# Authors: Alexandre Gramfort # Vincent Michel # Gilles Louppe # # License: BSD 3 clause """Recursive feature elimination for feature ranking""" import numpy as np from ..utils import check_X_y, safe_sqr from ..utils.metaestimators import if_delegate_has_method from ..base import BaseEstimator from ..base import MetaEstimatorMixin from ..base import clone from ..base import is_classifier from ..externals.joblib import Parallel, delayed from ..model_selection import check_cv from ..model_selection._validation import _safe_split, _score from ..metrics.scorer import check_scoring from .base import SelectorMixin def _rfe_single_fit(rfe, estimator, X, y, train, test, scorer): """ Return the score for a fit across one fold. """ X_train, y_train = _safe_split(estimator, X, y, train) X_test, y_test = _safe_split(estimator, X, y, test, train) return rfe._fit( X_train, y_train, lambda estimator, features: _score(estimator, X_test[:, features], y_test, scorer)).scores_ class RFE(BaseEstimator, MetaEstimatorMixin, SelectorMixin): """Feature ranking with recursive feature elimination. Given an external estimator that assigns weights to features (e.g., the coefficients of a linear model), the goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features and weights are assigned to each one of them. Then, features whose absolute weights are the smallest are pruned from the current set features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached. Read more in the :ref:`User Guide `. Parameters ---------- estimator : object A supervised learning estimator with a `fit` method that updates a `coef_` attribute that holds the fitted parameters. Important features must correspond to high absolute values in the `coef_` array. For instance, this is the case for most supervised learning algorithms such as Support Vector Classifiers and Generalized Linear Models from the `svm` and `linear_model` modules. n_features_to_select : int or None (default=None) The number of features to select. If `None`, half of the features are selected. step : int or float, optional (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. verbose : int, default=0 Controls verbosity of output. Attributes ---------- n_features_ : int The number of selected features. support_ : array of shape [n_features] The mask of selected features. ranking_ : array 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. estimator_ : object The external estimator fit on the reduced dataset. Examples -------- The following example shows how to retrieve the 5 right informative features in the Friedman #1 dataset. >>> from sklearn.datasets import make_friedman1 >>> from sklearn.feature_selection import RFE >>> from sklearn.svm import SVR >>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0) >>> estimator = SVR(kernel="linear") >>> selector = RFE(estimator, 5, step=1) >>> selector = selector.fit(X, y) >>> selector.support_ # doctest: +NORMALIZE_WHITESPACE array([ True, True, True, True, True, False, False, False, False, False], dtype=bool) >>> selector.ranking_ array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5]) 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. """ def __init__(self, estimator, n_features_to_select=None, step=1, verbose=0): self.estimator = estimator self.n_features_to_select = n_features_to_select self.step = step self.verbose = verbose @property def _estimator_type(self): return self.estimator._estimator_type def fit(self, X, y): """Fit the RFE model and then the underlying estimator on the selected features. Parameters ---------- X : {array-like, sparse matrix}, shape = [n_samples, n_features] The training input samples. y : array-like, shape = [n_samples] The target values. """ return self._fit(X, y) def _fit(self, X, y, step_score=None): X, y = check_X_y(X, y, "csc") # Initialization n_features = X.shape[1] if self.n_features_to_select is None: n_features_to_select = n_features // 2 else: n_features_to_select = self.n_features_to_select if 0.0 < self.step < 1.0: step = int(max(1, self.step * n_features)) else: step = int(self.step) if step <= 0: raise ValueError("Step must be >0") support_ = np.ones(n_features, dtype=np.bool) ranking_ = np.ones(n_features, dtype=np.int) if step_score: self.scores_ = [] # Elimination while np.sum(support_) > n_features_to_select: # Remaining features features = np.arange(n_features)[support_] # Rank the remaining features estimator = clone(self.estimator) if self.verbose > 0: print("Fitting estimator with %d features." % np.sum(support_)) estimator.fit(X[:, features], y) # Get coefs if hasattr(estimator, 'coef_'): coefs = estimator.coef_ elif hasattr(estimator, 'feature_importances_'): coefs = estimator.feature_importances_ else: raise RuntimeError('The classifier does not expose ' '"coef_" or "feature_importances_" ' 'attributes') # Get ranks if coefs.ndim > 1: ranks = np.argsort(safe_sqr(coefs).sum(axis=0)) else: ranks = np.argsort(safe_sqr(coefs)) # for sparse case ranks is matrix ranks = np.ravel(ranks) # Eliminate the worse features threshold = min(step, np.sum(support_) - n_features_to_select) # Compute step score on the previous selection iteration # because 'estimator' must use features # that have not been eliminated yet if step_score: self.scores_.append(step_score(estimator, features)) support_[features[ranks][:threshold]] = False ranking_[np.logical_not(support_)] += 