text: """
  Forest of trees-based ensemble methods.
  
  Those methods include random forests and extremely randomized trees.
  
  The module structure is the following:
  
  - The ``BaseForest`` base class implements a common ``fit`` method for all
    the estimators in the module. The ``fit`` method of the base ``Forest``
    class calls the ``fit`` method of each sub-estimator on random samples
    (with replacement, a.k.a. bootstrap) of the training set.
  
    The init of the sub-estimator is further delegated to the
    ``BaseEnsemble`` constructor.
  
  - The ``ForestClassifier`` and ``ForestRegressor`` base classes further
    implement the prediction logic by computing an average of the predicted
    outcomes of the sub-estimators.
  
  - The ``RandomForestClassifier`` and ``RandomForestRegressor`` derived
    classes provide the user with concrete implementations of
    the forest ensemble method using classical, deterministic
    ``DecisionTreeClassifier`` and ``DecisionTreeRegressor`` as
    sub-estimator implementations.
  
  - The ``ExtraTreesClassifier`` and ``ExtraTreesRegressor`` derived
    classes provide the user with concrete implementations of the
    forest ensemble method using the extremely randomized trees
    ``ExtraTreeClassifier`` and ``ExtraTreeRegressor`` as
    sub-estimator implementations.
  
  Single and multi-output problems are both handled.
  """
  
  # Authors: Gilles Louppe <g.louppe@gmail.com>
  #          Brian Holt <bdholt1@gmail.com>
  #          Joly Arnaud <arnaud.v.joly@gmail.com>
  #          Fares Hedayati <fares.hedayati@gmail.com>
  #
  # License: BSD 3 clause
  
  
  import numbers
  from warnings import catch_warnings, simplefilter, warn
  import threading
  
  from abc import ABCMeta, abstractmethod
  import numpy as np
  from scipy.sparse import issparse
  from scipy.sparse import hstack as sparse_hstack
  from joblib import Parallel
  
  from ..base import is_classifier
  from ..base import ClassifierMixin, RegressorMixin, MultiOutputMixin
  from ..metrics import accuracy_score, r2_score
  from ..preprocessing import OneHotEncoder
  from ..tree import (DecisionTreeClassifier, HddDecisionTreeClassifier, DecisionTreeRegressor,
                      ExtraTreeClassifier, ExtraTreeRegressor)
  from ..tree._tree import DTYPE, DOUBLE
  from ..utils import check_random_state, compute_sample_weight, deprecated
  from ..exceptions import DataConversionWarning
  from ._base import BaseEnsemble, _partition_estimators
  from ..utils.fixes import delayed
  from ..utils.fixes import _joblib_parallel_args
  from ..utils.multiclass import check_classification_targets, type_of_target
  from ..utils.validation import check_is_fitted, _check_sample_weight
  from ..utils.validation import _deprecate_positional_args
  
  
  __all__ = ["RandomForestClassifier",
             "HddRandomForestClassifier",
             "RandomForestRegressor",
             "ExtraTreesClassifier",
             "ExtraTreesRegressor",
             "RandomTreesEmbedding"]
  
  MAX_INT = np.iinfo(np.int32).max
  
  
  def _get_n_samples_bootstrap(n_samples, max_samples):
      """
      Get the number of samples in a bootstrap sample.
  
      Parameters
      ----------
      n_samples : int
          Number of samples in the dataset.
      max_samples : int or float
          The maximum number of samples to draw from the total available:
              - if float, this indicates a fraction of the total and should be
                the interval `(0, 1)`;
              - if int, this indicates the exact number of samples;
              - if None, this indicates the total number of samples.
  
      Returns
      -------
      n_samples_bootstrap : int
          The total number of samples to draw for the bootstrap sample.
      """
      if max_samples is None:
          return n_samples
  
      if isinstance(max_samples, numbers.Integral):
          if not (1 <= max_samples <= n_samples):
              msg = "`max_samples` must be in range 1 to {} but got value {}"
              raise ValueError(msg.format(n_samples, max_samples))
          return max_samples
  
      if isinstance(max_samples, numbers.Real):
          if not (0 < max_samples < 1):
              msg = "`max_samples` must be in range (0, 1) but got value {}"
              raise ValueError(msg.format(max_samples))
          return round(n_samples * max_samples)
  
      msg = "`max_samples` should be int or float, but got type '{}'"
      raise TypeError(msg.format(type(max_samples)))
  
  
  def _generate_sample_indices(random_state, n_samples, n_samples_bootstrap):
      """
      Private function used to _parallel_build_trees function."""
  
      random_instance = check_random_state(random_state)
      sample_indices = random_instance.randint(0, n_samples, n_samples_bootstrap)
  
      return sample_indices
  
  
  def _generate_unsampled_indices(random_state, n_samples, n_samples_bootstrap):
      """
      Private function used to forest._set_oob_score function."""
      sample_indices = _generate_sample_indices(random_state, n_samples,
                                                n_samples_bootstrap)
      sample_counts = np.bincount(sample_indices, minlength=n_samples)
      unsampled_mask = sample_counts == 0
      indices_range = np.arange(n_samples)
      unsampled_indices = indices_range[unsampled_mask]
  
      return unsampled_indices
  
  
  def _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees,
                            verbose=0, class_weight=None,
                            n_samples_bootstrap=None):
      """
      Private function used to fit a single tree in parallel."""
      if verbose > 1:
          print("building tree %d of %d" % (tree_idx + 1, n_trees))
  
      if forest.bootstrap:
          n_samples = X.shape[0]
          if sample_weight is None:
              curr_sample_weight = np.ones((n_samples,), dtype=np.float64)
          else:
              curr_sample_weight = sample_weight.copy()
  
          indices = _generate_sample_indices(tree.random_state, n_samples,
                                             n_samples_bootstrap)
          sample_counts = np.bincount(indices, minlength=n_samples)
          curr_sample_weight *= sample_counts
  
          if class_weight == 'subsample':
              with catch_warnings():
                  simplefilter('ignore', DeprecationWarning)
                  curr_sample_weight *= compute_sample_weight('auto', y,
                                                              indices=indices)
          elif class_weight == 'balanced_subsample':
              curr_sample_weight *= compute_sample_weight('balanced', y,
                                                          indices=indices)
  
          tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
      else:
          tree.fit(X, y, sample_weight=sample_weight, check_input=False)
  
      return tree
  
  
  class BaseForest(MultiOutputMixin, BaseEnsemble, metaclass=ABCMeta):
      """
      Base class for forests of trees.
  
      Warning: This class should not be used directly. Use derived classes
      instead.
      """
  
      @abstractmethod
      def __init__(self,
                   base_estimator,
                   n_estimators=100, *,
                   estimator_params=tuple(),
                   bootstrap=False,
                   oob_score=False,
                   n_jobs=None,
                   random_state=None,
                   verbose=0,
                   warm_start=False,
                   class_weight=None,
                   max_samples=None):
          super().__init__(
              base_estimator=base_estimator,
              n_estimators=n_estimators,
              estimator_params=estimator_params)
  
          self.bootstrap = bootstrap
          self.oob_score = oob_score
          self.n_jobs = n_jobs
          self.random_state = random_state
          self.verbose = verbose
          self.warm_start = warm_start
          self.class_weight = class_weight
          self.max_samples = max_samples
  
      def apply(self, X):
          """
          Apply trees in the forest to X, return leaf indices.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples. Internally, its dtype will be converted to
              ``dtype=np.float32``. If a sparse matrix is provided, it will be
              converted into a sparse ``csr_matrix``.
  
          Returns
          -------
          X_leaves : ndarray of shape (n_samples, n_estimators)
              For each datapoint x in X and for each tree in the forest,
              return the index of the leaf x ends up in.
          """
          X = self._validate_X_predict(X)
          results = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
                             **_joblib_parallel_args(prefer="threads"))(
              delayed(tree.apply)(X, check_input=False)
              for tree in self.estimators_)
  
          return np.array(results).T
  
      def decision_path(self, X):
          """
          Return the decision path in the forest.
  
          .. versionadded:: 0.18
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples. Internally, its dtype will be converted to
              ``dtype=np.float32``. If a sparse matrix is provided, it will be
              converted into a sparse ``csr_matrix``.
  
          Returns
          -------
          indicator : sparse matrix of shape (n_samples, n_nodes)
              Return a node indicator matrix where non zero elements indicates
              that the samples goes through the nodes. The matrix is of CSR
              format.
  
          n_nodes_ptr : ndarray of shape (n_estimators + 1,)
              The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]]
              gives the indicator value for the i-th estimator.
  
          """
          X = self._validate_X_predict(X)
          indicators = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
                                **_joblib_parallel_args(prefer='threads'))(
              delayed(tree.decision_path)(X, check_input=False)
              for tree in self.estimators_)
  
          n_nodes = [0]
          n_nodes.extend([i.shape[1] for i in indicators])
          n_nodes_ptr = np.array(n_nodes).cumsum()
  
          return sparse_hstack(indicators).tocsr(), n_nodes_ptr
  
      def fit(self, X, y, sample_weight=None):
          """
          Build a forest of trees from the training set (X, y).
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The training input samples. Internally, its dtype will be converted
              to ``dtype=np.float32``. If a sparse matrix is provided, it will be
              converted into a sparse ``csc_matrix``.
  
          y : array-like of shape (n_samples,) or (n_samples, n_outputs)
              The target values (class labels in classification, real numbers in
              regression).
  
          sample_weight : array-like of shape (n_samples,), default=None
              Sample weights. If None, then samples are equally weighted. Splits
              that would create child nodes with net zero or negative weight are
              ignored while searching for a split in each node. In the case of
              classification, splits are also ignored if they would result in any
              single class carrying a negative weight in either child node.
  
          Returns
          -------
          self : object
          """
          # Validate or convert input data
          if issparse(y):
              raise ValueError(
                  "sparse multilabel-indicator for y is not supported."
              )
          X, y = self._validate_data(X, y, multi_output=True,
                                     accept_sparse="csc", dtype=DTYPE)
          if sample_weight is not None:
              sample_weight = _check_sample_weight(sample_weight, X)
  
          if issparse(X):
              # Pre-sort indices to avoid that each individual tree of the
              # ensemble sorts the indices.
              X.sort_indices()
  
          y = np.atleast_1d(y)
          if y.ndim == 2 and y.shape[1] == 1:
              warn("A column-vector y was passed when a 1d array was"
                   " expected. Please change the shape of y to "
                   "(n_samples,), for example using ravel().",
                   DataConversionWarning, stacklevel=2)
  
          if y.ndim == 1:
              # reshape is necessary to preserve the data contiguity against vs
              # [:, np.newaxis] that does not.
              y = np.reshape(y, (-1, 1))
  
          self.n_outputs_ = y.shape[1]
  
          y, expanded_class_weight = self._validate_y_class_weight(y)
  
          if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
              y = np.ascontiguousarray(y, dtype=DOUBLE)
  
          if expanded_class_weight is not None:
              if sample_weight is not None:
                  sample_weight = sample_weight * expanded_class_weight
              else:
                  sample_weight = expanded_class_weight
  
          # Get bootstrap sample size
          n_samples_bootstrap = _get_n_samples_bootstrap(
              n_samples=X.shape[0],
              max_samples=self.max_samples
          )
  
