8000 Merge pull request #3 from scikit-learn/master · arka204/scikit-learn@15a658f · GitHub
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Merge pull request #3 from scikit-learn/master
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‎README.rst

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@@ -51,6 +51,7 @@ scikit-learn requires:
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- NumPy (>= 1.13.3)
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- SciPy (>= 0.19.1)
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- joblib (>= 0.11)
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- threadpoolctl (>= 2.0.0)
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**Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.**
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scikit-learn 0.23 and later require Python 3.6 or newer.

‎azure-pipelines.yml

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- bash: sudo chown -R $USER $CONDA
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displayName: Take ownership of conda installation
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- bash: |
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set -ex
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conda create --name flake8_env --yes python=3.8
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conda activate flake8_env
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source activate flake8_env
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pip install flake8 mypy==0.770
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displayName: Install flake8
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- bash: |
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set -ex
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if [[ $BUILD_SOURCEVERSIONMESSAGE =~ \[lint\ skip\] ]]; then
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# skip linting
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echo "Skipping linting"
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exit 0
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else
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conda activate flake8_env
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source activate flake8_env
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./build_tools/circle/linting.sh
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fi
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displayName: Run linting
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- bash: |
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set -ex
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if [[ $BUILD_SOURCEVERSIONMESSAGE =~ \[lint\ skip\] ]]; then
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# skip linting
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echo "Skipping linting"
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exit 0
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else
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conda activate flake8_env
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source activate flake8_env
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mypy sklearn/ --ignore-missing-imports
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fi
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displayName: Run mypy

‎build_tools/circle/linting.sh

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@@ -161,3 +161,14 @@ then
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echo $bad_deprecation_property_order
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exit 1
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fi
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# Check for default doctest directives ELLIPSIS and NORMALIZE_WHITESPACE
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doctest_directive="$(git grep -nw -E "# doctest\: \+(ELLIPSIS|NORMALIZE_WHITESPACE)")"
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if [ ! -z "$doctest_directive" ]
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then
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echo "ELLIPSIS and NORMALIZE_WHITESPACE doctest directives are enabled by default, but were found in:"
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echo "$doctest_directive"
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exit 1
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fi

‎doc/conf.py

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# General information about the project.
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project = 'scikit-learn'
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copyright = '2007 - 2019, scikit-learn developers (BSD License)'
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copyright = '2007 - 2020, scikit-learn developers (BSD License)'
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# The version info for the project you're documenting, acts as replacement for
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# |version| and |release|, also used in various other places throughout the

‎doc/developers/advanced_installation.rst

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@@ -89,7 +89,8 @@ runtime:
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- Python (>= 3.6),
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- NumPy (>= 1.13.3),
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- SciPy (>= 0.19),
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- Joblib (>= 0.11).
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- Joblib (>= 0.11),
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- threadpoolctl (>= 2.0.0).
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Those dependencies are **automatically installed by pip** if they were missing
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when building scikit-learn from source.

‎doc/developers/contributing.rst

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@@ -22,7 +22,10 @@ See :ref:`new_contributors` to get started.
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We are a community based on openness and friendly, didactic,
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discussions.
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We aspire to treat everybody equally, and value their contributions.
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We aspire to treat everybody equally, and value their contributions. We
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are particularly seeking people from underrepresented backgrounds in Open
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Source Software and scikit-learn in particular to participate and
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contribute their expertise and experience.
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Decisions are made based on technical merit and consensus.
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‎doc/developers/develop.rst

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@@ -247,8 +247,9 @@ several internals of scikit-learn that you should be aware of in addition to
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the scikit-learn API outlined above. You can check whether your estimator
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adheres to the scikit-learn interface and standards by running
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:func:`~sklearn.utils.estimator_checks.check_estimator` on an instance. The
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:func:`~sklearn.utils.parametrize_with_checks` pytest decorator can also be
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used (see its docstring for details and possible interactions with `pytest`)::
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:func:`~sklearn.utils.estimator_checks.parametrize_with_checks` pytest
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decorator can also be used (see its docstring for details and possible
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interactions with `pytest`)::
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>>> from sklearn.utils.estimator_checks import check_estimator
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>>> from sklearn.svm import LinearSVC
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_xfail_checks (default=False)
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dictionary ``{check_name: reason}`` of common checks that will be marked
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:func:`~sklearn.utils.estimator_checks.parametrize_with_checks`. This tag
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currently has no effect on
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:func:`~sklearn.utils.estimator_checks.check_estimator`.
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:func:`~sklearn.utils.estimator_checks.parametrize_with_checks`. These
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checks will be simply ignored and not run by
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:func:`~sklearn.utils.estimator_checks.check_estimator`, but a
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`SkipTestWarning` will be raised.
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Don't use this unless there is a *very good* reason for your estimator
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not to pass the check.
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Also note that the usage of this tag is highly subject to change because

‎doc/modules/classes.rst

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.. autosummary::
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:toctree: generated/
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:template: class_without_init.rst
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:template: class.rst
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exceptions.ChangedBehaviorWarning
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exceptions.ConvergenceWarning
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Recently deprecated
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===================
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To be removed in 0.24
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To be removed in 0.25
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---------------------
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.. autosummary::
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:toctree: generated/
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:template: deprecated_function.rst
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model_selection.fit_grid_point
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utils.safe_indexing

‎doc/modules/compose.rst

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be applied to the target variable::
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>>> import numpy as np
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>>> from sklearn.datasets import load_boston
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>>> from sklearn.datasets import fetch_california_housing
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>>> from sklearn.compose import TransformedTargetRegressor
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>>> from sklearn.preprocessing import QuantileTransformer
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>>> from sklearn.linear_model import LinearRegression
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>>> from sklearn.model_selection import train_test_split
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>>> X, y = load_boston(return_X_y=True)
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>>> X, y = fetch_california_housing(return_X_y=True)
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>>> X, y = X[:2000, :], y[:2000] # select a subset of data
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>>> transformer = QuantileTransformer(output_distribution='normal')
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>>> regressor = LinearRegression()
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>>> regr = TransformedTargetRegressor(regressor=regressor,
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>>> regr.fit(X_train, y_train)
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TransformedTargetRegressor(...)
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>>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test)))
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R2 score: 0.67
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R2 score: 0.61
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>>> raw_target_regr = LinearRegression().fit(X_train, y_train)
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>>> print('R2 score: {0:.2f}'.format(raw_target_regr.score(X_test, y_test)))
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R2 score: 0.64
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R2 score: 0.59
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For simple transformations, instead of a Transformer object, a pair of
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functions can be passed, defining the transformation and its inverse mapping::
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>>> regr.fit(X_train, y_train)
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TransformedTargetRegressor(...)
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>>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test)))
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R2 score: 0.65
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R2 score: 0.51
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By default, the provided functions are checked at each fit to be the inverse of
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each other. However, it is possible to bypass this checking by setting
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>>> regr.fit(X_train, y_train)
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TransformedTargetRegressor(...)
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>>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test)))
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R2 score: -4.50
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R2 score: -1.57
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.. note::
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‎doc/modules/cross_validation.rst

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>>> y = np.array([1, 2, 3, 4, 5, 6])
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>>> tscv = TimeSeriesSplit(n_splits=3)
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>>> print(tscv)
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TimeSeriesSplit(max_train_size=None, n_splits=3)
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TimeSeriesSplit(gap=0, max_train_size=None, n_splits=3, test_size=None)
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>>> for train, test in tscv.split(X):
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... print("%s %s" % (train, test))
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