118 lines
3.3 KiB
C++
118 lines
3.3 KiB
C++
#include <xgboost/tree_updater.h>
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#include <xgboost/tree_model.h>
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#include <gtest/gtest.h>
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#include "../helpers.h"
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namespace xgboost {
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class UpdaterTreeStatTest : public ::testing::Test {
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protected:
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std::shared_ptr<DMatrix> p_dmat_;
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HostDeviceVector<GradientPair> gpairs_;
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size_t constexpr static kRows = 10;
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size_t constexpr static kCols = 10;
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protected:
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void SetUp() override {
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p_dmat_ = RandomDataGenerator(kRows, kCols, .5f).GenerateDMatrix(true);
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auto g = GenerateRandomGradients(kRows);
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gpairs_.Resize(kRows);
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gpairs_.Copy(g);
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}
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void RunTest(std::string updater) {
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auto tparam = CreateEmptyGenericParam(0);
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auto up = std::unique_ptr<TreeUpdater>{
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TreeUpdater::Create(updater, &tparam, ObjInfo{ObjInfo::kRegression})};
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up->Configure(Args{});
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RegTree tree;
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tree.param.num_feature = kCols;
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up->Update(&gpairs_, p_dmat_.get(), {&tree});
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tree.WalkTree([&tree](bst_node_t nidx) {
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if (tree[nidx].IsLeaf()) {
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// 1.0 is the default `min_child_weight`.
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CHECK_GE(tree.Stat(nidx).sum_hess, 1.0);
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}
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return true;
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});
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}
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};
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#if defined(XGBOOST_USE_CUDA)
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TEST_F(UpdaterTreeStatTest, GpuHist) {
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this->RunTest("grow_gpu_hist");
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}
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#endif // defined(XGBOOST_USE_CUDA)
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TEST_F(UpdaterTreeStatTest, Hist) {
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this->RunTest("grow_quantile_histmaker");
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}
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TEST_F(UpdaterTreeStatTest, Exact) {
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this->RunTest("grow_colmaker");
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}
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TEST_F(UpdaterTreeStatTest, Approx) {
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this->RunTest("grow_histmaker");
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}
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class UpdaterEtaTest : public ::testing::Test {
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protected:
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std::shared_ptr<DMatrix> p_dmat_;
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HostDeviceVector<GradientPair> gpairs_;
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size_t constexpr static kRows = 10;
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size_t constexpr static kCols = 10;
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size_t constexpr static kClasses = 10;
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void SetUp() override {
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p_dmat_ = RandomDataGenerator(kRows, kCols, .5f).GenerateDMatrix(true, false, kClasses);
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auto g = GenerateRandomGradients(kRows);
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gpairs_.Resize(kRows);
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gpairs_.Copy(g);
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}
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void RunTest(std::string updater) {
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auto tparam = CreateEmptyGenericParam(0);
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float eta = 0.4;
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auto up_0 = std::unique_ptr<TreeUpdater>{
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TreeUpdater::Create(updater, &tparam, ObjInfo{ObjInfo::kClassification})};
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up_0->Configure(Args{{"eta", std::to_string(eta)}});
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auto up_1 = std::unique_ptr<TreeUpdater>{
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TreeUpdater::Create(updater, &tparam, ObjInfo{ObjInfo::kClassification})};
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up_1->Configure(Args{{"eta", "1.0"}});
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for (size_t iter = 0; iter < 4; ++iter) {
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RegTree tree_0;
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{
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tree_0.param.num_feature = kCols;
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up_0->Update(&gpairs_, p_dmat_.get(), {&tree_0});
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}
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RegTree tree_1;
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{
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tree_1.param.num_feature = kCols;
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up_1->Update(&gpairs_, p_dmat_.get(), {&tree_1});
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}
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tree_0.WalkTree([&](bst_node_t nidx) {
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if (tree_0[nidx].IsLeaf()) {
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EXPECT_NEAR(tree_1[nidx].LeafValue() * eta, tree_0[nidx].LeafValue(), kRtEps);
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}
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return true;
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});
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}
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}
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};
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TEST_F(UpdaterEtaTest, Hist) { this->RunTest("grow_quantile_histmaker"); }
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TEST_F(UpdaterEtaTest, Exact) { this->RunTest("grow_colmaker"); }
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TEST_F(UpdaterEtaTest, Approx) { this->RunTest("grow_histmaker"); }
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#if defined(XGBOOST_USE_CUDA)
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TEST_F(UpdaterEtaTest, GpuHist) { this->RunTest("grow_gpu_hist"); }
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#endif // defined(XGBOOST_USE_CUDA)
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} // namespace xgboost
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