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

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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
2021-11-05 10:07:00 +08:00
committed by GitHub
parent 48aff0eabd
commit c968217ca8
11 changed files with 119 additions and 49 deletions
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12
src/c_api/c_api.cc
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@@ -1159,9 +1159,17 @@ XGB_DLL int XGBoosterFeatureScore(BoosterHandle handle, char const *json_config,
custom_feature_names = get<Array const>(config["feature_names"]);
}
auto& scores = learner->GetThreadLocal().ret_vec_float;
std::vector<int32_t> tree_idx;
if (!IsA<Null>(config["tree_idx"])) {
auto j_tree_idx = get<Array const>(config["tree_idx"]);
for (auto const &idx : j_tree_idx) {
tree_idx.push_back(get<Integer const>(idx));
}
}
auto &scores = learner->GetThreadLocal().ret_vec_float;
std::vector<bst_feature_t> features;
learner->CalcFeatureScore(importance, &features, &scores);
learner->CalcFeatureScore(importance, common::Span<int32_t const>(tree_idx), &features, &scores);
auto n_features = learner->GetNumFeature();
GenerateFeatureMap(learner, custom_feature_names, n_features, &feature_map);