skip the training of empty class in classification.

src/boosting/gbdt.cpp

@@ -162,6 +162,37 @@ void GBDT::ResetTrainingData(const BoostingConfig* config, const Dataset* train_
   if (train_data_ != nullptr) {
     // reset config for tree learner
     tree_learner_->ResetConfig(&new_config->tree_config);
+    class_need_train_ = std::vector<bool>(num_class_, true);
+    if (num_class_ > 1 || sigmoid_ > 0) {
+      // + 1 here for the binary classification
+      class_default_output_ = std::vector<double>(num_class_ + 1, 0.0f);
+      std::vector<data_size_t> cnt_per_class(num_class_, 0);
+      auto label = train_data_->metadata().label();
+      for (int i = 0; i < num_data_; ++i) {
+        ++cnt_per_class[static_cast<int>(label[i])];
+      }
+      if (num_class_ > 1) {
+        for (int i = 0; i < num_class_; ++i) {
+          if (cnt_per_class[i] == num_data_) {
+            Log::Warning("Only contain one class.");
+            class_need_train_[i] = false;
+            class_default_output_[i] = -std::log(kEpsilon);
+          } else if (cnt_per_class[i] == 0) {
+            class_need_train_[i] = false;
+            class_default_output_[i] = -std::log(1.0f / kEpsilon - 1.0f);
+          }
+        }
+      } else {
+        // binary classification. 
+        if (cnt_per_class[1] == 0) {
+          class_need_train_[0] = false;
+          class_default_output_[0] = -std::log(1.0f / kEpsilon - 1.0f);
+        } else if (cnt_per_class[1] == num_data_) {
+          class_need_train_[0] = false;
+          class_default_output_[0] = -std::log(kEpsilon);
+        }
+      }
+    }
   }
   gbdt_config_.reset(new_config.release());
 }
@@ -370,8 +401,11 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is
   #ifdef TIMETAG
     start_time = std::chrono::steady_clock::now();
   #endif
-    std::unique_ptr<Tree> new_tree(
+    std::unique_ptr<Tree> new_tree(new Tree(2));
+    if (class_need_train_[curr_class]) {
+      new_tree.reset(
         tree_learner_->Train(gradient + curr_class * num_data_, hessian + curr_class * num_data_));
+    }
   #ifdef TIMETAG
     tree_time += std::chrono::steady_clock::now() - start_time;
   #endif
@@ -383,6 +417,17 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is
       // update score
       UpdateScore(new_tree.get(), curr_class);
       UpdateScoreOutOfBag(new_tree.get(), curr_class);
+    } else {
+      // only add default score one-time
+      if (!class_need_train_[curr_class] && models_.size() < num_class_) {
+        auto output = class_default_output_[curr_class];
+        new_tree->Split(0, 0, BinType::NumericalBin, 0, 0, 0, 
+                        output, output, 0, num_data_, 1);
+        train_score_updater_->AddScore(output, curr_class);
+        for (auto& score_updater : valid_score_updater_) {
+          score_updater->AddScore(output, curr_class);
+        }
+      }
     }
     // add model
     models_.push_back(std::move(new_tree));

src/boosting/gbdt.h

@@ -343,6 +343,8 @@ protected:
   std::unique_ptr<Dataset> tmp_subset_;
   bool is_use_subset_;
   bool boost_from_average_;
+  std::vector<bool> class_need_train_;
+  std::vector<double> class_default_output_;
 };
 
 }  // namespace LightGBM

src/objective/binary_objective.hpp

@@ -42,6 +42,11 @@ public:
         ++cnt_negative;
       }
     }
+    if (cnt_negative == 0 || cnt_positive == 0) {
+      Log::Warning("Only contain one class.");
+      // not need to boost.
+      num_data_ = 0;
+    }
     Log::Info("Number of positive: %d, number of negative: %d", cnt_positive, cnt_negative);
     // use -1 for negative class, and 1 for positive class
     label_val_[0] = -1;