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LightGBM
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fix mismatch of number of features in feature_importances

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Yu Shi 2022-04-21 05:10:29 +00:00
Родитель b8e38ec1eb
Коммит 03979ad44a
1 изменённых файлов: 31 добавлений и 6 удалений
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33
src/boosting/gbdt_model_text.cpp
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@ -104,7 +104,16 @@ std::string GBDT::DumpModel(int start_iteration, int num_iteration, int feature_
for (size_t i = 0; i < feature_importances.size(); ++i) {
size_t feature_importances_int = static_cast<size_t>(feature_importances[i]);
if (feature_importances_int > 0) {
Log::Warning("i = %d, feature_names_.size() = %d", i, feature_names_.size());
if (i < feature_names_.size()) {
pairs.emplace_back(feature_importances_int, feature_names_[i]);
} else {
// with LibSVM format and continual training, the number of features in dataset can be fewer than in the intial model
// in that case FeatureImportance returns with the number of features in the intial model
std::stringstream str_buf;
str_buf << "Column_" << i << "_from_init_model";
pairs.emplace_back(feature_importances_int, str_buf.str());
}
}
}
str_buf << '\n' << "\"feature_importances\":" << "{";
@ -377,7 +386,15 @@ std::string GBDT::SaveModelToString(int start_iteration, int num_iteration, int
for (size_t i = 0; i < feature_importances.size(); ++i) {
size_t feature_importances_int = static_cast<size_t>(feature_importances[i]);
if (feature_importances_int > 0) {
if (i < feature_names_.size()) {
pairs.emplace_back(feature_importances_int, feature_names_[i]);
} else {
// with LibSVM format and continual training, the number of features in dataset can be fewer than in the intial model
// in that case FeatureImportance returns with the number of features in the intial model
std::stringstream str_buf;
str_buf << "Column_" << i << "_from_init_model";
pairs.emplace_back(feature_importances_int, str_buf.str());
}
}
}
// sort the importance
@ -609,10 +626,14 @@ std::vector<double> GBDT::FeatureImportance(int num_iteration, int importance_ty
for (int iter = 0; iter < num_used_model; ++iter) {
for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) {
if (models_[iter]->split_gain(split_idx) > 0) {
const int real_feature_index = models_[iter]->split_feature(split_idx);
#ifdef DEBUG
CHECK_GE(models_[iter]->split_feature(split_idx), 0);
CHECK_GE(real_feature_index, 0);
#endif
feature_importances[models_[iter]->split_feature(split_idx)] += 1.0;
if (static_cast<size_t>(real_feature_index) >= feature_importances.size()) {
feature_importances.resize(real_feature_index + 1);
}
feature_importances[real_feature_index] += 1.0;
}
}
}
@ -620,10 +641,14 @@ std::vector<double> GBDT::FeatureImportance(int num_iteration, int importance_ty
for (int iter = 0; iter < num_used_model; ++iter) {
for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) {
if (models_[iter]->split_gain(split_idx) > 0) {
const int real_feature_index = models_[iter]->split_feature(split_idx);
#ifdef DEBUG
CHECK_GE(models_[iter]->split_feature(split_idx), 0);
CHECK_GE(real_feature_index, 0);
#endif
feature_importances[models_[iter]->split_feature(split_idx)] += models_[iter]->split_gain(split_idx);
if (static_cast<size_t>(real_feature_index) >= feature_importances.size()) {
feature_importances.resize(real_feature_index + 1);
}
feature_importances[real_feature_index] += models_[iter]->split_gain(split_idx);
}
}
}