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
