class_docstr: Memory efficient feature selector. Identical functionality
  to classes in sklearn.feature_selection.univariate_selection but
  with more memory efficient attribute storage.
  
  Args:
      selector (object): any sklearn.feature_selection.univariate_selection._BaseFilter
          inheritor
      score_func (function): selector scoring function
      threshold (int/float): scoring threshold to use with scoring function and selector
class_docstr_tok: ["Memory","efficient","feature","selector",".","Identical","functionality","to","classes","in","sklearn.feature_selection.univariate_selection","but","with","more","memory","efficient","attribute","storage",".","Args",":","selector","(","object",")",":","any","sklearn.feature_selection.univariate_selection._BaseFilter","inheritor","score_func","(","function",")",":","selector","scoring","function","threshold","(","int\/float",")",":","scoring","threshold","to","use","with","scoring","function","and","selector"]
docstr: Reduce X to the selected features
  
  Args:
      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","Args",":","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, y=None):
      """
      Reduce X to the selected features
  
      Args:
          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
      """
      return X[:, self.mask]
  
imports: import warnings
  import pandas
  import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.preprocessing import MultiLabelBinarizer, FunctionTransformer, LabelEncoder, normalize
  from sklearn.feature_extraction.text import HashingVectorizer
  from sklearn import feature_selection
label_desc: Use image node_id 3 for calling the SelectorMem obj's underlying member method code with example usage: obj.transform(X, y) and returns: X