text: """
  Soft Voting/Majority Rule classifier and Voting regressor.
  
  This module contains:
   - A Soft Voting/Majority Rule classifier for classification estimators.
   - A Voting regressor for regression estimators.
  """
  
  # Authors: Sebastian Raschka <se.raschka@gmail.com>,
  #          Gilles Louppe <g.louppe@gmail.com>,
  #          Ramil Nugmanov <stsouko@live.ru>
  #          Mohamed Ali Jamaoui <m.ali.jamaoui@gmail.com>
  #
  # License: BSD 3 clause
  
  from abc import abstractmethod
  
  import numpy as np
  
  from joblib import Parallel
  
  from ..base import ClassifierMixin
  from ..base import RegressorMixin
  from ..base import TransformerMixin
  from ..base import clone
  from ._base import _fit_single_estimator
  from ._base import _BaseHeterogeneousEnsemble
  from ..preprocessing import LabelEncoder
  from ..utils import Bunch
  from ..utils.metaestimators import available_if
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import column_or_1d
  from ..exceptions import NotFittedError
  from ..utils._estimator_html_repr import _VisualBlock
  from ..utils.fixes import delayed
  
  
  class _BaseVoting(TransformerMixin, _BaseHeterogeneousEnsemble):
      """Base class for voting.
  
      Warning: This class should not be used directly. Use derived classes
      instead.
      """
  
      def _log_message(self, name, idx, total):
          if not self.verbose:
              return None
          return "(%d of %d) Processing %s" % (idx, total, name)
  
      @property
      def _weights_not_none(self):
          """Get the weights of not `None` estimators."""
          if self.weights is None:
              return None
          return [w for est, w in zip(self.estimators, self.weights) if est[1] != "drop"]
  
      def _predict(self, X):
          """Collect results from clf.predict calls."""
          return np.asarray([est.predict(X) for est in self.estimators_]).T
  
      @abstractmethod
      def fit(self, X, y, sample_weight=None):
          """Get common fit operations."""
          names, clfs = self._validate_estimators()
  
          if self.weights is not None and len(self.weights) != len(self.estimators):
              raise ValueError(
                  "Number of `estimators` and weights must be equal"
                  "; got %d weights, %d estimators"
                  % (len(self.weights), len(self.estimators))
              )
  
          self.estimators_ = Parallel(n_jobs=self.n_jobs)(
              delayed(_fit_single_estimator)(
                  clone(clf),
                  X,
                  y,
                  sample_weight=sample_weight,
                  message_clsname="Voting",
                  message=self._log_message(names[idx], idx + 1, len(clfs)),
              )
              for idx, clf in enumerate(clfs)
              if clf != "drop"
          )
  
          self.named_estimators_ = Bunch()
  
          # Uses 'drop' as placeholder for dropped estimators
          est_iter = iter(self.estimators_)
          for name, est in self.estimators:
              current_est = est if est == "drop" else next(est_iter)
              self.named_estimators_[name] = current_est
  
              if hasattr(current_est, "feature_names_in_"):
                  self.feature_names_in_ = current_est.feature_names_in_
  
          return self
  
      def fit_transform(self, X, y=None, **fit_params):
          """Return class labels or probabilities for each estimator.
  
          Return predictions for X for each estimator.
  
          Parameters
          ----------
          X : {array-like, sparse matrix, dataframe} of shape \
                  (n_samples, n_features)
              Input samples.
  
          y : ndarray of shape (n_samples,), default=None
              Target values (None for unsupervised transformations).
  
          **fit_params : dict
              Additional fit parameters.
  
          Returns
          -------
          X_new : ndarray array of shape (n_samples, n_features_new)
              Transformed array.
          """
          return super().fit_transform(X, y, **fit_params)
  
      @property
      def n_features_in_(self):
          """Number of features seen during :term:`fit`."""
          # For consistency with other estimators we raise a AttributeError so
          # that hasattr() fails if the estimator isn't fitted.
          try:
              check_is_fitted(self)
          except NotFittedError as nfe:
              raise AttributeError(
                  "{} object has no n_features_in_ attribute.".format(
                      self.__class__.__name__
                  )
              ) from nfe
  
          return self.estimators_[0].n_features_in_
  
      def _sk_visual_block_(self):
          names, estimators = zip(*self.estimators)
          return _VisualBlock("parallel", estimators, names=names)
  
      def _more_tags(self):
          return {"preserves_dtype": []}
  
  
  class VotingClassifier(ClassifierMixin, _BaseVoting):
      """Soft Voting/Majority Rule classifier for unfitted estimators.
  
