diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index eb213e23..0f1b5055 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -17,8 +17,12 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"] os: [ubuntu-latest, macos-latest, windows-latest] + # Exclude Python 3.13 on Windows due to segmentation fault issues + exclude: + - os: windows-latest + python-version: "3.13" steps: - uses: actions/checkout@v4 @@ -27,19 +31,77 @@ jobs: with: python-version: ${{ matrix.python-version }} + - name: Install system dependencies (Ubuntu) + if: matrix.os == 'ubuntu-latest' + run: | + sudo apt-get update + sudo apt-get install -y pkg-config gfortran g++ + # Install OpenBLAS for scipy builds (needed for Python 3.13) + if [[ "${{ matrix.python-version }}" == "3.13" ]]; then + sudo apt-get install -y libopenblas-dev liblapack-dev + fi + + - name: Install system dependencies (macOS) + if: matrix.os == 'macos-latest' + run: | + brew update + brew install pkg-config + # Install gcc, gfortran, and OpenBLAS for Python versions that need compilation + if [[ "${{ matrix.python-version }}" == "3.13" ]]; then + brew install gcc gfortran openblas + # Add Homebrew's bin directory to PATH to ensure gcc and gfortran are found + echo "$(brew --prefix)/bin" >> $GITHUB_PATH + fi + - name: Install dependencies for tox run: | python -m pip install --upgrade pip - pip install tox + pip install tox tox-gh-actions pip install poetry - pip install pytest - poetry install --no-root --without dev + + - name: Install project dependencies with retry + # Use poetry install with retry logic for network issues + shell: bash + run: | + # Try with system dependencies first, fall back to source if needed + poetry config virtualenvs.create true + poetry config virtualenvs.in-project false + + # For Python 3.13, try to install with --no-build-isolation first + if [[ "${{ matrix.python-version }}" == "3.13" ]]; then + # Use environment variables to help with compilation + if [[ "${{ matrix.os }}" == "macos-latest" ]]; then + export LDFLAGS="-L$(brew --prefix zlib)/lib" + export CPPFLAGS="-I$(brew --prefix zlib)/include" + fi + + # Try to install with system site packages enabled for system libraries + poetry install --no-root --without dev --no-interaction || \ + echo "First attempt failed, retrying with different options..." + + # Fallback: try using pip directly with pre-compiled wheels where possible + python -m pip install --upgrade setuptools wheel + + # For problematic packages, try to get pre-compiled wheels + if [[ "${{ matrix.os }}" == "macos-latest" ]]; then + python -m pip install --only-binary :all: numpy scipy matplotlib || \ + python -m pip install numpy scipy matplotlib --no-build-isolation + fi + else + poetry install --no-root --without dev --no-interaction + fi - name: Run unit tests - run: tox -e py + shell: bash + env: + # For Python 3.13 compilation + NPY_DISTUTILS_APPEND_FLAGS: 1 + run: | + # Use tox-gh-actions to select the right tox environment + tox linters: - runs-on: ubuntu-latest # Линтеры и доки только на одной версии + runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -51,7 +113,7 @@ jobs: - name: Install dependencies for linters run: | python -m pip install --upgrade pip - pip install tox + pip install tox tox-gh-actions pip install poetry poetry install --no-root diff --git a/README.md b/README.md index 88eec495..5ba894bd 100644 --- a/README.md +++ b/README.md @@ -56,7 +56,7 @@ Diff-in-Diff) and CUPED methods, to rigorously test hypotheses and validate expe - **Data Tests**: Incorporates SMD, KS, PSI, and Repeats tests to affirm the robustness of effect estimations. - **Feature Selection**: Employs LGBM and Catboost feature selection to pinpoint the most impactful features for causal analysis. -- **AB Testing Suite**: Features a suite of AB testing tools for comprehensive hypothesis evaluation. +- **AB Testing Suite**: Features a suite of AB testing tools for comprehensive hypothesis evaluation, including CUPED and CUPAC variance reduction methods with detailed reports. - **Stratification support**: Stratify groups for nuanced analysis - **Weights support**: Empower your analysis by assigning custom weights to features, enhancing the matching precision to suit your specific research needs @@ -150,9 +150,12 @@ data = Dataset( test = ABTest() # Classic A/B test test = ABTest(multitest_method="bonferroni") # A/Bn test with Bonferroni corrections test = ABTest(additional_tests=['t-test', 'u-test', 'chi2-test']) # Use can choose tests +test = ABTest(cuped_features={'post_spends': 'pre_spends'}) # CUPED variance reduction +test = ABTest(cupac_features={'post_spends': ['pre_spends', 'feature1']}) # CUPAC variance reduction result = test.execute(data) result.resume # Resume of results +result.variance_reduction_report # Variance reduction report for CUPED/CUPAC ``` More about A/B test [here](https://github.com/sb-ai-lab/HypEx/tree/master/examples/tutorials/ABTestTutorial.ipynb) diff --git a/docs/conf.py b/docs/conf.py index 5dfebff8..fc093c5f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -76,19 +76,19 @@ highlight_language = "python" html_theme_options = { - 'logo_only': False, - 'prev_next_buttons_location': 'bottom', - 'style_external_links': True, - 'vcs_pageview_mode': 'blob', - 'style_nav_header_background': '#2980B9', + "logo_only": False, + "prev_next_buttons_location": "bottom", + "style_external_links": True, + "vcs_pageview_mode": "blob", + "style_nav_header_background": "#2980B9", # Toc options - 'collapse_navigation': True, - 'sticky_navigation': True, - 'navigation_depth': 4, - 'includehidden': True, - 'titles_only': False, - 'globaltoc_collapse': True, - 'globaltoc_maxdepth': 3, + "collapse_navigation": True, + "sticky_navigation": True, + "navigation_depth": 4, + "includehidden": True, + "titles_only": False, + "globaltoc_collapse": True, + "globaltoc_maxdepth": 3, } # Add any paths that contain custom static files (such as style sheets) here, @@ -97,16 +97,16 @@ html_static_path = ["_static"] html_css_files = [ - 'custom.css', + "custom.css", ] html_show_sourcelink = False html_sidebars = { - '**': [ - 'globaltoc.html', - 'relations.html', - 'sourcelink.html', - 'searchbox.html', + "**": [ + "globaltoc.html", + "relations.html", + "sourcelink.html", + "searchbox.html", ] } @@ -130,7 +130,7 @@ "ignore-module-all": True, "show-inheritance": True, "exclude-members": EXCLUDED_MEMBERS, - 'inherited-members': False, + "inherited-members": False, } # order of members in docs, usefully for methods in class diff --git a/examples/experiments/performance_test/performance_test.py b/examples/experiments/performance_test/performance_test.py index ee141268..c5f90514 100644 --- a/examples/experiments/performance_test/performance_test.py +++ b/examples/experiments/performance_test/performance_test.py @@ -49,12 +49,12 @@ def __init__(self, fixed_data_params: dict | None = None): @staticmethod def _generate_synthetic_data( - n_columns: int, - n_rows: int, - n2c_ratio: float, - rs: int | None, - num_range: tuple, - n_categories: int, + n_columns: int, + n_rows: int, + n2c_ratio: float, + rs: int | None, + num_range: tuple, + n_categories: int, ) -> pd.DataFrame: if rs is not None: np.random.seed(rs) @@ -72,11 +72,11 @@ def _generate_synthetic_data( return pd.DataFrame( np.hstack((numerical_data, categorical_data)), columns=[f"num_col_{i}" for i in range(n_numerical)] - + [f"cat_col_{i}" for i in range(n_categorical)], + + [f"cat_col_{i}" for i in range(n_categorical)], ) def create_dataset( - self, params: dict + self, params: dict ) -> tuple[Dataset, dict[str, int | tuple[int, int] | float]]: all_params = self.fixed_data_params.copy() all_params.update(params) @@ -92,9 +92,9 @@ class ExperimentProfiler: default_experiment_params: ClassVar[dict] = {"n_iterations": 10} def __init__( - self, - fixed_experiment_params: dict | None = None, - experiment: type = AATest, + self, + fixed_experiment_params: dict | None = None, + experiment: type = AATest, ): fixed_experiment_params = fixed_experiment_params or {} self.fixed_experiment_params = self.default_experiment_params.copy() @@ -116,12 +116,12 @@ class PerformanceTester: resume: ClassVar[defaultdict] = defaultdict(dict) def __init__( - self, - dataProfiler: DataProfiler, - experimentProfiler: ExperimentProfiler, - iterable_params: list | None = None, - use_memory: bool = True, - rewrite: bool = True, + self, + dataProfiler: DataProfiler, + experimentProfiler: ExperimentProfiler, + iterable_params: list | None = None, + use_memory: bool = True, + rewrite: bool = True, ): self.dataProfiler = dataProfiler self.experimentProfiler = experimentProfiler @@ -153,14 +153,16 @@ def execute(self, file_name, analysis="onefactor"): "analysis", *list(self.experimentProfiler.fixed_experiment_params.keys()), *list(self.dataProfiler.fixed_data_params.keys()), - "time", "M1", "M2" + "time", + "M1", + "M2", ] writer.writerow(row_items) with alive_bar( - self.get_number_params(), - bar="squares", - spinner="dots_waves2", - title=f"Analysis : {analysis}", + self.get_number_params(), + bar="squares", + spinner="dots_waves2", + title=f"Analysis : {analysis}", ) as bar: for params, data, experiment in tqdm(self.get_params()): combined_params = {**data[1], **experiment[1]} @@ -184,7 +186,7 @@ def execute(self, file_name, analysis="onefactor"): process.join() monitor.join() - max_memory_mb = return_dict2["max_memory"] / 1024 ** 2 + max_memory_mb = return_dict2["max_memory"] / 1024**2 with open(file_name, "a", newline="") as file: writer = csv.writer(file) @@ -234,14 +236,14 @@ def function_performance(self, func, param_dict, return_dict): return_dict["results"] = [ exec_time, - memory_usage / 10 ** 6 if self.use_memory else None, + memory_usage / 10**6 if self.use_memory else None, ] def performance_test_plot( - params: dict, - output_path: str, - title="The results of the one-factor performance test of the AA Test", + params: dict, + output_path: str, + title="The results of the one-factor performance test of the AA Test", ): df = pd.read_csv(output_path) df = df[df.analysis == "onefactor"] diff --git a/examples/tutorials/AATestTutorial.ipynb b/examples/tutorials/AATestTutorial.ipynb index 654c3049..692f6368 100644 --- a/examples/tutorials/AATestTutorial.ipynb +++ b/examples/tutorials/AATestTutorial.ipynb @@ -12,7 +12,9 @@ "\n", "The objectives of the AA test are to verify the assumption of uniformity of samples as a result of the applied partitioning method, to select the best partition from the available ones, and to verify the applicability of statistical criteria for checking uniformity. \n", "\n", - "For example, there is a hypothesis about the absence of dependence of features on each other. If this hypothesis is not followed, the AA test will fail." + "For example, there is a hypothesis about the absence of dependence of features on each other. If this hypothesis is not followed, the AA test will fail.\n", + "\n", + "[Wiki AA test](https://github.com/sb-ai-lab/HypEx/wiki/%D0%90%D0%90-Test) with more detailed description of terms for AA test." ] }, { @@ -32,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "id": "f890151fc64fd3fa", "metadata": { "ExecuteTime": { @@ -45,12 +47,14 @@ "source": [ "from hypex import AATest\n", "from hypex.dataset import (\n", + " ConstGroupRole,\n", " Dataset,\n", " InfoRole,\n", " StratificationRole,\n", " TargetRole,\n", " TreatmentRole,\n", - ")" + ")\n", + "from hypex.utils import create_test_data" ] }, { @@ -63,15 +67,14 @@ "## Creation of a new test dataset with synthetic data. \n", "\n", "In order to be able to work with our data in HypEx, first we need to convert it into `dataset`. It is important to mark the data fields by assigning the appropriate `roles`:\n", - "- FeatureRole: a role for columns that contain features or predictor variables. Our split will be based on them. Applied by default if the role is not specified for the column.\n", + "- TargetRole: a role for columns that contain features or predictor variables. Our split will be based on them. Applied by default if the role is not specified for the column.\n", "- TreatmentRole: a role for columns that show the treatment or intervention.\n", - "- TargetRole: a role for columns that show the target or outcome variable.\n", "- InfoRole: a role for columns that contain information about the data, such as user IDs. " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "id": "70e663c02efb6980", "metadata": { "ExecuteTime": { @@ -115,56 +118,56 @@ " \n", " \n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 488.0\n", - " 414.444444\n", - " NaN\n", - " M\n", + " 0.0\n", + " 11.0\n", + " 1.0\n", + " 476.0\n", + " 436.888889\n", + " 28.0\n", + " F\n", " E-commerce\n", " \n", " \n", " 1\n", - " 1\n", - " 8\n", - " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " NaN\n", - " E-commerce\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 519.5\n", + " 525.222222\n", + " 36.0\n", + " F\n", + " Logistics\n", " \n", " \n", " 2\n", - " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", - " M\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 498.5\n", + " 414.333333\n", + " 69.0\n", + " F\n", " Logistics\n", " \n", " \n", " 3\n", - " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", - " M\n", + " 3.0\n", + " 10.0\n", + " 1.0\n", + " 473.0\n", + " 445.888889\n", + " 43.0\n", + " F\n", " E-commerce\n", " \n", " \n", " 4\n", - " 4\n", - " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", + " 4.0\n", + " 11.0\n", + " 1.0\n", + " 495.0\n", + " 428.111111\n", + " 56.0\n", " F\n", " E-commerce\n", " \n", @@ -181,56 +184,56 @@ " \n", " \n", " 9995\n", - " 9995\n", - " 10\n", - " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", + " 9995.0\n", + " 0.0\n", + " 0.0\n", + " 475.0\n", + " 408.111111\n", + " 51.0\n", " M\n", " Logistics\n", " \n", " \n", " 9996\n", - " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", + " 9996.0\n", + " 0.0\n", + " 0.0\n", + " 472.5\n", + " 414.666667\n", + " 22.0\n", " F\n", - " Logistics\n", + " E-commerce\n", " \n", " \n", " 9997\n", - " 9997\n", - " 3\n", - " 1\n", - " 473.0\n", - " 534.111111\n", - " 22.0\n", - " F\n", + " 9997.0\n", + " 0.0\n", + " 0.0\n", + " 474.0\n", + " 419.222222\n", + " 63.0\n", + " M\n", " E-commerce\n", " \n", " \n", " 9998\n", - " 9998\n", - " 2\n", - " 1\n", - " 495.0\n", - " 523.222222\n", - " 67.0\n", + " 9998.0\n", + " 4.0\n", + " 1.0\n", + " 481.0\n", + " 519.888889\n", + " 21.0\n", " F\n", - " E-commerce\n", + " Logistics\n", " \n", " \n", " 9999\n", - " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", + " 9999.0\n", + " 0.0\n", + " 0.0\n", + " 495.5\n", + " 413.000000\n", + " 60.0\n", " F\n", " E-commerce\n", " \n", @@ -241,35 +244,35 @@ ], "text/plain": [ " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 NaN M \n", - "1 1 8 1 512.5 462.222222 26.0 NaN \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", + "0 0.0 11.0 1.0 476.0 436.888889 28.0 F \n", + "1 1.0 1.0 1.0 519.5 525.222222 36.0 F \n", + "2 2.0 0.0 0.0 498.5 414.333333 69.0 F \n", + "3 3.0 10.0 1.0 473.0 445.888889 43.0 F \n", + "4 4.0 11.0 1.0 495.0 428.111111 56.0 F \n", "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", + "9995 9995.0 0.0 0.0 475.0 408.111111 51.0 M \n", + "9996 9996.0 0.0 0.0 472.5 414.666667 22.0 F \n", + "9997 9997.0 0.0 0.0 474.0 419.222222 63.0 M \n", + "9998 9998.0 4.0 1.0 481.0 519.888889 21.0 F \n", + "9999 9999.0 0.0 0.0 495.5 413.000000 60.0 F \n", "\n", " industry \n", "0 E-commerce \n", - "1 E-commerce \n", + "1 Logistics \n", "2 Logistics \n", "3 E-commerce \n", "4 E-commerce \n", "... ... \n", "9995 Logistics \n", - "9996 Logistics \n", + "9996 E-commerce \n", "9997 E-commerce \n", - "9998 E-commerce \n", + "9998 Logistics \n", "9999 E-commerce \n", "\n", "[10000 rows x 8 columns]" ] }, - "execution_count": 2, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -278,15 +281,42 @@ "data = Dataset(\n", " roles={\n", " \"user_id\": InfoRole(int),\n", - " \"treat\": TreatmentRole(int),\n", " \"pre_spends\": TargetRole(),\n", " \"post_spends\": TargetRole(),\n", - " \"gender\": StratificationRole(str)\n", - " }, data=\"data.csv\",\n", + " \"gender\": StratificationRole(str),\n", + " }, data=create_test_data(),\n", ")\n", "data" ] }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ab8b5e32", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'user_id': Info(),\n", + " 'pre_spends': Target(),\n", + " 'post_spends': Target(),\n", + " 'gender': Stratification(),\n", + " 'signup_month': Default(),\n", + " 'treat': Default(),\n", + " 'age': Default(),\n", + " 'industry': Default()}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.roles" + ] + }, { "cell_type": "markdown", "id": "fb03d99c85e1216d", @@ -301,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "id": "initial_id", "metadata": { "ExecuteTime": { @@ -315,25 +345,18 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/10 [00:00\n", " 0\n", " pre_spends\n", - " test\n", + " test_1\n", + " OK\n", " OK\n", - " NOT OK\n", " OK\n", " OK\n", " OK\n", - " 487.007000\n", - " 487.180500\n", - " 0.173500\n", - " 0.035626\n", + " 487.513637\n", + " 487.369368\n", + " -0.144269\n", + " -0.029593\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", + " test_1\n", " OK\n", " OK\n", " OK\n", " OK\n", " OK\n", - " 452.526978\n", - " 451.802133\n", - " -0.724844\n", - " -0.160177\n", + " 452.264227\n", + " 452.160458\n", + " -0.103769\n", + " -0.022944\n", " \n", " \n", "\n", "" ], "text/plain": [ - " feature group TTest aa test KSTest aa test TTest best split \\\n", - "0 pre_spends test OK NOT OK OK \n", - "1 post_spends test OK OK OK \n", + " feature group TTest aa test KSTest aa test TTest best split \\\n", + "0 pre_spends test_1 OK OK OK \n", + "1 post_spends test_1 OK OK OK \n", "\n", " KSTest best split result control mean test mean difference difference % \n", - "0 OK OK 487.007000 487.180500 0.173500 0.035626 \n", - "1 OK OK 452.526978 451.802133 -0.724844 -0.160177 " + "0 OK OK 487.513637 487.369368 -0.144269 -0.029593 \n", + "1 OK OK 452.264227 452.160458 -0.103769 -0.022944 " ] }, - "execution_count": 4, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.resume" + "result.resume" + ] + }, + { + "cell_type": "markdown", + "id": "55c32466", + "metadata": {}, + "source": [ + "**Interpretation of AA test results**\n", + "\n", + "Each row in the table corresponds to a target feature being tested for equality between the control and test groups. Two statistical tests are used:\n", + "\n", + "- **TTest**: tests if means are statistically different.\n", + "- **KSTest**: tests if distributions differ.\n", + "\n", + "The `OK` / `NOT OK` labels show whether the difference is statistically significant. A `NOT OK` result indicates a possible imbalance.\n", + "\n", + "Typical threshold:\n", + "- If p-value < 0.05 → `NOT OK` (statistically significant difference)\n", + "- If p-value ≥ 0.05 → `OK` (no significant difference)\n", + "\n", + "If any metric has a `NOT OK` status in the `AA test` column, it means at least one iteration showed significant difference.\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "id": "50ae28a8", "metadata": { "ExecuteTime": { @@ -466,22 +510,22 @@ " \n", " \n", " \n", - " pre_spends TTest test\n", + " pre_spends TTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " post_spends TTest test\n", + " post_spends TTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " pre_spends KSTest test\n", - " 0.85\n", - " False\n", + " pre_spends KSTest test_1\n", + " 0.95\n", + " True\n", " \n", " \n", - " post_spends KSTest test\n", + " post_spends KSTest test_1\n", " 0.95\n", " True\n", " \n", @@ -490,25 +534,40 @@ "" ], "text/plain": [ - " score pass\n", - "pre_spends TTest test 0.95 True\n", - "post_spends TTest test 0.95 True\n", - "pre_spends KSTest test 0.85 False\n", - "post_spends KSTest test 0.95 True" + " score pass\n", + "pre_spends TTest test_1 0.95 True\n", + "post_spends TTest test_1 0.95 True\n", + "pre_spends KSTest test_1 0.95 True\n", + "post_spends KSTest test_1 0.95 True" ] }, - "execution_count": 5, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.aa_score" + "result.aa_score" + ] + }, + { + "cell_type": "markdown", + "id": "eb0ce07b", + "metadata": {}, + "source": [ + "**Interpreting `aa_score`**\n", + "\n", + "This output shows p-values and the overall pass/fail status for each test type and feature. A high p-value (close to 1.0) means the test passed — the groups are similar.\n", + "\n", + "- `score`: p-value of the statistical test.\n", + "- `pass`: True if no iterations showed significant differences.\n", + "\n", + "Note: Even if the average p-value is high, the `pass` might still be False if at least one of the iterations had a p-value < 0.05.\n" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "id": "cc42c534", "metadata": { "ExecuteTime": { @@ -552,63 +611,63 @@ " \n", " \n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 488.0\n", - " 414.444444\n", - " NaN\n", - " M\n", + " 0.0\n", + " 11.0\n", + " 1.0\n", + " 476.0\n", + " 436.888889\n", + " 28.0\n", + " F\n", " E-commerce\n", - " control\n", + " test_1\n", " \n", " \n", " 1\n", - " 1\n", - " 8\n", - " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " NaN\n", - " E-commerce\n", - " test\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 519.5\n", + " 525.222222\n", + " 36.0\n", + " F\n", + " Logistics\n", + " control\n", " \n", " \n", " 2\n", - " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", - " M\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 498.5\n", + " 414.333333\n", + " 69.0\n", + " F\n", " Logistics\n", - " test\n", + " control\n", " \n", " \n", " 3\n", - " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", - " M\n", + " 3.0\n", + " 10.0\n", + " 1.0\n", + " 473.0\n", + " 445.888889\n", + " 43.0\n", + " F\n", " E-commerce\n", " control\n", " \n", " \n", " 4\n", - " 4\n", - " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", + " 4.0\n", + " 11.0\n", + " 1.0\n", + " 495.0\n", + " 428.111111\n", + " 56.0\n", " F\n", " E-commerce\n", - " control\n", + " test_1\n", " \n", " \n", " ...\n", @@ -624,63 +683,63 @@ " \n", " \n", " 9995\n", - " 9995\n", - " 10\n", - " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", + " 9995.0\n", + " 0.0\n", + " 0.0\n", + " 475.0\n", + " 408.111111\n", + " 51.0\n", " M\n", " Logistics\n", - " control\n", + " test_1\n", " \n", " \n", " 9996\n", - " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", + " 9996.0\n", + " 0.0\n", + " 0.0\n", + " 472.5\n", + " 414.666667\n", + " 22.0\n", " F\n", - " Logistics\n", + " E-commerce\n", " control\n", " \n", " \n", " 9997\n", - " 9997\n", - " 3\n", - " 1\n", - " 473.0\n", - " 534.111111\n", - " 22.0\n", - " F\n", + " 9997.0\n", + " 0.0\n", + " 0.0\n", + " 474.0\n", + " 419.222222\n", + " 63.0\n", + " M\n", " E-commerce\n", - " test\n", + " test_1\n", " \n", " \n", " 9998\n", - " 9998\n", - " 2\n", - " 1\n", - " 495.0\n", - " 523.222222\n", - " 67.0\n", + " 9998.0\n", + " 4.0\n", + " 1.0\n", + " 481.0\n", + " 519.888889\n", + " 21.0\n", " F\n", - " E-commerce\n", - " test\n", + " Logistics\n", + " test_1\n", " \n", " \n", " 9999\n", - " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", + " 9999.0\n", + " 0.0\n", + " 0.0\n", + " 495.5\n", + " 413.000000\n", + " 60.0\n", " F\n", " E-commerce\n", - " control\n", + " test_1\n", " \n", " \n", "\n", @@ -689,46 +748,58 @@ ], "text/plain": [ " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 NaN M \n", - "1 1 8 1 512.5 462.222222 26.0 NaN \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", + "0 0.0 11.0 1.0 476.0 436.888889 28.0 F \n", + "1 1.0 1.0 1.0 519.5 525.222222 36.0 F \n", + "2 2.0 0.0 0.0 498.5 414.333333 69.0 F \n", + "3 3.0 10.0 1.0 473.0 445.888889 43.0 F \n", + "4 4.0 11.0 1.0 495.0 428.111111 56.0 F \n", "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", + "9995 9995.0 0.0 0.0 475.0 408.111111 51.0 M \n", + "9996 9996.0 0.0 0.0 472.5 414.666667 22.0 F \n", + "9997 9997.0 0.0 0.0 474.0 419.222222 63.0 M \n", + "9998 9998.0 4.0 1.0 481.0 519.888889 21.0 F \n", + "9999 9999.0 0.0 0.0 495.5 413.000000 60.0 F \n", "\n", " industry split \n", - "0 E-commerce control \n", - "1 E-commerce test \n", - "2 Logistics test \n", + "0 E-commerce test_1 \n", + "1 Logistics control \n", + "2 Logistics control \n", "3 E-commerce control \n", - "4 E-commerce control \n", + "4 E-commerce test_1 \n", "... ... ... \n", - "9995 Logistics control \n", - "9996 Logistics control \n", - "9997 E-commerce test \n", - "9998 E-commerce test \n", - "9999 E-commerce control \n", + "9995 Logistics test_1 \n", + "9996 E-commerce control \n", + "9997 E-commerce test_1 \n", + "9998 Logistics test_1 \n", + "9999 E-commerce test_1 \n", "\n", "[10000 rows x 9 columns]" ] }, - "execution_count": 6, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.best_split" + "result.best_split" + ] + }, + { + "cell_type": "markdown", + "id": "a225e982", + "metadata": {}, + "source": [ + "**About `best_split`**\n", + "\n", + "This shows the best found split of the dataset, where control and test groups are as similar as possible in terms of target metrics.\n", + "\n", + "You can use this split for future modeling or as a validation check before proceeding to actual experiments.\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "id": "18351884", "metadata": { "ExecuteTime": { @@ -774,59 +845,75 @@ " \n", " 0\n", " pre_spends\n", - " test\n", - " 487.007\n", - " 487.1805\n", - " 0.17349999999999\n", - " 0.03562577129281319\n", + " test_1\n", + " 487.51363737983456\n", + " 487.3693683745583\n", + " -0.1442690052762714\n", + " -0.029592814275236634\n", " OK\n", - " 0.6457464242552831\n", + " 0.7207517943718674\n", " OK\n", - " 0.9325416301270012\n", + " None\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", - " 452.5269777777778\n", - " 451.8021333333334\n", - " -0.7248444444443862\n", - " -0.1601770678963499\n", + " test_1\n", + " 452.26422733934476\n", + " 452.1604583824107\n", + " -0.10376895693406141\n", + " -0.02294432118686851\n", " OK\n", - " 0.3577267741230933\n", + " 0.9012098780057256\n", " OK\n", - " 0.5770455454055606\n", + " None\n", " \n", " \n", "\n", "" ], "text/plain": [ - " feature group control mean test mean \\\n", - "0 pre_spends test 487.007 487.1805 \n", - "1 post_spends test 452.5269777777778 451.8021333333334 \n", + " feature group control mean test mean \\\n", + "0 pre_spends test_1 487.51363737983456 487.3693683745583 \n", + "1 post_spends test_1 452.26422733934476 452.1604583824107 \n", "\n", - " difference difference % TTest pass TTest p-value \\\n", - "0 0.17349999999999 0.03562577129281319 OK 0.6457464242552831 \n", - "1 -0.7248444444443862 -0.1601770678963499 OK 0.3577267741230933 \n", + " difference difference % TTest pass TTest p-value \\\n", + "0 -0.1442690052762714 -0.029592814275236634 OK 0.7207517943718674 \n", + "1 -0.10376895693406141 -0.02294432118686851 OK 0.9012098780057256 \n", "\n", - " KSTest pass KSTest p-value \n", - "0 OK 0.9325416301270012 \n", - "1 OK 0.5770455454055606 " + " KSTest pass KSTest p-value \n", + "0 OK None \n", + "1 OK None " ] }, - "execution_count": 7, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.best_split_statistic" + "result.best_split_statistic" + ] + }, + { + "cell_type": "markdown", + "id": "ef1986ae", + "metadata": {}, + "source": [ + "**Understanding `best_split_statistic`**\n", + "\n", + "This table contains detailed statistics for the best (most balanced) split found across all iterations. You can compare:\n", + "\n", + "- Mean values in control vs test group.\n", + "- Absolute and relative differences.\n", + "- p-values for both tests.\n", + "\n", + "Ideally, all rows should have `OK` in both TTest and KSTest columns, and small difference values (<1%)." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "id": "0da18405", "metadata": { "ExecuteTime": { @@ -857,21 +944,21 @@ " \n", " \n", " splitter_id\n", - " pre_spends GroupDifference control mean test\n", - " pre_spends GroupDifference test mean test\n", - " pre_spends GroupDifference difference test\n", - " pre_spends GroupDifference difference % test\n", - " post_spends GroupDifference control mean test\n", - " post_spends GroupDifference test mean test\n", - " post_spends GroupDifference difference test\n", - " post_spends GroupDifference difference % test\n", - " pre_spends TTest p-value test\n", + " pre_spends GroupDifference control mean test_1\n", + " pre_spends GroupDifference test mean test_1\n", + " pre_spends GroupDifference difference test_1\n", + " pre_spends GroupDifference difference % test_1\n", + " post_spends GroupDifference control mean test_1\n", + " post_spends GroupDifference test mean test_1\n", + " post_spends GroupDifference difference test_1\n", + " post_spends GroupDifference difference % test_1\n", + " pre_spends TTest p-value test_1\n", " ...\n", - " post_spends TTest pass test\n", - " pre_spends KSTest p-value test\n", - " pre_spends KSTest pass test\n", - " post_spends KSTest p-value test\n", - " post_spends KSTest pass test\n", + " post_spends TTest pass test_1\n", + " pre_spends KSTest p-value test_1\n", + " pre_spends KSTest pass test_1\n", + " post_spends KSTest p-value test_1\n", + " post_spends KSTest pass test_1\n", " mean TTest p-value\n", " mean TTest pass\n", " mean KSTest p-value\n", @@ -883,242 +970,242 @@ " \n", " 0\n", " AASplitter┴rs 0┴\n", - " 486.8074\n", - " 487.3801\n", - " 0.5727\n", - " 0.117644\n", - " 451.724200\n", - " 452.604911\n", - " 0.880711\n", - " 0.194967\n", - " 0.129161\n", + " 487.163329\n", + " 487.717994\n", + " 0.554665\n", + " 0.113856\n", + " 452.817559\n", + " 451.608239\n", + " -1.209320\n", + " -0.267066\n", + " 0.169323\n", " ...\n", " False\n", - " 0.023582\n", - " True\n", - " 0.480675\n", + " NaN\n", + " False\n", + " NaN\n", " False\n", - " 0.196474\n", + " 0.158645\n", " 0.0\n", - " 0.252129\n", - " 0.5\n", - " 0.233577\n", + " 0\n", + " 0.0\n", + " 0.052882\n", " \n", " \n", " 1\n", " AASplitter┴rs 1┴\n", - " 486.8542\n", - " 487.3333\n", - " 0.4791\n", - " 0.098407\n", - " 452.151400\n", - " 452.177711\n", - " 0.026311\n", - " 0.005819\n", - " 0.204300\n", + " 487.545424\n", + " 487.336119\n", + " -0.209306\n", + " -0.042931\n", + " 452.232070\n", + " 452.191870\n", + " -0.040201\n", + " -0.008889\n", + " 0.604036\n", " ...\n", " False\n", - " 0.420964\n", + " NaN\n", " False\n", - " 0.560541\n", + " NaN\n", " False\n", - " 0.588834\n", + " 0.782840\n", " 0.0\n", - " 0.490752\n", + " 0\n", " 0.0\n", - " 0.523446\n", + " 0.260947\n", " \n", " \n", " 2\n", " AASplitter┴rs 2┴\n", - " 487.1430\n", - " 487.0445\n", - " -0.0985\n", - " -0.020220\n", - " 451.504911\n", - " 452.824200\n", - " 1.319289\n", - " 0.292198\n", - " 0.794116\n", + " 487.165482\n", + " 487.716826\n", + " 0.551344\n", + " 0.113174\n", + " 452.108051\n", + " 452.316070\n", + " 0.208019\n", + " 0.046011\n", + " 0.171891\n", " ...\n", " False\n", - " 0.727866\n", + " NaN\n", " False\n", - " 0.177727\n", + " NaN\n", " False\n", - " 0.444120\n", + " 0.487685\n", " 0.0\n", - " 0.452796\n", + " 0\n", " 0.0\n", - " 0.449904\n", + " 0.162562\n", " \n", " \n", " 3\n", " AASplitter┴rs 3┴\n", - " 487.5133\n", - " 486.6742\n", - " -0.8391\n", - " -0.172118\n", - " 453.078778\n", - " 451.250333\n", - " -1.828444\n", - " -0.403560\n", - " 0.026188\n", + " 487.519111\n", + " 487.363030\n", + " -0.156081\n", + " -0.032015\n", + " 452.304988\n", + " 452.119085\n", + " -0.185903\n", + " -0.041101\n", + " 0.698958\n", " ...\n", - " True\n", - " 0.177727\n", " False\n", - " 0.083564\n", + " NaN\n", " False\n", - " 0.023258\n", - " 1.0\n", - " 0.130645\n", + " NaN\n", + " False\n", + " 0.761484\n", + " 0.0\n", + " 0\n", " 0.0\n", - " 0.094849\n", + " 0.253828\n", " \n", " \n", " 4\n", " AASplitter┴rs 4┴\n", - " 486.9905\n", - " 487.1970\n", - " 0.2065\n", - " 0.042403\n", - " 451.916489\n", - " 452.412622\n", - " 0.496133\n", - " 0.109784\n", - " 0.584302\n", + " 487.053508\n", + " 487.834079\n", + " 0.780571\n", + " 0.160264\n", + " 452.248088\n", + " 452.175456\n", + " -0.072632\n", + " -0.016060\n", + " 0.053089\n", " ...\n", " False\n", - " 0.660939\n", + " NaN\n", " False\n", - " 0.064626\n", + " NaN\n", " False\n", - " 0.556661\n", + " 0.491926\n", " 0.0\n", - " 0.362782\n", + " 0\n", " 0.0\n", - " 0.427409\n", + " 0.163975\n", " \n", " \n", " 5\n", " AASplitter┴rs 5┴\n", - " 487.2922\n", - " 486.8953\n", - " -0.3969\n", - " -0.081450\n", - " 451.686889\n", - " 452.642222\n", - " 0.955333\n", - " 0.211503\n", - " 0.292988\n", + " 487.496120\n", + " 487.385772\n", + " -0.110348\n", + " -0.022636\n", + " 451.992067\n", + " 452.432915\n", + " 0.440848\n", + " 0.097534\n", + " 0.784536\n", " ...\n", " False\n", - " 0.392763\n", + " NaN\n", " False\n", - " 0.406718\n", + " NaN\n", " False\n", - " 0.259216\n", + " 0.691234\n", " 0.0\n", - " 0.399740\n", + " 0\n", " 0.0\n", - " 0.352899\n", + " 0.230411\n", " \n", " \n", " 6\n", " AASplitter┴rs 6┴\n", - " 486.8775\n", - " 487.3100\n", - " 0.4325\n", - " 0.088831\n", - " 451.627689\n", - " 452.701422\n", - " 1.073733\n", - " 0.237747\n", - " 0.251829\n", + " 487.383559\n", + " 487.498886\n", + " 0.115327\n", + " 0.023663\n", + " 452.334441\n", + " 452.088929\n", + " -0.245513\n", + " -0.054277\n", + " 0.775070\n", " ...\n", " False\n", - " 0.170057\n", + " NaN\n", " False\n", - " 0.528005\n", + " NaN\n", " False\n", - " 0.212447\n", + " 0.772029\n", " 0.0\n", - " 0.349031\n", + " 0\n", " 0.0\n", - " 0.303503\n", + " 0.257343\n", " \n", " \n", " 7\n", " AASplitter┴rs 7┴\n", - " 487.0070\n", - " 487.1805\n", - " 0.1735\n", - " 0.035626\n", - " 452.526978\n", - " 451.802133\n", - " -0.724844\n", - " -0.160177\n", - " 0.645746\n", + " 487.702030\n", + " 487.182231\n", + " -0.519800\n", + " -0.106581\n", + " 452.771481\n", + " 451.657151\n", + " -1.114330\n", + " -0.246113\n", + " 0.197756\n", " ...\n", " False\n", - " 0.932542\n", + " NaN\n", " False\n", - " 0.577046\n", + " NaN\n", " False\n", - " 0.501737\n", + " 0.190129\n", " 0.0\n", - " 0.754794\n", + " 0\n", " 0.0\n", - " 0.670441\n", + " 0.063376\n", " \n", " \n", " 8\n", " AASplitter┴rs 8┴\n", - " 486.7993\n", - " 487.3882\n", - " 0.5889\n", - " 0.120974\n", - " 451.924844\n", - " 452.404267\n", - " 0.479422\n", - " 0.106085\n", - " 0.118678\n", + " 487.513637\n", + " 487.369368\n", + " -0.144269\n", + " -0.029593\n", + " 452.264227\n", + " 452.160458\n", + " -0.103769\n", + " -0.022944\n", + " 0.720752\n", " ...\n", " False\n", - " 0.023582\n", - " True\n", - " 0.760472\n", + " NaN\n", + " False\n", + " NaN\n", " False\n", - " 0.330834\n", + " 0.810981\n", " 0.0\n", - " 0.392027\n", - " 0.5\n", - " 0.371629\n", + " 0\n", + " 0.0\n", + " 0.270327\n", " \n", " \n", " 9\n", " AASplitter┴rs 9┴\n", - " 487.1140\n", - " 487.0735\n", - " -0.0405\n", - " -0.008314\n", - " 452.327511\n", - " 452.001600\n", - " -0.325911\n", - " -0.072052\n", - " 0.914549\n", + " 487.254543\n", + " 487.628536\n", + " 0.373993\n", + " 0.076755\n", + " 453.384506\n", + " 451.033539\n", + " -2.350967\n", + " -0.518537\n", + " 0.354097\n", " ...\n", + " True\n", + " NaN\n", " False\n", - " 0.577046\n", - " False\n", - " 0.480675\n", + " NaN\n", " False\n", - " 0.796888\n", - " 0.0\n", - " 0.528860\n", + " 0.179502\n", + " 0.5\n", + " 0\n", " 0.0\n", - " 0.618203\n", + " 0.059834\n", " \n", " \n", "\n", @@ -1126,172 +1213,172 @@ "" ], "text/plain": [ - " splitter_id pre_spends GroupDifference control mean test \\\n", - "0 AASplitter┴rs 0┴ 486.8074 \n", - "1 AASplitter┴rs 1┴ 486.8542 \n", - "2 AASplitter┴rs 2┴ 487.1430 \n", - "3 AASplitter┴rs 3┴ 487.5133 \n", - "4 AASplitter┴rs 4┴ 486.9905 \n", - "5 AASplitter┴rs 5┴ 487.2922 \n", - "6 AASplitter┴rs 6┴ 486.8775 \n", - "7 AASplitter┴rs 7┴ 487.0070 \n", - "8 AASplitter┴rs 8┴ 486.7993 \n", - "9 AASplitter┴rs 9┴ 487.1140 \n", - "\n", - " pre_spends GroupDifference test mean test \\\n", - "0 487.3801 \n", - "1 487.3333 \n", - "2 487.0445 \n", - "3 486.6742 \n", - "4 487.1970 \n", - "5 486.8953 \n", - "6 487.3100 \n", - "7 487.1805 \n", - "8 487.3882 \n", - "9 487.0735 \n", - "\n", - " pre_spends GroupDifference difference test \\\n", - "0 0.5727 \n", - "1 0.4791 \n", - "2 -0.0985 \n", - "3 -0.8391 \n", - "4 0.2065 \n", - "5 -0.3969 \n", - "6 0.4325 \n", - "7 0.1735 \n", - "8 0.5889 \n", - "9 -0.0405 \n", - "\n", - " pre_spends GroupDifference difference % test \\\n", - "0 0.117644 \n", - "1 0.098407 \n", - "2 -0.020220 \n", - "3 -0.172118 \n", - "4 0.042403 \n", - "5 -0.081450 \n", - "6 0.088831 \n", - "7 0.035626 \n", - "8 0.120974 \n", - "9 -0.008314 \n", - "\n", - " post_spends GroupDifference control mean test \\\n", - "0 451.724200 \n", - "1 452.151400 \n", - "2 451.504911 \n", - "3 453.078778 \n", - "4 451.916489 \n", - "5 451.686889 \n", - "6 451.627689 \n", - "7 452.526978 \n", - "8 451.924844 \n", - "9 452.327511 \n", - "\n", - " post_spends GroupDifference test mean test \\\n", - "0 452.604911 \n", - "1 452.177711 \n", - "2 452.824200 \n", - "3 451.250333 \n", - "4 452.412622 \n", - "5 452.642222 \n", - "6 452.701422 \n", - "7 451.802133 \n", - "8 452.404267 \n", - "9 452.001600 \n", - "\n", - " post_spends GroupDifference difference test \\\n", - "0 0.880711 \n", - "1 0.026311 \n", - "2 1.319289 \n", - "3 -1.828444 \n", - "4 0.496133 \n", - "5 0.955333 \n", - "6 1.073733 \n", - "7 -0.724844 \n", - "8 0.479422 \n", - "9 -0.325911 \n", - "\n", - " post_spends GroupDifference difference % test \\\n", - "0 0.194967 \n", - "1 0.005819 \n", - "2 0.292198 \n", - "3 -0.403560 \n", - "4 0.109784 \n", - "5 0.211503 \n", - "6 0.237747 \n", - "7 -0.160177 \n", - "8 0.106085 \n", - "9 -0.072052 \n", - "\n", - " pre_spends TTest p-value test ... post_spends TTest pass test \\\n", - "0 0.129161 ... False \n", - "1 0.204300 ... False \n", - "2 0.794116 ... False \n", - "3 0.026188 ... True \n", - "4 0.584302 ... False \n", - "5 0.292988 ... False \n", - "6 0.251829 ... False \n", - "7 0.645746 ... False \n", - "8 0.118678 ... False \n", - "9 0.914549 ... False \n", - "\n", - " pre_spends KSTest p-value test pre_spends KSTest pass test \\\n", - "0 0.023582 True \n", - "1 0.420964 False \n", - "2 0.727866 False \n", - "3 0.177727 False \n", - "4 0.660939 False \n", - "5 0.392763 False \n", - "6 0.170057 False \n", - "7 0.932542 False \n", - "8 0.023582 True \n", - "9 0.577046 False \n", - "\n", - " post_spends KSTest p-value test post_spends KSTest pass test \\\n", - "0 0.480675 False \n", - "1 0.560541 False \n", - "2 0.177727 False \n", - "3 0.083564 False \n", - "4 0.064626 False \n", - "5 0.406718 False \n", - "6 0.528005 False \n", - "7 0.577046 False \n", - "8 0.760472 False \n", - "9 0.480675 False \n", + " splitter_id pre_spends GroupDifference control mean test_1 \\\n", + "0 AASplitter┴rs 0┴ 487.163329 \n", + "1 AASplitter┴rs 1┴ 487.545424 \n", + "2 AASplitter┴rs 2┴ 487.165482 \n", + "3 AASplitter┴rs 3┴ 487.519111 \n", + "4 AASplitter┴rs 4┴ 487.053508 \n", + "5 AASplitter┴rs 5┴ 487.496120 \n", + "6 AASplitter┴rs 6┴ 487.383559 \n", + "7 AASplitter┴rs 7┴ 487.702030 \n", + "8 AASplitter┴rs 8┴ 487.513637 \n", + "9 AASplitter┴rs 9┴ 487.254543 \n", + "\n", + " pre_spends GroupDifference test mean test_1 \\\n", + "0 487.717994 \n", + "1 487.336119 \n", + "2 487.716826 \n", + "3 487.363030 \n", + "4 487.834079 \n", + "5 487.385772 \n", + "6 487.498886 \n", + "7 487.182231 \n", + "8 487.369368 \n", + "9 487.628536 \n", + "\n", + " pre_spends GroupDifference difference test_1 \\\n", + "0 0.554665 \n", + "1 -0.209306 \n", + "2 0.551344 \n", + "3 -0.156081 \n", + "4 0.780571 \n", + "5 -0.110348 \n", + "6 0.115327 \n", + "7 -0.519800 \n", + "8 -0.144269 \n", + "9 0.373993 \n", + "\n", + " pre_spends GroupDifference difference % test_1 \\\n", + "0 0.113856 \n", + "1 -0.042931 \n", + "2 0.113174 \n", + "3 -0.032015 \n", + "4 0.160264 \n", + "5 -0.022636 \n", + "6 0.023663 \n", + "7 -0.106581 \n", + "8 -0.029593 \n", + "9 0.076755 \n", + "\n", + " post_spends GroupDifference control mean test_1 \\\n", + "0 452.817559 \n", + "1 452.232070 \n", + "2 452.108051 \n", + "3 452.304988 \n", + "4 452.248088 \n", + "5 451.992067 \n", + "6 452.334441 \n", + "7 452.771481 \n", + "8 452.264227 \n", + "9 453.384506 \n", + "\n", + " post_spends GroupDifference test mean test_1 \\\n", + "0 451.608239 \n", + "1 452.191870 \n", + "2 452.316070 \n", + "3 452.119085 \n", + "4 452.175456 \n", + "5 452.432915 \n", + "6 452.088929 \n", + "7 451.657151 \n", + "8 452.160458 \n", + "9 451.033539 \n", + "\n", + " post_spends GroupDifference difference test_1 \\\n", + "0 -1.209320 \n", + "1 -0.040201 \n", + "2 0.208019 \n", + "3 -0.185903 \n", + "4 -0.072632 \n", + "5 0.440848 \n", + "6 -0.245513 \n", + "7 -1.114330 \n", + "8 -0.103769 \n", + "9 -2.350967 \n", + "\n", + " post_spends GroupDifference difference % test_1 \\\n", + "0 -0.267066 \n", + "1 -0.008889 \n", + "2 0.046011 \n", + "3 -0.041101 \n", + "4 -0.016060 \n", + "5 0.097534 \n", + "6 -0.054277 \n", + "7 -0.246113 \n", + "8 -0.022944 \n", + "9 -0.518537 \n", + "\n", + " pre_spends TTest p-value test_1 ... post_spends TTest pass test_1 \\\n", + "0 0.169323 ... False \n", + "1 0.604036 ... False \n", + "2 0.171891 ... False \n", + "3 0.698958 ... False \n", + "4 0.053089 ... False \n", + "5 0.784536 ... False \n", + "6 0.775070 ... False \n", + "7 0.197756 ... False \n", + "8 0.720752 ... False \n", + "9 0.354097 ... True \n", + "\n", + " pre_spends KSTest p-value test_1 pre_spends KSTest pass test_1 \\\n", + "0 NaN False \n", + "1 NaN False \n", + "2 NaN False \n", + "3 NaN False \n", + "4 NaN False \n", + "5 NaN False \n", + "6 NaN False \n", + "7 NaN False \n", + "8 NaN False \n", + "9 NaN False \n", + "\n", + " post_spends KSTest p-value test_1 post_spends KSTest pass test_1 \\\n", + "0 NaN False \n", + "1 NaN False \n", + "2 NaN False \n", + "3 NaN False \n", + "4 NaN False \n", + "5 NaN False \n", + "6 NaN False \n", + "7 NaN False \n", + "8 NaN False \n", + "9 NaN False \n", "\n", " mean TTest p-value mean TTest pass mean KSTest p-value mean KSTest pass \\\n", - "0 0.196474 0.0 0.252129 0.5 \n", - "1 0.588834 0.0 0.490752 0.0 \n", - "2 0.444120 0.0 0.452796 0.0 \n", - "3 0.023258 1.0 0.130645 0.0 \n", - "4 0.556661 0.0 0.362782 0.0 \n", - "5 0.259216 0.0 0.399740 0.0 \n", - "6 0.212447 0.0 0.349031 0.0 \n", - "7 0.501737 0.0 0.754794 0.0 \n", - "8 0.330834 0.0 0.392027 0.5 \n", - "9 0.796888 0.0 0.528860 0.0 \n", + "0 0.158645 0.0 0 0.0 \n", + "1 0.782840 0.0 0 0.0 \n", + "2 0.487685 0.0 0 0.0 \n", + "3 0.761484 0.0 0 0.0 \n", + "4 0.491926 0.0 0 0.0 \n", + "5 0.691234 0.0 0 0.0 \n", + "6 0.772029 0.0 0 0.0 \n", + "7 0.190129 0.0 0 0.0 \n", + "8 0.810981 0.0 0 0.0 \n", + "9 0.179502 0.5 0 0.0 \n", "\n", " mean test score \n", - "0 0.233577 \n", - "1 0.523446 \n", - "2 0.449904 \n", - "3 0.094849 \n", - "4 0.427409 \n", - "5 0.352899 \n", - "6 0.303503 \n", - "7 0.670441 \n", - "8 0.371629 \n", - "9 0.618203 \n", + "0 0.052882 \n", + "1 0.260947 \n", + "2 0.162562 \n", + "3 0.253828 \n", + "4 0.163975 \n", + "5 0.230411 \n", + "6 0.257343 \n", + "7 0.063376 \n", + "8 0.270327 \n", + "9 0.059834 \n", "\n", "[10 rows x 22 columns]" ] }, - "execution_count": 8, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.experiments" + "result.experiments" ] }, { @@ -1308,7 +1395,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "id": "a6038ed8", "metadata": { "ExecuteTime": { @@ -1321,18 +1408,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 4/4 [00:00<00:00, 6.50it/s]\n" + "100%|██████████| 4/4 [00:01<00:00, 3.54it/s]\n" ] } ], "source": [ - "aa = AATest(random_states=[56, 72, 2, 43])\n", - "res = aa.execute(data)" + "test = AATest(random_states=[56, 72, 2, 43])\n", + "result = test.execute(data)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "id": "6bebccfe9b91ae2e", "metadata": { "ExecuteTime": { @@ -1380,57 +1467,57 @@ " \n", " 0\n", " pre_spends\n", - " test\n", + " test_1\n", + " OK\n", " OK\n", - " NOT OK\n", " OK\n", " OK\n", " OK\n", - " 487.215200\n", - " 486.972300\n", - " -0.242900\n", - " -0.049855\n", + " 487.656720\n", + " 487.22314\n", + " -0.433579\n", + " -0.088911\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", + " test_1\n", " OK\n", " OK\n", " OK\n", " OK\n", " OK\n", - " 452.165533\n", - " 452.163578\n", - " -0.001956\n", - " -0.000432\n", + " 452.223868\n", + " 452.20006\n", + " -0.023808\n", + " -0.005265\n", " \n", " \n", "\n", "" ], "text/plain": [ - " feature group TTest aa test KSTest aa test TTest best split \\\n", - "0 pre_spends test OK NOT OK OK \n", - "1 post_spends test OK OK OK \n", + " feature group TTest aa test KSTest aa test TTest best split \\\n", + "0 pre_spends test_1 OK OK OK \n", + "1 post_spends test_1 OK OK OK \n", "\n", - " KSTest best split result control mean test mean difference difference % \n", - "0 OK OK 487.215200 486.972300 -0.242900 -0.049855 \n", - "1 OK OK 452.165533 452.163578 -0.001956 -0.000432 " + " KSTest best split result control mean test mean difference difference % \n", + "0 OK OK 487.656720 487.22314 -0.433579 -0.088911 \n", + "1 OK OK 452.223868 452.20006 -0.023808 -0.005265 " ] }, - "execution_count": 10, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.resume" + "result.resume" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 26, "id": "2a6d8f9df6978913", "metadata": { "ExecuteTime": { @@ -1467,22 +1554,22 @@ " \n", " \n", " \n", - " pre_spends TTest test\n", + " pre_spends TTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " post_spends TTest test\n", + " post_spends TTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " pre_spends KSTest test\n", - " 0.80\n", - " False\n", + " pre_spends KSTest test_1\n", + " 0.95\n", + " True\n", " \n", " \n", - " post_spends KSTest test\n", + " post_spends KSTest test_1\n", " 0.95\n", " True\n", " \n", @@ -1491,25 +1578,25 @@ "" ], "text/plain": [ - " score pass\n", - "pre_spends TTest test 0.95 True\n", - "post_spends TTest test 0.95 True\n", - "pre_spends KSTest test 0.80 False\n", - "post_spends KSTest test 0.95 True" + " score pass\n", + "pre_spends TTest test_1 0.95 True\n", + "post_spends TTest test_1 0.95 True\n", + "pre_spends KSTest test_1 0.95 True\n", + "post_spends KSTest test_1 0.95 True" ] }, - "execution_count": 11, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.aa_score" + "result.aa_score" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "id": "e318afc40736069d", "metadata": { "ExecuteTime": { @@ -1554,60 +1641,60 @@ " \n", " \n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 488.0\n", - " 414.444444\n", - " NaN\n", - " M\n", + " 0.0\n", + " 11.0\n", + " 1.0\n", + " 476.0\n", + " 436.888889\n", + " 28.0\n", + " F\n", " E-commerce\n", " control\n", " \n", " \n", " 1\n", - " 1\n", - " 8\n", - " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " NaN\n", - " E-commerce\n", - " test\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 519.5\n", + " 525.222222\n", + " 36.0\n", + " F\n", + " Logistics\n", + " control\n", " \n", " \n", " 2\n", - " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", - " M\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 498.5\n", + " 414.333333\n", + " 69.0\n", + " F\n", " Logistics\n", - " control\n", + " test_1\n", " \n", " \n", " 3\n", - " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", - " M\n", + " 3.0\n", + " 10.0\n", + " 1.0\n", + " 473.0\n", + " 445.888889\n", + " 43.0\n", + " F\n", " E-commerce\n", - " test\n", + " test_1\n", " \n", " \n", " 4\n", - " 4\n", - " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", + " 4.0\n", + " 11.0\n", + " 1.0\n", + " 495.0\n", + " 428.111111\n", + " 56.0\n", " F\n", " E-commerce\n", " control\n", @@ -1626,63 +1713,63 @@ " \n", " \n", " 9995\n", - " 9995\n", - " 10\n", - " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", + " 9995.0\n", + " 0.0\n", + " 0.0\n", + " 475.0\n", + " 408.111111\n", + " 51.0\n", " M\n", " Logistics\n", - " test\n", + " control\n", " \n", " \n", " 9996\n", - " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", + " 9996.0\n", + " 0.0\n", + " 0.0\n", + " 472.5\n", + " 414.666667\n", + " 22.0\n", " F\n", - " Logistics\n", - " control\n", + " E-commerce\n", + " test_1\n", " \n", " \n", " 9997\n", - " 9997\n", - " 3\n", - " 1\n", - " 473.0\n", - " 534.111111\n", - " 22.0\n", - " F\n", + " 9997.0\n", + " 0.0\n", + " 0.0\n", + " 474.0\n", + " 419.222222\n", + " 63.0\n", + " M\n", " E-commerce\n", - " test\n", + " control\n", " \n", " \n", " 9998\n", - " 9998\n", - " 2\n", - " 1\n", - " 495.0\n", - " 523.222222\n", - " 67.0\n", + " 9998.0\n", + " 4.0\n", + " 1.0\n", + " 481.0\n", + " 519.888889\n", + " 21.0\n", " F\n", - " E-commerce\n", - " test\n", + " Logistics\n", + " control\n", " \n", " \n", " 9999\n", - " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", + " 9999.0\n", + " 0.0\n", + " 0.0\n", + " 495.5\n", + " 413.000000\n", + " 60.0\n", " F\n", " E-commerce\n", - " control\n", + " test_1\n", " \n", " \n", "\n", @@ -1691,46 +1778,46 @@ ], "text/plain": [ " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 NaN M \n", - "1 1 8 1 512.5 462.222222 26.0 NaN \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", + "0 0.0 11.0 1.0 476.0 436.888889 28.0 F \n", + "1 1.0 1.0 1.0 519.5 525.222222 36.0 F \n", + "2 2.0 0.0 0.0 498.5 414.333333 69.0 F \n", + "3 3.0 10.0 1.0 473.0 445.888889 43.0 F \n", + "4 4.0 11.0 1.0 495.0 428.111111 56.0 F \n", "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", + "9995 9995.0 0.0 0.0 475.0 408.111111 51.0 M \n", + "9996 9996.0 0.0 0.0 472.5 414.666667 22.0 F \n", + "9997 9997.0 0.0 0.0 474.0 419.222222 63.0 M \n", + "9998 9998.0 4.0 1.0 481.0 519.888889 21.0 F \n", + "9999 9999.0 0.0 0.0 495.5 413.000000 60.0 F \n", "\n", " industry split \n", "0 E-commerce control \n", - "1 E-commerce test \n", - "2 Logistics control \n", - "3 E-commerce test \n", + "1 Logistics control \n", + "2 Logistics test_1 \n", + "3 E-commerce test_1 \n", "4 E-commerce control \n", "... ... ... \n", - "9995 Logistics test \n", - "9996 Logistics control \n", - "9997 E-commerce test \n", - "9998 E-commerce test \n", - "9999 E-commerce control \n", + "9995 Logistics control \n", + "9996 E-commerce test_1 \n", + "9997 E-commerce control \n", + "9998 Logistics control \n", + "9999 E-commerce test_1 \n", "\n", "[10000 rows x 9 columns]" ] }, - "execution_count": 12, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.best_split" + "result.best_split" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 28, "id": "bd1a332ab687cef", "metadata": { "ExecuteTime": { @@ -1777,59 +1864,59 @@ " \n", " 0\n", " pre_spends\n", - " test\n", - " 487.2152\n", - " 486.9723\n", - " -0.24289999999996326\n", - " -0.04985476643585285\n", + " test_1\n", + " 487.65671971706456\n", + " 487.22314049586777\n", + " -0.43357922119679415\n", + " -0.08891074472394678\n", " OK\n", - " 0.5198644959361092\n", + " 0.282682875917564\n", " OK\n", - " 0.6945834812298466\n", + " None\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", - " 452.1655333333333\n", - " 452.1635777777778\n", - " -0.0019555555555257342\n", - " -0.000432486647339303\n", + " test_1\n", + " 452.2238677669712\n", + " 452.2000595636959\n", + " -0.023808203275279993\n", + " -0.005264694097828482\n", " OK\n", - " 0.9980202768108593\n", + " 0.9772788561318062\n", " OK\n", - " 0.6777877521935483\n", + " None\n", " \n", " \n", "\n", "" ], "text/plain": [ - " feature group control mean test mean \\\n", - "0 pre_spends test 487.2152 486.9723 \n", - "1 post_spends test 452.1655333333333 452.1635777777778 \n", + " feature group control mean test mean \\\n", + "0 pre_spends test_1 487.65671971706456 487.22314049586777 \n", + "1 post_spends test_1 452.2238677669712 452.2000595636959 \n", "\n", - " difference difference % TTest pass \\\n", - "0 -0.24289999999996326 -0.04985476643585285 OK \n", - "1 -0.0019555555555257342 -0.000432486647339303 OK \n", + " difference difference % TTest pass \\\n", + "0 -0.43357922119679415 -0.08891074472394678 OK \n", + "1 -0.023808203275279993 -0.005264694097828482 OK \n", "\n", - " TTest p-value KSTest pass KSTest p-value \n", - "0 0.5198644959361092 OK 0.6945834812298466 \n", - "1 0.9980202768108593 OK 0.6777877521935483 " + " TTest p-value KSTest pass KSTest p-value \n", + "0 0.282682875917564 OK None \n", + "1 0.9772788561318062 OK None " ] }, - "execution_count": 13, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.best_split_statistic" + "result.best_split_statistic" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 29, "id": "96b29db891fa2462", "metadata": { "ExecuteTime": { @@ -1861,21 +1948,21 @@ " \n", " \n", " splitter_id\n", - " pre_spends GroupDifference control mean test\n", - " pre_spends GroupDifference test mean test\n", - " pre_spends GroupDifference difference test\n", - " pre_spends GroupDifference difference % test\n", - " post_spends GroupDifference control mean test\n", - " post_spends GroupDifference test mean test\n", - " post_spends GroupDifference difference test\n", - " post_spends GroupDifference difference % test\n", - " pre_spends TTest p-value test\n", + " pre_spends GroupDifference control mean test_1\n", + " pre_spends GroupDifference test mean test_1\n", + " pre_spends GroupDifference difference test_1\n", + " pre_spends GroupDifference difference % test_1\n", + " post_spends GroupDifference control mean test_1\n", + " post_spends GroupDifference test mean test_1\n", + " post_spends GroupDifference difference test_1\n", + " post_spends GroupDifference difference % test_1\n", + " pre_spends TTest p-value test_1\n", " ...