fix a bug in multi-class

include/LightGBM/tree.h

@@ -195,13 +195,21 @@ private:
 
 
 inline double Tree::Predict(const double* feature_values) const {
+  if (num_leaves_ > 1) {
     int leaf = GetLeaf(feature_values);
     return LeafOutput(leaf);
+  } else {
+    return 0.0f;
+  }
 }
 
 inline int Tree::PredictLeafIndex(const double* feature_values) const {
+  if (num_leaves_ > 1) {
     int leaf = GetLeaf(feature_values);
     return leaf;
+  } else {
+    return 0;
+  }
 }
 
 inline int Tree::GetLeaf(const double* feature_values) const {

include/LightGBM/utils/common.h

@@ -273,6 +273,9 @@ inline static std::string ArrayToString(const std::vector<T>& arr, size_t n, cha
 
 template<typename T>
 inline static std::vector<T> StringToArray(const std::string& str, char delimiter, size_t n) {
+  if (n == 0) {
+    return std::vector<T>();
+  }
   std::vector<std::string> strs = Split(str.c_str(), delimiter);
   if (strs.size() != n) {
     Log::Fatal("StringToArray error, size doesn't match.");

src/boosting/gbdt.cpp

@@ -336,6 +336,7 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is
     sub_gradient_time += std::chrono::steady_clock::now() - start_time;
 #endif
   }
+  bool shouldContinue = false;
   for (int curr_class = 0; curr_class < num_class_; ++curr_class) {
 #ifdef TIMETAG
     start_time = std::chrono::steady_clock::now();
@@ -345,10 +346,9 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is
 #ifdef TIMETAG
     tree_time += std::chrono::steady_clock::now() - start_time;
 #endif
-    // if cannot learn a new tree, then stop
-    if (new_tree->num_leaves() <= 1) {
-      Log::Info("Stopped training because there are no more leaves that meet the split requirements.");
-      return true;
+
+    if (new_tree->num_leaves() > 1) {
+      shouldContinue = true;
     }
 
     // shrinkage by learning rate
@@ -360,6 +360,13 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is
     // add model
     models_.push_back(std::move(new_tree));
   }
+  if (!shouldContinue) {
+    Log::Warning("Stopped training because there are no more leaves that meet the split requirements.");
+    for (int curr_class = 0; curr_class < num_class_; ++curr_class) {
+      models_.pop_back();
+    }
+    return true;
+  }
   ++iter_;
   if (is_eval) {
     return EvalAndCheckEarlyStopping();

src/io/tree.cpp

@@ -97,6 +97,7 @@ int Tree::Split(int leaf, int feature, BinType bin_type, uint32_t threshold_bin,
 }
 
 void Tree::AddPredictionToScore(const Dataset* data, data_size_t num_data, double* score) const {
+  if (num_leaves_ <= 1) { return; }
   if (has_categorical_) {
     if (data->num_features() > num_leaves_ - 1) {
       Threading::For<data_size_t>(0, num_data,
@@ -193,6 +194,7 @@ void Tree::AddPredictionToScore(const Dataset* data, data_size_t num_data, doubl
 void Tree::AddPredictionToScore(const Dataset* data,
   const data_size_t* used_data_indices,
   data_size_t num_data, double* score) const {
+  if (num_leaves_ <= 1) { return; }
   if (has_categorical_) {
     if (data->num_features() > num_leaves_ - 1) {
       Threading::For<data_size_t>(0, num_data,