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  • tests
  • feature_selection
  • kernel_ridge.py
  • linear_model
  • manifold
  • metrics
  • model_selection
  • semi_supervised
  • svm
  • tests

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    87 files changed

    +362
    -247
    lines changed

    ‎.gitignore

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    @@ -75,7 +75,7 @@ _configtest.o.d
    7575
    # files generated from a template
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    sklearn/utils/_seq_dataset.pyx
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    sklearn/utils/_seq_dataset.pxd
    78-
    sklearn/linear_model/sag_fast.pyx
    78+
    sklearn/linear_model/_sag_fast.pyx
    7979

    8080
    # 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
    131131
    sklearn/svm/liblinear.py
    132132

    133+
    sklearn/linear_model/base.py
    134+
    sklearn/linear_model/bayes.py
    135+
    sklearn/linear_model/cd_fast.py
    136+
    sklearn/linear_model/coordinate_descent.py
    137+
    sklearn/linear_model/huber.py
    138+
    sklearn/linear_model/least_angle.py
    139+
    sklearn/linear_model/logistic.py
    140+
    sklearn/linear_model/omp.py
    141+
    sklearn/linear_model/passive_aggressive.py
    142+
    sklearn/linear_model/perceptron.py
    143+
    sklearn/linear_model/ransac.py
    144+
    sklearn/linear_model/ridge.py
    145+
    sklearn/linear_model/sag.py
    146+
    sklearn/linear_model/sag_fast.py
    147+
    sklearn/linear_model/sgd_fast.py
    148+
    sklearn/linear_model/stochastic_gradient.py
    149+
    sklearn/linear_model/theil_sen.py
    150+
    151+
    sklearn/metrics/cluster/bicluster.py
    152+
    sklearn/metrics/cluster/supervised.py
    153+
    sklearn/metrics/cluster/unsupervised.py
    154+
    sklearn/metrics/cluster/expected_mutual_info_fast.py
    155+
    156+
    sklearn/metrics/base.py
    157+
    sklearn/metrics/classification.py
    158+
    sklearn/metrics/regression.py
    159+
    sklearn/metrics/ranking.py
    160+
    sklearn/metrics/pairwise_fast.py
    161+
    sklearn/metrics/scorer.py
    162+
    133163
    sklearn/inspection/partial_dependence.py
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    sklearn/inspection/permutation_importance.py
    135165

    ‎benchmarks/bench_rcv1_logreg_convergence.py

    Lines changed: 1 addition & 1 deletion
    Original file line numberDiff line numberDiff line change
    @@ -11,7 +11,7 @@
    1111

    1212
    from sklearn.linear_model import (LogisticRegression, SGDClassifier)
    1313
    from sklearn.datasets import fetch_rcv1
    14-
    from sklearn.linear_model.sag import get_auto_step_size
    14+
    from sklearn.linear_model._sag import get_auto_step_size
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    1616
    try:
    1717
    import lightning.classification as lightning_clf

    ‎benchmarks/bench_sparsify.py

    Lines changed: 1 addition & 1 deletion
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    @@ -45,7 +45,7 @@
    4545

    4646
    from scipy.sparse.csr import csr_matrix
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    import numpy as np
    48-
    from sklearn.linear_model.stochastic_gradient import SGDRegressor
    48+
    from sklearn.linear_model import SGDRegressor
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    from sklearn.metrics import r2_score
    5050

    5151
    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)]
    454454

    455455
    In linear models, coefficients are stored in an array called ``coef_``, and the
    456-
    independent term is stored in ``intercept_``. ``sklearn.linear_model.base``
    456+
    independent term is stored in ``intercept_``. ``sklearn.linear_model._base``
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    contains a few base classes and mixins that implement common linear model
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    patterns.
    459459

    ‎doc/modules/classes.rst

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    @@ -882,7 +882,7 @@ Miscellaneous
    882882
    manifold.locally_linear_embedding
    883883
    manifold.smacof
    884884
    manifold.spectral_embedding
    885-
    manifold.t_sne.trustworthiness
    885+
    manifold.trustworthiness
    886886

    887887

    888888
    .. _metrics_ref:
    @@ -1480,20 +1480,6 @@ Estimators
    14801480

    14811481
    svm.l1_min_c
    14821482

    1483-
    Low-level methods
    1484-
    -----------------
    1485-
    1486-
    .. autosummary::
    1487-
    :toctree: generated
    1488-
    :template: function.rst
    1489-
    1490-
    svm.libsvm.cross_validation
    1491-
    svm.libsvm.decision_function
    1492-
    svm.libsvm.fit
    1493-
    svm.libsvm.predict
    1494-
    svm.libsvm.predict_proba
    1495-
    1496-
    14971483
    .. _tree_ref:
    14981484

