class_docstr: Group time series cross-validator.
  
  Parameters
  ----------
  test_size : int
      Size of test dataset.
  train_size : int (default=None)
      Size of train dataset.
  n_splits : int (default=None)
      Number of the splits.
  gap_size : int (default=0)
      Gap size between train and test datasets.
  shift_size : int (default=1)
      Step to shift for the next fold.
  window_type : str (default="rolling")
      Type of the window. Possible values: "rolling", "expanding".
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/evaluate/GroupTimeSeriesSplit/
class_docstr_tok: ["Group","time","series","cross-validator",".","Parameters","--","--","--","--","--","test_size",":","int","Size","of","test","dataset",".","train_size",":","int","(","default=None",")","Size","of","train","dataset",".","n_splits",":","int","(","default=None",")","Number","of","the","splits",".","gap_size",":","int","(","default=0",")","Gap","size","between","train","and","test","datasets",".","shift_size",":","int","(","default=1",")","Step","to","shift","for","the","next","fold",".","window_type",":","str","(","default=","''","rolling","''",")","Type","of","the","window",".","Possible","values",":","``","rolling","''",",","``","expanding","''",".","Examples","--","--","--","--","--","-","For","usage","examples",",","please","see","https",":","\/\/rasbt.github.io\/mlxtend\/user_guide\/evaluate\/GroupTimeSeriesSplit\/"]
code: def __init__(
      self,
      test_size,
      train_size=None,
      n_splits=None,
      gap_size=0,
      shift_size=1,
      window_type="rolling",
  ):
      if (train_size is None) and (n_splits is None):
          raise ValueError(
              "Either train_size or n_splits should be defined"
          )
  
      if window_type not in ["rolling", "expanding"]:
          raise ValueError(
              'Window type can be either "rolling" or "expanding"'
          )
  
      if (train_size is not None) and (window_type == "expanding"):
          raise ValueError(
              "Train size can be specified only with rolling window"
          )
  
      self.test_size = test_size
      self.train_size = train_size
      self.n_splits = n_splits
      self.gap_size = gap_size
      self.shift_size = shift_size
      self.window_type = window_type
  
file_path: mlxtend/mlxtend/evaluate/time_series.py
imports: from itertools import accumulate, chain, groupby, islice
  import matplotlib.pyplot
  import numpy
  from matplotlib.patches import Patch
  from matplotlib.ticker import MaxNLocator
  from sklearn.utils import indexable
label_desc: Use image node_id 1 to create a new GroupTimeSeriesSplit object  with example: obj = GroupTimeSeriesSplit(test_size, train_size, n_splits, gap_size, shift_size, window_type)