766 lines
26 KiB
Python
766 lines
26 KiB
Python
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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Vincent Michel <vincent.michel@inria.fr>
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# Gilles Louppe <g.louppe@gmail.com>
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#
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# License: BSD 3 clause
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"""Recursive feature elimination for feature ranking"""
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import numpy as np
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from numbers import Integral, Real
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from joblib import effective_n_jobs
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from ..utils.metaestimators import available_if
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from ..utils.metaestimators import _safe_split
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from ..utils._param_validation import HasMethods, Interval
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from ..utils._tags import _safe_tags
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from ..utils.validation import check_is_fitted
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from ..utils.parallel import delayed, Parallel
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from ..base import BaseEstimator
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from ..base import MetaEstimatorMixin
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from ..base import clone
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from ..base import is_classifier
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from ..model_selection import check_cv
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from ..model_selection._validation import _score
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from ..metrics import check_scoring
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from ._base import SelectorMixin
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from ._base import _get_feature_importances
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def _rfe_single_fit(rfe, estimator, X, y, train, test, scorer):
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"""
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Return the score for a fit across one fold.
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"""
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X_train, y_train = _safe_split(estimator, X, y, train)
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X_test, y_test = _safe_split(estimator, X, y, test, train)
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return rfe._fit(
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X_train,
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y_train,
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lambda estimator, features: _score(
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estimator, X_test[:, features], y_test, scorer
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),
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).scores_
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def _estimator_has(attr):
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"""Check if we can delegate a method to the underlying estimator.
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First, we check the first fitted estimator if available, otherwise we
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check the unfitted estimator.
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"""
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return lambda self: (
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hasattr(self.estimator_, attr)
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if hasattr(self, "estimator_")
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else hasattr(self.estimator, attr)
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)
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class RFE(SelectorMixin, MetaEstimatorMixin, BaseEstimator):
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"""Feature ranking with recursive feature elimination.
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Given an external estimator that assigns weights to features (e.g., the
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coefficients of a linear model), the goal of recursive feature elimination
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(RFE) is to select features by recursively considering smaller and smaller
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sets of features. First, the estimator is trained on the initial set of
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features and the importance of each feature is obtained either through
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any specific attribute or callable.
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Then, the least important features are pruned from current set of features.
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That procedure is recursively repeated on the pruned set until the desired
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number of features to select is eventually reached.
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Read more in the :ref:`User Guide <rfe>`.
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Parameters
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----------
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estimator : ``Estimator`` instance
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A supervised learning estimator with a ``fit`` method that provides
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information about feature importance
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(e.g. `coef_`, `feature_importances_`).
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n_features_to_select : int or float, default=None
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The number of features to select. If `None`, half of the features are
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selected. If integer, the parameter is the absolute number of features
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to select. If float between 0 and 1, it is the fraction of features to
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select.
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.. versionchanged:: 0.24
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Added float values for fractions.
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step : int or float, default=1
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If greater than or equal to 1, then ``step`` corresponds to the
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(integer) number of features to remove at each iteration.
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If within (0.0, 1.0), then ``step`` corresponds to the percentage
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(rounded down) of features to remove at each iteration.
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verbose : int, default=0
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Controls verbosity of output.
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importance_getter : str or callable, default='auto'
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If 'auto', uses the feature importance either through a `coef_`
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or `feature_importances_` attributes of estimator.
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Also accepts a string that specifies an attribute name/path
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for extracting feature importance (implemented with `attrgetter`).
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For example, give `regressor_.coef_` in case of
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:class:`~sklearn.compose.TransformedTargetRegressor` or
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`named_steps.clf.feature_importances_` in case of
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class:`~sklearn.pipeline.Pipeline` with its last step named `clf`.
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If `callable`, overrides the default feature importance getter.
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The callable is passed with the fitted estimator and it should
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return importance for each feature.
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.. versionadded:: 0.24
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Attributes
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----------
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classes_ : ndarray of shape (n_classes,)
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The classes labels. Only available when `estimator` is a classifier.
