/*! * Copyright 2021-2022 XGBoost contributors */ #include #include #include "../../../src/data/gradient_index.h" #include "../helpers.h" namespace xgboost { namespace data { TEST(GradientIndex, ExternalMemory) { std::unique_ptr dmat = CreateSparsePageDMatrix(10000); std::vector base_rowids; std::vector hessian(dmat->Info().num_row_, 1); for (auto const &page : dmat->GetBatches({64, hessian, true})) { base_rowids.push_back(page.base_rowid); } size_t i = 0; for (auto const &page : dmat->GetBatches()) { ASSERT_EQ(base_rowids[i], page.base_rowid); ++i; } base_rowids.clear(); for (auto const &page : dmat->GetBatches({64, hessian, false})) { base_rowids.push_back(page.base_rowid); } i = 0; for (auto const &page : dmat->GetBatches()) { ASSERT_EQ(base_rowids[i], page.base_rowid); ++i; } } TEST(GradientIndex, FromCategoricalBasic) { size_t constexpr kRows = 1000, kCats = 13, kCols = 1; size_t max_bins = 8; auto x = GenerateRandomCategoricalSingleColumn(kRows, kCats); auto m = GetDMatrixFromData(x, kRows, 1); auto &h_ft = m->Info().feature_types.HostVector(); h_ft.resize(kCols, FeatureType::kCategorical); BatchParam p(max_bins, 0.8); GHistIndexMatrix gidx; gidx.Init(m.get(), max_bins, p.sparse_thresh, false, common::OmpGetNumThreads(0), {}); auto x_copy = x; std::sort(x_copy.begin(), x_copy.end()); auto n_uniques = std::unique(x_copy.begin(), x_copy.end()) - x_copy.begin(); ASSERT_EQ(n_uniques, kCats); auto const &h_cut_ptr = gidx.cut.Ptrs(); auto const &h_cut_values = gidx.cut.Values(); ASSERT_EQ(h_cut_ptr.size(), 2); ASSERT_EQ(h_cut_values.size(), kCats); auto const &index = gidx.index; for (size_t i = 0; i < x.size(); ++i) { auto bin = index[i]; auto bin_value = h_cut_values.at(bin); ASSERT_EQ(common::AsCat(x[i]), common::AsCat(bin_value)); } } } // namespace data } // namespace xgboost