class_docstr: A random forest classifier for time series.
  
  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.
  
  This transformer extracts 3 features from each window: the mean, the
  standard deviation and the slope. The total number of features is thus
  equal to ``3 * n_windows``. Then a random forest is built using
  these features as input data.
  
  Parameters
  ----------
  n_estimators : int (default = 500)
      The number of trees in the forest.
  
  n_windows : int or float (default = 1.)
      The number of windows from which features are extracted.
  
  min_window_size : int or float (default = 1)
      The minimum length of the windows. If float, it represents a percentage
      of the size of each time series.
  
  criterion : str (default = "entropy")
      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 : integer or None (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.
  
  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.
  
      - 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.
  
  min_weight_fraction_leaf : float (default = 0.)
      The minimum weighted fraction of the sum total of weights (of all
      the input samples) required to be at a leaf node.
  
  max_features : int, float, str or None (default = "sqrt")
      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
        ``int(max_features * n_features)`` features are considered at each
        split.
      - If "sqrt", then ``max_features=sqrt(n_features)``.
      - If "log2", then ``max_features=log2(n_features)``.
      - If None, then ``max_features=n_features``.
  
  max_leaf_nodes : int or None (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.)
      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.
  
  bootstrap : bool (default = True)
      Whether bootstrap samples are used when building trees. If False, the
      whole datset is used to build each tree.
  
  oob_score : bool (default = False)
      Whether to use out-of-bag samples to estimate
      the generalization accuracy.
  
  n_jobs : int or None, optional (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 ``joblib.parallel_backend``
      context. ``-1`` means using all processors.
  
  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``).
  
  verbose : int (default = 0)
      Controls the verbosity when fitting and predicting.
  
  class_weight : dict, "balanced", "balanced_subsample" or None (default = None)
      Weights associated with classes in the form ``{class_label: weight}``.
      If not given, all classes are supposed to have weight one.
  
      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.
  
  ccp_alpha : float (default = 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.
      It must be non-negative.
  
  max_samples : int, float or None (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)`.
  
  Attributes
  ----------
  estimator_ : DecisionTreeClassifier
      The child estimator template used to create the collection of fitted
      sub-estimators.
  
  classes_ : array, shape = (n_classes,)
      The classes labels.
  
  estimators_ : list of DecisionTreeClassifier
      The collection of fitted sub-estimators.
  
  feature_importances_ : array, shape = (n_features,)
      The feature importances (the higher, the more important the feature).
  
  indices_ : array, shape = (n_windows, 2)
      The indices for the windows.
      The first column consists of the starting indices (included)
      of the windows. The second column consists of the ending indices
      (excluded) of the windows.
  
  n_features_in_ : int
      The number of features when ``fit`` is performed.
  
  oob_decision_function_ : None or array, shape = (n_samples, n_classes)
      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 is not None
      only when ``oob_score`` is True.
  
  oob_score_ : None or float
      Score of the training dataset obtained using an out-of-bag estimate.
      This attribute is not None only when ``oob_score`` is True.
  
  Examples
  --------
  >>> from pyts.datasets import load_gunpoint
  >>> from pyts.classification import TimeSeriesForest
  >>> X_train, X_test, y_train, y_test = load_gunpoint(return_X_y=True)
  >>> clf = TimeSeriesForest(random_state=43)
  >>> clf.fit(X_train, y_train)
  TimeSeriesForest(...)
  >>> clf.score(X_test, y_test)
  0.97333...
  
  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] H. Deng, G. Runger, E. Tuv and M. Vladimir, "A Time Series
         Forest for Classification and Feature Extraction".
         Information Sciences, 239, 142-153 (2013).
  
  .. [2] Leo Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.
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docstr: Return the decision path in the forest.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_timestamps)
      Univariate time series.
  
  Returns
  -------
  indicator : sparse csr array, shape = (n_samples, n_nodes)
      Return a node indicator matrix where non zero elements
      indicates that the samples goes through the nodes.
  
  n_nodes_ptr : array, 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.
docstr_tok: ["Return","the","decision","path","in","the","forest",".","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","(","n_samples",",","n_timestamps",")","Univariate","time","series",".","Returns","--","--","--","-","indicator",":","sparse","csr","array",",","shape","=","(","n_samples",",","n_nodes",")","Return","a","node","indicator","matrix","where","non","zero","elements","indicates","that","the","samples","goes","through","the","nodes",".","n_nodes_ptr",":","array",",","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","."]
code: def decision_path(self, X):
      """Return the decision path in the forest.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_timestamps)
          Univariate time series.
  
      Returns
      -------
      indicator : sparse csr array, shape = (n_samples, n_nodes)
          Return a node indicator matrix where non zero elements
          indicates that the samples goes through the nodes.
  
      n_nodes_ptr : array, 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.
  
      """
      check_is_fitted(self)
      X = check_array(X, dtype="float64")
      X_new = self._pipeline["fe"].transform(X)
      return self._pipeline["rfc"].decision_path(X_new)
  
imports: from math import ceil
  from numba import njit
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.utils.validation import check_array, check_is_fitted, check_random_state
  from ..base import UnivariateClassifierMixin, UnivariateTransformerMixin
label_desc: Use image node_id 3 for calling the TimeSeriesForest obj's underlying member method code with example usage: obj.decision_path(X) and returns: self