5+//woULZv78+c2BAwcmqu4ZzdChQ83y5cvb/WzdutX84IMPzNq1a5tVqlQxX3zxRdvv8Za9e/earVq1Mp977jmzY8eO5qlTp2zbYmJizIEDB5pVqlQxGzRoYC5fvjy1q2XjrH/znbXed3L2NkjL9f/ztMVsOyrBNIJv99m+zyeYX65MXB/0b7uPWMwcjW4fq17fBPNGnCVNt0FqyEj1t1gs5vebLOaT7ey/65Vsk2D+de7uz82NOIvZbJB1nwodosxrkanXBs53ej+Z6JFoIikrV7a0/b5//PEHN27coFatWvfdXqZMGbuJrSpXrozFYuHgwYMEBATQunVrnn32WU6dOkWePHmYO3cuDRo0wN/fOuvr9u3bOXLkCHPnzrUdwzRNLBYLx48fp0SJEgBUqFDhrvdftGgRH374IUeOHCEyMpL4+Hj8/G7fF3fw4EG6du1q95qKFSuydu1aAM6fP09ERAQdOnSwDYkHuHnzpm128nbt2lGnTh2KFy9OvXr1aNSoEc8///x/tlupUqXshnIFBARQunRp27qrqyvZs2fn3LlziWpHT09PTp8+zbPPPmvb7ubmRoUKFWxD2Pfv309sbCx16tSxiyUuLo6yZcv+Z7z3cu3aNRo2bEjJkiUZOnRokl+fEQwbNoxhw4bdVV6hQgXblfF7KVWqFPPnz7/nNk9PT0aOHJlcIYpIBlMgl8HsAQbdm5m8Empy+C+IjIHXRpr8uM3k454Gvt6J+4K+eZ9Jg34ml69b16s8CYuGG7hnMjL87OrOxDAMGj4L9SrCl6ug36cm5y7D/j+hytsmqydB4bzWz0xUjEmrYSY//Gp97e5jHuw8DMFPpU6sDk3KL1++zLBhw9i2bRsBAQGEhIRQsWLFu/aLjY1l1KhRbNiwgcyZM9O9e3fq1avngIhFJLUkdgi5o3h5/ffkIqZp2t1Pe6db5RUrVqRw4cLMnz+ft956iyVLltgNS7dYLHTp0oUePXrcdYz8+fPblv89o/XmzZt5+eWXCQ0NpW7dumTJksU2PP1ecdwZ853vDdYh7JUqVbKV/f3337b3LleuHMePH2f58uWsXr2ali1bUrt27f+8VztTpkx3xXCvslvvn5h2fJBbx/rhhx/sJiID8PDwSNQxbomMjKR169b4+vqyZMmSu2IXEZGUVb64wY6Z0O1DkzkrrGVzVsCv+0wWDIOniv5337Bii0nzISZRMdb1SiXh+7GGnj2egbm6GrStD1XLQO1eJsdPw59n4Nm3TN5+EQ7/ZfLjNjh32bq/pztM73GOKk8GpFqMDv3WO27cOHLmzMmaNWvo0aMHISEhXLt27a79pk+fztWrV1m2bBmjR49m7NixnDhxwgERi4hYFS1aFC8vL9asWXPP7SVLlmTXrl12k7L98ssvuLi4UKxYMVvZq6++yty5cwkPD8fFxYWGDRvatpUrV459+/ZRpEiRu37+a4bpX375hQIFCjBw4EAqVKhA0aJF7/qbWbx4cX777Te7sm3bttmWAwICyJs3r+3Z0rd+ChYsaHffuZ+fH61atWLGjBksWLCAxYsXc+lS8k0G8KB2zJIlC7lz52bz5s227fHx8Wzfvt3uGB4eHpw8efKudgwMDEx0LNeuXaNt27a4u7uzdOlSu8d3iYhI6vH1Npg9wIUvBhn4/nOO/FAEVHrT5MOFJpHRd1/tvnzdpM/HFuq/ezshr1kOfvzAIIuvEnJnEJTH4OepBqX/+Rpz/goMCzOZ++PthNzXC34YB1WfiE3V2ByWlEdHR7NhwwbefPNNPD09qV69OoULF2bjxo137bts2TI6d+6Mr68vZcqUITg4mFWrVjkgahERK09PT/r370+/fv343//+x9GjR9m8eTOzZs0CoHXr1nh6etK2bVv27t3LunXr6N69O23atCEg4PaZ19atW7Njxw5GjRpF8+bN7RK9/v378+uvv/L222+za9cuDh8+zNKlS+nevft/xlakSBFOnjzJ/PnzOXr0KFOmTGHJkiV2+3Tv3p1Zs2YxZ84cDh8+zMiRI9m9e7fd1edhw4YxZswYJk+ezKFDh9izZw9ff/01kyZNAmDSpEnMnz+fAwcOcOjQIb7++mty5cplG96eHBLTju+88w5jx45lyZIlHDhwgK5du9rNap85c2b69u1Lr169mDNnDkePHmXnzp1MnTqVOXPmJCqO69evU69ePaKjo5kxYwbXrl3jzJkznDlzhoQETQgkIuIIrz1vsGOmQbl/znXH3YReH5tka2TydGcLNd6x8HRnC8VetZCjsckHC2+/tmlV+GGcQeZEDnmXjCFPDoMNHxlUKmlf7u0JzarB9pkG1ZN+Z9sjc9jw9ZMnT+Lr60uOHDlsZUWLFuXYsWN2+127do2LFy9SpEgRW1mxYsXYt2/ffY8dFxdHXFycXVlyPLv0zltMTIuFf0ZEOoVbwz8tzlTpOzh7/UFtAHe3wcCBA3F1dWXIkCGcOnWK3Llz06VLFywWC56enixfvpxevXrx9NNP4+3tzUsvvcTEiRPt2rBw4cI8/fTTbN26lQ8++MBuW+nSpVm3bh2DBg2iatWqmKZJ4cKFadmypd1+t+4zv6Vx48b07NmTbt26cePGDRo0aMCgQYMIDQ217ffKK69w9OhR+vbtS2xsLC1atKBt27Zs3brVts8bb7yBp6cnEydOpF+/fvj4+FC0aFHeffddLBYL3t7ejBs3jsOHD+Pq6srTTz/N999/b9dGdzJN865Y7xX/nWWJacdevXpx6tQp2rVrh4uLC+3bt6dp06ZcvXrVtk9oaCg5c+ZkzJgxHDt2jKxZs1K2bFnee+89LBaL3e/2XrFv3bqVLVu2ANiNdAA4evQoBQsWvOs1iZHYR6WIiMi9FQ002DQN3vvMZNI/SffNeOts2/fingnGdTHo0RxcXJSQO6NsfgY/fWydnf38FShRACo8DpncrJ8HR8wrYJim6ZDZDHbu3EloaCjffvutrWzq1KlERkbSv39/W9mZM2do0qSJ7csQwJIlS1i/fj2TJ0++57GnT5/OjBkz7MpatGhBy5YtHynmeet8GTg7OwDjOlygRXDUA14hIpJ+tGnThpw5c/LBBx84OhSnceetAJJ2WCwWTpw4QYECBZzqxImz1vtOzt4G6b3+P/1u8tVqk9Xb4OgpME1wcbEOSS6UG2qUhW4vGbbJve4lvbfBo3L2+oNj2sBhV8q9vLzs7hEEiIqKumvyJG9vbxISEoiNjbUN64yKisLb2/u+x27fvj2tW7e2K0uOK+W9W0OXlyz89fdfFA3Kh6dHjge/KIOwWCxEREQQGBjolP9Bnb3+oDaAjNUG0dHRTJ8+neeffx5XV1fmz5/PL7/8wsqVKylQoMA9X5OR6v8wnL3+IiJpXdUyBlXLWBPum/Em8QnWSbsSOzGoiKM4LCnPnz8/kZGRXLhwwTaE/fDhwzRp0sRuPz8/P7Jnz86RI0dsj805dOgQQUFB9z22u7v7Iyfg9+LpYR3ycu2yiaeHi1N+KXNxcc563+Ls9Qe1AWSMNnB1dWX58uWMGjWKGzduULx4cRYvXvzAR5pBxqj/o3D2+ouIpAeZ3Awy6eHPkk447FuFt7c3wcHBTJ8+ndjYWDZs2MDRo0cJDg6+a98GDRowc+ZMoqKi2LNnDxs3brzrebMiIpJ4Xl5erF69mkuXLhEVFcWOHTt46aWXHB2WiIiIiNNx6Kn+kJAQzp49S61atZg8eTJjxozBz8+P5cuX293/3aVLF3x9falXrx4hISGEhIQ89KQ6IiIiIiIiImmFQwd1+Pv7M2XKlLvK69evT/369W3rnp6ejBw5MjVDExEREREREUlxuilORERERERExEGUlIuIiIiIiIg4iJJyEREREREREQdRUi4iIiIiIiLiIErKRURERERERBxESbmIiIiIiIiIgygpFxEREREREXEQJeUiIiIiIiIiDqKkXERERERERMRBDNM0TUcHISIiIiIiIuKMdKVcRERERERExEGUlIuIiIiIiIg4iJJyEREREREREQdRUi4iIiIiIiLiIErKRURERERERBxESbmIiIiIiIiIgygpFxEREREREXEQJeUiIiIiIiIiDqKkXERERERERMRBlJSLyEM5deoUzz33nKPDEBERkRSivl4kdTh1Uh4XF0doaCgNGjSgWrVqdO7cmSNHjti2z549m9q1a1OzZk0mT56MaZoAxMfH8+6771K/fn0qVKjAhQsX7I7bsmVLqlatavt5+umn+fLLL1O1bknRuHFjqlWrRmxsrK0sMjKSypUr06xZMwdGlvKcue7/pXHjxuzZs8fRYaS6HTt20K5dO6pVq0atWrXo0qULf//9t6PDShWNGzemUaNG3Lx501Y2evRopk+f7sCoUk5K/f3/+++/efvtt6levTr169cnLCwsVesld1Nff5sz93nOXPf7UV+vvh4ydl8P6ae/d+qkPCEhgbx58xIWFsbatWsJDg6mT58+APz8888sWrSI2bNns3DhQn7++WeWLl1qe225cuUYP378PY+7cOFCfvrpJ3766SfCw8Nxc3OjWrVqqVKnh5U9e3Y2btxoW1+3bh0BAQFJPk58fHxyhpUqkqvukr5FRkbSt29f2rVrx7p16wgPD+fll1/G1dXV0aGlmujoaMLDwx0dRqpIqb//EyZMIG/evKxevZqZM2eyYMECfvvtt1Spk9yb+np76u/V3zsz9fXO1ddD+unvnTop9/LyomPHjgQEBODq6kqrVq04deoUV65cYdmyZTRv3px8+fKRI0cOXnvtNZYvXw6Am5sbr7zyCk888cQD32P16tU8/vjjBAYGpnR1HkndunVt9QNYvnw5devWta3PnDmTRo0aUa1aNdq3b8/hw4dt2xo3bsycOXN46aWXaNGiRarGnRwetu7Lly+nS5cudscaNGhQmr9SkhTDhg1j9uzZtvXw8HC6d+/uuIBS0IkTJ/D09KR69eq4uLjg7e1NjRo1yJUrFwkJCUyfPp1GjRpRt25dJk2aZPtCOn36dAYNGkTPnj2pVq0aXbt25eLFiw6uzcN59dVXCQsLu+eX7fnz59OkSRNq167NkCFDiIyMBOCtt97i+++/t+0XHR1NcHBwmm+DlPr7f/r0aZ5//nnc3NzImzcvTz31FMeOHUvNqsm/qK+3p/5e/f2/qa9XX39LRuvrIf30906dlP/b7t27yZYtG1mzZuX48eMUKVLEtq1YsWIP1dDLly+nXr16yRlmiqhUqRIHDx7k6tWrXLhwgYiICMqVK2fbXqhQIb744gvWrFlDpUqVGDp0qN3rN2zYwMyZM5k/f35qh/7IHrbuNWrU4MCBA5w/fx6A2NhYfvrpJ55//nmH1EMeTYECBYiNjWXUqFFs2rTJ1hEBzJ07l99//50vv/ySRYsWceDAARYtWmTbvmbNGl5++WVWrVpFQEAA48aNc0QVHlmlSpXImTPnXWfQf/31V+bMmcOHH35IeHg4MTExTJo0CYA6deqwevVq274bN26kVKlSZM+ePVVjf1TJ9fe/RYsWrFy5kri4OE6ePMmePXuoUKFCSoUtD8GZ+3pQf6/+3rmpr3fuvh7Sbn+vpPwfkZGRjB49mq5duwLWM0C+vr627T4+PkRHRyfpmKdOnWLfvn3UqVMnWWNNCa6urlSrVo3Vq1ezatUqateujWEYtu21atXC398fNzc329njO9vj1VdfJVu2bHh4eDgi/EfysHX39PQkODiYVatWAdY/UI8//jiPPfaYo6oij8DX15fPPvuM2NhYQkNDqVOnDoMHDyYqKorvvvuOrl27kjVrVjJnzsxrr73G2rVrba8tV64czzzzDB4eHrz55pts2LAhXQ7tBOjcufNdZ9BXrVpFs2bNKFSoEF5eXrz99tu2z33NmjXZtm0b169fB+DHH39MF3/z7pScf//LlCnDnj17qFq1Ki+99BJNmjSx6/DFsZy9rwf19+rvnZv6eitn7Oshbff3bg/9ygzkxo0b9OnThypVqtCkSRMAvL297c6eRUVF4e3tnaTjrlixgooVK5ItW7ZkjTel1K9fn48//pjY2FgGDhxo+48HsGTJEubNm8fZs2cxDAPTNLl69aqtTdJ7x/SwdW/QoAGffvoprVu3ZsWKFenmSoncW5EiRRgxYgQAf/zxByEhIXz++eecOXOGt99+2/blzTRNu8/8v5dN0+TKlSvkyJEjdSuQDJ555hly5MhhN0ztwoULlC9f3raeO3duYmJiiIyMJGvWrJQtW5b169dTo0YNtm7dyuDBgx0R+kNJzr//CQkJvPPOO7z++us0b96cc+fO0bNnT4KCgqhdu3aK1UESR339berv1d87M/X1ztfXQ9rv753+Snl8fDwDBgwgZ86c9OzZ01ZeqFAhu5n5Dh06RFBQUJKOvWLFCurXr59coaa4J598knPnzhETE0Px4sVt5adOnWLSpEkMHz6c9evXs2LFClxcXGyzEwJ2Z5rTo4ete8WKFTlz5gx//PEH27Zto1atWo6qQorw8vKym6k2Pdw7lFxKlChBjRo1OHr0KI899hgzZ85k/fr1rF+/ng0bNvD111/b9j137pzdsmEYZM2a1QFRJ49OnTrZnUHPkSMHZ86csW0/c+YMnp6etrPLt4a1bdiwgTJlyqSbuif33/9r165x/vx5mjdvjpubG3ny5KF69eps3749JcKXJFBfb0/9vfr7O6mvV18PGbevh/TR3zt9Uj5q1Chu3LjBsGHD7DqaBg0asHjxYv7++28uXLjA3Llz7TrduLg4bty4AcDNmzdty7ccPHiQ06dPU7169VSpR3KZMGECY8aMsSuLjo7GMAyyZMlCfHw806dPt+ugM4qHqburqyvPP/88Q4YMoUKFCvj5+aV22CmqWLFibNy4kcjISP766y+7GSkzmj///JO5c+fa7hk8ceKE7Z6pJk2aMG3aNC5cuIBpmpw6dcruD+/OnTvZsmULcXFxfPbZZwQHB+Pmln4HIj377LNky5aNDRs2AFC7dm2++eYb/vzzT2JiYpg2bZrdvZQ1atRg586dLFmyJF0NZ0vuv//+/v4EBATw7bffYrFYOHv2LBs2bKBw4cKpWzG5i/r6u6m/V39/i/p69fWQcft6SB/9ffr9JCWD06dPEx4ejoeHBzVq1LCVT5kyhSpVqnD48GFef/11LBYLTZs25YUXXrDt06xZM06fPg1YZyMF2LZtm237ihUrqFatGl5eXqlUm+RRtGjRu8qKFCnCiy++yMsvv2ybwTBTpkwOiC5lPWzd69evz7x58+jUqVNqhZoqDMOgQYMGbN68mYYNG1KwYEHq1q3L3r17HR1aivD29mb37t3873//IyoqiixZslCrVi3atWuHYRjEx8fToUMHrly5Qq5cuWjbtq3ttTVr1mTevHm8++67lCpVyjYsLj3r1KkTPXr0AKBy5cq0adOGHj16EBUVxXPPPUevXr1s+2bOnJny5cvz66+/8sEHHzgq5CRJqb//48aNY+LEiXz00Ud4enry/PPP8+KLL6ZizeTf1Nffm/p7e87a36uvV1+fkft6SD/9vWFmxFOgIqnowoULNGvWjJUrV+Lp6enocJJFrVq1CAsLI3/+/I4OJc2bPn06Fy9eZMCAAY4ORUREUlBG6+/V1yee+npJaU4/fF3kUVgsFubOnUudOnUyRAcNt88A5s6d28GRiIiIpA0Zrb9XXy+Stjj18HWRR/X888/j5+fHtGnTHB1Kshg1ahSbN29m4MCBGXLIooiIyMPISP29+nqRtEfD10VEREREREQcRMPXRURERERERBxESbmIiIiIiIiIgygpFxEREREREXEQJeUiIiIiIiIiDqKkXERERERERMRBlJSLpBPbtm2jQoUKVKhQgVOnTjk6HBEREUlm6utFnJOeUy6SBjRu3JjTp0//5z5Vq1aldOnSALi7u6dGWA+0bds23nzzTQCWLl1Knjx5HByRiIhI2qS+XkTuR0m5SBpQvHhxsmfPDsC5c+c4d+4cAMWKFbN1ytWqVaNp06aOClFEREQegfp6EbkfwzRN09FBiMht06dPZ8aMGYD9Gel7nakeNmwY33//Pblz56ZLly588sknREZG8sILL/D2228zdepUli5dSubMmWnXrh3Nmze3vc/58+eZNm0av/76K1euXCEgIIDGjRvTrl073Nys5+v27NnDtGnTOHToENHR0fj7+1O8eHH69OnDDz/8YIvzTo0aNWLYsGF88cUXLF++nDNnzhAVFYWfnx9PPfUU3bp1o0CBAgCEh4cTGhoKwNixY/n88885ceIE5cuXJzQ0lPXr1zNz5kxiY2OpU6cOffv2tcVWoUIFAHr27Mn+/fv56aef8PT0pFmzZnTp0gXDMFLi1yMiIvLI1Nerrxe5k+4pF8kALly4wNixY8mUKRNRUVHMmzePNm3asHTpUnx9fTlz5gzjx4/n+PHjAFy5coV27doRHh5OTEwMhQoV4syZM3z66aeMGjUKAIvFQs+ePdm6dStubm4UKlSImzdv8tNPP3HmzBkCAgIoVKiQLYZixYpRunRp8uXLB8D27duJiIgge/bsFCxYkGvXrrFu3Tq6du3KjRs37qrD0KFDiYuLIy4ujk2bNtG5c2fGjRuHh4cHV69eZdGiRXz33Xd3vW7atGns3LmTzJkzc/nyZWbOnMmCBQtSoplFREQcRn29+nrJuJSUi2QAN2/e5OOPP+abb74hICAAgIiICObNm8eiRYvw8PDAYrGwfft2ABYuXMjZs2fJnj073377LfPmzWPcuHEAfP/990RERHDt2jWuXr0KQFhYGF999RU//vgjCxYsICgoiKZNm9K/f39bDO+//z6zZ8+mY8eOAHTv3p1169bx9ddfs2DBAqZMmQLA2bNn+f333++qwxtvvMGiRYuoV68eAMePH2fo0KF88803PPXUU4D1CsK/lSpVivDwcJYuXUrZsmVt8YqIiGQk6uvV10vGpXvKRTKAW8PFAHLlysXZs2cpXLiwbTicv78/Z86c4dKlSwDs27cPgIsXL1KnTh27Y5mmyd69e6lfvz5PPvkku3fvpnnz5gQGBlK4cGGqVKli60z/y5kzZxg9ejRHjhwhOjqaO++UOX/+/F37BwcHA5A7d25bWdWqVQHImzcvu3btssV/p1q1atmGudWqVYudO3dy8eJFLl++jL+//wPjFBERSQ/U16uvl4xLSblIBuDj42NbdnV1vavs1j1XtzrLW//6+PjYDUu7xdPTE7AOF1uxYgW///47x48fZ82aNaxatYoLFy7w+uuv3zeev/76i759+3Lz5k18fHwoUaIE8fHxHDp0CLAOl7tfHW7FD+Dr63vP+EVERJyN+nqRjEtJuYgTKlWqFJs2bcLV1ZXRo0fbzrJHRUWxbt06atSogWma7N69m8aNG9tmgh0+fDhLly5l586dvP7667YOHSAmJsa2fPDgQW7evAnARx99xJNPPsnKlSsZOHBgstdlzZo1tklt1q5dC0D27Nl15lxERJya+nqR9ENJuYgTatmyJd999x3nzp2jWbNmFCpUiKioKM6ePUt8fDyNGjUiISGBrl274uPjQ0BAAIZh2CaPKVKkCAD58uXDzc2N+Ph4unbtSu7cuXnttdcoUqQIrq6uJCQk0L17d3LlysXFixdTpC4HDhygcePGGIZhe7xM27ZtU+S9RERE0gv19SLphyZ6E3FC/v7+hIWF0bhxY7JkycLRo0e5ceMGZcuWpXfv3oB1aFmzZs3IkycP586d46+//iJ37ty0adOGTp06AZA1a1b69u1LQEAAly5dYu/evVy8eJGCBQsyePBg8ubNS3x8PFmzZrXN9JrcunbtSoUKFYiMjCRLliy88cYbvPzyyynyXiIiIumF+nqR9EPPKReRdOnWs0uHDh1K48aNHRyNiIiIJDf19eIsdKVcRERERERExEGUlIuIiIiIiIg4iIavi4iIiIiIiDiIrpSLiIiIiIiIOIiSchEREREREREHUVIuIiIiIiIi4iBKykVEREREREQcREm5iIiIiIiIiIMoKRcRERERERFxECXlIiIiIiIiIg6ipFxERERERETEQf4Ps3aUJfxdQREAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "covs2 = compute_moving_average_metrics(hfcs2, metrics.ic)\n", + "widths2 = compute_moving_average_metrics(hfcs2, metrics.iw)\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 4.3))\n", + "covs.plot(ax=ax1, label=\"coverages model 1\")\n", + "covs2.plot(ax=ax1, label=\"coverages model 2\")\n", + "ax1.set_ylabel(\"Ratio covered [-]\")\n", + "ax1.set_title(\"Moving 4-week average of Interval Coverages\")\n", + "\n", + "widths.plot(ax=ax2, label=\"widths model 1\")\n", + "widths2.plot(ax=ax2, label=\"widths model 2\")\n", + "ax2.set_ylabel(\"Width [kWh]\")\n", + "ax2.set_title(\"Moving 4-week average of Interval Widths\")\n", + "\n", + "bts = pd.concat([bt, bt2], axis=0).round(3)\n", + "bts.index = [\"Model 1\", \"Model 2\"]\n", + "bts" + ] + }, + { + "cell_type": "markdown", + "id": "a451393c-35a3-4af9-81e6-48e197e74b9e", + "metadata": {}, + "source": [ + "Stable coverage over time for both models, but consistently lower interval widths for Model 2 -> we can clearly say that Model 2 is the winner (through **lower uncertainty**)." + ] + }, + { + "cell_type": "markdown", + "id": "49feed57-19b9-42d2-bb88-201c56034e96", + "metadata": {}, + "source": [ + "### Example 2: Multi-horizon forecasts\n", + "\n", + "Multi-horizon forecasts are supported out of the box. Simply set `n>1` (or `forecast_horizon`), and the model generates calibrated prediction intervals for each step." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9816887f-095e-44d8-afd2-67ced7698a37", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5581bbe9a69240718e7e746c28ccbb5f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/696 [00:00" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_horizon = 24\n", + "pred = cp_model.predict(n=multi_horizon, series=cal, **pred_kwargs)\n", + "\n", + "# plot\n", + "ax = series[pred.start_time() - 7 * 24 * series.freq : pred.end_time()].plot(\n", + " label=\"actual\"\n", + ")\n", + "pred.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "9970b109-f4c7-4784-999c-a47af6c23d3c", + "metadata": {}, + "source": [ + "Oh, why do we have such large intervals now? It's because we used Model 1 (the worse one) that was trained to predict only the next hour. Then under the hood we perform auto-regression to generate the 24-hour forecasts on the calibration set. Consequently, this results in larger errors / non-conformity scores the further ahead we predict, and ultimately in higher model uncertainty.\n", + "\n", + "We can perform much better if we use a base-forecaster that was trained on predicting the next 24 hours directly:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "db681fd0-5cca-435a-b4bb-72d1cb97aa7a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6b1cccc2e3bd441382af4022099b735e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_horizon = 24\n", + "\n", + "model = LinearRegressionModel(lags=input_length, output_chunk_length=multi_horizon).fit(\n", + " train\n", + ")\n", + "cp_model = ConformalNaiveModel(model=model, quantiles=quantiles, cal_length=four_weeks)\n", + "\n", + "pred = cp_model.predict(n=multi_horizon, series=cal, **pred_kwargs)\n", + "\n", + "# plot\n", + "ax = series[pred.start_time() - 7 * 24 * series.freq : pred.end_time()].plot(\n", + " label=\"actual\"\n", + ")\n", + "pred.