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[multi] Implement weight feature importance. #10700

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Aug 21, 2024
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[multi] Implement weight feature importance. #10700

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25 changes: 15 additions & 10 deletions src/gbm/gbtree.h
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Original file line number Diff line number Diff line change
Expand Up @@ -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;
});
Expand All @@ -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)
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30 changes: 30 additions & 0 deletions tests/python/test_with_sklearn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down
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