At least 2 features are chosen in subcolumn (#2409)

* at least 2 features are chosen in subcolumn

* Update serial_tree_learner.cpp

* rounding

src/treelearner/serial_tree_learner.cpp

@@ -277,9 +277,10 @@ std::vector<int8_t> SerialTreeLearner::GetUsedFeatures(bool is_tree_level) {
     return ret;
   }
   std::memset(ret.data(), 0, sizeof(int8_t) * num_features_);
+  const int min_used_features = std::min(2, static_cast<int>(valid_feature_indices_.size()));
   if (is_tree_level) {
-    int used_feature_cnt = static_cast<int>(valid_feature_indices_.size() * config_->feature_fraction);
-    used_feature_cnt = std::max(used_feature_cnt, 1);
+    int used_feature_cnt = static_cast<int>(std::round(valid_feature_indices_.size() * config_->feature_fraction));
+    used_feature_cnt = std::max(used_feature_cnt, min_used_features);
     used_feature_indices_ = random_.Sample(static_cast<int>(valid_feature_indices_.size()), used_feature_cnt);
     int omp_loop_size = static_cast<int>(used_feature_indices_.size());
     #pragma omp parallel for schedule(static, 512) if (omp_loop_size >= 1024)
@@ -290,8 +291,8 @@ std::vector<int8_t> SerialTreeLearner::GetUsedFeatures(bool is_tree_level) {
       ret[inner_feature_index] = 1;
     }
   } else if(used_feature_indices_.size() <= 0) {
-    int used_feature_cnt = static_cast<int>(valid_feature_indices_.size() * config_->feature_fraction_bynode);
-    used_feature_cnt = std::max(used_feature_cnt, 1);
+    int used_feature_cnt = static_cast<int>(std::round(valid_feature_indices_.size() * config_->feature_fraction_bynode));
+    used_feature_cnt = std::max(used_feature_cnt, min_used_features);
     auto sampled_indices = random_.Sample(static_cast<int>(valid_feature_indices_.size()), used_feature_cnt);
     int omp_loop_size = static_cast<int>(sampled_indices.size());
     #pragma omp parallel for schedule(static, 512) if (omp_loop_size >= 1024)
@@ -302,8 +303,8 @@ std::vector<int8_t> SerialTreeLearner::GetUsedFeatures(bool is_tree_level) {
       ret[inner_feature_index] = 1;
     }
   } else {
-    int used_feature_cnt = static_cast<int>(used_feature_indices_.size() * config_->feature_fraction_bynode);
-    used_feature_cnt = std::max(used_feature_cnt, 1);
+    int used_feature_cnt = static_cast<int>(std::round(used_feature_indices_.size() * config_->feature_fraction_bynode));
+    used_feature_cnt = std::max(used_feature_cnt, min_used_features);
     auto sampled_indices = random_.Sample(static_cast<int>(used_feature_indices_.size()), used_feature_cnt);
     int omp_loop_size = static_cast<int>(sampled_indices.size());
     #pragma omp parallel for schedule(static, 512) if (omp_loop_size >= 1024)