    14991485
    :mod:`sklearn.tree`: Decision Trees

    ‎doc/modules/computing.rst

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    @@ -297,7 +297,7 @@ decision function that is applied at prediction time is the same (a dot product)
    297297
    , so latency should be equivalent.
    298298

    299299
    Here is an example using
    300-
    :class:`sklearn.linear_model.stochastic_gradient.SGDClassifier` with the
    300+
    :class:`sklearn.linear_model.SGDClassifier` with the
    301301 10000
    ``elasticnet`` penalty. The regularization strength is globally controlled by
    302302
    the ``alpha`` parameter. With a sufficiently high ``alpha``,
    303303
    one can then increase the ``l1_ratio`` parameter of ``elasticnet`` to

    ‎doc/modules/cross_validation.rst

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    @@ -241,7 +241,7 @@ predefined scorer names::
    241241

    242242
    Or as a dict mapping scorer name to a predefined or custom scoring function::
    243243

    244-
    >>> from sklearn.metrics.scorer import make_scorer
    244+
    >>> from sklearn.metrics import make_scorer
    245245
    >>> scoring = {'prec_macro': 'precision_macro',
    246246
    ... 'rec_macro': make_scorer(recall_score, average='macro')}
    247247
    >>> scores = cross_validate(clf, X, y, scoring=scoring,

    ‎doc/templates/index.html

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    @@ -182,11 +182,6 @@ <h4 class="sk-landing-call-header">Community</h4>
    182182
    <li><strong>Gitter:</strong> <a href="https://gitter.im/scikit-learn/scikit-learn">gitter.im/scikit-learn</a></li>
    183183
    </ul>
    184184

    185-
    <form target="_top" id="paypal-form" method="post" action="https://www.paypal.com/cgi-bin/webscr">
    186-
    <input type="hidden" value="_s-xclick" name="cmd">
    187-
    <input type="hidden" value="74EYUMF3FTSW8" name="hosted_button_id">
    188-
    </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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    @@ -228,7 +228,7 @@ random sampling procedures.
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    - :func:`svm.SVC.decision_function` and
    229229
    :func:`multiclass.OneVsOneClassifier.decision_function`. |Fix|
    230230
    - :class:`linear_model.SGDClassifier` and any derived classifiers. |Fix|
    231-
    - Any model using the :func:`linear_model.sag.sag_solver` function with a `0`
    231+
    - Any model using the :func:`linear_model._sag.sag_solver` function with a `0`
    232232
    seed, including :class:`linear_model.LogisticRegression`,
    233233
    :class:`linear_model.LogisticRegressionCV`, :class:`linear_model.Ridge`,
    234234
    and :class:`linear_model.RidgeCV` with 'sag' solver. |Fix|

    ‎examples/applications/plot_model_complexity_influence.py

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    @@ -31,7 +31,7 @@
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    from sklearn.metrics import mean_squared_error
    3232
    from sklearn.svm import NuSVR
    3333
    from sklearn.ensemble import GradientBoostingRegressor
    34-
    from sklearn.linear_model.stochastic_gradient import SGDClassifier
    34+
    from sklearn.linear_model import SGDClassifier
    3535
    from sklearn.metrics import hamming_loss
    3636

    3737
    # #############################################################################

    ‎examples/linear_model/plot_sgd_penalties.py

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    @@ -6,8 +6,8 @@
    66
    Contours of where the penalty is equal to 1
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    for the three penalties L1, L2 and elastic-net.
    88
    9-
    All of the above are supported by
    10-
    :class:`sklearn.linear_model.stochastic_gradient`.
    9+
    All of the above are supported by :class:`~sklearn.linear_model.SGDClassifier`
    10+
    and :class:`~sklearn.linear_model.SGDRegressor`.
    1111
    1212
    """
    1313
    print(__doc__)

    ‎examples/preprocessing/plot_map_data_to_normal.py

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    @@ -3,8 +3,10 @@
    33
    Map data to a normal distribution
    44
    =================================
    55
    6+
    .. currentmodule:: sklearn.preprocessing
    7+
    68
    This example demonstrates the use of the Box-Cox and Yeo-Johnson transforms
    7-
    through :class:`preprocessing.PowerTransformer` to map data from various
    9+
    through :class:`~PowerTransformer` to map data from various
    810
    distributions to a normal distribution.
    911
    1012
    The power transform is useful as a transformation in modeling problems where
    @@ -22,7 +24,7 @@
    2224
    support inputs with negative values.
    2325
    2426
    For comparison, we also add the output from
    25-
    :class:`preprocessing.QuantileTransformer`. It can force any arbitrary
    27+
    :class:`~QuantileTransformer`. It can force any arbitrary
    2628
    distribution into a gaussian, provided that there are enough training samples
    2729
    (thousands). Because it is a non-parametric method, it is harder to interpret
    2830
    than the parametric ones (Box-Cox and Yeo-Johnson).