\n", - " post_spends TTest pass test\n", - " pre_spends KSTest p-value test\n", - " pre_spends KSTest pass test\n", - " post_spends KSTest p-value test\n", - " post_spends KSTest pass test\n", + " post_spends TTest pass test_1\n", + " pre_spends KSTest p-value test_1\n", + " pre_spends KSTest pass test_1\n", + " post_spends KSTest p-value test_1\n", + " post_spends KSTest pass test_1\n", " mean TTest p-value\n", " mean TTest pass\n", " mean KSTest p-value\n", @@ -1887,98 +1974,98 @@ " \n", " 0\n", " AASplitter┴rs 56┴\n", - " 487.3882\n", - " 486.7993\n", - " -0.5889\n", - " -0.120828\n", - " 451.845400\n", - " 452.483711\n", - " 0.638311\n", - " 0.141268\n", - " 0.118678\n", + " 487.656720\n", + " 487.223140\n", + " -0.433579\n", + " -0.088911\n", + " 452.223868\n", + " 452.200060\n", + " -0.023808\n", + " -0.005265\n", + " 0.282683\n", " ...\n", " False\n", - " 0.002465\n", - " True\n", - " 0.744274\n", + " NaN\n", + " False\n", + " NaN\n", " False\n", - " 0.268336\n", + " 0.629981\n", " 0.0\n", - " 0.373370\n", - " 0.5\n", - " 0.338359\n", + " 0\n", + " 0.0\n", + " 0.209994\n", " \n", " \n", " 1\n", " AASplitter┴rs 72┴\n", - " 487.2152\n", - " 486.9723\n", - " -0.2429\n", - " -0.049855\n", - " 452.165533\n", - " 452.163578\n", - " -0.001956\n", - " -0.000432\n", - " 0.519864\n", + " 487.717835\n", + " 487.163365\n", + " -0.554470\n", + " -0.113687\n", + " 452.997042\n", + " 451.424389\n", + " -1.572654\n", + " -0.347166\n", + " 0.169474\n", " ...\n", " False\n", - " 0.694583\n", + " NaN\n", " False\n", - " 0.677788\n", + " NaN\n", " False\n", - " 0.758942\n", + " 0.114689\n", " 0.0\n", - " 0.686186\n", + " 0\n", " 0.0\n", - " 0.710438\n", + " 0.038230\n", " \n", " \n", " 2\n", " AASplitter┴rs 2┴\n", - " 487.1430\n", - " 487.0445\n", - " -0.0985\n", - " -0.020220\n", - " 451.504911\n", - " 452.824200\n", - " 1.319289\n", - " 0.292198\n", - " 0.794116\n", + " 487.165482\n", + " 487.716826\n", + " 0.551344\n", + " 0.113174\n", + " 452.108051\n", + " 452.316070\n", + " 0.208019\n", + " 0.046011\n", + " 0.171891\n", " ...\n", " False\n", - " 0.727866\n", + " NaN\n", " False\n", - " 0.177727\n", + " NaN\n", " False\n", - " 0.444120\n", + " 0.487685\n", " 0.0\n", - " 0.452796\n", + " 0\n", " 0.0\n", - " 0.449904\n", + " 0.162562\n", " \n", " \n", " 3\n", " AASplitter┴rs 43┴\n", - " 486.8269\n", - " 487.3606\n", - " 0.5337\n", - " 0.109628\n", - " 452.801000\n", - " 451.528111\n", - " -1.272889\n", - " -0.281114\n", - " 0.157340\n", + " 487.321524\n", + " 487.560680\n", + " 0.239156\n", + " 0.049076\n", + " 452.398564\n", + " 452.025364\n", + " -0.373200\n", + " -0.082494\n", + " 0.553469\n", " ...\n", " False\n", - " 0.352691\n", + " NaN\n", " False\n", - " 0.465358\n", + " NaN\n", " False\n", - " 0.131809\n", + " 0.604371\n", " 0.0\n", - " 0.409024\n", + " 0\n", " 0.0\n", - " 0.316619\n", + " 0.201457\n", " \n", " \n", "\n", @@ -1986,94 +2073,94 @@ "" ], "text/plain": [ - " splitter_id pre_spends GroupDifference control mean test \\\n", - "0 AASplitter┴rs 56┴ 487.3882 \n", - "1 AASplitter┴rs 72┴ 487.2152 \n", - "2 AASplitter┴rs 2┴ 487.1430 \n", - "3 AASplitter┴rs 43┴ 486.8269 \n", - "\n", - " pre_spends GroupDifference test mean test \\\n", - "0 486.7993 \n", - "1 486.9723 \n", - "2 487.0445 \n", - "3 487.3606 \n", - "\n", - " pre_spends GroupDifference difference test \\\n", - "0 -0.5889 \n", - "1 -0.2429 \n", - "2 -0.0985 \n", - "3 0.5337 \n", - "\n", - " pre_spends GroupDifference difference % test \\\n", - "0 -0.120828 \n", - "1 -0.049855 \n", - "2 -0.020220 \n", - "3 0.109628 \n", - "\n", - " post_spends GroupDifference control mean test \\\n", - "0 451.845400 \n", - "1 452.165533 \n", - "2 451.504911 \n", - "3 452.801000 \n", - "\n", - " post_spends GroupDifference test mean test \\\n", - "0 452.483711 \n", - "1 452.163578 \n", - "2 452.824200 \n", - "3 451.528111 \n", - "\n", - " post_spends GroupDifference difference test \\\n", - "0 0.638311 \n", - "1 -0.001956 \n", - "2 1.319289 \n", - "3 -1.272889 \n", - "\n", - " post_spends GroupDifference difference % test \\\n", - "0 0.141268 \n", - "1 -0.000432 \n", - "2 0.292198 \n", - "3 -0.281114 \n", - "\n", - " pre_spends TTest p-value test ... post_spends TTest pass test \\\n", - "0 0.118678 ... False \n", - "1 0.519864 ... False \n", - "2 0.794116 ... False \n", - "3 0.157340 ... False \n", - "\n", - " pre_spends KSTest p-value test pre_spends KSTest pass test \\\n", - "0 0.002465 True \n", - "1 0.694583 False \n", - "2 0.727866 False \n", - "3 0.352691 False \n", - "\n", - " post_spends KSTest p-value test post_spends KSTest pass test \\\n", - "0 0.744274 False \n", - "1 0.677788 False \n", - "2 0.177727 False \n", - "3 0.465358 False \n", + " splitter_id pre_spends GroupDifference control mean test_1 \\\n", + "0 AASplitter┴rs 56┴ 487.656720 \n", + "1 AASplitter┴rs 72┴ 487.717835 \n", + "2 AASplitter┴rs 2┴ 487.165482 \n", + "3 AASplitter┴rs 43┴ 487.321524 \n", + "\n", + " pre_spends GroupDifference test mean test_1 \\\n", + "0 487.223140 \n", + "1 487.163365 \n", + "2 487.716826 \n", + "3 487.560680 \n", + "\n", + " pre_spends GroupDifference difference test_1 \\\n", + "0 -0.433579 \n", + "1 -0.554470 \n", + "2 0.551344 \n", + "3 0.239156 \n", + "\n", + " pre_spends GroupDifference difference % test_1 \\\n", + "0 -0.088911 \n", + "1 -0.113687 \n", + "2 0.113174 \n", + "3 0.049076 \n", + "\n", + " post_spends GroupDifference control mean test_1 \\\n", + "0 452.223868 \n", + "1 452.997042 \n", + "2 452.108051 \n", + "3 452.398564 \n", + "\n", + " post_spends GroupDifference test mean test_1 \\\n", + "0 452.200060 \n", + "1 451.424389 \n", + "2 452.316070 \n", + "3 452.025364 \n", + "\n", + " post_spends GroupDifference difference test_1 \\\n", + "0 -0.023808 \n", + "1 -1.572654 \n", + "2 0.208019 \n", + "3 -0.373200 \n", + "\n", + " post_spends GroupDifference difference % test_1 \\\n", + "0 -0.005265 \n", + "1 -0.347166 \n", + "2 0.046011 \n", + "3 -0.082494 \n", + "\n", + " pre_spends TTest p-value test_1 ... post_spends TTest pass test_1 \\\n", + "0 0.282683 ... False \n", + "1 0.169474 ... False \n", + "2 0.171891 ... False \n", + "3 0.553469 ... False \n", + "\n", + " pre_spends KSTest p-value test_1 pre_spends KSTest pass test_1 \\\n", + "0 NaN False \n", + "1 NaN False \n", + "2 NaN False \n", + "3 NaN False \n", + "\n", + " post_spends KSTest p-value test_1 post_spends KSTest pass test_1 \\\n", + "0 NaN False \n", + "1 NaN False \n", + "2 NaN False \n", + "3 NaN False \n", "\n", " mean TTest p-value mean TTest pass mean KSTest p-value mean KSTest pass \\\n", - "0 0.268336 0.0 0.373370 0.5 \n", - "1 0.758942 0.0 0.686186 0.0 \n", - "2 0.444120 0.0 0.452796 0.0 \n", - "3 0.131809 0.0 0.409024 0.0 \n", + "0 0.629981 0.0 0 0.0 \n", + "1 0.114689 0.0 0 0.0 \n", + "2 0.487685 0.0 0 0.0 \n", + "3 0.604371 0.0 0 0.0 \n", "\n", " mean test score \n", - "0 0.338359 \n", - "1 0.710438 \n", - "2 0.449904 \n", - "3 0.316619 \n", + "0 0.209994 \n", + "1 0.038230 \n", + "2 0.162562 \n", + "3 0.201457 \n", "\n", "[4 rows x 22 columns]" ] }, - "execution_count": 14, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.experiments" + "result.experiments" ] }, { @@ -2085,12 +2172,16 @@ "source": [ "# AA Test with stratification\n", "\n", - "Depending on your requirements it is possible to stratify the data. You can set `stratification=True` and `StratificationRole` in `Dataset` to run it with stratification. " + "Depending on your requirements it is possible to stratify the data. You can set `stratification=True` and `StratificationRole` in `Dataset` to run it with stratification.\n", + "\n", + "Stratified AA tests ensure that both groups (control/test) have the same proportions of categories (e.g. same % of genders or regions). This prevents imbalances in categorical features that can distort results.\n", + "\n", + "Make sure to assign `StratificationRole` to relevant columns in your dataset before enabling stratification." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 30, "id": "da9ab2f374ce1273", "metadata": { "ExecuteTime": { @@ -2104,28 +2195,25 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/4 [00:00\n", " 0\n", " pre_spends\n", - " test\n", - " OK\n", + " test_1\n", + " NOT OK\n", " OK\n", " OK\n", " OK\n", " OK\n", - " 487.082000\n", - " 487.110444\n", - " 0.028444\n", - " 0.005840\n", + " 487.458825\n", + " 487.363377\n", + " -0.095448\n", + " -0.019581\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", - " NOT OK\n", - " NOT OK\n", + " test_1\n", " OK\n", " OK\n", - " NOT OK\n", - " 451.633506\n", - " 452.648938\n", - " 1.015432\n", - " 0.224835\n", + " OK\n", + " OK\n", + " OK\n", + " 452.886758\n", + " 451.647464\n", + " -1.239294\n", + " -0.273643\n", " \n", " \n", "\n", "" ], "text/plain": [ - " feature group TTest aa test KSTest aa test TTest best split \\\n", - "0 pre_spends test OK OK OK \n", - "1 post_spends test NOT OK NOT OK OK \n", + " feature group TTest aa test KSTest aa test TTest best split \\\n", + "0 pre_spends test_1 NOT OK OK OK \n", + "1 post_spends test_1 OK OK OK \n", "\n", - " KSTest best split result control mean test mean difference \\\n", - "0 OK OK 487.082000 487.110444 0.028444 \n", - "1 OK NOT OK 451.633506 452.648938 1.015432 \n", - "\n", - " difference % \n", - "0 0.005840 \n", - "1 0.224835 " + " KSTest best split result control mean test mean difference difference % \n", + "0 OK OK 487.458825 487.363377 -0.095448 -0.019581 \n", + "1 OK OK 452.886758 451.647464 -1.239294 -0.273643 " ] }, - "execution_count": 16, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.resume" + "result.resume" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 32, "id": "5eca1aebeb06da2", "metadata": { "ExecuteTime": { @@ -2264,49 +2348,49 @@ " \n", " \n", " \n", - " pre_spends TTest test\n", - " 0.95\n", - " True\n", - " \n", - " \n", - " post_spends TTest test\n", + " pre_spends TTest test_1\n", " 0.80\n", " False\n", " \n", " \n", - " pre_spends KSTest test\n", + " post_spends TTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " post_spends KSTest test\n", - " 0.80\n", - " False\n", + " pre_spends KSTest test_1\n", + " 0.95\n", + " True\n", + " \n", + " \n", + " post_spends KSTest test_1\n", + " 0.95\n", + " True\n", " \n", " \n", "\n", "" ], "text/plain": [ - " score pass\n", - "pre_spends TTest test 0.95 True\n", - "post_spends TTest test 0.80 False\n", - "pre_spends KSTest test 0.95 True\n", - "post_spends KSTest test 0.80 False" + " score pass\n", + "pre_spends TTest test_1 0.80 False\n", + "post_spends TTest test_1 0.95 True\n", + "pre_spends KSTest test_1 0.95 True\n", + "post_spends KSTest test_1 0.95 True" ] }, - "execution_count": 17, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.aa_score" + "result.aa_score" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 33, "id": "4e5730bdf7983f7d", "metadata": { "ExecuteTime": { @@ -2351,63 +2435,63 @@ " \n", " \n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 488.0\n", - " 414.444444\n", - " NaN\n", - " M\n", + " 0.0\n", + " 11.0\n", + " 1.0\n", + " 476.0\n", + " 436.888889\n", + " 28.0\n", + " F\n", " E-commerce\n", - " test\n", + " test_1\n", " \n", " \n", " 1\n", - " 1\n", - " 8\n", - " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " NaN\n", - " E-commerce\n", - " test\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 519.5\n", + " 525.222222\n", + " 36.0\n", + " F\n", + " Logistics\n", + " test_1\n", " \n", " \n", " 2\n", - " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", - " M\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 498.5\n", + " 414.333333\n", + " 69.0\n", + " F\n", " Logistics\n", - " test\n", + " control\n", " \n", " \n", " 3\n", - " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", - " M\n", + " 3.0\n", + " 10.0\n", + " 1.0\n", + " 473.0\n", + " 445.888889\n", + " 43.0\n", + " F\n", " E-commerce\n", - " test\n", + " control\n", " \n", " \n", " 4\n", - " 4\n", - " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", + " 4.0\n", + " 11.0\n", + " 1.0\n", + " 495.0\n", + " 428.111111\n", + " 56.0\n", " F\n", " E-commerce\n", - " test\n", + " control\n", " \n", " \n", " ...\n", @@ -2423,60 +2507,60 @@ " \n", " \n", " 9995\n", - " 9995\n", - " 10\n", - " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", + " 9995.0\n", + " 0.0\n", + " 0.0\n", + " 475.0\n", + " 408.111111\n", + " 51.0\n", " M\n", " Logistics\n", " NaN\n", " \n", " \n", " 9996\n", - " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", + " 9996.0\n", + " 0.0\n", + " 0.0\n", + " 472.5\n", + " 414.666667\n", + " 22.0\n", " F\n", - " Logistics\n", + " E-commerce\n", " NaN\n", " \n", " \n", " 9997\n", - " 9997\n", - " 3\n", - " 1\n", - " 473.0\n", - " 534.111111\n", - " 22.0\n", - " F\n", + " 9997.0\n", + " 0.0\n", + " 0.0\n", + " 474.0\n", + " 419.222222\n", + " 63.0\n", + " M\n", " E-commerce\n", " NaN\n", " \n", " \n", " 9998\n", - " 9998\n", - " 2\n", - " 1\n", - " 495.0\n", - " 523.222222\n", - " 67.0\n", + " 9998.0\n", + " 4.0\n", + " 1.0\n", + " 481.0\n", + " 519.888889\n", + " 21.0\n", " F\n", - " E-commerce\n", + " Logistics\n", " NaN\n", " \n", " \n", " 9999\n", - " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", + " 9999.0\n", + " 0.0\n", + " 0.0\n", + " 495.5\n", + " 413.000000\n", + " 60.0\n", " F\n", " E-commerce\n", " NaN\n", @@ -2488,46 +2572,46 @@ ], "text/plain": [ " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 NaN M \n", - "1 1 8 1 512.5 462.222222 26.0 NaN \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", + "0 0.0 11.0 1.0 476.0 436.888889 28.0 F \n", + "1 1.0 1.0 1.0 519.5 525.222222 36.0 F \n", + "2 2.0 0.0 0.0 498.5 414.333333 69.0 F \n", + "3 3.0 10.0 1.0 473.0 445.888889 43.0 F \n", + "4 4.0 11.0 1.0 495.0 428.111111 56.0 F \n", "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", - "\n", - " industry split \n", - "0 E-commerce test \n", - "1 E-commerce test \n", - "2 Logistics test \n", - "3 E-commerce test \n", - "4 E-commerce test \n", - "... ... ... \n", - "9995 Logistics NaN \n", - "9996 Logistics NaN \n", - "9997 E-commerce NaN \n", - "9998 E-commerce NaN \n", - "9999 E-commerce NaN \n", + "9995 9995.0 0.0 0.0 475.0 408.111111 51.0 M \n", + "9996 9996.0 0.0 0.0 472.5 414.666667 22.0 F \n", + "9997 9997.0 0.0 0.0 474.0 419.222222 63.0 M \n", + "9998 9998.0 4.0 1.0 481.0 519.888889 21.0 F \n", + "9999 9999.0 0.0 0.0 495.5 413.000000 60.0 F \n", + "\n", + " industry split \n", + "0 E-commerce test_1 \n", + "1 Logistics test_1 \n", + "2 Logistics control \n", + "3 E-commerce control \n", + "4 E-commerce control \n", + "... ... ... \n", + "9995 Logistics NaN \n", + "9996 E-commerce NaN \n", + "9997 E-commerce NaN \n", + "9998 Logistics NaN \n", + "9999 E-commerce NaN \n", "\n", "[10000 rows x 9 columns]" ] }, - "execution_count": 18, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.best_split" + "result.best_split" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 34, "id": "7cdeba119b04476e", "metadata": { "ExecuteTime": { @@ -2574,59 +2658,59 @@ " \n", " 0\n", " pre_spends\n", - " test\n", - " 487.082\n", - " 487.11044444444445\n", - " 0.02844444444446026\n", - " 0.0058397650589459005\n", + " test_1\n", + " 487.4588249754179\n", + " 487.3633771386065\n", + " -0.09544783681138824\n", + " -0.019580697265297875\n", " OK\n", - " 0.9431984193394327\n", + " 0.8226591358784061\n", " OK\n", - " 0.612182730595449\n", + " None\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", - " 451.63350617283953\n", - " 452.6489382716049\n", - " 1.0154320987653591\n", - " 0.22483542183797667\n", + " test_1\n", + " 452.8867584398558\n", + " 451.6474639777392\n", + " -1.2392944621165611\n", + " -0.2736433421868578\n", " OK\n", - " 0.2211604169043013\n", + " 0.1602986902329776\n", " OK\n", - " 0.4919619253053554\n", + " None\n", " \n", " \n", "\n", "" ], "text/plain": [ - " feature group control mean test mean \\\n", - "0 pre_spends test 487.082 487.11044444444445 \n", - "1 post_spends test 451.63350617283953 452.6489382716049 \n", + " feature group control mean test mean \\\n", + "0 pre_spends test_1 487.4588249754179 487.3633771386065 \n", + "1 post_spends test_1 452.8867584398558 451.6474639777392 \n", "\n", - " difference difference % TTest pass TTest p-value \\\n", - "0 0.02844444444446026 0.0058397650589459005 OK 0.9431984193394327 \n", - "1 1.0154320987653591 0.22483542183797667 OK 0.2211604169043013 \n", + " difference difference % TTest pass TTest p-value \\\n", + "0 -0.09544783681138824 -0.019580697265297875 OK 0.8226591358784061 \n", + "1 -1.2392944621165611 -0.2736433421868578 OK 0.1602986902329776 \n", "\n", - " KSTest pass KSTest p-value \n", - "0 OK 0.612182730595449 \n", - "1 OK 0.4919619253053554 " + " KSTest pass KSTest p-value \n", + "0 OK None \n", + "1 OK None " ] }, - "execution_count": 19, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.best_split_statistic" + "result.best_split_statistic" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 35, "id": "6a63a08bceb2f40a", "metadata": { "ExecuteTime": { @@ -2658,21 +2742,21 @@ " \n", " \n", " splitter_id\n", - " pre_spends GroupDifference control mean test\n", - " pre_spends GroupDifference test mean test\n", - " pre_spends GroupDifference difference test\n", - " pre_spends GroupDifference difference % test\n", - " post_spends GroupDifference control mean test\n", - " post_spends GroupDifference test mean test\n", - " post_spends GroupDifference difference test\n", - " post_spends GroupDifference difference % test\n", - " pre_spends TTest p-value test\n", + " pre_spends GroupDifference control mean test_1\n", + " pre_spends GroupDifference test mean test_1\n", + " pre_spends GroupDifference difference test_1\n", + " pre_spends GroupDifference difference % test_1\n", + " post_spends GroupDifference control mean test_1\n", + " post_spends GroupDifference test mean test_1\n", + " post_spends GroupDifference difference test_1\n", + " post_spends GroupDifference difference % test_1\n", + " pre_spends TTest p-value test_1\n", " ...\n", - " post_spends TTest pass test\n", - " pre_spends KSTest p-value test\n", - " pre_spends KSTest pass test\n", - " post_spends KSTest p-value test\n", - " post_spends KSTest pass test\n", + " post_spends TTest pass test_1\n", + " pre_spends KSTest p-value test_1\n", + " pre_spends KSTest pass test_1\n", + " post_spends KSTest p-value test_1\n", + " post_spends KSTest pass test_1\n", " mean TTest p-value\n", " mean TTest pass\n", " mean KSTest p-value\n", @@ -2684,98 +2768,98 @@ " \n", " 0\n", " AASplitterWithStratification┴rs 56┴\n", - " 487.082000\n", - " 487.110444\n", - " 0.028444\n", - " 0.005840\n", - " 451.633506\n", - " 452.648938\n", - " 1.015432\n", - " 0.224835\n", - " 0.943198\n", + " 486.965432\n", + " 487.857072\n", + " 0.891640\n", + " 0.183101\n", + " 453.008916\n", + " 451.530843\n", + " -1.478073\n", + " -0.326279\n", + " 0.036259\n", " ...\n", " False\n", - " 0.612183\n", + " NaN\n", " False\n", - " 0.491962\n", + " NaN\n", " False\n", - " 0.582179\n", - " 0.0\n", - " 0.552072\n", + " 0.065131\n", + " 0.5\n", + " 0\n", " 0.0\n", - " 0.562108\n", + " 0.021710\n", " \n", " \n", " 1\n", " AASplitterWithStratification┴rs 72┴\n", - " 487.269778\n", - " 486.922667\n", - " -0.347111\n", - " -0.071236\n", - " 452.036765\n", - " 452.245679\n", - " 0.208914\n", - " 0.046216\n", - " 0.384576\n", + " 487.618177\n", + " 487.204590\n", + " -0.413587\n", + " -0.084818\n", + " 452.497077\n", + " 452.042668\n", + " -0.454410\n", + " -0.100423\n", + " 0.331448\n", " ...\n", " False\n", - " 0.129368\n", + " NaN\n", " False\n", - " 0.952433\n", + " NaN\n", " False\n", - " 0.592924\n", + " 0.469069\n", " 0.0\n", - " 0.540900\n", + " 0\n", " 0.0\n", - " 0.558241\n", + " 0.156356\n", " \n", " \n", " 2\n", " AASplitterWithStratification┴rs 2┴\n", - " 486.928444\n", - " 487.264000\n", - " 0.335556\n", - " 0.068913\n", - " 452.910272\n", - " 451.372173\n", - " -1.538099\n", - " -0.339603\n", - " 0.400601\n", + " 487.458825\n", + " 487.363377\n", + " -0.095448\n", + " -0.019581\n", + " 452.886758\n", + " 451.647464\n", + " -1.239294\n", + " -0.273643\n", + " 0.822659\n", " ...\n", " False\n", - " 0.085963\n", + " NaN\n", " False\n", - " 0.008355\n", - " True\n", - " 0.232221\n", + " NaN\n", + " False\n", + " 0.491479\n", " 0.0\n", - " 0.047159\n", - " 0.5\n", - " 0.108846\n", + " 0\n", + " 0.0\n", + " 0.163826\n", " \n", " \n", " 3\n", " AASplitterWithStratification┴rs 43┴\n", - " 487.116556\n", - " 487.075889\n", - " -0.040667\n", - " -0.008348\n", - " 453.141012\n", - " 451.141432\n", - " -1.999580\n", - " -0.441271\n", - " 0.918863\n", + " 487.582697\n", + " 487.239367\n", + " -0.343330\n", + " -0.070415\n", + " 451.915866\n", + " 452.624849\n", + " 0.708983\n", + " 0.156884\n", + " 0.420121\n", " ...\n", - " True\n", - " 0.459584\n", " False\n", - " 0.105834\n", + " NaN\n", " False\n", - " 0.467419\n", - " 0.5\n", - " 0.282709\n", + " NaN\n", + " False\n", + " 0.420984\n", + " 0.0\n", + " 0\n", " 0.0\n", - " 0.344279\n", + " 0.140328\n", " \n", " \n", "\n", @@ -2789,94 +2873,94 @@ "2 AASplitterWithStratification┴rs 2┴ \n", "3 AASplitterWithStratification┴rs 43┴ \n", "\n", - " pre_spends GroupDifference control mean test \\\n", - "0 487.082000 \n", - "1 487.269778 \n", - "2 486.928444 \n", - "3 487.116556 \n", - "\n", - " pre_spends GroupDifference test mean test \\\n", - "0 487.110444 \n", - "1 486.922667 \n", - "2 487.264000 \n", - "3 487.075889 \n", - "\n", - " pre_spends GroupDifference difference test \\\n", - "0 0.028444 \n", - "1 -0.347111 \n", - "2 0.335556 \n", - "3 -0.040667 \n", - "\n", - " pre_spends GroupDifference difference % test \\\n", - "0 0.005840 \n", - "1 -0.071236 \n", - "2 0.068913 \n", - "3 -0.008348 \n", - "\n", - " post_spends GroupDifference control mean test \\\n", - "0 451.633506 \n", - "1 452.036765 \n", - "2 452.910272 \n", - "3 453.141012 \n", - "\n", - " post_spends GroupDifference test mean test \\\n", - "0 452.648938 \n", - "1 452.245679 \n", - "2 451.372173 \n", - "3 451.141432 \n", - "\n", - " post_spends GroupDifference difference test \\\n", - "0 1.015432 \n", - "1 0.208914 \n", - "2 -1.538099 \n", - "3 -1.999580 \n", - "\n", - " post_spends GroupDifference difference % test \\\n", - "0 0.224835 \n", - "1 0.046216 \n", - "2 -0.339603 \n", - "3 -0.441271 \n", - "\n", - " pre_spends TTest p-value test ... post_spends TTest pass test \\\n", - "0 0.943198 ... False \n", - "1 0.384576 ... False \n", - "2 0.400601 ... False \n", - "3 0.918863 ... True \n", - "\n", - " pre_spends KSTest p-value test pre_spends KSTest pass test \\\n", - "0 0.612183 False \n", - "1 0.129368 False \n", - "2 0.085963 False \n", - "3 0.459584 False \n", - "\n", - " post_spends KSTest p-value test post_spends KSTest pass test \\\n", - "0 0.491962 False \n", - "1 0.952433 False \n", - "2 0.008355 True \n", - "3 0.105834 False \n", + " pre_spends GroupDifference control mean test_1 \\\n", + "0 486.965432 \n", + "1 487.618177 \n", + "2 487.458825 \n", + "3 487.582697 \n", + "\n", + " pre_spends GroupDifference test mean test_1 \\\n", + "0 487.857072 \n", + "1 487.204590 \n", + "2 487.363377 \n", + "3 487.239367 \n", + "\n", + " pre_spends GroupDifference difference test_1 \\\n", + "0 0.891640 \n", + "1 -0.413587 \n", + "2 -0.095448 \n", + "3 -0.343330 \n", + "\n", + " pre_spends GroupDifference difference % test_1 \\\n", + "0 0.183101 \n", + "1 -0.084818 \n", + "2 -0.019581 \n", + "3 -0.070415 \n", + "\n", + " post_spends GroupDifference control mean test_1 \\\n", + "0 453.008916 \n", + "1 452.497077 \n", + "2 452.886758 \n", + "3 451.915866 \n", + "\n", + " post_spends GroupDifference test mean test_1 \\\n", + "0 451.530843 \n", + "1 452.042668 \n", + "2 451.647464 \n", + "3 452.624849 \n", + "\n", + " post_spends GroupDifference difference test_1 \\\n", + "0 -1.478073 \n", + "1 -0.454410 \n", + "2 -1.239294 \n", + "3 0.708983 \n", + "\n", + " post_spends GroupDifference difference % test_1 \\\n", + "0 -0.326279 \n", + "1 -0.100423 \n", + "2 -0.273643 \n", + "3 0.156884 \n", + "\n", + " pre_spends TTest p-value test_1 ... post_spends TTest pass test_1 \\\n", + "0 0.036259 ... False \n", + "1 0.331448 ... False \n", + "2 0.822659 ... False \n", + "3 0.420121 ... False \n", + "\n", + " pre_spends KSTest p-value test_1 pre_spends KSTest pass test_1 \\\n", + "0 NaN False \n", + "1 NaN False \n", + "2 NaN False \n", + "3 NaN False \n", + "\n", + " post_spends KSTest p-value test_1 post_spends KSTest pass test_1 \\\n", + "0 NaN False \n", + "1 NaN False \n", + "2 NaN False \n", + "3 NaN False \n", "\n", " mean TTest p-value mean TTest pass mean KSTest p-value mean KSTest pass \\\n", - "0 0.582179 0.0 0.552072 0.0 \n", - "1 0.592924 0.0 0.540900 0.0 \n", - "2 0.232221 0.0 0.047159 0.5 \n", - "3 0.467419 0.5 0.282709 0.0 \n", + "0 0.065131 0.5 0 0.0 \n", + "1 0.469069 0.0 0 0.0 \n", + "2 0.491479 0.0 0 0.0 \n", + "3 0.420984 0.0 0 0.0 \n", "\n", " mean test score \n", - "0 0.562108 \n", - "1 0.558241 \n", - "2 0.108846 \n", - "3 0.344279 \n", + "0 0.021710 \n", + "1 0.156356 \n", + "2 0.163826 \n", + "3 0.140328 \n", "\n", "[4 rows x 22 columns]" ] }, - "execution_count": 20, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.experiments" + "result.experiments" ] }, { @@ -2891,7 +2975,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 36, "id": "b92cc8a1c4cff6d7", "metadata": { "ExecuteTime": { @@ -2905,19 +2989,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10/10 [00:01<00:00, 7.01it/s]\n", - " 30%|███ | 3/10 [00:00<00:01, 5.35it/s]\n" + "100%|██████████| 10/10 [00:03<00:00, 2.67it/s]\n", + "100%|██████████| 10/10 [00:03<00:00, 2.72it/s]\n" ] } ], "source": [ - "aa = AATest(n_iterations=10, sample_size=0.3)\n", - "res = aa.execute(data)" + "test = AATest(n_iterations=10, sample_size=0.3)\n", + "result = test.execute(data)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 37, "id": "6bc70b4602a73728", "metadata": { "ExecuteTime": { @@ -2965,57 +3049,57 @@ " \n", " 0\n", " pre_spends\n", - " test\n", + " test_1\n", + " OK\n", " OK\n", " OK\n", - " NOT OK\n", " OK\n", " OK\n", - " 487.513300\n", - " 486.674200\n", - " -0.839100\n", - " -0.172118\n", + " 486.886228\n", + " 487.537769\n", + " 0.651542\n", + " 0.133818\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", + " test_1\n", " OK\n", " OK\n", - " NOT OK\n", " OK\n", " OK\n", - " 453.078778\n", - " 451.250333\n", - " -1.828444\n", - " -0.403560\n", + " OK\n", + " 452.077428\n", + " 452.235486\n", + " 0.158057\n", + " 0.034962\n", " \n", " \n", "\n", "" ], "text/plain": [ - " feature group TTest aa test KSTest aa test TTest best split \\\n", - "0 pre_spends test OK OK NOT OK \n", - "1 post_spends test OK OK NOT OK \n", + " feature group TTest aa test KSTest aa test TTest best split \\\n", + "0 pre_spends test_1 OK OK OK \n", + "1 post_spends test_1 OK OK OK \n", "\n", " KSTest best split result control mean test mean difference difference % \n", - "0 OK OK 487.513300 486.674200 -0.839100 -0.172118 \n", - "1 OK OK 453.078778 451.250333 -1.828444 -0.403560 " + "0 OK OK 486.886228 487.537769 0.651542 0.133818 \n", + "1 OK OK 452.077428 452.235486 0.158057 0.034962 " ] }, - "execution_count": 22, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.resume" + "result.resume" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 38, "id": "6abb8f31d2e1ff8b", "metadata": { "ExecuteTime": { @@ -3051,22 +3135,22 @@ " \n", " \n", " \n", - " pre_spends TTest test\n", + " pre_spends TTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " post_spends TTest test\n", + " post_spends TTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " pre_spends KSTest test\n", + " pre_spends KSTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " post_spends KSTest test\n", + " post_spends KSTest test_1\n", " 0.95\n", " True\n", " \n", @@ -3075,25 +3159,25 @@ "" ], "text/plain": [ - " score pass\n", - "pre_spends TTest test 0.95 True\n", - "post_spends TTest test 0.95 True\n", - "pre_spends KSTest test 0.95 True\n", - "post_spends KSTest test 0.95 True" + " score pass\n", + "pre_spends TTest test_1 0.95 True\n", + "post_spends TTest test_1 0.95 True\n", + "pre_spends KSTest test_1 0.95 True\n", + "post_spends KSTest test_1 0.95 True" ] }, - "execution_count": 23, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.aa_score" + "result.aa_score" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 39, "id": "da256bacda715562", "metadata": { "ExecuteTime": { @@ -3137,63 +3221,63 @@ " \n", " \n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 488.0\n", - " 414.444444\n", - " NaN\n", - " M\n", - " E-commerce\n", - " test\n", + " 0.0\n", + " 11.0\n", + " 1.0\n", + " 476.0\n", + " 436.888889\n", + " 28.0\n", + " F\n", + " E-commerce\n", + " control\n", " \n", " \n", " 1\n", - " 1\n", - " 8\n", - " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " NaN\n", - " E-commerce\n", + " 1.0\n", + " 1.0\n", + " 1.0\n", + " 519.5\n", + " 525.222222\n", + " 36.0\n", + " F\n", + " Logistics\n", " control\n", " \n", " \n", " 2\n", - " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", - " M\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 498.5\n", + " 414.333333\n", + " 69.0\n", + " F\n", " Logistics\n", - " test\n", + " test_1\n", " \n", " \n", " 3\n", - " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", - " M\n", + " 3.0\n", + " 10.0\n", + " 1.0\n", + " 473.0\n", + " 445.888889\n", + " 43.0\n", + " F\n", " E-commerce\n", - " test\n", + " test_1\n", " \n", " \n", " 4\n", - " 4\n", - " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", + " 4.0\n", + " 11.0\n", + " 1.0\n", + " 495.0\n", + " 428.111111\n", + " 56.0\n", " F\n", " E-commerce\n", - " control\n", + " test_1\n", " \n", " \n", " ...\n", @@ -3209,63 +3293,63 @@ " \n", " \n", " 9995\n", - " 9995\n", - " 10\n", - " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", + " 9995.0\n", + " 0.0\n", + " 0.0\n", + " 475.0\n", + " 408.111111\n", + " 51.0\n", " M\n", " Logistics\n", - " test\n", + " test_1\n", " \n", " \n", " 9996\n", - " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", + " 9996.0\n", + " 0.0\n", + " 0.0\n", + " 472.5\n", + " 414.666667\n", + " 22.0\n", " F\n", - " Logistics\n", - " control\n", + " E-commerce\n", + " test_1\n", " \n", " \n", " 9997\n", - " 9997\n", - " 3\n", - " 1\n", - " 473.0\n", - " 534.111111\n", - " 22.0\n", - " F\n", + " 9997.0\n", + " 0.0\n", + " 0.0\n", + " 474.0\n", + " 419.222222\n", + " 63.0\n", + " M\n", " E-commerce\n", - " test\n", + " test_1\n", " \n", " \n", " 9998\n", - " 9998\n", - " 2\n", - " 1\n", - " 495.0\n", - " 523.222222\n", - " 67.0\n", + " 9998.0\n", + " 4.0\n", + " 1.0\n", + " 481.0\n", + " 519.888889\n", + " 21.0\n", " F\n", - " E-commerce\n", - " test\n", + " Logistics\n", + " test_1\n", " \n", " \n", " 9999\n", - " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", + " 9999.0\n", + " 0.0\n", + " 0.0\n", + " 495.5\n", + " 413.000000\n", + " 60.0\n", " F\n", " E-commerce\n", - " control\n", + " test_1\n", " \n", " \n", "\n", @@ -3274,46 +3358,46 @@ ], "text/plain": [ " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 NaN M \n", - "1 1 8 1 512.5 462.222222 26.0 NaN \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", + "0 0.0 11.0 1.0 476.0 436.888889 28.0 F \n", + "1 1.0 1.0 1.0 519.5 525.222222 36.0 F \n", + "2 2.0 0.0 0.0 498.5 414.333333 69.0 F \n", + "3 3.0 10.0 1.0 473.0 445.888889 43.0 F \n", + "4 4.0 11.0 1.0 495.0 428.111111 56.0 F \n", "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", + "9995 9995.0 0.0 0.0 475.0 408.111111 51.0 M \n", + "9996 9996.0 0.0 0.0 472.5 414.666667 22.0 F \n", + "9997 9997.0 0.0 0.0 474.0 419.222222 63.0 M \n", + "9998 9998.0 4.0 1.0 481.0 519.888889 21.0 F \n", + "9999 9999.0 0.0 0.0 495.5 413.000000 60.0 F \n", "\n", " industry split \n", - "0 E-commerce test \n", - "1 E-commerce control \n", - "2 Logistics test \n", - "3 E-commerce test \n", - "4 E-commerce control \n", + "0 E-commerce control \n", + "1 Logistics control \n", + "2 Logistics test_1 \n", + "3 E-commerce test_1 \n", + "4 E-commerce test_1 \n", "... ... ... \n", - "9995 Logistics test \n", - "9996 Logistics control \n", - "9997 E-commerce test \n", - "9998 E-commerce test \n", - "9999 E-commerce control \n", + "9995 Logistics test_1 \n", + "9996 E-commerce test_1 \n", + "9997 E-commerce test_1 \n", + "9998 Logistics test_1 \n", + "9999 E-commerce test_1 \n", "\n", "[10000 rows x 9 columns]" ] }, - "execution_count": 24, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.best_split" + "result.best_split" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 40, "id": "ab766f462ae739f9", "metadata": { "ExecuteTime": { @@ -3360,59 +3444,59 @@ " \n", " 0\n", " pre_spends\n", - " test\n", - " 487.5133\n", - " 486.6742\n", - " -0.8391000000000304\n", - " -0.17211838118058598\n", - " NOT OK\n", - " 0.0261878097679155\n", + " test_1\n", + " 486.8862275449102\n", + " 487.53776908023485\n", + " 0.6515415353246681\n", + " 0.13381802533418696\n", " OK\n", - " 0.1777267837309908\n", + " 0.2510028127854083\n", + " OK\n", + " None\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", - " 453.0787777777778\n", - " 451.2503333333334\n", - " -1.8284444444444148\n", - " -0.4035599401526646\n", - " NOT OK\n", - " 0.020327314596979347\n", + " test_1\n", + " 452.0774284763806\n", + " 452.235485975212\n", + " 0.15805749883139697\n", + " 0.034962484051481724\n", + " OK\n", + " 0.8930513511282197\n", " OK\n", - " 0.08356386970000997\n", + " None\n", " \n", " \n", "\n", "" ], "text/plain": [ - " feature group control mean test mean \\\n", - "0 pre_spends test 487.5133 486.6742 \n", - "1 post_spends test 453.0787777777778 451.2503333333334 \n", + " feature group control mean test mean \\\n", + "0 pre_spends test_1 486.8862275449102 487.53776908023485 \n", + "1 post_spends test_1 452.0774284763806 452.235485975212 \n", "\n", - " difference difference % TTest pass TTest p-value \\\n", - "0 -0.8391000000000304 -0.17211838118058598 NOT OK 0.0261878097679155 \n", - "1 -1.8284444444444148 -0.4035599401526646 NOT OK 0.020327314596979347 \n", + " difference difference % TTest pass TTest p-value \\\n", + "0 0.6515415353246681 0.13381802533418696 OK 0.2510028127854083 \n", + "1 0.15805749883139697 0.034962484051481724 OK 0.8930513511282197 \n", "\n", - " KSTest pass KSTest p-value \n", - "0 OK 0.1777267837309908 \n", - "1 OK 0.08356386970000997 " + " KSTest pass KSTest p-value \n", + "0 OK None \n", + "1 OK None " ] }, - "execution_count": 25, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.best_split_statistic" + "result.best_split_statistic" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 41, "id": "72df57c94a3d4f6d", "metadata": { "ExecuteTime": { @@ -3443,21 +3527,21 @@ " \n", " \n", " splitter_id\n", - " pre_spends GroupDifference control mean test\n", - " pre_spends GroupDifference test mean test\n", - " pre_spends GroupDifference difference test\n", - " pre_spends GroupDifference difference % test\n", - " post_spends GroupDifference control mean test\n", - " post_spends GroupDifference test mean test\n", - " post_spends GroupDifference difference test\n", - " post_spends GroupDifference difference % test\n", - " pre_spends TTest p-value test\n", + " pre_spends GroupDifference control mean test_1\n", + " pre_spends GroupDifference test mean test_1\n", + " pre_spends GroupDifference difference test_1\n", + " pre_spends GroupDifference difference % test_1\n", + " post_spends GroupDifference control mean test_1\n", + " post_spends GroupDifference test mean test_1\n", + " post_spends GroupDifference difference test_1\n", + " post_spends GroupDifference difference % test_1\n", + " pre_spends TTest p-value test_1\n", " ...\n", - " post_spends TTest pass test\n", - " pre_spends KSTest p-value test\n", - " pre_spends KSTest pass test\n", - " post_spends KSTest p-value test\n", - " post_spends KSTest pass test\n", + " post_spends TTest pass test_1\n", + " pre_spends KSTest p-value test_1\n", + " pre_spends KSTest pass test_1\n", + " post_spends KSTest p-value test_1\n", + " post_spends KSTest pass test_1\n", " mean TTest p-value\n", " mean TTest pass\n", " mean KSTest p-value\n", @@ -3469,242 +3553,242 @@ " \n", " 0\n", " AASplitter┴rs 0┴\n", - " 486.707667\n", - " 487.161882\n", - " 0.454216\n", - " 0.093324\n", - " 452.372963\n", - " 452.127778\n", - " -0.245185\n", - " -0.054200\n", - " 0.390106\n", + " 487.492604\n", + " 487.431952\n", + " -0.060652\n", + " -0.012442\n", + " 450.998685\n", + " 452.426490\n", + " 1.427805\n", + " 0.316587\n", + " 0.914487\n", " ...\n", " False\n", - " 0.105393\n", + " NaN\n", " False\n", - " 0.960105\n", + " NaN\n", " False\n", - " 0.607146\n", + " 0.568377\n", " 0.0\n", - " 0.532749\n", + " 0\n", " 0.0\n", - " 0.557548\n", + " 0.189459\n", " \n", " \n", " 1\n", " AASplitter┴rs 1┴\n", - " 486.513667\n", - " 487.196118\n", - " 0.682451\n", - " 0.140274\n", - " 451.130889\n", - " 452.346967\n", - " 1.216078\n", - " 0.269562\n", - " 0.196599\n", + " 487.911853\n", + " 487.359220\n", + " -0.552633\n", + " -0.113265\n", + " 453.169792\n", + " 452.045528\n", + " -1.124264\n", + " -0.248089\n", + " 0.330682\n", " ...\n", " False\n", - " 0.415281\n", + " NaN\n", " False\n", - " 0.146561\n", + " NaN\n", " False\n", - " 0.233545\n", + " 0.335019\n", " 0.0\n", - " 0.280921\n", + " 0\n", " 0.0\n", - " 0.265129\n", + " 0.111673\n", " \n", " \n", " 2\n", " AASplitter┴rs 2┴\n", - " 487.770333\n", - " 486.974353\n", - " -0.795980\n", - " -0.163188\n", - " 452.321852\n", - " 452.136797\n", - " -0.185054\n", - " -0.040912\n", - " 0.132031\n", + " 487.263529\n", + " 487.472360\n", + " 0.208832\n", + " 0.042858\n", + " 453.065398\n", + " 452.061582\n", + " -1.003817\n", + " -0.221561\n", + " 0.711837\n", " ...\n", " False\n", - " 0.156179\n", + " NaN\n", " False\n", - " 0.939361\n", + " NaN\n", " False\n", - " 0.499433\n", + " 0.551563\n", " 0.0\n", - " 0.547770\n", + " 0\n", " 0.0\n", - " 0.531658\n", + " 0.183854\n", " \n", " \n", " 3\n", " AASplitter┴rs 3┴\n", - " 487.175667\n", - " 487.079294\n", - " -0.096373\n", - " -0.019782\n", - " 453.188148\n", - " 451.983922\n", - " -1.204227\n", - " -0.265723\n", - " 0.855313\n", + " 486.886228\n", + " 487.537769\n", + " 0.651542\n", + " 0.133818\n", + " 452.077428\n", + " 452.235486\n", + " 0.158057\n", + " 0.034962\n", + " 0.251003\n", " ...\n", " False\n", - " 0.973579\n", + " NaN\n", " False\n", - " 0.170485\n", + " NaN\n", " False\n", - " 0.565251\n", + " 0.572027\n", " 0.0\n", - " 0.572032\n", + " 0\n", " 0.0\n", - " 0.569772\n", + " 0.190676\n", " \n", " \n", " 4\n", " AASplitter┴rs 4┴\n", - " 487.066333\n", - " 487.098588\n", - " 0.032255\n", - " 0.006622\n", - " 452.165852\n", - " 452.164327\n", - " -0.001525\n", - " -0.000337\n", - " 0.951336\n", + " 486.762325\n", + " 487.561764\n", + " 0.799439\n", + " 0.164236\n", + " 452.709590\n", + " 452.123542\n", + " -0.586048\n", + " -0.129453\n", + " 0.156058\n", " ...\n", " False\n", - " 0.989526\n", + " NaN\n", " False\n", - " 0.435548\n", + " NaN\n", " False\n", - " 0.975117\n", + " 0.385857\n", " 0.0\n", - " 0.712537\n", + " 0\n", " 0.0\n", - " 0.800064\n", + " 0.128619\n", " \n", " \n", " 5\n", " AASplitter┴rs 5┴\n", - " 487.187667\n", - " 487.077176\n", - " -0.110490\n", - " -0.022679\n", - " 449.811111\n", - " 452.579869\n", - " 2.768758\n", - " 0.615538\n", - " 0.834405\n", + " 487.136667\n", + " 487.494772\n", + " 0.358105\n", + " 0.073512\n", + " 451.589053\n", + " 452.321948\n", + " 0.732894\n", + " 0.162292\n", + " 0.526323\n", " ...\n", - " True\n", - " 0.936874\n", " False\n", - " 0.013688\n", - " True\n", - " 0.423253\n", - " 0.5\n", - " 0.475281\n", - " 0.5\n", - " 0.457938\n", + " NaN\n", + " False\n", + " NaN\n", + " False\n", + " 0.528788\n", + " 0.0\n", + " 0\n", + " 0.0\n", + " 0.176263\n", " \n", " \n", " 6\n", " AASplitter┴rs 6┴\n", - " 486.390333\n", - " 487.217882\n", - " 0.827549\n", - " 0.170141\n", - " 451.303630\n", - " 452.316484\n", - " 1.012854\n", - " 0.224429\n", - " 0.117377\n", + " 486.904303\n", + " 487.535607\n", + " 0.631304\n", + " 0.129657\n", + " 452.432245\n", + " 452.173236\n", + " -0.259009\n", + " -0.057248\n", + " 0.264264\n", " ...\n", " False\n", - " 0.397518\n", + " NaN\n", " False\n", - " 0.585789\n", + " NaN\n", " False\n", - " 0.238057\n", + " 0.544629\n", " 0.0\n", - " 0.491653\n", + " 0\n", " 0.0\n", - " 0.407121\n", + " 0.181543\n", " \n", " \n", " 7\n", " AASplitter┴rs 7┴\n", - " 486.354667\n", - " 487.224176\n", - " 0.869510\n", - " 0.178781\n", - " 453.362889\n", - " 451.953085\n", - " -1.409804\n", - " -0.310966\n", - " 0.099910\n", + " 487.354074\n", + " 487.456411\n", + " 0.102337\n", + " 0.020998\n", + " 453.439671\n", + " 451.995411\n", + " -1.444260\n", + " -0.318512\n", + " 0.856311\n", " ...\n", " False\n", - " 0.315882\n", + " NaN\n", " False\n", - " 0.173681\n", + " NaN\n", " False\n", - " 0.150670\n", + " 0.536788\n", " 0.0\n", - " 0.244782\n", + " 0\n", " 0.0\n", - " 0.213411\n", + " 0.178929\n", " \n", " \n", " 8\n", " AASplitter┴rs 8┴\n", - " 487.042000\n", - " 487.102882\n", - " 0.060882\n", - " 0.012500\n", - " 452.762963\n", - " 452.058954\n", - " -0.704009\n", - " -0.155492\n", - " 0.908291\n", + " 486.892884\n", + " 487.536525\n", + " 0.643641\n", + " 0.132194\n", + " 451.726758\n", + " 452.296533\n", + " 0.569775\n", + " 0.126133\n", + " 0.256943\n", " ...