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.
132 lines
5.1 KiB
C++
132 lines
5.1 KiB
C++
// Copyright by Contributors
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#include <dmlc/io.h>
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#include <dmlc/filesystem.h>
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#include <xgboost/data.h>
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#include <string>
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#include <memory>
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#include "../../../src/data/simple_csr_source.h"
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#include "../helpers.h"
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TEST(MetaInfo, GetSet) {
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xgboost::MetaInfo info;
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double double2[2] = {1.0, 2.0};
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EXPECT_EQ(info.GetRoot(1), 0)
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<< "When no root_index is given, was expecting default value 0";
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info.SetInfo("root_index", double2, xgboost::kDouble, 2);
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EXPECT_EQ(info.GetRoot(1), 2.0f);
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EXPECT_EQ(info.labels_.Size(), 0);
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info.SetInfo("label", double2, xgboost::kFloat32, 2);
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EXPECT_EQ(info.labels_.Size(), 2);
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float float2[2] = {1.0f, 2.0f};
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EXPECT_EQ(info.GetWeight(1), 1.0f)
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<< "When no weights are given, was expecting default value 1";
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info.SetInfo("weight", float2, xgboost::kFloat32, 2);
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EXPECT_EQ(info.GetWeight(1), 2.0f);
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uint32_t uint32_t2[2] = {1U, 2U};
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EXPECT_EQ(info.base_margin_.Size(), 0);
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info.SetInfo("base_margin", uint32_t2, xgboost::kUInt32, 2);
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EXPECT_EQ(info.base_margin_.Size(), 2);
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uint64_t uint64_t2[2] = {1U, 2U};
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EXPECT_EQ(info.group_ptr_.size(), 0);
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info.SetInfo("group", uint64_t2, xgboost::kUInt64, 2);
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ASSERT_EQ(info.group_ptr_.size(), 3);
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EXPECT_EQ(info.group_ptr_[2], 3);
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info.Clear();
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ASSERT_EQ(info.group_ptr_.size(), 0);
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}
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TEST(MetaInfo, SaveLoadBinary) {
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xgboost::MetaInfo info;
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double vals[2] = {1.0, 2.0};
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info.SetInfo("label", vals, xgboost::kDouble, 2);
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info.num_row_ = 2;
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info.num_col_ = 1;
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dmlc::TemporaryDirectory tempdir;
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const std::string tmp_file = tempdir.path + "/metainfo.binary";
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{
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std::unique_ptr<dmlc::Stream> fs {
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dmlc::Stream::Create(tmp_file.c_str(), "w")
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};
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info.SaveBinary(fs.get());
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}
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ASSERT_EQ(GetFileSize(tmp_file), 92)
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<< "Expected saved binary file size to be same as object size";
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std::unique_ptr<dmlc::Stream> fs {
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dmlc::Stream::Create(tmp_file.c_str(), "r")
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};
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xgboost::MetaInfo inforead;
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inforead.LoadBinary(fs.get());
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EXPECT_EQ(inforead.labels_.HostVector(), info.labels_.HostVector());
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EXPECT_EQ(inforead.num_col_, info.num_col_);
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EXPECT_EQ(inforead.num_row_, info.num_row_);
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}
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TEST(MetaInfo, LoadQid) {
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dmlc::TemporaryDirectory tempdir;
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std::string tmp_file = tempdir.path + "/qid_test.libsvm";
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{
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std::unique_ptr<dmlc::Stream> fs(
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dmlc::Stream::Create(tmp_file.c_str(), "w"));
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dmlc::ostream os(fs.get());
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os << R"qid(3 qid:1 1:1 2:1 3:0 4:0.2 5:0
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2 qid:1 1:0 2:0 3:1 4:0.1 5:1
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1 qid:1 1:0 2:1 3:0 4:0.4 5:0
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1 qid:1 1:0 2:0 3:1 4:0.3 5:0
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1 qid:2 1:0 2:0 3:1 4:0.2 5:0
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2 qid:2 1:1 2:0 3:1 4:0.4 5:0
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1 qid:2 1:0 2:0 3:1 4:0.1 5:0
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1 qid:2 1:0 2:0 3:1 4:0.2 5:0
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2 qid:3 1:0 2:0 3:1 4:0.1 5:1
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3 qid:3 1:1 2:1 3:0 4:0.3 5:0
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4 qid:3 1:1 2:0 3:0 4:0.4 5:1
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1 qid:3 1:0 2:1 3:1 4:0.5 5:0)qid";
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os.set_stream(nullptr);
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}
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std::unique_ptr<xgboost::DMatrix> dmat(
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xgboost::DMatrix::Load(tmp_file, true, false, "libsvm"));
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const xgboost::MetaInfo& info = dmat->Info();
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const std::vector<xgboost::bst_uint> expected_group_ptr{0, 4, 8, 12};
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CHECK(info.group_ptr_ == expected_group_ptr);
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const std::vector<xgboost::bst_row_t> expected_offset{
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0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60
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};
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const std::vector<xgboost::Entry> expected_data{
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xgboost::Entry(1, 1), xgboost::Entry(2, 1), xgboost::Entry(3, 0),
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xgboost::Entry(4, 0.2), xgboost::Entry(5, 0), xgboost::Entry(1, 0),
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xgboost::Entry(2, 0), xgboost::Entry(3, 1), xgboost::Entry(4, 0.1),
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xgboost::Entry(5, 1), xgboost::Entry(1, 0), xgboost::Entry(2, 1),
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xgboost::Entry(3, 0), xgboost::Entry(4, 0.4), xgboost::Entry(5, 0),
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xgboost::Entry(1, 0), xgboost::Entry(2, 0), xgboost::Entry(3, 1),
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xgboost::Entry(4, 0.3), xgboost::Entry(5, 0), xgboost::Entry(1, 0),
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xgboost::Entry(2, 0), xgboost::Entry(3, 1), xgboost::Entry(4, 0.2),
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xgboost::Entry(5, 0), xgboost::Entry(1, 1), xgboost::Entry(2, 0),
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xgboost::Entry(3, 1), xgboost::Entry(4, 0.4), xgboost::Entry(5, 0),
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xgboost::Entry(1, 0), xgboost::Entry(2, 0), xgboost::Entry(3, 1),
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xgboost::Entry(4, 0.1), xgboost::Entry(5, 0), xgboost::Entry(1, 0),
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xgboost::Entry(2, 0), xgboost::Entry(3, 1), xgboost::Entry(4, 0.2),
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xgboost::Entry(5, 0), xgboost::Entry(1, 0), xgboost::Entry(2, 0),
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xgboost::Entry(3, 1), xgboost::Entry(4, 0.1), xgboost::Entry(5, 1),
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xgboost::Entry(1, 1), xgboost::Entry(2, 1), xgboost::Entry(3, 0),
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xgboost::Entry(4, 0.3), xgboost::Entry(5, 0), xgboost::Entry(1, 1),
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xgboost::Entry(2, 0), xgboost::Entry(3, 0), xgboost::Entry(4, 0.4),
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xgboost::Entry(5, 1), xgboost::Entry(1, 0), xgboost::Entry(2, 1),
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xgboost::Entry(3, 1), xgboost::Entry(4, 0.5), {5, 0}};
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for (const auto &batch : dmat->GetBatches<xgboost::SparsePage>()) {
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CHECK_EQ(batch.base_rowid, 0);
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CHECK(batch.offset.HostVector() == expected_offset);
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CHECK(batch.data.HostVector() == expected_data);
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}
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}
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