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Implement feature score in GBTree. (#7041)

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* Categorical data support.
* Eliminate text parsing during feature score computation.
This commit is contained in:
Jiaming Yuan
2021-06-18 11:53:16 +08:00
committed by GitHub
parent dcd84b3979
commit 7dd29ffd47
10 changed files with 285 additions and 84 deletions
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24
src/learner.cc
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@@ -1193,6 +1193,30 @@ class LearnerImpl : public LearnerIO {
*out_preds = &out_predictions.predictions;
}
void CalcFeatureScore(std::string const &importance_type,
std::vector<bst_feature_t> *features,
std::vector<float> *scores) override {
this->Configure();
std::vector<std::string> allowed_importance_type = {
"weight", "total_gain", "total_cover", "gain", "cover"
};
if (std::find(allowed_importance_type.begin(),
allowed_importance_type.end(),
importance_type) == allowed_importance_type.end()) {
std::stringstream ss;
ss << "importance_type mismatch, got: " << importance_type
<< "`, expected one of ";
for (size_t i = 0; i < allowed_importance_type.size(); ++i) {
ss << "`" << allowed_importance_type[i] << "`";
if (i != allowed_importance_type.size() - 1) {
ss << ", ";
}
}
LOG(FATAL) << ss.str();
}
gbm_->FeatureScore(importance_type, features, scores);
}
const std::map<std::string, std::string>& GetConfigurationArguments() const override {
return cfg_;
}