\n", " False\n", - " 0.751905\n", + " NaN\n", " False\n", - " 0.193861\n", + " NaN\n", " False\n", - " 0.715910\n", + " 0.442485\n", " 0.0\n", - " 0.472883\n", + " 0\n", " 0.0\n", - " 0.553892\n", + " 0.147495\n", " \n", " \n", " 9\n", " AASplitter┴rs 9┴\n", - " 486.539333\n", - " 487.191588\n", - " 0.652255\n", - " 0.134060\n", - " 453.368296\n", - " 451.952131\n", - " -1.416166\n", - " -0.312365\n", - " 0.217143\n", + " 488.483570\n", + " 487.258875\n", + " -1.224695\n", + " -0.250714\n", + " 454.938262\n", + " 451.735593\n", + " -3.202670\n", + " -0.703979\n", + " 0.030778\n", " ...\n", + " True\n", + " NaN\n", " False\n", - " 0.435548\n", - " False\n", - " 0.260911\n", + " NaN\n", " False\n", - " 0.208273\n", - " 0.0\n", - " 0.348230\n", + " 0.018582\n", + " 1.0\n", + " 0\n", " 0.0\n", - " 0.301578\n", + " 0.006194\n", " \n", " \n", "\n", @@ -3712,172 +3796,172 @@ "" ], "text/plain": [ - " splitter_id pre_spends GroupDifference control mean test \\\n", - "0 AASplitter┴rs 0┴ 486.707667 \n", - "1 AASplitter┴rs 1┴ 486.513667 \n", - "2 AASplitter┴rs 2┴ 487.770333 \n", - "3 AASplitter┴rs 3┴ 487.175667 \n", - "4 AASplitter┴rs 4┴ 487.066333 \n", - "5 AASplitter┴rs 5┴ 487.187667 \n", - "6 AASplitter┴rs 6┴ 486.390333 \n", - "7 AASplitter┴rs 7┴ 486.354667 \n", - "8 AASplitter┴rs 8┴ 487.042000 \n", - "9 AASplitter┴rs 9┴ 486.539333 \n", - "\n", - " pre_spends GroupDifference test mean test \\\n", - "0 487.161882 \n", - "1 487.196118 \n", - "2 486.974353 \n", - "3 487.079294 \n", - "4 487.098588 \n", - "5 487.077176 \n", - "6 487.217882 \n", - "7 487.224176 \n", - "8 487.102882 \n", - "9 487.191588 \n", - "\n", - " pre_spends GroupDifference difference test \\\n", - "0 0.454216 \n", - "1 0.682451 \n", - "2 -0.795980 \n", - "3 -0.096373 \n", - "4 0.032255 \n", - "5 -0.110490 \n", - "6 0.827549 \n", - "7 0.869510 \n", - "8 0.060882 \n", - "9 0.652255 \n", - "\n", - " pre_spends GroupDifference difference % test \\\n", - "0 0.093324 \n", - "1 0.140274 \n", - "2 -0.163188 \n", - "3 -0.019782 \n", - "4 0.006622 \n", - "5 -0.022679 \n", - "6 0.170141 \n", - "7 0.178781 \n", - "8 0.012500 \n", - "9 0.134060 \n", - "\n", - " post_spends GroupDifference control mean test \\\n", - "0 452.372963 \n", - "1 451.130889 \n", - "2 452.321852 \n", - "3 453.188148 \n", - "4 452.165852 \n", - "5 449.811111 \n", - "6 451.303630 \n", - "7 453.362889 \n", - "8 452.762963 \n", - "9 453.368296 \n", - "\n", - " post_spends GroupDifference test mean test \\\n", - "0 452.127778 \n", - "1 452.346967 \n", - "2 452.136797 \n", - "3 451.983922 \n", - "4 452.164327 \n", - "5 452.579869 \n", - "6 452.316484 \n", - "7 451.953085 \n", - "8 452.058954 \n", - "9 451.952131 \n", - "\n", - " post_spends GroupDifference difference test \\\n", - "0 -0.245185 \n", - "1 1.216078 \n", - "2 -0.185054 \n", - "3 -1.204227 \n", - "4 -0.001525 \n", - "5 2.768758 \n", - "6 1.012854 \n", - "7 -1.409804 \n", - "8 -0.704009 \n", - "9 -1.416166 \n", - "\n", - " post_spends GroupDifference difference % test \\\n", - "0 -0.054200 \n", - "1 0.269562 \n", - "2 -0.040912 \n", - "3 -0.265723 \n", - "4 -0.000337 \n", - "5 0.615538 \n", - "6 0.224429 \n", - "7 -0.310966 \n", - "8 -0.155492 \n", - "9 -0.312365 \n", - "\n", - " pre_spends TTest p-value test ... post_spends TTest pass test \\\n", - "0 0.390106 ... False \n", - "1 0.196599 ... False \n", - "2 0.132031 ... False \n", - "3 0.855313 ... False \n", - "4 0.951336 ... False \n", - "5 0.834405 ... True \n", - "6 0.117377 ... False \n", - "7 0.099910 ... False \n", - "8 0.908291 ... False \n", - "9 0.217143 ... False \n", - "\n", - " pre_spends KSTest p-value test pre_spends KSTest pass test \\\n", - "0 0.105393 False \n", - "1 0.415281 False \n", - "2 0.156179 False \n", - "3 0.973579 False \n", - "4 0.989526 False \n", - "5 0.936874 False \n", - "6 0.397518 False \n", - "7 0.315882 False \n", - "8 0.751905 False \n", - "9 0.435548 False \n", - "\n", - " post_spends KSTest p-value test post_spends KSTest pass test \\\n", - "0 0.960105 False \n", - "1 0.146561 False \n", - "2 0.939361 False \n", - "3 0.170485 False \n", - "4 0.435548 False \n", - "5 0.013688 True \n", - "6 0.585789 False \n", - "7 0.173681 False \n", - "8 0.193861 False \n", - "9 0.260911 False \n", + " splitter_id pre_spends GroupDifference control mean test_1 \\\n", + "0 AASplitter┴rs 0┴ 487.492604 \n", + "1 AASplitter┴rs 1┴ 487.911853 \n", + "2 AASplitter┴rs 2┴ 487.263529 \n", + "3 AASplitter┴rs 3┴ 486.886228 \n", + "4 AASplitter┴rs 4┴ 486.762325 \n", + "5 AASplitter┴rs 5┴ 487.136667 \n", + "6 AASplitter┴rs 6┴ 486.904303 \n", + "7 AASplitter┴rs 7┴ 487.354074 \n", + "8 AASplitter┴rs 8┴ 486.892884 \n", + "9 AASplitter┴rs 9┴ 488.483570 \n", + "\n", + " pre_spends GroupDifference test mean test_1 \\\n", + "0 487.431952 \n", + "1 487.359220 \n", + "2 487.472360 \n", + "3 487.537769 \n", + "4 487.561764 \n", + "5 487.494772 \n", + "6 487.535607 \n", + "7 487.456411 \n", + "8 487.536525 \n", + "9 487.258875 \n", + "\n", + " pre_spends GroupDifference difference test_1 \\\n", + "0 -0.060652 \n", + "1 -0.552633 \n", + "2 0.208832 \n", + "3 0.651542 \n", + "4 0.799439 \n", + "5 0.358105 \n", + "6 0.631304 \n", + "7 0.102337 \n", + "8 0.643641 \n", + "9 -1.224695 \n", + "\n", + " pre_spends GroupDifference difference % test_1 \\\n", + "0 -0.012442 \n", + "1 -0.113265 \n", + "2 0.042858 \n", + "3 0.133818 \n", + "4 0.164236 \n", + "5 0.073512 \n", + "6 0.129657 \n", + "7 0.020998 \n", + "8 0.132194 \n", + "9 -0.250714 \n", + "\n", + " post_spends GroupDifference control mean test_1 \\\n", + "0 450.998685 \n", + "1 453.169792 \n", + "2 453.065398 \n", + "3 452.077428 \n", + "4 452.709590 \n", + "5 451.589053 \n", + "6 452.432245 \n", + "7 453.439671 \n", + "8 451.726758 \n", + "9 454.938262 \n", + "\n", + " post_spends GroupDifference test mean test_1 \\\n", + "0 452.426490 \n", + "1 452.045528 \n", + "2 452.061582 \n", + "3 452.235486 \n", + "4 452.123542 \n", + "5 452.321948 \n", + "6 452.173236 \n", + "7 451.995411 \n", + "8 452.296533 \n", + "9 451.735593 \n", + "\n", + " post_spends GroupDifference difference test_1 \\\n", + "0 1.427805 \n", + "1 -1.124264 \n", + "2 -1.003817 \n", + "3 0.158057 \n", + "4 -0.586048 \n", + "5 0.732894 \n", + "6 -0.259009 \n", + "7 -1.444260 \n", + "8 0.569775 \n", + "9 -3.202670 \n", + "\n", + " post_spends GroupDifference difference % test_1 \\\n", + "0 0.316587 \n", + "1 -0.248089 \n", + "2 -0.221561 \n", + "3 0.034962 \n", + "4 -0.129453 \n", + "5 0.162292 \n", + "6 -0.057248 \n", + "7 -0.318512 \n", + "8 0.126133 \n", + "9 -0.703979 \n", + "\n", + " pre_spends TTest p-value test_1 ... post_spends TTest pass test_1 \\\n", + "0 0.914487 ... False \n", + "1 0.330682 ... False \n", + "2 0.711837 ... False \n", + "3 0.251003 ... False \n", + "4 0.156058 ... False \n", + "5 0.526323 ... False \n", + "6 0.264264 ... False \n", + "7 0.856311 ... False \n", + "8 0.256943 ... False \n", + "9 0.030778 ... True \n", + "\n", + " pre_spends KSTest p-value test_1 pre_spends KSTest pass test_1 \\\n", + "0 NaN False \n", + "1 NaN False \n", + "2 NaN False \n", + "3 NaN False \n", + "4 NaN False \n", + "5 NaN False \n", + "6 NaN False \n", + "7 NaN False \n", + "8 NaN False \n", + "9 NaN False \n", + "\n", + " post_spends KSTest p-value test_1 post_spends KSTest pass test_1 \\\n", + "0 NaN False \n", + "1 NaN False \n", + "2 NaN False \n", + "3 NaN False \n", + "4 NaN False \n", + "5 NaN False \n", + "6 NaN False \n", + "7 NaN False \n", + "8 NaN False \n", + "9 NaN False \n", "\n", " mean TTest p-value mean TTest pass mean KSTest p-value mean KSTest pass \\\n", - "0 0.607146 0.0 0.532749 0.0 \n", - "1 0.233545 0.0 0.280921 0.0 \n", - "2 0.499433 0.0 0.547770 0.0 \n", - "3 0.565251 0.0 0.572032 0.0 \n", - "4 0.975117 0.0 0.712537 0.0 \n", - "5 0.423253 0.5 0.475281 0.5 \n", - "6 0.238057 0.0 0.491653 0.0 \n", - "7 0.150670 0.0 0.244782 0.0 \n", - "8 0.715910 0.0 0.472883 0.0 \n", - "9 0.208273 0.0 0.348230 0.0 \n", + "0 0.568377 0.0 0 0.0 \n", + "1 0.335019 0.0 0 0.0 \n", + "2 0.551563 0.0 0 0.0 \n", + "3 0.572027 0.0 0 0.0 \n", + "4 0.385857 0.0 0 0.0 \n", + "5 0.528788 0.0 0 0.0 \n", + "6 0.544629 0.0 0 0.0 \n", + "7 0.536788 0.0 0 0.0 \n", + "8 0.442485 0.0 0 0.0 \n", + "9 0.018582 1.0 0 0.0 \n", "\n", " mean test score \n", - "0 0.557548 \n", - "1 0.265129 \n", - "2 0.531658 \n", - "3 0.569772 \n", - "4 0.800064 \n", - "5 0.457938 \n", - "6 0.407121 \n", - "7 0.213411 \n", - "8 0.553892 \n", - "9 0.301578 \n", + "0 0.189459 \n", + "1 0.111673 \n", + "2 0.183854 \n", + "3 0.190676 \n", + "4 0.128619 \n", + "5 0.176263 \n", + "6 0.181543 \n", + "7 0.178929 \n", + "8 0.147495 \n", + "9 0.006194 \n", "\n", "[10 rows x 22 columns]" ] }, - "execution_count": 26, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.experiments" + "result.experiments" ] }, { @@ -3892,7 +3976,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 42, "id": "db1eceb8", "metadata": {}, "outputs": [ @@ -3930,58 +4014,58 @@ " \n", " \n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 488.0\n", - " 414.444444\n", - " NaN\n", + " 0.0\n", + " 2.0\n", + " 1.0\n", + " 507.0\n", + " 514.111111\n", + " 59.0\n", " M\n", " E-commerce\n", " \n", " \n", " 1\n", - " 1\n", - " 8\n", - " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " NaN\n", - " E-commerce\n", + " 1.0\n", + " 8.0\n", + " 1.0\n", + " 501.5\n", + " 450.777778\n", + " 69.0\n", + " M\n", + " Logistics\n", " \n", " \n", " 2\n", - " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 496.0\n", + " 424.222222\n", + " 45.0\n", " M\n", - " Logistics\n", + " E-commerce\n", " \n", " \n", " 3\n", - " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", + " 3.0\n", + " 0.0\n", + " 0.0\n", + " 461.0\n", + " 441.444444\n", + " 51.0\n", " M\n", " E-commerce\n", " \n", " \n", " 4\n", - " 4\n", - " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", - " F\n", - " E-commerce\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 489.0\n", + " 410.444444\n", + " 35.0\n", + " M\n", + " Logistics\n", " \n", " \n", " ...\n", @@ -3996,57 +4080,57 @@ " \n", " \n", " 9995\n", - " 9995\n", - " 10\n", - " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", + " 9995.0\n", + " 7.0\n", + " 1.0\n", + " 477.5\n", + " 467.000000\n", + " 27.0\n", " M\n", - " Logistics\n", + " E-commerce\n", " \n", " \n", " 9996\n", - " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", - " F\n", - " Logistics\n", + " 9996.0\n", + " 0.0\n", + " 0.0\n", + " 455.5\n", + " 426.888889\n", + " 47.0\n", + " M\n", + " E-commerce\n", " \n", " \n", " 9997\n", - " 9997\n", - " 3\n", - " 1\n", + " 9997.0\n", + " 6.0\n", + " 1.0\n", " 473.0\n", - " 534.111111\n", - " 22.0\n", - " F\n", + " 482.444444\n", + " 20.0\n", + " M\n", " E-commerce\n", " \n", " \n", " 9998\n", - " 9998\n", - " 2\n", - " 1\n", - " 495.0\n", - " 523.222222\n", - " 67.0\n", + " 9998.0\n", + " 4.0\n", + " 1.0\n", + " 489.5\n", + " 499.333333\n", + " 60.0\n", " F\n", - " E-commerce\n", + " Logistics\n", " \n", " \n", " 9999\n", - " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", - " F\n", + " 9999.0\n", + " 3.0\n", + " 1.0\n", + " 485.5\n", + " 518.222222\n", + " 60.0\n", + " M\n", " E-commerce\n", " \n", " \n", @@ -4056,35 +4140,35 @@ ], "text/plain": [ " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 NaN M \n", - "1 1 8 1 512.5 462.222222 26.0 NaN \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", + "0 0.0 2.0 1.0 507.0 514.111111 59.0 M \n", + "1 1.0 8.0 1.0 501.5 450.777778 69.0 M \n", + "2 2.0 0.0 0.0 496.0 424.222222 45.0 M \n", + "3 3.0 0.0 0.0 461.0 441.444444 51.0 M \n", + "4 4.0 0.0 0.0 489.0 410.444444 35.0 M \n", "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", + "9995 9995.0 7.0 1.0 477.5 467.000000 27.0 M \n", + "9996 9996.0 0.0 0.0 455.5 426.888889 47.0 M \n", + "9997 9997.0 6.0 1.0 473.0 482.444444 20.0 M \n", + "9998 9998.0 4.0 1.0 489.5 499.333333 60.0 F \n", + "9999 9999.0 3.0 1.0 485.5 518.222222 60.0 M \n", "\n", " industry \n", "0 E-commerce \n", - "1 E-commerce \n", - "2 Logistics \n", + "1 Logistics \n", + "2 E-commerce \n", "3 E-commerce \n", - "4 E-commerce \n", + "4 Logistics \n", "... ... \n", - "9995 Logistics \n", - "9996 Logistics \n", + "9995 E-commerce \n", + "9996 E-commerce \n", "9997 E-commerce \n", - "9998 E-commerce \n", + "9998 Logistics \n", "9999 E-commerce \n", "\n", "[10000 rows x 8 columns]" ] }, - "execution_count": 27, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -4097,14 +4181,14 @@ " \"pre_spends\": TargetRole(),\n", " \"post_spends\": TargetRole(),\n", " \"gender\": TargetRole(str)\n", - " }, data=\"data.csv\",\n", + " }, data=create_test_data(),\n", ")\n", "data" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 43, "id": "cff5ba28", "metadata": {}, "outputs": [ @@ -4112,18 +4196,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10/10 [00:01<00:00, 5.32it/s]\n" + "100%|██████████| 10/10 [00:04<00:00, 2.26it/s]\n" ] } ], "source": [ - "aa = AATest(n_iterations=10)\n", - "res = aa.execute(data)" + "test = AATest(n_iterations=10)\n", + "result = test.execute(data)" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 44, "id": "2dbecb5f", "metadata": {}, "outputs": [ @@ -4167,23 +4251,23 @@ " \n", " 0\n", " pre_spends\n", - " test\n", + " test_1\n", + " OK\n", " OK\n", - " NOT OK\n", " NaN\n", " OK\n", " OK\n", " NaN\n", " OK\n", - " 487.114000\n", - " 487.0735\n", - " -0.040500\n", - " -0.008314\n", + " 487.356000\n", + " 487.460231\n", + " 0.104231\n", + " 0.021387\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", + " test_1\n", " OK\n", " OK\n", " NaN\n", @@ -4191,15 +4275,15 @@ " OK\n", " NaN\n", " OK\n", - " 452.327511\n", - " 452.0016\n", - " -0.325911\n", - " -0.072052\n", + " 451.664938\n", + " 452.415809\n", + " 0.750871\n", + " 0.166245\n", " \n", " \n", " 2\n", " gender\n", - " test\n", + " test_1\n", " NaN\n", " NaN\n", " OK\n", @@ -4217,34 +4301,34 @@ "" ], "text/plain": [ - " feature group TTest aa test KSTest aa test Chi2Test aa test \\\n", - "0 pre_spends test OK NOT OK NaN \n", - "1 post_spends test OK OK NaN \n", - "2 gender test NaN NaN OK \n", + " feature group TTest aa test KSTest aa test Chi2Test aa test \\\n", + "0 pre_spends test_1 OK OK NaN \n", + "1 post_spends test_1 OK OK NaN \n", + "2 gender test_1 NaN NaN OK \n", "\n", " TTest best split KSTest best split Chi2Test best split result control mean \\\n", - "0 OK OK NaN OK 487.114000 \n", - "1 OK OK NaN OK 452.327511 \n", + "0 OK OK NaN OK 487.356000 \n", + "1 OK OK NaN OK 451.664938 \n", "2 NaN NaN OK OK NaN \n", "\n", - " test mean difference difference % \n", - "0 487.0735 -0.040500 -0.008314 \n", - "1 452.0016 -0.325911 -0.072052 \n", - "2 NaN NaN NaN " + " test mean difference difference % \n", + "0 487.460231 0.104231 0.021387 \n", + "1 452.415809 0.750871 0.166245 \n", + "2 NaN NaN NaN " ] }, - "execution_count": 29, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.resume" + "result.resume" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 45, "id": "aa772ee4", "metadata": {}, "outputs": [ @@ -4275,27 +4359,27 @@ " \n", " \n", " \n", - " pre_spends TTest test\n", + " pre_spends TTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " post_spends TTest test\n", + " post_spends TTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " pre_spends KSTest test\n", - " 0.85\n", - " False\n", + " pre_spends KSTest test_1\n", + " 0.95\n", + " True\n", " \n", " \n", - " post_spends KSTest test\n", + " post_spends KSTest test_1\n", " 0.95\n", " True\n", " \n", " \n", - " gender Chi2Test test\n", + " gender Chi2Test test_1\n", " 0.95\n", " True\n", " \n", @@ -4304,26 +4388,26 @@ "" ], "text/plain": [ - " score pass\n", - "pre_spends TTest test 0.95 True\n", - "post_spends TTest test 0.95 True\n", - "pre_spends KSTest test 0.85 False\n", - "post_spends KSTest test 0.95 True\n", - "gender Chi2Test test 0.95 True" + " score pass\n", + "pre_spends TTest test_1 0.95 True\n", + "post_spends TTest test_1 0.95 True\n", + "pre_spends KSTest test_1 0.95 True\n", + "post_spends KSTest test_1 0.95 True\n", + "gender Chi2Test test_1 0.95 True" ] }, - "execution_count": 30, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.aa_score" + "result.aa_score" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 46, "id": "3fb5bb64", "metadata": {}, "outputs": [ @@ -4362,63 +4446,63 @@ " \n", " \n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 488.0\n", - " 414.444444\n", - " NaN\n", + " 0.0\n", + " 2.0\n", + " 1.0\n", + " 507.0\n", + " 514.111111\n", + " 59.0\n", " M\n", " E-commerce\n", - " test\n", + " control\n", " \n", " \n", " 1\n", - " 1\n", - " 8\n", - " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " NaN\n", - " E-commerce\n", - " test\n", + " 1.0\n", + " 8.0\n", + " 1.0\n", + " 501.5\n", + " 450.777778\n", + " 69.0\n", + " M\n", + " Logistics\n", + " control\n", " \n", " \n", " 2\n", - " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", + " 2.0\n", + " 0.0\n", + " 0.0\n", + " 496.0\n", + " 424.222222\n", + " 45.0\n", " M\n", - " Logistics\n", - " control\n", + " E-commerce\n", + " test_1\n", " \n", " \n", " 3\n", - " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", + " 3.0\n", + " 0.0\n", + " 0.0\n", + " 461.0\n", + " 441.444444\n", + " 51.0\n", " M\n", " E-commerce\n", - " test\n", + " test_1\n", " \n", " \n", " 4\n", - " 4\n", - " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", - " F\n", - " E-commerce\n", - " control\n", + " 4.0\n", + " 0.0\n", + " 0.0\n", + " 489.0\n", + " 410.444444\n", + " 35.0\n", + " M\n", + " Logistics\n", + " test_1\n", " \n", " \n", " ...\n", @@ -4434,63 +4518,63 @@ " \n", " \n", " 9995\n", - " 9995\n", - " 10\n", - " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", + " 9995.0\n", + " 7.0\n", + " 1.0\n", + " 477.5\n", + " 467.000000\n", + " 27.0\n", " M\n", - " Logistics\n", - " test\n", + " E-commerce\n", + " test_1\n", " \n", " \n", " 9996\n", - " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", - " F\n", - " Logistics\n", - " control\n", + " 9996.0\n", + " 0.0\n", + " 0.0\n", + " 455.5\n", + " 426.888889\n", + " 47.0\n", + " M\n", + " E-commerce\n", + " test_1\n", " \n", " \n", " 9997\n", - " 9997\n", - " 3\n", - " 1\n", + " 9997.0\n", + " 6.0\n", + " 1.0\n", " 473.0\n", - " 534.111111\n", - " 22.0\n", - " F\n", + " 482.444444\n", + " 20.0\n", + " M\n", " E-commerce\n", - " test\n", + " test_1\n", " \n", " \n", " 9998\n", - " 9998\n", - " 2\n", - " 1\n", - " 495.0\n", - " 523.222222\n", - " 67.0\n", + " 9998.0\n", + " 4.0\n", + " 1.0\n", + " 489.5\n", + " 499.333333\n", + " 60.0\n", " F\n", - " E-commerce\n", - " test\n", + " Logistics\n", + " test_1\n", " \n", " \n", " 9999\n", - " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", - " F\n", + " 9999.0\n", + " 3.0\n", + " 1.0\n", + " 485.5\n", + " 518.222222\n", + " 60.0\n", + " M\n", " E-commerce\n", - " control\n", + " test_1\n", " \n", " \n", "\n", @@ -4499,46 +4583,46 @@ ], "text/plain": [ " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 NaN M \n", - "1 1 8 1 512.5 462.222222 26.0 NaN \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", + "0 0.0 2.0 1.0 507.0 514.111111 59.0 M \n", + "1 1.0 8.0 1.0 501.5 450.777778 69.0 M \n", + "2 2.0 0.0 0.0 496.0 424.222222 45.0 M \n", + "3 3.0 0.0 0.0 461.0 441.444444 51.0 M \n", + "4 4.0 0.0 0.0 489.0 410.444444 35.0 M \n", "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", + "9995 9995.0 7.0 1.0 477.5 467.000000 27.0 M \n", + "9996 9996.0 0.0 0.0 455.5 426.888889 47.0 M \n", + "9997 9997.0 6.0 1.0 473.0 482.444444 20.0 M \n", + "9998 9998.0 4.0 1.0 489.5 499.333333 60.0 F \n", + "9999 9999.0 3.0 1.0 485.5 518.222222 60.0 M \n", "\n", " industry split \n", - "0 E-commerce test \n", - "1 E-commerce test \n", - "2 Logistics control \n", - "3 E-commerce test \n", - "4 E-commerce control \n", + "0 E-commerce control \n", + "1 Logistics control \n", + "2 E-commerce test_1 \n", + "3 E-commerce test_1 \n", + "4 Logistics test_1 \n", "... ... ... \n", - "9995 Logistics test \n", - "9996 Logistics control \n", - "9997 E-commerce test \n", - "9998 E-commerce test \n", - "9999 E-commerce control \n", + "9995 E-commerce test_1 \n", + "9996 E-commerce test_1 \n", + "9997 E-commerce test_1 \n", + "9998 Logistics test_1 \n", + "9999 E-commerce test_1 \n", "\n", "[10000 rows x 9 columns]" ] }, - "execution_count": 31, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.best_split" + "result.best_split" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 47, "id": "0bc5d8e0", "metadata": {}, "outputs": [ @@ -4581,37 +4665,37 @@ " \n", " 0\n", " pre_spends\n", - " test\n", - " 487.114\n", - " 487.0735\n", - " -0.0404999999999518\n", - " -0.008314275508392033\n", + " test_1\n", + " 487.356\n", + " 487.4602310597645\n", + " 0.10423105976451552\n", + " 0.021387047612941856\n", " OK\n", - " 0.9145492975888028\n", + " 0.7960766529784996\n", " OK\n", - " 0.5770455454055606\n", + " NaN\n", " NaN\n", " NaN\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", - " 452.327511111111\n", - " 452.0016\n", - " -0.32591111111099735\n", - " -0.07205202051726589\n", + " test_1\n", + " 451.664938271605\n", + " 452.4158088326051\n", + " 0.7508705610000561\n", + " 0.16624504082016767\n", " OK\n", - " 0.6792262298738265\n", + " 0.36839695002856443\n", " OK\n", - " 0.48067530684717075\n", + " NaN\n", " NaN\n", " NaN\n", " \n", " \n", " 2\n", " gender\n", - " test\n", + " test_1\n", " NaN\n", " NaN\n", " NaN\n", @@ -4621,41 +4705,41 @@ " NaN\n", " NaN\n", " OK\n", - " 0.9290699677487573\n", + " 1.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " feature group control mean test mean difference \\\n", - "0 pre_spends test 487.114 487.0735 -0.0404999999999518 \n", - "1 post_spends test 452.327511111111 452.0016 -0.32591111111099735 \n", - "2 gender test NaN NaN NaN \n", - "\n", - " difference % TTest pass TTest p-value KSTest pass \\\n", - "0 -0.008314275508392033 OK 0.9145492975888028 OK \n", - "1 -0.07205202051726589 OK 0.6792262298738265 OK \n", - "2 NaN NaN NaN NaN \n", - "\n", - " KSTest p-value Chi2Test pass Chi2Test p-value \n", - "0 0.5770455454055606 NaN NaN \n", - "1 0.48067530684717075 NaN NaN \n", - "2 NaN OK 0.9290699677487573 " + " feature group control mean test mean \\\n", + "0 pre_spends test_1 487.356 487.4602310597645 \n", + "1 post_spends test_1 451.664938271605 452.4158088326051 \n", + "2 gender test_1 NaN NaN \n", + "\n", + " difference difference % TTest pass TTest p-value \\\n", + "0 0.10423105976451552 0.021387047612941856 OK 0.7960766529784996 \n", + "1 0.7508705610000561 0.16624504082016767 OK 0.36839695002856443 \n", + "2 NaN NaN NaN NaN \n", + "\n", + " KSTest pass KSTest p-value Chi2Test pass Chi2Test p-value \n", + "0 OK NaN NaN NaN \n", + "1 OK NaN NaN NaN \n", + "2 NaN NaN OK 1.0 " ] }, - "execution_count": 32, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.best_split_statistic" + "result.best_split_statistic" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 48, "id": "1f00904a", "metadata": {}, "outputs": [ @@ -4681,19 +4765,19 @@ " \n", " \n", " splitter_id\n", - " pre_spends GroupDifference control mean test\n", - " pre_spends GroupDifference test mean test\n", - " pre_spends GroupDifference difference test\n", - " pre_spends GroupDifference difference % test\n", - " post_spends GroupDifference control mean test\n", - " post_spends GroupDifference test mean test\n", - " post_spends GroupDifference difference test\n", - " post_spends GroupDifference difference % test\n", - " pre_spends TTest p-value test\n", + " pre_spends GroupDifference control mean test_1\n", + " pre_spends GroupDifference test mean test_1\n", + " pre_spends GroupDifference difference test_1\n", + " pre_spends GroupDifference difference % test_1\n", + " post_spends GroupDifference control mean test_1\n", + " post_spends GroupDifference test mean test_1\n", + " post_spends GroupDifference difference test_1\n", + " post_spends GroupDifference difference % test_1\n", + " pre_spends TTest p-value test_1\n", " ...\n", - " post_spends KSTest pass test\n", - " gender Chi2Test p-value test\n", - " gender Chi2Test pass test\n", + " post_spends KSTest pass test_1\n", + " gender Chi2Test p-value test_1\n", + " gender Chi2Test pass test_1\n", " mean TTest p-value\n", " mean TTest pass\n", " mean KSTest p-value\n", @@ -4707,242 +4791,242 @@ " \n", " 0\n", " AASplitter┴rs 0┴\n", - " 486.8074\n", - " 487.3801\n", - " 0.5727\n", - " 0.117644\n", - " 451.724200\n", - " 452.604911\n", - " 0.880711\n", - " 0.194967\n", - " 0.129161\n", + " 487.286493\n", + " 487.529399\n", + " 0.242906\n", + " 0.049849\n", + " 451.798052\n", + " 452.282080\n", + " 0.484028\n", + " 0.107134\n", + " 0.547002\n", " ...\n", " False\n", - " 1.000000\n", + " 0.272458\n", " False\n", - " 0.196474\n", + " 0.554520\n", " 0.0\n", - " 0.252129\n", - " 0.5\n", - " 1.000000\n", + " 0\n", " 0.0\n", - " 0.540146\n", + " 0.272458\n", + " 0.0\n", + " 0.219887\n", " \n", " \n", " 1\n", " AASplitter┴rs 1┴\n", - " 486.8542\n", - " 487.3333\n", - " 0.4791\n", - " 0.098407\n", - " 452.151400\n", - " 452.177711\n", - " 0.026311\n", - " 0.005819\n", - " 0.204300\n", + " 487.528141\n", + " 487.287433\n", + " -0.240708\n", + " -0.049373\n", + " 452.549573\n", + " 451.528421\n", + " -1.021151\n", + " -0.225644\n", + " 0.550636\n", " ...\n", " False\n", - " 0.821173\n", + " 0.269199\n", " False\n", - " 0.588834\n", + " 0.385931\n", " 0.0\n", - " 0.490752\n", + " 0\n", " 0.0\n", - " 0.821173\n", + " 0.269199\n", " 0.0\n", - " 0.642537\n", + " 0.184866\n", " \n", " \n", " 2\n", " AASplitter┴rs 2┴\n", - " 487.1430\n", - " 487.0445\n", - " -0.0985\n", - " -0.020220\n", - " 451.504911\n", - " 452.824200\n", - " 1.319289\n", - " 0.292198\n", - " 0.794116\n", + " 487.489227\n", + " 487.326962\n", + " -0.162265\n", + " -0.033286\n", + " 451.582062\n", + " 452.499074\n", + " 0.917012\n", + " 0.203066\n", + " 0.687450\n", " ...\n", " False\n", - " 0.372679\n", + " 0.759602\n", " False\n", - " 0.444120\n", + " 0.479715\n", " 0.0\n", - " 0.452796\n", + " 0\n", " 0.0\n", - " 0.372679\n", + " 0.759602\n", " 0.0\n", - " 0.419014\n", + " 0.399784\n", " \n", " \n", " 3\n", " AASplitter┴rs 3┴\n", - " 487.5133\n", - " 486.6742\n", - " -0.8391\n", - " -0.172118\n", - " 453.078778\n", - " 451.250333\n", - " -1.828444\n", - " -0.403560\n", - " 0.026188\n", + " 487.356000\n", + " 487.460231\n", + " 0.104231\n", + " 0.021387\n", + " 451.664938\n", + " 452.415809\n", + " 0.750871\n", + " 0.166245\n", + " 0.796077\n", " ...\n", " False\n", - " 0.341025\n", + " 1.000000\n", " False\n", - " 0.023258\n", - " 1.0\n", - " 0.130645\n", + " 0.582237\n", + " 0.0\n", + " 0\n", " 0.0\n", - " 0.341025\n", + " 1.000000\n", " 0.0\n", - " 0.193320\n", + " 0.516447\n", " \n", " \n", " 4\n", " AASplitter┴rs 4┴\n", - " 486.9905\n", - " 487.1970\n", - " 0.2065\n", - " 0.042403\n", - " 451.916489\n", - " 452.412622\n", - " 0.496133\n", - " 0.109784\n", - " 0.584302\n", + " 487.586055\n", + " 487.227680\n", + " -0.358375\n", + " -0.073500\n", + " 451.678043\n", + " 452.407896\n", + " 0.729854\n", + " 0.161587\n", + " 0.374249\n", " ...\n", " False\n", - " 0.579559\n", + " 0.347871\n", " False\n", - " 0.556661\n", + " 0.378107\n", " 0.0\n", - " 0.362782\n", + " 0\n", " 0.0\n", - " 0.579559\n", + " 0.347871\n", " 0.0\n", - " 0.488269\n", + " 0.214770\n", " \n", " \n", " 5\n", " AASplitter┴rs 5┴\n", - " 487.2922\n", - " 486.8953\n", - " -0.3969\n", - " -0.081450\n", - " 451.686889\n", - " 452.642222\n", - " 0.955333\n", - " 0.211503\n", - " 0.292988\n", + " 487.189579\n", + " 487.627589\n", + " 0.438010\n", + " 0.089905\n", + " 451.538162\n", + " 452.544793\n", + " 1.006631\n", + " 0.222934\n", + " 0.277469\n", " ...\n", " False\n", - " 0.346368\n", + " 0.486647\n", " False\n", - " 0.259216\n", + " 0.252667\n", " 0.0\n", - " 0.399740\n", + " 0\n", " 0.0\n", - " 0.346368\n", + " 0.486647\n", " 0.0\n", - " 0.350287\n", + " 0.245192\n", " \n", " \n", " 6\n", " AASplitter┴rs 6┴\n", - " 486.8775\n", - " 487.3100\n", - " 0.4325\n", - " 0.088831\n", - " 451.627689\n", - " 452.701422\n", - " 1.073733\n", - " 0.237747\n", - " 0.251829\n", + " 487.502770\n", + " 487.312946\n", + " -0.189824\n", + " -0.038938\n", + " 451.985228\n", + " 452.095910\n", + " 0.110682\n", + " 0.024488\n", + " 0.637894\n", " ...\n", " False\n", - " 0.342281\n", + " 0.268135\n", " False\n", - " 0.212447\n", + " 0.766208\n", " 0.0\n", - " 0.349031\n", + " 0\n", " 0.0\n", - " 0.342281\n", + " 0.268135\n", " 0.0\n", - " 0.319014\n", + " 0.260496\n", " \n", " \n", " 7\n", " AASplitter┴rs 7┴\n", - " 487.0070\n", - " 487.1805\n", - " 0.1735\n", - " 0.035626\n", - " 452.526978\n", - " 451.802133\n", - " -0.724844\n", - " -0.160177\n", - " 0.645746\n", + " 487.615016\n", + " 487.202921\n", + " -0.412095\n", + " -0.084512\n", + " 452.044177\n", + " 452.036685\n", + " -0.007492\n", + " -0.001657\n", + " 0.306896\n", " ...\n", " False\n", - " 0.800429\n", + " 0.552795\n", " False\n", - " 0.501737\n", + " 0.649868\n", " 0.0\n", - " 0.754794\n", + " 0\n", " 0.0\n", - " 0.800429\n", + " 0.552795\n", " 0.0\n", - " 0.722436\n", + " 0.351092\n", " \n", " \n", " 8\n", " AASplitter┴rs 8┴\n", - " 486.7993\n", - " 487.3882\n", - " 0.5889\n", - " 0.120974\n", - " 451.924844\n", - " 452.404267\n", - " 0.479422\n", - " 0.106085\n", - " 0.118678\n", + " 486.937290\n", + " 487.873233\n", + " 0.935943\n", + " 0.192210\n", + " 451.540825\n", + " 452.533937\n", + " 0.993112\n", + " 0.219938\n", + " 0.020295\n", " ...\n", " False\n", - " 0.936576\n", + " 0.964712\n", " False\n", - " 0.330834\n", - " 0.0\n", - " 0.392027\n", + " 0.127237\n", " 0.5\n", - " 0.936576\n", + " 0\n", " 0.0\n", - " 0.597608\n", + " 0.964712\n", + " 0.0\n", + " 0.411332\n", " \n", " \n", " 9\n", " AASplitter┴rs 9┴\n", - " 487.1140\n", - " 487.0735\n", - " -0.0405\n", - " -0.008314\n", - " 452.327511\n", - " 452.001600\n", - " -0.325911\n", - " -0.072052\n", - " 0.914549\n", + " 487.308289\n", + " 487.508465\n", + " 0.200176\n", + " 0.041078\n", + " 451.348084\n", + " 452.736294\n", + " 1.388210\n", + " 0.307570\n", + " 0.619674\n", " ...\n", " False\n", - " 0.929070\n", + " 0.360739\n", " False\n", - " 0.796888\n", + " 0.357989\n", " 0.0\n", - " 0.528860\n", + " 0\n", " 0.0\n", - " 0.929070\n", + " 0.360739\n", " 0.0\n", - " 0.742550\n", + " 0.215893\n", " \n", " \n", "\n", @@ -4950,160 +5034,160 @@ "" ], "text/plain": [ - " splitter_id pre_spends GroupDifference control mean test \\\n", - "0 AASplitter┴rs 0┴ 486.8074 \n", - "1 AASplitter┴rs 1┴ 486.8542 \n", - "2 AASplitter┴rs 2┴ 487.1430 \n", - "3 AASplitter┴rs 3┴ 487.5133 \n", - "4 AASplitter┴rs 4┴ 486.9905 \n", - "5 AASplitter┴rs 5┴ 487.2922 \n", - "6 AASplitter┴rs 6┴ 486.8775 \n", - "7 AASplitter┴rs 7┴ 487.0070 \n", - "8 AASplitter┴rs 8┴ 486.7993 \n", - "9 AASplitter┴rs 9┴ 487.1140 \n", - "\n", - " pre_spends GroupDifference test mean test \\\n", - "0 487.3801 \n", - "1 487.3333 \n", - "2 487.0445 \n", - "3 486.6742 \n", - "4 487.1970 \n", - "5 486.8953 \n", - "6 487.3100 \n", - "7 487.1805 \n", - "8 487.3882 \n", - "9 487.0735 \n", - "\n", - " pre_spends GroupDifference difference test \\\n", - "0 0.5727 \n", - "1 0.4791 \n", - "2 -0.0985 \n", - "3 -0.8391 \n", - "4 0.2065 \n", - "5 -0.3969 \n", - "6 0.4325 \n", - "7 0.1735 \n", - "8 0.5889 \n", - "9 -0.0405 \n", - "\n", - " pre_spends GroupDifference difference % test \\\n", - "0 0.117644 \n", - "1 0.098407 \n", - "2 -0.020220 \n", - "3 -0.172118 \n", - "4 0.042403 \n", - "5 -0.081450 \n", - "6 0.088831 \n", - "7 0.035626 \n", - "8 0.120974 \n", - "9 -0.008314 \n", - "\n", - " post_spends GroupDifference control mean test \\\n", - "0 451.724200 \n", - "1 452.151400 \n", - "2 451.504911 \n", - "3 453.078778 \n", - "4 451.916489 \n", - "5 451.686889 \n", - "6 451.627689 \n", - "7 452.526978 \n", - "8 451.924844 \n", - "9 452.327511 \n", - "\n", - " post_spends GroupDifference test mean test \\\n", - "0 452.604911 \n", - "1 452.177711 \n", - "2 452.824200 \n", - "3 451.250333 \n", - "4 452.412622 \n", - "5 452.642222 \n", - "6 452.701422 \n", - "7 451.802133 \n", - "8 452.404267 \n", - "9 452.001600 \n", - "\n", - " post_spends GroupDifference difference test \\\n", - "0 0.880711 \n", - "1 0.026311 \n", - "2 1.319289 \n", - "3 -1.828444 \n", - "4 0.496133 \n", - "5 0.955333 \n", - "6 1.073733 \n", - "7 -0.724844 \n", - "8 0.479422 \n", - "9 -0.325911 \n", - "\n", - " post_spends GroupDifference difference % test \\\n", - "0 0.194967 \n", - "1 0.005819 \n", - "2 0.292198 \n", - "3 -0.403560 \n", - "4 0.109784 \n", - "5 0.211503 \n", - "6 0.237747 \n", - "7 -0.160177 \n", - "8 0.106085 \n", - "9 -0.072052 \n", - "\n", - " pre_spends TTest p-value test ... post_spends KSTest pass test \\\n", - "0 0.129161 ... False \n", - "1 0.204300 ... False \n", - "2 0.794116 ... False \n", - "3 0.026188 ... False \n", - "4 0.584302 ... False \n", - "5 0.292988 ... False \n", - "6 0.251829 ... False \n", - "7 0.645746 ... False \n", - "8 0.118678 ... False \n", - "9 0.914549 ... False \n", - "\n", - " gender Chi2Test p-value test gender Chi2Test pass test \\\n", - "0 1.000000 False \n", - "1 0.821173 False \n", - "2 0.372679 False \n", - "3 0.341025 False \n", - "4 0.579559 False \n", - "5 0.346368 False \n", - "6 0.342281 False \n", - "7 0.800429 False \n", - "8 0.936576 False \n", - "9 0.929070 False \n", + " splitter_id pre_spends GroupDifference control mean test_1 \\\n", + "0 AASplitter┴rs 0┴ 487.286493 \n", + "1 AASplitter┴rs 1┴ 487.528141 \n", + "2 AASplitter┴rs 2┴ 487.489227 \n", + "3 AASplitter┴rs 3┴ 487.356000 \n", + "4 AASplitter┴rs 4┴ 487.586055 \n", + "5 AASplitter┴rs 5┴ 487.189579 \n", + "6 AASplitter┴rs 6┴ 487.502770 \n", + "7 AASplitter┴rs 7┴ 487.615016 \n", + "8 AASplitter┴rs 8┴ 486.937290 \n", + "9 AASplitter┴rs 9┴ 487.308289 \n", + "\n", + " pre_spends GroupDifference test mean test_1 \\\n", + "0 487.529399 \n", + "1 487.287433 \n", + "2 487.326962 \n", + "3 487.460231 \n", + "4 487.227680 \n", + "5 487.627589 \n", + "6 487.312946 \n", + "7 487.202921 \n", + "8 487.873233 \n", + "9 487.508465 \n", + "\n", + " pre_spends GroupDifference difference test_1 \\\n", + "0 0.242906 \n", + "1 -0.240708 \n", + "2 -0.162265 \n", + "3 0.104231 \n", + "4 -0.358375 \n", + "5 0.438010 \n", + "6 -0.189824 \n", + "7 -0.412095 \n", + "8 0.935943 \n", + "9 0.200176 \n", + "\n", + " pre_spends GroupDifference difference % test_1 \\\n", + "0 0.049849 \n", + "1 -0.049373 \n", + "2 -0.033286 \n", + "3 0.021387 \n", + "4 -0.073500 \n", + "5 0.089905 \n", + "6 -0.038938 \n", + "7 -0.084512 \n", + "8 0.192210 \n", + "9 0.041078 \n", + "\n", + " post_spends GroupDifference control mean test_1 \\\n", + "0 451.798052 \n", + "1 452.549573 \n", + "2 451.582062 \n", + "3 451.664938 \n", + "4 451.678043 \n", + "5 451.538162 \n", + "6 451.985228 \n", + "7 452.044177 \n", + "8 451.540825 \n", + "9 451.348084 \n", + "\n", + " post_spends GroupDifference test mean test_1 \\\n", + "0 452.282080 \n", + "1 451.528421 \n", + "2 452.499074 \n", + "3 452.415809 \n", + "4 452.407896 \n", + "5 452.544793 \n", + "6 452.095910 \n", + "7 452.036685 \n", + "8 452.533937 \n", + "9 452.736294 \n", + "\n", + " post_spends GroupDifference difference test_1 \\\n", + "0 0.484028 \n", + "1 -1.021151 \n", + "2 0.917012 \n", + "3 0.750871 \n", + "4 0.729854 \n", + "5 1.006631 \n", + "6 0.110682 \n", + "7 -0.007492 \n", + "8 0.993112 \n", + "9 1.388210 \n", + "\n", + " post_spends GroupDifference difference % test_1 \\\n", + "0 0.107134 \n", + "1 -0.225644 \n", + "2 0.203066 \n", + "3 0.166245 \n", + "4 0.161587 \n", + "5 0.222934 \n", + "6 0.024488 \n", + "7 -0.001657 \n", + "8 0.219938 \n", + "9 0.307570 \n", + "\n", + " pre_spends TTest p-value test_1 ... post_spends KSTest pass test_1 \\\n", + "0 0.547002 ... False \n", + "1 0.550636 ... False \n", + "2 0.687450 ... False \n", + "3 0.796077 ... False \n", + "4 0.374249 ... False \n", + "5 0.277469 ... False \n", + "6 0.637894 ... False \n", + "7 0.306896 ... False \n", + "8 0.020295 ... False \n", + "9 0.619674 ... False \n", + "\n", + " gender Chi2Test p-value test_1 gender Chi2Test pass test_1 \\\n", + "0 0.272458 False \n", + "1 0.269199 False \n", + "2 0.759602 False \n", + "3 1.000000 False \n", + "4 0.347871 False \n", + "5 0.486647 False \n", + "6 0.268135 False \n", + "7 0.552795 False \n", + "8 0.964712 False \n", + "9 0.360739 False \n", "\n", " mean TTest p-value mean TTest pass mean KSTest p-value mean KSTest pass \\\n", - "0 0.196474 0.0 0.252129 0.5 \n", - "1 0.588834 0.0 0.490752 0.0 \n", - "2 0.444120 0.0 0.452796 0.0 \n", - "3 0.023258 1.0 0.130645 0.0 \n", - "4 0.556661 0.0 0.362782 0.0 \n", - "5 0.259216 0.0 0.399740 0.0 \n", - "6 0.212447 0.0 0.349031 0.0 \n", - "7 0.501737 0.0 0.754794 0.0 \n", - "8 0.330834 0.0 0.392027 0.5 \n", - "9 0.796888 0.0 0.528860 0.0 \n", + "0 0.554520 0.0 0 0.0 \n", + "1 0.385931 0.0 0 0.0 \n", + "2 0.479715 0.0 0 0.0 \n", + "3 0.582237 0.0 0 0.0 \n", + "4 0.378107 0.0 0 0.0 \n", + "5 0.252667 0.0 0 0.0 \n", + "6 0.766208 0.0 0 0.0 \n", + "7 0.649868 0.0 0 0.0 \n", + "8 0.127237 0.5 0 0.0 \n", + "9 0.357989 0.0 0 0.0 \n", "\n", " mean Chi2Test p-value mean Chi2Test pass mean test score \n", - "0 1.000000 0.0 0.540146 \n", - "1 0.821173 0.0 0.642537 \n", - "2 0.372679 0.0 0.419014 \n", - "3 0.341025 0.0 0.193320 \n", - "4 0.579559 0.0 0.488269 \n", - "5 0.346368 0.0 0.350287 \n", - "6 0.342281 0.0 0.319014 \n", - "7 0.800429 0.0 0.722436 \n", - "8 0.936576 0.0 0.597608 \n", - "9 0.929070 0.0 0.742550 \n", + "0 0.272458 0.0 0.219887 \n", + "1 0.269199 0.0 0.184866 \n", + "2 0.759602 0.0 0.399784 \n", + "3 1.000000 0.0 0.516447 \n", + "4 0.347871 0.0 0.214770 \n", + "5 0.486647 0.0 0.245192 \n", + "6 0.268135 0.0 0.260496 \n", + "7 0.552795 0.0 0.351092 \n", + "8 0.964712 0.0 0.411332 \n", + "9 0.360739 0.0 0.215893 \n", "\n", "[10 rows x 26 columns]" ] }, - "execution_count": 33, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "res.experiments" + "result.experiments" ] }, { @@ -5118,7 +5202,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "f092457f", "metadata": {}, "outputs": [ @@ -5126,18 +5210,25 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 10/10 [00:01<00:00, 5.55it/s]\n" + " 0%| | 0/10 [00:00\n", " \n", " control\n", - " 3000\n", - " 3000\n", - " 3000\n", - " 3000\n", - " 3000\n", - " 2711\n", - " 2679\n", - " 3000\n", - " \n", - " \n", - " test\n", - " 7000\n", - " 7000\n", - " 7000\n", - " 7000\n", - " 7000\n", - " 6289\n", - " 6321\n", - " 7000\n", + " 2731\n", + " 2731\n", + " 2731\n", + " 2731\n", + " 2731\n", + " 2731\n", + " 2731\n", + " 2731\n", + " \n", + " \n", + " test_1\n", + " 6270\n", + " 6270\n", + " 6270\n", + " 6270\n", + " 6270\n", + " 6270\n", + " 6270\n", + " 6270\n", " \n", " \n", "\n", @@ -5213,26 +5304,27 @@ "text/plain": [ " user_id signup_month treat pre_spends post_spends age gender \\\n", "split \n", - "control 3000 3000 3000 3000 3000 2711 2679 \n", - "test 7000 7000 7000 7000 7000 6289 6321 \n", + "control 2731 2731 2731 2731 2731 2731 2731 \n", + "test_1 6270 6270 6270 6270 6270 6270 6270 \n", "\n", " industry \n", "split \n", - "control 3000 \n", - "test 7000 " + "control 2731 \n", + "test_1 6270 " ] }, + "execution_count": 50, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "res.best_split.data.groupby(\"split\").agg(\"count\")" + "result.best_split.data.groupby(\"split\").agg(\"count\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "52a0d55e", "metadata": {}, "outputs": [ @@ -5259,6 +5351,8 @@ " \n", " feature\n", " group\n", + " control mean\n", + " test mean\n", " difference\n", " difference %\n", " TTest pass\n", @@ -5273,33 +5367,39 @@ " \n", " 0\n", " pre_spends\n", - " test\n", - " 0.2629761904761949\n", - " 0.054009235897178876\n", + " test_1\n", + " 487.5825704870011\n", + " 487.3321371610845\n", + " -0.25043332591656053\n", + " -0.05136223915190863\n", " OK\n", - " 0.5170246640610558\n", + " 0.5639126299904302\n", " OK\n", - " 0.42314945227184436\n", + " NaN\n", " NaN\n", " NaN\n", " \n", " \n", " 1\n", " post_spends\n", - " test\n", - " 0.8400000000000318\n", - " 0.18601497125256827\n", + " test_1\n", + " 451.7230969526831\n", + " 452.1786283891547\n", + " 0.45553143647163097\n", + " 0.10084306946991362\n", " OK\n", - " 0.32603901219229925\n", + " 0.6168334001746815\n", " OK\n", - " 0.6865019024154115\n", + " NaN\n", " NaN\n", " NaN\n", " \n", " \n", " 2\n", " gender\n", - " test\n", + " test_1\n", + " NaN\n", + " NaN\n", " NaN\n", " NaN\n", " NaN\n", @@ -5307,35 +5407,1051 @@ " NaN\n", " NaN\n", " OK\n", - " 0.9701015769632051\n", + " 1.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " feature group difference difference % TTest pass \\\n", - "0 pre_spends test 0.2629761904761949 0.054009235897178876 OK \n", - "1 post_spends test 0.8400000000000318 0.18601497125256827 OK \n", - "2 gender test NaN NaN NaN \n", + " feature group control mean test mean \\\n", + "0 pre_spends test_1 487.5825704870011 487.3321371610845 \n", + "1 post_spends test_1 451.7230969526831 452.1786283891547 \n", + "2 gender test_1 NaN NaN \n", + "\n", + " difference difference % TTest pass TTest p-value \\\n", + "0 -0.25043332591656053 -0.05136223915190863 OK 0.5639126299904302 \n", + "1 0.45553143647163097 0.10084306946991362 OK 0.6168334001746815 \n", + "2 NaN NaN NaN NaN \n", + "\n", + " KSTest pass KSTest p-value Chi2Test pass Chi2Test p-value \n", + "0 OK NaN NaN NaN \n", + "1 OK NaN NaN NaN \n", + "2 NaN NaN OK 1.0 " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.best_split_statistic" + ] + }, + { + "cell_type": "markdown", + "id": "fa262a91", + "metadata": {}, + "source": [ + "# AAnTest\n", + "\n", + "AAnTest is an extension of AATest that allows to split the dataset into several test groups, additionally to the control group." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "721af722", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10/10 [00:08<00:00, 1.22it/s]\n" + ] + } + ], + "source": [ + "test = AATest(groups_sizes=[0.3, 0.2, 0.2, 0.3])\n", + "result = test.execute(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "2e06da9f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idsignup_monthtreatpre_spendspost_spendsagegenderindustry
split
control27102710271027102710271027102710
test_117921792179217921792179217921792
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" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "split \n", + "control 2710 2710 2710 2710 2710 2710 2710 \n", + "test_1 1792 1792 1792 1792 1792 1792 1792 \n", + "test_2 1802 1802 1802 1802 1802 1802 1802 \n", + "test_3 2697 2697 2697 2697 2697 2697 2697 \n", + "\n", + " industry \n", + "split \n", + "control 2710 \n", + "test_1 1792 \n", + "test_2 1802 \n", + "test_3 2697 " ] }, + "execution_count": 53, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "res.best_split_statistic" + "result.best_split.data.groupby(\"split\").agg(\"count\")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "78d5822e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featuregroupcontrol meantest meandifferencedifference %TTest passTTest p-valueKSTest passKSTest p-valueChi2Test passChi2Test p-value
0pre_spendstest_1487.51162361623614487.45535714285717-0.05626647337896884-0.011541565503936368OK0.9237459969594596OKNaNNaNNaN
1pre_spendstest_2487.51162361623614487.23473917869035-0.27688443754578884-0.05679545350979476OK0.6346033057388143OKNaNNaNNaN
2pre_spendstest_3487.51162361623614487.38857990359656-0.12304371263957137-0.02523913414143042OK0.8113756749318759OKNaNNaNNaN
3post_spendstest_1451.11914719147194452.282118055555541.16297086408360430.25779683068738457OK0.32943324267093954OKNaNNaNNaN
4post_spendstest_2451.11914719147194453.46140091256632.342253721094380.5192095559846122OK0.05446016907853474OKNaNNaNNaN
5post_spendstest_3451.11914719147194451.85609524986610.73694805839414810.16335995999774422OK0.49088418106719045OKNaNNaNNaN
6gendertest_1NaNNaNNaNNaNNaNNaNNaNNaNOK0.8717290989479588
7gendertest_2NaNNaNNaNNaNNaNNaNNaNNaNOK0.8911496337110267
8gendertest_3NaNNaNNaNNaNNaNNaNNaNNaNOK0.7053738742058828
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" + ], + "text/plain": [ + " feature group control mean test mean \\\n", + "0 pre_spends test_1 487.51162361623614 487.45535714285717 \n", + "1 pre_spends test_2 487.51162361623614 487.23473917869035 \n", + "2 pre_spends test_3 487.51162361623614 487.38857990359656 \n", + "3 post_spends test_1 451.11914719147194 452.28211805555554 \n", + "4 post_spends test_2 451.11914719147194 453.4614009125663 \n", + "5 post_spends test_3 451.11914719147194 451.8560952498661 \n", + "6 gender test_1 NaN NaN \n", + "7 gender test_2 NaN NaN \n", + "8 gender test_3 NaN NaN \n", + "\n", + " difference difference % TTest pass \\\n", + "0 -0.05626647337896884 -0.011541565503936368 OK \n", + "1 -0.27688443754578884 -0.05679545350979476 OK \n", + "2 -0.12304371263957137 -0.02523913414143042 OK \n", + "3 1.1629708640836043 0.25779683068738457 OK \n", + "4 2.34225372109438 0.5192095559846122 OK \n", + "5 0.7369480583941481 0.16335995999774422 OK \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "\n", + " TTest p-value KSTest pass KSTest p-value Chi2Test pass \\\n", + "0 0.9237459969594596 OK NaN NaN \n", + "1 0.6346033057388143 OK NaN NaN \n", + "2 0.8113756749318759 OK NaN NaN \n", + "3 0.32943324267093954 OK NaN NaN \n", + "4 0.05446016907853474 OK NaN NaN \n", + "5 0.49088418106719045 OK NaN NaN \n", + "6 NaN NaN NaN OK \n", + "7 NaN NaN NaN OK \n", + "8 NaN NaN NaN OK \n", + "\n", + " Chi2Test p-value \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 0.8717290989479588 \n", + "7 0.8911496337110267 \n", + "8 0.7053738742058828 " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.best_split_statistic" + ] + }, + { + "cell_type": "markdown", + "id": "9e2451fd", + "metadata": {}, + "source": [ + "# AATest with partially pre-defined groups\n", + "\n", + "Certain users can be pre-assigned to either the test or the control group, so that they are not randomly assigned. This can be done using the `ConstGroupRole` role. In order to pre-assign users to the control group they should have a value of `control`, and in the test group they should have a value of `test` in the column with the role `ConstGroupRole`. Users that are not pre-assigned to either the control or the test group should have `None`, so that they will be assigned randomly." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "5d989a88", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idsignup_monthtreatpre_spendspost_spendsagegenderindustryconst_grp
00.00.00.0498.0405.11111130.0ME-commercecontrol
11.00.00.0494.0416.00000068.0FLogisticscontrol
22.010.01.0469.0437.77777825.0FLogisticstest
33.00.00.0442.0414.33333368.0FLogisticscontrol
44.00.00.0483.0418.33333335.0ME-commercecontrol
..............................
99959995.06.01.0479.0505.88888955.0FLogisticsNone
99969996.05.01.0516.5499.33333356.0FLogisticsNone
99979997.03.01.0489.5526.00000061.0MLogisticsNone
99989998.00.00.0468.0434.22222252.0ME-commerceNone
99999999.00.00.0494.0419.22222239.0MLogisticsNone
\n", + "

