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‎.gitignore

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# files generated from a template
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sklearn/utils/_seq_dataset.pyx
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sklearn/utils/_seq_dataset.pxd
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sklearn/linear_model/sag_fast.pyx
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sklearn/linear_model/_sag_fast.pyx
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# deprecated paths
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# TODO: Remove in 0.24
@@ -130,6 +130,36 @@ sklearn/svm/libsvm.py
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sklearn/svm/libsvm_sparse.py
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sklearn/svm/liblinear.py
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sklearn/linear_model/base.py
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sklearn/linear_model/bayes.py
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sklearn/linear_model/cd_fast.py
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sklearn/linear_model/coordinate_descent.py
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sklearn/linear_model/huber.py
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sklearn/linear_model/least_angle.py
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sklearn/linear_model/logistic.py
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sklearn/linear_model/omp.py
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sklearn/linear_model/passive_aggressive.py
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sklearn/linear_model/perceptron.py
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sklearn/linear_model/ransac.py
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sklearn/linear_model/ridge.py
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sklearn/linear_model/sag.py
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sklearn/linear_model/sag_fast.py
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sklearn/linear_model/sgd_fast.py
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sklearn/linear_model/stochastic_gradient.py
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sklearn/linear_model/theil_sen.py
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sklearn/metrics/cluster/bicluster.py
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sklearn/metrics/cluster/supervised.py
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sklearn/metrics/cluster/unsupervised.py
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sklearn/metrics/cluster/expected_mutual_info_fast.py
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sklearn/metrics/base.py
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sklearn/metrics/classification.py
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sklearn/metrics/regression.py
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sklearn/metrics/ranking.py
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sklearn/metrics/pairwise_fast.py
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sklearn/metrics/scorer.py
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sklearn/inspection/partial_dependence.py
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sklearn/inspection/permutation_importance.py
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‎benchmarks/bench_rcv1_logreg_convergence.py

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from sklearn.linear_model import (LogisticRegression, SGDClassifier)
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from sklearn.datasets import fetch_rcv1
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from sklearn.linear_model.sag import get_auto_step_size
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from sklearn.linear_model._sag import get_auto_step_size
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try:
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import lightning.classification as lightning_clf

‎benchmarks/bench_sparsify.py

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from scipy.sparse.csr import csr_matrix
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import numpy as np
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from sklearn.linear_model.stochastic_gradient import SGDRegressor
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from sklearn.linear_model import SGDRegressor
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from sklearn.metrics import r2_score
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np.random.seed(42)

‎doc/developers/develop.rst

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@@ -453,7 +453,7 @@ this can be achieved with::
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return self.classes_[np.argmax(D, axis=1)]
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In linear models, coefficients are stored in an array called ``coef_``, and the
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independent term is stored in ``intercept_``. ``sklearn.linear_model.base``
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independent term is stored in ``intercept_``. ``sklearn.linear_model._base``
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contains a few base classes and mixins that implement comm 10000 on linear model
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patterns.
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‎doc/modules/classes.rst

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@@ -882,7 +882,7 @@ Miscellaneous
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manifold.locally_linear_embedding
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manifold.smacof
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manifold.spectral_embedding
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manifold.t_sne.trustworthiness
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manifold.trustworthiness
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.. _metrics_ref:
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svm.l1_min_c
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Low-level methods
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-----------------
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.. autosummary::
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:toctree: generated
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:template: function.rst
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svm.libsvm.cross_validation
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svm.libsvm.decision_function
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svm.libsvm.fit
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svm.libsvm.predict
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svm.libsvm.predict_proba
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.. _tree_ref:
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:mod:`sklearn.tree`: Decision Trees

‎doc/modules/computing.rst

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, so latency should be equivalent.
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Here is an example using
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:class:`sklearn.linear_model.stochastic_gradient.SGDClassifier` with the
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:class:`sklearn.linear_model.SGDClassifier` with the
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``elasticnet`` penalty. The regularization strength is globally controlled by
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the ``alpha`` parameter. With a sufficiently high ``alpha``,
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one can then increase the ``l1_ratio`` parameter of ``elasticnet`` to

