120 lines
4.0 KiB
C++
120 lines
4.0 KiB
C++
/**
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* Copyright 2019-2023 by XGBoost Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/tree_model.h>
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#include <xgboost/tree_updater.h>
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#include "../../../src/tree/param.h" // for TrainParam
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#include "../helpers.h"
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namespace xgboost::tree {
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std::shared_ptr<DMatrix> GenerateDMatrix(std::size_t rows, std::size_t cols,
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bool categorical = false) {
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if (categorical) {
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std::vector<FeatureType> ft(cols);
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for (size_t i = 0; i < ft.size(); ++i) {
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ft[i] = (i % 3 == 0) ? FeatureType::kNumerical : FeatureType::kCategorical;
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}
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return RandomDataGenerator(rows, cols, 0.6f).Seed(3).Type(ft).MaxCategory(17).GenerateDMatrix();
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} else {
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return RandomDataGenerator{rows, cols, 0.6f}.Seed(3).GenerateDMatrix();
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}
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}
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TEST(GrowHistMaker, InteractionConstraint) {
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auto constexpr kRows = 32;
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auto constexpr kCols = 16;
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auto p_dmat = GenerateDMatrix(kRows, kCols);
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auto p_gradients = GenerateGradients(kRows);
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Context ctx;
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ObjInfo task{ObjInfo::kRegression};
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{
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// With constraints
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RegTree tree{1, kCols};
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std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create("grow_histmaker", &ctx, &task)};
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TrainParam param;
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param.UpdateAllowUnknown(
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Args{{"interaction_constraints", "[[0, 1]]"}, {"num_feature", std::to_string(kCols)}});
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std::vector<HostDeviceVector<bst_node_t>> position(1);
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updater->Update(¶m, p_gradients.get(), p_dmat.get(), position, {&tree});
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ASSERT_EQ(tree.NumExtraNodes(), 4);
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ASSERT_EQ(tree[0].SplitIndex(), 1);
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ASSERT_EQ(tree[tree[0].LeftChild()].SplitIndex(), 0);
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ASSERT_EQ(tree[tree[0].RightChild()].SplitIndex(), 0);
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}
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{
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// Without constraints
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RegTree tree{1u, kCols};
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std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create("grow_histmaker", &ctx, &task)};
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std::vector<HostDeviceVector<bst_node_t>> position(1);
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TrainParam param;
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param.Init(Args{});
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updater->Update(¶m, p_gradients.get(), p_dmat.get(), position, {&tree});
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ASSERT_EQ(tree.NumExtraNodes(), 10);
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ASSERT_EQ(tree[0].SplitIndex(), 1);
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ASSERT_NE(tree[tree[0].LeftChild()].SplitIndex(), 0);
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ASSERT_NE(tree[tree[0].RightChild()].SplitIndex(), 0);
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}
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}
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namespace {
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void VerifyColumnSplit(int32_t rows, bst_feature_t cols, bool categorical,
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RegTree const& expected_tree) {
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auto p_dmat = GenerateDMatrix(rows, cols, categorical);
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auto p_gradients = GenerateGradients(rows);
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Context ctx;
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ObjInfo task{ObjInfo::kRegression};
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std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create("grow_histmaker", &ctx, &task)};
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std::vector<HostDeviceVector<bst_node_t>> position(1);
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std::unique_ptr<DMatrix> sliced{
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p_dmat->SliceCol(collective::GetWorldSize(), collective::GetRank())};
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RegTree tree{1u, cols};
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TrainParam param;
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param.Init(Args{});
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updater->Update(¶m, p_gradients.get(), sliced.get(), position, {&tree});
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Json json{Object{}};
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tree.SaveModel(&json);
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Json expected_json{Object{}};
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expected_tree.SaveModel(&expected_json);
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ASSERT_EQ(json, expected_json);
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}
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void TestColumnSplit(bool categorical) {
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auto constexpr kRows = 32;
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auto constexpr kCols = 16;
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RegTree expected_tree{1u, kCols};
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ObjInfo task{ObjInfo::kRegression};
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{
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auto p_dmat = GenerateDMatrix(kRows, kCols, categorical);
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auto p_gradients = GenerateGradients(kRows);
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Context ctx;
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std::unique_ptr<TreeUpdater> updater{TreeUpdater::Create("grow_histmaker", &ctx, &task)};
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std::vector<HostDeviceVector<bst_node_t>> position(1);
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TrainParam param;
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param.Init(Args{});
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updater->Update(¶m, p_gradients.get(), p_dmat.get(), position, {&expected_tree});
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}
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auto constexpr kWorldSize = 2;
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RunWithInMemoryCommunicator(kWorldSize, VerifyColumnSplit, kRows, kCols, categorical,
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std::cref(expected_tree));
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}
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} // anonymous namespace
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TEST(GrowHistMaker, ColumnSplitNumerical) { TestColumnSplit(false); }
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TEST(GrowHistMaker, ColumnSplitCategorical) { TestColumnSplit(true); }
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} // namespace xgboost::tree
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