skip the boosting for the empty class.

src/boosting/gbdt.cpp

@@ -179,7 +179,11 @@ void GBDT::ResetTrainingData(const BoostingConfig* config, const Dataset* train_
             class_default_output_[i] = -std::log(kEpsilon);
           } else if (cnt_per_class[i] == 0) {
             class_need_train_[i] = false;
+            if (sigmoid_ > 0.0f) {
               class_default_output_[i] = -std::log(1.0f / kEpsilon - 1.0f);
+            } else {
+              class_default_output_[i] = std::log(kEpsilon);
+            }
           }
         }
       } else {

src/objective/multiclass_objective.hpp

@@ -29,14 +29,25 @@ public:
     label_ = metadata.label();
     weights_ = metadata.weights();
     label_int_.resize(num_data_);
-    #pragma omp parallel for schedule(static)
+    std::vector<data_size_t> cnt_per_class(num_class_, 0);
     for (int i = 0; i < num_data_; ++i) {
       label_int_[i] = static_cast<int>(label_[i]);
       if (label_int_[i] < 0 || label_int_[i] >= num_class_) {
         Log::Fatal("Label must be in [0, %d), but found %d in label", num_class_, label_int_[i]);
       }
+      ++cnt_per_class[label_int_[i]];
     }
-    hessian_nor_ = static_cast<score_t>(num_class_) / (num_class_ - 1);
+    int non_empty_class = 0;
+    is_empty_class_ = std::vector<bool>(num_class_, false);
+    for (int i = 0; i < num_class_; ++i) {
+      if (cnt_per_class[i] > 0) {
+        ++non_empty_class;
+      } else {
+        is_empty_class_[i] = true;
+      }
+    }
+    if (non_empty_class < 2) { non_empty_class = 2; }
+    hessian_nor_ = static_cast<score_t>(non_empty_class) / (non_empty_class - 1);
   }
 
   void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override {
@@ -51,6 +62,7 @@ public:
         }
         Common::Softmax(&rec);
         for (int k = 0; k < num_class_; ++k) {
+          if (is_empty_class_[k]) { continue; }
           auto p = rec[k];
           size_t idx = static_cast<size_t>(num_data_) * k + i;
           if (label_int_[i] == k) {
@@ -72,6 +84,7 @@ public:
         }
         Common::Softmax(&rec);
         for (int k = 0; k < num_class_; ++k) {
+          if (is_empty_class_[k]) { continue; }
           auto p = rec[k];
           size_t idx = static_cast<size_t>(num_data_) * k + i;
           if (label_int_[i] == k) {
@@ -100,6 +113,7 @@ private:
   std::vector<int> label_int_;
   /*! \brief Weights for data */
   const float* weights_;
+  std::vector<bool> is_empty_class_;
   double softmax_weight_decay_;
   score_t hessian_nor_;
 };