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "3138c737-2b42-48c9-812a-05fd0c42b963", + "metadata": {}, + "source": [ + "### Example 3: Multi-horizon Forecasts on a Scheduled Basis with valid Coverage\n", + "\n", + "But what if we want to apply multi-horizon forecasts on a scheduled basis?\n", + "\n", + "E.g. we want to make a one-day (24 hour) forecast every 24 hours.\n", + "\n", + "By default, the calibration set considers all possible historical forecasts on the calibration set (`cal_stride=1`).\n", + "This would use examples generated outside our 24-hour schedule, and might lead to invalid coverages.\n", + "\n", + "Setting `cal_stride=24` will extract the correct examples." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f616f864-2ab8-4d82-8a0e-90da8d43d640", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "03f79ddd66f84399aaa02edd0f89429a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IntervalCoverageWidth
00.90.9022834772.75975
\n", + "" + ], + "text/plain": [ + " Interval Coverage Width\n", + "0 0.9 0.902283 4772.75975" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# conformal model\n", + "cp_model = ConformalNaiveModel(\n", + " model=model,\n", + " quantiles=quantiles,\n", + " cal_length=100,\n", + " cal_stride=multi_horizon, # stride for calibration set\n", + ")\n", + "\n", + "hfcs = cp_model.historical_forecasts(\n", + " series=cal_test,\n", + " forecast_horizon=multi_horizon,\n", + " start=test.start_time(),\n", + " last_points_only=False, # return each multi-horizon forecast\n", + " stride=multi_horizon, # use the same stride for historical forecasts\n", + " **pred_kwargs,\n", + ")\n", + "\n", + "# concatenate the forecasts into a single TimeSeries\n", + "hfcs_concat = concatenate(hfcs, axis=0)\n", + "plot_historical_forecasts(hfcs_concat)\n", + "\n", + "bt = cp_model.backtest(\n", + " cal_test,\n", + " historical_forecasts=hfcs,\n", + " last_points_only=False,\n", + " metric=[metrics.mic, metrics.miw],\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + ")\n", + "pd.DataFrame({\"Interval\": q_range, \"Coverage\": bt[0], \"Width\": bt[1]})" + ] + }, + { + "cell_type": "markdown", + "id": "bfa1fa34-aa8e-433d-8998-612daceb22b8", + "metadata": {}, + "source": [ + "Great, we also achieve valid coverage when applying our model only once per day.\n", + "\n", + "Since we have multi-horizon forecasts, it's also important to check the coverage and width for each step in the horizon:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "db5a32f3-0a21-4be3-b23b-09647432f921", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_hfc_horizon_metric(metric=metrics.ic):\n", + " # computes the metric per historical forecast, horizon and component with\n", + " # shape `(n forecasts, horizon, n components, 1)`\n", + " residuals = cp_model.residuals(\n", + " cal_test,\n", + " historical_forecasts=hfcs,\n", + " last_points_only=False,\n", + " metric=metric,\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + " values_only=True,\n", + " )\n", + " # create array and drop component and sample axes\n", + " residuals = np.array(residuals)[:, :, 0, 0]\n", + "\n", + " # compute the mean over all forecasts (365 1-day forecasts) for each horizon\n", + " return np.mean(residuals, axis=0)\n", + "\n", + "\n", + "covs_horizon = compute_hfc_horizon_metric(metrics.ic)\n", + "widths_horizon = compute_hfc_horizon_metric(metrics.iw)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "699c9790-2fb2-445e-8983-0a3174ff23c5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 8.6), sharex=True)\n", + "\n", + "horizons = [i + 1 for i in range(24)]\n", + "ax1.plot(horizons, covs_horizon)\n", + "ax2.plot(horizons, widths_horizon)\n", + "\n", + "ax1.set_ylabel(\"coverage ratio [-]\")\n", + "ax1.set_title(\"Interval coverage per step in horizon\")\n", + "\n", + "ax2.set_xlabel(\"horizon\")\n", + "ax2.set_ylabel(\"width [kWh]\")\n", + "ax2.set_title(\"Interval width per step in horizon\");" + ] + }, + { + "cell_type": "markdown", + "id": "785c893b-ae78-48f4-982a-46ed0e5df748", + "metadata": {}, + "source": [ + "The coverages are valid for all steps in the horizon and range between 89% and 92%.