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xgboost/tests/cpp/data/test_simple_dmatrix.cc

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/**
* Copyright 2016-2023 by XGBoost Contributors
*/
#include <xgboost/data.h>
#include <array> // std::array
#include <limits> // std::numeric_limits
#include <memory> // std::unique_ptr
#include "../../../src/data/adapter.h" // ArrayAdapter
#include "../../../src/data/simple_dmatrix.h" // SimpleDMatrix
#include "../filesystem.h" // dmlc::TemporaryDirectory
#include "../helpers.h" // RandomDataGenerator,CreateSimpleTestData
#include "xgboost/base.h"
#include "xgboost/host_device_vector.h" // HostDeviceVector
#include "xgboost/string_view.h" // StringView
using namespace xgboost; // NOLINT
TEST(SimpleDMatrix, MetaInfo) {
dmlc::TemporaryDirectory tempdir;
const std::string tmp_file = tempdir.path + "/simple.libsvm";
CreateSimpleTestData(tmp_file);
xgboost::DMatrix *dmat = xgboost::DMatrix::Load(tmp_file);
// Test the metadata that was parsed
EXPECT_EQ(dmat->Info().num_row_, 2);
EXPECT_EQ(dmat->Info().num_col_, 5);
EXPECT_EQ(dmat->Info().num_nonzero_, 6);
EXPECT_EQ(dmat->Info().labels.Size(), dmat->Info().num_row_);
EXPECT_EQ(dmat->Info().data_split_mode, DataSplitMode::kRow);
delete dmat;
}
TEST(SimpleDMatrix, RowAccess) {
dmlc::TemporaryDirectory tempdir;
const std::string tmp_file = tempdir.path + "/simple.libsvm";
CreateSimpleTestData(tmp_file);
xgboost::DMatrix *dmat = xgboost::DMatrix::Load(tmp_file, false);
// Loop over the batches and count the records
int64_t row_count = 0;
for (auto &batch : dmat->GetBatches<xgboost::SparsePage>()) {
row_count += batch.Size();
}
EXPECT_EQ(row_count, dmat->Info().num_row_);
// Test the data read into the first row
auto &batch = *dmat->GetBatches<xgboost::SparsePage>().begin();
auto page = batch.GetView();
auto first_row = page[0];
ASSERT_EQ(first_row.size(), 3);
EXPECT_EQ(first_row[2].index, 2);
EXPECT_EQ(first_row[2].fvalue, 20);
delete dmat;
}
TEST(SimpleDMatrix, ColAccessWithoutBatches) {
dmlc::TemporaryDirectory tempdir;
const std::string tmp_file = tempdir.path + "/simple.libsvm";
CreateSimpleTestData(tmp_file);
xgboost::DMatrix *dmat = xgboost::DMatrix::Load(tmp_file);
ASSERT_TRUE(dmat->SingleColBlock());
// Loop over the batches and assert the data is as expected
int64_t num_col_batch = 0;
for (const auto &batch : dmat->GetBatches<xgboost::SortedCSCPage>()) {
num_col_batch += 1;
EXPECT_EQ(batch.Size(), dmat->Info().num_col_)
<< "Expected batch size = number of cells as #batches is 1.";
}
EXPECT_EQ(num_col_batch, 1) << "Expected number of batches to be 1";
delete dmat;
}
TEST(SimpleDMatrix, Empty) {
std::vector<float> data{};
std::vector<unsigned> feature_idx = {};
std::vector<size_t> row_ptr = {};
data::CSRAdapter csr_adapter(row_ptr.data(), feature_idx.data(), data.data(),
0, 0, 0);
std::unique_ptr<data::SimpleDMatrix> dmat(new data::SimpleDMatrix(