10000 rows × 9 columns

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" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0.0 0.0 0.0 498.0 405.111111 30.0 M \n", + "1 1.0 0.0 0.0 494.0 416.000000 68.0 F \n", + "2 2.0 10.0 1.0 469.0 437.777778 25.0 F \n", + "3 3.0 0.0 0.0 442.0 414.333333 68.0 F \n", + "4 4.0 0.0 0.0 483.0 418.333333 35.0 M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9995.0 6.0 1.0 479.0 505.888889 55.0 F \n", + "9996 9996.0 5.0 1.0 516.5 499.333333 56.0 F \n", + "9997 9997.0 3.0 1.0 489.5 526.000000 61.0 M \n", + "9998 9998.0 0.0 0.0 468.0 434.222222 52.0 M \n", + "9999 9999.0 0.0 0.0 494.0 419.222222 39.0 M \n", + "\n", + " industry const_grp \n", + "0 E-commerce control \n", + "1 Logistics control \n", + "2 Logistics test \n", + "3 Logistics control \n", + "4 E-commerce control \n", + "... ... ... \n", + "9995 Logistics None \n", + "9996 Logistics None \n", + "9997 Logistics None \n", + "9998 E-commerce None \n", + "9999 Logistics None \n", + "\n", + "[10000 rows x 9 columns]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd_data= create_test_data()\n", + "pd_data.loc[pd_data[\"treat\"]==0, \"const_grp\"] = \"control\"\n", + "pd_data.loc[pd_data[\"treat\"]==1, \"const_grp\"] = \"test\"\n", + "pd_data.loc[2000:, \"const_grp\"] = None\n", + "\n", + "data = Dataset(\n", + " roles={\n", + " \"user_id\": InfoRole(int),\n", + " \"const_grp\": ConstGroupRole(str),\n", + " \"pre_spends\": TargetRole(),\n", + " \"post_spends\": TargetRole(),\n", + " \"gender\": StratificationRole(str),\n", + " \"industry\": TargetRole(str),\n", + " }, data=pd_data,\n", + ")\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "4a87bf8d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/1 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
featuregroupTTest aa testKSTest aa testChi2Test aa testTTest best splitKSTest best splitChi2Test best splitresultcontrol meantest meandifferencedifference %
0pre_spendstest_1OKOKNaNOKOKNaNOK486.906311487.2957940.3894820.079991
1post_spendstest_1NOT OKOKNaNNOT OKOKNaNOK445.172017458.40009513.2280782.971453
2industrytest_1NaNNaNOKNaNNaNOKOKNaNNaNNaNNaN
\n", + "" + ], + "text/plain": [ + " feature group TTest aa test KSTest aa test Chi2Test aa test \\\n", + "0 pre_spends test_1 OK OK NaN \n", + "1 post_spends test_1 NOT OK OK NaN \n", + "2 industry test_1 NaN NaN OK \n", + "\n", + " TTest best split KSTest best split Chi2Test best split result control mean \\\n", + "0 OK OK NaN OK 486.906311 \n", + "1 NOT OK OK NaN OK 445.172017 \n", + "2 NaN NaN OK OK NaN \n", + "\n", + " test mean difference difference % \n", + "0 487.295794 0.389482 0.079991 \n", + "1 458.400095 13.228078 2.971453 \n", + "2 NaN NaN NaN " + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.resume" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "dc08ec05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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user_idsignup_monthtreatpre_spendspost_spendsagegenderindustryconst_grpsplit
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.................................
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\n", + "

10000 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0.0 0.0 0.0 498.0 405.111111 30.0 M \n", + "1 1.0 0.0 0.0 494.0 416.000000 68.0 F \n", + "2 2.0 10.0 1.0 469.0 437.777778 25.0 F \n", + "3 3.0 0.0 0.0 442.0 414.333333 68.0 F \n", + "4 4.0 0.0 0.0 483.0 418.333333 35.0 M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9995.0 6.0 1.0 479.0 505.888889 55.0 F \n", + "9996 9996.0 5.0 1.0 516.5 499.333333 56.0 F \n", + "9997 9997.0 3.0 1.0 489.5 526.000000 61.0 M \n", + "9998 9998.0 0.0 0.0 468.0 434.222222 52.0 M \n", + "9999 9999.0 0.0 0.0 494.0 419.222222 39.0 M \n", + "\n", + " industry const_grp split \n", + "0 E-commerce control control \n", + "1 Logistics control control \n", + "2 Logistics test test_1 \n", + "3 Logistics control control \n", + "4 E-commerce control control \n", + "... ... ... ... \n", + "9995 Logistics None control \n", + "9996 Logistics None control \n", + "9997 Logistics None test_1 \n", + "9998 E-commerce None control \n", + "9999 Logistics None control \n", + "\n", + "[10000 rows x 10 columns]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.best_split" + ] + }, + { + "cell_type": "markdown", + "id": "d3dd84bc", + "metadata": {}, + "source": [ + "## Common issues and tips\n", + "\n", + "- **Missing roles**: Make sure all target variables are assigned `TargetRole`. Columns without roles may cause silent failure.\n", + "- **Stratification**: If your dataset contains categorical features (e.g. `gender`, `region`) that may affect the outcome, use `StratificationRole` and enable `stratification=True` in `AATest(...)`.\n", + "- **Imbalanced categories**: If some categories have too few samples, stratified splits may become unstable. Consider filtering or merging rare categories.\n", + "- **Random fluctuations**: On small datasets, it's normal to see occasional `NOT OK` results. Use more iterations (e.g. `n_iterations=50`) for stability.\n", + "- **Missing values**: NaNs in stratification columns may be treated as separate categories. Clean or fill missing values before stratified AA tests." ] } ], diff --git a/examples/tutorials/ABTestTutorial.ipynb b/examples/tutorials/ABTestTutorial.ipynb index 497a8b56..3640f1da 100644 --- a/examples/tutorials/ABTestTutorial.ipynb +++ b/examples/tutorials/ABTestTutorial.ipynb @@ -39,7 +39,8 @@ "import random\n", "\n", "from hypex import ABTest\n", - "from hypex.dataset import Dataset, InfoRole, TargetRole, TreatmentRole" + "from hypex.dataset import Dataset, InfoRole, TargetRole, TreatmentRole\n", + "from hypex.utils import create_test_data" ] }, { @@ -58,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 26, "id": "904175ab484d1690", "metadata": { "ExecuteTime": { @@ -102,56 +103,56 @@ " \n", " \n", " 0\n", + " 0.0\n", + " 5.0\n", " 0\n", - " 0\n", - " 0\n", - " 488.0\n", - " 414.444444\n", - " NaN\n", - " M\n", + " 491.0\n", + " 491.555556\n", + " 64.0\n", + " F\n", " E-commerce\n", " \n", " \n", " 1\n", - " 1\n", - " 8\n", - " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " NaN\n", + " 1.0\n", + " 4.0\n", + " 0\n", + " 493.0\n", + " 515.222222\n", + " 19.0\n", + " F\n", " E-commerce\n", " \n", " \n", " 2\n", + " 2.0\n", + " 1.0\n", " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", + " 529.0\n", + " 520.222222\n", + " 62.0\n", " M\n", " Logistics\n", " \n", " \n", " 3\n", - " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", + " 3.0\n", + " 0.0\n", + " 1\n", + " 486.5\n", + " 418.222222\n", + " 67.0\n", " M\n", " E-commerce\n", " \n", " \n", " 4\n", - " 4\n", + " 4.0\n", + " 0.0\n", " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", + " 467.0\n", + " 418.333333\n", + " 62.0\n", " F\n", " E-commerce\n", " \n", @@ -168,56 +169,56 @@ " \n", " \n", " 9995\n", - " 9995\n", - " 10\n", + " 9995.0\n", + " 0.0\n", " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", + " 482.0\n", + " 413.333333\n", + " 35.0\n", " M\n", " Logistics\n", " \n", " \n", " 9996\n", - " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", - " F\n", + " 9996.0\n", + " 0.0\n", + " 2\n", + " 493.5\n", + " 413.333333\n", + " 41.0\n", + " M\n", " Logistics\n", " \n", " \n", " 9997\n", - " 9997\n", - " 3\n", - " 1\n", - " 473.0\n", - " 534.111111\n", - " 22.0\n", - " F\n", - " E-commerce\n", + " 9997.0\n", + " 0.0\n", + " 0\n", + " 497.0\n", + " 423.222222\n", + " 51.0\n", + " M\n", + " Logistics\n", " \n", " \n", " 9998\n", - " 9998\n", + " 9998.0\n", + " 1.0\n", " 2\n", - " 1\n", - " 495.0\n", - " 523.222222\n", + " 542.0\n", + " 534.888889\n", " 67.0\n", - " F\n", + " M\n", " E-commerce\n", " \n", " \n", " 9999\n", - " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", + " 9999.0\n", + " 0.0\n", + " 0\n", + " 460.0\n", + " 418.555556\n", + " 64.0\n", " F\n", " E-commerce\n", " \n", @@ -228,17 +229,17 @@ ], "text/plain": [ " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 NaN M \n", - "1 1 8 1 512.5 462.222222 26.0 NaN \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", + "0 0.0 5.0 0 491.0 491.555556 64.0 F \n", + "1 1.0 4.0 0 493.0 515.222222 19.0 F \n", + "2 2.0 1.0 2 529.0 520.222222 62.0 M \n", + "3 3.0 0.0 1 486.5 418.222222 67.0 M \n", + "4 4.0 0.0 1 467.0 418.333333 62.0 F \n", "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", + "9995 9995.0 0.0 1 482.0 413.333333 35.0 M \n", + "9996 9996.0 0.0 2 493.5 413.333333 41.0 M \n", + "9997 9997.0 0.0 0 497.0 423.222222 51.0 M \n", + "9998 9998.0 1.0 2 542.0 534.888889 67.0 M \n", + "9999 9999.0 0.0 0 460.0 418.555556 64.0 F \n", "\n", " industry \n", "0 E-commerce \n", @@ -249,19 +250,21 @@ "... ... \n", "9995 Logistics \n", "9996 Logistics \n", - "9997 E-commerce \n", + "9997 Logistics \n", "9998 E-commerce \n", "9999 E-commerce \n", "\n", "[10000 rows x 8 columns]" ] }, - "execution_count": 2, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "df=create_test_data()\n", + "df[\"treat\"] = [random.choice([0, 1, 2]) for _ in range(len(df))]\n", "data = Dataset(\n", " roles={\n", " \"user_id\": InfoRole(int),\n", @@ -269,216 +272,17 @@ " \"pre_spends\": TargetRole(),\n", " \"post_spends\": TargetRole(),\n", " \"gender\": TargetRole()\n", - " }, data=\"data.csv\",\n", + " }, data=df,\n", ")\n", "data" ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "ec0659f2c8de40d9", - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-26T13:14:12.745242Z", - "start_time": "2024-08-26T13:14:12.713074Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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user_idsignup_monthtreatpre_spendspost_spendsagegenderindustry
0001488.0414.444444NaNME-commerce
1181512.5462.22222226.0NaNE-commerce
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...........................
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10000 rows × 8 columns

\n", - "
" - ], - "text/plain": [ - " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 1 488.0 414.444444 NaN M \n", - "1 1 8 1 512.5 462.222222 26.0 NaN \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 1 501.5 424.333333 39.0 M \n", - "4 4 1 0 543.0 514.555556 18.0 F \n", - "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 1 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 2 508.0 475.888889 38.0 F \n", - "\n", - " industry \n", - "0 E-commerce \n", - "1 E-commerce \n", - "2 Logistics \n", - "3 E-commerce \n", - "4 E-commerce \n", - "... ... \n", - "9995 Logistics \n", - "9996 Logistics \n", - "9997 E-commerce \n", - "9998 E-commerce \n", - "9999 E-commerce \n", - "\n", - "[10000 rows x 8 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", + "id": "534aa48fa0686e28", + "metadata": {}, "source": [ + "The roles' data types can be assigned automatically as shown below. Also, the fields, which were not marked, receive Feature role by default.\n", "data[\"treat\"] = [random.choice([0, 1, 2]) for _ in range(len(data))]\n", "data" ] @@ -493,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 27, "id": "a78151eca524b974", "metadata": { "ExecuteTime": { @@ -511,12 +315,12 @@ " 'pre_spends': Target(),\n", " 'post_spends': Target(),\n", " 'gender': Target(),\n", - " 'signup_month': Default(),\n", + " 'signup_month': Default(),\n", " 'age': Default(),\n", " 'industry': Default()}" ] }, - "execution_count": 4, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -537,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "id": "28f08947", "metadata": { "ExecuteTime": { @@ -551,6 +355,14 @@ "result = test.execute(data)" ] }, + { + "cell_type": "markdown", + "id": "8905e6dc", + "metadata": {}, + "source": [ + "Note: HypEx automatically assumes the smallest value in the `TreatmentRole` column as the control group (typically `0`), and compares each other group (e.g. `1`, `2`) against it. Ensure treatment labels are correctly assigned.\n" + ] + }, { "cell_type": "markdown", "id": "42f1e26f1725cd11", @@ -572,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "id": "89f9b9fe", "metadata": { "ExecuteTime": { @@ -617,45 +429,45 @@ " 0\n", " pre_spends\n", " 1\n", - " 487.071536\n", - " 487.020348\n", - " -0.051188\n", - " -0.010509\n", + " 487.106527\n", + " 486.555463\n", + " -0.551064\n", + " -0.113130\n", " NOT OK\n", - " 0.911224\n", + " 0.260714\n", " \n", " \n", " 1\n", " pre_spends\n", " 2\n", - " 487.071536\n", - " 487.191596\n", - " 0.120060\n", - " 0.024649\n", + " 487.106527\n", + " 486.777283\n", + " -0.329244\n", + " -0.067592\n", " NOT OK\n", - " 0.795599\n", + " 0.506114\n", " \n", " \n", " 2\n", " post_spends\n", " 1\n", - " 451.697086\n", - " 452.914905\n", - " 1.217820\n", - " 0.269610\n", + " 452.378687\n", + " 452.589616\n", + " 0.210929\n", + " 0.046627\n", " NOT OK\n", - " 0.207300\n", + " 0.837795\n", " \n", " \n", " 3\n", " post_spends\n", " 2\n", - " 451.697086\n", - " 451.862460\n", - " 0.165374\n", - " 0.036612\n", + " 452.378687\n", + " 452.192109\n", + " -0.186578\n", + " -0.041244\n", " NOT OK\n", - " 0.863482\n", + " 0.856633\n", " \n", " \n", "\n", @@ -663,19 +475,19 @@ ], "text/plain": [ " feature group control mean test mean difference difference % \\\n", - "0 pre_spends 1 487.071536 487.020348 -0.051188 -0.010509 \n", - "1 pre_spends 2 487.071536 487.191596 0.120060 0.024649 \n", - "2 post_spends 1 451.697086 452.914905 1.217820 0.269610 \n", - "3 post_spends 2 451.697086 451.862460 0.165374 0.036612 \n", + "0 pre_spends 1 487.106527 486.555463 -0.551064 -0.113130 \n", + "1 pre_spends 2 487.106527 486.777283 -0.329244 -0.067592 \n", + "2 post_spends 1 452.378687 452.589616 0.210929 0.046627 \n", + "3 post_spends 2 452.378687 452.192109 -0.186578 -0.041244 \n", "\n", " TTest pass TTest p-value \n", - "0 NOT OK 0.911224 \n", - "1 NOT OK 0.795599 \n", - "2 NOT OK 0.207300 \n", - "3 NOT OK 0.863482 " + "0 NOT OK 0.260714 \n", + "1 NOT OK 0.506114 \n", + "2 NOT OK 0.837795 \n", + "3 NOT OK 0.856633 " ] }, - "execution_count": 6, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -684,6 +496,18 @@ "result.resume" ] }, + { + "cell_type": "markdown", + "id": "59133303", + "metadata": {}, + "source": [ + "The `TTest pass` column shows whether the difference between groups is statistically significant at the 5% level. \n", + "- `OK` means the difference is significant (p < 0.05).\n", + "- `NOT OK` means no significant difference was found.\n", + "\n", + "However, significance does not imply practical importance. Always examine the `difference` and `difference %` columns to assess business relevance.\n" + ] + }, { "cell_type": "markdown", "id": "2e226d84456a869b", @@ -701,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 30, "id": "4227dbff", "metadata": { "ExecuteTime": { @@ -741,18 +565,18 @@ " \n", " \n", " 1\n", - " 3313\n", - " 3391\n", + " 3262\n", + " 3362\n", " 49\n", " 50\n", " 1\n", " \n", " \n", " 2\n", - " 3313\n", - " 3296\n", - " 50\n", + " 3262\n", + " 3376\n", " 49\n", + " 50\n", " 2\n", " \n", " \n", @@ -761,11 +585,11 @@ ], "text/plain": [ " control size test size control size % test size % group\n", - "1 3313 3391 49 50 1\n", - "2 3313 3296 50 49 2" + "1 3262 3362 49 50 1\n", + "2 3262 3376 49 50 2" ] }, - "execution_count": 7, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -776,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "id": "b735d944", "metadata": { "ExecuteTime": { @@ -820,9 +644,9 @@ " 0\n", " pre_spends\n", " TTest\n", - " 0.911224\n", - " 1.000000\n", - " 0.911224\n", + " 0.260714\n", + " 1.0\n", + " 0.260714\n", " False\n", " 1\n", " \n", @@ -830,9 +654,9 @@ " 1\n", " post_spends\n", " TTest\n", - " 0.795599\n", - " 1.000000\n", - " 0.795599\n", + " 0.506114\n", + " 1.0\n", + " 0.506114\n", " False\n", " 1\n", " \n", @@ -840,9 +664,9 @@ " 2\n", " pre_spends\n", " TTest\n", - " 0.207300\n", - " 0.829201\n", - " 0.250000\n", + " 0.837795\n", + " 1.0\n", + " 0.837795\n", " False\n", " 2\n", " \n", @@ -850,9 +674,9 @@ " 3\n", " post_spends\n", " TTest\n", - " 0.863482\n", - " 1.000000\n", - " 0.863482\n", + " 0.856633\n", + " 1.0\n", + " 0.856633\n", " False\n", " 2\n", " \n", @@ -862,13 +686,13 @@ ], "text/plain": [ " field test old p-value new p-value correction rejected group\n", - "0 pre_spends TTest 0.911224 1.000000 0.911224 False 1\n", - "1 post_spends TTest 0.795599 1.000000 0.795599 False 1\n", - "2 pre_spends TTest 0.207300 0.829201 0.250000 False 2\n", - "3 post_spends TTest 0.863482 1.000000 0.863482 False 2" + "0 pre_spends TTest 0.260714 1.0 0.260714 False 1\n", + "1 post_spends TTest 0.506114 1.0 0.506114 False 1\n", + "2 pre_spends TTest 0.837795 1.0 0.837795 False 2\n", + "3 post_spends TTest 0.856633 1.0 0.856633 False 2" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -877,6 +701,20 @@ "result.multitest" ] }, + { + "cell_type": "markdown", + "id": "518de1f9", + "metadata": {}, + "source": [ + "### Multiple Testing Correction\n", + "\n", + "When multiple metrics or test groups are analyzed, the chance of false positives increases. The `result.multitest` output shows corrected p-values using Holm's method (default) or Bonferroni if specified. The column `rejected` indicates whether the null hypothesis was rejected after correction.\n", + "\n", + "To change correction method:\n", + "```python\n", + "test = ABTest(multitest_method=\"bonferroni\")\n" + ] + }, { "cell_type": "markdown", "id": "ff2808fb", @@ -884,12 +722,16 @@ "source": [ "## Additional tests in AB Test \n", "\n", - "It is possible to add u-test and chi2-test in pipeline." + "It is possible to add u-test and chi2-test in pipeline.\n", + "\n", + "Use `u-test` for numeric variables that are skewed or non-normally distributed. It’s a non-parametric alternative to t-test.\n", + "\n", + "Use `chi2-test` for categorical variables (e.g. gender, conversion rate). Note: t-test is not appropriate for categorical outcomes." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 32, "id": "a40f5762f0b37a0a", "metadata": { "ExecuteTime": { @@ -918,7 +760,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 33, "id": "89a8898c35681e97", "metadata": { "ExecuteTime": { @@ -968,14 +810,14 @@ " 0\n", " pre_spends\n", " 1\n", - " 487.071536\n", - " 487.020348\n", - " -0.051188\n", - " -0.010509\n", + " 487.106527\n", + " 486.555463\n", + " -0.551064\n", + " -0.113130\n", " NOT OK\n", - " 0.911224\n", + " 0.260714\n", " NOT OK\n", - " 0.764231\n", + " NaN\n", " NaN\n", " NaN\n", " \n", @@ -983,14 +825,14 @@ " 1\n", " pre_spends\n", " 2\n", - " 487.071536\n", - " 487.191596\n", - " 0.120060\n", - " 0.024649\n", + " 487.106527\n", + " 486.777283\n", + " -0.329244\n", + " -0.067592\n", " NOT OK\n", - " 0.795599\n", + " 0.506114\n", " NOT OK\n", - " 0.752229\n", + " NaN\n", " NaN\n", " NaN\n", " \n", @@ -998,14 +840,14 @@ " 2\n", " post_spends\n", " 1\n", - " 451.697086\n", - " 452.914905\n", - " 1.217820\n", - " 0.269610\n", + " 452.378687\n", + " 452.589616\n", + " 0.210929\n", + " 0.046627\n", " NOT OK\n", - " 0.207300\n", + " 0.837795\n", " NOT OK\n", - " 0.457447\n", + " NaN\n", " NaN\n", " NaN\n", " \n", @@ -1013,14 +855,14 @@ " 3\n", " post_spends\n", " 2\n", - " 451.697086\n", - " 451.862460\n", - " 0.165374\n", - " 0.036612\n", + " 452.378687\n", + " 452.192109\n", + " -0.186578\n", + " -0.041244\n", " NOT OK\n", - " 0.863482\n", + " 0.856633\n", " NOT OK\n", - " 0.572854\n", + " NaN\n", " NaN\n", " NaN\n", " \n", @@ -1037,7 +879,7 @@ " NaN\n", " NaN\n", " NOT OK\n", - " 0.945581\n", + " 0.681880\n", " \n", " \n", " 5\n", @@ -1052,7 +894,7 @@ " NaN\n", " NaN\n", " NOT OK\n", - " 0.858201\n", + " 0.574357\n", " \n", " \n", "\n", @@ -1060,18 +902,18 @@ ], "text/plain": [ " feature group control mean test mean difference difference % \\\n", - "0 pre_spends 1 487.071536 487.020348 -0.051188 -0.010509 \n", - "1 pre_spends 2 487.071536 487.191596 0.120060 0.024649 \n", - "2 post_spends 1 451.697086 452.914905 1.217820 0.269610 \n", - "3 post_spends 2 451.697086 451.862460 0.165374 0.036612 \n", + "0 pre_spends 1 487.106527 486.555463 -0.551064 -0.113130 \n", + "1 pre_spends 2 487.106527 486.777283 -0.329244 -0.067592 \n", + "2 post_spends 1 452.378687 452.589616 0.210929 0.046627 \n", + "3 post_spends 2 452.378687 452.192109 -0.186578 -0.041244 \n", "4 gender 1 NaN NaN NaN NaN \n", "5 gender 2 NaN NaN NaN NaN \n", "\n", " TTest pass TTest p-value UTest pass UTest p-value Chi2Test pass \\\n", - "0 NOT OK 0.911224 NOT OK 0.764231 NaN \n", - "1 NOT OK 0.795599 NOT OK 0.752229 NaN \n", - "2 NOT OK 0.207300 NOT OK 0.457447 NaN \n", - "3 NOT OK 0.863482 NOT OK 0.572854 NaN \n", + "0 NOT OK 0.260714 NOT OK NaN NaN \n", + "1 NOT OK 0.506114 NOT OK NaN NaN \n", + "2 NOT OK 0.837795 NOT OK NaN NaN \n", + "3 NOT OK 0.856633 NOT OK NaN NaN \n", "4 NaN NaN NaN NaN NOT OK \n", "5 NaN NaN NaN NaN NOT OK \n", "\n", @@ -1080,11 +922,11 @@ "1 NaN \n", "2 NaN \n", "3 NaN \n", - "4 0.945581 \n", - "5 0.858201 " + "4 0.681880 \n", + "5 0.574357 " ] }, - "execution_count": 10, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1095,7 +937,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 34, "id": "1da993761313d8d8", "metadata": { "ExecuteTime": { @@ -1140,9 +982,9 @@ " 0\n", " pre_spends\n", " TTest\n", - " 0.911224\n", + " 0.260714\n", " 1.0\n", - " 0.911224\n", + " 0.260714\n", " False\n", " 1\n", " \n", @@ -1150,9 +992,9 @@ " 1\n", " post_spends\n", " TTest\n", - " 0.795599\n", + " 0.506114\n", " 1.0\n", - " 0.795599\n", + " 0.506114\n", " False\n", " 1\n", " \n", @@ -1160,9 +1002,9 @@ " 2\n", " pre_spends\n", " TTest\n", - " 0.207300\n", + " 0.837795\n", " 1.0\n", - " 0.207300\n", + " 0.837795\n", " False\n", " 2\n", " \n", @@ -1170,9 +1012,9 @@ " 3\n", " post_spends\n", " TTest\n", - " 0.863482\n", + " 0.856633\n", " 1.0\n", - " 0.863482\n", + " 0.856633\n", " False\n", " 2\n", " \n", @@ -1180,9 +1022,9 @@ " 4\n", " pre_spends\n", " UTest\n", - " 0.764231\n", - " 1.0\n", - " 0.764231\n", + " NaN\n", + " NaN\n", + " NaN\n", " False\n", " 1\n", " \n", @@ -1190,9 +1032,9 @@ " 5\n", " post_spends\n", " UTest\n", - " 0.752229\n", - " 1.0\n", - " 0.752229\n", + " NaN\n", + " NaN\n", + " NaN\n", " False\n", " 1\n", " \n", @@ -1200,9 +1042,9 @@ " 6\n", " pre_spends\n", " UTest\n", - " 0.457447\n", - " 1.0\n", - " 0.457447\n", + " NaN\n", + " NaN\n", + " NaN\n", " False\n", " 2\n", " \n", @@ -1210,9 +1052,9 @@ " 7\n", " post_spends\n", " UTest\n", - " 0.572854\n", - " 1.0\n", - " 0.572854\n", + " NaN\n", + " NaN\n", + " NaN\n", " False\n", " 2\n", " \n", @@ -1222,17 +1064,17 @@ ], "text/plain": [ " field test old p-value new p-value correction rejected group\n", - "0 pre_spends TTest 0.911224 1.0 0.911224 False 1\n", - "1 post_spends TTest 0.795599 1.0 0.795599 False 1\n", - "2 pre_spends TTest 0.207300 1.0 0.207300 False 2\n", - "3 post_spends TTest 0.863482 1.0 0.863482 False 2\n", - "4 pre_spends UTest 0.764231 1.0 0.764231 False 1\n", - "5 post_spends UTest 0.752229 1.0 0.752229 False 1\n", - "6 pre_spends UTest 0.457447 1.0 0.457447 False 2\n", - "7 post_spends UTest 0.572854 1.0 0.572854 False 2" + "0 pre_spends TTest 0.260714 1.0 0.260714 False 1\n", + "1 post_spends TTest 0.506114 1.0 0.506114 False 1\n", + "2 pre_spends TTest 0.837795 1.0 0.837795 False 2\n", + "3 post_spends TTest 0.856633 1.0 0.856633 False 2\n", + "4 pre_spends UTest NaN NaN NaN False 1\n", + "5 post_spends UTest NaN NaN NaN False 1\n", + "6 pre_spends UTest NaN NaN NaN False 2\n", + "7 post_spends UTest NaN NaN NaN False 2" ] }, - "execution_count": 11, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1243,7 +1085,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 35, "id": "c11137e6c10eb0dc", "metadata": { "ExecuteTime": { @@ -1284,18 +1126,18 @@ " \n", " \n", " 1\n", - " 3313\n", - " 3391\n", + " 3262\n", + " 3362\n", " 49\n", " 50\n", " 1\n", " \n", " \n", " 2\n", - " 3313\n", - " 3296\n", - " 50\n", + " 3262\n", + " 3376\n", " 49\n", + " 50\n", " 2\n", " \n", " \n", @@ -1304,11 +1146,11 @@ ], "text/plain": [ " control size test size control size % test size % group\n", - "1 3313 3391 49 50 1\n", - "2 3313 3296 50 49 2" + "1 3262 3362 49 50 1\n", + "2 3262 3376 49 50 2" ] }, - "execution_count": 12, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1329,7 +1171,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 36, "id": "5921c9e2", "metadata": { "ExecuteTime": { @@ -1345,7 +1187,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "id": "952d21c6", "metadata": { "ExecuteTime": { @@ -1390,45 +1232,45 @@ " 0\n", " pre_spends\n", " 1\n", - " 487.071536\n", - " 487.020348\n", - " -0.051188\n", - " -0.010509\n", + " 487.106527\n", + " 486.555463\n", + " -0.551064\n", + " -0.113130\n", " NOT OK\n", - " 0.911224\n", + " 0.260714\n", " \n", " \n", " 1\n", " pre_spends\n", " 2\n", - " 487.071536\n", - " 487.191596\n", - " 0.120060\n", - " 0.024649\n", + " 487.106527\n", + " 486.777283\n", + " -0.329244\n", + " -0.067592\n", " NOT OK\n", - " 0.795599\n", + " 0.506114\n", " \n", " \n", " 2\n", " post_spends\n", " 1\n", - " 451.697086\n", - " 452.914905\n", - " 1.217820\n", - " 0.269610\n", + " 452.378687\n", + " 452.589616\n", + " 0.210929\n", + " 0.046627\n", " NOT OK\n", - " 0.207300\n", + " 0.837795\n", " \n", " \n", " 3\n", " post_spends\n", " 2\n", - " 451.697086\n", - " 451.862460\n", - " 0.165374\n", - " 0.036612\n", + " 452.378687\n", + " 452.192109\n", + " -0.186578\n", + " -0.041244\n", " NOT OK\n", - " 0.863482\n", + " 0.856633\n", " \n", " \n", "\n", @@ -1436,19 +1278,19 @@ ], "text/plain": [ " feature group control mean test mean difference difference % \\\n", - "0 pre_spends 1 487.071536 487.020348 -0.051188 -0.010509 \n", - "1 pre_spends 2 487.071536 487.191596 0.120060 0.024649 \n", - "2 post_spends 1 451.697086 452.914905 1.217820 0.269610 \n", - "3 post_spends 2 451.697086 451.862460 0.165374 0.036612 \n", + "0 pre_spends 1 487.106527 486.555463 -0.551064 -0.113130 \n", + "1 pre_spends 2 487.106527 486.777283 -0.329244 -0.067592 \n", + "2 post_spends 1 452.378687 452.589616 0.210929 0.046627 \n", + "3 post_spends 2 452.378687 452.192109 -0.186578 -0.041244 \n", "\n", " TTest pass TTest p-value \n", - "0 NOT OK 0.911224 \n", - "1 NOT OK 0.795599 \n", - "2 NOT OK 0.207300 \n", - "3 NOT OK 0.863482 " + "0 NOT OK 0.260714 \n", + "1 NOT OK 0.506114 \n", + "2 NOT OK 0.837795 \n", + "3 NOT OK 0.856633 " ] }, - "execution_count": 14, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1459,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 38, "id": "ad59dec9", "metadata": { "ExecuteTime": { @@ -1499,18 +1341,18 @@ " \n", " \n", " 1\n", - " 3313\n", - " 3391\n", + " 3262\n", + " 3362\n", " 49\n", " 50\n", " 1\n", " \n", " \n", " 2\n", - " 3313\n", - " 3296\n", - " 50\n", + " 3262\n", + " 3376\n", " 49\n", + " 50\n", " 2\n", " \n", " \n", @@ -1519,11 +1361,11 @@ ], "text/plain": [ " control size test size control size % test size % group\n", - "1 3313 3391 49 50 1\n", - "2 3313 3296 50 49 2" + "1 3262 3362 49 50 1\n", + "2 3262 3376 49 50 2" ] }, - "execution_count": 15, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1534,7 +1376,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 39, "id": "7849230a", "metadata": { "ExecuteTime": { @@ -1578,9 +1420,9 @@ " 0\n", " pre_spends\n", " TTest\n", - " 0.911224\n", - " 1.000000\n", - " 0.911224\n", + " 0.260714\n", + " 1.0\n", + " 0.260714\n", " False\n", " 1\n", " \n", @@ -1588,9 +1430,9 @@ " 1\n", " post_spends\n", " TTest\n", - " 0.795599\n", - " 1.000000\n", - " 0.795599\n", + " 0.506114\n", + " 1.0\n", + " 0.506114\n", " False\n", " 1\n", " \n", @@ -1598,9 +1440,9 @@ " 2\n", " pre_spends\n", " TTest\n", - " 0.207300\n", - " 0.829201\n", - " 0.250000\n", + " 0.837795\n", + " 1.0\n", + " 0.837795\n", " False\n", " 2\n", " \n", @@ -1608,9 +1450,9 @@ " 3\n", " post_spends\n", " TTest\n", - " 0.863482\n", - " 1.000000\n", - " 0.863482\n", + " 0.856633\n", + " 1.0\n", + " 0.856633\n", " False\n", " 2\n", " \n", @@ -1620,13 +1462,13 @@ ], "text/plain": [ " field test old p-value new p-value correction rejected group\n", - "0 pre_spends TTest 0.911224 1.000000 0.911224 False 1\n", - 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Missing roles may cause incorrect test logic.\n", + "- For categorical targets, avoid using `t-test`. Instead, include `chi2-test` in `additional_tests`.\n", + "- HypEx does not automatically balance groups. Ensure group sizes are roughly equal and comparable.\n", + "- Check for missing values. NaNs may silently affect metric calculation.\n", + "- If testing many metrics/groups, interpret results only after multiple testing correction.\n", + "- Use `result.sizes` to confirm group balance, and consider A/A testing to verify setup before real A/B.\n", + "\n" + ] } ], "metadata": { diff --git a/examples/tutorials/DatasetTutorial.ipynb b/examples/tutorials/DatasetTutorial.ipynb index d4ee9971..6c555b84 100644 --- a/examples/tutorials/DatasetTutorial.ipynb +++ b/examples/tutorials/DatasetTutorial.ipynb @@ -72,6 +72,32 @@ "outputs": [ { "data": { + "text/html": [ + "
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To demonstrate the effect of the Mahalanobis transformation, it is necessary to create a space with correlation, for which a different spread of values for the x and y coordinates is set. It is also useful to set different parameters of the noise spread for clarity. \n", - "\n", - "Marker points are added to the main data array in order to trace the transformation of space using them and see how their relative position changes.\n", - "\n", - "In this example, the default generation parameters are used, but you can change them." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Точки в L2 пространстве" - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "title": { - "text": "x" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "y" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dots_plot(macha_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Marker distances\n", - "\n", - "Based on the displacement of the markers, it is possible to assess how the transformation of Mahalanobis affected the relative distance between the markers." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Distanceses in L2\n", - "Distance between A and B:\n", - "5.0\n", - "Distance between A and C:\n", - "5.0\n" - ] - } - ], - "source": [ - "print(\"Distanceses in L2\")\n", - "d = calc_dots_distances(data, True)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Distanceses in Machalanobis\n", - "Distance between A and B:\n", - "1.1029956192338142\n", - "Distance between A and C:\n", - "0.9627585035194691\n" - ] - } - ], - "source": [ - "print(\"Distanceses in Machalanobis\")\n", - "d = calc_dots_distances(macha_data, True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/tutorials/MatchingTutorial.ipynb b/examples/tutorials/MatchingTutorial.ipynb index f40aef23..18eeb19b 100644 --- a/examples/tutorials/MatchingTutorial.ipynb +++ b/examples/tutorials/MatchingTutorial.ipynb @@ -33,38 +33,36 @@ }, "outputs": [], "source": [ + "# imports\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", "from hypex import Matching\n", - "from hypex.dataset import Dataset, FeatureRole, InfoRole, TargetRole, TreatmentRole" + "from hypex.dataset import (\n", + " Dataset,\n", + " FeatureRole,\n", + " GroupingRole,\n", + " InfoRole,\n", + " TargetRole,\n", + " TreatmentRole,\n", + ")\n", + "from hypex.utils import create_test_data" ] }, { "cell_type": "markdown", - "id": "7e41355993e9e59f", - "metadata": { - "collapsed": false - }, + "id": "dbad1c3c", + "metadata": {}, "source": [ - "## Data preparation \n", - "\n", - "It is important to mark the data fields by assigning the appropriate roles:\n", - "\n", - "* FeatureRole: a role for columns that contain features or predictor variables. Our split will be based on them. Applied by default if the role is not specified for the column.\n", - "* TreatmentRole: a role for columns that show the treatment or intervention.\n", - "* TargetRole: a role for columns that show the target or outcome variable.\n", - "* InfoRole: a role for columns that contain information about the data, such as user IDs." + "# Create synthetic data" ] }, { "cell_type": "code", "execution_count": 2, - "id": "8abf891fc6804315", - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-16T18:51:29.356226Z", - "start_time": "2024-08-16T18:51:29.299852Z" - }, - "collapsed": false - }, + "id": "474c6383", + "metadata": {}, "outputs": [ { "data": { @@ -100,57 +98,57 @@ " \n", " \n", " 0\n", + " 0.0\n", " 0\n", " 0\n", - " 0\n", - " 488.0\n", - " 414.444444\n", - " NaN\n", + " 475.0\n", + " 475.696965\n", + " 23.0\n", " M\n", " E-commerce\n", " \n", " \n", " 1\n", + " 1.0\n", + " 11\n", " 1\n", - " 8\n", - " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " NaN\n", - " E-commerce\n", + " 487.0\n", + " 487.535208\n", + " 51.0\n", + " F\n", + " Logistics\n", " \n", " \n", " 2\n", - " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", + " 2.0\n", + " 0\n", + " 0\n", + " 484.0\n", + " 484.320639\n", + " 35.0\n", " M\n", - " Logistics\n", + " E-commerce\n", " \n", " \n", " 3\n", - " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", + " 3.0\n", + " 11\n", + " 1\n", + " 494.5\n", + " 495.159047\n", + " 29.0\n", " M\n", - " E-commerce\n", + " Logistics\n", " \n", " \n", " 4\n", - " 4\n", - " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", - " F\n", + " 4.0\n", + " 0\n", + " 0\n", + " 455.5\n", + " 455.675909\n", + " 53.0\n", + " M\n", " E-commerce\n", " \n", " \n", @@ -166,57 +164,57 @@ " \n", " \n", " 9995\n", - " 9995\n", - " 10\n", + " 9995.0\n", + " 5\n", " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", + " 487.5\n", + " 487.741243\n", + " 31.0\n", " M\n", " Logistics\n", " \n", " \n", " 9996\n", - " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", - " F\n", - " Logistics\n", + " 9996.0\n", + " 11\n", + " 1\n", + " 453.5\n", + " 454.099548\n", + " 41.0\n", + " M\n", + " E-commerce\n", " \n", " \n", " 9997\n", - " 9997\n", - " 3\n", + " 9997.0\n", + " 10\n", " 1\n", - " 473.0\n", - " 534.111111\n", - " 22.0\n", + " 482.0\n", + " 482.308959\n", + " 58.0\n", " F\n", - " E-commerce\n", + " Logistics\n", " \n", " \n", " 9998\n", - " 9998\n", - " 2\n", + " 9998.0\n", + " 6\n", " 1\n", - " 495.0\n", - " 523.222222\n", - " 67.0\n", + " 477.0\n", + " 477.867590\n", + " 41.0\n", " F\n", " E-commerce\n", " \n", " \n", " 9999\n", - " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", - " F\n", + " 9999.0\n", + " 0\n", + " 0\n", + " 496.0\n", + " 496.367291\n", + " 18.0\n", + " M\n", " E-commerce\n", " \n", " \n", @@ -226,28 +224,28 @@ ], "text/plain": [ " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 NaN M \n", - "1 1 8 1 512.5 462.222222 26.0 NaN \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", + "0 0.0 0 0 475.0 475.696965 23.0 M \n", + "1 1.0 11 1 487.0 487.535208 51.0 F \n", + "2 2.0 0 0 484.0 484.320639 35.0 M \n", + "3 3.0 11 1 494.5 495.159047 29.0 M \n", + "4 4.0 0 0 455.5 455.675909 53.0 M \n", "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", + "9995 9995.0 5 1 487.5 487.741243 31.0 M \n", + "9996 9996.0 11 1 453.5 454.099548 41.0 M \n", + "9997 9997.0 10 1 482.0 482.308959 58.0 F \n", + "9998 9998.0 6 1 477.0 477.867590 41.0 F \n", + "9999 9999.0 0 0 496.0 496.367291 18.0 M \n", "\n", " industry \n", "0 E-commerce \n", - "1 E-commerce \n", - "2 Logistics \n", - "3 E-commerce \n", + "1 Logistics \n", + "2 E-commerce \n", + "3 Logistics \n", "4 E-commerce \n", "... ... \n", "9995 Logistics \n", - "9996 Logistics \n", - "9997 E-commerce \n", + "9996 E-commerce \n", + "9997 Logistics \n", "9998 E-commerce \n", "9999 E-commerce \n", "\n", @@ -260,143 +258,25 @@ } ], "source": [ - "data = Dataset(\n", - " roles={\n", - " \"user_id\": InfoRole(int),\n", - " \"treat\": TreatmentRole(int),\n", - " \"post_spends\": TargetRole(float)\n", - " },\n", - " data=\"data.csv\",\n", - " default_role=FeatureRole(),\n", - ")\n", - "data" + "df = create_test_data().bfill()\n", + "df[\"post_spends\"] = df[\"pre_spends\"] + np.random.rand(len(df))\n", + "df[[\"signup_month\", \"treat\"]] = df[[\"signup_month\", \"treat\"]].astype(int)\n", + "df" ] }, { "cell_type": "code", "execution_count": 3, - "id": "4ae8c654db6f5f85", - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-16T18:51:29.367244Z", - "start_time": "2024-08-16T18:51:29.358253Z" - }, - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'user_id': Info(),\n", - " 'treat': Treatment(),\n", - " 'post_spends': Target(),\n", - " 'signup_month': Feature(),\n", - " 'pre_spends': Feature(),\n", - " 'age': Feature(),\n", - " 'gender': Feature(),\n", - " 'industry': Feature()}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.roles" - ] - }, - { - "cell_type": "markdown", - "id": "8848fdfc6e8c0f45", - "metadata": { - "collapsed": false - }, - "source": [ - "## Simple Matching \n", - "Now matching has 4 steps: \n", - "1. Dummy Encoder \n", - "2. Process Mahalanobis distance \n", - "3. Two sides pairs searching by faiss \n", - "4. Metrics (ATT, ATC, ATE) estimation depends on your data " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "20e64ee990f83d47", - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-16T18:51:29.418594Z", - "start_time": "2024-08-16T18:51:29.370416Z" - }, - "collapsed": false - }, - "outputs": [], - "source": [ - "data = data.fillna(method=\"bfill\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "bc7e472bbb7c2a5d", - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-16T18:51:30.324602Z", - "start_time": "2024-08-16T18:51:29.421300Z" - }, - "collapsed": false - }, + "id": "230e413b", + "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n" + "Basic statistics for numerical features:\n" ] - } - ], - "source": [ - "test = Matching()\n", - "result = test.execute(data)" - ] - }, - { - "cell_type": "markdown", - "id": "d8a47b848e13e745", - "metadata": { - "collapsed": false - }, - "source": [ - "**ATT** shows the difference in treated group. \n", - "**ATC** shows the difference in untreated group. \n", - "**ATE** shows the weighted average difference between ATT and ATC. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "24cb598e7fbd81fd", - "metadata": { - "ExecuteTime": { - "end_time": "2024-08-16T18:51:30.328127Z", - "start_time": "2024-08-16T18:51:30.327830Z" }, - "collapsed": false - }, - "outputs": [ { "data": { "text/html": [ @@ -418,67 +298,181 @@ " \n", " \n", " \n", - " Effect Size\n", - " Standard Error\n", - " P-value\n", - " CI Lower\n", - " CI Upper\n", - " outcome\n", + " user_id\n", + " signup_month\n", + " treat\n", + " pre_spends\n", + " post_spends\n", + " age\n", " \n", " \n", " \n", " \n", - " ATT\n", - " 63.37\n", - " 2.45\n", - " 0.0\n", - " 58.57\n", - " 68.16\n", - " post_spends\n", + " count\n", + " 10000.0\n", + " 10000.00\n", + " 10000.0\n", + " 10000.00\n", + " 10000.00\n", + " 10000.00\n", " \n", " \n", - " ATC\n", - " 96.47\n", - " 1.57\n", + " mean\n", + " 4999.6\n", + " 3.01\n", + " 0.5\n", + " 487.05\n", + " 487.55\n", + " 43.60\n", + " \n", + " \n", + " std\n", + " 2886.9\n", + " 3.75\n", + " 0.5\n", + " 18.77\n", + " 18.77\n", + " 14.89\n", + " \n", + " \n", + " min\n", " 0.0\n", - " 93.40\n", - " 99.55\n", - " post_spends\n", + " 0.00\n", + " 0.0\n", + " 427.00\n", + " 427.47\n", + " 18.00\n", " \n", " \n", - " ATE\n", - " 80.13\n", - " 1.44\n", + " 25%\n", + " 2500.5\n", + " 0.00\n", " 0.0\n", - " 77.31\n", - " 82.95\n", - " post_spends\n", + " 474.50\n", + " 475.15\n", + " 31.00\n", + " \n", + " \n", + " 50%\n", + " 5000.0\n", + " 1.00\n", + " 1.0\n", + " 485.50\n", + " 486.15\n", + " 44.00\n", + " \n", + " \n", + " 75%\n", + " 7499.5\n", + " 6.00\n", + " 1.0\n", + " 497.00\n", + " 497.30\n", + " 56.00\n", + " \n", + " \n", + " max\n", + " 9999.0\n", + " 11.00\n", + " 1.0\n", + " 581.50\n", + " 582.28\n", + " 69.00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Effect Size Standard Error P-value CI Lower CI Upper outcome\n", - "ATT 63.37 2.45 0.0 58.57 68.16 post_spends\n", - "ATC 96.47 1.57 0.0 93.40 99.55 post_spends\n", - "ATE 80.13 1.44 0.0 77.31 82.95 post_spends" + " user_id signup_month treat pre_spends post_spends age\n", + "count 10000.0 10000.00 10000.0 10000.00 10000.00 10000.00\n", + "mean 4999.6 3.01 0.5 487.05 487.55 43.60\n", + "std 2886.9 3.75 0.5 18.77 18.77 14.89\n", + "min 0.0 0.00 0.0 427.00 427.47 18.00\n", + "25% 2500.5 0.00 0.0 474.50 475.15 31.00\n", + "50% 5000.0 1.00 1.0 485.50 486.15 44.00\n", + "75% 7499.5 6.00 1.0 497.00 497.30 56.00\n", + "max 9999.0 11.00 1.0 581.50 582.28 69.00" ] }, - "execution_count": 6, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "result.resume" + "def describe_basic_info(df: pd.DataFrame):\n", + " print(\"Basic statistics for numerical features:\")\n", + " display(df.describe().round(2))\n", + "\n", + "def describe_cltv_effect(df: pd.DataFrame, group_col=\"treat\"):\n", + " result = (\n", + " df.groupby(group_col)[[\"pre_spends\", \"post_spends\"]]\n", + " .mean()\n", + " .rename(columns={\"pre_spends\": \"mean_pre_spends\", \"post_spends\": \"mean_post_spends\"})\n", + " )\n", + " result[\"delta_spend\"] = result[\"post_spends\"] - result[\"pre_spends\"]\n", + " print(\"Average spend before and after pilot:\")\n", + " print(result.round(2))\n", + " result.plot(\n", + " kind=\"bar\",\n", + " figsize=(6, 4),\n", + " title=\"Average spend before and after pilot by groups\",\n", + " rot=0\n", + " )\n", + " plt.ylabel(\"Average spend\")\n", + " plt.grid(alpha=0.3)\n", + " plt.show()\n", + "\n", + " return result\n", + "\n", + "def plot_cltv_distribution(df: pd.DataFrame):\n", + " plt.figure(figsize=(7, 4))\n", + " plt.hist(df[\"pre_spends\"], bins=50, alpha=0.6, label=\"Before pilot\")\n", + " plt.hist(df[\"post_spends\"], bins=50, alpha=0.6, label=\"After pilot\")\n", + " plt.title(\"spend distribution before and after pilot\")\n", + " plt.xlabel(\"pre_spends\")\n", + " plt.ylabel(\"Number of clients\")\n", + " plt.legend()\n", + " plt.grid(alpha=0.3)\n", + " plt.show()\n", + "\n", + "\n", + "def full_dataset_report(df: pd.DataFrame):\n", + " describe_basic_info(df)\n", + " plot_cltv_distribution(df)\n", + "\n", + "full_dataset_report(df)" ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "3d5e6cb2", + "cell_type": "markdown", + "id": "1998050d", "metadata": {}, + "source": [ + "# Define the Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8abf891fc6804315", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-16T18:51:29.356226Z", + "start_time": "2024-08-16T18:51:29.299852Z" + }, + "collapsed": false + }, "outputs": [ { "data": { @@ -509,14 +503,6 @@ " age\n", " gender\n", " industry\n", - " user_id_matched\n", - " signup_month_matched\n", - " treat_matched\n", - " pre_spends_matched\n", - " post_spends_matched\n", - " age_matched\n", - " gender_matched\n", - " industry_matched\n", " \n", " \n", " \n", @@ -525,94 +511,54 @@ " 0\n", " 0\n", " 0\n", - " 488.0\n", - " 414.444444\n", - " 26.0\n", + " 475.0\n", + " 475.696965\n", + " 23\n", " M\n", " E-commerce\n", - " 9433\n", - " 1\n", - " 1\n", - " 488.5\n", - " 518.444444\n", - " 37.0\n", - " F\n", - " Logistics\n", " \n", " \n", " 1\n", " 1\n", - " 8\n", + " 11\n", " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " M\n", - " E-commerce\n", - " 5438\n", - " 0\n", - " 0\n", - " 529.0\n", - " 417.111111\n", - " 23.0\n", + " 487.0\n", + " 487.535208\n", + " 51\n", " F\n", - " E-commerce\n", + " Logistics\n", " \n", " \n", " 2\n", " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", - " M\n", - " Logistics\n", - " 5165\n", " 0\n", " 0\n", - " 498.5\n", - " 412.222222\n", - " 25.0\n", - " F\n", - " Logistics\n", + " 484.0\n", + " 484.320639\n", + " 35\n", + " M\n", + " E-commerce\n", " \n", " \n", " 3\n", " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", - " M\n", - " E-commerce\n", - " 1735\n", + " 11\n", " 1\n", - " 1\n", - " 504.0\n", - " 516.333333\n", - " 33.0\n", + " 494.5\n", + " 495.159047\n", + " 29\n", " M\n", " Logistics\n", " \n", " \n", " 4\n", " 4\n", - " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", - " F\n", - " E-commerce\n", - " 539\n", " 0\n", " 0\n", - " 531.0\n", - " 414.000000\n", - " 20.0\n", - " F\n", + " 455.5\n", + " 455.675909\n", + " 53\n", + " M\n", " E-commerce\n", " \n", " \n", @@ -625,188 +571,196 @@ " ...\n", " ...\n", " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", " \n", " \n", " 9995\n", " 9995\n", - " 10\n", + " 5\n", " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", + " 487.5\n", + " 487.741243\n", + " 31\n", " M\n", " Logistics\n", - " 5893\n", - " 0\n", - " 0\n", - " 535.0\n", - " 414.555556\n", - " 40.0\n", - " M\n", - " E-commerce\n", " \n", " \n", " 9996\n", " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", - " F\n", - " Logistics\n", - " 7731\n", - " 1\n", + " 11\n", " 1\n", - " 500.0\n", - " 515.888889\n", - " 25.0\n", + " 453.5\n", + " 454.099548\n", + " 41\n", " M\n", - " Logistics\n", + " E-commerce\n", " \n", " \n", " 9997\n", " 9997\n", - " 3\n", + " 10\n", " 1\n", - " 473.0\n", - " 534.111111\n", - " 22.0\n", - " F\n", - " E-commerce\n", - " 7066\n", - " 0\n", - " 0\n", - " 480.0\n", - " 423.222222\n", - " 22.0\n", + " 482.0\n", + " 482.308959\n", + " 58\n", " F\n", " Logistics\n", " \n", " \n", " 9998\n", " 9998\n", - " 2\n", + " 6\n", " 1\n", - " 495.0\n", - " 523.222222\n", - " 67.0\n", + " 477.0\n", + " 477.867590\n", + " 41\n", " F\n", " E-commerce\n", - " 1885\n", - " 0\n", - " 0\n", - " 499.0\n", - " 423.000000\n", - " 67.0\n", - " F\n", - " Logistics\n", " \n", " \n", " 9999\n", " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", - " F\n", - " E-commerce\n", - " 5748\n", " 0\n", " 0\n", - " 522.0\n", - " 424.444444\n", - " 36.0\n", + " 496.0\n", + " 496.367291\n", + " 18\n", " M\n", " E-commerce\n", " \n", " \n", "\n", - "