\n", + "\n", + "In general, the widths increase with higher horizon. After horizon 16 they drop again, due to the nature of the target series (low Electricity consumption during the night -> lower uncertainty.)" + ] + }, + { + "cell_type": "markdown", + "id": "b6563158-d607-4991-bec9-bbadc2a69326", + "metadata": {}, + "source": [ + "### Example 4: Conformalized Quantile Regression\n", + "\n", + "Finally, let's check out an example of our `ConformalQRModel`. The API is exactly the same. \n", + "\n", + "The only difference is that it requires a **probabilistic** base forecaster.\n", + "\n", + "Let's use a linear model with quantile regression and perform the same single step forecast as in example 1." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "59a7d058-241b-4fe3-87d1-baf77b3638a0", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ec085a67dc854b55a80d5ab3f9256734", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "historical forecasts: 0%| | 0/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IntervalCoverageWidth
00.90.900241770.154514
\n", + "" + ], + "text/plain": [ + " Interval Coverage Width\n", + "0 0.9 0.90024 1770.154514" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# probabilistic regression model (with quantiles)\n", + "model = LinearRegressionModel(\n", + " lags=input_length,\n", + " output_chunk_length=horizon,\n", + " likelihood=\"quantile\",\n", + " quantiles=quantiles,\n", + ").fit(train)\n", + "\n", + "# conformalized quantile regression model\n", + "cp_model = ConformalQRModel(model=model, quantiles=quantiles, cal_length=four_weeks)\n", + "hfcs = cp_model.historical_forecasts(\n", + " series=cal_test,\n", + " forecast_horizon=horizon,\n", + " start=test.start_time(),\n", + " last_points_only=True,\n", + " stride=horizon,\n", + " **pred_kwargs,\n", + ")\n", + "plot_historical_forecasts(hfcs)\n", + "\n", + "bt = cp_model.backtest(\n", + " cal_test,\n", + " historical_forecasts=hfcs,\n", + " last_points_only=True,\n", + " metric=[metrics.mic, metrics.miw],\n", + " metric_kwargs={\"q_interval\": q_interval},\n", + ")\n", + "pd.DataFrame({\"Interval\": q_range, \"Coverage\": bt[0], \"Width\": bt[1]})" + ] + }, + { + "cell_type": "markdown", + "id": "98998cdf-3c8e-48d6-86e0-b0ad908a988f", + "metadata": {}, + "source": [ + "Same coverage, but slightly larger intervals than in the naive conformal prediction case." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/__init__.py b/examples/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/static/images/ad_4_sub_modules.png b/examples/static/images/ad_4_sub_modules.png new file mode 100644 index 0000000000..d306e72e4b Binary files /dev/null and b/examples/static/images/ad_4_sub_modules.png differ diff --git a/examples/static/images/ad_inside_anomaly_model.png b/examples/static/images/ad_inside_anomaly_model.png new file mode 100644 index 0000000000..4acb62715c Binary files /dev/null and b/examples/static/images/ad_inside_anomaly_model.png differ diff --git a/examples/static/images/ad_windowing.png b/examples/static/images/ad_windowing.png new file mode 100644 index 0000000000..37315109bf Binary files /dev/null and b/examples/static/images/ad_windowing.png differ diff --git a/examples/static/images/covariates-highlevel.png b/examples/static/images/covariates-highlevel.png index b5a4013c55..c256218ef5 100644 Binary files a/examples/static/images/covariates-highlevel.png and b/examples/static/images/covariates-highlevel.png differ diff --git a/examples/utils/__init__.py b/examples/utils/__init__.py index e512746e94..203c98e715 100644 --- a/examples/utils/__init__.py +++ b/examples/utils/__init__.py @@ -1 +1,3 @@ from .utils import fix_pythonpath_if_working_locally + +__all__ = ["fix_pythonpath_if_working_locally"] diff --git a/gradle/wrapper/gradle-wrapper.jar b/gradle/wrapper/gradle-wrapper.jar deleted file mode 100644 index 62d4c05355..0000000000 Binary files a/gradle/wrapper/gradle-wrapper.jar and /dev/null differ diff --git a/gradle/wrapper/gradle-wrapper.properties b/gradle/wrapper/gradle-wrapper.properties deleted file mode 100644 index e750102e09..0000000000 --- a/gradle/wrapper/gradle-wrapper.properties +++ /dev/null @@ -1,5 +0,0 @@ -distributionBase=GRADLE_USER_HOME -distributionPath=wrapper/dists -distributionUrl=https\://services.gradle.org/distributions/gradle-7.3-bin.zip -zipStoreBase=GRADLE_USER_HOME -zipStorePath=wrapper/dists diff --git a/gradlew b/gradlew deleted file mode 100755 index fbd7c51583..0000000000 --- a/gradlew +++ /dev/null @@ -1,185 +0,0 @@ -#!