def __init__(self, axis=None): BaseStep.__init__(self) NonFittableMixin.__init__(self) if axis is None: axis = -2 self.axis = axis
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, hyperparams: HyperparameterSamples = None, hyperparams_space: HyperparameterSpace = None, name: str = None): NonFittableMixin.__init__(self) BaseStep.__init__(self, hyperparams, hyperparams_space, name) InputAndOutputTransformerMixin.__init__(self)
def __init__(self, hyperparams_space: HyperparameterSpace = None, output=AN_EXPECTED_OUTPUT): BaseStep.__init__(self, hyperparams=None, hyperparams_space=hyperparams_space) NonFittableMixin.__init__(self) self.output = output
def __init__(self, add=1): NonFittableMixin.__init__(self) BaseStep.__init__( self, hyperparams=HyperparameterSamples({ 'add': add }) )
def __init__(self, multiply_by=1): NonFittableMixin.__init__(self) BaseStep.__init__( self, hyperparams=HyperparameterSamples({ 'multiply_by': multiply_by }) )
def __init__(self, axis): """ Create a numpy concatenate on custom axis object. :param axis: the axis where the concatenation is performed. :return: NumpyConcatenateOnCustomAxis instance. """ self.axis = axis BaseStep.__init__(self) NonFittableMixin.__init__(self)
def __init__(self, plotting_function: Callable, max_plotted_predictions, enabled=False): NonFittableMixin.__init__(self) BaseStep.__init__(self) self.max_plotted_predictions = max_plotted_predictions self.enabled = enabled self.plotting_function = plotting_function
def __init__(self, column): BaseStep.__init__(self) NonFittableMixin.__init__(self) self.column = column
def __init__(self): BaseStep.__init__(self) NonFittableMixin.__init__(self)
def __init__(self, custom_message: str = ""): self.custom_message = custom_message BaseStep.__init__(self) NonFittableMixin.__init__(self)
def __init__(self, axis): NonFittableMixin.__init__(self) BaseStep.__init__(self) self.axis = axis
def __init__(self): BaseStep.__init__(self) InputAndOutputTransformerMixin.__init__(self) NonFittableMixin.__init__(self)
def __init__(self, window_size_past, window_size_future): BaseStep.__init__(self) InputAndOutputTransformerMixin.__init__(self) NonFittableMixin.__init__(self) self.window_size_past = window_size_past self.window_size_future = window_size_future
def __init__(self, sub_data_container_names=None): BaseStep.__init__(self) NonTransformableMixin.__init__(self) NonFittableMixin.__init__(self) self.data_sources = sub_data_container_names
def __init__(self, dtype): NonFittableMixin.__init__(self) BaseStep.__init__(self) self.dtype = dtype
def __init__(self, new_shape): BaseStep.__init__(self) NonFittableMixin.__init__(self) self.new_shape = new_shape
def __init__(self, concatenate_inner_features=False): BaseStep.__init__(self) NonFittableMixin.__init__(self) self.concatenate_inner_features = concatenate_inner_features