docstr: Parameters
  ----------
  dataset_properties : dict
      Describes the dataset to work on, this can change the
      configuration space constructed by auto-sklearn. Mandatory
      properties are:
      * target_type: classification or regression
  
  
      Optional properties are:
      * multiclass: whether the dataset is a multiclass classification
        dataset.
      * multilabel: whether the dataset is a multilabel classification
        dataset
docstr_tok: ["Parameters","--","--","--","--","--","dataset_properties",":","dict","Describes","the","dataset","to","work","on",",","this","can","change","the","configuration","space","constructed","by","auto-sklearn",".","Mandatory","properties","are",":","*","target_type",":","classification","or","regression","Optional","properties","are",":","*","multiclass",":","whether","the","dataset","is","a","multiclass","classification","dataset",".","*","multilabel",":","whether","the","dataset","is","a","multilabel","classification","dataset"]
code: def __init__(
      self,
      dataset_properties,
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      random_state=None,
  ):
      """
      Parameters
      ----------
      dataset_properties : dict
          Describes the dataset to work on, this can change the
          configuration space constructed by auto-sklearn. Mandatory
          properties are:
          * target_type: classification or regression
  
  
          Optional properties are:
          * multiclass: whether the dataset is a multiclass classification
            dataset.
          * multilabel: whether the dataset is a multilabel classification
            dataset
      """
  
      # Since all calls to get_hyperparameter_search_space will be done by the
      # pipeline on construction, it is not necessary to construct a
      # configuration space at this location!
      # self.configuration = self.get_hyperparameter_search_space(
      #     dataset_properties).get_default_configuration()
  
      self.random_state = random_state
  
      # Since the pipeline will initialize the hyperparameters, it is not
      # necessary to do this upon the construction of this object
      # self.set_hyperparameters(self.configuration)
      self.choice = None
  
imports: from typing import Dict, Optional
  import importlib
  import inspect
  import pkgutil
  import sys
  from collections import OrderedDict
  from sklearn.base import BaseEstimator, TransformerMixin
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.constants import SPARSE
label_desc: Use image node_id 1 to create a new AutoSklearnChoice object from inherited base classes: object with example: obj = AutoSklearnChoice(dataset_properties, feat_type, random_state)