def __init__(
self,
steps: List[QueuedPipelineStepsTuple],
batch_size,
n_workers_per_step=None,
max_queue_size=None,
data_joiner=None,
use_threading=False,
use_savers=False,
cache_folder=None
):
NonFittableMixin.__init__(self)
CustomPipelineMixin.__init__(self)
if data_joiner is None:
data_joiner = NumpyConcatenateOuterBatch()
self.data_joiner = data_joiner
self.max_queue_size = max_queue_size
self.batch_size = batch_size
self.n_workers_per_step = n_workers_per_step
self.use_threading = use_threading
self.use_savers = use_savers
MiniBatchSequentialPipeline.__init__(self, steps=self._initialize_steps_as_tuple(steps),
cache_folder=cache_folder)
self._refresh_steps()
def __init__(self,
scoring_function=r2_score,
joiner=NumpyConcatenateOuterBatch()):
MetaStepMixin.__init__(self)
BaseStep.__init__(self)
self.scoring_function = scoring_function
self.joiner = joiner
def __init__(self,
scoring_function=r2_score,
k_fold=3,
joiner=NumpyConcatenateOuterBatch()):
self.k_fold = k_fold
BaseCrossValidation.__init__(self,
scoring_function=scoring_function,
joiner=joiner)
def __init__(self, wrapped=None, scoring_function=r2_score, joiner=NumpyConcatenateOuterBatch(),
cache_folder_when_no_handle=None,
split_data_container_during_fit=True, predict_after_fit=True):
BaseValidation.__init__(self, wrapped=wrapped, scoring_function=scoring_function)
ForceHandleOnlyMixin.__init__(self, cache_folder=cache_folder_when_no_handle)
EvaluableStepMixin.__init__(self)
self.split_data_container_during_fit = split_data_container_during_fit
self.predict_after_fit = predict_after_fit
self.joiner = joiner
def __init__(self,
scoring_function=r2_score,
joiner=NumpyConcatenateOuterBatch(),
cache_folder_when_no_handle=None):
BaseValidation.__init__(self, scoring_function)
ForceHandleOnlyMixin.__init__(self,
cache_folder=cache_folder_when_no_handle)
EvaluableStepMixin.__init__(self)
self.joiner = joiner
def __init__(self,
scoring_function=r2_score,
k_fold=3,
joiner=NumpyConcatenateOuterBatch(),
cache_folder_when_no_handle=None):
self.k_fold = k_fold
BaseCrossValidationWrapper.__init__(
self,
scoring_function=scoring_function,
joiner=joiner,
cache_folder_when_no_handle=cache_folder_when_no_handle)
def __init__(self,
scoring_function=r2_score,
k_fold=3,
joiner=NumpyConcatenateOuterBatch(),
cache_folder_when_no_handle=None,
split_data_container_during_fit=True,
predict_after_fit=True):
self.k_fold = k_fold
BaseCrossValidationWrapper.__init__(
self,
scoring_function=scoring_function,
joiner=joiner,
cache_folder_when_no_handle=cache_folder_when_no_handle,
split_data_container_during_fit=split_data_container_during_fit,
predict_after_fit=predict_after_fit)
def __init__(
self,
steps: List[QueuedPipelineStepsTuple],
batch_size: int,
n_workers_per_step: int = None,
max_queue_size: int = None,
data_joiner=None,
use_threading: bool = False,
use_savers: bool = False,
include_incomplete_batch: bool = False,
default_value_data_inputs: Union[Any, AbsentValuesNullObject] = None,
default_value_expected_outputs: Union[Any,
AbsentValuesNullObject] = None,
cache_folder: str = None,
):
if data_joiner is None:
data_joiner = NumpyConcatenateOuterBatch()
self.data_joiner = data_joiner
self.max_queue_size = max_queue_size
self.batch_size = batch_size
self.n_workers_per_step = n_workers_per_step
self.use_threading = use_threading
self.use_savers = use_savers
self.batch_size: int = batch_size
self.include_incomplete_batch: bool = include_incomplete_batch
self.default_value_data_inputs: Union[
Any, AbsentValuesNullObject] = default_value_data_inputs
self.default_value_expected_outputs: Union[
Any, AbsentValuesNullObject] = default_value_expected_outputs
MiniBatchSequentialPipeline.__init__(
self,
steps=self._initialize_steps_as_tuple(steps),
cache_folder=cache_folder,
batch_size=batch_size,
include_incomplete_batch=include_incomplete_batch,
default_value_data_inputs=default_value_data_inputs,
default_value_expected_outputs=default_value_expected_outputs)
self._refresh_steps()
def __init__(self, scoring_function=r2_score, joiner=NumpyConcatenateOuterBatch()):
BaseValidation.__init__(self, scoring_function)
self.joiner = joiner
def __init__(self,
scoring_function=r2_score,
k_fold=3,
joiner=NumpyConcatenateOuterBatch()):
self.k_fold = k_fold
super().__init__(scoring_function=scoring_function, joiner=joiner)
def __init__(self,
scoring_function=r2_score,
joiner=NumpyConcatenateOuterBatch()):
super().__init__()
self.scoring_function = scoring_function
self.joiner = joiner
