class_docstr: Initialize the univariate feature selection.
  
  Parameters
  ----------
  score_func : callable
      Function taking two arrays X and y, and returning a pair of arrays
      (scores, pvalues) or a single array with scores.
class_docstr_tok: ["Initialize","the","univariate","feature","selection",".","Parameters","--","--","--","--","--","score_func",":","callable","Function","taking","two","arrays","X","and","y",",","and","returning","a","pair","of","arrays","(","scores",",","pvalues",")","or","a","single","array","with","scores","."]
docstr: Run score function on (X, y) and get the appropriate features.
  
  Parameters
  ----------
  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).
  
  Returns
  -------
  self : object
      Returns self.
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code: def fit(self, X, y):
      """Run score function on (X, y) and get the appropriate features.
  
      Parameters
      ----------
      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).
  
      Returns
      -------
      self : object
          Returns self.
      """
      X, y = check_X_y(X, y, ["csr", "csc"], multi_output=True)
  
      if not callable(self.score_func):
          raise TypeError(
              "The score function should be a callable, %s (%s) "
              "was passed." % (self.score_func, type(self.score_func))
          )
  
      self._check_params(X, y)
      score_func_ret = self.score_func(X, y)
      if isinstance(score_func_ret, (list, tuple)):
          self.scores_, self.pvalues_ = score_func_ret
          self.pvalues_ = np.asarray(self.pvalues_)
      else:
          self.scores_ = score_func_ret
          self.pvalues_ = None
  
      self.scores_ = np.asarray(self.scores_)
  
      return self
  
imports: import numpy
  import warnings
  from scipy import special, stats
  from scipy.sparse import issparse
  from ..base import BaseEstimator
  from ..preprocessing import LabelBinarizer
  from ..utils import as_float_array, check_array, check_X_y, safe_sqr, safe_mask
  from ..utils.extmath import norm, safe_sparse_dot, row_norms
  from ..utils.validation import check_is_fitted
  from .base import SelectorMixin
label_desc: Use image node_id 2 for calling the _BaseFilter obj's underlying member method code with example usage: obj.fit(X, y) and returns: self