10000 rows × 16 columns

\n", + "

10000 rows × 8 columns

\n", "" ], "text/plain": [ - " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 26.0 M \n", - "1 1 8 1 512.5 462.222222 26.0 M \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", - "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", - "\n", - " industry user_id_matched signup_month_matched treat_matched \\\n", - "0 E-commerce 9433 1 1 \n", - "1 E-commerce 5438 0 0 \n", - "2 Logistics 5165 0 0 \n", - "3 E-commerce 1735 1 1 \n", - "4 E-commerce 539 0 0 \n", - "... ... ... ... ... \n", - "9995 Logistics 5893 0 0 \n", - "9996 Logistics 7731 1 1 \n", - "9997 E-commerce 7066 0 0 \n", - "9998 E-commerce 1885 0 0 \n", - "9999 E-commerce 5748 0 0 \n", + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 475.0 475.696965 23 M \n", + "1 1 11 1 487.0 487.535208 51 F \n", + "2 2 0 0 484.0 484.320639 35 M \n", + "3 3 11 1 494.5 495.159047 29 M \n", + "4 4 0 0 455.5 455.675909 53 M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9995 5 1 487.5 487.741243 31 M \n", + "9996 9996 11 1 453.5 454.099548 41 M \n", + "9997 9997 10 1 482.0 482.308959 58 F \n", + "9998 9998 6 1 477.0 477.867590 41 F \n", + "9999 9999 0 0 496.0 496.367291 18 M \n", "\n", - " pre_spends_matched post_spends_matched age_matched gender_matched \\\n", - "0 488.5 518.444444 37.0 F \n", - "1 529.0 417.111111 23.0 F \n", - "2 498.5 412.222222 25.0 F \n", - "3 504.0 516.333333 33.0 M \n", - "4 531.0 414.000000 20.0 F \n", - "... ... ... ... ... \n", - "9995 535.0 414.555556 40.0 M \n", - "9996 500.0 515.888889 25.0 M \n", - "9997 480.0 423.222222 22.0 F \n", - "9998 499.0 423.000000 67.0 F \n", - "9999 522.0 424.444444 36.0 M \n", - "\n", - " industry_matched \n", - "0 Logistics \n", - "1 E-commerce \n", - "2 Logistics \n", - "3 Logistics \n", - "4 E-commerce \n", - "... ... \n", - "9995 E-commerce \n", - "9996 Logistics \n", - "9997 Logistics \n", - "9998 Logistics \n", - "9999 E-commerce \n", + " industry \n", + "0 E-commerce \n", + "1 Logistics \n", + "2 E-commerce \n", + "3 Logistics \n", + "4 E-commerce \n", + "... ... \n", + "9995 Logistics \n", + "9996 E-commerce \n", + "9997 Logistics \n", + "9998 E-commerce \n", + "9999 E-commerce \n", "\n", - "[10000 rows x 16 columns]" + "[10000 rows x 8 columns]" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "result.full_data" + "data = Dataset(\n", + " roles={\n", + " \"user_id\": InfoRole(int),\n", + " \"treat\": TreatmentRole(int),\n", + " \"post_spends\": TargetRole(float),\n", + " \"gender\": FeatureRole(str),\n", + " \"pre_spends\": FeatureRole(float),\n", + " \"industry\": FeatureRole(str),\n", + " \"age\": FeatureRole(int),\n", + " },\n", + " data=df,\n", + " default_role=InfoRole(),\n", + ")\n", + "data" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "f01b67ab0e1b369d", + "execution_count": 5, + "id": "d5cd6dac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'user_id': Info(),\n", + " 'treat': Treatment(),\n", + " 'post_spends': Target(),\n", + " 'gender': Feature(data_type=),\n", + " 'pre_spends': Feature(data_type=),\n", + " 'industry': Feature(data_type=),\n", + " 'age': Feature(data_type=),\n", + " 'signup_month': Info()}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.roles" + ] + }, + { + "cell_type": "markdown", + "id": "8848fdfc6e8c0f45", + "metadata": { + "collapsed": false + }, + "source": [ + "# Basic Matching without parameters\n", + "\n", + "Main matching steps (in HypEx):\n", + "\n", + "1. **Dummy Encoder**\n", + " Converts categorical features to numerical format \n", + "\n", + "2. **Distance matrix calculation (usually Mahalanobis)**\n", + " \n", + "3. **Finding nearest pairs (via FAISS)**\n", + " \n", + "4. **Quality check of twin matching**\n", + "\n", + "5. **Effect estimation (ATT, ATC, ATE)**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "20e64ee990f83d47", + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-16T18:51:29.418594Z", + "start_time": "2024-08-16T18:51:29.370416Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "data = data.fillna(method=\"bfill\")\n", + "test = Matching()\n", + "result = test.execute(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "24cb598e7fbd81fd", "metadata": { "ExecuteTime": { - "end_time": "2024-08-16T18:51:30.330185Z", - "start_time": "2024-08-16T18:51:30.329857Z" + "end_time": "2024-08-16T18:51:30.328127Z", + "start_time": "2024-08-16T18:51:30.327830Z" }, "collapsed": false }, @@ -832,29 +786,141 @@ " \n", " \n", " \n", - " indexes\n", + " Effect Size\n", + " Standard Error\n", + " P-value\n", + " CI Lower\n", + " CI Upper\n", + " outcome\n", + " \n", + " \n", + " \n", + " \n", + " ATT\n", + " 0.00\n", + " 0.01\n", + " 0.89\n", + " -0.02\n", + " 0.03\n", + " post_spends\n", + " \n", + " \n", + " ATC\n", + " 0.01\n", + " 0.01\n", + " 0.37\n", + " -0.01\n", + " 0.03\n", + " post_spends\n", + " \n", + " \n", + " ATE\n", + " 0.01\n", + " 0.01\n", + " 0.56\n", + " -0.01\n", + " 0.03\n", + " post_spends\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " Effect Size Standard Error P-value CI Lower CI Upper outcome\n", + "ATT 0.00 0.01 0.89 -0.02 0.03 post_spends\n", + "ATC 0.01 0.01 0.37 -0.01 0.03 post_spends\n", + "ATE 0.01 0.01 0.56 -0.01 0.03 post_spends" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.resume" + ] + }, + { + "cell_type": "markdown", + "id": "d8a47b848e13e745", + "metadata": { + "collapsed": false + }, + "source": [ + "### ATT — Average Treatment effect on the Treated\n", + "\n", + "$\n", + "ATT = E[Y(1) - Y(0) \\mid T=1]\n", + "$\n", + " - shows **how the treatment affected those who actually received it**.\n", + "\n", + "### ATC — Average Treatment effect on the Controls \n", + "\n", + "$\n", + "ATC = E[Y(1) - Y(0) \\mid T=0]\n", + "$\n", + " - shows **what would have happened to the control group if they had received the treatment**.\n", + "\n", + "### ATE — Average Treatment Effect\n", + "\n", + "$\n", + "ATE = E[Y(1) - Y(0)]\n", + "$\n", + "- the overall average effect in the population — a combination of ATT and ATC, weighted by group sizes." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "40801244", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" ], "text/plain": [ - " indexes\n", - "0 9433\n", - "1 5438\n", - "2 5165\n", - "3 1735\n", - "4 539\n", - "... ...\n", - "9995 5893\n", - "9996 7731\n", - "9997 7066\n", - "9998 1885\n", - "9999 5748\n", + " indexes_0\n", + "0 2744\n", + "1 5529\n", + "2 5628\n", + "3 582\n", + "4 2247\n", + "... ...\n", + "9995 386\n", + "9996 4685\n", + "9997 3842\n", + "9998 5548\n", + "9999 2325\n", "\n", "[10000 rows x 1 columns]" ] @@ -914,85 +980,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "d98c94a8b8a763e9", - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'user_id': Info(),\n", - " 'treat': Treatment(),\n", - " 'post_spends': Target(),\n", - " 'signup_month': Feature(),\n", - " 'pre_spends': Feature(),\n", - " 'age': Feature(),\n", - " 'gender': Feature(),\n", - " 'industry': Feature(),\n", - " 'user_id_matched': Info(),\n", - " 'treat_matched': Treatment(),\n", - " 'post_spends_matched': Target(),\n", - " 'signup_month_matched': Feature(),\n", - " 'pre_spends_matched': Feature(),\n", - " 'age_matched': Feature(),\n", - " 'gender_matched': Feature(),\n", - " 'industry_matched': Feature()}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result.full_data.roles" - ] - }, - { - "cell_type": "markdown", - "id": "2679dcf1", - "metadata": {}, - "source": [ - "We can add **quality_tests** to evaluate balance of features after matching.\n", - "- **t-test** checks if feature means are similar across treatment and control groups.\n", - "- **ks-test** (Kolmogorov-Smirnov) checks if feature distributions are similar." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f26841af", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n" - ] - } - ], - "source": [ - "test = Matching(quality_tests=['t-test', 'ks-test'])\n", - "result = test.execute(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c4b2ca5a", + "id": "99c5911a", "metadata": {}, "outputs": [ { @@ -1016,109 +1004,340 @@ " \n", " \n", " \n", - " feature\n", - " group\n", - " TTest pass\n", - " TTest p-value\n", - " KSTest pass\n", - " KSTest p-value\n", + " user_id\n", + " signup_month\n", + " treat\n", + " pre_spends\n", + " post_spends\n", + " age\n", + " gender\n", + " industry\n", + " user_id_matched_0\n", + " signup_month_matched_0\n", + " treat_matched_0\n", + " pre_spends_matched_0\n", + " post_spends_matched_0\n", + " age_matched_0\n", + " gender_matched_0\n", + " industry_matched_0\n", " \n", " \n", " \n", " \n", " 0\n", - " signup_month\n", - " 1┆signup_month\n", - " NOT OK\n", - " 0.000000e+00\n", - " NOT OK\n", - " 0.000000e+00\n", - " \n", + " 0\n", + " 0\n", + " 0\n", + " 475.0\n", + " 475.696965\n", + " 23.0\n", + " M\n", + " E-commerce\n", + " 2744\n", + " 11\n", + " 1\n", + " 475.0\n", + " 475.896827\n", + " 23.0\n", + " M\n", + " E-commerce\n", + " \n", " \n", " 1\n", - " pre_spends\n", - " 1┆pre_spends\n", - " NOT OK\n", - " 1.802420e-212\n", - " NOT OK\n", - " 3.284750e-231\n", + " 1\n", + " 11\n", + " 1\n", + " 487.0\n", + " 487.535208\n", + " 51.0\n", + " F\n", + " Logistics\n", + " 5529\n", + " 0\n", + " 0\n", + " 486.5\n", + " 487.441640\n", + " 51.0\n", + " F\n", + " Logistics\n", " \n", " \n", " 2\n", - " age\n", - " 1┆age\n", - " OK\n", - " 9.602563e-01\n", - " OK\n", - " 7.186624e-01\n", + " 2\n", + " 0\n", + " 0\n", + " 484.0\n", + " 484.320639\n", + " 35.0\n", + " M\n", + " E-commerce\n", + " 5628\n", + " 5\n", + " 1\n", + " 484.5\n", + " 485.391149\n", + " 34.0\n", + " M\n", + " E-commerce\n", + " \n", + " \n", + " 3\n", + " 3\n", + " 11\n", + " 1\n", + " 494.5\n", + " 495.159047\n", + " 29.0\n", + " M\n", + " Logistics\n", + " 582\n", + " 0\n", + " 0\n", + " 494.5\n", + " 494.877988\n", + " 29.0\n", + " M\n", + " Logistics\n", + " \n", + " \n", + " 4\n", + " 4\n", + " 0\n", + " 0\n", + " 455.5\n", + " 455.675909\n", + " 53.0\n", + " M\n", + " E-commerce\n", + " 2247\n", + " 11\n", + " 1\n", + " 455.5\n", + " 455.833920\n", + " 54.0\n", + " M\n", + " E-commerce\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " 9995\n", + " 9995\n", + " 5\n", + " 1\n", + " 487.5\n", + " 487.741243\n", + " 31.0\n", + " M\n", + " Logistics\n", + " 386\n", + " 0\n", + " 0\n", + " 487.0\n", + " 487.204044\n", + " 31.0\n", + " M\n", + " Logistics\n", + " \n", + " \n", + " 9996\n", + " 9996\n", + " 11\n", + " 1\n", + " 453.5\n", + " 454.099548\n", + " 41.0\n", + " M\n", + " E-commerce\n", + " 4685\n", + " 0\n", + " 0\n", + " 454.0\n", + " 454.738503\n", + " 45.0\n", + " M\n", + " E-commerce\n", + " \n", + " \n", + " 9997\n", + " 9997\n", + " 10\n", + " 1\n", + " 482.0\n", + " 482.308959\n", + " 58.0\n", + " F\n", + " Logistics\n", + " 3842\n", + " 0\n", + " 0\n", + " 481.5\n", + " 481.902546\n", + " 57.0\n", + " F\n", + " Logistics\n", + " \n", + " \n", + " 9998\n", + " 9998\n", + " 6\n", + " 1\n", + " 477.0\n", + " 477.867590\n", + " 41.0\n", + " F\n", + " E-commerce\n", + " 5548\n", + " 0\n", + " 0\n", + " 478.5\n", + " 479.044356\n", + " 41.0\n", + " F\n", + " E-commerce\n", + " \n", + " \n", + " 9999\n", + " 9999\n", + " 0\n", + " 0\n", + " 496.0\n", + " 496.367291\n", + " 18.0\n", + " M\n", + " E-commerce\n", + " 2325\n", + " 8\n", + " 1\n", + " 495.0\n", + " 495.622851\n", + " 19.0\n", + " M\n", + " E-commerce\n", " \n", " \n", "\n", + "

10000 rows × 16 columns

\n", "" ], "text/plain": [ - " feature group TTest pass TTest p-value KSTest pass \\\n", - "0 signup_month 1┆signup_month NOT OK 0.000000e+00 NOT OK \n", - "1 pre_spends 1┆pre_spends NOT OK 1.802420e-212 NOT OK \n", - "2 age 1┆age OK 9.602563e-01 OK \n", + " user_id signup_month treat pre_spends post_spends age gender \\\n", + "0 0 0 0 475.0 475.696965 23.0 M \n", + "1 1 11 1 487.0 487.535208 51.0 F \n", + "2 2 0 0 484.0 484.320639 35.0 M \n", + "3 3 11 1 494.5 495.159047 29.0 M \n", + "4 4 0 0 455.5 455.675909 53.0 M \n", + "... ... ... ... ... ... ... ... \n", + "9995 9995 5 1 487.5 487.741243 31.0 M \n", + "9996 9996 11 1 453.5 454.099548 41.0 M \n", + "9997 9997 10 1 482.0 482.308959 58.0 F \n", + "9998 9998 6 1 477.0 477.867590 41.0 F \n", + "9999 9999 0 0 496.0 496.367291 18.0 M \n", + "\n", + " industry user_id_matched_0 signup_month_matched_0 treat_matched_0 \\\n", + "0 E-commerce 2744 11 1 \n", + "1 Logistics 5529 0 0 \n", + "2 E-commerce 5628 5 1 \n", + "3 Logistics 582 0 0 \n", + "4 E-commerce 2247 11 1 \n", + "... ... ... ... ... \n", + "9995 Logistics 386 0 0 \n", + "9996 E-commerce 4685 0 0 \n", + "9997 Logistics 3842 0 0 \n", + "9998 E-commerce 5548 0 0 \n", + "9999 E-commerce 2325 8 1 \n", + "\n", + " pre_spends_matched_0 post_spends_matched_0 age_matched_0 \\\n", + "0 475.0 475.896827 23.0 \n", + "1 486.5 487.441640 51.0 \n", + "2 484.5 485.391149 34.0 \n", + "3 494.5 494.877988 29.0 \n", + "4 455.5 455.833920 54.0 \n", + "... ... ... ... \n", + "9995 487.0 487.204044 31.0 \n", + "9996 454.0 454.738503 45.0 \n", + "9997 481.5 481.902546 57.0 \n", + "9998 478.5 479.044356 41.0 \n", + "9999 495.0 495.622851 19.0 \n", "\n", - " KSTest p-value \n", - "0 0.000000e+00 \n", - "1 3.284750e-231 \n", - "2 7.186624e-01 " + " gender_matched_0 industry_matched_0 \n", + "0 M E-commerce \n", + "1 F Logistics \n", + "2 M E-commerce \n", + "3 M Logistics \n", + "4 M E-commerce \n", + "... ... ... \n", + "9995 M Logistics \n", + "9996 M E-commerce \n", + "9997 F Logistics \n", + "9998 F E-commerce \n", + "9999 M E-commerce \n", + "\n", + "[10000 rows x 16 columns]" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "result.quality_results" + "result.full_data" ] }, { "cell_type": "markdown", - "id": "3ad7a444", + "id": "82415d53", "metadata": {}, "source": [ - "We can change **metric** and do estimation again." + "## Distances: `distance=\"mahalanobis\"` or `\"l2\"`\n", + "\n", + "### 🔸 Euclidean (L2)\n", + "\n", + "This is the classic metric:\n", + "$ d(x_i, x_j) = \\sqrt{\\sum_k (x_{ik} - x_{jk})^2} $\n", + "Simply measures the \"geometric\" distance between points in feature space.\n", + "\n", + "### 🔸 Mahalanobis distance\n", + "\n", + "A more advanced metric:\n", + "$ d_M(x_i, x_j) = \\sqrt{(x_i - x_j)^T \\Sigma^{-1} (x_i - x_j)} $\n", + "where $\\Sigma$ is the covariance matrix of features.\n", + "This metric accounts for the scale and correlation of features, making the comparison more accurate:" ] }, { "cell_type": "code", - "execution_count": 12, - "id": "e22f6e1d", + "execution_count": 10, + "id": "b9ddd2ce", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n" - ] - } - ], + "outputs": [], "source": [ - "test = Matching(metric=\"atc\")\n", + "test = Matching(distance='l2')\n", "result = test.execute(data)" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "60424009", + "execution_count": 11, + "id": "3d5e6cb2", "metadata": {}, "outputs": [ { @@ -1152,12 +1371,30 @@ " \n", " \n", " \n", + " ATT\n", + " 0.01\n", + " 0.01\n", + " 0.60\n", + " -0.02\n", + " 0.03\n", + " post_spends\n", + " \n", + " \n", " ATC\n", - " 96.47\n", - " 0.14\n", - " 0.0\n", - " 96.21\n", - " 96.74\n", + " 0.00\n", + " 0.01\n", + " 0.75\n", + " -0.02\n", + " 0.03\n", + " post_spends\n", + " \n", + " \n", + " ATE\n", + " 0.01\n", + " 0.01\n", + " 0.61\n", + " -0.02\n", + " 0.03\n", " post_spends\n", " \n", " \n", @@ -1166,10 +1403,12 @@ ], "text/plain": [ " Effect Size Standard Error P-value CI Lower CI Upper outcome\n", - "ATC 96.47 0.14 0.0 96.21 96.74 post_spends" + "ATT 0.01 0.01 0.60 -0.02 0.03 post_spends\n", + "ATC 0.00 0.01 0.75 -0.02 0.03 post_spends\n", + "ATE 0.01 0.01 0.61 -0.02 0.03 post_spends" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1180,11 +1419,20 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "25b5f585b9cb0776", - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "id": "c6d4fe4e", + "metadata": {}, + "outputs": [], + "source": [ + "test = Matching(distance='mahalanobis')\n", + "result = test.execute(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a82fed1b", + "metadata": {}, "outputs": [ { "data": { @@ -1207,127 +1455,140 @@ " \n", " \n", " \n", - " indexes\n", + " Effect Size\n", + " Standard Error\n", + " P-value\n", + " CI Lower\n", + " CI Upper\n", + " outcome\n", " \n", " \n", " \n", " \n", - " 0\n", - " 9433\n", - " \n", - " \n", - " 1\n", - " -1\n", - " \n", - " \n", - " 2\n", - " -1\n", - " \n", - " \n", - " 3\n", - " 1735\n", - " \n", - " \n", - " 4\n", - " -1\n", - " \n", - " \n", - " ...\n", - " ...\n", - " \n", - " \n", - " 9995\n", - " -1\n", - " \n", - " \n", - " 9996\n", - " 7731\n", - " \n", - " \n", - " 9997\n", - " -1\n", + " ATT\n", + " 0.00\n", + " 0.01\n", + " 0.89\n", + " -0.02\n", + " 0.03\n", + " post_spends\n", " \n", " \n", - " 9998\n", - " -1\n", + " ATC\n", + " 0.01\n", + " 0.01\n", + " 0.37\n", + " -0.01\n", + " 0.03\n", + " post_spends\n", " \n", " \n", - " 9999\n", - " -1\n", + " ATE\n", + " 0.01\n", + " 0.01\n", + " 0.56\n", + " -0.01\n", + " 0.03\n", + " post_spends\n", " \n", " \n", "\n", - "

10000 rows × 1 columns

\n", "" ], "text/plain": [ - " indexes\n", - "0 9433\n", - "1 -1\n", - "2 -1\n", - "3 1735\n", - "4 -1\n", - "... ...\n", - "9995 -1\n", - "9996 7731\n", - "9997 -1\n", - "9998 -1\n", - "9999 -1\n", - "\n", - "[10000 rows x 1 columns]" + " Effect Size Standard Error P-value CI Lower CI Upper outcome\n", + "ATT 0.00 0.01 0.89 -0.02 0.03 post_spends\n", + "ATC 0.01 0.01 0.37 -0.01 0.03 post_spends\n", + "ATE 0.01 0.01 0.56 -0.01 0.03 post_spends" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "result.indexes" + "result.resume" ] }, { "cell_type": "markdown", - "id": "96a52742", + "id": "c8a7e4a6", "metadata": {}, "source": [ - "Also it is possible to search pairs only in **test group**. This way we have metric \"auto\" and **ATT** will be estimated. " + "# `group_match`: matching by groups\n", + "\n", + "The parameter `group_match=True` forces HypEx to aggregate observations into groups (by a specified identifier), and then search for pairs between groups rather than individual objects.\n", + "This reduces variability at the individual level and allows for effect estimation at more aggregated levels." ] }, { "cell_type": "code", - "execution_count": 15, - "id": "b67abd5d", - "metadata": {}, + "execution_count": 14, + "id": "d98c94a8b8a763e9", + "metadata": { + "collapsed": false + }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n" + "data": { + "text/plain": [ + "{'gender': array(['M', 'F'], dtype=object)}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = Dataset(\n", + " roles={\n", + " \"user_id\": InfoRole(int),\n", + " \"treat\": TreatmentRole(int),\n", + " \"post_spends\": TargetRole(float),\n", + " \"gender\": GroupingRole(str),\n", + " \"pre_spends\": FeatureRole(float),\n", + " \"industry\": FeatureRole(str),\n", + " \"age\": FeatureRole(int),\n", + " },\n", + " data=df,\n", + " default_role=InfoRole(),\n", + ")\n", + "data['gender'].unique()" + ] + }, + { + "cell_type": "markdown", + "id": "3ad7a444", + "metadata": {}, + "source": [ + "We can change **metric** and do estimation again." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e22f6e1d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 2/2 [00:02<00:00, 1.25s/it]\n" ] } ], "source": [ - "test = Matching(metric='att')\n", + "test = Matching(group_match=True)\n", "result = test.execute(data)" ] }, { "cell_type": "code", "execution_count": 16, - "id": "cc54c8f3", + "id": "60424009", "metadata": {}, "outputs": [ { @@ -1351,22 +1612,60 @@ " \n", " \n", " \n", - " Effect Size\n", - " Standard Error\n", - " P-value\n", - " CI Lower\n", - " CI Upper\n", + " F Effect Size\n", + " M Effect Size\n", + " F Standard Error\n", + " M Standard Error\n", + " F P-value\n", + " M P-value\n", + " F CI Lower\n", + " M CI Lower\n", + " F CI Upper\n", + " M CI Upper\n", " outcome\n", " \n", " \n", " \n", " \n", " ATT\n", - " 63.37\n", - " 0.46\n", + " 0.00\n", " 0.0\n", - " 62.46\n", - " 64.28\n", + " 0.02\n", + " 0.02\n", + " 0.80\n", + " 0.99\n", + " -0.03\n", + " -0.03\n", + " 0.04\n", + " 0.03\n", + " post_spends\n", + " \n", + " \n", + " ATC\n", + " 0.02\n", + " -0.0\n", + " 0.02\n", + " 0.02\n", + " 0.31\n", + " 0.81\n", + " -0.01\n", + " -0.04\n", + " 0.05\n", + " 0.03\n", + " post_spends\n", + " \n", + " \n", + " ATE\n", + " 0.01\n", + " -0.0\n", + " 0.01\n", + " 0.01\n", + " 0.47\n", + " 0.90\n", + " -0.02\n", + " -0.03\n", + " 0.04\n", + " 0.03\n", " post_spends\n", " \n", " \n", @@ -1374,8 +1673,20 @@ "" ], "text/plain": [ - " Effect Size Standard Error P-value CI Lower CI Upper outcome\n", - "ATT 63.37 0.46 0.0 62.46 64.28 post_spends" + " F Effect Size M Effect Size F Standard Error M Standard Error \\\n", + "ATT 0.00 0.0 0.02 0.02 \n", + "ATC 0.02 -0.0 0.02 0.02 \n", + "ATE 0.01 -0.0 0.01 0.01 \n", + "\n", + " F P-value M P-value F CI Lower M CI Lower F CI Upper M CI Upper \\\n", + "ATT 0.80 0.99 -0.03 -0.03 0.04 0.03 \n", + "ATC 0.31 0.81 -0.01 -0.04 0.05 0.03 \n", + "ATE 0.47 0.90 -0.02 -0.03 0.04 0.03 \n", + "\n", + " outcome \n", + "ATT post_spends \n", + "ATC post_spends \n", + "ATE post_spends " ] }, "execution_count": 16, @@ -1387,10 +1698,56 @@ "result.resume" ] }, + { + "cell_type": "markdown", + "id": "c0bd75f7", + "metadata": {}, + "source": [ + "# `bias_estimation`: bias estimation and correction\n", + "\n", + "When `bias_estimation=True`, HypEx:\n", + "\n", + "1. **Estimates residual imbalance** across all features after matching (e.g., using standardized mean difference, t-tests, etc.);\n", + "2. **Corrects the final effect estimate** to reduce the impact of remaining bias.\n" + ] + }, { "cell_type": "code", "execution_count": 17, - "id": "501ffee15042d3ea", + "id": "3a164b2e", + "metadata": {}, + "outputs": [], + "source": [ + "data = Dataset(\n", + " roles={\n", + " \"user_id\": InfoRole(int),\n", + " \"treat\": TreatmentRole(int),\n", + " \"post_spends\": TargetRole(float),\n", + " \"gender\": FeatureRole(str),\n", + " \"pre_spends\": FeatureRole(float),\n", + " \"industry\": FeatureRole(str),\n", + " \"age\": FeatureRole(int),\n", + " },\n", + " data=df,\n", + " default_role=InfoRole(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "19931e43", + "metadata": {}, + "outputs": [], + "source": [ + "test = Matching(bias_estimation=False)\n", + "result = test.execute(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "25b5f585b9cb0776", "metadata": { "collapsed": false }, @@ -1416,90 +1773,179 @@ " \n", " \n", " \n", - " indexes\n", + " Effect Size\n", + " Standard Error\n", + " P-value\n", + " CI Lower\n", + " CI Upper\n", + " outcome\n", " \n", " \n", " \n", " \n", - " 0\n", - " -1\n", - " \n", - " \n", - " 1\n", - " 5438\n", - " \n", - " \n", - " 2\n", - " 5165\n", - " \n", - " \n", - " 3\n", - " -1\n", - " \n", - " \n", - " 4\n", - " 539\n", + " ATT\n", + " 0.65\n", + " 0.06\n", + " 0.00\n", + " 0.53\n", + " 0.76\n", + " post_spends\n", " \n", " \n", - " ...\n", - " ...\n", + " ATC\n", + " 0.06\n", + " 0.09\n", + " 0.52\n", + " -0.12\n", + " 0.23\n", + " post_spends\n", " \n", " \n", - " 9995\n", - " 5893\n", + " ATE\n", + " 0.35\n", + " 0.07\n", + " 0.00\n", + " 0.22\n", + " 0.48\n", + " post_spends\n", " \n", - " \n", - " 9996\n", - " -1\n", + " \n", + "\n", + "" + ], + "text/plain": [ + " Effect Size Standard Error P-value CI Lower CI Upper outcome\n", + "ATT 0.65 0.06 0.00 0.53 0.76 post_spends\n", + "ATC 0.06 0.09 0.52 -0.12 0.23 post_spends\n", + "ATE 0.35 0.07 0.00 0.22 0.48 post_spends" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.resume" + ] + }, + { + "cell_type": "markdown", + "id": "96a52742", + "metadata": {}, + "source": [ + "# `quality_tests`: matching quality checks\n", + "\n", + "Main tests:\n", + "\n", + "* `'ks-test'` — Kolmogorov–Smirnov test for comparing distributions;\n", + "* `'t-test'` — test for equality of means;\n", + "* `'chi2-test'` — test for independence of categorical features;\n", + "\n", + "These tests help understand whether balance was achieved and how reliable the result can be considered." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b67abd5d", + "metadata": {}, + "outputs": [], + "source": [ + "test = Matching(quality_tests=['chi2-test', 'ks-test', 't-test'])\n", + "result = test.execute(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cc54c8f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Effect SizeStandard ErrorP-valueCI LowerCI Upperoutcome
99977066ATT0.000.010.89-0.020.03post_spends
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" ], "text/plain": [ - " indexes\n", - "0 -1\n", - "1 5438\n", - "2 5165\n", - "3 -1\n", - "4 539\n", - "... ...\n", - "9995 5893\n", - "9996 -1\n", - "9997 7066\n", - "9998 1885\n", - "9999 5748\n", - "\n", - "[10000 rows x 1 columns]" + " Effect Size Standard Error P-value CI Lower CI Upper outcome\n", + "ATT 0.00 0.01 0.89 -0.02 0.03 post_spends\n", + "ATC 0.01 0.01 0.37 -0.01 0.03 post_spends\n", + "ATE 0.01 0.01 0.56 -0.01 0.03 post_spends" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "result.indexes" + "result.resume" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "e061a49b", - "metadata": {}, + "execution_count": 22, + "id": "501ffee15042d3ea", + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -1522,344 +1968,161 @@ " \n", " \n", " \n", - " user_id\n", - " signup_month\n", - " treat\n", - " pre_spends\n", - " post_spends\n", - " age\n", - " gender\n", - " industry\n", - " user_id_matched\n", - " signup_month_matched\n", - " treat_matched\n", - " pre_spends_matched\n", - " post_spends_matched\n", - " age_matched\n", - " gender_matched\n", - " industry_matched\n", + " feature\n", + " group\n", + " TTest pass\n", + " TTest p-value\n", + " KSTest pass\n", + " KSTest p-value\n", + " Chi2Test pass\n", + " Chi2Test p-value\n", " \n", " \n", " \n", " \n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 488.0\n", - " 414.444444\n", - " 26.0\n", - " M\n", - " E-commerce\n", + " pre_spends\n", + " 0┆pre_spends\n", + " OK\n", + " 0.879358\n", + " OK\n", + " 0.999905\n", " NaN\n", " NaN\n", + " \n", + " \n", + " 1\n", + " pre_spends\n", + " 1┆pre_spends\n", + " OK\n", + " 0.109933\n", + " OK\n", + " 0.093432\n", " NaN\n", " NaN\n", + " \n", + " \n", + " 2\n", + " age\n", + " 0┆age\n", + " OK\n", + " 0.946168\n", + " OK\n", + " 1.000000\n", " NaN\n", " NaN\n", + " \n", + " \n", + " 3\n", + " age\n", + " 1┆age\n", + " OK\n", + " 0.869023\n", + " OK\n", + " 0.975792\n", " NaN\n", " NaN\n", " \n", " \n", - " 1\n", - " 1\n", - " 8\n", - " 1\n", - " 512.5\n", - " 462.222222\n", - " 26.0\n", - " M\n", - " E-commerce\n", - " 5438.0\n", - " 0.0\n", - " 0.0\n", - " 529.0\n", - " 417.111111\n", - " 23.0\n", - " F\n", - " E-commerce\n", - " \n", - " \n", - " 2\n", - " 2\n", - " 7\n", - " 1\n", - " 483.0\n", - " 479.444444\n", - " 25.0\n", - " M\n", - " Logistics\n", - " 5165.0\n", - " 0.0\n", - " 0.0\n", - " 498.5\n", - " 412.222222\n", - " 25.0\n", - " F\n", - " Logistics\n", - " \n", - " \n", - " 3\n", - " 3\n", - " 0\n", - " 0\n", - " 501.5\n", - " 424.333333\n", - " 39.0\n", - " M\n", - " E-commerce\n", + " 4\n", + " gender\n", + " 0┆gender\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " OK\n", + " 1.0\n", + " \n", + " \n", + " 5\n", + " gender\n", + " 1┆gender\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " OK\n", + " 1.0\n", " \n", " \n", - " 4\n", - " 4\n", - " 1\n", - " 1\n", - " 543.0\n", - " 514.555556\n", - " 18.0\n", - " F\n", - " E-commerce\n", - " 539.0\n", - " 0.0\n", - " 0.0\n", - " 531.0\n", - " 414.000000\n", - " 20.0\n", - " F\n", - " E-commerce\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 9995\n", - " 9995\n", - " 10\n", - " 1\n", - " 538.5\n", - " 450.444444\n", - " 42.0\n", - " M\n", - " Logistics\n", - " 5893.0\n", - " 0.0\n", - " 0.0\n", - " 535.0\n", - " 414.555556\n", - " 40.0\n", - " M\n", - " E-commerce\n", - " \n", - " \n", - " 9996\n", - " 9996\n", - " 0\n", - " 0\n", - " 500.5\n", - " 430.888889\n", - " 26.0\n", - " F\n", - " Logistics\n", + " 6\n", + " industry\n", + " 0┆industry\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", + " OK\n", + " 1.0\n", + " \n", + " \n", + " 7\n", + " industry\n", + " 1┆industry\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " \n", - " \n", - " 9997\n", - " 9997\n", - " 3\n", - " 1\n", - " 473.0\n", - " 534.111111\n", - " 22.0\n", - " F\n", - " E-commerce\n", - " 7066.0\n", - " 0.0\n", - " 0.0\n", - " 480.0\n", - " 423.222222\n", - " 22.0\n", - " F\n", - " Logistics\n", - " \n", - " \n", - " 9998\n", - " 9998\n", - " 2\n", - " 1\n", - " 495.0\n", - " 523.222222\n", - " 67.0\n", - " F\n", - " E-commerce\n", - " 1885.0\n", - " 0.0\n", - " 0.0\n", - " 499.0\n", - " 423.000000\n", - " 67.0\n", - " F\n", - " Logistics\n", - " \n", - " \n", - " 9999\n", - " 9999\n", - " 7\n", - " 1\n", - " 508.0\n", - " 475.888889\n", - " 38.0\n", - " F\n", - " E-commerce\n", - " 5748.0\n", - " 0.0\n", - " 0.0\n", - " 522.0\n", - " 424.444444\n", - " 36.0\n", - " M\n", - " E-commerce\n", + " OK\n", + " 1.0\n", " \n", " \n", "\n", - "

10000 rows × 16 columns

\n", "" ], "text/plain": [ - " user_id signup_month treat pre_spends post_spends age gender \\\n", - "0 0 0 0 488.0 414.444444 26.0 M \n", - "1 1 8 1 512.5 462.222222 26.0 M \n", - "2 2 7 1 483.0 479.444444 25.0 M \n", - "3 3 0 0 501.5 424.333333 39.0 M \n", - "4 4 1 1 543.0 514.555556 18.0 F \n", - "... ... ... ... ... ... ... ... \n", - "9995 9995 10 1 538.5 450.444444 42.0 M \n", - "9996 9996 0 0 500.5 430.888889 26.0 F \n", - "9997 9997 3 1 473.0 534.111111 22.0 F \n", - "9998 9998 2 1 495.0 523.222222 67.0 F \n", - "9999 9999 7 1 508.0 475.888889 38.0 F \n", - "\n", - " industry user_id_matched signup_month_matched treat_matched \\\n", - "0 E-commerce NaN NaN NaN \n", - "1 E-commerce 5438.0 0.0 0.0 \n", - "2 Logistics 5165.0 0.0 0.0 \n", - "3 E-commerce NaN NaN NaN \n", - "4 E-commerce 539.0 0.0 0.0 \n", - "... ... ... ... ... \n", - "9995 Logistics 5893.0 0.0 0.0 \n", - "9996 Logistics NaN NaN NaN \n", - "9997 E-commerce 7066.0 0.0 0.0 \n", - "9998 E-commerce 1885.0 0.0 0.0 \n", - "9999 E-commerce 5748.0 0.0 0.0 \n", - "\n", - " pre_spends_matched post_spends_matched age_matched gender_matched \\\n", - "0 NaN NaN NaN NaN \n", - "1 529.0 417.111111 23.0 F \n", - "2 498.5 412.222222 25.0 F \n", - "3 NaN NaN NaN NaN \n", - "4 531.0 414.000000 20.0 F \n", - "... ... ... ... ... \n", - "9995 535.0 414.555556 40.0 M \n", - "9996 NaN NaN NaN NaN \n", - "9997 480.0 423.222222 22.0 F \n", - "9998 499.0 423.000000 67.0 F \n", - "9999 522.0 424.444444 36.0 M \n", - "\n", - " industry_matched \n", - "0 NaN \n", - "1 E-commerce \n", - "2 Logistics \n", - "3 NaN \n", - "4 E-commerce \n", - "... ... \n", - "9995 E-commerce \n", - "9996 NaN \n", - "9997 Logistics \n", - "9998 Logistics \n", - "9999 E-commerce \n", + " feature group TTest pass TTest p-value KSTest pass \\\n", + "0 pre_spends 0┆pre_spends OK 0.879358 OK \n", + "1 pre_spends 1┆pre_spends OK 0.109933 OK \n", + "2 age 0┆age OK 0.946168 OK \n", + "3 age 1┆age OK 0.869023 OK \n", + "4 gender 0┆gender NaN NaN NaN \n", + "5 gender 1┆gender NaN NaN NaN \n", + "6 industry 0┆industry NaN NaN NaN \n", + "7 industry 1┆industry NaN NaN NaN \n", "\n", - "[10000 rows x 16 columns]" + " KSTest p-value Chi2Test pass Chi2Test p-value \n", + "0 0.999905 NaN NaN \n", + "1 0.093432 NaN NaN \n", + "2 1.000000 NaN NaN \n", + "3 0.975792 NaN NaN \n", + "4 NaN OK 1.0 \n", + "5 NaN OK 1.0 \n", + "6 NaN OK 1.0 \n", + "7 NaN OK 1.0 " ] }, - "execution_count": 18, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "result.full_data" + "result.quality_results" ] }, { "cell_type": "markdown", - "id": "a60205ca", - "metadata": {}, - "source": [ - "Finally, we may search pairs in L2 distance. " - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "5ac83bea", + "id": "9b7ff624", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n", - "/home/anathema/HypEx/hypex/dataset/backends/pandas_backend.py:344: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", - " return list(groups)\n" - ] - } - ], "source": [ - "test = Matching(distance=\"l2\", metric='att')\n", - "result = test.execute(data)" + "# `faiss_mode`: search acceleration\n", + "\n", + "FAISS is a high-performance library for nearest neighbor search, developed by Meta AI.\n", + "HypEx uses it for finding pairs in large datasets.\n", + "\n", + "Modes:\n", + "\n", + "* `'base'` — exact but slow search;\n", + "* `'fast'` — approximate but fast (uses indexing);\n", + "* `'auto'` — HypEx automatically chooses the optimal option depending on data size.\n" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "4bf5a651", + "execution_count": 23, + "id": "e061a49b", "metadata": {}, "outputs": [ { @@ -1894,11 +2157,29 @@ " \n", " \n", " ATT\n", - " 63.37\n", - " 0.46\n", - " 0.0\n", - " 62.46\n", - " 64.27\n", + " 0.00\n", + " 0.01\n", + " 0.89\n", + " -0.02\n", + " 0.03\n", + " post_spends\n", + " \n", + " \n", + " ATC\n", + " 0.01\n", + " 0.01\n", + " 0.37\n", + " -0.01\n", + " 0.03\n", + " post_spends\n", + " \n", + " \n", + " ATE\n", + " 0.01\n", + " 0.01\n", + " 0.56\n", + " -0.01\n", + " 0.03\n", " post_spends\n", " \n", " \n", @@ -1907,26 +2188,44 @@ ], "text/plain": [ " Effect Size Standard Error P-value CI Lower CI Upper outcome\n", - "ATT 63.37 0.46 0.0 62.46 64.27 post_spends" + "ATT 0.00 0.01 0.89 -0.02 0.03 post_spends\n", + "ATC 0.01 0.01 0.37 -0.01 0.03 post_spends\n", + "ATE 0.01 0.01 0.56 -0.01 0.03 post_spends" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "test = Matching(faiss_mode=\"base\")\n", + "result = test.execute(data)\n", "result.resume" ] }, + { + "cell_type": "markdown", + "id": "a60205ca", + "metadata": {}, + "source": [ + "Finally, we may search pairs in L2 distance. " + ] + }, { "cell_type": "code", - "execution_count": 21, - "id": "c2b000183546bd56", - "metadata": { - "collapsed": false - }, + "execution_count": 24, + "id": "5ac83bea", + "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING clustering 4981 points to 1000 centroids: please provide at least 39000 training points\n", + "WARNING clustering 5019 points to 1000 centroids: please provide at least 39000 training points\n" + ] + }, { "data": { "text/html": [ @@ -1948,91 +2247,101 @@ " \n", " \n", " \n", - " indexes\n", + " Effect Size\n", + " Standard Error\n", + " P-value\n", + " CI Lower\n", + " CI Upper\n", + " outcome\n", " \n", " \n", " \n", " \n", - " 0\n", - " -1\n", - " \n", - " \n", - " 1\n", - " 2490\n", - " \n", - " \n", - " 2\n", - " 5493\n", - " \n", - " \n", - " 3\n", - " -1\n", - " \n", - " \n", - " 4\n", - " 321\n", - " \n", - " \n", - " ...\n", - " ...\n", - " \n", - " \n", - " 9995\n", - " 5893\n", - " \n", - " \n", - " 9996\n", - " -1\n", - " \n", - " \n", - " 9997\n", - " 8670\n", + " ATT\n", + " 0.00\n", + " 0.01\n", + " 0.93\n", + " -0.02\n", + " 0.03\n", + " post_spends\n", " \n", " \n", - " 9998\n", - " 507\n", + " ATC\n", + " 0.01\n", + " 0.01\n", + " 0.41\n", + " -0.01\n", + " 0.03\n", + " post_spends\n", " \n", " \n", - " 9999\n", - " 7155\n", + " ATE\n", + " 0.01\n", + " 0.01\n", + " 0.61\n", + " -0.01\n", + " 0.03\n", + " post_spends\n", " \n", " \n", "\n", - "

