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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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6
tests/cpp/gbm/test_gbtree.cc
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@@ -430,7 +430,7 @@ TEST(GBTree, FeatureScore) {
std::vector<bst_feature_t> features_weight;
std::vector<float> scores_weight;
learner->CalcFeatureScore("weight", &features_weight, &scores_weight);
learner->CalcFeatureScore("weight", {}, &features_weight, &scores_weight);
ASSERT_EQ(features_weight.size(), scores_weight.size());
ASSERT_LE(features_weight.size(), learner->GetNumFeature());
ASSERT_TRUE(std::is_sorted(features_weight.begin(), features_weight.end()));
@@ -438,11 +438,11 @@ TEST(GBTree, FeatureScore) {
auto test_eq = [&learner, &scores_weight](std::string type) {
std::vector<bst_feature_t> features;
std::vector<float> scores;
learner->CalcFeatureScore(type, &features, &scores);
learner->CalcFeatureScore(type, {}, &features, &scores);
std::vector<bst_feature_t> features_total;
std::vector<float> scores_total;
learner->CalcFeatureScore("total_" + type, &features_total, &scores_total);
learner->CalcFeatureScore("total_" + type, {}, &features_total, &scores_total);
for (size_t i = 0; i < scores_weight.size(); ++i) {
ASSERT_LE(RelError(scores_total[i] / scores[i], scores_weight[i]), kRtEps);