e089e16e3d
* Pass pointer to model parameters. This PR de-duplicates most of the model parameters except the one in `tree_model.h`. One difficulty is `base_score` is a model property but can be changed at runtime by objective function. Hence when performing model IO, we need to save the one provided by users, instead of the one transformed by objective. Here we created an immutable version of `LearnerModelParam` that represents the value of model parameter after configuration.
127 lines
3.4 KiB
C++
127 lines
3.4 KiB
C++
#include <gtest/gtest.h>
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#include <vector>
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#include <string>
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#include <utility>
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#include "../../../src/common/hist_util.h"
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#include "../helpers.h"
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namespace xgboost {
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namespace common {
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TEST(CutsBuilder, SearchGroupInd) {
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size_t constexpr kNumGroups = 4;
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size_t constexpr kRows = 17;
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size_t constexpr kCols = 15;
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auto pp_dmat = CreateDMatrix(kRows, kCols, 0);
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std::shared_ptr<DMatrix> p_mat {*pp_dmat};
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std::vector<bst_int> group(kNumGroups);
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group[0] = 2;
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group[1] = 3;
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group[2] = 7;
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group[3] = 5;
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p_mat->Info().SetInfo(
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"group", group.data(), DataType::kUInt32, kNumGroups);
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HistogramCuts hmat;
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size_t group_ind = CutsBuilder::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 0);
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ASSERT_EQ(group_ind, 0);
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group_ind = CutsBuilder::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 5);
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ASSERT_EQ(group_ind, 2);
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EXPECT_ANY_THROW(CutsBuilder::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 17));
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delete pp_dmat;
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}
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namespace {
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class SparseCutsWrapper : public SparseCuts {
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public:
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std::vector<uint32_t> const& ColPtrs() const { return p_cuts_->Ptrs(); }
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std::vector<float> const& ColValues() const { return p_cuts_->Values(); }
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};
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} // anonymous namespace
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TEST(SparseCuts, SingleThreadedBuild) {
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size_t constexpr kRows = 267;
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size_t constexpr kCols = 31;
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size_t constexpr kBins = 256;
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auto pp_dmat = CreateDMatrix(kRows, kCols, 0);
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std::shared_ptr<DMatrix> p_fmat {*pp_dmat};
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common::GHistIndexMatrix hmat;
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hmat.Init(p_fmat.get(), kBins);
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HistogramCuts cuts;
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SparseCuts indices(&cuts);
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auto const& page = *(p_fmat->GetBatches<xgboost::CSCPage>().begin());
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indices.SingleThreadBuild(page, p_fmat->Info(), kBins, false, 0, page.Size(), 0);
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ASSERT_EQ(hmat.cut.Ptrs().size(), cuts.Ptrs().size());
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ASSERT_EQ(hmat.cut.Ptrs(), cuts.Ptrs());
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ASSERT_EQ(hmat.cut.Values(), cuts.Values());
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ASSERT_EQ(hmat.cut.MinValues(), cuts.MinValues());
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delete pp_dmat;
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}
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TEST(SparseCuts, MultiThreadedBuild) {
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size_t constexpr kRows = 17;
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size_t constexpr kCols = 15;
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size_t constexpr kBins = 255;
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omp_ulong ori_nthreads = omp_get_max_threads();
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omp_set_num_threads(16);
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auto Compare =
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#if defined(_MSC_VER) // msvc fails to capture
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[kBins](DMatrix* p_fmat) {
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#else
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[](DMatrix* p_fmat) {
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#endif
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HistogramCuts threaded_container;
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SparseCuts threaded_indices(&threaded_container);
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threaded_indices.Build(p_fmat, kBins);
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HistogramCuts container;
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SparseCuts indices(&container);
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auto const& page = *(p_fmat->GetBatches<xgboost::CSCPage>().begin());
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indices.SingleThreadBuild(page, p_fmat->Info(), kBins, false, 0, page.Size(), 0);
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ASSERT_EQ(container.Ptrs().size(), threaded_container.Ptrs().size());
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ASSERT_EQ(container.Values().size(), threaded_container.Values().size());
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for (uint32_t i = 0; i < container.Ptrs().size(); ++i) {
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ASSERT_EQ(container.Ptrs()[i], threaded_container.Ptrs()[i]);
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}
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for (uint32_t i = 0; i < container.Values().size(); ++i) {
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ASSERT_EQ(container.Values()[i], threaded_container.Values()[i]);
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}
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};
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{
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auto pp_mat = CreateDMatrix(kRows, kCols, 0);
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DMatrix* p_fmat = (*pp_mat).get();
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Compare(p_fmat);
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delete pp_mat;
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}
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{
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auto pp_mat = CreateDMatrix(kRows, kCols, 0.0001);
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DMatrix* p_fmat = (*pp_mat).get();
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Compare(p_fmat);
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delete pp_mat;
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}
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omp_set_num_threads(ori_nthreads);
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}
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} // namespace common
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} // namespace xgboost
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