‎doc/modules/cross_validation.rst

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Or as a dict mapping scorer name to a predefined or custom scoring function::
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>>> from sklearn.metrics.scorer import make_scorer
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>>> from sklearn.metrics import make_scorer
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>>> scoring = {'prec_macro': 'precision_macro',
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... 'rec_macro': make_scorer(recall_score, average='macro')}
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>>> scores = cross_validate(clf, X, y, scoring=scoring,

‎doc/templates/index.html

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<li><strong>Gitter:</strong> <a href="https://gitter.im/scikit-learn/scikit-learn">gitter.im/scikit-learn</a></li>
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</ul>
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<form target="_top" id="paypal-form" method="post" action="https://www.paypal.com/cgi-bin/webscr">
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<input type="hidden" value="_s-xclick" name="cmd">
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<input type="hidden" value="74EYUMF3FTSW8" name="hosted_button_id">
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</form>
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<a class="btn btn-warning btn-big sk-donate-btn mb-1" onclick="document.getElementById('paypal-form').submit(); ">Help us, <strong>donate!</strong></a>
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<a class="btn btn-warning btn-big mb-1" href="about.html#citing-scikit-learn"><strong>Cite us!</strong></a>
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</div>
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<div class="col-md-4">

‎doc/themes/scikit-learn/static/nature.css_t

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font-weight: bold;
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}
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#paypal-form {
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margin: 30px 0;
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padding: 0;
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}
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div.container.index-lower a.cite-us {
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margin-left: 60px;
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padding-right: 20px;

‎doc/whats_new/v0.21.rst

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- :func:`svm.SVC.decision_function` and
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:func:`multiclass.OneVsOneClassifier.decision_function`. |Fix|
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- :class:`linear_model.SGDClassifier` and any derived classifiers. |Fix|
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- Any model using the :func:`linear_model.sag.sag_solver` function with a `0`
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- Any model using the :func:`linear_model._sag.sag_solver` function with a `0`
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seed, including :class:`linear_model.LogisticRegression`,
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:class:`linear_model.LogisticRegressionCV`, :class:`linear_model.Ridge`,
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and :class:`linear_model.RidgeCV` with 'sag' solver. |Fix|

‎examples/applications/plot_model_complexity_influence.py

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from sklearn.metrics import mean_squared_error
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from sklearn.svm import NuSVR
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from sklearn.ensemble import GradientBoostingRegressor
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from sklearn.linear_model.stochastic_gradient import SGDClassifier
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from sklearn.linear_model import SGDClassifier
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from sklearn.metrics import hamming_loss
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# #############################################################################

‎examples/linear_model/plot_sgd_penalties.py

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Contours of where the penalty is equal to 1
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for the three penalties L1, L2 and elastic-net.
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All of the above are supported by
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:class:`sklearn.linear_model.stochastic_gradient`.
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All of the above are supported by :class:`~sklearn.linear_model.SGDClassifier`
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and :class:`~sklearn.linear_model.SGDRegressor`.
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"""
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print(__doc__)

‎examples/preprocessing/plot_map_data_to_normal.py

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Map data to a normal distribution
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=================================
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.. currentmodule:: sklearn.preprocessing
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This example demonstrates the use of the Box-Cox and Yeo-Johnson transforms
7-
through :class:`preprocessing.PowerTransformer` to map data from various
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through :class:`~PowerTransformer` to map data from various
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distributions to a normal distribution.
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The power transform is useful as a transformation in modeling problems where
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support inputs with negative values.
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For comparison, we also add the output from
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:class:`preprocessing.QuantileTransformer`. It can force any arbitrary
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:class:`~QuantileTransformer`. It can force any arbitrary
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distribution into a gaussian, provided that there are enough training samples
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(thousands). Because it is a non-parametric method, it is harder to interpret
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than the parametric ones (Box-Cox and Yeo-Johnson).

