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Merge pull request #673 from EducationalTestingService/release/2.5
Preparing for v2.5 release
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‎conda-recipe/README.md

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How to create and test conda package.
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### How to create and test conda package
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1. To create the SKLL conda package run:
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`conda build -c defaults -c conda-forge --numpy=1.17 .`
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2. Upload the package to anaconda.org using `anaconda upload --user ets <path>`.
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3. Test the package:
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`conda create -n foobar -c conda-forge -c ets skll=2.1`
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1. To create the SKLL conda package run: `conda build -c conda-forge .`
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2. This will create python 3.7, 3.8, and 3.9 packages for your native platform, e.g., `osx-64`.
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3. Convert these built packages for the other two platforms. For example, if you ran the above command on macOS, run `conda convert -p linux-64 -p win-64 <packages files>`, where `<packages_files>` are the package files that were created in step 2.
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4. Upload all 9 package files (3 Python versions x 3 platforms) to anaconda.org using `anaconda upload --user ets <path_to_files>`.
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5. Test the package: `conda create -n foobar -c conda-forge -c ets python=3.9 skll`. This should _always_ install the latest package.

‎conda-recipe/skll/conda_build_config.yaml

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python:
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- 3.7
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- 3.8
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- 3.9
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numpy: 1.20.0
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pin_run_as_build:
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numpy:
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max_pin: x
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ignore_version: numpy

‎conda-recipe/skll/meta.yaml

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package:
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name: skll
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version: 2.1
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version: 2.5
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source:
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path: ../../../skll
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build:
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number: 0
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noarch: python
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script:
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- cd $SRC_DIR
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- "{{ PYTHON }} -m pip install . --no-deps -vv"
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entry_points:
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- compute_eval_from_predictions = skll.utilities.compute_eval_from_predictions:main
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- filter_features = skll.utilities.filter_features:main
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- generate_predictions = skll.utilities.generate_predictions:main
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- join_features = skll.utilities.join_features:main
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- plot_learning_curves = skll.utilities.plot_learning_curves:main
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- print_model_weights = skll.utilities.print_model_weights:main
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- run_experiment = skll.utilities.run_experiment:main
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- skll_convert = skll.utilities.skll_convert:main
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- summarize_results = skll.utilities.summarize_results:main
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- compute_eval_from_predictions = skll.utilities.compute_eval_from_predictions:main
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- filter_features = skll.utilities.filter_features:main
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- generate_predictions = skll.utilities.generate_predictions:main
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- join_features = skll.utilities.join_features:main
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- plot_learning_curves = skll.utilities.plot_learning_curves:main
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- print_model_weights = skll.utilities.print_model_weights:main
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- run_experiment = skll.utilities.run_experiment:main
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- skll_convert = skll.utilities.skll_convert:main
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- summarize_results = skll.utilities.summarize_results:main
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- compute_eval_from_predictions = skll.utils.commandline.compute_eval_from_predictions:main
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- filter_features = skll.utils.commandline.filter_features:main
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- generate_predictions = skll.utils.commandline.generate_predictions:main
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- join_features = skll.utils.commandline.join_features:main
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- plot_learning_curves = skll.utils.commandline.plot_learning_curves:main
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- print_model_weights = skll.utils.commandline.print_model_weights:main
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- run_experiment = skll.utils.commandline.run_experiment:main
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- skll_convert = skll.utils.commandline.skll_convert:main
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- summarize_results = skll.utils.commandline.summarize_results:main
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- compute_eval_from_predictions = skll.utils.commandline.compute_eval_from_predictions:main
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- filter_features = skll.utils.commandline.filter_features:main
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- generate_predictions = skll.utils.commandline.generate_predictions:main
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- join_features = skll.utils.commandline.join_features:main
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- plot_learning_curves = skll.utils.commandline.plot_learning_curves:main
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- print_model_weights = skll.utils.commandline.print_model_weights:main
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- run_experiment = skll.utils.commandline.run_experiment:main
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- skll_convert = skll.utils.commandline.skll_convert:main
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- summarize_results = skll.utils.commandline.summarize_results:main
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requirements:
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build:
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- python
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- pip
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- setuptools
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- beautifulsoup4
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- joblib >=0.8
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- numpy
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- joblib
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- numpy {{ numpy }}
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- pandas
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- python >=3.6
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- ruamel.yaml
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- scikit-learn >=0.23.1,<=0.23.2
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- scikit-learn >=0.24.1,<=0.24.2
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- scipy
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- seaborn
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- setuptools
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- tabulate
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run:
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- python
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- beautifulsoup4
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- joblib >=0.8
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- joblib
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- numpy
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- pandas
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- python >=3.6
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- ruamel.yaml
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- scikit-learn >=0.23.1,<=0.23.2
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- seaborn
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- scikit-learn >=0.24.1,<=0.24.2
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- scipy
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- seaborn
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- tabulate
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test:
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about:
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home: http://github.com/EducationalTestingService/skll
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license: BSD-3-Clause
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license: BSD
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license_file: LICENSE.txt
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summary: SciKit-Learn Laboratory makes it easier to run machine learning experiments with scikit-learn.