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estimator_ : ``Estimator`` instance
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The fitted estimator used to select features.
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n_features_ : int
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The number of selected features.
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n_features_in_ : int
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Number of features seen during :term:`fit`. Only defined if the
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underlying estimator exposes such an attribute when fit.
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.. versionadded:: 0.24
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X`
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has feature names that are all strings.
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.. versionadded:: 1.0
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ranking_ : ndarray of shape (n_features,)
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The feature ranking, such that ``ranking_[i]`` corresponds to the
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ranking position of the i-th feature. Selected (i.e., estimated
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best) features are assigned rank 1.
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support_ : ndarray of shape (n_features,)
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The mask of selected features.
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See Also
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--------
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RFECV : Recursive feature elimination with built-in cross-validated
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selection of the best number of features.
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SelectFromModel : Feature selection based on thresholds of importance
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weights.
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SequentialFeatureSelector : Sequential cross-validation based feature
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selection. Does not rely on importance weights.
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Notes
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-----
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Allows NaN/Inf in the input if the underlying estimator does as well.
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References
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----------
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.. [1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., "Gene selection
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for cancer classification using support vector machines",
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Mach. Learn., 46(1-3), 389--422, 2002.
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Examples
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--------
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The following example shows how to retrieve the 5 most informative
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features in the Friedman #1 dataset.
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>>> from sklearn.datasets import make_friedman1
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>>> from sklearn.feature_selection import RFE
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>>> from sklearn.svm import SVR
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>>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
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>>> estimator = SVR(kernel="linear")
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>>> selector = RFE(estimator, n_features_to_select=5, step=1)
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>>> selector = selector.fit(X, y)
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>>> selector.support_
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array([ True, True, True, True, True, False, False, False, False,
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False])
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>>> selector.ranking_
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array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
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"""
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_parameter_constraints: dict = {
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"estimator": [HasMethods(["fit"])],
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"n_features_to_select": [
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None,
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Interval(Real, 0, 1, closed="right"),
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Interval(Integral, 0, None, closed="neither"),
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],
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"step": [
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Interval(Integral, 0, None, closed="neither"),
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Interval(Real, 0, 1, closed="neither"),
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],
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"verbose": ["verbose"],
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"importance_getter": [str, callable],
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}
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def __init__(
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self,
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estimator,
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*,
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n_features_to_select=None,
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step=1,
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verbose=0,
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importance_getter="auto",
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):
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self.estimator = estimator
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self.n_features_to_select = n_features_to_select
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self.step = step
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self.importance_getter = importance_getter
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self.verbose = verbose
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@property
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def _estimator_type(self):
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return self.estimator._estimator_type
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@property
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def classes_(self):
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"""Classes labels available when `estimator` is a classifier.
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Returns
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-------
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ndarray of shape (n_classes,)
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"""
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return self.estimator_.classes_
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def fit(self, X, y, **fit_params):
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"""Fit the RFE model and then the underlying estimator on the selected features.
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Parameters
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----------
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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The training input samples.
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y : array-like of shape (n_samples,)
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The target values.
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**fit_params : dict
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Additional parameters passed to the `fit` method of the underlying
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estimator.
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Returns
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-------
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self : object
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Fitted estimator.