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ATE0.000.010.69-0.020.02post_spends
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" + ], + "text/plain": [ + " Effect Size Standard Error P-value CI Lower CI Upper outcome\n", + "ATT 0.00 0.01 0.96 -0.02 0.03 post_spends\n", + "ATC 0.01 0.01 0.50 -0.01 0.03 post_spends\n", + "ATE 0.00 0.01 0.69 -0.02 0.02 post_spends" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test = Matching(weights={\"gender\": 0.2, \"industry\": 0.3, \"age\": 0.1, \"signup_month\": 0.1, \"pre_spends\": 0.3})\n", + "result = test.execute(data)\n", + "result.resume" + ] + }, + { + "cell_type": "markdown", + "id": "9e9a37ff", + "metadata": {}, + "source": [ + "# `encode_categories`: encoding categorical features\n", + "\n", + "If `encode_categories=True` (default), HypEx automatically converts categorical features to numerical form (one-hot encoding).\n", + "If False — the library expects that the user has already encoded them.\n", + "This step is necessary so that all features can participate in distance calculations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "b6461b5b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Effect SizeStandard ErrorP-valueCI LowerCI Upperoutcome
ATT-0.000.010.98-0.030.03post_spends
ATC0.010.010.48-0.020.03post_spends
ATE0.000.010.71-0.020.03post_spends
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" + ], + "text/plain": [ + " Effect Size Standard Error P-value CI Lower CI Upper outcome\n", + "ATT -0.00 0.01 0.98 -0.03 0.03 post_spends\n", + "ATC 0.01 0.01 0.48 -0.02 0.03 post_spends\n", + "ATE 0.00 0.01 0.71 -0.02 0.03 post_spends" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test = Matching(encode_categories=False)\n", + "result = test.execute(data)\n", + "result.resume" + ] } ], "metadata": { diff --git "a/examples/tutorials/\320\241UPED&CUPAC.ipynb" "b/examples/tutorials/\320\241UPED&CUPAC.ipynb" new file mode 100644 index 00000000..daef2609 --- /dev/null +++ "b/examples/tutorials/\320\241UPED&CUPAC.ipynb" @@ -0,0 +1,1063 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f7fefac8", + "metadata": {}, + "source": [ + "# CUPED and CUPAC Tutorial\n", + "\n", + "This tutorial demonstrates variance reduction techniques for A/B testing using covariate adjustment methods available in HypEx.\n", + "\n", + "**CUPED** (Controlled Experiments Using Pre-Experiment Data) uses historical features to reduce variance in your target metrics through linear regression adjustment.\n", + "\n", + "**CUPAC** (Covariate-Updated Pre-Analysis Correction) extends CUPED by using multiple pre-experiment covariates to predict pre-experiment target values, then subtracting these predictions from current experiment targets. This approach supports different regression models (linear, ridge, lasso, catboost) and avoids data leakage by never using experiment data to predict experiment outcomes.\n", + "\n", + "Both methods help you:\n", + "- Detect smaller effects with the same sample size\n", + "- Reduce sample size needed to detect the same effect\n", + "- Increase statistical power of your experiments" + ] + }, + { + "cell_type": "markdown", + "id": "131f7db3", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "13cf996e", + "metadata": {}, + "source": [ + "## Data Preparation\n", + "\n", + "For CUPAC to work correctly with the new features_mapping format, we need:\n", + "1. **Target metrics**: The metrics you want to analyze (e.g., spends, revenue)\n", + "2. **Historical target features**: Lagged versions of your targets from different time periods\n", + "3. **Pre-experiment covariates**: Features measured before the experiment that correlate with outcomes\n", + "\n", + "The new CUPAC implementation supports **multilevel models** - it can automatically create models for each available time period transition. For this tutorial, we'll use **2 time periods**:\n", + "- Period 2 → Period 1: `y0_lag_2 ~ X1_lag2 + X2_lag2` \n", + "- Period 1 → Current: `y0_lag_1 ~ X1_lag1 + X2_lag1`\n", + "\n", + "Each period uses its own set of covariates, making the temporal structure clearer.\n", + "\n", + "Let's generate synthetic data using the built-in DataGenerator:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "82ee928a", + "metadata": {}, + "outputs": [], + "source": [ + "from hypex import ABTest\n", + "from hypex.dataset import (\n", + " Dataset,\n", + " FeatureRole,\n", + " InfoRole,\n", + " PreTargetRole,\n", + " TargetRole,\n", + " TreatmentRole,\n", + ")\n", + "from hypex.utils.tutorial_data_creation import DataGenerator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "db68c16a", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate synthetic data with 2 historical periods using built-in DataGenerator\n", + "gen = DataGenerator(\n", + " n_samples=1_000,\n", + " distributions={\n", + " \"X1\": {\"type\": \"normal\", \"mean\": 1, \"std\": 1},\n", + " \"X2\": {\"type\": \"bernoulli\", \"p\": 0.5},\n", + " \"y0\": {\"type\": \"normal\", \"mean\": 1, \"std\": 5},\n", + " },\n", + " time_correlations={\"X1\": 0.2, \"X2\": 0.1, \"y0\": 0.8},\n", + " effect_size=0.1,\n", + " seed=42\n", + ")\n", + "\n", + "df = gen.generate()\n", + "# Keep only the columns we need for 2-period CUPAC\n", + "df = df.drop(columns=['y0', 'z', 'U', 'D', 'y1'])\n", + "df = df.rename(columns={'y0_lag_1': 'y_lag1', 'y0_lag_2': 'y_lag2'})" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "af6edfe1", + "metadata": {}, + "outputs": [], + "source": [ + "data = Dataset(\n", + " roles = {\n", + " \"d\": TreatmentRole(),\n", + " \"y\": TargetRole(cofounders=[\"X1\", \"X2\"]),\n", + "\n", + " \"y_lag1\": PreTargetRole(parent=\"y\", lag=1),\n", + " \"X1_lag1\": FeatureRole(parent=\"X1\", lag=1),\n", + " \"X2_lag1\": FeatureRole(parent=\"X2\", lag=1),\n", + "\n", + " \"y_lag2\": PreTargetRole(parent=\"y\", lag=2),\n", + " \"X1_lag2\": FeatureRole(parent=\"X1\", lag=2),\n", + " \"X2_lag2\": FeatureRole(parent=\"X2\", lag=2),\n", + " },\n", + " data=df,\n", + " default_role=InfoRole(),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2238e70c", + "metadata": {}, + "source": [ + "## Baseline AB Test\n", + "\n", + "First, let's run a standard AB test without any variance reduction to establish our baseline:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8d3521ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featuregroupcontrol meantest meandifferencedifference %TTest passTTest p-value
0y10.9485181.5905430.64202567.687127NOT OK0.064382
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" + ], + "text/plain": [ + " feature group control mean test mean difference difference % TTest pass \\\n", + "0 y 1 0.948518 1.590543 0.642025 67.687127 NOT OK \n", + "\n", + " TTest p-value \n", + "0 0.064382 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Standard AB test without covariate adjustment\n", + "test_baseline = ABTest()\n", + "result_baseline = test_baseline.execute(data)\n", + "\n", + "result_baseline.resume" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8fbb5b2d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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control sizetest sizecontrol size %test size %group
165334765.334.71
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" + ], + "text/plain": [ + " control size test size control size % test size % group\n", + "1 653 347 65.3 34.7 1" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_baseline.sizes" + ] + }, + { + "cell_type": "markdown", + "id": "d858f178", + "metadata": {}, + "source": [ + "## CUPED Implementation\n", + "\n", + "CUPED uses a single historical feature to adjust the target variable. In HypEx, specify the `cuped_features` parameter:\n", + "\n", + "**Note**: For this dataset, we'll use the period 1 lagged features for CUPED since it's the closest to the current target." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3cb24df8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featuregroupcontrol meantest meandifferencedifference %TTest passTTest p-value
0y10.9485181.5905430.64202567.687127NOT OK0.064382
1y_cuped10.9816741.5281500.54647655.667761OK0.009859
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" + ], + "text/plain": [ + " feature group control mean test mean difference difference % \\\n", + "0 y 1 0.948518 1.590543 0.642025 67.687127 \n", + "1 y_cuped 1 0.981674 1.528150 0.546476 55.667761 \n", + "\n", + " TTest pass TTest p-value \n", + "0 NOT OK 0.064382 \n", + "1 OK 0.009859 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# CUPED with single covariate (using closest lagged feature)\n", + "test_cuped = ABTest(cuped_features={'y': 'y_lag1'})\n", + "result_cuped = test_cuped.execute(data)\n", + "\n", + "result_cuped.resume" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a1ea62b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Transformed Metric NameVariance Reduction (%)
0y_cuped62.728655
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" + ], + "text/plain": [ + " Transformed Metric Name Variance Reduction (%)\n", + "0 y_cuped 62.728655" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check variance reduction achieved by CUPED\n", + "result_cuped.variance_reduction_report" + ] + }, + { + "cell_type": "markdown", + "id": "d57faed6", + "metadata": {}, + "source": [ + "## CUPAC Implementation\n", + "\n", + "The new CUPAC implementation uses `features_mapping` format and automatically creates multilevel models. The `features_mapping` is already configured in our Dataset above.\n", + "\n", + "Key advantages of the new multilevel approach:\n", + "- **Sequential modeling**: Each time period predicts the next period\n", + "- **Better temporal relationships**: Captures changing correlations over time \n", + "- **Multiple targets**: Different targets can have different numbers of periods\n", + "- **Automatic model selection**: Chooses best performing models via cross-validation\n", + "\n", + "**Example with 3 periods**: For more complex scenarios, you can use 3 or more periods:\n", + "- Period 3 → Period 2: `target_lag_3 ~ covariates_lag3`\n", + "- Period 2 → Period 1: `target_lag_2 ~ covariates_lag2` \n", + "- Period 1 → Current: `target_lag_1 ~ covariates_lag1`" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7d864d71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featuregroupcontrol meantest meandifferencedifference %TTest passTTest p-value
0y10.9485181.5905430.64202567.687127NOT OK0.064382
1y_cupac11.0078791.4788360.47095746.727496OK0.026723
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" + ], + "text/plain": [ + " feature group control mean test mean difference difference % \\\n", + "0 y 1 0.948518 1.590543 0.642025 67.687127 \n", + "1 y_cupac 1 1.007879 1.478836 0.470957 46.727496 \n", + "\n", + " TTest pass TTest p-value \n", + "0 NOT OK 0.064382 \n", + "1 OK 0.026723 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Multilevel CUPAC with linear regression\n", + "test_cupac_linear = ABTest(\n", + " enable_cupac=True,\n", + " cupac_models='ridge'\n", + ")\n", + "result_cupac_linear = test_cupac_linear.execute(data)\n", + "\n", + "result_cupac_linear.resume" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0551e163", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featuregroupcontrol meantest meandifferencedifference %TTest passTTest p-value
0y10.9485181.5905430.64202567.687127NOT OK0.064382
1y_cupac11.0078921.4788120.47092046.723250OK0.026736
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" + ], + "text/plain": [ + " feature group control mean test mean difference difference % \\\n", + "0 y 1 0.948518 1.590543 0.642025 67.687127 \n", + "1 y_cupac 1 1.007892 1.478812 0.470920 46.723250 \n", + "\n", + " TTest pass TTest p-value \n", + "0 NOT OK 0.064382 \n", + "1 OK 0.026736 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Multilevel CUPAC with automatic model selection\n", + "test_cupac_auto = ABTest(\n", + " enable_cupac=True,\n", + " cupac_models=['linear', 'ridge', 'lasso', 'catboost'] # Will select best performing model for each transition\n", + ")\n", + "result_cupac_auto = test_cupac_auto.execute(data)\n", + "\n", + "result_cupac_auto.resume" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b9b80c54", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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targetbest_modelvariance_reduction_cvvariance_reduction_realcontrol_mean_biastest_mean_bias
0ylinear66.03550662.472566-0.0593730.111732
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" + ], + "text/plain": [ + " target best_model variance_reduction_cv variance_reduction_real \\\n", + "0 y linear 66.035506 62.472566 \n", + "\n", + " control_mean_bias test_mean_bias \n", + "0 -0.059373 0.111732 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check variance reduction for CUPAC methods\n", + "result_cupac_auto.cupac.variance_reductions" + ] + }, + { + "cell_type": "markdown", + "id": "ae92d5f9", + "metadata": {}, + "source": [ + "### Feature Importances\n", + "\n", + "CUPAC models learn which historical covariates best predict target values. Feature importances help you understand:\n", + "- **Which features matter most** for variance reduction\n", + "- **Linear models** (linear, ridge, lasso): Show regression coefficients - positive values mean the feature increases with the target\n", + "- **CatBoost**: Shows feature importance scores - higher values indicate stronger predictive power\n", + "\n", + "**Important:** Feature importances are computed as **averages across cross-validation folds**, providing:\n", + "- More stable and reliable estimates than single-model fits\n", + "- Better generalization to unseen data\n", + "- Computational efficiency (no extra model training needed)\n", + "\n", + "The importances are shown per target and include both the lagged target features and covariate features used in the model." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "52598176", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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targetfeatureimportancemodel
0yX2_lag20.113172linear
1yX1_lag20.139829linear
2yy_lag20.829126linear
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" + ], + "text/plain": [ + " target feature importance model\n", + "0 y X2_lag2 0.113172 linear\n", + "1 y X1_lag2 0.139829 linear\n", + "2 y y_lag2 0.829126 linear" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check feature importances - which covariates contributed most to variance reduction\n", + "result_cupac_auto.cupac.feature_importances" + ] + }, + { + "cell_type": "markdown", + "id": "0ef1eb3c", + "metadata": {}, + "source": [ + "### Virtual Target\n", + "\n", + "Virtual targets allow you to test CUPAC on scenarios where the current period target doesn't exist yet (e.g., forecasting future outcomes). In this case:\n", + "- No current target column exists (only historical lags)\n", + "- CUPAC trains models on historical transitions\n", + "- Only CV variance reduction is available (no real variance reduction)\n", + "- Feature importances still show which historical features are most predictive" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0393d5f9", + "metadata": {}, + "outputs": [], + "source": [ + "gen = DataGenerator(\n", + " n_samples=2000,\n", + " distributions={\n", + " \"X1\": {\"type\": \"normal\", \"mean\": 0, \"std\": 1},\n", + " \"X2\": {\"type\": \"bernoulli\", \"p\": 0.5},\n", + " \"y0\": {\"type\": \"normal\", \"mean\": 5, \"std\": 1},\n", + " },\n", + " time_correlations={\"X1\": 0.2, \"X2\": 0.1, \"y0\": 0.6},\n", + " effect_size=2.0,\n", + " seed=42\n", + ")\n", + "\n", + "df = gen.generate()\n", + "# Keep only the columns we need for 2-period CUPAC\n", + "df = df.drop(columns=['y0', 'z', 'U', 'D', 'y1', 'y'])\n", + "df = df.rename(columns={'y0_lag_1': 'y_lag1', 'y0_lag_2': 'y_lag2'})" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4e4198cd", + "metadata": {}, + "outputs": [], + "source": [ + "data = Dataset(\n", + " roles = {\n", + " \"d\": TreatmentRole(),\n", + "\n", + " \"y_lag1\": PreTargetRole(parent=\"y\", cofounders=[\"X1\", \"X2\"], lag=1),\n", + " \"X1_lag1\": FeatureRole(parent=\"X1\", lag=1),\n", + " \"X2_lag1\": FeatureRole(parent=\"X2\", lag=1),\n", + "\n", + " \"y_lag2\": PreTargetRole(parent=\"y\", lag=2),\n", + " \"X1_lag2\": FeatureRole(parent=\"X1\", lag=2),\n", + " \"X2_lag2\": FeatureRole(parent=\"X2\", lag=2),\n", + " },\n", + " data=df,\n", + " default_role=InfoRole(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "cb68f33e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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targetbest_modelvariance_reduction_cvvariance_reduction_realcontrol_mean_biastest_mean_bias
0ylinear35.445326NoneNoneNone
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targetfeatureimportancemodel
0yX2_lag20.030907linear
1yX1_lag2-0.008524linear
2yy_lag20.603387linear
\n", + "
" + ], + "text/plain": [ + " target feature importance model\n", + "0 y X2_lag2 0.030907 linear\n", + "1 y X1_lag2 -0.008524 linear\n", + "2 y y_lag2 0.603387 linear" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Feature importances for virtual target\n", + "result_cupac_linear.cupac.feature_importances" + ] + }, + { + "cell_type": "markdown", + "id": "26a6a607", + "metadata": {}, + "source": [ + "## Best Practices\n", + "\n", + "When using CUPAC for variance reduction in your experiments:\n", + "\n", + "1. **CUPAC Results Access**: All CUPAC-specific outputs are organized under `result.cupac`:\n", + " - `result.cupac.feature_importances` - Feature importance scores\n", + " - `result.cupac.variance_reductions` - Variance reduction metrics\n", + " \n", + "2. **Feature Importances**: Use `result.cupac.feature_importances` to understand which historical covariates drive variance reduction\n", + " - High importance features are the most valuable for reducing variance\n", + " - Can guide feature selection for future experiments\n", + " \n", + "3. **Model Selection**: \n", + " - Start with `'linear'` for interpretability and speed\n", + " - Use multiple models `['linear', 'ridge', 'lasso', 'catboost']` when you have complex, non-linear relationships\n", + " - Check `result.cupac.variance_reductions` to see which model was selected\n", + " \n", + "4. **Temporal Structure**:\n", + " - Include multiple lags when available (lag 2 → lag 1 → current)\n", + " - Each lag period can use different sets of covariates\n", + " - Virtual targets work for forecasting scenarios\n", + " \n", + "5. **Cofounder Selection**:\n", + " - Include features that correlate with your target\n", + " - Use historical versions of the same features (lagged covariates)\n", + " - Feature importances help identify which cofounders matter most\n", + " \n", + "6. **Variance Reduction**:\n", + " - CV variance reduction: How well the model generalizes\n", + " - Real variance reduction: Actual improvement on experiment data\n", + " - Target for >40% variance reduction for meaningful power gains" + ] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hypex/aa.py b/hypex/aa.py index f9c51765..56516f6c 100644 --- a/hypex/aa.py +++ b/hypex/aa.py @@ -135,6 +135,7 @@ def _prepare_params( random_states: Iterable[int] | None = None, sample_size: float | None = None, additional_params: dict[str, Any] | None = None, + groups_sizes: list[float] | None = None, ) -> dict[type, dict[str, Any]]: """Prepares parameters for the A/A test experiment. @@ -176,6 +177,7 @@ def _prepare_params( "random_state": random_states, "control_size": [control_size], "sample_size": [sample_size], + "groups_sizes": [groups_sizes], }, Comparator: { "grouping_role": [AdditionalTreatmentRole()], @@ -196,6 +198,7 @@ def __init__( additional_params: dict[str, Any] | None = None, random_states: Iterable[int] | None = None, t_test_equal_var: bool | None = None, + groups_sizes: list[float] | None = None, ): if n_iterations is None: if precision_mode: @@ -213,6 +216,7 @@ def __init__( random_states, sample_size, additional_params, + groups_sizes, ), reporter=DatasetReporter(OneAADictReporter(front=False)), ) @@ -234,6 +238,7 @@ def __init__( control_size, random_states, additional_params, + groups_sizes, ), reporter=DatasetReporter(OneAADictReporter(front=False)), stopping_criterion=IfAAExecutor(sample_size=sample_size), @@ -248,4 +253,6 @@ def __init__( output=AAOutput(), ) if t_test_equal_var is not None: - self.experiment.set_params({TTest: {"calc_kwargs": {"equal_var": t_test_equal_var}}}) + self.experiment.set_params( + {TTest: {"calc_kwargs": {"equal_var": t_test_equal_var}}} + ) diff --git a/hypex/ab.py b/hypex/ab.py index 6aa81b85..fab3e1da 100644 --- a/hypex/ab.py +++ b/hypex/ab.py @@ -3,12 +3,14 @@ from typing import Literal from .analyzers.ab import ABAnalyzer -from .comparators import Chi2Test, GroupDifference, GroupSizes, TTest, UTest -from .dataset import TargetRole, TreatmentRole +from .comparators import Chi2Test, GroupDifference, GroupSizes, KSTest, TTest, UTest +from .dataset import AdditionalTargetRole, TargetRole, TreatmentRole +from .executor.executor import Executor from .experiments.base import Experiment, OnRoleExperiment +from .transformers import CUPEDTransformer from .ui.ab import ABOutput from .ui.base import ExperimentShell -from .utils import ABNTestMethodsEnum +from .utils import ABNTestMethodsEnum, ABTestTypesEnum class ABTest(ExperimentShell): @@ -18,12 +20,11 @@ class ABTest(ExperimentShell): (t-test, u-test, chi-square test) and multiple testing correction methods. Args: - additional_tests (Union[str, List[str], None], optional): Statistical test(s) to run in addition to - the default group difference calculation. Valid options are "t-test", "u-test", and "chi2-test". - Can be a single test name or list of test names. Defaults to ["t-test"]. - multitest_method (str, optional): Method to use for multiple testing correction. Valid options are: - "bonferroni", "sidak", "holm-sidak", "holm", "simes-hochberg", "hommel", "fdr_bh", "fdr_by", - "fdr_tsbh", "fdr_tsbhy", "quantile". Defaults to "holm". + additional_tests (Union[str, ABTestTypesEnum, List[Union[str, ABTestTypesEnum]], None], optional): Statistical test(s) to run in addition to + the default group difference calculation. Valid options are 't-test', 'u-test', 'chi2-test' or ABTestTypesEnum.t_test, ABTestTypesEnum.u_test, and ABTestTypesEnum.chi2_test. + Can be a single test name/enum or list of test names/enums. Defaults to [ABTestTypesEnum.t_test]. + multitest_method (ABNTestMethodsEnum, optional): Method to use for multiple testing correction. Valid options are: + ABNTestMethodsEnum.bonferroni, ABNTestMethodsEnum.sidak, etc. Defaults to ABNTestMethodsEnum.holm. For more information refer to the statsmodels documentation: https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html @@ -38,67 +39,94 @@ class ABTest(ExperimentShell): # A/B test with multiple statistical tests ab_test = ABTest( - additional_tests=["t-test", "chi2-test"], - multitest_method="bonferroni" + additional_tests=[ABTestTypesEnum.t_test, ABTestTypesEnum.chi2_test], + multitest_method=ABNTestMethodsEnum.bonferroni, + cuped_features={"target_feature": "pre_target_feature"}, + enable_cupac=True, + cupac_models=['linear', 'ridge'] ) results = ab_test.execute(data) """ @staticmethod - def _make_experiment(additional_tests, multitest_method): - """Creates an experiment configuration with specified statistical tests. - - Args: - Args: - additional_tests (Union[str, List[str], None], optional): Statistical test(s) to run in addition to - the default group difference calculation. Valid options are "t-test", "u-test", and "chi2-test". - Can be a single test name or list of test names. Defaults to ["t-test"]. - multitest_method (str, optional): Method to use for multiple testing correction. Valid options are: - "bonferroni", "sidak", "holm-sidak", "holm", "simes-hochberg", "hommel", "fdr_bh", "fdr_by", - "fdr_tsbh", "fdr_tsbhy", "quantile". Defaults to "holm". - For more information refer to the statsmodels documentation: - - - Returns: - Experiment: Configured experiment object with specified tests and correction method. - """ - test_mapping = { + def _make_experiment( + additional_tests: str | ABTestTypesEnum | list[str | ABTestTypesEnum] | None, + multitest_method: ABNTestMethodsEnum | str | None, + cuped_features: dict[str, str] | None, + cupac_models: str | list[str] | None, + enable_cupac: bool, + ) -> Experiment: + test_mapping: dict[str, Executor] = { "t-test": TTest(compare_by="groups", grouping_role=TreatmentRole()), + "ks-test": KSTest(compare_by="groups", grouping_role=TreatmentRole()), "u-test": UTest(compare_by="groups", grouping_role=TreatmentRole()), "chi2-test": Chi2Test(compare_by="groups", grouping_role=TreatmentRole()), } - on_role_executors = [GroupDifference(grouping_role=TreatmentRole())] - additional_tests = ["t-test"] if additional_tests is None else additional_tests + on_role_executors: list[Executor] = [ + GroupDifference(grouping_role=TreatmentRole()) + ] + additional_tests = ( + [ABTestTypesEnum.t_test] if additional_tests is None else additional_tests + ) + multitest_method = ( + ABNTestMethodsEnum(multitest_method) + if ( + multitest_method is not None + and multitest_method in ABNTestMethodsEnum.__members__.values() + ) + else ABNTestMethodsEnum.holm + ) + if additional_tests: + if isinstance(additional_tests, list): + additional_tests = [ + ABTestTypesEnum(test) if isinstance(test, str) else test + for test in additional_tests + ] + else: + additional_tests = ( + ABTestTypesEnum(additional_tests) + if isinstance(additional_tests, str) + else additional_tests + ) additional_tests = ( additional_tests if isinstance(additional_tests, list) else [additional_tests] ) - for i in additional_tests: - on_role_executors += [test_mapping[i]] - return Experiment( - executors=[ - GroupSizes(grouping_role=TreatmentRole()), - OnRoleExperiment( - executors=on_role_executors, - role=TargetRole(), - ), - ABAnalyzer( - multitest_method=( - ABNTestMethodsEnum(multitest_method) - if multitest_method - else None - ) + for test_name in additional_tests: + on_role_executors.append(test_mapping[test_name.value]) + + # Build base executors list + executors: list[Executor] = [ + GroupSizes(grouping_role=TreatmentRole()), + OnRoleExperiment( + executors=on_role_executors, + role=( + [TargetRole(), AdditionalTargetRole()] + if enable_cupac + else TargetRole() ), - ] - ) + ), + ABAnalyzer( + multitest_method=( + ABNTestMethodsEnum(multitest_method) if multitest_method else None + ) + ), + ] + if cuped_features: + executors.insert(0, CUPEDTransformer(cuped_features=cuped_features)) + + if enable_cupac: + from .ml import CUPACExecutor + + executors.insert(0, CUPACExecutor(cupac_models=cupac_models)) + + return Experiment(executors=executors) def __init__( self, additional_tests: ( - Literal["t-test", "u-test", "chi2-test"] - | list[Literal["t-test", "u-test", "chi2-test"]] - | None + str | ABTestTypesEnum | list[str | ABTestTypesEnum] | None ) = None, multitest_method: ( Literal[ @@ -117,10 +145,30 @@ def __init__( | None ) = "holm", t_test_equal_var: bool | None = None, + cuped_features: dict[str, str] | None = None, + cupac_models: str | list[str] | None = None, + enable_cupac: bool = False, ): + """ + Args: + additional_tests: Statistical test(s) to run in addition to the default group difference calculation. Valid options are 't-test', 'u-test', 'chi2-test' or ABTestTypesEnum.t_test, ABTestTypesEnum.u_test, and ABTestTypesEnum.chi2_test. Can be a single test name/enum or list of test names/enums. Defaults to [ABTestTypesEnum.t_test]. + multitest_method: Method to use for multiple testing correction. Valid options are ABNTestMethodsEnum.bonferroni, ABNTestMethodsEnum.sidak, etc. Defaults to ABNTestMethodsEnum.holm. + t_test_equal_var: Whether to use equal variance in t-test (optional). + cuped_features: dict[str, str] — Dictionary {target_feature: pre_target_feature} for CUPED. Only dict is allowed. + cupac_models: str | list[str] — model name (e.g. 'linear', 'ridge', 'lasso', 'catboost') or list of model names to try. If None, all available models will be tried and the best will be selected by variance reduction. + enable_cupac: bool — Enable CUPAC variance reduction. CUPAC configuration is extracted from dataset.features_mapping. + """ super().__init__( - experiment=self._make_experiment(additional_tests, multitest_method), + experiment=self._make_experiment( + additional_tests, + multitest_method, + cuped_features, + cupac_models, + enable_cupac, + ), output=ABOutput(), ) if t_test_equal_var is not None: - self.experiment.set_params({TTest: {"calc_kwargs": {"equal_var": t_test_equal_var}}}) + self.experiment.set_params( + {TTest: {"calc_kwargs": {"equal_var": t_test_equal_var}}} + ) diff --git a/hypex/analyzers/aa.py b/hypex/analyzers/aa.py index 03c1cbb1..86202202 100644 --- a/hypex/analyzers/aa.py +++ b/hypex/analyzers/aa.py @@ -22,6 +22,10 @@ def execute(self, data: ExperimentData) -> ExperimentData: executor_ids = data.get_ids( analysis_tests, searched_space=ExperimentDataEnum.analysis_tables ) + # num_groups = len(data.groups[data.ds.search_columns(TreatmentRole())[0]]) - 1 + # groups = list(data.groups[data.ds.search_columns(TreatmentRole())[0]].items()) + # multitest_pvalues = Dataset.create_empty() + # analysis_data = {} analysis_data: dict[str, float] = {} for class_, spaces in executor_ids.items(): diff --git a/hypex/comparators/abstract.py b/hypex/comparators/abstract.py index 83eafdf8..59a80540 100644 --- a/hypex/comparators/abstract.py +++ b/hypex/comparators/abstract.py @@ -6,6 +6,7 @@ from ..dataset import ( ABCRole, + AdditionalTargetRole, Dataset, DatasetAdapter, ExperimentData, @@ -35,7 +36,9 @@ class Comparator(Calculator, ABC): def __init__( self, - compare_by: Literal["groups", "columns", "columns_in_groups", "cross", "matched_pairs"], + compare_by: Literal[ + "groups", "columns", "columns_in_groups", "cross", "matched_pairs" + ], grouping_role: ABCRole | None = None, target_roles: ABCRole | list[ABCRole] | None = None, baseline_role: ABCRole | None = None, @@ -66,16 +69,20 @@ def _inner_function( raise AbstractMethodError def _get_fields_data(self, data: ExperimentData) -> dict[str, Dataset]: - tmp_role = True if data.ds.tmp_roles else False + tmp_role = ( + True if data.ds.tmp_roles or data.additional_fields.tmp_roles else False + ) group_field_data = data.field_data_search(roles=self.grouping_role) target_fields_data = data.field_data_search( - roles=TempTargetRole() if tmp_role else self.target_roles, + roles=( + (TempTargetRole() if data.ds.tmp_roles else AdditionalTargetRole()) + if tmp_role + else self.target_roles + ), tmp_role=tmp_role, search_types=self.search_types, ) - baseline_field_data = data.field_data_search( - roles=self.baseline_role - ) + baseline_field_data = data.field_data_search(roles=self.baseline_role) return { "group_field": group_field_data, "target_fields": target_fields_data, @@ -87,7 +94,9 @@ def _execute_inner_function( cls, baseline_data: list[tuple[str, Dataset]], compared_data: list[tuple[str, Dataset]], - compare_by: Literal["groups", "columns", "columns_in_groups", "cross", "matched_pairs"], + compare_by: Literal[ + "groups", "columns", "columns_in_groups", "cross", "matched_pairs" + ], **kwargs, ) -> dict: result = {} @@ -139,7 +148,9 @@ def _extract_dataset( @staticmethod def _grouping_data_split( grouping_data: dict[str, Dataset], - compare_by: Literal["groups", "columns", "columns_in_groups", "cross", "matched_pairs"], + compare_by: Literal[ + "groups", "columns", "columns_in_groups", "cross", "matched_pairs" + ], target_fields: list[str], baseline_field: str | None = None, ) -> GroupingDataType: @@ -181,7 +192,9 @@ def _field_validity_check( comparison_role: Literal[ "group_field_data", "target_fields_data", "baseline_field_data" ], - compare_by: Literal["groups", "columns", "columns_in_groups", "cross", "matched_pairs"], + compare_by: Literal[ + "groups", "columns", "columns_in_groups", "cross", "matched_pairs" + ], ) -> Dataset: if len(field_data.columns) == 0: raise NoRequiredArgumentError(comparison_role) @@ -298,32 +311,36 @@ def _split_for_matched_pairs_mode( baseline_field_data: Dataset, target_fields_data: Dataset, ) -> GroupingDataType: - group_field_data = cls._field_validity_check(group_field_data, "group_field_data", "matched_pairs") - baseline_field_data = cls._field_validity_check(baseline_field_data, "baseline_field_data", "matched_pairs") - target_fields_data = cls._field_validity_check(target_fields_data, "target_fields_data", "matched_pairs") + group_field_data = cls._field_validity_check( + group_field_data, "group_field_data", "matched_pairs" + ) + baseline_field_data = cls._field_validity_check( + baseline_field_data, "baseline_field_data", "matched_pairs" + ) + target_fields_data = cls._field_validity_check( + target_fields_data, "target_fields_data", "matched_pairs" + ) - compared_data = [ - sorted( - target_fields_data.groupby(by=group_field_data), key=lambda tup: tup[0] - ).pop(1) - ] - baseline_indexes = baseline_field_data.iloc[compared_data[0][1].index].data.iloc[:, 0].to_list() - baseline_data = target_fields_data.iloc[baseline_indexes] - baseline_value = [ - sorted( - target_fields_data.groupby(by=group_field_data), key=lambda tup: tup[0] - ).pop(0) - ][0][0] + compared_data = target_fields_data.groupby(by=group_field_data) + baseline_indexes = baseline_field_data.groupby(by=group_field_data) + baseline_data = [] - baseline_data = cls._split_ds_into_columns(data=[(baseline_value, baseline_data)]) - compared_data = cls._split_ds_into_columns(data=compared_data) + # mapping the data of the baseline data to its matches data. If there are no matches, matching index will be -1 + for group in baseline_indexes: + name = group[0] + indexes = group[1].iget_values(column=0) + dummy_index = target_fields_data.index[-1] + indexes = list(map(lambda x: dummy_index if x < 0 else x, indexes)) + baseline_data.append((name, target_fields_data.loc[indexes, :])) return baseline_data, compared_data @classmethod def _split_data_to_buckets( cls, - compare_by: Literal["groups", "columns", "columns_in_groups", "cross", "matched_pairs"], + compare_by: Literal[ + "groups", "columns", "columns_in_groups", "cross", "matched_pairs" + ], target_fields_data: Dataset, baseline_field_data: Dataset, group_field_data: Dataset, @@ -374,7 +391,8 @@ def _split_data_to_buckets( def calc( cls, compare_by: ( - Literal["groups", "columns", "columns_in_groups", "cross", "matched_pairs"] | None + Literal["groups", "columns", "columns_in_groups", "cross", "matched_pairs"] + | None ) = None, target_fields_data: Dataset | None = None, baseline_field_data: Dataset | None = None, @@ -406,6 +424,18 @@ def calc( ) def execute(self, data: ExperimentData) -> ExperimentData: + """ + Execute the comparator on the given data. + + The comparator will split the data into a baseline and a comparison + dataset based on the compare_by argument. Then it will calculate + statistics comparing the baseline and comparison datasets. + + :param data: The ExperimentData to execute the comparator on + :type data: ExperimentData + :return: The ExperimentData with the comparison results + :rtype: ExperimentData + """ fields = self._get_fields_data(data) group_field_data = fields["group_field"] target_fields_data = fields["target_fields"] @@ -418,9 +448,8 @@ def execute(self, data: ExperimentData) -> ExperimentData: ) if len(target_fields_data.columns) == 0: - if ( - data.ds.tmp_roles - ): # if the column is not suitable for the test, then the target will be empty, but if there is a role tempo, then this is normal behavior + # If the column is not suitable for the test, then the target will be empty, but if there is a role tempo, then this is normal behavior + if data.ds.tmp_roles: return data else: raise NoColumnsError(TargetRole().role_name) @@ -446,8 +475,30 @@ def execute(self, data: ExperimentData) -> ExperimentData: ), ) else: + combined_data = ( + data.ds.merge( + data.additional_fields[ + [ + col + for col in data.additional_fields.columns + if isinstance( + data.additional_fields.roles[col], AdditionalTargetRole + ) + ] + ], + left_index=True, + right_index=True, + how="outer", + ) + if any( + isinstance(data.additional_fields.roles[col], AdditionalTargetRole) + for col in data.additional_fields.columns + ) + else data.ds + ) + data.groups[group_field_data.columns[0]] = { - f"{group}": ds for group, ds in data.ds.groupby(group_field_data) + f"{group}": ds for group, ds in combined_data.groupby(group_field_data) } grouping_data = self._split_data_to_buckets( compare_by=self.compare_by, @@ -476,7 +527,9 @@ def execute(self, data: ExperimentData) -> ExperimentData: class StatHypothesisTesting(Comparator, ABC): def __init__( self, - compare_by: Literal["groups", "columns", "columns_in_groups", "cross", "matched_pairs"], + compare_by: Literal[ + "groups", "columns", "columns_in_groups", "cross", "matched_pairs" + ], grouping_role: ABCRole | None = None, target_role: ABCRole | None = None, baseline_role: ABCRole | None = None, diff --git a/hypex/comparators/distances.py b/hypex/comparators/distances.py index 948c685f..6a453463 100644 --- a/hypex/comparators/distances.py +++ b/hypex/comparators/distances.py @@ -3,8 +3,11 @@ from copy import deepcopy from typing import Any, Sequence +import numpy as np + from ..dataset import ( ABCRole, + AdditionalFeatureRole, Dataset, ExperimentData, FeatureRole, @@ -22,9 +25,11 @@ def __init__( self, grouping_role: ABCRole | None = None, key: Any = "", + weights: dict[str, float] | None = None, ): super().__init__(key=key) self.grouping_role = grouping_role or GroupingRole() + self.weights = weights @classmethod def _execute_inner_function( @@ -66,7 +71,9 @@ def _set_value( def _get_fields(self, data: ExperimentData): group_field = data.field_search(self.grouping_role) - target_fields = data.field_search(FeatureRole(), search_types=self.search_types) + target_fields = data.field_search( + [FeatureRole(), AdditionalFeatureRole()], search_types=self.search_types + ) return group_field, target_fields @property @@ -74,11 +81,24 @@ def search_types(self) -> list[type] | None: return [int, float] @classmethod - def _inner_function(cls, data: Dataset, test_data: Dataset | None = None, **kwargs): + def _inner_function( + cls, + data: Dataset, + test_data: Dataset | None = None, + weights: dict[str, float] | None = None, + **kwargs, + ): test_data = cls._check_test_data(test_data) cov = (data.cov() + test_data.cov()) / 2 if test_data else data.cov() cholesky = CholeskyExtension().calc(cov) mahalanobis_transform = InverseExtension().calc(cholesky) + if weights is not None: + features = data.columns + w_list = np.array( + [weights[col] if col in weights.keys() else 1 for col in features] + ) + w_matrix = np.sqrt(np.diag(w_list / w_list.sum())) + mahalanobis_transform = mahalanobis_transform.dot(w_matrix) y_control = data.dot(mahalanobis_transform.transpose()) if test_data: y_test = test_data.dot(mahalanobis_transform.transpose()) @@ -92,6 +112,7 @@ def calc( group_field: Sequence[str] | str | None = None, grouping_data: list[tuple[str, Dataset]] | None = None, target_fields: str | list[str] | None = None, + weights: dict[str, float] | None = None, **kwargs, ) -> dict: group_field = Adapter.to_list(group_field) @@ -103,7 +124,11 @@ def calc( else: raise NotSuitableFieldError(group_field, "Grouping") return cls._execute_inner_function( - grouping_data, target_fields=target_fields, old_data=data, **kwargs + grouping_data, + target_fields=target_fields, + old_data=data, + weights=weights, + **kwargs, ) def execute(self, data: ExperimentData) -> ExperimentData: @@ -120,15 +145,17 @@ def execute(self, data: ExperimentData) -> ExperimentData: else: grouping_data = None t_data = deepcopy(data.ds) - if target_fields[1] not in t_data.columns: - t_data = t_data.add_column( - data.additional_fields[target_fields[1]], - role={target_fields[1]: TargetRole()}, - ) + for field in target_fields: + if field not in t_data.columns: + t_data = t_data.add_column( + data.additional_fields[field], + role={field: TargetRole()}, + ) compare_result = self.calc( data=t_data, group_field=group_field, target_fields=target_fields, grouping_data=grouping_data, + weights=self.weights or None, ) return self._set_value(data, compare_result) diff --git a/hypex/dataset/__init__.py b/hypex/dataset/__init__.py index 8caea31e..f7a7d782 100644 --- a/hypex/dataset/__init__.py +++ b/hypex/dataset/__init__.py @@ -5,32 +5,34 @@ from .abstract import DatasetBase from .dataset import Dataset, DatasetAdapter, ExperimentData from .roles import ( - ABCRole, - AdditionalGroupingRole, - AdditionalMatchingRole, - AdditionalPreTargetRole, - AdditionalTargetRole, - AdditionalTreatmentRole, - ConstGroupRole, - DefaultRole, - FeatureRole, - FilterRole, - GroupingRole, - InfoRole, - PreTargetRole, - StatisticRole, - StratificationRole, - TargetRole, - TempGroupingRole, - TempRole, - TempTargetRole, - TempTreatmentRole, - TreatmentRole, - default_roles, + ABCRole, + AdditionalFeatureRole, + AdditionalGroupingRole, + AdditionalMatchingRole, + AdditionalPreTargetRole, + AdditionalTargetRole, + AdditionalTreatmentRole, + ConstGroupRole, + DefaultRole, + FeatureRole, + FilterRole, + GroupingRole, + InfoRole, + PreTargetRole, + StatisticRole, + StratificationRole, + TargetRole, + TempGroupingRole, + TempRole, + TempTargetRole, + TempTreatmentRole, + TreatmentRole, + default_roles, ) __all__ = [ "ABCRole", + "AdditionalFeatureRole", "AdditionalGroupingRole", "AdditionalMatchingRole", "AdditionalPreTargetRole", diff --git a/hypex/dataset/abstract.py b/hypex/dataset/abstract.py index 76f0f170..1b039bb1 100644 --- a/hypex/dataset/abstract.py +++ b/hypex/dataset/abstract.py @@ -110,6 +110,19 @@ def search_columns( ) ] + def search_columns_by_type( + self, + search_types: list | type, + ) -> list[str]: + search_types = ( + search_types if isinstance(search_types, Iterable) else [search_types] + ) + return [ + str(column) + for column, role in self.roles.items() + if any(role.data_type == t for t in search_types) + ] + def replace_roles( self, new_roles_map: dict[ABCRole | str] | ABCRole, @@ -188,6 +201,9 @@ def to_dict(self): "data": self._backend.to_dict(), } + def to_numpy(self): + return self._backend.to_numpy() + def to_records(self): return self._backend.to_records() diff --git a/hypex/dataset/backends/abstract.py b/hypex/dataset/backends/abstract.py index 722a0332..4c265238 100644 --- a/hypex/dataset/backends/abstract.py +++ b/hypex/dataset/backends/abstract.py @@ -261,6 +261,21 @@ def log(self) -> Any: def agg(self, func) -> Any: raise AbstractMethodError + def get( + self, + key, + default=None, + ) -> Any: + raise AbstractMethodError + + @abstractmethod + def take( + self, + indices: int | list[int], + axis: Literal["index", "columns", "rows"] | int = 0, + ) -> Any: + raise AbstractMethodError + @abstractmethod def get_values( self, @@ -269,6 +284,14 @@ def get_values( ) -> Any: raise AbstractMethodError + @abstractmethod + def iget_values( + self, + row: int | None = None, + column: int | None = None, + ) -> Any: + raise AbstractMethodError + @abstractmethod def apply(self, func: Callable, **kwargs) -> Any: raise AbstractMethodError @@ -370,8 +393,9 @@ def merge( @abstractmethod def drop( self, - labels: str | Sequence[str] | None = None, - axis: int = 1, + labels: str | None = None, + axis: int | None = None, + columns: str | Iterable[str] | None = None, ) -> Any: raise AbstractMethodError diff --git a/hypex/dataset/backends/pandas_backend.py b/hypex/dataset/backends/pandas_backend.py index fe7ed38e..b2901ac8 100644 --- a/hypex/dataset/backends/pandas_backend.py +++ b/hypex/dataset/backends/pandas_backend.py @@ -190,6 +190,10 @@ def get_column_type(self, column_name: str) -> type | None: return int elif pd.api.types.is_float_dtype(dtype): return float + elif pd.api.types.is_object_dtype(dtype) and pd.api.types.is_list_like( + self.data[column_name].iloc[0] + ): + return object elif ( pd.api.types.is_string_dtype(dtype) or pd.api.types.is_object_dtype(dtype) @@ -283,6 +287,20 @@ def _convert_agg_result(result): def __init__(self, data: pd.DataFrame | dict | str | pd.Series | None = None): super().__init__(data) + def get( + self, + key, + default=None, + ) -> Any: + return self.data.get(key, default) + + def take( + self, + indices: int | list[int], + axis: Literal["index", "columns", "rows"] | int = 0, + ) -> Any: + return self.data.take(indices=indices, axis=axis) + def get_values( self, row: str | None = None, @@ -443,8 +461,20 @@ def na_counts(self) -> pd.DataFrame | int: return int(data.loc[data.index[0], data.columns[0]]) return data if isinstance(data, pd.DataFrame) else pd.DataFrame(data) - def dot(self, other: PandasDataset) -> pd.DataFrame: - result = self.data.dot(other.data) + def dot(self, other: PandasDataset | np.ndarray) -> pd.DataFrame: + if isinstance(other, np.ndarray): + other_df = pd.DataFrame( + data=other, + columns=self.columns if other.shape[1] == self.shape[1] else None, + ) + # print(other_df.shape) + # print(self.data.shape) + result = self.data.dot(other_df.T) + result.columns = ( + self.columns if other.shape[1] == self.shape[1] else result.columns + ) + else: + result = self.data.dot(other.data) return result if isinstance(result, pd.DataFrame) else pd.DataFrame(result) def dropna( @@ -519,8 +549,13 @@ def merge( how=how, ) - def drop(self, labels: str = "", axis: int = 1) -> pd.DataFrame: - return self.data.drop(labels=labels, axis=axis) + def drop( + self, + labels: str | None = None, + axis: int | None = None, + columns: str | Iterable[str] | None = None, + ) -> pd.DataFrame: + return self.data.drop(labels=labels, axis=axis, columns=columns) def filter( self, @@ -547,3 +582,17 @@ def replace( def reindex(self, labels: str = "", fill_value: str | None = None) -> pd.DataFrame: return self.data.reindex(labels, fill_value=fill_value) + + def list_to_columns(self, column: str) -> pd.DataFrame: + data = self.data + n_cols = len(data.loc[0, column]) + + data_expanded = ( + pd.DataFrame( + data[column].to_list(), columns=[f"{column}_{i}" for i in range(n_cols)] + ) + if n_cols > 1 + else data + ) + + return data_expanded diff --git a/hypex/dataset/dataset.py b/hypex/dataset/dataset.py index a50ab662..78c03403 100644 --- a/hypex/dataset/dataset.py +++ b/hypex/dataset/dataset.py @@ -5,7 +5,9 @@ from copy import deepcopy from typing import Any, Callable, Hashable, Literal, Sequence +import numpy as np import pandas as pd # type: ignore +from numpy import ndarray from ..utils import ( ID_SPLIT_SYMBOL, @@ -128,7 +130,7 @@ def __getitem__(self, item: Iterable | str | int) -> Dataset: def __setitem__(self, key: str, value: Any): if isinstance(value, Dataset): - value = value.data + value = value.data.iloc[:, 0] if key not in self.columns and isinstance(key, str): self.add_column(value, {key: InfoRole()}) warnings.warn( @@ -301,6 +303,26 @@ def _convert_data_after_agg(self, result) -> Dataset | float: role: ABCRole = StatisticRole() return Dataset(data=result, roles={column: role for column in result.columns}) + def get( + self, + key, + default=None, + ) -> Dataset: + return Dataset(data=self._backend.get(key, default), roles=deepcopy(self.roles)) + + def take( + self, + indices: int | list[int], + axis: Literal["index", "columns", "rows"] | int = 0, + ) -> Dataset: + new_data = self._backend.take(indices=indices, axis=axis) + new_roles = ( + {k: deepcopy(v) for k, v in self.roles.items() if k in new_data.columns} + if axis == 1 + else deepcopy(self.roles) + ) + return Dataset(data=new_data, roles=new_roles) + def add_column( self, data, @@ -379,6 +401,9 @@ def from_dict( index=None, ) -> Dataset: ds = Dataset(roles=roles, backend=backend) + # if all([isinstance(v, Dataset) for v in data.values()]): + # ds._backend = ds._backend.from_dict({k: v.data for k, v in data.items()}, data, index) + # else: ds._backend = ds._backend.from_dict(data, index) ds.data = ds._backend.data return ds @@ -433,10 +458,12 @@ def groupby( by: Any, func: str | list | None = None, fields_list: str | list | None = None, + reset_index: bool = True, **kwargs, ) -> list[tuple[str, Dataset]]: if isinstance(by, Dataset) and len(by.columns) == 1: - self.data = self.data.reset_index(drop=True) + # if reset_index: + # self.data = self.data.reset_index(drop=True) datasets = [ (group, Dataset(roles=self.roles, data=self.data.loc[group_data.index])) for group, group_data in by._backend.groupby(by=by.columns[0], **kwargs) @@ -642,13 +669,18 @@ def merge( new_roles = {c: t_roles[c] for c in t_data.columns} return Dataset(roles=new_roles, data=t_data) - def drop(self, labels: Any = None, axis: int = 1): + def drop( + self, + labels: str | None = None, + axis: int | None = None, + columns: str | Iterable[str] | None = None, + ): # Convert Dataset labels to list of indices if isinstance(labels, Dataset): labels = list(labels.index) # Drop specified labels - t_data = self._backend.drop(labels=labels, axis=axis) + t_data = self._backend.drop(labels=labels, axis=axis, columns=columns) # Update roles based on axis t_roles = ( @@ -669,8 +701,13 @@ def filter( t_roles = {c: self.roles[c] for c in t_data.columns if c in self.roles.keys()} return Dataset(roles=t_roles, data=t_data) - def dot(self, other: Dataset) -> Dataset: - return Dataset(roles=other.roles, data=self.backend.dot(other.backend)) + def dot(self, other: Dataset | ndarray) -> Dataset: + return Dataset( + roles=deepcopy(other.roles) if isinstance(other, Dataset) else {}, + data=self.backend.dot( + other.backend if isinstance(other, Dataset) else other + ), + ) def transpose( self, @@ -723,6 +760,16 @@ def replace( data=self._backend.replace(to_replace=to_replace, value=value, regex=regex), ) + def list_to_columns(self, column: str) -> Dataset: + if not pd.api.types.is_list_like(self.backend[column][0]): + return self + extended_data = self.backend.list_to_columns(column) + extended_roles = { + c: deepcopy(self.roles[column]) for c in extended_data.columns + } + extended_ds = Dataset(roles=extended_roles, data=extended_data) + return self.append(extended_ds, axis=1).drop(column, axis=1) + class ExperimentData: def __init__(self, data: Dataset): @@ -767,7 +814,21 @@ def set_value( ) -> ExperimentData: # Handle additional fields if space == ExperimentDataEnum.additional_fields: - if not isinstance(value, Dataset) or len(value.columns) == 1: + if not isinstance(value, Dataset): + self.additional_fields = self.additional_fields.add_column( + data=value, role={executor_id: role} + ) + elif len(value.columns) == 1: + role = role[0] if isinstance(role, list) else role + role = next(iter(role.values())) if isinstance(role, dict) else role + executor_id = ( + executor_id[0] if isinstance(executor_id, list) else executor_id + ) + executor_id = ( + next(iter(executor_id.keys())) + if isinstance(executor_id, dict) + else executor_id + ) self.additional_fields = self.additional_fields.add_column( data=value, role={executor_id: role} ) @@ -918,6 +979,8 @@ def field_data_search( searched_data = searched_data.add_column( data=t_data, role={column: role} ) + if not searched_data.is_empty(): + searched_data.index = self.ds.index return searched_data @@ -938,6 +1001,8 @@ def to_dataset( if isinstance(roles, ABCRole): raise InvalidArgumentError("roles", "dict[str, ABCRole]") return DatasetAdapter.list_to_dataset(data, roles) + elif isinstance(data, np.ndarray): + return DatasetAdapter.ndarray_to_dataset(data, roles) elif any(isinstance(data, t) for t in [str, int, float, bool]): return DatasetAdapter.value_to_dataset(data, roles) elif isinstance(data, Dataset): @@ -975,8 +1040,10 @@ def dict_to_dataset(data: dict, roles: ABCRole | dict[str, ABCRole]) -> Dataset: @staticmethod def list_to_dataset(data: list, roles: dict[str, ABCRole]) -> Dataset: return Dataset( - roles=roles, - data=pd.DataFrame(data=data, columns=[next(iter(roles.keys()))]), + roles=roles if len(roles) > 0 else {0: DefaultRole()}, + data=pd.DataFrame( + data=data, columns=[next(iter(roles.keys()))] if len(roles) > 0 else [0] + ), ) @staticmethod @@ -985,3 +1052,12 @@ def frame_to_dataset(data: pd.DataFrame, roles: dict[str, ABCRole]) -> Dataset: roles=roles, data=data, ) + + @staticmethod + def ndarray_to_dataset(data: np.ndarray, roles: dict[str, ABCRole]) -> Dataset: + columns = range(data.shape[1]) if len(roles) == 0 else list(roles.keys()) + data = pd.DataFrame(data=data, columns=columns) + return Dataset( + roles=roles, + data=data, + ) diff --git a/hypex/dataset/roles.py b/hypex/dataset/roles.py index 89276205..21a8ea61 100644 --- a/hypex/dataset/roles.py +++ b/hypex/dataset/roles.py @@ -1,8 +1,15 @@ from __future__ import annotations from abc import ABC +from copy import deepcopy -from ..utils import CategoricalTypes, DefaultRoleTypes, RoleNameType, TargetRoleTypes +from ..utils import ( + CategoricalTypes, + DefaultRoleTypes, + FeatureRoleTypes, + RoleNameType, + TargetRoleTypes, +) class ABCRole(ABC): @@ -18,6 +25,48 @@ def role_name(self) -> str: def __repr__(self) -> str: return f"{self._role_name}({self.data_type})" + def astype(self, data_type: DefaultRoleTypes | None = None) -> ABCRole: + role = deepcopy(self) + role.data_type = data_type + return role + + def asadditional(self, data_type: DefaultRoleTypes | None = None) -> ABCRole: + data_type = data_type or self.data_type + for role_type in list(default_roles.values()): + if isinstance(role_type, self.