Make sure the number of features is correct.

src/c_api/c_api_utils.h

@@ -206,8 +206,14 @@ inline void GenerateFeatureMap(Learner const *learner,
     // Use the feature names and types from booster.
     std::vector<std::string> feature_names;
     learner->GetFeatureNames(&feature_names);
+    if (!feature_names.empty()) {
+      CHECK_EQ(feature_names.size(), n_features) << "Incorrect number of feature names.";
+    }
     std::vector<std::string> feature_types;
     learner->GetFeatureTypes(&feature_types);
+    if (!feature_types.empty()) {
+      CHECK_EQ(feature_types.size(), n_features) << "Incorrect number of feature types.";
+    }
     for (size_t i = 0; i < n_features; ++i) {
       feature_map.PushBack(
           i,

tests/python/test_basic.py

@@ -154,6 +154,23 @@ def test_dump(self):
         dump4j = json.loads(dump4[0])
         assert 'gain' in dump4j, "Expected 'gain' to be dumped in JSON."
 
+    def test_feature_score(self):
+        rng = np.random.RandomState(0)
+        data = rng.randn(100, 2)
+        target = np.array([0, 1] * 50)
+        features = ["F0"]
+        with pytest.raises(ValueError):
+            xgb.DMatrix(data, label=target, feature_names=features)
+
+        params = {"objective": "binary:logistic"}
+        dm = xgb.DMatrix(data, label=target, feature_names=["F0", "F1"])
+        booster = xgb.train(params, dm, num_boost_round=1)
+        # no error since feature names might be assigned before the booster seeing data
+        # and booster doesn't known about the actual number of features.
+        booster.feature_names = ["F0"]
+        with pytest.raises(ValueError):
+            booster.get_fscore()
+
     def test_load_file_invalid(self):
         with pytest.raises(xgb.core.XGBoostError):
             xgb.Booster(model_file='incorrect_path')