class_docstr: Cross validation splitter for time series data
class_docstr_tok: ["Cross","validation","splitter","for","time","series","data"]
code: def _set_defaults(self):
      if self.date_col is None:
          self._full_len = self.df.shape[0]
      else:
          self._full_len = len(self.dt_array)
  
      if self.incremental_len is None:
          self.incremental_len = self.forecast_len
  
      # if n_splits is specified, set min_train_len internally
      if self.n_splits:
          # if self.n_splits == 1:
          #     # set incremental_len internally if it's None
          #     # this is just to dodge error and it's not used actually
          self.min_train_len = (
              self._full_len
              - self.forecast_len
              - (self.n_splits - 1) * self.incremental_len
          )
  
imports: import pandas
  import numpy
  import matplotlib.pyplot
  from copy import deepcopy
  import inspect
  from .metrics import smape, wmape, mape, mse, mae, rmsse
  from ..exceptions import BacktestException
  from ..constants.constants import TimeSeriesSplitSchemeKeys, BacktestFitKeys
  from ..constants.palette import OrbitPalette
  from orbit.utils.plot import orbit_style_decorator
label_desc: Use image node_id 2 for calling the TimeSeriesSplitter obj's underlying member method code with example usage: obj._set_defaults() without return types