&csr_adapter, std::numeric_limits<float>::quiet_NaN(), 1));
CHECK_EQ(dmat->Info().num_nonzero_, 0);
CHECK_EQ(dmat->Info().num_row_, 0);
CHECK_EQ(dmat->Info().num_col_, 0);
for (auto &batch : dmat->GetBatches<SparsePage>()) {
CHECK_EQ(batch.Size(), 0);
}
data::DenseAdapter dense_adapter(nullptr, 0, 0);
dmat.reset( new data::SimpleDMatrix(&dense_adapter,
std::numeric_limits<float>::quiet_NaN(), 1) );
CHECK_EQ(dmat->Info().num_nonzero_, 0);
CHECK_EQ(dmat->Info().num_row_, 0);
CHECK_EQ(dmat->Info().num_col_, 0);
for (auto &batch : dmat->GetBatches<SparsePage>()) {
CHECK_EQ(batch.Size(), 0);
}
data::CSCAdapter csc_adapter(nullptr, nullptr, nullptr, 0, 0);
dmat.reset(new data::SimpleDMatrix(
&csc_adapter, std::numeric_limits<float>::quiet_NaN(), 1));
CHECK_EQ(dmat->Info().num_nonzero_, 0);
CHECK_EQ(dmat->Info().num_row_, 0);
CHECK_EQ(dmat->Info().num_col_, 0);
for (auto &batch : dmat->GetBatches<SparsePage>()) {
CHECK_EQ(batch.Size(), 0);
}
}
TEST(SimpleDMatrix, MissingData) {
std::vector<float> data{0.0, std::nanf(""), 1.0};
std::vector<unsigned> feature_idx = {0, 1, 0};
std::vector<size_t> row_ptr = {0, 2, 3};
data::CSRAdapter adapter(row_ptr.data(), feature_idx.data(), data.data(), 2,
3, 2);
std::unique_ptr<data::SimpleDMatrix> dmat{new data::SimpleDMatrix{
&adapter, std::numeric_limits<float>::quiet_NaN(), 1}};
CHECK_EQ(dmat->Info().num_nonzero_, 2);
dmat.reset(new data::SimpleDMatrix(&adapter, 1.0, 1));
CHECK_EQ(dmat->Info().num_nonzero_, 1);
{
data[1] = std::numeric_limits<float>::infinity();
data::DenseAdapter adapter(data.data(), data.size(), 1);
EXPECT_THROW(data::SimpleDMatrix dmat(
&adapter, std::numeric_limits<float>::quiet_NaN(), -1),
dmlc::Error);
}
}
TEST(SimpleDMatrix, EmptyRow) {
std::vector<float> data{0.0, 1.0};
std::vector<unsigned> feature_idx = {0, 1};
std::vector<size_t> row_ptr = {0, 2, 2};
data::CSRAdapter adapter(row_ptr.data(), feature_idx.data(), data.data(), 2,
2, 2);
data::SimpleDMatrix dmat(&adapter, std::numeric_limits<float>::quiet_NaN(),
1);
CHECK_EQ(dmat.Info().num_nonzero_, 2);
CHECK_EQ(dmat.Info().num_row_, 2);
CHECK_EQ(dmat.Info().num_col_, 2);
}
TEST(SimpleDMatrix, FromDense) {
int m = 3;
int n = 2;
std::vector<float> data = {1, 2, 3, 4, 5, 6};
data::DenseAdapter adapter(data.data(), m, n);
data::SimpleDMatrix dmat(&adapter, std::numeric_limits<float>::quiet_NaN(),
-1);
EXPECT_EQ(dmat.Info().num_col_, 2);
EXPECT_EQ(dmat.Info().num_row_, 3);
EXPECT_EQ(dmat.Info().num_nonzero_, 6);
for (auto &batch : dmat.GetBatches<SparsePage>()) {
auto page = batch.GetView();
for (auto i = 0ull; i < batch.Size(); i++) {
auto inst = page[i];
for (auto j = 0ull; j < inst.size(); j++) {
EXPECT_EQ(inst[j].fvalue, data[i * n + j]);
EXPECT_EQ(inst[j].index, j);
}
}
}
}
TEST(SimpleDMatrix, FromCSC) {
std::vector<float> data = {1, 3, 2, 4, 5};
std::vector<unsigned> row_idx = {0, 1, 0, 1, 2};
std::vector<size_t> col_ptr = {0, 2, 5};
