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"]
code: def __init__(
      self,
      selector="fpr",
      score_func=feature_selection.f_classif,
      threshold=0.05,
  ):
      self.selector = selector
      self.score_func = score_func
      self.threshold = threshold
  
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 1 to create a new SelectorMem object from inherited base classes: TransformerMixin, BaseEstimator with example: obj = SelectorMem(selector, score_func, threshold)