text: from typing import Optional
  from typing import Union
  from typing import Callable
  
  from sklearn.feature_selection import f_classif
  
  from .typing import EstimatorType
  from .typing import Literal
  from .typing import CVType
  
  
  class GenericUnivariateSelect:
      def __init__(
          self,
          score_func: Callable = f_classif,
          mode: Literal["percentile", "k_best", "fpr", "fdr", "fwe"] = "percentile",
          param: Union[float, int] = 1e-05,
      ):
          ...
  
  
  class RFE:
      def __init__(
          self,
          estimator: EstimatorType,
          n_features_to_select: Union[int, None] = None,
          step: Union[int, float] = 1,
          verbose: int = 0,
      ):
          ...
  
  
  class RFECV:
      def __init__(
          self,
          estimator: EstimatorType,
          step: Union[int, float] = 1,
          min_features_to_select: int = 1,
          cv: CVType = None,
          scoring: Union[str, Callable, None] = None,
          verbose: int = 0,
          n_jobs: Union[int, None] = None,
      ):
          ...
  
  
  class SelectFdr:
      def __init__(self, score_func: Callable = f_classif, alpha: float = 0.05):
          ...
  
  
  class SelectFpr:
      def __init__(self, score_func: Callable = f_classif, alpha: float = 0.05):
          ...
  
  
  class SelectFromModel:
      def __init__(
          self,
          estimator: EstimatorType,
          threshold: Union[str, float, None] = None,
          prefit: bool = False,
          norm_order: Union[int, float] = 1,
          max_features: Optional[None] = None,
      ):
          ...
  
  
  class SelectFwe:
      def __init__(self, score_func: Callable = f_classif, alpha: float = 0.05):
          ...
  
  
  class SelectKBest:
      def __init__(
          self, score_func: Callable = f_classif, k: Union[Literal["all"], int] = 10
      ):
          ...
  
  
  class SelectPercentile:
      def __init__(self, score_func: Callable = f_classif, percentile: int = 10):
          ...
  
  
  class VarianceThreshold:
      def __init__(self, threshold: float = 0.0):
          ...
  