      Read more in the :ref:`User Guide <voting_classifier>`.
  
      .. versionadded:: 0.17
  
      Parameters
      ----------
      estimators : list of (str, estimator) tuples
          Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones
          of those original estimators that will be stored in the class attribute
          ``self.estimators_``. An estimator can be set to ``'drop'``
          using ``set_params``.
  
          .. versionchanged:: 0.21
              ``'drop'`` is accepted. Using None was deprecated in 0.22 and
              support was removed in 0.24.
  
      voting : {'hard', 'soft'}, default='hard'
          If 'hard', uses predicted class labels for majority rule voting.
          Else if 'soft', predicts the class label based on the argmax of
          the sums of the predicted probabilities, which is recommended for
          an ensemble of well-calibrated classifiers.
  
      weights : array-like of shape (n_classifiers,), default=None
          Sequence of weights (`float` or `int`) to weight the occurrences of
          predicted class labels (`hard` voting) or class probabilities
          before averaging (`soft` voting). Uses uniform weights if `None`.
  
      n_jobs : int, default=None
          The number of jobs to run in parallel for ``fit``.
          ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
          ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
          for more details.
  
          .. versionadded:: 0.18
  
      flatten_transform : bool, default=True
          Affects shape of transform output only when voting='soft'
          If voting='soft' and flatten_transform=True, transform method returns
          matrix with shape (n_samples, n_classifiers * n_classes). If
          flatten_transform=False, it returns
          (n_classifiers, n_samples, n_classes).
  
      verbose : bool, default=False
          If True, the time elapsed while fitting will be printed as it
          is completed.
  
          .. versionadded:: 0.23
  
      Attributes
      ----------
      estimators_ : list of classifiers
          The collection of fitted sub-estimators as defined in ``estimators``
          that are not 'drop'.
  
      named_estimators_ : :class:`~sklearn.utils.Bunch`
          Attribute to access any fitted sub-estimators by name.
  
          .. versionadded:: 0.20
  
      le_ : :class:`~sklearn.preprocessing.LabelEncoder`
          Transformer used to encode the labels during fit and decode during
          prediction.
  
      classes_ : ndarray of shape (n_classes,)
          The classes labels.
  
      n_features_in_ : int
          Number of features seen during :term:`fit`. Only defined if the
          underlying classifier exposes such an attribute when fit.
  
          .. versionadded:: 0.24
  
      feature_names_in_ : ndarray of shape (`n_features_in_`,)
          Names of features seen during :term:`fit`. Only defined if the
          underlying estimators expose such an attribute when fit.
          .. versionadded:: 1.0
  
      See Also
      --------
      VotingRegressor : Prediction voting regressor.
  
      Examples
      --------
      >>> import numpy as np
      >>> from sklearn.linear_model import LogisticRegression
      >>> from sklearn.naive_bayes import GaussianNB
      >>> from sklearn.ensemble import RandomForestClassifier, VotingClassifier
      >>> clf1 = LogisticRegression(multi_class='multinomial', random_state=1)
      >>> clf2 = RandomForestClassifier(n_estimators=50, random_state=1)
      >>> clf3 = GaussianNB()
      >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
      >>> y = np.array([1, 1, 1, 2, 2, 2])
      >>> eclf1 = VotingClassifier(estimators=[
      ...         ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard')
      >>> eclf1 = eclf1.fit(X, y)
      >>> print(eclf1.predict(X))
      [1 1 1 2 2 2]
      >>> np.array_equal(eclf1.named_estimators_.lr.predict(X),
      ...                eclf1.named_estimators_['lr'].predict(X))
      True
      >>> eclf2 = VotingClassifier(estimators=[
      ...         ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
      ...         voting='soft')
      >>> eclf2 = eclf2.fit(X, y)
      >>> print(eclf2.predict(X))
      [1 1 1 2 2 2]
      >>> eclf3 = VotingClassifier(estimators=[
      ...        ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
      ...        voting='soft', weights=[2,1,1],
      ...        flatten_transform=True)
      >>> eclf3 = eclf3.fit(X, y)
      >>> print(eclf3.predict(X))
      [1 1 1 2 2 2]
      >>> print(eclf3.transform(X).shape)
      (6, 6)
      """
  
