class HeteroBinningGuestWorkflow(WorkFlow):
def _initialize(self, config_path):
self._initialize_role_and_mode()
self._initialize_model(config_path)
self._initialize_workflow_param(config_path)
def _initialize_role_and_mode(self):
self.role = consts.GUEST
self.mode = consts.HETERO
def _initialize_intersect(self, config):
pass
def _initialize_model(self, runtime_conf_path):
binning_param = FeatureBinningParam()
self.binning_param = ParamExtract.parse_param_from_config(
binning_param, runtime_conf_path)
FeatureBinningParamChecker.check_param(self.binning_param)
self.model = HeteroFeatureBinningGuest(self.binning_param)
LOGGER.debug("Guest model started")
def save_binning_result(self):
meta_table = self.model.save_model(self.workflow_param.model_table,
self.workflow_param.model_namespace)
return meta_table
@status_tracer_decorator.status_trace
def run(self):
self._init_argument()
if self.workflow_param.method == "binning":
if self.binning_param.process_method == 'fit':
train_data_instance = self.gen_data_instance(
self.workflow_param.train_input_table,
self.workflow_param.train_input_namespace,
mode='fit')
if self.binning_param.local_only:
self.model.fit_local(train_data_instance)
else:
self.model.fit(train_data_instance)
self.save_binning_result()
else:
train_data_instance = self.gen_data_instance(
self.workflow_param.train_input_table,
self.workflow_param.train_input_namespace,
mode='transform')
self.load_model()
if self.binning_param.local_only:
self.model.transform_local(train_data_instance)
else:
self.model.transform(train_data_instance)
self.save_binning_result()
else:
raise TypeError("method %s is not support yet" %
(self.workflow_param.method))
LOGGER.info("Task end")
class HeteroBinningGuestWorkflow(WorkFlow):
def _initialize(self, config_path):
self._initialize_role_and_mode()
self._initialize_model(config_path)
self._initialize_workflow_param(config_path)
def _initialize_role_and_mode(self):
self.role = consts.GUEST
self.mode = consts.HETERO
def _initialize_intersect(self, config):
pass
def _initialize_model(self, runtime_conf_path):
binning_param = FeatureBinningParam()
self.binning_param = ParamExtract.parse_param_from_config(binning_param, runtime_conf_path)
FeatureBinningParamChecker.check_param(self.binning_param)
self.model = HeteroFeatureBinningGuest(self.binning_param)
LOGGER.debug("Guest model started")
def save_binning_result(self):
meta_table = self.model.save_model(self.workflow_param.model_table, self.workflow_param.model_namespace)
return meta_table
@status_tracer_decorator.status_trace
def run(self):
self._init_argument()
if self.workflow_param.method == "binning":
if self.binning_param.process_method == 'fit':
train_data_instance = self.gen_data_instance(self.workflow_param.train_input_table,
self.workflow_param.train_input_namespace,
mode='fit')
LOGGER.debug("After dataio, header is : {}".format(train_data_instance.schema))
if self.binning_param.local_only:
self.model.fit_local(train_data_instance)
else:
LOGGER.debug("Start model fit")
self.model.fit(train_data_instance)
self.model.save_model(self.workflow_param.model_table, self.workflow_param.model_namespace)
train_data_instance = self.one_hot_encoder_fit_transform(train_data_instance)
else:
train_data_instance = self.gen_data_instance(self.workflow_param.train_input_table,
self.workflow_param.train_input_namespace,
mode='transform')
LOGGER.debug("After dataio, header is : {}".format(train_data_instance.schema))
self.model.load_model(self.workflow_param.model_table, self.workflow_param.model_namespace)
if self.binning_param.local_only:
self.model.transform_local(train_data_instance)
else:
self.model.transform(train_data_instance)
self.save_binning_result()
train_data_instance = self.one_hot_encoder_transform(train_data_instance)
self._show_data(train_data_instance)
else:
raise TypeError("method %s is not support yet" % (self.workflow_param.method))
LOGGER.info("Task end")
def _show_data(self, data_instances):
local_data = data_instances.collect()
LOGGER.debug("data header: {}".format(data_instances.schema))
n = 0
for k, v in local_data:
LOGGER.debug("new data is :{}".format(v.features))
n += 1
if n >= 20:
break
