class_docstr: Makes sure the input data complies with Auto-sklearn requirements.
  Categorical inputs are encoded via a Label Encoder, if the input
  is a dataframe.
  
  This class also perform checks for data integrity and flags the user
  via informative errors.
  
  Attributes
  ----------
  feat_type: Optional[List[str]] = None
      In case the dataset is not a pandas DataFrame:
          +   If provided, this list indicates which columns should be treated as
              categorical it is internally transformed into a dictionary that
              indicates a mapping from column index to categorical/numerical.
          +   If not provided, by default all columns are treated as numerical
  
      If the input dataset is of type pandas dataframe, this argument
      must be none, as the column type will be inferred from the pandas dtypes.
  
  is_classification: bool
      For classification task, this flag indicates that the target data
      should be encoded
  
  feature_validator: FeatureValidator
      A FeatureValidator instance used to validate and encode feature columns to match
      sklearn expectations on the data
  
  target_validator: TargetValidator
      A TargetValidator instance used for classification to validate and encode the
      target values
code: def __init__(
      self,
      feat_type: Optional[List[str]] = None,
      is_classification: bool = False,
      logger_port: Optional[int] = None,
      allow_string_features: bool = True,
  ) -> None:
      self.feat_type = feat_type
      self.is_classification = is_classification
      self.logger_port = logger_port
      if self.logger_port is not None:
          self.logger = get_named_client_logger(
              name="Validation",
              port=self.logger_port,
          )
      else:
          self.logger = logging.getLogger("Validation")
  
      self.allow_string_features = allow_string_features
      self.feature_validator = FeatureValidator(
          feat_type=self.feat_type,
          logger=self.logger,
          allow_string_features=self.allow_string_features,
      )
      self.target_validator = TargetValidator(
          is_classification=self.is_classification, logger=self.logger
      )
      self._is_fitted = False
  
file_path: auto-sklearn/autosklearn/data/validation.py
imports: from typing import List, Optional, Tuple, Union
  import logging
  import numpy
  import pandas
  from scipy.sparse import spmatrix
  from sklearn.base import BaseEstimator
  from sklearn.exceptions import NotFittedError
  from autosklearn.data.feature_validator import SUPPORTED_FEAT_TYPES, FeatureValidator
  from autosklearn.data.target_validator import SUPPORTED_TARGET_TYPES, TargetValidator
  from autosklearn.util.logging_ import get_named_client_logger
label_desc: Use image node_id 1 to create a new InputValidator object from inherited base classes: BaseEstimator with example: obj = InputValidator(feat_type, is_classification, logger_port, allow_string_features)