          # Check parameters
          self._validate_estimator()
          # TODO: Remove in v1.2
          if (
              isinstance(self, (RandomForestRegressor, ExtraTreesRegressor))
              and self.criterion == "mse"
          ):
              warn(
                  "Criterion 'mse' was deprecated in v1.0 and will be "
                  "removed in version 1.2. Use `criterion='squared_error'` "
                  "which is equivalent.",
                  FutureWarning
              )
  
          if not self.bootstrap and self.oob_score:
              raise ValueError("Out of bag estimation only available"
                               " if bootstrap=True")
  
          random_state = check_random_state(self.random_state)
  
          if not self.warm_start or not hasattr(self, "estimators_"):
              # Free allocated memory, if any
              self.estimators_ = []
  
          n_more_estimators = self.n_estimators - len(self.estimators_)
  
          if n_more_estimators < 0:
              raise ValueError('n_estimators=%d must be larger or equal to '
                               'len(estimators_)=%d when warm_start==True'
                               % (self.n_estimators, len(self.estimators_)))
  
          elif n_more_estimators == 0:
              warn("Warm-start fitting without increasing n_estimators does not "
                   "fit new trees.")
          else:
              if self.warm_start and len(self.estimators_) > 0:
                  # We draw from the random state to get the random state we
                  # would have got if we hadn't used a warm_start.
                  random_state.randint(MAX_INT, size=len(self.estimators_))
  
              trees = [self._make_estimator(append=False,
                                            random_state=random_state)
                       for i in range(n_more_estimators)]
  
              # Parallel loop: we prefer the threading backend as the Cython code
              # for fitting the trees is internally releasing the Python GIL
              # making threading more efficient than multiprocessing in
              # that case. However, for joblib 0.12+ we respect any
              # parallel_backend contexts set at a higher level,
              # since correctness does not rely on using threads.
              trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
                               **_joblib_parallel_args(prefer='threads'))(
                  delayed(_parallel_build_trees)(
                      t, self, X, y, sample_weight, i, len(trees),
                      verbose=self.verbose, class_weight=self.class_weight,
                      n_samples_bootstrap=n_samples_bootstrap)
                  for i, t in enumerate(trees))
  
              # Collect newly grown trees
              self.estimators_.extend(trees)
  
          if self.oob_score:
              y_type = type_of_target(y)
              if y_type in ("multiclass-multioutput", "unknown"):
                  # FIXME: we could consider to support multiclass-multioutput if
                  # we introduce or reuse a constructor parameter (e.g.
                  # oob_score) allowing our user to pass a callable defining the
                  # scoring strategy on OOB sample.
                  raise ValueError(
                      f"The type of target cannot be used to compute OOB "
                      f"estimates. Got {y_type} while only the following are "
                      f"supported: continuous, continuous-multioutput, binary, "
                      f"multiclass, multilabel-indicator."
                  )
              self._set_oob_score_and_attributes(X, y)
  
          # Decapsulate classes_ attributes
          if hasattr(self, "classes_") and self.n_outputs_ == 1:
              self.n_classes_ = self.n_classes_[0]
              self.classes_ = self.classes_[0]
  
          return self
  
      @abstractmethod
      def _set_oob_score_and_attributes(self, X, y):
          """Compute and set the OOB score and attributes.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              The data matrix.
          y : ndarray of shape (n_samples, n_outputs)
              The target matrix.
          """
  
      def _compute_oob_predictions(self, X, y):
          """Compute and set the OOB score.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              The data matrix.
          y : ndarray of shape (n_samples, n_outputs)
              The target matrix.
  
          Returns
          -------
          oob_pred : ndarray of shape (n_samples, n_classes, n_outputs) or \
                  (n_samples, 1, n_outputs)
              The OOB predictions.
        """
          X = self._validate_data(X, dtype=DTYPE, accept_sparse='csr',
                                  reset=False)
  
          n_samples = y.shape[0]
          n_outputs = self.n_outputs_
          if is_classifier(self) and hasattr(self, "n_classes_"):
              # n_classes_ is a ndarray at this stage
              # all the supported type of target will have the same number of
              # classes in all outputs
              oob_pred_shape = (n_samples, self.n_classes_[0], n_outputs)
          else:
              # for regression, n_classes_ does not exist and we create an empty
              # axis to be consistent with the classification case and make
              # the array operations compatible with the 2 settings
              oob_pred_shape = (n_samples, 1, n_outputs)
  
          oob_pred = np.zeros(shape=oob_pred_shape, dtype=np.float64)
          n_oob_pred = np.zeros((n_samples, n_outputs), dtype=np.int64)
  
          n_samples_bootstrap = _get_n_samples_bootstrap(
              n_samples, self.max_samples,
          )
          for estimator in self.estimators_:
              unsampled_indices = _generate_unsampled_indices(
                  estimator.random_state, n_samples, n_samples_bootstrap,
              )
  
              y_pred = self._get_oob_predictions(
                  estimator, X[unsampled_indices, :]
              )
              oob_pred[unsampled_indices, ...] += y_pred
              n_oob_pred[unsampled_indices, :] += 1
  
          for k in range(n_outputs):
              if (n_oob_pred == 0).any():
                  warn(
                      "Some inputs do not have OOB scores. This probably means "
                      "too few trees were used to compute any reliable OOB "
                      "estimates.", UserWarning
                  )
                  n_oob_pred[n_oob_pred == 0] = 1
              oob_pred[..., k] /= n_oob_pred[..., [k]]
  
          return oob_pred
  
      def _validate_y_class_weight(self, y):
          # Default implementation
          return y, None
  
      def _validate_X_predict(self, X):
          """
          Validate X whenever one tries to predict, apply, predict_proba."""
          check_is_fitted(self)
  
          return self.estimators_[0]._validate_X_predict(X, check_input=True)
  
      @property
      def feature_importances_(self):
          """
          The impurity-based feature importances.
  
          The higher, the more important the feature.
          The importance of a feature is computed as the (normalized)
          total reduction of the criterion brought by that feature.  It is also
          known as the Gini importance.
  
          Warning: impurity-based feature importances can be misleading for
          high cardinality features (many unique values). See
          :func:`sklearn.inspection.permutation_importance` as an alternative.
  
          Returns
          -------
          feature_importances_ : ndarray of shape (n_features,)
              The values of this array sum to 1, unless all trees are single node
              trees consisting of only the root node, in which case it will be an
              array of zeros.
          """
          check_is_fitted(self)
  
          all_importances = Parallel(n_jobs=self.n_jobs,
                                     **_joblib_parallel_args(prefer='threads'))(
              delayed(getattr)(tree, 'feature_importances_')
              for tree in self.estimators_ if tree.tree_.node_count > 1)
  
          if not all_importances:
              return np.zeros(self.n_features_in_, dtype=np.float64)
  
          all_importances = np.mean(all_importances,
                                    axis=0, dtype=np.float64)
          return all_importances / np.sum(all_importances)
  
      # TODO: Remove in 1.2
      # mypy error: Decorated property not supported
      @deprecated(  # type: ignore
          "Attribute n_features_ was deprecated in version 1.0 and will be "
          "removed in 1.2. Use 'n_features_in_' instead."
      )
      @property
      def n_features_(self):
          return self.n_features_in_
  
  
  def _accumulate_prediction(predict, X, out, lock):
      """
      This is a utility function for joblib's Parallel.
  
      It can't go locally in ForestClassifier or ForestRegressor, because joblib
      complains that it cannot pickle it when placed there.
      """
      prediction = predict(X, check_input=False)
      with lock:
          if len(out) == 1:
              out[0] += prediction
          else:
              for i in range(len(out)):
                  out[i] += prediction[i]
  
  
  class ForestClassifier(ClassifierMixin, BaseForest, metaclass=ABCMeta):
      """
      Base class for forest of trees-based classifiers.
  
      Warning: This class should not be used directly. Use derived classes
      instead.
      """
  
      @abstractmethod
      def __init__(self,
                   base_estimator,
                   n_estimators=100, *,
                   estimator_params=tuple(),
                   bootstrap=False,
                   oob_score=False,
                   n_jobs=None,
                   random_state=None,
                   verbose=0,
                   warm_start=False,
                   class_weight=None,
                   max_samples=None):
          super().__init__(
              base_estimator,
              n_estimators=n_estimators,
              estimator_params=estimator_params,
              bootstrap=bootstrap,
              oob_score=oob_score,
              n_jobs=n_jobs,
              random_state=random_state,
              verbose=verbose,
              warm_start=warm_start,
              class_weight=class_weight,
              max_samples=max_samples)
  
      @staticmethod
      def _get_oob_predictions(tree, X):
          """Compute the OOB predictions for an individual tree.
  
          Parameters
          ----------
          tree : DecisionTreeClassifier object
              A single decision tree classifier.
          X : ndarray of shape (n_samples, n_features)
              The OOB samples.
  
          Returns
          -------
          y_pred : ndarray of shape (n_samples, n_classes, n_outputs)
              The OOB associated predictions.
          """
          y_pred = tree.predict_proba(X, check_input=False)
          y_pred = np.array(y_pred, copy=False)
          if y_pred.ndim == 2:
              # binary and multiclass
              y_pred = y_pred[..., np.newaxis]
          else:
              # Roll the first `n_outputs` axis to the last axis. We will reshape
              # from a shape of (n_outputs, n_samples, n_classes) to a shape of
              # (n_samples, n_classes, n_outputs).
              y_pred = np.rollaxis(y_pred, axis=0, start=3)
          return y_pred
  
      def _set_oob_score_and_attributes(self, X, y):
          """Compute and set the OOB score and attributes.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              The data matrix.
          y : ndarray of shape (n_samples, n_outputs)
              The target matrix.
          """
          self.oob_decision_function_ = super()._compute_oob_predictions(X, y)
          if self.oob_decision_function_.shape[-1] == 1:
              # drop the n_outputs axis if there is a single output
              self.oob_decision_function_ = self.oob_decision_function_.squeeze(
                  axis=-1
              )
          self.oob_score_ = accuracy_score(
              y, np.argmax(self.oob_decision_function_, axis=1)
          )
  
      def _validate_y_class_weight(self, y):
          check_classification_targets(y)
  
          y = np.copy(y)
          expanded_class_weight = None
  
          if self.class_weight is not None:
              y_original = np.copy(y)
  
          self.classes_ = []
          self.n_classes_ = []
  
          y_store_unique_indices = np.zeros(y.shape, dtype=int)
          for k in range(self.n_outputs_):
              classes_k, y_store_unique_indices[:, k] = \
                  np.unique(y[:, k], return_inverse=True)
              self.classes_.append(classes_k)
              self.n_classes_.append(classes_k.shape[0])
          y = y_store_unique_indices
  
          if self.class_weight is not None:
              valid_presets = ('balanced', 'balanced_subsample')
              if isinstance(self.class_weight, str):
                  if self.class_weight not in valid_presets:
                      raise ValueError('Valid presets for class_weight include '
                                       '"balanced" and "balanced_subsample".'
                                       'Given "%s".'
                                       % self.class_weight)
                  if self.warm_start:
                      warn('class_weight presets "balanced" or '
                           '"balanced_subsample" are '
                           'not recommended for warm_start if the fitted data '
                           'differs from the full dataset. In order to use '
                           '"balanced" weights, use compute_class_weight '
                           '("balanced", classes, y). In place of y you can use '
                           'a large enough sample of the full training set '
                           'target to properly estimate the class frequency '
                           'distributions. Pass the resulting weights as the '
                           'class_weight parameter.')
  
              if (self.class_weight != 'balanced_subsample' or
                      not self.bootstrap):
                  if self.class_weight == "balanced_subsample":
                      class_weight = "balanced"
                  else:
                      class_weight = self.class_weight
                  expanded_class_weight = compute_sample_weight(class_weight,
                                                                y_original)
  
          return y, expanded_class_weight
  
      def predict(self, X):
          """
          Predict class for X.
  
          The predicted class of an input sample is a vote by the trees in
          the forest, weighted by their probability estimates. That is,
          the predicted class is the one with highest mean probability
          estimate across the trees.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples. Internally, its dtype will be converted to
              ``dtype=np.float32``. If a sparse matrix is provided, it will be
              converted into a sparse ``csr_matrix``.
  
