def export_model(self):
meta_obj = lr_model_meta_pb2.LRModelMeta()
param_obj = self._get_param()
result = {
self.model_meta_name: meta_obj,
self.model_param_name: param_obj
}
return result
def _get_meta(self):
meta_protobuf_obj = lr_model_meta_pb2.LRModelMeta(penalty=self.model_param.penalty,
tol=self.model_param.tol,
alpha=self.alpha,
optimizer=self.model_param.optimizer,
batch_size=self.batch_size,
learning_rate=self.model_param.learning_rate,
max_iter=self.max_iter,
early_stop=self.model_param.early_stop,
fit_intercept=self.fit_intercept,
need_one_vs_rest=self.need_one_vs_rest)
return meta_protobuf_obj
def _get_meta(self):
model = self.model_fit
result = {
'penalty': model.penalty,
'tol': model.tol,
'fit_intercept': model.fit_intercept,
'optimizer': model.solver,
'need_one_vs_rest': self.need_one_vs_rest,
'max_iter': model.max_iter
}
meta_protobuf_obj = lr_model_meta_pb2.LRModelMeta(**result)
return meta_protobuf_obj
def _get_meta(self):
model = self.model_fit
predict_param = lr_model_meta_pb2.PredictMeta(**{"threshold": self.predict_param.threshold})
result = {'penalty': model.penalty,
'tol': model.tol,
'fit_intercept': model.fit_intercept,
'optimizer': model.solver,
'need_one_vs_rest': self.need_one_vs_rest,
'max_iter': model.max_iter,
'predict_param': predict_param
}
meta_protobuf_obj = lr_model_meta_pb2.LRModelMeta(**result)
return meta_protobuf_obj