def one_hot_encoder_fit_transform(self, data_instance):
if data_instance is None:
return data_instance
if self.workflow_param.need_one_hot:
LOGGER.info("Start one-hot encode")
one_hot_param = param_generator.OneHotEncoderParam()
one_hot_param = self._load_param(one_hot_param)
param_checker.OneHotEncoderParamChecker.check_param(one_hot_param)
one_hot_encoder = OneHotEncoder(one_hot_param)
data_instance = one_hot_encoder.fit_transform(data_instance)
save_result = one_hot_encoder.save_model(
self.workflow_param.model_table,
self.workflow_param.model_namespace)
# Save model result in pipeline
for meta_buffer_type, param_buffer_type in save_result:
self.pipeline.node_meta.append(meta_buffer_type)
self.pipeline.node_param.append(param_buffer_type)
LOGGER.info("Finish one-hot encode")
return data_instance
else:
LOGGER.info("No need to do one-hot encode")
return data_instance
def one_hot_encoder_transform(self, data_instance):
if data_instance is None:
return data_instance
if self.workflow_param.need_one_hot:
LOGGER.info("Start one-hot encode")
one_hot_param = param_generator.OneHotEncoderParam()
one_hot_param = ParamExtract.parse_param_from_config(one_hot_param, self.config_path)
param_checker.OneHotEncoderParamChecker.check_param(one_hot_param)
one_hot_encoder = OneHotEncoder(one_hot_param)
one_hot_encoder.load_model(self.workflow_param.model_table, self.workflow_param.model_namespace)
data_instance = one_hot_encoder.transform(data_instance)
LOGGER.info("Finish one-hot encode")
return data_instance
else:
LOGGER.info("No need to do one-hot encode")
return data_instance