          Returns
          -------
          y : ndarray of shape (n_samples,) or (n_samples, n_outputs)
              The predicted classes.
          """
          proba = self.predict_proba(X)
  
          if self.n_outputs_ == 1:
              return self.classes_.take(np.argmax(proba, axis=1), axis=0)
  
          else:
              n_samples = proba[0].shape[0]
              # all dtypes should be the same, so just take the first
              class_type = self.classes_[0].dtype
              predictions = np.empty((n_samples, self.n_outputs_),
                                     dtype=class_type)
  
              for k in range(self.n_outputs_):
                  predictions[:, k] = self.classes_[k].take(np.argmax(proba[k],
                                                                      axis=1),
                                                            axis=0)
  
              return predictions
  
      def predict_proba(self, X):
          """
          Predict class probabilities for X.
  
          The predicted class probabilities of an input sample are computed as
          the mean predicted class probabilities of the trees in the forest.
          The class probability of a single tree is the fraction of samples of
          the same class in a leaf.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples. Internally, its dtype will be converted to
              ``dtype=np.float32``. If a sparse matrix is provided, it will be
              converted into a sparse ``csr_matrix``.
  
          Returns
          -------
          p : ndarray of shape (n_samples, n_classes), or a list of such arrays
              The class probabilities of the input samples. The order of the
              classes corresponds to that in the attribute :term:`classes_`.
          """
          check_is_fitted(self)
          # Check data
          X = self._validate_X_predict(X)
  
          # Assign chunk of trees to jobs
          n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
  
          # avoid storing the output of every estimator by summing them here
          all_proba = [np.zeros((X.shape[0], j), dtype=np.float64)
                       for j in np.atleast_1d(self.n_classes_)]
          lock = threading.Lock()
          Parallel(n_jobs=n_jobs, verbose=self.verbose,
                   **_joblib_parallel_args(require="sharedmem"))(
              delayed(_accumulate_prediction)(e.predict_proba, X, all_proba,
                                              lock)
              for e in self.estimators_)
  
          for proba in all_proba:
              proba /= len(self.estimators_)
  
          if len(all_proba) == 1:
              return all_proba[0]
          else:
              return all_proba
  
      def predict_log_proba(self, X):
          """
          Predict class log-probabilities for X.
  
          The predicted class log-probabilities of an input sample is computed as
          the log of the mean predicted class probabilities of the trees in the
          forest.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples. Internally, its dtype will be converted to
              ``dtype=np.float32``. If a sparse matrix is provided, it will be
              converted into a sparse ``csr_matrix``.
  
          Returns
          -------
          p : ndarray of shape (n_samples, n_classes), or a list of such arrays
              The class probabilities of the input samples. The order of the
              classes corresponds to that in the attribute :term:`classes_`.
          """
          proba = self.predict_proba(X)
  
          if self.n_outputs_ == 1:
              return np.log(proba)
  
          else:
              for k in range(self.n_outputs_):
                  proba[k] = np.log(proba[k])
  
              return proba
  
  
  class ForestRegressor(RegressorMixin, BaseForest, metaclass=ABCMeta):
      """
      Base class for forest of trees-based regressors.
  
      Warning: This class should not be used directly. Use derived classes
      instead.
      """
  
      @abstractmethod
      def __init__(self,
                   base_estimator,
                   n_estimators=100, *,
                   estimator_params=tuple(),
                   bootstrap=False,
                   oob_score=False,
                   n_jobs=None,
                   random_state=None,
                   verbose=0,
                   warm_start=False,
                   max_samples=None):
          super().__init__(
              base_estimator,
              n_estimators=n_estimators,
              estimator_params=estimator_params,
              bootstrap=bootstrap,
              oob_score=oob_score,
              n_jobs=n_jobs,
              random_state=random_state,
              verbose=verbose,
              warm_start=warm_start,
              max_samples=max_samples)
  
      def predict(self, X):
          """
          Predict regression target for X.
  
          The predicted regression target of an input sample is computed as the
          mean predicted regression targets of the trees in the forest.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples. Internally, its dtype will be converted to
              ``dtype=np.float32``. If a sparse matrix is provided, it will be
              converted into a sparse ``csr_matrix``.
  
          Returns
          -------
          y : ndarray of shape (n_samples,) or (n_samples, n_outputs)
              The predicted values.
          """
          check_is_fitted(self)
          # Check data
          X = self._validate_X_predict(X)
  
          # Assign chunk of trees to jobs
          n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs)
  
          # avoid storing the output of every estimator by summing them here
          if self.n_outputs_ > 1:
              y_hat = np.zeros((X.shape[0], self.n_outputs_), dtype=np.float64)
          else:
              y_hat = np.zeros((X.shape[0]), dtype=np.float64)
  
          # Parallel loop
          lock = threading.Lock()
          Parallel(n_jobs=n_jobs, verbose=self.verbose,
                   **_joblib_parallel_args(require="sharedmem"))(
              delayed(_accumulate_prediction)(e.predict, X, [y_hat], lock)
              for e in self.estimators_)
  
          y_hat /= len(self.estimators_)
  
          return y_hat
  
      @staticmethod
      def _get_oob_predictions(tree, X):
          """Compute the OOB predictions for an individual tree.
  
          Parameters
          ----------
          tree : DecisionTreeRegressor object
              A single decision tree regressor.
          X : ndarray of shape (n_samples, n_features)
              The OOB samples.
  
          Returns
          -------
          y_pred : ndarray of shape (n_samples, 1, n_outputs)
              The OOB associated predictions.
          """
          y_pred = tree.predict(X, check_input=False)
          if y_pred.ndim == 1:
              # single output regression
              y_pred = y_pred[:, np.newaxis, np.newaxis]
          else:
              # multioutput regression
              y_pred = y_pred[:, np.newaxis, :]
          return y_pred
  
      def _set_oob_score_and_attributes(self, X, y):
          """Compute and set the OOB score and attributes.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              The data matrix.
          y : ndarray of shape (n_samples, n_outputs)
              The target matrix.
          """
          self.oob_prediction_ = super()._compute_oob_predictions(X, y).squeeze(
              axis=1
          )
          if self.oob_prediction_.shape[-1] == 1:
              # drop the n_outputs axis if there is a single output
              self.oob_prediction_ = self.oob_prediction_.squeeze(axis=-1)
          self.oob_score_ = r2_score(y, self.oob_prediction_)
  
      def _compute_partial_dependence_recursion(self, grid, target_features):
          """Fast partial dependence computation.
  
          Parameters
          ----------
          grid : ndarray of shape (n_samples, n_target_features)
              The grid points on which the partial dependence should be
              evaluated.
          target_features : ndarray of shape (n_target_features)
              The set of target features for which the partial dependence
              should be evaluated.
  
          Returns
          -------
          averaged_predictions : ndarray of shape (n_samples,)
              The value of the partial dependence function on each grid point.
          """
          grid = np.asarray(grid, dtype=DTYPE, order='C')
          averaged_predictions = np.zeros(shape=grid.shape[0],
                                          dtype=np.float64, order='C')
  
          for tree in self.estimators_:
              # Note: we don't sum in parallel because the GIL isn't released in
              # the fast method.
              tree.tree_.compute_partial_dependence(
                  grid, target_features, averaged_predictions)
          # Average over the forest
          averaged_predictions /= len(self.estimators_)
  
          return averaged_predictions
  
  
  class RandomForestClassifier(ForestClassifier):
      """
      A random forest classifier.
  
      A random forest is a meta estimator that fits a number of decision tree
      classifiers on various sub-samples of the dataset and uses averaging to
      improve the predictive accuracy and control over-fitting.
      The sub-sample size is controlled with the `max_samples` parameter if
      `bootstrap=True` (default), otherwise the whole dataset is used to build
      each tree.
  
      Read more in the :ref:`User Guide <forest>`.
  
      Parameters
      ----------
      n_estimators : int, default=100
          The number of trees in the forest.
  
          .. versionchanged:: 0.22
             The default value of ``n_estimators`` changed from 10 to 100
             in 0.22.
  
      criterion : {"gini", "entropy"}, default="gini"
          The function to measure the quality of a split. Supported criteria are
          "gini" for the Gini impurity and "entropy" for the information gain.
          Note: this parameter is tree-specific.
  
      max_depth : int, default=None
          The maximum depth of the tree. If None, then nodes are expanded until
          all leaves are pure or until all leaves contain less than
          min_samples_split samples.
  
      min_samples_split : int or float, default=2
          The minimum number of samples required to split an internal node:
  
          - If int, then consider `min_samples_split` as the minimum number.
          - If float, then `min_samples_split` is a fraction and
            `ceil(min_samples_split * n_samples)` are the minimum
            number of samples for each split.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_samples_leaf : int or float, default=1
          The minimum number of samples required to be at a leaf node.
          A split point at any depth will only be considered if it leaves at
          least ``min_samples_leaf`` training samples in each of the left and
          right branches.  This may have the effect of smoothing the model,
          especially in regression.
  
          - If int, then consider `min_samples_leaf` as the minimum number.
          - If float, then `min_samples_leaf` is a fraction and
            `ceil(min_samples_leaf * n_samples)` are the minimum
            number of samples for each node.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_weight_fraction_leaf : float, default=0.0
          The minimum weighted fraction of the sum total of weights (of all
          the input samples) required to be at a leaf node. Samples have
          equal weight when sample_weight is not provided.
  
      max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
          The number of features to consider when looking for the best split:
  
          - If int, then consider `max_features` features at each split.
          - If float, then `max_features` is a fraction and
            `round(max_features * n_features)` features are considered at each
            split.
          - If "auto", then `max_features=sqrt(n_features)`.
          - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
          - If "log2", then `max_features=log2(n_features)`.
          - If None, then `max_features=n_features`.
  
          Note: the search for a split does not stop until at least one
          valid partition of the node samples is found, even if it requires to
          effectively inspect more than ``max_features`` features.
  
      max_leaf_nodes : int, default=None
          Grow trees with ``max_leaf_nodes`` in best-first fashion.
          Best nodes are defined as relative reduction in impurity.
          If None then unlimited number of leaf nodes.
  
      min_impurity_decrease : float, default=0.0
          A node will be split if this split induces a decrease of the impurity
          greater than or equal to this value.
  
          The weighted impurity decrease equation is the following::
  
              N_t / N * (impurity - N_t_R / N_t * right_impurity
                                  - N_t_L / N_t * left_impurity)
  
          where ``N`` is the total number of samples, ``N_t`` is the number of
          samples at the current node, ``N_t_L`` is the number of samples in the
          left child, and ``N_t_R`` is the number of samples in the right child.
  
          ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
          if ``sample_weight`` is passed.
  
          .. versionadded:: 0.19
  
      min_impurity_split : float, default=None
          Threshold for early stopping in tree growth. A node will split
          if its impurity is above the threshold, otherwise it is a leaf.
  
          .. deprecated:: 0.19
             ``min_impurity_split`` has been deprecated in favor of
             ``min_impurity_decrease`` in 0.19. The default value of
             ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
             will be removed in 1.0 (renaming of 0.25).
             Use ``min_impurity_decrease`` instead.
  
      bootstrap : bool, default=True
          Whether bootstrap samples are used when building trees. If False, the
          whole dataset is used to build each tree.
  
      oob_score : bool, default=False
          Whether to use out-of-bag samples to estimate the generalization score.
          Only available if bootstrap=True.
  
      n_jobs : int, default=None
          The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
          :meth:`decision_path` and :meth:`apply` are all parallelized over the
          trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
          context. ``-1`` means using all processors. See :term:`Glossary
          <n_jobs>` for more details.
  
      random_state : int, RandomState instance or None, default=None
          Controls both the randomness of the bootstrapping of the samples used
          when building trees (if ``bootstrap=True``) and the sampling of the
          features to consider when looking for the best split at each node
          (if ``max_features < n_features``).
          See :term:`Glossary <random_state>` for details.
  
      verbose : int, default=0
          Controls the verbosity when fitting and predicting.
  
      warm_start : bool, default=False
          When set to ``True``, reuse the solution of the previous call to fit
          and add more estimators to the ensemble, otherwise, just fit a whole
          new forest. See :term:`the Glossary <warm_start>`.
  
      class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
              default=None
          Weights associated with classes in the form ``{class_label: weight}``.
          If not given, all classes are supposed to have weight one. For
          multi-output problems, a list of dicts can be provided in the same
          order as the columns of y.
  