    ‎sklearn/_build_utils/deprecated_modules.py

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    @@ -86,6 +86,61 @@
    8686
    'set_verbosity_wrap'),
    8787
    ('_liblinear', 'sklearn.svm.liblinear', 'sklearn.svm', 'train_wrap'),
    8888

    89+
    ('_base', 'sklearn.linear_model.base', 'sklearn.linear_model',
    90+
    'LinearRegression'),
    91+
    ('_cd_fast', 'sklearn.linear_model.cd_fast', 'sklearn.linear_model',
    92+
    'sparse_enet_coordinate_descent'),
    93+
    ('_bayes', 'sklearn.linear_model.bayes', 'sklearn.linear_model',
    94+
    'BayesianRidge'),
    95+
    ('_coordinate_descent', 'sklearn.linear_model.coordinate_descent',
    96+
    'sklearn.linear_model', 'Lasso'),
    97+
    ('_huber', 'sklearn.linear_model.huber', 'sklearn.linear_model',
    98+
    'HuberRegressor'),
    99+
    ('_least_angle', 'sklearn.linear_model.least_angle',
    100+
    'sklearn.linear_model', 'LassoLarsCV'),
    101+
    ('_logistic', 'sklearn.linear_model.logistic', 'sklearn.linear_model',
    102+
    'LogisticRegression'),
    103+
    ('_omp', 'sklearn.linear_model.omp', 'sklearn.linear_model',
    104+
    'OrthogonalMatchingPursuit'),
    105+
    ('_passive_aggressive', 'sklearn.linear_model.passive_aggressive',
    106+
    'sklearn.linear_model', 'PassiveAggressiveClassifier'),
    107+
    ('_perceptron', 'sklearn.linear_model.perceptron', 'sklearn.linear_model',
    108+
    'Perceptron'),
    109+
    ('_ransac', 'sklearn.linear_model.ransac', 'sklearn.linear_model',
    110+
    'RANSACRegressor'),
    111+
    ('_ridge', 'sklearn.linear_model.ridge', 'sklearn.linear_model',
    112+
    'Ridge'),
    113+
    ('_sag', 'sklearn.linear_model.sag', 'sklearn.linear_model',
    114+
    'get_auto_step_size'),
    115+
    ('_sag_fast', 'sklearn.linear_model.sag_fast', 'sklearn.linear_model',
    116+
    'MultinomialLogLoss64'),
    117+
    ('_sgd_fast', 'sklearn.linear_model.sgd_fast', 'sklearn.linear_model',
    118+
    'Hinge'),
    119+
    ('_stochastic_gradient', 'sklearn.linear_model.stochastic_gradient',
    120+
    'sklearn.linear_model', 'SGDClassifier'),
    121+
    ('_theil_sen', 'sklearn.linear_model.theil_sen', 'sklearn.linear_model',
    122+
    'TheilSenRegressor'),
    123+
    124+
    ('_bicluster', 'sklearn.metrics.cluster.bicluster',
    125+
    'sklearn.metrics.cluster', 'consensus_score'),
    126+
    ('_supervised', 'sklearn.metrics.cluster.supervised',
    127+
    'sklearn.metrics.cluster', 'entropy'),
    128+
    ('_unsupervised', 'sklearn.metrics.cluster.unsupervised',
    129+
    'sklearn.metrics.cluster', 'silhouette_score'),
    130+
    ('_expected_mutual_info_fast',
    131+
    'sklearn.metrics.cluster.expected_mutual_info_fast',
    132+
    'sklearn.metrics.cluster', 'expected_mutual_information'),
    133+
    134+
    ('_base', 'sklearn.metrics.base', 'sklearn.metrics', 'combinations'),
    135+
    ('_classification', 'sklearn.metrics.classification', 'sklearn.metrics',
    136+
    'accuracy_score'),
    137+
    ('_regression', 'sklearn.metrics.regression', 'sklearn.metrics',
    138+
    'max_error'),
    139+
    ('_ranking', 'sklearn.metrics.ranking', 'sklearn.metrics', 'roc_curve'),
    140+
    ('_pairwise_fast', 'sklearn.metrics.pairwise_fast', 'sklearn.metrics',
    141+
    'np'),
    142+
    ('_scorer', 'sklearn.metrics.scorer', 'sklearn.metrics', 'get_scorer'),
    143+
    89144
    ('_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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    404404
    """
    405405