1 # Set final attributes features = np.arange(n_features)[support_] self.estimator_ = clone(self.estimator) self.estimator_.fit(X[:, features], y) # Compute step score when only n_features_to_select features left if step_score: self.scores_.append(step_score(self.estimator_, features)) self.n_features_ = support_.sum() self.support_ = support_ self.ranking_ = ranking_ return self @if_delegate_has_method(delegate='estimator') def predict(self, X): """Reduce X to the selected features and then predict using the underlying estimator. Parameters ---------- X : array of shape [n_samples, n_features] The input samples. Returns ------- y : array of shape [n_samples] The predicted target values. """ return self.estimator_.predict(self.transform(X)) @if_delegate_has_method(delegate='estimator') def score(self, X, y): """Reduce X to the selected features and then return the score of the underlying estimator. Parameters ---------- X : array of shape [n_samples, n_features] The input samples. y : array of shape [n_samples] The target values. """ return self.estimator_.score(self.transform(X), y) def _get_support_mask(self): return self.support_ @if_delegate_has_method(delegate='estimator') def decision_function(self, X): return self.estimator_.decision_function(self.transform(X)) @if_delegate_has_method(delegate='estimator') def predict_proba(self, X): return self.estimator_.predict_proba(self.transform(X)) @if_delegate_has_method(delegate='estimator') def predict_log_proba(self, X): return self.estimator_.predict_log_proba(self.transform(X)) class RFECV(RFE, MetaEstimatorMixin): """Feature ranking with recursive feature elimination and cross-validated selection of the best number of features. Read more in the :ref:`User Guide `. Parameters ---------- estimator : object A supervised learning estimator with a `fit` method that updates a `coef_` attribute that holds the fitted parameters. Important features must correspond to high absolute values in the `coef_` array. For instance, this is the case for most supervised learning algorithms such as Support Vector Classifiers and Generalized Linear Models from the `svm` and `linear_model` modules. step : int or float, optional (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. cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 3-fold cross-validation, - integer, to specify the number of folds. - An object to be used as a cross-validation generator. - An iterable yielding train/test splits. 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 ` for the various cross-validation strategies that can be used here. scoring : string, callable or None, optional, 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, default 1 Number of cores to run in parallel while fitting across folds. Defaults to 1 core. If `n_jobs=-1`, then number of jobs is set to number of cores. Attributes ---------- n_features_ : int The number of selected features with cross-validation. support_ : array of shape [n_features] The mask of selected features. ranking_ : array 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. grid_scores_ : array of shape [n_subsets_of_features] The cross-validation scores such that ``grid_scores_[i]`` corresponds to the CV score of the i-th subset of features. estimator_ : object The external estimator fit on the reduced dataset. Notes ----- The size of ``grid_scores_`` is equal to ceil((n_features - 1) / step) + 1, where step is the number of features removed at each iteration. 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_ # doctest: +NORMALIZE_WHITESPACE array([ True, True, True, True, True, False, False, False, False, False], dtype=bool) >>> selector.ranking_ array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5]) 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. """ def __init__(self, estimator, step=1, cv=None, scoring=None, verbose=0, n_jobs=1): self.estimator = estimator self.step = step self.cv = cv self.scoring = scoring self.verbose = verbose self.n_jobs = n_jobs def fit(self, X, y): """Fit the RFE model and automatically tune the number of selected features. Parameters ---------- X : {array-like, sparse matrix}, 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, shape = [n_samples] Target values (integers for classification, real numbers for regression). """ X, y = check_X_y(X, y, "csr") # Initialization cv = check_cv(self.cv, y, is_classifier(self.estimator)) scorer = check_scoring(self.estimator, scoring=self.scoring) n_features = X.shape[1] n_features_to_select = 1 rfe = RFE(estimator=self.estimator, n_features_to_select=n_features_to_select, step=self.step, verbose=self.verbose - 1) # 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 self.n_jobs == 1: parallel, func = list, _rfe_single_fit else: parallel, func, = Parallel(n_jobs=self.n_jobs), delayed(_rfe_single_fit) scores = parallel( func(rfe, self.estimator, X, y, train, test, scorer) for train, test in cv.split(X, y)) scores = np.sum(scores, axis=0) n_features_to_select = max( n_features - (np.argmax(scores) * self.step), n_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) 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) # Fixing a normalization error, n is equal to get_n_splits(X, y) - 1 # here, the scores are normalized by get_n_splits(X, y) self.grid_scores_ = scores[::-1] / cv.get_n_splits(X, y) return self