‎sklearn/_build_utils/deprecated_modules.py

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'set_verbosity_wrap'),
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('_liblinear', 'sklearn.svm.liblinear', 'sklearn.svm', 'train_wrap'),
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89+
('_base', 'sklearn.linear_model.base', 'sklearn.linear_model',
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'LinearRegression'),
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('_cd_fast', 'sklearn.linear_model.cd_fast', 'sklearn.linear_model',
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'sparse_enet_coordinate_descent'),
93+
('_bayes', 'sklearn.linear_model.bayes', 'sklearn.linear_model',
94+
'BayesianRidge'),
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('_coordinate_descent', 'sklearn.linear_model.coordinate_descent',
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'sklearn.linear_model', 'Lasso'),
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('_huber', 'sklearn.linear_model.huber', 'sklearn.linear_model',
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'HuberRegressor'),
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('_least_angle', 'sklearn.linear_model.least_angle',
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'sklearn.linear_model', 'LassoLarsCV'),
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('_logistic', 'sklearn.linear_model.logistic', 'sklearn.linear_model',
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'LogisticRegression'),
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('_omp', 'sklearn.linear_model.omp', 'sklearn.linear_model',
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'OrthogonalMatchingPursuit'),
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('_passive_aggressive', 'sklearn.linear_model.passive_aggressive',
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'sklearn.linear_model', 'PassiveAggressiveClassifier'),
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('_perceptron', 'sklearn.linear_model.perceptron', 'sklearn.linear_model',
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'Perceptron'),
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('_ransac', 'sklearn.linear_model.ransac', 'sklearn.linear_model',
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'RANSACRegressor'),
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('_ridge', 'sklearn.linear_model.ridge', 'sklearn.linear_model',
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'Ridge'),
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('_sag', 'sklearn.linear_model.sag', 'sklearn.linear_model',
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'get_auto_step_size'),
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('_sag_fast', 'sklearn.linear_model.sag_fast', 'sklearn.linear_model',
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'MultinomialLogLoss64'),
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('_sgd_fast', 'sklearn.linear_model.sgd_fast', 'sklearn.linear_model',
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'Hinge'),
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('_stochastic_gradient', 'sklearn.linear_model.stochastic_gradient',
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'sklearn.linear_model', 'SGDClassifier'),
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('_theil_sen', 'sklearn.linear_model.theil_sen', 'sklearn.linear_model',
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'TheilSenRegressor'),
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('_bicluster', 'sklearn.metrics.cluster.bicluster',
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'sklearn.metrics.cluster', 'consensus_score'),
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('_supervised', 'sklearn.metrics.cluster.supervised',
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'sklearn.metrics.cluster', 'entropy'),
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('_unsupervised', 'sklearn.metrics.cluster.unsupervised',
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'sklearn.metrics.cluster', 'silhouette_score'),
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('_expected_mutual_info_fast',
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'sklearn.metrics.cluster.expected_mutual_info_fast',
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'sklearn.metrics.cluster', 'expected_mutual_information'),
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('_base', 'sklearn.metrics.base', 'sklearn.metrics', 'combinations'),
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('_classification', 'sklearn.metrics.classification', 'sklearn.metrics',
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'accuracy_score'),
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('_regression', 'sklearn.metrics.regression', 'sklearn.metrics',
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'max_error'),
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('_ranking', 'sklearn.metrics.ranking', 'sklearn.metrics', 'roc_curve'),
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('_pairwise_fast', 'sklearn.metrics.pairwise_fast', 'sklearn.metrics',
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'np'),
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('_scorer', 'sklearn.metrics.scorer', 'sklearn.metrics', 'get_scorer'),
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('_partial_dependence', 'sklearn.inspection.partial_dependence',
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'sklearn.inspection', 'partial_dependence'),
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('_permutation_importance', 'sklearn.inspection.permutation_importance',

