def __init__(self, seed=None, increment_seed_after_each_fit=True):
BaseTransformer.__init__(self)
InputAndOutputTransformerMixin.__init__(self)
if seed is None:
seed = 42
self.seed = seed
self.increment_seed_after_each_fit = increment_seed_after_each_fit
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
BaseTransformer.__init__(self)
def __init__(self,
sleep_time=0.1,
hyperparams=None,
hyperparams_space=None):
BaseTransformer.__init__(self,
hyperparams=hyperparams,
hyperparams_space=hyperparams_space)
self.sleep_time = sleep_time
def __init__(self):
BaseTransformer.__init__(self)
def __init__(self, axis):
BaseTransformer.__init__(self)
self.axis = axis
def __init__(self):
BaseTransformer.__init__(self)
ForceHandleMixin.__init__(self)
def __init__(self):
BaseTransformer.__init__(self)
TransformHandlerOnlyMixin.__init__(self)
def __init__(self, sub_data_container_names=None):
BaseTransformer.__init__(self)
ForceHandleOnlyMixin.__init__(self)
self.data_sources = sub_data_container_names
def __init__(self):
BaseTransformer.__init__(self, hashers=[HashlibMd5ValueHasher()])
InputAndOutputTransformerMixin.__init__(self)
def __init__(self,
hyperparams: HyperparameterSamples = None,
hyperparams_space: HyperparameterSpace = None,
name: str = None):
BaseTransformer.__init__(self, hyperparams, hyperparams_space, name)
InputAndOutputTransformerMixin.__init__(self)