‎doc/internal/release.rst

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e. update the README and this release documentation, if necessary.
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3. Run the following command in the ``conda-recipe`` directory to build the conda package::
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3. Build and upload the conda packages by following instructions in ``conda-recipe/README.md``.
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conda build -c conda-forge --numpy=1.17 .
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4. Build the PyPI source distribution using ``python setup.py sdist build``.
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4. Upload the package to anaconda.org using ``anaconda upload --user ets <package tarball>``. You will need to have the appropriate permissions for the ``ets`` organization.
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5. Upload the source distribution to TestPyPI using ``twine upload --repository testpypi dist/*``. You will need to have the ``twine`` package installed and set up your ``$HOME/.pypirc`` correctly. See details `here <https://packaging.python.org/guides/using-testpypi/>`__.
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5. Build the PyPI source distribution using ``python setup.py sdist build``.
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6. Test the conda package by creating a new environment on different platforms with this package installed and then running SKLL examples or tests from a SKLL working copy. If the package works, then move on to the next step. If it doesn't, figure out why and rebuild and re-upload the package.
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6. Upload the source distribution to TestPyPI using ``twine upload --repository testpypi dist/*``. You will need to have the ``twine`` package installed and set up your ``$HOME/.pypirc`` correctly. See details `here <https://packaging.python.org/guides/using-testpypi/>`__.
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7. Test the conda package by creating a new environment on different platforms with this package installed and then running SKLL examples or tests from a SKLL working copy. If the package works, then move on to the next step. If it doesn't, figure out why and rebuild and re-upload the package.
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8. Test the TestPyPI package by installing it as follows::
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7. Test the TestPyPI package by installing it as follows::
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pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple skll
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9. Then run some SKLL examples or tests from a SKLL working copy. If the TestPyPI package works, then move on to the next step. If it doesn't, figure out why and rebuild and re-upload the package.
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8. Then run some SKLL examples or tests from a SKLL working copy. If the TestPyPI package works, then move on to the next step. If it doesn't, figure out why and rebuild and re-upload the package.
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10. Create pull requests on the `skll-conda-tester <https://github.com/EducationalTestingService/skll-conda-tester/>`_ and `skll-pip-tester <https://github.com/EducationalTestingService/skll-pip-tester/>`_ repositories to test the conda and TestPyPI packages on Linux and Windows.
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9. Create pull requests on the `skll-conda-tester <https://github.com/EducationalTestingService/skll-conda-tester/>`_ and `skll-pip-tester <https://github.com/EducationalTestingService/skll-pip-tester/>`_ repositories to test the conda and TestPyPI packages on Linux and Windows.
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11. Draft a release on GitHub while the Linux and Windows package tester builds are running.
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10. Draft a release on GitHub while the Linux and Windows package tester builds are running.
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12. Once both builds have passed, make a pull request with the release branch to be merged into ``main`` and request code review.
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11. Once both builds have passed, make a pull request with the release branch to be merged into ``main`` and request code review.
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13. Once the build for the PR passes and the reviewers approve, merge the release branch into ``main``.
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12. Once the build for the PR passes and the reviewers approve, merge the release branch into ``main``.
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14. Upload source and wheel packages to PyPI using ``python setup.py sdist upload`` and ``python setup.py bdist_wheel upload``
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13. Upload source and wheel packages to PyPI using ``python setup.py sdist upload`` and ``python setup.py bdist_wheel upload``
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15. Make sure that the ReadTheDocs build for ``main`` passes.
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14. Make sure that the ReadTheDocs build for ``main`` passes.
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16. Tag the latest commit in ``main`` with the appropriate release tag and publish the release on GitHub.
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15. Tag the latest commit in ``main`` with the appropriate release tag and publish the release on GitHub.
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17. Send an email around at ETS announcing the release and the changes.
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16. Send an email around at ETS announcing the release and the changes.
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18. Post release announcement on Twitter/LinkedIn.
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17. Post release announcement on Twitter/LinkedIn.

‎setup.py

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'Operating System :: Unix',
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'Operating System :: MacOS',
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'Programming Language :: Python :: 3',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7',
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'Programming Language :: Python :: 3.8',
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'Programming Language :: Python :: 3.9',
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],
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zip_safe=False)

‎skll/version.py

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in one place. Based on the suggestion `here. <http://bit.ly/16LbuJF>`_
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:author: Dan Blanchard ([email protected])
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:author: Nitin Madnani ([email protected])
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:organization: ETS
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"""
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