class_docstr: Tranformer 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`.
class_docstr_tok: ["Tranformer","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","`","."]
docstr: Reduce 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.
docstr_tok: ["Reduce","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","."]
code: def transform(self, X):
      """Reduce 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.
      """
      X = check_array(X, accept_sparse="csr")
      mask = self.get_support()
      if not mask.any():
          warn(
              "No features were selected: either the data is"
              " too noisy or the selection test too strict.",
              UserWarning,
          )
          return np.empty(0).reshape((X.shape[0], 0))
      if len(mask) != X.shape[1]:
          raise ValueError(
              "X has a different shape than during fitting."
          )
      return X[:, safe_mask(X, mask)]
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/feature_selection/base.py
imports: from abc import ABCMeta, abstractmethod
  from warnings import warn
  import numpy
  from scipy.sparse import issparse, csc_matrix
  from ..base import TransformerMixin
  from ..utils import check_array, safe_mask
  from ..externals import six
label_desc: Use image node_id 3 for calling the SelectorMixin obj's underlying member method code with example usage: obj.transform(X) and returns: X, np