def __init__(self, **kwargs):
self._selection = dict()
for arg in kwargs:
self._selection[arg] = kwargs[arg]
Transformer.__init__(self, name='sample_slicer', **kwargs)
def __init__(self,
selection_dictionary=None,
stack_attr=['targets', 'chunks'],
**kwargs):
self._selection = selection_dictionary
self._attr = stack_attr
Transformer.__init__(self, name='sample_stacker', **kwargs)
def __init__(self, balancer=RandomUnderSampler(return_indices=True), attr='chunks', **kwargs):
self._attr = attr
self._balancer_algorithm = balancer
self._balancer = self._check_balancer(balancer)
Transformer.__init__(self, name='balancer', **kwargs)
def __init__(self, **kwargs):
self._selection = dict()
for arg in kwargs:
self._selection[arg] = kwargs[arg]
Transformer.__init__(self, name='sample_slicer',**kwargs)
def __init__(self, name='pipeline', nodes=[Transformer()]):
self.nodes = []
if nodes != None:
self.nodes = nodes
if isinstance(nodes[0], str):
self.nodes = [function_mapper(node)() for node in nodes]
Transformer.__init__(self, name)
def __init__(self, name='pipeline', nodes=[Transformer()]):
self.nodes = []
if nodes != None:
self.nodes = nodes
if isinstance(nodes[0], str):
self.nodes = [function_mapper(node)() for node in nodes]
Transformer.__init__(self, name)
def __init__(self, name='zfisher', **kwargs):
Transformer.__init__(self, name=name, **kwargs)
def __init__(self, target=None, **kwargs):
self._attribute = target
Transformer.__init__(self, name='target_transformer', **kwargs)
def __init__(self, name='sample_normalizer', **kwargs):
Transformer.__init__(self, name=name, **kwargs)
def __init__(self, chunks_attr='chunks', param_est=None, **kwargs):
self.node = ZScoreMapper(chunks_attr=chunks_attr, param_est=param_est)
Transformer.__init__(self, name='feature_normalizer', **kwargs)
def __init__(self, attributes, **kwargs):
self.node = mean_group_sample(attributes)
Transformer.__init__(self, name='sample_averager', **kwargs)
def __init__(self, selection_dictionary=None, stack_attr=['targets', 'chunks'], **kwargs):
self._selection = selection_dictionary
self._attr = stack_attr
Transformer.__init__(self, name='sample_stacker',**kwargs)
def __init__(self, degree=1, chunks_attr='chunks', **kwargs):
self._degree = degree
self.node = PolyDetrendMapper(chunks_attr=chunks_attr, polyord=degree)
Transformer.__init__(self, name='detrending', **kwargs)
def __init__(self, degree=1, chunks_attr='chunks', **kwargs):
self._degree = degree
self.node = PolyDetrendMapper(chunks_attr=chunks_attr, polyord=degree)
Transformer.__init__(self, name='detrending', **kwargs)
def __init__(self, name='upper_matrix', **kwargs):
Transformer.__init__(self, name=name, **kwargs)
def __init__(self, target=None, **kwargs):
self._attribute = target
Transformer.__init__(self, name='target_transformer', **kwargs)
def __init__(self, name='sample_normalizer', **kwargs):
Transformer.__init__(self, name=name, **kwargs)
def __init__(self, chunks_attr='chunks', param_est=None, **kwargs):
self.node = ZScoreMapper(chunks_attr=chunks_attr, param_est=param_est)
Transformer.__init__(self, name='feature_normalizer', **kwargs)
def __init__(self, attributes, **kwargs):
self.node = mean_group_sample(attributes)
Transformer.__init__(self, name='sample_averager', **kwargs)
def __init__(self, name='zfisher', **kwargs):
Transformer.__init__(self, name=name, **kwargs)
def __init__(self, balancer, attr='chunks', name='node', **kwargs):
self._attr = attr
self._balancer = balancer
Transformer.__init__(self, name=name, **kwargs)