def __init__(self,
hidden_units=None,
reduce_factor=None,
dnn_dropout=None,
use_bn=None,
dnn_layers=None,
activation=None,
space=None,
name=None,
**hyperparams):
if hidden_units is None:
hidden_units = Choice([100, 200, 300, 500, 800, 1000])
hyperparams['hidden_units'] = hidden_units
if reduce_factor is None:
reduce_factor = Choice([1, 0.8, 0.5])
hyperparams['reduce_factor'] = reduce_factor
if dnn_dropout is None:
dnn_dropout = Choice([0, 0.1, 0.3, 0.5])
hyperparams['dnn_dropout'] = dnn_dropout
if use_bn is None:
use_bn = Bool()
hyperparams['use_bn'] = use_bn
if dnn_layers is None:
dnn_layers = Choice([1, 2, 3])
hyperparams['dnn_layers'] = dnn_layers
if activation is None:
activation = 'relu'
hyperparams['activation'] = activation
ModuleSpace.__init__(self, space, name, **hyperparams)
def __init__(self, batch_size=128, epochs=None, space=None, name=None, **hyperparams):
if batch_size is None:
batch_size = Choice([128, 256, 512])
hyperparams['batch_size'] = batch_size
if epochs is not None:
hyperparams['epochs'] = epochs
ModuleSpace.__init__(self, space, name, **hyperparams)
self.space.fit_params = self
def __init__(self,
filters,
name_prefix,
data_format=None,
space=None,
name=None,
**hyperparams):
self.filters = filters
self.name_prefix = name_prefix
self.data_format = data_format
ModuleSpace.__init__(self, space, name, **hyperparams)
def __init__(self, space=None, name=None, **hyperparams):
ModuleSpace.__init__(self, space, name, **hyperparams)
self.space.DT_Module = self
self.config = None
def __init__(self, transformer=None, space=None, name=None, **hyperparams):
self.transformer = transformer
ModuleSpace.__init__(self, space, name, **hyperparams)
def __init__(self, task, fit_kwargs, space=None, name=None, **hyperparams):
ModuleSpace.__init__(self, space, name, **hyperparams)
self.task = task
self.fit_kwargs = fit_kwargs
self.estimator = None