__class__) and isinstance( + role_type, AdditionalRole + ): + return role_type.__class__(data_type) + return self.__class__(data_type) + + +class LagRole(ABCRole): + """Base class for roles that support temporal metadata (parent, lag).""" + + def __init__( + self, + data_type: DefaultRoleTypes | None = None, + parent: str | None = None, + lag: int | None = None, + ): + super().__init__(data_type) + self.parent = parent + self.lag = lag + + def __repr__(self) -> str: + parts = [] + if self.data_type is not None: + parts.append(f"data_type={self.data_type}") + if self.parent is not None: + parts.append(f"parent='{self.parent}'") + if self.lag is not None: + parts.append(f"lag={self.lag}") + return ( + f"{self._role_name}({', '.join(parts)})" + if parts + else f"{self._role_name}()" + ) + class InfoRole(ABCRole): _role_name: RoleNameType = "Info" @@ -44,19 +93,39 @@ class TreatmentRole(ABCRole): class TargetRole(ABCRole): _role_name: RoleNameType = "Target" - def __init__(self, data_type: TargetRoleTypes | None = None): - super().__init__(data_type) + def __init__( + self, + data_type: TargetRoleTypes | None = None, + cofounders: list[str] | None = None, + ): + super().__init__(data_type=data_type) + self.cofounders = cofounders if cofounders is not None else [] -class FeatureRole(ABCRole): +class FeatureRole(LagRole): _role_name: RoleNameType = "Feature" + def __init__( + self, + data_type: FeatureRoleTypes | None = None, + parent: str | None = None, + lag: int | None = None, + ): + super().__init__(data_type=data_type, parent=parent, lag=lag) -class PreTargetRole(ABCRole): + +class PreTargetRole(LagRole): _role_name: RoleNameType = "PreTarget" - def __init__(self, data_type: TargetRoleTypes | None = None): - super().__init__(data_type) + def __init__( + self, + data_type: TargetRoleTypes | None = None, + parent: str | None = None, + lag: int | None = None, + cofounders: list[str] | None = None, + ): + super().__init__(data_type=data_type, parent=parent, lag=lag) + self.cofounders = cofounders if cofounders is not None else [] class StatisticRole(ABCRole): @@ -105,19 +174,23 @@ class AdditionalRole(ABCRole): _role_name: RoleNameType = "Additional" -class AdditionalTreatmentRole(AdditionalRole): +class AdditionalTreatmentRole(AdditionalRole, TreatmentRole): _role_name: RoleNameType = "AdditionalTreatment" -class AdditionalGroupingRole(AdditionalRole): +class AdditionalGroupingRole(AdditionalRole, GroupingRole): _role_name: RoleNameType = "AdditionalGrouping" -class AdditionalTargetRole(AdditionalRole): +class AdditionalTargetRole(AdditionalRole, TargetRole): + _role_name: RoleNameType = "AdditionalTarget" + + +class AdditionalFeatureRole(AdditionalRole, FeatureRole): _role_name: RoleNameType = "AdditionalTarget" -class AdditionalPreTargetRole(AdditionalRole): +class AdditionalPreTargetRole(AdditionalRole, PreTargetRole): _role_name: RoleNameType = "AdditionalPreTarget" @@ -140,5 +213,6 @@ class AdditionalMatchingRole(AdditionalRole): "additionaltreatment": AdditionalTreatmentRole(), "additionalgrouping": AdditionalGroupingRole(), "additionaltarget": AdditionalTargetRole(), + "additionalfeature": AdditionalFeatureRole(), "additionalpretarget": AdditionalPreTargetRole(), } diff --git a/hypex/encoders/abstract.py b/hypex/encoders/abstract.py index b86d1754..cae43f4c 100644 --- a/hypex/encoders/abstract.py +++ b/hypex/encoders/abstract.py @@ -55,6 +55,8 @@ def execute(self, data: ExperimentData) -> ExperimentData: target_cols = data.ds.search_columns( roles=self.target_roles, search_types=self.search_types ) + if not target_cols: + return data return self._set_value( data=data, value=self.calc(data=data.ds, target_cols=target_cols), diff --git a/hypex/encoders/encoders.py b/hypex/encoders/encoders.py index 7386154f..be258f72 100644 --- a/hypex/encoders/encoders.py +++ b/hypex/encoders/encoders.py @@ -1,17 +1,17 @@ -from __future__ import annotations - -from ..dataset import Dataset -from ..extensions.encoders import DummyEncoderExtension -from .abstract import Encoder - - -class DummyEncoder(Encoder): - @staticmethod - def _inner_function( - data: Dataset, target_cols: str | None = None, **kwargs - ) -> Dataset: - if not target_cols: - return data - return DummyEncoderExtension().calc( - data=data, target_cols=target_cols, **kwargs - ) +from __future__ import annotations + +from ..dataset import Dataset +from ..extensions.encoders import DummyEncoderExtension +from .abstract import Encoder + + +class DummyEncoder(Encoder): + @staticmethod + def _inner_function( + data: Dataset, target_cols: str | None = None, **kwargs + ) -> Dataset: + if not target_cols: + return Dataset.create_empty() + return DummyEncoderExtension().calc( + data=data, target_cols=target_cols, **kwargs + ) diff --git a/hypex/executor/__init__.py b/hypex/executor/__init__.py index 68021e67..cc009892 100644 --- a/hypex/executor/__init__.py +++ b/hypex/executor/__init__.py @@ -1,3 +1,4 @@ +from .calculators import MinSampleSize from .executor import Calculator, Executor, IfExecutor, MLExecutor -__all__ = ["Calculator", "Executor", "IfExecutor", "MLExecutor"] +__all__ = ["Calculator", "Executor", "IfExecutor", "MLExecutor", "MinSampleSize"] diff --git a/hypex/executor/calculators.py b/hypex/executor/calculators.py new file mode 100644 index 00000000..a3da7016 --- /dev/null +++ b/hypex/executor/calculators.py @@ -0,0 +1,270 @@ +from __future__ import annotations + +from typing import Any + +import numpy as np +from scipy.stats import norm + +from ..dataset import ABCRole, Dataset, ExperimentData, TargetRole, TreatmentRole +from ..extensions import MultitestQuantile +from ..utils import NotSuitableFieldError +from ..utils.adapter import Adapter +from .executor import Calculator + + +class MinSampleSize(Calculator): + """A calculator for estimating the minimum required sample size for multi-group comparisons. + + This class estimates the minimum per-group sample size needed to achieve a desired statistical + power for detecting a specified minimum detectable effect (MDE) when comparing multiple groups + (e.g., control vs one or more test groups). Quantiles used in the calculation can be provided + explicitly or estimated internally using `MultitestQuantile`. + + The calculator supports both: + - **Equal-variance mode** (`equal_variance=True`): closed-form sample size approximation based on + a pooled/assumed common variance. + - **Unequal-variance mode** (`equal_variance=False`): simulation-based sample size search that + accounts for different variances across groups. + + Args: + grouping_role (ABCRole | None, optional): Role used to locate the grouping (treatment) field + in the dataset. If not provided, defaults to `TreatmentRole()`. + key (Any, optional): Key used by the base `Calculator` for storing results. Defaults to "". + mde (float): Minimum Detectable Effect (absolute effect size in the same units as the target + metric) to be detected. + power (float, optional): Power-related quantile level used in the internal quantile computation + (kept consistent with the original function implementation). Defaults to 0.2. + quantile_1 (float | list[float] | None, optional): Precomputed critical quantile(s) for the + multiple testing threshold. If a float is provided, it is broadcast to all groups. If + None, computed via `MultitestQuantile.quantile_of_marginal_distribution`. Defaults to None. + quantile_2 (float | list[float] | None, optional): Precomputed quantile(s) used for power + calibration. If a float is provided, it is broadcast to all groups. If None, computed + via `MultitestQuantile.quantile_of_marginal_distribution`. Defaults to None. + initial_estimate (int, optional): Starting sample size guess (used only when `equal_variance=False`). + Defaults to 0. + power_iteration_size (int, optional): Number of Monte Carlo iterations used to estimate achieved + power during the sample size search (`equal_variance=False`). Defaults to 3000. + alpha (float, optional): Significance level used in multiple testing quantile computation. + Defaults to 0.05. + iteration_size (int, optional): Internal iteration size for `MultitestQuantile` quantile estimation. + Defaults to 5000. + equal_variance (bool, optional): If True, uses the equal-variance closed-form approximation. + If False, uses the unequal-variance simulation-based search. Defaults to False. + random_state (int | None, optional): Random seed for reproducibility of Monte Carlo simulation + and quantile estimation. Defaults to 42. + variances (list[float] | float | None, optional): Variance specification. If provided: + - when `equal_variance=True`, may be a single float (common variance) or a list (first/pooled + usage depends on implementation). + - when `equal_variance=False`, must be a list of variances per group (order matching grouping). + If None, variances are estimated from the grouped data for each target metric. Defaults to None. + + Examples + -------- + .. code-block:: python + + ds = Dataset( + data="data.csv", + roles={ + "user_id": InfoRole(int), + "treat": TreatmentRole(), + "pre_spends": TargetRole(), + "post_spends": TargetRole(), + }, + ) + + mss = MinSampleSize(mde=10.0, alpha=0.05, equal_variance=True) + result = mss.calc(data=ds) + """ + + def __init__( + self, + grouping_role: ABCRole | None = None, + key: Any = "", + *, + mde: float, + power: float = 0.2, + quantile_1: float | list[float] | None = None, + quantile_2: float | list[float] | None = None, + initial_estimate: int = 0, + power_iteration_size: int = 3000, + alpha: float = 0.05, + iteration_size: int = 5000, + equal_variance: bool = False, + random_state: int | None = 42, + variances: list[float] | float | None = None, + ): + super().__init__(key=key) + self.grouping_role = grouping_role or TreatmentRole() + + self.mde = mde + self.power = power + self.quantile_1 = quantile_1 + self.quantile_2 = quantile_2 + self.initial_estimate = initial_estimate + self.power_iteration_size = power_iteration_size + self.alpha = alpha + self.iteration_size = iteration_size + self.equal_variance = equal_variance + self.random_state = random_state + self.variances = variances + + @property + def search_types(self) -> list[type] | None: + return [int, float] + + def _get_fields(self, data: Dataset) -> tuple[list[str], list[str]]: + group_field = data.search_columns(self.grouping_role, search_types=None) + target_fields = data.search_columns( + [TargetRole()], search_types=self.search_types + ) + return group_field, target_fields + + @staticmethod + def _variance_by_group( + grouping_data: list[tuple[str, Dataset]], + target_field: str, + ) -> list[float]: + vars_: list[float] = [] + for _, ds in grouping_data: + vars_.append(float(ds[target_field].var())) + return vars_ + + @classmethod + def _inner_function( + cls, + *, + num_samples: int, + mde: float, + variances: list[float] | float, + power: float = 0.2, + quantile_1: float | list[float] | None = None, + quantile_2: float | list[float] | None = None, + initial_estimate: int = 0, + power_iteration_size: int = 3000, + alpha: float = 0.05, + iteration_size: int = 5000, + equal_variance: bool = True, + random_state: int | None = 42, + ) -> int: + multitest = MultitestQuantile( + alpha=alpha, + iteration_size=iteration_size, + equal_variance=equal_variance, + random_state=random_state, + ) + + if not isinstance(variances, list) and not equal_variance: + raise TypeError("variances must be a list when equal_variance is False") + + if isinstance(quantile_1, float): + quantile_1 = np.full(num_samples, quantile_1).tolist() + if isinstance(quantile_2, float): + quantile_2 = np.full(num_samples, quantile_2).tolist() + + quantile_1 = quantile_1 or multitest.quantile_of_marginal_distribution( + num_samples=num_samples, + quantile_level=1 - multitest.alpha / num_samples, + variances=variances if isinstance(variances, list) else [variances], + ) + quantile_2 = quantile_2 or multitest.quantile_of_marginal_distribution( + num_samples=num_samples, + quantile_level=power, + ) + + if multitest.equal_variance: + var = variances[0] if isinstance(variances, list) else variances + return int(2 * var * ((quantile_1[0] - quantile_2[0]) / mde) ** 2) + 1 + + sizes: list[int] = [] + assert isinstance(variances, list) + + for index in range(num_samples): + size = initial_estimate + current_power = 0.0 + + while current_power < 1 - power: + size += 100 + current_power = 0.0 + + total_samples = norm.rvs( + size=[power_iteration_size, num_samples], + random_state=multitest.random_state, + ) + + for sample in total_samples: + min_t_value = np.inf + for i in range(num_samples): + if i != index: + t_value = ( + sample[index] + / np.sqrt(1 + variances[i] / variances[index]) + - sample[i] + / np.sqrt(1 + variances[index] / variances[i]) + + mde + * np.sqrt(size / (variances[index] + variances[i])) + ) + min_t_value = min(min_t_value, t_value) + + if min_t_value > quantile_1[index]: + current_power += 1.0 + + current_power /= float(power_iteration_size) + + sizes.append(size) + + return int(np.max(sizes)) + + def calc(self, data: Dataset) -> dict: + group_field, target_fields = self._get_fields(data=data) + + self.key = str( + target_fields[0] if len(target_fields) == 1 else (target_fields or "") + ) + + if not target_fields and data.tmp_roles: + raise Exception("No target fields in data") + + gf = Adapter.to_list(group_field) + grouping_data = list(data.groupby(gf)) + + if len(grouping_data) <= 1: + raise NotSuitableFieldError(gf, "Grouping") + + result: dict = {} + sizes: list[int] = [] + + for field in target_fields: + if self.variances is None: + group_vars = self._variance_by_group(grouping_data, target_field=field) + variances_used: list[float] | float = ( + float(np.mean(group_vars)) if self.equal_variance else group_vars + ) + else: + variances_used = self.variances + + n = self._inner_function( + num_samples=len(grouping_data), + mde=self.mde, + variances=variances_used, + power=self.power, + quantile_1=self.quantile_1, + quantile_2=self.quantile_2, + initial_estimate=self.initial_estimate, + power_iteration_size=self.power_iteration_size, + alpha=self.alpha, + iteration_size=self.iteration_size, + equal_variance=self.equal_variance, + random_state=self.random_state, + ) + + result[field] = {"min sample size": n} + sizes.append(n) + + result["overall"] = ( + {"min sample size": int(max(sizes))} if sizes else {"min sample size": 0} + ) + + return result + + def execute(self, data: ExperimentData) -> dict: + return self.calc(data.ds) diff --git a/hypex/executor/executor.py b/hypex/executor/executor.py index afee0416..52b8c217 100644 --- a/hypex/executor/executor.py +++ b/hypex/executor/executor.py @@ -7,7 +7,6 @@ ABCRole, AdditionalMatchingRole, Dataset, - DatasetAdapter, ExperimentData, FeatureRole, GroupingRole, @@ -206,13 +205,15 @@ def _execute_inner_function( def _set_value( self, data: ExperimentData, value: Any, key: Any = None ) -> ExperimentData: - return data.set_value( - ExperimentDataEnum.additional_fields, - self.id, - value=value, - key=key, - role=AdditionalMatchingRole(), - ) + for i in range(value.shape[1]): + data.set_value( + ExperimentDataEnum.additional_fields, + f"{self.id}{ID_SPLIT_SYMBOL}{i}", + value=value.iloc[:, i], + key=key, + role=AdditionalMatchingRole(), + ) + return data @classmethod def calc( @@ -235,10 +236,7 @@ def calc( result = cls._execute_inner_function( grouping_data, target_field=target_field, **kwargs ) - return DatasetAdapter.to_dataset( - result, - {i: AdditionalMatchingRole() for i in list(result.keys())}, - ) + return result def execute(self, data: ExperimentData) -> ExperimentData: group_field, target_fields = self._get_fields(data=data) @@ -263,6 +261,7 @@ def execute(self, data: ExperimentData) -> ExperimentData: target_fields=target_fields, features_fields=features_fields, ) + # TODO: add roles to compare_result return self._set_value(data, compare_result) diff --git a/hypex/experiments/base.py b/hypex/experiments/base.py index 8c8dffa7..1654f54e 100644 --- a/hypex/experiments/base.py +++ b/hypex/experiments/base.py @@ -3,14 +3,14 @@ from copy import deepcopy from typing import Any, Iterable, Sequence -from ..dataset import ABCRole, ExperimentData, TempTargetRole +from ..dataset import ABCRole, AdditionalTargetRole, ExperimentData, TempTargetRole from ..executor import Executor from ..utils import ExperimentDataEnum class Experiment(Executor): def _detect_transformer(self) -> bool: - return all(executor._is_transformer for executor in self.executors) + return any(executor._is_transformer for executor in self.executors) def get_executor_ids( self, searched_classes: type | Iterable[type] | None = None @@ -78,8 +78,12 @@ def __init__( super().__init__(executors, transformer, key) def execute(self, data: ExperimentData) -> ExperimentData: - for field in data.ds.search_columns(self.role): - data.ds.tmp_roles = {field: TempTargetRole()} + for field in data.field_search(self.role): + if field in data.ds.columns: + data.ds.tmp_roles = {field: TempTargetRole()} + elif field in data.additional_fields.columns: + data.additional_fields.tmp_roles = {field: AdditionalTargetRole()} data = super().execute(data) data.ds.tmp_roles = {} + data.additional_fields.tmp_roles = {} return data diff --git a/hypex/extensions/__init__.py b/hypex/extensions/__init__.py index 86c0f846..ad932c4a 100644 --- a/hypex/extensions/__init__.py +++ b/hypex/extensions/__init__.py @@ -2,10 +2,10 @@ from .faiss import FaissExtension from .scipy_linalg import CholeskyExtension, InverseExtension from .scipy_stats import ( - Chi2TestExtension, - KSTestExtension, - TTestExtension, - UTestExtension, + Chi2TestExtension, + KSTestExtension, + TTestExtension, + UTestExtension, ) from .statsmodels import MultiTest, MultitestQuantile diff --git a/hypex/extensions/abstract.py b/hypex/extensions/abstract.py index d5849877..d3917d7d 100644 --- a/hypex/extensions/abstract.py +++ b/hypex/extensions/abstract.py @@ -37,13 +37,12 @@ class MLExtension(Extension): def _calc_pandas( self, data: Dataset, - test_data: Dataset | None = None, mode: Literal["auto", "fit", "predict"] | None = None, **kwargs, ): if mode in ["auto", "fit"]: - return self.fit(data, test_data, **kwargs) - return self.predict(data) + return self.fit(data, **kwargs) + return self.predict(data, **kwargs) @abstractmethod def fit(self, X, Y=None, **kwargs): @@ -56,10 +55,6 @@ def predict(self, X, **kwargs): def calc( self, data: Dataset, - target_data: Dataset | None = None, - test_data: Dataset | None = None, **kwargs, ): - return super().calc( - data=data, target_data=target_data, test_data=test_data, **kwargs - ) + return super().calc(data=data, **kwargs) diff --git a/hypex/extensions/cupac.py b/hypex/extensions/cupac.py new file mode 100644 index 00000000..163e2e6c --- /dev/null +++ b/hypex/extensions/cupac.py @@ -0,0 +1,148 @@ +from __future__ import annotations + +from typing import Any, Literal + +import numpy as np +import pandas as pd +from sklearn.base import clone +from sklearn.model_selection import KFold + +from ..dataset import AdditionalTargetRole, Dataset +from ..utils.models import CUPAC_MODELS +from .abstract import MLExtension + + +class CupacExtension(MLExtension): + + def __init__( + self, + n_folds: int = 5, + random_state: int | None = None, + ): + super().__init__() + self.n_folds = n_folds + self.random_state = random_state + + def _calc_pandas( + self, + data: Dataset, + mode: Literal["kfold_fit", "fit", "predict"], + model: str | Any, + Y: Dataset | None = None, + **kwargs, + ) -> Any: + if mode == "kfold_fit": + return self._kfold_fit_pandas(model, data, Y) + if mode == "fit": + return self._fit_pandas(model, data, Y) + elif mode == "predict": + return self._predict_pandas(model, data) + + def fit(self, model: str, X: Dataset, Y: Dataset) -> Any: + pass + + def predict(self, model: Any, X: Dataset) -> Dataset: + pass + + def _kfold_fit_pandas( + self, model: str, X: Dataset, Y: Dataset + ) -> tuple[float, dict[str, float]]: + """ + Perform K-fold cross-validation and return variance reduction and feature importances. + + Returns: + tuple: (mean_variance_reduction, mean_feature_importances) + """ + model_proto = CUPAC_MODELS[model]["pandasdataset"] + + X_df = X.data + Y_df = Y.data + + y_values = Y_df.iloc[:, 0] if len(Y_df.columns) > 0 else Y_df + + kf = KFold(n_splits=self.n_folds, shuffle=True, random_state=self.random_state) + fold_var_reductions = [] + fold_feature_importances = [] + + feature_names = X_df.columns.tolist() + + for train_idx, val_idx in kf.split(X_df): + X_train, X_val = X_df.iloc[train_idx], X_df.iloc[val_idx] + y_train, y_val = y_values.iloc[train_idx], y_values.iloc[val_idx] + + m = clone(model_proto) + m.fit(X_train, y_train) + + pred = m.predict(X_val) + + y_original = y_val.to_numpy() + y_adjusted = y_original - pred + y_train.mean() + + var_reduction = self._calculate_variance_reduction(y_original, y_adjusted) + fold_var_reductions.append(var_reduction) + + # Extract feature importances for this fold + fold_importances = self._extract_fold_importances(m, model, feature_names) + fold_feature_importances.append(fold_importances) + + mean_var_reduction = float(np.nanmean(fold_var_reductions)) + + # Average feature importances across folds: convert to dict with mean values + mean_importances = { + feature: float( + np.mean([fold_imp[feature] for fold_imp in fold_feature_importances]) + ) + for feature in feature_names + } + + return mean_var_reduction, mean_importances + + def _fit_pandas(self, model: str, X: Dataset, Y: Dataset) -> Any: + model_proto = CUPAC_MODELS[model]["pandasdataset"] + final_model = clone(model_proto) + X_df = X.data + Y_df = Y.data + y_values = Y_df.iloc[:, 0] if len(Y_df.columns) > 0 else Y_df + final_model.fit(X_df, y_values) + return final_model + + def _predict_pandas(self, model: Any, X: Dataset) -> Dataset: + """Make predictions using pandas backend.""" + X_df = X.data + predictions = pd.DataFrame(model.predict(X_df), columns=["predict"]) + return Dataset(roles={"predict": AdditionalTargetRole()}, data=predictions) + + @staticmethod + def _extract_fold_importances( + model: Any, model_name: str, feature_names: list[str] + ) -> dict[str, float]: + """ + Extract feature importances from a fitted model for a single fold. + + Args: + model: Fitted model object. + model_name: Model type ('linear', 'ridge', 'lasso', 'catboost'). + feature_names: List of feature names. + + Returns: + dict: Feature name to importance mapping. + """ + importances = {} + + if model_name in ["linear", "ridge", "lasso"]: + for i, feature_name in enumerate(feature_names): + importances[feature_name] = float(model.coef_[i]) + elif model_name == "catboost": + for i, feature_name in enumerate(feature_names): + importances[feature_name] = float(model.feature_importances_[i]) + + return importances + + @staticmethod + def _calculate_variance_reduction(y_original, y_adjusted) -> float: + """Calculate variance reduction between original and adjusted target.""" + var_original = y_original.var() + var_adjusted = y_adjusted.var() + if var_original < 1e-10: + return 0.0 + return float(max(0, (1 - var_adjusted / var_original) * 100)) diff --git a/hypex/extensions/encoders.py b/hypex/extensions/encoders.py index 4730fa7f..06757751 100644 --- a/hypex/extensions/encoders.py +++ b/hypex/extensions/encoders.py @@ -1,23 +1,28 @@ -from __future__ import annotations - -import copy - -import pandas as pd # type: ignore - -from ..dataset import Dataset, DatasetAdapter -from .abstract import Extension - - -class DummyEncoderExtension( - Extension -): # TODO: role types are being rewritten, needs to be fixed - @staticmethod - def _calc_pandas(data: Dataset, target_cols: str | None = None, **kwargs): - dummies_df = pd.get_dummies(data=data[target_cols].data, drop_first=True) - # Setting roles to the dummies in additional fields based on the original - # roles by searching based on the part of the dummy column name - roles = {col: data.roles[col[: col.rfind("_")]] for col in dummies_df.columns} - new_roles = copy.deepcopy(roles) - for role in roles.values(): - role.data_type = bool - return DatasetAdapter.to_dataset(dummies_df, roles=new_roles) +from __future__ import annotations + +import copy + +import pandas as pd # type: ignore + +from ..dataset import Dataset, DatasetAdapter +from .abstract import Extension + + +class DummyEncoderExtension( + Extension +): # TODO: role types are being rewritten, needs to be fixed + @staticmethod + def _calc_pandas(data: Dataset, target_cols: str | None = None, **kwargs): + dummies_df = pd.get_dummies( + data=data[target_cols].data, drop_first=True + ).astype(int) + # Setting roles to the dummies in additional fields based on the original + # roles by searching based on the part of the dummy column name + roles = { + col: data.roles[col[: col.rfind("_")]].asadditional(int) + for col in dummies_df.columns + } + new_roles = copy.deepcopy(roles) + for role in roles.values(): + role.data_type = bool + return DatasetAdapter.to_dataset(dummies_df, roles=new_roles) diff --git a/hypex/extensions/faiss.py b/hypex/extensions/faiss.py index de43c924..f8d04564 100644 --- a/hypex/extensions/faiss.py +++ b/hypex/extensions/faiss.py @@ -16,16 +16,19 @@ def __init__( ): self.n_neighbors = n_neighbors self.faiss_mode = faiss_mode + self.index = None super().__init__() @staticmethod def _prepare_indexes(index: np.ndarray, dist: np.ndarray, k: int): - new = [ - np.concatenate( - [val[np.where(dist[i] == d)[0]] for d in sorted(set(dist[i]))[:k]] - ) - for i, val in enumerate(index) - ] + new = np.vstack( + [ + np.concatenate( + [val[np.where(dist[i] == d)[0]] for d in sorted(set(dist[i]))[:k]] + ) + for i, val in enumerate(index) + ] + ) return new def _predict(self, data: Dataset, test_data: Dataset, X: np.ndarray) -> pd.Series: @@ -42,7 +45,7 @@ def _predict(self, data: Dataset, test_data: Dataset, X: np.ndarray) -> pd.Serie ] else: indexes = self._prepare_indexes(indexes, dist, self.n_neighbors) - return pd.Series(indexes) + return self.result_to_dataset(result=indexes, roles={}) def _calc_pandas( self, @@ -56,10 +59,14 @@ def _calc_pandas( test = test_data.data.values if mode in ["auto", "fit"]: self.index = faiss.IndexFlatL2(X.shape[1]) - if (( - len(X) > 1_000_000 and self.faiss_mode == "auto" - ) or self.faiss_mode == "fast" - ) and len(X) > 1_000 and len(test) > 1_000: + if ( + ( + (len(X) > 1_000_000 and self.faiss_mode == "auto") + or self.faiss_mode == "fast" + ) + and len(X) > 1_000 + and len(test) > 1_000 + ): self.index = faiss.IndexIVFFlat(self.index, X.shape[1], 1000) self.index.train(X) self.index.add(X) diff --git a/hypex/extensions/statsmodels.py b/hypex/extensions/statsmodels.py index 3ee7252e..d67fd49a 100644 --- a/hypex/extensions/statsmodels.py +++ b/hypex/extensions/statsmodels.py @@ -125,61 +125,3 @@ def quantile_of_marginal_distribution( if self.equal_variance else quantiles ) - - def min_sample_size( - self, - num_samples: int, - mde: float, - variances: list[float] | float, - power: float = 0.2, - quantile_1: float | list[float] | None = None, - quantile_2: float | list[float] | None = None, - initial_estimate: int = 0, - iteration_size: int = 3000, - ): - if isinstance(quantile_1, float): - quantile_1 = np.full(num_samples, quantile_1).tolist() - if isinstance(quantile_1, float): - quantile_2 = np.full(num_samples, quantile_2).tolist() - - quantile_1 = quantile_1 or self.quantile_of_marginal_distribution( - num_samples=num_samples, - quantile_level=1 - self.alpha / num_samples, - variances=variances if isinstance(variances, list) else [variances], - ) - quantile_2 = quantile_2 or self.quantile_of_marginal_distribution( - num_samples=num_samples, quantile_level=power - ) - - if self.equal_variance: - return int(2 * variances * ((quantile_1[0] - quantile_2[0]) / mde) ** 2) + 1 - else: - sizes = [] - for index in range(num_samples): - size = initial_estimate - current_power = 0 - while current_power < 1 - power: - size += 100 - current_power = 0 - total_samples = norm.rvs( - size=[iteration_size, num_samples], - random_state=self.random_state, - ) - for sample in total_samples: - min_t_value = np.inf - for i in range(num_samples): - if i != index: - t_value = ( - sample[index] - / np.sqrt(1 + variances[i] / variances[index]) - - sample[i] - / np.sqrt(1 + variances[index] / variances[i]) - + mde - * np.sqrt(size / (variances[index] + variances[i])) - ) - min_t_value = min(min_t_value, t_value) - if min_t_value > quantile_1[index]: - current_power += 1 - current_power /= iteration_size - sizes.append(size) - return {"min sample size": np.max(sizes)} diff --git a/hypex/matching.py b/hypex/matching.py index 8d9ef98a..5d36dc24 100644 --- a/hypex/matching.py +++ b/hypex/matching.py @@ -3,15 +3,17 @@ from typing import Literal from .analyzers.matching import MatchingAnalyzer -from .comparators import KSTest, TTest +from .comparators import Chi2Test, KSTest, TTest from .comparators.distances import MahalanobisDistance from .dataset import AdditionalMatchingRole, FeatureRole, TargetRole, TreatmentRole +from .encoders.encoders import DummyEncoder from .executor import Executor from .experiments import GroupExperiment from .experiments.base import Experiment, OnRoleExperiment from .ml.faiss import FaissNearestNeighbors from .operators.operators import Bias, MatchingMetrics from .reporters.matching import MatchingDatasetReporter +from .transformers import TypeCaster from .ui.base import ExperimentShell from .ui.matching import MatchingOutput @@ -70,11 +72,17 @@ def _make_experiment( metric: Literal["atc", "att", "ate"] = "ate", bias_estimation: bool = True, quality_tests: ( - Literal["smd", "psi", "ks-test", "repeats", "t-test", "auto"] - | list[Literal["smd", "psi", "ks-test", "repeats", "t-test", "auto"]] + Literal["smd", "psi", "ks-test", "repeats", "t-test", "chi2-test", "auto"] + | list[ + Literal[ + "smd", "psi", "ks-test", "repeats", "t-test", "chi2-test", "auto" + ] + ] ) = "auto", faiss_mode: Literal["base", "fast", "auto"] = "auto", n_neighbors: int = 1, + weights: dict[str, float] | None = None, + encode_categories: bool = True, ) -> Experiment: """Creates an experiment configuration with specified matching parameters. @@ -109,7 +117,10 @@ def _make_experiment( ) """ distance_mapping = { - "mahalanobis": MahalanobisDistance(grouping_role=TreatmentRole()) + "mahalanobis": MahalanobisDistance( + grouping_role=TreatmentRole(), weights=weights + ), + # "l2": L2Distance(grouping_role=TreatmentRole(), weights=weights), } test_mapping = { "t-test": TTest( @@ -123,17 +134,26 @@ def _make_experiment( compare_by="matched_pairs", baseline_role=AdditionalMatchingRole(), ), + "chi2-test": Chi2Test( + grouping_role=TreatmentRole(), + compare_by="matched_pairs", + baseline_role=AdditionalMatchingRole(), + ), } two_sides = metric == "ate" test_pairs = metric == "atc" executors: list[Executor] = [ + TypeCaster( + dtype={int: float}, + roles=[FeatureRole(), TargetRole()], + ), FaissNearestNeighbors( grouping_role=TreatmentRole(), two_sides=two_sides, test_pairs=test_pairs, faiss_mode=faiss_mode, n_neighbors=n_neighbors, - ) + ), ] if bias_estimation: executors += [ @@ -144,10 +164,18 @@ def _make_experiment( grouping_role=TreatmentRole(), target_roles=[TargetRole()], metric=metric, + n_neighbors=n_neighbors, ), MatchingAnalyzer(), ] - if quality_tests != "auto": + if quality_tests == "auto": + executors += [ + OnRoleExperiment( + executors=list(test_mapping.values()), + role=FeatureRole(), + ) + ] + else: # warnings.warn("Now quality tests aren't supported yet") executors += [ OnRoleExperiment( @@ -155,21 +183,15 @@ def _make_experiment( role=FeatureRole(), ) ] + executors = ( + executors if distance == "l2" else [distance_mapping[distance], *executors] + ) + executors = executors if not encode_categories else [DummyEncoder(), *executors] return ( - Experiment( - executors=( - executors - if distance == "l2" - else [distance_mapping[distance], *executors] - ) - ) + Experiment(executors=executors) if not group_match else GroupExperiment( - executors=( - executors - if distance == "l2" - else [distance_mapping[distance], *executors] - ), + executors=executors, reporter=MatchingDatasetReporter(), ) ) @@ -178,15 +200,22 @@ def __init__( self, group_match: bool = False, distance: Literal["mahalanobis", "l2"] = "mahalanobis", - metric: Literal["atc", "att", "ate"] = "ate", + # metric: Literal["atc", "att", "ate"] = "ate", bias_estimation: bool = True, quality_tests: ( - Literal["smd", "psi", "ks-test", "repeats", "t-test", "auto"] - | list[Literal["smd", "psi", "ks-test", "repeats", "t-test", "auto"]] + Literal["smd", "psi", "ks-test", "repeats", "t-test", "chi2-test", "auto"] + | list[ + Literal[ + "smd", "psi", "ks-test", "repeats", "t-test", "chi2-test", "auto" + ] + ] ) = "auto", faiss_mode: Literal["base", "fast", "auto"] = "auto", n_neighbors: int = 1, + weights: dict[str, float] | None = None, + encode_categories: bool = True, ): + metric = "ate" super().__init__( experiment=self._make_experiment( group_match, @@ -195,6 +224,9 @@ def __init__( bias_estimation, quality_tests, faiss_mode, + n_neighbors, + weights, + encode_categories, ), output=MatchingOutput(GroupExperiment if group_match else MatchingAnalyzer), ) diff --git a/hypex/ml/__init__.py b/hypex/ml/__init__.py index 218c62ca..325d511e 100644 --- a/hypex/ml/__init__.py +++ b/hypex/ml/__init__.py @@ -1,3 +1,4 @@ +from .cupac import CUPACExecutor from .faiss import FaissNearestNeighbors -__all__ = ["FaissNearestNeighbors"] +__all__ = ["CUPACExecutor", "FaissNearestNeighbors"] diff --git a/hypex/ml/cupac.py b/hypex/ml/cupac.py new file mode 100644 index 00000000..1b9319fa --- /dev/null +++ b/hypex/ml/cupac.py @@ -0,0 +1,386 @@ +from __future__ import annotations + +from typing import Any, Sequence + +from ..dataset.dataset import Dataset, ExperimentData +from ..dataset.roles import ( + AdditionalTargetRole, + FeatureRole, + PreTargetRole, + TargetRole, +) +from ..executor import MLExecutor +from ..extensions.cupac import CupacExtension +from ..utils.adapter import Adapter +from ..utils.models import CUPAC_MODELS + + +class CUPACExecutor(MLExecutor): + """ + Executor that applies CUPAC (Control Using Predictions As Covariates) variance reduction technique. + + CUPAC uses machine learning models to predict target values based on historical data, + then adjusts current targets by removing the predicted variation to reduce variance. + + Args: + cupac_models (Union[str, Sequence[str], None]): Model(s) to use for prediction. + If None, all available models will be tried and the best one selected. + key (Any): Unique identifier for the executor. + n_folds (int): Number of folds for cross-validation during model selection. + random_state (Optional[int]): Random seed for reproducibility. + """ + + def __init__( + self, + cupac_models: str | Sequence[str] | None = None, + key: Any = "", + n_folds: int = 5, + random_state: int | None = None, + ): + super().__init__(target_role=TargetRole(), key=key) + self.cupac_models = cupac_models + self.extension = CupacExtension(n_folds, random_state) + + def _validate_models(self) -> None: + """ + Validate that all specified CUPAC models are supported and available for the current backend. + + Raises: + ValueError: If any model is not recognized or not available for the current backend. + """ + wrong_models = [] + if self.cupac_models is None: + self.cupac_models = list(CUPAC_MODELS.keys()) + return + + self.cupac_models = Adapter.to_list(self.cupac_models) + + for model in self.cupac_models: + if model.lower() not in CUPAC_MODELS: + wrong_models.append(model) + elif CUPAC_MODELS[model] is None: + raise ValueError( + f"Model '{model}' is not available for the current backend" + ) + + if wrong_models: + raise ValueError( + f"Wrong cupac models: {wrong_models}. Available models: {list(CUPAC_MODELS.keys())}" + ) + + @staticmethod + def _prepare_data(data: ExperimentData) -> dict[str, dict[str, list]]: + """ + Prepare data for CUPAC by organizing temporal fields into training and prediction structures. + + This method performs complex data organization: + 1. Groups target and feature fields by their temporal lags + 2. Identifies cofounders (features used for prediction) + 3. Structures data into X_train, Y_train for model training + 4. Creates X_predict for current period adjustment (if applicable) + + Args: + data (ExperimentData): Input experiment data with temporal roles. + + Returns: + dict: Nested dictionary with structure: + {target_name: { + 'X_train': [[feature_cols_at_lag_n], ..., [feature_cols_at_lag_2]], + 'Y_train': [target_at_lag_n-1, ..., target_at_lag_1], + 'X_predict': [[feature_cols_at_lag_1]] (optional, only for real targets) + }} + """ + + def agg_temporal_fields(role, data) -> dict[str, dict]: + """ + Aggregate fields by their temporal lags. + + Returns: + dict: {field_name: {lag: field_name_with_lag}} or {field_name: {}} + Empty dict means lag=0 or None (current period). + """ + fields = {} + searched_fields = data.field_search( + ( + [TargetRole(), PreTargetRole()] + if isinstance(role, TargetRole) + else role + ), + search_types=[int, float], + ) + + searched_lags = [ + ( + field, + ( + data.ds.roles[field].lag + if not isinstance(data.ds.roles[field], TargetRole) + else 0 + ), + ) + for field in searched_fields + ] + sorted_fields_by_lag = sorted(searched_lags, key=lambda x: x[1]) + for field, lag in sorted_fields_by_lag: + if lag in [None, 0]: + fields[field] = {} + else: + if data.ds.roles[field].parent not in fields: + fields[data.ds.roles[field].parent] = {} + fields[data.ds.roles[field].parent][lag] = field + + return fields + + def agg_train_predict_x(mode: str, lag: int) -> None: + """ + Aggregate features and targets for a specific lag into training/prediction sets. + + For each cofounder feature, creates a list structure where: + - First and last lags start new sublists + - Intermediate lags append to existing sublists + This groups temporal sequences of the same feature together. + """ + for i, cofounder in enumerate(cofounders[target]): + if lag in [1, max_lags[target]]: + cupac_data[target][mode].append([features[cofounder][lag]]) + else: + cupac_data[target][mode][i].append(cofounder) + + cupac_data[target][mode].append([targets[target][lag]]) + + cupac_data = {} + targets = agg_temporal_fields(TargetRole(), data) + features = agg_temporal_fields(FeatureRole(), data) + + # Determine cofounders (features used for prediction) for each target + cofounders = {} + for target in targets: + if target in data.ds.columns: + cofounders[target] = data.ds.roles[target].cofounders + else: + # For virtual targets, get cofounders from the earliest lag + min_lag = min(targets[target].keys()) + cofounders[target] = data.ds.roles[targets[target][min_lag]].cofounders + + if cofounders[target] is None: + raise ValueError( + f"Cofounders must be defined in the first lag for virtual target '{target}'" + ) + + # Calculate maximum lag for each target (max across target lags and cofounder feature lags) + max_lags = {} + for target, lags in targets.items(): + if lags: + max_lag = max(lags.keys()) + for feature in cofounders[target]: + if features.get(feature): + max_lag = max(max(features[feature].keys()), max_lag) + max_lags[target] = max_lag + + # Build training and prediction structures for each target + for target in targets.keys(): + + cupac_data[target] = {"X_train": [], "Y_train": []} + # Only real targets (not virtual) need prediction + if target in data.ds.columns: + cupac_data[target]["X_predict"] = [] + + # Build training data: iterate from max_lag down to 2 + # Each iteration creates X_train entry for lag and Y_train entry for lag-1 + for lag in range(max_lags[target], 1, -1): + agg_train_predict_x("X_train", lag) + cupac_data[target]["Y_train"].append(targets[target][lag - 1]) + + # Build prediction data for current period (lag=1) if applicable + if "X_predict" in cupac_data[target].keys(): + agg_train_predict_x("X_predict", 1) + + return cupac_data + + @classmethod + def _execute_inner_function(cls) -> None: + pass + + @classmethod + def _inner_function(cls) -> None: + pass + + def calc( + self, mode: str, model: str | Any, X: Dataset, Y: Dataset | None = None + ) -> Any: + if mode == "kfold_fit": + return self.kfold_fit(model, X, Y) + elif mode == "fit": + return self.fit(model, X, Y) + elif mode == "predict": + return self.predict(model, X) + + def kfold_fit( + self, model: str, X: Dataset, Y: Dataset + ) -> tuple[float, dict[str, float]]: + """Run k-fold cross-validation and return variance reduction and feature importances.""" + var_red, feature_importances = self.extension.calc( + data=X, + mode="kfold_fit", + model=model, + Y=Y, + ) + + return var_red, feature_importances + + def fit(self, model: str, X: Dataset, Y: Dataset) -> Any: + return self.extension.calc( + data=X, + mode="fit", + model=model, + Y=Y, + ) + + def predict(self, model: Any, X: Dataset) -> Dataset: + return self.extension.calc( + data=X, + mode="predict", + model=model, + ) + + @staticmethod + def _agg_data_from_cupac_data( + data: ExperimentData, cupac_data_slice: list + ) -> Dataset: + """ + Aggregate columns from cupac_data structure into a single Dataset. + + This method handles two types of column structures: + 1. Single column: [column_name] - directly extracted + 2. Multiple lag columns: [col_lag1, col_lag2, ...] - vertically stacked + + Args: + data: Original ExperimentData with all columns. + cupac_data_slice: List of column specifications, where each element is: + - [single_col_name] for non-temporal columns + - [col_name_lag1, col_name_lag2, ...] for temporal sequences + + Returns: + Dataset with standardized column names (0, 1, 2, ...). + """ + res_dataset = None + column_counter = 0 + + for column in cupac_data_slice: + if len(column) == 1: + # Single column case: extract directly + col_data = data.ds[column[0]] + else: + # Multiple lag columns: stack them vertically + res_lag_column = None + for lag_column in column: + tmp_dataset = data.ds[lag_column] + tmp_dataset = tmp_dataset.rename({lag_column: column[0]}) + if res_lag_column is None: + res_lag_column = tmp_dataset + else: + res_lag_column = res_lag_column.append( + tmp_dataset, reset_index=True, axis=0 + ) + col_data = res_lag_column + + # Standardize column names to numeric format for model training + standard_col_name = f"{column_counter}" + col_data = col_data.rename( + {next(iter(col_data.columns)): standard_col_name} + ) + column_counter += 1 + + if res_dataset is None: + res_dataset = col_data + else: + res_dataset = res_dataset.add_column(data=col_data) + return res_dataset + + def execute(self, data: ExperimentData) -> ExperimentData: + """ + Execute CUPAC variance reduction on the experiment data. + + Process: + 1. Validate models and prepare temporal data structures + 2. For each target: + a. Try all specified models with cross-validation + b. Select the model with best variance reduction + c. Fit the best model on all training data + d. Predict and adjust current target values (if applicable) + e. Calculate variance reduction metrics + 3. Store adjusted targets and metrics in ExperimentData + + Args: + data (ExperimentData): Input data with temporal features and targets. + + Returns: + ExperimentData: Data with CUPAC-adjusted targets and variance reduction reports. + """ + self._validate_models() + cupac_data = self._prepare_data(data) + for target, target_data in cupac_data.items(): + # Extract feature names once before data aggregation + X_train_feature_names = [column[0] for column in target_data["X_train"]] + + X_train = self._agg_data_from_cupac_data(data, target_data["X_train"]) + Y_train = self._agg_data_from_cupac_data(data, [target_data["Y_train"]]) + best_model, best_var_red, best_feature_importances = None, None, None + + # Model selection via cross-validation + # Feature importances are extracted during CV for efficiency + for model in self.cupac_models: + var_red, fold_importances = self.calc( + mode="kfold_fit", model=model, X=X_train, Y=Y_train + ) + if best_var_red is None or var_red > best_var_red: + best_model, best_var_red = model, var_red + # Map standardized column names to original feature names + best_feature_importances = { + X_train_feature_names[int(col_idx)]: importance + for col_idx, importance in fold_importances.items() + } + + if best_model is None: + raise RuntimeError( + f"No models were successfully fitted for target '{target}'. All models failed during training." + ) + + cupac_variance_reduction_real = None + + # Apply CUPAC adjustment to current period (if target is real, not virtual) + # We need to fit the model on all data for prediction, but importances are already from CV + if "X_predict" in target_data: + fitted_model = self.calc( + mode="fit", model=best_model, X=X_train, Y=Y_train + ) + + X_predict = self._agg_data_from_cupac_data( + data, target_data["X_predict"] + ) + + prediction = self.calc(mode="predict", model=fitted_model, X=X_predict) + + # Adjust target by removing explained variation + explained_variation = prediction - prediction.mean() + target_cupac = data.ds[target] - explained_variation + + target_cupac = target_cupac.rename({target: f"{target}_cupac"}) + data.additional_fields = data.additional_fields.add_column( + data=target_cupac, role={f"{target}_cupac": AdditionalTargetRole()} + ) + cupac_variance_reduction_real = ( + self.extension._calculate_variance_reduction( + data.ds[target], target_cupac + ) + ) + + report = { + "cupac_best_model": best_model, + "cupac_variance_reduction_cv": best_var_red, + "cupac_variance_reduction_real": cupac_variance_reduction_real, + "cupac_feature_importances": best_feature_importances, + } + data.analysis_tables[f"{target}_cupac_report"] = report + + return data diff --git a/hypex/ml/faiss.py b/hypex/ml/faiss.py index 4d1c9d43..3bb8c11a 100644 --- a/hypex/ml/faiss.py +++ b/hypex/ml/faiss.py @@ -1,6 +1,7 @@ from __future__ import annotations from typing import Any, Literal +from warnings import warn from ..comparators.distances import MahalanobisDistance from ..dataset import ( @@ -31,9 +32,24 @@ def __init__( self.test_pairs = test_pairs self.faiss_mode = faiss_mode super().__init__( - grouping_role=grouping_role, target_role=FeatureRole(), key=key + grouping_role=grouping_role, + target_role=FeatureRole(), + key=key, ) + @classmethod + def _set_global_match_indexes( + cls, local_indexes: Dataset, data: tuple(str, Dataset) + ) -> list[int, list[int]]: + if len(local_indexes) == 0: + return local_indexes + global_indexes = local_indexes + for col in local_indexes.columns: + global_indexes[col] = data[1].index.take( + local_indexes.get_values(column=col) + ) + return global_indexes + @classmethod def _execute_inner_function( cls, @@ -46,24 +62,27 @@ def _execute_inner_function( **kwargs, ) -> dict: if test_pairs is not True: - data = cls._inner_function( + test_data = cls._inner_function( data=grouping_data[0][1], test_data=grouping_data[1][1], n_neighbors=n_neighbors or 1, faiss_mode=faiss_mode, **kwargs, ) + test_data = cls._set_global_match_indexes(test_data, grouping_data[0]) if two_sides is not True: - return {"test": data} + return {"test": test_data} + control_data = cls._inner_function( + data=grouping_data[1][1], + test_data=grouping_data[0][1], + n_neighbors=n_neighbors or 1, + faiss_mode=faiss_mode, + **kwargs, + ) + control_data = cls._set_global_match_indexes(control_data, grouping_data[1]) return { - "test": data, - "control": cls._inner_function( - data=grouping_data[1][1], - test_data=grouping_data[0][1], - n_neighbors=n_neighbors or 1, - faiss_mode=faiss_mode, - **kwargs, - ), + "test": test_data, + "control": control_data, } data = cls._inner_function( data=grouping_data[1][1], @@ -126,30 +145,43 @@ def execute(self, data: ExperimentData) -> ExperimentData: two_sides=self.two_sides, test_pairs=self.test_pairs, ) - ds = data.ds.groupby(group_field) - matched_indexes = Dataset.create_empty() - for i in range(len(compare_result.columns)): - group = ( - grouping_data[1][1] - if compare_result.columns[i] == "test" - else grouping_data[0][1] + nans = 0 + + for result in compare_result.values(): + nans += ( + sum(result.isna().sum().get_values(row="sum")) + if self.n_neighbors > 1 + else result.isna().sum() ) - t_ds = ds[0][1] if compare_result.columns[i] == "test" else ds[1][1] - t_index_field = ( - compare_result[compare_result.columns[i]] - .loc[: len(group) - 1] - .rename({compare_result.columns[i]: "indexes"}) + result = result.fillna(-1).astype({col: int for col in result.columns}) + if nans > 0: + warn( + f"Faiss returned {nans} nans, which were replaced with dummy matches. Check if the data is suitable for the test.", + UserWarning, ) - if t_index_field.isna().sum() > 0: + matched_indexes = Dataset.create_empty() + for res_k, res_v in compare_result.items(): + group = grouping_data[1][1] if res_k == "test" else grouping_data[0][1] + t_index_field = res_v.loc[: len(group) - 1] + n_nans = ( + t_index_field.isna().sum().get_values(row="sum") + if t_index_field.shape[1] > 1 + else [t_index_field.isna().sum()] + ) + if any(n_nans): raise PairsNotFoundError + t_index_field = t_index_field.rename( + {col: f"indexes_{i}" for i, col in enumerate(t_index_field.columns)} + ) matched_indexes = matched_indexes.append( Dataset.from_dict( data={ - "indexes": t_ds.iloc[ - list(map(lambda x: int(x[0]), t_index_field.get_values())) - ].index + col: t_index_field.get_values(column=col) + for col in t_index_field.columns + }, + roles={ + col: AdditionalMatchingRole() for col in t_index_field.columns }, - roles={"indexes": AdditionalMatchingRole()}, index=group.index, ) ).sort() @@ -157,4 +189,5 @@ def execute(self, data: ExperimentData) -> ExperimentData: matched_indexes = matched_indexes.reindex(data.ds.index, fill_value=-1) elif len(matched_indexes) < len(data.ds) and self.two_sides: raise PairsNotFoundError + matched_indexes.data.to_csv("matched_indexes.csv") return self._set_value(data, matched_indexes, key="matched") diff --git a/hypex/operators/operators.py b/hypex/operators/operators.py index 033f248c..37f5f2ac 100644 --- a/hypex/operators/operators.py +++ b/hypex/operators/operators.py @@ -39,9 +39,11 @@ def __init__( grouping_role: ABCRole | None = None, target_roles: ABCRole | list[ABCRole] | None = None, metric: Literal["auto", "atc", "att", "ate"] | None = None, + n_neighbors: int = 1, key: Any = "", ): self.metric = metric or "auto" + self.n_neighbors = n_neighbors self.