          Note that for multioutput (including multilabel) weights should be
          defined for each class of every column in its own dict. For example,
          for four-class multilabel classification weights should be
          [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
          [{1:1}, {2:5}, {3:1}, {4:1}].
  
          The "balanced" mode uses the values of y to automatically adjust
          weights inversely proportional to class frequencies in the input data
          as ``n_samples / (n_classes * np.bincount(y))``
  
          The "balanced_subsample" mode is the same as "balanced" except that
          weights are computed based on the bootstrap sample for every tree
          grown.
  
          For multi-output, the weights of each column of y will be multiplied.
  
          Note that these weights will be multiplied with sample_weight (passed
          through the fit method) if sample_weight is specified.
  
      ccp_alpha : non-negative float, default=0.0
          Complexity parameter used for Minimal Cost-Complexity Pruning. The
          subtree with the largest cost complexity that is smaller than
          ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
          :ref:`minimal_cost_complexity_pruning` for details.
  
          .. versionadded:: 0.22
  
      max_samples : int or float, default=None
          If bootstrap is True, the number of samples to draw from X
          to train each base estimator.
  
          - If None (default), then draw `X.shape[0]` samples.
          - If int, then draw `max_samples` samples.
          - If float, then draw `max_samples * X.shape[0]` samples. Thus,
            `max_samples` should be in the interval `(0, 1)`.
  
          .. versionadded:: 0.22
  
      Attributes
      ----------
      base_estimator_ : DecisionTreeClassifier
          The child estimator template used to create the collection of fitted
          sub-estimators.
  
      estimators_ : list of DecisionTreeClassifier
          The collection of fitted sub-estimators.
  
      classes_ : ndarray of shape (n_classes,) or a list of such arrays
          The classes labels (single output problem), or a list of arrays of
          class labels (multi-output problem).
  
      n_classes_ : int or list
          The number of classes (single output problem), or a list containing the
          number of classes for each output (multi-output problem).
  
      n_features_ : int
          The number of features when ``fit`` is performed.
  
          .. deprecated:: 1.0
              Attribute `n_features_` was deprecated in version 1.0 and will be
              removed in 1.2. Use `n_features_in_` instead.
  
      n_outputs_ : int
          The number of outputs when ``fit`` is performed.
  
      feature_importances_ : ndarray of shape (n_features,)
          The impurity-based feature importances.
          The higher, the more important the feature.
          The importance of a feature is computed as the (normalized)
          total reduction of the criterion brought by that feature.  It is also
          known as the Gini importance.
  
          Warning: impurity-based feature importances can be misleading for
          high cardinality features (many unique values). See
          :func:`sklearn.inspection.permutation_importance` as an alternative.
  
      oob_score_ : float
          Score of the training dataset obtained using an out-of-bag estimate.
          This attribute exists only when ``oob_score`` is True.
  
      oob_decision_function_ : ndarray of shape (n_samples, n_classes) or \
              (n_samples, n_classes, n_outputs)
          Decision function computed with out-of-bag estimate on the training
          set. If n_estimators is small it might be possible that a data point
          was never left out during the bootstrap. In this case,
          `oob_decision_function_` might contain NaN. This attribute exists
          only when ``oob_score`` is True.
  
      See Also
      --------
      DecisionTreeClassifier, ExtraTreesClassifier
  
      Notes
      -----
      The default values for the parameters controlling the size of the trees
      (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
      unpruned trees which can potentially be very large on some data sets. To
      reduce memory consumption, the complexity and size of the trees should be
      controlled by setting those parameter values.
  
      The features are always randomly permuted at each split. Therefore,
      the best found split may vary, even with the same training data,
      ``max_features=n_features`` and ``bootstrap=False``, if the improvement
      of the criterion is identical for several splits enumerated during the
      search of the best split. To obtain a deterministic behaviour during
      fitting, ``random_state`` has to be fixed.
  
      References
      ----------
      .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
  
      Examples
      --------
      >>> from sklearn.ensemble import RandomForestClassifier
      >>> from sklearn.datasets import make_classification
      >>> X, y = make_classification(n_samples=1000, n_features=4,
      ...                            n_informative=2, n_redundant=0,
      ...                            random_state=0, shuffle=False)
      >>> clf = RandomForestClassifier(max_depth=2, random_state=0)
      >>> clf.fit(X, y)
      RandomForestClassifier(...)
      >>> print(clf.predict([[0, 0, 0, 0]]))
      [1]
      """
      @_deprecate_positional_args
      def __init__(self,
                   n_estimators=100, *,
                   criterion="gini",
                   max_depth=None,
                   min_samples_split=2,
                   min_samples_leaf=1,
                   min_weight_fraction_leaf=0.,
                   max_features="auto",
                   max_leaf_nodes=None,
                   min_impurity_decrease=0.,
                   min_impurity_split=None,
                   bootstrap=True,
                   oob_score=False,
                   n_jobs=None,
                   random_state=None,
                   verbose=0,
                   warm_start=False,
                   class_weight=None,
                   ccp_alpha=0.0,
                   max_samples=None):
          super().__init__(
              base_estimator=DecisionTreeClassifier(),
              n_estimators=n_estimators,
              estimator_params=("criterion", "max_depth", "min_samples_split",
                                "min_samples_leaf", "min_weight_fraction_leaf",
                                "max_features", "max_leaf_nodes",
                                "min_impurity_decrease", "min_impurity_split",
                                "random_state", "ccp_alpha"),
              bootstrap=bootstrap,
              oob_score=oob_score,
              n_jobs=n_jobs,
              random_state=random_state,
              verbose=verbose,
              warm_start=warm_start,
              class_weight=class_weight,
              max_samples=max_samples)
  
          self.criterion = criterion
          self.max_depth = max_depth
          self.min_samples_split = min_samples_split
          self.min_samples_leaf = min_samples_leaf
          self.min_weight_fraction_leaf = min_weight_fraction_leaf
          self.max_features = max_features
          self.max_leaf_nodes = max_leaf_nodes
          self.min_impurity_decrease = min_impurity_decrease
          self.min_impurity_split = min_impurity_split
          self.ccp_alpha = ccp_alpha
  
  
  class HddRandomForestClassifier(ForestClassifier):
      """
      A random forest classifier.
  
      A random forest is a meta estimator that fits a number of decision tree
      classifiers on various sub-samples of the dataset and uses averaging to
      improve the predictive accuracy and control over-fitting.
      The sub-sample size is controlled with the `max_samples` parameter if
      `bootstrap=True` (default), otherwise the whole dataset is used to build
      each tree.
  
      Read more in the :ref:`User Guide <forest>`.
  
      Parameters
      ----------
      n_estimators : int, default=100
          The number of trees in the forest.
  
          .. versionchanged:: 0.22
             The default value of ``n_estimators`` changed from 10 to 100
             in 0.22.
  
      criterion : {"gini", "entropy"}, default="gini"
          The function to measure the quality of a split. Supported criteria are
          "gini" for the Gini impurity and "entropy" for the information gain.
          Note: this parameter is tree-specific.
  
      max_depth : int, default=None
          The maximum depth of the tree. If None, then nodes are expanded until
          all leaves are pure or until all leaves contain less than
          min_samples_split samples.
  
      min_samples_split : int or float, default=2
          The minimum number of samples required to split an internal node:
  
          - If int, then consider `min_samples_split` as the minimum number.
          - If float, then `min_samples_split` is a fraction and
            `ceil(min_samples_split * n_samples)` are the minimum
            number of samples for each split.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_samples_leaf : int or float, default=1
          The minimum number of samples required to be at a leaf node.
          A split point at any depth will only be considered if it leaves at
          least ``min_samples_leaf`` training samples in each of the left and
          right branches.  This may have the effect of smoothing the model,
          especially in regression.
  
          - If int, then consider `min_samples_leaf` as the minimum number.
          - If float, then `min_samples_leaf` is a fraction and
            `ceil(min_samples_leaf * n_samples)` are the minimum
            number of samples for each node.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_weight_fraction_leaf : float, default=0.0
          The minimum weighted fraction of the sum total of weights (of all
          the input samples) required to be at a leaf node. Samples have
          equal weight when sample_weight is not provided.
  
      max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
          The number of features to consider when looking for the best split:
  
          - If int, then consider `max_features` features at each split.
          - If float, then `max_features` is a fraction and
            `round(max_features * n_features)` features are considered at each
            split.
          - If "auto", then `max_features=sqrt(n_features)`.
          - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
          - If "log2", then `max_features=log2(n_features)`.
          - If None, then `max_features=n_features`.
  
          Note: the search for a split does not stop until at least one
          valid partition of the node samples is found, even if it requires to
          effectively inspect more than ``max_features`` features.
  
      max_leaf_nodes : int, default=None
          Grow trees with ``max_leaf_nodes`` in best-first fashion.
          Best nodes are defined as relative reduction in impurity.
          If None then unlimited number of leaf nodes.
  
      min_impurity_decrease : float, default=0.0
          A node will be split if this split induces a decrease of the impurity
          greater than or equal to this value.
  
          The weighted impurity decrease equation is the following::
  
              N_t / N * (impurity - N_t_R / N_t * right_impurity
                                  - N_t_L / N_t * left_impurity)
  
          where ``N`` is the total number of samples, ``N_t`` is the number of
          samples at the current node, ``N_t_L`` is the number of samples in the
          left child, and ``N_t_R`` is the number of samples in the right child.
  
          ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
          if ``sample_weight`` is passed.
  
          .. versionadded:: 0.19
  
      min_impurity_split : float, default=None
          Threshold for early stopping in tree growth. A node will split
          if its impurity is above the threshold, otherwise it is a leaf.
  
          .. deprecated:: 0.19
             ``min_impurity_split`` has been deprecated in favor of
             ``min_impurity_decrease`` in 0.19. The default value of
             ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
             will be removed in 1.0 (renaming of 0.25).
             Use ``min_impurity_decrease`` instead.
  
      bootstrap : bool, default=True
          Whether bootstrap samples are used when building trees. If False, the
          whole dataset is used to build each tree.
  
      oob_score : bool, default=False
          Whether to use out-of-bag samples to estimate the generalization score.
          Only available if bootstrap=True.
  
      n_jobs : int, default=None
          The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
          :meth:`decision_path` and :meth:`apply` are all parallelized over the
          trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
          context. ``-1`` means using all processors. See :term:`Glossary
          <n_jobs>` for more details.
  
      random_state : int, RandomState instance or None, default=None
          Controls both the randomness of the bootstrapping of the samples used
          when building trees (if ``bootstrap=True``) and the sampling of the
          features to consider when looking for the best split at each node
          (if ``max_features < n_features``).
          See :term:`Glossary <random_state>` for details.
  
      verbose : int, default=0
          Controls the verbosity when fitting and predicting.
  
      warm_start : bool, default=False
          When set to ``True``, reuse the solution of the previous call to fit
          and add more estimators to the ensemble, otherwise, just fit a whole
          new forest. See :term:`the Glossary <warm_start>`.
  
      class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
              default=None
          Weights associated with classes in the form ``{class_label: weight}``.
          If not given, all classes are supposed to have weight one. For
          multi-output problems, a list of dicts can be provided in the same
          order as the columns of y.
  
