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[backport] [R] Fix global feature importance and predict with 1 sample. (#7394) (#7397)

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* [R] Fix global feature importance.

* Add implementation for tree index.  The parameter is not documented in C API since we
should work on porting the model slicing to R instead of supporting more use of tree
index.

* Fix the difference between "gain" and "total_gain".

* debug.

* Fix prediction.
This commit is contained in:
Jiaming Yuan
2021-11-06 00:07:36 +08:00
committed by GitHub
parent a3d195e73e
commit e7ac2486eb
11 changed files with 119 additions and 49 deletions
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7
src/learner.cc
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@@ -1214,11 +1214,10 @@ 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 {
void CalcFeatureScore(std::string const& importance_type, common::Span<int32_t const> trees,
std::vector<bst_feature_t>* features, std::vector<float>* scores) override {
this->Configure();
gbm_->FeatureScore(importance_type, features, scores);
gbm_->FeatureScore(importance_type, trees, features, scores);
}
const std::map<std::string, std::string>& GetConfigurationArguments() const override {