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"""
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self._validate_params()
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return self._fit(X, y, **fit_params)
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def _fit(self, X, y, step_score=None, **fit_params):
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# Parameter step_score controls the calculation of self.scores_
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# step_score is not exposed to users
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# and is used when implementing RFECV
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# self.scores_ will not be calculated when calling _fit through fit
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tags = self._get_tags()
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X, y = self._validate_data(
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X,
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y,
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accept_sparse="csc",
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ensure_min_features=2,
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force_all_finite=not tags.get("allow_nan", True),
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multi_output=True,
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)
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# Initialization
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n_features = X.shape[1]
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if self.n_features_to_select is None:
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n_features_to_select = n_features // 2
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elif isinstance(self.n_features_to_select, Integral): # int
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n_features_to_select = self.n_features_to_select
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else: # float
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n_features_to_select = int(n_features * self.n_features_to_select)
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if 0.0 < self.step < 1.0:
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step = int(max(1, self.step * n_features))
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else:
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step = int(self.step)
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support_ = np.ones(n_features, dtype=bool)
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ranking_ = np.ones(n_features, dtype=int)
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if step_score:
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self.scores_ = []
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# Elimination
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while np.sum(support_) > n_features_to_select:
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# Remaining features
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features = np.arange(n_features)[support_]
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# Rank the remaining features
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estimator = clone(self.estimator)
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if self.verbose > 0:
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print("Fitting estimator with %d features." % np.sum(support_))
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estimator.fit(X[:, features], y, **fit_params)
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# Get importance and rank them
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importances = _get_feature_importances(
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estimator,
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self.importance_getter,
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transform_func="square",
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)
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ranks = np.argsort(importances)
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# for sparse case ranks is matrix
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ranks = np.ravel(ranks)
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# Eliminate the worse features
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threshold = min(step, np.sum(support_) - n_features_to_select)
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# Compute step score on the previous selection iteration
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# because 'estimator' must use features
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# that have not been eliminated yet
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if step_score:
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self.scores_.append(step_score(estimator, features))
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support_[features[ranks][:threshold]] = False
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ranking_[np.logical_not(support_)] += 1
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# Set final attributes
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features = np.arange(n_features)[support_]
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self.estimator_ = clone(self.estimator)
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self.estimator_.fit(X[:, features], y, **fit_params)
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# Compute step score when only n_features_to_select features left
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if step_score:
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self.scores_.append(step_score(self.estimator_, features))
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self.n_features_ = support_.sum()
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self.support_ = support_
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self.ranking_ = ranking_
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return self
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@available_if(_estimator_has("predict"))
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def predict(self, X):
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"""Reduce X to the selected features and predict using the estimator.
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Parameters
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----------
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X : array of shape [n_samples, n_features]
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The input samples.
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Returns
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-------
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y : array of shape [n_samples]
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The predicted target values.
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"""
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check_is_fitted(self)
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return self.estimator_.predict(self.transform(X))
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@available_if(_estimator_has("score"))
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def score(self, X, y, **fit_params):
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"""Reduce X to the selected features and return the score of the estimator.
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Parameters
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----------
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X : array of shape [n_samples, n_features]
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The input samples.
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y : array of shape [n_samples]
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The target values.
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**fit_params : dict
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Parameters to pass to the `score` method of the underlying
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estimator.
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.. versionadded:: 1.0
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Returns
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-------
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score : float
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Score of the underlying base estimator computed with the selected
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features returned by `rfe.transform(X)` and `y`.
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"""
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check_is_fitted(self)
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return self.estimator_.score(self.transform(X), y, **fit_params)
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def _get_support_mask(self):
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check_is_fitted(self)
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return self.support_
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@available_if(_estimator_has("decision_function"))
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def decision_function(self, X):
|
||
|
|
"""Compute the decision function of ``X``.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {array-like or sparse matrix} of shape (n_samples, n_features)
|
||
|
|
The input samples. Internally, it will be converted to
|
||
|
|
``dtype=np.float32`` and if a sparse matrix is provided
|
||
|
|
to a sparse ``csr_matrix``.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
score : array, shape = [n_samples, n_classes] or [n_samples]
|
||
|
|
The decision function of the input samples. The order of the
|
||
|
|
classes corresponds to that in the attribute :term:`classes_`.
|
||
|
|
Regression and binary classification produce an array of shape
|
||
|
|
[n_samples].