          Note that for multioutput (including multilabel) weights should be
          defined for each class of every column in its own dict. For example,
          for four-class multilabel classification weights should be
          [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
          [{1:1}, {2:5}, {3:1}, {4:1}].
  
          The "balanced" mode uses the values of y to automatically adjust
          weights inversely proportional to class frequencies in the input data
          as ``n_samples / (n_classes * np.bincount(y))``
  
          The "balanced_subsample" mode is the same as "balanced" except that
          weights are computed based on the bootstrap sample for every tree
          grown.
  
          For multi-output, the weights of each column of y will be multiplied.
  
          Note that these weights will be multiplied with sample_weight (passed
          through the fit method) if sample_weight is specified.
  
      ccp_alpha : non-negative float, default=0.0
          Complexity parameter used for Minimal Cost-Complexity Pruning. The
          subtree with the largest cost complexity that is smaller than
          ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
          :ref:`minimal_cost_complexity_pruning` for details.
  
          .. versionadded:: 0.22
  
      max_samples : int or float, default=None
          If bootstrap is True, the number of samples to draw from X
          to train each base estimator.
  
          - If None (default), then draw `X.shape[0]` samples.
          - If int, then draw `max_samples` samples.
          - If float, then draw `max_samples * X.shape[0]` samples. Thus,
            `max_samples` should be in the interval `(0, 1)`.
  
          .. versionadded:: 0.22
  
      Attributes
      ----------
      base_estimator_ : DecisionTreeClassifier
          The child estimator template used to create the collection of fitted
          sub-estimators.
  
      estimators_ : list of DecisionTreeClassifier
          The collection of fitted sub-estimators.
  
      classes_ : ndarray of shape (n_classes,) or a list of such arrays
          The classes labels (single output problem), or a list of arrays of
          class labels (multi-output problem).
  
      n_classes_ : int or list
          The number of classes (single output problem), or a list containing the
          number of classes for each output (multi-output problem).
  
      n_features_ : int
          The number of features when ``fit`` is performed.
  
          .. deprecated:: 1.0
              Attribute `n_features_` was deprecated in version 1.0 and will be
              removed in 1.2. Use `n_features_in_` instead.
  
      n_outputs_ : int
          The number of outputs when ``fit`` is performed.
  
      feature_importances_ : ndarray of shape (n_features,)
          The impurity-based feature importances.
          The higher, the more important the feature.
          The importance of a feature is computed as the (normalized)
          total reduction of the criterion brought by that feature.  It is also
          known as the Gini importance.
  
          Warning: impurity-based feature importances can be misleading for
          high cardinality features (many unique values). See
          :func:`sklearn.inspection.permutation_importance` as an alternative.
  
      oob_score_ : float
          Score of the training dataset obtained using an out-of-bag estimate.
          This attribute exists only when ``oob_score`` is True.
  
      oob_decision_function_ : ndarray of shape (n_samples, n_classes) or \
              (n_samples, n_classes, n_outputs)
          Decision function computed with out-of-bag estimate on the training
          set. If n_estimators is small it might be possible that a data point
          was never left out during the bootstrap. In this case,
          `oob_decision_function_` might contain NaN. This attribute exists
          only when ``oob_score`` is True.
  
      See Also
      --------
      DecisionTreeClassifier, ExtraTreesClassifier
  
      Notes
      -----
      The default values for the parameters controlling the size of the trees
      (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
      unpruned trees which can potentially be very large on some data sets. To
      reduce memory consumption, the complexity and size of the trees should be
      controlled by setting those parameter values.
  
      The features are always randomly permuted at each split. Therefore,
      the best found split may vary, even with the same training data,
      ``max_features=n_features`` and ``bootstrap=False``, if the improvement
      of the criterion is identical for several splits enumerated during the
      search of the best split. To obtain a deterministic behaviour during
      fitting, ``random_state`` has to be fixed.
  
      References
      ----------
      .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
  
      Examples
      --------
      >>> from sklearn.ensemble import RandomForestClassifier
      >>> from sklearn.datasets import make_classification
      >>> X, y = make_classification(n_samples=1000, n_features=4,
      ...                            n_informative=2, n_redundant=0,
      ...                            random_state=0, shuffle=False)
      >>> clf = RandomForestClassifier(max_depth=2, random_state=0)
      >>> clf.fit(X, y)
      RandomForestClassifier(...)
      >>> print(clf.predict([[0, 0, 0, 0]]))
      [1]
      """
      @_deprecate_positional_args
      def __init__(self,
                   n_estimators=100, *,
                   criterion="gini",
                   max_depth=None,
                   min_samples_split=2,
                   min_samples_leaf=1,
                   min_weight_fraction_leaf=0.,
                   max_features="auto",
                   max_leaf_nodes=None,
                   min_impurity_decrease=0.,
                   min_impurity_split=None,
                   bootstrap=True,
                   oob_score=False,
                   n_jobs=None,
                   random_state=None,
                   verbose=0,
                   warm_start=False,
                   class_weight=None,
                   ccp_alpha=0.0,
                   max_samples=None):
          super().__init__(
              base_estimator=HddDecisionTreeClassifier(),
              n_estimators=n_estimators,
              estimator_params=("criterion", "max_depth", "min_samples_split",
                                "min_samples_leaf", "min_weight_fraction_leaf",
                                "max_features", "max_leaf_nodes",
                                "min_impurity_decrease", "min_impurity_split",
                                "random_state", "ccp_alpha"),
              bootstrap=bootstrap,
              oob_score=oob_score,
              n_jobs=n_jobs,
              random_state=random_state,
              verbose=verbose,
              warm_start=warm_start,
              class_weight=class_weight,
              max_samples=max_samples)
  
          self.criterion = criterion
          self.max_depth = max_depth
          self.min_samples_split = min_samples_split
          self.min_samples_leaf = min_samples_leaf
          self.min_weight_fraction_leaf = min_weight_fraction_leaf
          self.max_features = max_features
          self.max_leaf_nodes = max_leaf_nodes
          self.min_impurity_decrease = min_impurity_decrease
          self.min_impurity_split = min_impurity_split
          self.ccp_alpha = ccp_alpha
  
  
  class RandomForestRegressor(ForestRegressor):
      """
      A random forest regressor.
  
      A random forest is a meta estimator that fits a number of classifying
      decision trees on various sub-samples of the dataset and uses averaging
      to improve the predictive accuracy and control over-fitting.
      The sub-sample size is controlled with the `max_samples` parameter if
      `bootstrap=True` (default), otherwise the whole dataset is used to build
      each tree.
  
      Read more in the :ref:`User Guide <forest>`.
  
      Parameters
      ----------
      n_estimators : int, default=100
          The number of trees in the forest.
  
          .. versionchanged:: 0.22
             The default value of ``n_estimators`` changed from 10 to 100
             in 0.22.
  
      criterion : {"squared_error", "mse", "mae"}, default="squared_error"
          The function to measure the quality of a split. Supported criteria
          are "squared_error" for the mean squared error, which is equal to
          variance reduction as feature selection criterion, and "mae" for the
          mean absolute error.
  
          .. versionadded:: 0.18
             Mean Absolute Error (MAE) criterion.
  
          .. deprecated:: 1.0
              Criterion "mse" was deprecated in v1.0 and will be removed in
              version 1.2. Use `criterion="squared_error"` which is equivalent.
  
      max_depth : int, default=None
          The maximum depth of the tree. If None, then nodes are expanded until
          all leaves are pure or until all leaves contain less than
          min_samples_split samples.
  
      min_samples_split : int or float, default=2
          The minimum number of samples required to split an internal node:
  
          - If int, then consider `min_samples_split` as the minimum number.
          - If float, then `min_samples_split` is a fraction and
            `ceil(min_samples_split * n_samples)` are the minimum
            number of samples for each split.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_samples_leaf : int or float, default=1
          The minimum number of samples required to be at a leaf node.
          A split point at any depth will only be considered if it leaves at
          least ``min_samples_leaf`` training samples in each of the left and
          right branches.  This may have the effect of smoothing the model,
          especially in regression.
  
          - If int, then consider `min_samples_leaf` as the minimum number.
          - If float, then `min_samples_leaf` is a fraction and
            `ceil(min_samples_leaf * n_samples)` are the minimum
            number of samples for each node.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_weight_fraction_leaf : float, default=0.0
          The minimum weighted fraction of the sum total of weights (of all
          the input samples) required to be at a leaf node. Samples have
          equal weight when sample_weight is not provided.
  
      max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
          The number of features to consider when looking for the best split:
  
          - If int, then consider `max_features` features at each split.
          - If float, then `max_features` is a fraction and
            `round(max_features * n_features)` features are considered at each
            split.
          - If "auto", then `max_features=n_features`.
          - If "sqrt", then `max_features=sqrt(n_features)`.
          - If "log2", then `max_features=log2(n_features)`.
          - If None, then `max_features=n_features`.
  
          Note: the search for a split does not stop until at least one
          valid partition of the node samples is found, even if it requires to
          effectively inspect more than ``max_features`` features.
  
      max_leaf_nodes : int, default=None
          Grow trees with ``max_leaf_nodes`` in best-first fashion.
          Best nodes are defined as relative reduction in impurity.
          If None then unlimited number of leaf nodes.
  
      min_impurity_decrease : float, default=0.0
          A node will be split if this split induces a decrease of the impurity
          greater than or equal to this value.
  
          The weighted impurity decrease equation is the following::
  
              N_t / N * (impurity - N_t_R / N_t * right_impurity
                                  - N_t_L / N_t * left_impurity)
  
          where ``N`` is the total number of samples, ``N_t`` is the number of
          samples at the current node, ``N_t_L`` is the number of samples in the
          left child, and ``N_t_R`` is the number of samples in the right child.
  
          ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
          if ``sample_weight`` is passed.
  
          .. versionadded:: 0.19
  
      min_impurity_split : float, default=None
          Threshold for early stopping in tree growth. A node will split
          if its impurity is above the threshold, otherwise it is a leaf.
  
          .. deprecated:: 0.19
             ``min_impurity_split`` has been deprecated in favor of
             ``min_impurity_decrease`` in 0.19. The default value of
             ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
             will be removed in 1.0 (renaming of 0.25).
             Use ``min_impurity_decrease`` instead.
  
      bootstrap : bool, default=True
          Whether bootstrap samples are used when building trees. If False, the
          whole dataset is used to build each tree.
  
      oob_score : bool, default=False
          Whether to use out-of-bag samples to estimate the generalization score.
          Only available if bootstrap=True.
  
      n_jobs : int, default=None
          The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
          :meth:`decision_path` and :meth:`apply` are all parallelized over the
          trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
          context. ``-1`` means using all processors. See :term:`Glossary
          <n_jobs>` for more details.
  
      random_state : int, RandomState instance or None, default=None
          Controls both the randomness of the bootstrapping of the samples used
          when building trees (if ``bootstrap=True``) and the sampling of the
          features to consider when looking for the best split at each node
          (if ``max_features < n_features``).
          See :term:`Glossary <random_state>` for details.
  
      verbose : int, default=0
          Controls the verbosity when fitting and predicting.
  
      warm_start : bool, default=False
          When set to ``True``, reuse the solution of the previous call to fit
          and add more estimators to the ensemble, otherwise, just fit a whole
          new forest. See :term:`the Glossary <warm_start>`.
  
      ccp_alpha : non-negative float, default=0.0
          Complexity parameter used for Minimal Cost-Complexity Pruning. The
          subtree with the largest cost complexity that is smaller than
          ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
          :ref:`minimal_cost_complexity_pruning` for details.
  
          .. versionadded:: 0.22
  
      max_samples : int or float, default=None
          If bootstrap is True, the number of samples to draw from X
          to train each base estimator.
  
