97abcc7ee2
* Extract interaction constraints from split evaluator. The reason for doing so is mostly for model IO, where num_feature and interaction_constraints are copied in split evaluator. Also interaction constraint by itself is a feature selector, acting like column sampler and it's inefficient to bury it deep in the evaluator chain. Lastly removing one another copied parameter is a win. * Enable inc for approx tree method. As now the implementation is spited up from evaluator class, it's also enabled for approx method. * Removing obsoleted code in colmaker. They are never documented nor actually used in real world. Also there isn't a single test for those code blocks. * Unifying the types used for row and column. As the size of input dataset is marching to billion, incorrect use of int is subject to overflow, also singed integer overflow is undefined behaviour. This PR starts the procedure for unifying used index type to unsigned integers. There's optimization that can utilize this undefined behaviour, but after some testings I don't see the optimization is beneficial to XGBoost.
127 lines
3.3 KiB
C++
127 lines
3.3 KiB
C++
#include <gtest/gtest.h>
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#include <dmlc/filesystem.h>
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#include <fstream>
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#include <memory>
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#include <vector>
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#include "xgboost/data.h"
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#include "../helpers.h"
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namespace xgboost {
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TEST(SparsePage, PushCSC) {
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std::vector<bst_row_t> offset {0};
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std::vector<Entry> data;
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SparsePage page;
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page.offset.HostVector() = offset;
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page.data.HostVector() = data;
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offset = {0, 1, 4};
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for (size_t i = 0; i < offset.back(); ++i) {
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data.emplace_back(Entry(i, 0.1f));
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}
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SparsePage other;
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other.offset.HostVector() = offset;
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other.data.HostVector() = data;
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page.PushCSC(other);
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ASSERT_EQ(page.offset.HostVector().size(), offset.size());
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ASSERT_EQ(page.data.HostVector().size(), data.size());
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for (size_t i = 0; i < offset.size(); ++i) {
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ASSERT_EQ(page.offset.HostVector()[i], offset[i]);
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}
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for (size_t i = 0; i < data.size(); ++i) {
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ASSERT_EQ(page.data.HostVector()[i].index, data[i].index);
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}
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page.PushCSC(other);
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ASSERT_EQ(page.offset.HostVector().size(), offset.size());
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ASSERT_EQ(page.data.Size(), data.size() * 2);
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for (size_t i = 0; i < offset.size(); ++i) {
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ASSERT_EQ(page.offset.HostVector()[i], offset[i] * 2);
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}
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auto inst = page[0];
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ASSERT_EQ(inst.size(), 2);
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for (auto entry : inst) {
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ASSERT_EQ(entry.index, 0);
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}
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inst = page[1];
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ASSERT_EQ(inst.size(), 6);
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std::vector<size_t> indices_sol {1, 2, 3};
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for (size_t i = 0; i < inst.size(); ++i) {
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ASSERT_EQ(inst[i].index, indices_sol[i % 3]);
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}
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}
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TEST(SparsePage, PushCSCAfterTranspose) {
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dmlc::TemporaryDirectory tmpdir;
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std::string filename = tmpdir.path + "/big.libsvm";
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const int n_entries = 9;
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std::unique_ptr<DMatrix> dmat =
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CreateSparsePageDMatrix(n_entries, 64UL, filename);
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const int ncols = dmat->Info().num_col_;
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SparsePage page; // Consolidated sparse page
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for (const auto &batch : dmat->GetBatches<xgboost::SparsePage>()) {
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// Transpose each batch and push
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SparsePage tmp = batch.GetTranspose(ncols);
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page.PushCSC(tmp);
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}
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// Make sure that the final sparse page has the right number of entries
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ASSERT_EQ(n_entries, page.data.Size());
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// The feature value for a feature in each row should be identical, as that is
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// how the dmatrix has been created
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for (size_t i = 0; i < page.Size(); ++i) {
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auto inst = page[i];
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for (size_t j = 1; j < inst.size(); ++j) {
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ASSERT_EQ(inst[0].fvalue, inst[j].fvalue);
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}
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}
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}
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TEST(DMatrix, Uri) {
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size_t constexpr kRows {16};
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size_t constexpr kCols {8};
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std::vector<float> data (kRows * kCols);
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for (size_t i = 0; i < kRows * kCols; ++i) {
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data[i] = i;
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}
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dmlc::TemporaryDirectory tmpdir;
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std::string path = tmpdir.path + "/small.csv";
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std::ofstream fout(path);
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ASSERT_TRUE(fout);
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size_t i = 0;
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for (size_t r = 0; r < kRows; ++r) {
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for (size_t c = 0; c < kCols; ++c) {
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fout << data[i];
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i++;
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if (c != kCols - 1) {
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fout << ",";
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}
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}
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fout << "\n";
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}
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fout.flush();
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fout.close();
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std::unique_ptr<DMatrix> dmat;
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// FIXME(trivialfis): Enable the following test by restricting csv parser in dmlc-core.
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// EXPECT_THROW(dmat.reset(DMatrix::Load(path, false, true)), dmlc::Error);
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std::string uri = path + "?format=csv";
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dmat.reset(DMatrix::Load(uri, false, true));
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ASSERT_EQ(dmat->Info().num_col_, kCols);
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ASSERT_EQ(dmat->Info().num_row_, kRows);
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}
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} // namespace xgboost
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