docstr: Parameters:
  random state : ignored
  
  score_func : callable, Function taking two arrays X and y, and
               returning a pair of arrays (scores, pvalues).
docstr_tok: ["Parameters",":","random","state",":","ignored","score_func",":","callable",",","Function","taking","two","arrays","X","and","y",",","and","returning","a","pair","of","arrays","(","scores",",","pvalues",")","."]
code: def __init__(
      self, percentile, score_func="f_regression", random_state=None
  ):
      """Parameters:
      random state : ignored
  
      score_func : callable, Function taking two arrays X and y, and
                   returning a pair of arrays (scores, pvalues).
      """
      import sklearn.feature_selection
  
      self.random_state = random_state  # We don't use this
      self.percentile = int(float(percentile))
      if score_func == "f_regression":
          self.score_func = sklearn.feature_selection.f_regression
      elif score_func == "mutual_info":
          self.score_func = partial(
              sklearn.feature_selection.mutual_info_regression,
              random_state=self.random_state,
          )
      else:
          raise ValueError(
              "Don't know this scoring function: %s" % score_func
          )
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/select_percentile_regression.py
imports: from typing import Optional
  from functools import partial
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.components.feature_preprocessing.select_percentile import SelectPercentileBase
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 1 to create a new SelectPercentileRegression object from inherited base classes: SelectPercentileBase, AutoSklearnPreprocessingAlgorithm with example: obj = SelectPercentileRegression(percentile, score_func, random_state)