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