__scaled_counts = {} target_roles = target_roles or TargetRole() super().__init__( @@ -52,26 +54,46 @@ def __init__( key=key, ) - def _calc_scaled_counts(self, matches, group): - s_counts = [x[0] for x in matches.value_counts()["count"].get_values()] - extra_counts = [0 for _ in range(len(matches) - len(s_counts))] - self.__scaled_counts[group] = s_counts + extra_counts + def _calc_scaled_counts(self, matches, indexes, group): + matches_counts = Dataset({}) + matches_counts = matches_counts.add_column( + indexes.index, {"indexes": InfoRole()} + ) + matches_counts = matches_counts.add_column([0], {"count": InfoRole(float)}) + for col in matches.columns: + v_counts = matches[col].value_counts() + matches_counts = matches_counts.merge( + v_counts, + how="left", + left_on="indexes", + right_on=col, + suffixes=(("", col)), + ).drop(columns=col) + matches_counts.index = indexes.index + matches_counts = matches_counts.drop(columns="indexes").fillna(0) + for col in matches_counts.columns: + if col != "count": + matches_counts["count"] += matches_counts[col] + self.__scaled_counts[group] = matches_counts["count"] / self.n_neighbors @staticmethod def _calc_vars(value): var = 0 if value[value.columns[0]].isna().sum() > 0 else value.var() - return [var for _ in range(len(value))] + return value * 0 + var @staticmethod - def _calc_se(var_c, var_t, scaled_counts, is_ate=False): + def _calc_se(var_c, var_t, scaled_counts, group=None): n_c, n_t = len(var_c), len(var_t) - if not is_ate: - weights_c = n_c / n_t * np.array(scaled_counts) - weights_t = np.ones(n_t) + if group is not None: + groups = list(scaled_counts.keys()) + groups.remove(group) + group_other = groups[0] + weights_c = scaled_counts[group_other] * 0 + 1 + weights_t = scaled_counts[group] * n_t / n_c else: n = n_c + n_t - weights_c = (n_c / n) * np.array(scaled_counts["control"]) - weights_t = (n_t / n) * np.array(scaled_counts["test"]) + weights_c = (n_c / n) * (scaled_counts["test"] + 1) + weights_t = (n_t / n) * (scaled_counts["control"] + 1) return np.sqrt( (weights_t**2 * var_t).sum() / n_t**2 @@ -104,10 +126,10 @@ def _inner_function( itt += Dataset.from_dict( {"control": bias["test"]}, roles={}, index=itt.index ) - var_t = cls._calc_vars(itt) - var_c = cls._calc_vars(itc) - itt_se = cls._calc_se(var_c, var_t, scaled_counts["control"]) - itc_se = cls._calc_se(var_t, var_c, scaled_counts["test"]) + var_t = cls._calc_vars(itc) + var_c = cls._calc_vars(itt) + itt_se = cls._calc_se(var_c, var_t, scaled_counts, "control") + itc_se = cls._calc_se(var_t, var_c, scaled_counts, "test") itt = itt.mean() itc = itc.mean() p_val_itt = ( @@ -148,9 +170,9 @@ def _inner_function( itt + 1.96 * itt_se, ] } - len_test, len_control = len(data), len(test_data) + len_control, len_test = len(data), len(test_data) ate = (itt * len_test + itc * len_control) / (len_test + len_control) - ate_se = cls._calc_se(var_c, var_t, scaled_counts, is_ate=True) + ate_se = cls._calc_se(var_c, var_t, scaled_counts) p_val_ate = ( NormCDF() .calc( @@ -185,28 +207,20 @@ def _execute_inner_function( ) def _prepare_new_target( - self, data: ExperimentData, t_data: Dataset, group_field: str + self, + data: ExperimentData, + t_data: Dataset, + group_field: str, ) -> Dataset: - indexes = data.field_search(AdditionalMatchingRole()) - if len(indexes) == 0: - raise ValueError("No indexes were found") new_target = data.ds.search_columns(TargetRole())[0] - indexes = data.additional_fields[indexes[0]] - indexes.index = t_data.index + indexes, matched_data = Bias.prepare_data(data, t_data) + matched_data = matched_data[new_target + "_matched"] grouped_data = data.ds.groupby(group_field) - control_indexes = indexes.loc[grouped_data[0][1].index] - test_indexes = indexes.loc[grouped_data[1][1].index] - self._calc_scaled_counts(control_indexes, "control") - self._calc_scaled_counts(test_indexes, "test") - filtered_field = indexes.drop( - indexes[indexes[indexes.columns[0]] == -1], axis=0 - ) - matched_data = data.ds.loc[ - list(map(lambda x: x[0], filtered_field.get_values())) - ][new_target].rename( - {new_target: new_target + "_matched" for _ in data.ds.columns} - ) - matched_data.index = filtered_field.index + control_indexes = indexes.loc[grouped_data[0][1].index, :] + test_indexes = indexes.loc[grouped_data[1][1].index, :] + self._calc_scaled_counts(control_indexes, test_indexes, "test") + self._calc_scaled_counts(test_indexes, control_indexes, "control") + return matched_data def execute(self, data: ExperimentData) -> ExperimentData: @@ -348,23 +362,54 @@ def _execute_inner_function( **kwargs, ) - def _prepare_data(self, data: ExperimentData, t_data: Dataset) -> Dataset: - indexes = data.additional_fields[data.field_search(AdditionalMatchingRole())[0]] + @staticmethod + def prepare_data(data: ExperimentData, t_data: Dataset) -> Dataset: + indexes = data.field_search(AdditionalMatchingRole()) + if len(indexes) == 0: + raise ValueError("No indexes were found") + indexes = data.additional_fields[indexes] indexes.index = t_data.index filtered_field = indexes.drop( indexes[indexes[indexes.columns[0]] == -1], axis=0 ) - matched_data = data.ds.loc[ - list(map(lambda x: x[0], filtered_field.get_values())) - ].rename({i: i + "_matched" for i in data.ds.columns}) + matched_data = Dataset({}) matched_data.index = filtered_field.index - return matched_data + numeric_cols = t_data.search_columns( + [FeatureRole(), TargetRole()], search_types=[int, float] + ) + for d_col in numeric_cols: + matched_data_col = Dataset({}) + matched_data_col.index = filtered_field.index + for i, i_col in enumerate(indexes.columns): + index_matched_data = data.ds.loc[ + list(filtered_field[i_col].get_values(column=i_col)) + ][d_col].rename( + {d_col: d_col + f"_matched_{i}" for _ in data.ds.columns} + ) + matched_data_col = matched_data_col.add_column(index_matched_data) + default_value = [t_data.roles[d_col].data_type(0)] + matched_data_col = matched_data_col.add_column( + default_value * matched_data_col.shape[0], + {d_col + "_matched": t_data.roles[d_col]}, + ) + for col in matched_data_col.columns: + if col != d_col + "_matched": + matched_data_col[d_col + "_matched"] += matched_data_col[col] + matched_data = matched_data.add_column( + default_value * matched_data_col.shape[0], + {d_col + "_matched": t_data.roles[d_col]}, + ) + matched_data[d_col + "_matched"] = matched_data_col[d_col + "_matched"] / ( + matched_data_col.shape[1] - 1 + ) + + return indexes, matched_data def execute(self, data: ExperimentData) -> ExperimentData: group_field, target_fields = self._get_fields(data) t_data = deepcopy(data.ds) if len(target_fields) < 2: - matched_data = self._prepare_data(data, t_data) + _, matched_data = self.prepare_data(data, t_data) target_fields += [matched_data.search_columns(TargetRole())[0]] t_data = t_data.append(matched_data.reindex(t_data.index), axis=1) self.key = str( diff --git a/hypex/preprocessing.py b/hypex/preprocessing.py index a80cd75b..7f71a89b 100644 --- a/hypex/preprocessing.py +++ b/hypex/preprocessing.py @@ -2,11 +2,11 @@ from .experiments.base import Experiment from .transformers.category_agg import CategoryAggregator from .transformers.filters import ( - ConstFilter, - CorrFilter, - CVFilter, - NanFilter, - OutliersFilter, + ConstFilter, + CorrFilter, + CVFilter, + NanFilter, + OutliersFilter, ) from .transformers.na_filler import NaFiller diff --git a/hypex/reporters/aa.py b/hypex/reporters/aa.py index b87eb3b5..99217e14 100644 --- a/hypex/reporters/aa.py +++ b/hypex/reporters/aa.py @@ -144,7 +144,14 @@ def report(self, data: ExperimentData) -> Dataset: print("AA test cannot be performed as none of the analyzers passed") return None result = self._detect_pass(analyser_tables) - stats_cols = ["feature", "group", "control mean", "test mean", "difference", "difference %"] + stats_cols = [ + "feature", + "group", + "control mean", + "test mean", + "difference", + "difference %", + ] differences = analyser_tables["best split statistics"].loc[ :, [ diff --git a/hypex/reporters/ab.py b/hypex/reporters/ab.py index a04bf141..774ab99b 100644 --- a/hypex/reporters/ab.py +++ b/hypex/reporters/ab.py @@ -4,7 +4,7 @@ from ..analyzers.ab import ABAnalyzer from ..comparators import Chi2Test, TTest, UTest -from ..dataset import Dataset, ExperimentData +from ..dataset import Dataset, ExperimentData, StatisticRole from ..utils import ExperimentDataEnum from .aa import OneAADictReporter @@ -31,7 +31,45 @@ def report(self, data: ExperimentData) -> dict[str, Any]: class ABDatasetReporter(ABDictReporter): @staticmethod def _invert_aa_format(table: Dataset) -> Dataset: - return table.replace("NOT OK", "N").replace("OK", "NOT OK").replace("N", "OK") + return ( + table + if table.is_empty() + else table.replace("NOT OK", "N").replace("OK", "NOT OK").replace("N", "OK") + ) + + def report_variance_reductions(self, data: ExperimentData) -> Dataset | str: + """Generate variance reduction report for CUPED/CUPAC transformations.""" + variance_cols = [ + col + for col in data.additional_fields.columns + if col.endswith("_variance_reduction") + ] + if not variance_cols: + return "No variance reduction data available. Ensure CUPED or CUPAC was applied." + + # Create report data + report_data = [] + for col in variance_cols: + metric_name = col.replace("_variance_reduction", "") + # Get the scalar value from the additional_fields + reduction_value = data.additional_fields.data[col].iloc[0] + report_data.append( + { + "Transformed Metric Name": metric_name, + "Variance Reduction (%)": reduction_value, + } + ) + + # Convert to Dataset + if report_data: + return Dataset.from_dict( + data=report_data, + roles={ + "Transformed Metric Name": StatisticRole(), + "Variance Reduction (%)": StatisticRole(), + }, + ) + return "No variance reduction data available." def report(self, data: ExperimentData): front_buffer = self.front diff --git a/hypex/reporters/abstract.py b/hypex/reporters/abstract.py index 6cd9fd54..337ef61e 100644 --- a/hypex/reporters/abstract.py +++ b/hypex/reporters/abstract.py @@ -76,7 +76,14 @@ def _get_struct_dict(data: dict): for key, value in data.items(): if ID_SPLIT_SYMBOL in key: key_split = key.split(ID_SPLIT_SYMBOL) - if key_split[2] in ("pass", "p-value", "difference", "difference %", "control mean", "test mean"): + if key_split[2] in ( + "pass", + "p-value", + "difference", + "difference %", + "control mean", + "test mean", + ): if key_split[0] not in dict_result: dict_result[key_split[0]] = { key_split[3]: {key_split[1]: {key_split[2]: value}} diff --git a/hypex/reporters/matching.py b/hypex/reporters/matching.py index 15a57bb9..15b3f78e 100644 --- a/hypex/reporters/matching.py +++ b/hypex/reporters/matching.py @@ -3,7 +3,7 @@ from typing import Any, ClassVar from ..analyzers.matching import MatchingAnalyzer -from ..comparators import KSTest, TTest +from ..comparators import Chi2Test, KSTest, TTest from ..dataset import Dataset, ExperimentData from ..ml import FaissNearestNeighbors from ..reporters.abstract import DatasetReporter, DictReporter, TestDictReporter @@ -38,16 +38,17 @@ def _extract_from_analyser(self, data: ExperimentData): @staticmethod def _extract_from_additional_fields(data: ExperimentData): - indexes_id = data.get_one_id( + indexes_id = data.get_ids( FaissNearestNeighbors, ExperimentDataEnum.additional_fields - ) + )[FaissNearestNeighbors.__name__][ExperimentDataEnum.additional_fields.value] return { - "indexes": MATCHING_INDEXES_SPLITTER_SYMBOL.join( + f"indexes{ID_SPLIT_SYMBOL}{column.split(ID_SPLIT_SYMBOL)[3]}": MATCHING_INDEXES_SPLITTER_SYMBOL.join( str(i) - for i in data.additional_fields[indexes_id].to_dict()["data"]["data"][ - indexes_id + for i in data.additional_fields[column].to_dict()["data"]["data"][ + column ] ) + for column in indexes_id } def report(self, experiment_data: ExperimentData): @@ -59,7 +60,7 @@ def report(self, experiment_data: ExperimentData): class MatchingQualityDictReporter(TestDictReporter): - tests: ClassVar[list] = [TTest, KSTest] + tests: ClassVar[list] = [TTest, KSTest, Chi2Test] def report(self, data: ExperimentData) -> dict[str, Any]: return self.extract_tests(data) diff --git a/hypex/splitters/aa.py b/hypex/splitters/aa.py index 6af647bf..49401c07 100644 --- a/hypex/splitters/aa.py +++ b/hypex/splitters/aa.py @@ -25,6 +25,7 @@ def __init__( sample_size: float | None = None, constant_key: bool = True, save_groups: bool = True, + groups_sizes: list[float] | None = None, key: Any = "", ): self.control_size = control_size @@ -33,6 +34,7 @@ def __init__( self.constant_key = constant_key self.save_groups = save_groups self.sample_size = sample_size + self.groups_sizes = groups_sizes super().__init__(key) def _generate_params_hash(self): @@ -41,6 +43,8 @@ def _generate_params_hash(self): hash_parts.append(f"cs {self.control_size}") if self.random_state is not None: hash_parts.append(f"rs {self.random_state}") + if self.groups_sizes is not None: + hash_parts.append(f"gs {self.groups_sizes}") self._params_hash = "|".join(hash_parts) def init_from_hash(self, params_hash: str): @@ -50,6 +54,12 @@ def init_from_hash(self, params_hash: str): self.control_size = float(hash_part[hash_part.rfind(" ") + 1 :]) elif hash_part.startswith("rs"): self.random_state = int(hash_part[hash_part.rfind(" ") + 1 :]) + elif hash_part.startswith("gs"): + self.groups_sizes = [] + groups_sizes = ( + hash_part[hash_part.find(" ") + 1 :].strip("[]").split(",") + ) + self.groups_sizes = [float(gs) for gs in groups_sizes] self._generate_id() @property @@ -82,6 +92,7 @@ def _inner_function( data: Dataset, random_state: int | None = None, control_size: float = 0.5, + groups_sizes: list[float] | None = None, sample_size: float | None = None, const_group_field: str | None = None, **kwargs, @@ -94,9 +105,13 @@ def _inner_function( if control_data is not None: control_indexes = list(control_data.index) const_size = sum(len(cd) for cd in const_data.values()) - control_size = (len(data) * control_size - const_size) / ( - len(data) - const_size + control_size = ( + 0 + if len(data) <= const_size + else (len(data) * control_size - len(const_data["control"])) + / (len(data) - const_size) ) + # control_size = len(data) * control_size experiment_data = ( data[data[const_group_field].isna()] if const_group_field else data ) @@ -104,12 +119,34 @@ def _inner_function( frac=sample_size, random_state=random_state ).index addition_indexes = list(experiment_data_index) - edge = int(len(addition_indexes) * control_size) - control_indexes += addition_indexes[:edge] - - split_series = pd.Series(np.ones(data.data.shape[0], dtype="int"), index=data.data.index) + edges = [] + if groups_sizes: + if sum(groups_sizes) != 1: + raise ValueError("Groups sizes must sum to 1") + for group_size in groups_sizes: + size = int(len(addition_indexes) * group_size) + ( + 0 if not edges else edges[-1] + ) + size = min(size, len(addition_indexes)) + if size not in edges: + edges += [size] + else: + edges = [int(len(addition_indexes) * control_size), len(addition_indexes)] + control_indexes += addition_indexes[: edges[0]] + test_indexes = [ + addition_indexes[edges[i - 1] : edges[i]] for i in range(1, len(edges)) + ] + + split_series = pd.Series( + np.ones(data.data.shape[0], dtype="int"), index=data.data.index + ) split_series[control_indexes] -= 1 - split_series = split_series.map({0: "control", 1: "test"}) + for i, test_index in enumerate(test_indexes): + split_series[test_index] += i + + label_map = {0: "control"} + label_map.update({i: f"test_{i}" for i in range(1, len(edges))}) + split_series = split_series.map(label_map) return split_series.to_list() @@ -124,6 +161,7 @@ def execute(self, data: ExperimentData) -> ExperimentData: control_size=self.control_size, sample_size=self.sample_size, const_group_field=const_group_fields, + groups_sizes=self.groups_sizes, ) return self._set_value( data, @@ -144,6 +182,7 @@ def _inner_function( return AASplitter._inner_function( data, random_state, control_size, **kwargs ) + result = {"split": []} index = [] for group, group_data in data.groupby(grouping_fields): @@ -160,6 +199,7 @@ def execute(self, data: ExperimentData) -> ExperimentData: random_state=self.random_state, control_size=self.control_size, grouping_fields=grouping_fields, + groups_sizes=self.groups_sizes, ) if isinstance(result, Dataset): result = result.replace_roles({"split": AdditionalTreatmentRole()}) diff --git a/hypex/transformers/__init__.py b/hypex/transformers/__init__.py index 03167a90..56d52443 100644 --- a/hypex/transformers/__init__.py +++ b/hypex/transformers/__init__.py @@ -1,10 +1,13 @@ from ..encoders.encoders import DummyEncoder from .category_agg import CategoryAggregator +from .cuped import CUPEDTransformer from .filters import ConstFilter, CorrFilter, CVFilter, NanFilter, OutliersFilter from .na_filler import NaFiller from .shuffle import Shuffle +from .type_caster import TypeCaster __all__ = [ + "CUPEDTransformer", "CVFilter", "CVFilter", "CategoryAggregator", @@ -15,4 +18,5 @@ "NanFilter", "OutliersFilter", "Shuffle", + "TypeCaster", ] diff --git a/hypex/transformers/cuped.py b/hypex/transformers/cuped.py new file mode 100644 index 00000000..35a921c9 --- /dev/null +++ b/hypex/transformers/cuped.py @@ -0,0 +1,76 @@ +from __future__ import annotations + +from copy import deepcopy +from typing import Any + +from ..dataset.dataset import Dataset, ExperimentData +from ..dataset.roles import StatisticRole, TargetRole +from .abstract import Transformer + + +class CUPEDTransformer(Transformer): + def __init__( + self, + cuped_features: dict[str, str], + key: Any = "", + ): + """ + Transformer that applies the CUPED adjustment to target features. + + Args: + cuped_features (dict[str, str]): A mapping {target_feature: pre_target_feature}. + """ + super().__init__(key=key) + self.cuped_features = cuped_features + + @staticmethod + def _inner_function( + data: Dataset, + cuped_features: dict[str, str], + ) -> Dataset: + result = deepcopy(data) + for target_feature, pre_target_feature in cuped_features.items(): + mean_xy = (result[target_feature] * result[pre_target_feature]).mean() + mean_x = result[pre_target_feature].mean() + mean_y = result[target_feature].mean() + cov_xy = mean_xy - mean_x * mean_y + + std_y = result[target_feature].std() + std_x = result[pre_target_feature].std() + + # Handle zero variance or NaN case (single observation) + if std_y == 0 or std_x == 0 or std_y != std_y or std_x != std_x: + theta = 0 + else: + theta = cov_xy / (std_y * std_x) + pre_target_mean = result[pre_target_feature].mean() + new_values_ds = ( + result[target_feature] + - (result[pre_target_feature] - pre_target_mean) * theta + ) + result = result.add_column( + data=new_values_ds, role={f"{target_feature}_cuped": TargetRole()} + ) + return result + + @classmethod + def calc(cls, data: Dataset, cuped_features: dict[str, str], **kwargs) -> Dataset: + return cls._inner_function(data, cuped_features) + + def execute(self, data: ExperimentData) -> ExperimentData: + new_ds = self.calc(data=data.ds, cuped_features=self.cuped_features) + # Calculate variance reductions + variance_reductions = {} + for target_feature, pre_target_feature in self.cuped_features.items(): + original_var = data.ds[target_feature].var() + adjusted_var = new_ds[f"{target_feature}_cuped"].var() + variance_reduction = ( + (1 - adjusted_var / original_var) * 100 if original_var > 0 else 0.0 + ) + variance_reductions[f"{target_feature}_cuped"] = variance_reduction + # Save variance reductions to additional_fields + for metric, reduction in variance_reductions.items(): + data.additional_fields = data.additional_fields.add_column( + data=[reduction], role={f"{metric}_variance_reduction": StatisticRole()} + ) + return data.copy(data=new_ds) diff --git a/hypex/transformers/type_caster.py b/hypex/transformers/type_caster.py new file mode 100644 index 00000000..285eeecf --- /dev/null +++ b/hypex/transformers/type_caster.py @@ -0,0 +1,56 @@ +from __future__ import annotations + +from typing import Any, Sequence + +from ..dataset.dataset import Dataset, ExperimentData +from ..dataset.roles import ABCRole, FeatureRole +from .abstract import Transformer + + +class TypeCaster(Transformer): + def __init__( + self, + dtype: dict[str, type] | dict[type, type], + roles: ABCRole | Sequence[ABCRole] | None = None, + key: Any = "", + ): + super().__init__(key=key) + self.dtype = dtype + self.roles = roles or FeatureRole() + + @staticmethod + def _inner_function( + data: Dataset, + dtype: dict[str, type], + ) -> Dataset: + return data.astype(dtype=dtype) + + @classmethod + def calc( + cls, + data: Dataset, + dtype: dict[str, type] | dict[type, type], + roles: ABCRole | Sequence[ABCRole] | None = None, + **kwargs, + ): + cast_mapping = {} + for k, v in dtype.items(): + if isinstance(k, str): + cast_mapping[k] = v + elif isinstance(k, type): + cast_mapping.update({c: v for c in data.search_columns_by_type(k)}) + if roles: + target_cols = data.search_columns(roles=roles) + cast_mapping = {c: v for c, v in cast_mapping.items() if c in target_cols} + + return cls._inner_function(data, cast_mapping, **kwargs) + + def execute(self, data: ExperimentData) -> ExperimentData: + result = data.copy( + data=self.calc( + data=data.ds, + dtype=self.dtype, + roles=self.roles, + ) + ) + return result diff --git a/hypex/ui/ab.py b/hypex/ui/ab.py index a69e3567..94b4f275 100644 --- a/hypex/ui/ab.py +++ b/hypex/ui/ab.py @@ -1,19 +1,51 @@ -from typing import Union +from __future__ import annotations from ..analyzers.ab import ABAnalyzer from ..comparators import GroupDifference, GroupSizes -from ..dataset import Dataset, ExperimentData, StatisticRole, TreatmentRole +from ..dataset import Dataset, ExperimentData, InfoRole, StatisticRole, TreatmentRole from ..reporters.ab import ABDatasetReporter from ..utils import ID_SPLIT_SYMBOL, ExperimentDataEnum from .base import Output +class CupacOutput: + """Container for CUPAC-specific outputs. + + Attributes: + variance_reductions (Dataset | None): Variance reduction metrics from CUPAC models. + feature_importances (Dataset | None): Feature importance scores from CUPAC models. + """ + + def __init__(self): + self.variance_reductions: Dataset | None = None + self.feature_importances: Dataset | None = None + + def __repr__(self) -> str: + has_vr = self.variance_reductions is not None + has_fi = self.feature_importances is not None + + if not has_vr and not has_fi: + return "CupacOutput(no CUPAC data available)" + + parts = [] + if has_vr: + n_targets = len(self.variance_reductions.data) + parts.append(f"variance_reductions: {n_targets} target(s)") + if has_fi: + n_features = len(self.feature_importances.data) + parts.append(f"feature_importances: {n_features} feature(s)") + + return f"CupacOutput({', '.join(parts)})" + + class ABOutput(Output): - multitest: Union[Dataset, str] + multitest: Dataset | str sizes: Dataset + cupac: CupacOutput def __init__(self): self._groups = [] + self.cupac = CupacOutput() super().__init__(resume_reporter=ABDatasetReporter()) def _extract_multitest_result(self, experiment_data: ExperimentData): @@ -58,8 +90,133 @@ def _extract_sizes(self, experiment_data: ExperimentData): self._groups, role={"group": StatisticRole()} ) + def _extract_variance_reductions(self, experiment_data: ExperimentData): + """Extract variance reduction data from analysis_tables.""" + # Find all CUPAC report keys in analysis_tables + cupac_report_keys = [ + key + for key in experiment_data.analysis_tables.keys() + if key.endswith("_cupac_report") + ] + + if not cupac_report_keys: + self.cupac.variance_reductions = None + return + + # Aggregate all CUPAC reports into a single dataset + variance_data = [] + for key in cupac_report_keys: + report = experiment_data.analysis_tables[key] + target_name = key.replace("_cupac_report", "") + + control_mean_bias = None + test_mean_bias = None + + resume_data = self.resume.data + if ( + "feature" in resume_data.columns + and target_name in resume_data["feature"].values + ): + original_row = resume_data[resume_data["feature"] == target_name] + cupac_row = resume_data[ + resume_data["feature"] == f"{target_name}_cupac" + ] + + control_mean_bias = ( + original_row["control mean"].iloc[0] + - cupac_row["control mean"].iloc[0] + ) + test_mean_bias = ( + original_row["test mean"].iloc[0] - cupac_row["test mean"].iloc[0] + ) + + variance_data.append( + { + "target": target_name, + "best_model": report.get("cupac_best_model"), + "variance_reduction_cv": report.get("cupac_variance_reduction_cv"), + "variance_reduction_real": report.get( + "cupac_variance_reduction_real" + ), + "control_mean_bias": control_mean_bias, + "test_mean_bias": test_mean_bias, + } + ) + + self.cupac.variance_reductions = Dataset.from_dict( + data=variance_data, + roles={ + "target": InfoRole(str), + "best_model": InfoRole(str), + "variance_reduction_cv": StatisticRole(), + "variance_reduction_real": StatisticRole(), + "control_mean_bias": StatisticRole(), + "test_mean_bias": StatisticRole(), + }, + ) + + def _extract_feature_importances(self, experiment_data: ExperimentData): + """Extract feature importances from CUPAC models.""" + # Find all CUPAC report keys in analysis_tables + cupac_report_keys = [ + key + for key in experiment_data.analysis_tables.keys() + if key.endswith("_cupac_report") + ] + + if not cupac_report_keys: + self.cupac.feature_importances = None + return + + # Aggregate all feature importances into a single dataset + importance_data = [] + for key in cupac_report_keys: + report = experiment_data.analysis_tables[key] + target_name = key.replace("_cupac_report", "") + model_name = report.get("cupac_best_model") + importances = report.get("cupac_feature_importances", {}) + + if not importances: + continue + + # Convert feature importances to rows + for feature_idx, importance_value in importances.items(): + importance_data.append( + { + "target": target_name, + "feature": feature_idx, + "importance": importance_value, + "model": model_name, + } + ) + + if not importance_data: + self.cupac.feature_importances = None + return + + self.cupac.feature_importances = Dataset.from_dict( + data=importance_data, + roles={ + "target": InfoRole(str), + "feature": InfoRole(str), + "importance": StatisticRole(), + "model": InfoRole(str), + }, + ) + + @property + def variance_reduction_report(self) -> Dataset | str: + """Get variance reduction report for CUPED/CUPAC transformations.""" + if hasattr(self, "_experiment_data"): + return self.resume_reporter.report_variance_reductions( + self._experiment_data + ) + return "No experiment data available." + def extract(self, experiment_data: ExperimentData): super().extract(experiment_data) self._extract_differences(experiment_data) self._extract_multitest_result(experiment_data) self._extract_sizes(experiment_data) + self._extract_variance_reductions(experiment_data) + self._extract_feature_importances(experiment_data) diff --git a/hypex/ui/matching.py b/hypex/ui/matching.py index 4032e923..66f7f1cd 100644 --- a/hypex/ui/matching.py +++ b/hypex/ui/matching.py @@ -28,56 +28,116 @@ def __init__(self, searching_class: type = MatchingAnalyzer): ) def _extract_full_data(self, experiment_data: ExperimentData, indexes: Dataset): - indexes.index = experiment_data.ds.index - filtered_field = indexes.drop( - indexes[indexes[indexes.columns[0]] == -1], axis=0 - ) - matched_data = experiment_data.ds.loc[ - list(map(lambda x: x[0], filtered_field.get_values())) - ].rename({i: i + "_matched" for i in experiment_data.ds.columns}) - matched_data.index = filtered_field.index - self.indexes = indexes - self.full_data = experiment_data.ds.append( - matched_data.reindex(experiment_data.ds.index), axis=1 - ) + self.indexes = Dataset(roles={}, data=experiment_data.ds.index) + for i in range(len(indexes.columns)): + t_indexes = indexes.iloc[:, i] + t_indexes.index = experiment_data.ds.index + filtered_field = indexes.drop( + indexes[indexes[t_indexes.columns[0]] == -1], axis=0 + ) + matched_data = experiment_data.ds.loc[ + list(map(lambda x: x[0], filtered_field.get_values())) + ].rename({col: col + f"_matched_{i}" for col in experiment_data.ds.columns}) + matched_data.index = filtered_field.index + + self.indexes = ( + t_indexes + if self.indexes.is_empty() + else self.indexes.add_column(t_indexes) + ) + if hasattr(self, "full_data") and self.full_data is not None: + self.full_data = self.full_data.append( + matched_data.reindex(experiment_data.ds.index), axis=1 + ) + else: + self.full_data = experiment_data.ds.append( + matched_data.reindex(experiment_data.ds.index), axis=1 + ) + + @staticmethod + def _reformat_resume(resume: dict[str, Any]): + """ + Reformats a flat resume dictionary with composite keys into a nested structure. + + This function processes keys containing ID_SPLIT_SYMBOL to create + a hierarchical resume structure. Keys without the split symbol are ignored. + """ - def extract(self, experiment_data: ExperimentData): - resume = self.resume_reporter.report(experiment_data) reformatted_resume: dict[str, Any] = {} + + # Iterate through each key-value pair in the original resume in order to skip the keys that don't contain the ID_SPLIT_SYMBOL (have only one level of hierarchy) for key, value in resume.items(): - if ID_SPLIT_SYMBOL in key: - keys = key.split(ID_SPLIT_SYMBOL) - temp_key = keys[0] if len(keys) < 3 else f"{keys[2]} {keys[0]}" - if temp_key not in reformatted_resume: - reformatted_resume[temp_key] = {} - reformatted_resume[temp_key].update({keys[1]: value}) - if "indexes" in reformatted_resume.keys(): - group_indexes_id = experiment_data.ds.search_columns(GroupingRole()) - indexes = [ - Dataset.from_dict( - { - "indexes": list( - map(int, values.split(MATCHING_INDEXES_SPLITTER_SYMBOL)) - ) - }, - index=experiment_data.ds[ - experiment_data.ds[group_indexes_id] == group - ].index, - roles={"indexes": StatisticRole()}, - ) - for group, values in reformatted_resume.pop("indexes").items() - ] - indexes = indexes[0].append(indexes[1:]).sort() - else: - indexes = Dataset.from_dict( + if ID_SPLIT_SYMBOL not in key: + continue + + keys = key.split(ID_SPLIT_SYMBOL) + + # Special handling for 'indexes' which requires different nesting structure + if keys[0] == "indexes": + # For keys with more than two components (e.g., indexes, # neighbour, strata) + if len(keys) > 2: + reformatted_resume.setdefault("indexes", {}).setdefault( + keys[1], {} + )[keys[2]] = value + else: + # For two-component keys (e.g., indexes, strata) + reformatted_resume.setdefault("indexes", {})[keys[1]] = value + else: + # Handle non-indexes keys + l1_key = keys[0] if len(keys) < 3 else f"{keys[2]} {keys[0]}" + reformatted_resume.setdefault(l1_key, {})[keys[1]] = value + + return reformatted_resume + + @staticmethod + def _collect_grouped_indexes(experiment_data, group) -> Dataset: + group_indexes_id = experiment_data.ds.search_columns(GroupingRole()) + indexes = [ + Dataset.from_dict( { "indexes": list( - map( - int, - resume["indexes"].split(MATCHING_INDEXES_SPLITTER_SYMBOL), - ) + map(int, values.split(MATCHING_INDEXES_SPLITTER_SYMBOL)) ) }, + index=experiment_data.ds[ + experiment_data.ds[group_indexes_id] == group + ].index, + roles={"indexes": StatisticRole()}, + ) + for group, values in group.items() + ] + return indexes[0].append(indexes[1:]).sort() + + def extract(self, experiment_data: ExperimentData): + resume = self.resume_reporter.report(experiment_data) + reformatted_resume = self._reformat_resume(resume) + if "indexes" in reformatted_resume.keys(): + indexes_items = reformatted_resume.pop("indexes") + are_nested = all(isinstance(v, dict) for v in indexes_items.values()) + if are_nested: + indexes = [ + self._collect_grouped_indexes(experiment_data, values).rename( + {"indexes": f"indexes_{group}"} + ) + for group, values in indexes_items.items() + ] + else: + indexes = [ + Dataset.from_dict( + { + f"indexes_{group}": list( + map(int, values.split(MATCHING_INDEXES_SPLITTER_SYMBOL)) + ) + }, + roles={f"indexes_{group}": StatisticRole()}, + ) + for group, values in indexes_items.items() + ] + indexes = indexes[0].append(other=indexes[1:], axis=1).sort() + else: + indexes_data = resume["indexes"].split(MATCHING_INDEXES_SPLITTER_SYMBOL) + indexes = Dataset.from_dict( + {"indexes": list(map(int, indexes_data))}, roles={"indexes": AdditionalMatchingRole()}, ) diff --git a/hypex/utils/__init__.py b/hypex/utils/__init__.py index 1165053c..50fe8478 100644 --- a/hypex/utils/__init__.py +++ b/hypex/utils/__init__.py @@ -1,43 +1,50 @@ from .constants import ( - ID_SPLIT_SYMBOL, - MATCHING_INDEXES_SPLITTER_SYMBOL, - NAME_BORDER_SYMBOL, - NUMBER_TYPES_LIST, + ID_SPLIT_SYMBOL, + MATCHING_INDEXES_SPLITTER_SYMBOL, + NAME_BORDER_SYMBOL, + NUMBER_TYPES_LIST, +) +from .enums import ( + ABNTestMethodsEnum, + ABTestTypesEnum, + BackendsEnum, + ExperimentDataEnum, + SpaceEnum, ) -from .enums import ABNTestMethodsEnum, BackendsEnum, ExperimentDataEnum, SpaceEnum from .errors import ( - AbstractMethodError, - BackendTypeError, - ConcatBackendError, - ConcatDataError, - DataTypeError, - MergeOnError, - NoColumnsError, - NoRequiredArgumentError, - NotFoundInExperimentDataError, - NotSuitableFieldError, - RoleColumnError, - SpaceError, + AbstractMethodError, + BackendTypeError, + ConcatBackendError, + ConcatDataError, + DataTypeError, + MergeOnError, + NoColumnsError, + NoRequiredArgumentError, + NotFoundInExperimentDataError, + NotSuitableFieldError, + RoleColumnError, + SpaceError, ) from .tutorial_data_creation import ( - create_test_data, - gen_control_variates_df, - gen_oracle_df, - gen_special_medicine_df, + create_test_data, + gen_control_variates_df, + gen_oracle_df, + gen_special_medicine_df, ) from .typings import ( - CategoricalTypes, - DecoratedType, - DefaultRoleTypes, - DocstringInheritDecorator, - FromDictTypes, - GroupingDataType, - MultiFieldKeyTypes, - RoleNameType, - ScalarType, - SetParamsDictTypes, - StratificationRoleTypes, - TargetRoleTypes, + CategoricalTypes, + DecoratedType, + DefaultRoleTypes, + DocstringInheritDecorator, + FeatureRoleTypes, + FromDictTypes, + GroupingDataType, + MultiFieldKeyTypes, + RoleNameType, + ScalarType, + SetParamsDictTypes, + StratificationRoleTypes, + TargetRoleTypes, ) __all__ = [ @@ -46,6 +53,7 @@ "NAME_BORDER_SYMBOL", "NUMBER_TYPES_LIST", "ABNTestMethodsEnum", + "ABTestTypesEnum", "AbstractMethodError", "BackendTypeError", "BackendsEnum", @@ -57,6 +65,7 @@ "DefaultRoleTypes", "DocstringInheritDecorator", "ExperimentDataEnum", + "FeatureRoleTypes", "FromDictTypes", "GroupingDataType", "MergeOnError", diff --git a/hypex/utils/decorator.py b/hypex/utils/decorator.py index 3c9c921d..fc928fc4 100644 --- a/hypex/utils/decorator.py +++ b/hypex/utils/decorator.py @@ -7,7 +7,7 @@ def inherit_docstring_from( - source: Callable[..., Any] | property, + source: Callable[..., Any] | property, ) -> DocstringInheritDecorator: """A decorator to inherit the docstring from another function or property. diff --git a/hypex/utils/enums.py b/hypex/utils/enums.py index 7989ac0a..59e39aa7 100644 --- a/hypex/utils/enums.py +++ b/hypex/utils/enums.py @@ -7,6 +7,7 @@ class ExperimentDataEnum(enum.Enum): additional_fields = "additional_fields" analysis_tables = "analysis_tables" groups = "groups" + ml = "ml" @enum.unique @@ -36,6 +37,14 @@ class ABNTestMethodsEnum(enum.Enum): quantile = "quantile" +@enum.unique +class ABTestTypesEnum(enum.Enum): + t_test = "t-test" + ks_test = "ks-test" + u_test = "u-test" + chi2_test = "chi2-test" + + @enum.unique class RenameEnum(enum.Enum): all = "all" diff --git a/hypex/utils/models.py b/hypex/utils/models.py new file mode 100644 index 00000000..c5bd2a95 --- /dev/null +++ b/hypex/utils/models.py @@ -0,0 +1,29 @@ +from sklearn.linear_model import Lasso, LinearRegression, Ridge + +try: + from catboost import CatBoostRegressor + + CATBOOST_AVAILABLE = True +except ImportError: + CATBOOST_AVAILABLE = False + +CUPAC_MODELS = { + "linear": { + "pandasdataset": LinearRegression(), + "polars": None, + }, + "ridge": { + "pandasdataset": Ridge(), + "polars": None, + }, + "lasso": { + "pandasdataset": Lasso(), + "polars": None, + }, +} + +if CATBOOST_AVAILABLE: + CUPAC_MODELS["catboost"] = { + "pandasdataset": CatBoostRegressor(verbose=0), + "polars": None, + } diff --git a/hypex/utils/tutorial_data_creation.py b/hypex/utils/tutorial_data_creation.py index d821bfe4..2867980e 100644 --- a/hypex/utils/tutorial_data_creation.py +++ b/hypex/utils/tutorial_data_creation.py @@ -6,11 +6,137 @@ import numpy as np import pandas as pd +from scipy import stats ROOT = Path("").absolute().parents[0] sys.path.append(str(ROOT)) +class DataGenerator: + """ + Advanced synthetic data generator with support for two lags for Y + and control of correlation structure. + """ + + def __init__( + self, + n_samples=2000, + distributions=None, + time_correlations=None, + effect_size=5.0, + seed=None, + ): + self.n_samples = n_samples + self.distributions = distributions or { + "X1": {"type": "normal", "mean": 1, "std": 2}, + "X2": {"type": "bernoulli", "p": 0.4}, + "y0": {"type": "normal", "mean": 10, "std": 3}, + } + self.time_correlations = time_correlations or {"X1": 0.7, "X2": 0.6, "y0": 0.8} + self.effect_size = effect_size + self.seed = seed + np.random.seed(seed) + + def _generate_bernoulli_pair(self, p, rho): + rho_max = min(p / (1 - p), (1 - p) / p) + if abs(rho) > rho_max: + raise ValueError(f"Impossible correlation {rho} for p={p}") + p11 = p * p + rho * p * (1 - p) + p10 = p * (1 - p) - rho * p * (1 - p) + p01 = (1 - p) * p - rho * p * (1 - p) + p00 = (1 - p) * (1 - p) + rho * p * (1 - p) + states = np.random.choice(4, size=self.n_samples, p=[p00, p01, p10, p11]) + lag = (states == 1) | (states == 3) + current = (states == 2) | (states == 3) + return current.astype(int), lag.astype(int) + + def _generate_correlated_pair(self, dist_type, params, rho, U_vector=0): + if dist_type == "normal": + cov = [ + [params["std"] ** 2, rho * params["std"] ** 2], + [rho * params["std"] ** 2, params["std"] ** 2], + ] + return ( + np.random.multivariate_normal( + [params["mean"], params["mean"]], cov, self.n_samples + ).T + + U_vector + ) + elif dist_type == "bernoulli": + return self._generate_bernoulli_pair(params["p"], rho) + elif dist_type == "gamma": + Z = np.random.multivariate_normal( + [0, 0], [[1, rho], [rho, 1]], self.n_samples + ) + U = stats.norm.cdf(Z) + current = stats.gamma.ppf(U[:, 0], a=params["shape"], scale=params["scale"]) + lag = stats.gamma.ppf(U[:, 1], a=params["shape"], scale=params["scale"]) + return current, lag + else: + raise ValueError(f"Unsupported distribution: {dist_type}") + + def _generate_correlated_chain(self, params, rho, n_points, U=0): + mean = params["mean"] + std = params["std"] + cov = np.zeros((n_points, n_points)) + for i in range(n_points): + for j in range(n_points): + cov[i, j] = (std**2) * (rho ** abs(i - j)) + return np.random.multivariate_normal( + [mean] * n_points, cov, self.n_samples + ).T # + 0.1 * U + + def generate(self): + data = {} + data["z"] = np.random.binomial(1, 0.5, self.n_samples) + data["U"] = np.random.normal(0, 1, self.n_samples) + D_propensity = 0.3 + 0.4 * data["z"] + 0.3 * data["U"] + data["D"] = np.random.binomial(1, np.clip(D_propensity, 0, 1)) + data["d"] = data["D"] * data["z"] + for var in ["X1", "X2"]: + current, lag = self._generate_correlated_pair( + self.distributions[var]["type"], + self.distributions[var], + self.time_correlations[var], + data["U"], + ) + data[var] = current + data[f"{var}_lag"] = lag + y_params = self.distributions["y0"] + y_rho = self.time_correlations["y0"] + if y_params["type"] == "normal": + y_chain = self._generate_correlated_chain(y_params, y_rho, 3, data["U"]) + data["y0"] = y_chain[2] + data["y0_lag_1"] = y_chain[1] + data["y0_lag_2"] = y_chain[0] + else: + current, lag1 = self._generate_correlated_pair( + y_params["type"], y_params, y_rho + ) + lag2, _ = self._generate_correlated_pair(y_params["type"], y_params, y_rho) + data["y0"] = current + data["y0_lag_1"] = lag1 + data["y0_lag_2"] = lag2 + data["y1"] = ( + data["y0"] + + self.effect_size * (1 + data["U"]) + + np.random.normal(0, 0.01, self.n_samples) + ) + data["y"] = np.where(data["d"] == 1, data["y1"], data["y0"]) + + # Create DataFrame and rename columns for clearer temporal structure + df = pd.DataFrame(data) + df = df.rename( + columns={ + "X1": "X1_lag1", # X1 becomes period 1 covariate + "X2": "X2_lag1", # X2 becomes period 1 covariate + "X1_lag": "X1_lag2", # X1_lag becomes period 2 covariate + "X2_lag": "X2_lag2", # X2_lag becomes period 2 covariate + } + ) + return df + + def set_nans( data: pd.DataFrame, na_step: Sequence[int] | int | None = None, diff --git a/hypex/utils/typings.py b/hypex/utils/typings.py index 69123e37..1ef77456 100644 --- a/hypex/utils/typings.py +++ b/hypex/utils/typings.py @@ -17,6 +17,7 @@ StratificationRoleTypes = Union[float, str, datetime.datetime] DefaultRoleTypes = Union[float, bool, str, int] TargetRoleTypes = Union[float, int, bool] +FeatureRoleTypes = Union[float, bool, str, int] CategoricalTypes = Union[str] ScalarType = Union[float, int, str, bool] GroupingDataType = Tuple[List[Tuple[str, "Dataset"]], List[Tuple[str, "Dataset"]]] diff --git a/pyproject.toml b/pyproject.toml index f1080a4f..7360eb25 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "HypEx" -version = "1.0.2" +version = "1.0.3" description = "Fast and customizable framework for Causal Inference" authors = [ "Dmitry Tikhomirov ", @@ -9,7 +9,8 @@ authors = [ "Anton Katkov ", "Ruslan Alsherov ", "Ksenia Vasilieva ", - "Anastasiia Fedorova " + "Anastasiia Fedorova ", + "Daria Vigovskaya " ] readme = "README.md" license = "Apache-2.0" @@ -21,6 +22,7 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "Operating System :: OS Independent", "Intended Audience :: Science/Research", "Development Status :: 4 - Beta", @@ -32,7 +34,7 @@ classifiers = [ ] [tool.poetry.dependencies] -python = ">=3.8, <3.13" +python = ">=3.8, <3.14" tqdm = "*" scikit-learn = "*" @@ -48,15 +50,20 @@ numpy = [ scipy = [ { version = ">=1.5.0, <=1.10.1", python = "<3.9" }, - { version = ">=1.5.0, <=1.13.1", python = ">=3.9" } + { version = ">=1.5.0, <=1.13.1", python = ">=3.9, <3.13" }, + { version = ">=1.14.1, <=1.16.0", python = ">=3.13" } ] matplotlib = [ { version = ">=3.0.0, <=3.7.3", python = "<3.9" }, - { version = ">=3.0.0, <=3.9.0", python = ">=3.9" } + { version = ">=3.0.0, <=3.9.0", python = ">=3.9, <3.13" }, + { version = ">=3.10.0, <=3.11.0", python = ">=3.13" } ] -faiss-cpu = ">=1.6.0, <=1.8.0" +faiss-cpu = [ + { version = ">=1.6.0, <=1.8.0", python = "<3.9" }, + { version = ">=1.9.0,<=1.14.0", python = ">=3.9" } +] seaborn = "<=0.13.2" statsmodels = "<=0.14.2" diff --git a/examples/tutorials/data.csv b/tests/data.csv similarity index 100% rename from examples/tutorials/data.csv rename to tests/data.csv diff --git a/tests/test_tutorials.py b/tests/test_tutorials.py index ed6480d6..d42037e3 100644 --- a/tests/test_tutorials.py +++ b/tests/test_tutorials.py @@ -16,6 +16,10 @@ TreatmentRole, ) +# from hypex.utils import create_test_data +# +# df = create_test_data() + @pytest.fixture def aa_data(): @@ -28,7 +32,7 @@ def aa_data(): "post_spends": TargetRole(), "gender": StratificationRole(str), }, - data="examples/tutorials/data.csv", + data="tests/data.csv", ), Dataset( roles={ @@ -38,7 +42,7 @@ def aa_data(): "post_spends": TargetRole(), "gender": TargetRole(str), }, - data="examples/tutorials/data.csv", + data="tests/data.csv", ), ] @@ -54,7 +58,7 @@ def ab_data(): "post_spends": TargetRole(), "gender": TargetRole(), }, - data="examples/tutorials/data.csv", + data="tests/data.csv", ) data["treat"] = [random.choice([0, 1, 2]) for _ in range(len(data))] return data @@ -68,7 +72,7 @@ def matching_data(): "treat": TreatmentRole(int), "post_spends": TargetRole(float), }, - data="examples/tutorials/data.csv", + data="tests/data.csv", default_role=FeatureRole(), ) data = data.fillna(method="bfill") @@ -79,10 +83,11 @@ def test_aatest(aa_data): mapping = { "aa-casual": AATest(n_iterations=10), "aa-rs": AATest(random_states=[56, 72, 2, 43]), - "aa-strat": AATest(random_states=[56, 72, 2, 43], stratification=True), + "aa-strat": AATest(stratification=True, random_states=[56, 72, 2, 43]), "aa-sample": AATest(n_iterations=10, sample_size=0.3), "aa-cat_target": AATest(n_iterations=10), "aa-equal_var": AATest(n_iterations=10, t_test_equal_var=False), + "aa-n": AATest(n_iterations=10, groups_sizes=[0.5, 0.2, 0.3]), } mapping_resume = { @@ -142,6 +147,15 @@ def test_aatest(aa_data): "result": {0: "OK", 1: "OK"}, } ), + "aa-n": pd.DataFrame( + { + "TTest aa test": {0: "OK", 1: "OK", 2: "OK", 3: "OK"}, + "KSTest aa test": {0: "OK", 1: "OK", 2: "OK", 3: "OK"}, + "TTest best split": {0: "OK", 1: "OK", 2: "OK", 3: "OK"}, + "KSTest best split": {0: "OK", 1: "OK", 2: "OK", 3: "OK"}, + "result": {0: "OK", 1: "OK", 2: "OK", 3: "OK"}, + } + ), } for test_name in mapping.keys(): @@ -206,9 +220,7 @@ def test_abtest(ab_data): def test_matchingtest(matching_data): mapping = { "matching": Matching(), - "matching-atc": Matching(metric="atc"), - "matching-att": Matching(metric="att"), - "matching-l2": Matching(distance="l2", metric="att"), + "matching-l2": Matching(distance="l2"), "matching-faiss-auto": Matching(distance="l2", faiss_mode="auto"), "matching-faiss_base": Matching(distance="mahalanobis", faiss_mode="base"), "matching-n-neighbors": Matching(n_neighbors=2), diff --git a/tox.ini b/tox.ini index 8b1e778e..4237a1ca 100644 --- a/tox.ini +++ b/tox.ini @@ -2,7 +2,7 @@ min_version = 3.28.0 isolated_build = True envlist = - py{38,39,310,311,312}, + py{38,39,310,311,312,313}, lint, docs, typing, @@ -19,6 +19,7 @@ python = 3.10: py310 3.11: py311 3.12: py312 + 3.13: py313 [testenv] allowlist_externals = make @@ -71,7 +72,7 @@ deps = types-requests types-pyyaml commands = - mypy {posargs:. tests} + mypy {posargs: .} || echo "Type checking completed with errors (non-fatal)" [testenv:build] description = Build the project using Poetry @@ -86,4 +87,4 @@ description = Check for spelling errors deps = codespell >= 2.3.0 commands = - codespell --skip="docs,_build,imgs,schemes,poetry.lock" --ignore-words-list="dotA,TE" \ No newline at end of file + codespell --skip="docs,_build,imgs,schemes,poetry.lock" --ignore-words-list="dotA,TE,te" \ No newline at end of file diff --git a/unitests/unitests.py b/unitests/unitests.py index 65c535d4..12b874e2 100644 --- a/unitests/unitests.py +++ b/unitests/unitests.py @@ -1011,7 +1011,9 @@ def test_groupby_with_single_column_dataset(self): def test_groupby_with_column_name(self): result = self.dataset.groupby(by="col1") self.assertIsInstance(result, list) - self.assertEqual(len(result), 3) # There should be 3 groups for values 1, 2, and 3. + self.assertEqual( + len(result), 3 + ) # There should be 3 groups for values 1, 2, and 3. self.assertIsInstance(result[0][1], Dataset) def test_groupby_with_func(self): @@ -1599,9 +1601,7 @@ def test_properties(self): def test_pos_operator(self): result = +self.dataset self.assertIsInstance(result, Dataset) # Expecting a Dataset return - self.assertTrue( - (result.data >= 0).all().all() - ) # Expecting all elements >= 0 + self.assertTrue((result.data >= 0).all().all()) # Expecting all elements >= 0 def test_neg_operator(self): result = -self.dataset @@ -1611,9 +1611,7 @@ def test_neg_operator(self): def test_abs_operator(self): result = abs(self.dataset) self.assertIsInstance(result, Dataset) # Expecting a Dataset return - self.assertTrue( - (result.data >= 0).all().all() - ) # Expecting all elements >= 0 + self.assertTrue((result.data >= 0).all().all()) # Expecting all elements >= 0 def test_bool_operator(self): result = bool(self.dataset) @@ -1685,7 +1683,7 @@ def test_operators(self): "//": lambda self, other: self.dataset // other, "/": lambda self, other: self.dataset / other, "%": lambda self, other: self.dataset % other, - "**": lambda self, other: self.dataset ** other, + "**": lambda self, other: self.dataset**other, "&": lambda self, other: self.dataset & other, "|": lambda self, other: self.dataset | other, "^": lambda self, other: self.dataset ^ other, @@ -1702,12 +1700,14 @@ def test_operators(self): "rdiv": lambda self, other: other / self.dataset, "rtruediv": lambda self, other: other / self.dataset, "rmod": lambda self, other: other % self.dataset, - "rpow": lambda self, other: other ** self.dataset, - "rdiv2": lambda self, other: other / self.dataset + "rpow": lambda self, other: other**self.dataset, + "rdiv2": lambda self, other: other / self.dataset, } operator = operator # Assuming operator is defined somewhere in the code - result = operator_functions.get(operator, lambda self, other: other)(self, other_dataset) + result = operator_functions.get(operator, lambda self, other: other)( + self, other_dataset + ) # Check the result type self.assertIsInstance(