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
return self.estimator_.decision_function(self.transform(X))
|
||
|
|
|
||
|
|
@available_if(_estimator_has("predict_proba"))
|
||
|
|
def predict_proba(self, X):
|
||
|
|
"""Predict class probabilities for X.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {array-like or sparse matrix} of shape (n_samples, n_features)
|
||
|
|
The input samples. Internally, it will be converted to
|
||
|
|
``dtype=np.float32`` and if a sparse matrix is provided
|
||
|
|
to a sparse ``csr_matrix``.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
p : array of shape (n_samples, n_classes)
|
||
|
|
The class probabilities of the input samples. The order of the
|
||
|
|
classes corresponds to that in the attribute :term:`classes_`.
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
return self.estimator_.predict_proba(self.transform(X))
|
||
|
|
|
||
|
|
@available_if(_estimator_has("predict_log_proba"))
|
||
|
|
def predict_log_proba(self, X):
|
||
|
|
"""Predict class log-probabilities for X.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array of shape [n_samples, n_features]
|
||
|
|
The input samples.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
p : array of shape (n_samples, n_classes)
|
||
|
|
The class log-probabilities of the input samples. The order of the
|
||
|
|
classes corresponds to that in the attribute :term:`classes_`.
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
return self.estimator_.predict_log_proba(self.transform(X))
|
||
|
|
|
||
|
|
def _more_tags(self):
|
||
|
|
return {
|
||
|
|
"poor_score": True,
|
||
|
|
"allow_nan": _safe_tags(self.estimator, key="allow_nan"),
|
||
|
|
"requires_y": True,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
class RFECV(RFE):
|
||
|
|
"""Recursive feature elimination with cross-validation to select features.
|
||
|
|
|
||
|
|
See glossary entry for :term:`cross-validation estimator`.
|
||
|
|
|
||
|
|
Read more in the :ref:`User Guide <rfe>`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
estimator : ``Estimator`` instance
|
||
|
|
A supervised learning estimator with a ``fit`` method that provides
|
||
|
|
information about feature importance either through a ``coef_``
|
||
|
|
attribute or through a ``feature_importances_`` attribute.
|
||
|
|
|
||
|
|
step : int or float, default=1
|
||
|
|
If greater than or equal to 1, then ``step`` corresponds to the
|
||
|
|
(integer) number of features to remove at each iteration.
|
||
|
|
If within (0.0, 1.0), then ``step`` corresponds to the percentage
|
||
|
|
(rounded down) of features to remove at each iteration.
|
||
|
|
Note that the last iteration may remove fewer than ``step`` features in
|
||
|
|
order to reach ``min_features_to_select``.
|
||
|
|
|
||
|
|
min_features_to_select : int, default=1
|
||
|
|
The minimum number of features to be selected. This number of features
|
||
|
|
will always be scored, even if the difference between the original
|
||
|
|
feature count and ``min_features_to_select`` isn't divisible by
|
||
|
|
``step``.
|
||
|
|
|
||
|
|
.. versionadded:: 0.20
|
||
|
|
|
||
|
|
cv : int, cross-validation generator or an iterable, default=None
|
||
|
|
Determines the cross-validation splitting strategy.
|
||
|
|
Possible inputs for cv are:
|
||
|
|
|
||
|
|
- None, to use the default 5-fold cross-validation,
|
||
|
|
- integer, to specify the number of folds.
|
||
|
|
- :term:`CV splitter`,
|
||
|
|
- An iterable yielding (train, test) splits as arrays of indices.
|
||
|
|
|
||
|
|
For integer/None inputs, if ``y`` is binary or multiclass,
|
||
|
|
:class:`~sklearn.model_selection.StratifiedKFold` is used. If the
|
||
|
|
estimator is a classifier or if ``y`` is neither binary nor multiclass,
|
||
|
|
:class:`~sklearn.model_selection.KFold` is used.
|
||
|
|
|
||
|
|
Refer :ref:`User Guide <cross_validation>` for the various
|
||
|
|
cross-validation strategies that can be used here.
|
||
|
|
|
||
|
|
.. versionchanged:: 0.22
|
||
|
|
``cv`` default value of None changed from 3-fold to 5-fold.