/usr/bin/env sh - -# -# Copyright 2015 the original author or authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -############################################################################## -## -## Gradle start up script for UN*X -## -############################################################################## - -# Attempt to set APP_HOME -# Resolve links: $0 may be a link -PRG="$0" -# Need this for relative symlinks. -while [ -h "$PRG" ] ; do - ls=`ls -ld "$PRG"` - link=`expr "$ls" : '.*-> \(.*\)$'` - if expr "$link" : '/.*' > /dev/null; then - PRG="$link" - else - PRG=`dirname "$PRG"`"/$link" - fi -done -SAVED="`pwd`" -cd "`dirname \"$PRG\"`/" >/dev/null -APP_HOME="`pwd -P`" -cd "$SAVED" >/dev/null - -APP_NAME="Gradle" -APP_BASE_NAME=`basename "$0"` - -# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. -DEFAULT_JVM_OPTS='"-Xmx64m" "-Xms64m"' - -# Use the maximum available, or set MAX_FD != -1 to use that value. -MAX_FD="maximum" - -warn () { - echo "$*" -} - -die () { - echo - echo "$*" - echo - exit 1 -} - -# OS specific support (must be 'true' or 'false'). -cygwin=false -msys=false -darwin=false -nonstop=false -case "`uname`" in - CYGWIN* ) - cygwin=true - ;; - Darwin* ) - darwin=true - ;; - MINGW* ) - msys=true - ;; - NONSTOP* ) - nonstop=true - ;; -esac - -CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar - - -# Determine the Java command to use to start the JVM. -if [ -n "$JAVA_HOME" ] ; then - if [ -x "$JAVA_HOME/jre/sh/java" ] ; then - # IBM's JDK on AIX uses strange locations for the executables - JAVACMD="$JAVA_HOME/jre/sh/java" - else - JAVACMD="$JAVA_HOME/bin/java" - fi - if [ ! -x "$JAVACMD" ] ; then - die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME - -Please set the JAVA_HOME variable in your environment to match the -location of your Java installation." - fi -else - JAVACMD="java" - which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. - -Please set the JAVA_HOME variable in your environment to match the -location of your Java installation." -fi - -# Increase the maximum file descriptors if we can. -if [ "$cygwin" = "false" -a "$darwin" = "false" -a "$nonstop" = "false" ] ; then - MAX_FD_LIMIT=`ulimit -H -n` - if [ $? -eq 0 ] ; then - if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then - MAX_FD="$MAX_FD_LIMIT" - fi - ulimit -n $MAX_FD - if [ $? -ne 0 ] ; then - warn "Could not set maximum file descriptor limit: $MAX_FD" - fi - else - warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT" - fi -fi - -# For Darwin, add options to specify how the application appears in the dock -if $darwin; then - GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\"" -fi - -# For Cygwin or MSYS, switch paths to Windows format before running java -if [ "$cygwin" = "true" -o "$msys" = "true" ] ; then - APP_HOME=`cygpath --path --mixed "$APP_HOME"` - CLASSPATH=`cygpath --path --mixed "$CLASSPATH"` - - JAVACMD=`cygpath --unix "$JAVACMD"` - - # We build the pattern for arguments to be converted via cygpath - ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null` - SEP="" - for dir in $ROOTDIRSRAW ; do - ROOTDIRS="$ROOTDIRS$SEP$dir" - SEP="|" - done - OURCYGPATTERN="(^($ROOTDIRS))" - # Add a user-defined pattern to the cygpath arguments - if [ "$GRADLE_CYGPATTERN" != "" ] ; then - OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)" - fi - # Now convert the arguments - kludge to limit ourselves to /bin/sh - i=0 - for arg in "$@" ; do - CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -` - CHECK2=`echo "$arg"|egrep -c "^-"` ### Determine if an option - - if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then ### Added a condition - eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"` - else - eval `echo args$i`="\"$arg\"" - fi - i=`expr $i + 1` - done - case $i in - 0) set -- ;; - 1) set -- "$args0" ;; - 2) set -- "$args0" "$args1" ;; - 3) set -- "$args0" "$args1" "$args2" ;; - 4) set -- "$args0" "$args1" "$args2" "$args3" ;; - 5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;; - 6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;; - 7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;; - 8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;; - 9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;; - esac -fi - -# Escape application args -save () { - for i do printf %s\\n "$i" | sed "s/'/'\\\\''/g;1s/^/'/;\$s/\$/' \\\\/" ; done - echo " " -} -APP_ARGS=`save "$@"` - -# Collect all arguments for the java command, following