@@ -210,7 +210,9 @@ void GBDT::AddValidDataset(const Dataset* valid_data,
210210
211211 if (early_stopping_round_ > 0 ) {
212212 auto num_metrics = valid_metrics.size ();
213- if (es_first_metric_only_) { num_metrics = 1 ; }
213+ if (es_first_metric_only_) {
214+ num_metrics = 1 ;
215+ }
214216 best_iter_.emplace_back (num_metrics, 0 );
215217 best_score_.emplace_back (num_metrics, kMinScore );
216218 best_msg_.emplace_back (num_metrics);
@@ -452,7 +454,9 @@ bool GBDT::TrainOneIter(const score_t* gradients, const score_t* hessians) {
452454}
453455
454456void GBDT::RollbackOneIter () {
455- if (iter_ <= 0 ) { return ; }
457+ if (iter_ <= 0 ) {
458+ return ;
459+ }
456460 // reset score
457461 for (int cur_tree_id = 0 ; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {
458462 auto curr_tree = models_.size () - num_tree_per_iteration_ + cur_tree_id;
@@ -588,15 +592,19 @@ std::string GBDT::OutputMetric(int iter) {
588592 msg_buf << tmp_buf.str () << ' \n ' ;
589593 }
590594 }
591- if (es_first_metric_only_ && j > 0 ) { continue ; }
595+ if (es_first_metric_only_ && j > 0 ) {
596+ continue ;
597+ }
592598 if (ret.empty () && early_stopping_round_ > 0 ) {
593599 auto cur_score = valid_metrics_[i][j]->factor_to_bigger_better () * test_scores.back ();
594600 if (cur_score - best_score_[i][j] > early_stopping_min_delta_) {
595601 best_score_[i][j] = cur_score;
596602 best_iter_[i][j] = iter;
597603 meet_early_stopping_pairs.emplace_back (i, j);
598604 } else {
599- if (iter - best_iter_[i][j] >= early_stopping_round_) { ret = best_msg_[i][j]; }
605+ if (iter - best_iter_[i][j] >= early_stopping_round_) {
606+ ret = best_msg_[i][j];
607+ }
600608 }
601609 }
602610 }
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