/*! * Copyright 2019-2022 by XGBoost Contributors */ #include #include #include #include #include #include "xgboost/data.h" #include "../helpers.h" namespace xgboost { TEST(SparsePage, PushCSC) { std::vector offset {0}; std::vector data; SparsePage batch; batch.offset.HostVector() = offset; batch.data.HostVector() = data; offset = {0, 1, 4}; for (size_t i = 0; i < offset.back(); ++i) { data.emplace_back(Entry(i, 0.1f)); } SparsePage other; other.offset.HostVector() = offset; other.data.HostVector() = data; batch.PushCSC(other); ASSERT_EQ(batch.offset.HostVector().size(), offset.size()); ASSERT_EQ(batch.data.HostVector().size(), data.size()); for (size_t i = 0; i < offset.size(); ++i) { ASSERT_EQ(batch.offset.HostVector()[i], offset[i]); } for (size_t i = 0; i < data.size(); ++i) { ASSERT_EQ(batch.data.HostVector()[i].index, data[i].index); } batch.PushCSC(other); ASSERT_EQ(batch.offset.HostVector().size(), offset.size()); ASSERT_EQ(batch.data.Size(), data.size() * 2); for (size_t i = 0; i < offset.size(); ++i) { ASSERT_EQ(batch.offset.HostVector()[i], offset[i] * 2); } auto page = batch.GetView(); auto inst = page[0]; ASSERT_EQ(inst.size(), 2ul); for (auto entry : inst) { ASSERT_EQ(entry.index, 0u); } inst = page[1]; ASSERT_EQ(inst.size(), 6ul); std::vector indices_sol {1, 2, 3}; for (size_t i = 0; i < inst.size(); ++i) { ASSERT_EQ(inst[i].index, indices_sol[i % 3]); } } TEST(SparsePage, PushCSCAfterTranspose) { size_t constexpr kPageSize = 1024, kEntriesPerCol = 3; size_t constexpr kEntries = kPageSize * kEntriesPerCol * 2; std::unique_ptr dmat = CreateSparsePageDMatrix(kEntries); const int ncols = dmat->Info().num_col_; SparsePage page; // Consolidated sparse page for (const auto &batch : dmat->GetBatches()) { // Transpose each batch and push SparsePage tmp = batch.GetTranspose(ncols, common::OmpGetNumThreads(0)); page.PushCSC(tmp); } // Make sure that the final sparse page has the right number of entries ASSERT_EQ(kEntries, page.data.Size()); page.SortRows(common::OmpGetNumThreads(0)); auto v = page.GetView(); for (size_t i = 0; i < v.Size(); ++i) { auto column = v[i]; for (size_t j = 1; j < column.size(); ++j) { ASSERT_GE(column[j].fvalue, column[j-1].fvalue); } } } TEST(SparsePage, SortIndices) { auto p_fmat = RandomDataGenerator{100, 10, 0.6}.GenerateDMatrix(); auto n_threads = common::OmpGetNumThreads(0); SparsePage copy; for (auto const& page : p_fmat->GetBatches()) { ASSERT_TRUE(page.IsIndicesSorted(n_threads)); copy.Push(page); } ASSERT_TRUE(copy.IsIndicesSorted(n_threads)); for (size_t ridx = 0; ridx < copy.Size(); ++ridx) { auto beg = copy.offset.HostVector()[ridx]; auto end = copy.offset.HostVector()[ridx + 1]; auto& h_data = copy.data.HostVector(); if (end - beg >= 2) { std::swap(h_data[beg], h_data[end - 1]); } } ASSERT_FALSE(copy.IsIndicesSorted(n_threads)); copy.SortIndices(n_threads); ASSERT_TRUE(copy.IsIndicesSorted(n_threads)); } TEST(DMatrix, Uri) { size_t constexpr kRows {16}; size_t constexpr kCols {8}; std::vector data (kRows * kCols); for (size_t i = 0; i < kRows * kCols; ++i) { data[i] = i; } dmlc::TemporaryDirectory tmpdir; std::string path = tmpdir.path + "/small.csv"; std::ofstream fout(path); size_t i = 0; for (size_t r = 0; r < kRows; ++r) { for (size_t c = 0; c < kCols; ++c) { fout << data[i]; i++; if (c != kCols - 1) { fout << ","; } } fout << "\n"; } fout.flush(); fout.close(); std::unique_ptr dmat; // FIXME(trivialfis): Enable the following test by restricting csv parser in dmlc-core. // EXPECT_THROW(dmat.reset(DMatrix::Load(path, false, true)), dmlc::Error); std::string uri = path + "?format=csv"; dmat.reset(DMatrix::Load(uri, false, true)); ASSERT_EQ(dmat->Info().num_col_, kCols); ASSERT_EQ(dmat->Info().num_row_, kRows); } } // namespace xgboost