d9a47794a5
* Fix CPU hist init for sparse dataset. * Implement sparse histogram cut. * Allow empty features. * Fix windows build, don't use sparse in distributed environment. * Comments. * Smaller threshold. * Fix windows omp. * Fix msvc lambda capture. * Fix MSVC macro. * Fix MSVC initialization list. * Fix MSVC initialization list x2. * Preserve categorical feature behavior. * Rename matrix to sparse cuts. * Reuse UseGroup. * Check for categorical data when adding cut. Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu> * Sanity check. * Fix comments. * Fix comment.
85 lines
2.3 KiB
C++
85 lines
2.3 KiB
C++
#include <gtest/gtest.h>
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#include <dmlc/filesystem.h>
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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<size_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 (int64_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->GetRowBatches()) {
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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 (int 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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} // namespace xgboost
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