data::CSCAdapter adapter(col_ptr.data(), row_idx.data(), data.data(), 2, 3);
data::SimpleDMatrix dmat(&adapter, std::numeric_limits<float>::quiet_NaN(),
-1);
EXPECT_EQ(dmat.Info().num_col_, 2);
EXPECT_EQ(dmat.Info().num_row_, 3);
EXPECT_EQ(dmat.Info().num_nonzero_, 5);
auto &batch = *dmat.GetBatches<SparsePage>().begin();
auto page = batch.GetView();
auto inst = page[0];
EXPECT_EQ(inst[0].fvalue, 1);
EXPECT_EQ(inst[0].index, 0);
EXPECT_EQ(inst[1].fvalue, 2);
EXPECT_EQ(inst[1].index, 1);
inst = page[1];
EXPECT_EQ(inst[0].fvalue, 3);
EXPECT_EQ(inst[0].index, 0);
EXPECT_EQ(inst[1].fvalue, 4);
EXPECT_EQ(inst[1].index, 1);
inst = page[2];
EXPECT_EQ(inst[0].fvalue, 5);
EXPECT_EQ(inst[0].index, 1);
}
TEST(SimpleDMatrix, FromFile) {
dmlc::TemporaryDirectory tempdir;
std::string filename = tempdir.path + "test.libsvm";
CreateBigTestData(filename, 3 * 5);
// Add an empty row at the end of the matrix
{
std::ofstream fo(filename, std::ios::app | std::ios::out);
fo << "0\n";
}
constexpr size_t kExpectedNumRow = 6;
std::unique_ptr<dmlc::Parser<uint32_t>> parser(
dmlc::Parser<uint32_t>::Create(filename.c_str(), 0, 1, "auto"));
auto verify_batch = [kExpectedNumRow](SparsePage const &page) {
auto batch = page.GetView();
EXPECT_EQ(batch.Size(), kExpectedNumRow);
EXPECT_EQ(page.offset.HostVector(),
std::vector<bst_row_t>({0, 3, 6, 9, 12, 15, 15}));
EXPECT_EQ(page.base_rowid, 0);
for (auto i = 0ull; i < batch.Size() - 1; i++) {
if (i % 2 == 0) {
EXPECT_EQ(batch[i][0].index, 0);
EXPECT_EQ(batch[i][1].index, 1);
EXPECT_EQ(batch[i][2].index, 2);
} else {
EXPECT_EQ(batch[i][0].index, 0);
EXPECT_EQ(batch[i][1].index, 3);
EXPECT_EQ(batch[i][2].index, 4);
}
}
};
constexpr bst_feature_t kCols = 5;
data::FileAdapter adapter(parser.get());
data::SimpleDMatrix dmat(&adapter, std::numeric_limits<float>::quiet_NaN(),
1);
ASSERT_EQ(dmat.Info().num_col_, kCols);
for (auto &batch : dmat.GetBatches<SparsePage>()) {
verify_batch(batch);
}
}
TEST(SimpleDMatrix, Slice) {
size_t constexpr kRows {16};
size_t constexpr kCols {8};
size_t constexpr kClasses {3};
auto p_m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true);
auto& weights = p_m->Info().weights_.HostVector();
weights.resize(kRows);
std::iota(weights.begin(), weights.end(), 0.0f);
auto& lower = p_m->Info().labels_lower_bound_.HostVector();
auto& upper = p_m->Info().labels_upper_bound_.HostVector();
lower.resize(kRows);
upper.resize(kRows);
std::iota(lower.begin(), lower.end(), 0.0f);
std::iota(upper.begin(), upper.end(), 1.0f);
auto& margin = p_m->Info().base_margin_;
margin = decltype(p_m->Info().base_margin_){{kRows, kClasses}, Context::kCpuId};
std::array<int32_t, 3> ridxs {1, 3, 5};
std::unique_ptr<DMatrix> out { p_m->Slice(ridxs) };
ASSERT_EQ(out->Info().labels.Size(), ridxs.size());
ASSERT_EQ(out->Info().labels_lower_bound_.Size(), ridxs.size());
ASSERT_EQ(out->Info().labels_upper_bound_.Size(), ridxs.size());
