[multi] Implement weight feature importance. (#10700)

src/gbm/gbtree.h

@@ -236,12 +236,11 @@ class GBTree : public GradientBooster {
     auto add_score = [&](auto fn) {
       for (auto idx : trees) {
         CHECK_LE(idx, total_n_trees) << "Invalid tree index.";
-        auto const& p_tree = model_.trees[idx];
-        p_tree->WalkTree([&](bst_node_t nidx) {
-          auto const& node = (*p_tree)[nidx];
-          if (!node.IsLeaf()) {
-            split_counts[node.SplitIndex()]++;
-            fn(p_tree, nidx, node.SplitIndex());
+        auto const& tree = *model_.trees[idx];
+        tree.WalkTree([&](bst_node_t nidx) {
+          if (!tree.IsLeaf(nidx)) {
+            split_counts[tree.SplitIndex(nidx)]++;
+            fn(tree, nidx, tree.SplitIndex(nidx));
           }
           return true;
         });
@@ -253,12 +252,18 @@ class GBTree : public GradientBooster {
         gain_map[split] = split_counts[split];
       });
     } else if (importance_type == "gain" || importance_type == "total_gain") {
-      add_score([&](auto const &p_tree, bst_node_t nidx, bst_feature_t split) {
-        gain_map[split] += p_tree->Stat(nidx).loss_chg;
+      if (!model_.trees.empty() && model_.trees.front()->IsMultiTarget()) {
+        LOG(FATAL) << "gain/total_gain " << MTNotImplemented();
+      }
+      add_score([&](auto const& tree, bst_node_t nidx, bst_feature_t split) {
+        gain_map[split] += tree.Stat(nidx).loss_chg;
       });
     } else if (importance_type == "cover" || importance_type == "total_cover") {
-      add_score([&](auto const &p_tree, bst_node_t nidx, bst_feature_t split) {
-        gain_map[split] += p_tree->Stat(nidx).sum_hess;
+      if (!model_.trees.empty() && model_.trees.front()->IsMultiTarget()) {
+        LOG(FATAL) << "cover/total_cover " << MTNotImplemented();
+      }
+      add_score([&](auto const& tree, bst_node_t nidx, bst_feature_t split) {
+        gain_map[split] += tree.Stat(nidx).sum_hess;
       });
     } else {
       LOG(FATAL)

tests/python/test_with_sklearn.py

@@ -336,6 +336,36 @@ def test_feature_importances_weight():
         cls.feature_importances_
 
 
+def test_feature_importances_weight_vector_leaf() -> None:
+    from sklearn.datasets import make_multilabel_classification
+
+    X, y = make_multilabel_classification(random_state=1994)
+    with pytest.raises(ValueError, match="gain/total_gain"):
+        clf = xgb.XGBClassifier(multi_strategy="multi_output_tree")
+        clf.fit(X, y)
+        clf.feature_importances_
+
+    with pytest.raises(ValueError, match="cover/total_cover"):
+        clf = xgb.XGBClassifier(
+            multi_strategy="multi_output_tree", importance_type="cover"
+        )
+        clf.fit(X, y)
+        clf.feature_importances_
+
+    clf = xgb.XGBClassifier(
+        multi_strategy="multi_output_tree",
+        importance_type="weight",
+        colsample_bynode=0.2,
+    )
+    clf.fit(X, y, feature_weights=np.arange(0, X.shape[1]))
+    fi = clf.feature_importances_
+    assert fi[0] == 0.0
+    assert fi[-1] > fi[1] * 5
+
+    w = np.polynomial.Polynomial.fit(np.arange(0, X.shape[1]), fi, deg=1)
+    assert w.coef[1] > 0.03
+
+
 @pytest.mark.skipif(**tm.no_pandas())
 def test_feature_importances_gain():
     from sklearn.datasets import load_digits