‎sklearn/base.py

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from .metrics.regression import _check_reg_targets
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from .metrics._regression import _check_reg_targets
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y_pred = self.predict(X)
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# XXX: Remove the check in 0.23
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y_type, _, _, _ = _check_reg_targets(y, y_pred, None)

‎sklearn/covariance/_graph_lasso_.py

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from ..exceptions import ConvergenceWarning
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from ..utils.validation import check_random_state, check_array
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from ..linear_model import cd_fast
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from ..linear_model import _cd_fast as cd_fast
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from ..model_selection import check_cv, cross_val_score
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‎sklearn/discriminant_analysis.py

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from scipy.special import expit
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from .base import BaseEstimator, TransformerMixin, ClassifierMixin
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from .linear_model.base import LinearClassifierMixin
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from .linear_model._base import LinearClassifierMixin
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from .covariance import ledoit_wolf, empirical_covariance, shrunk_covariance
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from .utils.multiclass import unique_labels
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from .utils import check_array, check_X_y

‎sklearn/ensemble/_base.py

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from abc import ABCMeta, abstractmethod
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import warnings
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import numpy as np
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# defined by MetaEstimatorMixin
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self._validate_names(names)
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# FIXME: deprecate the usage of None to drop an estimator from the
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# ensemble. Remove in 0.24
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if any(est is None for est in estimators):
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warnings.warn(
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"Using 'None' to drop an estimator from the ensemble is "
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"deprecated in 0.22 and support will be dropped in 0.24. "
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"Use the string 'drop' instead.", DeprecationWarning
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)
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raise ValueError(
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for est in estimators:
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if est not in (None, 'drop') and not is_estimator_type(est):
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raise ValueError(
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"The estimator {} should be a {}."
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.format(
249+
"The estimator {} should be a {}.".format(
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est.__class__.__name__, is_estimator_type.__name__[3:]
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)
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)

‎sklearn/ensemble/_voting.py

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estimators : list of (string, estimator) tuples
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Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones
9090
of those original estimators that will be stored in the class attribute
91-
``self.estimators_``. An estimator can be set to ``None`` or ``'drop'``
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``self.estimators_``. An estimator can be set to ``'drop'``
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using ``set_params``.
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.. deprecated:: 0.22
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Using ``None`` to drop an estimator is deprecated in 0.22 and
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support will be dropped in 0.24. Use the string ``'drop'`` instead.
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voting : str, {'hard', 'soft'} (default='hard')
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96100
Else if 'soft', predicts the class label based on the argmax of
@@ -119,7 +123,7 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):
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----------
120124
estimators_ : list of classifiers
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The collection of fitted sub-estimators as defined in ``estimators``
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that are not `None`.
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that are not 'drop'.
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named_estimators_ : Bunch object, a dictionary with attribute access
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Attribute to access any fitted sub-estimators by name.
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Invoking the ``fit`` method on the ``VotingRegressor`` will fit clones
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of those original estimators that will be stored in the class attribute
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``self.estimators_``. An estimator can be set to ``None`` or ``'drop'``
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using ``set_params``.
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``self.estimators_``. An estimator can be set to ``'drop'`` using
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``set_params``.
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.. deprecated:: 0.22
333+
Using ``None`` to drop an estimator is deprecated in 0.22 and
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support will be dropped in 0.24. Use the string ``'drop'`` instead.
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weights : array-like, shape (n_regressors,), optional (default=`None`)
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Sequence of weights (`float` or `int`) to weight the occurrences of
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----------
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The collection of fitted sub-estimators as defined in ``estimators``
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that are not `None`.
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that are not 'drop'.
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named_estimators_ : Bunch object, a dictionary with attribute access
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Attribute to access any fitted sub-estimators by name.
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.. versionadded:: 0.20
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Examples
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--------
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>>> import numpy as np

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