          - If None (default), then draw `X.shape[0]` samples.
          - If int, then draw `max_samples` samples.
          - If float, then draw `max_samples * X.shape[0]` samples. Thus,
            `max_samples` should be in the interval `(0, 1)`.
  
          .. versionadded:: 0.22
  
      Attributes
      ----------
      base_estimator_ : DecisionTreeRegressor
          The child estimator template used to create the collection of fitted
          sub-estimators.
  
      estimators_ : list of DecisionTreeRegressor
          The collection of fitted sub-estimators.
  
      feature_importances_ : ndarray of shape (n_features,)
          The impurity-based feature importances.
          The higher, the more important the feature.
          The importance of a feature is computed as the (normalized)
          total reduction of the criterion brought by that feature.  It is also
          known as the Gini importance.
  
          Warning: impurity-based feature importances can be misleading for
          high cardinality features (many unique values). See
          :func:`sklearn.inspection.permutation_importance` as an alternative.
  
      n_features_ : int
          The number of features when ``fit`` is performed.
  
          .. deprecated:: 1.0
              Attribute `n_features_` was deprecated in version 1.0 and will be
              removed in 1.2. Use `n_features_in_` instead.
  
      n_outputs_ : int
          The number of outputs when ``fit`` is performed.
  
      oob_score_ : float
          Score of the training dataset obtained using an out-of-bag estimate.
          This attribute exists only when ``oob_score`` is True.
  
      oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs)
          Prediction computed with out-of-bag estimate on the training set.
          This attribute exists only when ``oob_score`` is True.
  
      See Also
      --------
      DecisionTreeRegressor, ExtraTreesRegressor
  
      Notes
      -----
      The default values for the parameters controlling the size of the trees
      (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
      unpruned trees which can potentially be very large on some data sets. To
      reduce memory consumption, the complexity and size of the trees should be
      controlled by setting those parameter values.
  
      The features are always randomly permuted at each split. Therefore,
      the best found split may vary, even with the same training data,
      ``max_features=n_features`` and ``bootstrap=False``, if the improvement
      of the criterion is identical for several splits enumerated during the
      search of the best split. To obtain a deterministic behaviour during
      fitting, ``random_state`` has to be fixed.
  
      The default value ``max_features="auto"`` uses ``n_features``
      rather than ``n_features / 3``. The latter was originally suggested in
      [1], whereas the former was more recently justified empirically in [2].
  
      References
      ----------
      .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
  
      .. [2] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized
             trees", Machine Learning, 63(1), 3-42, 2006.
  
      Examples
      --------
      >>> from sklearn.ensemble import RandomForestRegressor
      >>> from sklearn.datasets import make_regression
      >>> X, y = make_regression(n_features=4, n_informative=2,
      ...                        random_state=0, shuffle=False)
      >>> regr = RandomForestRegressor(max_depth=2, random_state=0)
      >>> regr.fit(X, y)
      RandomForestRegressor(...)
      >>> print(regr.predict([[0, 0, 0, 0]]))
      [-8.32987858]
      """
      @_deprecate_positional_args
      def __init__(self,
                   n_estimators=100, *,
                   criterion="squared_error",
                   max_depth=None,
                   min_samples_split=2,
                   min_samples_leaf=1,
                   min_weight_fraction_leaf=0.,
                   max_features="auto",
                   max_leaf_nodes=None,
                   min_impurity_decrease=0.,
                   min_impurity_split=None,
                   bootstrap=True,
                   oob_score=False,
                   n_jobs=None,
                   random_state=None,
                   verbose=0,
                   warm_start=False,
                   ccp_alpha=0.0,
                   max_samples=None):
          super().__init__(
              base_estimator=DecisionTreeRegressor(),
              n_estimators=n_estimators,
              estimator_params=("criterion", "max_depth", "min_samples_split",
                                "min_samples_leaf", "min_weight_fraction_leaf",
                                "max_features", "max_leaf_nodes",
                                "min_impurity_decrease", "min_impurity_split",
                                "random_state", "ccp_alpha"),
              bootstrap=bootstrap,
              oob_score=oob_score,
              n_jobs=n_jobs,
              random_state=random_state,
              verbose=verbose,
              warm_start=warm_start,
              max_samples=max_samples)
  
          self.criterion = criterion
          self.max_depth = max_depth
          self.min_samples_split = min_samples_split
          self.min_samples_leaf = min_samples_leaf
          self.min_weight_fraction_leaf = min_weight_fraction_leaf
          self.max_features = max_features
          self.max_leaf_nodes = max_leaf_nodes
          self.min_impurity_decrease = min_impurity_decrease
          self.min_impurity_split = min_impurity_split
          self.ccp_alpha = ccp_alpha
  
  
  class ExtraTreesClassifier(ForestClassifier):
      """
      An extra-trees classifier.
  
      This class implements a meta estimator that fits a number of
      randomized decision trees (a.k.a. extra-trees) on various sub-samples
      of the dataset and uses averaging to improve the predictive accuracy
      and control over-fitting.
  
      Read more in the :ref:`User Guide <forest>`.
  
      Parameters
      ----------
      n_estimators : int, default=100
          The number of trees in the forest.
  
          .. versionchanged:: 0.22
             The default value of ``n_estimators`` changed from 10 to 100
             in 0.22.
  
      criterion : {"gini", "entropy"}, default="gini"
          The function to measure the quality of a split. Supported criteria are
          "gini" for the Gini impurity and "entropy" for the information gain.
  
      max_depth : int, default=None
          The maximum depth of the tree. If None, then nodes are expanded until
          all leaves are pure or until all leaves contain less than
          min_samples_split samples.
  
      min_samples_split : int or float, default=2
          The minimum number of samples required to split an internal node:
  
          - If int, then consider `min_samples_split` as the minimum number.
          - If float, then `min_samples_split` is a fraction and
            `ceil(min_samples_split * n_samples)` are the minimum
            number of samples for each split.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_samples_leaf : int or float, default=1
          The minimum number of samples required to be at a leaf node.
          A split point at any depth will only be considered if it leaves at
          least ``min_samples_leaf`` training samples in each of the left and
          right branches.  This may have the effect of smoothing the model,
          especially in regression.
  
          - If int, then consider `min_samples_leaf` as the minimum number.
          - If float, then `min_samples_leaf` is a fraction and
            `ceil(min_samples_leaf * n_samples)` are the minimum
            number of samples for each node.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_weight_fraction_leaf : float, default=0.0
          The minimum weighted fraction of the sum total of weights (of all
          the input samples) required to be at a leaf node. Samples have
          equal weight when sample_weight is not provided.
  
      max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
          The number of features to consider when looking for the best split:
  
          - If int, then consider `max_features` features at each split.
          - If float, then `max_features` is a fraction and
            `round(max_features * n_features)` features are considered at each
            split.
          - If "auto", then `max_features=sqrt(n_features)`.
          - If "sqrt", then `max_features=sqrt(n_features)`.
          - If "log2", then `max_features=log2(n_features)`.
          - If None, then `max_features=n_features`.
  
          Note: the search for a split does not stop until at least one
          valid partition of the node samples is found, even if it requires to
          effectively inspect more than ``max_features`` features.
  
      max_leaf_nodes : int, default=None
          Grow trees with ``max_leaf_nodes`` in best-first fashion.
          Best nodes are defined as relative reduction in impurity.
          If None then unlimited number of leaf nodes.
  
      min_impurity_decrease : float, default=0.0
          A node will be split if this split induces a decrease of the impurity
          greater than or equal to this value.
  
          The weighted impurity decrease equation is the following::
  
              N_t / N * (impurity - N_t_R / N_t * right_impurity
                                  - N_t_L / N_t * left_impurity)
  
          where ``N`` is the total number of samples, ``N_t`` is the number of
          samples at the current node, ``N_t_L`` is the number of samples in the
          left child, and ``N_t_R`` is the number of samples in the right child.
  
          ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
          if ``sample_weight`` is passed.
  
          .. versionadded:: 0.19
  
      min_impurity_split : float, default=None
          Threshold for early stopping in tree growth. A node will split
          if its impurity is above the threshold, otherwise it is a leaf.
  
          .. deprecated:: 0.19
             ``min_impurity_split`` has been deprecated in favor of
             ``min_impurity_decrease`` in 0.19. The default value of
             ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
             will be removed in 1.0 (renaming of 0.25).
             Use ``min_impurity_decrease`` instead.
  
      bootstrap : bool, default=False
          Whether bootstrap samples are used when building trees. If False, the
          whole dataset is used to build each tree.
  
      oob_score : bool, default=False
          Whether to use out-of-bag samples to estimate the generalization score.
          Only available if bootstrap=True.
  
      n_jobs : int, default=None
          The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
          :meth:`decision_path` and :meth:`apply` are all parallelized over the
          trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
          context. ``-1`` means using all processors. See :term:`Glossary
          <n_jobs>` for more details.
  
      random_state : int, RandomState instance or None, default=None
          Controls 3 sources of randomness:
  
          - the bootstrapping of the samples used when building trees
            (if ``bootstrap=True``)
          - the sampling of the features to consider when looking for the best
            split at each node (if ``max_features < n_features``)
          - the draw of the splits for each of the `max_features`
  
          See :term:`Glossary <random_state>` for details.
  
      verbose : int, default=0
          Controls the verbosity when fitting and predicting.
  
      warm_start : bool, default=False
          When set to ``True``, reuse the solution of the previous call to fit
          and add more estimators to the ensemble, otherwise, just fit a whole
          new forest. See :term:`the Glossary <warm_start>`.
  
      class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \
              default=None
          Weights associated with classes in the form ``{class_label: weight}``.
          If not given, all classes are supposed to have weight one. For
          multi-output problems, a list of dicts can be provided in the same
          order as the columns of y.
  
          Note that for multioutput (including multilabel) weights should be
          defined for each class of every column in its own dict. For example,
          for four-class multilabel classification weights should be
          [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
          [{1:1}, {2:5}, {3:1}, {4:1}].
  
          The "balanced" mode uses the values of y to automatically adjust
          weights inversely proportional to class frequencies in the input data
          as ``n_samples / (n_classes * np.bincount(y))``
  
          The "balanced_subsample" mode is the same as "balanced" except that
          weights are computed based on the bootstrap sample for every tree
          grown.
  
          For multi-output, the weights of each column of y will be multiplied.
  
          Note that these weights will be multiplied with sample_weight (passed
          through the fit method) if sample_weight is specified.
  
      ccp_alpha : non-negative float, default=0.0
          Complexity parameter used for Minimal Cost-Complexity Pruning. The
          subtree with the largest cost complexity that is smaller than
          ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
          :ref:`minimal_cost_complexity_pruning` for details.
  
          .. versionadded:: 0.22
  
      max_samples : int or float, default=None
          If bootstrap is True, the number of samples to draw from X
          to train each base estimator.
  
          - If None (default), then draw `X.shape[0]` samples.
          - If int, then draw `max_samples` samples.
          - If float, then draw `max_samples * X.shape[0]` samples. Thus,
            `max_samples` should be in the interval `(0, 1)`.
  
          .. versionadded:: 0.22
  
      Attributes
      ----------
      base_estimator_ : ExtraTreesClassifier
          The child estimator template used to create the collection of fitted
          sub-estimators.
  
      estimators_ : list of DecisionTreeClassifier
          The collection of fitted sub-estimators.
  
      classes_ : ndarray of shape (n_classes,) or a list of such arrays
          The classes labels (single output problem), or a list of arrays of
          class labels (multi-output problem).
  
      n_classes_ : int or list
          The number of classes (single output problem), or a list containing the
          number of classes for each output (multi-output problem).
  
      feature_importances_ : ndarray of shape (n_features,)
          The impurity-based feature importances.
          The higher, the more important the feature.
          The importance of a feature is computed as the (normalized)
          total reduction of the criterion brought by that feature.  It is also
          known as the Gini importance.
  