the shell quoting and substitution rules -eval set -- $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS "\"-Dorg.gradle.appname=$APP_BASE_NAME\"" -classpath "\"$CLASSPATH\"" org.gradle.wrapper.GradleWrapperMain "$APP_ARGS" - -exec "$JAVACMD" "$@" diff --git a/gradlew.bat b/gradlew.bat deleted file mode 100644 index 5093609d51..0000000000 --- a/gradlew.bat +++ /dev/null @@ -1,104 +0,0 @@ -@rem -@rem Copyright 2015 the original author or authors. -@rem -@rem Licensed under the Apache License, Version 2.0 (the "License"); -@rem you may not use this file except in compliance with the License. -@rem You may obtain a copy of the License at -@rem -@rem https://www.apache.org/licenses/LICENSE-2.0 -@rem -@rem Unless required by applicable law or agreed to in writing, software -@rem distributed under the License is distributed on an "AS IS" BASIS, -@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -@rem See the License for the specific language governing permissions and -@rem limitations under the License. -@rem - -@if "%DEBUG%" == "" @echo off -@rem ########################################################################## -@rem -@rem Gradle startup script for Windows -@rem -@rem ########################################################################## - -@rem Set local scope for the variables with windows NT shell -if "%OS%"=="Windows_NT" setlocal - -set DIRNAME=%~dp0 -if "%DIRNAME%" == "" set DIRNAME=. -set APP_BASE_NAME=%~n0 -set APP_HOME=%DIRNAME% - -@rem Resolve any "." and ".." in APP_HOME to make it shorter. -for %%i in ("%APP_HOME%") do set APP_HOME=%%~fi - -@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script. -set DEFAULT_JVM_OPTS="-Xmx64m" "-Xms64m" - -@rem Find java.exe -if defined JAVA_HOME goto findJavaFromJavaHome - -set JAVA_EXE=java.exe -%JAVA_EXE% -version >NUL 2>&1 -if "%ERRORLEVEL%" == "0" goto init - -echo. -echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH. -echo. -echo Please set the JAVA_HOME variable in your environment to match the -echo location of your Java installation. - -goto fail - -:findJavaFromJavaHome -set JAVA_HOME=%JAVA_HOME:"=% -set JAVA_EXE=%JAVA_HOME%/bin/java.exe - -if exist "%JAVA_EXE%" goto init - -echo. -echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME% -echo. -echo Please set the JAVA_HOME variable in your environment to match the -echo location of your Java installation. - -goto fail - -:init -@rem Get command-line arguments, handling Windows variants - -if not "%OS%" == "Windows_NT" goto win9xME_args - -:win9xME_args -@rem Slurp the command line arguments. -set CMD_LINE_ARGS= -set _SKIP=2 - -:win9xME_args_slurp -if "x%~1" == "x" goto execute - -set CMD_LINE_ARGS=%* - -:execute -@rem Setup the command line - -set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar - - -@rem Execute Gradle -"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS% - -:end -@rem End local scope for the variables with windows NT shell -if "%ERRORLEVEL%"=="0" goto mainEnd - -:fail -rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of -rem the _cmd.exe /c_ return code! -if not "" == "%GRADLE_EXIT_CONSOLE%" exit 1 -exit /b 1 - -:mainEnd -if "%OS%"=="Windows_NT" endlocal - -:omega diff --git a/pyproject.toml b/pyproject.toml index ffb95ed3a0..112b16fb8d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,46 @@ build-backend = "setuptools.build_meta" [tool.pytest.ini_options] addopts = [ "--strict-markers", + "--color=yes" ] markers = [ "slow: marks tests as slow (deselect with `-m 'not slow'`)", -] \ No newline at end of file +] + + +[tool.ruff] +target-version = "py39" +line-length = 88 + +[tool.ruff.format] +preview = true + +[tool.ruff.lint] +select = [ + "E", + "W", # see: https://pypi.org/project/pycodestyle + "F", # see: https://pypi.org/project/pyflakes + "I", #see: https://pypi.org/project/isort/ + "UP", # see: https://docs.astral.sh/ruff/rules/#pyupgrade-up +# "D", # see: https://pypi.org/project/pydocstyle +] +ignore = [ + "E203", + "E402", # todo: use noqa per line + "E731", # Do not assign a `lambda` expression, use a `def` +] +unfixable = ["F401"] + +[tool.ruff.lint.pydocstyle] +# Use Google-style docstrings. +convention = "google" + +#[tool.ruff.pycodestyle] +#ignore-overlong-task-comments = true + +[tool.ruff.lint.mccabe] +# Unlike Flake8, default to a complexity level of 10. +max-complexity = 10 + +[tool.ruff.lint.pycodestyle] +max-line-length = 120 # E501 reports lines that