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Always use partition based categorical splits. (#7857)

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This commit is contained in:
Jiaming Yuan
2022-05-03 22:30:32 +08:00
committed by GitHub
parent 90cce38236
commit 317d7be6ee
13 changed files with 79 additions and 104 deletions
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13
tests/cpp/tree/hist/test_evaluate_splits.cc
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@@ -24,8 +24,8 @@ template <typename GradientSumT> void TestEvaluateSplits() {
auto dmat = RandomDataGenerator(kRows, kCols, 0).Seed(3).GenerateDMatrix();
auto evaluator = HistEvaluator<GradientSumT, CPUExpandEntry>{
param, dmat->Info(), n_threads, sampler, ObjInfo{ObjInfo::kRegression}};
auto evaluator =
HistEvaluator<GradientSumT, CPUExpandEntry>{param, dmat->Info(), n_threads, sampler};
common::HistCollection<GradientSumT> hist;
std::vector<GradientPair> row_gpairs = {
{1.23f, 0.24f}, {0.24f, 0.25f}, {0.26f, 0.27f}, {2.27f, 0.28f},
@@ -97,8 +97,7 @@ 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<float, CPUExpandEntry>{param, dmat->Info(), 4, sampler,
ObjInfo{ObjInfo::kRegression}};
auto evaluator_ = HistEvaluator<float, CPUExpandEntry>{param, dmat->Info(), 4, sampler};
CPUExpandEntry entry{0, 0, 10.0f};
entry.split.left_sum = GradStats{0.4, 0.6f};
@@ -125,7 +124,7 @@ TEST_F(TestPartitionBasedSplit, CPUHist) {
std::vector<FeatureType> ft{FeatureType::kCategorical};
auto sampler = std::make_shared<common::ColumnSampler>();
HistEvaluator<double, CPUExpandEntry> evaluator{param_, info_, common::OmpGetNumThreads(0),
sampler, ObjInfo{ObjInfo::kRegression}};
sampler};
evaluator.InitRoot(GradStats{total_gpair_});
RegTree tree;
std::vector<CPUExpandEntry> entries(1);
@@ -156,8 +155,8 @@ auto CompareOneHotAndPartition(bool onehot) {
int32_t n_threads = 16;
auto sampler = std::make_shared<common::ColumnSampler>();
auto evaluator = HistEvaluator<GradientSumT, CPUExpandEntry>{
param, dmat->Info(), n_threads, sampler, ObjInfo{ObjInfo::kRegression}};
auto evaluator =
HistEvaluator<GradientSumT, CPUExpandEntry>{param, dmat->Info(), n_threads, sampler};
std::vector<CPUExpandEntry> entries(1);
for (auto const &gmat : dmat->GetBatches<GHistIndexMatrix>({32, param.sparse_threshold})) {