class_docstr: Bagging ensemble of time series classifiers.
  
  Fits ``n_estimators`` clones of a classifier on
  datasets which are instance sub-samples and/or variable sub-samples.
  
  On ``predict_proba``, the mean average of probabilistic predictions is returned.
  For a deterministic classifier, this results in majority vote for ``predict``.
  
  The estimator allows to choose sample sizes fir instances, variables,
  and whether sampling is with or without replacement.
  
  Direct generalization of ``sklearn``'s ``BaggingClassifier``
  to the time series classification task.
  
  Note: if n_features=1, BaggingClassifier turns a univariate classifier into
  a multivariate classifier (as slices seen by ``estimator`` are all univariate).
  
  Parameters
  ----------
  estimator : sktime classifier, descendant of BaseClassifier
      classifier to use in the bagging estimator
  n_estimators : int, default=10
      number of estimators in the sample for bagging
  n_samples : int or float, default=1.0
      The number of instances drawn from ``X`` in ``fit`` to train each clone
      If int, then indicates number of instances precisely
      If float, interpreted as a fraction, and rounded by ``ceil``
  n_features : int or float, default=1.0
      The number of features/variables drawn from ``X`` in ``fit`` to train each clone
      If int, then indicates number of instances precisely
      If float, interpreted as a fraction, and rounded by ``ceil``
      Note: if n_features=1, BaggingClassifier turns a univariate classifier into
      a multivariate classifier (as slices seen by ``estimator`` are all univariate).
  bootstrap : boolean, default=True
      whether samples/instances are drawn with replacement (True) or not (False)
  bootstrap_features : boolean, default=False
      whether features/variables are drawn with replacement (True) or not (False)
  random_state : int, RandomState instance or None, optional (default=None)
      If int, ``random_state`` is the seed used by the random number generator;
      If ``RandomState`` instance, ``random_state`` is the random number generator;
      If None, the random number generator is the ``RandomState`` instance used
      by ``np.random``.
  
  Attributes
  ----------
  estimators_ : list of of sktime classifiers
      clones of classifier in `estimator` fitted in the ensemble
  
  Examples
  --------
  >>> from sktime.classification.ensemble import BaggingClassifier
  >>> from sktime.classification.kernel_based import RocketClassifier
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train") # doctest: +SKIP
  >>> X_test, y_test = load_unit_test(split="test") # doctest: +SKIP
  >>> clf = BaggingClassifier(
  ...     RocketClassifier(num_kernels=100),
  ...     n_estimators=10,
  ... ) # doctest: +SKIP
  >>> clf.fit(X_train, y_train) # doctest: +SKIP
  BaggingClassifier(...)
  >>> y_pred = clf.predict(X_test) # doctest: +SKIP
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docstr: Predicts labels probabilities for sequences in X.
  
  Parameters
  ----------
  X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
      The data to make predict probabilities for.
  
  Returns
  -------
  y : array-like, shape = [n_instances, n_classes_]
      Predicted probabilities using the ordering in classes_.
docstr_tok: ["Predicts","labels","probabilities","for","sequences","in","X.","Parameters","--","--","--","--","--","X",":","3D","np.array","of","shape","=","[","n_instances",",","n_dimensions",",","series_length","]","The","data","to","make","predict","probabilities","for",".","Returns","--","--","--","-","y",":","array-like",",","shape","=","[","n_instances",",","n_classes_","]","Predicted","probabilities","using","the","ordering","in","classes_","."]
code: def _predict_proba(self, X) -> np.ndarray:
      """Predicts labels probabilities for sequences in X.
  
      Parameters
      ----------
      X : 3D np.array of shape = [n_instances, n_dimensions, series_length]
          The data to make predict probabilities for.
  
      Returns
      -------
      y : array-like, shape = [n_instances, n_classes_]
          Predicted probabilities using the ordering in classes_.
      """
      classes = pd.Index(self.classes_)
      y_probas = [est.predict_proba(X) for est in self.estimators_]
  
      est_shape = (len(y_probas[0]), len(classes))
      y_proba_np = np.zeros((len(y_probas), est_shape[0], est_shape[1]))
      y_proba_np = np.zeros((est_shape[0], est_shape[1], len(y_probas)))
  
      for i, y_proba in enumerate(y_probas):
          cls_ix = self.estimators_[i].classes_
          ixer = classes.get_indexer_for(cls_ix)
          y_proba_np[:, ixer, i] = y_proba
  
      y_proba = np.mean(y_proba_np, axis=2)
  
      return y_proba
  
file_path: sktime/sktime/classification/ensemble/_bagging.py
imports: from math import ceil
  import numpy
  import pandas
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 3 for calling the BaggingClassifier obj's underlying member method code with example usage: obj._predict_proba(X) and returns: y_proba