460 lines
16 KiB
Python
460 lines
16 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 ..utils import check_X_y, safe_sqr
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from ..utils.metaestimators import if_delegate_has_method
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from ..utils.validation import check_is_fitted
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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 ..externals.joblib import Parallel, delayed
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from ..model_selection import check_cv
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from ..model_selection._validation import _safe_split, _score
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from ..metrics.scorer import check_scoring
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from .base import SelectorMixin
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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, y_train, lambda estimator, features:
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_score(estimator, X_test[:, features], y_test, scorer)).scores_
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class RFE(BaseEstimator, MetaEstimatorMixin, SelectorMixin):
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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 a
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``coef_`` attribute or through a ``feature_importances_`` attribute.
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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 : object
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A supervised learning estimator with a ``fit`` method that provides
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information about feature importance either through a ``coef_``
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attribute or through a ``feature_importances_`` attribute.
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n_features_to_select : int or None (default=None)
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The number of features to select. If `None`, half of the features
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are selected.
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step : int or float, optional (default=1)
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If greater than or equal to 1, then `step` corresponds to the (integer)
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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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Attributes
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----------
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n_features_ : int
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The number of selected features.
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support_ : array of shape [n_features]
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The mask of selected features.
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ranking_ : array 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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estimator_ : object
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The external estimator fit on the reduced dataset.
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Examples
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--------
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The following example shows how to retrieve the 5 right 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, 5, step=1)
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>>> selector = selector.fit(X, y)
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>>> selector.support_ # doctest: +NORMALIZE_WHITESPACE
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array([ True, True, True, True, True,
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False, False, False, False, False], dtype=bool)
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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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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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"""
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def __init__(self, estimator, n_features_to_select=None, step=1,
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verbose=0):
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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.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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def fit(self, X, y):
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"""Fit the RFE model and then the underlying estimator on the selected
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features.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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The training input samples.
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y : array-like, shape = [n_samples]
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The target values.
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"""
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return self._fit(X, y)
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def _fit(self, X, y, step_score=None):
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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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X, y = check_X_y(X, y, "csc")
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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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else:
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n_features_to_select = 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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if step <= 0:
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raise ValueError("Step must be >0")
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support_ = np.ones(n_features, dtype=np.bool)
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ranking_ = np.ones(n_features, dtype=np.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)
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# Get coefs
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if hasattr(estimator, 'coef_'):
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coefs = estimator.coef_
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else:
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coefs = getattr(estimator, 'feature_importances_', None)
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if coefs is None:
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raise RuntimeError('The classifier does not expose '
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'"coef_" or "feature_importances_" '
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'attributes')
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# Get ranks
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if coefs.ndim > 1:
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ranks = np.argsort(safe_sqr(coefs).sum(axis=0))
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else:
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ranks = np.argsort(safe_sqr(coefs))
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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)
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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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@if_delegate_has_method(delegate='estimator')
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def predict(self, X):
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"""Reduce X to the selected features and then predict using the
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underlying 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, 'estimator_')
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return self.estimator_.predict(self.transform(X))
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@if_delegate_has_method(delegate='estimator')
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def score(self, X, y):
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"""Reduce X to the selected features and then return the score of the
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underlying 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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"""
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check_is_fitted(self, 'estimator_')
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return self.estimator_.score(self.transform(X), y)
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def _get_support_mask(self):
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check_is_fitted(self, 'support_')
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return self.support_
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@if_delegate_has_method(delegate='estimator')
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def decision_function(self, X):
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check_is_fitted(self, 'estimator_')
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return self.estimator_.decision_function(self.transform(X))
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@if_delegate_has_method(delegate='estimator')
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def predict_proba(self, X):
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check_is_fitted(self, 'estimator_')
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return self.estimator_.predict_proba(self.transform(X))
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@if_delegate_has_method(delegate='estimator')
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def predict_log_proba(self, X):
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check_is_fitted(self, 'estimator_')
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return self.estimator_.predict_log_proba(self.transform(X))
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class RFECV(RFE, MetaEstimatorMixin):
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"""Feature ranking with recursive feature elimination and cross-validated
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selection of the best number of features.
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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 : object
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A supervised learning estimator with a ``fit`` method that provides
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information about feature importance either through a ``coef_``
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attribute or through a ``feature_importances_`` attribute.
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step : int or float, optional (default=1)
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If greater than or equal to 1, then `step` corresponds to the (integer)
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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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cv : int, cross-validation generator or an iterable, optional
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Determines the cross-validation splitting strategy.
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Possible inputs for cv are:
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- None, to use the default 3-fold cross-validation,
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- integer, to specify the number of folds.
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- An object to be used as a cross-validation generator.
