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: Run score function on (X, y) and get the appropriate features
  
  Args:
      X (array-like, shape = [n_samples, n_features])
          The training input samples
      y (array-like, shape = [n_samples])
          The target values (class labels in classification, real numbers in
          regression)
docstr_tok: ["Run","score","function","on","(","X",",","y",")","and","get","the","appropriate","features","Args",":","X","(","array-like",",","shape","=","[","n_samples",",","n_features","]",")","The","training","input","samples","y","(","array-like",",","shape","=","[","n_samples","]",")","The","target","values","(","class","labels","in","classification",",","real","numbers","in","regression",")"]
code: def fit(self, X, y=None):
      """
      Run score function on (X, y) and get the appropriate features
  
      Args:
          X (array-like, shape = [n_samples, n_features])
              The training input samples
          y (array-like, shape = [n_samples])
              The target values (class labels in classification, real numbers in
              regression)
      """
      selector = _SELECTOR_LOOKUP[self.selector.lower()](
          self.score_func, self.threshold
      )
      selector.fit(X, y)
      mask_indices = selector.get_support(indices=True)
      mask_bool = selector.get_support(indices=False)
      if np.array(mask_bool).nbytes > np.array(mask_indices).nbytes:
          self.mask = mask_indices
      else:
          self.mask = mask_bool
      return self
  
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 2 for calling the SelectorMem obj's underlying member method code with example usage: obj.fit(X, y) and returns: self