          Warning: impurity-based feature importances can be misleading for
          high cardinality features (many unique values). See
          :func:`sklearn.inspection.permutation_importance` as an alternative.
  
      n_features_ : int
          The number of features when ``fit`` is performed.
  
          .. deprecated:: 1.0
              Attribute `n_features_` was deprecated in version 1.0 and will be
              removed in 1.2. Use `n_features_in_` instead.
  
      n_outputs_ : int
          The number of outputs when ``fit`` is performed.
  
      oob_score_ : float
          Score of the training dataset obtained using an out-of-bag estimate.
          This attribute exists only when ``oob_score`` is True.
  
      oob_decision_function_ : ndarray of shape (n_samples, n_classes) or \
              (n_samples, n_classes, n_outputs)
          Decision function computed with out-of-bag estimate on the training
          set. If n_estimators is small it might be possible that a data point
          was never left out during the bootstrap. In this case,
          `oob_decision_function_` might contain NaN. This attribute exists
          only when ``oob_score`` is True.
  
      See Also
      --------
      sklearn.tree.ExtraTreeClassifier : Base classifier for this ensemble.
      RandomForestClassifier : Ensemble Classifier based on trees with optimal
          splits.
  
      Notes
      -----
      The default values for the parameters controlling the size of the trees
      (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
      unpruned trees which can potentially be very large on some data sets. To
      reduce memory consumption, the complexity and size of the trees should be
      controlled by setting those parameter values.
  
      References
      ----------
      .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized
             trees", Machine Learning, 63(1), 3-42, 2006.
  
      Examples
      --------
      >>> from sklearn.ensemble import ExtraTreesClassifier
      >>> from sklearn.datasets import make_classification
      >>> X, y = make_classification(n_features=4, random_state=0)
      >>> clf = ExtraTreesClassifier(n_estimators=100, random_state=0)
      >>> clf.fit(X, y)
      ExtraTreesClassifier(random_state=0)
      >>> clf.predict([[0, 0, 0, 0]])
      array([1])
      """
      @_deprecate_positional_args
      def __init__(self,
                   n_estimators=100, *,
                   criterion="gini",
                   max_depth=None,
                   min_samples_split=2,
                   min_samples_leaf=1,
                   min_weight_fraction_leaf=0.,
                   max_features="auto",
                   max_leaf_nodes=None,
                   min_impurity_decrease=0.,
                   min_impurity_split=None,
                   bootstrap=False,
                   oob_score=False,
                   n_jobs=None,
                   random_state=None,
                   verbose=0,
                   warm_start=False,
                   class_weight=None,
                   ccp_alpha=0.0,
                   max_samples=None):
          super().__init__(
              base_estimator=ExtraTreeClassifier(),
              n_estimators=n_estimators,
              estimator_params=("criterion", "max_depth", "min_samples_split",
                                "min_samples_leaf", "min_weight_fraction_leaf",
                                "max_features", "max_leaf_nodes",
                                "min_impurity_decrease", "min_impurity_split",
                                "random_state", "ccp_alpha"),
              bootstrap=bootstrap,
              oob_score=oob_score,
              n_jobs=n_jobs,
              random_state=random_state,
              verbose=verbose,
              warm_start=warm_start,
              class_weight=class_weight,
              max_samples=max_samples)
  
          self.criterion = criterion
          self.max_depth = max_depth
          self.min_samples_split = min_samples_split
          self.min_samples_leaf = min_samples_leaf
          self.min_weight_fraction_leaf = min_weight_fraction_leaf
          self.max_features = max_features
          self.max_leaf_nodes = max_leaf_nodes
          self.min_impurity_decrease = min_impurity_decrease
          self.min_impurity_split = min_impurity_split
          self.ccp_alpha = ccp_alpha
  
  
  class ExtraTreesRegressor(ForestRegressor):
      """
      An extra-trees regressor.
  
      This class implements a meta estimator that fits a number of
      randomized decision trees (a.k.a. extra-trees) on various sub-samples
      of the dataset and uses averaging to improve the predictive accuracy
      and control over-fitting.
  
      Read more in the :ref:`User Guide <forest>`.
  
      Parameters
      ----------
      n_estimators : int, default=100
          The number of trees in the forest.
  
          .. versionchanged:: 0.22
             The default value of ``n_estimators`` changed from 10 to 100
             in 0.22.
  
      criterion : {"squared_error", "mse", "mae"}, default="squared_error"
          The function to measure the quality of a split. Supported criteria
          are "squared_error" and "mse" for the mean squared error, which is
          equal to variance reduction as feature selection criterion, and "mae"
          for the mean absolute error.
  
          .. versionadded:: 0.18
             Mean Absolute Error (MAE) criterion.
  
          .. deprecated:: 1.0
              Criterion "mse" was deprecated in v1.0 and will be removed in
              version 1.2. Use `criterion="squared_error"` which is equivalent.
  
      max_depth : int, default=None
          The maximum depth of the tree. If None, then nodes are expanded until
          all leaves are pure or until all leaves contain less than
          min_samples_split samples.
  
      min_samples_split : int or float, default=2
          The minimum number of samples required to split an internal node:
  
          - If int, then consider `min_samples_split` as the minimum number.
          - If float, then `min_samples_split` is a fraction and
            `ceil(min_samples_split * n_samples)` are the minimum
            number of samples for each split.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_samples_leaf : int or float, default=1
          The minimum number of samples required to be at a leaf node.
          A split point at any depth will only be considered if it leaves at
          least ``min_samples_leaf`` training samples in each of the left and
          right branches.  This may have the effect of smoothing the model,
          especially in regression.
  
          - If int, then consider `min_samples_leaf` as the minimum number.
          - If float, then `min_samples_leaf` is a fraction and
            `ceil(min_samples_leaf * n_samples)` are the minimum
            number of samples for each node.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_weight_fraction_leaf : float, default=0.0
          The minimum weighted fraction of the sum total of weights (of all
          the input samples) required to be at a leaf node. Samples have
          equal weight when sample_weight is not provided.
  
      max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
          The number of features to consider when looking for the best split:
  
          - If int, then consider `max_features` features at each split.
          - If float, then `max_features` is a fraction and
            `round(max_features * n_features)` features are considered at each
            split.
          - If "auto", then `max_features=n_features`.
          - If "sqrt", then `max_features=sqrt(n_features)`.
          - If "log2", then `max_features=log2(n_features)`.
          - If None, then `max_features=n_features`.
  
          Note: the search for a split does not stop until at least one
          valid partition of the node samples is found, even if it requires to
          effectively inspect more than ``max_features`` features.
  
      max_leaf_nodes : int, default=None
          Grow trees with ``max_leaf_nodes`` in best-first fashion.
          Best nodes are defined as relative reduction in impurity.
          If None then unlimited number of leaf nodes.
  
      min_impurity_decrease : float, default=0.0
          A node will be split if this split induces a decrease of the impurity
          greater than or equal to this value.
  
          The weighted impurity decrease equation is the following::
  
              N_t / N * (impurity - N_t_R / N_t * right_impurity
                                  - N_t_L / N_t * left_impurity)
  
          where ``N`` is the total number of samples, ``N_t`` is the number of
          samples at the current node, ``N_t_L`` is the number of samples in the
          left child, and ``N_t_R`` is the number of samples in the right child.
  
          ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
          if ``sample_weight`` is passed.
  
          .. versionadded:: 0.19
  
      min_impurity_split : float, default=None
          Threshold for early stopping in tree growth. A node will split
          if its impurity is above the threshold, otherwise it is a leaf.
  
          .. deprecated:: 0.19
             ``min_impurity_split`` has been deprecated in favor of
             ``min_impurity_decrease`` in 0.19. The default value of
             ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
             will be removed in 1.0 (renaming of 0.25).
             Use ``min_impurity_decrease`` instead.
  
      bootstrap : bool, default=False
          Whether bootstrap samples are used when building trees. If False, the
          whole dataset is used to build each tree.
  
      oob_score : bool, default=False
          Whether to use out-of-bag samples to estimate the generalization score.
          Only available if bootstrap=True.
  
      n_jobs : int, default=None
          The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
          :meth:`decision_path` and :meth:`apply` are all parallelized over the
          trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
          context. ``-1`` means using all processors. See :term:`Glossary
          <n_jobs>` for more details.
  
      random_state : int, RandomState instance or None, default=None
          Controls 3 sources of randomness:
  
          - the bootstrapping of the samples used when building trees
            (if ``bootstrap=True``)
          - the sampling of the features to consider when looking for the best
            split at each node (if ``max_features < n_features``)
          - the draw of the splits for each of the `max_features`
  
          See :term:`Glossary <random_state>` for details.
  
      verbose : int, default=0
          Controls the verbosity when fitting and predicting.
  
      warm_start : bool, default=False
          When set to ``True``, reuse the solution of the previous call to fit
          and add more estimators to the ensemble, otherwise, just fit a whole
          new forest. See :term:`the Glossary <warm_start>`.
  
      ccp_alpha : non-negative float, default=0.0
          Complexity parameter used for Minimal Cost-Complexity Pruning. The
          subtree with the largest cost complexity that is smaller than
          ``ccp_alpha`` will be chosen. By default, no pruning is performed. See
          :ref:`minimal_cost_complexity_pruning` for details.
  
          .. versionadded:: 0.22
  
      max_samples : int or float, default=None
          If bootstrap is True, the number of samples to draw from X
          to train each base estimator.
  
          - If None (default), then draw `X.shape[0]` samples.
          - If int, then draw `max_samples` samples.
          - If float, then draw `max_samples * X.shape[0]` samples. Thus,
            `max_samples` should be in the interval `(0, 1)`.
  
          .. versionadded:: 0.22
  
      Attributes
      ----------
      base_estimator_ : ExtraTreeRegressor
          The child estimator template used to create the collection of fitted
          sub-estimators.
  
      estimators_ : list of DecisionTreeRegressor
          The collection of fitted sub-estimators.
  
      feature_importances_ : ndarray of shape (n_features,)
          The impurity-based feature importances.
          The higher, the more important the feature.
          The importance of a feature is computed as the (normalized)
          total reduction of the criterion brought by that feature.  It is also
          known as the Gini importance.
  
          Warning: impurity-based feature importances can be misleading for
          high cardinality features (many unique values). See
          :func:`sklearn.inspection.permutation_importance` as an alternative.
  
      n_features_ : int
          The number of features.
  
          .. deprecated:: 1.0
              Attribute `n_features_` was deprecated in version 1.0 and will be
              removed in 1.2. Use `n_features_in_` instead.
  
      n_outputs_ : int
          The number of outputs.
  
      oob_score_ : float
          Score of the training dataset obtained using an out-of-bag estimate.
          This attribute exists only when ``oob_score`` is True.
  
      oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs)
          Prediction computed with out-of-bag estimate on the training set.
          This attribute exists only when ``oob_score`` is True.
  
      See Also
      --------
      sklearn.tree.ExtraTreeRegressor : Base estimator for this ensemble.
      RandomForestRegressor : Ensemble regressor using trees with optimal splits.
  
      Notes
      -----
      The default values for the parameters controlling the size of the trees
      (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
      unpruned trees which can potentially be very large on some data sets. To
      reduce memory consumption, the complexity and size of the trees should be
      controlled by setting those parameter values.
  
      References
      ----------
      .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
             Machine Learning, 63(1), 3-42, 2006.
  
