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Optional normalization for learning to rank. (#10094)

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This commit is contained in:
Jiaming Yuan
2024-03-08 12:41:21 +08:00
committed by GitHub
parent bc516198dc
commit e14c3b9325
8 changed files with 44 additions and 5 deletions
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5
src/common/ranking_utils.h
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@@ -78,6 +78,7 @@ struct LambdaRankParam : public XGBoostParameter<LambdaRankParam> {
// unbiased
bool lambdarank_unbiased{false};
bool lambdarank_normalization{true};
double lambdarank_bias_norm{1.0};
// ndcg
bool ndcg_exp_gain{true};
@@ -86,6 +87,7 @@ struct LambdaRankParam : public XGBoostParameter<LambdaRankParam> {
return lambdarank_pair_method == that.lambdarank_pair_method &&
lambdarank_num_pair_per_sample == that.lambdarank_num_pair_per_sample &&
lambdarank_unbiased == that.lambdarank_unbiased &&
lambdarank_normalization == that.lambdarank_normalization &&
lambdarank_bias_norm == that.lambdarank_bias_norm && ndcg_exp_gain == that.ndcg_exp_gain;
}
bool operator!=(LambdaRankParam const& that) const { return !(*this == that); }
@@ -134,6 +136,9 @@ struct LambdaRankParam : public XGBoostParameter<LambdaRankParam> {
DMLC_DECLARE_FIELD(lambdarank_unbiased)
.set_default(false)
.describe("Unbiased lambda mart. Use extended IPW to debias click position");
DMLC_DECLARE_FIELD(lambdarank_normalization)
.set_default(true)
.describe("Whether to normalize the leaf value for lambda rank.");
DMLC_DECLARE_FIELD(lambdarank_bias_norm)
.set_default(1.0)
.set_lower_bound(0.0)

2
src/objective/lambdarank_obj.cc
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@@ -222,7 +222,7 @@ class LambdaRankObj : public FitIntercept {
};
MakePairs(ctx_, iter, p_cache_, g, g_label, g_rank, loop);
if (sum_lambda > 0.0) {
if (sum_lambda > 0.0 && param_.lambdarank_normalization) {
double norm = std::log2(1.0 + sum_lambda) / sum_lambda;
std::transform(g_gpair.Values().data(), g_gpair.Values().data() + g_gpair.Size(),
g_gpair.Values().data(), [norm](GradientPair const& g) { return g * norm; });

3
src/objective/lambdarank_obj.cu
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@@ -266,12 +266,13 @@ void CalcGrad(Context const* ctx, MetaInfo const& info, std::shared_ptr<ltr::Ran
*/
auto d_weights = common::MakeOptionalWeights(ctx, info.weights_);
auto w_norm = p_cache->WeightNorm();
auto norm = p_cache->Param().lambdarank_normalization;
thrust::for_each_n(ctx->CUDACtx()->CTP(), thrust::make_counting_iterator(0ul), d_gpair.Size(),
[=] XGBOOST_DEVICE(std::size_t i) mutable {
auto g = dh::SegmentId(d_gptr, i);
auto sum_lambda = thrust::get<2>(d_max_lambdas[g]);
// Normalization
if (sum_lambda > 0.0) {
if (sum_lambda > 0.0 && norm) {
double norm = std::log2(1.0 + sum_lambda) / sum_lambda;
d_gpair(i, 0) *= norm;
}