|
||
|
|
|
||
|
|
scoring : str, callable or None, default=None
|
||
|
|
A string (see model evaluation documentation) or
|
||
|
|
a scorer callable object / function with signature
|
||
|
|
``scorer(estimator, X, y)``.
|
||
|
|
|
||
|
|
verbose : int, default=0
|
||
|
|
Controls verbosity of output.
|
||
|
|
|
||
|
|
n_jobs : int or None, default=None
|
||
|
|
Number of cores to run in parallel while fitting across folds.
|
||
|
|
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
|
||
|
|
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
|
||
|
|
for more details.
|
||
|
|
|
||
|
|
.. versionadded:: 0.18
|
||
|
|
|
||
|
|
importance_getter : str or callable, default='auto'
|
||
|
|
If 'auto', uses the feature importance either through a `coef_`
|
||
|
|
or `feature_importances_` attributes of estimator.
|
||
|
|
|
||
|
|
Also accepts a string that specifies an attribute name/path
|
||
|
|
for extracting feature importance.
|
||
|
|
For example, give `regressor_.coef_` in case of
|
||
|
|
:class:`~sklearn.compose.TransformedTargetRegressor` or
|
||
|
|
`named_steps.clf.feature_importances_` in case of
|
||
|
|
:class:`~sklearn.pipeline.Pipeline` with its last step named `clf`.
|
||
|
|
|
||
|
|
If `callable`, overrides the default feature importance getter.
|
||
|
|
The callable is passed with the fitted estimator and it should
|
||
|
|
return importance for each feature.
|
||
|
|
|
||
|
|
.. versionadded:: 0.24
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
classes_ : ndarray of shape (n_classes,)
|
||
|
|
The classes labels. Only available when `estimator` is a classifier.
|
||
|
|
|
||
|
|
estimator_ : ``Estimator`` instance
|
||
|
|
The fitted estimator used to select features.
|
||
|
|
|
||
|
|
cv_results_ : dict of ndarrays
|
||
|
|
A dict with keys:
|
||
|
|
|
||
|
|
split(k)_test_score : ndarray of shape (n_subsets_of_features,)
|
||
|
|
The cross-validation scores across (k)th fold.
|
||
|
|
|
||
|
|
mean_test_score : ndarray of shape (n_subsets_of_features,)
|
||
|
|
Mean of scores over the folds.
|
||
|
|
|
||
|
|
std_test_score : ndarray of shape (n_subsets_of_features,)
|
||
|
|
Standard deviation of scores over the folds.
|
||
|
|
|
||
|
|
.. versionadded:: 1.0
|
||
|
|
|
||
|
|
n_features_ : int
|
||
|
|
The number of selected features with cross-validation.
|
||
|
|
|
||
|
|
n_features_in_ : int
|
||
|
|
Number of features seen during :term:`fit`. Only defined if the
|
||
|
|
underlying estimator exposes such an attribute when fit.
|
||
|
|
|
||
|
|
.. versionadded:: 0.24
|
||
|
|
|
||
|
|
feature_names_in_ : ndarray of shape (`n_features_in_`,)
|
||
|
|
Names of features seen during :term:`fit`. Defined only when `X`
|
||
|
|
has feature names that are all strings.
|
||
|
|
|
||
|
|
.. versionadded:: 1.0
|
||
|
|
|
||
|
|
ranking_ : narray of shape (n_features,)
|
||
|
|
The feature ranking, such that `ranking_[i]`
|
||
|
|
corresponds to the ranking
|
||
|
|
position of the i-th feature.
|
||
|
|
Selected (i.e., estimated best)
|
||
|
|
features are assigned rank 1.
|
||
|
|
|
||
|
|
support_ : ndarray of shape (n_features,)
|
||
|
|
The mask of selected features.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
RFE : Recursive feature elimination.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The size of all values in ``cv_results_`` is equal to
|
||
|
|
``ceil((n_features - min_features_to_select) / step) + 1``,
|
||
|
|
where step is the number of features removed at each iteration.
|
||
|
|
|
||
|
|
Allows NaN/Inf in the input if the underlying estimator does as well.
