Esempio n. 1
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    def _get_param(self):
        if self.need_cv:
            param_protobuf_obj = lr_model_param_pb2.LRModelParam()
            return param_protobuf_obj

        self.header = self.header if self.header else []
        LOGGER.debug("In get_param, self.need_one_vs_rest: {}".format(
            self.need_one_vs_rest))

        if self.need_one_vs_rest:
            one_vs_rest_result = self.one_vs_rest_obj.save(
                lr_model_param_pb2.SingleModel)
            single_result = {
                'header': self.header,
                'need_one_vs_rest': True,
                "best_iteration": -1
            }
        else:
            one_vs_rest_result = None
            single_result = self.get_single_model_param()
            single_result['need_one_vs_rest'] = False
        single_result['one_vs_rest_result'] = one_vs_rest_result

        param_protobuf_obj = lr_model_param_pb2.LRModelParam(**single_result)

        return param_protobuf_obj
Esempio n. 2
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    def _get_param(self):
        header = self.header
        LOGGER.debug("In get_param, header: {}".format(header))
        if header is None:
            param_protobuf_obj = lr_model_param_pb2.LRModelParam()
            return param_protobuf_obj
        if self.need_one_vs_rest:
            # one_vs_rest_class = list(map(str, self.one_vs_rest_obj.classes))
            one_vs_rest_result = self.one_vs_rest_obj.save(
                lr_model_param_pb2.SingleModel)
            single_result = {
                'header': header,
                'need_one_vs_rest': True,
                "best_iteration": -1
            }
        else:
            one_vs_rest_result = None
            single_result = self.get_single_model_param()
            single_result['need_one_vs_rest'] = False
        single_result['one_vs_rest_result'] = one_vs_rest_result
        # LOGGER.debug("in _get_param, single_result: {}".format(single_result))

        param_protobuf_obj = lr_model_param_pb2.LRModelParam(**single_result)

        return param_protobuf_obj
Esempio n. 3
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 def _get_param(self):
     header = self.header
     if header is None:
         param_protobuf_obj = lr_model_param_pb2.LRModelParam()
         return param_protobuf_obj
     result = self._get_model_param()
     param_protobuf_obj = lr_model_param_pb2.LRModelParam(**result)
     return param_protobuf_obj
Esempio n. 4
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    def _get_param(self):
        header = self.header
        LOGGER.debug("In get_param, header: {}".format(header))
        if header is None:
            param_protobuf_obj = lr_model_param_pb2.LRModelParam()
            return param_protobuf_obj
        if self.need_one_vs_rest:
            result = self._get_model_param_ovr()
            param_protobuf_obj = lr_model_param_pb2.LRModelParam(**result)

        else:
            result = self._get_model_param()
            param_protobuf_obj = lr_model_param_pb2.LRModelParam(**result)

        LOGGER.debug("in _get_param, result: {}".format(result))

        return param_protobuf_obj
Esempio n. 5
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    def _get_param(self):
        if self.need_cv:
            param_protobuf_obj = lr_model_param_pb2.LRModelParam()
            return param_protobuf_obj

        if self.need_one_vs_rest:
            one_vs_rest_result = self.one_vs_rest_obj.save(
                lr_model_param_pb2.SingleModel)
            single_result = {
                'header': self.header,
                'need_one_vs_rest': True,
                "best_iteration": -1
            }
        else:
            one_vs_rest_result = None
            single_result = self.get_single_model_param()

            single_result['need_one_vs_rest'] = False
        single_result['one_vs_rest_result'] = one_vs_rest_result
        LOGGER.debug(f"saved_model: {single_result}")
        param_protobuf_obj = lr_model_param_pb2.LRModelParam(**single_result)
        return param_protobuf_obj
Esempio n. 6
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    def _get_param(self):
        header = self.header

        weight_dict = {}
        intercept = 0
        if not self.use_encrypt and not self.component_properties.need_cv:
            lr_vars = self.model_weights.coef_
            for idx, header_name in enumerate(header):
                coef_i = lr_vars[idx]
                weight_dict[header_name] = coef_i
            intercept = self.model_weights.intercept_

        param_protobuf_obj = lr_model_param_pb2.LRModelParam(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
Esempio n. 7
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    def _get_param(self):
        header = self.header

        weight_dict = {}
        intercept = 0
        if not self.use_encrypt:
            lr_vars = self.model_weights.coef_
            for idx, header_name in enumerate(header):
                coef_i = lr_vars[idx]
                weight_dict[header_name] = coef_i
            intercept = self.model_weights.intercept_

        param_protobuf_obj = lr_model_param_pb2.LRModelParam(iters=self.n_iter_,
                                                             loss_history=self.loss_history,
                                                             is_converged=self.is_converged,
                                                             weight=weight_dict,
                                                             intercept=intercept,
                                                             header=header)
        from google.protobuf import json_format
        json_result = json_format.MessageToJson(param_protobuf_obj)
        LOGGER.debug("json_result: {}".format(json_result))
        return param_protobuf_obj