// Copyright 2023 Ant Group Co., Ltd.
// 
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// 
//   http://www.apache.org/licenses/LICENSE-2.0
// 
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//

syntax = "proto3";

package secretflowapis.v1.sdc.teeapps.params;

option java_package = "com.alipay.secretflow.secretflowapis.v1.sdc.teeapps.params";
option java_outer_classname = "LrProto";

message LrHyperParams {

  // 线性回归迭代次数
  //   范围: [1, 10000]
  //   默认: 10
  int32 max_iter = 1;
  // 训练目标:
  // "linear":
  //    线性回归目标
  // "logistic" (默认):
  //    二分类目标
  string regression_type = 2;
  // 权重L2正则项，该参数越大算法越保守。
  //   范围: [0, 1e4)
  //   默认: 0
  double l2_norm = 3;

  // 最小收敛误差, 支持输入（0，1）的数值
  // 默认值：0.0001
  double tol = 4;

  // 是否report特征权重
  // [True, False]
  //   默认: False
  //
  // WARN: 这个参数存在安全隐患，以后决定是否删除
  bool should_report_weights = 5;
}

message LrReport {
  message Weight {
    string feature_name = 1;
    double value = 2;
    double pvalue = 3;
  }
  // weight for each feature.
  repeated Weight weights = 1;
}