    406406
    from .metrics import r2_score
    407-
    from .metrics.regression import _check_reg_targets
    407+
    from .metrics._regression import _check_reg_targets
    408408
    y_pred = self.predict(X)
    409409
    # XXX: Remove the check in 0.23
    410410
    y_type, _, _, _ = _check_reg_targets(y, y_pred, None)

    ‎sklearn/covariance/_graph_lasso_.py

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    2020
    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
    22+
    from ..linear_model import _cd_fast as cd_fast
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    from ..linear_model import lars_path_gram
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    from ..model_selection import check_cv, cross_val_score
    2525

    ‎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
    19+
    from .linear_model._base import LinearClassifierMixin
    2020
    from .covariance import ledoit_wolf, empirical_covariance, shrunk_covariance
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    from .utils.multiclass import unique_labels
    2222
    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 numbers
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    import warnings
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    1112
    import numpy as np
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    @@ -223,6 +224,15 @@ def _validate_estimators(self):
    223224
    # defined by MetaEstimatorMixin
    224225
    self._validate_names(names)
    225226

    227+
    # FIXME: deprecate the usage of None to drop an estimator from the
    228+
    # ensemble. Remove in 0.24
    229+
    if any(est is None for est in estimators):
    230+
    warnings.warn(
    231+
    "Using 'None' to drop an estimator from the ensemble is "
    232+
    "deprecated in 0.22 and support will be dropped in 0.24. "
    233+
    "Use the string 'drop' instead.", DeprecationWarning
    234+
    )
    235+
    226236
    has_estimator = any(est not in (None, 'drop') for est in estimators)
    227237
    if not has_estimator:
    228238
    raise ValueError(
    @@ -236,8 +246,7 @@ def _validate_estimators(self):
    236246
    for est in estimators:
    237247
    if est not in (None, 'drop') and not is_estimator_type(est):
    238248
    raise ValueError(
    239-
    "The estimator {} should be a {}."
    240-
    .format(
    249+
    "The estimator {} should be a {}.".format(
    241250
    est.__class__.__name__, is_estimator_type.__name__[3:]
    242251
    )
    243252
    )

    ‎sklearn/ensemble/_voting.py

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    @@ -88,9 +88,13 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):
    8888
    estimators : list of (string, estimator) tuples
    8989
    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'``
    91+
    ``self.estimators_``. An estimator can be set to ``'drop'``
    9292
    using ``set_params``.
    9393
    94+
    .. deprecated:: 0.22
    95+
    Using ``None`` to drop an estimator is deprecated in 0.22 and
    96+
    support will be dropped in 0.24. Use the string ``'drop'`` instead.
    97+
    9498
    voting : str, {'hard', 'soft'} (default='hard')
    9599
    If 'hard', uses predicted class labels for majority rule voting.
    96100
    Else if 'soft', predicts the class label based on the argmax of
    @@ -119,7 +123,7 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):
    119123
    ----------
    120124
    estimators_ : list of classifiers
    121125
    The collection of fitted sub-estimators as defined in ``estimators``
    122-
    that are not `None`.
    126+
    that are not 'drop'.
    123127
    124128
    named_estimators_ : Bunch object, a dictionary with attribute access
    125129
    Attribute to access any fitted sub-estimators by name.
    @@ -322,8 +326,12 @@ class VotingRegressor(RegressorMixin, _BaseVoting):
    322326
    estimators : list of (string, estimator) tuples
    323327
    Invoking the ``fit`` method on the ``VotingRegressor`` will fit clones
    324328
    of those original estimators that will be stored in the class attribute
    325-
    ``self.estimators_``. An estimator can be set to ``None`` or ``'drop'``
    326-
    using ``set_params``.
    329+
    ``self.estimators_``. An estimator can be set to ``'drop'`` using
    330+
    ``set_params``.
    331+
    332+
    .. deprecated:: 0.22
    333+
    Using ``None`` to drop an estimator is deprecated in 0.22 and
    334+
    support will be dropped in 0.24. Use the string ``'drop'`` instead.
    327335
    328336
    weights : array-like, shape (n_regressors,), optional (default=`None`)
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    Sequence of weights (`float` or `int`) to weight the occurrences of
    @@ -339,11 +347,13 @@ class VotingRegressor(RegressorMixin, _BaseVoting):
    339347
    ----------
    340348
    estimators_ : list of regressors
    341349
    The collection of fitted sub-estimators as defined in ``estimators``
    342-
    that are not `None`.
    350+
    that are not 'drop'.
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    named_estimators_ : Bunch object, a dictionary with attribute access
    345353
    Attribute to access any fitted sub-estimators by name.
    346354
    355+
    .. versionadded:: 0.20
    356+
    347357
    Examples
    348358
    --------
    349359
    >>> import numpy as np

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