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 save_step(self, step: BaseTransformer, context: 'ExecutionContext') -> BaseTransformer: step.queue = None step.observers = [] return step
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)