class_docstr: Random interval feature extractor transform.
  
  Transformer that segments time-series into random intervals
  and subsequently extracts series-to-primitives features from each interval.
  
  n_intervals: str{'sqrt', 'log', 'random'}, int or float, optional (
  default='sqrt')
      Number of random intervals to generate, where m is length of time
      series:
      - If "log", log of m is used.
      - If "sqrt", sqrt of m is used.
      - If "random", random number of intervals is generated.
      - If int, n_intervals intervals are generated.
      - If float, int(n_intervals * m) is used with n_intervals giving the
      fraction of intervals of the
      time series length.
  
      For all arguments relative to the length of the time series,
      the generated number of intervals is
      always at least 1.
  
  features: list of functions, optional (default=None)
      Applies each function to random intervals to extract features.
      If None, the mean is extracted.
  
  random_state: : int, RandomState instance, 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`.
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docstr: Fit transformer, generating random interval indices.
  
  Parameters
  ----------
  X : pandas DataFrame of shape [n_samples, n_features]
      Input data
  y : pandas Series, shape (n_samples, ...), optional
      Targets for supervised learning.
  
  Returns
  -------
  self : RandomIntervalSegmenter
      This estimator
docstr_tok: ["Fit","transformer",",","generating","random","interval","indices",".","Parameters","--","--","--","--","--","X",":","pandas","DataFrame","of","shape","[","n_samples",",","n_features","]","Input","data","y",":","pandas","Series",",","shape","(","n_samples",",","...",")",",","optional","Targets","for","supervised","learning",".","Returns","--","--","--","-","self",":","RandomIntervalSegmenter","This","estimator"]
code: def _fit(self, X, y=None):
      """Fit transformer, generating random interval indices.
  
      Parameters
      ----------
      X : pandas DataFrame of shape [n_samples, n_features]
          Input data
      y : pandas Series, shape (n_samples, ...), optional
          Targets for supervised learning.
  
      Returns
      -------
      self : RandomIntervalSegmenter
          This estimator
      """
      # We use composition rather than inheritance here, because this transformer
      # has a different transform type (returns tabular) compared to the
      # RandomIntervalSegmenter (returns panel).
      self._interval_segmenter = RandomIntervalSegmenter(
          self.n_intervals,
          self.min_length,
          self.max_length,
          self.random_state,
      )
      self._interval_segmenter.fit(X, y)
      self.intervals_ = self._interval_segmenter.intervals_
      self.input_shape_ = X.shape
      self._time_index = self._interval_segmenter._time_index
      return self
  
imports: import numpy
  import pandas
  from joblib import Parallel, delayed
  from sktime.datatypes import convert_to
  from sktime.transformations.base import BaseTransformer
  from sktime.transformations.panel.segment import RandomIntervalSegmenter
  from sktime.utils.pandas import df_map
label_desc: Use image node_id 2 for calling the RandomIntervalFeatureExtractor obj's underlying member method code with example usage: obj._fit(X, y) and returns: self