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
