text: from skmultiflow.trees import HoeffdingTreeRegressor
  from skmultiflow.trees.nodes import RandomLearningNodeForRegression
  from skmultiflow.trees.nodes import InactiveLearningNodeForRegression
  from skmultiflow.trees.nodes import RandomLearningNodePerceptron
  from skmultiflow.trees.nodes import InactiveLearningNodePerceptron
  from skmultiflow.utils import check_random_state
  
  
  class ARFHoeffdingTreeRegressor(HoeffdingTreeRegressor):
      """ ARF Hoeffding Tree regressor.
  
      Parameters
      ----------
      max_byte_size: int (default=33554432)
          Maximum memory consumed by the tree.
      memory_estimate_period: int (default=2000000)
          Number of instances between memory consumption checks.
      grace_period: int (default=50)
          Number of instances a leaf should observe between split attempts.
      split_confidence: float (default=0.01)
          Allowed error in split decision, a value closer to 0 takes longer to decide.
      tie_threshold: float (default=0.05)
          Threshold below which a split will be forced to break ties.
      binary_split: boolean (default=False)
          If True, only allow binary splits.
      stop_mem_management: boolean (default=False)
          If True, stop growing as soon as memory limit is hit.
      remove_poor_atts: boolean (default=False)
          If True, disable poor attributes.
      no_preprune: boolean (default=False)
          If True, disable pre-pruning.
      leaf_prediction: string (default='perceptron')
          | Prediction mechanism used at leafs.
          | 'mean' - Target mean
          | 'perceptron' - Perceptron
      nominal_attributes: list, optional (default: None)
          List of Nominal attributes. If emtpy, then assume that all attributes are numerical.
      learning_ratio_perceptron: float (default: 0.02)
          The learning rate of the perceptron.
      learning_ratio_decay: float (default: 0.001)
          Decay multiplier for the learning rate of the perceptron
      learning_ratio_const: boolean (default: True)
          If False the learning ratio will decay with the number of examples seen
      max_features: int (default: 2)
          Number of randomly selected features to act as split candidates at each attempt.
      random_state: int, RandomState instance or None, optional (default=None)
         If int, random_state is the seed used by the random number generator;
         If RandomState instance, random_state is the random number generator;
         If None, the random number generator is the RandomState instance used
         by `np.random`. Used when leaf_prediction is 'perceptron' and to randomly pick subsets of
         features at each split.
  
      This is the base-estimator of the Adaptive Random Forest Regressor ensemble learner (see
      skmultiflow.meta.adaptive_random_forest_regressor). This Hoeffding Tree Regressor includes a
      max_features parameter, which defines the number of randomly selected features to be considered
      at each split.
      """
  
      def __init__(self,
                   max_byte_size=33554432,
                   memory_estimate_period=2000000,
                   grace_period=50,
                   split_confidence=0.01,
                   tie_threshold=0.05,
                   binary_split=False,
                   stop_mem_management=False,
                   remove_poor_atts=False,
                   leaf_prediction="perceptron",
                   no_preprune=False,
                   nominal_attributes=None,
                   learning_ratio_perceptron=0.02,
                   learning_ratio_decay=0.001,
                   learning_ratio_const=True,
                   max_features=2,
                   random_state=None):
          super().__init__(max_byte_size=max_byte_size,
                           memory_estimate_period=memory_estimate_period,
                           grace_period=grace_period,
                           split_confidence=split_confidence,
                           tie_threshold=tie_threshold,
                           binary_split=binary_split,
                           stop_mem_management=stop_mem_management,
                           remove_poor_atts=remove_poor_atts,
                           leaf_prediction=leaf_prediction,
                           no_preprune=no_preprune,
                           nominal_attributes=nominal_attributes,
                           learning_ratio_perceptron=learning_ratio_perceptron,
                           learning_ratio_decay=learning_ratio_decay,
                           learning_ratio_const=learning_ratio_const,
                           )
  
          self.max_features = max_features
          self.random_state = random_state
          self._random_state = check_random_state(self.random_state)
  
      def _new_learning_node(self, initial_class_observations=None, parent_node=None,
                             is_active_node=True):
          """Create a new learning node. The type of learning node depends on the tree
          configuration."""
          if initial_class_observations is None:
              initial_class_observations = {}
  
          # Generate a random seed for the new learning node
          random_state = self._random_state.randint(0, 4294967295, dtype='u8')
          if is_active_node:
              if self.leaf_prediction == self._TARGET_MEAN:
                  return RandomLearningNodeForRegression(
                      initial_class_observations, max_features=self.max_features,
                      random_state=random_state
                  )
              elif self.leaf_prediction == self._PERCEPTRON:
                  return RandomLearningNodePerceptron(
                      initial_class_observations, max_features=self.max_features,
                      parent_node=parent_node, random_state=random_state
                  )
          else:
              if self.leaf_prediction == self._TARGET_MEAN:
                  return InactiveLearningNodeForRegression(initial_class_observations)
              elif self.leaf_prediction == self._PERCEPTRON:
                  return InactiveLearningNodePerceptron(initial_class_observations, parent_node,
                                                        random_state=random_state)
  
      @staticmethod
      def is_randomizable():
          return True
  
      def reset(self):
          super().reset()
          self._random_state = check_random_state(self.random_state)
  
      def new_instance(self):
          return ARFHoeffdingTreeRegressor(max_byte_size=self.max_byte_size,
                                           memory_estimate_period=self.memory_estimate_period,
                                           grace_period=self.grace_period,
                                           split_confidence=self.split_confidence,
                                           tie_threshold=self.tie_threshold,
                                           binary_split=self.binary_split,
                                           stop_mem_management=self.stop_mem_management,
                                           remove_poor_atts=self.remove_poor_atts,
                                           leaf_prediction=self.leaf_prediction,
                                           no_preprune=self.no_preprune,
                                           nominal_attributes=self.nominal_attributes,
                                           learning_ratio_perceptron=self.learning_ratio_perceptron,
                                           learning_ratio_decay=self.learning_ratio_decay,
                                           learning_ratio_const=self.learning_ratio_const,
                                           max_features=self.max_features,
                                           random_state=self.random_state
                                           )
  