| 138 | } |
| 139 | |
| 140 | void CreateObjectiveAndMetrics() { |
| 141 | // create objective function |
| 142 | objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective, |
| 143 | config_)); |
| 144 | if (objective_fun_ == nullptr) { |
| 145 | Log::Warning("Using self-defined objective function"); |
| 146 | } |
| 147 | // initialize the objective function |
| 148 | if (objective_fun_ != nullptr) { |
| 149 | objective_fun_->Init(train_data_->metadata(), train_data_->num_data()); |
| 150 | } |
| 151 | |
| 152 | // create training metric |
| 153 | train_metric_.clear(); |
| 154 | for (auto metric_type : config_.metric) { |
| 155 | auto metric = std::unique_ptr<Metric>( |
| 156 | Metric::CreateMetric(metric_type, config_)); |
| 157 | if (metric == nullptr) { continue; } |
| 158 | metric->Init(train_data_->metadata(), train_data_->num_data()); |
| 159 | train_metric_.push_back(std::move(metric)); |
| 160 | } |
| 161 | train_metric_.shrink_to_fit(); |
| 162 | } |
| 163 | |
| 164 | void ResetTrainingData(const Dataset* train_data) { |
| 165 | if (train_data != train_data_) { |