ASSERT_EQ(out->Info().base_margin_.Size(), ridxs.size() * kClasses);
for (auto const& in_batch : p_m->GetBatches<SparsePage>()) {
auto in_page = in_batch.GetView();
for (auto const &out_batch : out->GetBatches<SparsePage>()) {
auto out_page = out_batch.GetView();
for (size_t i = 0; i < ridxs.size(); ++i) {
auto ridx = ridxs[i];
auto out_inst = out_page[i];
auto in_inst = in_page[ridx];
ASSERT_EQ(out_inst.size(), in_inst.size()) << i;
for (size_t j = 0; j < in_inst.size(); ++j) {
ASSERT_EQ(in_inst[j].fvalue, out_inst[j].fvalue);
ASSERT_EQ(in_inst[j].index, out_inst[j].index);
}
ASSERT_EQ(p_m->Info().labels_lower_bound_.HostVector().at(ridx),
out->Info().labels_lower_bound_.HostVector().at(i));
ASSERT_EQ(p_m->Info().labels_upper_bound_.HostVector().at(ridx),
out->Info().labels_upper_bound_.HostVector().at(i));
ASSERT_EQ(p_m->Info().weights_.HostVector().at(ridx),
out->Info().weights_.HostVector().at(i));
auto out_margin = out->Info().base_margin_.View(Context::kCpuId);
auto in_margin = margin.View(Context::kCpuId);
for (size_t j = 0; j < kClasses; ++j) {
ASSERT_EQ(out_margin(i, j), in_margin(ridx, j));
}
}
}
}
ASSERT_EQ(out->Info().num_col_, out->Info().num_col_);
ASSERT_EQ(out->Info().num_row_, ridxs.size());
ASSERT_EQ(out->Info().num_nonzero_, ridxs.size() * kCols); // dense
{
HostDeviceVector<float> data;
auto arr_str = RandomDataGenerator{kRows, kCols, 0.0}.GenerateArrayInterface(&data);
auto adapter = data::ArrayAdapter{StringView{arr_str}};
auto n_threads = 2;
std::unique_ptr<DMatrix> p_fmat{
DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), n_threads, "")};
std::unique_ptr<DMatrix> slice{p_fmat->Slice(ridxs)};
ASSERT_LE(slice->Ctx()->Threads(), n_threads);
}
}
TEST(SimpleDMatrix, SliceCol) {
size_t constexpr kRows {16};
size_t constexpr kCols {8};
size_t constexpr kClasses {3};
auto p_m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true);
auto& weights = p_m->Info().weights_.HostVector();
weights.resize(kRows);
std::iota(weights.begin(), weights.end(), 0.0f);
auto& lower = p_m->Info().labels_lower_bound_.HostVector();
auto& upper = p_m->Info().labels_upper_bound_.HostVector();
lower.resize(kRows);
upper.resize(kRows);
std::iota(lower.begin(), lower.end(), 0.0f);
std::iota(upper.begin(), upper.end(), 1.0f);
auto& margin = p_m->Info().base_margin_;
margin = decltype(p_m->Info().base_margin_){{kRows, kClasses}, Context::kCpuId};
auto constexpr kSlices {2};
auto constexpr kSliceSize {4};
for (auto slice = 0; slice < kSlices; slice++) {
std::unique_ptr<DMatrix> out { p_m->SliceCol(kSlices, slice) };
ASSERT_EQ(out->Info().labels.Size(), kRows);
ASSERT_EQ(out->Info().labels_lower_bound_.Size(), kRows);
ASSERT_EQ(out->Info().labels_upper_bound_.Size(), kRows);
ASSERT_EQ(out->Info().base_margin_.Size(), kRows * kClasses);
for (auto const &in_batch : p_m->GetBatches<SparsePage>()) {
auto in_page = in_batch.GetView();
for (auto const &out_batch : out->GetBatches<SparsePage>()) {