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- An iterable yielding train/test splits.
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For integer/None inputs, if ``y`` is binary or multiclass,
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:class:`sklearn.model_selection.StratifiedKFold` is used. If the
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estimator is a classifier or if ``y`` is neither binary nor multiclass,
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:class:`sklearn.model_selection.KFold` is used.
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Refer :ref:`User Guide <cross_validation>` for the various
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cross-validation strategies that can be used here.
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scoring : string, callable or None, optional, default: None
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A string (see model evaluation documentation) or
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a scorer callable object / function with signature
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``scorer(estimator, X, y)``.
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verbose : int, default=0
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Controls verbosity of output.
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n_jobs : int, default 1
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Number of cores to run in parallel while fitting across folds.
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Defaults to 1 core. If `n_jobs=-1`, then number of jobs is set
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to number of cores.
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Attributes
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----------
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n_features_ : int
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The number of selected features with cross-validation.
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support_ : array of shape [n_features]
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The mask of selected features.
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ranking_ : array of shape [n_features]
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The feature ranking, such that `ranking_[i]`
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corresponds to the ranking
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position of the i-th feature.
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Selected (i.e., estimated best)
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features are assigned rank 1.
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grid_scores_ : array of shape [n_subsets_of_features]
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The cross-validation scores such that
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``grid_scores_[i]`` corresponds to
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the CV score of the i-th subset of features.
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estimator_ : object
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The external estimator fit on the reduced dataset.
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Notes
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-----
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The size of ``grid_scores_`` is equal to ceil((n_features - 1) / step) + 1,
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where step is the number of features removed at each iteration.
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Examples
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--------
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The following example shows how to retrieve the a-priori not known 5
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informative 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 RFECV
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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 = RFECV(estimator, step=1, cv=5)
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>>> selector = selector.fit(X, y)
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>>> selector.support_ # doctest: +NORMALIZE_WHITESPACE
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array([ True, True, True, True, True,
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False, False, False, False, False], dtype=bool)
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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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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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"""
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def __init__(self, estimator, step=1, cv=None, scoring=None, verbose=0,
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n_jobs=1):
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self.estimator = estimator
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self.step = step
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self.cv = cv
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self.scoring = scoring
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self.verbose = verbose
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self.n_jobs = n_jobs
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def fit(self, X, y):
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"""Fit the RFE model and automatically tune the number of selected
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features.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape = [n_samples, n_features]
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Training vector, where `n_samples` is the number of samples and
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`n_features` is the total number of features.
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y : array-like, shape = [n_samples]
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Target values (integers for classification, real numbers for
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regression).
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"""
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X, y = check_X_y(X, y, "csr")
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# Initialization
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cv = check_cv(self.cv, y, is_classifier(self.estimator))
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scorer = check_scoring(self.estimator, scoring=self.scoring)
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n_features = X.shape[1]
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n_features_to_select = 1
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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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if step <= 0:
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raise ValueError("Step must be >0")
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rfe = RFE(estimator=self.estimator,
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n_features_to_select=n_features_to_select,
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step=self.step, verbose=self.verbose)
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# Determine the number of subsets of features by fitting across
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# the train folds and choosing the "features_to_select" parameter
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# that gives the least averaged error across all folds.
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# Note that joblib raises a non-picklable error for bound methods
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# even if n_jobs is set to 1 with the default multiprocessing
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# backend.
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# This branching is done so that to
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# make sure that user code that sets n_jobs to 1
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# and provides bound methods as scorers is not broken with the
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# addition of n_jobs parameter in version 0.18.
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if self.n_jobs == 1:
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parallel, func = list, _rfe_single_fit
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else:
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parallel, func, = Parallel(n_jobs=self.n_jobs), delayed(_rfe_single_fit)
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scores = parallel(
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func(rfe, self.estimator, X, y, train, test, scorer)
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for train, test in cv.split(X, y))
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scores = np.sum(scores, axis=0)
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n_features_to_select = max(
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n_features - (np.argmax(scores) * step),
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n_features_to_select)
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# Re-execute an elimination with best_k over the whole set
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rfe = RFE(estimator=self.estimator,
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n_features_to_select=n_features_to_select, step=self.step)
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rfe.fit(X, y)
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# Set final attributes
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self.support_ = rfe.support_
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self.n_features_ = rfe.n_features_
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self.ranking_ = rfe.ranking_
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self.estimator_ = clone(self.estimator)
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self.estimator_.fit(self.transform(X), y)
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# Fixing a normalization error, n is equal to get_n_splits(X, y) - 1
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# here, the scores are normalized by get_n_splits(X, y)
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self.grid_scores_ = scores[::-1] / cv.get_n_splits(X, y)
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return self
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