def _get_param(self): if self.need_cv: param_protobuf_obj = linr_model_param_pb2.LinRModelParam() return param_protobuf_obj single_result = self.get_single_model_param() param_protobuf_obj = linr_model_param_pb2.LinRModelParam( **single_result) return param_protobuf_obj
def _get_param(self): header = self.header LOGGER.debug("In get_param, header: {}".format(header)) if header is None: param_protobuf_obj = linr_model_param_pb2.LinRModelParam( best_iteration=-1) return param_protobuf_obj weight_dict, intercept_ = self.get_weight_intercept_dict(header) best_iteration = -1 if self.validation_strategy is None else self.validation_strategy.best_iteration param_protobuf_obj = linr_model_param_pb2.LinRModelParam( iters=self.n_iter_, loss_history=self.loss_history, is_converged=self.is_converged, weight=weight_dict, intercept=intercept_, header=header, best_iteration=best_iteration) return param_protobuf_obj
def _get_param(self): header = self.header LOGGER.debug("In get_param, header: {}".format(header)) if header is None: param_protobuf_obj = linr_model_param_pb2.LinRModelParam() return param_protobuf_obj weight_dict = {} for idx, header_name in enumerate(header): coef_i = self.model_weights.coef_[idx] weight_dict[header_name] = coef_i intercept_ = self.model_weights.intercept_ param_protobuf_obj = linr_model_param_pb2.LinRModelParam( iters=self.n_iter_, loss_history=self.loss_history, is_converged=self.is_converged, weight=weight_dict, intercept=intercept_, header=header) return param_protobuf_obj