Implement feature score in GBTree. (#7041)

* Categorical data support.
* Eliminate text parsing during feature score computation.

tests/cpp/gbm/test_gbtree.cc

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2019-2020 XGBoost contributors
+ * Copyright 2019-2021 XGBoost contributors
  */
 #include <gtest/gtest.h>
 #include <dmlc/filesystem.h>
@@ -410,4 +410,41 @@ TEST(Dart, Slice) {
   auto const& trees = get<Array const>(model["learner"]["gradient_booster"]["gbtree"]["model"]["trees"]);
   ASSERT_EQ(weights.size(), trees.size());
 }
+
+TEST(GBTree, FeatureScore) {
+  size_t n_samples = 1000, n_features = 10, n_classes = 4;
+  auto m = RandomDataGenerator{n_samples, n_features, 0.5}.GenerateDMatrix(true, false, n_classes);
+
+  std::unique_ptr<Learner> learner{ Learner::Create({m}) };
+  learner->SetParam("num_class", std::to_string(n_classes));
+
+  learner->Configure();
+  for (size_t i = 0; i < 2; ++i) {
+    learner->UpdateOneIter(i, m);
+  }
+
+  std::vector<bst_feature_t> features_weight;
+  std::vector<float> 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()));
+
+  auto test_eq = [&learner, &scores_weight](std::string type) {
+    std::vector<bst_feature_t> features;
+    std::vector<float> 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);
+
+    for (size_t i = 0; i < scores_weight.size(); ++i) {
+      ASSERT_LE(RelError(scores_total[i] / scores[i], scores_weight[i]), kRtEps);
+    }
+  };
+
+  test_eq("gain");
+  test_eq("cover");
+}
 }  // namespace xgboost