def __init__(self,
training_window_size,
validation_window_size=None,
padding_between_training_and_validation=0,
drop_remainder=False,
scoring_function=r2_score,
joiner=NumpyConcatenateOnCustomAxis(axis=1)):
"""
Create a classic walk forward time series cross validation object.
The difference in start position between two consecutive validation split are equal to one
`validation_window_size`.
:param training_window_size: the window size of training split.
:param validation_window_size: the window size of each validation split and also the time step taken between
each forward roll, by default None. If None : It takes the value `training_window_size`.
:param padding_between_training_and_validation: the size of the padding between the end of the training split
and the start of the validation split, by default 0.
:param drop_remainder: drop the last split if the last validation split does not coincide
with a full validation_window_size, by default False.
:param scoring_function: scoring function use to validate performance if it is not None, by default r2_score,
:param joiner the joiner callable that can join the different result together.
:return: WalkForwardTimeSeriesCrossValidation instance.
"""
AnchoredWalkForwardTimeSeriesCrossValidationWrapper.__init__(
self,
training_window_size,
validation_window_size=validation_window_size,
padding_between_training_and_validation=
padding_between_training_and_validation,
drop_remainder=drop_remainder,
scoring_function=scoring_function,
joiner=joiner)
def __init__(self,
minimum_training_size,
validation_window_size=None,
padding_between_training_and_validation=0,
drop_remainder=False,
scoring_function=r2_score,
joiner=NumpyConcatenateOnCustomAxis(axis=1)):
"""
Create a anchored walk forward time series cross validation object.
The size of the validation split is defined by `validation_window_size`.
The difference in start position between two consecutive validation split is also equal to
`validation_window_size`.
:param minimum_training_size: size of the smallest training split.
:param validation_window_size: size of each validation split and also the time step taken between each
forward roll, by default None. If None : It takes the value `minimum_training_size`.
:param padding_between_training_and_validation: the size of the padding between the end of the training split
and the start of the validation split, by default 0.
:param drop_remainder: drop the last split if the last validation split does not coincide
with a full validation_window_size, by default False.
:param scoring_function: scoring function use to validate performance if it is not None, by default r2_score,
:param joiner the joiner callable that can join the different result together.
:return: WalkForwardTimeSeriesCrossValidation instance.
"""
BaseCrossValidationWrapper.__init__(self,
scoring_function=scoring_function,
joiner=joiner)
self.minimum_training_size = minimum_training_size
# If validation_window_size is None, we give the same value as training_window_size.
self.validation_window_size = validation_window_size or self.minimum_training_size
self.padding_between_training_and_validation = padding_between_training_and_validation
self.drop_remainder = drop_remainder
self._validation_initial_start = self.minimum_training_size + self.padding_between_training_and_validation