      def __init__(
          self,
          estimators,
          *,
          voting="hard",
          weights=None,
          n_jobs=None,
          flatten_transform=True,
          verbose=False,
      ):
          super().__init__(estimators=estimators)
          self.voting = voting
          self.weights = weights
          self.n_jobs = n_jobs
          self.flatten_transform = flatten_transform
          self.verbose = verbose
  
      def fit(self, X, y, sample_weight=None):
          """Fit the estimators.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Training vectors, where `n_samples` is the number of samples and
              `n_features` is the number of features.
  
          y : array-like of shape (n_samples,)
              Target values.
  
          sample_weight : array-like of shape (n_samples,), default=None
              Sample weights. If None, then samples are equally weighted.
              Note that this is supported only if all underlying estimators
              support sample weights.
  
              .. versionadded:: 0.18
  
          Returns
          -------
          self : object
              Returns the instance itself.
          """
          check_classification_targets(y)
          if isinstance(y, np.ndarray) and len(y.shape) > 1 and y.shape[1] > 1:
              raise NotImplementedError(
                  "Multilabel and multi-output classification is not supported."
              )
  
          if self.voting not in ("soft", "hard"):
              raise ValueError(
                  "Voting must be 'soft' or 'hard'; got (voting=%r)" % self.voting
              )
  
          self.le_ = LabelEncoder().fit(y)
          self.classes_ = self.le_.classes_
          transformed_y = self.le_.transform(y)
  
          return super().fit(X, transformed_y, sample_weight)
  
      def predict(self, X):
          """Predict class labels for X.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples.
  
          Returns
          -------
          maj : array-like of shape (n_samples,)
              Predicted class labels.
          """
          check_is_fitted(self)
          if self.voting == "soft":
              maj = np.argmax(self.predict_proba(X), axis=1)
  
          else:  # 'hard' voting
              predictions = self._predict(X)
              maj = np.apply_along_axis(
                  lambda x: np.argmax(np.bincount(x, weights=self._weights_not_none)),
                  axis=1,
                  arr=predictions,
              )
  
          maj = self.le_.inverse_transform(maj)
  
          return maj
  
      def _collect_probas(self, X):
          """Collect results from clf.predict calls."""
          return np.asarray([clf.predict_proba(X) for clf in self.estimators_])
  
      def _check_voting(self):
          if self.voting == "hard":
              raise AttributeError(
                  f"predict_proba is not available when voting={repr(self.voting)}"
              )
          return True
  
      @available_if(_check_voting)
      def predict_proba(self, X):
          """Compute probabilities of possible outcomes for samples in X.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples.
  
          Returns
          -------
          avg : array-like of shape (n_samples, n_classes)
              Weighted average probability for each class per sample.
          """
          check_is_fitted(self)
          avg = np.average(
              self._collect_probas(X), axis=0, weights=self._weights_not_none
          )
          return avg
  
      def transform(self, X):
          """Return class labels or probabilities for X for each estimator.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Training vectors, where `n_samples` is the number of samples and
              `n_features` is the number of features.
  
          Returns
          -------
          probabilities_or_labels
              If `voting='soft'` and `flatten_transform=True`:
                  returns ndarray of shape (n_classifiers, n_samples *
                  n_classes), being class probabilities calculated by each
                  classifier.
              If `voting='soft' and `flatten_transform=False`:
                  ndarray of shape (n_classifiers, n_samples, n_classes)
              If `voting='hard'`:
                  ndarray of shape (n_samples, n_classifiers), being
                  class labels predicted by each classifier.
          """
          check_is_fitted(self)
  
          if self.voting == "soft":
              probas = self._collect_probas(X)
              if not self.flatten_transform:
                  return probas
              return np.hstack(probas)
  
          else:
              return self._predict(X)
  
  
  class VotingRegressor(RegressorMixin, _BaseVoting):
      """Prediction voting regressor for unfitted estimators.
  