auto out_page = out_batch.GetView();
for (size_t i = 0; i < kRows; ++i) {
auto out_inst = out_page[i];
auto in_inst = in_page[i];
ASSERT_EQ(out_inst.size() * 2, in_inst.size()) << i;
for (size_t j = 0; j < kSliceSize; ++j) {
auto const slice_start = kSliceSize * slice;
ASSERT_EQ(in_inst[slice_start + j].fvalue, out_inst[j].fvalue);
ASSERT_EQ(in_inst[slice_start + j].index, out_inst[j].index);
}
ASSERT_EQ(p_m->Info().labels_lower_bound_.HostVector().at(i),
out->Info().labels_lower_bound_.HostVector().at(i));
ASSERT_EQ(p_m->Info().labels_upper_bound_.HostVector().at(i),
out->Info().labels_upper_bound_.HostVector().at(i));
ASSERT_EQ(p_m->Info().weights_.HostVector().at(i), out->Info().weights_.HostVector().at(i));
auto out_margin = out->Info().base_margin_.View(Context::kCpuId);
auto in_margin = margin.View(Context::kCpuId);
for (size_t j = 0; j < kClasses; ++j) {
ASSERT_EQ(out_margin(i, j), in_margin(i, j));
}
}
}
}
ASSERT_EQ(out->Info().num_col_, out->Info().num_col_);
ASSERT_EQ(out->Info().num_row_, kRows);
ASSERT_EQ(out->Info().num_nonzero_, kRows * kSliceSize); // dense
ASSERT_EQ(out->Info().data_split_mode, DataSplitMode::kCol);
}
}
TEST(SimpleDMatrix, SaveLoadBinary) {
dmlc::TemporaryDirectory tempdir;
const std::string tmp_file = tempdir.path + "/simple.libsvm";
CreateSimpleTestData(tmp_file);
xgboost::DMatrix * dmat = xgboost::DMatrix::Load(tmp_file);
data::SimpleDMatrix *simple_dmat = dynamic_cast<data::SimpleDMatrix*>(dmat);
const std::string tmp_binfile = tempdir.path + "/csr_source.binary";
simple_dmat->SaveToLocalFile(tmp_binfile);
xgboost::DMatrix * dmat_read = xgboost::DMatrix::Load(tmp_binfile);
EXPECT_EQ(dmat->Info().num_col_, dmat_read->Info().num_col_);
EXPECT_EQ(dmat->Info().num_row_, dmat_read->Info().num_row_);
EXPECT_EQ(dmat->Info().num_row_, dmat_read->Info().num_row_);
// Test we have non-empty batch
EXPECT_EQ(dmat->GetBatches<xgboost::SparsePage>().begin().AtEnd(), false);
auto row_iter = dmat->GetBatches<xgboost::SparsePage>().begin();
auto row_iter_read = dmat_read->GetBatches<xgboost::SparsePage>().begin();
// Test the data read into the first row
auto first_row = (*row_iter).GetView()[0];
auto first_row_read = (*row_iter_read).GetView()[0];
EXPECT_EQ(first_row.size(), first_row_read.size());
EXPECT_EQ(first_row[2].index, first_row_read[2].index);
EXPECT_EQ(first_row[2].fvalue, first_row_read[2].fvalue);
delete dmat;
delete dmat_read;
}
TEST(SimpleDMatrix, Threads) {
size_t constexpr kRows{16};
size_t constexpr kCols{8};
HostDeviceVector<float> data;
auto arr_str = RandomDataGenerator{kRows, kCols, 0.0}.GenerateArrayInterface(&data);
auto adapter = data::ArrayAdapter{StringView{arr_str}};
std::unique_ptr<DMatrix> p_fmat{
DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), 0, "")};
ASSERT_EQ(p_fmat->Ctx()->Threads(), AllThreadsForTest());
}
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