7b9043cf71
* Upgrade gtest for clang-tidy. * Use CMake to install GTest instead of mv. * Don't enforce clang-tidy to return 0 due to errors in thrust. * Add a small test for tidy itself. * Reformat.
119 lines
4.2 KiB
C++
119 lines
4.2 KiB
C++
// Copyright by Contributors
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#include <gtest/gtest.h>
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#include <vector>
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#include "helpers.h"
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#include "xgboost/learner.h"
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#include "dmlc/filesystem.h"
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namespace xgboost {
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TEST(Learner, Basic) {
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using Arg = std::pair<std::string, std::string>;
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auto args = {Arg("tree_method", "exact")};
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auto mat_ptr = CreateDMatrix(10, 10, 0);
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std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {*mat_ptr};
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auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
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learner->Configure(args);
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delete mat_ptr;
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}
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TEST(Learner, SelectTreeMethod) {
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using Arg = std::pair<std::string, std::string>;
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auto mat_ptr = CreateDMatrix(10, 10, 0);
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std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {*mat_ptr};
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auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
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// Test if `tree_method` can be set
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learner->Configure({Arg("tree_method", "approx")});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_histmaker,prune");
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learner->Configure({Arg("tree_method", "exact")});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_colmaker,prune");
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learner->Configure({Arg("tree_method", "hist")});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_quantile_histmaker");
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learner->Configure({Arg{"booster", "dart"}, Arg{"tree_method", "hist"}});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_quantile_histmaker");
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#ifdef XGBOOST_USE_CUDA
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learner->Configure({Arg("tree_method", "gpu_exact")});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_gpu,prune");
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learner->Configure({Arg("tree_method", "gpu_hist")});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_gpu_hist");
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learner->Configure({Arg{"booster", "dart"}, Arg{"tree_method", "gpu_hist"}});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_gpu_hist");
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#endif
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delete mat_ptr;
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}
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TEST(Learner, CheckGroup) {
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using Arg = std::pair<std::string, std::string>;
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size_t constexpr kNumGroups = 4;
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size_t constexpr kNumRows = 17;
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size_t constexpr kNumCols = 15;
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auto pp_mat = CreateDMatrix(kNumRows, kNumCols, 0);
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auto& p_mat = *pp_mat;
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std::vector<bst_float> weight(kNumGroups);
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std::vector<bst_int> group(kNumGroups);
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group[0] = 2;
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group[1] = 3;
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group[2] = 7;
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group[3] = 5;
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std::vector<bst_float> labels (kNumRows);
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for (size_t i = 0; i < kNumRows; ++i) {
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labels[i] = i % 2;
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}
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p_mat->Info().SetInfo(
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"weight", static_cast<void*>(weight.data()), DataType::kFloat32, kNumGroups);
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p_mat->Info().SetInfo(
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"group", group.data(), DataType::kUInt32, kNumGroups);
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p_mat->Info().SetInfo("label", labels.data(), DataType::kFloat32, kNumRows);
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std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {p_mat};
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auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
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learner->Configure({Arg{"objective", "rank:pairwise"}});
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learner->InitModel();
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EXPECT_NO_THROW(learner->UpdateOneIter(0, p_mat.get()));
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group.resize(kNumGroups+1);
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group[3] = 4;
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group[4] = 1;
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p_mat->Info().SetInfo("group", group.data(), DataType::kUInt32, kNumGroups+1);
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EXPECT_ANY_THROW(learner->UpdateOneIter(0, p_mat.get()));
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delete pp_mat;
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}
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TEST(Learner, SLOW_CheckMultiBatch) {
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using Arg = std::pair<std::string, std::string>;
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// Create sufficiently large data to make two row pages
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dmlc::TemporaryDirectory tempdir;
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const std::string tmp_file = tempdir.path + "/big.libsvm";
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CreateBigTestData(tmp_file, 5000000);
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std::shared_ptr<DMatrix> dmat(xgboost::DMatrix::Load( tmp_file + "#" + tmp_file + ".cache", true, false));
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EXPECT_TRUE(FileExists(tmp_file + ".cache.row.page"));
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EXPECT_FALSE(dmat->SingleColBlock());
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size_t num_row = dmat->Info().num_row_;
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std::vector<bst_float> labels(num_row);
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for (size_t i = 0; i < num_row; ++i) {
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labels[i] = i % 2;
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}
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dmat->Info().SetInfo("label", labels.data(), DataType::kFloat32, num_row);
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std::vector<std::shared_ptr<DMatrix>> mat{dmat};
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auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
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learner->Configure({Arg{"objective", "binary:logistic"}});
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learner->InitModel();
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learner->UpdateOneIter(0, dmat.get());
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}
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} // namespace xgboost
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