exceed the length of 100. diff --git a/requirements/core.txt b/requirements/core.txt index c88794026a..0245c46194 100644 --- a/requirements/core.txt +++ b/requirements/core.txt @@ -2,9 +2,8 @@ holidays>=0.11.1 joblib>=0.16.0 matplotlib>=3.3.0 nfoursid>=1.0.0 -numpy>=1.19.0 -pandas>=1.0.5,<2.0.0; python_version < "3.9" -pandas>=1.0.5; python_version >= "3.9" +numpy>=1.19.0,<2.0.0 +pandas>=1.0.5 pmdarima>=1.8.0 pyod>=0.9.5 requests>=2.22.0 diff --git a/requirements/dev.txt b/requirements/dev.txt index 1894e4f4f8..f6b5c64736 100644 --- a/requirements/dev.txt +++ b/requirements/dev.txt @@ -1,7 +1,3 @@ -black[jupyter]==22.3.0 -flake8==4.0.1 -isort==5.11.5 pre-commit pytest-cov -pyupgrade==2.31.0 testfixtures diff --git a/requirements/release.txt b/requirements/release.txt index 5571b3c1b7..fa071632d3 100644 --- a/requirements/release.txt +++ b/requirements/release.txt @@ -1,11 +1,12 @@ bump2version==1.0.1 docutils==0.17.1 -ipython==8.10.0 -ipykernel==5.3.4 -ipywidgets==7.5.1 -jupyterlab==4.0.11 +ipython==8.18.1 +ipykernel==6.29.5 +ipywidgets==8.1.5 +jupyterlab==4.2.5 ipython_genutils==0.2.0 -jinja2==3.1.3 +jinja2==3.1.5 +lxml_html_clean==0.4.0 m2r2==0.3.2 nbsphinx==0.8.7 numpydoc==1.1.0 @@ -16,3 +17,4 @@ sphinx==5.0.0 sphinx-automodapi==0.14.0 sphinx_autodoc_typehints==1.12.0 twine==3.3.0 +tenacity<=8.3.0 diff --git a/requirements/torch.txt b/requirements/torch.txt index b38e319e03..617ef86948 100644 --- a/requirements/torch.txt +++ b/requirements/torch.txt @@ -1,3 +1,3 @@ -pytorch-lightning>=1.5.0,<=2.1.2 +pytorch-lightning>=1.5.0 tensorboardX>=2.1 torch>=1.8.0 diff --git a/settings.gradle b/settings.gradle deleted file mode 100644 index 7001fe30f3..0000000000 --- a/settings.gradle +++ /dev/null @@ -1 +0,0 @@ -rootProject.name = 'darts' diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index a0a67764d2..0000000000 --- a/setup.cfg +++ /dev/null @@ -1,11 +0,0 @@ -[flake8] -exclude = - .git, - __pycache__, - .pytest_cache, - __init__.py -max-line-length = 120 -extend-ignore = E203 - -[isort] -profile = black diff --git a/setup.py b/setup.py index aefd06f660..5737f98fef 100644 --- a/setup.py +++ b/setup.py @@ -30,7 +30,7 @@ def read_requirements(path): setup( name="darts", - version="0.27.2", + version="0.33.0", description="A python library for easy manipulation and forecasting of time series.", long_description=LONG_DESCRIPTION, long_description_content_type="text/markdown", @@ -45,7 +45,7 @@ def read_requirements(path): "darts": ["py.typed"], }, zip_safe=False, - python_requires=">=3.8", + python_requires=">=3.9", classifiers=[ "Intended Audience :: Science/Research", "Intended Audience :: Developers", @@ -57,9 +57,9 @@ def read_requirements(path): "Operating System :: Unix", "Operating System :: MacOS", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", "Programming Language :: Python :: Implementation :: PyPy", ], keywords="time series forecasting", diff --git a/setup_u8darts.py b/setup_u8darts.py index 2b1ef21104..d6ea42ee5d 100644 --- a/setup_u8darts.py +++ b/setup_u8darts.py @@ -29,7 +29,7 @@ def read_requirements(path): setup( name="u8darts", - version="0.27.2", + version="0.33.0", description="A python library for easy manipulation and forecasting of time series.", long_description=LONG_DESCRIPTION, long_description_content_type="text/markdown", @@ -45,7 +45,7 @@ def read_requirements(path): "darts": ["py.typed"], }, zip_safe=False, - python_requires=">=3.8", + python_requires=">=3.9", classifiers=[ "Intended Audience :: Science/Research", "Intended Audience :: Developers", @@ -57,9 +57,9 @@ def read_requirements(path): "Operating System :: Unix", "Operating System :: MacOS", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", "Programming Language :: Python :: Implementation :: PyPy", ], keywords="time series forecasting", diff --git a/static/images/ad_4_sub_modules.png b/static/images/ad_4_sub_modules.png new file mode 100644 index 0000000000..2f0518afff Binary files /dev/null and b/static/images/ad_4_sub_modules.png differ diff --git a/static/images/ad_inside_anomaly_model.png b/static/images/ad_inside_anomaly_model.png new file mode 100644 index 0000000000..b4260d430d Binary files /dev/null and b/static/images/ad_inside_anomaly_model.png differ diff --git a/static/images/ad_windowing.png b/static/images/ad_windowing.png new file mode 100644 index 0000000000..7d1b8b977e Binary files /dev/null and b/static/images/ad_windowing.png differ