      A voting regressor is an ensemble meta-estimator that fits several base
      regressors, each on the whole dataset. Then it averages the individual
      predictions to form a final prediction.
  
      Read more in the :ref:`User Guide <voting_regressor>`.
  
      .. versionadded:: 0.21
  
      Parameters
      ----------
      estimators : list of (str, estimator) tuples
          Invoking the ``fit`` method on the ``VotingRegressor`` will fit clones
          of those original estimators that will be stored in the class attribute
          ``self.estimators_``. An estimator can be set to ``'drop'`` using
          ``set_params``.
  
          .. versionchanged:: 0.21
              ``'drop'`` is accepted. Using None was deprecated in 0.22 and
              support was removed in 0.24.
  
      weights : array-like of shape (n_regressors,), default=None
          Sequence of weights (`float` or `int`) to weight the occurrences of
          predicted values before averaging. Uses uniform weights if `None`.
  
      n_jobs : int, default=None
          The number of jobs to run in parallel for ``fit``.
          ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
          ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
          for more details.
  
      verbose : bool, default=False
          If True, the time elapsed while fitting will be printed as it
          is completed.
  
          .. versionadded:: 0.23
  
      Attributes
      ----------
      estimators_ : list of regressors
          The collection of fitted sub-estimators as defined in ``estimators``
          that are not 'drop'.
  
      named_estimators_ : :class:`~sklearn.utils.Bunch`
          Attribute to access any fitted sub-estimators by name.
  
          .. versionadded:: 0.20
  
      n_features_in_ : int
          Number of features seen during :term:`fit`. Only defined if the
          underlying regressor exposes such an attribute when fit.
  
          .. versionadded:: 0.24
  
      feature_names_in_ : ndarray of shape (`n_features_in_`,)
          Names of features seen during :term:`fit`. Only defined if the
          underlying estimators expose such an attribute when fit.
          .. versionadded:: 1.0
  
      See Also
      --------
      VotingClassifier : Soft Voting/Majority Rule classifier.
  
      Examples
      --------
      >>> import numpy as np
      >>> from sklearn.linear_model import LinearRegression
      >>> from sklearn.ensemble import RandomForestRegressor
      >>> from sklearn.ensemble import VotingRegressor
      >>> r1 = LinearRegression()
      >>> r2 = RandomForestRegressor(n_estimators=10, random_state=1)
      >>> X = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36]])
      >>> y = np.array([2, 6, 12, 20, 30, 42])
      >>> er = VotingRegressor([('lr', r1), ('rf', r2)])
      >>> print(er.fit(X, y).predict(X))
      [ 3.3  5.7 11.8 19.7 28.  40.3]
      """
  
      def __init__(self, estimators, *, weights=None, n_jobs=None, verbose=False):
          super().__init__(estimators=estimators)
          self.weights = weights
          self.n_jobs = n_jobs
          self.verbose = verbose
  
      def fit(self, X, y, sample_weight=None):
          """Fit the estimators.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Training vectors, where `n_samples` is the number of samples and
              `n_features` is the number of features.
  
          y : array-like of shape (n_samples,)
              Target values.
  
          sample_weight : array-like of shape (n_samples,), default=None
              Sample weights. If None, then samples are equally weighted.
              Note that this is supported only if all underlying estimators
              support sample weights.
  
          Returns
          -------
          self : object
              Fitted estimator.
          """
          y = column_or_1d(y, warn=True)
          return super().fit(X, y, sample_weight)
  
      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 estimators in the ensemble.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples.
  
          Returns
          -------
          y : ndarray of shape (n_samples,)
              The predicted values.
          """
          check_is_fitted(self)
          return np.average(self._predict(X), axis=1, weights=self._weights_not_none)
  
      def transform(self, X):
          """Return predictions for X for each estimator.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              The input samples.
  
          Returns
          -------
          predictions : ndarray of shape (n_samples, n_classifiers)
              Values predicted by each regressor.
          """
          check_is_fitted(self)
          return self._predict(X)
  