      Examples
      --------
      >>> from sklearn.datasets import load_diabetes
      >>> from sklearn.model_selection import train_test_split
      >>> from sklearn.ensemble import ExtraTreesRegressor
      >>> X, y = load_diabetes(return_X_y=True)
      >>> X_train, X_test, y_train, y_test = train_test_split(
      ...     X, y, random_state=0)
      >>> reg = ExtraTreesRegressor(n_estimators=100, random_state=0).fit(
      ...    X_train, y_train)
      >>> reg.score(X_test, y_test)
      0.2708...
      """
      @_deprecate_positional_args
      def __init__(self,
                   n_estimators=100, *,
                   criterion="squared_error",
                   max_depth=None,
                   min_samples_split=2,
                   min_samples_leaf=1,
                   min_weight_fraction_leaf=0.,
                   max_features="auto",
                   max_leaf_nodes=None,
                   min_impurity_decrease=0.,
                   min_impurity_split=None,
                   bootstrap=False,
                   oob_score=False,
                   n_jobs=None,
                   random_state=None,
                   verbose=0,
                   warm_start=False,
                   ccp_alpha=0.0,
                   max_samples=None):
          super().__init__(
              base_estimator=ExtraTreeRegressor(),
              n_estimators=n_estimators,
              estimator_params=("criterion", "max_depth", "min_samples_split",
                                "min_samples_leaf", "min_weight_fraction_leaf",
                                "max_features", "max_leaf_nodes",
                                "min_impurity_decrease", "min_impurity_split",
                                "random_state", "ccp_alpha"),
              bootstrap=bootstrap,
              oob_score=oob_score,
              n_jobs=n_jobs,
              random_state=random_state,
              verbose=verbose,
              warm_start=warm_start,
              max_samples=max_samples)
  
          self.criterion = criterion
          self.max_depth = max_depth
          self.min_samples_split = min_samples_split
          self.min_samples_leaf = min_samples_leaf
          self.min_weight_fraction_leaf = min_weight_fraction_leaf
          self.max_features = max_features
          self.max_leaf_nodes = max_leaf_nodes
          self.min_impurity_decrease = min_impurity_decrease
          self.min_impurity_split = min_impurity_split
          self.ccp_alpha = ccp_alpha
  
  
  class RandomTreesEmbedding(BaseForest):
      """
      An ensemble of totally random trees.
  
      An unsupervised transformation of a dataset to a high-dimensional
      sparse representation. A datapoint is coded according to which leaf of
      each tree it is sorted into. Using a one-hot encoding of the leaves,
      this leads to a binary coding with as many ones as there are trees in
      the forest.
  
      The dimensionality of the resulting representation is
      ``n_out <= n_estimators * max_leaf_nodes``. If ``max_leaf_nodes == None``,
      the number of leaf nodes is at most ``n_estimators * 2 ** max_depth``.
  
      Read more in the :ref:`User Guide <random_trees_embedding>`.
  
      Parameters
      ----------
      n_estimators : int, default=100
          Number of trees in the forest.
  
          .. versionchanged:: 0.22
             The default value of ``n_estimators`` changed from 10 to 100
             in 0.22.
  
      max_depth : int, default=5
          The maximum depth of each tree. If None, then nodes are expanded until
          all leaves are pure or until all leaves contain less than
          min_samples_split samples.
  
      min_samples_split : int or float, default=2
          The minimum number of samples required to split an internal node:
  
          - If int, then consider `min_samples_split` as the minimum number.
          - If float, then `min_samples_split` is a fraction and
            `ceil(min_samples_split * n_samples)` is the minimum
            number of samples for each split.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_samples_leaf : int or float, default=1
          The minimum number of samples required to be at a leaf node.
          A split point at any depth will only be considered if it leaves at
          least ``min_samples_leaf`` training samples in each of the left and
          right branches.  This may have the effect of smoothing the model,
          especially in regression.
  
          - If int, then consider `min_samples_leaf` as the minimum number.
          - If float, then `min_samples_leaf` is a fraction and
            `ceil(min_samples_leaf * n_samples)` is the minimum
            number of samples for each node.
  
          .. versionchanged:: 0.18
             Added float values for fractions.
  
      min_weight_fraction_leaf : float, default=0.0
          The minimum weighted fraction of the sum total of weights (of all
          the input samples) required to be at a leaf node. Samples have
          equal weight when sample_weight is not provided.
  
      max_leaf_nodes : int, default=None
          Grow trees with ``max_leaf_nodes`` in best-first fashion.
          Best nodes are defined as relative reduction in impurity.
          If None then unlimited number of leaf nodes.
  
      min_impurity_decrease : float, default=0.0
          A node will be split if this split induces a decrease of the impurity
          greater than or equal to this value.
  
          The weighted impurity decrease equation is the following::
  
              N_t / N * (impurity - N_t_R / N_t * right_impurity
                                  - N_t_L / N_t * left_impurity)
  
          where ``N`` is the total number of samples, ``N_t`` is the number of
          samples at the current node, ``N_t_L`` is the number of samples in the
          left child, and ``N_t_R`` is the number of samples in the right child.
  
          ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
          if ``sample_weight`` is passed.
  
          .. versionadded:: 0.19
  
      min_impurity_split : float, default=None
          Threshold for early stopping in tree growth. A node will split
          if its impurity is above the threshold, otherwise it is a leaf.
  
          .. deprecated:: 0.19
             ``min_impurity_split`` has been deprecated in favor of
             ``min_impurity_decrease`` in 0.19. The default value of
             ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it
             will be removed in 1.0 (renaming of 0.25).
             Use ``min_impurity_decrease`` instead.
  
      sparse_output : bool, default=True
          Whether or not to return a sparse CSR matrix, as default behavior,
          or to return a dense array compatible with dense pipeline operators.
  
      n_jobs : int, default=None
          The number of jobs to run in parallel. :meth:`fit`, :meth:`transform`,
          :meth:`decision_path` and :meth:`apply` are all parallelized over the
          trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
          context. ``-1`` means using all processors. See :term:`Glossary
          <n_jobs>` for more details.
  
      random_state : int, RandomState instance or None, default=None
          Controls the generation of the random `y` used to fit the trees
          and the draw of the splits for each feature at the trees' nodes.
          See :term:`Glossary <random_state>` for details.
  
      verbose : int, default=0
          Controls the verbosity when fitting and predicting.
  
      warm_start : bool, default=False
          When set to ``True``, reuse the solution of the previous call to fit
          and add more estimators to the ensemble, otherwise, just fit a whole
          new forest. See :term:`the Glossary <warm_start>`.
  
      Attributes
      ----------
      base_estimator_ : DecisionTreeClassifier instance
          The child estimator template used to create the collection of fitted
          sub-estimators.
  
      estimators_ : list of DecisionTreeClassifier instances
          The collection of fitted sub-estimators.
  
      feature_importances_ : ndarray of shape (n_features,)
          The feature importances (the higher, the more important the feature).
  
      n_features_ : int
          The number of features when ``fit`` is performed.
  
          .. deprecated:: 1.0
              Attribute `n_features_` was deprecated in version 1.0 and will be
              removed in 1.2. Use `n_features_in_` instead.
  
      n_outputs_ : int
          The number of outputs when ``fit`` is performed.
  
      one_hot_encoder_ : OneHotEncoder instance
          One-hot encoder used to create the sparse embedding.
  
      References
      ----------
      .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
             Machine Learning, 63(1), 3-42, 2006.
      .. [2] Moosmann, F. and Triggs, B. and Jurie, F.  "Fast discriminative
             visual codebooks using randomized clustering forests"
             NIPS 2007
  
      Examples
      --------
      >>> from sklearn.ensemble import RandomTreesEmbedding
      >>> X = [[0,0], [1,0], [0,1], [-1,0], [0,-1]]
      >>> random_trees = RandomTreesEmbedding(
      ...    n_estimators=5, random_state=0, max_depth=1).fit(X)
      >>> X_sparse_embedding = random_trees.transform(X)
      >>> X_sparse_embedding.toarray()
      array([[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
             [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.],
             [0., 1., 0., 1., 0., 1., 0., 1., 0., 1.],
             [1., 0., 1., 0., 1., 0., 1., 0., 1., 0.],
             [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.]])
      """
  
      criterion = "squared_error"
      max_features = 1
  
      @_deprecate_positional_args
      def __init__(self,
                   n_estimators=100, *,
                   max_depth=5,
                   min_samples_split=2,
                   min_samples_leaf=1,
                   min_weight_fraction_leaf=0.,
                   max_leaf_nodes=None,
                   min_impurity_decrease=0.,
                   min_impurity_split=None,
                   sparse_output=True,
                   n_jobs=None,
                   random_state=None,
                   verbose=0,
                   warm_start=False):
          super().__init__(
              base_estimator=ExtraTreeRegressor(),
              n_estimators=n_estimators,
              estimator_params=("criterion", "max_depth", "min_samples_split",
                                "min_samples_leaf", "min_weight_fraction_leaf",
                                "max_features", "max_leaf_nodes",
                                "min_impurity_decrease", "min_impurity_split",
                                "random_state"),
              bootstrap=False,
              oob_score=False,
              n_jobs=n_jobs,
              random_state=random_state,
              verbose=verbose,
              warm_start=warm_start,
              max_samples=None)
  
          self.max_depth = max_depth
          self.min_samples_split = min_samples_split
          self.min_samples_leaf = min_samples_leaf
          self.min_weight_fraction_leaf = min_weight_fraction_leaf
          self.max_leaf_nodes = max_leaf_nodes
          self.min_impurity_decrease = min_impurity_decrease
          self.min_impurity_split = min_impurity_split
          self.sparse_output = sparse_output
  
      def _set_oob_score_and_attributes(self, X, y):
          raise NotImplementedError("OOB score not supported by tree embedding")
  
      def fit(self, X, y=None, sample_weight=None):
          """
          Fit estimator.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples. Use ``dtype=np.float32`` for maximum
              efficiency. Sparse matrices are also supported, use sparse
              ``csc_matrix`` for maximum efficiency.
  
          y : Ignored
              Not used, present for API consistency by convention.
  
          sample_weight : array-like of shape (n_samples,), default=None
              Sample weights. If None, then samples are equally weighted. Splits
              that would create child nodes with net zero or negative weight are
              ignored while searching for a split in each node. In the case of
              classification, splits are also ignored if they would result in any
              single class carrying a negative weight in either child node.
  
          Returns
          -------
          self : object
  
          """
          self.fit_transform(X, y, sample_weight=sample_weight)
          return self
  
      def fit_transform(self, X, y=None, sample_weight=None):
          """
          Fit estimator and transform dataset.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Input data used to build forests. Use ``dtype=np.float32`` for
              maximum efficiency.
  
          y : Ignored
              Not used, present for API consistency by convention.
  
          sample_weight : array-like of shape (n_samples,), default=None
              Sample weights. If None, then samples are equally weighted. Splits
              that would create child nodes with net zero or negative weight are
              ignored while searching for a split in each node. In the case of
              classification, splits are also ignored if they would result in any
              single class carrying a negative weight in either child node.
  
          Returns
          -------
          X_transformed : sparse matrix of shape (n_samples, n_out)
              Transformed dataset.
          """
          X = self._validate_data(X, accept_sparse=['csc'])
          if issparse(X):
              # Pre-sort indices to avoid that each individual tree of the
              # ensemble sorts the indices.
              X.sort_indices()
  
          rnd = check_random_state(self.random_state)
          y = rnd.uniform(size=X.shape[0])
          super().fit(X, y, sample_weight=sample_weight)
  
          self.one_hot_encoder_ = OneHotEncoder(sparse=self.sparse_output)
          return self.one_hot_encoder_.fit_transform(self.apply(X))
  
      def transform(self, X):
          """
          Transform dataset.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Input data to be transformed. Use ``dtype=np.float32`` for maximum
              efficiency. Sparse matrices are also supported, use sparse
              ``csr_matrix`` for maximum efficiency.
  
          Returns
          -------
          X_transformed : sparse matrix of shape (n_samples, n_out)
              Transformed dataset.
          """
          check_is_fitted(self)
          return self.one_hot_encoder_.transform(self.apply(X))
  