2775c2a1ab
This PR prepares the GHistIndexMatrix to host the column matrix which is used by the hist tree method by accepting sparse_threshold parameter. Some cleanups are made to ensure the correct batch param is being passed into DMatrix along with some additional tests for correctness of SimpleDMatrix.
72 lines
2.0 KiB
C++
72 lines
2.0 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/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;
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gidx.Init(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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} // namespace data
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} // namespace xgboost
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