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

Support learning rate for zero-hessian objectives. (#8866)

Browse Source
This commit is contained in:
Jiaming Yuan
2023-03-06 20:33:28 +08:00
committed by GitHub
parent 173096a6a7
commit 228a46e8ad
34 changed files with 464 additions and 434 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
tests/cpp/tree/hist/test_evaluate_splits.cc
View File

@@ -24,7 +24,7 @@ void TestEvaluateSplits(bool force_read_by_column) {
auto dmat = RandomDataGenerator(kRows, kCols, 0).Seed(3).GenerateDMatrix();
auto evaluator = HistEvaluator<CPUExpandEntry>{&ctx, param, dmat->Info(), sampler};
auto evaluator = HistEvaluator<CPUExpandEntry>{&ctx, &param, dmat->Info(), sampler};
common::HistCollection hist;
std::vector<GradientPair> row_gpairs = {
{1.23f, 0.24f}, {0.24f, 0.25f}, {0.26f, 0.27f}, {2.27f, 0.28f},
@@ -96,7 +96,7 @@ TEST(HistEvaluator, Apply) {
param.UpdateAllowUnknown(Args{{"min_child_weight", "0"}, {"reg_lambda", "0.0"}});
auto dmat = RandomDataGenerator(kNRows, kNCols, 0).Seed(3).GenerateDMatrix();
auto sampler = std::make_shared<common::ColumnSampler>();
auto evaluator_ = HistEvaluator<CPUExpandEntry>{&ctx, param, dmat->Info(), sampler};
auto evaluator_ = HistEvaluator<CPUExpandEntry>{&ctx, &param, dmat->Info(), sampler};
CPUExpandEntry entry{0, 0, 10.0f};
entry.split.left_sum = GradStats{0.4, 0.6f};
@@ -123,7 +123,7 @@ TEST_F(TestPartitionBasedSplit, CPUHist) {
// check the evaluator is returning the optimal split
std::vector<FeatureType> ft{FeatureType::kCategorical};
auto sampler = std::make_shared<common::ColumnSampler>();
HistEvaluator<CPUExpandEntry> evaluator{&ctx, param_, info_, sampler};
HistEvaluator<CPUExpandEntry> evaluator{&ctx, &param_, info_, sampler};
evaluator.InitRoot(GradStats{total_gpair_});
RegTree tree;
std::vector<CPUExpandEntry> entries(1);
@@ -153,7 +153,7 @@ auto CompareOneHotAndPartition(bool onehot) {
RandomDataGenerator(kRows, kCols, 0).Seed(3).Type(ft).MaxCategory(n_cats).GenerateDMatrix();
auto sampler = std::make_shared<common::ColumnSampler>();
auto evaluator = HistEvaluator<CPUExpandEntry>{&ctx, param, dmat->Info(), sampler};
auto evaluator = HistEvaluator<CPUExpandEntry>{&ctx, &param, dmat->Info(), sampler};
std::vector<CPUExpandEntry> entries(1);
for (auto const &gmat : dmat->GetBatches<GHistIndexMatrix>({32, param.sparse_threshold})) {
@@ -204,7 +204,7 @@ TEST_F(TestCategoricalSplitWithMissing, HistEvaluator) {
info.num_col_ = 1;
info.feature_types = {FeatureType::kCategorical};
Context ctx;
auto evaluator = HistEvaluator<CPUExpandEntry>{&ctx, param_, info, sampler};
auto evaluator = HistEvaluator<CPUExpandEntry>{&ctx, &param_, info, sampler};
evaluator.InitRoot(GradStats{parent_sum_});
std::vector<CPUExpandEntry> entries(1);