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
|
||
|
|
.. [1] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., "Gene selection
|
||
|
|
for cancer classification using support vector machines",
|
||
|
|
Mach. Learn., 46(1-3), 389--422, 2002.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
The following example shows how to retrieve the a-priori not known 5
|
||
|
|
informative features in the Friedman #1 dataset.
|
||
|
|
|
||
|
|
>>> from sklearn.datasets import make_friedman1
|
||
|
|
>>> from sklearn.feature_selection import RFECV
|
||
|
|
>>> from sklearn.svm import SVR
|
||
|
|
>>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
|
||
|
|
>>> estimator = SVR(kernel="linear")
|
||
|
|
>>> selector = RFECV(estimator, step=1, cv=5)
|
||
|
|
>>> selector = selector.fit(X, y)
|
||
|
|
>>> selector.support_
|
||
|
|
array([ True, True, True, True, True, False, False, False, False,
|
||
|
|
False])
|
||
|
|
>>> selector.ranking_
|
||
|
|
array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
|
||
|
|
"""
|
||
|
|
|
||
|
|
_parameter_constraints: dict = {
|
||
|
|
**RFE._parameter_constraints,
|
||
|
|
"min_features_to_select": [Interval(Integral, 0, None, closed="neither")],
|
||
|
|
"cv": ["cv_object"],
|
||
|
|
"scoring": [None, str, callable],
|
||
|
|
"n_jobs": [None, Integral],
|
||
|
|
}
|
||
|
|
_parameter_constraints.pop("n_features_to_select")
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
estimator,
|
||
|
|
*,
|
||
|
|
step=1,
|
||
|
|
min_features_to_select=1,
|
||
|
|
cv=None,
|
||
|
|
scoring=None,
|
||
|
|
verbose=0,
|
||
|
|
n_jobs=None,
|
||
|
|
importance_getter="auto",
|
||
|
|
):
|
||
|
|
self.estimator = estimator
|
||
|
|
self.step = step
|
||
|
|
self.importance_getter = importance_getter
|
||
|
|
self.cv = cv
|
||
|
|
self.scoring = scoring
|
||
|
|
self.verbose = verbose
|
||
|
|
self.n_jobs = n_jobs
|
||
|
|
self.min_features_to_select = min_features_to_select
|
||
|
|
|
||
|
|
def fit(self, X, y, groups=None):
|
||
|
|
"""Fit the RFE model and automatically tune the number of selected features.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {array-like, sparse matrix} of shape (n_samples, n_features)
|
||
|
|
Training vector, where `n_samples` is the number of samples and
|
||
|
|
`n_features` is the total number of features.
|
||
|
|
|
||
|
|
y : array-like of shape (n_samples,)
|
||
|
|
Target values (integers for classification, real numbers for
|
||
|
|
regression).
|
||
|
|
|
||
|
|
groups : array-like of shape (n_samples,) or None, default=None
|
||
|
|
Group labels for the samples used while splitting the dataset into
|
||
|
|
train/test set. Only used in conjunction with a "Group" :term:`cv`
|
||
|
|
instance (e.g., :class:`~sklearn.model_selection.GroupKFold`).
|
||
|
|
|
||
|
|
.. versionadded:: 0.20
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self : object
|
||
|
|
Fitted estimator.
