-i$SrSSKrSSKJrJr SSKJr SSKrSSK J r J r SSK J r SSKJrJrJr SS KJr SS KJr SS KJrJrJrJr "S S \ \S9rSSjrg)zGeneric feature selection mixinN)ABCMetaabstractmethod) attrgetter) csc_matrixissparse)TransformerMixin)_safe_indexing check_arraysafe_sqr)_get_output_config)get_tags)_check_feature_names_in _is_pandas_dfcheck_is_fitted validate_datacN\rSrSrSrS Sjr\S5rSrSr Sr S S jr S r g) SelectorMixinaM Transformer mixin that performs feature selection given a support mask This mixin provides a feature selector implementation with `transform` and `inverse_transform` functionality given an implementation of `_get_support_mask`. Examples -------- >>> import numpy as np >>> from sklearn.datasets import load_iris >>> from sklearn.base import BaseEstimator >>> from sklearn.feature_selection import SelectorMixin >>> class FeatureSelector(SelectorMixin, BaseEstimator): ... def fit(self, X, y=None): ... self.n_features_in_ = X.shape[1] ... return self ... def _get_support_mask(self): ... mask = np.zeros(self.n_features_in_, dtype=bool) ... mask[:2] = True # select the first two features ... return mask >>> X, y = load_iris(return_X_y=True) >>> FeatureSelector().fit_transform(X, y).shape (150, 2) cfUR5nU(dU$[R"U5S$)ak Get a mask, or integer index, of the features selected. Parameters ---------- indices : bool, default=False If True, the return value will be an array of integers, rather than a boolean mask. Returns ------- support : array An index that selects the retained features from a feature vector. If `indices` is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. If `indices` is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector. r)_get_support_masknpnonzero)selfindicesmasks R/var/www/html/venv/lib/python3.13/site-packages/sklearn/feature_selection/_base.py get_supportSelectorMixin.get_support4s-(%%'"t; 4(8(;;cg)z Get the boolean mask indicating which features are selected Returns ------- support : boolean array of shape [# input features] An element is True iff its corresponding feature is selected for retention. N)rs rrSelectorMixin._get_support_maskKsr c [SUS9SnUS:g=(a [U5n[UUSS[U5RR (+USS9nUR U5$) zReduce X to the selected features. Parameters ---------- X : array of shape [n_samples, n_features] The input samples. Returns ------- X_r : array of shape [n_samples, n_selected_features] The input samples with only the selected features. transform) estimatordensedefaultNcsrF)dtype accept_sparseensure_all_finiteskip_check_arrayreset)r rrr input_tags allow_nan _transform)rXoutput_config_dense preserve_Xs rr%SelectorMixin.transformWso1MgV(I5J-:J    "*4.";";"E"EE' q!!r c\UR5nUR5(d~[R"S[5 [ US5(aUR SS2SS24$[R"SURS9RURSS45$[XSS9$)z"Reduce X to the selected features.zYNo features were selected: either the data is too noisy or the selection test too strict.ilocNrr*axis) ranywarningswarn UserWarninghasattrr7remptyr*reshapeshaper )rr2rs rr1SelectorMixin._transformvs!xxzz MMC  q&!!vva!e}$88AQWW-55qwwqz1oF FaA..r c[U5(aUR5nUR[R"UR 5R SS55nUR5n[R"S/[R"U5/5n[URURU4URS[U5S- 4URS9nU$UR!5n[#USS9nUR%5URS:wa ['S5eUR(S:Xa USSS24n[R*"URSUR,4URS9nXSS2U4'U$)a>Reverse the transformation operation. Parameters ---------- X : array of shape [n_samples, n_selected_features] The input samples. Returns ------- X_original : array of shape [n_samples, n_original_features] `X` with columns of zeros inserted where features would have been removed by :meth:`transform`. r9r)rCr*Nr8z,X has a different shape than during fitting.)rtocscinverse_transformrdiffindptrrBravel concatenatecumsumrdatarrClenr*rr sum ValueErrorndimzerossize)rr2it col_nonzerosrJXtsupports rrHSelectorMixin.inverse_transforms8 A;; A''(9(A(A!R(HIB88:L^^aS"))L*A$BCFF+wwqz3v;?3ggB I""$  & ;;=AGGAJ &KL L 66Q;$' A XXqwwqz7<<0 @1g: r NcR[U5 [X5nXR5$)auMask feature names according to selected features. Parameters ---------- input_features : array-like of str or None, default=None Input features. - If `input_features` is `None`, then `feature_names_in_` is used as feature names in. If `feature_names_in_` is not defined, then the following input feature names are generated: `["x0", "x1", ..., "x(n_features_in_ - 1)"]`. - If `input_features` is an array-like, then `input_features` must match `feature_names_in_` if `feature_names_in_` is defined. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. )rrr)rinput_featuress rget_feature_names_out#SelectorMixin.get_feature_names_outs(( 0F..011r r")F)N) __name__ __module__ __qualname____firstlineno____doc__rrrr%r1rHr\__static_attributes__r"r rrrs54<.    ">/ &P2r r) metaclassc[U[5(aoUS:Xa][US5(a [S5nOg[US5(a [S5nOJ[ SUR R S35e[U5nO[U5(d [ S5eU"U5nUcU$US:XaJURS:Xa[R"U5nU$[RRUS US 9nU$US :Xa8URS:Xa [U5nU$[U5RS S 9nU$[ S 5e)aF Retrieve and aggregate (ndim > 1) the feature importances from an estimator. Also optionally applies transformation. Parameters ---------- estimator : estimator A scikit-learn estimator from which we want to get the feature importances. getter : "auto", str or callable An attribute or a callable to get the feature importance. If `"auto"`, `estimator` is expected to expose `coef_` or `feature_importances`. transform_func : {"norm", "square"}, default=None The transform to apply to the feature importances. By default (`None`) no transformation is applied. norm_order : int, default=1 The norm order to apply when `transform_func="norm"`. Only applied when `importances.ndim > 1`. Returns ------- importances : ndarray of shape (n_features,) The features importances, optionally transformed. autocoef_feature_importances_z;when `importance_getter=='auto'`, the underlying estimator z should have `coef_` or `feature_importances_` attribute. Either pass a fitted estimator to feature selector or call fit before calling transform.z4`importance_getter` has to be a string or `callable`normr9r)r;ordsquarer:zpValid values for `transform_func` are None, 'norm' and 'square'. Those two transformation are only supported now) isinstancestrr@rrQ __class__r^callablerRrabslinalgrir rP)r&gettertransform_func norm_order importancess r_get_feature_importancesrvs^8&# V y'**#G,$:;;#$:; !!*!4!4!=!= >?00 'F f  OPP#K 6 !   q &&-K ))..1*.MK  8 #   q ";/K #;/333;K   4  r )Nr9)rbr=abcrroperatorrnumpyr scipy.sparserrbaser utilsr r r utils._set_outputr utils._tagsrutils.validationrrrrrrvr"r rrsJ% '-#992"k2$k2\Dr