4a4e5c7c18
* Prepare gradient index for Quantile DMatrix. - Implement push batch with adapter batch. - Implement `GetFvalue` for prediction.
112 lines
3.4 KiB
C++
112 lines
3.4 KiB
C++
/*!
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* Copyright 2021-2022 XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/data.h>
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#include "../../../src/common/column_matrix.h"
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#include "../../../src/data/gradient_index.h"
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#include "../helpers.h"
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namespace xgboost {
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namespace data {
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TEST(GradientIndex, ExternalMemory) {
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std::unique_ptr<DMatrix> dmat = CreateSparsePageDMatrix(10000);
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std::vector<size_t> base_rowids;
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std::vector<float> hessian(dmat->Info().num_row_, 1);
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for (auto const &page : dmat->GetBatches<GHistIndexMatrix>({64, hessian, true})) {
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base_rowids.push_back(page.base_rowid);
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}
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size_t i = 0;
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for (auto const &page : dmat->GetBatches<SparsePage>()) {
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ASSERT_EQ(base_rowids[i], page.base_rowid);
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++i;
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}
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base_rowids.clear();
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for (auto const &page : dmat->GetBatches<GHistIndexMatrix>({64, hessian, false})) {
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base_rowids.push_back(page.base_rowid);
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}
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i = 0;
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for (auto const &page : dmat->GetBatches<SparsePage>()) {
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ASSERT_EQ(base_rowids[i], page.base_rowid);
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++i;
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}
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}
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TEST(GradientIndex, FromCategoricalBasic) {
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size_t constexpr kRows = 1000, kCats = 13, kCols = 1;
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size_t max_bins = 8;
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auto x = GenerateRandomCategoricalSingleColumn(kRows, kCats);
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auto m = GetDMatrixFromData(x, kRows, 1);
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auto &h_ft = m->Info().feature_types.HostVector();
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h_ft.resize(kCols, FeatureType::kCategorical);
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BatchParam p(max_bins, 0.8);
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GHistIndexMatrix gidx(m.get(), max_bins, p.sparse_thresh, false, common::OmpGetNumThreads(0), {});
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auto x_copy = x;
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std::sort(x_copy.begin(), x_copy.end());
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auto n_uniques = std::unique(x_copy.begin(), x_copy.end()) - x_copy.begin();
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ASSERT_EQ(n_uniques, kCats);
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auto const &h_cut_ptr = gidx.cut.Ptrs();
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auto const &h_cut_values = gidx.cut.Values();
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ASSERT_EQ(h_cut_ptr.size(), 2);
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ASSERT_EQ(h_cut_values.size(), kCats);
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auto const &index = gidx.index;
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for (size_t i = 0; i < x.size(); ++i) {
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auto bin = index[i];
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auto bin_value = h_cut_values.at(bin);
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ASSERT_EQ(common::AsCat(x[i]), common::AsCat(bin_value));
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}
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}
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TEST(GradientIndex, PushBatch) {
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size_t constexpr kRows = 64, kCols = 4;
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bst_bin_t max_bins = 64;
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float st = 0.5;
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auto test = [&](float sparisty) {
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auto m = RandomDataGenerator{kRows, kCols, sparisty}.GenerateDMatrix(true);
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auto cuts = common::SketchOnDMatrix(m.get(), max_bins, common::OmpGetNumThreads(0), false, {});
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common::HistogramCuts copy_cuts = cuts;
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ASSERT_EQ(m->Info().num_row_, kRows);
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ASSERT_EQ(m->Info().num_col_, kCols);
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GHistIndexMatrix gmat{m->Info(), std::move(copy_cuts), max_bins};
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for (auto const &page : m->GetBatches<SparsePage>()) {
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SparsePageAdapterBatch batch{page.GetView()};
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gmat.PushAdapterBatch(m->Ctx(), 0, 0, batch, std::numeric_limits<float>::quiet_NaN(), {}, st,
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m->Info().num_row_);
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gmat.PushAdapterBatchColumns(m->Ctx(), batch, std::numeric_limits<float>::quiet_NaN(), 0);
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}
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for (auto const &page : m->GetBatches<GHistIndexMatrix>(BatchParam{max_bins, st})) {
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for (size_t i = 0; i < kRows; ++i) {
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for (size_t j = 0; j < kCols; ++j) {
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auto v0 = gmat.GetFvalue(i, j, false);
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auto v1 = page.GetFvalue(i, j, false);
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if (sparisty == 0.0) {
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ASSERT_FALSE(std::isnan(v0));
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}
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if (!std::isnan(v0)) {
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ASSERT_EQ(v0, v1);
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}
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}
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}
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}
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};
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test(0.0f);
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test(0.5f);
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test(0.9f);
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}
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} // namespace data
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} // namespace xgboost
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