Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix pairwise objective with NDCG metric along with custom gain. (#10100)

Browse Source 
* Fix pairwise objective with NDCG metric.

- Allow setting `ndcg_exp_gain` for `rank:pairwise`.

This is useful when using pairwise for objective but ndcg for metric.
This commit is contained in:
Jiaming Yuan 2024-03-11 14:54:10 +08:00 committed by GitHub
parent 06c9702028
commit 1450aebb74
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 26 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
src/objective/lambdarank_obj.cc
View File

@ -474,7 +474,6 @@ class LambdaRankMAP : public LambdaRankObj<LambdaRankMAP, ltr::MAPCache> {
public: public:
void GetGradientImpl(std::int32_t iter, const HostDeviceVector<float>& predt, void GetGradientImpl(std::int32_t iter, const HostDeviceVector<float>& predt,
const MetaInfo& info, linalg::Matrix<GradientPair>* out_gpair) { const MetaInfo& info, linalg::Matrix<GradientPair>* out_gpair) {
CHECK(param_.ndcg_exp_gain) << "NDCG gain can not be set for the MAP objective.";
if (ctx_->IsCUDA()) { if (ctx_->IsCUDA()) {
return cuda_impl::LambdaRankGetGradientMAP( return cuda_impl::LambdaRankGetGradientMAP(
ctx_, iter, predt, info, GetCache(), ti_plus_.View(ctx_->Device()), ctx_, iter, predt, info, GetCache(), ti_plus_.View(ctx_->Device()),
@ -564,7 +563,6 @@ class LambdaRankPairwise : public LambdaRankObj<LambdaRankPairwise, ltr::Ranking
public: public:
void GetGradientImpl(std::int32_t iter, const HostDeviceVector<float>& predt, void GetGradientImpl(std::int32_t iter, const HostDeviceVector<float>& predt,
const MetaInfo& info, linalg::Matrix<GradientPair>* out_gpair) { const MetaInfo& info, linalg::Matrix<GradientPair>* out_gpair) {
CHECK(param_.ndcg_exp_gain) << "NDCG gain can not be set for the pairwise objective.";
if (ctx_->IsCUDA()) { if (ctx_->IsCUDA()) {
return cuda_impl::LambdaRankGetGradientPairwise( return cuda_impl::LambdaRankGetGradientPairwise(
ctx_, iter, predt, info, GetCache(), ti_plus_.View(ctx_->Device()), ctx_, iter, predt, info, GetCache(), ti_plus_.View(ctx_->Device()),
@ -610,6 +608,13 @@ class LambdaRankPairwise : public LambdaRankObj<LambdaRankPairwise, ltr::Ranking
[[nodiscard]] const char* DefaultEvalMetric() const override { [[nodiscard]] const char* DefaultEvalMetric() const override {
return this->RankEvalMetric("ndcg"); return this->RankEvalMetric("ndcg");
} }
[[nodiscard]] Json DefaultMetricConfig() const override {
Json config{Object{}};
config["name"] = String{DefaultEvalMetric()};
config["lambdarank_param"] = ToJson(param_);
return config;
}
}; };
#if !defined(XGBOOST_USE_CUDA) #if !defined(XGBOOST_USE_CUDA)

5
tests/cpp/common/test_parameter.cc
View File

@ -97,4 +97,9 @@ TEST(XGBoostParameter, Update) {
ASSERT_NEAR(p.f, 2.71828f, kRtEps); ASSERT_NEAR(p.f, 2.71828f, kRtEps);
ASSERT_NEAR(p.d, 2.71828, kRtEps); // default ASSERT_NEAR(p.d, 2.71828, kRtEps); // default
} }
// Just in case dmlc's use of global memory has any impact in parameters.
UpdatableParam a, b;
a.UpdateAllowUnknown(xgboost::Args{{"f", "2.71828"}});
ASSERT_NE(a.f, b.f);
} }

14
tests/python/test_ranking.py
View File

@ -54,6 +54,20 @@ def test_ndcg_custom_gain():
assert byxgb.evals_result() == bynp.evals_result() assert byxgb.evals_result() == bynp.evals_result()
assert byxgb_json == bynp_json assert byxgb_json == bynp_json
# test pairwise can handle max_rel > 31, while ndcg metric is using custom gain
X, y, q, w = tm.make_ltr(n_samples=1024, n_features=4, n_query_groups=3, max_rel=33)
ranknet = xgboost.XGBRanker(
tree_method="hist",
ndcg_exp_gain=False,
n_estimators=10,
objective="rank:pairwise",
)
ranknet.fit(X, y, qid=q, eval_set=[(X, y)], eval_qid=[q])
history = ranknet.evals_result()
assert (
history["validation_0"]["ndcg@32"][0] < history["validation_0"]["ndcg@32"][-1]
)
def test_ranking_with_unweighted_data(): def test_ranking_with_unweighted_data():
Xrow = np.array([1, 2, 6, 8, 11, 14, 16, 17]) Xrow = np.array([1, 2, 6, 8, 11, 14, 16, 17])