class_docstr: Helper class for orchestration that uses a single split for training and testing.
  
  Wrapper for sklearn.model_selection.train_test_split
  
  Parameters
  ----------
  *arrays : sequence of indexables with same length / shape[0]
      Allowed inputs are lists, numpy arrays, scipy-sparse
      matrices or pandas dataframes.
  test_size : float, int or None, optional (default=0.25)
      If float, should be between 0.0 and 1.0 and represent the proportion
      of the dataset to include in the test split. If int, represents the
      absolute number of test samples. If None, the value is set to the
      complement of the train size. By default, the value is set to 0.25.
      The default will change in version 0.21. It will remain 0.25 only
      if ``train_size`` is unspecified, otherwise it will complement
      the specified ``train_size``.
  train_size : float, int, or None, (default=None)
      If float, should be between 0.0 and 1.0 and represent the
      proportion of the dataset to include in the train split. If
      int, represents the absolute number of train samples. If None,
      the value is automatically set to the complement of the test size.
  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`.
  shuffle : boolean, optional (default=True)
      Whether or not to shuffle the data before splitting. If shuffle=False
      then stratify must be None.
  stratify : array-like or None (default=None)
      If not None, data is split in a stratified fashion, using this as
      the class labels.
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code: def __init__(
      self,
      test_size=0.25,
      train_size=None,
      random_state=None,
      shuffle=True,
      stratify=None,
  ):
      self._test_size = test_size
      self._train_size = train_size
      self._random_state = random_state
      self._shuffle = shuffle
      self._stratify = stratify
  
file_path: sktime/sktime/series_as_features/model_selection/_split.py
imports: import numpy
  import pandas
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 1 to create a new SingleSplit object  with example: obj = SingleSplit(test_size, train_size, random_state, shuffle, stratify)