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Fix CPU hist init for sparse dataset. (#4625)

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* 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.
This commit is contained in:
Jiaming Yuan
2019-07-04 19:27:03 -04:00
committed by Philip Hyunsu Cho
parent b7a1f22d24
commit d9a47794a5
33 changed files with 681 additions and 299 deletions
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102
tests/cpp/common/test_hist_util.cc
View File

@@ -9,15 +9,7 @@
namespace xgboost {
namespace common {
class HistCutMatrixMock : public HistCutMatrix {
public:
size_t SearchGroupIndFromBaseRow(
std::vector<bst_uint> const& group_ptr, size_t const base_rowid) {
return HistCutMatrix::SearchGroupIndFromBaseRow(group_ptr, base_rowid);
}
};
TEST(HistCutMatrix, SearchGroupInd) {
TEST(CutsBuilder, SearchGroupInd) {
size_t constexpr kNumGroups = 4;
size_t constexpr kNumRows = 17;
size_t constexpr kNumCols = 15;
@@ -34,18 +26,102 @@ TEST(HistCutMatrix, SearchGroupInd) {
p_mat->Info().SetInfo(
"group", group.data(), DataType::kUInt32, kNumGroups);
HistCutMatrixMock hmat;
HistogramCuts hmat;
size_t group_ind = hmat.SearchGroupIndFromBaseRow(p_mat->Info().group_ptr_, 0);
size_t group_ind = CutsBuilder::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 0);
ASSERT_EQ(group_ind, 0);
group_ind = hmat.SearchGroupIndFromBaseRow(p_mat->Info().group_ptr_, 5);
group_ind = CutsBuilder::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 5);
ASSERT_EQ(group_ind, 2);
EXPECT_ANY_THROW(hmat.SearchGroupIndFromBaseRow(p_mat->Info().group_ptr_, 17));
EXPECT_ANY_THROW(CutsBuilder::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 17));
delete pp_mat;
}
namespace {
class SparseCutsWrapper : public SparseCuts {
public:
std::vector<uint32_t> const& ColPtrs() const { return p_cuts_->Ptrs(); }
std::vector<float> const& ColValues() const { return p_cuts_->Values(); }
};
} // anonymous namespace
TEST(SparseCuts, SingleThreadedBuild) {
size_t constexpr kRows = 267;
size_t constexpr kCols = 31;
size_t constexpr kBins = 256;
// Dense matrix.
auto pp_mat = CreateDMatrix(kRows, kCols, 0);
DMatrix* p_fmat = (*pp_mat).get();
common::GHistIndexMatrix hmat;
hmat.Init(p_fmat, kBins);
HistogramCuts cuts;
SparseCuts indices(&cuts);
auto const& page = *(p_fmat->GetColumnBatches().begin());
indices.SingleThreadBuild(page, p_fmat->Info(), kBins, false, 0, page.Size(), 0);
ASSERT_EQ(hmat.cut.Ptrs().size(), cuts.Ptrs().size());
ASSERT_EQ(hmat.cut.Ptrs(), cuts.Ptrs());
ASSERT_EQ(hmat.cut.Values(), cuts.Values());
ASSERT_EQ(hmat.cut.MinValues(), cuts.MinValues());
delete pp_mat;
}
TEST(SparseCuts, MultiThreadedBuild) {
size_t constexpr kRows = 17;
size_t constexpr kCols = 15;
size_t constexpr kBins = 255;
omp_ulong ori_nthreads = omp_get_max_threads();
omp_set_num_threads(16);
auto Compare =
#if defined(_MSC_VER) // msvc fails to capture
[kBins](DMatrix* p_fmat) {
#else
[](DMatrix* p_fmat) {
#endif
HistogramCuts threaded_container;
SparseCuts threaded_indices(&threaded_container);
threaded_indices.Build(p_fmat, kBins);
HistogramCuts container;
SparseCuts indices(&container);
auto const& page = *(p_fmat->GetColumnBatches().begin());
indices.SingleThreadBuild(page, p_fmat->Info(), kBins, false, 0, page.Size(), 0);
ASSERT_EQ(container.Ptrs().size(), threaded_container.Ptrs().size());
ASSERT_EQ(container.Values().size(), threaded_container.Values().size());
for (uint32_t i = 0; i < container.Ptrs().size(); ++i) {
ASSERT_EQ(container.Ptrs()[i], threaded_container.Ptrs()[i]);
}
for (uint32_t i = 0; i < container.Values().size(); ++i) {
ASSERT_EQ(container.Values()[i], threaded_container.Values()[i]);
}
};
{
auto pp_mat = CreateDMatrix(kRows, kCols, 0);
DMatrix* p_fmat = (*pp_mat).get();
Compare(p_fmat);
delete pp_mat;
}
{
auto pp_mat = CreateDMatrix(kRows, kCols, 0.0001);
DMatrix* p_fmat = (*pp_mat).get();
Compare(p_fmat);
delete pp_mat;
}
omp_set_num_threads(ori_nthreads);
}
} // namespace common
} // namespace xgboost