|
||
|
|
"""
|
||
|
|
self._validate_params()
|
||
|
|
tags = self._get_tags()
|
||
|
|
X, y = self._validate_data(
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
accept_sparse="csr",
|
||
|
|
ensure_min_features=2,
|
||
|
|
force_all_finite=not tags.get("allow_nan", True),
|
||
|
|
multi_output=True,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Initialization
|
||
|
|
cv = check_cv(self.cv, y, classifier=is_classifier(self.estimator))
|
||
|
|
scorer = check_scoring(self.estimator, scoring=self.scoring)
|
||
|
|
n_features = X.shape[1]
|
||
|
|
|
||
|
|
if 0.0 < self.step < 1.0:
|
||
|
|
step = int(max(1, self.step * n_features))
|
||
|
|
else:
|
||
|
|
step = int(self.step)
|
||
|
|
|
||
|
|
# Build an RFE object, which will evaluate and score each possible
|
||
|
|
# feature count, down to self.min_features_to_select
|
||
|
|
rfe = RFE(
|
||
|
|
estimator=self.estimator,
|
||
|
|
n_features_to_select=self.min_features_to_select,
|
||
|
|
importance_getter=self.importance_getter,
|
||
|
|
step=self.step,
|
||
|
|
verbose=self.verbose,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Determine the number of subsets of features by fitting across
|
||
|
|
# the train folds and choosing the "features_to_select" parameter
|
||
|
|
# that gives the least averaged error across all folds.
|
||
|
|
|
||
|
|
# Note that joblib raises a non-picklable error for bound methods
|
||
|
|
# even if n_jobs is set to 1 with the default multiprocessing
|
||
|
|
# backend.
|
||
|
|
# This branching is done so that to
|
||
|
|
# make sure that user code that sets n_jobs to 1
|
||
|
|
# and provides bound methods as scorers is not broken with the
|
||
|
|
# addition of n_jobs parameter in version 0.18.
|
||
|
|
|
||
|
|
if effective_n_jobs(self.n_jobs) == 1:
|
||
|
|
parallel, func = list, _rfe_single_fit
|
||
|
|
else:
|
||
|
|
parallel = Parallel(n_jobs=self.n_jobs)
|
||
|
|
func = delayed(_rfe_single_fit)
|
||
|
|
|
||
|
|
scores = parallel(
|
||
|
|
func(rfe, self.estimator, X, y, train, test, scorer)
|
||
|
|
for train, test in cv.split(X, y, groups)
|
||
|
|
)
|
||
|
|
|
||
|
|
scores = np.array(scores)
|
||
|
|
scores_sum = np.sum(scores, axis=0)
|
||
|
|
scores_sum_rev = scores_sum[::-1]
|
||
|
|
argmax_idx = len(scores_sum) - np.argmax(scores_sum_rev) - 1
|
||
|
|
n_features_to_select = max(
|
||
|
|
n_features - (argmax_idx * step), self.min_features_to_select
|
||
|
|
)
|
||
|
|
|
||
|
|
# Re-execute an elimination with best_k over the whole set
|
||
|
|
rfe = RFE(
|
||
|
|
estimator=self.estimator,
|
||
|
|
n_features_to_select=n_features_to_select,
|
||
|
|
step=self.step,
|
||
|
|
importance_getter=self.importance_getter,
|
||
|
|
verbose=self.verbose,
|
||
|
|
)
|
||
|
|
|
||
|
|
rfe.fit(X, y)
|
||
|
|
|
||
|
|
# Set final attributes
|
||
|
|
self.support_ = rfe.support_
|
||
|
|
self.n_features_ = rfe.n_features_
|
||
|
|
self.ranking_ = rfe.ranking_
|
||
|
|
self.estimator_ = clone(self.estimator)
|
||
|
|
self.estimator_.fit(self._transform(X), y)
|
||
|
|
|
||
|
|
# reverse to stay consistent with before
|
||
|
|
scores_rev = scores[:, ::-1]
|
||
|
|
self.cv_results_ = {}
|
||
|
|
self.cv_results_["mean_test_score"] = np.mean(scores_rev, axis=0)
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self.cv_results_["std_test_score"] = np.std(scores_rev, axis=0)
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for i in range(scores.shape[0]):
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self.cv_results_[f"split{i}_test_score"] = scores_rev[i]
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return self
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