def __init__(self,
name=None,
max_iter=100,
batch_size=-1,
secure_aggregate=True,
aggregate_every_n_epoch=1,
early_stop="diff",
encode_label=False,
predict_param=None,
cv_param=None,
**kwargs):
explicit_parameters = kwargs["explict_parameters"]
explicit_parameters["optimizer"] = None
explicit_parameters["loss"] = None
explicit_parameters["metrics"] = None
explicit_parameters["nn_define"] = None
explicit_parameters["config_type"] = "keras"
Component.__init__(self, **explicit_parameters)
if "name" in explicit_parameters:
del explicit_parameters["name"]
for param_key, param_value in explicit_parameters.items():
setattr(self, param_key, param_value)
self.optimizer = None
self.loss = None
self.metrics = None
self.nn_define = None
self.config_type = "keras"
self.input = Input(self.name, data_type="multi")
self.output = Output(self.name, data_type='single')
self._module_name = "HomoNN"
self._model = Sequential()
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
new_kwargs = self.erase_component_base_param(**kwargs)
ReaderParam.__init__(self, **new_kwargs)
self.output = Output(self.name, data_type='single', has_model=False)
self._module_name = "Reader"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
# print(self.name)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
SBTTransformerParam.__init__(self, **new_kwargs)
self.input = Input(self.name, data_type="multi")
self.output = Output(self.name)
self._module_name = "SBTFeatureTransformer"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
new_kwargs = self.erase_component_base_param(**kwargs)
SampleWeightParam.__init__(self, **new_kwargs)
self.input = Input(self.name)
self.output = Output(self.name, data_type='single', has_model=True)
self._module_name = "SampleWeight"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
# print (self.name)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
DataSplitParam.__init__(self, **new_kwargs)
self.input = Input(self.name, )
self.output = Output(self.name, has_model=False, data_type="multi")
self._module_name = "HeteroDataSplit"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
ScorecardParam.__init__(self, **new_kwargs)
self.input = Input(self.name)
self.output = Output(self.name, data_type='single', has_model=False)
self._module_name = "Scorecard"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
# print (self.name)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
SampleParam.__init__(self, **new_kwargs)
self.input = Input(self.name)
self.output = Output(self.name, has_model=False)
self._module_name = "FederatedSample"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
FeldmanVerifiableSumParam.__init__(self, **new_kwargs)
self.input = Input(self.name)
self.output = Output(self.name, has_model=False)
self._module_name = "FeldmanVerifiableSum"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
# print(self.name)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
HeteroFastSecureBoostParam.__init__(self, **new_kwargs)
self.input = Input(self.name, data_type="multi")
self.output = Output(self.name)
self._module_name = "HeteroFastSecureBoost"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
# print (self.name)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
FeatureBinningParam.__init__(self, **new_kwargs)
self.input = Input(self.name)
self.output = Output(self.name)
self._module_name = "HeteroFeatureBinning"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
# print (self.name)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
KmeansParam.__init__(self, **new_kwargs)
self.input = Input(self.name, data_type="multi")
self.output = Output(self.name, data_type="no_limit", output_unit=2)
self._module_name = "HeteroKmeans"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
#print (self.name)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
StatisticsParam.__init__(self, **new_kwargs)
self.input = Input(self.name)
self.output = Output(self.name, has_model=True)
self._module_name = "DataStatistics"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
#print (self.name)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
DataTransformParam.__init__(self, **new_kwargs)
self.input = Input(self.name)
self.output = Output(self.name, data_type='single')
self._module_name = "DataTransform"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
# print (self.name)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
HomoOneHotParam.__init__(self, **new_kwargs)
self.input = Input(self.name)
self.output = Output(self.name)
self._module_name = "HomoOneHotEncoder"
def __init__(self, **kwargs):
Component.__init__(self, **kwargs)
#print (self.name)
LOGGER.debug(f"{self.name} component created")
new_kwargs = self.erase_component_base_param(**kwargs)
ColumnExpandParam.__init__(self, **new_kwargs)
self.input = Input(self.name)
self.output = Output(self.name, data_type='single', has_model=True)
self._module_name = "ColumnExpand"
def __init__(self,
task_type="classification",
epochs=None,
batch_size=-1,
early_stop="diff",
tol=1e-5,
encrypt_param=None,
predict_param=None,
cv_param=None,
interactive_layer_lr=0.1,
validation_freqs=None,
early_stopping_rounds=None,
use_first_metric_only=None,
floating_point_precision=23,
drop_out_keep_rate=1,
selector_param=None,
**kwargs):
explicit_parameters = kwargs["explict_parameters"]
explicit_parameters["optimizer"] = None
explicit_parameters["loss"] = None
explicit_parameters["metrics"] = None
explicit_parameters["bottom_nn_define"] = None
explicit_parameters["top_nn_define"] = None
explicit_parameters["interactive_layer_define"] = None
explicit_parameters["config_type"] = "keras"
Component.__init__(self, **explicit_parameters)
if "name" in explicit_parameters:
del explicit_parameters["name"]
for param_key, param_value in explicit_parameters.items():
setattr(self, param_key, param_value)
self.input = Input(self.name, data_type="multi")
self.output = Output(self.name, data_type='single')
self._module_name = "HeteroNN"
self.optimizer = None
self.loss = None
self.config_type = "keras"
self.metrics = None
self.bottom_nn_define = None
self.top_nn_define = None
self.interactive_layer_define = None
self._bottom_nn_model = Sequential()
self._interactive_layer = Sequential()
self._top_nn_model = Sequential()
def __init__(self,
epochs=1,
batch_size=-1,
encrypt_param=None,
predict_param=None,
cv_param=None,
intersect_param={'intersect_method': consts.RSA},
validation_freqs=None,
early_stopping_rounds=None,
use_first_metric_only=None,
mode='plain',
communication_efficient=False,
n_iter_no_change=False,
tol=1e-5,
local_round=5,
**kwargs):
explicit_parameters = kwargs["explict_parameters"]
explicit_parameters["optimizer"] = None
# explicit_parameters["loss"] = None
# explicit_parameters["metrics"] = None
explicit_parameters["nn_define"] = None
explicit_parameters["config_type"] = "keras"
Component.__init__(self, **explicit_parameters)
if "name" in explicit_parameters:
del explicit_parameters["name"]
for param_key, param_value in explicit_parameters.items():
setattr(self, param_key, param_value)
self.input = Input(self.name, data_type="multi")
self.output = Output(self.name, data_type='single')
self._module_name = "FTL"
self.optimizer = None
self.loss = None
self.config_type = "keras"
self.metrics = None
self.bottom_nn_define = None
self.top_nn_define = None
self.interactive_layer_define = None
self._nn_model = Sequential()
self.nn_define = None